diff --git a/applications/lofar2/model/lofar2_station_sdp_firmware_model.ipynb b/applications/lofar2/model/lofar2_station_sdp_firmware_model.ipynb index df48f6bf1e72e43dbf6a69c6559c7ba3d76e16fd..a1468a4f5cd1509f376dc42530418b69d6a9a375 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", @@ -23,7 +23,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "id": "2b477516", "metadata": {}, "outputs": [], @@ -42,7 +42,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "id": "e1b6fa12", "metadata": {}, "outputs": [ @@ -51,7 +51,8 @@ "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 +69,17 @@ "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": 4, "id": "eb325c9c", "metadata": {}, "outputs": [ @@ -105,7 +109,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "id": "3e71626f", "metadata": {}, "outputs": [ @@ -142,7 +146,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "id": "0ec00484", "metadata": {}, "outputs": [ @@ -159,14 +163,14 @@ " 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 +192,6 @@ "\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 +206,33 @@ "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": "code", - "execution_count": 6, + "execution_count": 7, "id": "ac73d7e3", "metadata": {}, "outputs": [ @@ -337,7 +339,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "id": "98f1917e", "metadata": {}, "outputs": [ @@ -373,7 +375,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "id": "f66c5028", "metadata": {}, "outputs": [ @@ -395,7 +397,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "id": "a9fca052", "metadata": {}, "outputs": [ @@ -427,7 +429,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "id": "d9972b6b", "metadata": {}, "outputs": [ @@ -465,7 +467,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "id": "be2d952f", "metadata": {}, "outputs": [ @@ -492,7 +494,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "id": "a9e7fabc", "metadata": {}, "outputs": [ @@ -516,7 +518,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "id": "92852a53", "metadata": {}, "outputs": [ @@ -579,7 +581,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, "id": "a04af043", "metadata": {}, "outputs": [ @@ -587,9 +589,9 @@ "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 = 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" ] } ], @@ -598,20 +600,26 @@ "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\")" ] }, { @@ -650,7 +658,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 16, "id": "5ba30659", "metadata": {}, "outputs": [ @@ -742,7 +750,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 17, "id": "5ec1330a", "metadata": {}, "outputs": [ @@ -808,7 +816,7 @@ "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." @@ -816,7 +824,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 63, "id": "33f37393", "metadata": {}, "outputs": [ @@ -827,7 +835,6 @@ "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 +845,22 @@ ". 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 noise SST level is 4.0 bit = 24.1 dB below the sine SST level 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 1152x432 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" } ], "source": [ @@ -850,7 +871,6 @@ "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 +901,35 @@ "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=(16, 6))\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", + "\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 noise SST level is {diff_SST_bits:.1f} bit = {diff_SST_dB:.1f} dB \" \\\n", + " f\"below the sine SST level when ni_sigma = si_ampl. \" \\\n", + " f\"Note that typically ni_sigma < FS / 4 to avoid ADC input overflow.\")" ] }, { @@ -889,9 +937,9 @@ "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." ] }, { @@ -899,12 +947,12 @@ "id": "ba543d00", "metadata": {}, "source": [ - "## 3.5 Beamlet statistics (BST)" + "## 3.4 Beamlet statistics (BST)" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 21, "id": "f0b09a83", "metadata": {}, "outputs": [ @@ -1013,7 +1061,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 22, "id": "06c7b393", "metadata": {}, "outputs": [ @@ -1094,7 +1142,7 @@ "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", "\n", @@ -1111,7 +1159,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 23, "id": "def6eba7", "metadata": {}, "outputs": [ @@ -1152,7 +1200,7 @@ }, { "data": { - "image/png": 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\n", 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qMQxaOpIq5p0iEDF/5807v6vdjs/+ZWXOyy06Jetnr2/BvuNtWLXnRL5FsRF3y08+uPznb+O8H71pOnbVL9/BxQ/Py5NEwYl6OjiO7ecnr2zGnvpWrNuvAjmGwRf+tgo3PbIQDW3qJVHhjzj2D3HiJ69uwsvrDuW83KJTspjVQrW36ImyjltDiPuifOCih9VxQpkLQ2HtvhMAgI6kinuk8Ifq7+JJESpZWmevnACjo9CG01z5xJ2MTY79ZqVjhYWqSEUwTsb+pxAoOiWLodpb9MRdkc2XePGulXA5mX5rTolhxcb9eVco4kjRKVkJ9iKo+oPIIAVitmBNoEDELUjYwJtQdRwKyt1BERSlA8eTolOyMjpWfFqc6jgV4aC1oDjpM5k2HSepCpc416IaxONNnMY8RYbiU7IMn6w8C6I46TgZ25zyyYqGOLalGIqk4Ihjm1FIKlmEkKsJIZsJIdsIIbMF5+8mhGwkhKwlhLxJCBnFnUsRQlbr/14KU3ihrPpf1eCio9DG00KTt5AwpmTzKoU7BdV/GVbv+HVgyidLofCPp5JFCCkB8AiAawCMB3ArIWS8JdkqANWU0rMBPA/gIe5cG6V0iv7vhpDkdpFX+xtFd/DnxbvwyT8t931dnAegbIh7n5vrQSFXA6Nfn7im9i7UPDwPa6IIcqnXcVz99Aqu/4pxbxHzx/2kJ1f3p6UjiUt/WouVu4/nqMTCRsaSNR3ANkrpDkppJ4BnAdzIJ6CUzqOUtupflwAYEa6YftA6qXQEA+x9L27A3PeOhJ5voRHT8dSRXMsbN+Vzxe7j2HWsFT9/Y0voeafZdGHoOYdGgfVfGnFrQ4r4k6uXynX7G7CjrgU/eWVTTsordEol0gwHsJf7vg/AeS7pPwHgFe57JSFkBYAkgAcppS9YLyCE3AXgLgAYPHgwamtrJcQSc+yYtkXL+vXrUXE0u0bQ3NwslMWvfI1NbQCAd1euxIntJVnJ5AaTy0nusDh8WKvjje+9hz4NW0PJs7a21iZ3tr+htVUbN5ctW459VdG6HzY3N+Odd+Yb36Oo/7o61rbXoezIe9LXrTuqbXlzrL7eJle2baWJte13V+J4hG07CyLvv4DgfZi1/js7te2kFi9ejP7d4uUy+/bbb6NUX0YadR8TJW6yx/k3edX5/PkL0L0s+tedzfVaoNwTDSek6itubSXosxkUGSVLGkLIbQCqAVzMHR5FKd1PCBkL4C1CyDpK6Xb+OkrpEwCeAIDq6mpaU1MTWIa/7lkBHDmMCRMmombikMD5ANrNMMny6hwAgF/5frF+AdDQgHPOPRdTRvbJSiYhFrlscnO8d7AR1/xqPl783ExMDijLvw+tAg4ewFlnnYmaqVm+9HOyG3IHrGcr3VbWAi0tmD59Gk4b1DNQHr99ayt++voW7HrwOtd0tbW1mDbjQmDuawCyl13E/+1eDhw9gokTJ6Fm/GDp6+jmI8DK5ejXrx9qaqabzrm1FRmq1s0HGhtRfW41Jo3oHTifOBC0/wKC92HW+q9Y/CbQ3o7zZ8zA8D7dAv+WUNGfx4tmzUJFqaZIZ9tugvCBRxcCAP712ZlZ5SOUPaQ+J0oc61yXfeaFF6J3t7LI5ei24xiwbAn69O6DmpoZnunz0VaE+LzHYckt86q0H8BI7vsI/ZgJQsjlAL4N4AZKqbG7L6V0v/53B4BaAFOzkNeTzNSQsreLeGuTNt356obgezgVzOICQ77gb3c/fV2bYpMxxce9OqKgAFYXFlb/lSk3ymICkW+R3t1zAu/GcE/a2BC/JqOAnJK1HMA4QsgYQkg5gFsAmFbZEEKmAngcWgd1hDvelxBSoX8eAGAmgI1hCS+COY7mu0MQEceOMwhxdXJ2Igxx43Dr4iCDlTjKZKGw+q8Ce7YU8SGOK1IVEtOFlNIkIeTzAF4DUALgSUrpBkLI/QBWUEpfAvAwgCoA/9A7iT36SpyzADxOCElDU+gepJRG3Elpf9Nxam8x6jiZohcfiTTmbjyMUgBPLdyZb1GEyDSnXCnRcbp3aWN1YZ4FcaBQ+q+F2+rQ0pHk5I6iFEUxo9pMPJHyyaKUvgzgZcux+7jPlztctwjApGwE9Euc48wonPnkn1fgiSu64/uvRqqDByZNKUpipd7IkwupYx16