bluuebunny/crossref_metadata_2025 huggingface dataset:
Evaluation of Weighted Mean of Vectors Algorithm for Identification of Solar Cell Parameters
Authors: Hassan Amir Y. , Ismaeel Alaa A. K. , Said Mokhtar , Ghoniem Rania M. , Deb Sanchari , Elsayed Abeer Galal
Published: May 2022

The environmental and technical benefits of renewable energy sources make expanding their use essential in our lives. The main source of renewable energy used in this work is photovoltaic energy. Photovoltaic cells are a clean energy source dependent on solar irradiance to generate electricity from sunlight. The identification of solar cell variables is one of the main items in the simulation and modeling of photovoltaic models. The models used in this work are triple-diode, double-diode, and single-diode solar cells. A novel optimization method called weighted mean of vectors (INFO) is applied for estimating the solar cell variables in the three models. The fitness function of identification is to minimize the root-mean-square error (RMSE) between the measured data of current and the data of simulated current based on the parameters identified from the algorithms. The INFO technique is compared with another seven methods: Harris hawk optimization (HHO), tunicate swarm algorithm (TSA), sine—cosine algorithm (SCA), moth–flame optimizer (MFO), grey wolf optimization (GWO), chimp optimization algorithm (ChOA), and Runge–Kutta optimization (RUN).
Efficient Removal of Dyes from Aqueous Solution by Adsorption on L-Arginine-Modified Mesoporous Silica Nanoparticles
Authors: Alswieleh Abdullah M.
Published: May 2022

The use of mesoporous silica modified with L-arginine (Ar-MSNPs) for the removal of ionic dyes from aqueous solutions has been investigated. Several analytical techniques have been used to determine the characteristics of nanoadsorbents. The removal of crystal violet and fluorescein was performed using the batch method to investigate the effects of cultivation pH, initial concentrations of dyes, and exposure time on adsorption efficiency. The optimum adsorption of fluorescein was achieved at pH 2, whereas the optimum adsorption of crystal violet was achieved at pH 13. The equilibrium was established in both systems at 20 min at low concentrations, and approximately 30 min at high concentrations. The equilibrium adsorption data was analyzed using Langmuir and Freundlich isotherm models. The correlation coefficient (R2) values of the isotherms presented the best fit with the Langmuir isotherm. The adsorption kinetic data was fitted with the pseudo second-order model for both systems.
Detection and Isolation of Incipiently Developing Fault Using Wasserstein Distance
Authors: Lu Cheng , Zeng Jiusun , Luo Shihua , Cai Jinhui
Published: May 2022

This paper develops an incipient fault detection and isolation method using the Wasserstein distance, which measures the difference between the probability distributions of normal and faulty data sets from the aspect of optimal transport. For fault detection, a moving window based approach is introduced, resulting in two monitoring statistics that are constructed based on the Wasserstein distance. From analysis of the limiting distribution under multivariate Gaussian case, it is proved that the difference measured by the Wasserstein distance is more sensitive than conventional quadratic statistics like Hotelling’s T2 and Squared Prediction Error (SPE). For non-Gaussian distributed data, a project robust Wasserstein distance (PRW) model is proposed and the Riemannian block coordinate descent (RBCD) algorithm is applied to estimate the Wasserstein distance, which is fast when the number of sampled data is large. In addition, a fault isolation method is further proposed once the incipiently developing fault is detected. Application studies to a simulation example, a continuous stirred tank reactor (CSTR) process and a real-time boiler water wall over-temperature process demonstrate the effectiveness of the proposed method.
Optimization of the Oxidative Coupling of Methane Process for Ethylene Production
Authors: Alkathiri Raed , Alshamrani Ali , Wazeer Irfan , Boumaza Mourad , Hadj-Kali Mohamed K.
Published: May 2022

The oxidative coupling of methane (OCM) process is considered an intriguing route for the production of ethylene, one of the most demanded petrochemical products on the market. Ethylene can be produced by various methods, but the most widely used is the steam cracking process. However, due to the current instability of the crude oil market and the shale gas revolution, the production of olefins from natural gas has opened a new path for companies to mitigate the high demand for crude oil while utilizing an abundant amount of natural gas. In this work, the OCM process was compared with other existing processes, and the process was simulated using Aspen HYSYS. The flowsheet was divided into four sections, namely (i) the reaction section, (ii) the water removal section, (iii) the carbon dioxide capture section, and (iv) the ethylene purification section. Each section was thoroughly discussed, and the heat integration of the process was performed to ensure maximum energy utilization. The heat exchanger network was constructed, and the results show that the heating utility can be reduced by more than 95% (from 76567 kW to 2107.5 kW) and the cooling utility can be reduced by more than 60% (from 116398 kW to 41939.2 kW) at an optimum minimum temperature difference of 25 °C. In addition, a case study on the recovery of the high exothermic heat of reaction for power production shows that 16.68 MW can be produced through the cycle, which can cover the total cost of compression.
Drug Carriers: A Review on the Most Used Mathematical Models for Drug Release
Authors: Trucillo Paolo
Published: May 2022

