Former Arizona gubernatorial candidate Kari Lake weighs in as Democratic Gov. Katie Hobbs vetoes legislation limiting Chinese land ownership near U.S. military bases and strategic assets and warns how the move puts national security at risk. Texas has become the latest state to cement a ban on land and property purchases by individuals or entities from adversarial nations. Republican Gov. Greg Abbott signed Senate Bill 17 into law over the weekend, prohibiting countries identified as security threats in the intelligence community's 2025 Annual Threat Assessment, from acquiring "real property" in the state. The countries include China, Russia, Iran and North Korea, and the bill identified "real property" as agricultural land, commercial or industrial properties, residential properties and land used for mining or water use. Amid heightened global tensions, there has been an increased appetite for protecting foreign asset acquisitions in the United States.

A fragile ceasefire remains in place between Israel and Iran, one day after US President Donald Trump announced a truce, ending 12 days of fighting that erupted following Israeli strikes on Tehran's nuclear and military sites. An analysis of data from the Armed Conflict Location and Event Data Project (ACLED) shows that between October 7, 2023, and just before Israel attacked Iran on June 13, 2025, Israel carried out nearly 35,000 recorded attacks across five countries: the occupied Palestinian territory, Lebanon, Syria, Yemen, and Iran. These attacks include air and drone strikes, shelling and missile attacks, remote explosives, and property destruction. The majority of attacks have been on Palestinian territory with at least 18,235 recorded incidents, followed by Lebanon (15,520), Syria (616), Iran (58) and Yemen (39). While the bulk of Israel's attacks have concentrated on nearby Gaza, the occupied West Bank, and Lebanon, its military operations have also reached far beyond its immediate borders.

Automated detection of small and rare wildlife in aerial imagery is crucial for effective conservation, yet remains a significant technical challenge. Prairie dogs exemplify this issue: their ecological importance as keystone species contrasts sharply with their elusive presence--marked by small size, sparse distribution, and subtle visual features--which undermines existing detection approaches. To address these challenges, we propose RareSpot, a robust detection framework integrating multi-scale consistency learning and context-aware augmentation. Our multi-scale consistency approach leverages structured alignment across feature pyramids, enhancing fine-grained object representation and mitigating scale-related feature loss. Complementarily, context-aware augmentation strategically synthesizes challenging training instances by embedding difficult-to-detect samples into realistic environmental contexts, significantly boosting model precision and recall. Evaluated on an expert-annotated prairie dog drone imagery benchmark, our method achieves state-of-the-art performance, improving detection accuracy by over 35% compared to baseline methods. Importantly, it generalizes effectively across additional wildlife datasets, demonstrating broad applicability. The RareSpot benchmark and approach not only support critical ecological monitoring but also establish a new foundation for detecting small, rare species in complex aerial scenes.

The increasing miniaturization of Unmanned Aerial Vehicles (UAVs) has expanded their deployment potential to indoor and hard-to-reach areas. However, this trend introduces distinct challenges, particularly in terms of flight dynamics and power consumption, which limit the UAVs' autonomy and mission capabilities. This paper presents a novel approach to overcoming these limitations by integrating Neural 3D Reconstruction (N3DR) with small UAV systems for fine-grained 3-Dimensional (3D) digital reconstruction of small static objects. Specifically, we design, implement, and evaluate an N3DR-based pipeline that leverages advanced models, i.e., Instant-ngp, Nerfacto, and Splatfacto, to improve the quality of 3D reconstructions using images of the object captured by a fleet of small UAVs. We assess the performance of the considered models using various imagery and pointcloud metrics, comparing them against the baseline Structure from Motion (SfM) algorithm. The experimental results demonstrate that the N3DR-enhanced pipeline significantly improves reconstruction quality, making it feasible for small UAVs to support high-precision 3D mapping and anomaly detection in constrained environments. In more general terms, our results highlight the potential of N3DR in advancing the capabilities of miniaturized UAV systems.

--Relative localization in the near-field scenario is critically important for unmanned vehicle (UxV) applications. Although related works addressing 2D relative localization problem have been widely studied for unmanned ground vehicles (UGVs), the problem in 3D scenarios for unmanned aerial vehicles (UA Vs) involves more uncertainties and remains to be investigated. Inspired by the phenomenon that animals can achieve swarm behaviors solely based on individual perception of relative information, this study proposes an infrastructure-free 3D relative localization framework that relies exclusively on onboard ultra-wideband (UWB) sensors. Leveraging 2D relative positioning research, we conducted feasibility analysis, system modeling, simulations, performance evaluation, and field tests using UWB sensors. The key contributions of this work include: derivation of the Cram er-Rao lower bound (CRLB) and geometric dilution of precision (GDOP) for near-field scenarios; development of two localization algorithms - one based on Euclidean distance matrix (EDM) and another employing maximum likelihood estimation (MLE); comprehensive performance comparison and computational complexity analysis against state-of-the-art methods; simulation studies and field experiments; a novel sensor deployment strategy inspired by animal behavior, enabling single-sensor implementation within the proposed framework for UxV applications. The theoretical, simulation, and experimental results demonstrate strong generalizability to other 3D near-field localization tasks, with significant potential for a cost-effective cross-platform UxV collaborative system. I. INTRODUCTION Precise localization is essential in diverse domains, including multi-agent robotic systems, the Internet of Things, intelligent vehicular networks, and logistics [1]-[3].

