bluuebunny/crossref_metadata_2025 huggingface dataset:
Life Cycle Energy Consumption and Greenhouse Gas Emissions Analysis of Primary and Recycled Aluminum in China
Authors: Peng Tianduo , Ren Lei , Du Ershun , Ou Xunmin , Yan Xiaoyu
Published: November 2022

Aluminum production is a major energy consumer and important source of greenhouse gas (GHG) emissions globally. Estimation of the energy consumption and GHG emissions caused by aluminum production in China has attracted widespread attention because China produces more than half of the global aluminum. This paper conducted life cycle (LC) energy consumption and GHG emissions analysis of primary and recycled aluminum in China for the year 2020, considering the provincial differences on both the scale of self-generated electricity consumed in primary aluminum production and the generation source of grid electricity. Potentials for energy saving and GHG emissions reductions were also investigated. The results indicate that there are 157,207 MJ of primary fossil energy (PE) consumption and 15,947 kg CO2-eq of GHG emissions per ton of primary aluminum ingot production in China, with the LC GHG emissions as high as 1.5–3.5 times that of developed economies. The LC PE consumption and GHG emissions of recycled aluminum are very low, only 7.5% and 5.3% that of primary aluminum, respectively. Provincial-level results indicate that the LC PE and GHG emissions intensities of primary aluminum in the main production areas are generally higher while those of recycled aluminum are lower in the main production areas. LC PE consumption and GHG emissions can be significantly reduced by decreasing electricity consumption, self-generated electricity management, low-carbon grid electricity development, and industrial relocation. Based on this study, policy suggestions for China’s aluminum industry are proposed. Recycled aluminum industry development, restriction of self-generated electricity, low-carbon electricity utilization, and industrial relocation should be promoted as they are highly helpful for reducing the LC PE consumption and GHG emissions of the aluminum industry. In addition, it is recommended that the central government considers the differences among provinces when designing and implementing policies.
Comparison of the Preparation Process of Rare Earth Oxides from the Water Leaching Solution of Waste Nd-Fe-B Magnets’ Sulfate Roasting Products
Authors: Chen Feixiong , Liu Fupeng , Wang Longjun , Wang Jinliang
Published: November 2022

The new process developed here consisting of sulfurization roasting transformation and water immersion can effectively realize the separation of rare earth elements (REEs) and impurities from spent Nd-Fe-B magnets. For the industrial application of the new process, it is critical to determine how to economically and efficiently prepare rare earth oxide (RExOy) products with higher purity from the obtained water leaching solution. Therefore, according to rare earth sulfate (RE2(SO4)3) solution characteristics, the oxalic acid precipitation–calcination method, sodium carbonate precipitation–calcination method, and double sulfates precipitation–alkali conversion–calcination method were optimized and compared. The results show that the recovery efficiency of REE recovery via the oxalic acid precipitation–calcination method is 99.44%, and the purity of RExOy is 99.83% under optimal technological conditions. However, the cost of oxalic acid precipitation is higher. The process consisting of the double sulfates precipitation–alkali conversion–calcination method is relatively complicated, the recovery efficiency of REEs is 97.95%, and the purity of the RExOy is 98.04%. The recovery efficiency of the REEs and the purity of the RExOy obtained from the sodium carbonate precipitation–calcination method are 99.12% and 98.33%, respectively. Moreover, the recycling cost of sodium carbonate precipitation is the lowest among the three processes for preparing RExOy, so it has industrial application potential. The obtained results for REE recovery from spent Nd-Fe-B magnets in this research can provide theoretical guidance for the innovation of the recycling process for REEs as secondary resources.
Application of Beetle Colony Optimization Based on Improvement of Rebellious Growth Characteristics in PM2.5 Concentration Prediction
Authors: Zhang Yizhun , Yan Qisheng
Published: November 2022

Aiming at the shortcomings of the beetle swarm algorithm, namely its low accuracy, easy fall into local optima, and slow convergence speed, a rebellious growth personality–beetle swarm optimization (RGP–BSO) model based on rebellious growth personality is proposed. Firstly, the growth and rebellious characters were added to the beetle swarm optimization algorithm to dynamically adjust the beetle’s judgment of the optimal position. Secondly, the adaptive iterative selection strategy is introduced to balance the beetles’ global search and local search capabilities, preventing the algorithm from falling into a locally optimal solution. Finally, two dynamic factors are introduced to promote the maturity of the character and further improve the algorithm’s optimization ability and convergence accuracy. The twelve standard test function simulation experiments show that RGP–BSO has a faster convergence speed and higher accuracy than other optimization algorithms. In the practical problem of PM2.5 concentration prediction, the ELM model optimized by RGP–BSO has more prominent accuracy and stability and has obvious advantages.
Disputed Emptiness: Vimalamitra’s Mādhyamika Interpretation of the Heart Sutra in the Light of His Criticism on Other Schools
Authors: Horiuchi Toshio
Published: November 2022

