bluuebunny/crossref_metadata_2025_split huggingface dataset:
Tungsten and molybdenum oxide nanostructures: two-dimensional layers and nanoclusters
Authors: Surnev Svetlozar , Netzer Falko P 
Published: January 2022

Abstract
W- and Mo-oxides form an interesting class of materials, featuring structural complexities, stoichiometric flexibility, and versatile physical and chemical properties that render them attractive for many applications in diverse fields of nanotechnologies. In nanostructured form, novel properties and functionalities emerge as a result of quantum size and confinement effects. In this topical review, W- and Mo-oxide nanosystems are examined with particular emphasis on two-dimensional (2D) layers and small molecular-type clusters. We focus on the epitaxial growth of 2D layers on metal single crystal surfaces and investigate their novel geometries and structures by a surface science approach. The coupling between the oxide overlayer and the metal substrate surface is a decisive element in the formation of the oxide structures and interfacial strain and charge transfer are shown to determine the lowest energy structures. Atomic structure models as determined by density functional theory (DFT) simulations are reported and discussed for various interface situations, with strong and weak coupling. Free-standing (quasi-)2D oxide layers, so-called oxide nanosheets, are attracting a growing interest recently in the applied research community because of their easy synthesis via wet-chemical routes. Although they consist typically of several atomic layers thick—not always homogeneous—platelet systems, their quasi-2D character induces a number of features that make them attractive for optoelectronic, sensor or biotechnological device applications. A brief account of recently published preparation procedures of W- and Mo-oxide nanosheets and some prototypical examples of proof of concept applications are reported here. (MO3)3 (M = W, Mo) clusters can be generated in the gas phase in nearly monodisperse form by a simple vacuum sublimation technique. These clusters, interesting molecular-type structures by their own account, can be deposited on a solid surface in a controlled way and be condensed into 2D W- and Mo-oxide layers; solid-state chemical reactions with pre-deposited surface oxide layers to form 2D ternary oxide compounds (tungstates, molybdates) have also been reported. The clusters have been proposed as model systems for molecular studies of reactive centres in catalytic reactions. Studies of the catalysis of (MO3)3 clusters in unsupported and supported forms, using the conversion of alcohols as model reactions, are discussed. Finally, we close with a brief outlook of future perspectives.
ADMIRA project: teaching particle physics at high school with Timepix detectors
Authors: Parcerisas D , Ballabriga R , Amorós E , Argudo A , Campbell M , Casas L , Christodoulou P , Colomé R , Corrons D , Curcó V , Enajas M , Granja C , Grauges E , Gou A , Lleó E , Llopart X , Pallares E , Pino H , Serra S , Valero G 
Published: January 2022

Abstract
This paper presents the case for how students can be helped to increase their scientific vocation by experimental work and the introduction of particle physics into pre-university studies. These two ideas are the two main lines of work of the ADMIRA initiative, which has been created by individuals belonging to different and complementary educational and research institutions. The initiative consists of a network of schools that share Minipix devices, a readout system for the Timepix detector designed at CERN in the framework of the Medipix2 Collaboration. The schools receive logistical and technical support from the local University (the University of Barcelona) and from CERN. The centres involved and the objectives, as well as the characteristics of the project are presented. The technical and didactic materials available are also shown. Finally, some initiative data has been analysed to show its rapid growth, and some students’ research is presented showing the diversity of tasks that can be done with such detectors.
A polarization-insensitive, wide-angle dual-band tunable graphene metamaterial perfect absorber with T-shaped strips and square ring
Authors: Yuan Yujiao , Qi Yunping , Zhang Baohe , Ding Jinghui , Liu Weiming , Chen Haowen , Wang Xiangxian 
Published: January 2022

