bluuebunny/crossref_metadata_2025 huggingface dataset:
A Unified Algorithm for the Sliding Spotlight and TOPS Modes Data Processing in Bistatic Configuration of the Geostationary Transmitter with LEO Receivers
Authors: Tian Feng , Suo Zhiyong , Wang Yuekun , Lu Zheng , Wang Zhen , Li Zhenfang
Published: April 2022

This paper deals with the imaging problem for sliding spotlight (SS) and terrain observation by progressive scan (TOPS) modes in bistatic configuration of the geostationary (GEO) transmitter with a low earth orbit satellite (LEO) receiver, named GTLR-BiSAR system. A unified imaging algorithm is proposed to process the GTLR-BiSAR data acquired in SS or TOPS modes. Our main contributions include four aspects. Firstly, the imaging geometry of this novel configuration is described in detail. Furthermore, the GTLR-BiSAR signal expressions were deduced in both time and frequency domains. These signal expressions provide great support for the design of processing the algorithm theoretically. Secondly, we present a unified deramping-based technique according to the special geometry of GTLR-BiSAR to overcome the azimuth spectrum aliasing phenomenon, which typically affects SS and TOPS data. Thirdly, the spatial variance of GTLR-BiSAR data were thoroughly analyzed based on the range-Doppler (RD) geolocation functions. On the basis of a former analysis, we put forward the azimuth variance correction strategy and modified the conventional chirp scaling function to solve the range variance problem. Finally, we completed the derivation of the two-dimensional spectrum after the range chirp scaling. On the basis of spectrum expressions, we compensated for the quadratic and residue phase, and the azimuth compression was completed by SPECAN operation. In addition, we provide a flow diagram to visually exhibit the processing procedures. At the end of this paper, the simulation and real data experiment results are presented to validate the effectiveness of the proposed algorithm.
Direct and Legacy Effects of Spring Temperature Anomalies on Seasonal Productivity in Northern Ecosystems
Authors: Marsh Hanna , Zhang Wenxin
Published: April 2022

Warmer or cooler spring in northern high latitudes will, for the most part, directly impact gross primary productivity (GPP) of ecosystems, but also carry consequences for the upcoming seasonal GPP. Spatiotemporal patterns of these legacy effects are still largely unknown but important for improving our understanding of how plant phenology is associated with vegetation dynamics. In this study, impacts of spring temperature anomalies on spring, summer and autumn GPP were investigated, and the dominant drivers of summer and autumn GPP including air temperature, vapor pressure deficit and soil moisture have been explored for northern ecosystems (>30°N). Three remote sensing products of seasonal GPP (GOSIF-GPP, NIRv-GPP and FluxSat-GPP) over 2001–2018, all based on a spatial resolution of 0.05°, were employed. Our results indicate that legacy effects from spring temperature are most pronounced in summer, where they have stimulating effects on the Arctic ecosystem productivity. Spring warming likely lessens the harsh climatic constraints that govern the Arctic tundra and extends the growing season length. Further south, legacy effects are mainly negative. This strengthens the hypothesis that enhanced vegetation growth in spring will increase plant water demand and stress in summer and autumn. Soil moisture is the dominant control of summer GPP in temperate regions. However, the dominant meteorological variables controlling vegetation growth may differ depending on the GPP products, highlighting the need to address uncertainties among different methods of estimating GPP.
Analysis of Water Yield Changes from 1981 to 2018 Using an Improved Mann-Kendall Test
Authors: Gao Han , Jin Jiaxin
Published: April 2022

Water yield (WY) refers to the difference between precipitation and evapotranspiration (ET), which is vital for available terrestrial water. Climate change has led to significant changes in precipitation and evapotranspiration on a global scale, which will affect the global WY. Nevertheless, how terrestrial WY has changed during the past few decades and which factors dominated the WY changes are not fully understood. In this study, based on climate reanalysis and remote sensing data, the spatial and temporal patterns of terrestrial WY were revisited from 1981 to 2018 globally using an improved Mann-Kendall trend test method with a permutation test. The response patterns of WY to precipitation and ET are also investigated. The results show that the global multi-year mean WY is 297.4 mm/a. Based on the traditional Mann-Kendall trend test, terrestrial WY showed a significant (p < 0.05) increase of 5.72% of the total valid grid cells, while it showed a significant decrease of 7.68% of those. After correction using the calibration method, the significantly increasing and decreasing areas are reduced by 10.52% and 10.58% of them, respectively. After the correction, the confirmed increase and decrease in WY are mainly located in Africa, eastern North America and Siberia, and parts of Asia and Oceania, respectively. The dominant factor for increasing WY is precipitation, while that for decreasing WY was the combined effect of precipitation and evapotranspiration. The achievements of this study are beneficial for improving the understanding of WY in response to hydrological variables in the context of climate change.
Assessing the Performance of the Phase Difference Bathymetric Sonar Depth Uncertainty Prediction Model
Authors: Mohammadloo Tannaz H. , Geen Matt , S. Sewada Jitendra , Snellen Mirjam , G. Simons Dick
Published: April 2022

