No. 6 (2024)

The problem of timely and accurate detection of fire hazardous situations is critical to ensure the safety of people and property. Traditional monitoring methods based on simple threshold values for smoke and temperature sensors are often insufficiently effective, as they can lead to false alarms or miss real fire hazardous situations. Modern methods using neural networks can significantly improve the accuracy of classifying an emergency situation by analyzing complex patterns in sensor data, which are complex nonlinear functions with dynamically changing parameters. The development of such models requires attention to the collection, labeling and processing of data, to the choice of neural network architecture for a specific task, because high-quality data labeling and the choice of the desired neural network architecture directly affect the selection of the desired patterns, as well as the detection of hidden patterns that are impossible or difficult to determine by traditional methods. The article examines an algorithm for classifying fire hazardous situations based on the Kolmogorov-Arnold network (KAN). This algorithm is used to process data from a complex of interconnected fire sensors and is designed to detect and classify various types of fire hazardous situations. The key element of the development is the use of the Kolmogorov-Arnold network, which, due to its architecture, is capable of modeling complex functional dependencies between input data. Readings from a complex of interconnected fire sensors, such as temperature and smoke sensors, are used as input data. To improve the accuracy of classification, data is labeled using expert knowledge. The Python programming language was used to implement the algorithm, together with the Pytorch, pykan, and scikit-learn libraries. The article presents the results of testing the model on real data and discusses possible directions for further improvement of the algorithm. During the experiments, it was shown that the proposed model demonstrates high accuracy in classifying fire hazardous situations, which is not inferior to traditional methods of data classification.

This article describes the problem of three-dimensional packaging of goods in various types of containers
during maritime cargo transportation. Maritime cargo transportation plays a significant role in
international trade, is carried out in specific and non-standard conditions, is characterized by increased
humidity, contact with sea salt, vibration, temperature interference and is carried out by container ships
transporting various categories of goods in containers selected taking into account the specifics of the
cargo being transported, which ensures reliability and safety. Of particular importance is the presence of
protection of goods from a variety of negative and man-made environmental factors, which confirms the
importance of properly designed marine cargo packaging, ensuring the preservation of goods, equipment,
raw materials, or materials throughout the entire time of transportation by sea, as well as reliable fastening
on deck or inside cargo compartments, excluding the possibility of damage to cargo, through exposure
vibration and static loads. The article describes the task of three-dimensional packaging in containers
for marine cargo transportation. Criteria and constraints are considered, and a modified combined
multi-criteria objective function is constructed. Its value should tend to 1, which corresponds to 100%
filling of voids. Also, the paper provides a brief overview and analysis of methods and algorithms for finding
solutions to the problem of three-dimensional packaging, their features, advantages and disadvantages
are revealed. Taking into account the analysis, it is noted that metaheuristic methods and search algorithms
are effective for solving the NP-complex problem of three-dimensional packaging, as they allow
obtaining sets of quasi-optimal solutions in polynomial time.

This article presents the mathematical justification of a path planning algorithm for a mobile robotic
complex (MRC) following an operator during autonomous control tasks using artificial intelligence
(AI). A proposed approach implements a "follow me" autonomous following task for the MRC. A pursuit
method is selected as the primary method, ensuring the MRC follows the leading operator at a specified
distance. The MRC's movement simulation is performed in a moving coordinate system to more accurately
describe the movement of a material point along a curvilinear trajectory. The input data consists of two
dynamic arrays containing information about the distance from the MRC's camera to the leading operator
and the course angle between the complex's longitudinal axis and the line of sight. Path planning is performed
with a delay, after the leading operator has conditionally taken one step away from the robot. The
introduction of fuzziness in the control process implies evaluating actions and reactions with a set of terms
that are associated with a certain degree of confidence with specific intervals of physical quantities. Based
on this approach, an algorithm was developed and implemented in the Python programming environment
using the Skfuzzy library's built-in fuzzy logic functions. Simulation modeling was conducted to evaluate
the accuracy of the target function implementation. Analysis of the results revealed the main advantages of
using fuzzy logic for automation tasks compared to traditional approaches in automatic control theory

One of the main conditions for the successful functioning of the enterprise is a well-organized production
planning process. Production planning systems of the APS/MES class, the basis of which are algorithms
for building production plans, allow automating this activity. The paper examines the problem of
scheduling for enterprises of a discrete type of production, related to the field of multi-criteria optimization
problems. A formal description of the planning task is given, taking into account the main production
constraints (time constraints, equipment requirements and the order of operations). The main methods of
solving problems of this class are briefly considered; their main advantages and disadvantages are noted.
To solve this problem, an approach based on the generation of heuristic rules used in planning production
operations for specified resources has been chosen. Based on this approach, a two-stage algorithm for
building production schedules is proposed, which includes the generation of dispatching rules and their
further application in building schedules. A genetic algorithm is responsible for generating dispatch rules.
The implementation of its genetic operators is described in detail, as well as the composition of the chromosome and the tree representation of the dispatch rules included in the chromosome. The algorithm is
implemented in C# 12 using a free platform.NET 8. The implemented algorithm has shown its effectiveness
in comparison with the greedy algorithm on small generated datasets. Further research in this area is
aimed at evaluating the effectiveness of the constructed algorithm with more complex genetic operators
and the structure of the expression tree, as well as reducing the duration of the process of generating heuristic
rules for large data sets.

Recently, neural network models have become one of the most promising directions in the field of automatic
feature extraction from digital signals. Traditional approaches, such as statistical, time-domain,
frequency-domain, and time-frequency analysis, require significant expert knowledge and often prove insufficiently
effective when dealing with non-stationary and complex signals, such as biomedical signals (ECG,
EEG, EMG) or industrial signals (e.g., currentgrams). These methods have several limitations when it comes
to analyzing multichannel data with varying frequency structures or when signal labeling is too laborintensive
or expensive. Modern neural network architectures, such as transformers, have demonstrated high
efficiency in automatic feature extraction from complex data. Transformers have outperformed traditional
convolutional and recurrent neural networks in many key metrics, particularly in tasks involving time series
forecasting, multimodal data classification, and feature extraction from sequences. Their ability to model
complex temporal dependencies and nonlinear relationships in data makes them ideal for tasks such as noise
filtering and multimodal signal processing. This paper proposes a method for feature extraction from digital
signals based on a modified transformer architecture that incorporates a nonlinear layer after the selfinspection
module. This approach improved the ability of the model to detect complex and nonlinear dependencies
in the data, which is particularly important when dealing with biomedical and signals obtained from
industrial systems. A description of the architecture and the experiments performed are presented, demonstrating
the high performance of the model in solving signal classification, prediction and filtering problems.
It is expected that the model can be applied to a wide range of applications including disease and fault
diagnosis, signal parameter prediction and system modelling.

The task of restoring lost fragments of monumental painting is relevant in the context of preserving
cultural heritage sites. Modern artificial intelligence technologies, including convolutional neural networks
(CNN), significantly expand the possibilities of restoration, allowing for the automation of complex
image restoration processes. In particular, the restoration of lost elements of frescoes requires precise
analysis tools that can predict missing fragments with minimal errors, while preserving the artistic style of
the original. The purpose of this study is to develop a technique of automated restoration of lost fragments
of monumental painting images using CNN (using frescoes as an example). This goal was achieved by
solving the following problems: obtaining fresco images using appropriate methodological and technical
tools, applying the U-Net architecture for image segmentation and reconstruction, predicting lost areas
based on color characteristic analysis. The photogrammetry method and the designed device, which were
used to perform multi-angle shooting, provided high-quality source data for subsequent processing. Adaptation
of the U-Net architecture to the image segmentation task has proven its effectiveness in identifying
key structural elements of frescoes, which contributed to the accurate reconstruction of lost areas.
To predict the lost areas, color characteristics were analyzed in the HSL system, which allowed the CNN
to predict the missing colors with a high degree of accuracy. Brief conclusions of the study show that the
proposed technique allows restoring both the shape and color of lost fragments of frescoes. The proposed
technique is planned to be used for the restoration of other types of art works, which makes it promising
for further research.

When design satellite communication complexes that are placed on board space satellites, it is required
to ensure a given communication quality within the established service area. The workspace in
such tasks can have a complex border shape. To cover a given area, on-board antenna systems are used,
which implement a contour pattern. The quality of communication is directly related to the parameters of
the main lobe of the directional pattern. The directional pattern should take this factor into account, and
the main lobe should be as close in shape as possible to the contour of the border of the serviced area.
One of the possible options for design an antenna system with a contour pattern is the use of a reflector
antenna. The antenna has a single source and a reflector with a profiled surface. The law of profiling the
reflector surface is determined by the shape of the boundary of the serviced area. At the antenna design
stage, it becomes necessary to model and analyze the parameters of the radiation pattern. This requires a
3D model of a profiled reflector. This 3D model is used as input data for electrodynamic modeling programs.
The construction of a 3D model consists of solving the equation that describes the reflector and
forming the results of solving the equation in the form of a solid. The analysis of the published articles
showed that currently the issues of forming 3D models, taking into account the design features of reflector
antennas, are not considered in sufficient detail. The goal of the work was to build a 3D model of a profiled
reflector for electrodynamic modeling, taking into account the features of the construction of reflector
antennas. To achieve this goal, the task of developing an appropriate algorithm has been solved. In the
course of the conducted research, an algorithm for forming a profiled reflector has been developed, which
allows creating an appropriate 3D model that can be used in electrodynamic modeling tasks. The developed
algorithm converts the results of solving an equation containing information about the shape of the
reflector into a discontinuous surface on which boundary conditions can be set.

