esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Method
### CSI Feature Extraction  
The modules involved in the feature extraction process are explained as follows:  
* Relative vertices localization: As mentioned above, to enhance the classification performance at each vertex, the representation of a single SI is augmented with the SIs of its neighboring vertices. To estimate the position of the vertices along the principal curvatures of \(\mathbf{v}_{i}\), the curvature tensors and the curvature derivatives are initially determined, and then the principal curvatures whose directions are denoted by \(\mathbf{t}_{max}\) and \(\mathbf{t}_{min}\) are obtained [33]. 3D points at a distance of \(d_{CSI}\) from \(\mathbf{v}_{i}\) along \(\mathbf{t}_{max}\), \(\mathbf{t}_{min}\), \(-\mathbf{t}_{max}\), and \(-\mathbf{t}_{min}\) are identified. Since the points are probably not on the mesh surface, we project the 3D points on the mesh and find the vertices closest to the projections. For clarity, we illustrate in Figure 4 how \(\mathbf{v}_{i}^{1}\) and \(\mathbf{v}_{i}^{2}\) are searched along the maximal principal direction; the same procedures are repeated in the minimal principal direction to search for \(\mathbf{v}_{i}^{3}\) and \(\mathbf{v}_{i}^{4}\). Once \(\mathbf{v}_{i}^{1}\), \(\mathbf{v}_{i}^{2}\), \(\mathbf{v}_{i}^{3}\), and \(\mathbf{v}_{i}^{4}\) are determined, the SI information of these surface points and \(\mathbf{v}_{i}\) are retrieved from the SI database.
* Sum-pooling: To reduce the dimension of \(S(\mathbf{v}_{i}^{1}),\cdots,S(\mathbf{v}_{i}^{4})\), each SI is subsampled by using a sum-pooling operation with \(s\times s\) filters and stride \(s\). That is, adjacent \(s\times s\) bins of the SI are summed up to yield a single bin of the subsampled SI. Therefore, the dimensions of each SI reduces from \(N_{\gamma}N_{\rho}\) to \(\dfrac{N_{\gamma}N_{\rho}}{s^{2}}\). The purpose of this operation is to reduce the size of the SIs while preserving important information before they are vectorized. The sum-pooling operation is inspired by the multi-resolution SI, which applies the concept of pyramid structure of an image to compress a high resolution SI [27]. It is notable that the sum-pooling operation is actually identical to generating SI by partitioning the same 3D space into smaller number of larger subvolumes. In our experiments, \(s\) was set to 2.
* Vectorization: This operation converts the original SI information to vector of length \(N_{\gamma}N_{\rho}\) for easy merging with the four subsampled SIs. The operation is also repeated for the four subsampled SIs before they are merged. For each of the subsampled SI, the length of the vector is \(\dfrac{N_{\gamma}N_{\rho}}{s^{2}}\).
* Concatenation: All five vectors obtained from the output of the vectorization stages are concatenated to a single feature vector of length \(N_{\gamma}N_{\rho}+4\dfrac{N_{\gamma}N_{\rho}}{s^{2}}\). The concatenated array forms the CSI and it is denoted as \(C(\mathbf{v}_{i})\).
# Analytical Computation of the Spatial Resolution in GNSS-R and Experimental Validation at L1 and L5
###### Abstract  
Global navigation satellite systems reflectometry (GNSS-R) is a relatively novel remote sensing technique, but it can be understood as a multi-static radar using satellite navigation signals as signals of opportunity. The scattered signals over sea ice, flooded areas, and even under dense vegetation show a detectable coherent component that can be separated from the incoherent component and processed accordingly. This work derives an analytical formulation of the response of a GNSS-R instrument to a step function in the reflectivity using well-known principles of electromagnetic theory. The evaluation of the spatial resolution then requires a numerical evaluation of the proposed equations, as the width of the transition depends on the reflectivity values of two regions. However, it is found that results are fairly constant over a wide range of reflectivities, and they only vary faster for very high or very low reflectivity gradients. The predicted step response is then satisfactorily compared to airborne experimental results at L1 (1575.42 MHz) and L5 (1176.45 MHz) bands, acquired over a water reservoir south of Melbourne, in terms of width and ringing, and several examples are provided when the transition occurs from land to a rough ocean surface, where the coherent scattering component is no longer dominant.  
