bluuebunny/crossref_metadata_2025_split huggingface dataset:
Energy-dependent boosted dark matter from diffuse supernova neutrino background
Authors: Das Anirban , Herbermann Tim , Sen Manibrata , Takhistov Volodymyr 
Published: July 2024

AbstractDiffuse neutrinos from past supernovae in the Universe present us with a unique opportunity to test dark matter (DM) interactions. These neutrinos can scatter and boost the DM particles in the Milky Way halo to relativistic energies allowing us to detect them in terrestrial laboratories. Focusing on generic models of DM-neutrino and electron interactions, mediated by a vector or a scalar boson, we implement energy-dependent scattering cross-sections and perform detailed numerical analysis of DM attenuation due to electron scattering in-medium while propagating towards terrestrial experiments. We set new limits on DM-neutrino and electron interactions for DM with masses in the range ∼ (0.1, 104) MeV, using recent data from XENONnT, LUX-ZEPLIN, and PandaX-4T direct detection experiments. We demonstrate that consideration of energy-dependent cross-sections for DM interactions can significantly affect constraints previously derived under the assumption of constant cross-sections, modifying them by multiple orders of magnitude.
Optical appearance of black holes surrounded by a dark matter halo
Authors: Macedo Caio F.B. , Rosa João Luís , Rubiera-Garcia Diego 
Published: July 2024

Abstract
Black holes in General Relativity are described by space-time metrics that are simpler in comparison to non-vacuum compact objects. However, given the universality of the gravitational pull, it is expected that dark matter accumulates around astrophysical black holes, which can have an impact in the overall gravitational field, especially at galactic centers, and induce non-negligible effects in their observational imprints. In this work, we study the optical appearance of a spherically symmetric black hole both when orbited by isotropically emitting light sources and when surrounded by a (geometrically and optically thin) accretion disk, while immersed in a dark matter halo. The black hole geometry plus the dark matter halo come as a solution of Einstein's field equations coupled to an anisotropic fluid whose density component follows a Hermquist-type distribution. We analyze in some depth the circular geodesic structure in both perturbative and non-perturbative regimes, investigating particular possible consequences for the structure of accretion disks. Despite this, however, even in situations in which the geodesic description differs profoundly from the isolated black hole case, we find minor modifications to the primary and secondary tracks of the isotropic orbiting sources, and to the width, location, and relative luminosity of the corresponding photon rings as compared to the Schwarzschild black hole at equal black hole mass and emission models. This shows that physical structures are crucial for understanding black hole images and points the limitations of drawing conclusions from more artificial imaging profiling. More profoundly, this fact points towards troubles distinguishing between both geometries using present observations of very-long baseline interferometry.
Local clustering of relic neutrinos: comparison of kinetic field theory and the Vlasov equation
Authors: Holm Emil Brinch , Zentarra Stefan , Oldengott Isabel M. 
Published: July 2024

AbstractGravitational clustering in our cosmic vicinity is expected to lead to an enhancement of the local density of relic neutrinos. We derive expressions for the neutrino density, using a perturbative approach to kinetic field theory and perturbative solutions of the Vlasov equation up to second order. Our work reveals that both formalisms give exactly the same results and can thus be considered equivalent. Numerical evaluation of the local relic neutrino density at first and second order provides some fundamental insights into the frequently applied approach oflinear responseto neutrino clustering (also known as the Gilbert equation). Against the naive expectation, including the second-order contribution does not lead to an improvement of the prediction for the local relic neutrino density but to a dramatic overestimation. This is because perturbation theory breaks down in a momentum-dependent fashion and in particular for densities well below unity.
Heavy dark matter in white dwarfs: multiple-scattering capture and thermalization
Authors: Bell Nicole F. , Busoni Giorgio , Robles Sandra , Virgato Michael 
Published: July 2024

