Search

The aim of this paper is to study the effect of variation of the parameters of a small-sized quadrotor blade on the level of aerodynamic noise and its frequency spectrum. There is a certain discrepancy between the calculated and experimental data available today. The reason for this is that the study of quadrotor rotor noise is carried out using various theoretical models that do not consider certain factors of sound generation or additional artificial sound sources, which result in an overestimated noise level. Therefore, there is a need for an accurate model, the calculation of which more closely matches the experimental data. In the paper below, an aerodynamic noise model is proposed to study the noise of a quadrocopter blade, which considers the non-stationarity and three-dimensionality of the sound generation and propagation process. The research methods are based on the numerical calculation of the characteristics of the near and far sound fields in the potential approximation: pressure coefficient, sound pressure level, and spectrum of the generated sound. The main parameters that were varied during numerical calculations were the rotor speed of the quadrotor, angle of attack, torsion angle, and blade position in the plane of rotation. The NASA parabolic profile was used as the test profile. Results and conclusions. The results of numerical calculations in the near field revealed three areas of sound generation above the blade surface. The first region, which is more unstable, is caused by blade torsion. The other two sound generation areas are smoothly distributed along the blade’s swing, and the pressure change level in each of them doubles. The level of generated aerodynamic noise is largely dependent on the blade speed and the distance from the blade at which the noise is calculated. The angle of attack and blade pitch have a smaller effect on the generated noise level, and in the far field, they have almost no effect on the maximum noise level. The dependence of noise on the distance to the blade, as well as the blade parameters, which were chosen similar to those studied by other authors, showed a fairly good agreement with the calculations of these authors and with experimental studies. Because of aerodynamic calculations and noise generation around the quadrotor blade using ANSYS software, it was found that sound vibrations occur during the flow around the blade tip, with a level of 120 dB in the immediate vicinity of the blade surface. Calculations using a 3D model of the propeller confirmed that the volume reached 80 dB at the nearest reference point, which closely matches the data from the calculation based on the potential model and the experimental data.

The use of unmanned aerial vehicles is gaining wide popularity in various areas of research and information acquisition. More and more often, unmanned aerial vehicles are used to obtain various types of images of the Earth’s surface for its study. In particular, such data can be used in law enforcement, localization of crowds, etc. Typically, such systems operate independently of humans and provide information about objects in an automatic mode, with humans working only under the control of the aircraft. One of the main components of such systems is a neural network for localization and classification of objects, the parameters of which determine both the accuracy of the system as a whole and the design of the aircraft for shooting. In particular, the accuracy of the neural network determines the profitability of such a system, because if the accuracy is insufficient, the use of such systems will not make sense. Therefore, the main subject of research in this paper is a neural network for object localization, in particular YOLO v5, and its accuracy parameters on images obtained from unmanned aerial vehicles. The main focus of this paper is on the parameters of the neural network and the study of its metrics, which are important parameters of a trained neural network. Another important parameter for the further use of a neural network is its training parameters, as well as the data used for training. This study also pays attention to the details of the training process. The main goal of this study is to train a neural network on a selected dataset and to study the accuracy metrics of the trained neural network. The main goal of this study is to determine the dependence of localization accuracy on the area of the object, which will allow for more detailed development of unmanned systems with automatic object detection, as well as to assess the profitability of using such systems in task planning. On the basis of the data obtained, conclusions were drawn about the dependence of localization accuracy on the area of an object in images from unmanned aerial vehicles. These data can serve as a reference for unmanned aerial vehicle developers, particularly when selecting photo modules or planning the system architecture.

The need to develop new systems for detecting and counteracting malware remains relevant. In addition to malware detection methods, the need to develop new systems for detecting and counteracting malware has become increasingly important. The use of various detection systems and the formation of a variable architecture in them significantly improves the effectiveness of detection, since both for attackers in computer attacks and for malware, understanding the system is significantly complicated. In addition, such systems may contain baits, traps, and, accordingly, modifiable operating environments to deceptively execute programs for research. This paper develops a conceptual model of multicomputer systems, which is designed to ensure the functioning of antivirus bait and traps to detect malware and computer attacks in corporate networks. The proposed approach is intended to prevent and counteract metamorphic virus penetration. This paper presents the conceptual model of multicomputer systems and introduces a defining characteristic responsible for the control of decisions and other defining characteristics of the system. Methods for detecting metamorphic viruses with the possibility of their implementation in the architecture of multi-computer systems with bait and traps are developed so that the system directly joins the detection procedure through its components and decides on the presence of metamorphic code in the executable file. An implementation of a multi-computer malware detection system with metamorphic functionality was developed to prove the feasibility of the proposed conceptual architecture model and the developed methods for detecting metamorphic viruses. An experiment on the functioning of a multi-computer malware detection system was set up, and experimental studies were conducted. The conducted experiments included metamorphic virus detection. In addition, an experiment on the effectiveness of detecting the metamorphic code of viruses was conducted. The efficiency of detecting metamorphic virus code using the developed multi-computer system was also investigated, and the presence of improved detection was established. The directions of further work are to extend the results of this work to new types of malware.

The number of different types and the actual number of malware and computer attacks is constantly increasing. Therefore, detecting and counteracting malware and computer attacks remains a pressing issue. Users of corporate networks suffer the greatest damage. Many effective tools of various kinds have been developed to detect and counteract these effects. However, the dynamism in the development of new malware and the diversity of computer attacks encourage detection and countermeasure developers to constantly improve their tools and create new ones. The object of research in this paper is deception systems. The task of this study is to develop the elements of the theory and practice of creating such systems. Deception systems occupy a special place among the means of detecting and counteracting malware and computer attacks. These systems confuse attackers, but they also require constant changes and updates, as the peculiarities of their functioning become known over time. Therefore, the problem of creating deception systems whose functioning would remain incomprehensible to attackers is relevant. To solve this problem, we propose a new principle for the synthesis of such systems. Because the formation of such systems will be based on computer stations of a corporate network, the system is positioned as a multi-computer system. The system proposes the use of combined baits and traps to create false attack targets. All components of such a system form a shadow computer network. This study develops a principle for synthesizing multi-computer systems with combined baits and traps and a decision-making controller for detecting and countering IEDs and spacecraft. The principle is based on the presence of a controller for decisions made in the system and the use of specialized functionality for detection and counteraction. According to the developed principle of synthesizing such systems, this paper identifies a subset of systems with deception technologies that must have a controller and specialized functionality. The decision-making controller in the system is separate from the decision-making center. Its task is to choose the options for the next steps of the system, which are formed in the center of the system, depending on the recurrence of events. Moreover, prolonged recurrence of external events requires the system center to form a sequence of next steps. If they are repeated, the attacker has the opportunity to study the functioning of the system. The controller in the system chooses different answers from different possible answers for the same repeated suspicious events. Thus, an attacker, when investigating a corporate network, receives different answers to the same queries. Specialized functionality, in accordance with the principle of synthesis of such systems, is implemented in the system architecture. It affects the change of system architecture in the process of its functioning as a result of internal and external influences. This paper also considers a possible variant of the architecture of such deception systems, in particular, the architecture of a system with partial centralization. To synthesize such systems, a new method for synthesizing partially centralized systems for detecting malware in computer environments has been developed based on analytical expressions that determine the security state of such systems and their components. In addition, the experiments showed that the loss of 10-20% of the components does not affect the performance of the task. The results of the experiments were processed using ROC analysis and the algorithm for constructing the ROC curve. The results of the experiments made it possible to determine the degree of degradation of the systems constructed in this manner. Conclusions. This paper presents a new principle for the synthesis of multi-computer systems with combined decoys and traps and a decision-making controller for detecting and counteracting IEDs and spacecraft, as well as methods for synthesizing partially centralized systems for detecting malware in computer networks.

Acquired images are often noisy. Since the amount of such images increases, they should be compressed where lossy compression is often applied for several reasons. Such compression is associated with the phenomena of specific image filtering due to lossy compression and the possible existence of an optimal operation point (OOP). However, such filtering is not perfect, and residual noise can be quite intensive even if an image is compressed at the so-called optimal operation point. Then, additional post-filtering can be applied. Thus, the basic subject of this paper is the post-processing of noisy images compressed in a lossy manner. The main goal of this paper is to consider the possible application of a block-matching 3-dimensional (BM3D) filter to images corrupted by additive white Gaussian noise compressed by a better portable graphics (BPG) coder with a compression ratio smaller than that for the optimal operation point and in OOP neighborhood. The tasks of this paper are to analyze the efficiency of compressed image post-processing depending on noise intensity, image complexity, coder compression parameter Q, and filter threshold parameter β according to different quality metrics and to provide practical recommendations on setting the filter and coder parameters. The main result is that the post-processing efficiency decreases when the coder compression parameter increases and becomes negligible for a coder compression parameter slightly larger than its value for OOP. The post-processing efficiency is larger for simpler structure images and larger noise intensity. Compressed image quality due to post-processing improves according to the standard criterion peak signal-to-noise ratio and visual quality metrics. For larger coder compression parameters, the optimal threshold shifts toward smaller values. In conclusion, we demonstrate the efficiency of post-processing and show that the BM3D filter outperforms the standard discrete cosine-based (DCT) filter. We also provide recommendations for filter parameter setting. We also outline possible research directions for the future.

The modern gas turbine engines (GTEs) development is associated with an increase in the compressor pressure ratio and the gas temperature at the turbine inlet with a simultaneous reduction in the size and weight of the engine. The reliable operation of the GTE largely depends on the oil system excellence. In circulating oil systems, to ensure the lubrication and cooling of engine friction units, a high multiplicity of oil circulation is necessary. The preparation of oil for the next lubrication cycle is related to its cooling, cleaning of mechanical impurities formed during operation, and air separation. Oil coolers are used to cool the oil in GTEs. Air or fuel can be used as the cooling medium. Regardless of the cooling medium choice, a two-phase mixture (oil-air) and not a single-phase oil, as is considered when designing oil coolers, affects the course of thermo-hydraulic processes. Therefore, the subject of this paper is thermohydraulic processes in two-phase media. The goal of this study is to determine the specific aspects of thermohydraulic processes during the oil-air mixture cooling in an aviation gas turbine engine to further improve oil cooler design approaches. This paper aims to show the differences in the course of thermohydraulic processes in two-phase media from single-phase media and to emphasize the importance of considering them when designing oil coolers. The main results are as follows. For a two-phase flow, changes in temperature and pressure change not only the thermophysical properties of the phases but also the gas content, density, and speed of the mixture, which affects the course of thermohydraulic processes in the oil cooler. Under certain combinations of parameters, the structure of the two-phase flow may change. A low value of the equilibrium speed of sound can lead to unpredictable operation of the oil cooler and the oil system as a whole. In addition, the presence of an oil-air mixture reduces the heat transfer capacity of the oil cooler compared with the results of calculations using standard methods. In conclusion, it can be stated that to increase the efficiency of the oil system, it is extremely important to establish the regularities of thermo-hydraulic processes for the two-phase flow of the oil-air mixture and consider this during the design of oil coolers.

