Om Mishra | Searcharxiv

arXiv2026

From Design to Deorbit: A Solar-Electric Autonomous Module for Multi-Debris Remediation

The escalating accumulation of orbital debris threatens the sustainability of space operations, necessitating active removal solutions that overcome the limitations of current fuel-dependent methods. To address this, this study introduces a novel remediation architecture that integrates a mechanical clamping system for secure capture with a high-efficiency, solar-powered NASA Evolutionary Xenon Thruster (NEXT) and autonomous navigation protocols. High-fidelity simulations validate the architecture's capabilities, demonstrating a successful retrograde deorbit from 800 km to 100 km, <10m position Root Mean Square Errors (RMSE) via radar-based Extended Kalman Filter (EKF) navigation, and a 93\% data delivery efficiency within 1 second using Delay/Disruption Tolerant Network (DTN) protocols. This approach significantly advances orbital management by establishing a benchmark for renewable solar propulsion that minimizes reliance on conventional fuels and extends mission longevity for multi-target removal.

cs.DC↗

arXiv2026

Explainable Galaxy Interaction Prediction with Hybrid Attention Mechanisms

Galaxy interaction classification remains challenging due to complex morphological patterns and the limited interpretability of deep learning models. We propose an attentive neural ensemble that combines AG-XCaps, H-SNN, and ResNet-GRU architectures, trained on the Galaxy Zoo DESI dataset and enhanced with LIME to enable explainable predictions. The model achieves Precision = 0.95, Recall = 1.00, F1 = 0.97, and Accuracy = 96%, outperforming a Random Forest baseline by significantly reducing false positives (23 vs. 70). This lightweight (0.45 MB) and scalable framework provides an interpretable and efficient solution for large-scale surveys such as Euclid and LSST, advancing data-driven studies of galaxy evolution.

astro-ph.IM↗