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The Data Compression as a Service (DCaaS) study aimed to bring the benefits of data compression to the experimenters on ESA's OPS-SAT platform. It focused on using the POCKET+ algorithm for the compression of housekeeping telemetry data. The project contained a space component, which performed the compression of provided user data, and a ground component, for the decompression of transmitted user data. Three use cases were selected within the scope of the project: file-to-file, packet-to-packet, and stream-to-stream lossless data transfers.

With the 'democratisation of space', and the proliferation of large non-geostationary orbit constellations, the pace at which human-made objects are being deployed into orbit demands urgent action in defining and supporting a global space traffic management plan.

The 'SaaSy ML' project 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 onboard 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.