GNSS-R; spatial resolution; diffraction; experiment; airborne; L1; L5 +
Footnote †: journal: Remote Sensing, 2020, 12, 3910
# Automatic Era Identification in Classical Arabic Poetry
## 4 Experiments and Results
### Experiment 3: Assessing the Contribution of Different Feature Types
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **P** & **R** & **F1** \\ \hline
**Logitboost** & & & \\ ‘Abbasid & 0.831 & 0.891 & 0.860 \\ Umayyad & 0.784 & 0.799 & 0.791 \\ Pre-Islamic & 0.678 & 0.573 & 0.621 \\ Weighted AVG & 0.780 & 0.786 & 0.781 \\ \hline
**Naïve Bayes** & **P** & **R** & **F1** \\ ‘Abbasid & 0.806 & 0.612 & 0.696 \\ Umayyad & 0.53 & 0.521 & 0.526 \\ Pre-Islamic & 0.434 & 0.639 & 0.516 \\ Weighted AVG & 0.642 & 0.597 & 0.608 \\ \hline
**SVM (SMO)** & P & R & F1 \\ ‘Abbasid & 0.916 & 0.883 & 0.899 \\ Umayyad & 0.799 & 0.824 & 0.811 \\ Pre-Islamic & 0.72 & 0.749 & 0.734 \\ Weighted AVG & 0.837 & 0.834 & 0.835 \\ \hline
**ADABoost** & **P** & **R** & **F1** \\ ‘Abbasid & 0.605 & 0.947 & 0.738 \\ Umayyad & 0.724 & 0.663 & 0.692 \\ Pre-Islamic & 0 & 0 & 0 \\ Weighted AVG & 0.476 & 0.631 & 0.534 \\ \hline
**PART (DL)** & **P** & **R** & **F1** \\ ‘[PERSON] & 0.848 & 0.878 & 0.863 \\ [PERSON] & 0.777 & 0.747 & 0.762 \\ Pre-Islamic & 0.611 & 0.591 & 0.601 \\ Weighted AVG & 0.769 & 0.772 & 0.77 \\ \hline
**KNN** & P & R & F1 \\ ‘[PERSON] & 0.801 & 0.536 & 0.642 \\ [PERSON] & 0.492 & 0.744 & 0.592 \\ Pre-Islamic & 0.507 & 0.587 & 0.544 \\ Weighted AVG & 0.65 & 0.599 & 0.604 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results of Experiment 2. (Table 6). While their contribution is low, it is not the lowest (see Table 7). In addition, it is encouraging that both the topical and domain-specific features, which model literary phenomena according to domain experts, are informative for era classification purposes, and contribute to classification performance both individually and in combination with the other features.
## 3 Results
### Higher Probability Area for Tsunami Detection
Figure 6 shows the results from the MLE, which matches tsunami energy distribution probabilities assuming various conditions. In Figure 6, it is clear that tsunami energy is mainly distributed in the eastern sea of Korea, which is indicated by the black line. The upper boundary of the black outlined area is because the South Korean territorial sea restricts the area. To confirm whether tsunamis with the same fault parameters but different source locations have higher propagation probability in the tsunami detection region, the maximum tsunami height distributions with the same fault parameters except source location and strike angle were compared. Figure 7a shows a location map of epicenters. The detailed fault parameters are indicated in Table 1 (case 28, 33, 38). Figure 7b-d show the maximum tsunami height distribution in three different epicenters from Table 1. As shown in Figure 7b-d, before the tsunamis propagate into the eastern territorial sea of Korea, they are refracted by the Yamato Rise located in the eastern sea and mainly propagated to the higher probability tsunami detection region in the eastern territorial sea. Therefore,  
Figure 6: MLE results for maximum tsunami wave height distribution from COMCOT numerical modeling of 39 potential tsunami scenarios. Probability results for (**a**) 0.1 m (**b**) 0.2 m and (**c**) 0.3 m maximum height tsunami wave. The left side displays the results in the entire numerical model domain, and the right side displays the results in the eastern sea of Korea in water depth greater than 300 m. The black line indicates the higher tsunami detection probability boundary. it was confirmed that tsunamis are mainly propagated to the higher probability tsunami detection region than other regions in the eastern territorial sea.
