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T1fTUDSBr9VXm3lRRa+atXTnFsX8ZL6qdNG0V2D70sgpsdUY0V53JhWBbBBCg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\nziuZYfqQcuK5A7UNAVxglYWqnavSyZT5545Xku7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AAQQQQAABBBBAYA4KkKiG60WPpETV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\nPWcdk/zf1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h5U/Mw9EsTMgqGgctasFyNdTKCh+rjSNWB0Asm6wXpLRyhmSu/DjahckU0TVThw75kBEG\npZOy4jZUo5vyk+EjMKmaOOeJOMrYYu2+JN7WHf/ST2P0tsxgM1UeQ99UtH0a5QZ32Rqp/Ln3ppNT\nnUMVqUbYA1G0Y05xYMLbnB3NWNIOp5z5ivahif3c5z7Xyqi2NP30674iPnF8YUqFFrQb3BS5VWFL\nqqohPDscsMhmxsp0iWrA4SwIjUeQOmdTHQdJwX+9tyY61THV8hLO0NbL+CiDiQDBAdTR1pkS6ZGm\njLPzNxlVnE0dQ30iQcuCZWElWPI5EoYrVxkhmSYR6BMBRjyGsbMcYWeZo+P3zgmk49YWIpAQV2AM\n7XMWnYop09XkrmBLHQ5UHZpDFRUFkbK0sKUQnBNXFwuU6Rx1xpbaGZ7WVaxzwZbIqyyG7Y/V0uKA\n8bT/ifwGsWOtGCJk1bM7FDVXq2BLloXObMnCa0EOttRut2qBKtiS4FIdAEQrgy0xnbQi9Tly8vPx\nQWCCIwgQdZoJOIr5w5TSvutfhANXIFBh0BcTtetDimstYyvgX+r5YlVq+dAuSy7Nw0J60qyQOZvD\nIQXxm2zJsYZQhx5dmvnKNWPFZCNBsevHFQGy8hXrTscgghC6JBq0rtXukIBwmwrAnCepYqBDTt5P\nbs1+ixRqcsiWoMcIA7nUcYidZRcCbLAcxFEZL2lUvdTLIWOPmlgKMeMw2GwJauKYa50KKhZ2ozJp\n0ajGSupzmWRMlGY7N3ObDwHqsz322MN4Y84Syql4UHnjkOE2DY6lLIau8dkPksXn9fzti5hGRVTM\nqIz/Rs7mTjEZvYyVsKk6U/9ZDNsfUh9KRmfa9j+xT2JKVdTK+l+EDukVxuIU1+uHc6ZvNrdGqpSZ\n9IPApLIlU5ouhngJGwgBrMMZrX9EskZWqmuLGQ0IHGBlsdc6UjjlxJ3eLbBa1wiBcSZFKyv+agKT\nc5irfls16MWZLpFqzHdBUtQNA4hAA86aGAM5k69e//rXYwwooMowNeinU4tvKQSJykloGsmtkUyI\n3wSpii4DhZAHYZOEIKJKfvgvChWnWGK/eInxFEu/BZoZQVSmfY1GtiIunwV9zkWz+EQmLftBIw3M\nTBKBdgQsEfw0SZXC3q5IYBBaakgg4k2z0RcxMBGMyG75z1Z/6LaiMhTiVo/4TQ9FrUZSy84BcanH\nw6oMDOaYFsP2B4ty/lH0nH8l9cFEY63IJxEYEAKTypbsc+SrIhAiK+YwfRZpDRs9BzVIVdwFkR6S\nZBbHPCBYwDArFrZfVg4rKEuLgEeewYFCMFjuj9iVvSxYVPwVWQlf3PJDmEzWHcIVgpAPfehDRPF+\nKy4oGgpVsf6dx0QRzJfQa0Cjp162JN6xrmkviCKTYp9Q7Q53mNQrMb9KBEaOgGlovrMccAawTGHz\nTIbNej5oDQZpKzdTiWy9nfEoqqo/ZN5838LbX2xJFjyWRFEPPMTV1kannepn0V5hn08n4ABpubB0\nzJehBaRZrtlrzTP91CMwqWwpOoZHKJEMKTetOYGQcPWsiGIbrtJzTlHshLjZ038xXaLLi/lGwMPo\nB2EqZxKh9+fMtiBPLbcNsOhkglCE4Y4frDWtYkG8LDqFFCrspbxs/Nw2botI0dI5ewoIFbuvShdn\nmkRgTBBgLeeKMcckXm+uyGAzJzgI80q6OZZ8Ktn4XSUR1ZbVthhC1R/mSpzvSNy53BLVx+rKsgpV\nErCbu9lA8ZwPhHjfOEQDbUtmPmUITCpbMm1MYFcBcCtl3Ef5xZ2KsUtY+HZVojvYYS1MiXlXsTqy\nLjj24S7slmjT5CAESP9XBzhBWg1Fhys/3ljFQnrU+bQ0ZUMtm5MIzDICtMnWGYsMeQ+HEj+YA7Ow\n5KzQQXHfD2Jk3jR9PEOVW/0h6qbYYpPgQ4dJhs9WVx71RO9MDNtDAPRTw/w2EZggBCaVLeFDYopw\nwheJhEiJVp5izgIUmp2ubCnC5JMwm/yFtptQB9/CuqL/5rv5pHrvYmAy5Edafij7FFSYSVbPLVMG\nAohyofHsIDYzBuZUaBafDPnm0ey+RIA6SZQvywJ9kyBtTPSY4xS6J5Y3oYPuUxhcfF7PjodNAnEX\n4TpZO7+WMHhyLiUXb+/Bxutc20C7/9FlxSg0ff10QfFtBD3pv2KZw5ggMKlsqYCvrK6mcY/lBh3h\nj8Cm0mUmc6p1fBWqdz9aBvR8flKS9br6kFoxjWQXVX7YNbMHb8qOu8owGjfFlq4JHlM4Afld/aXO\nZfsFw6IT5wQhHFLYzxamUZFMv/vQ58SQgzO/qNIvmWbWECBeYgmEdpAqoSPMg9CRwqyQSVPcExDT\noQWcQglVaOqtdeGg3pLS8Da/mD/WE1kx7hZ90cpp/Sw/lizFxVPMqXp1bu/3rnXWfMtvB00cxIKm\naHsZvTkXljkHng+dvXXHnEYXxfpfuAEidoVvSjlDHSQHyPDgacQCddamydi2d4LZUiwThQiB4ozt\nkRtLvCQW4g8i3iMBMvuADrvpnKelOdObqNaO2OMrnjxsyeEBV35UzBQqpn2RVTnPivl3HlVFJuPG\nlqxHBVsqbr31o+JLWw4vYodyGM4JVAwMyaxl1v2jjz66DJRPfMj8wnF5vrvEx3a6ZsUmGoHwFImQ\nFjiHZcp/izFM5sRthdE3aVP7wC5OcQXFpyajHWs/3VmmeJ6K4sbZtgZcFIWx0ImWVLa5FELTNdXx\nCLbCuFMasVTUWbzyOY9/7XVmsNWhzqL7zmcXJSu16rCUFWHEW9iSShb8skgzJyz+yslaMxGsdlpm\nVQlYgon6gVYKVsyPpyU3KZXIHBZK/SsoavRgfjIgBCb45pOg9iI9Wlyc1UTHdh7i4R9HLiMbT+Js\nwuW1cDgvQKQI4yTCaEACt2eUwyPxX0OzpOTxK3B+8UnQMqZI4fdeXs6ETeI5YgoxORermhUnIXbM\nqDkf6x2jKxJvhpOCMwVjkHn49PGu93K+WB0uJwoXX/YEDAs6DAuWB+LdUSwKUtIh3v+ABtZ82bqP\nRYwGCw1FmO7Td+qmO0R/8JLs2ksrrwCV/hQv/ddL6UmDbAPQA698BN60hnrjRFgUZxFHjxxDfWUA\nOATbhET9jgRCzNkDOEJyRWaAn7KlIfd+FgcBS4fVSRwBMd48BSZ21ggny8wxBnl5DTHUDWmrk1Am\n4gI4FgpCRpcnfGshwDDsObKJ1mvuOCcw8a4BOKJgPirCVEIF5nwstpbfiBWpzoxHNceSRTbWUmc1\nMemizu6MUmefWJOLOisr6gwT99RGbJH2R/OdbVhfme/lv1oxLJti0tJsyt+fLP7qJuw4VirgnHC+\nHIBsDRYQANopog7FOa3ITZVkzmNRVpDUOtH7ir/G2cz6bNfQHIH3GHI4BsO/vH24zVfEPmuv5cUS\nnbKlGiNwbD+ZVLZk4DrQGNzYEqmS9cXccHBhh2SPtH06IWEJxN3tVElnGPphYmmycUgR3Scmj29R\nH7upLZzJUQRSiscqgDyxCjfZXIpkhhQnFezK8iFP50LzxEQSllrEuTl73cYvAasp09L6QhpPYGt6\nC20Ss1q2EaqxfC6x6wtWa6661CViSlkOzGdOyJK1nGCcwKxNeIaYs/x+yyvyyAeiVnAF0n1arZus\nyHyF7A1e4qNews1La5Cqemm58RLayKUej3C6HnBZIvUUdSfYi3ZZyAwJ4qswbyJAIoUqqK3O3Wuv\nvYApOHjcV5NPIjBkBCw1Jr6ZbuQXPD7qgBNYTOz61hm21eUb05AnJMPUiOXOacFIZqxZdqHFBmj6\nOPMqgoxkzgvXujYWk+A3Y4EyayyD5mP7QySGrlmjhEeSoYXLf62BhZ9NlKLOJrIKR52tnBZkNKLM\nIazARMUmpjq7KWG+Sy1lZcK2UCVFmMviv+BbVrxYUqyfSJKzrsQWDScujC3+5CytGpgQqRuzDSdP\ni7D35aOvcy/qxhfHIgzwAq5Y/y3UvsL/5OO/LLpabotD3fBUK49lR/92RTsTTBICET1oQp84oiEo\nfPWdurQCPzCOMSHDvf2O7qKZ+IQJbFhba6wIcZWYWUQSa7qameZ/+3XZ0hC92omxqHIQfeG8dbkl\nQFaWMKUXl5q1A3v66afzIg5jBQsE4mWhsff7EYzNnySQrPytWyHNf9SqWAFNcv/lwMLUvaUUK4h4\n2dY4CYoLQ8aki7EZO4HQD0U3wZk40EGt/FI/elmkdGLToeR2RSvcgRXTjFd2uWlx/pa5tVuGLXdX\nFVo5WowxASSrMWsICLGGCRFgtC9QNmNkwqh2xpvzmg6Ew6Sw3JkO7YuMTZpDic8xHoO/NrCKoGKj\nSGrx543/WnKRGydMUWSjCK7ECnVe7VxnC7Xp2b5Yubg36hz3Zvb0EE05HQUgPlc9DbeYWD3kYyW0\nDniK1UZK9QcdIw1HL2yPf2K5xGKJcKdCe02sS/KXiQf3ak/AQD7WpTj+5TNNCLTaOE8S0etYV+oe\nRIE0wuojWIBliFVTyxcIkzuV7Lvux8BFSCOcKgi3CYRdNjSnmobaDrWSj/NfcYk94XnMc0JgLm9l\n/V17HU1OqjrSIOfLUMZHzAL8DE9SaIjcSXHxp+Jzq6QgKFZDaULWbcI7MzlIsS3AisoFYSQOeeQu\nqJVTFxCmpluLhjgCCgwjTrGOEy7Pel3lngF9h0e6WUU/dtZjTh9i2aIxQQAL4c9LexUB68uPUxzh\nsQ3baiDuriuSgv1UfwhgSKBJmOia5xNvd87tzDPPdJkdnZolqGVhKT50CCGSJ9M1B720yKgzcZQ6\nOzpG0LuKj2XTQkoaRAxcu84Vy4pkpFCcb6yxlkf/Oq1R8DEzir/iUieeeKLIUmRL4oaLNdXBpqK9\nXLf42T6YZPh2KhfenqCetsTTRP2KthBCmLQmA809MmTRYZloSreLi/AP0iAnDLozj3lOrtsBE38N\n9RyDRGea8UTPWmbRJC3XcKL78axk/7WywgqdR6SHoVbJjQKU7BC1CjFkPonA8BHA8qmBHMlIO+Yr\nndzC/m2RERuleg2p+HEXZydm1NW/Kqd0pFQ0FRJNXIeLY8lUCHTRCyfS4nPmAaHxF+y7eulRZ1kx\nFa/+VZ8pie0J5tmeIoX0dCT6hZAsciaXesMb3sCog/ApZFQVHwxYVs5jlqaKn2SyCULg/93jMUE1\nrlJV4hkKaddYUrQ5wDnJmdi0yEZz++ektVgUAyCPNaJ8Q3h7YmcRhgWs/7i2keVWqcyQ02g7Gves\nZz3LqYisq3wd95BrMoTidJzjLEm+aKIdOs6fCOfI8Agay+q8IdQwi5hxBBxdyHotRx72LiYmWQV+\nb0DOhwwJE1JlvbL1+qoKgBYx0bfJWTkxtGu7quQgDc0gqoTxUIrRXs35lbbQJBKfsxosqwLVmS5M\nnZ3Qeq0zs+jada7YtHIyqgALeFgyUDgyvWjnl/AkHGK3BAfKvq6l6GVCKdE7SZV6Ilhdc84E44PA\npFp5dxbxkTdQdbEHIlkhOiJSNpSdJDbbbDMeUi3fUmY5RljCPH509pOi7kG/aO7OOussPhpMKcdN\n2MjCicadPRNlk3pOt1OG/rLkOaFSoeoXlu9zdoe9h40aS0/OhgTs49ZlWZ8pRoA5tpu/URleFx5b\nNapkNFqI5tMdMzpmoRwBtWn8caCuVz2SkaAv2267LWVW7bgYtnn5OIE4ZfHGIABr1xUy+CNGsrYQ\nw5dVVOpMeafO1h8LLzrV7nTW0suuUrAm06EjGRFVYTgPM+2vf/3rjljES/5lKR+S+HLp4WrjqClU\nHmrFRbdz3XgfW3V9QsvfDtpw2pWlDBqB6WRLgRo9uiOCa5JMRYsOgyFLyXweqtWBtsZRSFspyKKo\nwJmE14sCV73E6ik1GW9gImohKNzHqn8+iSkdDW0POoIhRdnjt9wWh1282bocrnb5JAJDQ4A8iWuI\nHZoZJRctajgyaZY6Xc3sHHVILBB9omIz2jozZ52tQqxwuIkQhCioJyOb9gxRHDTOPMInyGsJwNC7\n4nFENI9UntxlTjKkztR5XevM9Zg8iWGlOtMAkNMPrTsUxGXEOrnLLrswz+BPQ3oUcUxa6sDqiy2H\nYzb+p1FSzhcGj0WpxyrE/CNtlYbZlUMua5rZkrnN4JG9ttnoPGRWUJDPJ37oFXcyZ8ppRbB2IrTo\n9fMBpXeEJSo/+OCDux6GBlSBkWTrIC6o3XxUSZWsehLMt9+MpM5Z6Iwg4MBGvERWQfWz2mqrMaMM\nm6Qqjx3a0LW1W7XKPh/lbyOkkFVowYIFfUa1RYC4PuBzTDNFV8HDSJjKD/7E8o+xYIfKO6RZD9WZ\niGW+OrN8UmesC3GsgkOzaQjPsEC0kv4BsWOKirkCsL0U0QG8Z/bOQoO5/XxyemxVL1PnpVSp2Z4a\nt9ym1ieOspMKmYCUmRF5krBDXDo9Bn2fa0rRhaaQQxJ59ZDPRh3GkHWZAUG7tnHchl3WJxGYEQTY\nzBF8xr2QdmVrUU/KcVINQin8Y74jGYsfs75Dgno4yzOsHsufWzkZgHcVX0VQIkLfznWWoBxXtl49\na3zFs4c3tB3BEZragUAOJepwikYc7SY6br6NQwL+1Lnq1uiLyfpkOtkSzQvfcu7ijkeMllgJiCDC\nGYrgl8uGqJWT1UlZ20QgEUgEEoEGEeAZxyyM+owt4/gcdxtsYGbVOALTqYlzehD8jfTbScjxKOK6\nElk7HBCGl0M/Nw5oZjhQBJhNIL5E6HOGaB9o0Zl5IpAITA0CREGMuxkydr48bmramw3pH4HplC2R\ni4YbJ6E3tmRnFUoAhfLbRsuRqn/gMoeRIMAwi9m+K26E3xxJBbLQRCARSAQSgRlE4F5T2WYkiYjV\njbnU0iz1nB6wpfidVGmiexzlJSOc88LziW5XVj4RSAQSgURgnBGYTrY0HMRdisQPVrTD4RSXpUAA\n8RWvoav3dWKVCCQCiUAikAg0iECypfpgulrygAMO4F5RP4v8MhFIBBKB/4sAF3d++5zSE5hEIBEY\nHwSSLdXvC776ccV9/Szyy0QgEUgE/i8CIkOK2SiIYgKTCCQC44NAsqX6fUErxGa8p+gp9QvLLxOB\nRGA2EGCZd9lll91yyy2z0dxsZSIwGQgkW5qMfspaJgKJwIwg4AzGQyU922eku7OZk4JAsqVJ6ams\nZyKQCCQCiUAikAiMBoFkS6PBPUtNBBKBRCARSAQSgUlBINnSpPRU1jMRSAQSgUQgEUgERoNAsqXR\n4J6lJgKJQCIwJwL3vve9b7/99pYbbROrRCARGC0CyZZGi3+WnggkAonA/4+AW5vEJXFZU4ZgzWGR\nCIwVAsmWxqo7sjKJQCIwuwjcddddhx122FVXXbXffvu5/3t2gciWJwLjh0CypfHrk6xRIpAIzCQC\npEqnnnrq9ddfv8MOOyy//PIziUE2OhEYUwSSLY1px2S1EoFEYNYQWGihhRZbbDFXgBMyzVrbs72J\nwJgjkGxpzDsoq5cIJAIzhAATb9cDpIn3DHV5NnVCEEi2NCEdldVMBBKBRCARSAQSgREhkGxpRMBn\nsYlAIpAIJAKJQCIwIQgkW5qQjspqJgKJQCKQCCQCicCIEEi2NCLgs9hEIBFIBBKBRCARmBAEki1N\nSEdlNROBRCARSAQSgURgRAgkWxoR8FlsIpAIJAKJQCKQCEwIAsmWJqSjspqJQCKQCCQCiUAiMCIE\nki2NCPgsNhFIBBKBRCARSAQmBIFkSxPSUVnNRCARSAQSgUQgERgRAsmWRgR8FpsIJAKJQCKQCCQC\nE4JAsqUJ6aisZiKQCCQCiUAikAiMCIFkSyMCPotNBBKBROD/IrDEEkv84x//uO222+6+++7EJhFI\nBMYKgWRLY9UdWZlEIBGYUQTuvPPOn/3sZ0suueRKK63kYt0ZRSGbnQiMKwI5J8e1Z7JeiUAiMEsI\nXHHFFfvss88aa6yxYMGCRRdddJaanm1NBCYAgWRLE9BJWcVEIBGYegR+97vfnXPOOcsss8xqq622\n8MILT317s4GJwGQhkGxpsvora5sIJALTicAiiyyy1FJL/f3vf5/O5mWrEoEJRyDZ0oR3YFY/EUgE\npgWBhRZaaFqaku1IBKYNgWRL09aj2Z5EIBFIBBKBRCARaBaBZEvN4pm5JQKJQCKQCCQCicC0IZBs\nadp6NNuTCCQCiUAikAgkAs0ikGypWTwzt0QgEUgEEoFEIBGYNgSSLU1bj2Z7EoFEIBFIBBKBRKBZ\nBJItNYtn5pYIJAKJQCKQCCQC04ZAsqVp69FsTyKQCCQCiUAikAg0i0CypWbxzNwSgUQgEUgEEoFE\nYNoQSLY0bT2a7UkEEoFEIBFIBBKBZhFIttQsnplbIpAIJAKJQCKQCEwbAsmWpq1Hsz2JQCIwiQi4\n9uTue55JrHzWORGYegSSLU19F2cDE4FEYAIQWHzxxW+77bY777xzAuqaVUwEZg+B/z3NzF6rm2nx\nfvvtd+qppx533HHrrLNOMzlmLt0QOProow888MCDDjpoq6226pY2/54ITAwC11133VlnnXX22Wdv\nt912szy2f/rTn15//fV33HHHv//974npvFFUFLdedtllV1tttUUWWWQU5c9imcmW6vd6sqX62NX9\nMtlSXeTyu7FG4G