diff --git a/applications/lofar2/model/lofar2_station_sdp_firmware_model.ipynb b/applications/lofar2/model/lofar2_station_sdp_firmware_model.ipynb
index cdc9a2e25cbef3d96cf0fa2c973ca7d2f637e276..df48f6bf1e72e43dbf6a69c6559c7ba3d76e16fd 100644
--- a/applications/lofar2/model/lofar2_station_sdp_firmware_model.ipynb
+++ b/applications/lofar2/model/lofar2_station_sdp_firmware_model.ipynb
@@ -50,7 +50,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "N_int = 200000000\n",
+      "N_int_adc = 200000000\n",
       "N_int_sub = 195312.5\n"
      ]
     }
@@ -66,10 +66,10 @@
     "f_adc = 200e6 # Hz\n",
     "f_sub = f_adc / N_fft\n",
     "T_int = 1 # s\n",
-    "N_int = f_adc * T_int\n",
+    "N_int_adc = f_adc * T_int\n",
     "N_int_sub = f_sub * T_int\n",
     "\n",
-    "print(f\"N_int = {N_int:.0f}\")\n",
+    "print(f\"N_int_adc = {N_int_adc:.0f}\")\n",
     "print(f\"N_int_sub = {N_int_sub:.1f}\")"
    ]
   },
@@ -156,17 +156,17 @@
       "subband_weight_im = 0\n",
       "\n",
       "Coherent WG sine input:\n",
-      "  G_subband_ampl = 1 * 0.5 * 2**4 * 1.0 = 8.0 = 3.00 bits\n",
+      "  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_sub_gain = 4\n",
+      "  . W_fsub_gain = 5.0\n",
       "  . subband_weight_gain = 1.0\n",
       "\n",
       "Incoherent white noise input:\n",
-      "  G_subband_sigma = 1 * 0.03125 * 2**4 * 1.0 = 0.5 = -1.00 bits\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_sub_gain = 4\n",
+      "  . W_fsub_gain = 5.0\n",
       "  . subband_weight_gain = 1.0\n",
       "\n"
      ]
@@ -187,8 +187,9 @@
     "G_fft_real_input_noise = 1 / np.sqrt(N_fft)  # incoherent, so proportional to 1/sqrt(N)\n",
     "\n",
     "# . Signal level bit growth to accomodate processing gain of FFT\n",
-    "W_sub_proc = np.log2(np.sqrt(N_sub))\n",
-    "W_sub_gain = 4  # use W_sub_gain instead of W_sub_proc\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",
@@ -203,26 +204,26 @@
     "print()\n",
     "\n",
     "# Expected factor from real signal input amplitude to subband amplitude\n",
-    "G_subband_ampl = G_fir_dc * G_fft_real_input_sine * 2**W_sub_gain * subband_weight_gain\n",
+    "G_subband_sine = G_fir_dc * G_fft_real_input_sine * 2**W_fsub_gain * subband_weight_gain\n",
     "\n",
     "print(\"Coherent WG sine input:\")\n",
-    "print(f\"  G_subband_ampl = {G_fir_dc} * {G_fft_real_input_sine} * 2**{W_sub_gain} * {subband_weight_gain} \\\n",
-    "= {G_subband_ampl} = {np.log2(G_subband_ampl):.2f} bits\")\n",
+    "print(f\"  G_subband_sine = {G_fir_dc} * {G_fft_real_input_sine} * 2**{W_fsub_gain} * {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_sub_gain =\", W_sub_gain)\n",
+    "print(\"  . W_fsub_gain =\", W_fsub_gain)\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_sigma = G_fir_dc * G_fft_real_input_noise * 2**W_sub_gain * subband_weight_gain\n",
+    "G_subband_noise = G_fir_dc * G_fft_real_input_noise * 2**W_fsub_gain * subband_weight_gain\n",
     "\n",
     "print(\"Incoherent white noise input:\")\n",
-    "print(f\"  G_subband_sigma = {G_fir_dc} * {G_fft_real_input_noise} * 2**{W_sub_gain} * {subband_weight_gain} \\\n",
-    "= {G_subband_sigma} = {np.log2(G_subband_sigma):.2f} bits\")\n",
+    "print(f\"  G_subband_noise = {G_fir_dc} * {G_fft_real_input_noise} * 2**{W_fsub_gain} * {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_sub_gain =\", W_sub_gain)\n",
+    "print(\"  . W_fsub_gain =\", W_fsub_gain)\n",
     "print(\"  . subband_weight_gain =\", subband_weight_gain)\n",
     "print()\n"
    ]
@@ -261,17 +262,17 @@
       "\n",
       "Gain from signal input to beamlet:\n",
       "\n",
-      "  N_ant =  1 : G_beamlet_sum_ampl = 8.00 *  1.00 * 1.0 =    8.00 = 3.0 bits\n",
-      "  N_ant = 12 : G_beamlet_sum_ampl = 8.00 * 12.00 * 1.0 =   96.00 = 6.6 bits\n",
-      "  N_ant = 24 : G_beamlet_sum_ampl = 8.00 * 24.00 * 1.0 =  192.00 = 7.6 bits\n",
-      "  N_ant = 48 : G_beamlet_sum_ampl = 8.00 * 48.00 * 1.0 =  384.00 = 8.6 bits\n",
-      "  N_ant = 96 : G_beamlet_sum_ampl = 8.00 * 96.00 * 1.0 =  768.00 = 9.6 bits\n",
+      "  N_ant =  1 : G_beamlet_sum_sine = 16.00 *  1.00 * 1.0 =   16.00 = 4.0 bits\n",
+      "  N_ant = 12 : G_beamlet_sum_sine = 16.00 * 12.00 * 1.0 =  192.00 = 7.6 bits\n",
+      "  N_ant = 24 : G_beamlet_sum_sine = 16.00 * 24.00 * 1.0 =  384.00 = 8.6 bits\n",
+      "  N_ant = 48 : G_beamlet_sum_sine = 16.00 * 48.00 * 1.0 =  768.00 = 9.6 bits\n",
+      "  N_ant = 96 : G_beamlet_sum_sine = 16.00 * 96.00 * 1.0 = 1536.00 = 10.6 bits\n",
       "\n",
-      "  N_ant =  1 : G_beamlet_sum_sigma = 0.50 *  1.00 * 1.0 =   0.50 = -1.0 bits\n",
-      "  N_ant = 12 : G_beamlet_sum_sigma = 0.50 *  3.46 * 1.0 =   1.73 = 0.8 bits\n",
-      "  N_ant = 24 : G_beamlet_sum_sigma = 0.50 *  4.90 * 1.0 =   2.45 = 1.3 bits\n",
-      "  N_ant = 48 : G_beamlet_sum_sigma = 0.50 *  6.93 * 1.0 =   3.46 = 1.8 bits\n",
-      "  N_ant = 96 : G_beamlet_sum_sigma = 0.50 *  9.80 * 1.0 =   4.90 = 2.3 bits\n",
+      "  N_ant =  1 : G_beamlet_sum_noise = 1.00 *  1.00 * 1.0 =   1.00 = 0.0 bits\n",
+      "  N_ant = 12 : G_beamlet_sum_noise = 1.00 *  3.46 * 1.0 =   3.46 = 1.8 bits\n",
+      "  N_ant = 24 : G_beamlet_sum_noise = 1.00 *  4.90 * 1.0 =   4.90 = 2.3 bits\n",
+      "  N_ant = 48 : G_beamlet_sum_noise = 1.00 *  6.93 * 1.0 =   6.93 = 2.8 bits\n",
+      "  N_ant = 96 : G_beamlet_sum_noise = 1.00 *  9.80 * 1.0 =   9.80 = 3.3 bits\n",
       "\n"
      ]
     }
@@ -315,22 +316,22 @@
     "print()\n",
     "\n",
     "# Expected factor from real signal input amplitude to beamlet amplitude\n",
-    "G_beamlet_sum_ampl = G_subband_ampl * bf_proc_coh * beamlet_weight_gain\n",
+    "G_beamlet_sum_sine = G_subband_sine * bf_proc_coh * beamlet_weight_gain\n",
     "\n",
     "# Expected factor from real signal input sigma to beamlet sigma\n",
-    "G_beamlet_sum_sigma = G_subband_sigma * bf_proc_incoh * beamlet_weight_gain\n",
+    "G_beamlet_sum_noise = G_subband_noise * bf_proc_incoh * beamlet_weight_gain\n",
     "\n",
     "print(\"Gain from signal input to beamlet:\")\n",
     "print()          \n",
     "for ni, na in enumerate(N_ant_arr):\n",
-    "    print(f\"  N_ant = {na:2d} : G_beamlet_sum_ampl = {G_subband_ampl:.2f} * \" \\\n",
+    "    print(f\"  N_ant = {na:2d} : G_beamlet_sum_sine = {G_subband_sine:.2f} * \" \\\n",
     "          f\"{bf_proc_coh[ni]:5.2f} * {beamlet_weight_gain} \" \\\n",
-    "          f\"= {G_beamlet_sum_ampl[ni]:7.2f} = {np.log2(G_beamlet_sum_ampl[ni]):.1f} bits\")\n",
+    "          f\"= {G_beamlet_sum_sine[ni]:7.2f} = {np.log2(G_beamlet_sum_sine[ni]):.1f} bits\")\n",
     "print()          \n",
     "for ni, na in enumerate(N_ant_arr):\n",
-    "    print(f\"  N_ant = {na:2d} : G_beamlet_sum_sigma = {G_subband_sigma:.2f} * \" \\\n",
+    "    print(f\"  N_ant = {na:2d} : G_beamlet_sum_noise = {G_subband_noise:.2f} * \" \\\n",
     "          f\"{bf_proc_incoh[ni]:5.2f} * {beamlet_weight_gain} \" \\\n",
-    "          f\"= {G_beamlet_sum_sigma[ni]:6.2f} = {np.log2(G_beamlet_sum_sigma[ni]):.1f} bits\")\n",
+    "          f\"= {G_beamlet_sum_noise[ni]:6.2f} = {np.log2(G_beamlet_sum_noise[ni]):.1f} bits\")\n",
     "print()"
    ]
   },
@@ -344,18 +345,18 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "N_ant =  1 : si_ampl_max = 2.000000 =  16384 =  14.0 bits\n",
-      "N_ant = 12 : si_ampl_max = 0.166667 =   1365 =  10.4 bits\n",
-      "N_ant = 24 : si_ampl_max = 0.083333 =    683 =   9.4 bits\n",
-      "N_ant = 48 : si_ampl_max = 0.041667 =    341 =   8.4 bits\n",
-      "N_ant = 96 : si_ampl_max = 0.020833 =    171 =   7.4 bits\n",
+      "N_ant =  1 : si_ampl_max = 1.000000 =   8192 =  13.0 bits\n",
+      "N_ant = 12 : si_ampl_max = 0.083333 =    683 =   9.4 bits\n",
+      "N_ant = 24 : si_ampl_max = 0.041667 =    341 =   8.4 bits\n",
+      "N_ant = 48 : si_ampl_max = 0.020833 =    171 =   7.4 bits\n",
+      "N_ant = 96 : si_ampl_max = 0.010417 =     85 =   6.4 bits\n",
       "\n"
      ]
     }
    ],
    "source": [
     "# Maximum coherent signal input amplitude\n",
-    "si_ampl_max = 2**(W_beamlet_sum - 1) / G_beamlet_sum_ampl\n",
+    "si_ampl_max = 2**(W_beamlet_sum - 1) / G_beamlet_sum_sine\n",
     "for ni, na in enumerate(N_ant_arr):\n",
     "    print(f\"N_ant = {na:2d} : si_ampl_max = {si_ampl_max[ni] / FS:f} \" \\\n",
     "          f\"= {si_ampl_max[ni]:6.0f} = {np.log2(si_ampl_max[ni]):5.1f} bits\")\n",
@@ -524,11 +525,11 @@
      "output_type": "stream",
      "text": [
       "Power at -50dBFS = 25.26 dB corresponds to:\n",
-      "  . sigma = 18.3 q\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 corresponds to:\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"
@@ -539,20 +540,22 @@
     "# 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\")\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",
     "\n",
-    "# Assume signal with sigma = 16 q\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 corresponds to:\")\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"
@@ -584,9 +587,9 @@
      "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, 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"
      ]
     }
    ],
@@ -594,20 +597,20 @@
     "# Signal input power statistic for ADC / WG (AST)\n",
     "si_sigma = sigma_fs_sine\n",
     "si_sigma_bits = np.log2(si_sigma)\n",
-    "P_ast = (si_sigma)**2 * N_int\n",
-    "print(f\"SI sigma = {si_sigma:6.1f} q = {si_sigma_bits:4.1f} bits: P_ast = {P_ast:e},\\\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",
     "\n",
     "si_sigma = sigma_50dBFS\n",
     "si_sigma_bits = np.log2(si_sigma)\n",
-    "P_ast = (si_sigma)**2 * N_int\n",
