Miracle-dz/UniFAR-FTUs huggingface dataset:
Neutral current reduction control for smart transformer under the imbalanced load in distribution system

Imbalanced loads arouse neutral current looping in the distribution system, which increases power loss and results in neutral potential variation. Compared to the conventional power transformer, the smart transformer (ST) has advantages on the downstream voltage regulation. Thus, this paper proposes a voltage control strategy based on ST to reduce the LV grid neutral current according with EN 50160 imbalanced voltage standard. The proposed control has been validated in the Matlab/Simulink, and the system performance under the proposed control has been simulated under the imbalanced loading profile in a 400 kVA, 10 kV/400 V distribution network. The results prove the proposed control can practically reduce the neutral current.
Environmental friendly and cost effective approach to provide highly reliable electric supply to rural business and industries

The healthy growth of economy lies on the balance between rural and urban development. While urban India has grown rapidly, rural infrastructure has been largely neglected until recently. Unreliable electricity supply is a major hurdle and has been identified by business owners as the second-most important obstacle to business development in India. The solutions proposed so far in the literature promote renewable energy microgrids and storage to improve the reliability of supply locally. Since the rural regions do not have the technical man-power to operate and manage a microgrid, this approach may not be effective in practice. The solution must be simple, affordable and tailored to the local needs. This article focuses on finding the optimum solution to improve the reliability of electrical supply for a small business/industry connected to a rural grid. The average cost of electricity has been calculated when 1) diesel generator, 2) battery, and 3) the most cost effective combination of solar PV panels and batteries, are used to improve reliability. Different grid reliability levels and different sell-back prices for the excess solar electricity have been used for the simulations. It was found that, even including installation cost, the combination of solar PV panels and battery is much cheaper than the commonly used diesel generator or the battery back-up. Affordable, reliable electricity will be a critical enabler for the development of rural areas.
State of the art of telepresence with a virtual reality headset

Today we are forced to collaborate even remotely using collaborative working environment. In such an environment, non-verbal communication is not taken into account. According to Judee K. Burgoon, sixty percent of the communication are non-verbal. To bridge this gap, we suggest the use of virtual reality, namely, telepresence tools that use virtual reality Headsets. In this article we present a state of the art of the telepresence devices for capturing tones, facial expressions and body language, all of which improve the communication and understanding of the message.
Pipelining user trajectory analysis and visual process maps for habit mining

Models of human habits in smart spaces can be expressed by using a multitude of formalisms whose readability influences the possibility of being validated by human experts. In this paper we present a visual analysis pipeline that allows, starting from the sensor log of a smart space, to graphically visualize human habits. The intuition here is to apply techniques borrowed from the area of business process automation and mining on a version of the sensor log preprocessed in order to translate raw sensor measurements into human actions. The paper also presents some hints of how the proposed method can be employed to automatically extract models to be reused for ambient intelligence.
Smart cities research in Portugal and Spain: An exploratory biliometric analysis

In this paper it is presented a bibliometric analysis on smart cities research in Portugal and Spain performed using the Scopus® database as a source. Number of articles, citations to those articles, types of sources, most productive and impacting authors and institutions, most cited articles and more relevant subject areas are analyzed and discussed. It is concluded that there is a significant gap between Portugal and Spain in terms of the impact of the research produced, partially explained with the subjects addressed and type of publications used. The results presented here can be of use to researchers and policy makers in the field of Smart Cities' research mainly as a basis for benchmarking.
Design and control of power conversion system for electric vehicle application

The performance of Electric Vehicle (EV) is largely dependent on effective power flow within its power conversion system. This paper presents the design and control of power conversion system for EV which mainly comprises of DC-DC converter and three phase PWM inverter. Hybrid energy storage system (HESS) and other components are sized based on the Indian driving cycle. The multi-input bidirectional DC-DC converter is controlled so that the battery shares the base load and the ultracapacitor (UC) shares the peaky loads during traction mode while UC absorbs the regenerated power during braking mode. The DC link voltage of the inverter is maintained constant. An indirect vector controlled induction motor is used as the propulsion unit. The simulation results of the system during acceleration and deceleration under various load conditions are found to be satisfactory.
On the performance of a deep learning-based anomaly detection system for 5G networks

