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Does this timeframe match the creation timeframe of the data associated with the instances (e.g., recent crawl of old news articles)? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . LCCS comprises of 23 classes and 14 sub-classes. The dataset, along with its machine-readable metadata, is hosted on CERN-backed Zenodo data repository: https://zenodo.org/record/6810792 Its long-term maintenance is discussed in the Datasheet. This includes reproducible code for the Benchmarks of Section 4 of [Cornebise et al., 2022a], following the ML Reproducibility Checklist [Pineau et al., 2021a,b]. The project also has its own website available at https://worldstrat.github.io/, The authors hereby state that they bear all responsibility in case of violation of rights, etc., and confirm that the data license is as follows: The low-resolution imagery, labels, metadata, and pretrained models are released under Creative Commons with Attribution 4.0 International (CC BY 4.0) The mean of the cloud coverage over the Sentinel 2 product areas is 7.98 %, with a standard deviation of 14.22. The quantiles are: 0.025: 0.00% 0.25: 0.00% 0.5: 0.66% 0.75: 10.05% 0.975: 49.95% It is important to note that this cloud cover percentage, as mentioned in the article and datasheet, is calculated on the entire product size of the provider, which varies in size but is much larger than the 2.5km we target. This means that even an image with a large cloud cover percentage can be cloud free, and in extreme cases (though unlikely), vice-versa. Also there are indeed considerable difference across sampled regions and land cover types. A simple example would be rainforests and non-desert equatorial regions. Using a strict no-cloud policy would make sampling enough low-resolution images either impossible or would make the temporal difference extremely large (up to 7 years for some AOIs). With that in mind, we strived to keep the cloud coverage as low as possible, ideally under 5%, while maintaining the temporal difference as small as possible.

Technology Engineering The'Star Trek' technology that came to real life Breakthroughs, discoveries, and DIY tips sent every weekday. To celebrate Star Trek Day on September 8, the European Space Agency (ESA) released a video of the Star Trek technology that's made it real-life space. So while we still don't have teleporters or deflector shields, ISS astronauts kind of have tricorders like the one used by Captain Christopher Pike in the first episode of the original series. We've also seen the development of technology that resembles Replicators, VISOR, and PADDs. The original premiered on network television in the United States on September 8, 1966.

The "Fake or Real" competition hosted on Kaggle (https://www.kaggle.com/competitions/fake-or-real-the-impostor-hunt ) is the second part of a series of follow-up competitions and hackathons related to the "Assurance for Space Domain AI Applications" project funded by the European Space Agency (https://assurance-ai.space-codev.org/ ). The competition idea is based on two real-life AI security threats identified within the project -- data poisoning and overreliance in Large Language Models. The task is to distinguish between the proper output from LLM and the output generated under malicious modification of the LLM. As this problem was not extensively researched, participants are required to develop new techniques to address this issue or adjust already existing ones to this problem's statement.

The rapid expansion of satellite constellations in near-Earth orbits presents significant challenges in satellite network management, requiring innovative approaches for efficient, scalable, and resilient operations. This paper explores the role of Artificial Intelligence (AI) in optimizing the operation of satellite mega-constellations, drawing from the ConstellAI project funded by the European Space Agency (ESA). A consortium comprising GMV GmbH, Saarland University, and Thales Alenia Space collaborates to develop AI-driven algorithms and demonstrates their effectiveness over traditional methods for two crucial operational challenges: data routing and resource allocation. In the routing use case, Reinforcement Learning (RL) is used to improve the end-to-end latency by learning from historical queuing latency, outperforming classical shortest path algorithms. For resource allocation, RL optimizes the scheduling of tasks across constellations, focussing on efficiently using limited resources such as battery and memory. Both use cases were tested for multiple satellite constellation configurations and operational scenarios, resembling the real-life spacecraft operations of communications and Earth observation satellites. This research demonstrates that RL not only competes with classical approaches but also offers enhanced flexibility, scalability, and generalizability in decision-making processes, which is crucial for the autonomous and intelligent management of satellite fleets. The findings of this activity suggest that AI can fundamentally alter the landscape of satellite constellation management by providing more adaptive, robust, and cost-effective solutions.

