bluuebunny/crossref_metadata_2025 huggingface dataset:
Investigation of Thermal Radiation from Soot Particles and Gases in Oxy-Combustion Counter-Flow Flames
Authors: Wang Chaoyang , Tang Guangtong , Yan Huibo , Li Lujiang , Yan Xiaopei , Li Zhicong , Lou Chun
Published: October 2021

Oxy-combustion with high flame temperature, low heat loss, high combustion efficiency, and low NOx emissions is being extensively studied. The thermal radiation from soot particles and gases in oxy-combustion accounts for the vast majority of the total heat transfer. Based on a detailed chemical reaction mechanism coupled with the soot particle dynamics model and optically thin radiation model, the influence of the flame structure and temperature distribution on the thermal radiation in oxygen-enriched counterflow diffusion flames was studied in this paper. The results revealed that reasonable assignment of total recycled flue gas and the degree of dilution of fuel and oxidant were critical, which can be used to adjust the overall radiation situation of the flame. At the same adiabatic flame temperature, as the fuel concentration decreased and the oxidant concentration increased (the stoichiometric mixture ratio is from 0.3 to 0.6), the soot formation decreased, which led to the particle radiation disappearing while the main radiation zone of gases moved 0.04 cm toward the fuel side. At the same stoichiometric mixture fraction (0.4), the radiation area was broadened and the radiation of soot particles was gradually enhanced with the adiabatic flame increasing from 2300 K to 2700 K.
Feature Selection and Uncertainty Analysis for Bubbling Fluidized Bed Oxy-Fuel Combustion Data
Authors: Marzova Katerina , Bukovsky Ivo
Published: October 2021

This paper presents a novel feature extraction and validation technique for data-driven prediction of oxy-fuel combustion emissions in a bubbling fluidized bed experimental facility. The experimental data were analyzed and preprocessed to minimize the size of the data set while preserving patterns and variance and to find an optimal configuration of the feature vector. The Boruta Feature Selection Algorithm (BFSA) finds feature vector’s configuration and the Multiscale False Neighbours Analysis (MSFNA) is newly extended and proposed to validate the BFSA’s design for emission prediction to assure minimal uncertainty in mapping between feature vectors and corresponding outputs. The finding is that the standalone BFSA does not reflect various sampling period setups that appeared significantly influencing the false neighborhood in the design of feature vectors for possible emission prediction, and MSFNA resolves that.
Attention-Based STL-BiLSTM Network to Forecast Tourist Arrival
Authors: Adil Mohd , Wu Jei-Zheng , Chakrabortty Ripon K. , Alahmadi Ahmad , Ansari Mohd Faizan , Ryan Michael J.
Published: October 2021

Tourism makes a significant contribution to the economy of almost every country, so accurate demand forecasting can help in better planning for the government and a range of stakeholders involved in the tourism industry and can aid economic sustainability. Machine learning models, and in particular, deep neural networks, can perform better than traditional forecasting models which depend mainly on past observations (e.g., past data) to forecast future tourist arrivals. However, search intensities indices (SII) indicators have recently been included as a forecasting model, which significantly enhances forecasting accuracy. In this study, we propose a bidirectional long short-term memory (BiLSTM) neural network to forecast the arrival of tourists along with SII indicators. The proposed BiLSTM network can remember information from left to right and right to left, which further adds more context for forecasting in memory as compared to a simple long short- term memory (LSTM) network that can remember information only from left to right. A seasonal and trend decomposition using the Loess (STL) approach is utilized to decompose time series tourist arrival data suggested by previous studies. The resultant approach, called STL-BiLSTM, decomposes time series into trend, seasonality, and residual. The trend provides the general direction of the overall data. Seasonality is a regular and predictable pattern which re-occurs at fixed time intervals, and residual is a random fluctuation that is something which cannot be forecast. The proposed BiLSTM network achieves better accuracy than the other methods considered under the current study.
Set Membership Estimation with Dynamic Flux Balance Models
Authors: Shen Xin , Budman Hector
Published: October 2021

Dynamic flux balance models (DFBM) are used in this study to infer metabolite concentrations that are difficult to measure online. The concentrations are estimated based on few available measurements. To account for uncertainty in initial conditions the DFBM is converted into a variable structure system based on a multiparametric linear programming (mpLP) where different regions of the state space are described by correspondingly different state space models. Using this variable structure system, a special set membership-based estimation approach is proposed to estimate unmeasured concentrations from few available measurements. For unobservable concentrations, upper and lower bounds are estimated. The proposed set membership estimation was applied to batch fermentation of E. coli based on DFBM.
Extraction of Chlorobenzenes and PCBs from Water by ZnO Nanoparticles
Authors: Zhang Yuntao , Chen Ran , Riviere Jim E. , Comer Jeffrey
Published: October 2021