oAD6r4/+YSkAxMcPS4AaxONNrm+PGmPliNfylRCI9XRhvgUImbDrOOz6CTM/md8a9f2NY/spAJ+sgiSe/VcMhVLkHLWrij+KTsmCYclSuJHNoFgoe3CHOTUaRdy1XBPFdCYbeJWSFQ7KEq8ISs7cFdSz7ouiU7LivDonDoRSLVE9ZBHdMuVMHB2GJSvG04WFRCx9SnVUlxpv1O2JJ8WnZKkBVQo1KPpDxpJ1Mg5Cxt6FqjmFShxfEuMnkYInhk1GgWJUsvS/qsEpwkS1JzFxXa1aqLCXn3hasmIolMJATTHHk+JTsmLo0xAnxc9JhHmbj+DXb/rbIifuna4f6TqTadz93GrsrW8VnpfyyYq4OmgMwyUoS1ZUxPvZCgNKKb7/nw1YHcXG5Scjxd9kCpLiU7L0vzEf//OOdVC886nl0hsIGys4Q5Yp9Px8bF68eMcx/Ovd/bjn3+uE5+NoWZAl0in0EKLqKzIYL4kxbG9hi5RMUzy1cBc++LtFIed8cpKrJlMoC5/iQvEpWTFeXrr/RBuONnV4J4yQONYLI8aiSQkXpfV0x9FmNLYnvRPmGCr4pAgOG8DiqNSH3XeoRUrBqWvuwJ5jZqt7rqox8yKg7psMoW4QHQfiqGWzRvnFv60CAM/NhnOBsjuYcaoPQrTOK98hHC792dvG5zhNzamONhri5O5gEFFcvBj+0thz8UPz0NKZisVYonCn6CxZUFp25JA4arICgsRwcmo2ctvqyJdTLBgD5Un426MkjvUZtuLHfmMcf2vcaelM2Y7FUjFXFJ+SFZW/kCKDH0PKwYY2zN14OLSy02mK55bvRVcqLX2NTCfupYjl25IVV1S1hAtzdwjS3iil+Ne7+9DaGb9pZUU08MYE9SzGk6JTshIFYmXJF2G+7cjk9YFHF+GTf14hl5+EaP9YuRff+Oda/GG+90bSQTodp98Ug8WFsUS9PYdLNgt3Vuw+jrufW4PvvrghVJkY4W+jpdpOtrRyFi1Vm/Gk6JSsOIZwiBNGR5kjx56DDe3SaWXuWENbFwCgvkV+AYFMvk57XirnXHeMKZ/8ilE8ZLG6kA24hxrlnzk/RLX6VxGcxvYu47Pqo+JJ0SlZjOYO+5y1ovDJ1wbgMqu94trJRalOn2jt8k6k8E2Ql8RExOEf4tq+T2Ya2zJTw7m+PYXUGhry2E8VnZLFBuEf/HdjniXJEKPFYKEQ51g+PGHKFyfLaFy2RGrpSKK5Q+vk494WCoVspgsz/qiFcTNUmwlOVYUWGIC3ZOWO+IZJcmLy/a/nreziU7LiMf7EnmyqKapB3o+Dup/nW+YN3KndZByRJcrxIVM+CLtT7EzKLz5QyJGN43vULz9xb98nEz0rdSWrjZ8uzE3Zaoz1RxEqWaoFuFEsHWV0UyJOx4ul5sIjkVDPWlQEaW2ZQKbRtFXl+B4fyku1oZt/0VH1GU+KUMnKtwSFQRj1lM9tdcLuUDKLUqn4uJRPVqgiFRSqgw+XQNOFEe92EVWcLIV/SvUXnE4foWxOVvL9glx8Sla+BShAOpLOiwQopbZNk4MoaDINXWoVYIDC+Xy7UmkcONEmyFj709KRQlowN6gGBAGqTkInm9Ws+faVbOlIoq452Krf9q6Uaz+kMFNWIrBkqedRSL7rpfiUrBhqWVFMYabSFMkgbzGCFnfNL+c7Jv/Xu/tx0UPzsHj7MZmsskPGJ8tH2aKB6jsvrMcFD76FJgeH0XX7G/DQa5ttx2WmYKxv+p3JdDRvUTFs44pwCOJzyEiQiB3fPbK97tfzUf3AXPnsuGfjzO+8ig/9brHv66KmK5UWvnTlm9IS7V53pbhgpPkSJubku16KT8k6SUagG367AKd9+5VQ8tpR1+J4btXe4wCArUeaAABHmoLF4AmrXwyir7Z3pYwlvG9uOgLAHMTPyttbjtqO+fVzaWjrwun3voJHa7f7ui4Konrx4AfzfL8tFgus/woysLP7HJVOwLI90dqJzpS9kF2WDYv9sm5/g1S6VA6VnnHffgWf+cvKnJUnC7Nk8Ttf5HparFAeeTVdGDJB3+JqHp6HLz27KmRpomPDgcZA1xmxSAMoo7vqWjD9h2/i2eV7A5XtRdgrBhkfeHSRryW8vbtl9k33ZVngEp1o7QQAPLt8j3S5+Wbq/a/jvhfXS6dXilV0BLNk6ddG7Pg+5f438PDy7AOeBpUy14al1zaEty1YWJQlBEpWroUIoZ19+dlVuPjheSEI40y+u6miU7Lau4I5Au461ooXVx8IWZr4EsS6sfe4+U3Vj0Irk1ImPz9is9w6JEINOCmdmeCn/h7VyrISreyA7TEfHG/twp8X7w50rVK4wiVYfcqHGwkC/3xuPZF9u/bzG3nLntpHNDNd2JnKvU9WmOW8sPoAdmdpAfUi3+2l6JSsNpdpoDjx2oZDaOmI50auhxvbbUEmczIJKxUnK7sNwK0+XUea2tHY3mVSOkUKl9+9C0t0s4KMgufEnmOtWLn7eODro0YNdeFDsrBGJbLw54qaZCqN3ccsbgl+lCyuPnI5XRgH5m06YotYXipwfM/dnS+s+s+3Tl50Sla/HuX5FsHEeT+aaxsoNx1qxKeeWYlv/3tdnqQCXt94CHf/fTVGz55jO3fej97Edb+2OMMLTF9+Gm94qwvlyxalsf6M6T98EzMffMt0bNOhRoyePQfPLNltvCkG7dezCdg56+F5+ODvFtmOf/b/3g00ta3CLBQOWTm+RzSqdCUp/ro02PT3D19+Dxc/XIvD3L6KTu1xb30rRs+eg5fWaDML87cexZGmzKrFVL5HzRxS39KJO59ejrueWWE6zrqxLosla+mOYxg9ew6O+Vjl6ZeTqPpDoeiUrLEDewCIj7J1uNHe2JvbNSvR3uPmUAJ1zR1o78pY4lo7k/jcX98N7Gwugj0g6/c34l+r9jumi9qEmy1hKgxN7UmT7eq4/tb4nRcy/kky5fGdD/ssWpa+aHsdfvTye6ZjJ1o7DeuhF21dqVhMbfODuVLgwiVYnCztb1TTI0/M3457Ar4YLtqmrU4+rvsquvHeQc3f9KXVB5BMpfG/f1yG2/641DhPYz4D/8i8bXh1/SHXNKk0xcEGQSgZC+wlbZfFCsjusHV14ePv7AAArNpzQlpevxTak55vpbDolCxGIQajrn5gLm79/RLj+4urD2DO2oP4+etbQisjX4NhWKVms7dbNqQDduwiC9hHfr8UT+idIWPK/W9gmo/l74riJJttddglUT0bx1vC3SdPRs4W3f1jF7cCOu6WrIdf24xP/5/7isSHXt2EGT9+C0ca3V+gnfpr9oJjtZSzqdSSkugHwHjfhQysDstLE3kJ8VR0SlbMnz8TIrN+lG8gQXCrTn+rAbMry8DHU+KmUNoiu3vkGyROll/auqLxJ4xsr0n+cwE9d4VAkOpk10R1L8IeoGTEZH6rFaUlxrF8+GSFXWbtZi1MTL2EZQ9wfoat04VMztIIrQyF9qwzeRMkP7JLKVmEkKsJIZsJIdsIIbMF5+8mhGwkhKwlhLxJCBnFnbudELJV/3d7mMKfLMSlUUcdg8yXj1d0YoSCmj6LD4XUf2Wz/yC7JqqWF0ZQZfOUurekrZ2aklVZlhmq8rFaLBnUlB0RrArMIRyoIWdJpEoWNckQd5iYiTxFKvdUsgghJQAeAXANgPEAbiWEjLckWwWgmlJ6NoDnATykX9sPwHcBnAdgOoDvEkL6hie+M4XSAAoJm+Lgo5LDUjp8hXAIsQ1IWbJOsjZXCL+30PovYxwIULeZ6cJobkw2Q5RofJORsrlDs+6ykChA/pfkxwHWn1q31clYsqKbpCq02mfPQ748iGTuxHQA2yilOyilnQCeBXAjn4BSOo9SyjyllwAYoX++CsAblNJ6SulxAG8AuDoc0cUUWgPwy7f+tRa3/WGpd0IH/PZPuQzhEPbqQuH1Rtwrcb5OZNOvn/mdV/DiaudFBopIKaj+y5ApiwnDqHSQfPi5thrThZmhKh/ThaxOb/jtAvz4lffcE+eAjCWLmo4lmU9WhI5AhabjWi1ZuY4AX+qdBMMB8CG+90F7s3PiEwDYfi+ia4dbLyCE3AXgLgAYPHgwamtrJcQSs22X5pzZ2dUZKB/+mubmZmEetbW1aEtSPL2+A7eNr0DPcn+9zzf+pilJdccbcfZ9c/DJSRU4e2DmVrAyt+zVfsvBQwdRW1sPAPjbMvMqE14+9tlJbgDYs8fdB8Ca38ED2urIrVu2oKG7+cndsnUrajt2uebHePvtd1Du4YzZ2tIKXp0T/Yatep0cOHAAtbX2/RR5OjvNv7W2thadndrvWbx4Mfp3y/yeVavcQyKsWLkSx7eXOJ5vbm7G4sWZvdcWLcyEXmjvSuO7/16D3ie22uSxUltbi+e3dGJPY+YN9RtPiqPVu7XvrcdTWHE4iVvPrMCGOs0aUF9/3HaNqK3IPjcnOjIyrlixAnVbnesnj0TefwHB+zBr/Tc3NwMA1qxdh8Qhf4P51uPafW5qcX7+s+HIkSOm705liI43N2s67PLly3G4l9ZOGjrsg11tbS3WH9YUq2PH6rBk5QkAQLIjsxJv0eIlGNQ9uBbh1j86HX/nHa3/WruvBWv3NWBGN+8o8G73oKWF1ccKHOqZ+S3tSYqvv9OKz0yuxPj+Jahv156xjo4O1O5MY+3RuTh7YCmO6yvTDxzO3JPlK5bjRIPW561ZtQpNO7N7Hv+zvRPDqxI4Z7BZTXjvmN7Ompqk2plbfTOiaK+Mli6tnaVSWruaV1srNXUoI7cMMkqWNISQ2wBUA7jYz3WU0icAPAEA1dXVtKamJrAM2xfsBDZtRHlZOXzl86oWL4q/pra21pwHl+aJd7Zj6aFNmHjaYHznSuvsgz1fnh0N2oNzvDOBps40/rO3DF+8+WKbDIeW7QE2rMPQIUNRU3O2ML+amhrbdTa5OZa2bwJ2OO+nZ83vtfp1wL49OP2M0zGwqgJYkVk1M27cONTMGO382zl5Z82aZTL5W88DwH0rzA1f9BsOLNXrZChXJw6ULZwLdGRCaMy8aBYqFs8D2tsxY8YMDOvTzSj/nKlTgaXOG9ROPeccnHOK80xRbW0tpk45D3hbi7l1wQUXALVvZmQp59qjoK3xx+6w3OPntohXdbm17zv0+GePfepKlG47BqxYir59+6Km5nyb3K5yuXCkqR2Yp/3G6upqTBzeW+q6uBK0/wKC92Ff+v3reHF75sWpZ88qoLEREydOQs34wb5k6LGrHli6GN2790BNje+f4IzeLoYMGQwcyFhkq2dciKqKUls60W+vWv0O0NyE6uppGD+sFwBz+2HU1NSgc8MhYNVK9O/fH2NPHwasWo3+fXtjX/MJAMC06edhzIAegX+OsH90kl0/ftFFs9CtvETuGZFIk6mPapw1tJdxfMWuejTNXYy5hyvx2Q/O1MI81L6FiooKPL25HUAHdj14OR7bshior0fvvv2AI5oT/bnnVuO5XWuBhkZMq67GpBHZPY+sH9r14OWm4+Xb6oDlS9GzZ0/U1FzomY/beOS3zwlCQ2sX8ObrKC8vQ2uyCxdfXCPls+Yqtw9kXgf2AxjJfR+hHzNBCLkcwLcB3EAp7fBzbZjkyhTItu/hHTL9ktBvdNyXJDPueib/G6Vm47v48aeXB85XKpiqKW5UPMgm4rwngrhgMSTW/deL280KdNBtnLRrtL9R+SxZF75M/O5r2WXoICbvYM9ix63Ze8I4lg+frFwtZGHTf6Ue830ix3cgM5WaCx/vQlncw9pLSZ6mC2U0hOUAxhFCxhBCygHcAuAlPgEhZCqAx6F1ULxN+TUAVxJC+uoOo1fqxwoeFjS0sjS4SZZp060dKWHk9bBIptLoSKZwqKHd2LhYnng9SNnEyZq/tc747PfyPfWtaGrvQltnKtKH9NKf1YaST7nuw9LWmcpLbJgYUVD9VyagqP9rjXZZoCEcRPmLtklLpyka27uwtz4TMLm9K2Xa39CXHHlc1HKosR2jZ8/BvM1as2OrA8s8XCuYOE4+WVHqEfEaEbxh8uarH/ScLqSUJgkhn4fWuZQAeJJSuoEQcj+AFZTSlwA8DKAKwD/0t5A9lNIbKKX1hJAfQOvoAOB+Sml9JL8kxzALgXAKTMfr4WXzwl4R3bN9Y7j190uwfNfxQNdmHN/tLXT1nhM43rIVX7p8nHQ+2WI4vrvUSVtnCvf/d4Ntvy+AV9L8CfSVv69Bj/IStHSm8L3rx+OOmWN8XS9b3I6jLd6JJKgsTaAzmY4s9hZgiZMV0663cPuvAJYs/W+2lp5kKo3v/2cjPlNzqjalrhP1GCXKv0sQOiFFKd736wXYU9+KXQ9eBwA48zuv4pZpI/HgB91dCESEFsMvAMxC97ele3DJGYOQ1JWmMi/PdWr5q8MsWVE+jzG2WgsxVhdmue9tUKR8siilLwN42XLsPu7z5baLMueeBPBkUAGDEnVFGpasLKYL2XMU9WKZoAqWF2xbHhklK2zcHvR/rNyLvy3b65wgICz69MvrDwmVLNG2OtyR0OVxo7KsBI3tyWiVrALpbAup/8rEyfJ/bWa6MDsZlu2qxzNLdmP70Wb89f9lfPjCjjNkX+Frn85Jpew/JpWm2MNZsVj6Z5fvDaZk+b4iOtheqSwEg9MzxpSoupYO