bluuebunny/crossref_metadata_2025_split huggingface dataset:
Space Weather with Quantified Uncertainties: Improving Space Weather Predictions with Data-Driven Models of the Solar Atmosphere and Inner Heliosphere
Authors: Pogorelov Nikolai V. , Arge Charles N. , Caplan Ronald M. , Colella Phillip , Linker Jon A. , Singh Talwinder , Van Straalen Brian , Upton Lisa , Downs Cooper , Gebhart Christopher , Hegde Dinesha V. , Henney Carl , Jones Shaela , Johnston Craig , Kim Tae K. , Marble Andrew , Raza Syed , Stulajter Miko M. , Turtle James 
Published: April 2024

Abstract
To address Objective II of the National Space Weather Strategy and Action Plan “Develop and Disseminate Accurate and Timely Space Weather Characterization and Forecasts” and US Congress PROSWIFT Act 116–181, our team is developing a new set of open-source software that would ensure substantial improvements of Space Weather (SWx) predictions. On the one hand, our focus is on the development of data-driven solar wind models. On the other hand, each individual component of our software is designed to have accuracy higher than any existing SWx prediction tools with a dramatically improved performance. This is done by the application of new computational technologies and enhanced data sources. The development of such software paves way for improved SWx predictions accompanied with an appropriate uncertainty quantification. This makes it possible to forecast hazardous SWx effects on the space-borne and ground-based technological systems, and on human health. Our models include (1) a new, open-source solar magnetic flux model (OFT), which evolves information to the back side of the Sun and its poles, and updates the model flux with new observations using data assimilation methods; (2) a new potential field solver (POT3D) associated with the Wang–Sheeley–Arge coronal model, and (3) a new adaptive, 4-th order of accuracy solver (HelioCubed) for the Reynolds-averaged MHD equations implemented on mapped multiblock grids (cubed spheres). We describe the software and results obtained with it, including the application of machine learning to modeling coronal mass ejections, which makes it possible to improve SWx predictions by decreasing the time-of-arrival mismatch. The tests show that our software is formally more accurate and performs much faster than its predecessors used for SWx predictions.
Modeling electron acceleration during the contraction of a magnetic island
Authors: Deuja Atit , Che Haihong 
Published: April 2024

Abstract
Magnetic reconnection releases the magnetic energy through the contraction of multi-magnetic island leading to the electron acceleration as proposed by Drake et. al in 2006. However, how the released magnetic energy is converted into electron’s kinetic energy is still theoretically not well understood. We model in particular the kinetic process assuming the adiabatic contraction of magnetic island that induces electric field which is proportional to the vector potential of the magnetic island and approximate the magnetic island with an ellipse. Under this model, we show that the energy gain is achieved through the work of inductive electric field. We further show that the curvature drift which is along the inductive electric field dominates the energy gain. We compared our model with the magnetic island formed by tearing instability in a 2.5D particle-in-cell simulation of magnetic reconnection and found the results from the model consistent with that of the simulation.
Peculiarities of charged particle kinetics in spherical plasma
Authors: Kolobov V I , Arslanbekov R R , Liang H 
Published: April 2024

Abstract
We describe transient kinetic simulations of partially ionized weakly-collisional plasma around spherical bodies absorbing or emitting charged particles. Numerical solutions of kinetic equations for electrons and ions in 1D2V phase space are coupled to an electrostatic solver using the Poisson equation or quasineutrality condition for small Debye lengths. The formation of particle groups and their contributions to electric current flow and screening of charged bodies by plasma are discussed for applications to Langmuir probes and solar wind.
An operator-splitting numerical scheme for relativistic magnetohydrodynamics
Authors: Phillips David , Komissarov Serguei 
Published: April 2024

