The article addresses the problem of increasing the capacity of the Baikal–Amur Mainline, which is part of the Northern Eurasian Corridor and provides the shortest route from Europe to the countries of the Asia-Pacific region via the ports of the Russian Far East. A key focus of the study is the Severobaikalsk section, which currently operates as a single-track system and is located in a specially protected natural area. In this regard, it is a limiting element since its reconstruction projects require environmental expertise. To tackle this issue, we develop a mathematical model based on semi-open queuing networks as a research tool. This modeling apparatus allows us to describe the features of the considered transport system, including single-track traffic, long sections between stations, which leads to the appearance of additional sidings, the presence of through and ring routes, and the significant influence of random factors on train operation. Based on the results of a numerical study of the model, we evaluate the current and maximum permissible section loads and point out bottlenecks in its structure. We compare two alternative ways of increasing the section capacity: creating a double-track connection on the most problematic sections and using a partially batch train schedule. Based on the results of the calculations, we conclude about the possibility and conditions of their implementation.

A geographical atlas is a systematic collection of geographical maps, prepared according to general goals as an integral work. Considering the general demand, complexity, and systematicity of the information presented in atlases, it becomes relevant not only for a visual and convenient presentation of maps for users, but also for integrating the atlas with other information systems within a single information space. The purpose of the study is to develop and apply Web services for publishing spatial data. Within the service-oriented information and analytical environment, the entry point to which is the geoportal of the IDSTU SB RAS, a technology has been created for developing services for publishing spatial data based on a data model that combines requirements and specifications for the data structure, legend, and user interface for data editing. The technology developed for creating services for publishing spatial data will make it possible to create atlas maps and provide the ability to use atlas data for computing services that are implemented by users and use them in various modeling, analysis, and forecasting systems, including various geographic information systems, for example QGIS, ArcGIS, etc.

Russia is faced with the tasks of apperceiving the historical, modern, and predicted role of the country’s eastern regions in the new geopolitical and economic environment. The production of a new atlas of Asian Russia by using newly developed and created spatial databases, as well as previously issued maps and atlases based on the system approach to creating cartographic models at different scale levels, can be an important tool for solving these tasks. The new atlas will make it possible to make an inventory and perform a visualization and a scientific analysis of the geopolitical, historical–geographical, resource–economic, sociodemographic, and ecological situation in the largest macroregion of Russia. New types and themes of the maps will be proposed for the first time and will make it possible to determine the transition models of different territories in Siberia and the Far East to sustainable balanced development. The atlas will help in a scientific understanding of the processes in nature and society; contribute to the development of naturelike technologies and human–machine systems for environmental and ecosystem management; and display the regional uniqueness of the ethical aspects of technological development—the main changes and transformations of social, political, and economic relations. The atlas will consist of two volumes: (1) the archaeology, history, and natural and cultural heritage of Asian Russia and (2) the nature, economy, population, and environmental situation of Asian Russia. The volumes will be accompanied by texts, plots, and photos. The atlas is planned to have an electronic version in 2024–2026 and will be issued in print in 2027–2028. Institutes of the Siberian and Far East Branches of the Russian Academy of Sciences, higher educational institutions of Siberia and the Far East, and the Faculty of Geography of Moscow State University will be involved in preparing the atlas. The principal coordinator of the project is the Sochava Institute of Geography, Siberian Branch, Russian Academy of Sciences, which has extensive experience in the production of atlases.

Предложен новый подход к созданию сервис-ориентированной вычислительной среды для моделирования и оценки функционирования и развития природно-технических систем с использованием высокопроизводительных вычислительных систем. Представлена схема прогнозирования развития и оценки состояния локального энергетического комплекса. Апробация предложенного подхода выполнена на примере решения задачи комплексной оценки критичности элементов транспорта локальной системы газоснабжения.