The paper considers the problem of determining the current coordinates of moving object in the
conditions of unstable signal from the global navigation satellite system (GNSS). The relevance of the
work is due to the fact that in recent years moving object are increasingly used in virtually all sectors of
industry, agriculture, transportation, solving a variety of tasks of surveillance, reconnaissance, monitoring
the state of controlled objects, search and rescue operations, cargo delivery and much more. At the same
time, the success of flight missions largely depends on how accurately and efficiently its onboard navigation
system works in real time. The existing solutions for creating onboard positioning systems involve the
use of inertial and GNSS. However, they have the disadvantage of partial or complete absence of data
from the GNSS (Global Positioning System). This paper describes a method for maintaining a given accuracy
of moving object spatial positioning under conditions of partial or complete absence of data from the
object's GSP. This approach is based on a combination of computer vision methods for processing video
stream frames from the moving object on-board vision system (OVS) in order to ensure positioning accuracy
under conditions of partial or complete absence of data from satellite navigation systems. Based on
the advanced method, an algorithm has been developed for automated determination of moving object
coordinates in the absence of georeferencing data from global positioning systems (GPS). Experiments
have been carried out, which demonstrated the reduction of time costs for description and matching of key
points and improvement of the accuracy of image matching. The developed algorithm was used to solve
the problem of satellite image matching, which is an important step in the moving object positioning problem
without the use of global geo-referencing data.

The aim of this work is to develop a trajectory planning system for the movement of a delta robot
used for weed cultivation. The delta robot is mounted on a mobile platform that moves between rows of
cultivated plants. A vision system detects weeds and determines their coordinates. The system is tasked
with planning the trajectory of the robot's gripper during weed removal, ensuring no damage is done to
either the robot or the plants. This research is highly relevant due to the growing global population, decreasing
arable land, rural depopulation, and a reduction in the availability of agricultural machinery.
To achieve this goal, the work presents a solution to both the forward and inverse kinematics of the delta
robot using an analytical approach. A model for determining the structural parameters of the delta robot
is proposed, which allows the evaluation of how these parameters affect the robot’s working area.
The lengths of the delta robot's arms are determined, tailored to the task of weed removal in corn fields.
The trajectory planning problem is addressed by decomposing the motion into horizontal movement of the
gripper and vertical movement, considering the size of soil clumps and the magnitude of weed extraction.
Experimental results demonstrate the possibility of significantly reducing the number of trajectory points,
thus lowering the computational complexity of the proposed methods and simplifying their implementation
in the robot's onboard computer.

This article focuses on automation systems used in car dealerships for the sale and repair of vehicles.
This requires consideration not only of existing processes but also their optimization using modern
technologies, which complicates the analysis of such systems. The implementation of such solutions can
lead to the creation of more efficient models that reflect the real operating conditions of car dealerships.
Understanding the key concepts of automation helps not only to structure the research but also to identify
directions for further development. Studying existing models and systems allows for the identification of
best practices and potential shortcomings. Comparative analysis helps not only to adapt proven solutions
to new conditions but also to avoid mistakes made in previous studies. System analysis and modeling of
queuing systems represent key aspects in the management and optimization of business processes, including
such complex areas as automation of the sales and repair process of cars. In the modern world of high
technology, where competition in the market of goods and services is constantly growing, the use of system
analysis allows enterprises to find effective solutions to improve their operations. Queuing systems (hereinafter
referred to as QMS) are a central element in various sectors of the economy, including car dealerships
and service centers. They are aimed at optimizing customer flows and resources in order to improve
the quality of service and minimize waiting times. The main task of system analysis in this context is to
study the structure, behavior and interaction of system components in order to identify weaknesses and
find optimal strategies to overcome them. To automate the work of the car dealership, a subject area was
selected, including key elements: staff, customers, cars, services and contracts. These elements are interconnected
and form the basis for the projected database. Each of the elements has its own essence, and
their interaction through contracts becomes the basis for the development of a relational database.

This article is devoted to the study of the influence of atmospheric precipitation on
hydrolithospheric processes using mathematical modeling. Hydrolithospheric processes involve interactions
between water, the atmosphere, and the Earth's crust, playing an important role in shaping the landscape,
water cycle, and Earth's climate. Using historical data on precipitation and hydrolithospheric processes,
the authors calibrate and validate their model. Results show that the model can accurately predict
changes in water flow, soil erosion, and water quality in response to changes in the precipitation regime.
This paper presents the development and application of mathematical models to study the effects of precipitation
on runoff generation, soil erosion, water table changes, and geomorphologic processes occurring
in the hydrolithosphere. The paper analyzes different types of models, including: – surface runoff
models, which describe the formation and movement of runoff over the land surface; – soil erosion models,
which predict the intensity of erosion processes caused by precipitation; – groundwater models, which
study the effect of precipitation on the water table and its movement in groundwater aquifers; – models of
geomorphologic processes, which study the influence of precipitation on the formation of relief, formation
of ravines, slopes and other geomorphologic elements. Problems of model validation and calibration, as
well as uncertainties associated with precipitation variability, were considered and studied. The results of
the study provide a better understanding of the interaction of precipitation with the hydrolithosphere and
present opportunities for using mathematical modeling to predict hydrolithospheric processes and develop
water management strategies. The article has important implications for understanding and managing
hydrolithospheric processes in a changing climate. The mathematical model developed in the article can
be used to assess the potential impacts of changing precipitation amounts and patterns, and to develop
adaptation strategies to mitigate these impacts.

