Time series data analysis and forecasting tool for studying the data on the use of network traffic is very important to provide acceptable and good quality network services, including network monitoring, resource management, and threat detection. More and more, the behavior of network traffic is described by the theory of deterministic chaos. The traffic of a modern network has a complex structure, an uneven rate of packet arrival for service by network devices. Predicting network traffic is still an important task, as forecast data provide the necessary information to solve the problem of managing network flows. Numerous studies of actually measured data confirm that they are nonstationary and their structure is multicomponent. This paper presents modeling using Nonlinear Autoregression Exogenous (NARX) algorithm for predicting network traffic datasets. NARX is one of the models that can be used to demonstrate non-linear systems, especially in modeling time series datasets. In other words, they called the categories of dynamic feedback networks covering several layers of the network. An artificial neural network (ANN) was developed, trained and tested using the LM learning algorithm (Levenberg-Macwardt). The initial data for the prediction is the actual measured network traffic of the packet rate. As a result of the study of the initial data, the best value of the smallest mean-square error MSE (Mean Squared Error) was obtained with the epoch value equal to 18. As for the regression R, its output ANN values in relation to the target for training, validation and testing were 0.97743. 0.9638 and 0.94907, respectively, with an overall regression value of 0.97134, which ensures that all datasets match exactly. Experimental results (MSE, R) have proven the method's ability to accurately estimate and predict network traffic

Currently, it is more efficient to use LEO small spacecraft as radio control points to determine the compliance of the radiation parameters of electronic means with the relevant norms and conditions of permits for the use of the radio frequency spectrum. Such satellite radio monitoring systems make it possible to determine the parameters of signals and the location of radio-electronic means over a large area with a diverse terrain, which will increase the efficiency of radio monitoring systems. When performing such tasks, one of the most important parameters is the detection and isolation of a useful signal against the background of noise and interference. For such purposes, it is effective to use Kalman filters. It was found that the Kalman filter can detect and isolate useful signals against the background of noise and interference with very high accuracy. However, when solving such problems, problems arise related to the stability of the method to the choice of the initial state of the filter and the inevitable distortion of the frequency of the desired signal due to the presence of the Doppler effect. In the course of this study, it was found that the similarity coefficient depends on the noise level, but it does not actually depend on frequency distortion. It was also found that when the SNR ratio is greater than 0 dB, the radio signal at the input of the on-board measuring receiver will be received with a reliability of more than 90 %. Therefore, it can be concluded that the signal detection method for satellite radio monitoring based on the use of the Kalman filter is resistant to possible frequency distortions of the desired signal due to the Doppler effect and does not affect the correctness and speed of decisions made

The object of this study is the process of deorbiting the KazEOSat-1 spacecraft, which has completed its active service life in low Earth orbit. The main problem is the lack of an effective technique to deorbit KazEOSat-1, taking into account its technical characteristics, orbital parameters, and the need to minimize risks to the environment and other objects in orbit. As part of the work, a software model was built that takes into account the initial orbital parameters of the device, which are essential for planning and performing deorbiting maneuvers. The model is designed to accurately calculate the descent trajectory, taking into account the laws of celestial mechanics and the influence of atmospheric conditions. The optimal deorbiting strategy was selected based on an analysis of various methods for calculating orbital maneuvers aimed at reducing fuel consumption and minimizing environmental risks. This included a comparative analysis of existing approaches and the selection of the most suitable ones under the given mission parameters. The results of the simulation using precise modeling methods in the MATLAB software environment allowed us to determine the main deorbiting parameters, such as the altitude at which the maneuvers begin, the required velocity impulses, the total fuel consumption, and the expected time before entering the dense layers of the atmosphere. Based on the obtained data, practical recommendations were formulated for the KazEOSat-1 deorbit. The first stage, the active controlled deorbit, is carried out by operating the low-thrust engine and braking by the Earth’s atmosphere, allowing the spacecraft to descend from 758 km to 444 km in 2.5 days. The second stage, the passive uncontrolled deorbit, continues the descent to 103 km in 969 days, using only atmospheric braking. The third stage, the uncontrolled drop, begins after reaching 103 km and ends with a drop to the Earth in 834 seconds

In this paper influence of the excess Ca and Cu cations on the critical temperature (Tc) and critical transport current density (J_c) of high-temperature superconducting ceramics of the compositions (HTSC) Bi_1.6Pb_0.4Sr₂Ca_2.1Cu_3.1O_y, Bi_1.6Pb_0.4Sr₂Ca_2.25Cu_3.25O_y and Bi_1.6Pb_0.4Sr₂Ca₃Cu₄O_y synthesized by the glass-ceramic method has been studied. The synthesis of superconducting ceramics was carried out on the basis of the glass phase, obtained by ultra-fast quenching of the melt. Melting of the mixture of starting components was carried out without the use of a crucible under the influence of IR radiant heating. Analysis of the elemental composition of the samples of the initial precursors showed a significant deviation from stoichiometry in oxygen (increase), as well as a decrease in calcium content. The synthesis of HTSC ceramics was carried out at a temperature of 849–850 °C for 96 h with intermediate grinding every 24 h. Studies of the phase composition of ceramic samples by X-ray diffraction have shown that HTSC ceramics consist only of a superconducting high-temperature phase Bi-2223. Studies of current-carrying characteristics by the four-point probe method according to the criterion of 1 µV/cm² have shown that high-temperature superconducting ceramics of the compositions Bi_1.6Pb_0.4Sr₂Ca_2.1Cu_3.1O_y, Bi_1.6Pb_0.4Sr₂Ca_2.25Cu_3.25O_y and Bi_1.6Pb_0.4Sr₂Ca₃Cu₄O_y have an increased density of critical transport current of 9.12 A/cm², 7.62 A/cm² and 7.26 A/cm², respectively. At the same time, it was found that with a decrease in the content of Ca and Cu cations in HTSC ceramics, an increase in the critical current density is observed.