1ve9u3v/3tQw45ZI011rARjnVdB1m5fffd99hjj11sscXoJQdZzsTnfddddz35\nyU8+8sgjl1lmmYlvzIQ0INlS/Y5KtlQfu7pfJluqi1x+N9YI7Ljjjueff/4555zzsIc9bKwrOuDK\nbbPNNueee+7ee+/9wAc+cMBFTXb2hx9+OMJ0+umnr7DCCpPdksmpfbKl+n2VbKk+dnW/TLZUF7n8\nbqwR2H333bGlz372s6ussspYV3TAldt2221vvvnmM844Y9FFFx1wUZOd/a677nrRRRedfPLJj370\noye7JZNT+7Tynpy+ypomAolAIjDVCNzrXvf65z//eeutt051KxtoHMESZWXqKxuAsnIWyZYqQ5UJ\nE4FEIBFIBAaMAMejf/3rXwMuZOKzT/es4XdhsqXhY54lJgKJQCKQCCQCicAkIZBsaZJ6K+uaCCQC\niUAikAgkAsNHINnS8DHPEhOBRCARSAQSgURgkhBItjRJvZV1TQQSgUQgEUgEEoHhI5BsafiYZ4mJ\nQCKQCCQCiUAiMEkIJFuapN7KuiYCiUAikAgkAonA8BFItjR8zLPERCARSAQSgUQgEZgkBJItTVJv\nZV0TgUQgEUgEEoFEYPgIJFsaPuZZYiKQCCQCrQjcdtttGZUxh0UiMLYIJFsa267JiiUCicD0I/Dv\nf//7L3/5i3Yuv/zyiyyyyPQ3OFuYCEwmAsmWJrPfstaJQCIwFQi4RHb//fd3Odq73/3uhz70oVPR\npmxEIjCFCCRbmsJOzSYlAonApCBAAXfaaaeRMG200Ub3u9/9JqXaWc9EYNYQSLY0az2e7U0EEoEx\nQmDhhRe+//3vP0YVyqokAonAXAgkW8pxkQgkAonAKBG4171yHR4l/ll2IlAFgZylVVDKNIlAIpAI\nJAKJQCIwuwgkW5rdvs+WJwKJQCKQCCQCiUAVBJItVUEp0yQCiUAikAgkAonA7CKQbGl2+z5bnggk\nAolAIpAIJAJVEEi2VAWlTJMIJAKJQCKQCCQCs4tAsqXZ7ftseSKQCCQCiUBXBP76179ecc/z+9//\nvmviTDCtCCRbmtaezXYlAolAIpAI1Efg7rvvFjXU5X1f/epXt7nn8aN+dvnlhCOQbGnCOzCrnwgk\nAolAIjAABH70ox/ttNNO2267rUtprr7n+fWvfz2AcjLLyUBgIfR5TGqKxbsB4I9//GPn+qy77rqP\nf/zjx6HO++2336mnnnrcccets846fdbHJPz+97/vcs1//OMffWY12s/ve9/7PvjBD9ZHD3jAAwZR\nk6OPPvrAAw886KCDttpqq0HkXz3PG2+88dprr9X1Sy21VPWvMiUEfnzPQ7thyk86IIsuuqgBYMA/\n5CEPqdeWn/70p4QWBtKRRx5ZL4dp+mr77bc3sz7/+c8vt9xyI2/XZZddhif9+c9/vummm0x29Tnk\nkEP23nvvkVdMBXbccUeawZNPPvlRj3rUONSnkTrccMMNZ599dpWszJc111yzSsoG04yYLRFy/u53\nv/OvJv3iF794+9vfDq/Ozdtuu+3w/UjjWqUR7lUNsqXPfe5zu+++u5V30i8hv+OOO5ZZZplPfvKT\na621VoPDtMhqfNjSKaec8q53vWvXXXfdbLPN9JpWD6K9U5nnW9/61o9//ONLLrmkSz8mvYF33nmn\n3j/qqKM22WSTem1JtlTGbazYkor96U9/wukdid/4xjcmW6o3wjt/5dSEj0aaL37xix/+8IerlPKy\nl71s5513HjINGDFb+sEPfrDPPvvceuutmr300ks///nP929nsMhgzj///Eiz6aabvvOd76wC7iDS\nNMiWDJF9991X9/cvphpES6vn6azz7W9/+6STTnrGM55R/avqKceHLX3hC1+wsrs03iH4sY997Ac+\n8IHa0oXqzZ+OlDvssMO3vvUt04cYctJbpCFG+xFHHEFfU68tP/vZz7beeuu1114b5aqXwzR9NW5s\nKbA9/vjj//u//zvZ0iBGGpEqMho5P/GJT3zmM59ZpZRLL730vPPOi5TPfvaziQCrfNVnmhGwpauu\nuurTn/50aAD//ve/u4I7fj/ykY/EnKhyOjcJRp/61KciDWFMcSHlYx7zmELm1CcoFT9vkC196EMf\nsuAaNOuvv37F0scz2eGHH475aUvFQd9rK8aHLX3pS196/etfv8oqqzz60Y++z33us8QSS/iXRI1W\npddGzVr6l7/85UTI3/jGN7pO9vFH5itf+QrVjDMbmXe92nKz2njjjZ/0pCedeOKJ9XKYpq+qsCXm\nCieccAJdxN/+9jdX7NkCHJurnzN9+JnPfKaQZ3RAzykIszdKEdnXve51UqYmrqnBxgjs2GOPtfWT\nLenHyHbzzTd/yUteUqWICy644JhjjilogD6y/JK2bLTRRlU+r5dm2FbeP/nJT4gfvva1r51+z3PX\nXXfZXKHmseJUWT2tLJHew3jlnHPOiazsXsyeCE7rATHar8x58l52S6OtRv+l08TJZKGFFuo/qzHP\n4fbbb8eQECbj8C1veQuzBoOQHRs2709jXvnRVs9o/+c//3nLLbeMthqNlG6tt+LXHvB/+MMfGMes\nuOKKq622WiP1mfpMWG4Q65LEU+aacV/+8pcpxA877LBrrrnG2btK8+0RJIKxa3R+Lr74YgO1Sp6Z\npicEggZwMIQ/AUGxoVekSsracMMNi69o5YIGsI6greqpJj0lHipbcooiMaOnJ1tyOPAQJjFf6KnG\n5cQgQzAjK5B98IMfxJkm13R0cmtedMr4OA3UHlQVP4w90oFGeuIlY88gNPORflPX8Tes8fKZE4Hw\nzZ4CcPoc8ARsBxxwgE3if/7nf6YAjSE0gTBvr732YnZNNUYaR9i88sorO6XYSm6++eYqFVh11VU/\n9rGPfbbC87a3vW3xxRevkmemqYKAyWJh5Mj1vve9j58HXYo1c8stt6zybYc0G2ywQWTlGEbhI2el\nDGJ5GR5b4o25YMECg49sk8KChtKzwgor9IMUWRTD+MjqBS94AVGTo8b73//+fvLMbxOBKgigSiY/\n4WgkppIzCI3A5z3veeYtSxTLNxPgKlllmplFwAbvnP2IRzwivQQqjoHrr7/+N7/5jcRI0hOe8ISn\nPvWp0CPh++EPf1hxutmDzFaO1V0fp6ApcESoCOwQkjHitjC+8pWvfOADH4gGUJ5aM/v301psscWM\nBFmxgtCn5I5KYRPSeIuGwZYoyJwAbCHG9Kte9arnPve5jTdDhjqAgxL1PzNwglklks0OoqDMMxGY\nDwHsn+8MxQqrCNa7ZqxxSMWeiCUCcyJgoWd2E/rrfKogsN566730pS+1j9ggKSvYCFNW+NCmW5HZ\nYKjkSpQSXZ8zzjijonavSs1nOU2EB6IsszA6Yeo+JkqNA0K0v9tuu5GhKOU73/kO0WMV67Tq1Rgs\nW7IKfPe732WZJEYOu86DDz74KU95SvXK1Uj55je/2fme0bQS+apUlM3WKCg/SQTmRIDMiQCfaYWh\nSNJpHH7iE5+woKcBRA6YRKB/BFjy8vIRa4b39FlnnfWRj3wkIkbSwlTM/Je//CVNHAVo18cOUlFe\nVbHo2UzGiJujtE35YQ97mIVRuBxyoAFBoQg+RkrhZoRJs5AOj/tGnqojrF5hdguGSgLS0I694Q1v\nEEer+piuVyIpK9kVd24l4md0f13DXdYrKL9KBOZDgPU3SaeIhZToxqEJzG3qkksuScQSgUSgTwTs\nIDTgfNNe/epXkx/ssccea6yxhjyrG5DRrznPmJhdH07WVRyP+mzR1H9OqoSYPuc5z2FqZmH01PaK\nqIIVYa0imEO95jWvYc9Eul/lqyppBsKWkHdE0lZx5pln0k3C6EUvepHNo0qF+k8DqRe+8IVK3GKL\nLfSKU8h73vMeMtVBmH31X9vMYVoRsKwLBGIc8j9gWkHsT0lHN1d9WZ9WZLJdiUBtBBhaOAN/9KMf\nFXqG0ZLNZdlll+2JLRFKOVGbmF0fXkThxpFPDQR+9atfkfyhATbfJz/5ya94xStE+amRT71PjAqR\nSkiYRDwXDjfsyutlVXw1ELbEYoMagkCMAQfZ0qhi0L34xS92pr/yyit1mOjSgxZr9dkT+fm0IsCW\n7r3vfS+S5PBA2sp/hxf0tDY225UIDBQBDqf0aIrYc889xbgSSD0CdjBaItMdUNHMyyJnEf4GVMT0\nZSuuFV6LBrDmJLAIUjvMx5DgaiMCk+CipInf+973+iy9ebZEhGMzwJCEonnta1/bZ/36/FwUE1Iu\nYi2hjBrUX/ZZq/x81hBwa97+++8vJh5PEMdihImpYxqQztowyPb2j0ARmINRrBOIgFUR25CBkUBK\nzYbtsJeZpx5yrKi5fSTepKaic1cGDXjQgx6EA2AC/fd77RyE5+Al59IFPgF9Do/m2RJNoTsRiXPA\nNHK3WPIkJ3tK05VWWgnNxHZrg54fJgL9ICBUPe9W2nTRitlb8HEVHZiwup8889tEYNYQKDa8Qw89\nlCqNd1XIDOgQXLV57rnnNgiIqDdMl0zVkGZ56NP9l8mUmKINFjR9WXGBdzgUttcqN3ypUhlPzpKW\nXPHw6HD92w/UTbIlwhu6BpJSVhoG8fLLL99PzRr8lvLSLoXqchwdaKzPBuucWU0lAoRMLg/nBU19\n8Nvf/lY4Wo4bYtpOZWOzUYlA4whwpxJBwMMIxiTiaspLzn/ZXbAxalZThpmRYClFWVHo4x73OP+N\nchtv2nRkSA6HBoiWjgCglcjAyNt1v/vdzzWOvAEQJnJ9C2+9KjXJltAR8hs1Y6VRKHrrVavxr9jb\nqpsADAhv45lnholATwgI3CxIv8gaLBB59zD9dsdCBt3pCcNMPJsIMNAWQcDDvd8k8rBK8V9ru+CH\nbnb7+c9/fuONN4YvBZUZz2heR2HbJKCMP1HeVdTI2F8ZvCpCWeVC3UVR/Vq6Wesm8BIpsSdjWD1W\nkdDf9KY3CYutbrXNvZtkS1SVVINMq8YKoxisauWKeLS30EDP2iCejvaG6+lAHVCHAJQTMCGTld0E\nplB4xjOewXNT6LYhFJ1FJAITjcC9731v+4vHlVkmEXdxxt3+63xuWkUYfacR9zZqJn8jRjNCPLMP\nsTfZv/1+xzveUTGsjLKiFP9GoWQBUag/TTSMg6s8JgpqNjDjRgOMEFUi0ykuYOgVhMbY0kUXXUTP\nxVnazQ+9VmI46U0qmxPa5LjQbIjP4dQ/S4GA1UoP9nO34FjBSCUnzgW1+tprry2QGjcf18zl4Byr\nPsrKTAQCbFLJaClZxPVhq0rUZELZHcmHXJbCld3lu1R1zEW++c1vTsH95ePZKQxd9IJbyKhHx7CG\nAhmwoyKDdD9jjeo1xpbOP/98pnDCmYtFWaMeQ/iEbMnVKGRLQoqHG0U+k4WAVc9tUITql156qfFG\nezUd/Sh8JeU1M3A2qsITk/z79/LLL5+s3snaJgIjRABbOuiggyzvm2yyCbbEHNA8Eg/ZS7Jb7hQ0\ndByPmDehUOnR1nhPESm5aoxVkHsLWC2PJ1tylYgTqRvY1JMJf6+B2pthS678tW8RUdJWNt4NzWZI\nOEFgy9d0BoMEclmfz2vd8mHojPkiIirrUUcdxfiAa4OrGa2DdOTNDo8R5rbLLrswj0CbvvSlL/lN\na8DkYgZHaZUuAEuvK12VbNvTWNlqy+2rlEinbJvJXq6CVec0VnU9Ze7stddeJpEz1XLLLRef2JsY\nrIjcwVjbTklJVPFGuf5rNTs5MHu3ZHEVtCy7wm9sG85Bz21U5rWQkDSGPdWzGbYkhDzLKWOUo35P\nxQ8/sdiv/ONU1QV2wy99hCViQmTRlow566D7kG4ijRHWsGvR2AODA9PSKCcpdCEgA4KuX01KAuE2\neNxsvPHGgt4SFxM1CUFLhDYp9R9mPS+88ELDldP4oAsltBfh9qabbhpQQXRD9m9r94Dyn51sUSW8\nk/DAvaosLmjizKNovvf2SBee4KbBlqZGlT8+/YutXn311Y6vYhyOORm1tBoSlIa91rNftuRGQ9GV\nFEz4yWipoPPj04stNTGdGPpZpKyDrvobc2lKUzD+5je/wZN003yOr063lhuyja9//etNFdp4PmXj\nbhFamfsQZzZeysgz5KjsfMxSlaTW5NJxvH4GKuEYeZN7qgB9pYs5WWuyizdigcNUAkoeP/grtb+U\nxkuHgV4dD62qPKpkSwXcUyWrJGbrSUNEPbTyyitXSZ9pOiDg+OSmi9VXX92ssQvqaKzI5s1KlVKb\n1t7AcMqiW6Aw4o4aZuD5NIKA4wS9p3hyFJ0TEXTXoXTTTTdl2dbTMOiXLV111VVOwIxVXZpbxh0L\nsUNfd911tJicNq04lF9zdoz1COUqUrJN8Zsvw5zrWuRZPPVukDCjyOKEXjBn+rkOhVuE/bvZCB+N\njN2WTFgNu9fQtczkagK4xV8JaawdBVmMoOdWmY9//OPgrehhO4jadshTAA+XAILdTU+eIZc+5OJW\nXXVVCkcs1ii1WzNN1S+DE3IMuXW1i7NW2P+o4Xg2mcKGtPhVLBGh5BGOgWiB2QRVJoF3vPQD9ZSS\npmC+VSjqQ8bDTLhsZU9+KRCwgkQeaUrty7bGemiJcyvUeeed5/A2zPuzaiM/5h+Sy9IYOA26ZCMe\nUSt1mfOGRd6DMVvxKImEK+SQ1NM2OeZtH3n1SHl59TKoRwPG3xoHXHF3LX/ks88+uzp6/bIlRNIC\n5KLmFowsZ3ZfC0E8YkNZquY8H1Mw03RGmnj8troJRdPSDPzJHCjy9EMR1ZtaTukgouZ93oWCKsVT\nrw5D+8qZ2yncOOYtEnGwkCS7iMW68A3Bk8hpaDds0iJSMKMeWvWqF+ROaQODxko9+QlX/3BCU6KG\nO++8s0HuX9ctGfA85qgSJrQ5/VebVIC1AVi4a7iDMtSyFh8HNih5LNn+ap2hanGdQLz0g8ZWSuk7\nHwOQmIjKVlTVssZd0a01hEAyj7A9fT7uCrTKWa9F8MPDrGkMkPvMMz/Hh4wERBk5jsedu/wnjI0I\nleSmMMctLtuETIcddhhLpgStKQQcM0wuk2VSLiF25OYjf8stt/TkJ9QXWyLIIhUnruCY14K73Vc4\nGTIn2mJGBq56MF5x//Ywmupth5bSyGZLZP/22wpVVrIggMa6K9yV4q/Fg+4Ip+HwR9DaU8dbOn2o\nXKShxnUoILakqhWLYxPvkksu6an0oSV2liKW+MQnPmEos9YqILWyIKMUkS1SU4cDoXLpKPFXS/nQ\n6lmxIEdwMjB1VkneLhW/muhk5KD8TN0ux6/HsNd2e/YQ7HXGEDTLiAWEVMAwMFDNQaJlhpIf+MAH\nXE8BJQ9CiUWpvIs8GQbESwGF7Z0mu086WwiZFxa0FqWbl/Zda6tdFlvtXyZBIW49tNY5UqJfWoSl\nNQg4Zbq7nuwEDeY5KVnp5eh0jyUCS6ayf/p/Hv9lwEQLgzPZbialUeNfT8A6gXeIy0DIRwDskMPG\ngFTYPCIsn7NdtlSCFSlt99Y6//rtTZk5nHzyyU413hMkF1IVBIONf3Ws1NZqQDNb/ZO+2BLlDlMA\nJ/52tkQ/RUJuqxZWgPsSeyYbtja76qHlbiz+nI7OUgLRWHfq8tvqZl/UDPIkZvY+9NgkiBb8tXhY\neFxwwQW2duE0rHHVbTuIWJztLKmUHUTi1fGKlNY4JAnbQEeorojTe81hOOmNPLoJmtAwJi0KJToy\nVlw06CDeUhOD3sPBUvT64VSyp1Io4PCGWTPSJL9ka2zYG//kK5QLZ5xxBgPwGbG6ixGivZQpmhzS\nXCRmgw02wJOsP8UQIucOPuQ4VFa6MfhlHrThhht20JtTkJnRsp3T+NK52QyyLjFl7WnEtiemDML2\n4hRuPPO1NhP7zLP8Oe6F8znOGSR50VODwGZWcyJAMvqTn/yEQXAcVOZ8HAxs0E4IOAOqZK93yJkz\nSIojjUErJdtEbkn+9dubsMyRj1FNPmLH996DISFe5LUS+7d6H+HNWLQtUuUrLqR9saUqSih6E0ZC\nwlVpBjE4gok/zXnCC61WiyGRmU+FxBzyWc96lg/ZkZXh0ENeOmvqA/L2oYX1U8my6IsEuHonNZvS\n2Ip7i8KhWq/DtnBIJuGLPipq66/YJ00WaZy/ovy+LUuYbCfjbIllfNOttLPzZlEddG728npuUBpu\nBpGGEuhaMnTleFqY1QAQJjGSPdEob9qdEoiiQ5vM+oQqGQcql1V2xW9xyydhcuKaj5eE87kDq7Oc\nKaMOVucytmRLZn37AlWjpS984Qtp31zP7luEj3ibzU2NfOb7hKUtfaUDj0FisrB1y3vNGoQ3s2pB\ngAQEp1mwYIFLaeYDBzUhGOZpgdPEEZ3uwjbUzubJ/CxxUhIdIQ/+9dsbWlSrgVtomSRaAFlieM+a\nkBTGkcnD4rknpx9+aZiJJdQRqOJq3BdbcjzydJXoQKq4hRihIdBmB9B+A4l8LHAt9faS6Nu1uNaX\nNddcsyXevH2dzo50SjLm5L3al+EWVt4aht7EdxZWq97DH/5w3e8SupFMIaSesCGue1QfsndKNFtC\nIYTXO8BRzz322CMonQtcjTYCuRDaEbD51pG9qD88yeroT8dzG4a86TRuMfV76n0j3MGIULNGxCAd\nSuZKWEsEjTia7eRMPZU+tomtfTGSPV/5ylfweMtr+bBotppuTqU0KVqB66M+1ddHygJCozntKqwe\nTrpWGE4keodqTB04WDmkFXDR69HdG3tVjoidQVYTB7w4ouBMTuTNnk/IqyyYeKRB4k5TMv6WOT62\nYyArNokIOGBg5HTiHUT+5h1NziqrrGIeEXA49Rn/RLk4FjloudU2cSpUKSmanIj867c33hOXOiBh\nRbYw5wHvPZTvcgt/L0ea6gDaEE1Dxjy20WHIltRMYVW2VVJxKx2ZM7YIWbuFExUJWOe2CQKEtFqe\nqJnXX3/9ORNb3Ri3M2QhZaG57Mle0icUUj1BHHXQc/RBbEowrZe85CUjcQAmhLRT0g6ETA7CrLlx\ncDtNME4qAzTIe7uLnTX2CSmDQvmNm8ZxubwBiEXhFG5XYH/KNnyWbYqrT7yeUpovOCtJRm1XW3JW\nYgMyV91EYUp+QC9czz+0p5oPKLHToUHLLKlwm0BTHEJ4rISFlvFM2E4T9+AHPxi/ZxDZeE1MhPJF\n4CFDKs8LRsEE5F6qKmerPiugLLZoqJ6dphHL8Zb6qLnDt0GC85ns/qvOYfPOs7LPyufniUAZgbin\nr+IFrDgKY5vCCdTyhQnMacpi67FUljcgvMoqYdbYoQpxLOks1Z5d2CGh16kkc1s5klfxCFRTthSu\ntspA7qocjFRL84g9IEU8QLChhahl52EHSlIyBXWwtIcdfoowkc6Rr0Cz+lCOkGUWrHoqPN2j+aOK\nw8uP2qaLd4oOEEoE/IkmOC6YBAJRJ2NPx+UyeowfMarQR6DkLptEuSzcZdActcnwbE6IfFNe09U7\nZepTmjX6yBJTcYrOB4iDl650pHNW86C2TJpquCyMHHB1JmzmpmokR6gLfEj4FiexOMmEg21c7VRR\nZt5To6xgzMJEBI2vtttuO5PC1GBWX85Hf6mSeUf+31P+7YktOxZDKrNBB6hzkCPL1xwy4xgnxHXG\nCUn8fKHX+mxafp4IdEaAkCK8VkPjZPNCBpyLurqnRTBJFEricoAh8idy6LCNHij4NdmSqCc4ij3V\nflzFRJGky+Qki4uwe9HmrpHiwocFHenASAphiTQO613VgmU0HRatkiRSZVVUdbjxWTbUhDHVP2kw\nJakYKu207bwbChpjjgQOYiHtmxM9KX0SCk2bhPFqQ2rRYMYyqsuQyCqCwwYbNSNZmfa9hpGdExm0\nmPCATJFKjr8FoTRTgIkjTEYsCWgExQkZD/9N4Zjxp1hADcWQYZvjFWXmvQ4k86JQ6hXX2rf0UThA\nmB39C1ytWg6N/HxF6yjLtHqtdpX0Qc1BGgE8KTKME8rcNACvgl6mqYJATyIDe7QZRFlM/R0iIidz\n9kldw9bEfLTr2ens2uWK2dTsYjUWh6h5xYNrTbaktaRnyuDcUWW2S2nh8+AWFOr0jjZsRjZMBDp0\nRrGLd+2MgiT1FO+BImOdddZxu54Ym1XGREsaNgHcW3pyQaxRynyfEGkSqhMpMW8Pb8x111037F6j\nR+ZDz2iLUWXdn8/nMwAPy9YG65xZNY6AE4gwWkzT/Pu0pz3NquEA01PItcar1GuGWDsmRKpkJKN9\nvFTo6+kZ/eb+HWypsFTruhR0Lt2Y536soPLjYMoxthjq89mTKTpW1SorXhUQyAWHdk0ESI0QD+my\ncaJcLpadl98qTRhmGpsOUbrzdvSdEXLCCSfwSnaBbvHSaYHkLyKncD4XbIk6sogvQ+lDgsici/Vb\n+VBBIyFDezDhBFsOO7dkPi9H3jIIFSQZ4ZyRoBRppFSEHWSYOIxhWT2dq0OuYaWiaOKqYg81Kyma\nukpPaJBIKHxOOEpOoS/IpMNHlf2frncM6AkcWVEgOq1V0Y/JueZeiOVRBPRUMwtNSNFpr8RRcG2F\ndloQOQQG15Ggz6Wwp/pE4rBbqmG6VKOs+MRMNjI85QASNRpOjMRNgMEK61TLvQwxdPJ28dkm2gi6\nNrCD+5DVvAA5EBaLoSilRpcNroZyZtBjL7F2E8Pwh0I+zKz+pSB91tmeFCOzvBS2QEej7RYCzhxG\nMlmLkBzSE7azTmBvpwIN6oxkRZVWhHuOH5Zsa9Gojj19Itzr55tttlk4GWCHVPmh/RyJ0RvMe4oc\npu+cD9GU6DXqRaPL3oHpojXxkhrXbZLh9HPMMcc4ORh+zoeEl+w62GLKgY+LU6UodMRs/sRIjkbb\ntygUTSVMCDmE0OPq5XP/NaHYM7jP27YlGRWqISRbabhoSSNyLMLU4CjttU+HkB6/iRB95WhkxB+w\nMp1ZUvY0fcLQ1gik9SY6CQmTjigvsO2N4nNHo8WIGd3XcfpCPxrMasUsj5R9iy226AkKIhtRRciw\nLe9V4pPVZEtBbnqqWTmx+jFIctDRAcAy5jB3spAWSUYhWKuutuhVFhfnxer5125y8aF+xRSNErJE\np9g4yPYkEousDA4kCUP32+GJ2wve3b9FRf8NhGeL62L/eY42B6JHQX10mQNrucvqTQF9HebMNTq9\nKw4crJy6CD+ctimzCICdB7oe2rpmWzsBQQ7cjFWKQtCFYXvL8OD/QSbvoBmrio3cSCYSKOJA1sN5\nzjrLqj3GGB2cw2WvS0dtTMbhQ3ubnUZAFuPEI86wcTJkbo1/IMcoi4FRRePP0kV6i2cASLQjUB9b\nAtYUxUvCHsJ1xiFS2lapMmw0fMVDoMjfxeyw49hrWMLhQyGaKmSxJjjuRWRF6kZey82INzEmhCJg\nVMEpaTD5H/DykcZYdWRlGeavvZoY9z8MhrnSBjsxl2kzMM7oMlatpDv20A6xOeZrpq+i0+WJhqIE\ncc9jZ1sauiAkmP9sETpEZ3Fo8F9mu71Cap1Ev8hZ/VuFuNdnS73WrCU9dGLZMluIQBF/EhFP+dxZ\nOA11FX0XNKtXgh/F9Rp6oJ+2Gw3syhl+OUOzHjDnDT7Ha1tIT+IK8jCXWJnwJi3pupns4fzvqeGa\n3k+Lyt8G+xxhAKqmGlLOx6Cyc+sy9m26zGP1JPtFd3rqssjTWNXXRdygxits+Wa2T09BNsOlFPOw\nHdbzY+i/blQbcHPcx+zhZqarSdzxV0DnrAJM25uRbAdFXIxk/i/2QhyrnV31UytdRhGjoPJDVMCi\noEMk4n5KHNtvWcU6YhknEdDOONFBRDJDq7CRYNlHi5VroEa5HSaUccJ2wvCOlKhezEHciNV8+aXJ\nZX+RkgKEnbtRRALhmMpyH60hiLJCOkLYIO1BVlGx2uNzCykXdxpSTluYlspQzGFU/huewtI4kIju\nI2qMl4LaKALLJOWqQvgaxBa5B8jQtCJmJcGPuazL6Lx0GdkefEjd1ISet9cTsm0izieaQCaEgELS\nAks63nkmGrfOV6yfTWG0SdfT0qCwGA/bnp4QVgfiBicHW3CVo8L/WvuO6hFb0ni1LJqrNMpctOLK\nuaI+iCRDbJInw91IJX+b7ziu24x7cvte47zFhdUk/44gw8GBOE0rKCXRRE8MF/uxydYTaSN+lImg\nDHIwYiLWBeULWXSYLhWcaT7bI6DN+aeQfMitJ210oIfv6sSjjz5arw0HzyGUgippl9OV4BexrPsv\nW2AId1B42/J5m7cLeHW02Blx/Wf757K1dvR5LYPDtEc9ndXiWh4qhp5GV1Oo0n1EVn54tM6qSlan\nMsXyajeiHDHZ4+4/ZyTifauwGzyc4DfZZBOjsfBP7lPOZMBbdjztDSxccuYTMhXre1mKEMeDCQ0u\njxagpKDAF205xqTTl03L8CukNU2NhPZ82H1awai6/AnbsICTBiFAduUOnIkkwKbA4ZcvleFEB+Tz\nou/ooOXASYq4HR8it9DjJGekRKG78FICsd1jJBArmm4OPwabfkQfC+YUeRqlxgOgRNuytEZgIRKs\nWDnN7iBJ1b3Qm8LTHu/Ubdtql5U2VUQ5HwwmPM60l/utH45AuLU1sH/DDzkYb7isjZ6ESadAO7yR\nipHgB0m5TmEebtk0eGK50GvMUajSrCFxCkUDepqPBqESq0j6R8mWtJYcVR/gTAYrpIzLsvrTQmlk\nW+6JOolkIipd+xPBy9FbMVrivpTqD3CxJRWwOlf/qp+Uhl2LAMyWJkNHmZ5EMk5IkCFPFnCCfbdH\nJiC1tYdMDiBoeIcQAMacOR/Bi7H1olGxreoIYFY0fyu+xdJsbJanntrSD55D+NYK2yKus6ko1+Ds\ncLYzsMO0oqWGcSDzL1VsO1u1UenKPtlSUaKzMjd4Xew00nLj0BBwU0SLT6+xYZJ6b2oXy1NcWMug\nm/0QOQFFiQR2O6QqdIhmKAmBOd5VfWnUBaT+7XUEFn2hTy2g9mz9W96KVMPUkG3ZDdYCYjqrWyHe\nGA6w7aWYsGZxr0f8yAd7QCx0lgM6pZW26Igqzs79NLZ8O57uJlyMm87h3MG5JDYFgiKbAuTtnZY4\nW7i1zrRC++BAXmXA6yaW2rIKQuMrcneTy1fhR1xUvtD+tOusi6OjdbtwjjGMezXb7QeoOb81DjWc\ncKXKNt9/6TBs2SLJOCJ8K2lc//mjQXHmhzMDMvCGoqkwkvGb4FmTLRTlXUnpvqKNpUIleZEPoWNP\nbKl65YfEluJE2DIH/Je4CJc0pp07Y0yXz44SFKTPAJWg3cCI/MMii+1yDiJ97dWHy/w0eYhVW65Q\nqI5gryn1KGlwe2TCwuOmYoZaKhg3Do5sheuQxxSK5dtvNnGmt82pPW5YTHuJEU2VweJjD4vH2uFx\naKM9Qbkq1ieSkWfqBcp+8UJ7+nCcE2OlFPNzwthB1EERQPHUvv7aiZmMxP247Ts6utnhuqWeUMLw\n2J86delfugP90tPnjSQOI4P2rIpNyJ+MWLOYEN5LozEwQVlAEfB6Qz7vh3BinYOyGPkxtuMKoJ6a\nUJxiFa0gDigUmkIiFZkY24QZvCioIYqXEntvEWM/0VNxjScmKTFK40KVXp8IaYFsCZFM1Ie/ymrQ\nsdZadl9Cd3ueWSYQaAcGUN4UdFnIV+yORg6qR/ajO0wuI4r4obxfxIdzBu8opIkdtGkRwAKwhcFN\nryA3mx6rQAetIcU9Gc3m35IbKRpZeHGFbfmvve65c9bTOQQNwHSZgkGYeZlZrxPLx1E9a3i0yBos\nEbYqE1Dvk0kX0r5BoDEktoQMmgDtMkMv7fTijhCmhaldy/YTwSf91fplj7ENh11zPOQobPec4Jkd\nFJd79ARTqMBIbkOiO4RHiSZ2FGTxRXe0kYWak3RP9ka2YXhaWXi0OmYVGTpXBbM2iygyDD6ybrIK\nYqcw/1KB2PjFHdB2eJYFckwabbEkIhGsvFdA0Cwj3oEvNEHT8cCzUNTa2nWZg6yFIy6Qma+NvppT\n4yM9DqGvQ4s6oIeTraOYkzoFt13Qrj8STVzhvK2ZZhnHPT9EntT8gs1EhDCD0GnSfhkHCesA4XzM\nSjscR1o2Ig5F5g5Tv5CkFo/dDtmiT7eehu7PcsEcx/wiWsNvqkhcCgEDbyllqU9ZwqdocgvV5ovq\nYFYUrfIWMcdZNRxQV1bMVgX672INVxxVI8Wco1TFousli1vJi29VXmcZtwZDZ4t7CyZbY6JZIj1W\nxlY/Q8L6ZuQ4Oup6e60/FQe20JyaCxgwzWNcC0E2r+/KxLdeK0b1laGIGVtDDPIh1MGuYV4UBSmX\nTgM1AWPFKN6dK2mOi0lLZqEgdvchdyRhKnizBcF5z6qrE03t8jEeu7BQhCi0fE1q47AMli1RbWoe\nquhfkm3IajCBW8zJ4okrBRxlJDOmy3+y5ZsJpgEzPdzWelrsQE6Z0pO/cQJk8xULca9PnHG7RhHt\nNdsO6Z1+DAg7h95F8kLAYHzwp+2JLbHQsmcbN4yEiqjoFhFGi0XpNg/0HAEyo/glxXtDkH2A4Whg\nwdxpstwdSFtoBnu1l4/MQ+7d0okNojeSrByCjRD2Ck6rRi+CbhgzYnCUqTFyDOZ49E5PbrcV224w\nWMWYPTpLGB5DOwbMWT2j3fzC2Ir7CiVj7mCYFQPMnyJklOUSW4od1N4fzk1FtjFc6UDN9xa2JI3x\nbDVQlj9ZeQ1Ci49NVw4Vnd30rBOCxQptsv46wpUlfyKJs0JTUMskRZcdf7lMlqVQFXtqbJNpkTPq\noKtHXhgBCfW1pcyCFgG3vOy8+NgUnFhIfPFX4xxVFbPHtsoQm7kkIR/CZE0rBonhx9bCToFSGxh6\n0JCw8JL+BlsqNDtlE5yWlw4/IanCvAtFjx8hWSm/HDRuxUoLJTN9OGwJdTYx7R04KBzMU+ZBZrfw\nE706A4bxov5qlyBSuNMUyZC9NvvX8sw1ta2WprkRAgEzrrg+xRxke265syxTE/Vq+a4y7eYxc3ei\n1aHGw2gOarxLOn/Lal2TbDMEGIB2N57f4XPY8lihWChjD8JXtP/VvuI4DmWnCg41crMIUlggSc4Q\nDPXhVaMVPjGLFKrv631e4ysMnczGlLYHGxCRA4ICKMZb1TM0TzzSszYIg3FPCw66ifBJxzP5CpYm\nvX2aAbLSCbENvnhZPGHFbADRIlWvTJHS0d+IZ4NZ49ux/QQx0mX6CGg27KgnoHSZA0Cv1bZYC6ws\nwpAzWa/fdkiv640H+4SczTLqfCw5bqoe4cMe1lHexgY6S1LUhLDHS2Mv/mtAWohV1U7mTBUj2XBt\nqTZLlJCdCDcAw5a/yiE+tBooy7/xX0PdmlsFAQD60KMO9oaWT6x1cdKwTJX/ZAM2DNj6VCki05QR\n4KoJUks6DMFuHfNX9AUbbu/9duicBMKOjaq3mEqM3rwkRKdwKMabeeE4GqzCTm8H8RhLDtiGnG85\necXuiH4ZBl76Nnrcw/pYDsZwXFBIGcJRDnc3boW1DL05aTp5vDdD62J0gWOEET6cEp09NBwfjTkS\nIhxTWGcRZwRoXWsSa5Q6E3kwFDE3/bd9LltjiQD1LMLqiFJkG9Yy9iZCX2Q3dkAPmkHUhLkaEtaB\nlh2tQ62sDI679izcmmqra/3/Vwtb46nIllAZfvI0AjYVYxpR8JvAfM4SdQBDb0v8nH8lIzGUw/c4\nHmtuhMbq5xk+W+Ks6JzaslPaCXplS1VaTcajLIcAK4Xt0zms61doHG5u2M3XEZ1zmEq2ZKVAZAue\nFAgACikR4KorpC0JBsSW6KmJkfAwZy+zjJq114oNIj1rXJ4sLTnbpcpsqWK5dDSaRt5DlkAx2r7I\nVsynXjKbE/mHFaxlI689DOpVY5q+Ylfu0GuDKC+GwjFXZEsWf5s3/mrXCFhsfvSwLNaZKDAPLWOF\nipkUZdWqkzYCZHNFsArTH1IuXYwEOD8Xup6w0yguDUSzwo2OHKsQX9nXhf002ofWQUNmSwQWlpSW\nTQGn9BKzYXJUheDCFpKMlTEeYjyiXP+NSAQtD00LEkaYZH4VnUt8S7qPvGICqLZv4wnCShpqb+0J\nf1TJsolI4GRUz12/XZj9eQ3JobMXkbixRafT4XNCINGlPeQ31jhyDr/nu1iNSFOz5zMrdlxgCuNf\nRhgGPVwwaxS1Txs3eNFkqdJAjUjKEFE222VbfM2Yd5hp5lshXazRKe2fKEVZTm9GuWHNQ4rgp0PO\nzs2WHgppE6CeHQYDLLbnwBy5i1AjAEYmANQvLY6yEfzCmdLY7qkshzCiCAcg/LVXl8M5C3LUMxlJ\nlZyqjWRBKXlBN2Uq3lPT2hObrWUrn0jA99vupfk9KRGclBwBnal4XPq8uAS3zxp2/ZzAwGEPV6Mr\nJG9ocbepPQy6ljuJCehldI0trUrlSQUoKNhZlmcBFYwzHk/+rm5NyKvR7mhnWwkaZPxHQCYvbTRl\nNS6SLZnt0Cd+oD7sXOnvrP8WXkPLG+/RLApBa6aDpd/IkE3HDxIpi4D/2nQkkD9KZ2AQB3ipLIzK\n+mDlHPQ1yQWwNni7sEk0HO88HLF9d46Xdg2VEa+rbFI85wCwDdGHQi9g1F9y8Bt6LellZQcBqX4p\nhhNVDKWqia9TsAWad5979ClVPv7UvtR0Hod6n4xKP+JMlYwiuvKpOROwPMCvidlJrYcpfnR6sNkY\nyvgp0PFKY91CZoLVaAj0kQMMd071X40Ma38yINlSUR+6JDOZxXfEMml/HB2s+0a8cz8xXu2GTKVs\naU40agsVCEXMbbrReoO2XBnT0JIhlhLxIXE0ZdYwJ2PtQVJPthTFaaPlEoC8l/sZqBUrb0Myd8Br\nYM+p0as9DCpWYFKSkQ+Rv1JYs4OsWOew5m5JTOBUUbYUH86ZyZwvJY7tI55CYmHKhJ+BJ1ykw6Gy\n/DJmVrgE+dfvyKrlZRVtVEVwuibrU7ZEqNZuhtG10DkTEM45xtiFu34OunC49qP8e74PW/qx/F/4\nR1bRNfWQJxtj50SIUHHZrBnL25FR7AoNFiSjfN9ZlSNFP2mwUaTSmcAOQbBEFkrPjV/Xc9/lTcYG\ngiCusIDup27j/C0lMZdg4qWw4G5/HJJIMrF4duLFZezj3KKJrlu4MXeIPlCldUxz4iIIFo76Thc7\nZg0n+EqV6g0ojTayDsY1mT86KQ2olCJbYjAOubZw0QT67K9BV3W0+fMPZf3DeL/6LVJW8v4hnTOT\n+XKO7SOegCssnyiG4qXfMTEjzmHxMmZWBILyb1xeVKQvXjbiSz+cfhSoj1qpqfj1FVeziFgWEaHL\nv+drcks/lv8bcql4dEdt5KPmyFYV2GuyJRoxkjT8jpnbSG6hov7gtEKMRmTieM1djg5bUBn1qdLs\nSMPcjJiKGLAcm7H65xOUkkjTBkOV0OKNWDSBtFkCW2+vwdAnCIQxqapV2LmQxWI/ajiOb8zwaXVJ\nWVzNjSrNDsfF6Vkw0IEOOnyiAUMpQ4juPDY0DcuYjNJeq0G2FLfBi+/FQqgRr/Je65DpqyBAXMrs\nT/wF/dWTKrxK5tOdpiZbKog5pWn1w0SzUBKPO1+KLMAiz6LG8NzJpqf7sCizacd7dYBsthXDzI1h\nY9xd2v4AkAidAn6Y9ZnNsswXm70NuEZ0HFo8Vjt0Qx5D3WleaBlmrbOGJKM64rReLcZqoETXzx5i\ndphoDYjCtrq46QiPZ1cwZTFEasAytp8w1qFhIJmmGuID1BJtv0a1ZWhd6tVvv0ZBjX/CVom+FQGg\nCqySeV9sKfSIVSK/ValKvTQYTwSoZDnLdKOIal0lN+K7UEVXSZxpEoHRImBVshUZ7YiCeUeWXs8S\nf7StyNKnDAHrJ39nIXDtl6G6Iocb7aYwZQg32By0JsyV5Mkdh8kR7Uqf+dNLOE5wGW6/oKLPnAf6\nORCI2fAHFuUVNcJ9sSUGVqhZRIge4aOrzE/yeU9PDNcp3w5UMXjdCBuYRScCEOC86VDIhoZtGXkS\n95+KkzzRSwQGh4BVVNwKjjIsIzklUQozkBjOVa+Da9S05sx/TXSYuDIybnbvX2fK6ldIZBbAXKYm\nCDcGPHRTvDIBUtHcsy+2xGaCrzK5DhXYBMGkqlgwjQZpJAuS+SIaTFaLsrbTioAVzYV9HmEmhOEw\nt3fccUdut9Pa3mzXZCFAmMQowpZJucwV3xAl4B/5EXqyMBxabSOmjF3Pv0QMQqL3L5/mdMWxn0ek\nAAFDa0j/BXEPP/XUU+3+3MUqWhP1xZYYBYu3xLeWZV//tR9yDrgwRzBx1vsfLkOueRY3CwhQcFh9\nSMvjHkDxjp1MXH5ccWLPAkTZxrFCgKqBqL5/WcVYNWrKKkPyJ6AuM3z8Rgw5bnHlS0JrN5bpj3Wp\nJ8VO7bKa+lBtWY725KPWF1tSb2cIy3pPRTbV2n7yob71RIz8fvLJbxOBASEgWLAYoUITEdyKgevm\nHyH7ajvKDqiSmW0iUCCALYVneGIytghEsAMPwzL/tkTc7afadtKKttL9lNLgt3gLSVtPK2q/bMnV\nOcQzfCIEsG+wJQPNimqDZp3Ckhd3mngPFOrMvB4CXBYOPfRQIm5R0RmFUG0QLHUNlVuvrPwqEegf\nAf4H7OpEW5im24X7h2U8c2CqG+5NTYk5xJ1xhQBdjTEwEbbefIpPP/10BECY6+p91C9bov5kVy9e\nkciH7lLt3x2xetXrpXRStxUJgvyCe548CdWDcaa+crWI4HtDcIo2fVgBuq+XUR0psXj8bkazA80U\n2tnYSURAaHWecSIji4M1hvV3i5nd0URuqm5U5MhBU7lNej5uKcE8uNe5xK1GYJThN98C67YW8XRc\nolK99H7ZUpQkui526Wq9CFA2zg/yi1dyw3bhyTjXM+s2PggwrxbZKG6wckVMVKxxH2lxdc1h4bmF\nXXUIESt/ODdAjQ/OWZPJRSCUO+MpqjeznJBFJrv22mu7Ikw0IpIZf/jf3fO4IcR9dia+l37HS9uc\necrxolnpAEmPDKOIeNzNpcKc1zRhzH23CauYjVPt8Z0ac5VcBGWseJthecA0w5Ys6+IWENhEIIdx\nfgxBURZ4uk4EBR5nJGenbia/2KdWW1ZEqEwImei82b01uC7wlnDcIdBG5d3Ezr1odhDOlk46AnEf\nyHiu/0JXU31wBKtyZzmVomm+9dZbMxOMhzOTN5z+ipeu25KhU7fZilc10ncokRAM4oMU5frBAZZU\nzLLAdGSYN4zVaBEDIGgIGCvKcRVWWqOIRj6xemO6skJ2e11jm2FLyubS7MK18847b2z94xwR6DgE\nZ2I8mwq4RkbejGQispEzEx2uZYs7CamPawq5qnEU6N9L4JJLLnGHCems27kZJ7kyViwl5oAzgm02\nMxEYHAIc9OiGbN5isiM9VW64EobntNNOY7HuXh2Py4XsaCa+8JsIQbz02Gg5n0vGWbXsOc8cxRJh\nZeipUQ7wAlZZWC6//PKiCD/sU5Ya5yhqxF7z7KkC/ScmWVx//fVdruUHH97zzz+//zwbz4HrDI8Z\nsiW+gW766tWJrzG2RGMtDIyhg3E33shGMoQRh0lZIb9pMNsIpOOWCTm2lQv/4HbQ4OP21uLQbH20\nru26664uWuI32+t8KxAjdY8aurdVDHpqPidXR8lxgzTrkwhMKAKkPmYW+xDe8jTpFVthUtMoiVvI\nDMtz7LHHiiblW6Y5ZFTx8sgjj3zUox7F98I6wLTD7VsCZIcW0n7M9NB0NrW5E1UplLrDYkJ6tOyy\nyxJdRxHxsFyksbnmmmtobypGUKxS4uDSbLjhhiRMEPAMrpTaOeNwH/zgB5kriQ1WI5PG2JKyKePo\nJsaWAtufSDsnYszV6Mj8BAKijZkJZDNE5Q0+dHCFuVJTOBP4Rw2NyU/e8zj+NpV55pMIJAJWA7et\n40wPechDqqNhg3B9mPtD4xYXP+L263hfvLTZkTeHKgczIBYSa8pvLoHEw0cccYSpbWPuegkpVT7p\nkVuMiGREU6PyiyLiIQzDotAyq4SneitGmDIuwHGqHGEd5is6+qh23IQm2ZJo97qWPZoxOm4maaSp\nwlHSBKeD6xgO4qaqRLBE6r7BBhvo5QYfCrJYMYuHHGinnXai1e26Gs7ZNOsjKazIripJdi2YrMda\n3BQOmU8ikAiQyrCSJupYb731qqOxySabiIlTmACb4CFXtqMV/vaEArvtttuLXvQiOgpWhmRCl156\naSjlqee4WhNdmNoIFuZEA+ixM7a71gsn7T3xlbl/2GGHsVZsvzFG9MhlllmGGclEeOZDwKqoUYQm\nyN9Y2bF99KMf5cmoblXM1+YeMLhtsw+a7EYU4Ze4azabc+3cuH8btXY4trq1Mxnoh27t1oWCxw+0\nlCFkTsW+8sorU94Poaz2IixhPEKtQc2WfvbZZ0cQM4fUze556OZ0Fj/QE088sXpZP//5z0844QRi\nJKydOrj6h1OW0gGaiQPlxRS0S4dyYBQfeQra0k8ThAdjbNf/CsbMmTaHaXN7ZWzAZiLvNq6jHj+Y\nySIxUjomUbqxLvKSBytJMHW8WGVrr712uGjF408+ZBvkN3d3fafjGCC6N22+trN8worMfeuzT+ZM\nxn7cUkBxRqdWTkAZZyvUHI9Dkd1aiUItFGkuvPDC8HvdYYcdOoDvw913373cEIkVylaYsq9Gr1Ea\nYmZCDbGXqvF5lU+Qv8033xy8HAmrpB9oGiKlM888E39lHtpPQU3KloKOsV6yGrLzorslZ6pO6geR\nEjQWZXpllvC0zquuuuogSsk8xwSBcFJr/BDmjLj88ss79lliyIQ8VmHLKNl49XttnQ5J7J1sSF6d\nX8MvI59EIBGoiADRjqnHAol7moeRH4YUSgznYcePV7/61f7KRtsObbV3yWvhzYNpMd826XzIehWP\n8a3JyE7IhsVsqB8Xfbqn6667bu+992a0VG6Lkxsb8zDiUVuaQX5YgvLbkkJR5QwWQmv0pYM2hlKP\nARMJVkWgxiEZly+rJfGSLqsngG+qFaBGkQ2MtdZayyDpJ9vm2ZKh4/Dtuj5OBBS3/VSu/2+dOQxi\n8lLcnHC1+t7Wf9GZw/ARGBBbQrLJSp0LjSI+Mp5QzGNmFdtovXCsMRq5v1lBhOfOWEoVoctkiUAg\nwNwE16FWo1/z8GlipBhCX2ISezM6te6664rm6njDwzRmaExSZoLUC2TePjzjjDNYZ9///vc3GcP6\n2+x2hokwPPUeKifysJbQ2LgaisNm3KOGdHbMm3i6YU6E0wqSIALZoEodzni2VLWdLD9udaaUFJhK\nfF1G8fVQbeQrbjQLFiygVoI8LWE/eTbPltTGTkCgykuO6JJiblT3LBLbkmGaYyH57wem/HaWEaCA\nI1F3BigrvCsybxJgqyRLCIdXbqthxDBZx8RZ7vps+/ggQAwjUDitVlSJbRAKEtOQcQ/NFOmvvyJM\nUhaGhsGWbN7Ew+EKjXmIt0dPZCYSLJnUdPd0iLWvASmK6OBChE4RKdHFh88dI3E1qX7cGp9eqF4T\ngBPm0YeiAdQ7/ZDR6oW2pCTJ07PGCX7srrPa+cSHA2FLkTUGzY6VXJQEksKYrLLPulb83HBUnIf8\nk0OEA0RPVn4VS8lkiUBnBKyPJPMMKcTUYKDApsrqnKAlAolAPwhwQ2MoRiTD/CjsjeTG/oYBL+Oh\ncFIjqikOM+HYz1GDoiPCmK2yyio8ftZcc02/xRyJBChUTxesVmkCcxnmU7EZsUthsCjcgLqRMDE5\nmnq2tOKKK4q3ia+4NgMHOOWUU4YZYJPtDe0WYzLMmB09OyrmE1V6rUOaAbIlpb7kJS/BmWguaV5F\nqYk47oN+zjnnnPDNJnajJwbWoEvM/BOBFgSsiVbzffbZh1yK7QJZNK15opQIJAJ9IkAmhG2QEplT\nQnsEK2ImK+rgXnvtRZLhv3NewELJEMos/xY+5GEk7mWzVEmecuaIzSIlNiMbtuohTHR/RemSRVW1\nYrIUbT11IjN5Ngwsvq2EBeA95dBT4gBfPE/Dg3E95+WePh8ZW2LhYXDTylEqkz3aPJjaNVX1OfPh\nXKZXYKRETnBp1j1QtDPzORHgGMiiwlzFkOgLBMcjBM5AXzlaEoH+ESA9MpvYJBFUFDdssFJyOCE0\nCtnSnBouiragJv6trXTrUP/CRopyQ9gCKwBDcup7O5GHGywmZykon97VJK5RIiqb4vWBhpQxGRrg\nHmKwcAAUb7P/kTBnDiQy+KhSWHYzD7X8uomhqbIGK1sK1kyjzPsAvyb2RLG5UDZV+3I+vEZdbOL6\nHlFWybGU2L/kbRD1zDynFYG4gltgOgu3pZxo06LZv7J8WuHKdk0TAjb78BIdQqPIgXiDox38TGm4\nBA4QQ9Ie3PhF1z21RelID2ti6jb+HFYAhjvsQOxEHv567ZaOJGS2cwzvyiuvtLvPh15cxjJyB/Oe\n0GhJbHgwEaOSA8sNN9xAFvjd7363nwzn/NblVMDHyZTClI0wT6iqBksZOFsq6uqcTYIKKQ4I7N14\nEHgM934awzREVKfICk+yUQmInL7Z/UCa3/aEQHEi5LSMKnH0NQjZJzFXImHq0wWjp5pk4kRgJAiQ\nqeBJpDV8oGpHSe6p5goiuBXhmj2QQzihhTowuiiugfPfduaByoS6zb/Flep+BIkpvyxXhrV4fCXZ\nfBexRw4ERQgTWQDeQ4tiBeCO3bldLHtYULFXYf3NZc8W1u4Zx/SW6Q8SMBw7lp46otfEtD1gEa+B\nUNBtesiAjbv/yz8KGsBtGYYYqlLo/nqtXtf0w2NL9LK8Dwwg6jmE2hDxsALrJ+o3P0w7U2RlSPF9\nI/B0zuja7PFMUNHNajwrP2u1siLrrzjOmvbEvxZoS7a56mIpZ5qMEdB5SEzHaJ+OVvQzeSnFHAxI\nRyzmYTo96MdWwihb7OwgajRfmIplv2z6U7ClooPKqq7itx9zJiiaUE7QWXbFptvGz5ycRJlrnhWg\n606kwrRURCA4H4t1HIs1dBk92hghgvy7yy67hJJxoh9dBhY0YI899uAXjDzZuBkY9dkogdQjK4QS\nXPzflTKIqxEWjotmh/Y4Fhgf1Mz63oMVMoXj6o8SEqCRP3U1duNlwB1Reg8RJe6PfslKKDBeghN6\nmuczpcuptwlvh9YXgyhIjzjwMVZzCeUg8u+cJ2fRX/ziF6ZNTzdD1aunexVEARa9V5NFc7FwOwNQ\nz3PMme8MWq+g6fvKlkCtwPpyCmw1BFMWJAI/XmONNaavp6q0CEUgUsWZbCX9y5aMDaY8XQ+9EYRW\nSkxi0003ld4VAuXa2kcEv8ZvKIBs0rTkpDgiLWFR9lQci2NdBLH00jYkHy7ult8iuofFhETHXu5h\n5uErxVnchBtgIFUeuoRJynI9paA5LGXl3JPNuM1L5RWhRF60sordzSNAFKHdVlttZUVtWVXoH3kC\nWnNqnMqgF3PQ57XvBa8yNuZLgwZotSXUv7RD1s9oL9EJ9KrkzAWSjXJ8pXN1tDWfflaUlsEdYP43\n9nGVyg0oDaqEDIalmygUr33ta/3bdUekq440ouA40LTc4TWgqg40Wx0vaKGzhTvOBlrQoDMnZbX6\n8IKkoh50We35E/Cce+65vEZRlkGXbhCSazrjMj7gmezmBwZzgy50OvJnYsJqgTJlCuJO2UqZFji/\nMSydjt7ptRVEO7Zz/MAGHHel9fOYU+iI4C9dNwKlWGesmZzD57z9ky5GZEJ/FcdSsO/999+fCCoC\n2NoyTFgkia4jIvJ7iQCRDdtTov4S0Ff4ERfcFv5rpEdMSoKj4IiOZxzfcBqcqR/xD7qmMmQBZfSA\nUAQ7aEEVC3ROo3KqsewgJXBGy3j1N2vZU6PrrQN2jfjQqaPiPBKoXeXjKyRVDMz+x17Xyo+YLTkf\nMI8PqSlRpEXHv50rzdCdDCbS2Kiw1K6NHP8ErN523nlnUreRMP0G8XECQyC4JYoR12C2FbMaJlvi\n8MJCjoDE8qrXHDcrVjKTiSrCaoGvymjNchvpCFIKmy7nFaf/RjKcuEywJaIdhMPO3b9sqSe2ZNkU\ng9tRU2C/dtxQH0dQEZyDMKE1YbqEK9hxkAwEiA4dSYqXNqNQfURWyAThjXahViRVEpOiSWZ9820M\nXfZG1m1UzDZv8+5zw1Ziy+25MiSsmlNSNTVsiXi+2PRxIFOpyhSI4KJDpgEjZktlXCw67GS7BrBi\nxz19gbmpJkkUrbyN33FWZeQ1mEacCOch96BF2NwhP8NkSwwV+b5ZJadAQDLkbqLOcIY22vuxWRxy\nnecrzlZKomDAT7oOvTaeo2JLBBJUE/YCAb7n1PubnmIykfx97GMfw2lqN7DDh4764kyy1hKvXzWG\nqYKfGrZUhtdNKWL5Vukpuovh62HGiC1VwSjTJAIdEBgmW8qOSAQSAQgMmS0xduGf72hNsksOxOKz\ng6mrxBQ95EAMdPjQNdtfbqegfGRAQ9chAGOzmXfNbSrZUtdWjzbB8HziRtvOLD0RSAQSgURg0hEQ\neQj1ETWA8znzRLSpQ4u4X9HHkWK6MBRz6t9ZPcpi/0R/xF+dgT/3Lr4dk45q1r8KAsmWqqCUaRKB\nRCARSARGjwCXIFZHHr4CfPW7mr7RkzL3Zr30xje+kW6ukQagX1RvgiaQbwnP3bUOjRSamYwcgWRL\nI++CrEAikAgkAolAJQSYByFJHhzo8Y9/fNeIMzJlWoRaMZduyodGoQy9X/CCF/DGmoIoGJVwz0S8\nIxOERCARSAQSgURgIhBgfiQYpmf11VevXmEuupzMmwqeyS2OVEmApeoVyJRTgECypSnoxGxCIpAI\nJAKJQCKQCAwQgWRLAwQ3s04EEoFEIBFIBBKBKUAg2dIUdGI2IRFIBBKBRCARSAQGiECypQGCm1kn\nAolAIpAIJAKJwBQgkGxpCjoxm5AIJAKJQCKQCCQCA0Qg2dIAwc2sE4FEIBFIBBKBRGAKEEi2NAWd\nmE1IBBKBRGA0CAhiJKD2HXfc4d7Z0dQgS00EhoJAsqWhwJyFJAKJQCIwdQhgSAJbi9C45JJLLrTQ\nQlPXvmxQIvD/I5BsKUdDIpAIJAKJQB0EXHD7rne9S0zt3XbbbZFFFqmTRX6TCEwIAsmWJqSjspqJ\nQCKQCIwZAtdff/3xxx/vGpBNNtkkr0sbs87J6jSMQLKlhgHN7BKBRCARmBEEMKTFF1+c3dKMtDeb\nOcsIJFua5d7PticCiUAiUB8BtkoIU9p310cwv5wcBJItTU5fZU0TgUQgEUgEEoFEYBQIJFsaBepZ\nZiKQCCQCiUAikAhMDgLJlianr7KmiUAikAgkAolAIjAKBJItjQL1LDMRSAQSgUQgEUgEJgeBZEuT\n01dZ00QgEUgEEoFEIBEYBQLJlkaBepaZCCQCiUAikAgkApODQLKlyemrrGkikAgkAjOAQF6i0rWT\nE6KuEDWeINlS45BmholAIpAIJAJ1EPj3v//t1rkHPehBdT6epW8WXXRRYa4y0tUw+3yhhHuYcGdZ\nA0Vgzz33PPfcc0866aTHPe5xAy0oM08EEgEInHHGGbvvvrtL4vzbCCDbbLPNxRdf/O53v3vppZdu\nJMNpzeTAAw/805/+dNppp7l2ptc2oqTbb7/9r3/961NOOWWZZZbp9fOZTZ9saWa7fgobnmxpCjs1\nmzTGCDTOlvbaa69jjjkGVVp44YXHuN2jrxqqtNZaa7mk76EPfWivtUm21CtikT7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    }
   },
   "cell_type": "markdown",
   "id": "113ae567-f3f1-44d0-ab24-235f522feab3",
   "metadata": {},
   "source": [
    "|                                                                                                                                                                      |\n",
    "|----------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n",
    "| <img src=\"attachment:media/image1.emf\" style=\"width:2.42103in;height:0.56977in\" /><img src=\"attachment:media/image2.png\" style=\"width:8.26778in;height:1.87209in\" /> |\n",
    "\n",
    "**Rechnerübung zur Signalübertragung II**\n",
    "\n",
    "***Versuch 2: Nyquist-Kriterium***\n",
    "\n",
    "**Teil 2.1 Sendesignal und Ausgangssignal des Korrelationsfilters\n",
    "erzeugen**\n",
    "\n",
    "Abbildung 1 zeigt ein allgemeines Datenübertragungssystem. Mit diesem\n",
    "System soll nun ein binärer Datenstrom übertragen werden. Die Quelle\n",
    "(NQ) erzeugt zu den diskreten Zeitpunkten $\\text{nT}$ jeweils einen\n",
    "Binärwert $a_{n}$. Die Folge der $a_{n}$ kann als Musterfunktion eines\n",
    "binären, zeitdiskreten Zufallsprozesses angesehen werden.\n",
    "\n",
    "<img src=\"attachment:media/image3.png\" style=\"width:5.30091in;height:2.03876in\" />\n",
    "\n",
    "Abbildung 1: Modell eines Übertragungssystems\n",
    "\n",
    "Im Sender werden die zeitdiskreten Binärwerte dann einer Filterung zur\n",
