bluuebunny/crossref_metadata_2025 huggingface dataset:
3D Data Fusion for Historical Analyses of Heritage Buildings Using Thermal Images: The Palacio de Colomina as a Case Study
Authors: Patrucco Giacomo , Gómez Antonio , Adineh Ali , Rahrig Max , Lerma José Luis
Published: November 2022

In the framework of built heritage monitoring techniques, a prominent position is occupied by thermography, which represents an efficient and non-invasive solution for these kinds of investigations, allowing the identification of phenomena detectable only in the non-visible range of the electromagnetic spectrum. This is of extreme interest, especially considering the possibility of integrating the radiometric information with the 3D models achievable from laser scanning or photogrammetric techniques, characterised by a high spatial resolution. This paper aims to illustrate how combining different geomatics techniques (in particular, by merging thermal images, laser scanning point clouds, and traditional visible colour photogrammetric data) can efficiently support historical analyses for studying heritage buildings. Additionally, a strategy for generating HBIM models starting from the integration of 3D thermal investigations and historical sources is proposed, concerning both the multi-temporal modification of the volumes of the building and the individual architectural elements. The case study analysed for the current research was the Palacio de Colomina in Valencia, Spain, a noble palace—now the headquarters of a university—that, during the last few centuries, has been subjected to considerable transformations in terms of rehabilitation works and modification of its volume.
Mapping Soil Organic Carbon Content in Patagonian Forests Based on Climate, Topography and Vegetation Metrics from Satellite Imagery
Authors: Martínez Pastur Guillermo , Aravena Acuña Marie-Claire , Silveira Eduarda M. O. , Von Müller Axel , La Manna Ludmila , González-Polo Marina , Chaves Jimena E. , Cellini Juan M. , Lencinas María V. , Radeloff Volker C. , Pidgeon Anna M. , Peri Pablo L.
Published: November 2022

Soil organic carbon (SOC) content supports several ecosystem services. Quantifying SOC requires: (i) accurate C estimates of forest components, and (ii) soil estimates. However, SOC is difficult to measure, so predictive models are needed. Our objective was to model SOC stocks within 30 cm depth in Patagonian forests based on climatic, topographic and vegetation productivity measures from satellite images, including Dynamic Habitat Indices and Land Surface Temperature derived from Landsat-8. We used data from 1320 stands of different forest types in Patagonia, and random forest regression to map SOC. The model captured SOC variability well (R² = 0.60, RMSE = 22.1%), considering the huge latitudinal extension (36.4° to 55.1° SL) and the great diversity of forest types. Mean SOC was 134.4 ton C ha−1 ± 25.2, totaling 404.2 million ton C across Patagonia. Overall, SOC values were highest in valleys of the Andes mountains and in southern Tierra del Fuego, ranging from 53.5 to 277.8 ton C ha−1 for the whole Patagonia region. Soil organic carbon is a metric relevant to many applications, connecting major issues such as forest management, conservation, and livestock production, and having spatially explicit estimates of SOC enables managers to fulfil the international agreements that Argentina has joined.
Routine Processing and Automatic Detection of Volcanic Ground Deformation Using Sentinel-1 InSAR Data: Insights from African Volcanoes
Authors: Albino Fabien , Biggs Juliet , Lazecký Milan , Maghsoudi Yasser
Published: November 2022

Since the launch of Sentinel-1 mission, automated processing systems have been developed for near real-time monitoring of ground deformation signals. Here, we perform a regional analysis of 5 years over 64 volcanic centres located along the East African Rift System (EARS). We show that the correction of atmospheric signals for the arid and low-elevation EARS volcanoes is less important than for other volcanic environments. We find that the amplitude of the cumulative displacements exceeds three times the temporal noise of the time series (3σ) for 16 of the 64 volcanoes, which includes previously reported deformation signals, and two new ones at Paka and Silali volcanoes. From a 5-year times series, uncertainties in rates of deformation are ∼0.1 cm/yr, whereas uncertainties associated with the choice of reference pixel are typically 0.3–0.6 cm/yr. We fit the time series using simple functional forms and classify seven of the volcano time series as ‘linear’, six as ‘sigmoidal’ and three as ‘hybrid’, enabling us to discriminate between steady deformation and short-term pulses of deformation. This study provides a framework for routine volcano monitoring using InSAR on a continental scale. Here, we focus on Sentinel-1 data from the EARS, but the framework could be expanded to include other satellite systems or global coverage.
GIS-Based Simulation for Landfill Site Selection in Mekong Delta: A Specific Application in Ben Tre Province
Authors: Nguyen Dinh-Thanh , Truong Minh-Hoang , Ngo Thi-Phuong-Uyen , Le Anh-Minh , Yamato Yuya
Published: November 2022

