bluuebunny/crossref_metadata_2025 huggingface dataset:
Analysis of Spatial and Temporal Variability of Ecosystem Service Values and Their Spatial Correlation in Xinjiang, China
Authors: Zhang Shuai , Wang Yang , Xu Wenzhe , Sheng Ziyi , Zhu Zhen , Hou Yifeng
Published: October 2023

Xinjiang is located in arid northwest China, which is a key area for promoting the high-quality development of the regional ecological environment. In recent years, against a background of increasing human activities and rapid natural changes, Xinjiang has faced enormous ecological challenges. This paper utilizes land-use data from 2000 to 2020 to verify the region’s current state of the ecosystem. Additionally, it uses the value equivalent factor per unit area, ecosystem service value (ESV) loss and gain matrix, and double-factor spatial autocorrelation analysis to study the spatial and temporal variabilities of ESV in Xinjiang and its attribution to spatial correlation. The results show that (1) the ESV in Xinjiang exhibits an overall increasing trend during 2000–2020, with a total increase of about CNY 18.202 billion. Regulation-service ESV takes the main position in the single-service function, accounting for about 67.18% of the total ESV. In northern Xinjiang, the ESV demonstrates a decreasing trend, dropping by about CNY 16.885 billion, while in southern Xinjiang, the ESV shows an increasing trend, rising by CNY 35.086 billion. (2) For the study period, the main loss of ESV in Xinjiang is the conversion of ecological land with a high ESV into cropland or barren land with a low ESV. The conversion of bare land to grassland led to the largest increase in ESV (about CNY 209.308 billion), whereas the conversion of grassland to barren land led to the largest loss (about CNY 183.046 billion). (3) There are positive correlations among ESV, net primary productivity (NPP), and human activity intensity (HAI). However, all of the relationships weaken year by year. The spatial agglomeration of ESV ∩ NPP is significantly greater than that of ESV ∩ HAI, so NPP is the dominant factor in the spatial correlation of ESV in Xinjiang. The findings of this study provide a scientific basis for promoting high-quality regional ecological development in China’s arid northwest.
The Latest Desertification Process and Its Driving Force in Alxa League from 2000 to 2020
Authors: Xie Jiali , Lu Zhixiang , Xiao Shengchun , Yan Changzhen
Published: October 2023

Alxa League of Inner Mongolia Autonomous Region is a concentrated desert distribution area in China, and the latest desertification process and its driving mechanism under the comprehensive influence of the extreme dry climate and intense human activities has attracted much attention. Landsat data, including ETM+ images obtained in 2000, TM images obtained in 2010, and OLI images obtained in 2020, were used to extract three periods of desertification land information using the classification and regression tree (CART) decision tree classification method in Alxa League. The spatio-temporal variation characteristics of desertification land were analyzed by combining the transfer matrix and barycenter migration model; the effects of climate change and human activities on regional desertification evolution were separated and recombined using the multiple regression residual analysis method and by considering the influence of non-zonal factors. The results showed that from 2000 to 2020, the overall area of desertification land in Alxa League was reduced, the desertification degree was alleviated, the desertification trend was reversed, and the desertification degree in the northern part of the region was more serious than in the southern part. The barycenter of the slight, moderate, and severe desertification land migrated to the southeast, whereas the serious desertification land’s barycenter migrated to the northwest in the period of 2000–2010; however, all of them hardly moved from 2010 to 2020. The degree of desertification reversal in the south was more significant than in the north. Regional desertification reversal was mainly influenced by the combination of human activities and climate change, and the area accounted for 61.5%; meanwhile, the localized desertification development was mainly affected by human activities and accounted for 76.8%.
Application of Sparse Regularization in Spherical Radial Basis Functions-Based Regional Geoid Modeling in Colorado
Authors: Yu Haipeng , Chang Guobin , Zhang Shubi , Zhu Yuhua , Yu Yajie
Published: October 2023

Spherical radial basis function (SRBF) is an effective method for calculating regional gravity field models. Calculating gravity field models with high accuracy and resolution requires dense basis functions, resulting in complex models. This study investigated the application of sparse regularization in SRBFs-based regional gravity field modeling. L1-norm regularization, also known as the least absolute shrinkage selection operator (LASSO), was employed in the parameter estimation procedure. LASSO differs from L2-norm regularization in that the solution obtained by LASSO is sparse, specifically with a portion of the parameters being zero. A sparse model would be advantageous for improving the numerical efficiency by reducing the number of SRBFs. The optimization problem of the LASSO was solved using the fast iterative shrinkage threshold algorithm, which is known for its high efficiency. The regularization parameter was selected using the Akaike information criterion. It was specifically tailored to the L1-norm regularization problem. An approximate covariance matrix of the estimated parameters in the sparse solution was analytically constructed from a Bayesian viewpoint. Based on the remove–compute–restore technique, a regional geoid model of Colorado (USA) was calculated. The numerical results suggest that the LASSO adopted in this study provided competitive results compared to Tikhonov regularization; however, the number of basis functions in the final model was less than 25% of the Tikhonov regularization. Without significantly reducing model accuracy, the LASSO solution provides a very simple model. This is the first study to apply the LASSO to SRBFs-based modeling of the regional gravity field in real gravity observation data.
Revealing the Potential of Deep Learning for Detecting Submarine Pipelines in Side-Scan Sonar Images: An Investigation of Pre-Training Datasets
Authors: Du Xing , Sun Yongfu , Song Yupeng , Dong Lifeng , Zhao Xiaolong
Published: October 2023