Abstract
Metamaterial perfect absorbers play an essential role in many optoelectronic devices. In this paper, a dual-band tunable metamaterial perfect absorber based on graphene is proposed. The simulation results present that under normal incidence two absorption peaks of 99.9% and 99.9% occur at the frequencies 1.69 THz and 4.30 THz, respectively. Impedance matching theory is employed to elaborate this dual-band perfect absorption phenomenon. While at oblique incidence, the absorption of the absorber remains more than 90% over a wide incident angle from 0° to 75° for the transverse electric (TE) polarization and 60° for the transverse magnetic (TM) polarization separately. Furthermore, it is also independent to the polarization angles. In addition, the effects of different geometrical parameters and the chemical potential of graphene on the resonant frequencies are investigated in detail. The two peaks of the absorber can be dynamically tuned by the variation of the chemical potential of graphene. Due to its good performances, the designed metamaterial perfect system has great potential applications in biosensing, photodetectors, stealth, and imaging devices.
Cosmological perturbations and invariant observables in geodesic lightcone coordinates
Authors: Fröb Markus B. , Lima William C.C. 
Published: January 2022

Abstract
We consider a recent approach to the construction of gauge-invariant relational observables in gravity in the context of cosmological perturbation theory. These observables are constructed using a field-dependent coordinate system, which we take to be geodesic lightcone coordinates. We show that the observables are gauge-independent in the fully non-linear theory, and that they have the expected form when one adopts the geodesic lightcone gauge for the metric. We give explicit expressions for the Sasaki-Mukhanov variable at linear order, and the Hubble rate — as measured both by geodesic observers and by observers co-moving with the inflaton — to second order. Moreover, we show that the well-known linearised equations of motion for the Sasaki-Mukhanov variable and the scalar constraint variables follow from the gauge-invariant Einstein's equations.
Stochastic gravitational wave background from PBH-ABH mergers *
Authors: Cui Wenfeng , Huang Fei , Shu Jing , Zhao Yue 
Published: January 2022

Abstract
The measurement of gravitational waves produced by binary black-hole mergers at the Advanced LIGO has encouraged extensive studies on the stochastic gravitational wave background. Recent studies have focused on gravitational wave sources made of the same species, such as mergers from binary primordial black holes or those from binary astrophysical black holes. In this paper, we study a new possibility – the stochastic gravitational wave background produced by mergers of one primordial black hole and one astrophysical black hole. Such systems are necessarily present if primordial black holes exist. We study the isotropic gravitational wave background produced through the history of the universe. We find it is very challenging to detect such a signal. We also demonstrate that it is improper to treat the gravitational waves produced by such binaries in the Milky Way as a directional stochastic background due to a very low binary formation rate.
Comparison of novel SCADA Data Cleaning Technique for Wind Turbine Electric Pitch System
Authors: McKinnon C , Tartt K , Carroll J , McDonald A , Plumley C , Ferguson D 
Published: January 2022

Abstract
Wind turbines typically do not operate in the ideal operating conditions, leading to abnormal behaviour that is reflected in their power curves. This abnormal behaviour can affect the performance of condition monitoring processes, as it may mask faulty behaviour. By cleaning other abnormal data, such as curtailment, models can learn the normal behaviour of the turbines.
This paper presents a novel cleaning technique that utilises a combination of data binning and the Mahalanobis distance. This removes between 5 to 6% of the data, without great loss of normal data. When compared against other data cleaning techniques, the one presented in this paper produces a more ideal power curve. This technique could improve the performance of data-based condition monitoring techniques.
How do Humans decide under Wind Power Forecast Uncertainty — an IEA Wind Task 36 Probabilistic Forecast Games and Experiments initiative
Authors: Möhrlen Corinna , Giebel Gregor , Bessa Ricardo J. , Fleischhut Nadine 
Published: January 2022

