bluuebunny/crossref_metadata_2025_split huggingface dataset:
Economic impacts of variable rate nitrogen fertilization: Input saving and yield increase in cereals
Authors: Medici Marco , Canavari Maurizio 
Published: December 2022

Abstract
The purpose of this study is to discuss the economic impacts stemming from variable-rate (VR) nitrogen fertilization, one of the main precision agriculture practices. Economic impacts are related to reduced nitrogen use and increased crop yields thanks to a better distribution of inputs, net from possible spatial and temporal uncertainty. This paper reviews N=31 case studies (in 29 articles) that compared the performance of VR nitrogen applications versus uniform treatments. The comparisons covered nitrogen fertilization for cereal crops, in particular wheat and maize, for different years and in different countries. Findings highlight relevant changes in amounts of nitrogen applied, with the evidence of higher nitrogen efficiency resulting in reduced operating costs, while changes in crop yields are less evident: VR applications and uniform applications substantially reach the same production level, and higher nitrogen use efficiencies are achievable without significantly compromising yields. Overall, net economic impacts are in favour of VR fertilization. This work can raise farmers’ and other stakeholders’ knowledge of the actual economic impacts stemming from the adoption of VR fertilization and helps policymakers to understand the economic impact of precision agriculture and the need to foster sustainability-based policies.
Memory for polarization state of light based on atomic frequency comb in a 153Eu:Y2SiO5 crystal
Authors: Akhmedzhanov R A , Gushchin L A , Kalachev A A , Nizov N A , Nizov V A , Sobgayda D A , Zelensky I V 
Published: December 2022

Abstract
In this work we realize an optical memory for polarization state of light that is based on atomic frequency comb protocol in a 153Eu:Y2SiO5 crystal. In order to compensate for the absorption and refraction anisotropy of the crystal we use a setup with multiple passes through the sample with intermediary rotation of polarization. We obtain memory efficiency of ∼25% with a storage time of ∼165 ns and fidelity of over 85%.
A Double-Layer Voltage Control Model for Distributed Photovoltaic in Distribution Network
Authors: Zhao Zhiyu , Zhao Tianqi , Xu Peng , Wang Zesen , Xie Huan , Li Xuan , Li Yu 
Published: December 2022

Abstract
With the increasing penetration of distributed photovoltaic (PV) in distribution network (DN), and the randomness of PV power output, so the DN with PV is prone to voltage fluctuation or even overvoltage. In this paper, a double-layer distributed PV voltage control strategy is proposed. This strategy takes full advantage of reactive power of PV inverter connected to DN and aims at reducing the loss of uploading power of PV, so as to achieve collaborative control of buses voltage. It adopts the double-layer control strategy of PV system in DN, as well as the three modes switching of maximum power point tracking (MPPT), reactive power regulating and power factor regulating of PV. On the premise of realizing the effective control of buses voltage, it can reduce the loss of uploading power of PV to the greatest extent. Finally, the proposed distributed PV voltage control strategy is verified through a typical DN case based on the real time digital simulation (RTDS).
A Large-Range Ultrasonic Distance Measuring Device Based on Improved Time Difference Method
Authors: Liao Yubin , Ye Qingzhou , Zhang Maolin 
Published: December 2022

Abstract
Ultrasonic ranging by using the time difference method generally has the problems of inaccurate measurement of echo arrival time and small range. To solve the above problems, firstly, this paper proposes a method to detect the arrival time of the echo by using the inflection point of the echo envelope with the combination of logarithmic characteristics of the logarithmic amplifier and improves the peak detection of the traditional TDOA by changing the characteristic point of the arrival time of the echo. Secondly, an ultrasonic distance measuring device is designed, in which the transmitting power and the sensitivity of the receiving end can be adjusted automatically with the echo signal strength to improve the measurement accuracy and broaden the measuring range. The experimental results show that the range of the device is expanded from 0.5-12 m to 0.3-20 m, and the distance measurement error is reduced from 1% to 0.5% when the improved time difference method is adopted and the self-tuning of the transmitting and receiving parameters is enabled. It shows that the improved time difference method can improve the measurement accuracy and can effectively expand the ultrasonic ranging range by using the self-tuning of the transmitting and receiving parameters.
Distribution Network Distributed Energy Storage Configuration Optimization Method Considering Variance of Network Loss Sensitivity
Authors: Shi Yan , Huang Maoyi 
Published: December 2022

