Electric energy supply at home is an issue, which offers more solutions than years ago. The objective is to present an evaluation of self-sufficient electric energy supply at home. The method bases on the solar power plant as the primary source of power generation. The storage of electric energy with direct current electric battery is considered as the secondary power source, knowing that night hours are without the primary power generation. The real case time dependent home consumption is determined and the real case of solar power plant generation is considered in this evaluation. The size of solar power plant and the size of the battery are optimised, which is based on consumption and on 100 % self-sufficient power system. One parameter optimisation bases on minimisation of costs for such a system. The model includes realistic yearly time dependent home consumption curve, realistic yearly time dependent solar power plant generation curve and realistic yearly time dependent curve of state of charge of battery, which is a function of solar power and home consumption. The time resolution of this model can vary based on density of data points of solar power generation and density of points of determined power consumption. Results include comparison of costs related with different size of solar power plant and with different size of battery. The most important result is a combination of the size of solar power plant and the size of battery, which are both related with the smallest overall costs. Consideration of different years gives different results due to different sun irradiation through the hours of the year due to weather changes. Consideration of different locations gives different results due to the same reason. Results show relatively large cost of all cases, which exceeds the cost of supply of electrical energy from actual provider at the current conditions.
The article explores using very high-resolution RGB data from unmanned aerial vehicles (UAVs) to explore the possibilities of utilizing data to monitor vegetation. The Baroch nature reserve, located in the Pardubice region of the Czech Republic, is used as an area of interest. Data is collected using the UAV DJI Mavic 2 DUAL Enterprise. The reserve is overgrown mainly with reed and cattail and is used as a pasture. The paper aims to determine the effectiveness of utilizing UAV technology and very high-resolution RGB data to identify, monitor, and classify vegetation types in this grassy habitat and to provide insights into the practicality of using such data for ecological research and landscape management purposes.
The study aims to evaluate the Landsat 8 Thermal Infrared Sensor land surface temperature (LST) products and Visible Infrared Imager Radiometer Suite (VIIRS) LST environmental data record against ground observations in Armenia and Belarus. The data from the Landsat-8 satellite is used due to its high spatial resolution. In contrast, the VIIRS information is obtained due to its high temporal resolution, accessible with a minimal delay after the flyby of satellites. Algorithms to calculate LST were analyzed to find the best performance for both daytime and nighttime data. The evaluation shows that the current VIIRS LST products demonstrate a reasonable accuracy, with a root mean squared error average of 2.77 $\mathbf{K}$ and an average coefficient of determination of 0.92 K.
This paper deals with the exploration of a system which acts on demand in case of emergency matter. To verify functionality, each component of the system must be regularly inspected with a specified inspection interval and tested to find latent failures. A failure-based preventive maintenance (PM) is considered, in context with the imperfect corrective maintenance model. This means that PM brings full renew which is realized after a beforehand setting the number of failures. If a failure occurs, it is detected during the first follow-up inspection and the reparation process starts. The imperfect corrective maintenance model is intended where each repair deteriorates the system life-time whose probability distribution is gradually altered through rising failure rate. Reliability mathematics for unavailability analysis is briefly demonstrated. The new renewal process model, including the failure-based PM, has been termed as a real ageing process. The imperfect corrective maintenance as well as increasing length of the inspection interval result in unwanted increase of system unavailability function which can be eliminated as well as optimized by the properly selected failure-based PM applied to all system components. If the PM is fixed due to cost, inspection intervals of system components can be optimized to keep the maximal value of the unavailability function in its original level. Sensitivity analysis applied to the length of the optimal inspection intervals with regard to gradually changing PM is demonstrated. This optimization process is demonstrated on a four-component system adopted from references.
