diff --git a/applications/lofar2/libraries/sdp/src/vhdl/sdp_pkg.vhd b/applications/lofar2/libraries/sdp/src/vhdl/sdp_pkg.vhd
index cfd90432042d1c0a8599cc0bc38df728afd8c5c5..f23ce1bf869a2bbdc8d2fb2327d680e7f9a23bf0 100644
--- a/applications/lofar2/libraries/sdp/src/vhdl/sdp_pkg.vhd
+++ b/applications/lofar2/libraries/sdp/src/vhdl/sdp_pkg.vhd
@@ -99,8 +99,8 @@ PACKAGE sdp_pkg is
   CONSTANT c_sdp_W_statistic               : NATURAL := 64;
   CONSTANT c_sdp_W_statistic_sz            : NATURAL := 2;   -- = c_sdp_W_statistic / c_word_w
   CONSTANT c_sdp_W_sub_weight              : NATURAL := 16;  -- = w in s(w, p), s = signed
-  CONSTANT c_sdp_W_sub_weight_fraction     : NATURAL := 13;  -- = p in s(w, p)
-  CONSTANT c_sdp_W_sub_weight_magnitude    : NATURAL := c_sdp_W_sub_weight - c_sdp_W_sub_weight_fraction - 1;  -- = 2
+  CONSTANT c_sdp_W_sub_weight_fraction     : NATURAL := 14;  -- = p in s(w, p)
+  CONSTANT c_sdp_W_sub_weight_magnitude    : NATURAL := c_sdp_W_sub_weight - c_sdp_W_sub_weight_fraction - 1;  -- = 1
   CONSTANT c_sdp_W_beamlet_scale           : NATURAL := 16;  -- = w in u(w, p), u = unsigned
   CONSTANT c_sdp_W_beamlet_scale_fraction  : NATURAL := 15;  -- = p in u(w, p)
   CONSTANT c_sdp_W_beamlet_scale_magnitude : NATURAL := c_sdp_W_beamlet_scale - c_sdp_W_beamlet_scale_fraction;  -- = 1
@@ -159,9 +159,11 @@ PACKAGE sdp_pkg is
   -- . g_fft_guard_w = 1 (was 2)
   --CONSTANT c_sdp_wpfb_subbands : t_wpfb :=
   --  (1, c_sdp_N_fft, 0, c_sdp_P_pfb,
-  --  c_sdp_N_taps, 0, c_sdp_W_adc, 17, c_sdp_W_fir_coef,
-  --  true, false, true, 17, c_sdp_W_subband, 0, 22, 1, true, 54, c_sdp_W_statistic_sz, 195313,
-  --  c_fft_pipeline, c_fft_pipeline, c_fil_ppf_pipeline);  -- = c_wpfb_lofar2_subbands_lts_2021
+  --   c_sdp_N_taps, 0, c_sdp_W_adc, 17, c_sdp_W_fir_coef,
+  --   true, false, true,
+  --   17, c_sdp_W_subband, 0, 22, 1, true,
+  --   54, c_sdp_W_statistic_sz, 195313,
+  --   c_fft_pipeline, c_fft_pipeline, c_fil_ppf_pipeline);  -- = c_wpfb_lofar2_subbands_lts_2021
 
   -- DTS 2022-04-04, changes based on results from  in tb_tb_verify_pfb_wg.vhd:
   -- . fil_backoff_w = 1
@@ -169,11 +171,52 @@ PACKAGE sdp_pkg is
   -- . g_fft_out_gain_w = 1 (compensate for fil_backoff_w = 1)
   -- . g_fft_stage_dat_w = 24
   -- . g_fft_guard_w = 1
+  --CONSTANT c_sdp_wpfb_subbands : t_wpfb :=
+  --  (1, c_sdp_N_fft, 0, c_sdp_P_pfb,
+  --   c_sdp_N_taps, 1, c_sdp_W_adc, 23, c_sdp_W_fir_coef,
+  --   true, false, true,
+  --   23, c_sdp_W_subband, 1, 24, 1, true,
+  --   54, c_sdp_W_statistic_sz, 195313,
+  --   c_fft_pipeline, c_fft_pipeline, c_fil_ppf_pipeline);  -- = c_wpfb_lofar2_subbands_dts_18b
+
+  -- L2TS
+  -- . Use fft_guard_w = 1, instead of 2 to avoid overflow in first FFT stage,
+  --   because fil_backoff_w = 1 already provides sufficient FFT input margin
+  -- . Use fft_out_gain_w = 1 + 1 = 2. One to compensate for fil_backoff_w
+  --   = 1 and one to preserve the W_fft_proc = 5b while using fft_out_dat_w
+  --   = c_sdp_W_subband = 18b instead of 19b, to fit a 18x19 multiplier for
+  --   SST.
+  -- . From hdl/libraries/base/common/python/try_round_weight.py it follows
+  --   that using -r = 6 extra internal bits per stage is sufficient to have
+  --   < 1% disturbance on the sigma of the subband noise. The disturbance
+  --   on the sigma is about proportional to 1/2**r, so with -r = 4 it is
+  --   about < 4%. Therefore use fft_stage_dat_w = fft_out_dat_w +
+  --   fft_out_gain_w + 6b = 26b.
+  -- . The raw_dat_w for FFT output of real input is fft_stage_dat_w + 1,
+  --   because the use_separate in the FFT feature does not divide by 2.
+  --   This implies that preferrably fft_stage_dat_w <= 26, to fit the 27b
+  --   multiplier resources.
+  -- . Increasing fft_stage_dat_w from 24b to 26b does increase M20K usage:
+  --         24b           26b
+  --     FFT 6 x  27 M20K, 6 x  28 M20K, due to separate
+  --     BF  2 x 403 M20K, 2 x 397 M20K, due to reorder_col
+  --     where 6 = c_sdp_P_pfb and 2 = c_sdp_N_beamsets.
+  --   The total design increase is 18 m20K = 2.5 % and 4000 FF = 1.2 %. The
+  --   nof DSP remains 611.
+  CONSTANT c_sdp_W_fil_backoff             : NATURAL := 1;
+  CONSTANT c_sdp_W_fft_guard               : NATURAL := 1;
+  CONSTANT c_sdp_W_fft_stage_dat           : NATURAL := 25;  -- TODO try 26b, compare synthesis results
+  CONSTANT c_sdp_W_fft_in_dat              : NATURAL := c_sdp_W_fft_stage_dat - c_sdp_W_fft_guard;
+  CONSTANT c_sdp_W_fft_out_gain            : NATURAL := 2;
+  CONSTANT c_sdp_W_stat_data               : NATURAL := c_sdp_W_subband * 2 + ceil_log2(c_sdp_N_int_sub_hi);  -- = 54
+
   CONSTANT c_sdp_wpfb_subbands : t_wpfb :=
     (1, c_sdp_N_fft, 0, c_sdp_P_pfb,
-    c_sdp_N_taps, 1, c_sdp_W_adc, 23, c_sdp_W_fir_coef,
-    true, false, true, 23, c_sdp_W_subband, 1, 24, 1, true, 54, c_sdp_W_statistic_sz, 195313,
-    c_fft_pipeline, c_fft_pipeline, c_fil_ppf_pipeline);  -- = c_wpfb_lofar2_subbands_dts_18b
+     c_sdp_N_taps, c_sdp_W_fil_backoff, c_sdp_W_adc, c_sdp_W_fft_in_dat, c_sdp_W_fir_coef,
+     true, false, true,
+     c_sdp_W_fft_in_dat, c_sdp_W_subband, c_sdp_W_fft_out_gain, c_sdp_W_fft_stage_dat, c_sdp_W_fft_guard, true,
+     c_sdp_W_stat_data, c_sdp_W_statistic_sz, c_sdp_N_int_sub_hi,
+     c_fft_pipeline, c_fft_pipeline, c_fil_ppf_pipeline);  -- = c_wpfb_lofar2_subbands_l2ts_18b
 
   CONSTANT c_sdp_wpfb_complex_subbands : t_wpfb := func_wpfb_map_real_input_wpfb_parameters_to_complex_input(c_sdp_wpfb_subbands);
 
