Nine people have been killed and dozens more wounded in a Russian attack on the western city of Ternopil, Ukraine's president Volodymyr Zelensky has said. Nine-storey blocks of flats were hit in the strikes, as Russia fired more than 470 drones and 47 missiles at Ukraine overnight in a brazen attack, Zelensky said. Three districts of Ukraine's second city, Kharkiv, were also hit by a massive drone attack which injured more than 30 people, including children. Photos posted online showed buildings and cars ablaze. Power cuts are affecting a number of regions across the country, Ukraine's energy ministry said.

Can Ukraine restore its pre-war borders? Why are Tomahawk missiles for Ukraine a'red line' for Russia? Is Russia testing NATO with aerial incursions in Europe? Five people have been killed in Ukraine after Russia launched hundreds of drones and missiles across the country overnight, which officials said targeted civilian infrastructure. Ukraine's President Volodymyr Zelenskyy said on Sunday that Russia fired approximately 50 missiles and 500 attack drones.

Russia pounded Ukraine with missile and drone attacks overnight on Saturday and into Sunday morning, focusing on the major western city of Lviv. Ukraine's neighbour Poland scrambled fighter jets in order to ensure the safety of Polish airspace, the Polish military confirmed. Allied Nato aircraft were also deployed. Lviv's regional head Maksym Kozytskyi said two people were killed in strikes in the region, and two more injured. Elsewhere, Russia again targeted Ukraine's power plants - and one was struck in an overnight attack on Zaporizhzhia, where the mayor said one person died and more than 73,000 people were without electricity.

A chaotic modulation scheme is an efficient wideband communication method. It utilizes the deterministic chaos to generate pseudo-random carriers. Chaotic bifurcation parameter modulation is one of the well-known and widely-used techniques. This paper presents the machine learning based demodulation approach for the bifurcation parameter keying. It presents the structure of a convolutional neural network as well as performance metrics values for signals generated with the chaotic logistic map. The paper provides an assessment of the overall accuracy for binary signals. It reports the accuracy value of 0.88 for the bifurcation parameter deviation of 1.34% in the presence of additive white Gaussian noise at the normalized signal-to-noise ratio value of 20 dB for balanced dataset.

Abstract--This paper presents the machine learning approach to the automated classification of a dog's emotional state based on the processing and recognition of audio signals. It offers helpful information for improving human-machine interfaces and developing more precise tools for classifying emotions from acoustic data. The presented model demonstrates an overall accuracy value above 70% for audio signals recorded for one dog. I.Introduction Scientists suggest that canines are far more intelligent than people realize. Over the years, there have been a lot of publications about studies focused on dogs.

Russia launched a "massive missile and drone" attack on Ukraine's energy infrastructure, a Ukrainian minister said, after Washington said talks with Kyiv were back on track to secure a truce in the three-year conflict. The attack damaged natural gas production facilities of Ukraine's state-run oil and gas firm Naftogaz, the company said in a statement. In the northeastern city of Kharkiv, Russian forces struck a civilian enterprise and injured at least five people, according to its governor Oleh Syniehubov. In the northern region of Chernihiv, an attack damaged one of the production facilities, according to its governor Viacheslav Chaus who did not provide additional details. The governor of the western region of Ivano-Frankivsk, Svitlana Onyshchuk, said the air defence repelled an attack on infrastructure facilities.

Object recognition and detection are well-studied problems with a developed set of almost standard solutions. Identity documents recognition, classification, detection, and localization are the tasks required in a number of applications, particularly, in physical access control security systems at critical infrastructure premises. In this paper, we propose the new original architecture of a model based on an artificial convolutional neural network and semantic segmentation approach for the recognition and detection of identity documents in images. The challenge with the processing of such images is the limited computational performance and the limited amount of memory when such an application is running on industrial oneboard microcomputer hardware. The aim of this research is to prove the feasibility of the proposed technique and to obtain quality metrics. The methodology of the research is to evaluate the deep learning detection model trained on the mobile identity document video dataset. The dataset contains five hundred video clips for fifty different identity document types. The numerical results from simulations are used to evaluate the quality metrics. We present the results as accuracy versus threshold of the intersection over union value. The paper reports an accuracy above 0.75 for the intersection over union (IoU) threshold value of 0.8. Besides, we assessed the size of the model and proved the feasibility of running the model on an industrial one-board microcomputer or smartphone hardware.

In this paper we proposed the use of JT65A radio communication protocol for data exchange in wide-area monitoring systems in electric power systems. We investigated the software demodulation of the multiple frequency shift keying weak signals transmitted with JT65A communication protocol using deep convolutional neural network. We presented the demodulation performance in form of symbol and bit error rates. We focused on the interference immunity of the protocol over an additive white Gaussian noise with average signal-to-noise ratios in the range from -30 dB to 0 dB, which was obtained for the first time. We proved that the interference immunity is about 1.5 dB less than the theoretical limit of non-coherent demodulation of orthogonal MFSK signals.

In this paper we present the symbol and bit error rate performance of the weak signal digital communications system. We investigate orthogonal multiple frequency shift keying modulation scheme with supervised machine learning demodulation approach using simple dense end-to-end artificial neural network. We focus on the interference immunity over an additive white Gaussian noise with average signal-to-noise ratios from -20 dB to 0 dB.

In the current study, the fatigue life of QSTE340TM steel was modelled using a machine learning method, namely, a neural network. This problem was solved by a Multi-Layer Perceptron (MLP) neural network with a 3-75-1 architecture, which allows the prediction of the crack length based on the number of load cycles N, the stress ratio R, and the overload ratio Rol. The proposed model showed high accuracy, with mean absolute percentage error (MAPE) ranging from 0.02% to 4.59% for different R and Rol. The neural network effectively reveals the nonlinear relationships between input parameters and fatigue crack growth, providing reliable predictions for different loading conditions.