Miracle-dz/UniFAR-FTUs huggingface dataset:
A Ladder Architecture for Power Improvement and Multi-Peaks Elimination in PV Arrays during Non-Uniform Irradiance Scenarios

Solar Photovoltaic (PV) arrays are vulnerable to non-uniform irradiances that diminish their power generation and introduce complexities by creating multiple peaks in the power-voltage curves, ultimately leading to reduction in overall performance. Various mitigation techniques are proposed in the literature but, each exhibits limitations of cost, complexity, and application. Hence, to deal with such problems, in this paper, a ladder architecture for PV arrays is proposed to improve the power generation capability during non-uniform irradiance scenarios by using the charge redistribution approach. Also, the proposed ladder approach ensures the PV array to operates with convex characteristics and eliminates multiple peaks from the power curves. The proposed architecture is modeled and validated in the MATLAB/Simulink platform using a 9x3 array with system size of 8.8kW. The performance comparison under four partial shading cases is carried out with the conventional configurations using various comparison parameters. From the conducted analysis, the approach is found to significantly improve the array power generation and generate convex curves with higher than 99% conversion efficiency.
Fine-grained Lung Function Sensing based on Millimeter-Wave Radar

The COVID-19 pandemic has posed a significant threat to the health of elderly individuals, particularly those with respiratory conditions. Therefore, daily monitoring of lung capacity is necessary to assess the pulmonary condition and initiate timely treatment measures. This paper aims to explore an effective non-contact method for fine-grained pulmonary function sensing. Specifically, we propose to utilize millimeter-wave radar to contactless sensing of the overall chest and abdominal motion. Then the LUNet (Lung Unet) is constructed to recover the Expiratory Volume (EV) curves using the entire motion information of the chest and abdomen. Finally, the pulmonary function indicators are extracted based on the predicted EV curve directly. The experimental results demonstrate that the average correlation of the predicted EV curve is 96.74% and the mean relative errors for FEV1, FVC, and FEV1/FVC are 7.73%, 8.15%, and 6.70%, respectively. These results suggest that our method has the potential for clinical monitoring and assessment of respiratory diseases.
Auxiliary Based Circular Coil for Wireless Power Transfer System

In a wireless power system, the design of coils plays a crucial job in power transfer. The effectiveness of the coupler depends on shape, size, coupling coefficient, mutual inductance, and the distance between the couplers. This paper presents an innovative coil structure for Wireless Power Transfer (WPT) systems, with a 3D model developed in Ansys Maxwell. The study focuses on evaluating key performance metrics, specifically the coupling coefficient (k) and Mutual inductance (M). Comparative analysis against existing coil designs, such as circular, square, Square with auxiliary, DD, and DD with auxiliary configurations, demonstrates that the proposed coil structure outperforms its counterparts in terms of both coupling coefficients despite having similar dimensions and turn counts. These findings signify the potential for significantly improved WPT system coupling Coefficient and effectiveness with the adoption of this novel coil structure.
Self-Synchronizing Robust Control Scheme for Harmonic Current Supression of Grid-Interfaced PV System Under Distorted Grid Voltages

This paper presents a self-synchronizing robust control scheme for a single stage three-phase grid-interfaced photovoltaic (GIPV) system that does not requires a phase locked loop (PLL). Under the harmonically distorted grid voltages caused by nonlinear load the proposed control scheme utilizes an enhanced proportional multi-resonant (EPMR) current controller to effectively address the issue of grid current harmonics. The computational burden on the digital controller of the GCPV system reduces due to the elimination of a dedicated synchronization unit (PLL) in the proposed robust control scheme. The efficacy of the proposed robust control scheme is verified by experimentation in various grid scenarios, including situations with distorted grid voltages and changes in grid frequency. Further, an modified incremental conductance (MINC) maximum power point tracking (MPPT) algorithm is utilized to ensures the reduction of power oscillations to a minimum, particularly when faced with swiftly changing atmospheric conditions. To validate the effectiveness of the proposed robust control scheme, real-time digital simulations are conducted using the OPAL-RT real time simulator.
A Security Framework for Internet of Things Systems Based on Dynamic Watermarking for Data Packet Authentication and Anomaly Detection

With the widespread use of Internet of Things (IoT) technology in the supply chain, the threat of attacks targeting the transmission of IoT data packets is getting more serious. Existing solutions used in traditional network security cannot be deployed in IoT networks due to the simple structure and low computational capacity of IoT devices. Hence, an IoT-specific security framework is required. This paper presents a security framework for IoT networks that includes a dynamic watermark authentication and detection module as well as an anomalous cause analysis module. The former employs the Long Short-Term Memory (LSTM) method to extract features from transmitted data packets, creating watermarks dynamically to encrypt data and detect attacks during transmission. The latter introduces the game theory to model the interactions between the attacker and the gateway on each IoT device in the network to analyze the root anomaly cause and the most vulnerable device to attack. Simulation results have shown the effectiveness of the proposed framework.
Simulation Study on Discharge Characteristics of Metal-Enclosed Liquid Nitrogen Switch

