At least seven people have been killed including two children during intense Russian drone and missile strikes on Ukraine, says President Volodymyr Zelensky. A kindergarten was hit in Ukraine's second biggest city Kharkiv and there was widespread damage in Kyiv, in a series of attacks that Zelensky said proved Moscow had not come under enough pressure for its continued war. At least 27 people including children are reported hurt. Hours earlier, US President Donald Trump said his plans for an imminent summit in Budapest with Russia's Vladimir Putin had been shelved as he did not want a wasted meeting. The Kremlin has rejected calls for a ceasefire along the current front lines made both by Trump and European leaders.

How successful is Ukraine's'gas war' against Russia? How will Putin travel to Hungary with an ICC arrest warrant? How much of Europe's oil still comes from Russia? A "massive" Russian attack killed four people and injured seven in the town of Novhorod-Siverskyi, in Ukraine's Chernihiv region, Governor Viacheslav Chaus wrote in a post on Telegram. Chaus said that Russian forces launched about 20 Shahed drones in the attack and that there was "a lot of destruction in the city".

Abstract--The aim of this paper is to design a new type of grasping and perching unmanned aerial vehicle (UA V), Flexbee, characterized by its soft vector-propulsion nozzle (SVPN). Compared to previous UA Vs, Flexbee integrates flight, grasping, and perching functionalities into the four SVPNs, offering advantages such as decoupled position and attitude control, high structural reuse, and strong adaptability for grasping and perching. A dynamics model of Flexbee has been developed, and the nonlinear coupling issue of the moment has been resolved through lin-earization of the equivalent moment model. Hierarchical control strategy was employed to design the controllers for Flexbee's two operational modes. Finally, flight, grasping, and perching experiments were conducted to validate Flexbee's kinematic capabilities and the effectiveness of the control strategy. UL TI-ROTOR unmanned aerial vehicles (UA Vs), with their three-dimensional maneuverabilities, have demonstrated remarkable effectiveness in environments that are difficult for humans to reach [1]-[5]. As people's requirements for UA V endurance performance and adaptability to complex environments offer greater advantages, compared with large UA Vs, small UA Vs have the characteristics of small size, light weight, low cost, and high maneuverability, which play a greater advantage in complex environments [6]-[8].

Achieving high-quality reconstruction requires capturing images of keypoints within the target scene from diverse viewing angles, and coverage control offers an effective framework to meet this requirement. Meanwhile, recent advances in real-time 3D reconstruction algorithms make it possible to render an evolving map during flight, enabling immediate feedback to guide drone motion. Building on this, we present Coverage-Recon, a novel coordinated image sampling algorithm that integrates online map feedback to improve reconstruction quality on-the-fly. In Coverage-Recon, the coordinated motion of drones is governed by a Quadratic Programming (QP)-based angle-aware coverage controller, which ensures multi-viewpoint image capture while enforcing safety constraints. The captured images are processed in real time by the NeuralRecon algorithm to generate an evolving 3D mesh. Mesh changes across the scene are interpreted as indicators of reconstruction uncertainty and serve as feedback to update the importance index of the coverage control as the map evolves. The effectiveness of Coverage-Recon is validated through simulation and experiments, demonstrating both qualitatively and quantitatively that incorporating online map feedback yields more complete and accurate 3D reconstructions than conventional methods.

The integration of Unmanned Aerial Vehicles (UAVs) into Open Radio Access Networks (O-RAN) enhances communication in disaster management and Search and Rescue (SAR) operations by ensuring connectivity when infrastructure fails. However, SAR scenarios demand stringent security and low-latency communication, as delays or breaches can compromise mission success. While UAVs serve as mobile relays, they introduce challenges in energy consumption and resource management, necessitating intelligent allocation strategies. Existing UAV-assisted O-RAN approaches often overlook the joint optimization of security, latency, and energy efficiency in dynamic environments. This paper proposes a novel Reinforcement Learning (RL)-based framework for dynamic resource allocation in UAV relays, explicitly addressing these trade-offs. Our approach formulates an optimization problem that integrates security-aware resource allocation, latency minimization, and energy efficiency, which is solved using RL. Unlike heuristic or static methods, our framework adapts in real-time to network dynamics, ensuring robust communication. Simulations demonstrate superior performance compared to heuristic baselines, achieving enhanced security and energy efficiency while maintaining ultra-low latency in SAR scenarios.

