State University of Telecommunications

The study substantiates the importance of enterprise awareness of its environment for making managerial decisions under conditions of uncertainty. The object of the study is information and its asymmetry in the enterprise awareness of its environment when making decisions under conditions of uncertainty. Making management decisions in conditions of uncertainty requires a high-quality information base, which necessitates the need to improve approaches to assessing the impact of challenges on the enterprise functioning. The SPOD matrix is used to analyze enterprise challenges. (Strengths, Problems, Opportunities, Definite) is applied. It is emphasized that the classical SPOD matrix has a static nature and does not account for informational influences; therefore, it is proposed to extend it with a component U (Uncertainty) to reflect the dynamics of uncertainty. The entropic nature of economic processes is identified, revealing that increasing entropy reduces the resilience of economic systems and necessitates adaptive management. The proposed approach, based on the entropic measurement of uncertainty, enables a quantitative assessment of the degree of unpredictability of challenges. The variability of the enterprise environment under uncertainty imposes certain limitations on the applicability of the research results, particularly those related to the integration of non-formalized factors – socio-psychological, reputational, communicative – which are difficult to represent quantitatively. Ensuring the relevance of data is required to prevent overloading the matrix with excessive variables. The combining the SPOD/U method with an entropic assessment establishes a methodological foundation for evaluating environmental uncertainty of enterprise functioning. The research findings are particularly relevant for enterprises operating in the field of electronic communications.

The construction industry plays a vital role in the economic development and overall progress of any country. Construction activities have significant impact on the economy, but their environmental consequences cannot be overlooked. The excessive use of construction materials particularly cement and steel, which are among the most commonly used construction materials, has become a major environmental concern, as these materials are also key sources of carbon emissions. Moreover, the raw materials required for the preparation of cement and Steel are also depleting at a rapid pace. Therefore, it is necessary to conduct research studies to find new alternative materials which can reduce the consumption of cement and steel in the concrete. Fly ash can be used as binding agent in concrete as it has good cementation properties and is abundantly available. To enhance the mechanical performance of geopolymer concrete (GPC), polypropylene fibers (PPFs) were incorporated in varying ratios (0.5% to 1.5% by volume). The samples were prepared to test the mechanical and durability properties of the concrete. Compressive Strength, Flexural Strength, and Split Tensile Strength test was carried out to conclude the mechanical properties of the geopolymer concrete against different percentages of polypropylene Fiber. Acid attack and rapid chloride permeability tests were conducted out to evaluate the durability of the concrete. The research findings depicted that the greatest compressive strength and split tensile strength are obtained at 1% PPFs GPC. The least amount of chloride penetration was demonstrated by GPC, at 1.5% PPFs.

Handover management in 5G/6G networks presents significant challenges mainly due to ultradense deployments, high mobility and diversity of scenarios. This work presents a digital twin-based Deep Q-Learning (DQL) strategy to optimize handover processes and resource management in these networks. It is proposed to consider digital twins to support the implementation of real-time network conditions where DQL agents are trained to improve quality of service (QoS) parameters related to throughput. The suggested approach, deployed with the ns-3 mmWave module and ns3-ai framework, shows relevant benefits, including reduced handovers. Moreover, the consideration of digital twins allows adaptive learning and scalability, allowing networks to respond more effectively to dynamic user behavior and environmental modifications. These results underline the potential of combining DQL and digital twins as an efficient solution for smart handover management, and thus, for sustainable and efficient communication in 5G/6G networks.