Realistic predictions of the contribution of the uncertainty sources affecting the quality of the bathymetric measurements prior to a survey is of importance. To this end, models predicting these contributions have been developed. The objective of the present paper is to assess the performance of the bathymetric uncertainty prediction model for Phase Difference Bathymetric Sonars (PDBS) which is an interferometric sonar. Two data sets were acquired with the Bathyswath-2 system with a frequency of 234 kHz at average water depths of around 26 m and 8 m with pulse lengths equal to 0.0555 ms and 0.1581 ms, respectively. The comparison between the bathymetric uncertainties derived from the measurements and those predicted using the current model indicates a relatively good agreement except for the across-track distances close to the nadir. The performance of the prediction model can be improved by modifying the term addressing the effect of footprint shift, i.e., spatial decorrelation, on the bottom due to fact that at a given time the footprints seen by different receiving arrays are slightly different.
Data Augmentation for Building Footprint Segmentation in SAR Images: An Empirical Study
Authors: Wangiyana Sandhi , Samczyński Piotr , Gromek Artur
Published: April 2022

Building footprints provide essential information for mapping, disaster management, and other large-scale studies. Synthetic Aperture Radar (SAR) provides consistent data availability over optical images owing to its unique properties, which consequently makes it more challenging to interpret. Previous studies have demonstrated the success of automated methods using Convolutional Neural Networks to detect buildings in Very High Resolution (VHR) SAR images. However, the scarcity of such datasets that are available to the public can limit research progress in this field. We explored the impact of several data augmentation (DA) methods on the performance of building detection on a limited dataset of SAR images. Our results show that geometric transformations are more effective than pixel transformations. The former improves the detection of objects with different scale and rotation variations. The latter creates textural changes that help differentiate edges better, but amplifies non-object patterns, leading to increased false positive predictions. We experimented with applying DA at different stages and concluded that applying similar DA methods in training and inference showed the best performance compared with DA applied only during training. Some DA can alter key features of a building’s representation in radar images. Among them are vertical flips and quarter circle rotations, which yielded the worst performance. DA methods should be used in moderation to prevent unwanted transformations outside the possible object variations. Error analysis, either through statistical methods or manual inspection, is recommended to understand the bias presented in the dataset, which is useful in selecting suitable DAs. The findings from this study can provide potential guidelines for future research in selecting DA methods for segmentation tasks in radar imagery.
Evaluation of Land Surface Phenology for Autumn Leaf Color Change Based on Citizen Reports across Japan
Authors: Tsutsumida Narumasa , Shin Nagai , Miura Tomoaki
Published: April 2022

Autumn foliage color is an important phenological characteristic associated with climate and appeals to populations as a cultural ecosystem service (CES). Land surface phenology (LSP) analyzed using time-series remotely sensed imagery can facilitate the monitoring of autumn leaf color change (ALCC); however, the monitoring of autumn foliage by LSP approaches is still challenging because of complex spatio-temporal ALCC patterns and observational uncertainty associated with remote sensing sensors. Here, we evaluated the performance of several LSP analysis approaches in estimation of LSP-based ALCCs against the ground-level autumn foliage information obtained from 758 sightseeing (high CES) sites across Japan. The ground information uniquely collected by citizens represented ALCC stages of greening, early, peak, late, and defoliation collected on a daily basis. The ALCC was estimated using a second derivative approach, in which normalized difference vegetation index (NDVI), kernel normalized difference vegetation index (kNDVI), enhanced vegetation index (EVI), two-band enhanced vegetation index (EVI2), and green red vegetation index (GRVI) were applied based on MODerate resolution Imaging Spectroradiometer (MODIS) MOD09A1 with four (Beck, Elmore, Gu, and Zhang) double logistic smoothing methods in 2020. The results revealed inconsistency in the estimates obtained using different analytical methods; those obtained using EVI with the Beck model estimated the peak stage of the ALCC relatively well, while the estimates obtained using other indices and models had high discrepancies along with uncertainty. Our study provided insights on how the LSP approach can be improved toward mapping the CESs offered by autumn foliage.
ST-PN: A Spatial Transformed Prototypical Network for Few-Shot SAR Image Classification
Authors: Cai Jinlei , Zhang Yueting , Guo Jiayi , Zhao Xin , Lv Junwei , Hu Yuxin
Published: April 2022

Few-shot learning has achieved great success in computer vision. However, when applied to Synthetic Aperture Radar Automatic Target Recognition (SAR-ATR), it tends to demonstrate a bad performance due to the ignorance of the differences between SAR images and optical ones. What is more, the same transformation on both images may cause different results, even some unexpected noise. In this paper, we propose an improved Prototypical Network (PN) based on Spatial Transformation, also known as ST-PN. Cascaded after the last convolutional layer, a spatial transformer module implements a feature-wise alignment rather than a pixel-wise one, so more semantic information can be exploited. In addition, there is always a huge divergence even for the same target when it comes to pixel-wise alignment. Moreover, it reduces computational cost with fewer parameters of the deeper layer. Here, a rotation transformation is used to reduce the discrepancies caused by different observation angles of the same class. Thefinal comparison of four extra losses indicates that a single cross-entropy loss is good enough to calculate the loss of distances. Our work achieves state-of-the-art performance on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset.
Impact of Drought on Isoprene Fluxes Assessed Using Field Data, Satellite-Based GLEAM Soil Moisture and HCHO Observations from OMI
Authors: Opacka Beata , Müller Jean-François , Stavrakou Trissevgeni , Miralles Diego G. , Koppa Akash , Pagán Brianna Rita , Potosnak Mark J. , Seco Roger , De Smedt Isabelle , Guenther Alex B.
Published: April 2022

