The footprint of Russian President Vladimir Putin's war on Ukraine is growing fast after a weekend in which sea drones crippled a Russian naval vessel and oil tanker. For the first time, the attacks put at risk Russia's commodity exports via the Black Sea, a route that accounts for most of the grain and 15% to 20% of the oil Russia sells daily on global markets. Significantly higher insurance and shipping costs are likely to follow for Moscow, but there are risks to European and global markets, too. The expansion comes as Ukraine's counteroffensive advances more slowly than Kyiv officials planned, and as Saudi Arabia's attempt to catalyze peace talks by hosting a multinational conference showed just how hard it is likely to be to end the bloodshed on terms both sides can accept.

A Russian tanker was damaged in a Ukrainian drone attack in the Kerch Strait, briefly halting traffic on the strategic bridge linking Crimea to Russia on Saturday, a day after one of Moscow's warships was hit in the Black Sea. The number of attacks in the Black Sea has increased from both sides since Moscow exited a deal last month that had allowed Ukrainian grain exports via the shipping hub during the conflict between the two countries. The Russian tanker SIG was hit around 11:20 p.m. Friday south of the Kerch Strait, Russia's Federal Agency for Sea and Inland Water Transport said.

Ukrainian sea drones have attacked a key Russian port on the Black Sea, damaging a naval ship, according to a Ukrainian official, speaking about the latest in a series of strikes inside Russia after Kyiv promised to bring the fight home to the Kremlin. Moscow said it repelled Friday's attack on Novorossiysk, which marked the first time a commercial Russian port has been targeted in the 18-month war. Olenegorsky Gornyak, a landing ship, suffered a serious breach in the attack, carried out by Ukraine's navy and security service, according to a security service official. As a result, the ship is unable to carry out its combat missions, said the official who spoke on the condition of anonymity because he was not authorised to give the information to the media. Ukrainian news agencies carried footage from social media channels that they suggested showed the Olenegorsky Gornyak listing to one side. The ship is designed to transport troops and heavy equipment and was sent for repairs in 2014, according to Russian media reports.

This is based on announcements by Russian and Ukrainian authorities, and local media reports. Ukrainian defence sources have told CNN that sea drones had also been used in an attack on the Kerch Bridge to Crimea in July.

Ukrainian President Volodymyr Zelenskyy warned this week that war is coming to Russia after kamikaze drone attacks targeted skyscrapers in Moscow's financial district, as his country's forces continued to score small-scale territorial successes against Russian troops in Ukraine's east and south. Here is a round-up of the main battlefield events during the 75th week of the war. On July 30, a suspected Ukrainian long-range drone hit a Moscow high-rise building that houses the Ministry of Digital Development, the Economy Ministry and the Ministry of Industrial Development, responsible for military industry. Two days later, another pair of drones was shot down outside Moscow, but a third made it through to the city where it was intercepted by electronic jammers and crashed into a skyscraper, damaging the facade. The attacks came just days after a previous drone raid on the centre of the Russian capital.

With the rapid amassing of spatial-temporal (ST) ocean data, many spatial-temporal data mining (STDM) studies have been conducted to address various oceanic issues, including climate forecasting and disaster warning. Compared with typical ST data (e.g., traffic data), ST ocean data is more complicated but with unique characteristics, e.g., diverse regionality and high sparsity. These characteristics make it difficult to design and train STDM models on ST ocean data. To the best of our knowledge, a comprehensive survey of existing studies remains missing in the literature, which hinders not only computer scientists from identifying the research issues in ocean data mining but also ocean scientists to apply advanced STDM techniques. In this paper, we provide a comprehensive survey of existing STDM studies for ocean science. Concretely, we first review the widely-used ST ocean datasets and highlight their unique characteristics. Then, typical ST ocean data quality enhancement techniques are explored. Next, we classify existing STDM studies in ocean science into four types of tasks, i.e., prediction, event detection, pattern mining, and anomaly detection, and elaborate on the techniques for these tasks. Finally, promising research opportunities are discussed. This survey can help scientists from both computer science and ocean science better understand the fundamental concepts, key techniques, and open challenges of STDM for ocean science.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Russian drones on Wednesday hit a Ukrainian port city along the border with Romania, causing significant damage and a huge fire at facilities that are key to Ukrainian grain exports. The attacks followed the end of a deal with Russia that had allowed Ukrainian shipments to world markets from the Black Sea port of Odesa. Since scrapping the deal, Russia has hammered the country's ports with strikes, compounding the blow to the key industry.

Ukraine's coastal region of Odesa was rattled by Russian drones which hit grain storage facilities in the south of the region, according to authorities in Kyiv. The grain port of Izmail, an inland port across the Danube River from NATO-member Romania, was the main target of Moscow's drone attack. "As a result of the attack, fires broke out at the facilities of the port and industrial infrastructure of the region, and an elevator was damaged," Odesa region Governor Oleh Kiper said in a statement on the Telegram messaging app. Russia's continued attacks against the Ukrainian civilian infrastructure on #Danube, in the proximity of Romania, are unacceptable. These are war crimes and they further affect UA's capacity to transfer their food products towards those in need in the world.

The Russian military said anti-aircraft units thwarted a Ukrainian "terrorist attack" and downed drones targeting Moscow, but that one drone struck a high-rise tower that was hit earlier in the week. Russia said it had repelled an overnight Ukrainian drone attack aimed at its patrol boats in the Black Sea. Mykhailo Podolyak, an adviser to Ukrainian President Volodymyr Zelenskyy, told the Reuters news agency that Kyiv did not attack and would not attack civilian vessels in the Black Sea, calling Russian claims "fictitious". Drones struck populated areas in the Ukrainian city of Kharkiv, destroying two floors of a college dormitory, according to local officials. Kharkiv Mayor Ihor Terekhov said there had been three separate attacks on Ukraine's second-largest city.

Satellite altimeter observations retrieved since 1993 show that the global mean sea level is rising at an unprecedented rate (3.4mm/year). With almost three decades of observations, we can now investigate the contributions of anthropogenic climate-change signals such as greenhouse gases, aerosols, and biomass burning in this rising sea level. We use machine learning (ML) to investigate future patterns of sea level change. To understand the extent of contributions from the climate-change signals, and to help in forecasting sea level change in the future, we turn to climate model simulations. This work presents a machine learning framework that exploits both satellite observations and climate model simulations to generate sea level rise projections at a 2-degree resolution spatial grid, 30 years into the future. We train fully connected neural networks (FCNNs) to predict altimeter values through a non-linear fusion of the climate model hindcasts (for 1993-2019). The learned FCNNs are then applied to future climate model projections to predict future sea level patterns. We propose segmenting our spatial dataset into meaningful clusters and show that clustering helps to improve predictions of our ML model.