Carriers are protective transporters of drugs to target cells, facilitating therapy under each points of view, such as fast healing, reducing infective phenomena, and curing illnesses while avoiding side effects. Over the last 60 years, several scientists have studied drug carrier properties, trying to adapt them to the release environment. Drug/Carrier interaction phenomena have been deeply studied, and the release kinetics have been modeled according to the occurring phenomena involved in the system. It is not easy to define models’ advantages and disadvantages, since each of them may fit in a specific situation, considering material interactions, diffusion and erosion phenomena, and, no less important, the behavior of receiving medium. This work represents a critical review on main mathematical models concerning their dependency on physical, chemical, empirical, or semi-empirical variables. A quantitative representation of release profiles has been shown for the most representative models. A final critical comment on the applicability of these models has been presented at the end. A mathematical approach to this topic may help students and researchers approach the wide panorama of models that exist in literature and have been optimized over time. This models list could be of practical inspiration for the development of researchers’ own new models or for the application of proper modifications, with the introduction of new variable dependency.
Dance Movement Psychotherapy in Breast Cancer: “Throwing the Βall to Μount Olympus”
Authors: Karaferi Spyridoula , Papaliagka (Papaliaga) Maria N. , Tolia Maria , Poultsidi Antigoni , Felemegas Panagiotis D. , Bonotis Konstantinos
Published: May 2022

The present study is a Dance Movement Psychotherapy (DMP) pilot intervention designed to address the psychological challenges faced by Greek breast cancer patients, after surgery and around radiotherapy treatment. It was designed as a 12 weeks’ project, involving patients of diverse age and educational level, mostly living in rural areas. The personality and psychopathology inventory Symptoms’ Check List-90 (SCL-90), the therapist’s records and video self-observation were used in a mixed method approach. Thirteen subjects initiated the study, but they were gradually reduced and only four completed it. Statistical analysis was performed using SPSS-25 addressing this fact. Positive results of qualitative interpretation of the therapist’s data and SCL-90, as well as changes in movement patterns and self-perception evaluated via video self-observation, showed that the intervention was beneficial for the patients who completed it. However, the fact that not all the participants were able to finish the program, implies that although DMT intervention at an early stage can help some patients deal with strong emotions, careful evaluation of eligible individuals should precede. Besides the patients’ medical condition, demographic and personality characteristics should be considered in the design of future similar studies.
The Religious and Technological History of the Tang Dynasty Spherical Incense Burner
Authors: Huang Bing
Published: May 2022

This article introduces and explores, for the first time in any Western language, the gilded-silver xiangnang 香囊 (spherical incense burner) from Famen Temple, one of the largest xiangnang incense burners found in the Tang dynasty. The spherical incense burner evolved from censers for bedclothes known as beizhong xianglu 被中香爐 (literally “perfume burner [to be placed] among the covers [of the bed], used as a warming device), which are chronicled as early as the Han dynasty in texts such as Xijing zaji 西京雜記 (Miscellaneous Records of the Western Capital). The silver spherical incense burner spread from China to the Islamic world and Venice, possibly influencing the development of the gyroscope for maritime navigation in Europe. This paper further examines the spherical incense burner’s relation to a device known as the Cardan Suspension (used to facilitate seafaring) and to the ritual of incense burning (imagined as a way to figuratively reach another world). It also discusses the spherical incense burner’s impact on similar objects from the Islamic world and Venice.
The Reactions to Muslim Identity Building through Social Media: User Comments on YouTube Street Interview Videos
Authors: Abanoz Enes
Published: May 2022

With the expanding digital public sphere, social media have worked as tools that construct and maintain the boundaries and descriptors of social identity among members, by portraying and reinforcing content shared by communities and cultures. Religious leaders and groups have used social media to make themselves more visible and to communicate their faith’s mission. The scope of this study is to use Henri Tajfel’s social identity theory to examine the viewers’ comments on the content created by a religious individual/group for the production, shaping, and reproduction of the Muslim identity through YouTube. We have used Corpus Linguistics (CL) methods to examine relative frequencies and emerging statistical significance of lexical patterns in viewers’ comments. This analysis shows that a strong “we” versus “you” dichotomization exists between the in-group, Muslims, and the out-group, persons who are systematically degraded as being low-informed religious, or non-religious, in street interviews.
The Impact of the Great War on Scottish Christianity
Authors: Roxburgh Kenneth Boyle Emery
Published: May 2022

This paper explores the impact of the Great War (1914–1918) on Christianity in Scotland. This includes the attitudes of various denominations to the war, the rise and fall of religious attendance, and the impact of the war on Sabbatarianism, Sunday schools, church attendance, theology, and social and moral issues in Scottish society.
With Friends Like These: Does American Soft Power Advance International Religious Freedom?
Authors: Zellman Ariel , Fox Jonathan
Published: May 2022