    "Gestaltung der Sendesignalform unterzogen. Das Sendesignal hat die Form\n",
    "\n",
    ", wobei$\\text{\\ a}_{n}$die Werte 0 und 1 annehmen kann.\n",
    "\n",
    "Das Trägersignal kann beispielsweise rechteckförmig sein. Dies soll nun\n",
    "an einem Beispiel dargestellt werden.\n",
    "\n",
    "Öffnen Sie die Datei „Versuch2.m“ und führen Sie das Programm aus."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "48d2f5b7-b8ac-462a-84b9-4ee7fbfef593",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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"
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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yO3fuqK4U4rrAb7zxRnBwsI+Pj5mZ2fr1648dO3bw4EEHBwfyQAAAIBXEWa2oqGjBggX83FBVun9U2H7379/39vZ+//33lb/IAwCADojgTjSqVq1a5evrO3v2bP4ydAZTW1vb2Nho4KAAACA2xFntypUr6enpbbiiVTutWrXK0dFx5MiRBo4LAACiQpzV7O3tHzx4QFtmi9atW5ebm/vpp59qOyOqvHAiY+zo0aOGqhcAgKnhr/8pZsRZbfz48Vu2bFm2bJn+V8Zrp6SkpM8//3zr1q3K27JoJPSJPRIKhUL8LYYnlapKpZ5MOlWVSj2ZdKoqlXoyvS/dblzEWe3s2bP5+fk5OTnOzs6q81DS09NpA/GSkpLS09O3bt0qxBxLAACQHPoRyAkTJtCWqc3GjRuR0gAAQBVxVlNedswANm7c+PDhQ9Ub28+fP1/PWzQBAIBJIs5qhnTu3DljVwEAAMRF3/urAQAAiB99X+3IkSP/+te/rl+/zt/hm3fo0CHyQAAAAGqI+2q7d+9etmxZjx49Ll68OGrUqMcff/zy5cu659wDAABQIe6r7dixY+3ataNHj965c+eSJUsYY2vWrLlx4wZtFAAAAI2I+2q//PKLn58fY8zc3LyhoYExNnPmzFOnTtFGAQAA0Ig4qz148IC/bFW3bt0qKioYY2ZmZnx6AwAAEJpQM/uHDh26evXq0NDQL774on///gJFAQAAUEXcV5s/fz6/sHDhwurq6nnz5v3444/vvfcebRQAAACNiPtqykt7ODk57d+/v76+3sbGhjYEAACANsR9tbNnz6o+REoDAABDIs5qoaGhL7744u7du+vq6mhLBgAAaBFxVsvKyvLw8Fi9evVf//rXFStWXLp0ibZ8AAAAHYiz2sCBAz/44IN///vfb731VkFBwcSJE0NDQw8cOHD//n3aQAAAAM0JcnVjuVweFhZ25MiR999//8cff4yJifH399+yZYvqlSEBAADICfJ7tcbGxpMnT+7evTsvL8/d3T00NPTXX3/dtm1baWnpunXrhIgIAADAyLParVu3MjMzMzMzq6urn3vuuZ07dypvVD1hwoQXX3yRNhwAAIAq4qzm7+/ftWvXqVOnTps2zd7eXvUpZ2dnFxcX2nAAAACqiLPa2rVrx44da25urvHZL774gjYcAACAKuLZIgEBAd99953qmry8PEyABAAAwyDOasnJyefOnVNdc/78+c2bN9NGAQAA0Ig4q+3fv/+ll15SXfPSSy9h4BEAAAyDOKv99ttvFhZ/OldnZmZ269Yt2igAAAAaEWc1Nze3gwcPqq45ePBg3759aaMAAABoRDwHcsaMGUuWLCkvLx8+fHhTU1N+fn5GRkZCQgJtFAAAAI2Is9rEiRNramqSkpJ27tzJGHviiSfee++98ePH00YBAADQiP6KWa+88kpoaOj169dlMpmjo6OZmSCXmgQAAGhOkOtAmpmZPf3000KUDAAAoAM6UgAAYDros1q/fv3UFgAAAAyDPqs1NTWpLQAAABgGRiABAMB0IKsBAIDpQFYDAADTgawGAACmA1kNAABMB7IaAACYDmQ1AAAwHfRZbeXKlWoLAAAAhkGf1UJDQ9UWAAAADAMjkAAAYDqIr9l/9+7d06dPKxSK2tpaW1tbFxcXPz8/Ozs72igAAAAakWW1oqKi1NTUEydONDU12dra2tra1tbW1tbWmpmZBQQEhIeHDx48mCoWAACARjRZLSoqKjc3d9y4cZs3b/b29pbL5fz6mpqaoqKiw4cPh4eH+/v7b9iwgSQcAACARjRZzdHR8eTJk81HGuVyub+/v7+//9KlS1NSUkhiAQAAaEMzW+T555/XffLMzs4uJiaGJBYAAIA2NFlt0qRJJOUAAAC0B2b2AwCA6UBWAwAA00E2s//o0aM6ng0MDKQKBAAAoA1ZVnvrrbd0PFtaWkoVCAAAQBuyrHb48GGqogAAANqGLKs988wzVEUBAAC0DWaLAACA6UBWAwAA00GT1YqKikjKAQAAaA+a82o2NjaMsYcPHzLGLC0t+eWCggKZTObr68uvAQAAEBpNVmtoaPjHP/5x7NgxjuNmzZo1d+7cl19++cKFC4yxPn367Nq1C7dYAwAAA6DJaps2bTp16tS8efPMzc0///zzn3/+2czMLCMj48GDB7Gxsdu3b9f9azYAAAASNFnt8OHD69ev9/PzY4x5eXm99tpre/bs8fT0ZIwtX748NjYWWQ0AAAyAZrbIjRs3vL29+WUvLy/GmLu7O/9wwIAB//nPf0iiAAAA6EaT1R49emRlZcUvW1lZWVpaKh/ev3//8ccf17+o+vr6zMzMoKAgd3f3AwcOaNssOTnZXYWPj0976g8AAKaB7Noiqs6dO6dcLikp8fX11f9vs7OzL1y4EBcXFxwcrHtLT0/PPXv2tLGKAABgimiyWkREhLanDhw4MGfOHP2Lmj59OkWNAACgI6LJatHR0dqeWr9+PUmI5i5evOjt7W1lZTVw4MCYmBg3NzeBAgEAgFQIdcWsefPmCVQyz97ePj4+Pjc3Nysry9bWdtq0aZWVlYJGBAAA8RPkvBpj7Pjx4wKVzJsyZQq/IJfLV69eHRAQkJmZGRUVpW175ZxM3Xc3Na6KigpjV0FfUqmqVOrJpFNVqdSTSaeqUqknY6yiosLFxcXYtWiBUFnNkCwtLXv37l1eXq5jG6nctlT8LUZJKlWVSj2ZdKoqlXoy6VRVKvWUBFO4Zv/Dhw/Ly8sdHByMXREAADAyobLatm3byMtMSkpS/i4tMjKysLCwrq6uoqJi8eLFtbW1yjFJAADosIiz2oYNG/iFESNGNF+pj1OnTvE/rGaMxcTEuLu7L1u2rPlmM2fO3Lx5s7+//7Rp0+7du7dnzx5XV9f21R0AACRPxnEcYXHu7u7Nz2BpXGlIRq+AnhQKhVSG16VSVanUk0mnqlKpJ5NOVaVSTyaRqgp+Xq2qqkoulwsdBQAAgBHOgXz11VfVFhhjjY2NCoWiVVfMAgAAaDOyrNarVy/GWEFBAb/As7S0DAgImDp1KlUUAAAAHciyWlxcHGPMwsIiNjaWqkwAAIBWIT6vhpQGAABGRJPVMjMzGxsbdWzQ2NiYmZlJEgsAAEAbmqyWkZERGBiYmpp67do1tacUCsXGjRvHjh2bkZFBEgsAAEAbmvNqWVlZ+/btS0tLS0hI6Nq1q5OTU+fOnfkLf/z2229ubm7z588PCgoiiQUAAKANTVYzMzMLCQkJCQkpLi7Oz89XKBS1tbU9evQIDAwcPny4h4cHSRQAAADdiK/Z7+Xl5eXlRVsmAACAnkzhmv0AAAA84r7alStXmq+0srJycHCwtLSkjQUAAKCGOKu98MILGtdbWVlNmjRp6dKlFhamcJ9SAAAQJ+Ics3z58sTExIiIiEGDBnEcV1JSkpKSMnfuXGtr66SkJAcHh9mzZ9NGBAAAUCLOanv37l23bt3IkSP5h8OGDXNzc/v444/37t3bo0ePhIQEZDUAABAO8WyRS5cuDR48WHWNj48Pf28zX1/fyspK2nAAAACqiLOara1tYWGh6prCwkL+/mrV1dU9e/akDQcAAKCKeARy8uTJ0dHRM2bM8PT0lMlkJSUlaWlp/B3XsrOzg4ODacMBAACoIs5qUVFRtra2KSkpNTU1jDE7O7s5c+aEh4czxiZOnOjq6kobDgAAQBVxVjMzM4uIiIiIiKiqqmKMOTg4KJ/q27cvbSwAAAA1Qv16TDWfAQAAGIbgV8zKyckpKSkROgoAAAATrq+mNGfOHMbYwIEDX3/99cDAQKHDAQBARyZ4Vtu5c+e9e/eKior4O4sKHQ4AADoywbPakCFDGGPKq40AAAAIh/i82oYNG/RcCQAAQI44q23atEnPlQAAAOQEnwNZVVXFXzELAABAaGTn1fjLYqkuMMYaGxsVCoWvry9VFAAAAB3IslqvXr0YYwUFBfwCz9LSMiAgYOrUqVRRAAAAdCDLanFxcYwxCwuL2NhYqjIBAABahXhmP5/SKisrr1y5whh75plnHB0daUMAAABoQ5zV7t+/v3LlyuzsbOWa4ODglStXWllZ0QYCAABojngO5Pr16/Py8uLj448dO3bs2LEPP/wwLy9v/fr1tFEAAAA0Iu6rHTp0aO3atcOGDeMfOjs7Ozk5vfPOO0uWLKENBAAA0BxxX+327dseHh6qa/r373/79m3aKAAAABoRZzVHR8e8vDzVNbm5uZgwAgAAhkE8Avnyyy+/++67586dGzx4cFNTU1FR0Y4dO95++23aKAAAABoRZ7UZM2aYmZlt3LgxNTWVMdalS5d33nnntddeo40CAACgEXFWu3z5clhYWFhY2K1btxhj9vb2tOUDAADoQHxebcKECfyCvb09UhoAABgYcVbr3r17dXU1bZkAAAB6Is5qwcHBSUlJjx49oi0WAABAH8Tn1YqKigoLC7/++msXF5dOnTop16enp9MGAgAAaI44qzk4OChPrQEAABgYcVZbu3YtbYEAAAD6Iz6vBgAAYETIagAAYDros1q/fv3UFgAAAAyDPqs1NTWpLQAAABgGRiABAMB0IKsBAIDpQFYDAADTgawGAACmA1kNAABMB7IaAACYDmQ1AAAwHfRZbeXKlWoLAAAAhkGf1UJDQ9UWAAAADAMjkAAAYDqI70Rz9+7d06dPKxSK2tpaW1tbFxcXPz8/Ozs72ii8+vr6gwcP7tq168KFCwkJCS+++KIQUQAAQELIslpRUVFqauqJEyeamppsbW1tbW1ra2tra2vNzMwCAgLCw8MHDx5MFYuXnZ194cKFuLi44OBg2pIBAECiaLJaVFRUbm7uuHHjNm/e7O3tLZfL+fU1NTVFRUWHDx8ODw/39/ffsGEDSTje9OnTCUsDAAATQJPVHB0dT5482XykUS6X+/v7+/v7L126NCUlhSQWAACANjSzRZ5//nndJ8/s7OxiYmJIYgEAAGhD01ebNGlSaWkpSVECcXd35xeOHj1q3Jro4Oriauwq6M2FcTKmKCszdj1aUFFRYewq6EsqVXV1cS1TiP1150nlkEqlnoyxiooKFxcXY9eiBcRzIEVL5EmXMSZjMsYYxzhjV6QlMhljTMYxxpj42zeTSCV54q8q30pdXVwl0FAZY1I4pDyp1FMS8Hs1UZDJ+P+k8EnBSaGSIBipdNSgwyLrq+ke2QsMDKQKBMbHcZxMJuMYJ5MhyQGAqJBltbfeekvHs0IMAJ46dWrmzJn8ckxMTExMzKRJk+Li4sgDCU0mY4yTMRlXVqZgDAMRIFIyJuMYp2AKjm+vEhmEhI6GLKsdPnyYqig9jRgxQvxnywAAwJDIstozzzxDVVSHw8nUVoh/YE9RVsbJXPF1vePg54morcHrDyKE2SJGppwnokxj/BqZ+meI+Ig88QI11RyGfAaihawGbcefXzF2LQAA/ocmqxUVFZGU0xFxMo5xqt0etU6bWMhkIqsQGI7GwUZ8pwFxoslqNjY2Op6tr68niQJGg3wGABJBk9U4jvvkk09GjRo1fPjwBQsWXL16VfVZb29vkiimxwS+6uILu8nT/fri1Qexoclqe/bs2bx588iRI59//vnCwsKXXnrp1KlTJCWbPI1n3UU6CAkdlba5IZgzAiJEk9V27NixevXqDz74YPny5ceOHfP19X3jjTeOHz9OUjgYmTK1apn0+Ht3DRkYAESAJqv98ssvo0eP5pflcvmmTZuCgoIWLFgg5gvkG53un/tw3O//JAD5zHS1+KM0DEGD2ND8Crtbt25Xr17t378//9Dc3HzVqlWdO3d+++23Hzx4QBICjKOljhoAgKjQ9NVGjhy5evXqxsZG1ZVLliyZN2/eokWLSEKYGMl0b/RJZhzHXxZQOnsFACaLJquFhYVVV1enpqaqrY+MjHz33Xe7detGEsVkKC9nbOyK6EcyI6FATM9rYmEQEkSFZgTS1dX10KFDGp8KCwsLCwsjiQLihXvTAIA44IpZhqbsqOHDH8SsVRf/aVInAAAgAElEQVQvRncNxEOorDZv3jyBSgYAANBGqKyGH6vRksA8DI7jGCeVc4UAYKowAmlQbRt+lMy9acBUtOHeaRiEBJFAVgNK+GgDAOMSKqtt27ZNoJKlrdl9Z/T6I8NfFhL3nenA2nyTa3ynATEgzmobNmzgF0aMGNF8JUgD8hkASBZxVtu0aZOeK8FU4Qs7ABiR4OfVqqqq5HK50FEkoc0DOwz3pgFDaU8rZfhOAyJAc20Rxtirr76qtsAYa2xsVCgUvr6+VFFAcBSXM+Y/2jgZrokMAIZGltV69erFGCsoKOAXeJaWlgEBAVOnTqWKIl3t/ArMpJUgZDLcTlKK2t9KmfI7DVoAGAlZVouLi2OMWVhYxMbGUpUJhobxTQCQOOLzakhpGkkmWSj7g+3sGOLeNABgJDRZLTMzU+3mamoaGxszMzNJYkkO7jsDkkA4bIg5I2BENFktIyMjMDAwNTX12rVrak8pFIqNGzeOHTs2IyODJBZIA8cxhu4aABgazXm1rKysffv2paWlJSQkdO3a1cnJqXPnznV1dRUVFb/99pubm9v8+fODgoJIYklLR77vjIQ6qEA+vwNzRsBYaLKamZlZSEhISEhIcXFxfn6+QqGora3t0aNHYGDg8OHDPTw8SKIAAADoRjYHkufl5eXl5UVbJmgkgZtOcxzHmAx3pwEAAyLOaleuXGm+0srKysHBwdLSkjaW+Ak3/Ki8N43YExtIgUBDhRiEBKMgzmovvPCCxvVWVlaTJk1aunSphQVxRDHjOMwDY0ywD00AgOaIc8zy5csTExMjIiIGDRrEcVxJSUlKSsrcuXOtra2TkpIcHBxmz55NG1HMfv80F//nOcVVsrTBJG+RE/Q7B7prYHjEWW3v3r3r1q0bOXIk/3DYsGFubm4ff/zx3r17e/TokZCQ0KGymnA4TmKDkPhoAwDDIL62yKVLlwYPHqy6xsfHp7S0lDHm6+tbWVlJGw4ICNlR+71g5DMAMBTirGZra1tYWKi6prCwkL8TTXV1dc+ePWnDiZnQvRPcmwbazwB9aAxBg4ERj0BOnjw5Ojp6xowZnp6eMpmspKQkLS2NvzdNdnZ2cHAwbThoL+E7av8LhXvTAIDwiLNaVFSUra1tSkpKTU0NY8zOzm7OnDnh4eGMsYkTJ7q6utKGEy3DnEaSUILAdUZEyGAnOzFnBAyJOKuZmZlFRERERERUVVUxxhwcHJRP9e3blzYWSA4+2ABAaEL9ekw1n3U0MhmTzIc3P5nSAJ0+juNkst8vdiyhPiYASA3xbJHmcnJySkpKhI4iHhK77wyT1Dgm0DHwkCDmjIDBCH6ljzlz5jDGBg4c+PrrrwcGBgodDsSL4xiTyTjGobsGAIIRPKvt3Lnz3r17RUVF/D3YhA5nXAacUShJ0urEAoAUCZ7VhgwZwhhTXm3E9GG2V0tweIzOKG0UMyHBMAQ/rwaGIZNJ4efYHIcPNQAQFHFf7erVqxrXOzs70wYSITHc9hpnrKBFRuwwobsGBkCc1caNG6dxPX8pSNOG+87oDx9tACAQ4qyWlZWlXOY4rqysbN26dXPnzqWNImYS6CoZe04LJnkDgHCIs9rAgQNVH3p6ejo6On7yySehoaG0gUTIuP0P3JsG9GH0w45BSBCa4LNFBg4c+NNPPwkdBfRl7I7a78HxoQYAwhA8qxUVFVlbWwsdxejE8PUT96YB3cTQShmGoEFgxCOQixcvVn1YU1OTl5cXFhZGGwV009oNE0dHTQn3pgEAcsRZ7datW6oPu3Tpsnz58pCQENoooI2EEgSuMwIAQiDOaunp6bQFSoJIBnakCAfOYETVSjFnBISDa4u0l3LmoQTwXTmRdOg4juMPmjSOHQBIA/11II8cOfKvf/3r+vXrjx49Uq48dOgQeSAxEMP1RFpHMhUFSiLsGKG7BgIh7qvt3r172bJlPXr0uHjx4qhRox5//PHLly97enrSRgETwXGM4ewaAFAi7qvt2LFj7dq1o0eP3rlz55IlSxhja9asuXHjBm0UMBm/zxmRyk/HAUD0iPtqv/zyi5+fH2PM3Ny8oaGBMTZz5sxTp07RRhEJ6Q0/ihaOoJBEO9CHH66BEIiz2oMHD/jfXHfr1q2iooIxZmZmxqc3MDAJ3ZsGH20AQEWoOZBDhw5dvXr18ePHY2Nj+/fv36q/3b9//3PPPTdgwIDx48cfP35c4zbJycnuKnx8fChq3QpS6ahJILGBkETbUePhOw2QI85q8+fP5xcWLlxYXV09b968H3/88b333tO/hLy8vKVLl7755punTp0KCQmJjIw8f/68xi09PT1L//D9998T1L41RJ7MlKRyexx8tAEACeKsFhkZyS84OTnt37+/qKgoJyfHw8ND/xLS0tJGjx4dFBQkl8tnzZrVr1+/7du301aSkDRym+jHIsXcmQAAaRH2V9g2Njat2p7juOLi4qFDhyrXDB8+/OzZsxo3vnjxore3t5+fX0RExKVLl9pV0dYT+cAOk0rGBSGJv5UyDEICNZqZ/S3ePm337t36lFNXV9fQ0NC1a1flmq5du