Biogenic volatile organic compounds (BVOCs), primarily emitted by terrestrial vegetation, are highly reactive and have large effects on the oxidizing potential of the troposphere, air quality and climate. In terms of global emissions, isoprene is the most important BVOC. Droughts bring about changes in the surface emission of biogenic hydrocarbons mainly because plants suffer water stress. Past studies report that the current parameterization in the state-of-the-art Model of Emissions of Gases and Aerosols from Nature (MEGAN) v2.1, which is a function of the soil water content and the permanent wilting point, fails at representing the strong reduction in isoprene emissions observed in field measurements conducted during a severe drought. Since the current algorithm was originally developed based on potted plants, in this study, we update the parameterization in the light of recent ecosystem-scale measurements of isoprene conducted during natural droughts in the central U.S. at the Missouri Ozarks AmeriFlux (MOFLUX) site. The updated parameterization results in stronger reductions in isoprene emissions. Evaluation using satellite formaldehyde (HCHO), a proxy for BVOC emissions, and a chemical-transport model, shows that the adjusted parameterization provides a better agreement between the modelled and observed HCHO temporal variability at local and regional scales in 2011–2012, even if it worsens the model agreement in a global, long-term evaluation. We discuss the limitations of the current parameterization, a function of highly uncertain soil properties such as porosity.
Analysis on Seasonal Variation and Influencing Mechanism of Land Surface Thermal Environment: A Case Study of Chongqing
Authors: Wang Rongxiang , Min Jie , Li Yuechen , Hu Yunyun , Yang Shiqi
Published: April 2022

Rapid urbanization has brought about many problems in urban environments, including the deterioration of the thermal environment, which greatly affects the sustainable development of cities and the health of urban residents. Therefore, a comprehensive and thorough study of the thermal environment is necessary. In this study, quantitative and qualitative analyses of the interannual and seasonal variation of the thermal environment in the urban area of Chongqing were conducted using a combination of equal sector analysis, mathematical statistics, and principal component analysis. In addition, the mechanism by which multiple integrated human and natural factors affect the urban thermal environment in different seasons was explored. The thermal environment of the land surface has a clear spatial correlation with built-up areas. From 2010 to 2020, the Liangjiang New Area and the western high-tech zone were the main areas of thermal environment area expansion. From 2010 to 2020, a decrease in the area of low-temperature zones and an increase in the area of high-temperature zones were the main trends. In particular, during the summer of 2020, the low-temperature zones almost disappeared, and the area of high-temperature zones was nearly twice as large as in the other seasons. The combined influence of multiple factors on surface temperature has certain seasonal characteristics. The slope, elevation, normalized water body index, fractional vegetation cover, normalized imperviousness index, and nighttime light intensity were the main factors affecting the surface temperature, whereas landscape pattern indicators, as well as the population and points of interest (POI) density, had a low influence. The strength of the combined influence of these multiple factors of the different seasons exhibited the following order: winter > spring > summer > autumn.
Assessing the Impact of the Loss Function and Encoder Architecture for Fire Aerial Images Segmentation Using Deeplabv3+
Authors: Harkat Houda , Nascimento José , Bernardino Alexandre , Thariq Ahmed Hasmath
Published: April 2022

Wildfire early detection and prevention had become a priority. Detection using Internet of Things (IoT) sensors, however, is expensive in practical situations. The majority of present wildfire detection research focuses on segmentation and detection. The developed machine learning models deploy appropriate image processing techniques to enhance the detection outputs. As a result, the time necessary for data processing is drastically reduced, as the time required rises exponentially with the size of the captured pictures. In a real-time fire emergency, it is critical to notice the fire pixels and warn the firemen as soon as possible to handle the problem more quickly. The present study addresses the challenge mentioned above by implementing an on-site detection system that detects fire pixels in real-time in the given scenario. The proposed approach is accomplished using Deeplabv3+, a deep learning architecture that is an enhanced version of an existing model. However, present work fine-tuned the Deeplabv3 model through various experimental trials that have resulted in improved performance. Two public aerial datasets, the Corsican dataset and FLAME, and one private dataset, Firefront Gestosa, were used for experimental trials in this work with different backbones. To conclude, the selected model trained with ResNet-50 and Dice loss attains a global accuracy of 98.70%, a mean accuracy of 89.54%, a mean IoU 86.38%, a weighted IoU of 97.51%, and a mean BF score of 93.86%.
Ocean Color Remote Sensing of Suspended Sediments along a Continuum from Rivers to River Plumes: Concentration, Transport, Fluxes and Dynamics
Authors: Ody Anouck , Doxaran David , Verney Romaric , Bourrin François , Morin Guillaume P. , Pairaud Ivane , Gangloff Aurélien
Published: April 2022

