Miracle-dz/UniFAR-FTUs huggingface dataset:
Gadolinium trisilicide − a paramagnetic representative of the YbSi3 type series

Abstract Binary gadolinium trisilicide GdSi3 was prepared by high-pressure high-temperature synthesis at typically 9.5(5) GPa and 870 K before quenching to ambient conditions. At ambient pressure, GdSi3 exhibits an exothermic decomposition at 647(10) K into the thermodynamically stable phases GdSi2–x and Si, indicating its metastable character. Powder X-ray diffraction data is consistent with the YbSi3-type crystal structure comprising slabs of condensed Si2 dumbbells, which enclose layered arrangements of gadolinium cations. Quantum chemical analysis of the chemical bonding shows, that the framework is formed by silicon dumbbells with homopolar bonds. The magnetic moment of 8.13(8) µB is consistent with Gd4f7 (Gd3+ state) and antiferromagnetic ordering is observed below 64(1) K.
Ticks of Alabama: the fauna and spatial distribution of medically important species across the state

ABSTRACT: The last statewide survey of hard ticks in Alabama was in 1972. To address this deficit, we examined the distribution of the medically important species across the state, Ixodes scapularis (Say), Dermacentor variabilis (Say), Amblyomma americanum (L.), and A. maculatum (Koch), between April, 2018 and February, 2021. Collections primarily involved dragging (April to July) and examination of harvested deer (November to February). A total of 2,927 ticks was collected from 110 sites; three species, I. scapularis, A. americanum, and D. variabilis, represented 91.70% of all ticks collected. Amblyomma americanum and D. variabilis were the most common species encountered in drags; I. scapularis dominated deer collections. Dermacentor variabilis was never found on deer, whereas D. albipictus was only found on deer. Stepwise regression (AIC) of drag data was linked to several site variables. Results suggest a linear response along a south (low abundance) to north (high abundance) gradient, in addition to increased abundance at sites with lower temperatures and greater precipitation and canopy cover.
Same, Same, but Dissimilar: Exploring Measurements for Workload Time-series Similarity

Benchmarking is a core element in the toolbox of most systems researchers and is used for analyzing, comparing, and validating complex systems. In the quest for reliable benchmark results, a consensus has formed that a significant experiment must be based on multiple runs. To interpret these runs, mean and standard deviation are often used. In case of experiments where each run produces a time series, applying and comparing the mean is not easily applicable and not necessarily statistically sound. Such an approach ignores the possibility of significant differences between runs with a similar average. In order to verify this hypothesis, we conducted a survey of 1,112 publications of selected performance engineering and systems conferences canvassing open data sets from performance experiments. The identified 3 data sets purely rely on average and standard deviation. Therefore, we propose a novel analysis approach based on similarity analysis to enhance the reliability of performance evaluations. Our approach evaluates 12 (dis-)similarity measures with respect to their applicability in analysing performance measurements and identifies four suitable similarity measures. We validate our approach by demonstrating the increase in reliability for the data sets found in the survey.
Extracting information from X-ray diffraction patterns containing Laue oscillations

Abstract The presence of Laue oscillations in a film grown on a solid surface is broadly taken as indicating a high quality, crystallographically aligned film of the targeted compound. In this paper we briefly review the origins of both Laue oscillations and Kiessig fringes and show how they can be used together to determine if extra thickness exists above or below the coherently diffracting domains. The differences between experimental and “ideal” films are discussed and the effect of structural features (roughness, different thickness coherently diffracting domains and thickness in addition to the coherently diffracting domains) are illustrated with experimental and simulated data for metal and mixed-metal chalcogenide films of titanium, bismuth, vanadium/iron, and bismuth/molybdenum. Examples are given showing how quantitative information can be extracted from experimental diffraction patterns.
Interdecadal change of South China Sea summer monsoon intensity in late 1990s: Interdecadal change of SCS summer monsoon

This study analysed the interdecadal variation of the South China Sea summer (June–September) monsoon (SCSSM) in recent 32 years (1979–2010). The SCSSM showed a strengthening interdecadal variability starting from 1995, which was also verified in time series of precipitation and precipitable water averaged over the SCSSM domain. Thus, 32 years of total analysis period was divided into two: first half of past 16 years (1979–1994, hereafter 7994) and recent 16 years (1995–2010, hereafter 9510) thereby analysing a mean difference between 9510 and 7994 periods to determine causes of the strengthened SCSSM since the mid-1990s. The spatial distribution of difference in outgoing longwave radiation, precipitable water, and precipitation between the two periods showed a typical La Niña pattern. The pattern which occurred during La Niña event has been verified through analysis on differences in 850and 200-hPa stream flows between the two periods. At 850 hPa, anomalous huge anticyclones were strengthened in the both hemispheres so that anomalous easterlies were strengthened in the equatorial central and western Pacific. As a result, the SCS was affected by anomalous southeasterlies. On the contrary, anomalous huge cyclones were strengthened in the both hemispheres at 200 hPa so that anomalous westerlies were strengthened in the equatorial western and central Pacific. This means that rising air from the tropical western Pacific since the mid-1990s was moved to the east and then Walker circulation, which was descended at the tropical central and eastern Pacific, was strengthened. Due to the strengthened Walker circulation, La Niña events have been more dominant than before in recent years and as a result of this, SCSSM was more strengthened than before.
experiment consisted in encoding three synchronized binary waveforms of 1549.32, 1554.13, and 1558.98 nm in a single eight-level signal of 1564.68 nm. In order to obtain an encoded signal with eight equidistant power levels keeping a linear rela-