/7666/Nt7S3t4+Pj/f396+pqVm3bt20adMOHjzo6OiorWR3d3d+4ejRo/rUpGUuTKFQ0BT1B35+DSGOufILirIyRlpb8qqWsTKZi6xMUUZbLHk9hSNIVaXQShkTpJ5MOq++VOrJGKuoqHBxcTF2LVpAk9Xc3NxIytHflClT+AW5XL569eqAgIDMzMyoqCht25eWlhJG//0rsAAvrkAtxtXVhbzrJkRVpVKmQGirKqFWyjFO5iJIt1Iqr75U6ikJNFktNjaWpJzOnTtbW1vfvn1bueb27dvdunXT/VeWlpa9e/cuLy8nqYPp+ONcmoyJfaKmkiRGzABAzMR1dWOZTObl5VVQUKBcc/r06cGDB+v+q4cPH5aXlzs4OAhcO6mSSkrDxY4BoP3os1pNTc3WrVsXLVq0aNGi1NTU2traVv15eHh4Tk7O/v37a2pqtm3b9tNPP82YMYN/KikpSfm7tMjIyMLCwrq6uoqKisWLF9fW1irHJAUlk6E/ARIguVaKOSNAhfg6kOfPn581a9ajR4/4M23Hjx9PTU1NT0/X/4fYo0aNio+PT05O/sc//tG7d++kpCSNPwyYOXNmcnJycXGxtbW1p6fnnj17XF1dKfdEJ2lctpDjJFLRP3AcJ5PJOMZJq9oAICbE18aYNm1az5494+Li+Dn99fX1y5Ytu3HjRkZGBmGU1nJ3dyeZLSL09UQUCoVUThoLVdXfsxrZsGnHPKSCdtQEPaS0NZfKqy+VejKJVJV4BPLcuXNLlixR/kzNxsZmyZIl2i4OAqAR7k0DAG1GnNU0TtnAPA7QF26QDQDtQ5zVxo0bFx8fX1dXxz+sq6uLj48fO3YsbRSjkMrljE0EjnJbSW6eiCrMGYH2o5ktMmvWLH7hwYMHhYWFeXl5ffr0YYxdvnz5v//9r6+vL0kUaA9l50fs+YLjOIl/NAOAEdFktSeeeEK5PGHCBOXy008/TVK+0f3eURN/StCOnxHJpDKBE9rENDo6+E4D7UGT1dauXUtSjmhx/A1dxP9eM5WUxY9Eif5wi5HUDxoGIaGdxHVtEZGTRr4Q/X1nAACEg6ymF9PoNyizMrKeSTKNVsrQXYP2oc9q/fr1U1sAA5HMhBC94KMNANqAPqs1NTWpLUidKX22ortmqkyplfJMb4/AMDACqRcJDOy0pqPGcdLoznEyfLS1ggRaqd5MaV/AwJDVOhxJ5LM/Qb8SAPSGrKYL7jsDkmCSrRQnVqFtkNVaJo2uAj+qKL2OmE4cx/9sTSKvAQAYH7KaViZwPRHoCEy7Q2PaewdCQFZrCW6LYmy/d9dAO9MbfuSZ6n6BoJDVQNzwWwQAaA36rLZy5Uq1BSnqOPedkUyyMPlXok1Mcp6IKswZgdaiubqxqtDQULUFECcJ5TPcmwYA9IQRSA06yDwR0967jqDjdGI6zp5C+xH31e7evXv69GmFQlFbW2tra+vi4uLn52dnZ0cbRWj8fWek0TOguPWMqdy+piOSRittHwxCQquQZbWioqLU1NQTJ040NTXZ2tra2trW1tbW1taamZkFBASEh4cPHjyYKhb8riPdBhR3XAMAfdBktaioqNzc3HHjxm3evNnb21sul/Pra2pqioqKDh8+HB4e7u/vv2HDBpJwQus4n564QbZ0dZxWyvCdBlqDJqs5OjqePHmy+UijXC739/f39/dfunRpSkoKSSz4nWndd0Yf+GgDgBbRzBZ5/vnndZ88s7Ozi4mJIYklNIzgg/h1zFbaMfcaWosmq02aNImkHJHoUL0Bvqcnjf6ecrQUOlgrZR1vf6HNMLNfmkiHH6WR0hgudgwALUNW+x/cdwYkocO2UkzxB32Qzew/evSojmcDAwOpAglNGrMBJVBFYXAcJ5PJOMZJ43UCAEMjy2pvvfWWjmdLS0upAgmkg1xPBKQOnZUO21UFPZFltcOHD1MVZUx4v4DodeQ2ikFIaBFZVnvmmWeoigLQBYOQAKAdZosw1pHuO6MPyUww7HivFgbfGLpr0BL6O9GAdEkon+HeNACgEU1fraioiKQco8E8ERA9dFBU4WiANjRZzcbGRm3NvHnzSEo2GGl86xe4M6XM65LptHUw0milwsNxAB2EOq92/PhxgUruuHC9qD/D+RUAaA6zRTCU8SforokTWmlzOCagEbIaY5IY0Oh4953RR4fqrkmglRoQjgZoI1RW27Ztm0Al0+o4n4mmzNT7lWil2uDIQHPEWU15t+sRI0Y0XylO+NKnRnr3pukA0EqbwzEBjYiz2qZNm/RcCa1g8OFHaaQ0hnvTAIA6wc+rVVVVyeVyoaO0AX/fGWPXAqAFaKW64fiAGrJri7z66qtqC4yxxsZGhULh6+tLFYWeJC5PIZmukzF0gMtCSqGNGkeHmi4EeiLLar169WKMFRQU8As8S0vLgICAqVOnUkWhgvvOgCTgI1sfuHYaqCLLanFxcYwxCwuL2NhYqjIFh/cCiB7aqG7oroEa4vNqUkppYDIwZwQA/kCT1TIzMxsbG3Vs0NjYmJmZSRKr/TD82FqSSRam9YqiC6I/HCtQoslqGRkZgYGBqamp165dU3tKoVBs3Lhx7NixGRkZJLHIyEzqE1AgMplEUhrHmeRIFIYf9YGjBKpozqtlZWXt27cvLS0tISGha9euTk5OnTt3rqurq6io+O2339zc3ObPnx8UFEQSiwBnap99YHpML0MLDXNGgEeT1czMzEJCQkJCQoqLi/Pz8xUKRW1tbY8ePQIDA4cPH+7h4UEShRAnia93fC/JqKNqHPe/WwWY1vCeBEihjYqFSfbUoW2I74Xt5eXl5eVFW2YHJY2BP3HhP9qQDAA6MuKsduXKleYrraysHBwcLC0taWO1DT71WgvdNcNDK20DfKcBHnFWe+GFFzSut7KymjRp0tKlSy0siCOaJtx3BgCgTYhzzPLlyxMTEyMiIgYNGsRxXElJSUpKyty5c62trZOSkhwcHGbPnk0bsVUw8m7yfv/CLpPwFwK00vZAdw2Is9revXvXrVs3cuRI/uGwYcPc3Nw+/vjjvXv39ujRIyEhwbhZjeEMfJtIKUHIZCbwCqOVtg3mjAAjv7bIpUuXBg8erLrGx8entLSUMebr61tZWUkbzjRh+LE9cJ0RgI6NOKvZ2toWFhaqriksLOTvRFNdXd2zZ0/acPrj7zsjja/ASGYdmGRaqVihuwbEI5CTJ0+Ojo6eMWOGp6enTCYrKSlJS0vj702TnZ0dHBxMG661JDOLTxq1BAAQHeKsFhUVZWtrm5KSUlNTwxizs7ObM2dOeHg4Y2zixImurq604VoB1xPpOFTvuFZWZuzatAI6GVTQ5e3IiLOamZlZREREREREVVUVY8zBwUH5VN++fWljMcb279+fnJx848aN3r17L1y4cMyYMRo3u3SplG/n6AKByOGzuP0wCNnBEZ9XU3JwcFBNaULIy8tbunTpm2++eerUqZCQkMjIyPPnzwsaESTjjzkjLkYcHgAAYxAqqxlAWlra6NGjg4KC5HL5rFmz+vXrt3379uab/X7fGXTU6MhkzNXVxdi1aAnHMcYU0hmBdHVxRUeNCrprHZlUsxrHccXFxUOHDlWuGT58+NmzZ7VuzxiTNfunTfMtsb1yWxnjWnM8ZdoKN9L2/HRYMW7PRFYfqW/PmIuri4jqYxrbS4FUr19VV1fX0NDQtWtX5ZquXbv++uuvrSpEoVBoXO/CXIyyfUVFhajqo2V7zRuLcPsyRZmrS7MRyN8Tst6Mt73G/dW6t4baXq2VGr0+OraX6TNHTAztQbXSYqiP9u3/nfdvFxcdr4koSDWr6Y/jmLYbhLb25RF6+6eeeqpVf2KU+nOc1jeF2I6ni4tLmaJM/a9kMqZtoE/jauNtL8Lj2ao/NG79uebvepG9vsrVCoXif/sigvro2F5RpvmbsahIdQSyc+fO1tbWt2/fVq65fft2t27dNG7s7ub++6iZ2j9tNG6M7VW2V5Qp9Nye01a4kbZnHCfO7TUcUknVX4Tbqx9SY9fHFLaXAkGy2rVr17766qvPPvuMMcZP8Scnk8m8vLwKCgqUa06fPq12sS4AAOhoiLNaQ0PDokWLnnvuuaioqLi4OMZYbGxsTk4ObRReeHh4Tk7O/v37a2pqtm3b9tNPP82YMUOIQAAAIBXEWe3DDz8sLS3dvXt3SUkJvyYsLCwtLY02Cm/UqFHx8fHJyckjRozIyspKSkry8PAQIhAAAEgF8WyRI0eO7Nq1q0+fPso1/fr1U2Y4ckFBQUFBQQIVDgAAkkPcV6uvr7e3t1dd09jYaGYm1TkpAAAgLcT5xs3Nbc+ePapr8vPz+/fvTxsFAABAI+IRyJkzZ0ZHR5eUlPj7+zPGsrKyEhIS4uPjaaMAAABoRJzVJk6c2NDQsGHDhmPHjjHGPvroo5iYGG2X0gcAAKBFf22RKVOmTJ48ubKysqmpycnJCSfVAADAYOiz2sOHD8vLy+/cucNUfoI9ZMgQ8kAAAABqiLNaUVHRggULbt68qba+tLSUNhAAAEBzxFlt1apVvr6+s2fPlsvltCUDAAC0iDirXblyJT093c7OjrZYAAAAfRBP5bC3t3/w4AFtmQAAAHoizmrjx4/fsmULx0nkjgUAAGBaiEcgz549m5+fn5OT4+zsrJrb0tPTaQMBAAA0R5zV7O3tJ0yYQFsmAACAnoiz2tq1a2kLBAAA0B8u/AEAAKYDWQ0AAEwHzQhkaGio7g12795NEggAAEAHmqzm5uZGUg4AAEB70GS12NhYknIAAADaQ5DzateuXfvqq68+++wzpnLZfgAAAKERz+xvaGhYsWLFgQMHzM3NHz169Nprr8XGxk6ePHn06NG0gQAAAJoj7qt9+OGHpaWlu3fvLikp4deEhYWlpaXRRgEAANCIuK925MiRXbt29enTR7mmX79+ygwHAAAgKOK+Wn19vb29veqaxsZGMzP8Kg4AAAyBON+4ubnt2bNHdU1+fn7//v1powAAAGhEPAI5c+bM6OjokpISf39/xlhWVlZCQkJ8fDxtFAAAAI2Is9rEiRMbGho2bNhw7NgxxthHH30UExMzZswY2igAAAAaEWc1xtiUKVMmT55cWVnZ1NTk5OSEk2oAAGAwxCnnzJkzt2/flslkTk5OTz/9NJ/Szpw5QxsFAABAI+Ks9sorr4SEhFy+fFltJW0UAAAAjeiHB/v27Ttt2rT8/HzykgEAAHSjz2qbNm0aP358eHj4v/71L/LCAQAAdKCfLWJubh4bG+vo6BgTE1NZWTl79mzyEAAAABrRZzXe7NmzHR0d33333Rs3bggUAgAAQI1QWY0xNnHiRAcHh3nz5gkXAgAAQBVxVjt48KDqw6FDh2ZlZeHqxgAAYBjEWa1v375qa5ydnZ2dnWmjAAAAaEQ/Avnw4cPy8vI7d+6orhwyZAh5IAAAADXEWa2oqGjBggU3b95UW19aWkobCAAAoDnirLZq1SpfX9/Zs2fL5XLakgEAAFpEnNWuXLmSnp5uZ2dHWywAAIA+iK8tYm9v/+DBA9oyAQAA9ESc1caPH79lyxaO42iLBQAA0AfxCOTZs2fz8/NzcnKcnZ1Vc1t6ejptIAAAgOaIs5q9vf2ECRNoywQAANATcVZbu3YtbYEAAAD6o78TDQAAgLHQ9NVCQ0MZY7t37+YXmtu9ezdJIAAAAB1ospqbm5vaAgAAgOHRZLXY2Fi1BQAAAMMT5LzatWvXvvrqq88++4wxVlVVJUQIAACA5ojnQDY0NKxYseLAgQPm5uaPHj167bXXYmNjJ0+ePHr0aNpAAAAAzRH31T788MPS0tLdu3cr7xQaFhaWlpZGGwUAAEAj4r7akSNHdu3a1adPH+Wafv364V7YAABgGMR9tfr6ent7e9U1jY2NZmb4VRwAABgCcb5xc3Pbs2eP6pr8/Pz+/fvTRgEAANCIeARy5syZ0dHRJSUl/v7+jLGsrKyEhIT4+HjaKAAAABoRZ7WJEyc2NDRs2LDh2LFjjLGPPvooJiZmzJgxtFEAAAA0Is5qjLEpU6ZMnjy5srKyqanJyckJJ9UAAMBg6LMaY0wmkzk5OQlRMgAAgA5i7Ejt37//ueeeGzBgwPjx448fP65xm+TkZHcVPj4+Bq4kAACIkOiyWl5e3tKlS998881Tp06FhIRERkaeP39e45aenp6lf/j+++8NXE8hBAYGGrsK+pJKVaVSTyadqkqlnkw6VZVKPZlEqiq6rJaWljZ69OigoCC5XD5r1qx+/fpt377d2JUCAABpEFdW4ziuuLh46NChyjXDhw8/e/asxo0vXrzo7e3t5+cXERFx6dIlQ9URAADES1xZra6urqGhoWvXrso1Xbt2/fXXX5tvaW9vHx8fn5ubm5WVZWtrO23atMrKSgPWFAAAxEiQOZCqcnJyunXr5unpSVvslClT+AW5XL569eqAgIDMzMyoqCht27u7u9NWQCBSqSeTTlWlUk8mnapKpZ5MOlWVSj0lQfCsNmfOHMbYwIEDX3/99eZnGr/66qv58+fzy/Hx8cHBwdbW1rdv31ZucPv27W7duukOYWlp2bt37/Lycm0blJaWtrH2AAAgKYJntZ07d967d6+oqCgjI6N5VnvuuefUUo6Xl1dBQUFYWBj/8PTp04MHD9Yd4uHDh+Xl5QMGDCCsNgAASJHg59WGDBkycuTIyMhIPacyhoeH5+Tk7N+/v6amZtu2bT/99NOMGTP4p5KSkpS/S4uMjCwsLKyrq6uoqFi8eHFtba1yTBIAADoswftqrTVq1Kj4+Pjk5OR//OMfvXv3TkpK8vDwaL7ZzJkzk5OTi4uLra2tPT099+zZ4+rqavjaAgCAqMg4jqMtkR8PvHPnjurKIUOG0EYBAABojrivVlRUtGDBgps3b6qtx3wNAAAwAOK+WkhISO/evWfPni2Xy1XX9+zZkzAKAACARsRZzcvLKzc3187OjrBMAAAAPRHPgbS3t3/w4AFtme2hz+X/xaC+vj4zMzMoKMjd3f3AgQPGro5W58+fj4qKGjFixNChQ2fNmqXtwtNicOvWrbi4uNGjR3t5eQUFBYn5qPI+/fRTd3f3BQsWGLsimknrLhlVVVWLFi0aNmyYn59fXFxcfX29sWukWWBgoPufvfnmm8aulAYNDQ1xcXH+/v4DBgwYM2ZMYmJiY2OjsSulFXFWGz9+/JYtW8hnoLSN/pf/N7rs7OwffvghLi7O2BVpQWJiYmBg4BdffHHkyBFHR8cZM2ZUVVUZu1Ka7dy5093dPSMj45tvvnn55ZcXLVr0zTffGLtSWv3000/bt28fOHCgsSuii1TuknH37t1p06bV1NRkZ2efOHHC1dX122+/NXalNDt69KjykB49epSJ9aL469atO3r06MaNG7///vsPPvjg008/TU1NNXaltCKeLXL27Nn8/PycnBxnZ2fV3Jaenk4bSB/Ky/8zxmbNmnXo0KHt27cnJCQYviYtmj59urGroJeUlBTl8nvvvZednf3NN9+EhIQYsUraqF4+bcqUKcnJyUVFRSNHjjRilbSpr69fuHDhypUrd+7caey6mIKUlJQHDx6sX7/e2tqaMRYaGmrsGullz549dnZ2Y8eONXZFNDh//vxf//pX/kdWQ4cO9fHxOXfunLErpRX9COSECRO8vLy6dOnyhAraKPpo1eX/oQ1qa2sbGxvVpgWJUH19/b59++7evRsQEGDsumi2cuXKESNG+Pv7G7siLZDKXTK+/PLLv//973xKk4rGxsYvvvjixRdftLKyMnZdNHjhhRfy8vLOnz9///79wsLCH374YcKECcaulFbEfbW1a9fSFthm+l/+H9pm1apVjo6O4uz98H7++Wf+vdepU6e4uDiNP+c3ugMHDvz444/Z2dnGrkgL+Ltk+Pv719TUrFu3btq0aQcPHnR0dDR2vTS4fv36E088ERERcfr06e7du48bN27BggU2NjbGrpcuJ0+erK6unjp1qrErotn06dOvXbsWHBzMGJPJZO+88864ceOMXSmtiLPa1atXNa53dnamDQTGtW7dutzc3E8//VTM34j79u1bWlpaU1Nz5MiRd9999/HHHx8zZoyxK/UnFRUVcXFx6enpYj6MvNbeJcOIOI5LTU1NSEhITEy8fPlyZGRkbW2tyE9aZ2VleXt79+3b19gV0WzNmjVffvnlP//5T3d39x9++GHBggWdOnVSXq1XbIizmrYEbvhfYXfu3LkNl/8HfSQlJX3++edbt24lv8GQEORy+dSpU3NzczMyMsSW1S5cuHDnzh3+K7CSu7v7iRMnnJycjFWrFrV4lwzj6t69+4ABA55//nnGmKenZ1hYWHJyspiz2q1bt3Jzc99//31jV0SzpqamHTt2vPPOO15eXowxPz+/qVOnfvrpp/ZCdIIAAAQ4SURBVCae1caPH88YO3ToUFZWlnIlx3FlZWXr1q2bO3cuSZRWkclkbbj8P7QoKSkpPT1969at0roK2sOHD41dBQ3U7lnxxhtvPPbYYx9//LERq6QPkd8lw8fH5/79+6przMzEdXtkNdnZ2Y899hifhkVIJpOZm5urruE4TsyHlKZmbm5ubm5ujLGBKjw9PYOCgj766KMjR46QRGktHZf/h7bZuHGjVFJaZGTkmTNn6urqfv31123btuXl5YlzrqZUSOguGbNmzfrmm2+OHDnS0NBQUlLy6aefinlqA2MsKyvrxRdfFO2ZP5lM9ve//3379u3FxcUNDQ2nT5/OzMwU83k1+qsbq2loaBg1atSZM2cEjaLN/v37k5OTb9y40bt374ULF4ptAErp1KlTM2fOVF0zadIkEY6ZDBgwQK3TM3/+/MjISGPVR4czZ85s2rSpuLjY0tKyb9++s2bNGj16tLEr1QIx99XOnj2repeMt99+W7QngRhjOTk569evLysre/LJJ8ePHz9//nxxzi1kjBUWFk6fPj07O1ucs5l4dXV1iYmJX3/9dXV1tYODw8SJE+fMmSPaQ0qT1fixCI2/YDh16tTixYvz8vLaHwUAAEA3mvNqr7/+Or+wePFi1fU1NTV5eXmiPakIAAAmhngEctasWaoPu3Tp4ufnFxISonayEQAAQAiCn1cDAAAwGOLZmT///DNtgQAAAPojzmoin0ELAACmjTirde/evbq6mrZMAAAAPRFnteDg4KSkpEePHtEWCwAAoA/i2SLTp08vLCzs3r27i4tLp06dlOuNcn81AADoaIivbuzg4IBTawAAYCyY2Q9gZMuWLfvll18+//xzY1cEwBQQ99WuXLnSfKWVlZWDg4OlpSVtLAAAADXEfTV3d3eN662srCZNmrR06VILC+I8CiBpK1asyMjIUD6cO3euOG/FCSAVxDlm+fLliYmJERERgwYN4jiupKQkJSVl7ty51tbWSUlJDg4Os2fPpo0IIGmxsbGPHj3CCCQAFeKstnfv3nXr1o0cOZJ/OGzYMDc3t48//njv3r09evRISEhAVgMAAOEQ/17t0qVLaveb9vHx4e/26+vrW1lZSRsOAABAFXFWs7W1LSwsVF1TWFgol8sZY9XV1T179qQNBwAAoIp4BHLy5MnR0dEzZszw9PSUyWQlJSVpaWmvvvoqYyw7Ozs4OJg2HIAJsLS0bGxsNHYtAEwEcVaLioqytbVNSUmpqalhjNnZ2c2ZMyc8PJwxNnHiRFdXV9pwACbAycnp2LFjVVVVDg4Oxq4LgOQJ9SvsqqoqxhjepQAtqqmpiYmJ+f7772trazGzH6CdcG0RAAAwHcSzRQAAAIyIPqv169dPbQEAAMAw6LNaU1OT2gIAAIBhYAQSAABMB7IaAACYDmQ1AAAwHchqAABgOpDVAADAdPx/p40BPR8ibPAAAAAASUVORK5CYII="