The article proposes an approach to modeling social interactions in organizational systems, which
consists of several stages: obtaining data about system users, for example, using network parsing; forming
a GH-model of the system based on fuzzy graphs with different types of vertices and multiple different
types of edges; calculating graph characteristics taking into account a certain type of edges; using values
of graph characteristics to analyze the system taking into account the inherent semantic load. The expediency
of using the GH graph for the study of social relations in organizational systems is substantiated,
since it has a number of advantages. The GH-graph allows you to set all the necessary multi-type relationships
and at the same time reduce the time of system analysis by 1.9 times by using multiple edges in the
form of a vector, allowing you to combine several different types of edges. Modification of the model consists
in using different types of vertices. The type of vertices in the graph is determined by calculating their
characteristics. The paper shows the process of forming a graph model of a subsystem and calculating its
characteristics. The results of calculating the degrees of vertices and their centrality by degrees are
shown. To calculate the metric characteristics of the graph model, a modified algorithm for finding shortest
paths in the GH-graph was used, which was previously developed. A special feature of this algorithm
is the ability to use filters based on the type of vertices and edges. Numerical indices of the radius and
diameter of the graph are obtained, groups of central and peripheral vertices are determined, the centrality
of vertices in proximity is calculated, taking into account the selected types of edges for the study of
different types of relations in the system. The analysis of the subsystem is carried out using the example of
solving two practical problems. Groups of employees of the enterprise were identified among the network
users, their possible statuses and communicative activities were determined. The user status refers to belonging
to groups of managers of different levels, a group of ordinary employees of the enterprise. A solution
to the problem of identifying users (groups of users) most suitable for the dissemination (or, conversely,
non-proliferation) of information on the network is proposed

This paper demonstrates how to construct a complete Remaining Useful Life (RUL) estimation workflow,
including the steps of preprocessing, selecting trend features, constructing a health indicator by fusing sensors,
training RUL similarity estimators, and verifying the prediction performance. The method was tested in a
MATLAB demo program implementing this method for predicting the occurrence of faults in technical systems
(https://www.mathworks.com/help/predmaint/ug/similarity-based-remaining-useful-life-estimation.html) based
on data from the "PHM08 Challenge Data Set", NASA Ames Prognostics Data Repository
(http://ti.arc.nasa.gov/project/prognostic-data-repository), NASA Ames Research Center, Moffett Field, CA. The
method is focused on the use of reasonable technical characteristics of the equipment being estimated, which are
sufficiently covered in the reference literature. Therefore, the method gives good results when assessing equipment
whose operating conditions are close to the statistical average. This paper uses the Predictive Maintenance
Toolbox™ in MATLAB, which includes several specialized models developed for calculating RUL from various
types of measured system data. These models are useful when you have historical data and information, such as:
‒ failure histories of machines similar to the one to be diagnosed. The historical data for each member of the
data ensemble is fitted to a model of identical structure; ‒ a known threshold value of some condition indicator
indicating failure; ‒ data on how much time or how much use it took for similar machines to fail (service life).
RUL estimation models provide methods for training a model using historical data and using it to make a remaining
service life prediction. The term service life here refers to the useful life of a machine defined in terms of
any quantity used to measure the service life of a system. Similarly, time evolution can mean the evolution of a
value with usage, distance traveled, number of cycles, or another quantity that describes the service life. A general
workflow for using RUL estimation models is: ‒ create and configure the corresponding model object;
‒ train the estimation model using the available historical data; ‒ using test data of the same type as the available
historical data, estimate the RUL of the test component. It is also possible to use the test data recursively to
update the model as new data becomes available, i.e. track the evolution of the RUL prediction as new data
becomes available.

method In this paper, we propose a novel method for the automated assessment of knee osteoarthritis
severity, utilizing advanced machine learning techniques, specifically a deep neural network. Osteoarthritis
is one of the most prevalent degenerative joint diseases, and its timely diagnosis is crucial for
ensuring effective treatment. Traditional methods for visually assessing X-ray images of the knee joint
present several limitations, including subjectivity and reliance on the experience of the clinician. Therefore,
the development of automated medical image analysis techniques has become increasingly relevant.
Osteoarthritis of the knee joint is one of the most common and severe degenerative diseases leading to a
significant decrease in the quality of life of patients. Traditional methods of diagnosing osteoarthritis,
such as visual assessment of X-ray images, depend on the subjective opinion of a specialist and his experience,
which can lead to variations in the accuracy of diagnosis and timely detection of pathology. Therefore,
the development and implementation of methods for automated analysis of medical images is highly
relevant and has potential clinical value. In this study, we designed and trained a specialized neural network
based on the ResNet-34 architecture, which has demonstrated significant effectiveness in solving
computer vision problems. The network was modified to incorporate two parallel branches, each contain
ing a spiral linear structure and four hidden layers. This design enables more precise identification of the
knee joint area. Additionally, the architecture facilitates optimization of the loss function to account for
varying pathological characteristics, such as different degrees of joint degradation, and to address the
issue of class imbalance—a common challenge in medical imaging datasets. To further enhance model
performance, the neural network was trained on two distinct datasets stratified by gender (male and female).
This approach improved overall image quality and reduced the impact of noise introduced by artifacts
during radiographic imaging. Moreover, we employed the ImagePixelSpacing technique during data
preparation to standardize image resolution at 256 × 256 pixels, allowing for more accurate processing
of fine details and structures within the knee joint. The network training employed state-of-the-art optimization
techniques, resulting in a high level of classification accuracy. To evaluate the effectiveness of the
proposed model, the Kappa test was utilized, confirming the reliability of baseline determinations.
The model achieved an average accuracy of 93.76%, as demonstrated by the multiclass T-test, indicating
its strong potential for clinical application. Additionally, the model’s area under the curve (AUC) score
was 0.97, surpassing the results reported in previous studies in this domain. In conclusion, this research
contributes significantly to the field of medical informatics and computer-based medical image analysis by
offering an innovative solution for the automated assessment of osteoarthritis. This method has the potential
to profoundly improve diagnostic accuracy and treatment outcomes in clinical settings. In addition,
these results demonstrate the potential of the model as a reliable tool for automated assessment of the
degree of osteoarthritis, which can not only improve the accuracy of diagnosis, but also facilitate the work
of medical specialists. Further research may include adapting the model to analyze other joints and integrating
additional functionality, such as predicting disease progression based on sequential scans.