This study introduces a novel approach to the critical task of submarine pipeline or cable (POC) detection by employing GoogleNet for the automatic recognition of side-scan sonar (SSS) images. The traditional interpretation methods, heavily reliant on human interpretation, are replaced with a more reliable deep-learning-based methodology. We explored the enhancement of model accuracy via transfer learning and scrutinized the influence of three distinct pre-training datasets on the model’s performance. The results indicate that GoogleNet facilitated effective identification, with accuracy and precision rates exceeding 90%. Furthermore, pre-training with the ImageNet dataset increased prediction accuracy by about 10% compared to the model without pre-training. The model’s prediction ability was best promoted by pre-training datasets in the following order: Marine-PULSE ≥ ImageNet > SeabedObjects-KLSG. Our study shows that pre-training dataset categories, dataset volume, and data consistency with predicted data are crucial factors affecting pre-training outcomes. These findings set the stage for future research on automatic pipeline detection using deep learning techniques and emphasize the significance of suitable pre-training dataset selection for CNN models.
Record Low Arctic Stratospheric Ozone in Spring 2020: Measurements of Ground-Based Differential Optical Absorption Spectroscopy in Ny-Ålesund during 2017–2021
Authors: Li Qidi , Luo Yuhan , Qian Yuanyuan , Dou Ke , Si Fuqi , Liu Wenqing
Published: October 2023

The Arctic stratospheric ozone depletion event in spring 2020 was the most severe compared with previous years. We retrieved the critical indicator ozone vertical column density (VCD) using zenith scattered light differential optical absorption spectroscopy (ZSL-DOAS) from March 2017 to September 2021 in Ny-Ålesund, Svalbard, Norway. The average ozone VCD over Ny-Ålesund between 18 March and 18 April 2020 was approximately 274.8 Dobson units (DU), which was only 64.7 ± 0.1% of that recorded in other years (2017, 2018, 2019, and 2021). The daily peak difference was 195.7 DU during this period. The retrieved daily averages of ozone VCDs were compared with satellite observations from the Global Ozone Monitoring Experiment-2 (GOME-2), a Brewer spectrophotometer, and a Système d’Analyze par Observation Zénithale (SAOZ) spectrometer at Ny-Ålesund. As determined using the empirical cumulative density function, ozone VCDs from the ZSL-DOAS dataset were strongly correlated with data from the GOME-2 and SAOZ at lower and higher values, and ozone VCDs from the Brewer instrument were overestimated. The resulting Pearson correlation coefficients were relatively high at 0.97, 0.87, and 0.91, respectively. In addition, the relative deviations were 2.3%, 3.1%, and 3.5%, respectively. Sounding and ERA5 data indicated that severe ozone depletion occurred between mid-March and mid-April 2020 in the 16–20 km altitude range over Ny-Ålesund, which was strongly associated with the overall persistently low temperatures in the winter of 2019/2020. Using ZSL-DOAS observations, we obtained ozone VCDs and provided evidence for the unprecedented ozone depletion during the Arctic spring of 2020. This is essential for the study of polar ozone changes and their effect on climate change and ecological conditions.
A Robust InSAR Phase Unwrapping Method via Improving the pix2pix Network
Authors: Zhang Long , Huang Guoman , Li Yutong , Yang Shucheng , Lu Lijun , Huo Wenhao
Published: October 2023

The main core of InSAR (interferometric synthetic aperture radar) data processing is phase unwrapping, and the output has a direct impact on the quality of the data processing products. Noise introduced from the SAR system and interferometric processing is unavoidable, causing local phase inaccuracy and limiting the unwrapping results of traditional unwrapping methods. With the successful implementation of deep learning in a variety of industries in recent years, new concepts for phase unwrapping have emerged. This research offers a one-step InSAR phase unwrapping method based on an improved pix2pix network model. We achieved our aim by upgrading the pix2pix network generator model and introducing the concept of quality map guidance. Experiments on InSAR phase unwrapping utilizing simulated and real data with different noise intensities were carried out to compare the method with other unwrapping methods. The experimental results demonstrated that the proposed method is superior to other unwrapping methods and has a good robustness to noise.
Misaligned RGB-Infrared Object Detection via Adaptive Dual-Discrepancy Calibration
Authors: He Mingzhou , Wu Qingbo , Ngan King Ngi , Jiang Feng , Meng Fanman , Xu Linfeng
Published: October 2023

