Miracle-dz/UniFAR-FTUs huggingface dataset:
Maximum Hosting Capacity Calculation Method of Grid-connected PV in Distribution Networks Based on Improved Big Bang-Big Crunch Algorithm

With fast growing integration of distributed renewable photovoltaics (PV) in distribution system, negative impacts will be caused on the planning and operation of distribution system. Study of maximum allowable capacity of grid-connected PV is able to provide theoretical foundation and technical support for the planning, construction and operation of the future distribution system. This paper proposes a maximum hosting capacity model of grid-connected PV in distribution system considering the constraints of nodal voltage, line capacity, total harmonic voltage distortion and short-circuit current. The improved Big Bang-Big Crunch algorithm is adopted to solve this problem. Finally, the proposed model is tested on a modified IEEE 33-bus test system. The effectiveness of the proposed method is demonstrated by comparing with several existing methods.
Consistent Surface-Potential-Based Modeling of Drain and Gate Currents in AlGaN/GaN HEMTs

In this article, the gate current in AlGaN/GaN high-electron mobility transistors is modeled in a surface potential-based compact model. The thermionic emission, the Poole–Frenkel emission, and the Fowler–Nordheim tunneling are the dominant mechanisms for the gate current in the forward- and reverse-bias regions. These conduction mechanisms are modeled within the framework of the ASM-GaN compact model, which is a physics-based industry-standard model for GaN HEMTs, hence yielding a consistent model for the drain and gate currents. The proposed model captures the gate voltage, drain voltage, temperature, and gate-length dependencies of the gate current. The results of dc gate-leakage measurements of two GaN HEMT, differing only in terms of gate length, over a wide range of temperature, showing these current-conduction mechanisms, are presented, and the proposed model is validated accordingly. The developed gate current model, implemented in Verilog-A, is in excellent agreement with the experimental data.
A novel Interference-aided Energy Harvesting Scheme for Cooperative Network

A novel Interference-aided Energy Harvesting (EH) scheme for Decode -and-Forward (DF) relaying system has been presented in this work. In this scheme, interference is exploited for the purpose of harvesting energy in a Cooperative Network (CN), where a power constrained relay driven by a novel hybrid protocol, harvests energy from Radio-Frequency (RF) signals received from source node (S) as well as an interferer node (I). The results of simulation illustrate the effect of harvesting energy from the interferer on the performance of the system in terms of a number of parameters such as, “throughput”, location of relay, position of interferer and efficiency. Finally, an approximation for the optimal “EH time” and “power-spitting ratio” have been derived by maximizing the“throughput” of the system.
A Review of Geothermal Energy for Future Power Generation

Renewable power generation is rapidly increasing due to the depletion and unfavorable environmental impact of fossil fuels. Geothermal energy is a form of renewable energy that can be effectively used for electric power generation. Besides, geothermal power provides considerable advantages compared to other renewable resources such as solar and wind power. Geothermal energy provides reliable, stable and efficient power compared to the lack of inertia, lack of efficiency and the intermittent nature of solar and wind resources. Moreover, geothermal power plants must be integrated with energy storage devices to improve the stability and flexibility of the power system. Gravity-fed energy storage and flywheel energy storage systems are two reliable technologies that can be integrated with geothermal power for improved stability and flexibility. Furthermore, the disadvantages of geothermal energy such as higher initial cost and geographic dependency can be compensated using recent research and developments in the geothermal technology. These recent developments include enhanced geothermal systems, small-scale geothermal power generation and geothermal power generation using abandoned oil and gas wells. Therefore, geothermal energy has the potential to become a major power generating source in the future
Unified Deep Learning Architecture for the Detection of All Catenary Support Components

With the rapid development of deep learning technologies, researchers have begun to utilize convolutional neural network (CNN)-based object detection methods to detect multiple catenary support components (CSCs). The literature has focused on the detection of specified large-scale CSCs. Additionally, CNN architectures have faced difficulties in identifying overlapping CSCs, especially small-scale components. In this paper, a unified CNN architecture is proposed for detecting all components at various scales of CSCs. First, a detection network for CSCs with large scales is proposed by optimizing and improving Faster R-CNN. Next, a cascade network for the detection of CSCs with small scales is proposed and is integrated into the detection network for CSCs with large scales to construct the unified network architecture. The experimental results demonstrate that the detection accuracy of the proposed CNN architecture can reach 92.8%; hence, it outperforms the popular CNN architectures.
A 2.92-Gb/s/W and 0.43-Gb/s/MG Flexible and Scalable CGRA-Based Baseband Processor for Massive MIMO Detection

