esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussions
### Evaluation of Findings  
The development and implementation of the immersive hydroinformatics framework yielded insights that directly respond to the research questions posed in this paper. Regarding the question about the most effective data sources for creating immersive hydrological visualizations, the integration of the USGS API and weather API proved to be paramount. The selection of Mapbox as the map provider, as detailed in the consideration of various map providers, facilitated a rich and dynamic visualization platform. This choice was initiated by the accurate rendering of hydrological phenomena and the seamless integration of real-time data, pointing to the effectiveness of these data sources in enhancing the immersive experience.  
In response to focusing on how XR can be utilized to enhance hydrological data visualizations, the use of the Unity engine and Mapbox Maps SDK, alongside VR devices, created a multi-dimensional and interactive environment. The application's ability to dynamically generate 3D models and visualize real-time data within an immersive setting exemplifies the potential of XR technologies in transforming hydrological data visualization.  
Concerning the development, maintenance, and leverage of user immersion in data visualization, the incorporation of real-time weather conditions and fluid simulations significantly contributed to user engagement. The visualization of rainfall and snowfall animations, as well as the simulation of water behavior using the Zibra Liquids asset, not only enhanced the realism of the environment but also facilitated a deeper understanding of hydrological processes, thereby addressing the role of immersion in data visualization. Table 1 provides an overview of the core components of the proposed framework in terms of their core value and strengths.  
Lastly, with respect to identifying future prospects and potential improvements in the use of XR systems for hydrological data visualization. The discussion on integrating additional hydrological phenomena, such as percolation and soil moisture, into the framework suggests a pathway for expanding the application's capabilities. Moreover, the emphasis on refining user interfaces and ensuring cross-platform compatibility highlights the ongoing need to enhance accessibility and user experience. These findings underscore the framework's capacity to address the initial research questions, demonstrating the considerable promise of XR technologies in hydrological visualization and the potential for future advancements in this field.
# Using Allan Variance Based Semi-Parameter Model to Calibrate Pointing Errors of Alt-az Telescopes
## 4 Experiment and Results
The mount model is then utilized to compensate for the geometric errors. Figure 5 and Table 1 present the remaining residual errors after compensation of mount model. It was shown that the mount model had remarkably compensated for geometric errors and improved the LOS pointing precision of alt-az telescopes. It was improved to 2.2376\" in azimuth and 2.569\" in elevation. The precision  
Figure 4: Original pointing errors of the alt-az telescope: (**a**) in azimuth; and (**b**) in elevation. between mount model and spherical harmonic function model is similar. On the other hand, due to impacts of nonlinear factors excluded in the mount model, residual errors are not white noises. Consequently, the AVSPM is adopted to compensate for residual nonlinear errors. As shown in Figure 6 and Table 1, the result indicates that the AVSPM outperform the mount model remarkably in LOS pointing precision, namely 1.1261\({}^{\prime\prime}\) in azimuth and 1.3054\({}^{\prime\prime}\) in elevation. Furthermore, the residual errors of AVSPM are assessed according to the criterions provided by Eubank [12]. Because both geometric and nonlinear errors were removed by AVSPM, residual errors were determined to be white noises. It was proven that the LOS pointing precision of alt-az telescopes remarkably improved after calibration, and the proposed AVSPM achieved higher precision than the classical mount model. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Pointing Model**} & \multicolumn{2}{c}{**Pointing Precision (RMSE) of 1 m Alt-az Telescope**} \\ \cline{2-4}  & \(\Delta\)**\(\Delta\)**\(\Delta\)**\(\Delta\)**\(\Delta\)**\(\Delta\)**\(\Delta\)** \\ \hline OE & 18.1477\({}^{\prime\prime}\) & 26.3599\({}^{\prime\prime}\) & 20.6719\({}^{\prime\prime}\) \\ SHFM & 2.3655\({}^{\prime\prime}\) & 2.7226\({}^{\prime\prime}\) & 1.7112\({}^{\prime\prime}\) \\ MM & 2.2376\({}^{\prime\prime}\) & 2.569\({}^{\prime\prime}\) & 1.6923\({}^{\prime\prime}\) \\ AVSPM & 1.1261\({}^{\prime\prime}\) & 1.3054\({}^{\prime\prime}\) & 1.1542\({}^{\prime\prime}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Pointing precision of the alt-az telescope after calibration. Figure 5: Residual errors and fitting surface of mount model: (**a**) in azimuth; and (**b**) in elevation. Figure 6: Residual errors and fitting surface of AVSPM: (**a**) in azimuth; and (**b**) in elevation.