Abstract
We describe a novel operator-splitting approach to numerical relativistic magnetohydrodynamics designed to expand its applicability to the domain of ultra-high magnetisation. In this approach, the electromagnetic field is split into the force-free component, governed by the equations of force-free degenerate electrodynamics (FFDE), and the perturbation component, governed by the perturbation equations derived from the full system of relativistic magnetohydrodynamics (RMHD). The combined system of the FFDE and perturbation equations is integrated simultaneously, for which various numerical techniques developed for hyperbolic conservation laws can be used. At the end of every time-step of numerical integration, the force-free and the perturbation components of the electromagnetic field are recombined and the result is regarded as the initial value of the force-free component for the next time-step, whereas the initial value of the perturbation component is set to zero. To explore the potential of this approach, we build a 3rd-order WENO code, which was used to carry out 1D and 2D test simulations. Their results show that this operator-splitting approach allows us to bypass the stiffness of RMHD in the ultra-high-magnetisation regime where the perturbation component becomes very small. At the same time, the code performs very well in problems with moderate and low magnetisation too.
Challenges Modeling the Low-Luminosity Type Iax Supernovae
Authors: Feldman C , Eisenberg E , Townsley D M , Calder A C 
Published: April 2024

Abstract
Numerical models allow the investigation of phenomena that cannot exist in a laboratory. Computational simulations are therefore essential for advancing our knowledge of astrophysics, however, the very nature of simulation requires making assumptions that can substantially affect their outcome. Here, we present the challenges faced when simulating dim thermonuclear explosions, Type Iax supernovae. This class of dim events produce a slow moving, sparse ejecta that presents challenges for simulation. We investigate the limitations of the equation of state and its applicability to the expanding, cooling ejecta. We also discuss how the“fluff”, i.e. the low-density gas on the grid in lieu of vacuum, inhibits the ejecta as it expands. We explore how the final state of the simulation changes as we vary the character of the burning, which influences the outcome of the explosion. These challenges are applicable to a wide range of astrophysical simulations, and are important to discuss and overcome as a community.
Tackling exascale systems for astrophysics
Authors: Roussel-Hard Lou , Audit Édouard , Dessart Luc , Padioleau Thomas , Wang Yushan 
Published: April 2024

Abstract
Exascale systems are now appearing in the US, Japan, in Europe with two exascale procurements in the coming years and very likely in China. These systems offer great opportunities for the numerical astrophysics community and also raise serious technical challenges. Many legacy codes need to be fully rewritten to benefit from exascale architectures. Performance portability is a key issue as well as the management of the huge amount of data generated by exascale simulations. In this paper we discuss the path we have chosen to exascale and the software engineering solution chosen to address performance portability in the long run and data management. A new radiation hydrodynamics code based on the Kokkos library is presented as well as test runs and preliminary performance analysis. The first target of this new code will be to study supernova ejecta. They are multidimensional fluids, structured on both small and large scales following the non-linear development of complex fluid instabilities during and after the explosion. They contain a wealth of information about stellar composition and the evolution of elements in the interstellar medium. Their study requires significant computing resources that can be met through the use of exascale machines and suitable software.
Methods and Challenges of Including Dust Evolution in Magnetohydrodynamic Simulations
Authors: Slavin Jonathan D. 
Published: April 2024

Abstract
Dust plays many important roles in the interstellar medium, affecting the heating, cooling and chemistry in all the different phases of the medium. Up until recently though, if dust was included in simulations at all, it was typically assumed that it was tightly coupled to the gas. More recent work has allowed for the dust to decouple from the gas, though this brings with it challenges for including all the dust destruction processes, especially grain-grain collisions. We discuss the various methods of simulating dust evolution in the context of magneto-hydrodynamical simulations of the interstellar medium and their strengths and weaknesses. In particular we present a method for improving on the tight coupling approach that shows promise for simulating the evolution of small grains subject to shocks.
Does Central Asian forest growth benefit from a warming-wetting climate? Insights from tree-ring records
Authors: Wang Ting , Bao Anming , Xu Wenqiang , Zheng Guoxiong , Du Weibing , Yu Tao , Huang Xiaoran , Gao Ziqian , Bao Jiayu , Stoffel Markus , Maeyer Philippe De , Wulf Alain De 
Published: April 2024

