bluuebunny/crossref_metadata_2025 huggingface dataset:
Approach to the Technical Processes of Incorporating Sustainability Information—The Case of a Smart City and the Monitoring of the Sustainable Development Goals
Authors: Parra-Domínguez Javier , López-Blanco Raúl , Pinto-Santos Francisco
Published: August 2022

Currently, the concern for achieving and fulfilling the Sustainable Development Goals (SDGs) is a constant in advanced societies. The scientific community and various organisations are working on obtaining an information system that will make it possible to offer the necessary value to this type of sustainability information. The article aims to incorporate criteria on the technology used in the reporting system, specifically in collecting the different types of data and generating other interfaces. The methods described here are carried out on a specific case study, a Smart City, showing the different types of data that exist and the possible interfaces that allow objective monitoring of the achievement of the SDGs. It is, therefore, a descriptive study of a process whose results are the establishment of criteria concerning the different data sources as well as the generation of a set of interfaces that motivate the monitoring that can be carried out in a specific city to observe its compliance and deviations from critical values, for example, environmental. The main conclusions of this research establish the importance of incorporating and sizing the technology needed to develop the criteria for monitoring the SDGs. There is a need for convergence between the correct, objective and universal provision of this type of sustainability information and the technology used for the collection and presentation of the data.
A Smart Sensors-Based Solar-Powered System to Monitor and Control Tube Well for Agriculture Applications
Authors: Ullah Sana , Hafeez Ghulam , Rukh Gul , Albogamy Fahad R. , Murawwat Sadia , Ali Faheem , Khan Farrukh Aslam , Khan Sheraz , Rehman Khalid
Published: August 2022

Agricultural productivity plays a vital role in a country’s economy, which can be increased by providing the proper water needed for crops. Proper water provision ensures suitable moisture and appropriate conditions essential for crops, water resource preservation, minimized water wastage, and energy consumption. However, adequate water provision is challenging due to intermittent and uncertain environmental and weather conditions. On this note, a model with uncertain and stochastic conditions (rain, wet, dry, humidity, and moisture) capturing abilities is needed. Thus, a smart-sensors-based solar-powered system is developed for monitoring and controlling the tube well that ensures proper water provision to crops. The developed system properly checks weather and environmental conditions (rain, temperature, irradiance, humidity, etc.), soil conditions (wet or dry), and crop conditions to monitor and regulate water flow accordingly to minimize water and energy consumption wastage. The developed system is an integrated system of four modules: Arduino with a built-in Atmel AT mega microcontroller, sensors, solar power, and a global system for mobile communication (GSM). The GSM module exchanges acknowledgement messages with the operator and controller about the various statuses, such as weather and environmental conditions, soil conditions (wet or dry), crop conditions, and the toggle status of the motor (OFF, ON/main power supply, or solar power). In order for the controller module to determine the motor state, the sensors module computes many parameters, including rain, wet, dry, humidity, and moisture. In addition, the sensor module also prevents the motor from dry running. The developed smart irrigation system is superior to existing irrigation systems in aspects of water wastage and energy consumption minimization.
Characterization of Mean-Field Type H− Index for Continuous-Time Stochastic Systems with Markov Jump
Authors: Ma Limin , Song Caixia , Zhang Weihai , Liu Zhenbin
Published: August 2022

In this brief, we consider the mean-field type H− index problem for stochastic Markovian jump systems. A sufficient condition is derived for stochastic Markovian jump systems with (x,u)-dependent noise based on generalized differential Riccati equations. Especially for stochastic Markovian jump systems with only x-dependent noise, a sufficient and necessary condition is developed to characterize H− index larger than some ξ>0. Finally, a numerical example is addressed to verify the effectiveness of our obtained results.
In Situ Synthesis of Zero-Valent Iron-Decorated Lignite Carbon for Aqueous Heavy Metal Remediation
Authors: Samaraweera Hasara , Nawalage Samadhi , Nayanathara R. M. Oshani , Peiris Chathuri , Karunaratne Tharindu N. , Gunatilake Sameera R. , Thirumalai Rooban V. K. G. , Zhang Jilei , Zhang Xuefeng , Mlsna Todd
Published: August 2022

Lignite’s large abundance, physicochemical properties and low cost are attractive for industrial wastewater remediation. However, directly applying lignite for wastewater treatment suffers low efficiency. Here, we synthesize highly efficient zero-valent iron (ZVI)-decorated lignite carbon through the in-situ carbonization of a lignite and FeCl2 mixture for heavy metal removal. The effect of carbonization temperature on the morphology, structure and crystallite phases of ZVI-decorated lignite carbons (ZVI-LXs) was investigated. At an optimized temperature (i.e., 1000 °C), ZVI particles were found evenly distributed on the lignite matrix with the particles between 20 to 190 nm. Moreover, ZVI particles were protected by a graphene shell that was formed in situ during the carbonization. The synthesized ZVI-L1000 exhibited higher Cu2+, Pb2+ and Cd2+ stripping capacities than pristine lignite in a wide pH range of 2.2–6.3 due to the surface-deposited ZVI particles. The maximum Langmuir adsorption capacities of ZVI-L1000 for Cd2+, Pb2+ and Cu2+ were 38.3, 55.2 and 42.5 mg/g at 25 °C, respectively, which were 7.8, 4.5 and 10.6 times greater than that of pristine lignite, respectively. ZVI-L1000 also exhibited a fast metal removal speed (~15 min), which is ideal for industrial wastewater treatment. The pseudo-second-order model fits well with all three adsorptions, indicating that chemical forces control their rate-limiting adsorption steps. The reduction mechanisms of ZVI-L1000 for heavy metals include reduction, precipitation and complexation.
Parameter Matching and Performance Analysis of a Master-Slave Electro-Hydraulic Hybrid Electric Vehicle
Authors: Jia Qingxiao , Zhang Hongxin , Zhang Yanjun , Yang Jian , Wu Jie
Published: August 2022

