esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 System Development and Experimental Verification
### Experimental Verification
#### 4.2.1 Experimental Verification of Point Cloud Registration and Densification Method  
To verify the effectiveness of the NDT point cloud registration and densification method fused with ORB-SLAM3 technology, the spatial ROI filtering algorithm was first applied to remove the interfering point cloud of the car body. The experimental results, as shown in Figure 14, effectively remove the point cloud information of the car body after filtering and only retain the basic point cloud of the tunnel and tunnel wall, making the point cloud registration more focused on the target area.  
Secondly, to balance the detection efficiency and obstacle recognition, we set the mapping frame threshold N to 10. The experimental results are shown in Figure 15: images a and b show the tunnel point cloud registration situation with registration error scores of 21.18 and 0.04, respectively. The point cloud information of the tunnel wall and contact network line in these two pictures is clearly visible.  
After completing the point cloud registration of all collected adjacent frame data, we obtained the analysis result in Figure 16. Figure 16 shows that during the forward process of the detection device, the registration error score of the adjacent frames gradually drops from the highest of 21.18 to close to zero, indicating that the registration accuracy continues to improve.  
Figure 14: Point cloud data before and after interference removal. (**a**) Original point cloud data. (**b**) Point cloud data after interference removal.  
Figure 15: Tunnel Point Cloud Registration Results. (**a**) Tunnel point cloud registration image with a score of 21.18. (**b**) Tunnel point cloud registration image with a score of 0.04.  
Finally, a point cloud densification enhancement experiment was conducted based on single-frame registration results. The results are shown in Figure 17: The left image is the original point cloud information image, with a total of 24,000 point cloud data, and the right image is the point cloud information image after densification, with the point cloud data increased to 240,000. As can be seen from the figure, the tunnel point cloud, especially the contact network line point cloud, is significantly densified, which further enhances the recognition of small targets.  
It can be seen that the point cloud registration and densification method combining ORB-SLAM3 and NDT technology has good effects in the application of high-speed rail tunnel obstacle detection and can meet the detection requirements of small-volume obstacles in high-speed rail tunnels.
# Investigation on Intelligent Early Warning of Rock Burst Disasters Using the PCA-PSO-ELM Model
## 3 Example Analysis
### Results Analysis  
After training the PSO-ELM model, the obtained curve of the variation of the model iteration error was achieved, as shown in Figure 6, indicating that the fitness value did not change after 35 iterations, i.e., the algorithm converged and the optimal parameter combination was identified.  
The parameter combination obtained through optimization was input into the ELM super parameter so that the rock burst grade could be predicted. The prediction results of the model for the test set are displayed in Figure 7.  
Notably, it was evidently discovered from the analysis in Figure 7 that the prediction accuracy of the PSO-ELM model reached 100%. In order to further investigate the performance for predicting the rock burst model of the PSO-ELM algorithm, this paper employed  
Figure 6: Variation curve of the fitness value of the PSO-ELM model.  
Figure 7: PSO–ELM model prediction results: (**a**) the comparison of the PSO–ELM model prediction results; (**b**) the test set confusion matrix of the PSO–ELM model.  
the BP neural network, the radial basis function, and the extreme learning machine to learn the training set samples and predict the rock burst grade of the test set. The prediction results are exhibited in Figure 8, Figure 9, and Figure 10, respectively.  
Comparing the prediction results of the PSO-ELM neural network with those of the BP neural network, the radial basis function, and the ELM, as shown in Figures 7-11 and Table 7, the prediction accuracy of the BP neural network, RBF, and traditional ELM referred to 75%, 75%, and 87.5%, respectively. The ELM rock burst prediction model optimized using PSO reached 100%, which overcame the shortcomings of randomness of the ELM input weights and the hidden layer thresholds so that the prediction accuracy of the combined PCA-PSO-ELM model could be tremendously improved and a novel idea for the early warning exploration of rock burst in mines could be evidently provided.  
Figure 8: BP Model prediction results: (**a**) the comparison of the BP model prediction results; (**b**) the test set confusion matrix of the BP model.  
Figure 9: RBF model prediction results: (**a**) the comparison of the RBF model prediction results; (**b**) the test set confusion matrix of the RBF model.  
