esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Materials and Methods
### De Novo Assembly and Unigene Annotation  
Cutadapt [82] and in-house Perl scripts at Hangzhou Lianchuan Biotechnology Co. Ltd. (LC Sciences, Houston, TX, USA), were used to remove adaptor sequences and low-quality bases. The sequence quality was evaluated by FastQC ([[https://www.bioinformatics](https://www.bioinformatics)]([https://www.bioinformatics](https://www.bioinformatics)). babraham.ac.uk/projects/fastqc/, accessed on 16 March 2020). The de novo assembly of transcriptome from _H. pluvialis_ was performed using Trinity2.4.0 [83]. All assembled unigenes were aligned against the non-redundant (nr) protein ([[http://www.ncbi.nlm](http://www.ncbi.nlm)]([http://www.ncbi.nlm](http://www.ncbi.nlm)).  
nih.gov/, accessed on 16 March 2020), Gene Ontology (GO) ([[http://www.geneontology.org/](http://www.geneontology.org/)]([http://www.geneontology.org/](http://www.geneontology.org/)), accessed on 16 March 2020), SwissProt ([[http://www.expasy.ch/sprot/](http://www.expasy.ch/sprot/)]([http://www.expasy.ch/sprot/](http://www.expasy.ch/sprot/)), accessed on 16 March 2020), Kyoto Encyclopedia of Genes and Genomes (KEGG) ([[http://www.genome.jp/kegg/](http://www.genome.jp/kegg/)]([http://www.genome.jp/kegg/](http://www.genome.jp/kegg/)), accessed on 16 March 2020), and EggNOG ([[http://eggnogdb.embl.de/](http://eggnogdb.embl.de/)]([http://eggnogdb.embl.de/](http://eggnogdb.embl.de/)), accessed on 16 March 2020) databases using DIAMOND [84] with a threshold of E value < 0.00001. Transcription factors were identified using iTAK [85]. The selection of de novo assembly over genome-based analysis was based on two lines of evidence. First, the results of several biosynthetic pathways based on the genome-based analysis showed variations in the expression patterns of genes from those based on the de novo assembly. Therefore, we further investigated the cause of these inconsistent results by conducting the qRT-PCR analysis of genes involved in these biosynthetic pathways. Our results of qRT-PCR confirmed the expression patterns of genes based on the de novo assembly, whereas the results based on the genome-based assembly were largely not confirmed by qRT-PCR analysis. Second, our transcriptomic sequencing results based on the alignment program HISAT2 ([[https://daehwankimlab.github.io/hisat2/](https://daehwankimlab.github.io/hisat2/)]([https://daehwankimlab.github.io/hisat2/](https://daehwankimlab.github.io/hisat2/)), accessed on 16 March 2020) revealed the overall alignment rate of only \(\sim\)50% with the reference genome of _H. pluvialis_ deposited at the NCBI database (GenBank accession number GCA_011766145.1 of BioProject PRJDB8952).
## 3 Results
### Main Outcomes of the Top 20 Highly Cited Articles from Each Thematic Area
#### 3.2.8 Marine Safety and Security
The validation carried out with the reference mucilage datasets derived from Sentinel-2A imagery and with in situ spectroradiometer measurements showed that the produced MODIS-based maps accurately depicted the mucilage-covered areas. [PERSON] and [PERSON] [188] combined GIS, MCDM, and remote sensing techniques (SPOT 7 optical imagery) for the identification of suitable habitats for Posidonia oceanica in Gulluk Bay, Turkey. The authors used the following criteria: depth, sheltered area, slope, sediment yield, and topographic position index. Their model identified suitable habitats for seagrass with an overall accuracy of 76%. Human activities have resulted in negative effects on sea turtle populations in the Mediterranean, thus sufficient conservation and monitoring efforts are essential [187]. Towards this direction, satellite telemetry and conventional GPS data loggers have been proven to be valuable tools for mapping the spatial ecology of sea turtles [185; 186]. [PERSON] et al. [190] studied the relative sea-level rise and potential submersion risk for 2100 on 16 coastal plains of the Mediterranean Sea (France, Spain, Tunisia, Cyprus) by analyzing LiDAR data and different climate projection scenarios. They estimated a potential loss of land for the above areas between about 148 km\({}^{2}\) for the IPCC-RCP8.5 scenario and 192 km\({}^{2}\) for the Rahmstorf scenario, impacting a coastline length of about 400 km. However, the low resolution (25 m \(\times\) 25 m) of the digital terrain model map of Cyprus prevented any further detailed coastal hazard assessment.