We propose the agent-based approach to intellectualize data processing and analysis in modeling the operations of interconnected microgrids. Microgrids are modern energy systems with a large share of environmentally friendly and resource-saving equipment. The modeling of different aspects for the organization and operation of microgrids is relevant for their structural and parametric optimization. The study of these aspects in the interaction of microgrids deserves special attention. In this case, it becomes possible to take into account the synergistic effect in the distribution and consumption of electric power. We implement the modeling process using a multi-agent system. In this regard, we describe the structure of this system, methods of its construction, and goals and processes of agent operations. In addition, we provide a model of agent behavior developed based on the basis of a finite-state control machine. The advantages of the proposed approach are demonstrated on a model example of balancing generation and consumption of electric power.

Unknown actuator failures are inevitable in practical systems. At the same time, time delay exists in many physical actuators, and the system performance will be affected by such actuator delay and faults. However, the results of studies that attempt to compensate for unknown failures of actuators with time delay are still very limited. In this paper, such a problem is studied, and an adaptive control scheme is proposed based on backstepping approaches. First, the input delay of actuator faults and output disturbances are transformed into unknown effects on the output signal. In the backstepping recursive design, these unknown effects will accumulate to the last step of the controller design. Then, a new Lyapunov function is constructed by introducing auxiliary signals to prove the stability of the system. It is shown that the proposed control scheme can compensate for the effects caused by unknown actuator failures and input delays. The stability of the closed-loop system can be guaranteed by this adaptive controller. Finally, simulation studies are used to verify the effectiveness of the proposed scheme.

The Baikal Natural Territory (BNT) and Khuvsgul region have similar environmental problems. It is relevant to carry out integrated scientific studies and monitor the state of the components of the natural environment in these areas. This paper presents the results of a number of joint Russian–Mongolian projects aimed at studying and developing new methods and technologies for integrated environmental monitoring and prediction. A digital platform has been created to support scientific research and environmental monitoring. This platform makes it possible to collect, store, process, and analyze large arrays of heterogeneous spatiotemporal data and predict environmental situations using a set of mathematical and information models, services, and machine learning methods. The authors have also developed methods and web services for environmental monitoring based on the processing of Earth remote sensing (RS) data. A technology for classifying multispectral Sentinel-2 satellite images has been created that makes it possible to distinguish 12 classes of the land cover using artificial intelligence methods. A service for monitoring the state of the atmosphere over large areas has been created based on the processing of Sentinel-5P satellite data. This service makes it possible to display the concentrations of SO 2 , NO 2 , CO, CH 4 , H 2 O, O 3 , formaldehydes, and aerosols in the air.

Forecasts of the forest ecosystem dynamics are important for environmental protection and forest resource management. Such forecasts can support decisions about where and how to restore damaged forests and plan felling, and in forest conservation. Forest landscape models (FLM) are used to predict changes in forests at the landscape level. FLM initialization usually requires detailed tree species and age data; so, in the absence of forest inventory data, it is extremely difficult to collect initial data for FLM. In our study, we propose a method for combining data from open sources, including remote sensing data, to solve the problem of the lack of initial data and describe initializing the LANDIS-II model. We collected land cover classification and above-ground biomass products, climate, soil, and elevation data to create initial vegetation and ecoregion maps. Our method is based on some simplifications of the study object—some tree species are replaced by groups of species; the forest stand is considered homogeneous. After initialization, the natural dynamics without harvesting and disturbances were simulated by the Biomass Succession extension for 200 years. The study presents a detailed methodology that can be used to initialize other study areas and other FLMs with a lack of field data.

Статья посвящена проблемам взаимодействия сервисов, создаваемых разными разработчиками при формировании информационно-аналитических систем поддержки научных исследований. Для унификации параметров вычислительных сервисов предложены требования и рекомендации к создаваемым сервисам. Рассмотрены компоненты разработанной авторами цифровой платформы экологического мониторинга Байкальской природной территории, обеспечивающие алгоритмизированное взаимодействие всех участников исследований на основе композиций сервисов. To implement digital environmental monitoring of the Baikal natural territory, a digital platform has been developed at IDSTU SB RAS. Researchers from 16 scientific institutes, as well as employees of departmental organizations are collaborators of the project that provides support for a comprehen- sive analysis of monitoring data, forecasting and development of recommendations for the conservati- on of Lake Baikal and surrounding areas. The CPU is implemented on the basis of a service- oriented architecture that provides the ability to create services for collecting, presenting and processing data to all research participants. New services can be formed, among other things, based on a composition of services previously created by various platform participants. The article discusses the problems of interaction between services created by different developers. To unify the parameters of computing services, requirements and recommendations for the services being created are proposed. The components of the digital platform for environmental monitoring of the Baikal Natural Territory, developed by the authors, are considered, providing algorithmic interaction of all research participants based on service architecture. The approach proposed by the authors allows partially solving the problem of unifying services according to the types and formats of input and output data and the appropriate sources. The developed set of software components is actively used in practice. More than 200 data services and more than 40 data processing services have been created.

