esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results & Discussion
### Intensive Sampling  
The temporal concentrations using 24 h integrated PAH samples every six days, BC (one-minute resolution), EC (72 h integrated), and OC (72 h integrated) were analyzed to characterize the PM\({}_{2.5}\) collected from the study area. BC is emitted from a variety of combustion processes, mainly incomplete combustion [48; 49; 50]. On the other hand, OC is classified as primary organic carbon (POC) or secondary organic carbon (SOC) formed by photochemical reactions, depending on the formation process [51; 52]. Estimations of primary and secondary particles based on the proportions of OC, EC, and BC contained in PM\({}_{2.5}\) have been conducted in various studies [26; 53]. The overall averaged BC in the intensive periods was 0.84 ug/m\({}^{3}\). It is interesting that the maximum BC concentration reached 14.17 ug/m\({}^{3}\) on 11 August when the asphalt mix plant was running.  
The correlation between OC and EC was evaluated based on the BC concentration (Figure 5). OC showed a correlation of at least 0.82 with BC concentration. These results indicate that PM\({}_{2.5}\) in the study area is composed of the primary particles generated by combustion. The overall linear regression between the EC and BC concentrations in the study area showed a slope of 3.56 and an r\({}^{2}\) of 0.83. Previous studies reported that the relationship between EC and BC depends on the emission source and atmospheric conditions [54]. In addition, the relationship between EC and BC can depend on the presence of atmospheric compounds related to time-related source differences [55]. Therefore, the relationship between EC and BC is sensitive to the source of carbonaceous aerosols. Thus, the study area appears to be affected by emissions from the asphalt mix plant. Time series concentrations of OC, EC, and BC were measured to confirm the effect of the asphalt mix plant on PM\({}_{2.5}\), and the time series concentrations were compared based on the operational period of the asphalt mix plant (Figure 6). As shown in Figure 6, OC, EC, and BC increased when the asphalt mix plant was operational. The BC-based OC and EC correlations, along with the increases in BC, OC, and EC during the operational period, prove that the primary particles produced by the asphalt mix plant account for PM\({}_{2.5}\) in the study area.  
Figure 4: Scatter plots between PAHs.
###### Abstract  
Crop canopy temperature has proven beneficial for qualitative and quantitative assessment of plants' biotic and abiotic stresses. In this two-year study, water stress identification in okra crops was evaluated using thermal-RGB imaging and AI approaches. Experimental trials were developed for two irrigation types, sprinkler and flood, and four deficit treatment levels (100, 50, 75, and 25% crop evapotranspiration), replicated thrice. A total of 3200 thermal and RGB images acquired from different crop stages were processed using convolutional neural network architecture-based deep learning models (1) ResNet-50 and (2) MobileNetV2. On evaluation, the accuracy of water stress identification was higher with thermal imagery inputs (87.9% and 84.3%) compared to RGB imagery (78.6% and 74.1%) with ResNet-50 and MobileNetV2 models, respectively. In addition, irrigation treatment and levels had significant impact on yield and crop water use efficiency; the maximum yield of 10,666 kg ha\({}^{-1}\) and crop water use efficiency of 1.16 kg m\({}^{-3}\) was recorded for flood irrigation, while 9876 kg ha\({}^{-1}\) and 1.24 kg m\({}^{-3}\) were observed for sprinkler irrigation at 100% irrigation level. Developments and observations from this study not only suggest applications of thermal-RGB imagery with AI for water stress quantification but also developing and deploying automated irrigation systems for higher crop water use efficiency.  
okra crop; water stress; thermal-RGB imagery; deep learning; precision irrigation
# DBMF: A Novel Method for Tree Species Fusion Classification Based on Multi-Source Images
## 1 Introduction
### Research Objectives  
This study aimed to improve the network identification ability for challenging tree species classification tasks by proposing a network for tree species classification wherein CNN and LSTM are fused with multi-source data based on HJ-1A and Sentinel-2 remote sensing images. Sentinel-2 data are used as an MSI, which has HR, and HJ-1A data are used as an HSI, which has LR. To exploit the correlation between the HSI and MSI, we used the spectrum of LR HSI and the corresponding spatial neighborhoods in HR MSI as the input pairs of the network. Features were extracted from the corresponding neighborhoods in the LR HSI and the MSI with two CNN branches. Then, these branches were connected and fed to the fusion activation layer. The final fusion layer output the spectrum classification map.  