This study investigates the capability of high and medium spatial resolution ocean color satellite data to monitor the transport of suspended particulate matter (SPM) along a continuum from river to river mouth to river plume. An existing switching algorithm combining the use of green, red and near-infrared satellite wavebands was improved to retrieve SPM concentrations over the very wide range (from 1 to more than 1000 g.m−3) encountered over such a continuum. The method was applied to time series of OLI, MSI, and MODIS satellite data. Satisfactory validation results were obtained even at the river gauging station. The river liquid discharge is not only related to the SPM concentration at the gauging station and at the river mouth, but also to the turbid plume area and SPM mass estimated within the surface of the plume. The overall results highlight the potential of combined field and ocean color satellite observations to monitor the transport and fluxes of SPM discharged by rivers into the coastal ocean.
Non-Least Square GNSS Positioning Algorithm for Densely Urbanized Areas
Authors: Demkowicz Jerzy
Published: April 2022

The paper introduces an essentially new algorithm for calculating the GNSS position as an alternative to the least-square method. The proposed approach can be widely applied to any positioning method that uses multiple position lines for position calculation and is an example of how using a numerical solution can improve position accuracy without access to historical data. In essence, the method is based on the adaptation of the median filtering method widely used in the field of image processing, while at the same time applying a combinatorial approach and order statistics. The proposed solution makes it possible to improve on and assess the credibility of a single measurement. The article highlights the differences between the proposed and currently used approaches, as well as their advantages and disadvantages. The algorithm has been extensively tested under various environmental and weather conditions. The tests were carried out in typical and also in very demanding conditions, thus taking into account the real application context, i.e., pedestrian and car navigation in densely urbanized areas.
Image Reconstruction of Multibranch Feature Multiplexing Fusion Network with Mixed Multilayer Attention
Authors: Cai Yuxi , Gao Guxue , Jia Zhenhong , Lai Huicheng
Published: April 2022

Image super-resolution reconstruction achieves better results than traditional methods with the help of the powerful nonlinear representation ability of convolution neural network. However, some existing algorithms also have some problems, such as insufficient utilization of phased features, ignoring the importance of early phased feature fusion to improve network performance, and the inability of the network to pay more attention to high-frequency information in the reconstruction process. To solve these problems, we propose a multibranch feature multiplexing fusion network with mixed multilayer attention (MBMFN), which realizes the multiple utilization of features and the multistage fusion of different levels of features. To further improve the network’s performance, we propose a lightweight enhanced residual channel attention (LERCA), which can not only effectively avoid the loss of channel information but also make the network pay more attention to the key channel information and benefit from it. Finally, the attention mechanism is introduced into the reconstruction process to strengthen the restoration of edge texture and other details. A large number of experiments on several benchmark sets show that, compared with other advanced reconstruction algorithms, our algorithm produces highly competitive objective indicators and restores more image detail texture information.
Tracking a Rain-Induced Low-Salinity Pool in the South China Sea Using Satellite and Quasi-Lagrangian Field Observations
Authors: Cao Zhiyong , Hu Zhendong , Bai Xiaolin , Liu Zhiyu
Published: April 2022

A low-salinity pool (LSP) was observed in the northeastern South China Sea on 8–10 August 2018. Employing satellite and field observations, as well as widely used HYbrid Coordinate Ocean Model (HYCOM) Analysis data, we investigated the distribution, origin and evolution of the LSP. A bowl-like structure of the LSP was observed from field observations and is also indicated by the HYCOM Analysis data. Spatially, the LSP extended 20 m deep vertically and spread at least 45 km laterally. Particle tracking simulations using satellite-observed precipitation and surface currents revealed the origin and evolution of the LSP. It is found that the LSP was induced by a heavy rainfall event two days prior to the field observations, evidenced by the significant correlation between the rainfall and salinity anomaly. The vertical expansion of the LSP was favored by nocturnal convection, but was restricted by the strong stratification at its base, which appeared to have prohibited development of convective instabilities as indicated by the observed vertical variation of the turbulent dissipation rate. The formation of a barrier layer due to the LSP restricted vertical heat exchanges, and as a result a thin temperature inversion layer was formed as the surface temperature dropped due to the nighttime cooling and mixing with the cold rainwater. The thermohaline structure favored development of diffusive convection, which is evidenced by the observation that the diapycnal diffusivity for heat (KT) was one order of magnitude larger than that for density (Kρ). Overall, this study provides novel insights into how the upper ocean responds to rainfall with satellite and field observations.
WHUVID: A Large-Scale Stereo-IMU Dataset for Visual-Inertial Odometry and Autonomous Driving in Chinese Urban Scenarios
Authors: Chen Tianyang , Pu Fangling , Chen Hongjia , Liu Zhihong
Published: April 2022

