ABSTRACT Compared to traditional combustion‐based systems, hydrogen fuel cells (HFCs) offer several significant advantages. They are increasingly being utilized in modern automobiles and power plants due to their environmental friendly nature. Unlike combustion engines, fuel cells produce little to no emissions, HFCs, in particular, emit only water vapor, thereby contributing positively to the fight against climate change by eliminating carbon dioxide emissions. HFCs function similar to traditional Lithium‐ion batteries; however, they do not deplete or require recharging, and their high‐energy density makes them a compelling alternative for energy storage, especially in High‐Altitude Platform Station (HAPS) energy management systems. HAPS are unteathered stratospheric platforms lying at an altitude of 17–22 km that act as an communication or sensing stations. HAPS can cater better communication, transit operations, intelligence, surveillance, and rescue missions, which is a significant advancement in the next generation of wireless systems. This study focuses on the reliability analysis of HFCs within the Energy Management Subsystem (EMS) of HAPS, employing both stochastic modeling and machine learning (ML) techniques. We utilized out of system models for both nonrepairable and repairable configurations to conduct an analytical evaluation of HFCs. Discrete event simulation is employed to validate these analytical findings. Furthermore, widely used ML models, for example, and ‐nearest neighbors (KNN) regression are applied to accurately predict reliability metrics and temporal behaviors based on key input parameters. The study also explores various network topologies to identify the most efficient configuration for low earth orbit (LEO) satellite networks.
Gautam et al. (Fri,) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: