{"id":"W4416650073","doi":"10.1109/tcomm.2025.3637046","title":"Two-Timescales Optimization of Content Placement and Delivery in Satellite-Terrestrial Edge Computing Networks","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Communications","topic":"Satellite Communication Systems","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Science Foundation of Beijing Municipality; Queen's University; National Natural Science Foundation of China; Queen's University Belfast","keywords":"Markov decision process; Leverage (statistics); Reinforcement learning; Optimization problem; Cache; Enhanced Data Rates for GSM Evolution; Edge device; Integer programming; Edge computing; Quality of service","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002931685,0.0001545915,0.0002514964,0.0003429589,0.0001571029,0.00003614744,0.0004643099,0.00009245562,0.000008638427],"category_scores_gemma":[0.000006901759,0.0001807357,0.00006120541,0.0005169606,0.0001348546,0.0001106657,0.00001451944,0.0003244264,0.000002956819],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001321969,"about_ca_system_score_gemma":0.00002974888,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001131554,"about_ca_topic_score_gemma":0.0002857849,"domain_scores_codex":[0.9987326,0.0002337031,0.0006384767,0.0001426198,0.00009494061,0.0001576638],"domain_scores_gemma":[0.9978528,0.0007479038,0.00007642635,0.001213076,0.0000678123,0.00004200652],"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":[0.00002537868,0.0001166992,0.0002231269,0.00002693032,0.00006243417,1.185115e-7,0.0003712862,0.9766135,0.0004094833,0.0002211533,0.00001147884,0.02191843],"study_design_scores_gemma":[0.001099779,0.00002044425,0.0005608606,0.0003336563,0.00003544298,0.000001607789,0.0006267645,0.9952451,0.001251748,0.00001336948,0.0006620069,0.0001492189],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01881804,0.009380345,0.9659111,0.0002145804,0.0005125914,0.0006890448,0.00001827792,0.000220095,0.0042359],"genre_scores_gemma":[0.977459,0.01238322,0.009963349,0.00002954235,0.000009907509,0.00006272882,0.00002368935,0.00001959468,0.00004897108],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9586409,"threshold_uncertainty_score":0.7370187,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05139023337721987,"score_gpt":0.2743349580219171,"score_spread":0.2229447246446972,"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."}}