Abstract: In the modern information society, the volume of data is constantly growing, and its effective processing is becoming key for enterprises. The transmission and storage of this data also plays a critical role. Big data used in analytics systems is most often transmitted in one of two popular formats: CSV for structured data and JSON for unstructured data. However, existing file formats may not be effective or flexible enough for certain data analysis tasks. For example, they may not support complex data structures or provide sufficient control over metadata. Alternatively, analytical tasks may require additional information about the data, such as metadata, data schema, etc. Based on the above, the subject of this study is a data format based on the combined use of CSV and JSON for processing and analyzing large amounts of information. The option of sharing the designated data types for the implementation of a new data format is proposed. For this purpose, designations have been introduced for the data structure, which includes CSV files, JSON files, metadata and a dependency graph. Various types of functions are described, such as aggregating, transforming, filtering, etc. Examples of the application of these functions to data are given. The proposed approach is a technique that can significantly facilitate the processes of information analysis and processing. It is based on a formalized approach that allows you to establish clear rules and procedures for working with data, which contributes to their more efficient processing. Another aspect of the proposed approach is to determine the criteria for choosing the most appropriate data storage format. This criterion is based on the mathematical principles of information theory and entropy. The introduction of a criterion for choosing a data format based on entropy makes it possible to evaluate the information content and compactness of the data. This approach is based on the calculation of entropy for selected formats and weights reflecting the importance of each data value. By comparing entropies, you can determine the required data transmission format. This approach takes into account not only the compactness of the data, but also the context of their use, as well as the possibility of including additional meta-information in the files themselves and supporting data ready for analysis.

Keywords: Apache Parquet, Integration of data formats, Data analysis, Analysis Ready Data, Metadata, Data processing, CSV, JSON, Data storage formats, Big Data

Abstract: The article focuses on security and fire alarm systems (SFAS) as means of ensuring the safety of facilities, viewing them as integrated complexes for promptly detecting potential threats. The main emphasis is on detectors, including their classification and role within the system. The article examines various configurations of SFAS and ways of connecting and processing signals from detectors, allowing for an evaluation of how these factors affect the system's effectiveness. The lifecycle of SFAS is described, highlighting the importance of each stage from design to operation. The article provides an overview of regulatory documents, emphasizing the importance of compliance with standards and requirements when implementing SFAS. Recommended for security professionals and individuals interested in delving into this topic. Additionally, the article addresses issues related to the placement of SFAS and their impact on system effectiveness. It analyzes vulnerabilities arising from irrational placement of components and presents a methodology for optimizing placement to enhance security. The methodology is described step by step, considering input and output processes at each stage. The authors conduct practical testing of the methodology in an educational laboratory with an installed SFAS, identifying placement errors and formulating recommendations for correction. The article is beneficial for professionals in the design and installation of SFAS, as well as for those seeking to improve the level of protection of facilities, accentuating the critical importance of proper component placement.

Keywords: Protection, Installation, Components, Optimization, Effectiveness, Methodology, Placement, Fire alarm, Security systems, Design

Abstract: The paper considers the problem of automated recognition of single emergencies in chemical and oil refining industries. Modern chemical and technological production facilities are maintained and managed by a small number of personnel, which increases the burden on each operator. To reduce the number of operator errors, their training is regularly conducted on simulators equipped with a set of both standard situations (routine start-up, shutdown, normal process management, switching from one mode to another) and emergency scenarios (column depressurization, pump failure, failure of the power supply system). Nevertheless, it is impossible to foresee all possible failures during operator training, and even a trained operator may not notice the first signs of an accident, and therefore it is necessary to create a decision support system that helps the operator to recognize failures of technological equipment in a timely manner. To recognize failures, it is proposed to use a neural network trained on an array of simulated accident data. An industrial simulator based on the RTsim platform was used to simulate typical accidents. The novelty of the research lies in the use of artificial intelligence methods to diagnose the property of the technological process according to the SCADA system and the use of data for training a neural network not from a real object (which will always be insufficient), but from a model that exactly corresponds to a specific technological site. The number of simulated scenarios used to train a neural network can be quite large, which reduces the proportion of erroneous system responses. The developed system confidently copes with the recognition of individual equipment failures. The results obtained can be used to help process operators and to improve emergency protection systems. The analysis of the time required by the system to recognize an emergency situation can be used to design new production facilities, modify the control and management system.