Abstract
The need to take into account and explicitly model forecast uncertainty is today at the heart of many scientific and applied enterprises. For instance, the ever-increasing accuracy of weather forecasts has been driven by the development of ensemble forecasts, where a large number of forecasts are generated either by generating forecasts from different models or by repeatedly perturbing the initial conditions of a single forecast model. Importantly, this approach provides robust estimates of forecast uncertainty, which supports human judgement and decision-making. Although weather forecasts and their uncertainty are also crucial for the weather-to-power conversion for RES forecasting in system operation, power trading and balancing, the industry has been reluctant to adopt ensemble methods and other new technologies that can help manage highly variable and uncertain power feed-ins, especially under extreme weather conditions.
In order to support the energy industry in the adaptation of uncertainty forecasts into their business practices, the IEA Wind Task 36 has started an initiative in collaboration with the Max Planck Institute for Human Development and Hans-Ertel Center for Weather Research to investigate the existing barriers in the industry to the adoption of such forecasts into decision processes. In the first part of the initiative, a forecast game was designed as a demonstration of a typical decision-making task in the power industry. The game was introduced in an IEA Wind Task 36 workshop and thereafter released to the public. When closed, it had been played by 120 participants. We will discuss the results of our first experience with the experiment and introduce some new features of the second generation of experiments as a continuation of the initiative. We will also discuss specific questions that emerged when we started and after analysing the experiments. Lastly we will discuss the trends we found and how we will fit these into the overall objective of the initiative which is to provide training tools to demonstrate the use and benefit of uncertainty forecasts by simulating decision scenarios with feedback and allowing people to learn from experience, rather than reading articles, how to use such forecasts.
An Existence Condition for Power-Flow of DC Microgrids with CPLs Considering Voltage Disturbance and Distributed Generations under MPPT Control
Authors: Xia Ziqing , Su Mei , Liu Zhangjie , Sun Yao , Han Hua , Liu Ruisong 
Published: January 2022

Abstract
For the power-flow equation of a power network, whether a feasible power-flow solution exists is necessarily important for the correct operation of the system. Besides, it is directly related to the system’s voltage stability. Therefore, it is of great importance to investigate the existence of the feasible power-flow solution of DC microgrids, especially for those with CPLs and a large amount of distributed generations under MPPT control (MPPPT-DGs). In this paper, a mathematical power-flow equation of the DC microgrid with CPLs is built firstly. Next, a solvability condition for the power-flow equation of the DC networks considering voltage disturbance and MPPT-DGs is derived based on Brouwer’s fixed-point theorem. Moreover, simulations are performed using MALTAB to verify the effectiveness of the proposed theorem.
On-line Remaining Charging-discharging Cycle Prediction of Lithium-ion Batteries using Cumulative Indicator
Authors: Luo Zeyu , Chen Hao , Wang Xian-Bo , Yang Zhi-Xin 
Published: January 2022

Abstract
Prediction of the effective number of full charging-discharging cycle is valuable for lithium-ion battery (LIB) replacement and recycling. This paper proposes to construct a cumulative degradation indicator (CDI) to work as a more predictable indicator. The proposed CDI is better than the original degradation indicator (DI) in multiple criteria. In the stage of determining the end-of-life (EoL) threshold, a relevance vector machine (RVM) is introduced to screen a small number of available samples, and to reduce the prediction error. In the experimental verification stage, this paper uses LIB full-life data from NASA to verify the early and long-term prediction performance of RCDC using a small sample. The experimental results show that when the proportion of training data approaches 50%, the prediction error gradually converges to the actual value.
A Construction Method of Power Grid Monitoring Knowledge Graph
Authors: Liu Yi , Huang Xinjian , Li Sen , Gao Shang , Ji Kun , Li Huihui , Xu Hanyang 
Published: January 2022