## 5 Results
### Stress-Dependent Capillary Pressure  
The Brooks and Corey method is used to compute the capillary pressure as a function of water saturation at the fracture-matrix interface after running CFD to determine the saturation values. The capillary pressure (\(Pc\)) can be expressed as:  
\[Pc=Pec\ S_{w}^{-1/\lambda_{p}} \tag{17}\]  
where \(Pec\) is the entry capillary pressure and \(\lambda_{p}\) is the pore size distribution index.  
Figure 10 depicts the variations in capillary pressure concerning the injected water volume (\(V_{w}\)) under different stress-loading conditions. It can be seen that as the volume of injected water increases, the capillary pressure decreases nonlinearly. At lower \(V_{w}\) values, the decrease in the capillary pressure is quite significant due to the highest amount of oil remaining. At higher \(V_{w}\) values, as the capillary pressure approaches close to zero, the stress loading has a negligible effect on it because the capillary forces are dominant. It can be seen that the minimum capillary pressure recorded, at \(V_{w}\) = 0.7, is 0.36 kPa. In terms of stress loading, \(Pc\) = 5.46 kPa at a stress loading of 6.9 MPa for \(V_{w}\) = 0.1 (10%). \(Pc\) reduces to 4.96 kPa, 4.47 kPa, 4.01 kPa, 3.47 kPa, and 2.98 kPa at the same \(V_{w}\) when the stress loading increases to 9 MPa, 11 MPa, 13.1 MPa, 15.2 MPa, and 17.2 MPa. The same trend has been observed for other stress-loading conditions considered here.  
Figure 9: Variations in water saturation at the fracture–matrix interface for different injected water volumes and stress loadings.  
Further analyzing the effects of external stress on the capillary pressure, Figure 11 depicts capillary pressure variations against stress loading for \(V_{w}=0.1\). It can be seen that as the stress loading increases, the capillary pressure decreases linearly. Thus, fracture closure with effective stress results in a considerable downward shift in the capillary pressure, leading to an increase in water saturation.
# Distributed Fuzzy Cognitive Maps for Feature Selection in Big Data Classification
## 4 Results
### Comparison of Computational Time for Classification with and without Feature Selection  
Figure 7 depicts the comparison of computational time of the model when feature selection was applied and when feature selection was not applied. From the results, it can be observed that applying the feature selection model along with classification considerably reduced the computational time. Using a feature selection method reduces the dimension of the feature space by selecting the most relevant features with respect to the problem under consideration, thereby reducing the computational load on the classification system.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Accuracy** & \begin{tabular}{c} **Accuracy** \\ **after Feature** \\ **Selection** \\ \end{tabular} & \
\begin{tabular}{c} **Total** \\ **Features** \\ \end{tabular} &
\begin{tabular}{c} **Selected** \\ **Features** \\ \end{tabular} \\ \hline Breast Cancer Wisconsin & 96.25 & 97.32 & 32 & 14 \\ (Diagnostic) Data Set & 97.2 & 99.66 & 16 & 7 \\ US Congressional Voting & 97.2 & 99.66 & 16 & 7 \\ Records dataset & 78.9 & 80.5 & 8 & 3 \\ Diabetes dataset & 86.02 & 88.97 & 45 & 17 \\ Polycystic ovary syndrome & 87.65 & 91.83 & 23 & 9 \\ Detection dataset & 92.6 & 93.34 & 13 & 5 \\ Zoo dataset & 88.15 & 89.36 & 17 & 6 \\ Connectionist Bench (Sonar, & 75.3 & 86.47 & 60 & 26 \\ Mines vs. Rocks) Data Set & 83.5 & 88.63 & 23 & 10 \\ Climate Model Simulation & 88 & 92.41 & 18 & 9 \\ Crashes Data Set & 86.8 & 91.82 & 294 & 101 \\ Page Blocks Classification & 91 & 95.33 & 10 & 3 \\ Data Set & 81 & 82.125 & 21 & 10 \\ pc1 & 80 & 93.1 & 21 & 8 \\ cm1 & 83 & 89.67 & 35 & 12 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results obtained from proposed FCM-based feature selection model.