Abstract
With the wide application of distributed generation technology, in order to maintain the stable and safe operation of the distribution network, this paper considers the uncertainty of power generation and a load of distributed energy storage. It takes the distribution network with distributed energy storage as the research object, models and analyzes the optimization problem, and studies the problem of DG configuration by using the reinforcement learning method. In the case of distributed grid connection, the voltage distribution change of the distribution network and the impact of network loss are analyzed. The line power flow of the distribution network is calculated by using the MATLAB platform as a platform, and the power flow calculation of the distribution network is simulated and analyzed by the MATLAB platform. The system loss is significantly reduced. The experimental results show that the system loss variance is 29.19. After adding energy storage, the loss variance is 14.77, which is reduced by 49.41%. According to the scheme, the voltage stability and operating loss of the system can be effectively improved.
Identification Technology of Relationship Between User Meter and Transformer Based on Spatial-Temporal Correlation of Data
Authors: Ye Aliaosha , Zheng Guoquan , Zhu Enguo , Lu Jizhe 
Published: December 2022

Abstract
Due to the inability to accurately gain the changes of transformer archives, there are often problems such as confusion and loss of control of the station area archives in the electricity consumption information collection system, which seriously affects the work of line loss management and fault operation and maintenance, and it is difficult to achieve refined management. Existing manual identification methods are time-consuming and labor-intensive, while the dedicated device identification method is costly and requires a device-independent identification technology. Aiming at the above problems, a data analysis method based on temporal and spatial correlation is studied to analyze the temporal and spatial correlation of the voltage data of the power collection equipment. On this basis, use the classification method in data mining to divide collection equipment of each station area into one category, and calculate the probability that the acquisition equipment to be identified belongs to each category, to determine the belonging relationship of its station area. By simulating the user situation of the real station area, the simulation verifies the effectiveness of the intelligent station area identification method based on the spatial and temporal correlation of data, which can not only ensure the accuracy and efficiency of station area identification, but also does not depend on equipment and labor, and can meet the application requirements.
Land Subsidence Time Series Prediction Method Based on LSTM-AMSGD
Authors: Qian Cheng , Shi Menglu , Lv Xiaoxia , Wu Dicong , Du Xiang , Liu Jing 
Published: December 2022

Abstract
Accurate prediction of geological subsidence is of great importance for geological hazard risk assessment. Various existing prediction models do not take into account the time correlation between geological subsidence, and the prediction effect lacks practical significance. In this paper, an LSTM-AMSGD-based land subsidence prediction method is proposed. Firstly, the high-precision time series inversion results of large-area land surface deformation are obtained by the small baseline interference technique with multiple principal image coherent targets. Secondly, a recurrent neural network (LSTM-AMSGD) is used as the network architecture. The final cumulative subsidence prediction error is within 0.3 mm, and the single-step prediction of more than 400,000 observation points can be completed in 126s. Therefore, the LSTM-AMSGD model in this paper is effective for the prediction of geological subsidence.
Detection and Tracking of Infrared Small Targets in Complex Dynamic Backgrounds by Using Image Registration and Pipeline Filter
Authors: Zhu Zhiping , Li Hongxia , Xia Mingxia 
Published: December 2022

Abstract
This paper propses an infrared small target detection method in complex dynamic backgrounds in sequence images by using image registration and pipeline filter. For dynamic scenes caused by camera movements, registration of image frames and reference frame images is conducted by using the feature point matching algorithm. Next, the frame difference method is used to detect small mobile target in video image sequences. Finally, the impact of ground clutter noise and random noise are eliminated by using a pipeline filter to precisely track the trajectory of small moving targets. The experiment results show that the proposed method is superior to the classic infrared small target detection algorithm in terms of accuracy and stability in complex ground backgrounds.
A Blockchain-Based IoT Terminal Trust Mechanism Management Scheme
Authors: Zhang Guanqun , Liu Mengxi , Zai Guangjun 
Published: December 2022

