esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 DE Algorithm  
Differential evolution is a robust, stochastic, nonlinear global optimization method [24]. Due to the comparatively large collection of points it maintains, it is reasonably resilient but typically slower than other approaches. This approach was utilized in this study to identify the best optimized tuned mass damper (TMD) design parameters because of its resilience and success for global optimization. Moreover, this method is not widely used for the optimal design of TMD for response reduction of the cantilever beam.  
No optimization algorithm exists that always ensures global resolution, particularly for nonlinear, non-differentiable, and non-continuous issues. The proposed DE method is an effective and rigorous stochastic population-based algorithm that generally tries to achieve a globally optimal solution. Four fundamental steps are undertaken in evolutionary algorithms, including initialization, mutation, recombination, and selection, which are expressed as follows [25; 26; 27].  
Definition of the Target Vector under the Lower and Upper Bounds and Determination of the Population Matrices  
Let \(\overline{\mathbf{X}}_{i}^{g}\) be the target vector for the \(g^{th}\) generation and \(i^{th}\) population. This vector can be expressed as  
\[\overline{\mathbf{X}}_{i}^{g}=\{x_{1,i^{\prime}}^{g}\,x_{2,i^{\prime}}^{g},\ldots, x_{D,i}^{g}\},\hskip 28.452756 pti=1,\ldots,\,\text{NP} \tag{17}\]  
in which \(D\) shows the dimension of the problem (number of variables to be found) and NP is the number of populations, which can usually be chosen between \(2D\) and \(4D\)[28]. In this study, the stiffness, mass, and damping coefficients of TMD are the dimensions of the problems. Therefore, the number of the population (NP) can be determined between \(2\times 3=6\) and \(4\times 3=12\). For the \(i^{th}\) population, the target vector can be determined as \(x_{1,\ i}^{g}=m_{d,\ i^{\prime}}^{g}\), \(x_{2,\ i}^{g}=c_{d,\ i}^{g}\)\(x_{3,\ i}^{g}=k_{d,\ i}^{g}\) for the coefficients of the TMD.  
In the beginning, the generation number \(g\) is set equal to zero. The \(j^{th}\) dimension of the \(i^{th}\) population of the problem can be calculated as  
\[x_{ij}^{0}=x_{j,Low}+rand_{ij}(0,1).\,\left(x_{j,Up}-x_{j,Low}\right) \tag{18}\]  
In this equation, \(x_{j,Low}\), and \(x_{j,Up}\) are the lower and upper bounds of the \(j^{th}\) dimension, respectively. \(rand_{ij}(0,1)\) is a random real number between [0] and [1] for the \(i^{th}\) population.
## 2 Methodology  
### Object-Based Image Analysis  
A high-resolution (50-cm) land cover map of Chattanooga was used and created by the University of Tennessee at Chattanooga IGTLab using object-based image analysis classification [33]. Stakeholders requested the most recent and highest-resolution multispectral imagery available and within the scope of the project budget. Funding from the Lyndhurst Foundation facilitated the tasking of high-spatial-resolution multispectral images of Chattanooga acquired from Planet's SkySat constellation, a series of 21 high-resolution space-based Earth-imaging satellites in orbit around the Earth. The spatial resolution of SkySat is approximately 50 cm, meaning that every picture element, or \"pixel\" in a SkySat image, represents 50 cm on the Earth's surface. Over the course of the late summer and early fall of 2021, Planet tasked its satellites to collect imagery on cloud-free days, supplying the research team with thousands of raw images in the form of 6 mosaic raster datasets to analyze and classify into land cover data.  
