esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Project Overview
### Building a Numerical Model with Different Angles Between Tectonic Stress and the Roadway
At an angle of 30\({}^{\circ}\), the shear failure of the roadway's roof and floor is exacerbated. The roadway's roof strata are experiencing shear failure. When the angle is 45\({}^{\circ}\), the deepest point of failure on both sides of the roadway moves from the middle to the shoulder angle. At this point, the shoulder angle of the roadway looks like it is experiencing tensile-shear composite failure. At an angle of 60\({}^{\circ}\), the failure range on both sides of the roadway tends to stay the same. The shear failure at the top and bottom angles moves deeper, and the plastic zone's maximum depth is at the shoulder angle. When the angle is 90\({}^{\circ}\), the plastic zone of roof shear failure spreads even more into the deep rock stratum. The plastic zone has the biggest failure range, and the rock around the roadway is the most damaged. Quantitative analysis shows that, at an angle of 0\({}^{\circ}\), the plastic zone extends approximately 4.0 m into the roof and floor, and up to 8.0 m into both sidewalls. At 45\({}^{\circ}\), the maximum shear failure depth at the shoulder angle is about 5.5 m, and 4.5 m in the roof. When the angle reaches 90\({}^{\circ}\), the failure zone penetrates the deepest, extending to 7.0 m in the roof, 6.4 m in the floor, 7.2 m at the shoulder angles, and approximately 6.0 m into both sidewalls. In conclusion, when the roadway is positioned within the tectonic stress zone, the roof and floor strata are directly influenced by horizontal tectonic stress. The roof and floor are subjected to horizontal compression, resulting in shear failure of the roof and buckling failure of the floor. The two sides of the roadway are currently exhibiting a yield state following the damage incurred. As the angle progressively rises from 0\({}^{\circ}\) to 90\({}^{\circ}\), there is a noticeable increase in the plastic zones of the roof, floor, and both sides. Following the shear failure at the shoulder angle, the plastic zone extends into the deeper rock layer, exhibiting the most significant growth. The angle of the roadway roof and shoulder was identified as the location of maximum plastic strain accumulation. Although the observed relationship between tectonic stress angle and plastic zone depth remains generally valid, specific values and failure patterns may vary depending on rock mass properties, burial depth, and local structural features. Therefore, extrapolation to other sites should account for differences in geological conditions.
## 2 Results of Literature Analysis
### Content Analysis
#### 2.2.3 Constraints
This will reduce the turnaround time and increase the profitability of the company. The authors mentioned that over time, SC configurations with AM are going to pay off due to the rapid advancement in technology development. However, [PERSON] et al. [19] mentioned that the distributed AM facilities are not a prescription for all spare parts, as they cannot guarantee a faster turnaround time when there is a high demand from different clients. Therefore, a mixed SC configuration could still be an option for the time being. [PERSON] et al. [23] studied the technical barriers related to AM technology, such as materials, accuracy levels, additive manufacturing chamber, 3D model, and postprocessing capabilities. The author investigated the economic barriers related to digital spare production costs, investment costs, employees' skills, and supplier contracts. [PERSON] et al. [52] utilized three different AM technologies, namely vat photopolymerization (VP), material extrusion (ME), and powder bed fusion (PBF), to produce medical spare parts. These spares included face shields, facemasks, nasal swabs, and venturi valves, which were in high demand, especially during the COVID-19 pandemic. Vat photopolymerization (VP) is used the most in the medical field as it supports biocompatible materials. However, additive manufacturing technologies are being adopted in various applications and industries; however, some limitations still exist, such as the printing time, raw material cost, and the need for post-processing. [PERSON] [43] mentioned the availability and use of metal powders for laser-based AM. The authors mentioned that with this type of technology, owing to the optimized geometry of the parts for manufacturing, the cost of production and the management of SC could be cheaper. Technology is advancing in AM; however, the main factors in spare parts manufacturing could be the suitability of the technology, material supply, and efficiency in terms of costs and emissions. As AM technology is considered a constraint, decision makers need to analyze the available technology and match it with the demand pattern, lifecycle costs of operation, and the quality of spare parts produced. Once the investment is made, changes to be made in manufacturing and SC configurations can be very costly.
# Battery Surface and Edge Defect Inspection Based on Sub-Regional Gaussian and Moving Average Filter
## 3 Principle of Defect Inspection Algorithm  
### ROI Extraction  
Region of interest (ROI) extraction is the first step of image processing. The original image captured by the linear CCD array camera is a constant 2D image (Figure 4a). The size of the original acquired image is \(8192\,\times\,10,240\) pixel according to the parameter settings of the linear CCD array camera used. The original image also includes invalid areas. Determining the vertical line that best fits the ROI position of the battery edge in the image is necessary because of the battery's constant width and the different luminance between the background and the battery surface. The specific ROI extraction processing can be described as follows:  
Figure 3: Software structure.  
