bluuebunny/crossref_metadata_2025 huggingface dataset:
A Destriping Algorithm for SDGSAT-1 Nighttime Light Images Based on Anomaly Detection and Spectral Similarity Restoration
Authors: Zhang Degang , Cheng Bo , Shi Lu , Gao Jie , Long Tengfei , Chen Bo , Wang Guizhou
Published: November 2022

Remote sensing nighttime lights (NTLs) offers a unique perspective on human activity, and NTL images are widely used in urbanization monitoring, light pollution, and other human-related research. As one of the payloads of sustainable development science Satellite-1 (SDGSAT-1), the Glimmer Imager (GI) provides a new multi-spectral, high-resolution, global coverage of NTL images. However, during the on-orbit testing of SDGSAT-1, a large number of stripes with bad or corrupted pixels were observed in the L1A GI image, which directly affected the accuracy and availability of data applications. Therefore, we propose a novel destriping algorithm based on anomaly detection and spectral similarity restoration (ADSSR) for the GI image. The ADSSR algorithm mainly consists of three parts: pretreatment, stripe detection, and stripe restoration. In the pretreatment, salt-pepper noise is suppressed by setting a minimum area threshold of the connected components. Then, during stripe detections, the valid pixel number sequence and the total pixel value sequence are analyzed to determine the location of stripes, and the abnormal pixels of each stripe are estimated by a clustering algorithm. Finally, a spectral-similarity-based method is adopted to restore all abnormal pixels of each stripe in the stripe restoration. In this paper, the ADSSR algorithm is compared with three representative destriping algorithms, and the robustness of the ADSSR algorithm is tested on different sizes of GI images. The results show that the ADSSR algorithm performs better than three representative destriping algorithms in terms of visual and quantitative indexes and still maintains outstanding performance and robustness in differently sized GI images.
The Temporal Evolution of Coastlines in the Bohai Sea and Its Impact on Hydrodynamics
Authors: Lu Jingfang , Zhang Yibo , Lv Xianqing , Shi Honghua
Published: November 2022

Over the past 40 years, increasing coastal reclamation and natural sedimentation has changed coastline positions and resulted in variation in the hydrodynamic environment in the Bohai Sea (BHS), China. Based on the Landsat series images, an interpretative identifier for identifying the coastline was proposed to assess the hydrodynamic changes caused by the coastline change and was applied to a typical case of the Bohai Sea (BHS), China. We combined a grid-based coastline position with an adjoint data assimilation method to seamlessly map the distribution of the amplitude, phase lag, and tidal current of the M2 tidal constituent along the BHS’s coast from 1985 to 2018. Our findings reveal that the coastline change at long time scales dominated reclamation, and around 72.9% of the coastline of the BHS mapped in 2018 had seaward movement compared with its position in 1985. From 1985 to 2018, the BHS volume decreased by 0.17%, the sea surface area decreased by 4.54%, and the kinetic energy increased by 2.53%. The change in the coastline increased the amplitude of the M2 tidal constituent in the Bohai Bay by 6–14 cm and increased the residual current in the eastern coast of the Liaodong Bay by up to 0.07 (0.01) m/s.
Increasing Global Flood Risk in 2005–2020 from a Multi-Scale Perspective
Authors: Duan Yu , Xiong Junnan , Cheng Weiming , Li Yi , Wang Nan , Shen Gaoyun , Yang Jiawei
Published: November 2022

In the context of global climate change, floods have become one of the major natural disasters affecting the safety of human life, economic construction, and sustainable development. Despite significant improvements in flood risk and exposure modeling in some studies, there is still a lack of evidence on the spatiotemporal distribution patterns associated with flood risk across the globe. Meanwhile, numerous studies mostly explore flood risk distribution patterns based on specific spatial scales, ignoring to some extent the fact that flood risk has different distribution patterns on different scales. Here, on the basis of hazard–vulnerability components quantified using game theory (GT), we proposed a framework for analyzing the spatiotemporal distribution patterns of global flood risk and the influencing factors behind them on multiple scales. The results revealed that global flood risk increased during 2005–2020, with the percentages of high-risk areas being 4.3%, 4.48%, 4.6%, and 5.02%, respectively. There were 11 global risk hotspots, mainly located in areas with high population concentration, high economic density, abundant precipitation, and low elevation. On the national scale, high-risk countries were mainly concentrated in East Asia, South Asia, Central Europe, and Western Europe. In our experiment, developed countries accounted for the majority of the 20 highest risk countries in the world, with Singapore being the highest risk country and El Salvador having the highest positive risk growth rate (growing by 19.05% during 2015–2020). The findings of this study offer much-needed information and reference for academics researching flood risk under climate change.
Impact of Smoke Plumes Transport on Air Quality in Sydney during Extensive Bushfires (2019) in New South Wales, Australia Using Remote Sensing and Ground Data
Authors: Attiya Ali A. , Jones Brian G.
Published: November 2022

