esa-sceva/satcom-chunk-collection huggingface dataset:
# Rolling Bearing Fault Diagnosis Based on Wavelet Packet Transform and Convolutional Neural Network
## 3 Proposed CNN-Based Fault Diagnosis Method
### Data-to-Image Conversion Method  
An effective way to improve the fault diagnosis performance is to extrude condition features and eliminate noise interference for input data. As discussed in Section 2.2, the WPT could capture the low-frequency information to obtain the fault information. Moreover, the noise component also can be filtered by manually discarding from obtained wavelet packet coefficients.  
In this study, a 2-D CNN model is selected to achieve the fault diagnosis. However, the wavelet packet coefficient is not appropriate as the input of CNN model. In order to obtain the appropriate input, a data-to-image method is designed to form the two-dimension input data for CNN model. As shown in Figure 1, it consists of two steps. Firstly, the wavelet packet coefficient, which is obtained from the raw signal by using WPT, is reshaped into a 2-D matrix with a size of \(\mathrm{M}\ \times\ \mathrm{N}\) where \(\mathrm{M}\) and \(\mathrm{N}\) could be selected manually according to the length of wavelet packet coefficients. Then, the matrix elements are normalized to \(-255\) to \(255\). Secondly, each element of obtained matrix is converted to a pixel, and a gray image with a size of \(\mathrm{M}\ \times\ \mathrm{N}\) is obtained. In this study, the \(\mathrm{n}\times\mathrm{n}\) pooling window iscommonly applied, and the length and width of feature map in each pooling layer is reduced by same, so it is recommended to get the gray image with a size of \(\mathrm{M}\times\mathrm{M}\).
## 4 Results
### Perceived Trust of Latent Dirichlet Allocation (LDA)
#### 4.1.2 ANOVA and Post-Analysis  
The results of ANOVA analysis and post-analysis using SPSS are as follows. First, our ANOVA analysis indicated that the influence of the affective level on the perceived trust of the LDA evaluation algorithm was statistically significant (\(p\)-value \(<\) 0.05), as shown in Table 1. In other words, there is a perceived trust difference between the users according to the affective level of the paragraph. In addition, SNK as the post-hoc analysis was conducted to verify the differences in affective levels, as shown in Table 2. As a result of this analysis, paragraphs with low affective levels were classified into one group, and those of medium and high affective levels were classified into one group. Figure 2 depicts the average LDA confidence score for each affective level in the paragraphs.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & \multicolumn{2}{c}{**Sum of Squares**} & \multirow{2}{*}{**DF**} & \multirow{2}{*}{**Mean Square**} & \multirow{2}{*}{**F**} & \multirow{2}{*}{**\(p\)-Value**} \\  & & **(Type III)** & & & & \\ \hline intercept & theory & 7688.995 & 1 & 7688.995 & 1387.875 & 0.000 \\  & error & 110.802 & 20 & 5.540 & & \\ \hline \multirow{3}{*}{affective level} & theory & 23.952 & 2 & 11.976 & 13.153 & 0.000 \\  & error & 36.421 & 40 & 0.911 & & \\ \hline \multirow{3}{*}{subject} & theory & 110.802 & 20 & 5.540 & 6.086 & 0.000 \\  & error & 36.746 & 40.366 & 0.910 & & \\ \hline \multirow{3}{*}{affective level\(\times\)subject} & theory & 36.421 & 40 & 0.911 & 1.055 & 0.382 \\  & error & 616.491 & 714 & 0.863 & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Analysis of variance (ANOVA) results corresponding to the application of Latent Dirichlet Allocation (LDA).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Affective Level**} & \multirow{2}{*}{**N**} & \multicolumn{2}{c}{**Subset**} \\ \cline{3-4}  & & **1** & **2** \\ \hline Medium & 231 & 2.9740 & \\ High & 294 & 3.1054 & \\ Low & 252 & & 3.4048 \\ \(p\)-value & & 0.110 & 1.000 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Student–Newman–Keuls (SNK) analysis of LDA results.
