Miracle-dz/UniFAR-FTUs huggingface dataset:
THE COMPACTNESS INDICATORS OF SOLIDS APPLIED TO ANALYSIS OF GEOMETRIC EFFICIENCY OF BUILDINGS

The authors redefine and generalize the so-called relative ratio of compactness of solid with respect to sphere and cube introduced by Mahdavi and Gurtekin. The authors propose such new indicators as a relative ratio of solid compactness in the shape of a prism-shaped solid related to a cuboid with a square base and a given height (e.g. the height of the analyzed storey), a solid compactness indicator defined for the basis of the prism relative to the square. They introduce also other indicators: a relative defect of the perimeter and area. The test of indicators formulated and described in the paper was carried out in two groups of buildings: existing (spotted locally) and those whose designs can be found in the selected catalog available on the website. In addition to the compactness ratios calculated for existing and designed buildings, an analysis of the size of the indicators for the adopted building research models was made. Proposed indicators allow for a description of the compactness of the building model with an indication of the deviation from the real ideal solid. The introduced geometric determinants of solids may be useful in the initial phase of searching for satisfactory design solutions.
Stepwise Design Methodology and Heterogeneous Integration Routine of Air-Cooled SiC Inverter for Electric Vehicle

Carrying on SiC devices, the air-cooled inverter of the electric vehicle (EV) can eliminate the traditional complicated liquid-cooling system in order to obtain a light and compact performance of the powertrain, which is considered as the trend of next-generation EV. However, the air-cooled SiC inverter lacks strategic design methodology and heterogeneous integration routine for critical components. In this article, a stepwise design methodology is proposed for the air-cooled SiC inverter in the power module, dc-link capacitor, and heat sink levels. In the power module level, an electrical–thermal–mechanical multiphysics model is proposed. The multidimension stress distribution principles in a six-in-one SiC power module are demonstrated. An improved power module is presented and confirmed by using the observed multiphysics design principles. In the dc-link capacitor level, ripple modeling of the inverter and capacitor are created. Considering the tradeoffs among ripple voltage, ripple current, and cost, optimal strategies to determine the material and minimize the capacitance of the dc-link capacitor are proposed. In the heat sink level, thermal resistance of air-cooled heat sink is modeled. Structure and material properties of the heat sink are optimally designed by using a comprehensive electro-thermal analysis. Based on the optimal design results, the prototypes of the customized SiC power module and heterogeneously integrated air-cooled inverter are fabricated. Experimental results are presented to demonstrate the feasibility of the designed and manufactured air-cooled SiC inverter.
The Use of Acidic Hydrolysates after Furfural Production from Sugar Waste Biomass as a Fermentation Medium in the Biotechnological Production of Hydrogen

This study investigates a simultaneous processing of sugar beet pulp (SBP) for furfural, hydrogen and methane production using various pretreatment methods. In the experiments, sugar beet pulp was first subjected to thermal and thermochemical pretreatment at 140 °C. Then hydrolysates from these operations were investigated for their potential for methane and hydrogen production in batch tests. The experiments showed that thermal pretreatment of SBP resulted in the highest biogas and methane yields of 945 dm3/kg volatile solids (VS) and 374 dm3 CH4/kg VS, respectively, and a moderate hydrogen production of 113 dm3 H2/kg VS, which corresponded to a calculated energy production of 142 kWh/t; however, only low amount of furfural was obtained (1.63 g/L). Conversely, the highest furfural yield of 12 g/L was achieved via thermochemical pretreatment of SBP; however, biogas production from hydrolysate was much lower (215 dm3/kg VS) and contained only 67 dm3/kg VS of hydrogen. Meanwhile, in the experiment with lower amounts of sulfuric acid (2%) used for pretreatment, a moderate furfural production of 4 g/L was achieved with as high as 220 dm3/kg VS of hydrogen and the corresponding energy yield of 75 kWh/t.
Fault-Tolerant Control of Doubly-Fed Wind Turbine Generation Systems under Sensor Fault Conditions

This paper studies the fault-tolerant control problem of uncertain doubly-fed wind turbine generation systems with sensor faults. Considering the uncertainty of the system, a fault-tolerant control strategy based on a T-S fuzzy observer is proposed. The fuzzy observer is established based on the T-S fuzzy model of the uncertain nonlinear system. According to the comparison and analysis of residual between the state estimation of the fuzzy observer output and the measured value of the real sensor, a fault detection and isolation (FDI) based on T-S fuzzy observer is designed. Then by using a Parallel Distributed Compensation (PDC) method we design the robust fuzzy controller. Finally, the necessary and sufficient conditions for the stability of the closed-loop system are proved by quoting Lyapunov stability theory. The simulation results verify the effectiveness of the proposed control method.
Decentralized Robust State Estimation of Active Distribution Grids Incorporating Microgrids Based on PMU Measurements