In this paper, we present a challenging stereo-inertial dataset collected onboard a sports utility vehicle (SUV) for the tasks of visual-inertial odometry (VIO), simultaneous localization and mapping (SLAM), autonomous driving, object detection, and other computer vision techniques. We recorded a large set of time-synchronized stereo image sequences (2 × 1280 × 720 @ 30 fps RGB) and corresponding inertial measurement unit (IMU) readings (400 Hz) from a Stereolabs ZED2 camera, along with centimeter-level-accurate six-degree-of-freedom ground truth (100 Hz) from a u-blox GNSS-IMU navigation device with real-time kinematic correction signals. The dataset comprises 34 sequences recorded during November 2020 in Wuhan, the largest city of Central China. Further, the dataset contains abundant unique urban scenes and features of a complex modern metropolis, which have rarely appeared in previously released benchmarks. Results from milestone VIO/SLAM algorithms reveal that methods exhibiting excellent performance on established datasets such as KITTI and EuRoC perform unsatisfactorily when moved outside the laboratory to the real world. We expect our dataset to reduce this limitation by providing more challenging and diverse scenarios to the research community. The full dataset with raw and calibrated data is publicly available along with a lightweight MATLAB/Python toolbox for preprocessing and evaluation. The dataset can be downloaded in its entirety from the uniform resource locator (URL) we provide in the main text.
Remote Sensing—Based Assessment of the Water-Use Efficiency of Maize over a Large, Arid, Regional Irrigation District
Authors: Jiang Lei , Yang Yuting , Shang Songhao
Published: April 2022

Quantitative assessment of crop water-use efficiency (WUE) is an important basis for high-efficiency use of agricultural water. Here we assess the WUE of maize in the Hetao Irrigation District, which is a representative irrigation district in the arid region of Northwest China. Specifically, we firstly mapped the location of the maize field by using a remote sensing/phenological–based vegetation classifier and then quantified the maize water use and yield by using a dual-source remote-sensing evapotranspiration (ET) model and a crop water production function, respectively. Validation results show that the adopted phenological-based vegetation classifier performed well in mapping the spatial distributions and inter-annual variations of maize planting, with a kappa coefficient of 0.86. In addition, the ET model based on the hybrid dual-source scheme and trapezoid framework also obtained high accuracy in spatiotemporal ET mapping, with an RMSE of 0.52 mm/day at the site scale and 26.21 mm/year during the maize growing season (April–October) at the regional scale. Further, the adopted crop water production function showed high accuracy in estimating the maize yield, with a mean relative error of only 4.3%. Using the estimated ET, transpiration, and yield of maize, the mean maize WUE based on ET and transpiration in the study region were1.94 kg/m3 and 3.06 kg/m3, respectively. Our results demonstrate the usefulness and validity of remote sensing information in mapping regional crop WUE.
A Model-Based Temperature Adjustment Scheme for Wintertime Sea-Ice Production Retrievals from MODIS
Authors: Preußer Andreas , Heinemann Günther , Schefczyk Lukas , Willmes Sascha
Published: April 2022

Knowledge of the wintertime sea-ice production in Arctic polynyas is an important requirement for estimations of the dense water formation, which drives vertical mixing in the upper ocean. Satellite-based techniques incorporating relatively high resolution thermal-infrared data from MODIS in combination with atmospheric reanalysis data have proven to be a strong tool to monitor large and regularly forming polynyas and to resolve narrow thin-ice areas (i.e., leads) along the shelf-breaks and across the entire Arctic Ocean. However, the selection of the atmospheric data sets has a large influence on derived polynya characteristics due to their impact on the calculation of the heat loss to the atmosphere, which is determined by the local thin-ice thickness. In order to overcome this methodical ambiguity, we present a MODIS-assisted temperature adjustment (MATA) algorithm that yields corrections of the 2 m air temperature and hence decreases differences between the atmospheric input data sets. The adjustment algorithm is based on atmospheric model simulations. We focus on the Laptev Sea region for detailed case studies on the developed algorithm and present time series of polynya characteristics in the winter season 2019/2020. It shows that the application of the empirically derived correction decreases the difference between different utilized atmospheric products significantly from 49% to 23%. Additional filter strategies are applied that aim at increasing the capability to include leads in the quasi-daily and persistence-filtered thin-ice thickness composites. More generally, the winter of 2019/2020 features high polynya activity in the eastern Arctic and less activity in the Canadian Arctic Archipelago, presumably as a result of the particularly strong polar vortex in early 2020.
Forecasting of Built-Up Land Expansion in a Desert Urban Environment
Authors: Mansour Shawky , Alahmadi Mohammed , Atkinson Peter M. , Dewan Ashraf
Published: April 2022

