esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Crystallinity  
The DSC thermograms of as-spun and annealed nanofibers are shown in Figure 1. An exothermic peak at 85 \({}^{\circ}\)C, related to the polymer cold crystallization, is seen in the as-spun fibers indicating that, as expected, those samples have a low crystallinity because of the fast evaporation of the solvent during the electro-spinning process. In contrast, there is no sign of a cold crystallization peak in the annealed samples, which refers to the fact that the annealing process increases the overall crystallinity. Table 1 displays the values of the characteristic temperature (\(T_{c}\) and \(T_{m}\)), the associated enthalpies (\(\Delta H_{c}\) and \(\Delta H_{m}\)) and \(X_{c}\) for each sample. The crystallinity of the nanofibers increases dramatically from 2% in the as-spun sample to about 50% in the annealed sample.  
Interestingly, there is no clear shifting in melting temperature or a large difference in values of \(\text{X}_{c}\) between samples annealed at 90, 100 and 120 \({}^{\circ}\)C, indicating that the crystallinity content is expected to be maximized in the samples after the thermal post-treatment. The increase of the crystalline content for all annealed samples compared to the as-prepared sample confirms the positive effect of the annealing process on the crystallinity of the electrospun nanofibers. It should be noted that the samples have a glass transition (\(T_{g}\)) around 60 \({}^{\circ}\)C, well below the annealing temperature.
# Improving the Robustness of DTW to Global Time Warping Conditions in Audio Synchronization
## 3 Results
### Experimental Results
The adaptive weighting does not seem to provide additional benefit in handling global time warping conditions. Finally, consider the four red systems (SelectiveTransitions-max\(\{W\}\) where \(W=2,3,4,5\)). These systems all have \((0,1)\), \((1,0)\), \((1,1)\) nominal transitions but selectively remove the \((0,1)\) and \((1,0)\) transitions from selected locations \((i,j)\) in the pairwise cost matrix. We can see in Figure 3 that this scheme does indeed provide robustness to extreme time warping. For example, SelectiveTransitions-max\(2\) has reasonable performance for global time warp factors \(<2.0\), SelectiveTransitions-max\(3\) has reasonable performance for global time warp factors \(<3.0\), etc. However, compared to the best-performing systems, these variants show a moderate degradation in alignment accuracy. One difference between this approach and DTW1-add3 and DTW1-add4 is the computational cost: since we are removing transitions (rather than adding additional transitions), we do not need to consider as many possibilities and the resulting computational cost is lower (in theory). So, non-uniform transition patterns do seem to make DTW more robust to global time warping conditions, but they come with a moderate reduction in accuracy.
# A Novel Discriminating and Relative Global Spatial Image Representation with Applications in CBIR
## 3 Proposed Methodology
### BoVW Model  
The Bag-of-Words (BoW) methodology was first proposed in textual retrieval systems [11] and was further applied in the form of BoVW representation for image analysis. In BoVW, the final image representation is a histogram of visual words. It is termed a bag, as it counts how many times a word occurs in a document. A histogram does not have any order and does not retain any information regarding the location of visual words in the 2D image space [9; 16]. The similarity of two images is determined by histogram intersection. In the case of dissimilar images, the result of the intersection is small.  
As a first step in BoVW, the local features are extracted from the image \(Im\), and the image is represented as a set of image descriptors, such as \(Im=\{d_{1},d_{2},d_{3},...,d_{I}\}\), where \(d_{i}\) denotes the local image features and \(I\) represents total image descriptors. The feature extraction can be done by applying some local descriptors such as SIFT descriptors [36]. The key points can be acquired automatically by using interest point detectors or by applying dense sampling [16].  
Consequently, there are numerous local descriptors created for each image for a given dataset. The extracted descriptors are vector quantized by applying \(k\)-means [11] clustering technique to construct the visual vocabulary, as in  
\[v=\{w_{1},w_{2},w_{3},...,w_{K}\} \tag{1}\]where \(K\) shows the specified number of clusters or visual words and \(v\) denotes the constructed visual vocabulary.  
