bluuebunny/crossref_metadata_2025 huggingface dataset:
Exploratory Mapping of Blue Ice Regions in Antarctica Using Very High-Resolution Satellite Remote Sensing Data
Authors: Jawak Shridhar D. , Luis Alvarinho J. , Pandit Prashant H. , Wankhede Sagar F. , Convey Peter , Fretwell Peter T.
Published: February 2023

Mapping spatiotemporal changes in the distribution of blue ice regions (BIRs) in Antarctica requires repeated, precise, and high-resolution baseline maps of the blue ice extent. This study demonstrated the design and application of a newly-developed semi-automatic method to map BIRs in the Antarctic environment using very high-resolution (VHR) WorldView-2 (WV-2) satellite images. We discussed the potential of VHR satellite data for the mapping of BIRs in the Antarctic environment using a customized normalized-difference blue-ice index (NDBI) method devised using yellow, green, and near-infrared spectral bands of WV-2 data. We compared the viability of the newly developed customized NDBI approach against state-of-the-art target detection (TD), spectral processing (SP) and pixel-wise supervised (PSC) feature extraction (FE) approaches. Four semi-automatic FE approaches (three existing plus one newly developed) consisting of 16 standalone FE methods (12 existing + four customized) were evaluated using an extensive quantitative and comparative assessment for mapping BIRs in the vicinity of Schirmacher Oasis, on the continental Antarctic coastline. The results suggested that the customized NDBI approach gave a superior performance and the highest statistical stability when compared with existing FE techniques. The customized NDBI generally rendered the lowest level of misclassification (average RMSE = 654.48 ± 58.26 m2), followed by TD (average RMSE = 987.81 ± 55.05 m2), SP (average RMSE = 1327.09 ± 127.83 m2) and PSC (average RMSE = 2259.43 ± 115.36 m2) for mapping BIRs. Our results indicated that the use of the customized NDBI approach can greatly improve the semi-automatic mapping of BIRs in the Antarctic environment. This study presents the first refined map of distribution of BIRs around the Schirmacher Oasis. The total area of blue ice in the study area was estimated to be 106.875 km2, approximately 61% of the study area. The WV-2 derived BIR map area presented in this study locally refined the existing BIR map derived using Landsat Enhanced Thematic Mapper Plus (ETM+) and the Moderate Resolution Imaging Spectroradiometer (MODIS)-based mosaic of Antarctica (MOA) dataset by ~31% (~33.40 km2). Finally, we discussed the practical challenges and future directions in mapping BIRs across Antarctica.
Bathymetry Refinement over Seamount Regions from SAR Altimetric Gravity Data through a Kalman Fusion Method
Authors: Wu Yihao , Wang Junjie , Shen Yueqian , Jia Dongzhen , Li Yu
Published: February 2023

Seafloor topography over seamount areas is crucial for studying plate motions, seafloor seismicity, and seamount ecosystems. However, seamount bathymetry modeling is difficult due to the complex hydrodynamic environment, biodiversity, and scarcity of shipborne echo sounding data. The use of satellite altimeter-derived gravity data is a complementary way of bathymetry computation; in particular, the incorporation of synthetic aperture radar (SAR) altimeter data may be useful for seamount bathymetry modeling. Moreover, the widely used filtering method may have difficulty in combing different bathymetry data sets and may affect the quality of the computed bathymetry. To mitigate this issue, we introduce a Kalman fusion method for weighting and combining gravity-derived bathymetry data and the reference bathymetry model. Numerical experiments in the seamount regions over the Molloy Ridge show that the use of SAR-based altimetric gravity data improves the local bathymetry model, by a magnitude of 14.27 m, compared to the result without SAR data. In addition, the developed Kalman fusion method outperforms the traditionally used filtering method, and the bathymetry computed from the Kalman fusion method is improved by a magnitude of 9.34 m. Further comparison shows that our solution has improved quality compared to a recently released global bathymetry model, namely, GEBCO 2022 (GEBCO: General Bathymetric Chart of the Oceans), by a magnitude of 34.34 m. The bathymetry model in this study may be substituted for existing global bathymetry models for geophysical investigations over the target area.
In Search of the Max Coverage Region in Road Networks
Authors: Fang Lanting , Kou Ze , Zhou Yuzhang , Zhang Yudong , Yuan George Y.
Published: February 2023

The widespread use of mobile devices has resulted in the generation of vast amounts of spatial data. The availability of such large-scale spatial data facilitates the development of data-driven approaches to address real-life problems. This paper introduces the max coverage region (MCR) problem in road networks and provides efficient solutions. Given a set of spatial objects and a coverage radius, the MCR problem aims to identify a location from the road network, so that we can reach as many spatial objects as possible within the given coverage radius from the location. This problem is fundamental to supporting many real-world applications. Given a road network and a set of sensors, this problem can be used to find the best location for a sensor maintenance station. This problem can also be applied in medical research, such as in a protein–protein interaction network, where the nodes represent proteins, the edges represent their interactions, and the weight of an edge represents confidence. We can use the MCR problem to find the set of interacting proteins with a confidence budget. We propose an efficient exact solution to solve the problem, where we reduce the MCR problem to an equivalent problem named the most overlapped interval and design an edge-level upper bound estimation method to reduce the search space. Furthermore, we propose two approximate solutions that sacrifice a little accuracy for much better efficiency. Our experimental study on real-road network datasets demonstrates the effectiveness and superiority of the proposed approaches.
Fuzzy Assessment of Ecological Security on the Qinghai–Tibet Plateau Based on Pressure–State–Response Framework
Authors: Lu Tong , Li Changjia , Zhou Wenxin , Liu Yanxu
Published: February 2023

