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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
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
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
Early space missions consisted of bespoke components communicating with facilities on Earth. Today this model has been updated with off-the-shelf components and reusable launch systems that lower mission cost into low-earth orbit. Extending this model to develop a sustainable off-earth economy requires bringing the concept of reconfigurable computing agents to deep space.
Mission Control Space...
"Don't try this at home" (named after one of the AI ships from I.Banks Culture series) is a concept demonstrating the benefits of having a Cognitive Cloud Computing infrastructure in Space. Such an infrastructure will provide users a comprehensive and scalable set of commercial services to EO sensors and space-based data providers. It enables several applications by efficiently collecting and fusing information on target objects from observations of different platforms at various wavelengths, resolutions, and times. As a result, it leads to timely, precise information.
Planetek Italia
The “SaaSy ML” idea will provide experimenters with on-board Machine Learning (ML) functionalities that any app can subscribe to via a Software as a Service (SaaS) app hosted on the Satellite Experimental Processing Platform (SEPP). The ML features provided by the SaaS app will cover both training and prediction operations. The JSAT open-source java library for ML will be re-used thus making accessible over 100 training algorithms on-board a flying mission. Re-using the JSAT library will ensure the development to focus on the idea's novel aspect: a spacecraft SaaS app for in-orbit ML.
VISIONSPACE TECHNOLOGIES...
Today’s satellites are inherently inflexible and purpose-built for a single mission. In contrast, the community is exploring disruptive approaches towards in-orbit reconfiguration and on-board data processing. One of the most promising approaches is to use COTS components and open source frameworks, which decrease time-to-market and could even increase the pool of developers/users on the satellite.
National Technical...
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