The aim of this research is to develop a GIS-based simulation for selecting the most suitable site of solid waste landfill which could help to minimize harmful impacts to the environment and society in the extreme sensitive and complex delta by an integration of geographic information system (GIS) and analysis hierarchy process (AHP) and nine criteria (distance from surface water; depth of ground water table; distance from residential area, land use, distance from main roads, geo-environmental and geotechnical characteristics, distance from historical and tourism sites, and distance from industrial zones). Different from most of the previous studies on the landfill site selection, geology-related criteria including soil types/lithology, soil permeability, and soil depth/soil thickness (soil-structure), which are called geo-environmental and geotechnical characteristics in this research, will be carefully considered, integrated, and evaluated. The AHP was employed to determine the weight of each criterion based on pair weight comparison and its matrix, while a land suitability index (LSI) score was calculated to determine the most suitable site. Moreover, the suitability map was also created which indicated very advantageous, advantageous, rather advantageous, and disadvantageous areas in the study area for landfill siting. Finally, the developed model could be used for supporting planners, managers, policy makers, and local government to make decisions on suitable and effective planning strategies for landfill site selection and could be applied anywhere and especially in other deltas around the world.
Monitoring Spatial-Temporal Variations of Lake Level in Western China Using ICESat-1 and CryoSat-2 Satellite Altimetry
Authors: Chen Jun , Duan Zheng
Published: November 2022

The lakes in the arid or semi-arid regions of western China are more sensitive to climate changes, and lake levels are considered as a direct indicator of regional climate variability. In this study, we combined satellite altimetry data from ICESat-1 with a smaller footprint and higher accuracy (compared to radar altimetry) and CryoSat-2 with a higher resolution in the along-track direction to monitor lake levels in western China and their trends over a long time period from 2003 to 2021. Our satellite altimetry derived lake levels were well-validated by comparing them against in situ measurements for a lake and independent altimetry-derived product from the DAHITI database for the common lakes. Furthermore, the commonly used linear model was applied to our derived lake level time-series to estimate the overall change trends in 67 typical lake levels over western China. Our results showed that 55 (82%) of these lakes displayed an increasing tendency in water levels, and the remaining 12 (18%) lakes showed a decreasing trend. Overall, the mean water level changing rate in western China was +0.15 m/yr (−1.40 to +0.58 m/yr) during the studied time period. The spatial patterns of the lake level variations can be grouped into three subregions: lake level changes between 2003 and 2021 showed general rising lake levels for the central–northern TP (Tibetan Plateau) endorheic region and Xinjiang, but declining levels for the southern TP exorheic region. The seasonal characteristic of lake level changes showed a significant increase during the summer monsoon season, followed by decreases during the non-monsoon season. The precipitation variations play a leading role in the lake level changes in the context of warm and humid climate states. There were good correspondences between the monthly variations in the lake level and monthly mean precipitation. Additionally, the lake levels also showed a relationship with the air temperature change, in particular, the lake level increase showed a small degree of hysteresis behavior compared with the rising temperatures. Geographically, the precipitation increase in the westerlies regions led to widespread lake expansion in the central–northern TP and Xinjiang. Conversely, precipitation decrease in the Indian monsoon regions caused lake shrinkage in the exorheic region of the southern TP. This study helps us achieve a better understanding of the spatial-temporal patterns of lake level changes in the arid or semi-arid region of western China.
Impacts of FY-4A GIIRS Water Vapor Channels Data Assimilation on the Forecast of “21·7” Extreme Rainstorm in Henan, China with CMA-MESO
Authors: Yin Ruoying , Han Wei , Wang Hao , Wang Jincheng
Published: November 2022

A record-breaking extreme rainstorm occurred in Henan Province of China on 20 July 2021. To investigate the impacts of the Geostationary Interferometric Infrared Sounder (GIIRS) data assimilation on model analysis and forecasts of this rainfall event, the high temporal resolution GIIRS water vapor (WV) channel data were assimilated in the high-resolution CMA-MESO (Mesoscale Weather Numerical Forecast System of China Meteorological Administration) in this study. The results showed that the GIIRS WV radiance assimilation could improve the model WV analysis, which in turn adjusted the distributions of hydrometeors (radar composite reflectivities) and wind field, and finally improved the precipitation forecast. Additionally, although barely any GIIRS observations were assimilated over the cloudy area, the precipitation forecast errors of “21·7” extreme rainstorm events could be reduced by improving the structure of atmospheric circulations through the assimilation of neighboring data around Henan, especially over the upstream region. With the GIIRS WV data assimilation, the location error of maximum 24-h accumulated precipitation forecasts decreased from 128.48 km to 28.97 km (improved by 77.45%) for the cold start at 0000 UTC (Universal Time Coordinated) on 19 July 2021, and it was also reduced by about 60.52% for the warm start experiment at 0600 UTC on 19 July 2021. In addition, the GIIRS assimilation experiment showed an extraordinarily heavy rainfall area (above 250 mm/24 h) around Zhengzhou station, which did not appear in the control experiment, and was closer to the observed extreme precipitation. This study demonstrates the potential value of geostationary hyperspectral infrared sounders data assimilation in extreme weather early warning and forecasting.
Use of Airborne Radar Images and Machine Learning Algorithms to Map Soil Clay, Silt, and Sand Contents in Remote Areas under the Amazon Rainforest
Authors: Ferreira Ana Carolina de S. , Ceddia Marcos B. , Costa Elias M. , Pinheiro Érika F. M. , Nascimento Mariana Melo do , Vasques Gustavo M.
Published: November 2022