Intelligent reflecting surface (IRS) assisted unmanned aerial vehicle (UAV) systems provide a new paradigm for reconfigurable and flexible wireless communications. To enable more energy efficient and spectrum efficient IRS assisted UAV wireless communications, this paper introduces a novel IRS-assisted UAV enabled spectrum sharing system with orthogonal frequency division multiplexing (OFDM). The goal is to maximize the energy efficiency (EE) of the secondary network by jointly optimizing the beamforming, subcarrier allocation, IRS phase shifts, and the UAV trajectory subject to practical transmit power and passive reflection constraints as well as UAV physical limitations. A physically grounded propulsion-energy model is adopted, with its tight upper bound used to form a tractable EE lower bound for the spectrum sharing system. To handle highly non convex, time coupled optimization problems with a mixed continuous and discrete policy space, we develop a deep reinforcement learning (DRL) approach based on the actor critic framework. Extended experiments show the significant EE improvement of the proposed DRL-based approach compared to several benchmark schemes, thus demonstrating the effectiveness and robustness of the proposed approach with mobility.

Small Unmanned Aerial Vehicles (UAVs) exhibit immense potential for navigating indoor and hard-to-reach areas, yet their significant constraints in payload and autonomy have largely prevented their use for complex tasks like high-quality 3-Dimensional (3D) reconstruction. To overcome this challenge, we introduce a novel system architecture that enables fully autonomous, high-fidelity 3D scanning of static objects using UAVs weighing under 100 grams. Our core innovation lies in a dual-reconstruction pipeline that creates a real-time feedback loop between data capture and flight control. A near-real-time (near-RT) process uses Structure from Motion (SfM) to generate an instantaneous pointcloud of the object. The system analyzes the model quality on the fly and dynamically adapts the UAV's trajectory to intelligently capture new images of poorly covered areas. This ensures comprehensive data acquisition. For the final, detailed output, a non-real-time (non-RT) pipeline employs a Neural Radiance Fields (NeRF)-based Neural 3D Reconstruction (N3DR) approach, fusing SfM-derived camera poses with precise Ultra Wide-Band (UWB) location data to achieve superior accuracy. We implemented and validated this architecture using Crazyflie 2.1 UAVs. Our experiments, conducted in both single- and multi-UAV configurations, conclusively show that dynamic trajectory adaptation consistently improves reconstruction quality over static flight paths. This work demonstrates a scalable and autonomous solution that unlocks the potential of miniaturized UAVs for fine-grained 3D reconstruction in constrained environments, a capability previously limited to much larger platforms.

How successful is Ukraine's'gas war' against Russia? How will Putin travel to Hungary with an ICC arrest warrant? How much of Europe's oil still comes from Russia? How Russia's new tactics pose new winter threat to Ukraine The Russian drone strike was surgically precise and destroyed a giant transformer at a key power station in the Ukrainian capital. "There's nothing left to repair," Mykola Svyrydenko, who lives close to Thermal Station 5, a sprawling, Soviet-era structure with two giant steam pipes that provides electricity and heat to hundreds of thousands of Kyiv's residents, told Al Jazeera.

Drone attack in Sudan threatens Khartoum airport's reopening: Reports A series of drone attacks has hit areas in Sudan's capital, including near Khartoum international airport, a day before its long-awaited reopening, according to the AFP news agency and Sudanese media reports. Witnesses told AFP they heard drones over central and southern Khartoum early on Tuesday. A wave of explosions was reported near the airport between 4am and 6am (02:00-04:00 GMT). The airport has been shut since fighting erupted in April 2023 between the Sudanese army and the paramilitary Rapid Support Forces (RSF), badly damaging infrastructure. Sudan's Rakoba News, citing witnesses, reported more than eight blasts in and around the airport.

A tornado tore through Val-d'Oise, north of Paris, on Monday, toppling construction cranes, damaging properties and uprooting trees in its path. One person was killed and four others critically injured, authorities said. The town of Ermont, about 20 km (13 miles) northeast of Paris was hardest hit by the sudden twister, which caused damage in multiple districts. Interior Minister Laurent Nunez said on the X social media platform that it had been a storm of rare intensity. Drone footage shows blaze destroying the historic Bernaga Monastery in Italy.