Keywords: industrial safety, decision-support system, simulation modeling, Digital Twin, RTsim, computer trainer, artificial intelligence, accident, failure, oil refining

Abstract: In this paper, the static strength of a UAV blade made of composite material was calculated. Composite materials have an advantage over traditional materials (metals and alloys) in the field of aviation – gain in weight, low sensitivity to damage, high rigidity, high mechanical characteristics. At the same time, the identification of vulnerabilities in a layered structure is a difficult task and in practice is solved with the help of destructive control. Composite materials available in the ANSYS materials library were used in the modeling: Epoxy Carbon Woven (230 Gpa) Prepreg woven carbon fiber in the form of a semi–finished prepreg impregnated with epoxy resin carbon fiber with Young's modulus E=230 GPa and Epoxy Carbon (230 Gpa) Prepreg is a unidirectional carbon fiber prepreg impregnated with epoxy resin with a Young's modulus E=230 GPa. Modern software products, such as ANSYS WorkBench, allow comprehensive investigation of the layered structure. Several variants of blade designs with different fillers as the median material were investigated. The forward and reverse destruction criteria based on the Tsai-Hill theory were used. The influence of gravity was not taken into account. It is shown that the developed blade design meets the requirements. Balsa wood, pine, aspen and polyurethane foam were chosen as the middle material of the blade. Pine and aspen wood were selected according to the criteria of their availability and having the lowest density. The materials library of the ANSYS WorkBench software package used does not have characteristics for all of them, so the characteristics of the selected materials (pines and aspens) were added manually. For modeling and calculations in the ANSYS WorkBench program, such characteristics as density, axial elastic modulus, Poisson's coefficients, shear modulus and tensile and compressive strength limits are required.

Keywords: middle fillers, fiberglass, carbon fabrics, Tsai-Hill theory, failure criterion, stress, ANSYS WorkBench, static strength, blade, composite material

Abstract: Stochastic packet generation models are models that are used to generate traffic in computer networks with certain characteristics. These models can be used to simulate network activity and test network performance. Standard data transmission on the network is packet generation with delays, in which packets are sent at certain intervals. Various stochastic models can be used to generate delayed packets, including uniform distribution, exponential distribution, and Erlang distribution. In this work, an experimental setup was assembled and a client-server application was developed to conduct research and analyze the performance of the data transmission channel. An algorithm has been proposed that allows to restore the moment characteristics of a random value of the interval between packets for further use of queuing models. The analysis of the distribution laws on the performance of the experimental network sample was performed and estimates of the efficiency of channel use and the average packet generation time in network segments, as well as histograms of delays according to the distribution laws, were obtained. An experimental setup was created, and a client-server application was developed to analyze the performance of the data transmission channel. An algorithm for restoring the moment characteristics of the time intervals between packets is proposed. The analysis of the distribution laws on network performance was carried out, estimates of the efficiency of channel use and the average packet generation time in network segments were obtained, as well as histograms of delays according to the distribution laws. The generation of packets with delays according to stochastic distribution laws (uniform, exponential, Erlang) is of great importance in modeling and analyzing the operation of network systems. Also, the generation of packets with delays according to the above-mentioned distribution laws allows testing and debugging of network applications and devices in conditions close to real ones. This allows to identify possible problems and improve the operation of network systems. As a result of the experiment, an algorithm was proposed that allows to restore the moment characteristics of a random value of the interval between packets for further use of queuing models. Also, the analysis of the influence of distribution laws on the performance of the experimental network sample was performed and estimates of the efficiency of channel use and the average packet generation time in network segments, as well as histograms of delays according to distribution laws, were obtained.

Keywords: generating packages, data transmission, traffic analysis, client-server application, delays, packet switching, Erlang distribution, exponential distribution, uniform distribution, Stochastic models

Abstract: The objects of this study are data storage devices of various types and levels of complexity, as well as the principles of their operation. They are complex technical systems that include many components and are characterized by a high degree of integration. The subject of the research is to study the main characteristics of hard drives and solid-state drives. Their structure, functional features, principles of operation and ways of optimization are important. The purpose of the study is to determine the most effective methods for optimizing the operation of these devices. This includes aspects such as memory management, load balancing, power management, and others. The results of this research can be used to improve data efficiency, improve the performance of data storage systems and create new technologies in this area. This study examines the performance of various disk storage solutions through a series of tests aimed at understanding speed and dependence on external factors. The main conclusions of the study reflect the importance of the integrated use of optimization approaches to improve the speed of reading and writing data. Optimizing the processes of reading and writing data is critically important for modern high-performance computing systems, as well as for applications that require quick access to large amounts of information. The improved techniques used in the course of the study contribute to a significant increase in the performance of data storage devices. They take into account the specifics of various types of storage devices, including hard drives and solid-state drives, and offer optimization approaches that take into account their unique characteristics. Overall, the results of this study provide valuable insights into the principles of optimizing data storage, and they can serve as a basis for developing new strategies and solutions in this important area of information technology. This study represents a significant contribution to the scientific understanding of optimizing data reading and writing processes, and its findings may have long-term implications for the development of data storage technologies.