Smoke aerosol dispersion and transport have a significant impact on air quality levels and can be examined by environmental monitoring and modelling techniques. The purpose of this study is to determine the characteristics of the smoke aerosols and the level of air quality during November and December 2019 under the influence of extensive bushfires in the Sydney area, New South Wales (NSW), Australia. To achieve this goal, air quality and meteorological data were analysed in combination with remote sensing satellite measurements. Meteorological and air quality data were obtained from the Bureau of Meteorology (BOM) and Environmental Protection Agency monitoring sites in NSW. In Richmond the daily maximum average hourly concentration of particulate matter (PM10) was 848.9 μg/m3 at 07:00 UTC on 26 November 2019 and 785 μg/m3 at 07:00 UTC on 12 December 2019. On 10 December 2019, the highest PM10 recorded in the Sydney region was 961.5 μg/m3 in St Marys at 01:00 UTC, while the highest PM2.5 concentration was 714.6 μg/m3 in Oakdale in southwest Sydney at 18:00 UTC. These values all decreased again to the standard level (<50 μg/m3) in a few days. The potential sources of smoke aerosols originated from bushfires to the northwest of Sydney (Blue Mountains) as well as from southwest and northwest NSW. The smoke plumes were revealed by the combined AOD values from Aqua and Terra sensors on the MODIS satellite. In each case, the smoke travelled towards the east coast of Australia and out over the Pacific Ocean. The NAAPS model displays the existence of smoke at ground level, while the CALIPSO satellite data showed that the plumes extended 14 km up into the stratosphere layer. Backward trajectories obtained from the HYSPLIT model agree well with the movement of smoke plumes observed in the MODIS satellite images.
Analysis of Spatial and Temporal Variability of Global Wetlands during the Last 20 Years Using GlobeLand30 Data
Authors: Li Mengjuan , Ti Peng , Zhu Xiuli , Xiong Tao , Mei Yuting , Li Zhilin
Published: November 2022

Knowing the distributions and changes in global wetlands and their conversion to other land cover types could facilitate our understanding of wetland development, causes of variations, and decision-making for restoration and protection. This study aimed to comprehensively analyze the changes in wetland distributions at global, continental, typical regional, and national scales and the conversions between wetlands and other land cover types in the last 20 years. This study used GlobeLand30 (GL30) data with a 30 m resolution for the years 2000, 2010, and 2020. The main findings of this study are as follows: (1) the area of wetlands continued to increase globally from 2000 to 2020, with a total increase of approximately 4%. Wetland changes from 2010 to 2020 were more significant than those from 2000 to 2010. The regions with significant wetland changes were mainly in the north middle- and high-latitude, and the equatorial middle- and low-latitude, and Oceania and North America were the continents with the highest increase and decrease, respectively; (2) the major conversion of wetlands was mainly natural land cover types, including forest, grassland, water, and tundra, and there were minor conversions due to human activities, including the conversion of wetlands to cropland (~4600 km2) and artificial land (~3400 km2); (3) from 2000 to 2020, the increase in global wetlands was uneven, while the decrease was nearly even at a national scale. Australia had the highest increase due to the conversions from grass, bare land, and water, and Canada had the highest decrease due to the conversion into tundra and forest. The analysis results could more comprehensively characterize the distributions and changes of global wetlands, which may provide basic information and knowledge for related research work and policymaking.
Research on the Spatiotemporal Evolution of Mangrove Forests in the Hainan Island from 1991 to 2021 Based on SVM and Res-UNet Algorithms
Authors: Fu Chang , Song Xiqiang , Xie Yu , Wang Cai , Luo Jianbiao , Fang Ying , Cao Bing , Qiu Zixuan
Published: November 2022

Mangrove ecosystems play a dominant role in global, tropical, and subtropical coastal wetlands. Remote sensing plays a central role in mangrove conservation, as it is the preferred tool for monitoring changes in spatiotemporal distribution. To improve correlated estimation accuracies and explore the influencing mechanisms based on the mangrove ground survey, this study used a support vector machine (SVM) machine learning and Res-UNet deep learning algorithms to identify the land area of mangrove forests and the crown surface cover area of mangrove forests in the Hainan Island from 1991 to 2021. Both classification techniques were verified by a confusion matrix, which from 1991 to 2021, revealed overall accuracies of 93.11 ± 1.54% and 96.43 ± 1.15% for SVM and Res-UNet, respectively. Res-UNet was more accurate in identifying the crown surface cover area, whereas SVM was more suitable for obtaining the land area. Furthermore, based on the crown surface cover area of the mangrove forests on the Hainan Island, influencing mechanisms were analyzed through dynamic changes and landscape patterns. Since 1991, the Hainan Island mangrove forest area has increased, with the center of mass moving from coastal areas to the ocean and increasing the overall landscape fragmentation. Moreover, the change in the mangrove forests area was correlated with economic development and the increasingly urban population of the entire island. Altogether, the reliable assessment of the tropical mangrove forest land area and crown surface cover provides an important research foundation for the protection and restoration plans of tropical mangrove forests.
SAOCNN: Self-Attention and One-Class Neural Networks for Hyperspectral Anomaly Detection
Authors: Wang Jinshen , Ouyang Tongbin , Duan Yuxiao , Cui Linyan
Published: November 2022

