Miracle-dz/UniFAR-FTUs huggingface dataset:
Evolution of the Trieste Port: a real-time system for a coordinated cold ironing

The continuous growing in environment requirements combined with the proximity of ports to city centers make it necessary to take actions in favor of the site decarbonization. Therefore the electrical distribution grid of port needs to be analyzed and renewed for reducing the CO2 equivalent mass produced. This goal can be reached by using an integrated monitor systems in order to impose a desired power-flow and proper voltages on the different grid nodes. Not only the possibility of supervising the grid but also some notable functionalities are enabled by these innovative systems: for example cold ironing hubs, electrical vehicle recharge hubs, optimization of power generation and storage functionality. In this paper, the Port of Trieste is taken as case of study and possible evolution scenarios will be presented using a mathematical model of the medium voltage power system. Particularly, an innovative real-time hierarchical monitor system architecture will be discussed, while a patched Linux PC will be presented as possible monitor device implementation.
An Opportunistic Routing Protocol based on Link Quality and Local Topology in Mobile Ad hoc Networks

In mobile ad hoc networks (MANETs), the frequent changes of network topology due to node mobility increases the cost of maintaining deterministic routing. As one state-less routing protocol, opportunistic routing utilizes the nature of broadcast and does not have to maintain deterministic routes. However, broadcast may cause interference to some nodes and affect the transmission quality between nodes. Thus, how to adapt opportunistic routing to the dynamic environment and avoid unreliable transmissions are two important issues to solve. In this paper, we propose an opportunistic routing protocol based on link quality and local topology (OR-LqT) in MANETs. In the protocol, we measure link qualities by considering both interference and nodes' mobility to find the candidate set, and select a next forwarding node by prioritizing the neighbor nodes based on their locations, local topology and moving adaptivity. The proposed OR-LqT can avoid the number of unreliable transmissions and the number of dead-ends. The performance results show that compared to the conventional routing protocols, such as AODV, $\mathbf{PRR}^{\ast}\mathbf{d}$ and GPSR, the OR-LqT decreases the number of dead-ends by more than 33.5%, packet drop ratio by more than 21%, average delay by more than 22% and overhead by more than 21%.
Energy Dissipation Characteristics and Failure Patterns of Freeze-Thawed Granite in Dynamic Flexural Tension

Deformation and failure of freeze-thawed rocks under dynamic loads are of practical meaning in studying the stability of slopes and tunnels in cold regions. The rock failure process is essentially actuated by the transfer and transformation of energy. In this paper, the energy dissipation characteristics and failure patterns of freeze-thawed granite under dynamic loads were investigated. A split Hopkinson pressure bar (SHPB) system was used to conduct the experiments on semicircular bend (SCB) specimens subjected to 30, 60, 90, and 120 freeze-thaw (F-T) cycles. The results found that the incident energy, reflected energy, and dissipation energy were loading rate-dependent, while the transmitted energy was not correlated with the loading rate. The reflected energy accounted for the largest proportion (greater than 60%) of the incident energy. In addition, the energy reflection rate (ERR) decreased when the loading rate or porosity increased, whereas the energy dissipation rate increased when the loading rate or porosity increased. A model was established to predict the ERR and EDR evolution. Finally, the failure patterns of freeze-thawed granite under dynamic loads demonstrated that a higher loading rate caused a change in failure patterns, whereas a larger porosity led to greater fragmentation in the specimens.
E-Learning in Developing Countries: Is it a Panacea? A Case Study of Sudan

Despite the increasing costs of education and the growing use of information communication technologies (ICTs) in developing countries, the need for more educational opportunities is increasing. This paper aims to answer the question: is e-learning a panacea for education in developing countries? It is based on the case country Sudan, and analyzes students’, academics’, and policymakers’ attitudes toward e-learning, ICTs, and educational technology. This study uses an exploratory research approach, and relies mainly on two data collection instruments: questionnaire and semi-structured interview. A survey was conducted on a population of academics and students at five public universities in Sudan, as well as policymakers from these institutions and the higher education ministry. A convenience sample of 400 was chosen as the target population representation. A total of 388 valid questionnaires were returned. The results show more conservative attitudes toward e-learning and ICTs among academics and policymakers. The study concludes that there is an increasing demand in developing countries for more authentic learning opportunities and simulated experiences that the e-learning can provide. E-learning, however, is a paradigm entirely different from traditional face-to-face education. To do e-learning work, a different roles and practice is required for the educator.
Classification of diabetic retinopathy based on hard exudates patterns, using images processing and SVM