The main objectives of this work can be summarized as follows:  
1. To design a framework for tree species classification with multi-source data and a DL algorithm.
2. To assess the performance of the proposed method for tree species classification using HSI data and MSI data.
3. To analyze the advantages of the proposed model with other models to provide new ideas for the application of the DL method in forestry.
# Biosynthesis of Natural Rubber: Current State and Perspectives
## 2 Biosynthesis of Natural Rubber and the Involved Genes/Proteins
### Genes/Proteins Involved in Natural Rubber Biosynthesis
#### 2.3.4 Other Involved Genes/Proteins/Pathways  
Genes of the MEP and MVA pathways from rubber-producing plants were cloned and functionally analyzed [84; 85; 86; 87]. While MVA pathway was thought to be the main provider of the IPP monomer, there were different opinions about whether MEP pathway was directly involved in rubber biosynthesis. Some MEP pathway genes have high expression levels in the latex, but the seedling feeding experiment with [1-\({}^{13}\)C] 1-deoxy-d-xylulose triacetate, an intermediate derivative of the MEP pathway, indicated that the MEP pathway is responsible for carotenoids biosynthesis rather than rubber biosynthesis in _H. brasiliensis_ latex [84]. However, in a following study, expression profiles of MEP and MVA pathway genes suggested that MEP pathway may be an alternate contributor of IPP in mature rubber trees or in clones, which do not produce a large amount of carotenoids [88]. Farnesyl diphosphate synthases (FPSs) were identified and demonstrated to be the main preryltransferase involved in _trans_-1,4-polyisoprene (Eu-rubber) biosynthesis in _E. ulmoides_[5; 6; 89]. Gene co-expression network analysis showed correlated expression pattern of MEP pathway genes with _EuFPS1_ and MVA pathways genes with _EuFPS5_, indicating that both MEP and MVA pathways may be involved in Eu-rubber biosynthesis [90]. However, expression patterns of MEP pathway genes were not correlated with the accumulation of Eu-rubber in fruits, while MVA pathway genes did, implying that MVA pathway rather than MEP pathway is mainly responsible for IPP supply during Eu-rubber biosynthesis in fruits [5].  
Figure 4: Schematic model of rubber particles. Rubber particles are made of polyisoprene, surrounded by a monolayer of lipids with proteins and other compounds. The synthesized polyisoprene chains are elongated inward and stored in the interior of the rubber particles. The _H. brasiliensis_ rubber biosynthetic machinery is supposed to consist of HRT1, HRBP and REF. HRBP may work as a scaffold tethering HRT1 on the surface of rubber particles. REF may function in stabilizing the rubber biosynthetic machinery, while both REF and SRPP may play roles in rubber particle stabilization and coagulation. REF contains one predicted transmembrane domain and may be inserted deeply into the rubber particle lipidic monolayer, while SRPP has no predicted transmembrane domain and may just associate with the lipidic monolayer. CPT, _cis_-premyltransferase; HRT1, _Hewea_ rubber transferase 1; HRBP, HRT1-REF bridging protein; REF, rubber elongation factor; SRPP, small rubber particle protein; RP, rubber particle.
# A Novel Suspended-Sediment Sampling Method: Depth-Integrated Grab (DIG)
###### Abstract  
Measuring suspended sediment in fluvial systems is critical to understanding and managing water resources. Sampling suspended sediment has been the primary means of understanding fluvial suspended sediment. Specialized samplers, sampling methods, and laboratory methods developed by select U.S. Federal Agencies are more representative of river and stream conditions than commonly used grab sampling and total suspended solids (TSS) laboratory methods but are not widely used because they are expensive, time consuming, and not required as part of water quality standards in the United States. A new suspended-sediment sampling method called a depth-integrated grab (DIG) was developed by combining certain elements from both grab and depth-integrating sampling methods and suspended-sediment concentration (SSC) laboratory methods. The goal of the DIG method was to provide more accurate results than Grab-TSS while being easier and cheaper to sample than specialized samplers and methods. Approximately 50 paired comparison samples were collected at 9 sites in Minnesota from 2018 through 2019. Results showed no significant difference between the DIG and specialized sampling methods and a significant difference between both methods and the Grab-TSS method. The DIG-SSC provided an improved alternative to the Grab-TSS method, but additional research and testing is important to evaluate if this method is appropriate in different conditions than were observed in this study.  