Metal oxide nanoparticles have great potential for selective adsorption and catalytic degradation of contaminants from aqueous solutions. In this study, we employ mass spectrometry and molecular dynamics simulations to better understand the chemical and physical mechanisms determining the affinity of chlorobenzenes and polychlorinated biphenyls (PCBs) for zinc oxide nanoparticles (ZnO NPs). The experiments and simulations both demonstrate that the adsorption coefficients for chlorobenzenes increase steadily with the number of chlorine atoms, while, for PCBs, the relation is more complex. The simulations link this complexity to chlorine atoms at ortho positions hindering coplanar conformations. For a given number of chlorine atoms, the simulations predict decreasing adsorption affinity with increasing numbers of ortho substitutions. Consequently, the simulations predict that some of the highest adsorption affinities for ZnO NPs are exhibited by dioxin-like PCBs, suggesting the possibility of selective sequestration of these most acutely toxic PCBs. Remarkably, the experiments show that the PCB adsorption coefficients of ZnO NPs with diameters ≤ 80 nm exceed those of a soil sample by 5–7 orders of magnitude, meaning that a single gram of ZnO NPs could sequester low levels of PCB contamination from as much as a ton of soil.
Effective Analysis of Different Gas Diffusers on Bubble Hydrodynamics in Bubble Column and Airlift Reactors towards Mass Transfer Enhancement
Authors: Ham Phaly , Bun Saret , Painmanakul Pisut , Wongwailikhit Kritchart
Published: October 2021

Even bubble column reactors (BCR) and airlift reactors (ALR) have been developed in terms of various related aspects towards mass transfer enhancement, the effective analysis of gas diffuser types on mass transfer and gas–liquid hydrodynamic characteristics is still limited. Therefore, the present study aims to analyze the relative effect of different types of air diffusers on bubble hydrodynamics and mass transfer performance to understand their behaviors and define the best type. The experiments were conducted by varying different diffuser types, reactor types (BCR and ALR), and superficial gas velocity (Vg) (0.12 to 1.00 cm/s). Five air diffusers including commercial fine sand (F-sand) and coarse sand (C-sand) diffusers, and acrylic perforated diffusers with orifice sizes of 0.3 mm (H-0.3), 0.6 mm (H-0.6), and 1.2 mm (H-1.2), were used in this study. For every condition, it was analyzed in terms of bubble hydrodynamics and oxygen mass transfer coefficient (KLa). Lastly, the selected diffusers that provided the highest KLa coefficient were evaluated with a solid media addition case. The results of both reactor classes showed that F-sand, the smallest orifice diffuser, showed the smallest air bubbles (3.14–4.90 mm) compared to other diffusers, followed by C-sand, which larger about 22–28% on average than F-sand. ALR exhibited a better ability to maintain smaller bubbles than BCR. Moreover, F-sand and C-sand diffusers showed a slower rising velocity through their smaller bubbles and the tiny bubble recirculation in ALR. Using F-sand in ALR, the rising velocity is about 1.60–2.58 dm/s, which is slower than that in BCR about 39–54%. F-sand and C-sand were also found as the significant diffusers in terms of interfacial area and gas hold-up. Then, the KLa coefficient was estimated in every diffuser and reactor under the varying of Vg. Up to 270% higher KLa value was achieved from the use of F-sand and C-sand compared to other types due to their smaller bubbles generated/maintained and longer bubble retention time through slower rising velocity. After adding 10% ring shape plastic media into the reactors with F-sand and C-sand diffusers, a better performance was achieved in terms of KLa coefficient (up to 39%) as well as gas hold-up and liquid mixing. Lastly, ALR also had a larger portion of mixed flow pattern than BCR. This eventually promoted mass transfer by enhancing the mixed flow regime.
Evaluation of Hammermill Tip Speed, Air Assist, and Screen Hole Diameter on Ground Corn Characteristics
Authors: Braun Michaela , Wecker Haley , Dunmire Kara , Evans Caitlin , Sodak Michael W. , Kapetanovich Maks , Shepherd Jerry , Fisher Randy , Coble Kyle , Stark Charles , Paulk Chad
Published: October 2021

This study was performed to evaluate hammermill tip speed, assistive airflow, and screen hole diameter on hammermill throughput and characteristics of ground corn. Corn was ground using two Andritz hammermills measuring 1 m in diameter each equipped with 72 hammers and 300 HP motors. Treatments were arranged in a 3 × 3 × 3 factorial design with three tip speeds (3774, 4975, and 6176 m/min), three screen hole diameters (2.3, 3.9, and 6.3 mm), and three air flow rates (1062, 1416, and 1770 fan revolutions per minute). Corn was ground on three separate days to create three replications and treatments were randomized within day. Samples were collected and analyzed for moisture, particle size, and flowability characteristics. There was a 3-way interaction (p = 0.029) for standard deviation (Sgw). There was a screen hole diameter × hammer tip speed interaction (p < 0.001) for geometric mean particle size dgw (p < 0.001) and composite flow index (CFI) (p < 0.001). When tip speed increased from 3774 to 6176 m/min, the rate of decrease in dgw was greater as screen hole diameter increased from 2.3 to 6.3 mm. For CFI, increasing tip speed decreased the CFI of ground corn when ground using the 3.9 and 6.3 mm screen. However, when grinding corn using the 2.3 mm screen, there was no evidence of difference in CFI when increasing tip speed. In conclusion, the air flow rate did not influence dgw of corn, but hammer tip speed and screen size were altered and achieved a range of dgw from 304 to 617 µm.
Evaluation of Pb, Mg, Al, Zn, and Cu as Electrode Materials in the Electrocoagulation of Microalgae
Authors: Phiri Jesse T. , Pak Hun , We Junhyung , Oh Sanghwa
Published: October 2021

