Aerodrag is a dominant force acting on spacecraft operating in Very Low Earth orbit (VLEO). Accurately measuring aerodrag is essential for precise orbit determination and orbit control, consequently, precise attitude determination, better collision avoidance, and longer life missions. Conventional methods for aerodrag estimation rely on GNSS measurements with simplified onboard models, which can be unreliable or inaccurate due to environmental factors, such as atmospheric disturbances or ionospheric scintillation.

The In-Orbit Refurbishment and Upgrading Service (IRUS) is a first-of-its-kind refurbishment and upgrading mission concept, proposed by Astroscale and BAE Systems, aligned with ESA’s Circular Economy strategy. The outcome of the systems study was a servicer design, taking elements of Astroscale’s ELSA-M and COSMIC platforms, and a modified, prepared BAE Systems client platform. DHV Technology assessed the potential to refurbish and upgrade solar cells and solar arrays.

The Team evaluated a mission concept to robotically refurbish a GEO satellite by replacing a component of the Electric Propulsion subsystem. The Team performed a case-study to evaluate the suitability of existing propulsion systems for refurbishment. Further, they created a set of circular economy system maps to envisage what the future in-orbit circular economy would look like, and identified a business case that supports refurbishment based on purely commercial value.

When parachutes are deployed, the parachute material is extracted from the deployment bag at high speed. Interaction with the parachute bag during deployment can cause damage which can propagate and cause parachute failure during inflation. After a parachute has failed during inflation, it is impossible to determine whether the initial damage was caused during deployment or inflation. A parachute extraction rig thus provides an invaluable tool for testing parachute deployment before committing to expensive system flight tests of the system.

The URINIT (intensified and robust water and nutrient recovery from urine for space exploration) project aimed to develop a robust and sustainable approach to treat urine in space for crewed space flights and extended durations. The approach built on several innovative inputs:

Cyberattacks on aerospace systems are becoming a growing concern for space missions, alth

Array Fed Reflector (AFR) antennas provide a favourable trade-off between performance (e.g. gain) and complexity (e.g. number of antenna ports) for emerging broadband satellites in geostationary orbit. In light of emerging digital AFR payloads, satellite integrators and operators are in need of a) efficient optimization processes to define the antenna configuration (Optics, Focal Array) that is the most flexible and best suited for user distribution, and b) practical techniques for the dynamic reconfiguration of the payload during operation in a reduced timescale.

The purpose of this activity was to evaluate the readiness and applicability of Orbex launch services to meet ESA's needs, identify any gaps, and formulate a roadmap to address them. It focused on three key areas: (i) an assessment of the company’s robustness; (ii) a mission feasibility analysis of four ESA missions; and (iii) a gap analysis of the current launch system relative to ESA requirements, followed by a high-level roadmap to close the identified gaps. 

Robots are the most important assistants for astronauts during extra-terrestrial missions. All successful exploration missions in the past have proven the importance and the durability that robots have in extreme environments. For our next step on the lunar surface, it’s essential to redesign a more complete and multifunctional robotic system that can provide support for astronauts during the construction, exploration, research and everyday life tasks. This research study aims to create a new framework for reconfigurable robotics, going beyond the “traditional” intransigent robot.

This project was primarily focused on exploring and enhancing the state-of-the-art in raw Synthetic Aperture Radar (SAR) data compression by incorporating machine learning (ML) techniques. The overarching goal was to evaluate the potential improvements in compression efficacy through the integration of ML algorithms with the aim to on-board these algorithms in future.