Soil texture has a great influence on the physical–hydric and chemical behavior of soils. In the Amazon regions, due to the presence of dense forest cover and limited access to roads, carrying out surveys and mapping of soils is challenging. When data exist, they are relatively sparse and the distribution is quite uneven. In this context, machine learning algorithms (ML) associated with remote sensor covariates offer a framework to derive digital maps of soil attributes. The objective of this study was to produce maps of surface and subsurface soil clay, silt, and sand contents in a 13.440 km2 area in the Amazon. The specific objectives were to (a) evaluate the gain in prediction accuracy when using the P-band of airborne radar as a covariate; (b) evaluate two sampling approaches (Reference Area—RA and Total Area—TA); and (c) evaluate the transferability and performance of three ML algorithms: regression tree (RT), random forest (RF), and support vector machine (SVM). The study site was divided into three blocks, called Urucu, Araracanga, and Juruá, respectively. The soil dataset consisted of 151 surface and subsurface sand, silt, and clay observations and 21 covariates (20 relief variables and the backscattering coefficient from the P-band). Both the RA and TA sampling approach used 114 observations for training the prediction models (75%) and 37 for validation (25%). The RA approach was better for the development of sand and silt models. Overall, RF derived the most accurate predictions for all variables. The effect of introducing the P-band backscattering coefficient improved the sand prediction accuracy at the surface and subsurface in Araracanga, which had the highest sand content, with relative improvements (RI) of the R2, root mean square error (RMSE), and mean absolute error (MAE) of 46%, 3%, and 4% at the surface, respectively, and 66.7%, 4.4%, and 5.2% at the subsurface, respectively. For silt, the P-band improved the predictions at the surface in Araracanga, which had the lowest silt contents among the blocks. For clay, adding the P-band improved the RF predictions at the subsurface, with RI of the R2, RMSE, and MAE of 29%, 5%, and 5%, respectively. Despite the low observation density, inherently hindered by the low accessibility of the area and high costs of sampling thereof, the results showed the potential of ML algorithms boosted by airborne radar P-band to map soil clay, silt, and sand contents in the Amazon.
Oceanic Mesoscale Eddies Identification Using B-Spline Surface Fitting Model Based on Along-Track SLA Data
Authors: Xu Luochuan , Gao Miao , Zhang Yaorong , Guo Junting , Lv Xianqing , Zhang Anmin
Published: November 2022

Gridded reanalysis data has been widely used in oceanic mesoscale eddy identification. However, there is little research on identifying oceanic mesoscale eddies based on along-track data. In this paper, the B-spline surface fitting model is established for oceanic mesoscale eddy identification. This innovative model can embody along-track data’s advantages in accuracy, universality, and instantaneity, and directly identify oceanic mesoscale eddies solely based on along-track data. We start with ideal experiments using simulated data to explore the error sources of B-spline surface fitting for one oceanic mesoscale eddy, followed by practical experiments with measured data to analyze the effect of the B-spline surface fitting and the fitting errors further. Compared with eddies obtained from gridded reanalysis data, results show that the B-spline surface fitting model based on along-track data has an ideal effect in identifying oceanic mesoscale eddies; little difference exists between the fitting result and measured data. The model proposes a new technological means to deal with along-track data, which contributes to oceanic mesoscale eddy identification and marine element data analysis.
Development and Utilization of a Mirror Array Target for the Calibration and Harmonization of Micro-Satellite Imagery
Authors: Ichikawa Dorj , Nagai Masahiko , Tamkuan Nopphawan , Katiyar Vaibhav , Eguchi Tsuyoshi , Nagai Yumiko
Published: November 2022

The utilization of multi-sensor and constellation satellite data with appropriate geometric and radiometric calibration and validation is required for effective satellite data applications for various monitoring tasks. In this paper, we present the research and development of optical calibration sites using both natural surface and ground point-source-mirror reflectors constructed at The Center for Research and Application of Satellite Remote Sensing of Yamaguchi University (YUCARS), Japan. The YUCARS calibration sites experimented with GRUS-1A (Axelspace Corporation, Tokyo, Japan) and PlanetScope (Planet Lab, San Francisco, CA, USA) images for the verification of radiometric and geometric performance following the harmonized reflectance product. The top of atmosphere (TOA) radiance and reflectance of optical micro-satellite imageries were simulated by MODTRAN6 based on the in-situ data of the ground point-source-mirror reflector, ground surface and atmospheric measurements. The YUCARS mirror arrays were used to verify geometric accuracy and better band co-registration. The TOA reflectance derived from the ground measurements and acquired by satellite instruments were correlated to derive harmonization coefficients. The results show an improvement in image accuracy and harmonization of the different sensor data for the multi-temporal application. The preliminary results show that the mirror-arrays method can efficiently solve the limits of the external environment, time, and space. Furthermore, it can be used for improving radiometric performance and image quality using deblurring from a point spread function created from YUCARS mirror arrays.
SAR Target Recognition Based on Inception and Fully Convolutional Neural Network Combining Amplitude Domain Multiplicative Filtering Method
Authors: Chen He , Fu Xiongjun , Dong Jian
Published: November 2022