В статье рассматривается задача оценки пропускной способности Улан-Баторской железной дороги (УБЖД), которая является частью наиболее короткого транспортного коридора из Центральной России в Северо-Восточный Китай. Отличительной ее особенностью является то, что УБЖД в основном имеет однопутное сообщение, вследствие чего применяется пакетный график движения поездов, существенно усложняющий технологию работы. Исследование проводится методами математического моделирования с применением теории массового обслуживания. Модель имеет вид сети массового обслуживания, поступление пакетов поездов описывается при помощи BMAP-потоков, для учета различных маршрутов движения поездов используется несколько типов заявок. Выполняются численные расчеты, на их основе определяется текущая пропускная способность УБЖД и проводится сравнительная оценка эффективности возможных вариантов ее модернизации. The article considers the problem of assessing the capacity of the Ulaanbaatar Railway, which is a part of the shortest transport corridor from Central Russia to Northeast China. In its current state, Mongolian railways cannot satisfy the growing demand for transit traffic, so the problem of increasing their capacity is essential. To find ways of solving this problem, we analyze the running of trains along the northern (most loaded) part of the trunk railway. Mathematical modelling based on the queuing theory is used as a research tool. The model has the form of a queuing network with 40 nodes, four BMAP flows, and four types of requests, for each of which a separate route matrix is constructed. The model describes the most significant properties of the Ulaanbaatar railway for the study, such as a single-track system of running, the arrival of train packages from several directions, the non-linear structure of the network, and the different routes of the train traffic. The mathematical model is implemented in the form of a simulation model and numerically studied. Based on the results obtained, we can draw the following conclusions. First, to meet the demand for container transportation in the short term, it is enough to increase the number of tracks at the sidings. Secondly, the complete reconstruction of the Ulaanbaatar Railway with the construction of double- track lines will allow bringing the railway corridor for passing through the territory of Mongolia. The major outcome is expected to be the volume of container transit among all land transport corridors between Russia and China. In addition to solving the important practical problem, the research results suggest that the presented highly adaptive model-algorithmic apparatus allows to studying a wide range of transport systems affected by random factors.

Different problems of strategy planning and control of a mobile robot group under complex dynamicconditions with incomplete information about the external environment are considered. Approachesto solving problems of effective work scheduling under conditions of inconstant active groupcomposition, searching for the source of a nonstationary concentration field, supervisory control ofdiscrete-event systems are presented. An original mathematical model formulated in terms of work-shiftscheduling problems and a problem-oriented modification of evolutionary algorithms with a specializedset of heuristics for its efficient solution are developed for the problem of scheduling top-level groupwork. Searching and monitoring the source of the nonstationary concentration field is carried out usinga decentralized multi-agent control strategy that combines elements of bionic and gradient approaches,as well as a method for generating artificial potential fields. The considered control strategy has lowcomputational complexity, high variability with respect to the types of fields surveyed, and is easily scalableto control any available number of mobile robots. The latter is of special importance, in particularwhen considering the problem of parallel and independent monitoring of multiple sources. It is proposedto use the means of logical inference, namely automatic theorem proving in the calculus of positivelyconstructed formulas, to solve various problems of the supervised control theory of discrete-eventsystems used at different levels of the robotic complex hierarchical control system. Features of the calculusallows solving complex problems of dynamic systems control, as well as processing and controllingevents based on environmental data in real time in the process of logical inference efficiently. Theapproach based on positively constructed formulas allows studying the properties of automata-baseddiscrete-event systems, as well as to synthesize and model finite automata for the construction and realizationof monolithic and modular supervisors. A general scheme combining the considered approachesfor controlling a group of mobile robots at different levels and time scales within a single hierarchicalcontrol system is proposed.

