bluuebunny/crossref_metadata_2025_split huggingface dataset:
Research on key technology of smart grid based on holographic data
Authors: Zhang Tao , Li Shuang , Fu Yuhui , Wu Yiming , Wang Liang 
Published: July 2024

Abstract
With the increasing scale and complexity of power projects, designers face more and more challenges in terms of design quality and efficiency. In order to improve design quality and reduce human-caused errors, utilizing technologies such as big data and strong artificial intelligence, this manuscript proposes a key technology for smart grid based on holographic data. Firstly, the concept and key technologies of holographic intelligent design were proposed. Then, the overall plan of holographic smart grid was constructed, and the implementation methods of the main functions of holographic smart grid were elaborated. Finally, the construction process of the holographic digital twin platform was explored.
Research on Intelligent Heating for Urban Systems Based on Digital Twin
Authors: Jing Gao , Zhongxiao Du , Shuxiang Yang , Yingqi Xu , Sibo Xu 
Published: July 2024

Abstract
This article focuses on the intelligent heating platform driven by digital twins, analyzing the overall framework of the system according to the sensor layer, application layer, and network layer, and building an information service platform for intelligent heating networks. The data of the entire heating system, including heat sources, primary networks, heating stations, secondary networks, and heat users, is remotely collected, analyzed, and diagnosed. Subsequently, statistical analysis is conducted on the energy consumption of each heat source and heating station, achieving data sharing and data mining among various business systems. This article conducts research on several key technologies for optimizing decision-making methods of heating system operation scheduling based on simulation models and develops software systems to support the intelligent upgrading of heating systems. The results show that intelligent heating system based on digital twins can better meet the balance between supply and demand in urban heating systems, and optimize the overall operating costs and environmental benefits of the system under multiple constraints.
Research on optimal investment decision of distribution network projects under the construction of the new power system
Authors: Jiao Jie , Zhang Jiyuan , Yang Jie , Zhang Wenwen , Wen Xin 
Published: July 2024

Abstract
This article establishes a ranking model of distribution network projects to make optimal investment decisions. The project optimization ranking model consists of the LCC model and the project comprehensive evaluation model. When constructing the evaluation model of distribution network projects, the evaluation index system is first set, and then the weight allocation for each index is calculated based on the G1-antientropy weight comprehensive integrated weighting method, so as to calculate the comprehensive benefit of the unit cost of a project for optimal sorting. Taking a distribution network planning project in Sichuan Province as an example, the empirical analysis is conducted, and the ranking results are obtained by using the above optimization ranking model for distribution network projects depending on the comprehensive benefit index of unit cost of the project A-E, which confirms the reasonability and practicability of the decision-making system and realizes the optimization of investment decisions for distribution network projects.
Privacy-preserving method for sensitive partitions of electricity consumption data based on hybrid differential privacy and k-anonymity
Authors: Zhai Feng , Liang Xiaobing , Qin Yu , Li Baofeng , Shen Lixiang , Xie Jingyi 
Published: July 2024

Abstract
With the development of smart grid, traditional privacy protection methods are no longer sufficient to cope with the mining and analysing of users’ electricity consumption data, which can easily lead to the leakage of users’ privacy. In this paper, for the challenge of privacy protection of user electricity data in smart grid, we propose a privacy protection method for sensitive partition of electricity data based on hybrid differential privacy and k-anonymity technique, which identifies pattern-specific quasi-identifiers by random forest technique and divides the data into privacy-invasive and non-privacy-invasive partitions, and adopts the Laplacian noise addition (numerical quasi-identifiers) and data generalisation (sub-types of quasi-identifiers) techniques to achieve differential privacy protection for different partitions. Finally, it is experimentally verified that the method performs better in terms of information reduction accuracy and execution time, effectively striking a balance between protecting user privacy and maintaining data utility.
Correlation analysis of wind power and meteorology based on the ramp detection method with adaptive sliding window
Authors: Ming Huang Ruan , En Li Hao , Xin Miao Lin , Bing Li Can , Fei Xu Yu , Deng Xiang , Ying Zhang Qi 
Published: July 2024

