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Polymer materials are an integral part of our life, and in recent decades they have found an increasing number of applications in electrical engineering. They are used not only as insulators, protective materials and flexible substrates, but also as electrically conductive materials for instance in batteries, capacitors, sensing systems and photovoltaics. These materials are light, low-cost, and easy to manufacture. Their properties can be tailored according to the demands of the particular application. The drawback of polymeric materials, especially petroleum-based plastics, is their impact on the environment due to difficult and/or costly recycling. A possible solution to this problem could be replacing conventional plastics with biodegradable polymer materials obtained from nature like proteins and polysaccharides or synthesised from biological products like poly(lactic acid) or from petroleum like poly(butyleneadipate-co terephthalate). This paper provides an overview of the most commonly used conventional polymeric materials and their potential biodegradable substitutes used in electrotechnical applications.

Generative Adversarial Network (GAN) is an exciting innovation in machine learning within the neural network field. These models are able to generate a realistic image, video or even voice output. One of the useful applications is its possibility to enrich data sets for better learning of neural network models. In the presented work, we focus on image augmentation with the use of several variations of GAN to improve the classification of convolutional neural network. Accordingly, to prove the advantage of GAN-based image augmentation in comparison with methods of classical augmentation, we used specifically three different degrees of image rotation and compared classification results of convolutional neural network that use images from these augmentation methods. Mentioned methods of image augmentation are applied to five datasets belonging to three different domains, specifically medical, astronomical and geological domain. The architecture and settings of the convolutional neural network are the same for all datasets. To evaluate classification results, we used confusion matrix, accuracy, precision, recall and F1-score.

The temperature field variation of overhead conductors in the operating environment of the Qinghai-Tibet Plateau is significant, causing the stress on the conductor wires to fluctuate with climate changes, leading to fatigue and wire breakage. Conducting numerical simulation and analysis of the temperature field of overhead conductors in this environment is crucial for enhancing the safety of transmission lines. This paper establishes a numerical algorithm for solving the radial temperature distribution of overhead conductors in operation based on the two-dimensional steady-state heat balance theory. This method is applied to the numerical simulation of the temperature field of the heat-resistant aluminum alloy conductor NRLH58GJ630-55 in the Qinghai-Tibet Plateau environment, analyzing the effects of current load, wind speed, and ambient temperature on the radial temperature distribution of the overhead conductor.

We explore the use of radiance fields (RFs) to reconstruct photorealistic 3D urban scenes, creating digital twins (DTs) for autonomous driving (AD) by leveraging Nerfacto and Splatfacto models integrated with the CARLA simulator. Our research demonstrates that publicly available RFs can be utilized through Nerfstudio library to create photorealistic urban scenes and extract arbitrary images based on the camera pose. These scenes can serve as simulations for AD or as DT repositories for static environments within the vehicular metaverse. Additionally, we quantitatively evaluate RF models and use masking to remove dynamic objects, successfully simulating real-world scenarios. Quantitative evaluation shows that the Splatfacto model achieves a peak signal-to-noise ratio (PSNR) of up to 26.40, a structural similarity index measure (SSIM) of 0.84, and a learned perceptual image patch similarity (LPIPS) score of 0.21, consistently outperforming the Nerfacto model.

Abstract The global need for diabetic patient self-care is increasing. The current global epidemiology of diabetes calls for an improved healthcare management system, particularly in poorer countries, to minimize the burden of diabetes complications and mortality. The main objective of this study seeks to increase diabetic patients’ access to healthcare by utilizing internet of things-based smart self-care high-tech devices. The proposed model was integrated with a Blynk software and hosted in NodeMCU, allowing user to access diabetic health metric from the smartphones or interconnected devices. The e-monitoring system has an inbuilt feedback mechanism that provides online access to healthcare provider for immediate insulin therapy. Furthermore, patients suffering from acute diabetic metabolic issues may have difficulty visiting the hospital. Therefore, they can use the proposed model to track and monitor their blood glucose levels, as well as communicate remotely with their doctor, so improving their health while saving money and time. Also, diabetic patients can use this system to record their health condition on their phone, decreasing the need for doctor visits and clinical medical procedure. The proposed system has several advantages, including speedy internet access and remote monitoring from anywhere in the world. This research also helps to achieve the United Nations’ Sustainable Development Goals, which include good health and well-being, equitable healthcare access, and sustainable cities and communities.

Abstract Series compensation is widely used to enhance the transmittable power through an existing transmission line. This is achieved by controlling series reactance of the transmission line by injecting a voltage in series with transmission line and is perpendicular to the line current. However, X/R ratio of the transmission line gets deteriorated with series compensation. X/R ratio plays an important role in maintaining stability and damping oscillation in power system. In the proposed scheme, X/R ratio of the line has been included as one of the constraints along with midpoint voltage and power balance equation, with the objective of minimizing power system losses while enhancing the power. It is found that by maintaining X/R ratio of the transmission line same as the original designed value, stability of the system can be improved while making an attempt to enhance the transmittable power capacity. Steady state and transient stability, voltage stability and damping oscillations are analyzed with proposed optimal control of statis synchronous series compensator (SSSC), and results are produced. Validation of proposed optimal control of SSSC is done by using MATLAB platform for enhancing transmittable power of a 400 kV, 400 km transmission line and results are presented here. The paper investigates the effectiveness of the proposed optimization technique on improvement of power system stability and oscillations damping as well.