Relevance. In long-haul decameter-band (LDB) communication systems, the number of bit errors is simultaneously influenced by two key factors: the signal-to-noise ratio and the degree of phase distortions caused by the Doppler shift, which arises from the random movement of ionospheric inhomogeneities. The challenge of improving the interference resistance of such systems is complicated by the fact that even with a high signal level at the demodulator's input, reception can be hindered by phase distortions, leading to a sharp increase in the number of bit errors and a degradation of the BER (bit error rate) coefficient. Despite the sufficient number of classical works, the problem of enhancing the interference resistance of modern domestic LDB radio communication systems under specified operating scenarios using contemporary methods and digital signal processing tools remains relevant and in demandich lead to a sharp increase in the number of bit errors and a deterioration in the BER coefficient. Despite a sufficient number of classical works, the problem of improving the noise immunity of modern domestic LDB or short-wave (HF) radio communication systems in specified operating scenarios using modern methods and means of digital signal processing (DSP) remains relevant and in demand. The object of the research is modern domestic decameter-band radio communication systems, which often exhibit known shortcomings, including low adaptability to changes in the ionosphere and issues with signal interference. For example, the R-016 system has limitations in its frequency range, making it less effective under varying ionospheric conditions that affect signal levels. Prototypes may also have problems with signal processing, which leads to bit errors of up to 10-3 even in the absence of noticeable interference. The subject of the study is the models and methods of functioning of LDB decameter-band communication lines.The objective of the research is to evaluate the impact of various factors, such as changes in preamble length and the use of adaptive filters, on the interference resistance of the system. The analysis of the obtained results shows that increasing the preamble length in such systems contributes to enhancing the interference resistance of long-haul communication. The scientific novelty lies in the improvement of existing radio propagation models in the decameter range by applying a complex of parameters that includes the signal-to-noise ratio set for a specific communication session in the radio line, and to increase the accuracy of field strength calculations at the receiving point, recalculated values of critical frequencies based on electron concentration forecasts, as well as Doppler shift for each layer of the ionosphere. The practical significance of the results lies in the enhancement of the interference resistance of existing decameter-band communication systems in ionospheric propagation conditions.

Currently, data mining based on machine learning plays a key role in decision support in various industries. An important practical problem of machine learning is the implementation of object classification in real time, which can be achieved by parallelizing data processing algorithms both for input data and for decision function data. To improve the efficiency of parallelizing machine learning methods, a unified decision function model has been developed. The Relevance of this research is to present a unified decision function model in the framework of machine learning algorithms and functions for its parallelization both in terms of input data and decision function data.The essence of the presented approach is that the features of the operation of metric methods of machine learning are analyzed, independent data for processing are presented using different categories of the analyzed property, developed decision function model describes the object features for input data and decision function data using standardized elements and including functions for their parallel processing based on group parallelization of objects. The proposed approach is based on the use of methods for analyzing algorithms and computational complexity, mathematical statistics and the methodology of designing parallel algorithms. Experiments have shown that parallelization of the proposed decision function model for the potential function method allows increasing the classification efficiency for one object using additional computing resources, and for a group of objects within the limits of the computer's memory size or planning horizon.The novelty of the proposed approach is that the model differs from existing ones in a method of formalizing objects and their features using unified elements for training and classification objects and has a structure and functions oriented towards its parallel processing by pattern recognition methods based on decision functions within the framework of group parallelization of objects. Theoretical significance: the model is unified and can be used to parallelize other pattern recognition methods that can be described by similar parameters, architecture, and classification features. The practical significance of the proposed approach is that the model allows decomposing the pattern classification problem into separate subtasks of finding regularities between input data and decision function data.

The article discusses methodological approaches to the construction of multicommodity hierarchical communication networks and identifies the main tasks of processing and distribution of discrete correspondence flows, which allow to create favorable conditions for reducing material, financial and labor costs in transport systems with further mechanization and automation of production. Multicommodity communication networks are characterized by the presence of a multitude of sources and drains of correspondence flows (products or requirements). Correspondence is understood as a pair of different network nodes, between which there is a directed (addressed) discrete flow of elements (for example, indivisible loads of uniform size, bits or symbols) of a given size. In a multicommodity network, all correspondence flows are subject to a one-time transfer from sources to drains. In general, a certain set of types (categories) of correspondence can be defined on the network, differing in weight, dimensions and other characteristics, but having common sources and drains. The hierarchical structure of the network and the principles of sorting, distribution and routing of flows are given.