diff --git a/applications/lofar2/model/lofar2_station_sdp_firmware_model.ipynb b/applications/lofar2/model/lofar2_station_sdp_firmware_model.ipynb
index df48f6bf1e72e43dbf6a69c6559c7ba3d76e16fd..4997638a71b174b6d322f0c4248bfd253700fa09 100644
--- a/applications/lofar2/model/lofar2_station_sdp_firmware_model.ipynb
+++ b/applications/lofar2/model/lofar2_station_sdp_firmware_model.ipynb
@@ -7,7 +7,7 @@
    "source": [
     "# LOFAR2.0 Station SDP Firmware quantization model\n",
     "\n",
-    "Author: Eric Kooistra, Aug 2022\n",
+    "Author: Eric Kooistra, Aug - Dec 2022\n",
     "\n",
     "Purpose: Model the expected signal levels in the SDP firmware and clarify calculations in [1].\n",
     "\n",
@@ -23,13 +23,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 24,
    "id": "2b477516",
    "metadata": {},
    "outputs": [],
    "source": [
     "import numpy as np\n",
-    "import matplotlib.pyplot as plt"
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "dpi = 254  # 10 dots per mm"
    ]
   },
   {
@@ -42,7 +44,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 25,
    "id": "e1b6fa12",
    "metadata": {},
    "outputs": [
@@ -51,7 +53,8 @@
      "output_type": "stream",
      "text": [
       "N_int_adc = 200000000\n",
-      "N_int_sub = 195312.5\n"
+      "N_int_sub = 195312.5\n",
+      "log2(N_int_sub) = 17.6 bits = 41.4 dB\n"
      ]
     }
    ],
@@ -68,14 +71,17 @@
     "T_int = 1 # s\n",
     "N_int_adc = f_adc * T_int\n",
     "N_int_sub = f_sub * T_int\n",
+    "N_int_sub_bits = np.log2(N_int_sub)\n",
+    "N_int_sub_dB = 10 * np.log2(N_int_sub_bits)\n",
     "\n",
     "print(f\"N_int_adc = {N_int_adc:.0f}\")\n",
-    "print(f\"N_int_sub = {N_int_sub:.1f}\")"
+    "print(f\"N_int_sub = {N_int_sub:.1f}\")\n",
+    "print(f\"log2(N_int_sub) = {N_int_sub_bits:4.1f} bits = {N_int_sub_dB:4.1f} dB\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 26,
    "id": "eb325c9c",
    "metadata": {},
    "outputs": [
@@ -105,7 +111,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 27,
    "id": "3e71626f",
    "metadata": {},
    "outputs": [
@@ -140,9 +146,17 @@
     "print(\"Unit_beamlet_scale =\", Unit_beamlet_scale)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "8e1d2552",
+   "metadata": {},
+   "source": [
+    "## 1.1 Subband gain factor"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 28,
    "id": "0ec00484",
    "metadata": {},
    "outputs": [
@@ -159,14 +173,14 @@
       "  G_subband_sine = 1 * 0.5 * 2**5.0 * 1.0 = 16.0 = 4.00 bits\n",
       "  . G_fir_dc = 1\n",
       "  . G_fft_real_input_sine = 0.5 = 0.5\n",
-      "  . W_fsub_gain = 5.0\n",
+      "  . W_fft_proc = 5.0\n",
       "  . subband_weight_gain = 1.0\n",
       "\n",
       "Incoherent white noise input:\n",
       "  G_subband_noise = 1 * 0.03125 * 2**5.0 * 1.0 = 1.0 = 0.00 bits\n",
       "  . G_fir_dc = 1\n",
       "  . G_fft_real_input_noise = 0.03125 = 0.03125\n",
-      "  . W_fsub_gain = 5.0\n",
+      "  . W_fft_proc = 5.0\n",
       "  . subband_weight_gain = 1.0\n",
       "\n"
      ]
@@ -188,8 +202,6 @@
     "\n",
     "# . Signal level bit growth to accomodate processing gain of FFT\n",
     "W_fft_proc = np.log2(np.sqrt(N_fft))\n",
-    "W_fsub_gain = W_fft_proc  # use W_fsub_gain instead of W_fft_proc\n",
-    "#W_fsub_gain = 4\n",
     "\n",
     "# . Subband equalizer (E_sub)\n",
     "subband_weight_gain = 1.0\n",
@@ -204,33 +216,41 @@
     "print()\n",
     "\n",
     "# Expected factor from real signal input amplitude to subband amplitude\n",
-    "G_subband_sine = G_fir_dc * G_fft_real_input_sine * 2**W_fsub_gain * subband_weight_gain\n",
+    "G_subband_sine = G_fir_dc * G_fft_real_input_sine * 2**W_fft_proc * subband_weight_gain\n",
     "\n",
     "print(\"Coherent WG sine input:\")\n",
-    "print(f\"  G_subband_sine = {G_fir_dc} * {G_fft_real_input_sine} * 2**{W_fsub_gain} * {subband_weight_gain} \\\n",
+    "print(f\"  G_subband_sine = {G_fir_dc} * {G_fft_real_input_sine} * 2**{W_fft_proc} * {subband_weight_gain} \\\n",
     "= {G_subband_sine} = {np.log2(G_subband_sine):.2f} bits\")\n",
     "print(\"  . G_fir_dc =\", G_fir_dc)\n",
     "print(\"  . G_fft_real_input_sine =\", G_fft_real_input_sine, \"=\", 1 / N_sidebands)\n",
-    "print(\"  . W_fsub_gain =\", W_fsub_gain)\n",
+    "print(\"  . W_fft_proc =\", W_fft_proc)\n",
     "print(\"  . subband_weight_gain =\", subband_weight_gain)\n",
     "print()\n",
     "\n",
-    "# Expected factor from real signal input white noise sigma to subband amplitude\n",
-    "G_subband_noise = G_fir_dc * G_fft_real_input_noise * 2**W_fsub_gain * subband_weight_gain\n",
+    "# Expected factor from real signal input white noise sigma to subband noise sigma\n",
+    "G_subband_noise = G_fir_dc * G_fft_real_input_noise * 2**W_fft_proc * subband_weight_gain\n",
     "\n",
     "print(\"Incoherent white noise input:\")\n",
-    "print(f\"  G_subband_noise = {G_fir_dc} * {G_fft_real_input_noise} * 2**{W_fsub_gain} * {subband_weight_gain} \\\n",
+    "print(f\"  G_subband_noise = {G_fir_dc} * {G_fft_real_input_noise} * 2**{W_fft_proc} * {subband_weight_gain} \\\n",
     "= {G_subband_noise} = {np.log2(G_subband_noise):.2f} bits\")\n",
     "print(\"  . G_fir_dc =\", G_fir_dc)\n",
     "print(\"  . G_fft_real_input_noise =\", G_fft_real_input_noise, \"=\", 1 / np.sqrt(N_fft))\n",
-    "print(\"  . W_fsub_gain =\", W_fsub_gain)\n",
+    "print(\"  . W_fft_proc =\", W_fft_proc)\n",
     "print(\"  . subband_weight_gain =\", subband_weight_gain)\n",
     "print()\n"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "ec61a040",
+   "metadata": {},
+   "source": [
+    "## 1.2 Beamlet gain factor"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 29,
    "id": "ac73d7e3",
    "metadata": {},
    "outputs": [
@@ -335,9 +355,17 @@
     "print()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "bda7e827",
+   "metadata": {},
+   "source": [
+    "## 1.3 Maximum input level for beamlet_sum and BST"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 30,
    "id": "98f1917e",
    "metadata": {},
    "outputs": [
@@ -363,17 +391,48 @@
     "print()"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "7310c2cc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "N_ant =  1 : ni_sigma_max = 4.000000 =  32768 =  15.0 bits\n",
+      "N_ant = 12 : ni_sigma_max = 1.154701 =   9459 =  13.2 bits\n",
+      "N_ant = 24 : ni_sigma_max = 0.816497 =   6689 =  12.7 bits\n",
+      "N_ant = 48 : ni_sigma_max = 0.577350 =   4730 =  12.2 bits\n",
+      "N_ant = 96 : ni_sigma_max = 0.408248 =   3344 =  11.7 bits\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Maximum incoherent signal input sigma, for beamlet_sum_sigma * 4 = 2**(W_beamlet_sum - 1)\n",
+    "# Use 4 sigma to avoid overflow\n",
+    "ni_sigma_max = 2**(W_beamlet_sum - 1) / 4 / G_beamlet_sum_noise\n",
+    "for ni, na in enumerate(N_ant_arr):\n",
+    "    print(f\"N_ant = {na:2d} : ni_sigma_max = {ni_sigma_max[ni] / FS:f} \" \\\n",
+    "          f\"= {ni_sigma_max[ni]:6.0f} = {np.log2(ni_sigma_max[ni]):5.1f} bits\")\n",
+    "print()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "d942fcc6",
    "metadata": {},
    "source": [
-    "# 2 Quantization model"
+    "# 2 Quantization noise\n",
+    "\n",
+    "## 2.1 dB full scale (dBFS)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 32,
    "id": "f66c5028",
    "metadata": {},
    "outputs": [
@@ -395,104 +454,121 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "id": "a9fca052",
+   "execution_count": 33,
+   "id": "be2d952f",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\n",
-      "P_quant = 0.083333\n",
-      "P_quant_dB = -10.79 dB = -1.8 bit\n",
-      "sigma_quant = 0.29 q\n"
+      "W_adc = 14 bits\n",
+      "FS = 8192\n",
+      "sigma_fs_sine = 5792.6 q\n",
+      "P_fs_sine_dB = 75.26 dB = 12.5 bit\n"
      ]
     }
    ],
    "source": [
-    "# Quantization noise\n",
-    "# . The quantization noise power is q**2 * 1 / 12, so the standard deviation\n",
-    "#   of the quantization noise is q * sqrt(1 / 12) < q = one LSbit\n",
-    "# . The quantization noise power is at a level of -10.79 dB or -1.8 bit.\n",
-    "# . The 0 dB power level or 0 bit level corresponds to the power of one LSbit, so q**2 \n",
-    "P_quant = 1 / 12  # for W >> 1 [2]\n",
-    "P_quant_dB = 10 * np.log10(P_quant)\n",
-    "sigma_quant = np.sqrt(P_quant)\n",
-    "print()\n",
-    "print(f\"P_quant = {P_quant:.6f}\")\n",
-    "print(f\"P_quant_dB = {P_quant_dB:.2f} dB = {P_quant_dB / P_bit_dB:.1f} bit\")\n",
-    "print(f\"sigma_quant = {sigma_quant:.2f} q\")"
+    "# Full scale (FS) sine\n",
+    "P_fs_sine = FS**2 / 2\n",
+    "P_fs_sine_dB = 10 * np.log10(P_fs_sine)\n",
+    "print(f\"W_adc = {W_adc} bits\")\n",
+    "print(\"FS =\", FS)\n",
+    "print(f\"sigma_fs_sine = {sigma_fs_sine:.1f} q\")\n",
+    "print(f\"P_fs_sine_dB = {P_fs_sine_dB:.2f} dB = {P_fs_sine_dB / P_bit_dB:.1f} bit\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "id": "d9972b6b",
+   "execution_count": 34,
+   "id": "c827851e",
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Power at -50dBFS = 25.26 dB corresponds to:\n",
+      "  . sigma = 18.3 q (= 4.2 bits)\n",
+      "  . Noise range 3 sigma = +-55 q\n",
+      "  . Sine with amplitude A = = sigma * sqrt(2) = 25.9 q\n",
+      "\n",
+      "sigma = 16 q (= 4.0 bits) corresponds to:\n",
+      "  . Power = 24.08 dB, so at -51.2 dBFS\n",
+      "  . Noise range 3 sigma = +-48 q\n",
+      "  . Sine with amplitude A = sigma * sqrt(2) = 22.6 q\n"
+     ]
     }
    ],
    "source": [
-    "# Impact of quantization on the system noise\n",
-    "# The quantization noise has sigma_quant = 0.29 q, this increases the system noise.\n",
-    "# The system noise has sigma_sys = n * q. For n = 2 the quantization increases the\n",
-    "# total power by 2% (so sigma_sys increase by sqrt(2 %) is about 1 %).\n",
-    "n = np.arange(1,9)\n",
-    "sigma_sys = n  # = n * q, so sigma of n LSbits\n",
-    "P_sys = sigma_sys**2\n",
-    "P_tot = P_sys + P_quant\n",
-    "sigma_tot = np.sqrt(P_tot)\n",
+    "# dBFS: Signal level relative to FS sine\n",
+    "power_50dBFS = P_fs_sine_dB - 50  \n",
+    "sigma_50dBFS = 10**(power_50dBFS / 20)\n",
+    "sigma_50dBFS_bits = np.log2(sigma_50dBFS)\n",
+    "ampl_50dBFS = sigma_50dBFS * np.sqrt(2)\n",
     "\n",
-    "plt.figure()\n",
-    "plt.plot(n, (P_tot / P_sys - 1) * 100)\n",
-    "plt.title(\"Increase in total noise power due to quantization\")\n",
-    "plt.xlabel(\"sigma_sys [q]\")\n",
-    "plt.ylabel(\"[%]\")\n",
-    "plt.grid()"
+    "print(f\"Power at -50dBFS = {power_50dBFS:.2f} dB corresponds to:\")\n",
+    "print(f\"  . sigma = {sigma_50dBFS:.1f} q (= {sigma_50dBFS_bits:.1f} bits)\")\n",
+    "print(f\"  . Noise range 3 sigma = +-{3 * sigma_50dBFS:.0f} q\")\n",
+    "print(f\"  . Sine with amplitude A = = sigma * sqrt(2) = {ampl_50dBFS:.1f} q\")\n",
+    "print()\n",
+    "\n",
+    "# Assume signal with sigma = 16 q is 4 bits noise\n",
+    "sigma_16q = 16\n",
+    "sigma_16q_bits = np.log2(sigma_16q)\n",
+    "power_16q = sigma_16q**2\n",
+    "power_16q_dB = 10 * np.log10(power_16q)\n",
+    "dBFS_16q = power_16q_dB - P_fs_sine_dB\n",
+    "print(f\"sigma = {sigma_16q:.0f} q (= {sigma_16q_bits:.1f} bits) corresponds to:\")\n",
+    "print(f\"  . Power = {power_16q_dB:.2f} dB, so at {dBFS_16q:.1f} dBFS\")\n",
+    "print(f\"  . Noise range 3 sigma = +-{3 * sigma_16q:.0f} q\")\n",
+    "print(f\"  . Sine with amplitude A = sigma * sqrt(2) = {np.sqrt(2) * sigma_16q:.1f} q\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "982937ab",
+   "metadata": {},
+   "source": [
+    "## 2.2 Signal to noise ratio (SNR)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "id": "be2d952f",
+   "execution_count": 35,
+   "id": "a9fca052",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "W_adc = 14 bits\n",
-      "FS = 8192\n",
-      "sigma_fs_sine = 5792.6 q\n",
-      "P_fs_sine_dB = 75.26 dB = 12.5 bit\n"
+      "\n",
+      "P_quant = 0.083333\n",
+      "P_quant_dB = -10.79 dB = -1.8 bit\n",
+      "sigma_quant = 0.29 q\n"
      ]
     }
    ],
    "source": [
-    "# Full scale (FS) sine\n",
-    "P_fs_sine = FS**2 / 2\n",
-    "P_fs_sine_dB = 10 * np.log10(P_fs_sine)\n",
-    "print(f\"W_adc = {W_adc} bits\")\n",
-    "print(\"FS =\", FS)\n",
-    "print(f\"sigma_fs_sine = {sigma_fs_sine:.1f} q\")\n",
-    "print(f\"P_fs_sine_dB = {P_fs_sine_dB:.2f} dB = {P_fs_sine_dB / P_bit_dB:.1f} bit\")"
+    "# Quantization noise\n",
+    "# . The quantization noise power is q**2 * 1 / 12, so the standard deviation\n",
+    "#   of the quantization noise is q * sqrt(1 / 12) < q = one LSbit\n",
+    "# . The quantization noise power is at a level of -10.79 dB or -1.8 bit.\n",
+    "# . The 0 dB power level or 0 bit level corresponds to the power of one LSbit, so q**2 \n",
+    "P_quant = 1 / 12  # for W >> 1 [2]\n",
+    "P_quant_dB = 10 * np.log10(P_quant)\n",
+    "sigma_quant = np.sqrt(P_quant)\n",
+    "print()\n",
+    "print(f\"P_quant = {P_quant:.6f}\")\n",
+    "print(f\"P_quant_dB = {P_quant_dB:.2f} dB = {P_quant_dB / P_bit_dB:.1f} bit\")\n",
+    "print(f\"sigma_quant = {sigma_quant:.2f} q\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 36,
    "id": "a9e7fabc",
    "metadata": {},
    "outputs": [
@@ -514,51 +590,52 @@
     "print(f\"SNR_dB = P_fs_sine_dB - P_quant_dB = {P_fs_sine_dB:.2f} - {P_quant_dB:.2f} = {SNR_dB:.2f} dB\")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "c1c19077",
+   "metadata": {},
+   "source": [
+    "## 2.3 Impact of quantization on the system noise"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "id": "92852a53",
+   "execution_count": 37,
+   "id": "d9972b6b",
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Power at -50dBFS = 25.26 dB corresponds to:\n",
-      "  . sigma = 18.3 q (= 4.2 bits)\n",
-      "  . Noise range 3 sigma = +-55 q\n",
-      "  . Sine with amplitude A = = sigma * sqrt(2) = 25.9 q\n",
-      "\n",
-      "sigma = 16 q (= 4.0 bits) corresponds to:\n",
-      "  . Power = 24.08 dB, so at -51.2 dBFS\n",
-      "  . Noise range 3 sigma = +-48 q\n",
-      "  . Sine with amplitude A = sigma * sqrt(2) = 22.6 q\n"
-     ]
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "# Signal level relative to FS sine\n",
-    "power_50dBFS = P_fs_sine_dB - 50  \n",
-    "sigma_50dBFS = 10**(power_50dBFS / 20)\n",
-    "sigma_50dBFS_bits = np.log2(sigma_50dBFS)\n",
-    "ampl_50dBFS = sigma_50dBFS * np.sqrt(2)\n",
-    "\n",
-    "print(f\"Power at -50dBFS = {power_50dBFS:.2f} dB corresponds to:\")\n",
-    "print(f\"  . sigma = {sigma_50dBFS:.1f} q (= {sigma_50dBFS_bits:.1f} bits)\")\n",
-    "print(f\"  . Noise range 3 sigma = +-{3 * sigma_50dBFS:.0f} q\")\n",
-    "print(f\"  . Sine with amplitude A = = sigma * sqrt(2) = {ampl_50dBFS:.1f} q\")\n",
+    "# Impact of quantization on the system noise\n",
+    "# The quantization noise has sigma_quant = 0.29 q, this increases the system noise.\n",
+    "# The system noise has sigma_sys = n * q. For n = 2 the quantization increases the\n",
+    "# total power by 2% (so sigma_sys increase by sqrt(2 %) is about 1 %).\n",
+    "step = 0.1\n",
+    "n = np.arange(1, 9, step)\n",
+    "sigma_sys = n  # = n * q, so sigma of n LSbits\n",
+    "P_sys = sigma_sys**2\n",
+    "P_tot = P_sys + P_quant\n",
+    "sigma_tot = np.sqrt(P_tot)\n",
     "\n",
-    "# Assume signal with sigma = 16 q is 4 bits noise\n",
-    "sigma_16q = 16\n",
-    "sigma_16q_bits = np.log2(sigma_16q)\n",
-    "power_16q = sigma_16q**2\n",
-    "power_16q_dB = 10 * np.log10(power_16q)\n",
-    "dBFS_16q = power_16q_dB - P_fs_sine_dB\n",
-    "print()\n",
-    "print(f\"sigma = {sigma_16q:.0f} q (= {sigma_16q_bits:.1f} bits) corresponds to:\")\n",
-    "print(f\"  . Power = {power_16q_dB:.2f} dB, so at {dBFS_16q:.1f} dBFS\")\n",
-    "print(f\"  . Noise range 3 sigma = +-{3 * sigma_16q:.0f} q\")\n",
-    "print(f\"  . Sine with amplitude A = sigma * sqrt(2) = {np.sqrt(2) * sigma_16q:.1f} q\")\n"
+    "plt.figure(figsize=(12, 8))\n",
+    "plt.plot(n, (P_tot / P_sys - 1) * 100)\n",
+    "plt.title(\"Increase in total noise power due to quantization\")\n",
+    "plt.xlabel(\"sigma_sys [q]\")\n",
+    "plt.ylabel(\"[%]\")\n",
+    "plt.grid()\n",
+    "plt.savefig('plots/lofar2_station_sdp_firmware_model_incr_sigma_sys.jpg', dpi=dpi)"
    ]
   },
   {
@@ -574,12 +651,12 @@
    "id": "f7fff7a0",
    "metadata": {},
    "source": [
-    "## 3.1 Signal input power and DC level"
+    "## 3.1 ADC Statistics (AST)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 38,
    "id": "a04af043",
    "metadata": {},
    "outputs": [
@@ -587,31 +664,37 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "SI sigma = 5792.6 q = 12.5 bits: P_ast = 6.710886e+15, uses 52.6 bits, is 0 dBFS = FS sine\n",
-      "SI sigma =   18.3 q =  4.2 bits: P_ast = 6.710886e+10, uses 36.0 bits, is -50 dBFS\n",
-      "SI sigma =   16.0 q =  4.0 bits: P_ast = 5.120000e+10, uses 35.6 bits, is -51.2 dBFS\n"
+      "SI sigma = 5792.6 q = 12.5 bits: P_ast = 6.710886e+15 = 158.3 dB, uses 52.6 bits, is 0 dBFS = FS sine\n",
+      "SI sigma =   18.3 q =  4.2 bits: P_ast = 6.710886e+10 = 108.3 dB, uses 36.0 bits, is -50 dBFS\n",
+      "SI sigma =   16.0 q =  4.0 bits: P_ast = 5.120000e+10 = 107.1 dB, uses 35.6 bits, is -51.2 dBFS\n"
      ]
     }
    ],
    "source": [
-    "# Signal input power statistic for ADC / WG (AST)\n",
+    "# Signal input power and DC level statistic for ADC / WG\n",
     "si_sigma = sigma_fs_sine\n",
     "si_sigma_bits = np.log2(si_sigma)\n",
     "P_ast = (si_sigma)**2 * N_int_adc\n",
-    "print(f\"SI sigma = {si_sigma:6.1f} q = {si_sigma_bits:4.1f} bits: P_ast = {P_ast:e}, \\\n",
-    "uses {np.log2(P_ast):.1f} bits, is 0 dBFS = FS sine\")\n",
+    "P_ast_bits = np.log2(P_ast)\n",
+    "P_ast_dB = 10 * np.log10(P_ast)\n",
+    "print(f\"SI sigma = {si_sigma:6.1f} q = {si_sigma_bits:4.1f} bits: P_ast = {P_ast:e} = {P_ast_dB:.1f} dB, \\\n",
+    "uses {P_ast_bits:.1f} bits, is 0 dBFS = FS sine\")\n",
     "\n",
     "si_sigma = sigma_50dBFS\n",
     "si_sigma_bits = np.log2(si_sigma)\n",
     "P_ast = (si_sigma)**2 * N_int_adc\n",
-    "print(f\"SI sigma = {si_sigma:6.1f} q = {si_sigma_bits:4.1f} bits: P_ast = {P_ast:e}, \\\n",
-    "uses {np.log2(P_ast):.1f} bits, is -50 dBFS\")\n",
+    "P_ast_bits = np.log2(P_ast)\n",
+    "P_ast_dB = 10 * np.log10(P_ast)\n",
+    "print(f\"SI sigma = {si_sigma:6.1f} q = {si_sigma_bits:4.1f} bits: P_ast = {P_ast:e} = {P_ast_dB:.1f} dB, \\\n",
+    "uses {P_ast_bits:.1f} bits, is -50 dBFS\")\n",
     "\n",
     "si_sigma = sigma_16q\n",
     "si_sigma_bits = np.log2(si_sigma)\n",
     "P_ast = (si_sigma)**2 * N_int_adc\n",
-    "print(f\"SI sigma = {si_sigma:6.1f} q = {si_sigma_bits:4.1f} bits: P_ast = {P_ast:e}, \\\n",
-    "uses {np.log2(P_ast):.1f} bits, is {dBFS_16q:.1f} dBFS\")"
+    "P_ast_bits = np.log2(P_ast)\n",
+    "P_ast_dB = 10 * np.log10(P_ast)\n",
+    "print(f\"SI sigma = {si_sigma:6.1f} q = {si_sigma_bits:4.1f} bits: P_ast = {P_ast:e} = {P_ast_dB:.1f} dB, \\\n",
+    "uses {P_ast_bits:.1f} bits, is {dBFS_16q:.1f} dBFS\")"
    ]
   },
   {
@@ -639,7 +722,7 @@
    "id": "f842d856",
    "metadata": {},
    "source": [
-    "For a complex signal (like subbands and beamlets):\n",
+    "For a complex signal (like subbands and beamlets), assume mean complex = 0 so rms = std and power = var (= std^2):\n",
     "\n",
     "* power complex = power real + power imag = (std real)^2 + (std imag)^2\n",
     "* power real = power imag = power complex / 2\n",
@@ -648,9 +731,17 @@
     "* ampl real = ampl imag = std complex = std real * sqrt(2) = std imag * sqrt(2)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "22c393ba",
+   "metadata": {},
+   "source": [
+    "### 3.2.1 Coherent, narrow band,  sine input"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 39,
    "id": "5ba30659",
    "metadata": {},
    "outputs": [
@@ -740,9 +831,17 @@
     "      f\"at {dBFS_16q:.1f} dBFS (= FS / {10**(-dBFS_16q/20):.0f})\")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "b6378f26",
+   "metadata": {},
+   "source": [
+    "### 3.2.2 Incoherent, wide band, noise input"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 40,
    "id": "5ec1330a",
    "metadata": {},
    "outputs": [
@@ -808,15 +907,23 @@
    "id": "d6c867ae",
    "metadata": {},
    "source": [
-    "Conclusion (for W_fsub_gain = W_fft_proc = 5 bits):\n",
+    "Conclusion (for W_fft_proc = 5 bits):\n",
     "* For FS sine input the subband amplitude is 17 bits, so including the sign bit this fits in W_subband = 18b. It does not fit all special test signals (e.g. first harmonic of FS square wave input).\n",
     "* For XST the W_crosslet = 16b subband samples can only fit sine signal input <= 0.25 FS\n",
     "* For sigma = FS / 4 white noise input the subband sigma uses 11 bits, so 10.5 bits for the subband real and imaginary parts. The 4 sigma just fits in FS and corresponds to 2 bits, so including the sign bit the 4 sigma range of the subband real and imag fits in 1 + 10.5 + 2 = 13.5 bits."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "e9bcdc19",
+   "metadata": {},
+   "source": [
+    "### 3.2.3 From SST level to input level"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 41,
    "id": "33f37393",
    "metadata": {},
    "outputs": [
@@ -827,7 +934,6 @@
       "G_subband_sine = 16.0 = 4.0 bits\n",
       "G_subband_noise = 1.0 = 0.0 bits\n",
       "\n",
-      "Calculate \n",
       "sub_SST = 2.097152e+14 (= 143.2 dB)\n",
       ". sub_power = 1073741824.0\n",
       ". sub_ampl = 32768.0\n",
@@ -838,8 +944,22 @@
       ". sub_sigma = 2048.0\n",
       ". sub_sigma_re = 1448.1546878700492\n",
       ". sub_sigma_im = 1448.1546878700492\n",
-      ". si_sigma = 2048.0 (si_sigma_exp = 2048.0) = FS/4\n"
+      ". si_sigma = 2048.0 (si_sigma_exp = 2048.0) = FS/4\n",
+      "\n",
+      "The SST level for noise input is G_subband_sine / G_subband_noise = 16.0 = 4.0 bit = 24.1 dB below the SST level for sine input when ni_sigma = si_ampl. Note that typically ni_sigma < FS / 4 to avoid ADC input overflow.\n"
      ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
     }
    ],
    "source": [
@@ -850,7 +970,6 @@
     "print(f\"G_subband_sine = {G_subband_sine} = {np.log2(G_subband_sine)} bits\")\n",
     "print(f\"G_subband_noise = {G_subband_noise} = {np.log2(G_subband_noise)} bits\")\n",
     "print()\n",
-    "print(\"Calculate \")\n",
     "\n",
     "# Coherent (WG sine) signal: from SST to subband amplitude and signal input amplitude in q units\n",
     "sub_SST = SST_fs4  # SST in WG sine frequency subband for si_ampl = FS / 4 = 2048\n",
@@ -881,7 +1000,37 @@
     "print(f\". sub_sigma = {sub_sigma}\")\n",
     "print(f\". sub_sigma_re = {sub_sigma_re}\")\n",
     "print(f\". sub_sigma_im = {sub_sigma_im}\")\n",
-    "print(f\". si_sigma = {si_sigma} (si_sigma_exp = {si_sigma_exp}) = FS/4\")"
+    "print(f\". si_sigma = {si_sigma} (si_sigma_exp = {si_sigma_exp}) = FS/4\")\n",
+    "print()\n",
+    "\n",
+    "# SST in dB as function of input signal level\n",
+    "si_ampls_bits = np.arange(1, W_adc-1, 0.1)\n",
+    "si_ampls = 2**si_ampls_bits\n",
+    "si_sigmas = si_ampls / np.sqrt(2)\n",
+    "si_sub_ampls = si_ampls * G_subband_sine  # subband amplitude for signal input sine\n",
+    "si_SSTs = si_sub_ampls**2 * N_int_sub\n",
+    "si_SSTs_dB = 10 * np.log10(si_SSTs)\n",
+    "\n",
+    "ni_sigmas_bits = np.arange(1, W_adc-1, 0.1)\n",
+    "ni_sigmas = 2**ni_sigmas_bits\n",
+    "ni_sub_sigmas = ni_sigmas * G_subband_noise\n",
+    "ni_SSTs = ni_sub_sigmas**2 * N_int_sub\n",
+    "ni_SSTs_dB = 10 * np.log10(ni_SSTs)\n",
+    "\n",
+    "plt.figure(figsize=(12, 8))\n",
+    "plt.plot(si_ampls_bits, si_SSTs_dB, 'r', ni_sigmas_bits, ni_SSTs_dB, 'b')\n",
+    "plt.title(f\"SST as function of input sine amplitude and input noise sigma\")\n",
+    "plt.xlabel(\"si_ampl, ni_sigma [bits]\")\n",
+    "plt.ylabel(\"SST [dB]\")\n",
+    "plt.grid()\n",
+    "plt.savefig('plots/lofar2_station_sdp_firmware_model_sst_db_si_bits.jpg', dpi=dpi)\n",
+    "\n",
+    "diff_SST_bits = np.log2(G_subband_sine / G_subband_noise)\n",
+    "diff_SST_dB = 20 * np.log10(G_subband_sine / G_subband_noise)\n",
+    "print(f\"The SST level for noise input is G_subband_sine / G_subband_noise = \\\n",
+    "{G_subband_sine / G_subband_noise} = {diff_SST_bits:.1f} bit = {diff_SST_dB:.1f} dB \\\n",
+    "below the SST level for sine input when ni_sigma = si_ampl. \\\n",
+    "Note that typically ni_sigma < FS / 4 to avoid ADC input overflow.\")"
    ]
   },
   {
@@ -889,9 +1038,9 @@
    "id": "66d49365",
    "metadata": {},
    "source": [
-    "## 3.4 Crosslet statistics (XST)\n",
+    "## 3.3 Crosslet statistics (XST)\n",
     "\n",
-    "The crosslet statistics have W_crosslet = 16b, but use the same LSbit level as the subbands. The subbands have W_subband = 18b and the maximum subband sine amplitude is 17b (for W_fsub_gain = W_fft_proc =5 bits). Therefore the maximum sine input for no XST overflow is A = 0.25. If subband_weight = 1.0 then the auto correlations of the XST are equal to the SST."
+    "The crosslet statistics have W_crosslet = 16b, but use the same LSbit level as the subbands. The subbands have W_subband = 18b and the maximum subband sine amplitude is 17b (for W_fft_proc = 5 bits). Therefore the maximum sine input for no XST overflow is A = 0.25. If subband_weight = 1.0 then the auto correlations of the XST are equal to the SST. Hence the crosslets have the same sensitivity as the subbands, but less dynamic range."
    ]
   },
   {
@@ -899,12 +1048,14 @@
    "id": "ba543d00",
    "metadata": {},
    "source": [
-    "## 3.5 Beamlet statistics (BST)"
+    "## 3.4 Beamlet statistics (BST)\n",
+    "\n",
+    "### 3.4.1 Coherent, narrow band,  sine input"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 42,
    "id": "f0b09a83",
    "metadata": {},
    "outputs": [
@@ -914,8 +1065,8 @@
      "text": [
       "Coherent (WG sine) signal input level --> Expected beamlet level and BST level:\n",
       "\n",
-      "N_ant  si_ampl         si_sigma       beamlet_sigma =         BST\n",
-      "                                       beamlet_ampl\n",
+      "N_ant  si_ampl         si_sigma          beamlet_sum_sigma =           BST\n",
+      "                                         beamlet_sum_ampl\n",
       "         value  #bits     value  #bits        value  #bits           value     dB  #bits\n",
       "    1   8192.0   13.0    5792.6   12.5     131072.0   17.0    3.355443e+15  155.3   51.6, at 0 dBFS (= FS sine)\n",
       "   12   8192.0   13.0    5792.6   12.5    1572864.0   20.6    4.831838e+17  176.8   58.7, at 0 dBFS (= FS sine)\n",
@@ -949,40 +1100,40 @@
     "# . uses BF weights to form beamlets from sum of weighted subbands\n",
     "\n",
     "# Beamlet_sum level and BST level for coherent (WG sine) input (similar as for SST)\n",
-    "beamlet_ampl_fs = si_ampl_fs * G_beamlet_sum_sine  # beamlet amplitude for FS signal input sine\n",
-    "beamlet_ampl_fs_bits = np.log2(beamlet_ampl_fs)\n",
-    "BST_fs = beamlet_ampl_fs**2 * N_int_sub\n",
+    "beamlet_sum_ampl_fs = si_ampl_fs * G_beamlet_sum_sine  # beamlet amplitude for FS signal input sine\n",
+    "beamlet_sum_ampl_fs_bits = np.log2(beamlet_sum_ampl_fs)\n",
+    "BST_fs = beamlet_sum_ampl_fs**2 * N_int_sub\n",
     "BST_fs_dB = 10 * np.log10(BST_fs)\n",
     "BST_fs_bits = np.log2(BST_fs)\n",
     "\n",
-    "beamlet_ampl_fs4 = si_ampl_fs4 * G_beamlet_sum_sine  # beamlet amplitude for FS signal input sine\n",
-    "beamlet_ampl_fs4_bits = np.log2(beamlet_ampl_fs4)\n",
-    "BST_fs4 = beamlet_ampl_fs4**2 * N_int_sub\n",
+    "beamlet_sum_ampl_fs4 = si_ampl_fs4 * G_beamlet_sum_sine  # beamlet amplitude for FS signal input sine\n",
+    "beamlet_sum_ampl_fs4_bits = np.log2(beamlet_sum_ampl_fs4)\n",
+    "BST_fs4 = beamlet_sum_ampl_fs4**2 * N_int_sub\n",
     "BST_fs4_dB = 10 * np.log10(BST_fs4)\n",
     "BST_fs4_bits = np.log2(BST_fs4)\n",
     "\n",
-    "beamlet_ampl_50dBFS = si_ampl_50dBFS * G_beamlet_sum_sine  # beamlet amplitude -50dBFS signal input sine\n",
-    "beamlet_ampl_50dBFS_bits = np.log2(beamlet_ampl_50dBFS)\n",
-    "BST_50dBFS = beamlet_ampl_50dBFS**2 * N_int_sub\n",
+    "beamlet_sum_ampl_50dBFS = si_ampl_50dBFS * G_beamlet_sum_sine  # beamlet amplitude -50dBFS signal input sine\n",
+    "beamlet_sum_ampl_50dBFS_bits = np.log2(beamlet_sum_ampl_50dBFS)\n",
+    "BST_50dBFS = beamlet_sum_ampl_50dBFS**2 * N_int_sub\n",
     "BST_50dBFS_dB = 10 * np.log10(BST_50dBFS)\n",
     "BST_50dBFS_bits = np.log2(BST_50dBFS)\n",
     "\n",
-    "beamlet_ampl_s16q = si_ampl_s16q * G_beamlet_sum_sine  # beamlet amplitude for signal input sine with sigma = 16 q\n",
-    "beamlet_ampl_s16q_bits = np.log2(beamlet_ampl_s16q)\n",
-    "BST_s16q = beamlet_ampl_s16q**2 * N_int_sub\n",
+    "beamlet_sum_ampl_s16q = si_ampl_s16q * G_beamlet_sum_sine  # beamlet amplitude for signal input sine with sigma = 16 q\n",
+    "beamlet_sum_ampl_s16q_bits = np.log2(beamlet_sum_ampl_s16q)\n",
+    "BST_s16q = beamlet_sum_ampl_s16q**2 * N_int_sub\n",
     "BST_s16q_dB = 10 * np.log10(BST_s16q)\n",
     "BST_s16q_bits = np.log2(BST_s16q)\n",
     " \n",
     "print(\"Coherent (WG sine) signal input level --> Expected beamlet level and BST level:\")\n",
     "print()\n",
-    "print(\"N_ant  si_ampl         si_sigma       beamlet_sigma =         BST\")\n",
-    "print(\"                                       beamlet_ampl\")\n",
+    "print(\"N_ant  si_ampl         si_sigma          beamlet_sum_sigma =           BST\")\n",
+    "print(\"                                         beamlet_sum_ampl\")\n",
     "print(\"         value  #bits     value  #bits        value  #bits           value     dB  #bits\")\n",
     "for ni, na in enumerate(N_ant_arr):\n",
     "    print(f\"{na:5d} \" \\\n",
     "          f\"{si_ampl_fs:8.1f} {si_ampl_fs_bits:6.1f} \" \\\n",
     "          f\"{si_sigma_fs:9.1f} {si_sigma_fs_bits:6.1f} \" \\\n",
-    "          f\"{beamlet_ampl_fs[ni]:12.1f} {beamlet_ampl_fs_bits[ni]:6.1f} \" \\\n",
+    "          f\"{beamlet_sum_ampl_fs[ni]:12.1f} {beamlet_sum_ampl_fs_bits[ni]:6.1f} \" \\\n",
     "          f\"{BST_fs[ni]:15e} {BST_fs_dB[ni]:6.1f} {BST_fs_bits[ni]:6.1f}, \" \\\n",
     "          f\"at 0 dBFS (= FS sine)\")\n",
     "print()\n",
@@ -990,7 +1141,7 @@
     "    print(f\"{na:5d} \" \\\n",
