--Intrusion detection in unmanned-aerial-vehicle (UA V) swarms is complicated by high mobility, non-stationary traffic, and severe class imbalance. Leveraging a 120 k-flow simulation corpus that covers five attack types, we benchmark three quantum-machine-learning (QML) approaches--quantum kernels, variational quantum neural networks (QNNs), and hybrid quantum-trained neural networks (QT -NNs)--against strong classical baselines. All models consume an 8-feature flow representation and are evaluated under identical preprocessing, balancing, and noise-model assumptions. Results reveal clear trade-offs: quantum kernels and QT -NNs excel in low-data, nonlinear regimes, while deeper QNNs suffer from trainability issues, and CNNs dominate when abundant data offset their larger parameter count. The complete codebase and dataset partitions are publicly released to enable reproducible QML research in network security.

The trajectory data of traffic participants (TPs) is a fundamental resource for evaluating traffic conditions and optimizing policies, especially at urban intersections. Although data acquisition using drones is efficient, existing datasets still have limitations in scene representativeness, information richness, and data fidelity. This study introduces FLUID, comprising a fine-grained trajectory dataset that captures dense conflicts at typical urban signalized intersections, and a lightweight, full-pipeline framework for drone-based trajectory processing. FLUID covers three distinct intersection types, with approximately 5 hours of recording time and featuring over 20,000 TPs across 8 categories. Notably, the dataset averages two vehicle conflicts per minute, involving roughly 25% of all motor vehicles. FLUID provides comprehensive data, including trajectories, traffic signals, maps, and raw videos. Comparison with the DataFromSky platform and ground-truth measurements validates its high spatio-temporal accuracy. Through a detailed classification of motor vehicle conflicts and violations, FLUID reveals a diversity of interactive behaviors, demonstrating its value for human preference mining, traffic behavior modeling, and autonomous driving research.

Tens of thousands of military personnel will march in formation through Beijing's historic Tiananmen Square on the day of the parade, which will mark the 80th anniversary of Japan's formal surrender in World War Two and the end of the conflict. The 70-minute parade is likely to feature China's latest weaponry, including hundreds of aircraft, tanks and anti-drone systems - the first time its military's new force structure is being fully showcased in a parade. Most Western leaders are not expected to attend the parade, due to their opposition to Russia's invasion of Ukraine, which has driven the sanctions against Putin's regime. But it will see leaders from Indonesia, Malaysia, Myanmar and Vietnam in attendance - further proof of Beijing's concerted efforts to ramp up ties with neighbouring South East Asia. Just one EU leader will be attending - Slovak Prime Minister Robert Fico - while Bulgaria and Hungary will send representatives.

Former U.S. Army Intel and Special Ops soldier Brett Velicovich joins'America's Newsroom' to discuss the Defense Department's push to increase military drone production and Ukraine's drone strike on Russia. EXCLUSIVE: Army Secretary Dan Driscoll said U.S. soldiers are improvising with government credit cards to buy and test battlefield gear as they adapt to the exploding drone threat -- as the Army shifts its long-term posture toward countering China in the Indo-Pacific. In an interview with Fox News Digital, Driscoll described how elite units like the 75th Ranger Regiment are bypassing the Pentagon's cumbersome procurement system to test new drones, sensors and weapons in real time. At the same time, he said the Army is aligning with the Pentagon's assessment of China as the nation's "pacing threat," building a force optimized for the Indo-Pacific but still capable of deploying worldwide at a moment's notice. After a visit with the regiment at Hunter Army Airfield in Savannah, Georgia, on Tuesday, Driscoll said Rangers "basically just use their corporate credit card to go online and purchase things to test, and they will find what works."