The *Āryaprajñāpāramitāhṛdayaṭīkā (hereafter PHT), Vimalamitra’s (ca. the 8th- to 9th cent.) commentary on the long Heart Sutra (hereafter HS), is not merely a commentary on words and phrases of the HS, but it also refers to and criticizes non-Buddhist schools and other schools within Buddhism. However, due to its textual situation, namely, the original Sanskrit being lost and it remains only as the Tibetan translation, the discussion there has not always been well understood. In particular, it has been suggested in recent years that Vimalamitra endorsed the Yogācāra perspective in the PHT. In this paper, I will primarily examine Vimalamitra’s interpretation of the famous four sets of phrases, such as “rūpa (form) is emptiness/empty” in the PHT, and clarify Vimalamitra’s understanding of key Buddhist concepts such as “emptiness” or “dependent origination”. In doing so, I argue that the PHT is written sorely from the Madhyamaka perspective, and Vimalamitra criticizes other schools, including the Yogācāra. Furthermore, by comparison with the writings of Kamalaśīla, an older contemporary of Vimalamitra, I further clarify his position as a Mādhyamika adherent. Finally, I will discuss the significance of those philosophical arguments in the PHT to the modern readers of the HS.
Major Gaps in Sterba’s New Atheological Argument from Evil
Authors: Collins Robin
Published: November 2022

In this essay, I first offer several scenarios where Sterba’s argument based on the Pauline Principle fails: specifically, one in which we all consent to living an earthly life in some prior existence (prior-consent scenario), one in which the victims would approve of the evil being done to them for some greater good (would-approve scenario), and one that combines one of these two scenarios with the stipulation that the greater goods redound to the victims (victim-beneficiary scenario). Along the way, I claim that a version of the Kantian principle that persons should not be treated as mere means, but as ends in themselves, better captures the intuitions used in support of the Pauline Principle. After this, I present two further significant problems with Sterba’s arguments. First, I argue that his claim that God should prevent the serious evil consequences of our free choices fails to consider the degree to which such a policy would make us aware of God’s monitoring of our every move. This in turn would greatly diminish our ability to make morally significant choices. Second, I point out flaws with his argument for the applicability of the Pauline Principle to God’s choices, particularly objecting to his argument that any greater-good theodicy implies that God would desire that people sin so that good may come, something he claims is morally perverse.
Granular Content Distribution for IoT Remote Sensing Data Supporting Privacy Preservation
Authors: Zhang Xiaoshuai , Zhang Guangyuan , Huang Xingru , Poslad Stefan
Published: November 2022

Facilitated by the Internet of Things (IoT) and diverse IoT devices, remote sensing data are evolving into the multimedia era with an expanding data scale. Massive remote sensing data are collected by IoT devices to monitor environments and human activities. Because IoT devices are involved in the data collection, there are probably private data contained in the collected remote sensing data, such as the device owner information and the precise location. Therefore, when data analysts, researchers, and other stakeholders require remote sensing data from numerous IoT devices for different analyses and investigations, how to distribute massive remote sensing data efficiently and regulate different people to view different parts of the distributed remote sensing data is a challenge to be addressed. Many general solutions rely on granular access control for content distribution but do not consider the low computational efficiency caused by the huge file size of the remote sensing data or certain IoT devices only have a constrained computational performance. Therefore, we propose a new granular content distribution scheme, which is more lightweight and practical for the distribution of multimedia remote sensing data with the consideration of the large data size to avoid complicated operations to the data. Furthermore, a dual data integrity check (hash summary and watermark) designed in our scheme can detect tampering or forgery from encrypted remote sensing data before decrypting it and validate it again after decryption. The security analyses and experimental results manifest that our new scheme can maintain high computational efficiency and block tampering and forgery during the granular content distribution for IoT remote sensing data.
An Integrated InSAR and GNSS Approach to Monitor Land Subsidence in the Po River Delta (Italy)
Authors: Fabris Massimo , Battaglia Mattia , Chen Xue , Menin Andrea , Monego Michele , Floris Mario
Published: November 2022

Land subsidence affects many areas of the world, posing a serious threat to human structures and infrastructures. It can be effectively monitored using ground-based and remote sensing techniques, such as the Global Navigation Satellite System (GNSS) and Interferometric Synthetic Aperture Radar (InSAR). GNSS provides high precision measurements, but in a limited number of points, and is time-consuming, while InSAR allows one to obtain a very large number of measurement points, but only in areas characterized by a high and constant reflectivity of the signal. The aim of this work is to propose an approach to combine the two techniques, overcoming the limits of each of them. The approach was applied in the Po River Delta (PRD), an area located in Northern Italy and historically affected by land subsidence. Ground-based GNSS data from three continuous stations (CGNSS) and 46 non-permanent sites (NPS) measured in 2016, 2018, and 2020, and Sentinel-1 and COSMO-SkyMed SAR data acquired from 2016 to 2020, were considered. In the first phase of the method, InSAR processing was calibrated and verified through CGNSS measurements; subsequently, the calibrated interferometric data were used to validate the GNSS measurements of the NPS. In the second phase, the datasets were integrated to provide an efficient monitoring system, extracting high-resolution deformation maps. The results showed a good agreement between the different sources of data, a high correlation between the displacement rate and the age of the emerged surfaces composed of unconsolidated fine sediments, and high land subsidence rates along the coastal area (up to 16–18 mm/year), where the most recent deposits outcrop. The proposed approach makes it possible to overcome the disadvantages of each technique by providing more complete and detailed information for a better understanding of the ongoing phenomenon.
Assessment of Land Use Land Cover Changes for Predicting Vulnerable Agricultural Lands in River Basins of Bangladesh Using Remote Sensing and a Fuzzy Expert System
Authors: Alam Kazi Faiz , Ahamed Tofael
Published: November 2022