     "          f\"{si_ampl_fs4:8.1f} {si_ampl_fs4_bits:6.1f} \" \\\n",
     "          f\"{si_sigma_fs4:9.1f} {si_sigma_fs4_bits:6.1f} \" \\\n",
-    "          f\"{beamlet_ampl_fs4[ni]:12.1f} {beamlet_ampl_fs4_bits[ni]:6.1f} \" \\\n",
+    "          f\"{beamlet_sum_ampl_fs4[ni]:12.1f} {beamlet_sum_ampl_fs4_bits[ni]:6.1f} \" \\\n",
     "          f\"{BST_fs4[ni]:15e} {BST_fs4_dB[ni]:6.1f} {BST_fs4_bits[ni]:6.1f}, \" \\\n",
     "          f\"at {20*np.log10(1 / 4**2):.1f} dBFS (= FS / 4)\")\n",
     "print()\n",
@@ -998,7 +1149,7 @@
     "    print(f\"{na:5d} \" \\\n",
     "          f\"{si_ampl_50dBFS:8.1f} {si_ampl_50dBFS_bits:6.1f} \" \\\n",
     "          f\"{si_sigma_50dBFS:9.1f} {si_sigma_50dBFS_bits:6.1f} \" \\\n",
-    "          f\"{beamlet_ampl_50dBFS[ni]:12.1f} {beamlet_ampl_50dBFS_bits[ni]:6.1f} \" \\\n",
+    "          f\"{beamlet_sum_ampl_50dBFS[ni]:12.1f} {beamlet_sum_ampl_50dBFS_bits[ni]:6.1f} \" \\\n",
     "          f\"{BST_50dBFS[ni]:15e} {BST_50dBFS_dB[ni]:6.1f} {BST_50dBFS_bits[ni]:6.1f}, \" \n",
     "          f\"at -50 dBFS (= FS / {10**(50/20):.0f})\")\n",
     "print()\n",
@@ -1006,14 +1157,22 @@
     "    print(f\"{na:5d} \" \\\n",
     "          f\"{si_ampl_s16q:8.1f} {si_ampl_s16q_bits:6.1f} \" \\\n",
     "          f\"{si_sigma_s16q:9.1f} {si_sigma_s16q_bits:6.1f} \" \\\n",
-    "          f\"{beamlet_ampl_s16q[ni]:12.1f} {beamlet_ampl_s16q_bits[ni]:6.1f} \" \\\n",
+    "          f\"{beamlet_sum_ampl_s16q[ni]:12.1f} {beamlet_sum_ampl_s16q_bits[ni]:6.1f} \" \\\n",
     "          f\"{BST_s16q[ni]:15e} {BST_s16q_dB[ni]:6.1f} {BST_s16q_bits[ni]:6.1f}, \" \\\n",
     "          f\"at {dBFS_16q:.1f} dBFS (= FS / {10**(-dBFS_16q/20):.0f})\")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "29e97579",
+   "metadata": {},
+   "source": [
+    "### 3.4.2 Icoherent, wide band, noise input"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 43,
    "id": "06c7b393",
    "metadata": {},
    "outputs": [
@@ -1023,8 +1182,8 @@
      "text": [
       "Incoherent white noise input level --> Expected beamlet level and BST level:\n",
       "\n",
-      "N_ant  si_sigma     beamlet_sigma        beamlet_sigma_re =         BST\n",
-      "                                         beamlet_sigma_im\n",
+      "N_ant  si_sigma        beamlet_sum_sigma    beamlet_sum_sigma_re =           BST\n",
+      "                                            beamlet_sum_sigma_im\n",
       "          value  #bits      value  #bits            value  #bits           value     dB  #bits\n",
       "    1    2048.0   11.0     2048.0   11.0           1448.2   10.5    8.192000e+11  119.1   39.6\n",
       "   12    2048.0   11.0     7094.5   12.8           5016.6   12.3    9.830400e+12  129.9   43.2\n",
@@ -1044,40 +1203,40 @@
     "# Beamlet level and BST level for incoherent white noise input (similar as for SST)\n",
     "\n",
     "# si_std = FS / 4\n",
-    "nbeamlet_sigma_fs4 = ni_sigma_fs4 * G_beamlet_sum_noise\n",
-    "nbeamlet_sigma_fs4_bits = np.log2(nbeamlet_sigma_fs4)\n",
-    "nbeamlet_sigma_re_fs4 = nbeamlet_sigma_fs4 / np.sqrt(N_complex)\n",
-    "nbeamlet_sigma_re_fs4_bits = np.log2(nbeamlet_sigma_re_fs4)\n",
-    "nBST_fs4 = nbeamlet_sigma_fs4**2 * N_int_sub\n",
+    "nbeamlet_sum_sigma_fs4 = ni_sigma_fs4 * G_beamlet_sum_noise\n",
+    "nbeamlet_sum_sigma_fs4_bits = np.log2(nbeamlet_sum_sigma_fs4)\n",
+    "nbeamlet_sum_sigma_re_fs4 = nbeamlet_sum_sigma_fs4 / np.sqrt(N_complex)\n",
+    "nbeamlet_sum_sigma_re_fs4_bits = np.log2(nbeamlet_sum_sigma_re_fs4)\n",
+    "nBST_fs4 = nbeamlet_sum_sigma_fs4**2 * N_int_sub\n",
     "nBST_fs4_dB = 10 * np.log10(nBST_fs4)\n",
     "nBST_fs4_bits = np.log2(nBST_fs4)\n",
     "\n",
     "# si_std = 16\n",
-    "nbeamlet_sigma_s16q = ni_sigma_s16q * G_beamlet_sum_noise\n",
-    "nbeamlet_sigma_s16q_bits = np.log2(nbeamlet_sigma_s16q)\n",
-    "nbeamlet_sigma_re_s16q = nbeamlet_sigma_s16q / np.sqrt(N_complex)\n",
-    "nbeamlet_sigma_re_s16q_bits = np.log2(nbeamlet_sigma_re_s16q)\n",
-    "nBST_s16q = nbeamlet_sigma_s16q**2 * N_int_sub\n",
+    "nbeamlet_sum_sigma_s16q = ni_sigma_s16q * G_beamlet_sum_noise\n",
+    "nbeamlet_sum_sigma_s16q_bits = np.log2(nbeamlet_sum_sigma_s16q)\n",
+    "nbeamlet_sum_sigma_re_s16q = nbeamlet_sum_sigma_s16q / np.sqrt(N_complex)\n",
+    "nbeamlet_sum_sigma_re_s16q_bits = np.log2(nbeamlet_sum_sigma_re_s16q)\n",
+    "nBST_s16q = nbeamlet_sum_sigma_s16q**2 * N_int_sub\n",
     "nBST_s16q_dB = 10 * np.log10(nBST_s16q)\n",
     "nBST_s16q_bits = np.log2(nBST_s16q)\n",
     "\n",
     "print(\"Incoherent white noise input level --> Expected beamlet level and BST level:\")\n",
     "print()\n",
-    "print(\"N_ant  si_sigma     beamlet_sigma        beamlet_sigma_re =         BST\")\n",
-    "print(\"                                         beamlet_sigma_im\")\n",
+    "print(\"N_ant  si_sigma        beamlet_sum_sigma    beamlet_sum_sigma_re =           BST\")\n",
+    "print(\"                                            beamlet_sum_sigma_im\")\n",
     "print(\"          value  #bits      value  #bits            value  #bits           value     dB  #bits\")\n",
     "for ni, na in enumerate(N_ant_arr):\n",
     "    print(f\"{na:5d} \" \\\n",
     "          f\"{ni_sigma_fs4:9.1f} {ni_sigma_fs4_bits:6.1f} \" \\\n",
-    "          f\"{nbeamlet_sigma_fs4[ni]:10.1f} {nbeamlet_sigma_fs4_bits[ni]:6.1f} \" \\\n",
-    "          f\"{nbeamlet_sigma_re_fs4[ni]:16.1f} {nbeamlet_sigma_re_fs4_bits[ni]:6.1f} \" \\\n",
+    "          f\"{nbeamlet_sum_sigma_fs4[ni]:10.1f} {nbeamlet_sum_sigma_fs4_bits[ni]:6.1f} \" \\\n",
+    "          f\"{nbeamlet_sum_sigma_re_fs4[ni]:16.1f} {nbeamlet_sum_sigma_re_fs4_bits[ni]:6.1f} \" \\\n",
     "          f\"{nBST_fs4[ni]:15e} {nBST_fs4_dB[ni]:6.1f} {nBST_fs4_bits[ni]:6.1f}\")\n",
     "print()\n",
     "for ni, na in enumerate(N_ant_arr):\n",
     "    print(f\"{na:5d} \" \\\n",
     "          f\"{ni_sigma_s16q:9.1f} {ni_sigma_s16q_bits:6.1f} \" \\\n",
-    "          f\"{nbeamlet_sigma_s16q[ni]:10.1f} {nbeamlet_sigma_s16q_bits[ni]:6.1f} \" \\\n",
-    "          f\"{nbeamlet_sigma_re_s16q[ni]:16.1f} {nbeamlet_sigma_re_s16q_bits[ni]:6.1f} \" \\\n",
+    "          f\"{nbeamlet_sum_sigma_s16q[ni]:10.1f} {nbeamlet_sum_sigma_s16q_bits[ni]:6.1f} \" \\\n",
+    "          f\"{nbeamlet_sum_sigma_re_s16q[ni]:16.1f} {nbeamlet_sum_sigma_re_s16q_bits[ni]:6.1f} \" \\\n",
     "          f\"{nBST_s16q[ni]:15e} {nBST_s16q_dB[ni]:6.1f} {nBST_s16q_bits[ni]:6.1f}, at {dBFS_16q:.1f} dBFS\")"
    ]
   },
@@ -1094,11 +1253,11 @@
    "id": "cdb20624",
    "metadata": {},
    "source": [
-    "## 3.6 Beamlet output\n",
+    "## 3.5 Beamlet output\n",
     "\n",
-    "The beamlet output is W_beamlet = 8 bit. The beamlet has a sign bit, about 1 bit for the sigma and about 2 bits to fit a range of 4 sigma, so about 4 bits can carry the noise signal. The extra 4 bits are for some RFI and to fit differences in subband noise level due to the antenna and RCU2 band filter shape, in case these differences are not fully equalized by the subband weights. The subband noise level can be equalized using the subband weights, to have more dynamic range for RFI the beamlet.\n",
+    "The beamlet output is W_beamlet = 8 bit. The beamlet has a sign bit, about 1 bit for the sigma and about 2 bits to fit a range of 4 sigma, so about 4 bits can carry the noise signal. The extra 4 bits are for some RFI and to fit differences in subband noise level due to the antenna and RCU2 band filter shape, in case these differences are not fully equalized by the subband weights. The subband noise level can be equalized using the subband weights, to have more dynamic range for RFI in the beamlet output.\n",
     "\n",
-    "Choosing FPGA_beamlet_output_scale_RW = 1 sqrt(N_ant) makes that the beamlet output level for noise input is equal to that of N_ant = 1 for all N_ant. The BST can be used to check whether the beamlet output will fit."
+    "Choosing FPGA_beamlet_output_scale_RW = 1 / sqrt(N_ant) makes that the beamlet output level for noise input is equal to that of N_ant = 1 for all N_ant. The BST can be used to check whether the beamlet output will fit."
    ]
   },
   {
@@ -1111,7 +1270,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 44,
    "id": "def6eba7",
    "metadata": {},
    "outputs": [
@@ -1119,11 +1278,42 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "ampl = 1.0\n",
+      "sigma = 4\n",
+      "sigma_y = 4.000000\n",
+      "sigma_z = 4.000000\n",
+      "mean_y = 0.000000\n",
+      "mean_z = -0.000000+0.000000j\n",
+      "\n",
       "The DFT of the sine plot shows:\n",
       ". G_fft_real_input_dc = 1\n",
       ". G_fft_real_input_sine = 0.5\n",
       "\n",
-      "The DFT of the block plot shows that the first harmonic has an amplitude of 4/pi * A/2 = 0.6364949321522198, which is larger than A / 2 = 0.5000000000000007 for sine input. Hence the bin samples need 1 bit more than for a full scale sine, because to also fit e.g. this harmonic of a block wave.\n"
+      "The DFT of the block plot shows that the first harmonic has an amplitude of 4/pi * A/2 = 0.6364949321522198, which is larger than A / 2 = 0.5000000000000007 for sine input. Hence the bin samples need 1 bit more than for a full scale sine, because to also fit e.g. this harmonic of a block wave.\n",
+      "\n",
+      "The rfft = fft without the negative frequencies.\n",
+      ". len(Y_fft) = 1024\n",
+      ". len(Y_rfft) = 513\n",
+      ". Y_fft[512-3:512] = \n",
+      "[ 0.00144621-0.01266498j -0.04157605+0.11406241j -0.0599346 +0.00323098j]\n",
+      ". Y_fft[512:512+3] = \n",
+      "[ 0.        +0.j         -0.0599346 -0.00323098j -0.04157605-0.11406241j]\n",
+      ". Y_rfft[0:3] = \n",
+      "[ 0.        +0.j         -0.0599346 -0.00323098j -0.04157605-0.11406241j]\n",
+      "\n",
+      "For the DFT of the real input noise the expected std() = 0.125000:\n",
+      ". mean(Y_fft) = -0.000981+0.000000j\n",
+      ". mean(Y_rfft) = -0.001093-0.006394j\n",
+      ". std(Y_fft) = 0.1249961482342099\n",
+      ". std(Y_rfft) = 0.12476274308800336\n",
+      ". rms(Y_fft) = 0.125\n",
+      ". rms(Y_rfft) = 0.12493127757893327\n",
+      "The slight difference with fft() and rfft() for std() and rms() results is due to that mean_Y_fft and mean_Y_rfft are not 0, so rms != std and due to that rfft has length N_fft//2 + 1.\n",
+      "\n",
+      "For the DFT of the complex input noise the expected std() = 0.125000:\n",
+      ". mean(Z_fft) = (-0.00032388896071808065-0.00165549535192123j)\n",
+      ". std(Z_fft) = 0.12498861720605157\n",
+      ". rms(Z_fft) = 0.125\n"
      ]
     },
     {
@@ -1152,7 +1342,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 720x288 with 2 Axes>"
       ]
@@ -1183,7 +1373,7 @@
     "N_bins = N_fft // 2 + 1  # positive frequency bins including DC and f_s/2\n",
     "\n",
     "# . select a bin\n",
-    "i_bin = 200   # bin index in range(N_bins)\n",
+    "cw_bin = 200   # carrier wave bin index in range(N_bins)\n",
     "dc_bin = 0  # DC\n",
     "\n",
     "# . time and frequency axis\n",
@@ -1197,23 +1387,42 @@
     "f_axis_rfft = f_axis[0:N_bins]  # positive frequency bins\n",
     "\n",
     "bw_bin = f_s / N_fft  # bin band width\n",
-    "f_bin = i_bin * bw_bin  # bin frequency\n",
+    "f_bin = cw_bin * bw_bin  # bin frequency\n",
     "\n",
-    "# . create sine at bin + DC, use cos to see DC at i_bin = 0  \n",
+    "# . create sine at cw_bin and with DC, use cos to create DC at dc_bin = 0  \n",
     "s = ampl * np.cos(2 * np.pi * f_bin * t_axis)\n",
     "dc = ampl * np.cos(2 * np.pi * dc_bin * t_axis)  # equivalent to dc = ampl\n",
     "noise = np.random.randn(N_fft)\n",
     "noise *= sigma / np.std(noise)  # apply requested sigma\n",
+    "noise -= np.mean(noise)  # apply zero mean to have std = rms for input\n",
     "b = ampl * np.sign(s)  # block wave, sign: -1 if x < 0, 0 if x==0, 1 if x > 0\n",
     "\n",
     "x = s + dc\n",
     "y = noise\n",
+    "mean_y = np.mean(y)\n",
+    "sigma_y = np.std(y)\n",
+    "\n",
+    "noise_complex = np.random.randn(N_fft) + np.random.randn(N_fft) * 1j\n",
+    "noise_complex *= sigma / np.std(noise_complex)  # apply requested sigma\n",
+    "noise_complex -= np.mean(noise_complex)  # apply zero mean to have std = rms for input\n",
+    "z = noise_complex\n",
+    "mean_z = np.mean(z)\n",
+    "sigma_z = np.std(z)\n",
+    "\n",
+    "print(f\"ampl = {ampl}\")\n",
+    "print(f\"sigma = {sigma}\")\n",
+    "print(f\"sigma_y = {sigma_y:.6f}\")\n",
+    "print(f\"sigma_z = {sigma_z:.6f}\")\n",
+    "print(f\"mean_y = {mean_y:.6f}\")\n",
+    "print(f\"mean_z = {mean_z:.6f}\")\n",
+    "print()\n",
     "\n",
     "# . DFT using complex input fft()\n",
     "S_fft = np.fft.fftshift(np.fft.fft(s) / N_fft)\n",
     "B_fft = np.fft.fftshift(np.fft.fft(b) / N_fft)\n",
     "X_fft = np.fft.fftshift(np.fft.fft(x) / N_fft)\n",
     "Y_fft = np.fft.fftshift(np.fft.fft(y) / N_fft)\n",
+    "Z_fft = np.fft.fftshift(np.fft.fft(z) / N_fft)\n",
     "\n",
     "# . DFT using real input rfft()\n",
     "S_rfft = np.fft.rfft(s) / N_fft\n",
@@ -1233,6 +1442,7 @@
     "plt.title('DFT of real input sine using rfft (SSB)')\n",
     "plt.plot(f_axis_rfft, abs(X_rfft))\n",
     "plt.grid()\n",
+    "plt.savefig('plots/lofar2_station_sdp_firmware_model_sine_spectrum.jpg', dpi=dpi)\n",
     "\n",
     "# Plot block spectrum\n",
     "# . DSB = double sideband\n",
@@ -1246,8 +1456,9 @@
     "plt.title('DFT of real input block using rfft (SSB)')\n",
     "plt.plot(f_axis_rfft, abs(B_rfft))\n",
     "plt.grid()\n",
+    "plt.savefig('plots/lofar2_station_sdp_firmware_model_block_spectrum.jpg', dpi=dpi)\n",
     "\n",
-    "# Plot noise spectrum\n",
+    "# Plot real input noise spectrum\n",
     "plt.figure(figsize=(10, 4))\n",
     "plt.subplot(1, 2, 1)\n",
     "plt.title('DFT of real input noise using fft (DSB)')\n",
@@ -1257,6 +1468,7 @@
     "plt.title('DFT of real input noise using rfft (SSB)')\n",
     "plt.plot(f_axis_rfft, abs(Y_rfft))\n",
     "plt.grid()\n",
+    "plt.savefig('plots/lofar2_station_sdp_firmware_model_noise_spectrum.jpg', dpi=dpi)\n",
     "\n",
     "print(\"The DFT of the sine plot shows:\")\n",
     "print(f\". G_fft_real_input_dc = {G_fft_real_input_dc}\")\n",
@@ -1267,12 +1479,49 @@
     "B_max = max(abs(B_rfft)) \n",
     "print(f\"The DFT of the block plot shows that the first harmonic has an amplitude of 4/pi * A/2 = {B_max}, \\\n",
     "which is larger than A / 2 = {S_max} for sine input. Hence the bin samples need 1 bit more than for a full \\\n",
-    "scale sine, because to also fit e.g. this harmonic of a block wave.\")"
+    "scale sine, because to also fit e.g. this harmonic of a block wave.\")\n",
+    "print()\n",
+    "\n",
+    "print(\"The rfft = fft without the negative frequencies.\")\n",
+    "print(f\". len(Y_fft) = {len(Y_fft)}\")\n",
+    "print(f\". len(Y_rfft) = {len(Y_rfft)}\")\n",
+    "print(f\". Y_fft[512-3:512] = \\n{Y_fft[512-3:512]}\")\n",
+    "print(f\". Y_fft[512:512+3] = \\n{Y_fft[512:512+3]}\")\n",
+    "print(f\". Y_rfft[0:3] = \\n{Y_rfft[0:3]}\")\n",
+    "print()\n",
+    "\n",
+    "mean_Y_fft = np.mean(Y_fft)\n",
+    "mean_Y_rfft = np.mean(Y_rfft)\n",
+    "sigma_Y_fft = np.std(Y_fft)\n",
+    "sigma_Y_rfft = np.std(Y_rfft)\n",
+    "rms_Y_fft = np.sqrt(sigma_Y_fft**2 + np.abs(mean_Y_fft)**2)\n",
+    "rms_Y_rfft = np.sqrt(np.sum(np.abs(Y_rfft)**2) / (N_fft // 2 + 1))  # equivalent\n",
+    "rms_Y_rfft = np.sqrt(sigma_Y_rfft**2 + np.abs(mean_Y_rfft)**2)   # equivalent\n",
+    "\n",
+    "print(f\"For the DFT of the real input noise the expected std() = {sigma_y / np.sqrt(N_fft):.6f}:\")\n",
+    "print(f\". mean(Y_fft) = {mean_Y_fft:.6f}\")\n",
+    "print(f\". mean(Y_rfft) = {mean_Y_rfft:.6f}\")\n",
+    "print(f\". std(Y_fft) = {sigma_Y_fft}\")\n",
+    "print(f\". std(Y_rfft) = {sigma_Y_rfft}\")\n",
+    "print(f\". rms(Y_fft) = {rms_Y_fft}\")\n",
+    "print(f\". rms(Y_rfft) = {rms_Y_rfft}\")\n",
+    "print(\"The slight difference with fft() and rfft() for std() and rms() results is due to that mean_Y_fft \\\n",
+    "and mean_Y_rfft are not 0, so rms != std and due to that rfft has length N_fft//2 + 1.\")\n",
+    "print()\n",
+    "\n",
+    "mean_Z_fft = np.mean(Z_fft)\n",
+    "sigma_Z_fft = np.std(Z_fft)\n",
+    "rms_Z_fft = np.sqrt(np.sum(np.abs(Z_fft)**2) / N_fft)  # equivalent\n",
+    "rms_Z_fft = np.sqrt(sigma_Z_fft**2 + np.abs(mean_Z_fft)**2)  # equivalent\n",
+    "print(f\"For the DFT of the complex input noise the expected std() = {sigma_z / np.sqrt(N_fft):.6f}:\")\n",
+    "print(f\". mean(Z_fft) = {mean_Z_fft}\")\n",
+    "print(f\". std(Z_fft) = {sigma_Z_fft}\")\n",
+    "print(f\". rms(Z_fft) = {rms_Z_fft}\")\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 45,
    "id": "2e386180",
    "metadata": {},
    "outputs": [
@@ -1294,9 +1543,9 @@
     },
     {
      "data": {
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\n",
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\n",
       "text/plain": [
-       "<Figure size 720x288 with 1 Axes>"
+       "<Figure size 864x576 with 1 Axes>"
       ]
      },
      "metadata": {
@@ -1315,7 +1564,7 @@
     "# . The amplitude of the phasor is equal to the std() of a rotating phasor.\n",
     "# . The amplitude of the bin phasor is A/2, so the bin phasor A/2 exp(jwt) has power\n",
     "#   (A/2)**2\n",
-    "# . The total power in the N_sidebands = 2 bins is eual to the input power as expected\n",
+    "# . The total power in the N_sidebands = 2 bins is equal to the input power as expected\n",
     "\n",
     "# . input sine\n",
     "sin_std = np.std(s)\n",
@@ -1337,10 +1586,11 @@
     "bin_phasor = bin_ampl * np.exp(2 * np.pi * 1j * bw_bin * t_axis)\n",
     "bin_std = np.std(bin_phasor)\n",
     "\n",
-    "plt.figure(figsize=(10, 4))\n",
+    "plt.figure(figsize=(12, 8))\n",
     "plt.title('Bin phasor to model bin_std')\n",
     "plt.plot(t_axis, bin_phasor.real, 'r', t_axis, bin_phasor.imag, 'b')\n",
     "plt.grid()\n",
+    "plt.savefig('plots/lofar2_station_sdp_firmware_model_bin_phasor.jpg', dpi=dpi)\n",
     "\n",
     "print(f\"sine bin ampl = {bin_ampl:.4f}\")\n",
     "print(f\"sine bin re = {bin_re:.4f}\")\n",
@@ -1352,7 +1602,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 46,
    "id": "97e9a32d",
    "metadata": {},
    "outputs": [
@@ -1360,21 +1610,22 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "noise sigma = 4.0000 (= 4.0000)\n",
-      "noise power = 16.0000 (= 16.0342)\n",
+      "noise mean = 0.000000)\n",
+      "noise sigma = 4.000000\n",
+      "noise var = 16.000000)\n",
+      "noise power = 16.000000)\n",
       "\n",
       "N_fft = 1024\n",
       "sqrt(N_fft) = 32.0\n",
-      "sigma / std(Y_fft) = 31.979117\n",
-      "sigma / std(Y_rfft) = 31.988146\n",
+      "sigma / std(Y_fft) = 32.000986\n",
+      "\n",
+      "noise bin std(fft) = 0.124996\n",
+      "noise bin power = 0.015625\n",
+      "noise bins power = 16.000000 (= noise power)\n",
       "\n",
-      "noise bin std (fft) = 0.125082\n",
-      "noise bin std (rfft) = 0.125046\n",
-      "noise bin.re std = 0.091900\n",
-      "noise bin.im std = 0.084799\n",
-      "noise bin power = 0.015637\n",
-      "noise bin.re power + bin.im power = 0.015637\n",
-      "noise bins power = 16.011861 (= 16.034232)\n",
+      "noise bin.re std = 0.084893\n",
+      "noise bin.im std = 0.091746\n",
+      "noise bin.re power + bin.im power = 0.015625 (= bin power)\n",
       "\n",
       "The ratio of real input noise std and DFT bin noise std shows:\n",
       ". G_fft_real_input_noise = 0.03125 = (1 / sqrt(1024))\n"
@@ -1384,37 +1635,49 @@
    "source": [
     "# Log sigma and power of the noise bins (incoherent signal input)\n",
     "\n",
-    "# . input noise\n",
+    "# . real input noise\n",
+    "noise_mean = np.mean(y)\n",
     "noise_std = np.std(y)\n",
+    "noise_var = noise_std**2\n",
     "noise_power = np.sum(y**2) / N_fft\n",
-    "print(f\"noise sigma = {noise_std:.4f} (= {sigma:.4f})\")\n",
-    "print(f\"noise power = {noise_std**2:.4f} (= {noise_power:.4f})\")\n",
+    "print(f\"noise mean = {noise_mean:f})\")\n",
+    "print(f\"noise sigma = {noise_std:f}\")\n",
+    "print(f\"noise var = {noise_var:f})\")\n",
+    "print(f\"noise power = {noise_power:f})\")\n",
     "print()\n",
     "\n",
     "# . fft bin\n",
     "# . The white noise will appear equally in all bins, therefore the bin_std can\n",
     "#   be modelled by averaging over all bins. This however does cause small \n",
-    "#   differences in fft input and output power, due to the DC bin (?)\n",
-    "bin_std = np.std(Y_rfft)\n",
-    "bin_power = bin_std**2\n",
-    "bin_re_std = np.std(Y_rfft.real)\n",
-    "bin_re_power = bin_re_std**2\n",
-    "bin_im_std = np.std(Y_rfft.imag)\n",
-    "bin_im_power = bin_im_std**2\n",
+    "#   differences in fft input and output std, due to that fft output mean != 0,\n",
+    "#   so rms != std.\n",
+    "bin_std = np.std(Y_fft)\n",
+    "bin_var = bin_std**2\n",
+    "bin_mean = np.mean(Y_fft)\n",
+    "bin_power = bin_var + np.abs(bin_mean)**2\n",
     "bins_power = bin_power * N_fft\n",
     "\n",
+    "bin_re_mean = np.mean(Y_fft.real)\n",
+    "bin_re_std = np.std(Y_fft.real)\n",
+    "bin_re_var = bin_re_std**2\n",
+    "bin_re_power = bin_re_var + np.abs(bin_re_mean)**2\n",
+    "bin_im_mean = np.mean(Y_fft.imag)\n",
+    "bin_im_std = np.std(Y_fft.imag)\n",
+    "bin_im_var = bin_im_std**2\n",
+    "bin_im_power = bin_im_var + np.abs(bin_im_mean)**2\n",
+    "\n",
+    "\n",
     "print(f\"N_fft = {N_fft}\")\n",
     "print(f\"sqrt(N_fft) = {np.sqrt(N_fft)}\")\n",
-    "print(f\"sigma / std(Y_fft) = {sigma / np.std(Y_fft):f}\")\n",
-    "print(f\"sigma / std(Y_rfft) = {sigma / bin_std:f}\")\n",
+    "print(f\"sigma / std(Y_fft) = {sigma / bin_std:f}\")\n",
+    "print()\n",
+    "print(f\"noise bin std(fft) = {bin_std:f}\")\n",
+    "print(f\"noise bin power = {bin_power:f}\")\n",
+    "print(f\"noise bins power = {bins_power:f} (= noise power)\")\n",
     "print()\n",
-    "print(f\"noise bin std (fft) = {np.std(Y_fft):f}\")\n",
-    "print(f\"noise bin std (rfft) = {bin_std:f}\")\n",
     "print(f\"noise bin.re std = {bin_re_std:f}\")\n",
     "print(f\"noise bin.im std = {bin_im_std:f}\")\n",
-    "print(f\"noise bin power = {bin_power:f}\")\n",
-    "print(f\"noise bin.re power + bin.im power = {bin_re_power + bin_im_power:f}\")\n",
-    "print(f\"noise bins power = {bins_power:f} (= {noise_power:f})\")\n",
+    "print(f\"noise bin.re power + bin.im power = {bin_re_power + bin_im_power:f} (= bin power)\")\n",
     "\n",
     "print()\n",
     "print(\"The ratio of real input noise std and DFT bin noise std shows:\")\n",
@@ -1428,8 +1691,16 @@
    "source": [
     "Conclusion:\n",
     "* For coherent sine input is easiest to calculate power via the amplitude of the single bin phasor\n",
-    "* For incoherent white noise input is easiest to calculate power via the std of all bins"
+    "* For incoherent white noise input is easiest to calculate power via the std of all bins, but this is an approximation because the mean of the input and the output bins will typically not be exactly 0."
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "247ae61b",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
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diff --git a/applications/lofar2/model/signal_statistics.ipynb b/applications/lofar2/model/signal_statistics.ipynb
index daea338f5256a4e615e15cfe0d07212a77322f2e..088fe13610733b6a820e6b521e86f5d7f76df161 100644
--- a/applications/lofar2/model/signal_statistics.ipynb
+++ b/applications/lofar2/model/signal_statistics.ipynb
@@ -12,7 +12,7 @@
     "Purpose: Model the SNR of a beamformer and a correlator\n",
     "\n",
     "Description:\n",
-    "* SNR: This model shows two different SNR measures. one regarding the 'coherent' SNR of the coherent signal versus the incoherent signal (e.g. in a voltage beamformer, in a correlator) that indicates the strength of the coherent signal with respect to the incoherent noise, and one regarding the 'incoherent' SNR of the power measurement itself, that indicates the accuracy if the measured power (e.g. in power statistics, in a powers beamformer). The 'coherent' SNR makes use of phase information of the input voltage signals. The 'incoherent' SNR is based on input powers, so the input phase information is lost, and therefore the 'incoherent' SNR can only improve the accuracy of the mean power measurement, it cannot improve (distinguish) between signal and noise.\n",
+    "* SNR: This model shows two different SNR measures. one regarding the 'coherent' SNR of the coherent signal versus the incoherent signal (e.g. in a voltage beamformer, in a correlator) that indicates the strength of the coherent signal with respect to the incoherent noise, and one regarding the 'incoherent' SNR of the power measurement itself, that indicates the accuracy if the measured power (e.g. in power statistics, in a powers beamformer). The 'coherent' SNR makes use of phase information of the input voltage signals. The 'incoherent' SNR is based on input powers, so the input phase information is lost, and therefore the 'incoherent' SNR can only improve the accuracy of the mean power measurement, it cannot improve (distinguish) between coherent signal and incoherent noise.\n",
     "* Coherent summator (sums voltages, e.g. voltage beamformer): The 'coherent' SNR of coherent input versus the incoherent input improves by the number of inputs N.\n",
     "* Incoherent summator (sums powers, e.g. auto power statistics, power beamformer): The 'coherent' SNR of coherent input versus incoherent input does not improve, because the coherent phase information is lost in the powers. However, the accuracy of mean power measurement, so the 'incoherent' SNR, does improve by factor N, because the std of the mean power measurement reduces by N.\n",
     "* Correlator: The 'coherent' SNR of coherent input versus the incoherent input improves by sqrt(N) for integration over N cross powers in time. The mean correlation of the coherent input is constant and non-zero. The mean correlation of the incoherent input is zero. The power of the mean correlation of the incoherent input reduces by N, so the std of the mean correlation of the incoherent input reduces by sqrt(N). For example, if the input SNR of the input signal is -20 dB (i.e. sigma_coh / sigma_sys = 0.1), then it takes integration over N = 10000 cross powers in time to improve the SNR of the correlator output by a factor 100 = +20 dB to 0 dB.\n",
@@ -38,7 +38,9 @@
    "outputs": [],
    "source": [
     "import numpy as np\n",
-    "import matplotlib.pyplot as plt"
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "dpi = 254  # 10 dots per mm"
    ]
   },
   {
@@ -58,7 +60,7 @@
     "\n",
     "If mean = 0 then var = power and std = rms.\n",
     "\n",
-    "For a complex signal (like subbands and beamlets):\n",
+    "For a complex signal (like subbands and beamlets), assume mean complex = 0 so rms = std and power = var (= std^2):\n",
     "\n",
     "* power complex = power real + power imag = (std real)^2 + (std imag)^2\n",
     "* power real = power imag = power complex / 2\n",
@@ -103,9 +105,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "mean(si) = -0.199139, expected -0.2\n",
+      "mean(si) = -0.195478, expected -0.2\n",
       "std(si) = 0.500000, expected 0.5\n",
-      "rms(si) = 0.538197, expected 0.538516\n"
+      "rms(si) = 0.536854, expected 0.538516\n"
      ]
     }
    ],
@@ -162,9 +164,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 864x576 with 1 Axes>"
       ]
      },
      "metadata": {