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TD:\n",
      "     1\n",
      "\n",
      "die gesendeten Datenbits a[i]:\n",
      "     1     1     0     0     1     0     0     0\n",
      "\n",
      "die empfangenen Datenbits g[i]:\n",
      "     1     1     0     0     1     0     0     0\n",
      "\n",
      "Anzahl der fehlerhaften Datenbits (ohne Rauschen):\n",
      "     0\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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"
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "n_max:\n",
      "    0.2900\n",
      "\n",
      "die gesendeten Datenbits a[i]:\n",
      "     1     1     0     0     1     0     0     0\n",
      "\n",
      "die verrauschten Abtastwerte y[i]:\n",
      "    1.0531    0.9004    0.1843   -0.1416    0.7245    0.1568   -0.1884    0.0487\n",
      "\n",
      "die geschätzten Datenbits a_en[i]:\n",
      "     1     1     0     0     1     0     0     0\n",
      "\n",
      "Anzahl der fehlerhaften Datenbits (mit Rauschen):\n",
      "     0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "Versuch2;"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5445e254-a69c-4c87-b90d-7f4b970f797c",
   "metadata": {},
   "source": [
    "Einige Definitionen der verwendeten Matlab-Funktionen:\n",
    "\n",
    "-   Definition der Taktzeit:\n",
    "\n",
    "-   Definition der Rechteckfunktion:\n",
    "\n",
    "<!-- -->\n",
    "\n",
    "-   Definition der Form des Sendesignals:\n",
    "\n",
    "-   Sendesignal:\n",
    "\n",
    "-   Dargestellter Zeitbereich:\n",
    "\n",
    "-   Definition einer Beispielbinärfolge:\n",
    "\n",
    "( In dem Matlab-Code können Sie die Binärfolge ändern. Gegebenes\n",
    "Beispiel:$a_{i} = 11001110$. In „Figure 1“ wird Das Sendesignal für die\n",
    "gegebene Folge dargestellt.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "27ab1c3c-091b-4366-8379-159d2e03fa21",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = [1 1 0 0 1 1 1 0];\n",
    "\n",
    "%  Sendesignal m(t)\n",
    "m = 0;\n",
    "for i = 0:7\n",
    "    m = m + a(i+1)*(heaviside(t-i)-heaviside(t-i-1));\n",
    "end\n",
    "\n",
    "%  m(t) plotten\n",
    "figure(1)\n",
    "plot(t,m,'LineWidth',2.5,'Color',[1 0 0]);\n",
    "axis([0 8 -0.5 1.5]);\n",
    "grid on\n",
    "hold off\n",
    "title('Abbildung 2: Verlauf des Sendesignals m(t)')\n",
    "xlabel('t')\n",
    "ylabel('m(t)')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbd591d4-22ba-4fcb-bab3-90a06aa0e174",
   "metadata": {},
   "source": [
    "Vorausgesetzt, dass der Kanal störungsfrei ist, erhält man am\n",
    "Empfängerausgang ein Signal der Form , wobei die\n",
    "Autokorrelationsfunktion der Filterstoßantwort ist.\n",
    "\n",
    "-   Definition der Dreiecksfunktion:\n",
    "\n",
    "-   Autokorrelationsfunktion:\n",
    "\n",
    "-   Ausgangssignal des Korrelationsfilters:\n",
    "\n",
    "> „Figure 2“ zeigt den Verlauf des zugehörigen Ausgangssignals im Falle\n",
    "> des ungestörten Kanals."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6a1d603f-5c9e-4cfa-8263-72682c71c22c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%  Ausgangssignal des Korrelationsfilters g(t)\n",
    "g = 0;\n",
    "for i = 0:7\n",
    "    g = g + a(i+1)*(((heaviside(t-i)-heaviside(t-i-1)).*(t-i))...\n",
    "                  +((heaviside(t-i-1)-heaviside(t-i-2)).*(i+2-t)))  ;\n",
    "end\n",
    "\n",
    "%  g(t) plotten\n",
    "figure(2)\n",
    "plot(t,g,'LineWidth',2.5,'Color',[0 0 1]);\n",
    "axis([0 8 -0.5 1.5]);\n",
    "grid on\n",
    "title('Abbildung 3: Ausgangssignal des Korrelationsfilters im Falle des ungestörten Kanals')\n",
    "xlabel('t')\n",
    "ylabel('g(t)')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd47774c-34a5-491b-a3ae-ba161864c8cd",
   "metadata": {},
   "source": [
    "Tastet man nun zu den Zeitpunkten$\\text{\\ nT}$ ab, so erhält man wieder\n",
    "die Ausgangsbinärfolge. Die Abtastung wird in Matlab durch erreicht.\n",
    "(Die Verzögerung um T ist notwendig, da man keine nichtkausalen Filter\n",
    "realisieren kann.)\n",
    "\n",
    "Zum Vergleich sind auf Matlab-Command-Window gesendete$a_{i}$und\n",
    "empfangene$g_{i}$Binärfolge dargestellt.\n",
    "\n",
    "„Figure 3“ zeigt erneut das Ausgangssignal des Korrelationsempfängers,\n",
    "wobei die Einzelimpulse für n=0…3 gestrichelt dargestellt sind. Man kann\n",
    "daran gut erkennen, dass die Autokorrelationsfunktion an allen anderen\n",
    "Abtastwerten außer den Abtastwert Null besitzt. Wäre dies nicht der\n",
    "Fall, würden sich diese Störterme anderen Abtastwerten überlagern und\n",
    "dadurch Eigeninterferenzen hervorrufen. Diese Interferenz wird auch als\n",
    "Intersymbolinterferenz bezeichnet."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "101c9716-52b0-4ad9-bf3e-f7b60524430d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%  TD ist die Dauer der Autokorrelationsfunktion\n",
    "%  Grundeinstellung TD = 1.0, hier kann die Dauer beispielsweise auf den\n",
    "%  Wert 1.4 erhöht werden.\n",
    "%  Sie können TD ändern -->\n",
    "TD = 1.0;\n",
    "\n",
    "Dif = TD - 1;\n",
    "\n",
    "%  Ausgangssignal des Korrelationsfilters g(t)\n",
    "figure(3); \n",
    "hold on;\n",
    "g_TD = 0;\n",
    "g_einzel = zeros(8,1801);\n",
    "for i = 0:7\n",
    "    g_einzel(i+1,:) = a(i+1)*(((heaviside(t-i+Dif)-heaviside(t-i-1)).*(t/TD-i/TD+(1-1/TD)))...\n",
    "                +((heaviside(t-i-1)-heaviside(t-i-2-Dif)).*(i/TD-t/TD+(1+TD)/TD)));\n",
    "    % die einzelnen Autokorrelationsfunktionen plotten\n",
    "    switch i\n",
    "        case 0\n",
    "            plot(t,g_einzel(i+1,:),'b--','LineWidth',2);\n",
    "        case 1\n",
    "            plot(t,g_einzel(i+1,:),'r--','LineWidth',2);\n",
    "        case 2\n",
    "            plot(t,g_einzel(i+1,:),'c--','LineWidth',2);\n",
    "        case 3\n",
    "            plot(t,g_einzel(i+1,:),'m--','LineWidth',2);\n",
    "            \n",
    "    end\n",
    "    \n",
    "    g_TD = g_TD + a(i+1)*(((heaviside(t-i+Dif)-heaviside(t-i-1)).*(t/TD-i/TD+(1-1/TD)))...\n",
    "                +((heaviside(t-i-1)-heaviside(t-i-2-Dif)).*(i/TD-t/TD+(1+TD)/TD)));\n",
    "  \n",
    "end\n",
    "\n",
    "%  g(t) plotten\n",
    "\n",
    "plot(t,g_TD,'g','LineWidth',1.5);\n",
    "axis([0 8 -0.5 2]);\n",
    "grid on\n",
    "title('Abbildung 4: Ausgangssignal des Korrelationsfilter im Falle des ungestörten Kanals mit 4 einzeln eingezeichneten verschobenen Autokorrelationsfunktionen')\n",
    "xlabel('t')\n",
    "ylabel('Summe g(t)=grün, einzelne g(t-T)=blau,g(t-2T)=rot,g(t-3T)=cyan,g(t-4T)=magenta')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c848f0b-f36d-4938-af87-808999857da3",
   "metadata": {},
   "source": [
    "-   Ausgangssignal des Korrelationsfilters:\n",
    "\n",
    "**  \n",
    "**\n",
    "\n",
    "**Teil 2.2 Erhöhung der Dauer der Autokorrelationsfunktion**\n",
    "\n",
    "Erhöht man nun die Dauer der Rechteckfunktion und damit auch die der\n",
    "Autokorrelationsfunktion, so kann man Intersymbolinterferenz bewusst\n",
    "hervorrufen.\n",
    "\n",
    "Mit dem Parameter $T_{D}$, der in der Grundeinstellung auf 1.0 normiert\n",
    "ist, kann die Dauer beispielsweise auf den Wert 1.4 erhöht werden.\n",
    "\n",
    "In dem Matlab-Code können Sie die Dauer $T_{D}$ändern und das Programm\n",
    "erneut ausführen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "303d7dec-e7a3-4f15-8a19-0b8f6f70726b",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "TD=1.4;\n",
    "Dif = TD - 1;\n",
    "g_TD = 0;\n",
    "for i = 0:7 \n",
    "    g_TD = g_TD + a(i+1)*(((heaviside(t-i+Dif)-heaviside(t-i-1)).*(t/TD-i/TD+(1-1/TD)))...\n",
    "                +((heaviside(t-i-1)-heaviside(t-i-2-Dif)).*(i/TD-t/TD+(1+TD)/TD)));\n",
    "end"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "602726f3-0652-4b77-ba10-e979554e3831",
   "metadata": {},
   "source": [
    "Wie sieht die Autokorrelationsfunktion nun aus?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "8790aadb-8538-4747-a3d2-2bcbad30ba2c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "axis([0 8 -0.5 2]);\n",
    "grid on\n",
    "hold on\n",
    "plot(t,g_TD,'g','LineWidth',1.5);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71b72126-bacf-4193-b431-d0e9af7f967e",
   "metadata": {},
   "source": [
    "Welchen Einfluss hat die Erhöhung der Dauer auf die Abtastwerte$\\text{\\ g}_{i}(nT)$?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc45a2f2-55cb-410f-80ce-941f9165bb88",
