The integration of emerging uncrewed aerial vehicles (UAVs) with artificial intelligence (AI) and ground-embedded robots (GERs) has transformed emergency rescue operations in unknown environments. However, the high computational demands often exceed a single UAV's capacity, making it difficult to continuously provide stable high-level services. To address this, this paper proposes a cooperation framework involving UAVs, GERs, and airships. The framework enables resource pooling through UAV-to-GER (U2G) and UAV-to-airship (U2A) links, offering computing services for offloaded tasks. Specifically, we formulate the multi-objective problem of task assignment and exploration as a dynamic long-term optimization problem aiming to minimize task completion time and energy use while ensuring stability. Using Lyapunov optimization, we transform it into a per-slot deterministic problem and propose HG-MADDPG, which combines the Hungarian algorithm with a GDM-based multi-agent deep deterministic policy gradient. Simulations demonstrate significant improvements in offloading efficiency, latency, and system stability over baselines.

National Review editor-in-chief Rich Lowry and FOX Business' Liz Claman join'MediaBuzz' to discuss Hegseth's heated press conference where he called out the media's'hatred' of President Donald Trump. FIRST ON FOX: Defense Secretary Pete Hegseth has issued sweeping new orders to fast-track drone production and deployment, allowing commanders to procure and test them independently and requiring drone combat simulations across every branch of the military. As part of an aggressive push to outpace Russia and China in unmanned warfare, "the Department's bureaucratic gloves are coming off," Hegseth wrote. "Lethality will not be hindered by self-imposed restrictions... Our major risk is risk-avoidance." In a pair of memos first obtained by Fox News Digital, Hegseth rescinded legacy policies that he believes restricted innovation.

The organization is modeled after an agency the Pentagon formed two decades ago to counter improvised explosive devices that insurgents used against U.S. troops in Iraq and Afghanistan. The Army oversees drone defense for the military. But critics of the Army's past approaches have said its counter-drone defenses are built on older technology and are not adaptable enough, given how quickly the drone threat has evolved on the battlefield in Ukraine. New technologies can detect and identify incoming drones, then take them out more efficiently. Older technology, critics say, is poor at identifying drones, including which ones pose the most acute and immediate threat.

A Ukrainian intelligence officer was gunned down in broad daylight in Kyiv following another night of Russian drone attacks on the capital.

June saw a new monthly high of 5,429 drones, July has seen more than 2,000 in just the first nine days. With production in Russia ramping up, some reports suggest Moscow may soon be able to fire over 1,000 missiles and drones in a single night. Experts in Kyiv warn that the country is in danger of being overwhelmed. "If Ukraine doesn't find a solution for how to deal with these drones, we will face great problems during 2025," says former intelligence officer Ivan Stupak. "Some of these drones are trying to reach military objects - we have to understand it - but the rest, they are destroying apartments, falling into office buildings and causing lots of damage to citizens."

Ukrainian President Volodymyr Zelenskyy has opened a conference in Italy on rebuilding his war-battered country, as it comes under relentless ground and air attacks from Russia. The Rome gathering will see the Ukrainian leader hold a flurry of meetings on Thursday, including a video call with leaders from about 30 countries in the so-called "coalition of the willing", as he seeks to secure financing to bolster his country's air defence systems, which were this week strained by Russia's largest missile and drone attack in more than three years of war. The United Kingdom and France are spearheading talks among the coalition on how to support a possible ceasefire in Ukraine, including potentially deploying peacekeeping forces to police any future peace agreement with Russia. This week, the office of UK Prime Minister Keir Starmer said the call would cover "stepping up support for Ukraine and further increasing pressure on Russia". The success of the coalition's operation hinges on United States backup with airpower or other military assistance, but the administration of President Donald Trump has made no public commitment to provide support. Amid growing uncertainty about US commitment to Kyiv's defence, despite Trump's recent U-turn on pausing critical weapons deliveries, Zelenskyy had a "substantive" meeting with Trump's Ukraine envoy, Keith Kellogg, on Wednesday.