Diabetic retinopathy (DR) is an ocular disease very common on diabetic patients. It is developed when the patient present a high level of glucose in blood which in consequence results in damage on the retina bloods vessels. The diagnostic of DR consists on the detection of different characteristics presented on the back of the eyeball, like microaneurysms, exudates, retina hemorrhages and thickness of veins. On this article we propose the detection of hard exudates and the segmentation of blood vessels through image processing for retinopathy detection. The main goal is the automatic classification images that present exudates or not. On an early step, the isolated characteristics by means of the image segmentation can be used for a support vector machine to determine the exudates presence. This proposal has been tested on a database of 130 images and the results obtained a maximum sensitivity of the 87.3% and a specificity 84.6%. Although the proposal is focuses on detected diabetic retinopathy it has been found that it could be used for the progression and diagnosis of macular edema.
Thermal Conductivity of Silicene Nanoribbons: An Equilibrium Molecular Dynamics Study

In order to gain insight into the thermal transport properties of silicene nanoribbon (SiNR), we compute the thermal conductivity of both armchair and zigzag SiNRs using Equilibrium Molecular Dynamics (EMD) simulation. Applying Modified Embedded Atom Method (MEAM) potential, the room temperature thermal conductivities of 10 nm × 2 nm armchair and zigzag silicene nanoribbon are estimated to be 2.52 W/m-K and 2.59 W/m-K, respectively. We also investigate SiNR's thermal conductivity as a function of ribbon length, width and temperature. For both armchair and zigzag SiNRs, increasing length and width have a positive effect on thermal conductivity but for increasing temperature, it shows a decreasing trend. The very low thermal conductivity of SiNR validates the conclusion that it is a potential thermo-electric material and our study will be helpful for its use in modern small-scale silicene-based device.
Systematic Review of Technology Enabled Active Learning Classrooms in Higher Education

Technology enabled active learning classrooms have become a prominent blended learning environment that has been highly recommended for mass expansions and normalization in higher education. Early research on the topic has shown that the environment offers good potential for increasing student engagement and learning achievement in higher education. However, no literature reviews have summarized the available knowledge documented in existing research. This study presents a synthesis of comparative research [1-12] which examines the technology enabled active learning classroom in higher education.
The Method of Increasing the Detection Range of Unmanned Aerial Vehicles In Multiradar Systems Based on Surveillance Radars

The paper analyses a model of a small base synchronous multiradar system on the basis of two-coordinate surveillance radar, which will enable to increase the detection range of unmanned aerial vehicles. Separate positions of elements of such systems are spacious apart from each other, but the processing of radar information is carried out in a single point. A special feature will be the using of radars with mechanical rotation in the azimuth plane as elements of the system. It will increase the efficiency of detection due to the using of radar information contained in the structure of the electromagnetic field more efficiently than the monostatic two-coordinate radars. The advantage will be an increasing in the energy potential of the system as a result of the addition of the power of the pulses of individual radars and an increasing in the gain of the antenna array due to the more directional properties of the antenna array compared to single antenna. Calculations of the multiradar system parameters are carried out by the method of mathematical modelling.
Analytical Threshold Voltage Model of Schottky-source/drain (Schottky-S/D) double gate-all-around (DGAA) Field-Effect-Transistors (FETs)

This paper reports an analytical model of threshold voltage for the Schottky-source/drain (Schottky-S/D) double gate-all-around (DGAA) FETs. In order to develop the threshold voltage model, the method of parabolic potential approximation is used to solve the 3-D Poisson's equation to formulate the surface potentials and threshold voltage expressions. The proposed model of threshold voltage is based on a modified definition of the threshold voltage. The influence on threshold voltage due to variation in device physical parameters like core and oxide thickness, channel thickness, and source-drain metal work function variation, and drain voltages have been investigated. The proposed analytical model results have been compared and verified against the results obtained from 3-D ATLAS device simulator from SILVACO.
Lexicon Based Sentiment Analysis of Twitter Data

: Sentiment Analysis of twitter data is an active area of Natural Language Processing research. This study explores a unsupervised lexicon based approach to calculate polarity of tweets fro a publicly available twitter corpus. Along with lexicon based search of sentiment bearing words, several rule based methods are used to get the final polarity count of tweets. This study takes into account effect of negation, capitalization, multiple punctuation, slang, and degree modifier
Granger Causality Inference and Time Reversal

A fundamental issue in the study of a complex system is the detection and characterization of causal dependence among components of the system. The system's components are represented by a set of selected and simultaneously recorded variables. Omitting observations of a relevant variable in a system, the Granger causality analysis can lead to false detections. Haufe et al. (2013) suggested using time reversal for testing Granger causality to alleviate the problem of spurious causality. Here we investigate the behavior of the so-called time-reversed Grange causality on different nonlinear dynamical systems with a known causal structure. As our experimental results show, the impact of a hidden influential variable cannot be excluded by using the time-reversed Granger causality. On the other hand, the time-reversed Granger causality, by the definition, can lead to false rejections in the case of a bidirectional causal connection between variables.
Group Consensus of Networking Heterogeneous Agents with Parametric Uncertainties

