Nebula Public Library

Space weather observations and prediction of expected disturbances on ground- and space-based infrastructures are critical for the safety and evolution of our modern society. Space weather events, like solar storms, can have crucial impact on navigation, communication, power grids and other electrical conducting infrastructures.

With the “democratisation of space”, and the proliferation of large non-geostationary orbit (NGO) constellations, the pace at which human-made objects are being deployed in orbit demands urgent action in defining and supporting a global Space Traffic Management (STM) plan. Promptly identifying, tracking, and cataloguing of resident space objects (RSOs), in particular after orbit insertion or break-up and fragmentation events, as the recent Russian anti-satellite weapon (ASAT) test put in evidence, is of critical importance to enable a safe and sustainable use of space.

The “SaaSy ML” idea will provide experimenters with on-board Machine Learning (ML) functionalities that any app can subscribe to via a Software as a Service (SaaS) app hosted on the Satellite Experimental Processing Platform (SEPP). The ML features provided by the SaaS app will cover both training and prediction operations. The JSAT open-source java library for ML will be re-used thus making accessible over 100 training algorithms on-board a flying mission. Re-using the JSAT library will ensure the development to focus on the idea's novel aspect: a spacecraft SaaS app for in-orbit ML.

In order to enable lunar exploration in a sustainable manner, it is necessary to develop technologies that allow the use of lunar resources in-situ. Additive manufacturing techniques are being studied for in-situ resources utilization on the Moon (e.g.: solar or laser sintering). The results of these studies, presented samples with low mechanical properties due to the formation of defects and cracks within the final product.