The development of active distribution grids brings with it a demand for a higher accuracy of state estimation. Micro-phasor measurement units ( ${\boldsymbol{\mu }}$ PMUs) deployed in distribution grids provide necessary but insufficient support for the state estimation of enhanced performance. This paper deals with a decentralized PMU-based robust state estimation of active distribution grids incorporating microgrids. The PMU-based robust state estimation is formulated as a quadratic programming problem and solved in a decentralized manner to accommodate the autonomous operation mode among microgrids, as well as to preserve information privacy between different operators. During each iteration of the decentralized method, each microgrid evaluates its bad measurements and sends its local optimization objective with respect to boundary states to the utility grid for a unified optimization. The proposed iteration scheme between the utility grid and microgrids has significant advantages over regular iteration methods, in terms of its convergence rates and minimal data exchange between operators. Case studies of different systems are presented that demonstrate the exactness and efficiency of the proposed method.
Temperatures, food riots, and adaptation: A long-term historical analysis of England

A large body of research indicates that environmental conditions can influence the risk of social unrest. However, we know little about how these effects may change in the long run. Are they likely to remain constant or do they change over time – for example as a consequence of human adaptation? To investigate this question, we rely on a disaggregated analysis of England over a period of more than 300 years. Combining data on geo-referenced food riots with reconstructed climate data, we first assess the impact of annual temperatures on social unrest over the period 1500–1817. We then use our long-term time-series dataset to assess the temporal heterogeneity of year-to-year associations between temperatures and social conflict. Our models show a substantive negative correlation between temperatures and food riots in the aggregate. This association, however, seems to be highly inconsistent over time and largely confined to the 18th century. In addition, we find evidence of decadal processes of adaptation: past exposure to adverse weather conditions dampens the effect of current exposure. Taken together, these findings underline the importance of considering temporal heterogeneities when assessing the climate–conflict nexus and caution against any simple extrapolations of observable present-day effects of environmental conditions into the future.
Using Virtual Reality to Enforce Principles of Cybersecurity

The Cyberinfrastructure Security Education for Professionals and Students (CiSE-ProS) virtual reality environment is an exploratory project that uses engaging approaches to evaluate the impact of learning environments produced by augmented reality (AR) and virtual reality (VR) technologies for teaching cybersecurity concepts. The program is steeped in well-reviewed pedagogy; the refinement of the educational methods based on constant assessment is a critical factor that has contributed to its success. In its current implementation, the program supports undergraduate student education. The overarching goal is to develop the CiSE-ProS VR program for implementation at institutions with low cyberinfras-tructure adoption where students may not have access to a physical data center to learn about the physical aspects of cybersecurity.
An Improved Hybrid Particle Swarm Optimization and Tabu Search Algorithm for Expansion Planning of Large Dimension Electric Distribution Network

Optimal expansion of medium-voltage power networks is a common issue in electrical distribution planning. Minimizing the total cost of the objective function with technical constraints make it a combinatorial problem which should be solved by powerful optimization algorithms. In this paper, a new improved hybrid Tabu search/particle swarm optimization algorithm is proposed to optimize the electric expansion planning. The proposed method is analyzed both mathematically and experimentally and it is applied to three different electric distribution networks as case studies. Numerical results and comparisons are presented and show the efficiency of the proposed algorithm. As a result, the proposed algorithm is more powerful than the other algorithms, especially in larger dimension networks.
Computing-Aware Base Station Sleeping Mechanism in H-CRAN-Cloud-Edge Networks

In this paper, a power minimization problem using base station sleeping is proposed for heterogeneous cloud radio access networks (H-CRANs) taking into account the computing delay constraints. In the proposed system, which is modeled using M/M/k queues, the edge device coexists with the small base station (SBS) to provide computing capabilities beside the central cloud. In general, the SBS sleeping is governed by the availability of resources provided the macro base station (MBS) which is in charge of accommodating offloaded users from sleeping SBSs. However, switching off lightly loaded SBSs can impose significant burdens on cloud servers. Here, the proposed sleeping scheme allows SBSs serving more computing tasks to remain active in order to fulfill the task completion deadlines requested by mobile users and to keep the cloud response time within a predefined limit. In other words, the proposed scheme aims to save power by undertaking a centralized selection of active and sleeping SBSs taking into account the delay constraints of both cloud and mobile devices. First, we consider a disjoint cloud-edge system, where computing services can be provided by either the cloud or the edge device, and aim to minimize the number of active SBSs. The problem is formulated as a 0-1 knapsack problem with SBS utilization considered as the weight while the ratio of computing tasks to all incoming tasks is considered as the value of that SBS. In this problem, which is solved using dynamic programming, SBSs processing less computing tasks are given higher values; and as a result, higher chance to sleep compared to others. Second, a shared computing system is proposed whereby active SBSs (edge devices) contribute to the total computing capability. Here, an exhaustive search approach is used to achieve the optimal power saving. We also proved that the shared computing system performs better in terms of response time compared to the disjoint system depending on the number of active SBSs.
Compensation of Mismatched Disturbances for Nonlinear Plants with Distributed Time-delay