The needs of practice constantly require improving the performance of computing systems. For
quite a long time, multiprocessor systems have been the main way to build ultra-high performance computing
systems. When creating such systems, many difficult problems arise. They are related to the need to
parallelize the computing process in order to efficiently load the system processors, overcome conflicts
when several processors try to use the same system resource, reduce the impact of conflicts on system
performance, etc. With microelectronics overcoming the milestone of a billion transistors on a silicon
chip, a new paradigm of multicore processors has emerged. At the same time, the problem of the ratio of
multicore and multithreading in modern computers arose. This is due to the dilemma of preference between
them. A multicore processor contains two or more electronic computing cores placed on a single
semiconductor crystal. Each core of a multicore processor is a full-fledged microprocessor. Multicore is
an obvious and traditional method of distributed solution of many complex tasks. But this cannot be said
about multithreading, which relies on the use of very fast cache memory associated with the main memory
and serves to reduce the average access time to the main memory of the processor. The relative novelty of
modern approaches to the construction of computing systems requires comparative experimental studies
of their capabilities. A promising and convenient mathematical object for these purposes is the distributed
multiplication of matrices of large dimensions. The article presents practical results of distributed multiplication
of square matrices with sizes from 300*300 to 2000*2000 and randomly generated values of
elements in the matrices in the range from -100 to +100. Based on the experimental data presented in the
corresponding tables and graphs, hyperbolic relations are obtained for the dependence of the matrix multiplication
time on the number of virtual machines (cores) in the laptop used. Similar results were obtained
by multiplying square matrices on single-processor computers connected to a local network. Analytical
expressions in this case also represent hyperbolic time dependencies. But the numerical values in
them significantly exceed those for the hyperbolic formula obtained for the laptop. Based on the results
obtained, the conducted research allows us to conclude that the use of a single-processor computer connected
to a local network for multiplying matrices of large dimensions is inferior to the performance of a
laptop. This is due to the significant time spent moving data over the local network.

The article considers algorithms for generating unmanned aerial vehicles motion trajectories during
search and rescue and liquidation operations. The methods and algorithms for controlling the motion of a
unmanned aerial vehicles group in formation, when deployed in a line, when deployed in a rank, when
turning, in a column are described. Control is carried out using alternative collective adaptation algorithms
based on the ideas of collective behavior. The operating principles of one adaptation machine are
considered. The purpose of controlling slave robots is to minimize deviations. To implement the adaptation
mechanism, the parameters of the vector are matched with adaptation machines that model the behavior
of adaptation objects in the environment. A structure has been developed for the process of alternative
collective adaptation of parameters that control the motion of a group of unmanned aerial vehicles in
formation. Original rules for controlling parameters have been developed that have a number of advantages
over other methods: complete decentralization of control in combination with dynamic correction
of robot parameters that set the position and orientation of the robot in an absolute coordinate system,
and the linear velocity of the robot, respectively. A structure of a maneuver performed by a robot to correct
parameter deviations is proposed. Control is performed using an alternative collective adaptation algorithm
based on the ideas of collective behavior of adaptation objects, which allows for efficient processing
of emergency situations, such as agent failure, changes in the number of agents due to failure or sudden
acquisition of communication with the next agent, as well as in conditions of measurement errors and
noise that satisfy certain restrictions.

