bluuebunny/crossref_metadata_2025_split huggingface dataset:
Detecting GNSS spoofing and Re-localization on UAV based on imagery matching
Authors: Liu Ruihang , Liu Haiying , Meng Xiaolin , Li Tiexiang , Hancock Craig M 
Published: October 2024

Abstract
Due to rapid advancements in global navigation satellite system (GNSS), computer, and microelectronics technologies, there is a growing popularity and widespread promotion of high-performance, cost-effective intelligent UAVs across various applications. Recently, GNSS spoofing attacks have emerged as a significant obstacle hindering the long-term development of UAVs. UAVs rely heavily on unprotected GNSS signals for navigation, making them highly vulnerable to spoofing. This paper presents an algorithm that employs image matching for detecting GNSS spoofing and re-localization on UAVs using a deep learning methodology. This method functions autonomously solely reliance on camera and does not require alterations to the antenna configuration. Utilizing a camera-equipped UAV, we evaluate the likeness between real-time aerial photographs and satellite imagery by leveraging the position information provided by UAV. By identifying disparities between images taken by a spoofing affected drone and authentic ones, the spoofing can be identified using deep neural network models. Upon detecting spoofing, this paper presents a vision-based re-localization method for UAVs. Experimental results demonstrate an approximately 88.4% accuracy of our model in detecting GNSS spoofing attacks within 100 ms and 88% success rate of re-localization with the accuracy of less than 15 m. Our algorithm exclusively depends on publicly accessible satellite imagery, offering an intelligent and efficient approach for detecting UAV GNSS spoofing and re-localization in GNSS denied environment.
MODVORTEx: computer vision-driven automation for magnetic domain wall velocity analysis
Authors: Raj Rakhul , Reddy V Raghavendra 
Published: October 2024

Abstract
This paper presents Magneto Optic Domain Velocity Observation and Real-Time Extraction (MODVORTEx), a comprehensive software solution for automating domain wall (DW) velocity measurements in magnetic materials using magneto-optic Kerr effect microscopy. Building upon our previous work on bubble domain structures, we introduce a versatile graphical user interface (GUI) that accommodates arbitrary domain shapes and employs advanced computer vision techniques. The software provides different methods for DW detection and velocity calculation, catering to various domain structures of arbitrary shape. Our approach significantly reduces the time and effort required for data extraction, transforming a process that previously took days of manual work into a task completable within minutes. We provide the details of the algorithmic implementation which is organized into pre-processing, DW detection, displacement measurement, and velocity extraction. This tool is applicable to a wide range of scenarios, including bubble domain dynamics, Dzyaloshinskii–Moriya interaction studies in perpendicular magnetic anisotropy systems, current-driven DW motion in patterned strips etc. By providing both GUI and Application Programing Interface, our software offers flexibility for integration into existing measurement systems and adaptability for specific research needs. This automation promises to accelerate research in spintronics and magnetic materials, enabling more comprehensive and accurate studies of DW dynamics.
Synthesis mechanism of TiO2 nanoparticles via high frequency electrical arc discharge
Authors: Tavakolian Amir Hossein , Abdullah Amir , Hoshmand Moini Saleh 
Published: October 2024

Abstract
The rapid advancement of nanofabrication techniques has significantly increased the utilization of nanoparticles in recent years. This study investigates the synthesis of titanium dioxide (TiO2) nanoparticles, highlighting their unique properties and diverse applications across scientific and industrial fields. These nanoparticles are valued for their biocompatibility and advantageous optical, electrical, and physical properties. Various synthesis methods—chemical, physical, and biological—are reviewed, with a particular focus on the electric arc discharge method. This method is distinguished by its efficiency and environmental friendliness, enabling the production of highly pure nanoparticles. Utilizing continuous and alternating sparks between two electrodes, the technique generates spherical nanoparticles with adjustable sizes, controlled by the energy of each spark. An RC circuit-based device was designed for this electrical discharge process. Dynamic light scattering (DLS) measurements revealed an average particle size of 164.51 nm with a standard deviation of 44.08 nm. Field emission scanning electron microscopy (FESEM) images showed both solid and hollow spherical TiO2 nanoparticles. Energy dispersive spectroscopy (EDS) confirmed that the TiO2 particles contained only titanium and oxygen, with no other elements detected. X-ray diffraction (XRD) analysis verified the crystal structure, predominantly identifying the anatase phase of the synthesized nanoparticles. This research enhances the understanding of TiO2 nanoparticle synthesis and characterization, providing a foundation for future innovations in their extensive applications.
A plaintext-related and ciphertext feedback mechanism for medical image encryption based on a new one-dimensional chaotic system
Authors: Xu Jianwu , Liu Kun , Huang Qingye , Li Quanjun , Huang Linqing 
Published: October 2024