The aim of this study was to assess the LULC changes over 26 years from 1995 to 2021 to find the most changed land use conditions within the 25 km territory of the main river systems of Bangladesh. In addition, the prediction of vulnerable areas for agricultural land use in terms of inundation by river water was also analyzed. The study area includes river networks distributed through eight administrative divisions (Rangpur, Rajshahi, Mymensingh, Sylhet, Dhaka, Khulna, Barishal and Chittagong) of Bangladesh, covering an area of 64,556 km2. The study was conducted by identifying permanent water bodies from NDWI indices and preparing LULC maps that include the five main land use classes (water body, bare land, vegetation, agricultural land, and urban area) in the Google Earth Engine platform using supervised classification. The LULC maps were then analyzed in the ArcGIS® environment. A vulnerability map for agricultural land use was also prepared using a fuzzy expert-based system applying multicriteria analysis. From the land use land cover map of the study area, it was found that among the five land use classes, water bodies, bare land, vegetation, and urban areas increased in size by 3.65%, 2.18%, 3.31% and 2.55%, respectively, whereas agricultural land use significantly decreased by 11.68%. This decrease in agricultural land use was common for the analyzed area of all administrative divisions. According to the vulnerable area map of the eight divisions, more than 50% of the analyzed area of the Khulna and Dhaka divisions and more than 40% of the analyzed area of the Rajshahi, Mymensingh, Sylhet, Barishal and Chittagong divisions were highly vulnerable to agricultural land use due to the possibility of inundation by water. However, approximately 44% of the analyzed area of the Rangpur division was not vulnerable for agricultural land use. The prepared LULC and vulnerability maps can be helpful for the future land use planning of Bangladesh to meet the increasing demand for food production and livelihoods for increasing populations.
A Novel Near-Real-Time GB-InSAR Slope Deformation Monitoring Method
Authors: Su Yuhan , Yang Honglei , Peng Junhuan , Liu Youfeng , Zhao Binbin , Shi Mengyao
Published: November 2022

In the past two decades, ground-based synthetic aperture radars (GB-SARs) have developed rapidly, providing a large amount of SAR data in minutes or even seconds. However, the real-time processing of big data is a challenge for the existing GB-SAR interferometry (GB-InSAR) technology. In this paper, we propose a near-real-time GB-InSAR method for monitoring slope surface deformation. The proposed method uses short baseline SAR data to generate interferograms to improve temporal coherence and reduce atmospheric interference. Then, based on the wrapped phase of each interferogram, a network method is used to estimate and remove systematic errors (such as atmospheric delay, radar center shift error, etc.). After the phase unwrapping, a least squares estimator is used for the overall solution to obtain the initial deformation parameters. When new data are added, a sequential estimator is used to combine the previous processing results and dynamically update the deformation parameters. Sequential estimators could avoid repeated calculations and improve data processing efficiency. Finally, the method is validated with the measured data. The results show that the average deviation between the proposed method and the overall estimation was less than 0.01 mm, which could be considered a consistent estimation accuracy. In addition, the calculation time of the sequential estimator was less sensitive than the total amount of data, and the time-consuming growth rate of each additional period of data was about 1/10 of the overall calculation. In summary, the new method could quickly and effectively obtain high-precision surface deformation information and meet the needs of near-real-time slope deformation monitoring.
Dynamics of Spatiotemporal Variation of Groundwater Arsenic Due to Salt-Leaching Irrigation and Saline-Alkali Land
Authors: Yin Shuhui , Tian Yuan , Yang Linsheng , Wen Qiqian , Wei Binggan
Published: November 2022

Determining the link between the evolution of salt-leaching irrigation, saline-alkali land, and groundwater arsenic (As) is essential to prevent groundwater arsenic pollution and implement appropriate soil salinization control projects. The objectives of our study were to explore the spatiotemporal correlation of saline-alkali land and salt-leaching irrigation with groundwater As in the Hetao Plain. Therefore, groundwater As concentrations during Period I (2006–2010) and Period II (2016–2020) were collected by historical data and chemical measurements. Salt-leaching irrigation area and saline-alkali land area in Period I and Period II were extracted through remote sensing data. With the increase of the salt-leaching irrigation area level (SLIAL) and saline-alkali land area level (SALAL), the variation trend in groundwater As concentration slightly fluctuated, with an increase in the SLIAL at the low SALAL, which may be because short-term flooding may not considerably enhance As mobilization. Lower groundwater As concentrations appeared in regions with higher SLIAL and lower SALAL. A larger saline-alkali land area (higher SALAL) increased the groundwater As concentration. The path analysis model confirmed that salt-leaching irrigation may increase groundwater salinity to affect groundwater As levels and to decrease the saline-alkali land area. From Periods I to II, the difference in path analysis results may imply that the decrease in the saline-alkali land area may have influenced As mobilization due to competitive adsorption caused by the increase in total dissolved solids (TDS) in groundwater. Our results provide new insights for the impacts of saline-alkali land and salt-leaching irrigation both on groundwater As concentration and the geochemical processes of As enrichment in arid and semi-arid areas with more serious salinization.
Processing and Validation of the STAR COSMIC-2 Temperature and Water Vapor Profiles in the Neutral Atmosphere
Authors: Ho Shu-peng , Kireev Stanislav , Shao Xi , Zhou Xinjia , Jing Xin
Published: November 2022

