The main objective of this activity was to develop the technology of crystal growth and testing of mercurous bromide (Hg2Br2) single crystals potentially suitable for broadband infrared (SWIR, LWIR) polarization optic and acousto-optics, incl. the acousto-optic tuneable filter (AOTF). The crystal exhibits unique optical characteristics in the full 0.5 - 30 μm spectral range: broad optical transmission, high birefringence, extremely low velocity of acoustic wave propagation and high coefficient of acousto-optical interaction.

In recent years, research in the space community has shown a growing interest in AI, mostly driven by systems miniaturization and commercial competition [1]. FPGAs have proven to be competitive accelerators for these algorithms and works proposing methods for automating the design on these devices have acquired relevance [2]. The common purpose is to enable a wide range of users without specific skills to accelerate AI models on FPGAs with reduced development times. Among the emerging frameworks, FPG-AI constitutes a valuable option for promoting the deployment of AI onboard the satellite.

The work carried out reports on a new machine learning-based software prototype for designing and optimising phased array antennas. The prototype can rapidly and accurately compute excitation coefficients for large-scale antenna arrays across diverse configurations and far-field requirements. The prototype relies on tailored neural networks integrating structural physics and domain knowledge using an encoder-decoder structure.

The SPOTTED project addresses the critical issue of marine debris, particularly plastic pollution, by enhancing detection and monitoring capabilities. Utilizing advanced spatio-temporal fusion (STF) techniques and Artificial Intelligence (AI) with high-resolution satellite imagery from Sentinel-2 (S2) and PlanetScope (PS), the project aims to refine S2 data and develop AI algorithms for identifying landfills and floating debris.

Super-resolution (SR) is aimed at reconstructing high-resolution (HR) images from low-resolution (LR) observations. Developing effective SR techniques is particularly important for enhancing the capabilities of remotely-sensed data, including Sentinel-2 images, as their 10m resolution remains a serious limitation in many cases. Importantly, most of the state-of-the-art SR techniques based on deep networks are evaluated in an artificial setup: LR images are simulated from an HR image (later treated as a reference), and the goal of SR is to reverse this degradation process.

Conventional high-speed video cameras present major limitations for space applications, they are heavy, bulky and require a lot of resources to operate and to space qualify. The study proposes the use of the neuromorphic camera technology for high-speed imaging in space. Neuromorphic cameras, also called event-cameras, offer advantages such as small size, low power consumption, and high temporal resolution, making them suitable for detecting fast phenomena like lightning and space debris. The advantages also make them relatively easy to space-qualify.

In this study we explore the potential of Large Language Models (LLMs) to interact with Earth Observation (EO) data with some promising first results. We source a first testing set for the BIG Bench framework. With 20+ subtasks, we aim for a large variety of questions which are currently beyond the capabilities of LLM and other models. Further, we implement a methodology to test proprietary state of the art models where token distribution may not be available.

With the development of vibration-free AOCS technologies (magnetic bearing reaction wheels, microthrusters), cryocoolers (Stirling, Pulse Tube) will become the main source of microvibrations. To overcome this difficulty and cope with missions (Science and Earth Observation) that have more and more stringent pointing requirements, it is necessary to develop alternate cooling solution or to adapt current technologies to generate no mechanical disturbances.