{"id":"W3200448595","doi":"10.1109/jiot.2021.3112907","title":"Deep Dyna-Reinforcement Learning Based on Random Access Control in LEO Satellite IoT Networks","year":2021,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"IoT Networks and Protocols","field":"Engineering","cited_by":41,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"National Key Research and Development Program of China; Fundamental Research Funds for the Central Universities; University of Science and Technology Beijing; National Natural Science Foundation of China","keywords":"Computer science; Reinforcement learning; Satellite; Internet of Things; Computer network; Random access; Access control; Distributed computing; Artificial intelligence; Computer security","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":[0.001092668,0.0005637,0.001039666,0.000268915,0.0004051711,0.000809002,0.0009809101,0.0007125526,0.001757535],"category_scores_gemma":[0.003074396,0.0003328228,0.0003523635,0.0003119388,0.0009161168,0.0008213303,0.0008903552,0.001257279,0.0001638695],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001388619,"about_ca_system_score_gemma":0.001262479,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01549926,"about_ca_topic_score_gemma":0.008463363,"domain_scores_codex":[0.9996353,0.0001399105,0.00001578339,0.00006591457,0.00005554688,0.00008759511],"domain_scores_gemma":[0.9983702,0.001156694,0.0001497025,0.00005604286,0.0001774665,0.00008989834],"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":[0.00003563877,0.00002120606,0.0003822755,0.00001919266,0.00001167661,0.00003378564,0.00002643104,0.9850265,0.0001970025,0.006794206,0.0003149995,0.007137025],"study_design_scores_gemma":[0.000003015393,0.000005142591,0.00002323723,8.834315e-7,0.00000128984,0.000002006887,0.000001841017,0.9986993,0.00003539104,0.001182419,0.00004428802,0.000001133757],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.148128,0.0008382575,0.8411193,0.0009751316,0.00009387273,0.00006506325,0.00008185984,0.0006517849,0.008046919],"genre_scores_gemma":[0.981571,0.0001290644,0.01572749,0.00009053112,0.0000127938,0.00005482994,0.00004127476,0.0000237149,0.002349229],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01549926,"threshold_uncertainty_score":0.0308181,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009935644652283906,"score_gpt":0.2479618417234167,"score_spread":0.2380261970711328,"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."}}