bluuebunny/crossref_metadata_2025 huggingface dataset:
Quantum Control Landscapes for Generation of H and T Gates in an Open Qubit with Both Coherent and Environmental Drive
Authors: Petruhanov Vadim N. , Pechen Alexander N.
Published: October 2023

An important problem in quantum computation is the generation of single-qubit quantum gates such as Hadamard (H) and π/8 (T) gates, which are components of a universal set of gates. Qubits in experimental realizations of quantum computing devices are interacting with their environment. While the environment is often considered as an obstacle leading to a decrease in the gate fidelity, in some cases, it can be used as a resource. Here, we consider the problem of the optimal generation of H and T gates using coherent control and the environment as a resource acting on the qubit via incoherent control. For this problem, we studied the quantum control landscape, which represents the behavior of the infidelity as a functional of the controls. We considered three landscapes, with infidelities defined by steering between two, three (via Goerz–Reich–Koch approach), and four matrices in the qubit Hilbert space. We observed that, for the H gate, which is a Clifford gate, for all three infidelities, the distributions of minimal values obtained with a gradient search have a simple form with just one peak. However, for the T gate, which is a non-Clifford gate, the situation is surprisingly different—this distribution for the infidelity defined by two matrices also has one peak, whereas distributions for the infidelities defined by three and four matrices have two peaks, which might indicate the possible existence of two isolated minima in the control landscape. It is important that, among these three infidelities, only those defined with three and four matrices guarantee the closeness of the generated gate to a target and can be used as a good measure of closeness. We studied sets of optimized solutions for the most general and previously unexplored case of coherent and incoherent controls acting together and discovered that they form sub-manifolds in the control space, and unexpectedly, in some cases, two isolated sub-manifolds.
Analysis of the Potential Range of Mountain Pine-Broadleaf Ecotone Forests and Its Changes under Moderate and Strong Climate Change in the 21st Century
Authors: Fedorov Nikolay , Zhigunova Svetlana , Shirokikh Pavel , Baisheva Elvira , Martynenko Vasiliy
Published: October 2023

Climatic changes have a significant impact on the composition and distribution of forests, especially on ecotone ones. In the Southern Ural, pine-broadleaf ecotone forests were widespread during the early Holocene time, but now have persisted as relic plant communities. This study aimed to analyze the current potential range and to model changes in habitat suitability of relic pine-broadleaf ecotone forests of the suballiance Tilio-Pinenion under scenarios of moderate (RCP4.5) and strong (RCP8.5) climate change. For modelling, we used MaxEnt software with the predictors being climate variables from CHELSA Bioclim, the global digital soil mapping system SoilGrids and the digital elevation model. In the Southern and Middle Urals, climate change is expected to increase the areas with suitable habitat conditions of these forests by the middle of the 21st century and decrease them in the second half of the century. By the middle of the 21st century, the eastern range boundary of these forests will shift eastward due to the penetration of broad-leaved tree species into coniferous forests of the Southern Ural. In the second half of the century, on the contrary, it is expected that climate aridization will again shift the potential range border of these forests to the west due to their gradual replacement by hemiboreal coniferous forests. The relationship between the floristic composition of pine-broadleaf forests and habitat suitability was identified. In low and medium habitat suitability, pine-broadleaf forests contain more nemoral species characteristic of deciduous forests of the temperate zone, and can be replaced by broadleaf forests after thinning and removal of pine. In the Volga Upland, suitable habitats are occupied by pine-broadleaf forests of the vicariant suballiance Querco robori-Tilienion cordatae. Projected climatic changes will have a significant impact on these ecotone forests, which remained completely unaltered for a long time.
Decoding the Chemical Signatures and Sensory Profiles of Enshi Yulu: Insights from Diverse Tea Cultivars
Authors: Guo Yating , Shen Yili , Hu Boya , Ye Huichun , Guo Haowei , Chu Qiang , Chen Ping
Published: October 2023

Enshi Yulu, a renowned Chinese steamed green tea, is highly valued for its unique sensory attributes. To enhance our comprehensive understanding of the metabolic variation induced by steaming fixation, we investigated the overall chemical profiles and organoleptic quality of Enshi Yulu from different tea cultivars (Longjing 43, Xiapu Chunbolv, and Zhongcha 108). The relationships between sensory traits and non-volatiles/volatiles were evaluated. A total of 58 volatiles and 18 non-volatiles were identified as characteristic compounds for discriminating among the three tea cultivars, and the majority were correlated with sensory attributes. The “mellow” taste was associated with L-aspartic acid, L-asparagine, L-tyrosine, L-valine, EGC, EC, and ECG, while gallic acid and theobromine contributed to the “astringent” taste. “Kokumi” contributors were identified as L-methionine, L-lysine, and GCG. Enshi Yulu displayed a “pure” and “clean and refreshing” aroma associated with similar volatiles like benzyl alcohol, δ-cadinene, and muurolol. The composition of volatile compounds related to the “chestnut” flavor was complex, including aromatic heterocycles, acids, ketones, terpenes, and terpene derivatives. The key contributors to the “fresh” flavor were identified as linalool oxides. This study provides valuable insights into the sensory-related chemical profiles of Enshi Yulu, offering essential information for flavor and quality identification of Enshi Yulu.
On the Mechanical, Thermal, and Rheological Properties of Polyethylene/Ultra-High Molecular Weight Polypropylene Blends
Authors: Gavande Vishal , Jeong Mingi , Lee Won-Ki
Published: October 2023

