In this paper, differences of infrared spectra of hydrate formation inhibitors of thermodynamic and kinetic action types were investigated. The method was proposed for determining the action type of a hydrate formation inhibitor by its infrared spectrum. The relevance of the proposed method is due to its expressiveness in comparison with the testing of inhibitors in laboratory tests. It is proposed to use the method of Fourier transform infrared spectrometry. The method allows us to obtain data on the molecular and intermolecular interactions of the substance under study. The spectra obtained in the mode of attenuated total reflection were analyzed by the principal component analysis and the regression method of projection on latent structures, which are related to chemometric methods of analysis and make it possible to identify the key features of the inhibitor compositions that affect the mechanism of their action. The separation of the sample of infrared spectra of the studied inhibitors into two subgroups, which represent two different types of inhibitor action, was obtained. The principal component analysis makes it possible to identify the key features of the compositions of reagents that affect the mechanism of their action. For kinetic inhibitors, a characteristic ratio of the amplitudes of vibrations of the –OH and N–H functional groups in the internal standard of the spectrum was revealed. It is shown that the decisive factor in the division of inhibitors into groups is the difference between the resonant vibration frequencies corresponding to the valence vibrations of C–O, C–N bonds and the resonant vibration frequencies of organofluorine compounds. At the same time, the similarity in the amplitude of the indicated spectral regions was noted. For the group of thermodynamic inhibitors, the most influential bands in the IR spectrum were the bands of symmetric and asymmetric stretching vibrations of the C–H bonds in the CH2 and CH3 groups. There was a significant increase in the amplitude in the spectral range of 2950–2750 cm–1 compared with the signal amplitude in the regions of 3300–3400 cm–1 and 1200–1100 cm–1, also found in the spectra of this group of inhibitors. The method of projection on latent structures was used to develop a regression model to determine the mechanism of action of the studied inhibitors. The proposed method allows for express determination of the action type of hydrate formation inhibitors. Results could be used in oilfield chemistry to determine the action type of hydrate formation inhibitors used to prevent the formation of gas hydrates during the production, preparation or transportation of hydrocarbons.

Raman spectroscopy (RS) is one of the promising approaches for structural and functional studies of various biological objects, including bacterial microorganisms. Both traditional biochemical tests and genetic methods which require expensive reagents, consumables and are time-consuming are used for bacterial analysis. Spectroscopic methods are positioned as noninvasive, highly sensitive, and requiring minimal sample preparation. In this work we investigated the possibility of using the RS method using optical sensors based on gold anisotropic nanoparticles. The applicability of the method was demonstrated by studying the effect of a broad-spectrum cephalosporin antibiotic and an extract of Viburnum opulus L (VO) on Escherichia coli (E. Coli) colonies. The studies were performed by Raman spectroscopy using a Virsa spectrometer (Renishaw). Raman signal amplification was carried out using two original optical sensors proposed by the authors. To create sensors, we used a chemical method of depositing gold nanostars on APTES-modified quartz glasses and a physical method for creating sensors based on anodizing titanium surfaces. The results of the study showed the high sensitivity and information content of the proposed method. The possibility of using the RS method for studying the inactivation of bacterial microorganisms is shown. Spectral Raman bands of E. Coli were determined and identified before and after exposure to VO extract and antibiotic as a control. A decrease in the intensity of spectral modes corresponding to amino acids and purine metabolites was found in the average Raman spectrum of E. Coli after exposure to VO extract. For the first time, a study of the antimicrobial effect of an aqueous extract of VO fruits was carried out by the method of Raman scattering. It has been shown that the use of plant extracts, including VO fruit extracts, to inactivate the vital activity of bacterial colonies is a promising approach to the search for new alternative antibacterial agents. The results obtained are in good agreement with the already known scientific studies and confirm the effectiveness of the proposed method.