Communication systems’ development requires service customization in aspects, such as standards, multiple-input multiple-output (MIMO) scales, and algorithms. The existing hardware designs for massive MIMO detection have difficulty in achieving both high flexibility and scalability with high hardware efficiency. This article proposes a baseband processor based on a dynamic coarse-grained reconfigurable array (CGRA) for massive MIMO detection. To efficiently support various algorithm features and requirements, three optimization techniques are proposed to achieve high flexibility and scalability. First, an on-demand matrix–vector systolic array is proposed to enable flexible and scalable matrix and vector operations, reducing memory accesses by 82%. Second, distributed multi-interaction data storage is designed for flexible data access and reusability. Finally, a continuable adaptive context information format is proposed to support different bit widths, operations, and extensions of MIMO systems, reducing context information by 67%. These techniques achieve the improvements of 1.33<inline-formula> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula>, 1.34<inline-formula> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula>, and 1.29<inline-formula> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> in energy efficiency and 1.21<inline-formula> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula>, 1.18<inline-formula> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula>, and 1.18<inline-formula> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> in area efficiency, evaluated by removing one technique at a time from the proposed architecture. Fabricated in a 28-nm CMOS technology, the chip achieves high flexibility and scalability in supporting various detection algorithms; various MIMO scales, such as <inline-formula> <tex-math notation="LaTeX">$4\,\,\times $ </tex-math></inline-formula> 4, 32 <inline-formula> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> 32, and 128 <inline-formula> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> 8; and baseband processing tasks, such as filtering and fast Fourier transformation. When benchmarked on various detection algorithms, the processor achieves 1.64–2.92-Gb/s/W energy efficiency and 0.25–0.43-Gb/s/MG area efficiency, which are 2.78–28.54<inline-formula> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> and 2.05–14.43<inline-formula> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> those of state-of-the-art programmable designs, respectively. To our knowledge, this is the first flexible and scalable CGRA-based baseband processor for massive MIMO detection.
Current State of Honeypots and Deception Strategies in Cybersecurity

This article surveys honeypot problematics and deception-based defensive strategies in the cyberworld. First, we define the honeypot phenomenon and summarize it’s advantages and disadvantages, as well as legal and ethical issues. We classify honeypots to different categories and present examples of both actively developed honeypots and ones that had a significant impact. Finally, we discuss the current challenges in this field.
Improved Ferroelectric Switching in Sputtered HfZrOx Device Enabled by High Pressure Annealing

HfZrO<sub>x</sub> (HZO) materials exhibiting ferroelectricity in ultra-thin layers can be deposited in various ways. Specifically, in sputtered HZO layers, only weak ferroelectric switching with gradual transition and small remnant polarization (<inline-formula> <tex-math notation="LaTeX">$\text {P}_{\text {r}}$ </tex-math></inline-formula>) is demonstrated using conventional rapid thermal annealing at high temperatures of at least 750 °C. Here we show that rapidly transited ferroelectric switching with a larger 2 P<sub>r</sub> of <inline-formula> <tex-math notation="LaTeX">$24~\mu $ </tex-math></inline-formula> C/cm<sup>2</sup> is achieved by introducing a high pressure annealing (HPA) at even lower temperature of 550 °C. Our findings reveal that the HPA enhances the crystallinity of grains in the HZO, thereby enlarging the P<sub>r</sub> and strengthening breakdown conditions. Through short pulse techniques, how interface and bulk region in the HZO are involved in the switching depending on the HPA is also investigated.
Spatio-Temporal GRU for Trajectory Classification

Spatio-temporal trajectory classification is a fundamental problem for location-based services with many real-world applications such as travel mode classification, animal mobility detection, and location recommendation. In the literature, many approaches have been proposed to solve this classification task including deep learning models like LSTM recently for sequence classification. However, these approaches fail to consider both spatial and temporal interval information simultaneously, but share some common drawbacks: omitting either the spatial information or the temporal interval information out. Some models like Time-LSTM, have been proposed to handle the temporal interval information for spatio-temporal trajectories, but they do not take into account the spatial information. Note that, considering both spatial and temporal interval information is crucial for spatio-temporal data mining in order not to miss any spatio-temporal pattern. In this study, we propose a trajectory classifier called Spatio-Temporal GRU to better model the spatio-temporal correlations and irregular temporal intervals prevalently present in spatio-temporal trajectories. We introduce a novel segmented convolutional weight mechanism to capture short-term local spatial correlations in trajectories and propose an additional temporal gate to control the information flow related to the temporal interval information. Performance evaluation demonstrates that our proposed model outperforms popular deep learning approaches for the travel model classification problem.
Fractional Order Derivatives Evaluation in Computerized Assessment of Handwriting Difficulties in School-aged Children