@@ -174,9 +176,9 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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uFsOZPRsOOKCYya6G7QO1Aky+JUnS4FSptCRz6fu+2D5QK8DkW5IkDS7dpSazZy//2O6uJj1nw20fqBVk8i1Jktpfd8IdUZSWVJN4Q1HPnQlnn+3FleoXthqUJEntrWfbwGpKSrp1l5ZMmGCyrX7hzLckSWpvfbUNLGdpierA5FuSJLW3arqSdHRYWqK6sOxEkiS1tw03hIcf7n1/9wy3pSWqA2e+JUlS+ylfPOexx5bd311i4gy36syZb0mS1F56XmC5aFHRMnCddeDJJ4uLKLtnuqU6M/mWJEntpdIFlosWwYgR8PjjjYlJKrHsRJIktZfeLrB0OXg1AZNvSZLUmsrruseMgcMOK+576+PtcvBqApadSJKk1tOzrnv2bDjppN6Pt2e3moQz35IkqTWUz3QfeGB1C+eAHU3UVJz5liRJza9SB5NqRMCsWQMWllQrZ74lSVLzq3aJ+J6s81aTMfmWJEnNq7vUZPbs2s+1zltNyORbkiQ1p+5Sk74S76FDi9KSjg741KeK++7n1nmrCVnzLUmSmkdnZ1FiMmdOcWFlX7Xdw4ebYKvlOPMtSZIao7OTt+6339J9urtnujP7Tryd2VaLcuZbkiTVX6mkZPVq+3SX6+iwg4laVtPNfEfEtyPioYi4tXTbrZfjdo2Iv0fEvRHx1XrHKUmSVsKKdi/xIkq1uKZLvkuOzcxtS7dLe+6MiKHAicB7gS2Aj0TEFvUOUpIkraA5c6o/tvyiSktN1OKaNflenjcD92bm/Zn5IjAN2LPBMUmSpGr11n87Yunnw4fDmWfC4sVFqYmJt1pcsybfh0fE7RFxekSsW2H/JsADZc8fLG2TJEmt4H3vW3bb8OFw6KG2C1Rbi8ys/5tGTAc2rLBrMnAD8DiQwPeAjTLzEz3O3xvYNTMnlp4fALwlMw+v8F6TgEkAo0aN2n7atGn9+VGqMnfuXEaMGFH3921VjlftHLPaOF61cbxq43gtscH06bzy1FNZ7bHHWLDBBjz+1rey/g03sNqjj0IEL669NotWXZU1Hn+cBRtswP0TJ/LYLrs0Ouym5verNo0cr/Hjx9+cmWN7bm9I8l2tiBgD/CEz39Bj+9uAb2fme0rPvwaQmT/s6/XGjh2bM2bMGKBoe9fV1cW4cePq/r6tyvGqnWNWG8erNo5XbRyvku4Fcvq6qHKNNbjryCPZ4vvfr19cLc7vV20aOV4RUTH5brqyk4jYqOzpB4A7Kxx2E/CaiHhFRAwD9gMurkd8kiSpCtV0M5k/n1eeemp94pGaRDP2+T4qIralKDuZBRwCEBEbA6dm5m6ZuTAiDgcuB4YCp2fmzAbFK0mSeqqym8lqjz02wIFIzaXpku/MPKCX7f8Gdit7fimwTBtCSZLUAOXLwm+0UXHBZBWlrQs22IDV6xCe1CyaruxEkiS1mO767u5l4f/976I14Kqr9n3e8OHcP3FifWKUmoTJtyRJWjm91Xe/7GVLtw381KeWaSNodxMNNk1XdiJJklpMb/XdTz4Jjz/e97ldXf0ejtTMnPmWJEm16+yEMWNgyJBlV6Xs1tsqltIg5sy3JEmqTc8e3pUurBw+HKZMqW9cUgtw5luSJPWtfJZ7zBg44ojKNd5Dh7osvLQcznxLkqTe9Zzlnj2792MXLy5uknrlzLckSepdNStVdrPGW1ouk29JkrREeYnJ6NF9z3SXs8ZbqorJtyRJKvRcLOeBB3o/duTIZXp2W+MtLZ8135IkqdBbiUnPpeKHD4fjjjPZllaAM9+SJA1W5SUmHR29l5hkOsst9RNnviVJGox6djHpbZVKKBLuWbPqEpbU7pz5liRpMOqrxKScF1JK/crkW5Kkwai3mW5LTKQBZdmJJEmD0dprw9NPL7vdEhNpQDnzLUnSYHPGGUXiPXTo0tstMZEGnMm3JEntrryryQYbwCc/Ce96F5x2miUmUp1ZdiJJUjvr2dXkP/8pkvB994UDDyxukurGmW9JktpJ+Sz3mDFwxBHLdjVZvBi+971GRCcNes58S5LULnrOcve2aA703ddb0oBx5luSpHbRW+/uSkaPHthYJFVk8i1JUruodjbbriZSw5h8S5LUispruzfZBLbbrlggp5KRI+1qIjUJa74lSWo1PWu7//3v4vbWt8Ltty9dejJ8OBx3nMm21CSc+ZYkqdV8/euVa7sffriY1XaWW2paJt+SJDWznq0Df/jD3mu758wpEu1Zs4p2grNmmXhLTcayE0mSmlWl1oFf/3oxq12pvtsOJlLT6zX5jog3VnH+S5l5Rz/GI0mSuvXWOnCddWDBgmVru+1gIjW9vma+rwFuAqKPY14BjOnPgCRJUklv5SVPPw1nn10k53PmFDPeU6ZYYiK1gL6S75syc+e+To6Iq/o5HkmSBp/OzqUT6SOPhBtv7L114OjRRaJtsi21nF6T7+Ul3tUeI0mS+lCprvuII2CVVWDPPeGKK2D+/CXHW14itbQ+u51ExCoREaXHm0XE3hGxXX1CkyRpEOitrnvUKPh//w9OOcXWgVIb6TX5joiDgceA2aXHVwJ7A9Mi4it1ik+SpPbWW133v/9d3Ns6UGorfdV8fw54FbAWcDfQkZmPR8Rwigsxfzzw4UmS1OY22AAefXTZ7bYNlNpSX8n3i5n5FPBURNybmY8DZOa8iHixPuFJktTGurrgySeX7dttXbfUtvqq+V4jIraLiO2BYaXHbyw9X71O8UmS1J66uuB974PXvAZOOMG6bmmQ6Gvm+xHgmAqPu59LkqRalLcUBNhoI7jqquLiysMOa2xskuqir1aD4+oYhyRJ7a1nS0GAp56C6dOd5ZYGkb66nXywr1s9g5QkqSV0dvLW/faDIUNgzJgi4YaiU8kXvrBsS8H584uZcEmDRl9lJ3uU7jcA3g50r2Y5HvgLcMFABBQR5wKvLT1dB3g6M7etcNws4DlgEbAwM8cORDySJFWlNLO9evliOQcfDBdcAH/7W+WOJtB7q0FJbamvspOPA0TEFcAWmflw6flGwK8GKqDM3Lf7cUT8FHimj8PHd3dhkSSpoSotljN/fpF877ILPPccPPHEsufZUlAaVPpc4bJks+7Eu+RRYMB/UpRW1twHOGeg30uSpKp1dhYlJeWlJS+80PsMdgT86U9w3HFFC8FythSUBp1qku8rI+LyiDgoIg4CLgGmD2xYAPwP8Ghm/rOX/QlcERE3R8SkOsQjSRrsui+anD276Ms9ezYceCCst97SfbrLdc9sT5hQtBC0paA0qEX29sOi/KCIDwA7lp5em5kXrtSbRkwHNqywa3JmXlQ65iTg3sz8aS+vsUlmPhQRGwB/Aj6TmddWOG4SMAlg1KhR20+bNm1lQl8hc+fOZcSIEXV/31bleNXOMauN41Ubx2uJt+63H6tXqN1euPrqPLTXXmx64YUMXbDgv9sXrbYaf//iF3lsl13qGWZL8ftVG8erNo0cr/Hjx99c6ZrEqpLveouIVYCHgO0z88Eqjv82MDczf9LXcWPHjs0ZM2b0T5A16OrqYty4cXV/31bleNXOMauN41Ubx6vMkCGVZ7gjio4mnZ288IUvsPpjjxUz3lOmOLO9HH6/auN41aaR4xURFZPvvloN/qGKF13uMStoF+Ce3hLviFgzItbqfgy8G7hzgGKRJKlIutdaq/K+stKSG6ZNKxLxWbNMvCUto69WgztExMV97A9gi36Op9t+9LjQMiI2Bk7NzN2AUcCFxTWZrAL8JjMvG6BYJEmC//1fePZZWGUVWLhwyXYvmpRUg76S7z2rOP/F/gqkXGYeVGHbv4HdSo/vB7YZiPeWJGkZ3/tecZs4EXbaCb7xjaK7iaUlkmrUV5/va+oZiCRJTenHP4ZvfavoavLLXxZ13/vv3+ioJLWoaloNSpI0eJT38V5vPfjqV+GjH4XTTiu2SdJK6KvsRJKkwaW7j3f3SpVPPQVDh8Kuuxb3krSS/C+8JEndKi0Rv2gRfPObjYlHUtvpdeY7Iu6gWEVymV1AZubWAxaVJEmN0NsS8b1tl6Qa9VV2snvdopAkqZEy4dRTe9/f3cdbklZSX91OZtczEEmSGuL+++Hgg+Gqq+D1r4d//QteeGHJfvt4S+pHy635joi3RsRNETE3Il6MiEUR8Ww9gpMkqV+VdzLp6IADDoCttoKbbiraCN55ZzED3tFRLBnf0QFTp9rHW1K/qabbyQkUK07+FhgLfAzYfCCDkiSp3/XsZDJnDvz617DNNvD738NmmxXbJ0ww2ZY0YKrqdpKZ9wJDM3NRZp4B7DqwYUmS1M8qdTKBop1gd+ItSQOsmpnveRExDLg1Io4CHsYWhZKkVtNbx5IHHqhvHJIGtWqS6ANKxx0OPA9sBnxoIIOSJKnfrb125e12MpFUR8ud+S7revIC8J2BDUeSpH6WWZScPP10sUrlokVL9tnJRFKdVdPt5B0R8aeI+EdE3N99q0dwkiStlMWL4bOfhR/+sLjY8owz7GQiqaGqqfk+Dfg8cDOwaDnHSpLUHBYuhIkT4cwz4QtfgKOPLpLuAw5odGSSBrFqku9nMvOPAx6JJEn9ZcGCYkb7d7+D734XvvGNIvGWpAarJvm+OiKOBi4AFnRvzMy/DVhUkiTVqrOzqO2eMwdWW61YpfLYY+Fzn2t0ZJL0X9Uk328p3Y8t25bAzv0fjiRJK6DnAjovvADDhsHLX97YuCSph2q6nYyvRyCSJK2wr3xl2QV0XnyxmAn3gkpJTWS5yXdEHFlh8zPAzZl5a79HJElSte66C37yE3joocr7e1tYR5IapJpFdsYChwKblG6HUCwvf0pEfHkAY5MkqSgpGTMGhgwp7js74dprYY89YMstYdo0GDGi8rkuoCOpyVSTfG8KvDEzv5CZXwC2BzYAdgQOGsDYJEmDXXct9+zZxWI5s2fDxz4GO+0EN9wA3/lOMbt98snFgjnlXEBHUhOq5oLLDSjrcgK8BIzKzPkRsaCXcyRJWnmTJy9by714May3XpGIdyfc3XXd3d1ORo8uEm/rvSU1mWqS707gxoi4qPR8D+A3EbEmcNeARSZJGtxuuqlIsCt56qllZ7onTDDZltT0llt2kpnfAyYBT5duh2bmdzPz+cz0p5wkacVUquV+/nk49VQYOxbe/ObeF8axlltSi+p15jsiXpaZz0bEesD9pVv3vvUy88l6BChJakM9+3LPng0HHQSrrgrz58Mb3gAnnlj06j7iiKVLT6zlltTC+io7+Q2wO3AzxaI63aL0/JUDGJckqZ1VquVeuLBItq+7Dt7xjiWz3musYS23pLbRa/KdmbuX7l9Rv3AkSYNCb/2358+HHXZYepu13JLayHJrviPiHaWLK4mI/SPimIiw2E6StGIWLYI116y8z1puSW2umj7fJwHzImIb4AvAfcDZAxqVJKk9vfAC7LMPzJ0Lq/T45au13JIGgWqS74WZmcCewAmZeSKw1sCGJUlqO089Be9+N1xwARx7LPzqV9DRUdR2d3TA1KmWl0hqe9X0+X4uIr4G7A/sGBFDgFUHNixJUlt54AHYdVe4995iOfh99y22m2xLGmSqmfnel2KFy09m5iMUy80fPaBRSZLax513wtveBg8+CJddtiTxlqRBaLkz36WE+5iy53OAswYyKElSm7jmGthzz6Ke+7rrYOutGx2RJDVUNTPfkiTV7vzzixrvjTaCv/7VxFuSMPmWJPWX8uXi11sPPvzhYpn4668vLqiUJFV1wSURsQYwOjP/PsDxSJJaUc/l4p96CoYOhYkTi0RckgRUt8jOHsCtwGWl59tGxMUDHJckqZV8/evLLhe/aBF85zuNiUeSmlQ1ZSffBt4MPA2QmbcCLjkvSYLFi+HCC3tfLr637ZI0SFWTfL+Umc/02JYDEYwkqUUsXlxcULnddvDBDy67WmU3l4uXpKVUk3zPjIiPAkMj4jUR8XPgLwMclySpGS1eDOedB9tsU1xQ+cILcPbZcPrpRTvBci4XL0nLqCb5/gywJcVCO+cAzwKfW9k3jogPR8TMiFgcEWN77PtaRNwbEX+PiPf0cv4rIuLG0nHnRsSwlY1JklSm1L1kp513LrqVHH44bLVVsUjOwoXF/rvugv33hwMOKJaHd7l4SepTNYvszAMml2796U7gg8AvyzdGxBbAfhQJ/8bA9IjYPDMX9Tj/x8CxmTktIk4GPgmc1M8xStLgVNa9JKCo3T7xRNh4YzjnnGLWe+jQpc+ZMMFkW5KWY7nJd0T8nmVrvJ8BZgC/zMwXVuSNM/Pu0uv33LUnMC0zFwD/ioh7KS74/GtZTAHsDHy0tOlMigtDTb4lqT9U6l4CRW33fvvVPx5JahPV9Pm+H3g5RckJwL7Ac8DmwCnAAf0c0ybADWXPHyxtKzcSeDozF/ZxDAARMQmYBDBq1Ci6urr6NdhqzJ07tyHv26ocr9o5ZrVxvPqwaBEbdHXx+jlzWGZqBMgHHuAax65Pfr9q43jVxvGqTTOOVzXJ99sz801lz38fETdl5psiYmZfJ0bEdGDDCrsmZ+ZFtQS6ojJzKjAVYOzYsTlu3Lh6vO1Surq6aMT7tirHq3aOWW0crwoWLYLf/ha++124+25YdVV46aVlDovRox275fD7VRvHqzaOV22acbyqSb5HRMTozJwDEBGjgRGlfS/2dWJm7rICMT0EbFb2fNPStnJPAOtExCql2e9Kx0iSlqe7ZeB3vlNcPLnFFnDuufDii3DIIUuXnti9RJJWWjXdTr4A/Dkiro6ILuA64IsRsSZFrXV/uxjYLyJWi4hXAK8B/q/8gMxM4Gpg79KmA4G6zKRLUltYvLiY6d5666J7SSZMmwa33w777FN0MCl1L0m7l0hSv1lu8p2Zl1IkwJ8DjgBem5mXZObzmfmzFX3jiPhARDwIvA24JCIuL73fTOA84C6KJe0/3d3pJCIujYiNSy/xFeDI0gWZI4HTVjQWSWprpZaBDBlSJNFHHFH06d5nn6Lc5Jxz4I47iiS8vIPJhAkwaxbXXHUVzJpl4i1J/aCashMoku/XAqsD20QEmXnWyrxxZl4IXNjLvinAMr/bzMzdyh7fT9EFRZLUm7KWgUDRMvD442HDDYt9PRNuSdKAqqbV4P8C44AtgEuB9wJ/BlYq+ZYk1cHkyZVbBg4bBh/96LLbJUkDqpqa772BdwKPZObHgW2AtQc0KklS/5gzp/L2Bx6obxySJKC65Ht+Zi4GFkbEy4DHWLobiSSp2WTCSScV95WMHl3feCRJQHXJ94yIWIdiQZ2bgb9RttqkJKnJvPgiHHooHHZYcWHlGmssvd+WgZLUMNV0OzksM5/OzJOBdwEHlspPJEnN5tFHYeedi7aAX/sa3HwznHJK0eXEloGS1HDVXHB5ZWa+EyAzZ/XcJklqEjNmwAc+AE88UfTs3nffYvuECSbbktQkek2+I2J1YDiwfkSsC0Rp18uATeoQmySpWp2dMHEibLABXH89bLddoyOSJFXQ18z3IRQL62xMUefd7VnghAGMSZJUrUWL4KtfhZ/8BHbcsVgq/uUvb3RUkqRe9Jp8Z+ZxwHER8ZnM/HkdY5IkVeOpp+AjH4HLLy8urvzZz2DVVRsdlSSpD32VnXyw9PChssf/lZkXDFhUkqS+3X03vP/9MHt2cQHlwQc3OiJJUhX6KjvZo499CZh8S1I9dHYWK1XOmVP05/7Qh4oOJmusAVdfDe94R6MjlCRVqa+yE9sJSlKjdXbCpElLloifPRuOOQbGjIFrr4XNXPNMklrJcvt8R8TaEXFMRMwo3X4aES4vL0n1MHnyksS73KJFJt6S1IKqWeHydOA5YJ/S7VngjIEMSpJUMmdO5e0PPljfOCRJ/WK5i+wAr8rMD5U9/05E3DpA8UiSut1yCwwbBgsWLLtv9Oj6xyNJWmnVzHzPj4gdup9ExDuA+QMXkiQNcv/5DxxyCGy/fdE6cNiwpfcPHw5TpjQmNknSSqkm+f4UcGJEzIqIWRQL7BwyoFFJ0mD00ktw/PGw+eZw2mlwxBHwwANw+unQ0QERxf3UqS4XL0ktqpqykzsyc5uIeBlAZj47wDFJ0uBz5ZVFsj1zJuyyCxx3HGyxRbFvwgSTbUlqE9XMfP8rIqYCb6K48FKS1F/+9a+ib/cuuxRdTS68EK64YkniLUlqK9Uk368DpgOfpkjETyivAZckrYDnn4dvfQte/3q47DL4/vfhrrtgr72K8hJJUltabtlJZs4DzgPOi4h1geOAa4ChAxybJLWfTDj3XPjSl4p2gR/5CBx1FGy6aaMjkyTVQTUz30TEThHxC+BmYHWKft+SpOXp7CxWoxwyBDbeuJjp/shHYP31ixUqf/MbE29JGkSWO/Nd6nByC8Xs95cy8/mBDkqS2kLPpeEffri4feITRceSof4CUZIGm2q6nWxthxNJWgFf/3rlpeGvvNLEW5IGqeWWnZh4S9IKmDOn96Xhe9suSWp7VdV8S5KqtHgxnHQSbLll711LXBpekgatXpPviDiidP+O+oUjSS3s3nth553hsMPgLW+BY44ploIv59LwkjSo9TXz/fHS/c/rEYgktaxFi4pEe+ut4ZZb4NRT4U9/gs99rriw0qXhJUklfV1weXdE/BPYOCJuL9seQGbm1gMbmiS1gLvuKrqX3Hgj7LFHUXKyySZL9rs0vCSpTK/Jd2Z+JCI2BC4H3l+/kCSpBbz0UrE4zne/C2utVfTr3m8/V6eUJPWpz1aDmfkIsE1EDAM2L23+e2a+NOCRSVKzuuWWYrb71lthn33g5z+HDTZodFSSpBaw3G4nEbET8E/gROAXwD8iYseBDkySms6CBfCNb8Cb3gSPPAIXXFAsFW/iLUmqUjWL7BwDvDsz/w4QEZsD5wDbD2RgktRUbrihmO2++2446KDiAst11210VJKkFlNNn+9VuxNvgMz8B7DqwIUkSU1k3jw48kh4+9th7lz44x/hjDNMvCVJK6Sa5HtGRJwaEeNKt1OAGQMdmCTVXWcnjBkDQ4YU95Mnw1ZbwbHHwqGHwp13wq67NjpKSVILq6bs5FPAp4HPlp5fR1H7LUnto7MTJk0qZroBZs+GH/ygqOfu6oKddmpoeJKk9rDc5DszF1DUfR8z8OFIUoNMnrwk8S632mom3pKkflNN2Ykktb85cypvf/DB+sYhSWprJt+SNHMmrNLLLwJHj65vLJKktrZCyXdE+K+RpNaXCSecAGPHwuqrFyUm5YYPhylTGhObJKkt9Zl8R8TbImLviNig9HzriPgNcP3KvGlEfDgiZkbE4ogYW7b9XRFxc0TcUbrfuZfzvx0RD0XEraXbbisTj6RB6NFHYffd4TOfgZ13hn/+E047DTo6iiXiOzpg6lSYMKHRkUqS2kivF1xGxNHA7sCtwFci4nJgIvBD4BMr+b53Ah8Eftlj++PAHpn574h4A3A5sEkvr3FsZv5kJeOQNAit99e/FsvCP/dcMfN92GFFwj1hgsm2JGlA9dXt5H3Adpn5QkSsCzwAvCEzZ63sm2bm3QAR0XP7LWVPZwJrRMRqpY4rkrRy5s+HL32JrU88EbbeGq6+GrbcstFRSZIGkcjMyjsi/paZbyx7fktmbtevbx7RBXwxM5dZtCci9gYOzcxdKuz7NnAQ8CzFgj9fyMynenmPScAkgFGjRm0/bdq0/gq/anPnzmXEiBF1f99W5XjVzjFbvhH33svrv/991pw9m/v22osHP/UpctiwRofVEvx+1cbxqo3jVRvHqzaNHK/x48ffnJlje27vK/l+Gri2bNOO5c8z8/19vWFETAc2rLBrcmZeVDqmiwrJd0RsCVwMvDsz76vw2qMoSlQS+B6wUWYutxRm7NixOWNG/Rfn7OrqYty4cXV/31bleNXOMevD4sXws5/B174GI0fCmWfSteqqjlcN/H7VxvGqjeNVG8erNo0cr4iomHz3VXayZ4/nP63lDSvNWFcjIjYFLgQ+VinxLr32o2XHnwL8YUXeS1Kbe+ghOOggmD4d9toLTjkF1l+/WLFSkqQG6DX5zsxr6hkIQESsA1wCfDUze+2oEhEbZebDpacfoLiAU5KWuPBCmDgRXnihSLo/+cniokpJkhqor24nV1OUdVSSmfnOFX3TiPgA8HPg5cAlEXFrZr4HOBx4NfCtiPhW6fB3Z+ZjEXEqcHKpROWoiNi2FN8s4JAVjUVSm5k7Fz7/eTj1VNh+e/jNb2DzzRsdlSRJQN9lJ1+ssO2twJeBx1bmTTPzQorSkp7bvw98v5dzJpY9PmBl3l9Sm7rppqJV4L33FjXe3/42eFGlJKmJ9FV2cnP344jYCfgmsDpFB5I/1iE2SarOokVw1FHwrW/BRhsVLQR32qnRUUmStIy+Zr6JiPcA3wAWAFMy8+q6RCVJ1ZozBw44AK69FvbdF046CdZdt9FRSZJUUV813zdR1GQfDfy1tO2/fb8z828DHp0klevshMmTi4R79GjYYw84++yineBZZ8H++3tRpSSpqfU18/08MBfYG/gQUP4vWgI7D2BckrS0zk6YNAnmzSuez55dLA3/6lfD5ZfDK1/Z2PgkSapCXzXf4+oYhyT1bfLkJYl3uRdfNPGWJLWMIb3tiIg3RcSGZc8/FhEXRcTxEbFefcKTpJI5cypvf+CB+sYhSdJK6DX5Bn4JvAgQETsCPwLOAp4Bpg58aJJUcuutsEovv6gbPbquoUiStDL6Sr6HZuaTpcf7AlMz83eZ+U2KhXAkaWAtXAjf/z686U0wfDisttrS+4cPhylTGhObJEkroM/kOyK6p5reCVxVtq/PFoWStNLuuQfe8Q745jfhwx+G+++H006Djo6io0lHB0ydWiyqI0lSi+griT4HuCYiHgfmA9cBRMSrKUpPJKn/LV4MP/85fPWrsOaacN55RfINRaJtsi1JamF9dTuZEhFXAhsBV2RmlnYNAT5Tj+AkDTKzZsHHPw5dXbD77nDKKbDhhss7S5KkltFn+Uhm3lBh2z8GLhxJg1ImnH46fP7zxfPTTiuScBfMkSS1GWu3JTXWww/DwQfDJZfAuHFwxhkwZkyjo5IkaUD0dcGlJA2s886DN7wBrrwSjjuuuDfxliS1MZNvSfX3xBPwkY/AvvsWy8Pfcgt89rMwxB9JkqT25r90kurr0kuL2e7zzy96eF9/PbzudY2OSpKkujD5llQfzz1X1Ha/732w/vpw000weXLvK1dKktSGTL4lDbyuLth666KjyVe+AjNmwLbbNjoqSZLqzuRb0sCZP79oHzh+fDHDfd118KMfLbtMvCRJg4S/75U0MG66CT72sWKZ+E9/Gn7842LFSkmSBjFnviX1rxdfhG99C972Npg7F664Ak44wcRbkiSc+ZbUn+68s5jtvuWW4v6442CddRodlSRJTcOZb0krprOzWBBnyBDo6ID99oPtt4cHH4QLL4QzzzTxliSpB2e+JdWusxMmTYJ584rnc+YUt7Fji2XiN9igsfFJktSknPmWVLvJk5ck3uUee8zEW5KkPph8S6rdnDmVtz/wQH3jkCSpxZh8S6re448X5SaZlfePHl3feCRJajEm35KWb+FCOPFE2HxzOOMM2G03WGONpY8ZPhymTGlMfJIktQiTb0l9+/OfiwspDz8cttsObrutuKjylFOKLicRxf3UqTBhQqOjlSSpqdntRFJlDz8MX/4y/PrXsNlm8Nvfwoc+VCTbUCTaJtuSJNXEmW9JS3vpJfjpT+G1r4Xzzis6m9x9N+y995LEW5IkrRBnviUtMX06fPazRbK9227FCpWvfnWjo5IkqW048y2paB344Q/Du94FCxbA739f1HWbeEuS1K9MvqXB7IUXig4lr3tdkWx/73swcybsvnujI5MkqS1ZdiINVpdcAkccAffdV1xI+dOfFl1LJEnSgHHmWxps7rsP9tijmN1edVW44go4/3wTb0mS6sDkWxos5s2Db34TttgCurrg6KOLnt3velejI5MkadCw7ERqd5lwwQVw5JHFhZUTJsBRR8HGGzc6MkmSBh1nvqV2dvfd8O53Fz2611kHrrmmWDTHxFuSpIYw+Zba0bPPwpe+BFtvDTNmwM9/DjffDDvu2OjIJEka1BqSfEfEhyNiZkQsjoixZdvHRMT8iLi1dDu5l/PXi4g/RcQ/S/fr1i96qYllQmdn0TrwJz+BAw+Ev/8dDj8cVrHKTJKkRmvUzPedwAeBayvsuy8zty3dDu3l/K8CV2bma4ArS8+lwe2224qZ7f33h002gRtvhFNPhQ02aHRkkiSppCHJd2benZl/X4mX2BM4s/T4TGCvlQ5KalVPPcVrjjsO3vhGuOceOOWUIvF+85sbHZkkSeqhGWu+XxERt0TENRHxP70cMyozHy49fgQYVafYpOaxeHExs7355mx88cVw2GHwj3/AxIkwpBn/akuSpMjMgXnhiOnAhhV2Tc7Mi0rHdAFfzMwZpeerASMy84mI2B74f8CWmflsj9d+OjPXKXv+VGZWrPuOiEnAJIBRo0ZtP23atJX9aDWbO3cuI0aMqPv7tirHa/nWuvtuXnP88bzsnnt4equtuO3gg8mttmp0WC3D71htHK/aOF61cbxq43jVppHjNX78+Jszc2zP7QN2BVZm7rIC5ywAFpQe3xwR9wGbAzN6HPpoRGyUmQ9HxEbAY3285lRgKsDYsWNz3LhxtYa10rq6umjE+7Yqx6uHzk6YPLno0b3JJvCqVxUtAzfcEM4+m3UmTCCvucYxq4Hfsdo4XrVxvGrjeNXG8apNM45XU7U/iIiXA09m5qKIeCXwGuD+CodeDBwI/Kh0f1H9opTqqLMTJk0qVqcEePDB4rbbbnDOOfCylzU2PkmSVJNGtRr8QEQ8CLwNuCQiLi/t2hG4PSJuBc4HDs3MJ0vnnFrWlvBHwLsi4p/ALqXnUvuZPHlJ4l1u5kwTb0mSWlBDZr4z80Lgwgrbfwf8rpdzJpY9fgJ454AFKDWDuXNh9uzK++bMqW8skiSpX9gSQWpGl10Gb3hD7/tHj65fLJIkqd+YfEvN5PHH4YAD4L3vhTXWgG9+E4YPX/qY4cNhypTGxCdJklaKybfUDLqXhX/96+Hcc4uk+5Zb4LvfhalToaMDIor7qVNhwoRGRyxJklZAU3U7kQal2bPh0EOLUpO3vKVYOKe85GTCBJNtSZLahDPfUqMsWgTHHQdbbgnXXVc8vv76vmu9JUlSS3PmW2qEO+8sloG/8caivvukk4qSEkmS1Nac+Zbq6YUX4Fvfgu22g/vuK+q8L7nExFuSpEHCmW+pXv78Zzj4YLjnnqKjyTHHwPrrNzoqSZJUR858SwPtmWfgsMPgf/4H5s8vLqw86ywTb0mSBiGTb2kgXXxxcUHlL38Jn/98Uev9nvc0OipJktQgJt/SQHjkEdhnH9hzT1hvPfjrX4sykxEjGh2ZJElqIJNvqT9lwumnF4vlXHxxsRLlzTfDm9/c6MgkSVIT8IJLqb/cey8ccghcdRXsuGOxEuVrX9voqCRJUhNx5ltaWQsXwlFHwVZbwYwZRX331VebeEuSpGU48y2tjL/9rVgs55ZbYK+94MQTYeONGx2VJElqUs58Syti3jz48peLWu6HH4bf/Q4uvNDEW5Ik9cmZb6lWV14JkybB/fcXi+YcdRSss06jo5IkSS3AmW+pWk8+CZ/4BOyyCwwdWtR1T51q4i1Jkqpm8i0tTyacd17RPvCss+BrX4PbboNx4xodmSRJajGWnUh9efDBYmn43/8ett8eLr8ctt220VFJkqQW5cy3VMnixXDSSbDFFjB9Ovz0p3DDDSbekiRppTjzLfV0993FhZTXXw/vehecfDK88pWNjkqSJLUBZ76lbi++CN/7XjG7fddd8KtfFWUmJt6SJKmfOPMtQVFSMnEizJwJ++0HP/sZjBrV6KgkSVKbceZbg9vcuXDEEfD2t8MzzxQXVp5zjom3JEkaEM58a/D64x/h0EPhgQfg05+GH/wA1lqr0VFJkqQ25sy3Bp///AcmTIDddoM114Q//xl+/nMTb0mSNOBMvjV4ZMLZZxeL5fz2t/Dtb8MttxQlJ5IkSXVg2YkGh1mz4JBD4Ior4G1vg1NPLXp4S5Ik1ZEz32pvixbBscfCllvCX/4CJ5xQlJmYeEuSpAYw+Vb76OyEMWNgyJDi/oc/LGa5jzwSdt656N396U8X+yVJkhrAshO1h85OmDQJ5s0rns+eDV//enER5bRpsM8+ENHYGCVJ0qBn8q32MHnyksS73Nprw7771j8eSZKkCvz9u9rDnDmVtz/0UH3jkCRJ6oPJt1rfZZf1Xsc9enR9Y5EkSeqDybda15NPwoEHwnvfWywHv/rqS+8fPhymTGlMbJIkSRWYfKs1nX9+sVjOb34D3/gG3H9/0bu7o6O4sLKjA6ZOLVaylCRJahJecKnW8sgjRbvACy6AN76xWDRnm22KfRMmmGxLkqSm5sy3WkMmnHlmsTjOJZfAj34EN964JPGWJElqAc58q/nNmVP08L78cthhh6K85LWvbXRUkiRJNWvIzHdEfDgiZkbE4ogYW7Z9QkTcWnZbHBHbVjj/2xHxUNlxu9X1A6g+Fi+GX/yiWBr+z38uloa/5hoTb0mS1LIaNfN9J/BB4JflGzOzE+gEiIitgP+Xmbf28hrHZuZPBjJINdA//gETJ8J118G7311cPNnR0eioJEmSVkpDku/MvBsg+l7u+yPAtLoEpOaxcCGbnXMOnHVW0TrwjDOKdoIuDS9JktpAM19wuS9wTh/7D4+I2yPi9IhYt15BaQDdfju89a28aupU2G03uPtuOOggE29JktQ2IjMH5oUjpgMbVtg1OTMvKh3TBXwxM2f0OPctwKmZuVUvrz0KeBxI4HvARpn5iV6OnQRMAhg1atT206bVfzJ97ty5jBgxou7v2yrixRfp+PWvGf2b37DwZS/jjkMO4bn3vKfRYbUUv2O1cbxq43jVxvGqjeNVG8erNo0cr/Hjx9+cmWN7bh+wspPM3GUlTt+PPma9M/PR7scRcQrwhz6OnQpMBRg7dmyOGzduJcJaMV1dXTTifVvCjTcWfbvvugsOOIBhxx7Lc3fc4XjVyO9YbRyv2jhetXG8auN41cbxqk0zjlfTlZ1ExBBgH/qo946IjcqefoDiAk61knnz4Mgj4W1vg2efLXp3n3UWjBzZ6MgkSZIGTKNaDX4gIh4E3gZcEhGXl+3eEXggM+/vcc6pZW0Jj4qIOyLidmA88Pm6BK7+cfXVsNVWcOyxcOihMHNmUeMtSZLU5hrV7eRC4MJe9nUBb62wfWLZ4wMGLDgNnGeegS9/uWgb+OpXQ1cX7LRTo6OSJEmqm6YrO1Gb+sMfisVyTj0VvvhFuO02E29JkjTomHxrYD3+OOy/P+yxB6y7LtxwAxx9NAwf3ujIJEmS6s7kWwMjE849F7bYAs47D779bbj5ZnjTmxodmSRJUsM0anl5tbN//xsOOwwuuqhItk87rbjAUpIkaZBz5lv9J7NItLfYAi6/vCgv+ctfTLwlSZJKnPlW//jXv2DSJJg+HXbcsbiw8jWvaXRUkiRJTcWZb62cRYvg+OPhDW8oVqs86aSij7eJtyRJ0jKc+daKu/tumDixKC1573vhl7+EzTZrdFSSJElNy5lv1e6ll+AHP4Btt4V77oGzzy6WhzfxliRJ6pMz36rNLbfAJz4Bt94KH/4w/PznMGpUo6OSJElqCc58qzovvACTJxetAx95BC64oOjfbeItSZJUNWe+tXx/+Qt88pNFiclBB8ExxxSrVUqSJKkmznyrd3PnwhFHwA47wLx5cNllcMYZJt6SJEkryJlvVTZ9Ohx8MMyaBYcfXlxgudZajY5KkiSppTnzraU9/XRRYvKud8GwYXDddcVFlSbekiRJK83kW0tcdFGxNPyZZ8JXv1p0NNlhh0ZHJUmS1DYsOxE89hh89rNw7rmwzTbw+9/D9ts3OipJkqS248z3YJYJnZ3FbPeFF8L3vgc33WTiLUmSNECc+R6sHnwQDj20WJnyLW+B008vknBJkiQNGGe+B5vFi2HqVNhyS7jqqqJn9/XXm3hLkiTVgTPfg8l998HEidDVBTvvDKecAq98ZaOjkiRJGjSc+R4MFi0qZri32gr+9rdi5nv6dBNvSZKkOnPmu93NnFn07b7xRth9dzjpJNh000ZHJUmSNCg5892uXnyx6F6y3XZFuclvfgMXX2ziLUmS1EDOfLejGTOK2e7bb4f99oPjj4eXv7zRUUmSJA16zny3k/nz4StfKVoH/uc/xYqV55xj4i1JktQknPluF9ddV8x2//Ofxf1PfgLrrNPoqCRJklTGme9W99xz8OlPw447wsKFRReTU0818ZYkSWpCJt+t7PLL4Q1vKDqYHHEE3HEHvPOdjY5KkiRJvTD5bkVPPgkHHQS77grDh8Of/ww/+xmsuWajI5MkSVIfTL5bze9+VywF/+tfw+TJcMst8Pa3NzoqSZIkVcELLlvFI4/A4YcXyfd228Fll8G22zY6KkmSJNXAme9mlwlnnVXMdv/hD/CDHxSrVZp4S5IktRxnvpvZnDlwyCHFLPfb3w6nnQave12jo5IkSdIKcua7GS1eDL/4BWy5ZdG/+/jj4dprTbwlSZJanDPfzeaf/4SJE4tke5dd4JRTYMyYRkclSZKkfuDMd7NYuBCOPhq23hpuu60oMbniChNvSZKkNuLMdzO4/fZiSfgZM2CvveDEE2HjjRsdlSRJkvqZM9+NtGAB/O//wvbbw+zZcO65cMEFJt6SJEltypnvRrnxxmK2e+ZM2H9/OPZYWH/9RkclSZKkAdSwme+IODoi7omI2yPiwohYp2zf1yLi3oj4e0S8p5fzXxERN5aOOzcihtUt+JUxbx584QtF68Cnny56d599tom3JEnSINDIspM/AW/IzK2BfwBfA4iILYD9gC2BXYFfRMTQCuf/GDg2M18NPAV8si5R16KzE8aMYaeddy4unPzGN4oLKo85Bg4+uJj1ft/7Gh2lJEmS6qRhyXdmXpGZC0tPbwA2LT3eE5iWmQsy81/AvcCby8+NiAB2Bs4vbToT2GvAg65FZydMmgSzZxOZRU33lCnw3HNw1VVw8smw9tqNjlKSJEl11CwXXH4C+GPp8SbAA2X7HixtKzcSeLosea90TGNNnlyUmPS02mowfnz945EkSVLDDegFlxExHdiwwq7JmXlR6ZjJwEKgc4BimARMAhg1ahRdXV0D8TbL2GnOHKLC9nzwQa6pUwytau7cuXX7c2oXjlltHK/aOF61cbxq43jVxvGqTTOO14Am35m5S1/7I+IgYHfgnZmZpc0PAZuVHbZpaVu5J4B1ImKV0ux3pWO6Y5gKTAUYO3Zsjhs3rsZPsYJGjy5KTXqI0aOpWwwtqquryzGqkWNWG8erNo5XbRyv2jhetXG8atOM49XIbie7Al8G3p+Z5fUZFwP7RcRqEfEK4DXA/5WfW0rUrwb2Lm06ELho4KOuwZQpMHz40tuGDy+2S5IkaVBqZM33CcBawJ8i4taIOBkgM2cC5wF3AZcBn87MRQARcWlEdK9A8xXgyIi4l6IG/LR6f4A+TZgAU6dCRwcZAR0dxfMJExodmSRJkhqkYYvslFoE9rZvCrDMFHFm7lb2+H56dEFpOhMmwIQJXNOEv/KQJElS/TVLtxNJkiSp7Zl8S5IkSXVi8i1JkiTVicm3JEmSVCcm35IkSVKdmHxLkiRJdWLyLUmSJNWJybckSZJUJybfkiRJUp2YfEuSJEl1YvItSZIk1YnJtyRJklQnJt+SJElSnZh8S5IkSXVi8i1JkiTVSWRmo2Oom4j4DzC7AW+9PvB4A963VTletXPMauN41cbxqo3jVRvHqzaOV20aOV4dmfnynhsHVfLdKBExIzPHNjqOVuF41c4xq43jVRvHqzaOV20cr9o4XrVpxvGy7ESSJEmqE5NvSZIkqU5MvutjaqMDaDGOV+0cs9o4XrVxvGrjeNXG8aqN41Wbphsva74lSZKkOnHmW5IkSaoTk++VFBG7RsTfI+LeiPhqhf2rRcS5pf03RsSYsn1fK23/e0S8p66BN0gV43VkRNwVEbdHxJUR0VG2b1FE3Fq6XVzfyBujivE6KCL+UzYuE8v2HRgR/yzdDqxv5I1RxXgdWzZW/4iIp8v2Dcbv1+kR8VhE3NnL/oiI40vjeXtEvLFs32D8fi1vvCaUxumOiPhLRGxTtm9WafutETGjflE3ThXjNS4inin7e/etsn19/l1uR1WM15fKxurO0s+s9Ur7BuP3a7OIuLqUM8yMiCMqHNOcP8My09sK3oChwH3AK4FhwG3AFj2OOQw4ufR4P+Dc0uMtSsevBryi9DpDG/2ZmmC8xgPDS48/1T1epedzG/0ZmnC8DgJOqHDuesD9pft1S4/XbfRnavR49Tj+M8DpZc8H1fer9Jl3BN4I3NnL/t2APwIBvBW4sbR90H2/qhyvt3ePA/De7vEqPZ8FrN/oz9Bk4zUO+EOF7TX9XW6X2/LGq8exewBXlT0fjN+vjYA3lh6vBfyjwr+RTfkzzJnvlfNm4N7MvD8zXwSmAXv2OGZP4MzS4/OBd0ZElLZPy8wFmfkv4N7S67Wz5Y5XZl6dmfNKT28ANq1zjM2kmu9Xb94D/Ckzn8zMp4A/AbsOUJzNotbx+ghwTl0ia1KZeS3wZB+H7AmclYUbgHUiYiMG5/drueOVmX8pjQf486ua71dvVuZnX8uqcbz8+ZX5cGb+rfT4OeBuYJMehzXlzzCT75WzCfBA2fMHWfYP/r/HZOZC4BlgZJXntptaP/MnKf7H2m31iJgRETdExF4DEF+zqXa8PlT6ddr5EbFZjee2k6o/c6mc6RXAVWWbB9v3qxq9jelg/H7VqufPrwSuiIibI2JSg2JqRm+LiNsi4o8RsWVpm9+vPkTEcIpE8Xdlmwf19yuKkt7tgBt77GrKn2Gr1OuNpFpExP7AWGCnss0dmflQRLwSuCoi7sjM+xoTYdP4PXBOZi6IiEMofsuyc4NjagX7Aedn5qKybX6/1C8iYjxF8r1D2eYdSt+vDYA/RcQ9pZnOwexvFH/v5kbEbsD/A17T2JBawh7A9ZlZPks+aL9fETGC4j8in8vMZxsdTzWc+V45DwGblT3ftLSt4jERsQqwNvBElee2m6o+c0TsAkwG3p+ZC7q3Z+ZDpfv7gS6K/+W2s+WOV2Y+UTZGpwLbV3tuG6rlM+9Hj1/ZDsLvVzV6G9PB+P2qSkRsTfF3cc/MfKJ7e9n36zHgQtq/zHC5MvPZzJxbenwpsGpErI/fr+Xp6+fXoPp+RcSqFIl3Z2ZeUOGQpvwZZvK9cm4CXhMRr4iIYRR/IXp2SbgY6L6Kdm+KCySytH2/KLqhvILif/v/V6e4G2W54xUR2wG/pEi8Hyvbvm5ErFZ6vD7wDuCuukXeGNWM10ZlT99PUfMGcDnw7tK4rQu8u7StnVXz95GIeB3FBTZ/Lds2GL9f1bgY+FipY8BbgWcy82EG5/druSJiNHABcEBm/qNs+5oRsVb3Y4rxqtjRYjCJiA1L10AREW+myEmeoMq/y4NRRKxN8Rvhi8q2DcrvV+m7cxpwd2Ye08thTfkzzLKTlZCZCyPicIo/sKEUnRNmRsR3gRmZeTHFF+PsiLiX4kKK/UrnzoyI8yj+gV8IfLrHr8DbTpXjdTQwAvht6WfynMx8P/B64JcRsZjiB/SPMrOtk6Mqx+uzEfF+iu/QkxTdT8jMJyPiexT/iAF8t8evKNtOleMFxd/BaaX/BHcbdN8vgIg4h6LjxPoR8SDwv8CqAJl5MnApRbeAe4F5wMdL+wbd9wuqGq9vUVzT84vSz6+FmTkWGAVcWNq2CvCbzLys7h+gzqoYr72BT0XEQmA+sF/p72XFv8sN+Ah1VcV4AXwAuCIzny87dVB+vygmSQ4A7oiIW0vbvg6Mhub+GeYKl5IkSVKdWHYiSZIk1YnJtyRJklQnJt+SJElSnZh8S5IkSXVi8i1JkiTVicm3pEEhIjIiflr2/IsR8e1+eu1fRcTe/fFay3mfD0fE3RFxdZXHXxoR6/RzDGMiYpkewhGxcUSc35/vVXrdbUurH9ZyzhoRcU1EDO0t3hpea0pEPBARc3tsXy0izo2IeyPixiiWtyYitoqIX63o+0lqfybfkgaLBcAHS4voNI0oVr6t1ieBgzNzfDUHZ+Zumfn0CgVWo8z8d2YOxH9AtqXo01uLTwAX9NPaCb+n8mqBnwSeysxXA8cCPwbIzDuATUsL7kjSMky+JQ0WC4GpwOd77ug5c909yxkR40ozqBdFxP0R8aOImBAR/xcRd0TEq8peZpeImBER/4iI3UvnD42IoyPipoi4PSIOKXvd6yLiYiqspBkRHym9/p0R8ePStm8BOwCnRcTRPY7fKCKujYhbS+f8T2n7rO7/bETENyPi7xHx54g4JyK+WNreFRE/Ln2mf5SdO6YU499Kt7f3NbjlM8wRcVBEXBARl0XEPyPiqPKxjYhjI2JmRFwZES8vi2Ns6fH6pdiHAd8F9i19tn0jYqfS41sj4pYorezXwwTKVgAse+/VI+KM0tjeEhHjS9uHR8R5EXFXRFxYmskeC5CZN5RWxOtpT+DM0uPzgXdGaZUTioR9v77GS9LgZfItaTA5EZgQxRLN1doGOJRiFcwDgM0z883AqcBnyo4bQzFD+j7g5IhYnWJ29JnMfBPwJuDgiHhF6fg3Akdk5ublbxYRG1PMou5MMev7pojYKzO/C8wAJmTml3rE+FHg8szcthTvrT1e803Ah0r73guM7XH+KqXP9DmKVfUAHgPelZlvBPYFju9rkCrYtnTeVhTJ82al7WtSrDi6JXBN2fstIzNfpFg18tzM3DYzzwW+SLEi8LbA/1CsjFj+WYcBr8zMWRVe8tPFy+ZWwEeAM0t/TodRzGJvAXwT2L6Kz7cJ8EApzoXAMxSrW0Lx5/Q/VbyGpEHI5FvSoJGZzwJnAZ+t4bSbMvPhzFwA3AdcUdp+B0XC3e28zFycmf8E7gdeB7wb+FgUSx/fSJGcvaZ0/P9l5r8qvN+bgK7M/E8pqesEdlxejMDHo6hh3yozn+ux/x3ARZn5Qmnf73vsv6B0f3PZZ1oVOCUi7gB+C2yxnBh6ujIzn8nMFyhm9ztK2xcD55Ye/5piNr8W1wPHRMRngXVKY1RufeDpXs7dofSeZOY9wGxg89L2aaXtdwK31xhTT48BG6/ka0hqUybfkgabn1HMSK9Ztm0hpZ+HETEEGFa2b0HZ48VlzxcD5fXa2eN9EgjgM6VZ220z8xWZ2Z28P78yH2KpN8q8liJBfwj4VUR8rMaX6P5Mi1jymT4PPEoxWz6Wpcekltfs+bo9dY/bf/8MgNV7e9HM/BEwEVgDuD4iXtfjkPl9nd+PHgI2g//W7a8NPFHatzo9ZuQlqZvJt6RBJTOfBM6jSMC7zWJJqcH7KWZ9a/XhiBhSqgN/JfB34HLgUxGxKkBEbB4Ra/b1IsD/ATuV6p6HUpRHXNPXCRHRATyamadQlMO8scch1wN7lGqeRwC7V/F51gYezszFFOU2Q6s4pxpDgO76+o8Cfy49nsWSP4PyCzefA/5b1x0Rr8rMOzLzxxQz/ksl35n5FDC0VE7S03UU9eBExObAaIo/p+uBfUrbt6AolVmei4EDy+K9KjO7/yOxObDCHVYktTeTb0mD0U8pyhO6nUKR8N4GvI0Vm5WeQ5E4/xE4tFRucSpFycXfShcj/pLeZ4ABKF3c91XgauA24ObMXObiwR7GAbdFxC0UddbH9XjNmyiSxdtL8d1BUaPcl18AB5bG5HX030z988CbS+OxM8UFlQA/ofiPyi0s/WdzNbBF9wWXwOdKF5XeDrxU+jw9XUHlcpZfAENKpTTnAgeVyol+Abw8Iu4Cvg/MpDQ+EXFURDwIDI+IB2NJe8rTgJERcS9wJMWfWbfxwCXVD4mkwSSW/EddktSuImJEZs6NiOHAtcCkzPxbA+KYm5kjBvg93gh8PjMPqPL4ocCqmflC6TcX04HXli74rPW9V6P4TcUOFerRJanvGRhJUtuYWiqpWB04sxGJd71k5t8i4uqIGFplr+/hwNWl8qAADluRxLtkNPBVE29JvXHmW5IkSaoTa74lSZKkOjH5liRJkurE5FuSJEmqE5NvSZIkqU5MviVJkqQ6MfmWJEmS6uT/AyHALekVj1jKAAAAAElFTkSuQmCC\n",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 864x576 with 1 Axes>"
       ]
      },
      "metadata": {
@@ -227,20 +229,22 @@
     "    coh_SNR_dB = 20 * np.log10(bf_coh_std / bf_incoh_std)\n",
     "    coh_SNR_dB_arr.append(coh_SNR_dB)\n",
     "\n",
-    "plt.figure(1)\n",
+    "plt.figure(figsize=(12, 8))\n",
     "plt.plot(S_arr, bf_coh_std_arr, 'g', S_arr, bf_incoh_std_arr, 'b', S_arr, bf_sys_std_arr, 'r')\n",
     "plt.title(\"Summator\")\n",
     "plt.xlabel(\"Number of signal inputs\")\n",
     "plt.ylabel(\"BF std\")\n",
     "plt.legend(['bf_coh', 'bf_incoh', 'bf_sys'])\n",
     "plt.grid()\n",
+    "plt.savefig('plots/signal_statistics_summator_std.jpg', dpi=dpi)\n",
     "\n",
-    "plt.figure(2)\n",
+    "plt.figure(figsize=(12, 8))\n",
     "plt.plot(S_arr_log, coh_SNR_dB_arr, 'r-o')\n",
     "plt.title(\"Summator\")\n",
     "plt.xlabel(\"Number of signal inputs (log10)\")\n",
     "plt.ylabel(\"SNR of voltage signal [dB]\")\n",
-    "plt.grid()"
+    "plt.grid()\n",
+    "plt.savefig('plots/signal_statistics_summator_snr.jpg', dpi=dpi)"
    ]
   },
   {
@@ -273,7 +277,7 @@
    "id": "84b8930c",
    "metadata": {},
    "source": [
-    "## 3 Correlation\n"
+    "## 3 Correlator\n"
    ]
   },
   {
@@ -292,9 +296,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 864x576 with 1 Axes>"
       ]
      },
      "metadata": {
@@ -304,9 +308,9 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 864x576 with 1 Axes>"
       ]
      },
      "metadata": {
@@ -316,9 +320,9 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 864x576 with 1 Axes>"
       ]
      },
      "metadata": {
@@ -387,15 +391,15 @@
     "    auto_mean_std_log10 = 10 * np.log10(auto_mean_std)\n",
     "    auto_mean_std_log10_arr.append(auto_mean_std_log10)\n",
     "\n",
-    "\n",
-    "plt.figure(1)\n",
+    "plt.figure(figsize=(12, 8))\n",
     "plt.plot(N_samples_arr, auto_mean_arr, 'g')\n",
     "plt.title(\"Auto correlator mean\")\n",
     "plt.xlabel(\"Number of samples\")\n",
     "plt.ylabel(\"Auto power mean\")\n",
     "plt.grid()\n",
+    "plt.savefig('plots/signal_statistics_auto_correlator_mean.jpg', dpi=dpi)\n",
     "\n",
-    "plt.figure(2)\n",
+    "plt.figure(figsize=(12, 8))\n",
     "fit_coef = np.polyfit(N_samples_arr_log, auto_mean_SNR_dB_arr, 1)\n",
     "fit_p = np.poly1d(fit_coef)\n",
     "fit_line = fit_p(N_samples_arr_log)\n",
@@ -404,8 +408,9 @@
     "plt.xlabel(\"Number of samples (log10)\")\n",
     "plt.ylabel(\"SNR of power measurement [dB]\")\n",
     "plt.grid()\n",
+    "plt.savefig('plots/signal_statistics_auto_correlator_snr.jpg', dpi=dpi)\n",
     "                           \n",
-    "plt.figure(3)\n",
+    "plt.figure(figsize=(12, 8))\n",
     "fit_coef = np.polyfit(N_samples_arr_log, auto_mean_std_log10_arr, 1)\n",
     "fit_p = np.poly1d(fit_coef)\n",
     "fit_line = fit_p(N_samples_arr_log)\n",
@@ -413,7 +418,8 @@
     "plt.title(\"Auto correlator mean power std (%3.1f dB/decade)\" % fit_p[1])\n",
     "plt.xlabel(\"Number of samples (log10)\")\n",
     "plt.ylabel(\"Auto mean power std (log10)\")\n",
-    "plt.grid()"
+    "plt.grid()\n",
+    "plt.savefig('plots/signal_statistics_auto_correlator_mean_power_std.jpg', dpi=dpi)"
    ]
   },
   {
@@ -451,9 +457,9 @@
     },
     {
      "data": {
-      "image/png": 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VbSuv6YADDuCxxx7jxhtvZMGCBXTs2JEOHTpw5JFH8q9//YtPPvmEsrIyhgwZwpAhQ3jttde45pprmD179rZh35uLeiAikv6a+IEgJY0YyiTe4dxnzZrFiy++yC233MKCBQuYOHEixx13HNOmTWPkyJG8+uqr7LXXXjuse/jhhzNr1ixeeuklzj33XCZMmMC4ceO48MILufXWW9lrr70477zzABg4cCAffPAB06ZN4/rrr2fMmDHccMMNO2wzWdQDERGJIVXDuX/55Zf06tWLiy66iAsvvJAPPvgAgIMOOogVK1bw1FNPceaZZwLwzTffkJeXx9lnn81VV121rW5zUQ9ERCSGVA3nnp+fv+1cR4cOHXjiiSe2LTv99NOZP38+Xbt2BYLxtK666ioyMjLIysriL3/5S3I+jFoogYiI1GL8+PGMHz++1uXDhg3jnXfe2aF8zpw5O5Tde++9jd7nnDlzuPLKK7fNjx07lrFjx8a13WTQISwRkTRXVFTEwIEDadeuHWPGjEl1ONuoByIikgINGc69S5cufPbZZ80VWtyUQEREUqA1DOeuQ1gikrZS9cjttqqhn7cSiIikpdzcXAoLC5VEmom7U1hYWOsd8rHoEJaIpKV+/fpRUFDAmjVrmnzbW7ZsadAXZUsXb3tzc3Pp169f3NtVAmmIggJowIcrIonLyspiwIABSdl2fn5+zPs6WqtktVeHsBqiljtHRUTaIiWQhpgwIdURiIikDSUQERFJiBJIQ0QiqY5ARCRtKIE0RFUCmTEDPv00tbGIiKSYrsJqiIww3x51VPCu69NFpA1TD6Qhtm6FsrJURyEikhbq7YGYWQ7w/4D+0fXd/abkhZWmPvoIVq/ePl9eDrU8llJEpLWLpwfyAnAiUA5sjnq1Td98s336669hwQJYtSqxbS1dCpWVTROXiEgzi+fncz93PzrpkbQUK1dun375ZfjFL6BbN7jvPjjlFPjjH2GvveDYY+vezq23wnXXwZlnwuTJkJUVlM+ZA8uXw+mnQ3Z208RcVgaLFwfTkUhwLicjI5ju3Bl69mya/YhIm2L1DVRmZpOAe919QfOElDwjRozwefPmNXxFs22TpXk55BSXAlCRFaG0Qzu2ts+lS8F3zBt3FCOemE5FZoS554+l61ffsvi/D6Zg+B50WL2ObstW8d0efRn8/Fsc/NDLrN7r+/T6ZAXLRg5i1i9O5pBJ0xg4/UMA1u3ck9m/OInl/zWo2v4bIlJaxqAX32H4kzPotGpdrfU2d+/Emj368t3AvqwZ2I81A/tS1K8HSz5fwpCdetNhzXrarymiw5r1dPi2iPZr1pNdXEpZu2y25uVSlpdDWV4OW/Nygvl22ZS1z6U8JyvmqyInm4qsSNAud7KLS8nZUEzOxmJyNpaQs7GY3I0lQdmmEnI2lpBVXIq5Y+7BxQvhP1sjmLawzMIFjlEZycAzM6iMZFAZieCRcDozEqMs6IyvW72Gnp06k1FeQaSsgozyinC6PHgP5zPKqsoqiZSX42aUtcuhPDeLspxsyttlU5abHcznBuXluVVlwausqk74uUTKK8jcspXM0rJt71lbyoiUlpFVozyztOq9jMwt4fyWYD6ytTz4HCrDz4uoz6fae7C8bGsZ2VmZQXmlB59edD2odsGIVU1WK4v9PVKtPLpKPBeg1PbvPkaxx/l/xDMy8Axja2UlkZxsPMO2lVVGMqpNY0ZlRgYeCcuqps3wSLBezeVsq1d9eVWMnmFgYbxmQZkZZBhu4JYRLM8wIKi/rU6M8qBNMbZVo3z1mm855neP0LV3YkPDmNn77j5ih/I4EshiYHdgGVBK8Odzdx+aUCQp1BQJpKYnh8D4k2HeJNhvFVQYfN4N9iqEkkzIrIQp+8CpiyG3Yvt6j+8LF5wIF34Af/lXcCyxJBNuOxQ+/B78/rVgG6/3h0U9oTgreDnQrSR4dS8Jtv9eH5i9C7zdDzbmQvtS+Mn78Mu3oPcmePP78MAI2JwFGV791XMzDFsVvPZZA1nhEbXiMPbsGkfYKgxWt4cNOdC+DDpshY6lkNnAC9IqgZIsyC2HSB3rVgJFubAxByotaL/b9u8ir1FW9Z7hwXYzK7e/siqqz2dWxo67EtgagbJI8L41AmUZdZdlOOSVQfutwXteWfD5tN/a8M+mPuUWfHbFWcG/meKs6vOlmTt+VtHvtS2rtNo/z+jPvOpzh9hliZTXZLV8ZrFWiVW3tnpV/y4ildWna77Xtiyjlvr1LTMPYqqKwQjfa5lPxtVNS996iV0PqefISC0ak0B2iVXu7l8mFEkKJSOBbL7jVkov/Qk5kx6l/RVXsfWoI9k8+SEyvlpB5S470+Hs88icOYutPz6d0jNOI/LpZxDJpPSi87ZdFpz1z6lk/XsGWyb+D5W77BxsuKyMnAcfIfcvD2Hr12Obi7HSoOdT2akj3rUr3q0rVDqRhYuwigo8I4OKIYPJKCggo3AtZUccTsk1/0P5YSPj68WUlhL5+FMi/1lA5sLFrFjzLX0OPIDK3r2p7BO8/Hu9drxwwB1KS7GNm7DNm7e9U1KCFZdgW7YE0yVbYMsWLGqanBy8S2cqu3TBu3TGu3bBO4fvXbrgHTtsv3w6GdyhoiK4IMKdOXPncuioUU27j61bseISKC7GiouDz6SkBDZv3j5dXAI52XhuLuTl4e1y8XbtoF07PK9dtXKyshLulcYyZ84cDj300CbbXkvQItpc1duurKzz3Sq93rrvvfsuY049i0hOYiMQ15ZA6j0HUpUozKwn0HbGP45T+8OOpH27bnDexfDUM2Rfcy3ZfXaFPrsGFf49A1atIqdvX3IAjg/Xi97Ij8+FH59b/cNtB1z1q+BVpaICKivJqDpfUmXTJnjnHWzOHDLnzIFdd4Orrybr4IOpUbNu7YBDesMhowH4Oj+fPUaPjm/dPKBrQ3aWnjrldqVbu25Nu9F2QOem3WRT6pTVqenbnObaWpuzC75LOHnUJZ7LeE8A7gL6AN8CuwAfA/s0eTQtxBn8nfOy/sbYcb3gwAODwk6dIMazjIlEoG/fptlxJBJ7OJUOHYKbG6tucBQRaQbxHBv4HXAw8Jm7DwDGAO8kNao0VvbKDKZwBkeXvQgPP9ykhxJERFqSeBJImbsXAhlmluHuM4EdjoW1Fd+t2X7OSLdwiEhbFk8CKTKzDsBs4G9m9iea6EZCMzvazD41syVmNjHG8hwzmxIuf9fM+kctuzYs/9TMxjZFPPFYs3p71li1Ct58Ex54YPv9hSUl8V2huGED/O53wb2EIiItUTw3Ep4IlABXAGcRnA5s9DAmZhYB7gd+ABQA75nZVHdfHFXtAmCdu+9uZmcAtwM/MrNBwBkE52H6ANPNbKC7V5Bk6wq3J5DLLoN//COYvukmmDkTjjsOBg6EF17Yfm9gTWvXwjHHwNy58Kc/wbPPwujR8MEHcP318NVXcNVVcPbZjR9BvrQUpkwJYsvOhtxcCC/uITcXunaF3XcPXv36xXfBU0VFMCxYbm7zHMErL4fNm7f3+KouOKlr2iz4/KNfjbmYq7w8aHPVq7S0+rxZ9c+26pXsC8jKyoJYSkuDi9pKS4OyqotwYl2YU7Ns0aJOZGfXv05D4mpoO+pbv6nqmAV/kwULulJWtv2e2livqrpN/Yq+lzd6Xy1RvZfxwrZLefdw9+lmlgdE3H1jo3Zsdghwo7uPDeevBXD3/42q82pY520zywRWATsBE6PrRtera59NcRnv0bzMq2y/Mf/gg+EPf4Ajjgi+hBctCsoHDoQ1a2DiRLj66uBpuG++GXyJ33UXfPxxcNP6vffCkiVw5JHw739D9+7BF/lHH8Hee8ONN8Khh0Lv3tvDcA+SzOLF8O23MHw4DBpU/ctqzZqgZ/TnPwc9pZ49g+UlJcFr69Ydm5mTA7vuGrRjt93gq68KyMvrR2EhFBYGia+wEIqKghjMIC+v9ldubt2vnJzgC6+oCNavr/19cxMNnJORsWNSqZlgNmwoISOj3Q5JItHDldnZ25N1zeQSnchzc6sng+iEUNe0BoRuPWIllnjL6qu7efMGXn65E/37JxZbwpfxmtlFwMVAN2A3oC/wAMHJ9MboC6yImi8ADqqtjruXm9l6oHtY/k6NdWNe6mRmF4fx06tXL/Lz8xsc6Oio6Qyqf5P07/8VpaVLGTlyb15/vRcdO5Zxyilf8/bb3dl55wquuaYLU6asY/78LlRWBhkgJ6eC3/1uIYMGreOOOyLcfPMgZs3qwjnnrOD001fQvn0Fs2b14NFHB/CjHwUX/ObmVtC7dwk5OZV8+WUeJSXV/3QdOpSxzz4bGDx4PStXtuO113pRVpbBgQcWMmFCASNGrKv2KyfoRWSwfn0W33zTjq+/Dl7ffNOORYva8dpr7YhEetGpUwmdOpXRsWM5O+9cxuDB5XTqVEZ2diWlpRmUlkbYsmX7+5YtETZuzGDNmghbt2ZQVpbB1q3VX2Vl1X+WZ2ZW0qFDOR06lNO+ffDeo0cF/ftvn8/NrQgTpId/1+0JNXivXl5ZCRUVGZSXG+XlRkWFUV5e93xlpdG791by8iJkZlaSleVkZVWSlVVJZmbVtFdbVjUd3AqTwdatkfBzCdpaWhoJ36uXFRdnUFSUsW3Z1q0Z1faRnR3sNzu7ki5dKncor1oWzPu2+apYzXzbL2jwqF+5O05v2VJCXl7uts+uat3t79U/43g09Bd1zfqx1/d669S3napeVXFxCTk5ebhb2MuK/Q51L4/nvaJi+3YqKy2MYft78Npx3aaoWzXdvn0F7723iOXLSxv0d6mXu9f5AuYD2cCHUWUL6lsvju2eCjwcNX8OcF+NOgsJxuKqmv8C6AHcB5wdVf4IcGp9+xw+fLgnZHsv3nvwbfSsT5kSVHn11WD+kku2r1Ze7n7OOe6Zme6XX+7+2Wfun3/uXlhYffOVle4lJTvutqzMffp09/vvd58wwf2EE9yPOsr9ssvcH3jAffZs98WL3SdPdr/oIve99w5iaNfO/Sc/CZY1xsyZMxu3gVpUVATtXbfOvbg4aH+6SFab05na3Po1tr3API/xnRrPOZBSd99qYSoPDyU1Rcf5a+D7UfP9wrJYdQrC/XYGCuNct8ntz/t8x04MGxb8gl+4MDj0BDBmDNx8c3DeokokEoyTeP/90LFj7dutOn5eU2ZmsN0x9fT19t4bxo0Lpteu3T5GYrrKyNh+2EZEWq54EsgbZvYroJ2Z/QD4GfBiE+z7PWAPMxtA8OV/BvDjGnWmAuOBtwl6LK+7u5vZVOApM/sDwUn0PYC5TRBTnT5kf6ZMgbFjgy/B7OzgOD4EX9rXXbfjOmZ1J4+m1q3t3FwrIikWTwKZSHA11ALgJ8A04OHG7tiDcxo/B14FIsCj7r7IzG4i6C5NJTg09VczWwKsJUgyhPX+D1hM8JySS70ZrsCC4CR3Ov+6FxFpLvGMhVUJPBS+mpS7TyNISNFlN0RNbwFOq2XdW4Bbmjqm+ujmQRGRQL1XqZvZ8Wb2oZmtNbMNZrbRzDY0R3DpSJdNiogE4jmEdTdwCsGVV23+61M9EBGRQDz3ya4AFip5BPQpiIgE4umBXA1MM7M3CJ5ICIC7/yFpUaUx9UBERALxJJBbgE0ED5PKTm446W/kyFRHICKSHuJJIH3cfXDSI2kBdPhKRGS7eM6BTDOzHyY9EhERaVHiSSA/BV4xsxJdxisiIlXiuZGwGQfiEBGRliKJj7sREZHWTAlEREQSogQiIiIJqTOBmFnEzD5prmBERKTlqDOBhEOkf2pmOzdTPCIi0kLEcyNhV2CRmc0FNlcVuvsJSYtKRETSXjwJ5NdJj0JERFqceO4DecPMdgH2cPfpZpZH8ARBERFpw+J5oNRFwLPAg2FRX+D5JMYkIiItQDyX8V4KjAQ2ALj750DPZAYlIiLpL54EUuruW6tmzCwT0Li0IiJtXDwJ5A0z+xXQzsx+ADwDvJjcsEREJN3Fk0AmAmuABcBPgGnA9ckMSkRE0l88l/EeATzp7g8lOxgREWk54umBjAM+MrN3zOwOM/tvM+ua7MBERCS9xXMfyHgAM+sDnArcD/SJZ10REWm96k0CZnY2cBgwBPgOuA+YneS4REQkzcXTi7gb+AJ4AJjp7suTGZCIiLQM9Z4DcfcewPlALnCLmc01s78mPTIREUlr8Qxl0gnYGdgF6A90BiqTG5aIiKS7eA5hzYl63efuBckNSUREWoJ4rsIaCmBmHZIfjoiItBTxHMIabGYfAouAxWb2vpkNTn5oIiKSzuK5kXASMMHdd3H3nYH/CctERKQNiyeBtHf3mVUz7p4PtE9aRCIi0iLEk0CWmtmvzax/+LoeWNqYnZpZNzN7zcw+D99jDo1iZuPDOp+b2fio8nwz+9TM5ocvPZ9ERKSZxZNAzgd2Av4RvnYKyxpjIjDD3fcAZoTz1ZhZN+A3wEHAgcBvaiSas9x9WPj6tpHxiIhIA8VzFdY64HIz6wxUuvvGJtjvicDocHoykA9cU6POWOA1d18LYGavAUcDf2+C/YuISCPFMxbWAcCjQMdwfj1wvru/34j99nL3leH0KqBXjDp9gRVR8wVhWZXHzKwCeA642d1jPiXRzC4GLgbo1asX+fn5DQ52dPieyLot2aZNm9TmNkBtbv2S1d54biR8BPiZu88GMLNDgceAoXWtZGbTge/FWHRd9Iy7u5k19BG5Z7n712bWkSCBnAM8Eauiu08ivGpsxIgRPnr06AbuarvGrNsS5efnq81tgNrc+iWrvfEkkIqq5AHg7nPMrLy+ldz9qNqWmdlqM+vt7ivNrDcQ6xzG12z/8Q/Qj+BQF+7+dfi+0cyeIjhHEjOBiIhIcsT7TPQHzWy0mY0ysz8D+Wa2v5ntn+B+pwJVV1WNB16IUedV4Idm1jU8ef5D4FUzyzSzHgBmlgUcDyxMMA4REUlQPD2QfcP339Qo3w9w4MgE9nsb8H9mdgHwJXA6gJmNAC5x9wvdfa2Z/Q54L1znprCsPUEiyQIiwHRAj9sVEWlm8VyFdURT79TdC4ExMcrnARdGzT9KcAI/us5mYHhTxyQiIg0TzyEsERGRHSiBiIhIQpRAREQkIfEM535aeL8FZna9mf2jEVdfiYhIKxFPD+TX4f0WhwJHEdxY+JfkhiUiIukungRSEb4fB0xy95eA7OSFJCIiLUE8CeRrM3sQ+BEwzcxy4lyv1XiXA1kZc1QWEZG2K55EcDrBXeFj3b0I6AZclcyg0s0aduLrauM4iohIPHei9wZecvdSMxtNMIiixp0SEWnj4umBPAdUmNnuBKPafh94KqlRiYhI2osngVS6ezlwCnCvu19F0CsREZE2LJ4EUmZmZwLjgH+FZVnJC0lERFqCeBLIecAhwC3uvszMBgB/TW5YIiKS7upNIO6+GPglsMDMBgMF7n570iMTEZG0Fs8z0UcDk4HlgAHfN7Px7j4rqZGJiEhai+cy3ruAH7r7pwBmNhD4O3omh4hImxbPOZCsquQB4O6foZPoIiJtXjw9kPfN7GHgyXD+LGBe8kISEZGWIJ4EcglwKXB5OD8b+HPSIkpDPXtC7vrKVIchIpJW6kwgZhYBPnL3vYA/NE9IIiLSEtR