Climate change and human activities have caused a wide range of ecological risks in the Qinghai–Tibet Plateau (QTP) over the past two decades, such as land degradation and biodiversity loss. Therefore, it is imperative to assess the ecological security and drivers for its sustainable development. However, there still lacks a spatial understanding of ecological security in the QTP, as well as the geographic driving forces. In this study, a pressure–state–response (PSR) framework and the coupled fuzzy and obstacle degree models were used to evaluate the spatial pattern and factors that modulate ecological security of the QTP. The southeast of the plateau exhibited high pressure and state levels, indicating that population and economic development activities were concentrated in these regions owing to the good natural conditions. The ecological security evaluation value of the QTP is moderately low, with a median value of 47.4 (the full mark is 100). Seven regions with low ecological security were identified where targeted planning and governance measures should be implemented based on the local natural and economic conditions. Population density, net primary productivity index (NPP) of vegetation, and GDP per unit area were the main factors that modulated ecological security in the QTP, with obstacles accounting for 17.52%, 13.20%, and 12.97%, respectively. These results improve our understanding of the major vulnerable areas and main driving forces of ecological security, providing key information for optimization of ecological security pattern in the QTP.
Air Quality Index (AQI) Did Not Improve during the COVID-19 Lockdown in Shanghai, China, in 2022, Based on Ground and TROPOMI Observations
Authors: Ma Qihan , Wang Jianbo , Xiong Ming , Zhu Liye
Published: February 2023

The lockdowns from the coronavirus disease of 2019 (COVID-19) have led to a reduction in anthropogenic activities and have hence reduced primary air pollutant emissions, which were reported to have helped air quality improvements. However, air quality expressed by the air quality index (AQI) did not improve in Shanghai, China, during the COVID-19 outbreak in the spring of 2022. To better understand the reason, we investigated the variations of nitrogen dioxide (NO2), ozone (O3), PM2.5 (particular matter with an aerodynamic diameter of less than 2.5 μm), and PM10 (particular matter with an aerodynamic diameter of less than 10 μm) by using in situ and satellite measurements from 1 March to 31 June 2022 (pre-, full-, partial-, and post-lockdown periods). The results show that the benefit of the significantly decreased ground-level PM2.5, PM10, and NO2 was offset by amplified O3 pollution, therefore leading to the increased AQI. According to the backward trajectory analyses and multiple linear regression (MLR) model, the anthropogenic emissions dominated the observed changes in air pollutants during the full-lockdown period relative to previous years (2019–2021), whereas the long-range transport and local meteorological parameters (temperature, air pressure, wind speed, relative humidity, and precipitation) influenced little. We further identified the chemical mechanism that caused the increase in O3 concentration. The amplified O3 pollution during the full-lockdown period was caused by the reduction in anthropogenic nitrogen oxides (NOx) under a VOC-limited regime and high background O3 concentrations owing to seasonal variations. In addition, we found that in the downtown area, ground-level PM2.5, PM10, and NO2 more sensitively responded to the changes in lockdown measures than they did in the suburbs. These findings provide new insights into the impact of emission control restrictions on air quality and have implications for air pollution control in the future.
Possible Future Climate Change Impacts on the Meteorological and Hydrological Drought Characteristics in the Jinghe River Basin, China
Authors: Huang Tingting , Wu Zhiyong , Xiao Peiqing , Sun Zhaomin , Liu Yu , Wang Jingshu , Wang Zhihui
Published: February 2023

Revealing the impact of future climate change on the characteristics and evolutionary patterns of meteorological and hydrological droughts and exploring the joint distribution characteristics of their drought characteristics are essential for drought early warning in the basin. In this study, we considered the Jinghe River Basin in the Loess Plateau as the research object. The standardized precipitation index (SPI) and standardized runoff index (SRI) series were used to represent meteorological drought and hydrological drought with monthly runoff generated by the SWAT model. In addition, the evolution laws of the JRB in the future based on Copula functions are discussed. The results showed that: (1) the meteorological drought and hydrological drought of the JRB displayed complex periodic change trends of drought and flood succession, and the evolution laws of meteorological drought and hydrological drought under different spatiotemporal scales and different scenario differ significantly. (2) In terms of the spatial range, the JRB meteorological and hydrological drought duration and severity gradually increased along with the increase in the time scale. (3) Based on the joint distribution model of the Copula function, the future meteorological drought situation in the JRB will be alleviated when compared with the historical period on the seasonal scale, but the hydrological drought situation is more serious. The findings can help policy-makers explore the correlation between meteorological drought and hydrological drought in the background of future climate change, as well as the early warning of hydrological drought.
Assessment of Estimated Phycocyanin and Chlorophyll-a Concentration from PRISMA and OLCI in Brazilian Inland Waters: A Comparison between Semi-Analytical and Machine Learning Algorithms
Authors: Lima Thainara Munhoz Alexandre de , Giardino Claudia , Bresciani Mariano , Barbosa Claudio Clemente Faria , Fabbretto Alice , Pellegrino Andrea , Begliomini Felipe Nincao
Published: February 2023