s +
Footnote †: journal: Applied sciences  
ediment transport; suspended sediment; suspended-sediment concentration; isokinetic; depth-integrating; grab sampling; total suspended solids; water quality; Minnesota; Wilcoxon test
# A Multivariate Machine Learning Model of Adsorptive Lindane Removal from Contaminated Water
## 2 Materials and Methods
### Stepwise Linear Regression  
Estimates of RA values were made using multiple stepwise regression (SR) in MATLAB (version 2019a). An onward and reverse selection was used in SR to zero in on the model's most crucial parameters. The method was proposed to overcome problems with conventional multiple regression computation [16; 17]. Multiple SR analysis has become a popular and helpful method in recent years. The technique is simple, flexible, and widely applicable [18]. SR is recommended for multiple independent variables [19]. The selection of SR was based on its capacity to handle many predictor factors, exhibit high computational efficiency, enable model fine-tuning, and facilitate prompt execution [20].  
In SR, two stages are involved in determining the best possible set of predictors: Step-by-step reverse removal of insignificant parameters according to predetermined limit [21] and onward choice of input parameters in a \"greedy style\" to reduce the remainder of the squares at every stage. In this type of calculation, two distinct value thresholds are used for introducing and dropping parameters, respectively. If the importance of any potential parameters falls below a particular limit when an additional parameter is introduced to the set of factors that predict, SR will reject the new parameter. _t_-tests and F-tests are used in SR models to incorporate or eliminate variables that predict. The ideal combination of \"N\" predictor parameters is achieved in the final model. The Gauss  
Figure 1: One-variable-at-time experimental setup.  
Jordan algorithm transforming rules, which consider the recurrent relationship among the regression coefficient, the remaining covariance variables, and the reverse of the covariance matrix's partitioning elements, provide the conceptual foundation for a stepwise regression model. Assume mode begins with a forward choice with no variable that predicts (Equation (1)) to illustrate SR's conceptual framework. Regression formulae can be completed by adding variables one at a time (Equation (2)) if they satisfy specific criteria (I F-ratio). To get rid of unnecessary predictors, sequential backward regression is performed.  
\[y=\beta_{0}+\varepsilon \tag{1}\]  
\[y=\beta_{0}+\beta_{1}x_{1}+\beta_{2}x_{2}+\ldots+\beta_{n}x_{n}+\varepsilon \tag{2}\]  
In the current study, the independent variable is denoted by \(y\), which represents the RA value. However, each of the dependent or explanatory variables, including retroreflective sheeting brand, grade, color, orientation, observation angle, age, and others, are represented by \(x_{1}\), \(x_{2}\), and \(x_{n}\). The regression coefficients, \(\beta_{0}\), \(\beta_{1}\), \(\beta_{2}\), and so on, estimate the relationship between the independent and dependent variables. Additionally, the random error is represented by \(\varepsilon\).
## 2 Materials and Methods
### Data Analysis
#### 2.3.2 Statistical Analysis  
After preprocessing the spectrum, the calibration set was input into the multiple regression model for analysis. According to the generated regression model, the nutrient content of the prediction set was predicted, so as to evaluate the performance of the model. The models used include partial least squares (PLS) and extreme learning machines (ELM). The competitive adaptive reweighted sampling algorithm (CARS) was used to select the characteristic wavelength of the spectral data.  
PLS is a universal chemometric modeling method proposed by [PERSON] in 1983. PLS can not only decompose the spectral information matrix and the concentration matrix, but also greatly reduce the interference of noise signals on the effective spectral signals [16]. PLS respectively projects the predictor variable X and the observed variable Y into a new space, and tries to find the multidimensional direction of the X space to explain the multidimensional direction of the largest variance in the Y space. The number of principal components in the PLS is directly related to the prediction performance of the regression model. Too few principal components will lose the effective information in the spectrum, and too many principal components will cause the noise to be retained and reduce the prediction accuracy.  
ELM is a machine learning method based on feedforward neural network (FNN), which can be used for supervised learning and unsupervised learning problems [17]. Its characteristic is that the weights of hidden layer nodes are randomly or artificially given and do not need to be updated. The learning process only calculates the output weights [18]. In many cases, ELM shows better generalization performance than gradient-based machine learning methods, and because ELM does not require gradient operations, it has a very fast training speed.  