-    "print(f\"SI sigma = {si_sigma:6.1f} q = {si_sigma_bits:4.1f} bits: P_ast = {P_ast:e},\\\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",
     "\n",
     "si_sigma = sigma_16q\n",
     "si_sigma_bits = np.log2(si_sigma)\n",
-    "P_ast = (si_sigma)**2 * N_int\n",
-    "print(f\"SI sigma = {si_sigma:6.1f} q = {si_sigma_bits:4.1f} bits: P_ast = {P_ast:e},\\\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\")"
    ]
   },
@@ -618,8 +621,8 @@
    "source": [
     "From measured P_ast and DC_ast to signal input sigma in q units:\n",
     "\n",
-    "* si_rms = sqrt(P_ast / N_int)\n",
-    "* si_mean = DC_ast / N_int\n",
+    "* si_rms = sqrt(P_ast / N_int_adc)\n",
+    "* si_mean = DC_ast / N_int_adc\n",
     "* si_sigma = sqrt(si_rms^2 - si_mean^2)"
    ]
   },
@@ -660,10 +663,10 @@
       "  si_ampl         si_sigma           sub_sigma =         SST\n",
       "                                      sub_ampl\n",
       "    value  #bits     value  #bits        value  #bits           value     dB  #bits\n",
-      "   8192.0   13.0    5792.6    4.0      65536.0   16.0    8.388608e+14  149.2   49.6, at 0 dBFS (= FS sine)\n",
-      "   2048.0   11.0    1448.2    4.0      16384.0   14.0    5.242880e+13  137.2   45.6, at -24.1 dBFS (= FS / 4)\n",
-      "     25.9    4.7      18.3    4.2        207.2    7.7    8.388608e+09   99.2   33.0, at -50 dBFS (= FS / 316)\n",
-      "     22.6    4.5      16.0    4.0        181.0    7.5    6.400000e+09   98.1   32.6, at -51.2 dBFS (= FS / 362)\n"
+      "   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",
+      "   2048.0   11.0    1448.2   10.5      32768.0   15.0    2.097152e+14  143.2   47.6, at -24.1 dBFS (= FS / 4)\n",
+      "     25.9    4.7      18.3    4.2        414.5    8.7    3.355443e+10  105.3   35.0, at -50 dBFS (= FS / 316)\n",
+      "     22.6    4.5      16.0    4.0        362.0    8.5    2.560000e+10  104.1   34.6, at -51.2 dBFS (= FS / 362)\n"
      ]
     }
    ],
@@ -673,8 +676,8 @@
     "si_ampl_fs = FS\n",
     "si_ampl_fs_bits = np.log2(si_ampl_fs)\n",
     "si_sigma_fs = si_ampl_fs / np.sqrt(2)\n",
-    "si_sigma_fs_bits = np.log2(si_sigma)\n",
-    "sub_ampl_fs = si_ampl_fs * G_subband_ampl  # subband amplitude for FS signal input sine\n",
+    "si_sigma_fs_bits = np.log2(si_sigma_fs)\n",
+    "sub_ampl_fs = si_ampl_fs * G_subband_sine  # subband amplitude for FS signal input sine\n",
     "sub_ampl_fs_bits = np.log2(sub_ampl_fs)\n",
     "SST_fs = sub_ampl_fs**2 * N_int_sub\n",
     "SST_fs_dB = 10 * np.log10(SST_fs)\n",
@@ -683,8 +686,8 @@
     "si_ampl_fs4 = FS / 4\n",
     "si_ampl_fs4_bits = np.log2(si_ampl_fs4)\n",
     "si_sigma_fs4 = si_ampl_fs4 / np.sqrt(2)\n",
-    "si_sigma_fs4_bits = np.log2(si_sigma)\n",
-    "sub_ampl_fs4 = si_ampl_fs4 * G_subband_ampl  # subband amplitude for FS signal input sine\n",
+    "si_sigma_fs4_bits = np.log2(si_sigma_fs4)\n",
+    "sub_ampl_fs4 = si_ampl_fs4 * G_subband_sine  # subband amplitude for FS signal input sine\n",
     "sub_ampl_fs4_bits = np.log2(sub_ampl_fs4)\n",
     "SST_fs4 = sub_ampl_fs4**2 * N_int_sub\n",
     "SST_fs4_dB = 10 * np.log10(SST_fs4)\n",
@@ -694,7 +697,7 @@
     "si_ampl_50dBFS_bits = np.log2(si_ampl_50dBFS)\n",
     "si_sigma_50dBFS = sigma_50dBFS\n",
     "si_sigma_50dBFS_bits = np.log2(si_sigma_50dBFS)\n",
-    "sub_ampl_50dBFS = si_ampl_50dBFS * G_subband_ampl  # subband amplitude -50dBFS signal input sine\n",
+    "sub_ampl_50dBFS = si_ampl_50dBFS * G_subband_sine  # subband amplitude -50dBFS signal input sine\n",
     "sub_ampl_50dBFS_bits = np.log2(sub_ampl_50dBFS)\n",
     "SST_50dBFS = sub_ampl_50dBFS**2 * N_int_sub\n",
     "SST_50dBFS_dB = 10 * np.log10(SST_50dBFS)\n",
@@ -704,7 +707,7 @@
     "si_ampl_s16q_bits = np.log2(si_ampl_s16q)\n",
     "si_sigma_s16q = sigma_16q\n",
     "si_sigma_s16q_bits = np.log2(sigma_16q)  # = 16\n",
-    "sub_ampl_s16q = si_ampl_s16q * G_subband_ampl  # subband amplitude for signal input sine with sigma = 16 q\n",
+    "sub_ampl_s16q = si_ampl_s16q * G_subband_sine  # subband amplitude for signal input sine with sigma = 16 q\n",
     "sub_ampl_s16q_bits = np.log2(sub_ampl_s16q)\n",
     "SST_s16q = sub_ampl_s16q**2 * N_int_sub\n",
     "SST_s16q_dB = 10 * np.log10(SST_s16q)\n",
@@ -752,8 +755,8 @@
       " si_sigma         sub_sigma         sub_sigma_re =         SST\n",
       "                                    sub_sigma_im\n",
       "    value  #bits      value  #bits         value  #bits           value     dB  #bits\n",
-      "   2048.0   11.0     1024.0   10.0         724.1    9.5    2.048000e+11  113.1   37.6\n",
-      "     16.0    4.0        8.0    3.0           5.7    2.5    1.250000e+07   71.0   23.6, at -51.2 dBFS\n"
+      "   2048.0   11.0     2048.0   11.0        1448.2   10.5    8.192000e+11  119.1   39.6\n",
+      "     16.0    4.0       16.0    4.0          11.3    3.5    5.000000e+07   77.0   25.6, at -51.2 dBFS\n"
      ]
     }
    ],
@@ -766,7 +769,7 @@
     "# si_std = FS / 4\n",
     "ni_sigma_fs4 = FS / 4\n",
     "ni_sigma_fs4_bits = np.log2(ni_sigma_fs4)\n",
-    "nsub_sigma_fs4 = ni_sigma_fs4 * G_subband_sigma\n",
+    "nsub_sigma_fs4 = ni_sigma_fs4 * G_subband_noise\n",
     "nsub_sigma_fs4_bits = np.log2(nsub_sigma_fs4)\n",
     "nsub_sigma_re_fs4 = nsub_sigma_fs4 / np.sqrt(N_complex)\n",
     "nsub_sigma_re_fs4_bits = np.log2(nsub_sigma_re_fs4)\n",
@@ -777,7 +780,7 @@
     "# si_std = 16\n",
     "ni_sigma_s16q = sigma_16q\n",
     "ni_sigma_s16q_bits = np.log2(ni_sigma_s16q)  # = 16\n",
-    "nsub_sigma_s16q = ni_sigma_s16q * G_subband_sigma\n",
+    "nsub_sigma_s16q = ni_sigma_s16q * G_subband_noise\n",
     "nsub_sigma_s16q_bits = np.log2(nsub_sigma_s16q)\n",
     "nsub_sigma_re_s16q = nsub_sigma_s16q / np.sqrt(N_complex)\n",
     "nsub_sigma_re_s16q_bits = np.log2(nsub_sigma_re_s16q)\n",
@@ -805,10 +808,10 @@
    "id": "d6c867ae",
    "metadata": {},
    "source": [
-    "Conclusion:\n",
-    "* For FS sine input the subband amplitude is 16 bits, so including the sign bit this fits in W_subband = 18b. The one spare bit is to fit 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 <= 0.5 FS\n",
-    "* For sigma = FS / 4 white noise input the subband sigma uses 10 bits, so 9.5 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 + 9.5 + 2 = 12.5 bits."
+    "Conclusion (for W_fsub_gain = 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."
    ]
   },
   {
@@ -821,20 +824,21 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "G_subband_ampl = 8.0 = 3.0 bits\n",
-      "G_subband_sigma = 0.5 = -1.0 bits\n",
+      "G_subband_sine = 16.0 = 4.0 bits\n",
+      "G_subband_noise = 1.0 = 0.0 bits\n",
       "\n",
-      "sub_SST = 5.242880e+13\n",
-      ". sub_power = 268435456.0\n",
-      ". sub_ampl = 16384.0\n",
-      ". si_ampl = 2048.0 (si_ampl_exp = 2048.0)\n",
+      "Calculate \n",
+      "sub_SST = 2.097152e+14 (= 143.2 dB)\n",
+      ". sub_power = 1073741824.0\n",
+      ". sub_ampl = 32768.0\n",
+      ". si_ampl = 2048.0 (si_ampl_exp = 2048.0) = FS/4\n",
       "\n",
-      "sub_SST = 2.048000e+11\n",
-      ". sub_power = 1048576.0\n",
-      ". sub_sigma = 1024.0\n",
-      ". sub_sigma_re = 724.0773439350246\n",
-      ". sub_sigma_im = 724.0773439350246\n",
-      ". si_sigma = 2048.0 (si_sigma_exp = 2048.0)\n"
+      "sub_SST = 8.192000e+11 (= 119.1 dB)\n",
+      ". sub_power = 4194304.0\n",
+      ". 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"
      ]
     }
    ],
@@ -843,9 +847,10 @@
     "# . depends on whether the input was a coherent sine like signal or a white noise like signal\n",
     "\n",
     "# Fsub gain implementation factors\n",
-    "print(f\"G_subband_ampl = {G_subband_ampl} = {np.log2(G_subband_ampl)} bits\")\n",
-    "print(f\"G_subband_sigma = {G_subband_sigma} = {np.log2(G_subband_sigma)} bits\")\n",
+    "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",
@@ -853,12 +858,12 @@
     "\n",
     "sub_power = sub_SST / N_int_sub\n",
     "sub_ampl = np.sqrt(sub_power)   # ampl real = ampl imag = std complex = sqrt(power complex)\n",
-    "si_ampl = sub_ampl / G_subband_ampl\n",
+    "si_ampl = sub_ampl / G_subband_sine\n",
     "\n",
-    "print(f\"sub_SST = {sub_SST:e}\")\n",
+    "print(f\"sub_SST = {sub_SST:e} (= {SST_fs4_dB:5.1f} dB)\")\n",
     "print(f\". sub_power = {sub_power}\")\n",
     "print(f\". sub_ampl = {sub_ampl}\")\n",
-    "print(f\". si_ampl = {si_ampl} (si_ampl_exp = {si_ampl_exp})\")\n",
+    "print(f\". si_ampl = {si_ampl} (si_ampl_exp = {si_ampl_exp}) = FS/4\")\n",
     "print()\n",
     "\n",
     "# Incoherent white noise signal: from SST to subband sigma and signal input sigma in q units:\n",
@@ -869,14 +874,14 @@
     "sub_sigma = np.sqrt(sub_power)   # std complex = sqrt(power)\n",
     "sub_sigma_re = sub_sigma / np.sqrt(N_complex)\n",
     "sub_sigma_im = sub_sigma / np.sqrt(N_complex)\n",
-    "si_sigma = sub_sigma / G_subband_sigma\n",
+    "si_sigma = sub_sigma / G_subband_noise\n",
     "\n",
-    "print(f\"sub_SST = {sub_SST:e}\")\n",
+    "print(f\"sub_SST = {sub_SST:e} (= {nSST_fs4_dB:5.1f} dB)\")\n",
     "print(f\". sub_power = {sub_power}\")\n",
     "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})\")"
+    "print(f\". si_sigma = {si_sigma} (si_sigma_exp = {si_sigma_exp}) = FS/4\")"
    ]
   },
   {
@@ -886,7 +891,7 @@
    "source": [
     "## 3.4 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 16b. Therefore the maximum sine input for no XST overflow is A = 0.5. If subband_weight = 1.0 then the auto correlations of the XST are eqaul 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_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."
