Classical control design techniques used for flexible spacecraft are based on single axis design approaches to control the rigid-body motion complemented by low-pass and notch filters to suppress the resonant peaks of the low-frequency flexible modes. These techniques are inherently relying on a priori knowledge of the structural flexible modes and introduce significant conservatism in the control and structural design, dictated by the need to separate the controller bandwidth from the flexible modes frequency.

The main advantage of the proposed design of middle resolution class analog to digital conversion for space application is minimal demand for additional special function/parameters components (single middle class op-amp, resistors and capacitor are enough). All other required functionality, including analog signal comparator and reference switching, can be implemented in FPGA digital subsystem for middle resolution range applications. This makes the choice of space mature/proven components easier.

In this activity, Mission Control Space Services deployed a low-level implementation of the OPS-SAT SmartCam model using a Field Programmable Gate Array (FPGA), comparing against a high-level CPU model using Tensorflow Lite. Experiments showed that the FPGA implementation reproduced the precision and accuracy of the high-level model, while running at a slower speed. Further optimisations of the FPGA are expected to close the gap in timing and unlock new methods for deploying deep learning on spacecraft.

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.

In this project the authors looked at event-based sensing and processing for space situational awareness (SSA). Many advantages exist with the new paradigm of neuromorphic engineering, the authors investigate whether event-based optical data is suitable for the task and if processing with Spiking Neural Networks (SNN) provides advantages in terms of efficiency and efficacy. With the implementation of a novel heterogeneous LIF SNN, authors claim to surpass the state of the art in event-based SSA with a 15% increase in accuracy.

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 LEO-GEO4GHG project studied the feasibility of new frontiers of cloud computing in space and artificial intelligence (AI) combining multiple sources of data such as the SATLANTIS GEI-SAT constellation and other external meteorological and atmospheric data with the final aim of providing near real-time methane detection and quantification.

State-of-the-art planetary science missions have demanding requirements regarding orbit knowledge prediction ; for example, for ExoMars TGO, the prediction requirement is around 100m (cross track) and 10s (along track) 2 weeks in advance during the science phase. EnVision M5 candidate mission has similar requirement with a 300 m cross-track and 10s along-track orbit knowledge prediction up to 2 weeks in advance of a given pass. Aerobraking operations also require accurate orbit determination e.g.