The novel ultra-high molecular weight polypropylene (UHMWPP) as a dispersed component was melt blended with conventional high-density polyethylene (PE) and maleic anhydride grafted-polyethylene (mPE) in different proportions through a kneader. Ultra-high molecular weight polypropylene is a high-performance polymer material that has excellent mechanical properties and toughness compared to other polymers. Mechanical, thermal, and rheological properties were presented for various UHMWPP loadings, and correlations between mechanical and rheological properties were examined. Optimal comprehensive mechanical properties are achieved when the UHMWPP content reaches approximately 50 wt%, although the elongation properties do not match those of pure PE or mPE. However, it is worth noting that the elongation properties of these blends did not match those of PE or mPE. Particularly, for the PE/UHMWPP blends, a significant drop in tensile strength was observed as the UHMWPP content decreased (from 30.24 MPa for P50U50 to 13.12 MPa for P90U10). In contrast, the mPE/UHMWPP blends demonstrated only minimal changes in tensile strength (ranging from 29 MPa for mP50U50 to 24.64 MPa for mP90U10) as UHMWPP content varied. The storage modulus of the PE/UHMWPP blends increased drastically with the UHMWPP content due to the UHMWPP chain entanglements and rigidity. Additionally, we noted a substantial reduction in the melt index of the blend system when the UHMWPP content exceeded 10% by weight.
Optimization and Performance Evaluation of an Atomized Acid System for the Expansion of Carbonate Gas Injection
Authors: Zhang Jianpeng , He Jiayuan , Zhang Rusheng , Zhang Lufeng , Xu Wenjun
Published: October 2023

The conventional liquid acid has several shortcomings in the acidizing process of fractured-vuggy carbonate reservoirs, including high filtration loss, fast reaction rate, high friction resistance, and difficult flowback. To address these issues, a new atomizing acid acidizing technology is proposed, combining the gas injection development practice from the fractured-vuggy carbonate reservoir in the Tahe oilfield. The laboratory experiments were conducted to optimize the type and concentration of atomized acid, iron ion stabilizer, corrosion inhibitor, and atomization stabilizer. The acid atomization rate was evaluated under different combinations of gas and liquid injection flows using a self-made atomized acid well migration simulator, and the best atomization scheme was selected. Furthermore, a kinetic experiment for the acid–rock reaction was carried out to evaluate the retarding performance of the atomized acid. The optimized formula for the atomizing acid system consists of 15~25% hydrochloric acid, 0.005% atomizing stabilizer (AEO-7), 1% iron ion stabilizer (EET), 1.5% corrosion inhibitor (EEH-160), and water. The optimal gas and acid injection scheme is gas injection at 2m3/min and acid injection at 10 mL/min, which maintains an atomization rate of over 80% after the acid mist migrates through the wellbore. Compared with gelling acid, the acid–rock reaction rate of atomized acid is 8.5, 9.1, and 10.6 times slower under acid concentrations of 15%, 20%, and 25% respectively. The retarding effect of atomized acid is superior, facilitating etching and initiating underdeveloped gas drive channels and thereby increasing the probability of gas communication with new reservoirs. The research findings presented in this paper establish a theoretical foundation for the practical implementation of the atomized acid acidizing process in the field.
A New Method for Measuring the Effective Length of Acid-Fracturing Fractures
Authors: Xu Wenjun , Li Shengxiang , Zhang Jianpeng , Wang Lei , Feng Yan , Liao Yuanai
Published: October 2023

Acid fracturing as an important stimulation technique, provides strong technical support for the exploration breakthrough and efficient development of carbonate oil and gas reservoirs. Accurately predicting the effective length of acid-fracturing fractures is of great significance for guiding the acid-fracturing design and improving the stimulation effect of acid fracturing. This article fully considers the essential requirement that the long-term conductivity of acid-fracturing fractures is not zero within the effective length segment. Based on the principle of the same acid concentration and acid dissolution amount, the long-term conductivity testing experiment of acid-fracturing fractures under different residual acid concentrations was designed and carried out with the consideration of the common ion effect. The critical acid concentration with long-term conductivity of 0 was obtained. This method overcomes the shortcomings of the existing methods that result in the overestimation of the effective length of acid-fracturing fractures due to inaccurate values of residual acid concentration or short-term conductivity as the determining criterion. The experimental results show that the higher the acid concentration, the deeper the acid etching groove, and the higher the initial conductivity of acid-fracturing fractures. The long-term conductivity decline rate of different acid concentrations is above 80%, which means that using short-term conductivity as an evaluation indicator alone will overestimate the effective length of acid-fracturing fracture and the yield-increasing effect of acid-fracturing treatment. In the case presented in this paper, the critical acid concentration for acid-fracturing fracture with long-term conductivity of 0 is 4%, and the effective length of acid-fracturing fractures is 120 m.
Numerical Modeling and Economic Analysis of Ultrasonic-Assisted CO2 Absorption Process for Offshore Application
Authors: Mohd Tamidi Athirah , Lau Kok Keong , Ng Li Huey , Mhd Yusof Siti Munirah , Azmi Nurulhuda , Zakariya Shahidah , Khalit Siti Hajar , Quek Ven Chian
Published: October 2023

