{"id":"W4406090099","doi":"10.1109/ojcoms.2025.3526759","title":"Active RIS-NOMA Uplink in URLLC, Jamming Mitigation via Surrogate and Deep Learning","year":2025,"lang":"en","type":"article","venue":"IEEE Open Journal of the Communications Society","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; Ministère de la Défense Nationale","keywords":"Noma; Telecommunications link; Jamming; Computer science; Computer network; Physics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006463548,0.00053779,0.0005083854,0.0002269869,0.0002039183,0.0006033338,0.0005504256,0.0006920986,0.0006407415],"category_scores_gemma":[0.001729229,0.0002611151,0.0003594115,0.0002620092,0.0004837607,0.0005918595,0.0006662625,0.0007756104,0.0002135845],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003431246,"about_ca_system_score_gemma":0.0005408138,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001841629,"about_ca_topic_score_gemma":0.002260365,"domain_scores_codex":[0.9997849,0.00009281597,0.000006794607,0.00003150471,0.00004682189,0.00003714078],"domain_scores_gemma":[0.9995381,0.00026123,0.00006216259,0.00003132965,0.00007919531,0.00002801593],"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":[0.00007062988,0.00003881209,0.000579035,0.00003417227,0.00002236955,0.00005414157,0.00002645995,0.9682721,0.00293093,0.00333302,0.0004573333,0.02418088],"study_design_scores_gemma":[0.000001068474,0.00001316118,0.00004779594,0.000001688704,0.000001723039,0.000005086257,0.000002479262,0.9989595,0.0003881468,0.0005027773,0.00007490463,0.000001632517],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1184992,0.0004881735,0.8746196,0.0003663313,0.00004171993,0.00002244744,0.00004978391,0.000477243,0.005435485],"genre_scores_gemma":[0.9469762,0.0001782317,0.04974466,0.0001148596,0.00002662107,0.0000383644,0.00008865662,0.00003036165,0.002802052],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001841629,"threshold_uncertainty_score":0.003661752,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01676295254077728,"score_gpt":0.2831495680726141,"score_spread":0.2663866155318368,"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."}}