The aim of this work is to test the state-of-the-art of water constituent retrieval algorithms for phycocyanin (PC) and chlorophyll-a (chl-a) concentrations in Brazilian reservoirs from hyperspectral PRISMA images and concurrent in situ data. One near-coincident Sentinel-3 OLCI dataset has also been considered for PC mapping as its high revisit time is a relevant element for mapping cyanobacterial blooms. The testing was first performed on remote sensing reflectance (Rrs), as derived by applying two atmospheric correction methods (6SV, ACOLITE) to Level 1 data and as provided in the corresponding Level 2 products (PRISMA L2C and OLCI L2-WFR). Since PRISMA images were affected by sun glint, the testing of three de-glint models was also performed. The applicability of Semi-Analytical (SA) and Mixture Density Network (MDN) algorithms in enabling PC and chl-a concentration retrieval was then tested over three PRISMA scenes; in the case of PC concentration estimation, a Random Forest (RF) algorithm was further applied. Regarding OLCI, the SA algorithm was tested for PC estimation; notably, only SA was calibrated with site-specific data from the reservoir. The algorithms were applied to the Rrs spectra provided by PRISMA L2C products—and those derived with ACOLITE, in the case of OLCI—as these data showed better agreement with in situ measurements. The SA model provided low median absolute error (MdAE) for PRISMA-derived (MdAE = 3.06 mg.m−3) and OLCI-derived (MdAE = 3.93 mg.m−3) PC concentrations, while it overestimated PRISMA-derived chl-a (MdAE = 42.11 mg.m−3). The RF model for PC applied to PRISMA performed slightly worse than SA (MdAE = 5.21 mg.m−3). The MDN showed a rather different performance, with higher errors for PC (MdAE = 40.94 mg.m−3) and lower error for chl-a (MdAE = 23.21 mg.m−3). The results overall suggest that the model calibrated with site-specific measurements performed better and indicates that SA could be applied to PRISMA and OLCI for remote sensing of PC in Brazilian reservoirs.
Deep Learning Method Based on Spectral Characteristic Rein-Forcement for the Extraction of Winter Wheat Planting Area in Complex Agricultural Landscapes
Authors: Sun Hanlu , Wang Biao , Wu Yanlan , Yang Hui
Published: February 2023

Winter wheat is one of the most important food crops in the world. Remote sensing technology can be used to obtain the spatial distribution and planting area of winter wheat in a timely and accurate manner, which is of great significance for agricultural management. Influenced by the growth conditions of winter wheat, the planting structures of the northern and southern regions differ significantly. Therefore, in this study, the spectral and phenological characteristics of winter wheat were analyzed in detail, and four red-edge vegetation indices (NDVI, NDRE, SRre, and CIred-edge) were included after band analysis to enhance the ability of the characteristics to extract winter wheat. These indices were combined with a deep convolutional neural network (CNN) model to achieve intelligent extraction of the winter wheat planting area in a countable number of complex agricultural landscapes. Using this method, GF-6 WFV and Sentinel-2A remote sensing data were used to obtain full coverage of the region to evaluate the geographical environment differences. This spectral characteristic enhancement method combined with a CNN could extract the winter wheat data well for both data sources, with average overall accuracies of 94.01 and 93.03%, respectively. This study proposes a method for fast and accurate extraction of winter wheat in complex agricultural landscapes that can provide decision support for national and local intelligent agricultural construction. Thus, our study has important application value and practical significance.
A Multilevel Spatial and Spectral Feature Extraction Network for Marine Oil Spill Monitoring Using Airborne Hyperspectral Image
Authors: Wang Jian , Li Zhongwei , Yang Junfang , Liu Shanwei , Zhang Jie , Li Shibao
Published: February 2023