In the quest for net zero carbon emissions by 2050, Carbon Capture Utilization and Storage (CCUS) is indispensable. The development of more efficient CO2 capture processes is essential. High-frequency ultrasonic irradiation is an emerging, intensified technique that can enhance the CO2 absorption process. To advance this technology toward commercialization, it is crucial to conduct a thorough economic analysis to allow the identification of the key cost component. While equipment sizing is essential in this economic assessment, there is a lack of numerical models for estimating the size and power consumption of ultrasonic absorbers. This study introduces a numerical model for these predictions. The model was then used to determine the economic feasibility of this emerging technique against the packed bed columns based on capital expenditure (CAPEX), operational expenditure (OPEX), and unit technical cost (UTC) for 20 years of plant operation. According to the economic analysis, ultrasonic intensification requires 34% less CAPEX due to its compact design. Although its OPEX is 11% higher due to the additional electricity needed for the ultrasonic transducers, the UTC is still 3% lower than the conventional packed bed column, demonstrating a potential cost savings in implementing the ultrasonic irradiation-assisted technique during the CO2 absorption process offshore.
Quantitative Prediction of Deep Coalbed Methane Content in Daning-Jixian Block, Ordos Basin, China
Authors: Ouyang Zheyuan , Wang Haichao , Sun Bin , Liu Yunxuan , Fu Xuehai , Dou Wei , Du Liang , Zhang Beixi , Luo Bing , Yang Mengmeng , Zeng Zhiwei
Published: October 2023

The potential of deep coalbed methane resources is substantial. Gas content is an important parameter for resource assessment. At present, the gas content test method commonly used for shallow coal reservoirs is not suitable for determining deep coalbed methane gas content. Therefore, it is urgent to establish a prediction method for deep coalbed methane gas content. This study aims to quantitatively predict the gas content of coalbed methane in deep coal reservoirs and uncover its influencing factors. For this purpose, we selected the Daning-Jixian area, a region in China with relatively advanced development of deep coalbed methane, as a case study. It established a prediction model for adsorbed gas and free gas content in deep coal reservoirs through a series of experimental tests, encompassing gas content, coal quality, isothermal adsorption, and nuclear magnetic resonance. The model sheds light on the impact of coal-rock characteristics, coal quality attributes, and pore characteristics on adsorbed gas and free gas content. The results show that adsorbed gas dominates in deep coal reservoirs with a high metamorphic degree and that the average proportion of adsorbed gas under reservoir burial depth is 80.15%. At a depth of approximately 1800~2000 m, a turning point is observed where adsorbed gas content begins to decline. Beyond this depth range, the detrimental effect of temperature on coalbed methane content surpasses the beneficial impact of pressure. Coal quality characteristics play a major role in controlling adsorbed gas content, and an increase in water content and ash yield significantly reduces the adsorption performance of coal reservoirs. The content of free gas increases with the increase in burial depth, with its controlling factors primarily being confining pressure and porosity. The increase in the proportion of micropores in the pores of deep coal reservoirs has an adverse effect on the content of free gas. The proportion of adsorbed gas in deep coal reservoirs gradually decreases with the increase in burial depth, while the proportion of free gas gradually rises with the increase in burial depth. The development potential of free gas cannot be overlooked in the exploration and development of deep coalbed methane.
Modern Chinese Buddhist Culture in the Greater Hangzhou Region in Yu Dafu’s Travel Notes
Authors: Yang Yi , Xu Xiaoya
Published: October 2023

Buddhism has been a significant part of Hangzhou’s rich history. Throughout the twentieth century, Hangzhou’s Buddhist culture continued to inspire many Chinese writers, one of the most prominent being Yu Dafu. The writer stayed in Hangzhou several times during the 1920s and 1930s and wrote numerous travel notes, including many describing his and his friends’ visits to temples in and around Hangzhou. These short travel notes, written in modern Chinese with the characteristics of modern prose, opened a relationship between Buddhism and Chinese literature, effectively inaugurating a fresh genre of Chinese Buddhist literature. This paper focuses on Yu Dafu’s travel notes, considers extensive historical sources, and explores how they recorded and represented Chinese Buddhist culture in Hangzhou and more broadly. This paper also explores the reciprocal influence of contemporary Hangzhou’s Buddhist culture on writers’ cognitive frameworks, spiritual solace, and literary choices.
Low-Cost Computer-Vision-Based Embedded Systems for UAVs
Authors: Ortega Luis D. , Loyaga Erick S. , Cruz Patricio J. , Lema Henry P. , Abad Jackeline , Valencia Esteban A.
Published: October 2023