1. The pixel value \(px_{i}\) of each line is identified, and the maximum value \(px_{\text{max}}\) is obtained. The pixel value of each point is divided by the maximum value \(\frac{px_{i}}{px_{\text{max}}}\). Correspondingly, the same procedure is performed for column \(\frac{py_{i}}{py_{\text{max}}}\).
2. The pixel value between ROI and the background dramatically changes. The boundary point \(px_{0}=\frac{1}{n}\sum\limits_{i=1}^{n}\frac{px_{i}}{px_{\text{max}}},py_{0}= \frac{1}{m}\sum\limits_{i=1}^{m}\frac{py_{i}}{py_{\text{max}}}\) is determined using this feature, where n is the total number of rows in the image, and m is the total number of columns.
3. The column width of ROI was extracted from left to right (Figure 4b).
4. The row width of ROI was extracted from top to bottom (Figure 4c).
5. ROI extraction is completed, and ROI is divided into two sections, namely, edge part \(W_{1}\) and inner part \(W_{2}\) (Figure 4d).  
After ROI extraction, a series of image processing algorithms, including Gaussian filtering [25], moving average filtering (MAF) [26], blob analysis [27], and defect characteristic analysis [28], are applied. The sub-regional processing method is creatively proposed on the basis of Gaussian filtering and MAF considering the effect of nonuniform background illumination of the battery edge and the difference between the edge background and the internal surface defects of the battery. Gaussian filtering and MAF are carried out row-by-row in the inner area \(W_{2}\) and column-by-column in the edge area \(W_{1}\). This approach is different from most module or global processing methods. The proposed method has higher defect detection accuracy and lower omission detection rate than the traditional unpartitioned processing methods. The detailed principles of the proposed algorithm are described in  
Figure 4: Processing of Region of interest (ROI) extraction. (**a**) Original image (8192 \(\times\) 10, 240 pixels); (**b**) ROI extracted of columns; (**c**) ROI extracted of rows; (**d**) ROI extraction finished (5525 \(\times\) 9291 pixels).  
the following section. To prove the advancement of our proposed algorithm, we compared it with the traditional unpartitioned processing.
## 4 Experiments
### Ablation Study
#### 4.3.3 Analysis of Attention with Euclidean Distance  
For sequence modeling, we explain the shortcomings of the dot product attention mechanism when applied to video sequences. It is affected by the magnitude of feature vectors in the sequence, causing attention weights to overly focus on local segments and thus undermining the model's ability to perceive the global context. Therefore, we use the Euclidean distance between feature vectors to measure attention in sequence modeling. In Table 3, we observe that using Euclidean distance for sequence modeling in Transformer, whether with single or fused features, results in lower WER (1.5-3.9 lower) compared to the scale-dot product. The results demonstrate that our improvements to the Transformer are more effective in learning and understanding the semantics of continuous sign language gestures. Furthermore, we visualized the attention weight matrices of the Transformer using Euclidean distance and applied it to the same sample data as in Figure 4, as shown in Figure 7. Compared to Figure 4, it is evident from Figure 7 that, after using Euclidean distance to measure similarity, the attention weight distribution is more uniform, effectively alleviating the sparsity issue.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Fusion Methods**} & \multicolumn{3}{c}{**Dev**} & \multicolumn{2}{c}{**Test**} \\ \cline{2-6}  & **del/ins** & **WER** & **del/ins** & **WER** \\ \hline \multirow{3}{*}{Concatenation} & max-pooling & 14.6/3.3 & 26.7 & 14.4/3.9 & 27.1 \\  & avg-pooling & 12.1/4.9 & 25.9 & 12.8/3.6 & 26.2 \\  & **self-attention** & **7.4/3.3** & **22.4** & **7.3/3.2** & **22.6** \\ \hline \multirow{3}{*}{Sum} & max-pooling & 13.7/3.7 & 26.5 & 13.5/4.0 & 26.9 \\  & avg-pooling & 12.8/4.2 & 26.1 & 12.2/3.7 & 25.8 \\ \cline{1-1}  & self-attention & 7.6/3.7 & 22.8 & 7.4/4.1 & 23.1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Evaluation of different feature fusion methods on PHOENIX-2014.
## 2 Materials and Methods
### Feature Integration and Transformation/Reduction  
Integrating the spectral and image features for each seed is needed to achieve the accurate classification of maize seed. In this study, the integrating features from each seed were created by connecting the spectral features and image features together in a vector. As the features were greatly different in their scales of value, an autoscaling method was used to overcome the problem of large disparity in values among the features. Autoscaling is essentially a normalization procedure expressed as follows [26]:  
\[\widehat{Y}_{ij}=\frac{Y_{ij}-\overline{Y_{j}}}{SD_{j}} \tag{2}\]  
where \(\widehat{Y}_{ij}\) and \(Y_{ij}\) denote the _j-th_ rescaled and original features for sample \(i\), respectively, \(\overline{Y_{j}}\) is the mean value of all the samples, and \(SD_{j}\) is the corresponding standard deviation. This preprocessing procedure rescales each feature to zero means and unit variance.  