The International Religious Freedom Act, passed by Congress in 1998, set international religious freedom promotion (IRF) as a core objective of American foreign policy. Although formally empowering the president to enact punitive sanctions in instances of extreme religious repression, IRF is primarily a soft power instrument, with the expressed intent to persuade rather than coerce states into greater respect for religious freedom. Nearly a quarter century since its enactment, however, religious discrimination has markedly increased worldwide. This paper therefore seeks to quantitatively evaluate the extent to which American soft power, measured via levels of popular approval for the United States in countries surveyed by various polling agencies from 2002 to 2014, has correlated with shifts in governmental religious discrimination (GRD) since 1998. We find that not only do higher levels of approval of the United States correlate with greater increases in GRD, but this effect is particularly robust in more democratic states, in which American soft power should presumably have a greater influence. These findings should be deeply troubling for IRF advocates, empirically validating prevalent concerns regarding the efficacy, priority, and viability of IRF as a foreign policy instrument.
Early Successful Eye Movement Desensitization and Reprocessing (EMDR) Therapy for Verbal Memory Impairment in an Adjustment Disorder: A Case Report in a Newly-Diagnosed Multiple Sclerosis Patient
Authors: Virgilio Eleonora , Solara Valentina , Sarnelli Maria Francesca , Vecchio Domizia , Comi Cristoforo
Published: May 2022

Multiple sclerosis (MS) is a chronic inflammatory disease of the immune system affecting the central nervous system. Several phenotypes are possible, and cases usually present with a relapsing-remitting (RR) course with disease onset at a young age. MS diagnosis can represent a traumatic event for the patient, possibly evolving into adjustment disorder (AD). AD is defined by the presence of emotional or behavioral symptoms in response to identifiable stress occurring within the prior three months and similarly to post-traumatic stress disorder (PTSD) can significantly affect quality of life. Usually, neuropsychological disorders are not associated with AD. Several treatments are available for AD, and among them, eye movement desensitization and reprocessing (EMDR) is one of the most effective in relieving depression and anxiety. However, little is known about AD and PTSD in the MS population and no data are available on the effectiveness of EMDR for cognitive impairment associated with AD. We describe a 25-year-old patient with RR MS developing an AD with a verbal memory deficit after being diagnosed. Both the psychological and cognitive deficits were diagnosed using an extensive neuropsychological battery. Considering the high impact of the verbal memory deficit, on the patient’s quality of life, an EMDR intervention was planned. After a six-month EMDR intervention performed by two trained neuropsychologists, the patient was retested. There was an improvement in verbal memory tests and depression anxiety scales and the Dissociative Experiences Scale. It is recognized that emotional changes and psychiatric disorders, frequently affect MS patients at diagnosis. It is imperative to recognize this and promptly set a neuropsychological treatment. Moreover, we suggest checking cognition along with depression and anxiety. Finally, to our knowledge, this is the first report of AD with an isolated neuropsychological deficit (verbal memory) developed after the MS diagnosis and treated beneficially with e EMDR. More studies are needed to confirm the efficacy of EMDR in treating cognitive impairment associated with AD in MS patients.
Fine Wine and Gout
Authors: Pritzker Kenneth P. H. , Pritzker Andrea R.
Published: May 2022

From ancient times to the present day, gout has been associated in the popular and scientific literature with wealthy men who overindulge in fancy foods, fine wine, and debauchery. Curiously, amongst diseases, gout was thought to be good, a malady to be accepted because of otherwise beneficial effects on health, and longevity. This narrative review critically examines the history of these associations and explores in detail the pathogenic factors contributing to development of gout prior to the 20th century. While lead toxicity has been previously implicated with wine, the specific association of gout and fine wine can be attributed to lead complexes in products such as sapa, a grape extract used to sweeten wine, in addition to lead nanoparticles leached from crystal glassware and lead glazed dinner plates. The health benefits of gout can be attributed to lead complexes in fine wine and lead nanoparticles from glazed dinnerware. These compounds have excellent antibacterial properties, thereby inhibiting the presence of pathogenic bacteria in foodstuffs. Probing the association of gout and fine wine provides a very well documented example of how the pathogenesis of disease becomes better understood with the passage of time and continuing, persistent scientific enquiry.
A New Class of Counting Distributions Embedded in the Lee–Carter Model for Mortality Projections: A Bayesian Approach
Authors: Awad Yaser , Bar-Lev Shaul K. , Makov Udi
Published: May 2022

