Novel GNC systems are based on on-board real-time optimized GNC systems to enable safe ascent, agile manoeuvring as well as ultraprecise stage landing on ships and ground. Modern space transportation and future exploration systems will be re-usable to contribute in reducing mission costs, while improving reliability, safety and performance. This activity shall establish novel GNC architectures and systems implementing Real-time Optimised GNC system for reliable and fault tolerant ascent flight, precise orbital guidance and control as well as for precision entry descent and landing.

Detecting anomalies from satellite telemetry is critical for the safe operation of that satellite. Although many approaches to autonomous onboard anomaly detection have already been proposed, most of them have so far only been tested on non-satellite or simulated data. This activity tackled this research gap and proposed an end-to-end machine learning-powered approach for detecting abnormal events in real-life OPS-SAT telemetry data, and deployed it on board a satellite.

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.

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.

• • Ship Detection: Reduce the processing time; ;

Commonly used classical cryptographic protocols, such as the RSA-protocol, rely on the hardness of computational problems. With the advent of quantum computing (QC), the hardness assumptions of these problems is nullified, since they can be solved efficiently on a QC. On the other hand, the security of Quantum key distribution (QKD) rests upon the very laws of physics and guarantees information-theoretic security, which not only enables tap-proof communication at present, but also prevents any future technology to crack encrypted messages.

Tables and graphs are knowingly used to organise data within a company with different levels of depth and complexity. Knowledge graphs (KGs) are particularly useful because they can cope with data diversity (high-quality complete data and sparse and incomplete data), they have a high degree of scalability and flexibility (the semantic data model can be inter-operational, large, wide and as deep as needed) and, last but not least, they provide reasoning and inference capabilities.

The goal of the project was to develop a service as a product to simplify fitting DNN (Deep Learning Neural Networks) in on board HW to make better use of AI on space missions The role of the service is to support data processing engineers in reducing HW resources requirement of powerful DNN for image analysis to be executed onboard.