{"id":"W4417118667","doi":"10.1049/icp.2025.4051","title":"Leveraging orbital dynamics with RF signal features for satellite multi-orbit proximity threat detection","year":2025,"lang":"en","type":"article","venue":"IET conference proceedings.","topic":"Space Satellite Systems and Control","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Random forest; Kinematics; Satellite; Classifier (UML); SIGNAL (programming language); Feature extraction; Covert; Pattern recognition (psychology); Set (abstract data type); Interference (communication)","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":[0.0004150372,0.0006716464,0.00043208,0.001284996,0.000263694,0.0004963512,0.0005977725,0.0005552775,0.0006172442],"category_scores_gemma":[0.001785958,0.0002129564,0.0006483232,0.0006388979,0.0002868281,0.0005862126,0.0005888641,0.00063094,0.0003662247],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004361471,"about_ca_system_score_gemma":0.0004051999,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00706701,"about_ca_topic_score_gemma":0.01078875,"domain_scores_codex":[0.9997748,0.00004568893,0.00001210948,0.00006824473,0.00006682641,0.00003232414],"domain_scores_gemma":[0.9994919,0.0002174849,0.00009463638,0.00008101652,0.0000746869,0.00004025984],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002548531,0.000269773,0.06174785,0.00008358708,0.000111336,0.0001752288,0.00009961356,0.8281474,0.008419415,0.0009298683,0.002968716,0.09679233],"study_design_scores_gemma":[0.000007253389,0.00005867108,0.00532834,0.0000062009,0.00001013483,0.00004097624,0.00001725098,0.9910737,0.002034694,0.0007450297,0.0006694067,0.000008447882],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6852198,0.0005175302,0.3007579,0.0005254123,0.0001368541,0.0001675758,0.003541811,0.005175349,0.003957741],"genre_scores_gemma":[0.9573233,0.0001035636,0.03744749,0.00007499896,0.00003911559,0.00005726444,0.004151959,0.00007201116,0.0007303834],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00706701,"threshold_uncertainty_score":0.0140518,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01096345658630802,"score_gpt":0.2163891651361944,"score_spread":0.2054257085498864,"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."}}