Projects

• The ORCHIDE project addresses satellite communication limitations by developing an edge computing ecosystem for autonomous data processing and AI integration in space.

• The system implements a software framework for in-orbit reprogramming and deploys machine learning models directly on satellites, enabling autonomous operations without constant ground contact.

• The solution focuses on real-time decision-making and decentralized computing architecture, supporting extended missions through enhanced system autonomy and resource optimization.

• The project addresses critical challenges in spacecraft thermal management, focusing on reducing micro-vibrations from Pulsating Heat Pipes (PHPs) that can affect sensitive instruments in microgravity conditions.

• MITIGATE aims to advance PHP technology through optimized design and manufacturing, developing prototypes that deliver improved thermal performance while minimizing operational vibrations.

• The initiative focuses on miniaturizing PHP systems for integration with high-power components like Data Processing Units, working to establish new standards in spacecraft thermal management.

• The project addresses critical vulnerabilities in satellite operations, focusing on cybersecurity threats and radiation effects, where traditional FPGA systems lack real-time adaptation capabilities.

• QUASAR delivers a solution enabling secure in-flight FPGA reprogramming with post-quantum signatures, incorporating Triple Modular Redundancy and EDAC for enhanced reliability and data integrity.

• The initiative demonstrated system robustness through extensive breadboard testing, establishing new standards for secure and adaptable satellite systems that can respond to emerging threats and mission requirements.

• The project addresses the challenge of efficiently handling growing satellite data volumes, as transferring all data to Earth for processing leads to delays and high bandwidth costs.

• KP Labs developed autonomous, in-orbit data processing systems for the Space-Based Data Centers project, designing distributed architectures for large-scale data storage and processing directly in space.

• The project validated machine learning models for space-based systems, explored innovative uses in space exploration, and enabled real-time processing of Earth observation data, reducing latency and bandwidth requirements.

• The PIGEON project develops advanced super-resolution algorithms to enhance hyperspectral satellite imagery while preserving spectral information.

• Using deep learning techniques, the project improves image clarity and detail interpretation for environmental and agricultural monitoring applications.

• The solution aims to enhance monitoring capabilities for applications like crop health assessment and environmental analysis through improved spatial resolution.

• The project addresses traditional soil monitoring challenges by developing satellite-based solutions using hyperspectral imaging and machine learning algorithms for soil composition analysis.

• GENESIS project aims to enable continuous monitoring of soil parameters through the Intuition-1 satellite platform, offering potential for large-scale data collection without ground sampling requirements.

• The proposed system is designed to provide insights for agricultural management through space-based soil analysis, supporting more precise and sustainable farming practices.

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• The ESA Anomaly Detection Benchmark addresses inconsistent methods in satellite telemetry anomaly detection across the space industry by creating a standardized framework and comprehensive dataset.

• The project delivered a large-scale satellite telemetry anomaly dataset containing 31 GB of curated data from 3 missions, along with a reproducible evaluation pipeline tailored for operational needs.

• The initiative fostered industry collaboration through public dataset accessibility and introduced specialized algorithms for satellite telemetry anomaly detection, enabling effective comparison and assessment of detection methods.

• The project addresses the need for real-time anomaly detection in satellite telemetry data to enhance performance and reduce reliance on ground interventions.

• Utilizing ESA's OPS-SAT, the project implemented an onboard anomaly detection system with machine learning capabilities, featuring a RandomForest classifier with 95.7% accuracy.

• The system enables autonomous satellite monitoring and fast inference for immediate response, demonstrating the feasibility of onboard anomaly detection for mission autonomy.