Hyperspectral anomaly detection is a popular research direction for hyperspectral images; however, it is problematic because it separates the background and anomaly without prior target information. Currently, deep neural networks are used as an extractor to mine intrinsic features in hyperspectral images, which can be fed into separate anomaly detection methods to improve their performances. However, this hybrid approach is suboptimal because the subsequent detector is unable to drive the data representation in hidden layers, which makes it a challenge to maximize the capabilities of deep neural networks when extracting the underlying features customized for anomaly detection. To address this issue, a novel unsupervised, self-attention-based, one-class neural network (SAOCNN) is proposed in this paper. SAOCNN consists of two components: a novel feature extraction network and a one-class SVM (OC-SVM) anomaly detection method, which are interconnected and jointly trained by the OC-SVM-like loss function. The adoption of co-training updates the feature extraction network together with the anomaly detector, thus improving the whole network’s detection performance. Considering that the prominent feature of an anomaly lies in its difference from the background, we designed a deep neural extraction network to learn more comprehensive hyperspectral image features, including spectral, global correlation, and local spatial features. To accomplish this goal, we adopted an adversarial autoencoder to produce the residual image with highlighted anomaly targets and a suppressed background, which is input into an improved non-local module to adaptively select the useful global information in the whole deep feature space. In addition, we incorporated a two-layer convolutional network to obtain local features. SAOCNN maps the original hyperspectral data to a learned feature space with better anomaly separation from the background, making it possible for the hyperplane to separate them. Our experiments on six public hyperspectral datasets demonstrate the state-of-the-art performance and superiority of our proposed SAOCNN when extracting deep potential features, which are more conducive to anomaly detection.
A Rotary Platform Mounted Doppler Lidar for Wind Measurements in Upper Troposphere and Stratosphere
Authors: Zhao Ming , Xie Chenbo , Wang Bangxin , Xing Kunming , Chen Jianfeng , Fang Zhiyuan , Li Lu , Cheng Liangliang
Published: November 2022

A Doppler lidar mounted on a rotary platform has been developed for measuring wind fields in the upper troposphere and stratosphere. The rotating platform was used to support a large system for the detection of wind velocities of sight (VOS) in four directions. The principle, structure, and parameters of the lidar system are introduced. The Fabry-Perot interferometer (FPI), the core component of the wind measurement system, was designed after comprehensively considering the measurement uncertainty and the influence of Mie scattering. Its dual-edge channel bandwidth is 1.05 GHz with 3.48 GHz spacing. In operation, the FPI channels are locked to the laser frequency with a stability of 14.8 MHz. Compared with the local radiosonde, it was found that the deviation in wind speed below 28 km was generally less than 10 m/s, and the deviation in wind direction below 19 km was less than 10 degrees. The 42-day profile comparison between lidar in Hefei and radiosondes in Anqing and Fuyang was analyzed. The statistical results show that the wind speed and wind direction deviations between lidar and radiosondes below 20 km were approximately 10 m/s and 20 degrees, respectively, which are comparable to the regional differences in the wind field. However, as altitudes exceed 20 km, the deviations increased rapidly with height. The experiments indicate that the Doppler lidar could measure wind fields from 7 km to 30 km, with better detection accuracy below 20 km.
Ice Aprons in the Mont Blanc Massif (Western European Alps): Topographic Characteristics and Relations with Glaciers and Other Types of Perennial Surface Ice Features
Authors: Kaushik Suvrat , Ravanel Ludovic , Magnin Florence , Trouvé Emmanuel , Yan Yajing
Published: November 2022

Ice aprons are poorly studied and not well-defined thin ice bodies adhering to high altitude steep rock faces, but are present in most Alpine-type high mountain environments worldwide. This study aims to precisely define ice aprons based on a detailed analysis of their topographical characteristics in the Mont Blanc massif (western European Alps). For this, we accurately identified and precisely mapped 423 ice aprons using a combination of high-resolution optical satellite images from 2019. To better understand their relationship with other types of glaciers, especially the steep slope glaciers and other surface ice bodies, we built a detailed inventory at the scale of the massif that incorporates nine different types of perennial surface ice bodies. In addition, an analysis using different topographic factors helped us to better understand the preferred locations of the ice aprons. We show that they predominantly occur on west-oriented steep and topographically rugged rock slopes above the local Equilibrium Line Altitude (~3200 m a.s.l.), with concave profile curvatures around them that facilitate snow accumulation. They are also found in areas underlain by permafrost. The extensive inventory also helped us to identify different types of ice aprons based on their relationships with glaciers/ice bodies. The analysis shows that ice aprons existing at the headwall of large glaciers above a bergschrund are the most dominant ice apron type in the study area, with ~82% of the total.
Improved Estimation of O-B Bias and Standard Deviation by an RFI Restoration Method for AMSR-2 C-Band Observations over North America
Authors: Shen Wangbin , Lin Zhaohui , Qin Zhengkun , Bai Xuesong
Published: November 2022