Lasers are widely used in dermatology to remove telangiectasias. Increasing the efficiency of sclerosis of deep-lying and large telangiectasias with laser radiation is possible by changing the optical transmission of the skin when it is heated and converting the hemoglobin of the blood contained in it into methemoglobin. The influence of the concentration of methemoglobin in the blood on the absorption of light in human skin is poorly understood, which determines the relevance of this study in the context of finding ways to improve the efficiency of laser removal of telangiectasias. Seven-layer optical models of human skin without telangiectasia and with it for numerical simulation were developed. The extinction coefficients and the degree of change in the optical transmission of whole blood and skin layers were calculated in the range of wavelength from 400 to 1600 nm for skin model without and with arteriolar and venular telangiectasias at various concentrations of methemoglobin in the blood. Based on the analysis of these data, the wavelengths with the biggest change in the optical transmission of whole blood and skin layers occurred during the transformation of hemoglobin to methemoglobin were selected. At the selected wavelengths, the Monte Carlo method was used in optical modelling to get the distribution of the absorbed optical power in each layer of the skin model without and with telangiectasia at various concentrations of methemoglobin. It has been shown that the spectra of extinction coefficients for arteriolar and venular telangiectasias do not differ significantly. During the transformation of hemoglobin to methemoglobin, the largest decrease in the degree of change in the optical transmission of whole blood occurs at wavelengths of 629 nm and 1105 nm, and the largest increase occurs at wavelengths of 447 nm and 578 nm. The part of absorbed optical power in the layer of superficial vascular plexus without and with telangiectasia at wavelengths of 629 nm and 1105 nm increases, and at wavelengths of 441 nm and 574 nm it decreases with a growth of the methemoglobin concentration from 0 to 100 % in the skin model. At the same time, in the layer of deep vascular plexus the value of part of absorbed optical power increases at wavelengths of 441 nm, 574 nm and 1105 nm, but at a wavelength of 629 nm first increases with a growth of the methemoglobin concentration up to 25 %, and then decreases, but to values exceeding the value of part of absorbed optical power without methemoglobin. The change in optical transmission associated with the replacement of blood hemoglobin with methemoglobin is more pronounced for the superficial vascular plexus layer, which is associated with high blood content in it and a limited contribution of the overlying skin layers to the deformation of the spectrum of light incident on this layer. In skin with telangiectasia, a change in the concentration of methemoglobin changes the proportion of absorbed optical power by a greater amount than in skin without telangiectasia, which can be associated with an increase in the volume concentration of blood in skin layers with telangiectasia and an increase in their thickness. The obtained results can be applied in the development of laser systems and technologies for the treatment of skin diseases, including laser sclerosis of telangiectasias.

A specialized low-temperature measuring cell with a cryogenic capillary system for infrared spectral analysis of ethanol developed by the authors is presented. The use of the created low-temperature cell is possible for further studies of the low-temperature properties of both pure ethanol and mixtures with its contents, which is currently an urgent task, and the data obtained with its help can be used for ice research. Two methods of ethanol research at low temperature are presented in comparison. In the first method proposed by the authors, a specially developed low-temperature measuring cell based on a diffuse reflection prefix of the Fourier spectrometer FSM 2203 with a cryogenic capillary system is used. This system allows you to achieve the required low-temperature regime at normal atmospheric pressure. The results of the experiment are compared with the traditional method of gas-phase condensation of the test sample under low temperature conditions at the pressure P = 1.0·10–5 Torr. Infrared spectra of low molecular weight amorphous and crystalline ethanol were obtained at a temperature of 150 K at normal atmospheric pressure and in vacuum. Comparison of experimental results confirmed the operability of the new installation. In the experiments, peaks were observed in the absorption bands from 2850 to 3000 cm–1 and from 2950 to 3100 cm–1, corresponding to the valence CH vibrations of ethanol as well as in the absorption bands from 3150 to 3400 cm–1 and from 3300 to 3500 cm–1, which corresponds to the valence vibrations of OH. The results of the study showed the prospects of the proposed method and can be useful by researchers in the field of low-temperature spectroscopy at normal pressure.

