Miracle-dz/UniFAR-FTUs huggingface dataset:
Techno-Economic Optimization of the Sizing of Large-Scale Linear Photovoltaic Systems

Basic Energy generation using solar photovoltaic requires large area. As cost of the land is growing day by day, there is a strong requirement to use the available land as efficiently as possible. The objective of this paper is to propose a new solution to install high-power photovoltaic (PV) systems in order to take advantage of linear surfaces, extending from hundreds of meters to tens, or even hundreds of kilometers, and only a few meters or few tens of meters in width or height such as cycle paths, motorway edges, railways, river dikes, etc. This system is called large-scale linear PV system. The possibilities and opportunities of these new kinds of large-scale PV plants are infinite.The work of this paper consists of defining and optimizing the electrical architecture of these large-scale innovative linear photovoltaic systems. We first presented a simplified modeling tool to meet both the precision and the speed criteria for optimization studies. Then, the technical and economic optimization of large linear PV systems was subsequently studied by optimizing the LCOE of different possible electrical architectures of this type of PV system.
Enhanced Frequency Adaptive Demodulation Technique for Grid-Connected Converters

This article presents an enhanced frequency adaptive demodulation technique for grid-synchronization of grid-connected converters in variable frequency condition. Demodulation works by generating demodulated voltages which contain undesired double frequency components. As a result, high-order low-pass filters with high cutoff frequency are required to eliminate the undesired components. This reduces the dynamic performance. Frequency adaptive demodulation technique enhances the dynamic performance by rejecting the double frequency components as opposed to filtering, however, at the cost of additional computational complexity. This article overcomes this problem by using double demodulation without recreating the double frequency component for rejection purposes. This reduces the computational complexity significantly. Suitability of the proposed method is verified through numerical simulation and experimental study. Comparative study with existing frequency adaptive demodulation and second-order generalized integrator phase-locked loop techniques demonstrate the validity and performance improvement by the proposed technique.
Price Forecast Methodologies Comparison for Microgrid Control with Multi-Agent Systems

Multi-Agent systems offer a way to control distributed generation in microgrids, reliability and cost minimisation capabilities can be improved by price forecast methodologies that can be deployed without the need of external control signals. This paper presents and compares two suitable electricity price forecast methodologies for use in distributed control of Microgrids’ resources using Multi-Agents: Markov Chain Monte Carlo simulations with heuristic and numerical optimisation and price prediction with Non-linear Auto Regressive Artificial Neural Networks with different internal architectures. The methods are evaluated using MAPE and RMSE functions for the UK electricity market data. It was found that the proposed heuristic model has less error than the Neural Networks only when the price data contains outliers.
QoS-Forecasting-Based Intelligent Flow-Control Scheme for Multi-Connectivity in 5G Heterogeneous Networks

In the fifth-generation (5G) wireless-network system, the convergence of multiple networks of different standards as well as that of high- and low-frequency networks exists since a long time. Owing to the inability of 5G networks to predict the user quality of service (QoS) accurately, these networks are prone to issues such as access congestion, low QoS, and frequent congestion in one network while other network resources remain idle. Therefore, 5G networks fail to meet the QoS requirements and also prevent effective resource utilization. The deployment of multi-connectivity technologies can facilitate the optimization of the multinetwork convergence-system architecture. However, such technologies are faced with several challenges. Existing literature mainly focuses on the development of a multi-connectivity flow-control scheme to determine the secondary nodes (SNs) to which the master node (MN) should distribute data. This paper presents a three-step, QoS-forecasting, intelligent flow-control scheme, wherein the user equipment (UE) determines the data-flow direction based on the network characteristics and load handled by each node. Subsequently, the MN determines the initial user priority based on load balancing, user characteristics, and fairness. Finally, the MN allocates data to each SN in accordance with the QoS and average transmission capability of UE. The simulation results reveal that the proposed algorithm improves the system throughput significantly compared to the single connectivity and traditional fixed-data-split methods. Furthermore, the proposed method facilitates transmission-delay reduction, radio-link failure-probability control, and improved system robustness.
Exploratory analysis of the fire statistics using automatic time series decomposition

The aim of the work was to determine the possibility of using software based on machine learning technologies at the stage of exploratory analysis of data on the situation with fires in settlements, to assess the possibility of using the results obtained earlier by other researchers. Exploratory analysis of data and comparative assessment with results from other sources was carried out.
Design and Implementation of Vehicle Speed Recorder using GPS Tracker and Internet-of-Things Platform

