Hugging Face Trending Papers
Space Generative AI with Solar Energy Harvesting
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arXiv AI
Sep 2
Space Generative AI with Solar Energy Harvesting
arXiv:2609.01062v1 Announce Type: new Abstract: Satellites are emerging as promising platforms to extend generative \emph{artificial intelligence} (AI) services to remote areas lacking terrestrial in...
arXiv Machine Learning
Aug 11
ML-Based Hierarchical Prediction for Practical Energy Scheduling in Dynamic NTN-WPT Systems
arXiv:2608. 08804v1 Announce Type: cross Abstract: With advancements in long-distance wireless power transfer (WPT) and space-based energy technologies, integrating WPT into non-terrestrial networks (NTNs), referred to as NTN-WPT, is emerging as a promising approach for next-generation wireless networks.
arXiv AI
Sep 21
SpaceDiffusion: Over-the-Orbit Diffusion for Space Generate-and-Forward Communications
arXiv:2609.20899v1 Announce Type: cross Abstract: Satellite communications are an essential component of sixth-generation (6G) mobile networks, which provide ubiquitous connectivity for global servic...
arXiv Machine Learning
Oct 12
Goal-Oriented Bundle Management and Flow Control for Deep-Space Communications via Reinforcement Learning
The paper introduces a reinforcement‑learning framework for managing bundles and flow control in deep‑space networks, where relay nodes must schedule and drop data under intermittent connectivity and limited storage. It defines a goal‑oriented loss based on excess age to prioritize mission‑relevant information and formulates relay control as a constrained semi‑Markov decision process solved with masked Lagrangian PPO. Simulations show that the approach improves age‑based utility and inter‑flow fairness compared to traditional schedulers.
arXiv AI
Aug 26
Reinforcement Learning-Guided Evolutionary Policy Optimization for Preference-Adjustable Heterogeneous Agile Earth Observation Satellite Scheduling
The paper introduces a reinforcement‑learning‑guided evolutionary policy optimization framework for scheduling heterogeneous agile Earth observation satellites, addressing task selection, satellite assignment, and sequencing under diverse visibility windows, maneuvering constraints, energy use, and storage limits. It combines assignment‑based indirect encoding with decoder‑based cost evaluation to capture satellite‑dependent constraints while integrating task gain, energy savings, and load balance into a single utility metric. The resulting RLOSMEA algorithm uses reinforcement learning to select high‑level search operators, achieving higher weighted utility and more stable convergence than baseline metaheuristics across varied AEOS scenarios.
arXiv Machine Learning
Jul 8
Joint Energy Management and Coordinated AIGC Workload Scheduling for Distributed Data Centers: A Diffusion-Aided Reward Shaping Approach
arXiv:2605. 02965v2 Announce Type: replace Abstract: Artificial intelligence-generated content (AIGC) has emerged as a transformative paradigm for automating the creation of diverse and customized content, giving rise to rapidly growing computational workloads in cloud data centers.