The research of Synthetic aperture radar (SAR) target recognition plays a significant role in military and civilian fields. However, for small sample SAR target recognition, there are some problems that need to be solved urgently, including low recognition accuracy, slow training convergence rate, and serious overfitting. Aiming at the above problems, we propose a recognition method based on Inception and Fully Convolutional Neural Network (IFCNN) combined with Amplitude Domain Multiplicative Filtering (ADMF) image processing. To improve the recognition accuracy and convergence rate, the ADMF method is utilized to construct the pretraining set, and the initial parameters of the network are optimized by pretraining. In addition, this paper builds the IFCNN model by introducing the Inception structure and the mixed progressive convolution layer into the FCNN. The full convolution structure of FCNN is effective to alleviate the problem of network overfitting. The Inception structure can enhance the sparsity of features and improve the network classification ability. Meanwhile, the mixed progressive convolution layers can accelerate training. Based on the MSTAR dataset, the experimental results show that the method proposed achieves an average precision of 88.95% and the training convergence rate is significantly improved in small sample scenarios.
Comparing Gaofen-5, Ground, and Huanjing-1A Spectra for the Monitoring of Soil Salinity with the BP Neural Network Improved by Particle Swarm Optimization
Authors: Jiang Xiaofang , Xue Xian
Published: November 2022

Most of the world’s saline soils are found in arid or semiarid areas, where salinization is becoming serious. Ground laboratory hyperspectral data (analytical spectral devices, ASD) as well as spaceborne hyperspectral data, including Gaofen-5 (GF-5) and Huanjing-1A (HJ-1A), provide convenient salinity monitoring. However, the difference among ASD, GF-5, and HJ-1A spectra in salinity monitoring remains unclear. So, we used ASD, GF-5, and HJ-1A spectra as data sources in Gaotai County of Hexi Corridor, which has been affected by salinization. For a more comprehensive comparison of the three spectra datum, four kinds of band screening methods, including Pearson correlation coefficient (PCC), principal component analysis (PCA), successive projections algorithm (SPA), and random forest (RF) were used to reduce the dimension of hyperspectral data. Particle swarm optimization (PSO) was used to improve the random initialization of weights and thresholds of the back propagation neural network (BPNN) model. The results showed that root mean square error (RMSE) and determination of the coefficients (R2) of models based on ASD and HJ-1A spectra were basically similar. ASD spectra (RMSE = 4 mS·cm−1, R2 = 0.82) and HJ-1A (RMSE = 2.98 mS·cm−1, R2 = 0.93) performed better than GF-5 spectra (RMSE = 6.45 mS·cm−1, R2 = 0.67) in some cases. The good modelling result of HJ-1A and GF-5 data confirmed that spaceborne hyperspectral imagery has great potential in salinity mapping. Then, we used HJ-1A and GF-5 hyperspectral imagery to map soil salinity. The results of GF-5 and HJ-1A showed that extremely and highly saline soil mainly occurred in grassland and the southern part of arable land in Gaotai County. Other lands mainly featured non-saline and slightly saline soil. This can provide a reference for salinity monitoring research.
Anomalous Zenith Total Delays for an Insular Tropical Location: The Tahiti Island Case
Authors: Zhang Fangzhao , Feng Peng , Xu Guochang , Barriot Jean-Pierre
Published: November 2022

The weighted mean temperature of the troposphere, Tm, is a key parameter in GNSS meteorology. It can be routinely derived based on meteorological data from radiosonde (RS) or numerical weather models. Alternatively, it can be also derived through a least-squares model of the ratio between the precipitable water vapor from RS data and the zenith wet delay estimates from GNSS measurement in the precise point positioning mode. In this last case, we found anomalous Tm values for the remote sub-tropical humid location of the Tahiti Island in the South Pacific Ocean and traced these anomalous values to anomalous zenith total delays (ZTD) that seem to have an accuracy poorer by one order of magnitude than the claimed accuracy of ZTD delays from worldwide databases. The possible causes of these discrepancies are discussed.
Radar Intra–Pulse Signal Modulation Classification with Contrastive Learning
Authors: Cai Jingjing , Gan Fengming , Cao Xianghai , Liu Wei , Li Peng
Published: November 2022

The existing research on deep learning for radar signal intra–pulse modulation classification is mainly based on supervised leaning techniques, which performance mainly relies on a large number of labeled samples. To overcome this limitation, a self–supervised leaning framework, contrastive learning (CL), combined with the convolutional neural network (CNN) and focal loss function is proposed, called CL––CNN. A two–stage training strategy is adopted by CL–CNN. In the first stage, the model is pretrained using abundant unlabeled time–frequency images, and data augmentation is used to introduce positive–pair and negative–pair samples for self–supervised learning. In the second stage, the pretrained model is fine–tuned for classification, which only uses a small number of labeled time–frequency images. The simulation results demonstrate that CL–CNN outperforms the other deep models and traditional methods in scenarios with Gaussian noise and impulsive noise–affected signals, respectively. In addition, the proposed CL–CNN also shows good generalization ability, i.e., the model pretrained with Gaussian noise–affected samples also performs well on impulsive noise–affected samples.
Multisensor Assessment of Leaf Area Index across Ecoregions of Ardabil Province, Northwestern Iran
Authors: Andalibi Lida , Ghorbani Ardavan , Darvishzadeh Roshanak , Moameri Mehdi , Hazbavi Zeinab , Jafari Reza , Dadjou Farid
Published: November 2022