Subject of study. In this paper, for the first time, an original method for estimating the change in the brightness of video data under the influence of changes in the lighting conditions of the scene and external noise is proposed. Algorithms for stabilizing the brightness of video data are also proposed. An objective assessment of the quality of video data pre-processed is given. The purpose of the research is to create a methodology for analyzing the variability of video data parameters under the influence of negative factors and to develop effective algorithms for stabilizing the parameters of the received video stream. The reliability of the method is tested using real video recordings pictured through various conditions. Objectives: To determine the most universal, resistant to external influences, and informative indicator necessary for an objective assessment of the quality of video data under various shooting conditions and scene lighting features; develop and programmatically implement algorithms for stabilizing video parameters based on modern programming tools. Research methods. Statistical analysis and pre-processing of video stream parameters as a random spatio-temporal process, algorithms for processing video data by digital filtering, and adaptive stabilization of video stream parameters. Research results. It has been proposed and experimentally proven that the optimal indicator of video stream quality is the average frame brightness (AFB). An algorithm for spatiotemporal processing of video data is proposed that generates a sequence of AFB values from the original video stream. The paper also proposes digital algorithms for filtering and stabilizing the brightness of a video stream and investigates the effectiveness of their application. Conclusions. The scientific novelty of the results obtained lies in a new method for analyzing and evaluating the parameters of video surveillance data and algorithms for filtering and stabilizing the brightness of the video stream. The performance of the proposed algorithms has been tested on real data. The algorithms are implemented in the Python software environment using the functions of the OpenCV library.

The evaluation of risk and uncertainty in the context of software project management is the subject of this paper. This paper discusses the difficulties faced by project managers in handling uncertainty brought on by the complex nature of software projects and the ever evolving requirements of technology. A review of the literature, data production, visualization, statistical analysis, and mathematical modeling are included in this study. The goal of this study is to create a methodical approach to assist project managers in making decisions by considering the inherent uncertainty in software development and to find approaches and procedures that may successfully reduce risks, improve decision-making, and eventually result in the implementation of successful projects. The following tasks were carried out: to evaluate risk and uncertainty by examining the state-of-the-art in decision theory and its applications in software project management; to develop an integrated strategy that blends Monte Carlo Simulation with Decision Trees to assess risk and uncertainty in software project management; to generate data, visualize it, and perform statistical analysis to comprehend how project outcomes, costs, and time are affected; to identify important variables affecting project results and decision-making using decision trees; to use Monte Carlo simulation to create project scenarios and weigh the likelihood of each; and to supply project managers with knowledge and suggestions to help them make informed decisions and successfully manage risks. Methods. To evaluate risk and uncertainty in software project management, this paper analyzes the decision theory approaches currently used as well as Decision Trees and Monte Carlo Simulation techniques. Results. This study offers thorough insights into how project results, costs, and duration vary among various techniques. The critical factors that have a substantial influence on project success are shown through decision trees. According to the study’s findings, combining decision theory and statistical analysis equips project managers to make wise decisions despite uncertainty. Conclusions. Project managers may improve decision making, risk reduction, and overall project success by applying these cutting-edge approaches. To adapt these techniques to unique software project management contexts and real-world situations, further study and implementation in practice are necessary. With the use of such techniques, the software development sector would be better able to manage the complexity of projects and provide good results within set financial and time parameters.

Heat transfer pump circuits with liquid or two-phase heat transfer fluids are widely used in thermal management systems in both space and ground applications. These systems play an important role in maintaining the optimal temperature and heat distribution in various processes and devices. Such systems use throttles to ensure efficient distribution of coolant flows through the hydraulic network. It is especially important to ensure the reliability of the system at low coolant consumption. Therefore, a rational approach is to manufacture throttles in the form of capillary segments. Capillary throttles can regulate the flow of coolant with high accuracy, which makes it possible to improve control over the thermal regime of the system. To calculate and determine the characteristics of such throttles, it is important to know the coefficient of hydraulic friction resistance λ, which is used in the Darcy formula. This coefficient depends on various factors, including the geometry of the throttle, physical properties of the heat transfer fluid, and flow conditions. Therefore, the aim of this study was to determine the value of the hydraulic friction resistance coefficient λ. In this work, experiments were performed with the shedding of copper capillaries of different internal diameters (0.8-1.2 mm) in water and isopropyl alcohol. The experiments were carried out in the laminar and transient regions, in the range of Reynolds numbers from 250 to 6050. Conclusions. In the laminar region (Re < 1185), it is recommended to use the Poiseuille formula. In the transitional and turbulent regions (1185 < Re < 6050), it is recommended to use the Blasius formula. The results of the experiments made it possible to obtain data on the value of the hydraulic friction resistance coefficient λ. These data can be used to calculate throttles in similar heat transfer systems. Moreover, the paper provides recommendations on the use of well-known formulas for calculating throttles, which allows engineers and designers to effectively implement these elements in their projects. Thus, the research conducted in this paper has important practical implications for the development and optimization of heat transfer systems in space and terrestrial applications.

The subject of this work is a study of the current state of modeling the flow around the blades of quadcopters, models of aerodynamic sound generation, formulation, and numerical solution of the problem of generation of rotation noise by the blades of a quadrotor type aerotaxi. The models describing the flow field around the quadrotor blades include the model of nonlinear vortices in lattices, the Reynolds-averaged Navier-Stokes equation (RANS, URANS), the large eddy simulation method (LES), and the direct numerical simulation (DNS) of the system of aerodynamic equations. This paper analyzes the main noise models of different types of aerodynamic origin. Gutin’s model is used to describe the noise of the quadrotor rotation, and the Ffowcs -Williams-Hawkings equation in the formulation of Farassat is used to model the noise taking into account various sound sources. However, these approaches have certain drawbacks that limit their application. The following paper uses a modern approach to modeling noise of aerodynamic origin based on the three-dimensional unsteady equation of sound propagation from a thin blade in the potential approximation, previously proposed by one of the authors of the work. Using this approach, a numerical calculation of the problem of sound generation (rotational noise) of the aerodynamic origin of a quadrotor type aerotaxi was performed. The purpose of the study. Despite the approaches described above, there is a problem associated with achieving an acceptable noise level, i.e. its further reduction. To solve this problem, there is a need to use more accurate models that will allow research on reducing air taxi noise. Tasks of the study. In this regard, the task of modeling noise of aerodynamic origin was set and solved using a refined model of the sound generated by the interaction of the flow and air taxi blades. Research methods are based on the construction and use of a mathematical model for the generation of rotation sound generated by the joint operation of aerotaxi rotors. On this basis, the calculations of the near and far sound fields were performed. A new model for calculating the long-range sound field of a quadrotor type aerotaxi is proposed, which considers the mutual formation of the resulting sound field from the joint operation of 4 propellers. The pressure coefficient and the sound pressure level in the distant sound field were calculated, and the frequency filling of the spectrum of the generated sound wave was investigated. Results and Conclusions. Numerical calculations of the problem of aerotaxi rotation noise generation showed that the maximum pressure level in the generated waves is in the immediate vicinity of the location of the quadrotor screws. However, the maximum value of the pressure level depends on the parameters of the problem, which vary: the thickness of the blade and the speed of the horizontal flight of the aerotaxi. As one moves away from the screws, the local maxima disappear and the wave takes the form of a flat wave. The general level of generated sound (rotational noise) is in the range of 70dB-102dB, which coincides with the results of studies of quadrotor aerotaxi, as well as taxis with the arrangement of propellers according to the aircraft type. The generated rotational noise energy is concentrated in the first 4-5 harmonics. Therefore, the noise model of aerodynamic origin proposed in this study can be used to study the rotation noise of a quadrotor type aerotaxi.

This paper investigates the possibility of manufacturing the working blade of the 2nd stage of the free turbine from an experimental alloy based on titanium aluminide of the Ti-28Al-7Nb-2Mo-0.3 (Y, Re, B) system. Alloys of this class are important structural materials with a unique set of physical and mechanical characteristics. Alloys based on aluminide are characterized by low density, high heat strength, and heat resistance and have a high potential to replace nickel-based alloys designed for operation at temperatures no higher than 850°C. However, the use of a new alloy for the manufacture of a turbine blade involves calculations of the strength and oscillations of the blades. The purpose of the calculations is to determine the possibility of using an aluminide-based alloy of the Ti-28Al-7Nb-2Mo-0.3 (Y, Re, B) system instead of the VZHL12E-VI alloy for the selected working blade. The research was carried out by performing a modal analysis, determining the stress-deformed state, and checking the strength and mass characteristics. The physical properties of the materials were used as the initial data for the calculation. The geometry and temperature distribution of the blade and its fixing remained constant for the two options. A three-dimensional model of the experimental vane was built using the Unigraphics NX system, and a finite element model was developed using the Ansys software complex. It was established that when an experimental alloy is used, the mass of one blade is reduced by almost 40%. Using the finite element method, the natural frequencies and forms of blade oscillations were calculated, and Campbell’s diagrams were constructed for the original and experimental blades for the nominal operation of the engine. When using an experimental alloy, the frequencies of the blades’ natural oscillations are further from the resonant frequency. The distribution of equivalent stresses was determined, and it was shown that the maximum stresses act in the root section of the blade feather. When using the experimental alloy based on titanium aluminide, the equivalent stresses are reduced by 35% compared with the initial values. It is shown that the safety margin of the proposed material is not less than that of the VZHL12E-VI alloy.

Distributed replicated databases play a crucial role in modern computer systems enabling scalable, fault-tolerant, and high-performance data management. However, achieving these qualities requires resolving a number of trade-offs between various properties during system design and operation. This paper reviews trade-offs in distributed replicated databases and provides a survey of recent research papers studying distributed data storage. The paper first discusses a compromise between consistency and latency that appears in distributed replicated data storages and directly follows from CAP and PACELC theorems. Consistency refers to the guarantee that all clients in a distributed system observe the same data at the same time. To ensure strong consistency, distributed systems typically employ coordination mechanisms and synchronization protocols that involve communication and agreement among distributed replicas. These mechanisms introduce additional overhead and latency and can dramatically increase the time taken to complete operations when replicas are globally distributed across the Internet. In addition, we study trade-offs between other system properties including availability, durability, cost, energy consumption, read and write latency, etc. In this paper we also provide a comprehensive review and classification of recent research works in distributed replicated databases. Reviewed papers showcase several major areas of research, ranging from performance evaluation and comparison of various NoSQL databases to suggest new strategies for data replication and putting forward new consistency models. In particular, we observed a shift towards exploring hybrid consistency models of causal consistency and eventual consistency with causal ordering due to their ability to strike a balance between operations ordering guarantees and high performance. Researchers have also proposed various consistency control algorithms and consensus quorum protocols to coordinate distributed replicas. Insights from this review can empower practitioners to make informed decisions in designing and managing distributed data storage systems as well as help identify existing gaps in the body of knowledge and suggest further research directions.