The global navigation satellite system (GNSS) radio occultation (RO) is becoming an essential component of National Oceanic and Atmospheric Administration (NOAA) observation systems. The constellation observing system for meteorology, ionosphere, and climate (COSMIC) 2 mission and the Formosa satellite mission 7, a COSMIC follow-on mission, is now the NOAA’s backbone RO mission. The NOAA’s dedicated GNSS RO SAtellite processing and science Application Center (RO-SAAC) was established at the Center for Satellite Applications and Research (STAR). To better quantify how the observation uncertainty from clock error and geometry determination may propagate to bending angle and refractivity profiles, STAR has developed the GNSS RO data processing and validation system. This study describes the COSMIC-2 neutral atmospheric temperature and moisture profile inversion algorithms at STAR. We used RS41 and ERA5, and UCAR 1D-Var products (wetPrf2) to validate the accuracy and uncertainty of the STAR 1D-Var thermal profiles. The STAR-RS41 temperature differences are less than a few tenths of 1 K from 8 km to 30 km altitude with a standard deviation (std) of 1.5–2 K. The mean STAR-RS41 water vapor specific humidity difference and the standard deviation are −0.35 g/kg and 1.2 g/kg, respectively. We also used the 1D-Var-derived temperature and water vapor profiles to compute the simulated brightness temperature (BTs) for advanced technology microwave sounder (ATMS) and cross-track infrared sounder (CrIS) channels and compared them to the collocated ATMS and CrIS measurements. The BT differences of STAR COSMIC-2-simulated BTs relative to SNPP ATMS are less than 0.1 K over all ATMS channels.
An Operational Atmospheric Correction Framework for Multi-Source Medium-High-Resolution Remote Sensing Data of China
Authors: Zhang Hao , Yan Dongchuan , Zhang Bing , Fu Zhengwen , Li Baipeng , Zhang Shuning
Published: November 2022

Land surface reflectance (LSR) data form the basis of quantitatively remotely sensed applications. For accurate LSR retrieval, atmospheric correction has been investigated by many researchers and implemented in typical processing systems, including common atmospheric correction software for various types of datasets and automatic operating systems for application to certain individual data sources. In recent years, China has launched multiple medium–high-resolution satellites but has not provided standard LSR products partly because of the lack of an appropriate operational system. In this paper, a multi-source remote sensing LSR product system for medium- and high-resolution data is proposed, called the “Operational Atmospheric Correction Framework for multi-source Medium-high-resolution Remote Sensing data of China” (ACFrC). The AC algorithm, processing flow, and design of the multi-source LSR system were described in detail. A practical atmospheric correction algorithm was proposed specially for data in only the visible and near-infrared (VNIR) bands. The entire processing chain was divided into modules for multi-source data ingestion, apparent reflectance calculation, cloud and water identification, atmospheric correction, and standard LSR product generation. To date, most types of multi-source data have been tested using the ACFrC system, with reasonable results being obtained. From the preliminary results, the 313 scenes of LSR products from the GaoFen-2 (GF-2) satellite over China for the period from 2015 to 2018 were cross-compared with Landsat-8 LSR acquired on the same day, showing an overall uncertainty less than 0.112 × LSR + 0.0112. Further, the ACFrC data processing efficiency was found to be suitable for automatic operation. System improvement is ongoing and future refinements will include online cloud parallel computing functionality and services, more robust algorithms, and other radiometric processing functions.
FY-4A/AGRI Aerosol Optical Depth Retrieval Capability Test and Validation Based on NNAeroG
Authors: Ding Haonan , Zhao Limin , Liu Shanwei , Chen Xingfeng , de Leeuw Gerrit , Wang Fu , Zheng Fengjie , Zhang Yuhuan , Liu Jun , Li Jiaguo , She Lu , Si Yidan , Gu Xingfa
Published: November 2022