Abstract
Centralized monitoring and integrated intelligent alarm functions have been implemented on the control system of smart power grid, which provides technical support for the judgment and disposal of power grid accident. However, there are still some problems such as lack of systematic analysis tools and cannot realize the correlation of alarms. This paper proposes a power grid accident analysis method based on monitoring information knowledge graph, which is constructed with power grid topology and historical alarm data rules. Using this method, the staff can accurately and quickly judge the power grid accident with the support of grid topology and alarm correlation, so as to improve the efficiency of power grid accident analysis and reliability of power grid control system.
Research on Reactive Power Compensation Control of V2G Vehicle-Mounted Converter Suitable for Sharing Scenarios
Authors: Yue Hao , Wu Bingqing , Ding Jianmin , Yue Yunli , Chen Dan , Xin Dixi 
Published: January 2022

Abstract
The paper proposes an electric drive reconfigurable on-board converter for V2G energy management in a shared scenario. The proposed convert is obtained by adding an auxiliary circuit to the existing PEV driving hardware. In V2G mode, the converter uses drive motor as a power inductor to reduce the volume and increase power density. This paper proposes a method to adjustment active power and reactive power separately, which have able to be used for reactive power compensation on the load side. The feasibility of the system is verified by experiments.
A Data-driven Model of Pipe Diameter and Insulation Thickness Optimization for District Heating Systems
Authors: Chen Kailun , Hu Jiayi , Yu Libin , Zheng Menglian , Sun Shien , He Dong , Lin Junguang 
Published: January 2022

Abstract
One of the major downsides of district heating systems is the large thermal loss through the distribution pipe network, which is expected to be alleviated through the optimization for the thickness of the insulation layer and the pipe diameter. Although a few previous studies have developed a diversity of techno-economic models for optimizing the pipe diameter or the insulation thickness of the district heating pipeline, few of them were able to capture the complicated relationship among the heat loss, pipe diameter, insulation thickness, operation parameters, and complex exterior conditions in the real world (such as the conditions of the soil and degradation condition of the insulation layer). The present study devises a combined model that first bases on the heat transfer model to theoretically estimate heat losses and then bases on the data-driven artificial-neural-network model to fill in the gap between the measured and theoretical heat losses. Results show that the predicted heat losses via the developed combined model reproduce the measured data well. Furthermore, compared with the original pipe diameter and insulation layer thickness, the optimized diameters of the pipe and thickness values of the insulation layers via the proposed combined model are all elevated.
Research on Static Var Generator Detection Method for New Energy Fields and Stations in Hunan Area
Authors: Yao Xinrui , Sheng Jie , Huang Hualin , Yu Da , Hu Jinhao 
Published: January 2022

Abstract
With the strategy of constructing new power system with new energy as the main body put forward, the installed capacity of new energy in Hunan area will usher in explosive growth during the 14th five-year Plan period. The safe and stable operation of new energy stations to power grid will also be increasingly intensified. The paper briefly introduces the working principle, working mode and application of SVG(Static Var Generator) device supporting new energy stations. Based on relevant standards, a test scheme suitable for new energy stations in Hunan province is proposed. Taking the SVG device supporting a new energy station in Hunan province as an example, the grid-connection performance test is carried out to comprehensively and scientifically evaluate the network-related performance of SVG devices in the new energy station.
Requirement Analysis of Wind Turbine High Voltage Ride-through Capability Detection and Simulation
Authors: Lu Kunpeng , Tang Wei , Zhang Bo , Guo Yutong , Li Yan , Yu Xiaoqing , Li Dongqi , He Fei , Yuan Qiujie 
Published: January 2022