The paper deals with the robust algorithm for compensation of unknown mismatched disturbances depending on a state vector of plants with distributed time-delay. The algorithm based on generalization of a backstepping method and disturbance compensation method. The proposed control system compensates disturbances with required accuracy. The simulation results illustrate the efficiency and robustness of the suggested control system. System". MIMO System".
A New Predictive Control Strategy for Multilevel Current-Source Inverter Grid-Connected

The DC/AC converters—commonly called inverters—transform the DC into AC and are classified as Voltage-Source Inverters (VSIs) or Current-Source Inverters (CSIs). A variant of the CSIs are the Multilevel Current-Source Inverters (MCSIs). In this paper, a new predictive control strategy for an MCSI with multiple inputs and grid-connected is proposed. The control technique uses the advantages of the Sliding Mode Control (SMC) for the balance of current in the input and Predictive Control (PC) to obtain a suitable grid current, since it separates both functions. The calculations are based on conventional Kirchhoff’s Voltage Law (KVL) and knowledge of the mathematical model of the system is not required. Generally, traditional MCSIs use large input inductors (100–1000 mH). In this paper, it is achieved a reduction in size of the input inductors. Simulation results are shown to validate the proposed control.
Micrometer and Millimeter Wave P-to-P Links Under Dust Storm Effects in Arid Climates

A dust storm is the main attenuation factor that can disturb receiving radio signals in arid climate condition as in Saudi Arabia. This paper presents a study on the effect of dust storms on the received radio frequency power in a homogenous environment in the city of Riyadh. A number of micrometer and millimeter wave links have been considered along with several measured dust storm data to investigate the dust storm effects. The results showed that dust storm can critically influence the communication link and this effect grows up as the physical distance between the transmitter and the receiver increases. The negative effect of the dust storm apparently appears at high-frequency bands allocated for the next communication generation (5G) which imposes finding solutions to mitigate the effects of this phenomenon.
Delayed hearing loss following vestibular schwannoma surgery: Behavioural and electrophysiological responses in the early postoperative period

Some patients suffer hearing loss in the early postoperative period following vestibular schwannoma excision despite having intact hearing immediately after surgery. As this phenomenon has rarely been documented or described, the putative mechanism remains vague. The objective of the current study was to document the patterns of change in behavioural and electrophysiological responses in patients following VS surgery to better describe the phenomenon of delayed hearing loss. In particular, we aimed to determine whether the impairment that eventually leads to delayed hearing loss is neural or cochlear in origin. Auditory function was monitored in six adult patients who underwent surgery at Christchurch Public Hospital for excision of unilateral vestibular schwannoma through the retrosigmoid approach. Patients were assessed preand postoperatively by puretone audiometry, speech audiometry, tympanometry, distortion product otoacoustic emissions (DPOAEs), and auditory brainstem response (ABR). When measurable hearing was demonstrated postoperatively, pure-tone audiometry, speech audiometry and ABR were assessed at 24 hour intervals following surgery. Transtympanic electrocochleography (ECochG) was carried out if wave I of the ABR was lost during the postoperative period. Postoperative monitoring revealed that 4 patients suffered permanent anacusis and the remaining 2 patients had permanent hearing preservation. There were no patients who
A Signaling-Free Underwater Code Division Multiple Access Scheme

In this paper, we propose an underwater code division multiple access system where each sensor node independently evaluates whether a channel is available or not without control message exchanges with a central data-gathering node named a sink. A sensor node is able to estimate how large power is currently received at a sink in the distance based on the overheard power at the node from neighbors. If the estimated power is below a certain threshold level, the sensor node is allowed to transmit data in a p-persistent manner, where the probability p depends on the available capacity. Simulation results show the traffic estimation works well as demonstrated by a success probability of approximately 100%, and the data throughput improves in most of the offered traffic region because of the removal of the control signaling related to channel allocation.
Reconfigurable Distribution Network for Managing Transactive Energy in a Multi-Microgrid System