Spaceborne microwave radiometer observations play vital roles in surface parameter retrievals and data assimilation, but widespread radio-frequency interference (RFI) signals in the C-band channel result in a lack of valuable data over large areas. Establishing repaired data based on existing observation information is crucial. In this study, Advanced Microwave Scanning Radiometer (AMSR)-2 C-band data affected by RFI were accurately repaired through the iterative principal component analysis (PCA) method in 2016 over the U.S. land area. The standard deviation (STD) and bias characteristics of the brightness temperature in the C-band vertical polarization channel were compared and analyzed before and after the restoration to verify the assimilation application prospect of the repaired data. Not only was the spatial continuity of the microwave imager observations significantly improved following restoration; the STD and bias of the observation minus background (OMB) of the restored data were basically consistent with those of the RFI-free data. The STD of OMB exhibited obvious seasonal variations, which were approximately 4.0 K from January to May and 3.0 K from June to December, whereas the biases were near zero in winter but negative (approximately −2.0 K) in summer. The surface type and terrain height also critically affected the STD and bias. The STD decreased with increasing terrain height, whereas the bias exhibited the opposite trend. The STD was largest in low-vegetation areas (4.0 K) but only approximately 2.0–3.0 K in pine forest and brush areas. These results show that the restored data have a high prospect for retrieval application and assimilation, and the STD and bias estimation results also provide a reference for land-based AMSR-2 data assimilation.
Energy-Efficient Controller Placement in Software-Defined Satellite-Terrestrial Integrated Network
Authors: Wei Linhui , Chang Chen , Liu Yu , Wang Yumei
Published: November 2022

The satellite-terrestrial integrated network (STIN), as an integration of the satellite network and terrestrial, has become a promising architecture to support global coverage and ubiquitous connection. The architecture of software-defined networking (SDN) is utilized to intelligently coordinate the global STIN, in which the placement schemes of SDN controllers, including the locations, number, and roles, would produce various performances. However, the uneven distribution of global users leads to the unbalanced energy consumption of satellite resources, which brings a heavy burden for satellites to maintain the control flows for network management. To provide green communication for international economic trade in the countries along the Belt and Road, in this paper, we focus on the energy-efficient controller placement (EECP) problem in the software-defined STIN. The satellite gateways are located in the countries along the Belt and Road, which accounts for a large number of traffic demands and a dense population. The controllers are deployed on the LEO satellites, where each LEO satellite is a candidate controller. The energy consumption for the control paths and the user data links is modeled and then formulated as the flow processing-oriented optimization problem. A modified simulated annealing placement (MSAP) algorithm is developed to solve the EECP problem, in which we use the greedy way to obtain the initial set of controllers, and then the final optimal controller placement result is obtained by the simulated annealing algorithm. Extensive simulations are conducted on the simulated Iridium satellite network topology and statistics data. Compared with other algorithms, the results show that MSAP reduces network energy consumption by 20% and average latency by 25%.
Neuro-Fuzzy-AHP (NFAHP) Technique for Copper Exploration Using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Geological Datasets in the Sahlabad Mining Area, East Iran
Authors: Shirazi Aref , Hezarkhani Ardeshir , Beiranvand Pour Amin , Shirazy Adel , Hashim Mazlan
Published: November 2022

Fusion and analysis of thematic information layers using machine learning algorithms provide an important step toward achieving accurate mineral potential maps in the reconnaissance stage of mineral exploration. This study developed the Neuro-Fuzzy-AHP (NFAHP) technique for fusing remote sensing (i.e., ASTER alteration mineral image-maps) and geological datasets (i.e., lithological map, geochronological map, structural map, and geochemical map) to identify high potential zones of volcanic massive sulfide (VMS) copper mineralization in the Sahlabad mining area, east Iran. Argillic, phyllic, propylitic and gossan alteration zones were identified in the study area using band ratio and Selective Principal Components Analysis (SPCA) methods implemented to ASTER VNIR and SWIR bands. For each of the copper deposits, old mines and mineralization indices in the study area, information related to exploration factors such as ore mineralization, host-rock lithology, alterations, geochronological, geochemistry, and distance from high intensity lineament factor communities were investigated. Subsequently, the predictive power of these factors in identifying copper occurrences was evaluated using Back Propagation Neural Network (BPNN) technique. The BPNN results demonstrated that using the exploration factors, copper mineralizations in Sahlabad mining area could be identified with high accuracy. Lastly, using the Fuzzy-Analytic Hierarchy Process (Fuzzy-AHP) method, information layers were weighted and fused. As a result, a potential map of copper mineralization was generated, which pinpointed several high potential zones in the study area. For verification of the results, the documented copper deposits, old mines, and mineralization indices in the study area were plotted on the potential map, which is particularly appearing in high favorability parts of the potential map. In conclusion, the Neuro-Fuzzy-AHP (NFAHP) technique shows great reliability for copper exploration in the Sahlabad mining area, and it can be extrapolated to other metallogenic provinces in Iran and other regions for the reconnaissance stage of mineral exploration.
Mapping Irregular Local Climate Zones from Sentinel-2 Images Using Deep Learning with Sequential Virtual Scenes
Authors: Yao Qianxiang , Li Hui , Gao Peng , Guo Haojia , Zhong Cheng
Published: November 2022