Handwriting difficulties (HD) affects some of the school-aged children and its current prevalence rate is between 5–34%. Children at primary schools have to face rising cognitive demands that the handwriting represents, and some of them are not able to do so. As a result, they tend to make mistakes and their written product is dysfluent and has poor legibility. HD can also lead them to lower self-esteem, learning difficulties and ultimately to less academic achievements. For this reason occupational therapists are trying to identify HD through examination as early as possible. We extracted online handwriting signals of children using digitizing tablets. Handwriting Proficiency Screening Questionnaire for Children (HPSQ–C) was used to score severity of HD in children’s written product. To advance current computerized analysis of online handwriting, we employed fractional order derivative features (FD) together with conventional measures. We selected significant features for HD identification and utilized correlation analysis together with Mann-Whitney U-test to evaluate their discriminative power. We can conclude that FD-based features bring benefits of more robust quantification of in-air movements as opposed to the conventionally used ones. Finally, we have shown that utilization of FD can be beneficial for computerized assessment of HD but should be further optimized and evaluated with advanced statistical or machine learning methods.
FABRIC: A National-Scale Programmable Experimental Network Infrastructure

FABRIC is a unique national research infrastructure to enable cutting-edge and exploratory research at-scale in networking, cybersecurity, distributed computing and storage systems, machine learning, and science applications. It is an everywhere-programmable nationwide instrument comprised of novel extensible network elements equipped with large amounts of compute and storage, interconnected by high speed, dedicated optical links. It will connect a number of specialized testbeds for cloud research (NSF Cloud testbeds CloudLab and Chameleon), for research beyond 5G technologies (Platforms for Advanced Wireless Research or PAWR), as well as production high-performance computing facilities and science instruments to create a rich fabric for a wide variety of experimental activities.
The Audio-Visual Arabic Dataset for Natural Emotions

Emotions are a crucial aspect of human life and the researchers have tried to build an automatic emotion recognition system that helps to provide important real-world applications. The psychologists have shown that emotions differ across culture, considering this fact, we provide and describe the first audio-visual Arabic emotional dataset which called (AVANEmo). In this work we aim to fill the gap between studies of emotion recognition for Arabic content and other languages by provided an Arabic dataset which is a major and fundamental part of build emotion recognition application. Our dataset contains 3000 clips for video and audio data, and it covers six basic emotional labels (Happy, Sad, Angry, Surprise, Disgust, Neutral). Also, we provide some baseline experiments to measure the primitive performance for automated audio and visual emotion recognition application using the AVANEmo dataset. The best accuracy that we achieved was 54.5% and 57.9% using the audio and visual data respectively. The data will be available for distribution to researchers.
Fault Location in Active Distribution Networks Using Block Sparse Bayesian Learning

Fault location is one of critical issues in the operation of distribution networks. Timely and accurate fault location can avoid time-consuming examinations and huge economic losses. Facing the issue, this paper introduces a block sparse Bayesian learning (BSBL) based fault location approach for active distribution networks. This approach utilizes the synchronized phasor measurement information provided by a limited number of phasor measurement units (PMUs) placed in the distribution network. By formulating the fault location problem into a block sparse signal estimation model, a BSBL framework is adopted to recover the sparse signal and thus determine the fault type along with accurate fault position. The utilization of BSBL method is able to exploit both block structure of the sparse signal and correlation among the non-zero variables, which helps further improve the recovery performance. In addition, the principle of the PMU placement is described in this paper, which offers an insight into the application of fault location using only a few PMUs. The effectiveness of the proposed approach has been verified by testing different fault types, fault resistances, fault positions and noise levels.
Small Signal Modeling and Analysis of Parallel Inverters Controlled by Robust Droop Controller

Robust droop controller belongs to the virtual synchronous machine family, which guarantees the accurate power sharing and an autonomous grid support for the inverters in parallel. In this paper, a precise small signal model of the parallel inverter system controlled by the robust droop controller is constructed. Problems such as the influence of the control and system parameters and system harmonic responses are analyzed based on eigenvalue evaluations. The results reveal the operation characteristics of the system and are further verified through simulations in PSCAD/EMTDC.
Recent Developments in Pedestrian Detection Using Deep Learning