Autonomous navigation of unmanned aerial vehicles (UAVs) has become in the past decade an extremely attracting topic, also due to the increasing availability of affordable equipment and open-source control and processing software environments. This demand has also raised a strong interest in developing accessible experimental platforms to train engineering students in the rapidly evolving area of autonomous navigation. In this paper, we describe a platform based on low-cost off-the-shelf hardware that takes advantage of the Matlab/Simulink programming environment to tackle most of the problems related to UAV autonomous navigation. More specifically, the subject of this paper is the autonomous control of the flight of a small UAV, which must explore and patrol an indoor unknown environment. Objectives: to analyse the existing hardware platforms for autonomous flight indoors, choose a flight exploration scenario of unknown premises, to formalize the procedure for obtaining a model of knowledge for semantic classification of premises, to formalize obtaining distance to obstacles using data camera horizontally employment and building on its barrier map. Namely, we use the method of image segmentation based on the brightness threshold, a method of training the semantic segmentation network, and computer algorithms in probabilistic robotics for mobile robots. We consider both the case of navigation guided by structural visual information placed in the environment, e.g., contrast markers for flight (such as path marked by a red tape), and the case of navigation based on unstructured information such as recognizable objects or human gestures. Basing on preliminary tests, the most suitable method for autonomous in-door navigation is by using\ object classification and segmentation, so that the UAV gradually analyses the surrounding objects in the room and makes decisions on path planning. The result of our investigation is a method that is suitable to allow the autonomous flight of a UAV with a frontal video camera. Conclusions. The scientific novelty of the obtained results is as follows: we have improved the method of autonomous flight of small UAVs by using the semantic network model and determining the purpose of flight only at a given altitude to minimize the computational costs of limited autopilot capabilities for low-cost small UAV models. The results of our study can be further extended by means of a campaign of experiments in different environments.

Images are mainly viewed and analyzed by humans. Because of this, in the characterization of image quality and effectiveness of image processing, it is necessary to take into account the peculiarities of the human vision system and cognition that are very complex. Saliency maps as well as priority and meaning maps introduced recently are the attempts to incorporate specific features of human vision into image analysis and processing fields. Many authors that consider the aforementioned maps consider them from different viewpoints. Thus, the basic subject of this paper is the factors that influence and determine these maps. Among such factors, there are low-level features as well as social and psychological ones such as emotions, age, and life values. The main goal of this paper is to give a brief survey of these factors and to consider how maps are already used in image quality assessment and processing as well as how they can be employed in the future. The tasks of the paper are to provide a definition of saliency, priority, and meaning maps, to analyze the factors that influence these maps, and to evaluate what improvement can be obtained due to taking maps into account in the assessment of image visual quality and such image processing operations as quality assessment, denoising, and lossy compression. The main result is that, by taking saliency maps into account, image quality assessment and processing efficiency can be sufficiently improved, especially for applications oriented on image viewing and analysis by observers or customers. This can be done by the simple weighting of local estimates of a given metric with further aggregation as well as by approaches based on neural networks. Using different quantitative criteria, we show what positive results can be got due to incorporating maps into quality assessment and image processing. As conclusion, we present possible directions of future research that are mainly related to an adaptation of denoising and lossy compression parameters to peculiarities of human attention.

Remote sensing data are widely used in numerous applications. A conventional task solved using remote sensing images is their classification. The classification maps are commonly produced by some pre-trained classifiers applied either to uncompressed or compressed images where lossy compression is often needed and employed in practice due to the necessity to reduce data volume at stages of image transfer and storage. Then, the classification accuracy depends on the characteristics of an image, a classifier, and a coder used. The main subject of this paper is the factors that determine classification accuracy. One of them is compressed image quality. We fix the quality of compressed image quality characterized by the peak signal-to-noise ratio for several coders and rely on the same training approach. Our goal is twofold. First, we would like to consider classification accuracy for two approaches to classifier training: based on undistorted data and images with simulated distortions. Second, our desire is to compare the performance of different techniques of image compression. The task of this paper is to obtain an idea is it worth training the neural network classifier for uncompressed images or images of similar quality to the quality of compressed data to be classified. The coder’s influence on classification results is of special interest as well. The main results are the following. First, the classification accuracy is almost the same for classifiers trained for uncompressed and simulated compressed data for the general distortion model. Second, there is a certain difference in the classification results for different compression techniques studied. Lightly better classification results are observed for data produced by more sophisticated (modern) coders. Experiments have been carried out for two real-life three-channel Sentinel-2 images of Kharkiv and the Kharkiv region having different complexity. Four typical classes have been considered. As a conclusion, it is possible to state that either the general model of distortions must be modified or the classifier training should be performed for data produced by the corresponding compression technique.

In the modern science and practice of remote sensing of any surface, including various types of land cover, radio systems are widely used. Due to low energy consumption and high stealth, the most promising direction of technical tools for remote sensing is the development of passive remote sensing systems. To estimate the specific parameters of the surface, information on the relation between the signals of the thermal radiation and these parameters is needed. Therefore, it is relevant to study and analyze the existing types of such relations that connect the brightness temperature and the parameters of the Earth’s surfaces; this is the subject matter of the study in this paper. The goal of this study is to analyze existing relation models and study the conditions of their application in terms of operating frequencies, geometric characteristics, type of surface coverage (with vegetation, without vegetation, etc.), as well as the method of obtaining mathematical relations (electrodynamic models or empirical models). Due to the large number of different types of earth surface coverings and different types of surface models, in order to simplify remote sensing tasks for passive systems, as well as to improve the results of modeling or experiments, the following tasks are formulated: to study and classify existing models of the relation between surface parameters and brightness temperature, and to calculate and analyze the potential accuracy of estimating surface parameters by passive remote sensing systems. The following results were obtained: the impact of the atmosphere on the surface’s brightness temperature, described by the electrodynamic flat model, for the operating frequencies 75–110 GHz of the radiometer was analyzed. The impact of the atmosphere on the brightness temperature appears at sighting angles greater than 50° at horizontal polarization and greater than 80° at vertical polarization. According to the results, it can be said that the estimation accuracy of the imaginary part of the complex permittivity decreases with an operating wavelength decrease, and the estimation accuracy of the real part is the opposite. Conclusions. The results of simulation modeling show that for radiometers with the above-mentioned operating frequencies, it is possible to estimate the real permittivity or only the real component separately in the case of describing the surface with a complex permittivity parameter. It is recommended that the study be conducted at vertical and horizontal polarizations, without and with the impact of the atmosphere. To estimate the surface conductivity (the imaginary part of the complex permittivity of a surface), systems with lower operating frequencies are required. Thus, this study presents an analysis of existing models, calculates the potential accuracy of parameters estimation when using these models, and provides recommendations for practical experiments or modeling.

In the realm of long-term investment, strategic portfolio allocation is an essential tool, especially in relation to risk management and return optimisation. There are many ways to pursue optimal portfolio composition, and their effectiveness depends on many factors, including the investor’s goals, risk appetite, and investment horizon. One of the primary means of portfolio optimisation is diversification. The core idea of diversification is to maintain a diverse portfolio with weakly correlated assets that can vastly reduce portfolio exposure to different market stress factors. Diversification is a fundamental strategy in investment and portfolio management that is essential for mitigating risk and enhancing potential returns over the long term. By spreading investments across various asset classes, sectors, geographies, and investment styles, diversification helps reduce the volatility of the overall portfolio. The main subject of this study is the theoretical basis of portfolio diversification and the analysis of historical data to derive optimal strategies for using uncorrelated assets to improve portfolio performance. This paper examines the correlation dynamics between different asset classes, such as stocks, bonds, and alternative investments, and their response to changes in inflation, interest rates, and market volatility, and tests it with historical data to deduce the optimal strategies for using uncorrelated assets to improve portfolio performance. The findings of this study prove the variable relationship between asset classes under specific economic conditions. This study uses historical data to show how different asset classes can be optimally leveraged or adjusted to mitigate risks and capitalise on opportunities presented by shifting economic indicators. This reveals that the hedging benefits of equities, bonds, and gold depend greatly on interest rates, market volatility, and inflation. It also provides guidelines for investors on optimal portfolio allocation and risk management. In conclusion, dynamic portfolio management is an essential tool for reducing the portfolio’s overall volatility while maximising returns. The diversification performance of different financial asset classes depends on major economic indicators such as inflation, interest rates, and market volatility. Investors seeking to optimise their portfolios in anticipation of or in response to economic changes, aiming to maximise returns while controlling for risk, can leverage these results.

The subject matter of this article is finite state machines (FSMs), which are used as control devices in unmanned aerial vehicles (UAVs). The goal of this study is to develop description styles for fault-tolerant FSMs in hardware description languages (HDLs) that prevent failures in the state register and in the input vector of the FSM. The tasks to be solved are as follows: development of description methods for FSM transitions from illegal states in case of failure in the state register, as well as for FSM transitions from each state in case of failure in the input vector; determination of FSM output vector values in case of the above failures; development of description styles for fault-tolerant FSMs; and investigation of the efficiency of the proposed description styles for fault-tolerant FSMs. The methods used are: the theory of finite state machines, state encoding methods of FSMs, description styles of FSMs, and Verilog hardware description language. The following results were obtained: two styles of describing fault-tolerant FSMs have been developed, safe0 and safe1, which do not increase the area and do not decrease the performance of FSMs, and in some cases allow the area to be reduced (for some examples by a factor of 4.8) and increase the performance (for some examples by a factor of 2.355). In addition, the description styles of fault-tolerant FSMs neutralize design errors when transitions are described in each state but not for all possible values of input variables. Conclusions. In this paper, the problem of designing fault-tolerant FSMs when the values of bits in the state register or in the input vector of the FSM change because of the negative external impact is described. Different ways of solving the problem at the level of FSM description in HDL are considered. Two description styles for fault-tolerant FSMs are proposed: safe0 and safe1. The fault tolerance of FSMs is provided in the following manner. When the input vector is not defined in the FSM specification for a specific state, the FSM will remain in the initial transition state, i.e. the FSM will not transit to another state. If the code of the illegal state is set in the state register, the FSM will transition to the start state. For all these faults, the safe0 style provides a zero output vector at the FSM output, whereas the safe1 style preserves the value of the previous output vector. A promising direction for future research seems to be the development of new styles and methods of FSM description, aimed at improving the FSM parameters (an area, a performance and a power consumption), as well as improving the reliability and fault tolerance of FSMs.

