Ultimate is a sport where points are scored by passing a disc and catching it in the opposing team's end zone. In Ultimate, the player holding the disc cannot move, making field dynamics primarily driven by other players' movements. However, current literature in team sports has ignored quantitative evaluations of when players initiate such unlabeled movements in game situations. In this paper, we propose a quantitative evaluation method for movement initiation timing in Ultimate Frisbee. First, game footage was recorded using a drone camera, and players' positional data was obtained, which will be published as UltimateTrack dataset. Next, players' movement initiations were detected, and temporal counterfactual scenarios were generated by shifting the timing of movements using rule-based approaches. These scenarios were analyzed using a space evaluation metric based on soccer's pitch control reflecting the unique rules of Ultimate. By comparing the spatial evaluation values across scenarios, the difference between actual play and the most favorable counterfactual scenario was used to quantitatively assess the impact of movement timing. We validated our method and show that sequences in which the disc was actually thrown to the receiver received higher evaluation scores than the sequences without a throw. In practical verifications, the higher-skill group displays a broader distribution of time offsets from the model's optimal initiation point. These findings demonstrate that the proposed metric provides an objective means of assessing movement initiation timing, which has been difficult to quantify in unlabeled team sport plays.

Lt. Gen. Keith Kellogg discusses the latest with the Ukraine and Russia war after a deadly Russian attack on'America Reports.' Russian officials said Ukrainian drones ignited an overnight fire at a nuclear plant in Russia's Kursk region. The strikes coincided with Ukraine's 34th Independence Day, marking its 1991 break from the Soviet Union. Russia said the strikes hit several power facilities. The plant fire was quickly extinguished. A transformer was damaged, but radiation levels remained normal, and no injuries were reported.

Urban Air Mobility (UAM) envisions the widespread use of small aerial vehicles to transform transportation in dense urban environments. However, UAM faces critical operational challenges, particularly the balance between minimizing noise exposure and maintaining safe separation in low-altitude urban airspace, two objectives that are often addressed separately. We propose a reinforcement learning (RL)-based air traffic management system that integrates both noise and safety considerations within a unified, decentralized framework. Under this scalable air traffic coordination solution, agents operate in a structured, multi-layered airspace and learn altitude adjustment policies to jointly manage noise impact and separation constraints. The system demonstrates strong performance across both objectives and reveals tradeoffs among separation, noise exposure, and energy efficiency under high traffic density. The findings highlight the potential of RL and multi-objective coordination strategies in enhancing the safety, quietness, and efficiency of UAM operations.

--With the increasing deployment of small Unmanned Aircraft Systems (sUAS) in unfamiliar and complex environments, Environmental Digital Twins (EDT) that comprise weather, airspace, and terrain data are critical for safe flight planning and for maintaining appropriate altitudes during search and surveillance operations. With the expansion of sUAS capabilities through edge and cloud computing, accurate EDT are also vital for advanced sUAS capabilities, like geolocation. However, real-world sUAS deployment introduces significant sources of uncertainty, necessitating a robust validation process for EDT components. This paper focuses on the validation of terrain models, one of the key components of an EDT, for real-world sUAS tasks. These models are constructed by fusing U.S. Geological Survey (USGS) datasets and satellite imagery, incorporating high-resolution environmental data to support mission tasks. V alidating both the terrain models and their operational use by sUAS under real-world conditions presents significant challenges, including limited data granularity, terrain discontinuities, GPS and sensor inaccuracies, visual detection uncertainties, as well as onboard resources and timing constraints. We propose a 3-Dimensions validation process grounded in software engineering principles, following a workflow across granularity of tests, simulation to real world, and the analysis of simple to edge conditions. We demonstrate our approach using a multi-sUAS platform equipped with a T errain-A ware Digital Shadow. As swarms of small Unmanned Aircraft Systems (sUAS) are increasingly deployed in complex, unstructured environments such as disaster zones, wilderness areas, and wildfire regions, the need for accurate environmental models becomes critical. Effective sUAS mission planning requires awareness not only of dynamic airspace and weather conditions but also of the underlying terrain. In such settings, terrain is often the dominant factor influencing flight safety, sensor placement, line-of-sight communications, and search effectiveness. This paper focuses specifically on the role of terrain models that enable mission-level decision-making and flight planning for sUAS operations. However, terrain inaccuracies or blind spots, such as missing elevation data, undetected peaks, or mismatched georeferencing, can result in ineffective or even hazardous behavior by autonomous vehicles. To minimize these issues, we construct and maintain a terrain model by fusing multiple sources of environmental data, including public USGS datasets [1], [2], and satellite imagery [3].