In recent years, socioeconomic transformation and social modernisation in the Gulf Cooperation Council (GCC) states have led to tremendous changes in lifestyle and, subsequently, expansion of urban settlements. This accelerated growth is pronounced not only across vegetated coasts, plains, and mountains, but also in desert cities. Nevertheless, spatial simulation and prediction of desert urban patterns has received little attention, including in Oman. While most urban settlements in Oman are located in desert environments, research exploring and monitoring this type of urban growth is rare in the scientific literature. This research focuses on analysing and predicting land use–land cover (LULC) changes across the desert city of Ibri in Oman. A methodology was employed involving integrating the multilayer perceptron (MLP) and Markov chain (MC) techniques to forecast spatiotemporal LULC dynamics and map urban growth patterns. The inputs were three Landsat images from 2010 and 2020, and a series of covariate layers based on transforms of elevation, slope, population settlements, urban centres, and points of interest that proxy the driving forces of change. The findings indicated that the observed LULC changes were predominantly rapid across the city during 2010 to 2020, transforming desert, bare land, and vegetation into built-up areas. The forecast showed that area of land conversion from desert to urban would be 5666 ha during the next two decades and 7751 ha by 2050. Similarly, vacant land is expected to contribute large areas to urban expansion (2370 ha by 2040, and 3266 ha by 2050), although desert cities confront numerous environmental challenges, including water scarcity, shrinking vegetation cover, and being converted into residential land. Massive urban expansion has consequences for biodiversity and natural ecosystems—particularly in green areas, which are expected to decline by approximately 107 ha by 2040 (i.e., 10%) and 166 ha by 2050. The outcomes of this research provide fundamental guidance for decision-makers and planners in Oman and elsewhere to effectively monitor and manage desert urban dynamics and sustainable desert cities.
Context-Unsupervised Adversarial Network for Video Sensors
Authors: Canet Tarrés Gemma , Pardàs Montse
Published: April 2022

Foreground object segmentation is a crucial first step for surveillance systems based on networks of video sensors. This problem in the context of dynamic scenes has been widely explored in the last two decades, but it still has open research questions due to challenges such as strong shadows, background clutter and illumination changes. After years of solid work based on statistical background pixel modeling, most current proposals use convolutional neural networks (CNNs) either to model the background or to make the foreground/background decision. Although these new techniques achieve outstanding results, they usually require specific training for each scene, which is unfeasible if we aim at designing software for embedded video systems and smart cameras. Our approach to the problem does not require specific context or scene training, and thus no manual labeling. We propose a network for a refinement step on top of conventional state-of-the-art background subtraction systems. By using a statistical technique to produce a rough mask, we do not need to train the network for each scene. The proposed method can take advantage of the specificity of the classic techniques, while obtaining the highly accurate segmentation that a deep learning system provides. We also show the advantage of using an adversarial network to improve the generalization ability of the network and produce more consistent results than an equivalent non-adversarial network. The results provided were obtained by training the network on a common database, without fine-tuning for specific scenes. Experiments on the unseen part of the CDNet database provided 0.82 a F-score, and 0.87 was achieved for LASIESTA databases, which is a database unrelated to the training one. On this last database, the results outperformed by 8.75% those available in the official table. The results achieved for CDNet are well above those of the methods not based on CNNs, and according to the literature, among the best for the context-unsupervised CNNs systems.
Data-Driven Assessment of Wind Turbine Performance Decline with Age and Interpretation Based on Comparative Test Case Analysis
Authors: Astolfi Davide , Pandit Ravi , Celesti Ludovica , Vedovelli Matteo , Lombardi Andrea , Terzi Ludovico
Published: April 2022

An increasing amount of wind turbines, especially in Europe, are reaching the end of their expected lifetimes; therefore, long data sets describing their operation are available for scholars to analyze the performance trends. On these grounds, the present work is devoted to test case studies for the evaluation and the interpretation of wind turbine performance decline with age. Two wind farms were studied, featuring widely employed wind turbine models: the former is composed of 6 Senvion MM92 and the latter of 11 Vestas V52 wind turbines, owned by the ENGIE Italia company. SCADA data spanning, respectively, 10 and 7 years were analyzed for the two test cases. The effect of aging on the performance of the test case wind turbines was studied by constructing a data-driven model of appropriate operation curves, selected depending on the working region. For the Senvion MM92, we found that it is questionable to talk about performance aging because there is no evident trend in time: the performance variation year by year is in the order of a few kW and is therefore irrelevant for practical applications. For the Vestas V52 wind turbines, a much wider variability is observed: two wind turbines are affected by a remarkable performance drop, after which the behavior is stable and under-performing with respect to the rest of the wind farm. Particular attention is devoted to the interpretation of the results: the comparative discussion of the two test cases indicates that the observed operation curves are compatible with the hypothesis that the worsening with age of the two under-performing Vestas V52 can be ascribed to the behavior of the hydraulic blade pitch. Furthermore, for both test cases, it is estimated that the gearbox-aging contributes negligibly to the performance decline in time.
Machine Learning for Touch Localization on an Ultrasonic Lamb Wave Touchscreen
Authors: Bahrami Sahar , Moriot Jérémy , Masson Patrice , Grondin François
Published: April 2022