To improve the battery state of charge (SOC) of the electric vehicle (EV), this paper proposes a master–slave electro-hydraulic hybrid electric vehicle (MSEH-HEV). The MSEH-HEV uses a planetary row as the core transmission component to realize the interconversion between mechanical energy, hydraulic energy and electrical energy. Meanwhile, this paper introduces the six working modes in vehicle operation, matches the parameters of key components to the requirements of the vehicle’s performance and designs a rule-based control strategy to dominate the energy distribution and the operating mode switching. The research uses AMESim and Simulink to perform a co-simulation of the MSEH-HEV, and the superiority of MSEH-HEV is testified by comparing it with an AMESim licensed EV. The simulation results show that in the Economic Commission for Europe (ECE) and the Extra Urban Driving Cycle (EUDC), the MSEH-HEV has a 15% reduction in battery consumption, and the motor peak torque is greatly reduced. Moreover, a fuzzy control strategy is designed to optimize the rule-based control strategy. Ultimately, the optimized strategy further reduces the motor torque while maintaining the battery SOC. In this paper, the applicable research consists of the necessary references for the design matching of future electro-hydraulic hybrid electricity systems.
Does the Tough Stuff Make Us Stronger? Spiritual Coping in Family Caregivers of Persons with Early-Stage Dementia
Authors: McGee Jocelyn Shealy , Davie Morgan , Meraz Rebecca , Myers Dennis , Boddie Stephanie Clintonia
Published: August 2022

Spiritual coping may be conceptualized as relying upon spiritual beliefs, practices and/or relationships as an aspect of navigating stressful or traumatic experiences. There is evidence that spirituality may be used as a resource and may cultivate growth for some in the midst of difficult life circumstances. There is limited research, however, on this phenomenon among family caregivers of persons living with a dementia, particularly those in the early stages. The purpose of the current study, therefore, was to gain insight into the nature of spiritual coping among caregivers of persons living with a dementia in the early part of their caregiving journey. Using interpretative phenomenological analysis, interviews with 28 caregivers were analyzed. Five spiritual coping themes and corresponding subthemes emerged from their narratives: (1) spiritual relational coping; (2) spiritual behavioral coping; (3) spiritual belief coping; (4) spiritual coping to gain a sense of control; and (5) spiritual coping for constancy or life transformation. Three-fourths of the sampled caregivers reported growth in their spiritual lives during the early part of their caregiving journey. A sense of being “spiritually grounded” in the Divine (e.g., God, a higher power, a life philosophy) and oneself (as a spiritual being) was an important aspect of spiritual coping in this sample. Additionally, caregivers tended to use multiple forms of spiritual coping simultaneously while also navigating spiritual struggles. Finally, some caregivers viewed caregiving as a spiritual path that they were actively following rather than a passive spiritual experience. These findings speak to the importance of identifying and encouraging spiritual coping among caregivers as well as identifying spiritual struggles.
VLP Landmark and SLAM-Assisted Automatic Map Calibration for Robot Navigation with Semantic Information
Authors: Wang Yiru , Hussain Babar , Yue Chik Patrick
Published: August 2022

With the rapid development of robotics and in-depth research of automatic navigation technology, mobile robots have been applied in a variety of fields. Map construction is one of the core research focuses of mobile robot development. In this paper, we propose an autonomous map calibration method using visible light positioning (VLP) landmarks and Simultaneous Localization and Mapping (SLAM). A layout map of the environment to be perceived is calibrated by a robot tracking at least two landmarks mounted in the venue. At the same time, the robot’s position on the occupancy grid map generated by SLAM is recorded. The two sequences of positions are synchronized by their time stamps and the occupancy grid map is saved as a sensor map. A map transformation method is then performed to align the orientation of the two maps and to calibrate the scale of the layout map to agree with that of the sensor map. After the calibration, the semantic information on the layout map remains and the accuracy is improved. Experiments are performed in the robot operating system (ROS) to verify the proposed map calibration method. We evaluate the performance on two layout maps: one with high accuracy and the other with rough accuracy of the structures and scale. The results show that the navigation accuracy is improved by 24.6 cm on the high-accuracy map and 22.6 cm on the rough-accuracy map, respectively.
An Improved Aerosol Optical Depth Retrieval Algorithm for Multiangle Directional Polarimetric Camera (DPC)
Authors: Ge Bangyu , Li Zhengqiang , Chen Cheng , Hou Weizhen , Xie Yisong , Zhu Sifeng , Qie Lili , Zhang Ying , Li Kaitao , Xu Hua , Ma Yan , Yan Lei , Mei Xiaodong
Published: August 2022

The DPC is a multiangle sensor that detects atmospheric parameters. However, the retrieval of high-precision and high-spatial-resolution aerosol products from the DPC remains a great challenge due to the ill-posed nature of the problem. Thus, a novel aerosol optical depth (AOD) retrieval algorithm was proposed using visible surface reflectance relationships (VISRRs). The VISRR algorithm accounts for the surface anisotropy and needs neither a shortwave infrared band nor a surface reflectance database that can retrieve AOD over dark and bright land cover. Firstly, moderate-resolution imaging spectroradiometer (MODIS) surface reflectance (MYD09) products were used to derive the preceding surface reflectance relationships (SRRs), which are related to surface types, scattering angle, and normalized difference vegetation index (NDVI). Furthermore, to solve the problem of the NDVI being susceptible to the atmosphere, an innovative method based on an iterative atmospheric correction was proposed to provide a realistic NDVI. The VISRR algorithm was then applied to the thirteen months of DPC multiangle data over the China region. AOD product comparison between the DPC and MODIS showed that they had similar spatial distribution, but the DPC had both high spatial resolution and coverage. The validation between the ground-based sites and the retrieval results showed that the DPC AOD performed best, with a Pearson correlation coefficient (R) of 0.88, a root mean square error (RMSE) of 0.17, and a good fraction (Gfrac) of 62.7%. Then, the uncertainties regarding the AOD products were discussed for future improvements. Our results revealed that the VISRR algorithm is an effective method for retrieving reliable, simultaneously high-spatial-resolution and full-surface-coverage AOD data with good accuracy.
Timely Plastic-Mulched Cropland Extraction Method from Complex Mixed Surfaces in Arid Regions
Authors: Fu Chenhao , Cheng Lei , Qin Shujing , Tariq Aqil , Liu Pan , Zou Kaijie , Chang Liwei
Published: August 2022

Plastic mulch is extensively applied in agricultural production in arid regions. It significantly influences the interactions between land and atmosphere by altering underlying surface characteristics. An accurate and timely extraction method for Plastic-Mulched Cropland (PMC) is required to understand land surface energy transfer processes, eco-hydrological cycle, the climate effect of PMC, and in the management of water resources. In this study, we proposed a Timely Plastic-mulched cropland Extraction Method (TPEM) from complex mixed surfaces with multi-source remote sensing data in the Shiyanghe River Basin (SRB), a typical representation of a complex and inhomogeneous arid region in the northwest of China. We defined TPEM in three phases; in the first phase, the spectral characteristic curves were drawn from ground object points labeled by visual interpretation with multi-source remote sensing data. In the second phase, a spectral characteristic analysis of the modified index was proposed to amplify the difference between PMC and non-PMC ground objects. Finally, the Classification and Regression Tree (CART) classifier was used to generate thresholds of indices as PMC extraction rules. The results showed that it can extract the boundary of PMC in large-scale farmland, distinguish PMC from ground objects in complex mixed surfaces, and separate the PMC from desert land that shares same spectral characteristics with PMC. The TPEM is verified to be efficient and robust, with an overall accuracy of 0.9234, quantity disagreement of 0.0541, and allocation disagreement of 0.0224, and outperformed two extensively used PMC extraction methods, especially for timely PMC extraction when satellite data only during the period that ground surface incomplete covered by plastic mulch is available. This study will provide us with an accurate and timely method to extract PMC, especially in the widely distributed complex mixed surfaces.
Exploration of the Contribution of Fire Carbon Emissions to PM2.5 and Their Influencing Factors in Laotian Tropical Rainforests
Authors: Su Zhangwen , Xu Zhenhui , Lin Lin , Chen Yimin , Hu Honghao , Wei Shujing , Luo Sisheng
Published: August 2022

It is of great significance to understand the drivers of PM2.5 and fire carbon emission (FCE) and the relationship between them for the prevention, control, and policy formulation of severe PM2.5 exposure in areas where biomass burning is a major source. In this study, we considered northern Laos as the area of research, and we utilized space cluster analysis to present the spatial pattern of PM2.5 and FCE from 2003–2019. With the use of a random forest and structural equation model, we explored the relationship between PM2.5 and FCE and their drivers. The key results during the target period of the study were as follows: (1) the HH (high/high) clusters of PM2.5 concentration and FCE were very similar and distributed in the west of the study area; (2) compared with the contribution of climate variables, the contribution of FCE to PM2.5 was weak but statistically significant. The standardized coefficients were 0.5 for drought index, 0.32 for diurnal temperature range, and 0.22 for FCE; (3) climate factors are the main drivers of PM2.5 and FCE in northern Laos, among which drought and diurnal temperature range are the most influential factors. We believe that, as the heat intensifies driven by climate in tropical rainforests, this exploration and discovery can help regulators and researchers better integrate drought and diurnal temperature range into FCE and PM2.5 predictive models in order to develop effective measures to prevent and control air pollution in areas affected by biomass combustion.
Radiation Sensitivity Analysis of Ocean Wake Information Detection System Based on Visible Light Remote Sensing
Authors: Ying Shipeng , Qu Hongsong , Tao Shuping , Zheng Liangliang , Wu Xiaobin
Published: August 2022