Imagine a future where humans live in huge'space oases' on Mars – luxury indoor habitats made of heat-reflective material that grow their own food. Robots are sent into the vast Martian wilderness, where they explore without the risk of exhaustion, radiation poisoning or dust contamination. Enormous space stations and satellites are manufactured in orbit, AI is trusted to make critical decisions, and the whole solar system is connected by a vast internet network. While this sounds like science-fiction, the European Space Agency (ESA) hopes it will become a reality in just 15 years. In a new report, the agency – which represents more than 20 countries including the UK – outlines an ambitious vision for space exploration by 2040.

This competition hosted on Kaggle (https://www.kaggle.com/competitions/trojan-horse-hunt-in-space) is the first part of a series of follow-up competitions and hackathons related to the "Assurance for Space Domain AI Applications" project funded by the European Space Agency (https://assurance-ai.space-codev.org/). The competition idea is based on one of the real-life AI security threats identified within the project -- the adversarial poisoning of continuously fine-tuned satellite telemetry forecasting models. The task is to develop methods for finding and reconstructing triggers (trojans) in advanced models for satellite telemetry forecasting used in safety-critical space operations. Participants are provided with 1) a large public dataset of real-life multivariate satellite telemetry (without triggers), 2) a reference model trained on the clean data, 3) a set of poisoned neural hierarchical interpolation (N-HiTS) models for time series forecasting trained on the dataset with injected triggers, and 4) Jupyter notebook with the training pipeline and baseline algorithm (the latter will be published in the last month of the competition). The main task of the competition is to reconstruct a set of 45 triggers (i.e., short multivariate time series segments) injected into the training data of the corresponding set of 45 poisoned models. The exact characteristics (i.e., shape, amplitude, and duration) of these triggers must be identified by participants. The popular Neural Cleanse method is adopted as a baseline, but it is not designed for time series analysis and new approaches are necessary for the task. The impact of the competition is not limited to the space domain, but also to many other safety-critical applications of advanced time series analysis where model poisoning may lead to serious consequences.

State-of-the-art space science missions increasingly rely on automation due to spacecraft complexity and the costs of human oversight. The high volume of data, including scientific and telemetry data, makes manual inspection challenging. Machine learning offers significant potential to meet these demands. The Euclid space telescope, in its survey phase since February 2024, exemplifies this shift. Euclid's success depends on accurate monitoring and interpretation of housekeeping telemetry and science-derived data. Thousands of telemetry parameters, monitored as time series, may or may not impact the quality of scientific data. These parameters have complex interdependencies, often due to physical relationships (e.g., proximity of temperature sensors). Optimising science operations requires careful anomaly detection and identification of hidden parameter states. Moreover, understanding the interactions between known anomalies and physical quantities is crucial yet complex, as related parameters may display anomalies with varied timing and intensity. We address these challenges by analysing temperature anomalies in Euclid's telemetry from February to August 2024, focusing on eleven temperature parameters and 35 covariates. We use a predictive XGBoost model to forecast temperatures based on historical values, detecting anomalies as deviations from predictions. A second XGBoost model predicts anomalies from covariates, capturing their relationships to temperature anomalies. We identify the top three anomalies per parameter and analyse their interactions with covariates using SHAP (Shapley Additive Explanations), enabling rapid, automated analysis of complex parameter relationships. Our method demonstrates how machine learning can enhance telemetry monitoring, offering scalable solutions for other missions with similar data challenges.

Detecting anomalous events in satellite telemetry is a critical task in space operations. This task, however, is extremely time-consuming, error-prone and human dependent, thus automated data-driven anomaly detection algorithms have been emerging at a steady pace. However, there are no publicly available datasets of real satellite telemetry accompanied with the ground-truth annotations that could be used to train and verify anomaly detection supervised models. In this article, we address this research gap and introduce the AI-ready benchmark dataset (OPSSAT-AD) containing the telemetry data acquired on board OPS-SAT -- a CubeSat mission which has been operated by the European Space Agency which has come to an end during the night of 22--23 May 2024 (CEST). The dataset is accompanied with the baseline results obtained using 30 supervised and unsupervised classic and deep machine learning algorithms for anomaly detection. They were trained and validated using the training-test dataset split introduced in this work, and we present a suggested set of quality metrics which should be always calculated to confront the new algorithms for anomaly detection while exploiting OPSSAT-AD. We believe that this work may become an important step toward building a fair, reproducible and objective validation procedure that can be used to quantify the capabilities of the emerging anomaly detection techniques in an unbiased and fully transparent way.