Classification and regression employing a simple Deep Neural Network (DNN) are investigated to perform touch localization on a tactile surface using ultrasonic guided waves. A robotic finger first simulates the touch action and captures the data to train a model. The model is then validated with data from experiments conducted with human fingers. The localization root mean square errors (RMSE) in time and frequency domains are presented. The proposed method provides satisfactory localization results for most human–machine interactions, with a mean error of 0.47 cm and standard deviation of 0.18 cm and a computing time of 0.44 ms. The classification approach is also adapted to identify touches on an access control keypad layout, which leads to an accuracy of 97% with a computing time of 0.28 ms. These results demonstrate that DNN-based methods are a viable alternative to signal processing-based approaches for accurate and robust touch localization using ultrasonic guided waves.
Magnetic Reference Mark in a Linear Positioning System Generated by a Single Wiegand Pulse
Authors: Lien Hung-Lin , Chang Jen-Yuan
Published: April 2022

A Wiegand sensor is composed of a strip of Wiegand wire and a pick-up coil. The research presented in this paper examines and characterizes the fast magnetization reversal in a Wiegand wire, which leads to changes in magnetic flux density in its pick-up coil to produce the so-called Wiegand pulse to be used as a reference mark in a linear positioning system. It was observed in this research that the magnitude and duration of the pulse voltage were independent of driving frequency, indicating that Wiegand effect sensors could be ideal for use as zero-speed transducers. The repeatability of the Wiegand pulse was found to vary with different magnetic flux intensities of external magnetic field, as well as the angle between the magnetic induction line and the Wiegand wire. Through calibrated experimental and numerical parametric studies, the mechanism for producing repeatable Wiegand pulses to be used as a reference mark for precision liner positioning systems was revealed, which represents the novelty of this research. On the basis of this mechanism, the optimal design combination of the Wiegand sensor’s position with respect to the magnetization source can be obtained. Utilizing commercially available Wiegand sensors, it was demonstrated in this research that with a Wiegand pulse serving as a magnetic reference mark, positioning repeatability of 0.3 um could be achieved, which is on the same order as optical scales. The work presented in this research has engineering implications as well as offering scientific insights into magnetization mechanisms for generating enough magnetic remanence to produce a Barkhausen jump, resulting in repeatable Wiegand for use as a reference mark in a linear positioning system.
Assessment of the Seafloor Topography Accuracy in the Emperor Seamount Chain by Ship-Based Water Depth Data and Satellite-Based Gravity Data
Authors: Liu Pengpeng , Jin Shuanggen , Wu Ziyin
Published: April 2022

The seafloor topography estimation is very important, while the bathymetry data and gravity data are scarce and uneven, which results in large errors in the inversion of the seafloor topography. In this paper, in order to reduce the influence of errors and improve the accuracy of seafloor inversion, the influence of different resolution data on the inversion topography in the Emperor Seamount Chain are investigated by combining ship water depth data and satellite gravity anomaly data released by SIO V29.1. Through the comparison of different resolution models, it is found that the choice of resolution affects the accuracy of the inversion terrain model. An external comparison is presented by using the international high-precision topography data and check points observations. The results show that with the increase in resolution, the fitting residuals obtained by the scale factor are optimized, and the precision of the terrain model is gradually approaching the S&S V19.1 and GEBCO-2020 models, but is better than the ETOPO1 and SRTM 30 models. By external validation using the check points, the standard deviation of the difference was reduced from 58.92 m to 47.01 m, and the correlation between the inverted terrain and the NGDC grid model was increased from 0.9545 to 0.9953. For recovering the Emperor Seamount Chain terrain, the relative error was gradually decreased with the improvement of resolution. The maximum relative error is reduced from 1.09 of 2′ topography to 0.74 of 10″ topography, and the average error is reduced from 0.04 to 0.01 with an improvement by 32.11%. The terrain error between the inverted terrain model and the NGDC grid model is gradually reduced, while the error percentage is increasing by 25.51% and 21.49% in the range of −50 to 50 m and −100 to 100 m, respectively. Furthermore, the sparse area can effectively reduce the terrain standard deviation and improve the terrain correlation by increasing the resolution through the analysis of different density subsets. The error was decreased most significantly in sparse and dense homogeneous regions with increasing resolution.
Monitoring of Ultra-High Dose Rate Pulsed X-ray Facilities with Radioluminescent Nitrogen-Doped Optical Fiber
Authors: Vidalot Jeoffray , Campanella Cosimo , Dachicourt Julien , Marcandella Claude , Duhamel Olivier , Morana Adriana , Poujols David , Assaillit Gilles , Gaillardin Marc , Boukenter Aziz , Ouerdane Youcef , Girard Sylvain , Paillet Philippe
Published: April 2022