    ]
   },
   {
@@ -912,29 +917,29 @@
       "N_ant  si_ampl         si_sigma       beamlet_sigma =         BST\n",
       "                                       beamlet_ampl\n",
       "         value  #bits     value  #bits        value  #bits           value     dB  #bits\n",
-      "    1   8192.0   13.0    5792.6    4.0      65536.0   16.0    8.388608e+14  149.2   49.6, at 0 dBFS (= FS sine)\n",
-      "   12   8192.0   13.0    5792.6    4.0     786432.0   19.6    1.207960e+17  170.8   56.7, at 0 dBFS (= FS sine)\n",
-      "   24   8192.0   13.0    5792.6    4.0    1572864.0   20.6    4.831838e+17  176.8   58.7, at 0 dBFS (= FS sine)\n",
-      "   48   8192.0   13.0    5792.6    4.0    3145728.0   21.6    1.932735e+18  182.9   60.7, at 0 dBFS (= FS sine)\n",
-      "   96   8192.0   13.0    5792.6    4.0    6291456.0   22.6    7.730941e+18  188.9   62.7, at 0 dBFS (= FS sine)\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",
+      "   24   8192.0   13.0    5792.6   12.5    3145728.0   21.6    1.932735e+18  182.9   60.7, at 0 dBFS (= FS sine)\n",
+      "   48   8192.0   13.0    5792.6   12.5    6291456.0   22.6    7.730941e+18  188.9   62.7, at 0 dBFS (= FS sine)\n",
+      "   96   8192.0   13.0    5792.6   12.5   12582912.0   23.6    3.092376e+19  194.9   64.7, at 0 dBFS (= FS sine)\n",
       "\n",
-      "    1   2048.0   11.0    1448.2    4.0      16384.0   14.0    5.242880e+13  137.2   45.6, at -24.1 dBFS (= FS / 4)\n",
-      "   12   2048.0   11.0    1448.2    4.0     196608.0   17.6    7.549747e+15  158.8   52.7, at -24.1 dBFS (= FS / 4)\n",
-      "   24   2048.0   11.0    1448.2    4.0     393216.0   18.6    3.019899e+16  164.8   54.7, at -24.1 dBFS (= FS / 4)\n",
-      "   48   2048.0   11.0    1448.2    4.0     786432.0   19.6    1.207960e+17  170.8   56.7, at -24.1 dBFS (= FS / 4)\n",
-      "   96   2048.0   11.0    1448.2    4.0    1572864.0   20.6    4.831838e+17  176.8   58.7, at -24.1 dBFS (= FS / 4)\n",
+      "    1   2048.0   11.0    1448.2   10.5      32768.0   15.0    2.097152e+14  143.2   47.6, at -24.1 dBFS (= FS / 4)\n",
+      "   12   2048.0   11.0    1448.2   10.5     393216.0   18.6    3.019899e+16  164.8   54.7, at -24.1 dBFS (= FS / 4)\n",
+      "   24   2048.0   11.0    1448.2   10.5     786432.0   19.6    1.207960e+17  170.8   56.7, at -24.1 dBFS (= FS / 4)\n",
+      "   48   2048.0   11.0    1448.2   10.5    1572864.0   20.6    4.831838e+17  176.8   58.7, at -24.1 dBFS (= FS / 4)\n",
+      "   96   2048.0   11.0    1448.2   10.5    3145728.0   21.6    1.932735e+18  182.9   60.7, at -24.1 dBFS (= FS / 4)\n",
       "\n",
-      "    1     25.9    4.7      18.3    4.2        207.2    7.7    8.388608e+09   99.2   33.0, at -50 dBFS (= FS / 316)\n",
-      "   12     25.9    4.7      18.3    4.2       2486.9   11.3    1.207960e+12  120.8   40.1, at -50 dBFS (= FS / 316)\n",
-      "   24     25.9    4.7      18.3    4.2       4973.8   12.3    4.831838e+12  126.8   42.1, at -50 dBFS (= FS / 316)\n",
-      "   48     25.9    4.7      18.3    4.2       9947.7   13.3    1.932735e+13  132.9   44.1, at -50 dBFS (= FS / 316)\n",
-      "   96     25.9    4.7      18.3    4.2      19895.3   14.3    7.730941e+13  138.9   46.1, at -50 dBFS (= FS / 316)\n",
+      "    1     25.9    4.7      18.3    4.2        414.5    8.7    3.355443e+10  105.3   35.0, at -50 dBFS (= FS / 316)\n",
+      "   12     25.9    4.7      18.3    4.2       4973.8   12.3    4.831838e+12  126.8   42.1, at -50 dBFS (= FS / 316)\n",
+      "   24     25.9    4.7      18.3    4.2       9947.7   13.3    1.932735e+13  132.9   44.1, at -50 dBFS (= FS / 316)\n",
+      "   48     25.9    4.7      18.3    4.2      19895.3   14.3    7.730941e+13  138.9   46.1, at -50 dBFS (= FS / 316)\n",
+      "   96     25.9    4.7      18.3    4.2      39790.7   15.3    3.092376e+14  144.9   48.1, at -50 dBFS (= FS / 316)\n",
       "\n",
-      "    1     22.6    4.5      16.0    4.0        181.0    7.5    6.400000e+09   98.1   32.6, at -51.2 dBFS (= FS / 362)\n",
-      "   12     22.6    4.5      16.0    4.0       2172.2   11.1    9.216000e+11  119.6   39.7, at -51.2 dBFS (= FS / 362)\n",
-      "   24     22.6    4.5      16.0    4.0       4344.5   12.1    3.686400e+12  125.7   41.7, at -51.2 dBFS (= FS / 362)\n",
-      "   48     22.6    4.5      16.0    4.0       8688.9   13.1    1.474560e+13  131.7   43.7, at -51.2 dBFS (= FS / 362)\n",
-      "   96     22.6    4.5      16.0    4.0      17377.9   14.1    5.898240e+13  137.7   45.7, at -51.2 dBFS (= FS / 362)\n"
+      "    1     22.6    4.5      16.0    4.0        362.0    8.5    2.560000e+10  104.1   34.6, at -51.2 dBFS (= FS / 362)\n",
+      "   12     22.6    4.5      16.0    4.0       4344.5   12.1    3.686400e+12  125.7   41.7, at -51.2 dBFS (= FS / 362)\n",
+      "   24     22.6    4.5      16.0    4.0       8688.9   13.1    1.474560e+13  131.7   43.7, at -51.2 dBFS (= FS / 362)\n",
+      "   48     22.6    4.5      16.0    4.0      17377.9   14.1    5.898240e+13  137.7   45.7, at -51.2 dBFS (= FS / 362)\n",
+      "   96     22.6    4.5      16.0    4.0      34755.7   15.1    2.359296e+14  143.7   47.7, at -51.2 dBFS (= FS / 362)\n"
      ]
     }
    ],
@@ -944,25 +949,25 @@
     "# . 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_ampl  # beamlet amplitude for FS signal input sine\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",
     "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_ampl  # beamlet amplitude for FS signal input sine\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",
     "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_ampl  # beamlet amplitude -50dBFS signal input sine\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",
     "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_ampl  # beamlet amplitude for signal input sine with sigma = 16 q\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",
     "BST_s16q_dB = 10 * np.log10(BST_s16q)\n",
@@ -1021,17 +1026,17 @@
       "N_ant  si_sigma     beamlet_sigma        beamlet_sigma_re =         BST\n",
       "                                         beamlet_sigma_im\n",
       "          value  #bits      value  #bits            value  #bits           value     dB  #bits\n",
-      "    1    2048.0   11.0     1024.0   10.0            724.1    9.5    2.048000e+11  113.1   37.6\n",
-      "   12    2048.0   11.0     3547.2   11.8           2508.3   11.3    2.457600e+12  123.9   41.2\n",
-      "   24    2048.0   11.0     5016.6   12.3           3547.2   11.8    4.915200e+12  126.9   42.2\n",
-      "   48    2048.0   11.0     7094.5   12.8           5016.6   12.3    9.830400e+12  129.9   43.2\n",
-      "   96    2048.0   11.0    10033.1   13.3           7094.5   12.8    1.966080e+13  132.9   44.2\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",
+      "   24    2048.0   11.0    10033.1   13.3           7094.5   12.8    1.966080e+13  132.9   44.2\n",
+      "   48    2048.0   11.0    14189.0   13.8          10033.1   13.3    3.932160e+13  135.9   45.2\n",
+      "   96    2048.0   11.0    20066.2   14.3          14189.0   13.8    7.864320e+13  139.0   46.2\n",
       "\n",
-      "    1      16.0    4.0        8.0    3.0              5.7    2.5    1.250000e+07   71.0   23.6, at -51.2 dBFS\n",
-      "   12      16.0    4.0       27.7    4.8             19.6    4.3    1.500000e+08   81.8   27.2, at -51.2 dBFS\n",
-      "   24      16.0    4.0       39.2    5.3             27.7    4.8    3.000000e+08   84.8   28.2, at -51.2 dBFS\n",
-      "   48      16.0    4.0       55.4    5.8             39.2    5.3    6.000000e+08   87.8   29.2, at -51.2 dBFS\n",
-      "   96      16.0    4.0       78.4    6.3             55.4    5.8    1.200000e+09   90.8   30.2, at -51.2 dBFS\n"
+      "    1      16.0    4.0       16.0    4.0             11.3    3.5    5.000000e+07   77.0   25.6, at -51.2 dBFS\n",
+      "   12      16.0    4.0       55.4    5.8             39.2    5.3    6.000000e+08   87.8   29.2, at -51.2 dBFS\n",
+      "   24      16.0    4.0       78.4    6.3             55.4    5.8    1.200000e+09   90.8   30.2, at -51.2 dBFS\n",
+      "   48      16.0    4.0      110.9    6.8             78.4    6.3    2.400000e+09   93.8   31.2, at -51.2 dBFS\n",
+      "   96      16.0    4.0      156.8    7.3            110.9    6.8    4.800000e+09   96.8   32.2, at -51.2 dBFS\n"
      ]
     }
    ],
@@ -1039,7 +1044,7 @@
     "# 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_sigma\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",
@@ -1048,7 +1053,7 @@
     "nBST_fs4_bits = np.log2(nBST_fs4)\n",
     "\n",
     "# si_std = 16\n",
-    "nbeamlet_sigma_s16q = ni_sigma_s16q * G_beamlet_sum_sigma\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",
@@ -1081,7 +1086,7 @@
    "id": "8829fd41",
    "metadata": {},
    "source": [
-    "If subband_weight = 1.0 and beamlet_weight = 1.0 and N_ant = 1 then the BST are eqaul to the SST"
+    "If subband_weight = 1.0 and beamlet_weight = 1.0 and N_ant = 1 then the BST are equal to the SST"
    ]
   },
   {
@@ -1091,7 +1096,7 @@
    "source": [
     "## 3.6 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. 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 the beamlet.\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."
