Miracle-dz/UniFAR-FTUs huggingface dataset:
A Wind Farm Modeling in IEEE-24 Bus Reliability Test System on DIgSILENT Power Factory

Reliable and secure operation of a power system network is an indispensable concern for the system operator. Continuous works are being done towards achieving and holding the status of reliable and secure operation. However, the complexity of the power networks has increased so much that the direct implementation of any new technology on the system can be very complex and sometimes quite risky also. Therefore, the choice of an appropriate Simulation software is needed to implement and test new technology efficiently. In this paper, DIgSILENT power factory simulation software has been used to simulate an SO MW wind farm, using sixteen wind turbines (5 MW each). The performance analysis of the developed wind farm has been carried out on the IEEE 24-bus reliability test system. Comparative analysis of the results during validation of the network as well as optimization of total cost and loss has been carried out.
Electromagnetic Parameter Calibration for a Broadband Ray-Launching Simulator With SAGE Algorithm for Millimeter-Wave Communications

The millimeter-wave (mmWave) communication is a promising solution to future high-speed data transmission. To study the impact of its wireless channel, a ray-launching simulator is developed to generate deterministic channel models according to realistic scenario descriptions. For such channel modeling, the material parameters play an important role in the electromagnetic numerical computing of multi-path components (MPCs) and should be calibrated according to channel measurement results. Our contribution in this paper is three-fold: we conduct the channel measurement in an indoor scenario for extracting mmWave channel characteristics, apply the Hungarian algorithm to pair measured and simulated propagation MPCs and apply the SAGE algorithm for the parameter calibration of materials impacting on the MPCs. Note that the proposed material parameter calibration method is general for the various radio propagation environment, not limited by the indoor or outdoor scenarios. Moreover, it avoids independent measurement of specific materials in a given scenario, thus greatly reducing the difficulty of obtaining material parameters. The result shows that the space alternating generalized expectation-maximization (SAGE) algorithm achieves a good match between measurement and simulation with a reasonable computational effort.
Modeling Temperature Behavior in the Helper Data for Secret-Key Binding with SRAM PUFs

We use a statistical model for SRAM PUFs to study achievable performance of secret-key binding schemes in terms of secret-key capacity. We show that capacity increases when the temperature dependent behavior of individual SRAM cells is observed during enrollment. We introduce a temperature helper data scheme that models temperature dependent behavior of the SRAM cells in the helper data without revealing information about the key. We prove that the scheme is secure and achieves secret-key capacity for the given model. We evaluate the key reconstruction error probability for an implementation of the scheme in MATLAB through Monte Carlo simulations.
Arbitrarily Varying Wiretap Channels with and without Non-Causal Side Information at the Jammer

We investigate the Arbitrarily Varying Wiretap Channel (AVWC) with non-causal side information at the jammer for the case that there exists a best channel to the eavesdropper. Non-causal side information means that codewords are known at an active adversary before they are transmitted. By considering the maximum error criterion, we allow also messages to be known at the jammer before the corresponding codeword is transmitted. A multi letter formula for the common randomness secrecy capacity is derived. Furthermore, we compare our results to the random code secrecy capacity for the cases of maximum error criterion but without non-causal side information at the jammer, maximum error criterion with non-causal side information of the messages at the jammer, and the case of average error criterion without non-causal side information at the jammer.
Using Ensemble Decision Tree Model to Predict Student Dropout in Computing Science

Science, Technology, Engineering and Mathematics (STEM) professionals play a key role in the development of an economy. STEM workers are critical thinkers as they contribute immensely by driving innovations. There is a high demand for professionals in the STEM fields but there is also a shortage of human resource in these areas. One way to reduce this problem is by identifying students who are at-risk of dropping out and then intervening with focused strategies that will ensure that these students remain in same the programme till graduation. Therefore, this research aims to use a data mining classification technique to identify students who are at-risk of dropping out from their Computing Science (CS) degree programmes. The Random Forest (RF) decision tree algorithm is used to learn patterns from historical data about first-year undergraduate CS students who are enrolled in a tertiary institute in the South Pacific. A number of factors are used which comprise of students demographic information, previous education background, financial information as well as data about students’ academic interaction. Feature selection is performed to determine which factors have greater influence in students’ decision in dropping out. Cross-validation techniques are used to ensure that the models are not over-fitted. Two models were built using a 5fold and 10-fold cross-validation and the results were compared using several measures of model performance. The results show that the factors corresponding to students’ academic performance in a first-year programming course had the greatest impact student attrition in CS.
Optimized RTPS platform for IIoT based smart farming systems