Leaf area index (LAI), one of the most crucial vegetation biophysical variables, is required to evaluate the structural characteristic of plant communities. This study, therefore, aimed to evaluate the LAI of ecoregions in Iran obtained using Sentinel-2B, Landsat 8 (OLI), MODIS, and AVHRR data in June and July 2020. A field survey was performed in different ecoregions throughout Ardabil Province during June and July 2020 under the satellite image dates. A Laipen LP 100 (LP 100) field-portable device was used to measure the LAI in 822 samples with different plant functional types (PFTs) of shrubs, bushes, and trees. The LAI was estimated using the SNAPv7.0.4 (Sentinel Application Platform) software for Sentinel-2B data and Google Earth Engine (GEE) system–based EVI for Landsat 8. At the same time, for MODIS and AVHRR, the LAI products of GEE were considered. The results of all satellite-based methods verified the LAI variations in space and time for every PFT. Based on Sentinel-2B, Landsat 8, MODIS, and AVHRR application, the minimum and maximum LAIs were respectively obtained at 0.14–1.78, 0.09–3.74, 0.82–4.69, and 0.35–2.73 for shrubs; 0.17–5.17, 0.3–2.3, 0.59–3.84, and 0.63–3.47 for bushes; and 0.3–4.4, 0.3–4.5, 0.7–4.3, and 0.5–3.3 for trees. These estimated values were lower than the LAI values of LP 100 (i.e., 0.4–4.10 for shrubs, 1.6–7.7 for bushes, and 3.1–6.8 for trees). A significant correlation (p < 0.05) for almost all studied PFTs between LP 100-LAI and estimated LAI from sensors was also observed in Sentinel-2B (|r| > 0.63 and R2 > 0.89), Landsat 8 (|r| > 0.50 and R2 > 0.72), MODIS (|r| > 0.65 and R2 > 0.88), and AVHRR (|r| > 0.59 and R2 > 0.68). Due to its high spatial resolution and relatively significant correlation with terrestrial data, Sentinel-2B was more suitable for calculating the LAI. The results obtained from this study can be used in future studies on sustainable rangeland management and conservation.
Gaussian Dynamic Convolution for Semantic Segmentation in Remote Sensing Images
Authors: Feng Mingzhe , Sun Xin , Dong Junyu , Zhao Haoran
Published: November 2022

Different scales of the objects pose a great challenge for the segmentation of remote sensing images of special scenes. This paper focuses on the problem of large-scale variations of the target objects via a dynamical receptive field of the deep network. We construct a Gaussian dynamic convolution network by introducing a dynamic convolution layer to enhance remote sensing image understanding. Moreover, we propose a new Gaussian pyramid pooling (GPP) for multi-scale object segmentation. The proposed network can expand the size of the receptive field and improve its efficiency in aggregating contextual information. Experiments verify that our method outperforms the popular semantic segmentation methods on large remote sensing image datasets, including iSAID and LoveDA. Moreover, we conduct experiments to demonstrate that the Gaussian dynamic convolution works more effectively on remote sensing images than other convolutional layers.
Zero-Shot Remote Sensing Image Dehazing Based on a Re-Degradation Haze Imaging Model
Authors: Wei Jianchong , Wu Yi , Chen Liang , Yang Kunping , Lian Renbao
Published: November 2022

Image dehazing is crucial for improving the advanced applications on remote sensing (RS) images. However, collecting paired RS images to train the deep neural networks (DNNs) is scarcely available, and the synthetic datasets may suffer from domain-shift issues. In this paper, we propose a zero-shot RS image dehazing method based on a re-degradation haze imaging model, which directly restores the haze-free image from a single hazy image. Based on layer disentanglement, we design a dehazing framework consisting of three joint sub-modules to disentangle the hazy input image into three components: the atmospheric light, the transmission map, and the recovered haze-free image. We then generate a re-degraded hazy image by mixing up the hazy input image and the recovered haze-free image. By the proposed re-degradation haze imaging model, we theoretically demonstrate that the hazy input and the re-degraded hazy image follow a similar haze imaging model. This finding helps us to train the dehazing network in a zero-shot manner. The dehazing network is optimized to generate outputs that satisfy the relationship between the hazy input image and the re-degraded hazy image in the re-degradation haze imaging model. Therefore, given a hazy RS image, the dehazing network directly infers the haze-free image by minimizing a specific loss function. Using uniform hazy datasets, non-uniform hazy datasets, and real-world hazy images, we conducted comprehensive experiments to show that our method outperforms many state-of-the-art (SOTA) methods in processing uniform or slight/moderate non-uniform RS hazy images. In addition, evaluation on a high-level vision task (RS image road extraction) further demonstrates the effectiveness and promising performance of the proposed zero-shot dehazing method.
A New Global Bathymetry Model: STO_IEU2020
Authors: Fan Diao , Li Shanshan , Feng Jinkai , Sun Yongqi , Xu Zhenbang , Huang Zhiyong
Published: November 2022