5DsTdK4BPzWznZopHRERaiHgOYXUFFpnZXGBzVaG7n5C0qEREJO3Fk0B+nfQoRESkxak1gYSj7/Zy9zdqlB8KrEx2YCIikt7qOgdyN7AhRvn6cFmb0WNLAX3Llqc6DBGRtFLXIaxe7r6gZqG7LzCz/skLKf3suuGjVIcgIpJ26uqBdKljWbsmjkNERFqYuhLIPDO7qGahmV0IvJ+8kEREpCWo6xDWFcA/zewstieMEUA2cHKS4xIRkTRXawJx99XAf5nZEcDgsPgld3+9WSITEZG0Vu99IO4+E5jZDLGIiEgLEs9w7iIiIjtQAhERkYSkJIGYWTcze83MPg/fu9ZS7xUzKzKzf9UoH2Bm75rZEjObYmbZzRO5iIhUSVUPZCIww933AGaE87HcAZwTo/x24I/uvjuwDrggKVGKiEitUpVATgQmh9OTgZNiVXL3GcDG6DIzM+BI4Nn61hcRkeRJVQLp5e5VAzKuAno1YN3uQJG7l4fzBUDfpgxORETqF89w7gkxs+nA92Isui56xt3dzDyJcVwMXAzQq1cv8vPzG7yN0eF7Iuu2ZJs2bVKb2wC1ufVLVnuTlkDc/ajalpnZajPr7e4rzaw38G0DNl0IdDGzzLAX0g/4uo44JgGTAEaMGOGjR49uwK6qa8y6LVF+fr7a3Aaoza1fstqbqkNYU4Hx4fR44IV4V3R3J7ix8dRE1hcRkaaRqgRyG/ADM/scOCqcx8xGmNnDVZXMbDbwDDDGzArMbGy46BpggpktITgn8kizRi8iIsk7hFUXdy8ExsQonwdcGDV/WC3rLwUOTFqAIiJSL92JLiIiCVECERGRhCiBiIhIQpRAREQkIUogIiKSECUQERFJiBKIiIgkRAlEREQSogQiIiIJUQIREZGEKIGIiEhClEBERCQhSiAiIpIQJRAREUmIEoiIiCRECURERBKiBCIiIglRAhERkYQogYiISEKUQEREJCFKICIikhAlkDgsZm820iHVYYiIpJXMVAfQEixjAJtpzwGpDkREJI2oBxKH7t0gO7sy1WGIiKQVJZA4RCKQkeGpDkNEJK0ogYiISEKUQOKwNqc3qzL7pjoMEZG0opPocbhn6MN88cUGPkl1ICIiaUQ9EBERSYgSiIiIJEQJREREEqIEIiIiCVECERGRhCiBiIhIQlKSQMysm5m9Zmafh+9da6n3ipkVmdm/apQ/bmbLzGx++BrWLIGLiMg2qeqBTARmuPsewIxwPpY7gHNqWXaVuw8LX/OTEKOIiNQhVQnkRGByOD0ZOClWJXefAWxspphERKQBUnUnei93XxlOrwJ6JbCNW8zsBsIejLuXxqpkZhcDFwP06tWL/Pz8Bu+osHAIFRWRhNZtyTZt2qQ2twFqc+uXrPYmLYGY2XTgezEWXRc94+5uZg0d6vZagsSTDUwCrgFuilXR3SeFdRgxYoSPHj26gbuC7t2hqGgDiazbkuXn56vNbYDa3Polq71JSyDuflRty8xstZn1dveVZtYb+LaB267qvZSa2WPALxsRar0OPRQ6dy4COiVzNyIiLUqqzoFMBcaH0+OBFxqycph0MDMjOH+ysCmDq+naa+Hii5cmcxciIi1OqhLIbcAPzOxz4KhwHjMbYWYPV1Uys9nAM8AYMysws7Hhor+Z2QJgAdADuLlZoxcRkdScRHf3QmBMjPJ5wIVR84fVsv6RyYtORETioTvRRUQkIUogIiKSECUQERFJiBKIiIgkRAlEREQSogQiIiIJMfeGjiLScpnZGuDLBFfvAXzXhOG0BGpz26A2t36Nbe8u7r5TzcI2lUAaw8zmufuIVMfRnNTmtkFtbv2S1V4dwhIRkYQogYiISEKUQOI3KdUBpIDa3Daoza1fUtqrcyAiIpIQ9UBERCQhSiAiIpIQJZAazOxoM/vUzJaY2cQYy3PMbEq4/F0z65+CMJtUHG2eYGaLzew/ZjbDzHZJRZxNqb42R9X7f2bmZtaiL/mMp71mdnr4d15kZk81d4xNLY5/1zub2Uwz+zD8t31sKuJsSmb2qJl9a2YxH7JngXvCz+Q/ZrZ/o3bo7nqFLyACfAHsSvC89Y+AQTXq/Ax4IJw+A5iS6riboc1HAHnh9E/bQpvDeh2BWcA7wIhUx53kv/EewIdA13C+Z6rjboY2TwJ+Gk4PApanOu4maPfhwP7AwlqWHwu8DBhwMPBuY/anHkh1BwJL3H2pu28FngZOrFHnRGByOP0swdMSrRljbGr1ttndZ7p7cTj7DtCvmWNsavH8nQF+B9wObGnO4JIgnvZeBNzv7usA3P3bZo6xqcXTZgc6hdOdgW+aMb6kcPdZwNo6qpwIPOGBd4AuVY8IT4QSSHV9gRVR8wVhWcw67l4OrAe6N0t0yRFPm6NdQPALpiWrt81h1/777v5ScwaWJPH8jQcCA83sTTN7x8yObrbokiOeNt8InG1mBcA04LLmCS2lGvr/vU4peaSttExmdjYwAhiV6liSycwygD8A56Y4lOaUSXAYazRBD3OWmQ1x96JUBpVkZwKPu/tdZnYI8FczG+zulakOrKVQD6S6r4HvR833C8ti1jGzTIKub2GzRJcc8bQZMzsKuA44wd1Lmym2ZKmvzR2BwUC+mS0nOFY8tQWfSI/nb1wATHX3MndfBnxGkFBaqnjafAHwfwDu/jaQSzDoYGsW1//3eCmBVPcesIeZDTCzbIKT5FNr1JkKjA+nTwVe9/DsVAtVb5vNbD/gQYLk0dKPjUM9bXb39e7ew937u3t/gvM+J7j7vNSE22jx/Lt+nqD3gZn1IDiktbQZY2xq8bT5K2AMgJntTZBA1jRrlM1vKjAuvBrrYGC9u69MdGM6hBXF3cvN7OfAqwRXcTzq7ovM7CZgnrtPBR4h6OouIThZdUbqIm68ONt8B9ABeCa8XuArdz8hZUE3UpxtbjXibO+rwA/NbDFQAVzl7i22Zx1nm/8HeMjMriQ4oX5uC/8xiJn9neCHQI/w3M5vgCwAd3+A4FzPscASoBg4r1H7a+Gfl4iIpIgOYYmISEKUQEREJCFKICIikhAlEBERSYgSiIhIK1Xf4Io16v7RzOaHr8/MrKi+dZRApNUIR829K2r+l2Z2YxNt+3EzO7UptlXPfk4zs4/NbGay91VPHMvD+0GkZXsciGtYGne/0t2Hufsw4F7gH/WtowQirUkpcEq6ffGFIxbE6wLgInc/IlnxSNsRa3BFM9vNzF4xs/fNbLaZ7RVj1TOBv9e3fSUQaU3KCYbovrLmgpo9CDPbFL6PNrM3zOwFM1tqZreZ2VlmNtfMFpjZblGbOcrM5oXd++PD9SNmdoeZvRc+X+EnUdudbWZTgcUx4jkz3P5CM7s9LLsBOBR4xMzuqFG/t5nNCg8vLDSzw8Lyv4QxLTKz30bVX25m/xvWn2dm+5vZq2b2hZldEhXjLDN7yYLnZjwQjgNWM9azw89jvpk9GLY5En6mC8N27PCZS9qaBFzm7sOBXwJ/jl5owfN+BgCv17ch3Ykurc39wH/M7PcNWGdfYG+CX2pLgYfd/UAz+wXBCK1XhPX6EwwTvhsw08x2B8YRDAdxgJnlAG+a2b/D+vsDg8OxpbYxsz4Ew8QPB9YB/zazk9z9JjM7EvhljGFTfgy86u63mFkEyAvLr3P3tWHZDDMb6u7/CZd95e7DzOyPBIcyRhIM17EQeCCscyDBszC+BF4BTiF4TEFVrHsDPwJGunuZmf0ZOAtYBPR198FhvS71f8ySambWAfgvto8qAZBTo9oZwLPuXlHf9pRApFVx9w1m9gRwOVAS52rvVY0HZGZfAFUJYAHBw7Sq/F84UuvnZrYU2Av4ITA0qnfTmWAQwq3A3JrJI3QAkO/ua8J9/o3gQUDP1xUj8KiZZQHPu/v8sPx0M7uY4P9yb4JkUJVAqoZkWQB0cPeNwEYzK436wp/r7kvDOP5O0APalkAIxooaDrwXfuG0A74FXgR2NbN7gZeiPjNJbxlAUXieozZnAJfGuzGR1uZugnMJ7aPKygn/vYeHabKjlkWPLlwZNV9J9R9ZNcf9cYInu11WdfLR3Qe4e9WX6ebGNKLajoJj2YcTjJz6uJmNM7MBBIcgxrj7UIIv8tyo1aLbUbONVe2K1aZoBkyOat+e7n5j+OCpfYF84BLg4UY1UJqFu28AlpnZabDtEbf7Vi0Pz4d0Bd6OZ3tKINLquPtagmG6L4gqXk7wSxrgBMIB5hroNDPLCM+L7Ap8SjBY30/DngFmNtDM2te1EWAuMMrMeoSHns4E3qhrhfC49Gp3f4jgy3p/gqfpbQbWm1kv4JgE2nSgBSPWZhAcqppTY/kM4FQz6xnG0c3MdgkvVMhw9+eA68N4JM2Evcq3gT3NrMDMLiA4BHmBmX1EcCgy+kmNZwBPxzuopA5hSWt1F/DzqPmHgBfC/zSvkFjv4CuCL/9OwCXuvsXMHiY4N/KBBcd41gAn1bURd19pZhOBmQS/8F9y9xfq2fdo4CozKwM2AePcfZmZfQh8QvCUuTcTaNN7wH3A7mE8/6wR62Izu57gPE0GUEZweKMEeCzqpPu1Cexbkszdz6xlUcxLe939xoZsX6PxirRRZjaa4IT98SkORVooHcISEZGEqAciIiIJUQ9EREQSogQiIiIJUQIREZGEKIGIiEhClEBERCQh/x/Jr32Jz+Bq0wAAAABJRU5ErkJggg==\n",
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\n",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 864x576 with 1 Axes>"
       ]
      },
      "metadata": {
@@ -463,9 +469,9 @@
     },
     {
      "data": {
-      "image/png": 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Ax9LXJI+nDlJcbHM/r19fPbdStOXgYw65y7qg66yj4vAxcEqSfT5IU1s8nrpLUVFEHNJhOfiYQ+5Tly0HVb24phri8dRp3CQ9UD3LwbuVag+13HKowjBNj8dTaZzlAOkJSDdpYqOsvTjkLrXccvDi4PFkGtVIzAGqbjmE3UoifsKfXMdZDhs2ZLcdVSSpOASD3s6qicZ4PHWSXbvsPZ2WA/iy3bmMat13K6lqGXBdDbTF46mbODFIp+UAfja4XKawMPJ3rqviEPChiPxWRHYXkbbuldGWeTx1hWhxqIrloGoupLA4eMshd3FWg0itFYdUZwn5cfB+eWid4meD83iS454gqyMOO3aYQHi3Uu3ABaO7d6/b4qCqvTLdEI+nzpIOt1K4XLfDB6RzF2c59OsHY8ZUfQ6PLJJSa0WkkYhcKSKjgtcVItIo043zeOoE6bAcwrPAOXzMIXdxlkPfviYMW7Zktz1VIFUpexTYB3gkeO0TrPN4PMlwYlCdQXDhuRwc3q2Uu4QtB6iVrqVUYw77qerQ0PJHIvJtJhpU55gyBfbYw0/lWJ9Jh+UQz63kxSE3WbcOGjWCHj1seeNG6FW7vPOpWg6lItLHLYhIb6A0M02qQ5SVWT33hx/Odks82cSJgZuLIZ2Wg4855CZr10L79tA2SOrMlOVQVpaZ85K6OPwOGCsi40TkY+Aj4LfVuXCQFjtWRGaIyHQRuSpY31ZEPhCRucF7m+pcJ6sUFcHOnVbP3VN/ceLQpImV7U6X5eBjDrnLunXQoQO0CbqvTIiDKnTrRo9nn03/uUndrfQp0BfoHyzPTsO1S4BrVXWKiLQAJovIB8BPgDGq+kcRuQG4Abg+DdereVwnsGlTVpvhyTLOUmjSxF7pCkg3awYlJeXnivDkBs5yyKQ4rFkDK1dSEv5NpJFULYfPVbVIVb8LXkXA59W5sKquVNUpweetwEygKzbjnJPCZ4HTqnOdrOI6gVoYjPKkEfc7aNzYXul0K4W3eXKHaMshE/WVZs0CYLuLa6SZhJaDiHTCOuzdRGRvbHIfgJZAfroaISI9gb2BL4ECVXV+mFVAQZxjLgMuAygoKGDcuHFVunZhYWGVj01Gk7VrOQjYtHgx32ToGlUhk/ecq2TznttPnsxgYOJ33zEE2LB4MbMr2ZZu33zDHsCnX39Nydy5AHRetoz+wIQPP6S4Q4cKx/i/c/Y4eOVK1gwcyNyvvuKwhg1Z9u23LEhzu7qMHk0/YG379pm5Z1WN+wIuAsYCW4N39xoNnJ7o2FRfQHNgsjsfsClq+8Zk59hnn320qowdO7bKxyZl/nxVUB00KHPXqAIZveccJav3/OKL9juYNUu1Vy/V88+v/DnuvNPOUVwcWff887Zu9uyYh/i/c5bYtcv+LrfdZssdO6pedln6r3PllarNmunYjz6q8imASRqnX0022c+zwLMicoaqvppuYQoG0r0KvKCqrwWrV4tIZ1VdKSKdgTXpvm6N4WMOHoi4kRo3rnrMobDQjm8UGnvqZ4PLTZwLyVlzbdpkxrU8cybsuadlwGWAVMtnvCoiJwGDgKah9XdU9cIiIsCTwExV/Uto02jMYvlj8P5mVa+RdXzMwQMVs5WqGnOIDjz6mENu4kZHt29v723bZk4cRoxI/3kDUi2f8Q+s+N6vsbjDmUB1oyAHAxcAR4rIN8HrREwUjhGRucDRwXLtxHUC27dXvYa/p/aTDsshei4HiIiDH+uQW7jR0Zm0HLZuhWXLYMCA9J43RKqprD9Q1SEi8p2q/l5E7gferc6FVfVTIgHuaI6qzrlzhnAnsGkTdOyYtaZ4skg6LIfouRzAWw65SrTl0KaNPeWnk9nBaIIMikOqqaw7gvftItIF2AV0zkyT6hDR4uCpn0SnsqbLcvAxh9ykJiwHJzZ77pne84ZIVRzeEpHWwJ+BKcAi4KUMtanuEH5C9HGH+ku6AtLecqgdOMuhXTt7b9MGNm9Ob6mLmTOhYUOr25YhUg1I3xl8fFVE3gKaqurmjLWqruAtBw/Y76BRI8sqqU5AuiBqyI8Xh9xk3Tpo1Soyar1NGyt1sXlzZFBcdZk504QhnL2WZpINgjs9wTZC6aeeWITFwVsO9ZeiokhH4QPStZfCQpg/H4YOTbzfunWReAOUL6GRTnEYODA954pDMsvhlATbFPDikAhvOXjAfgdNmtjndAakGzUy10JtsxxU4bXX4IQTInGTXKekBE45BT7/3Dr53XaLv6+rq+RId2XWXbtMpE6P++yeFpINgrs4o1ev6/iYgwfSYzls3AitW1dcXxvndPjoIxg5Ep5/Hs47L9utSY0bbgBXomL+fBg8OP6+69ZB166R5XQX35s3Dy0pYWqzg1j+bmKdqg6pjnMoEJEnReTdYHmgiFySmSbVIbzl4IHqWw67dpkAuCfQMLVRHF4Kcllc4DbXeflluP/+yICzOXMS7x9tOaS7+N7MmVzCkwy9+RQuv9wMsUyQarbSM8D7QJdgeQ5wdQbaU7dw4tCokbcc6jPhktpVsRzcbyeWv7q2TfhTXAyvBpV4MlGpNN1Mnw4//alN2jVqlK0LCh/GRDVSkdVRDctBFSZNMsOlXz9YtQqYOZMzeYV//nUnX3yRseoZKYtDe1X9D1BmDdYS/ExwyXFPiB07esuhPlNUVD3LwXWi8cTBTQRUG3j//cj/Qm0QhwcesLjOK69YamqnThUtB9XIvWzbZhN8xQtIp8jy5fC730Hv3rDffnDffTbL6MaNwMyZnLD7dC67smlGx9WmKg7bRKQdFoRGRA4EfCprMtwTYkGBtxzqM+myHGK5lVq1gi1bqte+muTf/7b76NGjdojDJ5/A4YdD52DMb9++FcXhpZdMDJ56quIAOLCgQOPGCfuAsjKYMAEmTows/+1vlpD01FOwerXp6oAB2DwOGRz85ki1fMY1WEG8PiLyGdABGJmxVtUVXCfQsWPkR+Opf0RbDmVlUFoKDRqkdnwit1KrVrBoUVqamXG2b4c337Qg9Dff5L44rFplLqRLL42s69cP3nqr/H5jx5r1cMklkX3DloNIzFHSZWXw2WfmrXr1VbMWfvhDeOMN2H13WL++YvYyZWUmDpdkPuSbVBxEpAFwePDqj9VDmq2quzLcttqPG/zUpg3Mm5ft1niyRdhycO9FRamncSZyK7VubYOragNvvWVul7PPhiVLrPfLZT791N4PPTSyrl8/e4zfvNmEGWDKFNundWt4/HFbFz35UlCZVTUSIzjuOPjwQ3tuOOEEuPdeOPnkyCEVhAHMatm2LflYizSQ1K2kqqXAOapaoqrTVXWaF4YUcSmMmarn7qkdRKeyQuVcS8ncSrUlnvXvf5t75rDD7F5y3XIYP95cQsOHR9b17WvvLihdXAxTp8IPfmAmwBln2Ppu3b4/pKQEPso7il9+fgF9+8KOoFLdz38OL75oyU2vv24GldObuHzxhb0feGD17y8JqcYcPhORh0TkUBEZ7l4ZbVldwD0xtm5t/8CZyjnz5DbRqaxQuaC0E4dY4xxat7aYQzrr9mSCzZvhnXfgrLPMnVZbxOHAAyN/MzDLASLiMH26pRoPH277vfyyVUzt1o3Zs00AunSBo6b/nX+tPIbhw2Hj14vg8MMZWTCec86BFi0q0aYvv4SWLWsk5pCqOAzDJvq5A7g/eN2XoTbVHVyn0KaN+ZhrU1aJJ31EB6TdulTZsMF6kIYxvMCtW9tDx9at1W5mRpk61QTxuONsuW1be2AqzdGkxy1b4Ntvy7uUAPr0Mb+QC0pPnmzvw4ezaxe890EDphaZgBQWwgsvwFFHwajD/srabsP5z3+gy0M3WaD7pJMix6fKF1/A/vtDXqpdd9VJeoUg5jBaVY+Ieh2Z8dbVdsKWA9Qe89+TXqID0m5dqmzcGNulBBE/RK7/tlautHfnbmnbNlKMLheZMMGsscMOK7++aVPo3v17cSie+C3v7HYGF9/Vh4ICix08+qjtOny4uYxeegnOGDqf/M0rYdo0c6/95Cf2HRx3HMyYkVqbtm83kT3ggPTdZwJSjjnUQFvqHuGYA/i4Q32lupZDooJt7sEjVztZhxOHTp3s3YldrrqWPvnELLUYvn3t2w/mzEEVhv7rWk7aMYrX3xBOOQVGj4a/BJMei4RKW7iy3bfeapHm++6zaHSjRnDMMbBiRfI2TZ5sllYNxBsg9VTWz0TkIeBl4Pux+qo6JSOtqis4t5K3HOo3sSyHyrqVkolDrv+2Vq2yztbNcZDr4jB+vD36B8UOd+yA996zmPM3k59laulA8kpL+L/S39P2tB9w1L8v/f5PHBP393v9dbjlFvse2rWD//0P9tkH7rkHHnoocZtcMDpXLIeAYfiYQ+VxT4zecqjfxLIc6qNbqVOniK88l8Vh50746is49FAmTrTM2w4drAjqe+/BQf3XU7ilFD75hPN3PcOJZ+yWWBggcr+tW8M110TW77UXXHghPPmkpcgm4ssvbch0dJpshkhJHGLEG3zMIRWcW6m2PN15MkM4lbUqlkNdcSs5lxLkrDgUFsJ/7l3I/OJucOihrFkDY8ZYmukHH5gB9MStS2nJVosdQPlU13i4+7322opZZ9ddZ7+RBx9MfI4vv6wxqwFSdCuJSCvgNsBFZz4G7vCzwSXBWw4eqH4qayK3Um2xHFatskCuI4fEYetWG583ahS8+y7s2DGAOzmHmw85hONbm66VSxRz6ayvvmpBhf79k1/kyCPhj3+EK66ouK1fPyth/sgjcP31sVOWly+HZctqVBxSdSs9BWwFzgpeW4CnM9WoOoPrFFq2tOVc/wf2pB9Vy4OvakB6xw4TkmRupdpmOaS7jHUlccNCdu2Cnj3h3HMtQemnP4VxA3/FjcPeg3btaNAgRgZxjx62csMGGDYstTIo+fnW8UdP2OS48UZLn33kkdjbv/zS3msoGA2pi0MfVb1NVRcEr98DvTPZsDqBsxwaNLB/Ym851D+cCFTVckhUV8mdLz8/tx88Skosp9MVrwPrXFu2rFFx2LQJ/vUvm9DtkENsXaNG8Oc/W/x5+XJ46IZlHD7jURqcmWCWtYYNbbwDWDA5Hey9Nxx/vLmWZs0ysQ8Pmv3yS/tbDxuWnuulQKrisENEDnELInIwsCMzTapDhH3NbpS0p37hxKGqlkOiukqOXC+hsWaNdXRhcYAaGyX9wQc23qxjR7joIhvbduCBpllg1sIhhwSx8teCmY9HJqkr6lxLqcQbUuWmm0xEBwyw/iI/38pxvPuumTV7703yyHf6SDWV9ZfAs0HsAWAj8JOMtKguEfY1+/pK9RNnIVTXcojnVoLcL74XPcbBkSFxWLfOKpuecopVy18yr4gZ0xtx9dV5jBxp8yPEnSBn1CjLIHKdfzxcjaV0isOhh9pYhunTLXNp4UL4z38ignXllem7VgqkJA6q+g0wVERaBsu1qIB8FgmnMHrLoX5SXcshmVsJct9yWLXK3lOxHJ55xspDDBwY/3y/+hUcfbTllgasWWNDCB5/fAjffGNjxZ580qyCi97+MT8d3gD506uJ27lypVVivf325Pd02mlWaTlRO6vC8OHlBeeBB2xk3euvw8UXp/daSUg1W+ke4E+quilYbgNcq6o3Z7BttZ+wW6lNm8TTC3rqJs5CqGoqaypupdatc7v8tbMcYonD0qWR5eXLrQNs1syCA6fH8Pvv2gX//CesWEHJqafTsKHdeteu5ibq1q0p119vXqFhwwBVGn7xaWrV7V57zdxfyVxKYE/50XWXMkHjxtaeVNqUZlKNOZzghAFAVTcCJ2akRXUJbznUbVKphBodkK7sILhU3Uq5/Nty4lBQUH59tOXg5jxp1cp87bfcUuE7XjFlFX8v+xWHv38jP/qRrWvXzgYXf/st/OtfX3H33eaeF8HcM+vXmwi5IEM8Ro0yf3+6rYFaSqri0EBEvo+EiMhuQM1FRmorPuZQd/nuOwsYJrMGo91KlbUcNm60Xs6lQ8eiNriV2ratGEx14uCycubPt/cPPzR/0F13mZWAVcI+5BDodlA3ruTvrNvZnIOGRwT25z+HIUNixBKmT7f30tLyVko0q1dbPaUsPKHnKqmKwwvAGBG5REQuAT4Ans1cs+oI0ZbDtm1mFntqP7Nn29P/558n3q+6AekNG+y3k6hEswtI5+p8IdFjHBxt21qn7cqNz59vaaJ9+7Lk1id4oPktbPvKOvd582y3O07/lhkMYDqDuen4FEq7TZsW+bxwYfz93njDrJQzz0z9vuo4qZbPuBe4CxgQvO5U1T9lsmG1HtWKMQfI7Sc8T+q4Dm3mzMT7RVsODRrYqzKWQyKXEpjlUFxsNYFykZUrK8YboMIo6YXfbuG+VndwwMEN6dFTuKbwDj792gaN3XCDuY1uHvYWA5hlx333XfJrT58eGaQWTxxKS80vNXAgDB5cmTur06Sayoqqvge8l8G21C1KS00gwpYDmDjUUOEsTwbZEiTsJavFH205gP0mKhNzSBSMhvL1lb6vEZ1DrFoVGXUWpm1bdtGQRhs2MGN7Twa9/XcA9u1llSbOmHAte3z3GnBvZBDykiX2/1NUVFEcHnuMths2wIgRkXXTp9ughi++iC8OL70UmWchbo5r/SPz0wnVV6IDkb6+Ut0iVXGIthzAfhPxLIeLLoK3344sJ6qr5Mjlwo6qFdxKs2djQePfHcUv+Ads2MCAAfBw/u9YcO7NTJxolSb2GNrMxCDsil282Opd7LWXTXzj2LkTrr6ans88U/7a06bB0KGw++6xxaG42OZYGDbMu5Si8OKQKaJTGHP5H9hTeZw4LFiQ2J1TGcuhpMRSOF98MbIuVbcS5OZva9Mmu9fOnXn4YQsa77kn3Hwz7Jafx4F8ARs2IJs38avt99Fr79aRY3v3tjjAkiWRdYsXWwG/IUPMcnBxlvHjYccOWsyZE8mAWr7c/k6DBkGvXrHF4YknbP0999TI1Ju1iZS+DRG5KpV1nhDRT4w+5lC3cDGHsrLIfMKxqIzl4ATn228j6yrrVsoR3EP7H+/cRRkCnTszZ47p2IMPWuLQhHc3cylPWGfuMpVczSIwcQATYHfSJUus8N2QIXa/LgPpPfN4iyqMHWvrXDB68ODY4rBtG9x5p41XOP749H8JtZxUpfKiGOt+Ut2Li8hTIrJGRKaF1rUVkQ9EZG7wnuQ/I0eJdiu5f2DvVqobbNkSedJM5FqKJQ6NG8cWB9e5z5pl1ohqam6lHLEcVO1h/pZbLLa7115w04MdmMZg6NSJBx6wB/yrrgqmkg5XZo0lDu6z27ZunVWpdeIAkbjDe+/BEUdQkp9vqbAQSWN1lsOqVXa84+GHbd0f/uBjDTFIKA4ico6I/BfoJSKjQ6+xQDqKojwDREv2DcAYVe0LjAmWax/xLAcvDnWDLVtswFReXmJxiOVWatIktlvJiUNpqZ2zsNA+J3MrZdFlqQpFU+fAkUfy0ZtbGTrUPDSdO1v16RUPvc4QpkLnzhW9Nk2b2liRsDj06hXZ3rmzfVfOcnDupe7dI1lF331n1sOMGXDyyWwaNiwiDtOmWayjXbvIeRctipz/lVfgBz+Agw9O4zdSd0hmOUzApgSdRWR60PuBa4HjqntxVf2EiiLzQyJjKJ4FTqvudbJCdMyhaVPrSAoLs9emTDJ3bu7m2WeCrVsta6ZPn8TprFWxHMA6vVTqKkGNu5VUYdIkSy/dYw+444o1MHYsh5Z9zGOPWfz5o4/gl7+ETjsCV06sVFaIDISbP9/KpobLXOTlWafuxGHxYnvv0cMGBfbsaUHp99+39ccdx8bhw21QxKJFZjkMGmTbnDg419K2bfD11+UzmzzlSJjKqqqLgcXAQTXTHAAKVDUYb88qoCDWTiJyGXAZQEFBAePGjavSxQoLC6t8bCKaz5vHvsC0OXNYF5z/kKZNWTl7NvMzcL3KkO57brJqFQeeey5T776bDQfV5E8lddJ9z/uuWMHOggLo2JHdJk5kYpxzd502jb7ApxMnUhKMch6+cyclK1fyXdQx7SZMYK/g89K332Z1SYn9hpYv//43FBNVDs/LY8m337IwtF8mfttPP92T//2vgFWrdqNBgzKGD99Ev+WjAVj2zhv0Pb85M2ZEjKk+X31Fl8aNGT9lSkzXzb6NG7NzzhwabN9OXocOfB3V3r1ataLxd98xedw4uo0Zwx7Ap0uWULJ5M4O7dGG3zz9n+4oVtOjQgS/WrIEBA+gLzH7oIfaYOpWVJ57IvHHjaLx+PT8A5rz/Pivy82k9ZQrDSkv5rkULNmT5/7G6ZKoPQ1WTvoDTgbnAZmwWuK3AllSOTeHcPYFpoeVNUds3JjvHPvvso1Vl7NixVT42IV9+qQqqb78dWdepk+qll2bmepUg7ff8xRd2rw8+mN7zppG033OvXqrnnad6442qDRuqFhfH3u/Pf7bvZuvWyLpDD1U94oiK+z73nO3btq1t/+gjW06l7e3aqf7qV+VWVfeeS0tVP/tM9d57I+vOPVf1xBNVn3pKdf16VS0pUW3Rwtp59tkVT3LeefZdxWPECPs+undXPf/8ituvuEK1ZUvVsjLVq69WbdbMPquq3nyzaoMGtv1nP1NV1bEffaTaubPqAQdYmx57zPYtK1Nt2lT12mtt+Y47VEVUN2yo/BeTY1Tn7wxM0jj9aqqD4P4EnKKqSYaDpoXVItJZVVeKSGdgTQ1cM/3Ecic0a2bmbF3DZe6sXp3ddtQkW7eaa2PgQEtBnT/fcjSjiZfKGg6MOpxb6NBDLXKbSkVWR5qK75WW2rwyo0bZFMnLl1tzL7rI6uY9/3yUAfDNVPsumjSJPWI5XukMR9u2kbiBy04K06ePxXc2bIiksboGDBliDd6yJZJtJGLlvJ97zpadW0nE3FDOrfTZZ7Ytle+2npJqttLqGhIGgNFEsqMuAt6soeuml+iYA9RdcXBxlPokDlu2mDgMGGDLzo+yYwf86U+RtFT3kBCeiDheKmtYHDZsiKRiptKBVaP4XmlpZKjGiy/CYYdZvbv99jMxWLuq9PuCqhU8Q59+au9nnRWpNxUmXukMR9u2FiNQLZ+p5Ains7o0VofLWGrQAI46KrL+6KMjn8MVVl06q1PAWKO2Pd+TqjhMEpGXg+yl092ruhcXkZeAz4H+IrIsKOr3R+AYEZkLHB0s1z7qouVQWmpPZKWl5dfXN8uhqMj+vi1bRqwFJw5/+IMN733rLVt2xRfDvWq8gPSWLSYc++9vyx9/bO/JspWg0rPBlZREgsZdusDjj9v6k0+2ahJr19r8MueV/ouWfQsiE/ZE8+mnNvr4pJPsdxEdnF+1Krk4OJKJg7McHHvsYYkeBx0UCcpDRCi6dSu/3onDtGn2m/VZSglJ1a3UEtgOHBtap8Br1bm4qp4TZ9NRcdbXHqLHOUDtF4fPPoMLL7R/uiOOiKyvb+LgrIIWLexv2rOndYoLFpjVAJHMmqKiiqWqE6WytmoVeSKeMMEsjmbNkrepVavEg/ECyspsIrVXX7VhA/n51q+7zNA2beDss0MHvP22zYfwhz/AX/9a/mSq5v46/PBIm6dODWbZwcyRjRuTu5UcscTBZRlNnWoNDlsODRrY9+2sN0fXrtaecFqsO9emTRHh9pZDQlKdJrRm56erC8RzK61dm532pAP3ZBo9VqO+uZWcGLo5FgYMMMvhN7+xzrxRo4g4hMu2OxKlsrZsaR19jx52jg4dUhugFcdy2LULxoyxcXVXX23ZoUuWmOdl5Eg44QQTiLhMmGDX/8c/4Le/NSvBsXgxrFhhnWzfvhXjDvGmBw3jxCE/v+JkQGD/MwUF4LJxwpYDwK9/Hfu8771nf4cwTiyef97MpbDQeCqQavmMfiIyxo1kFpEhIuKnCE1EXXQrbd9u7+7J2RG2HOrDWAd3/04cBg60khejR1sRt379qmc5gBWLg9RcSlAuIF1cDO+8A/fe25+CAhOAu+6KxBXefttcR2eckUQYli6FZcvg2mtt+a67ym938YZDDjFRHDiwfDE89x2kIg69e8cXwT594Kuv7HOqHXrnztC+ffl1ThxmzTKXkh8VnZBUYw6PAzcCuwBU9Tvg7IRH1HfqolspmTgUF2e9hEONEEscVE0Urr468tQPlbccosUhxWyanfltKS4sgpISHnjAXEXjx3fglFPgv/+1Pr5pU9s35T5xwgR7P/tsuOwyeOqpyEhmMHFo1SqSEbTXXuUth9des99/orEvThxiuZQcvXtHKrNGWw6VIexm8i6lpKQqDvmq+lXUuiQTstZz6qLl4NruxMARHvVdH1xL7v7daN6DDrJ5FB56yP7eThzchE+pWg5btlRKHHbsCILG50HH+37Hm/wQtmzhvPPMOnj99c949lkLMjthqBQTJphpMWQI3HSTWQe//31k+6efWvkJN9nCkCGWnbRunXXmL74Ip56aWOBSFQew63TtWoUbCWjTJvL9+mB0UlIVh3Ui0gcLQiMiI4GViQ+p58SKOeTnWwdbW10vySwHqBviMHWqJfbHm5A+2nIYMMC+g2OOseUePey7Wr+++pZDDLdSYaE9zHfoAKefbu71s/ZfTB/mw6ZNdOsGJ54IjRpV83c2YYJlTjVqZG6aK66wbLUjjrD36dPLP4HvFYzvnjrVGrVunSUwJKJTJwuEhFNOo3Hi0LVr+ZTgqtCrlz2kue/XE5dUv+nLgceAPUVkObAQOC9jraoLxLMcSkttW/TTZG3AWQ6xxGG33exRti6Iw+jRNq/CbbfFHpgVLQ4QeXqGiF988eL44lBUZA8JYR+PC0iDXbdtW+jalcJCiyGsX2+pp82aWTjgvPMsqDxiBDR6ewaM/zp99ZVc7aHrr4+su+suU6SHH450+uEn8HDG0vjxtu9xSUqwFRRYPMEdGwv3N6iOS8lx+ukmWtUVmXpA0m9IRBoAv1LVo0WkGZCnqluTHVfviRdzAPvHq43iEM9yKCw0t8C0aXVDHNwcAWvXpi4OYcLiEM+tBGaZuIyasrJybqWt2/J46+bpjBrbjnc7mu7uuSf84hemJ599FnXNdFdmnTTJHmR+8IPy7b7uOrjmGgtkTJlSXhwKCiwI/Mkntv2Xv6yYMRSLffZJvN25nNKRXXTLLdU/Rz0hqVtJVUuBQ4LP27wwpIhzK4WfUMLiUBtx4hAdc9i61f5x8/LqhjgsW2bva+JUbtm61XroeOMPUrEcoLxrqbCQzdqC0hatAevDzr2mExMmNuKnP7VMzmnTEgST0z2ngwtGH3hgxW0NG8KPfmQT5YR/3yLmWnr1Vbu3ZC6lVOnUydxabvyEp0ZI1bb6WkRGA68A3/dsqlqtQXB1mlgjY2u7OCRyK7VqZW6EmhaHv/7Vrn9zGjOrneUQTxy2bLFgdLyeum1b+1s7yyE8ShcilkNREZt2NWP0aHjluUb8jzV8uPZ/HApcfrm5jA46qLzHKi6JynbfeKONOXj66RROFDBhgpkq7dqlfgyYe2jsWMtg2nvvyh0bj7w8K82x227pOZ8nJVIVh6bAeuDI0Lpqj5Cu08SKK9R2cUjkVmrRwtwKNS0Ozz1nHV86xcFZDvEGLLq6SvEQiWQsxbEcVtKJS85sxofjLbGne+cGXMHf6dzNRvv27WuvlIlnOSxcCPfdZ7+9p55KLY9V1cThtNMq0YAAF5S+8ML0jiMIz/PgqRH8COlMEatTqKvisHUrNG+eHXFYutSe8FOZazkVtm+PVENN5FZK1lk5cQhiDuvWwRtvmAv+oiZNaM86Vq+xKTNHjoT9SyYhh/wW+r9XtXY7sYq2HO65x2Ibmzeb2HXsmPxcc+bYdxCON6TKSSdZEb6LfZdR2/EjpDNFUVFuisOHHzLottuqlk4ba5xDaal1qNmwHHbujHTgbr7g6uKsBkjsVkpkOQD06MGahdv459rTOXr8bXTqBJdeCi+8ADRuTCNKmPzaEv78ZzjgAJAtQafuLIDK0rCh/Q1ClkPTVavgmWcixQHnzk3tXGPG2HtVxKFTJ3j5ZXMxemo1foR0pshVy+GNN+jwySdVS3mMZTm4ewmLQ02N4wh35K68dXVx8QaRKrmV1q0LPvTowZWbfs8v1t7Jkh3tuf56S+55/33KxRy+Z3M1xcEdGxKH7i++aP76Rx+1FSkU5uO11ywbaehQ6N+/6m3x1Hr8COlMkasxBzfZyfe9WMCOHfDBB4mPdeKwc2ck08ZZEc6ttHNnxWymTOE6cki/OOy5Z2K3UkgcVqywwdGHH25emzlzgB49uJm7+IahzD77du6+2+KzIsTOVkqWHpsKrVvbCOVt22DJEjq9+y5cckmk9lG05bB1K3zxhR2janW7zzzTGjpmjAmLp97iR0hnily1HJw4rF9ffv0LL8Cxx9rUX/EIt9sJgCud4SwHSO5aUrUn0wceSL3dsViyxN47dkyfODhrZO+9k2YrzZpl/W63blYcdP16GzfXogXQoweDmc5QvkOaxEllTbfl0LmzmSbNm0fqHd1wgwlD794VLYcbb7R0qC5dbPT+ZZfZoLUPP6x8lpKnzlGdEdLnZ6xVdYFcjDmoxrccVqyw9zVr4tev2b49Uvph61brQMJ1htz9rl6dONVm+XIr0HbLLXDOOfHr/d9zj53nzDNjb3dP+ccdZ0OIo0ccV4WlS20gV/fu5lYKnXPJEps+s/v6EYxs2ZJOncxQ+v3vLahcblqBstCArXiD4MKWw+bNlrOaytwN8XjuOZsgaO5cmDuXeW3b0s+NKu7bt6LlMGGCDUC7+GKbi6J1axOTVAaueeo8qWYrLQD8COnKEMtycPWRsyUOq1ZF6jZHi4Pzr0dbFGG2b7eOfMmSiBsk7FZy2ULxZg1zzJ5t79u2we2321wBsfjLXyxam0gcOnSAffe1jnH16sQTy6TCsmU2Z0HHjrBrFwu/28qo/7Vk1KhI1eifMoKRLRfSurUNJI5J587Wye7aldogOFc6ozriVlBgmUIBK8aNo59b6NfPpn4rKzN30c6dZm1de60NqvB4okg1W6mJiJwLXAX8RkRuFZFbM9u0Wk6smENeng3kyZY4LFgQ+RxPHFwaZzSq1m7X+TpxqIpbyYnD6afDE09UnFoSIoXrErm5liyxjtxNY5YO19LSpazsMOT7bJuLL2vIdddZn/rHP8K877bzJJckjw3k5UUmxolnOYTdSqlkQFWHvn0truQsxKlTTbj23Tdz1/TUalKNObwJ/BALQm8LvTzxiOVWguyW7XYuJahoITixiCcOxcXWQ0aLQ9it1L69PfmmIg7NmpnF0KxZ+eJuDuf7TyQOS5ea+ycN4jB7Ntx9Nwyb8QLdP3yS9U3NtfbAL+awYAFMnGjN7NM+iA2kMijLldFI1XKoTrwhGf0CG8LFHSZPtvdkdY089ZZUYw7dVPX4jLakrhHLrQQ5IQ67mjenUWXdSi5TyYmDE4WwW6lhQxOIVMShXz97Or/xRnuNG2flRR0unrBuXezidW6fI480F1CHDlUShwkTrJidm8DsB2zhTyeNo0GBDRbbu/VC6DUsckBlsoqcOKRiOWRaHFwMaO5c+84mTbKYkZ8q0xOHVC2HCSKyV0ZbUteIV5Y7m+KwYAF07kxRhw6Vdyu5NidyK0FqA+Fmz44MzLrqKguEvvhi+X1cJhJYqmU0mzdbG5zrZvDgpOKgarvcdlsw3iBobsuW8OCDsHTcfD7jEH5z9kpa9w7mUYjOWIqePzoRuWQ5dOtmM/44y2HSJHMp+akyPXFIKA4iMlVEvsOqsk4Rkdki8l1ovSceuWo59O7NrlatyouDamQ5Vcsh2q3ksmySicOOHVZWwg2w2m03E4rw9JNQfgxDLNeS2+6ycQYPtlHSUQPwVG1656ee6snAgVb65847IyWv+/SxCc2uugq67QrcbrvvHhnhGz0QriqWQ/TvIBuWQ14e7LGHWQ47dth35V1KngQkcyudXCOtqIvkaszhsMMoWbKkvDhs2RKZozee5eDEwQWdw+LQrFlkwFRBAXz+efw2zJ1rPXZ49G3v3hUnKEhVHMKWQ2EhLFmCdu/xfdIRWALPvHk9GDECrrzSqk3HTGpycY5u3exv17p1RcvB3XdlYg7RFmSscQ6ZDkiDufKmT7c04pISH4z2JCSh5aCqi90LaA2cErxaB+s88cg1y6G42DrUXr0qWg7hp+NkbqXmze0VHgQX7iiTWQ4uUyksDn36WNvCbpalSyMT7bgMmzDO7RQogA4azCT24Ybf7qJvX7MQiorMa/L88zBq1ATGjLH5Z+JmuzrBceM8OnSonltp8GATmD32KL++dWtLc3X3pZp5ywFMHBYsgC+/tGVvOXgSkGoq61XAC0DH4PW8iPw6kw2r9eRazGHJEuuEnFtp/fqIC8aJQ/Pmyd1K+fnWMYYth2hx2L49EouIxomDy54BE4eyMnM3OZYutVHUTZrEtxwaNIDOnRk9GnqdcyD7MYn7X+tF375w//2R29tvP2jTZlf878axbJkJQtOmttyxY/XcSh07WrXY6AJ2DRva/c+YYcs7d5rllmlx6NvXrvPaa3afzrTyeGKQakD6EuAAVb1VVW8FDgQuzVyz6gC55lZyYxyc5VBaGinZ4KyI/v2Tu5WaNasoDs2bR/ZLNtZh9mxz24RHAjsLwcUdVE3Mune30g4hcSgrswyja147hC/anQQNG9KxIwzaK4+n217D6jMux5UUcn18yixdWr7D7Nixem6lRAwcGBGHdJTOSAUnyJ98YlaDD0Z7EpCqOAhQGlouDdZ54pHIreQ62prEjXHo3Ztd7qnXiYJ7Ou7Xz8QhVlVVJ2j5+dYxhrOVoi0HSCwO0dU+3RzBThw2b7bz7r47dO1K2fKVjB9v8YLdd7dpix+edSTfND8EsJks334bfnLALNrOjJ5cuRIsW2bC5YjlVtqyxVxC1Z0DfOBAE+wdOyLikOmYg0tnVfXxBk9SUhWHp4EvReR2Ebkd+AJ4MmOtqu2o5p5baeFC69S6dDHLASqKw557WqAyVlXVaMshPM4hbDk4h34scVAtn8bq6NzZspacdbN0KaXksahJf+jaldLlqzjtNHjsMdh/f4shrO25P7/Yf0r58xxyiOWqxiuYl4xYlsO6dWauOFxF1uo+dQ8YYOedMycitJm2HDp2jAiQFwdPElISB1X9C3AxsCF4XayqD2awXbWbkqCaeSzLIT/fXE6lpRW3ZZIFCyx7pkGDiuKwbp35YFzHGCvuELYcEsUcevSwjvPbbyueY/VqOy7achCB3r0pmbvQgsY3tKQLKzjqD0ejXbrSaMVi3n1HWbsWXn8dzjunjJYrZlX0mR9zjL27yWoqQ2GhzYUQthw6drQOPOxqS1dW0cCB9j5jRs25lUQi1oMPRnuSkGpA+kBgrqr+TVX/BswXkQMy27RajEtRjOdWgpq3HoIxDkDEreREYO1ac6G4Ms2x4g7xAtLRbqW2ba0M9OjRFc8RK1Mp4MmGP6fzO09w9NHwrw+7cDgf88dbtlHWpRvs2MH+/TZFLrN2rX3HboyDY/hwK/734Yfxv4dVqyIVXMO4NNaw4LixDmFLJCjXXW369bP035kza04cwDKounaNX3nX4wlI1a30KBBOPykM1nli4VIyc00cevUCiO1Wat8+uTjk5ZmrLBxziHYrAfzwh/D11+VHOcP34rCrd3/ee8+Cxm7AbqeeTTmGD3l1lLL26nv4T4NzOfPS1jTo1tl2CKezRo9xcDRoYKUhPvgg/mx0N91k8xz/8peRsR1QfoyDw823HM5Yiprop8o0aWIprmHLIdMxB4A//9mqs/pgtCcJKQekVSP/bapaRup1meofThzixRygZsVhyxazEgLLoTQ/3+IPYXHo0MGe+iG+Wyk/3zoVF3NQrWg5gIkDwH//+/2qXbvg7XfzuLjBvyjYvzsnnACvvBJJ2Dnp6CJeLP0xpx+8mvxVC+zJtkGDyBNuOJ01enR0mKOPtu2x5ktWNZdTx47wz3/CiSdaqum8efDuu7ZPdMwBKloO6erEXcZSTcUcwP7O4TRijycOqXbwC0TkSiLWwq+ABQn2r9/kmlvJZSoFlgMiZimEYw79+kXEIZ7l4OajaNnSYibr1lmHGy0O/ftDv37sfO0dVp54Ob162eE/evMi8mUHPzxFOPNMCxF8r5/hdFZXihtii0PUALhyuLjDBx9U7AQXLrRjH3rI7uXnP7fvwQWcO3eumK0EFcUhelBbVRk4EN56K/J3qAnLweNJkVTF4RfA34CbsalCxwCXZapRtZ5ccyuFxjh8T7t25WMO7dsnthy2b4+03XVirsMOuZV27ID33oNRDf7Nfz/qw17nlPDZFw1p1Qo+63IWQw9sSuNnX6p4fpfOumCBPfnvt58td+lS/lpg25s2jT2VZe/e0LOnxR2iJ7H56CN7P/JIyxbq188GhA0caPGKQYPK/83atTMhjXYrpSPmAHbdkhIrgte8uVlKHk+OkOpMcGuAszPclrpDLonDihXWAULk6RwilsPOneYa6tDBXE0tWsS2HJxbCSKdo4sDBMv33GOvbdugbcvBnMWznHXkHsAImDqV/VaOhkE3x25nz57WEc+bZ/7/00+39U4EomMO3bvH9puLmPXw8svW8TYM/cTHjrVUW5dKe/DB9opHw4Z27Uy6lcDKWdSES8njqQSpxhxqHBE5PqgCO09Ebsh2eypFLsQcpk6F448318vzz8MJJ0Sm8YSIODiXhnOhtGuXklupkGa8/GZTzuQ/rFN7gt99dzjvPPjf/2DV6jye6HATxy56zDr7E04wt81lcQzOJk3MpfPll+aWC7uMokZJM2NGeSsomqOPtk48PIenqonDiBGVC8aGB8KVltrfLV3i0L+/tWXTJu9S8uQcORlUFpEGwMPAMcAyYKKIjFbVGdltWYrkQszhb3+zMgk33ggXXljR/+7cSs5l4sShbdu4bqVtTdsx+iV45dGDeJe17HxsNwpYxewNy2gPXHCBvYwGcPLJZrVMn26d9fjxiVMoXf1sKC8OXbtGxGHBAhvo9tOfxj/PkUdap/vBBzZ8GshfutTmhTjyyPjHxSJcXyk86106yM83i2nhQm85eHKOZPM5XBW8J7C9M8L+wDxVXaCqxcC/sWlKawe54FaaPx+GDYO77oqdndK+vYmAeypu397e27YtZzls3gyLFll71zTozLnnwhczW/EznmDcyfexnK4cfFBZhdMDlrW0ebM96b/6qhXSS0SfPpHvJZyJFBaH11+399NOi3+e9u1h773LjWdo/fXXtu2IIxK3IZpwfaXKVGRNFeda8uLgyTGSWQ4XA38F/g4Mz3xzvqcrECrozzKg3KA7EbmMICheUFDAuHHjqnShwsLCKh8bjzaTJzMU+HrGDDZHBRkbFhZyCDDv229Zlubrhjlwxgw2DRnCrBjXKCwsZN6mTexRWsrcd96hL/DVwoVsLy1lYGkpu5Zs5e83zOSTTzowaVJbDjxwPe+uW0ez5gv4xz8mMWS3WRx00ZWsW38QDSjjq5kz2R6jXlRekyYM3ndfVh13HGsaNbKpQBPQXQQXFflsyRJ2BSmePXftosfq1XwyZgxDn32Whn36MGnx4vJVXKPoetBB9H34YWZddx2rTjqJfhMnsrNDB75YujQypiEF+hYX03HFCj4bN478hQvZH5i+dClr0/S36928Od2BNUVFzEjz7yETv+1cx99zGlHVuC/gJWAusA34LvSaCnyX6NjqvICRwBOh5QuAh+Ltv88++2hVGTt2bJWPjcvbb6uC6pdfVtxWXGzb7rwz/dd1FBWpiqjeemvMzWPHjlV97jlrx+WX2/u6daqqevngsdqIIgXV7t1Vr7kmuI0ePVQvushOsGqVHbP33va+ZEl62v3vf9v5mjZVLSuLrP/nP2395Ml2X7fdlvxcpaWqRx2lmp+vOmOGFrVqpXrhhZVv0+2327V37VKdMME+v/de5c8Tj6eftnP+7GfpO2dARn7bOY6/58oBTNI4/WpCy0FVzxGRTsD7wKnpl6a4LAfCSezdgnW1g0Qxh0aN7JVJt9KiReZOcemhsWjXjnW0440xvXiLN3g5vw1NgD27FnL1tL9yxoRr2f/AvEjsNpyt5NwqUdlK1ca1t1u38kFjl8766KN2Xz/6UfJz5eXBv/4FQ4bAccfRePPmyruUIDIQ7oILKmZrpQPvVvLkKEkD0qq6ChgqIo0B57yeraopzJ5SZSYCfUWkFyYKZwPnZvB66SVRzAGqVpn1tddsmPGPf5x8XzeuIYY4bNgAo0d35q5POzGOVZTOakjvvEUsWpJH//5wxXFz4f3rYMClIK0jB4bHOTRtammezhcfXT6jqrj2Rg9uc0HsF16wLKUhQ1I7X5cu8MwzcMoptlwVcTj+eCu3MX58JO7hxCodDBhgDwuu1LnHkyOklK0kIocD/wIWYfM47C4iF6nqJ5lolKqWiMgVmMXSAHhKVadn4loZIVEqK1RNHP70J7NIUhEHNy9CMK5h5UpL+d99d6tl9MAD/enbcxfXcy8jGcWw/sVI/+DrDY+Sbt3aPquWT2UVsafnjRsjQpEO2rSxYHJ0mqoThx07LBBdmVTUk0+GG29k47vv0sbN6VwZevWyUcxgAfyNGy3DKF20aAFffBGplurx5Aip/lf/BThWVWcDiEg/LB6Rsbq/qvoO8E6mzp9RErmVoGrisHJl6mW+FyxgRdPevPpKJ14ZZdmhv/gFPPKIzYfw5JMTufiM/kjrYEBax8Mjx7pRx6FaTOzYYe9OHMBcSxs3ptfFAtYRRz+Zt29vT9e7dqXmUormnnv49thjGVHdtrVrF3tUdnUZXpO5Hh5PaqQqDo2cMACo6hwRaZShNtV+0u1WKiszcQB7ik/y5PyjF8/kjZ33w1VWofm22+Css2xbXh707r0Nadki0uG6MQ4Qu75SeKIfh4s7pMul5DggRiX4vDwbQLdjR8X5mD0eT0ZIVRwmicgTwPPB8nnApAT712/SLQ7r10fKS2/eHHH3YNmco0bBZ5/ZUAIR2K/sS/bZcx4jXz+/wqRr3+OK761cGRnjALHLdofncnA4cUi35RCP88834fL1hzyeGiFVcfglcDlwZbA8HngkIy2qC6QSc9i4MfXzOasBYM0alm9rzYsvWsnriRNt9bBhNpC3YwflpsKb4NzLYM/zE5/XiUMsyyE8Sjo8C5zDiUJNicPdd9fMdTweD5D6NKFFqvoXVT09eD2gqkWZblytpToxh1deqTjN5YoVzKMPqyiANWuYOBGuu848TPfu9Rzzuh7O11PUsi5Xr7Yn/URprA5nJYTFwdVfypZbyePx5AQ5W3ivVlNcbG6beC6QROJw3XVW2hSbOO3uu2Hvn+9HX+bxGJfBmjUcf7xlq06cCNc1+Rt9ln9i1UwhksYarsAaD+dOCruVGja0nPtklkNNu5U8Hk+NkpOF92o9xcXmUooXOI4nDiUlsHQpZXkNOWC/SFHRH3Tfzl+4i5GMgjX/R9OmoWxPNxDtww8tHdKlsaZiOThRCFsOUKG+Uk7EHDweT41SZctBRGLM0egBzK0Uz6UE5cRB1YqM3n47XHDWTigtJW/ZEo49RvnrX23qgs9O/iO/afkUu7Os/NwCpaWwapV9dq6oBQtMlFLJxY/lVnLrw5ZDLLeSEwXvVvJ46iRJLQcROQgrhPeJqq4RkSHADcChlC9x4XEUFycVhznbu/Lczcoro4TZs60/P3xoCUU0pklxMXdfsTKS779ypY1gW7myvDisWWNprk2a2CxnpaVmOXTrFj8YHqZTJ3uPHp0bbTl4t5LHU+9IVrL7z8BTwBnA2yJyF/A/4EvAD+mMRwxxUIUpU4Ikpfx8PuJI7vmDDf595BHzDo29+k2aEGQ6LVoUOXjFChOKcPloiGQxnXqqnfjrr81ySMWlBDbPw3//GxEJh3creTz1nmRupZOAvVX1HOBY4GrgQFX9q6ruzHTjai1BzEHVgsbXX29z0u+zjyUj0awZ5/ECK6euZ8wY+OUvg/45LAjhctTxxMHFG9wMO2PGmOWQSjAarIM/+eSK61NxK/lsJY+nTpPMrbTTiYCqbhSRuaq6KPPNquUUFVHYsDVD+tgkXw0b2syVN94YVH8Y3YwWFNIivxAIZQotWmSppBs3RsShrMziCp07m3tneqjElBOHvfe2odBvvmn7pmo5xKNtW2tDWZmNTs6FcQ4ej6dGSSYOvUVkdGi5V3hZVWuyjHfOUlZmtdNeecVi0Y8UF9O8aQmnHW0FRE89NTK2DIg/G9yiRVbCedasiDi40dFduthUm2PHRvZfscKCFQUFpj4PPmjrU7Uc4tGunfnBNm2yhm/fHik17vBuJY+nTpNMHKKn5rw/Uw2pjUyebFMGvPqqVXNu3NiKhmpRMdKkCX/5S5wDE4nDwQdbDSEnDi6u0LmzxQHWr7eU14YNI6ObGzWCo46KiEM6LAeAdesi4hC2GsCslZ/9DA47rHrX8ng8OUmyyX4+rqmG1AZKS62s/4EHWqXq//4X/vlPOOEEq6h98snBA/UxybOVgPLiEIxxoGdP64xnzbL1znXUpUtkovt16yxI4WIRAIcfboPuSkurbzm4QRTz5tn80+GJfhz5+fD449W7jsfjyVkSioOIjAU0zmZV1aPS36TcoqQEPv7Yitu99prFg99801xFV10F114bw7OSyjgHKC8Oy5dbx96zp61//31z7YTFwVkRa9ZExKFzZ1vXooWp1rRpUT6sKjBokL1PmwYnnlh+oh+Px1MvSOZW+m2MdQcC1wFrYmyrUyxeDPvuaw/q+flmGYwcaR4ciJQhqkBxceJpH2OJg8tUcuKwfbu5kJwgdOoUmbLSZSytWGHuHcftt9vTfmUmw4lFmzYmRi74Hcut5PF46jTJ3EqT3edgNrhbgKbAL1T13Qy3rUYpLrZM0FGjrA/+wx+ge3c480yL9R5/fCX6R1c+Ix7JxKGw0D4vXmwC0Lat+bHC4lBSYu/hiXGOPtpe6WDQoIg4xHIreTyeOk0qI6SPA24GioC7VXVskkNqFVOmtOaZZ8xVtGmTxQwuvti2idgAtUpTFbfSokV2we7dYevWyLqw6ygsDm50dDrnMw4zeDD84x92De9W8njqHcliDhOBDsCfgc+Ddd/PaaiqUzLauhpg3LiOfPKJZRmdcQYce2xqlScSkkL5DKCiOHTtase5uY4XLza3khOA1q0tS2nNmkgswglHuhk0yLKmFi40cahuHMPj8dQqklkO24BCYCRWQiPszFbgyAy1q8a45JIFjBrVJWFfHpMVKywA8fzzFbODkolD06ZmJUSLgyuW16aNjTx2biU3nZtIZJR0OFCdCVxQevp071byeOohyWIOI2qoHVmjVauSygsDwOef2+uZZ+COO8pvSxZzEKlYtnvhQjj00Mj2nj1tnRsd7XDi4ALVmRKHgQPtfdo071byeOohyQrv7ScinULLF4rImyLyNxGp334GF0B+7bWK25LFHMBiC27ChpISWLasfJntHj1slJ0bHe0IWw5udHQmaNnS2jh9us9W8njqIckK7/0TrEyoiBwG/BH4F7AZeCyzTctxFi609+nTbcq2MMncSmAVUcePt2OXLYuMcXD06BE7rhAWh44dLQaRKVzGkncreTz1jmTi0EBVXe3mHwOPqeqrqnoLsEdmm5bjLFoU6bRffbX8tmRuJYCLLrKO/YknyqexOlxQGuJbDplyKTkGDYKZM80S8m4lj6dekVQcRMQ9mh4FfBTaVr+nGF20CA44wF5h11JZmbmCklkOnTrBKafAs8/CnDm2LlVx2LbNBrtlWhwGDzahA285eDz1jGTi8BLwsYi8CewAxgOIyB6Ya6l+ompupZ49Lf918uTI0/+uXfaeSpT70kutXtJDD1n8YPfQxHphoQhPxuPGOsydm7k0VofLWAJvOXg89YyE4qCqdwPXAs8Ah6iqq7OUB/w6s03LYdatsyBtr14mDhCxHtyTdiricOyxJghTp0bGODic5eBGRzucOKhm3nIYMCDy2VsOHk+9IpnlgKp+oaqvq+q20Lo5dWEAXJUJxwh694ZhwyJxBycOqYyka9AAfvrTyLnCdOxo54i2Dpw4QObFoVmzyBgOLw4eT70iqTh4YhAdQD79dJgwAcaNi2QYpTp44uKLI+MawuTlmfXQtWv59WFxyLRbCSKuJe9W8njqFfU7qFxVXBqr69DPOgtuuw2OOCKyT6o1OHr0gCefhKFDK277xz8q1gPv0CHyOdOWA5g4/Pe/3nLweOoZXhyqwqJFFgtwU2X2728pnzNnWsmLdevgpJNSP5+r9BdNWGwc+flWWqOwsGbEYfBge/fTgXo89QovDlUhXAfJ0b+/vWqCjh0tIB52MWWKkSNh587y80Z4PJ46jxeHqrBoUflMnprGiUMmR0c7mjSBSy7J/HU8Hk9O4QPSlUU1tuVQkwwaBEOGZO/6Ho+nzuMth8qyZo3Nc9CrV/ba8OijNhLb4/F4MkRWLAcROVNEpotImYjsG7XtRhGZJyKzg1nocotYdZBqmkaN0jAjkcfj8cQnW26lacDpwCfhlSIyEDgbGAQcDzwiIg1qvnkhFiyAE0+EJUtsORfEwePxeDJMVsRBVWeq6uwYm34I/FtVi1R1ITAP2L9mWxdCFX71K3j3XbjvPlsXPcbB4/F46iC5FnPoCnwRWl4WrKuAiFwGXAZQUFDAuHHjqnTBwsLCuMd2+PhjBr3/PkXt2tHw8cf5/Jhj6D1hAu1btWKCm6inFpLonusq/p7rB/6e00fGxEFEPgQ6xdj0f6r6ZnXPr6qPEUw4tO++++qIESOqdJ5x48YR89itW+H882HYMJo88QTsuy+HzJxptZP69o19TC0h7j3XYfw91w/8PaePjImDqh5dhcOWA6G61XQL1tU8v/+91UkaNQr22QeOPBL+9jcLBA8fnpUmeTweT02Ra+McRgNni0gTEekF9AW+qvFWfPopPPigzbdw4IG27pprYPlyC1BnM43V4/F4aoBspbL+SESWAQcBb4vI+wCqOh34DzADeA+4XFVLa6xhxcVw880wYgR06wb33BPZdsIJkfIYPhjt8XjqONnKVnpdVbupahNVLVDV40Lb7lbVPqraX1XfrbFGTZwI++8Pd98NF1wA33wD7dpFtuflwW9+Y5/dHAcej8dTR8m1bKUap9HmzXDZZfDEE1BQAG++CaeeGnvnSy6BNm3gmGNqtpEej8dTw9RvcXjnHfa/4AIrh3HNNXDrrZEy3LFo2NDmbvB4PJ46Tv0Wh3792DJgAO2efhoGDsx2azwejydnyLVspZpljz2Yeu+9Xhg8Ho8nivotDh6Px+OJiRcHj8fj8VTAi4PH4/F4KuDFwePxeDwV8OLg8Xg8ngp4cfB4PB5PBbw4eDwej6cCXhw8Ho/HUwFR1Wy3odqIyFpgcRUPbw+sS2NzagP+nusH/p7rB9W55x6q2iHWhjohDtVBRCap6r7ZbkdN4u+5fuDvuX6QqXv2biWPx+PxVMCLg8fj8Xgq4MUBHst2A7KAv+f6gb/n+kFG7rnexxw8Ho/HUxFvOXg8Ho+nAl4cPB6Px1OBeisOIrK7iIwVkRkiMl1Ersp2mzKNiDQVka9E5Nvgnn+f7TbVBCLSQES+FpG3st2WmkJEFonIVBH5RkQmZbs9mUZEWovIKBGZJSIzReSgbLcpk4hI/+Bv615bROTqtF6jvsYcRKQz0FlVp4hIC2AycJqqzshy0zKGiAjQTFULRaQR8Clwlap+keWmZRQRuQbYF2ipqidnuz01gYgsAvZV1XoxIExEngXGq+oTItIYyFfVTVluVo0gIg2A5cABqlrVwcAVqLeWg6quVNUpweetwEyga3ZblVnUKAwWGwWvOv10ICLdgJOAJ7LdFk9mEJFWwGHAkwCqWlxfhCHgKGB+OoUB6rE4hBGRnsDewJdZbkrGCVws3wBrgA9Uta7f84PAdUBZlttR0yjwPxGZLCKXZbsxGaYXsBZ4OnAfPiEizbLdqBrkbOCldJ+03ouDiDQHXgWuVtUt2W5PplHVUlUdBnQD9heRwVluUsYQkZOBNao6OdttyQKHqOpw4ATgchE5LNsNyiANgeHAo6q6N7ANuCG7TaoZAhfaqcAr6T53vRaHwO/+KvCCqr6W7fbUJIHZPRY4PstNySQHA6cG/vd/A0eKyPPZbVLNoKrLg/c1wOvA/tltUUZZBiwLWcGjMLGoD5wATFHV1ek+cb0VhyA4+yQwU1X/ku321AQi0kFEWgefdwOOAWZltVEZRFVvVNVuqtoTM70/UtXzs9ysjCMizYIkCwL3yrHAtOy2KnOo6ipgqYj0D1YdBdTZxJIoziEDLiUwc6y+cjBwATA18MED3KSq72SvSRmnM/BskN2QB/xHVetNemc9ogB43Z5/aAi8qKrvZbdJGefXwAuBm2UBcHGW25NxAuE/Bvh5Rs5fX1NZPR6PxxOfeutW8ng8Hk98vDh4PB6PpwJeHDwej8dTAS8OHo/H46mAFwePx+PxVMCLgycjiIiKyP2h5d+KyO1pOvczIjIyHedKcp0zgwqfYzN9rSTtWCQi7dNwnqtF5MLgc5W/QxHZU0Q+F5EiEflt1LbjRWS2iMwTkRtC6/8tIn2rdweemsSLgydTFAGnp6NTSyciUpmxPZcAl6rqEZlqT00R3PdPgRfTcLoNwJXAfVHXaAA8jI3aHQicIyIDg82PYjWuPLUELw6eTFGCzW37m+gN0U+tIlIYvI8QkY9F5E0RWSAifxSR84I5KKaKSJ/QaY4WkUkiMieooeSKCv5ZRCaKyHci8vPQeceLyGhijJwVkXOC808TkXuDdbcChwBPisifo/bvLCKfBHX0p4nIocH6R4M2lZsrI3jy/4ObW0FEhovI+yIyX0R+EWrjJyLydvDk/Q8RqfD/KSLnB9/HNyLyz+CeGwTf6bTgPip858CRWJmFkhjnPCooWDdVRJ4SkSbB+hPF5keYLCJ/k2A+DFVdo6oTgV1Rp9ofmKeqC1S1GCtZ8sNg2/jgb1afB97WKrw4eDLJw8B5YiWVU2Uo8AtgADaCvZ+q7o+V3P51aL+eWGd0EvAPEWmKPelvVtX9gP2AS0WkV7D/cGzuin7hi4lIF+BerPMcBuwnIqep6h3AJOA8Vf1dVBvPBd4PChgOBb4J1v+fqu4LDAEOF5EhoWOWBPuPB54BRgIHAuEJl/YP7nEg0Ac4PaqtA4AfAwcH5yoFzgva3VVVB6vqXsDTVORgbM6ScgTf2zPAj4NjGwK/DNb/EzhBVfcBOsQ4ZzRdgaWh5WXBOlS1DJiHfV+eWoAXB0/GCKrc/gtzQaTKxGCujSJgPvC/YP1UTBAc/1HVMlWdi5VL2BOrIXRhUA7lS6Ad4PzcX6nqwhjX2w8Yp6prg6fqF7C5ARK2Ebg4iKHsFcwHAnCWiEwBvgYGYZ28Y3ToPr5U1a2quhYokqDeVdDGBapaitXLOSTqukcB+wATg3s8Cugd3H9vEfm7iBwPxKou3Bkrax1Nf2Chqs4Jlp8N7n9PYEHoO0tH/Z41QJc0nMdTA3gTz5NpHgSmUP5ptoTgwSRwnTQObSsKfS4LLZdR/vcaXfdFAQF+rarvhzeIyAisjHNaUNVPxEpgnwQ8IyJ/wSyC3wL7qepGEXkGaBo6LHwf0ffo7ivWPYUR4FlVvTG6TSIyFDgOs7rOwuILYXZEtScTLAd2Dy13C9Y5mgbt8NQCvOXgySiqugH4D+bycSzCnoDBatE3qsKpzxSRvCAO0RuYDbyPuUQaAYhIP0k+6ctXmAuofRBQPQf4ONEBItIDWK2qj2PuruFAS0yANotIARaUrSz7i0ivQDB/jE3jGmYMMFJEOgbtaCsiPYKgf56qvgrcTOxy1TOBPWKsnw30FBG37QLs/mdj1kjPYP2PU2j/RKBvcA+NsUq4o0Pb+1GHq8PWNbzl4KkJ7geuCC0/DrwpIt8C71G1p/olWMfeEviFqu4UkScw19MUERHMjXJaopOo6kqxlMux2JP526r6ZpJrjwB+JyK7gELgQlVdKCJfYyXQlwKfVeGeJgIPYZ34WGwehnBbZ4jIzdgMb3lYQPhy7Gn86VAAu4JlAbwLPBe9MvjeLgZeCYLFE4F/qGqRiPwKeE9EtgXrARCRTlg8piVQJjax/UBV3SIiV2Ai3QB4SlWnB8cUADuC8tqeWoCvyurx5ACB6+u3qnpyBq/xOnBdEKdJZf/mqloYCO3DwFxVfaCK1/4NsEVVn6zK8Z6ax7uVPJ76ww1YYDpVLg0C39OBVlj2UlXZhAW7PbUEbzl4PB6PpwLecvB4PB5PBbw4eDwej6cCXhw8Ho/HUwEvDh6Px+OpgBcHj8fj8VTg/wEqNqmYRIcANwAAAABJRU5ErkJggg==\n",