Marine oil spills can cause serious damage to marine ecosystems and biological species, and the pollution is difficult to repair in the short term. Accurate oil type identification and oil thickness quantification are of great significance for marine oil spill emergency response and damage assessment. In recent years, hyperspectral remote sensing technology has become an effective means to monitor marine oil spills. The spectral and spatial features of oil spill images at different levels are different. To accurately identify oil spill types and quantify oil film thickness, and perform better extraction of spectral and spatial features, a multilevel spatial and spectral feature extraction network is proposed in this study. First, the graph convolutional neural network and graph attentional neural network models were used to extract spectral and spatial features in non-Euclidean space, respectively, and then the designed modules based on 2D expansion convolution, depth convolution, and point convolution were applied to extract feature information in Euclidean space; after that, a multilevel feature fusion method was developed to fuse the obtained spatial and spectral features in Euclidean space in a complementary way to obtain multilevel features. Finally, the multilevel features were fused at the feature level to obtain the oil spill information. The experimental results show that compared with CGCNN, SSRN, and A2S2KResNet algorithms, the accuracy of oil type identification and oil film thickness classification of the proposed method in this paper is improved by 12.82%, 0.06%, and 0.08% and 2.23%, 0.69%, and 0.47%, respectively, which proves that the method in this paper can effectively extract oil spill information and identify different oil spill types and different oil film thicknesses.
Urban Remote Sensing with Spatial Big Data: A Review and Renewed Perspective of Urban Studies in Recent Decades
Authors: Yu Danlin , Fang Chuanglin
Published: February 2023

During the past decades, multiple remote sensing data sources, including nighttime light images, high spatial resolution multispectral satellite images, unmanned drone images, and hyperspectral images, among many others, have provided fresh opportunities to examine the dynamics of urban landscapes. In the meantime, the rapid development of telecommunications and mobile technology, alongside the emergence of online search engines and social media platforms with geotagging technology, has fundamentally changed how human activities and the urban landscape are recorded and depicted. The combination of these two types of data sources results in explosive and mind-blowing discoveries in contemporary urban studies, especially for the purposes of sustainable urban planning and development. Urban scholars are now equipped with abundant data to examine many theoretical arguments that often result from limited and indirect observations and less-than-ideal controlled experiments. For the first time, urban scholars can model, simulate, and predict changes in the urban landscape using real-time data to produce the most realistic results, providing invaluable information for urban planners and governments to aim for a sustainable and healthy urban future. This current study reviews the development, current status, and future trajectory of urban studies facilitated by the advancement of remote sensing and spatial big data analytical technologies. The review attempts to serve as a bridge between the growing “big data” and modern urban study communities.
Fitness for Purpose of Several Fractional Vegetation Cover Products on Monitoring Vegetation Cover Dynamic Change—A Case Study of an Alpine Grassland Ecosystem
Authors: Huang Renjie , Chen Jianjun , Feng Zihao , Yang Yanping , You Haotian , Han Xiaowen
Published: February 2023

Long-time series global fractional vegetation cover (FVC) products have received widespread international publication, and they supply the essential data required for eco-monitoring and simulation study, assisting in the understanding of global warming and preservation of ecosystem stability. However, due to the insufficiency of high-precision FVC ground-measured data, the accuracy of these FVC products in some regions (such as the Qinghai–Tibet Plateau) is still unknown, which brings a certain impact on eco-environment monitoring and simulation. Here, based on current international mainstream FVC products (including GEOV1 and GEOV2 at Copernicus Global Land Services, GLASS from Beijing Normal University, and MuSyQ from National Earth System Science Data Center), the study of the dynamic change of vegetation cover and its influence factors were conducted in the three-rivers source region, one of the core regions on the Qinghai–Tibet Plateau, via the methods of trend analysis and partial correlation analysis, respectively. Our results found that: (1) The discrepancy in the eco-environment assessment results caused by the inconsistency of FVC products is reflected in the statistical value and the spatial distribution. (2) About 70% of alpine grassland in the three-rivers source region changing trend is controversial. (3) The limiting or driving factors of the alpine grassland change explained via different FVC products were significantly discrepant. Thus, before conducting these studies in the future, the uncertainties of the FVC products utilized should be validated first to acquire the fitness of the FVC products if the accuracy information of these products is unavailable within the study area. In addition, more high-precision FVC ground-measured data should be collected, helping us to validate FVC product uncertainty.
Optical Fiber Magnetic Field Sensors Based on 3 × 3 Coupler and Iron-Based Amorphous Nanocrystalline Ribbons
Authors: Lou Minggan , Zhang Wentao , Huang Wenzhu , Xi Xuekui
Published: February 2023

Optical fiber interferometric magnetic field sensors based on magnetostrictive effects have several advantages, e.g., high sensitivity, strong adaptability to harsh environments, long distance transmission, etc. They also have great application prospects in deep wells, oceans, and other extreme environments. In this paper, two optical fiber magnetic field sensors based on iron-based amorphous nanocrystalline ribbons and a passive 3 × 3 coupler demodulation system were proposed and experimentally tested. The sensor structure and the equal-arm Mach–Zehnder fiber interferometer were designed, and the experimental results showed that the magnetic field resolutions of the optical fiber magnetic field sensors with sensing length of 0.25 m and 1 m were 15.4 nT/√Hz @ 10 Hz and 4.2 nT/√Hz @ 10 Hz, respectively. This confirmed the sensitivity multiplication relationship between the two sensors and the feasibility of improving the magnetic field resolution to the pT level by increasing the sensing length.
Millimeter Wave Attenuation Due to Wind and Heavy Rain in a Tropical Region
Authors: Mankong Ukrit , Chamsuk Pakawat , Nakprasert Sitthichok , Potha Sangdaun , Weng Zu-Kai , Dat Pham Tien , Kanno Atsushi , Kawanishi Tetsuya
Published: February 2023

