examples/Introduction_to_Iterative_Detection_and_Decoding.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction to Iterative Detection and Decoding\n",
    "In this notebook, you will learn how to set-up an iterative detection and decoding (IDD) scheme (first presented in [1]) by combining multiple available components in Sionna.\n",
    "\n",
    "For a gentle introduction to MIMO simulations, we refer to the notebooks [\"Simple MIMO Simulations\"](https://nvlabs.github.io/sionna/examples/Simple_MIMO_Simulation.html) and [\"MIMO OFDM Transmissions over CDL\"](https://nvlabs.github.io/sionna/examples/MIMO_OFDM_Transmissions_over_CDL.html).\n",
    "\n",
    "You will evaluate the performance of IDD with OFDM MIMO detection and soft-input soft-output (SISO) LDPC decoding and compare it againts several non-iterative detectors, such as soft-output LMMSE, K-Best, and expectation propagation (EP), as well as iterative SISO MMSE-PIC detection [2].\n",
    "\n",
    "For the non-IDD models, the signal processing pipeline looks as follows:"
   ]
  },
  {
   "attachments": {
    "block_diagram.png": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![block_diagram.png](attachment:block_diagram.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Iterative Detection and Decoding\n",
    "The IDD MIMO receiver iteratively exchanges soft-information between the data detector and the channel decoder, which works as follows:"
   ]
  },
  {
   "attachments": {
    "idd_diagram.png": {
     "image/png": 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gwYONWvfr108SEhK82oK33npLduzYIQULFpRBgwZ59VwcHAEEEEAAAQQQQAABBBBAAAEEEEAAgXAVSOtoLoziEq49hnYjgAACCCCAAAIIIIAAAv4XuG6Qi1bx2Weflbp168qFCxekU6dOXqv1+vXrZcSIEcbxdQSZPHnyeO1cHBgBBBBAAAEEEEAAAQQQQAABBBBAAIFwFkjLaC6M4hLOPYa2I4AAAggggAACCCCAAAL+F0hRkEtERIRMmjRJIiMjZfHixTJ9+nSP1/zKlSvSoUMHuXTpkjRu3FgeeeQRj5+DAyKAAAIIIIAAAggggAACCCCAAAIIIIDANYHUjubCKC7X7FhCAAEEEEAAAQQQQAABBBDwvUCKgly0WpUqVZKuXbsaNezSpYscP37co7WdMGGCrFq1SrJlyybjx4/36LE5GAIIIIAAAggggAACCCCAAAIIIIAAAgg4C6RmNBdGcXH2owQBBBBAAAEEEEAAAQQQQMC3AikOctFq9erVS0qXLi2HDh2S1157zWM13b9/v3Tv3t04Xu/evY1zeOzgHAgBBBBAAAEEEEAAAQQQQAABBBBAAAEE3AqkdDQXRnFxS8gKBBBAAAEEEEAAAQQQQAABHwmkKshFR1kZN26cUbX3339fli1b5pFqvvTSS3Ly5EljtBgdJYaEAAIIIIAAAggggAACCCCAAAIIIIAAAr4RSMloLozi4ptrwVkQQAABBBBAAAEEEEAAAQSSF0hVkIseqkmTJtK6dWu5evWqdOjQQRITE5M/w3XWLliwQObMmSMREREyadIkiYyMvM4erEYAAQQQQAABBBBAAAEEEEAAAQQQQAABTwpcbzQXRnHxpDbHQgABBBBAAAEEEEAAAQQQSKtAqoNc9EQjRoyQ3Llzy99//y2DBw9O67nl3Llz8txzzxn7a8BM3bp103wsdkQAAQQQQAABBBBAAAEEEEAAAQQQQACBtAkkN5oLo7ikzZS9EEAAAQQQQAABBBBAAAEEPC+QpiCXggULypAhQ4zaDBgwQLZu3ZqmmvXu3Vt27dolsbGxMnDgwDQdg50QQAABBBBAAAEEEEAAAQQQQAABBBBAIP0C7kZzYRSX9NtyBAQQQAABBBBAAAEEEEAAAc8IpCnIRU/dvn17iY+PlwsXLkinTp1SXZt169bJqFGjjP30VUeGISGAAAIIIIAAAggggAACCCCAAAIIIICAfwRcjebCKC7+uRacFQEEEEAAAQQQQAABBBBAwLVAmoNcIiIiZOLEiRIZGSnff/+9TJs2zfUZXJReuXJFnn32Wbl06ZLcfffd8vDDD7vYiiIEEEAAAQQQQAABBBBAAAEEEEAAAQQQ8KWA42gujOLiS33OhQACCCCAAAIIIIAAAgggcD2BNAe56IFvuukmef31141zdO3aVY4dO3a98xnrx40bJ6tXr5aoqCgZO3ZsivZhIwQQQAABBBBAAAEEEEAAAQQQQAABBBDwroD9aC6M4uJda46OAAIIIIAAAggggAACCCCQeoF0Bbno6Xr27CllypSRw4cPiwa6XC/t27dPevToYWzWp08fKVWq1PV2YT0CCCCAAAIIIIAAAggggAACCCCAAAII+EjAOpoLo7j4CJzTIIAAAggggAACCCCAAAIIpFgg3UEuWbNmlfHjxxsnnDp1qixdujTZk7/44oty6tQpiYuLk1dffTXZbVmJAAIIIIAAAggggAACCCCAAAIIIIAAAr4V0NFcNNBF/5EQQAABBBBAAAEEEEAAAQQQCCSBdAe5aGPuvPNOeeyxx4x2dezYURITE122cf78+fL5559LhgwZZNKkSZIpUyaX21GIAAIIIIAAAggggAACCCCAAAIIIIAAAv4TqFy0lWiwCwkBBBBAAAEEEPC2wFVvn4DjI4AAAgiElIBHglxUZPjw4RITEyObNm2SgQMHOiGdPXtWXnjhBaNcA2Fq167ttA0FCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCAQPgIR4dNUWooAAggg4AEBjw2lUqBAARkyZIi0b9/eCHJp3bq1lCtXzlbFXr16ye7du6VQoUIyYMAAW3k4LFy5csUI/tEAoM2bN8uWLVvk6NGjcubMGTl9+rScP39erl4lTjUc+gJtRCBcBSIjIyU6Otr4lytXLilVqpSUL1/e+Fe5cmXJnj17uNLQ7jAT0KDfDRs2GPcDek+wfft2YxpHvR/Qf0lJSWEmQnMRQCDcBXT62xw5chj3CHny5JEyZcoY9wcVKlSQm266STJnzhzuRLQ/RAUO7D8oWzZtk4StO2Tb1u1y8MAhOXP6rJw9Y/l39pzo5wgkBBBAIFwEIiIiJCoqm+SIziHZc2SXfPljpHSZUlLmhlJSrkJZKVGyWLhQ0E4E/CLAfYlf2Dmpg8DtT0RK2eoZHUrdZ/fs3iu3dmjqfgPWIJBCAZ19JHv2KOMeJEd0dikYW0DK3lDadh8SW6hgCo/EZggg4EsBjwW5aKWfeuop+fDDD2XZsmWio7UsWbLEaMvvv/8uo0ePNpb1Vb/gDPW0Y8cO+eqrrwyDpUuXyvHjx0O9ybQPAQQQSJOABsDUrFlTGjVqJE2aNJH4+HjRD7hICISCgAaxLl++XBYtWmTcE6xZs4ZAllC4sLQBAQR8IhAVFWXcF+g9QtOmTSUuLs4n5+UkCHhDQINXvl+8VJYv/VlWLF0lO3fs9sZpOCYCCCAQkgL6ZVO9W2pLfIM60vjuRpI3X0xItpNGIeArAe5LfCXNeVIjUOtUEcvmuVO8S1JikmzdnJDi7dkQgbQKlCxVXOIb1pH6DevK7Xc2NIJh0nos9kMAAc8JRFi+fPHoECJ///23VKlSRRITE2Xq1KnyxBNPGFMT/frrr3LPPfcYgR+eq35gHenUqVMya9YsmTZtmvGFlodpA6ux1AYBBBDwkkCJEiWkTZs2xt8P+xHBvHQ6DouAVwR01Da9H5g+fbrs2rXLK+fgoAgggEC4Cejob/p8+dhjjxkjhIZb+wO1vXsP7ZCeY9qYqte/y1RTPlwz+pnAsh9XyuxPvpSFXy2W8+fOhysF7UYAAQQ8JpAxY0a57Y5bpGXr5paAl9slSxZGffMYLgcKaQHuS0L68oZE4woUyyzReVL+u/zEC1dlzxbur0Pi4gdRI7JZRp67+9475SHLfcgtt9YLyx/rHjq6V0Z90NN01aa+s8KUJ4OALwQ8HuSile7du7f07dtX8ubNK88995yxrFNRbNy4UfTLy1BLhw8flpEjR8qYMWOMKQdCrX20BwEEEPCHgI7m0rx5c+nevbvUqFHDH1XgnAikWkCDenVaxi+//JKpCFOtxw4IIIBAygR0CqMnn3xS3nzzTSldunTKdmIrrwkQ5OJMe/nyZflyzlcyetgE2bZlu/MGlCCAAAIIeERAR3jp+OJT8ni7R0S/dCIhgICzAPclziaUIIAAAp4QKFuutLzUpaM0b3mvaBBuuCSCXMLlSgd+O70S5HLhwgVjKOmtW7faBIYOHSpdu3a15UNh4ezZs0YAz3vvvSfnzp27bpOKFi1qzC9fvnx5KVasmDHvfHR0tGTLxkPYdfHYAAEEglogKSlJTp8+bfw7cuSIbN682fi3bdu2FE3dcvfdd8vw4cOlQoUKQe1A5UNXYNOmTfLqq6/KwoULr9tInaKrbNmytnuCfPny2e4JdB0JAQQQCCeBixcv2u4RDhw4YLtH0Olfr1y5kiyFfoj0+OOPy6BBg6RgQebIThbLiysJcjHjzvtigQx8e5js3vmPeYWLXHTOHFKmbCnLXO+lpUSpYpapnXMac8FH5YgSnReehAACCISLgI4wcf7cBdEpVE6fPiN7du+VhK3bjUDBY0evPwV8npg88sprnaTds23C6kumcOkftDPtAvO/XCgD3no3TfclOXNGS44c2YX7krT7sycCCASHgH72cO7MOTljuQ85deq07Nqxx7gPSdi2Q06fOnPdRhQvWVS6v9VV7mt+93W3DYUNCHIJhasYGm3wSpCL0nz//fdyxx13GEo6fdGaNWskU6aUDzUW6Lyff/65vPLKK7Jnzx63VdUvrZo1ayY6h7z+K1SokNttWYEAAgiEo4AGCK5YsUKWLFkiCxYskPXr17tl0F9td+nSRXr27ClRUVFut2MFAr4U0D7cr18/GTZsmDFVo7tzx8XFSdOmTY37gfj4ePqwOyjKEUAAgf8Ejh8/LkuXLjXuEebPny87d+50a5MrVy7jvbhTp058seVWyXsrCHL513Z7wjpQXSMAAEAASURBVE7p3uVtY3oid9r6mYgOad3gtnoS36CuVKxUPiyHt3bnQzkCCCDgSmDH9l2y4qdVsvzHn+W7RT8mO/Wbvq8OGPaW1KxdzdWhKEMgbAS4LwmbS01DEUDAiwIahPvXn5st9yE/y08/rDSe9S5duuT2jPqsN2BYHyldpqTbbUJhBUEuoXAVQ6MNXgtyUZ7atWvL6tWrZfr06ca86aFAdvLkSWnfvr3MmTPHZXP014T333+/MXy2jjzAL7JdMlGIAAIIuBRYt26dfPTRRzJ16lQ5duyYy23KlCkjn376qVSvXt3legoR8JXA2rVrpVWrVpKQkODylDptY9u2bY1RBm6++WaX21CIAAIIIHB9Af1gadmyZTJt2jSZMWOGnD/vet71unXrysyZM6V48eLXPyhbeEyAIBeRqZOmyzs9B1kCXpNcuuqXrq0ff8gyjPU9EpM3xuU2FCKAAAIIXF/g3NlzsmD+Ipk5/TP5eflqtzt0eKGddOvThc9l3QqxIpQFuC8J5atL2xBAwJ8CR48ck7mffS0zps2WvzdudlmVzJkjpXe/N43R5VxuEAKFBLmEwEUMkSZ4NcilQYMGxoeRs2fPlpYtWwY9mX6Z9fDDD8v27c5zamswiw6VrfPC33DDDUHfVhqAAAII+FPgzJkzMmHCBGN0DJ26wDHpqC46csYLL7zguIo8Aj4RGDNmjDGyUGJiotP5YmNjjXUdO3a0DO2bw2k9BQgggAACaRc4ePCgMYXh+PHjjWmOHI8UExMjH3zwgdx3332Oq8h7SSCcg1xOnTwtXV7sLgvmLXKpW71mFXmpaye5o8mtLtdTiAACCCCQdoHffl0no98dL4u/+cHlQarVuFnGvz9CihYv4nI9hQiEmgD3JaF2RWkPAggEssB33/5o3IesXfOHy2o2bdZYhr03QHLmina5PpgLCXIJ5qsXWnVnkucUXk8N1KlXr57LAJfGjRvLxo0bZcqUKQS4pNCTzRBAAIHkBDQwoGvXrsYIGd27dxcNarFPGljw4osvSrt27SS5IQLt92EZAU8IaH/Tfqf9zzHARfup9lcd2UX7LwEunhDnGAgggIBZoGDBgjJ48GDjvVZHy4qIiDBtoCPB6ZSxAwYMMJWTQcDTAnt2/SP3NGrpMsClcJFYmfzRGJm3+FMCXDwNz/EQQACB/wQ0iOWDmRNk7qKZclPlCk4uGgRzV8MWoq8kBEJdgPuSUL/CtA8BBAJNQH/IoM97+tynz3+OSX8Ioc+L+v5MQgAB7wgQ5JIC17Fjx8ojjzzi9GVWvnz5ZNasWfLtt98S3JICRzZBAAEEUisQFRUl/fv3l/Xr10t8fLzT7vpL7ebNm7udtsBpBwoQSIeATo+h/U37nWPS/qn9VPur9lsSAggggIB3BfLnz29Mb/jTTz+5fBbr0aOHvPzyy6JTHZEQ8LSADk19f5PWsj1hp9OhdYqMpasXyt333em0jgIEEEAAAc8L1KhVVRb++Lm8NaCbZMli/oHM8eMn5OFmT8qSxUs9f2KOiECACHBfEiAXgmoggEBYCuhznz7/6XOgY9LnRX1udDe1keP25BFAIHUCBLlcx0t/JajTYVy5csW0Zf369eWPP/6Qhx56yFROBgEEEEDA8wLly5eXH3/80ZgSzvEX219//bU0adKEQBfPs3NEOwENcNGR27S/2SftjzpVofZP7ackBBBAAAHfCuhzmU4r27p1a6cTjx492hh9i0AXJxoK0iGw4Y+N0qLpY3LwwCHTUfLE5JHpc/5nzL8elZ2AVxMOGQQQQMDLAhkzZpRnnmsr87+bLaXLlDSd7fy589Ku9XMuR94ybUgGgSAU4L4kCC8aVUYAgZAT0Oe/3v3eNJ4H9bnQPulzoz4/6vs1CQEEPCtAkEsynjr9kH5x5Zh0igL9MqtIEeZ0dbQhjwACCHhLIFOmTDJw4ECZN2+e00gZy5YtM4IOmbrIW/rhfVztVxrUunz5chOEjtii/VH7pfZPEgIIIICAfwSio6NlxowZMmrUKMmQwfyI++GHHxpTyPmnZpw11AR2bN8lj7VsL6dOnjY1rWKl8rJ4+Vy57Y4GpnIyCCCAAAK+FdBpixb++Jnc3rih6cT6TPd8+1dl5bJfTOVkEAhmAe5LgvnqUXcEEAhFAX0e1OdCfT60T/r8qM+R+r5NQgABzwmYPwH03HGD/kj6pVWHDh2c2qHTEOgvAvUXAiQEEEAAAd8L3HvvvfL9999L3rx5TSfXETaeeeYZUxkZBDwhoP3KcQQX7X/aD7U/khBAAAEEAkPgpZdekk8++UQyZzZPVTB8+HAZOnRoYFSSWgStwOFDR+TRFk/J0SPHTG2oE19TPvv6YylUuKCpnAwCCCCAgH8EckTnkPdnjJOHWjc3VSAxMUnaPdpJNm7YZCong0AwCnBfEoxXjTojgEA4COhzoT4f6nOifdLnSH2e1PdvEgIIeEaAIBcXjlu3bpU2bdrI5cuXTWs1uKV79+6mMjIIIIAAAr4XqFOnjixdutQp0OWDDz6Q9957z/cV4owhK6D9SfuVfdIAF+1/2g9JCCCAAAKBJfDwww/L3LlzJTIy0lQxHaHzu+++M5WRQSClAvrZQKenOsvunf+Ydrnl1noy4/P3JWeuaFM5GQQQQAAB/wroSJsjxg2SJ54yT2d45vRZaf/4804jcvm3tpwdgdQJcF+SOi+2RgABBHwtoM+H+pyoz4v2SZ8n9bnS8btn+21YRgCBlAsQ5OJgdfHiRdEPRk+fNg8/3KtXL9FpikgIIIAAAoEhcNNNN8lXX33lNHVR165dZe3atYFRSWoR1ALaj7Q/2Sedokj7nfY/EgIIIIBAYArcddddotMURURE2Cp45coV44cMBw4csJWxgEBKBUYMHis/L19t2jyuSiWZMn2MZMliHjnItBEZBBBAAAG/Ceh9QP93e8u9ze8y1UG/YOryIj9iNKGQCSoB7kuC6nJRWQQQCFMBfU7U50V9brRP+lyp7+MkBBBIvwBBLg6Gr7/+uvzxxx+m0rZt28o777xjKiODAAIIIOB/AR1J49NPPzV9iZWYmCitWrWSc+fO+b+C1CBoBbT/aD/S/mRN+iGp9jdGcLGK8IoAAggErkDr1q1l8ODBpgoePHhQHn/8cVMZGQSuJ7BqxRoZOXScabMixQrL9Dn/k+w5spvKySCAAAIIBJZAhgwZ5L1JQ6Vmneqmii2Yt0imf/CpqYwMAsEgwH1JMFwl6ogAAgj8K6DPi/rcqM+P9kmfL/X9nIQAAukTIMjFzk9/sT1mzBi7EpFKlSrJuHHmD7RMG5BBAAEEEPCrwL333itvvPGGqQ4JCQnSr18/UxkZBFIjoP1H+5F90n6m/Y2EAAIIIBAcAq+99po0a9bMVFmdsujjjz82lZFBwJ1AUlKSvNG5t1y9etW2iU6BMWHqSMmbL8ZWxgICCCCAQOAKZM6cWca/P0LyxOQxVbJ/n6Fy5PBRUxkZBAJZgPuSQL461A0BBBBwLaDPjfr8qM+R1qTPl/qcqe/rJAQQSLsAQS7/2enw1Z06dRJ9tSadkmD27NmSLVs2axGvCCCAAAIBKNC3b1+Jj4831WzYsGGyadMmUxkZBFIioP1G+4990v6l/YyEAAIIIBBcAh988IEUL17cVOkuXbrIyZMnTWVkEHAlMGnsVNm2ZbtpVfe3uki1GjebysgggAACCAS2QKHCBWX0RPMIb6dOnpZ3eprLArsV1C7cBVzdl3Trw31JuPcL2o8AAoEvoM+P+n5tn/Q5U9/XSQggkHYBglz+s9M529esMQ8P1adPH6lQoULaddkTAQQQQMAnAhoJPWXKFNFfaFmTTjPz6quvWrO8IpBiAe039tMUab/S/mUfcZ/ig7EhAggggIBfBfLkyeM0MqdOW8SIb369LEFx8sOHjsiIIeZRXatUqyzPPNc2KOpPJRFAAAEEzAKN7mwoLR+531T42adz5bdf15nKyCAQiALu7kuefb5tIFaXOiGAAAIIOAjo+7U+T9onfd7U93cSAgikTYAgF4vb5cuXZcCAASbBihUrSufOnU1lZBBAAAEEAlegfPny0rVrV1MFFy5cKL/++qupjAwCyQlof9F+Y5+0X2n/IiGAAAIIBKfAPffcI/ffb/5Sa/z48XLkCB8mBecV9U2tJ1p+VXf+3HnbySIiImTg8LckQwY+RrGhsIAAAggEmUCvvm9IzlzRplqPGmoOaDStJINAgAhwXxIgF4JqIIAAAmkU0OdIfZ7U50pr0udNfX8nIYBA2gT4dMbiNmvWLNm2bZtJcMSIERIZGWkqI4MAAgggENgCPXr0kNjYWFMl+/fvb8qTQSA5Acf+ov1J+xUJAQQQQCC4BfT5zn5ErrNnz8rIkSODu1HU3msCJ06clGlTPjEdv1WbByWuSiVTGRkEEEAAgeASyJc/r3R+/XlTpb/79kfZuIGpjk0oZAJKgPuSgLocVAYBBBBIs4A+T+pzpX3S5059nychgEDqBQhysZgNGTLEJFerVi1p3LixqYwMAggggEDgC0RFRUmXLub5LefOnStbtmwJ/MpTQ78LaD/R/mKftD9pvyIhgAACCAS3QKlSpaRNmzamRowZM0bOn782UodpJZmwFtAPGs+eOWsz0F/dvdSloy3PAgIIIIBA8Ao83u4Ricmbx9SA8aMnm/JkEAgkAe5LAulqUBcEEEAgfQIvvtrBNDqoPnd+9P7M9B2UvREIU4GwD3JZt26d/PHHH6bLzy+2TRxkEEAAgaAS6Nixo8TExNjqfPXqVZk2bZotzwIC7gS0n2h/saa8efOK9icSAggggEBoCHTr1s30YdLJkyflyy+/DI3G0QqPCsye8YXpeM1b3islShYzlZFBAAEEEAhOgWxR2eTZ59uZKr9w/iI5c/qMqYwMAoEiwH1JoFwJ6oEAAgikX6BkqeKiz5f2adbHn9tnWUYAgRQKhH2Qy0cffWSiKlGihNx3332mMjIIIIAAAsEjkCNHDmnXzvyB1fTp003BC8HTGmrqKwENbtF+Yp/atm0r2p9ICCCAAAKhIVCuXDmnETsJhA2Na+vJVvz26zrZnrDTdMh2z5pHATKtJIMAAgggEHQCbdq1Mk1Tf+HCRZn/5TdB1w4qHPoC3JeE/jWmhQggEH4Cjs+X+vyp7/ckBBBInUBYB7noF1ozZswwiekQ1hEREaYyMggggAACwSXw+OOPmyq8a9cuWb58uamMDAL2Ato/tJ/YJ8d+ZL+OZQQQQACB4BR44oknTBVfvHixHDp0yFRGJrwFPp81zwRQukxJqVbjZlMZGQQQQACB4BbIkye3NGrc0NSIL2bPN+XJIBAIAtyXBMJVoA4IIICAZwX0+VKfM+2T4/u9/TqWEUDAtUBYB7ls2LBB9u/fb5LhCy0TBxkEEEAgKAVuvvlmiYuLM9V90aJFpjwZBOwFHPuH9h/tRyQEEEAAgdASaN68uURHR9sadfnyZVmyZIktzwICPy1ZYUJ48JH7TXkyCCCAAAKhIdCylfn9ffXPa+X8+Quh0ThaETIC3JeEzKWkIQgggIBJwPE50/H93rQxGQQQcCkQ1kEuP/zwgwmlTJkyUr58eVMZGQQQQACB4BRo2rSpqeJ8gWXiIOMg4Ng/HPuPw+ZkEUAAAQSCVCBbtmxy2223mWrv+DfAtJJMWAkc2H9QErbtMLX59sa3mvJkEEAAAQRCQ6Bho3jJlCmTrTFJSUmyZtVaW54FBPwtwH2Jv68A50cAAQS8J+D4nKnPofq+T0IAgZQLhHWQi+OHmY0aNUq5HFsigAACCAS0gON7+po1a+Ts2bMBXWcq5x8B7RfaP+yTY/+xX8cyAggggEBwCxDkEtzXz5u1X/HTKtPhc+fOJTdVrmAqI4MAAgggEBoC2XNklyrVKpsa4/h3wLSSDAI+FnDsj9yX+PgCcDoEEEDAiwL6nKnv6/bJ8X3ffh3LCCDgLBDWQS6//fabScTxw07TSjIIIIAAAkElEB8fL5GRkbY666+ydJo6EgKOAtovtH9Yk/Yb7T8kBBBAAIHQFHB87ktISJBTp06FZmNpVaoE/lz/t2n72vE1JUOGsP7YxORBBgEEEAg1gXq31DY1acO6v0x5Mgj4U4D7En/qc24EEEDAuwL6nKnPm/bJ8X3ffh3LCCDgLBC2n9bor7b37t1rEomLizPlySCAAAIIBK9AVFSUlC1b1tSAzZs3m/JkEFABx36h/Ub7DwkBBBBAIDQFKlasaJqeQFu5ZcuW0GwsrUqVQMLW7abtb6rEKC4mEDIIIIBAiAncWMk8bb3jlHUh1lyaE2QC3JcE2QWjuggggEAqBRyfNx3f91N5ODZHIOwErk086sWmL1iwQPbt2+fFM6Tu0PXr15eIiAi5evWqbUeNmnP8MtS2kgUEEEAAgaAUKF++vPz997Vf5DoGMwRlo6i0xwUc+4X2GxICCCCAQOgK6IhdpUuXNgW26N+CGjVqhG6jaVmKBBK27jBtV+aGUqY8GQQQQACB0BIoU7a0qUF79+yTCxcuStasWUzlZBDwhwD3Jf5Q55wIIICA7wQcnzcd3/d9VxPOhEBwCng1yKVBgwbGr6OnTp0aUDpDhw41PtS0r1SJEiUkSxYeYOxNWEYAAQSCXcAxWGH7dvOvc4O9fdTfMwKO/cKx33jmLBwFAQQQQCCQBMqVK2cKcnH8WxBIdaUuvhHQH8Hs3vWP6WSly5Y05ckggAACCISWgOP7vP4t+GfPXil7gzn4JbRaTWuCQYD7kmC4StQRAQQQSJ+A432IPo/q+78O0kBCAIHrC3g1yKVfv35y+vRpWbly5fVr4sMtYmNj5eTJk6Yz5s+f35QngwACCCAQ/AL58uUzNeLUqVOmPBkEVMCxXzj2G5QQQAABBEJPwPH5z/FvQei1mBZdT+D8ufNy5coV02Z588aY8mQQQAABBEJLIFu2rJItKpvo3wBrOn3qjHWRVwT8JsB9id/oOTECCCDgMwHH5019HtX3/6jsUT6rAydCIJgFvBrkojCjRo0KSJ/Ro0eb6hUdHW3Kk0EAAQQQCH4Bx/d2DbwkIeAo4NgvHPuN4/bkEUAAAQSCX8Dxvd7xb0Hwt5AWpFbgzJmzTrtkj87uVEYBAggggEBoCeTIkd0U5HLmNEEuoXWFg7M13JcE53Wj1ggggEBqBFw9b+r7P0EuqVFk23AWyBCujT9zxvzA4vghZ7i60G4EEEAglAQc39v5AiuUrq7n2uLYLxz7jefOxJEQQAABBAJFIEeOHKaqOP4tMK0kExYCLr9M4hd0YXHtaSQCCIS3gAa52KezLoIe7dezjIAvBLgv8YUy50AAAQT8K5DdxfOmq/d//9aSsyMQuAJhG+SSmJhouiqRkZGmPBkEEEAAgeAXcHxvT0pKCv5G0QKPCzj2C8d+4/ETckAEEEAAAb8LOL7XO/4t8HsFqYDPBS4lXXI6p2M/cdqAAgQQQACBoBfIFGke6DzpkvPfg6BvJA0IOgHuS4LuklFhBBBAINUCrp43Xb3/p/rA7IBAmAiY7+LDpNE0EwEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQACBcBP4eflq6dOtf6qbXb1mVRk4/K1U78cOCCCAAAIIIIAAAgh4WoAgF0+LcjwEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBAJQIHuOKClavKh8+/V3qapdXNXKqdqejRFAAAEEEEAAAQQQ8JZA2E5X5C1QjosAAggggAACCCCAAAIIIIAAAggggAACCCCAQCAKxFWpJBM/GCmZM0faqjdgWB/Ztn+d6d/av3+SZg80tW1TvWYV2zILCCCAAAIIIIAAAgj4U4AgF3/qc24EEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBHwocOH8RUlMTLKdsW58LcmWLavpX2yhgvLM821t29SoVdW2zAICCCCAAAIIIIAAAv4UYLoif+pzbgQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEfCiw/o8/bWfLmStabihfxpa3XyhZqpgx4kvOXDmlbLnS9qtYRgABBBBAAAEEEEDAbwIEufiNnhMjgAACCCCAAAIIIIAAAggggAACCCCAAAIIIOBbgV9X/247YbUaVSQiIsKWt1+IyRsjOw5dC4ixX8cyAggggAACCCCAAAL+EiDIxV/ynBcBBBBAAAEEEEAAAQQQQCCsBC5fvizbtm2TPXv2SP78+aVy5cqSIYPrWYR1m+zZs0tMTExYGdFYBBBAAAEEEPhXYN3vG+TM6bPX5dAAlbr1a7kNVHF1gLV2QS41apunITp54pQM7jtCbqhQRto908bV7pQhgAACCCCAAAIIIOBXAYJc/MrPyRFAAAEEEEAAAQQQQAABBMJBYMqUKdK5c2c5ffq0rbmFCxeW1157TTp16iRZsmSxlf/5559Su3ZtuXr1qowfP16efPJJ2zoWEEAAAQQQQCA8BJo3aS2JiUkpauySn7+S8jfekKJt9f5i7eo/bNvWqHUtyEXXvdNzkMyc/pnc/+A9BLnYlFhAAAEEEEAAAQQQCCQB1z8ZC6QaUhcEEEAAAQQQQAABBBBAAAEEgligS5cu0r59e1OAizZn3759RuBL+fLlZeLEibJr1y6ZN2+ePPDAA3Lu3DnjF9l16tQJ4pZTdQQQQAABBBBIq0CluIop2rVEyWISnTM6RdvqRgnbdsiJEydt2783fKK0a91Jnnj4WalfrbER4KIra9apZtuGBQQQQAABBBBAAAEEAkmAkVwC6WpQFwQQQAABBBBAAAEEEEAAgZASmD17tgwfPtxoU8aMGUVHbzlw4IAkJV37ZbYGt3Ts2NGp3cOGDRMNgCEhgAACCCCAQPgJzP9ulnzy0Rzp+mIPt41v2/4x6Tukp9vpD13t+Osvv5uKV/y0ypS3ZmrVqWFd5BUBBBBAAAEEEEAAgYASYCSXgLocVAYBBBBAAAEEEEAAAQQQQCBUBI4ePSrPP/+80ZzixYvL+vXrZffu3bJ371558803JSoqym1Tn376aZeBL253YAUCCCCAAAIIhJxA68dbSt58MW7b9XznZ1MV4KIHWrvmWpBLsweaytI1C41/P6z6Wp575RnjXDmis8uNN5Vze15WIIAAAggggAACCCDgTwGCXPypz7kRQAABBBBAAAEEEEAAAQRCViAxMVHq168vmTNnlpkzZ0rFiv9OO5A/f34ZOHCgJCQkyKuvvipFihQxDCIjIyUuLk6mTJkikydPDlkXGoYAAggggAACKRdILsgltlCBlB/ovy3tR3Jp0Cheyt5Q2vhXrkJZaf7gvcZW1WtWTXXwTKorwg4IIIAAAggggAACCKRRgOmK0gjHbggggAACCCCAAAIIIIAAAggkJ1CoUCH5/PPP5eLFi5IlSxanTWNjY0WnJNJ/58+fl0yZMokGupAQQAABBBBAAAGrQObMru8NMmTIkOpAlJMnTsmWTdush5aatavalnWhzA2lZMEPcyRfvrymcjIIIIAAAggggAACCASSAEEugXQ1qAsCCCCAAAIIIIAAAggggEDICbgKcHFsZLZs2RyLyCOAAAIIIIAAAm4FIiIi3K5zt+L3tetsq/LkyW0Jailty+tC1qxZ5OaqlY2y5Ut/lkuXLsktt9aTjBkzmrYjgwACCCCAAAIIIICAPwUIcvGnPudGAAEEEEAAAQQQQAABBBAIKYFRo0bJihUrrtsm/bLok08+sW03ffp0mTdvni3vuFCrVi3p2rWrYzF5BBBAAAEEEEAgxQL2UxVVr1VF3AXKfL9oqTzx8LOSO3cu2bB9VYqPz4YIIIAAAggggAACCPhCwGNBLlevXpWNGzdKUlKSVKni/gbZF43iHAgggAACCCCAAAIIIIAAAgj4Q+CXX36R2bNnX/fUOjWRfZDLxx9/LN98843b/fSX1CQEEEAAAQQQQCA9AqtWrrHtXqN2Nduy/cK5s+ekz5v9jaK6t9RK9ZRI9sdiGQEEEEAAAQQQQAABbwhk8NRBmzdvLpUrV5Zq1apJuXLlZM+ePZ46NMdBAAEEEEAAAQQQQAABBBBAICgEXnvtNWnYsGGyda1UqZJ89NFHpm26desmNWvWNJVZMxUrVpSXX37ZmuUVAQQQQAABBBBItcDx4ydk7erfbfvVdBHkots8/0xX2bF9l7Fd/YZ1bduzgAACCCCAAAIIIIBAoAh4ZCSXNWvWmIZV3rZtmwwZMkTee++9QGkn9UAAAQQQQAABBBBAAAEEEEDA6wJVq1aV+fPnS1xcnOzcudPl+fr27Sv6QxH71KBBA5kzZ46UKFHCvlgKFCggixcvlsKFC5vKySCAAAIIIIAAAikV2Lo5QQa8PUwSE5Nsu7z+Sm/Jli2LLX/u7HnZ+88+uXgx0VZWv0Ed2zILCCCAAAIIIIAAAggEioBHglyWL1/u1J6TJ086lVGAAAIIIIAAAggggAACCCCAQKgLREdHS+fOnd2OvnLixAmXBMWLF5fs2bPL2bNnbeuHDx9OgItNgwUEEEAAAQQQSIvAsIHvyaIF35t2Tdi63ZR3zBSMLSBly5VxLCaPAAIIIIAAAggggIDfBTwS5FK7dm2nhtxxxx1OZRQggAACCCCAQPoFRo0aJStWrLjugTJmzCiffPKJbbvp06ebRl6zrfhvoVatWtK1a1fHYvIIIIAAAgggkAaBxx57THTqosTEa7+Gth5m9erV0rZtW2vW9qqjotoHuNx4443SunVr23oWEEAAAQQQQACBtAhM+GCkTJCRadmVfRBAAAEEEEAAAQQQCDgBjwS51KtXT0aOHCkjRoyQQ4cOGcMuP/zwwwHXWCqEAAIIIIBAKAj88ssvMnv27Os2JVOmTKYgl48//li++eYbt/tdunTJ7TpWIIAAAggggEDqBPLmzSt33XWXywBTd3+P7YNT9WwaJJMhQ4bUnZitEUAAAQQQQAABBBBAIOwFDh44JAf2H7Q5ZMmSRSpULGfLs+BfgfPnL8iWTVtNlSh/YznJmvXaNHKmlWQQQAABBEwCHgly0SO+/PLLbodiNp2RTMgLHD58WH799VejnbfeeqtlbtdsqWqzjk5w6tQp0aG6b7rppmT3TUhIkC1btpi20VGEIiMjTWWuMr6sp6vza9nevXtl1qxZsn79etm3b58RJJYjRw4pUKCAFCxYUDSA7O677xb9gNyfyVv11OucktEoUtL2uLg4KVKkiNF3lixZIhcuXJDz588brxcvXrTMJ3xR9Av8q1evSu7cuSV//vwSGxsrNWvWlKioqJScwuU2gdCPXFaMwpAW0C+89D1j6dKlbttZqVIl6dGjh2l9t27d5OjRo7JmzRpTuWYqVqzI33EnFQoQSF7A+ncsIiJC7rzzTtHRk1Kb/vrrL9m1a5dtNx21oWTJkra8txd2794tCxculKJFixr3HHyZ7m1xjh9uAjoKy7x585yavWPHDlm7dq1Ur17dtu7KlSsydepUW75w4cKio8GQEEAg+AVOnzojK376WS5cTJQLli809Pk00bKsIz1dunTZeE7NlSunxOTLY/k8IL9UqVZZskWl7rOU4FeiBQgggAACCCBgFfh742b55uvvZPfOPZZAlUNy4vhJyWIJfoiy3B8UKVZIbqxY3ghYqVG7qmTOnNm6m+n14w9niU5VZk2lSpeQ5b8tsmZ59bNAwtYd0vS2lqZaLF29gGniTCJkEEAAAfcCHgtycX8K1oSbwGeffSadOnUymq0BHA899FCKCTQooXHjxnLu3DnjV4/6pUty6dNPP3X6EvfIkSMpCgrxZT0d26DDkL/44ouyaNEi0Q+z3aXx48cbv9xUk6FDh4p+ae3L5O16bt++XZo2beqRJjVv3ly++OIL0WM+8MADKT6mRrDHx8dLq1atpF27dikKkLI/uD/7kX09WA4vgapVq8r8+fNFg7t27tzpsvF9+/Y1RlazX9mgQQOZM2eOlChRwr7YCKxbvHix6JdpJAQQSLmA/d+xt956S/r06ZPynf/bcuLEiTJ69GjbfvXr15dly5bZ8t5c0C/eW7RoIZcvXzZOo38Ply9f7s1TcmwEwk7gvvvuk+zZs5umILIi6N9k+yAXffbR4Bdr0ucFdx9YW7fhFQEEgkNgl+ULqqfbvJDiymbOHCk161SXZi2aSqvHWqT6OTXFJ2JDBBBAAAEEEAgogflfLpR3B4yWbVu2p6heMXnzSOvHH5LnX3lGcuXOmaJ92AgBBBBAAIFQEGDc41C4igHWhj/++MNWIw0ASE36/vvvjQAX3cc6Gkxq9k/Ntv6q54QJE+Tmm282pgxJLsDF2hbdRoczr1KlinTp0iXZoBjrPp54DZZ6WtualJRkXUzVq/6CTkd+6dChg5QrV05mzpyZqv391Y9SVUk2DkmB6Oho6dy5s9u2nThxwuU6HSVLv2yzT8OHDyfAxR6EZQTSIKCBZZ4IEPHltGEffvihLcBFm6yjq61atSoNrWcXBBBwJ6B/czXQxVXSIBf7NG7cOFtW99P7UxICCISnQGJikmXkl1Xyxiu9pX71JvLlZ18HDIT+spyEAAIIIIAAAp4VOHL4qDzZqoN0bPtKigNctAbHjh6XsSMnyb69+z1bIY6GAAIIIIBAgAswkkuAX6BgrJ79l/5ff/21MV1M1qxZU9QUHZnAmnREFh2+33HEAev69L76o546Mstzzz2Xpqrrr6z1i+gDBw6IfimVKZP3/vcNlnqmCTKZnXREDB1SfvXq1fLuu+8ao+gks7mxyh/96Hp1Yn34COgUBjp1kQ5z7pi0H7dt29axWHSEprNnz9rKdWoU7fckBBBIn4D+ndb/J/XvQp48edJ3MB/t7SoY7tixYz46O6dBIHwE9O+sq0Bq/Zus7xkazK4jQ2lguzXpCIPB8l5irTOvCCDgHYF/du+V559+Vf5Yu15693sjRc+pnq6JfvE2Z+ZcmTl9jmzdnCBzF82UGrWqevo0HA8BBHwssD1hp7zc4XW3Z81gmY41W7Yski9/Pql4U3mpWuNmqVW3epqmaXV7ElYggIDo39mW9z5u/I1NC0eZG0rLjZb/R0kIIIAAAgiEk4D3viUPJ0XaahPQUUc2bNhgy585c0a+/fZbuf/++21l7hauXr0qX331lWm1jubijSAXf9Rz7ty58sILrocn1jbqFAHVqlWTgwcPyu+//278O3r0qMlDMzNmzDA+8B4zZozTOk8U+LueBQsWFB2hIrUpuWlWihQpIiVLlpSYmBg5deqUbN682QgWcneOESNGyL59+1x+GWG/jz/6kf35WUYgb968xtRuOuWIY7L/osx+3SeffGKfNYJkMmRgYDcTChkE0iiwe/dueeaZZ4xpwdJ4CJ/u1rJlS2M0M+tJixYtKk2aNLFmeUUAAQ8J3HXXXcb9+/Hjx52OqKO5aJCLjqKo95aa9O9ycqO1OR2EAgQQCEqB2EIFpGjxopb3h1xy+vQZSdi6Qw4fOuK2Lf8b94Ec3H9Ixk8d4XYbb6w4dPCw1LzpVrEfbe7Kf1MdeuN8HBMBBHwnkC1bNsmZK6f8+P31p0v9Yva/P0wsXLSQdHi+nTzV4XGfBd3t/We/HDtyTDJFZuKLfN91D87kI4GTJ07JQ/c94TLAJSIiQuo3rGuZwrCa5C+QT86cOmP57uCwHDpwWFYu/8UIjtFqPvrEQz6qLadBAAEEEEAgcAQIcgmcaxESNdmyZYttuiFrg/SD25QEufz2229GYIF1P33VIJcHH3zQvsgjy76u58mTJ6Vjx462D66tjdAvqHW0m9q1a1uLTK8a0NK+fXs5f/68qVyHMtcvpm699VZTeXozgVBPHUGlTZs26W2Kaf9BgwY5HVN/vf7333/L1KlT5X//+59pe818+umnUqNGDenatavTOmuBr/uR9by8ImAvoL8OdxXksmPHDlm7dq1Ur17dtrl+eaZ93po0OExHniAhgIDnBHSqRv2yWv/uB3rSgBy9F/noo4+kYsWK0qlTJ36VGegXjfoFpUDmzJmlRYsWMmXKFKf667NSz5495f3337eta968uZQuXdqWZwEBBEJToPtbXeXBVuYfBOkXXVu3JMinH38mMz6c7dTweV8skLiqlaTTS087rfNWwaVLl00BLt46D8dFAAHPC+iorx2efNn4PFKDaN8e2F2KlyxmO1GhwgXl488mS+83+8uUCdNs5d+vnG+Z5jhKNLhk29btsvnvrZaRnD6Tc2fPyT5LWZ9uA+Srud/Kx3P+J9lzmKdDth3EgwvPPP6irPt9g+TOnUs27lztwSNzKAT8L/BW9wGyZdM2p4pocMu7o/tJsRJFndZpgf7/Pf+LhcZIaw+1bu5yGwoRQAABBBAIZQF+uh3KV9cPbVu3bp3TWXUKIldTaThuaD9VkXWdBrl4I/m6nvrBtU4zZJ9iY2Plxx9/dBvgots++uijsmLFCilevLj9rqKj3nTo0MF4Na1IZyZY6pnOZhq7586dW+rWrSuTJk0yRhsqVuzaQ771+N26dRNXfcW63tW6QOzv1vryGpoC9913n+XDJ9cfKukXZ/Zp4cKFosEv1vTiiy+KfvFGQgABzwq8+uqrsnHjRs8e1AtH06kPH374YdG/XYMHDzZGPfPCaTgkAghYBNxNDagjDPbq1UvsR3Ds0qULZgggEKYCuXLnNKYBGjqqn8z4fIoULhLrJDHw7WGyccMmp3IKEEAAAUeB39ask0ULl8h33/5ovG528UW67lPhxhtMu5YsXcL4Yr1OfE1p07aV9B3cU35c9bXcdkcD23ZrVq2V3pZgF28nDbDRABdN1Wre7O3TcXwEfCrw/aKlMmvGF07nfPLpR2Xml1PdBrjoDvp5ngbLfvLF+5I3X4zTMShAAAEEEEAg1AUIcgn1K+zj9umc8o5JRwf57rvvHIud8q6CXHQUAm8kX9ZTP7B2HClEv1T64YcfpFKlStdtXtWqVWX58uVOX0TrKCIpcb3uCf7bIFjqmdL2pGa7xo0by59//ikVKlQw7abDMffr189UZp/xZT+yPy/LCNgLaICLBrq4So5BLjoKlDXpfhosR0IAAc8L6AhsjzzyiFy4cMHzB+eICCAQlAI6AqMGubtKOpKhNdWpU0fq1atnzfKKAAJhLNCwUX1Z8vPXUuaG0iaFy5apgka9e+2+3rSSDAIIIGAnsHTJcrucWKYHz2HKWzORKfjxS5FihWW6ZeSWO++6zbqbzPxojnz79fU/87XtkIaFUe+Ot+1VvVZV2zILCISCQP8+Q52aocFk/Yb2Ep2qiIQAAggggAAC7gWYrsi9DWvSIODqS389jH7R2rRpU7dH/Oeff0SnK3JMOm99QkKClClTxnFVuvK+rKdOAXDx4kVTfZs1a+YUUGHawCGjo4zorz8//PBD0xodheTOO+80laU1Eyz1TGv7rrdfzpw5ZeDAgfLAAw+YNv38889FA4rKlStnKteML/uR08kpQMBOQN8fZs6caVfy7+K2bduMflqlShXZvn27fPPNN7Zt2rVrJ3ny5LHlWUAAAc8KaPCkjuhiH1zmqTPofcWGDRuMe6fff/9dNKD4xhtvlLi4OONfyZIl3X4gduzYMWMqM1d10amLqlWr5mqVUaZTni1evFj++usv4z1FR6nTL+31Pk0Ddxs2bCiRkZFu93dcoSPT6ehSeg+o//Q9S6dR06BT/adtKliwoONu5BEISoGMGTMaIyeNHj062fozikuyPKxEIOwEonPmkO59XpWn27xgavuCeYskYdsOKVO2lKncmrlw4aJl6oOtsumvrZZpRrbInt17RX9sky9/XqlSrbJUrXGzlLKM1JBc2vTXFjl08LAxXYnjdr+vXW8J5jV/zqHbVKkWJzlzRTtubst7ol62g7GAQBgJ6H1zapPeu382a55ptxxuglxS8116r35vyA/fLbNNY9bz9b7S5J47TOexZi5eTJTPLXVY99sG+XP9X3L69Bkpbxk1pkLFcsYIMQUK5rduanrVaZF0miQd4WLuZ1/b1q1asUZ6HHzHlteFtwZ2S9UziGlnMgj4UWDlsl+MqcAcq9Cn/5ui04t5Ox3Yf1AWzl9s/L92+NARKRhbQCpWqiA3Va4glW++ye1nCvb18tTf9fPnzsuaX/79bigmb4xUirvRdhprPbcn7JTDB49I9ujscqPlPUTfS6pZ7mfcTZnmjWPaKmW3oO/Pu3f9Ixv+2Cgb1m2UnTt2G5Zly5WWspZA5RvKl5H8BfLZ7cEiAggggICnBAhy8ZQkxzEE3H3pP3fuXOPhRz9UcZW+/vraA4vjeh3NxVdBLt6opwaPOKaOHTs6Fl03r1+UOQa5LFiwQPRXXPqheXpTsNQzve1Mbv/mzZsbUxj9/PPPts30Q4F58+ZJ165dbWXWhWDp79b68hq6AnfddZcRsKKBgY5Jgww1yGXChAnGPNy6Xh+WO3fu7LgpeQQQ8LDA+PHjjWBUxwDKtJ5Gg1v075H+/6yjjblLhQoVkvfff1/0vcEx9ejRw9jfsVzzOrLZt99+62qVfPbZZ9KnT59kp2GKiYmRFi1ayJNPPin169d3eRxr4bJly0SD7TSYObnUsmVLGTVqlBH8ktx2rEMgGAQ0KDW5IJdSpUo5BVwHQ7uoIwIIeFfgrnvvlOo1q8jaNddGztUvVBYtWCKdXnradPK9e/bJ5AnT5OMPZ8nZM2dN6xwzGuQyeOQ7Et+gjuMqIz9u1GT57NO5Lte903Owy/J5iz816uq40pP1cjw2eQTCQeDChcRUN3P4oDGi/+/ZJw2cS2/S4LpWbVrIxx/MMg61b+8BOXbsuMTEmH9Eo9MMvfBMF/nrz83GdtbPLrdt2S5fz/1Wpk6aLiPHD5bbGzd0qtJ7wyfK6GETnMqX/bhS9J81xRYqIP3f7W3N8opAUAlMm/KJU311pCQNivBm0uCP3t36yyfT5oi7ALoGt8XL6IlD3AZnePrvut67DHpnuNHs3Llzya9/LTWW+3QfIDM+nO22njrC1NjJw6Rmbecf63jjmI7X5ZeVv0rn596UXTv3OK4y5e+5v4m8M6iHxBbiRzwmGDIIIIBAOgW8HxKazgqye/AIHDx4UPQXva6S/mpYp+dxl1xNVWTd9tdff7UueuTVl/U8c+aMrFu3zlRvDdi54w7Xv3AwbeiQ0V9n33bbbabSc+fOGb+mNhWmIRMs9UxD01K9y9tvv+20j6u+68t+5FQhChBwENB5ePWLZVdJg1x0yhT9wtuaNKCrdGnzsOfWdbwigED6BMqWLWs6QPv27WXPnuQ/8DDt4CazdetWIxBzzJgxyQa46O779+83RtDTYDbH0eQSE1P/AfnIkSNFg002btzopnb/Fuv93uTJk2XoUOchl6076vuRjlShU7dcL8BF99H3MB3RZezYsbZAPeuxeEUg2AR0KiINZHGXXnnlFY8Er7s7PuUIIBC8Al27v+RU+ZXLVpnKdHSXulXukEljp143wEV33LF9lzzc7El5u8cg03E8nQnUenm6nRwPAW8JnD9/Qfbvc/15q6tz6mgHGiAyYshYp9XRlhEQPJGqVK1sOszWTebA9W1bEuTuW1sYAS76g8cBw/rI5n9+M/51erm9se+xo8fl6ceet4w6tc10LM20eLiZjJ86Qlo+cr9tXdHiRYwyLbf+Gz3R/XOHbUcWEAhAAQ0uWb702o8srVV87r//P6x5T7/q3/6mjVomGzii5/zphxVyR3wz+eO39U5V8Mbf9aSkJNt5Tpw4KTOmzZaH7nvCCKZzF4ijO2iwzYNN28jM6Z/Z9rcueOOY1mPrCDZ6//TgPW2uG+Ci+2hgX8Nad8vU/03ncw0rIq8IIICABwQ8FuSif2x0WHYdMj25PzweqDOHCFABd6NaWKurvwB2lTRQ4/vvv3e1yijzdJCLL+upo9DoSCv2SacXSuucmvrBuGNas2aNY1Gq88FSz1Q3LA071KhRw2kv+5FdrCt92Y+s5+QVgeQE9NfhrtLmzZulV69ecvToUdtqpkKwUbCAgMcFpkyZYppiRwM/2rRp43Q/kJoTr1y50phGSO+zU5r0flyDU/S+Iz335jo1kY4ml5rkborKs2fPSnx8vAwfPjxVH+ycOnVKXnjhBWNEl9TUg20RCESBRx55xGW1cufOLU899ZTLdRQigAACcVUrOSHYj+yiK2MLF0zT/YYGxegvkR1TZOaUT0HouK993tP1sj82ywiEuoCOkNLHMuKCTt/jKulnjlXKxdv+VSheXW6p3kQG9x3hanNxN12Ry42TKSxRqrhp7WbL9Gj2qfebAyzB9v8G17/Rq7M8+fSjki1bVuNfz7dfs0yJUt7YXL+Enmh5D3JMOpJFsweamkaRuOXWekaZllv/uRuJyvF45BEINIEEy3Rcx4+fMFWr0s0VpVZd58+lTRt5IOMqsMzVYY8cPipvdO7j9HmCL/6u936zv+jUiClJ+j7Yr/dQt++T1mN46pj6fnx/k0eMoOLUfNZy5vRZ6flaX5k8/kNrlXhFAAEEEEingOu5Y9JwUP1VuE7poUl/wbpkyRIpVqxYGo7ELsEqcL0v/b/44gsZN26c05yS3333nTHKgLt2//bbb8bNVFoDQxyP68t6btiwwfH0UqRIEaeylBa42nf79u0p3d3tdoFUTw0ocTetlX0DcubMafxK3b7ME8t58uSRXLlyycmTJ22H0ylg9JfvOlqGNfmyH1nPySsCyQnoqAixsbEuR9R69913bbtqsFy9evVseRYQQMCzAgUKFDBGTrrnnntsB/7pp5+kX79+xnQ/tsIULui0eRrgoaOu2afixYtLz549jf+f9f99DYDRaQw1sMX+gxadFmjmzJliDYTTEVnKlStnHGr27Nmiga7JpQ8++MB0PN1WR6Jo27at3H777caXaXoMvcfbtm2bEcjbrFkzp0NqnXQaI72vs0/6N79Tp07SqFEjKV++vBGQp4HzOnKLvtonba8e29PTWNqfg2UEvC2g/y8OHDjQ6TQdOnSQHDlyOJVTgAACCKiADt2v04ycPnXtfuDkiVOm59RqNW6WRnc2kCWLfzLQorJHya231xedWqRUmZJyxfIl0GbLiAn662jHqYx6v9lPFv74uenzmh5vd5XmD94jB/Yfklc6vWG6EG8N6CYVKv57P2G/olx584h2us7T9bI/H8sIhLpA7cqNrvvF7eFDR1LEkNkSuGb/uVaKdnKzUYmS5s/8t2/badtSpxNaumS5kc+ZK1raPdvGts660LzlvbZpjH787t/3LOs6+9c1q649O9Sp5/0v/+3PzTIC3hT4fa3zdwYVb/o3+Mub57U/dtNmjeXRJx4SDVr7xzIiykLLiHDT3v/EfhP5c91f8sXs+cboStYVvv67fsut/2fvLMCjuLo+fqC4BA0hIbhLcIfi7lYcXijFCxSpYsWKFC0UbXFoS3GKU1rcXYMTggSX4NB++59+Mxnbzdqsnvs8y1y/5/5mydyde+45ZUzrm4pUpnwpin4WTXt3H6CpE2aR3FLLI5NC4PyflpK1lnDs7RPvNfqY1kTgIg9wx9a+Uysqb3IBmT1nNoI8500u2+bPWUJw3SYPY0dOphp1qlIWlbKgvA7HmQATYAJMwDoCTlFygSUJUcEFw+IF97hx42jq1KnWScG1fIKAetMfmxC7d+82+WV9KMzv7t27hI2WihWVvlbl3x1UbN68ufB9gjl7BCgbwES/uCEjZDrwjyvlFOcuFzckJESetCmup+QiV8awqTNZZU+SE5tk+MQW8uTJY4iSC8bF5p36ewIrGMHBwZJY6nJP/b5LAnPE5wngBxX+fv7www8W58pWXCzi4UIm4BQCsGTSs2dPQVFD7HDEiBGCUkj58uXFLKuuCxYsEBRY5JULFy4sKLTIn0twhYgPlNjat29PL1++lJoMHDiQmjZtKrzUrl27NuGDcP78+ViVXNTWYxImTCis7+TrmQoVKlCPHj0ICnWwOiOXSxQCbofUVv0KFChAixcvpkKFConVhCsYdezYUbBCJXd9BOt/nTp1or///ltRnxNMwJsIhIWFUf78+RXuv+LHj0+9evXypmmwrEyACbiBQKbMoXTm1HnFyI8ePqag9OmkvP5f9xKUXGrVq0Yjxg6mkAzppTIx8km39tShVXc6dyZczKLTJ88RNqYrVolZp6ROnYpgOeHWTa2blEImyzK2nDZ3plyS0BxhAn5A4LXJJYazgrOsuECeOHHjKsRKmDDmUJjcyhT+VsCCizrklCnEQZEOFi1SpUqpqAZLMCePxyi9lylXUlHOCSbgzQT0lNNCM9p/MNYWFvjtMXnGGIKymRiymZRhK5ie+WU+LEndO/YVs4UrrC3BhZg8uOK5DjknTBtFTVvEuC2DDGXKlzQp8X5IDWu0VBzI+W3JiliVXBzt8481mwjumuQhd96cNHX2eMoflkeeLayTWrRpSt+PmkwzfvhZKntlckHX/9NvaMX6xVIeR5gAE2ACTMA+AsoVqX19CC+61U2dsfGu7pPTnk3gxIkTCgGLFSsmbKjIM7HBIQ/Qfl2/fr08i1q3bi28+JVnOtNlkSvlfPxYaXYQc9JTVJHP1VJcr63eGJb60CvT60NvLL22enl6bfXG0GvrCXlZsmTRiKGW35XfI40wnMEEzBAQLTWYKRYUuBo3bmyumPOZABNwIgEoZ+TNm1fqESZ027RpY3qB+0jKiy0C6y1QUJGHJEmS0NatW3UVSVAPllq+/FJ52vrq1as0c+ZMeTdWx2HJTB7iml5op0mTRp4lxKH8AlnV6zoUwhrNsGHDFG3gmuWPP/7QKLiIldAflObVVmF27NhBkZGRYjW+MgGvJADFf1gOFANcGOmtn8VyvjIBJsAEQCA0U6gGxNMnzxR5hYsWpB2HNtLPi3/UVXD5r58MNHLcYEU7JOSWGDSFDmZ4qlwOToubMwHDCUQ8OEs3H4c75XPq8n6nyXvr5m1FX2kDY34fXAy/LJXt+nsf5c9SUvPp3E6p3PvA5BZFHU4eO2WyVvVWyM6QMYTw4cAEfIXA40cxFsTFOYVmMl7JJVGihLRkxU8KBRdxfFzhCqxqDeUB5csXr8qrCHGjn+uQc/HyORoFF1GQYiUKC9brxDSukRE35UlN3NE+8V5j4phpin5hrWrhb7M0Ci5iJSgADhr+BdWoXUXMEq779xzSVSJWVOIEE2ACTIAJxErAKUoupUqV0gyEk6Qc/IcATgtfuHBBMeGCBQtSixYtFHkrV65UaNjCCtCdOzGngpInT041a9bUbHg4S8nF1XJiI0gdEiXSnmBQ1zGXDgwM1BSp3RdoKliR4S1yWjEVp1TRc40lv2+u/h45ZVLciV8QgCsiWCIyFz777DOCxRcOTIAJGE8gceLEtGTJEoVJ8IiICPrkk0+sHhwuHW/fVr5AhiWTtGnTWuyjW7duinFRGa6M7AlwISQPeAauXr1anhVrfP/+/QqrFWgwceJEypw5c6xt9axTnTlzJtZ2XIEJeDIBKHCJh0KwDv/mm288WVyWjQkwAQ8hECeOVhC59QSxNIfJTH5soXS5EoSTx/IQcd1YJVJPlUvOgONMgAlYR0Ct5BKYLub3ydXL16VOkiVPSulDgjSfHLmyCS7P4PYMn7g67ymUropKSH1yhAn4AoEnj3WUXFygyBUckp7KmVzqWAqt2n2kKH754iXdjbqnyEPCyOc65CxfsYxmTHlGQ5NLRXl4ZbJ89fDBfx4F5Pli3NE+YaXqgsntozwMHfW1SQk5duWkEWMHyZsJ8QvnL2ryOIMJMAEmwARsIxDPtur6tWEWffLkyTRp0iSCS5pGjRoJLhP0a3OuLxI4deoU4YSyPMD0fKZMmSgoKIiioqKEolu3btG+ffsEU/rIWLdunbwJ1a9fn6BMoDZb7ywlF1fLCRN46vDu3Tt1ltVptTURNMQmmqPBk+SsXLmyVa6p0qfXml12lIPYHpuQ6oDqzTcpAABAAElEQVQT52Jw9fdIHJevTMAaAjgNPnr0aE1VfIc//vhjTT5nMAEmYByBIkWK0MiRI+mLL76QBoHCL6yqQBEltqBWIEb9fv36xdZMWHvVq1ePMJYYLl+OOVEp5llz/fDDD2nOnDmKqu3atSOs6fr27avIN5eA5Rl5gLKdtValoAiTMWNGunHjhtTF6dOnBaVoKYMjTMCLCMANK343iwEWnuCGkwMTYAJMIDYCNyOViq+oH5AyILZmJksIbwgKLNeuRFDEtRumdy4JKX/BvITNnvBzMRss101lrgyeKpcrGfBYTMBbCYSfjfnbgTmUKhujhJI0WRJpWnDVMXzMQCltS+TQwWNS9VJliktxjjABXyCgd8Ayoen57AkhS7ZMGjGuXblO6YICNfnyDFc/10MyBMuHF+KRN25R6jSpNfnWZljqc+dfexTd4LBC7XrVFXnmElCEgQtJuQvI86a/o+UrxigImmvL+UyACTABJmCegFOUXNB9nz59hI/5objElwkcP35cMT1YZIHLFyzYYDb/xx9/lMrhsgiKUQhqJZePPvpPU7hw4cJSfUSOHTsmmLrXsziiqBhLwtVygoE6OGJiX6+t3NS5eixr054kJzbh27Zta63ohtS7fj3m1AkGwPdYztnV3yNDJsmd+iwBuCzSU3Lp2rUrJUuWzGfnzRNjAp5KoH///rRx40b666+/JBGhqALlkfz580t5ehG1kkuqVKlI75mt1zZnTuXpbDzboJBsqzUnbMBDmf3o0aPSMG/fvhWUbZYuXUqff/654J7SUr9QcJYHyAGlVmuDXMEFbc6dO2dtU67HBNxCANaGxowZI4wNK2s9e/YU4rDACEuXr1+/FtLx4sWjoUOHukVGHpQJMAHvIxAZcUsjdEBAck0eMuAaesPaLTRl/HQ6ezpcYVFXt4Ep886t/w4nmSt3Rr6nyuWMuXEfTMBfCEQ/i6YFPy2Vplu4aJhJaS5ISmfKHEp7dx0Q0ndu2/935YhMyaVwsYJS/xxhAr5AICCFVkkVChrFSxZx+/SCgtNpZHj+/IUmDxnufK7L3aSJwqndOIr51l4t9XnkoHL/C+6LPqrfztquFQouaHTxwuVYrdVY3TlXZAJMwC8IwB39pUsxFqVgMKJcuXLk6L65N8NzmpKLN0Ng2R0noN70DwsLExQD0DOsCsiVXHCqGCbqYS3jxIkT0uBQjKlVq5aQhqsjecAL4fDwcMqbN6882+a4q+XUcxty8+ZNm+UWG+i1lStfiPVsvXqLnLbOy576L168oPv37yuagrH8QeHq75FCGE4wgVgI4O8vNs7l7jxgralXL6XP61i64WImwAScRADPjwULFghW6h49eiT0Cpc/WB/BbaOloFZygYU8a0NIiNJnPRRTsPbSe+Zb6hPy//LLL1SjRg1SK4HC0h427NHnsGHDBCVVvRNpokU/+TjqZ6m8LLY4FAM4MAFPJgB3rIsXLxZEhII/fttA0fTLL78UlPdF2eG+LHv27GKSr0yACTABswTgKuDRw//WEWKlgBTJFb9TxXycNP5u2AQ6dfyMmGXVFRtVRgZPlcvIOXPfTMAXCfw0cyE9lrla6dyjg2KaGU1KLmLYu/MAvXz5ymSFOpGYpbg+expNsPwif+eGCg/uPxQ+YmW4N5IHS33K63GcCXgqgRQptUqqsLbmCUH9/9GcTO5+rqdMlcKcaHbnW+rz/j3lfgEGOXPqvN1jxdNx02Z3Z9yQCTABnyYAxRZYCl+yZAmpPYXAzTxcYLdu3Zr88X1pXJ++8zw5lxFQb1TIlVSgSRYaGvMDBxsk2NT5448/FPKJroqQCbcaME8vD85wWeRqOfVOW+tZY5HP01Jcr60z3PZ4i5yW2Dir7Nq1a5qu5N9nFLr6e6QRiDOYQCwEdu3apbA+hM30DBkyxNKKi5kAEzCKANztzJgxQ9E9XO7E5npIrXQJhWBrA9xFqoPeM05dRy+dK1cuwd1k+fLl9YoJJwnat29PJUuWpLNnz2rq3Lt3T5PnSIatijqOjMVtmYCjBF69ekUVKlSgokWLktx1V758+Wj8+PGOds/tmQAT8BMCNyK0h2Xy5s+tmf3q5X9Q6yadzCq4QBkV7opcHTxVLldz4PGYgKcSePdO6YJeT05YY/xx8mya8v10qbhK9QrUqFk9KY1I1eoVpfSjR49p3qxFUloeOX3yHFUuXYdGDB4nzxbi9+89UOS9NbldE8MfqzdR4Vxl6a9tO8UsvjIBryOQJm0ajcyudhuoEcCGDE94rusdsLFhCrpVLfUJ5TtnhkxZYvbLnNkv98UEmIDvEIByS4cOHQQX1zhAqVZwwUxhHOJ///sf4d0t3M3DdZw/BT4G6U9326C5wjTbyZMnFb3LlQKwOGjevLlgvUWstGLFCoUVF+SLrorEOnBZJD8xDCWXdu2sNwEn9iNe3SEnFHUwf/mJqD179ogi2XzV2zjCC3NHg7fI6eg8rWkPbUh1wKadGNzxPRLH5isTsJbAuHHj6MmTJ0J1nMCANi8HJsAE3EsAFk82bNhACxculASB4gsUYMwFWF+Rh8SJE8uTFuOwTKYOSZIkUWdZnQ4ODqadO3cSLPLh9IBa4RMdYa0GN0xwzyR/dqpPgkH5uVu3blaPra5Yp04ddRanmYBXEYDCGv4vJU2a1KvkZmGZABNwH4GVy9ZqBi+ict+BDd/eXb9QvH9Ao5DQYPpsQA8qVbYYZcqSkRIkSCCYzB82cDRhs9jo4KlyGT1v7p8JeBMBPQsFkB+WVq5cvkqXL16ln00WXI4fPSVNK6xwfvr+h5FSWowgv0HjOrR21QYha9S34+lm5G1q3Lw+ZcmaiaLu3KPlv66muSblF2zWHNx3WGwqXdWWX2ZNm0cNmtQhbKxPGT9D+DvnChdrkkAcYQJOJhBWKL+mxwvnLmryPDHDX5/r6vcacNPW7uNWdt+iKiaFwPfv/7G7PTdkAkzAdwlcvHhRstwCJWNrAg4gdunShYYPHy5YEYblYLgz8vXASi6+foddMD9okz1//lwxklzJBQWwIgAXRWKYP3++ybTlYzFJcldFYmahQoVozZo1YlLYOJESdkTcIWfChAkJm0K3bsX4zoZC0MGDBxWbP9ZMB32sW7dOURWLK2couXiLnIrJG5CAUoDctZY4RKlSpcSo4PPOG77vksAc8TsCWARNmjRJmnebNm0EbV8pgyNMgAm4jcDUqVMJlpbww0MMN26YN0mcI0cOha/V27dvi81ivepZf0N/jgQo7jZt2lT4rF27lgYPHqxRdH748KGguAxXS1hfIMDdpHye2FwbOHCgI6JwWybgtQTy5MlD8+bNI5iU5cAEmAATsIbA0yfPaP5P2sMYRYoVUjT/9pvRps0S5UvQLj070ldD+pmeyQkUdUMypKeQDMGKPKMSniqXUfPlfpmANxGAEsu8OYtp1tS5CrGL5zNZYzG5MIMlFnVIkCA+dezSVvjbgnW9Xvjm2/50/twFunD+klCMv2F6f8fqNKhBP8zUWnIJzZSB0gUF0t2o/yxCThw7jfBB+MDk4mPk94OpVfuPhDT/wwS8kUCefDkpcZLEBHeEYjh6+AQdPniMipcsImZ55NVfn+s5cmUXlITFmxLf9Lewz4DuYtKuKyxacWACTIAJiASsUW4JCAigevXqEaxmb9++XfP7D++De/XqRaNGjaL+/ftT9+7dffqAFbsrEr89fLWbwIkTJxRtsQGiVnIpUaIEZcuWTaoXFRVFr1+/ltL4T6nWKoOSizzgxLD6hY28PLa4u+SsXr26RjSYjbI14LS3+kQ3GCVLlszWrnTre4ucusI7KRMKLqL1C7FLKGDVrFlTTGosEHnq910SmCM+SeDMmTOCZStYt5IrZkVHRxOsRYh/X+GHcejQoR7H4OytP+jFG+ea+fS4SbJATECHAH6ILFq0SHgxq1MsZMmtv2XPnl1RTa40qyjQSciVSlAMV5Bp0mhNIus0tSqrQYMGdOzYMZo8ebLG52tERARBoVkMBQoUEKPCFW6TxL9TigJOMAEfIZAlSxbCqZnKlSsT3GvhtxGezxMmTBCsIJUuXdpHZur8aVyK2kLv3r9yfsfcIxPwYgLYGMZGtDwkTZaUKlWNcSO4b/dBunThirwKNWxal4aO+kqj4KKoZGfin3/+taqlq+WySiiuxASYgEQA7lHGjphkOoj4nyVYseDRw0f0yvTeFpYK8uTLZbIEVZzadmhB836ZQWevHaIhI78SrEKJ9dXXjJlDadOOVdS9dycKSp9OXUwFCxegWfOn0JyFU4WNfnUFHOr78acJwrjysqLFC9GqTUupvQPWE+T96cWv3ttBr94qeejV4zwm4AgBKGuVKKW1zj5h9FRHujW8rT8/1/G3UB5uXL9peq/hX25B5PPnOBPwBALXH+yj+8+8wwqWJV5QboEbeBwShAVw9T449oBbt25Nq1evprt37xK8UWzZsoVwGHLmzJnCuye1tak7d+7Q559/Tng/BYWXp0+fWhLBa8tYycVrb53nCK42V4//NFAMUAe82DUX1K6KUA/uiuQBZvf13PXI61iKu0vOr776itR/YH799VfCZrS1ARtBs2bN0lSHPzZnBW+R01nzVffz6NEjYaNOnY+Hi/z77K7vkVouTvs3ASxSFi9eLHwGDBggWIbAZnOTJk2ETWeRDjbY1JvkYpk7r6ciV9CcHTVoR/gEVnZx543gsd1CoFy5cvT1119bNbba8gqspOCHT2wBP4bwY0ce1H3Jy+yNY33Tp08fgiKuOhw4cEDKUiu5wPUfLNpwYAK+SgDPXii141TNlStXBCVprP/79YM1hf8sHPnq3B2d18kbv9DaY93o3K3VrOziKExu7xMEsPE8Z/p8zVw+atmIkiWPOfDy95/a5+rwMcZZTXtw3zqFdVfLpQHFGUyACVgkUKBgXrr5OFz3c+nWcTp8dif9uXcdrdywhMZOHk41alfRVUrRGwQWpAYN/4KOnt9F564fpnXbltH67b/TsfDdtPHvFVSvUS29ZlJe2Q9LCeMeOrNDuB489ZfQR7ESyvfFUgMnRS7f3SKsRY5fX8jKLk5iyt3oE2jVvpmmYOdfewRrLpoCD8nw5+d67rw5FXcBB5QO6LhbU1SyI2GtIrEdXXMTJuBzBG4+OkYL9jamdcf7eaWyC4xBiMotOBQpV26By3nsmy9fvlxSbGnYsKHinVJgYCB17dpVePeEg5HTpk0T3MjjYL4Y7t+/T4MGDaLMmTPTkCFDFGOIdbz5ykou3nz3PER29aa/2oqLKCZcFukFKBDUrl1bU4RTj3LlAlQ4fFjrp1XT0EyGu+SESfLGjRsrpIKCC7TorAnYCOrbt69gfkpeHy/I27ZtK89yKO4tcjo0STONnz17RrVq1dIwhlZ9z549Fa3c9T1SCMEJJiAj8OrVK6pQoYLgumzr1q1SSb58+Wj8+PFS2tMi7/55RYevzWNlF0+7MSyPSwjAwlLJkiVjHUvPnYncepO5DlauXEnXr19XFOv1pajgQAIKdenSKU9oYmNfDGolF+T37t2b3rzhU08iI74yASYQQ+D1u6d0PGIhK7vEIOGYnxKIfhZNbZp0oocPHikIQMm0Q+c2iryHJqsL8gArCmkDnWPB7YMPtK8ORRci8jH14kbKpTce5zEBJuCZBAJSJCdYYSlctKDghsgWKeFeDZZkMmQMsaWZQ3Xf//Oazt1ezcouDlHkxrERqFW3GqVJm1pT7Ys+gxVucTQVVBlrV22gTX/EvA9UFTs16c/PdbiYUofBX4xw+nuNyBs31cNwmgkwgVgIXDBZhPVGZRcopsiVW+DtBHvJOCQFiy3Lli0TXMYnTpw4FgJEQUFBwl7mzp07CS6LYHm7TJkyJCq8PH78WPAIILcgHmunXlBB+0vVC4RmET2LgLWb/lB+gbklddBzVYQ6em5gjhw5om5uddqdcuqd2IYZqenTp1uU/+XLl9SsWTPdE9KffvoppU6tXQhb7DCWQm+RM5Zp2FS8d+9eKlWqFB08eFDTbuDAgZrvrDu/RxoBOYMJmCEABUFscidNmtRMDc/JZmUXz7kXLInrCMCVGKwxxfZ/tEaNGprn0Lx58+jBgwdmhcXaYcyYMZpy+GO1N2zcuJHCw8Ntag4lXTEULVpUo9Rz7tw5Gj16tFjF4vXkyZNOf3FkcUAuZAJMwCMIsLKLR9wGFsJNBA4dOEp1q35Ex4+e0kjQZ0B3yplb6dIwKEipbPryxUtNO3nGxfDLtH7tZnkWPVG5KxELA9OlpUSJE4lJ4frnlh2KtLmEkXKZG5PzmQATYALOIsDKLs4iyf3oEUiQIAH1/VJ5uBL1ws9dpLpVmtHJ46f1mkl5+M09cuj31L1jX+rV9Qu6ekV50EWq6MSIPz/XwwrlNynqhSlowlXk1Ila6/uKSv+fOHv6vOa9RrqgtJqq1q6xNA05gwkwAfI2ZZciRYoQ9sfr168vKLtAsQV7OvCKEts7Y0u3OyQkRLC8jb1PuIzHQWi4zYZHALyT9qXASi6+dDfdMJd79+4RtM3kwZwlF9TRc1mk56pI7K9QoUJiVLjaa8nF3XIWK1aMsFGlDrASgtPPV69eVRTBTNW6deuoatWqtGrVKkUZEnAJNXz4cE2+oxneIqet84QCy+bNm2n//v0EJZVt27bR1KlTqWLFilS+fHnCRps6lC1bVjDfJc939/dILgvHmYA5ArDKBDclRlptMDe2I/ms7OIIPW7rjQRy5syp6yZPPhdYFFMrgsCHKjTx9dwWwfVe9erV6ejRo/JuqHnz5oJCpyLTygRMZWKtAmss3bt3J7hLUwf4i8UPMXnA/MSAeUA5R+2m5dtvvxXMcsJ0pl6IiIggWALEenDixIl6VTiPCTABPyDAyi5+cJP9dIrHjpwkmP0/cug4nT55jnb+vZfmzlpETeq0oUY1WxE2TtSheMkiuhti+QrkUVS9f+8BLf91tSJPTOzesY8a1GhBN28o3+Vcv3aDXjx/IVaTrrAckyNXNimNyF/bdgqyKzJNiZcvX9G7d++kbCPlkgbhCBNgAkzAYAKs7GIwYD/uvsMnbahE6WIaArCY1rh2G/ppxgJB6UV+8h6uDHeZ1gztPupCM6b8JLTF87tnp/709u1bTV/OzPDn5zrea0yaPoYSJIivQDpxzDTqbVIyevhA35Uj1ltQRKpeviHN/nGeom26oECNC7iFP5vct5qs83BgAkzAfgLeoOwyatQoCg4OFryXHDp0SPD8kStXLiEP+eY+2bJlo7CwMOHdcM2aNalHjx70ww8/CG7h5c8KkV6mTJmof//+tG/fPtIzciDW89ar01R2AO/MmTPCg7Rw4cKSCRxvBcNyW0dAbdUCrdSKKfKeoOSCDQ0xJEuWTNdVkViO75I8nDhxQnhhYqu2mSfIiT805cqV05y+/vnnn4WNHygHwdQ/TkqrXQzIGcAXG0xY4WpE8BY5bZk7FFrwsTZUqlSJVqxYQVi8yoMnfI/k8nDcfwlA0Q2bzpcvXxa0cWG5BZay4P4EynPqjWRvIiUquxyP+IUKZ2pFJbJ2pCQJnGu1ypt4sKy+TQD/j9evX0+rV+tvQmH28LeK9cOePXskGFBwgRWyRo0aERRUsZ6CQif6Uq8hcDpMrSgjdWRFBG7QRIVmWKGDQguek1C0wd+e3bt3C89MdVdNmzZVZMGFGtaA6h9UWNNs2LCBKleuLCjSpEqVSvjbBgVU9A3LNAj48Qc/tTiNwIEJMAH/JCAqu5y7tZryhjSinEG1KN4HSusS/kmGZ+2tBObNXkz4WBvKlC9JcxZN1fxORftiJQtTokQJ6dWr11J3/T8dSEcOHqeadauaTgImEazC7N11gLZv3alQRJEamCJ7dx+kajUrybOEeMXK5en0ibOK/LbNOlPjj+pT/rC8dP/efdq/55Dp1PkZmr1gCtWqV12oa7RcCoE4wQSYABMwmICo7HIhaiPlCqpNeUIaUqL4KQwelbv3ZQKwZD95xhhqXKs1qV0BvjIpjg79+jth+nD5lSdfLrpzO4oirkXqIjlx7JSwrujSs6NuuTMy/f25nitPDur/dS8aPUx5CGfFb2tou8nKXdkKpSh33pyUMmUKunY1gmA579D+I9L6bMr4mdSsZSNKHxwk3Q64ctuzc7+UhoUeKMXMnjaPqteuQrDgx4EJMAH7CEDZBZ9cQTWoTPbulDZ5Tvs6MqDV7du3dQ8SOjJUjhw5qFu3btSxY0enewFxRC4j28ZzVud4yb527VqhO4Dcvn07ZcyY0Vndcz8eSkC96Q8TStmzK83mykWHhQEowUBZBcGcqyKxjVph5tWrV3T69GlSK7+I9c1dPUFOWFXA5hOsszx//lwhKhYvahkVFf4/AcUW9AHrI0YFb5HTiPnjhFqXLl0Ezcf48eNrhlDfI0/9vmsE5wyfI4C/s3PmzPG5ecknxMouchoc92UC+L984MABwo8bcwHKJTBdec1kYlIMsNoC6yj4mAt4lk2aNImg5W9vWLBggaLpixcvBKUUKKaYC7CGBosy6vD5558LvxGgOCMPcL+0fPly4SPPl8ejo6MFN0xQxuXABJiAfxNgZRf/vv/+OHtsgLXt0IJGjBtEer9TwSQofTr6akg/+vabGFeAsKiycO4vwsdabn1MJ5HXbPnFZLlF+V6nz4ButHLZGrp9K0rqCofdVi5bK3ykTFMk/PwlScnFaLnk43KcCTABJuAqAqzs4irS/jFOlqyZ6Pd1C6hZvfZ0766+ldOnT57RwX1HLAIpWLgA1WtU22IdRwv5uU7UvfcntHvHfsGajpzno0ePaf2azcJHni+Pw+LOtEmzaeS4wVI21m/1qzWX0mIEFv/emtZyrOQiEuErE7CfgCcqu7Rp04YiIyNpzZo19k9M1fLSpUuCS6JBgwYJXlXgngiWuX05xHXG5GBKR1RwQX8AOW7cOGd0zX14OAH1pn/+/PkJigKWAszOi8GSqyLUwX9AdX/2uCzyFDlx6hp+0GxV0gELnJjGKW6cnjY6eIuczuKA71irVq3o1KlTNGPGDLMvDj3le+SseXM/TMAbCIjKLnN21KAd4RPoxRt985/eMBeWkQnoEUibNi3Nnz/fohVErIeOHTtGausoev2JeVBsgSUUmK10JKjdEMXWF2TFDzS9TThYSIP7QFhXs9W3LNYmnTt3jm14LmcCTMCPCIjKLmuPdSNYd3n3/pUfzZ6n6g8EoNzSsGld2r7vDxozaZjus1XOoVO39lSngdZNsryOGC9QMC/tPrqF6jasKWYJV7hAWL18vSIPiaTJktLEH0cLV02hKiP83EVFjpFyKQbiBBNgAkzAxQREZResRY5fX0iv3j5xsQQ8nK8QgHLpH9uWUdUaFW2eEizew+3Rqk1LKSRDepvb29rA35/reK/xy6q5gqJK4iSJbcJXpFhBavM/pUILLLl8Megzi++EbBqEKzMBJmCWgCe5McJ+Lyx7w5VQbAEKMTg4iAP6DRo0oMyZM1tsAmMROLRYokQJi4cjLXbiJYWWtRGsnAReoKvDkye8qFMz8cX0zZs3FdOCy53YAlwWIcTmqgh1YLkEfsjkQW6GP0UKpUlIuOhIlEhrstrdcsrlByO4FJg8eTIVKVJEXqSJw8VAjRo1aPHixYKCiz3KMZpOrczwFjmtnI5QDRtqcAkFi0KwhtO7d29auXIlYQNv6dKlBFcKloInfY8sycllTMAXCbCyiy/eVd+bEzaj4MLH1oBnPZ5JlkLKlCkFSyew6gL3RXrj4GULnmVQbIFSDFyYORq2bdtGmzZtEtwmqd34yfuGhSko60AhFIo75gIYffrpp4JiabNmzQQfs+bqBgYGCicPYPll//79gs9Zc3U5nwkwAf8lwMou/nvvfWXm2CBJkzY1Zc+ZjUqULkYfd21HPy2aRicv76PpP08kmMa3JuDwxpyFU2n81FGUPCCZpgme4+ire+9OtHbrMsqaLTNNnj5GUKSxxiV0hcrlaPPOVVS4aJimbzEDyjBksvAiD0bLJR+L40yACTABdxBgZRd3UPe9MUMzZaCFy2bT3CU/CsouegdH5LOGVRVYett1eBONGj9EcFsoL9eLJw+w/X0F+pG3c8VzXT6e3jzsyXNmn3iv0bFLW9q+d52gMJwuKNCsSKnTpKIGjevQL6vn0R9//k558+fW1IW1lt/XLaTS5UrEqtSsacwZTIAJ2EzAk5RdPvvss1jl/+KLLwTDIrNmzRIOFsLSN4xBtG3b1mJbKLt8/PHHNHToUIv1vLkwjsm8qPLXpx2zgWUKvGyXB2gJtW/fXp7lUfFvv/2Whg0bJskEiyLLli2T0hwxlgCUNgICAgStM2NHcqx3V8h58eJFOnPmDN26dUtQtsCmFRQx0qdPTzi1DE6eELxFTk9gpZbBFd8j9Zic/o/A77//Ts2bx2jIY/MX/984uJfAbwc7UuSjQw4JES9uIiqcqRWVyNqRkiRI7VBfsEJ29uxZqQ+sB2KzNCZV5ggTcBIBUcsea4DGjRvH2iuW8FeuXBFcQD59+pTwPYYVlcSJbTtJFOtAsgpv3rwhKBvjx1RERIRgjSVLliyUNWtWCgqK8Skta2JV9N69e8I8rl69KijvQEkmQ4YMglIqXh5xYAJGEODfg86jevPuVRo0TflyZVT/eXYNsPJwR3r9zrEDMwnjBVDekEaUM6gWxftAewDCnGAXTC5WKpeuqyi++ThckeYEE/AWAu/fv6dLF67QmVPnBOu4ufPmNCnRZCUcpNELcINw7MgJevTwMWXPkZXCCufXqyblRd25SyePn6F7UfconWmTLUNosOkTQgEpLG+eGS2XJCBHmIANBCqVqkMXwy9LLWbOn0z1DXb5IQ3GEY8jsO3MQLr37JxDcn0QNyHlCqpNeUIaUqL4ygOa1nbM6xJrSfluvWdPo4Vn853bd01ujO7R69dvKFXqlIJibK7cOShPPuXBYHeQ4Od6DPUH9x/SmdPn6ca1SEqaPClBuSU4OMjkAjKbTVZa4NLo8KHjdP/ufeEwt7VW+mIk4Zi3EciQUqn49Nf+9VYrubtrrncf3KQp8wcphp83fI8i7YrE3kvTad/l6Q4PlSuoBpXJ3p3SJs/pcF+2doD3u1AetBROnDhB5gxM4HAi9jEeP35sqQvBokuHDh0s1vHGwnjOELps2bKCVYpJkyYJm/SNGjVSbCo6Ywzuw7cIxKZh5imzdYWcOXPmJHw8PXiLnJ7I0RXfI0+cN8vEBIwkIFp2OR7xi9OUXYyUl/tmArERgCW6rl27xlZNKofyB6yn4OOqgI0xI9YDsNhSrVo1V02Dx2ECTMDHCYiWXeDCyB5lFx/Hw9PzEwKw2gLFFnysCVBOqVilvDVVhTo4PV69Vjqr64sVjZZLHIevTIAJMAF3EhAtu1yI2uiwsos758Fju5cArLLBiponB36ux9wdWOWrUKlsTIadsSRJkzilHzuH52ZMwC8JwLILPu5QdsH7XVjuevv2rV3s8T5148aNVL16dYqOjjbbB6xqQ3cDlsJ9KVhWD7Jhpn369BFOdb548UJw+6HnMsaG7rgqE2ACTIAJMAEmwARiJSAqu8zZUYN2hE+gF28extqGKzABJsAEmAATYAK+T0BUdll7rBtB4eXd+1e+P2meIRNgAkyACTABJuAxBERlF6xFjl9fSK/eOmatzmMmxoIwASbABJgAE/BBAu5yYwSFQUdC6dKlaeLEiRa7eP78Oc2ePdtiHW8sdIolF2+cOMvMBJgAE2AC/kMgfsI4lDooAaU0edN4EB1jhth/CHjWTN++f+l0gURlF7bs4nS03CETYAJMwKcJJE4WlwJSx6fEKV/zGsHOO/3kZSQlTPxe0frJixuKtLWJf/9V9mNtO0v1RGUXtuxiiRKXMQEmwASYQKqg+JQgYVz654NHZO9zjCl6P4F3/7x2+iREZRe27OJ0tNwhE2ACTMBnCASGJjC5rYlDL99HmdYhCT16XtGvozTvANyx5/LSoMOu7rTsYu+N79SpE82aNYuOHDlitotp06ZRv379KF4831EN8Z2ZmL1tXMAEmAATYAL+TiBT7sTUfVxWAcP8PQ39HYdPz5+VXXz69vLkmAATYAJOJ1CkUgpq8mmIqd/LxGsE+/HmLKpsu+FkH2WGB6RY2cUDbgKLwASYABPwYAIt+2eg7AWT0gtaThtOLvdgSVk0byXAyi7eeudYbibABJiA8QR6js9KyVLGo7PPJtDZk8aP5+gI6ncAvvg+xZuUXeLGjUv9+/en1q1bm721N27coOXLl1PLli3N1vG2Aqe5K/K2ibO8TIAJMAEmwASYgO8SEJVd2I2R795jnhkTYAJMgAkwAXsIiMou7MbIHnrchgkwASbABJgAE3CUgKjswm6MHCXJ7ZkAE2ACTIAJGE/AXW6MbJ1ZgwYNKEmSJBabzZs3z2K5txWykou33TGWlwkwASbABJgAE7CaACu7WI2KKzIBJsAEmAAT8CsCamWX9/++8av582SZABNgAkyACTAB9xJgZRf38ufRmQATYAJMgAnYQsDTlV2SJk1K1apVszily5cvWyz3tkJWcvG2O8byMgEmwASYABNgAjYTYGUXm5FxAybABJgAE2ACfkFAVHY5/XQ0VWqWhhIkjOMX8+ZJMgEmwASYABNgAp5BgJVdPOM+sBRMgAkwASbABKwh4MnKLoUKFbI4hcjISPr3338t1vGmQlZy8aa7xbIyASbABJgAE2ACDhFgZReH8HFjJsAEmAATYAI+S+Ddv8+p3ifp6ZsFuVjZxWfvMk+MCTABJsAEmIDnEpAru0S+XE9JU3zgucKyZEyACTABJsAE/JyAJyq7FCxY0OJdef36Nd27d89iHW8qZCUXb7pbLCsTYAJMgAkwASbgFAJyZZcS9YhfHjmFKnfCBJgAE2ACTMD7CSRLGU9Sdjl3azW9e//K+yfFM2ACTIAJMAEmwAS8hgCUXaJe7xAUb+t+HMTvK7zmzrGgTIAJMAEm4I8EPEnZJVOmTLHeAlhz8ZXASi6+cid5HkyACTABJsAEmIDNBKDskiqEKFVgfJvbcgMmwASYABNgAkzAdwkkSf4BPX5xnV69feK7k+SZMQEmwASYABNgAh5LIGGiuJQ+a0JKye8rPPYesWBMgAkwASbABEQCD6Iv04Pnl8WkW64BAQGxjnvjxo1Y63hLhXjeIijLyQSYABNgAkyACTABZxLIkrYclc3ek6r1a0WRl/iUtjPZcl9MgAkwASbABLyVwD/v/6Wjfz2hbUvv0Ymzq7x1Giw3E2ACTIAJMAEm4MUEAuLlphE9NtCNC/yuwotvI4vOBJgAE2ACfkAgTdLsVDp7N8qdvibFieNe2yLJkiWLlfjTp09jreMtFVjJxVvuFMvJBJgAE2ACTIAJOIWAqNwSnNKyj0qnDMadMAEmwASYABNgAl5CIA4d3vZIUG65f+uNl8jMYjIBJsAEmAATYAK+RCA4ZREKC21JDyLjmBRcVvrS1HguTIAJMAEmwAR8ioAnKbeIYN+9eydGzV5TpUpltszbCljJxdvuGMvLBJgAE2ACTIAJ2EWAlVvswsaNmAATYAJMgAn4NIE4FJcyp/2Qkr4uSQPGd/DpufLkmAATYAJMgAkwAc8kICq3pEmWUxDwAV3yTEFZKibABJgAE2ACfk7AE5VbxFvy+PFjMWr2mi5dOrNl3lbASi7edsdYXibABJgAE2ACTMAmAv8pt3xKwSnDbGrHlZkAE2ACTIAJMAHfJSAqtxTI8BElTxxCF87zZpLv3m2eGRNgAkyACTABzySgVm7xTClZKibABJgAE2ACTMCTlVvEu2ONkktgYKBY3euvrOTi9beQJ8AEmAATYAJMgAnoEWDlFj0qnMcEmAATYAJMwL8JSMotoc0peaJg/4bBs2cCTIAJMAEmwATcQoCVW9yCnQdlAkyACTABJmAzgRjllloUJ04cm9u7ssGjR49iHc6nlVyWLl1KUVFRsUJwV4WwsDCqVq2au4bncZkAE2ACTMALCdy5/poWjrpBoaGhNHHiRK+bwa3HJ+jo9YU2yV0x9+emjZv0NrVxVeV9l6bTg+eXDRuOlVsMQ8sdMwEmwAR8jkD4kWhhjVCmTBnq27evz83PFRN6+OQu/bppqmKoVvV7KNLWJg5c+ZHevX9pbXWb6v2n3FKBCoSaLLewcotN7LgyE2ACTMAfCGxZfJeSpohHXXp2oGIlivjDlHmOOgRORf5CT1/e1ClxTlZwyqIUFtqCRLdEzumVe2ECTIAJMAFvJ7Bs8i2KFz8OfTvqawrO4Jnv9EXGT549pA1//yomhWuPFiMUaVckLtzZTBeithg2FJRbyuToTrmCanq8cosI4dy5c2JU95o2bVpKliyZbpk3Zmosubx48YL69evnkXNJmTIlnT592iNlY6GYABNgAkzAcwk8f/KeTu56Su/yEeVOX9NzBbUg2dHrFgp1irKmLW96aZJdp8T9WccjTItgA5RcWLnF/feWJWACTIAJeBuBB7ffEj650wd47RrB3cxvxr1KTx/MVIiRKU1ZRdraxOGrc+gdOVfJhZVbrKXP9ZgAE2AC/k3g8skXAoB4nbKRvc8x/yboG7O/cGe9aSLOV3Jh5Rbf+H7wLJgAE2ACRhE4u/+Z0HWqBAVN65AcRg3jlH7vmp6TTx+sUPTljj2XB9GmQ7QGKLl4k+UWxU0wJXbv3q3OUqRr1aqlSHt7QqPk8vHHH9OcOXPo4MGDlC5dOhoyZIhb53j27FmaPn26IMOUKVMoQ4YMbpWHB2cCTIAJMAEmwAQ8iwArt3jW/fBlaW7fvk03b8a88EyUKBEVKFDAl6fMc2MCTIAJeC0BVm7x2lvHgjMBJsAEmAAT8BkCrNziM7eSJ+KjBK5djaAnj59Is8uaLQsFpEgupTnCBJiA/xDwZuUW3KV//vmH9u3bZ/GG1atXz2K5txVqlFzixo1L8+bNo6JFi9LduyYzjUmTUocOHdwyr/fv3xPMRiPUrVuX2rdv7xY5eFDPJbB27Vp69uyZ8ImOjiZ8nj9/Tq9fv6a3b98KZpeCgoIoffr0hGupUqUoICDAcyfkoZJFRETQxo0bBVcvtWvXJvyd4MAEmAATcDeBLCZrNWWz96TglGHuFoXHdxKBTZs20b///iv1Bit+4lpQyrQQefDggaConSZNGipZsqSFmsqi+/fv06FDhxSZFStWpCRJkijyoAg+dOhQKS9nzpx04cIFKe1IhJ+15ukxG/NsuIQJMAEtAVZu0TLxt5xnT6Npz8599Or1G3r18pXwfuCNKf7mzRt69+69sNZIkSKAUqdNZTrcFUiFi4ZR4iSJ/Q0Tz5cJMAEmwAQMJMDKLQbC9dKueX3imTduxOCxtOmPbZJwzVo2pCkzx0lpjjABJuD7BLzRLZHeXTlw4AA9fvxYr0jIixcvHtWs6Z1eDsxNSqPkgor58uWjYcOG0VdffSX4Ja9evbpbLKiMHTtW2HDABsfs2bPNzYHz/ZhAkyZNCMpQ1gacuK5Tpw61bt2a0DZOnDjWNvXbelAkknMuV65crCav/BYWT5wJMAGXEGDlFpdgdssg3bp1o+vXY3xzQTEVCijx48e3Sp6ff/6ZvvzyS4KSS1RUFH3wwQdWt8O6Vx4WL15Mbdq0kWcZFudnrXm0zMY8Gy5hAkxASYCVW5Q8/Dl1/doN6tT2U6sRJEgQn0qULkYNmtShFm2aWL3usHoArsgEmAATYAJ+Q4CVW/zmVts8UV6f2IzMLQ1emhSkOTABJuAfBHxFuUW8W6NHjxajulfsjUPfwpeCWXMMAwYMEE7AQuunS5cuLp/zmTNnBEUbDPzDDz9QSEiIy2XgAX2PwKtXr2jlypXUrFkzQXEDVmA4WCawYMEChSLRnj17aP/+/ZYbcSkTYAJMwAACUG5pXeoXalpsJltvMYCvJ3TZoEEDhRhPnz4lPHesDatXrxaqwqLLrl27rG1G69fD73pMgMWyGjVqxGQYHHP0WXvq1CmDJXRf946ycZ/kPDITYAKuIgDllixpK1HdwlOpTI7elDxRsKuG5nF8hMCbN29Nll/205efDaHyxWrS6hXKdYE7p3nuTLg7h3fZ2P4yT5cB5YGYABNwOQEot9QoMJYq5RlEaZLldPn4PKDvEfDk9Ynv0eYZMQEm4E8EoNxSr9B4+l+51ZQ7fS2fMIZw7NgxWrdundnbiAOk33//vdlyby0wq+SCk6/z58+nhAkT0oYNGwQXRq6a5Lt37wQXSTAlC/9Q7dq1c9XQPI4fEcBGWOnSpQkbaBzME9Azb/Xw4UPzDbiECTABJuBkAqzc4mSgHtxdo0aNNNJhHWpNuH37tkIJU1R4ia3to0ePaO/evYpqcJEUGBioyDMyYc+zFm5FJ0yYQPnz56eCBQvG6nPVSPmN7NseNkbKw30zASbgOQRYucVz7oUvSRIZcZN6dupH334zWvBp7o653b/3gGZOnUuVStWhauUa0OGDx9whhuFj+ss8DQfJAzABJuBWAqzc4lb8fjO4J6xP/AY2T5QJMAGfJeCpyi3//POPQ8yfPHlCnTp1sthHnz59KFeuXBbreGOhrrsicSJ58+al4cOHC2bf+/btK5xozZAhg1hs2BVuig4fPkypUqViN0WGUfbNjlOnTi1s9uAaHR1N9+7dI2wC3blzR3fCZ8+epXHjxtHIkSN1yzmTBKs327dvl1CEhob6nN82aXIcYQJMwKMIsFsij7odLhGmQoUKwvoPiidi2Lhxo/CsFtPmrnBr8++//0rFa9asocmTJ0tpc5HNmzcrLJahntqijLm2zsqHhTlbnrVY12TKlInevn0riWCL+0apkRdEbGXjBVNiEZkAE3CQALslchCgnzZPH5yOQjOFmtYZKejZs2i6fPEq3bt73yyNOdPnU9TtuzRj3iSzdYwouBt1j0rkr0Q4/CWGf2xw0Sy28fSrv8zT0+8Dy8cEmID9BNgtkf3suGUMAW9Zn8RIzDEmwASYgPcR8GS3RHiXDYMf9gbsg9euXZtgycVcKFy4MA0ZMsRcsVfnW1Rywcz69+8vuHc5cOAAde7cWbDqYuSMT58+LSjWYAy4KQoOZlPDRvL2tb5h9UdvQwvKLIsXL6aZM2eSfOMM80f9Xr16UVBQkK/hcMp88P8+TZo0tGjRIsqXLx91796dYOmJAxNgAkzAKAKs3GIUWc/vN168eFS3bl3hmS1Ki7XhjRs3KGPGjGKW7nXVqlWK/GvXrtGJEyeoUKFCinx1Qu2qCOUNGzZUVzM0beuzFhtfcgUXQ4Vzc+e2snGzuDw8E2ACBhL4z3JLBcof+hG7JDKQs692/c23A6hpC+Xz/cnjp3TxwmX6bckKWrrgd83U167aQAWLFKDuvS2fitM0dCDj3bv3CgUXB7ry6Kb+Mk+PvgksHBNgAnYRCElZjAqENmeXRHbR40ZqAt6yPlHLzWkmwASYgDcQ8GTlFpFfVFSUGLXp+uzZM/rpp5+E/e2IiAizbYsVK0Zbtmyh5MmTm63jzQWxKrlgM3vevHlUtGhRwknauXPn0scff2zInOVuiurXr09t27Y1ZBzu1P8IQDnju+++EzatqlWrJlh5ESk8f/6c/vrrL2rZsqWYxVcZAWw4Nm/eXPjIsjnKBJgAE3A6AVZucTpSr+wQLougmCoPWIN26dJFnqWIwywjnuXqAJdFlpRcYA5y06ZNima5c+cmfFwZ+FlrnjazMc+GS5iAvxBg5RZ/udOun2eKlAFUvGQR4VOvYS0a0Gsg3bqptAI7etgEqlC5HOUPy+N6AXlEJsAEmAAT8BgCrNziMbfC5wXh9YnP32KeIBNgAgYT8AblFhHBnDlzxKjZ6/Tp0wledqDMgv1sHAY9efIkPX361GwbFHz44YcEy+cpU6a0WM+bC+NaI7zotgh1+/XrR5GRkdY0s7nOmDFj6MiRI4KZ+lmzZtncnhswgdgIlCpVigYOHKipdvnyZU0eZzABJsAEmIBrCEC5pXWpX6hpsZkUnDLMNYPyKB5LoGbNmpQoUSKFfBs2bFCk1QmU65l2hJKLpQBLhffvK10VuNpVkSX5uMx7CXz//fe0bt06750AS84EPIAAlFuypq1EdQtPpdI5erP1Fg+4J74sQsUq5Wn7vvWUPWc2xTThDnDK+OmKPE4wASbABJiA/xCAckuNAmOpYp6BbL3Ff267x8yU1ycecytYECbABLyAAJRb6hUaT/8rt5pyp69FceLE8Vip4X3k008/pREjRsQqI/Ql4G4Illt++eUX2r17t0UFF1hDnzJlCv39998+reACcLFachHpQrllxYoVJLotwolaZwaYohdvJrspciZZ7ktNoEyZMuosunTpkibPXAZ8pF29epWOHj0qfNA2JCSE8uTJI3ygFGaP66OXL1/SuXPn6MyZM8LnypUrFBoaKlhRgiUlnCq3xU2QI3I+fPhQUDjTYwDXRZBHDHv37qWbN2+KScG1UZUqVaS0pcjjx49p69atiiown5Utm/LForyCI/MS+4HM0HgUQ9myZSlp0qRCcv/+/bR582aCmwv4s0udOjVlz55dsALk6pP9onx8ZQK+SiBr2g+pTPYerNjiqzfYznklS5aMqlatSnI3Qn/++aegxJIgQQLdXtWuisRKx48fp+vXr1PmzJnFLMVVPoZYYK+Sy61btwQXn+Hh4XTnzh1hbQArMvjguan+YWXLs1aUDevl27dvCxr7Yp54PXjwIGEtoQ4lS5akFClSqLM1aUeery9evKA9e/YIfQYGBhJ8vYpB5HLx4kWBC8xjhoWFUYECBQjKx7jf6mAPG/ThbDnUciGN+WANiBMT+EEKS5T4fuXIkYMqVapEOXPmpPnz59MXX3xBsEKzYMECat26tV5XnMcEmIAZAmy5xQwYzjacQPKAZPTN0H7Uqe2nirE2rN1Cly9dpew5siry1Qk8SyOuR9Kp42fo1IkzdO1qBAWlT0c5cmWjHCblmZy5s1NgurTqZkL6/NkLdDfqHt2MvK0pP3bkJL169VqTX7hoQQpIYdnstCMyaQaUZbx8+YouhV+m8PMXTZ9LFHHtBgWHpKewQvlMn/wmZaGsuu8vjJonRHv9+g2dPxtuYn+WTps+T58+E5jny5+b8po+GTOHatZjsikJ0UMHjtLLFzHrKVj6SZI0iVB25NBx+vvPXRQZcdP0PuEFpUyVgrJkyUQ16lYV7q+6L04zASbg3QT+s9zSwqTYksO7J8LSez0Bd65PLMGzdy1grk9nPMf1+sa67M+tO0xrrFv06OFjSpcukLJmz0y58uSgDyuV1WtiU56jay1ee9iEmyszAV0CeFaXyd6dcgXViHW9r9uBGzJhneXHH3902sgBAQHUtGlTateunfB+Uv0e2mkDeVhHViu5YHMdL2yLFCkimHX/+eefqVMn5/glxsvh//3vf8LmBTYW2E2Rh31LfEwcPdNMeptCetPetWsXdezYkWKz/NKsWTNBUw7KL7EFbPrABcPOnTsJbhPMBciN/yewRIMNJEvBUTkxxsyZM3WHqFGjhqAEIhZ27txZ2OQR0zh9jw04Pc5iHfGKvyMDBgwQk8IV2oXmlFwcnRcGgBJRuXLlFGNCuShx4sTCJhQUYPQCNuNYyUWPDOcxAdsJZE5Tmirk6sfKLbaj85sWcFkkV0CB0iGeAVB+UYfXr18LLjXV+WJ6zZo11Lt3bzGpuMrHQAGer1B8tCVAseKzzz4jPNPMPcfx7Fy4cKFCCdaWZ60oz7hx42jRokViUnHt37+/Ii0m9u3bR6VLlxaTuldHn684HfDNN98IfUM5FGYzEfr27SucMjDHBcohS5cu1TC3hw3Gc7Yc6FMM+J6NHDmSxo4dS2/fvhWzFde4ceMSFH137Ngh5OM3Dn5c4qVXmzZtFHU5wQSYgD6BLIEVKGdQLbbaoo+Hc11AoFa96lSsRGGCQoMY8Hd8y4bt1L23+XdgB/Yepr49vqLrJmUPS6Fuw5o0fMxASh8cpKg2fcpPtOK3NYo8MTF80Fgxqriu3fqbIKsiU5ZwVCZZV1L0yuVr9EWfwbR/zyHh+SYVqCJQvvmoVWPqM6A7pUmbWio1Yp7YFBs+aAwtmvsrwfKOuZAuKJAm/vgdVa5WQbdK+LmL1KhmK0XZjoMbBAuDPT/pT4cPHlOUiYnc+XKxkosIg69MwAcIpE9RiIpk7sjKLT5wL31pCu5an+gxdHQtoO7TWc9xdb94bn/dbyidPR2uLpLScEf58MEjKW1rxNG1Fq89bCXO9ZmAkkCqJJlNllsmeJVyizgD8dC9mI7tCh2NJEmSCPuY2MtMmzatcDgfB/Sht1GvXj2hLLZ+fK08ri0TgqUK0dqKM90WwU0RTkTipTi7KbLljnBdewhcuHBB0wynbi2FV69eETaPcEI3NgUX9LN8+XKCRRdo4pnb2EH+hAkThBPeUOwwV0+UC1ZPsHmDk8KHDh0SsxVXZ8mp5/JBMZAs0bVrV1mKTCfMXgkmsxSZZhJz585VlBQsWJAqVqyoyEPCWfNCX3obUziJjdPk5hRc8KCoXbs2mnNgAkzACQRKZ+/KCi5O4OjLXdSvX5+gMCAP5lwWbdu2TbC8Ja8rj5tzWQQrZLD0Ig/4QaAeV16ujkNRFZZS4D/V0nN8y5YtwvNe/vy25VmrHtdZaWc9X+XPVlhhgflMKHvMnj3bIhdY2cFzf968eYop2cvG2XKIQkFBFtZpoOQiH0MsF6/4DuD7KK8DC3jWKD2LffCVCfg7gaKmTaXkiYL9HQPP380EBnyjVY7du2u/rlSwsDJs4BhqWrdtrAou6GD9ms1UsWRtmjdnscVnpO5gVmYaIROecTOnzqXq5RrQvt0HLSq4QMynT57RzzMXUtki1ej40ZNWSm57NWy0NajenObPWWJRwQU9w1JO22adaejX3wlWX9SjvX37Tp1FF85fprpVPzKr4JI5S0aqUl1faUbTGWcwASbgFQQKhDZnBRevuFP+J6S71ydGrAWc+RyXfyOmTZolKK5aUnBB/TOnztPtW1HyplbFnbXW4rWHVbi5EhMwSyBvSF2TW6KaXmO9RT4RHKLDYQprPzhI9/TpU4qKihK8UBw+fJh+++03+u677+ijjz7ySwUX8FTuHsgJm4lDuQWnQQHzk08+MVPL+uxTp05JijNwU5Q+fXrrG3NNJmAHge3bt2ta5cuXT5MnZsCtDSx/TJw40aaXUPg/Ap9qUExRB5z6Ll++vGDFxForMmIfMDOVLl06MSldjZBT6txCBNZl1K4GYPUptgC3QFAukYdevXrJk0LcFfPC6Wo8HMwF3Ed/Me9ljgHnMwEmwARcSQBu/9TWR8y5yjSnxCLKCyslULxQBz2lGXtcFUEBwpqA50y3bt1i3RSy1Jc5d02W2pgrM/L52qdPH8HFqbmx5fn4kQbXPpDH2cEZcmCdhh+L58+ft1k8HBA4duwYVa5c2ea23IAJMAEmwATcR6BgkQKaweWWXcTCFyaXNQ1rtqTZP86z6fke/ew5Dfp8BP00Y4HYFcVPEF+KOxJxpkyiHHDfAwsnIwaP1XWbJNbTu+J3dNq0aaQiZ80THcK8f80Kjen0yXNS/9ZEwL1low5W3bNPO/en+/cemO22Y5e2/K7ALB0uYAJMgAkwAWcScMf6RJTfmWsBsU+jnuOTv59Oo4dNtOo5L8piy9WItZZ8fF57yGlwnAkwASZgmYDV7orEbnC6FRvYOM24efNm4aSmvcoueKndoUMHwU1Rw4YN2Yy3CJmvhhFYt26dxloQFDSqVaumOya06KDEAUtD8hAvXjzq3r27cEoZLmwePHhAp0+fFiy34CoPgwYNImyawRqIGIYPH05wH6AOFSpUIPxfKFq0qOAyARsqJ0+epFWrVhEUwhCmTZtGMO8vD86WE+6WcuXKJQzx+++/05Eje7901wAAQABJREFUR+TDKeIpUqQQXIzJ3RsdPHhQUGCxpDwEtw7ygJPOalP+zp6XfDx5HCfZEWDyCxZ7atWqRalSpSK4uFiwYIHgokpen+NMgAkwASZgPAG4LJJb2IJruWvXrlGWLFmkwXGSaO3atVIaEVgPgTIJyhCw3oRbIriNkQe1qyKYeoRbIXsCNnCaNGkiKIDjeQ8LJStXrqQZM2YousN6Au558Lyz5VkrdgIt/1atWhGs0GB9Ig+TJk2isLAweZYQ13sWu+r5Ci5wMVWnTh3BGh4UgGG9DqcM5NZa7t+/T/BF+/nnnwsy28NGM3FZhr1yoAu4VcR3Tx6wRgBvrBdgXnT37t3Uo0cPunr1qrya4N6ArbgokHCCCTABJuAVBFKmTEHJA5LRs6fRkrxPHj8Vnl2iwimepX26f0mnTygPbuA3ZftOrah8hdKUPWc2evTwEZ03ucGBpRGYpJeHsSMnU406VSlL1kw0cNgAatS0Lt25fZc+M/UrD99+9zXlMbnEUYdcuXMospwtk9j5xHE/Ktw3ifmlyhanmib5wwrlF1wSXbpwhc6dCaeNf2yl82f/s6A76vshFJopg9jEKfNEZ1jnDfp8OGGjSR5CQoPpswE9qHipIhQYlFZQgNm+ZQfNmT5fXo0O7jtCa1asp0bN6iny1Qm4UEDAu9CuvT6mylU/pBSm78fmDdvo919WU4s2TdVNOM0EmAATYAJMwBAC7lifiBNx5loAfRr1HD95/DR9P0p74DhtYBpq3qYJFS4SRlmzZ6YL4ZdNrij/FNYC4hytuRq11pKPzWsPOQ2OMwEmwAQsE7BZyQXdYVMf5rrx0hcbwjVr1qSMGTNaHkmndPTo0ZKbIvkGuU5VzmICDhGAkga+Y0uWLNFo8WJDJTAwULd/uB1asWKFoqxAgQK0ePFiwe2AvACWWTp27EiDBw+m77//XiqC1ZZOnToJmzrIxIlvWIWRB7wwGTp0KEEhRu4mIX/+/NS0aVMaNmwYLVu2TFA2adu2rbypEHe2nHDNI7rngaKNJSUXCNCzZ0+Br1wwKMONGzdOniXFcVobprTkAcpy2GCUB2fPS963Oh4QECAwxt8zMUCZD/eFAxNgAkyACbieAJQ+YeFDHmDNBUqmYtizZw/dvXtXTBL+lkP5A8/7HTt2SPmw9iJXcnn9+rXgVkaqYIpAGQO+TW0N2OzCMw/KJ2KAG0Qo0FYyuTls0aKFmC1csQaAkoutz1o0hkIo5IyMjFT0iUTx4sUFK3GaAp0MVzxfwQUKrep1C9wT4VkLK3miIhJEhMsiUcnFHjY60xSyHJEDFoDUikpQcIGyklzZCsouyIPrQ7lbTLjDgsWgunXrmhOP85kAE2ACTMBDCWTKHCqYsJeL9+jhYwpKn07I+mPNJtqwdou8mHLnzUlTZ4+n/GF5FPklyxQXlCG+HzWZZvwQc9jj1UuTW+RPv6EV6xeb3Henog8rlaVbN+8o2iJRyGRZBn3EFpwtE8aDYs7safMUQ0N5tN9Xn9Jnn/dQvL/A/Os2rElwp7B21QY6efwMNW3RUNHWGfNEh8uWrtJYcAH3Rb/Pke4R6lUwMcWneMki1KfbFwpLNGNGTKI6DWqQqLiE+nohWfKkNGv+FKpkUnARQ4GCean/V73EJF+ZABNgAkyACbiEgKvXJ5iUs9cC6NOo5/jgL0ehe0UoVqIwzV5o8iARHCTl5yuQR1AuhhLxWNN6wNpgxFpLb2xee+hR4TwmwASYgJaAze6KxC769u1LZcqUsdttEaxTjBgxQuhu6tSp7KZIBMtXhwjMmjVL2HSAJRRs9mTKlIkSJkwoxH/66SdSuwbKli2boKilNyg2XqBcIg8pU6akP/74Q6PgItbBWFDsULs7wEYbNqSg7YvT5W/fvhWbCFecOh8yZIjiBZGiginRvHlzwgludTBCTvUYsaWh+INNK3lYtGiRWZ/YsA7z7NkzqTpOu+EEtDy4el4TJkwQNt3kMnCcCTABJsAE3EcAFsXy5s2rEEDtYkjtqqh+/frCRoVc4QQdwPqgaLULaTyX1e5xoFRja4By5qZNmxQKLvI+8OxWKziEh4fLq7g87ornK7hAIUmt4CJOFq6oRGVaMQ9WepwdHJUDlumwdpMHWKGRK7iIZVgjwvWqOsA9IwcmwASYABPwPgKhmUI1Qj998t9vWDxLJ46ZpigPSJGcFv42S6PgIlZKmDABDRr+BdWoXUXMEq779xzSVWxRVLIiYYRMeAZ+2XeoYBVPLsL3U0ZQvy8/tfj+okHjOjRo2H8W2uRtnRF/Hv1csyGVKHEi+nX1PIWCi3yseo1qUY/POsuz6Mb1SFo091dFnl5i6MivFAouenU4jwkwASbABJiAKwi4en1ixFrAqOf4mpXr6bDJlaE8VKxSnlZsWKxQcJGX9+7fzepnvBFrLbks8jivPeQ0OM4EmAATME/AbiUXWJvAictEiRLRli1baM6cOeZHUZWIboqw0Q9T9K1bt1bV4CQTsI8ANrDgIuDYsWOC9ZEbN24ozOHLe8XG2datWwUz8/J8MY5NCVhdkQecvla7CpKXi3G9TQ70hc0SmLSXB7gWgAUYe4MRctojC6y5yMOdO3eEjT95nhhXuyrC3wEoJMmDK+cFJR1H7oFcbo4zASbABJiA8wjg+SAP27dvJ1hhEYNayQUugxBgBQ2uBcUAhZZt27aJScF9kZQwRXAiGQoytobQ0FCqXLmyxWaw5iYPkAXPSHcFVzxfwaVKFeUmnnq+LVu2VGRBERlui5wZHJUDrNTB0u8WWKiBpRd5ULswkpdxnAkwASbABDyXgGlpoAlQVEE4cug4XTh/SVE+dNTXCrc8ikJZYsTYQbLUf9EL55VujDQVrMgwQqZjR07Sof1HFKPjxHOLtu510bPr7710N+qeQq5W7ZpR6jSpFXnqRPuPW1H8+PEV2X+aXBlZCrBO4+75WpKPy5gAE2ACTMC/CLh6fWLEWsCI5ziUcUYOibGsj28F9i+Hjf5G8+xXf2MSJU6oztJNG7HW0huI1x56VDiPCTABJqBPwG4lF3Qnui1CHK6LoFBgTcAJSCghpE6dWuPixJr2XIcJOEIArgg6d+5M2LiAJRdzAQow8gBrI40bN5ZnmY1DEUbtwuv06dMapRl0MGrUKIsnoMwO8v8FRsgZ25h65WATEhKiKIIinDrAjL9a0ad3797qaoICkjzTGfzl/cnjcBuB/jkwASbABJiAZxFQW1eBC0DRDRGsAl65ckUSGM93uI1BSJs2LVWvXl0qQ0SuEKO2CAM3M0FBMaZrFQ0dTOTIkUPTw6VLyo0xTQUDMzxl3aBeJ2HKUFR2dbAkh9otFH67wCWWpZA1a1ZFcUREhCLNCSbABJgAE/AOAjcjb2sEDUj53zNg5197FGXYRKldT7nuUFSQJUIzZaCQDOllOUTnzzqu5GKETHBPoA5fDu7r0PsLdX/2pK9cuqZp1rVnR02eOiMwXVqqVquSIvv6VcvP6SbN6/O7AgUxTjABJsAEmIA7Cbh6fWLEWsCI53hkxE26pVq7tWrfjHLmzu6022XEWktPOF576FHhPCbABJiAPoGYI6765bHmwm3RypUrae/evfTJJ58I5uAtNcKGxMiRI4Uq06ZNM2xDwZIMXObfBOC2SO3GQI/Ivn37FNnv37+P9bS2vIFa6evcuXOa0714GYZTv44EI+S0Rx6cmO/atSsNHTpUar5u3Tp68OABpUmTRsqbO3euFEekUKFCVKFCBUUeEq6cV548eTTjcwYTYAJMgAm4n0DJkiUpODiYbt+O2WiCgkqNGjVo1apVCgGh4AJFFzHAUghc5ogBzySYl4WCiVrJRK1MI7ZxxlWtAIo+o6OjndG1XX0Y8XxVW2OzRrB06dJpqj158kSTZ3SGJTkCAwMVw0O+N2/eCC6xFAWyBNY98gDFGA5MgAkwASbgfQQiI25phA4ISC7kHTl4XFGG9cVH9dsp8iwlbt1UWnS7eOGypepWlRkhU8r/V+oRBYDlu0pVy4tJt10vX76mGDuFSc6MmUMVeeYSWbNnURRF3rgluFk2d+glR07zB6MUHXGCCTABJsAEmIALCLh6fWLEWsCI57jeWqrJRw2cekeMWGuVr1hGIyOvPTRIOIMJMAEmYJaAw0ouotuiwoULS26LYCVDL8A90f/+9z/CFVYfrFE00OuH85iAOQLdunWj4cOHEzYhYPZ+zZo1NHjwYEX1b775hrCZJd8IU1T4/0RUVJQm+/hx5cssTQULGVACgaKLPOD0cIIE/5k8lufbEjdCTlvGl9ft0qWLoMSG/+MI2AhaunQp9erVS0jDVdnChQuFuPiPnhUXlLlyXtmzO0+rW5wXX5kAE2ACTMBxAthMadCgAc2aNUvqDIorkydPVlhmQSFcFMkDXB3BrSZcGSLcvXtXUKCE60B1wBhGBayVPSm48vlqad6eovxhSQ618g4Ung8dOkTlypXTnRrcLamVnOESkQMTYAJMgAl4F4GXL17So4ePFEIHpEguWTC5f0/rXu/MqfOK+rYk4jnBqqgRMl0MVyrfhIQGO/z+whYu5upeuXRVUZTBJJe1IX16pZIt3lHcNCm6ZMqSUbeLzFmVbpV1K3EmE2ACTIAJMAEXEHDH+sSItYARz3G1nLgdWbNndupdMWKtpScgrz30qHAeE2ACTECfgFPeuufKlUuyztK/f38yZ5YbboqgJACrDjNmzNCXiHOZgAMEEiZMSDh1C9cAYWFhBIUWnAKXh2vXrtGQIUPkWbrxe/eUPp51K9mQCfP1aiUXS+6SrO3aCDmtHVtdL3369JpNRrnLImxMyk/jw51E69at1d0IaVfOS+2XW1cgzmQCTIAJMAG3EICyijzA7d22bduENaWYD4XRevXqiUnhCrcyderUUeTBZdH69esVeVgz5MuXT5HnywlXPl8tcYQCkycES3IUK1ZMI6JaeVpeYcyYMYK1IHle/vz55UmOMwEmwASYgBcQuGEyea8OefPnlrIe3H8oxZ0RyZTFOisklsYyQib1qejMZhRBLMllRNnDB0oFpKTJklk9TNp0MVZmxUZ691ssix/f4bOBYld8ZQJMgAkwASbgEAG955XR6xMj1gJGPMcvqBRzkyRNQkEqxVaH4JsaG7HW0pOJ1x56VDiPCTABJqBPwGm/1j777DNasWKF5LZoy5YtihFPnDhBo0aNEvLYTZECDScMJIDT0zj9Xbx4ccEErTgUToC3adOGihQpImZpruqT16GhoQRLMfYGbLRNnz5d0dycSVxFpVgSRsgZy5AWi3v27Em//vqrVOfYsWMEN2UFCxakn3/+WcpHBFafcMpeL3javPRk5DwmwASYABMwnkCVKlUoefLk9OzZM2kwuMeTh2rVqhGUWtQBLovgVlMMixYtoocPlRtTRlpxEcf1pKsRz1e16yhPmq8jskBRumjRonT06FGpm7/++kuwUDdx4kSSK8nOnz+fpk6dKtVDBMq/akUrRQVOMAEmwASYgEcSWLlsrUauIsUKSnnqZ2lwSBC1+7iVVG5rpEr1irY20dQ3Qqb5Py1VjPPBB045J6fo057Eu7fvFM0SJUqoSFtKvHzxn4U/eZ3ESRLLkxxnAkyACTABJuCRBNyxPjFiLWDEc/xelPKwcrx4Hzj9Hhqx1nr//h+ny8kdMgEmwAT8iYDTlFzwRx4WG+C2aOvWrTR79myC6xIEuC7p0KGD5KYIGw4cmICrCOA7CZc4kyZNkoaEuXl8P/fv30/mFE3y5s2rMDmPU+IDBw6U+rAnAqtHcktHV65csacbRRsj5FQMYGOifPnyVKhQIYJimxjwt+Grr75SnJ6H+6bu3buLVTRXT5uXRkDOYAJMgAkwAZcQwPO3du3atGzZMmk89fNT7apIrAjrLslMp3ujo6OFLD1XPXBh6E/BiOerryq54HsBN5hQhPrnn5iXT1DYx++dSpUqCd+vw4cP044dOzRfI6w9U6RIocnnDCbABJgAE/BcAk+fPKP5Py3RCFikWCEpL0eu7HTr5h0pHT9BfOozwPxvW6migREjZMqeIwvdirwtSX392g0p7s5IlmyZ6drVCEmEu6qNLalAJ3L7Vsx9E4uzZMskRvnKBJgAE2ACTMAjCbhrfWLEWsCI53hIBqXrQvB69jSakgdYb+0tthtvxFrr9MlzsQ3L5UyACTABJmCBgFOPYWADX7TWMmDAALp+/bowNPJEN0UzZ860IA4XMQFjCGCDImNGpY9lbEioT9zKRy9QoIA8SXBz9Pr1a0WerQlsLMkD/o9ACcyRYIScjsiDtrDmIg9LliwRrLjA37UY4H5CfU/EMlw9cV5y+TjOBJgAE2ACriOgdlkkHxnKquYUVRInTmy2DH3AhWa5cuXk3XltXK6EYWkS/Hy1REdbVrduXfr+++81BeHh4YK1wAkTJugquLRo0YJYsV+DjTOYABNgAh5PAAou2BSRh6TJklKlquWlrDz5cklxRG5cv2l6V/BGkefMxD///Btrd0bIhM0ceYiMuOXw+wt5f+q4NfNEmyxZlUopUbfvqrsym751M0ZpB5UCUiSn1KlTma3PBUyACTABJsAEPIGAu9YnRqwFjHiOZ82eWXObnK2ca8RaSyM0ZzABJsAEmIBNBJyq5IKR+/TpI2wWwKT8J598QnBV8t133wlC/fjjj5QuXTqbBOTKTMAZBHCKW0+hZfDgwQprLfKx1JtA2DzatWuXvIrN8Tx58ijawKLMtm3bFHm2JoyQ01YZ1PXhCiplypRS9r179zRWcGBdx1LwxHlZkpfLmAATYAJMwDgCcPkidw0jH6lixYqCsoo8Tx5v1cq8+wAoMJiz6CbvwxvieNZaE/j5ag0lZZ1+/frR8uXLBastyhJtKk6cOMKa55dfftEWcg4TYAJMgAl4NIHHj5/QnOnzNTJ+1LIRJUsecxI4d96cijr//vsvHdh3WJHnzMSD+0pXi3p9GyFTjlzZFEMJ70T+3qvIc2bCmnliPLXlFdy3K5evxSoK3r/s2L5HUS9LVu2mmKICJ5gAE2ACTIAJuJmAO9cnRqwFjHiOZ82eRXOX9u0+oMlzJMOItZYj8nBbJsAEmAATIHK6kovotggnZ7F5X7VqVeGkR5MmTQgnGjkwAXcRwClv9UlvuC9QWx0R5VNvAiEfihlv3th/Qitfvnxi99K1f//+JLdwIhVYGTFCTiuHNlstSZIk1LFjR7PlRYoUoQ8//NBsOQo8cV4WBeZCJsAEmAATMIwAXL7ANYxeMOeqSKxbo0YN0wnd1GJScYUbGm8Meoo5t28rTyabmxc/X82RMZ9/9+5dOnr0KD1//ly3EhRbsmbNSn379qULFy7QyJEjCXkcmAATYAJMwHsIRD+LpjZNOtHDB48UQuMdV4fObRR5efIplVxQOPiLEQ69KxAH+OAD7Ws6a9zxGCFTrtw5RLGk67CBYxx6fyF2ZO880T57zqxiN9J1/pwlUtxcZMO6LXTzxi1FsV5figqcYAJMgAkwASbgRgLuXp8YsRbQe/Y6+hzPpmPJ5YcJMzXW+Ry5lUastRyRh9syASbABJiAAUougJozZ07JesujR48obdq0NGPGDObNBNxOANZcYNVFHtatWyeczpXnIV60aFEqWbKkIvvcuXM0evRoRZ65xMmTJzUvuaDYAQUPeUCfY8eOlWeZjf/555/08uVLRbkRcioGsDPRvXt3sxs8vXr1irVXT51XrIJzBSbABJgAEzCEgJ7LIigSNG7c2OJ4sACjpwiTMGFCqlmzpsW2nloYFBREUCiVhw0bNsiTZuP8fDWLRrfgzp07BCVlWKbESf2QkBDq0aMHLViwgLZu3Upnzpyhp0+f0pUrV2jixImUI4d2Q1C3Y85kAkyACTABjyFw6MBRqlv1Izp+9JRGpj4DulPO3Eq3PWGF8lPhomGKupcuXKGpE2cp8swlzp4+r3lXINYNTJeWEiVOJCaF659bdijSegkjZCpVtjgVKKh0uYx5/jh5jp4ImrxdJqsvL1++0uQjw955om3FKuVJfbL8tyUr6OFDpYIS6ooBckybNFtMStePu7aT4hxhAkyACTABJuBJBDxhfWLEWsCI53i2HFkpX4HcitsHxeXxo39Q5OklrHWXaMRaS08ezmMCTIAJMAHrCWiPiFjf1mJNWLwQXbN8++237KbIIi0udBWBjBkz0rBhwzTD4fv65MkTRT5OSc+bN4+wCSYP+D63b9+e7t+/L8+W4hEREdSyZUsqVKiQsNkhFZgi6HPWrFmE02DyMGjQIGGTDhspeuHEiRNUuXJlqlatGs2dO1dRxQg5FQPYmYCyG07Pq0NgYCBZch0h1vfUeYny8ZUJMAEmwARcSwBWV9TWMcqUKUPBwcGxCoLnsjrA2qBa8VVdx1PTWEeI62xRxk2bNtHmzZvFpHSFcqzcYhw/XyU0VkVgneXBgwdCXViqPHv2LMEFK9aCUFzG+nH16tU0ZcoUmj17tmDJ8urVq1b1zZWYABNgAkzAdQSOHTlJf/+5i44cOk6nT56jnSYFjLmzFlGTOm2oUc1WBOUNdShesgj1/bKnOlv4XT9p+hhKkCC+omzimGnUu+sXJmsw+u6FYEWke8e+VL18Q5r94zxFWzGBZ7xageOvbTsF2cU64hWKG+IzHs93Z8uEPsdOHq5Zf40bOZk+btOTzFmYOXPqPDWr145aNupIvy5eLoqruNo7T3QCub4Z2l/RX/Sz59SgWgtdt0Vw8wBZTp84q2hTv1FtKlq8kCKPE0yACTABJsAEXEnA09cnRqwFjHiOY10xZORXmlv304wF1NpkqU9tyQ0VcYjl18UraLsVysSoD7mdvdZCvxyYABNgAkzAfgLx7G9quSUeLNjMPn/+POG0KQcm4CkE+vTpQ4sWLaLjx49LIsHE/1dffaWxOISTu1Bq+frrr6W6iKA9TkxD8QRm/1OlSkWXL18mWGXZvXu3ZG1l1KhRwiYITv2KoUSJEgQrJ9ggkQdskPz1119UtmxZyp8/P0EhB6eCYR5/z549BP/XCOPHj6euXbtSvHgx/32NkFMum73xTz/9VLPh1rlzZ0qUSHkyzVz/njovc/JyPhNgAkyACRhHIDQ0lIoVK0aHDx+WBtGz0CIVyiKVTK6O0qdPT3JlUm91VSROC4qkWCOIAS9o6tSpQ61bt6bChQtTVFQU7dy5k44cOUK///47yS3h8PNVpBb79ebNm1IlKAzhdw0+UG5RK0hLFU0RWAMcM2aMsFaU53OcCTABJsAE3ENg3uzFhI+1oUz5kjRn0VRhQ0OvTa48Oaj/171o9LCJiuIVv60RNkvKVihFufPmpJQpU9C1qxF0MfwyHdp/hF69ei3UnzJ+JjVr2YjSB2vfl1WsXF6jkNG2WWdq/FF9yh+Wl+7fu0/79xyik8fP0OwFU6hWvepCn0bIVLhoQfpfp9Y0/yelO6DN67fR3l37qXjJoqZ55qCQDMF0/doNOnXijGmeR4WNIwg1c+pcatexpeL9hSCs6R9754n2NetWo+KlitJhkxUeMVy9cp3qmSzy1DKVhRXOT0mTJqXjJuWmbVv+1mxuwdLf198qFWXEfvjKBJgAE2ACTMBVBLxhfWLEWsCI5/iHlcpS1RoVSW0Bb8f23VSpdF3TmqWIoEicMVMGunLpGu38a4+wdrHlXhux1rJlfK7LBJgAE2ACSgIxu+TKfE4xAZ8lAK1bWFPB6W9RcQSTRV67du0EJRP55D///HPavn27YJJeno9TvcuXL9d1dSTWi46OFjY4fvhBaRoPJu9PnTolbD6JdXHFZsnGjRuFjzxfHr927RotW7ZM2MSS5xshp7x/e+LYbMuaNSuJJ5qhmAMT/7YET5yXLfJzXSbABJgAE3AeAShqyJVcmjRpYlXnUL5u3rw5ic9jWISpX7++VW09tdLAgQNp8eLFFBkZKYmIdQ3y8JEHuNSRK7mgjJ+vckLm41Am2rVrl1Th9evXBKt9sYWDBw9SlSpVaPLkyQQFaw5MgAkwASbgHQSwRmjboQWNGDeIoAhhKXTv/Qnt3rGf4JZHHh49ekzr12wWPvJ8efzF8xeC+5yR4wbLs4V4nwHdaOWyNXT7VpRUBmXWlcvWCh8p0xQJP39JUnJBvhEyfTWkH507G04H9sYoGmOsZ0+jCVZm8DEXIiNu0rpVGwUFHXUdR+aJvsZOGkb/a9mNMIYYnjx+Sr8tWSl8xDz1Fe8lvh39NWXO8n/s3Qd8FGX6wPEnnXQSSpBeAhEpKtKRKqABKRbOXv6eJ3goYL1T7xT1bHeCevY79ARR4TxFBEGkKII0EaQTEEINNZAKgbT/vqsT9t3dkLZldvc3ftaZ953yvu93hs3M7rPv28R+FWkEEEAAAQRMKeDt+xN33Au44+/4U88/Jtu2pEnGQb23fHXfpYJa1KumkzvutWpaJ/ZHAAEEAlVAHzMlUBVod8AJqF/XjhkzRmu3+tDonnvukcLCQi1fBcWo7v9ff/116y+BtJUVJLp16yaq5xL7KS4uztpry+TJkyvdq4lxDDU8QZ06dYxk2dwd9Sw7eDUX1JeKqtcaY1JfRjZq1MhIVmpuxnZVquJshAACCCDgcoERI0aUHbNTp07SvHnzsnRFC7ZDFnXu3Flse1mraF8zrldDLakhDGNjYyusngpysZ/4+2ov4jw9atQop/ddzrd2zH300Uetvek4riEHAQQQQMBMAurLoxHXDZUlK+fKi5bgiYoCXFTd1d/ST2a9LypQJTIqskrNufSyjnLLHb9zuk90TLRMfvMFUfOKprRtO7VN3FGn2LgY+d/cD+Wp5/5sGc45XCuvokSr1i0lIbG2081q0k51wAsvaiPffP+FDBnuOEyy0wItmU2bN5YvFnwid959S3mbkI8AAggggIBpBMxyf+KOewF3/B1vldxCFiz7wtqjS2VOovruoqqTO+61qloHtkcAAQQQ+FWg6u/iyCHgJwKqN5ULLrhAa436Ekj1zmI/qRtKNfSO6n3l+uuvd9jPdns1TNcNN9xg7fll1apV0qFDB9vVZcvqJuqBBx6Q9evXiwr+aNq0adk6+wU1vE9qaqq1h5etW7fKlVdeab+JNe2OejotqAqZd911V1kgz7hx46qw57lN3dkudezKfEF4rjYsIYAAAgh4S0ANEdiqVStr8ZUdqsioa/fu3aX5b0ExtsEyxvrqzOPj46uzm1R3P/vCBg0aZB2ySAXvljepv3EqkNfZ5K6/r65qn7M6VyXPFfVISUmRnTt3Wntjadu2rahAZRVgpJbVvZnqpUUFQqseAdXwlvb3c2fPnpUpU6ZUpdpsiwACCCDgRgEVjFKnbqKo4Isu3S+Tu0bfJlM+fEM27lopb703WVQ39FWZ1N/S/7vnVlmyYo4MHXGl1E+qV+7uiXUSZPg1Q+STL/4jcxd/Km3bpZS7bZ/+vWTB97Pkkk7OP09QO1qDYJz8jXdHndTnF/eM/T/5xvLFUeqwQdKwsf5Zim1DVCBM/4F9ZPr//i1L18yTflf0tl2tLdeknepA8bXj5N/TXrcGJqnhi5wFBqm6t05pZR126ZvvZ4sKMKrsFG35m8+EAAIIIICAuwV84f7EHfcC7vg7npiYIFNnvmsNzm1/8UVOA5dVW7r17CxfL50lN90+yjqsourpzfqyBDFXNLnjXssok3sPQ4I5AgggULFAkOVDb+efele8b4Vb9OnTx9q996effmoNDKhwBw9uMHHiRHn66afLSlS/0lRDwDAhUFmBY8eOyYYNG6xD8agvkOrWrWvtpUT1tKJudKoznTx5UjZu3Gj9MqV27drSrFkz65cl9evXr/Yx3VHPqrZtzpw5ooZ3uvPOO6u6a7nbm6Fd5VaOFaYRUH9/1BAlxnTRRReJsx4NjPVmnacdXiBzN1RtzPg7e82WOjG/BgOYtV1mqVe7du1EBRAak7ofUPcFTOYUUEMI7tixQ4YPH17l3liMfdVQRVXtWcycGudqdejQIWuPIYcPH7YG46pgC/WqaqAHf1/PmVZn6fTp09K7d2+t9xYVYLVy5crqHI593CjA86DrcA8eTZe/vHGrdsDnHvqPlvaFxA7LsC/9uw/VqnowK01Lk0CgMgKZx0/Ils3bZf+eAxIdGy0quOWCC5IkuU3Laj3XHzl8VDb+vEWOHTkm9RvUl0aWAJNGjRtKXHzFvbkZ9XV1ndRxs7KyZdvmNEnftcdSlzhp3LShtV5169XxWjvVR5x79+yXrZu2S25unqS0Tba82khkZC2DgjkCDgL9ug2RnWm7yvLf+eBVGTYytSzNAgLeEOC+xBvq/l2mL9wLuOPvuPrhSdrWnZYhHnda71daJje3DllYmV77qnJFuMO3KuWzre8KNKqtB75/u+qrKgfee7r1RzMPymsf/EUr9j/P1Hw4MO2AJBCohEBoJbZhEwQQcCKgemwZOHCgkzXVz0pISJC+fftaX9U/ir6nO+qpl1BxSn2Z6OrJDO1ydZs4HgIIIIBAxQIDBgwQ9arOVJN9q1OeJ/dRvdNdffXVNS6Sv681I4yMjJSbbrpJC3JRPcEwIYAAAggEjoDqKaZPv54ua3CSJbBl0FX1a3Q8V9dJVaZ27XjpcXlX66tGlfttZ1e0U/3gqHmLptaXK+rEMRBAAAEEEPAXAV+4F3DH3/Hw8HDpcEk768ud59Idvu6sL8dGAAEE/EGA4Yr84SzSBgQQQAABBBBAAAEEEHCLwNKlS+W5556TnJycSh1/7dq12nZJSUlamgQCCCCAAAIIIIAAAggggAACCCCAAAIIIIBA9QUIcqm+HXsigAACCCCAAAIIIICAHwtkZ2fLrbfeKn/5y1+kRYsW8ve//13UkETOptzcXHnggQdkxowZ2uoOHTpoaRIIIIAAAggggAACCCCAAAIIIIAAAggggAAC1RdguKLq27EnAggggAACCCCAAAII+LHA+PHj5cCBA9YWnjhxQv70pz/JCy+8IJ06dZJLL71UmjVrJio/LS1NvvzyS8nPz9c0VHfLjz76qJZHAgEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQKD6AgS5VN+OPRFAAAEEEEAAAQQQQMBPBVTgytSpUx1al5WVJUuWLLG+HFbaZTz++OPWgBi7bJIIIIAAAggggAACCCCAAAIIIIAAAggggAAC1RRguKJqwrEbAggggAACCCCAAAII+K9AcnKyvPrqqxIXF1flRkZFRckrr7wif/vb36q8LzsggAACCCCAAAIIIIAAAggggAACCCCAAAIIlC9AkEv5NqxBAAEEEEAAAQQQQACBABUICQkRNVzR/v375e2335aePXuKCl4535SUlCTPPvusdZ8JEyacb1PWIYAAAggggAACCCCAAAIIIIAAAggggAACCFRDgOGKqoHGLggggAACCCCAAAIIIBAYAqonlzFjxlhfpaWlkp6eLlu2bLG+cnJypEmTJtKsWTNp3ry5qN5fwsPDAwOGViKAAAIIIIAAAggggAACCCCAAAIIIIAAAl4QIMjFC+gUiQACCCCAAAIIIIAAAr4nEBQUJC1btrS+hg0b5nsNoMYIIIAAAggggAACCCCAAAIIIIAAAggggICPCzBckY+fQKqPAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggEggBBLoFwlmkjAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCDg4wIEufj4CaT6CCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIBAIAgQ5BIIZ5k2IoAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACPi5AkIuPn0CqjwACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIBIIAQS6BcJZpIwIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggg4OMCBLn4+Amk+ggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAQCAIEOQSCGeZNiKAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAj4uEOqJ+j/77LPyr3/9yxNFVaqMe+65p1LbsRECCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIICAOQTcGuTy3HPPyfjx42X9+vXmaO1vtRg8eLCp6kNlEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBM4v4NYgl969e8ukSZMkIyPj/LXw8NpOnTrJzJkzPVwqxSGAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAghUV8CtQS6qUv37969u3dgPAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAGrQDAOCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgiYXYAgF7OfIeqHAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgggIAS5cBEggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIImF6AIBfTnyIqiAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIECQC9cAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAgOkFAjbIJThYb3pJSYnpTxYVRAABBBComoD9e7v9e3/VjsbW/ipgf13YXzf+2m7ahQACCASygP17vf3fgkC2CdS2BwcHOTTd/jpx2IAMBBBAAAGfFygpKdXaEBykf2asrSSBgIcEuC/xEDTFIIAAAl4UcPa86ez934tVpGgETC0QsHftMTEx2onJy8vT0iQQQAABBHxfwP693f693/dbSAtcIWB/XdhfN64og2MggAACCJhLID8/X6uQ/d8CbSWJgBCIio52aOep/FMOeWQggAACCPiXwCm7e4LomCj/aiCt8UkB7kt88rRRaQQQQKBKAs6eN529/1fpoGyMQAAJBGyQS2xsrHaac3NztTQJBBBAAAHfF7B/b7d/7/f9FtICVwjYXxcEubhClWMggAAC5hawv0cgyMXc58sTtYuOdvxSMy9PD4byRD0oAwEEEEDAswL5dgGN0TGOQY+erRGlISDCfQlXAQIIIOD/As6eN529//u/BC1EoHoCBLn85padnV09QfZCAAEEEDCtgP17u30wg2krTsU8KmB/XWRlZXm0fApDAAEEEPC8gP17vf3fAs/XiBK9LRAT6/ilZm4OPb56+7xQPgIIIOBOATVMQF6uHtAYQ5CLO8k5diUFuC+pJBSbIYAAAj4skJPt2PmCs/d/H24iVUfArQIBG+TSsGFDDTY9PV1KS/UxWLUNSCCAAAII+JzArl27tDrbv/drK0kErID9dWF/3QQsDA1HAAEE/Fhg9+7dWusaNWqkpUkEnkBISIjUqZuoNXxP+j4tTQIBBBBAwL8E9u87KCrQxXZKuqC+bZJlBLwioO5L6taro5XNfYnGQQIBBBDweYG9e/ZrbVDv++r9nwkBBConELBBLikpKZrQqVOnZP9+/Q1F24AEAggggIDPCaSlpWl1tn/v11aSCFgB++vC/roJWBgajgACCPixgP17vf3fAj9uOk07j0Bym5ba2l0707U0CQQQQAAB/xLYtVMPek1IqC2JiQn+1Uha47MCrVq30OrOfYnGQQIBBBDweQH793X7932fbyANQMDNAgEb5JKUlCTx8fEa77Zt27Q0CQQQQAAB3xVQvXPxBZbvnj9P1tz+i0113dC7myfPAGUhgAACnhVQP27Iy9OHobH/W+DZGlGaWQTsP1TcmfaLWapGPRBAAAEE3CCwM03v/dX+74AbiuSQCFRawP565L6k0nRsiAACCPiEgP37uv37vk80gkoi4EWBgA1yUebt27fX6JctW6alSSCAAAII+K7Axo0bJTs7W2tAu3bttDQJBJSA/XWhrht1/TAhgAACCPingP1zX0JCgtgPXeefLadVFQmkXNha22T1irVamgQCCCCAgH8JrF75k9agNm2TtTQJBLwpwH2JN/UpGwEEEHC/gP3zpv37vvtrQAkI+LZAQAe59O3bVzt7S5Ys0dIkEEAAAQR8V8D+PT05OZkvsHz3dLq15uqLTXV92E7214/tOpYRQAABBHxb4Ntvv9Ua0KdPHy1NInAFuvfqqjU+ffdeyTh4WMsjgQACCCDgHwIlJSWy6oc1WmN62P0d0FaSQMDDAtyXeBic4hBAAAEPCqjnTPW8aTvZv+/brmMZAQQcBQI6yKV///6ayI8//ihZWVlaHgkEEEAAAd8UWLhwoVbxAQMGaGkSCNgK2F8f9teP7bYsI4AAAgj4toD9e7z93wDfbh21r4lAuw4XSkJCbe0Qy75boaVJIIAAAgj4h8CG9ZslOytHa0yvPt21NAkEvCnAfYk39SkbAQQQcK+A/XOmeg5V7/tMCCBQeYGADnLp1auXRERElGkVFRXJp59+WpZmAQEEEEDANwWOHTsmixYt0ip/xRVXaGkSCNgK2F8f6vo5fvy47SYsI4AAAgj4gcCKFStk717911IEufjBiXVRE4KCgqRHb703l1mfznHR0TkMAggggICZBL7431ytOsltWkpSg/paHgkEvCnAfYk39SkbAQQQcK+A/XNmzz7dRL3vMyGAQOUFAjrIJTIyUoYNG6Zpffjhh1qaBAIIIICA7wnMmDFDCgsLyyoeHR0tQ4YMKUuzgIC9gLo+1HViTOr6+eSTT4wkcwQQQAABPxGYNm2a1pK2bdtK+/bttTwSgS0w7JpUDWD50pVy+NARLY8EAggggIBvC6gfOtp/uTRspP7+79stpPb+IsB9ib+cSdqBAAIInBNQz5fqOdN2unrkVbZJlhFAoBICAR3konxuu+02jWn58uWyfft2LY8EAggggIBvCUyZMkWr8DXXXCMxMTFaHgkEbAXU9aGuE9vJ/jqyXccyAggggIDvCeTl5cnMmTO1it9+++1amgQCg1OvkNi4c/eNpaWlMmP6Z8AggAACCPiRwML530rm8RNai667cYSWJoGAGQS4LzHDWaAOCCCAgGsF1POles40JvX8qd7vmRBAoGoCAR/kkpqaKnXr1i1TU28sL774YlmaBQQQQAAB3xL46quvZOPGjVql+QJL4yBRjoD9daKuI3U9MSGAAAII+IfAW2+9JVlZWWWNCQ4OlltuuaUszQICSqBWrQi52u7X/FPenianT50GCAEEEEDATwT+OekdrSWdu14qLVo20/JIIGAGAe5LzHAWqAMCCCDgOgH1XKmeL20n9fyp3u+ZEECgagIBH+QSFhYm9957r6b20UcfyZ49e7Q8EggggAACviHw3HPPaRVVwxAMHDhQyyOBgDMBdZ2o68V2sr+ebNexjAACCCDgOwIFBQUyefJkrcIjR46UJk2aaHkkEFACd91zqwZx8sRJ+fA/M7Q8EggggAACvimwdMly2fjzZq3y/zda7+lbW0kCAS8LcF/i5RNA8QgggIALBdRzpXq+tJ3s3+dt17GMAALlCwR8kIuiGT9+vERHR5cpqXFZH3vssbI0CwgggAACviHw+eefy8qV+niWjz/+uAQFBflGA6ilVwXUdaKuF9tJXU+zZs2yzWIZAQQQQMAHBV5++WU5cuSIVvMnnnhCS5NAwBC4qP2FMuiq/kbSOn998ruWnoCytTwSCCCAAAK+JVBcXCzPT3xZq3TLVs1l+DWpWh4JBMwkwH2Jmc4GdUEAAQSqL6CeJ9Vzpe2knjvV+zwTAghUXYAgF4tZnTp1ZMyYMZrejBkzZPHixVoeCQQQQAAB8wrk5+fLhAkTtAq2bNlSbrrpJi2PBALnE1DXi7pubCcVDKuuLyYEEEAAAd8USE9Pl+eff16rvBq2tlOnTloeCQRsBcY9rPf4eiLzpLwwcZLtJiwjgAACCPiYwNQpH8vmjdu0Wo994B5RQxgyIWBmAe5LzHx2qBsCCCBQOQH1PKmeK22n8Y/80TbJMgIIVEGAO/jfsFTPLSrYxXYaO3asnD7NuNu2JiwjgAACZhV48sknZf/+/Vr1XnzxRQkJCdHySCBwPgF1vajrxnZS19VTTz1lm8UyAggggIAPCdg/16n3+hdeeMGHWkBVvSHQqfPFMnTElVrRH039r6xds17LI4EAAggg4BsCGQcPy9+fe1WrbNt2KXL9jSO0PBIImFGA+xIznhXqhAACCFReQD1HqudJ20k9b156WUfbLJYRQKAKAgS5/IalAlzsP+hMS0uT+++/vwqcbIoAAggg4A2Br7/+Wl555RWt6EGDBsmoUaO0PBIIVEZAXTfq+rGdJk+eLOo6Y0IAAQQQ8C0B9f49f/58rdLqGe/iiy/W8kgg4Exg4vOPS1R0VNmq0tJS+ePvH2TYojIRFhBAAAHfEFDDFKn379ycPK3CL0x6SkJDQ7U8EgiYVYD7ErOeGeqFAAIInF9ADVOk7kPU86QxqedM9b7OhAAC1RcgyMXG7u6775bu3bvb5Ii89957Mn36dC2PBAIIIICAeQQOHjwot912m3aTGBERIW+++aZ5KklNfE5AXT/qOjIm9RCirjN1vTEhgAACCPiGwOrVq+XPf/6zVtmGDRvKM888o+WRQKA8gYaNGshDf9Z/+HJwf4Y88MfHtHvP8vYnHwEEEEDAHAIvPfuq/LjqJ60yN9xyrXTpfpmWRwIBMwtwX2Lms