{'fraction_non_alphanumeric': 0.029159519725557463, 'fraction_numerical': 0.005145797598627788, 'mean_word_length': 5.020618556701031, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
An ML Approach for Decoding Collision Slots

The tag inventory process in an EPCglocal Class-1 Generation-2 (EPCglobal Gen2) long-range Radio Frequency Identification (RFID) system is based on Framed Slotted ALOHA (FSA). Collisions between tags are inevitable in an FSA-based system and limit its maximal throughput. In this work we describe a simple Maximum Likelihood (ML) scheme for jointlydecoding R tag replies in a collision-slot, allowing the reader to decode all of the colliding tag replies and greatly increasing the probability of decoding at least on tag reply correctly.