CARS is an efficient variable selection method, whose main idea is derived from the principle of \"survival of the fittest\" in [PERSON]'s theory of evolution. Regarding each variable as an independent unit, adaptive reweighting sampling technology is used to select variables with larger absolute values of regression coefficients in the model, and variables with small weights are eliminated to obtain a series of subsets containing multiplevariables. Finally, RMSE is calculated for each subset interactive verification, and the subset with the smallest RMSE value is selected and output [19]. Usually the CARS method uses the PLS model for modeling calculations, and the CARS method has been widely used in the selection of characteristic wavelengths of spectral data.
## 3 Results  
### Shrimp Growth, Survival, and FCR after 35 Days  
Shrimp weight, carapace length, and specific growth rates increased linearly in response to dietary PRSE levels over the first 35 days of feeding the experimental diets (Figure 1a-c). The shrimp FCR decreased linearly in response to increasing dietary levels of PRSE over the same period (Figure 1f). No relationship was found between shrimp condition factor, survival, and dietary PRSE level after 35 days of feeding the diets (Figure 1d). Overall, the fitted linear models found that shrimp fed the highest level of PRSE (6 g kg\({}^{-1}\) diet) were 65% heavier (6.1 vs. 3.7 g), 11% longer (23.4 vs. 21.0 mm), had an SGR 32% higher (5.0 vs. 3.8% day\({}^{-1}\)), and an FCR 39% lower (1.9 vs. 3.1) than shrimp fed control diets without PRSE.  
Figure 1: (**a**) Weight; (**b**) carapace length; (**c**) specific growth rate; (**d**) condition factor; (**e**) survival; and (**f**) feed conversion ratios of juvenile black tiger shrimp _Peneus monodon_ fed diets with increasing levels of a polyphenol-rich sugarcane extract (PRSE) (Polygain\({}^{\text{TM}}\)) for 35 days. Dashed lines represent best fit models for the data. Equations and adjusted R\({}^{2}\) values represent the relationship between the measured parameter and dietary PRSE level. Horizontal lines indicate no relationship was present. Data are mean \(\pm\) SEM, \(n\) = 4.
## 4 Remote Sensing Platforms Used in Peatland Research
### Satellite-Based Remote Sensing
#### Challenges and Limitations
\begin{tabular}{c} **Peatland** \\ **Type**\({}^{\star}\) \\ \end{tabular}  & \ \begin{tabular}{c} **Peatland** \\ **Status** \\ \end{tabular}  & **Dominant Vegetation** & **Platform** & **Sensor/Product** & **Indices** & \begin{tabular}{c} **Sensing** \\ **Type**\({}^{\star}\)** \\ \end{tabular}  & **Multi/** & **Application** \\ \hline
[88] & \begin{tabular}{c} **Peatland** \\ **Peatland** \\ **Natural** \\ \end{tabular}  & \begin{tabular}{c} **Sphayamin spp., _Carac spp.,_ \\ _Plaverism sterkerk,_ \\ _Hycosium sterk,_ \\ _Hycosium sterk,_ \\ \end{tabular}  & \ \begin{tabular}{c} ALOS PALSAR, \\ EES-1 or \(2\), Landsat 5 \\ TM \\ \end{tabular}  & \begin{tabular}{c} SAR L-band, \\ C-band, ETM+ \\ \end{tabular}  & NA & Mixed & Multi & \\ \hline
[31] & \begin{tabular}{c} **Peatland** \\ **Natural** \\ \end{tabular}  & \begin{tabular}{c} **Sphayamin spp., _Carac spp.,_ \\ \end{tabular}  & \begin{tabular}{c} Sentinel-2 \\ \end{tabular}  & \ \begin{tabular}{c} MSI \\ \end{tabular}  & \begin{tabular}{c} NVLV, EVL \\ NDWI \\ \end{tabular}  & Passive & Multi & \\ \hline
[69] & \begin{tabular}{c} **Bog** \\ **Peatland** \\ \end{tabular}  & \ \begin{tabular}{c} **N** \\ \end{tabular}  & \begin{tabular}{c} Sentinel-1, \\ RADASAT-2 \\ \end{tabular}  & C-band & NA & Active & NA & \\ \hline
[89] & \begin{tabular}{c} **Peatland** \\ **Peatland** \\ \end{tabular}  & NA & NA & Landsat & OLI & NA & Passive & Multi & \\ \hline
[90] & \ \begin{tabular}{c} **Peatland** \\ **Peatland** \\ \end{tabular}  & NA & NA & Sentinel-1 & C-band SAR & \begin{tabular}{c} Coherence \\ Assessment \\ \end{tabular}  & Active & NA & \\ \hline