A modified corrugated balanced antipodal Vivaldi antenna with director (CBAVA-D) is presented for ultrawideband (UWB) applications in this article. The main features of the antenna, besides an UWB width are high gain, low cross-polarization, and excellent group delay. The new antenna concept, including the hybrid structure of exponential tapered corrugation edge and curving dielectric director with high-dielectric constant is demonstrated, based on simulation and measurement results in both frequency and time domain. The antenna prototype input match is better than 10 dB, the maximal gain of 12.6 dB and the cross-polarization level less than 30 dB in the main beam direction at 1.5–15 GHz band are achieved. In the time domain measured group delay with variation less than 60.25 ns in the whole operating band is also obtained and exhibited.VC 2013 Wiley Periodicals, Inc. Microwave Opt Technol Lett 55:1321–1325, 2013; View this article online at wileyonlinelibrary.com. DOI 10.1002/mop.27558
Effects of Sports Public Goods on the Health of the Elderly: Empirical Evidence from China

ABSTRACT Sports public service can promote the elderly to participate in sport exercises and have a positive impact on health of the elderly. The aim of this study was to identify the effect of sport public service on the health and to study the meditation effect of physical activity. We conducted a questionnaire survey on sports public service and the health of the elderly in China, and established a mediation effect model to measure the impact of five categories of sports public service on the health of the elderly. The final sample comprised 1089 older adults (548 males and 541 females). Cronbach’s α was over 0.7. Sport public service have positive effect on the health of the elderly, but they have meditation effect on both frequency and duration of physical exercise of the elderly. Among which the instruction of physical activity and service of sport exercises are the most significant factors. Sports public service for the elderly in China are not well balanced both in regions and among people, with more goods in towns and cities but less in rural areas. This study clarifies the impact of sports public service on the health of the elderly, and empirically analyses its meditation effect on physical exercise of the elderly. It is emphasized that the provision of sports public service in China is not to build sports facilities, but to provide more instruction and sports public services for residents physical exercise.
Single-Ended-to-Balanced Rat-Race Coupler With Wideband Common-Mode Suppression

This brief presents a novel design methodology for a single-ended-to-balanced (SETB) rat-race coupler. The proposed coupler relies on the slotted-microstrip star-junction; it exploits the characteristics of the slotline to form differential ports with high common-mode rejection ratio (CMRR), and wide common-mode (CM) and mode conversion rejection fractional bandwidths (FBWs), it benefits from the junction to realize miniaturized transmission lines (TLs), and, finally, it provides the single-ended signal through the microstrip ports. The proposed rat-race coupler has 32.5% and 31% 15-dB return loss (RL)/isolation and 3-dB FBWs, respectively, $0.45\boldsymbol{\lambda } _{g} \mathbf {\times } 0.5\boldsymbol{\lambda } _{g}$ size, and 0.6 dB insertion loss (IL). The CMRR exceeds 40 dB at the operating frequency, while the CM as well as the mode conversion rejection FBWs exceed 375%. Measurements, which are in good agreement with EM and circuit simulations, confirm the theoretical predictions.
A Fault Section Location Method for Distribution Network Based on Discrete Bayesian Network Model

Multi-line distribution networks that are composed by cables and overhead lines have the characteristic of high structure complexity, high equipment diversity and strong environmental interference, which requires a robustness fault section location (FSL) algorithm. In this paper, an approach based on the discrete Bayesian network for distribution network FSL is proposed. Based on the data collected by remote terminal unit (RTU) installed on distribution lines and feeders, the influence between line section faults and the data collected by RTUs is analyzed. Then, the discrete Bayesian network for FSL is constructed. The structure of the discrete Bayesian network is determined by the physical model of the distribution network, and the parameters are obtained by the maximum expectation algorithm from RTU measurements. Based on which, the belief propagation algorithm is utilized to the inference even under situations when there are only partial data available. Finally, the experiments executed on Pgmpy verify the validity and robustness of the proposed discrete Bayesian network.
Short-term Wind Power Prediction Method Based on TCN-GRU Combined Model