Apart from the conventionally used iron (Fe) and aluminum (Al) electrodes in microalgae harvesting, experiments were designed to examine the viability of lead (Pb), magnesium (Mg), zinc (Zn), and copper (Cu) as electrode materials in the harvesting of microalgae. The effect of voltage on the flocculation efficiency (FE) of the electrode materials was examined and compared. By dividing the optimal FE values by their corresponding periods, a simple yet practical approach was used to rank the electrode materials. From highest to lowest flocculation efficiency, the results were as follows: Cu, Zn, Mg, Al, and Pb at 10 V; Mg, Zn, Cu, Al, and Pb at 20 V; and Mg, Zn, Al, Cu, and Pb at 30 V. Important factors such as temperature, periodic FE, consumption of electrode material, pH, and metallic concentrations in the effluent were evaluated. The temperature increase proposed to have been affected by electric resistance drop and anodic corrosion, between 1.7 °C and 3.3 °C, 5 °C and 8.9 °C, and 10.5 °C and 18.4 °C was recorded at 10 V, 20 V, and 30 V respectively. Except for the supernatants of the experiments from Al electrodes, which remained relatively unaffected by voltage and time, the pH of all the other supernatants increased with time and voltage. The effluents recorded metallic concentrations between 0.513 mg/L and 43.8 mg/L for Pb, 7.02 mg/L and 20.5 mg/L for Mg, 1.34 mg/L and 9.09 mg/L for Al, 0.079 mg/L and 0.089 mg/L for Zn, and 0.252 mg/L and 0.434 mg/L for Cu electrodes.
Diagnostics of a Domestic Hot Water Storage Tank under Operating Conditions
Authors: Obstawski Paweł , Janaszek-Mańkowska Monika , Ratajski Arkadiusz
Published: October 2021

This paper presents a new method for the diagnostics of a hot water storage tank under operating conditions. Depending on the operating point of the tank, the method enables determination of thermal conductivity coefficients of the coil heat exchanger, which allows us to determine the intensity of heat transfer between the transfer medium and water in the tank as well as of tank walls, which consequently enables determination of heat losses to the environment. Furthermore, the dynamic properties of the tank may also be determined by applying this method. The advantage of this method is possibility of analyzing changes in the material constants of the coil heat exchanger, tank walls, and dynamic properties of the tank as a function of mass flow of the medium supplying the coil heat exchanger. The possibility of determining coefficients of thermal conductivity as well as the inertia of tank and exchanger, based on temperature measurements acquired in operating conditions is a novelty in this paper. Knowing the variability of material constants and of dynamic properties of the tank as a function of medium flow rate allows multicriteria optimization to be performed which, with a conventional design of the tank, results in a reduction of up to 10% in the time taken to prepare domestic hot water.
Microwave Radiation Influence on Dairy Waste Anaerobic Digestion in a Multi-Section Hybrid Anaerobic Reactor (M-SHAR)
Authors: Zieliński Marcin , Dębowski Marcin , Kazimierowicz Joanna
Published: October 2021

Whey is a primary by-product of dairy plants, and one that is often difficult to manage. As whey processing units are costly and complicated, only 15–20% of whey is recycled for use in the food industry. The difficulties in managing waste whey are particularly pronounced for small, local dairy plants. One possible solution to this problem is to use advanced and efficient digesters. The aim of this study was to present an innovative multi-section hybrid anaerobic bioreactor (M-SHAR) design and to identify how microwave radiation heating (MRH) affects methane fermentation of liquid dairy waste (LDW) primarily composed of acid whey. The MRH reactor was found to perform better in terms of COD removal and biogas production compared with the convection-heated reactor. The heating method had a significant differentiating effect at higher organic load rates (OLRs). With OLRs ranging from 15 to 25 kgCOD∙m−3∙d−1, the M-SHAR with MRH ensured a 5% higher COD removal efficiency and 12–20% higher biogas yields.
Application of Ultrasonic Atomization in a Combined Circulation System of Spray Evaporative Cooling and Air Cooling for Electric Machines
Authors: Wang Yu , Ruan Lin
Published: October 2021

A combined circulation system of spray evaporative cooling and air cooling (CCSSECAC) is a way to enhance the cooling performance of an air-cooled electric machine while maintaining its existing structure. Based on a traditional air-cooled machine, when the discrete evaporative cooling medium particles are scattered into the airflow, they will reach the heat source with the air circulation. The cooling capacity of the cooling system is enhanced simultaneously through the phase transition and convection heat transfer. Ultrasonic atomization is a simple way to produce tiny droplets and a good way to improve the performance of CCSSECAC. To verify the effectiveness of such a system, a principle test model was built and a multi-operational condition experiment was carried out as an exploratory study. The experimental results showed that the new cooling system was feasible for horizontal machines, and the stator coil temperature was significantly reduced compared with the air-cooled mode.
Research on Structural–Mechanical Properties during the Castor Episperm Breaking Process
Authors: Yang Liu , Chen Huan , Xiao Junyu , Fan Yuchao , Song Shaoyun , Zhang Yonglin , Liu Xiaopeng
Published: October 2021

