Comprised of GRD and SLC images from Sentinel-1; AIS used to create labels.

Even after a century beneath the water, the Titanic's bow remains one of the most magnificent and haunting sights in the ocean. However, a new survey of the wreck site has revealed that the railing, made famous by Jack and Rose, has now collapsed into rust. Haunting images snapped by underwater robots through the years show the great ship's bow has gradually eroded. Experts say that its metal construction and frequent human visits mean it is only a matter of time before the Titanic collapses. Dr Rodrigo Pacheco-Ruiz, archaeological data manager for HMS Victory and maritime archaeologist from the University of Southampton, told MailOnline: 'The realistic view is that because she's such a big metal object, she won't be there for very long.' Haunting pictures reveal how the Titanic's iconic bow has decayed in the 37 years between 1987 and 2010 Earlier this week, RMS Titanic Inc, the company which holds the salvage rights for the ship, released new images and footage of the sunken liner.

After four years without an incident, SpaceX engineers had good reason to be confident about a routine launch this week. But that confidence was literally blown out of the water this morning after a Falcon 9 rocket booster dramatically exploded shortly after landing. Booster 1062 had just broken the record for the most consecutive launches without failure when it failed to touch down a SpaceX drone ship in the Atlantic Ocean. A shocking video captured the moment the booster suddenly tipped over and was engulfed in a ball of purple flames. This marks the first time since 2021 that a SpaceX booster stage has failed to land after taking its payload into orbit.

Russia launched its fifth drone attack on Kyiv in two weeks, with Ukraine's air defence systems destroying all the air weapons before they could reach the capital, Ukraine's military said. No casualties or damage was reported, Serhiy Popko, head of Kyiv's military administration, said on Telegram. Russia's air defence systems destroyed eight Ukrainian drones overnight, the Russian Ministry of Defence said. Three of the drones were destroyed over the Belgorod region, which borders Ukraine, and three were intercepted in the Black Sea, the ministry said on Telegram.

Large language models (LLMs) frequently generate non-factual content, known as hallucinations. Existing retrieval-augmented-based hallucination detection approaches typically address this by framing it as a classification task, evaluating hallucinations based on their consistency with retrieved evidence. However, this approach usually lacks detailed explanations for these evaluations and does not assess the reliability of these explanations. Furthermore, deficiencies in retrieval systems can lead to irrelevant or partially relevant evidence retrieval, impairing the detection process. Moreover, while real-world hallucination detection requires analyzing multiple pieces of evidence, current systems usually treat all evidence uniformly without considering its relevance to the content. To address these challenges, we introduce Halu-J, a critique-based hallucination judge with 7 billion parameters. Halu-J enhances hallucination detection by selecting pertinent evidence and providing detailed critiques. Our experiments indicate that Halu-J outperforms GPT-4o in multiple-evidence hallucination detection and matches its capability in critique generation and evidence selection. We also introduce ME-FEVER, a new dataset designed for multiple-evidence hallucination detection. Our code and dataset can be found in https://github.com/GAIR-NLP/factool .

A Russian group is cobbling together hoverboards, a form of personal transport, to create four-wheeled robots capable of carrying out kamikaze attacks, moving supplies or laying a smokescreen. Both sides in the Russia-Ukraine war are using improvised aerial drones by the thousand, while in the Black Sea, Ukraine has deployed an armada of uncrewed vessels developed from Jet Skis and speedboats. Both sides are also developing cheap ground-based robots, and Russia's latest effort is an extreme example.

Advances in underwater imaging enable the collection of extensive seafloor image datasets that are necessary for monitoring important benthic ecosystems. The ability to collect seafloor imagery has outpaced our capacity to analyze it, hindering expedient mobilization of this crucial environmental information. Recent machine learning approaches provide opportunities to increase the efficiency with which seafloor image datasets are analyzed, yet large and consistent datasets necessary to support development of such approaches are scarce. Here we present BenthicNet: a global compilation of seafloor imagery designed to support the training and evaluation of large-scale image recognition models. An initial set of over 11.4 million images was collected and curated to represent a diversity of seafloor environments using a representative subset of 1.3 million images. These are accompanied by 2.6 million annotations translated to the CATAMI scheme, which span 190,000 of the images. A large deep learning model was trained on this compilation and preliminary results suggest it has utility for automating large and small-scale image analysis tasks. The compilation and model are made openly available for use by the scientific community at https://doi.org/10.20383/103.0614.

The Russian navy's Black Sea Fleet has been forced to rebase nearly all its combat-ready warships from occupied Crimea to other locations, and its main naval hub is becoming ineffectual because of attacks by Kyiv, Ukraine's navy chief said. Vice-Admiral Oleksiy Neizhpapa said Ukrainian missile and naval drone strikes had caused heavy damage to the Sevastopol base, a logistics hub for repairs, maintenance, training and ammunition storage among other important functions for Russia. "They were established over many decades, possibly centuries. And clearly they are now losing this hub," Neizhpapa told Reuters in a rare interview in the port city of Odesa ahead of Ukraine Navy Day on Sunday. More than 28 months since Russia's full-scale invasion, Kyiv has dealt a series of stinging blows to Moscow in the Black Sea although Ukrainian ground troops are on the back foot across a sprawling front.

Russia says it destroyed two Ukrainian sea drones targeting the Black Sea port of Novorossiysk, a key naval base and oil shipping outlet. The Ministry of Defence in Moscow said on Wednesday that Russian forces had destroyed the naval drones as they advanced on the port in an overnight attack. Ukraine has reported success in targeting Russian ships and infrastructure in the Black Sea over recent months. "Two unmanned boats travelling in the direction of Novorossiysk were destroyed in the waters of the Black Sea," the ministry said in a post on Telegram. The attack caused no damage or shipping disruptions, the local city administration reported, according to Russian state news agencies.

This paper proposes a novel Reinforcement Learning (RL) approach for sim-to-real policy transfer of Vertical Take-Off and Landing Unmanned Aerial Vehicle (VTOL-UAV). The proposed approach is designed for VTOL-UAV landing on offshore docking stations in maritime operations. VTOL-UAVs in maritime operations encounter limitations in their operational range, primarily stemming from constraints imposed by their battery capacity. The concept of autonomous landing on a charging platform presents an intriguing prospect for mitigating these limitations by facilitating battery charging and data transfer. However, current Deep Reinforcement Learning (DRL) methods exhibit drawbacks, including lengthy training times, and modest success rates. In this paper, we tackle these concerns comprehensively by decomposing the landing procedure into a sequence of more manageable but analogous tasks in terms of an approach phase and a landing phase. The proposed architecture utilizes a model-based control scheme for the approach phase, where the VTOL-UAV is approaching the offshore docking station. In the Landing phase, DRL agents were trained offline to learn the optimal policy to dock on the offshore station. The Joint North Sea Wave Project (JONSWAP) spectrum model has been employed to create a wave model for each episode, enhancing policy generalization for sim2real transfer. A set of DRL algorithms have been tested through numerical simulations including value-based agents and policy-based agents such as Deep \textit{Q} Networks (DQN) and Proximal Policy Optimization (PPO) respectively. The numerical experiments show that the PPO agent can learn complicated and efficient policies to land in uncertain environments, which in turn enhances the likelihood of successful sim-to-real transfer.