The problem of multi-criteria optimization of the choice of an unprepared helidrom to plant the
unmanned aerial vehicle (UAV) helicopter type on it is considered in this article. The problem of multi -
criteria optimization of the choice of an unprepared helidrom is formalized based on satisfying requirements
of the International Civil Aviation Organization (ICAO) to an unprepared helidrom by mi nimizing
the original loss function taking into account the following data: the probability of availability
of an unprepared helidrom, the probability of failure of the UAV’s helicopter type onboard system, the
error of a digital elevation map (DEM) positional information, the error of the UAV’s helicopter type
coordinates information and the technical characteristics of the UAV helicopter type. It is proposed to
determine the suitability of an unequipped helidrom based on the maximum height of terrain elements
of it’s surface using statistical processing of a lidar earth scanning data. The mathematical formulations
of the problem of decision-making on UAV helicopter type landing are proposed based on requirements
for an unprepared helidrom in terms of maximum height of terrain elements and soil hardness.
The comparison of the computational time of algorithms of the choice of an unprepared helidrom
is completed using Raspberry Pi 3 Model B. The result of a simulation modelling of the proposed opt imal
algorithm of the choice of an unprepared helidrom for the estimation of its ef ficiency under conditions
of variability of parameters of the probabilistic loss function using OpenStreetMap and SRTM is
presented. The result of solving the problem of decision-making on UAV helicopter type landing based
on a lidar earth scanning data is presented

The operation of robots on vertical and close to them surfaces has broad prospects due to the need
to perform a sufficiently large number of technological operations on them on the one hand and the complexity
of using manual labor on the other hand. The movement of a mobile robot along a vertical surface
is considered. The movement of the robot and its retention on the surface is carried out through the operation
of two linear actuators that exert pressure on it and rely on platforms capable of moving along a horizontal
surface. The robot and the platform have piano‒type wheels operating in one of two modes – free and brake. At the same time, the braking devices ensure reliable adhesion of the wheels to the corresponding
surfaces. A design scheme and a mathematical model of a robotic system using the force of linear actuators
to move the robot along a vertical flat surface are proposed. The problem of the dynamics of the movement
of a mobile robot has been solved, the movement of which along the working surface is carried out by
controlling the magnitude and direction of the efforts developed by the actuators and the choice of inhibited
supports that ensure a stable mode of movement. The process of movement is considered, consisting of three
stages, at each of which one of the robot's supports is braked, while all the supports of the platforms on the
horizontal surface are also braked. During the transition between the stages of movement, the mobile robot
makes a stop before changing the braked wheel, after which movement resumes. The friction forces between
the disinhibited robot supports and the work surface are neglected. The equations and trajectories of the
motion of the center of mass of the mobile robot are obtained. The dependences of the lengths of the linear
drives of the clamping mechanism on the coordinates of the center of mass of the robot are presented. Simulation
modeling was carried out, as a result of which the ranges of changes in the lengths of linear actuators
and the forces developed to ensure the required displacement were determined.

The article considers the applicability of deceptive information systems and their components in
building an automated system for deploying and managing the applied implementation of deceptive component
technology to improve the attack prevention system. The main advantages and the role of technology
in the information security strategy setting the specifics and the area of technology means and tools
practical appliance are suggested. The article considers the fundamentals of the architecture and features
of the technology application, as well as its limitations. The purpose and the objective of using the present
technology is pointed in terms of key principles of implementation disclosure. In addition, regulatory publications
and other recommendations constituting the best practices in the field of its use were analyzed.
The concept and architecture of the final automated solution for integration into information systems and
security systems are considered, and the functional content of the final solution is described. A distinctive
feature of the proposed solution is the use of controlled containerization mechanisms, that provide ample
opportunities for scaling the solution and isolating compromised system components as a result of an
intruder's actions. A formulated process of the automation system practical implementation in perspective
of solution subsystems is schematically described in relation to dependent components (such as suggested
document pieces and outer tools and systems) and included operations processing conditions. A model of
deployment and operation of a distributed automation system is also provided in the following sequence:
setting up a deployment server (including provisioning), deploying a network of false decoy components
based on containerization, deploying external baits, integrating with systems and instances of the information
security stack external to the composition of the solution. The solution is implemented by means of
the principle: fake assets and resources of the fictive environment are deployed in an information technology
infrastructure using controls and are intended to be affected by the adversary. The deployed set of
subsystem tools was tested using a third-party node with the appropriate tools and scanning scenarios.
Recommendations are given for further improvement of the automation system for deployment and management
of tools and measures for deceptive component technology.

IT asset management is the foundation for building an effective vulnerability management process.
Without an understanding of the IT assets under control, it is technically impossible to start building a
vulnerability management process. With an existing IT asset management process in place, one of the
tasks that is essential to vulnerability management is to uniquely name software as an asset. This unambiguous
naming allows the software and its vulnerabilities to be identified without actively scanning IT
infrastructure nodes, but only by interacting with the IT asset management system. Technically, this approach
can be called “passive vulnerability detection,” but it is extremely labor-intensive to implement
using existing naming systems. In order to make the possibility of passive detection more realistic, the
authors propose to create a common foundation by forming a conceptual scheme and then creating a system
of standardized naming and identification of software, the regulation of which will be centralized at
the state level. As part of the review of existing software naming systems, attention is paid to CPE problems
both on the part of on-site specialists, namely obtaining CPE identifiers and translating software
information into a CPE identifier, and on the part of a vulnerability data aggregator, namely obtaining
vulnerability information via a CPE identifier. The problems of CPE application, as well as the problems
of interaction with vulnerability data aggregators from unfriendly countries, discovered in the course of
the research form the prerequisites for the formation of a national system for state regulation of software
naming and identification, which will eliminate the problems of existing software naming systems. In conclusion,
advantages of the national system of software naming and identification are given in case of its
creation and use in real conditions by all participants of the vulnerability management process