Abstract
In recent years, Plaintext-Related Image Encryption (PRIE) algorithms have been introduced, demonstrating a commendable level of plaintext sensitivity to resist chosen plaintext attack (CPA). However, these approaches suffer from several drawbacks, including inability to fully reconstruct the original image, limited practical value and excessive computational demands etc.. Moreover, the exponential expansion of medical data necessitates the formulation of more secure and efficient encryption algorithms. In this paper, firstly, a novel one-dimensional chaotic map, designated as 1D-SAM, which strikes an excellent balance between structural complexity and chaotic performance is proposed. The 1D-SAM achieve a larger chaotic range and an elevated Lyapunov exponent, signifying enhanced dynamical complexity. Subsequently, we devise a lightweight medical image encryption system leveraging the 1D-SAM and an innovative diffusion architecture, termed the plaintext-related and ciphertext feedback mechanism(PRCFM). This encryption system is a symmetric-key cryptosystem, eliminating the need for transmitting supplementary data beyond the secret keys to the recipient. Notably, the encrypted image maintains identical dimensions to its original counterpart and is fully recoverable. Complete simulation experiments were conducted on a personal computer equipped with MATLAB
R2021a, OS Windows 11, 2.60 GHz CPU and 16GB
RAM. The experimental results indicate that our encryption system, employing a single permutation-diffusion round, efficiently encrypts a 512 × 512 image in approximately 0.2854 seconds. Leveraging the advantages of the PRCFM, our approach demonstrates superior plaintext sensitivity, achieving an average number of pixels changing rate (NPCR) of 99.6051% and a unified average changed intensity (UACI) of 33.4452%. In summary, our work addresses key limitations of contemporary encryption frameworks, exhibiting acceptable performance in both encryption speed and security strength.
Construction and analysis of symmetric Sprott B multi-attractors with electric implementation
Authors: Doungmo Goufo Emile F 
Published: October 2024

Abstract
In chaos theory, a number of systems and models which apparently contain simple ordinary differential equations (ODEs) turn out to show a dynamic characterized by complicated behaviors and complex trajectories. One of such systems is the Sprott B model. We construct some set of multi-attractors based on the Sprott B model where additional parameters and operators are considered. After summarizing important preliminaries relevant to simple chaotic differential systems, the model is firstly solved analytically and numerically, then graphical simulations are provided. The later show coexistence and evolution of two chaotic attractors in a symmetrical representation. Lastly, similar results and expected outcomes are recovered via an electrical circuit implementation, realized using the Field Programmable Gate Array (FPGA) board, the Digital-to-Analog Converter (DAC) and the Rigol Oscilloscope. They also show progressing sets of coexisting multi-attractors.
Disassembling one-dimensional chains in molybdenum oxides
Authors: Du 杜 Xian 宪 , Li 李 Yidian 义典 , Zhao 赵 Wenxuan 文轩 , Xu 许 Runzhe 润哲 , Zhai 翟 Kaiyi 恺熠 , Chen 陈 Yulin 宇林 , Yang 杨 Lexian 乐仙 
Published: October 2024