Abstract This paper presents a novel method for identifying critical transmission lines (CTLs) in power systems subjected to cascading overload outages, leveraging the Natural Connectivity (NC) index. Unlike previous studies that employed suboptimal weights for the edges, this work introduces a new weighting strategy that enhances the effectiveness of NC in CTLs identification. The proposed Weighted Natural Connectivity-based (WNC-based) method is validated through comparisons with established methods, including the catastrophic expectation index, and the cascading failure simulation index, using IEEE test systems of 30, 39, 57, and 118 buses and the 400 kV Mexican power system. The experimental results demonstrate that the WNC-based method accurately identifies critical lines, providing a practical balance between simplicity and precision. This study challenges prior conclusions that natural connectivity is unsuitable for CTLs identification, showcasing its potential in power system applications. Furthermore, the method's applicability extends to interconnected systems, such as the integration of power transmission, gas distribution, and telecommunication networks, paving the way for future research. The WNC-based method offers a practical tool for preventing blackouts and enhancing power grid resilience, contributing significantly to the field of power system reliability and stability.

Abstract The rapid evolution of battery technology has sparked an urgent need for advanced monitoring and diagnostic capabilities. This comprehensive review explores the emerging concept of Lab-on-Cell (LoC), a paradigm shift in battery management that integrates sophisticated sensing technologies directly into electrochemical cells. Through meticulous analysis, the study examines cutting-edge sensor technologies, including resistive and thermoelectric sensors, piezoelectric devices, electrochemical impedance spectroscopy, and optical fiber sensors. It delves into their principles, applications, and limitations within the context of battery diagnostics. Uniquely, this review intertwines technological assessment with geopolitical and economic context, charting the evolution of LoC technologies against a backdrop of global events and policy shifts. It sheds light on the complex drivers of innovation in this field, drawing connections between research trends, industrial needs, and regulatory changes. The study introduces a novel dual-reference system, separating general and LoC-specific sources to cater to a diverse readership. The review culminates in a forward-looking analysis of LoC technologies’ potential impact on battery management systems, cell design, and manufacturing processes. By weaving together technological advancements, market forces, and future projections, this in-depth examination provides a holistic view of the LoC landscape. It stands as a vital resource for researchers, industry professionals, and policymakers navigating the intricate future of energy storage technologies.

Abstract Quantum state teleportation represents a pillar of quantum information and a milestone on the roadmap towards quantum networks with a large number of nodes. Successful photonic demonstrations of this protocol have been carried out employing different qubit encodings. However, demonstrations in the Fock basis encoding are challenging, due to the impossibility of generating a coherent superposition of vacuum-one photon states on a single mode with linear optics. Indeed, previous realizations only allowed the teleportation of dual-rail entangled states, by exploiting ancillary electromagnetic modes. Here, instead, we enable the quantum teleportation of pure vacuum-one-photon qubits encoded in a single spatial mode, by exploiting coherent control of a resonantly excited semiconductor quantum dot in a micro-cavity. Within our setup, we can both teleport genuine single-rail vacuum-one-photon qubits and perform entanglement swapping. Our results may disclose new quantum information processing potentialities for this encoding, whose manipulation is achievable via quantum dot single-photon sources.

Magnetic Resonance Imaging (MRI) is a very versatile diagnostic tool for non-invasive analysis of human organ functions, without use of ionizing radiations. Loud operating sound is the major challenge associated with the MRI technology, reaching up to 130 dB. This paper analyses and compares the spectral properties of acoustic noise produced in the examination room of the mobile imaging trailer based 1.5 Tesla MRI system, during different scanning sequences of image acquisition. The analysis is useful in understanding the dynamic behavior of the sound generated inside the examination room to develop the noise reduction strategy.

This article presents an approach to building a bitexact simulation environment, capable of handling complex wireless communication stack such as 6TiSCH with enough performance, to be useful in large scale network topologies. The architecture of the simulator and the stack is presented along with the discussion of key features and design choices. The results of the simulation are compared to the results of extensive testbed experiments, which conclude that the designed tool can be used for practical design and verification of large distributed IoT applications.

Despite advancements in encryption technology, the issue of covert data transmission remains relevant. Researchers are actively enhancing established methods of concealing information with software tools that utilize mathematical models and developing new data entry methods. Steganographic techniques can be used with cryptographic methods to enhance the confidentiality of information within a container or as a standalone software tool. This article examines the steganographic integrity of concealing confidential information within audio files. The steganographic technique was selected as the phase coding method, which involves substituting the audio element’s phase with the secret message’s relative phase. An implementation of the chosen method was developed for the experiment. During the experiment, secret data of varying lengths were used to assess the impact of the length of the concealed information on the distortion of the audio file that may be perceptible to the listener. The study also investigated the resilience of the concealed information to noise attacks, allowing for the determination of the threshold of its robustness. The findings provide insight into the reliability of this steganographic method for hiding classified information within media objects.