Figure 11: Comparison of the precision between the different models.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Test Sample** & **Actual Grade** & **BP** & **RBF** & **ELM** & **PSO–ELM** \\ \hline
1 & IV & IV & IV & IV & IV \\
2 & I & I & I & I & I \\
3 & IV & IV & IV & IV & IV \\
4 & III & III & III & III & III \\
5 & III & IV & IV & III & III \\
6 & IV & IV & IV & IV & IV \\
7 & III & III & III & IV & III \\
8 & III & IV & II & III & III \\ \hline \end{tabular}
\end{table}
Table 7: Comparison of the prediction results of the different models.  
Figure 10: ELM model prediction results: (**a**) the comparison of the ELM model prediction results; (**b**) the test set confusion matrix of the ELM model.
## 4 Spatial Positioning and Postural Estimation Method  
### Preprocessing  
The preprocessing includes two phases: 3D CAD model sampling and scene point cloud acquisition, filtering, clustering, and segmentation. Since the CAD models have a higher resolution than model point clouds, in order to achieve matching between point cloud pairs, the CAD models are down-sampled before matching. We convert the CAD models into model surface point clouds (MSPCs) by a sampling interval equal to the resolution of the stereo camera (0.055 mm). Figure 8 shows the surface point cloud obtained from the 3D CAD model. Compared with traditional point cloud approaches, the MSPC sampled from a design model contains richer information and more sufficient features, which improves the accuracy of point cloud matching. The type, position, and posture can be accurately matched in a scene with multiple randomly postured components.  
A scene point cloud is the point cloud of the area to be matched. It is obtained by the 3D point cloud camera. The preprocessing of the scene point cloud can avoid global matching and improve the matching efficiency. Before matching, the scene point cloud is filtered, clustered, and segmented to obtain candidate-matching regions. In the subsequent matching process, only the candidate-matching regions are matched. Hence, the matching efficiency is greatly improved. The original scene point cloud is shown in Figure 9.  
The original scene point cloud is down-sampled by voxel filtering, which simplifies the point cloud without the loss of shape features. In the original scene point cloud (OSPC) acquired by the camera, a large part of the point cloud is the background of the trap, which is not useful and leads to mismatches, as well as increases the calculation. SAC segmentation [24] combined with parameter tuning is used to remove the background plane of the point cloud. The point cloud after removing the background plane is shown in Figure 10.  
Figure 8: Down-sampling for the magnetic system component: (**a**) 3D design model; (**b**) sampled surface point cloud.  
The scene point cloud is clustered and segmented by the Euclidean distance. The upper and lower thresholds of the number of points in a point cloud cluster are determined according to the distribution of the number of points of the component. The point cloud clusters that are too large, too small, or too far from the center of clusters are further filtered out. Finally, point cloud clusters meeting the requirements are kept. The segmented and filtered point cloud subsets (termed \"candidate point clouds\", CPC) are rendered in different colors to distinguish different point cloud regions, as shown in Figure 11. The colored regions are selected as the candidates for rough matching and fine registration of the following stages.  
Figure 10: Point cloud after removing background.  
Figure 9: Original scene point cloud.
## 5 Results
### PCA and HCA Results
#### 5.3.3 PCA-2 Results  
PCA-2 was conducted on a set of seven variables including four statistical variables (mode, sorting, skewness, kurtosis) and three textural variables (sand, silt, and clay fractions). The correlation matrix resulting from PCA-2 reveals several positive correlations: between sorting and clay fraction, mode and sand fraction, and kurtosis and sand fraction (Table 5). On the other hand, sand and silt fractions exhibit negative correlations with clay fraction and strong negative correlations with kurtosis and mode.  