Millimeter wave fixed wireless systems in future backhaul and access network applications can be affected by weather conditions. The losses caused by rain attenuation and antenna misalignment due to wind-induced vibrations have greater impacts on the link budget reduction at E-band frequencies and higher. The current International Telecommunications Union Radiocommunication Sector (ITU-R) recommendation has been widely used to estimate rain attenuation, and the recent Asia Pacific Telecommunity (APT) report provides the model to estimate the wind-induced attenuation. This article provides the first experimental study of the combined rain and wind effects in a tropical location using both models at a frequency in the E band (74.625 GHz) and a short distance of 150 m. In addition to using wind speeds for attenuation estimation, the setup also provides direct antenna inclination angle measurements using the accelerometer data. This solves the limitation of relying on the wind speed since the wind-induced loss is dependent on the inclination direction. The results show that the current ITU-R model can be used to estimate the attenuation of a short fixed wireless link under heavy rain, and the addition of wind attenuation via the APT model can estimate the worst-case link budget during high wind speeds.
Physical Structure Expression for Dense Point Clouds of Magnetic Levitation Image Data
Authors: Zhang Yuxin , Zhang Lei , Shen Guochen , Xu Qian
Published: February 2023

The research and development of an intelligent magnetic levitation transportation system has become an important research branch of the current intelligent transportation system (ITS), which can provide technical support for state-of-the-art fields such as intelligent magnetic levitation digital twin. First, we applied unmanned aerial vehicle oblique photography technology to acquire the magnetic levitation track image data and preprocessed them. Then, we extracted the image features and matched them based on the incremental structure from motion (SFM) algorithm, recovered the camera pose parameters of the image data and the 3D scene structure information of key points, and optimized the bundle adjustment to output 3D magnetic levitation sparse point clouds. Then, we applied multiview stereo (MVS) vision technology to estimate the depth map and normal map information. Finally, we extracted the output of the dense point clouds that can precisely express the physical structure of the magnetic levitation track, such as turnout, turning, linear structures, etc. By comparing the dense point clouds model with the traditional building information model, experiments verified that the magnetic levitation image 3D reconstruction system based on the incremental SFM and MVS algorithm has strong robustness and accuracy and can express a variety of physical structures of magnetic levitation track with high accuracy.
A Condition Evaluation Simplified Method for Traction Converter Power Module Based on Operating Interval Segmentation
Authors: Wang Lei , Zhou Mingchao , Dongye Zhonghao , Sha Yanbei , Chen Jingcao
Published: February 2023

In the actual operation of urban rail vehicles, it is essential to evaluate the condition of the traction converter IGBT modules. Considering the fixed line and the similarity of operation conditions between adjacent stations, this paper proposes an efficient and accurate simplified simulation method to evaluate IGBT conditions based on operating interval segmentation (OIS). Firstly, this paper proposes the framework for a condition evaluation method by segmenting operating intervals based on the similarity of average power loss between neighboring stations. The framework makes it possible to reduce the number of simulations to shorten the simulation time while ensuring the state trend estimation accuracy. Secondly, this paper proposes a basic interval segmentation model that uses the operating conditions as inputs to implement the segmentation of the line and is able to simplify the operation conditions of entire line. Finally, the simulation and analysis of the temperature and stress fields of IGBT modules based on segmented intervals completes the IGBT module condition evaluation and realizes the combination of lifetime calculation with actual operating conditions and internal stresses. The validity of the method is verified by comparing the interval segmentation simulation with actual test results. The results show that the method can effectively characterize the temperature and stress trends of traction converter IGBT modules in the whole line, which could support the fatigue mechanism and lifetime assessment reliability study of IGBT modules.
TLS Protocol Analysis Using IoTST—An IoT Benchmark Based on Scheduler Traces
Authors: Salles Rafael , Farias Ricardo
Published: February 2023

The Internet of Things (IoT) envisions billions of everyday objects sharing information. As new devices, applications and communication protocols are proposed for the IoT context, their evaluation, comparison, tuning and optimization become crucial and raise the need for a proper benchmark. While edge computing aims to provide network efficiency by distributed computing, this article moves towards sensor nodes in order to explore efficiency in the local processing performed by IoT devices. We present IoTST, a benchmark based on per-processor synchronized stack traces with the isolation and precise determination of the introduced overhead. It produces comparable detailed results and assists in determining the configuration that has the best processing operating point so that energy efficiency can also be considered. On benchmarking applications which involve network communication, the results can be influenced by the constant changes that occur in the state of the network. In order to circumvent such problems, different considerations or assumptions were used in the generalization experiments and the comparison to similar studies. To present IoTST usage on a real problem, we implemented it on a commercial off the-shelf (COTS) device and benchmarked a communication protocol, producing comparable results that are unaffected by the current network state. We evaluated different Transport-Layer Security (TLS) 1.3 handshake cipher suites at different frequencies and with various numbers of cores. Among other results, we could determine that the selection of a specific suite (Curve25519 and RSA) can improve the computation latency by up to four times over the worst suite candidate (P-256 and ECDSA), while both providing the same security level (128 bits).
Algorithms for Vision-Based Quality Control of Circularly Symmetric Components
Authors: Brambilla Paolo , Conese Chiara , Fabris Davide Maria , Chiariotti Paolo , Tarabini Marco
Published: February 2023