Unmanned Aerial Vehicles (UAVs) are versatile, adapting hardware and software for research. They are vital for remote monitoring, especially in challenging settings such as volcano observation with limited access. In response, economical computer vision systems provide a remedy by processing data, boosting UAV autonomy, and assisting in maneuvering. Through the application of these technologies, researchers can effectively monitor remote areas, thus improving surveillance capabilities. Moreover, flight controllers employ onboard tools to gather data, further enhancing UAV navigation during surveillance tasks. For energy efficiency and comprehensive coverage, this paper introduces a budget-friendly prototype aiding UAV navigation, minimizing effects on endurance. The prototype prioritizes improved maneuvering via the integrated landing and obstacle avoidance system (LOAS). Employing open-source software and MAVLink communication, these systems underwent testing on a Pixhawk-equipped quadcopter. Programmed on a Raspberry Pi onboard computer, the prototype includes a distance sensor and basic camera to meet low computational and weight demands.Tests occurred in controlled environments, with systems performing well in 90% of cases. The Pixhawk and Raspberry Pi documented quad actions during evasive and landing maneuvers. Results prove the prototype’s efficacy in refining UAV navigation. Integrating this cost-effective, energy-efficient model holds promise for long-term mission enhancement—cutting costs, expanding terrain coverage, and boosting surveillance capabilities.
Characterizing the 2022 Extreme Drought Event over the Poyang Lake Basin Using Multiple Satellite Remote Sensing Observations and In Situ Data
Authors: Liu Sulan , Wu Yunlong , Xu Guodong , Cheng Siyu , Zhong Yulong , Zhang Yi
Published: October 2023

With advancements in remote sensing technology and the increasing availability of remote sensing platforms, the capacity to monitor droughts using multiple satellite remote sensing observations has significantly improved. This enhanced capability facilitates a comprehensive understanding of drought conditions and early warnings for extreme drought events. In this study, multiple satellite datasets, including Gravity Recovery and Climate Experiment (GRACE), the Global Precipitation Measurement (GPM) precipitation dataset, and the Global Land the Data Assimilation System (GLDAS) dataset, were used to conduct an innovative in-depth characteristic analysis and identification of the extreme drought event in the Poyang Lake Basin (PLB) in 2022. Furthermore, the drought characteristics were also supplemented by processing the synthetic aperture radar (SAR) image data to obtain lake water area changes and integrating in situ water level data as well as the Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation index dataset, which provided additional instances of utilizing multi-source remote sensing satellite data for feature analysis on extreme drought events. The extreme drought event in 2022 was identified by the detection of non-seasonal negative anomalies in terrestrial water storage derived from the GRACE and GLDAS datasets. The Mann–Kendall (M-K) test results for water levels indicated a significant abrupt decrease around July 2022, passing a significance test with a 95% confidence level, which further validated the reliability of our finding. The minimum area of Poyang Lake estimated by SAR data, corresponding to 814 km2, matched well with the observed drought characteristics. Additionally, the evident lower vegetation index compared to other years also demonstrated the severity of the drought event. The utilization of these diverse datasets and their validation in this study can contribute to achieving a multi-dimensional monitoring of drought characteristics and the establishment of more robust drought models.
An Adaptive Multiscale Generative Adversarial Network for the Spatiotemporal Fusion of Landsat and MODIS Data
Authors: Pan Xiaoyu , Deng Muyuan , Ao Zurui , Xin Qinchuan
Published: October 2023

The monitoring of rapidly changing land surface processes requires remote sensing images with high spatiotemporal resolution. As remote sensing satellites have different satellite orbits, satellite orbital velocities, and sensors, it is challenging to acquire remote sensing images with high resolution and dense time series within a reasonable temporal interval. Remote sensing spatiotemporal fusion is one of the effective ways to acquire high-resolution images with long time series. Most of the existing STF methods use artificially specified fusion strategies, resulting in blurry images and poor generalization ability. Additionally, some methods lack continuous time change information, leading to poor performance in capturing sharp changes in land covers. In this paper, we propose an adaptive multiscale network for spatiotemporal fusion (AMS-STF) based on a generative adversarial network (GAN). AMS-STF reconstructs high-resolution images by leveraging the temporal and spatial features of the input data through multiple adaptive modules and multiscale features. In AMS-STF, for the first time, deformable convolution is used for the STF task to solve the shape adaptation problem, allowing for adaptive adjustment of the convolution kernel based on the different shapes and types of land use. Additionally, an adaptive attention module is introduced in the networks to enhance the ability to perceive temporal changes. We conducted experiments comparing AMS-STF to the most widely used and innovative models currently available on three Landsat-MODIS datasets, as well as ablation experiments to evaluate some innovative modules. The results demonstrate that the adaptive modules significantly improve the fusion effect of land covers and enhance the clarity of their boundaries, which proves the effectiveness of AMS-STF.
ConCs-Fusion: A Context Clustering-Based Radar and Camera Fusion for Three-Dimensional Object Detection
Authors: He Wei , Deng Zhenmiao , Ye Yishan , Pan Pingping
Published: October 2023