## Appendix A  
Definition: Given two functions f(x) and g(x), the convolution of f(x) and g(x), denoted h(x) = f'g(x), is defined as:  
\[f*g(x)=\int_{R}f(x)g(x-t)dt \tag{1}\]  
Wavelets are signals that meet different characteristics:  
1. Finite;
2. Zero energy or zero average. _Mother wavelet_\(\psi_{a,b}(t)\): \[\psi_{a,b}(t)=1/\sqrt{a}\cdot\psi(t-b/a)\] (2)  
where \(a\) represents the scaling, and \(b\) is the displacement.  
Continuous Wavelet transform is defined as:  
\[CWT(a,b)=1/\sqrt{a}\int_{-\infty}^{\infty}x(t)\psi(t-b/a)dt \tag{3}\]  
where \(x(t)\) represents the input function.  
The Discrete Wavelet transform appears when the parameters \(a\) and \(b\) take the values: \(a=2^{-j}\) and \(b=k2^{-j}\), with \(j\) and \(k\) in the set of integers.  
In this way, the Discrete Wavelet transform is defined as:  
\[DWT(a,b)=1/2^{j/2}\int_{-\infty}^{\infty}x(t)\psi(2^{j}t-k)dt \tag{10}\]  
where \(x(t)\) represents the input vector.  
The signal reconstruction is obtained from the following equation:  
\[x(t)=\sum_{k}\sum_{j}c_{j,k}\phi(t)+\sum_{k}\sum_{j}d_{j,k}\psi(t);j,k\in\mathbb{ Z} \tag{11}\]  
where \(c_{jk}\) represents the scale or approximation coefficients and \(d_{j,k}\) the _wavelet or detail coefficients_.  
Each input vector is convolved with a Wavelet type at a certain level. This is analogous to the use of a filter or kernel in a conventional CNN [10; 22; 31] such as the filters used in [28]. In this step, each data vector is transformed or decomposed into vectors of shorter lengths, depending on the Wavelet level used. Each output vector expresses _features_ differently from the original vector (in the case of signals, different frequencies are represented in each signal). In this step, two additional results tend to describe the information in a focused way; one is the shift, and the other is the fading, which reduces or increases the amount of data in the original vector at the time of the convolution [3]. It is interesting to note that the use of the _Discrete Wavelet_ represents a main advantage, which is to build a convolution in all areas of the information to be explored such that it is proposed as the substitution of the _Pooling_ tool for this purpose. Likewise, the main action in using this type of Wavelet is to decompose the information in a multiscale manner, allowing the analysis of approximations that can separate and delimit a particular characteristic.
# A Review of Ensemble Learning Algorithms Used in Remote Sensing Applications
## 4 Applications of Ensemble Learning Algorithms
### Other Applications  
Other applications have used ensemble learning algorithms. In this section, we briefly mention some studies that recognized the importance of stacking.  
[PERSON] et al. [93] developed a two-layer stacking and blending ensemble method to predict daily reference evapotranspiration in which level 0 models included RF, SVR, multilayer perceptron neural network (MLP), and kNN. Both stacking and blending were significantly more accurate than the base models, and this approach is thus highly recommended for predicting reference evapotranspiration. [PERSON] et al. [46] used a stacking model to predict daily maximum air temperature that consisted of multiple linear regression (MLR) and support vector regression (SVR), and RF was optimized by SVR. The stacking ensemble method produced more accurate predictions than cokriging, three distinct data-driven methods, and a simple average ensemble model. [PERSON] et al. [71] showed that stacking was a suitable approach for short term electricity consumption forecasting. [PERSON] et al. [79] showed that stacking increased the accuracy of detection of forest change. They investigated a stacking model using both parametric and RF-based image fusion rules as the meta learner to combine several forest disturbance detection algorithms and found that stacking using an RF model to build the meta learner reduced the rates of errors of omission and errors of commission by 50% in some instances when compared to individual change detection methods and by 25% when compared with stacking using a logistic regression model as the meta learner.