## 4 Experiments and Results
### Evaluation Indicators  
For the method proposed in this study, an entity was considered correctly identified when and only when its boundary and class were correctly identified. To facilitate comparison with traditional models, the precision rate Precision (\(P\)), recall rate Recall (\(R\)), and F1 value (\(F1\)-score) were used as quantitative evaluation metrics of the model.  
\[P=\frac{TP}{TP+FP}\times 100\% \tag{6}\]  
\[R=\frac{TP}{TP+FN}\times 100\% \tag{7}\]  
\[F_{1}=\frac{2\times P\times R}{P+R}\times 100\% \tag{8}\]  
where \(TP\) (True Positives) indicates the number of entities in the test set for which the category was correctly identified, \(FP\) (False Positives) indicates the number of entities for which the category was incorrectly identified, and \(FN\) (False Negatives) represents the number of entities that were not identified.
# A Parallel Two-Stage Iteration Method for Solving Continuous Sylvester Equations
## 5 Numerical Examples
Here we focus on comparing the parallel performance with the TS iteration method and the MCG method. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**n**} & \multicolumn{3}{c}{**HSS**} & \multirow{2}{*}{**MCG**} \\ \cline{2-2} \cline{4-6}  & & **r = 0.01** & **r = 0.1** & **r = 1.0** \\ \hline
32 & 0.40 & 0.40 & 0.95 \\
64 & 0.17 & 0.23 & 0.81 \\
128 & 0.09 & 0.13 & 0.62 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The optimal values \(\alpha_{exp}\) for HSS. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**n**} & \multicolumn{3}{c}{**TS**} & \multicolumn{3}{c}{**HSS**} & \multirow{2}{*}{**MCG**} \\ \cline{2-2} \cline{4-6}  & **ITs** & **T** & **ITs** & **T** & **ITs** & **T** \\ \hline
32 & 31 & 0.043 & 48 & 0.061 & 183 & 0.059 \\
64 & 54 & 0.395 & 125 & 0.443 & 254 & 0.415 \\
128 & 158 & 5.796 & 247 & 8.872 & 458 & 8.236 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of ITs and T when choosing \(r=1.0\). \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**n**} & \multicolumn{3}{c}{**TS**} & \multicolumn{3}{c}{**MCG**} \\ \hline \(p\) & 1 & 2 & 4 & 6 & 1 & 2 & 4 & 6 \\ T & 140.25 & 77.62 & 39.83 & 30.45 & 297.47 & 160.89 & 84.29 & 63.56 \\ ITs & 1067 & 1067 & 1067 & 1067 & 4992 & 4992 & 4992 & 4992 \\ S & & 1.81 & 3.52 & 4.61 & & 1.85 & 3.53 & 4.68 \\ \(E_{1}\) & 1.00 & 0.90 & 0.88 & 0.77 & 0.47 & 0.44 & 0.42 & 0.37 \\ \(\Delta\) & \(9.66\times 10^{-7}\) & \(9.66\times 10^{-7}\) & \(9.66\times 10^{-7}\) & \(9.14\times 10^{-7}\) & \(9.01\times 10^{-7}\) & \(9.13\times 10^{-7}\) & \(9.11\times 10^{-7}\) & \(9.80\times 10^{-7}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Numerical results of Example 2 with \(h=1/101\). From the results Tables 5 and 6, we observe that both iteration steps and computational time using TS are much less than those with the MCG. Furthermore, parallel efficiency with the TS method is higher than with the MCG. In addition, the advantages of the TS method increase over the MCG method with increasing scale-size of equations from \(10^{4}\) (\(n=100\)) to \(10^{6}\) (\(n=1000\)). **Example 3**. : _Let \(A=(a_{ij})_{n\times n}\) and \(B=(b_{ij})_{n\times n}\) where_  
\[a_{ij}= \begin{cases}-n+\sin(i+j),&i=j\\ i,&\text{others}\end{cases} ,\;\;b_{ij}= \begin{cases}3+2\sin(i+j),&i=j\\ \cos(i+j),&j-i=1.\\ \sin(i+j),&i-j=1\end{cases} \]  
In the Sylvester matrix equation \(AX+XB=F\), let \(n=1000\) and \(F\) is an any given matrix. The numerical results are listed in Table 7.  
From Table 7 we observe that the two-stage iteration method is still efficient in the case that the coefficient matrices are indefinite matrices. This indicates that the condition for the convergence is only a sufficient condition in Theorem 1.