Characteristic features of the Baikal natural territory (BNT) are analyzed and the problems of forest monitoring are highlighted. An approach is proposed for the digital transformation of forest resource monitoring using a service-oriented paradigm, an infrastructure approach, and declarative specifications, as well as end-to-end and Web technologies for collecting and processing large amounts of spatiotemporal data. A scheme of a digital forest monitoring platform based on an information–analytical geoportal environment is described, including a system for processing and storing spatiotemporal data and a catalog of basic and thematic services for assessing the consequences of natural and anthropogenic impacts on forests of the BNT. The experience of using deep learning methods to monitor changes in the state of forests is presented. An automated determination of land cover types is carried out on the basis of Sentinel-2 images. The composition of classes of the training data set created for the BNT is described. The result of satellite image classification with the identified land cover classes is given. The digital platform (DP) thus created can be used to assess and predict the state of forest resources of the BNT and make managerial decisions on effective forest management.

The solution of environmental monitoring problems reasonably requires the collection, digitization, storage, and analysis of a large volume of spatiotemporal data. Their processing frequently necessitates the application of distributed calculations. The known geoinformation systems do not usually contain the means, which could support the calculations of this kind to a full extent. An approach to the solution of this problem is proposed on the basis of a geoinformation system integrated with the means of the development and use of distributed scientific applications presented by web data processing services in a heterogeneous computational environment. An advantage of this approach consists in a decrease in the labour efforts in the preparation and implementation of experiments with geodata.

Статья посвящена проблеме интеллектуализации обработки и анализа данных в исследовании процессов функционирования микросетей как экологически чистых и ресурсосберегающих систем энергетики. Процесс моделирования взаимодействия микросетей реализуется мультиагентной системой. Рассмотрены структура мультиагентной системы, средства ее построения, назначение и процессы функционирования агентов. Разработана модель поведения агента, базирующаяся на использовании конечного управляющего автомата. Мультиагентная система ориентирована на поддержку исследования живучести автономных энергетических систем инфраструктурных объектов Байкальской природной территории. A microgrid is a network having a diverse spectrum of energy generators and storage devices. Generally, it has a relatively small local loads. Energy management in such networks has a number of advantages, such as reducing power losses and simplifying the management process. To this end, the need to study various aspects of organizing and operating microgrids from the point of view for managing their functioning is significantly increases. In this regards, the paper addresses an intellectualization of data processing and analyzing within the study of micro-grids as environmentally friendly and resource-saving energy systems. We consider microgrid interactions under external disturbances. The process of modelling the interaction of microgrids is realized by a multi-agent system. The multi-agent system structure, tools of its construction, and purpose and functioning of agents are considered. Moreover, we developed and presented a model of agent behavior that is based on the use of a finite control machine. The multi-agent system is implemented using the JADE framework. To reduce labor costs for creating agents, we have developed a special add-on to JADE for determining the agent behavior. The considered multi-agent system is focused on supporting the study of the resilience of autonomous energy systems for infrastructure objects located on the Baikal natural territory.

Various combinations of neural network parameters and sets of input data for satellite image classification are considered in the article. The training set is completed with a NDVI (normalized difference vegetation index) and local binary patterns. Testing of classifiers created on a different number of epochs and samples is carried out. Values of the neural network hyperparameters are determined that allow a classification accuracy of 0.70 and an F-measure of 0.65 to be achieved. Separation into classes with similar spectral characteristics is shown to offer low classification quality at different parameters and input data sets. Additional information is required. For exam-ple, for forests to be divided into more detailed classes, one needs to employ classifiers that use images from different seasons and vegetation periods. In addition, the training set needs to be ex-tended to take into account various natural zones, soils, etc.