0PdEEAAAecC6jNl9fyoniNtJ/Wcqd7XmRBAoPoCBLnY2AUFBcmHH34osbGxNrkif/jDH2TZsmVaHgkEEEAAAe8LZGdny9ChQ+X48eNaZV5++WVp3bq1lkcCgaoIqOtHXUe2k7rO1PWmrjsmBBBAAAFzC+zevVtGjhwphYWFZRUNDg6WadOmOTzvlW3AAgJOBO4Ze6f07N1NW/PNvMXy3FP/0PJIIIAAAgiYU2DmR5/Lm6/+S6tc0+aN+fW0JkLCVwS4L/GVM0U9EUAAgV8F1HOjen60ndTzpXo/Z0IAgZoJEORi55ecnCz//ve/tdyCggIZPny4bNq0ScsngQACCCDgPYEzZ87IiBEjZMOGDVolrrvuOrnvvvu0PBIIVEdAXUfqerKd1PWmrjt1/TEhgAACCJhT4OjRozJ48GA5fPiwVsEnnnhCrrjiCi2PBAIVCajgqDf+/bLUqZuobfr2P9+Td15/X8sjgQACCCBgLoGFX38rj4z7i1apsLAwefeD1yQuXv+Ro7YRCQRMKsB9iUlPDNVCAAEEnAio50X13Gg7qedK9Xyp3s+ZEECgZgL8K3Lid8MNNzh8QZqVlSUDBw6UdevWOdmDLAQQQAABTwrk5eXJsGHDZOnSpVqxbdq0sQ4zp2WSQKAGAmrYQnVd2U7qulPXn7oOmRBAAAEEzCVw4MAB6d+/v+zatUurmHqWe+qpp7Q8EghUViCpQX15c8okCQ0N1XZ59q8vEeiiiZBAAAEEzCOw4KtFMvqOcVJcXKxV6pmXnpCOl7TX8kgg4EsC3Jf40tmirgggEKgCKsBFPS/aTup5Uj1XqvdxJgQQqLkAQS7lGL722mty7bXXamvVLwL79esnixfrXUtpG5FAAAEEEHCrwLFjx6xfXi1cuFArp2HDhrJgwQKJj4/X8kkgUBMBdT2p60pdX7aTuv7Ul6jqemRCAAEEEDCHwLZt26Rnz56ydetWrUKdOnWSzz//XEJCQrR8EghURaB3v54y+c3nHXZRH1z+7cm/ixprnQkBBBBAwBwCH0/9r9x92/2WHjjPahW6/8HRcvtdN2l5JBDwRQHuS3zxrFFnBBAIBAH1XKieD+0DXFTb1fOkev9mQgAB1wgQ5FKOo+oq6uOPP5a+fftqW+Tm5kpqaqq88sorWj4JBBBAAAH3C6xevVq6dOkia9eu1QqrXbu2fP3119K8eXMtnwQCrhBQ15W6vtR1Zjup61Bdj+q6ZEIAAQQQ8K6ACmJRAS779+/XKqKGo50/f77ExjIkgQZDoloC190wQp78258c9lVdUN91y1jJysp2WEcGAggggIDnBM6ePSt//dPf5JHxf5WSkhKt4Btvu17+/OSDWh4JBHxZgPsSXz571B0BBPxRQD0PqudC+yGKVFvVc6R632ZCAAHXCRDkch7LiIgImTt3rgwaNEjbqrCwUB588EEZMWKEZGZmautIIIAAAgi4XkB9ODVp0iTp3bu37N27Vyugfv361h62OnTooOWTQMCVAur6Uj25qevNdlLXo7ou1fVp/yGq7XYsI4AAAgi4R6CgoEDGjRsn1113nSXAIEsr5KKLLpJvv/3W4b1b24gEAlUUGH3fXU4DXb6Zt1gG9x4pa9esr+IR2RwBBBBAwBUC6bv3yojBN8n7737ocLhb7vyd/OO1Zx3yyUDA1wW4L/H1M0j9EUDAXwTUc6B6HlTPhfaTCnBR79dMCCDgWgGCXCrwjImJka+++kpuvPFGhy2//PJLSUlJkSlTptA1sYMOGQgggIBrBH766Sfp0aOHPPzww6KCDG2nli1byg8//CBqGAImBNwtoK4zdb2p6852Uteluj7VdaquVyYEEEAAAc8IzJs3T9q1ayevv/66Q4GqV5fly5dL48aNHdaRgUBNBdQHlP989++ixlS3nQ7uz5CRV94kf5rwJL262MKwjAACCLhRQA1JNPmlN+SKHlfLxp83O5Q04ZE/yt9ffVZUr91MCPijAPcl/nhWaRMCCPiKgOq9RT3/qedA9TxoO6nnRfXcSICLrQrLCLhOgLv7SliGhYVZhy7661//6vBApHpy+cMf/mD9YksNZcCEAAIIIOAagd27d8vdd98tXbt2lTVr1jgctF+/frJixQpRwxAwIeApAXW9qetOXX/2k7pO1fWqrlt1/TIhgAACCLhHQA0XN3z4cBk6dKjT99vbbrtNFi1aJAkJCe6pAEdFwCKgupqe/r9/S526iZqHGoN9+gczpfdlV8q/3vyPnD51WltPAgEEEEDANQLFxcXy2czZMqD7UJn0wuuigl1sp1q1ImTymy/II0+Mt81mGQG/FOC+xC9PK41CAAETC6jnPPW8p5771POfeg60ndRzonpeZIgiWxWWEXCtAEEulfQMCgqSZ555RhYsWCBJSUkOe61evVpSU1Olc+fOMmPGDFHdZjMhgAACCFRdQL2fqi+n2rRpI++9957DEDDq11dPPvmk9csrZ+/HVS+RPRComoC67tSXp+o6tP81oBqySF236vpV17G6npkQQAABBGouoL7IUs9iV111lXTp0kXmzJnjcNCoqCh5//33Zdq0aRIZGemwngwEXC3Qu19PWbh8tvTs3c3h0CcyT8rTT7woXdr3l1f/8ZYcPnTEYRsyEEAAAQSqLpCdlSMfTPlILr9ssIwb/ajsSd/ncJDkNi1l3refyQ23XOuwjgwE/FWA+xJ/PbO0CwEEzCSgnuvU8516zlPPe+q5z35Sz4fqOVG9LzMhgID7BIIs0WV6eJn7yvKbIx8+fFgmTJggM2fOLLdN8fHxMmrUKOvY8L1795bo6Ohyt2UFAgggEMgCKihgw4YN1qHhPvzwQ9mxY0e5HG3btpV33nlH+vTpU+42/rgi7fACmbvhoSo17c5es6VOTKsq7cPGVRf4/vvvZcyYMbJt27ZydzYCXlSPAxdffLFDYEy5O7ICAQQQCHABNRycChacPXu2fPTRR3Lo0KFyRdS9gbpHUPcKTJ4VOHg0Xf7yxq1aoc899B8t7e8JdT/7rzc/kEkvvi6n8k85ba764czlfXvINaOGWT/sbNiogdPtyEQAAQQQcBQ4eTJLfli6SvvKhqMAAEAASURBVOZ8MV++mbdYzp7VhzI29ggJCZG7Rt8mj1p6b4mKjjKymSMQUALclwTU6aaxCCDgAYGMg4dl2XcrZNanc2T50pUOvbYYVVD3Hg/9+X65Z+ydfv3579HMg/LaB38xmm2d/+eZH7Q0CQQ8IUCQSw2UFy9eLGPHjpW0tLTzHkUNd6SGL+jWrZukpKRYX2ps+NjYWOuLXxmel4+VCCDgBwLqS6q8vDzJzc2VY8eOWQNZ1HunCm5RQQInTpw4bytVoKAaMu7BBx8U9Z4aaBNBLuY+4+r6njx5sjz77LOSn59/3somJiZag7RUsIu6J1ABMPXq1bPeD8TExATk9X1eMFYigIDfC5w5c8Z6f6DuE1QQi7o/UK9169bJ8uXL5dQp5wEDBozqXesf//iHtfcsI4+5ZwUIcjnnrT78nPj48/LV7AXnMstZatGymXTr2VlapyRLq9YtpHmLphIbFyMxMdESbXmpoBgmBBBAIJAEVJBgXl6+5b4gTw7sOyi7dqbLLzt2y9o162TLpu0VUnTu1klenDxR2rZLqXBbNkAgEAS4LwmEs0wbEUDAFQIqOLDsPiQnz9pLnLoP2Zn2i6xesVbSd++tsJihI66Uic8/LoHwYwaCXCq8HNjAQwIEudQQuqioyPqrwhdeeKHCYJcaFsXuCCCAQMAJqGDAe++91xrcEshDExHk4huX/pEjR6zBLm+//bb1C1vfqDW1RAABBHxToEGDBvLQQw9Ze9NSQYJM3hMgyMXRft3aDfLPl9+WhV9/67iSHAQQQAABlwpc1uUSGffwvTLwyn4uPS4HQ8BfBLgv8ZczSTsQQMCMAoOu6m+9D+nU+WIzVs8tdSLIxS2sHLQaAsHV2IddbARCQ0PljjvukK1bt8p///tfGTRokF93Q2XTdBYRQAABtwmo3i2ef/552bt3r7z00ksSyAEubkPmwC4XUNepul7VdauuX3UdMyGAAAIIuFagS5cuooIJ09PT5eGHH7b0ekGAi2uFOZorBNQHnB/MeMc6DvtNt4+y9tDiiuNyDAQQQACBXwXCw8Pk6pFXyX+/nCpfLpxJgAsXBgLnEeC+5Dw4rEIAAQSqIaB64FTPeQuXz7Y+9wVSgEs1uNgFAbcJ0JOLG2gzMjKsvbvMmTPHOob82bNn3VAKh0QAAQT8S0AN3TJ48GC55ZZbrMO7+VfratYaenKpmZ839169erX1nuCbb76hxzdvngjKPq9Am8uiJTOjUDIPcc96XihWekUgODhYLrnkEklNTZVbb71VLrzwQq/Ug0LLF6Anl/JtjDUFBWfkm/mLZc6s+bJy2Ro5eTLLWMUcAVMKXHZFvGz6IVfOFpSYsn5UKjAFIqMipVuPzpI6bJAMG5kq8bXjAhOCViNQQwHuS2oIyO4IIBCQAgkJtaVH764y7JpUGZx6hdSqFRGQDqrR9OQSsKfedA0PNV2N/KBCDRs2lEceecT6UmPIq7HkV6xYUTa+/I4dOyQ/P98PWkoTEEAAgaoLhISESIsWLUQFtajXpZdeKgMGDBD13smEgL8JdOvWrSxoSwXBLlmyRNavX192T6B6IiguLva3ZtMeHxO46vYkObKvQGZOyvCxmlNdfxOIiIiQVq1ald0jdO3aVfr16ycJCQn+1lTaE2AC6gPQ4dcMsb5KS0tly6btsuqHNZK2faeosd5/2bFbMo+fCDAVmmtWgdDwIBn6+ySJrxsmS2YeN2s1qZefC8TFx0qr5BbSqnULaZ3SyhrccsllHSUsLMzPW07zEHC/APcl7jemBAQQ8G2BOnUTJblNS+t9SMqFraV7r67SrsOFEhQU5NsNo/YI+JkAPbl46YSeOXNGcnNzra/Tp097qRYUiwACCHhGQH0QFRsba31FRUVxQ1hFdnpyqSKYD22uvuhSAbHGPUFhYaEP1Z6q+oPA0dPr5KfjL1qaEix9Grwq0WEN/KFZtMGHBFRgi3GPEBkZ6UM1p6qGAD25GBI1m6ug17zcfMnLy5fTlnuDkpLSmh2QvRGopsCRgu/lQMFcCQmKkg5xj1nmgfsr1WoSsls1BNSXRpGRtSQ6NtoyFGE0wSzVMGQXBFwpwH2JKzU5FgIImF0gONhyH2L5zkLdg8RY7kXUj3SZyhegJ5fybVjjWQF6cvGsd1lp6sNc9apbt25ZHgsIIIAAAgggEFgC6sPc6Oho66tBA4ILAuvsm6O161Y+81tFSuR48CLpctHz5qgYtUAAAQQCTEB9kKqG3mD4jQA78SZrblHJGdmyfrm1VsWllmCruO3SttF1Jqsl1UEAAQQQcLcA9yXuFub4CCCAAAIIIFBTgeCaHoD9EUAAAQQQQAABBBBAwPcEdh/7Xo7kbC6r+LaMuXIyf19ZmgUEEEAAAQQQCCyBX44skILCrLJGbz80WwqL6X24DIQFBBBAAAEEEEAAAQQQQAABUwgQ5GKK00AlEEAAAQQQQAABBBDwrMCKX97SCiyVElm1+x0tjwQCCCCAAAIIBIaA6sVlW8YXWmPPFuXJjsPztDwSCCCAAAIIIIAAAggggAACCHhbgCAXb58BykcAAQQQQAABBBBAwMMC9r24GMXTm4shwRwBBBBAAIHAErDvxcVoPb25GBLMEUAAAQQQQAABBBBAAAEEzCJAkItZzgT1QAABBBBAAAEEEEDAQwL2vbgYxdKbiyHBHAEEEEAAgcARcNaLi9F6enMxJJgjgAACCCCAAAIIIIAAAgiYRYAgF7OcCeqBAAIIIIAAAggggIAHBMrrxcUomt5cDAnmCCCAAAIIBIZAeb24GK2nNxdDgjkCCCCAAAIIIIAAAggggIAZBAhyMcNZoA4IIIAAAggggAACCHhIoLxeXIzi6c3FkGCOAAIIIICA/wucrxcXo/X05mJIMEcAAQQQQAABBBBAAAEEEDCDAEEuZjgL1AEBBBBAAAEEEEAAAQ8IVNSLi1EFenMxJJgjgAACCCDg3wIV9eJitJ7eXAwJ5ggggAACCCCAAAIIIIAAAt4WIMjF22eA8hFAAAEEEEAAAQQQ8JBARb24GNWgNxdDgjkCCCCAAAL+K1CZXlyM1tObiyHBHAEEEEAAAQQQQAABBBBAwNsCBLl4+wxQPgIIIIAAAggggAACHhCobC8uRlXozcWQYI4AAggggIB/ClS2Fxej9fTmYkgwRwABBBBAAAEEEEAAAQQQ8KYAQS7e1KdsBBBAAAEEEEAAAQQ8JFDZXlyM6tCbiyHBHAEEEEAAAf8TqEovLkbr6c3FkGCOAAIIIIAAAggggAACCCDgTQGCXLypT9kIIIAAAggggAACCHhAoKq9uBhVojcXQ4I5AggggAAC/iVQ1V5cjNbTm4shwRwBBBBAAAEEEEAAgcATKC0NvDbTYnMKEORizvNCrRBAAAEEEEAAAQQQcJlAVXtxMQqmNxdDgjkCCJQnEBIS6rCquLjIIY8MBBAwj0B1enExak9vLoYEcwQQQAABBBBAAAEEAk+guKRYa3RIcIiWJoGApwQIcvGUNOUggAACCCCAAAIIIOAFger24mJUld5cDAnmCCDgTCAsJMwh2/5DL4cNyEAAAa8KVLcXF6PS9OZiSDBHAAEEEEAAAQQQQCCwBOx/1BLi5DOBwBKhtd4SIMjFW/KUiwACCCCAAAIIIICABwSq24uLUTV6czEkmCOAgDOBUCcfaBUVFTrblDwEEDCBQE16cTGqT28uhgRzBBBAAAEEEEAAAQQCS6C4WH/eDwt1/OFLYInQWm8JEOTiLXnKRQABBBBAAAEEEEDAzQI17cXFqB69uRgSzBFAwF4g1MkHWvTkYq9EGgHzCNS0FxejJfTmYkgwRwABBBBAAAEEEEAgcASKi+2GK3Lyw5fA0aCl3hQgyMWb+pSNAAIIIIAAAggggIAbBWrai4tRNXpzMSSYI4CAvUB4WC37LDlz5rRDHhkIIOB9AVf04mK0gt5cDAnmCCCAAAIIIIAAAggEjkDBWf15PyIsMnAaT0tNJUCQi6lOB5VBAAEEEEAAAQQQQMA1Aq7qxcWoDb25GBLMEUDAViAsNFwiwvUPtU4V5NluwjICCJhEwFW9uBjNoTcXQ4I5AggggAACCCCAAAKBIXDqtP68HxsdHxgNp5WmEyDIxXSnhAohgAACCCCAAAIIIFBzAVf14mLUhN5cDAnmCCBgLxATGadl5Z/O1dIkEEDA+wKu7MXFaA29uRgSzBFAAAEEEEAAAQQQCAwB+yCXmEiCXALjzJuvlQS5mO+cUCMEEEAAAQQQQAABBGok4OpeXIzK0JuLIcEcAQRsBWKia9sm5dTpfC1NAgEEvC/g6l5cjBbRm4shwRwBBBBAAAEEEEAAAf8XsO+5NSaKIBf/P+vmbCFBLuY8L9QKAQQQQAABBBBAAIFqC7i6FxejIvTmYkgwRwABW4FYuw+18vKzbVezjAACXhZwRy8uRpPozcWQYI4AAggggAACCCCAgP8L5No978fa/ejF/wVooVkECHIxy5mgHggggAACCCCAAAIIuEDAXb24GFWjNxdDgjkCCBgCifENjEXr/GTOMS1NAgEEvCvgrl5cjFbRm4shwRwBBBBAAAEEEEAAAf8WyMo5rjUwMT5JS5NAwFMCBLl4SppyEEAAAQQQQAABBBDwgIC7enExqk5vLoYEcwQQMATqJVxgLFrnJ7P1D720lSQQQMCjAu7sxcVoCL25GBLMEUAAAQQQQAABBBDwb4GT2fqPWuw/D/Dv1tM6MwkQ5GKms0FdEEAAAQQQQAABBBCogYC7e3ExqkZvLoYEcwQQUAJ1axPkwpWAgFkF3N2Li9FuenMxJJgjgAACCCCAAAIIIOCfAkXFRZKbpw9PbP95gH+2nFaZUYAgFzOeFeqEAAIIIIAAAggggEA1BNzdi4tRJXpzMSSYI4CAErD/5Zbqvri4pBgcBBDwsoAnenExmkhvLoYEcwQQQAABBBBAAAEE/FPgZNYxKbX8ZzvVtevZ1XYdywi4U4AgF3fqcmwEEEAAAQQQQAABBDwk4KleXIzm0JuLIcEcAQQa1G2qIagAl8yTh7U8Eggg4HkBT/XiYrSM3lwMCeYIIIAAAggggAACCPifwJHMA1qj4mPqSGREtJZHAgFPCRDk4ilpykEAAQQQQAABBBBAwI0CnurFxWgCvbkYEswRQCA6Mk4S4uppEIeP6R9+aStJIICA2wU82YuL0Rh6czEkmCOAAAIIIIAAAggg4H8Ch4/t1xrVpEErLU0CAU8KEOTiSW3KQgABBBBAAAEEEEDADQKe7sXFaAK9uRgSzBFAoElSsoZw+DhBLhoICQQ8LODpXlyM5tGbiyHBHAEEEEAAAQQQQAAB/xKw/zFL4ySCXPzrDPtWawhy8a3zRW0RQAABBBBAAAEEEHAQ8HQvLkYF6M3FkGCOAAKNk1pqCIeO7tPSJBBAwHMCv/biMstzBdqURG8uNhgsIoAAAggggAACCCDgRwL2z/n2P3bxo6bSFB8QIMjFB04SVUQAAQQQQAABBBBAoDwBb/XiYtSH3lwMCeYIBLZA0wvaaAD7D+2S0tJSLY8EAgh4RuDXXlyyPVOYk1LozcUJClkIIIAAAggggAACCPiwQE5elmTlZmotsP8cQFtJAgE3CxDk4mZgDo8AAggggAACCCCAgDsFvNWLi9EmenMxJJgjENgCrZt21ABOF+TLsROHtDwSCCDgfoFfe3H5wv0FnacEenM5Dw6rEEAAAQQQQAABBBDwQYF9GTu1WkfWipGG9ZpreSQQ8KQAQS6e1KYsBBBAAAEEEEAAAQRcKODtXlyMptCbiyHBHIHAFUiMry+J8UkawN6D+odg2koSCCDgFoFfe3HJcsuxq3JQenOpihbbIoAAAggggAACCCBgbgH75/vkJu0lOJgwA3OfNf+uHVeff59fWocAAggggAACCCDgxwLe7sXFoKU3F0OCOQKBLdC6aQcNYO/BHVqaBAIIuFfADL24GC2kNxdDgjkCCCCAAAIIIIAAAr4vsMfu+d7++d/3W0gLfE2AIBdfO2PUFwEEEEAAAQQQQAABi4BZenExTga9uRgSzBEIXIE2zS7WGr9zz2YpLS3V8kgggID7BMzSi4vRQnpzMSSYI4AAAggggAACCCDguwL5p3Ik48herQGtm+rP/9pKEgh4QCDUA2VQBAIIIIAAAggggAACCLhYIK/giFzc5MZKHzX71H7Zk/lDpbdXG6Y0SJVaYfGV3udE/i5JiG5a6e3ZEAEE/EugfXJXrUF5lg/CDh3dJw2Tmmn5JBBAwD0CRcUFkpx0VaUPfiLvFzmR/0ult1cbJiddafl/UKX3UfcfdWPbVHp7NkQAAQQQQAABBBBAAAFzCagfsNhOtcKjJNmuJ1fb9Swj4AkBglw8oUwZCCCAAAIIIIAAAgi4WKBjk1FVOmLa4QVVDnLp0WqM1IlpVaVy2BgBBAJXoH5iY0myvI6cOFCGsCN9I0EuZRosIOBegfaNf1elAjYf+LTKQS6XNb9bgoNCqlQOGyOAAAIIIIAAAggggIDvCqSlb9Iq37blZRIaQoiBhkLC4wIMV+RxcgpEAAEEEEAAAQQQQAABBBBAwD8FOrTprjUszRLkwoQAAggggAACCCCAAAIIIIAAAr4nUFJSIjv36EEuHdv08L2GUGO/EyDIxe9OKQ1CAAEEEEAAAQQQQAABBBBAwDsCHVvrH3bty/hFsnNPeKcylIoAAggggAACCCCAAAIIIIAAAtUW2L1vq5wuyNf275DcTUuTQMAbAgS5eEOdMhFAAAEEEEAAAQQQQAABBBDwQ4G2LTtLVK1YrWWbd/yopUkggAACCCCAAAIIIIAAAggggID5BTbZPc+3aHSh1KndwPwVp4Z+L0CQi9+fYhqIAAIIIIAAAggggAACCCCAgGcE1Ljcndr21grblLZGS5NAAAEEEEAAAQQQQAABBBBAAAFzCxQXF8mWnT9plezafqCWJoGAtwQIcvGWPOUigAACCCCAAAIIIIAAAggg4IcC9h967T+0W05kHfXDltIkBBBAAAEEEEAAAQQQQAABBPxTYOeezQ5DFXVp198/G0urfE6AIBefO2VUGAEEEEAAAQQQQAABBBBAAAHzClzU8jKJiYrXKrh28zItTQIBBBBAAAEEEEAAAQQQQAABBMwrsHbT91rlkpu0Z6giTYSENwUIcvGmPmUjgAACCCCAAAIIIIAAAggg4GcCIZYhi3pcfKXWqnWWIJfikmItjwQCCCCAAAIIIIAAAggggAACCJhPICcvS9J2b9AqdvmlQ7Q0CQS8KUCQizf1KRsBBBBAAAEEEEAAAQQQQAABPxToe9kwrVW5+dkOH5BpG5BAAAEEEEAAAQQQQAABBBBAAAFTCKgfqpSUlpTVJSI8Urp1GFSWZgEBbwsQ5OLtM0D5CCCAAAIIIIAAAggggAACCPiZQKP6LUV1ZWw7rfp5iW2SZQQQQAABBBBAAAEEEEAAAQQQMJmA6oV1zcbvtFp1az9QakVEaXkkEPCmAEEu3tSnbAQQQAABBBBAAAEEEEAAAQT8VKBv5+Fay3bt3SKHj+3X8kgggAACCCCAAAIIIIAAAggggIB5BDan/SjZuSe0CvXtPEJLk0DA2wIEuXj7DFA+AggggAACCCCAAAIIIIAAAn4ooLoyjotO0Fq27Mf5WpoEAggggAACCCCAAAIIIIAAAgiYR2D5T19rlVG9tLZs3FbLI4GAtwUIcvH2GaB8BBBAAAEEEEAAAQQQQAABBPxQICw0XK7odr3Wso1payy/CDup5ZFAAAEEEEAAAQQQQAABBBBAAAHvC+zev10yjuzVKnJlr5u0NAkEzCBAkIsZzgJ1QAABBBBAAAEEEEAAAQQQQMAPBQZ0vUbCwyLKWlZiGdt76Zq5ZWkWEEAAAQQQQAABBBBAAAEEEEDAHAJLVnyhVaR+YiPpdGEfLY8EAmYQIMjFDGeBOiCAAAIIIIAAAggggAACCCDghwIxUfFy+aVDtZat3bhUsnIytTwSCCCAAAIIIIAAAggggAACCCDgPYFd+7ZK+oE0rQJX9rxRgoMJJ9BQSJhCgKvSFKeBSiCAAAIIIIAAAggggAACCCDgnwJX97ldQkPCyhpXbOnN5dtVX5alWUAAAQQQQAABBBBAAAEEEEAAAe8KLPphllaBxLj60qfTMC2PBAJmESDIxSxngnoggAACCCCAAAIIIIAAAggg4IcCCXH1pH+XkVrL1m1eLsdPHtbySCCAAAIIIIAAAggggAACCCCAgOcFtu/eIPsyftEKHt7vTgkNPfeDFW0lCQS8LECQi5dPAMUjgAACCCCAAAIIIIAAAggg4O8CQy29uYSHRZQ1s6S0ROZ/N7MszQICCCCAAAIIIIAAAggggAACCHheoLi4yPJ8PkMruF5CQ+llN/SwtgEJBLwsQJCLl08AxSOAAAIIIIAAAggggAACCCDg7wLxMYkysPsorZnbd/8sv+zZrOWRQAABBBBAAAEEEEAAAQQQQAABzwms+nmJQ0+rIwf83jLscKjnKkFJCFRRgCCXKoKxOQIIIIAAAggggAACCCCAAAIIVF3gaktvLnGWYBfb6avvPpHikmLbLJYRQAABBBBAAAEEEEAAAQQQQMADAvmncmXJyi+0klo0ais9Ol6p5ZFAwGwCBLmY7YxQHwQQQAABBBBAAAEEEEAAAQT8UCAyIlquHzhGa9nRzAz5Ye0CLY8EAggggAACCCCAAAIIIIAAAgi4X2D+0hlScOa0VtAtQyZIUFCQlkcCAbMJEORitjNCfRBAAAEEEEAAAQQQQAABBBDwU4Fel6RKs4YpWusWW341lnnyiJZHAgEEEEAAAQQQQAABBBBAAAEE3Cew0zJ88PqtK7QCunccLK2atNfySCBgRgGCXMx4VqgTAggggAACCCCAAAIIIIAAAn4oEBwcLLdf/bD2q7CiokKZtfADP2wtTUIAAQQQQAABBBBAAAEEEEDAfAJnC8/I7IVTtYpF1oqRG64cq+WRQMCsAqFmrRj1QgABBBBAAAEEEEAAAd8X2L59u+zZs0f27dtnfWVnZ0tYWJjExMRIUlKSNG/eXLp06SL169f3/cbSAgQQqJRAy8YXycDuo2Thyv+WbZ++f7us3vCtdLu4f1keCwgggAACCCCAAAIIIIAAAggg4HqBBd9/KidzjmsHVgEutWPrankkEDCrAEEuZj0z1AsBBBBAAAEEEEAAAR8VyMvLk+nTp8tbb70lmzZtqlQrmjVrJg8++KCMGzeuUtuzEQII+LbAdVfcI+u2fS+ZWYfLGjL/uxnSssmFUi/xgrI8FhBAAAEEEEAAAQQQQAABBBBAwHUCaekbZdXPi7UDpjS/VPp0GqblkUDAzAIMV2Tms0PdEEAAAQQQQAABBBDwMYEZM2ZIo0aN5N577610gItq4t69e2XhwoU+1lqqiwAC1RWICI+UO4c/qu1eWHRW/vvVu1JcXKTlk0AAAQQQQAABBBBAAAEEEEAAgZoL5J/Kkc+/fk87UHhYhNw54k/asMLaBiQQMKEAQS4mPClUCQEEEEAAAQQQQAABXxMoLS2ViRMnyk033SQ5OTkO1W/ZsqWMGTNG3nnnHXnooYckKirKYRsyEEAgsATaJ3eTK7pdrzU64+he+Wb5Z1oeCQQQQAABBBBAAAEEEEAAAQQQqJmA+uzuf5YAlzxLoIvtdOOV90uDOk1ss1hGwPQCDFdk+lNEBRFAAAEEEEAAAQQQML/A+PHj5fXXX3daUTVskerZxXYaPXq09OjRQzIzM22zWUYAgQATuGHwH2Xb7p8k41h6WcuXr/1amlzQStq36VyWxwICCCCAAAIIIIAAAggggAACCFRf4NtVc2SHZagi2+mSlMulf9drbLNYRsAnBOjJxSdOE5VEAAEEEEAAAQQQQMC8AkuXLpU33njDaQUfe+wxhwAXtWHr1q3l1VdfdboPmQggEDgCYZZukceMmiihIWFaoz+z/LrsWOYhLY8EAggggAACCCCAAAIIIIAAAghUXWBH+iZZsuILbce4mES5a+RjWh4JBHxFgCAXXzlT1BMBBBBAAAEEEEAAARMKFBQUyD333COqy1P7qW/fvvLcc8/ZZ5elb7zxRklMTCxLs4AAAoEp0KRBstw8ZILW+LOFBTL9y39KwZnTWj4JBBBAAAEEEEAAAQQQQAABBBCovMCJrKMy86t3pNTynzEFBQXLvaOeltjo2kYWcwR8SoAgF586XVQWAQQQQAABBBBAAAFzCUyZMkV27NjhtFK33nqrBAUFOV2nMkNDQ+W+++6T3r17W1/t2rUrd1tWIICAfwv07zJSLr90iNbI4ycOy8dz3pTikmItnwQCCCCAAAIIIIAAAggggAACCFQscLogX6bOesXyA5JT2sa/swwdfGGLTloeCQR8SSDUlypLXRFAAAEEEEAAAQQQQMBcAp999pnTCoWEhMiIESOcrrPNfPrpp0W9mBBAAIHbr35Y9h/ZJXsz0sowdu3dIrMXTpVrr7yrLI8FBBBAAAEEEEAAAQQQQAABBBA4v0BRcZFMn/26qB+Q2E5d2g2Qq3rdZJvFMgI+J0CQi8+dMiqMAAIIIIAAAggggIA5BDIzM2XZsmVOK6N6Z6lXr57Tdd7KVPWdN29eWfExMTFyzTXXlKVZQAAB7wqEhUXIfTc+L8++e7fk5J8sq8xPm5dJQnw96d99WFkeCwgggAACCCCAAAIIIIAAAggg4FxADSv++YL3ZM+Bcz8iUVs2SUqWu0Y+5nwnchHwIQGCXHzoZFFVBBBAAAEEEEAAAQTMJDB37lwpLnY+jEifPn3MVFVrXebPny+33357Wb0aNmxIkEuZBgsImEOgbu0GMv6Wf8hL/xkrZwvPlFVq0Q+fS2StKOl+yRVleSwggAACCCCAAAIIIIAAAggggICjwJwl02XDtlXaitqxdeWB216WWhFRWj4JBHxRINgXK02dEUAAAQQQQAABBBBAwPsC27ZtK7cSZuvFRVV0xYoVWn07dOigpUkggIA5BFo2biujr58oQUFBWoXmLJ4u67Ys1/JIIIAAAggggAACCCCAAAIIIIDAOYEF338qq39eci7DslQrPMoa4JIQZ65el7VKkkCgCgIEuVQBi00RQAABBBBAAAEEEEDgnMCRI0fOJeyW6tata5fj/aR9kMu9997r/UpRAwQQcCrQqW0fuTl1gsO6zxe8Lxu3r3bIJwMBBBBAAAEEEEAAAQQQQACBQBdYvOIL+f7Hc0N1K4+Q4BD54w1/k6YNWgc6D+33IwGCXPzoZNIUBBBAAAEEEEAAAQQ8KeBLQS45OTmyadOmMp6mTZvK1VdfXZZmAQEEzCcwsPv1cu0Vf9AqpsYV/++8d+nRRVMhgQACCCCAAAIIIIAAAgggEOgC3yz7nyxZOVtjCAoKljGjnpYOrbtp+SQQ8HUBglx8/QxSfwQQQAABBBBAAAEEvCTgS0Euq1evlpKSkjKp0aNHS0hISFmaBQQQMKfAsL53ypDet2qVU4Eun339nqz6ebGWTwIBBBBAAAEEEEAAAQQQQACBQBNQz8hzlkyXpWu+cmj67695XDq36++QTwYCvi5AkIuvn0HqjwACCCCAAAIIIICAlwTOnj1bbsm1atUqd503VtgOVRQeHi533323N6pBmQggUA2BUYPulUHdRznsOWfxdPl21RyHfDIQQAABBBBAAAEEEEAAAQQQCASB4uKiX38Est7xRyC3D3tEel2SGggMtDEABUIDsM00GQEEEEAAAQQQQAABBFwgkJCQUO5RTp48We46b6ywDXK57rrrpH79+t6oBmUigEA1BW4eMkHCwiJk3rLp2hEW/fC5nMw+JiMG3WEdZ1xbSQIBBBBAAAEEEEAAAQQQQAABPxUoOHNKPvryDdm9b5vWQjVE0V0jH5PLLx2i5ZNAwJ8ECHLxp7NJWxBAAAEEEEAAAQQQ8KBAnTp1yi0tMzOz3HWeXqGGKVLDFRnTH//4R2OROQII+JCA6tGlVnikfL7431qtf9q8TLJyT8jNw8ZKrYhIbR0JBBBAAAEEEEAAAQQQQAABBPxNICsnU6Z+PlmOZmZoTQsJDpHR10+ULu0HaPkkEPA3AYYr8rczSnsQQAABBBBAAAEEEPCQwPmCXI4fP+6hWlRczJYtWyQ7O9u6YceOHeXyyy+veCe2QAABUwoM63un3DLkAQkKCtLqt2vvFnn742fkWOYhLZ8EAggggAACCCCAAAIIIIAAAv4koHpueWv60w4BLuFhteT+m18kwMWfTjZtKVeAIJdyaViBAAIIIIAAAggggAAC5xOoV69euavN1JOL7VBF9OJS7iljBQI+IzCw+/Vy343PS7hl+CLb6fiJw/LWR8/I5h1rbbNZRgABBBBAAAEEEEAAAQQQQMAvBJav/Vre/98/JP90rtaeuJhEeez3b8rFbXpq+SQQ8FcBglz89czSLgQQQAABBBBAAAEE3CzQs2f5D85r15rnS2YjyCUuLk5uueUWN6tweAQQ8IRAp7Z95E//96bERSdoxZ0tLJBP5rwp85fOlOLiIm0dCQQQQAABBBBAAAEEEEAAAQR8UeB0wSn5+Mtfn3VLS0u1JjSs10KevOff0rzhhVo+CQT8WSDUnxtH2xBAAAEEEEAAAQQQQMB9Av369ZOwsDApLCx0KGTevHly9uxZCQ8Pd1hnm/Hjjz9Kenq6NatLly7SokUL29UuWTaCXG6//XaJiYlxyTE5CAIIeF+gZeO28tfRU+SNGY/L3ow0rULq122qC+ffDR0t9RIv0NaRQAABBBBAAAEEEEAAAQQQQMBXBNIPpMmn8/4l2bknHKp8SUov+cN1T0pULT7vcsAhw68FCHLx69NL4xBAAAEEEEAAAQQQcJ9AbGysdO/eXZYtW+ZQSE5OjixZskSuuuoqh3W2Gffee6/89NNP1qzhw4fL7Nmzy1a/+uqrcvLkSSkoKJAzZ85IUdGvvTI8++yzEh8fL99884217K1bt0rTpk1l8ODBMnTo0LL9jYUJEyaICrphqCJDhDkC/iNQt3YDeeL3b8u0uS/L8vXztIZlHN0rb344UVL73SjdLu6vrSOBAAIIIIAAAggggAACCCCAgJkFiiy9ky5ZOVu+X/2VlFr+s52CgoJkZP+7ZVjfO0QtMyEQaAIEuQTaGae9CCCAAAIIIIAAAgi4UOCuu+5yGuSiipg2bdp5g1z27Nkj69atK6tNSUlJ2bJaeOWVV2Tfvn1ankqMHz9err/+emsQje3Kf/7zn/LQQw/Jyy+/bJstY8eOtb60TBIIIOA3AmFhEfL7a56Qlo3bycfzXpWi4nO9SxUWnZUvF02TTWlr5JpBd0qdhCS/aTcNQQABBBBAAAEEEEAAAQQQ8E+BfRm7ZNY378vRzAyHBkZHxsro6ydKh9bdHdaRgUCgCAQHSkNpJwIIIIAAAggggAACCLhe4I477pAePXo4PfAnn3wib731ltN1KnPixIliP46w7cbbt2+X1NRU2yzrsuo9RvUSo4ZKsp8mT54sO3bssM8mjQACASDQv8tIedIyfJEaj9x+St+/Xf457a/y/Zp5UlxSbL+aNAIIIIAAAggggAACCCCAAAJeFzhztkDmLJku//rkOacBLm1bXibPjv2QABevnykq4G0Bgly8fQYoHwEEEEAAAQQQQAABHxZQXaK++eabEhrqvJPIcePGyUcffSSFhed6VlCBLWoIoalTp5635ZGRkXLttdc6bKOONXfuXOsQRpMmTdLWq2PPmjVLyyOBAAKBI9CkQbJMHPOeXNHtOodGFxUVyoJln8ob056UX/ZsdlhPBgIIIIAAAggggAACCCCAAALeEFCfZ63b8oO88v5jsmr9YofhiUJCQuV3g8fKI3e8Jglx9bxRRcpEwFQCBLmY6nRQGQQQQAABBBBAAAEEfE/g0ksvlfnz50tCQoJD5YuLi+XWW2+VxMREGTp0qAwcONC6/Nprrzls6ywjOjraIfull16yHksF2Nx///0OPbpkZDh25epwEDIQQMBvBdTwRbcOfVAeun2y1KndwKGdqrvn/3w2ST6c9ZocP3nYYT0ZCCCAAAIIIIAAAggggAACCHhKYF/GL/LOx8/KZ19Pkdz8LIdimzVMsfZamnr5zaI+C2NCAAER5z+3RAYBBBBAAAEEEEAAAQQQqIKACl5ZvXq1/O53v5Off/7ZYc+8vDyZN2+eQ35FGc56iFFlGZMasqhp06aya9cuI0uOHz9etswCAggErkD75G7y3H3T5bPF/5JFqz51GB5t++6fZUf6RunU/nLp33241I6rE7hYtBwBBBBAAAEEEEAAAQQQQMCjAoeP7ZdFP8ySbbvWOy033PIDjpH975Yre94gwcEhTrchE4FAFSDIJVDPPO1GAAEEEEAAAQQQQMDFAq1bt5b169fLypUr5YMPPpDPPvtMMjMzz1tK/fr15ZJLLrG+Bg8efN5tjZURERHGonUeFRWlpVUXr0wIIICAEogIj5SbU8dL9w6DZNrcl2VvRpoGU1JaIms3fS/rLd1Cd+7YV/p2vVriYx17pdJ2IoEAAggggAACCCCAAAIIIIBANQWOHD8oS1bOls07fiz3CB1ad7f2UFo/sVG527ACgUAWIMglkM8+bUcAAQQQQAABBBBAwA0CPXr0EPV69913RfXgcvDgQesrKytL1PBDtWvXtg5tpIYwqlu3bo1r4Ky3lxoflAMggIBfCbRsfJE8ec8UWf7zPPls0buSk3dCa19xSbGs/nmJ/LhxqVx8YTe5vPNV0qBeE20bEggggAACCCCAAAIIIIAAAghUV2D3vm2ybO3X1h5FyztGg7pN5aarxknHNj3K24R8BBCwCBDkwmWAAAIIIIAAAggggAACbhOIiYmRlJQU68tdhTAesbtkOS4C/iUQHBwsfTpdLV3a9Ze530+zDmF0tvCM1sgSS7DL+q0rrK9WzdpJ90sGSErLiyWErqE1JxIIIIAAAggggAACCCCAAAIVC6hnzk1pP8qq9Ysk4+jecneIiYqXYX3vlCu6XishIXx9Xy4UKxD4TYB/JVwKCCCAAAIIIIAAAggggAACCCAQMAKREdEyatC9MrjHDfKVJdjl2x+/kKLiQof279q7RdQrNjpeOrXvLZ0tr8Ta9R22IwMBBBBAAAEEEEAAAQQQQAABW4GMI3vlx01LZcO2lXLmbIHtKm05qlasXNXrJhnUfZTUitCH49Y2JIEAApoAQS4aBwkEEEAAAQQQQAABBBAwk0BpaalDdezz7NMOO5CBAAIIOBGIj0mUm4dMkNTLb7H27LJ8/Vdi37OL2i03P1uWrp5rfTW5oKV0SOkq7dt0kfjYRCdHJQsBBBBAAAEEEEAAAQQQQCAQBY5lHpKNaastPbeskWMnDp2XIDoyTgZ2v97644uoWjHn3ZaVCCDgKECQi6MJOQgggAACCCCAAAIIIGASgcJCx94VioqKtNrZb2Of1jYmgQACCNgJJMTVk9uufkiuGXC3LFkzSxav/p/k5J+02+rX5P5Du0W95n03Q5o2TJaUFh2ljeV1Qf2mwtBpTsnIRAABBBBAAAEEEEAAAQT8UqDYMtzt/oxfZEf6Jtm+e4McOX6gwnYmJTaWwT1vkMsvHSrhYREVbs8GCCDgXIAgF+cu5CKAAAIIIIAAAggggIAJBHbv3u1Qi/T0dGnVqpU1XwW0HDigf4jgbB+Hg5CBAAII2AmoMdCH97vT0rPLzbJ600JZuvZL+WX/ZrutziX3WT7MVK+FP3wuMVFx0rp5e2nWqI3l1VrqJV5A0Ms5KpYQQAABBBBAAAEEEEAAAZ8XUEEth4/tl70Hd0r6/jTZtW+rZSii0xW2S/0gokNyd+nXZYRc3KaXBAcHV7gPGyCAwPkFCHI5vw9rEUAAAQQQQAABBBBAwEsCM2fOlNdee82h9AcffFCmTp0qrVu3ltGjR0tWVpa2zbp16+T++++XSZMmSXh4uLaOBAIIIFCRQFhouPVXdeqXdQeP7palP82RFT9/Lfmnc8rdNe9UjqzfusL6UhtF1oqWJhe0svbw0qBuY2lQr7HUSWggIcEh5R6DFQgggAACCCCAAAIIIIAAAuYQKCoqlKOZGXL4+H5LYMsByTi6Vw5YevUsLDpb6QrWiU+S3p2ulss7DRW1zIQAAq4TIMjFdZYcCQEEEEAAAQQQQAABBFwoMGXKFMnMzHQ44qZNm2TRokUSGRkpH3/8scN6lfHGG2/IxIkTpU6dOk7Xk4kAAghURqBR/ZZyc+p4uWHwWNm6+ydZs3mRrNu2TE4V5J5399MF+ZYuqzdaX8aGKsCldnxdSYizvOLrWecx0fESFRkt0ZGxosZhj4iItAbChIaGWechIXxsY/gxRwABBBBAAAEEEEAAAQSqK1BUXCTFv72Kigul4MxpOXU61/LKtzzf5UlOXpaczD4mJ3OOW+fZuSektLS0ysXVjq0rXdoPkK7tBkirJu3p4bPKguyAQOUE+LSkck5shQACCCCAAAIIIIAAAh4WWLhwYYUlVucDhwoPygYIIICAnYAKNunQupv1dcewQtmW/pNs3LlSNu1YJUdOHLDb2nlSdW2defKI9eV8C3IR8KxAvcYFktSsamX+dfLvLTsEVW0ntkYAAQQQQAABBBBAwI8FmjZoLR3b9JCOrXtIctMOBLb48bmmaeYRIMjFPOeCmiCAAAIIIIAAAggggAACCCCAgMkFVC8rHVp3t75kiMhRS5DL5l/WyI69G2Tnvk1yIvuIyVtA9RBAAAEEEEAAAQQQQAABBKor0KBuU2ndtKOkNL9E2rfqKvGx9CJcXUv2Q6C6AgS5VFeO/RBAAAEEEEAAAQQQQAABBBBAIOAF6ic2lgFd1etaq8WJ7KOWYJeNsu/QDjlwZLfsP/KLpcvrYwHvBAACCCCAAAIIIIAAAggg4GsC9RMbSeOkVtIkKVmaNUyxBLd0kJioeF9rBvVFwO8ECHLxu1NKgxBAAAEEEEAAAQQQQAABBBBAwFsCifH1pVuHgdaXUYf80zly+Pg+OXbykBzPOmSdn8g+LLmnsiUvP0vyLOvPnD1tbM4cAQQQQAABBBBAAAEEEEDAzQKREdGWgJXallecxEUnSJ3aDaRu7QukXsIFUjehoVxg6bElIjzSzbXg8AggUB0Bglyqo8Y+CCCAAAIIIIAAAggggAACCCCAQCUFoiPjpFWT9tZXebsUFp21BLoUSHFxoRRZXoWWV3FxUXmbk49AjQW2HJ4hmw59VKXjPDN2mgQHhVRpHzZGAAEEEEAAAQQQQMCbAkFBQRISHCpq6NnQkDAJs7xU8EpICF+Te/O8UDYCNRHgX29N9NgXAQQQQAABBBBAAAEEEEAAAQQQcIFAWGi4qBcTAp4S2J+bUOWiGtVvQZBLldXYAQEEEEAAAQQQQAABBBBAwJUCwa48GMdCAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQMAdAgS5uEOVYyKAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgi4VIAgF5dycjAEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABdwgQ5OIOVY6JAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggg4FIBglxcysnBEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBNwhQJCLO1Q5JgIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggIBLBQhycSknB0MAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBwhwBBLu5Q5ZgIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACLhUgyMWlnBwMAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAwB0CBLm4Q5VjIoAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCLhUgCAXl3JyMAQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAF3CBDk4g5VjokAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCDgUgGCXFzKycEQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEE3CFAkIs7VDkmAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAgEsFCHJxKScHQwABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEHCHAEEu7lDlmAgggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIuFSDIxaWcHAwBBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEDAHQIEubhDlWMigAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIuFSAIBeXcnIwBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAXcIhLrjoBwTAQQQQAABBBBAQBcoKSmRDRs2yLp16yQtLc36Sk9Pl+zsbMnLy5Pc3FwpLCzUdyKFgAsFQsODJDoupEpHfPREslguXSYE3CYQEREhsbGx1lft2rUlOTlZUlJSrK+uXbtKmzZt3FY2B0YAAQQQQAABBBBAAAEEEEAAAQQQQAAB3xMgyMX3zhk1RgABBBBAAAEfEThx4oR8+umn8s0338h3330nKs2EgLcEis6WSvbxIm8VT7kIOBU4c+aMqNfx48et69evX69t16hRIxkwYICkpqbKiBEjJCoqSltPAgEEEEAAAQQQQAABBBBAAAEEEEAAAQQCS4Agl8A637QWAQQQQAABBNwsoHpsmTNnjkydOlW++uorOXv2rJtL5PAIIICA/wocPHhQPvzwQ+tL9fhy3XXXyV133SW9e/f230bTMgQQQAABBBBAAAEEEEAAAQQQQAABBBAoVyC43DWsQAABBBBAAAEEEKi0QFFRkUybNk3atWsnI0eOlFmzZhHgUmk9NkQAAQQqFlDDun3wwQfSp08f6dWrl8yfP7/indgCAQQQQAABBBBAAAEEEEAAAQQQQAABBPxKgJ5c/Op00hgEEEAAAQQQ8IbA3LlzZcKECbJr164Ki2/Tpo107NhRUlJSRC3Xq1dPVO8E6hUWFlbh/myAAAII+JOAGqpIBa+o16FDhyQtLc36UsMWHThwoNymrlixQoYMGSJdu3aVN954Q7p06VLutqxAAAEEEEAAAQQQQAABBBBAAAEEEEAAAf8RIMjFf84lLUEAAQQQQAABDwvs3btXxo0bJ19++WW5JUdGRsqIESNk6NChMmDAAGnYsGG527ICAQQQQOCcwM6d/9/evYDZOd2L4//mSiRB4hKXiKhEEkLJkZBEVJQopbRxrdBWjhYHrfSotpxf9fk5qlqq+JVSGuKUupUiRDQoGrfQICQuEYncGoJIRO7/rv3v7DPv3jOTkcxM9t7zeZ9nP/Ou9a53re/6rJhpM9+s9UZMmDAh7r333nj44Ydj1apV//vwX3fPPvts7LvvvvHtb387Lr744ujUqVNRGxUECBAgQIAAAQIECBAgQIAAAQIECFSOgOOKKmctzYQAAQIECBBoQoE//vGPsfvuu9ea4NKvX7+44YYbYt68eXHrrbfGiBEjJLg04foYigCB8hfo2bNnfOc734mxY8fG7Nmz47LLLouddtqpaGKrV6+Oa6+9NrdL1lNPPVX0XAUBAgQIECBAgAABAgQIECBAgAABApUjIMmlctbSTAgQIECAAIEmEPj000/j9NNPj+OPPz53vEbhkAMHDsz9QnbSpElxyimnxKabblrYRJkAAQIEPqNAly5dYtSoUfH666/HTTfdlDvyrbCLdLzRAQccED//+c9jzZo1hY+VCRAgQIAAAQIECBAgQIAAAQIECBCoAAFJLhWwiKZAgAABAgQINI3Ahx9+GMOGDcvtGFA44jbbbBO33HJL/O1vf4tDDz208LEyAQIECDSAQOvWrePkk0+OV155JS699NLo0KFDpteVK1fGD3/4w1ybFStWZJ4pECBAgAABAgQIECBAgAABAgQIECBQ/gKSXMp/Dc2AAAECBAgQaAKBOXPmxP777x9PPPFE0Whpx5apU6fGiSeeWPRMBQECBAg0vEBKdjn33HPjtddei4MPPrhogJR0+JWvfCWWLFlS9EwFAQIECBAgQIAAAQIECBAgQIAAAQLlKyDJpXzXTuQECBAgQIBAEwnMmzcvhgwZEi+//HJmxLSDwJgxY+KGG26IzTbbLPNMgQABAgQaX6Br167x0EMPxf/9v/83WrVqlRkw1aedtdIxcy4CBAgQIECAAAECBAgQIECAAAECBCpDQJJLZayjWRAgQIAAAQKNJLBo0aL40pe+FNOnT8+MsP3228fEiRNjxIgRmXoFAgQIEGhagZYtW8YFF1wQDzzwQNHxRWn3reOPPz5WrVrVtEEZjQABAgQIECBAgAABAgQIECBAgACBRhGQ5NIorDolQIAAAQIEKkFgxYoVceSRR8bkyZMz0+ndu3f87W9/i759+2bqFQgQIEBgwwkccsghMWHChNhyyy0zQdx7771x+umnZ+oUCBAgQIAAAQIECBAgQIAAAQIECBAoTwFJLuW5bqImQIAAAQIEmkDghz/8YTz22GOZkXbZZZf461//Gt26dcvUKxAgQIDAhhfo379/7vt2p06dMsFcf/31ceONN2bqFAgQIECAAAECBAgQIECAAAECBAgQKD8BSS7lt2YiJkCAAAECBJpA4L777ovLL788M9K2224b48aNi6222ipTr0CAAAECpSOw2267xf333x/t2rXLBHXWWWfFlClTMnUKBAgQIECAAAECBAgQIECAAAECBAiUl4Akl/JaL9ESIECAAAECTSCwYMGC+OY3v5kZaeONN46xY8dG9+7dM/UKBAgQIFB6AoMGDYoxY8ZkAvvkk0/i+OOPj3QUnYsAAQIECBAgQIAAAQIECBAgQIAAgfIUkORSnusmagIECBAgQKARBc4999xYuHBhZoQrrrgi9txzz0ydAgECBAiUrsDw4cPj7LPPzgT4yiuvRPp+7iJAgAABAgQIECBAgAABAgQIECBAoDwFJLmU57qJmgABAgQIEGgkgSeeeCJuuummTO/HHntsfOc738nUKRAgQIBA6Qv84he/iL322isT6E9/+tOYNWtWpk6BAAECBAgQIECAAAECBAgQIECAAIHyEJDkUh7rJEoCBAgQIECgiQTOOeeczEibbbZZXHnllZk6BQIECBAoD4G2bdvGddddFy1b/u//9V2yZElccMEF5TEBURIgQIAAAQIECBAgQIAAAQIECBAgkBH437/py1QrECBAgAABAgSan8BDDz0UkyZNykz8v//7v6NLly6ZOgUCBAgQKB+Bvffeu2g3rj/84Q/x9ttvl88kREqAAAECBAgQIECAAAECBAgQIECAQE5Akos/CAQIECBAgACBfwmkhJbqV69eveL000+vXuWeAAECBMpQ4KKLLooOHTrkI1+5cmX8/Oc/z5fdECBAgAABAgQIECBAgAABAgQIECBQHgKSXMpjnURJgAABAgQINLLAc889F08++WRmlB/96EeZIy4yDxUIECBAoGwEOnfuXJS0OHr06Fi4cGHZzEGgBAgQIECAAAECBAgQIECAAAECBAhESHLxp4AAAQIECBAg8E+Bm266KeOw4447xoknnpipUyBAgACB8hUYNWpUbLzxxvkJLFu2LP74xz/my24IAKJ5AABAAElEQVQIECBAgAABAgQIECBAgAABAgQIECh9AUkupb9GIiRAgAABAgQaWWDFihVx2223ZUYZOXJktG7dOlOnQIAAAQLlK7DNNtvEUUcdlZnAmDFjMmUFAgQIECBAgAABAgQIECBAgAABAgRKW0CSS2mvj+gIECBAgACBJhAYP358vP/++/mRWrRoESeddFK+7IYAAQIEKkOg8Hv7xIkTY8aMGZUxObMgQIAAAQIECBAgQIAAAQIECBAg0AwEJLk0g0U2RQIECBAgQKBugUceeSTTYPDgwdG9e/dMnQIBAgQIlL/AsGHDYquttspM5C9/+UumrECAAAECBAgQIECAAAECBAgQIECAQOkKSHIp3bURGQECBAgQINBEAhMmTMiMdMghh2TKCgQIECBQGQLpGLqDDjooM5nCnwGZhwoECBAgQIAAAQIECBAgQIAAAQIECJSUgCSXkloOwRAgQIAAAQJNLbBw4cJ46aWXMsMeeOCBmbICAQIECFSOwNChQzOTefTRRzNlBQIECBAgQIAAAQIECBAgQIAAAQIESldAkkvpro3ICBAgQIAAgSYQePnll2PNmjX5kdq1axcDBgzIl90QIECAQGUJHHDAAZkJzZ07N957771MnQIBAgQIECBAgAABAgQIECBAgAABAqUpIMmlNNdFVAQIECBAgEATCUybNi0zUq9evSIdZ+EiQIAAgcoU2HnnnWPjjTfOTK7wZ0HmoQIBAgQIECBAgAABAgQIECBAgAABAiUjIMmlZJZCIAQIECBAgMCGECj8xWZKcnERIECAQOUKtGzZMnr06JGZYOHPgsxDBQIECBAgQIAAAQIECBAgQIAAAQIESkZAkkvJLIVACBAgQIAAgQ0hMHPmzMywPXv2zJQVCBAgQKDyBAq/1xf+LKi8GZsRAQIECBAgQIAAAQIECBAgQIAAgcoQkORSGetoFgQIECBAgMA6Cnz88ceZNzt37pwpKxAgQIBA5QkUfq8v/FlQeTM2IwIECBAgQIAAAQIECBAgQIAAAQKVISDJpTLW0SwIECBAgACBdRQo/MVmhw4d1rEnrxEgQIBAuQh07NgxE2rhz4LMQwUCBAgQIECAAAECBAgQIECAAAECBEpGQJJLySyFQAgQIECAAIENIVD4i83CX3xuiJiMSYAAAQKNK1CY0Fj4s6BxR9c7AQIECBAgQIAAAQIECBAgQIAAAQLrKiDJZV3lvEeAAAECBAhUhMCqVasy82jVqlWmrECAAAEClSdQ+L2+8GdB5c3YjAgQIECAAAECBAgQIECAAAECBAhUhoAkl8pYR7MgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECFS0gCSXil5ekyNAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIVIaAJJfKWEezIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAhUtIAkl4peXpMjQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECFSGgCSXylhHsyBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIVLSAJJeKXl6TI0CAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAhUhoAkl8pYR7MgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECFS0gCSXil5ekyNAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIVIaAJJfKWEezIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAhUtIAkl4peXpMjQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECFSGQOvKmIZZECBAgAABAgQIECBAgACB8hZ49dVXI33efffdaNmyZXTp0iX69esXPXv2rPfE1qxZEw8//HAcdNBB0apVq3q/pyEBAgQIECBAgAABAgQIECBAgACBchCQ5FIOqyRGAgQIECBAgAABAgQIEKhYgaeeeirOO++8SF9runbeeec49thj4xvf+Eb06tWrpia5umXLlsWpp54aY8aMiaOOOir+9Kc/1drWAwIECBAgQIAAAQIECBAgQIAAAQLlKOC4onJcNTETIECAAAECBAgQIECAQEUIjB49Og488MBaE1zSJN9666342c9+Fr17944BAwbEr371q3j99dfz80/JLXfddVdu15eU4JKuIUOG5J+7IUCAAAECBAgQIECAAAECBAgQIFApAnZyqZSVNA8CBAgQIECAAAECBAgQKCuBsWPHxsiRI2P16tW5uLfbbrvo379/bLTRRjFr1qyYOnVqfPDBB5k5Pffcc5E+o0aNivbt28fy5ctj1apV+T5S41NOOSX3PPOiAgECBAgQIECAAAECBAgQIECAAIEKEJDkUgGLaAoECBAgQIAAAQIECBAgUF4Cs2fPjhNPPDGXnJKSW6644oo45phjMpNIyStPP/103HbbbbnPe++9l3m+ZMmSTDkVzjnnnLj00kuL6lUQIECAAAECBAgQIECAAAECBAgQqAQBxxVVwiqaAwECBAgQIECAAAECBAiUlcAmm2yS27Vlp512iieffLIowSVNplWrVjF48OC46qqrYs6cOfGnP/0plxjTpUuXorkOHTo0Hnjggbj88sujdWv/nqUISAUBAgQIECBAgAABAgQIECBAgEBFCPibr4pYRpMgQIAAAQIECBAgQIAAgXIS6NSpU4wbNy5WrlwZbdq0WWvoqc1RRx2V+6TG8+bNiwULFkTbtm1jhx12iJQ04yJAgAABAgQIECBAgAABAgQIECBQ6QKSXCp9hc2PAAECBAgQIECAAAECBEpSoEWLFvVKcKkp+G222SbSx0WAAAECBAgQIECAAAECBAgQIECgOQk4rqg5rba5EiBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgTKVMBOLmW6cMImQIAAAQIECBAgQIAAgfIUmDp1asydO7dewQ8YMCDat2+fbztz5sx466238uW6btJOL3369KmriWcECBAgQIAAAQIECBAgQIAAAQIEykpAkktZLZdgCRAgQIAAAQIESlUg/cJ69uzZ+fA23njj6Nu3b77sZsMKLF26NKZMmZIJIq1PWicXgaYW+Ld/+7f45JNP6jXs5MmTY4899si3/eMf/xg/+MEP8uW6bk488cS45ZZb6mriGQECBAgQIECAAAECBAgQIECAAIGyEpDkUlbLJVgCBAgQIECAAIHGEHj55ZfjnnvuienTp8ecOXNi4cKFueSHtHtCt27dYvfdd899Bg0aFG3btq0xhOuvvz5+8pOf5J/17NkzXn/99XzZzYYVmDZtWvTv3z8TxGuvvRa9e/fO1CkQaAqBlLTy9NNPr3WonXbaKTp37pxpt8suu0THjh3j448/ztQXFtq1axd77bVXYbUyAQIECBAgQIAAAQIECBAgQIAAgbIWkORS1ssneAIECBAgQIAAgfURuOOOO3KJKSnZoT7XVlttFSNHjozzzjsvNt988/q8og0BAgSKBCZOnBg33nhj7vtJ0cN/VZx22mlx9dVXR6tWrTJNjjzyyJg1a1YMGTIkUoJeTdfWW28dkyZNiq5du9b0WB0BAgQIECBAgAABAgQIECBAgACBshVoWbaRC5wAAQIECBAgQIDAOgr84x//iCOOOCKOPfbYqG+CSxpqwYIFcckll+R+wbyOQ3uNAAECOYFTTjklUjJKbdePf/zjogSXqrabbbZZpCSY2q6UCCPBpTYd9QQIECBAgAABAgQIECBAgAABAuUsYCeXcl49sRMgQIAAAQIECHxmgZTgMnTo0Hj11Vc/87vphXS8TTq+yEWAAIH1FUi7Q6XvSTVd22+/fU3V+bouXbrk7wtv6npW2FaZAAECBAgQIECAAAECBAgQIECAQDkJSHIpp9USKwECBAgQIECAwHoJfPjhh3HggQfWmODSsmXL+OIXvxiDBw+O9AviRYsWxdy5c3Ofxx57LObPn58b+9///d/XKwYvEyBAoEqgbdu2VbeZr+mIovQ9qa6rTZs2tT6urd9aX/CAAAECBAgQIECAAAECBAgQIECAQJkISHIpk4USJgECBAgQIECAwPoLnHPOOTFlypSijlJyy+9+97vo3r170bNUsXz58rj99ttjzJgxcfLJJ9fYRiUBAgQaSqBFixZr7aquNnU9W2vHGhAgQIAAAQIECBAgQIAAAQIECBAoYYG6/2lYCQcuNAIECBAgQIAAAQKfRWDs2LExevToolfOOOOMGD9+fK0JLumFtCvCiBEjYty4cZGOF3ERIECAAAECBAgQIECAAAECBAgQIECAAAECTS8gyaXpzY1IgAABAgQIECCwAQTOO++8olEPPfTQuOqqq8KuB0U0KggQIECAAAECBAgQIECAAAECBAgQIECAQMkJOK6o5JZEQAQIECBAgAABAg0t8Nhjj8Urr7yS6TYltlx22WXRsmXj533PmTMn7r777pg2bVrMmzcvtttuu/j85z+f+/Tr169eSTaffvpp7qilNI/0mTFjRrRp0ya23nrr6N+/f+yzzz7Ro0ePzBxrKnzyySfx1FNP5R6lXWn23HPPfLOqON94441cnB07dozdd989+vbtm+u/Q4cO+bZVNw3dX1W/hV/XrFkTb7/9drzwwgu5z5tvvplz7N27d6RPnz59okuXLoWvKRMgQIAAAQIECBAgQIAAAQIECBAgQIBABQlIcqmgxTQVAgQIECBAgACBmgWuueaaogeHH354LjGi6EEDVqQEkO9973txww03xOrVq2vsediwYXHzzTfXmqAxc+bM+PWvfx3XX399fPzxxzX2UVXZs2fP+O1vfxtDhw6tqir6mvr68Y9/nKvv3LlzzJo1K3d/zjnnxO9+97ta49xxxx3jD3/4QwwaNCjTZ0P3l+n8X4UnnngivvWtb8Vbb71V0+N83dFHH52zSklELgIECBAgQIAAAQIECBAgQIAAAQIECBCoPAFJLpW3pmZEgAABAgQIECBQTSDtAPKXv/ylWs3/f/uDH/ygqK4hK9JuKAMGDMjtvlJXvw8//HBuR5f77rsvtyNL9bZp95fjjjsuVq5cWb261vs05oEHHhijRo3K7VJTU8MVK1bkqxcuXJhLbEnJK88880y+vqabd955J77whS/Eddddl0s4qWrT0P1V9Zu+pt1rzj///LjiiitqTb6p3v7OO++M5HnxxRfH6aef3iS79FQf3z2BhhJISXH33ntvnd3df//9dT73kAABAgQIECBAgAABAgQIECBAgEAlCkhyqcRVNScCBAgQIECAAIG8QDoi6P3338+X0006Imi//fbL1DVGYcqUKfXqdv78+XHaaafF888/nzm6aPvtt693gkv1gS6//PI46qijYsiQIdWra7z/7ne/W2N9TZUp2SYlBx177LHRvn37mppEQ/W3ZMmS2H///XNHE9U4UC2VixYtijPPPDOWL18eaXcaF4FyFEhJLum/YRcBAgQIECBAgAABAgQIECBAgAABAlmBltmiEgECBAgQIECAAIHKEnj22WeLJrTHHnsU1TVWRYsWLWL48OHx4IMPxuuvvx7jx4/P7TJSON4LL7yQOw6oev0+++wThx12WL6qQ4cOub7ScUO///3vc7uwpESOjh075ttU3aRkk9qOSKpqU/1rivOggw6KlCCTYnnsscfiwgsvjLZt21ZvFu+991785je/ydTVVFif/tLuO9/4xjeKElxat24dZ511VvzpT3+KV199NdIxRukoqr59+xaFcMEFF6z1eKOil1QQIECAAAECBAgQIECAAAECBAgQIECAQEkL2MmlpJdHcAQIECBAgAABAusrkHZJKbx23HHHwqpGKacEkdGjR8cJJ5yQ779nz565ZJIDDjggdxRR/sE/b1KCyYknnli9KpdoMnbs2PjqV78aV155ZXTt2jXzPBVSQstXvvKVeOmll/LPXnzxxXjkkUdi2LBh+brablKcN9xwQ4wYMSLTJB1PdMghh8TgwYMzCTMpwebcc8/NtK1eWN/+0rFDd911V/Uuc4kst9xyS+5op+oP0o483/rWt+K//uu/4he/+EX+0SeffBIjR46Mx/6ZrOMiUG4CKUksHT1W15USziZPnlxXE88IECBAgAABAgQIECBAgAABAgQIVJyAJJeKW1ITIkCAAAECBAgQqC6wcOHC6sXcfffu3YvqGrqiXbt28cADD8TQoUNr7Dod+XPzzTfn2lQ1SEcrFV79+/ePqVOnRq9evQof5cspaeeqq66KlJRS/Uo7x6wtySXFef/999f6C/V99903Dj300EycM2bMqD5M5n59+0u7z/z0pz/N9Ln55pvnYqwtOWmjjTaKSy+9NJLfn//85/y7jz/+eLz77rs1JgblG7khUIICrVq1yiWp1RXafffdl0tuq6uNZwQIECBAgAABAgQIECBAgAABAgQqTcBxRZW2ouZDgAABAgQIECCQEfjggw8y5VSoLVmiqOF6VKQdV2pLcKnqNu00Uv1asmRJzJs3r3pV7r6uBJeqxvvvv3/RsT1vv/121eNav6Y417ZjxPHHH595f+nSpbljizKV/yqsb39PP/10TJkyJdN12uGmPmuWdropvAr7KnyuTIAAAQIECBAgQIAAAQIECBAgQIAAAQLlI2Anl/JZK5ESIECAAAECBAisg0A69qPw2njjjQurNki5R48eReO++eabsc022xTVV69Yvnx5pASW1Hb69OmRdk/Zc889czuWvPLKK/mm6VlDXDvssENRN++8805sueWWRfX1qairv/Hjx2e6SDtapKOa6nOlRJjU96xZs/LNk0c6cslFgAABAgQIECBAgAABAgQIECBAgAABAuUvIMml/NfQDAgQIECAAAECBOoQSEfdFF4pQWPgwIGF1U1e3m677YrGXLx4cVFdqlizZk3cfffdcdFFF8VLL70U6ViftV2zZ89eW5N6Pd96662L2n300UdFdfWtqKu/iRMnZrpZtWrVWnfEqf5C9QSXVP/aa69Vf+yeAAECBAgQIECAAAECBAgQIECAAAECBMpYQJJLGS+e0AkQIECAAAECBNYuUFOSS0PtcLL20etu0bJl/U4PTbub/OhHP4pJkybV3WHB05QY0xBX586dG6KbfB919Td//vx8u6qbv//971W3n/lr69b+L89nRvMCAQIECBAgQIAAAQIECBAgQIAAAQIESlTA3/iW6MIIiwABAgQIECBAoGEEato1pFSSXOozw1tvvTVGjBhR684tKVFmo402iqVLl9anu3VqU9ORT+vU0b9eqqu/BQsWrE/XRe/utNNORXUqCBAgQIAAAQIECBAgQIAAAQIECBAgQKA8BSS5lOe6iZoAAQIECBAgQKCeAv369StqOWXKlKK6Uqx46KGH4uSTTy5KcOnWrVtccMEFMWTIkPjc5z4Xbdu2jXfffTdGjRoVd9xxRylOpd4xFe5u07Vr1zjttNPq/X5hw8MOO6ywSpkAAQIECBAgQIAAAQIECBAgQIAAAQIEylRAkkuZLpywCRAgQIAAAQIE6ifQt2/faN++fSxZsiT/wtNPPx0TJ06MgQMH5utK8SYlraxcuTITWqq7+OKLc7u3VH+QkkF22GGH6lVled+nT5+YNWtWPvaUwHP++efny24IVJJAbUeKrV69eq3TrO3d9GJdz9basQYECBAgQIAAAQIECBAgQIAAAQIESligZQnHJjQCBAgQIECgAgWmL3g85i96rQJnZkqlKtCqVasYPHhwUXgXXnhhUV0pVTz++OPx2mvZ/1ZOOOGEuOyyy4oSXEop7vWNJSUlVb9mzJgRy5Ytq17lnkDFCHz66ac1ziUluRQmuBU2XL58eWFVvuy/mTyFGwIECBAgQIAAAQIECBAgQIAAgQoTkORSYQtqOgQIECBAoNQF5nw4OW6ZeEzc88JZkl1KfbEqKL6RI0cWzebhhx/O7eZS9KBEKsaNG1cUyRVXXFFUV2kVhUku6Zf9TzzxRKNMsz67ZTTKwDol8E+BlOCSjhmr7Zo2bVptj3L1db371ltv1fmuhwQIECBAgAABAgQIECBAgAABAgTKVUCSS7munLgJECBAgECZC7y14FHJLmW+huUU/lFHHRVbb711Ucjf/va36/wlc+ELt99+e9xzzz2F1Y1Sfu+99zL97rTTTjXOIdOoAgqFSS5pSmeffXbUtWvFuk77nXfeWddXvUdgvQTef//9+O53vxuLFy+utZ/05762ZJWpU6fGr3/961rfvfPOO+O2226LVatW1drGAwIECBAgQIAAAQIECBAgQIAAAQLlKCDJpRxXTcwECBAgQKCCBCS7VNBilvBU2rZtG//n//yfoghfeeWVGDBgQEyaNKnoWfWKtOPHeeedF8cdd1ycdNJJ8eabb1Z/3Cj32267babfTz75JFMuLKSjje66665M9QcffJApl0OhX79+uTWpHmua289+9rPqVbXev/TSSzUmxGyzzTZF74wdO7aoTgWBphDo3r17XHfddXUONWHChNhll10iJbRUv6655pro06dPpKO8arvSUUfpeLPTTjuttibqCRAgQIAAAQIECBAgQIAAAQIECJSlgCSXslw2QRMgQIAAgcoTkOxSeWtaajM644wzYr/99isKa+7cubH//vvndkWYMmVKrFmzJt8mJYn85S9/iS9/+ctx6aWX5urTzgtf//rXY8WKFfl2jXHz+c9/PtPt/PnzY8yYMZm6qkL6ZfigQYOicGeS6dOnx5IlS6qalcXXVq1axe9///vYaKONMvFeeOGFcfLJJ0fhDjdVjWbOnBnHH398JLfLL7+8qjr/NSW5tG/fPl9ONylZIO3O4yLQ1ALpqKL6XCnBrnAXo/TfSH2vlODnIkCAAAECBAgQIECAAAECBAgQIFBJAq0raTLmQoAAAQIECJS/QEp2SZ+dtxoaA3ucEV027VP+kzKDkhBo0aJFjB49OoYMGRIpsaX6lXZJ+d73vper2nzzzWP33XeP2bNnR0oSqel67rnn4uqrr45zzjmnpscNUjdw4MBo165dLF26NN/fyJEjY+LEiXHkkUdGhw4dIsXx6KOPxoMPPlhj0k1K2HnsscdySTr5TsrgZtddd42U1PKjH/0oE21K8km7rwwdOjTSsUadOnXKHeeSdnp58skn81b//d//nUuI2W677TLv77PPPpESgqqudJRL2p0nJcUcccQRcf7551c98pVAowqsT5JcOmYtfVwECBAgQIAAAQIECBAgQIAAAQIEmqOAJJfmuOrmTIAAAQIEykBAsksZLFIZhrjzzjvnkhxSksS8efNqnMGHH34YTzzxRI3Pqir33nvvOOaYY6qKjfI1HVd08cUXZxJp0i/G0+4j6VPfK+1+8tRTT0Xv3r3r+0pJtDv33HNzazV+/PhMPO+//37ceeeduU/mQbVC2m3nkksuiSuvvLJabeQ8991330xdKjzzzDO5JCFJLkU0KggQIECAAAECBAgQIECAAAECBAgQIFBSAo4rKqnlEAwBAgQIECBQKOAYo0IR5fUVSMkeTz/99DrtbtKmTZv4j//4j1wSTNeuXdc3lLW+f/bZZ8fw4cPX2i412GuvveKNN96Io48+OtN+4cKFceutt2bqyqGQjmQZN25cXHXVVUXHDK0t/rRjy6mnnlrULNVfdNFF0bKl/xtUhKOCAAECBAgQIECAAAECBAgQIECAAAECZSDgb3fLYJGESIAAAQIECETuCKNbJh4T97xwVsxf9BoSAuslsOOOO8b9998f99xzTy7ZpW3btnX2l469+c53vhPTpk3LHVO08cYb19k+Pdxss83W2qamBtXfS8kYadeSG264ocb+WrduHbvttlukXU/SMUY9evTIHcl0wgknRErIqc9Vfbz6tF9bm4bsLx0xdeaZZ8bLL7+cS95Ju9vUdm211Va5o4fSzi8piSkdOVXTlXZrSUcWfeELX4i1rXtN76sjQIAAAQIECBAgQIAAAQIECBAgQIAAgQ0n0GLNP68NN7yRCRAgQIAAgbUJTJs3Lu6f/P21Ncs8/+bge2OLDjtn6kql8OQbV8Yz069b73B23mpoDOxxRnTZtM969ZUSBF599dV8H7fffnujH0OTH8xNyQgsWrQod2TNnDlzcscYLVu2LLbYYotIiRO77rpr9O3bd4PHumrVqpg6dWr8/e9/z+1EkmLq1atXrYkaH330UW5O6Xif1K5fv34bfA4NEcCCBQti8uTJ8fbbb0fHjh1jyy23jO233z53HFNKivks15IlS3LJQfPnz4927drF1772tc/yurZlLHDhhRfGT3/60/wM0vFj6fu/iwABAs1J4Om3ro2n3rz6M035nGGTo2WLVp/pHY0JECBAgAABAgQIECBAgEBDCrRuyM70RYAAAQIECBBoKoF0jFH6NFSyS1PFbZzSFNh0003j4IMPLs3g/hVVOr4nJWWlT32utKPKsGHD6tO0rNqkxKODDjqoQWJu3759g/XVIAHphAABAgQIECBAgAABAgQIECBAgAABAgTqFJDkUiePhwQIECBAgECpC0h2KfUVEh8BAgQIECBAgAABAgQIECBAgAABAgQIECBAoGEEJLk0jKNeCBAgQIBASQm8t/jNWLl6WUnFVBXMkmULqm4b9Ktklwbl1BkBAgQIECBAgAABAgQIECBAgAABAgQIECBAoOQEJLmU3JIIiAABAgQIrL/A/ZO/v/6dlGkPkl3KdOGETYAAgQ0g0K5Dq9h0i9bRbvNPIyWIuggQINCcBD5ZvrA5TddcCRAgQIAAAQIECBAgQKBCBCS5VMhCmgYBAgQIECCQFZDskvVQIkCAAIFigb0O2DS+duZ2/3wwPW566qjiBmoIECBAgAABAgQIECBAgAABAgQIECgpAUkuJbUcgiFAgAABAgQaWkCyS0OL6o8AAQIECBAgQIAAAQIECBAgQIAAAQIECBAgsGEEJLlsGHejEiBAgAABAk0sINmlicENR4AAAQIECBAgQIAAAQIECBAgQIAAAQIECBBoYAFJLg0MqjsCBAgQIECgtAUKk11KO1rRESBAgAABAgQIECBAgAABAgQIECBAgAABAgQIVAm0rLrxlQABAgQIECDQnARSssstE4+JL54Ssd3OGzenqZsrAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKAsBSS5lOWyCZoAAQIECBBoKIFNNo3YtJPN7RrKUz8ECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgcYSkOTSWLL6JUCAAAECBEpaoMumfeOr/X4T910RMfX5xSUdq+AIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQi/LNlfwoIECBAgACBZiWQklsG9TgjPrfV/s1q3iZLgAABAgQIECBAgAABAgQIECBAgAABAgQIECh3AUku5b6C4idAgAABAgTqJSC5pV5MGhEgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIESlZAkkvJLo3ACBAgQIAAgYYQkNzSEIr6IECAAAECBAgQIECAAAECBAgQIECAAAECBAhseAFJLht+DURAgAABAgQINIKA5JZGQNUlAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQGADCkhy2YD4hiZAgAABAvUR2L5Tvzhqr6vq07Qs2rw65/54ff64RotVckuj0eqYAAECBAgQIECAAAECBAgQIECAAAECBAgQILBBBSS5bFB+gxMgQIAAgbULdNhoq+iw9dC1NyyTFnM/erlRIpXc0iisOiVAgEBFC0ybtDhuvmhWDBw4MEaNGlXRczU5AgQINIRAi2jZEN3ogwABAgQIECBAgAABAgQIrLOAJJd1pvMiAQIECBAgUAoCkltKYRXEQIAAgfIUeH/uikifXttuGrtsM6w8JyFqAgQIECBAgAABAgQIECBAgAABAs1IQJJLM1psUyVAgAABApUkILmlklaz7rksWrQonnrqqbob1eNp586dY5999qlHy6ZvMnPmzHjwwQeja9euceihh0bLlv6VdNOvghEJECBAgAABAgQIECBAgAABAgQIECBAoNQFJLmU+gqJjwABAgQIEMgISG7JcDSLwvTp0+Owww5b77m2aNEi/vGPf8SWW2653n01ZAd//vOf42tf+1qsWrUq1+3gwYPjySefbMgh9EWAAAECBAgQIECAAAECBAgQIECAAAECBCpCwD8RrYhlNAkCBAgQIFD5Aim55av9fhMjBt4Wn9tq/8qfsBk2uMCaNWti5cqVDd5v9Q5ffvnl6sV63d900035BJf0Qtq15umnn67Xu5XcaF0sK9nD3AgQIECAAAECBAgQIECAAAECBAgQIEAgQpKLPwUECBAgQIBASQtIbinp5RHcPwXS7jCXXXZZ7LbbbrHHHnvExIkTP5PLhx9+WNR+4cKFRXXNoWJ9LZuDkTkSIECAAAECBAgQIECAAAECBAgQIECgOQs4rqg5r765EyBAgACBEhZwLFEJL04JhNalS5fo2LHjZ46kdeuG/Z+/8+bNi27dusWKFSvysVQdO5SvWMvN0UcfHRMmTMi36tq1axxyyCH5cnO5aQjL5mJlngQIECBAgAABAgQIECBAgAABAgQIEGiuAg37t/zNVdG8CRAgQIAAgQYTkNzSYJQV3dEvf/nLGDFixAafYzr+qHqCy7oEdOqpp8YWW2wRY8aMiV133TVOP/30aNWq1bp0VdbvNIRlWQMIngABAgQIECBAgAABAgQIECBAgAABAgTWKiDJZa1EGhAgQIAAAQJNISC5pSmUjVGKAml3mWOPPTb3KcX4xESAAAECBAgQIECAAAECBAgQIECAAAECBEpFQJJLqayEOAgQIECAQDMVkNzSTBfetAkQIECAAAECBAgQIECAAAECBAgQIECAAAECn1FAkstnBNOcAAECBAgQaBgByS0N46iX9RNYvXp1jB8/Pl599dWYPn16zJs3L7bZZpvYeeedo2/fvvGFL3wh2rRpUzTIK6+8EnPnzo1Zs2YVPXv22Wdj6dKlRfUDBgyIzTbbLFe/cOHCmDRpUlGbVJGOLurXr1+Nzz755JN46qmncs922GGH6N27d77d7Nmz44knnognn3wyPvjgg9htt93i85//fO7TtWvXfLuqm/feey/uvvvu3NznzJkTnTt3zr2Tjkzaf//9a5x31btVXz/99NOYMmVKJI/0mTFjRu69rbfeOvr37x/77LNP9OjRo6p5jV/X17LGTv9VuWzZsnj55ZfjhRdeiBdffDE++uij6NOnT+yxxx65T/fu3aNFixZ1dRF/+9vfYsmSJfk2gwYNivbt2+fKTz/9dIwbNy4378WLF+cM05+dI488Mnr16pV/xw0BAgQIECBAgAABAgQIECBAgAABAgQINIyAJJeGcdQLAQIECBAgUE+BLTr0iK/2+018bqv96/mGZgQaR+Cuu+6Kn/zkJ7kkjdpGSIkfX/va1+Ib3/hG7Lfffvlml156aYwZMyZfrn7z/e9/v3oxfz9x4sTYd999c+Xzzz8/rr322vyz6jfDhg3LJU5Ur6u6//Wvfx0//vGPc8Vu3brFG2+8EWvWrIlvfvObcdttt1U1K/q69957x5///OfYdtttIyV+/OAHP4jf/OY3sXLlyqK2qSIlgdx0002x55571vh85syZkWK5/vrr4+OPP66xTVVlz54947e//W0MHTq0qirzdX0tM539q5Dm+J//+Z8549rmmJomjxtvvDG+9KUv1dRN7s/G4MGDM89ee+21aNeuXXz961/PJcBkHv6rkBKkJLnUJKOOAAECBAgQIECAAAECBAgQIECAAAEC6yfQcv1e9zYBAgQIECBA4LMJ9Nn2MAkun41M60YQuOKKK+Loo4+uM8ElDZt2XPnd734Xv/jFLxo0iuXLl69TfytWrMi/lxJNUlwHH3xwnQku6YXnn38+0k4yDzzwQG53lSuvvLLWBJfU/qWXXsq1T4kxhVfa/SXtVnL55ZevNcElvZsScQ488MCoLfmnsP/1LafxBg4cGFdffXWdc0zjpN14DjvssDjnnHNyyT+FY1f3rnqWdv1JO9SkHV5qupLNoYceWtMjdQQIECBAgAABAgQIECBAgAABAgQIECCwngKSXNYT0OsECBAgQIAAAQLlJZCSFEaNGvWZgk6JENWvtm3bVi9usPsLLrggd0RRfQJ499134/DDD4/JkyfXp3mkBI/vfve7kY4kqn5tv/32a00eqd6+6j4lxaTjlAqvhrRMiSfpqKd0NFF9r7QTTkp6SslC6X5t14knnhjz58+vtdmZZ5651iOQan3ZAwIECBAgQIAAAQIECBAgQIAAAQIECBCoU8BxRXXyeEiAAAECBAgQIFCKAunon9at1/4/ZdPRP+nIoerX6NGji5IZdtppp9yRP1/84hdj1apVMWnSpNxxPm+++WYuYeErX/lK9S7i5z//eZxwwgkxe/bs3FFG1R/+6le/it133716Ve5+1113zdelXWR22WWXXPmOO+7IjZd/uA436ficlFyRYtpoo41yO9Rcdtll8cwzz9TaW9pdJR3p06NHj5g1a1ak45uuueaajM2MGTNyCSA//OEP8/2kXUxS0s/YsWNzdR06dIhDDjkkdzxPOpoo+U2ZMiW3A07hUUYpaSbtKtOy5f/m2q+vZVVgq1evzhksXry4qir3NR3rlJKBBg0aFNtss00uASbFnhJbqie1pAScdORTMqzrqkr6adWqVW53mnTUUadOneLee+/NHfH0rW99q67XPSNAgAABAgQIECBAgAABAgQIECBAgACB9RBY+28G1qNzrxIgQIAAAQIECBBoDIHf/OY3uSSUtfX97LPPFiW5FO7ykZJCnnzyydhuu+3y3e2///5xxhlnxC9/+cvcsTTbbrtt/lm62WKLLSIlxKTdUQqvvffeO/bbb7/C6kw5HWdTdaTN1KlT1yvJZZNNNomHHnoohgwZkh8jJdQcc8wxcdxxx8Xtt9+er6+6SbuR3HLLLVXFSMkpKeklJYKMGDEiX59uJkyYENWTXFLdhRdemEty+epXvxrp6KOuXbum6syVElpSclA6+qjqSvaPPPJIpOSjqmt9Lav6uemmm4p2cNlzzz1zcVZfv4MOOijSJ8315JNPjqVLl1Z1Eeeff34MHz481ra7zKabbppzTck9VVca6yc/+UlV0VcCBAgQIECAAAECBAgQIECAAAECBAgQaASB//0nlI3QuS4JECBAgAABAgQIlJrA8uXLMyGlXUVSokXhlZJfUtLDAw88UPioZMpt2rSJu+++O5PgUj24tFtJ4ZWSO2644YbC6lw5Jb/su+++mWdvvPFGppwK/fv3j5Sck8auKcEltdlxxx3jqquuSreZ6/XXX8+UG6KQdm9Ja1X9Ssk/48ePj+oJLtWfp910zjvvvOpV8fbbb8e1116bqaupkHbJqZ7gUlMbdQQIECBAgAABAgQIECBAgAABAgQIECDQ8AKSXBreVI8ECBAgQIAAAQIlLNCrV69MdGknj3vuuSdTVy6F7t2715lskRI8unTpkplO2l0lJfDUdh1xxBGZRzNnzoxly5Zl6lKh0LGowT8r0o44ffv2zTxKiSQNfaXdYebOnZvpduTIkbHllltm6goLp512WtGuLVXHMBW2rSqn+TiSqErDVwIECBAgQIAAAQIECBAgQIAAAQIECDStgOOKmtbbaAQIECBAgAABAg0gMHTo0Nhll13W2tPWW29d1CYd63P99ddn6k866aSYM2dOnHPOOZn6SijUldBS0/xS4kz1a/Xq1bFo0aLYaqutqlcX3acdclICy5tvvhnTp0+Pdu3aRTrCJ+308sorr+Tbp2cNfdW0O8yoUaPWOkxKADr88MNzO9JUNX7rrbeqbmv8mna7adWqVY3PVBIgQIAAAQIECBAgQIAAAQIECBAgQIBA4wpIcmlcX70TIECAAAECBAg0gsApp5wSI0aMWKeeU5JCOsbnhRdeyL+/YsWKSEkRf/jDH+Lcc8+N4cOHN9tEhrUls+TR/nmzZs2aXILIRRddFC+99FKkhJi1XbNnz15bk8/8vDDJpVOnTlGYrFNbpz179sw8euedd2LVqlW1rn/v3r0z7RUIECBAgAABAgQIECBAgAABAgQIECBAoOkEHFfUdNZGIkCAAAECBAgQKAGBli1bxq233ho77rhjUTTPP/98HHfccZESH8aMGZNL4ihqVOEVLVq0KJphTXXjx4+P/v37x9FHHx1///vf65XgkjpOiTENfRUmuXTr1q3eQ2y33XaZtinhKR3RVNu188471/ZIPQECBAgQIECAAAECBAgQIECAAAECBAg0soAkl0YG1j0BAgQIECBAgEDpCaSjjiZOnBj77bdfjcGlY3dOPvnkGDBgQLz66qs1tmnOlSlJ6Etf+lJMmjSpRoaUSJSOK2qq67333ssM1bFjx0y5rkI6sqjwmjFjRmFVvtymTZv8vRsCBAgQIECAAAECBAgQIECAAAECBAgQaFoBSS5N6200AgQIECBAgACBEhHYdttt469//Wvceeedseeee9YYVdrZZciQIfHss8/W+Lw5Vj700EO5BKDCo4nS7inXXXddvPbaa7F06dL45JNPYtasWXHMMcc0OlPafaX69VkSbFKchdcmm2xSWKVMgAABAgQIECBAgAABAgQIECBAgAABAiUgIMmlBBZBCAQIECBAgAABAhtGIB3DM3z48HjxxRfj3nvvjT322KMokIULF+YSNZYtW1b0rDlWjBo1KlauXJmZeqpLRwadeuqp0bt372jbtm3uedeuXWOHHXbItG2MQo8ePTLdzp07N1Ouq/Duu+8WPS7sr6iBCgIECBAgQIAAAQIECBAgQIAAAQIECBDYIAKSXDYIu0EJECBAgAABAgRKTeArX/lKLtnliiuuiNatW2fCmzlzZowePTpTV1uhcIeT2tqVY/3jjz+e26mleuwnnHBCXHbZZbHRRhtVr26Q+/pa7rzzzpnx5syZkynXVUi7zVS/Nt9889hiiy2qV7knQIAAAQIECBAgQIAAAQIECBAgQIAAgRIRkORSIgshDAIECBAgQIAAgQ0v0LJly/jud78b11xzTVEwzzzzTFFdTRULFiyoqboi6saNG1c0j5QU1FhXfS0Ld15Ju++88cYbaw1r1apV8fDDD2faFfaVeahAgAABAgQIECBAgAABAgQIECBAgAABAhtUQJLLBuU3OAECBAgQIECAQCkK/Pu//3tsvfXWmdCmT5+eKadCq1atiuo+y1E5RS+XeMV7772XiXCnnXYqcso0+AyF9bHs1atX0Uj/7//9v6K6woq777473nnnnUx1TX1lGigQIECAAAECBAgQIECAAAECBAgQIECAwAYTkOSywegNTIAAAQIECBAgsKEEHnzwwZg2bdpnGr6mo3O6dOkSm2yySaafsWPHZsqVVNh2220z0/nkk08y5cLCa6+9FnfddVem+oMPPsiUqwrrYzls2LDo06dPVVe5r7///e/j/fffz9RVLyxdujQuueSS6lW5+7POOquoTgUBAgQIECBAgAABAgQIECBAgAABAgQIlIaAJJfSWAdRECBAgAABAgQIfAaBefPm5Y6jSUfS1PczY8aM3AjpiJq0U0vfvn3j9NNPj9RX4XXzzTfHP/7xj0x1z549M+VUSMcb9e7dO1P/0EMPRU3H+qSkipUrV2ballvh85//fCbk+fPnx5gxYzJ1VYUJEybEoEGDinZKSTviLFmypKpZ/uv6WKZdYH72s5/l+0o3ixYtioEDB9Z4bFFKtDn44IPjhRdeyLxz7LHHxj777JOpUyBAgAABAgQIECBAgAABAgQIECBAgACB0hFoXTqhiIQAAQIECBAgQIBA/QTOPffcSJ/PcnXv3j3efvvtGD9+fMyZMyf36rXXXhspoeWAAw7IJUR07NgxnnzyyaLdR1Lj4cOH1zhc2kWkerLEmjVr4rDDDouvf/3rseeee0ZKBPnrX/8akyZNijvuuCOOOuqoGvsph8qUNNKuXbtICTtV18iRI2PixIlx5JFHRocOHeK5556LRx99NNJuOStWrKhqlv+afB577LH48pe/nK+rulkfyzT+4MGD46mnnqrqLpfgkpJWkvm//du/5eJ79tln44EHHihKvmnbtm1Roky+IzcECBAgQIAAAQIECBAgQIAAAQIECBAgUBICklxKYhkEQYAAAQIECBAg0FQCN910U2aodOROOmKormOG0o4kaeePmq7zzz8/brnllnj33Xfzj9PRRqkufapfU6ZMKeskl3Rc0cUXXxznnHNOflopkeWaa67JffKVa7k5+eSTc8kohbvgrK9lSlo64ogjomrXnhRG2rUlHV2UPrVdbdq0iV/96lfxuc99rrYm6gkQIECAAAECBAgQIECAAAECBAgQIECgBAQcV1QCiyAEAgQIECBAgACBphMoPIZobSOnY43uvffeSIkQNV1p95Ibb7wx0i4wa7tSkku5X2effXatu9oUzm2vvfbK7aZy9NFHZx4tXLgwbr311kxdKqyvZVqrF198sd7xpTFTYkvaveeMM85IRRcBAgQIECBAgAABAgQIECBAgAABAgQIlLCAJJcSXhyhESBAgAABAgQINLzAI488Eg899FDueJ1WrVrVOsDOO+8co0ePjr///e+x5ZZb1touPUi7vKQjiwYMGFBru5QEk47qachrs802W2t39WlTvZO1tW/ZsmXceeedccMNN0RNbVu3bh277bZb7jipdIxRjx49co4nnHBCrYlC1cdfX8vNN988F1/a1SUdX1RT8lFa91133TWX2JKSYupat+qxtWjRosb+qrdxT4AAAQIECBAgQIAAAQIECBAgQIAAAQKNJ9Din3/R3rB/0954seqZAAECBAgQINDgAumX8a+++mq+39tvvz2OOeaYfNlNZQssX7483nnnndzxNjNnzoz27dtH9+7dY6eddoouXbqs0+Tnzp0bkyZNinnz5kU63qdbt265T00JIes0QAm9tGrVqpg6dWouESglv6SdVHr16hVt27atMcqPPvoonnnmmXj//fdz7fr161dju6rKhrBM/3dn+vTpMXny5Fi0aFEuASfF2a5du6phfG2GAhdeeGH89Kc/zc88fd9P3/9dBAgQIECAAAECBAgQIECAAAECBAiUtkDr0g5PdAQIECBAgAABAgQaTyAlY/Ts2TP3aahRUmLL4Ycf3lDdlXQ/aUeUlCiWPvW5UqLPsGHD6tM016YhLNPuK2lXnvRxESBAgAABAgQIECBAgAABAgQIECBAgEB5CziuqLzXT/QECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgWYhIMmlWSyzSRIgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEyltAkkt5r5/oCRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQLNQkCSS7NYZpMkQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECJS3gCSX8l4/0RMgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEmoWAJJdmscwmSYAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAobwFJLuW9fqInQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECDQLAUkuzWKZTZIAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgUN4CklzKe/1ET4AAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBoFgKSXJrFMpskAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKC8BSS5lPf6iZ4AAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAg0CwEJLk0i2U2SQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAeQtIcinv9RM9AQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKBZCEhyaRbLbJIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgfIWkORS3usnegIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAsxCQ5NIsltkkCRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQLlLSDJpbzXT/QECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgWYhIMmlWSyzSRIgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEyltAkkt5r5/oCRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQLNQkCSS7NYZpMkQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECJS3gCSX8l4/0RMgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEmoWAJJdmscwmSYAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAobwFJLuW9fqInQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECDQLAUkuzWKZTZIAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgUN4CklzKe/1ET4AAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBoFgKSXJrFMpskAQIECBAgUJtA69atM49WrVqVKSsQIECAQOUJFH6vb9WqVeVN0owIECBAgAABAgQIECBAgAABAgQIVKCAJJcKXFRTIkCAAAECBOov0KFDh0zjjz/+OFNWIECAAIHKE1i8eHFmUh07dsyUFQgQIECAAAECBAgQIECAAAECBAgQKE0BSS6luS6iIkCAAAECBJpIoPAXm5JcmgjeMAQIENiAAoXf6wt/FmzA0AxNgAABAgQIECBAgAABAgQIECBAgEAdApJc6sDxiAABAgQIEKh8gcJfbH7wwQeVP2kzJECAQDMXWLhwYUag8GdB5qECAQIECBAgQIAAAQIECBAgQIAAAQIlIyDJpWSWQiAECBAgQIDAhhDo1q1bZtg33ngjU1YgQIAAgcoTKPxeX/izoPJmbEYECBAgQIAAAQIECBAgQIAAAQIEKkNAkktlrKNZECBAgAABAuso0KtXr8yb06ZNy5QVCBAgQKCyBFavXh1vvvlmZlKFPwsyDxUIECBAgAABAgQIECBAgAABAgQIECgZAUkuJbMUAiFAgAABAgQ2hEDhLzZTksvKlSs3RCjGJECAAIEmEHjrrbfi008/zYxU+LMg81CBAAECBAgQIECAAAECBAgQIECAAIGSEZDkUjJLIRACBAgQIEBgQwjsvvvu0aJFi/zQS5cujWeffTZfdkOAAAEClSXw2GOPZSa07bbbxpZbbpmpUyBAgAABAgQIECBAgAABAgQIECBAoDQFJLmU5rqIigABAgQIEGgigc6dO8cee+yRGW3ChAmZsgIBAgQIVI7Ao48+mpnM0KFDM2UFAgQIECBAgAABAgQIECBAgAABAgRKV0CSS+mujcgIECBAgACBJhI48MADMyONGzcuU1YgQIAAgcoQWLVqVTzyyCOZyRT+DMg8VCBAgAABAgQIECBAgAABAgQIECBAoKQEJLmU1HIIhgABAgQIENgQAgcddFBm2KeeeipmzJiRqVMgQIAAgfIXSEmMCxYsyEzki1/8YqasQIAAAQIECBAgQIAAAQIECBAgQIBA6QpIcindtREZAQIECBAg0EQCBx98cGy55Zb5EVI+CQAACRFJREFU0dasWRNjxozJl90QIECAQGUIFH5vHzhwYHTv3r0yJmcWBAgQIECAAAECBAgQIECAAAECBJqBgCSXZrDIpkiAAAECBAjULdCmTZs4/vjjM41uuOGGWLlyZaZOgQABAgTKV2DevHlxzz33ZCZw0kknZcoKBAgQIECAAAECBAgQIECAAAECBAiUtoAkl9JeH9ERIECAAAECTSRw8sknZ0Z655134pZbbsnUKRAgQIBA+Qpcfvnl8emnn+YnsNFGG8Vxxx2XL7shQIAAAQIECBAgQIAAAQIECBAgQKD0BSS5lP4aiZAAAQIECBBoAoH+/fvHfvvtlxnpkksuidWrV2fqFAgQIECg/AQWLlwY11xzTSbwb37zm9G5c+dMnQIBAgQIECBAgAABAgQIECBAgAABAqUtIMmltNdHdAQIECBAgEATCpx//vmZ0aZNm1b0S9FMAwUCBAgQKAuBCy64IBYvXpyPtXXr1nHeeefly24IECBAgAABAgQIECBAgAABAgQIECgPgRZr/nmVR6iiJECAAAECBAg0vsDee+8dkyZNyg+02WabRUp26dKlS77ODQECBAiUj8Dzzz8f++yzT2ZnrnRE3U033VQ+kxApAQIECBAgQIAAAQIECBAgQIAAAQI5ATu5+INAgAABAgQIEKgm8Ktf/SpatGiRr/noo4/irLPOypfdECBAgED5CCxfvjy+/e1vZxJc2rdvHxdddFH5TEKkBAgQIECAAAECBAgQIECAAAECBAjkBSS55CncECBAgAABAgQihgwZEulf+Fe/7rjjjvjtb39bvco9AQIECJSBwH/+53/Giy++mIn0Jz/5Seywww6ZOgUCBAgQIECAAAECBAgQIECAAAECBMpDwHFF5bFOoiRAgAABAgSaUOAf//hH9OnTJxYuXJgfdeONN46JEyfGnnvuma9zQ4AAAQKlK3DXXXfF0UcfnQmwb9++8cILL0SbNm0y9QoECBAgQIAAAQIECBAgQIAAAQIECJSHgJ1cymOdREmAAAECBAg0ocDWW28do0ePzoz46aefxmGHHRYzZszI1CsQIECAQOkJ/O1vf4uTTjopE9gmm2wSt912mwSXjIoCAQIECBAgQIAAAQIECBAgQIAAgfISkORSXuslWgIECBAgQKCJBI444oj4/ve/nxlt7ty5ccghh8SCBQsy9QoECBAgUDoCU6ZMicMPPzyWLl2aCeqqq66K3XbbLVOnQIAAAQIECBAgQIAAAQIECBAgQIBAeQk4rqi81ku0BAgQIECAQBMKrFixIoYNGxaPPfZYZtTevXvHuHHjolu3bpl6BQIECBDYsALPPfdcbtet9957LxPIqaeeGtddd12mToEAAQIECBAgQIAAAQIECBAgQIAAgfITkORSfmsmYgIECBAgQKAJBT766KP4whe+EJMnT86Muv3228dDDz0Uffv2zdQrECBAgMCGEUjJh8OHD48lS5ZkAjjyyCPjrrvuilatWmXqFQgQIECAAAECBAgQIECAAAECBAgQKD8BxxWV35qJmAABAgQIEGhCgc022yyXzPK5z30uM+rs2bNj4MCBccstt2TqFQgQIECgaQVWr14dF110UXz5y18uSnAZMmRI3HbbbRJcmnZJjEaAAAECBAgQIECAAAECBAgQIECg0QQkuTQarY4JECBAgACBShHYZptt4oknnojdd989M6XFixfHSSedFCNHjoxFixZlnikQIECAQOMLvPvuu/GlL30p/uu//itWrVqVGTDVP/jgg7Hxxhtn6hUIECBAgAABAgQIECBAgAABAgQIEChfAUku5bt2IidAgAABAgSaUGC77baLv/71r7HffvsVjXrjjTdGr1694n/+53+KnqkgQIAAgYYXWLlyZfzyl7+MPn36xPjx44sGGDFiRPz5z3+O9u3bFz1TQYAAAQIECBAgQIAAAQIECBAgQIBA+QpIcinftRM5AQIECBAg0MQCm2++ee6XqaeddlrRyPPmzYv0S9VBgwbldg4oaqCCAAECBNZbICW33HzzzdG3b98499xzI+2oVf1q3bp1XHLJJbk2bdq0qf7IPQECBAgQIECAAAECBAgQIECAAAECFSDQYs0/rwqYhykQIECAAAECBJpU4I9//GOceuqp8fHHH9c4br9+/eLMM8+Mo48+Ojp27FhjG5UECBAgUD+B+fPn53bLuvrqq+Ptt9+u8aWuXbvGbbfdFoMHD67xuUoCBAgQIECAAAECBAgQIECAAAECBMpfQJJL+a+hGRAgQIAAAQIbSOCdd96Js88+O3ckRm0htGvXLo488sj48pe/HAceeGCkY49cBAgQILB2gTfeeCMmTJgQ9957bzz88MOxatWqGl9q2bJlfPvb346LL744OnXqVGMblQQIECBAgAABAgQIECBAgAABAgQIVIaAJJfKWEezIECAAAECBDagwP333x/f+9734q233lprFLvsskvsscce0atXr0j3W221VW6nl7Tbi6M11sqnAQECFSawbNmy3I5YaVesdOzbtGnTcp8XXngh3n333bXOdsCAAZF2d+nfv/9a22pAgAABAgQIECBAgAABAgQIECBAgED5C0hyKf81NAMCBAgQIECgBARWrlwZf/jDH+JnP/tZTJ06tQQiEgIBAgQqV2DQoEFxwQUXxKGHHlq5kzQzAgQIECBAgAABAgQIECBAgAABAgSKBCS5FJGoIECAAAECBAisu8Dq1avjvvvui9GjR8fYsWNj+fLl696ZNwkQIEAgL5B2vBo+fHiccsopMWTIkHy9GwIECBAgQIAAAQIECBAgQIAAAQIEmo+AJJfms9ZmSoAAAQIECDSxwMKFC+OOO+6IcePGxeOPPx6p7CJAgACB+gtsv/32MXTo0DjssMPiyCOPjE022aT+L2tJgAABAgQIECBAgAABAgQIECBAgEDFCUhyqbglNSECBAgQIECgFAXSDi+TJ0+OF154IaZNm5b7vP322/HRRx/Fxx9/HIsXL44VK1aUYuhiIkCAQKMJbLTRRtGhQ4dIu7R06tQpevToEb169cp9BgwYELvsskujja1jAgQIECBAgAABAgQIECBAgAABAgTKT+D/A8Pw0U6NekS0AAAAAElFTkSuQmCC"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![idd_diagram.png](attachment:idd_diagram.png)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We denote by $\\mathrm{L}^{D}$ the *a posteriori* information (represented by log-likelihood ratios, LLRs) and by $\\mathrm{L}^{E} = \\mathrm{L}^{D} - \\mathrm{L}^{A}$ the extrinsic information, which corresponds to the information gain in $\\mathrm{L}^{D}$ relative to the *a priori* information $\\mathrm{L}^{A}$. The *a priori* LLRs represent soft information, provided to either the input of the detector (i.e., $\\mathrm{L}^{A}_{Det}$) or the decoder (i.e., $\\mathrm{L}^{A}_{Dec}$). While exchanging extrinsic information is standard for classical IDD, the SISO MMSE-PIC detector [2] turned out to work better when provided with the full *a posteriori* information from the decoder.\n",
    "\n",
    "Originally, IDD was proposed with a resetting (Turbo) decoder [1]. However, state-of-the-art IDD with LDPC message passing decoding showed better performance with a non-resetting decoder [3], particularly for a low number of decoding iterations. Therefore, we will forward the decoder state (i.e., the check node to variable node messages) from each IDD iteration to the next."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Table of contents\n",
    "* [GPU Configuration and Imports](#GPU-Configuration-and-Imports)\n",
    "* [Simulation Parameters](#Simulation-Parameters)\n",
    "* [Setting-up the Keras Models](#Setting-up-the-Keras-Models)\n",
    "* [Non-IDD versus IDD Benchmarks](#Non-IDD-versus-IDD-Benchmarks)\n",
    "* [Discussion-Optimizing IDD with Machine Learning](#Discussion-Optimizing-IDD-with-Machine-Learning)\n",
    "* [Comments](#Comments)\n",
    "* [List of References](#List-of-References)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## GPU Configuration and Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of GPUs available : 2\n",
      "Only GPU number 0 used.\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras import Model\n",
    "gpus = tf.config.list_physical_devices('GPU')\n",
    "print('Number of GPUs available :', len(gpus))\n",
    "if gpus:\n",
    "    gpu_num = 0 # Number of the GPU to be used\n",
    "    try:\n",
    "        tf.config.set_visible_devices(gpus[gpu_num], 'GPU')\n",
    "        print('Only GPU number', gpu_num, 'used.')\n",
    "        tf.config.experimental.set_memory_growth(gpus[gpu_num], True)\n",
    "    except RuntimeError as e:\n",
    "        print(e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# Import Sionna\n",
    "try:\n",
    "    import sionna\n",
    "except ImportError as e:\n",
    "    # Install Sionna if package is not already installed\n",
    "    import os\n",
    "    os.system(\"pip install sionna\")\n",
    "    import sionna\n",
    "\n",
    "from sionna.mimo import StreamManagement\n",
    "from sionna.utils import QAMSource, BinarySource, sim_ber, ebnodb2no, QAMSource, expand_to_rank\n",
    "from sionna.mapping import Mapper, Constellation\n",
    "from sionna.ofdm import ResourceGrid, ResourceGridMapper, LSChannelEstimator, LinearDetector, KBestDetector, EPDetector, \\\n",
    "    RemoveNulledSubcarriers, MMSEPICDetector\n",
    "from sionna.channel import GenerateOFDMChannel, OFDMChannel, RayleighBlockFading, gen_single_sector_topology\n",
    "from sionna.channel.tr38901 import UMa, Antenna, PanelArray\n",
    "from sionna.fec.ldpc import LDPC5GEncoder\n",
    "from sionna.fec.ldpc import LDPC5GDecoder"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simulation Parameters\n",
    "In the following, we set the simulation parameters. Please modify at will; adapting the batch size to your hardware setup might be beneficial.\n",
    "\n",
    "The standard configuration implements a coded 5G inspired MU-MIMO OFDM uplink transmission over 3GPP UMa channels, with 4 single-antenna UEs, 16-QAM modulation, and a 16 element dual-polarized uniform planar antenna array (UPA) at the gNB. We implement least squares channel estimation with linear interpolation. Alternatively, we implement iid Rayleigh fading channels and perfect channel state information (CSI), which can be controlled by the model parameter `perfect_csi_rayleigh`.\n",
    "As channel code, we apply a rate-matched 5G LDPC code at rate 1/2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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AAAAAUIzwDhIAAAAAWBCQAAAAAMCCgAQAAAAAFgQkAAAAALAgIAEAAACABQEJAAAAACyK/RfF5uTk6NixYypdurRMJpOzywEAAMA/GIahc+fOqWLFinJx4f/fw7mKfUA6duyYgoODnV0GAAAAriMxMVGVK1d2dhko4Yp9QCpdurQkqbF7G7mZiv3pAgAA3HKyjCxtu7zW+ncb4EzFPjFceazOzeRGQAIAACjCeB0CRQEPeQIAAACABQEJAAAAACwISAAAAABgQUACAAAAAAsCEgAAAABYEJAAAAAAwIKABAAAAAAWBCQAAAAAsCAgAQAAAIAFAQkAAAAALAhIAAAAAGBBQAIAAAAACwISAAAAAFgQkAAAAADAgoAEAAAAABYEJAAAAACwICABAAAAgAUBCQAAAAAsCEgAAAAAYEFAAgAAAAALAhIAAAAAWBCQAAAAAMCCgAQAAAAAFgQkAAAAALAgIAEAAACABQEJAAAAACycGpDWrVunLl26qGLFijKZTFqwYIG17/Lly3rppZcUFhYmb29vVaxYUX379tWxY8ecVzAAAACAYs3NmQe/cOGCwsPD9dhjj+mBBx6w6bt48aK2b9+uESNGKDw8XGfPntXQoUPVtWtXbd261UkVAwAAoKRJT09XZmam3eM9PDzk6elZgBWhIDk1IHXq1EmdOnXKtc/Pz08rVqywaXv//ffVrFkzHTlyRCEhIYVRIgAAAEqw9PR0hVbx04kk+wOSr6+vKlSoIBcXF0VHRys6OroAK4SjOTUg5VdqaqpMJpPKlCmT55iMjAxlZGRY19PS0gqhMgAAABRHmZmZOpGUqYM/N5evj+t1x6edz1b1ZpuVmJgoX1/fQqgQjnbLBKT09HS99NJL6tOnzzUvtvj4eI0ePboQKwMAAEBx5+vjKt/St8yfzrgJt8QsdpcvX1avXr1kGIamTp16zbGxsbFKTU21LomJiYVUJQAAAIBbXZGPwVfC0Z9//qlVq1Zd91al2WyW2WwupOoAAAAAFCdFOiBdCUf79+/X6tWrVa5cOWeXBAAAAKAYc2pAOn/+vA4cOGBdP3TokHbs2CF/f39VqFBBDz30kLZv367FixcrOztbJ06ckCT5+/vLw8PDWWUDAAAAKKacGpC2bt2qu+++27o+fPhwSVJUVJTi4uK0aNEiSdLtt99us93q1avVtm3bwioTAAAAQAnh1IDUtm1bGYaRZ/+1+gAAAADA0W6JWewAAAAAoDAQkAAAAADAgoAEAAAAABYEJAAAAACwKNLfg+RIad2aytWdL5AFAAAoarIvZ0jzVjq7DEASd5AAAAAAwIqABAAAAAAWBCQAAAAAsCAgAQAAAIAFAQkAAAAALAhIAAAAAGBBQAIAAAAACwISAAAAAFgQkAAAAADAgoAEAAAAABYEJAAAAACwICABAAAAcIiEhASVKVPG2WXcFAISAAAA4CT9+vWTyWSSyWSSu7u7AgMD1b59e02bNk05OTlXjf/ll1/Us2dPBQYGytPTUzVr1tTAgQP1xx9/OKH6q/Xu3bvI1HKjCEgAAACAE3Xs2FHHjx/X4cOHtXTpUt19990aOnSoOnfurKysLOu4xYsX684771RGRoZmzZqlvXv36osvvpCfn59GjBhRaPVmZmbm2n758mV5eXnptttuu6n9X758+aa2v1kEJAAAAMCJzGazgoKCVKlSJTVq1Egvv/yyFi5cqKVLlyohIUGSdPHiRfXv31/33XefFi1apMjISIWGhqp58+Z666239NFHH+W5/4yMDL300ksKDg6W2WxWjRo19Nlnn0mSsrOzNWDAAIWGhsrLy0u1a9fWu+++a7N9v3791L17d40dO1YVK1ZU7dq1dfjwYZlMJs2ZM0dt2rSRp6enZs2alesjdgsXLlSjRo3k6empatWqafTo0TbBz2QyaerUqeratau8vb01duxYx/xib5CbU48OAAAAFENpaWk262azWWaz2e7t77nnHoWHh+ubb77R448/ruXLlys5OVkvvvhiruOv9d5P3759tXHjRk2ePFnh4eE6dOiQkpOTJUk5OTmqXLmy5s2bp3LlymnDhg0aNGiQKlSooF69eln3sXLlSvn6+mrFihU2+46JidHbb7+tO+64Q56enlq+fLlN/48//qi+fftq8uTJatWqlQ4ePKhBgwZJkkaNGmUdFxcXp3HjxmnSpElyc3NuRCEgAQAAAA4WHBxssz5q1CjFxcXlax916tTRr7/+Kknav3+/tS0//vjjD82dO1crVqxQZGSkJKlatWrWfnd3d40ePdq6Hhoaqo0bN2ru3Lk2Acnb21uffvqpPDw8JEmHDx+WJA0bNkwPPPBAnscfPXq0YmJiFBUVZT32a6+9phdffNEmID388MPq379/vs6toBCQAAAAAAdLTEyUr6+vdT0/d4+uMAxDJpPJ+vON2LFjh1xdXdWmTZs8x0yZMkXTpk3TkSNHdOnSJWVmZur222+3GRMWFmYNR3/XpEmTax5/586d+umnn2wem8vOzlZ6erouXryoUqVK2bWfwkRAAgAAABzM19fXJiDdiL179yo0NFSSVKtWLUnS77//roiICLv34eXldc3+2bNn6/nnn9fbb7+tiIgIlS5dWm+++aY2b95sM87b2zvX7fNqv+L8+fMaPXp0rneZPD097d5PYSIgAQAAAEXMqlWrtGvXLj377LOSpHvvvVfly5fXhAkTNH/+/KvGp6Sk5PoeUlhYmHJycrR27VrrI3Z/99NPP6lFixYaMmSIte3gwYMOO49GjRpp3759qlGjhsP2WdAISAAAAIATZWRk6MSJE8rOztbJkye1bNkyxcfHq3Pnzurbt6+k/38HqGfPnurataueeeYZ1ahRQ8nJyZo7d66OHDmi2bNnX7XvqlWrKioqSo899ph1koY///xTSUlJ6tWrl2rWrKnPP/9cy5cvV2hoqGbOnKktW7ZY71zdrJEjR6pz584KCQnRQw89JBcXF+3cuVO7d+/W66+/7pBjOFqJCUi+C7fIzVRiThcAAOCWkWVkXX9QMbZs2TJVqFBBbm5uKlu2rMLDwzV58mRFRUXJxeX/v5WnW7du2rBhg+Lj4/Xwww8rLS1NwcHBuueee64ZNqZOnaqXX35ZQ4YM0enTpxUSEqKXX35ZkvTEE0/ol19+Ue/evWUymdSnTx8NGTJES5cudci5dejQQYsXL9aYMWM0fvx4ubu7q06dOnr88ccdsv+CYDJu9I2vW0RaWpr8/PzU3KMdAQkAAKAIyjKytDlzpVJTU2/6vR1Hu/K35KnfWsi39PX/lkw7l6WAehuK5LnAPnxRLAAAAABYEJAAAAAAwIKABAAAAAAWBCQAAAAAsCAgAQAAAICFUwPSunXr1KVLF1WsWFEmk0kLFiyw6TcMQyNHjlSFChXk5eWlyMhI7d+/3znFAgAAACj2nBqQLly4oPDwcE2ZMiXX/gkTJmjy5Mn68MMPtXnzZnl7e6tDhw5KT08v5EoBAAAAlARO/WKgTp06qVOnTrn2GYahSZMm6dVXX1W3bt0kSZ9//rkCAwO1YMEC/etf/yrMUgEAAACUAEX2HaRDhw7pxIkTioyMtLb5+fmpefPm2rhxY57bZWRkKC0tzWYBAAAAAHsU2YB04sQJSVJgYKBNe2BgoLUvN/Hx8fLz87MuwcHBBVonAAAAgOKjyAakGxUbG6vU1FTrkpiY6OySAAAAANwiimxACgoKkiSdPHnSpv3kyZPWvtyYzWb5+vraLAAAAABgjyIbkEJDQxUUFKSVK1da29LS0rR582ZFREQ4sTIAAAAAxZVTZ7E7f/68Dhw4YF0/dOiQduzYIX9/f4WEhGjYsGF6/fXXVbNmTYWGhmrEiBGqWLGiunfv7ryiAQAAABRbTg1IW7du1d13321dHz58uCQpKipKCQkJevHFF3XhwgUNGjRIKSkpuuuuu7Rs2TJ5eno6q2QAAAAAxZjJMAzD2UUUpLS0tL+mB/doJzeTU/MgAAAAcpFlZGlz5kqlpqYWuffHr/wteeq3FvItff2/JdPOZSmg3oYieS6wT5F9BwkAAAAAChsBCQAAAAAsCEgAAAAAYEFAAgAAAAALAhIAAAAAWBCQAAAAAMCCgAQAAAAAFgQkAAAAALDgm1MBAACA62izpaFcS5mvOy77YoakDWratKlcXV0VHR2t6Ojogi8QDkNAAgAAABxsy5Yt8vX1dXYZuAE8YgcAAAAAFgQkAAAAALAgIAEAAACABQEJAAAAACwISAAAAABgQUACAAAAAAsCEgAAAABYEJAAAAAAwIKABAAAAAAWBCQAAAAAsCAgAQAAAIAFAQkAAAAALAhIAAAAAGDhZs+gyZMn273DZ5555oaLAQAAAABnsisgvfPOO3btzGQyEZAAAAAA3LLsCkiHDh0q6DoAAAAAwOlu6h0kwzBkGIajagEAAAAAp7qhgPT5558rLCxMXl5e8vLyUsOGDTVz5kxH1wYAAAAAhcquR+z+buLEiRoxYoSeeuoptWzZUpK0fv16DR48WMnJyXr22WcdXiQAAAAAFIZ8B6T33ntPU6dOVd++fa1tXbt2Vf369RUXF0dAAgAAAHDLyvcjdsePH1eLFi2uam/RooWOHz/ukKIAAAAAwBnyHZBq1KihuXPnXtU+Z84c1axZ0yFFAQAAAIAz5PsRu9GjR6t3795at26d9R2kn376SStXrsw1OAEAAADArSLfd5AefPBBbd68WeXLl9eCBQu0YMEClS9fXj///LN69Ojh0OKys7M1YsQIhYaGysvLS9WrV9drr73G1OIAAAAACkS+7yBJUuPGjfXFF184uparjB8/XlOnTtWMGTNUv359bd26Vf3795efn5+eeeaZAj8+AAAAUBIdPnxYoaGh+uWXX3T77bc7uxwlJCRo2LBhSklJKfBj3VBAys7O1vz587V3715JUr169dStWze5ud3Q7vK0YcMGdevWTffff78kqWrVqvrqq6/0888/O/Q4AAAAgLOcOHFC8fHxWrJkif73v//Jz89PNWrU0KOPPqqoqCiVKlXK2SU6Xe/evXXfffcVyrHynWj27Nmjrl276sSJE6pdu7akv+70BAQE6Ntvv1WDBg0cVlyLFi308ccf648//lCtWrW0c+dOrV+/XhMnTsxzm4yMDGVkZFjX09LSHFYPAAAA4Ej//e9/1bJlS5UpU0ZvvPGGwsLCZDabtWvXLn388ceqVKmSunbtWmDHz8zMlIeHR4HtPz/yquXy5cvy8vKSl5fXTe3/8uXLcnd3v+64fL+D9Pjjj6t+/fr63//+p+3bt2v79u1KTExUw4YNNWjQoBsqNi8xMTH617/+pTp16sjd3V133HGHhg0bpkceeSTPbeLj4+Xn52ddgoODHVoTAAAA4ChDhgyRm5ubtm7dql69eqlu3bqqVq2aunXrpiVLlqhLly6S/nrkzWQyaceOHdZtU1JSZDKZtGbNGkl/PeU1YMAA6/v7tWvX1rvvvmtzvH79+ql79+4aO3asKlasaL3h8fPPP+uOO+6Qp6enmjRpol9++eW6tWdkZOill15ScHCwzGazatSooc8+++ymarlynnPmzFGbNm3k6empWbNmKSEhQWXKlLHZfuHChWrUqJE8PT1VrVo1jR49WllZWdZ+k8mkqVOnqmvXrvL29tbYsWPt+kzyfQdpx44d2rp1q8qWLWttK1u2rMaOHaumTZvmd3fXNHfuXM2aNUtffvml6tevrx07dmjYsGGqWLGioqKict0mNjZWw4cPt66npaURkgAAAFCo/vkUk9lsltlstmk7ffq0vv/+e73xxhvy9vbOdT8mk8nuY+bk5Khy5cqaN2+eypUrpw0bNmjQoEGqUKGCevXqZR23cuVK+fr6asWKFZKk8+fPq3Pnzmrfvr2++OILHTp0SEOHDr3u8fr27auNGzdq8uTJCg8P16FDh5ScnHxTtVwRExOjt99+2xrali9fbtP/448/qm/fvpo8ebJatWqlgwcPWm/WjBo1yjouLi5O48aN06RJk+x+HSjfAalWrVo6efKk6tevb9OelJSkGjVq5Hd31/TCCy9Y7yJJUlhYmP7880/Fx8fnGZByu/gAAACAwvTP/0E/atQoxcXF2bQdOHBAhmFY7+JcUb58eaWnp0uSoqOjNX78eLuO6e7urtGjR1vXQ0NDtXHjRs2dO9cmlHh7e+vTTz+1Ps728ccfKycnR5999pk8PT2tT4s9+eSTeR7rjz/+0Ny5c7VixQpFRkZKkqpVq3bTtRw+fFiSNGzYMD3wwAN5Hn/06NGKiYmxZoJq1arptdde04svvmgTkB5++GH1798/719aLuwKSH9PwPHx8XrmmWcUFxenO++8U5K0adMmjRkzxu4Pz14XL16Ui4vtU4Curq7Kyclx6HEAAAAAR0pMTJSvr691PT//A//nn39WTk6OHnnkEZt36+0xZcoUTZs2TUeOHNGlS5eUmZl51Sx0YWFhNu/67N27Vw0bNpSnp6e1LSIi4prH2bFjh1xdXdWmTRuH1nJFkyZNrnn8nTt36qeffrJ5bC47O1vp6em6ePGidWKL6+0nN3YFpDJlytjc3jMMQ7169bK2Xfleoi5duig7OzvfReSlS5cuGjt2rEJCQlS/fn398ssvmjhxoh577DGHHQMAAABwNF9fX5uAlJsaNWrIZDJp3759Nu1X7sT8fVKCKzcN/v59oJcvX7bZbvbs2Xr++ef19ttvKyIiQqVLl9abb76pzZs324zL63G+/LjehAk3W8v1ajx//rxGjx6d612mvwe9GzlXuwLS6tWr871jR3jvvfc0YsQIDRkyRElJSapYsaKeeOIJjRw50in1AAAAAI5Srlw5tW/fXu+//76efvrpa/4xHxAQIEk6fvy47rjjDkmymbBBkn766Se1aNFCQ4YMsbYdPHjwunXUrVtXM2fOVHp6ujVcbNq06ZrbhIWFKScnR2vXrrU+YueIWuzVqFEj7du3z+Gv+Eh2BqRr3TorSKVLl9akSZM0adIkpxwfAAAAKEgffPCBWrZsqSZNmiguLk4NGzaUi4uLtmzZot9//12NGzeW9NcdmzvvvFPjxo1TaGiokpKS9Oqrr9rsq2bNmvr888+1fPlyhYaGaubMmdqyZYtCQ0OvWcPDDz+sV155RQMHDlRsbKwOHz6st95665rbVK1aVVFRUXrssceskzT8+eefSkpKUq9evW64FnuNHDlSnTt3VkhIiB566CG5uLho586d2r17t15//fWb2vcNfbNrenq6fv31VyUlJV31PlBBztMOAAAAFCfVq1fXL7/8ojfeeEOxsbH63//+J7PZrHr16un555+3uQMzbdo0DRgwQI0bN1bt2rU1YcIE3Xvvvdb+J554Qr/88ot69+4tk8mkPn36aMiQIVq6dOk1a/Dx8dG3336rwYMH64477lC9evU0fvx4Pfjgg9fcburUqXr55Zc1ZMgQnT59WiEhIXr55ZdvqhZ7dejQQYsXL7bOg+Du7q46dero8ccfv+l9m4y/P8hoh2XLlqlv377WKfxsdmYyOfQdJEdIS0uTn5+fmnu0k5vphvIgAAAAClCWkaXNmSuVmpp63fd2CtuVvyXrzRgs11LXn2gh+2KGfov6sEieC+yT7y+Kffrpp9WzZ08dP35cOTk5NktRC0cAAAAAkB/5DkgnT57U8OHDFRgYWBD1AAAAAIDT5DsgPfTQQ1qzZk0BlAIAAAAAzpXvl3Lef/999ezZUz/++KPCwsLk7u5u0//MM884rDgAAAAAKEz5DkhfffWVvv/+e3l6emrNmjU2XyBrMpkISAAAAABuWfkOSK+88opGjx6tmJgY6zf6AgAAAEBxkO+Ek5mZqd69exOOAAAAABQ7+U45UVFRmjNnTkHUAgAAAABOle9H7LKzszVhwgQtX75cDRs2vGqShokTJzqsOAAAAAAoTPkOSLt27dIdd9whSdq9e7dN398nbAAAAACAW02+A9Lq1asLog4AAAAAcLp8v4M0ffp0Xbp0qSBqAQAAAACnyndAiomJUWBgoAYMGKANGzYURE0AAAAA4BT5DkhHjx7VjBkzlJycrLZt26pOnToaP368Tpw4URD1AQAAAEChyXdAcnNzU48ePbRw4UIlJiZq4MCBmjVrlkJCQtS1a1ctXLhQOTk5BVErAAAAABSom/q218DAQN11112KiIiQi4uLdu3apaioKFWvXl1r1qxxUIkAAAAAUDhuKCCdPHlSb731lurXr6+2bdsqLS1Nixcv1qFDh3T06FH16tVLUVFRjq4VAAAAAApUvgNSly5dFBwcrISEBA0cOFBHjx7VV199pcjISEmSt7e3nnvuOSUmJjq8WAAAAAAoSPn+HqTbbrtNa9euVURERJ5jAgICdOjQoZsqDAAAAAAKW74D0meffXbdMSaTSVWqVLmhggAAAADAWex+xG7jxo1avHixTdvnn3+u0NBQ3XbbbRo0aJAyMjIcXiAAAAAAFBa7A9KYMWO0Z88e6/quXbs0YMAARUZGKiYmRt9++63i4+MLpEgAAAAAKAx2B6QdO3aoXbt21vXZs2erefPm+uSTTzR8+HBNnjxZc+fOLZAiAQAAAKAw2B2Qzp49q8DAQOv62rVr1alTJ+t606ZNmbkOAAAAwC3N7kkaAgMDdejQIQUHByszM1Pbt2/X6NGjrf3nzp2Tu7t7gRQJAAAAONWSMpK75/XHXU6X9NfNA1dXV0VHRys6Orpga4ND2R2Q7rvvPsXExGj8+PFasGCBSpUqpVatWln7f/31V1WvXr1AigQAAABuJVu2bJGvr6+zy8ANsDsgvfbaa3rggQfUpk0b+fj4aMaMGfLw8LD2T5s2Tffee2+BFAkAAAAAhcHugFS+fHmtW7dOqamp8vHxkaurq03/vHnz5OPj4/ACAQAAAKCw5PuLYv38/HJt9/f3v+liAAAAAMCZ7J7FDgAAAACKOwISAAAAAFgU+YB09OhRPfrooypXrpy8vLwUFhamrVu3OrssAAAAAMVQvt9BKkxnz55Vy5Ytdffdd2vp0qUKCAjQ/v37VbZsWWeXBgAAAKAYsjsgrVu3zq5xrVu3vuFi/mn8+PEKDg7W9OnTrW2hoaEO2z8AAAAA/J3dAalt27YymUySJMMwch1jMpmUnZ3tmMokLVq0SB06dFDPnj21du1aVapUSUOGDNHAgQPz3CYjI0MZGRnW9bS0NIfVAwAAAKB4s/sdpLJlyyo4OFgjRozQ/v37dfbs2auWM2fOOLS4//73v5o6dapq1qyp5cuX68knn9QzzzyjGTNm5LlNfHy8/Pz8rEtwcLBDawIAAABQfNkdkI4fP67x48dr48aNCgsL04ABA7Rhwwb5+vraBBJHysnJUaNGjfTGG2/ojjvu0KBBgzRw4EB9+OGHeW4TGxur1NRU65KYmOjQmgAAAAAUX3YHJA8PD/Xu3VvLly/X77//roYNG+qpp55ScHCwXnnlFWVlZTm8uAoVKqhevXo2bXXr1tWRI0fy3MZsNsvX19dmAQAAAAB73NA03yEhIRo5cqR++OEH1apVS+PGjSuQd31atmypffv22bT98ccfqlKlisOPBQAAAAD5DkgZGRn68ssvFRkZqQYNGqh8+fJasmSJ/P39HV7cs88+q02bNumNN97QgQMH9OWXX+rjjz9WdHS0w48FAAAAAHbPYvfzzz9r+vTpmj17tqpWrar+/ftr7ty5BRKMrmjatKnmz5+v2NhYjRkzRqGhoZo0aZIeeeSRAjsmAAAAgJLL7oB05513KiQkRM8884waN24sSVq/fv1V47p27eq46iR17txZnTt3dug+AQAAACA3dgckSTpy5Ihee+21PPsd/T1IAAAAAFCY7A5IOTk5BVkHAAAAADjdDc1iBwAAAADFUb4esZOkVatW6ZtvvtHhw4dlMpkUGhqqhx56SK1bty6I+gAAAACg0OTrDtLgwYMVGRmpr776SqdPn9apU6c0a9Ys3X333Xr66acLqkYAAAAAKBR2B6T58+dr+vTpmjZtmpKTk7Vx40Zt2rRJp06d0ieffKKPP/5YixYtKshaAQAAAKBA2R2Qpk+fruHDh6tfv34ymUz/vwMXFz322GMaNmyYPvvsswIpEgAAAAAKg90Bafv27erRo0ee/Q888IC2bdvmkKIAAAAAwBnsDkjJycmqXLlynv2VK1fW6dOnHVIUAAAAUNL169dP3bt3t663bdtWw4YNc1o9JYXdASkzM1Pu7u559ru5uSkzM9MhRQEAAAAlwZXXV0wmkzw8PFSjRg2NGTNGWVlZevfdd5WQkHDD+05ISFCZMmUcVmtJka9pvkeMGKFSpUrl2nfx4kWHFAQAAACUJB07dtT06dOVkZGh7777TtHR0XJ3d1dsbKyzSyuR7A5IrVu31r59+647BgAAAID9zGazgoKCJElPPvmk5s+fr0WLFmnfvn1KSUnRggULct3u7NmzGjp0qL799ltlZGSoTZs2mjx5smrWrKk1a9aof//+kmSdYG3UqFGKi4srjFO6pdkdkNasWVOAZQAAAADFR1pams262WyW2Wy2a1svLy+73u3v16+f9u/fr0WLFsnX11cvvfSS7rvvPv32229q0aKFJk2apJEjR1pvcvj4+OT/REogu99BysnJKcg6AAAAgGIjODhYfn5+1iU+Pv662xiGoR9++EHLly/XPffcc82xV4LRp59+qlatWik8PFyzZs3S0aNHtWDBAnl4eMjPz08mk0lBQUEKCgoiINnJ7oDk7u6upKQk6/oLL7ygM2fOFEhRAAAAwK0sMTFRqamp1uVa7xMtXrxYPj4+8vT0VKdOndS7d+/rPgq3d+9eubm5qXnz5ta2cuXKqXbt2tq7d6+jTqNEsjsgGYZhs/7RRx8pJSXF0fUAAAAAtzxfX1+b5VqP1919993asWOH9u/fr0uXLmnGjBny9vYuxGrxd3YHpH/6Z2ACAAAAkH/e3t6qUaOGQkJC5OZm3xQBdevWVVZWljZv3mxtO336tPbt26d69epJkjw8PJSdnV0gNRdnNxyQAAAAADhHzZo11a1bNw0cOFDr16/Xzp079eijj6pSpUrq1q2bJKlq1ao6f/68Vq5cqeTkZL6Wx075+h6kkSNHWr8HKTMzU2PHjpWfn5/NmIkTJzquOgAAAAC5mj59uoYOHarOnTsrMzNTrVu31nfffSd3d3dJUosWLTR48GD17t1bp0+fZppvO5kMO5+Va9u2rXUO9Tx3ZjJp1apVDinMUdLS0uTn56fmHu3kZspXHgQAAEAhyDKytDlzpVJTU+Xr6+vscmxc+VuyXq8Yubp7Xnd89uV0/TZ3XJE8F9iH70ECAAAAAIsbfgcpOTlZycnJjqwFAAAAAJwqXwEpJSVF0dHRKl++vAIDAxUYGKjy5cvrqaeeYspvAAAAALc8ux+xO3PmjCIiInT06FE98sgjqlu3riTpt99+U0JCglauXKkNGzaobNmyBVYsAAAAABQkuwPSmDFj5OHhoYMHDyowMPCqvnvvvVdjxozRO++84/AiAQAAAKAw2P2I3YIFC/TWW29dFY4kKSgoSBMmTND8+fMdWhwAAAAAFCa7A9Lx48dVv379PPsbNGigEydOOKQoAAAAAHAGuwNS+fLldfjw4Tz7Dx06JH9/f0fUBAAAAABOYXdA6tChg1555RVlZmZe1ZeRkaERI0aoY8eODi0OAAAAAApTviZpaNKkiWrWrKno6GjVqVNHhmFo7969+uCDD5SRkaGZM2cWZK0AAAAAUKDsDkiVK1fWxo0bNWTIEMXGxsowDEmSyWRS+/bt9f777ys4OLjACgUAAACAgmZ3QJKk0NBQLV26VGfPntX+/fslSTVq1ODdIwAAAADFQr4C0hVly5ZVs2bNHF0LAAAAADiV3ZM0FAXjxo2TyWTSsGHDnF0KAAAAgGLolglIW7Zs0UcffaSGDRs6uxQAAAAAxdQtEZDOnz+vRx55RJ988onKli3r7HIAAAAAFFO3RECKjo7W/fffr8jIyOuOzcjIUFpams0CAAAAAPa4oUkaCtPs2bO1fft2bdmyxa7x8fHxGj16dAFXBQAAAKA4KtJ3kBITEzV06FDNmjVLnp6edm0TGxur1NRU65KYmFjAVQIAAAAoLor0HaRt27YpKSlJjRo1srZlZ2dr3bp1ev/995WRkSFXV1ebbcxms8xmc2GXCgAAAKAYKNIBqV27dtq1a5dNW//+/VWnTh299NJLV4UjAAAAALgZRToglS5dWg0aNLBp8/b2Vrly5a5qBwAAAICbVaTfQQIAAACAwlSk7yDlZs2aNc4uAQAAACWM78KtcjNd/0/nLCNLktS0aVO5uroqOjpa0dHRBV0eHOiWC0gAAABAUbdlyxb5+vo6uwzcAB6xAwAAAAALAhIAAAAAWBCQAAAAAMCCgAQAAAAAFgQkAAAAALAgIAEAAACABQEJAAAAACwISAAAAABgQUACAAAAAAsCEgAAAABYEJAAAAAAwIKABAAAAAAWBCQAAAAAsCAgAQAAAIAFAQkAAAAALAhIAAAAAGBBQAIAAAAACwISAAAAAFgQkAAAAADAgoAEAAAAABYEJAAAAACwICABAAAAgAUBCQAAAAAsCEgAAAAAYEFAAgAAAAALN2cXUFjSujWVq7vZ2WUAAADgH7IvZ0jzVjq7DKfo16+fZsyYIUlyc3OTv7+/GjZsqD59+qhfv35ycbHvfkZCQoKGDRumlJSUAqy2ZOAOEgAAAOBEHTt21PHjx3X48GEtXbpUd999t4YOHarOnTsrKyvL2eWVOAQkAAAAwInMZrOCgoJUqVIlNWrUSC+//LIWLlyopUuXKiEhQZI0ceJEhYWFydvbW8HBwRoyZIjOnz8vSVqzZo369++v1NRUmUwmmUwmxcXFSZJmzpypJk2aqHTp0goKCtLDDz+spKQkJ53prYGABAAAADhYWlqazZKRkZGv7e+55x6Fh4frm2++kSS5uLho8uTJ2rNnj2bMmKFVq1bpxRdflCS1aNFCkyZNkq+vr44fP67jx4/r+eeflyRdvnxZr732mnbu3KkFCxbo8OHD6tevn0PPtbgpMe8gAQAAAIUlODjYZn3UqFHWuzr2qlOnjn799VdJ0rBhw6ztVatW1euvv67Bgwfrgw8+kIeHh/z8/GQymRQUFGSzj8cee8z6c7Vq1TR58mQ1bdpU58+fl4+PT/5OqoQgIAEAAAAOlpiYKF9fX+u62Zz/ycIMw5DJZJIk/fDDD4qPj9fvv/+utLQ0ZWVlKT09XRcvXlSpUqXy3Me2bdsUFxennTt36uzZs8rJyZEkHTlyRPXq1ct3TSUBj9gBAAAADubr62uz3EhA2rt3r0JDQ3X48GF17txZDRs21H/+8x9t27ZNU6ZMkSRlZmbmuf2FCxfUoUMH+fr6atasWdqyZYvmz59/3e1KuiIdkOLj49W0aVOVLl1at912m7p37659+/Y5uywAAACgQK1atUq7du3Sgw8+qG3btiknJ0dvv/227rzzTtWqVUvHjh2zGe/h4aHs7Gybtt9//12nT5/WuHHj1KpVK9WpU4cJGuxQpAPS2rVrFR0drU2bNmnFihW6fPmy7r33Xl24cMHZpQEAAAAOkZGRoRMnTujo0aPavn273njjDXXr