The assignment of each descriptor to the nearest visual word is done by computing the minimum distance as follows:  
\[w(d_{j})=\underset{w\in\mathbb{C}}{argmin}Dist(w,d_{j}) \tag{2}\]  
here, \(w(d_{j})\) represents the visual word mapped to \(j\)th descriptor and \(Dist(w,d_{j})\) depicts the distance between the descriptor \(d_{j}\) and visual word \(w\).  
The histogram representation of an image is based on the visual vocabulary. The number of histogram bins equates the number of visual words in the code book or dictionary (i.e., \(K\)). Each histogram bin \(bin_{i}\) represents a visual word \(w_{i}\) in \(v\) and signifies the number of descriptors mapped to a particular visual word as shown in (3)  
\[bin_{i}=card(D_{i})\;\;\text{where}\;\;D_{i}=\{d_{j},j\in 1,....,n\mid w(d_{j})=w_{i}\} \tag{3}\]  
\(D_{i}\) is the set of descriptors mapped to a particular visual word \(w_{i}\) in an image, and the cardinality of this set is given by \(Card(D_{i})\). The final histogram representation for the image is created by repeating the process for each word in the image. The histograms hence created do not retain the spatial context of the interest points.
## 4 Numerical Simulation of Temperature Field of Spatial Grid Structure  
### Finite Element Model  
According to the experimental design of the spatial grid model, the ANSYS software (ANSYS Inc., Canonsburg, PA, USA) thermal analysis module was used to establish the finite element model for the analysis of the structural temperature field. The assembled triangular support frame was not considered in the numerical model. In the thermal analysis, the three-dimensional shell element, Shell57, was used to simulate the heat conduction of the steel member. The surface effect element, Surf152, which covered the heat conduction element, was used to simulate the convective heat transfer between the structure and the surrounding environment. The thermal radiation element, Link31, was used to simulate the radiation with the sky and ground. Appropriate mesh generation can ensure the perfect combination of the result accuracy and computation efficiency. Due to the large number of members of the test model, the grid sensitivity analysis determined that the mesh size of the finite-element model was 0.1 m. Thermal boundary parameters including solar radiation intensity, ambient temperature, and wind speed measured on 16 August were used as the experimental basis for numerical simulation.  
The flow chart of the temperature field simulation is shown in Figure 7. Heat transfer of the spatial grid model occurred through heat conduction, convection, and radiation. Heat conduction was calculated with the material properties by the software automatically. The material properties, i.e., the specific heat and thermal conductivity, were 465 J/kg\(\cdot\)K and 49.8 W/m\(\cdot\)K, respectively [21]. The measured ambient temperature and wind speed were used in heat convection calculation. Heat radiation is calculated according to the measured solar radiation intensity and shadow analysis of members is also involved. The numerical simulation method of the non-uniform temperature field under solar radiation, was carefully described in our previous studies [2]. The numerical simulation covered every hour of 16 August 2016.  
Figure 6: All-day temperature difference variation curves of various members. Red and black lines indicate the top and bottom test points, respectively.
# Sequential-Fault Diagnosis Strategy for High-Speed Train Traction Systems Based on Unreliable Tests
## 3 Methodology
### Optimization Objectives of Fault Diagnosis Strategy  
1. Problem components The problem of solving a fault diagnosis tree via system fault reasoning is mainly composed of five factors, including a system fault fuzzy set \(F\), fault probability set \(\lambda\), test set \(T\), diagnosis cost set, and system dependency matrix. (1) \(F=\left\{f_{0},f_{1},f_{2},\cdots,f_{m}\right\}\) is the fault set of the system, where \(f_{0}\) indicates that the system is in a normal condition without faults.  
\begin{table}
\begin{tabular}{c c} \hline \hline \(n\) & \(R_{I}\) \\ \hline
1 & 0 \\
2 & 0 \\
3 & 0.58 \\
4 & 0.9 \\
5 & 1.12 \\
6 & 1.24 \\
7 & 1.32 \\
8 & 1.41 \\
9 & 1.45 \\ \hline \hline \end{tabular}
\end{table}
Table 2: \(R_{I}\) of repetitive calculation for 1–9 dimensional matrices.  