The number of traffic accidents in Indonesia is still relatively high. According to the official data, there were 107,500 cases of accidents in 2019. The accident was caused by various factors, including human factors as much as 61%. This indicates the driver’s lack of awareness to comply with traffic regulations such as the maximum speed limit for vehicles. Based on these conditions, it is necessary to make a device that can observe the speed of the vehicle, so that the driver does not exceed the maximum speed limit of the vehicle on the highway. This paper describes on a prototype of the vehicle speed recorder using GPS tracker and Internet-of-Things (IoT) platform. GPS tracker determines the location of a vehicle any time. The speed of a vehicle is computed by using Arduino microcontroller and SIM808 module. Furthermore, the location and speed of a vehicle is sent to a dedicated webserver such that the IoT platform so called Adafruit IO could proceed and display how much the speed of a vehicle being traveled and its location in the real time. Experimental results show that the prototype of vehicle speed recorder can display a location with high accuracy and can render an average speed discrepancy of 3.9 kph during travelling at a certain route. The discrepancy between the value of speed and the location of a vehicle occurs due to the delay in sending latitude and longitude coordinate data from the SIM808 module to the IoT platform.
Identification of Tomato Maturity Based on HIS Color Space Using The K-Nearest Neighbour Method

Post-harvest activities, namely sorting and grading, are usually done by the visual method by looking at the color or the size of the fruit. Technological advancements by using computer assistance make harvesting and detecting the ripening process of tomatoes easier. The purpose of this research is to apply the Hue, Intensity, Saturation (HIS) color transformation method to detect the ripeness of vegetable tomatoes and determine the accuracy of the HIS and Red, Green, Blue (RGB) methods. In this research, some samples of tomatoes' RGB values were taken through image processing by ripening level, between unripe, under-ripe, and ripe. After the RGB value is obtained, it will be converted into HIS using means, variance, and range color feature extraction. In classifying this research using the K-Nearest Neighbour (KNN) and Support Vector Machine (SVM) method. With the results, the accuracy of the KNN method is better than SVM with an average of 87.80% and 74.38%. The result of this research indicates that the more accurate method is HIS with an average of 89.65% compared to the RGB method with an average of 89.35% and the CIElab method with an average of 84.41%. This is because it is easier for HIS to identify objects with different hues by providing a threshold value in the range of hue values (spectral wavelengths) that surround the object.
Development of a dynamic Combined Heat and Power Plant and Flywheel Energy Storage System Model validated with Field Tests

The current changes in the power system, caused by the migration from conventional to distributed energy resources, lead to a lower resilience of the future power grid and increase the risk of instabilities and blackouts. However, distributed generation also offers new possibilities such as providing fast frequency response or the operation of island grids. To research novel control concepts, it is necessary to consider dynamic simulation models. For this purpose, dynamic models of distributed energy resources must be evaluated to represent their real behaviour. This includes the control system of these units as well as its most important electrical and mechanical components that are influencing the power output. Because of their particular relevance and high suitability in future power grids, this paper introduces dynamic models of a CHP plant and a FESS, which are validated with field tests that are conducted in an industrial (island) grid.
A Survey and Quantitative Evaluation of Integrated Circuit-Based Antenna Interfaces and Self-Interference Cancellers for Full-Duplex

Full-duplex (FD) wireless – simultaneous transmission and reception on the same frequency – is a promising technique that can significantly improve spectrum efficiency and reduce communication latency in future wireless networks. To enable FD operation, the powerful self-interference (SI) signal leaking from the transmitter (TX) into the receiver (RX) needs to be suppressed and canceled to an extreme degree at the antenna interface as well as in the RF/analog and digital domains. Various approaches to achieve TX-RX isolation at the antenna interface and SI cancellation (SIC) in the RF/analog and digital domains have been demonstrated, with a focus on using bench-top off-the-shelf components. Recent advances in integrated circuit (IC) implementations of various types of shared antenna interfaces and cancellers in the RF and/or analog baseband (BB) domains have further pushed the frontier of realizing FD wireless in small-form-factor/hand-held devices. Moreover, it is still important to fundamentally study the SIC performance that can be achieved by different types of cancellers. In this paper, we present a first-of-its-kind comprehensive overview on the implementation and comparison of various state-of-the-art integrated shared antenna interfaces and SI cancellers in both the RF and analog BB domains. We define two figures of merit (FOM) for the IC-based shared antenna interfaces and SI cancellers, which capture various design considerations and performance tradeoffs including the achievable isolation/SIC, bandwidth, noise figure degradation, TX/SI power handling, and power consumption. In particular, we focus on two types of integrated shared antenna interfaces including electrical-balance duplexers and circulators. We also discuss two types of SI cancellers based on the time-domain and frequency-domain approaches, which respectively employ parallel delay lines and bandpass filters to emulate the SI channel. Based on realistic measurements, we perform extensive numerical evaluations to illustrate and compare the achievable RF SIC performance in different scenarios, as well as discuss various design tradeoffs. Finally, we provide an overview of the IC-based implementations and performance evaluations of both types of cancellers based on our recent work.
Biogas Plant Operation under Distribution Locational Marginal Prices