Cyber security defense systems have evolved in the last years with innovative approaches and new Intrusion Detection Systems (IDS) capable of identifying cyber-threats that had gone unnoticed to date. However, new protection challenges are raised by the upcoming fifth generation (5G) mobile technology with new and advanced features. 5G will make that existing detection procedures become obsolete in case they are not adapted accordingly. This paper proposes a 5G-oriented architecture with which to conduct the analysis of network flows to identify cyber-threats in 5G mobile networks efficiently and quickly, by making use of deep learning techniques. Experiments on the proposed system with inspection capabilities are also included, so as to analyze and determine how many network flows, gathered from the 5G subscribers' User Equipments (UE), we can inspect in real-time. These outcomes can give us clues about when and why protection systems in 5G will stop detecting cyber-attacks for overload reasons.
UDE-Based Robust Command Filtered Backstepping Control for Close Formation Flight

This paper presents an uncertainty and disturbance estimator (UDE)-based nonlinear robust controller for close formation flight in light of the command filtered backstepping technique. To maximize the formation aerodynamic benefits at different flight maneuvers, the formation geometry is described in the wind frame of the leader aircraft. A novel nonlinear robust close formation control algorithm with a two-degree-of-freedom nature is developed. The command filtered backstepping technique is employed to design the baseline formation controller, whereas the UDE is introduced to enhance the robustness of the baseline formation control. The proposed control law can lead to desired tracking performance for the close formation flight under different maneuvers without using the acceleration information of the leader aircraft, asymptotical stability for the close formation flight at a level and straight flight with constant speeds, and enough robustness against the formation aerodynamic effects by purely observing system states and inputs. Numerical simulations are performed to show the feasibility and efficiency of the proposed controller.
Transportable and scalable system for activities and exercises recognition in real-time

Patients concerned with neuromuscular disorders are usually given a program of multiple physical exercises to do. These exercises must be recognized by devices to ensure the therapist that the patient did it well. To this end, we developed a specific method allowing the recognition of two types of activities on different window sizes. The method consists of two devices communicating over Wi-Fi, to use both wearable components and powerful embedded computer. Data is collected during fixed windows by the first device. Then features are calculated on the retrieved data for the recognition algorithm. Participants took part in an experiment based on four physical exercises and six daily activities. The method induced good results.
Optimization of convolutional neural network hardware structure based on FPGA

In this paper, we optimize the hardware structure of the convolutional neural network based on FPGA hardware development platform. At the convolutional operation level, we propose a FPGA-based tree-shape convolutional operation structure to accelerate the convolutional operation, which can effectively reduce the time of convolutional operation. On this basis, we design a three-dimensional tree-based convolutional operation structure between feature maps for more complicated operation.
Microstrip Four-Way Reconfigurable Single/Dual/Wideband Filtering Power Divider With Tunable Frequency, Bandwidth, and PDR

In this paper, a microstrip reconfigurable four-way filtering power divider (FPD) with tunable center frequency (CF), bandwidth (BW), and power division ratio (PDR) is presented. A novel coupling scheme is proposed based on four stub-loaded quad-mode resonators (SL-QMRs), two three-line coupled structures, and two quarter-wavelength transformers, which can achieve tunable CF, BW, PDR, and wide isolation BW. Switchable single/dual/wideband filtering response and the CF and BW of a dual-band FPD can be tuned by controlling the varactor diodes loaded onto the open-ends of SL-QMRs. Meanwhile, the PDR is controlled by varying the coupling strength between the center line and sidelines of a three-line coupled structure. Compared with the conventional wideband FPDs, the capacitor loaded onto the parallel coupled line of two outputs enlarges the coupling, which can improve the return loss and, thus, relieve the limitation of the line space. Based on the coupled resonator theory, the parameters of the resonators and coupling sections are analytically determined. For the dual-band FPD, the measured results show that the 3-dB fractional BW of the lower passband can be tuned from 23.4% to 30.9%, while the one of the upper passbands is tuned from 12.3% to 18.5%. The measured CF for the upper passband can be tuned independently from 1.98 to 2.21 GHz, while the lower one remains fixed. Then, the measured PDR is controlled from 1:1:1:1 to 1:2:1:2. The proposed four-way reconfigurable FPD exhibits switchless single/dual/wideband filtering responses, high isolation level, wide isolation BW, and the control of CF, BW, and PDR.
Rigid and Elastic Microparticles Detection Using 3-D Suspended Polymeric Microfluidics (SPMF3) Sensor

