Miracle-dz/UniFAR-FTUs huggingface dataset:
Challenges of Isotropy in Standardized 3-Axes EMF Measurements

The paper proposes a new method that processes 3-axes E-field measurement data in order to achieve better isotropy and reduced uncertainty of the measurement system. Typically, the frequency selective E-field measurement procedure assumes ideal sine radiation pattern of dipoles involved in the 3-axis sensor. The difference between the ideal sine and the real radiation patterns of the sensor is then evaluated and expressed as an added uncertainty of the measurements across all angles of ray arrival to the sensor. The method proposed in this paper consists of post-processing of the measured values at each axis element, based on real radiation patterns of elements of a 3-axis probe. In this way, with a typical probe element radiation pattern, the uncertainty of the measurements due to difference between ideal and real radiation patterns of the sensor can be significantly reduced. The paper illustrates the method using examples of long (up to wavelength) dipole probes.
EMOTION TRANSPLANTATION APPROACH FOR VLSP 2022

Emotional speech synthesis is a challenging task in speech processing. To build an emotional Text-to-speech (TTS) system, one would need to have a quality emotional dataset of the target speaker. However, collecting such data is difficult, sometimes even impossible. This paper presents our approach that addresses the problem of transplanting a source speaker’s emotional expression to a target speaker, one of the Vietnamese Language and Speech Processsing (VLSP) 2022 TTS tasks. Our approach includes a complete data pre-processing pipeline and two training algorithms. We first train a source speaker’s expressive TTS model, then adapt the voice characteristics for the target speaker. Empirical results have shown the efficacy of our method in generating the expressive speech of a speaker under a limited training data regime.
Influence of Contextual Factors on EFL Mexican Teachers' Beliefs and the Use of Textbooks La influencia de factores contextuales en las creencias y en el uso de libros de texto de profesores de lenguas mexicanos *

While much has been written about the use of instructional materials in the field of languageeducation, very few studies have actually studied the impact of the context in the use of materials inteachers’ practices. This work shows the findings of a study about the contextual factors that a group ofexperienced Mexican teachers believe shaped their lessons. Drawing on qualitative data, the study showsthat factors such as the length of courses and lessons, the school facilities, the institutional practices toselect textbooks as well as the institutional procedures to supervise teachers’ practices and assessment,explicitly emerged as the most influential in the teachers’ lessons.Key words: Educational context, instructional materials, language lessons, teachers’ beliefs,textbooks.Mientras en el area de la ensenanza de lenguas mucho se ha escrito acer ca del uso de materiales deinstruccion, poco se ha investigado acer ca del impacto del contexto en el uso de materiales en las practi-cas de los profesores. Este trabajo muestra los hallaz gos de un estudio acer ca de los factores contextua-les que un grupo de profesores mexicanos experimentados cree que deter minan sus clases. Usando
Data-Enabled Finite Control-Set Model Predictive Control of a Grid-Tied Inverter

This paper presents a novel predictive control architecture for power converters that addresses the challenges of model mismatch and parameter sensitivity in the finite control-set model predictive control (FCS-MPC) framework. The proposed method employs a dynamic-linearization-based approach, which replaces the detailed model used in the FCS-MPC controller with a virtual equivalent data model, creating a data-enabled FCS-MPC architecture. By relying solely on input-output data, the proposed method exhibits considerable robustness against parameter perturbations while retaining the attractive features of the conventional FCS-MPC method. The effectiveness of the proposed design is demonstrated through comparative simulations results on a two-level grid-tied inverter.
Transient DC Bias Current Mitigation for an ANPC based Triple-Active-Bridge (TAB) Converter

A transient DC bias mitigation method is proposed in this work for an isolated three-port triple-active-bridge (TAB) DC-DC converter, which consists of a two-level H-bridge on the low-voltage side and two three-level active-neutral-point-clamped (ANPC) half-bridge converter on the high-voltage (HV) side. The conventional single-phase-shift modulation strategy developed for the dual-active-bridge (DAB) can be applied to TAB, which however may induce a significant transient DC bias current due to improper update of phase-shift angle. Besides, the cross-coupling among the DC currents in different ports can further worsen the DC bias currents in the three-winding transformer, thus leading to large overshoots and even saturate inductors and transformer. To address this issue, the transformer currents of TAB converter are modeled and analyzed in this study. A modified phase-shift modulation method is proposed to eliminate transient DC bias current without sacrificing dynamic performance. Finally, a dedicated control strategy is developed to verify the effectiveness of the proposed method in simulation and experimental studies.
Voltage Optimization Considering Integrated Photovoltaic 5G Base Station for ADNs