[91] & \begin{tabular}{c} **Peatland** \\ **Peatland** \\ \end{tabular}  & NA & NA & \ \begin{tabular}{c} RapidEye \\ \end{tabular}  & NA & \begin{tabular}{c} REUND, NDVI, \\ Kernel, \\ Red Edge, \\ \end{tabular}  & Passive & Multi & \\ \hline
[61] & \begin{tabular}{c} **Bog** \\ **Peatland** \\ \end{tabular}  & \begin{tabular}{c} **Sphayamin spp., shrub, grass,** \\ \end{tabular}  & \ \begin{tabular}{c} **Sphayamin spp.,** \\ \end{tabular}  & \begin{tabular}{c} Sentinel-1 \\ \end{tabular}  & C-band SAR & NA & Active & NA & \\ \hline
[74] & \begin{tabular}{c} **Bog** \\ **Peatland** \\ \end{tabular}  & \begin{tabular}{c} **Disturbed** \\ **Peatland** \\ \end{tabular}  & \begin{tabular}{c} **Willow herb, small reed, and small** \\ **Peatland** \\ \end{tabular}  & \ \begin{tabular}{c} Spot-5, Spot-6, \\ Landsat’ \end{tabular}  & \begin{tabular}{c} HRG/ETM+ \\ \end{tabular}  & NA & Passive & Multi & \\ \hline
[33] & \begin{tabular}{c} **Bog** \\ **Peatland** \\ \end{tabular}  & \begin{tabular}{c} **Eighayamin segment, _Sphayamin_ \\ **spp.,** _Monision** \\ \end{tabular}  & \begin{tabular}{c} **Sphayamin** \\ **dward shrobts** \\ \end{tabular}  & \ \begin{tabular}{c} Sentinel-1 \\ \end{tabular}  & NA & \begin{tabular}{c} NA \\ \end{tabular}  & Active & NA & \\ \hline
# Failure Mode and Effects Analysis Considering Consensus and Preferences Interdependence
## 1 Introduction
Clearly, it is preferable that decision-makers reach an acceptable group consensus before applying the selection process. However, the current approach of FMEA fails to consider the consensus-reaching process, which may lead to some FMEA team experts not accepting the ranking results of failure modes. Consequently, it is imperative to incorporate a consensus-reaching process in FMEA risk assessment. According to the analysis above, the ranking results of failure modes will inevitably be influenced by the consensus-reaching process and the interdependence of the experts' preferences. This study proposes an integrated approach for determining the risk priority of failure modes, which considers the interdependence of experts' preferences and introduces the consensus-reaching process into the FMEA process. The remainder of this paper is organized as follows. Section 2 reviews briefly some relevant literature. Based on the GBM operator, the interval-valued Pythagorean fuzzy GBM (IVPFGBM) operator and the interval-valued Pythagorean fuzzy weighted GBM (IVPFWGBM) operator are defined in Section 3. A new FMEA method for the risk evaluation of failure modes under the interval-valued Pythagorean fuzzy environment is presented in Section 4. In Section 5, the effectiveness and feasibility of the proposed approach are illustrated by a practical example. Finally, Section 6 provides directions for future work and a brief conclusion.
## 4 Methodology  
This study involved several steps and processes for creating landslide susceptibility (LSS) maps including (Figure 2):  
1. A total of 223 landslide locations were identified using the high-resolution Google earth images and afterward these locations were verified through field investigation with a global positioning system (GPS) which was conducted during April 2018 and September 2019. The same number of non landslide points as landslide locations were taken randomly for training the models. The 16 environmental factors were considered for modeling (Table 1).
2. Relief-F technique was used to judge the effectiveness of the landslide conditioning factors (LCFs) for LSS mapping.
3. LSS maps first were prepared using ANN, SVM, LR, and RF models. The ensemble models were prepared combining the two, three and four models subsequently.
4. The contribution of the LCFs was assessed using the random forest (RF) model,
5. The LSS model's performances were evaluated through the area under receiver operating characteristic curve (AUCROC), precision, accuracy, mean-absolute-error (MAE), and root-mean-square-error (RMSE).
6. Finally, compound factor (CF) method was used to choose the best model.  
Figure 1: Location map of the study area with landslides.