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Encoded Planetary Control - EPC
Posted: Wed Nov 20, 2024 7:32 pm
by Stin
This is the next generation of my
CPC - Central Planetary Control - System
The Design is functional but have not all features of the CPC yet.
Why a redesign?
The old system was very central and you had to do all your analysis at the CPC.
Very limited bandwidth. Above all, I would have liked to transmit more train signals and especially the percentage value that shows how much of the capacity was filled
Due to the high local signal density, but low possibility of transmitting signals, a modular design was not possible
Why encoded?
The red signal line is encoded bit by bit and cannot be read directly without a decoder. It is therefore not compatible with the old CPC system. All replay stations must first be deleted and replaced by production decoders
because they are encoded, they can be distributed planet-wide via the radar stations without any problems and in a large bandwidth
Signals.png (49.6 KiB) Viewed 198 times
EPC - Core
If there are logistical requests that are higher than the reported capacity, the capacity for the logistical request is temporarily increased. Signals can be excluded here
It calculates on the basis of the reported capacities via the green signal line with the available items in the logistics network. From this, a 1/1000 fulfillment wheel is formed and reported on the first 12 bits. This value is never less than 1 or greater than 4000 if there is a request. During the calculation, care is also taken to ensure that no overflow of the 32 bit variables leads to calculation errors
Based on the capacities, the number of trains required to fulfill the capacity is calculated for all items. In my case, these have 4 wagons. This value can be adjusted. Values from 0 to 15 are possible (4 bit)
The filling levels of the liquids are used to calculate which production methods would make sense. The solid full productions should be designed in such a way that they burn excess fuel
The calculations are made for all signals that are not ignored. There are constant combinators with an x that contain the list of the ignored signals
Tank fill levels must be reported via the uncommon quality level using the green signal line. Use a quality transfair for this
EPC.png (866.83 KiB) Viewed 210 times
EPC - Production Decoder
Converts the first 4 bits of the production control into an integer.
Most production plants should be started up with >0 of your manufactured item
Waste signals only for normally qualitie items
P-Decoder.png (516.27 KiB) Viewed 179 times
EPC - 1/1000 Decoder
This decoder is primarily used for displaying values and other modules that take this value as a basis for performing calculations
Since calculations become much more complicated when values assume 0, the most envious value of states that have a capacity is 1 and because of its 12 bit nature it cannot become larger than 4k.
The 5 display module shows the possibilities. Here, 5 selectable values are filtered and displayed from the largest to the smallest. The symbol of the object is automatically transmitted to the monitor
Why no percentage value? Because the value should normally be 10 times as accurate as you need it to be. It also makes it easier for me to turn the 0 values into 1 values without it interfering.
1_1000_Decoder.png (950.79 KiB) Viewed 180 times
EPC - Train Decoder
Calculates the difference between capacity and content of the logistics network / reported fill levels. From this, it calculates full wagons (including tank wagons) that are required
Then calculate how many full trains would be needed. For all items that have capacity.