The Lee–Carter model, the dominant mortality projection modeling in the literature, was criticized for its homoscedastic error assumption. This was corrected in extensions to the model based on the assumption that the number of deaths follows Poisson or negative binomial distributions. We propose a new class of families of counting distributions, namely, the ABM class, which belongs to a wider class of natural exponential families. This class is characterized by its variance functions and contains the Poisson and the negative binomial distributions as special cases, offering an infinite class of additional counting distributions to be considered. We are guided by the principle that the choice of distribution should be made from a pool of distributions as large as possible. To this end, and following a data mining approach, a training set of historical mortality data of the population could be modeled using the ABM’s rich choice of distributions, and the chosen distribution should be the one that proved to offer superior projection results on a test set of mortality data. As an alternative to parameter estimation via the singular value decomposition used in the classical Lee–Carter model, we adopted Bayesian estimation, harnessing the Markov Chain Monte Carlo methodology. A numerical study demonstrates that when fitting mortality data using this new class of distributions, while traditional distributions may provide desirable projections for some populations, for others, alternative distributions within the ABM class can potentially produce superior results for the entire population or particular age groups, such as the oldest-old.
Ozone Profiles, Precursors, and Vertical Distribution in Urban Lhasa, Tibetan Plateau
Authors: Yu Jiayan , Meng Lingshuo , Chen Yang , Zhang Huifang , Liu Jianguo
Published: May 2022

Near-surface ozone is one of the significant issues in the troposphere. Recently, ozone pollution in Lhasa at an altitude of 3600 m has caused attention. The current knowledge of ozone formation in Lhasa city is still minimal. In this work, the profile of VOCs during early summer was investigated, and alkanes were the most critical group of VOCs. The urban areas of Lhasa are under transition conditions and controlled by both VOCs and NOx. Moreover, the most effective way to decrease ozone formation is to reduce the emissions of anthropogenic VOCs and NOx. The vertical distribution of tropospheric ozone was investigated using differential absorption lidar (DIAL). The results show that ozone concentrations decreased with the elevation of altitudes over Lhasa. The vertical distribution showed clear diurnal trends and that a high ozone concentration appeared at night because of the afternoon’s accumulated O3 generated by photochemical reactions. Ozone in Lhasa is mainly distributed between 0.4 km and 0.6 km. Local generation, overnight accumulation, and NOx titration were identified as three ozone distribution modes. This work helps to understand ozone formation and distribution in the Tibetan Plateau.
Micro-Doppler Parameters Extraction of Precession Cone-Shaped Targets Based on Rotating Antenna
Authors: Wang Zhihao , Luo Ying , Li Kaiming , Yuan Hang , Zhang Qun
Published: May 2022

Micro-Doppler is regarded as a unique signature of a target with micro-motions. The sophisticated recognition of the cone-shaped targets can be realized through the micro-Doppler effect. However, it is difficult to extract the micro-motion features perpendicular to the radar line of sight (LOS) effectively. In this paper, a micro-Doppler parameters extraction method of the cone-shaped targets is put forward based on the rotating antenna. First, a new radar configuration is proposed, in which an antenna rotates uniformly on a fixed circle, thus producing Doppler frequency shift. Second, the expression of the micro-Doppler frequency shift induced by the precession cone-shaped target is derived. Then, the micro-Doppler curves of point scatterers at the cone top and bottom are separated by the smoothness of the curves, and the empirical mode decomposition (EMD) method is utilized for the detection and estimation of the coning frequency. Finally, the micro-motion components perpendicular to the radar LOS are inverted by the peak of micro-Doppler frequency curve. Simulation results prove the effectiveness and robustness of the proposed method.
A Conventional Cruise and Felled-Tree Validation of Individual Tree Diameter, Height and Volume Derived from Airborne Laser Scanning Data of a Loblolly Pine (P. taeda) Stand in Eastern Texas
Authors: Corrao Mark V. , Sparks Aaron M. , Smith Alistair M. S.
Published: May 2022

Globally, remotely sensed data and, in particular, Airborne Laser Scanning (ALS), are being assessed by the forestry industry for their ability to acquire accurate forest inventories at an individual-tree level. This pilot study compares an inventory derived using the ForestView® biometrics analysis system to traditional cruise measurements and felled tree measurements for 139 Pinus taeda sp. (loblolly pine) trees in eastern Texas. The Individual Tree Detection (ITD) accuracy of ForestView® was 97.1%. In terms of tree height accuracy, ForestView® results had an overall lower mean bias and RMSE than the traditional cruise techniques when both datasets were compared to the felled tree data (LiDAR: mean bias = 1.1 cm, RMSE = 41.2 cm; Cruise: mean bias = 13.8 cm, RMSE = 57.5 cm). No significant difference in mean tree height was observed between the felled tree, cruise, and LiDAR measurements (p-value = 0.58). ForestView-derived DBH exhibited a −2.1 cm bias compared to felled-tree measurements. This study demonstrates the utility of this newly emerging ITD software as an approach to characterize forest structure on similar coniferous forests landscapes.
Water Deficit May Cause Vegetation Browning in Central Asia
Authors: Hao Haichao , Chen Yaning , Xu Jianhua , Li Zhi , Li Yupeng , Kayumba Patient Mindje
Published: May 2022