After integrating the spectral and image features, the number of features for classification greatly increased. Many classification features provide useful information for the accurate classification of maize seeds, but they also bring the risk of dimension disaster resulting from feature redundancy. Therefore, the two feature transformation/reduction methods, including principle component analysis (PCA) and multidimensional scaling (MDS), were used to overcome the dimension disaster in this study. PCA [27] is a linear dimension transformation/reduction method that aims to convert multiple variables to a small number of integrated variables, namely, the principal component, based on the internal structure of the correlation matrix or the covariance matrix of the original variables. The goals of PCA are to maximize the internal information of the data after transformation/reduction and to measure the importance of the direction by measuring the variance of the data in the direction of the projection. Therefore, PCA is a linear dimension reduction method with the least loss of original data information. MDS [28] is a nonlinear dimensionality reduction method used to maintain the similarity or distance between data points. The purpose of MDS is to transfer a set of dissimilarity data among individualities into space composition through the MDS, thus retaining the relative relationship among the original data. That is, we can visually show the relative relationship among the original data by MDS.  
PCA is used mainly to determine the features that can best embody the characteristics of data. Conversely, MDS is more concerned with the relative relation among the original data.
## 3 Related Work
### Strategic Network Design Approaches  
There are many approaches in the scientific literature dealing with strategic network design. The approaches can be grouped into four main clusters [21; 22; 23]. First, process models such as [24; 25] focus on describing the chronological sequence and purpose of decision process steps rather than identifying or evaluating alternative network solutions [22]. Second, mathematical optimization approaches such as by [26] compute an optimal network design while neglecting all process-related requirements and creating multiple alternative network solutions [21]. Third, evaluation approaches such as [27; 28; 29] focus on evaluating alternative network solutions to act as decision support [21]. Fourth, combined approaches such as [21; 22; 30] use process and mathematical optimization models. Therefore, these approaches generate multiple alternative network solutions within the predefined solution space [21]. As a preliminary conclusion, only evaluation and combined approaches address generating and evaluating alternative network solutions, which is necessary for solution space management. Hence, this paper describes evaluation and combined approaches in more detail.  
[PERSON] [27] presents an evaluation approach based on simulating a limited number of network configurations and evaluating the simulation results to provide a multi-criteria decision-making aid. Thereby, [PERSON] [27] also considers uncertainty in strategic network design. [PERSON] et al. [29] propose a simulation-based evaluation approach with a process model. The approach evaluates a limited number of network configurations and analyzes different optimization measures to support decision-making. [PERSON] [28] provides a detailed evaluation approach to various costs within production networks, separated into module, site, and network levels.  
[PERSON] [21] develops a combined approach based on the decision process and integrates a multi-criteria optimization model to generate alternative network solutions. [PERSON] [30] suggests a five-step approach that addresses production networks' strategic, tactical, and operational planning levels. The approach provides tools for each level that are applied to an alternative network solution.
# A TSVD-Based Method for Forest Height Inversion from Single-Baseline PolInSAR Data
## 2 RVoG Model
### The Determination of Initial Values of Model Parameters  
From Equations (10) and (27), it can be seen that the proposed method is plagued by the initial value. If the initial value cannot be well determined, it is difficult to get reliable estimation. The classical six-dimensional nonlinear optimization method [6] is also confronted by this problem since it is difficult to obtain the priori information of the forest parameters (forest height and extinction). However, it is easy to obtain reliable initial values of the ground phase, the pure volume coherence and the ground-to-volume ratio according to the RVoG assumption and its linear geometrical expression in the complex plane [10]. Based on this, Equation (10) is used to estimate the pure volume coherence, which is important to invert the forest parameters. Compared to the six-dimensional nonlinear optimization method, this method can avoid significant biases caused by the unreliable initial values of forest parameters. Moreover, in comparison to the three-stage method, the proposed method can provide more accurate pure volume coherence because it is free from the assumption that there is one polarization whose ground-to-volume power ratio should be less than \(-10\) dB [10], which cannot be fulfilled for the low-frequency PolInSAR data or for the sparse forest [31]. In addition, only one polarimetric observation is used to calculate the pure volume coherence in the three-stage method. However, multi-polarization observations are used to estimate the pure volume coherence under the TSVD based least-squares estimation framework, which enhances the method's ability to alleviate the effect of observational errors in the estimations. As a result, the obtained pure volume coherence can support more accurate forest parameter estimation.  