0zFmCQvz2aSUmqZwWdkF8/zrf40N1HMsd1FEfF+xqx51EW6IaWX5rnpsPtQEwByJ2IrXzZQN7hl1ENBcEF6cLO+3kWAPkXwdF8LdYBbWtzcfzek+lFsPN2H70eaclVesBGnDxuCX5bPGnjFrqITwpwst+QskSQo0RmvdZDto+vW3jJKMJctcG9a6Z+Lwlm9KM0ppmNI2dySx51grFm8/BkppwShXjEzEdzZ22H9Ac0cSC7fV2Y6HQVEoWR96bDE+8Ki2ZN5vA1i5ux43P7bIOyHHzY8txordmnUokUXwGFmzfhymYmLj1yPxNuJ2S5yiVkc9gOT87Un/Pff/d6PvTX2v/80CNLR571PH/0b2+674xTu47Gdv+ypPweEyEHjCLFlZrnVgbcfaP4XxjIjajFv+ophYVsUr2+lRmatz9fgySxRzfHcqV3ScIrOIKszFASt2H8esh+fh1t8vwavrD0VWF3M3HnZdnBQU1ubcmu8X/7YKH/3DUk/XnSAUhZIFaI7Jje1d+OHL/mLLzP7nuqym09zecLwao5dZPw6KTVjPamg+WVKJvFNlI0+Q+5JrNVnYCUsKsW5/A2o3H/FOyPGTVzflfNVOMZLVdGFIMrAFOWG6MchsySOy2IssWbzilUyls5ZTyicrR02bRXAvS7ivhBMdp5RmfLJClHf7kYxlev+Jtsi21fnkn1fgrU1Hwu9HmF+x3gafWbwbb2w0xznbclibmWrvDH81dqhxsvLN2d8TB23MF96N5eQZlMSDfjS/P4huGoY+e7LpGPzvXbT9GG5+zDnOmMIf2YRwyPa5YoORfbpQ7imx+vA4prMVbE+TEpjleCtNZyqdta+YlNUwV0oW88kqEe9M4SYOBa+UhidwLiLsf+6v7xqfKQ3XwGD4ZOkmpQfmaIaYs4b2wt1XnI4rxg+O1KBRNJasfJFN88vD7hCRsedYKxZt9zenHWQsIC7TKS+u3h+bkAV2v5Hc3ux8KHxsCl2RH4JsED1v8xEcbjRPkZQ4WFFkPSNk2541f1H2IktWknOG70ymQ/DJkkiTIy2LxV+UjZNlPWb4ZIUoLh/HkRASSU3MWXvQ+Bx2HDSrTxbjvYON+Prza0ItS0TRKllug1oqTXH/fzbiwIk2xzShyOB1XtYnK4/KmMx8NgDMengePvJ75z0Vw1IyDDks2a3YVY8vPbsa3//PhkBvtrJv6W7EwXcuTBZtq8OfFu1yPF9cvzYeZOJkBbBkwf+1dz613PBnZTBlyhokWXYBjmzpIjEb27uw/kCj8V0U+6pFD1AK6EpWDlpiVH2wNV+2aj0zXehwncNRI05WOOIBMK/wJGFnLoC/5ek0xQP/3WiKieYX1j5kxoUo2lJRTRfKsmJXPZ5cuBPvHWz0ThwhhWTJmv0vf87TVu57cQO+de2ZGNSz0jjm9+cv21mP38/fIby2sV1z1D7c2O46FOyPWLF2ooButcFH9I3Ib79gdH4FOYkIEmz3/5bsxvC+3XxfyxQY6zPBBiNZB/qF2+pw34vrM/lSihKHp9BNNkKAj/1xGVZz0d1Flqy7nllhfO5IptHd8LmRk9ePTLmGxV8sYSEcnHoOoU8WQvPJMoXRsGSWWcUaDfxLwvoDDfjDgp1Ysfs4XvjczED5ZWI9up2LbvrjpFSy2C2Mejsbr+xlrTtxmP7Kln+v2o+OZAqPfvRcNLR24UhTu9T+Y+/uOY4pI/ogkSD4n8edfX7YgEAIiby+nB5IU5wsl3PFgnJ0Dx9mUbXqFuv3N+DUgVXa/nkW7n1BU3CeunOa8FonRAoMwClZlvvrZCG778X12G4JJWBF5pFMEGJSsOa+d9hyXvttvNidqbQhV9DH3rziUexPlquW3qFbsoypX1+WLHBxsrKT2GpNYhASfV8mijeYzRQiuzJf42jRThdKEUZjySKPbMzqhQj7He//3UJc8Yt3PNMv3n4MH3h0EZ7QrVfmvKxvVxoEwd5KikCPNVFsU5cnG3z7bmjrwvt+swBf/vsqj4tsH1xJOpiqEkaQZDkly4p0aBpLMq9nUOSn1JlMg3IvWEFwCiJs2os0ok7YmivbJiflMe0n9MlCRjnKdpaEv4e8Mk4cyg4TvuwwimJKosym0FH8tpNSyfLzKB5pasfRpmCBTr0GOq8buoHzTfjjgp2e+xu2d6Xw4ur9oToOht3oRs+eYwTQ88qaOeQ++Mom/I9l5ZrdUqS/zWpalidhhnCoa+7A2qNJYRRqRkNbF0bPnmOaot59LJwtdGRh7XFnXYtwTzjf+Sk9LjL4qmUD7pId9XhpzQGMnj0H+47bfVTY/fVryXIae6z5OK0yC9oMbMFIPZSkMoGgnck0PvrHJdr1kuXubUpjw4EG4/vi7cc4mZxktbN67wmMnj3HMfjuhgMNho+VLKzvZvfGqS8XjS1mS3p2Dyd/r62O75ny7GUcbGhDfYvf/XHNRLUBuJtPVma7tgjKjSDPomL6D9/EtB/ODXRtNtOFe+tb8efFu43vD726ybO8mx5ZiC89uxq1e5OeafOBU9RiJ/g3j2W7zFvG2VbvGWUEm13PxpT80d8vxc9XduBjTy7zTPsmNwVy8cO1wQvNgkt+WouPPem8SEGRP0R7a7LHoDOZxj9X7gMAbD1sH9j9Tq2wVXrWN3xm4LLmkxIYvsTxmqSKD8eSlUpj/X7txUX2Gf7OwjZc9+sFxvdP/jnj42W2XjnLCgB/X74HgFlJ47nu1wvwzX+udZXFSeS0xbfKmk68utDFX8En/L03Ob4T96xn/PgtnPODN7Is234sm5kGt/ZovJxnkb8XSsmy8MB/N+KJd7ZLp8/mjaHFxZpwvDXzNpBMU8Mh0g22WrKxM54mBr/qT6mLedfRkoVg0wZ+Xp6s2fvZRiasDaC98Po9fADeL/xtFV7Q96FU5BfjjZr3idE/dyRThoWhtMTexjNxsuTKSupak/UN32lvOkeFynJYpOSJHklbKo/H1rrVDJAJ3qldHsYKYafP9t90Qt+AvrKsBNuONAnzW+kzrAmrY2bJ+jwXP8qcTnDM4XMQeEXHZMmCuB3YZHFI88i8bfjxK+4Bw8OemvWzujAKilbJkr1N1nr/w4Kd+NHL3lYjoxyuoK5UGqNnz8EzS3Y7XxCAf/scALNpSmyVHkOmHufJRge3WrI8cnfbssjmk6V/TaYp3tp0WHCFe9mbDgVfaSocQBx+2r8iUGY+9cwKfODRhVLli/jPmgP48t9Xm45JBZOMpy5fFJgcj2lmGrArxVafOTtnS1uy0ubBp60zhXmbjhj31To9KFooJCrJrXQ+izlrD5jOaS9IzteWOfhkmTLIEiefLNGPYkrWvS+sw+U/fwftXamsFQR2dSqdRmN7FzYdEitvwnr3sLwB2t6io2fPwfr9DeIERvmZDExhNCTjZB1xcLF5+LXNePxtu48tT9rpHgTEiJPloO2k0xS79P1do/C9K3glK04rnFo7NMuUzNReXPnOC+u9E1m486lg+0153To3S5YtL/3vW5uO4OV1h3yX/c1/yoeoiNuG3a9tOIx395zItxiKEMhsgM4NcvzUDbNkCUYMoy+UtmSZpwu/8+J63Pn0cmNgt08X2jMWKXRuSh7/u376+hbTOa+pfpH1TsbC7wfTSkOPtCf0/T1ZAFFKRdY/f+WzuktRd6u30Kpokl1c8Ov6djJz1h0Unjfk4O71nzi3FVnHd7++aKayQ7dkaThZsh7zMXMVhIJXsvzQkcze2deKqTn49DlyzTdPuuPx1owla9nOepeUYs649xXsrAtnSszVksV9/ufKffjsX8RmdSfakynTcvGThaAvJZRS2/OjVjBGgGC6kP+cdFkpJWvJ+sF/N+KuP69Al+58xcYeNu3drFuzrYFAxX5AzseOt3Ris8US49X83CyoZQLFslPkKJYFbiETbItZWjstaahvBcHu9qD9TaXT2OHDDcGamWc9e2Tl9DuIZDTSbLbiEZYtaBfJVNqY8nYjsyhK/KvX7XO36mVLwStZsm26dvMRnHHvq1i1x98c+ckGv1fY/zy+2NGp04mOZBr/fnef8Jxf+4+rJYu7718LsDXCN/+5Djc9stA7oYBCjlsWVHn/yaubcca9r2b1hqrwRuT4blrppX8W+SfJGrL+uGAnXt942MjLuiG0U6wu0cApXuWmHbv+twtw1S/fsaR3hhD3PkI0Xcj3V2E8ls7hHOzPTlO7eYFRmjr7ikoVCH66kLr67AqzMskiLlf2JcsphiQBkepDsgkhIdtHjbv3FVz287e989P/OrWPqA0aBa9kyfLOFm1fPb+OiLK0d6XQwKxARmcX/O4FuTKMrWH4fcEAbUmuX5xkt8rnZ3Wha3kBKmtNEVqxDje2C7chYVDBICDLs/pKKj70Q4xm6osOpwGzy8FZnb9K9r4Y/l1sP1D9QvbYycTJErUplmzfcXvfYTh2CywQXj5Zbs7+8LhWFqcpNyroza2KSJraLVlet8LZkmXW6g40tAvTyeZrvY7V1ZGmdrHy7JABX8cyq/aC4CfO2u5j3tvtGD5ZEosvoujSCj7iu1OlpNMUGw40YMKw3tGWrwtw0yMLHZ0Uc0UYncxSyxQhCTHEr20ZskeTLnH5QXGaqtI8SbynVqLmvB+9iW9cfQY+W3OaYycXfLrQ/L0zmcbmw/lt78WM13ShCHZK9tlggyt7+bF+l1Wy7HK4KPoA3v/oQuHgqPlk2Z8lhiiEg0nJCmN1oUO93/7kMtQ1u8d/omn5597Y6N6i9LDnM5V2v4tecbK8mgABQV1zB6b/8E18atZYfOvas0znnab7nO+OmWwsWWFtN1fX3KG/SLCXB4edOiIeSwrekuU0aDS2J3HdrxcYfjdBFBCZASlNKQ41tAsVrGJ40w/SbYX1u2UtWUEo5Ck/N55auMu0ga6VbG8Nq7cH5mzEp55ZmWVuCiuiDaJFju9uCo/sINVlOL6b805kO13oUialwKo9J4QBK4nxnxhRMFK+rHAsWWK2HG72DLIpq4SayxO/nCXT1MNS5J6Xk+LAHz2mK41vbTqCvfWtpqDbjkqW5Dt3Ns7rbtZ4P1Q/MBczfvwWZ8lyULLUdGF2vLh6P+qaM43HT4XKJKUAzv/xm4Gvd+KHczYGvjZMBSLIb5B9Mwhq8pa5NtuyfZFFfb+05oB3Ih8cberAJ/7kvNozaOfHv3BsOdyEvy/fGygfhTvG6kLuNvFjTmZvOvu1xnMneYuTtulC83l7CAd7HtqslrfFK9MnOQvn5ZMlmi7kywrHJyt4x0Bh/+2HGttx/3+c+3LDj85ihUxT6iqL6IyTFU6URgsqSo3PFz00zxR023G6EESqf/dyfN9b7zzNl03f3NqZxLxN5pBCLDuZcTEKhavglSyvOnlq4S7c/Nji6CwyEanBfLDIfBJklYjMfD4g4a/g800uTsgqml/826rQy16yo97TJyMoBARX/uKd0JfOKzQ8LVm6puM2yMoq0oblynB8p7a/Xvv3CRUBal/Cz5RH9+7EfXN3keN7WFYPhmnGzWfWWn3Zjz+5cKfjNVa/LsOSlfKYLhTdC0E+MoimWZ0c3yFpyfJKc9FD8xzPZbN34b3/1sKQbOVcGVh+ztOF3OcIBpbCV7Ik6iRoSAGZ6q5v6XI8F6cYXnHAr6Lr/iYXn7qN48zjQYujrKIwEOosvOO723Sh/ldWyWJ5sWl5NrAamxNTS+Rv4XShQF44vzy4iaZZslxCOIhWF/KWrBBM+CZrkM8+RuT47nmNpU7Z15TXdKHomE/lxC1/p/16eZ8st/rJarpQoNjL3tmd+n6wfFBtY7rQQdsx3/PwKXglSxYWgVZm3toPv5i7xTtRjojLYO9Uq/bVhe71H+V0YTbYfocgTVz166Bysct25XhT65MVp+lCXgGyX+Ns5RJh3VaHDfisjOaOpGnLKKGSJTBopik17QLBP+du08xe/ZfIEsGvhg6l/8viuaXU/+XWRZZsTEpRj9FJbEA08FJy+KCiIt30fx5fLL7OtEG0c/7ZKVniz27sqmtBMpXmQqBkzrn6ZFnyj6LfLoLVhXK1wvu+8A3F7c0paH1Ty99cwX7X2qP5jWWUphSjZ8/BsN6VHunc83Fr8CdauzB69hzMGNs/gITh0daZMm/tEWMoRI624kp2eipuDBhbTOEPp+lCphi5Wnn1UzvrWvCbt7biX+/ux3+/cCEmDu9tSpfZVoeVYy+PV4rE1jPRFGLGqZ7PFwD+6RBDj+G21qUkocXO41dYmlZbemhZdc0d2O3xkmB6CQ8yXeizK7DVqcmS5WbJdz/o6JOV5aikKWfeeWQXJ4trOxIZHWxoQ81Pa/GJC8cIF21kfM8cI2XZ0oZJwVuyorQYBM27vSuV1V542bLtRBord/uP1h4W/12jbdlgje1ixduS5Xx+2S7t9y3e4S9Yahjwj+p3XvS/DVFYUEp9x/ty6NMVMcNkleAGbsOSJbrGcvCSn9biX+9qe2W+7zcLbOnZ6tDMwKRlwCsu/Apf0XiX4vZ9y6SjtjSyuE35ERDbLhB+fLLe/+hCfPB3YgsNQ3bqqLUzaatvLRip/ylGgAvhoB/3qjOvbXV46btSaWOvQuOygFOrspfl0pLFVkku3n5M6NPIXoJlgpGmI3hfLnglK44k0xRX/3K+LSJwLmGbl+bDL