Products from castor seeds have been widely used in various fields. In order to study the breaking behavior and rupture mechanism of castor seed episperm during coat shelling process, the force-structure property of coating castor seed was investigated by a self-developed texture analyzer with in situ optical microscopic observation. Influences of compression distance, velocity and working temperature were studied. The results showed that castor seed episperm rupture commonly happened from the tail end to the first end. Compression distance effect can change the episperm cracking degree. Under pressing distance 2–3 mm, the episperm easily cracked into two flaps, and the breaking force stabilized at 77 N. Pressing velocity has no significant effect on episperm breaking. Temperature changes the physical property. With an increase in temperature, breaking force presents a “slope” decline; under a temperature of 120 ℃, temperature effect on the breaking force decreased significantly and the breaking force fell to about 52 N. The research results can provide theoretical basis for the castor episperm peeling.
Synthesis and Water Treatment Applications of Nanofibers by Electrospinning
Authors: Agrawal Saumya , Ranjan Rashmi , Lal Bajrang , Rahman Ashiqur , Singh Swatantra , Selvaratnam Thinesh , Nawaz Tabish
Published: October 2021

In the past few decades, the role of nanotechnology has expanded into environmental remediation applications. In this regard, nanofibers have been reported for various applications in water treatment and air filtration. Nanofibers are fibers of polymeric origin with diameters in the nanometer to submicron range. Electrospinning has been the most widely used method to synthesize nanofibers with tunable properties such as high specific surface area, uniform pore size, and controlled hydrophobicity. These properties of nanofibers make them highly sought after as adsorbents, photocatalysts, electrode materials, and membranes. In this review article, a basic description of the electrospinning process is presented. Subsequently, the role of different operating parameters in the electrospinning process and precursor polymeric solution is reviewed with respect to their influence on nanofiber properties. Three key areas of nanofiber application for water treatment (desalination, heavy-metal removal, and contaminant of emerging concern (CEC) remediation) are explored. The latest research in these areas is critically reviewed. Nanofibers have shown promising results in the case of membrane distillation, reverse osmosis, and forward osmosis applications. For heavy-metal removal, nanofibers have been able to remove trace heavy metals due to the convenient incorporation of specific functional groups that show a high affinity for the target heavy metals. In the case of CECs, nanofibers have been utilized not only as adsorbents but also as materials to localize and immobilize the trace contaminants, making further degradation by photocatalytic and electrochemical processes more efficient. The key issues with nanofiber application in water treatment include the lack of studies that explore the role of the background water matrix in impacting the contaminant removal performance, regeneration, and recyclability of nanofibers. Furthermore, the end-of-life disposal of nanofibers needs to be explored. The availability of more such studies will facilitate the adoption of nanofibers for water treatment applications.
Investigating the Solubility and Activity of a Novel Class of Ice Recrystallization Inhibitors
Authors: Ampaw Anna A. , Newell Kayla , Ben Robert N.
Published: October 2021

O-aryl-β-d-glucosides and N-alkyl-d-gluconamides are two classes of effective ice recrystallization inhibitors (IRIs), however their solubilities limit their use in cryopreservation applications. Herein, we have synthesized and assessed phosphonate analogues of small-molecule IRIs as a method to improve their chemical and physical properties. Four sodium phosphonate compounds 4–7 were synthesized and exhibited high solubilities greater than 200 mM. Their IRI activity was evaluated using the splat cooling assay and only the sodium phosphonate derivatives of α-methyl-d-glucoside (5-Na) and N-octyl-d-gluconamide (7-Na) exhibited an IC50 value less than 30 mM. It was found that the addition of a polar sodium phosphonate group to the alkyl gluconamide (1) and aryl glucoside (2) structure decreased its IRI activity, indicating the importance of a delicate hydrophobic/hydrophilic balance within these compounds. The evaluation of various cation-phosphonate pairs was studied and revealed the IRI activity of ammonium and its ability to modulate the IRI activity of its paired anion. A preliminary cytotoxicity study was also performed in a HepG2 cell line and phosphonate analogues were found to have relatively low cytotoxicity. As such, we present phosphonate small-molecule carbohydrates as a biocompatible novel class of IRIs with high solubilities and moderate-to-high IRI activities.
Nonlinear Offset-Free Model Predictive Control based on Dynamic PLS Framework
Authors: Zhao Qiang , Jin Xin , Yu Huapeng , Lu Shan
Published: October 2021

A nonlinear offset-free model predictive control based on a dynamic partial least square (PLS) framework is proposed in this paper. A multi-output multi-input system is projected into latent variable space by a PLS outer model. For each latent variable model, the T–S fuzzy model is used to describe the nonlinear characteristics of the system; while the state-space model is used in T–S fuzzy model consequent parameters to describe the dynamic characteristics. A disturbance model is introduced in the state-space model. For model state variables, a state observer is used to compensate for the mismatch of the model. The case study results for the pH neutralization process show that the MPC controller based on this method can guarantee the tracking performance of the nonlinear system without static error.
A Bridge between Local GAAP and Solvency II Frameworks to Quantify Capital Requirement for Demographic Risk
Authors: Clemente Gian Paolo , Della Corte Francesco , Savelli Nino
Published: October 2021