The use of unmanned and manned aerial vehicles for remote object localization and classification is very common. These methods are used in various systems, ranging from territory surveys to law enforcement. Methods of object localization and classification using neural networks require a detailed study and research of the quality of their work on data that has certain specifics, such as vehicle detection. The use of neural networks to detect certain types of objects using images obtained from aircraft can also help in the study of hard-to-reach locations. Therefore, the main subject of this paper is the localization and classification of objects in images obtained using digital cameras mounted on aircraft. The main focus is on determining the accuracy of object localization and detection using selected types of neural networks, which are the most important indicators of neural network efficiency. The speed of a neural network is also an equally important characteristic as it directly affects its ability to be used in tasks that require fast object localization, such as video surveillance or automated car control systems. The main goal of this study is to study the accuracy of object localization and classification in images obtained with the help of cameras mounted on aircraft, as well as to study the speed of neural networks and determine the effectiveness of their application in real-world conditions. The objectives of this study are to train YOLO v5, SSD, and Faster RCNNs on the VisDrone dataset and to further study them on the vehicle localization dataset. The main goal of this work is to obtain statistics on the performance of neural networks trained on the VisDrone dataset. On the basis of the obtained statistics, conclusions are drawn about the effectiveness of the considered neural networks. The conclusions are drawn by considering the speed of the model, localization (IoU), and classification (Precision, Recall) metrics. Possible directions for further development of the topic under study are presented as conclusions.

The subject matter of this article is the influence of film-forming corrosion preventive compounds on aviation riveted joints fatigue life. The goal of this work is to develop a method for the assessment of negative side effects caused by the application of corrosion preventive compounds. Tasks of the work are as follows: analysis of the aircraft corrosion problem; critical review of existing publications concerning possible negative side effects caused by the application of corrosion preventive compounds for protection of aviation riveted joints against corrosion, where reduction of the fatigue life is considered as a result of the preventive compounds penetration into the gaps of riveted joints; development of the specimen for fatigue tests, with features of contemporary transport aircraft typical design; fatigue tests of the riveted specimens treated with preventive compounds and without treatment; investigation of the preventive compounds penetration into the gap of riveted joint. The main method of this research is fatigue loading of the riveted specimens at loads close to those the aircraft encounters in service. The main result is the experimentally proven possibility of negative side effects of corrosion preventive compound application. The observed effect is explained by the change in friction in joints and the corresponding redistributions of the forces between the joint elements. It was found that a factor influencing friction force is the viscosity of the preventive compounds. The possibility of preventing negative side effects by optimizing the protective procedure has been experimentally proven. Conclusions. The results presented in the paper show the probability of the negative side effects of corrosion preventive compound application. Considering the variety of these materials and their physical and chemical characteristics, the necessity of the grounded selection of the protective compounds is evident as well as the optimization of the treatment technology. Conducted research has revealed an example of the negative influence of preventive compounds on riveted joints fatigue life and has opened the way for this effect prevention. The described research procedure is a component of the complex method for exploring corrosion preventive compounds’ negative side effects.

Single and three-channel images are widely used in numerous applications. Due to the increasing volume of such data, they must be compressed where lossy compression offers more opportunities. Usually, it is supposed that, for a given image, a larger compression ratio leads to worse quality of the compressed image according to all quality metrics. This is true for most practical cases. However, it has been found recently that images are called “strange” for which a rate-distortion curve like dependence of the peak signal-to-noise ratio on the quality factor or quantization step, behaves non-monotonously. This might cause problems in the lossy compression of images. Thus, the basic subject of this paper are the factors that determine this phenomenon. The main among them are artificial origin of an image, possible presence of large homogeneous regions, specific behavior of image histograms. The main goal of this paper is to consider and explain the peculiarities of the lossy compression of strange images. The tasks of this paper are to provide definitions of strange images and to check whether non-monotonicity of rate-distortion curves occurs for different coders and metrics. One more task is to put ideas and methodology forward of further studies intended to detect strange images before their compression. The main result is that non-monotonous behavior can be observed for the same image for several quality metrics and coders. This means that not the coder but image properties determine the probability of an image to being strange. Moreover, both grayscale and color images can be strange, and both the natural scene and artificial images can be strange. This depends more on image properties than on image origin and number of channels. In particular, the percentage of pixels that belong to large homogeneous regions and image entropy play an important role. As conclusions, we outline possible directions of future research that, in the first order, relate to the analysis of images in large databases to establish parameters that show that a given image can be considered as strange.

Subject matter: Speech emotion recognition (SER) is an ongoing interesting research topic. Its purpose is to establish interactions between humans and computers through speech and emotion. To recognize speech emotions, five deep learning models: Convolution Neural Network, Long-Short Term Memory, Artificial Neural Network, Multi-Layer Perceptron, Merged CNN, and LSTM Network (CNN-LSTM) are used in this paper. The Toronto Emotional Speech Set (TESS), Surrey Audio-Visual Expressed Emotion (SAVEE) and Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) datasets were used for this system. They were trained by merging 3 ways TESS+SAVEE, TESS+RAVDESS, and TESS+SAVEE+RAVDESS. These datasets are numerous audios spoken by both male and female speakers of the English language. This paper classifies seven emotions (sadness, happiness, anger, fear, disgust, neutral, and surprise) that is a challenge to identify seven emotions for both male and female data. Whereas most have worked with male-only or female-only speech and both male-female datasets have found low accuracy in emotion detection tasks. Features need to be extracted by a feature extraction technique to train a deep-learning model on audio data. Mel Frequency Cepstral Coefficients (MFCCs) extract all the necessary features from the audio data for speech emotion classification. After training five models with three datasets, the best accuracy of 84.35 % is achieved by CNN-LSTM with the TESS+SAVEE dataset.

The subject of the paper is the process of joint use of false aircraft targets as part of a group of combat unmanned aerial vehicles to perform tasks to destroy enemy targets. The current paper determines the optimal number of false aircraft targets in a group of combat unmanned aerial vehicles to defeat targets with the desired degree of their defeat and acceptable losses of own combat unmanned aerial vehicles. The scientific task is to improve the methodology for determining the optimal number of false aircraft targets in a group of combat unmanned aerial vehicles to defeat targets with the desired degree of defeat and acceptable losses of own combat unmanned aerial vehicles. To achieve the purpose of the research paper, the following tasks were performed: the process of joint use of false aircraft targets as part of a group of combat unmanned aerial vehicles to defeat targets with the desired degree of their defeat has been formalized; a mathematical model for determining the optimal composition of false aircraft targets as part of a group of combat unmanned aerial vehicles to minimize the losses of real aircraft during their tasks has been developed; based on the conditions of a practical example, the functioning of the improved methodology has been tested and the relevant recommendations have been substantiated. Methods. The mathematical model uses combinatorics and binomial probability distribution. The following results were obtained. An improved methodology is presented, which is multifunctional since, on the one hand, its use makes it possible to determine the required number of false aircraft targets in a group of combat unmanned aerial vehicles to defeat targets with the desired degree of their defeat and acceptable losses of own combat unmanned aerial vehicles, and on the other hand, to determine the predicted level of losses of real aircraft targets from the group when using a certain number of false aircraft targets. Conclusions. The availability of an improved methodology with ready-made calculation formulas will allow the prediction of possible results of combat use of groups of unmanned aerial vehicles based on the initial parameters and substantiate recommendations on their possible composition.

Remote sensing images have found numerous applications nowadays. A traditional outcome or intermediate result of their processing is a classification map. Such maps are usually obtained from a pre-trained classifier and it is desired to have the produced classification maps as accurately as possible. The basic subject of this article is the factors determining this accuracy. The main among them are the quality of remote sensing data and classifier type, parameters and training approach. Image quality can be degraded due to several factors. One of them is distortions introduced by lossy compression that is widely used due to a huge volume of acquired data and the necessity to sufficiently decrease their size at transmission, storage and/or dissemination stages. Because of this, the main goal of this paper is to consider classification and lossy compression jointly. In particular, this means that the classifier learning can be performed for original (uncompressed, compressed in a lossless manner) images (if they are available) as well as for compressed data at hand (offered to a user for classification and further analysis). The task of this paper is to consider and compare these two options. The first one is the classifier learning for original images and further application to compressed data, where images can be compressed with different compression ratios while producing compressed data of different quality. The second option is the use of the classifier learning for compressed images, where compression parameters for training data can be approximately the same as for the images to which the classifier is applied. The main result is that the latter methodology can provide certain benefits compared to the classifier learning for original data if one has to classify compressed remote sensing data. Simulation data are obtained for a classifier based on a convolutional neural network. As images for training and verification, four real-life three-channel (visible range) Sentinel-2 remote sensing images of Kharkiv and Kharkiv region are employed that possess different complexity of the content and have four main classes. The practical recommendations are given. In conclusion, we can state that it is worth having classifiers trained for several degrees of compression and it is reasonable to compress complex structure images with special care.

The results of an analytical review of literature sources on the use of approximate boundary conditions for electromagnetic fields of impedance type in solving boundary value problems of electromagnetism for more than 80 recent years are presented. During this period, the impedance approach was generalized to various electrodynamic problems, in which its use made it possible to significantly expand the limits of mathematical modeling, which adequately considers the physical properties of real boundary surfaces. More than eighty years have passed since the publication of approximate boundary conditions for electromagnetic fields. The meaning and value of these conditions lies in the fact that they allow solving diffraction problems about fields outside well-conducting bodies without considering the fields inside them, which greatly simplifies the solution. Since then, numerous publications have been devoted to the application of impedance boundary conditions, the main of which (according to the authors) are presented in this paper. Particular attention is paid to the characteristics of electrically thin impedance vibrators and film-type surface structures as a personal contribution of the authors to the theory of impedance boundary conditions in electromagnetism. The subject of research in this article is the analysis of the limits and conditions for the correct application of impedance boundary conditions. The goal is to systematize the results of using the concept of approximate impedance boundary conditions for electromagnetic fields in problems of electrodynamics based on an analytical review of literature sources. The following results were obtained. The types of metal-dielectric structures are presented, for which methods of theoretical determination of the values of surface impedances for film-type structures are currently known, which are the most promising for creating technological control elements on their basis in centimeter and millimeter wavelength devices. Conclusions. The materials of this paper do not pretend to be a complete reference book covering all the results and aspects of the development of the concept of approximate impedance type boundary conditions in problems of electromagnetism over the past decades. Simultaneously, the authors hope that the information presented in this paper will be useful to specialists in the field of theoretical and applied electrodynamics, as well as graduate students, young scientists and students who are just mastering radiophysics and radio engineering specialties.