# Deep 3D Convolutional Neural Network for Facial Micro-Expression Analysis from Video Images
## 8 Results And Discussions
### Stratified K-Fold
#### 8.2.1 Scenario-1  
Scenario-1 is an intra-dataset experiment where both training and validation were performed on the SMIC dataset. Our Model-A reached an accuracy of 43.7% compared to 33.5% and 37.3% of Model-B and Model-C, respectively. Thus, the performance difference is 10.2% and 6.4%. The advantage of Model-A is that it is deeper and consists of more convolutional layers. Therefore, it is able to better identify spatio-temporal features related to micro-expressions. Another benefit is provided by our pre-processing technique, which involves selecting an Apex sequence and interpolating the first and last frames of each video, resulting in fewer samples being discarded because of too short length.  
Figure 4: Evaluation accuracies for our Split Model with train–test split for multiple runs.  
A overall low accuracy can be noticed for all three models. The main reason for this is that the videos were recorded at 100 FPS, which is low compared to the other benchmark datasets. Thus, there is a lag in the models' ability to capture the temporal information, which is an essential factor in micro-expression recognition. Consequently, poor temporal feature quality leads to low accuracies. Additionally, the average sample length in SMIC is only 34 frames, which is shorter than for the other databases. It is likely that this only allows the extraction of features with lower quality from the samples.
## 3 Results and Discussion
### Reinforcement  
Figure 7 shows the change in the safety factor based on the change in the amount of reinforcement. Inappropriate results for the design standard (e.g., minimum spacing of reinforcement) were not considered. The original design involved a cross section with seven D16 reinforcements. The safety factor was less than 1 when the number of reinforcements decreased from seven to six; thus, it was inappropriate with respect to Code 2010. However, in the case of Code 2015, the safety factor corresponded to 1.187 even when the number of reinforcements decreased to six, and this indicated that it was safe. Additionally, when the type of reinforcement was changed to D13, Code 2010 was also inappropriate for the safety factor, but Code 2015 was appropriate, with safety factors that corresponded to 1.150 and 1.027 when nine and eight D13 reinforcements, respectively, were applied. Under the flexural performance condition (i.e., safety factor) similar to the original one, the number of reinforcements could be decreased by 14.3%, and the diameter of the reinforcement could be decreased by up to 18.0% and 27.0% for nine and eight D13 reinforcements, respectively.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Load Type (kN\(\cdot\)m)**} & \multicolumn{2}{c}{**Code 2010**} & \multicolumn{2}{c}{**Code 2015**} \\ \cline{2-5}  & **Factor** & **Load (kN\(\cdot\)m)** & **Factor** & **Load (kN\(\cdot\)m)** \\ \hline Dead load & 1.30 & 1.066 & 1.50 & 1.066 \\ Live load & 2.15 & 31.272 & 1.80 & 30.070 \\ Factored load & & 68.621 & & 55.725 \\ Flexural strength & & 78.207 & & 76.543 \\ Safety factor & & 1.140 & & 1.374 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Load combination and flexural strength.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Height (mm)**} & \multicolumn{2}{c}{**Code 2010**} & \multicolumn{2}{c}{**Code 2015**} \\ \cline{2-5}  & **Strength (kN\(\cdot\)m)** & **Safety Factor** & **Strength (kN\(\cdot\)m)** & **Safety Factor** \\ \hline
220 & 78.207 & 1.140 & 76.543 & 1.374 \\
215 & 75.843 & 1.105 & 74.041 & 1.329 \\
210 & 73.480 & 1.071 & 71.538 & 1.284 \\
205 & 71.117 & 1.036 & 69.036 & 1.239 \\
200 & 68.753 & 1.002 & 66.534 & 1.194 \\
195 & 66.390 & - & 64.031 & 1.149 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Strength based on the change in the height of the cross section.
## 3 Results
### Numerical Simulation Result  
Numerical simulation was a method of investigating the relationship between discharge parameters and electron energy. Figure 7 demonstrated the electric potential distribution of two types of electrode structures in the TM-DBD and CC-DBD reactors. Similarly, the voltage was linearly decreasing from anode to cathode in the two reactors, and the electromagnetic field was uniform distribution in the CC-DBD reactor, while that was stronger in the central discharge zone in the TM-DBD reactor with nonuniform voltage distribution.  
Figure 6: FTIR spectra of final products for N\({}_{2}\)/O\({}_{2}\) system in the TM-DBD and CC-DBD reactors.  