Multi-modality three-dimensional (3D) object detection is a crucial technology for the safe and effective operation of environment perception systems in autonomous driving. In this study, we propose a method called context clustering-based radar and camera fusion for 3D object detection (ConCs-Fusion) that combines radar and camera sensors at the intermediate fusion level to achieve 3D object detection. We extract features from heterogeneous sensors and input them as feature point sets into the fusion module. Within the fusion module, we utilize context cluster blocks to learn multi-scale features of radar point clouds and images, followed by upsampling and fusion of the feature maps. Then, we leverage a multi-layer perceptron to nonlinearly represent the fused features, reducing the feature dimensionality to improve model inference speed. Within the context cluster block, we aggregate feature points of the same object from different sensors into one cluster based on their similarity. All feature points within the same cluster are then fused into a radar–camera feature fusion point, which is self-adaptively reassigned to the originally extracted feature points from a simplex sensor. Compared to previous methods that only utilize radar as an auxiliary sensor to camera, or vice versa, the ConCs-Fusion method achieves a bidirectional cross-modal fusion between radar and camera. Finally, our extensive experiments on the nuScenes dataset demonstrate that ConCs-Fusion outperforms other methods in terms of 3D object detection performance.
IPCONV: Convolution with Multiple Different Kernels for Point Cloud Semantic Segmentation
Authors: Zhang Ruixiang , Chen Siyang , Wang Xuying , Zhang Yunsheng
Published: October 2023

The segmentation of airborne laser scanning (ALS) point clouds remains a challenge in remote sensing and photogrammetry. Deep learning methods, such as KPCONV, have proven effective on various datasets. However, the rigid convolutional kernel strategy of KPCONV limits its potential use for 3D object segmentation due to its uniform approach. To address this issue, we propose an Integrated Point Convolution (IPCONV) based on KPCONV, which utilizes two different convolution kernel point generation strategies, one cylindrical and one a spherical cone, for more efficient learning of point cloud data features. We propose a customizable Multi-Shape Neighborhood System (MSNS) to balance the relationship between these convolution kernel point generations. Experiments on the ISPRS benchmark dataset, LASDU dataset, and DFC2019 dataset demonstrate the validity of our method.
Automated Method for SLAM Evaluation in GNSS-Denied Areas
Authors: Merkle Dominik , Reiterer Alexander
Published: October 2023

The automated inspection and mapping of engineering structures are mainly based on photogrammetry and laser scanning. Mobile robotic platforms like unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs), but also handheld platforms, allow efficient automated mapping. Engineering structures like bridges shadow global navigation satellite system (GNSS), which complicates precise localization. Simultaneous localization and mapping (SLAM) algorithms offer a sufficient solution, since they do not require GNSS. However, testing and comparing SLAM algorithms in GNSS-denied areas is difficult due to missing ground truth data. This work presents an approach to measuring the performance of SLAM in indoor and outdoor GNSS-denied areas using a terrestrial scanner Leica RTC360 and a tachymeter to acquire point cloud and trajectory information. The proposed method is independent of time synchronization between robot and tachymeter and also works on sparse SLAM point clouds. For the evaluation of the proposed method, three LiDAR-based SLAM algorithms called KISS-ICP, SC-LIO-SAM, and MA-LIO are tested using a UGV equipped with two light detection and ranging (LiDAR) sensors and an inertial measurement unit (IMU). KISS-ICP is based solely on a single LiDAR scanner and SC-LIO-SAM also uses an IMU. MA-LIO, which allows multiple (different) LiDAR sensors, is tested on a horizontal and vertical one and an IMU. Time synchronization between the tachymeter and SLAM data during post-processing allows calculating the root mean square (RMS) absolute trajectory error, mean relative trajectory error, and the mean point cloud to reference point cloud distance. It shows that the proposed method is an efficient approach to measure the performance of SLAM in GNSS-denied areas. Additionally, the method shows the superior performance of MA-LIO in four of six test tracks with 5 to 7 cm RMS trajectory error, followed by SC-LIO-SAM and KISS-ICP in last place. SC-LIO-SAM reaches the lowest point cloud to reference point cloud distance in four of six test tracks, with 4 to 12 cm.
GLTF-Net: Deep-Learning Network for Thick Cloud Removal of Remote Sensing Images via Global–Local Temporality and Features
Authors: Jia Junhao , Pan Mingzhong , Li Yaowei , Yin Yanchao , Chen Shengmei , Qu Hongjia , Chen Xiaoxuan , Jiang Bo
Published: October 2023

