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

This paper presents the results of numerical modeling and vibration testing of a nanosatellite’s optical payload, aimed at assessing its mechanical stability under the mechanical impacts of launch. The purpose of the study is to compare finite element modeling (FEM) data with experimental testing to refine the computational model and improve the reliability of mechanical stability predictions. The methodology included an FEM analysis with an average damping coefficient, an adapter blank test, a resonance study with a low-level sinusoidal run, random vibration tests, and a sinusoidal pulse test. The FEM results showed an average yield margin of safety MoS = 2.5 with a minimum MoS = 1.8 in the primary mirror mount area. The adapter blank test confirmed the absence of natural resonances in the operating range. The resonance study revealed modes in the 300–1340 Hz range, with the most pronounced peaks in the secondary mirror bracket (520–600 Hz) and the electronics unit (1030–1100 Hz). A comparison of the root mean square (RMS) acceleration values between calculations and tests revealed discrepancies due to the heterogeneous nature of the damping. The values of ζ determined by the half-power method varied from 0.9% to 4.8%, which confirms the dependence of the damping properties on the frequency and localization of the modes. The obtained results confirmed the structural integrity of the payload, allowed for the localization of structural elements, and substantiated the need to consider actual damping coefficients in FEM models. The presented data can be used to optimize the design and improve mechanical stability during payload integration into the satellite platform.

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

The article presents a voice control system for unmanned aerial vehicles (UAVs) and an integrated mobile robot, based on artificial intelligence (AI). The system recognizes voice commands in the Kazakh language, converted into Latin transliteration, providing intuitive control of the UAV and robot. The performance of the system in various scenarios including agriculture, environmental monitoring and search and rescue operations is investigated. The system showed high accuracy of command recognition (95%) and efficient control of the UAV and robot. The proposed system opens up new possibilities for the use of UAVs and robots in various fields, increasing their autonomy, flexibility and ease of use.

Nonlinear optical effects in optical waveguides play an important role in the development of fiber and integrated optics systems for optical communication and information processing. On the one hand, nonlinear effects impose restrictions on the radiation power that can be transmitted through an optical fiber or light guide. In this paper, the problem of the occurrence of the phenomenon from the stimulated Brillouin scattering (SBS) effect is investigated using two optical sources of rays in a single-mode optical fiber at joint waves of 1310 nm and 1550 nm. Due to the fact that in all trunk fiber-optic lines, the intensity and energy of input signals is limited due to the influence of SBS, methods are currently being sought to reduce the influence of this phenomenon. It was found that the energy of the input beam in the combined propagation of the compound did not reach the value of the threshold of SBS in the values of 25 dBm and 27 dBm due to the discrepancy between the experimental results and the results of the model. The SBS effect was not observed when the threshold threshold of 15 dBm and 27 dBm – SBS was reached when combining a dual beam along a single optical fiber in one direction. As a result, by double integration, the value of the SBS threshold was raised, and the direction for future scientific research was determined. If the possibility of increasing the threshold of SBS is proved, then increasing the distance of amplifiers in the main networks, respectively, its economic effect increases. In addition, it can be noted that there are no scientific papers devoted to the study of the effects of optical nonlinear effects by combining and distributing these two compounds along a single optical fiber. This article discusses the issues of improving the capacity and determination of the threshold for stimulated Brillouin scattering. To increase the power threshold is invited to consider the dependence of the phase modulation frequency of the spectral width of the laser radiation. In addition, the fact that the SBS threshold did not reach the value at an energy of 27 DB in the case of the double-beam distribution can be proved by discrepancies in comparison with the results of experimental studies and the results of the model that determines the SBS effect

This paper presents the design and numerical assessment of a compact short-wave infrared (SWIR) optical payload for a CubeSat nanosatellite platform. The proposed payload is intended to provide sequential imaging in selected SWIR bands using a revolver filter wheel while accommodating a long-focal-length optical system within a limited 3U volume. The optical layout is based on a modified Maksutov configuration with an effective focal length of approximately 550 mm and an aperture of 84 mm. The system is designed to achieve a ground sampling distance (GSD) of ≤15 m at an orbital altitude of 500–550 km. For sequential imaging in different regions of the SWIR range, filters with central wavelengths of 1.24, 1.6, 1.9, and 2.1 μm are used together with an additional broadband window. The optical performance was evaluated using spot diagrams and modulation transfer function (MTF) analysis for the selected spectral bands. A preliminary thermo-optical sensitivity assessment was also performed over the temperature range from −20 °C to +40 °C by considering temperature-induced changes in axial distances between optical elements and surface curvatures caused by material expansion. The results indicate that the MTF at the detector Nyquist frequency of 33 lp/mm remains above the selected threshold of 0.1 for the considered spectral bands and uniform temperature states. The proposed configuration demonstrates the feasibility of implementing a compact long-focal-length SWIR payload with filter-wheel-based band selection for CubeSat missions, while maintaining the required calculated image quality within the considered design-stage assumptions.