{"id":"W3143553543","doi":"","title":"Graph Attention Networks for Channel Estimation in RIS-assisted Satellite IoT Communications.","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Wireless Communication Technologies","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University; Polytechnique Montréal","funders":"","keywords":"Computer science; Channel (broadcasting); Internet of Things; Beamforming; Computer network; Graph; Transmission (telecommunications); Communications satellite; Satellite; Path (computing); Real-time computing; Telecommunications; Distributed computing; Embedded system; Engineering; Theoretical computer science","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":[0.0002160751,0.0002990964,0.0003689179,0.000455246,0.0001399028,0.00005754786,0.001346242,0.0005293075,0.0000043729],"category_scores_gemma":[0.00006632033,0.0004219433,0.0001841489,0.0008259513,0.0001522213,0.000189377,0.0009758859,0.0008602659,0.000004744975],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004525216,"about_ca_system_score_gemma":0.00003372021,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004142383,"about_ca_topic_score_gemma":0.0003972162,"domain_scores_codex":[0.9987222,0.0001034433,0.0003489153,0.0004460285,0.00004703866,0.0003323561],"domain_scores_gemma":[0.9970098,0.0002201686,0.0001988242,0.0023556,0.0001593361,0.00005630808],"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":[0.00001167696,0.00005734869,0.0006473723,0.0001442457,0.00006997105,0.000005378477,0.00006108302,0.9839739,0.00006636078,0.004806272,0.0000160628,0.01014032],"study_design_scores_gemma":[0.000423674,0.00001047355,0.006642444,0.0003078499,0.00004408257,0.000001239507,0.0004262353,0.9815341,0.0001071248,0.009973431,0.0001490795,0.0003802342],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1066292,0.002482854,0.8886947,0.00008763408,0.0002311951,0.0006122108,0.00002042584,0.0009449465,0.0002968218],"genre_scores_gemma":[0.9720554,0.01288313,0.01416877,0.00001179204,0.00001048199,0.00003475441,0.0007192246,0.00005267592,0.0000637159],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8745259,"threshold_uncertainty_score":0.9998232,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07699862093464215,"score_gpt":0.2119642561066862,"score_spread":0.1349656351720441,"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."}}