Russia has launched air attacks on targets in southern and eastern Ukraine using drones and possibly ballistic missiles, Ukraine's air force said. The southern port of Odesa and the Mykolaiv, Donetsk, Kherson, Zaporizhia and Dnipropetrovsk regions were all under threat of Russian drone attacks, the air force said on the Telegram messaging app in the early hours of Tuesday morning. Russia may also be using ballistic weaponry to attack the regions of Poltava, Cherkasy, Dnipropetrovsk, Kharkiv and Kirovohrad, the air force added. Oleh Kiper, the head of the Odesa region's military administration, said air defence systems there were engaged in repelling a Russian drone attack. "Several waves of attacks are likely," Kiper said on Telegram.

When applied to autonomous mobility applications, RL can be used to derive optimal control In the fast-paced world of technological advancements, strategies for maintaining safety, efficiency, and robustness in autonomous mobility has emerged as a transformative innovation, various traffic situations. Furthermore, in order to control the dramatically reshaping numerous aspects of human life, platoon, the use of single-agent RL is not suitable because such as transportation, logistics, and surveillance [1]. These all agents will identically operate when they are located in a complex systems depend on advanced algorithms, sensors, and same space and time with same action-reward settings. Therefore, communication networks to carry out their tasks smoothly for realizing the cooperation and coordination among and proficiently with their own objectives [2]. One crucial multiple agents, multi-agent RL (MARL) algorithms should element that supports the successful functioning of these be utilized [4]-[6]. Among various MARL algorithms, this systems, particularly when operating as a coordinated group, paper considers communication network (CommNet) which is the efficient sharing of information among multiple mobility is widely and actively used in modern distributed computing platforms.

With the increasing use of drones across various industries, the navigation and tracking of these unmanned aerial vehicles (UAVs) in challenging environments (such as GNSS-denied environments) have become critical issues. In this paper, we propose a novel method for a ground-based UAV tracking system using a solid-state LiDAR, which dynamically adjusts the LiDAR frame integration time based on the distance to the UAV and its speed. Our method fuses two simultaneous scan integration frequencies for high accuracy and persistent tracking, enabling reliable estimates of the UAV state even in challenging scenarios. The use of the Inverse Covariance Intersection method and Kalman filters allow for better tracking accuracy and can handle challenging tracking scenarios. We have performed a number of experiments for evaluating the performance of the proposed tracking system and identifying its limitations. Our experimental results demonstrate that the proposed method achieves comparable tracking performance to the established baseline method, while also providing more reliable and accurate tracking when only one of the frequencies is available or unreliable.

A congressional committee focused on national security threats from China said it had "grave concerns" about a research partnership between the University of California, Berkeley, and several Chinese entities, claiming that the collaboration's advanced research could help the Chinese government gain an economic, technological or military advantage. In a letter sent last week to Berkeley's president and chancellor, the House Select Committee on the Chinese Communist Party requested extensive information about the Tsinghua-Berkeley Shenzhen Institute, a collaboration set up in 2014 with China's prestigious Tsinghua University and the Chinese city of Shenzhen. The letter pointed to the institute's research into certain "dual-use technologies" that are employed by both civilian and military institutions, like advanced semiconductors and imaging technology used for mapping terrain or driving autonomous cars. The committee also questioned whether Berkeley had properly disclosed Chinese funding for the institute, and cited its collaborations with Chinese universities and companies that have been the subjects of sanctions by the United States in recent years, like the National University of Defense Technology, the telecom firm Huawei and the Chinese drone maker DJI.

A congressional committee focused on national security threats from China said it had "grave concerns" about a research partnership between the University of California, Berkeley, and several Chinese entities, claiming that the collaboration's advanced research could help the Chinese government gain an economic, technological or military advantage. In a letter sent last week to Berkeley's president and chancellor, the House Select Committee on the Chinese Communist Party requested extensive information about the Tsinghua-Berkeley Shenzhen Institute, a collaboration set up in 2014 with China's prestigious Tsinghua University and the Chinese city of Shenzhen. The letter pointed to the institute's research into certain "dual-use technologies" that are employed by both civilian and military institutions, like advanced semiconductors and imaging technology used for mapping terrain or driving autonomous cars. The committee also questioned whether Berkeley had properly disclosed Chinese funding for the institute, and cited its collaborations with Chinese universities and companies that have been the subjects of sanctions by the United States in recent years, like the National University of Defense Technology, the telecom firm Huawei and the Chinese drone maker DJI.