There is consistent evidence of vegetation greening in Central Asia over the past four decades. However, in the early 1990s, the greening temporarily stagnated and even for a time reversed. In this study, we evaluate changes in the normalized difference vegetation index (NDVI) based on the long-term satellite-derived remote sensing data systems of the Global Inventory Modelling and Mapping Studies (GIMMS) NDVI from 1981 to 2013 and MODIS NDVI from 2000 to 2020 to determine whether the vegetation in Central Asia has browned. Our findings indicate that the seasonal sequence of NDVI is summer > spring > autumn > winter, and the spatial distribution pattern is a semicircular distribution, with the Aral Sea Basin as its core and an upward tendency from inside to outside. Around the mid-1990s, the region’s vegetation experienced two climatic environments with opposing trends (cold and wet; dry and hot). Prior to 1994, NDVI increased substantially throughout the growth phase (April–October), but this trend reversed after 1994, when vegetation began to brown. Our findings suggest that changes in vegetation NDVI are linked to climate change induced by increased CO2. The state of water deficit caused by temperature changes is a major cause of the browning turning point across the study area. At the same time, changes in vegetation NDVI were consistent with changes in drought degree (PDSI). This research is relevant for monitoring vegetation NDVI and carbon neutralization in Central Asian ecosystems.
Spectral Estimation of In Vivo Wheat Chlorophyll a/b Ratio under Contrasting Water Availabilities
Authors: Mulero Gabriel , Bacher Harel , Kleiner Uri , Peleg Zvi , Herrmann Ittai
Published: May 2022

To meet the ever-growing global population necessities, integrating climate-change-relevant plant traits into breeding programs is required. Developing new tools for fast and accurate estimation of chlorophyll parameters, chlorophyll a (Chl-a) content, chlorophyll b (Chl-b) content, and their ratio (Chl-a/b), can promote breeding programs of wheat with enhanced climate adaptability. Spectral reflectance of leaves is affected by changes in pigment concentration and can be used to estimate chlorophyll parameters. The current study identified and validated the top known spectral indices and developed new vegetation indices (VIs) for Chl-a and Chl-b content estimation and used them to non-destructively estimate Chl-a/b values and compare them to hyperspectral estimations. Three wild emmer introgression lines, with contrasting drought stress responsiveness dynamics, were selected. Well-watered and water-limited irrigation regimes were applied. The wheat leaves were spectrally measured with a handheld spectrometer to acquire their reflectance in the 330 to 790 nm range. Regression models based on calculated VIs as well as all hyperspectral curves were calibrated and validated against chlorophyll extracted values. The developed normalized difference spectral indices (NDSIs) resulted in high accuracy of Chl-a (NDSI415,614) and Chl-b (NDSI406,525) estimation, allowing for indirect non-destructive estimation of Chl-a/b with root mean square error (RMSE) values that could fit 6 to 10 times in the range of the measured values. They also performed similarly to the hyperspectral models. Altogether, we present here a new tool for a non-destructive estimation of Chl-a/b, which can serve as a basis for future breeding efforts of climate-resilient wheat as well as other crops.
Synchronization, Decoupling, and Regime Shift of Urban Thermal Conditions in Xi’an, an Ancient City in China under Rapid Expansion
Authors: Guo Rui , Liu Shuguang , Shi Yi , Zhao Shuqing , Yuan Wenping , Li Yuanyuan , Wu Yiping
Published: May 2022

Urbanization has profound impacts on economic development and environmental quality. Some of the serious consequences of urbanization are the changes in the thermal environment, which directly affect the greater environment and quality of life. Although many studies have been performed on urban heat islands, few have specifically examined the thermal evolution of rapidly expanding ancient cities and the impacts of urbanization on the thermal environments of important heritage sites. In this study, we analyzed the temporal and spatial patterns of the thermal environment quantified as the surface urban heat island (SUHI) and land surface temperature (LST) values from 2000 to 2018 in Xi’an, an ancient city with rich cultural heritage in China. Specifically, we analyzed the temporal evolution of the thermal environments of the functional zones and heritage sites and explore their coupling relationships with the overall temperature of the study area using a statistical analysis approach. Furthermore, we revealed time-sensitive changes in temperature regimes using the newly proposed double temperature curve approach (DTCA). The results showed that the heat island phenomenon has been intensifying in Xi’an, as evidenced by the summer daytime mean SUHI values being greater than 7 °C continuously since 2010 and the increased frequency of high-intensity SUHI effects. Extreme heat conditions were more frequent in the old urban area (built-up and in existence before 2000) than in the new urban area, while SUHI values in the new area deteriorated more rapidly. The changes in temperature in the functional zones were strongly synchronized with the overall temperature changes in Xi’an, and the temperature differences increased linearly with the overall temperature. The LST values in the four major historical heritage sites investigated in this study were 2–8 °C higher than the background temperature and were decoupled from background temperature changes. From the DTCA, we found the time periods of the thermal environment regime changes for each functional zone or heritage site, which were largely the result of policy guidance. Regional synchronization, site decoupling, and regime shifts in LST suggest opportunities for regional planning and urban landscape optimization to reduce adverse effects of urbanization on the urban environment, particularly in cities with rich historical heritage sites.
Inversion of Ocean Subsurface Temperature and Salinity Fields Based on Spatio-Temporal Correlation
Authors: Song Tao , Wei Wei , Meng Fan , Wang Jiarong , Han Runsheng , Xu Danya
Published: May 2022

