Researchers present a hybrid temporal convolutional network and transformer architecture designed to forecast satellite collision probability early, using conjunction data messages as input within a dedicated analysis framework. The work targets the growing operational load on satellite operators caused by more frequent close approaches in low Earth orbit as satellites and debris multiply.
An arXiv preprint surveys how falling launch and hardware costs have led to more satellites and a fast-growing volume of data generated in orbit. Because downlinking that data to Earth is expensive and slow, the paper looks at processing it in space, including onboard edge AI and orbital data-center concepts. It is a cross-listed replacement submission and has not been peer reviewed.
A new arXiv preprint introduces an adaptive, distributed physical-layer authentication and attack detection method built on causal meta-learning for 6G non-terrestrial networks. The work targets settings such as satellite links, where Doppler shifts, long delays, and fast-changing channels create distribution shifts that undermine conventional learning-based defenses.