Solid solution Ga1–xInxAs is widely used in modern optoelectronics as a material for p-i-n photodetectors, lasers emitting in the spectral range 1.3–1.55 μm. In this paper, the features of obtaining Ga1–xInxAs films by the method of zone recrystallization with a temperature gradient, the essence of which is the sequential recrystallization of parts of the source melt moving under the action of a temperature gradient, are studied. Ga1–xInxAs films on GaAs substrates were obtained in a temperature gradient field through a thin gas zone in a specially designed graphite cassette. The films were prepared at a temperature of 1123 K with a temperature gradient of 30 K/cm. A 1:1 mixture of nitrogen and hydrogen was used as the carrier gas. The thickness of the gas zone between the source and the substrate was 1 mm. The deposition time for all films was 10 min. The growth kinetics, morphology, and structure of the chemical bonds of the obtained films have been studied. Based on the results of theoretical calculations, it was found that an increase in the concentration of indium leads to a decrease in the film growth rate to 0.3137 µm/min. A comparison of the results of theoretical calculations with experimental results showed a discrepancy between the growth rates for films with an indium concentration in the growth source of more than 20 %, which is primarily due to the segregation of indium on the film surface. The films have an RMS roughness from 9.1 to 24.2 nm. It is shown that the content of indium in the growth source significantly affects the properties of the grown films and leads to a decrease in the growth rate, an increase in the elastic stresses in the layer, and a nonstoichiometric composition of the film. It has been established that with an increase in the indium concentration in the film, a significant shift in the frequency of the LO and TO phonon modes of GaAs to the left by 13 and 16 cm–1, respectively, is observed due to the influence of elastic mechanical stresses. The presented results show that Ga1–xInxAs solid solution films with short-range order of chemical bonds were obtained by the method of zone recrystallization in a temperature gradient.

In the recent years, the devices in Internet of Things (IoT) are growing exponentially due to the emergence of many sophisticated applications. This tremendous growth leads to serious security challenges and the devices of Wireless Sensor Networks should be protected from various attacks. IoT can be configured dynamically without fixed infrastructure and the devices are communicated with one another in an Ad-hoc manner. The work presents the classification of various DDoS attacks in the IoT environment and provides a solution for replay attack. All variations of DDoS attacks are modeled using UML based activity modeling. This clearly understands the behavior of each version of attacks and their performance in the environment. The modeling also helps to construct a solution to prevent this attack from its execution. The work also proposed a trust based protocol for replay attacks which allows the attack inside the network and blocks it after identifying the attack based on its specific behavior. The network performance is improved after implementing this proposed protocol inside the network with help of simulation under realistic conditions. The performance metrics considered in the work are energy, packet loss, computational time and throughput. The paper compares the performance with the state-of-the-art schemes such as Efficient Distributed Deterministic Key and Hash- based Message Authentication Code. The experimental analysis proved that the proposed scheme outperforms the other state-of-the-works in terms of computational cost, throughput, and delay.

Systems implementing artificial intelligence technologies have become widespread due to their effectiveness in solving various applied tasks including computer vision. Image processing through neural networks is also used in security- critical systems. At the same time, the use of artificial intelligence is associated with characteristic threats including disruption of machine learning models. The phenomenon of triggering an incorrect neural network response by introducing perturbations that are visually imperceptible to a person was first described and attracted the attention of researchers in 2013. Methods of attacks on neural networks based on malicious perturbations have been continuously improved, ways of disrupting the operation of neural networks in processing various types of data and tasks of the target model have been proposed. The threat of disrupting the functioning of neural networks through these attacks has become a significant problem for systems implementing artificial intelligence technologies. Thus, research in the field of countering attacks based on malicious perturbations is very relevant. This article describes current attacks, provides an overview and comparative analysis of such attacks on image processing systems based on artificial intelligence. Approaches to the classification of attacks based on malicious perturbations are formulated. Defense methods against such attacks are considered, their shortcomings are revealed. The limitations of the applied defense methods that reduce the effectiveness of counteraction to attacks are shown. Approaches and practical measures to detect and eliminate harmful disturbances are proposed.

