Nebula Public Library

Cubesats have the potential to revolutionize Earth Observation by exploring different operating points in terms of spatial, temporal, and spectral resolution. However, physical limitations, mostly in terms of size, prevent them from obtaining high-quality images. OPS-SAT, while characterized by a small form factor, provides an impressive in terms of capabilities and flexibility hardware/software platform for executing demanding machine learning algorithms (inference stage).

University of Luxembourg studied and evaluated a new concept of connecting space missions via space internet providers. The system requirements, necessary adaptations, and possible limitations are provided and discussed. Three types of space internet provider are studied and evaluated (i.e. Starlink, Oneweb and O3b mpower). The study revealed that higher constellations have better coverage towards the space missions. The study also provided some options for terminals that can be plugged into the space mission satellites for connection.

The Sun regularly releases considerable amounts of energy resulting in coronal mass ejections (CMEs) and accelerated particles, which can have a variety of adverse space weather (SW) effects at Earth and in the near-Earth environment. A useful means of tracking SW activity is via solar radio bursts (SRBs) associated with CMEs and solar energetic particle events (SEPs); CME-driven shocks can be tracked via Type II SRBs, while energetic electrons escaping into the heliosphere can be tracked via Type III SRBs.

Mission Control deployed a low-level implementation of the OPS-SAT SmartCam model using a Field Programmable Gate Array (FPGA), comparing against a high-level CPU model using Tensorflow Lite. Experiments showed that the FPGA implementation reproduced the precision and accuracy of the high-level model, while running at a slower speed. Further optimizations of the FPGA are expected to close the gap in timing and unlock new methods for deploying deep learning on spacecraft.