This study presents a comprehensive analysis of a two-level battery charger for electric vehicles, focusing on modeling, simulation, and performance evaluation. The proposed charger topology employs two switches operating complementarily, along with essential components such as inductors, capacitors, and batteries. Detailed modeling in steadystate and dynamic regimes reveals the influence of nonlinearities, particularly switch and energy storage element characteristics, on charger efficiency and performance. Efficiency calculations highlight the significance of precise modeling and control strategies. Controller synthesis involves designing a robust proportional-integral compensator for effective battery charging current regulation. Analysis of non-idealities underscores the need for accurate component sizing and control strategies. The findings provide valuable insights for optimizing charger design and control, with implications for enhancing electric vehicle performance and reliability.

The paucity of orthogonal resource blocks has led to focused research on non-orthogonal multiple access (NOMA) signaling. This paper considers a multiuser downlink hybrid non-orthogonal multiple access (NOMA) framework with direct links to near-users (NUs) as well as far-users (FUs). It is shown that intelligent power allocation, together with mode switching and user selection, is the key to high spectral efficiency (SE) and energy efficiency (EE). To enhance the system throughput, intelligent NU and FU selection, and switching among NOMA Mode (NM), cooperative NOMA mode (CNM), and OMA mode (OM) have been considered. Switching to OM when both NM and CNM fail ensures performance equivalent to OM. The closedform expressions for the outage probability, mode probability, throughput, and EE are derived with mode switching and user selection. It is demonstrated that choosing the optimal target rate and NOMA power allocation coefficient is essential to maximize the EE. Only statistical channel knowledge is used for this purpose. Furthermore, we demonstrate that the proposed framework outperforms known schemes in terms of throughput and EE. In particular, the energy savings with the proposed scheme can be very significant. Monte Carlo simulations validate the derived expressions.

The sensitivity of distribution network losses to the change in net power at the network nodes (and phases of each node for unbalanced distribution systems) conveys key messages about the need to increase or decrease the net power to reduce the network losses. Similar information is provided by the total network losses allocated to nodes and phases, determined by a simple calculation based on the power flow solution. However, the allocated losses are not formally equivalent to the sensitivities of network losses to net load variation. This paper provides original insights into the interpretation and application of the allocated losses in radial distribution systems. New analytical formulations of the sensitivity of the total losses with respect to the node current magnitude are provided, starting from the power flow solution for balanced and unbalanced radial distribution networks. Two fundamental results are then presented for the first time: (i) the sign of the allocated losses is identical to the sign of total loss sensitivities with respect to the node current magnitudes, and (ii) the increase/reduction of the total losses for small net power changes in a node are determined by calculating the product of the sign of the allocated losses and the sign of the net node power, without the need of carrying out further power flow calculations. The results are discussed showing solutions obtained on balanced and unbalanced radial distribution systems with time-varying loads and local generations, even in reverse power flow conditions.

The study of nanoparticles has generated great interest in recent years in several areas of research. For example, in multiferroic composite materials, the combination of ferroelectric materials and ferrites may lead to magnetoelectric effect. In particular, CoFe2O4 and NiFe2O4 combined/fused/?? as magnetic phase is of interest, as these materials are isolating oxides with high transition temperatures and high saturation magnetization. In this work, the effective magnetic anisotropy was studied as a function of the temperature for nanocrystalline nickel doped cobalt ferrite samples NixCox-1Fe2O4., x = 0,0; 0,25; 0,50 and 1).

This paper investigates the physical layer performance of 5G New Radio (NR) in a cell-free (CF) massive MIMO system, focusing on the transmission of the Physical Uplink Shared Channel (PUSCH) over frequency-selective Rayleigh fading channels. A comprehensive link-level simulator, compliant with 3GPP standards, is developed to evaluate the system's performance across various scenarios. Key parameters such as subcarrier spacing (SCS), modulation and coding schemes (MCS) (QPSK, 16-QAM, 64-QAM, 256-QAM), and varying numbers of distributed radio units (RUs) are systematically analyzed. The results demonstrate the dual benefits of increasing the number of RUs: enhanced spatial diversity and improved proximity between user equipment (UE) and RUs, leading to significant improvements in reliability and error rate performance. The study also highlights the impact of higher SCS values in leveraging frequency diversity, particularly when signal bandwidth exceeds the channel's coherence bandwidth. Practical channel estimation is incorporated to validate performance under realistic conditions. Block Error Ratio (BLER) is used as the primary performance metric, providing valuable insights into the interplay of spatial and frequency diversity in CF 5G NR systems, thereby confirming their potential to achieve robust and efficient communication in challenging wireless environments.

An identity and data fiduciary model for source validation in social networks is needed, one that protects users' privacy and independence, while ensuring that data being transmitted is authentic and human-generated. Such a model will enable new types of reputation scoring services to emerge, utilizing data on the networks based on the pseudonymous user's posts and transaction history.