High Resolution (HR) images have numerous applications, such as video conferencing, remote sensing, medical imaging, etc. Furthermore, a few challenges with the super resolution algorithms of magnetic resonance brain images are now obtainable, namely, low sensitivity, significant frequency noise as well as poor resolution. To fix these problems, a Convolutional Neural Network (CNN) based Discrete Cosine Transform (DCT) singular frame quality improvement method is described. There are two stages in this proposed method, involving training and testing. During the training stage, the HR, and Low Resolution (LR) pictures are employed as input, and they are preprocessed to create blocks of images. The histogram and DCT are used for extracting the features from the LR and HR blocks, and these extracted features are assigned with class id. The CNN, which extracts the features and allocates class id, receives its feature extractor as its final input. An LR input image is once more divided into [2 × 2] blocks during the testing stage, so each block histogram and DCT feature are estimated. Each feature vector is fed into the neural network as well as the results are contrasted with a set of feature vectors that have been recorded, in addition to the class id that has been allocated to a certain vector. In order to generate a Super resolution image with an LR image, a relevant HR block is then swapped out for this LR block. These results indicated that the initial dataset can achieve 22.4 and 19.5 Peak Signal to Noise Ratio (PSNR) and Root Mean Square Error (RMSE) values while measuring the effectiveness of this proposed method using RMSE and PSNR. Then, the second dataset illustrates that the PSNR and RMSE values are 20.1 and 25.5. For the third dataset, the values are 45.7 and 12.3, respectively. However, the presented method works better than the neural method of Super Resolution Channel Spatial Modulation Network and resolution enhancement technique.

Currently, various natural language processing tasks often require large data sets. However, for many tasks, collecting large datasets is quite tedious and expensive, and requires the involvement of experts. An increase in the amount of data can be achieved using methods of data augmentation, however, the use of classical approaches can lead to the inclusion of phrases in the data corpus that differ in the speech style and vocabulary of the target person, which can lead to both a change in the target class as well as the appearance of replicas with unnatural vocabulary use and lack of meaning. In this context, a new method for test data enrichment is proposed that takes into account the person’s style and vocabulary. In this article, a new method for expanding text data that preserves individual language features and vocabulary is proposed. The core of the method is to create individual templates for each person based on the analysis of syntactic trees of propositions and then to create new replicas according to the generated templates. The method was tested on the task of assessing the user’s emotional state in a dialogue. The search was carried out for data sets in English and Russian. The proposed method made it possible to improve the quality of solving these problems for both the English and Russian languages. Up to a 2 % increase in accuracy and weighted F1 metrics has been noted for various models. The results of the work can be applied to improve the accuracy and weighted F1 metrics of models designed to solve various problems for the English and Russian languages.

The CIAO (Cooperative Interaction Automata Objects) specification language is intended to describe the behavior of distributed and parallel event-driven systems. This class of systems includes various software and hardware systems for control, monitoring, data collection, and processing. The ability to verify compliance with requirements is desirable competitive advantage for such systems. The CIAO language extends the concept of state machines of the UML (Unified Modeling Language) with the possibility of cooperative interaction of several automata through strictly defined interfaces. The cooperative interaction of automatа objects is defined by a link scheme that defines how the provided and required interfaces of different automatа objects are connected. Thus, the behavior of the system as a whole could be described as a set of execution protocols, each of which is a sequence of interface calls, possibly with guard conditions. We represent a set of protocols using a semantic graph in which all possible paths from the initial nodes to the final nodes define sequences of interface method calls. Because the interfaces are strictly defined in advance by the connection scheme, it is possible to construct a semantic graph automatically according to a given system of interacting automaton objects. To verify the system behavior, one only has to check if each path in the semantic graph does satisfy the requirements. System requirements are formally described using conditional regular expressions that define patterns of acceptable and forbidden behavior. This article proposes methods and algorithms that allow you to check the compliance of programs in the CIAO language with the requirements automatically and, thereby, check the semantics of the developed program. The proposed method narrows the specification formalism to the class of regular languages and the programming language to a language with a simple and predefined structure. In many practical cases, this is sufficient for effective verification.

Modern learning is impossible without automated knowledge testing systems. At present, the most progressive are adaptive testing models in which the complexity of tasks varies depending on the correctness of the patient’s answers. This article describes the development of an intelligent adaptive testing system using a fuzzy mathematics device. An intelligent adaptive testing system has been developed; the module that implements the expert system uses the production base of the rules. The input parameters of testing are the percentage of correct responses, the degree of correctness of the response, the duration of the response, and the number of attempts. The output is a change in the current level of training of the student on the basis of which test questions of related complexity are selected. As a method of logical inference, the Mamdani method is used which consists of six operational actions: phazification — conversion of exact values of input variables into values of linguistic variables through belonging functions, this served as the basis for designing a fuzzy base of rules of the expert system; aggregation of sub-conditions — determination of the truth of conditions for each linguistic rule of the fuzzy inference system; activating sub-conclusions — finding the degree of truth of each of the sub-conclusions in the linguistic rule; accumulation of conclusions — finding the belonging function for each of the output linguistic variables; defazzification — finding a numerical value for each of the output linguistic variables. A developed intelligent adaptive testing system (ISAT) is presented that allows, based on the analysis of test results, to determine the current level of training of students, to adapt the material to the level of their training. This system allows you to dynamically present questions of appropriate complexity in real time. When using the developed intelligent adaptive testing system, students will be provided with questions of the appropriate level of complexity, this will allow building an individual learning trajectory. The introduction of a predefined system will ensure the implementation of a personalized approach for organizing the learning process; will increase the accuracy of assessing students’ knowledge. The use of the technology of fuzzy expert systems allows for automated, intelligent control of students’ knowledge.