Recently, the local climate zone (LCZ) system has been presented to establish the connection between urban landscape and local thermal environment. However, LCZ entities are very difficult to be identified by pixel-based classifiers or object-oriented image analysis, as they are often a complicated combination of multiple ground objects (e.g., buildings, roads, grassland, etc.). Scene classifiers, especially deep learning methods can exploit the structure or contextual information of image scenes and then improve the performance of LCZ classification. However, the square and uniform-sized image patches often bring about extra challenges, as they cannot exactly match LCZ entities of diverse sizes and shapes in most cases. In this study, a sequential virtual scene method is presented to identify LCZ entities of diverse shapes and sizes, which consists of a small “core patch” for scanning diverse entities and sequential virtual scenes for providing abundant context. Specifically, the Bidirectional Long Short-Term Memory (Bi-LSTM) were used to learn the spatial relationship among virtual scenes, respectively. Importantly, a “self-attention” mechanism is designed to weigh the contribution of every virtual scene for alleviating the influences of mixed patches, according to the similarity between its hidden state and the final hidden state. Experiments prove SVS achieves better accuracies than random forest and ResNet and has the outstanding capacity of identifying irregular LCZ entities. It is a promising way to carry out LCZ mapping in cities of different types due to its flexibility and adaptability.
Editorial for the Special Issue “Air Quality Research Using Remote Sensing”
Authors: Costa Maria João , Bortoli Daniele
Published: November 2022

Air pollution is a worldwide environmental hazard with serious consequences for health and climate as well as for agriculture, ecosystems, and cultural heritage, among others [...]
DUPnet: Water Body Segmentation with Dense Block and Multi-Scale Spatial Pyramid Pooling for Remote Sensing Images
Authors: Liu Zhiheng , Chen Xuemei , Zhou Suiping , Yu Hang , Guo Jianhua , Liu Yanming
Published: November 2022

Water body segmentation is an important tool for the hydrological monitoring of the Earth. With the rapid development of convolutional neural networks, semantic segmentation techniques have been used on remote sensing images to extract water bodies. However, some difficulties need to be overcome to achieve good results in water body segmentation, such as complex background, huge scale, water connectivity, and rough edges. In this study, a water body segmentation model (DUPnet) with dense connectivity and multi-scale pyramidal pools is proposed to rapidly and accurately extract water bodies from Gaofen satellite and Landsat 8 OLI (Operational Land Imager) images. The proposed method includes three parts: (1) a multi-scale spatial pyramid pooling module (MSPP) is introduced to combine shallow and deep features for small water bodies and to compensate for the feature loss caused by the sampling process; (2) dense blocks are used to extract more spatial features to DUPnet’s backbone, increasing feature propagation and reuse; (3) a regression loss function is proposed to train the network to deal with the unbalanced dataset caused by small water bodies. The experimental results show that the F1, MIoU, and FWIoU of DUPnet on the 2020 Gaofen dataset are 97.67%, 88.17%, and 93.52%, respectively, and on the Landsat River dataset, they are 96.52%, 84.72%, 91.77%, respectively.
Deep-Learning-Based Feature Extraction Approach for Significant Wave Height Prediction in SAR Mode Altimeter Data
Authors: Atteia Ghada , Collins Michael J. , Algarni Abeer D. , Samee Nagwan Abdel
Published: November 2022

Predicting sea wave parameters such as significant wave height (SWH) has recently been identified as a critical requirement for maritime security and economy. Earth observation satellite missions have resulted in a massive rise in marine data volume and dimensionality. Deep learning technologies have proven their capabilities to process large amounts of data, draw useful insights, and assist in environmental decision making. In this study, a new deep-learning-based hybrid feature selection approach is proposed for SWH prediction using satellite Synthetic Aperture Radar (SAR) mode altimeter data. The introduced approach integrates the power of autoencoder deep neural networks in mapping input features into representative latent-space features with the feature selection power of the principal component analysis (PCA) algorithm to create significant features from altimeter observations. Several hybrid feature sets were generated using the proposed approach and utilized for modeling SWH using Gaussian Process Regression (GPR) and Neural Network Regression (NNR). SAR mode altimeter data from the Sentinel-3A mission calibrated by in situ buoy data was used for training and evaluating the SWH models. The significance of the autoencoder-based feature sets in improving the prediction performance of SWH models is investigated against original, traditionally selected, and hybrid features. The autoencoder–PCA hybrid feature set generated by the proposed approach recorded the lowest average RMSE values of 0.11069 for GPR models, which outperforms the state-of-the-art results. The findings of this study reveal the superiority of the autoencoder deep learning network in generating latent features that aid in improving the prediction performance of SWH models over traditional feature extraction methods.
Daytime Sea Fog Detection Based on a Two-Stage Neural Network
Authors: Tang Yuzhu , Yang Pinglv , Zhou Zeming , Zhao Xiaofeng
Published: November 2022