Quality inspection in the industrial production field is experiencing a strong technological development that benefits from the combination of vision-based techniques with artificial intelligence algorithms. This paper initially addresses the problem of defect identification for circularly symmetric mechanical components, characterized by the presence of periodic elements. In the specific case of knurled washers, we compare the performances of a standard algorithm for the analysis of grey-scale image with a Deep Learning (DL) approach. The standard algorithm is based on the extraction of pseudo-signals derived from the conversion of the grey scale image of concentric annuli. In the DL approach, the component inspection is shifted from the entire sample to specific areas repeated along the object profile where the defect may occur. The standard algorithm provides better results in terms of accuracy and computational time with respect to the DL approach. Nevertheless, DL reaches accuracy higher than 99% when performance is evaluated targeting the identification of damaged teeth. The possibility of extending the methods and the results to other circularly symmetrical components is analyzed and discussed.
Agent-Based Approach for (Peri-)Urban Inter-Modality Policies: Application to Real Data from the Lille Metropolis
Authors: Diallo Azise Oumar , Lozenguez Guillaume , Doniec Arnaud , Mandiau René
Published: February 2023

Transportation authorities have adopted more and more incentive measures (fare-free public transport, construction of park-and-ride facilities, etc.) to reduce the use of private cars by combining them with public transit. However, such measures remain difficult to assess with traditional transport models. This article proposes a different approach: an agent-oriented model. To reproduce realistic applications in an urban context (a metropolis), we investigate the preferences and choices of different agents based on utilities and focus on a modal choice performed through a multinomial logit model. Moreover, we propose some methodological elements to identify the individuals’ profiles using public data (census and travel surveys). We also show that this model, applied in a real case study (Lille, France), is able to reproduce travel behaviors when combining private cars and public transport. Moreover, we focus on the role played by park-and-ride facilities in this context. Thus, the simulation framework makes it possible to better understand individuals’ intermodal travel behavior and assess its development policies.
A New Gain-Phase Error Pre-Calibration Method for Uniform Linear Arrays
Authors: Liu Chang , Tang Xiao , Zhang Zhi
Published: February 2023

In this paper, we consider the gain-phase error calibration problem for uniform linear arrays (ULAs). Based on the adaptive antenna nulling technique, a new gain-phase error pre-calibration method is proposed, requiring only one calibration source with known direction of arrival (DOA). In the proposed method, a ULA with M array elements is divided into M−1 sub-arrays, and the gain-phase error of each sub-array can be uniquely extracted one by one. Furthermore, in order to obtain the accurate gain-phase error in each sub-array, we formulate an errors-in-variables (EIV) model and present a weighted total least-squares (WTLS) algorithm by exploiting the structure of the received data on sub-arrays. In addition, the solution to the proposed WTLS algorithm is exactly analyzed in the statistical sense, and the spatial location of the calibration source is also discussed. Simulation results demonstrate the efficiency and feasibility of our proposed method in both large-scale and small-scale ULAs and the superiority to some state-of-the-art gain-phase error calibration approaches.
A Survey of the Performance-Limiting Factors of a 2-Dimensional RSS Fingerprinting-Based Indoor Wireless Localization System
Authors: Yaro Abdulmalik Shehu , Maly Filip , Prazak Pavel
Published: February 2023

A receive signal strength (RSS) fingerprinting-based indoor wireless localization system (I-WLS) uses a localization machine learning (ML) algorithm to estimate the location of an indoor user using RSS measurements as the position-dependent signal parameter (PDSP). There are two stages in the system’s localization process: the offline phase and the online phase. The offline phase starts with the collection and generation of RSS measurement vectors from radio frequency (RF) signals received at fixed reference locations, followed by the construction of an RSS radio map. In the online phase, the instantaneous location of an indoor user is found by searching the RSS-based radio map for a reference location whose RSS measurement vector corresponds to the user’s instantaneously acquired RSS measurements. The performance of the system depends on a number of factors that are present in both the online and offline stages of the localization process. This survey identifies these factors and examines how they impact the overall performance of the 2-dimensional (2-D) RSS fingerprinting-based I-WLS. The effects of these factors are discussed, as well as previous researchers’ suggestions for minimizing or mitigating them and future research trends in RSS fingerprinting-based I-WLS.
A Novel Eco-Friendly and Highly Sensitive Solid Lead–Tin Microelectrode for Trace U(VI) Determination in Natural Water Samples
Authors: Gęca Iwona , Korolczuk Mieczyslaw
Published: February 2023