Visual speech recognition or automated lip-reading systems actively apply to speech-to-text translation. Video data proves to be useful in multimodal speech recognition systems, particularly when using acoustic data is difficult or not available at all. The main purpose of this study is to improve driver command recognition by analyzing visual information to reduce touch interaction with various vehicle systems (multimedia and navigation systems, phone calls, etc.) while driving. We propose a method of automated lip-reading the driver’s speech while driving based on a deep neural network of 3DResNet18 architecture. Using neural network architecture with bi-directional LSTM model and attention mechanism allows achieving higher recognition accuracy with a slight decrease in performance. Two different variants of neural network architectures for visual speech recognition are proposed and investigated. When using the first neural network architecture, the result of voice recognition of the driver was 77.68 %, which was lower by 5.78 % than when using the second one the accuracy of which was 83.46 %. Performance of the system which is determined by a real-time indicator RTF in the case of the first neural network architecture is equal to 0.076, and the second — RTF is 0.183 which is more than two times higher. The proposed method was tested on the data of multimodal corpus RUSAVIC recorded in the car. Results of the study can be used in systems of audio-visual speech recognition which is recommended in high noise conditions, for example, when driving a vehicle. In addition, the analysis performed allows us to choose the optimal neural network model of visual speech recognition for subsequent incorporation into the assistive system based on a mobile device.

It is evident that when the human brain stops functioning for a small period of time, it will lead to death. As a result, dealing with brain disorders should be done early and properly. A brain tumour is one of the most serious brain illnesses. The development of tumours can be detected using Magnetic Resonance Imaging (MRI). However, because an MRI image has loud noise, it can be hard to diagnose a tumour. The diagnosis process is slow, yet illness necessitates prompt and accurate medical attention in order for patients to survive. One of the solutions for tumour diagnosis is to employ MRI brain picture segmentation. In this designed model, MRI of the brain is collected and pre-processed with Non-Local Means (NLM) to reduce noise from captured raw data. This pre-processed image is first segmented with Region of Interest (ROI) for identifying regions of interest and then with a fusion deformable fuzzy system, which combines fuzzy C-means (FCM) and deformable systems. By analyzing the fitness value of α and β constants, segmented pictures from models are fused using the Dolphin Sine Cosine Algorithm (SCA) method to combine the model results. The integrated output from the algorithm is classified with the deep Convolutional Neural Network (CNN) classifier. The created model experimental findings are analyzed and compared to current methodologies. The proposed model performance measures are 0.90, 0.89, 0.88, and 0.10 in terms of selectivity, precision, accuracy and errors. As a result, when compared to previous strategies, the proposed approach outperforms them.

The problem of determining the optimal number and location of tracking points on the human body to ensure the necessary accuracy of reconstruction of kinematic parameters of human movements in virtual space is considered. Optimization of the human tracking system in virtual reality has been performed to reduce the amount of transmitted information, computational load and cost of motion capture systems by reducing the number of physical sensors. The task of optimizing the number and location of tracking points on the human body necessary for the reconstruction of a virtual body model from a limited set of input points using numerical approximation of the regression function is set. An algorithm has been developed for collecting a large amount of data from a human body model in a virtual scene and from a motion capture suit in the real world. The smallest number of human body tracking points and their location were obtained using the proposed algorithm. Various neural network topologies have been trained and tested to approximate the regression relationship between a vector of tracking points limited in size (from 3 to 13) and a vector of 18 virtual points used for the complete reconstruction of the human body model. The necessary accuracy of reconstruction of kinematic parameters of human movements is provided at 5 and 7 input points. The proposed approach made it possible to use 5 or 7 physical sensors to build a model of the human body and restore the kinetic parameters of its movements in virtual reality. The approach can be applied to solving inverse kinematics problems in order to reduce the number of physical sensors placed on the surface of the object under study, to simplify the processing and transmission of information. By combining data from both the motion capture suit and the virtual avatar, the process of collecting information has been significantly accelerated, the volume of the training sample has been expanded and various patterns of user body movements have been modeled.