Then, with the estimated pure volume coherence, Equation (27) is used to estimate forest height. The initial values for the forest height and the mean extinction coefficient are determined by the three-stage method. Although the three-stage method cannot give satisfactory results, especially for the low-frequency PolInSAR data or for sparse forest, these estimated results can be regarded as the initial values for Equation (27). The least-squares method estimates the corrections of initial values from the reliable pure volume coherence to improve the accuracy of the estimated unknown parameters. The final experiment has shown that with the accurate pure volume coherence and reasonable initial values, the proposed method can attain good forest height results.
## 3 Related Work  
### Trust Management Models  
On the Internet of Things, trust management and scalability are very important problems [21; 22]. Most trust-based networks build a centralized confidential system that can examine whether a particular node is trustable or not [23]. VANET is an ad hoc network of vehicles that trust management is the critical issue for cooperating between the nodes. Only a few models consider sharing information on VANET [24; 25]. Researchers focus on trust models that are dynamic and applicable to the network systems. In these models, nodes can cooperate based on the calculated trust and using exchanged messages. There are two type of trust management system for networks, specially VANET: central and decentralized systems. The benefit of central trust management systems is that they are easier to control and have reduced cost, but it has several big disadvantages. If the main system goes down, the whole system will fail. However, in the decentralized system, this problem has been fixed. Therefore, some researchers focused on the decentralized trust in VANET [26; 27; 28]. Many trust models have been used in P2P network to update node's belief based on the other node's trust value. For example, the system in [29] is one of the earlier distributed systems in VANET. They proposed a system for cars based on crowdsourcing in VANET. The system allows cars to hide votes, points, and lists of interacting vehicles under their own homogeneous encryption layer in order to stay out of reach of the malicious agents. It only uses the total credit of the cars at the end and uses the accumulated weight of the credits.
## 2 Theoretical Background
### Advantages Over the Auto-Regressive Model Coefficients  
The MFCCs, TECCs, and similar cepstral-based features can be compared to other DSFs to better describe their advantages. As done in [21], the discussion is here focused on the Auto-Regressive Model (AR) Coefficients, which are a classic feature widely used for SHM purposes [3,4]. An AR model of order \(p\) can be defined (for a time-discrete signal \(x[n]\)), as  
\[x[k]=\sum\
olimits_{j=1}^{p}a_{j}x[k-1]+\epsilon_{k} \tag{4}\]  
With \(x[k]\) being the signal value at the \(k\)-th integer multiple of the sampling interval \(\Delta t\), \(a_{j}\) the \(j\)-th AR coefficient, and \(\epsilon_{k}\) the modelling error at \(k\Delta t\). The Akaike Information Criterion (AIC) can be used to select a proper value of \(p\) to both prevent over- and under-fitting (i.e., to have a model complete enough to describe properly the observed data, but not unnecessarily overcomplicated), as  
\[AIC(p)=n\Big{(}\ln\big{(}o_{p}^{2}\big{)}+1\Big{)}+2p \tag{5}\]  
Figure 2: Conceptual relationships between the proposed methodology and the other tested approaches.  
where \(n\) is the total amount of estimated data point, \(p\) is the specific order under investigation, and \(\ln\!\left(\sigma_{p}^{2}\right)\) is the natural logarithm of the mean of the sum of squared residual errors \(\epsilon_{k}\) between the model and the data at any data point.  
However, with respect to AR coefficients, the cepstral features have the following useful qualities: (1) they do not require a somehow arbitrary definition of the model order (even if some arbitrariness does still exist in the definition of the \(L-1\) coefficients to be extracted from the \(M\) critical bands); (2) their results are much less affected by the choice of \(L\) and \(M\) with respect to AR's results accordingly to the order \(p\); and, (3) the effects of noise, echoes, and nonlinearities can be easier discerned and eventually removed in the quefrency domain, as they affect separate and distant regions in the logarithmic scale.  
A proper comparison between cepstral features and AR coefficients over some experimental data is postponed to Section 6.2.1.
# Optimal Thresholding for Multi-Window Computed Tomography (CT) to Predict Lung Cancer
## 2 Proposed Method
### Preprocessing  
CT scan images can be noisy, which can adversely affect the segmentation accuracy. Therefore, almost every existing lung nodule detection technique implements some preprocessing method to improve the image quality. To reduce the noise effect and enhance the contrast, we propose to use image normalization and image smoothing as preprocessing steps. To increase the pixel intensity and improve the contrast, image normalization is used. The images in our collection have a range of pixel intensities from \(-1000\) to \(1000\). Image normalization is employed to rescale the intensity values within the 0-255 range. Moreover, to reduce or eliminate the image noise, image smoothing is implemented. To this end, we use the Gaussian smoothing filter.