Keywords: Cache Buffer, Interface, Defragmentation, Reading data, Fragmentation, Efficiency, Optimization, Solid state drives, Hard drives, The file system

Abstract: Today, mobile devices play an important role in everyone's daily life, and one of the key technologies leading to significant benefits for mobile applications is machine learning. Optimization of machine learning algorithms for mobile devices is an urgent and important task, it is aimed at developing and applying methods that will effectively use the limited computing resources of mobile devices. The paper discusses various ways to optimize image recognition algorithms on mobile devices, such as quantization and compression of models, optimization of initial calculations. In addition to ways to optimize the machine learning model itself, various libraries and tools for using this technology on mobile devices are also being considered. Each of the described methods has its advantages and disadvantages, and therefore, in the results of the work, it is proposed to use not only a combination of the described options, but also an additional method of parallelization of image processing processes. The article discusses examples of specific tools and frameworks available for optimizing machine learning performance on iOS, and conducted its own experiments to test the effectiveness of various optimization methods. An analysis of the results obtained and a comparison of the performance of the algorithms are also provided. The practical significance of this article is as follows: Improving the performance of machine learning algorithms on iOS mobile devices will lead to more efficient use of computing resources and increase system performance, which is very important in the context of limited computing power and energy resources of mobile devices. Optimization of machine learning performance on the iOS platform contributes to the development of faster and more responsive applications, which will also improve the user experience and allow developers to create new and innovative features and capabilities. Expanding the applicability of machine learning on iOS mobile devices opens up new opportunities for application development in various fields such as pattern recognition, natural language processing, data analysis, and others.

Keywords: parallelization, performance, efficiency, OS Apple, optimization, image recognition, iOS, machine learning, mobile device, neural network

Abstract: The modern development of computer technologies and operating systems is accompanied by an increase in the need for software capable of ensuring the interaction of various programs and applications with each other, regardless of their source environment. In this study, a comparative analysis of two such programs will be conducted – Wine and PortProton. Wine is a program capable of running most applications developed for Windows on Unix-like systems. This is a compatibility layer that allows you to work with Windows applications. There is also a domestic version – PortProton, that offers the launch of Windows applications. This study aims to compare these two programs, analyze their features, advantages and disadvantages, determine which of them is the most convenient and functional for the end user in the context of Windows application emulation. The research methodology involves a comparative analysis of the Wine and PortProton platforms through benchmark testing and checking the performance of Windows applications on Linux. Benchmark testing includes evaluating the performance, stability, and speed of Windows applications on each platform. Due to the lack of scientific sources on the topic of comparing Wine and PortProton in the context of Windows application emulation, this study has a unique character. In conclusion Wine and PortProton successfully cope with the emulation of Windows applications, showing in some moments the best performance due to the optimization of the Linux operating system. PortProton copes best with the task of emulating programs due to stable operation and ease of use. Wine, despite a slight advance in the context of performance and the ability to run several programs at the same time, showed the worst efficiency due to the incorrect operation of some programs and the lack of an intuitive graphical interface. Based on the above conclusions, PortProton can be recommended for most users.

Keywords: Unix, Linux, Technical specifications, Windows, Performance, Wine, Portproton, Application compatibility, Emulation, cross platforms

Abstract: The subject of the study is the process of reverse engineering of programs in order to obtain their source code in low- or high-level languages for processors with x86 architecture, the software interface of which is developed by Intel and AMD. The object of the study is the technical specifications in the documentation produced by these companies. The intensity of updating documentation for processors is investigated and the need to develop technological approaches aimed at automated disassembler construction, taking into account regularly released and frequent updates of the processor software interface, is justified. The article presents a method for processing documentation in order to obtain a generalized, formalized and uniform specification of processor commands for further automated translation into the disassembler program code. The article presents two main results: the first is an analysis of the various options for describing commands presented in the Intel and AMD documentation, and a concise reduction of these descriptions to a monotonous form of representation; the second is a comprehensive syntactic analysis of machine code description notations and the form of representation of each command in assembly language. This, taking into account some additional details of the description of the commands, for example, the permissible operating mode of the processor when executing the command, made it possible to create a generalized description of the command for translating the description into the disassembler code. The results of the study include the identification of a number of errors in both the documentation texts and in the operation of existing industrial disassemblers, built, as shown by the analysis of their implementation, using manual coding. The identification of such errors in the existing reverse engineering tools is an indirect result of the author's research.

Keywords: CPU mode, x86 AMD documentation, x86 Intel documentation, assembler, microprocessor, assembler syntax, machine code syntax, command specification, software reengineering, disassembler