## 3 Machine Learning Approaches for Slope Deformation Prediction
### Time-Series Forecasting Strategy  
The time-series problem can generally be expressed as follows. For a time-series containing \(N\) historical observation values [\(y_{1}\), \(y_{2}\), \(\ldots\), \(y_{N}\)], the recent \(d\) historical observation values [\(y_{N-d+1}\), \(\ldots\), \(y_{N}\)] are used to predict the values [\(y_{N+h}\), \(\ldots\), \(y_{N+h}\)] at the next \(H\) time points. When predicting according to the direct strategy [34], first, the sliding window method [35] is used to obtain \(H\) datasets that contain input data and target data, i.e., the \(d\) historical data as the input and next \(h\) data as the output. Second, \(H\) independent regression models are trained according to the functional relationship shown in Equation (2). Finally, \(H\) historical datasets are input into each model to predict the value at the corresponding time.  
\[\hat{y}_{N+h}=\hat{f}_{h}(y_{N-d+1},\ldots,y_{N}),h\in 1,\ldots,H \tag{2}\]  
where [\(y_{N-d+1}\), \(\ldots\), \(y_{N}\)] is the input data, \(y_{N+h}\) is the target data, \(H\) is the length of the prediction time, and \(\hat{f}_{h}\) is the regression model.  
In this paper, first, the time-series displacement data at each monitoring point were divided into a training set and a test set. Second, each model was trained to obtain optimal parameters based on the training data. Third, the test data were predicted based on the trained model, as shown in Figure 4.  
Specifically, in the training process, first, the training data were divided into an array of size 283 \(\times\) 7 based on a sliding window with a window size of seven and a time step of one. Each array has a size of seven, where the first six data are used as inputs to the deep learning model, and the seventh data is used as the output. Second, the model learned the features of 283 displacement arrays and established a correspondence between the input data and output data. In the predicting process, first, the test data were divided into an array of size 66 \(\times\) 7 based on the sliding window method. Second, the first six data in each array were fed into the trained model to predict the seventh displacement data. Finally, the predicted displacements were compared with the true displacements to evaluate the model performance.
# Semantic Structure from Motion for Railroad Bridges Using Deep Learning
## 5 Conclusions and Discussion  
In this study, an automatic procedure for generation of a 3D point cloud model that contains bridge component information was proposed using deep learning and computer vision. The verification test was carried out at the Osong 3 rd test track located in Osong-eup, Heungdeok-gu, Cheongju-si, Korea, by applying the proposed technique to the collected images. The proposed method was able to automatically generate a 3D point cloud model containing information on bridge components with an accuracy of 74.23%, IoU of 65.90%, and average BF score of 55.59%.  
[PERSON] et al. [4] conducted a study to automatically extract design parameters of bridges using 3D Point Cloud Data, and the results showed high reliability. However, this study  
\begin{table}
\begin{tabular}{c c c c} \hline  & \multicolumn{3}{c}{**Class**} \\ \cline{2-4}  & **Girder** & **Pier** & **Average** \\ \hline Precision (\%) & 78.64 & 69.81 & 74.23 \\ \hline IoU (\%) & 75.05 & 56.74 & 65.90 \\ \hline BF-score (\%) & 59.29 & 51.88 & 55.59 \\ \hline \end{tabular}
\end{table}
Table 6: SsfM results.  
Figure 13: Semantic PCD of the Osong 3 rd test track bridge.  
\begin{table}
\begin{tabular}{c c c c} \hline  & \multicolumn{3}{c}{**Class**} \\ \cline{2-4}  & **Girder** & **Pier** & **Average** \\ \hline Accuracy (\%) & 85.00 & 76.73 & 80.87 \\ \hline IoU (\%) & 80.22 & 53.09 & 66.66 \\ \hline BF-score (\%) & 62.49 & 50.17 & 56.33 \\ \hline \end{tabular}
\end{table}
Table 5: Semantic segmentation results.  
used LiDAR to acquire 3D Point Cloud Data. There is a problem where the bridge must be shut down in order to obtain 3D point cloud data from LiDAR. In order to solve this problem, in this study was conducted to collect 2D image data with an unmanned aerial and generate 3D point cloud data using the 2D image data.  
It was confirmed that the proposed method in this study has a problem of increasing the error because errors are accumulated in the process of SgfM. However, if errors are minimized in the future by using deep learning models with improved modulus and big data, it is expected that time and cost in the modeling for the BIM of existing structures can be saved.  
Conceptualization, H.Y.; methodology, H.Y. and J.H.L.; software, J.H.L.; validation, J.H.L. and G.P.; formal analysis, J.H.L. and G.P.; writing--original draft preparation, G.P.; writing--review and editing, H.Y.; visualization, J.H.L.; supervision, H.Y.; project administration, H.Y.; funding acquisition, H.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Research Foundation of Korea, grant number NRF-2019R11A3A0104827.  
Not applicable.  
The authors declare no conflict of interest.