   "metadata": {},
   "source": [
    ">Werte werden auf über 1 angehoben; angrenzende Nullen werden auch angehoben"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "0b456e29-6a85-4509-89a1-c1a8e50c980d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TD:\n",
      "    1.4000\n",
      "\n",
      "die gesendeten Datenbits a[i]:\n",
      "     1     1     0     0     1     1     1     0\n",
      "\n",
      "die empfangenen Datenbits g[i]:\n",
      "     1     1     0     0     1     1     1     0\n",
      "\n",
      "Anzahl der fehlerhaften Datenbits (ohne Rauschen):\n",
      "     0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "bit_stats(g_TD, TD, a)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7404e619-be7a-4264-8fc9-b5ec23e69288",
   "metadata": {},
   "source": [
    "Nun wird ein Schwellwertentscheider am Ausgang des Filters im Empfänger\n",
    "angeschlossen. Dieser Schwellwertentscheider entscheidet mit der Grenze\n",
    "C=0.5 zwischen einer Null und einer Eins.\n",
    "\n",
    "Könnte die ursprüngliche Folge aus den Abtastwerten regeneriert werden,\n",
    "wenn $T_{D} = 1.5$ beträgt? Untersuchen Sie es mit unterschiedlichen\n",
    "Bitfolgen und vergleichen Sie die $a_{i}$und$\\text{\\ \\ g}_{i}$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "fb66b21f-2003-46a5-b805-1368a1372adf",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TD:\n",
      "    1.5000\n",
      "\n",
      "die gesendeten Datenbits a[i]:\n",
      "     1     1     0     0     1     1     1     0\n",
      "\n",
      "die empfangenen Datenbits g[i]:\n",
      "     1     1     0     0     1     1     1     0\n",
      "\n",
      "Anzahl der fehlerhaften Datenbits (ohne Rauschen):\n",
      "     0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "TD=1.5;\n",
    "Dif = TD - 1;\n",
    "g_TD = 0;\n",
    "for i = 0:7 \n",
    "    g_TD = g_TD + a(i+1)*(((heaviside(t-i+Dif)-heaviside(t-i-1)).*(t/TD-i/TD+(1-1/TD)))...\n",
    "                +((heaviside(t-i-1)-heaviside(t-i-2-Dif)).*(i/TD-t/TD+(1+TD)/TD)));\n",
    "end\n",
    "\n",
    "bit_stats(g_TD, TD, a)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3accbae2-eaec-484f-9d3b-d939ead49008",
   "metadata": {},
   "source": [
    "**Teil 2.3 Der Einfluss des Rauschens**\n",
    "\n",
    "Auf der Übertragungsstrecke wird nun zu dem\n",
    "Sendesignal$\\text{\\ m}\\left( t \\right)$noch Rauschen $n(t)$\n",
    "hinzuaddiert. Am Ausgang des Empfängers erhält man das Signal\n",
    "$y\\left( t \\right) = g\\left( t \\right) + n_{e}(t)$, wobei $n_{e}(t)$ das\n",
    "mit$\\ s(T - t)$gefaltete Rauschen ist (siehe Abbildung 1). Dieser\n",
    "Rauschanteil am Ausgang soll durch eine Matlab-Funktion erzeugt werden:\n",
    "\n",
    "In dem Matlab-Code können Sie den Maximalwert des Rauschens ändern\n",
    "\n",
    "z.B. Maximalwert des Rauschens: $n_{\\max} = 0.29$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "400ff5ab-ecdb-44ee-9fa4-eaf64527a810",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "n_max:\n",
      "    0.2900\n",
      "\n",
      "die gesendeten Datenbits a[i]:\n",
      "     1     1     0     0     1     1     1     0\n",
      "\n",
      "die verrauschten Abtastwerte y[i]:\n",
      "    1.0034    1.3408    0.2884    0.5288    1.3097    1.4034    1.4664    0.3263\n",
      "\n",
      "die geschätzten Datenbits a_en[i]:\n",
      "     1     1     0     1     1     1     1     0\n",
      "\n",
      "Anzahl der fehlerhaften Datenbits (mit Rauschen):\n",
      "     1\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%  Maximalwert des Rauschens\n",
    "%  Sie können n_max ändern -->\n",
    "n_max = 0.29;\n",
    "\n",
    "%  Rauschsignal\n",
    "n_e = 2*(n_max*rand(1,length(t))-n_max/2);\n",
    "\n",
    "%  Summensignal am Ausgang des Empfängsfilters\n",
    "y = g_TD + n_e;\n",
    "\n",
    "bit_stats_noise(a, y, C, n_max)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36079032-d03e-467c-9dfd-7e8f599bdab6",
   "metadata": {},
   "source": [
    "Rauschsignal:\n",
    "$n_{e}\\left( t \\right) = 2 \\bullet \\lbrack rand\\left( n_{\\max} \\right) - \\frac{n_{\\max}}{2}\\rbrack$\n",
    "\n",
    "Summensignal am Ausgang des Empfängerfilters:\n",
    "$y\\left( t \\right) = g\\left( t \\right) + n_{e}(t)$\n",
    "\n",
    "Das erzeugte Rauschsignal kann Werte zwischen ${- n}_{\\max}$und\n",
    "$+ n_{\\max}$annehmen und ist in diesem Bereich gleichverteilt. Den\n",
    "Verlauf einer Musterfunktion des Ausgangssignals\n",
    "$y\\left( t \\right)$zeigt „Figure 4“."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "b2627300-e7a0-4f77-aa2a-1f884e6aa403",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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"
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "% Verlauf einer Musterfunktion des Ausgangssignals y(t)\n",
    "figure(4)\n",
    "plot(t,y,'LineWidth',0.5,'Color',[1 0 0]);\n",
    "axis([0 8 -0.5 2]);\n",
    "grid on\n",
    "title('Abbildung 5: Das verrauschte Ausgangssignal des Empfängsfilters')\n",
    "xlabel('t')\n",
    "ylabel('y(t)')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe9129da-4826-4abd-8592-fa3935059f6f",
   "metadata": {},
   "source": [
    "Aus der Folge der verrauschten Abtastwerte $y(nT)$ wird im Empfänger\n",
    "eine Folge $a_{\\text{en}}$von Bits geschätzt.\n",
    "\n",
    "-   Abtastung des verrauschten Ausgangssignals:\n",
    "    $y_{i} = y(i \\bullet T + T)$\n",
    "\n",
    "-   Eingestellte Schwelle: C=0.5\n",
    "\n",
    "-   Schätzung der Datenbits aus den verrauschten Abtastwerten:\n",
    "\n",
    "$$a_{\\text{en}_{i}} = 1\\ ,\\ \\ wenn\\ y_{i} > C$$\n",
    "\n",
    "$$a_{\\text{en}_{i}} = 0,\\ \\ wenn\\ y_{i} \\leq C$$\n",
    "\n",
    "Zum Vergleich sind auf dem Matlab-Command-Window die gesendeten\n",
    "Datenbits$\\text{\\ a}_{i}$, die verrauschten Abtastwerte$y_{i}$ am\n",
    "Ausgang des Empfängers sowie die geschätzten\n",
    "Datenbits$\\text{\\ a}_{en,i}$dargestellt.\n",
    "\n",
    "Eine Bitfehlersumme lässt sich durch folgende Beziehung berechnen.\n",
    "\n",
    "Bitfehlersumme:\n",
    "\n",
    "Damit ergibt sich mit den obigen Einstellungen (ursprüngliche Folge mit\n",
    "$T_{D} = 1.0$und  \n",
    "$n_{\\max} = 0.29$) eine Fehlersumme zu error = 0.\n",
    "\n",
    "Experimentieren Sie mit verschiedenen Rausch-Stärken\n",
    "(Parameter$\\text{\\ n}_{\\max}$) und untersuchen Sie die\n",
    "Abtastwerte$\\text{\\ y}_{i}$. Beachten Sie die Anzahl der fehlerhaften\n",
    "Datenbits."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "42c3275d-5603-48b6-be95-b57233f1d54e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "n_max:\n",
      "    0.3400\n",
      "\n",
      "die gesendeten Datenbits a[i]:\n",
      "     1     1     0     0     1     1     1     0\n",
      "\n",
      "die verrauschten Abtastwerte y[i]:\n",
      "    1.5494    1.6487    0.3219    0.5940    1.5105    2.0342    1.5163    0.4233\n",
      "\n",
      "die geschätzten Datenbits a_en[i]:\n",
      "     1     1     0     1     1     1     1     0\n",
      "\n",
      "Anzahl der fehlerhaften Datenbits (mit Rauschen):\n",
      "     1\n",
      "\n"
     ]
    }
   ],
   "source": [
    "n_max = 0.34;\n",
    "n_e = 2*(n_max*rand(1,length(t))-n_max/2);\n",
    "y = g_TD + n_e;\n",
    "\n",
    "bit_stats_noise(a, y, C, n_max)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2402d268-6e96-4e7d-9a29-bb7fec41328b",
   "metadata": {},
   "source": [
    "Verändern Sie auch die Dauer der Autokorrelationsfunktion$T_{D}$ auf\n",
    "höhere Werte."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "cc154a2f-438d-42a5-a136-9df99c041f4d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<html><body><pre>TD = 1.8000</pre></body></html>"
      ],
      "text/plain": [
       "TD = 1.8000"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "n_max:\n",
      "    0.3900\n",
      "\n",
      "die gesendeten Datenbits a[i]:\n",
      "     1     1     0     0     1     1     1     0\n",
      "\n",
      "die verrauschten Abtastwerte y[i]:\n",
      "    1.7822    1.2764    0.2245    0.7083    1.5881    1.6103    1.1923    0.8163\n",
      "\n",
      "die geschätzten Datenbits a_en[i]:\n",
      "     1     1     0     1     1     1     1     1\n",
      "\n",
      "Anzahl der fehlerhaften Datenbits (mit Rauschen):\n",
      "     2\n",
      "\n"
     ]
    }
   ],
   "source": [
    "TD=1.8\n",
    "Dif = TD - 1;\n",
    "g_TD = 0;\n",
    "for i = 0:7 \n",
    "    g_TD = g_TD + a(i+1)*(((heaviside(t-i+Dif)-heaviside(t-i-1)).*(t/TD-i/TD+(1-1/TD)))...\n",
    "                +((heaviside(t-i-1)-heaviside(t-i-2-Dif)).*(i/TD-t/TD+(1+TD)/TD)));\n",
    "end\n",
    "y = g_TD + n_e;\n",
    "\n",
    "bit_stats_noise(a, y, C, n_max)"
   ]
  }
 ],
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   "display_name": "MATLAB Kernel",
   "language": "matlab",
   "name": "jupyter_matlab_kernel"
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   "file_extension": ".m",
   "mimetype": "text/x-matlab",
   "name": "matlab"
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