Russia has threatened to retaliate against Ukraine for a failed attempt to assassinate President Vladimir Putin in an alleged drone attack on the Kremlin citadel in Moscow. Kyiv has denied any involvement and accused Russia of readying itself for a major offensive in Ukraine. Here's what we know so far about Wednesday's alleged attack: Moscow said two drones had been used in the alleged attack on Putin's residence in the Kremlin citadel, but had been disabled by electronic defences. "We regard these actions as a planned terrorist act and an attempt on the president's life, carried out on the eve of Victory Day, the May 9 Parade, at which the presence of foreign guests is also planned," the Kremlin said in a statement. "The Russian side reserves the right to take retaliatory measures where and when it sees fit."

"Although Ukraine has not confirmed that its armed forces carried out the attacks, I think that the pre-emptive raids we have seen last year prove that Ukraine has the capability to launch long range attacks of that kind from within Ukrainian territory," says aviation expert David Cenciotti.

Fox News' Alex Hogan reports on Russia claiming Ukraine attacked the Kremlin in an attempt to assassinate Vladmir Putin as the war in Ukraine rages on. Russia has trimmed down annual Victory Day celebrations, with some claiming the Kremlin fears protests and dissent following continued and severe losses in Ukraine. Russian President Vladimir Putin has used the celebrations, which mark the Soviet Union's triumph over Nazi Germany in World War II, as propaganda opportunities. He used 2021 to warn that Russia's enemies once more followed "much of the ideology of the Nazis," a rallying cry he repeated throughout his invasion of Ukraine, and in 2022 he marched in the Immortal Regiment procession while holding a picture of his father in military attire. However, this year's celebrations will have much less fanfare as governors in Belgorod, Kursk, Voronezh, Oryol and Pskov as well as the Crimean Peninsula have all canceled their parades, The Guardian reported.

Oleksii says he's already lost five small Chinese-made drones and his Brigade "might lose three to four drones a day". He says the enemy have access to radio-electronic warfare stations and anti-drone guns which "can transmit interference and interrupt communications" to disable their drones.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Ukrainian President Volodymyr Zelenskyy traveled to Helsinki for talks with the prime ministers of four Nordic countries Wednesday as part of his effort to secure greater firepower for his country's armed forces as they figure out how to dislodge Russian troops from occupied areas of Ukraine. The Nordic countries -- Finland, Sweden, Norway and Denmark -- have been among Kyiv's strongest backers since Russia's full-scale invasion of Ukraine in February 2022. Before the meeting with Zelenskyy in Finland's capital, Nordic officials appeared ready to provide more aid as the war stretches into its 15th month.

Russia accused Ukraine on Wednesday of attacking the Kremlin with drones overnight in a failed bid to kill President Vladimir Putin. The Kremlin said two drones had been used in the alleged attack on Putin's residence in the Kremlin citadel, but had been disabled by electronic defenses. It said Russia reserved the right to retaliate -- a comment that suggested that Moscow might use the alleged incident to justify a further escalation in the 14-month-old war with Ukraine. This could be due to a conflict with your ad-blocking or security software. Please add japantimes.co.jp and piano.io to your list of allowed sites. If this does not resolve the issue or you are unable to add the domains to your allowlist, please see this FAQ.

With the world population rapidly increasing, transforming our agrifood systems to be more productive, efficient, safe, and sustainable is crucial to mitigate potential food shortages. Recently, artificial intelligence (AI) techniques such as deep learning (DL) have demonstrated their strong abilities in various areas, including language, vision, remote sensing (RS), and agrifood systems applications. However, the overall impact of AI on agrifood systems remains unclear. In this paper, we thoroughly review how AI techniques can transform agrifood systems and contribute to the modern agrifood industry. Firstly, we summarize the data acquisition methods in agrifood systems, including acquisition, storage, and processing techniques. Secondly, we present a progress review of AI methods in agrifood systems, specifically in agriculture, animal husbandry, and fishery, covering topics such as agrifood classification, growth monitoring, yield prediction, and quality assessment. Furthermore, we highlight potential challenges and promising research opportunities for transforming modern agrifood systems with AI. We hope this survey could offer an overall picture to newcomers in the field and serve as a starting point for their further research.