Pedestrian Detection is utilized in various applications such as driverless cars, traffic control system, etc. In this research work, four recent research works’ performances are analysed with several other research works. The first work is Parts and Context network, second is Scale Aware Fast R-CNN, third work is Extended Filtered Channel Framework, and the fourth is See Extensively while Focusing on Core Area (ExtAtt). All these four research works are described in detail. Further, the performances of these four research works are analysed using three datasets, KITTI, Caltech, and INRIA. Average Precision in percentage is used for KITTI dataset and Log average miss rate in percentage is used for Caltech and INRIA datasets, as evaluation metrics. These values are compared with over 20 other research works previously performed on the same datasets. Among all the pedestrian detection models considered, ExtAtt achieves the highest average precision with KITTI dataset and PCN achieves the lowest log average miss rate on Caltech and INRIA datasets.
Small-Signal Stability of Islanded-Microgrids with DC Side Dynamics of Inverters and Saturation of Current Controllers

This paper addresses the effects of current controller saturation on small-signal stability of microgrids when considering the dc side dynamics of inverters. The saturation effect is introduced with a sigmoid function, that restrains the output of the current controllers based on the physical limitations of the dc side of the DG units. A linearized state-space model is presented, and the equilibrium points of the state-space variables are obtained in the time domain. Then, the eigenvalues of the microgrid model are plotted on the s-plane, and their traces are investigated. A participation factor analysis indicates an increased sensitivity of the low-frequency modes to the gains of the voltage controllers that was not characterized before. This increased sensitivity points to a more restricted stability region, which imposes new limits to the bandwidth of the voltage controllers.
A New Bonobo optimizer (BO) for Real-Parameter optimization

This paper presents a new metaheuristic optimization algorithm, namely Bonobo optimizer (BO). It is inspired from the social behaviour and reproductive strategies of Bonobos. Bonobos adopt fission-fusion social strategy, which is nothing but forming several groups of variable sizes and compositions within a community and after a short period of time, they again reunite with their own community. Furthermore, bonobos show mainly four types of reproductive strategies, such as promiscuous, restrictive, consortship and extra-group mating. These natural behaviours of bonobos are artificially modelled in the proposed algorithm to solve optimization problems. The novelty of the proposed BO lies with the updating mechanisms of searching agents and their associated parameters, and the selection method of the mating partners. The performance of the proposed BO has been examined on a set of twenty optimization problems with varying attributes, and the results are compared with the reported ones by using seven other metaheuristic algorithms, in the literature. The outcome of the experiment clearly shows the superior performance of the proposed BO in both the aspects of exploration and exploitation capabilities compared to that of the others. The source code of BO is available at: https://sites.google. com/site/softcomputinglaboratory/Home.
Lexicon-Based Sentiment Analysis for Movie Review Tweets

Sentiment analysis is a computational process to identify and classify subjective information such as positive, negative and neutral from the source material. It is able to extract feeling and emotion from a piece of a sentence. This technology has been widely used to extract valuable information from people’s views on social media. Hence, this project aims to classify movie reviews into positives, negatives and neutral polarity using lexicon-based method which used R as the language and development framework. Twitter data is used as the source material. Firstly, tweets were extracted using RStudio and Twitter API. Then data pre-processing was done by removing all the stop words and noises. Next was the tokenization process, which separates the words and matches the separated words with positive and negative words vocabulary. Finally, the result of the sentiment analysis is produced into positive, negative and neutral polarities. The results were evaluated using standard evaluation metrics that are the precision, recall, F1 score and accuracy. After all, it is found that the basic lexicon-based method is able to classify sentiment quite well with 52% accuracy. Apparently, the accuracy value achieved in our experiment is not impressive enough, but it is worth corresponding to the simplicity and minimal cost of development for sentiment analysis on Twitter data for movies.
Effect of Seed Layer on Gate-All-Around Poly-Si Nanowire Negative-Capacitance FETs With MFMIS and MFIS Structures: Planar Capacitors to 3-D FETs

In this article, we successfully fabricated nanowire (NW) negative capacitance (NC)-related ferroelectric FETs (FE-FETs) with two structures: trigate (TG) and gate-all-around (GAA). Planar capacitors with a metal–FE–metal (MFM) structure were investigated first. Post-metal annealing (PMA) at 700 °C resulted in the best ferroelectricity. This condition was considerably different from that of directly stacking onto NWs because of the difference in size and curvature between planar and TG or GAA structures. Because of the addition of an underlying ZrO<sub>2</sub> seed layer, Hf<inline-formula> <tex-math notation="LaTeX">$_{{1}- {x}}$ </tex-math></inline-formula>Zr<sub><italic>x</italic></sub>O<sub>2</sub> in the gate-stack has been crystallized before the PMA process. In addition, two different gate-stack configurations, MFM–insulator–semiconductor (MFMIS) and metal–FE–insulator–semiconductor (MFIS), were investigated for the GAA structure. We determined that MFMIS displayed considerably more favorable subthreshold behavior and ON-state current compared with MFIS. NC-related phenomena, such as negative drain-induced barrier lowering and negative differential resistance, were observed.