Ocean observation is essential for studying ocean dynamics, climate change, and carbon cycles. Due to the difficulty and high cost of in situ observations, existing ocean observations are inadequate, and satellite observations are mostly surface observations. Previous work has not adequately considered the spatio-temporal correlation within the ocean itself. This paper proposes a new method—convolutional long short-term memory network (ConvLSTM)—for the inversion of the ocean subsurface temperature and salinity fields with the sea surface satellite observations (sea surface temperature, sea surface salinity, sea surface height, and sea surface wind) and subsurface Argo reanalyze data. Given the time dependence and spatial correlation of the ocean dynamic parameters, the ConvLSTM model can improve inversion models’ robustness and generalizability by considering ocean variability’s significant spatial and temporal correlation characteristics. Taking the 2018 results as an example, our average inversion results in an overall normalized root mean square error (NRMSE) of 0.0568 °C/0.0027 PSS and a correlation coefficient (R) of 0.9819/0.9997 for subsurface temperature (ST)/subsurface salinity (SS). The results show that SSTA, SSSA SSHA, and SSWA together are valuable parameters for obtaining accurate ST/SS estimates, and the use of multiple channels in shallow seas is effective. This study demonstrates that ConvLSTM is superior in modeling the subsurface temperature and salinity fields, fully taking global ocean data’s spatial and temporal correlation into account, and outperforms the classic random forest and LSTM approaches in predicting subsurface temperature and salinity fields.
Ultra-Low Sidelobe Waveforms Design for LPI Radar Based on Joint Complementary Phase-Coding and Optimized Discrete Frequency-Coding
Authors: Song Yuxiao , Wang Yu , Xie Jingyang , Yang Yiming , Tian Biao , Xu Shiyou
Published: May 2022

In this paper, in order to reduce the probability of the radar waveform intercepted by the passive detection system, the time-bandwidth product of the radar waveform is increased, and the detection probability of the radar waveform to the target is improved. This paper tackles the holographic RF stealth radar and proposes a joint coding waveform based on the linear frequency modulation (LFM) waveform. Joint coding uses complementary codes to perform phase-coding, and combines the codewords optimized by genetic algorithm in order to perform discrete frequency-coding waveform. The joint coding waveform model is theoretically analyzed, and the ambiguity function, pulse compression and target detection probability of the joint coding waveform are obtained by numerical simulation. In addition, the complexity of the algorithm and the low probability of intercept (LPI) characteristic of the joint coding waveform are analyzed. The results show that the joint coding waveform has an approximate “pushpin” ambiguity function, ultra-low sidelobe characteristics, better RF stealth and target detection performance. Finally, it has good application prospects in the current battlefield environment.
Forest Habitat Fragmentation in Mountain Protected Areas Using Historical Corona KH-9 and Sentinel-2 Satellite Imagery
Authors: Olariu Bogdan , Vîrghileanu Marina , Mihai Bogdan-Andrei , Săvulescu Ionuț , Toma Liviu , Săvulescu Maria-Gianina
Published: May 2022

Forest habitat fragmentation is one of the global environmental issues of concern as a result of forest management practices and socioeconomic drivers. In this context, a constant evaluation of natural habitat conditions still remains a challenge in order to achieve a general image of the environmental state of a protected area for proper sustainable management. The purpose of our study was to evaluate the evolution of forest habitat in the last 40 years, focusing on Bucegi Natural Park, one of the most frequented protected areas in Romania, as relevant for highly human-impacted areas. Our approach integrates a historical panchromatic Corona KH-9 image from 1977 and present-day Sentinel-2 multispectral data from 2020 in order to calculate a series of spatial metrics that reveal changes in the pattern of the forest habitat and illustrate forest habitat fragmentation density. Object-based oriented analysis with supervised maximum likelihood classification was employed for the production of forest cover fragmentation maps. Ten landscape metrics were adapted to the analysis context, from patch statistics to proximity index. The results show a general growth of the forest surface but also an increase in habitat fragmentation in areas where tourism was developed. Fragmentation indices explain that larger and compact patches feature natural park protected forests after the spruce–fir secondary canopies were grown during the last 4–5 decades. The number of patches decreased to half, and their average size is double that of before. The method can be of extensive use for environmental monitoring in protected areas management and for understanding the environmental history connected to present-day problems that are to be fixed under rising human pressure.
Comparison of the Effects of Regional and Global Dust Storms on the Composition of the Ionized Species of the Martian Upper Atmosphere Using MAVEN
Authors: Farahat Ashraf , Withers Paul , Mayyasi Majd , Dayeh Maher A.
Published: May 2022