During 2021 and early 2022, each of these 6 mosaics of orthorectified pansharpened imagery were segmented and classified into a thematic land cover map using object-based image analysis. In preparation for segmentation, the red, green, and blue bands were extracted from the imagery using the \"Extract Bands Raster Function\" within ArcGIS Pro. Segmentation was performed using the \"Segment Mean Shift\" geoprocessing tool after the optimal segmentation parameters were determined using the \"Segment Mean Shift\" raster function within ArcGIS Pro 3.2, Esri, USA, to save time and computational processing. Following imagery segmentation, training samples for image classification were collected using the \"Training Samples Manger\" within ArcGIS Pro 3.2, Esri, USA. Between 150 and 300 samples were collected for each land cover class, for each mosaic. Using these training samples, the segmented images were classified into a thematic land cover layer using the \"support vector machine\" supervised classification algorithm, as it is known to work well with an unbalanced number of classes and/or small sample sizes in each class. This resulted in 6 thematic land cover maps derived from the original imagery mosaics, which were combined into one land cover map of the city. This process was performed several times to achieve optimal results.  
The resulting raster was found to have a 91% accuracy rate with a 0.9 kappa coefficient by sampling 500 random points across imagery and checking for their correct land cover classification (Table 1) The resulting land classes included forests, non-forest vegetation, developed areas, water, and bare earth (Figures 2 and 3). These generalized classes were chosen to assess major land cover groups within the project duration, as a simplified alternative to the NLCD classes. This thematic 2021 land cover map of Chattanooga is the highest-resolution and most accurate map ever produced for the city.
## 2 Performance Metrics
### Color Gamut and Color Shift
The experimental procedure to measure color shift is described as follows: we first measured the white spectrum using a high-resolution spectrometer (HR2000 CG-UV-NIR); then, we rotated the angle of the optical fiber used for collecting white light into the spectrometer and measured the white spectrum again at different viewing angles. Figure 5a shows the measured white spectra of the mLCD and the OLED display at 0\({}^{\circ}\) and 60\({}^{\circ}\) viewing angles. From Figure 5a, we can see that the mLCD backlight adopted two phosphors to widen the color gamut, while the blue shift caused by the cavity effect was noticeable for the OLED panel. As Figure 5b depicts, the mLCD exhibited a weaker color shift than the OLED display. The weaker color shift of the mLCD benefited from the small color shift of the fringing field switching (FFS) mode [26]. It should be mentioned that the color shift  
Figure 4: (**a**) Measurement procedure and setting for the color gamut volume and (**b**) measured color gamut volumes of the mLCD and the OLED display. remains indistinguishable by human eyes if \(\Delta\)u'v' \(<\) 0.02. This means that both panels are fine in terms of color shift within the 60\({}^{\circ}\) viewing cone.
## 3 Results
### Effects of PGPB on Canola Exposed to Saline Conditions  
#### 3.3.1 Water Content and Osmolyte Synthesis  
Water content was reduced in plants grown in saline soil (Figure 2); nevertheless, the presence of bacteria significantly enhanced the plant water content in this condition (Figure 2). The presence of _A. globiformis_ significantly ameliorated the response to salt in the water content and osmolyte synthesis (Table 2). This improvement was also detected in the absence of added salt (Table 2). The other strains did not significantly influence proline synthesis.