Sulfur hexafluoride(SF6) is widely used in high-voltage circuit breakers due to its excellent insulation capability. At high temperatures, SF6 tends to break down and produce toxic gases, which exacerbates the greenhouse impact. Given its excellent insulating qualities and environmental friendliness, liquid nitrogen(LN2) has the potential to replace other insulating materials in circuit breakers in the future. In this paper, metal-enclosed LN2 switching discharge characteristics are simulated by altering three conditions: electrode shape, electrode material, and LN2 temperature. The experiments were conducted using DC and various electrode spacings based on local thermodynamic equilibrium(LTE), and eventually changes in potential and pressure inside the closed cavity were noticed. The experimental results found that the sphere symmetric electrode has the lowest potential mean value at the small gap, meanwhile, the rod-sphere electrode has the lowest electric potential mean value at the large gap. In addition, the average value of pressure inside the chamber using the rod electrode was the lowest. The average rate of change of simulated electric potential and pressure was always the lowest using electrodes made of stainless steel.
Effect of the Training Data Quantity on the Day-ahead Load Forecasting Performance in the Industrial Sector

Load forecasts are becoming increasingly important in an increasingly digitalized world, even for smaller companies, for energy procurement or operational optimization. At the same time, it is unclear how much historical data is required to calculate a sufficiently good forecast. To answer this question, this work investigates the impact of training set size (historical load, weather, and calendar information) on the predictive performance of a day-ahead load forecast in the industrial context. For this purpose, a use case study on the data of seven companies from the manufacturing sector using six model classes was conducted. The results suggest that a forecast can produce meaningful results on 18 months of data whereas a period of less than six months yields results of high variance. For six out of seven companies, the best model trained on historical data less than or equal to one year was at most 1.5% (MAPE) worse than the overall best model.
Research on User Profile and Personalized Recommendation Based on Collaborative Filtering Algorithm

Due to the rapid development of information technology, fundamental changes have taken place in the way of information production and dissemination, resulting in an increasingly dense information environment for people. In response to the above problems, a personalized recommendation system for immovable cultural relics based on user portraits is planned to be developed. This system can not only make full use of the problems of digital technology, but also reduce the time for users to find information and help users to discover new hobbies. Aiming at the problems of difficult item feature extraction and low degree of personalization in the content-based recommendation algorithm, and the problem of data sparseness and cold start in the collaborative filtering recommendation algorithm, according to the specific requirements of the recommendation scenario, the collaborative filtering recommendation algorithm is mainly adopted, and the content-based recommendation algorithm is mainly based on the collaborative filtering recommendation algorithm. The hybrid recommendation algorithm supplemented by the recommendation algorithm realizes the personalized recommendation function. At the same time, in order to improve system performance and alleviate the problem of cold start, the popular recommendation algorithm is specially adopted. This algorithm extracts the data of registered users in the database, comprehensively analyzes and counts high-frequency information, and recommends it to users after screening.
Temperature Dependence of DC Dielectric Strength and Voltage Endurance of BOPP

Biaxially oriented polypropylene (BOPP) is the current gold standard for DC thin film capacitors. However, one of its major disadvantages is the poor high-temperature stability. It is the aim of this study to contribute to the understanding of the failure of BOPP at elevated temperatures. Therefore, capacitor-grade PP was extruded into a cast film using a twin-screw extruder and a water-cooled chill roll. BOPP films were produced using a laboratory stretching machine. The films were tested for their short-term dielectric strength and voltage endurance between 23 and 140°C. DSC, shrinkage and TSDC measurements were conducted to analyze the morphological and electrical changes in the material at elevated temperatures. An increase in temperature caused a reduction in dielectric strength and voltage endurance. The onset of space charge release by thermal trap activation coincided with a slow deterioration in both properties. The decrease was further enhanced as crystalline regions of low stability started to melt and orientation in amorphous regions started to be relaxed. This shows that trap states and morphological changes in the operating temperature range of thin film capacitors between 60 and 100°C should be the focus of future research.
Documenting Indigenous Traditional Knowledge of the Asian Elephant in Captivity