By capturing gaze points and eye movement patterns, eye tracking technology enables researchers to gain insights into visual attention, cognitive processes, and human behavior. Raw eye tracking data is inherently noisy, containing coordinates with occasional invalid entries. Researchers commonly face the challenge of cleaning and refining this data before proceeding to calculate essential eye tracking metrics. Critical preprocessing steps involve filtering out noise, detecting events such as fixations and saccades, and deriving metrics such as fixation duration, saccade amplitude, and more. This paper introduces a tool that provides users with the ability to preprocess data from both Gazepoint and FOVIO eye trackers. The methodology adopted for this line of work is based on Nyström and Holmqvist's (2010) data and velocity-driven algorithm. Preliminary results show the methodology developed as part of this line of work is promising in terms of improved accuracy and reliability in detecting fixations and saccades. Furthermore, this code and its versatile Graphical User Interface (GUI) has been successfully employed in multiple studies. Finally, we present supplementary code for calculating various eye tracking metrics and percent gaze overlap, extending the utility of our tool for in-depth analysis of visual attention and collaborative gaze behavior.

There is growing tendency toward the application of high frequency electromagnetic waves in in areas such as wireless telecommunications and military technology. High frequency electromagnetic waves like microwave, millimeter wave and terahertz penetrate to the surface layer of a medium depending on the frequency and the conductivity. Moreover, the most important types of interaction between high frequency electromagnetic waves and the surrounding medium - when the medium is represented by a biological tissue model - are predominantly in the form of tissue heating. The objective of this study is to analyze the influence of exposure to electromagnetic fields in the frequency range of 1 MHz – 100 GHz on a biological model coated with soil samples. The biological model consists of three biological tissue: skin, fat and muscle. Further, four soil samples are used as a coating material based on their textual classifications which are silty clay, silty loam, sandy loam and loam. Results show that the different soil samples can reduce the amount of absorbed radiation, and heating in the skin tissue thus proved to be a good shielding material against high frequency electromagnetic waves, particularly for frequencies greater than 10 GHz.

This work addresses the challenge of modelling Hammerstein nonlinear systems iteratively, combining a kernel adaptive filter with a linear filter. We evaluate this approach in the context of acoustic echo cancellation (AEC), where a nonlinear speaker followed by a linear system models the loudspeaker enclosure microphone system (LEMS). Our contributions include a novel iterative online processing procedure and a detailed investigation of challenges and strategies, such as offset removal and normalization, for improving system stability and performance. Our results demonstrate that normalization is crucial for reducing fluctuations and achieving a 5 dB improvement in Echo Return Loss Enhancement (ERLE) compared to linear filters.

In this paper, we develop a Virtual Reality-based immersive learning environment that allows teachers to conduct a lesson in a virtual space. The proposed system allows teachers and students to be present in the same virtual space regardless of their actual physical locations. The teachers can verbally communicate with students in real-time, sharing/collecting learning materials, and conducting in-class assignments. The students can interactively learn about real-world objects by touching and manipulating 3D models with hand tracking. Evaluation results demonstrate the effectiveness of the proposed system.

State estimation based on Kalman Filter (KF) has been widely considered for state-of-charge (SoC) prediction of lithium-ion (Li-ion) batteries. However, the KF is a model-based approach, and its performance declines when faced with modeling inaccuracies resulting from the nonlinear and timevarying nature of Li-ion batteries. To tackle the modeling uncertainties, a novel hybrid variant of KF is deployed, in which priori estimations are attained similar to the KF algorithm while the posterior estimations are obtained using a recurrent neural network (RNN) integrated into the KF architecture. The proposed method yields enhanced robustness in maintaining the accuracy of SoC estimations in the presence of model mismatches, e.g. due to the aging of batteries. In the proposed method, named KalmanNet, the RNN learns from battery data to predict the optimum Kalman gains for the minimization of SoC estimation error. The proposed method is trained and tested using actual battery data of a high-capacity pouch Li-ion cell replicating real-life driving cycles of electric vehicles. The significance of the results is twofold: 1-When exposed to a model mismatch of ±5%, the SoC estimation accuracy is improved by about 0.5% compared to EKF. 2-The size of required training data is reduced by 20% compared to similar machine learning algorithms.

Evaluating the helpfulness of online reviews supports consumers who must sift through large volumes of online reviews. Online review platforms have increasingly adopted review evaluating systems, which let users evaluate whether reviews are helpful or not; in turn, these evaluations assist review readers and encourage review contributors. Although review helpfulness scores have been studied extensively in the literature, our knowledge regarding their counterpart, review unhelpfulness scores, is lacking. Addressing this gap in the literature is important because researchers and practitioners have assumed that unhelpfulness scores are driven by intrinsic review characteristics and that such scores are associated with low-quality reviews. This study validates this conventional wisdom by examining factors that influence unhelpfulness scores. We find that, unlike review helpfulness scores, unhelpfulness scores are generally not driven by intrinsic review characteristics, as almost none of them are statistically significant predictors of an unhelpfulness score. We also find that users who receive review unhelpfulness votes are more likely to cast unhelpfulness votes for other reviews. Finally, unhelpfulness voters engage much less with the platform than helpfulness voters do. In summary, our findings suggest that review unhelpfulness scores are not driven by intrinsic review characteristics. Therefore, helpfulness and unhelpfulness scores should not be considered as two sides of the same coin.