The results of the scree plot analysis indicate that PC1 explains 43.10% of the total variance while PC2 and PC3 contribute to 30.55% and 14.30% respectively (Table 6 and Figure 9). Based on these results, only the scores of PC1 and PC2, which account for 74% of the overall variance, were included in the analysis. PC1 shows a positive correlation with clay content, sorting, and skewness. PC2 demonstrates a positive correlation with mode, sand fraction, and skewness, while it is negatively correlated with silt and clay fractions. The interpretation of PC1 suggests that it distinguishes between homogeneous and heterogeneous samples, while PC2 is structured by the grain size gradient.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \
\begin{tabular}{c} **Principal** \\ **Component** \\ \end{tabular} & **Eigenvalue** & **\% of Variance** &
\begin{tabular}{c} **\% of Cumulative** \\ **Variance** \\ \end{tabular} \\ \hline
1 & 3.02 & 43.10 & 43.10 \\
2 & 2.14 & 30.55 & 73.65 \\
3 & 1 & 14.30 & 87.95 \\
4 & 0.49 & 6.99 & 94.94 \\
5 & 0.26 & 3.67 & 98.61 \\
6 & 0.10 & 1.39 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Total variance of the PCA-2.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Mode** & **Sorting** & **Skewness** & **Kurtosis** & **Sand** & **Silt** & **Clay** \\ \hline \begin{tabular}{c} **Mode** \\ **Sorting** \\ **Skewness** \\ \end{tabular} & \(1\) & \(1\) & \(1\) & \(1\) & \(1\) & \(1\) & \(1\) & \(1\) \\ \begin{tabular}{c} **Kurtosis** \\ **Sand** \\ **Silt** \\ \end{tabular} & \(0.71\) & \(-0.31\) & \(0.15\) & \(0.47\) & \(1\) & \(1\) & \(1\) \\ \
\begin{tabular}{c} **Silt** \\ \end{tabular} & \(-0.46\) & \(-0.54\) & \(-0.52\) & \(0.18\) & \(-0.36\) & \(1\) \\
\begin{tabular}{c} **Clay** \\ \end{tabular} & \(-0.33\) & \(0.72\) & \(0.26\) & \(-0.59\) & \(-0.69\) & \(-0.43\) & \(1\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Correlation matrix of seven statistical parameters.
## 3 Experiments
### Verification of Kinematic Model of Surgical Robot
#### 3.2.1 Accuracy of Vision-Based Measurements  
To assess the accuracy of the proposed vision-based measurement method, experiments were conducted on shoulder joint 1, shoulder joint 2, elbow joint, wrist joint 1, and wrist joint 2, as shown in Figure 10A. A camera was positioned above the robot arm. The visual angle measurement algorithm, alongside the IC Measure software, was employed to measure the angles of these joints. The maximum discrepancy between the visual angle measurements and the IC Measure software readings is 0.96\({}^{\circ}\), which is within an acceptable margin of error.  
Figure 10: Accuracy of vision-based measurements. (**A**) Angle of shoulder joint 1 measured using the AVM. (**B**) Comparison of joint angle measurements between the AVM and IC Measure. (**C**) Error of AVM measurements relative to the IC Measure results.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Measuring Times** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** \\ \hline Theoretical angle (\({}^{\circ}\)) & 0 & 5 & 10 & 15 & 20 & 25 & 30 & 35 & 40 \\ \hline Measuring angle (\({}^{\circ}\)) & 0.45 & 5.53 & 9.21 & 14.42 & 20.18 & 25.34 & 30.78 & 35.56 & 40.75 \\ \hline Absolute error (\({}^{\circ}\)) & 0.45 & 0.53 & 0.79 & 0.58 & 0.18 & 0.34 & 0.78 & 0.56 & 0.75 \\ \hline \end{tabular}
\end{table}
Table 3: Statistics of angle measurement results of shoulder joint 1 by IC software.  
Figure 9: Experiment for decoupling model of multi-joint.
## 3 Results
### Near-Infrared FEL Imaging  
To clarify the cause of the variation in the transmission of the tumor tissue in the THz region, near-infrared imaging using the LEBRA FEL was performed for sample #3, in which the difference in the transmission between the tumor tissue and the normal tissue was minimal. The FEL wavelength was set to 1.93 \(\upmu\)m, where there was a vibration mode of OH radicals in water. The absorption coefficient of water at a wavelength of 1.93 \(\upmu\)m was as large as 124 cm\({}^{-1}\), and the FEL was absorbed by the water rather than proteins and lipids, which were the main components of the liver. Figure 4. shows a near-infrared image and a photograph of the liver sample used in the experiment.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Sample** & **Number of** & **Transmission of** & **Transmission of** & **\(p\)-Value** \\
**Number** & **Data Point** & **Normal Tissue** & **Tumor Tissue** & \\ \hline \#1 & 55 & 36.07\% \(\pm\) 2.30\% & 37.88\% \(\pm\) 2.85\% & \textless{}0.001 \\ \#2 & 55 & 34.65\% \(\pm\) 1.34\% & 31.11\% \(\pm\) 1.38\% & \textless{}0.001 \\ \#3 & 55 & 36.74\% \(\pm\)  
Because an absorption of the near-infrared FEL due to the mounting medium Marinol is negligible in Figure 4a, the transmission of the sample at the wavelength of 1.93 \(\upmu\)m is calculated by normalizing the FEL intensity passing through the Marinol fixed sample sandwiched between quartz slides. The imaging graph is represented on a logarithmic scale owing to low transmission. The black and green frames in Figure 4b indicate the areas where the transmissions of the tumor tissue and the normal tissue are evaluated in Table 1. Images of the stained tissues inside the frames are also shown in this figure. The transmissions of the tumor tissue and the normal tissue for the near-infrared FEL were measured to be 3.03 \(\pm\) 0.61% and 3.56 \(\pm\) 1.50%, respectively. The difference in the transmission of the two tissues was not significant (\(p\) = 0.08 \(>\) 0.05). This experimental result indicates that the number of water molecules contained in the 2 mm\({}^{2}\) tumor tissue is almost equal to that in the 2 mm\({}^{2}\) normal tissue.  