"Elephants have been in captivity for nearly 3500 years now and recent estimates suggest that, globally, 15000 Asian elephants live in captive conditions. These elephants are housed in zoos, circuses, temples, government run elephant-camps/orphanages and with private owners, each with a unique management practice. Traditionally, in India, elephants have been captured and trained by a few tribes with an in-depth understanding of the behavior and ecology of the animal. In this study, we document the current status of elephant keepers and captive elephants in the forest elephant camps, highlight the importance of preserving the traditional knowledge and culture of elephant keeping and ways to strengthen the same by infusing nuanced scientific techniques of elephant management, and explore aspects of human-animal relationships, in this case, the relation between the elephant and its keeper. " "In this study we the document the status of this traditional knowledge among the present day elephant keepers and the current elephant keeping practices in order to improve the welfare of elephants in captivity. "
A new water-resistant snow index for the detection and mapping of snow cover on a global scale

ABSTRACT An up-to-date spatio-temporal change analysis of global snow cover is essential for better understanding of climate–hydrological interactions. The normalized difference snow index (NDSI) is a widely used algorithm for the detection and estimation of snow cover. However, NDSI cannot discriminate between snow cover and water bodies without use of an external water mask. A stand-alone methodology for robust detection and mapping of global snow cover is presented by avoiding external dependency on the water mask. A new spectral index called water-resistant snow index (WSI) with the capability of exhibiting significant contrast between snow cover and other cover types, including water bodies, was developed. WSI uses the normalized difference between the value and hue obtained by transforming red, green, and blue, (RGB) colour composite images comprising red, green, and near-infrared bands into a hue, saturation, and value (HSV) colour model. The superiority of WSI over NDSI is confirmed by case studies conducted in major snow regions globally. Snow cover was mapped by considering monthly variation in snow cover and availability of satellite data at the global scale. A snow cover map for the year 2013 was produced at the global scale by applying the random walker algorithm in the WSI image supported by the reference data collected from permanent snow-covered and non-snow-covered areas. The resultant snow-cover map was compared to snow cover estimated by existing maps: MODIS Land Cover Type Product (MCD12Q1 v5.1, 2012), Global Land Cover by National Mapping Organizations (GLCNMO v2.0, 2008), and European Space Agency’s GlobCover 2009. A significant variation in snow cover as estimated by different maps was noted, and was was attributed to methodological differences rather than annual variation in snow cover. The resultant map was also validated with reference data, with 89.46% overall accuracy obtained. The WSI proposed in the research is expected to be suitable for seasonal and annual change analysis of global snow cover.
Modified Hierarchical Clustering Algorithm for Partial Discharge Separation

To date, one of the main tools for evaluating the reliability of an insulation system is the continuous monitoring of those phenomena which, by interacting with the elements of the system, can induce aging processes or failures. For power grids, a signal that identifies possible aging or improper use of the component is Partial Discharge (PD) activity. Generally, the evaluation of the PD phenomenon is carried out through a two-step procedure: measurement and data analysis. To optimize the PD analysis process, increasingly sophisticated PD separation/classification algorithms are needed. Especially for the measurements carried out in HVDC systems for which the absence of a phase reference makes more difficult to identify the different types of discharge. The purpose of this article is to investigate the possibility of optimizing the input data to a hierarchical clustering algorithm in order to obtain a subdivision of the dataset more faithful to the real behavior of the phenomena. Specifically, the proposed approach is based on the use of the cross-correlation matrix to carry out the clustering operation. This matrix replaces the matrix of the distances among the points distributed in the map used for the representation of the data. Results show that with this modification it is possible to separate phenomena that present partially or completely overlapping patterns. Moreover, the algorithm turns out to be automatic and does not require the choice of references or thresholds to define the similarity among pulses.
A Method for Defects Inspection of Power Transmission Lines Based on DETA and Integrated Improvement Algorithm

Collecting images through drone and helicopter aerial photography is one of the main methods for transmission line inspection. Currently, the popular intelligent transmission defects recognition is mainly based on deep neural network object detection algorithms. Due to the outstanding performance in various public datasets and rapid development of DETR algorithms, there is a promising application prospect in the field of transmission inspection. This article proposes a transmission line inspection model based on DETA, which is optimized by integrated improvement algorithm, which inlcudes SNIP sampling algorithm, multi-scale inference strategy, and prior knowledge based box-selection algorithm. Experiments have shown that the method proposed in this article has the advantages of accurate defect localization, high detection accuracy, and strong scalability.
Comparison of Data Compression Methods for Implanted Real-Time Peripheral Nervous System