The voltage problem of active distribution networks (ADNs) is becoming more and more severe with the increase of the proportion for distributed energy resources (DERs) and new loads. This paper considers integrated photovoltaic (PVs) 5G base station (5G BS) as an emerging flexibility resource and uses it to optimize the voltage of ADNs. First, the voltage regulation potential of backup batteries and PVs in 5G BS is analyzed and evaluated. Then, a voltage optimization model of ADNs considering integrated PV 5G BS is established. This model is an integer second-order cone programming (MISOCP) problem, which can be easily solved by commercial software. Finally, an actual ADN is used to verify the effectiveness of the integrated PV 5G BS to participate in voltage optimization.
A Hybrid Deep Learning Framework for Efficient Sentiment Analysis

—In the era of Microblogging and the rapid growth of online platforms, an exponential rise is shown in the volume of data generated by internet users across various domains. Additionally, the creation of digital or textual data is expanding significantly. This is because consumers respond to comments made on social media platforms regarding events or products based on their personal experiences. Sentiment analysis is usually used to accomplish this kind of classification on a large scale. It is described as the process of going through all user reviews and comments that are discovered in product reviews, events, or similar sources in order to look for unstructured text comments. Our study examines how deep learning models like LSTM, GRU, CNN, and hybrid models (LSTM+CNN, LSTM+GRU, GRU+CNN) capture complex sentiment patterns in text data. Additionally, we study integrating BOW and TF-IDF as complementing features to improve model predictive power. CNN with RNNs consistently improves outcomes, demonstrating the synergy between convolutional and recurrent neural network architectures in recognizing nuanced emotion subtleties.In addition, TF-IDF typically outperforms BOW in enhancing deep learning model sentiment analysis accuracy.
Reconstruction of Fine Cross-Range Resolution ISAR Images of Targets in 3-D Motion using Compressed Sensing and Frame Selection

The Inverse Synthetic Aperture Radar (ISAR) is a powerful tool for target recognition and classification. However, generating focused ISAR images of moving targets is challenging because of motion compensation problems. In this study, we propose a novel approach to reconstruct an ISAR image of a noncooperative maneuvering target with 3-D motion using compressed sensing with a frame selection scheme. The proposed approach finds the time frames with only 2-D motion through a joint time-frequency analysis (JTFA) and Radon transform and reconstructs the ISAR image with an improved cross-range resolution by adopting compressed sensing. Simulation results demonstrate the effectiveness of the proposed approach in improving the image quality and reducing the impact of 3-D motion on ISAR imaging.
Investigation and Mitigation of Radiated EMI due to Near-field Coupling in a High-density Active-clamp Flyback Power Adapter

The article first measured the near electric field distribution of an active-clamp Flyback (ACF) power adapter under investigation with a near-field electromagnetic (EM) scanner, the critical near-field capacitive coupling issue between CM choke and transformer/ DM choke was then identified. Second, a radiated EMI model was developed and preliminarily quantified for the ACF power converter under investigation considering the near-field capacitive coupling effect. Third, two effective techniques were proposed to mitigate the radiated EMI due to the identified near-field capacitive coupling issue based on the developed EMI model. Both the analysis and proposed techniques were finally verified by experiments.
Trapezoidal Current Modulation based Zero Voltage Switching of Interleaved Converters

This paper presents a novel approach for soft switching in DC-DC converters based on a current modulation technique that enables zero voltage switching at all loads. Switching frequency and inductor RMS current equations are derived using the proposed current modulation technique. Three distinct operational modes of the converter are established. The proposed scheme is compared with the triangular current mode (TCM) to highlight desirable features. A laboratory prototype of the converter with IGBT and SiC switches is used for hardware results. It is observed that hardware and simulation waveforms follow analytical waveforms with SiC switches on all case points, while with IGBT switches deviate for specific cases. It is shown that improved accuracy of soft switching region is obtained with SiC MOSFETS.
A 6.9-17.0 GHz Low-noise Amplifier in 65nm CMOS

Due to the ever-increasing data transmission rate, the wideband LNAs are being required popularly. A wideband LNA operating at 6.9-17.0 GHz with two cascode amplification stages is proposed. Source inductance degeneration and Chebyshev filter matching are used to achieve low noise and wideband input matching simultaneously. The $\Pi- \text{type}$ inter-stage matching circuit is implemented to satisfy bandwidth requirements. Fabricated in 65nm CMOS, the amplifier finally obtained a gain of 19.7 dB and a minimum noise figure of 3.9 dB with good return loss and unconditional stability.
Gamification and programming: enhancing mathematics learning in students

In this study, a mobile application based on gamification was developed to support the learning of fractions in fourth grade students. Using the SCRUM methodology, 4 sprints were carried out and tools such as Unity, Firebase and C# were used. To determine its impact, a pre- and post-test research design was used with a group of 21 fourth grade elementary school students from an educational institution in Chiclayo-Peru. The results showed a successful gamification, with an attractive design and high satisfaction in usability. In summary, gamified mobile applications have great potential to improve learning.
Gray-Box Modeling for Distribution Systems with Inverter-Based Resources