In this paper, we study the errors of the homodyne demodulation method based on arctangent function solutions (PGC- ATAN) which are associated with the use of a low-pass filter (LPF) in this signal phase demodulation algorithm. The method of demodulation of an interference signal by PGC-ATAN method is investigated in order to detect and consider in more detail the errors at the filtering stage (the article considers the moving average method), and corrections to the signal are analytically calculated, taking into account the error introduced by the low-pass filter. We obtained formulas for calculating corrections to the signals S1(t), S2(t), S3(t), S4(t)  which received by filtering the original signal multiplied by the reference oscillator signal, the calculations were compared with the results of mathematical modeling of the interference signal processing by the PGC-ATAN method. The demodulation of the signal, taking into account the corrections, showed that, in general, the effect on the signal phase is small at a low heating rate, however, for high-speed processes, the error can lead to serious distortions of the desired signal phase. These calculated corrections for processed interference signal will improve the demodulation method based on the calculations of the arc tangent function and more accurately calculate the desired phase of the signal.

Improvement of the manufacturing technology for gyroscopic devices, which autonomously generate motion parameters of moving objects, has strategic importance and priority for various industries. The object of current research is a spherical rotor of an electrostatically suspended gyroscope which geometric parameters determine the accuracy characteristics of the device. The paper presents results of the process modeling of spherical form correction for rotors of electrostatically suspended gyroscopes at the stage of its manufacture during the coating deposition process. The proposed mathematical model of the deposition process is based on the placement of a movable screen with a hole between a rotor and a spray source. The axis of the hole lies on the dynamic axis of the rotor and it provides a formation of a spherical segment on the coating rotor surface. During deposition of an additional layer, the screen or rotor moves along the dynamic axis of the rotor changing the distance between the rotor and the screen, and there is additional rotation of the rotor around its dynamic axis. It allows adjusting the curvature of the formed coating on the rotor surface. An analytical model of the technological process for correcting the shape of spherical rotors of electrostatically suspended gyroscopes has been developed. A mathematical description, control factors and significant parameters of the process are given. The results of practical testing of the developed model are presented. The presented mathematical model makes it possible to correct the shape of the rotors during the deposition of a functional coating expanding the technological possibilities and increasing the accuracy of rotors.

Reliability of the communication system with spatial multiplexing has been studied. Increasing the bandwidth of radio communication channels due to spatial multiplexing is one of the most popular and relevant areas of modern research in the field of radio communications. Solving the problem of spatial multiplexing in the time domain with multipath propagation is associated with a significant increase in the dimension of the problem and redundant calculations. Detection in the time domain makes it difficult to take into account the frequency dependence of the amplitude and phase of the received signals, which in turn reduces the probability of correct signal recognition. In multipath propagation, a solution to the problem of spatial multiplexing in the frequency domain is proposed by applying the convolution theorem. The probability of error is estimated when using the proposed detection method. The stability of the solution is estimated depending on the conditionality of the matrix of amplitude-phase parameters. The expression of the estimation of the upper bound of the error probability in the subchannel is derived depending on the number of conditionality of the matrix of amplitude-phase parameters and the spectral density of noise in physical communication channels. An algorithm for adaptive formation of matrices of amplitude-phase parameters has been developed which selects such antennas among an excessive number of receiving antennas allowing to increase the stability of detection by reducing the number of conditionality of the matrix of coefficients of a system of linear equations. The theoretical basis of the spatial multiplexing method in multi-antenna communication systems has been developed. The proposed method makes it possible to increase the efficiency of calculations by reducing the dimension of the detection problem in comparison with the solution in the time domain. It is proposed to solve the detection problem only at frequencies at which a useful signal is expected to be received, which is especially useful for narrow-band frequency and phase, orthogonal and biorthogonal types of modulation often used in multi-antenna digital communication systems. Expressions for estimating the probability of error in the subchannel are derived. An algorithm for adaptive formation of matrices of amplitude-phase parameters has been developed, which makes it possible to increase the stability of the solution of the detection problem. The research results are applicable in the development of multi-antenna communication systems with spatial multiplexing.