(2) \(\partial=\{\partial_{0},\partial_{1},\partial_{2},\cdots,\partial_{m}\}\) is a set representing failure probability, which is calculated from the failure rate. \(\partial_{0}+\partial_{2}+\cdots+\partial_{n}=1\), where \(\partial_{0}\) represents the probability of no faults in the system.  
\[\partial(f_{0})=\frac{\sum\limits_{k=1}^{m}(1-\lambda_{k})}{\sum\limits_{k=1} ^{m}(1-\lambda_{k})+\sum\limits_{k=1}^{m}\left(\lambda_{k}\prod\limits_{k=1}^ {m}(1-\lambda_{k})\right)}=\frac{1}{1+\sum\limits_{k=1}^{m}\lambda_{k}/(1- \lambda_{k})} \tag{16}\]  
\[\partial(f_{i})=\frac{\lambda_{i}\prod\limits_{k=1,k\
eq i}^{m}(1-\lambda_{k}) }{\prod\limits_{k=1}^{m}(1-\lambda_{k})+\sum\limits_{i=1}^{m}\left(\lambda_{i} \prod\limits_{k=1,k\
eq i}^{m}(1-\lambda_{k})\right)}=\frac{\lambda_{i}/(1- \lambda_{i})}{1+\sum\limits_{k=1}^{m}\lambda_{k}/(1-\lambda_{k})},\;i\
eq 0 \tag{17}\]  
(3) \(T=\{t_{1},t_{2},\cdots,t_{n}\}\) is the available test set.  
(4) \(S=\{s_{1},s_{2},\cdots,s_{n}\}\) represents the cost of isolating the fault when the fault occurs, i.e., the cost of fault diagnosis.  
(5) \(FT=\left[ft_{ij}\right]_{(m+1)\times n}\) is the system fault-test dependency matrix. Considering the reliability of the test, this can be transformed into a fault-test dependency matrix \(FT=\left[d_{ij}\right]_{(m+1)\times n}\) based on reliability.  
(2) Optimization objectives  
The construction of a fault diagnosis strategy is a typical Nondeterministic polynomial (NP) problem. On the premise that only a single fault occurs, the objective function of the system is:  
\[D_{opt}=\min\sum\limits_{i=0}^{m}\partial(f_{i})\left(\sum\limits_{k=0}^{|D(i )|}s_{D_{(i)}|\mathbb{R}}\right) \tag{18}\]  
where \(D_{opt}\) represents generated diagnosis tree, \(m\) represents the node of the generated diagnosis tree, \(s_{D_{(i)}|\mathbb{R}}\) represents the diagnostic cost of the kth test in the sequence \(D(i)\), \(D(i)\) represents the test node required for fault isolation, and \(|D(i)|\) represents the length of the test sequence, which refers to the number of tests required to locate an isolated fault.
# Robust Control of an Inverted Pendulum System Based on Policy Iteration in Reinforcement Learning
## 2 Model Formulation
### State-Space Model with Uncertainty  
In Section 2.1, we established the dynamic model of the IPS as shown in Equation (8). Next, we will derive the state-space model of the IPS.  
As the rotation angle of the pendulum \(\theta\) is very small, it can be approximated that \(\dot{\theta}\approx 0\), \(\dot{\theta}^{2}\approx 0\). It follows from (8) that  
\[\left\{\begin{array}{l}F_{1}=(m_{1}+m_{2})\dot{x}+z\dot{x}-m_{1}L\ddot{ \theta}\\ (I+m_{1}L^{2})\ddot{\theta}-m_{1}gL\theta=m_{1}L\dot{x}\end{array}\right. \tag{9}\]  
Equation (9) is the linearized dynamic model of the system. The first equation comes from (1)-(3), which is the equilibrium force equation of the system in the horizontal direction. The second equation comes from (4)-(7), which is the equilibrium force equation of the system in the vertical direction. The state variables of the system can be defined as  
\[x_{1}=x,\quad x_{2}=\dot{x},\quad x_{3}=\theta,\quad x_{4}=\dot{\theta}.\]Therefore, the following state-space equation can be derived.  