To address the limitations in global seafloor topography model construction, a scheme is proposed that takes into account the efficiency of seafloor topography prediction, the applicability of inversion methods, the heterogeneity of seafloor environments, and the inversion advantages of sea surface gravity field element. Using the South China Sea as a study area, we analyzed and developed the methodology in modeling the seafloor topography, and then evaluated the feasibility and effectiveness of the modeling strategy. Based on the proposed modeling approach, the STO_IEU2020 global bathymetry model was constructed using various input data, including the SIO V29.1 gravity anomaly (GA) and vertical gravity gradient anomaly (VGG), as well as bathymetric data from multiple sources (single beam, multi-beam, seismic, Electronic Navigation Chart, and radar sensor). Five evaluation areas located in the Atlantic and Indian Oceans were used to assess the performance of the generated model. The results showed that 79%, 89%, 72%, 92% and 93% of the checkpoints were within the ±100 m range for the five evaluation areas, and with average relative accuracy better than 6%. The generated STO_IEU2020 model correlates well with the SIO V20.1 model, indicating that the proposed construction strategy for global seafloor topography is feasible.
Development of Real-Time Landmark-Based Emotion Recognition CNN for Masked Faces
Authors: Farkhod Akhmedov , Abdusalomov Akmalbek Bobomirzaevich , Mukhiddinov Mukhriddin , Cho Young-Im
Published: November 2022

Owing to the availability of a wide range of emotion recognition applications in our lives, such as for mental status calculation, the demand for high-performance emotion recognition approaches remains uncertain. Nevertheless, the wearing of facial masks has been indispensable during the COVID-19 pandemic. In this study, we propose a graph-based emotion recognition method that adopts landmarks on the upper part of the face. Based on the proposed approach, several pre-processing steps were applied. After pre-processing, facial expression features need to be extracted from facial key points. The main steps of emotion recognition on masked faces include face detection by using Haar–Cascade, landmark implementation through a media-pipe face mesh model, and model training on seven emotional classes. The FER-2013 dataset was used for model training. An emotion detection model was developed for non-masked faces. Thereafter, landmarks were applied to the upper part of the face. After the detection of faces and landmark locations were extracted, we captured coordinates of emotional class landmarks and exported to a comma-separated values (csv) file. After that, model weights were transferred to the emotional classes. Finally, a landmark-based emotion recognition model for the upper facial parts was tested both on images and in real time using a web camera application. The results showed that the proposed model achieved an overall accuracy of 91.2% for seven emotional classes in the case of an image application. Image based emotion detection of the proposed model accuracy showed relatively higher results than the real-time emotion detection.
Parasitic Effects on Electrical Bioimpedance Systems: Critical Review
Authors: Marcôndes David William Cordeiro , Paterno Aleksander Sade , Bertemes-Filho Pedro
Published: November 2022

Parasitic capacitance represents the main error source in measurement systems based on electrical impedance spectroscopy. The capacitive nature of electrodes’ impedance in tetrapolar configuration can give origin to phase errors when electrodes are coupled to parasitic capacitances. Nevertheless, reactive charges in tissue excitation systems are susceptible to instability. Based on such a scenario, mitigating capacitive effects associated with the electrode is a requirement in order to reduce errors in the measurement system. A literature review about the main compensation techniques for parasitic capacitance was carried out. The selected studies were categorized into three groups: (i) compensation in electronic instrumentation; (ii) compensation in measurement processing, and (iii) compensation by negative impedance converters. The three analyzed methods emerged as effective against fixed capacitance. No method seemed capable of mitigating the effects of electrodes’ capacitance, that changes in the frequency spectrum. The analysis has revealed the need for a method to compensate varying capacitances, since electrodes’ impedance is unknown.
Signal Recovery from Randomly Quantized Data Using Neural Network Approach
Authors: Al-Shaikhi Ali
Published: November 2022

We present an efficient scheme based on a long short-term memory (LSTM) autoencoder for accurate seismic deconvolution in a multichannel setup. The technique is beneficial for compressing massive amounts of seismic data. The proposed robust estimation ensures the recovery of sparse reflectivity from acquired seismic data that have been under-quantized. By adjusting the quantization error, the technique considerably improves the robustness of data to the quantization error, thereby boosting the visual saliency of seismic data compared to the other existing algorithms. This framework has been validated using both field and synthetic seismic data sets, and the assessment is carried out by comparing it to the steepest decent and basis pursuit methods. The findings indicate that the proposed scheme outperforms the other algorithms significantly in the following ways: first, in the proposed estimation, fraudulently or overbearingly estimated impulses are significantly suppressed, and second, the proposed guesstimate is much more robust to the quantization interval changes. The tests on real and synthetic data sets reveal that the proposed LSTM autoencoder-based method yields the best results in terms of both quality and computational complexity when compared with existing methods. Finally, the relative reconstruction error (RRE), signal-to-reconstruction error ratio (SRER), and power spectral density (PSD) are used to evaluate the performance of the proposed algorithm.
Analysis of and Reduction in Noise in Current Measurement of XCP under the Laboratory Condition
Authors: Liu Yunliang , Chen Guangyuan , Du Libin , Zhang Qisheng , Zhou Keyu , Lin Zucan , Du Sheng
Published: November 2022