+      "image/png": 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FEgPJspGXY8cCa9cCu3cnflwJhMKaEEIIIYRET3Fx4xiIwUxgXLQoceNJAhTWhBBCCCEkekLbmVtpIpVBKKwJIYQQQkj0uAnrbt3kRmFNCCGEEEKIByZj7cSYMRTWhBBCCCGEeOLmWAPAxInA0qUZnbOmsCaEEEIIIdFRVSU3N2F9zTXiaF99NaB14saWQCisCSGEEEJIdDg1h7HSsSNw333AN98Azz+fmHElGAprQgghhBASHcXFsnQT1gDwq18Bhx4K/OEPQGlpvEeVcCisCSGEEEJIdPhxrAFpHvPoo7L9X/4S/3ElGAprQgghhBASHX6FNSDVQX7zG+A//8m4NucU1oQQQgghJDqMsHYrt2flrrtEhF99NVBfH79xJRgKa0IIIYQQEh3hONYAkJcH/P3vwOzZwLPPxmtUCYfCmhBCCCGEREdREdCqldz8csEFwKRJwM03Byc/pjkU1oQQQgghJDq8msPYkZUlOeuSEuDPf47PuBIMhTUhhBBCCImO4uLwhTUAjBoFXHkl8PjjwO7dsR9XgqGwJoQQQggh0RGJY22YNEk6MW7ZEtsxJQEKa0IIIYQQEh3RCOv8fFlu3x678SQJCmtCCCGEEBIdRUX+S+2F0q2bLLdti914kgSFNSGEEEIIiZz6+sgz1gAda0IIIYQQQgDIpMP6+siFdceOQE4OhTUhhBBCCGnihNscJpSsLKBrV0ZBCCGEEEJIE8c0d4lUWAMSB6FjTQghhBBCmjTROtaATGCksCaEEEIIIU2aWAjr/HxGQQghhBBCSBPHCOtIy+0B4ljv2CGTINMYCmtCCCGEEBI5RUWAUkCHDpHvIz8fqKkBSkpiN64kQGFNCCGEEEIip6gIyMsDsrMj30eG1LKmsCaEEEIIIZETTTtzg+m+SGFNCCGEEEKaLNF0XTQYxzrNJzBSWBNCCCGEkMihY72fpAlrpVRvpdQ0pdRypdQypdS1gfUdlVKfK6VWB5ZRJOEJIYQQQkhciYWw7tABaNaMjnUU1AK4UWs9HMBEAFcrpYYD+COAL7XWgwF8GXhMCCGEEEJSkaKi6ErtAVJVpGtXOtaRorXeqrWeH7hfDqAAQE8ApwJ4LrDZcwBOS8oACSGEEEKIO/v2ARUV0TvWQEZ0X8xJ9gAAQCnVD8AYAN8DyNdabw08tQ1AvsNrrgBwBQDk5+dj+vTp8R+oDRUVFUk7NkkcPM+ZD89x04DnuWnA85w4mhcV4TAAq4qKsCXKz/zAnBw0X7MG83zsJ1XPcdKFtVKqDYC3AFyntS5TSu1/TmutlVLa7nVa6ycAPAEA48eP10cddVQCRtuY6dOnI1nHJomD5znz4TluGvA8Nw14nhPI0qUAgCGHHooh0X7mI0YAn3zi69yl6jlOalUQpVQziKh+SWv9dmD1dqVU98Dz3QHsSNb4CCGEEELiwrJlwHnnSZQinSkulmUsoiD5+Wnf1jyZVUEUgKkACrTWD1qeeg/ARYH7FwH4X6LHRgghhBASVz78EHj1VeDHH5M9kugoKpJlrIR1bW1QrKchyXSsJwG4AMAxSqmFgdtJAP4G4Hil1GoAxwUeE0IIIYRkDps3y3LnzuSOI1piKawzoJZ10jLWWutZAJTD08cmciyEEEIIIQmlsFCWmSKsoy23BzTsvjhiRPT7SwLsvEgIIYQQkmgySVg3bw7k5ka/rwxwrCmsCSGEEEISTSZFQTp1kgYv0WIcawprQgghhCSN3/0O+PLLZI+C+KW2Nti6e0eaFz+LRTtzQ16euN9p3NY86XWsCSGEEBIF9fXAv/8NZGcDx3KKUlqwfTtQVyf3092xLi6OnbBWSlxrOtaEEEIISQp79jRcktTH5KuB9BfWsXSsAQprQgghhCSRsjJZVlQkdxzEPyZf3bs3hXUo+flpHQWhsCaEEELSmfJyWdKxTh+MYz16dHoLa61FWMei1J6hWzc61oQQQghJEnSs04/Nm2WS3gEHiLDWOtkjiozycpmIGWvHOo3bmlNYE0IIIekMHev0o7AQ6NFDRGRNDbB7d7JHFBmx7Lpo6NZNJnaafacZFNaEEEJIOmOENR3r9KGwEOjVC+jSRR6naxwkHsI6zWtZU1gTQggh6YyJgtCxTh82bwZ69kx/YV1cLMt4COs0ncBIYU0IIYSkM3Ss0wut6Vi7keZtzSmsCSGEkHSGjnV6UVICVFVlhmPNKEgjKKwJIYSQdMY41lVVwW5+JHUxpfYyybHu0CF2+2zfHmjRglEQQgghhCQB41gDQGVl8sZB/GGaw/TsCbRqBeTmprewbtcOaNYsdvtM87bmFNaEEEJIOmMca4A563TA6lgDQNeuUrc5HYl110VDGndfpLAmhBBC0hmrY82cdeqzebO4st27y+MuXdLXsd65MxhniSUe3RffeANYubJN7I8bAyisCSGEkHSGjnV6UVgoLrWJT6SzsN68Oei8xxKbKMjGjcH7TzwBfPxx99gfNwZQWBNCCCHpTHk5kJMj9+lYpz6m1J4hXYW11sCmTUDv3rHfd7duwI4dKC+tw5NPAoccAgwYAGzZIk+/9BLwu9+tjv1xYwCFNSGEEJLOlJUFS5TRsU59THMYgxHWWidvTJGwe7d83+LgWG9oNgiX1j+B7j0VrrhCrhcfeEDmeQJi+GelqIJN0WERQgghxBfl5cG8Lh3r1MfOsd67N/0uikx1kxg51jt2ACtWyP3srp3wFs7AeSeU4LvvgCVLgOuuk0p8qQ6FNSGEEJLOlJUFhXW6ibOmRlWVtAG3OtZdu8oy3eIgmzbJMgphXVcHfPIJcOaZcq1x7bWyvteI9tiOfDx51XxMnChzPdMFCmtCCCEkXamvFzFNxzo9CC21BwSraqRbyT0jrCOMgjz2GNC/P3DiicDXXwPXXAM89FDgyfx8tMC+tKxlTWFNCCGEpCt79kg2l451emBtDmNI1+6LmzdL0LlHD1+b790LvP66mPaAfHWHD5d1mzcDDz4ojwHI5EUgLWtZU1gTQggh6YoptWcmL9KxTm3cHOt0E9abNskFnalI48DSpcD118u1xDnnAO+9J+tvuEFiIGedJR3MG9C2LdCyZVo61u6fBiGEEEJSFyOs27cHWremY53qGGGdCY71pk2uMZCSEuCkk4DZs6Vk92mnAZdeChx3nDzvmptO47bmFNaEEEJIumK6LrZrB7RpQ8c61dm8OXiuDLm5QKtW6SmsR43a/1Br4LvvgNWrgYsuAvLyRHc/+CBw/vkRNGjs1i0toyAU1oQQQki6Yhzrtm1FoNGxTm1CS+0ZunZNL2GttVwk/PSn2LkTeOEF4KmngIICSYf88peSEHnjjSiOkZ8PrF8fqxEnDGasCSGEkHTFONZt29KxTgdCm8MYunRJr6ogJSVAZSWeLpyCnj2BG28Uh/qpp4CVKz1j1/6gY00IIYSQhGIc63bt6FinA4WFwIgRjdenibDesAF45hnghIFFmAhg/ASFq3sAl11m/7aiIj8f2LVLil1nZ8d45/GDwpoQQkjT5vXXJS96443JHkn40LFOH2prga1bnR3rZcsSPyYf7N0L/O9/wNSpwOefy7rWF9ViIoBRk9vhoYlxOnB+vtRp37UrWPUmDWAUhBBCSNPm2WelW0U6Qsc6fdi+XYSiXca6S5fIM9b19cB//wuUlkY1PDu0BsaMkTJ5BQXAbbcBP/4I3HTI17JBhM1hfJGmtazpWBNCCGna7NqVvk5vWZn8TN6yJR3rVMeuOYyhSxfpnLJnj1wghcOsWcBvfiPfhZtvjmqIFRXAa68BH30kEw+zsoA//UnmVh53nCWRsWmTPDCNieKBcanTrOQeHWtCCCFNm6Ki9HV6y8vFrVaKjnWqY9ccxtC1qywjca1NPmPatIiGpbXUmr7sMtHJl10m7rQZ7gUXAFOmhMScN2+WjovxzD4bx5rCmhBCCEkjjGOtdbJHEj7l5ZKvBuhYpzp2zWEM0TSJ+eILWc6cCezbF9HLDz0UeOUV4OyzgW++kbh3794uL/JoDhMTjGOdZlEQCmtCCCHxIR2Eak2N/IReXy+ztNKNsjJxrAFxrNP1AqEpsHkz0Lw50Llz4+eMsA63MkhpKTBnDnDggUBlpdx3ob4e+OwzEdB33inrjj4aePppmVc5dSpw2GEeXREBEdauyjsGtGkjjXPoWBNCCGnyLF0qud8ff0z2SNwpKgreT0e3N9Sx1lqyuiT1KCwUt9pOtUbqWE+bJmr5rrtkv199ZbvZxo3AX/8K9O8vsY4vvwzWms7JAS6+OHh95olpDhNvYa1UWtayprAmhBASe1askJ+l165N9kjc2bUreD8d88llZUFhbSa9pfL72LwZuPZa+aWgqeHUHAaIXFh//rlcUJ10EjB6dANhXVsb3Oz3vwfuuAMYOlQmJ27ZAtxyS3iH2k9REVBdHf8oCCBxEDrWhBBCmjym9JcpB5eqZIJjbazGNm1kmcrv47nngIcfBpYsSfZIEo9TO3NAzl2LFpEJ66OOApo1A445BvjuOyyfX40bb5SJiAUFstndd8uPRyYG0qJFFO9j0yZZxtuxBsSxprAmhBDS5Nm9W5ap7J4CDR3rVBakTqSbYz1zpiy3bEnuOBKNiU84OdZKhV/Lev16YM0a4LjjUFUFPF17IQ7bNw0jxrXEww8DRx4ZjNsPGQL06xftmwhgygYmQljn5zMKEg5KqaeVUjuUUkst6+5QShUqpRYGbiclc4yEEEIiwAhrOtbxJZ0c67o64Ntv5b6pkNFUKCnxjk907RqWsNaff4EidASOPx7V1cDVjx+IEnTAA8d+hMJC4M03geHDYzD2UIxjnagoyK5dDXMtKU6yG8Q8C+ARAM+HrH9Ia/1A4odDCCEkJqSLsE5nx1rrhpMXU92xXrIk+H1oao61W3MYg0/Hetcu4MUXgadun4LWzcZhzgEHoIMCFi9WGHTBJVB7AHSNoye5aZPMeExEm/Fu3eR7vnNnfJvRxJCkOtZa6xkAipM5BkIIIXEgHaMgqT7WUExpvXRxrGfNkmWLFk3PsXZrDmPo0sW13N7330tr8Z49geuvB3Ird+Dy8QtRr6XKyODBgDr2GOCHH+J7QWsiLfFsDmMYMkSWP/wQ/2PFiGQ71k5co5S6EMBcADdqrUtCN1BKXQHgCgDIz8/H9OnTEzvCABUVFUk7NkkcPM+ZD89xbBmxdi26ANhcUIA1KfS5hp7nYcuWIT8rC6q+HivmzcO2RLhwMaJ5UREOA7Bq61ZsmT4drTZtwiEAls+Zgx3t2yd7eI0Y/vbbaNe1K2rat8e+pUuxJI7fi1T7e+7+1VcYCuC7jRux16Fe+sDqavTYtg0zLePesaMFcnNrkZtbhw8/7I5PPhmAk0/ejjNHfIdz7zwbBUffghkzgtvndeyI0XV1WPzooyieODEu72X0kiVAu3ZYmIDPV2mNw9q0wa7//hcrQ+oBpto53o/WOqk3AP0ALLU8zgeQDXHT7wHwtNc+xo0bp5PFtGnTknZskjh4njMf23NcX691dXXCx5IRHH201oDWl1yS7JE0oNF5Pukkrfv2lbE+/HAyhhQ5K1bIuF98UR4XFsrjxx9P7rjsqK/XukcPrc87T+uTT9Z61Ki4Hi7l/s2+/XatldJ63z7nbe69V2tA7y2t1G++qfUJJ8hLzNeyqkpuWmut//Y3OddbtzbcR2Wl1s2ba33jjfF4F8KAAVqfe2789h/KBRdo3bFjo88umecYwFztoElTriqI1nq71rpOa10P4EkAByd7TISQJsr//icTitItIpAKpEsUpKgI6NtX7qdqhMIJ83O/tfMikJqf+YYNkquePBno0aPpZawLCyWT3KyZ4yb1nbrgD7gfvQY2x5lnSo+lW28FTjlFnm/ZUm4ApMzegQdKBtlKq1bSn9yhUUzUJKo5jJXTTweKi4EZMxJ3zChIOWGtlLKm038OYKnTtoQQEldWr5ZyZuG2GSbpNXmxRw8gKys1BakbZWWyDJ28mIoXCKbM3uTJks/dtSs9W8hHikOpvYoK6YIIAFndumIBxuDwUWX48EOppnfnncHrvv1UVUle/fjj7Y91zDHAwoUiRmPNzp3S+CmRwvonP5ELhnfeSdwxoyDZ5fZeAfAdgKFKqc1KqUsB3K+UWqKUWgzgaADXJ3OMhJAmjBFaqS4OY8nu3cDy5dHvJ10axOzaJZPGcnNTU5C6EepY5+TIxMBUvECYNQto3x4YOVIuZABg69bkjimRWJrDaA3MmQNccYV8FCecECgG0qULPsUUvHXT9zjpJJe5gbNmyUXJccfZP3/MMXKQr7+O/ftIZKk9Q+vWwIknirCur0/ccSMk2VVBztNad9daN9Na99JaT9VaX6C1PlBrPUprfYrWugn95RFCUgojXFJdHMaSf/xDXMVo0Do9oiA1NTLOTp2koka6CetQxxpI3fcxaxYwaZL8MmCc26ZUGSTgWM+eDRx0EHDIIcBLL0nKYfp0oHNnAF26IBv13iX3Pv8caN4cOOII++cPPljEaDziIIlsDmPl9NMlPjRnTmKPGwEpFwUhhJCUoSk61lu3SjOLmprI91FVFWzokMqfnfmpvHPnzHCsAXkfqXYxU1Qkv4KYCzYjrJtAzrq+Hvjiw72YUzII6NUL3bpJquGxx+TtP/usXG8oBfnlBPCOnn3+OXDYYcHoTyjNm8tnHQ9hnch25lZ++lP5RSYN4iAU1oQQ4kRTFNaxyEabfWRlpfZnZ7ouduqUmoLUi3RxrE23RSOsTRQkgx3rzZuBu+4CBg4Ejv9ZCzyIG4CePdGvn9SjvvJKScY0oF07mdzo5ljv2CH5aad8teGYY+RiZvv2KN9JCJs2yRjNRUCiyMsDjj0WePvtYJ/2FIXCmhBCnDCiMN0EVzSYbLQRbZFghHW3bqn92ZnmMOnsWGdniwVqSMULhFmzxEWdMEEed+yY0U1ifv1rmXB4220irF/5y3I8i19555KV8u6+aFxop3y14ZhjZDltmu9x+2LzZnkfWUmQj6efDqxZI+VSUhgKa0IIcYKOdXT76NULqKwE6uqiH1c8sArrVHR6vTDtzJUKrkvF9zFzJjB+fLBWnFLRldyrrwdeeAEYMwb45pvYjdMve/c2+IxXrpSyeKbIyUEHAbfcAqxdC3zxBXDu0AVoib3u7cwNXbu6C+vPPwc6dADGjXPfz5gxYonbxUGKiyWLsnat93hC2bQp8TEQw6mnyncnxeMgFNaEEOJEJgjrwkKp2+WXWAtrIPWEniE0CpKq43SirKxhDARIPce6qgqYO7fxhNiePSNzrM2+LrxQIhEffBCTYYbFtddiz5En4dlngcMPB4YNA+67L9h1+6qrJAYyYEBge5NL9iOs3RxrrUVYH3OMdzvxnBzgyCMbCuuVK8VO79VLlvfd5z2eUDZtSmxFECv5+RJIf/vt5BzfJxTWhBDiRCZUBbn6ahEhfjGiOBZRECMkUvXzM451umasy8sbTlwEUs+x/uEHmQh7+OEN14frWO/YAVx2mVS8WLsWeOYZYMSIpMQC1s3agu7z3sfFF4sGvv9+SUg4FtNZvlz+Ftq08d65m7BesUKErVe+2nD00fJZvfiiTP4bNkw+t/POA0aPlguTcKivl4uhZDnWgMRBFi0C1q1L3hg8cBXWSqnFPm5fJmqwhBCSUDLBsd66Nbx6wbFwrE1O2zhbqfr5FRVJPrl1azrW8WLWLFkedljD9cax9jMR7ZFHgCFDgOeeA264AVi1CvjVr6TzYAKEdXEx8PDDwN//DkBr9F8/DVfhMcx8agUKCoA//EHMVEcWLwZGjfJ3sC5dnKuCvPCCZJtPPtnfvkzO+oILxOm/4w5g40Zg6lR5bsmSYPUeP+zYIRdJyRTWP/+5LFM4DpLj8Xw2gJNcnlcA3ovdcAghJIXIBGFdUhIUul7s2yc/3QOxjYKkktCzsmtXoIAwUs/p9UN5eePSEqn2PmbNEme5Y8eG63v0kPx9WZlNeQwLCxcCv/2tCMFHHxXX1TBiBPDqq/L98uMGh0F9vcz7e+op0XCmH8vvz98OtacC9+NmIKsLoIa572jfPnGsTzzR34G7dJHzunevTPA01NZKbb6TTgpWVfFi5EiZQdmvn7jU+/uhQ4Lg1dXSXfaAA/ztLxnNYULp10/y42+/7Z0zTxJeUZArtdYbXG7rAfwmAeMkhJDEonVmCOvSUrn5cQaNIAaij4JkZQHdu8vjVP38du2SGAggTm9lZcqX8mqAm2OdCu+jrk5K7dllJPw2iTGO9COPNBTVgAhHIDadQkO45RYR0p9+Kh0SFy6UeLNavSq40eLF3jtauVJc3nAca6BxHOSTT+SXp0sv9bcfQP4G//pX4OKLG4pqQKIgQHhxkGQ1hwnl9NOBb79FczNHIsVwFdZa61leO/CzDSEEYn1EMgubJIfq6mA1i1QVhl5oLaK6ttafi2kV1tE61u3aBUVfqn5+RUVBxzo3Vz4v49inA04Z67o6cUqTzbJl8l2wE9bGdfXKWa9aJQJx/0xAC0ZYRxkHqakRV/pnPwNmz5Z1F1wAvPyyDO/hh8XgBSAOLyDVO/wIa7ONX2HdtassQ4X11KmSN/npT/3tx4thw6Qe9aJF/l+TrOYwoZx+OgCgczIqwvjAK2M9WCn1rFLqQaVUL6XUx0qpPUqpRUqpCYkaJCFpT20tcPnlwJNPJnskxC/W+EKqCkMvKiuDHRT9xEFi6Vi3bx8U1ukQBTFd7FJ1rHY4OdZA4t7Ha68BRx0FzJvX+LmZM2UZjWO9ciXQv3/DWIShf3/JyEcorFeuBG66SZINp58OLFgAbNsmz40Y0Tg9AUCEfvPmEslYvNj7l4HFi2X7IUP8DcrOsd6+XaqfXHihiOFY0Ly5vMlwHOtNm+Q8mL+ZZHHAAcCQIehsvl8phlcU5BkA3wLYAuB7AE8D6ATg9wAeie/QCMkgjAsW6y5YJH5kgrC2imk/wtq6TbSOtVVYp+rnV1TUMAoCpFY+2Q0TVQoV1iZrnIj3oTVwzz3A118DhxwieV6rUz5rlgjovn0bv9avY71yJTB0qP1z2dkispYtC2vIgFxvHnYY8OCDwKGHAu+/D2zYAJx2mscOVq+Wri9jxsiFmde/6YsXA8OH+xfEdsL6+efFnLnkEn/78Mvo0eE51qY5jLVuejJQCjj9dOQtWCBzSFIML2HdRmv9hNb6AQBVWus3tNbVWuvPAdhcPhJCbKmulqXTbG+Sehgx2L596gpDL6z/6YTrWMdCWBuRl4qfX22tfD7WyYtA+gjrykqZYRcaBUmkY71okVSWuPtu4Je/lOLNhxwSdHJnzpQye3ZCrHVraVPt5ljX14uQdXN7R470dKy1lqIYV10lzR+1Fp372muiFd99V2IgOV7lHAAZz+DBwWiHVxxk0SL/MRCgsbDWWmIgkyY1zphHy0EHiUVvbHovktkcJpTTT4fOyQm/ZGAC8BLW9Zb7ob8L1oMQ4g861umHESbdu6emMPRDuI61EdYdOsQmCmLEairGK4qLZZmqjvWOHe4xA3N+kulYP/+8KNSrrpJSeO++KxPsxo+XsniFhS7FnSFutptjXVgoFxBOjjUgwnrLFlvnsqQE+Pe/xZidMEGGO3x48Ot43HFAt26+3qlQXy8ttYcMkVJ/gLuw3rlTPo/9AW0f5OWJwjcmzLffimsfzqRFv5gJjH5d62Q2hwll/Hh88+67Uqs7xfAS1sMCtaqXWO6bxy7fdEJIA+hYpx9WYZ0qVRbCJVJh3bt3bBzrrCwRrKl4YWIqCqRixrqwUATMhx86b2M+02Q51rW1wEsvidVrLk5OPVXc49NPB/75T1nnJqx79HB3rFeulKWbsB4xQpaBOEh9ffCf26++An73O9H+//2vaNznn298LeKbzZtl54MHy3vu2dNdWC9ZIstwHGul5DtpHOupU+Vi6ayzIhy0C0bw+xHWdXVyAZMqjrVSqG8UgE8NvH748FnckBDiitWx1jr5GTXijREuPXrI/9ZVVfLzdToRaRSkR4/oLgJLS4O1idu0SU1hbbouhgrrVHCs16yREPDSpSJc7Ui2Y/3ZZ/Idueiihus7d5ba0mecISU2TOUOO3r2dC+VtypQ2s4rCgKgcNaPePbryXj6aRnSEUdIH5UFC4LGbNSY8QweLMtRo9yFdbgVQQym+2J5OfD66zKLMsZ1ugHIL1N9+viLU2zfLhdTqSKsUxhXYa213pCogRCS0RgLZd8+74YIJDWwOtaA/CeXbsLaKqb9TPIpLZX/wDt0EHEXCVoHHWtAhF8quMChGMfauK2plLE2Lq6pG2xHsh3r55+Xz86p8clZZ3m7rD16SL63rk4mIoaycqW8H5eGKO8v7I3Hsz/Gx7f8BPVakgFjxshzzZvHUFQDwVJ7RuiPGgV88YVcBNlNTly8WErkmRJ6funaVYT1a6/J9zEeMRDDQQf5c6xToTlMmuBVbq9cKVXmdEvUIAlJe6y1cZmzTg/shHW6YYR1ixb+Heu8PBHDkb7fykoRSnl58jiafcWTVHas/QjrZDrWpaWSpz7vPFGvkdKzp3xXnH4dWbVKRGzIL3zWj+XpZxTmZ43DH3u/jDVrJP7ht+N32KxaJRfXRuiPGiWi2kRWQgl34qLBONZTp0oo/JBDIh+zF6NHAytWeNdvT5XmMGmAV4OYtlrrdgD+BeCPAHoC6AXgZgD/jPvoCMkUjGMNMGedLhgxmM7CuqREhFbnzv6Fdfv24oJG+n5NnCRdoiChkxdTwV03E/pS1bF+4w1puX3hhdHtx6vknqXUXmWlmORHHinpBdNr6/HHgY0X3Ip79lyHgQPiPA/CVAQxQt+tMkhtreS+IxXW69ZJlObSS+MbHRw9WqJuXrXAU6U5TBrgNXnRcIrW+j9a63KtdZnW+r8ATo3nwAjJKOhYpx8VFTL5zvyMm4ri0IvSUnGO8/LCE9Zt24qSqa0N/5ihwjqVoyAtWwbjPenmWJvvYzIc6+efl9Jv48dHtx+3JjHV1cD69djSYzx+/Wu5vr3oIpmA+H//F/xBpGtXIGfUcDmfkZoWb74JzJjhvd2qVcF8NSCiv1kze2G9erVcfIRTEcTQpYs4+c2aSQvIeOJ3AuOmTdKMp2PH+I4nA/ArrPcopX6plMpWSmUppX4JIAX+9SEkTbAKazrW6YFpvpHqTU7cKC2VvLRfYW0mHUbTMdFOWKfiZ2e6Lho3sHlzKXOWSsJ6+3bn1uROURDzPuJ1MbN2rTR+ueii6J1UI6xDHOuSEqDgs02A1mg+pB9eeUUKjnz9tZjYN98c/KEBQPStzX/7W+BPf3LfpqYG+PHHhhMpmzWTqIadsI504iIQrGV9yinB+/Gif3/5DnlNYEyV5jBpgF9h/QsAZwPYHridFVhHCPGDNQpCxzo9KC8X9y+dhXVJSfiOdV5eMF4QSS3rdImCWLsuGtq0SQ1hvWVLcDKfU0yivFx+UbGbUBvP9/HiiyKufvnL6PfVtau8h8JC1NcD06YB558v7vTlN3cAAHQe3w/btolJfsQRDroupOReWJSWygTKH35wzxmvXy+/4Fgda8C5MsjixXKBE0lTFxO3uOyy8F8bLllZ8h68HOuVKyWDQzzxJay11uu11qdqrTtrrbtorU/TWq+P89gIyRzMP9hZWXSs04WKivQX1tYoiJ+qILFoRZ4uURDjWFvJzU2+sNZaxLRpQOIUBykrk8/WTmnm5sbnM9daFO4xx8Qma5uTA3Trhue/7oshQ2S3H34oevLfx70n2wweDM9yxfn5cpEUiWNdUCDLmhppz+hEaEUQw6hR8guDqTJjWLRIRHWLCJpUn3CC2PNTpoT/2kgwrc3rHfr+LVkiz//0p4kZT5rjVRXkCq8d+NmGkCaPcax79qRjnS6YKEgqt+X2oqREoiAdOoSfsQYie8/mOKHCOtUa7Oza1dixjpcgDYdduyT+YSpBmEljoZSXN564aIiXY/3NNzKpLspJizU1wP/+F7gG69EDe3ZVoXdv4IUX5JrikUeAMRUzxbp2eo9WlPLV2twWI6wBeX9OGGFt51gDwWYwhsWLI4uBAPJrhaM9HwcOOki+T+vX2z//5JMSMYp2smoTwcux/qNS6nSX2xkArk3EQAlJa4xj3bcvhXW6YKIg6SysrY717t3OjhQgF3/79gWrggCxi4JonXwnOJSiotR0rE2+2ghrL8fajnhdIDz/vERPTj89opevXg388Y+SKDjtNJkziJ49cVX2k/tjIK1aBTa2VATxxYgREgUJ9wKuoEBc5cGDJTvuxKpV8ncU+p2xqwxSUiIXRJEK60Rjin3b5awrK+WK58wzG1+IElu8hPXXAE52uf0MwOfxHCAhGUF1tbgQPXsyCpIumChITo78b+9HWGsNvPeeu4D1y5dfSvYzUurrRXwZYV1f7y62rE5ztFGQrKzgBUk0EyHjRV2diJ9QkZQKGWuTqR42TD47J2GdaMe6qkq6AJ5xRthdACsrpUzekCHAAw8AEybIn8lFFwHo0QNqi01VkJUr3TsuhjJypHzf3Sqp2FFQIMc58khxrJ3+dkNL7Rny8+V7ZHWszf1IKoIkg5Ej5W/WTli/+ab823AFwwl+8eq8eHGiBkJIRlNVJaW98vPpWKcLJgoC+K9s8e23Ur5g2jTgqKMiP3ZREfCTn4i9d889ke1j924R+h06BN9HaamzGDNOs2kQA0QurNu3DwoQq+PfrVv4+4sHJSXy2dhFQYqLkzMmg3Gse/aUKgxujrXTuczNlQYjseT99+XchrYwd2D+fNFpl1wiJnevXlIm76KLgqXhAcj7LC4W88GEqYuKZF04jrW1Mkg4+e+CAikbOHky8NRT0mLdrg37qlXApEmN1yvVeAJjNBVBkkGrVvJZ201gfOIJufA44ojEjytN8VsVhBASDVVV8o9X167yH6K1SghJTUwUBPA/Ac9cNIVOZAqXzz8X5ywakWccaONYW9fZYY1wRBsFMTEQIDUnf4Z2XTSkQsa6sFDEWvfuIhDdHGunKEg8HOt335ULI5cLxpIS4NFHpaX4uHHAjTcGU3AvvSTXiQ1ENWDfJMZ0Mgw3CgKEVxmkqkpK6B1wgAhrwD4OUl0NbNzo7KCPGiWCvq5OHi9aJBdtjd5sCjN6dGPHetkycfEvv5xl9sKAwpqQRGDcmPx8ecw4SOpjoiCAf8faVN6IRJBa+eST6PdjRLSpYw24VwaxCutYONaGVIyCmAsfO8c6FaIgXbtKjWQ3x9otChKPC4T58yX3bcoAhvDaa6KRr7lGNNijj4pm3Z+ZdsKulvWqVbIMJwrSsaMI2XAmMK5aJb9cHHAAMGCAXDjYCeu1a2W70ImLhlGjJO+ybp08NhMX00mMHnSQXDxY/4146in5Hvr8lYIInsI60BDm7EQMhpCMxepYAxTWqU59vQiscKMg5j+laNxZrYFPP5X70QhrM5a8PBHXgLtjbc1YN28uE7piIaxTcfKnm2OdbGFdWBh0cXv1klaDNTWNt3ObvBhrx3rPHhGhZpIbZFh/+5tUhQPEob7kEmDePNHgv/lN8HrOFfNerd0XV66UuQ39+4c3znArg5iKIAccICJ48mT7yiBOpfYM1gmMdXUyhnSJgRjMuTVxkOpq4LnnZKJqvJvUZBiewlprXQ/gpgSMhZDMpbpahLVxrJmzTm2qqkTgJsOxXrw4OGkxFo51uFEQs23btk0vCpIKkxcLC4Mubq9e8j0MncSqdWId68WLAa1RO2os3ntPGgL27i3NCr/8UjYZNEhc6rFjw9y3XVvzlSuBgQNFXIfDyJGSkfY7ebigQCbtGcE8aZKUnAv9lcA46E6O9fDhsp/Fi8W1rqxMP2Ed2tr8rbfk3zNOWgwbv1GQL5RSv1dK9VZKdTS3uI6MkEzCTF6kY50eGBEYrrA2mehoRKRxq8eNi30UxG/GGoi8FXm6R0GqqoJZ2WSwZUtDYQ00rmVdWSni0c2x3rfP3umOhED2dvJdU3DqqdKk8A9/EL15551R7jsvT0yH0ChIOPlqw4gRwdy0HwoKxBU3kyZNzjrUtV69Wv7ttn6vrbRqJeJ88eLgxMV0qQhi6NZNjB+Ts37iCbm4iWYSdhPF7+XgOYHl1ZZ1GsCA2A6HkAyFjnV6YURgpFGQaATxJ5+I2zVsGPDdd5HvxxoFMW2vvYS1UsGLiXbtInsfpaXpEQVp0UKEtBXzuLLSWbTGk717pZqHNQoCNHZQzWfp5lgD4r77ymM0pqpKTMu33wZe67gYzTp0wHU3NUfrXODEEyV6GxOUkvdrHOu6OmDNGuCkk8Lfl7UyiJMItlJQIDEQw+jR8tnNmgWcc05w/apVzm61YdQo6dy4aJG418OHhz38pGMmMK5cCcyYIVmfLE7FCxe/Lc3729woqgnxi3GsW7cWoUHHOrUxwjrRUZCKCvlP/YQTIhe2htLSYD3pnBx5D17Cul274H+kkTjWWsuYUz0KUlQkbnXo5DKrIE0GW7fKMtSxDhXW5nvh5lgDEb2PBQuAq6+WeYAXXCAG7Po5O4DRo3HueQqnnBJDUW3o2TPoWG/YIBcY4UxcNBgx6ydnXVsrgtkqrHNygIkTG09gXL3aezyjRkkM5JtvZFvPWZspyEEHSZTm0Ufls/jVr5I9orTEl7BWSjVTSv1OKfVm4HaNUirWf1qEZC5m8iIgPynSsU5t7KIgFRXe2c1oJy9OmyY/30+ZIuI0WmGdlxcUynl53pMXQwVxuO9jzx5xHK37ycmRi8pUioLs2tU4Xw1EJUhjghGXRlibXxsidazD/My/+04y0k8/DZx8snwdVy2vxeDVH0kNvXhhdaxNnjmSKEjbtkC/fv5K7v34o8RlrMIakDjI4sXBv72KCrng8eNYA/KhpVu+2jB6tHwmjz0mrTHNL6wkLPx6/P8FMA7AfwK3cYF1hBA/WJsf5OfTsU51Qh1rv4IrWsf6k09EFE2aJKJp377Ia56XlDSMAXTo4F1uz7p9JI55aE7b0KZNajnWTsI6QkEaM6zNYQBx1O1qWcfAsdZaKnpceKFMQgSkmt5TT4m+f+EFiddmrV4p30FLRZCYYxxrrSOrYW1lxAh/jrW1IoiVSZPkAnr2bHnsVRHEYMS01ukrrE0uvKZGaleTiPArrCdorS/SWn8VuF0MYEI8B0ZIRkHHOr2wy1gD3uIwWsf600+Bo4+W/G80TVqAoGNt8HKs7SYdhvs+QiuLWPeVSo61iYKEkuwoiBHWJmMN2NeyNufFSVi7XCCYMnlDh4pwfu+94I8aWVnApZcGqzMCCE5mi7djXVUl38+VK+V7GGmJt5EjgRUroGpr3bdzEtYTJ8oHYeIgRlh7OdZ9+gT/ZtNVWA8ZIgZQv37AccclezRpi19hXaeUGmgeKKUGAEjitGlC0oxQx5rCOrWxi4JY19uhdVC4RiKG16yRRhQnnCCPoxXWJSUNFVK4wrpdu8iFdahjHWmFkXjh5VgnU1i3aCHNTgxuwtopChLiWFs15q23ikPdvbuUKd6yBbjnHpcxLVggY4rUQfaDtUmMqQgSaXOVkSOBmhq0spbvs6OgQD4Eu+/q6NFBYW2iKYMGue/PtDYH0q8iiCEnB7jjDuChhzhpMQr8fnJ/ADBNKTVdKfU1gK8A/D5+wyIkwwh1rHftSm5JL+KO3eRFwF0clpcHz2kkItKU2ZsyRZap4Fj7yZWH7gNI7ShIXZ1cdKSiY21K7VlFZa9est7674VXFCTwPtas0bjlFjFT58+Xp265RUxhEwMxBWMcWbgQOPDAOMxYtGBtErNyZWQTFw2B1ua5XiX3QiuCWJk8WaIgNTXiWPfq5eODgrjdphV9unLzzZKvJhHjV1jPAjAYwO8A/BbAUAA27YkIIbaYcnuAONb19cFauiT1iCQKYmpYd+wYmRj+5BOpG2ucsVgI63Ac61AhHkn9aTfHOkWiIDnmYiEVJy9auy4aevUSUW39lcvFsd63D3jpi3wcja8w+Hcn4b77pCS60eoDB4ahW7UWxzqe+Wog6FivXi3ufDTu+LBhQE4O2q5Y4byN1t7CuqpK3rufUnuGO++UK5h0amVOYo5fYf2d1nqv1npx4LYXQBQFVgWl1NNKqR1KqaWWdR2VUp8rpVYHlh3c9kFIylNTI/8xmigIm8SkPuXl4tA1by6P/YhMk6/u21f+U/bKeFrZuxf46qtgDASITRQk1LEuK7N3oLW2j4IA4TnN1rboVlIoCtLMiP9UnbxoRKbBrklMWZn8VG9xUc3Xr6YG+M1tnbARfXDPz77Dxo3A++9HGJHevFkuGOOZrwaCFxOmP3o0wrpVK+DYY9Fl5kz5XtuxZYt8H52E9aRJspw1y1+pPeuxu3ULf8wko3AV1kqpbkqpcQBaKaXGKKXGBm5HAfDxu4gnzwI4IWTdHwF8qbUeDODLwGNC0peqKllaHWuAOetUpqIi6F4C/hxrq7D22jaUb76RpiQmBgJEJ6z37pXvXaiwNgI6lMrKxmXyzHsO5/hpEAVpZt5PqkVBtG7YddFgV8u6vBxo2xaluxX++19xpCdNkl3k5gI/zNqH1RiMWybPaLS7sFiwQJbxdqxbtpRfeqZNk8fRREEA4Iwz0GrLlmAXxFCcJi4aevSQjozvvy+/LPp1rAmBt2M9BcADAHoBeBDAPwK3GwDcEu3BtdYzABSHrD4VwHOB+88BOC3a4xCSVEy5NDrW6UNFRcP8ajjCuk8fWYYjSD/5RBzyo48OrotGWBuBa42CmPt2cRA7QRxJY5fdu4Hs7MYdDVMoCuLLsU6GsN69Wy5wfAjrhT+2x4U1T6F7d+A3v5FrIrMEgCEHtkCWQvSf+cKFDSflxZOePWXuCRC9kD3tNOisLODNN+2f9xLWgMRBpk+X+9EKfdKkcG1prrV+DsBzSqkztNZvJWhM+VrrQPspbANgW6FcKXUFgCsAID8/H9PNH0CCqaioSNqxSeKI5jy32LYNhwJYsWEDtk2fjpyyMkwGsOabb7C5e/dYDpNEgfUcj1i3Dq2Vwg+Bx9mVlTgcwNqFC7HJ4XvQffZsDAWwpqYGgwD88NVX2NO/v69jj3/rLdSMHIlFc+fuX5e1dy+OALBu4UJsDPO712rjRhwCYPmWLdgReG3nzZsxEsDcL75ARYhwab1+PQ4GsLywcP/27deuxRgAi2bOREllpa/jDl6+HF1bt8Y35if9AP2Li9GnrAxfT5sW0/xpVlUV6ps3FzHvk7ydOwEAs1evRrWN8DyiWTNsLijAugT/u976xx/lHJSW7j8HAACtcXjz5ljw1XJsHTALbdvWYuXqHPyv6if4ycmFOOmkrRgypAJKNWwYOLlVK2xdsQJro3gfIz7/HLm9emGO5XsZL0a1bImOAKrz8zF7zpyo9zdy5Ei0euEF/HDssY2eG/zFF8jPzcWsFSuCdbND6N6lC0wgZU5JCSr5/3zKkbL6S2vt6wbgpwBuAnCbufl9rcd++wFYanlcGvJ8idc+xo0bp5PFtGnTknZskjiiOs8rVmgNaP3SS/K4rk7rnByt//SnmIyNxIYG53jKFK0PPjj4uK5OzuFttznv4P77ZZs335TlN9/4O3BhoWx/330N19fXR/49mT1b9vnhh8F106bJuq++arz9t9/Kcx9/HFy3YIGse/tt/8c9/3yt+/VrvP7ee2VflZX+9+XF3r1a9+6t9a23hvWyNVdeKWMpK7PfoGNHra++OgYDDJPPPpNxzZixf1VNjdbvv6/1qbmf6WxVq++9V9bvPfZEvWf8Ee7769ZN68svj25M/fppfc450e3DL5dcIu//uONisruV114r+1u2rPGTRx2l9cSJ7jtYtkxen5Ul3zWSciRTfwGYqx00qd+W5o8BOAdSEUQBOAtA31gKfAvblVLdA8ftDoC/l5P0JjRjnZXFJjGpTmgUJCvLOydcUiJ1YM2vEH4jFKbM3gkh002Uiqz7IRCMe4RWBbE+ZyWWUZDQfLV1X7GMg3z8sUzoC3HHvWi2e7dMSrVm6K3k5iYnCmLpuqg1cNttEtc/+WRg9r5xuLHHqzjzTNmk+Z4StM5r7r6/Nm2iex8lJcD69fHPVxvMBMYY1cvedfjh8jf0ls2P7W4VQQzDhknuu1+/4CRmQnzgtyrIYVrrCyHu8V8BHAogXqGj9wBcFLh/EYD/xek4hCQGk7E2whpgW/NUp7y8sfDyqmxRXCxCNtxs9Kefihg/8MDGz0UqrE3eO3TyIuBfWEeS8Q5ti26IRKR78fzzslywIKxa283KymTiolMkJUnCunr9NnyJY4Du3aGUxJvHjAHeeQfYdNYNuK/ZrcHocXm5c3MYQ25udBcyixbJMt4VQQwmWx6jPPO+Tp1kRmdozrqkREwNL2GdlQVcfDFw+ukxGQ9pOvgV1gHLDZVKqR4AagBEHQ5VSr0CKds3VCm1WSl1KYC/ATheKbUawHGBx4SkL8axNpMXATrWqU5oVRDAn2PdoUN4IrKuDvj8c6kGYif02rePzrG2E9ZGdFuJt2NtPstYCeviYqnY0L27nKs1a3y/tNnu3fYTFw0JFtaLFgG/+x3Q42+/xfH4HBt3ygX4O+8AH3wgvTqa9ekujra5gCgrc24OY4jWsU5URRCDmaQ5bFjs9nnGGVIZxLQlB/xNXDQ88ADw97/HbjykSeBXWH+glMoD8HcA8wGsB/BKtAfXWp+nte6utW6mte6ltZ6qtS7SWh+rtR6stT5Oax1aNYSQ9MLJsaawTl1CoyCAt2NthHU4Tu+OHSISJ0ywfz5ax9oaBWnXTsS7nWNtV3+6RQupVJKKUZDXXpOCzffeK4+NCPRBjnGsnWjTJiEVTBYulNM+ejTwxBPAiV3m4ov+V+zXlw3mY/bqJe/X/MqVCMd64UK5cMm3rR8Qe6ZMAZ58ErCZbBgxxm22xkHCEdaERIAvYa21vktrXaqlMkhfAMO01n+J79AIyRCcHOsdO5wbGJDkEkkUJNSx9iOIjQDu2NH++Xbt7OtOe1FaKsLY+p3LyhLR6xQFCS2Tp5S8l3CEfWmpu7COlWP9/PMSnfnFL0T8m37dPkiWY601MHMm8O238rh7dzGg//UvKV/9Urff45ghm5Fl97+yteSe1olzrBPlVgNyHi+7LKwKL5706QMcfHDDOEhBgfxt9OsXu+MQYsG13J5SyjFcpJSC1vrt2A+JkAwjdPIiIC5QdbU/54kklro6OWd2wtrapCOUkhLJh+bkyLn2IyLtJhlaadfOsRyY537t9unU1tw4zaFxlHA6JhrBF+8oyKpVwOzZ8hN98+YisFNYWG/bJtcBU6fK0E88EfjoI/knYN48y4aFhc71onv3luXmzcDw4aLI4+lYV1eLAD355Mhen0qceSZw000yEbNfP3lfQ4fGVsATYsHLsT7Z5faz+A6NkAwhtEEMwCYxqYwRVZE61oD/CIfdJEMr0URB7PbpJaztju9XDFdUiOCLdxTkhRfEff/FL+Tx2LHirvr59ae+Hs3Ky92jIDEU1n/4g2jim28GunQBnn0WeOMNmw1rayUa5tQm0epYm+9DPB3r5ctlTImauBhPzjhDliYO4qciCCFR4NUg5uJEDYSQjMXJsQbkP9NBgxI/JuKMEZLhZKzr6xu6xH6dXrsstJVoyu2FI6zdIhx+j+/UztzsB4jesa6vF2F9/PHB8mxjxwJPPSWl90zXSydKS6Hq690d6ygE6bp1Ip5vukl2M3w4cP31wCWXeMzJ275d3puTsO7cWdz5zZudv5+hRONYJ3riYjwZMEAuEN56S9pTrl8PXHSR58sIiRS/dazzlVJTlVIfBx4PD1TwIIR4YTd5kY516mLEiJ1j7SRUyspEGEXqWLsJ6+pqYN8+731ZcYuCOFUFcSqT51cMuwnrWEVBZs4ENmwALrwwuM64qn7iIEVFsvRyrMMQpNXVwCuvyJy7gQOBe+4JdkC8+GLg/vt9FLowNazNxUIoWVkiuq2OtVcUpE0buag3fc7DYeFCOfcDBoT/2lTkjDOA774DvvpKftmgY03iiN+qIM8C+BSA+atfBeC6OIyHkMzDbvKi1bEmqYWbsK6stBcqoQLZryC1K4tnxYincAWpUxSkQ4fwoyCxcKxNhZFooyDPPSfn5bTTgutGjRLh6UdY79olS6+M9b59EoXwYNs20cK/+IW41XfdJbo/tNePJ1u2yNLJsQYkDhKuYw3IdzZcFiwADjoI9jMp0xDTWeeee2RJYU3iiN+/ms5a69cB1AOA1roWQASXwYQ0QaqrZUJbjiV51aWLLOlYpx5G/NlFQazPWwkV1uE41rm5IjrtiKRJCxB+FMStTF64jrXTRYJXHXAvKisloHzWWUDr1sH1rVuLUPJTcs8Iay/HGrCNg5SVAY8/HtRn3boBV14JfPEFsHYtcOutwTh0WFi6LjoSKqz9ONZA+LGW+noprp0J+WrD0KHAiBHiWmdlxawJDSF2+BXWe5RSnQBoAFBKTQQQQQ0oQpogVVUN3WpAhFTHjnSsUxEjXOwca+vzViJ1rK0THu2IRFhr7R4Fqaho7MbGYvKim2MNhCfS7Xj3XRm7NQZiGDs2vCiIl2MN7BekWku041e/khJ5V10lvWlMr5b/+z+JgURl7hYWyoW3ueC2wwhr8zn7dazD/ZVg7Vp5TSbkq60Y13rAAPkFhZA44fefghsgrcYHKqW+AfA8gN/GbVSEZBJVVQ3z1QZTy5qkFm5REMCfsA7HsY61sN6zR4Szk2MNNKyNXV/v7Vj7qbhh12QmdF/RREGefx7o2xc44ojGz40ZI3EKrwtVP1EQc94DY733XuDww4G33wbOPx+YMydofMaMLVtEtbvttFcvYO9e4Mcf5XG8HOuFC2WZSY41EKwOwhgIiTOuVUEAQCmVDeDIwG0oAAVgpda6Js5jIyQzqK5u7FgD7L6YqnhFQdyEtWn04ldYOznLhkiEtVtu26wrLQ3GISoqRDg7TV7UWsRZ6IVGKF6OdTRRkC1bpPX7LbfYi8+xY2W5YIF7wLmoCPXZ2chycHvr6oBPVwzAU3gL10xTOGYIcPbZktA466yG/XNiSmGhewwECNayXr5clvFyrBcsEPd8+PDwXpfqjBwprvXPWCmYxBdPYa21rlNKnae1fgjAsgSMiZDMwsmxzs+XLCNJLZyiIG6VLeyiIHv3yiS45s2dj1VS4t4BLhJh7VZpxCqsDW6C2Dp50o+wzs5umH+2Ek0U5OWXxVm/4AL7501sYf58d2G9axdq2rdHi5BGOD/+CDz9NPDMM0Bh4aHogh04c4t8joMHyy2uFBZ6C1kT3i4okEY+Xio/Usd63jzJI2daXEIphyLihMQWvz9mfaOUekQpdbhSaqy5xXVkhGQK1dXOURA61qlHpFGQZs2CotJvNY94REH8ONbWknt+6k/7Ob5T90brviKNgjz/PDBxovOks/btpdadV8561y7UBj5Tk26pr5eox733SoGRt+5Zgc3ohV8ctj6ysUZCYaFzqT2DEdYrVshn6fQ5GyJxrGtrJecyaZL/1xBCGuDpWAcYHVjeaVmnARwT09EQkonYTV4ExLEuLRVnM9PcoXSmokLOV07IP49uwrq4WASyETvWbd0qUHgJayN2IxHWdvs16/w61uE0dnHKaRsijYJs2wYsWQI88ID7dmPHhvQID0FrYOVKLGh9MN64Fpg+XTbPyRHdPnhwIG2xeB/w55rYdIn0Q0WFnF+vKEjXrjLYvXvdM+KGSBzrRYvkHB1+uP/XEEIa4Ddj/V4gCkIICRc3xxoAdu6MsEYXiQtOsQcvx9oqZP04zbW1IqrchHXr1pIpjiQK4pWxNrg53OHU0fYS1pFGQUwZvQkT3LcbO1Z+6re5WCkrA169aw2mFjyDOTgEzddIKezSUtGox1gtIpdye3HBTw1rQGI2PXoAGzd6T1wEInOsZ8yQJYU1IRHjGQXRWtcBOC8BYyEkM3FzrAHGQVKNiorohbUfp9erOQwgDni4bc39Tl40xDoK4kSkURC/7bXNBMZAVQutg01Pv/sOuPKBwajMaotrrihAYSHw2msOxm+ihbVX10Ur5gLca+IiEJljPXOmRGq8RD4hxBFmrAmJN27l9gCW3Es1KirshYtxj2PlWHu1M7fuy1oezwu3sndt2sh7SFYUZN++8Nuzz58vYs/LpQ2Uh9vxdQEeeEDmAt52mzx13AGF+D77MCy+7mmccd529yRFqjrWQFBY+3GsTd7f78WM1uJY060mJCqYsSYk3riV2wPoWKcaTlEQpWS9U+fFoUODj/1EKNyy0FbCdaxLSkQQh2bEAXkPod0X/VQF8etYu7nv1s6VpiyhHxYsCLrRLny+sAsea/Uh3rvzJ6jVwKGHBtMj2VOfwMH1s4HfvABs2uS+IyNIE+1YhyOs/TjWWVnyXvy+j4ICaaBjVyecEOIbX8Jaa310vAdCSMZCxzq9cHKsAeeccElJQ7HoJ0LhloW2EkkUxG2feXkNq4KUlkpFE7uLv3Ac69JS7yiI2ZdfYb17N7BuHXDppbZPW8s/P/ccMKNuEq7t8AIunXVxsA/Ivn3AE08AJ50kzreXsM7Olr/XRE1eLCyUc+xVzhAI1rL241gD4r77fR8zZ8qSjjUhUeErCqKUaq+UelApNTdw+4dSyuVfUELIfpwmL7ZpI44SHevUIlxhXV/fuNFLrKMg4Qprt3126NDYsc7Lsy/f1qqVCE0vYV1fL2P0ioIA4U1gNF0ALY713r2Sjz7+eNGZS5bI+oceAgpvehgPlFyKA/pYXNq335bKIldf7f+4ubmJjYL4yVcD4TnWgHzmft/HjBnS/XHgQH/bE0Js8ZuxfhpAOYCzA7cyAM/Ea1CEZBROkxcBtjWPlqIiKXUXS9yaodgJ6927JZ9qFbN+RGS8hHVJibdjHSqsnQSxUvKevY5vujf6cazDcYLNxMUxY7BzJ3DddaJBzz0XWL0auOOO4A8/XboAzSccJONYvDi4j0ceEbE4ZYr/4yZSWPvpumgIV1j7daxNvvqII7zrYxNCXPGbsR6otT7D8vivSqmFcRgPIZmFKU1g51gDbGseLeefL27pp5/Gbp9OVUEAe2FtJ5Czs0XUuAnSeGWsS0uBvn2dn8/LA7ZuDT6ORZk8r3bmZj9AWI51+ZwCbOp8BIbn56NlOfDCC8BxxwGXXQYce6xNd3PjbM+fLyHrBQuAb74BHnzQvhW6E4kW1kcd5W/beDnWGzYAmzczBkJIDPArrKuUUpO11rMAQCk1CUBV/IZFSIZQUyPCz82x3rgxsWPKJDZskFttrf1kvUjwEta7djVc5+Q8ewnSkhJpd+703TBEIqzdStOF41ib48dCWPuMgmgt5fGmTgVee+0hDMzdhoVaPs7Nm52vUQGI89ulS7AD46OPStzqV79yH7/dWBORsa6vDy8K0rOnlDo580x/2/t1rE39ak5cJCRq/F7C/xrAo0qp9Uqp9QAeAXBV3EZFSKZQFbj+pGMdH0pKgMpKYOnS2OyvpkZCvE6OoF33QCdh7SWITYk+r5/e27UT17Guzn07637DiYJ4TXb0EwUJx7F2EXpvvgmMGCEdtV9/XeM8/TIeP+Oz/c+7impAPssxY8SpLi4GXn4Z+OUvvX8VCCVRjvWuXXJR6DcKohTw179Km0g/+HWsZ86Uz2jECH/7JYQ44ktYa60Xaq0PAjAKwCit9Rit9aL4Do2QDMB0qHByJfPzpfNifX3ixpRJGFE7e3Zs9mdEX7RREKdtQ1/nR/CF0/2wrk5EsNt+8/LkYsTUk05iFKSuDvjkk+CPAJWVMrypU4GtH8zHk/pyTPxZl/Biv2PHyoXWY4/JhW04kxYNiRLW4ZTai4RwHOvJk8OLyxBCbPFbFeRepVSe1rpMa12mlOqglLo73oMjJO3xcqy7dhV1EesJeE2BqipxlwHg++9js89ohHVoCTkvx9qreod1P4D/WtKAuwNtjmlc6yREQdavB26/HejfHzjxRODFF+WpCy4Avv0WuOQSoM3KebLSRw3rBowdK7883HuvZIYPOii81wOJE9bzAu9x+PD47N+PY71tG7BqFWMghMQIv5enJ2qtS80DrXUJgJPiMiJCMgk/jjXAOEgkWGsxx8qxNgLSrdze3r0i3ELHYRcF8XKsvWpYA0Gx6kdY+2mTbm1rXlcnY/Rymr2O7dbt0dCqFWpUc5zw5OkYMAC46y7Rk2+8AfzmN7JJA2d6wQIZa79+7scOxQjxPXuAa64J77WGRGWsZ8yQi+shQ+Kzfz+O9axZsuTERUJigl9hna2UamEeKKVaAWjhsj0hBPDnWAMsuRcJRtCOGgWsWNFQaEeKH8caaCiYi4tlEmLoOfYSpOFGQcIR1l5RELOt2WesHGsbQb90KfDUUwCUQrN2rdCjZTFuuw1Yv15iIGeeKR9fIxYskEmY4ZZ/699fxty9O/Dzn4f3WkOiHOuZM0XQxqvEnXGstXbeZsYMmeAZ7i8DhBBb/ArrlwB8qZS6VCl1KYDPATwXv2ERkiEYx9pt8iJAxzoSjJA+4QRZ/vBD9PuMRFg7TUL0O3nRi3CEtZ9ujlZh7Sc6Yi4Q3MTZ7t1SlSXwPS8vFzF96KHAgQcCv/td4FBt2uDpQ57AHXcAffq4vI/aWmDRIpmIGC5ZWcDdd0v96mbNwn89kBhhvXGjXF3EM4KRmyvnrcqliNeMGcBhh0X+WRFCGuB38uJ9AO4GcEDgdpfW+v54DoyQjMD8h+ZWbg+gYx0JRkT+5CciamMRBzHC2i0KAtgLa7ttnZze+npRmvFyrMMV1l5RkPp6d3FmctpK4YMPxCy+/HJZ/eCDUhGxffvAvvxELFaulIvSSIQ1APz2t8Dpp0f2WkAEaW1tcIJnPEhEC3Fzgeh0kVBaKs10GAMhJGb4Lvyqtf4EwCdxHAshmYeXY92xozQToWMdPkZY9+0rYd1YCGsjhL0ca6s4dBLW7dqJMNu7F2gRkpwrLxex6idjHc8oiB9hba1K0rp1o6d37gRemH0YRjbbg59A0hvnngtceikwcWKIke+nwggQ7LiYrHhCbq4sKyoaT0qNFTNnymc7alR89g80fB9dujR+/ptvxNHmxEVCYgZr6xAST7wc66wsoFOnxk1HiDfWSYMTJ0plELe4gh+iiYL42db6GiA8x9qIYDfCiYKUlIRXJs8i7E2ZvLPOkkpxNy44Hx/VScvwXr2CMZBG0WG7OuB2LFggfzNDh3pvGw+8nN5YYErcZWfH7xhe72PmTImAHHJI/MZASBODwpqQeOI1eRHwNzmMNMYaezjkEJlEuGZNdPuMZRTEzWkOR1gbceTXsc7Odr4wAMR1btZMtvVTzcPmPZ9wgpTJmzZNCm8sHXMB/jniSe/x+Y2CLFggTm6summGi3F64yWsd+4ECgriH8GwOtZ2zJgBTJjgo/MOIcQvfutYX+tnHSEkBK9ye0D4LauJUFIin112tjjWQPT1rI14tIk8AHAW1nZxATfH2k8W2pCV5a/kndlvXp57lQmlgt0XfUxe3NuyPd7AmTj9ut77rxOvugp4/XXpb/Lgg8CI2kXu4tzgJwqitQjrSPPVsSDewtqUuIt3BMPNsa6slAm/jIEQElP8OtYX2az7VQzHQUhm4sexbt+ewjoSrE7x8OEiIqLNWVdUiKh2+nne0uQEgGQinBq9xMqxNvvyWxXEj1gPFdY2onjZMuCGG4Be507C2XgDcwvaYPVqee6MMyQGsj867tVkxuAnCrJ+vYwtlYX1xo3ASy9Fvv8ZM+Rie/z4yPfhBzfH+vvvZYImJy4SElNcf2dTSp0H4BcA+iul3rM81RYAW8UR4oXX5EVARNP69QkZTkZhFdbZ2fKTdiyEtVMMBGjsQhthmirC2m83R6uwbtmyUSHpZcuAkSMlMXLqMdW49NOTcfxDFyF71HnOx/XrWHtFQczExWQKa3MB5TTWhx8G/vEPmVx5wAHh73/mTPmVxbaAdwxxc6xnzJBfLyZNiu8YCGlieDnW3wL4B4AVgaW53QhgSnyHRkgG4DV5EWAUJFJCs80TJ0rtY7eycF6Ul7vnk1u2FBFvhLWbQPYTBUkBx1q3a4/Zs4HLLgOuv16eHj5cJiAWFgJvPFOBE/Apsvc4HL++Xt6jn+O2bSvnp7bWeZsFC+QzPvBA7/3FCy/HevlyWZpe7OFQVibvMRERDCfHurAQePVVaffu54KIEOIbV2Gttd6gtZ6utT5Ua/215TZfa+3yLyMhBIA41s2auc/8p7COjFBhfcghItjmz498nxUV7sJaqYY5YTdh7eVYZ2W5Hyt0X+FkrL3Iy8OunRoPfX8YRpbOxKGHis6qr5enlZJyeV26wLYqSAPKyyUX7TcKAri71gsWiAuczAl1XsK6oECWL70U/ND88u238ppERDDsHOvPP5dfAzZtkr7yhJCY4nfy4ulKqdVKqd1KqTKlVLlSikqAEC+qqtzdaiAomqItFRcuO3YA77yT2GPGEjthDUQ3gdFLWAP+hbVXub28PBHXfohRFKS+PvA169ABd265DDcsugjtmlXjySeBrVuBf/3L5kW5uaK0nbLRfkr2GezqgIeS7ImLgLuw3rNHolvDh0vnGzMR0S8zZ0q1k0MPjXqYnlgd67o64PbbgSlTpDHV3LnAz34W/zEQ0sTwO3nxfgCnaK3ba63baa3baq3bxXNghGQEVVXezlu7dkBNTTCPnSieflq606WrWx4ae+jWDejXL7qcdXm5e8Ya8C+s3crk+W1nbogyCrJxI/DXvwIDBgQa/uXl4Ya6v2PJqF/iu8NuxGWXubxt49I7HT8cYR06+TOU7duBLVtSR1jbXQCsXCnLm2+W7cKNg8yYIdlsc4x4kpMjM0zXrRNBfeedwEUXycXnsGHxPz4hTRC/wnq71rogriMhJBOprvbnWAOJF7hGFBan4TzkvXvloiVUnB5ySHTCOpaOtYl6OGWswxHWfirHVFfLLSCsa2uBN9+UetP9+gF33AEMHizJJOTlod++VRhZ/l30ZfIicayd9pUKExcBuRhWyt6xNvnqCROAn/9c6g76vSiurgbmzElsibvcXODZZyWC8vTTwDPPJEbUE9JE8Sus5yqlXlNKnReIhZyulDo9ngNTSq1XSi1RSi1USs2N57EIiRt+HWsg8cLaHC8dhbWToJ04UbKjW7ZEtt9IhbVT22snp9nvJEPrfkwbdCcCAre0RT4A2fQ3vwGWLgVuvVVMy88/DyQQzLE3bfIniN2aGMUyCmKE9ejR3vuKJ1lZUnbRTlgXFIgTPGgQcMEF8v4//NDffufMkTb3iRTWPXpIB8vvvwcuvjhxxyWkieK3rVU7AJUAfmJZpwG8HfMRNeRorTV7PZP0pbo6dYW1EUqZJKytOeuf/zz8/fqNgmzdKveLi+Wndqdz7OT0lpQAvXv7H1e7dhKO3rPHdnwVFcDrT9XjKXyDrfeNwtrrpZLbzJmi/xrNnTXCurbWfzUPp++n+f7EIgqyYIHkVcK56IgXubnOjrWx/o85RiJIL74oxb29mDFDlokscTdjhlwk7C86TgiJJ76Etdaal7mERILfyYtA8hxrI1LTCSdhPWaMCJ5IhLXW/h1r47h6ZaXdHOtwM9aA7MsirJcvBx56SCp6VFR0xzCU4upTNqOmZhhatBCj0harcI02CjJvnnzHBwzwtx/AXVgnOwZiyM21d9YLCoARI+R+Tg7wi18A//43UFQEdOrkvs+ZM6WMoNMvHPEgnO8ZISRqfAlrpdQQAP8FkK+1HqmUGgWZzHh3HMemAXymlNIAHtdaPxEypisAXAEA+fn5mD59ehyH4kxFRUXSjk0SR6TnefT27dDZ2Vjk8to2q1ZhPIAl33yDIrdW1DHmoE2b0AHAyu++w9YuXRJ23FjQ8bvvMArAvHXrUB7y2Y4dOBD1n3yChSecENY+K0tLgdparNuxAxtdztfgsjJ0LSnBN9OnY8SqVWjdogV+cNj+oLo6ZG3ahAXW57XGESUl2FxejnU+v1NdNm3CCABzvvgCW/MGAQDat6/Bd991wosvDsdRR+3Aef2/xAX/vQALxj6K777b5rq/dj/+iLGB+6t37EChxzhG7NuHVtu2Ya7NduM++gi1BxyARd9+6/k+mhcV4TAAq+bPx5ZevRo8l1NRgclr1mDdkUe6fv7R4vdvebxSqNqwAcss26qaGhyxejU2HHww1gfWtxk2DONrarDqnnuw5ZRTHPen6uowecYMbJsyBav5f0bc4f/NmU/KnmOttecNwNcADgawwLJuqZ/XRnoD0DOw7ApgEYAjnLYdN26cThbTpk1L2rFJ4oj4PI8fr/WJJ7pvs3q11oDWzz8f2TEiZexYOe699yb2uLHghRdk7CtXNn7ut7/VunVrrWtqwtrlrHfflX0+/LD7hn/8o9bNmsn9Y47R+rDDnLc97TStDzyw4bo9e+Q4//d/vsdW98FH+lMcr88+dpdu3lzrP/9Z1tfUaL17d2Cjl1+W/RYUeO+woEC2BbR+9lnv7S+8UOu+fRuvLy3VWimtb7/d3xspK5Nj3n9/4+fMOf3mG3/7ihDff8sTJ2p9/PEN1y1dKmN86aXguvp6rUeMcP8eaK31nDny2ldfDWu8JDL4f3Pmk8xzDGCudtCkficvttZazwlZF9cGMVrrwsByB4B3IMKekPTCz+RF81M8Jy/6x60ax8SJQGWl9OUOg2zTsdErCtKmjZRH3Ls3sihImO3M77sPGHDZ0ZiCz/DFnLb49a+B8wKdxXNygimRsLo5WqMgfvLMTpMXv/lG5LnfyXimGoXdvl57TXLnEyf621e8sctYm4ogw4cH1ykFnH++VN1Yt855fzNnyjIRjWEIIUnDr7DepZQaCIlnQCl1JoCt8RqUUipXKdXW3IdMmlwar+OREOrrgVWrkj2KzCCVy+1lwuRFO1FoJjCGWXbPt7C25oS9hLVdNtlDWO/bB3z5ZfDxggXA4H41eBXnYMvjH+Cf/wxGfBtghLWfzHSkGevQJkam2YlfMWxKEIZml0tKgE8/Bc4+23/TnHhjJ6wLCkRIh4bXf/lLWb70kvP+ZswABg6UKh2EkIzF779gVwN4HMAwpVQhgOsAXBWvQQHIBzBLKbUIwBwAH2qtP4nj8YiV99+XlsKFhckeSfrjx7Fu0UJKONCx9k9JiQi0Zs0aPzdgANC5c/jCurJS7vipCgL4E9Z2XTWNAA65KCgoAH7/e6BXL+C444AVK2T9iy8Cn79ajHPwOlpUlTofq6RELuK8LuQA2cZUifArrI1Lb2XGDGD8eKk64Re72t7vviv7P/ts//uJN3aTF5cvB/r3b/w33bs3cNRRwAsv2HdQra+Xi5BEltkjhCQFT2GtlMoG8But9XEAugAYprWerLXeEK9Baa3Xaa0PCtxGaK3videxiA1btsh/BNvcJ0ARH/gptwf476wXK2prRfQD6SusnQStUsDkycDbbwfLm/kgbMe6tFRqGLtVeGjbVj5rqyANcaxXrJDqa8OHS0vxww+XssiDB8tmOTnw96tGuE1njLD3W8caaCiIKyuBH34IXyxaq6oYXn9dBOuECeHtK560aWPvWB9wgP32F1wArF4tn4mV6mrg5Zfl74wxEEIyHk9hrbWuAzA5cH+P1tqhThLJGMx/eqbxA4kcP+X2gMQLa6tAStdye24i8qGHgO7dgeOPB954w9cuwxbWmzfL0suxBhqcW11cgjmYgBmruwOQMsiVlcDf/y67fOst4KSTQmpPm2O6fUfCbTpjxh1px8TvvxeXORJhbd1PURHwxRfiViewKo4noVGQujppZ27NV1s54wz5W3/hBWDXLuC552Rd584iuvPzgTAr1RBC0g+/DWIWKKXeA/AGgP3/0mit490ghiQDCuvYoHV4jnUiP28jbLKzM8+xBqSH9zffAKecApxzjsSarrvOdZdhR0E2bpSlV8YaAMrLUZTdFS++CEy97yQswYU4/N81mPEL0cKm4aAjOTkSt/ByrMMR1uE41nbCfsYMEcLhNjsJjYK8/ba4+uecE95+4o0R1lrL+/zxR/nlwcmxbt9evm+PPw785z/yq1/PnsCFF8r6o47yd5FNCElr/ArrlgCKABxjWZeIzoskGRhhnejMb6axb5/8p5yKjrU5Vs+ewM6diTturCgpkZaCbnTqJE7oL38JXH+9COEHHnCcHBe2Y+1HWAcc6zsfaI17npavxIQee/E4rsC5H/7H/Th2+3L7jhQViUvvl7w8EY85Pv4bsIuCzJgBHHRQ+F0SrZ0rAakGMmhQ8tuYh5KbK+LYXBzbVQQJ5cYbge3bgSOPFDE9dmxqufCEkLjDzoukMXSsY4MRan4d60ROFjUCqV8/EYh+JlmmEn47F7ZqJVGQ66+XeEhhofxEb3OxE0thvXkz8MwzwNUHdkRHAIO7lODKK7vj0kuBg575O/D0q0DHJxq9zpX27Z2FtdaS75082f/+OnTwL4pDHet9+4DvvgMuv9z/8az7MlWHtm8Hpk0D/vSn1BOg5nuwZ09DYT1smPNrDj4YSMWGFYSQhJHKnRdJsqCwjg3V1bL061gXFMR3PFaMQOrbV5YlJZkprAGJu/zrX1K54aabRCh98EHjzaqqRNx5VbhwENb79slup04FPvlEzM7hf+uBMwCcd/A6nPez4eGP3YqbY71xo/zd2tbhc+D66/23fQ/NWM+fLxdjkVS5sEZB3n5bPqhUi4EAwZrbe/ZITrqgQH7h8ROdIYQ0WfyW23sSwJ8A1ACA1noxgHPjNSiSZMyEHQrr6AjXsU5GFKRfP1mmU8563z6Z7ReOOFUK+MMfgNtvl5IbRhRbyK6qEtHn5ZzaCOvSUqBPH5mrtnAhcMstwNq1wBmn1ck21nMbbvUOg9t3xDTDCUdYT5gAnHWW/2MDQUFsqq2E45AbrFVBXntNMssjR4a/n3hjFdaAONZO+WpCCAmQsp0XSRKhYx0bjGOdisLaCCTjWKeTsA6nw2AopmWhk2PtFQMBgObNsadZHp4tPB634w6gQwfk5Ukqwmj2u+6Sctq21TTCrd5hiLWwDofQKMiMGdIkJT8/sn1VVEgsZ8aM1KsGYjDCuqJCojYFBe75akIIQYp2XiRJhsI6NhjH2m8UZO/exg044kWoY51OJffCbAnegCFDZKLc++83espLWGsNzJkDXHkl0L12Iy7WT+NtdQZqcuTC6a67bMrk2dWfjkcUZPlymbgYyX79YD6X8nIpOzdrVuTNTsy+nntOPtRUjIEADR3rTZtkSceaEOJBNJ0Xfx2vQZEYs2ePNH3xC6uCxIZwHGuT2wztSBcv0tmxjkZYKwWcfDLw1VeNmpTkVFa6ltr797+lW/oLLwCnt/4UM3A4Fuf/xLb5435yc+WYoY51PKIg8XRTTSvy8nJgyRK56I5UWJvPeOpU4MADU1esWicvmvkPdKwJIR74EtaBToihnRfXx3VkJHbcdVd4/wnSsY4N4TrWQOIuZsrKRPB36SKPm4qwBkRY79snpfgsWB3r+nrgyy8lOWJSI6edBjz2mFSKe7b/X3E4ZkF19BiDUiIkQzPW0URBQltm19eLYx2vGIjBvA+Tr45WWK9bl7puNdDQsTYVQVL1IoAQkjL4rQrSAsAZAPoByFGBPJzW+s64jYzEjo0bg13i/EBhHRvCzVgDiRPW5eUicNq1S78mMdEK68mT5ReC998XtRwgu6oKmzuOwrN3A08/Lf1A8vKAYwLV+/v0kRgIgKA49DMGq7CuqRGhFqljXVcnEzeN6APk73vPnvgL63bt5HszY4b80tGnT2T7scZtzj47NmOLB9aMdUGBVAYxF6KEEOKA3wYx/wOwG8A8AAkKgZKYsXu3ZHf37QOaN/fensI6NqS6Y92unTiqHTqkp7COxPUFgGbNpLX0hx8C9fXQKgtKAVmVVTh27t+wapqI6bvvlmp0ttdF4QhrI0itY49UWANy7qzCOt4TFw3mAmHePGDKlOj2AwBjxgCDB8dmbPEg1LGmW00I8YFfYd1La31CXEdC4ocRyOXl0o3Ojdra4AQ6CuvoCLfcHpBYx9ocs2PH9BTW0UzUO/lkrHxtAaZetB0fLeiOefOAnOoqPH74S+j98B8wcKDH68MV1ua8xkpYWzssGmEd7/xv27YiqnfsAA4/PPL9mPeRyjEQoGHGevny1HbXCSEpg9/Ji98qpQ6M60hI/DAC2Y9oMzVb27eX+7Wsqhgx4TaIARJ3MVNWFhSH6SisW7f29+tLCHv2SDGKwx85G8OwEg+93BWDB8vbz66qwlGDC71FNRB+FMQ41qZUYKQZa6Dx33G8K4JYj79jh9yPNF8NiFN9333AVVfFZlzxokULmbT544/ynaNjTQjxgauwVkotUUotBjAZwHyl1Eql1GLLepIOWB1rL0wMpGdPWbIySOSksmNtoiCACOt0K7cXhojUOniNs3Ah8KtfATuKm+G+fv/FpgOm4J13gO7dtExedKkK0gDrRYkX8XCsrSxbFv8YCBB8z127StnCSMnOlg6Yqd7BUCmJg/zwgzymsCaE+MArCvKzhIyCxJdwHGursF6+XF7jRzyQxqTD5EVAzm8i26lHi09hXVwMvPSSVHWbNAl49FHgsMOAb78FJk4E1AMVwE1fSo3iTp2g6uv9NYgBInesYy2sTUWQyy8Pf3+RHv+II1KzoUs8yM2V8oIAS+0RQnzh6lhrrTeYG4A8ACcHbnmBdSTVqa8P/qceiWPNnHXkhDN5sVUrcfKS5VinWxTERZjOmAH84hdAjx7A734H5OQABx8szykFHHpoQBf+LOAbfPBB8HsfD2EdT8d6wwapEpJIxzqaGEi6kZsrlVjatg3+m0gIIS74ylgrpa4F8BKAroHbi0qp38ZzYCRGlJcH696G41j36CFLCuvIqa6WHHCWjz8zpRLb1tzqWHfoIOc5XfL0NsJ669bg1/zll4GPPwYuuwyYPx+YOxe46CKb/QwbBgwcKMLaXHSGGwUJx7HWOrqMtYlOWL8jiaoIAjRNYW0utA44oOm49ISQqPA7efFSAIdorW/TWt8GYCKABPz2SKLG+p9wuFEQgMI6Gqqq/LnVhkQJ65oaEf1WxxoIir5UJyCsa2qAd94R47lXL2D2bHn67rul0egjj8g8OUdMF8YvvwS2b5d18XKs6+rk+1BSIt+JcL4Xoce0E9aJiCmccgpw3XXSLbGpYEruMV9NCPGJX2GtANRZHtcF1pFUxyqMGQWJLTU17s9XVfnLVxsSJazN9yBUWKdJHKS0uB43L70AvXsDp58urvTNNwO9e8vznTuH8bH/7GdSXvLdd+WxX2E9apQc0E8dZmuEI9J25oD8+tGyZWNh3aNH5DW9w+HAA4GHHvL3C0ymYIQ189WEEJ/4rWP9DIDvlVLvBB6fBmBqXEZEYotVGIdTbo9REHe2bJEYwaefOv80Xl0dnjPZvn1ihLU5RmhlixQW1pWVUvVsxJAatNyzC8+uOASHHidxjxNOkBx1RBx+uAjfV1+Vx36F9ejR0vHQD+ZzLi+PTlgDjS++EtHKvClDx5oQEia+rAet9YMALgZQHLhdrLX+ZxzHRWJFtI41y+3Zs3SpCOelS523STfHOsVK7mkt/Uh+/Wsp03zaaYAuKUVL7MWPf30B774rhnPEohoQF/iEE6QyCOA/Yx0OVse6tDQ6d9n6Hamvl2ouFNbxw1xo0bEmhPjE7+TFiQBWa60f1lo/DGCtUuqQ+A6NxIRwHWsjrDt3FtFBx9qeDYGiODt3Om9TXZ2awjoNHOsPPpB89PjxwLPPSrz3ySexX/y3zo+hAP6ZpaqoX8c6HOLlWK9fn7iKIE2Vdu3kV6d+/ZI9EkJImuA3LPdfABWWxxWBdSTVMcLYr2irqACaNRNR3b49hbUT69fL0k1Yp+rkRXOMFMpY19cD06ZJdQ8A2LdPqg/+5z+y7oUXgKOOAlRpDNqZh3LSScHccDyEdawy1mZf5vwlsiJIU+X664G335YvIyGE+MD35EWtTTErQGtdD//5bJJMjDDu3dt/FMTkCimsnUlnxzq0tJyJJiRBWG/ZAtx7r8wBPOYYaeYCAD//eTAG0iA5EU0daCc6dZLOMUB8oiBWxzqWUZBEVgRpqgwaBJx4YrJHQQhJI/wK63VKqd8ppZoFbtcCWBfPgZEYsXu3hFC7dvXvWBvXLhOF9QcfAEOHSiWIaIiXY11Z6V1tJFpCHeucHLmfQGFdXy8VPXr3Bv78Z6BPH3Glb7xRnncsGRwPYQ0Al12G8iFDgBYtYrtfIPg5l5bK31O0jrX5m1y2TOZCpHprcEIIaUL4FdZXATgMQCGAzQAOAXBFvAZFYsju3fIfb7t2/h1rI6yt/4lnCt9/D6xa5S6I/eDHsY5k8iLg7zxFQ+jkRSAh3RdXrwYef1zuZ2XJtd7NN8v6adOA88/38XHFS1hfdBHmPf54fJqAGMd682aZkRmrKAgrghBCSMrhK86htd4B4Nw4j4XEA6uw9ltuz+pYr1kT3/ElGiOEd++WriKRsG+fZBis+7Mj3HJ71iyuyT3HA/M9sOaJ4ySsKyuBt94CnnpKWo3n5Eh1j/x84LHHItihaWITa2EdT1q3lisJczEWC2FdVycVQa66KjZjJIQQEhOaUKX/Jkq4wjo0CpJp5faMEI6my+DmzZJl6NYN2LVL7tsRqWMd78+8vFxy9NYJWR07xrzc3rRpUg79wgvlOuT//k9KP+fnR7HTkhL5TOMR2YgXpl29qXsdbca6pgZYsUK+X3SsCSEkpaCwznSMsG7bVgRVcA6qPZmesY6FsDbO44QJ4hw67SuSyYtA/IV1WVnjSXoxcKxLSoBHH5UYOyDNCU85BZg+XdI3f/yj1KOO+iDp5FYb2rYNCutoHWsA+O47WVJYE0JISuEqrAOTFKGUmpSY4ZCYY3Wsa2q8J+3ZOdZOjmw6EgthbSYujh/fcJ+hRDJ5EUiMY23NVwMRC2utgekfV+H8A+ahRw+Na64B3gn0Z+3UCXj+eeDII2MYXU5XYd2uHVBYKPejGb+ZqGiENSuCEEJISuHlWF8cWP473gMhccLqWAPeE+NCy+1pHWwakwnEyrFWChg7Vh7v2tV4G63Dd6yNaEqGY92hgwhrr180QjjzTODok1rhgxUDccnk1Zg3L1gyLy6kq7Bu2zZ4gRorx7pXr8YXSIQQQpKKl7AuUEqtBjBUKbXYcluilFqciAGSKLE61oC3aAt1rM0+MoHaWqCoSO5H61j37CkBYsDesTa/DKSiY11WZu9Y19W5XnjV1gLvvQeccUbwK3HRhRov9L4FW9ADjx795v5rjbiRrsLa+nlHm7EG2MqcEEJSFNeqIFrr85RS3QB8CuCUxAyJxAytRURZhbWbY23caWu5PUBUVO/e8R1rIjCiGojese7bF+jSRR7bCeuqKlmmYsa6vFzGb8XafTFEdK9eDTz9tLQW37ZN5myuWAEccghwSo+5wKb/kw1N9jyelJQABx4Y/+PEGvMLQXZ2dN0dreeGwpoQQlIOz3J7WuttAA5SSjUHMCSweqXWOs5dLEjUVFTIz8/WKIibaKuulu1DHetMqQxiFcDRuPDr10unPjdhXV0ty3Ac69xciZh4fd6LF0vJP5PxDhcnxxoQYd2v3/7V69cDQ4ZItbif/hS49FLpAN6sWWCDp56Si4fevRMnrNPZse7QIbrAOYU1IYSkNL7qWCuljgTwPID1ABSA3kqpi7TWM+I4NhItRjz6jYLs2SPLTI2CWAVwpI51ba2U2+vXT0Rzmzaxc6xNWTYvYX3ttcDKlSJk9yvcMHCavAhg/pxaTJ0qqZDHHpO3OXUqcMIJweTLfioqgJdfBs4+W/ZZUBD+WMKhrk4+m3QU1ubCNtqxU1gTQkhK47fc3oMAfqK1PlJrfQSAKQAeit+wSEywCms/kxfNJMVMF9Zt20YurLdsEXFtohRdurg71uEIa8Bft8uNG4GtW4N17cIlZPJiaSnwn88GYSzmYdyvD8bUqVJAxsxjvOQSG1ENAK+/Lt+Zyy+Xz2PDBv+TH3fuFKEcDunYHMZgBHE0+WrrfgDggAOi2xchhJCY41dYN9NarzQPtNarAERglZGEEq5jnenCescOWQ4aFLmwNnEHE5dwEtbGsQ4nCgJ4O9ZaB8u2mf7g4bB3L7BvH3TbdvuLVNx/P3D1PT1Qjyw8cu4sbN0qLrVnYuGpp0TcHXaYCOvKyoY5dieqqoCBA4FHHglv7PFqZ54IrFGQaGjRQn6l6N2bFUEIISQF8Sus5yqlnlJKHRW4PQlgbjwHRmKAnbD241hby+1Z95PuGAE8cGD0wjqejrWbsC4qEnHcvTvw2WfAjz+GtfutqyvwN9yMof/6NT75RNZdfTUwd1Y1FmAMrh4105/2W7ZMSr5ddpkocPN5+MlZr10r38Mvvghr7GktrGMVBTFxIcZACCEkJfErrH8NYDmA3wVuywPrSCpjBHG7dv4mxoU61q1bSxWDTBLWnToBnTtHLqxNc5g+fWTZuXNiHWvjVt90k5zPJ5/03GVdHfD++8CppwK9R3fEn/A3dO+4F61by/M9ewLjJrWEatnSf5OYp54S5/SCC+Sx+TxMd0E31q6V5ezZ4dXNTmdhHasoCABcdZXMIiWEEJJy+BLWWuu9WusHtdanB24Paa09WvhFh1LqBKXUSqXUGqXUH+N5rIzF6lgrJa5ZOMLauGOZJKy7dBFxU1oadjMUAOLI5ucHnWjjWIfuK16O9ebNspw4Ucp0PP20BKJtsO7m6quB778Hfn/hTqzEEHx977c46qiQF/jtvlhdLS0Vf/7zYGWUcB1rQBrrrFnjvb0hnYV1rBxrALj7bunMQwghJOXw61gnFKVUNoBHAZwIYDiA85RS7N0bLlZhDch/7uFMXjSvzaRye0ZY19QExW84rF/foBwdunSRaEZod8p4O9Y9ewJXXAFs3y5dWyyHfekl4OijpUxeTY386PD558CmTcDfLl6JIVhtn8/1K6zffVe2u+yyhq/NzfUnrNesCQa4TWtuP6SzsI5VxpoQQkhKk5LCGsDBANZorddprfcBeBXAqUkeU/qxe7eoKpOZ9hJtoeX2ABHWmehYA5HFQUxzGINTLetIyu0B/oS1UtKl5cQTZRLb449jzRrgmmukesf550si47e/lXLXADB0aKAyn7mwchLWRry68eSTcnFx7LHBdUpJHMSvYz16tIyhqQjrWDrWhBBCUhZfdaztUEr10Vr7CFRGRE8AmyyPNwM4JOT4VwC4AgDy8/Mxffr0OA3FnYqKiqQd24vBy5eja+vW+ObrrwEAYwHUbtiAxQ7j7bVoEQYBmLlgAepWShGY0VoDGzZgYYq+x3A4rLAQuwYORElhIUYAmPPZZ6gM7UDoQEVFBaZ/9RWOWL8em8eNw7rA59Fp2zYcCGDep5+i3FL+rPuiRRgK4NsFC7Bv0ybbfdrRt6QE/SsqMP3LL+WiKIShP/yAjh074rtvvkFFRQ7yDzsNE177Nz595gs88cQxOOKInTjppK0YPboUWVnADz80fH3X2bMxHMCcggJUVlY2eG5EfT1abdmCuS7numVhISZ+9RV+vOQSbJjRsIz9gW3bovmyZZjn8V05ZOlSlA8dipwhQ9D8889dj2dlwKJF6NWsGWZ8/72v7cMlnn/L2RUVGD1oEFYohT0Z8LeUzqTyv9kkdvA8Zz4pe4611q43AIcCOBNA18DjUQBeBrDJ67WR3gLHe8ry+AIAjzhtP27cOJ0spk2blrRje3L++Vr36xd8fNxxWh96qPP2d9yhNaB1bW1w3cknaz16dPzGmChqa7VWSuu//EXrjz+W9/ntt75fPm3aNK23bJHXPfpo8InZs2XdBx80fME//ynri4rCG+eDD8rrSkttn67/yRT99dDL9YUXat2qldbXXlqudXa2rrv5T/4O9cQTsv/Nmxs/d8klWvfs6f76P/1J66ws+9dfeaXWnTu7v37fPq2zs7W+5Ratb7tN9lVW5mPgWuvLLtO6Wzd/20ZASv8tk5jB89w04HnOfJJ5jgHM1Q6a1DUKopT6O4CnAZwB4EOl1N0APgPwPYDBcdD5hkIAvS2PewXWkXDYvTuYrwbkp3evjHWrVg2d0kyJghQXywTDaKIgpiKInyhINJMXAds4yL//DQyd/hiOXPkE3n0XuOgi4KKr2wA/+xmynpmKjm32ee/f7NfSIGY/Xhnr2lrgmWekp3nPno2f79tXJiSaSJEdGzdKmZKBA2UCZn19Y1vdiXRtZ04IIaTJ4JWx/imAMVrr8wD8BMB1ACZqrf+ltY5g5pdvfgAwWCnVXynVHMC5AN7zeA0JJVRY+6kKYs1XA5lTFcQI32iEdWhzGLM/6/4NJmPdokV4x7AI69pa4KuvggVHFi4EutVtwXPHPo+tW4H//hcYMwYyiXHHDuB///Pev7mwCj3PgIjWqqrg2EOZNg3Yts251JufknumIsigQSKsAf85awprQgghKY6XsK42AlprXQJgtdZ6fbwHpbWuBXANgE8BFAB4XWu9LN7HzTjsHOtwhbWpChJJabpUIhbC2s6xbtNGxLOdY92iBZAV5vzgdu2wFgPw5/vbo29fmR84f7489fiDezCjbhIuPLZwfw1qAMCUKSJq/XRiLCuTMduNq2NHWTpNYJw9WyYpHnOM/fPmc/EjrAcOFJE8bBiFNSGEkIzB63/9AUqp98wNQP+Qx3FDa/2R1nqI1nqg1vqeeB4rYykra+xYl5c7i2QnYV1b6+xipgumnblVWIfrxG/YIA1mrJ+RUvbdF6uqwi619+OPwDG3TMQgrMXfXuyJMWOAt98GRo2S53O2B9JQvXo1fGF2NnD55cCXX3rXhS4vd26FbYS1Uxxk7lwpL+L0ej+1rNeskc+le3d5fOih/hvFUFgTQghJcbyE9akA/mG5hT4mqYydY11X5yyS9+yxF9ZmX+mM1bFu2VLc5EiiIHZVRJyEtY989aJFEvcApO9MSWVz3I0/Y+OjH+CDD6QHS7NmgY2tNaxDueQSEdhenRjLyuzz1YC3Yz13LjB+vPO+e/SQMbgJ67VrgQEDgo75oYdKm3Y/jWIorAkhhKQ4ruX2tNZfJ2ogJMZobS+sAXEtG2QJAjg51oDsy7iM6YgRvp07y9J0XwyH9esBS0m9/dgJ6+pqR2G9ezfwyivSFXzePMlJz58vp2TBZ7uAPvcCOTYC2U1Y9+gBnHCCWNz33ef8HsrKInOst2yRm5uwzs4WN90rCjJoUPDxoYfK8rvvgMEu86Hr62XsFNaEEEJSGK+qINOUUl853L5M1CBJBFRWijsdGgUBnHPWXsI6ndm5U0SZsX/btw9PWAfqeds61p07+46C/OMfcn3y619LV8SHHwa++MKygUtVkP3tzO2ENQCMGCGitr7e+X1EGgWZN0+WbsIakM/HybHWWoT1wIHBdcOH+2sUs3u3vJ7CmhBCSArj1SDm9zbrJgK4CcCO2A+HxIzQduaAu2gDnKuCWPeXrpiui4YwHetmu3fLxYq1IojBxbHevh147jnphtijh2jKCy+UbuDjxgU7e+/HfP5256iwUM6nXUUPQLow7tsnY8nPt9+mrKzh52DFTVjPnSvxjdGj7V9r6NsX+Nrhh66tW+WCwyqss7KAQw7xFtbp3HWREEJIk8ErCjLP3FdKHQngLwBaArhKa/1xnMdGosFOWBvH2qmWdUVFsP25IZMc6yiEdcvt2+WOU8a6okLEdMuWqK0FPt00Ck9t+j0+6CVzP7t2BX71K+C00+TmSHa2CGcnYR06cdGKeW7TJmdh7eZYt20rx7cT1j/8IO5y6PcjlD59ZJy1tUBOyD8v1lJ7Vg49FLj7bhmbU/6bwpoQQkga4FkLTCk1RSk1EyKq79FaT6aojjFvveXs8kVKrBxr83q3Mn3pwI4d0QnrbdvkjpNjDQA7d+43ZH+26B58Uz4K118PFBSIqPaNU1nEzZudYyCAONaACGsn3CYvKiXCNVRYay2O9YQJ7uMG5MKjrk7y2KFYS+1ZOfRQ70YxRlibii6EEEJICuKVsf4BwOMAXoXEP3YrpcaaWyIG2CT405+Ae++N7T7dhLWdY22qhWRyxjoKYd3CwbGurgZeWTEGt+AeYNcutGolBTre6v97bD7uYtx/v5RqDgtTOzyUwsLohLXW7o41YN99cdMm+fy88tWAe8m9NWvEEQ91/Q85RJZucRA61oQQQtIAr4z1HgAVAM6EtDW3JkI1AIdOESQsioosNdVihFsUxE60mTbUocK6bVtxMtNZWNfXy2fctWtwXV5eWO+p5bZtIkgDjunixVLV48UXgZKSCRiIDrh103q0HgPcfjuA1z4CckdENl47x7q2VroeukVBunSRMoJOwnrvXpkx6eRYAyKsQ8vtzZ0ry3CF9eGHN3xu7VqJioR+1zt0kGorFNaEEELSHK+M9VEJGkfTpb5eRENNTWz3G24UxElYZ2WJEEtnYV1SIo58qGNdXb0/F+1Fy+3b98dAnnlGXOnmzYHTTwcum7IJR188BFnlLwRf4HO/ttgJ623b5Lvi5lgrJcLbSVibXyq8HGvjzhvmzpW8tOlU44Zxze0c69BSe1YmTgTee09c9UYzOkFhTQghJC3wioJMUEp1szy+UCn1P6XUw0qpjvEfXhPAlBErL49tjtlOWLdqJULZLgpSUSFLu4oT7dunt7C2Nocx+Oy+qDUwaxZw7eIb8Gaz8wAAJ50E/OtfEiN+5RXg2FNykQXdsDKIzwYxttgJa1PD2s2xBkTYOglrs89woyBz5wIHHujvQqF1a/mc7WpZr1nTOF9tMI1iVq+2f76kRJxuu/rrhBBCSIrgNXnxcQD7AEApdQSAvwF4HsBuAE/Ed2hNBKuIcZt0Fi67d4uIDm2/7TQxzk1Yt2uXusL6u++ADz5w38baztxgLjgcctbbtwN//7skFA4/HHi/4nhsbj0EgBTc+N3vpLs5ABHp2dkNhbVLgxhP7M6RVw1rQ+/ewW1DMfv0ioJYv5Nm4qKfGIjBrpZ1SYnc3IQ14BwHMV0X7dxsQgghJEXwEtbZWmvzv+w5AJ7QWr+ltf4LAIffdElYWEWMkyCKhN27RaCFCpF27dwda7tyak6T6VKBW2+VbituuDnWFmGtdfDpE08EbrpJer88/UgltqI7rjv1R/v9Z2U1bhLj0CDGF3YXMm5dF6307i3b1tU1fs5vFGT3bsl0A8C6dSJqwxHWffo0FtZOpfYMXo1i2M6cEEJIGuAprJVSJod9LICvLM95TXwkfigqCt6PtbC2xkAMbduG71inchSkoEA+NzN+OzyE9Y8/An/5izQuNLv5179k17NmARdPWoVcODSHMVibxGgtEwWjcazLyxt2UCwslFC3acnuRK9eIqpNeUArfhxrI17NBUc4ExcNxrG2Xqk4ldozeDWKobAmhBCSBngJ61cAfK2U+h+AKgAzAUApNQgSByHREs8oiJ2w9nKsIxXW8+bZu6TxZPdu6eYHAKtWOW9nI6yrW3XAqzgHx/3+IAwYANxzj+jmXbvk+cMPt5TJM+6rXXMYg1VYV1fLMhrHWuvghFIgWMPaKwrhVnLPr2MNBL+Xc+dKpZGRI/2NHZDPqaqq4UXjmjWyHDDA+XWHHgosXdr4+1leLueZwpoQQkiK4yqstdb3ALgRwLMAJmu934LKAvDb+A6tiWAETIsW8YmChBIPx3r1anE033knsrFGyooVwfsrVzpvt3OnvIfmzbF3b2DzXZ1wHl7Fmq25uPNO0c4ffeRgSq9fL0u/jnVVlSyjcayBhufJq4a1wU1Y+81YA8EqHHPnAgcdJG65X+xqWa9dC3Tv7t650TSK+fpr4PPPgVtukXUdOgDLlgH9+/sfAyGEEJIEPOMcWuvZNutc7EESFkZYjxgRe8e6R4/G69u1sz+OU7k9wFtYL1smSyNAE0VBQfC+i7Au21KBV5tfg6mHiAv93HPAQZPaYBYm4dDfn4qsP97kfpwNG1DXsiWy989WtMGasY6FYw2IEDZiurDQXxwjlo51fb38EnHBBf7GbejTR5YbNgDjxsn9tWudYyAG0yjm5JNlmZMDHHww8Mc/Akcf3bguNiGEEJJiMCedbIqLRej069fQgY2W3bulpEUokUZB9u6VW4sWjZ83P/Pb5XrjSUGBlGDr1s1WWM+ZA/z3v8Dr7zyKyvpWGJkfLD6BVq0wqdkPQNmR3sdZvx7V+fnIdYthdOkSrEcea8daa/k149RTvV/boYOUpHNyrJVyd42twnr1avmuhJOvBuwd6zVrgOOP9x773XfLOI8+Gpg82f77SAghhKQoFNbJpqhIxEzv3vLzd6yIZPJidra9cDZCb/fuht0LDab2cKKF9YoVwJAh8tkFLkp27BDzOCsLeP114M03gV+2/xCXjvweB3/992BEWSn5fPy0Nd+wAdXdusFFjgbz20VFQcc6VsK6pET26VXDGpD35VTLurw82EnTCauwjmTiotlHbm6wlnVVlRT99nKsAeDPfw7vWIQQQkgK4TV5kcSb4mIRIr16xa5JjNbekxetFRsAEda5ufaiy+zHaWzJEtYFBcABB6Bu8DB8XNAPZ5yu0bMn8FWgds2f/iRz3p5o8VscMrS08VvLy/MnrAOOtStGWO/aFXSsI42ChH7efkvtGZxqWZeVucdAgGC1lOJi4Icf5OLA7pcPN5RqWMt63TpZOpXaI4QQQjIEOtbJxghrazZ2xIjo9llVJXWInRxrU3HC+jN7RYXzz+5mP04560iiIGVlItqaNfP/Git792LPmq24L/9xPPPFBGze2wadv67Dtddm7zdGO3WCvNdduxqW2jP4EdYVFUBxMaq7dXPfzux/587ge4qVY+23OYyhd2/g008bry8rc5+4CEiuuV07+V7OmweMGSPrwsVay9qr1B4hhBCSIdCxTjbFxaIAzc/8sagMYtfO3GBXcQKIXFhXVQVjB+EI6zFjpMZdmOzdKxXZsGYNWugqPLP8YIwcshdv4gwUvjgdDzwQUjyitFQuMiIV1gFxuNevY71zZ/SOdeg58tvO3NC7t1j1NTUN15eXezvWgFzo7dwJLFgQfgzEYHWszYUXhTUhhJAMh8I62dg51tHiR1iHTmCMVFibn/mHDZN88b593uOrqpLXmQyvD5YsAa67TgqdTJkC1C5dgRzUYcX7a/DxO3txBt5G87UFjV9o1xzGkJfnXZ87UOnEdxRk587oM9bGVbY61kpJuTo/9OolTv2WLQ3X+3GsAfk+fvMNUFkJTJjgf9xW+vaV78OePeJY5+UF89uEEEJIhkJhnUzq64PCunt3EU/xdqxDRZshUmFt8tWmFNqOHd7jM01dzGtd+PJLqcI2apRU+DjuOOCZZ4CsFcsBALmjB8tn16aNfck9M55IHevAhU613aRNK6YUXywc65wcqexhday7dvUfm3G6SAvHsTYTDyN1rE3JvY0bg6X2vJrbEEIIIWkOhXUyKSsTcd2xY7BsXLIc69DMtd1r7IS1+Zl/8mRZ+omDGCf1xx8lpmFBa+Dbb4O6rq5OhvbQQ6IvX3sN+MlPgKyVBeKKtm4tgm3YMHth7eVY+xHWOTnY5+W25uQEIxTRltsD5DM3n3dhof8YCOAsrMNxrAH5PgwZ4v+4Vqwl9/zUsCaEEEIyAArrZGKawxgh41TNIVxi7Vg75bIBcZ07dw72/w5HWNfU7Bd/O3YA//gHMHw4MGkS8J//yCbHHx+MgXTubNnHihUNq1UMHeourO0c57w8iTu4xVc2bZL8SXa29/sy3RejjYIA8plboyB+Jy4CsXGsAWnukhXhPxFGWK9bJ3EaCmtCCCFNAArrZGKEtYkR9OqVmhnrZs3EGXZyrAcPFrcdCMY83LBkf/Wq1bjwQnnrv/+99Ah56ing1lvleaVsEgT19fbCeuPGYAdJg5tj7VXtBJDzYYSqF0ZYRxsFARoK63Ad63btGnfY1NpfuT0gKKwjjYEAwYuRWbPkVwmW2iOEENIEoLBOJqnmWLt15HNqa756tYgmM7nPh2O9fkU1/ourAABq7Rp07Aj89rfSGf3bb4FLL/VouLdxo4hX45IDIqzNeKzs3Cnv2a7xjanZ7BYHiURYx9KxrqqS70k4jjXQuElMVZXkasKJgkQjrLOz5WLAFBWnY00IIaQJQGGdTIqKZGmETKyaxOzeLTavnYiyi3Vo7e5YA/bC2pTaGzxYhGuHDo7Ceu9e6YT4k58AAx6/CVfjUfzYYhiwZg3++c9gDMQXpvW71bE2Ijs0DrJjh71bDXgLa9NK3K+w7ty5oWNtJ+b9YoR1uM1hDKEXaeYXCj+Odd++IownTgzvmHb72b5d7lNYE0IIaQJQWCcTO8caiD4OYiap2eVjW7SQaIc1CrJvn/xcH66wDu2o162brbCePVt04TnniO69vf/z+HHsmeg/pJmvyiCNKAiU1bMK68GD5WIiVFjv3OktrJ2iIDt3yhVBOI51UZHktlu2jK4KRqiwDicKAjR2rM2FlB/H+uc/l/PSr194xwzF5KxbtpRoCCGEEJLhUFgnEyOsO3SQZayaxDi1MweCTrbVsa6okGW4wtqI4sGDZdm9O7BtG8rLJSf99tuyesQIqT396aeixW9vcR/6DsiW15mqIuFQUCDusHU2Y6tWUuLNuNkGP8LaybE2wjQcYV1XJznzaPLVgHze0TjWvXqJW793rzwOx7HOzg7pshMhpuTegAGRT4IkhBBC0gj+b5dMiotF5DZvLo9j5Vi7CWtAxJXVsTYT/tyEtbX8m8GI4kGDpExe9uG4dMm16N4duPxy4JVX5Om2bYGXXpIYSHY2ZPJijx7idK9bJ2I0HAoKGuarDXYl93butK8IAsRHWAOSAY8mXw0EHetw25kbzJjN68NxrGOFcawZAyGEENJEoLBOJqY5jMFvk5hrrgHuvNP5eS9hHaljHZr9NqX28vJw8cXApM/vwGvlJ+HcczS+/VYy1Y3Ys0f2Y4T1vn3hX0iEVgQxDB0KrFol2WhAlol2rM3rYiGs6+rkM27XLnxBHHqRFo5jHSuMsGZFEEIIIU0ECutkUlTUUFg3aybi2k1o1tUBzz4rFrATfhzrKKIgdXUS6zjn3XOxo6+0vD7vPODJMz7BVnTHU/+swKGHOkSMTTm+7t2DEZJw4iC7dsnNSVhXVATL+ZWVSa1sJ2GdmysWupuwbtHC+fWhmO02b44+CmIE8PLl4bvVQGNhbc53IoX1gAGyNOeZEEIIyXAorJNJqGMNSDbWzbEuKBDXd9Uq5+oh4UZBjLD2KLe3YU8n3HFbPfr3B044Afhy10FY1lFamU+ZAlx26k60RYV7yT0jeo1jDYQ3gdFu4qLBlNwzOWu3duaAKP/27d2Fda9e/ichmuPU1sbGsQbk/YY7cRFwdqwTGQUZNAh44w3gwgsTd0xCCCEkiVBYJ5Pi4mBzGENoNYdQ5swJ3l+0yH6bOERBdmR1w0CsxZ13KxxwAPD6i3tRWN8dR0+uCW5kmsT4FdY9eoizG45jbUSzU8YaCOas3ZrDGNzamodTwzr0OLFyrEtKInOsW7eWi7ZkOtYAcOaZ7hdshBBCSAZBYZ1MInGs58wJ1keeP99+m0gda4uwXrYMuOEG4OKL5XHXXs3xFC7DjzM249NPgbNGr0EL7Gv4M78fYW2iID16SKWIQYPCE9YFBSIaTcUJKz16yHtIlrBu0SLoCMfKsQYiE9ZAw1rWZWXyeUc7LkIIIYQ4QmGdLLS2F9a9e4vodaqtPGcOcMQRImLthHV1tUwIjMCxrlBtMXUqcNhhwMiRwCOPSK+T+noA7drhV3gOfdsEmtpYKoLsx69j3apVcHyDBoUfBRk61L58m1LAkCGNhbVTVRBAhLXdZ11XJ6XuwhHWQFDEx8qxBiKLggANf/0oL5d9RlNbmxBCCCGuUFgni7IyEW92jjVg71pXVQGLFwMHHwyMG2cvrN3amRvatRMxXV8PrYH6MhHWD7/YEZddJgbuP/4huvLVVwMa1uzPCHIjhq3CulMnmQzoJaxN9RNAHO+1a/2X3HMqtWcYNiwYF4nGsd62TcYUqbBOFcfaGgVJZL6aEEIIaYJQWCeL0K6LBiOs7XLWCxaI2Dv4YGDsWKkYUVnZcBsjrN2ytO3aYRc64aH79uHAA4G3fpBYxaVX5uDbb4MxkAZ61Ahrs//Vq0VIm+Y2gCjw/HxvYW3twmdK7plGKG5UVgIbNthPXDQMHSp1pKuqZPJibq67yHUS1uGW2jPEQ1hH6lj36iXfs8rKoGNNCCGEkLhBYZ0sjLC2m7wI2DvWZuLihAkirOvrgSVLGm7j4lhrDXz2GXD2y6ehB7bghltaok0boG39bqBlS+T3zHEukxcqrNessS+j5tDWfD92whrwFwcxEQ8vYa217M+thrXBS1iHK2pjFQWxusvRONaAvJeyMgprQgghJM6knLBWSt2hlCpUSi0M3E5K9pjigpNjbWISdo71nDkilrp3F2ENNI6D2Ahr6zzF3/0O+GpVT1yNR7Hkf+swezZwQtf53pUb7Bxru8YfXsJ669aGwjqcWtZupfYM1sogfoR1+/YSi6mtbbg+UsfatFmP1rFu0UJuzZo1bN0eDlZhXV7OKAghhBASZ1JOWAd4SGs9OnD7KNmDiQtFgUmAocLaNIlxcqwPPlju9+4tbreDsN7XOg9vvin1pvv1k2SEUsB77wGFL0zDQ7gBI/MDGeSKCvfmMEBDYV1VJWItXMe6vFxuVmHds6cISD/CesWKYCURJ8yYVqxwb2duMN0XQycwbtok1UesURc/xMqxBuQz79nTfqKmH+hYE0IIIQklVYV15uPkWAMSPwh1rIuKZJKfEdZKiWsdIqw3rqvFjXgAPaeMwFlnSV76mmskxgxI0YwWnQIi2ljZfoS1cVB37wbWrZN1To719u2BUiIhWLsuGrKygIED/UVBCgpkW1Nu0A5Tis+vY+3U1tyU2gu3ikasMtaACOFIYyBAw7w+HWtCCCEk7uQkewAOXKOUuhDAXAA3aq1LQjdQSl0B4AoAyM/Px/Tp0xM7wgAVFRURHbvvvHnoD+DrJUugTRWLACNatkTrVavwg2W/Hb//HqMALGzeHKWB9QM6d0avadPw6Xtfoaq2BTp2rEHx9+vxMG7ApGHbcdKpuzBuXDGys2XeoyF37VpMALD022+xKycHozZuRE59PeZ7vI/DWrXCroICFL3zDg4EMK+sDOUhr+lZXo7BdXX45r33UGNEa4C8hQsxGsDCHTv2vwcAGNmhA1otWtTg/doxYe5cVPXogaUe243q2hU5c+eizfbt2FxVhXUu23fatAkHApj75ZeosFzMjF22DLVt2mBx4LV+z3PHrVsxCsDaLVuwKcrv5JChQ7G3SxdsiGI/h3XogF1z5qBrcTG2lZVhTZL+TtKBSP+WSXrB89w04HnOfFL2HGutE34D8AWApTa3UwHkA8iGuOn3AHjaa3/jxo3TyWLatGmRvfD667Vu08b+uWuv1bpt24br7rhDa6W0LivTWmtdX6/17Ls/15fhCd2mda2+7DLZrP622/VOdNK6ttb52OvWaQ1o/cwz8viww7Q+9ljvMQ8apPV552n997/L64uLG2/z+uvy3OLFjZ976SV5rqCg4fobb9S6ZUut6+qcj11To3WzZlrffLP3OH/7W62bN5dj3X+/+7Zffy3bffllw/Xdu2t98cX7H/o+z99/L/v717/8bR9vxo3TesoUrbOytL711mSPJqWJ+G+ZpBU8z00DnufMJ5nnGMBc7aBJk+JYa62P87OdUupJAB/EeTjJwa45jMHaJMZkm+fMAYYPB9pKE5d//hNYuvQ4tMYenDNmLS69dAgAQJXtRuc2e6WetBMma2uiIHv2eEcmABnL7t2Shw4ttWewNok58MCGz1m7LloZPFga27g1ZPnxR6Cmxr2GtWHo0GD2JZIoyL59Mv5wJy4CwZhLquSZe/cGFi6UaE6qjIkQQgjJUFIuY62UsgRw8XOIk515FBU5C+uQJjH1dRrTZ+VAH3wIAGDRIonwPvFYPba2HYqnD/oXJk4MvNarnTkQzNqaZi9+MtZAUFg7VQQB3LsvbtkiGejQrK/Zl9sERj8VQQxDhwbvRyKst2yRkn2RCOvevYFPPgHOPjv818aDXr2A9evlPjPWhBBCSFxJxYz1/Uqp0QA0gPUArkzqaOKFl2MNYNP8nXjmLeDpJ2qxoex/mN7hPRwJ6YrYrBkAZAGvDGo4gdGPsG7eXCYAWicvepXbA2S/q1fLMY480n4bI6yNO23F1LAOnRBoFdZHH22/XyOs/TjW1m28qoKYz8oqrCMttWeYMiWy18UD63ugY00IIYTElZRzrLXWF2itD9Raj9Jan6K1tlFoGYCLsN7SrC9OxEfoe9GRuP12YHDHIryCc3HIWdIhUUR1gLFjxcI2dZj9CGtARFYkjvX27eKkOznWbduKSHdyrENjIICIvxYt3CuDFBRIzMLPe+vZM3ih4OVYt20rQj+WwjqVoLAmhBBCEkbKCesmQ3Fxg66LBQXA55/L/c7Du2IHuuLWyV9j3Trg8+P/jnNb/g8tx41ovJ+xY6WutKks4ldYt20rwrq+XjLWfoX1zp0Sk7CrYW1wqmXtJKyzsoABA5yjIPv2SbzikEO8xwiIUB4imXNPYZ2VJe+rKQhrRkEIIYSQuEJhnQy0BoqLUdGmG555Bpg0SeYl/uY38lTz3GaY1+MU3Dn4BfTvD5m4OHZsiFUdILQDYziOdXk5UFkpj/0Ia6vj6dakxU5Ya92466KVQYOcHet33xWn/IorvMdoGDpUguh+Ii55eQ0bxGzaJJ9hJghROtaEEEJIwqCwTgbl5Xi89hL0eORPuOQSmcd4//3ArFmW+HHv3iLwamuBefOCjWFCGTpUJgSGK6yNY11RIY/9OtaGcB3r8nJxxq3NYawMHiwNcOway/znP0D//uFll6+9FnjgAX/b5uU1dqwzwa0GGmbaM+FCgRBCCElhKKwTRFER8K9/SdU4FBejH9bjjPEbMHOmxED+8AcgP9/ygl69JMu8bJlEPZyEdXY2MHp05I51JMK6Y0f3Vt92wnrLFlm6OdZVVY0nPS5bBnz9NXDVVeG19p44UX4C8EMmC+tmzVKvBCAhhBCSoVBYx5H6euCLL4BzzxU9ed11wIcfAiguxhR8hmd+vxyTJzt0zTZtzb//Xh47CWtA4iALFogw3bs3PMd6zx55HI6wdnOrARHWJSUyFoMfYQ00joM89phMbLzkEu/xRUqosN68OXOENRB8L3SsCSGEkLhCYR0namslN3388cBnn4nhumgRcM01kImLQIPJi43o3Vvc5C++EId4wADnbceOlW3nzZPH8Xas3fLVQLDk3vbtwXVOzWEMRqxbJzBWVADPPQecdRbQubP3+CLFOnmxulomaGaasM7JAVq2TPZICCGEkIwmFetYpyX79gEffAB89x3w97+LjrnwQtHDp50WommMsHaqYw0Em8R89BFw+OEOtnYAM4Fx2jRZhlNuzwhrv3WsAX+ONSBxkD5SInC/Y+2Use7dW+prW4X1yy+L+Pcb6YgUq2MdaMqTUcJ63Dhg+XL37xAhhBBCooaOdZRs3Ngaf/iD6OAzzgBeeUVSEABwyy0SA2lkFBYVydJNWBtht2ePewwEEGu8efPwhHXbtlIRxAhKP451jx6Scx492n07u+6LW7bIMZziCNnZchVioiBay6TFgw5CsK1knMjLk4uMurrMKrVnuOkm+bmEEEIIIXGFjnUUvPEGcNFFByMnBzj5ZOCyy6RwRXa2xwvDcawBb2HdrBkwapTY5YB/xxoIil8/wrp3bxG+/fu7b+ckrJ1iIIZBg4KO9ezZIgYffzz+Tqtpa15enpnCOisrvImfhBBCCIkI/m8bBccfD1x55Vps3gy8/TZw0kk+RDUgwjo3VyblOdG9e1AMTZjgvc+xYyUfDPh3rIFgRMOPsAbEVfYSuqaNeKTC2rjV7doBv/iFv3FFgxHWpaVBYW29sCGEEEII8QGFdRTk5QHnnrupYZk8P7i0M99Ps2bi/PbrFxSqbpicNRCeY20mFfoV1n5o3lwmZlqFtVtzGMPgwRJPWbIEeP11CanHclxOhArrzp2luQwhhBBCSBgwCpIM/AhrADjmGPhW7dEIa6ViLySttay1FsfaaeKiwVQbueUWmQ3661/HdkxOhArrTIqBEEIIISRhUFgng6Iif8L6hRf87/PAAyWHUlfnrxGINQrSpk3sc8xWYb17t9TY9hMFAaTY95FHyqTMRBAqrL0y5IQQQgghNjAKkgz8Otbh0LIlMGKEtDfP8XG9ZHWs/ZTaCxersPZqDmPo00ciMED8S+xZMQ4/HWtCCCGERAEd62RQXOzeHCZSJk+WCIUfjGNdUuLd8CUSjLA2MRDAW1jn5IhbvHu3FP9OFMax3rxZxDWFNSGEEEIigMI60WgdH8caAO6/P9jwxQtrXCQeEwS7dZP4R3m5d9dFK/fdJ9VSmjeP/ZicMJ/F0qWypLAmhBBCSARQWCeaPXuAmpr4COvcXP+xDmujlngJa0Bca6+ui1YS6VQbsrNFXC9ZIo8prAkhhBASAcxYJxo/XRcTQU5OsBJIIoR1u3bxyXLHirw8YOVKuU9hTQghhJAIoLBONH66LiYKE4FIhLD2EwNJJnl5UlFFKaBnz2SPhhBCCCFpCIV1ojHCOh6TF8PFxEHiIaxN7GPrVn/NYZKNmcDYrVuwMgkhhBBCSBhQWCeaVHSs4xHR6NBBBKpxrP3kq5OJKbnHGAghhBBCIoTCOtGkorCOh2OdlSVdI7duTZ8oCEBhTQghhJCIobBONGbyYocOyR0HEN8oCCCxioICYO9eCmtCCCGEZDwU1ommuFiqcZiKHMkkno41IMJ68WK5T2FNCCGEkAyHwjrRxKvrYiQkwrGurpb76SKse/VK6jAIIYQQkr5QWCeaeHVdjIREONaGVJ+8SMeaEEIIIVFCYZ1oiooorFORo44CzjgDOOigZI+EEEIIIWkKhXWiSSXHOhFREEDc4Nat43OMWDFgAPDmm6k/TkIIIYSkLBTWiSaVMtbxrGMNBIV1querCSGEEEJiAIV1ItE6tRzrESOk7F/fvvHZP4U1IYQQQpoQOckeQJOishLYty91hPWECcGGNfEgP1+WqZ6vJoQQQgiJAXSsE4lpDpMqwjretGkDDBoEjBmT7JEQQgghhMQdOtaJJJXamSeKFSukvTkhhBBCSIZDYZ1IjLBOlcmLiSA7O9kjIIQQQghJCLQSE0lTdKwJIYQQQpoIFNaJhMKaEEIIISRjobBOJE1t8iIhhBBCSBOCwjqRFBcDLVsCrVoleySEEEIIISTGUFgnklTqukgIIYQQQmIKhXUiSaWui4QQQgghJKYkRVgrpc5SSi1TStUrpcaHPPcnpdQapdRKpdSUZIwvblBYE0IIIYRkLMlyrJcCOB3ADOtKpdRwAOcCGAHgBAD/UUplTiHkoiIKa0IIIYSQDCUpwlprXaC1Xmnz1KkAXtVa79Va/whgDYCDEzu6OELHmhBCCCEkY0m1zos9Acy2PN4cWNcIpdQVAK4AgPz8fEyfPj3ug7OjoqLC37G1xhG7dmHznj1Yl6SxksjxfZ5J2sJz3DTgeW4a8DxnPql6juMmrJVSXwDoZvPUn7XW/4t2/1rrJwA8AQDjx4/XRx11VLS7jIjp06fD17ErK4GaGvQZPRp9kjRWEjm+zzNJW3iOmwY8z00DnufMJ1XPcdyEtdb6uAheVgigt+Vxr8C69IddFwkhhBBCMppUK7f3HoBzlVItlFL9AQwGMCfJY3JGa6i6On/b7tolSwprQgghhJCMJFnl9n6ulNoM4FAAHyqlPgUArfUyAK8DWA7gEwBXa619KtcEU1UFdOmC3q++6m/7NWtkOWBA/MZECCGEEEKSRlImL2qt3wHwjsNz9wC4J7EjioBWrYBWrdB6wwZ/269YIcshQ+I3JkIIIYQQkjRSLQqSXowYgdz16/1tu2IF0KcPkJsb1yERQgghhJDkQGEdDcOHo/XGjUB9vfe2K1cCQ4fGf0yEEEIIISQpUFhHw/DhyN67F/ByrbUWx3rYsIQMixBCCCGEJB4K62gYMUKWy5e7b7dlC1BRQWFNCCGEEJLBUFhHwwEHyHLZMvftzMRFCmtCCCGEkIyFwjoa8vKwt3Nnb8eawpoQQgghJOOhsI6SPf36eQvrlSuBNm2A7t0TMiZCCCGEEJJ4KKyjpLJvXxHWbpVBzMRFpRI3MEIIIYQQklAorKNkT9++QGUlsHGj80asCEIIIYQQkvFQWEfJnn795I5THKSiAti0icKaEEIIISTDobCOksq+feWOU2WQVatkSWFNCCGEEJLRUFhHSW27dkC3bs6O9cqVsmTXRUIIIYSQjIbCOhaMGOEsrFesALKygEGDEjsmQgghhBCSUCisY8Hw4SKstW783IoVQP/+QMuWiR8XIYQQQghJGBTWsWD48OAkxVBYEYQQQgghpElAYR0Lhg+XZWgcpL5eJi9SWBNCCCGEZDwU1rFgxAhZhlYG2bgRqK7mxEVCCCGEkCYAhXUs6NQJ6Nq1sWO9YoUs6VgTQgghhGQ8FNaxwkxgtEJhTQghhBDSZKCwjhWm5J61MsiKFUDHjkDnzv/f3t0H21HfdRx/f5oEURCoEgKFSqA8NekDhRDtUDGUtoJlEBkoIC0z6EDBtkIdylDLOOI/2qmtHZXyUCigUhgKRjqggA9QqKOQB0IhJKEQsIUJBUUIYTAF8vWP3Ts9XC4kudl7N73n/Zq5c8/57dndz+5vAt/7O7/d7S+XJEmSJoWFdVfmzIG1a+HJJ3/StnJlM7866S+XJEmSJoWFdVfGujPIqlVOA5EkSRoSFtZdGX1nkOeeg6eesrCWJEkaEhbWXZk5s5lLPTJivWpV89vCWpIkaShYWHdp8M4g3hFEkiRpqFhYd2nu3GYqSFVTWE+fDnvt1XcqSZIkTQIL6y7NmQPPPw9r1jRTQfbZB2bM6DuVJEmSJoGFdZcG7wyycqXTQCRJkoaIhXWXRu4Mcv/98MgjFtaSJElDxMK6S7vs0jxp8eab4eWXLawlSZKGiIV1l5JmOshddzXv99+/3zySJEmaNBbWXZs7FzZsaF5bWEuSJA0NC+uujVzAOGsWvPWt/WaRJEnSpLGw7tpIYe38akmSpKFiYd21kTuDWFhLkiQNlel9B5hydt0VzjoLTjqp7ySSJEmaRBbWXUvga1/rO4UkSZImmVNBJEmSpA5YWEuSJEkdsLCWJEmSOtBLYZ3khCTLk2xIMm+gfXaSl5Isa38u6SOfJEmStLn6unjxQeA44NIxlj1aVQdObhxJkiRpy/RSWFfVCoAkfexekiRJ6lyqqr+dJ3cC51bV4vb9bGA58DCwFrigqu5+g3XPAM4AmDVr1sHXXXfdZER+nXXr1rH99tv3sm9NHvt56rOPh4P9PBzs56mvzz4+/PDDl1TVvLGWTdiIdZJ/AXYdY9EXquqmN1htDfBLVfU/SQ4G/iHJ3KpaO/qDVXUZcBnAvHnzasGCBR0l3zx33nknfe1bk8d+nvrs4+FgPw8H+3nq21r7eMIK66r60DjWWQ+sb18vSfIosB+wuON4kiRJUqe2qtvtJZmZZFr7em9gX2B1v6kkSZKkjevrdnu/leQJ4P3ALUluaxcdBnwvyTLgBuDMqnq2j4ySJEnS5ujrriALgYVjtN8I3Dj5iSRJkqQts1VNBZEkSZJ+WllYS5IkSR2wsJYkSZI6YGEtSZIkdcDCWpIkSeqAhbUkSZLUgVRV3xm2WJJngP/qafc7A//d0741eeznqc8+Hg7283Cwn6e+Pvt4z6qaOdaCKVFY9ynJ4qqa13cOTSz7eeqzj4eD/Twc7Oepb2vtY6eCSJIkSR2wsJYkSZI6YGG95S7rO4Amhf089dnHw8F+Hg7289S3Vfaxc6wlSZKkDjhiLUmSJHXAwnockrw9yR1JHkqyPMnZfWdS95Jsm+TeJPe3/Xxh35k0cZJMS3Jfkpv7zqKJkeTxJA8kWZZkcd951L0kOyW5IcnKJCuSvL/vTOpWkv3bf8MjP2uTnNN3rhFOBRmHJLsBu1XV0iQ/DywBjq2qh3qOpg4lCbBdVa1LMgP4LnB2Vf1nz9E0AZL8ATAP2KGqju47j7qX5HFgXlV5f+MpKsnVwN1VdXmSbYCfq6rneo6lCZJkGvAk8MtV1dfzTF7DEetxqKo1VbW0ff0CsALYvd9U6lo11rVvZ7Q//iU6BSXZA/gocHnfWSSNT5IdgcOAKwCq6scW1VPeEcCjW0tRDRbWWyzJbOB9wD09R9EEaKcHLAOeBv65quznqemrwHnAhp5zaGIVcHuSJUnO6DuMOrcX8AxwZTut6/Ik2/UdShPqJODavkMMsrDeAkm2B24EzqmqtX3nUfeq6tWqOhDYA5if5F09R1LHkhwNPF1VS/rOogn3gao6CDgK+FSSw/oOpE5NBw4CLq6q9wEvAuf3G0kTpZ3qcwzwrb6zDLKwHqd2zu2NwDVV9fd959HEar9OvAM4suco6t6hwDHt/NvrgA8m+bt+I2kiVNWT7e+ngYXA/H4TqWNPAE8MfLN4A02hranpKGBpVf2o7yCDLKzHob2o7QpgRVV9pe88mhhJZibZqX39s8CHgZW9hlLnqurzVbVHVc2m+Vrx36rq4z3HUseSbNdebE47PeAjwIP9plKXquop4IdJ9m+bjgC8qcDUdTJb2TQQaL420eY7FPgE8EA7/xbgD6vqH/uLpAmwG3B1e9XxW4Drq8pbsUk/nWYBC5txEaYD36yqW/uNpAnwGeCadprAauC0nvNoArR/HH8Y+GTfWUbzdnuSJElSB5wKIkmSJHXAwlqSJEnqgIW1JEmS1AELa0mSJKkDFtaSJElSByysJQ2VJJXkywPvz03yxx1t+6okx3exrY3s54QkK5LcMdH72kiOx5Ps3MF2zklyavt63OcwyQFJ/iPJ+iTnjlp2ZJJVSR5Jcv5A+3VJ9t2yI5CkhoW1pGGzHjiui4KwS0k257kCvwucXlWHT1SeydIe9+8A3+xgc88Cvw/8+ah9TAMuonlS2xzg5CRz2sUXA+d1sG9JsrCWNHReAS4DPjt6wejR0iTr2t8LknwnyU1JVif5sySnJLk3yQNJ3jGwmQ8lWZzk4SRHt+tPS/KlJIuSfC/JJwe2e3eSbzPGE+KSnNxu/8EkX2zb/gj4AHBFki+N+vxuSe5Ksqxd51fb9ovbTMuTXDjw+ceT/Gn7+cVJDkpyW5JHk5w5kPGuJLe0I76XJHnd/zuSfLw9H8uSXNoe87T2nD7YHsfrzjnwQZrHEr8yxjaPSHJfu+43kvxM2/4bSVYmWZLkL5PcDM2jyqtqEfDyqE3NBx6pqtVV9WOaR9f/Zrvs7rbPfGCapC1mYS1pGF0EnJJkx81Y573AmcA7aZ68ul9VzQcup3na24jZNIXcR4FLkmxLM8L8fFUdAhwCnJ5kr/bzBwFnV9V+gztL8jbgizSF54HAIUmOrao/ARYDp1TV50Zl/G3gtqo6sM27rG3/QlXNA94D/FqS9wys84P283cDVwHHA78CXDjwmfntMc4B3gEcNyrrO4ETgUPbbb0KnNLm3r2q3lVV7wau5PUOBZaMbmzP21XAie2604Gz2vZLgaOq6mBg5hjbHG134IcD759o26iqDcAjNOdLkraIhbWkoVNVa4G/oZk2sKkWVdWaqloPPArc3rY/QFNMj7i+qjZU1fdpHql8APAR4NQky4B7gF8ERub13ltVj42xv0OAO6vqmXY09xrgsI1lBE5r54y/u6peaNs/lmQpcB8wl6ZAHvHtgeO4p6peqKpngPVJdhrIuLqqXgWupRkxH3QEcDCwqD3GI4C92+PfO8lfJTkSWDtG5t2AZ8Zo3x94rKoebt9f3R7/AcDqgXN27RuejU33NPC2DrYjacj51ZekYfVVYCmvHUV9hXbAoZ3usM3AsvUDrzcMvN/Aa/9bWqP2U0CAz1TVbYMLkiwAXhxP+LFU1V1JDqMZLb8qyVdoRqLPBQ6pqv9NchWw7cBqg8cx+hhHjmusYxoU4Oqq+vzoTEneC/w6zWj/x2jmUw96aVSeifAk8PaB93u0bSO2bXNI0hZxxFrSUKqqZ4HraaZpjHicZuQV4Bhgxjg2fUKSt7TzrvcGVgG30UxjmAGQZL8k221kO/fSTNvYub347mTgO2+2QpI9gR9V1ddppqgcBOxAU7w/n2QWzQV8m2t+kr3aPzZOBL47avm/Ascn2aXN8QtJ9mwvEH1LVd0IXNDmGW0FsM8Y7auA2UlGln2C5vhX0YyCz27bT9yE/IuAfdtj2AY4iZ+M1APsBzy4CduRpDfliLWkYfZl4NMD778O3JTkfuBWxjea/AOaongH4Myq+r8kl9NMF1maJDRTH459s41U1Zo0t4W7g2ZE+Jaqumkj+14AfC7Jy8A64NSqeizJfcBKmnnG/z6OY1oE/DVNAXwHsHBU1oeSXADc3hbfLwOfohkFvnLgYsfXjWgD/wT87ejG9rydBnyrvbBwEXBJVa1P8nvArUlebNsBSLIrzfzzHYANSc4B5lTV2iSfpvkDZxrwjapa3q4zC3ipqp4ax3mRpNdI1ehv9CRJarTTVc6tqqMncB8LgfPaeemb8vntq2pd+0fKRcD3q+ovxrnvzwJrq+qK8awvSYOcCiJJ6tv5NBcxbqrT24sklwM70twlZLyeo7kwUpK2mCPWkiRJUgccsZYkSZI6YGEtSZIkdcDCWpIkSeqAhbUkSZLUAQtrSZIkqQMW1pIkSVIH/h/GsJ+sGfbvwgAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 864x576 with 1 Axes>"
       ]
      },
      "metadata": {
@@ -556,15 +562,16 @@
     "    cross_SNR_dB_arr.append(cross_SNR_dB)\n",
     "    #print(f\"{N_samples}, correlator SNR = {cross_SNR_dB:.0f} dB\")\n",
     "    \n",
-    "plt.figure(1)\n",
+    "plt.figure(figsize=(12, 8))\n",
     "plt.plot(N_samples_arr, cross_coh_mean_arr, 'g', N_samples_arr, cross_incoh_mean_arr, 'b', N_samples_arr, cross_sys_mean_arr, 'r')\n",
     "plt.title(\"Correlator mean\")\n",
     "plt.xlabel(\"Number of samples\")\n",
     "plt.ylabel(\"Cross power mean\")\n",
     "plt.legend(['cross_coh', 'cross_incoh', 'cross_sys'])\n",
     "plt.grid()\n",
+    "plt.savefig('plots/signal_statistics_cross_correlator_mean.jpg', dpi=dpi)\n",
     "\n",
-    "plt.figure(2)\n",
+    "plt.figure(figsize=(12, 8))\n",
     "fit_coef = np.polyfit(N_samples_arr_log, cross_SNR_dB_arr, 1)\n",
     "fit_p = np.poly1d(fit_coef)\n",
     "fit_line = fit_p(N_samples_arr_log)\n",
@@ -572,7 +579,8 @@
     "plt.title(\"Correlator SNR (%3.1f dB/decade)\" % fit_p[1])\n",
     "plt.xlabel(\"Number of samples (log10)\")\n",
     "plt.ylabel(\"SNR of coherent correlator [dB]\")\n",
-    "plt.grid()"
+    "plt.grid()\n",
+    "plt.savefig('plots/signal_statistics_cross_correlator_snr.jpg', dpi=dpi)"
    ]
   },
   {
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diff --git a/libraries/base/common/python/try_round_weight.py b/libraries/base/common/python/try_round_weight.py
index 7b8a3ec60a04224192f8610714d77899e66a34e3..24715832be19db892977b4153dea8baeb68359cd 100644
--- a/libraries/base/common/python/try_round_weight.py
+++ b/libraries/base/common/python/try_round_weight.py
@@ -28,16 +28,29 @@
 #   . quantized subbands --> sigma_qq
 #   . unquantized subbands --> sigma_sq
 #   Preliminary conclusion:
-#   . for small input noise with sigma < 2 the output sigma gets disturbed
+#   . For small input noise with sigma < 2 the output sigma gets disturbed
 #     due to the weighting if the weighting is applied after the subband
 #     quantisation
-#   . increasing -N improves the results, for LOFAR subbands N = 195312
+#   . Increasing -N improves the results, for LOFAR subbands N = 195312
 #   . it may be preferred to apply the subband weights to the unquantized
 #     WPFB output.
+#   . Choosing sufficient intermediate resolution (-r) before applying
+#     weights:
+#     - Rounding noise changes the sigma of the noise anyway, as shown by
+#       sigmas_ratio_sq_input_T. Therefore choose resolution such that
+#       jumps in sigma due to rounding weighted noise, as shown by
+#       sigmas_ratio_qq_sq_T, is much smaller than sigmas_ratio_sq_input_T
+#       ==> -r 2.
+#     - For input sigma >= 1
+#       . Choose sigmas_ratio_qq_sq_T < 10%   ==> -r 3
+#       . Choose sigmas_ratio_qq_sq_T <  1%   ==> -r 6
+#       . Choose sigmas_ratio_qq_sq_T <  0.1% ==> -r 9
+#       . Hence the disturbance (jumps) on the sigma is about proportional
+#         to 1/2**r
 # Note:
-# . For values exactly halfway between rounded decimal values, NumPy rounds to
-#   the nearest even value. Thus 1.5 and 2.5 round to 2.0, -0.5 and 0.5 round
-#   to 0.0, etc.
+# . For values exactly halfway between rounded decimal values, NumPy of
+#   Python3 rounds to the nearest even value. Thus 1.5 and 2.5 round to 2.0,
+#   -0.5 and 0.5 round to 0.0, etc. Python2 rounds half away from zero.
 # Usage:
 #   > python3 try_round_weight.py -N 195312
 