Abstract
Since the penetration of wind power rises rapidly, the coupling between wind power and meteorology strengthens the dependence of the new power system on meteorology. The fluctuation of wind power and the ramp events become key challenges that affect the safe and stable operation of the power system. To balance the accuracy and efficiency of wind power ramp event detection, an adaptive sliding time window-based ramp event detection method is proposed, which includes three major steps: dataset preprocessing and compression, wind power ramp trend pattern extraction considering bump event screening, and wind power ramp detection based on adaptive sliding time window. The correlation between the ramp event of wind power and meteorological factors like temperature, wind speed and direction are analyzed, and the feasibility of the proposed method is verified. The results show that wind speed is the decisive meteorological factor influencing ramp events, and wind power is also affected by wind direction. However, the correlation between wind power and temperature is weak, which only works in a few ramp events.
Short-term Power Load Forecasting Based on EMD-GWO-BP
Authors: Bai Hong , Guan Yan , Cai Yinong , Wang Mingqi 
Published: July 2024

Abstract
The short-range prediction of electricity demand holds immense importance in the strategic planning and advancement of the energy sector. However, the actual load sequence data reflects multiple complex properties, such as nonlinearity, non-stationarity, and temporal variation. In order to accurately forecast the load, this paper presents a three-level hybrid integrated short-term load prediction method composed of empirical mode decomposition (EMD), Grey Wolf Optimization (GWO) and BP neural network (BP). EMD is used to decompose load data to obtain good power consumption characteristics; GWO is used to obtain the optimal weight and threshold required for BP prediction. The hybrid integration method (EMD-GWO-BP) was evaluated using the 2017 annual data of Chaoyang County, Liaoning Province. The EMD-GWO-BP method was compared with the other two mainstream coupling methods (BP, GWO-BP). The statistical analysis Indicates that the suggested method within this document shows better forecast precision on three standard scales of MAPE, MAD and RMSE, which reflects the advanced nature of this method.
The SHADOWS experiment at the CERN SPS
Authors: Cicero V. ,   the SHADOWS collaboration
Published: July 2024

Abstract
SHADOWS (Search for Hidden And Dark Objects With the SPS) is a proposed proton beam-dump experiment for the search of a large variety of Feebly-Interacting Particles (FIPs) at the CERN SPS. It will exploit the potential for searches and discoveries at the intensity frontier offered by the upgrade of the ECN3 beam line.
SHADOWS will be located off-axis, which allows the optimisation of the signal-to-background ratio, and will collect data from up to 5×1019 protons of 400 GeV on target in 4 years of operations.
The experiment has a transversal size of 2.5×2.5 m2 and is composed by an upstream veto, a 20 m long decay volume and a spectrometer with a tracking system in a dipole magnet, a timing detector, a calorimeter and a muon system. The conceived detector offers excellent
tracking and timing performance for the identification and reconstruction of most of the visible final states of FIP decays. SHADOWS will allow to explore a large parameter space region of many FIPs, like light dark scalars, axion-like particles and heavy neutral leptons, with masses ranging between 0.1 and 10 GeV.
This paper reports about the status of the proposal of the SHADOWS experiment, with focus on the detector challenges.
Status of edge electron density and temperature measurements with Helium Beam Emission Spectroscopy (He-BES) on EAST
Authors: Ding G.F. , Ye Y. , Chen R. , Xu G.S. , Yu Y. , Lin X. , Yang Q.Q. , Zhang W. , Li Y. , Yan N. , Liu S.C. , Wang L. , Zhang T. , Zhou T.F. , Wu D.G. 
Published: July 2024