Microsystems have gathered great interests and shown valuable results in the study of mechanobiology and biophysical analysis of cells due to their properties such as microsystems match cell dimensions, provide growth microenvironment such as in-vivo environment, and facilitate parallel analysis that can be done on cells through integration of other sensors or chemicals at low cost and with low amount of sample use. There are a variety of microsystems that measure cell properties such as physical, mechanical, and chemical. In this paper, a new 3-D suspended polymeric microfluidics (SPMF3) platform for microparticles detection is introduced and tested. The principle of the SPMF3 is based on bending of structure due to flow forces applied to the microcantilever which is modified when microparticles are passing through the suspended microfluidics. The SPMF3 is less complex and less expensive compared with the other optical, microfluidics, and microcantilever based techniques formerly employed for microparticles and cells. According to the experimental results, the SPMF3 is highly sensitive to the particles passing through the micro-nozzle without employing an external exciter. One can study and obtain the biophysical and elastic properties of the passing particles such as size, number, and viscoelasticity with the deflection behavior of the microcantilever.
30-Day Postoperative Outcome in Patients Undergoing Extensive Gynaecological Surgery

Purpose: Extensive gynaecological surgery is the treatment of choice for advanced ovarian cancer and borderline ovarian tumours. Postoperative complications are common and contribute to patient suffering and medical costs. The purpose of this prospective observation study was to investigate complications and subjective parameters, such as pain and nausea, in patients undergoing extensive cytoreduction surgical procedures at our hospital to lay ground for future improvement. Methods: Patients undergoing elective laparotomy for suspected advanced ovarian cancer and borderline ovarian tumours were included. Preand intra-operative parameters were drafted from anaesthesiology records. Medical records provided data regarding postoperative complications. Pre-formatted inquiries were answered by patients on the first, third, fifth and 30th postoperative day. Results: Twenty-three patients were included in the study. Fifteen patients experienced complications within 30 days postoperatively, with a total of 38 complications recorded in the study. Only 2 patients experienced severe complications. The most common complications were urinary tract infection (5 patients) and delayed gastric emptying (5 patients). Significant differences in operative time, perioperative intravenous fluid use, perioperative lactate and length of stay were found between patients suffering from complications compared to patients with an uncomplicated postoperative course. Conclusions: Postoperative complications following extensive cytoreduction surgical procedures are common and further research and resources should be devoted to their prevention, detection and treatment. Postoperative pain management was satisfactory while nausea management could be improved.
Eye Fixation Assisted Video Saliency Detection via Total Variation-Based Pairwise Interaction

As human visual attention is naturally biased toward foreground objects in a scene, it can be used to extract salient objects in video clips. In this paper, we proposed a weakly supervised learning-based video saliency detection algorithm utilizing eye fixations information from multiple subjects. Our main idea is to extend eye fixations to saliency regions step by step. First, visual seeds are collected using multiple color space geodesic distance-based seed region mapping with filtered and extended eye fixations. This operation helps raw fixation points spread to the most likely salient regions, namely, visual seed regions. Second, in order to seize the essential scene structure from video sequences, we introduce the total variance-based pairwise interaction model to learn the potential pairwise relationship between foreground and background within a frame or across video frames. In this vein, visual seed regions eventually grow into salient regions. Compared with previous approaches the generated saliency maps have two most outstanding properties: $integrity$ and $purity$ , which are conductive to segment the foreground and significant to the follow-up tasks. Extensive quantitative and qualitative experiments on various video sequences demonstrate that the proposed method outperforms the state-of-the-art image and video saliency detection algorithms.
Deep Learning of Path-Based Tree Classifiers for Large-Scale Plant Species Identification

In this paper, a deep learning framework is devel- oped to enable path-based tree classifier training for supporting large-scale plant species recognition, where a deep neural network and a tree classifier are jointly trained in an end-to-end fashion. First, a two-layer plant taxonomy is constructed to organize large numbers of plant species and their genus hierarchically in a coarse- to-fine fashion. Second, a deep learning framework is developed to enable path-based tree classifier training, where a tree classifier over the plant taxonomy is used to replace the flat softmax layer in traditional deep CNNs. A path-based error function is defined to optimize the joint process for learning deep CNN and tree classifier, where back propagation is used to update both the classifier parameters and the network weights simultaneously. We have also constructed a large-scale plant database of Orchid family for algorithm evaluation. Our experimental results have demonstrated that our path-based deep learning algorithm can achieve very competitive results on both the accuracy rates and the computational efficiency for large-scale plant species recognition.
Is there a case for radical change to weather satellite constellations?