Abstract
Climate warming has pronounced impacts on high-elevation regions, including arid Central Asia, and has multiple impacts on the environment. Forests in these mountainous areas provide essential services by regulating regional climate, sequestering carbon, and supporting soil and water conservation. However, trends in forest productivity and their response to climate change remain unclear. To address this knowledge gap, we collected tree cores from five sample plots in the western Tianshan region. We used tree-ring widths to reconstruct net primary productivity (NPP) and investigated the sensitivity of forest NPP to climate change by analysing weather station data and employing LASSO regression to identify climatic factors influencing forest growth. Our results demonstrate that the reconstructed forest NPP effectively captured significant carbon shifts and revealed a non-significant increase in forest productivity associated with climate warming and higher precipitation between 1970 and 2020 at low and middle elevations in the Tianshan mountains. Humidity is the primary limiting factor affecting forest growth in this region. Conversely, the relationship between temperature and forest growth is not consistent as precipitation increases. Our findings suggest that continued warming will exacerbate water stress in forests.
Characterizing the performances of different observational precipitation products and their uncertainties over Africa
Authors: Ayugi Brian Odhiambo , Chung Eun-Sung , Babaousmail Hassen , Lim Kam Sian Kenny Thiam Choy 
Published: April 2024

Abstract
Validation of observed gridded precipitation datasets sourced from satellites or reanalysis over Africa remains a challenge due to the dearth of in-situ products that can act as a true estimate. To address this gap, this study compares the performance of different precipitation products (gauge, reanalysis, and satellite-based) sourced from the Frequent Rainfall Observations on GridS (FROGS) database over Africa. Satellite products are classified as corrected (incorporating gauge observations into their algorithms) or uncorrected, which implies that temporal variations depend entirely on the satellite. The main aim is to identify regions where precipitation products depict minimal uncertainties, supporting the use of the datasets in understanding precipitation variability in the specific regions. This is achieved by applying the triple collocation approach, which takes advantage of three collocated datasets of the same variable to derive the mean square error without requiring knowledge of the true value. The results show that light precipitation (1–5 mm d−1) was prevalent in most regions of Africa during the study duration (2001–2016). Estimating the spatial distribution of daily precipitation greater than the 90th percentiles suggests that extreme precipitation is mainly detected over the Central Africa region and coastal regions of West Africa, where the majority of uncorrected satellite products show consistent performance. The satellite product CMORPH_V1_RAW shows higher estimates of 90th percentile precipitation among the uncorrected satellite products. The ability of precipitation products to detect rainy or non-rainy days shows that corrected satellite products depict notable agreement for probability of detection and false alarm ratio over most regions of Africa. Overall, better performance is demonstrated by the IMERG products, ARCv2, CHIRPSv2 and PERSIANN_CDRv1r1 (corrected), and GPCC, CPC_v1.0 and REGEN_ALL (gauge) during the study period. Among the reanalysis products, ERA5 datasets shows good performance in estimating daily precipitation over Africa. The optimal maps that show the classification of products in regions where they depict reliable performance can be recommended for various usage by different stakeholders.
Divergent responses of subtropical evergreen and deciduous forest carbon cycles to the summer 2022 drought
Authors: Han Lei , Chen Yanan , Wang Ying , Sun Yuan , Ding Zhi , Zhang Hongsheng , Tang Xuguang 
Published: April 2024

Abstract
Ongoing shifts in climate, especially extreme drought, is posing considerable threats to the forest carbon uptake worldwide. In China, summer 2022 was the warmest and driest since the beginning of meteorological measurements. This study synthesized the tower-based carbon fluxes and climate data from two subtropical evergreen and deciduous forest ecosystems to investigate the effects of such summer drought. Interestingly, the net ecosystem production (NEP = −NEE) only exhibited a slight decrease at the deciduous forest while it even enhanced at the evergreen forest during the summer 2022 drought. Further analysis revealed that although reductions in gross primary productivity (GPP) and ecosystem respiration (R
e) were found at both sites, larger decrease in R
e than GPP at the evergreen forest led to stronger NEP compared to the previous year. However, the NEP of two forest ecosystems sharply reduced in the following 2023, which can be ascribed to the legacy effects of the summer 2022 drought. The results of multiple linear regression revealed that soil water content (SWC) was recognized as the primary driver of GPP and R
e, and downwelling shortwave radiation (R
g) regulated the variability of NEP during the summer 2022. Across these forest carbon fluxes including GPP, R
e and NEP, the deciduous forest exhibited larger resistance, whereas the evergreen forest showed stronger resilience. All analyses emphasized the diverse adaptive strategies among vegetation types, which acted an important role in assessing ecosystem carbon sequestration in face of future climate change.
Computational insights into the electronic structure of functionalized monolayer hBN supercapacitor electrodes
Authors: Sruthi T , Mathew Vincent 
Published: April 2024