Sea fog detection has received widespread attention because it plays a vital role in maritime activities. Due to the lack of sea observation data, meteorological satellites with high temporal and spatial resolution have become an essential means of sea fog detection. However, the performance is unsatisfactory because low clouds and sea fog are hard to distinguish on satellite images because they have similar spectral radiance characteristics. To address this difficulty, a new method based on a two-stage deep learning strategy was proposed to detect daytime sea fog in the Yellow Sea and Bohai Sea. We first utilized a fully connected network to separate the clear sky from sea fog and clouds. Then, a convolutional neural network was used to extract the differences between low clouds and sea fog on 16 Advanced Himawari Imager (AHI) observation bands. In addition, we built a Yellow and Bohai Sea Fog (YBSF) dataset by pixel-wise labelling AHI images into three categories (i.e., clear sky, cloud, and sea fog). Five comparable methods were used on the YBSF dataset to appraise the performance of our method. The vertical feature mask (VFM) generated by Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) was also used to verify the detection accuracy. The experimental results demonstrate the effectiveness of the proposed method for sea fog detection.
Small Ultrasound-Based Corrosion Sensor for Intraday Corrosion Rate Estimation
Authors: Thibbotuwa Upeksha Chathurani , Cortés Ainhoa , Irizar Andoni
Published: November 2022

The conventional way of studying corrosion in marine environments is by installing corrosion coupons. Instead, this paper presents an experimental field study using an unattended corrosion sensor developed on the basis of ultrasound (US) technology to assess the thickness loss caused by general atmospheric corrosion on land close to the sea (coastal region). The system described here uses FPGA, low-power microcontroller, analog front-end devices in the sensor node, and a Beaglebone black wireless board for posting data to a server. The overall system is small, operates at low power, and was deployed at Gran Canaria to detect the thickness loss of an S355 steel sample and consequently estimate the corrosion rate. This experiment aims to demonstrate the system’s viability in marine environments and its potential to monitor corrosion in offshore wind turbines. In a day, the system takes four sets of measurements in 6 hour intervals, and each set consists of 5 consecutive measurements. Over the course of 5 months, the proposed experiment allowed for us to continuously monitor the corrosion rate in an equivalent corrosion process to an average thickness loss rate of 0.134 mm/year.
Treatment Outcome Prediction Using Multi-Task Learning: Application to Botulinum Toxin in Gait Rehabilitation
Authors: Khan Adil , Hazart Antoine , Galarraga Omar , Garcia-Salicetti Sonia , Vigneron Vincent
Published: November 2022

We propose a framework for optimizing personalized treatment outcomes for patients with neurological diseases. A typical consequence of such diseases is gait disorders, partially explained by command and muscle tone problems associated with spasticity. Intramuscular injection of botulinum toxin type A is a common treatment for spasticity. According to the patient’s profile, offering the optimal treatment combined with the highest possible benefit-risk ratio is important. For the prediction of knee and ankle kinematics after botulinum toxin type A (BTX-A) treatment, we propose: (1) a regression strategy based on a multi-task architecture composed of LSTM models; (2) to introduce medical treatment data (MTD) for context modeling; and (3) a gating mechanism to model treatment interaction more efficiently. The proposed models were compared with and without metadata describing treatments and with serial models. Multi-task learning (MTL) achieved the lowest root-mean-squared error (RMSE) (5.60°) for traumatic brain injury (TBI) patients on knee trajectories and the lowest RMSE (3.77°) for cerebral palsy (CP) patients on ankle trajectories, with only a difference of 5.60° between actual and predicted. Overall, the best RMSE ranged from 5.24° to 6.24° for the MTL models. To the best of our knowledge, this is the first time that MTL has been used for post-treatment gait trajectory prediction. The MTL models outperformed the serial models, particularly when introducing treatment metadata. The gating mechanism is efficient in modeling treatment interaction and improving trajectory prediction.
Predicting Blood Glucose Concentration after Short-Acting Insulin Injection Using Discontinuous Injection Records
Authors: Tang Baoyu , Yuan Yuyu , Yang Jincui , Qiu Lirong , Zhang Shasha , Shi Jinsheng
Published: November 2022

Diabetes is an increasingly common disease that poses an immense challenge to public health. Hyperglycemia is also a common complication in clinical patients in the intensive care unit, increasing the rate of infection and mortality. The accurate and real-time prediction of blood glucose concentrations after each short-acting insulin injection has great clinical significance and is the basis of all intelligent blood glucose control systems. Most previous prediction methods require long-term continuous blood glucose records from specific patients to train the prediction models, resulting in these methods not being used in clinical practice. In this study, we construct 13 deep neural networks with different architectures to atomically predict blood glucose concentrations after arbitrary independent insulin injections without requiring continuous historical records of any patient. Using our proposed models, the best root mean square error of the prediction results reaches 15.82 mg/dL, and 99.5% of the predictions are clinically acceptable, which is more accurate than previously proposed blood glucose prediction methods. Through the re-validation of the models, we demonstrate the clinical practicability and universal accuracy of our proposed prediction method.
Self-Mixing Laser Distance-Sensor Enhanced by Multiple Modulation Waveforms
Authors: Cavedo Federico , Esmaili Parisa , Norgia Michele
Published: November 2022