The use of agents across diverse domains within computer science and artificial intelligence is experiencing a notable surge in response to the imperatives of adaptability, efficiency, and scalability. The subject of this study is the application of formal methods to furnish a framework for knowledge interpretation with a specific focus on the agent-based paradigm in software engineering. This study aims to advance a formal approach to knowledge interpretation by leveraging the agent-based paradigm. The objectives are as follows: 1) to examine the current state of the agent-based paradigm in software engineering; 2) to describe the basic concepts of the knowledge interpretation approach; 3) to study the general structure of the rule extraction task; 4) to develop the reference structure of knowledge interpretation; 5) to develop a multi-agent system architecture; 6) and to discuss the research results. This study employs formal methods, including the use of closed path rules and predicate logic. Specifically, the integration of closed path rules contributes to the extraction and explication of facts from extensive knowledge bases. The obtained results encompass the following: 1) a rule mining approach grounded in closed path rules and tailored for processing extensive datasets; 2) a formalization of relevance that facilitates the scrutiny and automated exclusion of irrelevant fragments from the explanatory framework; and 3) the realization of a multi-agent system predicated on the synergy among five distinct types of agents, dedicated to rule extraction and the interpretation of acquired knowledge. This paper provides an example of the application of the proposed formal tenets, demonstrating their practical context. The conclusion underscores that the agent-based paradigm, with its emphasis on decentralized and autonomous entities, presents an innovative framework for handling the intricacies of knowledge processing. It extends to the retrieval of facts and rules. By distributing functions across multiple agents, the framework offers a dynamic and scalable solution to effectively interpret vast knowledge repositories. This approach is particularly valuable in scenarios where traditional methods may struggle to cope with the volume and complexity of information.

The problem of researching weapons distribution strategies to ensure effective combat operations in a military conflict zone is being formed and solved. The relevance of the study is related to the solution of the task of an operational-tactical nature in order to establish the military parity of forces in the conditions of the introduction of various equipment into the combat zone (CZ), which has different characteristics of combat capability from the set of warehouses and suppliers, with long supply chains. The problems that arise when establishing military parity of forces with the help of various weapons with different indicators of combat capability, the limited capabilities of suppliers, and the complications of supply logistics are analyzed. It is shown that asymmetric solutions, according to military equipment, are possible because of the use of technological innovations and high indicators of the combat effectiveness of weapons. It was concluded that the establishment of military parity of forces is possible thanks to the use of rational strategies for the distribution of weapons, which is an urgent problem in the conditions of modern hybrid warfare. Thus, this paper proposes a study of possible strategies for the distribution of weapons by CZ in conditions of threats of martial law of the country. The current study creates models that allow for the evaluation of possible arms distribution strategies, considering the diversity of military equipment, limited capabilities of suppliers, and risks associated with long logistics supply chains. Using set-theoretical analysis, methods of combinatorics, and enumeration theory, a systematic presentation of the distribution process of weapons is created. Possible strategies for the distribution of weapons by CZ to establish military parity of forces are formed and analyzed. Optimization models have been created for the rational distribution of military equipment under conditions of limited capabilities of manufacturers in terms of costs, production terms, and risks of arms supply. A simulation model is being developed to study the dynamics of the logistical process of distribution and supply of weapons in agent representation using the AnyLogic platform. Modeling allows you to analyze the impact of threats to the main indicators of the distribution and supply of weapons. An illustrative example of the distribution of weapons by CZ of a military conflict is presented. The scientific novelty of this research is related to the solution of the actual problem of the rational distribution of various military equipment by CZ in the conditions of modern hybrid warfare. The results of this study should be used for operational-tactical planning of the logistical process of distribution and supply of weapons to solve the problem of establishing military parity of forces in the CZ.

The subject of this study is a topological optimization algorithm for a lapped symmetric adhesive joint. The purpose of this research is to create a hybrid optimization algorithm that combines the advantages of a genetic algorithm and a particle swarm algorithm and, at the same time, reduces the time required to solve the problem. Task: to create a methodology for solving the optimization problem for a symmetric double-sided lapped adhesive joint, which consists of a main plate and two patches (the main plate has a constant thickness, and the thickness of the patches varies along the length of the joint, this is required to reduce the stress concentration in the joint and reduce its weight) with satisfaction of the optimality criterion, namely, to minimize the mass of the structure with the strength and thickness restrictions for the patch. The optimization problem is that we must find the optimal patch form, namely, the length of the patch and the thickness-on-length dependence for the patch. Methods: the modified Goland-Reissner model was used to describe the deflected mode of the joint. The finite difference method was used to solve the direct stress state problem for the structure. For the numerical solution of the optimization problem, a combination of the multi-population model of the genetic optimization algorithm and the particle swarm algorithm was used. To improve the performance of the genetic algorithm, a multi-population model with migration of the best individuals between populations was applied. The introduction of individuals from other populations into the population avoids homogenization of the genotype in a separate population and premature stopping of the optimization process. To describe the shape of the patch, the Fourier series expansion of the patch thickness dependence was used. Results: A hybrid algorithm is proposed based on the sequential application of a genetic algorithm and a particle swarm algorithm for three populations of solutions. The particle swarm algorithm makes it possible to improve the value of the objective function achieved at the previous stage by 20%. Conclusions: the scientific novelty lies in the improvement of the optimization algorithm compared with the known ones. To reduce the calculation time, a one-dimensional adhesive joint stress state mathematical model was used in this paper. The methods used made it possible to create a combined topological optimization algorithm that combines the advantages of both methods and allows us to find a solution to the problem quite quickly. The Python program run time is only a few minutes.

This paper analyses the topology of a regional power system distribution grid. This research improves the efficiency of the electric power system's operation by using upgrading the redesign (reengineering) methods of topological structures within distribution grids. The research object is an electric power system that consists of generation, transmission and distribution parts and requires reengineering. The subject of research is the re-engineering of the topology of the power system distribution grid. To achieve the research purpose, modifications of k-means algorithm as well as the small step algorithm based on the statistical analysis, clustering and minimum spanning tree development methods of Prim and Kraskal are used. The modifications described in this paper allow for optimization of the network based on user needs, properties of the operating grid elements, and other additional constraints for flexibility and generality. Given the varying parameters, this method provides the means to redesign parts of the distribution grid, keeping its certain elements safe from displacement, but also the means to redesign the whole distribution grid, including changes to the number and location of transformer substations and transmission lines. Conclusions. To solve the problem of determining the territorially close groups of consumers in the paper, it was proposed to use the k-means clustering algorithm. This algorithm allows us to divide consumer sets into clusters, so that coordinates of their centres will be recommended as locations of transformer substations. The modernization of the k-means algorithm was proposed by developing procedures for adding and combining clusters using different strategies for determining starting centroids. Based on this, a method for reengineering the topological structures of regional electric power systems in terms of the possibility of their fundamental restructuring was developed. The results of this research may be useful to various enterprises, organizations or institutes dealing with the elaboration or design of electric power system development on the corporate, regional or local level.

Lossy compression has been widely used in various applications due to its variable compression ratio. However, distortions are introduced unavoidably, and this decreases the image quality. Therefore, it is often required to control the quality of the compressed images. A two-step method has been proposed recently to provide the desired visual quality. The average rate-distortion curve was used to determine the proper parameter value that controls compression. However, its performance for the wavelet-based coder Set Partitioning in Hierarchical Trees (SPIHT) is insufficient because there are very wide limits of visual quality variation for different images for a given value of the compression control parameter (CCP). Additionally, previous work has demonstrated that the level of errors, which is the subject of our study relates to texture features of an image to be compressed, where texture presence is an inherent property of remote sensing images. In this paper, our goal is to develop an adaptive two-step method for SPIHT to improve accuracy. The following tasks were solved. First, a prediction of visual quality for a particular parameter value is conducted. The prediction scheme is based on the information extraction from a certain number of image blocks to perform a visual quality calculation of the image compressed for a given CCP value. A threshold is adopted as the complexity grouping; in this paper, images are divided into two groups: simple and complex images. Second, the results of the grouping determine the adaptive curve model adopted. Finally, a two-step compression method is applied according to this curve. The classical metric Peak signal-to-noise ratio (PSNR) is employed to evaluate the image quality. The research method is based on a validation experiment that is conducted for an image set covering different image complexity and texture features. The comparison results of four typical desired values prove that the accuracy has been generally improved, the variances of both the first and second steps have been reduced sufficiently, and the mean absolute error has also been improved. Conclusion: the improvement effects are significant, particularly in the low desired visual quality. A remote sensing image is taken as an example to analyze in detail; the quality of the decompressed images meets the user’s visual requirement, and the errors are acceptable.

The subject of the study in this paper is models and methods of optimization of the organization's project portfolio for the planning period, considering the effects of the previously made decisions. Project portfolio optimization is one of the responsible and complex tasks by company's top management solves. Based on the analysis of the known works in the field, the research purpose is described: to create a method that would allow solve the problem of multi-criteria project portfolio optimization for the planned period, considering the aftereffects of the previously made decisions. The research tasks are to enhance the method for solving the project portfolio optimization problem in terms of maximizing the difference between income and costs for all projects started during the planned period; to propose a method for solving the project portfolio optimization problem in terms of the social effects of projects that started during the planned period; create a method for solving the problem of project portfolio optimization for the planned period in a multi-criteria setting. There are the following results obtained in the paper. There is presents the mathematical model of the problem being solved, the problem objective functions include the difference between the receipt and expenditure of funds in time, the portfolio risks, and its implementation social effects. The mathematical model considers the provision of funds sufficiency for the implementation of projects in all periods, the required sequence of project implementation, and the mandatory inclusion of some projects in the portfolio for a given period. The problem under consideration belongs to the multi-criteria non-Markov dynamic discrete optimization problems. There is a proposed method for solving it in a multi-criteria formulation. The method is based on solving one criterion problem, and then a multi-criteria problem. The method is based on the minimax approach and implicit search. There has been developed solving method for the problem of enterprise project portfolio optimization for the planned period following the profit criterion. In contrast to the existing methods, this method considers the constraints on debt absence and the aftereffects of the previously made decisions. The method served as the basis for creating risk and social effect optimization methods. A method for enterprise project portfolio optimization of the planned period is provided, which, unlike previous, considers the criteria of profit, risks, and social effect, the constraints on debt absence, and the aftereffect of the previously made decisions. That makes it possible to improve the quality of the generated portfolio.

Today, there is a contradiction between the rapid increase in the complexity and size of modern software while increasing responsibility for the performance of their functions, the increasing requirements of customers and users to the quality and efficiency of software use and the imperfection of models, methods, tools of predicting software quality at the early stages of the life cycle.Therefore, the task of predicting the software quality level based on requirements is relevant. The aim of this study is to solve this task by developing information technology for prediction software quality levels based on requirements. The proposed information technology for prediction software quality level based on requirements provides analysis of quality attributes in requirements, reflects the dependence (equations) of quality characteristics on attributes, forms a quantitative assessment of quality characteristics, reflects the dependence (equation) of quality on its characteristics, forms a quantitative assessment of quality, performs quality level prediction, provides all the listed services simultaneously and the model, methods, and tools underlying the IT belong to common methodological approaches and are integrated. The developed system of predicting the software quality level based on requirements provides the user with predicted estimates of eight software quality characteristics, geometric interpretation of the software quality characteristics’ values, a comprehensive indicator of the predicted software quality, and a conclusion about the future software quality level. On the basis of this, it is possible to compare sets of requirements for software and make a reasonable choice of a set of requirements for further implementation. The information technology and the system of predicting the software quality level based on requirements, developed in this paper, provide the possibility of comparing sets of requirements for software, justified selection of requirements for further implementation of quality software (as experiments have shown, this is only one of the four proposed sets), and rejection or revision of unsuccessful sets of requirements that cannot be used to develop quality software.