Abstract
Helium Beam Emission Spectroscopy (He-BES) diagnostic has
been developed on EAST, which is able to measure the edge electron
density and temperature profiles simultaneously using a helium line
intensity ratio method. The diagnostic includes the beam injector
and the detection system. There are 20 observation channels within
an observation range of 80 mm in the detection system at the low
filed side, which can cover the whole scrape-off layer (SOL) and
part of the pedestal region of EAST. The beam injector system has
been upgraded to Supersonic Molecular Beam Injector (SMBI) system to
realize deeper helium injection since the 2021 campaign. Four
spectral lines at wavelengths of 728.1 nm, 706.5 nm, 667.8 nm and
656.3 nm are detected by the He-BES. The first three spectral
lines, including 728.1 nm, 706.5 nm, 667.8 nm, are measured for
calculating edge n
e and T
e profiles based on the
collisional-radiative model (CRM) model, and the last spectral line
(656.3 nm) is used for the measurement of D
α
emission. The edge electrostatic fluctuations can be obtained from
the power spectrum of D
α emission. The electron density
and temperature profiles calculated from the 667.8/728.1 and
728.1/706.5 nm line ratios are in good agreement with those from
other diagnostics in the edge region of plasma. The self-consistency
of He-BES diagnostic is also verified, such as the density pump out
caused by LHW and the lower edge temperature caused by the lower
heating power.
Expected performance of Cosmic Muon Veto Detector
Authors: Shah Raj , Majumder Gobinda , Shukla Prashant ,   on behalf of INO collaboration
Published: July 2024

Abstract
The India-based Neutrino Observatory (INO) collaboration has
established a miniICAL detector, at the transit campus of IICHEP,
Madurai, India, which serves as a prototype detector of the larger
Iron-Calorimeter detector (ICAL). The purpose of miniICAL lies in
unraveling the intricate physics and engineering challenges inherent
in constructing and operating a substantial ICAL-type detector. To
explore the feasibility of building a large-scale neutrino
experiment at shallow depths the collaboration has embarked upon the
construction of a Cosmic Muon Veto Detector (CMVD) around the
miniICAL detector. The primary objective of this endeavor revolves
around attaining a veto efficiency surpassing 99.99%, while
simultaneously maintaining a false-positive rate lower than
10-5. The CMVD system is based on extruded plastic
scintillators (EPS) and utilizes wavelength-shifting fibers to
collect scintillation photons and uses silicon photomultipliers
(SiPMs) as photo-transducers. A software tool is developed for CMVD
and is integrated with the existing miniICAL consisting of RPC
detectors. The simulation is tuned to include properties of EPSs and
WLS fibers, measured efficiencies, and time resolutions of
EPSs. Measured spectra and noise in SiPMs are also taken into
account. The muon tracks in the RPCs are used to estimate the muon
veto efficiency of CMVD to arrive at efficient muon veto
criteria. With improved veto efficiency of cosmic muons, the CMVD
experiment will help to pave the way for future large-scale
shallow-depth neutrino experiments e.g. INO-type experiments,
enhancing our understanding of neutrino properties.
Unsupervised deep learning bias correction of CMIP6 global ensemble precipitation predictions with cycle generative adversarial network
Authors: Huang Bohan , Liu Zhu , Duan Qingyun , Rajib Adnan , Yin Jina 
Published: July 2024

Abstract
Climate change significantly impacts agricultural production, ecosystem stability, and socioeconomic development. Global climate models (GCMs) serve as the primary tool for simulating historical and future precipitation patterns. However, due to issues such as coarse resolution, boundary condition, and parameterization, model outputs require bias correction (BC). With the evolution of deep learning techniques, supervised convolutional neural network (CNN) frameworks have gained popularity in the area of climate model BC but face limitations in spatial correlation assumptions and data sparsity, particularly for extreme precipitation This study proposed an unsupervised learning approach using cycle generative adversarial network (CycleGAN) to correct the ensemble mean bias of models and compare its performance with CNN and Quantile Mapping methods. The results demonstrate that the proposed CycleGAN approach outperforms both CNN and Quantile Mapping in ensemble mean BC. It effectively learns the overall distribution of precipitation through an adversarial process and yields better extreme precipitation predictions.
Anomalous diffusion of self-propelled particles
Authors: J Sevilla Francisco , Chacón-Acosta Guillermo , Sandev Trifce 
Published: July 2024

Abstract
The transport equation of active motion is generalised to consider time-fractional dynamics to describe the anomalous diffusion of self-propelled particles. In the present study, we consider an arbitrary active motion pattern modelled by a scattering function that defines the dynamics of the change of the self-propulsion direction. The exact probability density of the particle positions at a given time is obtained. From it, the time dependence of the firsts moments, i.e. the mean square displacement and the kurtosis for an arbitrary scattering function, are derived and analysed. Anomalous diffusion is found with a crossover of the scaling exponent from 2α in the short-time regime to α in the long-time one,

