Cargotec (Finland)

Characterization of uncooperative Resident Space Objects (RSOs) plays a crucial role in On-Orbit Servicing (OOS) and Active Debris Removal (ADR), allowing missions to assess the geometry and motion properties of the target. Addressing the specific challenge of reconstructing tumbling targets, this study evaluates the performance of existing state-of-the-art 3D reconstruction algorithms for dynamic scenes (D-NeRF, TiNeuVox, HexPlane), focusing on their ability to generate physically plausible representations with high fidelity. Specifically, we address a scene that is both geometrically dynamic, due to the tumbling target, and subject to dynamic lighting, where directional sunlight casts shifting shadows across the surface. To support our evaluation, we developed a simulation environment using Isaac Sim to generate physics-accurate 2D image sequences of tumbling satellites under high-fidelity orbital lighting conditions. We observe that while HexPlane excels in stationary scenarios (PSNR > 35 dB), all methods degrade substantially as complexity increases. In fly-around scenarios, D-NeRF produces blurred outputs, HexPlane exhibits ghosting artifacts, and TiNeuVox introduces foggy artifacts. Moreover, standard metrics fail to capture these structural inconsistencies due to the dominance of the black background in the images.

5G technology brings significant potential for enabling port automation, leading to improved flexibility, predictability, and safety in ports. In this article, we present main drivers and technologies required for port automation. We focus on future communication needs in ports and describe 5G communication networks with their ultra-reliable, low-latency, and high-capacity capabilities as a connectivity solution to forge the advanced technologies required to realize port automation. Extensive multi-cell simulations built on realistic measurement-based propagation models and prototype testing in lab and port environments demonstrate the 5G capabilities for ports.