Abstract
The dimensionality of quantum materials strongly affects their physical properties. Although many emergent phenomena, such as charge-density wave and Luttinger liquid behavior, are well understood in one-dimensional (1D) systems, the generalization to explore them in higher dimensional systems is still a challenging task. In this study, we aim to bridge this gap by systematically investigating the crystal and electronic structures of molybdenum-oxide family compounds, where the contexture of 1D chains facilitates rich emergent properties. While the quasi-1D chains in these materials share general similarities, such as the motifs made up of MoO6 octahedrons, they exhibit vast complexity and remarkable tunability. We disassemble the 1D chains in molybdenum oxides with different dimensions and construct effective models to excellently fit their low-energy electronic structures obtained by ab initio calculations. Furthermore, we discuss the implications of such chains on other physical properties of the materials and the practical significance of the effective models. Our work establishes the molybdenum oxides as simple and tunable model systems for studying and manipulating the dimensionality in quantum systems.
Experimental identification of ion cyclotron emission on HL-2A using YOLO neural network algorithm
Authors: Zhang Jiahao , Zhao Jun , Liu Linzi , Tong Ruihai , Zhong Wulyu , Luo Yi 
Published: October 2024

Abstract
Identification of magnetohydrodynamics (MHD) instabilities with neural networks has been extensively applied in the research of magnetically controlled fusion plasmas. Ion Cyclotron Emission (ICE) is a potential fast ion diagnostic method in burning plasmas. To assess ICE as a fast ion diagnostic for International Thermonuclear Experimental Reactor, real-time identification of ICE is required in the fast ion diagnostic flow. In the present work, we employed YOLO (You Only Look Once) to identify core and edge ICE in a large labeled database of HL-2A discharges, achieving a precision of 85.4% and a recall rate of 77.3%. Subsequent improvements to the YOLO model resulted in a noteworthy 8.3% increment in the recall rate. The developed identification method demonstrates significant potential for real-time application in identifying MHD instabilities.
Identification of Key Performance Metrics for Different Ultrasonic Surgical Devices
Authors: Zhang Yuli , Luo Xiaoning , Wu Wei , Zhang Qing , Xiong Shuanghui , Lin Sen , Zhou Pan , Xu Yang , Hu Min , Zhou Zhaoying 
Published: October 2024

Abstract
Ultrasonic surgical devices utilize high-frequency sound waves to perform surgical tasks, including cutting, fragmenting, emulsifying, and sealing tissues. Their multifunctional capabilities have facilitated their broad application across various surgical disciplines, such as general surgery, neurosurgery, and orthopedic surgery. While the current international standard, IEC 61847:1998 “Ultrasonics - Surgical Systems - Measurement and Declaration of the Basic Output Characteristics”, outlines a set of fundamental output characteristics for these devices, the diverse applications and performance requirements of each device necessitate the identification of key performance metrics. In this study, we delve into the applicability of these basic output characteristics for different ultrasonic surgical devices. Through comprehensive experimental evaluations on biological tissues, we assessed the influence of specific parameters, namely drive frequency, excursion amplitude, power output, duty cycle, and primary sound output area, on the cutting efficiency and coagulation results of three distinct ultrasonic surgical devices. Our results highlight the paramount parameters that govern the primary functions of these devices. This research offers invaluable insights for device manufacturers and evaluators, guiding them in prioritizing and emphasizing crucial parameters during the design, manufacturing, and evaluation phases of ultrasonic surgical devices.
Truncated Newton full waveform inversion method for the human brain imaging
Authors: Wu Faxuan , He Qinglong , Li Yang , Han Bo , Wang Yangfei 
Published: October 2024

Abstract
It has been shown that full-waveform inversion (FWI) method can be a competitive alternative for medical imaging problems. It offers high-resolution results while delivering the advantages of being fast, safe, portable, and affordable, compared to magnetic resonance imaging (MRI) and x-ray computed tomography. However, to the best of our knowledge, FWI applications in medical imaging only use the first-order derivative information. In this case, the high parameter contrasts between different tissues of human body and multi-scatterings problems may lead FWI to local minima. Thus, we present a competitive truncated Newton method for high-resolution imaging of the human brain. This truncated Newton method, based on the efficient linear solver MINRES-QLP, can make full use of multiple scattering wavefield information. Compared with the truncated Newton method based on conjugate gradient, the MINRES-QLP iterative method presents various advantages when solving linear systems, such as the capacity to handle both non-singular and singular systems, less computational cost, and efficiency even for ill-conditioned systems. Numerical experiments for imaging the brain with and without the skull are conducted. Numerical results indicate that, compared with the truncated Newton method based on conjugate gradient, the truncated Newton method based on MINRES-QLP exhibits computational efficiency while maintaining the same level of accuracy.
Ring-array Ultrasound CT Reflection Imaging Applied to Breast Tumor Detection
Authors: Shi Lifan , Huang Xiao , Yuan Xuting , Zhang Wendong 
Published: October 2024

