The Longest Solar Eclipse for 100 Years Is Coming. NASA has announced when the longest total solar eclipse of the century will occur--and you won't have to wait long. Here's what you should know. The duration of a total solar eclipse always varies. In April 2024, the eclipse that crossed North America lasted 4 minutes and 28 seconds.

After weeks of silence, NASA has officially dismissed speculation that 3I/ATLAS has anything to do with aliens. After the temporary shutdown of the US government, NASA has finally started its nonessential work back up. It's starting off with a bang: The agency called a press conference to show its hitherto reserved images of the interstellar object 3I/ATLAS. NASA scientists also confirmed that 3I/ATLAS is in fact a comet, contrary to the speculations about alien technology flooding the internet. During the broadcast, a panel of scientists showed the results of observations obtained by different NASA missions across various points in the journey 3I/ATLAS has taken .

An observatory detected the first radio signal from the interstellar object 3I/Atlas. An image of the interstellar comet 3I/Atlas, captured by the Hubble telescope on July 21, 2025. More evidence has emerged to support the natural origin of comet 3I/Atlas . After several weeks of conspiracy theories, social media debates, and speculation on popular podcasts such as Joe Rogan's, this interstellar object is still a comet . The most recent confirmation came from an observatory in South Africa that detected the first radio signal from 3I/Atlas.

As drones have risen to prominence on the battlefield, so too has electronic warfare, in which adversaries attempt to mask, jam or trace radio signals.

The increasing scarcity of spectrum resources and the rapid growth of wireless device have made efficient management of radio networks a critical challenge. Cognitive Radio Technology (CRT), when integrated with deep learning (DL), offers promising solutions for tasks such as radio signal classification (RSC), signal denoising, and spectrum allocation. However, existing DL-based CRT frameworks are often task-specific and lack scalability to diverse real-world scenarios. Meanwhile, Large Language Models (LLMs) have demonstrated exceptional generalization capabilities across multiple domains, making them a potential candidate for advancing CRT technologies. In this paper, we introduce RadioLLM, a novel framework that incorporates Hybrid Prompt and Token Reprogramming (HPTR) and a Frequency Attuned Fusion (FAF) module to enhance LLMs for CRT tasks. HPTR enables the integration of radio signal features with expert knowledge, while FAF improves the modeling of high-frequency features critical for precise signal processing. These innovations allow RadioLLM to handle diverse CRT tasks, bridging the gap between LLMs and traditional signal processing methods. Extensive empirical studies on multiple benchmark datasets demonstrate that the proposed RadioLLM achieves superior performance over current baselines.

This study presents an ML approach for classifying digital radio operating modes evaluated on real-world transmissions. We generated 98 different parameterized radio signals from 17 digital operating modes, transmitted each of them on the 70 cm (UHF) amateur radio band, and recorded our transmissions with two different architectures of SDR receivers. Three lightweight ML models were trained exclusively on spectrograms of limited non-transmitted signals with random characters as payloads. This training involved an online data augmentation pipeline to simulate various radio channel impairments. Our best model, EfficientNetB0, achieved an accuracy of 93.80% across the 17 operating modes and 85.47% across all 98 parameterized radio signals, evaluated on our real-world transmissions with Wikipedia articles as payloads. Furthermore, we analyzed the impact of varying signal durations & the number of FFT bins on classification, assessed the effectiveness of our simulated channel impairments, and tested our models across multiple simulated SNRs.

The ability to assess sleep at home, capture sleep stages, and detect the occurrence of apnea (without on-body sensors) simply by analyzing the radio waves bouncing off people's bodies while they sleep is quite powerful. Such a capability would allow for longitudinal data collection in patients' homes, informing our understanding of sleep and its interaction with various diseases and their therapeutic responses, both in clinical trials and routine care. In this article, we develop an advanced machine learning algorithm for passively monitoring sleep and nocturnal breathing from radio waves reflected off people while asleep. Validation results in comparison with the gold standard (i.e., polysomnography) (n=849) demonstrate that the model captures the sleep hypnogram (with an accuracy of 81% for 30-second epochs categorized into Wake, Light Sleep, Deep Sleep, or REM), detects sleep apnea (AUROC = 0.88), and measures the patient's Apnea-Hypopnea Index (ICC=0.95; 95% CI = [0.93, 0.97]). Notably, the model exhibits equitable performance across race, sex, and age. Moreover, the model uncovers informative interactions between sleep stages and a range of diseases including neurological, psychiatric, cardiovascular, and immunological disorders. These findings not only hold promise for clinical practice and interventional trials but also underscore the significance of sleep as a fundamental component in understanding and managing various diseases.

5G cellular networks are particularly vulnerable against narrowband jammers that target specific control sub-channels in the radio signal. One mitigation approach is to detect such jamming attacks with an online observation system, based on machine learning. We propose to detect jamming at the physical layer with a pre-trained machine learning model that performs binary classification. Based on data from an experimental 5G network, we study the performance of different classification models. A convolutional neural network will be compared to support vector machines and k-nearest neighbors, where the last two methods are combined with principal component analysis. The obtained results show substantial differences in terms of classification accuracy and computation time.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. The northeastern Ukrainian border region of Sumy said parts of its territory had lost television and radio signal on Thursday after Russia launched a mass overnight drone attack that damaged communications infrastructure. The attack with 36 drones hit four cities in Sumy region and television facilities in neighboring Kharkiv region, officials said, suggesting Moscow was trying a new tactic of striking at communications more than two years into its full-scale invasion. "As a result of the damage, part of the territory of the region (temporarily) cannot receive Ukrainian television and radio signal," the region's administration said in a statement on Telegram messenger.

In the realm of amateur radio, the effective classification of signals and the mitigation of noise play crucial roles in ensuring reliable communication. Traditional methods for signal classification and noise reduction often rely on manual intervention and predefined thresholds, which can be labor-intensive and less adaptable to dynamic radio environments. In this paper, we explore the application of machine learning techniques for signal classification and noise reduction in amateur radio operations. We investigate the feasibility and effectiveness of employing supervised and unsupervised learning algorithms to automatically differentiate between desired signals and unwanted interference, as well as to reduce the impact of noise on received transmissions. Experimental results demonstrate the potential of machine learning approaches to enhance the efficiency and robustness of amateur radio communication systems, paving the way for more intelligent and adaptive radio solutions in the amateur radio community.