esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Validation of ANN Models  
Several ANN models were trained using the 560 training sets and validated for 40 test sets. First, ANN models with two hidden layers were validated, as shown in the first graph of Figure 9. In the graph, nH means the number of the hidden layers. The \(x\)-axis represents the size of the first hidden layer, which is denoted as H\({}_{1}\). The \(y\)-axis describes the average points for the test sets, which is related to the accuracy of the ANN model. When the size of the first hidden layer is 6000, the accuracy of  
Figure 8: Twenty training sets for ANNs.  
Figure 7: Activation functions.  
the ANN model is 100%. However, the accuracy of the ANN model becomes 97.5% when the size of the hidden layer is 4000. Similarly, the number of the hidden layers increased from 2 to 11, so all generated ANN models were validated. In the case of the ANN models with 11 hidden layers, the size of the 11 th hidden layer became 5 or 6, even though the number of the first layer's hidden neurons was 5000 or 6000. When the number of hidden layers was 9, the accuracies of all generated ANN models were 100%.  
Figure 9: Accuracies of ANN models in the change of the number of hidden layers.
## 4 Experiments and Result Analyses
### Evaluation Metrics  
For each image, _Precision_ and _Recall_ can be calculated by comparing the detected defects with the ground truth. Then, the F1 score can be computed as an overall metric for performance evaluation.  
\[Precision=\frac{TP+TN}{TP+TN+FP+FN} \tag{10}\]  
\[Recall=\frac{TP}{TP+FN} \tag{11}\]  
\[\text{F1}=\frac{2\times Precision\times Recall}{Precision+Recall} \tag{12}\]  
where _TP_ (true positive) is correctly predicted positive examples; _FP_ (false positive) is wrongly predicted negative examples; _TN_(true negative) is correctly predicted negative examples; _FN_(false negative) is wrongly predicted positive examples.  
With precision as the vertical axis and recall as the horizontal axis, the precision-recall (PR) curve of the network can be obtained by changing the threshold for predicting a pixel as a defect. By calculating the area under the PR curve, the average precision (AP) of the network can also be obtained.  
The IoU of the defect area is one of the evaluation metrics of semantic segmentation. IoU calculates the ratio of the intersection and union between the defect and normal areas. IoU can be formulated as Equation (13).  
\[\text{IoU}=\frac{TP}{TP+FN+FP} \tag{13}\]
# Lithium Battery SOH Estimation Based on Manifold Learning and LightGBM
## 2 Algorithm Principle
### Landmark Isometric Mapping Algorithm (L-ISOMAP)  
The basic idea of landmark-isometric mapping (L-ISOMAP) is similar to MDS, that is, to find the \"geodesic distance\" between any two sample points from high-dimensional data, and to achieve the \"geodesic distance\" in the low-dimensional space by mapping. The \"geodesic distance\" remains approximately the same in the low-dimensional space. Compared with the ISOMAP algorithm, L- ISOMAP has a faster operation rate and wider application range and can represent the low-dimensional features of high-dimensional data well.  
The specific calculation steps of the L-ISOMAP algorithm are as follows:  
Select the appropriate landmark points. Select \(n\) landmark points from \(N\) samples, and then find the Euclidean distance between \(N\) all sample points and the selected \(n\) points to obtain the matrix \(d\), where \(d_{ij}\) represents the Euclidean distance between the sample points \(x_{i}\) and landmark points \(x_{j}\).  
Construct the adjacency graph. Each edge weight in the adjacency graph \(G\) is \(d_{E}(i,j)\), and if the sample points \(x_{i}\) and \(x_{j}\) are connected in \(G\), then the initial value of the shortest path between them is \(d_{G}(i,j)=d_{E}(i,j)\); otherwise, \(d_{G}(i,j)=\infty\). Let \(q=1,2,\ldots,N\) and calculate the following equation:  
\[d_{G}(i,j)=\min\{d_{G}(i,j),d_{G}(i,q),d_{G}(q,j)\} \tag{6}\]  
From this, the geodesic distances between all sample point pairs can be formed into a geodesic distance matrix at \(D_{G}=\{d_{G}(i,j)\}\) and the shortest paths can be calculated.  