In this paper, we develop a novel gray-box modeling approach for distribution systems with inverter-based resources (IBRs). The proposed gray-box modeling method aims to improve estimation accuracy by taking advantages of both physics-based (white-box) and data-driven (black-box) modeling approaches. To this end, the gray-box modeling framework is constructed by encoding prior physical knowledge of the system into a white-box model and then embedding the output variables of the white-box model into the input vector of a black-box model. Especially, the white-box modeling component is constructed alongside the equivalent network model simplified with Kron reduction. Furthermore, case studies demonstrate that our gray-box modeling approach effectively improves estimation accuracy compared to purely physics-based or data-driven methods.
Disturbance Rejection Ability Comparison for Different Solid-State Transformer Control Strategies

The conventional control of a three-stage cascaded solid-state transformer (SST) is designed assuming decoupled stages. With high-capacitance dc-link capacitors, it is possible to ignore interactions among the stages. However, in practice, when finite-size capacitance values are used, the interactions among stages are nonnegligible, potentially leading to over-voltage or under-voltage phenomena on the high-voltage dc-link capacitors. In this view, this paper compares various SST control strategies based on their disturbance rejection ability. The analysis illustrates the influence of the control variables and bandwidth chosen for each SST stage on the capacitor voltages during load transients. Small-signal analysis is provided to compare the different SST control methods. The analysis is confirmed by simulation results using PLECS.
A Low-Pass Filtering Cable Design for Sub-6 GHz Wireless Communication Application

This paper introduces a novel design concept for a filtering cable optimized for sub-6 GHz wireless communication applications. We introduce a groundbreaking approach that incorporates an insulated cylinder coated with a defected conductor transmission structure (DCTS). The integration of the DCTS enables distributed lowpass filtering advantages along the cable's axis, specifically tailored for the sub-6GHz frequency range. The DCTS, comprising six-slot-ring defected structures, functions as an equivalent lumped filtering circuit, allowing easy fine-tuning of the filtering performance by manipulating the geometrical parameters of the six-slot-ring defected structure. Our meticulous engineering efforts result in a meticulously engineered low-pass filtering cable, constructed with a polytetrafluoroethylene (PTFE) inner core wire, enveloped by a flexible printed circuit board (FPCB) featuring 40 etched six-slot-ring defected structures. A layer of PTFE serves as the transmission dielectric, while an outermost shielding layer enhances the shielding effectiveness (SE). The proposed filtering cable, with a radius of 2.413mm and a length of 340mm, exhibits exceptional performance characteristics tailored to sub-6GHz wireless communication applications. It achieves an insertion loss of below 0.38 dB within the passband range below 6 GHz and provides rejection levels exceeding 43 dB within the stopband range from 7.7 GHz to 25 GHz. Compared to conventional filters, the proposed filtering cable offers a more versatile and compact solution, delivering outstanding filtering capabilities specifically designed for sub-6 GHz wireless communication applications.
A Unified Numerical Modeling Method for Dual Active Bridge Type Converter

Dual Active Bridge (DAB) Type Converter is a widely adopted topology in modern electric energy systems. It offers galvanic isolation, high conversion efficiency, and facilitates bidirectional power flow. Various methods have been proposed to enhance its efficiency, such as control-side improvements like Double-Phase-Shift (DPS) and Triple-Phase-Shift (TPS), as well as circuit-side modifications like resonant mode operation. These methods aim to extend the Zero-Voltage Switching (ZVS) range and optimize the current RMS value for improved efficiency. However, these approaches rely on accurate steady-state models.Traditionally, modeling the DAB type converter in the time domain while considering different switching modes separately has been complex and inflexible. To address this issue, this paper presents a novel Fast-Fourier-Transform-based modeling method. This approach involves calculating the system waveforms in the discrete frequency domain and subsequently converting them back into the discrete time domain, effectively bypassing the challenges posed by multi-switching modes. This proposed algorithm can be easily integrated into optimization algorithms to achieve rapid hardware or modulation optimization. Moreover, This algorithm can also be applied to a wide range of isolated converters.
Two-Stage Piezoelectric Resonator / Switched Capacitor DC-DC Converter

Magnetics pose a significant challenge for power converter miniaturization. Approaches to address this include power converters based on piezoelectric resonators (PRs) rather than magnetics, and switched-capacitor (SC) converters which utilize only capacitors. However, PR-based converter efficiencies tend to decrease at high voltage conversion ratios, and purely SC-based converters are not capable of efficiently regulating voltage. Through the hybridization of PR and SC-based converters, there is an opportunity for each to address the drawbacks of the other to create a high power density, high efficiency power converter for applications requiring high voltage conversion ratios and wide, continuous voltage conversion ranges. In this paper, we propose a two-stage dc-dc converter with a PR-based regulation stage and a SC-based transformation stage. We detail a new simplified eight-stage switching sequence for the PR stage and experimentally validate this concept in a prototype converter.
A Space Vector Modulation Technique for H8 Transformerless Three-Phase Photovoltaic Inverter