Electric propulsion has been developed since the early 1960s, and its use onboard satellites, orbiting platforms, and interplanetary probes have increased significantly in the 21st century. The need for a detailed understanding of the working physics and a more accurate assessment of performance to create innovative designs has stimulated the development of several numerical simulation codes. The choice of method for modelling a specific thruster should be dictated by the physical characteristics of the flow in the device, and by the level of accuracy required from the simulation. There are various conditions in different types of thrusters. This means that different methods and computer codes must be developed for each of the different thrusters. The successful development of physically accurate numerical methods for simulating gas and plasma flows in electric propulsion thrusters can significantly improve the design process of these devices. In recent years, numerical simulations have increasingly benefited the basic understanding and engineering optimization of electric thrusters. This is due to several concurrent contributions: the evolution of computer hardware that has allowed the representation of multidimensional geometries and multiscale phenomena; implementation of sophisticated new algorithms and numerical diagnostic tools; and availability of new collisional and surface interaction data. There are two main directions for future work to continue to improve the numerical modelling of electric thrusters. First, the numerical methods themselves must be improved in terms of their physical accuracy and computational speed. The second main direction for improvement in the simulations involves more accurate determination of physical parameters that are required by the numerical formulations. This paper outlines efforts to develop models of various electrical propulsion concepts, from the first attempts in the early 90s to the latest sophisticated multidimensional simulations.

The subject of study in this article is the methods of parametric diagnostics of the technical condition of the units of an air vehicle's hydraulic and fuel systems, which make it possible to ensure the established reliability indicators of these systems throughout their entire life cycle. This work aims to analyze the existing literature on the methods of parametric diagnostics of hydraulic units, determine the basic requirements for the development of diagnostic methods for units of hydraulic, and fuel systems. The objective of the study is to classify the existing methods of parametric diagnostics, applied primarily to hydraulic systems, to analyze the advantages and disadvantages of the algorithms under consideration based on the requirements for diagnostic algorithms established by the authors, and to assess the possibility of effectively using these methodologies to diagnose the technical condition of a complex hydraulic distribution unit. The authors of this work formed the main requirements for the methods of diagnosing a hydraulic and fuel system's units based on the nature of the detected defects, the experience of using diagnostic algorithms, as well as the requirements of existing standards and scientific publications analysis. The authors developed a classification of parametric diagnostic algorithms to systematize existing works. This paper describes the main features and differences between diagnostic algorithms based on methods for identifying a mathematical model of an object and diagnostic algorithms in the space of measured parameters. Methods for forming diagnostic models of hydraulic units have been analyzed, such as the state-space model, the Hammerstein model, the Volterra model, the ARX model, and the matrix of influence coefficients. Established analysis of the application of defect identification algorithms such as a divided difference filter (DDF), a radial basis functions neural network RBF, and a cosine distance method. As a result, the advantages and disadvantages of the monitored diagnostic algorithms were identified and the main tasks for developing an algorithm for parametric diagnostics of the technical condition of a complex hydraulic distribution unit were formulated.

The paper presents a mathematical model of radio-electronic systems (RES), which include antennas and their excitation paths with nonlinear characteristics. The model provides acceptable accuracy of RES quality indicator analysis and electromagnetic compatibility (EMC) for further practical design. General purpose: the development of a mathematical model of a transmitting multi-input radiating structure with nonlinear characteristics under the Fresnel zone. Objective: choice justification of a structural schema of a radiating multi-input system with a radiator that has a distributed nonlinear surface impedance; obtaining the nonlinear integral equations (NIE) related to the current density for radiators with distributed nonlinearity, excited by an arbitrary field distribution for solving the general analysis problem; obtaining a ratio for calculating focused electromagnetic fields (EMF) created by multi-input radiating structures with nonlinear characteristics in the Fresnel zone. The methods used in the paper are mathematical methods of electrodynamics and antennas theory with nonlinear elements (ANE), theory of microwave circuits, and multipoles. The following results were obtained. An electrodynamics approach is proposed to analyze the entire set of nonlinear effects arising in transmitting multi-input radiating structures with nonlinear characteristics. It allows considering the mutual influence of the transmitting and receiving antennas with nonlinear characteristics in the system itself and the electrodynamics interaction of the transmitting antenna with nonlinear characteristics with RES for other purposes. Component equations (NIE) of multi-input radiating structures that establish the relationship of amplitude-phase distribution at the inputs of radiators with distributed nonlinearity and amplitude-phase distribution on their surfaces are obtained. A mathematical model of multi-input radiator structures with nonlinear characteristics in the Fresnel zone for analysis purposes has been produced. Conclusions. The scientific novelty of the obtained results is as follows: a generalized theory of transmitting antennas of arbitrary configuration with nonlinear characteristics in the Fresnel zone, which makes it possible to analyze the characteristics of these antennas considering the positive and negative (beneficial and adverse) nonlinear effects that arise in them.

The special class of atomic functions is considered. The atomic function is a solution with compact support of linear differential functional equation with constant coefficients and linear transformations of the argument. The functions considered are used in discrete atomic compression (DAC) of digital images. The algorithm DAC is lossy and provides better compression than JPEG, which is de facto a standard for compression of digital photos, with the same quality of the result. Application of high precision values of atomic functions can improve the efficiency of DAC, as well as provide the development of new technologies for data processing and analysis. This paper aims to develop a low complexity algorithm for computing precise values of the atomic functions considered. Precise values of atomic functions at the point of dense grids are the subject matter of this paper. Formulas of V. O. Rvachev and their generalizations are used. Direct application of them to the computation of atomic functions on dense grids leads to multiple calculations of a great number of similar expressions that should be reduced. In this research, the reduction required is provided. The goal is to develop an algorithm based on V. O. Rvachev’s formulas and their generalizations. The following tasks are solved: to convert these formulas to reduce the number of arithmetic operations and to develop a verification procedure that can be used to check results. In the current research, methods of atomic function theory and dynamic programming algorithms development principles are applied. A numerical scheme for computation of atomic functions at the points of the grid with the step, which is less than each predetermined positive real number, is obtained and a dynamic algorithm based on it is developed. Also, a verification procedure, which is based on the properties of atomic functions, is introduced. The following results are obtained: 1) the algorithm developed provides faster computation than direct application of the corresponding formulas; 2) the algorithm proposed provides precise computation of atomic functions values; 3) procedure of verification has linear complexity in the number of values to be checked. Moreover, the algorithms proposed are implemented using Python programming language and a set of tables of atomic functions values are obtained. Conclusions: results of this research are expected to improve existing data processing technologies based on atomic functions, especially the algorithm DAC, and accelerate the development of new ones.

The subject matter of the paper is the development of fingerprint local structures based on the new method of the minutia vicinity decomposition (MVD) for the solution to the task of fingerprint verification. It is an essential task because it is produced attempts to introduce biometric technology in different areas of social and state life: criminology, access control system, mobile device applications, banking. The goal is to develop real number vectors that can respond to criteria for biometric template protection schemes such as irreversibility with the corresponding accuracy of equal error rate (EER). The problem to be solved is the problem of accuracy in the case of verification because there are false minutiae, disappearing of truth minutiae and there are also linear and angular deformations. The method is the new method of MVD that used the level of graphs with many a point from 7 to 3. This scheme of decomposition is shown in this paper; such a variant of decomposition is never used in science articles. The following results were obtained: description of a new method for fingerprint verification. The new metric for creating vectors of real numbers were suggested – a minimal path for points in the graphs. Also, the algorithm for finding out minimal paths for points was proposed in the graphs because the classic algorithm has a problem in some cases with many points being 6. These problems are crossing and excluding arcs are in the path. The way of sorting out such problems was suggested and examples are given for several points are 20. Results of false rejection rate (FRR), false acceptance rate (FAR), EER are shown in the paper. In this paper, the level of EER is 33 % with full search. 78400 false and 1400 true tests were conducted. The method does not use such metrics as distances and angles, which are used in the classical method of MVD and will be used in future papers. This result is shown for total coincidences of real number, not a similarity that it is used at verifications. It is a good result in this case because the result from the method index-of-max is 40 %.

The subject matter of the article is pseudo-random number generators. Random numbers play the important role in cryptography. Using not secure pseudo-random number generators is a very common weakness. It is also a fundamental resource in science and engineering. There are algorithmically generated numbers that are similar to random distributions but are not random, called pseudo-random number generators. In many cases the tasks to be solved are based on the unpredictability of random numbers, which cannot be guaranteed in the case of pseudo-random number generators, true randomness is required. In such situations, we use real random number generators whose source of randomness is unpredictable random events. Quantum Random Number Generators (QRNGs) generate real random numbers based on the inherent randomness of quantum measurements. The goal is to develop a mathematical model of the generator, which generates fast random numbers at a lower cost. At the same time, a high level of randomness is essential. Through quantum mechanics, we can obtain true numbers using the unpredictable behavior of a photon, which is the basis of many modern cryptographic protocols. It is essential to trust cryptographic random number generators to generate only true random numbers. This is why certification methods are needed which will check both the operation of the device and the quality of the random bits generated. The goal of the research is also to develop the model of a hybrid semi self-testing certification method for quantum random number generators (QRNG). The tasks to be solved are to create the mathematical model of a random number generator, which generates the fast random numbers at a lower cost. To create the mathematical model of a hybrid semi self-testing certification method for quantum random number generators. To integrate a hybrid semi self-testing certification method to the hybrid random number generator. the methods used are mathematical optimization and simulation. The following results were obtained: we present the improved hybrid quantum random number generator, which is based on QRNG, which uses the time of arrival of photons. The model of a hybrid semi self-testing certification method for quantum random number generators (QRNG) is offered in the paper. This method combines different types of certification approaches and is rather secure and efficient. Finally, the hybrid certification method is integrated into the model of the new quantum random number generator. Conclusions. The scientific novelty of the results obtained is as follows: 1. The hybrid quantum random number generator is offered, which is based on QRNG, which uses the time of the arrival of photons. It uses the simple version of the detectors with few requirements. The hybrid QRNG produces more than one random bit per the detection of each photon. It is rather efficient and has a high level of randomness. 2. The hybrid semi self-testing certification method for quantum random number generators (QRNG) is offered. The Self-testing, as well as device-independent quantum random number generation methods, are analyzed. The advantages and disadvantages of both methods are identified. Based on the result the hybrid method is offered. 3. The hybrid semi self-testing certification method for quantum random number generators is integrated into the offered model of the quantum random number generator. The paper analyzes its security and efficiency. The paper offers to use the new random number generator in the crypto-schemes.