## 4 Results
### Validation for Product Recommendation System
The decision tree and bagging algorithm combination gave slightly worse results regarding the training model compared to the previous models (accuracy = 0.088, precision = 0.4, recall = 0.1, F1-score = 0.12). On the other hand, this combination showed a lower deviation of each metric than the two previous models (accuracy = 0.051, precision = 0.1, recall = 0.07, F1-score = 0.07), meaning that in this process, a combination of the decision tree and bagging algorithm showed a better result in overcoming the overfitting problem. Although all of them exhibited overfitting, with higher results in the training model compared to the testing model in terms of accuracy, the K-nearest neighbor algorithm demonstrated a good-enough result regarding the metrics both in the training set (accuracy = 0.06, precision = 0.25, recall = 0.1, F1-score = 0.13) and the testing set (accuracy = 0.025, precision = 0.24, recall = 0.08, F1-score = 0.11). The K-nearest neighbor performed better on the testing model, which delivered a non-overfitting classifier, so K-nearest neighbor is the most suitable classifier for implementing this recommendation system. While K-nearest neighbor was not the best-performing classifier in this study, the deviation of the training and testing result is the least when compared with another classifiers. In the real-world situation, by using K-nearest neighbor, the prediction will be more precise, stable, and reliable based on the training set. By predicting the recommendation as precisely as the purchasing behavior, the rate of transaction might be increased.
## 5 Multi-Objective Optimization Algorithm Based on DL-MOPSO
After performing the DL-MOPSO algorithm, the Pareto solution sets at different spring heights are illustrated in Figure 4, where \(F_{1}\) and \(F_{2}\) are values of \(F_{1}(\mathbf{X})\) and \(F_{2}(\mathbf{X})\) expressed in Equations (17) and (18). It is shown from Figure 4 that the \(DLSC\) and \(a\) are contradictory in the Pareto solution set. In order to select the optimal solution, the TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) method was used to get the optimal solutions of \(\mathbf{X}i\)[39], which are shown in Table 4.  
Figure 3: Flow chart of double-loop multi-objective particle swarm optimization (DL-MOPSO) algorithm. It is indicated from Table 4 that the optimal value of \(x_{1}\) (inner diameter of air line) is constant, while the optimal value of \(x_{2}\) (inner diameter of connector) changes with spring height. The change of the inside area of the connectors can be realized through control valves placed in the connectors to change their inside areas, along with the control of spring height. In previous studies, two traditional types of longitudinal connections were usually used to connect the passive air suspensions on the same side: Type 1 (three 6.5 mm inside diameter connectors connecting a 6.5 mm inside diameter air line) and Type 2 (three 20 mm inside diameter connectors connecting a 50 mm inside diameter air line) [8]. The longitudinal connection with the optimal parameters is defined as Type 3.
## 3 Results
### Variance Analysis  
When comparing the between-site CGA data from GL1 and GL2, the general linear model revealed significant differences (adjusted significance level: \(p\leq 0.002\)) for 39% of all parameters (12/31) (Table 4). Only three parameters (hip abduction swing phase (degree), hip extension stance phase (% gait cycle), knee flexion swing phase (% gait cycle)) fulfilled all three statistical criteria (\(p\leq 0.002\) and \(\eta_{\mathrm{p}}{}^{2}\geq 0.14\) and d \(\geq 0.80\)).
# Improving Juvenile Age Estimation Based on Facial Landmark Points and Gravity Moment
## 2 Related Work
### Feature Extraction  
Feature extraction attempts to estimate representations of the face, which are as close as possible to the ground truth. This is consistent with the knowledge that aging information is encoded in the face and can be represented by a set of facial landmarks [33]. However, extracting accurate features remains a difficult task. Consequently, various feature extraction methods are proposed for age estimation [14; 15; 25]. Pioneering works included that of [PERSON] et al. [15], who distinguished faces into baby, young-adult and senior-adult classes from geometrical features and wrinkle patterns. The authors used snakelets to detect curves and extracted wrinkle patterns from certain areas of the skin. The approach learned from a small private dataset of 47 images. Additionally, [PERSON] and [PERSON] [20] proposed a shape model to examine facial shape differences for children under 18 years of age. They identified face shape by a set of facial muscle landmarks and their corresponding coordinates, which formed 48 fiducial features. The authors presented a model for measuring childhood aging deformations by warping faces per the model for rejuvenation or aging. The model enabled face age estimation based on both childhood aging features and adulthood aging features. [PERSON] et al. [34] proposed an automatic facial landmark detection method that analyzed image intensities with adaptive bag-of-words. [PERSON], et al. [35] introduced a landmark extraction method by minimizing an objective function that was trained on both labeled and unlabeled face images. [PERSON] et al. [36] extracted facial landmarks with a method that combined relief curves and surface curvature. Facial landmark extraction continues to attract attention. Recently, [PERSON] and [PERSON] [37] proposed a multi-scale cascaded bivariate label distribution (BLD) learning method to improve facial landmarks. The authors used an ensemble of low and high-resolution images and an optimization mechanism to establish mappings from an input patch to the BLD for each image that most likely represented the true BLD.  