In this paper, a group consensus problem is investigated for the heterogeneous agents that are governed by the Euler-Lagrange system and the first-order system, respectively, while the parameters of the Euler-Lagrange system are uncertain. To achieve group consensus, a novel group consensus protocol and a time-varying estimator of the uncertain parameters are proposed. Then, several effective sufficient conditions are obtained. Finally, these theoretical results are demonstrated by the numerical simulations.
Weighted $l_{p}$ Minimization for Compressive Channel Estimation in FDD Massive MIMO

The paper proposes a weighted $l_{p}$ minimization algorithm for downlink channel estimation in frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) system, in which we take the popular $l_{p}$ minimization algorithm named as Iteratively Reweighted Least Squares(IRLS) method for example. The proposed algorithm involves two stages, in which it first diagnoses the downlink support by utilizing the channel sparsity in angular domain and spatial reciprocity for uplink and downlink channels, and then a weighted IRLS algorithm based on the diagnosed support is used for downlink channel recovery. The diagnosed support is used for generating the weighting matrix in the weighted IRLS. The restricted isometry property(RIP) based analysis results for the $l_{p}$ minimization are given out. The results has the general form for $0 < p\leq 1$ and can be specified to the RIP analysis results for standard $l_{1}$ and weighted $l_{1}$ minimization which are given in other literatures. Hence our analysis results have the more general form. Simulations show that the proposed method can achieve higher estimation accuracy compared with IRLS, weighted subspace pursuit, weighted $l_{1}$ minimization and standard $l_{1}$ minimization.
Electronics-Based Free-Space Terahertz Measurement Using Hemispherical Lens Antennas

This paper introduces a novel free-space terahertz (THz) characterization setup featuring an electronic-based technique and miniature additive-manufactured hemispherical lens antennas. By using in-house custom-made hemispherical lenses, radiation characteristics of the electromagnetic (EM) wave of the measurement setup, e.g. the radiation pattern as well as the collimated and focused beam size of the THz beam, can be precisely predicted and synthesized by appropriately selecting the radius size of the lenses. This technique eases design and measurement setup complexity as compared to the existing conventional THz measurement setups without compromizing the measurement accuracy and reliability. To demonstrate the THz measurement setup developed in this paper, an asymptotically single-mode hollow Bragg fiber operating from 0.246 to 0.276 THz was characterized for its EM transmission properties. The measurement results characterized by this setup are in good agreements with the results obtained from the analytical study and a conventional free-space THz measurement system. The novel THz measurement technique was also characterized for its critical properties, e.g. reliability, repeatability, sensitivity, and accuracy, indicating that the setup can achieve highly accurate measurement quality while still offers various advantages compared to all other existing THz measurement setups such as ease of measurement that allows non-THz expert to setup, setup flexibility, cost-effectiveness, smaller required measurement space, less sensitivity to measurement environments, etc.
A Novel Multi-Criteria Preamble Detection Algorithm for ADS-B Signals

Satellite-based automatic dependent surveillance broadcast (ADS-B) is of great importance in the next generation air traffic management system. However, there are two major challenges: interference and low signal to noise ratio (SNR). The interference can be mitigated by various separation algorithms. In this paper, we are committed to improving the detection performance for the ADS-B signals with downlink format field equal to 17 at low SNRs. At the moment, commercial receivers work well with sufficient SNRs but may not function properly in the case of the low SNRs. To this end, based on the characteristics of the ADS-B signal frame format, we propose a novel multi-criteria preamble detection algorithm. The proposed algorithm is a constant false alarm rate (CFAR) detection algorithm, and the determination of threshold is independent of noise power. We make a detailed theoretical analysis of each criterion, and develop the analytic expression of the detection probability. All the theoretical results are validated by the Monte Carlo simulations. The simulation shows that the proposed algorithm has an excellent probability of detection when the SNR is low, which is suitable for the satellite-based ADS-B application.
Image Feature Matching Based on Deep Learning

Image feature matching is an integral task for many computer vision applications such as object tracking, image retrieval, etc. The images can be matched no matter how the image changes owing into the geometric transformation (such as rotation and translation), illumination, etc. Also due to the successful application of the deep learning in image processing, the deep learning method has an advantage in feature extraction of images. In this paper, we adopt a deep Convolutional neural network (CNN) model, which attention on image patch, in image feature points matching. CNN obtains the feature by convolution kernel which parameters are achieved by learning. So it has strong ability to express feature. Compared with other methods, experimental results indicate the proposed method has higher accuracy and completed efficiently.
The Regression Parameters of Band Frequency Spectra of the Breath Sounds Vary in Course of a Respiratory Cycle