This document describes the architecture and algorithms of a high fidelity fixed wing flight simulator intended to test and validate novel guidance, navigation, and control (GNC) algorithms for autonomous aircraft. It aims to replicate the influence of as many factors as possible on the aircraft performances, the Earth model, the physics of flight and the associated equations of motion, and in particular the behavior of the onboard sensors, limiting the assumptions to the bare minimum, and including multiple relatively minor effects not usually considered in simulation that may play a role in the GNC algorithms not performing as intended. The author releases the flight simulator C ++ implementation as open-source software. The simulator modular design enables the replacement of the standard GNC algorithms with the objective of evaluating their performances when subject to specific missions and meteorological conditions (atmospheric properties, wind field, air turbulence). The testing and evaluation is performed by means of Monte Carlo simulations, as most simulation modules (such as the aircraft mission, the meteorological conditions, the errors introduced by the sensors, and the initial conditions) are defined stochastically and hence vary in a pseudo-random way from one execution to the next according to certain user-defined input parameters, ensuring that the results are valid for a wide range of conditions. In addition to modeling the outputs of all sensors usually present onboard a fixed wing platform, such as accelerometers, gyroscopes, magnetometers, Pitot tube, air vanes, and a Global Navigation Satellite System (GNCC) receiver, the simulator is also capable of generating realistic images of the Earth surface that resemble what an onboard camera would record if following the resulting trajectory, enabling the use and evaluation of visual and visual inertial navigation systems.

The protection of non-combatants in times of (fully) autonomous warfare raises the question of the timeliness of the international protective emblem. Incidents in the recent past indicate that it is becoming necessary to transfer the protective emblem to other dimensions of transmission and representation. (Fully) Autonomous weapon systems are often launched from a great distance to the aiming point and there may be no possibility for the operators to notice protective emblems at the point of impact. In this case, the weapon system would have to detect such protective emblems and, if necessary, disintegrate autonomously or request an abort via human-in-the-loop. In our paper, we suggest ways in which a cross-frequency protective emblem can be designed. On the one hand, the technical deployment, e.g. in the form of RADAR beacons, is considered, as well as the interpretation by methods of machine learning. With regard to the technical deployment, possibilities are considered to address different sensors and to send signals out as resiliently as possible. When considering different signals, approaches are considered as to how software can recognise the protective emblems under the influence of various boundary conditions and react to them accordingly. In particular, a distinction is made here between the recognition of actively emitted signals and passive protective signals, e.g. the recognition of wounded or surrendering persons via drone-based electro-optical and thermal cameras. Finally, methods of distribution are considered, including encryption and authentication of the received signal, and ethical aspects of possible misuse are examined.

Multi-robot system for manufacturing is an Industry Internet of Things (IIoT) paradigm with significant operational cost savings and productivity improvement, where Unmanned Aerial Vehicles (UAVs) are employed to control and implement collaborative productions without human intervention. This mission-critical system relies on 3-Dimension (3-D) scene recognition to improve operation accuracy in the production line and autonomous piloting. However, implementing 3-D point cloud learning, such as Pointnet, is challenging due to limited sensing and computing resources equipped with UAVs. Therefore, we propose a Digital Twin (DT) empowered Knowledge Distillation (KD) method to generate several lightweight learning models and select the optimal model to deploy on UAVs. With a digital replica of the UAVs preserved at the edge server, the DT system controls the model sharing network topology and learning model structure to improve recognition accuracy further. Moreover, we employ network calculus to formulate and solve the model sharing configuration problem toward minimal resource consumption, as well as convergence. Simulation experiments are conducted over a popular point cloud dataset to evaluate the proposed scheme. Experiment results show that the proposed model sharing scheme outperforms the individual model in terms of computing resource consumption and recognition accuracy. Index Terms Digital Twin, Distributed Model Sharing, Knowledge Distillation, Network Calculus, Multi-Robot System. HE advances in wireless communication, and machine learning technologies have boosted the research and development of the Industrial Internet of Things (IIoT). A multi-robot system is a typical IIoT paradigm, in which Unmanned Aerial Vehicles (UAVs) are employed to implement auto-production collaboratively without human intervention. It can significantly save operation costs and improve productivity [1].