Various ships and submerged moving objects in the ocean are key targets of numerous remote sensors. Wake has developed into one of the key detection targets of ocean visible light remote sensing as the visible trail information left by moving objects on the ocean surface. In the situation of slow ship speed, deep draft, and the existence of air clouds and fog, the wake target signal is weak, and the signal-to-noise ratio is low due to the low reflectivity of the sea surface and the interference of the background waves on the sea surface. This paper analyzes the radiative sensitivity of visible light imaging systems for the most crucial wake detection indicator in order to address the aforementioned issues. The noise equivalent reflectance difference, which is widely used to describe radiative sensitivity in engineering, is derived and numerically simulated by establishing the imaging link model based on TDICCD. We calculated the noise equivalent reflectivity difference for eight bands commonly used in ocean remote sensing; results show that the index is generally on the order of 10−4, and with the increase in the central wavelength, the value of noise equivalent reflectance difference also shows a downward trend and is stable within a certain value range. This research provides theoretical guidance for the engineering design of a visible spectrum imaging system for wake detection, aids in improving the imaging system’s capacity to detect weak wake signals, and provides a basis for subsequent wake detection and enhancement processing, removal of false wakes, and retrieval of ship information.
Hydro-Geophysical Evaluation of the Regional Variability of Senegal’s Terrestrial Water Storage Using Time-Variable Gravity Data
Authors: Mohamed Ahmed , Faye Cheikh , Othman Abdullah , Abdelrady Ahmed
Published: August 2022

The Gravity Recovery and Climate Experiment (GRACE) satellite data retrieval experiment has been instrumental in characterizing the global fluctuations in terrestrial water storage (ΔTWS) over the past 20 years. Given the limited availability of hydrological data, GRACE measurements are frequently combined with other climatic models, standardized precipitation index (SPI), and standardized temperature index (STI) data to examine the likelihood of such impacts on hydrology and calculate the groundwater storage changes (ΔGWS). The characterization of the intensity and variability of drought events has been identified based on the Terrestrial Water Storage Deficit Index (TWSI), derived from GRACE mass concentration blocks (mascons) over Senegal during the studied period (April 2002–December 2021). The results are: (1) The average annual precipitation (AAP) rate for the entire period was calculated at 692.5 mm/yr. (2) The GRACE-derived ΔTWS variations were calculated at +0.89 ± 0.34, +0.07 ± 0.36, +1.66 ± 1.20, and +0.63 ± 0.08 cm/yr for Periods I (April 2002–December 2009), II (January 2010–December 2017), III (January 2018–December 2021), and the entire period (April 2002–December 2021), respectively. (3) The ΔGWS changes were estimated to be +0.89 ± 0.31, +0.085 ± 0.33, +1.64 ± 1.11, and +0.63 ± 0.08 cm/yr for Periods I, II, II, and the entire period, respectively. (4) There is good agreement in some years and seasons according to the investigation of the link between the GRACE dataset, STI, and SPI. (5) Senegal’s groundwater storage is increasing at a rate of 0.63 ± 0.08 cm/yr (1.24 ± 0.16 km3/yr) between April 2002 and December 2021. (6) Considering the yearly extraction rates of 1.13 ± 0.11 cm/yr (2.22 ± 0.22 km3/yr), an average recharge rate of +1.76 ± 0.14 cm/yr (+3.46 ± 0.28 km3/yr) was calculated for the studied area. The integrated strategy is instructive and economical.
MsIFT: Multi-Source Image Fusion Transformer
Authors: Zhang Xin , Jiang Hangzhi , Xu Nuo , Ni Lei , Huo Chunlei , Pan Chunhong
Published: August 2022

Multi-source image fusion is very important for improving image representation ability since its essence relies on the complementarity between multi-source information. However, feature-level image fusion methods based on the convolution neural network are impacted by the spatial misalignment between image pairs, which leads to the semantic bias in merging features and destroys the representation ability of the region-of-interests. In this paper, a novel multi-source image fusion transformer (MsIFT) is proposed. Due to the inherent global attention mechanism of the transformer, the MsIFT has non-local fusion receptive fields, and it is more robust to spatial misalignment. Furthermore, multiple classification-based downstream tasks (e.g., pixel-wise classification, image-wise classification and semantic segmentation) are unified in the proposed MsIFT framework, and the fusion module architecture is shared by different tasks. The MsIFT achieved state-of-the-art performances on the image-wise classification dataset VAIS, semantic segmentation dataset SpaceNet 6 and pixel-wise classification dataset GRSS-DFC-2013. The code and trained model are being released upon the publication of the work.
No-Till Soil Organic Carbon Sequestration Patterns as Affected by Climate and Soil Erosion in the Arable Land of Mediterranean Europe
Authors: Baiamonte Giorgio , Gristina Luciano , Orlando Santo , Palermo Salvatore Samuel , Minacapilli Mario
Published: August 2022