Remote sensing images are very vulnerable to cloud interference during the imaging process. Cloud occlusion, especially thick cloud occlusion, significantly reduces the imaging quality of remote sensing images, which in turn affects a variety of subsequent tasks using the remote sensing images. The remote sensing images miss ground information due to thick cloud occlusion. The thick cloud removal method based on a temporality global–local structure is initially suggested as a solution to this problem. This method includes two stages: the global multi-temporal feature fusion (GMFF) stage and the local single-temporal information restoration (LSIR) stage. It adopts the fusion feature of global multi-temporal to restore the thick cloud occlusion information of local single temporal images. Then, the featured global–local structure is created in both two stages, fusing the global feature capture ability of Transformer with the local feature extraction ability of CNN, with the goal of effectively retaining the detailed information of the remote sensing images. Finally, the local feature extraction (LFE) module and global–local feature extraction (GLFE) module is designed according to the global–local characteristics, and the different module details are designed in this two stages. Experimental results indicate that the proposed method performs significantly better than the compared methods in the established data set for the task of multi-temporal thick cloud removal. In the four scenes, when compared to the best method CMSN, the peak signal-to-noise ratio (PSNR) index improved by 2.675, 5.2255, and 4.9823 dB in the first, second, and third temporal images, respectively. The average improvement of these three temporal images is 9.65%. In the first, second, and third temporal images, the correlation coefficient (CC) index improved by 0.016, 0.0658, and 0.0145, respectively, and the average improvement for the three temporal images is 3.35%. Structural similarity (SSIM) and root mean square (RMSE) are improved 0.33% and 34.29%, respectively. Consequently, in the field of multi-temporal cloud removal, the proposed method enhances the utilization of multi-temporal information and achieves better effectiveness of thick cloud restoration.
A Modified High-Precision Imaging Algorithm Based on Imaging Plane Optimization with Minimum Entropy Applied to UAV SAR
Authors: Du Xingwang , Xie Xin , Yang Zhijun , Tian Weiming
Published: October 2023

The miniaturized and lightweight unmanned aerial vehicle synthetic aperture radar (UAV SAR) is gradually becoming a research hotspot. The motion errors of UAVs lead to a deviation from a straight flight path couple with an unknown elevation of the target area, which leads to a degradation in SAR image quality. To achieve high-precision SAR imaging results, external terrain elevation information should be utilized. However, such data are challenging to obtain and limited in accuracy. In response to this problem, a modified high-precision imaging algorithm based on imaging plane optimization with minimum entropy is proposed. The proposed algorithm makes good use of the nonlinear trajectory of the UAV, which is unfriendly to imaging. Then, the image entropy is taken into account as the evaluation metric to acquire an approximated optimization imaging plane. Finally, the BP imaging is performed on the optimization imaging plane. The proposed method does not rely on external terrain information. Instead, it makes full use of the non-linear trajectory of the UAV and autonomously generates the optimal imaging plane for different terrain areas. By doing so, it achieves high-precision imaging results. Simulations and actual measurements have validated the effectiveness and enhancement of the proposed method.
Boosting Semantic Segmentation of Remote Sensing Images by Introducing Edge Extraction Network and Spectral Indices
Authors: Zhang Yue , Yang Ruiqi , Dai Qinling , Zhao Yili , Xu Weiheng , Wang Jun , Wang Leiguang
Published: October 2023

Deep convolutional neural networks have greatly enhanced the semantic segmentation of remote sensing images. However, most networks are primarily designed to process imagery with red, green, and blue bands. Although it is feasible to directly utilize established networks and pre-trained models for remotely sensed images, they suffer from imprecise land object contour localization and unsatisfactory segmentation results. These networks still need to explore the domain knowledge embedded in images. Therefore, we boost the segmentation performance of remote sensing images by augmenting the network input with multiple nonlinear spectral indices, such as vegetation and water indices, and introducing a novel holistic attention edge detection network (HAE-RNet). Experiments were conducted on the GID and Vaihingen datasets. The results showed that the NIR-NDWI/DSM-GNDVI-R-G-B (6C-2) band combination produced the best segmentation results for both datasets. The edge extraction block benefits better contour localization. The proposed network achieved a state-of-the-art performance in both the quantitative evaluation and visual inspection.
Reconfigurable Intelligent Surface-Assisted Radar Deception Electronic Counter-Countermeasures
Authors: Zhao Shanshan , Xie Biao , Liu Ziwei , An Jirui
Published: October 2023

A reconfigurable intelligent surface (RIS) is a promising technology for wireless communication and radar detection, owing to its superior ability to realize smart radio environments. Inspired by previous studies on RISs, this study deals with the use of RISs for radar electronic counter-countermeasures (ECCMs) in deception jamming scenarios. At first, a RIS was applied to a monostatic radar, constructing a virtual multi-radar system combined with multi-beam receiving technology. Then, a data-fusion-based deception ECCM method for the proposed virtual multi-radar system was studied to discriminate the active false targets generated by deception jamming. A theoretical analysis of the target discrimination probability was derived. Because the location of RISs is the key to determining the target discrimination ability, the location optimization of the RIS was considered based on the theoretical analysis. Simulation results corroborate the deception ECCM ability of the proposed RIS-assisted virtual multi-radar system, enhancing the survivability of a radar system in a complex electromagnetic environment.
Multi-Target Tracking AA Fusion Method for Asynchronous Multi-Sensor Networks
Authors: Wang Kuiwu , Zhang Qin , Zheng Guimei , Hu Xiaolong
Published: October 2023