0
<
α
<
1

being the order of the fractional derivative considered. It is shown that the exact solution found satisfies a fractional diffusion equation that accounts for the non-local and retarded effects of the Laplacian of the probability density function through a coupled temporal and spatial memory function. Such a memory function holds the complete information of the active-motion pattern. In the long-time regime, space and time are decoupled in the memory function, and the time fractional telegrapher’s equation is recovered. The theoretical framework presented here can be applied as model of active motion that exhibits anomalous diffusion.
Revisiting the spatially inhomogeneous condensates in the(1+1)-dimensional chiral Gross–Neveu model via the bosonic two-point function in the infinite-N limit
Authors: Koenigstein Adrian , Winstel Marc 
Published: July 2024

AbstractThis work shows that the known phase boundary between the phase with chiral symmetry and the phase of spatially inhomogeneous chiral symmetry breaking in the phase diagram of the(1+1)-dimensional chiral Gross–Neveu (GN) model can be detected from the bosonic two-point function alone and thereby confirms and extends previous results (Schön and Thies 2000At The Frontier of Particle Physics: Handbook of QCD, Boris Ioffe Festschriftvol 3 (World Scentific) ch 33, pp 1945–2032; Boehmeret al2008Phys. Rev.D78065043; Boehmer and Thies 2009Phys. Rev.D80125038; Thies 2018Phys. Rev.D98096019; Thies 2022Phys. Rev.D105116003). The analysis is referred to as the stability analysis of the symmetric phase and does not require knowledge about spatial modulations of condensates. We perform this analysis in the infinite-Nlimit at nonzero temperature and nonzero quark and chiral chemical potentials also inside the inhomogeneous phase. Thereby we observe an interesting relation between the bosonic 1-particle irreducible two-point vertex function of the chiral GN model and the spinodal line of the GN model.
Informationally overcomplete measurements from generalized equiangular tight frames
Authors: Siudzińska Katarzyna 
Published: July 2024

AbstractInformationally overcomplete measurements find important applications in quantum tomography and quantum state estimation. The most popular are maximal sets of mutually unbiased bases, for which trace relations between measurement operators are well known. In this paper, we introduce a more general class of informationally overcomplete positive, operator-valued measure (POVMs) that are generated by equiangular tight frames of arbitrary rank. This class provides a generalization of equiangular measurements to non-projective POVMs, which include rescaled mutually unbiased measurements and bases. We provide a method of their construction, analyze their symmetry properties, and provide examples for highly symmetric cases. In particular, we find a wide class of generalized equiangular measurements that are conical two-designs, which allows us to derive the index of coincidence. Our results show benefits of considering a single informationally overcomplete measurement over informationally complete collections of POVMs.
Critical behavior of dirty free parafermionic chains
Authors: Pandey Akshat , Cowsik Aditya 
Published: July 2024

Abstract
A family of

Z

n

-symmetric non-Hermitian models of Baxter was shown by Fendley to be exactly solvable via a parafermionic generalization of the Clifford algebra. We study these models with spatially random couplings, and obtain several exact results on thermodynamic singularities as the distributions of couplings are varied. We find that these singularities, independent of n, are identical to those in the random transverse-field Ising chain; correspondingly the models host infinite-randomness critical points. Similarities in structure to exact methods for random Ising models, a strong-disorder renormalization group, and generalizations to other models with free spectra, are discussed.
Conditional quantum thermometry—enhancing precision by measuring less
Authors: Sone Akira , Soares-Pinto Diogo O , Deffner Sebastian 
Published: July 2024

