The Russian capital has been hit by a rare drone attack that has caused "minor" damage to buildings and no casualties, the city's mayor said. "This morning, at dawn, a drone attack caused minor damage to several buildings. All the city's emergency services are on the scene … No one has been seriously injured so far," Moscow's mayor Sergei Sobyanin said in a statement on Tuesday. Moscow, located more than 1,000km (620 miles) from Ukraine, has only rarely been the target of drone attacks since the start of the conflict in Ukraine, even though such attacks have become more common elsewhere in Russia. Andrei Vorobyov, governor of the Moscow region, said on the Telegram channel that several drones were shot down on their approach to Moscow. It was not immediately clear who launched the drones.

President of Ukraine Volodymyr Zelenskyy held a meeting with U.S. Sen. Lindsey Graham on May 26, 2023, during the senator's third visit to Ukraine since Russia invaded the country. Sen. Lindsey Graham, R-S.C., is a wanted man in Russia for comments he made while visiting Ukrainian President Volodymyr Zelenskyy on Friday. Russia's Interior Ministry put out a warrant for Graham's arrest on Monday in response to an edited video released by Zelenskyy's office in which Graham praised U.S. support for Ukraine's defense and noted that Russians are dying as Ukraine fights for its freedom. In the video, Graham noted that "the Russians are dying" and described the U.S. military assistance to the country as "the best money we've ever spent." While Graham appeared to have made the remarks in different parts of the conversation, the short video by Ukraine's presidential office put them next to each other, causing outrage in Russia.

"Almost every night, the skies look and sound like another Star Wars episode, but we don't feel much of Russian rockets hitting their targets here within the city area. And this is all thanks to the decent countries, decent people of the world who gave us this air defence," he said.

In this paper, we develop a hierarchical aerial computing framework composed of high altitude platform (HAP) and unmanned aerial vehicles (UAVs) to compute the fully offloaded tasks of terrestrial mobile users which are connected through an uplink non-orthogonal multiple access (UL-NOMA). To better assess the freshness of information in computation-intensive applications the criterion of age of information (AoI) is considered. In particular, the problem is formulated to minimize the average AoI of users with elastic tasks, by adjusting UAVs trajectory and resource allocation on both UAVs and HAP, which is restricted by the channel state information (CSI) uncertainty and multiple resource constraints of UAVs and HAP. In order to solve this non-convex optimization problem, two methods of multi-agent deep deterministic policy gradient (MADDPG) and federated reinforcement learning (FRL) are proposed to design the UAVs trajectory, and obtain channel, power, and CPU allocations. It is shown that task scheduling significantly reduces the average AoI. This improvement is more pronounced for larger task sizes. On one hand, it is shown that power allocation has a marginal effect on the average AoI compared to using full transmission power for all users. Compared with traditional transmission schemes, the simulation results show our scheduling scheme results in a substantial improvement in average AoI.

Unmanned aerial vehicles (UAVs) are promising for providing communication services due to their advantages in cost and mobility, especially in the context of the emerging Metaverse and Internet of Things (IoT). This paper considers a UAV-assisted Metaverse network, in which UAVs extend the coverage of the base station (BS) to collect the Metaverse data generated at roadside units (RSUs). Specifically, to improve the data collection efficiency, resource allocation and trajectory control are integrated into the system model. The time-dependent nature of the optimization problem makes it non-trivial to be solved by traditional convex optimization methods. Based on the proposed UAV-assisted Metaverse network system model, we design a hybrid framework with reinforcement learning and convex optimization to {cooperatively} solve the time-sequential optimization problem. Simulation results show that the proposed framework is able to reduce the mission completion time with a given transmission power resource.

Dozens of patients are undergoing rehabilitation at the Superhumans Center, a newly established medical center aiming to become Ukraine's first venue for for such treatment. Russian forces pummeled the Ukrainian capital of Kyiv with "Kamikaze" drone attacks throughout the night as the city prepared to celebrate the anniversary of its founding Sunday. Russia launched 54 Iranian-made drones at Kyiv and elsewhere in Ukraine, but air defenses shot down 52 of the drones, according to Ukrainian officials. Two people were killed during Saturday night's attack, with falling debris landing on one 41-year-old man and another person dying of unspecified causes, Kyiv Mayor Vitali Klitschko said in a statement. Kyiv is marking the 1,541-year anniversary since its founding on Sunday.

Oleksiy Danilov, secretary of the powerful National Security and Defence Council of Ukraine, said an assault to retake territory from President Vladimir Putin's occupying forces could begin "tomorrow, the day after tomorrow or in a week".

Abstract--In addition to its crucial impact on customer satisfaction, last-mile delivery (LMD) is notorious for being the most time-consuming and costly stage of the shipping process. Pressing environmental concerns combined with the recent surge of e-commerce sales have sparked renewed interest in automation and electrification of last-mile logistics. To address the hurdles faced by existing robotic couriers, this paper introduces a customer-centric and safety-conscious LMD system for small urban communities based on AI-assisted autonomous delivery robots. The presented framework enables end-to-end automation and optimization of the logistic process while catering for realworld imposed operational uncertainties, clients' preferred time schedules, and safety of pedestrians. To this end, the integrated optimization component is modeled as a robust variant of the Cumulative Capacitated Vehicle Routing Problem with Time Windows, where routes are constructed under uncertain travel times with an objective to minimize the total latency of deliveries (i.e., the overall waiting time of customers, which can negatively affect their satisfaction). We demonstrate the proposed LMD system's utility through real-world trials in a university campus with a single robotic courier. Implementation aspects as well as the findings and practical insights gained from the deployment are discussed in detail. Lastly, we round up the contributions with numerical simulations to investigate the scalability of the developed mathematical formulation with respect to the number of robotic vehicles and customers.

We propose a novel failure-sentient framework for swarm-based drone delivery services. The framework ensures that those drones that experience a noticeable degradation in their performance (called soft failure) and which are part of a swarm, do not disrupt the successful delivery of packages to a consumer. The framework composes a weighted continual federated learning prediction module to accurately predict the time of failures of individual drones and uptime after failures. These predictions are used to determine the severity of failures at both the drone and swarm levels. We propose a speed-based heuristic algorithm with lookahead optimization to generate an optimal set of services considering failures. Experimental results on real datasets prove the efficiency of our proposed approach in terms of prediction accuracy, delivery times, and execution times.

The interest in dynamic vision sensor (DVS)-powered unmanned aerial vehicles (UAV) is raising, especially due to the microsecond-level reaction time of the bio-inspired event sensor, which increases robustness and reduces latency of the perception tasks compared to a RGB camera. This work presents ColibriUAV, a UAV platform with both frame-based and event-based cameras interfaces for efficient perception and near-sensor processing. The proposed platform is designed around Kraken, a novel low-power RISC-V System on Chip with two hardware accelerators targeting spiking neural networks and deep ternary neural networks.Kraken is capable of efficiently processing both event data from a DVS camera and frame data from an RGB camera. A key feature of Kraken is its integrated, dedicated interface with a DVS camera. This paper benchmarks the end-to-end latency and power efficiency of the neuromorphic and event-based UAV subsystem, demonstrating state-of-the-art event data with a throughput of 7200 frames of events per second and a power consumption of 10.7 \si{\milli\watt}, which is over 6.6 times faster and a hundred times less power-consuming than the widely-used data reading approach through the USB interface. The overall sensing and processing power consumption is below 50 mW, achieving latency in the milliseconds range, making the platform suitable for low-latency autonomous nano-drones as well.