Objective. When integrating the ELK stack into an information system, it is necessary to have a duplicate Java application in a closed circuit for hidden anomaly processing. It is necessary to develop the architecture of a Java application for hidden integration with the information system.Method. The research used methods of analyzing information in information system logs, static analysis methods, programming for application development, and data processing algorithms.Result. An example of implementing the Elasticsearch stack for processing and storing logs is presented. An implementation of anomaly analysis using the official Elasticsearch library is proposed. Options for using Elasticsearch for anomaly analysis are considered, an implementation of anomaly analysis using the official Elasticsearch library is proposed. The architecture of a Java application integrated into an information system for automated log analysis in order to detect computer attacks or signals of their onset by searching for anomalies is proposed. Variants of anomalies in information system logs are considered and actions for their detection are described. A generalized map of the Java application workflow is demonstrated.Conclusion. The architecture of a Java application implementing the analysis of logs of an information system for key anomalies has been developed.

In recent twenty years fiber optic sensors using Mandelstam Brillouin scattering effect have obtained a wide application in distributed measurements of temperature and deformation. Their application in engineering structures requires the analysis of functional parameters under conditions of noise interference onto backscatter signal spectrum. The research presents a mathematical model for measurement process based on stimulated scattering emphasizing on the measurement errors analysis. The formulas are presented for evaluation of mean quadratic errors in temperature and deformation measurements depending on noise-to-signal ratio and number of measurement cycles. In addition, methods to assess spatial resolution and dynamic range are proposed. Variations in methods for detection and processing backscattering spectra make it possible to produce efficient sensor systems which can monitor parameter change in real time.

The purpose of this study was to determine the market reaction to changes in stock prices on the Russian-Ukraine war event on the Indonesia Stock Exchange in 2022. This research using Bayesian analysis Approach on banking stocks. The results show that the AAR and CAR Before-After of the three models do not show significant results or market anomaly. The implications of the results of this research for investors and researchers that the events of the Russian-Ukraine war in 2022 can be taken into consideration for investors to be more rational in responding to political events in making investments in both the financial sector or other sectors, and for other researchers the EMH theory is no longer relevant to recent world developments. The limitations in this study are only in the financial sector in banking stocks, and the future big agenda will be expanded in several sectors, namely manufacturing, infrastructure and technology on the Indonesia stock exchange. The novelty in this research is that it uses the Bayesian analysis approach to the three models of approaches that have been and are often used, but in this case it is very different from previous researchers who have never used the Bayesian analysis approach

The object of the research is the process of ensuring the protection of data transmission in communication channels of critical infrastructure objects based on mobile and smart technologies. The development of quantum computing technologies based on Grover and Shor algorithms provides practical cracking of symmetric and asymmetric cryptosystems in polynomial time. The emergence of systems based on artificial intelligence allows creating hybrid systems for detecting weaknesses (critical points) in security systems not only on critical infrastructure objects. In addition, a full-scale quantum computer will open a new era of implementing post-quantum cryptography algorithms. Among the winners of post-quantum algorithms, the crypto-code constructions (CCC) of McEliece and Niederreiter are separately highlighted, which allow to provide the required level of protection and the required level of reliability of information transmission in an integrated manner. But a significant drawback is the possibility of cracking such systems on linear codes, as well as the need to build them on the Galois field 210–213, which significantly reduces their use in low-capacity systems based on smart and mobile technologies. To solve this drawback, the work proposes the use of a symmetric CCC based on the Rao-Nam scheme on algebrogeometric and flawed codes, which provides the possibility of significantly reducing the volume of key data (construction of CCC over the Galois field 24–26). When using the Rao-Nam CCC, a quantum symmetric algorithm is formed, which ensures the preservation of the level of stability and reliability of information transmission (safe time 1025–1035). This approach provides the possibility of forming intelligent information protection systems (IIPS). The given structural scheme of the IIPS construction ensures timely detection of threats with an assessment of the computational and financial and human capabilities of attackers, as well as the use of the necessary CCC/algebraic (flawed) codes to ensure the required level of security