In 2024, the "low-altitude economy" was written into China's Government Work Report for the first time [1], and flying robots have been rapidly popularized nationwide. From an environmental perspective, electrically powered air vehicles are attracting growing attention; key technologies include overall configuration design, integrated energy management, and high-efficiency, high power-to-weight electric propulsion [2]. For electric propulsion, mainstream systems use electric motors to drive propellers, but propeller noise is significant and hard to mitigate [3], which limits widespread use in cities--the main arena for the low-altitude economy--and is also unfavorable for silent reconnaissance. Hence, there is a pressing need for a new propulsion approach enabling quiet, fully electric flight. In the 1920s, Brown observed that an asymmetric capacitor under high voltage can generate thrust, known as the Biefeld-Brown effect. A leading explanation is ionic wind: a high electric field ionizes air, and the resulting ions accelerate and transfer momentum to neutral molecules, producing a net airflow (thrust) [4]. Xu et al. first mounted a plasma thruster on a fixed-wing UAV without other propulsion; the gliding distance with the thruster on was five times that with it off, but the maximum range was only 45m and no controller design was provided [5]. Zhang et al. realized altitude control for a micro ionic-wind-powered UA V using passive components, but the wingspan was at most 6 .3cm

Insufficient data volume and quality are particularly pressing challenges in the adoption of modern subsymbolic AI. To alleviate these challenges, AI simulation uses virtual training environments in which AI agents can be safely and efficiently developed with simulated, synthetic data. Digital twins open new avenues in AI simulation, as these high-fidelity virtual replicas of physical systems are equipped with state-of-the-art simulators and the ability to further interact with the physical system for additional data collection. In this article, we report on our systematic survey of digital twin-enabled AI simulation. By analyzing 22 primary studies, we identify technological trends and derive a reference framework to situate digital twins and AI components. Based on our findings, we derive a reference framework and provide architectural guidelines by mapping it onto the ISO 23247 reference architecture for digital twins. Finally, we identify challenges and research opportunities for prospective researchers.

Ukraine intends to strike deep into Russia following a large Russian drone attack that left 60,000 Ukrainians without electricity, President Volodymyr Zelenskyy has said. Speaking on Sunday after a meeting with his top general, Oleksandr Syrskii, the Ukrainian president confirmed the new planned strikes on X. Both sides have intensified their air strikes in recent weeks, with Moscow attacking Ukraine's energy and transport systems as well as launching deadly strikes in recent days on civilian areas in Kyiv and Zaporizhia, and Ukraine targeting Russian oil refineries and pipelines. Overnight, Russian drones hit four energy facilities in Ukraine's Odesa region, according to the private energy company DTEK. The strikes left 29,000 people without electricity, local authorities reported.

Tokyo is seeking another record-busting defense budget -- including spending to build a drone "shield" to defend Japan's southwestern periphery -- amid rising concerns over the Chinese military's moves near and inside the country's waters and airspace. The Defense Ministry said Friday that it is seeking a budget exceeding 8.8 trillion ( 60 billion) for fiscal 2026, up 4.4% from last year's record 8.5 trillion initial request. The budget is the fourth in a five-year spending plan of around 43 trillion, as Japan zeroes in on its target of spending 2% of gross domestic product on defense by 2027. Most prominent in this year's request is a 128.7 billion plan to build a multilayered coastal defense system covering the air, sea, and waters that incorporates unmanned assets as well as strengthened standoff defense capabilities to attack from outside an enemy's range.

A fire broke out at a unit of the Afipsky oil refinery in Russia's southern Krasnodar region following a Ukrainian drone attack, local authorities said. The extent of damage was not immediately clear at the refinery, which, together with the Krasnodar refinery, processed an estimated 7.2 million metric tonnes of crude oil in 2024.

In post-disaster scenarios, rapid and efficient delivery of medical resources is critical and challenging due to severe damage to infrastructure. To provide an optimized solution, we propose a cooperative trajectory optimization and task allocation framework leveraging unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs). This study integrates a Genetic Algorithm (GA) for efficient task allocation among multiple UAVs and UGVs, and employs an informed-RRT* (Rapidly-exploring Random Tree Star) algorithm for collision-free trajectory generation. Further optimization of task sequencing and path efficiency is conducted using Covariance Matrix Adaptation Evolution Strategy (CMA-ES). Simulation experiments conducted in a realistic post-disaster environment demonstrate that our proposed approach significantly improves the overall efficiency of medical rescue operations compared to traditional strategies. Specifically, our method reduces the total mission completion time to 26.7 minutes for a 15-task scenario, outperforming K-Means clustering and random allocation by over 73%. Furthermore, the framework achieves a substantial 15.1% reduction in total traveled distance after CMA-ES optimization. The cooperative utilization of UAVs and UGVs effectively balances their complementary advantages, highlighting the system's scalability and practicality for real-world deployment.