Vol 29, No 12 (2023)

The paper is devoted to the issues of organizing the interaction of the new generation National Research Computer Network (NIKS) with individual Russian regional research and education telecommunications networks in the course of the development of NIKS within the framework of the national project "Science and Universities". A brief excursion into the history of the creation and development of academic networks within the territory of St. Petersburg is given. The main attention is paid to the organizational, methodological and technical aspects of the implementation of activities for the integration of the Regional Unified Computer Network for Education and Science of St. Petersburg (ROKSON) into NIKS.

The COVID-19 pandemic in 2023 can be divided according to characteristics into groups of regional epidemics. Local processes demonstrate oscillatory dynamics in the form of special forms of oscillations. COVID waves and peaks cannot be considered classic damped relaxation oscillations. Often, the damping amplitude of infection waves formed after the initial outbreak again turns into an extreme peak. The reasons may be effects after crisis events: mass infections and/or an increase in virulence of a new strain that evades vaccine immunity. Using simulation tools, we analyzed a number of variants of epidemic dynamics with sudden changes. We have identified special important epidemic scenarios for the sudden occurrence of a short wave and computationally investigated on the basis of the proposed nonlinear equations with a deviating argument as the most likely development option for a given parametrically simulated situation. Each COVID outbreak is a positive feedback loop: the more infections, the higher the likelihood of further mutations in the virus and strains evading antibodies. At the end of 2023, after a long summer minimum, the incidence rate resumed due to strains of the new Omicron branch of BA.2.xx "Pirola".

The problem of recognition of an underwater pipeline (UP) from stereo images using an autonomous underwater robot (AUV) is considered in relation to the initial position of tracking the UP, or to situations where the previous section of the UP is hidden by interference (submerged in the ground, hidden by algae, etc.). The final result of the identification of the UP section, visible by the stereo camera, is the calculation of its center line and the detection of the relative position of the AUV and UP in the camera coordinate system. The article proposes a recognition method based on the selection of visible UP boundaries (contours) on vectorized images of a stereopair. At the stage of vectorization, noise is eliminated, illumination is equalized, and the image is processed using the Canny method to obtain a binary image. The construction of UP contours is performed using the algorithm proposed by the authors, which is a modification of the Hough method. The main feature of the proposed algorithm is a relatively high performance due to a multiple reduction in the amount of information being processed. Reducing the volume of processed data is done by pre-sorting the line segments in the vectorized image, and by optimizing the computational scheme in the algorithm. The experiments also showed that the algorithm can detect the visible boundaries of the UP on blurry, non-contrasting images. The algorithmic basis of the method is described in detail, including:

• search and construction of the most reliable UP boundaries using the method of the integral contribution of the line segments to the line formation;
• generation and selection of point features belonging to the surface of the UP (due to the constructed contours);
• calculation of the 3D direction of the center line;
• calculation of the center line of the visible section of UP;
• calculation of the AUV position parameters relative to the UP required for the AUV control system.

The centerline calculation is performed using the least squares method using point features belonging to the surface of the UP. The performed computational experiments on virtual scenes using the real texture of the seabed confirm the operability of the implemented approach and the possibility of its application for the inspection of underwater infrastructure.

Cardinality estimation (CardEst) plays an important role in creating high-quality quer y execution plans in the DBMS. In the last decade, a large number of methods have been developed: traditional methods (histograms, samples), machine learning methods based on queries or data. But all of them are based on different restrictions and assumptions, and the cardinality estimation with their help worsens with an inc rease in the number of joined tables. The article proposes two new methods based on the theory of approximate calculation of aggregates and allowing to remove most of the restrictions. Method 1 fetches blocks after executing the subquer ies of the original query and does not require a preliminary analysis of the filter conditions. The condition for joining tables can be arbitrary (not necessarily the equality of attributes). Method 2 allows you to calculate the probabilities of reading blocks based on the metadata accumulated in the process of populat ing the database. Metadata takes up little memory, and the overhead of maintaining it is low. Method 2, in contrast to method 1, takes into account the true cardinality of the connection of neighboring blocks in the sample obtained from metadata. Therefore, the prospect opens up for a more accurate assessment of the cardinality of joining a large number of tables. Method 1 (EVACAR method) is implemented and compared with modern machine learning methods BayesCard, DeepDB, FLAT on a special STATS test. The results of the experiments confirmed the effectiveness of the EVACAR method. The EVACAR method is more accurate or its maximum q-error is comparable to machine learning methods for 75–88 % of evaluated queries (subplans). In the future, it is planned to implement the 2nd method for assessing the cardinality of queries.