Plus: The data center backlash is scrambling the midterm elections. This company's plans to deploy space mirrors could jeopardize the night sky for many A company that plans to beam sunlight from space to Earth on demand might unintentionally brighten the night sky for many more people than intended, according to a new study. Later this year, Reflect Orbital plans to launch a test satellite that will extend an 18-by-18-meter mirror in orbit. The goal is to eventually launch up to 50,000 larger satellites that can reflect sunlight to Earth on demand. Reflect Orbital says the technology could extend sunlight for solar panel charging, emergency response, and military activities. But new research suggests the giant beams could shine as bright as 10,000 full moons and scatter light over tens of kilometers, raising concerns about dark skies, aviation, and wildlife.

CosmoCube would peer 13.5 billion years back in time from the far side of the moon. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. Breakthroughs, discoveries, and DIY tips sent six days a week. By signing up, you confirm you are 16+, will receive newsletters and promotional content and agree to our Terms of Use and acknowledge the data practices in our Privacy Policy . The big bang may have started everything we know and love, but there wasn't much to show for it at first.

House of Horrors mom insists her kids were NOT'feral', as lawyer reveals her incredulous excuse for their'messy' home Bargain hunter bought Connecticut mansion for a pittance only to find family decomposing inside. Why Tom Brady's midlife crisis after Gisele Bundchen divorce threatens to ruin his legacy... and he doesn't care! Jon Bon Jovi, 64, abruptly stops NYC concert and tells audience he is'hurt' before leaving the stage So much has never been made public': Insiders at very top of Hollywood tell me bombshell twist that flips Taylor Swift-Lively feud on its head: ROB SHUTER McDonald's copies Costco with its newest menu item and fans have mixed reactions Meghan and Harry share snaps of their European summer holiday as pro-Sussex People magazine reveals they stayed at Princess Diana's childhood home with Archie and Lilibet The age when you must give up smoking to escape the risk of lung cancer: Experts reveal the truth about what quitting does to your body in your 30s, 40s, and 50s... and the REAL impact of that one cheeky cigarette a week White House defends Sophie Cunningham amid woke backlash... as WNBA star doubles down on trans views with new Instagram photos Hot summer snaps of Nicole Kidman getting affectionate with handsome businessman on Italian getaway... as friends speak out about moving on after Keith Urban divorce Amal Clooney's diet and fitness regime revealed - and it's surprisingly simple if you don't mind seaweed soup for breakfast! Harry has reached a new low. It's such a slap in the face to his father... especially after what the King's friend told me: RICHARD EDEN Elon Musk and Jeff Bezos want to put data centres in SPACE - but experts warn they could be'catastrophic' for the planet Data centres in space could be'catastrophic' for the planet, scientsts have warned in response to plans laid out by some of the world's richest people.

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Plus: NATO is building a network to stop Russian attackers in their tracks. I was really looking forward to July 4, and not just because I love a poolside barbecue. This year the American holiday also marked a big symbolic deadline for US nuclear power. Last year the Trump administration set a goal to see three new microreactors achieve criticality, a technical milestone establishing that a reactor can sustain a chain reaction, by the nation's 250th birthday. And just in time, not just three, but four reactors did so. But achieving criticality doesn't mean a reactor is ready to provide electricity for the grid (or at all, for that matter).

The increasing number of satellites and orbital debris has made space congestion a critical issue, threatening satellite safety and sustainability. Challenges such as collision avoidance, station-keeping, and orbital maneuvering require advanced techniques to handle dynamic uncertainties and multi-agent interactions. Reinforcement learning (RL) has shown promise in this domain, enabling adaptive, autonomous policies for space operations; however, many existing RL frameworks rely on custom-built environments developed from scratch, which often use simplified models and require significant time to implement and validate the orbital dynamics, limiting their ability to fully capture real-world complexities. To address this, we introduce OrbitZoo, a versatile multi-agent RL environment built on a highfidelity industry standard library, that enables realistic data generation, supports scenarios like collision avoidance and cooperative maneuvers, and ensures robust and accurate orbital dynamics. The environment is validated against various real satellite constellations, including Starlink, achieving a Mean Absolute Percentage Error (MAPE) of 0.16% compared to real-world data. This validation ensures reliability for generating high-fidelity simulations and enabling autonomous and independent satellite operations. This project is open source1 and has a dedicated project page2.

The global rise in wildfire frequency and intensity over the past decade underscores the need for improved fire monitoring techniques. To advance deep learning research on wildfire detection and its associated human health impacts, we introduce SmokeViz, a large-scale machine learning dataset of smoke plumes in satellite imagery. The dataset is derived from expert annotations created by smoke analysts at the National Oceanic and Atmospheric Administration, which provide coarse temporal and spatial approximations of smoke presence. To enhance annotation precision, we propose pseudo-label dimension reduction (PLDR), a generalizable method that applies pseudo-labeling to refine datasets with mismatching temporal and/or spatial resolutions. Unlike typical pseudo-labeling applications that aim to increase the number of labeled samples, PLDR maintains the original labels but increases the dataset quality by solving for intermediary pseudo-labels (IPLs) that align each annotation to the most representative input data. For SmokeViz, a parent model produces IPLs to identify the single satellite image within each annotations time window that best corresponds with the smoke plume. This refinement process produces a succinct and relevant deep learning dataset consisting of over 160,000 manual annotations. The SmokeViz dataset is expected to be a valuable resource to develop further wildfire-related machine learning models and is publicly available at https://noaa-gsl-experimental-pds.s3.amazonaws.com/index.

More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. There are very few projects and companies currently aimed at tackling the issue. Breakthroughs, discoveries, and DIY tips sent six days a week. Nearly half of all known objects currently orbiting Earth technically classify as space junk, but the true amount may be even higher. Not only that, the debris continues amassing faster than it's being removed.

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Probabilistic techniques are central to data analysis, but different approaches can be challenging to apply, combine, and compare. This paper introduces composable generative population models (CGPMs), a computational abstraction that extends directed graphical models and can be used to describe and compose a broad class of probabilistic data analysis techniques. Examples include discriminative machine learning, hierarchical Bayesian models, multivariate kernel methods, clustering algorithms, and arbitrary probabilistic programs. We demonstrate the integration of CGPMs into BayesDB, a probabilistic programming platform that can express data analysis tasks using a modeling definition language and structured query language. The practical value is illustrated in two ways. First, the paper describes an analysis on a database of Earth satellites, which identifies records that probably violate Kepler's Third Law by composing causal probabilistic programs with nonparametric Bayes in 50 lines of probabilistic code. Second, it reports the lines of code and accuracy of CGPMs compared with baseline solutions from standard machine learning libraries.