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Telemetry data is the most valuable asset provided by any spacecraft. Increasing the data amount sent from space to ground increases a mission’s value, but often this comes with increased costs. Compression of Space generated data provides great benefits to both the large ESA missions by increasing the mission science throughput and small missions (which typically have low bandwidth) by increasing the traditional low downlink rates of these missions. One solution to this problem is the POCKET+ lossless compression algorithm, which is currently being standardized by CCSDS.
VisionSpace Technologies
Cubesats have the potential to revolutionize Earth Observation by exploring different operating points in terms of spatial, temporal, and spectral resolution. However, physical limitations, mostly in terms of size, prevent them from obtaining high-quality images. OPS-SAT, while characterized by a small form factor, provides an impressive in terms of capabilities and flexibility hardware/software platform for executing demanding machine learning algorithms (inference stage).
OHB Hellas
Mission Control 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 optimizations of the FPGA are expected to close the gap in timing and unlock new methods for deploying deep learning on spacecraft.
Mission Control Space...
In this project the authors look at event-based sensing and processing for space situational awareness (SSA). Many advantages exist with the new paradigm of neuromorphic engineering, the authors investigate: If 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.
UNIVERSITY OF STRATHCLYDE
With the “democratisation of space”, and the proliferation of large non-geostationary orbit (NGO) constellations, the pace at which human-made objects are being deployed in orbit demands urgent action in defining and supporting a global Space Traffic Management (STM) plan. Promptly identifying, tracking, and cataloguing of resident space objects (RSOs), in particular after orbit insertion or break-up and fragmentation events, as the recent Russian anti-satellite weapon (ASAT) test put in evidence, is of critical importance to enable a safe and sustainable use of space.
Vyoma
LEO-GEO4GHG studies the feasibility of new frontiers of Cloud Computing in Space and artificial intelligence (AI) combining multiple sources of data such as SATLANTIS GEI-SAT constellation and other external meteorological and atmospheric data with the final aim of providing near real time methane detection and quantification.
SATLANTIS MICROSATS SA
A proof of concept of an attitude control system based on Artificial Intelligence, as an alternative of current state-of-the-art systems based on model approaches, has been developed and tested in a simulator and in the EM of the OPS-SAT. The system relies on Computer Vision and Deep Reinforcement Learning algorithms to command the OPS-SAT reaction wheels in order to actively modify its attitude, so that the on-board optical camera keeps framed and focused on a target without the aid of any other instrument, such as the star tracker, the ADCS or the GPS receiver.
Adatica Engineering
Detecting anomalies from the satellite telemetry is critical for its safe operation. Although many approaches to autonomous on-board anomaly detection have already been proposed, most of them have so far only been tested on non-satellite or simulated data. We 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.
KP Labs
OPS-SAT mission is a key element for operational validation in flight (or IOD/IOV) of the DeepCube service of Deep Learning at the edge that our company is preparing with the support of the GSTP programme (GSTP Make, ESA-CNES) and the R&D performed in the CORTEX project (Permanent Open Call EOEP-4, ESA/Phi-Lab, https://esacortexproject.agenium-space.com). The goal of the proposed experiment is to execute on-board the inference of the simplified models defined in those projects.
Agenium Space
The main objective of this study is the evaluation of the merits of a dual-camera setup for the acquisition of both high spatial resolution images and high-resolution images of identified events of
interest, on-board, through AI methods. A mission analysis of the approach for a single-satellite, dual camera setup as well as for a two-satellite (leading-trailing), single camera setup was
OHB Hellas
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