From the preceding, the majority of the feature extraction methods utilize techniques that aim to locate landmarks on the face. In this regard, the Active Appearance Model (AAM) is predominately utilized [10].
## Materials and Methods
### Chemical Characterization of Essential Oil by GC/MS  
The identification of different chemical compounds contained in essential oils was carried out by gas chromatography coupled to a mass spectrometer. _W. frutescens_ oil was analyzed using a Thermo Fischer capillary gas chromatograph directly coupled to the mass spectrometer system (model GC ULTRA S/N 20062969; Polaris QS/N 210729) using two columns, a non-polar HP-5 MS capillary fused silica column (60 m 0.32 mm, 0.25 mm film thickness) and a DB-HeawyWAX column (30 m \(\times\) 0.25 mm, film thickness 0.25 \(\upmu\)m), in addition to GC-FID (flame ionization detector). GC-MS operating conditions were maintained as follows: initial temperature of 40 \({}^{\circ}\)C/2 min; speed of 2 C/min; a final temperature of 260 \({}^{\circ}\)C/10 min; injector temperature of 250 \({}^{\circ}\)C; and carrier gas of helium 1 mL/min. The essential oil was diluted in hexane with a dilution ratio of 10:100. The volume of the sample injected was 1 mL with the fractional injection technique; ionization energy was 70 eV, ionization mode; ion source temperature of 200 C, sweep mass range \(m/z\) 40-650, and interface line temperature of 300 C. Retention indices (RI) were determined with reference to a homologous series of \(n\)-alkanes and by matching their recorded mass spectra with those stored in the spectrometer database (NIST MS Library v. 2.0) [38; 54; 55].
## 8 Anonymity and Security Analysis
### Link Anonymity  
Link anonymity is to prevent the ADV from knowing the relationship between the sender and the receiver. If a message leaves a sender and subsequently leaves the recipient without change, the ADV would know the relationship between the two nodes. This is secured in our framework since every message is completely changed after each retransmission including the IDs. In addition, it maintains fixed size. Applying different delays and different next-hop direction should also increase the privacy of the link. Furthermore, the adversary cannot know if the link carries real or fake data.
# A Routing Algorithm Based on Real-Time Information Traffic in Sparse Environment for VANETs
## 4 Routing Algorithm  
It is a challenging task to design a fast and reliable routing protocol for VANETs especially in sparse urban scenes where the connection between vehicle nodes is not enough, while several aspects need to be considered to improve the routing performance.  
Our contribution aims at : (i) increasing the probability of successful transmission within the communication range, (ii) increasing the probability of establishing an effective route, (iii) selecting the  
Figure 3: Parameters of the node: (**a**) effective information traffic; (**b**) number of neighbors.  
bests relay with the lowest delay and the highest probability of reach destinations. To achieve these goals, we define link utility and node effective information traffic and use a variance-based weight calculation method to calculate vehicle utility. The node with the largest vehicle utility will be selected as the next hop.