We exploited the potential of radiation-induced emissions (RIEs) in the visible domain of a nitrogen-doped, silica-based, multimode optical fiber to monitor the very high dose rates associated with experiments at different pulsed X-ray facilities. We also tested this sensor at lower dose rates associated with steady-state X-ray irradiation machines (up to 100 keV photon energy, mean energy of 40 keV). For transient exposures, dedicated experimental campaigns were performed at ELSA (Electron et Laser, Source X et Applications) and ASTERIX facilities from CEA (Commissariat à l’Energie Atomique—France) to characterize the RIE of this fiber when exposed to X-ray pulses with durations of a few µs or ns. These facilities provide very large dose rates: in the order of MGy(SiO2)/s for the ELSA facility (up to 19 MeV photon energy) and GGy(SiO2)/s for the ASTERIX facility (up to 1 MeV). In both cases, the RIE intensities, mostly explained by the fiber radioluminescence (RIL) around 550 nm, with a contribution from Cerenkov at higher fluxes, linearly depend on the dose rates normalized to the pulse duration delivered by the facilities. By comparing these high dose rate results and those acquired under low-dose rate steady-state X-rays (only RIL was present), we showed that the RIE of this multimode optical fiber linearly depends on the dose rate over an ultra-wide dose rate range from 10−2 Gy(SiO2)/s to a few 109 Gy(SiO2)/s and photons with energy in the range from 40 keV to 19 MeV. These results demonstrate the high potential of this class of radiation monitors for beam monitoring at very high dose rates in a very large variety of facilities as future FLASH therapy facilities.
Electrochemical Detection of Ethanol in Air Using Graphene Oxide Nanosheets Combined with Au-WO3
Authors: Fauzi Aynul Sakinah Ahmad , Hamidah Nur Laila , Kitamura Shota , Kodama Taiga , Sonda Kosuke , Putri Ghina Kifayah , Shinkai Takeshi , Ahmad Muhammad Sohail , Inomata Yusuke , Quitain Armando T. , Kida Tetsuya
Published: April 2022

Detection, monitoring, and analysis of ethanol are important in various fields such as health care, food industries, and safety control. In this study, we report that a solid electrolyte gas sensor based on a proton-conducting membrane is promising for detecting ethanol in air. We focused on graphene oxide (GO) as a new solid electrolyte because it shows a high proton conductivity at room temperature. GO nanosheets are synthesized by oxidation and exfoliation of expanded graphite via the Tour’s method. GO membranes are fabricated by stacking GO nanosheets by vacuum filtration. To detect ethanol, Au-loaded WO3 is used as the sensing electrode due to the excellent activity of gold nanoparticles for the catalysis of organic molecules. Au-WO3 is coupled with rGO (reduced graphene oxide) to facilitate the electron transport in the electrode. Ce ions are intercalated into the GO membrane to facilitate proton transport. The sensor based on the Ce doped-GO membrane combined with Au-WO3/rGO as a sensing electrode shows good electric potential difference (ΔV) responses to ethanol in the air at room temperature. The sensor signal reaches more than 600 mV in response to ethanol at 40 ppm in air, making it possible to detect ethanol at a few ppb (parts per billion) level. The ethanol sensing mechanism was discussed in terms of the mixed-potential theory and catalysis of ethanol on Au-WO3.
Passive Attenuation of Mechanical Vibrations with a Superelastic SMA Bending Springs: An Experimental Investigation
Authors: Senko Richard , Almeida Vinícius S. , dos Reis Rômulo P. B. , Oliveira Andersson G. , Silva Antonio A. , Rodrigues Marcelo C. , de Carvalho Laura H. , Lima Antonio G. B.
Published: April 2022

This work presents an experimental study related to the mechanical performance of a special design spring fabricated with a superelastic shape memory alloy (SMA-SE). For the experimental testing, the spring was coupled in a rotor machine, aiming to attenuate the mechanical vibration when the system went through a natural frequency without any external power source. It was verified that the reduction in instabilities stemmed from the better distribution of vibration force in the proposed device, as well as the damping capacity of the spring material. These findings showed that the application of the M-Shape device of SMA-SE for three different cases could reduce vibration up to 23 dB when compared to the situations without, and with, 1.5 mm of preload. The M-Shape device was shown to be efficient in reducing the mechanical vibration in a rotor system. This was due to the damping capacity of the SMA-SE material, and because the application did not require any external source of energy to generate phase transformation.
Heart Rate Variability-Based Subjective Physical Fatigue Assessment
Authors: Ni Zhiqiang , Sun Fangmin , Li Ye
Published: April 2022

Accurate assessment of physical fatigue is crucial to preventing physical injury caused by excessive exercise, overtraining during daily exercise and professional sports training. However, as a subjective feeling of an individual, physical fatigue is difficult for others to objectively evaluate. Heart rate variability (HRV), which is derived from electrocardiograms (ECG) and controlled by the autonomic nervous system, has been demonstrated to be a promising indicator for physical fatigue estimation. In this paper, we propose a novel method for the automatic and objective classification of physical fatigue based on HRV. First, a total of 24 HRV features were calculated. Then, a feature selection method was proposed to remove useless features that have a low correlation with physical fatigue and redundant features that have a high correlation with the selected features. After feature selection, the best 11 features were selected and were finally used for physical fatigue classifying. Four machine learning algorithms were trained to classify fatigue using the selected features. The experimental results indicate that the model trained using the selected 11 features could classify physical fatigue with high accuracy. More importantly, these selected features could provide important information regarding the identification of physical fatigue.