The paper analyzes the vibroacoustic signals obtained by physical modeling of the rotating system, for example, an aircraft gas turbine engine, in the conditions of steady-state and non-steady-state modes. An air starter (supercharger) is used as a physical model of a rotating system, which is driven by a DC motor. The measuring system uses a dynamic microphone with an amplifier, a tachometer, a two-channel digital oscilloscope, a personal computer with technological and special software. The simulation of the ingress of foreign objects into the rotating system is performed by throwing paper balls during the rotation. The multilevel processing based on sequential application of methods of frequency-time analysis, multispectral analysis, and fractal analysis is proposed and substantiated for processing of measured vibroacoustic signals. The results of the frequency-time analysis showed that at the time of throwing the balls the intensity of the components at higher frequencies increases. For fragments of signal realization without throwing and with the throwing of balls the multispectral analysis is carried out and estimates of the bispectrum modulus are received in the form of contour images. At the third level of signal processing, the Minkowski dimension of the contour images of the bispectrum module estimates is determined. The Minkowski dimension is an integral quantitative indicator of the geometry of isolines and differs in value for the selected fragments of the vibroacoustic signal. So it can be used as a diagnostic sign of a foreign object entering the rotating system at the final level of processing. The obtained results can be used to improve the systems of condition monitoring of complex rotating systems, increase sensitivity, expand functionality and provide multi-class diagnostics in the event of damage and violation of normal operating modes

The paper notes the wide possibilities and high efficiency of vibration diagnostics of parts and assemblies of power units by the method of electronic speckle interferometry (ESI), shows the feasibility of developing and improving methods for obtaining speckle interferograms of vibrating objects. A patented by the authors' scheme of a digital speckle interferometer (DSI) for determining the dynamic parameters of products is presented. The diffuse reference wave greatly simplifies its alignment, mainly the aligned channels of propagation of interfering beams determine the resistance to mechanical and thermal disturbances, and the separate beam splitter and diffuser make it easy to optimize the optical scheme for the object under study. Speckle interferograms are obtained by speckle correlation methods and a developed method for determining the contrast of the dynamic speckle pattern. It does not impose special requirements on the parameters of the video system, does not require the use of a specialized video camera, and allows, by using a rotary platform, to organize a panoramic view of the oscillatory forms of asymmetric structures. The use of this method in combination with the proposed optical scheme provides an express analysis of products in off-bench conditions. The increased noise immunity of the installation allows the determination of the spectrum of frequencies and vibration modes (SFVM) of samples under free boundary conditions, which is rarely realized in interferometry. The paper presents examples of such speckle interferograms, the expediency of obtaining which is important in the resonance study of the elastic properties of the material and vibration resonance flaw detection. The implemented software and hardware solutions increase the resolution and visibility of the band patterns; facilitate their quantitative interpretation and the extraction of numerical information on the distribution of vibration amplitudes. The area of scientific interests and the specificity of the author's developments have determined the preferential adaptation of the proposed device with a complex of software and methodological developments for vibration testing of gas turbine engine (GTE) blades.

This article presents the structure of designing and modeling the flow of rotors and pusher propellers in a highly rarefied atmosphere. The aerodynamic characteristics of an airfoil are key factors in determining design accuracy and performance prediction. The blade elements operate at different Reynolds numbers and angles of attack. The blade profile was selected based on an estimate of the lift-to-drag ratio for expected Reynolds numbers under the design conditions. This paper considers the features of the operation of propellers in a highly rarefied atmosphere. When modeling propellers operate in rarefied atmosphere, it is necessary to pay special attention to the choice of a model for the turbulent viscosity of the medium. The greatest difficulties in the design of stratospheric propellers arise when solving the problems of excessive diameter, weight and shape of the blades. To solve this problem, in this paper, it is proposed to use joined double row blades. This approach made it possible to eliminate the aerodynamic shading of the blades and increase their aerodynamic load. The use of double-row blades will provide the necessary rigidity and strength of the blades and will lead to weight reduction. Additionally, due to the double-row design, it becomes possible to reduce the diametrical dimensions of the propeller. An analytical technique for profiling double-row blades has been developed. The proposed approach is based on the joint application of the one-dimensional propeller theory and numerical gas dynamics methods. The one-dimensional theory is used to obtain the original geometric shape of the propeller for given characteristics, and the methods of numerical gas dynamics, which are based on solving the Reynold averages of the Navier-Stokes equations, are used to solve the problems of spatial modeling of the flow and obtain refined propeller characteristics. Considering the design requirements and operational limitations, it is proposed to use propellers with joined double-row blades in conditions of rarefied atmosphere.

The subject of this work is two approaches to describe the laminar unsteady flow of an incompressible fluid in the boundary layer. In the first approach, the viscosity of the fluid and the acceleration with which the plane is set in motion are considered constant. In essence, this is Rayleigh's problem. The solution obtained on the basis of these assumptions asymptotically converges to the well-known self-similar Stokes solution. It is important that the solutions of Stokes and Rayleigh asymptotically at large values of time correspond to the disappearance of shear stresses between the liquid and moving plane after acceleration. A paradox emerges the equations derived by Stokes to describe internal friction indicate the absence of the same friction between a moving body and fluid. Since research using the calculus of variation methods revealed that the molecular viscosity inside the stationary boundary layer should depend on the distance to the moving surface, the corresponding non-steady problem was considered. As a result, as before for the steady case, solutions describing both non-gradient and gradient flows of incompressible fluid in the boundary layer are obtained. The asymptotic analysis of the transition to steady flow testifies the consistency of these solutions. For the case of non-gradient flow, a comparison of the classical solution with the solution corresponding to the extreme fluid flow rate carried by the moving surface is made. It is shown that according to the solution obtained on the basis of the calculus of variation approach, the shear stress on the surface does not disappear anywhere after the motion becomes steady but, as expected, acquires a constant value. The research methods are purely theoretical and the results are analyzed by comparison with available theoretical and experimental data and compliance with the fundamental laws of physics, in particular the law of conservation of energy. These methods are based on the construction of analytical mathematical models, which are differential equations in partial derivatives supplemented with appropriate physical initial and boundary conditions. In addition, Euler's differential equations for the extreme of functional theory are used (in this paper, this is the extreme of fluid flow rate across the cross-section of the boundary layer). When solving these equations, the well-known Fourier method of variable separation is used. Arbitrary functions of time arising during partial integration (by one of the variables – the spatial coordinate) are determined from the conditions of asymptotic convergence of the solutions of non-steady problems to the corresponding solutions of steady problems. Conclusions. The presented results are of fundamental importance for understanding the physics of the flow around aircraft parts, as they indicate the contradiction of the existing idea of the reversibility of direct and inverse problems: the motion of a body in a still fluid and the flow of a fluid around an immobile body.

This paper proposes an analysis of an air traffic control students’ survey describing self-assessment of their workload levels. The workload is defined as the number of aircraft simultaneously under control. The survey is performed over the gird with axes represented with aircraft number, their correspondent, and utility/satisfaction levels. The aspiration level values are calculated using workload differences. The safety background of risks in aviation activities is described. Risk and aspiration level links are highlighted. The aspiration level notion for the proposed research is inferred. Survey details and conditions are explained. The differences in four quarters at the workload charts specified by respondents are explained. The aspiration level parameters and basic statistics calculated for respondents are presented. Appropriate goodness-of-fit tests are performed with different sets of initially received answers. Whole samples and subsamples are considered. It is shown that for the full sample considered, the outlet removal provides significant increase of p-value, thus allowing to change the hypothesis approval status. Sub-samples list includes the whole sample without outlets and the whole sample aspiration level calculated for only positive values according to y-axis. Additionally, other values were considered. Namely regret values, i.e., the values on the descending half of the charts and a mixture of regret and aspiration level values in regard that is higher. Normal and exponential distribution significances are proven for the different options mentioned above. It is shown that the general chi-squared method provides the latter mixture to be insignificantly exponential, whereas specific Fisher’s test approves the significance of the data. The role of air traffic control students’ aspiration level in their estimation and possible education strategy personalization is described. The importance of human factor consideration during similar survey performance is once again proven. The connection of regret with aspiration level in a mixture is discussed. The conclusions on the results are provided. Further research directions are proposed.

The choice of this study has a significant impact on daily life. In various fields such as journalism, academia, business, and more, large amounts of text need to be processed quickly and efficiently. Text summarization is a technique used to generate a precise and shortened summary of spacious texts. The generated summary sustains overall meaning without losing any information and focuses on those parts that contain useful information. The goal is to develop a model that converts lengthy articles into concise versions. The task to be solved is to select an effective procedure to develop the model. Although the present text summarization models give us good results in many recognized datasets such as cnn/daily- mail, newsroom, etc. All the problems can not be resolved by these models. In this paper, a new text summarization method has been proposed: combining the Extractive and Abstractive Text Summarization technique. In the extractive-based method, the model generates a summary using Sentence Ranking Algorithm and passes this generated summary through an abstractive method. When using the sentence ranking algorithm, after rearranging the sentences, the relationship between one sentence and another sentence is destroyed. To overcome this situation, Pronoun to Noun conversion has been proposed with the new system. After generating the extractive summary, the generated summary is passed through the abstractive method. The proposed abstractive model consists of three pre-trained models: google/pegusus-xsum, face-book/bart-large-cnn model, and Yale-LILY/brio-cnndm-uncased, which generates a final summary depending on the maximum final score. The following results were obtained: experimental results on CNN/daily-mail dataset show that the proposed model obtained scores of ROUGE-1, ROUGE-2 and ROUGE-L are respectively 42.67 %, 19.35 %, and 39.57 %. Then, the result has been compared with three state-of-the-art methods: JEANS, DEATS and PGAN-ATSMT. The results outperform state-of-the-art models. Experimental results also show that the proposed model is qualitatively readable and can generate abstract summaries. Conclusion: In terms of ROUGE score, the model outperforms some art-of-the-state models for ROUGE-1 and ROUGE-L, but doesn’t achieve good result in ROUGE-2.