    ]
@@ -1147,7 +1152,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 720x288 with 2 Axes>"
       ]
@@ -1356,20 +1361,20 @@
      "output_type": "stream",
      "text": [
       "noise sigma = 4.0000 (= 4.0000)\n",
-      "noise power = 16.0000 (= 16.0020)\n",
+      "noise power = 16.0000 (= 16.0342)\n",
       "\n",
       "N_fft = 1024\n",
       "sqrt(N_fft) = 32.0\n",
-      "sigma / std(Y_fft) = 32.005621\n",
-      "sigma / std(Y_rfft) = 32.014420\n",
+      "sigma / std(Y_fft) = 31.979117\n",
+      "sigma / std(Y_rfft) = 31.988146\n",
       "\n",
-      "noise bin std (fft) = 0.124978\n",
-      "noise bin std (rfft) = 0.124944\n",
-      "noise bin.re std = 0.089603\n",
-      "noise bin.im std = 0.087076\n",
-      "noise bin power = 0.015611\n",
-      "noise bin.re power + bin.im power = 0.015611\n",
-      "noise bins power = 15.985590 (= 16.002000)\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",
       "\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"
@@ -1425,16 +1430,6 @@
     "* 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"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "17aee663",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# DFT of square wave\n"
-   ]
   }
  ],
  "metadata": {
diff --git a/applications/lofar2/model/signal_statistics.ipynb b/applications/lofar2/model/signal_statistics.ipynb
index 0dc6b1215a19b10b8f58bd32eb7d516233e8556b..daea338f5256a4e615e15cfe0d07212a77322f2e 100644
--- a/applications/lofar2/model/signal_statistics.ipynb
+++ b/applications/lofar2/model/signal_statistics.ipynb
@@ -7,7 +7,7 @@
    "source": [
     "# Signal statistics for beamformer and correlator\n",
     "\n",
-    "Author: Eric Kooistra, Aug 2022\n",
+    "Author: Eric Kooistra, Aug - Dec 2022\n",
     "\n",
     "Purpose: Model the SNR of a beamformer and a correlator\n",
     "\n",
@@ -103,9 +103,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "mean(si) = -0.200259, expected -0.2\n",
+      "mean(si) = -0.199139, expected -0.2\n",
       "std(si) = 0.500000, expected 0.5\n",
-      "rms(si) = 0.538613, expected 0.538516\n"
+      "rms(si) = 0.538197, expected 0.538516\n"
      ]
     }
    ],
@@ -156,13 +156,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "id": "89845ec3",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -174,7 +174,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -286,13 +286,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "id": "8713e865",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -304,7 +304,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -316,7 +316,7 @@
     },
     {
      "data": {
-      "image/png": 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alaRTVf1QVVuranVVbaaqx/nq/qmqHVW1q6p+UpJ+istxHY/j4NYHh/k8eGTGWFmbISVf+DXwlYFRZVd8dAX9X+ofOq6ZXROAnfnhyqH7M92548s7SiyDYRgGxDcrvQN8CDRX1c6q2gloAfwPeLsMZCsz7p98P//+7t+AE/6ic6POfHvJtzSo0SDU5oaDbgDg+E7Hl4eIIWpmucohYuTwyx+/8M/J/ywPkQzDqITEUw6NVfUdv1lHVQtU9W2gUfpFSz8HtjyQ/evtz/hF43l95usAjF80PrBtp706AUVObskSay7iwOcPjHtevax6geWeckhmQnrO2jmhdKfJsGnXJiYsnpB0e8MwKi/xlMOPIvK0iBwkIi3d7SAReRr4uawETCcdGnbgyV5Psm74Or69+FsAjmh3RDlL5bAlPzhIXyyzUhD7Pb0fg98cnLDd5KWT2Zm3k9NHnc4xrx0TmKvCMIyqRTw/h2HAJcA9FDmirQDGApUu9vTBbQ5G74rOt9CvVT+mrExuwdSu/F0UFDoDrZ9XO/rTSz9aWsQyKxWXBesXcPjIw7m016XM+mMWALsLdpfKtQ3D2HOJt1ppN/CMu1VZfrj0BwCemvJUwrabczczfnG4WWrzrs2lKk9otVISI4dkWL3N8fyes25OKCJtYWzfRsMwqgjFWmIjIv8obUH2dP7Y/keZ9BMyK5XSyMFTMp7SARs5GIZR/MB7l5aqFHs4zWo345c/fkncsATsyt9F92e6c90n1wHOS31r7lZa/6c1Xy/7Ou65hVrIB3M+CBwReBPbntIBUw6GYcT3c9gSY9uKE2fJcGlep3mpmXmCOKf7OWzcuTFMAe3M38n01dNZuXUlt31xW1j71dtWh+Wrfvz7xzlt1GmhFVl+QsohqyYijlnJlINhGPFGDpuAzqpaL2KrC/xeNuLtGWRlJBfc9rLelxXr+t4ktJ+deTsDTUyz186mxSMtwuZIvln+DRA8Oe6da2YlwzD8xFMOrwJtY9S9mQZZ9hi+HPYlK29MLh5gtcxqof19Gu9TajKMnjc6pDR+/P3HUPncdXMBuP7T60NlCzc4UU5a1Y2Ofu7lt66ZVTM0IW3KwTCMmMpBVe9Q1cA1nKp6a/pEqvgMbD+QlnWTs6w9O+RZTuh8Qui4Sa0mKfc3e+1sWv4nuj//177HrDWzospmrJkBQHZmdlTd1tytUdeatmoaC9bHj0JrGEblJuGEtIj0Dtg6ikhFSxRUIendojddGnUJHfdrlXxiO88r+7sV3wXWB8VzunvS3TGvd9qo05i/fn7oeNXWVbwx642oa133yXV0eapL1PmGYVQdklmt9DROdrbngReA74B3gXkicmwaZatQeCah7k27l+g6b532Fi+f+HJg3bndzw07HtB2QKApqLhs2rWJq8ddHZpnGPjKwFA2vLzCvNCEdDx25u1k5G8jzfRkGJWcZJTDKqCXqvZR1QOBXsBi4BjgoXQKV5E4usPRzL1mLlf0uSJU9vopr/PxOR8DkJ0RbbIJom71upy+3+lR5U8c/wSPHPtIWFnN7JphYbxLgy+WfEGt/6sFwLLNy0Ll8fJi3/7F7UxbNQ2Ah799mFeWvsIzU6u0b6RhVHqSUQ5dVPVX70BVZwP7qOri9IlVMenauGvY8bkHnMugzoOA8OxxkVzb71oAzjvgvJhtTt331DBfA4CbDr4p6bmN4lAru1ZoP68wLzQh7Se/MJ//+/r/6PtCX+Qe4YeVjsf4jrwdaZPLMIzyJ5l5g19F5BmKwnSfBcwWkepA7M/NKsT5B5xPver1uHjMxYH1HRp2CIzb5Kd+jfpRTmptG7SNqxzGzh+burA+/Etk8wqDf8rc/Nyw448XOCMlC7FhGJWbZEYOF+JkfPuLuy12y/KA6Ow0VZAb+t/ARb2C8z43rtU47rkD2zmPsHZ27cD6lnViK4cbPr0hSQmjKdRCMjOKEhfFMivlFuQGlptyMIzKTcKRg6ruFJEngc8BBeapqvcm2ZZO4fZkDmlzCJ+c+wl1q9eN2+6z8z5jc+7mmJPB6TIrXT728rA5h/zC/EAZIkcO2RnZ5BXmocQfCRmGsWeTUDmIyADgFeA3QIA2InKBqn6VVsn2cDIkI6FiAMf3IN7ookXdFqUpVoiXfg6Puh7LrBS5Kik701EONnIwjMpNMnMOjwDHquo8ABHpArwFxE9lZsTE++r2e0/HIt7IoWntpqUWDTavIHhCOtKs5IUK8cduMgyj8pHMnEO2pxgAVHU+kNy6TSMux3YMdxMJiqGUztVKfvIK88LmIABGzx1N16fCV2h5Cs1GDoZRuUlGOUwTkRdFZIC7vQBMS7dglZl61esx5dIpvHP6O2Hl2ZnZUauaWtRJj1kpkgmLJ/Dbpt/Cyk5+5+Sodp4/x/2T7+fR7x4Nlb/9y9vIPcLijVVuhbNhVEqSUQ5XAbOB691ttltmlIC+rfqG+RnEonpW9aiyEzqfQFZGVrl8vf++rSgg742f3xjav3fSvQB0fKJjmctkGEbpk1A5qGquqv5HVU91t0dVNXh9o5EW8u8sCrU9pMsQejTrQX5hPht2bihHqYqY9Nsk5qybE7dNXkEeH83/qIwkMgyjpMSckBaRWRB7vaKqHpAWiYwo/EtMN+7cGNqvCHZ/uSd+PKbzPjiP0/Y9jW+Wf8Mj3z3CuHPGMem3Sdx35H0s3bSU1dtWc1jbw8pIWsMwkiXeaqUhZSZFJeGz8z6jVnYtDvvvYRzd4ei09HHfwPuYsHhCVHnzOs1ZvW11sa7ZtVFX5q2fl7hhMXhj1hu8MesN2jVoB8At429h9trZvPDTC2zc5Si6RN7jhmGUPfGUwzJNsF5RRCRRm6qEt/ooXS87QRjYfmCYcqiVXYsdeTsYM3QMg98czLod61K+bvM6zdOmHDwiJ7s9xWAYRsUk3pzDRBG5TkT29heKSDUROVJEXgEuSK94RiJ6Nu8JOEtRt+/eXqxrtG/YvhQlgl//+DVKGXgELdc1DKPiEU85HA8UAG+JyCoRmS0ii4EFwNnAY6o6sjidisjDIjJXRGaKyIci0sBXd5uILBSReSJyXHGuX9nxrxj64MwP+Mfh/6B/6/5c1DM4vlMinh78dGmJBsD+z+xP+8fbl7mj3Opdq5m9dnaZ9mkYlZV4aUJ3qerTqnoITi7po4DeqtpWVS9T1Z9L0O94YH93Uns+cBuAiOwHDAW64Sinp0UkM+ZVqihTV00N7Ter04x7Bt5DhmTw5OAnQ+XfXvxt0termV2TC3teyODOg0tVzqD4SwVaEFVWWomDzv7hbLo93a1UrmUYVZ1k/BxQ1TxV/V1VN5VGp6r6uap66zO/B1q7+ycBb7vLZ5fgRINNPq9mJeL8vc+PWedN7n53SXj6UH+qz1Q9q/970n8Zd844Zl01i7Fnjy2VeZOg1VQFhdHK4ehX0zN5bxhG8akIeaAvBjxX4VY4ysJjhVsWhYhcDlwO0KxZM3JycorV+bZt24p9brqYeMTEMLn8L9mcnByuaHIFp9Q9hV0Ld5GzMCfwGot/jvZU7tOwD9M2Rju3R95/HeqQsyoHQUoUffWBDx6IKtuybUtU2eRlk2P+BqpKIYVkpjCArGi/Z0X8PwYmV6pUNbnSphxEZALQPKDqdlUd7ba5HcgH3kj1+qr6PE5ea/r06aMDBgwolpw5OTkU99x04perUAvBjYGbUNZJzj8DBw4MnbP979vJlExmrplJvxejB2KxrpnxVUagGShZ7vz1zqiymrVqQkASOU8GVSW3IJcaWTUAuP+r+7lz4p1su20btasF57wIMSn8WhWFPeH/WEXC5EqNdMmVlFmpOKjq0aq6f8DmKYYLcXwpzvUth10JtPFdprVbZkDohZksl/W+jI4NO1IruxbVs6onFQXWj+d817xOkI4vHpH5ISJ5+NuHqfnPmqzdvpbFGxeHQouv2b6m1GQwDCMxMZWDiGwVkS2xtpJ0KiLHA8OBE1XV/x05BhgqItVFpD3QGZhSkr4qAxmSwSW9LuHri79O6bzn//w8C69fGDr2lEOz2s2SOt8L4f3hWR+m1G88lmxaErMuvzCfWyfcCsCw/w2j4xMdWb55OUDUMl1VZeaamaUml2EY4cRbrVRXVesBjwN/w7H9twZuBR4rYb9PAXWB8SIyXUSedfv8FRiFE9zvU+Aa1RLYNSoRL574Ir1b9E7Y7uR9Tub6ftcH1vlHDtMuSxxY1xs5JOOb8Nf+f03YJh6FWhhSBACfLvwUKFrddMCz4dFaXpv5Gj2e7cG4+eNK1K9hGMEkY1Y60V3SulVVt6jqMzirioqNqnZS1Taq2tPdrvTV/VNVO6pqV1X9pCT9VEU+POtDHh/0eGCdF+FVUVrXax3Yxo83coiMDNumZpuotoM6DUpV1DCu+/g6OjzRIWE7L9f1wg3OiGjKytQGlsPHD6fJw01SF9AwqhjJKIftInKuiGSKSIaInAsUzxXXKFe8XAyqmtT8gzdy8LK/ebSvXboe1QBPT0vsiPe/uf+j2v3VmLtuLg1rNASKwnAE+UoUamGUI97D3z5crBAjhlHVSEY5nAOcCaxxtzPcMmMPw3vZK0p2ZuJkfp7fRKRyaFYjuTmL0sbLGXH52MtDuSTen/M+P//+c5SvxMadG8m8N5MnfniizOU0jMpAMsqhlaqepKqNVbWJqp5MDN8Do2JTt1pdAK7qc1VoFBEPz6yUlZEV5hR3aftLubbvtekRMg4/r3ac8icvmxwqW7V1Fb2f7x1WBrBp1yYA/v3dv8tMPsOoTCSjHJ5Mssyo4NTMrknenXncM+CepEYO3kgjUpFUy6jGpb0vTYuMpYU32lmzzZbAGkZxiJfs52DgT0ATEbnRV1UPsHhHeyjeS9MbFQz/0/Ckz/HjD9Xx6bmfhtWlMz9EsnirnPIK89LWx4L1C2hVr1VS6V4NY08j3sihGlAHR4HU9W1bgNPTL5qRbvQu5cFjHoxZ7ymQzIzY3wL7N92f4zodx4EtDwyVHd3haPQuDYUTLw8SZclLJYve5KWTuWj0RWGT24VaSJenunDaqNOKLaNhVGRijhxUdRIwSURGqupSABHJAOqoaomc4Iw9A//oIJLImEt71dyLpwY9xbWfXBtabtp5r85MXz09nSIGMnbe2ISrsR7//nFEhBsOuoHZa2czddVUhnQZQuNajaPaHvnqkeQX5vPckOdC180vdOJGfrbws9K/AcOoACQTW+lfInIlTm6HqUA9EXlcVR9Or2hGefPskGe5+fObqVOtTsw23ugC4KDWBwHQpr7jB5GML0U6OPHtE8OOv1j8BdNWTePWQ28NlXmrnQD++pnjwDe482DGnRPbqc4/cvCUg2FUVpJRDvup6hbXv+ETHG/pHwFTDpWcofsPZej+QwPrvFGF397ep2UfZl45k86NOgMVJ+vb0a85y1xP3ufkqLofVv4Q2t+4Mzh1qacA/UEIg0KPG0ZlIpnVStkikg2cDIxR1TwoQRxno1LQrUk37jriLkadMSqsvHuz7qEAgTWzU1MOx3VMnPgv1eCBfvYZsU9UmX/uoV71eoHneau2/ArBRg5GZScZ5fAc8BtQG/hKRNriTEobVRgR4e4Bd7N3/b1jtkl1Fc/RHRIn/QlaOVUS/Kai+jXqB7YJGjmYcjAqOwmVg6o+oaqtVHWwG1p7GTAw/aIZFY3Pz/ucBdctSLp9qsohmfalbc7xjxwSmcFs5GBUJVLO56AO9pdRBTmm4zF02qtT0u2H9RjGsB7D4k5Mt6zbkot6XgQkl68ityB+PohU8a+6irW8NWRWChg5lCRTnmFUZNKW7McwamXX4pWTX+HBo2P7UkDRSzeVVKAAPZr1SKl9kPLxK4TXZr5GvX9FzzuEzEqFBWzYuQGIHjmoalSQvy+XfMnpo06PKjeMPYG4ysGNwvqnshLGqJyc0/0cci7ICSs7t/u5oX3PXJOZkckzJzyT1DXfP/N9/jf0fynJEZTkKHK0sHX31qg23sjhP9/9h0YPNWLxxsVR6VMz7s3gkjGXhJUNemMQ7895n92F0RFjDaOiE1c5qGohMKKMZDEqMZHOZed0dwL7Zmdkh40cruxzZdS5QZy676kpT04HzRMEzWHEMi+9N+c9AJZtXhZ4rf9O/2/gtfPNCmvsgSRjVvpCRE4T7/PJMIpBt6bd+O6S70LHnp9EZkZm2MgBYOF1C6MvEIAXZTZZIr/2AcYtiHZ6GzNvDOt3rGf4+OFs3rU5VL4rfxcAA18ZyJbcxAv2vP5MORh7Isl8el0B3AgUiMhOQHDmpYMXhRtGDPq37h/a94cDj5xz6LhXx6SuV79GfeZfO59VW1cx4JUBCdsnu8LolHdOQRAUZWvu1pCsO/N2htos27wsqWsBZlYy9kiSWcpaV1UzVDVbVev5cksbRrHxJxI6psMxAOzTONpJLRGdG3XmiHZHsH74+oRtU8kA561C2rp7K9vznMSHO/OLlIM/bEgibNmrsSeSUDmIw3kicqd73EZE+qVfNKMy409BesWBV7Dm5jV0a9qt2Nfz0oaWNt+tKDKF+VOR+k1U/tVI01ZNi7pGnqYvbLhhpItk5hyeBg6mKDXoNmyS2igm3Zt25/bDbi+ac5BMRISmtZuW6LoiwoTDJ1D4j+RDcSdDrLmF3Pwif4uXf345tN/3hb48+HX40l0bORh7Iskoh4NU9RpgF4CqbsTJ9WAYKTPzqpncf+T9YXMOQZzY9UQePe7RqPLnhzzPm6e+GXiOp2imXja11OT1zzP48TvjzVk3J6zub1/8LUwh2MjB2BNJZkI6T0QycYPtiUgToHQ/z4wqh9+sFMTooaOBonDaHpcdeFnCa/dp2aeE0hXhzTdE4q1cAlizPToVqX+Vk40cjD2RZEYOTwAfAk1F5J/A18D/pVUqo9KTaORQWkQ635UWfrPSii0rouo9T2qANbnhymPllpUMHz880MdiwuIJlvfaqBAk/MtU1TdE5EfgKJxlrCer6pwEpxlGXLwJ3WSVw8wrZ1K3emp+DZB62PBk8Y8c/IrAY+OuotwQ9825j3u5l/oP1OeQNocA8MnCTxjSZQiHtz2czxd9zu6C3ZzQ+QSOee0YOu/VmfnXzU+L3IaRLAn/MkXkPuArYKSqBo+xDSNFPFNLvPzUfro3616sfqpnVi/WeYnwzE01s2oyc83MqPqDXjwoqmxL7hY+WfgJA9oNAIqewXGvO3ksvCW9CzYkH/nW48qPrmTOujlMunBSyucaRhDJmJUWA2cD00Rkiog8IiInpVkuo5LjmVTSbVYKUj6X976ctvXblui63y7/Fgj3fYjH3HVzQ/veSq3IuYjxi8cXW57nfnyOr5Z+VezzDSOSZJzg/quqF+PkcHgdOMP91zCKTWjkkGIk1lTxXsTJcla3szh7/7MTtpu01PlCTzYz3b4j9g3te/Mt23enbyC+dvta5B7hjZlvpK0Po3KTjBPciyLyLfAMjhnqdCA9HkdGlcFTDmkfOUhmlOe1ojGD62VmZHLavqclff32DdqnLNMXS74AYvtQlAbz1s8D4OlpT6etD6Nyk8xnVSMgE9gEbADWlTTZj4jcJyIzRWS6iHwuIi3dchGRJ0RkoVvfuyT9GBWXdCsHL6ubiDDnmjmc0PmEsPpYSXoESUmmj875qNgyzlgzw3I9GBWWZMxKp6jqQcBDQANgoohEr91LjYdV9QBV7Ql8BPzDLR8EdHa3y3FGK0YlJN3K4d6B9wJQr3q9sP48Yo0cMiQj6UnyUaePotNenWhSq0nMNvHMWmPnj2Xb7m0J+9lvxH5c+VFyocwNo7RIxqw0REQeBF7GidD6JUUv82Khqv7xdG0IfcadBLzqpiL9HmggIi1K0pdRMUl1KWuq3Pynm9n8t800r9M8rD+PWF/sIpL013y/Vk6IsYY1Y1tZPeUUxMINCwNjMfnZuHMjc9bN4bkfn0tKpljsLthNXoF5ahvJk8xf5vHAZOBxVV1VWh27DnXDgM04k90ArYDlvmYr3LLfA86/HGd0QbNmzcjJySmWHNu2bSv2uemkosoFpSPb4rWLAdi9cXfcaz3U/SHyNT+p/uLJ1TSvKHbTqlWryN0dnIt69erVzMqflbCvDrU7sGT6EpawhMzdsUca1TX+UtqHPn8osPy9z97jw5Uf0qJm0bdRMs/AazNrs3MPmzdvJicnh0GTB1EtoxqjDxkdaltR/4+ZXKmRLrmScYK7VkSaAX3dOYApqvpHovNEZALQPKDqdlUdraq3A7eLyG3AtcBdqQiuqs8DzwP06dNHBwwYkMrpIXJycijuuemkosoFpSPb4Xo4TaY14cKeF8Z1VBtA8v3Ek+vQww+l/if1eWbaM7Ro0YKuWV3DIq56tGzRku77dIdf4/fVuEHjUF9tV7ZlztY5tGvQjt82/Rberl5j1qyN7fG8IDfYp+GRFY/w/Yrvw8riPvNJ4W2ylmXBdKhfvz4DBgxg16Rd7CrcFXaNivp/zORKjXTJlYxZ6QxgCs4S1jOBH0Tk9ETnqerRqrp/wDY6oukbgLc8ZCXQxlfX2i0zKhkZksFVfa9KmwdzJFkZWfRs3jN07MVuiiTZPA01smqE9mtn1waCHe787fx4IcaXbl4aWL9ow6Kosnd/fZcfVvzARaMv4t/f/juufJ5pLJW8E+D4n6zetjqlc4zKSTKrle4A+qrqBao6DOgH3FmSTkWks+/wJMDzEBoDDHNXLfUHNqtqlEnJMIqD/0XZpHb0JHKTWk34a/+/RpUH4X/pexPYQRPZseZUPP+IWEH51u+MTl505ntn0v+l/oycPpJbxt8SeN6Z754ZFbMpyJ/ix1U/8sLiF6LK7/jyDlo80iJufKdJv01C7pFAz3Cj8pCMcsiIMCOtT/K8eDwgIr+IyEzgWOAGt/xjHI/shcALwNUl7McwQuzbxHFEO6h1dGgLgD9u+YPuzbqHLXNde8vawLbPDSmaIPZWJAWtgDq49cHFkjXWaqpEvDv7XVZvWx26h2+Wf0Odf9WJatfnhT68ufzNKEUyep4zooqXNe+92e8B0OPZHill1zP2LJJ5yX8qIp+JyIUiciEwDuclXmxU9TTXxHSAqv5ZVVe65aqq16hqR1Xtrqrxl3IYRgocuvehLLhuAZf0ugRwckMEmZf8q5WClqLWyq5FuwbtQsfeiKRutfDAgC3rtuSCnhcEypKdmZ2y/JHEWga7u2B3YMRXgCkrp9D7uSL3obzC8BVMnlLxQqoH4T/n599/TlpeY88imQnpW0TkVOBQt+h5Vf0wvWIZRnrotFen0H4yuSGCwntE2vE9BfLnLn9m6qqp1MquxY68Hdw74F5qZdcKvK4/qmtxmb9+Pr1bRPuJ7srfxa9rg2fUIwMC5hXkhZnIkpmrsCWxVYNkzUPf4qyHmAhEL/EwjEqE/6s+aB4h8qvaa7N3/b3JvzOfod2GAs5XuOepHUmsDHOpcPBLB9Pj2R5R5dvztnPdJ9cldY28wjxUNZQf2xs5fLrw05ijj5enF6VFjTfCMPZsklmtdCnOaqVTcOIqfS8iF6dbMMMoL47reFxoP2jkEGlq8s85ZGZkhr0wY61WSjaaazx2F+xm5pqZ9HuhX1h5KgH98gryuOPLO6h+f3V25u0MKYkbP7+Rh74J9sEwqgbJjBxuAXqp6oWqegFwIHBresUyjPLDP1oIGjkM6jQo7NgzwUROIqtqzKW6F/W8qKRihpi6KjxndiqTxHmFeTz/0/MAvDbztTA/jVimKT+pLpU19hySUQ7rga2+461umWFUejIlk223beOqPlcBcHWfqxl58siwNt7IwTPJ9G/dH4CujbsGmpW2/G1L2Gqn1vVal6rMp7+b0A0pRF5BXkj+sfPHhtWVxujG2HNJJnzGQhzHt9E4MZBOAmaKyI0AqvqfNMpnGGmnb8u+McNnZ0gGtavVDpmHOu7VMcpUFLmU9ZJel3BE2yPo3KgzQUSmO41leioL8gqLlEOkE583aZ6bn8uD3zzILX+6pcycFo3yJxnlsMjdPLy1f6kn9DWMCsiUy6bErPPmD+IF4/PmJTzlICJhiqFNzTYs3+mEDLvhoBuizk8lIVHdanXZurtoIN+sdjPWbI/tsJaImWtmhuSPXF7rTZq/MuMV7sq5i7yCPO478r5i92XsWSSzlPWeshDEMCoyofX/ATZ2T4HEclx7td+rXDzjYpZsWsI1fa+JvnYKOR3OO+A8nplWFMm+TrU6JVIOZ7x7Bm3qORFrIr25PbOSN6JYvGlxsfsx9jxK6ulsGJWSUaePYnDnwaHj0Pr/gKWb8TykPbzz/OfPuHIGI08aGeaRfVLXk7isd2z/i17Ne7HsL8tCx6Vh5vHkf2vWW2Hl23dv5+MFRf6uQTGXiuvJbVR8TDkYRgBndDuDceeMCx3HGzkkpRyINk8d0OwALuh5QVhZZkYmz//5+ZjXUTQsf0QsP4pU8IL/Rea8mPXHLE5484TQaqYg5RB5jlF5MOVgGElQWiOHoPSk/jmHoPmHTMmkb8u+oT68oH1QNpPZnlJYvW111D3GChxo7Pkk4wTXRET+LiLPi8jL3lYWwhlGReHiXo7f55AuQ6LqIiekU2Xs2WNpUadF2LX8XNLrEg5scSDgKKnsjKKJ47JQDos3OnMNO/N2Mnvt7LA6z4t6xuoZTFkZPrG/adcmTh91ugXn20NJZuQwGqgPTMAJuudthlFl6NWiF3qX0qFhh6i6RBPSUOR13aBGg6i6ro278tAxjjdypNPd2lvWMuKEEWF9iEhIQZTl0tKd+Tvp/kz3sLKT3zmZvII8ej7XMypu09NTn+b9Oe/zyLePlJmMRumRjHKopaq3quooVX3f29IumWHsIXgT14e3PTxmm0ePe5RF1y+iae2mgfXeF3jkyKFxrcZkZWRFOdp5piX/nEOt7FqcvM/JoWNvtOEvSwdHjDwisNxviisoLODhbx5mR96OtMpilB7JKIePRGRw4maGUTU5sv2R5N+ZT79W/WK2yc7MDhx1eHgTu97IoU29NlzQoyjcd2SIDi/aq3/kUFBYEFqWCnDjwTeidykndD4h1VsC4PPzPqdl3ZYJ2wWlW4UiRbZ8y3JG/TqK4ROGc+eX4XnCPpjzAXKPBGa2yyvIY966ecWQ3CgNklEON+AoiJ0iskVEtopIsDupYVRRgmIwpYI359C1UVcAlv11WViYjr1q7gU4fg1QpBRqZBbNORRoAcMPGR51bf8Edix6NIuO7nrI3ocUazXU1JVT2bBzQ2jk8PrM13nzlzcBZx7Cz105Tur4oMx2N31+E/uM2IcVW1akLINRcpJxgjNPaMNIM4M6D2L8+eMZ2G5gYP1th91Gw5oNQ6OJZZsdX4cZa2aE2hQUFtC6Xmsu7HkhI6ePDDm1BeW2BujdoDcHdz6YEVNHUD0ruk12RnbKcxoZ92SgKD2a9eDUfU8NlX80/6OUrgMwaekkwAkkWNrxp4zEJBM+AxFpCHQGQp8pqvpVuoQyjKrI0R2OjllXI6sGf+n/l6hyfxRVz4zz72P+TeOajTlln1NC5wahFK188q+A8sjKyEp5NZQnw4w1M0o81+Ept1h5JYz0klA5uPkcbgBaA9OB/jgJf45Mq2SGYSQkyJzVqFYjHj724dBx0KgA3GWxbjylyNAZ4Ewkl8TJLigsSJCfh8dLP73E79t+51A36aQ3Od/nhT4M3X8o01ZN49HjHg1cTmyUPsnOOfQFlqrqQKAXsCmdQhmGkRzeXEQ8Ys05FFIYUgr1a9QPq2tVtxUQrDSSJZ4iAGdy3a9ALh17KXdOLJqw9iu+t395m4UbFnLVuKuKLY+RGskoh12qugtARKqr6lyga3rFMgwjGUYPHZ2wTZDJyMN7+Xds2DFUtu22bSy4bkGJZVu0cVFUmV9hZN6bGTehUJBDYCITk6pyx5d3MGP1jLjtjMQkoxxWiEgD4H/AeDevw9J0CmUYRnwGtBsAQLsG7UJlH50dPOkbGYrbo1AL2ZrrhP/2L1mtXa12qTjXvTnrzaiyeevmsWTjkrjnnfvDuVwy+pJAk1mko+GKLSvYvGtz6F5yC3L55+R/csjLh5RMeCOp1UqnuLt3i8hEHG/pT9MqlWEYcfli2BdRNv0TugT7M8QaOSgaCstdO7t26QoYg+9WfEeHJzow88qZMdus2rWKl6e/HLhyK1I5tHm0DR0aduC0fU/j4W8f5o+b/wCcJEa5+blkZmSWyDRWlUkq8J6IHCoiF6nqJJzJ6FbpFcswjHhkSEbSvhX+l6PfIa5LnS6hbG9lnY3ugGcPSNhmwYZo09aGnRt4eurTYQH/Fm9czOszXwdg466NgKNEavyzBse8dkwpSVz1SGa10l1AH5x5hv8C2cDrgI3bDKMC0KFhB87Y74yY9Z5ZaZ/G+4RWLt16yK0clXEUL290YmjGWtFUngQ5vxVoAdd8fA278nfRaa9OUfWe0vBGGDm/5aRVxspMMuOtU3BWKP0EoKqrRMQc4wyjgrDo+uiJXz/1qtcDoH2D9lxx4BV8MOcDrul7DYt+XhRKBVojqwYTzp/Awg0Lw871JpB7Nu/J9NXTS1/4YrJuxzpu+vym0LEXmHB3wW4gtex6RjDJKIfdqqoiogAiUjbGScMwSoXW9Voz9uyxHLr3oTSo0QC9y3lxLmJRmFnpqA5HcVSHo8LO9V6yw/80nHM+OKdsBY9DZNIl79i7n0TLaI3EJDPnMEpEngMaiMhlOKG7X0ivWIZhlCZDugwJDBf+pzZ/ApxRRRDel3jd6hXLWPDL2l/Cjr2RgzcSimTQG4OQewS5Rzj1nVMD2xjhJFQOqvpv4D3gfZx5h3+o6pOl0bmI3CQiKiKN3WMRkSdEZKGIzBSR3qXRj2EYwdx+2O3Mu3Ye+zbZN7DeUw5ewL+KwqINRaa0JrWahEYO3uorP1NXTuXThUULLD+c+2H6BawEJLXGS1XHi8gPXnsR2UtVN5SkYxFpAxwLLPMVD8KJ4dQZOAh4xv3XMIw0kJmRSZdGXWLW5xbkAvGVw5Htj+SgVgfxr6//VeryxcLvPFegBaGRg18JePR7MXYodSM2yaQJvUJEVgMzgWnAj+6/JeVRYDiEGQdPAl5Vh+9xTFktSqEvwzCKQW6+oxwi/SB+vPxHRp0+CnCivnoJjxJxy5+iQ3OXlPzC/NDI4ckppWLUMEhu5HAzsL+qlloiWBE5CVipqjMiEra3Apb7jle4Zb8HXONy4HKAZs2akZOTUyxZtm3bVuxz00lFlQsqrmwmV2okI9eW7U7qlunTpoeVr5+znoUbnZVNGzZs4JfpRXMAHx/6MYO/DlYWh8vhPMzDgXXFZXfebnIlN6VzRoweQaPqjWheo3lY+Xsr3mPEohF8efiXRLybUv4dX17yMj9u/JERvUekJFuqpOv/VzLKYRGQcm4/EZkANA+ouh34O45Jqdio6vPA8wB9+vTRAQMGFOs6OTk5FPfcdFJR5YKKK5vJlRrJyNV8bnPWrFnDwMMGwhSnbEiXIRw98Gharm3J33/5O2cceAZXHHwFjy17jLU71nL8kcfD18HXO+qIo+Cb0r0PMqBmjZqwK/lTrp1+LUBo5ZbH0fc6YdP/dNifonw/Uv0dB05yPLzT/dun6/9XMsrhNuBbd84hpJ5V9fp4J6lqYHB6EekOtAe8UUNr4CcR6QesBNr4mrd2ywzDKAfGnj2Wj+Z/RPM6zcPKALo17cbSvyylTb02iAhzr52b8HrpCGWxK38XSzbFj9eULFkZWRQUFLArf1eFdAwsS5JZyvoc8CXwPc58g7cVC1WdpapNVbWdqrbDMR31VtXVwBhgmLtqqT+wWVWjTEqGYZQNbeq34aq+scNk711/7yjzC8CwHsM4qetJUeUlTaeaTt799d3QBLz3bxByj3Ddx9eVlVjlRjJqPFtVb0y7JA4fA4OBhTimrIvKqF/DMEqRV05+BVUl496i78+vLvyKDEkqnFuZMX31dHo27wnAA988ECr3JuJj8dTUp3hycOWe/E7ml/pERC4XkRYispe3lZYA7ghinbuvqnqNqnZU1e6qWhqrogzDKAciRxSRCYUqAr2e6xXa9ydF8jytI4nMJ7F993Ye+/6xqGixfhasX8CIKemdlE4HyYwcznb/vc1XpkCH0hfHMIzKSMeGHem8V+dSv26z2s1Ys31Nia+zeONifvr9p9BxbkEuBYUFbM7dHJZtL68wL+y827+8ncd/eJy96+/NqfsGe14f8vIhrN2xlssPvDxmbo2KSDIe0u0DNlMMhmEkzcLrFxY7gdDNB98csy4yFlRx6fhEx5A3ODhmpeHjh9PooUahhEgAeQVFymHSb5NCIcK37d4W89qbdm0CCAszvidQsQyAhmFUSfq07MMRbY8IrLvziDsDy8FxwEsHuQW5/Of7/wCwOXdzqNyvQAa8MoBx88cB0UmI/HjzLJGjjoqOKQfDMJJi3S3rWH3T6lK7Xv/W/UP7nffqHArRcUWHK8La1atejx7NeoSOvTwOHRt2ZO/6e4fKOzQsnkFjyJtDosqWby7yxd2RV+TmFfmCX79zPeAoh7sm3sXYeWOjrhVSDgWmHAzDqIQ0qtWIZnWaldr1vrvku1Au7Bf+/ELo67tVzehEk94S2CmXTuHc7ucCcFzH48LyNvRrVbwYSuMWjIsq25K7JbTvNyv5Rw5+Fm1YxL1f3ctZ750FwG+bfgvV7akjh6Q8UkTkROBw93CSqkarR8MwjAD8q4AimXjBxNC+pxyyJPq1lCmZoTbefnZmdpg5pzS/zP2T0xt2bgiNHmL18cd2J3e1FxXWvwpqTx05JJMm9F9AP+ANt+h6ETlYVf+eVskMw9jjmXD+hMB0nkF4L3rv5e/H86wu0ILQyzZTMsOS+sRzXEuVZ398NrR/7OtOpJ+JR0yM+fXv73vdjnWhSWgoWtJbGUcOJwA9VZ1fTkReAX7GiY9kGIYRk1RWE/25y58Zv3g8rWu2jqrzzEr5hfmh/cyMzDCzUiLHNY8Tu57ImHljkpbL470V71EtN3gU5PeLOOrV8Hv2IsbuaSOHZOccGvj2K54ni2EYezzX9ruWjbdupGXNlqGyaZc5frDeaKKgsKDI/JSRFWZW6t60OwDdmnSL209x80uPWDSCR79/NLDOP3KYuWZmeH/u6GZPGzkkoxz+BfwsIiPdUcOPwP+lVyzDMKoaIhKVyvTAlgcChHwkFA35C0SalY7rdBxLbljCL1f/Qs2s2D4V8ZadFpdY6UmhaHK70o0cVPUtoD/wAU6q0INV9e10C2YYhuHx0okvcWP/Gzmi7RGhEBaZGZlhL/rD9j6Mdg3aAfEdzgq0IGZdcdmetz1hm0o3chCRL1T1d1Ud426rReSLshDOMIyqi39iumXdljxy3CNkZmSGXu6ZUjTn8ODRD4Z5YMdLfZqOkcO3y79N2ObDOcG5q1duWcmW3C2Mmz+Obk93qzAjjJgT0iJSA6gFNBaRhoAXRaseTnY2wzCMtPDpuZ/GfMF7o4KsjKyQWUkID/I3YdgEPpjzAbvyd3HT5zeF1aVDOXjEi/X0wDcP8K+jo/Nst360NW3rtyW3IJfV21azdsdaWtZtGXCFsiXeyOEKnPmFfYCfKMrjMBp4Kv2iGYZRVTmu03G0b9g+sC5MObgjh8hQ4M3rNOfqvldzdIfonGPexHUQJ3Q+gX8f8+/iik3Dmg1Tav/lki8BWLp5aUjBFXfCvLSJqRxU9XFVbQ/cHBF0r4eqmnIwDKNc8M85NK3dFIDGtRoHto30mdjyty3s33T/mNc+q9tZnLRPdJKiIO4beF9UWcMa8ZXDqzNeZc22opFF5LJXIGySvTxJxs9hs4gMiyxU1VfTII9hGEZcvDmHrIwsrj/oelrUbcF5B5wX2NY/ohgzdAx1q9eNMkH5ERGyM5ILq92iTouoskQjhwv+dwH9WvXjh0t/COwbKk701mSWsvb1bYcBdwMnplEmwzAqMW+f9jbvnvFusc/3L2XNyshiWI9hMTPMeeWt67Xmz13/DBS9hPdrsh/gvOS9eE0QP9yHn3rV6/HX/n8NK4s3cvDycK/csjKwftXWVYATv2n55uV8sfgLNu/aHNZm/KLxyD3Cwg0Lk5KxJCQcOahqWLJUEWkA2FJWwzCKxVn7n1Wi8/1mpUQEtRm6/1C+WfYNV/S5gr4v9I3KWJeKcvjPcf8Jc4xLZFby2HfEvmEKyc+OvB10fapr6LjgH07IkHHzxzF8wnAAvl72ddJhSYpLUoH3ItgOBM8UGYZhpBn/UtZEBI0oamTV4IUTX2DZ5mVA+EonVU06W1vd6nWjyuKZlbzorooyd91c7pwYnKdi7fa1Ycd5BXlUz6rOkLeKQouXhekpmcB7YyE0Q5IB7AcUf0xoGIZRAvxzDomIZW7y10V6Zacycogk3sjBc5RLFAPKi/DqMWHxBAZ3HhxWlleQx+6C3UnLWhySGTn413XlA0tVdUWa5DEMw4hLaM4hCbNSvMnnVnVb8cBRD3DW/mdxx5d3OO1TmJCuXz06zFykogli6+6tcevP+zB8ct0/YvC48fMbufrjq8m/M30jiGTCZ0zybd8A7URkRNokMgzDiMMNB91AzayaHNvx2IRtvfmEICUhItx66K2hkBseySgdcJIf+enetDu1smslPC9WwqBU8KLAJlI0JSGpqKwi0ktEHhaR34D7gLlpk8gwDCMOfVr2YcftO0Krf+JRXIeyXs178dKJL8Vt4ymCfxz+DwCqZ1WnelZ6clrHouGDqTndpUJM5SAiXUTkLhGZCzwJLANEVQeq6pNpk8gwDKOc+emKn7i418XUyKqRsO0JXU4I7VfPLFvlkE7ijRzmAkcCQ1T1UFchlH44Q8MwjHImllfyrKtm8crJr0SVH7r3oYHty3rkkE7iTUifCgwFJorIpzi+DbFndwzDMCookb4MMdtFvOI67dWJTnt1Iisji3WL1nHDjBsAmHzR5MBzIlcPrb5pNc0fSWz+qojEi630P1UdihN4byLwF6CpiDwjIolnggzDMMqZOtXqALG/9D3qVXOWpcb68j+n+zm0rd02YX+RZqVmdZolI2aFJBkP6e3Am8CbbujuM4Bbgc/TLJthGEaJaFSrEbOumpXQm/ihYx5i7/p7c8o+p8RsUyszeCWS3yQVpFyW/WUZhVpIu8fbhcoyJbPUkg6lI3kRJJ9DGgBV3aiqz6tq8lnDDcMwypH9m+6fcGK5bvW63HbYbXGXscbyf2hdrzUAp+xzSuCEdJv6bdi7/t5hZZ75KRm/iES8uOTFEl8jiJSUQ2khIneLyEoRme5ug311t4nIQhGZJyLHlYd8hmEYydKybks2DN/AbYfeFtMsJSLcP/D+0LHX7oGjHogZbjxZMtL0Gi8X5eDyqKr2dLePAURkP5xJ8G7A8cDTIkkEUDEMwygDbjjoBt489c2o8oY1GyIicZey3n747dTMclKZeiOH6lnVWbdjXYlkSibGVHEoTuC9dHIS8Laq5gJLRGQh0A/4rnzFMgzDgMeOfyxufaKlrF48J085VMusRoMaDdi0a1NpiFeqlKdyuNZNIjQNuElVN+Lkpv7e12YFMfJVi8jlwOUAzZo1Iycnp1hCbNu2rdjnppOKKhdUXNlMrtQwuVIjGbl2FuwMO45sX1jo5K8u3O38O3/ufJ7o/gTvLH+HcavHhdoNaj6IQc0Hcf306xPKlZeXl5bnlTblICITgKAFvrcDz+CE4VD330eAi1O5vqo+DzwP0KdPHx0wYECx5MzJyaG456aTiioXVFzZTK7UMLlSIxm58gvz4eui48j22d9ns7NgJ/Xr1GfVrlV0268bZ3Q7g7ZL2zJuZJFyaN2yNdf9+ToG9B/AAc8eADhpSYPCfFerVi0tzyttykFVozN7ByAiLwAfuYcrgTa+6tZumWEYRoUnURhxz2HOMysVqjOCiFy15M0jdG/WPVR29v5nByqHeJFnS0J5rVbyJ189BfjF3R8DDBWR6iLSHugMTClr+QzDMNJB5JyD5yMRGc01aEltrOW46VIO5TXn8JCI9MQxK/0GXAGgqr+KyChgNk7uiGtU0+ThYRiGkUZGDI7ObOCF8YgcObSs2xKALo26MH/9/MAVSMkEASxNymXkoKrnq2p3VT1AVU9U1d99df9U1Y6q2lVVPykP+QzDMErK1X2vjiqLGjlo0chB71Iu7XUpEGyeqhLKwTAMoyoSOecQiRcD6vhOx0fVxXOwSwemHAzDMMqIprWbAvDIsY9wee/LOX2/08PqD25zMDtv38kxHY+JOtc/mlh43UKG9RgGVL45B8MwjCrHJ+d+wuh5o+nWtBvP/fm5wDbJmI867tWR1nWdmE6VarWSYRhGVaRN/TZc2+/aEl3j2r7O+bESFJUWNnIwDMPYQ9C70qsQ/JhyMAzDKEXGnj02baYeP95Kp3RNSJtyMAzDKEWGdBlSJv14ZiWbkDYMw6iCLP/rcrbmbo0q90YO6cKUg2EYRgXGyzQXSbpHDrZayTAMw4jClINhGMYeSLrNSqYcDMMw9kBCZiULn2EYhmFEYnMOhmEYRggveF9QeO/SwFYrGYZh7IH8/bC/k1+Yz9GZSSXdTBkbORiGYeyB1KlWh4eOeYhqGcHhv0uKKQfDMAwjClMOhmEYRhSmHAzDMIwoTDkYhmEYUZhyMAzDMKIw5WAYhmFEYcrBMAzDiMKUg2EYhhGFpDuyX1kgImuBpcU8vTGwrhTFKS0qqlxQcWUzuVLD5EqNyihXW1VtElRRKZRDSRCRaarap7zliKSiygUVVzaTKzVMrtSoanKZWckwDMOIwpSDYRiGEYUpB3i+vAWIQUWVCyqubCZXaphcqVGl5Krycw6GYRhGNDZyMAzDMKIw5WAYhmFEUWWUg4i8LCJ/iMgvMepFRJ4QkYUiMlNEelcQuQaIyGYRme5u/ygDmdqIyEQRmS0iv4rIDQFtyvx5JSlXeTyvGiIyRURmuHLdE9Cmuoi84z6vH0SkXQWR60IRWet7XpemWy5f35ki8rOIfBRQV+bPK0m5yvN5/SYis9x+pwXUl+7fpKpWiQ04HOgN/BKjfjDwCSBAf+CHCiLXAOCjMn5WLYDe7n5dYD6wX3k/ryTlKo/nJUAddz8b+AHoH9HmauBZd38o8E4FketC4KmyfF6+vm8E3gz6vcrjeSUpV3k+r9+AxnHqS/VvssqMHFT1K2BDnCYnAa+qw/dAAxFpUQHkKnNU9XdV/cnd3wrMAVpFNCvz55WkXGWO+wy2uYfZ7ha50uMk4BV3/z3gKBGRCiBXuSAirYETgBdjNCnz55WkXBWZUv2brDLKIQlaAct9xyuoAC8el4Nd08AnItKtLDt2h/O9cL46/ZTr84ojF5TD83JNEdOBP4DxqhrzealqPrAZaFQB5AI4zTVDvCcibdItk8tjwHCgMEZ9uTyvJOSC8nle4Cj2z0XkRxG5PKC+VP8mTTlUfH7CiX/SA3gS+F9ZdSwidYD3gb+o6pay6jcRCeQql+elqgWq2hNoDfQTkf3Lot9EJCHXWKCdqh4AjKfoaz1tiMgQ4A9V/THdfaVCknKV+fPycaiq9gYGAdeIyOHp7MyUQxErAf9XQGu3rFxR1S2eaUBVPwayRaRxuvsVkWycF/AbqvpBQJNyeV6J5Cqv5+XrfxMwETg+oir0vEQkC6gPrC9vuVR1varmuocvAgeWgTiHACeKyG/A28CRIvJ6RJvyeF4J5Sqn5+X1vdL99w/gQ6BfRJNS/Zs05VDEGGCYO+PfH9isqr+Xt1Ai0tyztYpIP5zfLK1/JG5/LwFzVPU/MZqV+fNKRq5yel5NRKSBu18TOAaYG9FsDHCBu3868KW6s4jlKVeETfpEnHmctKKqt6lqa1VthzPZ/KWqnhfRrMyfVzJylcfzcvutLSJ1vX3gWCByhWOp/k1mFVvaPQwReQtnJUtjEVkB3IUzQYeqPgt8jDPbvxDYAVxUQeQ6HbhKRPKBncDQdP+R4HxBnQ/Mcu3VAH8H9vbJVR7PKxm5yuN5tQBeEZFMHGU0SlU/EpF7gWmqOgZHqb0mIgtxFiAMTbNMycp1vYicCOS7cl1YBnIFUgGeVzJyldfzagZ86H73ZAFvquqnInIlpOdv0sJnGIZhGFGYWckwDMOIwpSDYRiGEYUpB8MwDCMKUw6GYRhGFKYcDMMwjChMORhpQURURB7xHd8sIneX0rVHisjppXGtBP2cISJzRGRiuvtKIMdvpeHIJyJ/EZFh7n6xn6GI7CMi34lIrojcHFF3vIjMEycy6N985W+LSOeS3YFRlphyMNJFLnBqWXonJ4PrbZsslwCXqerAdMlTVrj3fTFOtNGSsgG4Hvh3RB+ZwAic8A77AWeLyH5u9TM4MYuMPQRTDka6yMfJbfvXyIrIr1YR2eb+O0BEJonIaBFZLCIPiMi54uQkmCUiHX2XOVpEponIfDcmjhdk7mERmSpOYLQrfNedLCJjgNkB8pztXv8XEXnQLfsHcCjwkog8HNG+hYh8JU5c/V9E5DC3/BlXprDcCe6X/7/c9tNEpLeIfCYiizwnJlfGr0RknPvl/ayIRP19ish57vOYLiLPufec6T7TX9z7iHrmwJHAT24Qu8hrHiVO/oJZ4uQXqe6WDxaRueIEentC3PwGqvqHqk4F8iIu1Q9YqKqLVXU3TgiKk9y6ye5vVmUcb/d0TDkY6WQEcK6I1E/hnB7AlcC+ON7QXVS1H04cm+t87drhvIxOAJ4VkRo4X/qbVbUv0Be4TETau+17Azeoahd/ZyLSEngQ5+XZE+grIier6r3ANOBcVb0lQsZzgM/cgHY9gOlu+e2q2gc4ADhCRA7wnbPMbT8ZGInjyd0f8Cfg6efe435AR+DUCFn3Bc4CDnGvVQCc68rdSlX3V9XuwH+J5hAgKqCc+9xGAme552bheJjXAJ4DBqnqgUCTgGtGEjMqqKoW4nju9kjiOkYFwJSDkTbciKmv4pggkmWqm7chF1gEfO6Wz8JRCB6jVLVQVRcAi4F9cOLNDBMntMYPOCGePTv3FFVdEtBfXyBHVde6X9Vv4CRgiisjcJE7h9LdzS0BcKaI/AT8DHTDecl7jPHdxw+qulVV1wK54sY/cmVcrKoFwFs4Ixc/R+EEepvq3uNRQAf3/juIyJMicjwQFEG3BbA2oLwrsERV57vHr7j3vw+w2PfM3or5NJLnD6BlKVzHKANsiGekm8dwwmj7v2bzcT9MXNNJNV9drm+/0HdcSPj/18i4L4qTAes6Vf3MXyEiA4DtxRE+CFX9SpxwyScAI0XkPzgjgpuBvqq6UURGAjV8p/nvI/IevfsKuic/AryiqrdFyiQiPYDjcEZdZ+LML/jZGSFPOkgUFbSGK4exB2AjByOtqOoGYBSOycfjN4pCHZ+IG2gwRc4QkQx3HqIDMA/4DMckkg0gIl3EiWAZjyk4JqDG7oTq2cCkeCeISFtgjaq+gGPu6g3Uw1FAm0WkGc6kbKr0E5H2rsI8C/g6ov4L4HQRaerKsZeItHUn/TNU9X3gDleeSOYAnQLK5wHtRMSrOx/n/ufhjEbaueVnJSH/VKCzew/VcILljfHVdyE6kqhRQbGRg1EWPAJc6zt+ARgtIjOATyneV/0ynBd7PeBKVd0lIi/imJ5+EhHBMaOcHO8iqvq7OEsuJ+J8mY9T1dEJ+h4A3CIiecA2YJiqLhGRn3FCYi8HvinGPU0FnsJ5iU/Eidnvl3W2iNyBkw0sA2dC+Bqcr/H/+iawo0YWOLmFX4ssdJ/bRcC77mTxVJzczbkicjXwqYhsd8sBJyw6znxMPaBQRP6Ck8t7i4hci6OkM4GXVfVX95xmwE5VXV2M52KUAxaV1TAqAK7p62ZVHZLGPj4EhrvzNMm0r6Oq21xFOwJYoKqPFrPvvwJbVPWl4pxvlD1mVjKMqsPfcCamk+Uyd+L7V5xMbM+VoO9NlG1KTaOE2MjBMAzDiMJGDoZhGEYUphwMwzCMKEw5GIZhGFGYcjAMwzCiMOVgGIZhRPH/dhjA1sCDXNUAAAAASUVORK5CYII=\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -396,18 +396,24 @@
     "plt.grid()\n",
     "\n",
     "plt.figure(2)\n",
-    "plt.plot(N_samples_arr_log, auto_mean_SNR_dB_arr, 'r')\n",
-    "plt.title(\"Auto correlator\")\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",
+    "plt.plot(N_samples_arr_log, auto_mean_SNR_dB_arr, 'r', N_samples_arr_log, fit_line, 'b--')\n",
+    "plt.title(\"Auto correlator (%3.1f dB/decade)\" % fit_p[1])\n",
     "plt.xlabel(\"Number of samples (log10)\")\n",
     "plt.ylabel(\"SNR of power measurement [dB]\")\n",
     "plt.grid()\n",
     "                           \n",
     "plt.figure(3)\n",
-    "plt.plot(N_samples_arr_log, auto_mean_std_log10_arr, 'g')\n",