Abstract
We present an improved treatment for the scattering of heavy dark matter from the ion constituents of a white dwarf. In the heavy dark matter regime, multiple collisions are required for the dark matter to become gravitationally captured. Our treatment incorporates all relevant physical effects including the dark matter trajectories, nuclear form factors, and radial profiles for the white dwarf escape velocity and target number densities. Our capture rates differ by orders of magnitude from previous estimates, which have typically used approximations developed for dark matter scattering in the Earth.
We also compute the time for the dark matter to thermalize in the center of the white dwarf, including in-medium effects such as phonon emission and absorption from the ionic lattice in the case where the star has a crystallized core. We find much shorter thermalization timescales than previously estimated, especially if the white dwarf core has crystallized.
We illustrate the importance of our improved approach by determining the cross section required for accumulated asymmetric dark matter to self-gravitate.
Enhanced soft magnetic properties of SiO2-coated FeSiCr magnetic powder cores by particle size effect
Authors: Ge 葛 Mingyue 铭悦 , Xiao 肖 Likang 礼康 , Liu 刘 Xiaoru 潇如 , Pan 潘 Lin 嶙 , Zhou 周 Zhangyang 章洋 , Lan 蓝 Jianghe 江河 , Xiong 熊 Zhengwei 政伟 , Wu 吴 Jichuan 冀川 , Gao 高 Zhipeng 志鹏 
Published: July 2024

Abstract
It has been known that metal FeSiCr powders with large average particle sizes have been typically employed to prepare magnetic powder cores (SMCs), with few studies reported on the influence of magnetic properties for original powders with various average particle sizes less than 10 μm. In this work, SiO2-coated FeSiCr SMCs with different small particle sizes were synthesized using the sol–gel process. The contribution of SiO2 coating amount and voids to the soft magnetic properties was elaborated. The mechanism was revealed such that smaller particle sizes with less voids could be beneficial for reducing core loss in the SMCs. By optimizing the core structure, permeability and magnetic loss of 26 and 262 kW/cm3 at 100 kHz and 50 mT were achieved at a particle size of 4.8 μm and ethyl orthosilicate addition of 0.1 mL/g. The best DC stacking performance, reaching 87%, was observed at an ethyl orthosilicate addition rate of 0.25 mL/g under 100 Oe. Compared to other soft magnetic composites (SMCs), the FeSiCr/SiO2 SMCs exhibit significantly reduced magnetic loss. It further reduces the magnetic loss of the powder core, providing a new strategy for applications of SMCs at high frequencies.
Peak structure in the interlayer conductance of Moiré superlattices
Authors: Tao 陶 Yizhou 懿洲 , Liu 刘 Chao 超 , Xiao 肖 Mingwen 明文 , Fang 方 Henan 贺男 
Published: July 2024

Abstract
We investigate the peak structure in the interlayer conductance of Moiré superlattices using a tunneling theory we developed previously. The theoretical results predict that, due to the resonance of two different partial waves, the double-peak structure can appear in the curve of the interlayer conductance versus twist angle. Furthermore, we study the influences of the model parameters, i.e., the chemical potential of electrodes, the thickness of Moiré superlattice, and the strength of interface potential, on the peak structure of the interlayer conductance. In particular, the parameter dependence of the peak structure is concluded via a phase diagram, and the physical meanings of the phase diagram is formulized. Finally, the potential applications of the present work is discussed.
Enhancing VTOL Performance: Shrouded Rotor BLDC Motor Model and Validation
Authors: Dayhoum Abdallah , Etewa Mohamed , Ramirez-Serrano Alejandro , Martinuzzi Robert 
Published: July 2024