For the first time a solid state lead–tin microelectrode (diameter ϕ 25 µm) was utilized for U(VI) ion determination by adsorptive stripping voltammetry. The described sensor is characterized by high durability, reusability and eco-friendly features, as the need for using lead and tin ions for metal film preplating has been eliminated, and consequently, the amount of toxic waste has been limited. The advantages of the developed procedure resulted also from the utilization of a microelectrode as a working electrode, because a restricted amount of metals is needed for its construction. Moreover, field analysis is possible to perform thanks to the fact that measurements can be carried out from unmixed solutions. The analytical procedure was optimized. The proposed procedure is characterized by two orders of magnitude linear dynamic range of U(VI) determination from 1 × 10−9 to 1 × 10−7 mol L−1 (120 s of accumulation). The detection limit was calculated to be 3.9 × 10−10 mol L−1 (accumulation time of 120 s). RSD% calculated from seven subsequent U(VI) determinations at a concentration of 2 × 10−8 mol L−1 was 3.5%. The correctness of the analytical procedure was confirmed by analyzing a natural certified reference material.
A Survey on Reinforcement Learning for Reconfigurable Intelligent Surfaces in Wireless Communications
Authors: Puspitasari Annisa Anggun , Lee Byung Moo
Published: February 2023

A reconfigurable intelligent surface (RIS) is a development of conventional relay technology that can send a signal by reflecting the signal received from a transmitter to a receiver without additional power. RISs are a promising technology for future wireless communication due to their improvement of the quality of the received signal, energy efficiency, and power allocation. In addition, machine learning (ML) is widely used in many technologies because it can create machines that mimic human mindsets with mathematical algorithms without requiring direct human assistance. Meanwhile, it is necessary to implement a subfield of ML, reinforcement learning (RL), to automatically allow a machine to make decisions based on real-time conditions. However, few studies have provided comprehensive information related to RL algorithms—especially deep RL (DRL)—for RIS technology. Therefore, in this study, we provide an overview of RISs and an explanation of the operations and implementations of RL algorithms for optimizing the parameters of RIS technology. Optimizing the parameters of RISs can offer several benefits for communication systems, such as the maximization of the sum rate, user power allocation, and energy efficiency or the minimization of the information age. Finally, we highlight several issues to consider in implementing RL algorithms for RIS technology in wireless communications in the future and provide possible solutions.
False Data Injection Impact on High RES Power Systems with Centralized Voltage Regulation Architecture
Authors: Bragatto Tommaso , Bucarelli Marco Antonio , Bucarelli Maria Sofia , Carere Federico , Geri Alberto , Maccioni Marco
Published: February 2023

The increasing penetration of distributed generation (DG) across power distribution networks (DNs) is forcing distribution system operators (DSOs) to improve the voltage regulation capabilities of the system. The increase in power flows due to the installation of renewable plants in unexpected zones of the distribution grid can affect the voltage profile, even causing interruptions at the secondary substations (SSs) with the voltage limit violation. At the same time, widespread cyberattacks across critical infrastructure raise new challenges in security and reliability for DSOs. This paper analyzes the impact of false data injection related to residential and non-residential customers on a centralized voltage regulation system, in which the DG is required to adapt the reactive power exchange with the grid according to the voltage profile. The centralized system estimates the distribution grid state according to the field data and provides the DG plants with a reactive power request to avoid voltage violations. A preliminary false data analysis in the context of the energy sector is carried out to build up a false data generator algorithm. Afterward, a configurable false data generator is developed and exploited. The false data injection is tested in the IEEE 118-bus system with an increasing DG penetration. The false data injection impact analysis highlights the need to increase the security framework of DSOs to avoid facing a relevant number of electricity interruptions.
COVID-19 Detection Using Photoplethysmography and Neural Networks
Authors: Lombardi Sara , Francia Piergiorgio , Deodati Rossella , Calamai Italo , Luchini Marco , Spina Rosario , Bocchi Leonardo
Published: February 2023

The early identification of microvascular changes in patients with Coronavirus Disease 2019 (COVID-19) may offer an important clinical opportunity. This study aimed to define a method, based on deep learning approaches, for the identification of COVID-19 patients from the analysis of the raw PPG signal, acquired with a pulse oximeter. To develop the method, we acquired the PPG signal of 93 COVID-19 patients and 90 healthy control subjects using a finger pulse oximeter. To select the good quality portions of the signal, we developed a template-matching method that excludes samples corrupted by noise or motion artefacts. These samples were subsequently used to develop a custom convolutional neural network model. The model accepts PPG signal segments as input and performs a binary classification between COVID-19 and control samples. The proposed model showed good performance in identifying COVID-19 patients, achieving 83.86% accuracy and 84.30% sensitivity (hold-out validation) on test data. The obtained results indicate that photoplethysmography may be a useful tool for microcirculation assessment and early recognition of SARS-CoV-2-induced microvascular changes. In addition, such a noninvasive and low-cost method is well suited for the development of a user-friendly system, potentially applicable even in resource-limited healthcare settings.
Adaptive Reflection Detection and Control Strategy of Pointer Meters Based on YOLOv5s
Authors: Liu Deyuan , Deng Changgen , Zhang Haodong , Li Jinrong , Shi Baojun
Published: February 2023