The Advanced Geostationary Radiation Imager (AGRI) is one of the main imaging sensors on the Fengyun-4A (FY-4A) satellite. Due to the combination of high spatial and temporal resolution, the AGRI is suitable for continuously monitoring atmospheric aerosol. Existing studies only perform AOD retrieval on the dark target area of FY-4A/AGRI, and the full disk AOD retrieval is still under exploration. The Neural Network AEROsol Retrieval for Geostationary Satellite (NNAeroG) based on the Fully Connected Neural Network (FCNN) was used to retrieve FY-4A/AGRI full disk aerosol optical depth (AOD). The data from 111 ground-based Aerosol Robotic Network (AERONET) and Sun–Sky Radiometer Observation Network (SONET) sites were used to train the neural network, and the data from 28 other sites were used for independent validation. FY-4A/AGRI AOD data from 2017 to 2020 were validated over the full disk and three different surface types (vegetated areas, arid areas, and marine and coastal areas). For general validation, the AOD predicted by the application of NNAeroG to FY-4A/AGRI observations is consistent with the ground-based reference AOD data. The validation of the FY-4A/AGRI AOD versus the reference data set shows that the root-mean-square error (RMSE), mean absolute error (MAE), R squared (R2), and percentage of data with errors within the expected error ± (0.05 + 15%) (EE15) are 0.237, 0.145, 0.733, and 58.7%, respectively. The AOD retrieval accuracy over vegetated areas is high but there is potential for improvement of the results over arid areas and marine and coastal areas. AOD retrieval results of FY-4A/AGRI were compared under fine and coarse modes. The retrieved AOD has low accuracy in coarse mode but is better in coarse–fine mixed mode and fine mode. The current AOD products over the ocean of NNAeroG-FY4A/AGRI are not recommended. Further development of algorithms for marine areas is expected to improve the full disk AOD retrieval accuracy.
Deformation and Volumetric Change in a Typical Retrogressive Thaw Slump in Permafrost Regions of the Central Tibetan Plateau, China
Authors: Jiao Chenglong , Niu Fujun , He Peifeng , Ren Lu , Luo Jing , Shan Yi
Published: November 2022

Ice-rich permafrost in the Qinghai–Tibet Plateau (QTP), China, is becoming susceptible to thermokarst landforms, and the most dramatic among these terrain-altering landforms is retrogressive thaw slump (RTS). Concurrently, RTS development can in turn affect the eco-environment, and especially soil erosion and carbon emission, during their evolution. However, there are still a lack of quantitative methods and comprehensive studies on the deformation and volumetric change in RTS. The purpose of this study is to quantitatively assess the RTS evolution through a novel and feasible simulation framework of the GPU-based discrete element method (DEM) coupled with the finite difference method (FDM). Additionally, the simulation results were calibrated using the time series observation results from September 2021 to August 2022, using the combined methods of terrestrial laser scanning (TLS) and unmanned aerial vehicle (UAV). The results reveal that, over this time, thaw slump mobilized a total volume of 1335 m3 and approximately 1050 m3 moved to a displaced area. Additionally, the estimated soil erosion was about 211 m3. Meanwhile, the corresponding maximum ground subsidence and headwall retrogression were 1.9 m and 3.2 m, respectively. We also found that the amount of mass wasting in RTS development is highly related to the ground ice content. When the volumetric ice content exceeds 10%, there will be obvious mass wasting in the thaw slump development area. Furthermore, this work proposed that the coupled DEM-FDM method and field survey method of TLS-UAV can provide an effective pathway to simulate thaw-induced slope failure problems and complement the research limitations of small-scale RTSs using remote sensing methods. The results are meaningful for assessing the eco-environmental impacts on the QTP.
Time-Series FY4A Datasets for Super-Resolution Benchmarking of Meteorological Satellite Images
Authors: Wei Jingbo , Zhou Chenghao , Wang Jingsong , Chen Zhou
Published: November 2022

Meteorological satellites are usually operated at high temporal resolutions, but the spatial resolutions are too poor to identify ground content. Super-resolution is an economic way to enhance spatial details, but the feasibility is not validated for meteorological images due to the absence of benchmarking data. In this work, we propose the FY4ASRgray and FY4ASRcolor datasets to assess super-resolution algorithms on meteorological applications. The features of cloud sensitivity and temporal continuity are linked to the proposed datasets. To test the usability of the new datasets, five state-of-the-art super-resolution algorithms are gathered for contest. Shift learning is used to shorten the training time and improve the parameters. Methods are modified to deal with the 16-bit challenge. The reconstruction results are demonstrated and evaluated regarding the radiometric, structural, and spectral loss, which gives the baseline performance for detail enhancement of the FY4A satellite images. Additional experiments are made on FY4ASRcolor for sequence super-resolution, spatiotemporal fusion, and generalization test for further performance test.
Coastal High-Temporal Sea-Surface Altimetry Using the Posterior Error Estimations of Ionosphere-Free PPP and Information Fusion for Multi-GNSS Retrievals
Authors: Zhou Wei , Bian Shaofeng , Liu Yi , Huang Liangke , Liu Lilong , Chen Cheng , Li Houpu , Zhai Guojun
Published: November 2022