Further progress in microwave technology is inextricably linked with the creation of new precision
automatic measuring systems. In our country, microwave circuit vector analyzers that can measure the
amplitude and phase relationships of the S-parameters of the microwave networks under test are not
mass-produced. The use of multi-port reflectometers (MPR) as measuring devices in automatic microwave
circuit analyzers allows creating relatively cheap and high-precision devices for studying load parameters.
The paper provides an overview of the works in which the MPR method is developed, when the latter
can be represented by a multi-probe transmission line reflectometer (MTLR). The history of the development
of measurement methods using traditional MPR is briefly described and it is shown that the main
problem of their use is reflectometer calibration, which can be carried out accurately only using a set of
precision calibration standards. MTLR, which is a special case of MPR, is studied in detail. It is shown
that random measurement errors by the MTLR method are higher than those of a precisely calibrated MR.
However, the MTLR has important advantages that are discussed in the paper. A strategy for increasing
the measurement accuracy using the MTLR is described: 1) optimal methods for processing output signals
from the MTLR probes using the maximum likelihood method are proposed; 2) methods for calibrating the MTLR sensors are studied in detail and it is shown that it can be calibrated using a set of inaccurately known
loads with their parallel certification, therefore, systematic calibration errors are significantly reduced;
3) methods for optimizing the MTLR design by arranging the probes inside the microwave path for measuring
with maximum accuracy in narrow and wide frequency ranges are studied, and it is also shown how it is
possible to measure with potentially achievable accuracy due to the proper choice of weighting coefficients in
the MTLR probes. Random and systematic errors in measuring the complex reflection index of microwave
loads, as well as uncertainties in measuring types A and B by the MTLR method are investigated, and references
to relevant works are given. In conclusion, the possibilities of joint use of the MTLR and MPR methods
are considered, a combined MPR is briefly described, which measures with an accuracy characteristic of a
traditional MPR, but can be calibrated using a set of unknown loads, which is inherent in the MTLR method.
Automatic network analyzer, multi-pole reflectometer, multi-probe measuring line, maximum likelihood
method, error dispersion matrix, meter calibration.

For wireless and battery-free power supply of autonomous devices with low power consumption harvesting
of radio frequency energy from the environment is increasingly used: energy from cellular stations,
radio stations, microwave ovens, Wi-Fi, Bluetooth, etc. To convert the collected energy into a DC voltage,
devices consisting of an antenna, a rectifier and an impedance matching circuit of the antenna and the rectifier,
called rectennas, are used. The power density of the electromagnetic field can be very small: from hundreds
of microwatts to tens of picowatts per cm2. Therefore, the task of developing rectennas capable of operating
at ultra-low power levels is urgent. The parameters of components of the rectenna (antenna, impedance
matching circuit, rectifier) are strongly interconnected, therefore, to obtain optimal characteristics, it is
necessary to design the rectenna considering the mutual influence of all components and use appropriate
models. The paper analyzes the features of the construction and development of a rectenna model based on
MOSFETs for operation at ultra-low power levels. Expressions for estimating the output voltage of the
recntenna are obtained, considering the basic parameters of the antenna, the rectifier/voltage multiplier and
the impedance matching circuit. Calculations based on the obtained expressions and modeling are performed
for a typical 90 nm CMOS technology. The possibility of constructing rectennas based on MOSFETs at ultralow
power levels up to -50 dBm is shown. Recommendations are given on the choice of technological and
design parameters of rectennas for harvesting microwave energy.

Growing user requirements for data exchange rates in telecommunication systems have resulted in
the active adoption of mm-band wavelengths and the intensive development of broadband communication
systems. Designing mm-wave microwave devices using CAD requires accurate frequency-dependent relative
permittivity data for the used substrate to reduce the device design time. This paper focuses on determining
the relative dielectric constant of the Rogers 3003G2 substrate in the mm-wavelength range. Both
non-resonant and resonant methods were used to find the dielectric permittivity. The automation of the
measurement data processing was achieved by using the developed script in MATLAB. The relative permittivity
of the substrate in the band 1-42 GHz was determined by applying the phase difference method,
using two microstrip lines of different lengths. SIW resonators with waveguide excitation were developed
to avoid using a probe station with fragile probes for S-parameter measurements in the mm-length range.
The relative permittivity of the studied substrate in the 60-170 GHz range was found using three prototype
multi-mode SIW resonators. A set of single-mode SIW resonators with different waveguide excitation coupling
was produced to avoid ambiguity in longitudinal mode number determination in multi-mode SIW
resonators. Several loaded resonant frequencies were obtained by varying the length of SIW-resonators'
excitation slots to calculate the unloaded resonant frequency used to find the relative dielectric permittivity
of the substrate. Recommendations for developing SIW resonators for the determination of dielectric
properties of the substrates are given in the conclusion section.