\[\left\{\begin{array}{l}\dot{x_{1}}=x_{2}\\ \dot{x_{2}}=\frac{-(I+m_{1}L^{2})z}{I(m_{1}+m_{2})+m_{1}m_{2}L^{2}}x_{2}+\frac{m _{1}^{2}gL^{2}}{I(m_{1}+m_{2})+m_{1}m_{2}L^{2}}x_{3}+\frac{I+m_{1}L^{2}}{I(m_{ 1}+m_{2})+m_{1}m_{2}L^{2}}u\\ \dot{x_{3}}=x_{4}\\ \dot{x_{4}}=\frac{-m_{1}Lz}{I(m_{1}+m_{2})+m_{1}m_{2}L^{2}}x_{2}+\frac{m_{1}gL( m_{1}+m_{2})}{I(m_{1}+m_{2})+m_{1}m_{2}L^{2}}x_{3}+\frac{m_{1}L}{I(m_{1}+m_{2})+m_{1}m _{2}L^{2}}u\end{array}\right. \tag{10}\]  
where \(u\) represents the force \(F_{3}\) from the motor. Using \(W=I(m_{1}+m_{2})+m_{1}m_{2}L^{2}\), Equation (10) can be written as  
\[\dot{x}=Ax(t)+Bu(t)\]  
where  
\[A=\left[\begin{array}{cccc}0&1&0&0\\ 0&\frac{-(I+m_{1}L^{2})z}{W}&\frac{m_{1}^{2}gL^{2}}{W}&0\\ 0&0&0&1\\ 0&\frac{-m_{1}Lz}{W}&\frac{m_{1}gL(m_{1}+m_{2})}{W}&0\end{array}\right],B= \left[\begin{array}{c}0\\ \frac{I+m_{1}L^{2}}{W}\\ 0\\ \frac{m_{1}L}{W}\end{array}\right]\]  
However, the accurate model of the IPS is difficult to obtain, and all its parameters have uncertainties. In this paper, the friction coefficient \(z\) between the trolley and guide rail is selected as an uncertain parameter. The numerical values of the other parameters in Table 1 are known, where \(m_{1}=0.109,m_{2}=1.096,L=0.25\), and \(I=0.0034\). Therefore, the state-space model of the uncertain IPS can be abbreviated as  
\[\dot{x}=A(z)x+Bu \tag{11}\]  
where  
\[A(z) = \begin{bmatrix}0&1&0&0\\ 0&-0.8832z&0.6293&0\\ 0&0&0&1\\ 0&-2.3566z&27.8285&0\end{bmatrix},B=\begin{bmatrix}0\\ 0.8832\\ 0\\ 2.3566\end{bmatrix}\]  
Here we choose \(z_{0}=0.1\) as the nominal value and denote the nominal matrix of the system as \(A(z_{0})\). Therefore, the nominal system corresponding to the uncertain system (11) is  
\[\dot{x}=A(z_{0})x+Bu \tag{12}\]  
where  
\[A(z_{0}) = \begin{bmatrix}0&1&0&0\\ 0&-0.0883&0.6293&0\\ 0&0&0&1\\ 0&-0.2357&27.8285&0\end{bmatrix}\]
# Experimental Investigation into Permeable Asphalt Pavement Based on Small-Scale Accelerated Testing
## 3 Results and Discussion
### Results of Rut Depth
Moreover, the strength of CTB and CTPB might have increased after loading under dry conditions. The results of average rut depth confirmed that the thickness of the surface layer and the base materials were critical parameters. The rutting resistance of the dense asphalt mixture and porous asphalt mixture was compared; the thicker dense asphalt mixture may also lead to early rutting fatigue. This showed that surface thickness was one of the important factors affecting rutting deformation. And the study has shown that thinner asphalt surfaces are prone to rutting earlier [54,55]. A thinner surface layer could produce a more permanent deformation. However, pavements with a thin surface layer could also produce a less permanent deformation, mostly because the thicker layer has more material to deform [56]. Moreover, the denser or stiffer base provides a better rutting resistance performance. Therefore, a thicker surface layer or a stiffer base could be better for a fully permeable pavement; this conclusion was consistent with that obtained by the resilient tensile strain.