The aim of this paper is to provide a stochastic model useful for assessing the capital requirement for demographic risk in a framework coherent with the Solvency II Directive. The model extends to the market consistent context classical methodologies developed in a local accounting framework. The random variable demographic profit, defined in literatue under local accounting principles, is indeed analysed in a Solvency II framework. We provide a unique formulation for different non-participating life insurance contracts and we prove analytically that the valuation of demographic profit can be significantly affected by the financial conditions in the market. Regarding this topic, we implement the Vašíček model to add randomness to risk-free rates. A case study has also been developed considering a portfolio of life insurance contracts. Results prove the effectiveness of the model in highlighting the main drivers of capital requirement evaluation (e.g., the volatility of both mortality rates and risk-free rates), also compared to the local GAAP framework.
Modeling the Future Value Distribution of a Life Insurance Portfolio
Authors: Costabile Massimo , Viviano Fabio
Published: October 2021

This paper addresses the problem of approximating the future value distribution of a large and heterogeneous life insurance portfolio which would play a relevant role, for instance, for solvency capital requirement valuations. Based on a metamodel, we first select a subset of representative policies in the portfolio. Then, by using Monte Carlo simulations, we obtain a rough estimate of the policies’ values at the chosen future date and finally we approximate the distribution of a single policy and of the entire portfolio by means of two different approaches, the ordinary least-squares method and a regression method based on the class of generalized beta distribution of the second kind. Extensive numerical experiments are provided to assess the performance of the proposed models.
Regionalized Linear Models for River Depth Retrieval Using 3-Band Multispectral Imagery and Green LIDAR Data
Authors: Sundt Håkon , Alfredsen Knut , Harby Atle
Published: October 2021

Bathymetry is of vital importance in river studies but obtaining full-scale riverbed maps often requires considerable resources. Remote sensing imagery can be used for efficient depth mapping in both space and time. Multispectral image depth retrieval requires imagery with a certain level of quality and local in-situ depth observations for the calculation and verification of models. To assess the potential of providing extensive depth maps in rivers lacking local bathymetry, we tested the application of three platform-specific, regionalized linear models for depth retrieval across four Norwegian rivers. We used imagery from satellite platforms Worldview-2 and Sentinel-2, along with local aerial images to calculate the intercept and slope vectors. Bathymetric input was provided using green Light Detection and Ranging (LIDAR) data augmented by sonar measurements. By averaging platform-specific intercept and slope values, we calculated regionalized linear models and tested model performance in each of the four rivers. While the performance of the basic regional models was comparable to local river-specific models, regional models were improved by including the estimated average depth and a brightness variable. Our results show that regionalized linear models for depth retrieval can potentially be applied for extensive spatial and temporal mapping of bathymetry in water bodies where local in-situ depth measurements are lacking.
Estimation of Apple Tree Leaf Chlorophyll Content Based on Machine Learning Methods
Authors: Ta Na , Chang Qingrui , Zhang Youming
Published: October 2021

Leaf chlorophyll content (LCC) is one of the most important factors affecting photosynthetic capacity and nitrogen status, both of which influence crop harvest. However, the development of rapid and nondestructive methods for leaf chlorophyll estimation is a topic of much interest. Hence, this study explored the use of the machine learning approach to enhance the estimation of leaf chlorophyll from spectral reflectance data. The objective of this study was to evaluate four different approaches for estimating the LCC of apple tree leaves at five growth stages (the 1st, 2nd, 3rd, 4th and 5th growth stages): (1) univariate linear regression (ULR); (2) multivariate linear regression (MLR); (3) support vector regression (SVR); and (4) random forest (RF) regression. Samples were collected from the leaves on the eastern, western, southern and northern sides of apple trees five times (1st, 2nd, 3rd, 4th and 5th growth stages) over three consecutive years (2016–2018), and experiments were conducted in 10–20-year-old apple tree orchards. Correlation analysis results showed that LCC and ST, LCC and vegetation indices (VIs), and LCC and three edge parameters (TEP) had high correlations with the first-order differential spectrum (FODS) (0.86), leaf chlorophyll index (LCI) (0.87), and (SDr − SDb)/ (SDr + SDb) (0.88) at the 3rd, 3rd, and 4th growth stages, respectively. The prediction models of different growth stages were relatively good. The MLR and SVR models in the LCC assessment of different growth stages only reached the highest R2 values of 0.79 and 0.82, and the lowest RMSEs were 2.27 and 2.02, respectively. However, the RF model evaluation was significantly better than above models. The R2 value was greater than 0.94 and RMSE was less than 1.37 at different growth stages. The prediction accuracy of the 1st growth stage (R2 = 0.96, RMSE = 0.95) was best with the RF model. This result could provide a theoretical basis for orchard management. In the future, more models based on machine learning techniques should be developed using the growth information and physiological parameters of orchards that provide technical support for intelligent orchard management.
Generating 1 km Spatially Seamless and Temporally Continuous Air Temperature Based on Deep Learning over Yangtze River Basin, China
Authors: Li Rui , Huang Tailai , Song Yu , Huang Shuzhe , Zhang Xiang
Published: October 2021