Wireless technology is expected to undergo considerable transformation because of the numerous services offered by 6G communication networks, which virtually entirely encompass every part of everyday life and use a variety of devices. Channel modeling is an essential factor in designing 6G communication networks. To meet the channel requirements of future 6G communication networks, it is crucial to measure the channel to consider path loss, multi-band, fading, blocking effect, multipath clustering, transmitter, and receiver moving speed/direction/time. The goal of this paper is to design and evaluate a 6G communication network in Banda Aceh City using a statistical channel model. The channel model is associated with environmental conditions such as rainfall and humidity. The method is then based on computer simulation using the NYUSIM simulator to complete the channel modeling using an operating frequency of 95 GHz with a bandwidth of 800 MHz. In the simulator, the designed 6G channel model is evaluated in both line-of-sight (LOS) and non-line-of-sight (NLOS) network environments. In addition, the designated network parameters, such as the coverage area, angle of arrival (AoA), angle of departure (AoD), and power delay profile (PDP), are simulated. The results, at AoA, the value of received power for LOS conditions ranges from -86 dBm to -101 dBm, while the value for NLOS conditions ranges from -91 dBm to -111 dBm. Under LOS conditions, the received power for AoD ranges from -86 dBm to -101 dBm, whereas under NLOS conditions, it ranges from -91 dBm to -111 dBm. In the omnidirectional PDP, the pathloss value for the LOS condition is 99.8 dB and the delay is 17.9 ns, while the pathloss value for the NLOS condition is 104.2 dB with a delay of 28.1 ns. For the directional PDP, the LOS condition yields a path loss of 106.4 dB and a delay of 2.9 ns, while the NLOS condition yields a path loss of 110.5 dB and a delay of 3 ns. Conclusions. The simulation indicated that the AoA, AoD, and PDP in terms of received power, pathloss, and propagation delay are in acceptable conditions for a 6G network in Banda Aceh City in the two observed environments. Therefore, it is conceivable to establish a 6G network in Banda Aceh City in the future.

Remote sensing provides data (images) important for many modern applications. Image number and average size tend to increase. This makes their transfer via communication lines, storage, and dissemination problematic. Thus, compression should be applied where lossy compression is mostly used. Most methods of lossy compression assume that images do not contain noise. Meanwhile, images are often noisy, and this should be considered in the design and performance analysis of image compression techniques. Lossy compression has already been studied by several coders. However, it has not been investigated for the recently proposed atomic transform-based techniques that possess several advantages, in particular, the ability to provide privacy of compressed data. The main subject of this paper is the peculiarities of noisy image lossy compression by an atomic transform-based coder. Our goal is to analyze whether the considered compression method provides the noise filtering effect and the so-called optimal operation point. The task is to obtain rated distortion curves for the atomic transform-based coder applied to noisy images and to analyze their behavior for several performance characteristics such as quality metrics and compression ratio. In the first order, the monotonicity of the main dependence is of interest. The main results are as follows. First, it is shown that the dependencies have non-monotonic behavior and the appearance of analogs of optimal operation point is possible, at least, for such metric as maximal absolute error. Second, there is a specific dependence of compression ratio on a parameter called UBMAD that controls compression. Experiments have been performed on several noisy test images having different complexity and contaminated by noise of different intensities. In conclusion, it is demonstrated that one more coder might have optimal operation points for images having a rather simple structure. However, at the moment, it is difficult to predict its existence and the corresponding coder parameters.

Currently, there are no clinical decision support information technologies (CDSIT) that would consider civil-legal grounds when forming a decision for clinicians. Therefore, the design, development, and implementation of CDSIT, which considers civil-legal grounds when forming decisions, are actual problems. Methodology for the development and application of knowledge-driven, rule-based, clinical decisions support information technologies with consideration of civil-legal grounds has been developed, which provides a theoretical basis for developing clinical decisions support information technology with consideration of civil-legal grounds and partial CDSITs regarding the possibility of providing medical services of a certain type. In addition to the conclusion about the possibility or impossibility of providing certain medical services, the developed methodology ensures the presence of all essential terms (from the viewpoint of civil law regulation) in the contract for the certain medical service's provision and/or the data on potential patients for the provision of such a service, as well as minimization of the influence of the human factor when making clinical decisions. It is advisable to evaluate the CDSITs with consideration of civil-legal grounds, developed according to the proposed methodology, from the viewpoint of the correctness of the decisions generated by them, as well as from the viewpoint of their usefulness for clinics. In this paper, experiments with the methodology-based CDSIT regarding the possibility of performing a surrogate motherhood procedure with consideration of civil-legal grounds were conducted. Such experiments showed the correctness of the generated decisions at the level of 97 %. Experiments also demonstrated the usefulness of such IT for clinics from the viewpoint of eliminating adverse legal consequences, as they might arise due to violation or disregard of legal, and moral and ethical norms.

This paper formulates and solves a multivariate problem related to modeling the logistics of war reserve stockpiling for successful combat operations in the armed conflict area. The relevance of the study is related to a comprehensive solution to the problem of war reserve stockpiling to fulfill the objectives of a military operation, considering the capabilities of materiel suppliers, complex logistics of war materiel transportation to armed conflict areas, and military threats arising from the martial law in the country. The study creates a set of models that allow: prioritizing war materiel types for reserve stockpiling; formulating requirements for the size of reserves; selection of a rational structure of logistics supply chain, considering the risks of military threats; ensuring the success of combat operations in the armed conflict area. The article analyzes the existing problems of war reserve stockpiling under martial law, which arose due to: the variety of war materiel; small supply batches; different materiel manufacturers and suppliers located at a great distance from the armed conflict area; heterogeneous transport environment of supply; risks associated with military threats during war materiel reserve stockpiling. A method of presenting the importance of certain war materiel types for supply to the armed conflict area is proposed, considering the combat capability of individual weapons and assessments of military experts. The requirements for the amount of war materiel reserve in the interval representation, from the minimum to the maximum value of the inventory, have been formed. The minimum war materiel reserve to be used in the armed conflict area provides the necessary parity of military forces. Simultaneously, the nature of hostilities does not change, but the risks associated with the enemy's actions to destroy the war materiel reserve (war of reserves) may manifest themselves. The maximum war materiel reserve provides confidence in achieving the objectives of a military operation in the armed conflict area, but is difficult to implement due to the limited capabilities of weapons and materiel suppliers and existing military threats. An optimization model for the war reserve stockpiling was created, considering the limited capabilities of suppliers, permissible time for reserve stockpiling, and the risks that may arise from military threats and their impact on the logistics of war materiel supply (war of logistics). An optimization model for the war reserve stockpiling was developed, considering the combat capability of certain types of weapons, which makes it possible to create asymmetry in the military parity of forces due to the increased combat characteristics of modern weapons (quality over quantity). Due to the multitude of materiel manufacturers (possible suppliers) and various variants for the formation of supply chain, the task of enumeration of possible variants for the composition and structures of the logistics supply chain for war reserve stockpiling was formed and solved. The number of warehouse variants and logistics supply chain structures is estimated using the methods of enumeration theory, and a set of variants is formed for subsequent comparison and selection of a rational option. An optimization model for the formation of logistics links in the supply chain was created to form the necessary war reserve stocks in the armed conflict area. The example of HIMARS MLRS stockpiling illustrates the effectiveness of the proposed approach for successful combat operations in the armed conflict area, using both quantitative and qualitative assessments when comparing possible stockpiling variants. The scientific novelty of the study is related to the development of a set of original optimization models, models of the variant enumeration for the structures of the logistics supply chain, which allows scientifically sound formulation of requirements for the size of the war reserve stock for successful fulfillment of the objectives of a military operation in the armed conflict area, considering the capabilities of materiel suppliers, long supply chains in a heterogeneous transport environment, short delivery time, and the risks of military threats. It is advisable to use the results of the study for planning war reserve stock, for their effective use in the combat zone, justifying the composition of materiel manufacturers and suppliers, forming a logistics supply chain, which will ensure the success of combat operations in the armed conflict area.

COVID-19 pandemic has significantly impacted the world, with millions of infections and deaths, healthcare systems overwhelmed, economies disrupted, and daily life changed. Simulation has been recognized as a valuable tool in combating the pandemic, helping to model the spread of the virus, evaluate the impact of interventions, and inform decision-making processes. The accuracy and effectiveness of simulations depend on the quality of the underlying data, assumptions, and modeling techniques. Ongoing efforts to improve and refine simulation approaches can enhance their value in addressing future public health emergencies. The Russian full-scale military invasion of Ukraine on February 24, 2022, has created a significant humanitarian and public health crisis, with disrupted healthcare services, shortages of medical supplies, and increased demand for emergency care. The ongoing conflict has displaced millions of people, with Spain ranking 5th in the world for the number of registered refugees from Ukraine. The research aims to estimate the impact of the Russian war in Ukraine on COVID-19 transmission in Spain using means of machine learning. The research is targeted at COVID-19 epidemic process during the war. The research subjects are methods and models of epidemic process simulation based on machine learning. To achieve the study's aim, we used forecasting methods and built a model of COVID-19 epidemic process based on the XGBoost method. As a result of the experiments, the accuracy of forecasting new cases of COVID-19 in Spain for 30 days was 99.79 %, and the death cases of COVID-19 in Spain – were 99.86 %. The model was applied to data on the incidence of COVID-19 in Spain for the first 30 days of the war escalation (24.02.2022 – 25.03.2022). The calculated forecasted values showed that the forced migration of the Ukrainian population to Spain, caused by the full-scale invasion of Russia, is not a decisive factor affecting the dynamics of the epidemic process of COVID-19 in Spain. Conclusions. The paper describes the results of an experimental study assessing the impact of the Russian full-scale war in Ukraine on COVID-19 dynamics in Spain. The developed model showed good performance to use it in public health practice. The analysis of the obtained results of the experimental study showed that the forced migration of the Ukrainian population to Spain, caused by the full-scale invasion of Russia, is not a decisive factor affecting the dynamics of the epidemic process of COVID-19 in Spain.

A common task in computer forensics is to recover files that lack file system metadata. In the case of searching for file fragments in unallocated space, file carving is the most often used method, which is ideal for unfragmented data. However, such methods and the tools based on them are ineffective for recovering OOXML files with a high fragmentation level. These methods do not provide reliable determination of the correct order of fragments. Techniques for reconstructing documents based on the analysis of words and phrases are also ineffective in fragmented OOXML documents. The main reason is that OOXML files are ZIP archives and, as a result, store data on disk space in a compressed form. This paper proposes a syntactical method for reconstructing OOXML documents based on knowledge about the internal structure of this file type, regardless of their content. The details of the implementation of the reconstruction algorithm and the peculiarities of restoring certain types of local elements of the document were considered. The efficiency of the algorithm was tested on the Govdocs1 and NapierOne datasets. The proposed method was applied to 4096-byte data blocks, which correspond to the standard cluster size in different file systems. The experimental results confirmed the method's suitability for practical use with 82.97 % of recovered files, including 34.38 % reconstructed completely, 0.43 % excluding the last 21 bytes at most, and another 48.16 % excluding embeddings that require other approaches. In the latter case, obtaining a fully working document without displaying graphic images and other contents of different embeddings is possible. The presence in OOXML files of CRC-32 hashes of the uncompressed data stream of each local element allows us to confirm the correctness of information recovery and its integrity unambiguously. Simultaneously, the method's effectiveness depends mainly on data verification methods during the reconstruction of local elements that occupy at least three clusters in the file. Therefore, this method is supposed to be improved by developing new mechanisms for verifying XML elements.