Abstract
One prominent area of current research interest, driven by a multitude of factors and considerations, centers around drone technology. A primary challenge currently faced in the realm of small-scale Unmanned Aerial Vehicles (UAVs) is the increasing demand for increased power coupled with the constraint of limited flight duration provided by available battery technology. These vehicles fall into two overarching categories based on the presence or absence of shrouding around their rotor(s). Shrouded rotors play a pivotal role in augmenting aerodynamic performance by enhancing thrust while mitigating blade-tip vortex losses and numerous other aspects that are not presented in open rotors. Although such characteristics contribute to an expanded effective rotor diameter and optimized airflow within the shroud. There are numerous aspects related to power, shroud and rotor characterization, etc. that need to be understood before shrouded rotors are commonly used. This paper presents the implementation of an effective mathematical model for Brushless Direct Current (BLDC) motors, specifically tailored for application in conjunction with shrouded rotors aimed at enhancing thrust and reducing power consumption. The proposed model serves to predict the performance characteristics of the utilized motor within the context of the specified shrouded rotor combination. In order to identify the effects of the induced load torque on the rotor’s dynamic response, a methodical analysis is conducted on the numerical simulation of the proposed model. The results are experimentally verified for hover flying in a case study of a scalable and highly maneuverable vertical takeoff and landing aircraft developed for operations in highly confined spaces. The adoption of this modeling technique is anticipated to significantly streamline the shrouded rotor combination design and selection process, particularly in the selection of an appropriate motor.
Gradient-based aerodynamic shape optimization using a discrete adjoint approach on a graphics processing unit
Authors: Yang L , Yang J 
Published: July 2024

Abstract
This paper presents a gradient-based aerodynamic shape optimization framework that utilizes a Graphics Processing Unit (GPU) for solving both flow and adjoint equations. It is built based on a GPU-accelerated flow solver that has been developed previously. Hence, the focus of this work is on how to solve the adjoint equations on the GPU and subsequently compute the gradients. The adjoint equations are right-preconditioned by a block Incomplete Lower Upper (ILU) preconditioner and solved by a restarted Generalized Minimum Residual (GMRES) method. The exact residual Jacobian matrix in the adjoint equations is computed using finite difference and a distance-2 graph coloring algorithm. With the adjoint-based gradients, the steepest descent method with momentum is employed for constrained aerodynamic shape optimization of a wing-body configuration at a transonic flow condition.
Micro-vibration simulation of laser communication satellite subjected to the disturbance of momentum wheels
Authors: Zhang Jie , Yin Jiacong , Ye Sang , Pei Shengwei , Wang Yihong 
Published: July 2024

Abstract
As the laser gradually replaces the microwave for satellite communication, micro-vibration environment of satellites in orbit become the key factor to keep laser link and communication quality. Due to the limited use of vibration suppression measures, micro-vibration simulation covering the whole frequency range up to 1000 Hz is required for laser communication satellites, which is inevitably more difficult than optical satellites focusing on low-frequency characteristics. This paper proposes an engineering algorithm that can quickly and efficiently simulate the micro-vibration environment of satellites within 1000Hz, and the simulation results are confirmed by experimental data.
A Hybrid Algorithm for Predicting Head Related Transfer Function based on Physiological Parameters
Authors: Lu Dongdong , Zhu Jianbin , Shen Zhiqiang , Liu Chuang , Xin Qi 
Published: July 2024

Abstract
The Head Related Transfer Function (HRTF) characterizes the interaction between sound and the physiological structure of the listener, therefore, HRTFs have personalized characteristics. This paper proposes a hybrid algorithm for predicting HRTFs based on anthropometric parameters. This algorithm reduces the dimensionality of physiological parameters and HRTF, and then constructs a regression model between physiological parameters and HRTF. Firstly, principal component analysis is used to reduce the dimensionality of HRTF, followed by sparse principal component analysis to reduce the dimensionality of measured physiological parameters. Finally, a prediction model from physiological parameters to principal component coefficients is constructed through least squares regression. The predicted results were analyzed with spectral distortion. The result indicates that this prediction method can effectively predict HRTF.
AHA-Kriging surrogate model method for surface partition flaw tolerance assessment of turbine blisk
Authors: Wen Jiongran , Zheng Baiyang , Fei Chengwei 
Published: July 2024