Citing successes other police departments across the country have seen using drones, the Los Angeles Police Commission said it would allow the LAPD to deploy unmanned aircraft on routine emergency calls. The civilian oversight body approved an updated policy Tuesday allowing drones to be used in more situations, including "calls for service." The new guidelines listed other scenarios for future drone use -- "high-risk incident, investigative purpose, large-scale event, natural disaster" -- and transferred their command from the Air Support Division to the Office of Special Operations. Previously, the department's nine drones were restricted to a narrow set of dangerous situations, most involving barricaded suspects or explosives. Bryan Lium told commissioners the technology offers responding officers and their supervisors crucial, real-time information about what type of threats they might encounter while responding to an emergency.

Three Ukrainian soldiers raced across a field on a quad bike in eastern Ukraine, weaving at 100 kilometers an hour to avoid the attack drone chasing them from the sky. One fired a shotgun upward, blasting the tiny craft into pieces. This time it is just a training exercise. But with Russia having gained an upper hand in front line drone warfare for the first time since it invaded, Kyiv's troops are practicing hard.

Russia and Ukraine have swapped drone strikes, with at least three people reportedly killed by Moscow near the shared border. Strikes were reported overnight on Tuesday in several areas of Ukraine, as well as in Moscow. The attacks are the latest in a series of intensifying hostilities as the efforts of the United States to broker a ceasefire have stalled, with Russia appearing eager to take advantage, as global attention is dominated by the war between Israel and Iran. A Russian drone attack on a village in Sumy killed an eight-year-old boy and two adults, and injured another three people, the military administration of the region said. Drone strikes also wounded five people in Kharkiv and four others in the Dnipropetrovsk region, local authorities said.

This paper presents a novel high-level task planning and optimal coordination framework for autonomous masonry construction, using a team of heterogeneous aerial robotic workers, consisting of agents with separate skills for brick placement and mortar application. This introduces new challenges in scheduling and coordination, particularly due to the mortar curing deadline required for structural bonding and ensuring the safety constraints among UAVs operating in parallel. To address this, an automated pipeline generates the wall construction plan based on the available bricks while identifying static structural dependencies and potential conflicts for safe operation. The proposed framework optimizes UAV task allocation and execution timing by incorporating dynamically coupled precedence deadline constraints that account for the curing process and static structural dependency constraints, while enforcing spatio-temporal constraints to prevent collisions and ensure safety. The primary objective of the scheduler is to minimize the overall construction makespan while minimizing logistics, traveling time between tasks, and the curing time to maintain both adhesion quality and safe workspace separation. The effectiveness of the proposed method in achieving coordinated and time-efficient aerial masonry construction is extensively validated through Gazebo simulated missions. The results demonstrate the framework's capability to streamline UAV operations, ensuring both structural integrity and safety during the construction process.

We propose a dim and small target detection algorithm for drone broadcast frames based on the time-frequency analysis of communication protocol. Specifically, by analyzing modulation parameters and frame structures, the prior knowledge of transmission frequency, signal bandwidth, Zadoff-Chu (ZC) sequences, and frame length of drone broadcast frames is established. The RF signals are processed through the designed filter banks, and the frequency domain parameters of bounding boxes generated by the detector are corrected with transmission frequency and signal bandwidth. Given the remarkable correlation characteristics of ZC sequences, the frequency domain parameters of bounding boxes with low confidence scores are corrected based on ZC sequences and frame length, which improves the detection accuracy of dim targets under low signal-to noise ratio situations. Besides, a segmented energy refinement method is applied to mitigate the deviation caused by interference signals with high energy strength, which ulteriorly corrects the time domain detection parameters for dim targets. As the sampling duration increases, the detection speed improves while the detection accuracy of broadcast frames termed as small targets decreases. The trade-off between detection accuracy and speed versus sampling duration is established, which helps to meet different drone regulation requirements. Simulation results demonstrate that the proposed algorithm improves the evaluation metrics by 2.27\% compared to existing algorithms. The proposed algorithm also performs strong robustness under varying flight distances, diverse types of environment noise, and different flight visual environment. Besides, the broadcast frame decoding results indicate that 97.30\% accuracy of RID has been achieved.