## 4 Results
### Error Rate  
The bit error rate of the data transmission is less than 0.004%. The error of the data transmission can be checked out by the CRC (Cyclic Redundancy Check), which adds up all the data before transmission and pick up the 16 digits at the right-hand end of the row as check code to detect the data transmission. The data will be re-sent when the data goes wrong.
# Numerical and Experimental Characterization of a Coanda-Type Industrial Air Amplifier
## 5 Results
### Experimental Characterization
#### 5.1.2 Suction Mode: Analysis of the Flow Entrained from a Downstream Location  
In this configuration (see Figure 7), the Venturi tube is fitted with an additional part downstream from the restriction section, in order to fit the upper part of the ESAA, ensuring that only air passing through the Venturi tube is entrained by the device. The flow is discharged to the atmosphere as a free jet.  
The inlet total pressure was again varied from 0.35 to 0.70 MPa, and the same clearance gaps were analyzed as in Section 5.1.1. Finally, a total of 18 operating points were collected.  
Figure 17(left) reflects the dependence of the injected volumetric mass flow, \(Q_{c}\), on the total pressure provided by the high-pressure line. Differences between configurations with the same gap are believed to be due to the effects of manual adjustment of the screw. Under this operating condition, smaller gaps require a much higher total pressure to provide the same mass flow through the air amplifier.  
The aforementioned observations also hold true for the examination of the entrained volumetric mass flow, \(Q_{o}\), shown in Figure 17(middle), where the errors in the manual adjustment of the air amplifier gap seem to have a greater impact than the one observed on the high-pressure mass flow. A linear trend of the entrained mass flow with respect to the inlet total pressure was observed, without signs of mass flow limiting among the evaluated operating points. However, at the last measured point (\(p_{c}=0.7\) MPa), linearity was lost in the entrained volumetric mass flow, which also affected the amplification factor. The results reflect the extreme sensitivity of the entrained mass flow to changes in the nozzle gap, while at the same time show robustness on the performance of the air amplifier, which always maintains the same behavior despite the enlargement of the minimal outflow area.  
Figure 16: Blowing mode. High-pressure line volumetric mass flow, \(Q_{c}\); entrained volumetric mass flow, \(Q_{o}\); and amplification factor \(Q_{o}/Q_{c}\), against the total pressure measured at the inlet of the air amplifier \(p_{o}\).  
Finally, Figure 17(right) shows the relation between the entrained and injected volumetric mass flows, also referred to as the amplification factor. Similar to the manufacturer's tendency [24], the experimental measurements carried out at IDR/UPM facilities show a certain linearity as the inlet total pressure increases until it loses this linearity and remains almost constant. Smaller gaps provide higher amplification factors, reaching values above \(Q_{o}/Q_{c}=10\).
# Integrated Model Selection and Scalability in Functional Data Analysis Through Bayesian Learning
## Appendix E Fast Initialization
### Optimization of \(\beta,\bar{Z}\)  
**Derivation of Equation** (110)  
We differentiate \(\mathcal{P}\) with respect to \(\beta_{k}\)  
\[\frac{\partial\mathcal{P}}{\partial\beta_{k}} =\sum_{i=1}^{p}\frac{1}{2}\Big{(}\beta_{k}^{-1}-|\beta_{k}+s_{ik} |^{-1}-q_{ik}^{2}(\beta_{k}+s_{ik})^{2}\Big{)}\] \[=\frac{1}{2}\beta_{k}^{-1}\sum_{i=1}^{p}\Big{(}(\beta_{k}+s_{ik}) ^{-2}(\beta_{k}(s_{ik}-q_{ik}^{2})+s_{ik}^{2})\Big{)} \tag{115}\]  
We further adopt the approximation \(s_{1k}\approx s_{2k}\approx\ldots\approx s_{pk}\). Because \(s_{ik}\) is a discrete measure of the overlapping between the basis functions, it should remain invariant with respect to different sampling grids \(X_{i}\) given that the number of measurements is adequate and similar. Alternatively, the expectation maximization scheme can also be applied and is guaranteed to increase the likelihood \(\mathcal{P}\) in each iteration until convergence. However, we opt for this gradient descent with approximations for its advantage in speed to obtain a reasonable initialization. This way, we set the approximated differentiation to zero:  
\[\frac{\partial\mathcal{P}}{\partial\beta_{k}} \approx\frac{1}{2}\beta_{k}^{-1}(\beta_{k}+s_{1k})^{-2}\sum_{i=1 }^{p}\Big{(}(\beta_{k}(s_{ik}-q_{ik}^{2})+s_{ik}^{2})\Big{)}=0 \tag{116}\] \[\Rightarrow\beta_{k} \leftarrow\theta_{k}=\left(\frac{\sum_{i=1}^{p}s_{ik}^{2}}{\sum_ {i=1}^{p}(q_{ik}^{2}-s_{ik})}\right) \tag{117}\]  
Because \(\beta_{k}\) is a scale parameter, we need \(\beta_{k}>0\). Consequently, the optimal value for \(\beta_{k}\) to maximize \(\mathcal{P}\) depends on the sign of \(\theta_{k}\). When \(\theta_{k}>0\), the maximum of \(\mathcal{P}\) is achieved at \(\beta_{k}=\theta_{k}\).  