With the omission of subsidy schemes, Biogas Plants (BPs) in Germany are facing an uncertain future. However, due to their ability to provide flexible renewable energy, they could play a major role in the future power system, in which the share of uncontrollable energy resources is expected to grow further. This paper proposes a flexible BP operation under Distribution Locational Marginal Prices (DLMPs) by formulating linear approximate BP and electric grid models followed by the derivation and decomposition of DLMPs from the optimal operation problem. The model behavior is demonstrated in a test case on a radially operated distribution grid with fixed loads and one BP. It is shown that the BP optimally adapts its feeding regime and storage usage based on the DLMP price signals consistent with grid conditions.
Predictive Control in LV Networks: A 3-Stage Approach for Smart Sustainable Buildings

Through its environmental targets and policies, the E.U. is pushing for the integration of residential distributed energy resources (DERs) and smart sustainable buildings (SSBs). Originally passive low voltage (LV) networks will face fundamental challenges in trying to cope with said strategy. Achieving proper controllability is not easy; DER forecasting uncertainty is high, and distribution system operators (DSOs) rarely own flexible devices. The low carbon footprint of SSBs should also not be sacrificed in the pursuit of mitigating technical issues. We propose a 3-stage, LV network constraints management approach, based on model predictive control (MPC). In Stage 1, the DSO transmits MPC-driven optimal set-points to each SSB by solving a multi-phase, multi-period AC optimal power flow (AC-OPF). In Stage 2, each SSB performs its own MPC-driven optimization (quadratic programming), trying to maximize its compliance without increasing its environmental impact (net consumption). In Stage 3, the missing service (non-compliance cases) is automatically re-assigned among compliant SSBs, without DSO involvement. The approach is validated on a 3-phase (3Φ), 4-wire LV feeder, with high penetrations of DERs and SSBs. A comprehensive performance analysis is performed, regarding costs, horizon choice and alternative solution approaches.
A Survey on Centrality Metrics and Their Network Resilience Analysis

Centrality metrics have been studied in the network science research. They have been used in various networks, such as communication, social, biological, geographic, or contact networks under different disciplines. In particular, centrality metrics have been used in order to study and analyze targeted attack behaviors and investigated their effect on network resilience. Although a rich volume of centrality metrics has been developed from 1940s, only some centrality metrics (e.g., degree, betweenness, or cluster coefficient) have been commonly in use. This paper aims to introduce various existing centrality metrics and discusses their applicabilities in various networks. In addition, we conducted extensive simulation study in order to demonstrate and analyze the network resilience of targeted attacks using the surveyed centrality metrics under four real network topologies. We also discussed algorithmic complexity of centrality metrics surveyed in this work. Through the extensive experiments and discussions of the surveyed centrality metrics, we encourage their use in solving various computing and engineering problems in networks.
Topological Stability Analysis of High Renewable Penetrated Systems using Graph Metrics

This paper investigates the stability of high renewable penetrated power systems with an emphasis on grid structure. With both system dynamics and network topology taken into consideration, the influence of the increasing penetration of renewable resources to system stability is explored when the synchronous generators are gradually replaced by doubly fed induction generators (DFIGs). By virtue of a newly provided game-theoretic approach - SHAP (SHapley Additive exPlanations) values - the relationship between system small-signal stability and topological graph metrics is studied. Further, numerous statistical experiments are conducted on the available testing systems, including the standard IEEE test systems as well as the newly published synthetic networks with a wide variety of topologies. The results provide guidance on the further development of network growth models and control methods.
Robust Character Labeling in Movie Videos: Data Resources and Self-Supervised Feature Adaptation

Robust face clustering is a vital step in enabling computational understanding of visual character portrayal in media. Face clustering for long-form content is challenging because of variations in appearance and lack of supporting large-scale labeled data. Our work in this paper focuses on two key aspects of this problem: the lack of domain-specific training or benchmark datasets, and adapting face embeddings learned on web images to long-form content, specifically movies. First, we present a dataset of over 169000 face tracks curated from 240 Hollywood movies with weak labels on whether a pair of face tracks belong to the same or a different character. We propose an offline algorithm based on nearest-neighbor search in the embedding space to mine hard-examples from these tracks. We then investigate triplet-loss and multiview correlation-based methods for adapting face embeddings to hard-examples. Our experimental results highlight the usefulness of weakly labeled data for domain-specific feature adaptation. Overall, we find that multiview correlation-based adaptation yields more discriminative and robust face embeddings. Its performance on downstream face verification and clustering tasks is comparable to that of the state-of-the-art results in this domain. We also present the SAIL-Movie Character Benchmark corpus developed to augment existing benchmarks. It consists of racially diverse actors and provides face-quality labels for subsequent error analysis. We hope that the large-scale datasets developed in this work can further advance automatic character labeling in videos. All resources are available freely at https://sail.usc. edu/~ccmi/multiface.
Landfill runoff water and landfill leachate discharge and treatment