Abstract
Taking accurate measurements of the temperature of quantum systems is a challenging task. The mathematical peculiarities of quantum information make it virtually impossible to measure with infinite precision. In the present paper, we introduce a generalize thermal state, which is conditioned on the pointer states of the available measurement apparatus. We show that this conditional thermal state outperforms the Gibbs state in quantum thermometry. The origin for the enhanced precision can be sought in its asymmetry quantified by the Wigner–Yanase–Dyson skew information. This additional resource is further clarified in a fully resource-theoretic analysis, and we show that there is a Gibbs-preserving map to convert a target state into the conditional thermal state. We relate the quantum J-divergence between the conditional thermal state and the same target state to quantum heat.
Multi-material 3D nanoprinting for structures to functional micro/nanosystems
Authors: Duan Yongqing , Xie Wenshuo , Yin Zhouping , Huang YongAn 
Published: July 2024

Abstract
Multi-material 3D fabrication at the nanoscale has been a long-sought goal in additive manufacturing, with great potential for the direct construction of functional micro/nanosystems rather than just arbitrary 3D structures. To achieve this goal, researchers have introduced several nanoscale 3D printing principles, explored various multi-material switching and combination strategies, and demonstrated their potential applications in 3D integrated circuits, optoelectronics, biological devices, micro/nanorobots, etc. Although some progress has been made, it is still at the primary stage, and a serious breakthrough is needed to directly construct functional micro/nano systems. In this perspective, the development, current status and prospects of multi-material 3D nanoprinting are presented. We envision that this 3D printing will unlock innovative solutions and make significant contributions to various technologies and industries in the near future.
Rebuilding Ukraine’s energy supply in a secure, economic, and decarbonised way
Authors: Tröndle Tim , Melnyk Olena , Tutova Olena , Porieva Vira , Neumann Fabian , Staffell Iain , Patt Anthony 
Published: July 2024

Abstract
Since Russia’s 2022 invasion, Ukraine’s civilian energy infrastructure has faced systematic attack and requires urgent and strategic reconstruction. This study confronts the dual challenges of rebuilding Ukraine’s energy system rapidly to mitigate civilian and economic disruption while aligning this to long-term goals of sustainability and energy security. We demonstrate that Ukraine can readily meet future energy demands through a fully renewable electrified system at costs comparable to those from fossil fuels and nuclear power. Contrary to previous reliance on high-carbon energy sources, we find a diversified renewable energy portfolio, including significant solar photovoltaic and wind contributions, can efficiently meet growing energy demands and position Ukraine as an energy exporter, capitalising on its geographical advantages. This study’s approach, based on open data and models, extends beyond national borders and offers a model for post-conflict reconstruction that harmonizes immediate recovery with sustainable energy transition.
Notes on a Fragment: Zarina’s Dividing Line
Authors: Kumar Aparna 
Published: July 2024

Abstract
This essay provides a new reading of Zarina’s iconic woodcut print Dividing Line (2001), with an eye to unravelling the work’s relationship to the 1947 partition of the Indian subcontinent and the wider landscape of partition history. By considering Zarina’s use of paper, map and gouge, this reading argues that Dividing Line embodies a method and ethics of writing decolonial histories of partition, with important implications for how to understand and respond to the violent impact of postcolonial border-making on (art) history. The essay, written as a series of notes, seeks to account for the distinct forms and temporalities of writing for partition history that Dividing Line both produces and demands of the art historian. In its form, the essay mobilises the note as a fragmentary mode of writing, responsive to the specific dynamics of continuity and discontinuity endemic to the experience of lines, borders and partition in Dividing Line.
Land Art Submerged: Betty Beaumont’s Ocean Landmark and Art History Beyond Proximity
Authors: Curtis Francesca 
Published: July 2024

Abstract
Comprising 17,000 blocks of coal fly-ash, Betty Beaumont’s Ocean Landmark (1978–1980) is an artificial reef located off the coast of Fire Island, New York. This essay analyses the visual inaccessibility of the work and its reliance on an assemblage of media to highlight its significance for conceiving political, ecological and cultural meanings of the ocean. Land art’s fascination with the limits of visuality, the site/non-site dialectic and relationship with industry will provide a framework for this aim. Ocean Landmark’s situatedness and association with industrial ecology signals the wider juridical and political lack of transparency in industrial activity in the ocean spaces framed as sites of unseen environmental extraction. Demonstrating the political and epistemological consequences of a lack of visibility, it challenges the assumptions of total sensorial and epistemic access, highlighting the limitations of anthropocentric positions in and beyond the ocean.