{"id":"W4407374643","doi":"10.1109/tcomm.2025.3541092","title":"Robust and Secure Multi-User STAR-RIS-Aided Communications: Optimization Versus Machine Learning","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Communications","topic":"Space Satellite Systems and Control","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"Canada Excellence Research Chairs, Government of Canada; National Science and Technology Council; Canada Research Chairs","keywords":"Computer science; Star (game theory); Robustness (evolution); Electronic engineering; Engineering; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001906823,0.0002190304,0.0002294876,0.0002720254,0.0007819728,0.00010164,0.0006990036,0.0001472826,0.0000444483],"category_scores_gemma":[0.0000166611,0.0002433765,0.00008533234,0.0005190748,0.000138879,0.0002139041,0.00001494217,0.0007241759,0.00002370764],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001266371,"about_ca_system_score_gemma":0.00003481107,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002307995,"about_ca_topic_score_gemma":0.002002297,"domain_scores_codex":[0.9989243,0.0002117795,0.0003606184,0.0001890648,0.0001074998,0.0002068111],"domain_scores_gemma":[0.9972944,0.0004707851,0.00005560165,0.00198566,0.0001196449,0.00007387435],"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":[0.00003944678,0.0001643333,0.0000583343,0.00004572728,0.0002668017,2.526043e-7,0.0005946324,0.9859058,0.000383847,0.001380521,0.0001110057,0.01104932],"study_design_scores_gemma":[0.001594853,0.00003563566,0.00007675898,0.00009725218,0.0001176651,0.000001761608,0.0006265673,0.9610488,0.0002217013,0.00001449937,0.03594597,0.0002185831],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0005560426,0.007908905,0.9841118,0.001806734,0.0004463985,0.0005688047,0.00006987558,0.0006966508,0.003834814],"genre_scores_gemma":[0.9307004,0.01604166,0.05059348,0.00007019276,0.00001183386,0.0003218613,0.00007296586,0.00005354421,0.002134128],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9335183,"threshold_uncertainty_score":0.9924605,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03455601189354794,"score_gpt":0.2572922130072263,"score_spread":0.2227362011136784,"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."}}