Abstract
Breast tumor is one of the most common malignant tumors among women worldwide. Ultrasound imaging is a widely used modality for its detection and diagnosis. In this study, we derived the theory of focusing and steering technique based on curved array and verified it by simulation. We proposed an ultrasound CT imaging method based on ring-array with multi-subarray simultaneous transmission and reception. We performed simulation and experimental tests and evaluated the images using SSIM, PSNR and image entropy. The simulation results showed that the proposed focusing and steering technique could accurately focus on the set position. And when the ring-array diameter was 200 mm, the imaging effect reached a better level when the number of array elements increased to 1024, and increasing the number of elements had little effect on the imaging effect. In the experimental test, the best result was obtained when the sub-aperture element number of the ring-array was 1, and the result was better when the ring-array element number was 1024. This result provides an effective significance for ultrasound CT imaging of breast cancer.
Decoupling of Photoacoustic Spectroscopy Based on Machine Learning for Bone Chemical Composition Assessment: Simulation Study
Authors: Liu Jiayi , Feng Ting , Xie Weiya , Cheng Qian 
Published: October 2024

Abstract
Photoacoustic (PA) spectroscopy technology can be utilized to identification of molecules in biological tissue based on the contrast of optical absorption. However, it is susceptible to the interference of overlapped optical absorption peaks from different chemical components, especially for dense bone tissue which contains both organic and non-organic chemical components. To accurately extract the relative contents of chemical components in bone tissue, this study established a decoupling method based on the PA band ratio and machine learning to quantitatively analyze the proportion of chemical components in bone tissue from the PA spectra. The predicted quantification parameters of different chemical components were consistent with the simulated presets, and could be used to characterize the changes of bone tissue components. Considering that PA technology is non-invasive and radiation-free, this technique shows great potential for the early diagnosis and monitoring of bone disease progression.
Adaptive weight determination schemes in angle and sub-band compounding for ultrasonic beamforming
Authors: Yoneda Junna , Saito Yuta , Zheng Jie , Tagawa Norio 
Published: October 2024

Abstract
We have proposed the Adaptive Sub-Band Compound (A-SBC) method, which uses subbands of different frequencies to improve the resolution by adaptive compounding. A-SBC effectively suppresses the sidelobe level by mainly utilizing the principle that the phases of subbands of different frequencies are inconsistent off-axis. There are many specific methods that can realize A-SBC. In this study, we propose three additional methods based on a previous application of A-SBC to multi-angle plane-wave beamforming. We evaluate these four methods through simulation and human carotid artery experiments.
The mean scatterer spacing estimation with the Gabor atoms selected based on the Nakagami distribution
Authors: Zeng Xiuhua , Wu Keyan , Yang Taoxiang 
Published: October 2024

Abstract
Mean scatterer spacing (MSS) is a quantitative signature for disease diagnosis and tissue characterization. However, it is difficult to objectively extract the coherent components from the ultrasound RF echo signals to improve the accuracy performance of the MSS estimation. In the present study, a novel MSS estimation method with the matched Gabor atoms selected based on Nakagami parameters is proposed. Firstly, the ultrasound signals are decomposed into a series of Gabor atoms, and the envelopes of the residual signals are fitted with the Nakagami distribution. Then, the second order difference (SOD) of the shape parameters in Nakagami distribution is calculated to select the optimal atoms to match the coherent components. Finally, the locations of the selected Gabor atoms are used to estimate the MSS. In order to evaluate the performance of the proposed method, the simulated RF echo signals are modelled based on four regular degrees of the scatterer distributions, and then the optimally matched atoms are selected to estimate MSSs. The results based on simulated signals demonstrate that the propose method can provide accurate MSS estimation, which are advantageous for improving quantitative diagnosis of diseases and tissue characterization
A new inversion method for imaging near-wellbore radial slowness based on array acoustic logging data
Authors: Wang Z , Yue W Z , Zhu Y M 
Published: October 2024

