Abstract:

By the radiosounding of the ionosphere with short-wave signals, can be obtained information about the processes in the ionospheric plasma, about its structure and state; these data are also extremely important for radio engineering systems operating in the short-wave range. To date, a large amount of experimental data has been accumulated for various geo- and heliophysical, spatial and temporal conditions. The interest in large amounts of ionospheric radiosonde data is also motivated by the possibility of constructing statistical models using machine learning theory methods. The paper presents some Internet resources with ionospheric radiosonde data, shows the prospects for their application, and also identifies some problems, such as insufficient documentation of some data formats and the presentation of ionograms only in the form of raster images, most of which are also scanned from photographic films.

Abstract: Mass identification of a list of radio sources with the sky surveys of different ranges of the electromagnetic spectrum has undoubted interest to astronomers. Identification of the radio sources is not a straightforward procedure because of the different angular resolution, sensitivity limit, coordinate precision  of the radio catalogues, as well as due to the morphological structure of radio sources themselves.

Abstract:

The paper discusses the progress in the development of network services and technologies of the geoportal of satellite radiothermovision created by the authors earlier. The initial task of the geoportal was to provide potential users access to the results of the spatiotemporal interpolation of geophysical atmospheric fields. Practical implementation required the implementation of a technology of dynamic generation of processing products. The use of this technology had expanded the range of tasks, and led to the creation of a specialized service of the virtual integration of spatial geodata ICAR (Interactive Calculator for Atmospheric Research). The paper describes the interface and technological principles underlying the ICAR project, discusses some details of software and algorithmic implementation, provides examples of practical applications.

Abstract:

he ontology-based approach to multi-agent modeling involves the implementation of a modeling system through the creation of ontologies. An example of a holistic implementation of an ontology-based approach to agent-based modeling is the IEEE 1516 Standard for Modeling and Simulation High Level Architecture. The work is devoted to a multi-agent modeling system designed for modeling complex radio engineering systems (especially radar systems), its relevance is due to the need to replace part of the field tests of radio engineering systems with simulation experiments. The motivation for switching to the IEEE 1516 standard for a "heavy" multi-agent modeling system, among other things, is to ensure scalability, openness and multiple reuse of the developed agent models, which is completely logical to do based on the existing well-developed and proven standard that establishes rules for the interaction of models and the development of software interfaces. The general principles of construction and architecture of the modeling system are given. The basic requirements for the main modeling agents, their role and place in the complex modeling system are shown, a special place among which is occupied by the simulator of the background-target environment. The possibility of combining two simulation schemes is also discussed: discrete-event and step-by-step. The fact is that the step-by-step scheme has advantages such as simplicity and clarity, it is convenient to model processing algorithms, components of radio engineering systems. However, it is impossible to implement true autonomy and asynchrony of agents in it. Combining two modeling schemes allows you to combine their advantages.

Abstract:

Automatic processing of ionospheric radiosonde ionograms is necessary both for operational monitoring of the ionosphere and for analyzing archived radiosonde data arrays. The central place in this task is occupied by automatic detection and classification of tracks – traces of reflected radio signal modes on ionograms. This paper presents the methodology, results, and comparative analysis of the use of neural network models (neural network architectures) for Mask RCNN and YOLO instance segmentation. The most successful models were the YOLO family, which provide both high accuracy and computational efficiency. At the same time, the exact pixel–by-pixel matching of the masks is not critical – the key requirement is the correct localization of the useful signal. The use of these models to identify tracks on ionograms has demonstrated moderate but sufficient efficiency: the main structures of the tracks are reliably identified. The most successful models were the YOLO family, which provide both high accuracy and computational efficiency. In the future, the set of models used will be expanded, primarily due to visual transformers.

Abstract:

The process of applying Generative Adversarial Networks (GANs) for the synthesis of ionospheric sounding ionograms is considered. This task is relevant in the context of constructing a statistical model of the ionosphere through empirical search. This paper describes the specifics of oblique ionospheric sounding as a subject area, particularly outlining the origin of the data and its physical meaning. The dataset, which includes radio sounding session configurations, environmental parameters, and ionograms, is presented and characterized. A method for converting and preprocessing raw data to form a training corpus is described.

The paper presents the general organization of conditional GANs used in image generation tasks, discusses key modifications to their structure specifically for ionogram synthesis, and provides a schematic demonstration of the architecture. Furthermore, the specifics of loss function selection, based on knowledge of the physical meaning of ionograms, are identified. Based on these considerations, a composite loss function is formulated, combining the discriminator's response with heuristics. Finally, the work provides examples of the results obtained, formulates key existing implementation challenges, and proposes potential solutions.

Abstract:

The idea of using the available large arrays of ionogram processing results from vertical radiosonde of the ionosphere as training datasets for building predictive models using machine learning methods is put forward. The most common formats for saving the results of ionogram processing are considered, as well as some Internet resources with archives of freely available files of these formats. These datasets are used by us to build predictive models, including time series of critical frequencies of ionospheric layers. It is also possible to use some datasets of ionogram processing results to train models designed for automatic ionogram processing.