The continuously increasing requirements for environmental protection create problems for many countries in treating landfill runoff water. In the present paper, the creation, chemical composition and treatment possibilities for leachate in 5 Estonian landfills are studied. Generally, the younger the landfill, the higher concentrations of pollutants. In the Vaatsa landfill, the landfill runoff water flow rate has been measured up to 150 m3/d, average flow (Qaverage) = 10–20 m3/d. The composition of pollutants in the leachate depends on the character of the deposited waste material and on the age of the landfill. In Vaatsa landfill, the leachate biochemical oxygen demand (BOD7) is in the limits of 300–960 mgO2/l, in Uikala landfill the leachate BOD7 is in the limits of 231–1750 mgO2/l, but in Tallinn landfill it can reach up to 5500 mgO2/l, chemical oxygen demand (COD) is 580–2390 mgO/l and 9100 mgO/l correspondingly. Ammonia nitrogen in Vaatsa is between 50–330 mgN/l, in Uikala it is between 427–729 mgN/l, but it can reach up to 970 mgN/l in Tallinn. Total nitrogen (Ntot) in Vaatsa is 130–470 mgN/l and 1335 mgN/l correspondingly, and in Uikala 564–1567 mgN/l. The content of heavy metals is relatively low, caused by the high content of sulphates (500 – 700 mg/l), causing rapid sedimentation of heavy metals from the leachate. In the older landfills, the efficiency of nanofiltration (NF) and reverse osmosis (RO) were investigated, and the rather good results were obtained for BOD7 and COD. After RO treatment, the results were as follows: COD of 57 mgO/l, BOD7 of 35 mgO2/l. RO was able to reduce COD and BOD7 of biologically treated leachate by 97.9% and 93.2%. NF reduced 98 and 41% of leachate COD and BOD7. Neither RO nor NF was able to reduce the total nitrogen to the required discharge limit of 15 mg/l.
The Construction of Support Vector Machine Classifier Using the Artificial Bee Colony Algorithm for Multi-Classifications of Ultrasonic Supraspinatus Images

The setting of parameters in the support vector machines (SVM) is very important with regard to its accuracy and efficiency. In this paper, we employ the firefly algorithm to train all parameters of the SVM simultaneously, including the penalty parameter, smoothness parameter and Lagrangian multiplier. The proposed method is called the artificial bee colony-based SVM (ABC-SVM) classifier. The ABC-SVM classifier is not considered with the feature selection because the SVM together with feature selection is not suitable for being applied to a multi-class classification problem, especially for the one-against-all multi-class SVM. The experiments are on the multi-class diagnosis of ultrasonic supraspinatus images. The experimental results demonstrated that the use of ABC-SVM classifier in classifying the patterns has maximum accuracy compared with LIBSVM.
Total Quality Management and Innovation in Nursing Service Organizations

Organizations require providing high quality and innovative services to compete well and enhance their competitive advantage. The aim of this review was to discuss the literature regarding the total quality management and Innovation in service organizations. The electronic search was carried out in the Google Scholar, Pub Med and MEDLINE databases. The typical manufacturing organizations are more possible to attain better performance in the term of employee relations, consumer satisfaction, business performance and operational performance by means of total quality management. Different studies were carried out on the subject of total quality management and innovation. The association connecting of total quality management practices and the innovation performance is unclear. This review offers scientific evidence about effective use of total quality management and confirmed its applicability on the health care sector. The nurse manager is a major facilitator of total quality management programs. The total quality management implementation in hospital needs awareness about the quality management, preparation and development of both framework and customer awareness.
Thermal Biology and Physiology in Clinical Hyperthermia: Current Status and Future Needs1

The current status and needs of thermal biology and physiol ogy as related to clinical hyperthermia are summarized. Empha sis is placed on heat-induced modification of blood flow and microenvironment in tissues, on the biological effects of heat and X-rays, and on the relationship between drug resistance, heat resistance, and thermotolerance in thermochemotherapy. Re sults from recent studies investigating the relationships between thermotolerance and heat shock proteins in tissue culture cell lines, in rodent tumors, and in normal tissues are presented. These data strongly suggest that the levels of M, 70,000 heat shock protein can be used as an assay to predict the thermal sensitivity of tissues during fractionated hyperthermia.