Ocean tidal variation is a key parameter for ensuring coastal safety, monitoring marine climate, and maintaining elevation datum. Recently, the ground-based global navigation satellite system reflectometry (GNSS-R) technique has been applied for regional tidal measurements, which is somewhat restricted in terms of temporal and spatial resolutions. A convenient method to improve temporal resolution of measurements is to combine multi-GNSS observations. This paper proposes a new sea-surface altimetry method using the posterior errors (PE) of dual-frequency carrier-phase signals derived from the ionosphere-free Precise Point Positioning (IF-PPP). Considering that the number of initial retrievals is obviously unsuitable for minute-level tidal measurements, both the time sliding window based on the Lomb–Scargle periodogram and a weighted cubic spline smoothing function are significant processing steps for estimating the reflector heights between the sea surface and antenna center. Measurements from two coastal GNSS stations with different tidal amplitudes are used to test the proposed method and compare it with the tide gauge and the signal-to-noise ratio (SNR) methods, respectively. The experimental results show that the multi-GNSS PE combination method can be used to estimate a minute-level sea level time series, and its root-mean-squared errors (RMSE) are about 12.5 cm. In terms of correlation, for all results, the corresponding coefficients exceed 0.97. Moreover, this combined PE method demonstrates a significant advantage in increasing temporal resolution, which is beneficial for application on high-frequency sea-level monitoring.
Noised Phase Unwrapping Based on the Adaptive Window of Wigner Distribution
Authors: Chu Junqiu , Liu Xingling , Ma Haotong , Yu Xuegang , Ren Ge
Published: November 2022

A noised phase-unwrapping method is presented by using the Wigner distribution function to filter the phase noise and restore the gradient of the phase map. By using Poisson’s equation, the unwrapped phase map was obtained. Compared with the existing methods, the proposed method is theoretically simple, provides a more accurate representation, and can be implemented in light-field hardware devices, such as Shack-Hartmann sensors.
Analysis of the Microphysical Structure and Evolution Characteristics of a Typical Sea Fog Weather Event in the Eastern Sea of China
Authors: He Jianxin , Ren Xinyue , Wang Hao , Shi Zhao , Zhang Fugui , Hu Lijun , Zeng Qiangyu , Jin Xin
Published: November 2022

This study is the first to use the observation data of a fog monitor, a visibility meter, and an automatic weather station to carry out a comprehensive observation experiment from the perspective of microphysics on a severe sea fog process in Beilun District, China, from 14 to 15 June 2021. The results show the following: (1) Temperature is closely related to nucleation, condensation growth, and other processes. The decrease (increase) in temperature is the main reason for the enhancement (weakening) of nucleation and the growth of condensation (evaporation of droplets), which leads to an increase (or decrease) in microphysical quantities, such as droplet number concentration and liquid water content. (2) The average droplet number spectral distribution roughly conforms to the Gamma distribution, and the spectral distribution of the fog process presents a ”multi-peak” structure, with peak diameters of 6 μm, 12 μm, 16 μm, 24 μm, and 44 μm. Droplets with a diameter of less than 16 μm account for 75% of the droplet size distribution. (3) During this sea fog process, three microphysical parameters, namely, number concentration, liquid water content, and average diameter, are all positively correlated in pairs, but the positive correlation between the number concentration and the average diameter is weak. This shows that the condensation nucleation and the condensation growth of droplets are the main processes in this sea fog process and that the collision process occurs but is not the dominant process. The sea fog comprehensive observation experiment provides an important demonstration of the microphysics research of sea fog in the eastern coastal areas of China and provides more reference information for sea fog research and equipment comparisons between different regions. At the same time, it also provides an essential scientific basis for the short-term forecast of sea fog in the future and for the optimization of the microphysical parameters of related models.
Client-Oriented Blind Quality Metric for High Dynamic Range Stereoscopic Omnidirectional Vision Systems
Authors: Cao Liuyan , You Jihao , Song Yang , Xu Haiyong , Jiang Zhidi , Jiang Gangyi
Published: November 2022

A high dynamic range (HDR) stereoscopic omnidirectional vision system can provide users with more realistic binocular and immersive perception, where the HDR stereoscopic omnidirectional image (HSOI) suffers distortions during its encoding and visualization, making its quality evaluation more challenging. To solve the problem, this paper proposes a client-oriented blind HSOI quality metric based on visual perception. The proposed metric mainly consists of a monocular perception module (MPM) and binocular perception module (BPM), which combine monocular/binocular, omnidirectional and HDR/tone-mapping perception. The MPM extracts features from three aspects: global color distortion, symmetric/asymmetric distortion and scene distortion. In the BPM, the binocular fusion map and binocular difference map are generated by joint image filtering. Then, brightness segmentation is performed on the binocular fusion image, and distinctive features are extracted on the segmented high/low/middle brightness regions. For the binocular difference map, natural scene statistical features are extracted by multi-coefficient derivative maps. Finally, feature screening is used to remove the redundancy between the extracted features. Experimental results on the HSOID database show that the proposed metric is generally better than the representative quality metric, and is more consistent with the subjective perception.
An Effective Self-Configurable Ransomware Prevention Technique for IoMT
Authors: Tariq Usman , Ullah Imdad , Yousuf Uddin Mohammed , Kwon Se Jin
Published: November 2022