Compute the low-dimensional embedding coordinates. Applying the MDS algorithm to the geodesic distance matrix \(D_{G}\), the following objective function is minimized to obtain the low-dimensional embedding coordinates of the sample \(Y\).  
\[E= ||\tau(D_{G})-\tau(D_{Y})||_{L^{2}} \tag{7}\]  
where \(\tau(D_{G})=-HSH/2\), \(S_{ij}=D_{ij}^{2}\), \(H=I-\frac{1}{m}ee^{T}\), and \(I\) denote the unit matrix and \(e\) denotes the unit column vector. The optimal embedding coordinates of L-ISOMAP are obtained by minimizing the objective function and finding the maximum eigenvector \(Y\).
# Deploying Secure Distributed Systems: Comparative Analysis of GNS3 and SEED Internet Emulator
## 6 Results and Evaluation
### Bandwidth and Throughput  
In this subsection, we present the results for the bandwidth and throughput of the network models. Figure 4 illustrates that the SEED Internet Emulator exhibited significantly higher bandwidth and throughput compared to the other models. This can be attributed to the model's design, which utilizes Docker containers and tightly integrates all resources. To measure bandwidth and throughput, we employed industry-leading tools in networkmonitoring, namely Wireshark and Iperf. When conducting these measurements, we considered the farthest pair of endpoints and repeated the measurement five times.
# Deep Learning Based Lithology Classification Using Dual-Frequency Pol-SAR Data
## 2 Materials and Methods
### Stacked Sparse Autoencoder
#### 2.2.2 Stacked Sparse Autoencoder  
A hierarchical training strategy is used in SSAE to construct the deep neural network to generate deep features of the input data. First, we establish several SAEs, one by one. Then train the first SAE with input features. After the first SAE is trained, regard the features obtained from the hidden layer as the input of the next SAE, for training. In this way, each SAE can be trained separately, which is called pre-training [27]. After pre-training, the decoder layers of all SAEs are removed and a softmax classifier is connected to accomplish the classification. Hence, the trained SAEs and the softmax classifier are connected to establish a whole network, which is called the SSAE. Finally, in order to obtain a more accurate classification, back-propagation is applied to adjust the parameters of the SSAE with the training samples and their labels. This step is called fine-tuning, which treats all layers of the SSAE as a single model.  
After pre-training and fine-training, a trained SSAE can be obtained. We can extract optimized features from the input data to obtain favorable results in classification.
## 4 Results  
### Accuracy Assessment  
A two-stage data fusion approach was used in this study, utilizing a time-series of Sentinel-1 polarization datasets. The SVM and Xgboost accuracy assessment results are listed in Table 2. The SVM has an overall accuracy of 97.1%, cross-validation score value of 89%, kappa value of 93.3%, and the conditional kappa coefficient of agreement of 90.54% and 95.7% for maize and non-planted classes, respectively. The Xgboost has an overall accuracy of 96.8%, cross-validation score value of 96%, kappa value of 92.6%, and conditional kappa coefficient of agreement of 90.4% and 94.4% for maize and non-planted classes, respectively. The maize classified pixels were similar for both classifiers based on the confusion matrix. The precision, recall, and F1-Score values for both algorithms have similar values that are more than 90% for both classes. It can also be noted that the recall for the planted maize class in both cases is approximately 3.7% lower compared to the precision score value. This observation is also supported by the kappa statistic and suggests that the planted maize class is less accurately classified compared to the non-planted class.  
These results show that the SVM and Xgboost produced an acceptable performance in mapping smallholder farms and illustrated the capability of two-stage image fusion employed in this study. In particular, both algorithms classified the non-planted area class better by approximately 5% compared to the planted maize class. The cross-validation score indicates that the Xgboost algorithm is more consistent and stable compared to the SVM algorithm. The Xgboost algorithm cross-validation score outperformed the SVM algorithm cross-validation score by 7%. This is in contrast with the other statistical measures (Table 2), which seem to suggest that SVM has outperformed the Xgboost algorithm.  
In situations where statistical evaluation matrices seem to contradict each other, a non-parametric statistical test must be conducted. In our case, we used [PERSON]'s significance test. If the estimated test statistic is lower than the critical chi-square table value (i.e., 3.84 at 95% confidence level), the null-hypothesis is rejected and it is concluded that there is no significant difference between the two model results [64]. The McNemar's chi-square value of 64.62 and \(p\)-value of 9.085 \(\times\) 10\({}^{-16}\) were obtained by comparing the two algorithms. We, therefore, reject the null-hypothesis and conclude that the two results are statistically different from each other.