The target of carbon peak and carbon neutralization puts forward higher requirements for the accuracy of wind power prediction. In order to accurately predict the wind power timing, a short-term wind power prediction method based on the hybrid neural network model of temporal convolution network (TCN) and gate recurrent unit (GRU) was proposed. TCN was used to extract the sequential features of wind power time series and unidirectional space features, and GRU was used to further extract the sequential features of wind power series. The two models were trained together. The wind power timing series of a single fan and a single wind farm were collected respectively. After the pre-processing, the corresponding data sets were constructed for modeling respectively to verify the reliability of the proposed method. The results show that compared with the traditional model, this method has lower sensitivity to the time window, higher accuracy, and has certain engineering application value.
Galvannealed sheet steel with excellent press formability

Galvannealed steel sheet for exposed panels of automotives is required to exhibit the excellent mechanical properties and the superior coating adhesion. The former properties can be accomplished by the usage of ultra low carbon steel added with small amount of Ti and Nb as substrate. Surface defects such as streak are eliminated by the decrease in Ti content of substrate. On the other hand, the phase r deteriorates adhesion of galvannealed coating and causes pimple defects. This phase is suppressed through the precise control of Fe content in the coating which accompanies the optimization of Al content in the galvanizing bath and galvannealing temperature.
Power System Inertia Estimation Based on Long Short Term Memory Network

With the increasingly high penetration of renewable energy into the power system, traditional synchronous generators are gradually substituted by power-electronic-converter-based sources that commonly lack inertia support capability. Since deficient system inertia could cause larger frequency deviation under the same disturbance, commanding the inertia level of the system is beneficial and necessary for the operators to take precautions and ensure stable power supply. This paper puts forward a long short-term memory (LSTM) based inertia estimation method. Through massive training, the LSTM network successfully constructs the relationship between the system inertia and the system frequency sequence as well as the unbalanced power. The hyperparameters of the proposed deep learning model can be determined by the classic grid search. Case studies on the IEEE-39 system verify that the LSTM network can well handle the time-varying sequence and the proposed method can accurately estimate the system inertia.
Gamification Aspects in Detail: Collectanea of Studies to Renew Traditional Education

This work’s objective is to present the results of a systematic mapping that explored gamification in education to find good practices and lessons learned. There were identified a set of positive and negative aspects and tools to be reused by who wishes to use gamification to motivate students and to verify the effects of this technique. The results indicated the best practices and showed that, despite being very recent, gamification has already gained attention in many countries. In addition, gamification is overall effective and well received in education, attempting to the importance of planning the gamification design, the dynamic between groups and the participation of the teacher in the motivation and engagement of students.
Design of Demand Side Management Mechanism for Guangdong Province

As the largest power consumption province in China, the introduction of demand response mechanism in Guangdong Province is not perfect. With the increasing pressure of load peak shaving, it is particularly important to formulate a appropriate demand side manage scheme. Firstly, we study the general situation of power generation side and user side in Guangdong Province and analyze the corresponding potential of demand side response. Secondly, a win-win framework of demand side management is formulated to maximize the interests of power grid companies, demand response providers and the government. Finally, under the proposed framework, the effects of incentive-based demand response and price-based demand response are studied respectively.
Influence of Discrete and Continuous Action Spaces on Deep Reinforcement Learning-Based Pricing Strategy Optimization for Electricity Retailers

The pricing strategy optimization problem becomes important for electricity retailers in electricity market. Deep reinforcement learning (DRL) has been applied to solve the strategic decision-making problems in electricity market area. However, the influence of discrete and continuous action spaces on optimization results by using DRL-based methods to solve for optimal retail price is unknown. This paper applies two different DRL-based retail pricing strategies through deep Q network (DQN) and deep deterministic policy gradient (DDPG) for the electricity retailers. An in-depth comparative analysis between DQN and DDPG is conducted in terms of convergence and computational performance. The numerical results of optimal retail prices and responding loads show the influence of discrete and continuous actions space on optimization effect.
Detecting Botnet Victims Through Graph-Based Machine Learning

Botnets are one of the most devastating cybersecurity threats to modern organisations. A botnet is a distributed network of compromised devices that is leveraged to perform various malicious operations over the internet. The recent proliferation of unabated botnet activity has necessitated the investigation of novel botnet detection strategies. In this paper, we introduce BiSAGE; a graph-based machine learning technique capable of detecting the compromised hosts (bot victims) operating on a network. The advantage of our approach is that a bot victim can be detected not only through its actions but also through the actions of the devices it communicates with; an intrinsic characteristic of botnet activity. We provide an empirical evaluation of BiSAGE on CSE-CIC-IDS2018; a comprehensive dataset for evaluating intrusion detection systems. We show that BiSAGE is able to accurately identify bot victims without requiring any labelled samples of botnet activity.
Anomaly abduction reasoning of regional power flow operating index based on convergence cross mapping