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **Shoots (cm)** & **Roots (cm)** & **Fresh Weight (g)** \\ \hline N.I.C. & 12.68 \(\pm\) 0.34 a & 8.48 \(\pm\) 0.37 a & 1.52 \(\pm\) 0.12 a \\ N.I.S. & 10.71 \(\pm\) 0.21 b & 8.17 \(\pm\) 0.57 a & 0.95 \(\pm\) 0.07 b \\ A.C. & 15.62 \(\pm\) 0.37 a *** & 9.34 \(\pm\) 0.49 a & 1.74 \(\pm\) 0.12 a \\ A.S. & 12.87 \(\pm\) 0.17 b * & 6.88 \(\pm\) 0.62 b & 0.9 \(\pm\) 0.07 b \\ B.C. & 19.73 \(\pm\) 0.17 a *** & 16.92 \(\pm\) 0.57 a *** & 3.78 \(\pm\) 0.09 a *** \\ B.S. & 14.58 \(\pm\) 0.48 b *** & 8.28 \(\pm\) 0.31 b & 1.77 \(\pm\) 0.07 b *** \\ G.C. & 19.24 \(\pm\) 0.49 a *** & 18.98 \(\pm\) 0.51 a *** & 4.15 \(\pm\) 0.17 a *** \\ G.S. & 13.56 \(\pm\) 0.31 b *** & 8.77 \(\pm\) 0.29 b & 1.91 \(\pm\) 0.07 b *** \\ H.C. & 18.66 \(\pm\) 0.55 a *** & 10.35 \(\pm\) 0.41 a & 2.65 \(\pm\) 0.08 a *** \\ H.S. & 13.91 \(\pm\) 0.62 b *** & 7.64 \(\pm\) 0.28 b & 0.85 \(\pm\) 0.08 b \\ U.C. & 15.63 \(\pm\) 0.21 a *** & 8.6 \(\pm\) 0.39 a & 1.4 \(\pm\) 0.09 a \\ U.S. & 13.47 \(\pm\) 0.24 b *** & 7.41 \(\pm\) 0.45 a & 1.1 \(\pm\) 0.08 a \\ \hline \end{tabular}
\end{table}
Table 1: Shoot length, roots length, and fresh weight of _B. napus_ cv. SY Saveç grown for 30 days on non-saline soil (C) or saline soil (S). Plants were inoculated with plant growth-promoting bacteria (A = _A. brasilense_, B = _B. amblifaria_, G = _A. globiformis_, H = _H. serological_, U = _Pseudomonas_ sp. UW4) or not (N.I. = non-inoculated). Different letter means significant difference within the same bacterial treatment. Significant differences between inoculated or non-inoculated plants grown at the same soil condition are marked as * (_p_ < 0.05), *** (_p_ < 0.001) (_n_ = 16).  
Figure 1: Growth curves of _A. brasilense_ (**A**), _B. amblifaria_ (**B**), _H. serological_ (**C**), and _Pseudomonas_ sp. (**D**) in TSB media, along with different amounts of NaCl.
# Exploring the Cutting Process of Coaxial Phase Change Fibers under Optical Characterization Tests
## 2 Materials and Methods
### Fourier-Transform Infrared Spectroscopy (FTIR) and Index of Functional Groups  
#### 2.7.1 FTIR  
The chemical Fourier transform infrared spectroscopy (ATR-FTIR) test was employed to evaluate the chemical composition of the PCFs before and after hot cutting, as well as to investigate structural changes of the PEG 2000, with a specific focus on the calculation of the functional group area index as a standard approach to estimate possible PEG leakage from the inside of the PCFs.  
The chemical and surface compositions of PCFs pre and post-controlled washing were analyzed using attenuated total reflection Fourier transform infrared spectroscopy (ATR-FTIR) IRAffinity-IS (Shimadzu, Kyoto, Japan), coupled to a HATR 10 accessory with a diamond crystal. The transmittance spectra were obtained in a wavenumber range of 400-4000 cm\({}^{-1}\), with a scanning velocity of 200 scans and a resolution of 2 cm\({}^{-1}\) (Figure 8). The chemical peaks were identified to check the presence of PEG, CA, and DMF in each PCF.
# Comparison of Dimensionality Reduction Approaches and Logistic Regression for ECG Classification
## 3 Materials and Methods
### Dimensionality Reduction
#### 3.1.1 Principal Component Analysis (PCA)  
Principal Component Analysis (PCA) is a linear projection technique that transforms correlated input features into orthogonal components by maximizing variance [9]. The projection is defined by the eigenvectors of the covariance matrix. In our experiments, the first three principal components, explaining over 85% of the total variance, were retained to align with the 3D embedding used in the alternative method (UMAP) and ensure fair comparison.
## 2 Materials and Methods
### Model Evaluation and Validation  
One way to justify how well the model works for the SRT dataset is to evaluate the model performance using standard statistical metrics. The model is estimated using a testing dataset and then returns the model validation metrics for both regression and classification problems. As the SRT problem falls into both classification and regression, the validation metrics, such as mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), coefficient of determination (R\({}^{2}\)), and confusion matrix (CM), were used.  