Abstract
The slowness of near-wellbore formation is critical to geophysical exploration as it can be used to evaluate rock brittleness, wellbore stability, fracturing effect, and invasion depth. Conventional methods for investigating radial slowness profiles of both compression wave and shear wave near-wellbore are time-consuming and depend on the extracted arrival time of the first wave. Additionally, these methods cannot simultaneously conduct the profile inversion of the compression and shear waves. We develop a new inversion method for imaging near-wellbore radial slowness to overcome these limitations. It can adaptively extract slowness and its corresponding radial thickness from array acoustic logging data. The new method adaptively extracts slowness sequences with different radial depths by combining receivers with different source distances. The thickness corresponding to the slowness sequences can be obtained by combining the slow sequence with the ray theory, and finally, the radial slowness profile can be obtained. an example of synthesis using the FDTD method is presented. The results show that the algorithm is stable and reliable. Overall, this study highlights the value of radial slowness imaging as a powerful tool for wellbore analysis and reservoir characterisation.
Dipole acoustic remote detection (ARD) logging and its application in fracture-cave reservoir
Authors: Wu Baizhi , Dong Jingli , Xu Xiaokai , Du Qunjie , Zhai Yong , Chen Hao , Chao Yongsheng , Zhang Weifeng 
Published: October 2024

Abstract
Oil and gas from deep reservoirs are mostly enriched in the fracture-cave reservoirs. For this reason, fine evaluation of large-scale fracture-cave and effective detection of small scale fracture-cave are the key issues to be solved in the exploration of fracture-cave type carbonate reservoirs. The acoustic remote detection logging technology is based on acoustic reflection method to continuously image the formation around the well. The cross dipole array sonic data can be processed to image geological anomalies within a range of about 20 meters outside the well. Dipole S wave remote detection has the remarkable advantages of low emission frequency and larger investigation depth, but it has 180 degrees’ azimuthal ambiguity. This study is based on previously developed far field dipole S wave remote detection tool, finite difference is performed on formation models with and without are obtained for representative fracture-cave formations and remote detection processing and interpretation software is updated. These are applied on a well in the northwestern area. Through the comprehensive analysis of remote logging, conventional logging, electrical imaging logging and seismic data of the fracture-cave geological anomalies, a fine picture of the morphology and development of the geological anomalies from the near wellbore to the far end is formed
Reflection and transmission characteristic waves on the periodic rough interface of fluid-porous medium
Authors: Zhang Yujing , Han Qingbang , Su Nana , Wu Yang , Jin Qilin , Sun Liujia 
Published: October 2024

Abstract
Nondestructive testing (NDT) is a major method in practical applications such as material exploration and safety assessment. The interaction of ultrasonic waves with roughness material is crucial in NDT, thus the related ultrasonic diffraction theory at the rough interface is widely investigated and analyzed, especially for the periodic elastic medium interface. However, the actual surface is usually porous due to the long-term moisture infiltration. In this paper, the reflection and transmission characteristics of ultrasonic waves on the fluid-porous medium periodic rough surfaces are studied in details.
Adaptive adjustment method of segment linear frequency modulation excitation for wideband dipole logging
Authors: Cao Xueshen , Chen Hao , He Hongbin , Zhou Yinqiu , Chen DeHua 
Published: October 2024

Abstract
Wideband dipole signal is the basis for dipole dispersion correction and near-borehole imaging. Segment linear frequency modulation (SLFM) sound source signal is an effective method to broaden the dipole frequency band. In the actual logging process, the formation environment changes in real time, and the SLFM sound source with fixed parameters cannot compensate the bending wave excitation intensity curve at any time, which affects the quality of logging. This paper presents a method for adaptive adjustment of the SLFM sound source signal. Adaptive parameter adjustment can be made according to the change of downhole environment, so that the signal can be adapted to the changing downhole geological environment.