Transformer networks have attracted significant scholarly attention for their superior ability to model long-range dependencies, making them effective in video object-tracking applications. However, existing Transformer-based trackers still show limitations in handling scale variation, complex background transformations, and occlusion, primarily due to insufficient utilization of the spatiotemporal feature information of the target. Specifically, these trackers struggle with maintaining target semantic integrity, adequately perceiving crucial areas, and incorporating historical information. To address these challenges, this paper introduces several enhancements. First, it replaces the pixel-level attention mechanism with a multi-scale cyclicshifted window attention mechanism, preserving target semantic integrity and improving the model's ability to capture scale variations. Subsequently, the convolution theorem is applied to transform the attention computation of cyclically shifted spatial samples into entry-wise multiplication in the frequency domain for non-cyclically shifted samples, thus enhancing computational efficiency. Additionally, a selective elimination module is proposed to focus on critical areas resembling those in the search frame, effectively minimizing background interference. Finally, a head network assesses the reliability of candidate targets, reintegrating reliable samples as dynamic templates into the tracking network to fully leverage spatiotemporal information from historical frames. Extensive comparative experiments demonstrate that the proposed model outperforms current state-of-the-art trackers across several public datasets.

The increasing focus on cyber-physical security in Smart Grids (SGs) has catalyzed a surge in research over recent years. This paper comprehensively reviews SG cyber-physical security advancements, diverging from conventional studies that concentrate on specific attack types. It begins with a structured overview of SGs, delineating their cyber and physical layers and analyzing the key processes: generation, transmission, distribution, and consumption. Subsequent sections critique existing survey studies, identifying gaps and underscoring overlooked aspects in the current literature, particularly concerning the challenges faced. The review progresses to analyze current research trends in SG security, evaluating methodologies across both layers and categorizing them into Machine Learning-based, data-driven, and model-based approaches. The analysis includes a detailed classification of research focused on Control, Monitoring, and Protection across each component and stage of SGs. Additionally, the paper examines emerging cyberattack strategies in SGs that have not been extensively reviewed in existing literature. In conclusion, the paper reflects on significant gaps and challenges in SG cyber-physical security research, underscoring the need for further exploration and innovation in this domain. Thus, this review serves as a critical roadmap for future research, delineating the current state and potential directions in the rapidly evolving field of SG security.

This work proposes an efficient two-stage jamming detection and channel estimation algorithm for orthogonal frequency division multiplexing (OFDM)-based unmanned aerial vehicles (UAVs) communications. The proposed scheme is designed based on the unique time and frequency domain statistical characteristics of OFDM signals. In the time domain (TD), a likelihood ratio test (LRT)-based decision rule is derived as a function of the inherent correlation between the cyclic prefix (CP) samples and their counterparts in the OFDM symbol. In addition, in the frequency domain (FD), a closed-form joint jamming detection and channel estimation scheme is derived using the maximum a posteriori probability (MAP) principle as a function of the statistics of the received pilots and virtual subcarriers (VSCs) signals, which is then re-expressed using generalized MAP ratio test (GMAPRT). The system’s complexity is reduced by applying the two stages sequentially, where the possible implementation of the second stage is conditioned on the outcome of the first stage. The performance of the proposed algorithm is evaluated using Monte Carlo simulations, where the results demonstrate its effectiveness compared to the TD-only and FD-only approaches. The results confirm the superior performance of the proposed scheme compared to the cyclostationary feature (CF)-based technique under various operating scenarios.

A document by Biplov Paneru . Click on the document to view its contents.

Frequency coupling between impedances causes misleading stability assessment through single coordinate Bode plots (dd− and qq− axes) for power electronics-dominated systems. To perform accurate stability analysis, it is crucial to incorporate this frequency coupling. Traditionally, the accurate stability of such coupled systems is analysed using a Multi-Input-Multi-Output (MIMO) framework, which involves employing the generalized Nyquist plot to assess the stability. However, if the coupling strength in these systems could be quantified and used to correct the diagonal elements of the MIMO system, the stability evaluation could be greatly simplified using single coordinate Bode plots. This paper introduces exactly that - a corrective factor based on a quantified frequency coupling parameter, known as coupling strength. This approach allows MIMO systems to be analysed within a Single-Input-Single-Output (SISO) framework. The corrective factor ensures simpler and precise analysis using single coordinate Bode plots. The effects of frequency coupling can be directly observed in the Bode plots of the diagonal elements of the corrected MIMO system matrix. Additionally, this method provides an accurate estimation of resonance frequencies and stability margins by direct inspection of the Bode plots. Simulation and experimental results are presented to validate the proposed stability assessment technique.

Understanding the limitations of Large Language Models (LLMs) in code generation is crucial for optimizing their use in software development. This paper explores these limitations, discusses their impact on coding practices, and oers insights into potential solutions and areas for further research.