{"id":"W4404239091","doi":"10.1109/tvt.2024.3487015","title":"Cooperative Learning-Based Framework for VNF Caching and Placement Optimization Over Low Earth Orbit Satellite Networks","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Satellite Communication Systems","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure; Carleton University","funders":"National Research Foundation of Korea","keywords":"Low earth orbit; Satellite; Computer science; Medium Earth orbit; Communications satellite; Satellite broadcasting; Geocentric orbit; Computer network; Aerospace engineering; Engineering","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":[0.001601102,0.001094047,0.001775262,0.0004955931,0.000629434,0.001198874,0.002795424,0.001458215,0.003016033],"category_scores_gemma":[0.003551262,0.0006818228,0.0006540935,0.001032339,0.0009471055,0.001279953,0.001518208,0.001698222,0.0004107053],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001882775,"about_ca_system_score_gemma":0.002700347,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02143493,"about_ca_topic_score_gemma":0.01776791,"domain_scores_codex":[0.9991672,0.0002096185,0.00003607427,0.0002003216,0.0001834031,0.0002033928],"domain_scores_gemma":[0.9981427,0.001060696,0.0001863597,0.000101296,0.0003486682,0.0001603281],"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":[0.00003261894,0.00003034609,0.0002354466,0.00002483549,0.00001231169,0.00003308323,0.0000271589,0.9856825,0.0003012675,0.002679039,0.0004895621,0.01045164],"study_design_scores_gemma":[0.000003581608,0.000007501385,0.00001714935,0.000001224091,0.000001755228,0.000002742162,0.00000353264,0.9989076,0.00004335897,0.0009249972,0.00008538764,0.000001193737],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0162387,0.0003599028,0.980354,0.0002455338,0.00004843855,0.00005751486,0.00006962683,0.0004089126,0.002217305],"genre_scores_gemma":[0.848979,0.0003512944,0.1447672,0.0002637389,0.0001130577,0.0002778968,0.000231164,0.0001372421,0.004879434],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02143493,"threshold_uncertainty_score":0.04262036,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009330960579132007,"score_gpt":0.2413919651695499,"score_spread":0.2320610045904178,"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."}}