Object detection based on RGB and infrared images has emerged as a crucial research area in computer vision, and the synergy of RGB-Infrared ensures the robustness of object-detection algorithms under varying lighting conditions. However, the RGB-IR image pairs captured typically exhibit spatial misalignment due to sensor discrepancies, leading to compromised localization performance. Furthermore, since the inconsistent distribution of deep features from the two modalities, directly fusing multi-modal features will weaken the feature difference between the object and the background, therefore interfering with the RGB-Infrared object-detection performance. To address these issues, we propose an adaptive dual-discrepancy calibration network (ADCNet) for misaligned RGB-Infrared object detection, including spatial discrepancy and domain-discrepancy calibration. Specifically, the spatial discrepancy calibration module conducts an adaptive affine transformation to achieve spatial alignment of features. Then, the domain-discrepancy calibration module separately aligns object and background features from different modalities, making the distribution of the object and background of the fusion feature easier to distinguish, therefore enhancing the effectiveness of RGB-Infrared object detection. Our ADCNet outperforms the baseline by 3.3% and 2.5% in mAP50 on the FLIR and misaligned M3FD datasets, respectively. Experimental results demonstrate the superiorities of our proposed method over the state-of-the-art approaches.
Validating and Developing Hyperspectral Indices for Tracing Leaf Chlorophyll Fluorescence Parameters under Varying Light Conditions
Authors: Zhuang Jie , Wang Quan , Song Guangman , Jin Jia
Published: October 2023

Chlorophyll a fluorescence (ChlFa) parameters provide insight into the physiological and biochemical processes of plants and have been widely applied to monitor and evaluate the photochemical process and photosynthetic capacity of plants in a variety of environments. Recent advances in remote sensing provide new opportunities for the detection of ChlFa at large scales but demand further tremendous efforts. Among such efforts, application of the hyperspectral index is always possible, but the performance of hyperspectral indices in detecting ChlFa parameters under varying light conditions is much less investigated. The objective of this study is to investigate the performance of reported hyperspectral indices for tracking ChlFa parameters under different light conditions and to develop and evaluate novel spectral indices. Therefore, an experiment was conducted to simultaneously measure ChlFa parameters and spectral reflectance of sunlit and shaded leaves under varying light conditions, and 28 reported hyperspectral indices were examined for their performance in tracking the ChlFa parameters. Furthermore, we developed novel hyperspectral indices based on various spectral transformations. The results indicated that the maximum quantum efficiency of photosystem II (PSIImax), the cumulative quantum yield of photochemistry (ΦP), and the fraction of open reaction centers in photosystem II (qL) of sunlit leaves were significantly higher than those of shaded leaves, while the cumulative quantum yield of regulated thermal dissipation (ΦN) and fluorescence (ΦF) of shaded leaves was higher than that of sunlit leaves. Efficient tracing of ChlFa parameters could not be achieved from previously published spectral indices. In comparison, all ChlFa parameters were well quantified in shaded leaves when using novel hyperspectral indices, although the hyperspectral indices for tracing the non-photochemical quenching (NPQ) and ΦF were not stable, especially for sunlit leaves. Our findings justify the use of hyperspectral indices as a practical approach to estimating ChlFa parameters. However, caution should be used when using spectral indices to track ChlFa parameters based on the differences in sunlit and shaded leaves.
Preliminary Investigation of Sudden Ground Subsidence and Building Tilt in Balitai Town, Tianjin City, on 31 May 2023
Authors: Jiang Haonan , Balz Timo , Li Jianan , Mishra Vishal
Published: October 2023

A short-term rapid subsidence event occurred in the Bi Guiyuan community in Balitai Town, Tianjin City, leading to the tilting of high-rise buildings and the emergency evacuation of over 3000 residents. In response to this incident, InSAR (Interferometric Synthetic Aperture Radar) technology was swiftly employed to monitor the subsidence in the area before and after the event. Our observations indicate that the region had maintained stability for 8 months prior to the incident. However, over the course of the 15-day event, the ground experienced more than 10mm of subsidence. By integrating the findings from an InSAR analysis with geological studies, we speculate that the rapid subsidence in the region is related to the extraction of geothermal resources. It is suspected that during drilling operations, the wellbore mistakenly penetrated a massive underground karst cavity. Consequently, this resulted in a sudden rapid leakage of drilling fluid, creating a pressure differential that caused the overlying soil layers to collapse and rapidly sink into the cavity. As a result, short-term rapid subsidence on the ground surface and tilting of high-rise buildings occurred.
Dim Moving Multi-Target Enhancement with Strong Robustness for False Enhancement
Authors: Zhang Yuke , Chen Xin , Rao Peng , Jia Liangjie
Published: October 2023

In a space-based infrared system, the enhancement of targets plays a crucial role in improving target detection capabilities. However, the existing methods of target enhancement face challenges when dealing with multi-target scenarios and a low signal-to-noise ratio (SNR). Furthermore, false enhancement poses a serious problem that affects the overall performance. To address these issues, a new enhancement method for a dim moving multi-target with strong robustness for false enhancement is proposed in this paper. Firstly, multi-target localization is applied by spatial–temporal filtering and connected component detection. Then, the motion vectors of each target are obtained using optical flow detection. Finally, the consecutive images are convoluted in 3D based on the convolution kernel guided by the motion vectors of the target. This process allows for the accumulation of the target energy. The experimental results demonstrate that this algorithm can adaptively enhance a multi-target and notably improve the SNR under low SNR conditions. Moreover, it exhibits outstanding performance compared to other algorithms and possesses strong robustness against false enhancement.
Influence of Smart Sensors on Structural Health Monitoring Systems and Future Asset Management Practices
Authors: Preethichandra D. M. G. , Suntharavadivel T. G. , Kalutara Pushpitha , Piyathilaka Lasitha , Izhar Umer
Published: October 2023