Expendable current profiler (XCP) is one of the most vital devices detecting ocean currents. Compared with other methods, the expendable feature makes trials with XCP much faster and more hidden, while the accuracy of XCP is considerably influenced by electromagnetic noise all around. Aiming at researching the influence and reducing the noise, this study carried out laboratory simulation experiments. The designed laboratory experiments mainly have a self-developed rotation gear, an XCP prototype, a plastic flume, and two copper plates as power. Firstly, these experiments analyzed the main sources of electromagnetic noise for XCP detection. Secondly, we built a noise simulation environment and conducted XCP detection experiments under different noise in the flume. The data obtained by XCP were transmitted to the computer to be stored and processed. The results show the internal noise impact on XCP is significantly less than the external. For an excitation power of 1 mV, the offset of theoretical and actual data brought by internal noise is 12 times smaller than external and can be corrected.
Ultrasonic Quality Assurance at Magnesia Shotcrete Sealing Structures
Authors: Lay Vera , Effner Ute , Niederleithinger Ernst , Arendt Jennifer , Hofmann Martin , Kudla Wolfram
Published: November 2022

Engineered barriers are a key element to enable safe nuclear waste disposal. One method currently under research for their construction is magnesia concrete applied in a shotcrete procedure. In this study, the ultrasonic echo method is evaluated as a means for quality assurance. Imaging of internal structures (backwall, boreholes) and defects, such as delamination, has successfully been achieved in the shotcrete. Additionally, detailed information about the potential cause of selected reflectors are obtained by phase analysis. In several test blocks of various sizes, no consistent concrete section boundaries have been found by ultrasonic imaging, which was verified by subsequent drilling and complementary tests. An experiment with artificial defects imitating cracks, air-filled voids, and material with lower density has been challenging and shows the limitations of the current methods. Although significant defects, such as a large delamination, are reliably identified, several smaller defects are not identified. Generally, ultrasonic imaging provides a suitable base as a mean for quality assurance during and after the construction of sealing structures. However, further developments are required to enhance the reliability of the method and a full validation is still pending. Still, the method has potential to increase the safety of nuclear waste repositories.
Data Fusion Methods for Indoor Positioning Systems Based on Channel State Information Fingerprinting
Authors: Gidey Hailu Tesfay , Guo Xiansheng , Zhong Ke , Li Lin , Zhang Yukun
Published: November 2022

Indoor signals are susceptible to NLOS propagation effects, multipath effects, and a dynamic environment, posing more challenges than outdoor signals despite decades of advancements in location services. In modern Wi-Fi networks that support both MIMO and OFDM techniques, Channel State Information (CSI) is now used as an enhanced wireless channel metric replacing the Wi-Fi received signal strength (RSS) fingerprinting method. The indoor multipath effects, however, make it less robust and stable. This study proposes a positive knowledge transfer-based heterogeneous data fusion method for representing the different scenarios of temporal variations in CSI-based fingerprint measurements generated in a complex indoor environment targeting indoor parking lots, while reducing the training calibration overhead. Extensive experiments were performed with real-world scenarios of the indoor parking phenomenon. Results revealed that the proposed algorithm proved to be an efficient algorithm with consistent positioning accuracy across all potential variations. In addition to improving indoor parking location accuracy, the proposed algorithm provides computationally robust and efficient location estimates in dynamic environments. A Cramer-Rao lower bound (CRLB) analysis was also used to estimate the lower bound of the parking lot location error variance under various temporal variation scenarios. Based on analytical derivations, we prove that the lower bound of the variance of the location estimator depends on the (i) angle of the base stations, (ii) number of base stations, (iii) distance between the target and the base station, djr (iv) correlation of the measurements, ρrjai and (v) signal propagation parameters σC and γ.
TTQR: A Traffic- and Thermal-Aware Q-Routing for 3D Network-on-Chip
Authors: Liu Hanyan , Chen Xiaowen , Zhao Yunping , Li Chen , Lu Jianzhuang
Published: November 2022

The die-stacking structure of 3D network-on-chips (3D NoC) leads to high power density and unequal thermal conductance between different layers, which results in low reliability and performance degradation of 3D NoCs. Congestion-aware adaptive routing, which is capable of balancing the network’s traffic load, can alleviate congestion and thermal problems so as to improve the performance of the network. In this study, we propose a traffic- and thermal-aware Q-routing algorithm (TTQR) based on Q-learning, a reinforcement learning method. The proposed algorithm saves the local traffic status and the global temperature information to the Q1-table and Q2-table, respectively. The values of two tables are updated by the packet header and saved in a small size, which saves the hardware overhead. Based on the ratio of the Q1-value to the Q2-value corresponding to each direction, the packet’s output port is selected. As a result, packets are transferred to the chosen path to alleviate thermal problems and achieve more balanced inter-layer traffic. Through the Access Noxim simulation platform, we compare the proposed routing algorithm with the TAAR routing algorithm. According to experimental results using synthetic traffic patterns, our proposed methods outperform the TAAR routing algorithm by an average of 63.6% and 41.4% in average latency and throughput, respectively.
A Study of the Radiation Tolerance and Timing Properties of 3D Diamond Detectors
Authors: Anderlini Lucio , Bellini Marco , Cindro Vladimir , Corsi Chiara , Kanxheri Keida , Lagomarsino Stefano , Lucarelli Chiara , Morozzi Arianna , Passaleva Giovanni , Passeri Daniele , Sciortino Silvio , Servoli Leonello , Veltri Michele
Published: November 2022