Abstract
Traditional centralized Internet of Things (IoT) management cannot effectively defend against internal attacks from compromised devices due to its own limitations, while distributed IoT node management has certain requirements for the communication environment and resource consumption of the nodes themselves. And the trust authentication of the nodes cannot be easily resolved in a distributed manner. This paper presents a blockchain-based trust management solution. Trust management of nodes based on blockchain also greatly reduces resource consumption. Specifically, the trust value is derived by weighting the historical trust value and the direct trust value, so that the evaluation node does not rely solely on historical trust data to derive its own independent trust evaluation. A separate system node level mechanism exists in the scheme, and the system increases the system node level trust value for the node when this evaluation reaches a trusted level. If this evaluation is malicious, the system node-level trust value for the evaluation is initialized. Security analysis and experiments have shown that this scheme can effectively detect malicious nodes and takes less time.
Research on Optimal Scheduling of Power System Based on Ant Colony Algorithm
Authors: Wang Haiji , Hu Jiankun 
Published: December 2022

Abstract
With the development of new energy technology, the installation of new energy is becoming more and more. After a large number of new energy sources are connected to the power system, the capacity of peak regulation and frequency modulation is gradually insufficient, resulting in poor stability of the power system. Aiming at the problem of poor operation stability after new energy is connected to power system, a power system optimization scheduling method based on ant colony algorithm is proposed. In the model, the objective function is to minimize the total operating cost. The improved ant colony algorithm is used to solve the optimal scheduling model, and the effectiveness of the method is verified by simulation. The results show that this method has better ability of peak regulation and frequency modulation after new energy is added to the power system network, the frequency value and active power value of the power system are reduced, the load fluctuation value of the grid is reduced obviously, and the operation of the power system is more stable.
Simulation Research of Electricity-Hydrogen Coupled DC Microgrid Based on Parallel Computing
Authors: Wu Yetian , Bo Yaolong , Zhao Bo , Xia Yanghong , Wei Wei 
Published: December 2022

Abstract
Electricity-hydrogen coupled DC microgrid is of great significance for energy structure adjustment and efficient consumption of renewable energy. However, at present, such systems lack high-precision simulation support, and the transient simulation technology for large-scale microgrid systems is not perfect. In order to solve these problems, this paper form an electricity-hydrogen coupled DC microgrid simulation platform, and studies the function realization and the efficiency improvement of transient simulation. Firstly, the Euler algorithm is adopted to realize the transient simulation of the microgrid system for the functional requirements of the electricity-hydrogen coupled DC microgrid. Secondly, for different system sizes and computing platforms, an adaptive parallel computing optimal mode selection algorithm is proposed to improve the simulation efficiency. The experimental results show that the algorithm improves the computing efficiency of the large-scale electricity-hydrogen coupled DC microgrid system by about 40%.
Research on Power Grid Over-voltage Anomaly Data Management Based on the Improved Clustering Algorithm
Authors: Jia Lixia , Zeng Xiangming , Lin Fangman 
Published: December 2022

Abstract
The relational database uses distributed storage for grid over-voltage anomaly data, which lacks the division of the anomaly data, resulting in a long query time for anomaly data management. For this reason, the research of grid over-voltage anomaly data management based on the clustering algorithm is proposed. The clustering algorithm is combined with the outlier detection to divide the anomaly data and improve the query efficiency. The data are classified according to their characteristics. Row storage is selected as the main storage method for grid over-voltage anomaly data, and a three-dimensional model library is used to build out the management framework of the anomaly data to realize the efficient management of the anomaly data. In the experiment, the query time consumption of the proposed method is tested, and the analysis of the experimental results shows that the proposed method has a high query efficiency in managing the grid over-voltage anomaly data.
Estimation of Zernike polynomials for a highly focused electromagnetic field using polarimetric mapping images and neural networks
Authors: Ahmadi Kavan , Carnicer Artur 
Published: December 2022

