{"id":"W4315630157","doi":"10.1109/globecom48099.2022.10001422","title":"Joint Computation Offloading, UAV Trajectory, User Scheduling, and Resource Allocation in SAGIN","year":2022,"lang":"en","type":"article","venue":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","topic":"Satellite Communication Systems","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Computer science; Computation offloading; Trajectory optimization; Scheduling (production processes); Computation; Mathematical optimization; Optimization problem; Convex optimization; Resource allocation; Bandwidth allocation; Server; Real-time computing; Bandwidth (computing); Distributed computing; Edge computing; Regular polygon; Enhanced Data Rates for GSM Evolution; Optimal control; Computer network; Algorithm; Mathematics; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001109424,0.0003393453,0.0004161156,0.0002978457,0.0006338594,0.0001798694,0.001860407,0.0001090586,0.0002443817],"category_scores_gemma":[0.00006995991,0.0004572213,0.0000848716,0.001419136,0.0001915132,0.000290355,0.001034119,0.0009179026,0.00005327929],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001140767,"about_ca_system_score_gemma":0.0001550354,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000634837,"about_ca_topic_score_gemma":0.00115839,"domain_scores_codex":[0.9966808,0.001026114,0.0009049456,0.0004522362,0.0005060859,0.0004298103],"domain_scores_gemma":[0.9970184,0.0002094975,0.0002246707,0.002262704,0.0001338374,0.0001509102],"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":[0.0002224983,0.001991029,0.09517042,0.0007974136,0.0008099181,0.00003966572,0.02035988,0.5228271,0.02736732,0.1066751,0.04036669,0.183373],"study_design_scores_gemma":[0.001765758,0.0001299916,0.03661468,0.0001698449,0.00006164397,0.0001386811,0.008348512,0.7144953,0.0002582953,0.002114065,0.2346463,0.001256857],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8306965,0.0532487,0.02948413,0.005954859,0.002069023,0.003792757,0.001042043,0.002512193,0.07119978],"genre_scores_gemma":[0.9920558,0.001900024,0.004670937,0.0001953213,0.00002615189,0.0004925762,0.000484122,0.00004435891,0.0001307498],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1942796,"threshold_uncertainty_score":0.9997879,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05037661785141253,"score_gpt":0.2779998082432865,"score_spread":0.227623190391874,"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."}}