With the ever-growing population, food and water demands have risen exponentially. Almost 80-85% of food is produced by agriculture but the rise in demand needs to be met by an increase in food production. The agriculture sector tends to tackle this issue by incorporating the Industrial Internet of Things (IIoT) and cloud technologies to improve productivity and reduce wastage of resources needed for it. The prime target of incorporation of these technologies would be to facilitate farmers to grow crops and raise livestock with ease and lower cost with increasing profits. Smart farming systems can comprise of one or more sub-systems that are automated using IIoT technology and processing is done either in local server or cloud. Most researchers work on individual sub-system and improve the performance providing local optimization. These sub-systems can have different vendors and integration is quite difficult at times. Therefore, we propose to use Data Distribution Service (DDS) middleware to integrate these heterogeneous sub-systems and provide high performance with low latency system. In this paper, we introduce a novel approach to tackle the issue of heterogeneity and provide global optimization. A suitable mathematical model for our proposed system is given and experiments are conducted to show the efficiency of the system in terms of throughput and delay.
SymNav: Visually Assisting Symbolic Execution

Modern software systems require the support of automatic program analyses to answer questions about their correctness, reliability, and safety. In recent years, symbolic execution techniques have played a pivotal role in this field, backing research in different domains such as software testing and software security. Like other powerful machine analyses, symbolic execution is often affected by efficiency and scalability issues that can be mitigated when a domain expert interacts with its working, steering the computation to achieve the desired goals faster. In this paper we explore how visual analytics techniques can help the user to grasp properties of the ongoing analysis and use such insights to refine the symbolic exploration process. To this end, we discuss two real-world usage scenarios from the malware analysis and the vulnerability detection domains, showing how our prototype system can help users make a wiser use of symbolic exploration techniques in the analysis of binary code.
Chemicals profiling and antioxidants activities of Acacia seeds

This study reported investigations on phytochemical screening and antioxidants activities of seeds from seven Acacia species. Storage proteins and mineral contents were determined. The seed extracts of Acacia species were evaluated for their total phenols, flavonoids, carotenoids contents and total antioxidant capacity assessed by 2,2-azino-bis-3-ethylbenzothiazoline-6-sulfonic acid (ABTS) and 2,2diphenyl-1- picrylhydrazyl (DPPH) assays. Total protein content ranged from 99.49 ( Acacia cyclops ) to 142.77 ( Acacia cyanaphylla ) mg/g dry weight basis (DW) according to the Acacia species, having an average of 120.41 mg/g DW. The albumin constitutes the major fraction presenting 31.11%, followed by globulin (27.36%), prolamin (22.27%) and glutelin (19.27%). The Acacia seeds were rich in minerals. The total phenolic compounds (TPC) determinate ranged from 154.47 ( A. cyclops ) to 1217.99 ( Acacia horrida ) with an average of 632.40 mg gallic acid equivalents/100 g DW. The total flavonoid contents have an average of 11.21 mg rutin equivalents/100 g DW, and the carotenoids mean is 4.23 mg/kg. The Acacia seed extracts have an antioxidant activity reflected by the ABTS and DPPH tests. The Duncan’s test showed significant difference of proteinic, phenolics, flavonoid and carotenoid contents according to the Acacia species. Therefore, natural components from Acacia seeds could be incorporated as food ingredient, without causing detrimental effects to the food's palatability and the functional ingredient's efficacy.
Pathological Gait Classification Using Kinect v2 and Gated Recurrent Neural Networks

With the development of depth sensors and skeleton tracking algorithms, many skeleton-based pathological gait classification methods have recently been proposed. However, these methods classify only simple gait patterns, and there is no approach to classify complicated gait patterns. In this paper, we classify 1 normal and 5 pathological gaits (antalgic, stiff-legged, lurching, steppage, and Trendelenburg gaits) by using a gated recurrent unit (GRU)-based classifier and 3D skeleton data. We collected skeleton datasets for 1 normal and 5 pathological gaits by using a multiperspective Kinect system. We developed the GRU classifier to classify the pathological gaits and compared its performance with that of other machine learning-based classifiers. Furthermore, we considered various joint groups to identify important and irrelevant joints for pathological gait classification and to improve the performance of the GRU classifier. When all skeleton data are used, the GRU classifier achieves a classification accuracy of 90.13%. A long short-term memory (LSTM)-based classifier achieves the next highest accuracy of 87.25%. The classification accuracy of the GRU classifier depends on the joint groups considered, and the classification accuracy increases to 93.67% when only leg joints are considered. This study indicates that various pathological gaits can be classified by using skeleton data and the GRU classifier. The proposed method can be used to support medical and clinical decisions. Furthermore, the results for various joint groups can be used to develop other methods in cases where only specific joint data are available because of environmental limitations.
Connectance in stream food webs

