Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Creator Playbook Mashable Selects In My Bag Look Up Say More AI at School Safety Net Versus Trending Now Back to School Good Connection: Uplifting stories for a digital age All Series Elisha Sauers writes about space for Mashable, taking deep dives into NASA's moon and Mars missions, chatting up astronauts and history-making discoverers, and jetting above the clouds . Through 17 years of reporting, she's covered a variety of topics, including health, business, and government, with a penchant for public records requests. She previously worked for in Norfolk, Virginia, and in Annapolis, Maryland. Her work has earned numerous state awards, including the Virginia Press Association's top honor, Best in Show, and national recognition for narrative storytelling. For each year she has covered space, Sauers has won National Headliner Awards, including first place for her Sex in Space series.

Two X-ray space telescopes captured the never-before-seen blast 130 million light-years away. Breakthroughs, discoveries, and DIY tips sent every weekday. A never-before-seen blast from a supermassive black hole was spotted by two sophisticated X-ray space telescopes . This giant black hole about 130 million light-years away from Earth whipped up powerful winds, flinging material out into space at 37,282 miles per second. This particular supermassive black hole is lurking within the spiral galaxy NGC 3783.

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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.