This study examines the effectiveness of neural network architectures (multilayer perceptron MLP, convolutional neural network CNN, recurrent neural network RNN) for human voice recognition, with an emphasis on the Kazakh language. Problems related to language, the difference between speakers, and the influence of network architecture on recognition accuracy are considered. The methodology includes extensive training and testing, studying the accuracy of recognition in different languages, and different sets of data on speakers. Using a comparative analysis, this study evaluates the performance of three architectures trained exclusively in the Kazakh language. The testing included statements in Kazakhs and other languages, while the number of speakers varied to assess its impact on recognition accuracy. During the study, the results showed that CNN neural networks are more effective in recognizing human voice than RNN and MLP. Also, it was found that the CNN has a higher accuracy in recognizing the human voice in the Kazakh language, both for a small and for a large number of announcers. For example, for 20 speakers, the recognition error in Russian was 21.86 %, whereas in Kazakhs it was 10.6 %. A similar trend was observed for 80 speakers: 16.2 % Russians and 8.3 % Kazakhs. It can also be argued that learning one language does not guarantee high recognition accuracy in other languages. Therefore, the accuracy of human voice recognition by neural networks depends significantly on the language in which training is conducted. In addition, this study highlights the importance of different sets of speaker data to achieve optimal results. This knowledge is crucial for advancing the development of reliable human voice recognition systems that can accurately identify different human voices in different language contexts У цьому дослідженні розглядається ефективність архітектур нейронних мереж (багатошаровий персептрон БШП, згорткова нейронна мережа ЗНМ, рекурентна нейронна мережа РНМ) для розпізнавання голосу людини, з акцентом на казахську мову. Розглядаються проблеми, пов’язані з мовою, відмінністю мовців, впливом архітектури мережі на точність розпізнавання. Методологія включає тривале навчання та тестування, вивчення точності розпізнавання різними мовами та різні набори даних про носіїв мови. Використовуючи порівняльний аналіз, це дослідження оцінює продуктивність трьох архітектур, які навчаються виключно казахською мовою. Тестування включало висловлювання казахською та іншими мовами, у той час як кількість носіїв змінювалася, щоб оцінити його вплив на точність розпізнавання. Під час дослідження результати показали, що нейронні мережі ЗНМ більш ефективні в розпізнаванні людського голосу, ніж РНМ і БШП. Також виявилося, що ЗНМ має більш високу точність розпізнавання людського голосу казахською мовою, як для невеликої, так і для великої кількості дикторів. Наприклад, для 20 мовців помилка розпізнавання російською склала 21,86 %, а казахською – 10,6 %. Подібна тенденція спостерігалася для 80 носіїв мови: 16,2 % росіян і 8,3 % казахів. Можна також стверджувати, що вивчення однієї мови не гарантує високої точності розпізнавання іншими мовами. Тому точність розпізнавання людського голосу нейронними мережами істотно залежить від мови, якою ведеться навчання. Крім того, це дослідження підкреслює важливість різних наборів даних про спікерів для досягнення оптимальних результатів. Ці знання мають вирішальне значення для просування в розробці надійних систем розпізнавання людського голосу, які можуть точно ідентифікувати різні людські голоси в різних мовних контекстах

This paper considers the efficiency of neural networks for human voice recognition. The objects of the study are artificial neural networks used for human voice recognition. Their ability to effectively recognize a human voice regardless of language, trained on a small number of speakers in noisy conditions, has been considered. The task being solved is to enhance the accuracy of speech activity detection, which plays a significant role in improving the functioning of automatic speech recognition systems, especially under conditions of a low signal-to-noise ratio. The findings showed that the accuracy of human voice recognition in languages of different phonetic proximity could vary greatly. As a result of the study, it was found that the recurrent neural network (RNN) demonstrates high accuracy in voice recognition – 95 %, which exceeds the results of the convolutional neural network (CNN), reaching an accuracy of 94 %. Special features of the results are the adaptation of neural networks to multilingual features, which made it possible to increase the efficiency of their work. An important conclusion was that training neural networks on data with different languages and types of speakers significantly improves recognition accuracy. The study confirmed that training neural networks on different languages and speaker types could significantly affect recognition accuracy. The results are an important contribution to the development of speech recognition technologies and have the potential for application in various fields where high accuracy in human voice recognition is required