Baikal Natural Territory (BNT) is the territory that adjacent to Lake Baikal, which is a unique natural object and, in accordance with the UNESCO Convention, a "World Natural Heritage". Baikal is in the central part of the Baikal Rift Zone (BRZ) – the most active seismic zone located in the middle of Russia. The development of the BRZ leads to the emergence of dangerous geological processes that can lead to a violation of the balance in the Lake Baikal ecological system and the surrounding area. In addition, these processes and phenomena pose a real threat to the smooth functioning of mainline communications, hydroelectric power plants and strategically important industries in the region, which, according to the classification of the Ministry of Emergency Situations of Russia, belongs to the first category of danger. To ensure systematic monitoring and forecasting of the environmental situation of the BNT, systematic observations are organized, as well as obtaining and analysing information about the activity of hazardous geological processes in digital form. The digital transformation of monitoring of hazardous geological processes, resulting from the digitalization of processes and the development of appropriate infrastructure, provides the possibility of using new models and methods, more flexible approaches to the analysis of ongoing processes and the prediction of possible extreme events. In this paper, a digital platform is proposed that provides support for the digital transformation of the monitoring of hazardous geological processes using the example of BNT. The platform under consideration may be used for ecological monitoring of BNT area.

Nowadays, the development and use of workflow-based applications (distributed applied software packages) are some of the key challenges in terms of preparing and carrying out large-scale scientific experiments in distributed environments with heterogeneous computing resources. The environment resources can be represented by clusters of personal computers, supercomputers, and private or public cloud platforms and differ in their computational characteristics. Moreover, the composition and characteristics of resources change in dynamics. Therefore, computations planning and resource allocation in the considered environments are important problems. In this regard, we propose new algorithms for computation planning taking into account redundancy and uncertainty in such distributed applied software packages. Compared to other algorithms of a similar purpose, the proposed algorithms use evaluations of workflow execution makespan obtained in the process of continuous integration, delivery, and deployment of applied software. The proposed algorithms provide the construction of redundant problem-solving schemes that allow us to adapt them to the dynamic characteristics of computational resources and improve distributed computing reliability. The algorithms are based on a theory of conceptual modeling computational processes. We demonstrate the process of constructing problem-solving schemes on model examples. In addition, we show the utility in using redundancy for increasing the distributed computing reliability In comparison with some traditional meta-schedulers.

The rapid development of marine robotic technology in recent decades has resulted in significant improvements in the self-sufficiency of autonomous underwater vehicles (AUVs). However, simple scenario-based approaches are no longer sufficient when it comes to ensuring the efficient interaction of multiple autonomous vehicles in complex dynamic missions. The necessity to respond cooperatively to constant changes under severe operating constraints, such as energy or communication limitations, results in the challenge of developing intelligent adaptive approaches for planning and organizing group activities. The current study presents a novel hierarchical approach to the group control system designed for large heterogeneous fleets of AUVs. The high-level core of the approach is rendezvous-based mission planning and is aimed to effectively decompose the mission, ensure regular communication, and schedule AUVs recharging activities. The high-level planning problem is formulated as an original acyclic variation of the inverse shift scheduling problem, which is NP-hard. Since regular schedule adjustments are supposed to be made by the robots themselves right in the course of the mission, a meta-heuristic hybrid evolutionary algorithm is developed to construct feasible sub-optimal solutions in a short time. The high efficiency of the proposed approach is shown through a series of computational experiments.

The paper considers a problem of classifying Sentinel-2 multispectral satellite images for environmental monitoring of the Baikal Natural Territory (BNT). The specificity of the BNT required the creation of a new set of 12 classes, which takes into account current problems. The set was formed in such a way that the areas corresponding to these classes completely covered the BNT. A training dataset was formed using a web interface based on Sentinel-2 satellite images. The classification of satellite images was carried out using Random Forest algorithms and the ResNet50 neural network. The accuracy of the calculations showed that the classification results can be used to solve actual problems of the Baikal natural territory, in particular, to analyze changes in the forestland, assess the impact of climate change on the landscape, analyze the dynamics of development activities, create farmland inventory, etc.