{"id":"W4379087142","doi":"10.48550/arxiv.2305.19462","title":"Optimized Constellation Design for Two User Binary Sensor Networks Using NOMA","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Distributed Sensor Networks and Detection Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Constellation; Binary number; Computer science; Noma; Gaussian; Upper and lower bounds; Channel (broadcasting); Sensor fusion; Wireless sensor network; Algorithm; Topology (electrical circuits); Wireless; Rotation (mathematics); Real-time computing; Theoretical computer science; Mathematics; Telecommunications; Computer network; Physics; Telecommunications link; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002550263,0.001061097,0.000950035,0.0007036002,0.0005005228,0.001078281,0.000799762,0.0009438661,0.00114089],"category_scores_gemma":[0.008631242,0.0004797566,0.0004816196,0.001055617,0.001246205,0.001575437,0.002233509,0.001113588,0.0003945517],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008422651,"about_ca_system_score_gemma":0.0006637881,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000332447,"about_ca_topic_score_gemma":0.0003703698,"domain_scores_codex":[0.9976089,0.001499075,0.00008985232,0.0002177191,0.0004175312,0.0001668012],"domain_scores_gemma":[0.9963931,0.002341021,0.0004804082,0.0003366372,0.0003529903,0.00009568282],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002963983,0.00005226564,0.0006612846,0.0001574326,0.00005973445,0.0001447031,0.0002599512,0.8518478,0.01293925,0.07224748,0.0007490303,0.06058467],"study_design_scores_gemma":[0.00002758897,0.0001079062,0.0001414915,0.00001824292,0.0000112633,0.00008916232,0.00004262872,0.9595472,0.004569022,0.03448246,0.0009433027,0.00001989442],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01779234,0.0002324951,0.9802533,0.0001717537,0.000021065,0.00003245889,0.00003250331,0.0000748384,0.001389248],"genre_scores_gemma":[0.7401711,0.0005018658,0.257358,0.00009229039,0.00004961951,0.0001442236,0.0001202979,0.00004700512,0.001515641],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002550263,"threshold_uncertainty_score":0.01348722,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1407200401152836,"score_gpt":0.2256917330036049,"score_spread":0.08497169288832129,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}