8y6yTTDFifLS7SYagBvbzlqfHba+iIXov912R7fliWrXHGd1jzZMU8XcgMm88mSDKcggxHx3TJdCJinWEQD5+/n77TLYVM+5OTySpVI2HeB4BXCpvak66q1vfXegZVNaoqL3P/vzyvs11Lq24JjVaaMvQsD+GSZZcl8/tHL7+F9v1lg8k0OOrUqHcIhpG11/I5fTElv4xZesM9OVlKzaqosWTkl2+ruiMEWJDIdS9iUCxxUAdiebK/OV43/7mw9LFbw3N42rZ2WquN4wS/hZ4jiZHlZsvwMTkYwUraqkBsgeaVGNHA+9rZ9c12btVRSFErdB38CYgtQnLKYHvzuYWqXQU7YhdvsFnRK/SsFtgjx/HShy9Mp9snjPnPH1+p789U1d2StUMiGcMguGKl4qtkLQjKK1J1PZcLYtBtKlnucNSCal86TTsnyVYkFPAJlzM+5/xGiVUCA3f8tG5+suBDGiqZcYutUgl6oyBnyju/iz16wx9VY2cZdzE/POS7rt8phaSyy1wHuWhYhQEmJsyVL9N0v4gk3OdJezuoCrJYsfrrQ3ZIlsGQ63H/D547bqoe3SPnpw2QtWTLK5laHHSOcrLgyiHysW/WtwJx+pzlMia/ipJBSsgghVxNCNhNCthFCZgvOzyKEvEsISRJCPmQ5lyKErNb/vRSW4IxIfbK4zw1tzqEa4gIxfc7f4C+/56CXJSv+I7vzPH/0svvV75btrLc55haCIpstce6/nOCtRuJI4qLpQu56HzfWunchP/DzliPBdoNCrHqOn73o3Jp0ghDbdGGQOH5eMog+WxFZ69NU/reyX8HqlF1m3AMPhU2oZJs+Z745bfhtlUUW3tq2aHudMI3MbbniF+8IjztZcWUQhWloM5Qs8TV5ny4khJQAeATANQDGA7iVEDLekmwPgDsA/FWQRRuldIr+74Ys5bXht1KCGrI+9uQyX+UAebZy5LHo8lIHS5ZFptUeTtuFsGovn7c4yDYRbKNnI61kWYWqi8W9/7LC7t3P3tjCHXNXqETpfOkelr0LecUlyU3Hyb442Kak05LRtuHh+E7sbd5qucr25cZ0vUtWjtHnfRbvNF3oFNjUms58THz/mV5KOcUtjJfwHXUt+Mjvl2LRNruiJW+9tGNaGWhY25zTm6129oTtLj5ZtrYaQUcnE8JhOoBtlNIdAEAIeRbAjQCMvQIopbv0c/EfFX3AV7jfVVy54hC3go9vX6J4IbmiTNABiVi03X1lYEtn/n3agsI7x0dFkG5yj8Ux+CSwZBV8/yVSmLz2yfPz8lmpvxQZShaXER/ZX9ZqZE0lPc1IqetgmhNLFvfZLQZiSYKYYnQB4Ti+Gz556ewMCLzyIAprYJ4u9FWMTarDTfZV5GEFI/WTj9PvaHPxyQKym56UQUbJGg6AjyC3D8B5PsqoJISsAJAE8CCl9AVrAkLIXQDuAoDBgwejtrZWOvP2pL9K2bF9O8ixEgBAQ0MDWrvE19fW1qK5pQXZmIReeMt9ubATfn7/t//ytvG5szMzpbl27VrgYCkOt+R+3GhvFa/wOXzokOl7RfNB13w2bgi+f2PUsHvU0CBeWPB9l/3KnPLyy9Zd/vcP3H8gEy+utrYWTc3iNr5x40ZTumTS30rZoL8pAiLvv4Ds+jCeEydOGJ9ZHpvr7S8ba9asQWq/uft+b/Mm4/Pbb9unYqwy9SgDWrqAtqYG1NbWoku/x7t2Z6yd23dlPje1yAWiXbrUbPVfsGAhmhq9dyDo7Opy7W2PHD6MLsuLFy8rADQ3N0vXvSjdokWL0LdSUzqfXuT8e2kqaZs+XbpsObo5jKjWspqbtX7j0OHDAIC6ujrU1tbiyBGtntra27Fj+w7HvNra7fW5fn0mnMyz76wDPbQZfSoTaDihlbV6zWrsqteE3rlzJ3o179FlySyg8aq7DRvfsymG7723CX0btpmOrVmzFuRgqZF/bW2ttJVx5cp30bhDG6PXHtHaZGNDo6NsK1dqKz2bmppBOuwtaNNWbXFGXZ39xTeVSuFYfSbc0cp330WDXraftuRGLoKRjqKU7ieEjAXwFiFkHaXUtCSFUvoEgCcAoLq6mtbU1Ehn3tyRBOa+Jp1+zNhTMXVUX2DZYvTu3RukLQk02x3wampqMOeNeQCclwR78at3xVsTeFFTUwO8Okcq7Yjhw4E9uwEA5eXlQKdW5uTJk3Hx6QOxq64FmF8bSI6gVFVVAU32OGFDhw4B9meCEZ511lnA2tWO+ZzpcT6fsDb66KbFwPHsYpL5ud88b+/zHyJk6NBhwN49RrkvO7Tx8ePHA2tWGelK3noVSMlbFv08wzHHs/8CsujDLPe9d+8+wPF6VJYljDqs2H4MWLbElG7ipLNRc8YgbeB69WUAwBmnnwGsXwcAuOiiWcAbrxrp+/coz9wTvcySklKgK4k+/fqhpmY6Em+9CiCFocNGALt2AQAGDBoK7NF01MrKbkCLd384bdo0YEFGyTt/xgz03LoCaGxwva60tEzz5+zqFJ4fMmQIDnQex9G2jPIzdNhwYPdu43tVVRVqamaJC7DUtbU+mKxDe3cTpueprChHqiNleiaqq6ehqrIUqH3Llt7aHqrWzAeaGjFg4EDg0CEMHDgANTXVeHbvSuDwIZSWlWPM2NHA1s22vHqNnYz6VxfZjk+YMAFYpa2uXHwghf3tBO98owZ/3L4UOFaHiZPORtuuemDHdowdMwbnnDEQWLwQPXtWAY2NjnXCc9ZZZ2qBZtevNR2rmTrC1BYnTpyEmvGDAWiKW01NjRZI97VXxBXKMWXqVEwb3Q8A0LHhEPDuSvTq3Qs1NTPNCXUZzz23Gli8AD17VmFAVQVgUaZGjRoDbN2CIYMHA4cOmM6VlJSgX79+xjVTp56Dc0f1NcmdLTKO7/sBjOS+j9CPSUEp3a//3QGgFsBUH/JFjuxcb1yJo4iyMnmZZqMw3YZOYS0uhPXuONVwgS2adKOg+i82ReQVjoEa5+zHAPuz07dHuT0PgbO19dqOZEaJkJ0Kszu+yz0mDW1dqG8RK1iA5lMzoMr8O+w+WXIyOiF7vWhxj3VDbddy9L+O04UueX3gUbuCBdhlZ64BbJqMP2+aLvTRiVEKx07Da9pNdtqYX/QhU58iJ39TfmxFpdP1pjLy4PgOYDmAcYSQMYSQcgC3AJBaZUMI6UsIqdA/DwAwE5wvRBioTZgz1DVnLGeGT1Ye5HDeqoVY0nnlE5ZE0VFoukhLh9kaVQh1nCWx7r+sGIoPd0w0OFGBQuTmsy2zoTTzcecH/vauzJyYfFBRc7qwXpYShGBkv+6mY9n4ZKXTFB/5vdlCKJtbaSIh/J1+f6pIIWV/w3o2M9smieXz80KVSlN8459rTcdYngc41wmn3QFkkIn47jTGuDm3yyx6D9nFTyvXKwGlNAng8wBeA/AegOcopRsIIfcTQm4AAELINELIPgA3A3icELJBv/wsACsIIWsAzIPm0xBfRxsLhTD+OD2IhADPLd+LS35am1N5APlOtZAtWaNnz8He+taCs/jwe3gChdHGs6HQ+i/W5r1WCoqUMX7Qd1q1JiqLKVdpgeJmsmTJDpIWX6WwnNMJAU6xKFk2SxYoXly9H6Nnz8GJVmerGKDtTGFdfCP70i5aXUgDKEbWOuXDI/itNaf0RkR/kyWLBHr22xwCbK/f34ALfzLP+C7qu2VjmJnbvvgapzYlsmSlDCUrP8FIpXyyKKUvA3jZcuw+7vNyaGZ463WLAEzKUkZ32Xym/8mrm/C7j57jmW5XXUtBD0CNbUnbG0eucIzHYmnjhW7JuuiheXnfoDpbdjR4+1m9su5gQa/0jHP/ZYU9O15TL/xSf+sx62enPKxKlbF3IbdqjrdkBQ1GGtZzTAjBoJ7mTedFit8fF+wEAEy5/w3cc+2ZuGvWqWI5XZRXL0b07YbDFmf+NKW+XwxZnWYu46cLfWXl8sLNlCzL7oYe02iyZaQpcJNley9rPWw80IgeFSVSZYgsWbyM33x+Lf6+wr7ohzhMfBp5SKwujGJmrOAjvgepk61HxNuR8NT8tLYgXvOd3nf2nwjusJ8tjtOFlja+2SHiL8MabkARLsdbOvGLld6LM/6ydI9nGkU48NOAdc0d2lYoLgqS0xSheQm/+M0/45PF8tT+mkM4+PfJsoqbTcwkHkLsFqR/rTK711FqHuAfe1u8Qi9buUoEUS8p9T9k2Hyy2L3w2FZHhFN6U8R3AdkaGg81tNmsVNY8r/31fHzoMbnV9l57F4oULIbIWmXdhNuK2QIsJaIvCl7JCoLss3WiI5Zhc2KPbP2yN04nfjtvm+v5OFBo04U8y3fJrYoshMj7xQLl/lY/MBfVD8y1Tb+Z0jm8hfPHy0oSrlYbq9WBH4xNPlmSI9D7frPAnB+loTwoCQLb3oUi+Pri68R6qTXOlZZeTpb2rpTQWpjt3oXGBtE+LFnGFmoO6Zni0WWbWtVYt9991afpGkEhpYLo9yxdRzKFhfu10EJHm+RW24cd8Z1lIeOTpTaIFhGgTlhFtnamsOmQszXlwWXesV2G9+nmXwAPLvjxm6HnmUucH4wC1kgi5KKH7Eu+c8Fdz6x0PPf35Zm3xbhP2xYTIguV27Y6os2jAbP/S1lJwsHxXS/LcpzpHmUlxGTJCmr5CWsKJkGI0BfKVBac+x/rlV2CfYJkB9llO+tNgVoBtq2O8zV/XboHtzyx2CQLu2eZvWYzecnWmrEtksN5pmSl0mnpqbFelWJPIoFeKrSSsnvwy7lb8ft17r5xVlbuPo75W7WQCqIXDCv8zxBNGDL5hD5Z1NK/RdDX5SJOVmT85s2taHVwxHODVeqGA/ZYTjytEmGI9p8QB6PMhgMN3sqdFz96eZN3oohwVLGKUMfyilovw9768NtQtszfmtkqQylZuUM0qMg6vvODXWN7JjBxaQlx8Mlif83nUroQFaUlgSK+W5Hd89ALArl9Ufmfc7y1C8lUGpf9/G1bPYocsbNp65rS4pzBPf9eZztmFYGfLpQVJpEgQNrZisb63a5UZgqSEHG7uv8/G/G3ZXtQWZbAzNP6Y+E2c/8msmaKLILsnstar3h+85Y2g7HrweukLFl8CtEYk5kulPDJ0v8+/NomNBzqQo2EvF4UtJJVu+Wo6U1LFhX2IVpk3yQVhUGcV3kWG7KbQYsUJF5puOxnmZ0gShMJ1615rOeYMlVRmjApWbLThU7lZAsRbKtjhVIKaknysze2YPcxu39nUmjJCo6XJUt4jcMUntVJ3Q2vKdSMJYubOgUR6nBPLtRcOMpLE0K/M5E1MyV4M0inKR57ezuOu8Q98+JEa6eUnsn7XDlZqwDZEA5a4tc2HEYfEs5in4JWsgjkzIlWimnIiOP453RPYiiqQgJ133KH0+otWzpB0FKR0gBo035sakzkt0UpNYU7YNlUlCbQ1JEx5wd1Cg4rhEOCEOHA71XeRocZiy6hT1ZwWamLHxVvWeSxKi2UU3xlRUl4+mRpf5OptOlhdlN+05QKFVrZ0Ayr9h7H35b53/aLZ+uRZjlLFj9dKFCk3KYLAfMUMe+nKNHUpChonyzN5On/oYijYlJMKEthkaFuZ84Q2S/cQjjwz9pPXxdvaKxNF2qfRQrP2n0NmHL/G8Z3Y7qwrAQdAUI4OMmaLYTA25IlKM+peLFPVnA0S5Y4h7O/97r4GpeI9bL17aQ8WM/zSqXX2Empu1WIR9Sm+HYTlM5kWkqxF22GzZOJk+V0PfdZ/5tOUynLlwyFrWQ5mDy9UKulosUxTlZuxVCEhHpecofYkuWseMn0f2WJhOPUoIgkN13YySkiQV+ewov4LueTZQ/EKi4/KTC5R2XJcsIaJ4t/1mQtgIkEc3x38snKTBeaUrhk72TJEskk9G1zzlqarlTa058KsAZZtZ+nEnlk8so8J2H5EBe2khXQkhVFLAxFBqd7ouItFSbqeckdbgqViOdcYgYxSkuIYTGR6S9ZmopS8/AQ2PGdUtcXrAdumojzxvTzzEfGJws+/KLE04Vy14pws2SZ0qV5RSpzrbV82fpmiqfXdGFXOm0KQOqWfZpSlAhWcgr3JBRk5Ket9KwoxeVnDRLmIbV3IZdGtLqQ6dLiiO9ixTOVpkiEZBY4KZWsYprNEpm8840alIsLNf2bO4SWLOGKQy3hj1/xXkXMO77LDH4Zx3dzhO6gz7VX+zl1YBXOHdXXMx8S0JLlhKjvbOlMBVYmZcvlpwGZwuUVksMN0QbQovMpTqmkAJo7xH5imhziqVnh6kKR47uPPuPKCUNsbQ3QlGCZKuDTiJqHWzDS9q40lu3MxAvkN+gOa7qwwB3fg+2/VEyDxnMr9uVbBBvFVL8KpTTnEllLlp9HrKw0M10o4+fDxsyKsnDewWXeA2WmZmTjZMnWTUObXcm46ZGF+NC5I/DTmyfLZcIhuxUOrzwZ0776d9NCBslVXSwW6HGHvRqZA3dXOiPfL+duMQWatUIpFa5alN342U+XQSkV3tdUWm6bIt46F8Qny5yX9jdNqZouBLQHM5hPliJKVP0WF+p+5g5RXcuuOHSiLJGJkyUThiFpxMkKZ3jwGrAIEU/z2NJBvJ2NFdmXvM//dZXw+PMrg724UshZcHilJGWxYFFTOrlymWLxwJz3hOeZrpZKp4383RQsTR6x1VC4ujDLVZopSlEquK/JtJzju7dPlvbXa4EAn1cqHZ5yVNBKVoKIg+x5ETTei0IOFVepuFCWydwhb8mSvydeqwutsDFTNIUTBK/+gMDdksXG+oSMTxbs1jq//VG5YJsYGSiVWyIimi40bovJJ0tOy/JSHlh5ImXI8Zq0g3VJ0ifLT2ilVJqiTFBWMiXpk8VVmsi5XcZ53sjLWIigVhcCyMInKwJZFBmUEltcKB0rd/iN+C6Dtnehn+lC5pMVkiXLY8AlxN2OxZQI+dWF5u9+lAtA+933vbje1zWA9jtlHd/ZeJ/iBnXA6pMlV65XnbA8u1L+Vj+KrEtinyy5FwMnKLVv/K3lm5Z6KfD2yXI+Z5NF/5tS04UZAgUjVYNGpKj6LS5UCIfcIY7uLvJ58WHJSmQsWTL9JRvYykOcLnSDeJiyDEsNIZ4KhRZGwVyeX0f2irIS/Hnxbl/XAAF9sozVhaLpQlmfLA8lK82ULH+DpShfURbivQvly0k7ThdmHN/dfqGh+BEi3lbHsj+kG4ayq+JkaSQICTSVoQaNaFG1W1wopTl3iH2ysnN8Ly3x5/her2+FEpYlS6ZM1/HMmC4MaMnyqWRVBnT416K0+/TJYtOFuvLCXy8rt5fywLLpSKZ8jX3SPllZxhtzismVTFFuqs/5er6eRFOnLA8Zn6yM47vyyQLgvMmlF2rQiBblk1VcqNuZO2TjEPlyfC/RgjZTSqWm8g81ahvUV5SF45MlGnBH9utmfPbyySLGX4m9CwXl+bZkeSiXM8b2F5ct6ZP11KJdxmc3x3fZftRr70JWRkcy7XO6UBTxXc7x3c8YkKbai4AtX8k4WbzFz03JkvLJ4q5R04VgAdUCWLLUqBEpYe1VpogHSmnOHeKNnO3H/E0Xat08pf6mjCoj9Mn6wiXjjM9eqwtJji1ZXg7/n7vkNOFUEoWcP+rvarcbn404Wew7d7msL5mXMsDGO79b3QinC4WWLNF0tjxOTubJlNzqQq/7y5q8nxAOqTSVsnzJUNhKFiHBLFnhi6Lg6IxhgFSFohAQh2sQW7J+/LJ4yb4V5lScohTN3IbPXoRlyRINzAnTiCf2peHOGteIfHd4KLXXl6xvE8MrPpjTYJ2WtGRZrwG4F39+5aHky42XMsB+fnsy5Us28QbRgvwd2qcsqTRFa6ddtqRknCz2Uu9kdPEzXZjZVsd9lwI/FLSSlSDBrFLqzTxaVPUWF+p5yR3S/leU4vF3dkjlyUISpClFc7u8klUZ4XRhe5d5UHUb0PixUcaSZcW3T5ZX6ApHJct/32f4ZDEdizsn75MlOV3o05KVEClZotWFWcbJSlOgSdAuU5zju1t2fPluLykyTaczmTZkUo7vAACCYy3iKLduqDFDoZAnyPOy5XBT+IKcBIiqOltLAbNkUQo0+bBk9aoMZ0OQNLXrJTvrWozPhPiI+C4x8llT+HVf8HJ8d7KIfPFvq3y/9BurPg2LVuac/LY67ueZTK1dKTzN+YN5IbZkyfoM+nN8F23xk0xl9lp0y48vX2RLzCw+9G47X/3HGiNPpWQh+C7ZSsdSKOQJ8rxsPNAYuhwnA7IKla9gpAlnS5ZbH9q7W5l0GW6k0tQ2wE0Z2ScjA+QGwASBcNNiHtEg6zdOllfoCjcJ/D4rVktWNnsXepWxZu8JX7Lx0fW/e/14PS97ui7BdKwfxZY6WLL46UK37HiLn3A6My0/XajJwxzlpZJ7UthKVsDrlCVLoZAnyJR8WB3UyYZoOiZbSxaLpp2msPlkua1M69O9XL4QF440dRhhIRjvO3uo8dlLwTJWFxKxdcWW3pKf3+luL7+vRII4yuy3rDZ92jSzUXTmnOx0oVeVBF2HxNf1rdNPASDuC8KI+N6RtF/Ax8ly+wkpTinKdrqQyeMnvRcFrWQF9f5XqwsVCnmCPC4ylgmFnUbBG73QT8tHnqW8T5ZVyXIZScKyZP3gvxtN04OAHuWdxRj1uJ61pYRUMFK7gu+mrFx+1mAAwNiBPYxjXiswXS1ZAYcW5gvEXx7WzhlBfSp5nyxWp7J7F8pubs3y/OnNk/HBc0bY8rUtDBCQMt0v55cU2S5JJgCqHwpayQo8Xah0LIVCyOmDq2zH1OOSX7KdLizTB0uatk/LuFmGeobkk+WEaX2hTJwsIl5d+OHqkY75Au5TV0N7VwIwW/S8Vke7vUDIKjRWmTr0lX+Ui88k7bDvtbow4IDHtw1m0BDNvIrq1+904ZgBPfDATRNNx5PpTFwvt9/A15PYkqX9lTXK+LV8eXFyKllq2FAopAli+Q2rg1I4bRAtf73ZkmV2MBatIGMEWckXBNk4WQTidtWrW0YZ1EI4mM+7WaZYfvxv7RRMXYmuESF7W6wy8WUy5UZWUXESx9gfMaBFjK8T9km8d6F4qk+WlIOlKZnOBM91M4yZtikSTa0b2+rItWcjJIRSsoJPSShLlkIhZlDPStuxII+L+5a/Cj+IlARfwUgNnyxqW8bvZsnK1R0kHnGyynQlUVuFaE9otVBYFQG3aTd+KpIh8g8SXSNC9oWky2IS6jBCB1BDuZFVjrz81ILOOpqULOIsk0ih8qPYDajSfP+s95EPRipjySIIySeLpQ/pCShsJSvgdaL7/+/PXpCVLApFMfCj90+yHQsy3aBcssKjXRDfyM/AyeJkpSi1TYW5Waty5VenWbKc6a8Pwo4xviwXW4OfullV2E8My5Il64rEK84VpQlDyaI0M3UpvbpQcoNov/DZGpYsQV/QJagvWdlvnDIMD31osq08wLy60K0LModwsOPXxyqzGlHyAg+klCxCyNWEkM2EkG2EkNmC87MIIe8SQpKEkA9Zzt1OCNmq/7s9HLGNvANdJ3rbGDe4Z7biKBQFj8gPp9Atv3Htv2SxBu4E3O/J3+8632TdYJ8ptSsQViVr9X1XGJ9zqSi7lTWgqgIAHGMi2iwglgHebcBn1/KKirfju7Owsg7ffBlVFaUmx3d2T2Tz8tq7MKhPFj++Zhzf7ek6hY7vcmXefO5IY4GF3ZKV2btQ1idLPLWuK02SWlMy19OFhJASAI8AuAbAeAC3EkLGW5LtAXAHgL9aru0H4LsAzgMwHcB3CSF9sxdbzz/gdWFEsO7XI5zlzYr4E9Yqq3zATPGyiDqWII9LXHyy4tx/ySK2ZDnflPPG9jfdR94nyzoVZnUkF/nhRIVpEHcpjSlZdc0dAICn7pxmzof7LNoE223AN3yyuEy8LFlug6+scsGX0aOiFJ2pNNL6hsjsfslag7x854JOFyYIwaCeFfje9eONeyVcXShQBmX3yDRZy6wWSS6Eg1t7N91vkWO+4fPlzycrl5as6QC2UUp3UEo7ATwL4EY+AaV0F6V0LQBrzV4F4A1KaT2l9DiANwBcHYLcAIJXgqjR+c3q/hsnBCtcUXDERWHwy6dmjcW1k4Z6J+QQDXbBQp7EptJi23/J4nfPOcB8H/k4WVYFwhp4k1e6cjpd6FLUVROGAMgoWcP7dDOdt1pARHslOsGu5RUVb58s59YtqxjxPlndy7VtfDpT2mq6jCUrpOnCoCEcCLDs25fjjpljXPNKpqjRxvhjUpiULHMea/adwDNLdgPw2FZHr6d395zA4h3HbOeZvifbmnkfrzCQWaM7HMBe7vs+aG92MoiuHW5NRAi5C8BdADB48GDU1tZKZX74SLukGGYOHT5sO7Zg/nxfeWzcsDFQ2XHg/00qx9zdSexsVBs5ezGkO8GFw4Hnt+ZbEv/s3rMXXT5fYxcsXGA71tbe4bvsDRvWo7Juk+/rIiDy/gsI3ofJsP/gEdux7Tuc9y2sra1Fmmae7S2bNwMAFi1ajCPHzPeys73V9H3hgneMz4sWLrTlXULEy/j9Ultba4ycK1eswPZ6cV9021nlKD2yCaf0TODyga2ora3FgWZz2j17dhuf29o7fFle9+7Vbm9jQ4NxrKnVfVx5d+VK04vHxAElWF+nKcLrJMeFNm4KON2hxRC783dvoKklbdRva5vc+HbieL34BNXquaGxTSofK5s2bUJt4zbjOwFQV2cvq6MraVOSW9vlZF+7Zg0694r3itx3PCN3c2ur4zO1c3fm/h9syJR73ZgyzNnZhaYmbYuvLVu2SMm0cNFiAEBXZ0coz3G0gVAkoZQ+AeAJAKiurqY1NTVS1710eDVwYL/v8vr1HwgcOmQ6NmvWLGDuq9J5TJgwAVjzru+y48C3P3oFPt/ahcn3v55vUWLPkvuuxWsbDuH5rSvzLYpvRowYoZntuUHIi4suuhB409wuysrLgQ5/itbEiRNRo1sgTgaC9mF4dY5nkqrefYGjdaZjo0ePAbaKB42amhqUzH3FmMYZf9ZZwPo1OO+88/F/O1cBx08Yafv27on9zZktkC6tqQFefxkAcOGFM4G33jDlffsFY/D35XvQ0unfumaVkbz+MkAppk2bhq7tx4BNdgXljNPH4aoZo3HV5Zlj2440AwveNr6PHj0a2K69BZX6bKujR50C7NyOAf37YvNxzQpCEyUAnPd4PG/6NJDF8wFK8etbp+Li0wdi8ve1Z2bU2HHAug3S5QPAsEH9seX4USw+oNXp8D7dgPY2lJaVAxIvOIMG9AeO2hVxQrR67r5mPtDof5ur8WedhZqpmXcK8toc9OnbFzhmbospCvQoK0VniquzRCnc6pBxztSpmD6mX+aAw/NQVl4J45mypBk6bASwa5ftmjGjRwE7t6F7jx5AczNOP/10YON6T5mqp00H3nkb3SorIP0cuyAzXbgfAB/tbYR+TIZsrvUmoD3PjzlZoShUwprkCzLdEHQ3hgiIb/8lSXtXyuZ34+eWsBnANKW26UIWHiGT1ttPqqJMbHkIiuvqQkE7sh7iq8ZvTCjD8Z3L1MufaBg3XZmwTHW2BlA+e5SbbR0s5IZ0nCyPZy3oDifW6UpCiOMWT9Y9JWWnOmW7Cbe6cOqf/IbCsJYVVg8mo2QtBzCOEDKGEFIO4BYAL0nm/xqAKwkhfXWH0Sv1Y6EQtCMXVbrfrOIzhgSk0OVXeKJtMeLvRovS+wksaOTj+4rIiG3/Jdv5tydT6G5RbLwUX3YbLz9rkNFPakqWWQmwKlnmTOyHKGjgQdsqG9/UnJqp6LC13+eVwaSkw7W1XD8hHKoqSo0XmAQhJnkCKVkV5ntrBCOVrGev1YVBg5Fa65K45GWVQdYnS9bflfVBorbn1D8xf0O/zTWZa8d3SmkSwOehdS7vAXiOUrqBEHI/IeQGACCETCOE7ANwM4DHCSEb9GvrAfwAWke3HMD9+rFQCFoHQRudQlFIBIpvJTiWCsMJJ0/Euf+SXYHV0ZVGt3LzQOx1R5ji8Zma07iVYZpzNa9QlLsoWWGtNLXLZv3uvLZQJIP1ED8YivZ+dJWFOb5zBbkND6f06w4gM4aUJoip/NZOf+UD2upCHsMCI/ncVZa5D+NBZ26s/pxOGzADZiW1JEE8tybicpVKlUqzYK2Ccw71xJzxd1j2zfQuy99qRC+kfLIopS8DeNly7D7u83JopnTRtU8CeDILGR0JWgehWLKCFR0bCt4Sp4gEUbsIYskSbDGXN+Laf8kORO3JlEnJ0gY7OUtWgmSUEBbxvbI0YfhUWVeFmfJwOB62yu0UyV2TwX7casmSjX8kgl0qO6B2tyi7ZSUJV0vWXbPG4ol3nBcpAJpljKckkQkeK0NFqfv0bUdX2lVBciJltWQR4igTH5etrITIB1L1ackS7pPoKFOwTijnlqw4E+p0YcGrTQqFmWCWLPtzEMTyq54nb0SRskW0d6XRjZsuTBAiPWAmCDGsNGk94jvvU2UN4WC91oooDpVf7NN9zi99ooEuzBdEJotsnlbZSy1KaptFyZIZo6yWrNIAIRwqBPeRKY5tXSmbIieDzScLLtOFXD2U+VBu/MauEoeQED9Hbi8Q7mVp+SklC+FasnJVdlwocPEVEmg+Wf6uEVuyVKiPKJC2ZHWlTBYUAnkFmrcSpdOav1ElNyC7+WQJpwuRvSUrs+FzRsHxM11oJZtFFoYlSza9pbrKShKmqbIWy3Shm8sbw3G60Ifz+MLZl9qsbMza2dKRRK9K/wGVrfsrullQeatRmYvibsWvJUscDNVpujCgJSuVe8f3GBNQUxXcqLgrTbdMG+mdyAe5CjSoMGMNpBglYexsoOUT4CLVvDzpSspVbEcy45NVXppAMk3xaO1212tY9WuO2dpntrqwkrdkuSlZgpvo1abOGtrL9TzA9T3EdFBaBuv0YDYWB+LTkmWVp6yEoKwkgfvep20iYJ0uFDmlTx7Zx/S9R7mD47uPKbcBVRWYOKy37VwqrUX5F22X5YXd8d15urDEMl0oi6zFO+U2XRi2kqWmCzME1RNEmq//rHI7ivQq4K1dFBkG9KzIWVkU/qftQtuvSynxnshasjqTaUMZ6iYZPoF/iWL3IpWmSKap9HShKBI79TBljR3YA326u/dV9paROTK6f3evxHbH+SyamjFdKPmcWAdeZsFhm1hbpwutL7PvO3sohvQy9wHWqTy/PmaiqPWsbBb0NIiSJXJ8dzJq88qkn3c7PyEctKlq8TkR1qlc2bKUksUReFsdoeN7fgYj6fJinl8xE2Zd5bLeA00XhiRhtn47JwOyqwuBjHOztJKl/00