A The next generation of wireless systems, like 5G/6G, ought to accommodate a wide range of potential use cases like massive real-time and machine-type communications. Accordingly, such systems need to provide high throughput, low latency, and a low peak to average power ratio (PAPR). It is not feasible to fulfil each of these needs with a single 5G/6G technique. On one hand, MIMO technique can potentially improve spectral efficiency and hence support more date rates. On the other hand, the utilization of multi-carriers’ techniques like FBMC/OQAM, or offset quadrature amplitude modulation, offers a compelling substitute for the traditional cyclic prefix-based orthogonal frequency division multiplexing (CP-OFDM) because of its excellent spectral efficiency and extremely low out-of-band radiation. However, the FBMC/OQAM orthogonality constraint is loosened, confined to the real field alone, resulting in intrinsic interference. This interference causes incompatibilities between FBMC/OQAM and certain MIMO schemes. Also, equalizing the time-varying MIMO channel is even more difficult when the complex valued symbol—which includes both real and imaginary FBMC/OQAM task, primarily due to the general non-flatness of the subchannels. From this perspective, we first suggest in this paper an efficient pruned-DFT-based implementation of MIMO to restoring the complex orthogonality and also reduce the complexity of the whole system. Then, we optimize MIMO channel equalization by efficiently separating and equalizing multiple parallel channels using various equalization techniques. The numerical results, presented as MSE versus subcarrier index, show that Pruned DFT technique consistently achieves lower MSE values compared to the standard MIMO FBMC-OQAM across various configurations, which indicates better error performance and its robustness in dynamic channel conditions.

The Internet of Things has rapidly grown in the past years as emerging technology. Moreover, 5G networks start to offer communication infrastructure for applications of the Industrial Internet of Things (IIoT). However, due to energy limitations of IIoT devices and heterogeneity of 5G networks, managing IIoT networks is a challenging task. One of the most critical issues in IIoT that requires consideration is traffic routing that has a significant impact on energy consumption, and thus, lifetime of the network. Artificial Intelligence (AI) has been widely employed to solve complex scientific and practical problems. Such AI techniques as neural networks, fuzzy systems, genetic algorithms are commonly employed in wireless networks to promote their optimization, prediction, and management. This study suggests using an Adaptive Neuro-fuzzy Inference System (ANFIS) in 5G networks of IIoT for improving the routing process. A flow-chat of routing protocol was suggested. For input and output values of the ANFIS linguistic variables, terms and membership functions were defined. A rules base was developed. To improve the rule base of the ANFIS, a genetic algorithm was proposed. The operation of ANFIS was simulated in Matlab software.

One potential candidate technology for Beyond 5G (B5G) networks is the reconfigurable intelligent surface (RIS), which is easy to install on existing infrastructure such as vehicles and buildings. Creating additional paths between the transmitter and receiver to improve the received signal accessible to the system is one of the significant uses of RIS. A virtual line of sight (VLOS) is established through the Tx-RIS-Rx link without the line of sight (LOS). RIS technology solves the problem of low coverage in millimeter-wave communications. This paper presents the effect of the signal-to-noise ratio by changing dimensions. In addition to finding the optimal values for the dimensions that give the optimal SNR, simulation results prove that the RIS height is greater than or equal to half the dimension between the sender and the receiver. It is recommended that the RIS height be greater than half the distance between the sender and the receiver.

This paper focuses on extracting and understanding the acoustical features embedded in the soundscape used in ASMR (Autonomous Sensory Meridian Response) studies. To this aim, a dataset of the most common sound effects employed in ASMR studies is gathered, containing whispering stimuli but also sound effects such as tapping and scratching. Further, a comparative analytical survey is performed based on various acoustical features and two-dimensional representations in the form of mel spectrogram. A special interest is in whispering sounds uttered in different languages. That is why whispering sounds are compared in the language context, and the characteristics of speaking and whispering are investigated within languages. The results of the 2D analyses are shown in the form of similarity measures, such as Normalized Root Mean Squared Error (NRMSE), PSNR (peak signal-to-noise ratio), and SSIM (structural similarity index measure). The summary is produced, showing that the analytical aspect of the inherently experiential nature of ASMR is highly affected by the subjective, personal experience, so the evidence behind triggering certain brain waves cannot be unambiguous.

The study aimed to learn about the experiences and opinions of students and academic teachers regarding the use of artificial intelligence in higher education. The question was answered to what extent and in what way do students and academic teachers of APS use AI in their studies and work? A diagnostic survey method was used, and original survey questionnaires were used. The study involved 58 academic teachers and 139 students. A significant number of academic teachers do not use artificial intelligence, and some of them express open disapproval of its use in education. These concerns include the negative impact of AI on the teaching process and the risks associated with verifying knowledge. A small number of lecturers use AI mainly to search for or create teaching materials. They use AI to prepare lesson plans, generate images, or make classes more attractive. In turn, some of them fear that AI may encourage students to abuse technology in the educational process. Most students declare that they do not use AI, and some are afraid of its inaccuracy and the possibility of making mistakes. Of the students who use AI, they mainly use it to search for information, simplify and correct their work, and create new materials. There is a clear discrepancy between the needs and goals of lecturers and students in the context of AI use. Lecturers perceive AI as a tool to support teaching, but with concerns about its ethical and practical application, while students treat AI as a tool to facilitate everyday learning tasks. The results suggest the need to implement training on the ethical use of AI and its full potential for both lecturers and students to prepare them for the upcoming technological changes in education.