Research in the development of neuroprostheses aims to restore the loss bodily and motor functionalities. In order to have effective solutions, the physiological signals used to control neuroprostheses must be first measured and then properly reconstructed. Miniaturized electronic enables the realization of implantable devices suitable for the treatment of pathologies that cannot be cured with conventional medicine. However, given the limited data processing and transmission capabilities of microcontroller-based implantable devices, data compression algorithms must be developed with minimal distortion upon reconstructions. Although several compression techniques are available in the literature, a suitable strategy for compression of electroneurographic (ENG) signals has not yet been defined so far. The main goal of this work is to propose a pipeline to perform the compression of biomedical data. The approach is validated on available recordings of ENG signals. As main contribution, we applied different compression algorithms to the recorded ENG signals that were obtained in response to mechanical stimulation of a rat paw. A combination of lossy and lossless techniques was studied by measuring the performance in terms of time required for data compression, compression ratio, and distortion. To evaluate the use in real-time applications, the robustness of the techniques has been tested considering a temporal constraint of 300 ms.
Zeta DC-DC Converter with Multilevel Flying Capacitor Switching Cell

Multilevel power converters have been aim of research in the last years due to their advantages when compared to traditional two-level topologies: better harmonic performance, filters reduction, lower components voltage stress and higher power process capability. Following this trend, the present work proposes two Zeta DC-DC converters composed of multilevel flying capacitor asymmetric totem-pole switching cells. These structures are achieved by replacing one leg of switch/diode of the traditional two-level asymmetric totem-pole switching cell by a similar multilevel flying capacitor switching cell. A generalized structure of the flying capacitor switching cell for $k$ switches is presented. An operation analysis of the proposed converter is presented as well as simulation results to validate the theoretical approach.
Sensing and Beamtracking Scheme Design for UAV-Enabled ISAC Systems

In the unmanned aerial vehicle (UAV)-enabled systems, beamtracking is an important design issue. In this paper, we leverage the benefits of the integrated sensing and communication (ISAC) techniques to improve the beamtracking performance in the UAV-enabled systems. Specifically, we first obtain the angle-of-arrival/angle-of-departure (AoA/AoD) from the echo signals. Using the estimated angular information, we then propose an improved unscented Kalman filter (UKF)-based beamtracking scheme to predict the AoA/AoD from past information, taking the mobility of the UAV and the large accumulative estimation error of conventional UKF-based beamtracking scheme into account. Through numerical results, we show that our proposed beamtracking scheme can accurately estimate the AoA/AoD of our considered systems. We also demonstrate how our proposed scheme can be utilized to achieve nearly the same signal-to-noise ratio performance compared to the baseline assuming perfect AoA/AoD estimation.
Automation for Regulation of Deep Hypnosis by Delivery of Propofol and Remifentanil

The deep of hypnosis regulation has been studied for several years to help and assist the anesthesiologist in various general surgery processes. The present contribution presents a scheme for automating this process considering the infusion of two drugs, remifentanil, and propofol, as control variables and the bispectral index as the output variable. A description of virtual compartment-based dynamical modeling for both drugs is presented. Using this model, a proposal for closed-loop regulation based on a discrete model predictive control is presented. The proposed algorithm is tested in twelve virtual male patients, where the controller simulates and rejects two types of disturbances.
Adaptive Massive MIMO Hybrid Precoding Based on Meta Learning

Massive multiple-input multiple-output (MIMO) mmWave communication has emerged as a pivotal technology in the fifth generation (5G) mobile communication system, with hybrid precoding technology garnering significant attention. In this paper, a hybrid precoding scheme based on convolutional neural network (CNN) is proposed. By utilizing an analog precoder with a partial connection structure, the network size is reduced and convergence is facilitated. Compared to traditional algorithms, this approach reduces computational complexity and time overhead. However, similar to other deep learning algorithms, it lacks robustness as the trained network may not be adaptable to new environments. To address this limitation, this paper further proposes a hybrid precoding scheme based on meta-learning, enabling the CNN model to quickly adapt to changing environments.
Artificial Intelligence for Automotive Security: How to Support Developers in Automotive Solutions

With newer and more advanced technologies in the automotive industry come complex and sinister threats. To thwart attacks targeted at a vehicle's Controller Area Network (CAN), developing new and increasingly sophisticated algorithms exploiting Machine Learning and Deep Learning is not enough. Thus, where some of these techniques are impractical to embed into vehicles mounting Electronic Control Units (ECUs) with limited computational power, others may even require high prediction time, a critical factor in Cyber-Physical Systems (CPS) like modern vehicles. Moreover, help and support for developers during the automotive development life cycle are unorganized and inconsistent. This paper presents a study of Traditional and Quantum Machine Learning algorithms to improve the identification of in-vehicle attacks and redefine the security life cycle in this context serving as a baseline to define a vision model based on Artificial Intelligence that supports developers' decisions to integrate concrete design methods in the automotive development life-cycle.