1k2dO3dW3759VaNGDV2+fFnvvfee/vvf/2rmzJn68MMPbfZRtWpVnT9/XitXrlRycrIuXryokJAQeXh4WLdbtGiRXnvtNSed5a2jSAekZcuWqV+/fqpfv77Cw8OVkJCgI0eOaNu2bc4uDQAAAHCIZcuWqUKFCqpatao6duyo1atXa/LkyVq4cKFcXV0VHh6uiRMnavz48WrQoIFmzZql+Ph4m320aNFCgwcPVu/evRUQEKAJEyYoICBACQkJmjdvnurVq6dx48bprbfectJZ3jpMhmEYzi7CXgcOHFDNmjW1a9cuNWjQINcxGRkZNtMopqWlKTg4WHV7xsjVPf/PfgIAAKBgZV/O0N5545SammozsUFRkJaWJj8/P91pjpSb6frzm2UZWdqU8UORPBfYp0jfQfq7nJwcDRs2TC1btswzHEl/vbfk5+dnXf45xSIAAAAA5OWWCUjR0dHavXu3Zs+efc1xsbGxSk1NtS6JiYmFVCEAAACAW90t8T1ITz31lBYvXqx169apcuXK1xxrNptvaBpFAAAAACjSAckwDD399NOaP3++1qxZo9DQUGeXBAAAAKAYK9IBKTo6Wl9++aUWLlyo0qVL68SJE5IkPz8/eXl5Obk6AAAAAMVNkX4HaerUqUpNTVXbtm1VoUIF6zJnzhxnlwYAAACgGCrSd5BuoRnIAQAAABQDRfoOEgAAAAAUJgISAAAAAFgQkAAAAADAgoAEAAAAABZFepIGR/JduEVuphJzugAAALeMLCPL2SUAVtxBAgAAAAALAhIAAAAAWBCQAAAAAMCCgAQAAAAAFgQkAAAAALAgIAEAAACABQEJAAAAACwISAAAAABgQUACAAAAAAsCEgAAAABYEJAAAAAAwMLN2QUAAAAARV22siTDznG4pRGQAAAAgDx4eHgoKChIW06ssXsbX19fNWvWTC4uLoqOjlZ0dHTBFQiHIyABAAAAefD09NShQ4eUmZlp9zYeHh7y9PQswKpQkAhIAAAAwDV4enoSeEoQJmkAAAAAAAsCEgAAAABYEJAAAAAAwIKABAAAAAAWBCQAAAAAsCAgAQAAAIAFAQkAAAAALAhIAAAAAGBBQAIAAAAACwISAAAAAFgQkAAAAADAgoAEAAAAABYEJAAAAACwuCUC0pQpU1S1alV5enqqefPm+vnnn51dEgAAAIBiqMgHpDlz5mj48OEaNWqUtm/frvDwcHXo0EFJSUnOLg0AAABAMVPkA9LEiRM1cOBA9e/fX/Xq1dOHH36oUqVKadq0ac4uDQAAAEAxU6QDUmZmprZt26bIyEhrm4uLiyIjI7Vx48Zct8nIyFBaWprNAgAAAAD2KNIBKTk5WdnZ2QoMDLRpDwwM1IkTJ3LdJj4+Xn5+ftYlODi4MEoFAAAAUAwU6YB0I2JjY5WammpdEhMTnV0SAAAAgFuEm7MLuJby5cvL1dVVJ0+etGk/efKkgoKCct3GbDbLbDYXRnkAAAAAipkifQfJw8NDjRs31sqVK61tOTk5WrlypSIiIpxYGQAAAIDiqEjfQZKk4cOHKyoqSk2aNFGzZs00adIkXbhwQf3793d2aQAAAACKmSIfkHr37q1Tp05p5MiROnHihG6//XYtW7bsqokbAAAAAOBmmQzDMJxdREFKS0uTn5+fmnu0k5upyOdBAACAEifLyNLmzJVKTU2Vr6+vs8tBCVek30ECAAAAgMJEQAIAAAAACwISAAAAAFgU+5dyrrxilWVkObkSAAAA5ObK32nF/NV43CKKfUA6d+6cJGnb5bVOrgQAAADXcu7cOfn5+Tm7DJRwxX4Wu5ycHB07dkylS5eWyWSytqelpSk4OFiJiYnMllKCcR2AawBcA+AacD7DMHTu3DlVrFhRLi68AQLnKvZ3kFxcXFS5cuU8+319ffnHEFwH4BoA1wC4BpyMO0coKojoAAAAAGBBQAIAAAAAixIbkMxms0aNGiWz2ezsUuBEXAfgGgDXALgGAPxdsZ+kAQAAAADsVWLvIAEAAADAPxGQAAAAAMCCgAQAAAAAFgQkAAAAALAosQFpypQpqlq1qjw9PdW8eXP9/PPPzi4JBWTdunXq0qWLKlasKJPJpAULFtj0G4ahkSNHqkKFCvLy8lJkZKT279/vnGJRIOLj49W0aVOVLl1at912m7p37659+/bZjElPT1d0dLTKlSsnHx8fPfjggzp58qSTKoajTZ06VQ0bNrR+EWhERISWLl1q7efzL3nGjRsnk8mkYcOGWdu4DgBIJTQgzZkzR8OHD9eoUaO0fft2hYeHq0OHDkpKSnJ2aSgAFy5cUHh4uKZMmZJr/4QJEzR58mR9+OGH2rx5s7y9vdWhQwelp6cXcqUoKGvXrlV0dLQ2bdqkFStW6PLly7r33nt14cIF65hnn31W3377rebNm6e1a9fq2LFjeuCBB5xYNRypcuXKGjdunLZt26atW7fqnnvuUbdu3bRnzx5JfP4lzZYtW/TRRx+pYcOGNu1cBwAkSUYJ1KxZMyM6Otq6np2dbVSsWNGIj493YlUoDJKM+fPnW9dzcnKMoKAg480337S2paSkGGaz2fjqq6+cUCEKQ1JSkiHJWLt2rWEYf33m7u7uxrx586xj9u7da0gyNm7c6KwyUcDKli1rfPrpp3z+Jcy5c+eMmjVrGitWrDDatGljDB061DAM/h0A8P9K3B2kzMxMbdu2TZGRkdY2FxcXRUZGauPGjU6sDM5w6NAhnThxwuZ68PPzU/PmzbkeirHU1FRJkr+/vyRp27Ztunz5ss11UKdOHYWEhHAdFEPZ2dmaPXu2Lly4oIiICD7/EiY6Olr333+/zect8e8AgP/n5uwCCltycrKys7MVGBho0x4YGKjff//dSVXBWU6cOCFJuV4PV/pQvOTk5GjYsGFq2bKlGjRoIOmv68DDw0NlypSxGct1ULzs2rVLERERSk9Pl4+Pj+bPn6969eppx44dfP4lxOzZs7V9+3Zt2bLlqj7+HQBwRYkLSABKtujoaO3evVvr1693dikoZLVr19aOHTuUmpqqr7/+WlFRUVq7dq2zy0IhSUxM1NChQ7VixQp5eno6uxwARViJe8SufPnycnV1vWpWmpMnTyooKMhJVcFZrnzmXA8lw1NPPaXFixdr9erVqly5srU9KChImZmZSklJsRnPdVC8eHh4qEaNGmrcuLHi4+MVHh6ud999l8+/hNi2bZuSkpLUqFEjubm5yc3NTWvXrtXkyZPl5uamwMBArgMAkkpgQPLw8FDjxo21cuVKa1tOTo5WrlypiIgIJ1YGZwgNDVVQUJDN9ZCWlqbNmzdzPRQjhmHoqaee0vz587Vq1SqFhoba9Ddu3Fju7u4218G+fft05MgRroNiLCcnRxkZGXz+JUS7du20a9cu7dixw7o0adJEjzzyiPVnrgMAUgl9xG748OGKiopSkyZN1KxZM02aNEkXLlxQ//79nV0aCsD58+d14MAB6/qhQ4e0Y8cO+fv7KyQkRMOGDdPrr7+umjVrKjQ0VCNGjFDFihXVvXt35xUNh4qOjtaXX36phQsXqnTp0tb3Cfz8/OTl5SU/Pz8NGDBAw4cPl7+/v3x9ffX0008rIiJCd955p5OrhyPExsaqU6dOCgkJ0blz5/Tll19qzZo1Wr58OZ9/CVG6dGnre4dXeHt7q1y5ctZ2rgMAkkrmNN+GYRjvvfeeERISYnh4eBjNmjUzNm3a5OySUEBWr15tSLpqiYqKMgzjr6m+R4wYYQQGBhpms9lo166dsW/fPucWDYfK7fOXZEyfPt065tKlS8aQIUOMsmXLGqVKlTJ69OhhHD9+3HlFw6Eee+wxo0qVKoaHh4cREBBgtGvXzvj++++t/Xz+JdPfp/k2DK4DAH8xGYZhOCmbAQAAAECRUuLeQQIAAACAvBCQAAAAAMCCgAQAAAAAFgQkAAAAALAgIAEAAACABQEJAAAAACwISAAAAABgQUACAAAAAAsCEgDkYc2aNTKZTEpJSXF2KVZVq1bVpEmTnF1Gnvr166fu3bs7uwwAAG4YAQlAsXTq1Ck9+eSTCgkJkdlsVlBQkDp06KCffvrJ2aXdlC1btmjQoEEO329RDIMAADiDm7MLAICC8OCDDyozM1MzZsxQtWrVdPLkSa1cuVKnT592dmnXdfnyZbm7u9u0ZWZmysPDQwEBATe17yv7AQAAueMOEoBiJyUlRT/++KPGjx+vu+++W1WqVFGzZs0UGxurrl27SpIOHz4sk8mkHTt22GxnMpm0Zs0am/399NNPatiwoTw9PXXnnXdq9+7dV/W3bdtWpUqVUtmyZdWhQwedPXtWkrRs2TLdddddKlOmjMqVK6fOnTvr4MGD1m2v1DFnzhy1adNGnp6emjVrlvVRtbFjx6pixYqqXbu2pKsfsUtJSdHjjz+ugIAA+fr66p577tHOnTut/XFxcbr99tv16aefKjQ0VJ6ennb9DhMSElSmTBktX75cdevWlY+Pjzp27Kjjx49bx2RnZ2v48OHWc3vxxRdlGIbNfnJychQfH6/Q0FB5eXkpPDxcX3/9tSTJMAxFRkaqQ4cO1u3OnDmjypUra+TIkXbVCQCAoxGQABQ7Pj4+8vHx0YIFC5SRkXHT+3vhhRf09ttva8uWLQoICFCXLl10+fJlSdKOHTvUrl071atXTxs3btT69evVpUsXZWdnS5IuXLig4cOHa+vWrVq5cqVcXFzUo0cP5eTk2BwjJiZGQ4cO1d69e9WhQwdJ0sqVK7Vv3z6tWLFCixcvzrW2nj17KikpSUuXLtW2bdvUqFEjtWvXTmfOnLGOOXDggP7zn//om2++sQmE13Px4kW99dZbmjlzptatW6cjR47o+eeft/a//fbbSkhI0LRp07R+/XqdOXNG8+fPt9lHfHy8Pv/8c3344Yfas2ePnn32WT366KNau3atTCaTZsyYoS1btmjy5MmSpMGDB6tSpUoEJACA8xgAUAx9/fXXRtmyZQ1PT0+jRYsWRmxsrLFz505r/6FDhwxJxi+//GJtO3v2rCHJWL16tWEYhrF69WpDkjF79mzrmNOnTxteXl7GnDlzDMMwjD59+hgtW7a0u65Tp04Zkoxdu3bZ1DFp0iSbcVFRUUZgYKCRkZFh016lShXjnXfeMQzDMH788UfD19fXSE9PtxlTvXp146OPPjIMwzBGjRpluLu7G0lJSdes68q5nj171jAMw5g+fbohyThw4IB1zJQpU4zAwEDreoUKFYwJEyZY1y9fvmxUrlzZ6Natm2EYhpGenm6UKlXK2LBhg82xBgwYYPTp08e6PnfuXMPT09OIiYkxvL29jT/++OOatQIAUJC4gwSgWHrwwQd17NgxLVq0SB07dtSaNWvUqFEjJSQk5HtfERER1p/9/f1Vu3Zt7d27V9L/30HKy/79+9WnTx9Vq1ZNvr6+qlq1qiTpyJEjNuOaNGly1bZhYWHXfF9o586dOn/+vMqVK2e9a+bj46NDhw7ZPMZXpUqVG3p3qVSpUqpevbp1vUKFCkpKSpIkpaam6vjx42revLm1383NzeY8Dhw4oIsXL6p9+/Y29X3++ec29fXs2VM9evTQuHHj9NZbb6lmzZr5rhUAAEdhkgYAxZanp6fat2+v9u3ba8SIEXr88cc1atQo9evXTy4uf/3/IeNv78xceWwuP7y8vK7Z36VLF1WpUkWffPKJKlasqJycHDVo0ECZmZk247y9va/aNre2vzt//rwqVKhw1TtTklSmTBm795OXf04UYTKZrnrH6Hr1SdKSJUtUqVIlmz6z2Wz9+eLFi9q2bZtcXV21f//+G6oVAABH4Q4SgBKjXr16unDhgiRZ76j8fdKBvN7P2bRpk/Xns2fP6o8//lDdunUlSQ0bNtTKlStz3e706dPat2+fXn31VbVr105169a1Tt7gCI0aNdKJEyfk5uamGjVq2Czly5d32HFy4+fnpwoVKmjz5s3WtqysLG3bts26Xq9ePZnNZh05cuSq+oKDg63jnnvuObm4uGjp0qWaPHmyVq1aVaC1AwBwLdxBAlDsnD59Wj179tRjjz2mhg0bqnTp0tq6dasmTJigbt26Sfrrzs+dd96pcePGKTQ0VElJSXr11Vdz3d+YMWNUrlw5BQYG6pVXXlH58uWtX4YaGxursLAwDRkyRIMHD5aHh4dWr16tnj17yt/fX+XKldPHH3+sChUq6MiRI4qJiXHYeUZGRioiIkLdu3fXhAkTVKtWLR07dkxLlixRjx49cn1sz5GGDh2qcePGqWbNmqpTp44mTpxo8z1KpUuX1vPPP69nn31WOTk5uuuuu5SamqqffvpJvr6+ioqK0pIlSzRt2jRt3LhRjRo10gsvvKCoqCj9+uuvKlu2bIHWDwBAbriDBKDY8fHxUfPmzfXOO++odevWatCggUaMGKGBAwfq/ffft46bNm2asrKy1LhxYw0bNkyvv/56rvsbN26chg4dqsaNG+vEiRP69ttvre8G1apVS99//7127typZs2aKSIiQgsXLpSbm5tcXFw0e/Zsbdu2TQ0aNNCzzz6rN99802HnaTKZ9N1336l169bq37+/atWqpX/961/6888/FRgY6LDj5OW5557Tv//9b0VFRSkiIkKlS5dWjx49bMa89tprGjFihOLj41W3bl117NhRS5YsUWhoqE6dOqUBAwYoLi5OjRo1kiSNHj1agYGBGjx4cIHXDwBAbkxGfh4oBwAAAIBijDtIAAAAAGBBQAIAAAAACwISAAAAAFgQkAAAAADAgoAEAAAAABYEJAAAAACwICABAAAAgAUBCQAAAAAsCEgAAAAAYEFAAgAAAAALAhIAAAAAWPwfcf3wx2SUEhoAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1000x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "SIMPLE_SIM = False   # reduced simulation time for simple simulation if set to True\n",
    "if SIMPLE_SIM:\n",
    "    batch_size = int(1e1)  # number of OFDM frames to be analyzed per batch\n",
    "    num_iter = 5  # number of Monte Carlo Iterations (total number of Monte Carlo trials is num_iter*batch_size)\n",
    "    num_steps = 6\n",
    "    tf.config.run_functions_eagerly(True)   # run eagerly for better debugging\n",
    "else:\n",
    "    batch_size = int(64)  # number of OFDM frames to be analyzed per batch\n",
    "    num_iter = 128  # number of Monte Carlo Iterations (total number of Monte Carlo trials is num_iter*batch_size)\n",
    "    num_steps = 11\n",
    "\n",
    "ebno_db_min_perf_csi = -10  # min EbNo value in dB for perfect csi benchmarks\n",
    "ebno_db_max_perf_csi = 0\n",
    "ebno_db_min_cest = -10\n",
    "ebno_db_max_cest = 10\n",
    "\n",
    "\n",
    "NUM_OFDM_SYMBOLS = 14\n",
    "FFT_SIZE = 12*4 # 4 PRBs\n",
    "SUBCARRIER_SPACING = 30e3 # Hz\n",
    "CARRIER_FREQUENCY = 3.5e9 # Hz\n",
    "SPEED = 3. # m/s\n",
    "num_bits_per_symbol = 4 # 16 QAM\n",
    "n_ue = 4 # 4 UEs\n",
    "NUM_RX_ANT = 16 # 16 BS antennas\n",
    "num_pilot_symbols = 2\n",
    "\n",
    "# The user terminals (UTs) are equipped with a single antenna\n",
    "# with vertial polarization.\n",
    "UT_ANTENNA = Antenna(polarization='single',\n",
    "                     polarization_type='V',\n",
    "                     antenna_pattern='omni', # Omnidirectional antenna pattern\n",
    "                     carrier_frequency=CARRIER_FREQUENCY)\n",
    "\n",
    "# The base station is equipped with an antenna\n",
    "# array of 8 cross-polarized antennas,\n",
    "# resulting in a total of 16 antenna elements.\n",
    "BS_ARRAY = PanelArray(num_rows_per_panel=2,\n",
    "                      num_cols_per_panel=4,\n",
    "                      polarization='dual',\n",
    "                      polarization_type='cross',\n",
    "                      antenna_pattern='38.901', # 3GPP 38.901 antenna pattern\n",
    "                      carrier_frequency=CARRIER_FREQUENCY)\n",
    "\n",
    "# 3GPP UMa channel model is considered\n",
    "channel_model_uma = UMa(carrier_frequency=CARRIER_FREQUENCY,\n",
    "                    o2i_model='low',\n",
    "                    ut_array=UT_ANTENNA,\n",
    "                    bs_array=BS_ARRAY,\n",
    "                    direction='uplink',\n",
    "                    enable_shadow_fading=False,\n",
    "                    enable_pathloss=False)\n",
    "\n",
    "channel_model_rayleigh = RayleighBlockFading(num_rx=1, num_rx_ant=NUM_RX_ANT, num_tx=n_ue, num_tx_ant=1)\n",
    "\n",
    "constellation = Constellation(\"qam\", num_bits_per_symbol=num_bits_per_symbol)\n",
    "\n",
    "rx_tx_association = np.ones([1, n_ue])\n",
    "sm = StreamManagement(rx_tx_association, 1)\n",
    "\n",
    "# Parameterize the OFDM channel\n",
    "rg = ResourceGrid(num_ofdm_symbols=NUM_OFDM_SYMBOLS, pilot_ofdm_symbol_indices = [2, 11],\n",
    "                  fft_size=FFT_SIZE, num_tx=n_ue,\n",
    "                  pilot_pattern = \"kronecker\",\n",
    "                  subcarrier_spacing=SUBCARRIER_SPACING)\n",
    "\n",
    "rg.show()\n",
    "plt.show()\n",
    "\n",
    "# Parameterize the LDPC code\n",
    "R = 0.5  # rate 1/2\n",
    "N = int(FFT_SIZE * (NUM_OFDM_SYMBOLS - 2) * num_bits_per_symbol)\n",
    "# N = int((FFT_SIZE) * (NUM_OFDM_SYMBOLS - 2) * num_bits_per_symbol)\n",
    "# code length; - 12 because of 11 guard carriers and 1 DC carrier, - 2 becaues of 2 pilot symbols\n",
    "K = int(N * R)  # number of information bits per codeword\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setting-up the Keras Models\n",
    "\n",
    "Now, we define the baseline models for benchmarking. Let us start with the non-IDD models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "class NonIddModel(Model):\n",
    "    def __init__(self, num_bp_iter=12, detector='lmmse', cest_type=\"LS\", interp=\"lin\", perfect_csi_rayleigh=False):\n",
    "        super().__init__()\n",
    "        self._num_bp_iter = int(num_bp_iter)\n",
    "        ######################################\n",
    "        ## Transmitter\n",
    "        self._binary_source = BinarySource()\n",
    "        self._encoder = LDPC5GEncoder(K, N, num_bits_per_symbol=num_bits_per_symbol)\n",
    "        self._mapper = Mapper(constellation=constellation)\n",
    "        self._rg_mapper = ResourceGridMapper(rg)\n",
    "\n",
    "        # Channel\n",
    "        if perfect_csi_rayleigh:\n",
    "            self._channel_model = channel_model_rayleigh\n",
    "        else:\n",
    "            self._channel_model = channel_model_uma\n",
    "\n",
    "        self._channel = OFDMChannel(channel_model=self._channel_model,\n",
    "                                    resource_grid=rg,\n",
    "                                    add_awgn=True, normalize_channel=True, return_channel=True)\n",
    "        \n",
    "        # Receiver\n",
    "        self._cest_type = cest_type\n",
    "        self._interp = interp\n",
    "\n",
    "        # Channel estimation\n",
    "        self._perfect_csi_rayleigh = perfect_csi_rayleigh\n",
    "        if self._perfect_csi_rayleigh:\n",
    "            self._removeNulledSc = RemoveNulledSubcarriers(rg)\n",
    "        elif cest_type == \"LS\":\n",
    "            self._ls_est = LSChannelEstimator(rg, interpolation_type=interp)\n",
    "        else:\n",
    "            raise NotImplementedError('Not implemented:' + cest_type)\n",
    "\n",
    "        # Detection\n",
    "        if detector == \"lmmse\":\n",
    "            self._detector = LinearDetector(\"lmmse\", 'bit', \"maxlog\", rg, sm, constellation_type=\"qam\",\n",
    "                                            num_bits_per_symbol=num_bits_per_symbol, hard_out=False)\n",
    "        elif detector == \"k-best\":\n",
    "            k = 64\n",
    "            self._detector = KBestDetector('bit', n_ue, k, rg, sm, constellation_type=\"qam\",\n",
    "                                           num_bits_per_symbol=num_bits_per_symbol, hard_out=False)\n",
    "        elif detector == \"ep\":\n",
    "            l = 10\n",
    "            self._detector = EPDetector('bit', rg, sm, num_bits_per_symbol, l=l, hard_out=False)\n",
    "\n",
    "        # Forward error correction (decoder)\n",
    "        self._decoder = LDPC5GDecoder(self._encoder, return_infobits=True, hard_out=True, num_iter=num_bp_iter, cn_type='minsum')\n",
    "\n",
    "    def new_topology(self, batch_size):\n",
    "        \"\"\"Set new topology\"\"\"\n",
    "        if isinstance(self._channel_model, UMa):\n",
    "            # sensible values according to 3GPP standard, no mobility by default\n",
    "            topology = gen_single_sector_topology(batch_size,\n",
    "                                                  n_ue, max_ut_velocity=SPEED,\n",
    "                                                  scenario=\"uma\")\n",
    "            self._channel_model.set_topology(*topology)\n",
    "\n",
    "    @tf.function  # We don't use jit_compile=True to ensure better numerical stability\n",
    "    def call(self, batch_size, ebno_db):\n",
    "        self.new_topology(batch_size)\n",
    "\n",
    "        if len(ebno_db.shape) == 0:\n",
    "            ebno_db = tf.fill([batch_size], ebno_db)\n",
    "\n",
    "        ######################################\n",
    "        ## Transmitter\n",
    "        no = ebnodb2no(ebno_db=ebno_db, num_bits_per_symbol=num_bits_per_symbol,\n",
    "                       coderate=R)  # normalize in OFDM freq. domain\n",
    "        b = self._binary_source([batch_size, n_ue, 1, K])\n",
    "        c = self._encoder(b)\n",
    "        # Modulation\n",
    "        x = self._mapper(c)\n",
    "        x_rg = self._rg_mapper(x)\n",
    "\n",
    "        ######################################\n",
    "        ## Channel\n",
    "        # A batch of new channel realizations is sampled and applied at every inference\n",
    "        no_ = expand_to_rank(no, tf.rank(x_rg))\n",
    "        y, h = self._channel([x_rg, no_])\n",
    "\n",
    "        ######################################\n",
    "        ## Receiver\n",
    "        if self._perfect_csi_rayleigh:\n",
    "            h_hat = self._removeNulledSc(h)\n",
    "            chan_est_var = tf.zeros(tf.shape(h_hat),\n",
    "                                    dtype=tf.float32)  # No channel estimation error when perfect CSI knowledge is assumed\n",
    "        else:\n",
    "            h_hat, chan_est_var = self._ls_est([y, no])\n",
    "\n",
    "        llr_ch = self._detector((y, h_hat, chan_est_var, no))  # detector\n",
    "        b_hat = self._decoder((llr_ch))\n",
    "        return b, b_hat "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we implement the IDD model with a non-resetting LDPC decoder, as in [3], i.e., we forward the LLRs and decoder state from one IDD iteration to the following."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "class IddModel(NonIddModel):  # inherited from NonIddModel\n",
    "    def __init__(self, num_idd_iter=3, num_bp_iter_per_idd_iter=12, cest_type=\"LS\", interp=\"lin\", perfect_csi_rayleigh=False):\n",
    "        super().__init__(num_bp_iter=num_bp_iter_per_idd_iter, detector=\"lmmse\", cest_type=cest_type,\n",
    "                         interp=interp, perfect_csi_rayleigh=perfect_csi_rayleigh)\n",
    "        # first IDD detector is LMMSE as MMSE-PIC with zero-prior bils down to soft-output LMMSE\n",
    "        self._num_idd_iter = num_idd_iter\n",
    "        self._siso_detector = MMSEPICDetector(output=\"bit\", resource_grid=rg, stream_management=sm,\n",
    "                                              demapping_method='maxlog', constellation=constellation, num_iter=1,\n",
    "                                              hard_out=False)\n",
    "        self._siso_decoder = LDPC5GDecoder(self._encoder, return_infobits=False,\n",
    "                                           num_iter=num_bp_iter_per_idd_iter, stateful=True, hard_out=False, cn_type='minsum')\n",
    "        self._decoder = LDPC5GDecoder(self._encoder, return_infobits=True, stateful=True, hard_out=True, num_iter=num_bp_iter_per_idd_iter, cn_type='minsum')\n",
    "        # last decoder must also be statefull\n",
    "\n",
    "    @tf.function  # We don't use jit_compile=True to ensure better numerical stability\n",
    "    def call(self, batch_size, ebno_db):\n",
    "        self.new_topology(batch_size)\n",
    "\n",
    "        if len(ebno_db.shape) == 0:\n",
    "            ebno_db = tf.fill([batch_size], ebno_db)\n",
    "\n",
    "        ######################################\n",
    "        ## Transmitter\n",
    "        no = ebnodb2no(ebno_db=ebno_db, num_bits_per_symbol=num_bits_per_symbol,\n",
    "                       coderate=R)  # normalize in OFDM freq. domain\n",
    "        b = self._binary_source([batch_size, n_ue, 1, K])\n",
    "        c = self._encoder(b)\n",
    "        # Modulation\n",
    "        x = self._mapper(c)\n",
    "        x_rg = self._rg_mapper(x)\n",
    "\n",
    "        ######################################\n",
    "        ## Channel\n",
    "        # A batch of new channel realizations is sampled and applied at every inference\n",
    "        no_ = expand_to_rank(no, tf.rank(x_rg))\n",
    "        y, h = self._channel([x_rg, no_])\n",
    "\n",
    "        ######################################\n",
    "        ## Receiver\n",
    "        if self._perfect_csi_rayleigh:\n",
    "            h_hat = self._removeNulledSc(h)\n",
    "            chan_est_var = tf.zeros(tf.shape(h_hat),\n",
    "                                    dtype=tf.float32)  # No channel estimation error when perfect CSI knowledge is assumed\n",
    "        else:\n",
    "            h_hat, chan_est_var = self._ls_est([y, no])\n",
    "\n",
    "        llr_ch = self._detector((y, h_hat, chan_est_var, no))  # soft-output LMMSE detection\n",
    "        msg_vn = None\n",
    "\n",
    "        if self._num_idd_iter >= 2:\n",
    "            # perform first iteration outside the while_loop to initialize msg_vn\n",
    "            [llr_dec, msg_vn] = self._siso_decoder((llr_ch, msg_vn))\n",
    "            # forward a posteriori information from decoder\n",
    "\n",
    "            llr_ch = self._siso_detector((y, h_hat, llr_dec, chan_est_var, no))\n",
    "            # forward extrinsic information\n",
    "\n",
    "            def idd_iter(llr_ch, msg_vn, it):\n",
    "                [llr_dec, msg_vn] = self._siso_decoder([llr_ch, msg_vn])\n",
    "                # forward a posteriori information from decoder\n",
    "                llr_ch = self._siso_detector((y, h_hat, llr_dec, chan_est_var, no))\n",
    "                # forward extrinsic information from detector\n",
    "\n",
    "                it += 1\n",
    "                return llr_ch, msg_vn, it\n",
    "\n",
    "            def idd_stop(llr_ch, msg_vn, it):\n",
    "                return tf.less(it, self._num_idd_iter - 1)\n",
    "\n",
    "            it = tf.constant(1)     # we already performed initial detection and one full iteration\n",
    "            llr_ch, msg_vn, it = tf.while_loop(idd_stop, idd_iter, (llr_ch, msg_vn, it), parallel_iterations=1,\n",
    "                                               maximum_iterations=self._num_idd_iter - 1)\n",
    "        else:\n",
    "            # non-idd\n",
    "            pass\n",
    "\n",
    "        [b_hat, _] = self._decoder((llr_ch, msg_vn))    # final hard-output decoding (only returning information bits)\n",
    "        return b, b_hat"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Non-IDD versus IDD Benchmarks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/pyct/static_analysis/liveness.py:83: Analyzer.lamba_check (from tensorflow.python.autograph.pyct.static_analysis.liveness) is deprecated and will be removed after 2023-09-23.\n",
      "Instructions for updating:\n",
      "Lambda fuctions will be no more assumed to be used in the statement where they are used, or at least in the same block. https://github.com/tensorflow/tensorflow/issues/56089\n",
      "EbNo [dB] |        BER |       BLER |  bit errors |    num bits | block errors |  num blocks | runtime [s] |    status\n",
      "---------------------------------------------------------------------------------------------------------------------------------------\n",
      "    -10.0 | 2.1174e-01 | 1.0000e+00 |      249776 |     1179648 |         1024 |        1024 |         5.7 |reached target block errors\n",
      "     -9.0 | 1.8563e-01 | 1.0000e+00 |      218982 |     1179648 |         1024 |        1024 |         0.3 |reached target block errors\n",
      "     -8.0 | 1.0665e-01 | 9.5898e-01 |      125808 |     1179648 |          982 |        1024 |         0.3 |reached target block errors\n",
      "     -7.0 | 1.6909e-02 | 3.3828e-01 |       49867 |     2949120 |          866 |        2560 |         0.9 |reached target block errors\n",
      "     -6.0 | 1.1546e-03 | 3.3143e-02 |       33030 |    28606464 |          823 |       24832 |         8.3 |reached target block errors\n",
      "     -5.0 | 6.4903e-05 | 2.3804e-03 |        2450 |    37748736 |           78 |       32768 |        11.0 |reached max iter       \n",
      "     -4.0 | 2.6491e-07 | 1.2207e-04 |          10 |    37748736 |            4 |       32768 |        10.9 |reached max iter       \n",
      "     -3.0 | 0.0000e+00 | 0.0000e+00 |           0 |    37748736 |            0 |       32768 |        10.9 |reached max iter       \n",
      "\n",
      "Simulation stopped as no error occurred @ EbNo = -3.0 dB.\n",
