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
This is my balanced train unloader. I wanted to create a solution that was fully balanced and this is the most compact thing I manage to create.
It uses madzuri logic circuits connection but provides full chest, lane balancing and belt balancing. (as long as you add a 4to4 or 8to8) while providing full throughput (2 compressed blue belts, one per side, per wagon)