Convolutional neural networks (CNN) have been used efficiently in several fields, including environmental challenges. In fact, CNN can help with the monitoring of marine litter, which has become a worldwide problem. UAVs have higher resolution and are more adaptable in local areas than satellite images, making it easier to find and count trash. Since the sand is heterogeneous, a basic CNN model encounters plenty of inferences caused by reflections of sand color, human footsteps, shadows, algae present, dunes, holes, and tire tracks. For these types of images, other CNN models, such as CNN-based segmentation methods, may be more appropriate. In this paper, we use an instance-based segmentation method and a panoptic segmentation method that show good accuracy with just a few samples. The model is more robust and less

Canada's Prime Minister Mark Carney has expressed support for Ukraine's calls for security guarantees as part of any peace deal with Russia, including the possibility of deploying troops to the Eastern European country. During a visit to Kyiv, where he met Ukraine's President Volodymyr Zelenskyy on Sunday, Carney said a group of Ukraine's Western allies, known as the Coalition of the Willing, is working with the United States to bolster Ukrainian defences. "In Canada's judgment, it is not realistic that the only security guarantee could be the strength of the Ukrainian Armed Forces … that needs to be buttressed and reinforced," Carney told reporters. "We are working through – with our allies in Coalition of the Willing and with Ukraine – the modalities of those security guarantees on land, in the air and the sea, and I would not exclude the presence of troops." Three and a half years since Russia's full-scale invasion of Ukraine, US President Donald Trump is leading efforts to end the war.

Russia has accused Ukraine of carrying out a drone attack on a nuclear plant that has caused a fire and damage to an auxiliary transformer as Ukraine celebrates its Independence Day for the 34th time. Sunday's attack forced a 50 percent reduction in the operating capacity at reactor number three at the Kursk Nuclear Power Plant (NPP), close to the border with Ukraine, according to Russian officials, who added that several power and energy facilities were targeted in the overnight strikes. The fire at the nuclear facility was quickly extinguished with no injuries reported, the plant's news service said on Telegram. Two other reactors are operating without power generation, and one is undergoing scheduled repairs, it said, adding that radiation levels were normal. Alexander Khinshtein, the Kursk region's acting governor, said Ukrainian attacks on the plant, 60km (38 miles) from the Russia-Ukraine border, "are a threat to nuclear safety and a violation of all international conventions".

A drone attack has hit a convoy of 16 trucks carrying desperately needed food to Sudan's famine-hit North Darfur region, the United Nations said, as warring parties trade blame for the attack. UN spokesperson Daniela Gross told reporters on Thursday that all drivers and personnel travelling with the World Food Programme (WFP) convoy were safe. At least three of the trucks caught fire, according to a WFP statement quoted by the Reuters news agency. Gross said all trucks had caught fire, according to The Associated Press news agency. It was not yet clear who was responsible for Wednesday's attack, the second in the past three months to prevent a UN convoy from delivering to North Darfur.

At least 18 people have been killed and dozens injured in two attacks in Colombia attributed to dissident factions of the former Revolutionary Armed Forces of Colombia (FARC) rebel group. In Cali, the country's third most populated city, a vehicle packed with explosives detonated on Thursday near a military aviation school, in an incident that left six people dead and 71 injured, according to the mayor's office. Hours earlier, a National Police Black Hawk helicopter participating in a coca leaf crop eradication operation was downed by a drone in the municipality of Amalfi, in the department of Antioquia, killing 12 police officers. Colombian President Gustavo Petro blamed the attacks on dissident factions of the now-defunct FARC group that have rejected a 2016 peace agreement to end a prolonged internal conflict that has left more than 450,000 dead in the country. Petro said on X that the attack on the police helicopter occurred as the aircraft was transporting personnel to an area in Antioquia, in northern Colombia, to eradicate coca leaf crops, the raw material for cocaine.

Wildlife field operations demand efficient parallel deployment methods to identify and interact with specific individuals, enabling simultaneous collective behavioral analysis, and health and safety interventions. Previous robotics solutions approach the problem from the herd perspective, or are manually operated and limited in scale. We propose a decentralized vision-based multi-quadrotor system for wildlife monitoring that is scalable, low-bandwidth, and sensor-minimal (single onboard RGB camera). Our approach enables robust identification and tracking of large species in their natural habitat. We develop novel vision-based coordination and tracking algorithms designed for dynamic, unstructured environments without reliance on centralized communication or control.