I used 4 wagon trains with 2 locomotives so the default setting here is 4
Due to its 4bit nature, this signal can assume values from 0 to 15
T-Decoder.png (608.28 KiB) Viewed 171 times
The current EPC is being tested and is currently only running on Nauvis (Vulanus is my most importend planet) for me
0.52 - Added train Needed
0.51 - Added rarest items display
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b8fBNkxWV9ysG5YXID8ln7MnX4snXy7a+ybj18Q9M9mLi4qJ7jfoY8df4DQ7Z9vhfv/A97+137JsflmVbyJndy2azAUXvA4+Rs+DfvXrhdUXmZJBPvNfytW9kwV4zSFksTuH1dXHZno0uXqa6lSAyWAlNlStBbAAG0K4EKr3FT+tUUUJYHzH9XsX+Gi8DJF+ZSsPzW65a3l6KQUSz8XKYqj4CYsBYU91HQA3AAMqPQKVWkB0VnNLrjYJZAE1HoNLH3FdwoJCNo4cNN35yM1osZCxz6rUPCJmqMgJsAGY8BwQGYMZzADIAI1ZZ7jab8vuKTZOV92AY/lxJF5GF9+fqlgnto1nlDAcosvAMVdc6sgBLVGAWvqEkUoElFmCxCswieslQjR4/iQNQ9EnNZZRiz+rYYVQoCC1smYZHB0+jZcJP3DKjnLewShqeKTwNzuND5HxowXny1DhPDpHz2ILz4VPjfHSInCcWnMdPjfP0Ka7AFsZhw8Oqp9Ey8SGOiciC88FT43xyiJwf7FSWF4s26xx/1GFEGbNHgazCWB5IE27Yo+3RrhuFNsEj4+fGJRw4uQkcCpf4jm7Ipl//+vDXQGzjofHk9kLB/Zn46x9/c/gONYr2vK81/W5Zlpc8ucBsvIUHUeqzWaYLgarUJGGLHUQQPLupzCISnprlNgpPlVoRW8TCI1QFZqPwJCowcDS8eAh5WN5PnybqEzLNt7Gh3FW1rnnCjcgAzErbpmxg8kRk/UdeYcZ7BQktrLmVDYktzLmVYFZaEfCwf+rLCaEWltlKnkcWptlKsNjC8lgJlliYHivBbIwYVWDUxohRCYYs7ECUYKGFHYgSDNs6ID/OEqyM0fQwV9DEFGQzWNIDoHGXYGP0PtDTrpFVlUeQyu+vePtl605aXlV5dOCV16DvpAs9drfXCctsWinm+SCc0OxDVk3zGafBkxlO8wW33bOVm00jl2/om5SzKLUw+lKCReDcSwe2K+pj4x7GHonGYB+C8PN7O5F9hJWdZs1VzfrKFTQXL9maC/J13nrXeZMLs9qboipuljfeXzkV6agk7bBGdUY0sTCjU3bk1MKMTgU2jFquNVZD91uvM1b7tf/DBjsemqINm6R3N19c17dKh3MUIQsLP2U9QwsLPyUYttAjKcFsNFwq1U9ELfRIWAVmo+EKVWCxjeHcsIinUFOPGWoKwcPiDlVh4d5m901Xy13ECt+CCA+Cq8fsErnDgoNvweuytxsGutXjDVO2w10eUWRjIRtt9ptPNtB7IYsG+9VEDULvmQ4vcn94GUikzrLUAxfhnUTn345qPeo0uFYjT4NpNfo0mI4jMHY6ITgttY++1MahhVZaJbXF2AJMJbXFxELFrSwZtVBxK8EiC620Eiy2AFPyLLHzvwuHW6o/FqX3Uo467+smzzz/K3Mfs1WYMKNtVpxa6L1VjDSOxjXUyKsYmdio95UlA4cEHeiU8MdPp4JHFnmQ7iv5FnVZzEQgfZ9rd/x7xOCqnwS7WZjhAzFeoY+1fuwzyCNKiIVGSDlQqAWYcghHFoovZcksVAjcVRwoMj8vwWa/HTOxT2zLI28cVUY/5VyaWqgmVWPENPTchjZRNXpNY89tgClLFlpoEwMVGLYAQyowGyFXCWZjYaIEiyzAlDyzsTBRliyxkJiVYDYWJopqhgE0D2sQDyH3nSvO9Hx3H2LfPWJgsS8MkMVeAakayiaKixIMO4j3aP/i/VZTg3209QhBi/Ym4DPzTa4+scT0e0yjYJn2cR+JFJStTV3O89G+k8aapkWE6qRNE9tB08VCki1CU8ZC0xWC8Cks2SIIO4KliQRhA1MggrATeAJHEL5FGkQQvm0SRBgR8xSIMFxwAkQYvEX6QxgBu+SHMBrg1IcweOvEhzAyoLSHMGhY0kMYtl3KQ9hKZZnwEEbEIt0hjIBFskOHAMyheRBCbLDJiC3AlPtUeFxsdLTS1g6kIrxHyYLsUSKie5bmoj1LLruXutSDOXU5AAgO5gBg3Ch2qO//4knq+8NhuFDDPHoDhX9opDAYsonNmtP3/oJJrWfj5bFRYKtmf9OIoxtgqtkfWSiwcaICIxZgqQqMGmZDG5h7qktmocBWlyyG6+nVYAlcG64Gs1BgK8FCuAJ7APrxU6msEzqixdyFK1a6Tz8vvAM/Nxw8rPlunLPWBP1pw2Y6e7+scBCj09Qh9VE7lcL8xa1TKdor3ZdXHt6Bz+BYZwL3mzW2e79xMJv6hP1m6B+5VsD/j8WpSnEzL/mmmslXJU9WdZlJ6cSWndjWCREnB+6EaMRfu4OJ0MK+Sb28UrhJkhosctiRmDfpIZokPdEtShi7hd0KDsNykezXPSUME7hRknqUpPAEy8otBbawcFKDWWwQldXEFhZOOFaBYXgAKjUYgQegUoNReNwiNZhFflw1WGyx21SCJRa7TSVYarHbVIGZhxMLDMCQxdZVCRbCo/uowSwSM6nBCNzYSA1mEZdCDWaRJkkNFlsIN0qwxELsUoKlcG9+JZh5FCmD4URt1gABxqRdnl2Hy7pvmWSPJkwcRO8mb5m8Gk6YnBnya8p+j4n8nfLfk9Xv4YSHjxY3Ef84li/xa9TfhPxr8VLKrtnqR+Q1mRCJxH5CE7b2oNU1iVbX7H0k3md/2Dt4guU15u+vr1nTIHlN+O/i/VC+j1bXcfetuKbyd15LGsoyxPwai2vC34lkGdgYZoCy/PKarq7xhCbyHVaTCU3l75wrEV1dk0mE5HXM65WIMohr1mhIXuNJ3F0n/J1UvsN5xXpJuLqOJD4N+m/ZNebX/H3+h+OIa15+ilfXrOmwrBjFomUkBVZ4gUrkNXsUBhI25TdI8jTibRBJnkaBeEs2VITEjSwJe0HUWrzFucq4xwlGoo90Tch+w5yg+IATj2X1eM1EEYm8IZNY0ouDQXHZDeGNyL+IBW0sace8tqjrGqynUdH3KL+hA+CYNwa35xcAkXgimz6OBYCsVpps3vSv8Yyz7N9E9il+x7t/tH4mKzq4I90dEvULg/WdZDUvrmD5hMc+XN2Fgqdhdyc5LJ9Jel1ZQtQ1hXwWimekexaKZytMvPFM3tH13eabWDyTd3RYP/6ruJPPInEne7+YAibcZ5eIO8nAVf3kHVrf4TVzuzuCurt0yGsxDXBe4+5uwF0xzLllp7xLOl6T7o5hdp2M85WhdCMByd6fdNSpqG3ScZDyDsGP50h3RwR1QYHy/sGfie8i0Vsi2Sn5HcOLZK/id1hwQrwZBx1fxDNOd8KtfFF3h8WdQOH1nHDViryT9Gj3pqDA+i3p7vibUfcs4ZxAXc+Sd12LcSvynl531w1wftdT7+66Hsn5yr9LujeJeBZ2d7yNOAXS3RFRakEPBeKuWw5CSU9yQmCLO0GB/9pjip4cdj05lOsIQ8Hv2NrEc3yzRe2iXObzphDnsGV2kbOt4dnbbk/oy63gi25H+PKd9+2rl57vvayFp/yHvFmIFZBGYcrWP0pizCrOtRbd6a9QePYUhPppGDTpN2zjyn4RMZNkxkRJ6nPvW57XXS7CXrHwMu9yWZZeleezfOa1Mjkp8hH9jOeHf1lXFdtye3f1kr1dtXlZFlc8XTx7r/S6tAsyOtMt23l7DcMoeaL7rBWk53W5ymAJ1Ey9Oesri/iKL8SHIpcw9z0ZDCWJpJckIoXzjelhY5JooUyjFSWxFso0VlESaaFMY3EnVAtlmnowIVqo2BQq1EIlplBYC5Va9CuVs01ggaVyKUJmep2NTqoqV9/hb/IZtzSTdovF1O8G79b+qkKF+ykT7rTYV/uphsLYZ7zsvfqMEm16hY0pQdU1qcVAVnWiyAJLVa4Y3s2xvoSJ6UDEmyUc78/Topkui/Y8r7KLsm+81c/rPm7RxVehKTYift8zBROPy7o5v2GigzA2k/eiL4kPrpjgMTm7EP/P5BtdrIu6Uke6sJhFqUrXazGLUqWRDOxMsJ+gROn27bD4BLI1WKTBHDAxeSKz/JvjiHY0PALXhMcZCmxUnXzd+9U3RbvwQuSHROwW+LZBpPGRWw7n7OwWKSdJEh1I/1Ln0/rqqx0fGlqsucoJ1mLNVU6w0DjFw2XyYfMl+7G0V+fihsUwDtJo1OJenVEahJ+gNBy1ulfnTYaZf0c4IaOm9+r0wCACOIyjZNT+Xp2LFUQgohRHowb46jS7MBZhFMThqAm+OmkpiEIYhYiQUfdHdcIeEAUakjBJRh0g1UnnoAOBjjtAqid/WDPQOEwiEow7QKqIfA1sCYSDOIgIty24yX45X1UujBBBnPy4m6SK+itgV0Zks3764f4aWL+AC4xXTb2cswdsHf99fne2JQg7e+N9fud/J2ZSL3jxHV8bPRR6F0XrfeEhPxGx2LnOr2KigsdzGZQ5f7pgj9+Ih1J44EJDJzOwJ1+LJ18v2/qGLY9T7we2zBVVvuheoz5G/DV+g0PvhfevX/ie9/Y79s0Py7It5MzuZbPZgKL3ISuX+YJ/9+qF1xWZk0E+YbKLeO0bWbDXDFIWi1N4fV1ctmOB5EOLfV63SXh6+7xA7PMQeJ8X2uzzFIfDxsZGAw2XEgvB9bBKrBCuiFViYbgmVolF4KpYJZaNOKnCiuDKWPrgjB+Js9K4O3HjR6XypBSJozF5xi+uu5MbhMWBT3e2xg9qunfEwVqyul7bBCBxdCXPE7uDne6Uh7/Dz4asTnXerE515Awjj2Qao+MdZHi8M+MT7qumnsmYxt0hz89V6K2y5bJdWfcCu/SR93rZzMvlgl2H3l+zRZt7fOc2HZ7zzHu8m9V0LI54nA91Xu3jUCftbU2o46HOEAq7HeqkgRaKWMyX2PFQJ9FCwS2rCQ9+1/P/Keh00CGmuYwt1inseAYWaaFSay1peNKSwk7+iLYxjB1rhzID1p78ma/zWHveZ77Oq7CM50X9xG9+HKWf+Y2Po4ariP44yhxLfwhlMWM/lbNWfJCJiVMX1qMnwvrwEFkP9dYerEvotC45nn8GJw7eO/80sZlQTeCgk7lwc21Zn8wNNoHD4zkPYR9Fn/1cDfVeiv0qeqd4EKoeYNUD8s787O+V7uzPXOX23Ewrhid/y4vOHHOcSWtBye3UTy/ahLEFlmpkJHAxiahONlMLrMRRPTnQAiixLIxXlVgW1qtKLOwg25D0icg25BBlG3NtbqxvRgpXBSixIluJgZwspiQHY/j+XdkaCVyvoMRKwaYaZHPGPZlqnEw1TqYaJ1ONk6nGyVTjU5pqDII0wG1Gn4zYOuZ54j9JvxNi7MsUard4xOJEQyUREYsTDSWWhS6DPDfzIWJxVKPsCBb6DNl4Q7MTERCic/wX/r3dZch/FSEAJmQir4RPcdA5O/efiWu8cp7uPxTXXXQDJL2h5XWHI12heaiJ7lqEjog6Z2NprNIbqHQBL8I+5IMIfkGkazQnM8GSLg8qwHHENfeGJ9y12sq45dXKuGWgeIRYuIQaCxc21VeLy4x1Xy/rNJrSxRh9wYMMCq/lyyJn/d+bZvNsypqfG7L8Lm/yyeqd+lKscmzlGb7GvZ5v8myxZPPU596b2ps2edbmG+7NnR524bGlqBuC3uJuwbi0kKVguDcTbi7DYCtvZT7DqXEXaN6FvLbWWctsXaf2YCkTGUTWs4BK3CxlIn0kM1N9SBRooUxnGaoPPGY6yVB98GGwpwSNNvn/uBtu4D7DR6DtNnJAx7vezPsItNPGDuh01/t4H4H22NQBPd71Dt5HoL117ICe7nrn7iPYrjp1gd/5ftpHsA3zNy7w+vH60gVeP2B/4wKvH7HfusDrh+x3LvD6MftbF3j9oP2dC7x+1H7vskrpR+2/ucDrR+3vXeD1o/YPLvD6UfuDC7x+1P7oAq8ftX90gY92rWLchNeP2j+5wOtH7U8u8OmutaOb0t/ONdib8PpR+2cXeP2o/YsLvH7U/tUFXj9q/90FXj9q/8MFXj9q/9MFXj9qudbUgYB+3EpdrAMJ/djl+hyX7ZV++N7lZVnfutDQj+F5Ub13oaAfxtO7zKUliH4k314XrVNTEJP+dOdCgRp0p2zq1BL6QT29zt1I6Af2LK+c+KQf2UJr70BBP7Bn0Ayqm4qNbeP6Opvn/oe8aYtpVroQQToi13VT/L2uWjcyoY7MrOCZBd2IYB2R6bL54DK+KdFSaOrFwoUCNWWUO6lIW5m6qaAJVDdJxDoSTgMk0VagaKalU4NvGeTLuZ81jdOyGgVb4RuRuMuZyJYhviMKW0b3rL6tdlURrCHjTIBoCJT55Q6qsWWI74ZAtLVX7YaGfgUfpDt1oKNfx6/zrHGZRiL9Qr54vyxdVqZYL6JfXdcLl1rE/ShfQaxJrJGswUM1+LJiEDe1i2QeYzV8kzUu/Scmauh8XkxdoKkausyv8mqWNS4ibKyXw7Pqzu9pWxManqPO53nj85N4F8Dexlpsn53xejtrtt93RUsGbihzbjbfNjVbovKb2mnvl/QDUB5TDzqauHce4kmopMAtUXxWC27vWLloRBK8nUaT/22ZL1p/3tS/uPS5hCjptEWZu/OKKvFvlxe5X9ZXtb+YZ9Pcn5dZe8nAXahFQ1/E6XXWXDFBPb/Mq0W+g541TALYcrOc5s5vOY22qK5s8d9tMYb9/ooh5GuXSG5a+jIrp8tSZi741V+4/anno8/OHKJo0Fhr4GHqfDa060i1keSNLVxposx9eYqtvv/Y6qYGWpTqWz+0wEoco3ZE+qxlxlE7hliRNmqH0q860qcrQ1B3teHgi408mh7NXY3PeHDnwsluiP/ff/33mdX4scnFriuZUQBsRSSeGNohkmEXO/kvWsWa6ZkYPZG1JziOiO/GiReoQbrMwAIr0iZeMF8qqCrHsQUW0cbh2O6mMbRYps8tyqupL8twgabPOC6Hse07cYzIP2Q40cbmMM2CM7SDp9ooHabW68n9vnFyHj85j5+cx0/O4yfn8aNzHqcGzuPRjp3Hw3vO494fXwye403n8jOXaFHD9ZE4RosaLotEHy1qq/g11GVQbbQobdy5oYqTDuPOda6WL1fOlTInFPJR+NkgBJzO07ETqRbX9a1SqDKOlz8sK3GMlz9kI3GMsDTcGlHHePlDLHLfcVnmeu4Sfotc0oPreHXN8zNLj2XuaYy6lNm09y5GPJd2lygZcW/hsPtW5ItOeq9mEUZf40XMn70Ybf+VI/H9ngTxJsYab+KNPMZMQH2/duMtFp9zv+Fv8pbPDtwH+Krh3sB8hnu5LiN78CA2/jeDam7x7mXt2GRXuc+p2jn1GovqA2d2rHXvFXC1VgMWDBFNlDdDvUm3UPmtcOYeGmCdLxg3p/m5iFk4HbBrfQj/sX+XtWXLi8hA4QnTtjkmK7XLdBC3Dz1IRzERbvfcz34iEo1r+v63L1/xJBCs/ded26hTE02nfn19W9/KFN/Uuy6urhmbPLlS5rPVcOpOvobJvFde6WtPdnlXX3rZ5lDsR+C2/t0vQ1a921iPyG2z5IQXPuEzrCeaPBK6wx8qI8Ld+6cP10jgrmJz875pLVI3Dmh4gAa1UbrnZL6aC9m0yWd+p2L10knBrUMssN6pghkCxMf0fnfoxMdf+6/FQiRX10IE2Ghrr8mz2eciM00/s92XD7Im97jAKPxmDGMNr3SCJvImMYyt259vYE3ACbMlt5/nkMWSK4mcTbqLc7apXU0NokMM8pIGmqAWxupd9CnUu1l1x6S36uqeipd1kYcnZVvUuqhT627PxwJtuWC/LYc0OV+MWy44zpZLTTfJyUF24sSAE2PnN5rkM9tZkR4kK9IdsgJZsCI4TlaEFgMkOM4BYnroO9QqHdAAiXfICmKhWzwgVkTGC4ieFdSiVwTH2Ssii14RHGeviE1ZQQ9ygNAdssJU2qTkIFlBdsiK1KJXBEfZK8LAolcER9krQmNpEx/kAMHA3dg2VhhLm+FBsiLcISuwRa8IjrNXEIteERxnrzCWNtGhKt0esgJuWbnd+FGvYzbSSPIy3uTrI4XdM2OUFYbq6R7x47uPD6wn5GmHxq4TxKbgGNkkTl902eSMh1nwLIZZaq3qDtP9qrpDnUWVqa47TI9T1z20CIO2XbLftsO6HIXGbZccadthuEr6kPrxDlXSmMBV0ofEih2qpDG16BXJcfaKyKJXJMfZK2K4SvqQBsgOVdI4gaukD4kVO1RJ49SiVyRH2StIYNErkqPsFcZJ1QYq6UMaIDtUSZMQrpI+JFbsUCVNsEWvSI6zVxCLXpEcZ6+gcJX0IQ2QHaqkSQRXSR8SK3aokiaxRa9IjrNXJBa9IjnOXpHCNagHpnfbkwaVBlYa+C3MecIa+KHfwiizkEU3Sp5FNwrtulFynAc5a6ecUV5he8V3vF/FNxktr0XknK6