## 4 Methodology
### Gravity Field Survey and Bouguer Gravity Anomaly Data
The United States' Geological Survey's (USGS) Earthquake SRTM DEM data were used for terrain corrections and calculated for each measurement point using the Geosoft OASIS Montaj program (url-3: [[https://earthexplorer.usgs.gov/](https://earthexplorer.usgs.gov/)]([https://earthexplorer.usgs.gov/](https://earthexplorer.usgs.gov/)) accessed on 17 May 2018). In order to apply the best corrections, SRTM 1-Arc Second Global was used as topography reference. The cell size of the raster data is 1-arc second, which is approximately thirty  
Figure 3: Bouguer gravity anomaly map of the study area (datum: WGS84 UTM Zone 36 N). meters inside the study area. The Oasis Montaj program uses the vertical prism method for terrain correction [46]. In order to apply terrain correction with the vertical prism method, two nested areas were determined to facilitate the calculations. The topography height data of more frequently sampled inner area and less frequently sampled outer area were prepared. The reason behind this process is that the effect of the inner area on the measured gravity value is much greater, and the topographic changes in the outer area are relatively ineffective [46]. In this study, the center of the study area was accepted as being the center of the inner and outer circle; the radius of the inner circle was chosen to be fifty kilometers and the radius of the outer circle was chosen to be one hundred and fifty kilometers. The Bouguer anomaly map does not reflect the features of surface geology, but it is related to lateral changes in density, depending on the depth. Therefore, Bouguer anomalies are expected to reflect all heterogeneities of subsurface geological structure and density. The Bouguer anomaly map shows northwest-southeast trending gravity contours. The trend from the northwest to the center of the study area is characterized by high gravity values.
# Analyzing the Alignment between AI Curriculum and AI Textbooks through Text Mining
## 3 Research Methods
### Data Collection  
In Korea, a basic AI curriculum was set to be established for high schools by 2020 [10]. The Introduction to AI curriculum comprises four areas: \"1. Understanding of AI\", \"2. Principles and application of AI\", \"3. Data and machine learning\", and \"4. Social impact of AI\". The curriculum document delineates key concepts, content elements, and learning elements for each area. The contents of the Basic AI Curriculum are presented in Table 5.  
\"1. Understanding of AI\" encompasses topics such as \"social changes and changes in career and occupation due to advances in AI technology\" and \"AI forming relationships with humans and being utilized as an intelligent agent\". \"2. Principles and application of AI\" covers aspects like \"types and characteristics of various AI approaches for implementing recognition, search, inference, and learning\" and \"acquiring principles of and differences between approaches based on actual cases\". \"3. Data and machine learning\" includes topics on \"data attribute perspective, machine learning, and classification models\" and \"problem-solving and performance evaluation methods\". Lastly, \"4. Social impact of AI\" focuses on \"social values of AI and impact recognition\" and \"social responsibilities and fairness practice as a member of AI society\".  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
Based on the basic AI curriculum, eight AI textbooks were developed in 2021. This study analyzed the text data of the main text in four units from the eight textbooks.  
Table 6 presents the frequency (ratio) of each content area in the eight textbooks [45; 46; 47; 48; 49; 50; 51; 52]. This refers to the frequency (ratio) calculated by dividing the \"number of pages allocated to each area\" by the \"total number of pages\" for individual textbooks. This is significant as through this, the understanding of the importance of each section in individual textbooks can be achieved.  
Unit 2 of all textbooks exhibited the highest ratios, ranging from 35.4% to 40.5%. Unit 3 had the second-highest ratio, ranging from 25.1% to 35.0%. The ratio of Unit 1 ranged from 13.5% to 17.5%, while that of Unit 4 ranged from 13.0% to 21.3%. In other words, Units 2 and 3, which displayed a high frequency of content and learning elements in the curriculum documents, held significant weight across all textbooks.
## 3 Results
### Event Scale Assessment  
Figure 8 shows the PDF plots for the rainfall event depths, durations, and intensities over the 2-year period. Overall, the IMERG precipitation product presents a good agreement with rain gauge data in terms of duration (Figure 8a) and depth (Figure 8b). However, the rainfall intensities between 0-5 mm/h are underestimated, while the 5-10 mm/h rainfall intensity class is largely overestimated (Figure 8c).