No-tillage (NT) has been considered an agronomic tool to sequester soil organic carbon (SOC) and match the 4p1000 initiative requirements of conservative soil management. Recently, some doubts have emerged about the NT effect on SOC sequestration, often because observations and experimental data vary widely depending on climate and geographic characteristics. Therefore, a suitable SOC accounting method is needed that considers climate and morphology interactions. In this study, the yearly ratio between SOC in NT and conventional tillage (CT) (RRNT/CT) collected in a previous study for flat (96 samples) and sloping (44 samples) paired sites was used to map the overestimation of SOC sequestration. It was assumed that there would be an overestimation of NT capacity in sloping fields due to lower erosion processes with respect to CT. Towards this aim, Geographical Information System (GIS) techniques and an extensive input database of high spatial resolution maps were used in a simplified procedure to assess the overestimation of SOC stocks due to the sloping conditions and spatial variability of the Aridity Index (AI). Moreover, this also made it possible to quantify the effects of adopting NT practices on soil carbon sequestration compared to CT practices. The method was applied to the arable lands of five Mediterranean countries (France, Greece, Italy, Portugal and Spain) ranging between the 35° and 46° latitude. The results showed an overestimation of SOC sequestration, when the AI and soil erosion were considered. The average overestimation rate in the studied Mediterranean areas was 0.11 Mg ha−1 yr−1. Carbon stock overestimation ranged from 34 to 1417 Gg for Portugal and Italy, respectively. Even if overestimation is considered, 4p1000 goals are often reached, especially in the more arid areas. The findings of this research allowed us to map the areas suitable to meet the 4p1000 that could be achieved by adopting conservative practices such as NT.
Investigating the Potential of Infrared Thermography to Inform on Physical and Mechanical Properties of Soils for Geotechnical Engineering
Authors: Loche Marco , Scaringi Gianvito , Blahůt Jan , Hartvich Filip
Published: August 2022

Knowledge of physical and mechanical properties of geomaterials is fundamental to characterise their response to external forcings (mechanical, climatic) at various scales. This is true, for instance, in slope stability assessments, civil engineering works, and agriculture. The direct evaluation of these properties in situ can be difficult, especially in inaccessible or vast areas, and so can be the sampling and subsequent testing in the laboratory—where ensuring the representativeness of the acquired data at the scale of analysis poses an additional challenge. Thus, empirical correlations with more readily determinable quantities remain a powerful and practical tool. Recently, several sensors, able to inform on various geomaterial properties, have been developed. However, applications have typically targeted rocks, while studies on uncemented geomaterials (soils, geotechnically speaking) are lacking. Here, we propose a simple method to evaluate the porosity and critical state friction angle of soils via infrared thermography, consisting of periodic acquisitions of images in infrared wavelengths. To demonstrate the method’s capability, we analysed the cooling behaviour of samples of bentonite, kaolin, and sand (for which an extensive characterisation exists in the literature), after compaction to different porosities and pre-heating in an oven. We interpreted the results by seeking the optimal time interval for which a cooling rate index (CRI) could be defined, which is best linked with the target property. We found that the CRI correlates very well with the critical state friction angle (R2 > 0.85) and that different materials show unique and strong (R2 = 0.86–0.99) relationships between their porosity and the CRI, which also varies in a material-specific fashion according to the explored time interval. Although a systematic investigation on a wide range of natural soils is warranted, we argue that our method can be highly informative and could be used to calibrate remote sensing-based full-scale implementations in situ for various purposes.
Improved Ship Detection Algorithm Based on YOLOX for SAR Outline Enhancement Image
Authors: Li Sen , Fu Xiongjun , Dong Jian
Published: August 2022

Synthetic aperture radar (SAR) ship detection based on deep learning has the advantages of high accuracy and end-to-end processing, which has received more and more attention. However, SAR ship detection faces many problems, such as fuzzy ship contour, complex background, large scale difference and dense distribution of small targets. To solve these problems, this paper proposes a SAR ship detection method with ultra lightweight and high detection accuracy based on YOLOX. Aiming at the problem of speckle noise and blurred ship contour caused by the special imaging mechanism of SAR, a SAR ship feature enhancement method based on high frequency sub-band channel fusion which makes full use of contour information is proposed. Aiming at the requirement of light-weight detection algorithms for micro-SAR platforms such as small unmanned aerial vehicle and the defect of spatial pooling pyramid structure damaging ship contour features, an ultra-lightweight and high performance detection backbone based on Ghost Cross Stage Partial (GhostCSP) and lightweight spatial dilation convolution pyramid (LSDP) is designed. Aiming at the characteristics of ship scale diversity and unbalanced distribution of channel feature information after contour enhancement in SAR images, four feature layers are used to fuse contextual semantic information and channel attention mechanism is used for feature enhancement, and finally the improved ship target detection method based on YOLOX (ImYOLOX) is formed. Experimental tests on the SAR Ship Detection Dataset (SSDD) show that the proposed method achieves an average precision of 97.45% with a parameter size of 3.31 MB and a model size of 4.35 MB, and its detection performance is ahead of most current SAR ship detection algorithms.
A Novel Phase Compensation Method for Urban 3D Reconstruction Using SAR Tomography
Authors: Lu Hongliang , Sun Jili , Wang Jili , Wang Chunle
Published: August 2022