As identified from the photograph and the staining image inside the blue frame in Figure 4b, this sample had an area that was mostly occupied by fibrous tissues. Vacancies were found around the tumor tissues in this area. The transmission of the fibrous tissue in the blue frame was measured to be 10.82 \(\pm\) 6.82%, which was significantly higher than that of the other tissues (\(p\) < 0.001). The staining image inside the black frame in Figure 4b shows the presence of fibrous tissues and vacancies surrounding the tumor tissue. The proportion of fibrous tissues and vacancies in the black frame was 20-30%. Although small vacancies with a scale of 10 \(\upmu\)m are negligible for THz waves, larger vacancies influence the transmission of THz waves. Therefore, we considered that larger vacancies and fibrous tissues around the tumor tissue increase the transmission of the tumor tissue in the THz region.
# Text Normalization Using Encoder-Decoder Networks Based on the Causal Feature Extractor
## 2 Materials and Methods
### Encoder-Decoder Architecture
This vector \(a\) describes the characteristics that are expected to be obtained, so it is compared with each column of \(\mathbf{Z}\), \(z_{i}\), using the dot product as a function of similarity. So, a vector \(\alpha\in\mathbb{M}_{1\times l}\) is generated in this way:  
\[\alpha=\alpha_{1}\alpha_{2}\ldots\alpha_{l}=(a\cdot z_{1},a\cdot z_{2}\ldots a \cdot z_{l}) \tag{1}\]  
Then, this vector \(\alpha\) is transformed into a stochastic vector, i.e., a vector such that \(\sum\alpha_{i}=1\), via:  
\[\alpha_{i}^{\prime}=\frac{\exp\alpha_{i}}{\sum_{j}\exp\alpha_{j}} \tag{2}\]  
Now, \(\alpha_{i}^{\prime}\) represents the interest of the decoder with respect to the \(i\)-th element of the code, \(z_{i}\), at the given time step. With this information, a context vector is produced, that is, a vector representing the portion of the input that is actually interesting for the decoder at this instant. Traditionally, this context vector is given by a weighted sum of the elements of \(z_{i}\), weighted by \(\alpha^{\prime}\), i.e., \(c=\sum_{i}\alpha_{i}^{\prime}z_{i}\). Another possibility is to select only the code \(z_{i}\) corresponding to the highest \(\alpha_{i}^{\prime}\). However, we propose a different approach which consists in selecting several codes with the highest \(\alpha_{i}^{\prime}\) values. Thus, instead of performing a weighted sum or taking the maximum, a new hyperparameter \(\mathsf{d}\) is introduced to indicate the number of context elements that are considered. In this way, a context matrix \(c\in\mathbb{M}_{f\times d}\) is generated at each time step \(t\), where the \(i\)-th column, \(c_{i}\), corresponds to the vector \(\alpha_{i}^{\prime}z_{j}\), where \(\alpha_{j}^{\prime}\) is the \(i\)-th largest value of \(\alpha^{\prime}\). That is, \(c_{1}\) corresponds to the largest value of \(\alpha^{\prime}\), \(c_{2}\) to the second largest value, etc. Subsequently, the decoder receives this context matrix, \(c\), instead of just a context vector. The idea inspiring this modification is that taking the average of the feature vectors involves a significant loss of information. Instead, by using the \(\mathsf{d}\) greatest elements, the internal semantic of them is preserved.
