We aim to develop a novel control theory methodology using input-output process models for error mitigation in signal processing data-paths that operate in radiation prone environments.  In this approach, difference equations are used to characterize the process with perturbation corresponding to faulty digital circuits.

This activity developed high-performance data compression software with embedded data analysis features for radio-frequency, multi-band images and video. It offers excellent compression ratios and quality levels with very small computing requirements, which even allow for real-time operation on modest processors. Tests on data from ESA's OPS-SAT satellite reveal image ratios over 1:10 with excellent visual quality, and radio-frequency ratios over 1:3 with nearly identical spectrograms thanks to a smart lossy approach.

Up to now, the RF Safe Operating Area (SOA) of a process is evaluated by performing, at Room Temperature, RF step stress tests and RF life testing on specific parts and for specific applications only. In order to establish the complete RF SOA of a process, i.e. the RF SOA covering the main applications of a process, the two following main testing need to be done. Firstly, RF step stress tests and RF life testing will be performed on several MMIC designs and/or discrete parts, specifically chosen to be representative of the main applications of 2 major European millimeter-wave processes.

Radio Frequency Interference (RFI) impact heavily on a wide variety of space applications/services belonging to different areas, such as Telecommunications, Navigation and Earth Observation. Despite the huge number of interfering signal types, a preliminary classification of Interference may be performed according to the nature of interfering sources: - Intentional interferences. The transmitting events are deliberately performed for attacking other systems (e.g., jamming, spoofing, etc.); - Unintentional interferences.

Many use cases for insight generated in orbit require the integration and calibration of raw data from multiple instruments and vantage-points – a future vision sometimes referred to as ‘hybrid observation’. Such a capability would be a foundational requirement in the processing chain for rapid follow-up observations especially when we need to estimate quantitative variables (e.g. soil moisture or water pollutants).

‘STARCOP’ is an initiative to use machine learning and multiple satellites with diverse detection capabilities to quickly detect methane leaks and send notifications in near real-time. In this project Trillium Technologies developed machine learning models to automatically detect methane in hyperspectral and multispectral imagery. Their hyperspectral model (HyperSTARCOP) is able to capture more than 90% of plumes in test data while reducing the false positive rate by 39% when compared to state-of-the-art models.

The possibility to acquire and track signals for which the PRN code is not public (e.g. as for Galileo Public Regulated Service or any other non-public GNSS service) is of high interest in order to be able to assess, with real field signal, new acquisition and tracking techniques that otherwise can only be assessed with a full GNSS receiver with access to those PRN codes. In this context, the recovery of the unknown PRN codes from low-noise signal recordings (e.g.

The objective of the MARLISAT project was to further develop marine plastic monitoring through the use of multiple satellite technologies. In effect, the intention was to use satellite technology to detect marine litter, track it and forecast its pathways.

Four satellite technologies were combined:

Science missions such as SPICA (with the Safari instrument) and ATHENA (with the X-IFU instrument) require a complex cryogenic chain to ensure thermal bath temperatures in the order of 50 mK at the level of their respective detectors. The last cooling stage in these complex chains includes an Adiabatic Demagnetization Refrigerator (ADR) that takes advantage of the fact that the entropy of paramagnetic materials in a magnetic field is lower than when no field is present.