Heating of oil and oil products is widely used to reduce energy losses during transportation. The flow in the annular space of the heat exchanger is complex and depends on many factors. The use of thin tubes in helicoid-type heat exchangers makes it necessary to take into account the transition of the flow regime from laminar to turbulent. The semi-empirical turbulence models traditionally used in numerical calculations do not take into account the laminar-turbulent transition. An approach is developed to determine the effective length of the heat exchanger and the temperature of the cold coolant at its outlet in the case of a strong dependence of oil viscosity on temperature, taking into account the possibility of a laminar-turbulent transition. Oil is considered as a heated coolant, and water is considered as a heating component. The novelty of the developed approach lies in the application of the turbulence model, which takes into account the laminar-turbulent transition, to the calculation of helicoid-type heat exchangers. For numerical simulation, the Reynolds-averaged Navier–Stokes equations are used which are closed using γ–Reθt turbulence model that takes into account the laminar-turbulent transition. The results of numerical calculations are compared with the data obtained on the basis of the log-mean temperature difference method at constant and variable viscosity. In the case of variable oil viscosity, a transition from the laminar flow regime to the turbulent one is manifested which has a significant effect on the effective length of the heat exchanger. The results of numerical calculations can be useful in designing helicoid-type heat exchangers.

A variant of the generalized parameters process forming of the complex technical systems technical condition is considered. This approach is relevant for modern robotic systems equipped with built-in telemetry tools. The proposed approach to the generalized parameters formation is based on heterogeneous telemetry parameters weighted summation using information about structural and functional relationships in a complex technical system, followed by digital low-frequency filtering of the weighted summation results. This solution makes it possible to increase the complex technical systems technical condition reliability assessment by the values of generalized parameters in the external control loop. The form of generalized parameter representation in the technical condition gradations, which corresponds to the normal functioning, emergency situations and complex technical system partially operational state, is chosen. A multilevel hierarchical model of the generalized parameters formation of the complex technical systems technical condition on the basis of telemeasurements based on a variety of neural network structures allowing to take into account the nonlinear nature of the parameters being telemetered and the mutual influence between them has been developed. The model uses a variety of digital low-frequency filters that reduce the level of disturbances in the generalized parameters time series. The occurrence of disturbances is associated with the uncertainty of changes in the values of the telemetered and generalized parameters near the tolerance limits set by experts with expanded gradations according to technical condition. Information about the limits of tolerances characterizes not only the situations of regular and non-standard functioning, but also the complex technical systems partially operational state. The results of the generalized parameters formation of the spacecraft onboard system technical condition using multilayer neural networks, Kolmogorov–Gabor polynomials and digital filtering methods are presented. The advantages of using multilayer neural networks and median filters in the developed model are shown. The use of generalized parameters will significantly reduce the information load on the transmission channels of telemetry information as well as the means of its processing and analysis in the external control loop. The proposed solutions based on the basic operations of weighted summation and nonlinear transformation can be effectively implemented on promising vector-matrix and tensor processors that support their execution at the hardware level.

