The main objective of the project was to select the right IMU for our Future GNSS Receiver Unit (GRU). Two IMU were selected from their datasheet : µ-UMI-IC-HP from Litef and STIM318 from Sensonor. These 2 IMU were submitted to hard characterization tests applicable to our Launchers (and
HAPS) application.

'Don't try this at home' (named after one of the AI ships from I.Banks Culture series) is a concept demonstrating the benefits of having a cognitive cloud computing infrastructure in space. Such an infrastructure will provide a comprehensive and scalable set of commercial services to EO sensors and space-based data providers. It enables several applications by efficiently collecting and fusing information on target objects from observations of different platforms at various wavelengths, resolutions, and times. As a result, it leads to timely, precise information.

The project, named in short 'Plastic Monitor', was part of the ESA Discovery Campaign on Remote Sensing of Plastic Marine Litter, funded by the Discovery element of ESA’s Basic Activities. Plastic Monitor aimed to assess the feasibility of detecting heavy plastic pollution loads in an Indonesian river using satellite imagery and ground truth data, and to demonstrate how remote sensing can enhance the quantification and monitoring of plastic input into the marine environment.

The specific objectives of the project were to:

Today, Ge is used as a growth template for III-V based solar cells and also serves as the lowest active junction. While the thickness of the Ge on solar cell level is around 140µm, actually only 10-20µm are really active. Therefore, in order to become thinner and lighter, solar cell manufacturers start to thin down the Ge wafers after cell processing by grinding, etching or lift-off processes (to reduce weight). However, this Ge waste is today not recovered since no economical process exists.

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.

The market for life‐extension services for geostationary orbit (GEO) satellites is expected to remain healthy for the foreseeable future: over the last 20 years, an average of 22 new GEO satellites were launched per year. In total, ClearSpace expects there will be 370 potential client satellites that will reach their end of life due to the limited fuel they have remaining over the 2026–2041 period. ClearSpace believes a strong market penetration rate is feasible, as more than 30% of all the potential satellites belong to just seven satellite operators.

The space industry and allied agencies have long acknowledged the interest and added value of Model-Based System Engineering in expediting the design, development, deployment, and verification of space systems. The definition of models and equipment that can serve the diverse objectives of system engineering process has been the focus of numerous initiatives over the past few decades. The next stage is to employ MBSE broadly throughout the system life cycle across disciplines and the supply chain.

Recent studies have shown that remote sensing of floating marine plastic litter (MPL) is feasible from unmanned aerial systems, aircraft and satellite missions. However, in the infrared (IR) spectrum, water is a strong light absorber which makes the spectral detection and discrimination of plastics challenging. Additionally, MPL is often covered with living organisms and it is unclear what impact different thickness of biofouling may have on the spectral reflectance of floating plastic.

Today’s satellites are inherently inflexible and purpose-built for a single mission. In contrast, the community is exploring disruptive approaches towards in-orbit reconfiguration and on-board data processing. One of the most promising approaches is to use COTS components and open source frameworks, which decrease time-to-market and could even increase the pool of developers/users on the satellite.