Air temperature is one of the most essential variables in understanding global warming as well as variations of climate, hydrology, and eco-systems. However, current products and assimilation approaches alone can provide temperature data with high resolution, high spatio-temporal continuity, and high accuracy simultaneously (refer to 3H data). To explore this kind of potential, we proposed an integrated temperature downscaling framework by fusing multiple remotely sent, model-based, and in-situ datasets, which was inspired by point-surface data fusion and deep learning. First, all of the predictor variables were processed to maintain spatial seamlessness and temporal continuity. Then, a deep belief neural network was applied to downscale temperature with a spatial resolution of 1 km. To further enhance the model performance, calibration techniques were adopted by integrating station-based data. The results of the validation over the Yangtze River Basin indicated that the average Pearson correlation coefficient, RMSE, and MAE of downscaled temperature achieved 0.983, 1.96 °C, and 1.57 °C, respectively. After calibration, the RMSE and MAE were further decreased by ~20%. In general, the results and comparative analysis confirmed the effectiveness of the framework for generating 3H temperature datasets, which would be valuable for earth science studies.
Drought Assessment in the São Francisco River Basin Using Satellite-Based and Ground-Based Indices
Authors: Paredes-Trejo Franklin , Barbosa Humberto Alves , Giovannettone Jason , Kumar T. V. Lakshmi , Thakur Manoj Kumar , Buriti Catarina de Oliveira , Uzcátegui-Briceño Carlos
Published: October 2021

The São Francisco River Basin (SFRB) plays a key role for the agricultural and hydropower sectors in Northeast Brazil (NEB). Historically, in the low part of the SFRB, people have to cope with strong periods of drought. However, there are incipient signs of increasing drought conditions in the upper and middle parts of the SFRB, where its main reservoirs (i.e., Três Marias, Sobradinho, and Luiz Gonzaga) and croplands are located. Therefore, the assessment of the impacts of extreme drought events in the SFRB is of vital importance to develop appropriate drought mitigation strategies. These events are characterized by widespread and persistent dry conditions with long-term impacts on water resources and rain-fed agriculture. The purpose of this study is to provide a comprehensive evaluation of extreme drought events in terms of occurrence, persistence, spatial extent, severity, and impacts on streamflow and soil moisture over different time windows between 1980 and 2020. The Standardized Precipitation-Evapotranspiration Index (SPEI) and Standardized Streamflow Index (SSI) at 3- and 12-month time scales derived from ground data were used as benchmark drought indices. The self-calibrating Palmer Drought Severity Index (scPDSI) and the Soil Moisture and Ocean Salinity-based Soil Water Deficit Index (SWDIS) were used to assess the agricultural drought. The Water Storage Deficit Index (WSDI) and the Groundwater Drought Index (GGDI) both derived from the Gravity Recovery and Climate Experiment (GRACE) were used to assess the hydrological drought. The SWDISa and WSDI showed the best performance in assessing agricultural and hydrological droughts across the whole SFRB. A drying trend at an annual time scale in the middle and south regions of the SFRB was evidenced. An expansion of the area under drought conditions was observed only during the southern hemisphere winter months (i.e., JJA). A marked depletion of groundwater levels concurrent with an increase in soil moisture content was observed during the most severe drought conditions, indicating an intensification of groundwater abstraction for irrigation. These results could be useful to guide social, economic, and water resource policy decision-making processes.
Decay Assessment of Stone-Built Cultural Heritage: The Case Study of the Cosenza Cathedral Façade (South Calabria, Italy)
Authors: Donato Antonio , Randazzo Luciana , Ricca Michela , Rovella Natalia , Collina Matteo , Ruggieri Nicola , Dodaro Francesco , Costanzo Antonio , Alberghina Maria F. , Schiavone Salvatore , Buongiorno Maria F. , Russa Mauro F. La
Published: October 2021

This study aims to assess the different decay phenomena affecting the Cosenza Cathedral façade (Calabria, South Italy) through the evaluation of the relative damage indices. For this goal, a multidisciplinary approach was applied exploiting both nondestructive and microdestructive techniques. Such a combination enabled proposing an intervention priority scale that can be helpful to institutions when planning a prompt restoration intervention. The results suggest the efficiency of this approach to obtain a multidisciplinary diagnostic and conservation system for the management and valorization of the Cultural Heritage also in terms of monitoring, maintenance, and selection of the most suitable restoration procedures over time.
Automatic Landform Recognition from the Perspective of Watershed Spatial Structure Based on Digital Elevation Models
Authors: Lin Siwei , Chen Nan , He Zhuowen
Published: October 2021