AbstractThe high-pressure turbine blade in aero-engine power system may experience microstructural degradation due to uncertain flaws, multi-physical fields and loads during manufacturing, processing, installation, and maintenance, leading to serious structure deterioration that affects safety and reliability in service. Therefore, it is necessary to assess the influence of random flaws and loads on the fatigue performance of turbine blades from a probabilistic perspective. In this study, we propose a novel method based on the Artificial Hummingbird Algorithm and Kriging surrogate model (AHA-Kriging), for flaw tolerance assessment in the surface partition of the turbine blade. The results indicate that in the hazardous zone, the flaw tolerance reliability is 0.9984, corresponding to a LCF life of 1520 cycles. In the safe zone, the flaw tolerance reliability is 0.9991, corresponding to a LCF life of 2501 cycles. The primary factor influencing LCF life is flaw size, followed by factors such as the strength coefficient, gas temperature, and fatigue strength exponent. Besides, the AHA-Kriging approach exhibits higher modeling precision and simulation efficiency compared to other methods. This paper presents a practical engineering approach for assessing flaw tolerance in the surface partition of complex components, which is of significant value.
A proposal of drilling unit for robotic machining
Authors: Kondo Yasuo , Arimura Jho , Oh-hori Tom 
Published: July 2024

Abstract
In the assembly of large aircraft, drilling is often performed onsite after components are assembled, and many holes are still drilled by manual labor. In this unusual condition, it is difficult to apply conventional CNC machining. The authors have been proposing soft machining, a machining method that copies human drilling, as a machining method suitable for robotic drilling. In soft machining, thrust force is controlled to be smaller than that in CNC machining, and hole widening and burrs are less likely to be generated. In addition, vibration can be controlled by optimizing arm layout and drill shape. A twist drill with a 135° tip angle that was resharpened to a 115° tip angle at the center of the drill showed high performance in suppressing vibration. These indicate that soft machining can be applied to robotic drilling.
MSA-GAN: A novel method for inpainting EBSD image via cellular automation and deep learning
Authors: Zheng Baiyang , Wen Jiongran , Fei Chengwei 
Published: July 2024

Abstract
Electron backscatter diffraction (EBSD) is an important technique for analyzing the microstructure of materials. Unfortunately, EBSD images obtained by experimental means often suffer from different types of information loss, which affect the subsequent experimental analysis. Our study aims to study an EBSD image restoration method based on deep learning, which can solve the problem of various defects in EBSD imaging process. This paper proposes MSA-GAN: A multi-scale image restoration method based on generative adversarial networks (GAN) and feature pyramids (ASPP). To construct a dataset for training the model, a mask generation method based on cellular automata is proposed to simulate the information loss in EBSD images. The experimental results show that the MSA-GAN method can effectively restore large, medium and small types of defects in EBSD images, and achieve better results than other restoration methods, improving the quality and accuracy of the digital microstructure images. Our study provides a new technical means for the analysis of material microstructures, which has important theoretical and practical value.
Improvement of damage identification in GFRP plates using self-heating based vibrothermography and image processing
Authors: Katunin A , Amraei J , Lis K , Wachla D 
Published: July 2024

Abstract
Self-heating based vibrothermography (SHVT) is the non-destructive testing technique developed by the authors, which does not require external heat excitation. Thermal excitation is performed using viscoelastic heating induced by resonant vibration of a tested polymeric composite structure. This is especially useful in cases where direct thermal excitation cannot be applied. In this study, the authors implemented the SHVT technique for two-dimensional plate-like structures with simulated damage, which allowed initial detection of damage. Advanced image processing methods make it possible to significantly enhance the detectability of damage compared to the raw thermographic images acquired during inspection. The analysis of applied image processing methods allowed analysing the increase in damage detectability and selecting the most effective ones to achieve the best improvement of raw thermograms. The results of this study may find application in the inspection of polymeric composite components, especially in aircraft industry, where timely detection of damage is crucial for ensuring structural integrity and safety.
Effects of strong solar flares on the ionospheric electron density over the Philippines
Authors: Domingo Zane Nikia C. , Macalalad Ernest P. 
Published: July 2024