Optical rangefinders based on Self-Mixing Interferometry are widely described in literature, but not yet on the market as commercial instruments. The main reason is that it is relatively easy to propose new elaboration techniques and get results in controlled conditions, while it is very difficult to develop a reliable instrument. In this paper, we propose a laser distance sensor with improved reliability, realized through a wavelength modulation at a different frequency, able to decorrelate single measurement errors and obtain improvement by averages. A dedicated software is implemented to automatically calculate the modulation pre-emphasis, needed to linearize the wavelength modulation. Finally, data selection algorithms allow to overcome signal fading problems due to the speckle effect. A prototype demonstrates the approach with about 0.1 mm accuracy up to 2 m of distance at 200 measurements per second.
Optical Fiber Sensor for Curvature and Temperature Measurement Based on Anti-Resonant Effect Cascaded with Multimode Interference
Authors: Gui Yinqiu , Shu Qian , Lu Ping , Peng Jiajun , Zhang Jiangshan , Liu Deming
Published: November 2022

In this paper, a novel inline optical fiber sensor for curvature and temperature measurement simultaneously has been proposed and demonstrated, which can measure two parameters with very little crosstalk. Two combinational mechanisms of anti-resonant reflecting optical waveguide and inline Mach–Zehnder interference structure are integrated into a 3 mm-long single hole twin suspended core fiber (SHTSCF). The 85 μm hole core gives periodic several dominant resonant wavelengths in the optical transmission spectrum, acting as the anti-resonant reflecting optical waveguide (ARROW). The modes in two suspended cores and the cladding form the comb pattern. Reliable sensor sensitivity can be obtained by effective experiments and demodulation. Through intensity demodulation of the selected dip of Gaussian fitting, the curvature sensitivity can be up to −7.23 dB/m−1. Through tracking the MZI dip for wavelength demodulation, the temperature sensitivity can be up to 28.8 pm/°C. The sensor is simple in structure, compact, and has good response, which can have a bright application in a complex environment.
Three-Stage Pavement Crack Localization and Segmentation Algorithm Based on Digital Image Processing and Deep Learning Techniques
Authors: Yang Zhen , Ni Changshuang , Li Lin , Luo Wenting , Qin Yong
Published: November 2022

The image of expressway asphalt pavement crack disease obtained by a three-dimensional line scan laser is easily affected by external factors such as uneven illumination distribution, environmental noise, occlusion shadow, and foreign bodies on the pavement. To locate and extract cracks accurately and efficiently, this article proposes a three-stage asphalt pavement crack location and segmentation method based on traditional digital image processing technology and deep learning methods. In the first stage of this method, the guided filtering and Retinex methods are used to preprocess the asphalt pavement crack image. The processed image removes redundant noise information and improves the brightness. At the information entropy level, it is 63% higher than the unpreprocessed image. In the second stage, the newly proposed YOLO-SAMT target detection model is used to locate the crack diseases in asphalt pavement. The model is 5.42 percentage points higher than the original YOLOv7 model on mAP@0.5, which enhances the recognition and location ability of crack diseases and reduces the calculation amount for the extraction of crack contour in the next stage. In the third stage, the improved k-means clustering algorithm is used to extract cracks. Compared with the traditional k-means clustering algorithm, this method improves the accuracy by 7.34 percentage points, the true rate by 6.57 percentage points, and the false positive rate by 18.32 percentage points to better extract the crack contour. To sum up, the method proposed in this article improves the quality of the pavement disease image, enhances the ability to identify and locate cracks, reduces the amount of calculation, improves the accuracy of crack contour extraction, and provides a new solution for highway crack inspection.
Method to Develop Legs for Underwater Robots: From Multibody Dynamics with Experimental Data to Mechatronic Implementation
Authors: Pérez Bayas Miguel Ángel , Cely Juan , Sintov Avishai , García Cena Cecilia E. , Saltaren Roque
Published: November 2022

Exploration of the seabed may be complex, and different parameters must be considered for a robotic system to achieve tasks in this environment, such as soil characteristics, seabed gait, and hydrodynamic force in this extreme environment. This paper presents a gait simulation of a quadrupedal robot used on a typical terrigenous sediment seabed, considering the mechanical properties of the type of soil, stiffness, and damping and friction coefficients, referenced with the specialized literature and applied in a computational multibody model with many experimental data in a specific underwater environment to avoi hydrodynamic effects. The requirements of the positions and torque in the robot’s active joints are presented in accordance with a 5R mechanism for the leg and the natural pattern shown in the gait of a dog on the ground. These simulation results are helpful for the design of a testbed, with a leg prototype and its respective hardware and software architecture and a subsequent comparison with the real results.
Off-Road Detection Analysis for Autonomous Ground Vehicles: A Review
Authors: Islam Fahmida , Nabi M M , Ball John E.
Published: November 2022

