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The production process of full-CFRP struts, was before only suitable for low volume production rates (~10 struts/month) and including worker safety critical processes. This approach had to be reviewed and changed to enable production rates >100/month and improve worker safety. The activity focused on the: 

The Low-Cost Solar Panel Assembly GSTP Project has the high-level objective of increasing the European TRL level for innovative, high power to volume ratio, versatile, low-cost solar arrays, in this way contributing to cover the gap in this area with US manufacturers and to improve Europe global competitiveness. This new generation of solar power systems are also key to electrical-propelled missions in the whole range of orbits of interest

The ESA Deep Space Network consists of three 35m beam waveguide antennas located around the globe. The 35m beam waveguide antennas employ an X-Band feed covering the Deep Space uplink band near 7.2 GHz and down-link band near 8.45 GHz. Simultaneous uplink commanding at 20 kW and low noise reception (Generation #2) is supported. In order to increase the transmit power capability of ESA Deep Space antennas it is currently considered to add a new high power feed and associated 80 kW High Power Amplifier.

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.

In this activity, a proof of concept of an attitude control system based on artificial intelligence, as an alternative to current state-of-the-art systems based on model approaches, was developed and tested in a simulator and in the engineering model of ESA's OPS-SAT.

The driver behind the MoBaTe activity was to propose ways to improve the current software development and validation lifecycle using MBSE techniques to shift some of the system validation activities to the early stages of development. The roadmap to achieve this included identifying strategies, required model properties, modelling tools and languages needed to move from a requirements-based approach to a model-based approach. All this considering existing and established workflows and tools currently used by ESA.

As new software tools are developed for Model-Based Systems Engineering (MBSE) applications, parallel improvements are needed for Concurrent Engineering teams, since this can be one of the first steps for a model-based approach to build-up on throughout the continuation of the project. One of the main constraints during Concurrent Design (CD) studies is the capability of generating and evaluating a large amount of possible mission architectures without the implication of additional experts or time allocation.

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.

Due to the ongoing miniaturisation and increased performance of electronics devices, alternative cooling methods are required, specifically mechanical pumping loops (MPL). Passive two-phase loops are not capable of cooling ESA's expected power densities up to 300 W/m², so pumped two-phase loops are forecasted to replace their passive counterparts. According to Lapensée et al., “the most important component of a MPL is the pump and a pump failure directly results in MPL system failure, a reliable pump is therefore of vital importance". Wits et al.