gR6f4pjYqzvgoVZzURsUZH6WKk9qoOOOjVGZRGxVnfJTKLGphX3lIrNih2iKysK88JFbsUG0RWdhXHhIrdrhBjSzsKw+JFTvcoEbYYmcRP4edRUTsdhbxMe4s1p7w9wN7oEkX+mYd4gNN8ESGz+GRceLuCk9keJ2IR9QJxCUPuhOm3SWeyPA4CX+OxGXavyousSTEaaJQXoeDWD2YX3eFIYO4PXQQ20dQT1cxedAgJg8riqTKH09CSZb/4fF8xDWnG0q6/M8klHT541X8H/6Hx/YR1yIukKQbxoPfOT5Gq2s8wZIW/zPBkhZ/PKHra/aOpMv/TLCky1+dYEmX/5lgWV/+eIIlXf5ngmV9+eMJlmXgfyZEloE/nhBZBv5nQiRdItpW0uV/JkTS5Y8nRNLlfyZE0uWPJ0TS5X8mRNLljydE0uV/JlTS5Y8nVNKlor6SFqs26z8Sn/+Z0K4zcXwq8fmfCe26FsenEp8KfEmX/5lEEp//mUSSLv8ziSQt/mcSyfryP5OI0zWJTXM/yIJReBqqDU+zik7DM98wbBnd4VdfoVUoh8+8uvLa29qT4SiK3Ct5TDhNvJmRvb5V4BmjEOCUrGZCig8tBPgLm6DsjxkTfjQizaPEhB8Lhv9OE9kG2h/Cg+gPqtjGzvHhtUFftkZFofywWfJpVBlGgLq7AV7oEIhMRhMahCHrfmCv60PifNwe3kUnVlHSi1XdZDIeBEekrJl10+V13uQyDk43i6rD4Pxcve0q8UJW4d0/e+tf1nm83nl/Gpttdxw8JwIGjKIE3WfOQQeMso2lNB4uyj341N6CRtnEslIFjIrBgx492qAf5rkzGfcJuC69SoYio4jn2sOHbnk3ig6UgstLH6u8gS5wjWGBw8cqMNaFlzFd0h6rwKEuCIzpcHysAhNdqBbDAgePVWBtTiyzAuPksQoc68KeGBY4fawCR7rgJIYFjh6rwKkuhIhhgePHKnCiC/RhWOBHWzhQoInHoRfJ+0WZpEer6eR5QRWOwwGAV/TIeTXNSrajUjAKARgVHiujuq8uy2Ux67+5zrMPd35dKLyuQwDn8LEPx6mie2EAk9DRj8PmggeA3x40BKJueRbDcJ7zyuVs63+VLc40cUa03BtIosfZy+bFXDEWIwCX0iPnUl5dFVXuL6uidY08MhS9j5NZ2exDVk3zmd8ZCOiikOg5Fh979xI5TeqKJ3/YxjOASI/pcxDpr/Ks8W+v83xc6MKB3R4oOs64SYxN3SZoMqry3UlclQ1GJo+v4cWhg4p3zwUONNFPQJvP6Mg36oA+Shx0zntu8lAX2gSs1d9zgbEmAAlo9xodsUMOuJfGdrqTo+Qh671303J4HKnnX+JwULPnQUN0QUnABzV7LjDVhA4BqV+Oc5Rf5zfFNBMSk7AVNO2nBNkpZ46Si2VdXfnXGet9M7+oFnnTgsY8CR2ODvc8hGJdcBHw0eGeCxxpQoCA1DzRc9hTAjqqpU7xKNmYLRb5zUXJnvs3MsG1H0J4GTkcDu95DCW6KCHg4/c9FzjVxPIAKd+io9WAA3pnaqfqjY5XcVlM/Zui4sN91hQlZNqkgYNZxb61S4EmaAdIB/tcJnqkbP37LmZ47WIWrV3MOk8sdtX5Iq2dzXiG6d6trHOd4p5B6X0Ps4Qjp52HGV55fcms72hwHa6u8dpLTBRh6IWGV9d45TEmPdLW3ml4nSWeDH4feqqJ1Nh05bWGV95j0mstHlwnq+vNDPPh2psNrxzrpFcZ+2/lVbbyJEMD7zFe5pXnGRl4mPHyrDzJ6MB7jJdt5W2WDDzMON3Ok0x6leHVNV55m0lPMjy4jlbXeOBhhtceZtHAw4zXd+VJFvfeY4TXvfM2o7yNhIeZiRfXT9JB4HvuN2DkwRVpPLhWCcOLtk8V3jZZUXkzmRncY/sbbzHwXZCDR6TwlUbWnyucuTSj782GS9dkxA1scw7q3w537ABGklXQmCg4NAcwSx+sx3QBG/FAO2AXMMPk5kmvvIiCvfmquLuY7Cu/uaMXzWH5qkzOMtY2H/K1h+KQffoGeqgMd2siR7xhI42felo2FQNatEzevciaXTTVYlleLhu4Y9FGTbe2zsqacwdN0y6rK8bOnTl8cSvAHTSJwXEMtFm0p5CgoaTzINUGoojT+xPtuMek8JRkMkeTZ7PPPekuuenRPuYvuR/PR2LmIEviZFUztN2v1DC43VpOocknSD4f2Rd3zwmEgu2eiFnDJFu2C+UWSJpFH8rfHvqeKGgpfDEhgsFsbNQnG1z5p7OVEAUF17g4WjApfSZMSgGz2cC5y7QP7V1HkgYPpPr/BahJUqnd2kdYQadyJfsqV+xWrnhf5YrcyhXtq1zUrVx0X+UibuUi+yoXdisX3le5QrdyhfsqF3IrF9pXuUaLNebFoSmZ2i0etN6kp/XmtN6c1pvTenNab07rDWS9QYD1Jj7E/c2BLjcHutoc6GJzoGvNgS41B7rSHOhCc6DrzKdeZvIPeWO80BgXLIAtSvRME7kKtCilp0XptCidFqXTonRalE6L0s4XJci5dnR/p3TMnqKa0Iggbj3HIG3IwRYg2u/hOt5JUMZBefecmC/UxGS0OOiOnslBN3IyB4ifC5cs7QGi0/nM6XzmdD5zOp85nc88r/MZt6C8G2tIfFpDTmvIaQ05rSGnNeS0hlie20enI5LTEcnpiOR0RHI6IjkdkdgfkQSuqSA2lP7HEELELenDxhodn9bo0xp9WqNPa/RpjT6t0Ttfoy0P5uMjXaMdzt7pfs+yiSZ5jcUpLX0mp7SArDUbGnZ60rCfNOwnDftJw37SsJ807Ind7p2edu+n3ftp937avZ9276fd+85376nd7p0ew+79fpxtso6zHa7jbMv4zDz8c3+FVpe4C7lN1yG3eYzmZH2Fgi4O9zpAdsyJIIEuAnGTLhA34VG2Qxn5ek1AXA8jYq+iYNM+cLeIgr0RETtYRcRG6wjdnGooqfJX15G4Od0Qi9/DQcRteS1p8Z9W0bTZNeFRtkOj6NJvu0bwJe9frCI1v/N+zPOZiKaojzgdfxwh5V/U9fshvVcvvYs773VbVF7wOeUZNDbjUrMnZem9yRfsjStWNP6S9/WMFcLjZW1kHGwZFfILk4JviVC9mGfT3C/r6SqQY9cDPyzLaVYtF+pA1RqAqzK/yDYCV0O+vlyWV3Uz/B6Dvs/+tizKevA56cN3nvdhQZUt+vH/Aee0Yus=
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Re: Encoded Planetary Control - EPC
Posted: Wed Nov 20, 2024 8:43 pm
by Stin
Re: Encoded Planetary Control - EPC
Posted: Thu Nov 21, 2024 5:53 am
by Stin
I am still testing.
I showed some cool gadgets here in the second post.