Recent developments in networked and smart sensors have significantly changed the way Structural Health Monitoring (SHM) and asset management are being carried out. Since the sensor networks continuously provide real-time data from the structure being monitored, they constitute a more realistic image of the actual status of the structure where the maintenance or repair work can be scheduled based on real requirements. This review is aimed at providing a wealth of knowledge from the working principles of sensors commonly used in SHM, to artificial-intelligence-based digital twin systems used in SHM and proposes a new asset management framework. The way this paper is structured suits researchers and practicing experts both in the fields of sensors as well as in asset management equally.
Transparent Rule Enablement Based on Commonization Approach in Heterogeneous IoT Edge Networks
Authors: Jin Wenquan , Hong Yong-Geun , Song Jaeseung , Kim Jaeho , Kim Dohyeun
Published: October 2023

The paradigm of the Internet of Things (IoT) and edge computing brings a number of heterogeneous devices to the network edge for monitoring and controlling the environment. For reacting to events dynamically and automatically in the environment, rule-enabled IoT edge platforms operate the deployed service scenarios at the network edge, based on filtering events to perform control actions. However, due to the heterogeneity of the IoT edge networks, deploying a consistent rule context for operating a consistent rule scenario on multiple heterogeneous IoT edge platforms is difficult because of the difference in protocols and data formats. In this paper, we propose a transparent rule enablement, based on the commonization approach, for enabling a consistent rule scenario in heterogeneous IoT edge networks. The proposed IoT Edge Rule Agent Platform (IERAP) deploys device proxies to share consistent rules with IoT edge platforms without considering the difference in protocols and data formats. Therefore, each device proxy only considers the translation of the corresponding platform-specific and common formats. Also, the rules are deployed by the corresponding device proxy, which enables rules to be deployed to heterogeneous IoT edge platforms to perform the consistent rule scenario without considering the format and underlying protocols of the destination platform.
Full-Body Kinematics and Vertical Ground Reaction Forces in Elite Ten-Pin Bowling: A Field Study
Authors: Seiferheld Bo Eitel , Frost Jeppe , Østergaard Thorstein Brynildsen , Krog Mathias Sønder , Klitgaard Kent Kongsøre , de Zee Mark
Published: October 2023

The purpose was to investigate full-body kinematics and vertical ground reaction forces in the lower extremities of the delivery and to determine delivery changes over time after many deliveries in ten-pin bowling. Six male elite ten-pin bowlers completed six bouts of twelve bowling deliveries, all strike attempts, while measuring full-body kinematics and vertical ground reaction forces. Full-body joint angles, peak vertical ground reaction forces in the feet, vertical breaking impulse, centre of mass velocity, bowling score, and ball release velocity (BRvel) were measured. Results revealed that the BRvel was significantly decreased over bouts (p < 0.001). Additionally, increased flexion of the dominant wrist (p < 0.001) and elbow (p = 0.004) prior to ball release (BR) and increased pronation of the dominant wrist during BR (p = 0.034) were observed at later bouts. It was concluded that these kinematic changes in the dominant wrist and elbow prior to and during BR were performed to compensate for the change in traction between ball and lane during a bowling match. This, in turn, caused a decrease in BRvel. A conservation of energy perspective was discussed to highlight training applications and possibilities to enhance elite athletes’ bowling performance.
Display of WEDM Quality Indicators of Heat-Resistant Alloy Processing in Acoustic Emission Parameters
Authors: Grigoriev Sergey N. , Kozochkin Mikhail P. , Gurin Vladimir D. , Malakhinsky Alexander P. , Porvatov Artur N. , Melnik Yury A.
Published: October 2023

The widespread nature of heat-resistant alloys is associated with the difficulties in their mechanical machining. It forces the use of the wire electrical discharge machining to be wider. The productivity, roughness, and dimensions of the modified layer of the machined surfaces are indicators of the machining quality. The search for new diagnostic parameters that can expand the information content of the operational monitoring/diagnostics of wire electrical discharge machining and accompany the currently used electrical parameters’ data is an urgent research task. The article presents the studies of the relationship between the parameters of acoustic emission signals accompanying wire electrical discharge machining of heat-resistant alloys, process quality indicators, and characteristics of discharge pulses. The results are presented as mathematical expressions and graphs demonstrating the experimentally obtained dependencies. The research focuses on the formed white layer during wire electrical discharge machining. Pictures of thin cross-sections of the machined surfaces with traces of the modified layer are provided. The issues of crack formation in the modified layer and base materials are considered.
Terahertz Attenuated Total Reflection Spectral Response and Signal Enhancement via Plasmonic Enhanced Sensor for Eye Drop Detection
Authors: Hao Eugene Soh Jia , Zhang Nan , Zhu Qiang , Wang Xizu , Lin Karen Ke
Published: October 2023