The use of a transformerless three-phase inverter in grid-connected photovoltaic (PV) systems presents a cost-effective and compact solution. However, the absence of galvanic isolation can lead to the circulation of leakage currents, which are caused by parasitic capacitances in the PV panels. To address this issue, several topologies are proposed and combined with modulation techniques to reduce the leakage current. This paper proposes a space vector modulation technique applied to a transformerless three-phase inverter topology to further reduce the leakage current by assigning a new space vector, where the common-mode voltage will have a smaller variation in certain regions. Simulation results are presented in order to demonstrate the performance of the new modulation technique.
An Analysis of Pre-trained Models Versus Custom Deep Learning Models for Forest Fire Detection

The detection of forest fires is crucial for human and environmental safety due to their catastrophic impacts. Utilizing machine learning (ML) and deep learning (DL) enhances forest fire management by analyzing data, enabling early warning systems, monitoring fire behaviour, and optimizing resource allocation. The DL algorithms provide a significant influence in the context of forest fire detection. The result of many research studies in the recent past is evident that it is becoming increasingly important in future detection tasks. The majority of research studies use DL models that have been pre-trained. Nevertheless, an investigation should examine how custom models perform compared to pre-trained models in detection tasks. This study aims to compare the performance of the simple CNN model with that of the pre-trained DL model and also compare the pre-trained model as a feature extractor for the custom model. Five CNN pre-trained models, such as VGG16, ResNet50, MobileNetV2, Xception, and Inception were used for our study. The Xception model achieved high accuracy in both scenarios as a pre-trained model and feature extractor with custom layers for fire detection. Also, the result proved that combining CNN models as a feature extractor with custom layers performed well compared with the custom and pre-trained models.
UAV Trajectory Planning for Data Collection in Internet of Vehicles: A Reinforcement Learning Approach

Unmanned aerial vehicle (UAV) assisted data transmission is widely adopted in various internet of vehicles (IoV) applications. This paper is dedicated to the study of UAV-assisted data uploading in IoV with trajectory planning by considering factors including the energy consumption of the UAV, probability of reaching the destination, and the amount of collected data. In particular, we first present a system architecture for UAV-assisted vehicular data uploading. Next, we formulate the UAV trajectory planning problem with the goal of maximizing the amount of collected data. On this basis, we propose a deep reinforcement learning (DRL)-based algorithm, named as MACD-TD (maximizing the amount of collected data through trajectory design). Further, we transform the problem into maximizing the accumulative reward. In order to verify the effectiveness of the proposed architecture and algorithm, we built a 3D simulation platform for conducting experimental simulations. Finally, we give an extensive performance assessment and confirmed the efficacy of the proposed algorithm.
Reinforcement Learning Based Reactive Power Real-Time Dispatch Optimization in Distribution Networks

The access of large-scale distributed generation (DG) makes the reactive power optimal control strategy of distribution networks different from traditional distribution networks. Aiming at the problem of real-time reactive power dispatch for short-time scale scheduling, this paper proposes a Volt-Var control (VVC) method of distribution networks based on multi-agent deep reinforcement learning (MADRL). Firstly, the decentralized partially observable Markov decision process (Dec-POMDP) is designed based on the VVC model, and then the multi-agent twin delayed deep deterministic policy gradient (MATD3) algorithm is used to solve the Dec-POMDP model. The proposed method can intelligently generate the real-time VVC dispatch strategy according to the random change of generation and load. It has good real-time performance and control economy. Finally, the effectiveness of the proposed method is verified by simulation examples.
Zero Trust Security Framework for 5G MEC Applications: Evaluating UE Dynamic Network Behaviour

This paper presents a Zero Trust Security (ZTS) framework that enhances the security of Multi-Access Edge Computing (MEC) applications and services for 5G / 6G mobile networks. The ZTS framework incorporates a zero trust policy engine deployed in the 5G Core (5GC) network, which assesses User Equipment (UE) trustworthiness to access the MEC applications. The policy engine evaluates the UE Dynamic Network Behaviour (UDNB). Interactive procedures were designed to realise the proposed framework in a 3GPP-defined 5G network. Cooperation between mobile operators and vertical industries is achieved by leveraging the 5G Network Exposure Function and open interfaces. We propose that behaviour entropy be used as a quantitative measure to evaluate the UE trust value. An access control matrix was developed based on UE behaviour attributes. The performance evaluation demonstrates the effectiveness of our scheme in preventing unauthorised access to various MEC applications.