We present a study on the radiation tolerance and timing properties of 3D diamond detectors fabricated by laser engineering on synthetic Chemical Vapor Deposited (CVD) plates. We evaluated the radiation hardness of the sensors using Charge Collection Efficiency (CCE) measurements after neutron fluences up to 1016 n/cm2 (1 MeV equivalent.) The radiation tolerance is significantly higher when moving from standard planar architecture to 3D architecture and increases with the increasing density of the columnar electrodes. Also, the maximum applicable bias voltage before electric breakdown increases significantly after high fluence irradiation, possibly due to the passivation of defects. The experimental analysis allowed us to predict the performance of the devices at higher fluence levels, well in the range of 1016 n/cm2. We summarize the recent results on the time resolution measurements of our test sensors after optimization of the laser fabrication process and outline future activity in developing pixel tracking systems for high luminosity particle physics experiments.
G-DaM: A Distributed Data Storage with Blockchain Framework for Management of Groundwater Quality Data
Authors: Vangipuram Sukrutha L. T. , Mohanty Saraju P. , Kougianos Elias , Ray Chittaranjan
Published: November 2022

Groundwater overuse in different domains will eventually lead to global freshwater scarcity. To meet the anticipated demands, many governments worldwide are employing innovative and traditional techniques for forecasting groundwater availability by conducting research and studies. One challenging step for this type of study is collecting groundwater data from different sites and securely sending it to the nearby edges without exposure to hacking and data tampering. In the current paper, we send raw data formats from the Internet of Things to the Distributed Data Storage (DDS) and Blockchain (BC) edges. We use a distributed and decentralized architecture to store the statistics, perform double hashing, and implement access control through smart contracts. This work demonstrates a modern and innovative approach combining DDS and BC technologies to overcome traditional data sharing, and centralized storage, while addressing blockchain limitations. We have shown performance improvements with increased data quality and integrity.
Reverse Offset Printed, Biocompatible Temperature Sensor Based on Dark Muscovado
Authors: Aziz Shahid , Ali Junaid , Bhandari Krishna Singh , Chen Wenning , Li Sijia , Jung Dong Won
Published: November 2022

A reverse-offset printed temperature sensor based on interdigitated electrodes (IDTs) has been investigated in this study. Silver nanoparticles (AgNPs) were printed on a glass slide in an IDT pattern by reverse-offset printer. The sensing layer consisted of a sucrose film obtained by spin coating the sucrose solution on the IDTs. The temperature sensor demonstrated a negative temperature coefficient (NTC) with an exponential decrease in resistance as the temperature increased. This trend is the characteristic of a NTC thermistor. There is an overall change of ~2800 kΩ for the temperature change of 0 °C to 100 °C. The thermistor is based on a unique temperature sensor using a naturally occurring biocompatible material, i.e., sucrose. The active sensing material of the thermistor, i.e., sucrose used in the experiments was obtained from extract of Muscovado. Our temperature sensor has potential in the biomedical and food industries where environmentally friendly and biocompatible materials are more suitable for sensing accurately and reliably.
Unmanned Aerial Vehicle (UAV) Robot Microwave Imaging Based on Multi-Path Scattering Model
Authors: Chen Zhihua , Qiao Xinya , Wu Pei , Zhang Tiancai , Hong Tao , Fang Linquan
Published: November 2022

Unmanned Aerial Vehicle (UAV) robot microwave imaging systems have attracted comprehensive attention. Compared with visible light and infrared imaging systems, microwave imaging is not susceptible to weather. Active microwave imaging systems have been realized in UAV robots. However, the scattering signals of geographical objects from satellite transmitting systems received by UAV robots to process imaging is studied rarely, which reduces the need of load weight for the UAV robot. In this paper, a multi-path scattering model of vegetation on the earth surface is proposed, and then the microwave imaging algorithm is introduced to reconstruct the images from the UAV robot receiving the scattering data based on the multi-path model. In image processing, it is assumed that the orbit altitude of a transmitter loaded on the satellite remains unchanged, and the receiver loaded UAV robot obtains the reflective information from ground vegetation with different zenith angles. The imaging results show that the angle change has an impact on the imaging resolution. The combination of electromagnetic scattering model and image processing method contributes to understanding the image results and the multi-path scattering mechanisms of vegetation, which provide a reference for the research and development of microwave imaging systems of UAV robot networking using satellite transmitting signals.
A Malicious Code Detection Method Based on FF-MICNN in the Internet of Things
Authors: Zhang Wenbo , Feng Yongxin , Han Guangjie , Zhu Hongbo , Tan Xiaobo
Published: November 2022

It is critical to detect malicious code for the security of the Internet of Things (IoT). Therefore, this work proposes a malicious code detection algorithm based on the novel feature fusion–malware image convolutional neural network (FF-MICNN). This method combines a feature fusion algorithm with deep learning. First, the malicious code is transformed into grayscale image features by image technology, after which the opcode sequence features of the malicious code are extracted by the n-gram technique, and the global and local features are fused by feature fusion technology. The fused features are input into FF-MICNN for training, and an appropriate classifier is selected for detection. The results of experiments show that the proposed algorithm exhibits improvements in its detection speed, the comprehensiveness of features, and accuracy as compared with other algorithms. The accuracy rate of the proposed algorithm is also 0.2% better than that of a detection algorithm based on a single feature.