Remote healthcare systems and applications are being enabled via the Internet of Medical Things (IoMT), which is an automated system that facilitates the critical and emergency healthcare services in urban areas, in addition to, bridges the isolated rural communities for various healthcare services. Researchers and developers are, to date, considering the majority of the technological aspects and critical issues around the IoMT, e.g., security vulnerabilities and other cybercrimes. One of such major challenges IoMT has to face is widespread ransomware attacks; a malicious malware that encrypts the patients’ critical data, restricts access to IoMT devices or entirely disable IoMT devices, or uses several combinations to compromise the overall system functionality, mainly for ransom. These ransomware attacks would have several devastating consequences, such as loss of life-threatening data and system functionality, ceasing emergency and life-saving services, wastage of several vital resources etc. This paper presents a ransomware analysis and identification architecture with the objective to detect and validate the ransomware attacks and to evaluate its accuracy using a comprehensive verification process. We first develop a comprehensive experimental environment, to simulate a real-time IoMT network, for experimenting various types of ransomware attacks. Following, we construct a comprehensive set of ransomware attacks and analyze their effects over an IoMT network devices. Furthermore, we develop an effective detection filter for detecting various ransomware attacks (e.g., static and dynamic attacks) and evaluate the degree of damages caused to the IoMT network devices. In addition, we develop a defense system to block the ransomware attacks and notify the backend control system. To evaluate the effectiveness of the proposed framework, we experimented our architecture with 194 various samples of malware and 46 variants, with a duration of sixty minutes for each sample, and thoroughly examined the network traffic data for malicious behaviors. The evaluation results show more than 95% of accuracy of detecting various ransomware attacks.
Mediator Microbial Biosensor Analyzers for Rapid Determination of Surface Water Toxicity
Authors: Kharkova Anna , Arlyapov Vyacheslav , Medvedeva Anastasia , Lepikash Roman , Melnikov Pavel , Reshetilov Anatoly
Published: November 2022

Microbial mediator biosensors for surface water toxicity determination make it possible to carry out an early assessment of the environmental object’s quality without time-consuming standard procedures based on standard test-organisms, and provide broad opportunities for receptor element modifying depending on the required operational parameters analyzer. Four microorganisms with broad substrate specificity and nine electron acceptors were used to form a receptor system for toxicity assessment. Ferrocene was the most effective mediator according to its high rate constant of interaction with the microorganisms (0.33 ± 0.01 dm3/(g × s) for yeast Saccharomyces cerevisiae). Biosensors were tested on samples containing four heavy metal ions (Cu2+, Zn2+, Pb2+, Cd2+), two phenols (phenol and p-nitrophenol), and three natural water samples. The «ferrocene- Escherichia coli» and «ferrocene-Paracoccus yeei, E. coli association» systems showed good operational stability with a relative standard deviation of 6.9 and 7.3% (14 measurements) and a reproducibility of 7 and 5.2% using copper (II) ions as a reference toxicant. Biosensor analysis with these systems was shown to highly correlate with the results of the standard method using Chlorella algae as a test object. Developed biosensors allow for a valuation of the polluted natural water’s impact on the ecosystem via an assessment of the influence on bacteria and yeast in the receptor system. The systems could be used in toxicological monitoring of natural waters.
Enhancement of the CAST Block Algorithm Based on Novel S-Box for Image Encryption
Authors: Ali Rasha S. , Akif Omar Z. , Jassim Sameeh A. , Farhan Alaa Kadhim , El-Kenawy El-Sayed M. , Ibrahim Abdelhameed , Ghoneim Mohamed E. , Abdelhamid Abdelaziz A.
Published: November 2022

Background and Aim: due to the rapid growth of data communication and multimedia system applications, security becomes a critical issue in the communication and storage of images. This study aims to improve encryption and decryption for various types of images by decreasing time consumption and strengthening security. Methodology: An algorithm is proposed for encrypting images based on the Carlisle Adams and Stafford Tavares CAST block cipher algorithm with 3D and 2D logistic maps. A chaotic function that increases the randomness in the encrypted data and images, thereby breaking the relation sequence through the encryption procedure, is introduced. The time is decreased by using three secure and private S-Boxes rather than using six S-Boxes, as in the traditional method. Moreover, the CAST encryption algorithm was modified to be used on the private keys and substitution stage (S-Boxes), with the keys and S-Boxes of the encryption algorithm being generated according to the 2D and 3D chaotic map functions. The proposed system passed all evaluation criteria, including (MSE, PSNR, EQ, MD, SC, NC, AD, SNR, SIM, MAE, Time, CC, Entropy, and histograms). Results: Moreover, the results also illustrate that the created S-Boxes passed all evaluation criteria; compared with the results of the traditional method that was used in creating S-Box, the proposed method achieved better results than other methods used in the other works. The proposed solution improves the entropy which is between (7.991–7.999), reduces the processing time which is between (0.5–11 s/Images), and improves NCPR, which is between (0.991–1). Conclusions: The proposed solution focuses on reducing the total processing time for encryption and decryption and improving transmission security. Finally, this solution provides a fast security system for surgical telepresence with secure real-time communication. The complexity of this work needs to know the S-Box creation method used, the chaotic method, the values of the chaotic parameters, and which of these methods was used in the encryption process.
Development of an Artificial Neural Network Algorithm Embedded in an On-Site Sensor for Water Level Forecasting
Authors: Liu Cheng-Han , Yang Tsun-Hua , Wijaya Obaja Triputera
Published: November 2022