The distribution of electron velocity and electron drift in the space of the CC-DBD and TM-DBD reactors are shown in Figure 8. Once the plasma was generated and the number of micro-discharges inside the cell exceeded the threshold limit (10 macro-particles per particle), the particles were combined into larger micro-discharge [47]. Electron velocity distribution presented periodic drift as a variation of discharge voltage. According to Figure 8, electron velocity distribution was more balanced in the CC-DBD reactor. Summing up the electron velocity data of simulation, the largest electronic velocity and average electron velocity were 1.345 \(\times\) 10\({}^{6}\) m/s and 1.014 \(\times\) 10\({}^{6}\) m/s in the CC-DBD reactor, respectively. On the other hand, electron velocity distribution in the TM-DBD reactor was typical Poisson distribution [48] and stronger in the center area between the two electrodes. The maximum and average of electron velocity were 2.052 \(\times\) 10\({}^{6}\) m/s and 1.081 \(\times\) 10\({}^{6}\) m/s in the TM-DBD reactor, respectively.  
Considering that the motion energy of the electron is predominant in a plasma reactor, we determined the electron energy based on electron velocity distribution. The largest electron energy  
Figure 8: Distribution of electron velocity in the CC-DBD and TM-DBD reactors (5000 particles were randomly selected for this plot). (**a**) TM-DBD; (**b**) CC-DBD.  
Figure 7: Electric potential distribution of the TM-DBD and CC-DBD reactors (DBD was driven by 5 kV, 20 kHz AC power). (**a**) TM-DBD; (**b**) CC-DBD.  
and average electron energy in the TM-DBD reactor were 12.09 eV and 3.35 eV, respectively. While they were 5.25 eV and 2.96 eV in the CC-DBD reactor, respectively.  
The bond energies for N\({}_{2}\), NO and O\({}_{2}\) were 9.76, 6.5 and 5.12 eV, respectively [49]. Thus, O\({}_{2}\) could be dissociated all in TM-DBD and CC-DBD reactors in view of the largest electron energy of 12.09 eV and 5.25 eV respectively in the two reactors, but N\({}_{2}\) could be decomposed into N\(\cdot\) by the electron impact dissociation reaction only in the TM-DBD reactor.
# Optimal Design of Small-Aperture Optical Terminals for Free-Space Links
## 3 Results and Discussion  
The generalised optical terminal is shown in Figure 2. It receives a beam from free space and focuses it down onto an optical fibre tip. A fast-steering mirror is used to keep the beam on the centre of the fibre tip, providing TT compensation. The design is constructed in two stages. First is a maximisation of the total power throughput (i.e., the combination of coupling efficiency and geometric loss) and a minimisation of scintillation assuming a perfectly centred beam. Second is an optimisation of the TT compensation system to keep the coupling efficiency above some threshold value. Measurement of wavefront TT to provide error signals for the steering control loop is not discussed. This is already well understood and detailed in publications such as [27; 28].
# A Novel Multi-Objective Harmony Search Algorithm with Pitch Adjustment by Genotype
## 5 Experimentation and Results
### Performance Indicators  
Every problem was solved with 20 independent runs of each algorithm. An execution includes 10,000 evaluations of the objective function, with the exception of problem P6, which required 20,000 evaluations per run. In fact, the number of objective-function evaluations measures the computational cost. For the graphic appreciation of the results, the integrations of PSs and PFs of the 20 executions are made. In each figure, the number of solution vectors that make up each front is specified. Additionally to the graphical appreciation, the performance indicators described in the following subsection were applied for the quantitative comparison of the results. The performance indicators were calculated for each execution, and presented through its mean value (average), best value (best), worst value (worst), and standard deviation (Std. Dev). The best results are highlighted. Considering that metrics can be misleading in multi-objective optimization according to [PERSON] and [PERSON] [37], it can be illuminating to consider two main factors: (I) if the solutions belong to the real PF, and (II) how uniform the distribution of solutions along the Pareto front is.