Usually, the frequency content of the breath sounds is taken to be time-invariant and having the approximate decay of 12 dB per octave. This study deals with verification of this assumption by considering the short-time frequency spectra occurring in course of a respiration cycle. To approximate their shapes over the octave bands, a linear regression representation with respect to a logarithm of frequency is applied. The analysis of auscultation records shows the explicit time variation of linear regression parameters correlated with the respiratory activity. This property can be used as a marker in acoustic detection of the phases of a respiratory cycle.
Sliding Mode Observer Enhanced for Sensorless Permanent Magnet Synchronous Motor Control

A second order sliding-mode observer enhanced similar in structure and computational cost to a classical proportional-integral controller is proposed. This observer eliminates efficiently the chattering problem and preserves the robust properties of sliding mode. Owing to its advantages, the proposed observer is used for reconstructing the back electromotive force of a permanent magnet synchronous machine, it estimates the motor speed and position. These estimations are feedbacked in a vector control scheme, where the need for the mechanical position/speed sensor is eliminated. MATLAB™ simulations of the proposed observer demonstrate the accurate reconstruction and zero phase delay estimation of the back electromotive force. Experimental results of a sensorless speed control for a 7.46 kW permanent magnet synchronous motor validate the proposed observer. The advantages of the proposed observer are better compared to the sensorless control strategies LO and Q-SMO.
Time Series Forecasting using Recurrent Neural Networks modified by Bayesian Inference in the Learning Process

Typically, time series forecasting is done by using models based directly on the past observations from the same sequence. In these cases, when the model is learning from data, there is not an extra quantity of noiseless data available and computational resources are unlimited. In practice, it is necessary to deal with finite noisy datasets, which lead to uncertainty about what so appropriate the model is. For this, the employment of models based on Bayesian inference are preferable. Then, probabilities are treated as a way to represent the subjective uncertainty from rational agent, performing an approximated inference by maximizing a lower bound on the marginal likelihood. A modified algorithm using long-short memory recurrent neural networks for time series forecasting was presented. This new approach was chosen in order to be as close as possible to the original series in the sense of minimizing the associated Kullback-Leibler Information Criterion. A simulation study was conducted to evaluate and illustrate results, comparing this approach with Bayesian neural-networks-based algorithms for artificial chaotic time-series.
Minimum Infinity Norm-Based PWM for Three-Phase Four-Leg Converters

Control of four-leg voltage-source converters operating on three-phase systems traditionally faces the problem of transformation from a four-degrees of freedom (DOF) modulation space to a three-DOF output-voltage space. Contemporary, it is highly desirable to minimize the leg voltage, and consequently the dc-link voltage, especially because of positive impact on cost-effective dimensioning of power electronics modules. In order to meet these requirements, a novel universal PWM method is proposed. It is based on calculation of the minimum infinity norm and tailored for control of four-leg converters operating in applications with both, balanced and unbalanced output voltage. In order to verify its universality, it has been employed in two different applications, namely a three-phase four-leg inverter supplying an unbalanced load and a four-leg compensator of earth faults in distribution power grids. Capability of the proposed modulation algorithm to control the four-leg converters in various applications while minimizing the dc-link voltage has been verified by experiments on two designed laboratory prototypes.
Simulation and Analysis of Uniformity of Illuminance in Indoor VLC System

White LEDs are becoming an attractive choice in indoor visible light communication (VLC) and the uniformity of illuminance is a critical issue in VLC systems. In this paper, we have evaluated the illumination uniformity for three different LED lamp arrangements with varying semi-angles of the transmitter in MATLAB. In all the arrangements, (3×3) LEDs are symmetrically placed on the ceiling in a square room. The first order reflections are taken into account for evaluating the performance of the proposed system. Next, we discussed about the power received at the receiving plane considering reflection from the walls. The simulation results show that there is significant improvement in uniformity with the increase in the semi-angle of the transmitter up to a certain distance limited by the walls of the room.
Selected E-Learning Systems and Their Comparison

Specialized tools used to support teaching are exploited for effective learning. One of these tools and approaches is e-learning. The purpose of this article is to perform a partial evaluation of three selected e-learning systems. The systems are assessed predominantly from the point of view of users who normally come into contact with the system. These include: ATutor, Chamilo, and Moodle. The findings reveal that the best e-learning system is Moodle, closely followed by Chamilo, and the worst seems to be ATutor in this respect. It is not entirely clear from the score results that the only possible solution for the deployment of an e-learning system to communicate in Czech is the Moodle sys-tem, which has the only complete localization. However, its main advantage is that thanks to its popularity, many manuals are written about it in Czech.