As microgrids (MGs) interact with power distribution systems, the end-to-end transactive energy (TE) trading among MGs poses new challenges on operation and trading functions of distribution system operators (DSOs). This paper proposes a bi-level programming framework for the coordination of multi-MG energy TE trading and the operation of power distribution systems. The bi-level framework enables distributed TE trading decisions at the multi-MG level and effective market clearing decisions by coordinating MGs with DSO. At the lower level, MGs participate in a local TE trading market. The lower level will submit nodal equivalent loads to the DSO at the upper level, which applies a second-order cone programming (SOCP) to reconfigure the distribution network in respond to MG trading requirements. The updated topology is sent back to the lower level in an iterative process which will modify MGs’ electrical distances and corresponding power transfer costs. The second iterative process applies the Lagrangian relaxation at the lower level to represent the TE trading among MGs where Lagrangian multipliers denote TE price signals. Case studies considering the modified IEEE 33-bus show the effective applications of the proposed framework and algorithm.
A Validation Study of a Requirements Engineering Artefact Model for Big Data Software Development Projects

The elicitation, specification, analysis, prioritisation and management of system requirements for large projects are known to be challenging. It involves a number of diverse issues, such as: different types of stakeholders and their needs, relevant application domains, knowing about product and process technologies, regulatory issues, and applicable standards. The advent of “Big Data” and, in turn, the need for software applications involving Big Data, has further complicated requirements engineering (RE). In part, this is due to the lack of clarity in the RE literature and practices on how to treat Big Data and the “V” characteristics in the development of Big Data applications. Traditionally, researchers in the RE field have created domain models that help in understanding the context of the problem, and in supporting communication and analysis in a project. Analogously, for the emerging field of software applications involving Big Data, we propose an empirically derived RE artefact model. It has been validated for qualities such as: accuracy, completeness, usefulness, and generalisability by ten practitioners from Big Data software development projects in industry. The validation results indicate that the model captures the key RE elements and relationships involved in the development of Big Data software applications. The resultant artefact model is anticipated to help in such activities as: requirements elicitation and specification; definition of specific RE processes; customising and creating a common vision in Big Data RE projects; and creating traceability tools linking the artefacts.
Deep Learning-Based Machinery Fault Diagnostics With Domain Adaptation Across Sensors at Different Places

In the recent years, data-driven machinery fault diagnostic methods have been successfully developed, and the tasks where the training and testing data are from the same distribution have been well addressed. However, due to sensor malfunctions, the training and testing data can be collected at different places of machines, resulting in the feature space with significant distribution discrepancy. This challenging issue has received less attention in the current literature, and the existing approaches generally fail in such scenarios. This article proposes a domain adaptation method for machinery fault diagnostics based on deep learning. Adversarial training is introduced for marginal domain fusion, and unsupervised parallel data are explored to achieve conditional distribution alignments with respect to different machine health conditions. Experiments on two rotating machinery datasets are carried out for validations. The results suggest the proposed method is promising to address the fault diagnostic tasks with data from different places of machines, further enhancing applicability of data-driven methods in real industries.
Smart-Monitor: Patient Monitoring System for IoT-Based Healthcare System Using Deep Learning

Automated physiological signal monitoring to elderly sick patient is not only for fast access of data but also to get reliable service by accurate prediction by healthcare service provider. To address this challenge, this research focuses on novel Internet of Things (IoT) application-based physiological signal monitoring system to advance e-healthcare system. For the realization of the proposed system, Deep Neural Network-based accurate Signal Prediction and estimation algorithm was employed. The proposed system is prototyped as an advanced electronics component by using an intelligent sensor for signal measurement, National Instrument myRIO for smart data acquisition. Smart-Monitor is designed with intelligent sensor as the consumer product. To validate the proposed Smart-Monitor system, four physiological signal prediction accuracies for two users were computed. In prototype experimental set-up, an average accuracy of 97.2% was obtained. This shows that the proposed automated system is reliable and accurate monitoring is possible. From the experimental result, we validate the proposed system can provide reliable assist and accurate signal prediction.
Efficient hybrid side‐channel/machine learning attack on XOR PUFs

A novel hybrid side-channel (SC)/machine learning attack is explored in this Letter to leak the confidential information of non-linear physical unclonable functions (PUFs): XOR arbiter PUFs. In the proposed hybrid attack, SC analyses are utilised to pre-process the input challenge of XOR arbiter PUFs to add high correlation among all the input challenge bits. Subsequently, a convolutional neural network (CNN) attack is performed on the correlated input challenge bits to extract the critical feature among the neighbour data to significantly improve its training/testing accuracy. As shown in the results, after applying the SC analyses to add correlation for the input challenge of an XOR arbiter PUF, the training/testing accuracy of the hybrid attack can be boosted over 0.98. In contrast, the training/testing accuracy of a regular CNN attack on the XOR arbiter PUF is around 0.64 due to the lack of the corresponding correlation.