With chronic ocular diseases such as glaucoma and dry eye syndrome, patients have to apply eye drops over the long term. The therapeutic effects of eye drops depend on the amount of drug contained and the stability of the solution. In addition, contamination during usage and transport can also negatively affect the quality and efficacy of eye drops. The current techniques for the characterization of eye drops are often complicated and time-consuming. Developing a fast and non-invasive way of accurately measuring eye drop quality remains an ongoing challenge. The biggest challenge and primary prerequisite for the application of this new detection technique for eye drops is the obtention of a sufficient spectral response and resolvable signal, considering the large background signal contributed by water. In this work, we use terahertz (THz) attenuated total reflection (ATR) spectroscopy combined with a sensitive hybrid graphene oxide (GO) and carbon nanotube (CNT) thin-film sensors to obtain distinct THz spectral signals in commercial eye drops. Various commercial eye drop products have been tested, and we show that they can be differentiated via their spectral signals. Our results provide a solid foundation for the future fine analysis of eye drops and the detection of their quality. Furthermore, THz spectroscopy combined with GO/CNT films has significant potential and advantages for the non-destructive characterization of aqueous pharmaceutical products.
Cluster Content Caching: A Deep Reinforcement Learning Approach to Improve Energy Efficiency in Cell-Free Massive Multiple-Input Multiple-Output Networks
Authors: Tan Fangqing , Peng Yuan , Liu Qiang
Published: October 2023

With the explosive growth of micro-video applications, the transmission burden of fronthaul and backhaul links is increasing, and meanwhile, a lot of energy consumption is also generated. For reducing energy consumption and transmission delay burden, we propose a cell-free massive multiple-input multiple-output (CF-mMIMO) system in which the cache on the access point (AP) is used to reduce the load on the link. In this paper, a total energy efficiency (EE) model of a cache-assisted CF-mMIMO system is established. When optimizing EE, forming the co-operation cluster is critical. Therefore, we propose an energy-efficient joint design of content caching, AP clustering, and low-resolution digital-to-analog converter (DAC) in a cache-assisted CF-mMIMO network based on deep reinforcement learning. This scheme can effectively cache content in APs and select the appropriate DAC resolution. Then, taking into account the channel state information and user equipment (UE)’s content request preference, a deep deterministic policy gradient algorithm is used to jointly optimize the cache strategy, AP clustering, and DAC resolution decisions. Simulation results show that the energy efficiency of the proposed scheme is 4% higher than that of other schemes without the resolution optimization and is much higher than that of the only AP clustering without the joint design of content caching and channel quality.
DRGAN: Dense Residual Generative Adversarial Network for Image Enhancement in an Underwater Autonomous Driving Device
Authors: Qian Jin , Li Hui , Zhang Bin , Lin Sen , Xing Xiaoshuang
Published: October 2023

Underwater autonomous driving devices, such as autonomous underwater vehicles (AUVs), rely on visual sensors, but visual images tend to produce color aberrations and a high turbidity due to the scattering and absorption of underwater light. To address these issues, we propose the Dense Residual Generative Adversarial Network (DRGAN) for underwater image enhancement. Firstly, we adopt a multi-scale feature extraction module to obtain a range of information and increase the receptive field. Secondly, a dense residual block is proposed, to realize the interaction of image features and ensure stable connections in the feature information. Multiple dense residual modules are connected from beginning to end to form a cyclic dense residual network, producing a clear image. Finally, the stability of the network is improved via adjustment to the training with multiple loss functions. Experiments were conducted using the RUIE and Underwater ImageNet datasets. The experimental results show that our proposed DRGAN can remove high turbidity from underwater images and achieve color equalization better than other methods.
Structural Optimization Design of Dual Robot Gripper Unloading Device Based on Intelligent Optimization Algorithms and Generative Design
Authors: Jia Jiguang , Sun Xuan , Liu Ting , Tang Jiazhi , Wang Jiabing , Hu Xianxuan
Published: October 2023

The main aim of this paper is to explore new approaches to structural design and to solve the problem of lightweight design of structures involving multivariable and multi-objectives. An integrated optimization design methodology is proposed by combining intelligent optimization algorithms with generative design. Firstly, the meta-model is established to explore the relationship between design variables, quality, strain energy, and inherent energy. Then, employing the Non-dominated Sorting Genetic Algorithm III (NSGA-III), the optimal frameworks of the structure are sought within the entire design space. Immediately following, a structure is rebuilt based on the principle of cooperative equilibrium. Furthermore, the rebuilt structure is integrated into a generative design, enabling automatic iteration by controlling the initial parameter set. The quality and rigidity of the structure under different reconstructions are evaluated, resulting in solution generation for structural optimization. Finally, the optimal structure obtained is validated. Research outcomes indicate that the quality of structures generated through the comprehensive optimization method is reduced by 27%, and the inherent energy increases by 0.95 times. Moreover, the overall structural deformation is less than 0.003 mm, with a maximum stress of 3.2 MPa—significantly lower than the yield strength and meeting industrial usage standards. A qualitative study and analysis of the experimental results substantiate the superiority of the proposed methodology for optimized structural design.
Measuring and Energizing Sensor System for Digital Signal Monitoring of an Academic–Experimental CubeSat for Wireless Telemetry Purposes
Authors: Garcia-Fernandez Jose Pablo , Hernandez-Gonzalez Leobardo , Ramirez-Hernandez Jazmin , Guevara-Lopez Pedro , Juarez-Sandoval Oswaldo Ulises , Avalos-Arzate Guillermo
Published: October 2023