# A New Algorithm for Simultaneous Retrieval of Aerosols and Marine Parameters
## 2 Data and Methods
### Radial Basis Function Neural Network (RBF-NN)-Training and Evaluation  
#### 2.3.1 Radial Basis Function Neural Network  
The Radial Basis Function Neural Network (RBF-NN), first proposed by [PERSON] [43], consists of three main parts, namely an input layer, hidden layers, and an output layer [44; 45], and it employs radial basis functions (RBFs) as activation functions. The RBF's basic parameters are the center and width, and their response decreases (or increases) monotonically with distance from a central point [44]. There are two hidden layers of the RBF-NN, namely a RBF layer and a linear layer. The output \(O_{1i}\) from the first hidden layer (the RBF layer) is given by  
\[O_{1i}=\exp\Bigl{[}-((x_{1i}-w_{1i})b_{1})^{2}\Bigr{]}, \tag{19}\]  
where \(x_{1i},w_{1i}\), and \(b_{1}\) are the input, weight, and bias, respectively, of the RBF layer. The RBF layer is followed by the second hidden layer  
\[O_{2i}=w_{Li}x_{2i}+b_{2i}, \tag{20}\]  
where \(w_{Li}\), \(x_{2i}\), and \(b_{2i}\) are the weight, input, and bias of the linear layer, and \(O_{2i}\) is the final total output of the RBF-NN. If there are are \(N_{p}\) input parameters, Equation (19) for the first hidden layer becomes  
\[O_{1i}=\exp\Biggl{[}-b_{1}^{2}\sum_{k=1}^{N_{p}}(w_{1k}-x_{1k})^{2}\Biggr{]}. \tag{21}\]  
Replacing \(x_{2i}\) in Equation (20) by the output \(O_{1i}\) from the first hidden layer in Equation (21), which is the input to the second hidden layer (the linear layer), one obtains  
\[O_{2i}=w_{Li}\exp\Biggl{[}-b_{1}^{2}\sum_{k=1}^{N_{p}}(w_{1k}-x_{1k})^{2} \Biggr{]}+b_{2i}. \tag{22}\]  
If there are \(N\) neurons, the \(i^{\text{th}}\) output of the RBF-NN becomes  
\[O_{2i}=\sum_{j=1}^{N}w_{Lij}\exp\Biggl{[}-b_{1}^{2}\sum_{k=1}^{N_{p}}(w_{1 jk} -x_{1k})^{2}\Biggr{]}+b_{2i}. \tag{23}\]  
If we set \(a_{ij}=w_{Lij}\), \(b=b_{1}\), \(d_{i}=b_{2i}\), \(c_{jk}=w_{1 jk}\), \(p_{k}=x_{1k}\), \(R_{i}=O_{2i}\), Equation (23) simplifies to  
\[R_{i}=\sum_{j=1}^{N}a_{ij}\exp\Biggl{[}-b^{2}\sum_{k=1}^{N_{p}}\Bigl{(}c_{jk} -p_{k}\Bigr{)}^{2}\Biggr{]}+d_{i}, \tag{24}\]  
where \(N_{p}\) is the total number of input/retrieval parameters, the \(p_{k}\)'s are input/retrieval parameters (chlorophyll concentration, mineral concentration, CDOM absorption, aerosol fine-mode fraction, and aerosol volume fraction), and \(a_{ij}\), \(b\), \(c_{jk}\), and \(d_{i}\) are coefficients to be optimized by training the RBF-NN. The output parameters \(R_{i}\) are TOA radiances in the MODIS channels at 412, 442, 487, 530, 554, 666, 746, and 866 nm. Each term \(j=1,2,\ldots,N\)in the outer sum in Equation (24) is referred to as a neuron, so that \(N\) is the actual number of neurons after optimisation.
## 4 Discussion  
This study aimed to assess the effectiveness of stereolithography (SLA) printed models compared to traditional plaster casts in a dental practice setting. The use of computational intelligence, augmented reality, and 3D printing techniques has gained popularity in orthodontics, prosthetics, and maxillofacial surgery.  
The dataset for this experiment included 22 orthodontic patients with both upper and lower jaw SLA printed models and plaster casts. The SLA 3D printer utilized a blue 405 nm laser beam to harden a liquid polymer and create the upper dental arch model, while the plaster casts were prepared in a dental lab. These models were stored for long-term orthodontic therapy control.  
To evaluate the differences between the dental models, a laboratory scanner was employed to obtain casts, which were subsequently modified and pre-aligned using appropriate software. The final fine registration was conducted using an iterative closest point algorithm to facilitate the assessment of distances between the cloud and the mesh of the printed model. It was observed that SLA prints exhibited a smoother surface compared to plaster casts, although at higher resolutions, the layer thicknesses became visible [11]. This phenomenon can be attributed to the non-homogeneous nature of plaster casts, characterized by a grainy surface structure with sharp edges of orthorhombic crystals [6]. The findings are consistent with [34], which noted that laser printer intensity is influenced by color (gray and white resin) and layer thickness (optimal at approximately 100 \(\upmu\)m). In our study, it was confirmed that, after a three-year period, the plaster cast surface became smoother, with broken-off crystals and evident signs of long-term abrasion. On the other hand, stereolithography produced a different surface for the model, featuring a smooth depolymerization of the plastic surface. Moreover, this model could be used multiple times without any loss of precision. The results from the 22 experimental sets demonstrated a close agreement between the plaster cast and virtual surfaces, with a mean absolute difference of 0.018 mm and a standard deviation of 0.17 mm. These measurements were based on an average of 231,178 data points for each comparison.  