Abstract
In this study, we examined a collection of functionalized hBN monolayers’ electronic structure and quantum capacitance. Different ad-atom adsorption techniques have been used to functionalize the hBN monolayer. Density functional theory calculations are performed to precisely determine the electrical structure of an ad-atom doped hBN monolayer with a range of doping concentrations. The quantum capacitance of every functionalized system was then calculated. Above 500 μF/cm2, a noticeable quantum capacitance has been seen. Our calculations demonstrate that ad-atom doping at hollow position of hBN honeycomb lattice greatly increases the quantum capacitance of hBN monolayer. The microscopic cause of this system’s increased quantum capacitance has been investigated. Our DFT-based calculation shows that the system has a very large quantum capacitance due to the creation of new electronic states in the vicinity of the band edge and the Fermi level shift caused by the ad-atom adsorption.
Simultaneous synthesis of carbon quantum dots and porous carbon using one step hydrothermal method for supercapacitor applications
Authors: Sharanappa Sapna , Vijaykumar S P , Suresh D S , Ganesha H , Ba Shbil Abdullah , Basappa M 
Published: April 2024

Abstract
The present work reports a one-step hydrothermal carbonization process used to simultaneously produce carbon quantum dots and porous carbon. The synthesized materials were characterized by using a Fourier transform infrared spectrometer (FT-IR) to study the chemical interactions, the optical properties were studied with the help of UV-visible absorption spectroscopy, and the morphology analysis of CQDs and porous carbon was examined using FESEM, in which spherical morphology confirms the formation of CQDs, while XRD confirms the amorphous nature of the porous carbon. The application of CQDs and porous materials is evaluated as a supercapacitor material by using the cyclic voltammetry technique. The specific capacitance of porous carbon was 161.33 Fg−1 at 0.01mVs−1 scan rate. This makes CQDs and porous carbon a promising candidate for applications in energy storage devices such as supercapacitors.
Performance analysis of A3B2X9 (Cs3Bi2I9) based perovskite photovoltaic tandem structure with crystalline Silicon (c-Si)
Authors: Goutham Kumar Shreyus , Surya Teja Reddy Tadi , Suraj N , Prashanth C R , Honnavar Gajanan V 
Published: April 2024

Abstract
The efficiency of solar cells with single-junction utilizing organic-inorganic hybrid perovskites have attained a value more than 25.5%. The device power conversion efficiency (PCE) can be improved further either by optimizing the absorber layer (Perovskite film) or by investigating the novel device structures such as tandem based solar cells with perovskite and silicon. This combination of top cell (Perovskite solar cell) and bottom cell (Silicon solar cell) can improve the PCE which surpasses the Shockley-Queisser limit of single-junction solar cells by utilizing a wider range of solar spectrum. This paper presents an optimization and simulation of standalone Cs3Bi2I9 perovskite solar cell which was later integrated with a c-Si solar cell to simulate a tandem structure using SCAPS-1D software. The aim is to investigate the performance enhancement of the perovskite solar cell by optimizing it and stacking it on top of a high-efficiency c-Si solar cell using a four-terminal (4T) structure. At a short-circuit current density of 16.165 mA/cm2 and an open-circuit voltage of 1.41 V, the simulation findings demonstrate that the Cs3Bi2I9 perovskite solar cell exhibits a high-power conversion efficiency of 20.37%. The tandem structure demonstrates an enhanced power conversion efficiency of 31.59% which is significantly higher than the efficiency of the individual cells. The Cs3Bi2I9 perovskite solar cell is a great choice for application in tandem systems with c-Si solar cells for high-efficiency, according to the simulation results. This study provides valuable insights for the development of efficient perovskite/c-Si tandem solar cells.
Deep Learning Neural Network (DNN) Coupled Laser Induced Breakdown Spectroscopy for Quantitative Analysis of Copper Alloys
Authors: Pauly Leya , Mary John Lekha , Suryan Sivadas M S , Anoop K K 
Published: April 2024