Aiming at the problem that it is impossible to find out the abnormal reasons scientifically and intelligently when the power grid post-dispatch evaluation index has abnormal trend, this paper takes the regional power flow operation index as an example, and carries out the abducting reasoning research on the abnormal index based on convergence cross mapping method. Firstly, the regional power flow operation index and the corresponding correlation factor set are given. Then, the convergence cross mapping method is used to conduct causal analysis on regional power flow operation indicators and related factors, and the direction to solve the problem of abnormal regional power flow operation indicators is found out from the relevant indicators, so as to provide suggestions for optimizing the scheduling operation strategy. In the end, the proposed method is applied to IEEE39 node simulation network, and the results show that insufficient rotation reserve rate is an important reason for abnormal regional power flow operation index.
Air Quality Forecasting using decision trees algorithms

Forecasting air pollution is crucial as it not only affects the physical health of people but also provides guidance for pollution control. Particulate Matter with a diameter of less than $2.5\mu m$ (PM2.5) is one of the major contributors to air pollution which can cause acute and chronic effects on human health. Forecasting PM2.5 involves various meteorological factors as well as the influence of historical data. Therefore, the prediction of the surface PM2.5 concentration is of great importance for the protection of human health. This study uses machine learning models, namely Decision Tree (DT), Random Forest (RF), and Gradient Boosting Regressor (GBR) to predict PM2.5 hourly scale concentrations, using meteorological data and PM2.5 concentrations from adjacent stations. The dataset was collected in the city of Beijing in China as a study region. The experiments have shown that the gradient boosting regressor model achieves higher predictive precision than the other models proposed for estimating hourly PM2.5 concentrations with an $R^{2}$ value varying between 0.9 and 0.97. This work offers a promising and affordable approach to robustly predict PM2.5 concentrations.
Intelligent Detection Technology of Infrared Image of Substation Equipment Based on Deep Learning Algorithm

The safe and reliable operation of power equipment is the basis to ensure the reliability of power supply. The infrared detection technology of operating equipment has the advantages of no power failure, no contact, high sensitivity and mature technology, which can find equipment defects in time and effectively. However, the conventional manual inspection for infrared detection has the problems of large workload, long time-consuming and low efficiency, which restricts the large-scale popularization and application. Based on the above background, this paper constructs an accurate recognition model of substation equipment infrared image based on improved YOLOv3 algorithm, realizes the automatic real-time positioning of infrared image target image, and puts forward a complete fault diagnosis scheme for current and voltage overheating defects. The field test results show that the infrared image intelligent detection technology based on deep learning algorithm proposed in this paper can effectively realize the fault diagnosis of heating defects of substation equipment. This technology improves the state control ability of substation equipment, which is of great significance to promote the safe and stable operation of substation and improve the reliability of power supply.
Optimal day-ahead operation strategy of an electricity-hydrogen integrated energy system considering extra-day forecast information

In the context of global low-carbon development, hydrogen energy has become the focal point, and its integration with electric energy is deepening. This paper proposes an optimal day-ahead operation strategy for an electricity-hydrogen integrated energy system (E-H IES) using extra-day forecast information. HES is optimized with a more flexible way to realize inter-day energy transfer based on the different energy storage characteristics of battery energy storage (BES) and hydrogen energy storage (HES). A mixed-integer linear programming (MILP) problem is formulated to minimize the comprehensive costs including the electricity purchase and sale cost, battery lifetime depreciation cost, carbon emission cost, and correction and adjustment cost under different probability scenarios. Simulation results show that the long-cycle storage characteristics of HES are more significant, the overall system economy and battery lifetime are improved, and the abandoned renewable energy is greatly reduced.
A new multi-band fractal antenna using a triangular measured on the 1GHz to 6GHz band

This article examines the many properties of a new fractal antenna design based on triangular fractal geometry. The suggested antenna is built on a cheap FR4 substrate with a relative permittivity of 4.4, a thickness of 1.6mm, and a dielectric loss tangent of 0.02. The High Frequency Structure Simulator is used to design and simulate the antenna (HFSS). The basic structure of the designed antenna is optimized by applying a 50 Ω impedance microstrip line feed technique to achieve the high value of return gain and loss. The simulated results of the proposed antenna reveal that the antenna operates on five operating frequency bands 1.84GHz, 2.77GHz, 2.94GHz, 4.33GHz and 5.79GHz of gain which can reach 6.01db and a VSWR which varies between 1 and 2 in the operating bands. The antenna is built and then tested with a VNA (Vector Network Analyzer). The experimental and simulated results are very close to each other.