      "\n",
      "EbNo [dB] |        BER |       BLER |  bit errors |    num bits | block errors |  num blocks | runtime [s] |    status\n",
      "---------------------------------------------------------------------------------------------------------------------------------------\n",
      "    -10.0 | 2.1091e-01 | 1.0000e+00 |      248794 |     1179648 |         1024 |        1024 |         3.8 |reached target block errors\n",
      "     -9.0 | 1.8493e-01 | 1.0000e+00 |      218155 |     1179648 |         1024 |        1024 |         0.4 |reached target block errors\n",
      "     -8.0 | 9.8120e-02 | 9.5508e-01 |      115747 |     1179648 |          978 |        1024 |         0.4 |reached target block errors\n",
      "     -7.0 | 1.1955e-02 | 2.8027e-01 |       42309 |     3538944 |          861 |        3072 |         1.2 |reached target block errors\n",
      "     -6.0 | 4.5117e-04 | 1.6541e-02 |       17031 |    37748736 |          542 |       32768 |        12.7 |reached max iter       \n",
      "     -5.0 | 1.5471e-05 | 7.6294e-04 |         584 |    37748736 |           25 |       32768 |        12.5 |reached max iter       \n",
      "     -4.0 | 0.0000e+00 | 0.0000e+00 |           0 |    37748736 |            0 |       32768 |        12.7 |reached max iter       \n",
      "\n",
      "Simulation stopped as no error occurred @ EbNo = -4.0 dB.\n",
      "\n",
      "EbNo [dB] |        BER |       BLER |  bit errors |    num bits | block errors |  num blocks | runtime [s] |    status\n",
      "---------------------------------------------------------------------------------------------------------------------------------------\n",
      "    -10.0 | 2.1164e-01 | 1.0000e+00 |      249660 |     1179648 |         1024 |        1024 |         6.3 |reached target block errors\n",
      "     -9.0 | 1.8471e-01 | 1.0000e+00 |      217898 |     1179648 |         1024 |        1024 |         2.1 |reached target block errors\n",
      "     -8.0 | 1.1558e-01 | 9.9512e-01 |      136338 |     1179648 |         1019 |        1024 |         2.1 |reached target block errors\n",
      "     -7.0 | 1.8488e-02 | 5.6315e-01 |       32714 |     1769472 |          865 |        1536 |         3.2 |reached target block errors\n",
      "     -6.0 | 9.5951e-04 | 4.6762e-02 |       19525 |    20348928 |          826 |       17664 |        36.3 |reached target block errors\n",
      "     -5.0 | 1.9338e-05 | 1.4343e-03 |         730 |    37748736 |           47 |       32768 |        67.2 |reached max iter       \n",
      "     -4.0 | 1.5895e-07 | 6.1035e-05 |           6 |    37748736 |            2 |       32768 |        67.2 |reached max iter       \n",
      "     -3.0 | 0.0000e+00 | 0.0000e+00 |           0 |    37748736 |            0 |       32768 |        67.0 |reached max iter       \n",
      "\n",
      "Simulation stopped as no error occurred @ EbNo = -3.0 dB.\n",
      "\n",
      "EbNo [dB] |        BER |       BLER |  bit errors |    num bits | block errors |  num blocks | runtime [s] |    status\n",
      "---------------------------------------------------------------------------------------------------------------------------------------\n",
      "    -10.0 | 2.1164e-01 | 1.0000e+00 |      249662 |     1179648 |         1024 |        1024 |         8.2 |reached target block errors\n",
      "     -9.0 | 1.8639e-01 | 1.0000e+00 |      219878 |     1179648 |         1024 |        1024 |         0.6 |reached target block errors\n",
      "     -8.0 | 8.6596e-02 | 6.9844e-01 |      127691 |     1474560 |          894 |        1280 |         0.7 |reached target block errors\n",
      "     -7.0 | 3.4296e-03 | 4.6535e-02 |       69788 |    20348928 |          822 |       17664 |        10.3 |reached target block errors\n",
      "     -6.0 | 6.1750e-05 | 8.5449e-04 |        2331 |    37748736 |           28 |       32768 |        18.9 |reached max iter       \n",
      "     -5.0 | 1.0596e-06 | 3.0518e-05 |          40 |    37748736 |            1 |       32768 |        18.9 |reached max iter       \n",
      "     -4.0 | 0.0000e+00 | 0.0000e+00 |           0 |    37748736 |            0 |       32768 |        18.9 |reached max iter       \n",
      "\n",
      "Simulation stopped as no error occurred @ EbNo = -4.0 dB.\n",
      "\n",
      "EbNo [dB] |        BER |       BLER |  bit errors |    num bits | block errors |  num blocks | runtime [s] |    status\n",
      "---------------------------------------------------------------------------------------------------------------------------------------\n",
      "    -10.0 | 2.1188e-01 | 1.0000e+00 |      249945 |     1179648 |         1024 |        1024 |         7.8 |reached target block errors\n",
      "     -9.0 | 1.8657e-01 | 9.9805e-01 |      220084 |     1179648 |         1022 |        1024 |         0.8 |reached target block errors\n",
      "     -8.0 | 7.6331e-02 | 5.7943e-01 |      135065 |     1769472 |          890 |        1536 |         1.2 |reached target block errors\n",
      "     -7.0 | 1.6263e-03 | 1.7822e-02 |       61390 |    37748736 |          584 |       32768 |        25.7 |reached max iter       \n",
      "     -6.0 | 1.7325e-05 | 2.4414e-04 |         654 |    37748736 |            8 |       32768 |        25.5 |reached max iter       \n",
      "     -5.0 | 0.0000e+00 | 0.0000e+00 |           0 |    37748736 |            0 |       32768 |        25.7 |reached max iter       \n",
      "\n",
      "Simulation stopped as no error occurred @ EbNo = -5.0 dB.\n",
      "\n",
      "EbNo [dB] |        BER |       BLER |  bit errors |    num bits | block errors |  num blocks | runtime [s] |    status\n",
      "---------------------------------------------------------------------------------------------------------------------------------------\n",
      "    -10.0 | 2.9984e-01 | 1.0000e+00 |      353711 |     1179648 |         1024 |        1024 |        12.3 |reached target block errors\n",
      "     -8.0 | 2.6258e-01 | 1.0000e+00 |      309751 |     1179648 |         1024 |        1024 |         0.4 |reached target block errors\n",
      "     -6.0 | 2.2838e-01 | 1.0000e+00 |      269403 |     1179648 |         1024 |        1024 |         0.4 |reached target block errors\n",
      "     -4.0 | 1.4912e-01 | 9.0723e-01 |      175910 |     1179648 |          929 |        1024 |         0.4 |reached target block errors\n",
      "     -2.0 | 4.0182e-02 | 3.2930e-01 |      118503 |     2949120 |          843 |        2560 |         1.1 |reached target block errors\n",
      "      0.0 | 9.7169e-03 | 8.6571e-02 |      106028 |    10911744 |          820 |        9472 |         4.1 |reached target block errors\n",
      "      2.0 | 2.8845e-03 | 2.3529e-02 |      108885 |    37748736 |          771 |       32768 |        14.3 |reached max iter       \n",
      "      4.0 | 1.2734e-03 | 9.0942e-03 |       48069 |    37748736 |          298 |       32768 |        14.1 |reached max iter       \n",
      "      6.0 | 7.8371e-04 | 5.0354e-03 |       29584 |    37748736 |          165 |       32768 |        14.3 |reached max iter       \n",
      "      8.0 | 9.9370e-04 | 6.1340e-03 |       37511 |    37748736 |          201 |       32768 |        14.2 |reached max iter       \n",
      "     10.0 | 7.6546e-04 | 4.9744e-03 |       28895 |    37748736 |          163 |       32768 |        14.4 |reached max iter       \n",
      "EbNo [dB] |        BER |       BLER |  bit errors |    num bits | block errors |  num blocks | runtime [s] |    status\n",
      "---------------------------------------------------------------------------------------------------------------------------------------\n",
      "    -10.0 | 3.0051e-01 | 1.0000e+00 |      354491 |     1179648 |         1024 |        1024 |         6.8 |reached target block errors\n",
      "     -8.0 | 2.6452e-01 | 1.0000e+00 |      312037 |     1179648 |         1024 |        1024 |         0.5 |reached target block errors\n",
      "     -6.0 | 2.2600e-01 | 1.0000e+00 |      266602 |     1179648 |         1024 |        1024 |         0.5 |reached target block errors\n",
      "     -4.0 | 1.4565e-01 | 9.0625e-01 |      171821 |     1179648 |          928 |        1024 |         0.5 |reached target block errors\n",
      "     -2.0 | 4.0697e-02 | 3.4258e-01 |      120021 |     2949120 |          877 |        2560 |         1.3 |reached target block errors\n",
      "      0.0 | 7.6236e-03 | 6.9633e-02 |      103421 |    13565952 |          820 |       11776 |         5.8 |reached target block errors\n",
      "      2.0 | 1.9709e-03 | 1.5198e-02 |       74400 |    37748736 |          498 |       32768 |        16.0 |reached max iter       \n",
      "      4.0 | 9.2236e-04 | 6.6528e-03 |       34818 |    37748736 |          218 |       32768 |        16.1 |reached max iter       \n",
      "      6.0 | 7.6371e-04 | 5.6152e-03 |       28829 |    37748736 |          184 |       32768 |        16.1 |reached max iter       \n",
      "      8.0 | 1.0540e-03 | 6.3477e-03 |       39788 |    37748736 |          208 |       32768 |        15.9 |reached max iter       \n",
      "     10.0 | 9.5132e-04 | 6.2866e-03 |       35911 |    37748736 |          206 |       32768 |        16.1 |reached max iter       \n",
      "EbNo [dB] |        BER |       BLER |  bit errors |    num bits | block errors |  num blocks | runtime [s] |    status\n",
      "---------------------------------------------------------------------------------------------------------------------------------------\n",
      "    -10.0 | 3.0523e-01 | 1.0000e+00 |      360064 |     1179648 |         1024 |        1024 |         8.7 |reached target block errors\n",
      "     -8.0 | 2.6928e-01 | 1.0000e+00 |      317651 |     1179648 |         1024 |        1024 |         2.2 |reached target block errors\n",
      "     -6.0 | 2.3226e-01 | 1.0000e+00 |      273984 |     1179648 |         1024 |        1024 |         2.2 |reached target block errors\n",
      "     -4.0 | 1.6056e-01 | 9.7266e-01 |      189406 |     1179648 |          996 |        1024 |         2.2 |reached target block errors\n",
      "     -2.0 | 3.5856e-02 | 3.6936e-01 |       95168 |     2654208 |          851 |        2304 |         5.0 |reached target block errors\n",
      "      0.0 | 6.1384e-03 | 5.8168e-02 |       99566 |    16220160 |          819 |       14080 |        30.4 |reached target block errors\n",
      "      2.0 | 1.2737e-03 | 1.0498e-02 |       48079 |    37748736 |          344 |       32768 |        70.6 |reached max iter       \n",
      "      4.0 | 9.2064e-04 | 5.4932e-03 |       34753 |    37748736 |          180 |       32768 |        70.7 |reached max iter       \n",
      "      6.0 | 9.2936e-04 | 5.0049e-03 |       35082 |    37748736 |          164 |       32768 |        70.9 |reached max iter       \n",
      "      8.0 | 7.4246e-04 | 4.6082e-03 |       28027 |    37748736 |          151 |       32768 |        70.6 |reached max iter       \n",
      "     10.0 | 7.8665e-04 | 5.1270e-03 |       29695 |    37748736 |          168 |       32768 |        70.6 |reached max iter       \n",
      "EbNo [dB] |        BER |       BLER |  bit errors |    num bits | block errors |  num blocks | runtime [s] |    status\n",
      "---------------------------------------------------------------------------------------------------------------------------------------\n",
      "    -10.0 | 3.0156e-01 | 1.0000e+00 |      355731 |     1179648 |         1024 |        1024 |        11.2 |reached target block errors\n",
      "     -8.0 | 2.6622e-01 | 1.0000e+00 |      314046 |     1179648 |         1024 |        1024 |         0.7 |reached target block errors\n",
      "     -6.0 | 2.3190e-01 | 1.0000e+00 |      273565 |     1179648 |         1024 |        1024 |         0.7 |reached target block errors\n",
      "     -4.0 | 1.5164e-01 | 8.2324e-01 |      178884 |     1179648 |          843 |        1024 |         0.7 |reached target block errors\n",
      "     -2.0 | 3.0409e-02 | 2.0581e-01 |      143489 |     4718592 |          843 |        4096 |         2.8 |reached target block errors\n",
      "      0.0 | 4.0669e-03 | 3.0469e-02 |      125935 |    30965760 |          819 |       26880 |        18.0 |reached target block errors\n",
      "      2.0 | 1.4712e-03 | 9.0942e-03 |       55536 |    37748736 |          298 |       32768 |        22.0 |reached max iter       \n",
      "      4.0 | 6.7668e-04 | 3.9368e-03 |       25544 |    37748736 |          129 |       32768 |        21.9 |reached max iter       \n",
      "      6.0 | 7.9192e-04 | 4.2725e-03 |       29894 |    37748736 |          140 |       32768 |        21.7 |reached max iter       \n",
      "      8.0 | 8.3711e-04 | 5.1575e-03 |       31600 |    37748736 |          169 |       32768 |        22.3 |reached max iter       \n",
      "     10.0 | 7.4954e-04 | 4.7913e-03 |       28294 |    37748736 |          157 |       32768 |        22.0 |reached max iter       \n",
      "EbNo [dB] |        BER |       BLER |  bit errors |    num bits | block errors |  num blocks | runtime [s] |    status\n",
      "---------------------------------------------------------------------------------------------------------------------------------------\n",
      "    -10.0 | 3.0150e-01 | 1.0000e+00 |      355662 |     1179648 |         1024 |        1024 |        11.6 |reached target block errors\n",
      "     -8.0 | 2.6787e-01 | 1.0000e+00 |      315993 |     1179648 |         1024 |        1024 |         0.9 |reached target block errors\n",
      "     -6.0 | 2.3101e-01 | 1.0000e+00 |      272511 |     1179648 |         1024 |        1024 |         0.9 |reached target block errors\n",
      "     -4.0 | 1.4615e-01 | 7.9844e-01 |      215510 |     1474560 |         1022 |        1280 |         1.2 |reached target block errors\n",
      "     -2.0 | 2.3105e-02 | 1.4453e-01 |      156722 |     6782976 |          851 |        5888 |         5.3 |reached target block errors\n",
      "      0.0 | 3.5593e-03 | 2.1973e-02 |      134358 |    37748736 |          720 |       32768 |        29.4 |reached max iter       \n",
      "      2.0 | 1.3395e-03 | 7.1411e-03 |       50564 |    37748736 |          234 |       32768 |        29.2 |reached max iter       \n",
      "      4.0 | 7.4816e-04 | 3.9062e-03 |       28242 |    37748736 |          128 |       32768 |        29.4 |reached max iter       \n",
      "      6.0 | 7.6657e-04 | 3.9368e-03 |       28937 |    37748736 |          129 |       32768 |        29.4 |reached max iter       \n",
      "      8.0 | 8.2713e-04 | 4.1504e-03 |       31223 |    37748736 |          136 |       32768 |        29.3 |reached max iter       \n",
      "     10.0 | 7.2932e-04 | 4.2114e-03 |       27531 |    37748736 |          138 |       32768 |        29.4 |reached max iter       \n"
     ]
    }
   ],
   "source": [
    "# Range of SNR (dB)\n",
    "snr_range_cest = np.linspace(ebno_db_min_cest, ebno_db_max_cest, num_steps)\n",
    "snr_range_perf_csi = np.linspace(ebno_db_min_perf_csi, ebno_db_max_perf_csi, num_steps)\n",
    "\n",
    "def run_idd_sim(snr_range, perfect_csi_rayleigh):\n",
    "    lmmse = NonIddModel(detector=\"lmmse\", perfect_csi_rayleigh=perfect_csi_rayleigh)\n",
    "    k_best = NonIddModel(detector=\"k-best\", perfect_csi_rayleigh=perfect_csi_rayleigh)\n",
    "    ep = NonIddModel(detector=\"ep\", perfect_csi_rayleigh=perfect_csi_rayleigh)\n",
    "    idd2 = IddModel(num_idd_iter=2, perfect_csi_rayleigh=perfect_csi_rayleigh)\n",
    "    idd3 = IddModel(num_idd_iter=3, perfect_csi_rayleigh=perfect_csi_rayleigh)\n",
    "\n",
    "    ber_lmmse, bler_lmmse = sim_ber(lmmse,\n",
    "                                    snr_range,\n",
    "                                    batch_size=batch_size,\n",
    "                                    max_mc_iter=num_iter,\n",
    "                                    num_target_block_errors=int(batch_size * num_iter * 0.1))\n",
    "\n",
    "    ber_ep, bler_ep = sim_ber(ep,\n",
    "                              snr_range,\n",
    "                              batch_size=batch_size,\n",
    "                              max_mc_iter=num_iter,\n",
    "                              num_target_block_errors=int(batch_size * num_iter * 0.1))\n",
    "\n",
    "    ber_kbest, bler_kbest = sim_ber(k_best,\n",
    "                                    snr_range,\n",
    "                                    batch_size=batch_size,\n",
    "                                    max_mc_iter=num_iter,\n",
    "                                    num_target_block_errors=int(batch_size * num_iter * 0.1))\n",
    "\n",
    "    ber_idd2, bler_idd2 = sim_ber(idd2,\n",
    "                                  snr_range,\n",
    "                                  batch_size=batch_size,\n",
    "                                  max_mc_iter=num_iter,\n",
    "                                  num_target_block_errors=int(batch_size * num_iter * 0.1))\n",
    "\n",
    "    ber_idd3, bler_idd3 = sim_ber(idd3,\n",
    "                                  snr_range,\n",
    "                                  batch_size=batch_size,\n",
    "                                  max_mc_iter=num_iter,\n",
    "                                  num_target_block_errors=int(batch_size * num_iter * 0.1))\n",
    "\n",
    "    return bler_lmmse, bler_ep, bler_kbest, bler_idd2, bler_idd3\n",
    "\n",
    "\n",
    "BLER = {}\n",
    "\n",
    "# Perfect CSI\n",
    "bler_lmmse, bler_ep, bler_kbest, bler_idd2, bler_idd3 = run_idd_sim(snr_range_perf_csi, perfect_csi_rayleigh=True)\n",
    "BLER['Perf. CSI / LMMSE'] = bler_lmmse\n",
    "BLER['Perf. CSI / EP'] = bler_ep\n",
    "BLER['Perf. CSI / K-Best'] = bler_kbest\n",
    "BLER['Perf. CSI / IDD2'] = bler_idd2\n",
    "BLER['Perf. CSI / IDD3'] = bler_idd3\n",
    "\n",
    "# Estimated CSI\n",
    "bler_lmmse, bler_ep, bler_kbest, bler_idd2, bler_idd3 = run_idd_sim(snr_range_cest, perfect_csi_rayleigh=False)\n",
    "BLER['Ch. Est. / LMMSE'] = bler_lmmse\n",
    "BLER['Ch. Est. / EP'] = bler_ep\n",
    "BLER['Ch. Est. / K-Best'] = bler_kbest\n",
    "BLER['Ch. Est. / IDD2'] = bler_idd2\n",
    "BLER['Ch. Est. / IDD3'] = bler_idd3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we plot the simulation results and observe that IDD outperforms the non-iterative methods by about 1 dB in the scenario with iid Rayleigh fading channels and perfect CSI. In the scenario with 3GPP UMa channels and estimated CSI, IDD performs slightly better than K-best, at considerably lower runtime."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,2, figsize=(16,7))\n",
    "fig.suptitle(f\"{n_ue}x{NUM_RX_ANT} MU-MIMO UL | {2**num_bits_per_symbol}-QAM\")\n",
    "\n",
    "## Perfect CSI Rayleigh\n",
    "ax[0].set_title(\"Perfect CSI iid. Rayleigh\")\n",
    "ax[0].semilogy(snr_range_perf_csi, BLER['Perf. CSI / LMMSE'], 'x-', label='LMMSE', c='C0')\n",
    "ax[0].semilogy(snr_range_perf_csi, BLER['Perf. CSI / EP'], 'o--', label='EP', c='C0')\n",
    "ax[0].semilogy(snr_range_perf_csi, BLER['Perf. CSI / K-Best'], 's-.', label='K-Best', c='C0')\n",
    "ax[0].semilogy(snr_range_perf_csi, BLER['Perf. CSI / IDD2'], 'd:', label=r'IDD $I=2$', c='C1')\n",
    "ax[0].semilogy(snr_range_perf_csi, BLER['Perf. CSI / IDD3'], 'd:', label=r'IDD $I=3$', c='C2')\n",
    "\n",
    "ax[0].set_xlabel(r\"$E_b/N0$\")\n",
    "ax[0].set_ylabel(\"BLER\")\n",
    "ax[0].set_ylim((1e-4, 1.0))\n",
    "ax[0].legend()\n",
    "ax[0].grid(True)\n",
    "\n",
    "## Estimated CSI Rayleigh\n",
    "ax[1].set_title(\"Estimated CSI 3GPP UMa\")\n",
    "ax[1].semilogy(snr_range_cest, BLER['Ch. Est. / LMMSE'], 'x-', label='LMMSE', c='C0')\n",
    "ax[1].semilogy(snr_range_cest, BLER['Ch. Est. / EP'], 'o--', label='EP', c='C0')\n",
    "ax[1].semilogy(snr_range_cest, BLER['Ch. Est. / K-Best'], 's-.', label='K-Best', c='C0')\n",
    "ax[1].semilogy(snr_range_cest, BLER['Ch. Est. / IDD2'], 'd:', label=r'IDD $I=2$', c='C1')\n",
    "ax[1].semilogy(snr_range_cest, BLER['Ch. Est. / IDD3'], 'd:', label=r'IDD $I=3$', c='C2')\n",
    "\n",
    "ax[1].set_xlabel(r\"$E_b/N0$\")\n",
    "ax[1].set_ylabel(\"BLER\")\n",
    "ax[1].set_ylim((1e-3, 1.0))\n",
    "ax[1].legend()\n",
    "ax[1].grid(True)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Discussion-Optimizing IDD with Machine Learning\n",
    "Recent work [4] showed that IDD can be significantly improved by deep-unfolding, which applies machine learning to automatically tune hyperparameters of classical algorithms. The proposed *Deep-Unfolded Interleaved Detection and Decoding* method showed performance gains of up to 1.4 dB at the same computational complexity. A link to the simulation code is available in the [\"Made with Sionna\"](https://nvlabs.github.io/sionna/made_with_sionna.html#duidd-deep-unfolded-interleaved-detection-and-decoding-for-mimo-wireless-systems) section. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comments\n",
    "\n",
    "- As discussed in [3], IDD receivers with a non-resetting decoder converge faster than with resetting decoders. However, a resetting decoder (which does not forward `msg_vn`) might perform slightly better for a large number of message passing decoding iterations. Among other quantities, a scaling of the forwarded decoder state is optimized in the DUIDD receiver [4].\n",
    "- With estimated channels, we observed that the MMSE-PIC output LLRs become large, much larger as with non-iterative receive processing."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## List of References\n",
    "\n",
    "[1] B. Hochwald and S. Ten Brink, [*\"Achieving near-capacity on a multiple-antenna channel,\"*](https://ieeexplore.ieee.org/abstract/document/1194444) IEEE Trans. Commun., vol. 51, no. 3, pp. 389–399, Mar. 2003.\n",
    "\n",
    "[2] C. Studer, S. Fateh, and D. Seethaler, [*\"ASIC implementation of soft-input soft-output MIMO detection\n",
    "using MMSE parallel interference cancellation,\"*](https://ieeexplore.ieee.org/abstract/document/5779722) IEEE Journal of Solid-State Circuits, vol. 46, no. 7, pp. 1754–1765, Jul. 2011.\n",
    "\n",
    "[3] W.-C. Sun, W.-H. Wu, C.-H. Yang, and Y.-L. Ueng, [*\"An iterative detection and decoding receiver for LDPC-coded MIMO systems,\"*](https://ieeexplore.ieee.org/abstract/document/7272776) IEEE Trans. Circuits Syst. I, vol. 62, no. 10, pp. 2512–2522, Oct. 2015.\n",
    "\n",
    "[4] R. Wiesmayr, C. Dick, J. Hoydis, and C. Studer, [*\"DUIDD: Deep-unfolded interleaved detection and decoding for MIMO wireless systems,\"*](https://arxiv.org/abs/2212.07816) in Asilomar Conf. Signals, Syst., Comput., Oct. 2022."
   ]
  }
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