Summary 1 The properties of food webs are important both in theoretical ecology and environmental management, yet remain elusive. Here, we examined 12 new stream food webs of higher taxonomic resolution and completeness than any previously published data set and combined them with other 10 published stream webs. 2 Compared with most previously published food webs, these stream communities (containing between 22 and 212 species) had more feeding links per species, a higher fraction of intermediate species (0·78–0·88) and lower fractions of top (0·04–0·09) and ‘basal’ species (0·07–0·15). 3 The exponent of the log link-log species relationship (S1·3) differed significantly (ancova: P < 0·001) from the link-species ‘law’ (S1) and the constant connectance hypothesis (S2). Based on these results, the link scaling law and the constant connectance hypothesis must be rejected for food webs in stream systems. 4 Connectance was markedly lower than previously reported and decreased with web size. We attribute this to the body-size disparity of organisms at the top and the bottom of the web, flow disturbance and to the high physical heterogeneity and complexity of the sedimentary habitat. All these may reduce the fraction of possible feeding links that are realized.
An Obstacle-Aware Mobility Model for Campus Delay Tolerant Network

Mobility models are widely used for the evaluation of Delay Tolerant Networks (DTNs) performance, this mitigates the need for the model that can generate patterns that guarantee the contact opportunities for a reliable package forwarding. In this paper, we present a new mobility model that describes students’ daily activities in a campus environment. As opposed to the conventional models such as a Random walk, Random waypoint, Random direction, etc which uses a free space environment, our model includes obstacles of different shapes and sizes that obstruct movement and signal propagation. We evaluate the model using ONE simulator by comparing it with the Working Day Model and Random Waypoint. The model synthetically generates traces with the distribution of contact duration and inter-contact time similar to the distribution of real traces. We further investigate its impact on the performance of DTNs routing protocols and the result shows that, it performs better than the existing models in terms of average delivered message and message latency.
Online Clustering of Trajectories in Road Networks

The ubiquity of GPS-enabled smartphones and automotive navigation systems allows to monitor and collect massive streams of trajectory data in real-time. This enables real-time analyses on mobility data in urban settings, which in turn have the potential to substantially improve traffic conditions, analyze congested areas, detect events in (quasi) real-time, and so on. While many existing approaches characterize past movements of moving objects from historical trajectory data, or address the problem of finding out clusters of moving objects from data streams, such approaches fail to capture how movement behaviors unravel over time - for instance, they fail to capture typically trafficked routes or traffic jams. In this work we propose NET-CUTiS, a novel approach that addresses the problem of discovering and monitor the evolution of clusters of trajectories over road networks from trajectory data streams. We conduct several experiments that demonstrate the validity of our proposal in terms of clustering quality and run-time performance.
Transformerless Large-Scale Grid-tied PV System Using Sensorless Boost Modular Multilevel Inverter with DC Fault Blocking Capability

The scalability of Modular Multilevel Converters (MMCs) has eased the integration of large-scale Renewable Energy Sources (RESs) to Medium Voltage (MV) network. In this paper, the integration of a grid-connected large-scale Modular Multilevel Inverter (MMI)-based 1 MW PhotoVoltaic (PV) plant is analysed. The proposed MMI employs Full-Bridge-SubModules (FB-SMs) topology. Unlike the conventional Half-Bridge (HB)-based MMI, FB-SMs enable a boosting operation; avoiding typical configurations where step-up transformers are required. That is, better power density and specific power can be provided. Moreover, the FB circuit inherits DC faultblocking capability. Therefore, a DC-fault protection system is investigated as same as normal operation to foster the system reliability. A sensorless sorting algorithm is adopted to perform the MMI’s capacitors voltage balancing, hence the firing of FBSMs. The sensorless approach reduces hardware complexity and cost, especially in case of high number of MMI levels. The proposed system is investigated using MATLAB simulation platform.
Analysis of Factors Affecting Lecturer Acceptance of the E-Learning System in Universitas Lampung

Universitas Lampung uses e-learning to help lecturer and students for their learning process. The use of e-learning is expected to increase interaction between lecturer and college student because e-learning allows communication through audio and video, chat facility, file sharing, and discussion forum for lecturer and college student. However, the number of lecturers using this system is still low. The objective of this research is exploring factors that influenced the acceptance of e-learning system for the lecturer in Universitas Lampung. This research is based on Technology Acceptance Model (TAM) and Structural Equation Model (SEM). The result shows that the factor of motivation to use having a positive influence indirectly to behavioral intention through perceived usefulness factor. It also shows that internet self-efficacy factor having positive influence to perceived ease of use, meanwhile perceived of ease use factor have no positive influence to behavioral intention because it doesn't have a positive influence both directly and indirectly to behavioral intention.
Thermal Design of an Integrated Inductor for 45kW Aerospace Starter-Generator