Aiming at the problem of asynchronous multi-target tracking, this paper studies the AA fusion optimization problem of multi-sensor networks. Firstly, each sensor node runs a PHD filter, and the measurement information obtained from different sensor nodes in the fusion interval is flood communicated into composite measurement information. The Gaussian component representing the same target is associated with a subset by distance correlation. Then, the Bayesian Cramér–Rao Lower Bound of the asynchronous multi-target-tracking error, including radar node selection, is derived by combining the composite measurement information representing the same target. On this basis, a multi-sensor-network-optimization model for asynchronous multi-target tracking is established. That is, to minimize the asynchronous multi-target-tracking error as the optimization objective, the adaptive optimization design of the selection method of the sensor nodes in the sensor network is carried out, and the sequential quadratic programming (SQP) algorithm is used to select the most suitable sensor nodes for the AA fusion of the Gaussian components representing the same target. The simulation results show that compared with the existing algorithms, the proposed algorithm can effectively improve the asynchronous multi-target-tracking accuracy of multi-sensor networks.
A Perifacial EMG Acquisition System for Facial-Muscle-Movement Recognition
Authors: Zhang Jianhang , Huang Shucheng , Li Jingting , Wang Yan , Dong Zizhao , Wang Su-Jing
Published: October 2023

This paper proposes a portable wireless transmission system for the multi-channel acquisition of surface electromyography (EMG) signals. Because EMG signals have great application value in psychotherapy and human–computer interaction, this system is designed to acquire reliable, real-time facial-muscle-movement signals. Electrodes placed on the surface of a facial-muscle source can inhibit facial-muscle movement due to weight, size, etc., and we propose to solve this problem by placing the electrodes at the periphery of the face to acquire the signals. The multi-channel approach allows this system to detect muscle activity in 16 regions simultaneously. Wireless transmission (Wi-Fi) technology is employed to increase the flexibility of portable applications. The sampling rate is 1 KHz and the resolution is 24 bit. To verify the reliability and practicality of this system, we carried out a comparison with a commercial device and achieved a correlation coefficient of more than 70% on the comparison metrics. Next, to test the system’s utility, we placed 16 electrodes around the face for the recognition of five facial movements. Three classifiers, random forest, support vector machine (SVM) and backpropagation neural network (BPNN), were used for the recognition of the five facial movements, in which random forest proved to be practical by achieving a classification accuracy of 91.79%. It is also demonstrated that electrodes placed around the face can still achieve good recognition of facial movements, making the landing of wearable EMG signal-acquisition devices more feasible.
MCW: A Generalizable Deepfake Detection Method for Few-Shot Learning
Authors: Guan Lei , Liu Fan , Zhang Ru , Liu Jianyi , Tang Yifan
Published: October 2023

With the development of deepfake technology, deepfake detection has received widespread attention. Although some deepfake forensics techniques have been proposed, they are still very difficult to implement in real-world scenarios. This is due to the differences in different deepfake technologies and the compression or editing of videos during the propagation process. Considering the issue of sample imbalance with few-shot scenarios in deepfake detection, we propose a multi-feature channel domain-weighted framework based on meta-learning (MCW). In order to obtain outstanding detection performance of a cross-database, the proposed framework improves a meta-learning network in two ways: it enhances the model’s feature extraction ability for detecting targets by combining the RGB domain and frequency domain information of the image and enhances the model’s generalization ability for detecting targets by assigning meta weights to channels on the feature map. The proposed MCW framework solves the problems of poor detection performance and insufficient data compression resistance of the algorithm for samples generated by unknown algorithms. The experiment was set in a zero-shot scenario and few-shot scenario, simulating the deepfake detection environment in real situations. We selected nine detection algorithms as comparative algorithms. The experimental results show that the MCW framework outperforms other algorithms in cross-algorithm detection and cross-dataset detection. The MCW framework demonstrates its ability to generalize and resist compression with low-quality training images and across different generation algorithm scenarios, and it has better fine-tuning potential in few-shot learning scenarios.
Mass Reduction Techniques for Short Backfire Antennas: Additive Manufacturing and Structural Perforations
Authors: Aragbaiye Yewande Mariam , Isleifson Dustin
Published: October 2023

This paper presents novel approaches for reducing the mass of the classical short backfire (SBF) antenna by using additive manufacturing and structural perforations. We first investigated techniques to create a 3D-printed structure with a conductive coating material. This approach resulted in a significant mass reduction (70%) compared with the conventional metallic structure. We performed parametric simulation studies to investigate the effects of the manufacturing process and showed that there was practically no difference in the performance. The largest source of error was the surface roughness and the conductivity of the metal paint. In a second design, we created perforations in the structure to further reduce the mass. We performed parametric studies to optimize mass reduction and to characterize the effects of the perforations and the surface roughness introduced during the 3D-printing process on the antenna. Antenna prototypes were fabricated and tested. The masses of the perforated 3D printed antenna were approximately 30% and 20% of the original aluminum design, respectively (70% and 80% reductions in mass, respectively). The good agreement among the original design, simulation, and measurements demonstrated the effectiveness of the approach.
Detecting Helical Gearbox Defects from Raw Vibration Signal Using Convolutional Neural Networks
Authors: Lupea Iulian , Lupea Mihaiela
Published: October 2023