Landform recognition is one of the most significant aspects of geomorphology research, which is the essential tool for landform classification and understanding geomorphological processes. Watershed object-based landform recognition is a new spot in the field of landform recognition. However, in the relevant studies, the quantitative description of the watershed generally focused on the overall terrain features of the watershed, which ignored the spatial structure and topological relationship, and internal mechanism of the watershed. For the first time, we proposed an effective landform recognition method from the perspective of the watershed spatial structure, which is separated from the previous studies that invariably used terrain indices or texture derivatives. The slope spectrum method was used herein to solve the uncertainty issue of the determination on the watershed area. Complex network and P–N terrain, which are two effective methodologies to describe the spatial structure and topological relationship of the watershed, were adopted to simulate the spatial structure of the watershed. Then, 13 quantitative indices were, respectively, derived from two kinds of watershed spatial structures. With an advanced machine learning algorithm (LightGBM), experiment results showed that the proposed method showed good comprehensive performances. The overall accuracy achieved 91.67% and the Kappa coefficient achieved 0.90. By comparing with the landform recognition using terrain indices or texture derivatives, it showed better performance and robustness. It was noted that, in terms of loess ridge and loess hill, the proposed method can achieve higher accuracy, which may indicate that the proposed method is more effective than the previous methods in alleviating the confusion of the landforms whose morphologies are complex and similar. In addition, the LightGBM is more suitable for the proposed method, since the comprehensive manifestation of their combination is better than other machine learning methods by contrast. Overall, the proposed method is out of the previous landform recognition method and provided new insights for the field of landform recognition; experiments show the new method is an effective and valuable landform recognition method with great potential as well as being more suitable for watershed object-based landform recognition.
Retrieval of Water Quality from UAV-Borne Hyperspectral Imagery: A Comparative Study of Machine Learning Algorithms
Authors: Lu Qikai , Si Wei , Wei Lifei , Li Zhongqiang , Xia Zhihong , Ye Song , Xia Yu
Published: October 2021

The rapidly increasing world population and human activities accelerate the crisis of the limited freshwater resources. Water quality must be monitored for the sustainability of freshwater resources. Unmanned aerial vehicle (UAV)-borne hyperspectral data can capture fine features of water bodies, which have been widely used for monitoring water quality. In this study, nine machine learning algorithms are systematically evaluated for the inversion of water quality parameters including chlorophyll-a (Chl-a) and suspended solids (SS) with UAV-borne hyperspectral data. In comparing the experimental results of the machine learning model on the water quality parameters, we can observe that the prediction performance of the Catboost regression (CBR) model is the best. However, the prediction performances of the Multi-layer Perceptron regression (MLPR) and Elastic net (EN) models are very unsatisfactory, indicating that the MLPR and EN models are not suitable for the inversion of water quality parameters. In addition, the water quality distribution map is generated, which can be used to identify polluted areas of water bodies.
A Technical Review of Planet Smallsat Data: Practical Considerations for Processing and Using PlanetScope Imagery
Authors: Frazier Amy E. , Hemingway Benjamin L.
Published: October 2021

With the ability to capture daily imagery of Earth at very high spatial resolutions, commercial smallsats are emerging as a key resource for the remote sensing community. Planet (Planet Labs, Inc., San Francisco, CA, USA) operates the largest constellation of Earth imaging smallsats, which have been capturing multispectral imagery for consumer use since 2016. Use of these images is growing in the remote sensing community, but the variation in radiometric and geometric quality compared to traditional platforms (i.e., Landsat, MODIS, etc.) means the images are not always ‘analysis ready’ upon download. Neglecting these variations can impact derived products and analyses. Users also must contend with constantly evolving technology, which improves products but can create discrepancies across sensor generations. This communication provides a technical review of Planet’s PlanetScope smallsat data streams and extant literature to provide practical considerations to the remote sensing community for utilizing these images in remote sensing research. Radiometric and geometric issues for researchers to consider are highlighted alongside a review of processing completed by Planet and innovations being developed by the user community to foster the adoption and use of these images for scientific applications.
PolSAR Ship Detection with Optimal Polarimetric Rotation Domain Features and SVM
Authors: Li Haoliang , Cui Xingchao , Chen Siwei
Published: October 2021

Polarimetric synthetic aperture radar (PolSAR) can obtain fully polarimetric information, which provides chances to better understand target scattering mechanisms. Ship detection is an important application of PolSAR and a number of scattering mechanism-based ship detection approaches have been established. However, the backscattering of manmade targets including ships is sensitive to the relative geometry between target orientation and radar line of sight, which makes ship detection still challenging. This work aims at mitigating this issue by target scattering diversity mining and utilization in polarimetric rotation domain with the interpretation tools of polarimetric coherence and correlation pattern techniques. The core idea is to find an optimal combination of polarimetric rotation domain features which shows the best potential to discriminate ship target and sea clutter pixel candidates. With the Relief method, six polarimetric rotation domain features derived from the polarimetric coherence and correlation patterns are selected. Then, a novel ship detection method is developed thereafter with these optimal features and the support vector machine (SVM) classifier. The underlying physics is that ship detection is equivalent to ship and sea clutter classification after the ocean and land partition. Four kinds of spaceborne PolSAR datasets from Radarsat-2 and GF-3 are used for comparison experiments. The superiority of the proposed detection methodology is clearly demonstrated. The proposed method achieves the highest figure of merit (FoM) of 99.26% and 100% for two Radarsat-2 datasets, and of 95.45% and 99.96% for two GF-3 datasets. Specially, the proposed method shows better performance to detect inshore dense ships and reserve the ship structure.
Close-Range Sensing and Data Fusion for Built Heritage Inspection and Monitoring—A Review
Authors: Adamopoulos Efstathios , Rinaudo Fulvio
Published: October 2021