The issue of creating a wireless charging system for an on-board battery of an unmanned aerial vehicle
(UAV) is considered, taking into account the need to balance the voltages of its elements. When designing
the system, based on a brief overview of the principles of wireless energy transmission, the principle
of using magnetically coupled circuits is taken as the most suitable in terms of its technical and economic
properties. The aim of the work is to develop a circuit solution for a UAV's wireless battery charging
system with the ability to balance voltages both during charging and during load operation. The use of
such a system will improve the safety of battery operation and extend its service life by leveling the degree
of wear (aging) of the elements. As a result of the research, a circuit was developed and an experimental
sample of the specified wireless charging system was manufactured. When synthesizing the circuit, the
task was to minimize the number of components in the power circuits in order to reduce the mass of the
system and its cost. The maximum power of the experimental wireless charging system exceeds 100 Watts
(25 V · 4 A) and is somewhat excessive for an on-board battery with a capacity of 1,500 mAh. Forced
cooling of the receiving part is not required. The weight of the receiving part mounted on an unmanned
aerial vehicle is 79 g (40 g is the receiving coil and 39 g is the electronics unit) and has reserves for reduction
by reducing the cross–section of the receiving coil conductors, using a textolite with a lower
thickness in the electronics unit, sealing the installation and using a two–sided arrangement of components.
Laboratory tests have been carried out, confirming the operability of the proposed technical solutions,
and the effectiveness of balancing during charging has been evaluated. In order to evaluate the
effectiveness of the balancing system during the experiments, the output resistance of the receiver (U/I)
was calculated relative to one of the elements of the on-board battery when the voltage on it changes.
The result was 1.9 ohms with a charge current of 0.8 A (6S 1500 mAh battery).

The paper presents the results of experimental studies of the processes of formation of holes by the
method of focused ion beams on GaAs(111) substrates and their subsequent transformation during annealing
in an ultrahigh vacuum chamber of molecular beam epitaxy in an arsenic flux and in its absence.
It was found that at an ion beam exposure time of 1 ms, the processes of ion accumulation in the substrate
prevail over the processes of the material sputtering, whereas at an exposure time of 5 ms, intensive sputtering
of the substrate material occurs at the points of exposure to the ion beam with an increase in the
depth of the etched areas with an increase in the number of ion beam passes. After annealing of substrates
with ion beam-modified areas, the holes increase significantly in size as a result of local droplet etching
processes. Studies showed that the hole size after annealing in the arsenic flux exceeds the hole size after
annealing in the absence of an arsenic flux in almost the entire range of the number of ion beam passes.
The dependences of the depth and lateral size of the holes on the number of ion beam passes are nonmonotonic,
due to the competition of the processes of droplet etching and crystallization of ion beammodified
areas in the arsenic flux. The results of experimental studies show that to obtain highly symmetric
pyramidal holes with low surface density, it is required to create on the GaAs(111) surface an array of
focused ion beam treatment points with an interval of 2 μm at an exposure time of 5 ms and a number of
passes equal to 40. At the next stage, it is necessary to transform the ion beam processing points into pyramidal-
shaped holes by annealing the substrate in a molecular beam epitaxy chamber at a temperature
of 600°C and a time interval of 60 minutes. The technique proposed in this work, based on the combination
of ion-beam surface treatment and molecular beam epitaxy, makes it possible to obtain nanoholes
with the required symmetry, which can further serve as nucleation centers for InAs quantum dots with the
desired properties.

Expanding the range of materials available for processing by additive methods is of great interest to
industry. Technologies such as 3D polymer printing significantly expand the boundaries of design capabilities,
allowing a transition to next-generation devices. In view of the gradual implementation of such approaches
in practice, a new impetus for development has been given to the direction of metamaterials -
volumetric structures whose geometry allows for more complete use of the properties of the base material.
In particular, ceramics, common in modern electronics, can be introduced into a polymer molded by an
additive method as a functional additive. Subsequent heat treatment of such compositions allows obtaining
a macrostructured ceramic-polymer or purely ceramic framework with unique piezo- or dielectric properties.
However, additive particles can significantly change the technological properties of the base material,
which must be taken into account. At the same time, isolating the empirical features characterizing this
dynamics is a non-trivial task. Thus, in publications on UV-curable composites, the viscosity criterion of
the composition is recognized as the leading feature. At the same time, optical permittivity, which determines
the required equipment power, is not considered properly. In this regard, the presented work studies
the viscosity, dielectric, optical and temperature properties of composites based on UV-curable resin for
DLP 3D printing, containing additives of 5 vol. % Al2O3 and CTS-19 powders. A method for qualitative
express analysis of the technological suitability of the composition based on the Scotch test is presented. It
is shown that the viscosity of the composition is less significant in comparison with its optical permittivity
in the UV range. The considered compositions have temperature stability up to 300 ⁰С. The introduction of
powder additives makes it possible to increase the dielectric permittivity ε'/ε0 by 2.5 times and reduce
dielectric losses in the material when heated above 110 ⁰C. It is shown that composites containing aluminum
oxide have potential for use in electronics.