@@ -46,7 +59,7 @@ import textwrap
 
 import numpy as np
 import matplotlib
-matplotlib.use('tkagg')
+matplotlib.use('tkagg')  # to make X11 forwarding work
 import matplotlib.pyplot as plt
 
 import common as cm
@@ -72,8 +85,15 @@ _parser = argparse.ArgumentParser(
         # Zoom in at w = 0.75
         > python try_round_weight.py --w_lo 0.7 --w_hi 0.8 --w_step 0.0001 --s_lo 1 --s_hi 10 --s_step 1 -N 195312 -S 0
 
-        # Use -r = 6 to see effect of having more resolution before rounding
+        # Use -r = 6 to see effect of applying weights with more (intermediate)
+        # resolution before rounding
         > python try_round_weight.py --w_lo 0.7 --w_hi 0.8 --w_step 0.0001 --s_lo 1 --s_hi 10 --s_step 1 -N 195312 -S 0 -r 6
+        > python try_round_weight.py --w_lo 0.3 --w_hi 1.1 --w_step 0.001 --s_lo 1 --s_hi 10 --s_step 1 -N 195312 -S 0 -r 6
+
+        # Reproduce plots/
+        > python try_round_weight.py --w_lo 0.3 --w_hi 1.1 --w_step 0.001 --s_lo 1 --s_hi 10 --s_step 1 -N 195312 -S 0 -r 0 --noplot --save
+        > python try_round_weight.py --w_lo 0.3 --w_hi 1.1 --w_step 0.001 --s_lo 1 --s_hi 10 --s_step 1 -N 195312 -S 0 -r 4 --noplot --save
+        > python try_round_weight.py --w_lo 0.3 --w_hi 1.1 --w_step 0.001 --s_lo 1 --s_hi 10 --s_step 1 -N 195312 -S 0 -r 6 --noplot --save
         \n""")),
     formatter_class=argparse.RawTextHelpFormatter)
 _parser.add_argument('-S', default=0, type=int, help='Random number seed')
@@ -86,6 +106,8 @@ _parser.add_argument('--s_step', default=0.1, type=float, help='Step sigma')
 _parser.add_argument('--w_lo', default=0.3, type=float, help='Lowest weight')
 _parser.add_argument('--w_hi', default=2.0, type=float, help='Highest weight')
 _parser.add_argument('--w_step', default=0.1, type=float, help='Step weight')
+_parser.add_argument('--noplot', action="store_true", help='Do not show plots')
+_parser.add_argument('--save', action="store_true", help='Do save plots')
 args = _parser.parse_args()
 
 np.random.seed(args.S)
@@ -118,7 +140,8 @@ resolution = args.r
 resolution_factor = 2**resolution
 
 # Determine weighted rounded noise sigma / weighted noise sigma for range of weights and input noise sigmas
-sigmas_ratio = np.nan * np.zeros((N_weights, N_sigmas))  # w rows, s cols
+sigmas_ratio_qq_sq = np.nan * np.zeros((N_weights, N_sigmas))  # w rows, s cols
+sigmas_ratio_sq_input = np.nan * np.zeros((N_weights, N_sigmas))  # w rows, s cols
 sigmas_qq = np.zeros((N_weights, N_sigmas))
 sigmas_sq = np.zeros((N_weights, N_sigmas))
 for s, sigma in enumerate(sigmas):
@@ -143,26 +166,33 @@ for s, sigma in enumerate(sigmas):
        sigmas_qq[w][s] = s_qq
        sigmas_sq[w][s] = s_sq
        if s_sq != 0:
-           sigmas_ratio[w][s] = s_qq / s_sq  # weighted rounded noise sigma / weighted noise sigma
+           sigmas_ratio_qq_sq[w][s] = s_qq / s_sq  # weighted rounded noise sigma / weighted noise sigma
+           sigmas_ratio_sq_input[w][s] = s_sq / sigma  # weighted noise sigma / input noise sigma
 # Transpose [w][s] to have index ranges [s][w]
-sigmas_ratio_T = sigmas_ratio.transpose()
+sigmas_ratio_qq_sq_T = sigmas_ratio_qq_sq.transpose()
+sigmas_ratio_sq_input_T = sigmas_ratio_sq_input.transpose()
 sigmas_qq_T = sigmas_qq.transpose()
 sigmas_sq_T = sigmas_sq.transpose()
 
 # Plot results
 figNr = 0
+dpi = 254  # 10 dots per mm
+s_colors = plt.cm.jet(np.linspace(0, 1, N_sigmas))
+w_colors = plt.cm.jet(np.linspace(0, 1, N_weights))
 
 figNr += 1
 plt.figure(figNr)
 for s, sigma in enumerate(sigmas):
     # Plot sigma_qq of twice quantized noise as function of weight for
     # different input sigmas
-    plt.plot(weights, sigmas_qq_T[s], label='s = %4.2f' % sigma)
+    plt.plot(weights, sigmas_qq_T[s], color=s_colors[s], label='s = %4.2f' % sigma)
 plt.title("Sigma of weighted quantized noise")
 plt.xlabel("Weight")
 plt.ylabel("Sigma_qq")
 plt.legend(loc='upper right')
 plt.grid()
+if args.save:
+    plt.savefig('plots/try_round_weight_r%d_w_sigma_qq.jpg' % resolution, dpi=dpi)
 
 figNr += 1
 plt.figure(figNr)
@@ -171,13 +201,31 @@ for s, sigma in enumerate(sigmas):
     # different input sigmas.
     # Normalize the sigma_qq by the weight, so that it can be compared with
     # the input sigma that is shown by the horizontal sigma reference lines.
-    plt.plot(weights, sigmas_qq_T[s] / weights, label='s = %4.2f' % sigma)
-    plt.plot(weights, sigmas[s]*np.ones(N_weights))  # add sigma reference lines
+    plt.plot(weights, sigmas_qq_T[s] / weights, color=s_colors[s], label='s = %4.2f' % sigma)
+    plt.plot(weights, sigmas[s]*np.ones(N_weights), '--', color=s_colors[s])  # add sigma reference lines
 plt.title("Sigma of weighted quantized noise, normalized for weight")
 plt.xlabel("Weight")
 plt.ylabel("Sigma_qq")
 plt.legend(loc='upper right')
 plt.grid()
+if args.save:
+    plt.savefig('plots/try_round_weight_r%d_w_sigma_qq_normalized.jpg' % resolution, dpi=dpi)
+
+figNr += 1
+plt.figure(figNr)
+for s, sigma in enumerate(sigmas):
+    # Plot ratio of sigma_sq / sigma as function of weight for different
+    # input sigma. The ratio deviation from 1 tells how much quantized
+    # weighted noise deviates from the input noise. This shows that rounding
+    # noise cause a change in sigma even when weight is 1.
+    plt.plot(weights, sigmas_ratio_sq_input_T[s] / weights, color=s_colors[s], label='s = %4.2f' % sigma)
+plt.title("Relative sigma difference of weighted quantized data / input data")
+plt.xlabel("Weight")
+plt.ylabel("Relative sigma difference")
+plt.legend(loc='upper right')
+plt.grid()
+if args.save:
+    plt.savefig('plots/try_round_weight_r%d_w_sigmas_ratio_sq_input.jpg' % resolution, dpi=dpi)
 
 figNr += 1
 plt.figure(figNr)
@@ -186,32 +234,39 @@ for s, sigma in enumerate(sigmas):
     # input sigma. The ratio deviation from 1 tells how much the twice
     # quantized noise deviates from the noise that is only quantized after
     # the weighting.
-    plt.plot(weights, sigmas_ratio_T[s], label='s = %4.2f' % sigma)
-plt.title("Relative sigma difference of weighting after / before quantisation")
+    plt.plot(weights, sigmas_ratio_qq_sq_T[s], color=s_colors[s], label='s = %4.2f' % sigma)
+plt.title("Relative sigma difference of weighting before / after quantisation")
 plt.xlabel("Weight")
 plt.ylabel("Relative sigma difference")
 plt.legend(loc='upper right')
 plt.grid()
+if args.save:
+    plt.savefig('plots/try_round_weight_r%d_w_sigmas_ratio_qq_sq.jpg' % resolution, dpi=dpi)
 
 figNr += 1
 plt.figure(figNr)
 for w, weight in enumerate(weights):
     # Plot ratio of sigma_qq / sigma_sq as function of input sigma for
     # different weights
-    plt.plot(sigmas, sigmas_ratio[w], label='w = %4.2f' % weight)
-plt.title("Relative sigma difference of weighting after / before quantisation")
+    plt.plot(sigmas, sigmas_ratio_qq_sq[w], color=w_colors[w], label='w = %4.2f' % weight)
+plt.title("Relative sigma difference of weighting before / after quantisation")
 plt.xlabel("Sigma")
 plt.ylabel("Relative sigma difference (s_qq / s_sq)")
 plt.legend(loc='upper right')
 plt.grid()
+if args.save:
+    plt.savefig('plots/try_round_weight_r%d_s_sigmas_ratio_qq_sq.jpg' % resolution, dpi=dpi)
 
 figNr += 1
 plt.figure(figNr)
-plt.imshow(sigmas_ratio, origin='lower', interpolation='none', aspect='auto', extent=[sigma_lo, sigma_hi, weight_lo, weight_hi])
+plt.imshow(sigmas_ratio_qq_sq, origin='lower', interpolation='none', aspect='auto', extent=[sigma_lo, sigma_hi, weight_lo, weight_hi])
 plt.colorbar()
 plt.title("Relative sigma difference of weighting after / before quantisation")
 plt.xlabel("Sigma")
 plt.ylabel("Weight")
 plt.grid()
+if args.save:
+    plt.savefig('plots/try_round_weight_r%d_sw_sigmas_ratio_qq_sq.jpg' % resolution, dpi=dpi)
 
-plt.show()
+if not args.noplot:
+    plt.show()
diff --git a/libraries/base/common/python/try_wrap.py b/libraries/base/common/python/try_wrap.py
new file mode 100644
index 0000000000000000000000000000000000000000..a2cfcb4b50af0eb6bbcffbb1851fa95820d9824d
--- /dev/null
+++ b/libraries/base/common/python/try_wrap.py
@@ -0,0 +1,89 @@
+#! /usr/bin/env python3
+###############################################################################
+#
+# Copyright 2022
+# ASTRON (Netherlands Institute for Radio Astronomy) <http://www.astron.nl/>
+# P.O.Box 2, 7990 AA Dwingeloo, The Netherlands
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+###############################################################################
+
+# Author: Eric Kooistra
+# Date: jan 2023
+# Purpose:
+#   Try wrapping in a summator
+# Description:
+#   Usage:
+#   > python3 try_wrap.py -N 10
+#
+
+import argparse
+import textwrap
+
+import numpy as np
+import matplotlib
+matplotlib.use('tkagg')  # to make X11 forwarding work
+import matplotlib.pyplot as plt
+
+import random
+
+import common as cm
+
+# Parse arguments to derive user parameters
+_parser = argparse.ArgumentParser(
+    description="".join(textwrap.dedent("""\
+        The int_wrap function keeps w LSbits and remove MSbits from an integer,
+        so that the result is in range range -2**(w-1) to + 2**(w-1)-1. This
+        try_wrap.py show that wrap(sum) = wrap(sum(wrap)), so intermediate
+        wrapping in a summator does not change the final sum.
+
+        The wrap function is distributive, similar to modulo [1]:
+
+            (a + b) mod n = [(a mod n) + (b mod n)] mod n
+                 ab mod n = [(a mod n)(b mod n)] mod n
+
+        > python try_wrap.py -N 10 --w_inp 10 --w_sum 4
+
+        References:
+        [1] https://en.wikipedia.org/wiki/Modulo_operation
+        \n""")),
+    formatter_class=argparse.RawTextHelpFormatter)
+_parser.add_argument('-N', default=1000, type=int, help='Number of input samples')
+_parser.add_argument('--w_inp', default=10, type=int, help='Number bits of input samples')
+_parser.add_argument('--w_sum', default=4, type=int, help='Number bits of output sum')
+args = _parser.parse_args()
+
+N_samples = args.N
+W_input = args.w_inp
+W_sum = args.w_sum
+
+lo = -1 * 2**(W_input - 1)
+hi = 2**(W_input - 1) - 1
+x = [random.randint(lo, hi) for i in range(N_samples)]
+x_wrap = [cm.int_wrap(i, W_sum) for i in x]
+x_wrap_sum_wrap = cm.int_wrap(cm.add_list_elements(x_wrap), W_sum)
+x_wrap_sum      = cm.int_wrap(cm.add_list_elements(x), W_sum)
+
+print(x)
+print()
+print(x_wrap)
+print()
+
+if x_wrap_sum_wrap == x_wrap_sum:
+    print('OK')
+else:
+    print('Error:')
+    print(x_wrap_sum_wrap)
+    print(x_wrap_sum)
+
diff --git a/libraries/dsp/wpfb/src/vhdl/wpfb_pkg.vhd b/libraries/dsp/wpfb/src/vhdl/wpfb_pkg.vhd
index df393099f59b8a03f4552ca659018513b4ca3ad2..54aea050a7fb78f95fcc891712ad646d30ea5cff 100644
--- a/libraries/dsp/wpfb/src/vhdl/wpfb_pkg.vhd
+++ b/libraries/dsp/wpfb/src/vhdl/wpfb_pkg.vhd
@@ -82,6 +82,9 @@ package wpfb_pkg is
   -- LOFAR2 subband filter
   -----------------------------------------------------------------------------
 
+  -- Use guard_w = 1, instead of 2 to avoid overflow in first FFT stage,
+  -- because fil_backoff_w = 1 already provides sufficient FFT input margin.
+
   -- Fsub settings:
   -- . Settings used on LTS and DTS until at least March 2022
   constant c_wpfb_lofar2_subbands_lts_2021 : t_wpfb := (1, 1024, 0, 6,
@@ -97,7 +100,7 @@ package wpfb_pkg is
                                                        true, false, true, 23, 18, 1, 24, 1, true, 54, 2, 195313,
                                                        c_fft_pipeline, c_fft_pipeline, c_fil_ppf_pipeline);
 
-  -- . Settings used on DTS with fft_out_dat_w = 19b, to preserve FFT processing gain of 4.5 bits
+  -- . Settings used in tb_tb_verify_pfb_wg with fft_out_dat_w = 19b, to preserve FFT processing gain of 5 bits
   --   - use stage_dat_w = 25 --> fil_out_dat_w = fft_in_dat_w = 24
   --   - with fft_out_dat_w = 19 --> stat_data_w = 2*19 + 18 = 56 b
   constant c_wpfb_lofar2_subbands_dts_19b : t_wpfb := (1, 1024, 0, 6,
@@ -105,14 +108,26 @@ package wpfb_pkg is
                                                        true, false, true, 24, 19, 1, 25, 1, true, 56, 2, 195313,
                                                        c_fft_pipeline, c_fft_pipeline, c_fil_ppf_pipeline);
 
+  -- . Settings for L2TS with fft_out_gain_w = 2b, to have W_fsub_gain = W_fft_proc = 5b
+  constant c_wpfb_lofar2_subbands_l2ts_18b : t_wpfb := (1, 1024, 0, 6,
+                                                       16, 1, 14, 23, 16,
+                                                       true, false, true, 23, 18, 2, 24, 1, true, 54, 2, 195313,
+                                                       c_fft_pipeline, c_fft_pipeline, c_fil_ppf_pipeline);
+
   constant c_wpfb_lofar2_subbands : t_wpfb := c_wpfb_lofar2_subbands_dts_18b;
 
   -- The FFT output has more bits to be able to preserve the sensitivity of
   -- the processing gain of the FFT. The FFT has a processing gain of
-  -- sqrt(N_sub = N_fft / 2 = 512), so 4.5 bits. Therefore choose
-  -- fft_out_dat_w = fil_in_dat_w + 5 = 14 + 5 = 19b. Using fft_out_gain_w =
-  -- 1 compensates for the fil_backoff_w = 1 of the FIR filter. The
-  -- func_wpfb_subband_scale_w then thus returns 19 + 1 - (14 + 1) = 5 bits.
+  -- W_fft_proc = sqrt(N_fft = 1024), so 5 bits. Therefore choose
+  -- fft_out_dat_w = fil_in_dat_w + 5 = 14 + 5 = 19b. Using fft_out_gain_w
+  -- = 1 compensates for the fil_backoff_w = 1 of the FIR filter.
+  -- However, instead keep fft_out_dat_w = 18b to fit a 18x19 multiplier in
+  -- the SST. To preserve the sensitivity increase fft_out_gain_w by 1 at the
+  -- expense of loosing factor 2 (1 bit) in subband dynamic range. Therefore
+  -- fft_out_gain_w = 2 and the func_wpfb_subband_scale_w() then thus returns
+  -- (fft_out_dat_w + fft_out_gain_w) - (fil_in_dat_w + fil_backoff_w) =
+  -- (18 + 2) - (14 + 1) = 5 bits = W_fft_proc, to preserve the subband
+  -- sensitivity.
   function func_wpfb_subband_scale_w(wpfb : t_wpfb) return natural;
 
   -- The WPFB subband gain is the expected factor between subband amplitude
@@ -122,11 +137,12 @@ package wpfb_pkg is
   -- . DC gain of the FIR filter (= fir_filter_dc_gain ~= 1.0),
   -- . the FFT gain for a real input (= c_fft_real_input_gain_sine = 0.5) and
   -- . the extra bits to preserve the sensitivity of the FFT processing gain
-  --   (derived from wpfb).
+  --   W_fft_proc = 5b (derived from wpfb by func_wpfb_subband_scale_w()).
   -- For example:
   -- . func_wpfb_subband_gain() ~=  8 for c_wpfb_lofar2_subbands_lts_2021 and
   --                                  for c_wpfb_lofar2_subbands_dts_18b
   -- . func_wpfb_subband_gain() ~= 16 for c_wpfb_lofar2_subbands_dts_19b
+  --                                  for c_wpfb_lofar2_subbands_l2ts_18b
   function func_wpfb_subband_gain(wpfb : t_wpfb; fir_filter_dc_gain : real) return real;
 
   -- The expected WPFB SST level for subband amplitude A_sub and an integration