The densities of three ion species in the Martian upper atmosphere were compared during the MY33 and MY34 Martian regional and global dust storms (RDS 2016 and GDS 2018, respectively) using data from the neutral gas and ion mass spectrometer of the Mars atmosphere and volatile evolution mission. The trends of the ion species and their relative abundances in altitudes compared to some neutral species were examined from 10 September–4 October 2016 and 27 May–18 June 2018, at altitudes of 160–240 km. Both RDS 2016 and GDS 2018 caused variations in the ion species abundance of the upper atmosphere at their onsets in 18–21 September 2016 and 5–8 June 2018 respectively. The densities of O2+, CO2+, and O+ increased during RDS 2016. Meanwhile, O2+ and O+ densities decreased and CO2+ density increased during GDS 2018. Ion species’ relative abundances indicate that during RDS 2016, the increase in O2+ density may be caused by the increase of CO2+ or O+ densities rather than the increase of O or CO2 densities. Meanwhile, the decrease in O2+ density during GDS 2018 may be caused by the decrease of O or O+ densities rather than the decrease in CO2+ or CO2 densities.
UAVSwarm Dataset: An Unmanned Aerial Vehicle Swarm Dataset for Multiple Object Tracking
Authors: Wang Chuanyun , Su Yang , Wang Jingjing , Wang Tian , Gao Qian
Published: May 2022

In recent years, with the rapid development of unmanned aerial vehicles (UAV) technology and swarm intelligence technology, hundreds of small-scale and low-cost UAV constitute swarms carry out complex combat tasks in the form of ad hoc networks, which brings great threats and challenges to low-altitude airspace defense. Security requirements for low-altitude airspace defense, using visual detection technology to detect and track incoming UAV swarms, is the premise of anti-UAV strategy. Therefore, this study first collected many UAV swarm videos and manually annotated a dataset named UAVSwarm dataset for UAV swarm detection and tracking; thirteen different scenes and more than nineteen types of UAV were recorded, including 12,598 annotated images—the number of UAV in each sequence is 3 to 23. Then, two advanced depth detection models are used as strong benchmarks, namely Faster R-CNN and YOLOX. Finally, two state-of-the-art multi-object tracking (MOT) models, GNMOT and ByteTrack, are used to conduct comprehensive tests and performance verification on the dataset and evaluation metrics. The experimental results show that the dataset has good availability, consistency, and universality. The UAVSwarm dataset can be widely used in training and testing of various UAV detection tasks and UAV swarm MOT tasks.
Quantifying Water Consumption through the Satellite Estimation of Land Use/Land Cover and Groundwater Storage Changes in a Hyper-Arid Region of Egypt
Authors: Halipu Ayihumaier , Wang Xuechen , Iwasaki Erina , Yang Wei , Kondoh Akihiko
Published: May 2022

One of the areas that show the most visible effects of human-induced land alterations is also the world’s most essential resource: water. Decision-makers in arid regions face considerable difficulties in providing and maintaining sustainable water resource management. However, developing appropriate and straightforward approaches for quantifying water use in arid/hyper-arid regions is still a formidable challenge. Meanwhile, a better knowledge of the effects of land use land cover (LULC) changes on natural resources and environmental systems is required. The purpose of this study was to quantify the water consumption in a hyper-arid region (New Valley, Egypt) using two different approaches—LULC based on optical remote sensing data and groundwater storage changes based on Gravity Recovery Climate Experiment (GRACE) satellite data—and to compare and contrast the quantitative results of the two approaches. The LULC of the study area was constructed from 1986 to 2021 to identify the land cover changes and investigate the primary water consumption patterns. The analysis of groundwater storage changes utilized two GRACE mascon solutions from 2002 to 2021 in New Valley. The results showed an increase in agricultural areas in New Valley’s oases. They also showed an increased in irrigation water usage and a continuous decrease in the groundwater storage of New Valley. The overall water usage in New Valley for domestic and irrigation was calculated as 18.62 km3 (0.93 km3/yr) based on the LULC estimates. Moreover, the groundwater storage changes of New Valley were extracted using GRACE and calculated to be 19.36 ± 7.96 km3 (0.97 ± 0.39 km3/yr). The results indicated that the water use calculated from LULC was consistent with the depletion in groundwater storage calculated by applying GRACE. This study provides an essential reference for regional sustainability and water resource management in arid/hyper-arid regions.
Adversarial Representation Learning for Hyperspectral Image Classification with Small-Sized Labeled Set
Authors: Zhang Shuhan , Zhang Xiaohua , Li Tianrui , Meng Hongyun , Cao Xianghai , Wang Li
Published: May 2022