## 5 Experimental Results and Discussions
### Data Acquisition and Filtering  
The sinusoidal trajectory given by Equation (35) and shown in Figure 7 was used as the optimal exciting trajectory of the end effector. Its parameters were obtained by optimizing Equation (38) subject to Equation (37). The definition of the optimized exciting trajectory included the position, velocity, and acceleration of the end effector within a defined timespan (4 s). Using the inverse kinematics, the motion variables of the trajectories defined in the task space were transformed into the active joint space. While the active joints were moving to make the trajectories, their motion variables were measured using linear encoders. The active joint positions were directly measured by using the encoders. The uncertainty of the encoders was \(\pm 0.0005\) mm. The active joint velocities and accelerations were derived from the measured positions. The measured positions, velocities, and accelerations of the active joints are shown in Figures 8-10, respectively. It is shown that the measured active joint positions were so smooth that they did not need any filtering. However, some noise was found in the measured velocities and accelerations. Therefore, they were filtered, as shown in Figures 9 and 10. It should be noticed that the measured trajectory positions, velocities, and accelerations were used in the identification instead of the planned (commanded) ones.  
At the same time, the active joint forces were also measured. The active joint forces were not directly measured by using dedicated force sensors attached to the sliders. Instead, the measurement of the active joint forces was based on the voltage output from the controller to the servo drives. The active joint forces were calculated based on a proportional relationship between the voltage output from the controller to the servo drives and the resulting current in the servo drives, as well as a proportional relationship between the current drawn from the servo drives and the resulting actuation forces. This indirect measurement of the active joint forces was much cheaper than a direct measurement as no dedicated force sensors were required. The uncertainty of the active joint force measurement was \(\pm\)0.0381 N. The measured active joint forces are shown in Figure 11. As the measured active joint forces were noisy, they were filtered, as shown in Figure 11. The filtering of the velocities, accelerations, and forces were all performed by using a Savitzky-Golay smoothing filter in MATLAB. Thus, all the raw data were acquired from the hardware and subsequently filtered in MATLAB using the aforementioned filter.  
Figure 8: Raw measured active joint positions.  
Figure 7: Exciting trajectory: (**a**) position and (**b**) angle.  
Figure 10: Raw and filtered measured active joint accelerations.  
Figure 9: Raw and filtered measured active joint velocities.
## 2 Proposed Method
### Multi-Objective Instance-Weighting Strategy
#### 2.5.3 Multi-Objective Instance Weighting  
Two indicators, KLD and MMD, measure the dissimilarity between a source domain instance and target domain. The KLD measures how suitable the source domain is for the target domain task, and MMD more intuitively measures the distance between the two domain instances. Although both indicators measure domain discrepancy, they demonstrate inconsistent results. Since two indicators exhibit a low positive correlation, those indicators are combined as the multi-objective instance weight to play complementary roles in transfer learning.  
Both KLD and MMD of certain source instance have positive values. These two indicators were standardized and converted to be used as weights \(\mathcal{W}_{i}^{KLD}\) and \(\mathcal{W}_{i}^{MMD}\), respectively. Instances with a high value in a specific indicator should have a low weight value calculated from that indicator. The two weights have a value between 0 and 1, and the larger the indicator value, the smaller the weight value. In this process, the instance weight derived from each indicator is set to be 0 if corresponding indicator of that instance is larger than a standard deviation from domain mean value. Afterward, the final weight of \(i^{th}\) source domain instance \(\mathcal{W}_{i}\) is derived by the weighted sum of the two weights:  
\[\mathcal{W}_{i}=w_{1}*\mathcal{W}_{i}^{KLD}+w_{2}\,*\mathcal{W}_{i}^{MMD}, \tag{9}\]  
where \(w_{1}\) and \(w_{2}\) are weights of the KLD and MMD-based weight. In this research, two weights are equal at 0.5, because the importance of KLD and MMD are even.  
The multi-objective instance weights are used as training weights for the source domain in the training stage of the pre-trained model as shown in Equation (4). Through this process, source instances that are not useful for the target task are assigned very low training weights, so they are removed from the training stage, or their influence is significantly reduced. As a result, the effect of the dissimilarity between the two domains on model training is minimized so that transfer learning can proceed effectively.