-    "plt.title(\"Auto correlator mean power std\")\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",
+    "plt.plot(N_samples_arr_log, auto_mean_std_log10_arr, 'g', N_samples_arr_log, fit_line, 'b--')\n",
+    "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()\n"
+    "plt.grid()"
    ]
   },
   {
@@ -416,7 +422,7 @@
    "metadata": {},
    "source": [
     "**Conclusion:**\n",
-    "The summation of power values does not improve the 'coherent' SNR, but it does improve the 'incoherent' SNR, so the accuracy of the power measurement. Therefore the SNR for the auto correlation is defined as the accuracy of the mean power measurement. This 'incoherent' SNR improves by N, and applies to:\n",
+    "The summation of power values does not improve the 'coherent' SNR, but it does improve the 'incoherent' SNR, so the accuracy of the power measurement. Therefore the SNR for the auto correlation is defined as the accuracy of the mean power measurement. This 'incoherent' SNR improves by N, so by 10dB / decade, and applies to:\n",
     "* subband statistics (SST), averaging powers in time\n",
     "* beamlet statistics (SST), averaging powers in time\n",
     "* incoherent array (power) beamformer (IAB), averaging powers in space"
@@ -432,7 +438,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "id": "470fd269",
    "metadata": {},
    "outputs": [
@@ -445,7 +451,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -457,7 +463,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -519,25 +525,37 @@
     "    cross_sys_mean_arr.append(cross_sys_mean)\n",
     "    #print(f\"{N_samples}, {cross_coh_mean:9.6f}, {cross_incoh_mean:9.6f}, {cross_sys_mean:9.6f}\")\n",
     "\n",
-    "    # SNR definitions of the coherent correlator\n",
-    "    # . using cross_coh_mean shows the cross_SNR imrpovement for all N_max\n",
+    "    # SNR of the coherent correlator.\n",
+    "    # * The SNR of the correlator improves with sqrt(N), so factor 10 = 10dB when N increases by\n",
+    "    #   factor 100 = 20dB. Use select to try different SNR definitions. The SNR definitions are \n",
+    "    #   equivalent.\n",
+    "    # * The cross_sys_mean is available from measured data, but the cross_coh_mean and\n",
+    "    #   cross_incoh_mean cannot be distinghuised in measured data, therefore the SNR can only be\n",
+    "    #   calculated in a model.\n",
+    "    select = 1\n",
+    "    # . using cross_coh_mean / cross_incoh_mean shows the cross_SNR improvement for all N_max\n",
+    "    if select == 1:\n",
+    "        cross_SNR = np.abs(cross_coh_mean / cross_incoh_mean)\n",
+    "    # . the cross_coh_mean and cross_sys_mean become pow_coh, so constant > 0. Therefor it is\n",
+    "    #   also possible to define relative cross_SNR using 1 divided by the value of cross_incoh_mean\n",
+    "    #   or the error in cross_coh_mean, which both go to zero.\n",
+    "    if select == 20:\n",
+    "        cross_SNR = np.abs(1 / cross_incoh_mean)\n",
+    "    if select == 21:\n",
+    "        cross_SNR = np.abs(1 / (cross_sys_mean - cross_coh_mean))    \n",
     "    # . using cross_sys_mean requires that N_max > input_SNR to see the improvement in cross_SNR,\n",
     "    #   because for lower N_samples the cross_sys_mean is still dominated by cross_incoh_mean and\n",
     "    #   then thus cross_SNR = 1\n",
-    "    # . the cross_coh_mean and cross_sys_mean become pow_coh, so constant > 0. Therefor it is\n",
-    "    #   also possible to define relative cross_SNR using 1 divided by the error in cross_coh_mean\n",
-    "    #   or the value of cross_incoh_mean, which both go to zero.\n",
-    "    cross_SNR = np.abs(cross_coh_mean / cross_incoh_mean)\n",
-    "    #cross_SNR = np.abs(1 / (cross_coh_mean - pow_coh))\n",
-    "    #cross_SNR = np.abs(1 / cross_incoh_mean)\n",
-    "    #cross_SNR = np.abs(cross_sys_mean / cross_incoh_mean)\n",
-    "    #cross_SNR = np.abs(cross_sys_mean / (cross_sys_mean - cross_coh_mean))\n",
-    "    \n",
+    "    if select == 30:\n",
+    "        cross_SNR = np.abs(cross_sys_mean / cross_incoh_mean)\n",
+    "    if select == 31:\n",
+    "        cross_SNR = np.abs(cross_sys_mean / (cross_sys_mean - cross_coh_mean))\n",
+    "\n",
     "    cross_SNR_dB = 10 * np.log10(cross_SNR)\n",
     "    cross_SNR_arr.append(cross_SNR)\n",
     "    cross_SNR_dB_arr.append(cross_SNR_dB)\n",
     "    #print(f\"{N_samples}, correlator SNR = {cross_SNR_dB:.0f} dB\")\n",
-    "\n",
+    "    \n",
     "plt.figure(1)\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",
@@ -547,8 +565,11 @@
     "plt.grid()\n",
     "\n",
     "plt.figure(2)\n",
-    "plt.plot(N_samples_arr_log, cross_SNR_dB_arr, 'r')\n",
-    "plt.title(\"Correlator\")\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",
+    "plt.plot(N_samples_arr_log, cross_SNR_dB_arr, 'r', N_samples_arr_log, fit_line, 'b--')\n",
+    "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()"
@@ -579,7 +600,7 @@
    "metadata": {},
    "source": [
     "**Conclusion:**\n",
-    "The expected coherent cross power is pow_coh. The measurement of cross_coh_mean = pow_coh becomes more accurate when N_samples increases. The incoherent cross power is cross_incoh_mean and goes to zero. The cross power is a power statistics, but the two inputs are voltages so their phase information is preserved and therefore the correlator also has a 'coherent' SNR improvement. The 'coherent' SNR of the coherent correlator is proportional to 1 / cross_incoh_mean. Dividing by almost zero causes the 'coherent' SNR to fluctuate, but on average the 'coherent' SNR of the coherent signal improves by sqrt(N_samples)."
+    "The expected coherent cross power of the cross correlator is pow_coh. The measurement of cross_coh_mean = pow_coh becomes more accurate when N_samples increases. The incoherent cross power is cross_incoh_mean and goes to zero. The cross power is a power statistics, but the two inputs are voltages so their phase information is preserved and therefore the correlator also has a 'coherent' SNR improvement. The 'coherent' SNR of the coherent correlator is proportional to 1 / cross_incoh_mean. Dividing by almost zero causes the 'coherent' SNR to fluctuate, but on average the 'coherent' SNR of the coherent signal improves by sqrt(N_samples), so by 5dB / decade."
    ]
   },
   {