Today we are in a period of explosion of business taking advantage of the new opportunities offered by the new environment of economically open space. The period, sometimes called Space 4.0, is a paradigm shift, with changes in motivation and understanding, actors, and technology. For over a dozen years, under the name NewSpace, a revolution has been taking place in the space sector with the participation of new players, schools and universities, new commercial entrepreneurs and businesses. NewSpace entered the area traditionally occupied by OldSpace – government space agencies and large companies, testing new possibilities. These possibilities include new services, e.g. using data transmission from space, regarding communication, precise navigation, agriculture, surveillance, mapping, geology, climate, space weather, environmental monitoring, and security. The transformation of OldSpace into NewSpace was associated with the business risk of changing old, conservative business models into completely new ones, unknown in this area. The transformation is related to the need to maintain the changes in a reasonable legal system so as not to experience the „Wild West” again, this time in space. It is currently difficult to establish strict rules regarding the still distant colonization of Mars, but undoubtedly establishing rules for the use of the LEO area, i.e. the already crowded low Earth orbits, is becoming an increasingly urgent necessity. In the context of competition and technological cooperation between giga-regions, international cooperation, combating old prejudices and establishing equal opportunities, under the umbrella of social acceptance, we are building in Europe, not without difficulties, a common, democratic space.

The paper presents the results of measurements of illuminance. Twenty-two types of illuminance meter with known spectral sensitivity curves of photometric heads and eleven lamps emitting colored light were used. Laboratory measurements were conducted at a characteristic point, with a focus on assessing intrusive light, with a value of 3 lx. The considerations were supplemented with simulated illuminance calculations. For most illuminance meters, correlations were observed between the measurement results and the calculations. This allows the conclusion that, especially in the case of colored light, one of the main factors affecting the measurement result is the spectral sensitivity curve of the photometric head. The greatest discrepancies in the obtained illuminance values were found in the case of lamps emitting blue light. The paper highlights the fact that measuring low illuminance values produced by lamps emitting colored light is not an easy task. Depending on the illuminance meter used, conclusions regarding the assessment of intrusive light may vary. The aim of the paper is to indicate that not all calibrated illuminance meters are suitable for this assessment.

The interests of researchers in expanding network capacity and enhancing network quality have risen as a result of the growing demands for mobile multimedia traffics in cellular communication networks. Device-to-Device (D2D) communication has become a promising technology to enhance spectral efficiency and network capacity. However, since D2D devices share the frequency bandwidth with the traditional cellular networks, it causes interference problems. This paper proposes a soft Fractional Frequency Reuse (FFR) scheme to resolve the interference problems in such D2D communications. Extensive simulation experiment has been carried out. The performance of the system with proposed scheme is compared to a baseline system. The simulation results show that the proposed scheme is able to increase the value of SINR and throughput, achieving the values from 19.8 dB to 20.7 dB and from 66.1 to 68.8 Mbp, respectively. For CUE the scheme is able to provide an increase of 15.5% for SINR and 15% increase for throughput on downlink transmission.

Glacial landforms, created by the continuous movements of glaciers over millennia, are crucial topics in geomorphological research. Their systematic analysis affords invaluable insights into past climatic oscillations and augments understanding of long-term climate change dynamics. The classification of these types of terrain traditionally depends on labor-intensive manual or semi-automated methods. However, the emergence of automated techniques driven by deep learning and neural networks holds promise for enhancing efficiency of terrain classification workflows. This study evaluated the effectiveness of Convolutional Neural Network (CNN) architectures, particularly Residual Neural Network (ResNet) and VGG in comparison with Vision Transformer (ViT) architecture in the glacial landform classification task. By using preprocessed input data from Digital Elevation Model (DEM) which covers regions such as the Lubawa Upland and Gardno-Leba Plain in Poland, as well as the Elise Glacier in Svalbard, Norway, comprehensive assessments of those methods were conducted. The final results highlight the unique ability of deep learning methods to accurately classify glacial landforms. Classification process presented in this study can be the efficient, repeatable and fast solution for automatic terrain classification.

The article explores deep learning models in urological diagnostics to measure urinary bladder volume from medical images. It addresses the shortcomings of traditional methods by introducing advanced imaging techniques for more objective and precise analysis. The research employs Convolutional Neural Networks (CNNs) and the MONAI platform for image segmentation and analysis, using data from The Cancer Imaging Archive to focus on urological regions. Findings suggest these models enhance diagnostic accuracy but also highlight the need for further modifications to tailor them to specific medical data, underscoring machine learning’s significant role in accurate medical assessments for urology.