This paper uses a novel method for evaluating land degradation in mountain-protected reserve areas based on remotely-sensed data. A risk map is developed based on this method for the investigated area. The risk is computed based on quantifying drivers of land degradation that can be estimated remotely. This paper considers terrain slope, vegetation cover, and surface soil moisture (SSM) as drivers. The risk value for developing a risk map is computed, which should be from one of the indicated classes (typically, the number of classes is from three to five). Therefore, the threshold for risk should be introduced to indicate the risk class on the map. The Slovakian National Park of Pieniny is chosen as the study area. The data for the investigation has been obtained from the European remote sensing satellite system Copernicus Sentinel. The terrain slopes have been extracted from a digital surface model (DSM), which in turn was obtained from an interferometric pair of radar images of the Sentinel-1. The vegetation cover has been indicated by the leaf area index (LAI). LAI maps can be derived from optical Sentinel-2 satellite imagery and radar Sentinel-1, The specifics of this study and the presented result are the use of a short time series of Sentinel-2 multispectral images formed, acquired annually under the same phenological conditions - during the late June to early July period. Analysis of the risk map allows determining the terrain slope as the most significant driver of land degradation, which, moreover, has not changed much for decades. Vegetation cover acts as an additional land-strengthening agent. The SSM impact could not be disclosed, possibly due to small statistics and insufficient spatial resolution.
In recent decades, a significant increase in the share of renewable energy sources in power grids at various voltage levels has been observed. A number of articles have been published highlighting emerging problems in low-voltage grids with a large share of prosumers and in medium- and high-voltage grids to which photovoltaic (PV) plants are connected. The article analyzes the medium-voltage grid in terms of the possibility of connecting maximum PV power to it, while maintaining the criterion of the proper voltage level in the whole grid. The selection of suitable locations for PV power plants is analyzed, as well as the expected effect of the possible modernization of the grid - replacement of the main line with lines having a larger cross-section. As a result of the analysis, the percentage profits from both presented solutions (PV power plants optimal locations and main power line modernization) have been compared.
Priority queue is one of the fundamental data structures utilized in various areas of informatics such as graph theory, discrete-event simulation, and operating systems. Also, it finds an application in algorithms such as image segmenting used in remote sensing. The literature offers several basic implementations of the priority queue, like the binary heap, which usually offers good complexity to performance tradeoffs. However, the researchers have proposed several advanced implementations, such as the Fibonacci heap or Brodal queue, that offer better theoretical computational complexities. The existing practical comparison suggests that in some use cases, the simpler basic implementations perform better than theoretically better - yet more complicated ones. One of the not yet examined use cases is the OPTICS clustering algorithm. In the paper, we describe how the OPTICS algorithm utilizes the priority queue and we present an experimental comparison of basic and advanced priority queue implementations showing that this use case also favors simpler implementations with worse theoretical computational complexities of their operations.
The article discusses the possible effects of Directive 2022/2555 of the European Parliament (EU) and of the Council of 14 December 2022 on measures to ensure an ordinary high level of cyber security in the Union and amending Regulation (EU) No. 910/2014 and Directive (EU) 2018 /1972 and on the repeal of Directive (EU) 2016/1148 (NIS2 Directive) and subsequently to it on the Act on Cybersecurity and on the amendment of related laws (Act on cyber security), which will have to be amended concerning the NIS2 directive and their possible impacts on information security management systems (ISMS policies). The contribution aims to describe the current state of implementation of the NIS2 directive into the legal environment using the example of the Czech Republic. To outline the possible procedure of applying the implemented directive into practice and possible impacts on subjects.
This paper presents a comprehensive comparison of deep convolutional neural network (CNN) architectures and TensorFlow for full bee body identification in images with the purpose of using detected individual bees for future health analysis. The main focus is on five popular architectures: Faster R-CNN ResNet152, Faster R-CNN Inception ResNet, SSD ResNet50, SSD Mobilenet, and CenterNet Resnet50. The results show that SSD Mobilenet model offers the highest recall value of 80.5 percent but with the lowest precision among tested models of 95 percent. In contrast, Faster R-CNN ResNet152 is the second-best option with a recall value of 67.6 percent, the fastest image processing time, and a precision of 99.6 percent.