Synthetic aperture radar (SAR) tomography (TomoSAR) has been widely used in the three-dimensional (3D) reconstruction of urban areas using the multi-baseline (MB) SAR data. For urban scenarios, the MB SAR data are often acquired by repeat-pass using the spaceborne SAR system. Such a data stack generally has long time baselines, which result in different atmospheric disturbances of the data acquired by different tracks. These factors can lead to the presence of phase errors (PEs). PEs are multiplicative noise for observation data, which can cause diffusion and defocus in TomoSAR imaging and seriously affect the extraction of target 3D information. In this paper, we combine the methods of the block-building network (BBN) and phase gradient autofocus (PGA) to propose a novel phase compensation method called BBN-PGA. The BBN-PGA method can effectively and efficiently compensate for PEs of the MB SAR data over a wide area and improve the accuracy of 3D reconstruction of urban areas. The applicability of this proposed BBN-PGA method is proved by using simulated data and the spaceborne MB SAR data acquired by the TerraSAR-X satellite over an area in Barcelona, Spain.
Robust Correlation Tracking for UAV with Feature Integration and Response Map Enhancement
Authors: Lin Bin , Bai Yunpeng , Bai Bendu , Li Ying
Published: August 2022

Recently, correlation filter (CF)-based tracking algorithms have attained extensive interest in the field of unmanned aerial vehicle (UAV) tracking. Nonetheless, existing trackers still struggle with selecting suitable features and alleviating the model drift issue for online UAV tracking. In this paper, a robust CF-based tracker with feature integration and response map enhancement is proposed. Concretely, we develop a novel feature integration method that comprehensively describes the target by leveraging auxiliary gradient information extracted from the binary representation. Subsequently, the integrated features are utilized to learn a background-aware correlation filter (BACF) for generating a response map that implies the target location. To mitigate the risk of model drift, we introduce saliency awareness in the BACF framework and further propose an adaptive response fusion strategy to enhance the discriminating capability of the response map. Moreover, a dynamic model update mechanism is designed to prevent filter contamination and maintain tracking stability. Experiments on three public benchmarks verify that the proposed tracker outperforms several state-of-the-art algorithms and achieves a real-time tracking speed, which can be applied in UAV tracking scenarios efficiently.
Integration Data Model of the Bathymetric Monitoring System for Shallow Waterbodies Using UAV and USV Platforms
Authors: Lewicka Oktawia , Specht Mariusz , Stateczny Andrzej , Specht Cezary , Dardanelli Gino , Brčić David , Szostak Bartosz , Halicki Armin , Stateczny Marcin , Widźgowski Szymon
Published: August 2022

Changes in the seafloor relief are particularly noticeable in shallow waterbodies (at depths up to several metres), where they are of significance for human safety and environmental protection, as well as for which the highest measurement accuracy is required. The aim of this publication is to present the integration data model of the bathymetric monitoring system for shallow waterbodies using Unmanned Aerial Vehicles (UAV) and Unmanned Surface Vehicles (USV). As part of this model, three technology components will be created: a hydroacoustic and optoelectronic data integration component proposed by Dąbrowski et al., a radiometric depth determination component based on optoelectronic data using the Support Vector Regression (SVR) method, and a coastline extraction component proposed by Xu et al. Thanks to them, it will be possible to cover the entire area with measurements in the coastal zone, in particular between the shallow waterbody coastline and the min. isobath recorded by the echo sounder (the area is lacking actual measurement data). Multisensor data fusion obtained using Global Navigation Satellite System (GNSS)/Inertial Navigation System (INS), Light Detection And Ranging (LiDAR), Real Time Kinematic (RTK), UAV, and USV will allow to meet the requirements provided for the International Hydrographic Organization (IHO) Special Order (horizontal position error ≤ 2 m (p = 0.95), vertical position error ≤ 0.25 m (p = 0.95)). To this end, bathymetric and photogrammetric measurements shall be carried out under appropriate conditions. The water transparency in the tested waterbody should be at least 2 m. Hydrographic surveys shall be performed in windless weather and the water level is 0 in the Douglas sea scale (no waves or sea currents). However, the mission with the use of an UAV should take place in appropriate meteorological conditions, i.e., no precipitation, windless weather (wind speed not exceeding 6–7 m/s), sunny day.
Inter-Comparison of Diverse Heatwave Definitions in the Analysis of Spatiotemporally Contiguous Heatwave Events over China
Authors: Song Heyang , Kong Dongdong , Xiong Li , Gu Xihui , Liu Jianyu
Published: August 2022