Solar panels in enclosed areas are prone to suboptimal absorption of sunlight due to unstable sunshine. Two methods to optimize solar panel efficiency are available: dynamic and static. The dynamic method involves moving the panels towards the sun to maximize solar irradiation, while the static method uses a power converter to find the maximum power point. This research evaluates the performance of the MPPT system, which uses the P&O method with PSO, on solar panels. The objective is to determine the most appropriate MPPT algorithm to optimize the efficiency of solar panels. The MPPT system’s efficiency is tested under partial shading conditions of 100 w/m2, 300 w/m2, and 500 w/m2. The system’s output is evaluated based on the highest efficiency parameter value. The efficiency of the P&O and PSO methods is compared, and the most optimal efficiency is determined. The MPPT system is designed to measure parameters that are demonstrated qualitatively.

The paper presents and discusses the results of simulation of the current-voltage characteristics of an avalanche photodiode carried out in the SPICE program. The simulations take into account the influence of the directed energy of the electromagnetic field used in modern DEW (Directed-Energy Weapon), in the form of laser optical radiation and HPM (High Power Microwave) radiation, on the characteristics of the photodiode. The calculations were made using the model of the considered diode developed by the authors.

Underwater acoustic communications (UAC) in shallow water applications is a very difficult task. This task becomes even more difficult when there is a need to ensure reliable communication with an object buried in bottom sediments. The article presents a simulation of an acoustic transmission channel in conditions of strong multi-path propagation to an object buried in bottom sediments. The impulse response method was used, supported by a technique derived from the ray tracing image source method. Simulation results are presented for both narrowband and broadband signals with LFM frequency modulation. Based on the simulation, the conditions that should be met by the transmission signals to ensure correct communication were determined. Examples of data transmission to an object buried in bottom sediments in a simulated shallow channel with multi-path propagation were also presented.

The paper presents the methodology for evaluating mobile game players retention, which is the basis for generating economic income for its creators. The system for collecting and processing the in-game data (events related to the players’ actions) exploiting the Big Data cloud platform is described. The player profile with the crucial features allowing for the retention analysis is introduced. Datasets generated for the My Spa Resort mobile game by CherryPick company are described. The retention prediction approach based on the similarity estimation between the analyzed and already inactive players is presented. Results of the prediction using the k Nearest Neighbors (kNN) classifier are discussed.

The Internet of Things (IoT) has now permeated every aspect of modern life, requiring that all things be connected to one another and to the Internet using proper protocols. IoT, being an essential component of today's smart society is experiencing enormous problems from various security and interoperability attacks. Traditional encryption is unsuitable for low-cost IoTs because they are vulnerable to physical attacks. This paper proposes Lightweight mutual Authentication Protocol for IoT devices based on hash function using Elliptical Curve approach in which mutual authentication between RFID Tag and Reader is established through several rounds of communication. We also compare the proposed approach of authentication at both ends (Tag and Reader) in terms of efficiency and security.

In underwater communications, reciprocal motion between transmitter and receiver has a significant impact on reception quality. In orthogonal broadband systems that provide high bit rates, this problem becomes more important, especially in the higher frequency range, where the absolute Doppler shift is the greatest. Due to the low propagation speed of acoustic wave underwater, a substantial difference exists between the Doppler shift for lower and upper frequencies of the utilized spectrum. Consequently, a frequency-independent Doppler shift factor is employed. One of the most popular methods for determining the Doppler shift is the use of pilots. The problem of selecting the number and determining the frequency of pilots in such a way as to obtain the lowest possible error rate was identified. Real-world testing was conducted in a multipath propagation environment with relative speeds of up to 1.5 m/s. The effectiveness of Doppler shift determination was evaluated by analyzing the bit error rate. The results of the conducted tests indicate that, based on the achieved error rate, it is sufficient to employ 7 pilots positioned at low frequencies.

Present network monitoring systems need to cope with the ever-increasing amount of traffic in modern high-speed networks. These systems often perform sophisticated deep packet inspection (DPI) for anomaly detection, denial-of-service attacks detection and mitigation, intrusion detection and prevention, etc. Since DPI is resource-intensive, the monitoring devices are often not able to analyze all incoming traffic at link speeds. Consequently, sampling is employed to reduce the traffic volume and thus limit packet losses caused by resource exhaustion. Classical sampling methods select packets based on a fixed limiting parameter, regardless of the computational resource utilization of the monitoring device. This paper proposes a novel sampling approach for network traffic security monitoring that is based on an analytical model of the monitoring device. The model allows for testing adaptive sampling strategies that adjust the instantaneous sampling rate according to the input queue occupancy. The queue occupancy is used to drive the adaptation as it indicates the current relationship between available computational resources and the input traffic volume. Consequently, our approach maximizes the DPI ratio while simultaneously ensuring that the probability of packet loss due to resource exhaustion remains negligible. Analytical and simulation results are presented to demonstrate the impact of the proposed method on system parameters, along with a comparative studies.

This research aims to create a decision support system to identify retinal diseases using a four-class classification problem. To achieve this, the proposed system uses deep learning architecture to automatically recognize CNV, DME, and drusen from OCT images. The model employs two transfer learning architectures with several additional layers to classify retinal diseases. The purpose of model training, validation, and testing, the experiment uses 6,000 grayscale images labeled into four classes from the OCT data set. The Inception V3 model’s proposed additional layer exhibits an increase in accuracy of 3.08% and a reduction in the loss by 0.3767. The experiment’s results indicate that the Inception V3 model achieved an accuracy rate of 99.31%, and the VGG-16 model reached 98.83%, which outperformed other deep learning models using the OCT data set.

