{"id":"W4389115585","doi":"10.48550/arxiv.2311.15062","title":"Simultaneous Beam Training and Target Sensing in ISAC Systems with RIS","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Government of Jiangsu Province; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Scheme (mathematics); Computer science; Beam (structure); Domain (mathematical analysis); Line-of-sight; Real-time computing; Line (geometry); Base station; Doppler effect; Orientation (vector space); Simulation; Electronic engineering; Acoustics; Optics; Telecommunications; Aerospace engineering; Engineering; Physics; Geometry; Mathematics","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":[0.0002999133,0.0004191987,0.0004231663,0.0002443879,0.0002596012,0.0004168449,0.00046047,0.0004508445,0.0004641591],"category_scores_gemma":[0.001044401,0.0002077937,0.0002576126,0.0005971913,0.0005177478,0.0005870012,0.0006489516,0.0004632088,0.0001363115],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003383047,"about_ca_system_score_gemma":0.0005889171,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002985515,"about_ca_topic_score_gemma":0.003757628,"domain_scores_codex":[0.9995796,0.00008687131,0.00001123608,0.00007577469,0.0001756997,0.00007090154],"domain_scores_gemma":[0.9996215,0.0001594248,0.00006375424,0.00006472001,0.00006920814,0.00002130969],"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":[0.0003740682,0.0000803494,0.005664942,0.00007591335,0.00005274234,0.000278105,0.0002214223,0.7653816,0.07089225,0.009988747,0.0006279352,0.1463619],"study_design_scores_gemma":[0.00001051747,0.00009644417,0.0008702973,0.000002917869,0.000008360334,0.00008919399,0.00002717146,0.9890593,0.008486992,0.0008679819,0.0004711781,0.000009611174],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2053136,0.0002253083,0.7898024,0.0001302377,0.00004271771,0.00002837689,0.00002771747,0.0005646882,0.003864938],"genre_scores_gemma":[0.9128014,0.00006866079,0.08610441,0.00004879624,0.00001519161,0.00001929548,0.00002697553,0.00001255054,0.000902614],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002985515,"threshold_uncertainty_score":0.005936265,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04745922216901481,"score_gpt":0.1633279932261104,"score_spread":0.1158687710570955,"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."}}