Abstract
In this communication, we present a method to estimate the aberrated wavefront at the focal plane of a vectorial diffraction system. In contrast to the phase, the polarization state of optical fields is simply measurable. In this regard, we introduce an alternative approach for determining the aberration of the wavefront using polarimetric information. The method is based on training a convolutional neural network using a large set of polarimetric mapping images obtained by simulating the propagation of aberrated wavefronts through a high-NA microscope objective; then, the coefficients of the Zernike polynomials could be recovered after interrogating the trained network. On the one hand, our approach aims to eliminate the necessity of phase retrieval for wavefront sensing applications, provided the beam used is known. On the other hand, the approach might be applied for calibrating the complex optical system suffering from aberrations. As proof of concept, we use a radially polarized Gaussian-like beam multiplied by a phase term that describes the wavefront aberration. The training dataset is produced by using Zernike polynomials with random coefficients. Two thousand random combinations of polynomial coefficients are simulated. For each one, the Stokes parameters are calculated to introduce a polarimetric mapping image as the input of a neural network model designed and trained for predicting the polynomial coefficients. The accuracy of the neural network model is tested by predicting an unseen dataset (test dataset) with a high success rate.
Comparison between the scanning pentaprism and the Hartman method for wavefront analysis
Authors: Nuno M. Gonçalves , Abreu Manuel , Alves D. Castro 
Published: December 2022

Abstract
Techniques for wavefront measurement have many applications as optics systems for astronomy and as verification tests for optical surfaces. The development of large aperture optical systems drives the search for low cost and high-resolution wavefront detection sensors. The Hartman technique and its variations (i.e the Shack-Hartman sensor) are the current standards for the characterization of low-order optical aberrations, such as defocus and spherical. The scanning pentaprism method is presented as a simple and low-cost method for the verification of such aberrations. In this method, a transverse section of a wavefront is scanned and sampled into a series of sub-wavefronts with smaller apertures. The relative positions of the produced centroids are measured relative to a calibrated position. This allows for the determination of the optical path difference along the section and consequently the wavefront error. Both techniques were used as tools for collimating a telescope that is part of the on-ground support equipment of ESA PLATO mission. In this work, the results from both methodologies are compared.
Detection of Alzheimer’s by Machine Learning-assisted Vibrational Spectroscopy in Human Cerebrospinal Fluid
Authors: Arévalo Laura A. , Antonova Olga , O’Brien Stephen A. , Singh Gajendra Pratap , Seifert Andreas 
Published: December 2022

AbstractNowadays, the diagnosis of Alzheimer’s disease is a complex process that involves several clinical tests. Cerebrospinal fluid contains common Alzheimer-related biomarkers that include amyloid beta 1-42 (Aβ1-42) and tau proteins. In this work, we propose vibrational spectroscopy techniques supported by machine learning for the detection of biomarkers in cerebrospinal fluid that are related with Alzheimer’s by prediction models. Vibrational spectroscopy provides the entire biochemical composition of the body fluid, and thus, small but typical physiological changes related with the pathology can be ascertained. Within a machine learning framework, Raman and FTIR spectra were analyzed, which were taken from samples of healthy volunteers in comparison with samples from patients clinically diagnosed with Alzheimer’s. We find that a logistic regression model can discriminate between healthy control and Alzheimer’s patients with a precision of 98%, when the input for the model combines data from both vibrational spectroscopy methods. Our approach shows high discriminative capabilities and constitutes a proof of concept for an alternative and accurate tool for the diagnosis of Alzheimer’s disease.
Differences in the values of anaglyphs, vectograms and cheiroscopes on participants with low, normal, and high AC/A ratio
Authors: Pena-Verdeal Hugo , Noya-Padin Veronica , Nores-Palmas Noelia , Sabucedo-Villamarin Belen , Giraldez Maria Jesus 
Published: December 2022