Possibility of the use commercial carbon-polymer pastes for preparation thick-film resistors for linear strain sensors application was tested. The research showed that the polymer thick-film resistors are characterized by the piezoresistive effect up to 2% strain. For higher strain small cracks appeared in the resistive film. Presence of the cracks influenced resistance of the sensor. It is so called false piezoresistive effect. The cracks may cause instability of the sensor. Visual detection of the cracks is hardly possible because of their small dimensions. The indicator of cracks creation in resistive film was found.

The study on mobility is an inherent part of any generation of cellular technology. It is aimed at ensuring robust, reliable, and interruption-free handover of the mobile user connection. Such continuous improvement is necessary as the service requirements become more stringent which urge the mobility to meet the highest performance expectations. In this paper, it is described how such requirements are to be achieved by the 3GPP Release 18 components. It begins with the introduction to the world of mobility, covering the solutions that have been designed since the first generation of New Radio (NR). Then improvements pursued in consecutive 3GPP releases are outlined. Selected areas for Release 18 mobility enhancements have been evaluated using 5G-compliant system-level simulations and results thereof are presented below. These include wrong Primary Secondary Cell (PSCell) preparation in Conditional Handover (CHO) and the impacts of secondary cell setup delay in Dual Connectivity (DC). It is shown that preparing multiple PSCells in CHO ensures the user accesses the right cell in up to 96% of cases.

The development of technology in radiolink networks has grown rapidly and is suitable to be implemented on wide coverage area networks, but weather and physical environment factors greatly affect signal conditions. The background that has been stated, this study proposes migration to fiber optic access media because it is able to send data faster and is not affected by electromagnetic interference and is stable. Furthermore, this study conducted an optimization analysis on the SDWAN (Software Defined Wide Area Network) network, SDWAN functions as a firewall that can provide connectivity support and infrastructure network development in branch offices with system security in one centralized platform. The test method used in this study is a connectivity test from each remote fortigate located in the branch office. While the analysis method used is Quality of Service (QoS), which is a method to find out data delivery (Packet Loss, Delay and Jitter) using the FortiManager application, which is the analytical and experimental proof of concept of the SDWAN network model using the BGP method for auto failover testing.

The paper presents selected acoustic power amplifiers from among those currently available. The results of a series of measurements characteris ing the amplifiers are presented. The measured amplitude and phase characteristics as a function of frequency for four selected amplifiers are analysed. The spectra of the output signal in the band from 4 kHz to 30 kHz are presented. The usefulness of the selected amplifiers in an underwater communication system is assessed.

Ensuring universal and stable underwater communication in shallow waters for various environmental conditions is a difficult scientific and engineering task. This applies in particular to underwater communication systems that use acoustic waves in very shallow underwater channels, where multipath propagation permanently occurs. The article provides assumptions for a system working with incoherent M-ary Frequency-Shift Keying (MFSK) modulation, along with guidelines for eliminating the impact of the multipath phenomenon. The results of experimental tests carried out in a lake for two seasons and, therefore, different sound velocity profiles are presented. For comparison purposes, three transducers placed at different depths, including at the bottom of the reservoir, were used to receive the transmitted signals.

In this paper, we compare behaviors of two possible descriptions of the sampled signal in the case when the sampling period tends to zero, but remains all the time greater than zero. Note that this is the case we are dealing with in analog-to-digital conversion in microwave photonic systems. From this comparison, it follows that the description with the weighted step function is superior to the description with the weighted Dirac comb. A couple of useful comments and remarks associated with the results presented are also provided in the context of microwave photonic systems.

Underwater navigation is a research topic current undertaken in many areas of underwater research. The article presents an analysis resulting from MEMS modelling in the context of inertial navigation. The ideal approach was confronted with its limitations, but a non-linear approach, close to the real one, was also presented. Both models were compared in the context of inertial navigation. Random disturbances and their impact on linear and nonlinear dynamic systems, and in this context on Brownian noise motion, were also analysed. The linear velocity and displacement estimates generated by the presented models were compared to the ideal responses. The phenomenon of bifurcation in the context of inertial measurements is presented. Some of the analysis is performed on real data, but for greater clarity, some is performed on simulated data to highlight design issues and limitations.

As the capacity of renewable power plants connected to the grid increases year by year， optimizing the control strategies for their converters becomes crucial for enhancing system stability. Currently， the primary control strategies in renewable power plants focus on grid-following （GFL） and grid-forming （GFM） converters. The former tends to exhibit poor stability in weak grids， while the latter may lose stability in strong grids. To address this challenge， the paper first proposes a hybrid synchronous control structure for grid-connected converters that incorporates adaptive coefficients. This structure combines the phase-locked loop （PLL） commonly used by GFL converters with the power-synchronization loop （PSL） utilized by GFM converters， creating a hybrid synchronous control loop through a proportional coefficient. Subsequently， the corresponding small-signal model is developed， and based on the analysis results of small-signal stability， a method for determining adaptive parameter values is introduced. Finally， simulation results validate that the converter-connected system employing adaptive hybrid synchronous control maintains good stability in weak grids with a short-circuit ratio of 1 and in strong grids with a short-circuit ratio of 30. This provides a new control scheme suitable for the stable operation of grid-connected converters in renewable power plants under varying grid conditions.