A heatwave (HW) is a spatiotemporally contiguous event that is spatially widespread and lasts many days. HWs impose severe impacts on many aspects of society and terrestrial ecosystems. Here, we systematically investigate the influence of the selected threshold method (the absolute threshold method (ABS), quantile-based method (QTL), and moving quantile-based method (QTLmov)) and selected variables (heat index (HI), air temperature (Tair)) on the change patterns of spatiotemporally contiguous heatwave (STHW) characteristics over China from 1961–2017. Moreover, we discuss the different STHW change patterns among different HW severities (mild, moderate, and severe) and types (daytime and nighttime). The results show that (1) all threshold methods show a consistent phenomenon in most regions of China: STHWs have become longer-lasting (6.42%, 66.25%, and 148.58% HW days (HWD) increases were found from 1991–2017 compared to 1961–1990 corresponding to ABS, QTL, and QTLmov, respectively, as below), more severe (14.83%, 89.17%, and 158.92% increases in HW severity (HWS) increases), and more spatially widespread (14.92%, 134%, and 245.83% increases in the summed HW area (HWAsum)). However, the HW frequency (HWF) of moderate STHWs in some regions decreased as mild and moderate STHWs became extreme; (2) for threshold methods that do not consider seasonal variations (i.e., ABS and QTL), the spatial HI exceedance continuity was relatively weak, thus resulting in underestimated STHW characteristics increase rates; (3) for different variables defining STHWs, the relative changing ratio of the HI-based STHW was approximately 20% higher than that of the Tair-based STHW for all STHW characteristics, under the QTLmov threshold; (4) for different STHW types, the nighttime STHW was approximately 60% faster than the daytime STHW increase considering the QTL threshold and approximately 120% faster for the QTLmov method. This study provides a systematic investigation of different STHW definition methods and will benefit future STHW research.
Inversion of Aerosol Particle Size Distribution Using an Improved Stochastic Particle Swarm Optimization Algorithm
Authors: Nie Xin , Mao Qianjun
Published: August 2022

Aerosol particle size distribution (PSD) is one of the main influencing factors of the radiation effects and climate effects of aerosol. An improved stochastic particle swarm optimization (ISPSO) algorithm is proposed, and the PSD characteristics of aerosols were successfully retrieved from the aerosol optical depth (AOD). The performance analysis shows that the algorithm has good global search ability and convergence performance and will not fall into local optima. Then, the robustness and the ability to resist the noise of the algorithm were verified by adding random errors, using random initial values, and changing the number of samples and inversion parameters, and it was shown that the algorithm has a weak dependence on the initial value. The PSD characteristics of three typical aerosols were inverted, and the results show that the algorithm has good adaptability to the inversion of aerosol PSD. Finally, the PSD characteristics of aerosols from Xianghe and Mezaira under typical weather were inverted based on AERONET data, which shows the effectiveness and advancement of the ISPSO algorithm. This study can provide help for the obtaining of aerosol parameters under poor optical conditions.
On the Accuracy of Cadastral Marks: Statistical Analyses to Assess the Congruence among GNSS-Based Positioning and Official Maps
Authors: Dardanelli Gino , Maltese Antonino
Published: August 2022

Cadastral marks constitute a dense source of information for topographical surveys required to update cadastral maps. Historically, in Italy, cadastral marks have been the cartographic network for the implementation of mapping updates. Different sources of cadastral marks can be used by cadastral surveyors. In recent years, the cadastre is moving toward a digital world, and with the advancement of surveying technology, GNSS CORS technology has emerged in the positioning of cadastral marks. An analysis of congruence among cadastral marks using GNSS CORS and official maps is missing. Thus, this work aims to analyze the positional accuracy of some cadastral marks, located in Palermo, Italy, with regard to the official maps produced by the cadastral bureau, the local cartography, and Google Earth maps. A survey of 60 cadastral marks was carried out by conventional GNSS NRTK procedures, with the lateral offset method due to their materialization (mostly building edges), which is not always directly detectable. The cadastral marks’ positioning was obtained from different maps: cadastral maps and related monographic files, numerical technical maps, and Google Earth maps, to check their coordinate congruence. A statistical approach was performed to check whether the distribution frequencies of the coordinate’s differences belonged to the bivariate normal distribution for the planimetric coordinates and the univariate normal distribution for the altimetric component. The results show that the hypothesis of a normal distribution is confirmed in most of the pairs, and specifically, most of the analyses indicate that the highest congruencies seem to characterize the coordinates determined by using the GNSS and with those that can be deduced by the numerical technical maps. The results obtained experimentally show centimetric accuracies obtained by the GNSS NRTK survey, in both the planimetric and altimetric components, while the accuracies obtained from the georeferencing of the cadastral maps show differences in the order of 0.4–0.8 m. Meanwhile, the differences resulting from comparing the technical cartography produced by the local authority and Google Earth maps show greater criticalities, with a metric order of magnitude.
Three-Dimensional Coordinate Extraction Based on Radargrammetry for Single-Channel Curvilinear SAR System
Authors: Jiang Chenghao , Tang Shiyang , Ren Yi , Li Yinan , Zhang Juan , Li Geng , Zhang Linrang
Published: August 2022

With the rapid development of high-resolution synthetic aperture radar (SAR) systems, the technique that utilizes multiple two-dimensional (2-D) SAR images with different view angles to extract three-dimensional (3-D) coordinates of targets has gained wide concern in recent years. Unlike the traditional multi-channel SAR utilized for 3-D coordinate extraction, the single-channel curvilinear SAR (CLSAR) has the advantages of large variation of view angle, requiring fewer acquisition data, and lower device cost. However, due to the complex aerodynamic configuration and flight characteristics, important issues should be considered, including the mathematical model establishment, imaging geometry analysis, and high-precision extraction model design. In this paper, to address these challenges, a 3-D vector model of CLSAR was presented and the imaging geometries under different view angles were analyzed. Then, a novel 3-D coordinate extraction approach based on radargrammetry was proposed, in which the unique property of the SAR system, called cylindrical symmetry, was utilized to establish a novel extraction model. Compared with the conventional approach, the proposed one has fewer constraints on the trajectory of radar platform, requires fewer model parameters, and can obtain higher extraction accuracy without the assistance of extra ground control points (GCPs). Numerical results using simulated data demonstrated the effectiveness of the proposed approach.
Mining Is a Growing Threat within Indigenous Lands of the Brazilian Amazon
Authors: Mataveli Guilherme , Chaves Michel , Guerrero João , Escobar-Silva Elton Vicente , Conceição Katyanne , de Oliveira Gabriel
Published: August 2022