On the other hand, when \(\theta_{k}\leq 0\), \(\mathcal{P}\) is monotonically increasing with respect to \(\beta_{k}\), we should have \(\beta_{k}\leftarrow\infty\) in order to maximize \(\mathcal{P}\).  
More intuitively, Equation (117) can be regarded as a weighted summation of the estimation of \(\beta_{k}\) using each individual sample function, and it automatically assigns more weight to those with more measurements. Therefore, this optimization strategy is supposed to provide reasonable estimates even when the sampled functions have different numbers of measurements.  
**Derivation of Equation** (114)We differentiate \(\mathcal{P}\) with respect to \(\hat{Z}\) and zero the derivative to obtain  
\[\frac{\partial\mathcal{P}}{\hat{Z}}=-\frac{1}{2}\sum_{i=1}^{P}\Bigl{(} -2Y_{i}\mathcal{C}_{i}\Phi_{i}^{T}+2\hat{Z}\Phi_{i}\mathcal{C}_{i}^{-1}\Phi_{T} \Bigr{)}=0 \tag{1131}\] \[\Rightarrow Z\leftarrow\sum_{i=1}^{P}\Bigl{(}Y_{i}\mathcal{C}_{i}^{-1 }\[PERSON]_{i}^{T}\Bigr{)}\left(\sum_{i=1}^{P}(\Phi_{i}\mathcal{C}_{i}^{-1}\[PERSON]_{i} ^{T})\right)^{-1} \tag{1132}\]
# A Normalization Strategy for Weakly Supervised 3D Hand Pose Estimation
## 4 Experiments
### Evaluation of Normalization Strategy  
Table 1 provides a comprehensive comparison of the effects of the normalization strategy on the VPose3D and ST-GCN models across different 2D pose sources and datasets. In terms of **PJ** errors, both VPose3D and ST-GCN consistently exhibit lower errors on the STB and RHD datasets when trained with normalization (w/norm). Specifically, by employing the normalization strategy and utilizing GT 2D pose inputs, a substantial reduction in joint position errors of 45.1% and 43.4% is observed in the STB and RHD datasets, respectively. When MediaPipe 2D poses are used as input, the estimation errors of VPose3D decrease by 11.3% and 14.3% in the STB and RHD datasets, respectively. Higher AUC values are also observed across all scenarios, further emphasizing the effectiveness of the normalization approach. This consistent enhancement in performance metrics underscores the importance of the normalization strategy in improving the accuracy of lifting models. The lifting models serve as projection functions that capture mapping rules between 2D and 3D joints. Therefore, evaluating model accuracy based on GT 2D keypoints is more reliable, as the 2D-3D pose pairs \(\{X_{\text{GT}},x_{\text{GT}}\}\) adhere to true mapping rules, while \(\{X_{\text{GT}},x_{\text{mp}}\}\) pairs with 2D keypoints estimated by MediaPipe may not reflect accurate 2D-3D mappings. This signifies that there are inherent 2D errors from the keypoints detector, and experiments using 2D pose \(x_{\text{GT}}\) as input can more effectively assess the model performance. In Table 1, when the model is provided with GT 2D keypoints, the estimation accuracy significantly decreases, demonstrating that our model closely adheres to the actual mapping rules. When the model fed with MediaPipe 2D keypoints, the accuracy also improves, albeit to a lesser extent compared to when fed with GT data. It can be inferred that the errors primarily arise from inaccuracies in the 2D keypoints, given the strong performance of the model when using GT 2D keypoints. To comprehensively assess the efficacy of the proposed normalization method, the distribution of static biomechanical features in both the normalized and non-normalized datasets is visually depicted. By utilizing PCA to project features onto a 2D plane, as illustrated in Figure 4, a clear alignment emerges between the distribution in the normalized training dataset and the testing datasets, while the distribution of training data without normalization exhibits significant differences from that of the testing datasets. This illustrates the successful normalization of static biomechanical features to match the target individual.