Space technology for small satellites has made significant progress in the academic and industrial fields, and an alternative focused on educational institutions is the CubeSat standard, created to promote various scientific projects of space exploration. In this context, a fundamental module of any satellite is the telemetry subsystem, which controls the communication with the Earth through electronic circuits dedicated to remote communication; also, the measurement and power supply modules are integrated into a CubeSat. Its construction costs range from USD 2500 to 55,000, with suppliers from Europe and the United States. This motivates the development of the present project, aimed at an academic–experimental CubeSat-1U prototype, to limit this technological dependence, focusing on the measurement generated by the acceleration sensors, angular velocity, magnetic fields, barometric pressure, temperature and ultraviolet light intensity, and the energization of each of them. For this, the main objective of the research is to identify the four basic subsystems of the CubeSat-1U: (a) energization subsystem, (b) sensing subsystem, (c) transmission and reception subsystem, and (d) control subsystem. To describe in detail the construction of (a) and (b), a set of diagrams is performed, defining their operation and its interaction. To explain the subsystem’s construction, the components selection and integration are presented. As a result, the electrical measurements generated by the power system, the output of the sensors in laboratory conditions, and images of the developed circuits are presented, having as a contribution to the methodology of design, integration, and development of the four subsystems, the feasibility of construction and its implementation in an academic satellite.
Research on Development 3D Ground Penetrating Radar Acquisition and Control Technology for Road Underground Diseases with Dual-Band Antenna Arrays
Authors: Fang Liang , Yang Feng , Xu Maoxuan , Liu Fengyu
Published: October 2023

This paper describes the development of a new 3D ground-penetrating radar (GPR) acquisition and control technology for road underground diseases with dual-band antenna arrays. The 3D GPR system can be mounted on a vehicle-loading device and used by vehicles to detect road underground diseases at regular speeds. Compared with existing 3D GPR systems, this new type of 3D GPR has the following design features: it has dual-band antenna arrays, including a 16-channel 400 MHz antenna array and an 8-channel 200 MHz antenna array, which not only improves the detection efficiency, but also effectively balances the detection depth and detection resolution. A novel antenna switching method for time division step multiplexing (TDSM) is realized via field programmable gate array (FPGA), which not only avoids the crosstalk of antenna echo signals of different frequencies, but also ensures the interval of the same antenna working time. By combining the advantages of the FPGA and micro-control unit (MCU), and utilizing the high-speed transmission of the network port, the high-speed real-time transmission of the 3D GPR echo data is achieved. Finally, the integration of all software and hardware verified the correctness of the system, with good results.
Low-Power Long-Term Ambulatory Electrocardiography Monitor of Three Leads with Beat-to-Beat Heart Rate Measurement in Real Time
Authors: Martínez-Suárez Frank , García-Limón José Alberto , Baños-Bautista Jorge Enrique , Alvarado-Serrano Carlos , Casas Oscar
Published: October 2023

A low-power long-term ambulatory ECG monitor was developed for the acquisition, storage and processing of three simultaneous leads DI, aVF and V2 with a beat-to-beat heart rate measurement in real time. It provides long-term continuous ECG recordings until 84 h. The monitor uses a QRS complex detection algorithm based on the continuous wavelet transform with splines, which automatically selects the scale for the analysis of ECG records with different sampling frequencies. It includes a lead-off detection to continuously monitor the electrode connections and a real-time system of visual and acoustic alarms to alert users of abnormal conditions in its operation. The monitor presented is based in an ADS1294 analogue front end with four channels, 24-bit analog-to-digital converters and programmable gain amplifiers, a low-power dual-core ESP32 microcontroller, a microSD memory for data storage in a range of 4 GB to 32 GB and a 1.4 in thin-film transistor liquid crystal display (LCD) variant with a resolution of 128 × 128 pixels. It has programmable sampling rates of 250, 500 and 1000 Hz; a bandwidth of 0 Hz to 50% of the selected sampling rate; a CMRR of −105 dB; an input margin of ±2.4 V; a resolution of 286 nV; and a current consumption of 50 mA for an average battery life of 84 h. The ambulatory ECG monitor was evaluated with the commercial data-acquisition system BIOPAC MP36 and its module for ECG LABEL SS2LB, simultaneously comparing the morphologies of two ECG records and obtaining a correlation of 91.78%. For the QRS detection in real time, the implemented algorithm had an error less than 5%. The developed ambulatory ECG monitor can be used for the analysis of the dynamics of the heart rate variability in long-term ECG records and for the development of one’s own databases of ECG recordings of normal subjects and patients with cardiovascular and noncardiovascular diseases.
PSNet: A Deep Learning Model-Based Single-Shot Digital Phase-Shifting Algorithm
Authors: Qi Zhaoshuai , Liu Xiaojun , Pang Jingqi , Hao Yifeng , Hu Rui , Zhang Yanning
Published: October 2023