QYkxxsaksfjWa19u+yBLjemep2QlaKBuxf2eHqkf3M58TXG9zfM+iMxaFk3DDqgCxgZzJYLNk2axuxCavVcnyqj+bTA5KVipN8e7u48IyZEil7SEcvGJSAfb2UZ4ALho3QHidn3uXSlOhJc1JyXKz0rrR2qE9J2q6EMEHhCCrpUTIdnhhoKb38keY6kIu90GcNrqv72vCamZ3Pr08nIyKGF9Klq4YWUM5eEFIZrXafn2bkqG9K43zXj5Z1kGQQrPGOJGm1HPgtPbbBObVkDyivMJ1fHcuR5zeOl2o1R9TMvjtcvj8+e9WRa27VcnyqRw4KVkA8LEnlwEAegbwyZo8oo/pu1MwUsBsNbJJwfkFVlWU4p+fmcHlKS9PMk2FCpXTeO63Hhmf+Yu2k4uyZCH4wya0ZPktG8DvP1YdTIAAhK1jKZ0tP+RSWf7AOSMCtWtFbuCDXg7jFB8RbAC1riJzfFvnlAdmGdl9TNuncGTfzJSc63QhiM3Rm1KKB26aiM9PEU97Ux+WLGJ8z0TKsq08lGiQ2Tm+C/zDXLAmY781Y8kyO75bFSrCrfZkVFmCkfpdFMeKcKt3v9OFfbqX4UrLtlgELqsLXcpOgFdmgXNH9eOUan+WLJGSJz1d6LN3U0oWgg8IotVSQZ5Ttw4qbMK2gAS1AgZdFlvIhPmLc2nJCoKymOYO5pP1yw9PwfdvnOialr0YWi1Z7zt7KBbOvtSWPqPAZAbA3fWakjWiH6dkucXJIvbpQko1C0FVufg6CurpUyQKm8AGT2v7k1OyvNM4YTi+S6b3smS1drg7vmvWQXOe9tWF4nFl1gixosTq263e/U4XDu4pUPqJs78Vr+BZ71mSZurX7jcmzk/0U6yWLJbGafWz32lXuwzh9IWFrWQFrASR4htk+5Fcjkey2xREzacvPjXfIgRiSC93S4EbYdb8zrrWEHPzxr9PliJXsCXyI/t1t711W2GPf2WpfXsdvm3/8P2assb6swTJDDaHGrTpQj6963QhBNOFuhxDe4ivS1N3qwbL1/qdhQawiiPzMpjNi4HfYKSiEA5ARu6mDmsIB3O+mo+c+ZhTnCwrfSrEx5nobkqFNbSEF6LqIJD0ybIkSabte0Sy1E4ii+5HyqJksUUHjpasRMKUv3V6vuaMgeLCmQyuZ+UpaCUrKGFtq5PLAWnz4aZQ8wvaL8VBybrg1P6+r/nHp2d4J8oBdc0d+RbBFWXIyh0sGGl5ScKzLxnWR1OMLrQ4EKepeaD66HmjLFcSYwBs6UihrISYFDo3vxUiUAio/srRu4Jg14PX2a6hMkoWsX9nXXKw6ULvNM7X+rNkWZU+q+M7AHziwjGZ9DYnfbt1y+rba91smeE0arn5ZDFmnip2PPeDW8R32XvO2lOmXsTXiS1ZaZOSx8p09skipvq3vsh4WbrUdCG0+DFBCCUYKXI7IMVlv8UwfvMUS5wYN4KsihGRjdyFrHdYAwp6oaYLcwebLiwrJZ71PnFYb8z7Wg0+Y3nJSVMqvJZ3JGdv/K2dSZRbtoFxG2gI7IOn9/J86rlhsW1KEBmHapkpGluKUCxZkumtlqyEeboQMC8ssOpLCUJsdWq9B473xKHuWXInReeK8YMxLIT4fAkC7K1vE57jpzhFYlr3iAzDksV+r9NK5pJERiW+fvIwm/O/V4DW0MLZhJNNfvDaLd2J8BSW3A1IYQWWzBaR+X7coCpfeQz1cPLl6dej3FfeURCPmg9GQ1tX3spesuNY3souBNj0RZmEJausJIExA3rYLUsejZMf1Fs7U6goKzGVxef361unmq51Wl3ohh9LFt+XZDbltctvKwPWNB5CAfjG1Wc4yOLPkuW0rQ6vWPJpRLGr7H5n5u9OSqqnJcvhutKEXbELgtuLAK8YitokOyuaPhUxdkAP27FkigotWU4vkqWJjEvPmAE9jCnpS88chN999ByMH+YeODcs5aiglayg232EEcKBkMK2ZAWVXXTdVZZVKF74qf8gfYNIMTtZLTReGwmHhWhBRHO7Pz+Qk41ObrrQi/JScft16heMyRiSUQRaO1M2Sxa7bROH98INk4eZ8yDE9vx5NSe5EA6W7ySTr8hR3AsZ65eTAuJ3lZuTksWU1ZKE2VdXFJ3e63b7VYgM/zuH60oSxNPnzylP0zGX9F4ys3pjchiKtsNl91x7Fm6dforpmGbJynxnil170rzYgJcps2o1c7xHRSmumTTU856Htcl9QStZQQnDKkRIbqeRYjJb6LCfmT/h/CiMfqMfO5FNLoWsnuWq2Yi3P8lR4QUKewMvL014KhNODupOfVnG8Z23ZCVRUWYuq6SEOQ+Ly7VNF3q0KIrMQPrC52aaAp9aZRP9DlvIA9f48OLvIpwGVN8hHCzprNOFpQmzxKLAqV7TqU4KkdOw5RXCoawk4Xulnd8+z9O/SW8GrJ6cQnYwupWX4Mrxg03HkmmxJWvH0RZhHmUlCaO98uV4TVUyHN5rfHNSdoNOu3bHmbAjaAcN4SC6zktnPX+sOYqzyJLVt3sZrhpt978SPYTXTHIOhujESWrIyp1yLqhftbWOO0N7V2LywBJUlpV4Po9O1i5+0Ll2ktiizAbANNWd7In9nJPF03YPPdoTpeZtfMR5an95a0bKCOFgThuWJcsp3I4hi/S2OhalyRInq6wkYdog2apQJYi3Vd3JKmStTRYU1svxXTTt64UouVvw3H3Hxb5aRn7IKKH6AVdEqzDtqwu965E16wThw4RkzntdHwYnpZIVxsAT1vYjsjit6vBLkCjgPEHGzb988nws4mL5TBU4vg/uVYlbz7QHOEwQYETfjNPmph9cjdvOO8WWziSj8Fjw+1VzxqDA1+abMKYLg+pKYXVSxcolZw7CV86tRO9uZd6WLAclgfVlmx+4Gr+59RzjuPG2niCmFYQVZQnTs8CUAKdmYrdkuUO5a9KUOuRrnRLkB0OrJcvOwJ4VuH1GZhWlTPsc5LBnKKsLacd3h3SGJauEmIK9CoORejwXpQ4mYL4qf3DjBJyixztj+bnl65SnH9wW0ZgVMNG2ddpf5iDPt08RJQn7VLV1deE3rznTVd7SBDEk4duYV9nG9cqSFXwaJ6gvV5Scc0of1/NhWSS+cOk4AOFadrp7bPVRkiCmN8kvXjbOlsbZFE7w8pcuMr5XlpV4vgmGbUApL03gEo+YKmFhtfplSxjNpqI0EWgfMK9pEUUGr6ri63/h7Evxq1umAMi8nVeUlggHWQLzG7/mk5U5zzY2nuEQFuX2C0abvnu5WlBKM0vrU+LJRZExg1nqZXyyCCH4/o0TMaCqwvjuxSCnOHmEye2ZhWtZ7DeVlSQwdmDGadsW94uIlaEl37oM543pp+clLoOv+0SCGGOC1TLznfeNx28/MjVTJohjWAjGpOG9bXJacbNkebULVm9W300nqUqIfY9H3pL11B3TMH6ou+M6X8+m6UJuKt39etfT0hS0khV0AAlFx5Iw+/pheN/urufDmi7MVmS+YX5Mf5vsWVmGd75+iXu5+t+yEvGbnLOpm3guteVZ/u3LjdIe/99zuXyks8grpw/uGWp+YViy0mnv+hPdvrD86U4Gupe7hyrhfbKG9+mG3t20Z8KpW+Aduvlny6qMDe5Vidqv1eDb150lzOcTF47BynsvN75Xj/K2hLM4doN6OViPrFOC4ONkWdN6tyGZZjbYSRb9r6xfqVNZxnRhgmBY72624/x30U8a0rsSg3VFUMYnK0GI8Wxbp+JS6bTtxdc7rIbraQDuC5bM4ZTsmVnDTHgtOEgk7DLxPlmJhHfYk9JEwqijBIHNkuXpGxdS9xVOEKICI46WrDKPniKsEA6GeVwy/XOfmoH/eXyx8b0kQfD212tQkiB4/O0dxvFT+rsriZml0uKSnbbr8TNO96wsxUBuWmDqyD747xcuxMi+3dFhWYHy0udn4obfLpTPPEeEYdbnCWNVajKdRqXHZuhCx/dC0WxjgHX/OitWnyKvGEHgpsFMlqzShElhIwQYLVguz9O/qgILZ1+KrmQaozye8zSl+OKl43DTlOFavpx4I/t1w976NltbMftkyVk6rNe7ce91Z2GQaJsYvjzJx8SpTWemCxOmlwu7kuU8uLP7wlauUwpMHtEba/Y1ADAr1CUkMxXGimPlJtMU3cqsW/V4KFm27/6e3fYu8Qo/a/6sLXpN02rThXZLFr8KVcanKlNH3ObWlvpyuz4MCtqSFZRwfLLCxW17C8D/YHmFZWWGFVkr3Mh+9iB2o/r3wAjO8ibnnMoSi887RZ7209CpxXwOABOH90bv7mW2cr0GFlmevCPcTcK99ob0++CHscYjTYMpTGGZ208GvC1Z5vpnA7Xz6kL9L2CxZFmULMmebHifbhg9oIdnv0GpNniJni/2bmu3ZJHMdKHAh8kLr7b5yYvGorw0gT9/fLptxwq/rdpbybJbrqzfnZ5hFqaDgFMwuOv5O01IZkywxslKp6kt5Ee2+/hZYdPVjPauTEcjsp5bnfMFP8+ESIlKpjLThYmE9wt4WQkRjgmszXv1T2FtTVx03aCXf1Bc8dpsmlLgL588D3/4mPegXkqc68HvWOnWCfsJ3eBlQXPqePzu0s5j6pTg3vkF5ZR+7m/2jCf+91y8+dWLPdN5xbPx21eGZQH1Klc0qCpLljw9OCVL5J9p9YkjHkoWg8JsHS0vTZj6mrDDbFjF4fsIY3WX/t2wbpPMOZuS5V6aKR8vZp0+0NEBXvYpYUW99uVZeOqOacZx1veUJawWR+v1dgsNg7dkWSOjA+a6LUkQ22q5Us6SZd1k2ssnyzoweFXpSEu/x1uyhG3SkNEsl/N0od3xPcVNF4p8tqxYfbIG6PeeGQ48rw+p+ypoJUtUB/O+VmPTsiMpm4S7vtDTkkUpZp42AGeP7O2azgsPg1JWecokcu5gsp8uTFk6cVPxtrdnf2Srqlw5YQhOHegdGd9rutC6mawXYQWxdbOgPXXnNGFEbbW6UJ7u3HTh926YYDtvfQljVevk+cBq3hqBvaK0BBWl/i1ZslgHWP5rJqq7XYGwOnEb8kmIl80vMGYLJV9GWP91xpCeuOTMzKrjpL7yztuS5dYHJjJpEmb5APMMTIJbLcfuL9s2pqK0xGS5IiRTRz2cXr6FR52xKv28T5aoy7FZsjzKLSF2n6tkOm20IWvQVxF8X5ogwCVnDMKTd1Tj85ec5n6hcb1UMk8KWskSPRaDe1Vi8og+uRbFxNkjMorQj94/CQ9/6GzPa7ymidIWJ0dXiPxqGSv2qM/Oaf2UYY2NY8VJucjGGuImnzXboNv3hB1Q3asdfLh6JL5+1Rk4Td/KqF+PctdrwrJkuSlMl5wxSLj6UFmy5OFfskT1Zn0JY/4kXtOFgLtPlpcF3S9urW1QrwpMGNYLD35wkuk4AReMlBCTEujWhpzCPrjhpMTJPiVOj0GXru1a3R5E28g4vU8blixuutCkiHKfeesfS3v7BaPx9avOwMcvHG30D4yqilJ8bMYoPHvXDEwf472C2atG3SzuDwnGO5a6zIj4TkyyWxH5ZL205oDxIp0QnBflwWDPy6VnDjbukZcFNKz+q6CVLCdy0be7FfHS5y80Pn/o3BG4uXqkZ35elqzpo9nyXinxnPFQdm6Z5i2rPU8fnZzDcWdLlv+8RZdYD/HK6s3njsAZIa/qc8JLiXLyTcucJ/jcJafhU7PGAtAUHG1FpZiwLFlBVtIqJSs8bEqWXrVe8fMoqL4SS/teYZkuFEVkzwrbdGGGEkIw54sX4aJxlnAoxBzCYf43Lsk8JzLvlD6amVM/IPsu4tSm+3XXXtLOtygw1nw1K5U4j3JuyxljupD3yXKYLmTZlZcm8LlLTkNFqRbg9qEPZpQdQgjuv3EiJo3ojec+NcNWtu1neVSqtT0+dltmJbdomzVHS5ZDMaLpwn+9ux+vrDuo5eNzutDvNkFhUpxKVo4DhbohqxS5KVlz774Y9984Uc/PO0OZIsMIP+Fn+OaDwolwi1Ysi30z24yE9k1Z7bLlgmX3XI4l37rM8byXg6rfth2WkmUVy7qBq0gqNV0YDFGbtyrnrK07hnCw3BHWrirLSsxKVmm4PqxullPndZDmmE+DelVi6si++jlv/DQz1g9MPaUP3v56jW9LllP/NXpAD7z+lVn4+lXmafOzR/bGB88Zgev1GQI3nyz2gpVKU9PCBQYvo7Zazl0mP3zrGnMID68crb5nV09037824zfG5kHZH4fxwKGe6po7tfMJ4uhPeNG4AVh2z2UWnyxnmaKmOJWsHFQelSyHNZR/f/YCfNjFouVmtj9tUJVx3q1MmUHN7yDtltoad0REz8pSqbT9q8QOqUGmAkRvp24vajnaQxkA0LdHOYb0dgiMCHcl68Ypw/D/LtIsWG5O/TyhTRda7sPwvvZVp1aUjhUMUZu3DqQsjZMvEQsu2t2ylL97eYlpatcrNIdf3Fqb29SmdeqrQsLC5vXi5lQWAEwY1guj+mdeFKz1OGGYONClW5s+fXBPmyW6f48K/Ox/JmOo/sy77V3IpklPtHXZVuEBdp+sr15xOm6aMgwfOGe4s1CSTB/Tz+irZfC7YILdI9kXL231oD0t306cxobykoQt+KzwmcqRMaYolaxckJKMtcXu7dRT+uI71493