Abstract
Conventional Laser Induced Breakdown Spectroscopy (LIBS) quantitative analysis, employing both calibration and calibration-free techniques, is challenged by spectral overlapping, self-absorption, and spectral broadening effects, leading to decreased accuracy. Recently, integration of machine learning (ML) algorithms with LIBS has been increasingly employed to tackle these challenges. This article explores the augmentation of LIBS with deep learning neural networks (DNN) to enhance accuracy of quantitative analysis of multi-elemental copper alloys. Sufficient training data was acquired by simulating optical emission spectra for bronze (Cu-Sn) and admiralty brass (Cu-Zn-Sn) alloys under standard laser produced plasmas conditions, encompassing different alloy concentrations, electron temperatures, and densities. We designed a regularized DNN structure, fine-tuned using a validation dataset to optimize quantitative results. The model’s accuracy was assessed with test dataset. The quantitative results demonstrated reduced loss as training spectra increased from 500 to 5000 for both alloys. The decline in mean squared error, from 2.793×10−3 to 4.283×10−5 for bronze and from 3.245×10−2 to 5.598×10−4 for admiralty brass alloys, as training data increased from 500 to 5000, underscores the proposed DNN model’s potential for metallurgical alloy quantification.
Electrical nature and surface enhanced Raman spectroscopy of Ag nanoparticles decorated graphene Sheet
Authors: Sachidanand P S , Mohan Manu , Reshmi S , Viswan Gauthami , Bhattacharjee Kuntala 
Published: April 2024

Abstract
The interaction between graphene and silver nanoparticles (AgNPs) is investigated by studying the surface-enhanced Raman spectroscopy (SERS) that shows a large enhancement of Raman signal from the AgNPs coated graphene compared to the bare graphene. In addition, the electrical nature of the sample is examined using a two-probe probe station to acquire current-voltage (I-V) characteristics exhibiting a semi-conducting current behaviour of the AgNPs-graphene heterojunctions, in contrast to a semi-metallic electronic behaviour observed for the pristine graphene. We propose band gap engineering of graphene from semi-metallic to semi-conducting via breaking of A-B sub-lattice symmetry due to deposition of AgNPs which is believed to be associated by causing deformations at the regions where graphene is in close proximity with the AgNPs. Furthermore, there is also a possibility of doping induced by AgNPs to graphene and thereby, emergence of a bandgap due to tuning of the electronic structure. In this work, we report these investigations which possess profound implications for application of graphene as a semi-conducting material.
Nilgiri tree peels-derived activated porous carbon for high performance supercapacitor applications
Authors: Ba shbil Abdullah , Nagaraju Y S , Ganesha H , Veeresh S , Suresh D S , Vijaykumar S P , Sharanappa Sapna 
Published: April 2024

Abstract
In this study, the activated porous carbon (APC-x) was collected from Nilgiri tree peels, making it a sustainable and cost-effective green synthesis approach. The raw material undergoes carbonization and activating it using KOH as chemical activation agent. The chemical composition, crystallinity and morphology of the surface of ACP-x was examined by Fourier Transform infra-red (FTIR), X-ray diffraction and Field emission scanning electron microscope (FESEM). The cyclic voltammetry technique was used to measure the electrochemical performance of APC-x using PVA/KOH gel electrolyte in two-electrode system. The highest specific capacitance value 140 F/g was obtained for APC-0 at scan rate of 10 mV/s. The use of Nilgiri tree peels as precursor for activated porous carbon production presents a sustainable approach for developing high-performance supercapacitor materials.