The solar panels installed on a CubeSat are considered the main energy source of a nanosatellites. The deployment mechanism of a solar panel must be analyzed and tested extensively. Any suggested solar panel design should present a low vibrating free spinning deployment mechanism. This paper examines various types of solar panels to reach a conclusion of the efficient design when deployed on a 1U or 2U unit. However, calculations, analysis, simulations do not always give an extensive picture of how the satellite shall behave during deployment. Thus, testing in a microgravity environment gives a more accurate answer of how the satellite shall behave. In our work, various solar panels mechanisms are developed and eventually tested in microgravity. The first accordion structure for a 1U structure is tested in a microgravity environment through a parabolic flight with the National Research Council Falcon 20 aircraft. The results are recorded and analyzed to optimize the next design. The second design is based on a drag-sail mechanism for a 2U structure. The design is improved upon the first experiment results for the next parabolic flight. The simulated amount of power generated in orbit is also a main factor in our evaluation.

Integrating high-performance computer vision (CV) into Industry 4.0 environments remains a challenge due to the computational disparity between state-of-the-art (SOTA) models and resource-constrained edge hardware. This study proposes a hardware-aware optimization framework designed to bridge this gap, focusing on real-time object detection for high-speed, omnidirectional conveyor systems. Unlike conventional benchmarking, the proposed framework employs a multi-stage optimization pipeline—integrating backbone refinement, hyperparameter tuning, and quantization—to transition diverse architectures from baseline configurations ($M_{base}$) to hardware-optimized variants ($M_{opt}$).The framework’s efficacy is validated using a custom-built standalone experimental platform detecting package features, brands, and disruptions on an omnidirectional-wheeled conveyor. A comprehensive comparative analysis is conducted across a heterogeneous edge ecosystem, including the NVIDIA Jetson Nano (GPU), Raspberry Pi 4 (CPU), and Google Coral (TPU). Our findings demonstrate that through systematic tuning, the YOLOv10n variant emerged as the superior architecture, achieving a precision of 98.1% and an mAP_50:95 of 81.22%. Post-deployment characterization reveals that the optimized YOLOv10n model on the NVIDIA Jetson Nano achieved a peak inference speed of 25 frames per second (FPS), successfully striking the “Pareto-optimal” balance between predictive accuracy and real-time processing. The primary contributions of this work include a reproducible optimization methodology, a comparative performance map across three distinct hardware backends, and the release of a specialized industrial conveyor dataset.