Abstract
Solar flares are capable of generating extreme amounts of ionizing radiation which affects Earth’s atmosphere. These are examined most notably in the X-ray and EUV regimes. The eruption of strong solar flares of classes X6.9, X2.25, and M9.7 were recorded, during which the Philippines was on Earth’s sunlit side. In this paper, the observation of the effects of solar radiation on the ionospheric electron density is done by quantifying the change in the slant total electron content (ΔSTEC). This was calculated by finding the difference between the difference relative STEC and its fitted data. The data were acquired from two GNSS receiver stations (PIMO & PTGG) in the Philippines. Moreover, it was inferred that changes in the electron density are manifest during increased EUV enhancements on the occurrences of the X6.9 and M9.7 flares. The calculated ΔSTEC ranged from ∼0.43 – 0.67 TECU during the X6.9 (PIMO) and ∼0.45 – 0.71 TECU during the M9.7 (PTGG) flares. This signifies that the contribution of the EUV radiation to the ionospheric response in the E and F ionospheric regions is more immense than that of the X-ray.
Predicting the minimum energy pathway of 1H to 1T phase transition of select 2D transition metal dichalcogenides via density functional theory and machine learning approach
Authors: Diego KY M , Putungan D B , Santos-Putungan A B 
Published: July 2024

Abstract
This study predicts the minimum energy pathways (MEPs) and transition barriers of two-dimensional transition metal dichalcogenides (TMDs) during their 1H to 1T structural phase transitions. The investigation utilizes density functional theory (DFT) calculations and machine learning algorithms to predict the MEPs and transition barriers. Six TMDs, namely NbSe2, ScS2, ScSe2, TiTe2, VS2, and VSe2, are selected for analysis. The DFT calculations provide reference values for comparison with the machine learning predictions. The transition barriers obtained through DFT calculations range from 0.13 eV to 0.72 eV. The machine learning models, including Linear Regression, Random Forest Regression, K-Nearest Neighbor Regression, and Gaussian Process Regression, demonstrate varying degrees of accuracy in predicting the transition barriers. Gaussian Process Regression achieved the highest accuracy among all ML models, with the absolute mean error spanning all systems being 0.0093. It has been shown that the features that were identified in the study are sufficient for the prediction of the MEPs and transition barriers of TMDs using machine learning. Overall, this study contributes to the understanding of TMD phase transitions and provides insights into the integration of machine learning in materials science research.
Detection of microplastics in surface water using a DSLR lens-based UV imaging system
Authors: Jaruda RA J , Gabayno JL F 
Published: July 2024

Abstract
Microplastics are any synthetic solid particle or polymeric matrix, ranging from 1 μm to 5 mm in diameter. Due to their low density and size, microplastics can easily be transported through wind and water currents, contaminating the environment with the toxic compounds they contain, harming living organisms through microplastic consumption and exposure. Despite being the top global contributor of plastic waste in 2010, the Philippines still has very limited studies regarding microplastic contamination. There is a need, then, to develop an efficient and accessible means of identifying and quantifying microplastics. To do this, this study utilized a DSLR lens attached to a UV CCD camera to capture images of microplastics, which fluoresce under 395 nm illumination. An image processing algorithm was applied to the resulting images, including binarization, adaptive segmentation, and particle counting to quantify MPs as a fast and easily accessible testing method. Results from the study revealed that despite water samples having been taken within proximity of one another, the number of particles identified within the system still showed a wide spread in values. In addition, it was found that the size distribution of the microplastics obeys the power law. As a new application for UV-based imaging, our method showed that quantitative detection of microplastics is feasible for future in situ measurement of marine water samples. However, further refinement to the counting technique and comparison with standard testing for validation is still recommended for improvement.