Among all the models created using a different set of hyperparameters, the one having the smallest MSE, RMSE, and MAE but the highest R\({}^{2}\) was selected to be the best fit for a regression problem. MSE and RMSE are the most popular metrics for the regression task because of the theoretical relevance in statistical modelling despite being sensitive to outliers [51]. RMSE poses a high penalty on large errors through the defined least-square terms, which implied that it is useful for improving the model performance, especially when the model errors follow a normal distribution [52].  
MAE is the average of the absolute difference between the predicted and actual values. It is suitable to portray the errors that show a uniform distribution [51]. Besides, MAE is the most natural and precise measure of the average error magnitude [53].  
R\({}^{2}\) is a scale-free score that does not provide the model residuals' information because it only determines the data's dispersion, not the bias [54]. The computation of the regression validation metrics is shown in Equations (14)-(17).  
\[MSE=\frac{1}{n}\sum_{j=1}^{n}\left(y_{j}-\hat{y}_{j}\right)^{2} \tag{14}\]  
\[RMSE=\sqrt{\frac{1}{n}\sum_{j=1}^{n}\left(y_{j}-\hat{y}_{j}\right)^{2}} \tag{15}\]  
\[MAE=\frac{1}{n}\sum_{j=1}^{n}\left|y_{j}-\hat{y}_{j}\right| \tag{16}\]  
\[R^{2}=1-\frac{\sum_{j=1}^{n}\left(\hat{y}_{j}-\overline{y}\right)^{2}}{\sum_ {j=1}^{n}\left(y_{j}-\overline{y}\right)^{2}} \tag{17}\]  
In Equations (14)-(17); \(n\) is the total number of observations in the dataset; \(y_{j}\) is the actual value for the \(j^{th}\) observation; \(\hat{y}_{j}\) is the predicted value for the \(j^{th}\) observation; and \(\overline{y}\) is the mean of the actual value.  
In contrast, for the classification problem, the highest classification accuracy (ACC) was selected as the best fit model. The confusion matrix consists of four components, which are the true positive (TP), true negative (TN), false positive (FP), and false negative (FN). The ACC can be calculated using CM's component, as shown in Equation (18):  
\[ACC=\frac{TP+TN}{TP+TN+FP+FN} \tag{18}\]  
In Equation (18), \(TP\) is when the actual and predicted output are both 1; \(TN\) is when the actual and predicted output are both 0; \(FP\) is when the actual output is 0, but the predicted output is 1; and \(FN\) is when the actual output is 1, but the predicted output is 0. The result of the 1D-CNN models for each set in Table 2 is shown in Section 3.
## 2 Results and Discussion
### Sensory Analysis
In 2018, MS30 and MS33 wines were characterized as having aromas of fruit and exotic fruits like peach and apricot, while the MS82 wine showed more characteristics of aromatic herbs and some chemical aromas (sulfur or solvent notes). In this year, Verdejo still had marked floral and citrus notes although the MS30 and MS33 wines stood out for their fruity notes. Again in 2019, Syrah crosses (Figure 2C2) were characterized by the fruity wines they produced, especially MS33 with notes of young red fruit, some balsamic notes, and fresh herb with lemon leaf, all more intense than in Verdejo. The MS82 wine had aromas from the spice series (menthol), floral notes (citronella), some notes of green apple, and eucalyptus aromas and the MS30 wine had more aromas of green apple, lemon, pineapple, and fresh herbs than Verdejo wine. Finally, the wines obtained from the Cabernet crosses are shown in Figure 2D. In the 2018 season (D1), the wine from MC69 was considered as floral (white flowers) with pleasant notes of aromatic herbs like mint or peppermint, and scored better than the Verdejo wines for their organoleptic characteristics, while the MC180 wine showed the same notes but less pronounced, probably due to the maturity of the grape. MC180 also had peach and lychee aromas and generally had more subtle aromas than Verdejo. As regards the data obtained for 2019 (Figure 2D2), the mint and peppermint aromas were enhanced, as were white floral aromas (jasmine) in the MC69 wine and, probably due to the later harvest, it also had notes of red fruit (apricot), lychee, and sweet spices like ginger. MC180 was also valued positively for being a well-structured aromatic wine with aromas recalling peach, passion fruit, peppermint, some citric notes, and ginger and spicy notes. In summary, as can be seen in Figure 2D2 the aromatic quality of the wines obtained from these crosses was notably superior to that of Verdejo wine. According to the data outlined in Figure 2A2, B2, C2, and D2, and the views of the tasters, it could be seen that wines from 2019 were fresher and more pleasant than those from 2018, very probably as a result of the earlier harvest time in 2019.
