Israeli forces killed at least five Palestinians in the occupied West Bank's Tulkarem on Sunday morning, taking the toll in Israeli raids, including drone strikes, in the occupied territories to seven in the last 24 hours. Two of the victims were killed in drone strikes while several others were injured in an ongoing large-scale military offensive in Tulkarem, the Palestinian Wafa news agency reported quoting the local media and medical sources. At least two Palestinians were killed on Saturday in two separate incidents in the occupied West Bank. Israel's army confirmed its forces used aircraft to target Palestinians in the town, saying it struck and killed fighters who had launched explosives at them from the Nur Shams camp. Following the air raid, Israeli forces prevented ambulances from reaching the camp and arrested one paramedic, Wafa reported.

The tensions spilling over from the war in Gaza to merchant shipping in the Red Sea escalated on Saturday when Britain and the United States said their militaries had shot down more than a dozen attack drones. The Houthis, an armed group that controls much of northern Yemen, have been staging drone and missile assaults on Israeli and American targets since the Oct. 7 Hamas-led attacks on Israel. They have said they intend to prevent Israeli ships from sailing the Red Sea until Israel stops its war on Hamas, which rules Gaza. Both the Houthis and Hamas, like Hezbollah in Lebanon, are backed by Iran. The shipping industry was also bracing for potential economic fallout as the Red Sea, a vital sea lane, is increasingly drawn into the regional unrest.

The United States and United Kingdom authorities say their warships have shot down 15 attack drones over the Red Sea as Israel's war on Gaza threatens to spread in the region. The US Central Command (CENTCOM) on Saturday said its guided-missile destroyer responded to a wave of drones from "Houthi-controlled areas of Yemen" over the Red Sea, downing 14 suspected attack drones. It described the launches as "one-way attack drones", saying they were "shot down with no damage to ships in the area or reported injuries". UK Defence Secretary Grant Shapps also said the Royal Navy destroyer HMS Diamond fired a Sea Viper missile and destroyed a drone that was "targeting merchant shipping". Meanwhile, Yemen's Iran-aligned Houthis said the group attacked the Israeli city of Eilat on Saturday with a swarm of drones, according to spokesman Yahya Sarea who referred to the Red Sea resort city as being in "southern occupied Palestine".

Earlier this month, the US military said the Unity Explorer, sailing under the flag of the Bahamas and owned by a British company, was among three commercial vessels targeted in an attack by Iranian-backed Houthi rebels.

The Foreign Press Association (FPA), which represents several hundred journalists working for international news organisations, said it grieved the cameraman's death.

Hijackings, missile strikes and drone assaults on ships by Yemen's Houthi rebels have forced AP Moller-Maersk, a Danish shipping and logistics giant, and Hapag-Lloyd, a German shipping and container transportation company, to pause shipments through the Red Sea. Their decisions, announced on Friday, are a sign that major corporations are taking the security situation in the Red Sea increasingly seriously. But the consequences might also be felt by the world's oil markets and the cost of energy that consumers need to bear – though the extent of any disruption might depend on how major global players respond to the looming crisis, said experts. Maersk said in a statement that its decision stemmed from the company's concerns about the "highly escalated security situation in the southern Red Sea and Gulf of Aden" over the past few weeks. Recent missile and drone attacks on commercial vessels represent a "significant threat to the safety and security of seafarers," it said.

Autonomous robots need to be able to handle uncertainties when deployed in the real world. For the robot to be able to robustly work in such an environment, it needs to be able to adapt both its architecture as well as its task plan. Architecture adaptation and task plan adaptation are mutually dependent, and therefore require the system to apply runtime architecture and task plan co-adaptation. This work presents Metaplan, which makes use of models of the robot and its environment, together with a PDDL planner to apply runtime architecture and task plan co-adaptation. Metaplan is designed to be easily reusable across different domains. Metaplan is shown to successfully perform runtime architecture and task plan co-adaptation with a self-adaptive unmanned underwater vehicle exemplar, and its reusability is demonstrated by applying it to an unmanned ground vehicle.

Federated Learning (FL) is a decentralized machine learning (ML) technique that allows a number of participants to train an ML model collaboratively without having to share their private local datasets with others. When participants are unmanned aerial vehicles (UAVs), UAV-enabled FL would experience heterogeneity due to the majorly skewed (non-independent and identically distributed -IID) collected data. In addition, UAVs may demonstrate unintentional misbehavior in which the latter may fail to send updates to the FL server due, for instance, to UAVs' disconnectivity from the FL system caused by high mobility, unavailability, or battery depletion. Such challenges may significantly affect the convergence of the FL model. A recent way to tackle these challenges is client selection, based on customized criteria that consider UAV computing power and energy consumption. However, most existing client selection schemes neglected the participants' reliability. Indeed, FL can be targeted by poisoning attacks, in which malicious UAVs upload poisonous local models to the FL server, by either providing targeted false predictions for specifically chosen inputs or by compromising the global model's accuracy through tampering with the local model. Hence, we propose in this paper a novel client selection scheme that enhances convergence by prioritizing fast UAVs with high-reliability scores, while eliminating malicious UAVs from training. Through experiments, we assess the effectiveness of our scheme in resisting different attack scenarios, in terms of convergence and achieved model accuracy. Finally, we demonstrate the performance superiority of the proposed approach compared to baseline methods.

Ukraine's Air Force said Russia launched 42 drones and six missiles, mostly targeting the southern Odesa region. Air defence systems destroyed most of the Iranian-made Shahed drones but 11 people were injured by falling debris, which also damaged buildings and warehouses. The air force said Ukraine was also attacked by Russian fighter jets dropping Kinzhal hypersonic missiles. One missile was shot down over the Kyiv region, but another two hit the west of the capital where there is an air base. Kyiv regional governor Ruslan Kravchenko said no casualties were reported, or damage to critical and civilian infrastructure.

Unmanned aerial vehicle (UAV)-assisted sensor networks (UASNets), which play a crucial role in creating new opportunities, are experiencing significant growth in civil applications worldwide. UASNets improve disaster management through timely surveillance and advance precision agriculture with detailed crop monitoring, thereby significantly transforming the commercial economy. UASNets revolutionize the commercial sector by offering greater efficiency, safety, and cost-effectiveness, highlighting their transformative impact. A fundamental aspect of these new capabilities and changes is the collection of data from rugged and remote areas. Due to their excellent mobility and maneuverability, UAVs are employed to collect data from ground sensors in harsh environments, such as natural disaster monitoring, border surveillance, and emergency response monitoring. One major challenge in these scenarios is that the movements of UAVs affect channel conditions and result in packet loss. Fast movements of UAVs lead to poor channel conditions and rapid signal degradation, resulting in packet loss. On the other hand, slow mobility of a UAV can cause buffer overflows of the ground sensors, as newly arrived data is not promptly collected by the UAV. Our proposal to address this challenge is to minimize packet loss by jointly optimizing the velocity controls and data collection schedules of multiple UAVs.Furthermore, in UASNets, swift movements of UAVs result in poor channel conditions and fast signal attenuation, leading to an extended age of information (AoI). In contrast, slow movements of UAVs prolong flight time, thereby extending the AoI of ground sensors.To address this challenge, we propose a new mean-field flight resource allocation optimization to minimize the AoI of sensory data.