Abstract
With the number of wind farms connected to the power system increasing rapidly, the impact of wind power generation system on power grid system stability is becoming increasingly prominent. Especially when the power grid has transient fault, there will be problems such as reverse power flow, large-area power failure of wind farm and so on. This will directly lead to the power system instability and seriously affect the power grid security and stability. Especially in the stage of voltage recovery after the fault is eliminated, the low-voltage fault of the power grid might lead to a sharp rise in the voltage of power grid and overvoltage on the high-voltage line connected to the power grid with wind farm, resulting in the disconnection of the wind power grid connected system. However, China’s current standards do not specify the high-voltage crossing of wind power grid connected system, especially the high-voltage crossing detection device, detection method and detection procedure. Therefore, it is urgent to study the detection technology of wind power high voltage crossing and put forward the detection method. There are many reasons for high-voltage power grid fault, and the interaction between power grid and inverter is more complex in high-voltage fault. If the type test method is used to simulate various high-voltage fault conditions in power system and carry out the test and study on the HVRT( high voltage ride through) performance of wind turbine, the human and material cost is huge and there are many potential safety hazards. At the same time, the grid connection performance of wind turbines are largely related to the control strategy of the controller. Therefore, the semi physical simulation method can be used to research the HVRT detection technology of wind turbine. This can effectively avoid the safety problem of large current and high voltage in the primary circuit, and realize the physical connection between the real controller and the flexible high voltage fault platform model through hardware in the loop simulation. It can simulate the wind turbine capability of high-voltage ride through under various high-voltage fault conditions. It not only has low test cost, but also can deeply study the interaction between power grid and power grid.
Research and Application of Relation Extraction based on Triple Relation Graph Convolutional Networks
Authors: An Dezhi , Ma Xuejie , Jiang Cao , Liu Lei , Wang Yanxu 
Published: January 2022

Abstract
In recent years, data in non-Euclidean spaces is becoming more and more. Traditional methods cannot perform feature extraction on these data. Most of existing methods just extract contextual semantic features from relational instances. Their structural features in corpora are ignored. To solve this problem, the paper proposed a relation extraction method based on triple relation graph convolutional networks (TRGCN). Based on the extraction of semantic features of sentences using convolutional neural networks, this method used the concept of triple relation graphs to represent structural features. In other words, triple relation graphs were formed by considering triples formed by the relation between two entities in one sentence as nodes and triples with common entities and same relations as edges. Finally, multiple-layer graph convolutional networks were used for training. As shown by experimental results, the method proposed in this paper achieved an F1 value of 86.8% on the SemEval 2010 Tesk 8 data set, indicating that it is better than mainstream convolutional neural networks and recurrent neural networks.
Boundary Enhancement and Contrastive Alignment for Unsupervised Domain Adaptive Semantic Segmentation
Authors: Liu Hao , Ma Chenzhe , Hu Juan , Liu Chengzhao , Zheng Hantao , Xu Jianglong 
Published: January 2022

Abstract
In the actual construction process, the supervision work of concrete pouring has many problems, such as heavy workload, low efficiency, misjudgment and omission. Deep learning shows good performance in computer vision, such as semantic segmentation and object recognition. In this paper, semantic segmentation is used to identify the position of vibrating bar in concrete pouring to provide a basis for detecting whether the vibrating behavior is standardized. Existing semantic segmentation studies ignore whether the edges of objects are finely detected. Recently, contrastive learning has made progress in computer vision. In addition, the training data differs greatly from the actual construction scene, namely domain shift. Therefore, we proposed the domain-adaptation method BECA which consists of two parts: boundary enhancement for accurate detection of edges and contrastive alignment for domain shift. Experiments show that the proposed BECA has unique advantages compared with the previous methods.
Preparation of Octahedral Mn3O4 by Liquid Phase Method
Authors: Fu Dejing , Wang Haifeng , Gou Bibo , Li Mingdong , Wang Jiawei 
Published: January 2022

Abstract
Octahedral manganese trioxide was prepared from manganese sulfate solution with air as oxidant and compound additive as pH regulator. The results showed that the product had high crystallinity, stable baseline and significant diffraction peak. The micro morphology was formed by stacking regular octahedral structure, the particle size was uniform, and it was about 200nm, the pores were rich, the specific surface area was 6.5 m2/g, and the tap density was 2.11g/cm3. All performance indexes of the product met the excellent standard of soft ferrite manganese trioxide (GBT21836-2008).