## 4 Numerical Examples
### Example 2  
In order to verify the proposed hybrid boundary-type meshless method, we considered a homogeneous and isotropic example with a complex boundary shape. As shown in Figure 7, this example is a two-dimensional steady state heat conduction problem [39]. The governing equation of the example enclosed by \(\Omega\) can be expressed as  
\[\
abla^{2}u(\mathbf{x})=0\text{ in }\Omega \tag{31}\]  
The two-dimensional object boundary under consideration is defined as:  
\[\Gamma_{1}=\{\,(x_{1},y_{1})|x_{1}=\rho(\theta_{1})\cos\theta_{1},y_{1}=\rho( \theta_{1})\sin\theta_{1}\},\;\rho(\theta_{1})=1,\;0\leq\theta_{1}\leq 0.5\pi \tag{32}\]  
\[\Gamma_{2}=\{\,(x_{2},y_{2})|x_{2}=0,\;0\leq y_{2}\leq 1\} \tag{33}\]  
\[\Gamma_{3}=\{\,(x_{3},y_{3})|0\leq x_{3}\leq 2,\;y_{3}=0\} \tag{34}\]  
\[\Gamma_{4}=\{\,(x_{4},y_{4})|x_{4}=2,\;0\leq y_{4}\leq 2\} \tag{35}\]  
\[\Gamma_{5}=\{\,(x_{5},y_{5})|1\leq x_{5}\leq 2,\;y_{5}=2\} \tag{36}\]  
The analytical solution may be found as  
\[u=e^{x}\sin y \tag{37}\]  
For solving this example, the order of the particular non-singular basis function \(P\), is 15. The boundary condition is assumed to be the Dirichlet boundary condition using the analytical solution which is expressed as Equation (37). The boundary and source points are uniformly distributed on the boundary for the hybrid boundary-type meshless method, as demonstrated in Figure 8.  
We first compared the computed temperature of the inner collocation points in the computational domain with those from the analytical solution, as shown in Figure 9a. We obtained highly accurate numerical solutions in the order of \(10^{-11}\) for this problem. Figure 9b demonstrates the accuracy of the element-free Galerkin method (EFG) [39] and the improved interpolating element-free Galerkin (IIEFG) method [39]. It was found that the accuracy of the EFG and the IIEFG methods can only reach to the order of \(10^{-3}\) for the same problem. Figure 10 shows the comparison of the computed results with the analytical solution of temperature along line OA. In addition, Figure 11 depicts a plot of the comparison of the computed temperature distribution with the analytical solution. From these figures, it can be observed that the numerical solution agrees very well with the analytical solution. Again,  
Figure 6: Comparison of the temperature distribution with the analytical solution.  
Figure 10: The comparison of the computed results with the analytical solution of temperature along line OA.  
Figure 9: Comparison of computed results of the proposed method. **(a)** Comparison of results with the analytical solution and **(b)** comparison of results with the EFG and the IEFG methods.
## 3 Proposed Methodology
### Proposed Framework  
In the proposed framework, we perform the following steps for building a graph neural network model based on betweenness centrality.  