# An Application of Instantaneous Spectral Entropy for the Condition Monitoring of Wind Turbines
## 5 Results
### Signal #1
#### 5.1.2 Computational Efficiency  
The computational effort required by the feature extraction procedure was further tested. This is essential for online SHM since the whole algorithm (feature extraction and threshold validation) should be performed in real-time, i.e., during the acquisition of an uninterrupted data stream. This is generally performed via a small moving window of recent history. Therefore, the computational time needed should be smaller than the considered time window.  
Of the two main steps--TF transform and ISE(\(t\)) computation--the first one is the most demanding. Indeed, instantaneous SSE was performed (for the signal length considered here) in less than 0.6 s on average. This test, as well as the following ones, was performed on a laptop equipped with Windows 10 64-bit, Intel Core i7-7700 HQ with CPU 2.80 GHz and 16.0 GB RAM, and MatLab R2020b.  
The CWT was found to be slightly longer to perform. As it can be seen from Figure 12, the elapsed time is mostly equal for any pair of values in the inspected ranges of the two GWT parameters. Except for (\(\gamma=2\), \(\beta=40\)) and (\(\gamma=2.5\), \(\beta=10\)), which lasted, respectively, for 5.8 s and 3.5 s, the CWT ran in less than 2.0 s everywhere else. Considering  
Figure 11: Trends of ISE values for the normal conditions. (**a**): \(\mu\)(ISE) for varying \(\beta\); (**b**): \(\mu\)(ISE) for varying \(\gamma\); (**c**): \(\sigma\)(ISE) for varying \(\beta\); (**d**): \(\sigma\)(ISE) for varying \(\gamma\).  
all cases, the overall mean elapsed time is about 1.08 s (1.02 s excluding the two outliers), with many combinations running in <1 s.
# On the Synergy between Nonconvex Extensions of the Tensor Nuclear Norm for Tensor Recovery
## 3 Proposed Method  
To solve the above problem, we propose a method using \(\ell_{2}\) ball constraints and WPSPN minimization. Specifically, we formulate the following minimization problem:  
\[\min_{\mathcal{X}}\|\mathcal{X}\|_{\mathbf{w},\boldsymbol{\gamma},p}\quad \text{s.t.}\;\text{A}_{\Omega}(\mathcal{X})\in\text{B}(\mathcal{Y},\sigma_{n} \sqrt{|\Omega|}), \tag{9}\]  
where \(\text{B}(\mathcal{Y},r)\) is an \(\ell_{2}\) ball, and the \(\ell_{2}\) ball with center \(\mathcal{Y}\in\mathbb{R}^{n_{1}\times\cdots\times n_{N}}\) and radius \(r\in\mathbb{R}\) is defined as  
\[\text{B}(\mathcal{Y},r):=\{\mathcal{X}\in\mathbb{R}^{n_{1}\times\cdots\times n _{N}}\|\mathcal{X}-\mathcal{Y}\|_{2}\leq r\}. \tag{10}\]  
By using the ball constraint, it is possible to determine the appropriate parameters based only on the variance of the noise [25, 26, 27], which is convenient when comparing various regularization parameters, as in this paper. Additionally, \(|\Omega|\) is the number of elements of the set \(\Omega\). Since we assume that the standard deviation of noise \(\sigma_{n}\) is known, we can expect the realization of the noise \(\mathcal{V}\) added to the original tensor to exist inside the hypersphere determined by the standard deviation. The constraints of Equation (9) are in accordance with this fact. This method allows us to \"fairly compare the performance of different regularization terms (if the variance of the noise is known)\". In general, Equation (9) is a nonconvex optimization problem, which makes it difficult to find a globally optimal solution. As mentioned in Section 2, ADMM exhibits empirical performance on nonconvex optimization problems. Therefore, we propose solving Equation (9) using ADMM. The proposed algorithm is shown in Algorithm 1. The objective function in line 5 of Algorithm 1 is  
\[\underset{\mathbf{X}}{\operatorname{argmin}}\,\|\mathbf{X}\|_{\lambda\gamma_ {n}\mathbf{w}_{m},p}^{p}+\frac{1}{2}\|\text{unfold}_{m}(\mathcal{X}^{(k+1)})+ \mathbf{Z}_{1,m}^{(k)}-\mathbf{X}\|_{F}^{2} \tag{11}\]which is nonconvex, although one of the solutions can be written as [8; 18; 20]:  
\[\mathbf{US}_{\lambda\gamma_{m}\mathbf{w}_{m},p}(\Sigma)\mathbf{V}^{\top}, \tag{12}\]  
where \(\mathbf{U}\Sigma\mathbf{V}^{\top}=\text{unfold}_{m}(\mathcal{X}^{(k+1)})+ \mathbf{Z}_{1,m}^{(k)}\) is a singular value decomposition (SVD) and \(\mathbf{S}_{\mathbf{w},p}(\cdot)\) is a weighted thresholding operator.