Significantly, the model of the lower jaw demonstrated greater accuracy attributed to the utilization of less complex scanning technology and simpler anatomical structure. The  
Figure 7: Example of upper and lower SLA model and vacuum formed retainer.  
mean absolute difference between the the lower jaw models was 0.016 mm, whereas the upper jaw models exhibited a slightly higher difference of 0.021 mm. These findings highlight the remarkable efficiency of stereolithography (SLA) printed models when compared to traditional plaster casts.
# Review of Convective Heat Transfer Modelling in CFD Simulations of Fire-Driven Flows
## 3 Main Modelling Approaches
### Experimental Correlations  
A typical approach for determining convective heat transfer in CFD simulations is the use of experimental (empirical) correlations based on non-dimensional numbers (i.e., the Nusselt number \(Nu\)) for different types of flows [32; 33].
## 2 Materials and Methods
### Hybrid Control System Architecture
#### Overfitting Prevention Strategies  
To address potential overfitting issues, we implement multiple regularization techniques:  
* Dropout Layers: implementation of dropout with a rate of 0.2 between LSTM layers.
* Early Stopping: monitoring validation loss with patience of 10 epochs.
* L2 Regularization: weight decay coefficient of 1 \(\times\) 10\({}^{-4}\).
* Cross-validation: K-fold (K = 5) validation for hyperparameter tuning.  
High-Frequency Response Enhancement  
High-frequency environments present unique challenges for LSTM-based systems, particularly regarding response delays. Our solution implements the following:  
* Frequency-Aware Training:
* Dataset augmentation with high-frequency components;
* Adaptive sampling rates based on signal frequency content;
* Multi-rate processing architecture.
* Latency Optimization:
* Parallel processing pipeline for feature extraction;
* Optimized memory management for reduced I/O overhead;
* Hardware-specific optimizations for matrix operations.
# Combination Analysis of Future Polar-Type Gravity Mission and GRACE Follow-On
## 4 Discussion  
The advantages of GFO & retrograde-FPGs over GFO & prograde-FPGs are believed to be contributed by two aspects. On one hand, since the prograde and retrograde orbits sample the Earth in different directions, combining the two can obtain an integrated system that observes the Earth in a more multi-perspective view. Furthermore, as GFO is flying in a prograde orbit, adding retrograde FPGAs (rather than the prograde ones) can make the GFO & FPG integrated system more isotropic [37]. On the other hand, according to the [PERSON]'s linear perturbation theory [47], the RAANs of prograde and retrograde orbits precess in different directions in the inertial frame. Therefore, when projected into the Earth-fixed frame, the satellite ground tracks of the two evolve with different rates since the Earth rotates eastwards. As such, the signal sampling of the two differs in time domain. Once combined, they contribute to a more homogeneous system for signal sampling in terms of temporal resolution.  
Due to the designed polar orbit for polar-type satellites, the low- and medium-latitude areas suffer from much sparser ground tracks than the polar region. The former, however, is just the place where most of temporal aliasing errors appear. Therefore, improved ground tracks in these areas matter much to the final gravity solution, and that is where most of the gains of GFO & FPG-91 come from. However, we also observe the degradation of GFO & FPG-91 at high latitudes such as the Greenland compared with GFO & FPG-89, which is possibly due to the pattern change of GFO & FPG-joint ground tracks at the intersection of prograde FPG-89 and retrograde FPG-91. Similar degradations in the intersection of two Bender-type orbits are also found in [60] (pp. 108-111), which needs further investigations.
## 7 Lessons Learned and Discussion
The user perception category, however, is clearly the category with the highest challenges from an end-user perspective, as academic research suggests that much more progress is needed to address the lack of knowledge and awareness among end users. Therefore, we assign a rating of one for the domain improvements and three for the domain challenges. The overall rating for both domains, plotted against each other, can be seen in the radar plot in Figure 6. This graphical overview highlights the clear gap of improvements in the category of user perception.