Controlling a robot swarm with a single remote controller is a challenging task, especially under unstable communication conditions where agents can temporarily lose the control signal, necessitating robust decentralized mechanisms for formation maintenance. This paper presents and tests a semi-centralized control system that enables an operator to coordinate the entire swarm as a unified entity. The system integrates centralized commands from a base station with decentralized position correction via the ESP-NOW protocol. To compare performance in maintaining a rigid formation, the Local Voting Protocol (LVP) and its Accelerated version (ALVP) were applied. Their effectiveness was evaluated in a simulation environment with a group of four drones through experiments involving sharp maneuvers (50° and 75° turns) and significant data packet loss simulations (50% and 80%). The results demonstrate that the Accelerated Local Voting Protocol (ALVP) offers significant advantages over the standard LVP, including faster formation recovery, lower mean positioning error, and greater stability. Specifically, in a series of 20 flight tests with a 50° turn, ALVP successfully maintained the formation in 17 cases, compared to only 3 for LVP, and also showed superior robustness under packet loss conditions. Therefore, the proposed semi-centralized approach using the ALVP protocol is an effective and robust solution for swarm formation control. Future work will focus on conducting physical experiments and integrating obstacle avoidance mechanisms.

This paper proposes an algorithm for short-term rain rate forecasting, RainCast (Rain Rate NowCast), for up to two hours. This area of meteorology, known as 'nowcasting', is one of the most important tools in many areas of human activity. However, its availability may be severely limited by existing ground infrastructure. In this paper, the authors aim to create a precipitation forecasting algorithm for one such territory using the Asia-Pacific region as an example, based on satellite measurements from the Himawari-8/9 spacecraft. The algorithm combines the advantages of deterministic and statistical approaches to solve the forecasting problem and is based on two neural networks. The first model, a modified version of the physically constrained neural network NowcastNet, generates a preliminary forecast of the general direction of precipitation movement at the mesoscale level. The second model, based on the CasFormer architecture, employs diffusion methods to post-process the initial forecast, refining fine-scale details. The resulting hybrid algorithm, named RainCast, enables short-term precipitation forecasting (up to 2 hours) with high spatiotemporal resolution (2 km, updated every 10 minutes), utilizing solely infrared satellite measurements. Satellite data are converted into precipitation intensity using the algorithm previously developed by the authors. Based on precipitation maps, training, validation, and test datasets were compiled for the algorithm development and forecast quality assessment. The proposed RainCast algorithm was trained on these datasets and compared with other state-of-the-art solutions such as NowcastNet, Casformer, and Earthformer. Analysis of performance metrics demonstrated that the hybrid RainCast algorithm achieves comparable accuracy. For a 2-hour forecast, the Root Mean Square Error (RMSE) was 0.88, the Probability of Detection (POD) was 0.78, the Pearson Correlation Coefficient (PCC) was 0.75, the Structural Similarity Index Measure (SSIM) was 0.91, and the Peak Signal-to-Noise Ratio (PSNR) was 36.63. Visual analysis of the forecasts confirmed that RainCast produces results closest to actual observations, primarily due to the diffusion model's ability to refine fine-scale spatial and temporal precipitation patterns.

Decision-making on information infrastructure (II) security for its sustainable functioning in the face of threats requires a tool to assess the sustainability of its individual elements. The application of the semi-Markov model to assess the stability of the functioning of elements of II exposed to threats in a direct setting is associated with the increasing complexity of the description of the object of delineation (parametric space) in step progression from the number of the considered impacts, which reduces its practical significance. However, no studies have been found in the scientific literature to reduce the complexity of the semi-Markov model. The article presents an approach to reducing the complexity of modeling by adopting correct assumptions when forming the initial data. Given the conditions under which it is possible to take a series. It was a cost to limit the applicability of the model by significantly reducing the complexity of the modelling. The problem statement and the modified transition graph are given. The novelty of the problem statement is to take into account the limitations on the available resources for the restoration of functionality of the element. To explain the physical essence of the modeling process, a thought experiment with a model is introduced. To solve the problem, the following methods were used: a) expert methods for extraction of initial data; b) mathematical models of private semi-Markov processes; c) methods of transformation of Laplace; r) methods of planning of experiment. Illustrative examples and graphs accompany the task sequence demonstration. As a result of the experiment, the regularities of the studied process, the existence of which was proved formally. As a result of the experiment, the regularities of the studied process were revealed, the existence of which was officially proved. The results of the study broaden knowledge about the application of methods of Markov processes to assess the stability of the functioning of AI elements in relation to the conditions of the impact of threats.

The problem of finding the absolute error of stepwise and linear interpolation of the control signal from uniform samples from it using models of composite harmonic half-waves is solved. Previously, during the inspection of the control object, the maximum values of the signal parameters and half-waves are determined: speed, acceleration and sharpness, there are no spectrum parameters. To determine the values of the intervals of uniform sampling of time, two groups of models of "harmonic half-waves" are considered. The first group of models is described by harmonic time functions whose parameters are consistent. The second group of models is described by composite harmonic functions of time, thereby the time parameters of the signals are consistent. It is proved that with an increase in the entropy of the maximum values of the signal parameters, the value of the time sampling interval increases without increasing the interpolation error. Thus, the entropy value of the signal parameters serves as an indicator of their inconsistency. The results of modeling and graphs obtained in the environment of the mathematical package are presented. The results are intended to optimize the loading of input tasks and primary information processing of processors in robust real-time automation systems, for example, used to control high-speed trains when braking in sliding or skidding mode.

We apply a machine learning model to determine the optimal strategy in an online auction for the rent of computing resources using the best-choice model. The best-choice model allows clients to minimize the expected cost of renting a computing resource based on the spot price distribution function. The spot price dynamics platform is investigated. The most suitable price distributions in an auction are the normal distribution and its mixtures. In this case, the problems of determining the number of components in the mixture and estimating its parameters arise. One of the well-known methods for determining the number of components in a mixture of normal distributions is the BIC criterion. The EM algorithm is a basic tool for estimating the parameters of a mixture of distributions if we know the number of components. However, parameter estimation by this method takes more time when both the sample size and the number of components of the mixture increase. To automate and expedite the process of determining the number of components for a mixture of normal distributions and estimating its parameters, a classification machine learning model based on a convolutional neural network is developed. The results of the model training and validation are presented. The suggested model is compared with other algorithms which do not use neural networks. The results show that the suggested model performs well in determining the most appropriate number of components for a mixture of normal distributions and in reducing the time spent on applying the EM algorithm to estimate its parameters. This model can be used in different arias, for example, in finance or for determination of the optimal strategy in an online auction for the rent of computing resources.