## 6 Implementation and Experiment
### Experimental Results
\begin{table}
\begin{tabular}{c c c} \hline
**Test P\&ID** & **Precision** & **Recall** \\ \hline
1 & 0.9657 & 0.9038 \\
2 & 0.9732 & 0.9316 \\
3 & 0.9787 & 0.9150 \\
4 & 0.9531 & 0.8592 \\
5 & 0.9709 & 0.9009 \\
6 & 0.9183 & 0.8654 \\
7 & 0.9771 & 0.8649 \\
8 & 0.9412 & 0.9195 \\
9 & 0.9750 & 0.8931 \\ Average & 0.9614 & 0.8959 \\ \hline \end{tabular}
\end{table} 
Table 2: Performance evaluation result for test P&IDs. \begin{table}
\begin{tabular}{c c c c c} \hline
**Test P\&ID** &
\begin{tabular}{c} **Object Number** \\ **in Training Dataset** \\ \end{tabular} & **Precision** & **Recall** & **Rank** \\ \hline X\_sign\_line\_h & 840 & 1.0 & 1.0 & 1 \\ Dotted\_line\_h & 31,896 & 1.0 & 0.9647 & 8 \\ Dotted\_line\_v & 38,916 & 1.0 & 1.0 & 1 \\ Double\_slash\_sign\_line\_h & 684 & 1.0 & 1.0 & 1 \\ Double\_slash\_sign\_line\_v & 1722 & 1.0 & 0.8572 & 9 \\ Arrow\_e & 2268 & 1.0 & 1.0 & 1 \\ Arrow\_w & 2148 & 1.0 & 1.0 & 1 \\ Arrow\_s & 4914 & 1.0 & 0.9666 & 7 \\ Arrow\_n & 3414 & 1.0 & 1.0 & 1 \\ \hline \end{tabular}
\end{table} 
Table 3: Performance evaluation result by line sign and flow arrow types in test P&IDs. a diagonal line (error case 2) requires additional research for searching diagonal lines undetected in this study. Finally, the problem that a line included in an object other than a line is recognized (test case 3) is related to the results of symbol and text recognition. If there is no recognition information of a specific object in the process of removing the continuous lines overlapping with the symbol and text area, error case 3 occurs. Therefore, it is thought that error case 3 can be prevented by improving the recognition accuracy of symbols and texts.
## 2 Related Work
### Noise and Outlier Detection  
In the context of this work, the terms noise and outlier are used interchangeably. An outlier can be defined as \"an observation which deviates so much from the other observations as to arouse suspicions that it was generated by a different mechanism\" [26]. In [27], noise is defined as anything that obscures the relationship between the features of an instance and its class or observations resulting from non-systematic errors. Outliers are known to bias clustering results significantly, especially when the underlying assumption is that every data point has to reside in a cluster.  
In [28], clustering and outlier detection is modeled as an integer programming optimization task which requires prior knowledge of the number of outliers. [PERSON] and [PERSON] [29] present a method for simultaneously clustering and reporting outliers that is applicable for k-means clustering. [PERSON] et al. [30] present an iterative method based on visualization using hierarchical agglomerative methods to identify and remove outliers. Algorithmic solutions for outlier detection have also been proposed in [31; 32]. With NBR-Clust, most noise is identified as part of a resilience measure's critical attack set. Additional noise can be identified using separability, a graph-based internal validation measure, as will be further explained in Section 4.1.3.
## 4 Identification Method for SRRV Deviation Using Wheel-Rail Force Trend Component
### Selection of Wheel-Rail Force Trend Components for SRRV Deviation Identification  
In this study, the method of extracting trend components based on DWT is employed to analyze the characteristics of wheel-rail force data. The principles of extracting trend components using DWT have been explained in Section 2.2. Below, we will provide a detailed derivation of the selection of wheel-rail force trend components used for SRRV deviation identification.  
In Section 3, the sampling frequency is 5000 Hz, and the train speed is 275 km/h. Therefore, the distance traveled by the vehicle for each data sampling interval is  
\[275\times\frac{1000}{3600}\times\frac{1}{5000}=0.0152\,\mathrm{m} \tag{8}\]  
According to Equation (7), when passing through seven layers of decomposition, the resolution decreases by a factor of \(2^{7}\), and the scale increases by a factor of \(2^{7}\). At this point, the distance covered by a single sample is  
\[0.0152\times 2^{7}=1.95\,\mathrm{m} \tag{9}\]  
To effectively analyze the wavelength information of a set of data, at least three points or two segments of data are needed. Therefore, after seven layers of decomposition, the valid analytical wavelength of the data is  
\[1.95\times 2=3.9\,\mathrm{m} \tag{10}\]  
The valid analytical wavelength of other orders of DWT can also be calculated using the above method. The results indicate that in this case, only the effective analysis wavelength of the 7 th-order DWT, 3.9 m, falls within the turnout structural irregularity wavelength range of 2-6 m under different SRRV deviation conditions, as shown in Section 3.2. Therefore, the 7 th-order DWT was selected to analyze the wheel-rail force trend component.  
When a vehicle passes through a turnout without SRRV deviation, the trend component decomposition results of wheel-rail force on the switch rail side are shown in Figure 9.  
From the graph, it can be observed that after performing a seven-level decomposition of the calculated wheel-rail force, the 7 th-order trend component (A7) exhibits a \"U\"-shaped wave. The characteristics of the trend component are closely related to the wavelength and height of structural irregularities in the turnout area but are almost unaffected by track geometry. Therefore, the characteristics of the 7 th-order trend component of wheel-rail force can serve as a key indicator for identifying SRRV deviation.