Abstract
Purpose: Anaglyphs, Vectograms and Cheiroscopes are visual therapy materials based on red/green, polarized, or black/white targes that used similar but slightly different images for each eye to train fusion and vergence skills. This study aimed to analyse the differences in the results obtained on those devices on participants with low, normal, or high AC/A ratios. Material and methods: three groups of volunteer participants were recruited based on their recent clinical history among patients attending the Optometry Clinic of the centre: 15 participants with low AC/A, 15 participants with normal AC/A and 15 participants with High AC/A ratios. None of them was under any type of medication, have an ocular or systemic disease, or were performing any kind of visual training plan that could affect the study. In two sessions one week apart, following the manufacturer’s instructions, the participants performed in a random order three visual therapy device-based training: one red/green Fixed Demand Anaglyph [FDA], one Variable Demand Polarized Vectogram [VDPV], and one based on the Wheatstone W [WW]. Participants were instructed to indicate the maximum value base-out (BO) where both image fusion and clarity was lost. Results between both sessions were compared with an analysis of differences. Results: There was found higher BO vergences results with the three devices regarding the second to the first session in the Low and Normal AC/A groups (Wilcoxon test, all p ≤ 0.013), but none in the High AC/A group (Wilcoxon test, all p ≥ 0.162). Conclusion: There is an enhancement of BO vergences in Low and normal AC/A participants but not in high AC/A participants by performing visual training with Anaglyphs, Vectograms and Cheiroscopes devices.
Capillary based hybrid fiber sensor in a balloon-like shape for simultaneous measurement of displacement and temperature
Authors: Santos João P , Bierlich Jörg , Kobelke Jens , Ferreira Marta S 
Published: December 2022

Abstract
In this work, a hybrid sensor based on a silica capillary in a balloon-like shape for simultaneous measurement of displacement and temperature is proposed for the first time, to the best of our knowledge. The sensor is fabricated by splicing a segment of a hollow core fiber between two single mode fibers (SMF) and by bending the fiber in a balloon shape with the capillary at the top-center position. In a transmission scheme, the SMF-capillary-SMF configuration excites an antiresonant (AR) guidance and the balloon shape enhances a Mach-Zehnder interferometer (MZI). The different responses of the interferometers to external displacement and temperature variations are conducive to a hybrid application of the sensor for simultaneous measurement of these parameters. Experimental results show that, for a capillary length of 1.2 cm and a balloon length of 4 cm, AR is insensitive to displacement and its sensitivity to temperature is 14.3 pm/°C, while the MZI has a sensitivity to displacement of 1.68 nm/mm and twice the sensitivity of AR to temperature, of 28.6 pm/°C. The proposed fiber sensor consists of only one sensing element in one configuration exciting two interferometers at the same time, which makes it of simple fabrication as well as low cost.
Strongly coupled plasmonic systems on optical fiber sensors: A study on nanomaterial properties
Authors: dos Santos Paulo S. S. , Mendes João , Dias B. , Pastoriza-Santos I. , de Almeida J. M. M. M. , Coelho Luís C. C. 
Published: December 2022

Abstract
New paths to increase the sensing performance of plasmonic sensors have been reported in recent years. There are several methodologies to achieve such purpose, namely by optimizing the nanostructure, nanomaterial and even the sensing platform. Recently the use nanoparticles over plasmonic thin films have been reported and shown sensitivity enhancement, when compared to a bare thin film. Nevertheless, a nanomaterial combination between NP and thin film has not been studied. In this work it was studied such plasmonic materials in order to optimize not only refractometric sensitivity but also decrease the resultant plasmonic band width. It was found that for Au, Ag and Cu thin films, the deposition of plasmonic nanoparticles resulted in an overall refractometric sensitivity and figure of merit (FOM) increase. The larger FOM increase was obtained for the Ag thin film, from 42 to 162 when coupled to Si nanoparticles. The greater sensitivity increase was achieved for a Cu thin film coupled to a Si nanoparticle, with an increase from 1745 to 3230 nm/RIU.