When the rocket moves in the dense layers of the Earth’s atmosphere, classical nozzles operate in the jet overexpansion mode. In this mode, there is a partial decrease in the magnitude of the specific impulse. As a result, the amount of fuel consumed by the rocket engine increases. An increase in the efficiency of nozzle operation can be achieved by using designs of wide-range nozzles, in which case the replacement of a solid nozzle wall with a perforated one makes it possible to compensate for the loss of specific impulse. The paper presents a study of the effect of a porous insert on the operating modes of the nozzle. Numerical simulation was performed in the Ansys Fluent software package. At the first stage of the study, a geometric model of the computational zone is created which includes a two-dimensional model of the RD-107 rocket engine nozzle and a computational domain that simulates the external environment (air atmosphere). The calculation of the outflow of combustion products through the constructed nozzle at different pressures of atmospheric air is carried out. In the future, the classical nozzle is replaced by a nozzle with a porous insert, and the calculation is carried out at the same values of atmospheric pressure. The values of the specific impulse obtained in calculations with a classical and porous nozzle are compared. The amount of fuel saved when replacing a classic nozzle with a porous one is determined by the difference in the areas bounded by the curves on the plot of specific impulse versus the considered height above the Earth’s surface. Comparison of the values of the specific impulse of nozzles with an impenetrable wall and a porous insert made it possible to conclude that up to a height of 5.4 km the specific impulse of the nozzle with a perforated wall exceeds the values of the specific impulse of the classical nozzle. Evaluation of the effectiveness of the use of a gas-permeable insert in the nozzle design when the nozzle operates in dense layers of the Earth’s atmosphere showed that with the start of operation at a height of 0 km above sea level and up to the height at which the nozzle operates in the design mode – the value of the compensated specific impulse is 2.2 %. The results of the study can be applied in the design of nozzle devices of modern rocket engines operating in dense layers of the atmosphere.

The paper is devoted to solving the shockwave reflection problem from a wall shielded by a gas suspension layer. The dynamics of the gas suspension are described in a two-speed two-temperature formulation. In contrast to the known approximate models of dusty gas based on the application of classical self-similar solutions by correcting gas dynamic parameters and physical constants, an asymptotically exact solution is obtained. The analytical solution to the problem is constructed in the form of a composition of elementary decays discontinuities. The nonequilibrium solution converges to the exact one with a decrease in the characteristic times of dynamic and thermal relaxation of the carrier gas and suspended particles of arbitrary concentration. Calculations based on the nonequilibrium model are performed by the hybrid large-particle method of the second-order approximation in space and time. Both for the exact and calculate profiles of the relative values of the pressure and density of the mixture, the normalized velocity of the dispersed phase obtained from the nonequilibrium model are given. The influence of the intensity of the incident shock wave, as well as the concentration of particles in the gas suspension layer on the parameters of the impact of the shock wave pulse on the wall, is studied. The presence of a shielding layer leads to an increase in the reflection pressure from the wall compared to the reflection of the shock wave in a pure gas. The analysis of the influence of the relaxation properties of the gas suspension layer with a change in particle sizes from 1 to 8 µm is carried out. For sufficiently small particles of 1 micron and the accepted scales of the problem, the nonequilibrium solution reproduces the shock-wave structure well and corresponds to the asymptotics. With the increase in the size of dispersed inclusions, the spatial relaxation zones, smoothing the profiles of the parameters, increase. The error in calculating the velocity and other parameters for a nonequilibrium gas suspension with particles of 1 µm compared to the exact solution is in the range from 10–7 to 10–5. The results obtained are of practical importance in substantiating the influence of inert particle impurities on the dynamic loading of structures. The analytical solution to the problem may be in demand when testing various numerical schemes.

Unknown constant parameters estimation problem for a nonlinear time-varying system with delayed measurements is considered. The objective of this work is to design an adaptive observer for a nonlinear time-varying system. The observer must provide asymptotic convergence of the unknown constant parameters estimates to their true values. The main idea behind the method is to perform the parametrization of initial dynamical system based on GPEBO (Generalized Parameter Estimation Based Observer) technology and to build a linear regression model. The identification of linear regression model unknown parameters is performed using least square method with forgetting factor. This work develops the previously published approach for the case of nonlinear time-varying systems with delayed measurements. New parameters estimation algorithm can be applied for technical tasks, such as technical condition control and automatic control systems design.

We address the scarcity of datasets specifically tailored for legal NER in the Russian language and investigate the generalization capabilities of models towards unseen named entities. A rule-based program developed by legal experts at Tag-Consulting Company was employed to automatically annotate legal texts and create the RuLegalNER dataset. Part of the named entities only exists in the development and test splits, and they are unseen in the training set. RuBERT was utilized as the base architecture for experimental evaluation. Two different architectural extensions were explored: RuBERT with CRF and RuBERT with adapters. These architectures were used to train and evaluate NER models on the RuLegalNER dataset. Utilize RuLegalNER to train and evaluate legal NER models, enhancing performance in the legal domain and studying generalization on unseen entities. A published version of RuLegalNER is presented with detailed statistics and demonstration of the usefulness of RuLegalNER by evaluating modern architectures.