###### Abstract
Calculations of GHG emissions in our study align with the new ISO 14083 standard, supporting these goals. This paper's other contributions are as follows: Our study considers a wide range of operational and logistics conditions from the real-world business environment, intending to provide helpful conclusions for the researchers and the practitioners. Furthermore, a one-year period is analyzed to ensure that the inventory-related, logistic, economic, and environmental results are consistent, relevant, and applicable. Such an extended analysis period also contributed to establishing research conditions that are difficult to ensure in a real-world business environment. The research model acknowledges normally distributed stochastic market demand and deterministic determined fill rates (FR), inventory review periods (R), lead times (LT), and vehicle types used for inventory replenishments. Lastly, the study analyses the influence of various workweek schedules (WS) on the SC's economic and environmental performance. According to the authors' knowledge, this is the first research that simultaneously analyzes operational and economic aspects of periodic review (R, s, S) inventory systems in SCs, along with the new ISO 14083 standard for quantification and reporting of GHG emissions arising from transport chain operations. The rest of the paper is organized as follows: Literature review and research context are presented in Section 2. The Materials and Methods of the research are elaborated in Section 3. Section 4 presents economic and environmental outcomes according to the ISO 14083 standard for supply chain utilizing periodic review (R, s, S) inventory system with an extensive description per workweek schedules, inventory review period, lead time, demand variability, and fill rates on a level of individual simulation experiments (SEs) and grouped. Finally, the conclusions, managerial implications, and recommendations for optimal green inventory management practices are presented in Section 5.
# Non-Parametric Model-Based Estimation of the Effective Reproduction Number for SARS-CoV-2 +
## 2 Materials and Methods
### Estimation of Time-Dependent Parameters in Non-Linear ODE Models via the Augmented Kalman Smoother  
The Kalman Filter is an iterative algorithm commonly used to estimate the time courses of unobserved states within a linear model by utilizing noisy observations collected over time. Alongside the Kalman Filter, the Kalman Smoother is a two-step algorithm involving a forward pass with the Kalman Filter followed by a backward-pass procedure of a similar nature. In scenarios involving non-linear models, the Extended Kalman Filter and Smoother are applied to linearize such models incrementally, enabling analysis. For the examination of time-varying parameters in non-linear models, the _Augmented Kalman Smoother_ (AKS) serves as a notable approach. The AKS combines the Extended Kalman Smoother with the capability to consider time-dependent parameters.
## 2 Laser Triangulation Principle  
The _active triangulation system_ (ATRIS) 3D imaging approach developed in our group is based on laser triangulation [25], one of the most well known optical range measurement approaches. Its schematics are depicted in Figure 1.  
In this scenario, a laser strip is projected onto a measurement object. The reflected light falls onto a receiving element (sensor). The point \(O(x,y,z)\) belonging to the projection at the object's surface is reflected at position \(P(v,u)\) on the sensor's plane depending on the distance to the object and the angle \(\alpha\) between the base line and the plane of the projected light. The base line connects the optical center of the lens and the laser source. Given the lens' focal length \(f\) and the length of the base line \(b\), the coordinates of the original point \(O(x,y,z)\) can be calculated by the Equation (1):  
\[\begin{bmatrix}x\\ y\\ z\end{bmatrix}=\frac{b}{f\ \mathrm{ctg}\bigl{(}\alpha\bigr{)}-u}\begin{bmatrix}v\\ u\\ f\end{bmatrix} \tag{1}\]  
The above algorithm is then repeated for all points of the reflected profile, while the laser strip is scanned across the object. In this way a dense range map representing a shape of the object can be acquired.  
This method of range measurement is appropriate for indoor applications such as profilometry or surface quality control. It is affordable since it does not require any expensive high-frequency circuitry needed for _time-of-flight_; it is relatively robust since it is not based on quantification of accumulated charge, _i.e._, brightness of pixel. Instead, it relies on identifying the coordinates of the brightest pixels. However, in circumstances of intricate ambient light it is rather tricky to distinguish the pixels illuminated by the laser from those illuminated by the external light sources.  
It is obvious that ambient light can always be overpowered by the laser, although this is strongly undesirable because of high energy consumption and eye-safety concerns. In order to improve the robustness of the captured data, the concept of _modulated laser triangulation_ has been introduced in [26] by our research group, where we have proposed several possible designs of range detectors utilizing the sequential frame acquisition controlled by random binary sequences. In this paper we are  
Figure 1: Laser triangulation principle.  
demonstrating appropriate binary sequences which guarantee the immunity to environmental factors and propose the methodology for evaluation of the system performance.  