# State-of-the-Art Survey on Deep Learning-Based Recommender Systems for E-Learning
## 3 Classification Method
### Classification Based on Recommendation Techniques
#### 3.1.4 Hybrid Recommendation Technique  
RSs can be built using collaborative, content-based filtering or knowledge-based techniques. Still, each of them has its peculiar weaknesses, as observed by [PERSON] et al. in [7], where they highlighted some of the common pros and cons of each recommendation technique; for example, data sparsity, new user, and new item problems are some of the significant deficiencies of collaborative filtering, of which some of them can be controlled by content-based filtering. Therefore, combining two techniques with a hybrid recommendation technique can minimize such individual drawbacks. The term hybridization or, more formally, hybrid recommendation technique does not only mean it can be between different recommendation techniques (content-based, collaborative filtering, and knowledge-based) but also between algorithms under the same technique. Consider case-based reasoning and attribute-based algorithms; all of them are content-based techniques. The former suffers from a cold-start problem and new user/item problems, while the latter does not have such disadvantages. Conversely, domain dependencies are a severe problem of attribute-based algorithms that case-based reasoning does not have. The same complication can be revealed under collaborative filtering algorithms as both item-based, and user-based techniques suffer from cold-start problems. In contrast, stereotype collaborative filtering is believed to be free from cold-start problems [7]. Knowledge-based systems are unlike collaborative and content-based techniques that suffer from sparsity and cold-start problems at some level. It is a good alternative for hybrid RSs.
## 3 Research Results
### Questionnaire Survey Results
#### 3.2.4 Hierarchical Regression Analyses of Factors Influencing Puppetry Learning - An Introduction
That is, for each Y\(i\), \(i\) = 1, 2,..., 6, besides the _one-level_ regression conducted in Step 1, three _two-level hierarchical_ regressions were conducted in Step 2 so that a total of 24 regressions (i.e., 6 one-level regressions plus 18 two-level hierarchical regressions) have been carried out in this study by using the SPSS, resulting in a lot of statistical data which were condensed and rearranged into three tables, namely, Tables 11-13, as shown subsequently. In these tables, each _standardized regression coefficient_ yielded in the regression process is denoted by the Greek letter \(\beta\). Furthermore, the _coefficient of determination_ to indicate the _goodness of fit_ of the resulting linear regression model is denoted as R\({}^{2}\), with the value of the change of R\({}^{2}\) denoted as \(\Delta\)R\({}^{2}\). Furthermore, the mark of one to three asterisks, namely, \", \", and \", which appears as the superscript of \(\beta\), R\({}^{2}\), or \(\Delta\)R\({}^{2}\), is adopted to indicate that the corresponding \(p\) values are smaller than 0.05, 0.01, and 0.001, respectively, i.e., to mean that \(p\) < 0.05, \(p\) < 0.01, and \(p\) < 0.001, respectively. In short, the aforementioned three tables show the results of the 24 (from 6 + 18) regression analyses of the _factors_ (represented respectively by the control and independent variables C1 through C3 and X1 through X6, respectively) influencing the results of _puppetry-related activities_ (cognition, learning, promotion, inheritance, etc.) (represented by the dependent variables Y1 through Y6). The interpretations of the data in the three tables, Tables 11-13, are described in the following sections. \begin{table}
\begin{tabular}{c c c} \hline
**3E Indicator** & **Questions with Numberings** & **Independent Variable Label** \\ \hline Easy & 4. The system interface is easy to understand and simple to operate. & X1 \\ \cline{2-3}  & 5. It is easy to understand the role classification by using the system. & X2 \\ \hline Effective & 6. By using the system, I need only a short time to get to know various puppet roles. & X3 \\ \cline{2-3}  & 7. By using the system, I can remember the puppet roles’ characteristics quickly. & X4 \\ \hline Enjoyable & 8. It is interesting to use the system to learn puppet performances and narrations. & X5 \\ \cline{2-3}  & 9. By using the system, I can feel the sense of being a professional puppeteer. & X6 \\ \hline \end{tabular}
\end{table} 
Table 10: The 3E indicators and the respective questions of the corresponding independent variables.