When it comes to some essential abilities of autonomous ground vehicles (AGV), detection is one of them. In order to safely navigate through any known or unknown environment, AGV must be able to detect important elements on the path. Detection is applicable both on-road and off-road, but they are much different in each environment. The key elements of any environment that AGV must identify are the drivable pathway and whether there are any obstacles around it. Many works have been published focusing on different detection components in various ways. In this paper, a survey of the most recent advancements in AGV detection methods that are intended specifically for the off-road environment has been presented. For this, we divided the literature into three major groups: drivable ground and positive and negative obstacles. Each detection portion has been further divided into multiple categories based on the technology used, for example, single sensor-based, multiple sensor-based, and how the data has been analyzed. Furthermore, it has added critical findings in detection technology, challenges associated with detection and off-road environment, and possible future directions. Authors believe this work will help the reader in finding literature who are doing similar works.
Calibration of Deformable Mirrors for Open-Loop Control
Authors: Leutenegger Marcel , Hell Stefan W.
Published: November 2022

Deformable mirrors enable the control of wave fronts for the compensation of aberrations in optical systems and/or for beam scanning. Manufacturers of deformable mirrors typically provide calibration data that encode for the fabrication tolerances among the actuators and mirror segments to support open-loop control with high wave front fidelity and accuracy. We report a calibration method that enables users of the deformable mirrors to measure the response of the mirror itself to validate and improve the calibration data. For this purpose, an imaging off-axis Michelson interferometer was built that allowed measuring the mirror topography with high accuracy and sufficient spatial resolution. By calibrating each actuator over its entire range, the open-loop performance for our deformable mirror was improved.
Feature Pyramid U-Net with Attention for Semantic Segmentation of Forward-Looking Sonar Images
Authors: Zhao Dongdong , Ge Weihao , Chen Peng , Hu Yingtian , Dang Yuanjie , Liang Ronghua , Guo Xinxin
Published: November 2022

Forward-looking sonar is a technique widely used for underwater detection. However, most sonar images have underwater noise and low resolution due to their acoustic properties. In recent years, the semantic segmentation model U-Net has shown excellent segmentation performance, and it has great potential in forward-looking sonar image segmentation. However, forward-looking sonar images are affected by noise, which prevents the existing U-Net model from segmenting small objects effectively. Therefore, this study presents a forward-looking sonar semantic segmentation model called Feature Pyramid U-Net with Attention (FPUA). This model uses residual blocks to improve the training depth of the network. To improve the segmentation accuracy of the network for small objects, a feature pyramid module combined with an attention structure is introduced. This improves the model’s ability to learn deep semantic and shallow detail information. First, the proposed model is compared against other deep learning models and on two datasets, of which one was collected in a tank environment and the other was collected in a real marine environment. To further test the validity of the model, a real forward-looking sonar system was devised and employed in the lake trials. The results show that the proposed model performs better than the other models for small-object and few-sample classes and that it is competitive in semantic segmentation of forward-looking sonar images.
New Sensing and Radar Absorbing Laminate Combining Structural Damage Detection and Electromagnetic Wave Absorption Properties
Authors: Cozzolino Federico , Marra Fabrizio , Fortunato Marco , Bellagamba Irene , Pesce Nicola , Tamburrano Alessio , Sarto Maria Sabrina
Published: November 2022

Within the paradigm of smart mobility, the development of innovative materials aimed at improving resilience against structural failure in lightweight vehicles and electromagnetic interferences (EMI) due to wireless communications in guidance systems is of crucial relevance to improve safety, sustainability, and reliability in both aeronautical and automotive applications. In particular, the integration of intelligent structural health monitoring and electromagnetic (EM) shielding systems with radio frequency absorbing properties into a polymer composite laminate is still a challenge. In this paper, we present an innovative system consisting of a multi-layered thin panel which integrates nanostructured coatings to combine EM disturbance suppression and low-energy impact monitoring ability. Specifically, it is composed of a stack of dielectric and conductive layers constituting the sensing and EM-absorbing laminate (SEAL). The conductive layers are made of a polyurethane paint filled with graphene nanoplatelets (GNPs) at different concentrations to tailor the effective electrical conductivity and the functionality of the material. Basically, the panel includes a piezoresistive grid, obtained by selectively spraying onto mylar a low-conductive paint with 4.5 wt.% of GNPs and an EM-absorbing lossy sheet made of the same polyurethane paint but properly modified with a higher weight fraction (8 wt.%) of graphene. The responses of the grid’s strain sensors were analyzed through quasi-static mechanical bending tests, whereas the absorbing properties were evaluated through free-space and waveguide-based measurement techniques in the X, Ku, K, and Ka bands. The experimental results were also validated by numerical simulations.