SensorFault-Bench is a new CPS-grounded benchmark showing that clean-MSE rankings of forecasting models often disagree with their robustness under standardized sensor-fault scenarios across four real datasets.
Multi-Agent Reinforcement Learning-Based Fairness-Aware Scheduling for Bursty Traffic
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Bivariate bicycle codes enable a modular architecture that supports an order of magnitude more logical circuit volume per physical qubit than surface-code designs under circuit noise.
M-ASPM decouples receiver sensitivity gains from collision exposure in LPWANs via a single shared detection channel that handles synchronization, CFO estimation, and payload channel selection across multiple payload channels.
TF5 combines TESLA and GG09 IBS for efficient authentication of 5G system information broadcasts, with Tamarin verification and OpenAirInterface implementation.
AdaFair-MARL enforces workload fairness as an explicit second-order cone constraint in cooperative MARL via adaptive primal-dual optimization, achieving near-perfect constraint satisfaction while preserving team performance.
IDQS combines a recurrent predictive neural network for QoS forecasting with a pairwise decision model to detect LDoS attacks, reporting over 79% and 91% accuracy on two public datasets with 0.28s inference time.
A methodology constructs a glossary of viable urban site typologies for distributed PV-powered servers from legal, project, consultation, and literature sources, then evaluates them on energy, spatial, and qualitative criteria in Montpellier.
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Tour de gross: A modular quantum computer based on bivariate bicycle codes
Bivariate bicycle codes enable a modular architecture that supports an order of magnitude more logical circuit volume per physical qubit than surface-code designs under circuit noise.