Ukraine's capital Kyiv is again under a massive overnight Russian drone attack, local officials say, with at least 10 people reported injured and fires burning across the city. Authorities in Kyiv say drone wreckage has hit the roof of a residential building in the central Shevchenkivskyi district. Footage on social media, as yet unverified by the BBC, shows explosions in the night sky, as air defence units begin repelling the attack. Ukraine's military has also warned of a threat of a ballistic missile attack. Last night, Ukraine reported the biggest ever aerial attack from Russia, after 728 drones and 13 cruise or ballistic missiles struck cities around the country in multiple waves.

Hyper-parameters are essential and critical for the performance of communication algorithms. However, current hyper-parameters optimization approaches for Warm-Start Particles Swarm Optimization with Crossover and Mutation (WS-PSO-CM) algorithm, designed for radio map-enabled unmanned aerial vehicle (UAV) trajectory and communication, are primarily heuristic-based, exhibiting low levels of automation and improvable performance. In this paper, we design an Large Language Model (LLM) agent for automatic hyper-parameters-tuning, where an iterative framework and Model Context Protocol (MCP) are applied. In particular, the LLM agent is first set up via a profile, which specifies the boundary of hyper-parameters, task objective, terminal condition, conservative or aggressive strategy of optimizing hyper-parameters, and LLM configurations. Then, the LLM agent iteratively invokes WS-PSO-CM algorithm for exploration. Finally, the LLM agent exits the loop based on the terminal condition and returns an optimized set of hyperparameters. Our experiment results show that the minimal sum-rate achieved by hyper-parameters generated via our LLM agent is significantly higher than those by both human heuristics and random generation methods. This indicates that an LLM agent with PSO and WS-PSO-CM algorithm knowledge is useful in seeking high-performance hyper-parameters.

Unmanned Aerial Vehicles (UAVs) have emerged as versatile tools across various sectors, driven by their mobility and adaptability. This paper introduces SkyVLN, a novel framework integrating vision-and-language navigation (VLN) with Nonlinear Model Predictive Control (NMPC) to enhance UAV autonomy in complex urban environments. Unlike traditional navigation methods, SkyVLN leverages Large Language Models (LLMs) to interpret natural language instructions and visual observations, enabling UAVs to navigate through dynamic 3D spaces with improved accuracy and robustness. We present a multimodal navigation agent equipped with a fine-grained spatial verbalizer and a history path memory mechanism. These components allow the UAV to disambiguate spatial contexts, handle ambiguous instructions, and backtrack when necessary. The framework also incorporates an NMPC module for dynamic obstacle avoidance, ensuring precise trajectory tracking and collision prevention. To validate our approach, we developed a high-fidelity 3D urban simulation environment using AirSim, featuring realistic imagery and dynamic urban elements. Extensive experiments demonstrate that SkyVLN significantly improves navigation success rates and efficiency, particularly in new and unseen environments.

Collecting and annotating images for the purpose of training segmentation models is often cost prohibitive. In the domain of wildland fire science, this challenge is further compounded by the scarcity of reliable public datasets with labeled ground truth. This paper presents the Centralized Copy-Paste Data Augmentation (CCPDA) method, for the purpose of assisting with the training of deep-learning multiclass segmentation models, with special focus on improving segmentation outcomes for the fire-class. CCPDA has three main steps: (i) identify fire clusters in the source image, (ii) apply a centralization technique to focus on the core of the fire area, and (iii) paste the refined fire clusters onto a target image. This method increases dataset diversity while preserving the essential characteristics of the fire class. The effectiveness of this augmentation technique is demonstrated via numerical analysis and comparison against various other augmentation methods using a weighted sum-based multi-objective optimization approach. This approach helps elevate segmentation performance metrics specific to the fire class, which carries significantly more operational significance than other classes (fuel, ash, or background). Numerical performance assessment validates the efficacy of the presented CCPDA method in alleviating the difficulties associated with small, manually labeled training datasets. It also illustrates that CCPDA outperforms other augmentation strategies in the application scenario considered, particularly in improving fire-class segmentation performance.