Relative localization is a crucial functional block of any robotic swarm. We address it in a fleet of nano-drones characterized by a 10 cm-scale form factor, which makes them highly versatile but also strictly limited in their onboard power envelope. State-of-the-Art solutions leverage Ultra-WideBand (UWB) technology, allowing distance range measurements between peer nano-drones and a stationary infrastructure of multiple UWB anchors. Therefore, we propose an UWB-based infrastructure-free nano-drones swarm, where part of the fleet acts as dynamic anchors, i.e., anchor-drones (ADs), capable of automatic deployment and landing. By varying the Ads' position constraint, we develop three alternative solutions with different trade-offs between flexibility and localization accuracy. In-field results, with four flying mission-drones (MDs), show a localization root mean square error (RMSE) spanning from 15.3 cm to 27.8 cm, at most. Scaling the number of MDs from 4 to 8, the RMSE marginally increases, i.e., less than 10 cm at most. The power consumption of the MDs' UWB module amounts to 342 mW. Ultimately, compared to a fixed-infrastructure commercial solution, our infrastructure-free system can be deployed anywhere and rapidly by taking 5.7 s to self-localize 4 ADs with a localization RMSE of up to 12.3% in the most challenging case with 8 MDs.

Despite the advantages of having robot swarms, human supervision is required for real-world applications. The performance of the human-swarm system depends on several factors including the data availability for the human operators. In this paper, we study the human factors aspect of the human-swarm interaction and investigate how having access to high-quality data can affect the performance of the human-swarm system - the number of tasks completed and the human trust level in operation. We designed an experiment where a human operator is tasked to operate a swarm to identify casualties in an area within a given time period. One group of operators had the option to request high-quality pictures while the other group had to base their decision on the available low-quality images. We performed a user study with 120 participants and recorded their success rate (directly logged via the simulation platform) as well as their workload and trust level (measured through a questionnaire after completing a human-swarm scenario). The findings from our study indicated that the group granted access to high-quality data exhibited an increased workload and placed greater trust in the swarm, thus confirming our initial hypothesis. However, we also found that the number of accurately identified casualties did not significantly vary between the two groups, suggesting that data quality had no impact on the successful completion of tasks.

Terahertz communication networks and intelligent reflecting surfaces exhibit significant potential in advancing wireless networks, particularly within the domain of aerial-based multi-access edge computing systems. These technologies enable efficient offloading of computational tasks from user electronic devices to Unmanned Aerial Vehicles or local execution. For the generation of high-quality task-offloading allocations, conventional numerical optimization methods often struggle to solve challenging combinatorial optimization problems within the limited channel coherence time, thereby failing to respond quickly to dynamic changes in system conditions. To address this challenge, we propose a deep learning-based optimization framework called Iterative Order-Preserving policy Optimization (IOPO), which enables the generation of energy-efficient task-offloading decisions within milliseconds. Unlike exhaustive search methods, IOPO provides continuous updates to the offloading decisions without resorting to exhaustive search, resulting in accelerated convergence and reduced computational complexity, particularly when dealing with complex problems characterized by extensive solution spaces. Experimental results demonstrate that the proposed framework can generate energy-efficient task-offloading decisions within a very short time period, outperforming other benchmark methods.

Russia's air defence forces and fleet in the Black Sea have shot down eight Ukrainian drones over the Crimean port of Sevastopol, according to a Moscow-installed official. Mikhail Razvozhayev, the Russian-installed governor of Sevastopol, said the drone attack took place early on Sunday over the port of Sevastopol and the city's Balaklava and Khersones districts. "No objects, either in the city or in the water area were damaged," he said on the Telegram messaging app. One drone was shot down over the sea, five were intercepted by Russia's electronic warfare forces and two water surface drones were destroyed on the outer shore, he added. There was no immediate comment from Kyiv on the attack on the Crimean Peninsula, which Russia annexed from Ukraine in 2014.

Surfers at California's San Onofre Beach were seen surrounded by great white sharks while out enjoying the surf. Recently published footage from one of California's most popular surf beaches shows at least four sharks swimming beneath the waters as surfers nonchalantly chase waves. Photographer Kevin Christopherson captured the group of aquatic predators via drone camera over San Onofre State Beach in San Diego County, California. Surfers in the drone camera footage seem unaware of, or unconcerned about, the group of sharks just beneath their boards. "I counted 4 maybe 5 total great white sharks, and it didn't stop anyone from catching some waves!"