Reflective phenomena often occur in the detecting process of pointer meters by inspection robots in complex environments, which can cause the failure of pointer meter readings. In this paper, an improved k-means clustering method for adaptive detection of pointer meter reflective areas and a robot pose control strategy to remove reflective areas are proposed based on deep learning. It mainly includes three steps: (1) YOLOv5s (You Only Look Once v5-small) deep learning network is used for real-time detection of pointer meters. The detected reflective pointer meters are preprocessed by using a perspective transformation. Then, the detection results and deep learning algorithm are combined with the perspective transformation. (2) Based on YUV (luminance-bandwidth-chrominance) color spatial information of collected pointer meter images, the fitting curve of the brightness component histogram and its peak and valley information is obtained. Then, the k-means algorithm is improved based on this information to adaptively determine its optimal clustering number and its initial clustering center. In addition, the reflection detection of pointer meter images is carried out based on the improved k-means clustering algorithm. (3) The robot pose control strategy, including its moving direction and distance, can be determined to eliminate the reflective areas. Finally, an inspection robot detection platform is built for experimental study on the performance of the proposed detection method. Experimental results show that the proposed method not only has good detection accuracy that achieves 0.809 but also has the shortest detection time, which is only 0.6392 s compared with other methods available in the literature. The main contribution of this paper is to provide a theoretical and technical reference to avoid circumferential reflection for inspection robots. It can adaptively and accurately detect reflective areas of pointer meters and can quickly remove them by controlling the movement of inspection robots. The proposed detection method has the potential application to realize real-time reflection detection and recognition of pointer meters for inspection robots in complex environments.
SAR Image Simulations of Ocean Scenes Based on the Improved Facet TSM
Authors: Wang Tong , Li Ximin , Wang Yijin
Published: February 2023

The facet-based two scale model (FTSM) is widely applied in SAR image simulations of the anisotropic ocean surface. However, this model is sensitive to the cutoff parameter and facet size, and the choice of these two parameters is arbitrary. We propose to make an approximation of the cutoff invariant two scale model (CITSM) to improve the simulation efficiency while remaining the robustness to cutoff wavenumbers. Meanwhile, the robustness to facet sizes is obtained by correcting the geometrical optics (GO) solution, taking into account the slope probability density function (PDF) correction induced by the spectrum within an individual facet. The new FTSM, with less dependence on cutoff parameters and facet sizes, is proved to be reasonable in the comparisons with advanced analytical models and experimental data. Finally, SAR images of the ocean surface and ship wakes with various facet sizes are provided to prove the operability and applicability of our model.
Joint Optimization of Multi-User Partial Offloading Strategy and Resource Allocation Strategy in D2D-Enabled MEC
Authors: Yong Dongping , Liu Ran , Jia Xiaolin , Gu Yajun
Published: February 2023

With the emergence of more and more computing-intensive and latency-sensitive applications, insufficient computing power and energy of user devices has become a common phenomenon. Mobile edge computing (MEC) is an effective solution to this phenomenon. MEC improves task execution efficiency by offloading some tasks to edge servers for execution. In this paper, we consider a device-to-device technology (D2D)-enabled MEC network communication model, and study the subtask offloading strategy and the transmitting power allocation strategy of users. The objective function is to minimize the weighted sum of the average completion delay and average energy consumption of users, which is a mixed integer nonlinear problem. We first propose an enhanced particle swarm optimization algorithm (EPSO) to optimize the transmit power allocation strategy. Then, we utilize the Genetic Algorithm (GA) to optimize the subtask offloading strategy. Finally, we propose an alternate optimization algorithm (EPSO-GA) to jointly optimize the transmit power allocation strategy and the subtask offloading strategy. The simulation results show that the EPSO-GA outperforms other comparative algorithms in terms of the average completion delay, average energy consumption, and average cost. In addition, no matter how the weight coefficients of delay and energy consumption change, the average cost of the EPSO-GA is the least.
Advanced Computer Vision-Based Subsea Gas Leaks Monitoring: A Comparison of Two Approaches
Authors: Zhu Hongwei , Xie Weikang , Li Junjie , Shi Jihao , Fu Mingfu , Qian Xiaoyuan , Zhang He , Wang Kaikai , Chen Guoming
Published: February 2023

Recent years have witnessed the increasing risk of subsea gas leaks with the development of offshore gas exploration, which poses a potential threat to human life, corporate assets, and the environment. The optical imaging-based monitoring approach has become widespread in the field of monitoring underwater gas leakage, but the shortcomings of huge labor costs and severe false alarms exist due to related operators’ operation and judgment. This study aimed to develop an advanced computer vision-based monitoring approach to achieve automatic and real-time monitoring of underwater gas leaks. A comparison analysis between the Faster Region Convolutional Neural Network (Faster R-CNN) and You Only Look Once version 4 (YOLOv4) was conducted. The results demonstrated that the Faster R-CNN model, developed with an image size of 1280 × 720 and no noise, was optimal for the automatic and real-time monitoring of underwater gas leakage. This optimal model could accurately classify small and large-shape leakage gas plumes from real-world datasets, and locate the area of these underwater gas plumes.