Extreme weather events cause stream overflow and lead to urban inundation. In this study, a decentralized flood monitoring system is proposed to provide water level predictions in streams three hours ahead. The customized sensor in the system measures the water levels and implements edge computing to produce future water levels. It is very different from traditional centralized monitoring systems and considered an innovation in the field. In edge computing, traditional physics-based algorithms are not computationally efficient if microprocessors are used in sensors. A correlation analysis was performed to identify key factors that influence the variations in the water level forecasts. For example, the second-order difference in the water level is considered to represent the acceleration or deacceleration of a water level rise. According to different input factors, three artificial neural network (ANN) models were developed. Four streams or canals were selected to test and evaluate the performance of the models. One case was used for model training and testing, and the others were used for model validation. The results demonstrated that the ANN model with the second-order water level difference as an input factor outperformed the other ANN models in terms of RMSE. The customized microprocessor-based sensor with an embedded ANN algorithm can be adopted to improve edge computing capabilities and support emergency response and decision making.
Deep Reinforcement Learning for the Detection of Abnormal Data in Smart Meters
Authors: Sun Shuxian , Liu Chunyu , Zhu Yiqun , He Haihang , Xiao Shuai , Wen Jiabao
Published: November 2022

The rapidly growing power data in smart grids have created difficulties in security management. The processing of large-scale power data with the use of artificial intelligence methods has become a hotspot research topic. Considering the early warning detection problem of smart meters, this paper proposes an abnormal data detection network based on Deep Reinforcement Learning, which includes a main network and a target network composed of deep learning networks. This work uses the greedy policy algorithm to find the action of the maximum value of Q based on the Q-learning method to obtain the optimal calculation policy. It also uses the reward value and discount factor to optimize the target value. In particular, this study uses the fuzzy c-means method to predict the future state information value, which improves the computational accuracy of the Deep Reinforcement Learning model. The experimental results show that compared with the traditional smart meter data anomaly detection method, the proposed model improves the accuracy of meter data anomaly detection.
Vestibular Rehabilitation Improves Gait Quality and Activities of Daily Living in People with Severe Traumatic Brain Injury: A Randomized Clinical Trial
Authors: Tramontano Marco , Belluscio Valeria , Bergamini Elena , Allevi Giulia , De Angelis Sara , Verdecchia Giorgia , Formisano Rita , Vannozzi Giuseppe , Buzzi Maria Gabriella
Published: November 2022

Neurorehabilitation research in patients with traumatic brain injury (TBI) showed how vestibular rehabilitation (VR) treatments positively affect concussion-related symptoms, but no studies have been carried out in patients with severe TBI (sTBI) during post-acute intensive neurorehabilitation. We aimed at testing this effect by combining sensor-based gait analysis and clinical scales assessment. We hypothesized that integrating VR in post-acute neurorehabilitation training might improve gait quality and activity of daily living (ADL) in sTBI patients. A two-arm, single-blind randomized controlled trial with 8 weeks of follow-up was performed including thirty sTBI inpatients that underwent an 8-week rehabilitation program including either a VR or a conventional program. Gait quality parameters were obtained using body-mounted magneto-inertial sensors during instrumented linear and curvilinear walking tests. A 4X2 mixed model ANOVA was used to investigate session–group interactions and main effects. Patients undergoing VR exhibited improvements in ADL, showing early improvements in clinical scores. Sensor-based assessment of curvilinear pathways highlighted significant VR-related improvements in gait smoothness over time (p < 0.05), whereas both treatments exhibited distinct improvements in gait quality. Integrating VR in conventional neurorehabilitation is a suitable strategy to improve gait smoothness and ADL in sTBI patients. Instrumented protocols are further promoted as an additional measure to quantify the efficacy of neurorehabilitation treatments.
Reliability and Validity of Inertial Sensor Assisted Reaction Time Measurement Tools among Healthy Young Adults
Authors: Harper Brent , Shiraishi Michael , Soangra Rahul
Published: November 2022

The assessment of movement reaction time (RT) as a sideline assessment is a valuable biomarker for mild TBI or concussion. However, such assessments require controlled laboratory environments, which may not be feasible for sideline testing during a game. Body-worn wearable devices are advantageous as being cost-effective, easy to don and use, wirelessly transmit data, and ensure unhindered movement performance. This study aimed to develop a Drop-stick Test System (DTS) with a wireless inertial sensor and confirm its reliability for different standing conditions (Foam versus No Foam) and task types (Single versus Dual), and postures (Standing versus sitting). Fourteen healthy young participants (seven females, seven males; age 24.7 ± 2.6 years) participated in this study. The participants were asked to catch a falling stick attached to the sensor during a drop test. Reaction Times (RTs) were calculated from data for each trial from DTS and laboratory camera system (gold standard). Intraclass correlation coefficients (ICC 3,k) were computed to determine inter-instrument reliability. The RT measurements from participants using the camera system and sensor-based DTS showed moderate to good inter-instrument reliability with an overall ICC of 0.82 (95% CI 0.78–0.85). Bland–Altman plots and 95% levels of agreement revealed a bias where the DTS underestimated RT by approximately 50 ms.