In contrast to traditional phase-shifting (PS) algorithms, which rely on capturing multiple fringe patterns with different phase shifts, digital PS algorithms provide a competitive alternative to relative phase retrieval, which achieves improved efficiency since only one pattern is required for multiple PS pattern generation. Recent deep learning-based algorithms further enhance the retrieved phase quality of complex surfaces with discontinuity, achieving state-of-the-art performance. However, since much attention has been paid to understanding image intensity mapping, such as supervision via fringe intensity loss, global temporal dependency between patterns is often ignored, which leaves room for further improvement. In this paper, we propose a deep learning model-based digital PS algorithm, termed PSNet. A loss combining both local and global temporal information among the generated fringe patterns has been constructed, which forces the model to learn inter-frame dependency between adjacent patterns, and hence leads to the improved accuracy of PS pattern generation and the associated phase retrieval. Both simulation and real-world experimental results have demonstrated the efficacy and improvement of the proposed algorithm against the state of the art.
Pile Damage Detection Using Machine Learning with the Multipoint Traveling Wave Decomposition Method
Authors: Wu Juntao , El Naggar M. Hesham , Wang Kuihua
Published: October 2023

The in-hole multipoint traveling wave decomposition (MPTWD) method is developed for detecting and characterizing the damage of cast in situ reinforced concrete (RC) piles. Compared with the results of MPTWD, the results of the in-hole MPTWD reconstruction technique are found ideal for evaluating the lower-part pile integrity and are further utilized to establish a data-driven machine-learning framework to detect and quantify the degree of damage. Considering the relatively small number of field test samples of the in-hole MPTWD method at this stage, an analytical solution is employed to generate sufficient samples to verify the feasibility and optimize the performance of the machine learning modeling framework. Two types of features extracted by the distributed sampling and statistical and signal processing techniques are applied to three machine-learning classifiers, i.e., logistic regression (LR), extreme gradient boosting (XGBoost) and multilayer perceptron (MLP). The performance of the data-driven machine-learning framework is then evaluated through a specific case study. The results demonstrate that all three classifiers perform better when employing the statistical and signal processing techniques, and the total of 24 extracted features are sufficient for the machine-learning algorithms.
In-Wheel Motor Control System for Four-Wheel Drive Electric Vehicle Based on CR-GWO-PID Control
Authors: Xu Xiaoguang , Wang Miao , Xiao Ping , Ding Jiale , Zhang Xiaoyu
Published: October 2023

In order to improve the driving performance of four-wheel drive electric vehicles and realize precise control of their speed, a Chaotic Random Grey Wolf Optimization-based PID in-wheel motor control algorithm is proposed in this paper. Based on an analysis of the structural principles of electric vehicles, mathematical and simulation models for the whole vehicle are established. In order to improve the control performance of the hub motor, the traditional Grey Wolf Optimization algorithm is improved. In particular, an enhanced population initialization strategy integrating sine and cosine random distribution factors into a Kent chaotic map is proposed, the weight factor of the algorithm is improved using a sine-based non-linear decreasing strategy, and the population position is improved using the random proportional movement strategy. These strategies effectively enhance the global optimization ability, convergence speed, and optimization accuracy of the traditional Grey Wolf Optimization algorithm. On this basis, the CR-GWO-PID control algorithm is established. Then, the software and hardware of an in-wheel motor controller are designed and an in-wheel motor bench test system is built. The simulation and bench test results demonstrate the significantly improved response speed and control accuracy of the proposed in-wheel motor control system.
PEIPNet: Parametric Efficient Image-Inpainting Network with Depthwise and Pointwise Convolution
Authors: Ko Jaekyun , Choi Wanuk , Lee Sanghwan
Published: October 2023

Research on image-inpainting tasks has mainly focused on enhancing performance by augmenting various stages and modules. However, this trend does not consider the increase in the number of model parameters and operational memory, which increases the burden on computational resources. To solve this problem, we propose a Parametric Efficient Image InPainting Network (PEIPNet) for efficient and effective image-inpainting. Unlike other state-of-the-art methods, the proposed model has a one-stage inpainting framework in which depthwise and pointwise convolutions are adopted to reduce the number of parameters and computational cost. To generate semantically appealing results, we selected three unique components: spatially-adaptive denormalization (SPADE), dense dilated convolution module (DDCM), and efficient self-attention (ESA). SPADE was adopted to conditionally normalize activations according to the mask in order to distinguish between damaged and undamaged regions. The DDCM was employed at every scale to overcome the gradient-vanishing obstacle and gradually fill in the pixels by capturing global information along the feature maps. The ESA was utilized to obtain clues from unmasked areas by extracting long-range information. In terms of efficiency, our model has the lowest operational memory compared with other state-of-the-art methods. Both qualitative and quantitative experiments demonstrate the generalized inpainting of our method on three public datasets: Paris StreetView, CelebA, and Places2.
Non-Contact Adaptive Voltage Sensor Based on Electric Field Coupling Principle
Authors: Tan Xiangyu , Zhang Wenbin , He Mingxing , Li Wenyun , Ao Gang , Zhou Fangrong
Published: October 2023