Despite the fact that digitization is still a hot subject of debate, not many writers have addressed the use of digital tools to foster creativity in the classroom. Our paper focuses on digital resources, both online and offline, that foster students' imagination and innovation and may be thought of as creative applications. The article begins with a discussion of what creativity is, how it works, and why it's so crucial to have. We also provide a compilation of many writers' approaches to inspiring original thought and action. The article's second half focuses on clarifying the value of creative teaching and briefly describes a few useful digital tools. The article's key component is an analysis of a questionnaire survey of Slovak educators done in light of the training in the field of digital tools in the classroom that was put into effect; its objectives were to first familiarize educators with online and offline tools in the classroom, and then to ascertain the current state of the field and educators' interest in further training in this area. In Section 2.2, we give the analysis in both written and visual form. The survey results are presented as the last section of our study.
Modern world wants to have realistic and precious real-world models. One of the representations of the real world is $\mathbf{3d}$ models. This paper compares approaches and processing time for creating a 3D model based on UAV-borne image data. Image data for 3D model was collected by two approaches by an UAV. Both approaches were controlled by planned flights with specific parameters. Creating 3D models was processed in two software tools. One of them was freeware, and the second one was licensed. Results from the processing of 3D models show different processing times and quality of 3D models given by tools and data collection approaches.
Operational management at a railway node plays an important role in the smooth operation of the entire railway system. As part of operational management, dispatchers must find solutions to the problems of allocating scarce resources (e.g. personnel) in a fairly complex system. To support problem solving in a railway node, many different algorithms can be used, including algorithms based on the reinforcement learning paradigm. In this paper we examine the use of one of these algorithms, Proximal Policy Optimization (PPO). Experiments using this approach were conducted with a microscopic simulation model of a maintenance depot. The results confirmed that the trained PPO agent could be used for real-time decision support.
This article discusses the use of color and color space analysis in the space of 3D reconstruction of a real object. The text focuses on the area of point clouds as a carrier of color information in 3D space. This issue is applied to the 3D reconstruction of an original work of art using the method of photogrammetry into 3D point clouds to use the above procedures in the field of forensic science and the issue of falsification of works of art in digital and virtual environments.
The full-scale invasion of Ukraine by the Russian Federation has become a severe challenge for the entire Ukrainian society, including the higher education system. The decision to switch to distance learning was made due to the increased threat to the safety of students, teachers and staff of higher education institutions. The article examines the effectiveness of the introduction of distance learning in Ukrainian higher education institutions during martial law period; analyses the level of student satisfaction with the organization of the distance learning system during martial law period; analyses the difficulties and the indicators of success of students' learning outcomes in various specialities before and during martial law period. The authors created a questionnaire for students using Google Forms to solve these problems. The obtained results were processed with the help of content analysis, calculation of the share of elections, and ranking. In order to make a comparative characterization of students' learning outcomes before and during martial law period, the authors analyzed the overall and qualitative performance of students by speciality. It has been determined that the most significant difficulties that arise in Ukrainian distance learning in the context of war are: interruptions in the work of the Internet and communication, psychological stress of students and teachers, lack of effective interaction, lack of practical training, systematic physical and moral fatigue, and reduced motivation to learn. It was found that the indicators of qualitative performance decreased, while the indicators of overall performance remained almost unchanged. The authors conclude that although distance learning requires some compromises and adaptation, its implementation in Ukraine has shown that educational institutions can function effectively in a crisis and provide students with quality education. The results of the study can be useful for educational institutions and students to improve the quality of education in times of crisis.
IoT based systems must be continuously available to be used effectively and fulfill their purpose. Therefore, these systems must be designed to incorporate fault tolerance mechanisms. The successful use of IoT technology brings a number of devices connected in different ways, which increases the risk of network connection failure. The article deals with the implementation of FRR (Fast Reroute Mechanisms) to ensure reliability and availability in the network. In the context of IoT networks, it is important to consider the limitations that may affect the implementation of FRR mechanisms. IoT devices are often powered by batteries that have a limited capacity and therefore it is important to use energy efficiently. At the same time, IoT networks can be large and heterogeneous, which requires different approaches to ensure reliability and availability. The goal is to minimize the impact of an outage on the network and ensure that data transmission can continue without significant delays.