To address the difficulty in convergence encountered in power flow calculations of integrated electricity-gas energy systems using the traditional Newton-Raphson method for state evaluation and safety checks， this paper proposes a power flow calculation method based on an improved continuous Newton method. This approach transforms the nonlinear power flow model of the integrated electricity-gas energy systems into an ordinary differential equation model. By employing the trapezoidal integration method for large-step implicit integration， numerical stability is ensured. Additionally， the improved Euler method is used for small-step explicit integration to estimate iteration errors， with Richardson extrapolation correcting the iteration results and adaptively updating the iteration step size. Simulation tests on integrated electricity-gas energy systems demonstrate that the proposed method exhibits superior robustness compared to traditional methods in scenarios with ill-conditioned initial power flow values， and can serve as a tool for verifying power flow results when traditional methods fail to converge.

The super capacitor energy storage （SCES） system based on modular multilevel converter （MMC） is one of the key technologies for addressing issues with the grid integration of large-scale renewable energy. However， differences in supercapacitors can lead to inconsistencies in state of charge （SOC）， thereby affecting the utilization rate of energy storage capacity. In response to this issue， the paper proposes a SOC equilibrium control method for MMC-SCES based on capacitor voltage balancing. This balance is achieved by altering the energy storage power and equalizing the submodule capacitor voltage for all levels of SOC. The feasibility and effectiveness of the proposed control strategy are verified in a bipolar transmission system built in PSCAD/EMTDC. The results indicate that the proposed SOC equilibrium control strategy significantly reduces the number of controllers and simplifies the control system. The capacitor voltage balancing control strategy， which uses state sequencing and increment switching can effectively reduce the number of IGBT switch operations whilst balancing SOC， thus reducing switch loss.

To expedite the restoration of high-penetration new energy power systems following outages， a partitioning method for parallel restoration is proposed. This method takes into account the characteristics of new energy output. Multi-time-step series scenarios are generated by comprehensively considering spatiotemporal correlation of the output of new energy power supply. An expected multi-time-step series scenario of new energy power supply， considering occurrence probability of each series scenario， is constructed. Based on the expected multi-time-step series scenario of the new energy power supply， the available power of each charged domain is determined， and each charged domain is partitioned accordingly. In cases where there is overlap in partitioning， adjustments are made for objects to be restored in the overlapping areas. Factors considered include time inertia constants， ramp rate， adjustment rate， expected additional generation capacity， and the restoration value of the de-energized branches. Finally， using the IEEE118-bus system as a basis， it is demonstrated that the proposed partitioning method for parallel restoration can accelerate the grid restoration and reduce the power outage losses.

The VSC-HVDC transmission systems are the typical solution for integrating far offshore wind power. Using diode rectifier units （DRUs） at the rectifier stations can further enhance the system’s economic efficiency and reliability. Therefore， a steady-state power flow calculation method of AC system transmission system for offshore wind power， based on grid-forming （GFM） wind turbines and DRU， is proposed. The topology of the offshore wind power integration system using DRU is described， and the GFM control strategy of the wind turbines， utilizing Q-f（reactive power-frequency） droop control， is introduced. The proposed steady-state power flow calculation method iterates the power flow using the reactive power imbalance in AC bus at the DRU-based converter station and considers the Q-f droop control characteristics of the GFM wind turbines. The steady-state operational characteristics of AC transmission systems for offshore wind power， based on GFM wind turbines and DRU， are analyzed for both the low-frequency and intermediate-frequency AC transmission systems. The effectiveness of the proposed method is validated.

To address the issues of noise sensitivity and over-decomposition when decomposing harmonic signals using empirical wavelet transform （EWT）， a method based on adaptive wavelet denoising and improved EWT is proposed for detecting harmonics and interharmonics. Firstly， to enhance the effectiveness of traditional wavelet threshold functions， an adaptive parameterless threshold function is introduced. Then， by analyzing the signal spectrum using EWT， a series of filter banks is obtained to decompose the signal components. Finally， dynamic time warping （DTW） is employed to reconstruct the over-decomposed signals， resulting in the final components and identifying their frequencies and amplitudes. Simulation results demonstrate that the proposed method effectively suppresses noise in the harmonics and improves the over-decomposition of EWT. Comparison with empirical mode decomposition （EMD） and particle swarm optimization based variational mode decomposition （PSO-VMD） verifies the superiority of the proposed method in separating harmonics and detecting interharmonics.

Smoothing output power fluctuations of wind power generation systems is crucial for the stable operation of power grid. Based on the port-controlled Hamiltonian （PCH） model， an integrated control strategy for rotor speed tracking and power smoothing in directly-driven permanent magnet synchronous generator （D-PMSG） wind power generation systems is studied. To address the issue of low wind energy capture efficiency during power smoothing， a control strategy that integrates rotor speed smoothing control and PCH-based passive control is proposed. The strategy enables the rotor speed of the PMSG to quickly track the reference speed， improving wind energy capture efficiency while achieving DC link voltage control. Within the rated operating range of wind turbines， the strategy provides effective power smoothing support. Simulation results comparing this strategy with MPPT and other smoothing control strategies demonstrate the effectiveness and superiority of the proposed power smoothing strategy.