## 8 Empirical Findings  
For the analysis, both factor analysis and structural equation modeling were used. Confirmatory factor analysis (CFA) is considered a suitable technique to describe whether the listed items explain the exact construct or not. Figure 1 shows the structure of the confirmatory factor analysis for the present study. Four major constructs--eco-labeling (EL), green packaging and branding (GPB), green products, premium, and pricing (GPPP) and environmental concerns and beliefs (ECB)--were considered from a previous study [27]. The value for the goodness of fit was measured using chi-square tests, root mean square error approximation (RMSEA) goodness of fit index (GFI), adjusted goodness of fit index (AGFI), comparative fit index (CFI) and Tucker Lewis index (TLI), as they are accepted methods for our analysis, as explained in the literature [60; 61]. We calculated the goodness of fit indices and the value of composite reliability is presented in Table 1. For all constructs, the value of CR is >0.70 and explains the convergent validity for the model. Additionally, Table 2 presents the value of the symmetric matrix between the sample covariance and implied covariance of the model. For the goodness-of-fit analysis, a small difference is suggested such as in [62]. After deleting items which had a factor loading of <0.50, Figure 2 shows the appropriate loadings for the remaining items. The values of the factor loadings for the CBTE are also presented. After the consideration of the CFA, a structural equation model was developed, considering the green packaging and branding (GPB), green products, premium, and pricing (GPPP) and environmental concerns and beliefs (ECB) factors as latent variables. Figure 3 provides the structure model of the study. Findings for each construct with the consumer belief towards the environment (CBTE) are explained below. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Indices** & **Result** & **Decision** \\ \hline Chi-square & 344.798 & Accepted \\ Root mean squared error approximation (RMSEA) & 0.054 & Accepted \\ GFI & 0.907 & Accepted \\ Adjusted goodness of fit index (AGFI) & 0.881 & Accepted \\ Comparative fit index (CFI) & 0.904 & Accepted \\ Tucker Lewis index (TLI) & 0.899 & Accepted \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Fitted values for CFA. Figure 1: Measurement model of the study: confirmatory factor analysis (CFA), with the input and output path diagram. Note: Eco-Labeling (EL), Green Packaging and Branding (GPB), Green Products, Premium, and Pricing GPPP.
# The Role of Macrophage Dynamics in Atherosclerosis Analyzed Using a Petri Net-Based Model
## 2 Materials and Methods
### Petri Nets
#### 2.2.2 IL-23/IL-17/G-CSF Signaling Axis Leading to HSPCs Mobilization from the Bone Marrow  
The impaired regulation of cholesterol levels results in elevated IL-23 levels, a key pro-inflammatory cytokine. This stimulates the IL-23/IL-17/G-CSF pathway, increasing neutrophil production [32, 36]. Within the IL-23/IL-17/G-CSF axis, IL-23 triggers the transformation of naive forms of helper T cells into Th17 cells, known for their significant pathogenic potential and ability to secrete IL-17. The production of G-CSF, mediated by IL-17, further activates neutrophils and other myeloid cells. Their signaling to progenitor cells in the bone marrow results in an imbalance in myeloid cell production, with a preference to generate more neutrophils rather than monocyte lineages [5, 32, 33, 35, 37]. The excessive production of neutrophils subsequently leads to the initiation of two important processes. First, neutrophils create a more proteolytic environment within the bone marrow, leading to the cleavage of a crucial receptor, C-X-C motif chemokine receptor type 4 (CXCR4), which is responsible for the retention of HSPCs [38, 39, 40]. Second, shifting differentiation preference away from monocytes/macrophages leads to a decrease in the CXCR ligand CXCL12 in both mesenchymal stem cells and osteoblasts [35, 41, 42].  
Figure 1: An overview of the general framework outlining the necessary steps that should be taken to construct the model, perform the analysis, and achieve the outcomes.  
In summary, impaired cholesterol clearance pathways significantly impact the osteoblastic environment and HSPCs' mobilization via the IL-23/IL-17/G-CSF pathway [38]. This underscores the critical link between impaired lipid metabolism and immune signaling in the management of the bone marrow environment and stem cell activity.