Abstract:

Abstract. The problem of synthesizing ionograms of vertical ionosphere radiosonde using physically informed neural networks is solved. In the proposed approach, the neural network is used not for direct image generation, but for approximating the high-altitude profile of the electron concentration. Network training is performed taking into account a stationary one-dimensional balance equation, including an ionization source, recombination losses, and effective transfer of charged particles. The loss function additionally includes boundary conditions, a restriction on the smoothness of the profile, and diagnostic parameters of the F2 layer. The resulting electron concentration profile is transformed into a height distribution of the plasma frequency. Therefore, without taking into account the Earth's magnetic field and collisions of electrons with other particles, the reflection points, the group refractive index and the dependence of the effective height on the sounding frequency are calculated. Based on this dependence, a synthetic image of an ionogram with background noise and vertical interference structures is formed. The results confirm the possibility of constructing a single physically consistent chain from the height profile of the electron concentration to the image of the ionogram. The developed approach can be used to generate controlled synthetic data, study the effect of ionospheric parameters on the shape of ionogram trails, and subsequently develop algorithms for automatic processing of radiosonde results.

Abstract:

In this paper, we discuss monitoring of the state of murals, retrieval of data pertaining to this state and management and storing of the said data. We discuss the possibility of integration of traditional methods of mural mapping and modern methods of data visualization, including Project Tango for fixation of complex textures of inner 3D volumes of architectural monuments. We further discuss the express-scanning of automated cartogramming for further comparison of states and methods of assessing the damage done to the mural.

Abstract:

The transformation of the russian higher education system over the last thirty years is considered in terms of missions and indicators changes is examined through changes in the texts of of the effectiveness of the activities of universities. The qualitative reforms table of the university activity main aspects is resulted. It is shown that there is observed the pragmatizm and concretization of the university mission statement. The evolution of reporting and ranking indicators is discussed.

Abstract: International Virtual Observatory (IVO) is a collection of integrated astronomical data archives and software tools that utilize computer networks to create an environment in which research can be conducted. Several countries have initiated national virtual observatory programs that will combine existing databases from ground-based and orbiting observatories and make them easily accessible to researchers. As a result, data from all the world's major observatories will be available to all users and to the public. This is significant not only because of the immense volume of astronomical data but also because the data on stars and galaxies have been compiled from observations in a variety of wavelengths: optical, radio, infrared, gamma ray, X-ray and more. Each wavelength can provide different information about a celestial event or object, but also requires a special expertise to interpret. In a virtual observatory environment, all of this data is integrated so that it can be synthesized and used in a given study. The International Virtual Observatory Alliance (IVOA) represents 17 international projects working in coordination to realize the essential technologies and interoperability standards necessary to create a new research infrastructure. Russian Virtual Observatory is one of the founders and important members of the IVOA. The International Virtual Observatory project was launched about ten years ago, and major IVO achievements in science and technology in recent years are discussed in this presentation. Standards for accessing large astronomical data sets were developed. Such data sets can accommodate the full range of wavelengths and observational techniques for all types of astronomical data: catalogues, images, spectra and time series. The described standards include standards for metadata, data formats, query language, etc. Services for the federation of massive, distributed data sets, regardless of the wavelength, resolution and type of data were developed. Effective mechanisms for publishing huge data sets and data products, as well as data analysis toolkits and services are provided. The services include source extraction, parameter measurements and classification from data bases, data mining from image, spectra and catalogue domains, multivariate statistical tools and multidimensional visualization techniques. Development of prototype VO services and capabilities implemented within the existing data centers, surveys and observatories are also discussed. We show that the VO has evolved beyond the demonstration level to become a real research tool. Scientific results based on end-to-end use of VO tools are discussed in the presentation.

Abstract: Today the Virtual Observatory tools provide easy access to astronomical data and improve the efficiency of researches. Based on the existing web technologies the astronomical community is developing on realization greater data semantic connectivity. This will put the virtual observatory services to a new level of exchange of not only data but also knowledge. However, there is no development of tools for actualization information collected from web resource, and a means of organizing heterogeneous data prepared by the user for scientific analysis, which could improve further work. We consider the approach to organization of diverse information for a studying object, based on the development of the existing formats of astronomical data and tools to support semantic data connectivity.

Abstract: A model of propagation of thermal fields in permafrost from various engineering objects operating in Arctic regions is considered. The proposed model includes the most significant technical and climatic parameters affecting the formation of thermal fields in the surface layer of the soil. The main objective of the study is a long-term forecasting of changes in the dynamics of permafrost boundaries during operation of cluster sites of northern oil and gas fields. Such a forecast is obtained by simulation of complex system consisting of heat or cold sources and frozen soil, thawing of which can lead to the loss of the bearing capacity and possible technogenic and environmental accidents. For example, the sources of heat can be production wells, and the sources of cold can be seasonal cooling devices that are used to stabilize the soil. To minimize the impact of heat sources on permafrost, various options for thermal insulation are used, and to preserve the original temperature regime of the top layer of soil, riprap materials consisting of sand, concrete, foam concrete, or other heat insulating material are used. The developed set of programs was used in the design of 12 northern oil and gas fields. To solve the described problem in a complex three-dimensional area, substantial computational resources are required. The computing time of one variant can often exceed 10–20 hours of machine time on a supercomputer. To speed up the numerical calculations, multi-core processors are used. Numerical calculations illustrate the possibility of a developed set of programs for making long-term forecasts for determining changes in the boundaries of the permafrost zones, and show that on multi-core processors it is possible to achieve acceleration close to the theoretical one.