Built cultural heritage is under constant threat due to environmental pressures, anthropogenic damages, and interventions. Understanding the preservation state of monuments and historical structures, and the factors that alter their architectural and structural characteristics through time, is crucial for ensuring their protection. Therefore, inspection and monitoring techniques are essential for heritage preservation, as they enable knowledge about the altering factors that put built cultural heritage at risk, by recording their immediate effects on monuments and historic structures. Nondestructive evaluations with close-range sensing techniques play a crucial role in monitoring. However, data recorded by different sensors are frequently processed separately, which hinders integrated use, visualization, and interpretation. This article’s aim is twofold: i) to present an overview of close-range sensing techniques frequently applied to evaluate built heritage conditions, and ii) to review the progress made regarding the fusion of multi-sensor data recorded by them. Particular emphasis is given to the integration of data from metric surveying and from recording techniques that are traditionally non-metric. The article attempts to shed light on the problems of the individual and integrated use of image-based modeling, laser scanning, thermography, multispectral imaging, ground penetrating radar, and ultrasonic testing, giving heritage practitioners a point of reference for the successful implementation of multidisciplinary approaches for built cultural heritage scientific investigations.
Tracking a Low-Angle Isolated Target via an Elevation-Angle Estimation Algorithm Based on Extended Kalman Filter with an Array Antenna
Authors: Darvishi Hossein , Sebt Mohammad Ali , Ciuonzo Domenico , Salvo Rossi Pierluigi
Published: October 2021

In a low-angle tracking situation, estimating the elevation angle is challenging due to the entrance of the multipath signals in the antenna’s main lobe. In this article, we propose two methods based on the extended Kalman filter (EKF) and frequency diversity (FD) process to estimate the elevation angle of a low-angle isolated target. In the first case, a simple weighting of the per-frequency estimates is obtained (termed WFD). Differently, in the second case, a matrix-based elaboration of the per-frequency estimates is proposed (termed MFD). The proposed methods are completely independent of prior knowledge of geometrical information and the physical parameters. The simulation results show that both methods have excellent performance and guarantee accurate elevation angle estimation in different multipath environments and even in very-low SNR conditions. Hence, they are both suitable for low-peak-power radars.
Camouflaged Target Detection Based on Snapshot Multispectral Imaging
Authors: Shen Ying , Li Jie , Lin Wenfu , Chen Liqiong , Huang Feng , Wang Shu
Published: October 2021

The spectral information contained in the hyperspectral images (HSI) distinguishes the intrinsic properties of a target from the background, which is widely used in remote sensing. However, the low imaging speed and high data redundancy caused by the high spectral resolution of imaging spectrometers limit their application in scenarios with the real-time requirement. In this work, we achieve the precise detection of camouflaged targets based on snapshot multispectral imaging technology and band selection methods in urban-related scenes. Specifically, the camouflaged target detection algorithm combines the constrained energy minimization (CEM) algorithm and the improved maximum between-class variance (OTSU) algorithm (t-OTSU), which is proposed to obtain the initial target detection results and adaptively segment the target region. Moreover, an object region extraction (ORE) algorithm is proposed to obtain a complete target contour that improves the target detection capability of multispectral images (MSI). The experimental results show that the proposed algorithm has the ability to detect different camouflaged targets by using only four bands. The detection accuracy is above 99%, and the false alarm rate is below 0.2%. The research achieves the effective detection of camouflaged targets and has the potential to provide a new means for real-time multispectral sensing in complex scenes.
Temporally Generalizable Land Cover Classification: A Recurrent Convolutional Neural Network Unveils Major Coastal Change through Time
Authors: Gray Patrick Clifton , Chamorro Diego F. , Ridge Justin T. , Kerner Hannah Rae , Ury Emily A. , Johnston David W.
Published: October 2021

The ability to accurately classify land cover in periods before appropriate training and validation data exist is a critical step towards understanding subtle long-term impacts of climate change. These trends cannot be properly understood and distinguished from individual disturbance events or decadal cycles using only a decade or less of data. Understanding these long-term changes in low lying coastal areas, home to a huge proportion of the global population, is of particular importance. Relatively simple deep learning models that extract representative spatiotemporal patterns can lead to major improvements in temporal generalizability. To provide insight into major changes in low lying coastal areas, our study (1) developed a recurrent convolutional neural network that incorporates spectral, spatial, and temporal contexts for predicting land cover class, (2) evaluated this model across time and space and compared this model to conventional Random Forest and Support Vector Machine methods as well as other deep learning approaches, and (3) applied this model to classify land cover across 20 years of Landsat 5 data in the low-lying coastal plain of North Carolina, USA. We observed striking changes related to sea level rise that support evidence on a smaller scale of agricultural land and forests transitioning into wetlands and “ghost forests”. This work demonstrates that recurrent convolutional neural networks should be considered when a model is needed that can generalize across time and that they can help uncover important trends necessary for understanding and responding to climate change in vulnerable coastal regions.