Non-contact voltage sensors based on the principle of electric field coupling have the advantages of simple loading and unloading, high construction safety, and the fact that they are not affected by line insulation. They can accurately measure line voltage without the need to connect to the measured object. Starting from the principle of non-contact voltage measurement, this article abstracts a non-contact voltage measurement model into the principle of capacitive voltage sharing and deduces its transfer relationship. Secondly, it is theoretically inferred that the edge effect of the traditional symmetric structure sensor plate will cause the actual capacitance value between the sensor plates to be greater than the theoretically calculated capacitance value, resulting in a certain measurement error. Therefore, the addition of an equipotential ring structure is proposed to eliminate the edge additional capacitance caused by the edge effect in order to design the sensor structure. In addition, due to the influence of sensor volume, material dielectric constant, and other factors, the capacitance value of the sensor itself is only at pF level, resulting in poor low-frequency performance and imbuing the sensor with a low voltage division ratio. In this regard, this article analyzes the measurement principle of non-contact voltage sensors. By paralleling ceramic capacitors between the two electrode plates of the sensor, the capacitance of the sensor itself is effectively increased, improving the low-frequency performance of the sensor while also increasing the sensor’s voltage division ratio. In addition, by introducing a single pole double throw switch to switch parallel capacitors with different capacitance values, the sensor can have different voltage division ratios in different measurement scenarios, giving it a certain degree of adaptability. The final sensor prototype was made, and a high and low voltage experimental platform was built to test the sensor performance. The experimental results showed that the sensor has good linearity and high measurement accuracy, with a ratio error of within ±3%.
A Simulated Investigation of Lithium Niobate Orientation Effects on Standing Acoustic Waves
Authors: Janardhana Ranjith D. , Jackson Nathan
Published: October 2023

The integration of high-frequency acoustic waves with microfluidics has been gaining popularity as a method of separating cells/particles. A standing surface acoustic wave (sSAW) device produces constructive interference of the stationary waves, demonstrating an increase in cell separating efficiency without damaging/altering the cell structure. The performance of an sSAW device depends on the applied input signal, design of the IDT, and piezoelectric properties of the substrate. This work analyzes the characteristics of a validated 3D finite element model (FEM) of LiNbO3 and the effect on the displacement components of the mechanical waves under the influence of sSAWs by considering XY-, YX-, and 1280 YX-cut LiNbO3 with varying electrode length design. We demonstrated that device performance can be enhanced by the interference of multiple waves under a combination of input signals. The results suggest that 1280 YX-cut LiNbO3 is suitable for generating higher-amplitude out-of-plane waves which can improve the effectiveness of acoustofluidics-based cell separation. Additionally, the findings showed that the length of the electrode impacts the formation of the wavefront significantly.
Robust Symbol Timing Synchronization for Initial Access under LEO Satellite Channel
Authors: Kim Pansoo , Park Hyuncheol
Published: October 2023

This paper proposes a robust symbol timing synchronization scheme for return link initial access based on the Digital Video Broadcasting-Return Channel via Satellite 2nd generation (DVB-RCS2) system for the Low Earth Orbit (LEO) satellite channel. In most cases, the feedforward estimator structure is considered for implementing Time Division Multiple Access (TDMA) packet demodulators such as the DVB-RCS2 system. More specifically, the Non-Data-Aided (NDA) approach, without using any kind of preamble, pilot, and postamble symbols, is applicable for fine symbol timing synchronization. However, it hinders the improvement in estimation accuracy, especially when dealing with short packet lengths during the initial access from the User Terminal (UT) to the Gateway (GW). Moreover, when a UT sends a short random access packet for initial access or resource request to the LEO satellite channel, the conventional schemes suffer from a large Doppler error depending on UT’s location in a beam and satellite velocity. To ameliorate these problems, we propose a novel symbol timing synchronization algorithm for GW, and its advantage is confirmed through computer simulation.
Synthesis of TiO2-(B) Nanobelts for Acetone Sensing
Authors: Kumarage Gayan W. C. , Panamaldeniya Shasika A. , Maddumage Dileepa C. , Moumen Abderrahim , Maraloiu Valentin A. , Mihalcea Catalina G. , Negrea Raluca F. , Dassanayake Buddhika S. , Gunawardhana Nanda , Zappa Dario , Galstyan Vardan , Comini Elisabetta
Published: October 2023

Titanium dioxide nanobelts were prepared via the alkali-hydrothermal method for application in chemical gas sensing. The formation process of TiO2-(B) nanobelts and their sensing properties were investigated in detail. FE-SEM was used to study the surface of the obtained structures. The TEM and XRD analyses show that the prepared TiO2 nanobelts are in the monoclinic phase. Furthermore, TEM shows the formation of porous-like morphology due to crystal defects in the TiO2-(B) nanobelts. The gas-sensing performance of the structure toward various concentrations of hydrogen, ethanol, acetone, nitrogen dioxide, and methane gases was studied at a temperature range between 100 and 500 °C. The fabricated sensor shows a high response toward acetone at a relatively low working temperature (150 °C), which is important for the development of low-power-consumption functional devices. Moreover, the obtained results indicate that monoclinic TiO2-B is a promising material for applications in chemo-resistive gas detectors.