A study on the gearbox (speed reducer) defect detection models built from the raw vibration signal measured by a triaxial accelerometer and based on convolutional neural networks (CNNs) is presented. Gear faults such as localized pitting, localized wear on helical pinion tooth flanks, and lubricant low level are under observation for three rotating velocities of the actuator and three load levels at the reducer output. A deep learning approach, based on 1D-CNN or 2D-CNN, is employed to extract from the vibration image significant signal features that are used further to identify one of the four states (one normal and three defects) of the system, regardless of the selected load level or the speed. The best-performing 1D-CNN-based detection model, with a testing accuracy of 98.91%, was trained on the signals measured on the Y axis along the reducer input shaft direction. The vibration data acquired from the X and Z axes of the accelerometer proved to be less relevant in discriminating the states of the gearbox, the corresponding 1D-CNN-based models achieving 97.15% and 97% testing accuracy. The 2D-CNN-based model, built using the data from all three accelerometer axes, detects the state of the gearbox with an accuracy of 99.63%.
Coverage Strategy for Small-Cell UAV-Based Networks in IoT Environment
Authors: Aoueileyine Mohamed Ould-Elhassen , Allani Ramzi , Bouallegue Ridha , Yazidi Anis
Published: October 2023

In wireless communication, small cells are low-powered cellular base stations that can be used to enhance the coverage and capacity of wireless networks in areas where traditional cell towers may not be practical or cost-effective. Unmanned aerial vehicles (UAVs) can be used to quickly deploy and position small cells in areas that are difficult to access or where traditional infrastructure is not feasible. UAVs are deployed by telecommunication service providers to provide aerial network access in remote rural areas, disaster-affected areas, or massive-attendance events. In this paper, we focus on the scheduling of beaconing periods as an efficient means of energy consumption optimization. The conducted study provides a sub-modular game perspective of the problem and investigates its structural properties. We also provide a learning algorithm that ensures convergence of the considered UAV network to a Nash equilibrium operating point. Finally, we conduct extensive numerical investigations to assist our claims about the energy and data rate efficiency of the strategic beaconing policy (at Nash equilibrium).
Vehicular Network Intrusion Detection Using a Cascaded Deep Learning Approach with Multi-Variant Metaheuristic
Authors: Manderna Ankit , Kumar Sushil , Dohare Upasana , Aljaidi Mohammad , Kaiwartya Omprakash , Lloret Jaime
Published: October 2023

Vehicle malfunctions have a direct impact on both human and road safety, making vehicle network security an important and critical challenge. Vehicular ad hoc networks (VANETs) have grown to be indispensable in recent years for enabling intelligent transport systems, guaranteeing traffic safety, and averting collisions. However, because of numerous types of assaults, such as Distributed Denial of Service (DDoS) and Denial of Service (DoS), VANETs have significant difficulties. A powerful Network Intrusion Detection System (NIDS) powered by Artificial Intelligence (AI) is required to overcome these security issues. This research presents an innovative method for creating an AI-based NIDS that uses Deep Learning methods. The suggested model specifically incorporates the Self Attention-Based Bidirectional Long Short-Term Memory (SA-BiLSTM) for classification and the Cascaded Convolution Neural Network (CCNN) for learning high-level features. The Multi-variant Gradient-Based Optimization algorithm (MV-GBO) is applied to improve CCNN and SA-BiLSTM further to enhance the model’s performance. Additionally, information gained using MV-GBO-based feature extraction is employed to enhance feature learning. The effectiveness of the proposed model is evaluated on reliable datasets such as KDD-CUP99, ToN-IoT, and VeReMi, which are utilized on the MATLAB platform. The proposed model achieved 99% accuracy on all the datasets.
Microwave Imaging of Anisotropic Objects by Artificial Intelligence Technology
Authors: Liao Shu-Han , Chiu Chien-Ching , Chen Po-Hsiang , Jiang Hao
Published: October 2023

In this paper, we present the microwave imaging of anisotropic objects by artificial intelligence technology. Since the biaxial anisotropic scatterers have different dielectric constant components in different transverse directions, the problems faced by transverse electronic (TE) polarization waves are more complex than those of transverse magnetic (TM) polarization waves. In other words, measured scattered field information can scarcely reconstruct microwave images due to the high nonlinearity characteristic of TE polarization. Therefore, we first use the dominant current scheme (DCS) and the back-propagation scheme (BPS) to compute the initial guess image. We then apply a trained convolution neural network (CNN) to regenerate the microwave image. Numerical results show that the CNN possesses a good generalization ability under limited training data, which could be favorable to deploy in image processing. Finally, we compare DCS and BPS reconstruction images for anisotropic objects by the CNN and prove that DCS is better than BPS. In brief, successfully reconstructing biaxial anisotropic objects with a CNN is the contribution of this proposal.
An Institutional Analysis of Local Lifelong Learning Approaches to Early School Leaving in Italy and Spain
Authors: Rambla Xavier , Bartolini Maddalena
Published: October 2023

In this paper, we will analyze how a few initial vocational education and training (VET) schools have elaborated wide-ranging responses to early leaving from education and training in Liguria (Italy) and Catalonia (Spain). In contrast with many members of the EU and the OECD, the prevailing institutional arrangements in these two countries hardly support disadvantaged youths to catch up with basic academic performance and find an appealing pathway between compulsory and post-compulsory education. Despite this bias of official policies, we argue that some initial VET schools manage to navigate the interface between social structure and agency and accommodate the aspirations of disadvantaged students by involving local stakeholders and shaping school time organization in a particular way.