{"id":"W4404739496","doi":"10.1109/twc.2024.3502394","title":"Empowering ISAC Systems With Federated Learning: A Focus on Satellite and RIS-Enhanced Terrestrial Integrated Networks","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Wireless Communications","topic":"Satellite Communication Systems","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"National Science and Technology Council","keywords":"Computer science; Focus (optics); Satellite; Communications satellite; Telecommunications; Remote sensing; Engineering; Geology; Aerospace engineering","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0009764899,0.0007094766,0.0005668746,0.0002630168,0.0003645364,0.001046205,0.001026967,0.0007684833,0.001021126],"category_scores_gemma":[0.002222105,0.0002009237,0.0003318821,0.0003479072,0.001041779,0.001314947,0.001204642,0.00112831,0.0001925555],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007780322,"about_ca_system_score_gemma":0.001045192,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00280231,"about_ca_topic_score_gemma":0.003355352,"domain_scores_codex":[0.9995807,0.0001451424,0.00001108443,0.00007633034,0.0001122131,0.00007454623],"domain_scores_gemma":[0.999289,0.0003293472,0.00008033019,0.0001010029,0.0001403639,0.00005996028],"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":[0.00004890622,0.00004158462,0.000585291,0.00005155265,0.00002231922,0.00006325435,0.00004064983,0.9481199,0.002195101,0.01145906,0.0005444118,0.03682792],"study_design_scores_gemma":[0.00000297192,0.00003153561,0.00006185452,0.000004827106,0.000004228154,0.00001458087,0.00001181529,0.9956826,0.0007010243,0.002989562,0.0004917123,0.000003263823],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04628982,0.0006895479,0.9455113,0.000468137,0.00007567387,0.00005075162,0.00002672627,0.0004020588,0.006486027],"genre_scores_gemma":[0.9462844,0.0003747809,0.05132695,0.0001802289,0.00003516112,0.0000417266,0.00002749894,0.00002890935,0.001700358],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00280231,"threshold_uncertainty_score":0.005645037,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02311739805907575,"score_gpt":0.2658712228447254,"score_spread":0.2427538247856497,"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."}}