Step (i): We consider data that can be represented in terms of a graph structure (such as citation networks) as input. The set of nodes (\(A\)) and the feature matrix (\(X\)) of the underlying graph serve as input.  
Step (ii): After the input, the betweenness centrality (Bet) is calculated for all nodes using k Equation (11).  
Step (iii): Nodes are selected for the training based on betweenness centrality values. Preference is given to nodes with high betweenness centrality values. A subset of nodes (generally top 60% nodes in terms of betweenness centrality value) are selected for the training.  
Step (iv): The chosen training set nodes along with the feature matrix(\(X\)) are given to the Graph Attention Network (GAT) for training.  
Step (v): Finally, we obtain the classification of nodes as output. In addition, we display the visualization of GAT embeddings for the underlying dataset as \(t\)-distributed stochastic neighbour embedding (TSNE).  
We consider the Cora citation network to explain the whole process. The Cora dataset includes 2708 scientific publications classified into one of seven classes. In this dataset, the nodes and edges represent documents and citation links, respectively. This dataset is in the form of a set of nodes (\(A\)) and a feature matrix (\(X\)). Equation (10) is used to represent the betweenness centrality (Bet) of each node from smallest (blue) to largest (yellow) (as in Figure 5). Here, the set of nodes \(A\) is divided into two subsets \(A^{\prime}\) and \(A^{\prime\prime}\). The subset \(A^{\prime\prime}\) is the set of nodes selected for training (with high values for centrality, i.e., \(A^{\prime\prime}\subset A\)). For further training, we select the set of nodes with the feature matrix GAT Velickovic, P. et al. [29]. Then, we use ELU (exponential linear unit) [40] followed by Softmax activation. Lastly, the result is shown as t-distributed stochastic neighbor embedding (TSNE) visualization of GAT embedding for the Cora dataset. Here, each color represents a unique class (category) of the Cora dataset. The dataset has 7 classes and hence, 7 different colors are visible in the TSNE.
## 4 Conclusions  
In this paper, we aimed to improve the prediction accuracy, reduce the influence of uncertainty of driving intention and dynamics of the leading vehicle on the prediction results, and construct a prediction algorithm based on driving intention classification. In order to accurately predict the future actions of the leading vehicle, instead of just predicting the coordinate points, this paper used the LSTM algorithm to predict the heading angle of the leading vehicle and the GPR algorithm to predict the speed. It combined them to calculate the future trajectory.  
First, the impact on the future trajectory of the vehicle ahead under different driving intentions was fully considered, and the FCM algorithm was used to train the driving intentions of the vehicle ahead offline by taking the previous trajectory information and acceleration as input and using the information related to the speed and trajectory of the vehicle ahead under various complex working conditions. The output of FCM was the probability that the current input belongs to each category, and the final classification result was a probability set by the training dataset and the output classification probabilities to improve the resolution and accuracy of classification.  
Second, the GPR model and the LSTM model were trained with the historical and current moment speed data and trajectory data of different driving intentions, and after the training was completed, the models output the speed and heading angle prediction results under different driving intentions.  
Figure 5: Comparison figure of multistep prediction error.  
Figure 6: Figure of multistep trajectory prediction results.  
Third, the FCM output affiliation function was used as the weight of each model to realize the variable gain weighted fusion of different driving intention prediction results and the joint calculation of heading angle and speed. An iterative rolling method predicts the target's trajectory in the next 1s.  
Finally, the algorithm was verified using real vehicle data, and the results showed that none of the horizontal prediction errors of the future 1s trajectory prediction results were greater than 0.013 m. All prediction results' maximum values of horizontal distance prediction error reached 0.56 m. The vertical prediction error was no more than 0.025 m, and the maximum prediction error of the vertical distance among all the prediction results reached 0.63 m.  
Conceptualization, W.M. and Z.W.; software, W.M.; validation, W.M.; resources, W.M. and Z.W.; data curation, W.M.; writing--original draft preparation, W.M.; writing--review and editing, W.M. and Z.W.; visualization, W.M.; supervision, Z.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.