The National Oceanic and Atmospheric Administration (NOAA) has conducted a detailed study of its next generation satellite architecture, the NOAA Satellite Observing System Architecture (NSOSA) study. Among the goals for that study was to determine if there was a compelling case for making a radical change to the satellite architecture. Here “satellite architecture” is understood to mean the allocation of functions to platforms or orbits and the selection of measurements to make. The basic satellite architecture for weather observation from space has been largely stable for several decades and will continue in its current form for at least another decade. The study developed several general forms of radical change and determined that there is no compelling case for making any of them. The reasons are not merely unfavorable cost-benefit ratio in specific instances, but are inherent in the current understanding of relative collection priorities, instrument and data assimilation technologies, and division of mission responsibilities. While any of these could change, they are not likely to change in the foreseeable future. While the study concluded that there is no compelling case for radical structural change, the situation is quite different for significant change in business models and programmatic boundaries.
Optimal management of energy storage systems for residential customers with photovoltaic generation

The growing focus that some big companies are putting on the electrical storage market is leading to a decrease in the cost of these devices, which are now becoming appealing also for residential applications, often coupled with the installation of photovoltaic panels. This poses the problem of the optimal management of energy storage systems for residential customers in order to fully exploit the benefits of both photovoltaic generation and storage. The solution to this problem is an integral part of home energy management systems, which are expected to play a key role in the smart grid of the future. The purpose of this paper is to propose and analyze different energy storage system management policies in a framework with real time pricing for both energy purchase and sell. This pricing scheme is already present in some markets worldwide and will likely become more and more common in the next years.
Repositório ISCTE-IUL

— Blockchain technology is already being talked about as one of the megatrends for the next years. Researchers and organisations are starting to understand the potential benefits of this technology and are exploring how it can disrupt the world we live in with a diverse range of applications. But the truth is the ability to move blockchain from concept to adoption and production has been minimal yet. When it comes to auditing, blockchain solutions could have important benefits by reducing the workload of the auditors, helping in minimising fraud and optimising the existing processes but is also vital to have in mind other emerging technologies. Factom, Libra, and Verady are some examples of companies developing blockchain solutions that can be applied in the auditing environment, but much of the necessary development is still yet to be done.
In Defence of Sandcastles: Research Thinking Through Visualization In DH

Although recent research acknowledges the potential of visualization methods in DH, the predominant terminology used to describe visualizations (prototypes, tools) narrowly focuses on their use as a means to an end and, more importantly, as an instrument in the service of humanities research. While acknowledging the broad range of possible approaches to visualization, we introduce the metaphor of the sandcastle to highlight visualization as a research process in its own right. We argue that building visualization sandcastles provides a holistic approach to interdisciplinary knowledge generation that embraces visualization as (1) a dynamic interdisciplinary process where speculation and re-interpretation advance knowledge in all disciplines involved, (2) a mediator of ideas and theories within and across disciplines and (3) an aesthetic provocation to elicit critical insights, interpretation, speculation and discussions within and beyond scholarly audiences. We illustrate our argument based on our own research of an exceptional literary collection.
An improved PSO algorithm for node localization in indoor long-narrow confined space

Chain-type WSN is deployed to monitor long-narrow confined space, which is the common form of building structure. Considering the existing range-based algorithms can't satisfy the requirements of high accuracy, quick output and low energy consumption in chain-type WSN due to RSSI fluctuation in complex indoor environment, new distributed localization algorithm named WPE-PSO is presented in this paper, which is based on Particle Swarm Optimization (PSO). Firstly, PE-PSO is presented to improves proactive estimation and confines initial search range. Secondly, after analyzing how fitness function affects PSO localization algorithm, the concept of weight is applied to improve fitness function of PSO and WPE-PSO is proposed. The simulation adopts the modified RSSI range model, which is more consistent with the actual application. Compared with original PSO and Bounding Box PSO in different noise level, the simulation results show that WPE-PSO have better performance in terms of accuracy and compute complex.
Experimental Data Fitting Analysis on Frequency-Current-Temperature Relation

We study experimental data fitting to a frequency gradient curve for current and temperature simu lated with the Hodgkin-Hu xley (HH) oscillator undergoing saddle-node on invariant cycle bifurcation. In this study, one frequency gradient curve (referred to as the theoretical curve) are constructed by expanding the HH oscillator using small perturbations of the current and temperature wh ile the other gradient curve (referred to as the empirical plot) is obtained with frequency-current relations recorded from h ippocampal CA3 pyramidal cells at different environ mental tempera tures. The empirical p lot is best-fitted to the theoretical curve under a certain temperature parameter range using simu lations with the HH oscillator. In order to confirm the best-fit, we show that the theoretical curve is overlapped almost of the standard errors for the p lot and that a temperature coefficient in the HH oscillator is rescaled to be in a suitable range of the temperature coefficient calculated with the experimental data.