Currently, environmental protection is one of the main priorities. Vegetation cover as a complex system and water bodies are examples of land cover types that are observed and understood as important parts of our environment and need to be cared for. It is very important to collect up-to-date data on such systems. The use of unmanned aerial vehicles for data collection has become very used in recent years due to its scalability, efficiency, and instrumentality in various fields. Managing data collection projects using UAVs can be quite complex and challenging. This text examines the use of the Agile Scrum methodology in realization such a project.
Early detection and prevention of natural and anthropogenic degradation processes are especially relevant. This paper proposes a suitable architecture of a cloud-based early warning system for assessing land degradation based on satellite Earth observation and other necessary geospatial data. The early warning system is based on efficient methods for the time series of land degradation indicators fusion and algorithms for land degradation risk assessment. Earth observation data includes multispectral and radar satellite imagery and derived data products. Ground-truth data construct auxiliary data in concert with topographic., geoclimatic., cadastral., socioeconomic., and other statistical data. The use of the ArcGIS Online Portal is suggested for the processing of heterogeneous geospatial data. As a result., the authors intend to build a general knowledge-based model for assessing and predicting land degradation.
The skin potential level (SPL) of facial biologically active zones (BAZ) reflects the level of background brain activation and the level of mental stress. Emotional burnout syndrome is a reaction to everyday stress in interpersonal communication that includes functional changes in brain activity and corresponding dysfunctions of the autonomic nervous system. The aim of the study was to detect the changes of SPL in symmetric biologically active zones of face skin in the resting state depending on the level of emotional burnout. 31 healthy volunteers (women and men) - first-third year students (Mage= 19.07, SD = 1.91 years, from 17 to 23 years) were recruited from the Taras Shevchenko National University of Kyiv. Skin potential level was recorded by nonpolarizable silver electrodes from the symmetric biologically active zones of face skin with the palm surface as a reference area. To detect the severity of burnout we used V. Boyko's “Syndrome of Emotional Burnout” questionary. An inverse correlation was found between the formation of emotional burnout and the skin potential level in the right periotic BAZ (parotid BAZ). It detected links between the development of the Resistance stage of burnout and background SPL in the right and left frontal BAZ. Our data indicate that emotional exhaustion during the development of burnout, accompanied by a change in the background activity of brain structures and the level of psychic tension, varies emotionality in anticipation of emotionally significant events, which reduces the adaptive abilities and efficiency of future activities. It indicates that electrodermal potentials can serve as objective criteria for the formation of emotional burnout.
In recent years, low Earth orbit (LEO) satellites have emerged as a promising technology for various applications including communication, remote sensing, and navigation. While there are many benefits to using LEO satellites such as low latency, reliable and high-speed internet services, there are also several challenges such as Doppler shift and potential signal interference. In this analysis, we studied the behavior and performance of LEO satellites by implementing 5 ground stations and examining various factors such as the Latency and the Doppler Shift. Our findings revealed important insights into the stability and performance of LEO satellites, which can inform the development of future satellite communication systems. The results of this study highlight the potential of LEO satellites to meet the growing demand for high-speed and reliable communication networks, while also highlighting the need to address key challenges associated with their deployment and operation.
This paper deals with the overview of reliability statistical data of the main components of modern traction electric drives. The general focus is set on data acquisition, their statistical analysis, and the resulting failure statistics which can be used in various reliability calculation models and finally allow to derive tendencies and perspectives considering the reliability requirements on electric vehicles. The paper contains statistical data on the failures for various types of electric vehicles: cars, airplanes, helicopters, ships, and trains. With the development and acquisition of new materials and technologies, the resulting database of statistical data on reliability can be expanded and adjusted.