This paper presents a combined electromagnetic and thermal design of integrated inductor, for the application of a 45kW aircraft starter-generator. The inductor is designed at high current density by using the apporach of area product, followed by the finite element analysis, which validates the electromagnetic performance of integrated inductor. The total power losses at 8, 20, and 32kRPM are evaluated in order to investigate the thermal design of a combined starter-generator and integrated inductor system, whilst, achieving the full integration from a thermal management point of view. As both starter/generator and inductor share common cooling configuration; performance of a direct and an indirect cooling options are compared. The direct cooling configuration, based on a semi-flooded design, can lead to a temperature reduction up to 90°C in the most critical components.
A Systematic Literature Review of Deep Learning Algorithms for Personality Trait Recognition

Personality trait recognition has an essential role in the job screening process. The psychologies perform the analysis based on the survey, the handwriting of the participant, or conduct the interview. The process takes a lot of time and money. Consequently, it inspires that researchers develop a tool to help the screening process faster. The current review papers lack an explanation of deep learning algorithms in personality trait recognition. So we perform this study to classify the latest deep learning algorithms in personality trait recognition. Using Preferred Reporting Items for Systematic reviews and MetaAnalyses Method (PRISMA), we collected 25 key papers which are from Scopus, IEEE, Science Direct, Emerald Insight, and ACM. In this paper, we focus on discussing the problems, deep learning methods, and open issue in personality trait recognition. Based on the finding, the primary problem in personality trait recognition is the complexity of the text and audiovisual data. It induces the low performance of personality trait recognition. In detail, the text-based trait recognition has lower accuracy than the visual and audio based methods. Therefore we have a bigger opportunity to improve the performance of text-based personality recognition than audiovisual based. Moreover, the real experiment in audiovisual trait recognition still in the early stages and it can be explored in more detail.
Distributed Mobility Data Management in MobilityDB

Mobility applications involve large amounts of data that must be managed and queried in a scalable way. MobilityDB1 is an SQL moving object database system. It is an extension of PostgreSQL and PostGIS that supports storing and querying mobility data. This paper demonstrates the distributed query management capabilities in MobilityDB using a cluster that contains 2 billion real AIS ship trajectory points obtained from the Danish Maritime Authority.1https://github.com/ULB-CoDE-WIT/MobilityDB
Linearity-aware design of Doherty power amplifiers

The linearity is a crucial requirement of power amplifiers. At design level, it is typically assessed by means of AM/AM, AM/PM and IMR, which are extracted from 1- and 2-tone characterization. These however are not fully representative of the actual behaviour under modulated signal excitation, for which purpose other system-level metrics such as the NPR have been successfully introduced. In this paper, different linearity estimations are used to compare two GaN-on-Si Doherty MMIC power amplifiers for Ka-band space applications. One of the two amplifiers shows an AM/PM reduction of more than 30% with respect to the other. However, it is observed that this does not always lead to a reduction in NPR. The reasons for this disagreement between metrics are analysed and discussed.
Performance Analysis of Commercial PV for Remote Area in Tropical Island

Photovoitaics (PV) is a technology that directly generate electricity from solar energy resource. The PV stand-alone system can fulfil energy supply for the remote areas that are not powered to the main grid. The PV type can be selected based on the climate area to obtain a maximum and efficient energy production. In the tropical island, similar as the present study in the Desa Kamujan, Karimunjawa Islands, the PV testing was performed to analyze their performance. There are three types of commercial PV were used in this study with certain capacity: monocrystalline (2700 Wp), polycrystalline (2340 Wp), and thin film (200 Wp). Each PV was analyzed by the module efficiency, power output efficiency, and performance ratio. Based on this research polycrystalline PV has the best performance for remote area in tropical island. Polycrystalline PV has module efficiency 10.4%, followed by monocrystalline (10.24%) and thin film (4.19%). The power output efficiency of polycrystalline is 14.45%, thin film is 13.51 % and monocrystalline is 13.4%. While the performance ratio shows that polycrystalline reaches 66.57%, followed by monocrystalline 56.56% and thin film 32.52%.
A Flexible Step-up Modular Multilevel Converter for High-Power Drive Application

A flexible step-up modular multilevel converter (MMC) is proposed in this paper with a potential for high-power motor drive applications. Increasing the DC-link voltage utilization up to three times is the main advantage of the proposed structure over the conventional MMCs. Using the proposed structure in motor drive systems decreases the required DC-link voltage which leads to a significant reduction in the rate and size of the rectifier stage. Having one-degree of freedom in design, DC-link short-circuit fault blocking capability, and ease of design and control are among other features of the proposed structure. Detailed formulation of reference signals and circulating current of the proposed structure along with comparative simulation and experimental results validate the performance of the proposed structure.