Conserving tropical forests is crucial for the environment and future of our climate. Tropical rainforests worldwide, including the Brazilian Legal Amazon (BLA), offer exceptional ecosystem services. However, the disturbances that have been occurring more frequently within them are endangering their key role in tackling climate change. An alternative approach for preserving the intact forests that remain in the BLA is the delimitation of Indigenous Lands (ILs), which can, additionally, ensure the well-being of the traditional peoples inhabiting there. An increase in deforestation rates of the BLA in recent years, due to the weakening of the Brazilian environmental policy, is not confined to unprotected areas but is also occurring within ILs. Under this scenario, mining, not allowed in ILs, is a growing threat in these protected areas. Thus, using the freely available MapBiomas dataset, we have quantified for the first time the total mining area within ILs of the BLA from 1985 to 2020. Such activity jumped from 7.45 km2 in 1985 to 102.16 km2 in 2020, an alarming increase of 1271%. Three ILs (Kayapó, Mundurukú, and Yanomami) concentrated 95% of the mining activity within ILs in 2020 and, therefore, they require closer monitoring. Most of the mining in ILs in 2020 (99.5%) was related to gold extraction. A total of 25 of the 31 ILs of the BLA where mining activity was detected in at least one of 36 years analyzed (~81% of them) had a statistically significant increasing trend according to the Mann–Kendall test at 5%. The datasets used or cited in this study (MapBiomas, PRODES, and DETER) enable the monitoring of the current status of ILs, and the identification of emerging trends related to illegal activities. Therefore, they are critical tools for legal authorities.
Miniterm, a Novel Virtual Sensor for Predictive Maintenance for the Industry 4.0 Era
Authors: Garcia Eduardo , Montés Nicolás , Llopis Javier , Lacasa Antonio
Published: August 2022

This article introduces a novel virtual sensor for predictive maintenance called mini-term. A mini-term can be defined as the time it takes for a part of the machine to do its job. Being a technical sub-cycle time, its function has been linked to production. However, when a machine or component gets deteriorated, the mini-term also suffers deterioration, allowing it to be a multifunctional indicator for the prediction of machine failures as well as measurement of production. Currently, in Industry 4.0, one of the handicaps is Big Data and Data Analysis. However, in the case of predictive maintenance, the need to install sensors in the machines means that when the proposed scientific solutions reach the industry, they cannot be carried out massively due to the high cost this entails. The advantage introduced by the mini-term is that it can be implemented in an easy and simple way in pre-installed systems since you only need to program a timer in the PLC or PC that controls the line/machine in the production line, allowing, according to the authors’ knowledge, to build industrial Big Data on predictive maintenance for the first time, which is called Miniterm 4.0. This article shows evidence of the important improvements generated by the use of Miniterm 4.0 in a factory. At the end of the paper we show the evolution of TAV (Technical availability), Mean Time To Repair (MTTR), EM (Number of Work order (Emergency Orders/line Stop)) and OM (Labour hours in EM) showing a very important improvement as the number of mini-terms was increased and the Miniterm 4.0 system became more reliable. In particular, TAV is increased by 15%, OM is reduced in 5000 orders, MTTR is reduced in 2 h and there are produced 3000 orders less than when mini-terms did not exist. At the end of the article we discuss the benefits and limitations of the mini-terms and we show the conclusions and future works.
Improving EEG-Based Driver Distraction Classification Using Brain Connectivity Estimators
Authors: Perera Dulan , Wang Yu-Kai , Lin Chin-Teng , Nguyen Hung , Chai Rifai
Published: August 2022

This paper discusses a novel approach to an EEG (electroencephalogram)-based driver distraction classification by using brain connectivity estimators as features. Ten healthy volunteers with more than one year of driving experience and an average age of 24.3 participated in a virtual reality environment with two conditions, a simple math problem-solving task and a lane-keeping task to mimic the distracted driving task and a non-distracted driving task, respectively. Independent component analysis (ICA) was conducted on the selected epochs of six selected components relevant to the frontal, central, parietal, occipital, left motor, and right motor areas. Granger–Geweke causality (GGC), directed transfer function (DTF), partial directed coherence (PDC), and generalized partial directed coherence (GPDC) brain connectivity estimators were used to calculate the connectivity matrixes. These connectivity matrixes were used as features to train the support vector machine (SVM) with the radial basis function (RBF) and classify the distracted and non-distracted driving tasks. GGC, DTF, PDC, and GPDC connectivity estimators yielded the classification accuracies of 82.27%, 70.02%, 86.19%, and 80.95%, respectively. Further analysis of the PDC connectivity estimator was conducted to determine the best window to differentiate between the distracted and non-distracted driving tasks. This study suggests that the PDC connectivity estimator can yield better classification accuracy for driver distractions.