In the non-terrestrial network (NTN) being considered for Beyond 5G (B5G), high-altitude platform systems (HAPS) and low earth orbit (LEO) satellites will be used as communication platforms. This paper proposes a feeder link in HAPS and LEO using the THz band from the viewpoint of high speed and high capacity. the THz band feeder link has the feature of ensuring link performance even in cloudy or light rain compared to free space optical (FSO). In this paper, the link budget is clarified by estimating the propagation loss under various weather conditions based on the ITU-R model and other models. Based on the results, throughput evaluation results for each modulation scheme are presented.
Boron dust particles were injected by an impurity powder dropper to improve plasma confinement and perform wall conditioning in the Large Helical Device. A fast-framing camera for monitoring dust particle trajectories in the peripheral plasma detected a change in the ablation positions of the dust particles depending on the plasma density and heating power. An analysis using a three-dimensional edge plasma simulation code (EMC3-EIRENE) and a dust transport simulation code (DUSTT) was applied to understand these observations. The simulations proved that the dust particle trajectories are more deflected toward the outboard side of the torus by the effect of the plasma flow in an upper divertor leg for higher plasma densities and higher plasma heating powers. The simulation successfully reproduced observations of the change in the ablation positions in the peripheral plasma.
The elasticity problem of plates with holes is a classic problem in structural engineering and has significant implications for various industrial applications. Traditional numerical methods, such as finite element (FEM) analysis, require substantial computational resources and expertise. To solve this problem, we propose an innovative approach, a physics-informed neural network (PINN) -based surrogate model for solving elasticity problems in plates with holes. By training the PINN model on a dataset generated from FEM simulations with plates of different holes, we achieve accurate predictions of the stress and deformation fields, eliminating the need for laborious FEM computations. Our results demonstrate that the PINN-based surrogate model offers a computationally efficient and reliable approach for analyzing plates with holes of various sizes.