## 5 Our Contribution
### Our Training Algorithm  
The MLP training was carried out in a supervised manner using a backpropagation algorithm, whose objective is to adjust the weights and minimize the amount of quadrature error between the network output and the target result. The quadrature error is:  
\[E(x)=\left|\left|d(n)-PL\right|\right|^{2} \tag{1}\]  
where \(d(n)\) is the target value, and \(PL\) is the value of the network output. The backpropagation algorithm defines the error gradient in aim to achieve a minimum.  
The error gradient \(E(x)\) is calculated for each weight as follows:  
\[\frac{\partial E(n)}{\partial W_{ijk}\ (n)}=\frac{\partial E(n)}{\partial PL_{ ik}}\frac{\partial PL_{ik}}{\partial W_{ijk}}=\frac{\partial E(n)}{\partial PL _{ik}}x_{i-1\ j} \tag{2}\]  
where  
\(i\) varies between 1 and 3.  
\(K\) varies from 1 to 50.  
\(X\) the number of inputs varies between 6 to 11 according to each dataset.  
For the output layer i = L, the output error is denoted. \(\delta_{Lk}\) is calculated as follows:  
\[\delta_{Lk}=\frac{\partial E(n)}{\partial PL_{ik}}=2f^{\prime}(PL_{Lk})(d_{k} -x_{Lk}) \tag{3}\]  
where _f(x)_ is the activation function.  
For hidden layers, the error \(\delta_{ik}\) is given by:  
\[\delta_{ik}=f^{\prime}(PL_{ik})\sum_{j=1}^{N(j+1)}\delta_{i+1\ j}W_{i+1\ kj} \tag{4}\]  
The modification of the weights \(W(n)\) and the biases \(b(n)\) is obtained by the following two equations:  
\[W_{ijk}(n+1)=W_{ijk}(n)+0.7\delta_{ik}x_{i-1\ j}+0.6(W_{ijk}(n)-W_{ijk}(n-1) \tag{5}\]  
\[b_{ik}(n+1)=b_{ik}(n)+0.7\delta_{ik} \tag{6}\]  
0.7 is the learning step determining the convergence speed, and 0.6 is the momentum or inertia term that prevents the algorithm from getting stuck on a local minimum.  
Figure 2: MLP network architecture (with two hidden layers) for Dataset 2.  
[PERSON] et al. [32] designed the model with two hidden layers, while we tested three different architectures for the MLP models. However, their radial basis function (RBF) results were excellent. [PERSON] et al. [33] focused on developing the networks in the urban areas while we conducted our models in three different areas. Additionally, they used several training algorithms, while we used the same training algorithm in all cases (Figure 3).  
Figure 3: Flowchart of proposed predictive modelling with MLP neural network.
## 3 Results
### Data Preprocessing  
As mentioned in the previous section, there was a need for data pre-processing to obtain results with higher accuracy. Missing values and records containing outliers were removed from the data. Outliers were removed from the data by identifying the allowed values for each attribute. The used data in this section were ordinal data types. Ordinal discrete numbers are the same as categorical data, with the difference that the states in ordinal variable values have a meaningful order. To manage ordinal data, it is necessary first to establish correspondence between each data value and its corresponding rank so that the data falls in the interval of [1, Mf], where Mf is the number of different modes in the ordinal data for feature f. Since there are many other states for ordinal data, it is necessary to normalize the data to the interval [0,1] to prevent all features from having the same effect. So, if the i-th data rank in attribute f is rif, we have:  
Equation (7): Normalization of ordinal data  
\[z_{if}=\frac{r_{if}-1}{M_{f}-1} \tag{7}\]  
To calculate the degree of similarity between two data records, it is possible to do the same for continuous data [10].  
It should be noted that the available data that indicate the overall situation in a network are usually unbalanced because the network works well in most areas. There is a smaller percentage of points that have problems. Since the optimal range of all the used features in this research was included in the standard, we could separate the records in which the optimal degree of all variables were observed from the other data and label them as fault  
Figure 13: Architecture of working with drive test data.  
free to investigate the problem of data imbalance. Therefore, by doing this, a significant amount of data was reduced, and would be more suitable for analyzing the cause of a fault in the network.