In order to accelerate the acquisition of raw data required for 3D reconstruction, a multi-line pattern or another complex pattern can be projected onto a scene. Its reflection is then acquired as a _spatial distortion map_ in a single snapshot instead of scanning the scene by a single line step-by-step. This improvement coupled with modulated acquisition of spatial distortion maps makes the laser triangulation method promising for outdoors applications, such as collision avoidance, autonomous navigation, _etc._
# Research of Enzyme-Induced Carbonate Precipitation on Strength Behavior of Reinforced Sand
## 3 Results and Discussion
### Unconfined Compressive Strength
#### 3.1.5 Relationship Between Calcium Carbonate Content and Strength  
Figure 8 illustrates the variation in calcium carbonate content (\(W\)) of EICP-reinforced soil in relation to \(C\) across different \(D_{\rm r}\) and how this relates to the corresponding UCS. It can be seen that for a given \(D_{\rm r}\), \(W\) increases up to 7% with increasing \(C\), though this upward trend gradually slows. This is because a higher solution concentration provides  
Figure 6: Variation in UCS of EICP-reinforced sand with cementation solution concentration.  
Figure 7: Heatmap of Pearson correlation coefficients in this study.  
more reactants for the reaction, thereby producing more precipitate. Notably, at a \(C\) value of 1 mol/L and \(D_{\text{r}}\) = 0.7, the precipitation content peaks at 6%, coinciding with the maximum UCS value. This finding aligns with the results presented in Figure 6, suggesting a close correlation between the strength of EICP-reinforced soils and the quantity of calcium carbonate formed. Furthermore, for soil with the same \(C\), the calcium carbonate content diminishes as \(D_{\text{r}}\) increases, a trend that supports the observations from Figure 5. Based on the discussion above, one can find that for soil samples with a certain \(D_{\text{r}}\), soil strength is positively correlated to \(W\). However, strength is not solely determined by \(W\) when \(D_{\text{r}}\) varies.  
The relationship between calcium carbonate content and \(C\) shown in Figure 8 is nonlinear, indicating that only a portion of Ca\({}^{2+}\) participates in the reaction. To quantitatively describe the extent of the reaction, the average conversion rate of calcium ions (\(\eta\)) was calculated according to Equation (2).  
\[\eta=\frac{n_{\text{CaCO}_{3}}}{\eta_{0}^{\text{Ca}^{2+}}}\times 100\% \tag{2}\]  
where \(\eta_{\text{CaCO}_{3}}\) represents the molar amount of calcium carbonate produced and \(\eta_{0}^{\text{Ca}^{2+}}\) represents the initial molar amount of Ca\({}^{2+}\) in the cementation liquid.  
As shown in the Figure 9, with the increase in \(C\) from 0.5 to 2 mol\(\cdot\)L\({}^{-1}\), the conversion rate of calcium ions gradually decreased from 89.4% to 38.5%. This is because when the concentration of Ca\({}^{2+}\) exceeds 1 mol\(\cdot\)L\({}^{-1}\), Ca\({}^{2+}\) competitively binds to the active sites of urease, reducing the efficiency of urea hydrolysis by urease and thereby inhibiting the formation of calcium carbonate crystals. This is consistent with previous studies [31] that reported the inhibitory effect of Ca\({}^{2+}\) on urease activity.  
Figure 8: Relationships between \(W\) and UCS of different EICP-reinforced samples.
## 3 Results and Discussion
### California Bearing Ratio (CBR)  
California Bearing Ratio (CBR) tests were carried out to complement the Proctor test results. Figure 9a,b shows the stress applied of each specimen against the penetration of the undisturbed and oven-dried samples, respectively. For example, in the specimen prepared applying 56 hits per layer, the dry unit weight obtained for the undisturbed samples is equal to 9.1 kN/m\({}^{3}\), whereas for oven-dried samples it is equal to 10.7 kN/m\({}^{3}\). The CBR is determined at the penetration of 5.08 mm for both samples, with a value of 15.4 and 29.4%, for the undisturbed and oven-dried samples, respectively. Therefore, the CBR at 5.08 mm of penetration determined in the oven-dried specimen doubles the one obtained for the undisturbed samples. Thus, commercial laboratories should not dry allophanic soil samples in the oven to measure the CBR due to significant changes in their results. This is especially relevant as the obtained values for oven-dried samples are significantly higher, leading to unsafe designs. For example, for AASHTO technical standards, a CBR equal to 100 for the granular base layer and about 30 for the granular subbase is required. According to this standard, the oven-dried allophanic soil could be used as subbase material in pavement construction with a CBR of 29.4%, while the same soil in undisturbed conditions (with a CBR of 15.4%) could only be used as a subgrade. The variation of the CBR with the dry specific weight for both samples is presented in Figure 9c. The behavior of an intermediate state has not been evaluated in this work, such as drying in the air or under the shade.