TDegSs4nyK1DIdxfp2RRKKBOeS6cfSnmf+MSAJnOqb/gd9573Vn49rXam9Tcuy82RVd3epgXfPMSvPrli0zHXKcLLcf4B0xmlaSTrhJ2fU6wRF7m+dUtU7VwFD7I53Shmi30x58/Ph3/+uwFUvXGlCOnQL0PfnAS5t49y2gvbEuU7uUlJqtYRdg+WZYHhVesbM8Qsadj/USFbmHrTHr3s36aGUtLLVYgq2h9upcJFS3fewDqf9PpzO9zmi5k23e1d6VsGylrMmekTBDtxfSXt0z1DP8hCx9zzetnBt0LsdQyXejU95YQsaWKiSjy2cqUZT+eT0tWUQYjzUXldSblBi/+hjuJ9cLnZmLH0WbJ/IKdk8Zm8nFL7F4Hw/tkrB09K8vw4w9MwqzT7dvTfHzmGKPjOW1QFa6ZOBTzNh8FkHmYn/vUDLRxy4RH9O2OQw3tZlFt04XOmJLS+CgE00b3w69vnYov/m2VVHovuTtD2gw9SPwihT/Ys7HlcJNn2tMHV+G71483pqGsVJSW4LRBdj9D67ZUYUfltz5zvGJlVbL4l0JrCAdmyeqQULJ8DfgWpcrpSqfI7P4tONpf1nf1qCh1tIadMaQnnvnEdIwf2gvzt9bZBDQFI/Vx32RT+nkf4+vmi5d6r9Zj4pZaHN+dcFKi+AjuTlUgvG9x98kihFxNCNlMCNlGCJktOF9BCPm7fn4pIWS0fnw0IaSNELJa//dYyPI7yRt6ntZ4SNYI4jI4dQZTRvaRN6O6WrIyE/kfOGeEQ5rwkTW73jr9FJPiZVxvuZx/u2H1Mn1MP1xsUdBsW3BY/rrJGFY9DHbaEy0LLj1TfkNqr9lAPkggAPQPuIqykKf+Cq7/kklDCO6cOcbYv0+WqOMIurVH5+lCu38Rs7C5KVnGtjJEUzplsFqyMpmZv/qxkriWp6dv69TGi6qKUlfF9qJxA9G/qkIcJ0tCPh4/cQwBYOZpA4zPXrnz5d99pT18i5VMAFDtvk4b3ReAsy+yyPEd4CyeLtvqiI6Kp+C9pA4HTyWLEFIC4BEA1wAYD+BWQoh13usTAI5TSk8D8AsAP+HObaeUTtH/fTokud1lDnjd3Ltn4S+fPE94ztpBdHSljYbDm9y/ebXzzuDuVig5qV1TcSetCglTEsNUQKPyZbL6HjhiVbKsypqLgKZVOz5k+5Zl5/eePvZVlMWvQtOrWyneP1XslyH7MjD/G5dg6T3ODvm7jrX6EyomFGT/FWHn3y1kHywrQboEAt5CwfpUTU6Z9psgBP/49AV4/SuzbOfWfe9Kc1kOfYZVIUkQ8ctj0JeNls4kAKCqslRKQfL0ycryrefTF5+KRz5yjunYb26dKgy/IMJv6YYlS//w61unYs4XL3SM++fk+L5w2zHjvB9rntgCmRstS8aSNR3ANkrpDkppJ4BnAdxoSXMjgD/pn58HcBnJ43xC0JJPG9QTfRx8XqzjdTv38I/j3qI+U2PetkEW2fbiQ+cwMdAh0rEnLr0mG9iZk21Q3JqKW71YH0KWz+d083X/HtxvtuTD5+tnE2Vr/JkokLEM8ikIIfjFh6cI01ktWU6M7Nfdl1XOJmF8LV0F2H9FV3Q3CUvWHReMFsY9k8HtWXJzfB+jr1Yd1kdrg4Yly6X9Zhzfgd7dyoSbqzsrNOa4g1bRHFc7B2zorZ2Z6UKpBUrGpIT4ZTBby/Lsa87EdWcPNR3rVl5irBr2bIM+y7c6vncvL8WEYc7+p24+V5nz8uXn05Il45M1HMBe7vs+AFZzj5GGUpokhDQAYCPvGELIKgCNAO6llM63FkAIuQvAXQAwePBg1NbWSgl/+HC77VhtbS2Ot/v3Q2Fl7mkUvzm1tZvL2rBpK9JHtI6gqSnjT2WVnf/e5TDpXVtbi/cOJT1lA4COlMsSaeaMT6lNjoaGBgDA6tWr0Lrb3NHef0Elfr+uE3ub0lizeo3p3KJFixxlAYCnr+6B3euXY7eH3G6wdM3NzaitrcXmvV3GuWPHjjnm09hprotksgu1tbUYrsu1dFGmqbV0mdO+/fbbxudDhw+joUM7f8eEckwaUIKvvt1mkq2+Xrv/69aty+o3yqTrdLjHfB6b9ml1dOjQIdTWHndM39zWYTrW1dWZlXyMY/X1pmvfO2Z/bpYtW459VXlfWxN5/wUE78NYm+c51GLvv/zeHyc2b1gHHMw8/6J8a3oBNVd0cy1TJDcANDY2OV7X3NJiOpdKaX3eggULcVoZMHt6JXBwI2oPbsSRg1o7fW/LVtQmRb0LkOzSnoF1a9cCB8VD2YL581HB7eO3Ve9b9h84iNraemzU+90jR4/im9MqMWdHF9YfS6H+2DE0tdufwwMH9qO2tk5YFs/g7gSHWzP98MGjWn+yecNaHLTcX1F9dXZo/c2JE8cxc1gpFh1IoqsrCabdrFu7Fqn97gqzTB9hlWHLce05bmhocL3/SxYtdpRfdN3+/fsAAAcP7ENt7VFXWQBgwfx3UC+of8aypUvQs1ysJR2tq7PJ8N5776H3ia2mYxsOO4+5gHMb90vUju8HAZxCKT1GCDkXwAuEkAmU0kY+EaX0CQBPAEB1dTWtqamRynxx63tYcnCH6VhNTQ0ON7YDtW/6EpSVuelQI7DI3o+Wl1cAnKI14pTROPeMQcDiBaiqqgIaG0354NU55u8AulJp4PVXAAB/vL0an/jTCiMN2XIUj6xeZirzxc/NRP+qctNmzO1dKeCNV4W/gSQSQDoNQohWri7D2IE9UFZaApxoxAXTqzGRrWB7dQ4+W3MqPnb1mZhzcDH2NtVj8pTJwPKlRp4zLrjAVJeu90Yvz29alq62thY1NTXYt2Q3sGE9AKBP336oqZkuzKK+pRN46w3je0V5uWOZDa1dwJuvm8vUZRg8eDASTR1A/TFcfv5UXDhuAH6/+R2UlyZQU6MFln1270qsqzuE08+aALz7rjAfN6xyvXjqCfTrUY6LHppnS9eRzNzjH71/Eu759zpbHg2r9wPrV+O0UcNRU6PFUBPVP33zFfCxosvLy4FOu6Jlkk/i9/Tr1w+oO2pcW769ztRuAOCiC8637XFWYEj1X0DwPoy1eZ4dR5uB+W+bjsnm54h+Ty+YXo1JI3oL+yc/GHJb2kpVzyrU1HCrfrnz3bp1N5VX+vbrQFcXLrxwJvp0L8elXD59Tj2Blx9ZiNuvOi/TX1kYtLIWzUdbMGXKZCPA6Zmr30FnKo0dR1sAABdfPMsUpuLgsj3AhnUYOmQoamrORtu6g8DqdzFgwAB85oPVOPzSBqxftAuDBg5EsqENaGwwlXnKyBGoqbFve2RlTnUHth1pxvljNV39wdXvAGjCzPOqsfFAI7B+LQAt/pnoHvRYUQu0tqBfv774yyfPBwB88fevY229pjhNnTLFcwaB9RGnjnKR2dIOeuyqB5YuRp/evVFTc4EwLQDMnHkBMG+u6VpTXpZ2MXLESGD3TowedQpqaswxuURlXFpTg/0n2oB35gmTzbzgAvTtUSYcC/v3H4CammpTfhMnjEfN2eZFIsmNh4FVKxxFqaqqyv65g9x04X4AfICnEfoxYRpCSCmA3gCOUUo7KKXHAIBSuhLAdgCnZys04+4rxVllYwV0MlFaTd23zxydmc8XKNyvf2UWfnrzZEe5rGb7WeMGwMrkkX1MCpYs1l/w1ldr0NimPZy9u2WmQ3c9eB2+4eJDli/46ky6LHnpUWGuQ1l/NStJgeXo1S/PMkXu79VNex9pbOvCrgevcytJiskj+zgqIPwUwf9UixcwvO/sYfjalafj6x737x+fusD1fFBkplhjomDFtv9yIqSoG0Jkpguzwa1ZODmxi66ZMrIPdj14naOCBcDoG/kpxVe/PAtvfbXG+G7zwWJlGptLm2Vg37PdsH5AVYWhYAHc6sJy83Th3LsvdiiHyZtJe+OpZbbzbhh9xFXejukMt/t3/40ZRc1vCAcmrzWIqWP6BHH1O3OKo6Vh/xF+pwv/+4ULnU/6ROYXLwcwjhAyhhBSDuAWAC9Z0rwE4Hb984cAvEUppYSQgbrjKQghYwGMA7ADIVHhFK04Cy3L6b5ab5tTfBrG6YN74kPnmgdI1xhXko02aFwbpmQ5yZ1Z0myWw49zO+/HMSSbVXdcoV0uIQjY/Wd+dJedJb8qj6cjmXJ0gGWwemto6xKed+OOC0ZLpbtMX1XINwW3CPmfv3Qcqjw2jJ40wjpIheOI4NVeTx3Yw/V8Dolt/+UEe6ELc8ubW6dremYvLuBkFD4pbgpiQ6v52cm2/BF6QNx9x+2LMkRKCl9mposxnzf2DBTINrp/d9w+Y3QgWZNcnDKmZJ05pKcpICqPyPG9JEGM1dkyfl2sj/CzqTy/YtPKx7jf7vfeeSmvIpyCtrJzTkqW6Lgf/61BPStclXu/eNa+7qPweQCvASgB8CSldAMh5H4AKyilLwH4I4BnCCHbANRD68gAYBaA+wkhXdDmLD5NKa23lxIu2UVyFV/rxzlaJmc3Gb9+1Rk455S+4jwIwf994jzc9selwvNO3Hf9ePzw5fd8RfUF/C0DXvGdy5FKUbR2pXyXYy4zg8jKxLPkW5ehW1kJWruSvpezM2Scw2+ZPhJ/WLATV4wf7Dv/793gPL3wo/dPwoYDDfjL0j3GMb6TCH8cDMdMYg+fkTc/cVcKsf9iSlafbuU41GX3Ow3CAzdNwh0XjDEiYa++74rQY2QB7v1kU4fYByZoi7zzgtH4+/K9RhBPHkIIQKnAkkWEZbLvCRdloPbrlwSUFHjqzml4cfV+DOxZIfVC7aRAZJSgaJ43p5dtK36fd6Z8e20bxsOSJohdeXdzfBfdO3GAUnEGYVet1EhIKX0ZwMuWY/dxn9sB3Cy47p8A/pmljL7JppKcblwYJnxZuT550RhnK52PfIBMQNCbq0dKbVSdDczaI1YP5eH7abfpQgDGFjW9ETyUQntXynMbj9MG9bRNEw5z2R5Hlo+cdwrefO8w/rJ0j2eAxDAIayoqniqVmELrv9j6lV7dSnHI5v0VjJIEwRlDMqvv+nQPFi/NiVe+dBGu+dV8X5bvbNvQuME9sf1H1wrPXTVhMF5ed8g+kDpMD7LvLH2ChPvacPrgnvj6VdrUPrPQuG15lbGoOVuyI8Uje+LTyGoEmy2Rl5tw98JaV25xskQvD0KFykGUsF8YizPiexbXyvpk8cj2K6bo7y5CZnuTWd4r7r0867g4udzXL1NmptCk5PZF2dCe9FayrKy49/LQ9n3LdPR2U33Yb1Vh7WVoD58RSrYK8NHP874yUxqn6fZ+Pcq1BSo55hcfnoLvvK/TvlOBJV3mu9lC5NfnyA/Mq8Lthceyj7L9fETieXUPuoHQ9wjFtuwJYskS4TWV6CevqCmcp9gH2ZhSnTZqzmWcDa8G4XSadShsaeuAqgpf8/FW1tx3pXeiCOCf87CCJ4ru1Z8/rq1alI0lxTOgqsLTH0oW1l6Z1S7KOEnpHO1lqLbdCU7GqpJfOfzgFG/q7a/XYOW9l+dcnorSEgztbd9dgjn+M3cGY+9Cy3RWgiCyDp5FOXcLtGpEfHcQIUolEHBT7oIpoYcbtVAyfvZZNDZBF5yzKs8zT+vveI7Pi8fpF4RdtcWpZGVxrZPD9mdrTsXtM0ZlkbMZNxmtO7nLXtyjohQPfmASvjkt+DQWIZkd6Ht3LwvJg8fO85+e4TiInD2iDwDgnFP64De3niNO5BNRcMXJI7VybpoyLCvr4T8+PQNfqw4Y7BVAT11Zaxb4rISprHzxsnGhRahXOlR0DO6ltaXrzh6K39w6Ff/49Iw8S+RNxpJlpmdlGfrrvpLnj+0nvDYMf1dZrp04FP9zRhm+cbW24o41YyYBU4CitNQy5c+69yoPm/LyUnbCxssH11hQQIBf3TIFj90m1z9311eC+/FpdV1daPn9U/S+XHQOEN+/qBVVRnFOF2bjk5UgeOMrs3DFL94xHe9ZWYrv3zgRf1q8O0vpouWW6aegttb/AqgHPzAJP319M845pS9e+vyFtmml7uUleOcbwR0/rVSP7octD1wjXNZ97qi+WPPdK03hJrKlsqwE879xiSkuVe9uZdj8wNUoL0ngfWe34ievbsL0MeKBwI1po/uhZVcpgA7PtCJ66b+zqd09OF623H3F6fjXu/tsx5e5bKfjhLX/C8uqpwAG9arE2u9diZ4VpbG3CK6893IkCEFds9b2nRSmLQ9cY5sqevrO6XhmyW70Ddk/zI1EguDaMeWGRcU6Vc82ME5RalmoFB7D9VWRXS6Lepx8sgwH/YjNI+4WNAoCghuniLfyEvHd6yfg4zPH+NpVwn23D5FcGiIbhUih6uUwvoT9xBWpJSu7ahrHbc/ABg+3l60gb2LZdJ5Ovy+bXz12YBUe/ei5KC9NoCRBjDc6aqx0Kgu8es+J0pKE43RmmAoWQxS3qaK0BIQQjB7QA7+77VzXBQdRwX5ri8PqqzBhTfUD3F6HgwKE27C2QeuS53irBvGnV2VZ7BUsAOhfVYG+PcodLVmM8tKEzTIxeWQf/PTmyZGscpTFWsXM4m2dVg/zXsj0baUeliw3n6Ss8BjKMlN47gmfvKMaH585xvjeu1uZ77AIXtvqOKWVnS6cMrIPzgvwUu2XolKy2P5Xbj38uaP6mr6fPaI3/t9FYxxSA9dNGmo7NkMPMhfGKoS+3F6Jv751Kn5w08Ss8wyTzCqc+Hf4hcqgnhW4c+Zo/OH26sjKsE6Df+UK55iak4b3xpUWs751WkA1BwUP8dKyYk5mujBjyeIJu7nPvuZM/P5jzs/72IFsD0Hz8Vz1x05j2wfO0V7OSj1MaZeeORj3XW/dh92bL1x6Gs7UV8EaCp3lXvzp49Ntv5//6idO1jUTh9iOhV23RWnjd6uj88f2w8rdmX2c+KjeMqz73pW2IJjnjOqLTYeaAsnIb9x8w+RhDqnF18oeV8QbQgi+e733Vh2ylJck0MkFcd14/1W+/A/+84ULsbOuBa9vPGwcs26+myt/BkVhwFpDWKtXc4XVYZ/5w1pDx4Td3D998amu58cP6w1gLw6cEPttRRXCgY1HZ48UW53uv3EivnH1mY4LxLLlq1eega9eqfvLOfzEi08faDvmaclyqK9ctNbiVLJcz8k3zp6cVZfdDN5xeFifbnjlSxfh1IFV+CsXTFIG9lCH5YisUDCW3HOZaerRbUVP9WC56VFbx6V0LAWHsUovz3L4xmKA46cLTaFUctzgWTT7I01mP082TRfVDOu4wT0x54sX4gzLSxWjJEF8uXKUJAgqEsFahR9FMuFpyXJQsnLQYItqupCZFcN4y978wNX4xSXdPd9gzhraK5BWn9QtDWU+grMxHJeeRtARMJ80r81IFfGhX49yz30DCQHeu/9qfHaKnJ+dmw+EU/6Kkwe2Xc/00dH7uISJsbpQHzvKSvXpwogtWV700F+M2rvEYR6itCRPGNbbe4W7JBvvvwq/ujTYHqZ+fiHxsmQ5ZKYsWQGRbX+zBGZHRkVpCUoTBGeP6INnl+/N+HuFRJf+EJcFaMy59I/q26Mcb371YuPNqtBZ9Z0rHDeqPRm4aNwAPLt8L6oqStGtvMSxsx7auxK9u5UZezXalrRHLqmikOhfVYG5d1+Mkf0Kq5+w9qWsP7ZOe+Zayequx/Nq7RQvhsnnYgE/VJSWoCygrCUJgiG9KnH3lafjG8+vdU3L3x/ZbXUA8aK1vGyrU2i4WXT4CmTBKN24dfpInDe2H04dWBWGaAYZS1Z4xsSoOoKwf3s+6dsjd8vF48j9N07EZ2tO89xWpbKsBGu+eyVGz54DwN5xKUuVwsppgwqvn8hYsrS/zKE7mbaGcMhtg2dKllPs4DjqWKvvu8JmAcwGQgiW6OFlvJQs/iUw2w2iVTBSF4y93xwq6a//7zzfjwohJBIlg8VI8bPNAMPR8T0bgRQnBeWlCZzS37/5vqI0gZ/dPNn4rhzfFcVAJh6V9r2cmy6U3QYtCrwio8dxQ/Y+3cuNwLO54vH/PRevfvkikxIlGycLyM1CjaJSstwY2a8bLjh1QL7FMGB78oVhyRrauxKfvHAM/iRhmVNEx2O3nYsfvX9SvsWIBEIIPnjuCO67R/oYDgIKhZXzx/bDbeefgp988GwALtOFOZaLRUi3UmCLNyPnqglDcOaQXubVhVk6vqsNoiUQ1WfcOv0ks2QFcHxnpmRGghDc+z7/MUkU4XK1HnPlnn+vy7Mk4WNfXBiv50mhCEJpSQIP3JR5MWLT4tZpL6+FJGHT3WPPVmVINsNbHXl/tbISgq4Udawv5fgeENEAEHWjfPau831tD9GVhU/WhGGW6NrqgYstfGT1qPnzx6dHNhhY3wTj6BOiUGQLs4KkubUxn7xwDD7lEdcqbJxW94WtFMy9exaONAbbDixO8N0Rb8nq3a0cdc0d/ixZyifLmUw0XPs545B+8sPVI0Mt+/yx/XHGEHFsERHMJytICAcAplU8SsmKL+ePzV3oi1mnDwx9FSwjyg1zFYq4wCxZyXTaGDOumjjEFDQ6DoT1+J02qCcuOC0+bjROzPfYN9cUJ4v7wgKGO20D5LU9UBgUlZL1mRrtbUPUAJk5kZ0b0tv/fm1hMX5oL8Mny2t7AhmUE3Lx081j+iAq2KBjbaaiNnfVhMxWPKpJKgoRY7owpr5Pl54xCADQrTw//UG+8LLQ84oVvxK6jx44tbldHArj0jMH2Y6F3XUVzXThXbPG4tbppwDIDADnjuprbKFjrbh8PUPL7rkMVZWlSKUp3tlShy9celrWearxrPj4yyfPw2sbDgEAVt57Ocoi2sbCC6cAvyIl6vH/rcbZ33sNjQ4dmkIRd9gAnU7TvM+Jf+Xy03HqILNl+gc3TcQXLjtN7RRigTg4vp87ui9W7D6OSoeX1DOH9MKuB6/DF/62Cv17lOPpRbtCl61olKx7rj3L+JxIELz4uZkYM7AHzv7e63mUys4gbqPebDYE5ueSlSUrvgQ1R888bQBm6mb8XC+LFiHwcsT8b1xiW4X17F0zcO2v5+dKLIUiVFhfmuK21cnXir4vXT7Odqy8NIERfXPrhF8IOE0Xfv3KM1Bz+iBMHtnH9frf3DoV24824+lFu0IP9l1U04U8k0f2QS9O2x+rx7r60Lkj0K9HOT50zginSwsOpWMpouKXt0zFuEFVgm11NBP+qP7mN+1c+DgoFFHBry7MbB6t2nQcuGrCYNx9xenCcwO4F1Hezbm0JCG9JZzh0x1YQjFFY8lyYmDPChxt6sAvPqwFUhzZrzve/c4VeZYqXMKMGq9Q8NwweRhumDzM+P71q87Aw69txuBe7j6Nudz6SaEICxYcWjXf+PH4/zrP/Fw5fjAmDOuFDQca0S+w5T8aLavolaz/fP5CbDncVLRz2FdPGILZ15yZbzEUHE/dMQ13Pr0832JEwmdrTsXwPt1w3dlD8y2KQhE6pw6swl2zxuIj00/x3MpFER8IIXjhczMxZ+1BXJ9l3xS2fl30JpAhvStdN4IudO659iyMjmjZviIYl5w5CDefWzzT0TyEENw0dbij9VTNrCgKmUSC2PpU1aQLg7KSBG6aOjywFT2qvqvolaxiR5m148nJfl9O8p+vKAZUIz4pUY7vCgDAx2aMAgD07SEfZV6RO66ZpJmszzmlb54lUSgUQWDW6LFqpuCkICqLZdH7ZBUrd806FXfNyu1WDwp5LjljEHY9eF2+xcg5bHVWRZl6f1MUNjdXj8TNIe8MoogvbAF12IGfC17Jevau87H/eFu+xVAoFADOHNITX7psHD48TQ1OCoWicDh1YBW+cvnp+OC54e43W/BKVi73hlMoFO4QQvAVh1g2CoVCEVcIIcIAsNkiZdMnhFxNCNlMCNlGCJktOF9BCPm7fn4pIWQ0d+5b+vHNhJCrQpRdoVAoPFH9l0KhyBeeShYhpATAIwCuATAewK2EkPGWZJ8AcJxSehqAXwD4iX7teAC3AJgA4GoAj+r5KRQKReSo/kuhUOQTGUvWdADbKKU7KKWdAJ4FcKMlzY0A/qR/fh7AZURbB3kjgGcppR2U0p0Atun5KRQKRS5Q/ZdCocgbMj5ZwwHs5b7vA3CeUxpKaZIQ0gCgv358ieVam1cZIeQuAHcBwODBg1FbWyspfrQ0NzfHRhY/KLlzT6HKXqhy+yDy/gsI3ocVav0XqtxA4cqu5M4tYckdC8d3SukTAJ4AgOrqalpTU5NfgXRqa2sRF1n8oOTOPYUqe6HKHTeC9mGFWv+FKjdQuLIruXNLWHLLTBfuB8Cvxx6hHxOmIYSUAugN4JjktQqFQhEVqv9SKBR5Q0bJWg5gHCFkDCGkHJoj6EuWNC8BuF3//CEAb1FKqX78Fn31zhgA4wAsC0d0hUKh8ET1XwqFIm94ThfqPgqfB/AagBIAT1JKNxBC7gewglL6EoA/AniGELINQD20jgx6uucAbASQBPA5Smkqot+iUCgUJlT/pVAo8gmhUW09HRBCyFEAu/Mth84AAHX5FiIASu7cU6iyx0XuUZTSgfkWIgx89mFxqX+/FKrcQOHKruTOLX7kduy/YqdkxQlCyApKaXW+5fCLkjv3FKrshSp3sVCo9V+ocgOFK7uSO7eEJbfaxVWhUCgUCoUiApSSpVAoFAqFQhEBSsly54l8CxAQJXfuKVTZC1XuYqFQ679Q5QYKV3Yld24JRW7lk6VQKBQKhUIRAcqSpVAoFAqFQhEBSslSKBQKhUKhiAClZHEQQvoRQt4ghGzV//Z1SduLELKPEPLbXMroIIun3ISQKYSQxYSQDYSQtYSQD+dDVl2Wqwkhmwkh2wghswXnKwghf9fPLyWEjM6DmDYk5L6bELJRr983CSGj8iGnFS+5uXQfJIRQQkjBLbeOO0Xc5mcRQt4lhCQJIR/Kh4wiivVZJYR8mhCyjhCymhCygBAyPh9yiijUfkaizu8ghBzV63w1IeSTvgqglKp/+j8ADwGYrX+eDeAnLml/BeCvAH5bCHIDOB3AOP3zMAAHAfTJg6wlALYDGAugHMAaAOMtaT4L4DH98y0A/h6DOpaR+xIA3fXPnykUufV0PQG8A2AJgOp8y11M/4q8zY8GcDaAPwP4UL5l9iF3QT6rAHpxn28A8Gq+5ZaVXU8Xq35Gss7vyGacV5YsMzcC+JP++U8AbhIlIoScC2AwgNdzI5YnnnJTSrdQSrfqnw8AOAIgHxG2pwPYRindQSntBPAsNPl5+N/zPIDLCCEkhzKK8JSbUjqPUtqqf10CbUPhfCNT3wDwAwA/AdCeS+FOEoq5ze+ilK4FkM6HgA4U7bNKKW3kvvYAEJeVa4Xaz8jKHRilZJkZTCk9qH8+BE2RMkEISQD4GYCv5VIwDzzl5iGETIemtW+PWjABwwHs5b7v048J01BKkwAaAPTPiXTOyMjN8wkAr0QqkRyechNCzgEwklI6J5eCnUScLG0+LhTtswoAhJDPEUK2Q5vB+GKOZPOiUPsZ2bbyQX1q+XlCyEg/BXhuEF1sEELmAhgiOPVt/gullBJCRG8JnwXwMqV0Xy5fNEOQm+UzFMAzAG6nlMbp7bNoIITcBqAawMX5lsUL/aXh59BM4grFSUUhPasMSukjAB4hhHwEwL0Abs+zSJ4UeD/zHwB/o5R2EEI+Bc3ifKnsxSedkkUpvdzpHCHkMCFkKKX0oK6MHBEkmwHgIkLIZwFUASgnhDRTSh0d/cIgBLlBCOkFYA6Ab1NKl0Qkqhf7AfBvAiP0Y6I0+wghpQB6AziWG/EckZEbhJDLoSm+F1NKO3IkmxtecvcEMBFArf7SMATAS4SQGyilK3ImZXFT1G0+hhTrs2rlWQC/i1QieQq1n/Gsc0op/xz+AZoFUZ58O57F6R+Ah2F2IH/II/0diIfju6fc0KYH3wTw5TzLWgpgB4AxyDgaTrCk+RzMTsDPxaCOZeSeCm0Kdly+5fUjtyV9LWLgkFpM/4q5zXNpn0Z8HN+L9lnl5QVwPYAV+Zbbb1vR08ein5Gs86Hc5/cDWOKrjHz/yDj9g+YD8SaArQDmAuinH68G8AdB+rgoWZ5yA7gNQBeA1dy/KXmS91oAW/RO7tv6sfsB3KB/rgTwDwDbACwDMDbfdSwp91wAh7n6fSnfMsvIbUkbi86v2P4VcZufBs2PpQWa5W1DvmWWlLsgn1Voq9o36DLPg4siEzfZLWlj089I1PmP9Tpfo9f5mX7yV9vqKBQKhUKhUESAWl2oUCgUCoVCEQFKyVIoFAqFQqGIAKVkKRQKhUKhUESAUrIUCoVCoVAoIkApWQqFQqFQKBQRoJQshUKhUCgUighQSpZCoVAoFApFBPx/3aLh5YWUKHcAAAAASUVORK5CYII=\n", "text/plain": [ "<Figure size 720x288 with 2 Axes>" ] @@ -1272,7 +1320,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 24, "id": "2e386180", "metadata": {}, "outputs": [ @@ -1352,7 +1400,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 25, "id": "97e9a32d", "metadata": {}, "outputs": [ @@ -1361,20 +1409,20 @@ "output_type": "stream", "text": [ "noise sigma = 4.0000 (= 4.0000)\n", - "noise power = 16.0000 (= 16.0342)\n", + "noise power = 16.0000 (= 16.0053)\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) = 31.994717\n", + "sigma / std(Y_rfft) = 32.002165\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 std (fft) = 0.125021\n", + "noise bin std (rfft) = 0.124992\n", + "noise bin.re std = 0.087283\n", + "noise bin.im std = 0.089468\n", + "noise bin power = 0.015623\n", + "noise bin.re power + bin.im power = 0.015623\n", + "noise bins power = 15.997835 (= 16.005315)\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"