To effectively address the impact of uncertainties in wind turbine output on the safe and economical operation of microgrids， this paper proposes a two-stage distributed robust optimal scheduling model for microgrids that accounts for these uncertainties. First， an improved deep neural network （DNN） is used to forecast the output of wind farms， and the forecasting error is calculated； then， a non-parametric kernel density estimation method is employed to analyze the forecasting error data， and a box type uncertainty set is constructed using the cumulative probability density curve of the error. Next， a two-stage robust optimal scheduling model with a min-max-min structure for microgrids is established. Finally， strong duality theory is applied to decompose the original problem into a master problem and subproblem， both of which are formulated as mixed-integer linear programming problems， and the optimal scheduling scheme is obtained through iterative solving. The case study results show that the proposed model demonstrates stronger robustness compared to traditional two-stage robust optimal models.

The new cyber-physical power system is crucial for achieving dual carbon goals. However， novel false data injection attacks targeting state estimation can bypass existing security detection mechanisms， significantly challenging the secure operation of power systems. To detect false data in state estimation， the deceptive characteristics of malicious attacks are analyzed using the AC power grid model as the research object. By combining the data spatial feature extraction capabilities of convolutional neural networks （CNN） with the data classification abilities of least squares support vector machine （LSSVM）， this paper develops an attack detection model using CNN-LSSVM. The model’s effectiveness is verified using data from IEEE 14-bus power system， achieving a detection accuracy of 94.6%.

Under the same conditions， the ground capacitance of high-voltage AC submarine cables is over 20 times greater than that of overhead lines， making resonance phenomena more likely during no-load closing of submarine cables. This paper addresses an incident during the no-load closing of a 500 kV AC submarine cable in an offshore wind farm， where resonance caused a malfunction of the line differential protection. The paper analyzes the fault recording data and investigates the resonance mechanism during the closing process based on circuit theory. Simulation results indicate that the incident was caused by the integration of long-distance high-voltage AC cables into a weak system， leading to resonance overcurrents that were below 1.5 times the power frequency but of high magnitude. Despite current compensation from the protection device， significant capacitive resonant differential currents remained， resulting in the malfunction of the differential protection. In light of the engineering context， the paper proposes a response strategy that optimizes the wiring of the secondary circuit of the protection device and dynamically adjusts the threshold for differential current action based on system conditions and the magnitude of line capacitive currents. Finally， the effectiveness of the proposed countermeasures is validated through RTDS closed-loop testing.

As the rectification-side devices of the offshore wind power transmission systems， the diode uncontrolled rectification units are a technological hotspot in the offshore wind power sector. Multiplex rectifier circuits are usually employed for diode uncontrolled rectification， with a 12-pulse rectifier circuits being the most common. Theoretical analyses of serial and parallel multiplex wiring in the 12-pulse and the 24-pulse rectifier circuits within the multiplex rectifier circuits have been conducted. This entails studying the harmonic characteristics of the alternating current on the AC side and the voltage on the DC side， deriving the calculation methods for the harmonic numbers and the harmonic amplitudes， and determining the total current harmonic distortion rate and the voltage ripple factor. Comparative analyses are made on the characteristics of 12-pulse and 24-pulse rectifier circuits， investigating the feasibility of applying the 24-pulse rectifier circuits in the diode uncontrolled rectification units. Simulations are carried out on the PSCAD/EMTDC platform.

Wind turbines can contribute to frequency regulation by releasing rotor kinetic energy， which is an effective way to ensure frequency stability in power systems with high wind power penetration. However， the rotor speed recovery process may cause a secondary frequency drop （SFD） in the system. To address this issue， a wind turbine speed recovery strategy based on the asymptotic characteristics of an exponential function is proposed， following both qualitative and quantitative analysis of the frequency support and speed recovery. First， during the recovery period， the rotor speed is restored according to the power reference value defined by an exponential function. After the speed is recovered， the control switches to maximum power point tracking （MPPT） to manage the turbine's output power. Second， the parameters of the exponential decay coefficient are tuned based on various constraints. Finally， simulation results validate that the proposed strategy effectively mitigates SFDs while ensuring a smooth rotor speed recovery process.

Smart buildings， as prosumers， not only facilitate the local consumption of renewable energy sources （RES） but also coordinate internal flexible loads to reduce electricity consumption costs through peer-to-peer （P2P） electrical energy transactions. Based on this， the paper analyzes wind-photovoltaic （WPV） resources and flexible loads in smart buildings and proposes a P2P optimal scheduling strategy that incorporates joint transactions of electricity and regulation capacity. Initially， flexible load adjustments are used to provide regulation capacity， eliminating the prediction deviations of WPV outputs. Subsequently， the paper introduces a combined transaction mechanism for electricity and regulation capacity， where both electricity and regulation capacity are traded as market commodities in P2P transactions. Finally， case studies are conducted to analyze the proposed method and model. The results demonstrate that the proposed model not only effectively increases the local consumption of WPV but also encourages market participants to actively engage in P2P transactions.