Hyperspectral image (HSI) classification is one of the main research contents of hyperspectral technology. Existing HSI classification algorithms that are based on deep learning use a large number of labeled samples to train models to ensure excellent classification effects, but when the labeled samples are insufficient, the deep learning model is prone to overfitting. In practice, there are a large number of unlabeled samples that have not been effectively utilized, so it is meaningful to study a semi-supervised method. In this paper, an adversarial representation learning that is based on a generative adversarial networks (ARL-GAN) method is proposed to solve the small samples problem in hyperspectral image classification by applying GAN to the representation learning domain in a semi-supervised manner. The proposed method has the following distinctive advantages. First, we build a hyperspectral image block generator whose input is the feature vector that is extracted from the encoder and use the encoder as a feature extractor to extract more discriminant information. Second, the distance of the class probability output by the discriminator is used to measure the error between the generated image block and the real image instead of the root mean square error (MSE), so that the encoder can extract more useful information for classification. Third, GAN and conditional entropy are used to improve the utilization of unlabeled data and solve the small sample problem in hyperspectral image classification. Experiments on three public datasets show that the method achieved better classification accuracy with a small number of labeled samples compared to other state-of-the-art methods.
Shadow Removal from UAV Images Based on Color and Texture Equalization Compensation of Local Homogeneous Regions
Authors: Liu Xiaoxia , Yang Fengbao , Wei Hong , Gao Min
Published: May 2022

Due to imaging and lighting directions, shadows are inevitably formed in unmanned aerial vehicle (UAV) images. This causes shadowed regions with missed and occluded information, such as color and texture details. Shadow detection and compensation from remote sensing images is essential for recovering the missed information contained in these images. Current methods are mainly aimed at processing shadows with simple scenes. For UAV remote sensing images with a complex background and multiple shadows, problems inevitably occur, such as color distortion or texture information loss in the shadow compensation result. In this paper, we propose a novel shadow removal algorithm from UAV remote sensing images based on color and texture equalization compensation of local homogeneous regions. Firstly, the UAV imagery is split into blocks by selecting the size of the sliding window. The shadow was enhanced by a new shadow detection index (SDI) and threshold segmentation was applied to obtain the shadow mask. Then, the homogeneous regions are extracted with LiDAR intensity and elevation information. Finally, the information of the non-shadow objects of the homogeneous regions is used to restore the missed information in the shadow objects of the regions. The results revealed that the average overall accuracy of shadow detection is 98.23% and the average F1 score is 95.84%. The average color difference is 1.891, the average shadow standard deviation index is 15.419, and the average gradient similarity is 0.726. The results have shown that the proposed method performs well in both subjective and objective evaluations.
Dormant Season Vegetation Phenology and Eddy Fluxes in Native Tallgrass Prairies of the U.S. Southern Plains
Authors: Wagle Pradeep , Kakani Vijaya G. , Gowda Prasanna H. , Xiao Xiangming , Northup Brian K. , Neel James P. S. , Starks Patrick J. , Steiner Jean L. , Gunter Stacey A.
Published: May 2022

Carbon dioxide (CO2) fluxes and evapotranspiration (ET) during the non-growing season can contribute significantly to the annual carbon and water budgets of agroecosystems. Comparative studies of vegetation phenology and the dynamics of CO2 fluxes and ET during the dormant season of native tallgrass prairies from different landscape positions under the same climatic regime are scarce. Thus, this study compared the dynamics of satellite-derived vegetation phenology (as captured by the enhanced vegetation index (EVI) and the normalized difference vegetation index (NDVI)) and eddy covariance (EC)-measured CO2 fluxes and ET in six differently managed native tallgrass prairie pastures during dormant seasons (November through March). During December–February, vegetation phenology (EVI and NDVI) and the dynamics of eddy fluxes were comparable across all pastures in most years. Large discrepancies in fluxes were observed during March (the time of the initiation of growth of dominant warm-season grasses) across years and pastures due to the influence of weather conditions and management practices. The results illustrated the interactive effects between prescribed spring burns and rainfall on vegetation phenology (i.e., positive and negative impacts of prescribed spring burns under non-drought and drought conditions, respectively). The EVI better tracked the phenology of tallgrass prairie during the dormant season than did NDVI. Similar EVI and NDVI values for the periods when flux magnitudes were different among pastures and years, most likely due to the satellite sensors’ inability to fully observe the presence of some cool-season C3 species under residues, necessitated a multi-level validation approach of using ground-truth observations of species composition, EC measurements, PhenoCam (digital) images, and finer-resolution satellite data to further validate the vegetation phenology derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) during dormant seasons. This study provides novel insights into the dynamics of vegetation phenology, CO2 fluxes, and ET of tallgrass prairie during the dormant season in the U.S. Southern Great Plains.
Evolution of Realistic Organic Mixtures for the Origins of Life through Wet–Dry Cycling
Authors: Foster Kiernan , Hillman Brooke , Rajaei Vahab , Seng Kimsorn , Maurer Sarah
Published: May 2022

One of the challenges in understanding chemical evolution is the large number of starting organics and environments that were plausible on early Earth. Starting with realistic organic mixtures and using chemical analyses that are not biologically biased, understanding the interplay between organic composition and environment can be approached using statistical analysis. In this work, a mixture of 73 organics was cycled through dehydrating conditions five times, considering environmental parameters of pH, salinity, and rehydration solution. Products were analyzed by HPLC, amide and ester assays, and phosphatase and esterase assays. While all environmental factors were found to influence chemical evolution, salinity was found to play a large role in the evolution of these mixtures, with samples diverging at very high sea salt concentrations. This framework should be expanded and formalized to improve our understanding of abiogenesis.