esa-sceva/satcom-chunk-collection huggingface dataset:
# An Open-Source Algorithm for 3D ROck Slope Kinematic Analysis (ROKA)
## 2 The ROck Slope Kinematic Analysis (ROKA) Algorithm
### ROKA Results and Their Visualization  
The outputs of ROKA algorithm consist of (a) the identification of the discontinuities and intersections that could induce failures in all the different positions of the slope, (b) their classification based on their criticality, and (c) a stability classification of every point of the slope.  
1. To indicate the discontinuity planes and intersections that could activate an MOF (the critical discontinuities), the ROKA algorithm exports them as DXF files into specific  
Figure 4: Example of the real intersection between the discontinuities represented by Baecher’s discs (modified after [23]).  
directories representing all the different MOFs that could be activated, allowing a further simple 3D visualization of these results. 2. To classify the critical discontinuities, ROKA considers the dimensions of their intersections with the slope and normalizes these values using the maximum detected value. Then, every discontinuity is ranked by its critical value on a scale between 0 and 1 (0-100%), where one is assigned to the critical discontinuity that forms the longest intersection with the slope. 3. The description and stability classification of the slope consists of exporting a new 3D point cloud where nine pieces of information are assigned to each point: (1) dip direction and (2) dip angle of the slope (negative dip values for overhanging attitudes); (3) the overhanging nature of the scan plane (0 if normal dipping and 1 if overhanging); (4) the number of the discontinuities and (5) discontinuities intersections that intersect the local surface of the slope; (6) the critical value of the discontinuity planes that could cause planar failure or (7) flexural toppling considering the local orientation of the slope; (8) the critical value of the discontinuity intersections that could cause wedge failure; and (9) direct toppling.  
The visualization of the results of the ROKA algorithm allows understanding which part of the rock slope can be more critical, considering the possible MOFs and the dimension of the critical discontinuities and indicating the areas of the rock slope where an eventual further field-based analysis must be focused (e.g., concerning a detailed characterization of discontinuity properties that cannot be defined with remote sensing techniques).
# EEG-Based Measurement for Detecting Distraction in Coal Mine Workers
## 3 Methods
### EEG Features Selection  
Feature selection is essential for identifying the most predictive subset of features, enhancing model performance, simplifying structure, improving interpretability, and re ducing overfitting. In this study, we used a strategy combining statistical tests with Random Forests.  
Step 1: Preliminary screening via statistical tests  
We employed the Wilcoxon rank-sum test to identify features with significant differences between the two groups. This non-parametric test is suitable due to its lack of strict requirements regarding data distribution and sample size, making it ideal for our non-normally distributed data. Using MATLAB, we conducted the Wilcoxon rank-sum test on all EEG features extracted across all channels, setting the significance level at 0.05.  
Although multiple statistical tests were conducted in the feature screening process, we did not apply multiple comparison corrections at this stage. This decision was made because the aim of this step was exploratory: to identify potential EEG features associated with distraction. Applying strict corrections might have overly constrained the findings and limited the discovery of meaningful patterns. This procedure identified features and channels with statistically significant differences. The identified features were later validated during the model-building phase, where we carefully interpreted the features in the context of known neurophysiological mechanisms.  
Step 2: Feature selection based on feature importance  
Random Forest (RF), an ensemble learning method, was used for feature selection due to its ability to assess feature importance through the Mean Decrease Gini (MDG). MDG quantifies the importance of each feature by measuring its impact on classification accuracy. In this study, using the significant EEG features identified in the first step, we employed RF to select the most impactful features for our model's predictive performance.  
Due to the inherent randomness of the RF algorithm, which can lead to variability in accuracy, we trained 30 independent RF models using the filtered feature set to obtain a more stable estimate of feature importance. Averaging the feature importance scores from these models mitigated the randomness of individual models. We then selected the top 40 features based on average importance scores as the candidate subset. Using a stepwise feature addition strategy, we evaluated model performance. Starting with the highest-ranked feature, we sequentially added features to the model, reassessing accuracy after each addition. By monitoring the trend in accuracy, we identified the point at which it stabilized, indicating that adding more features no longer significantly improved performance. This set of features was determined to be the final feature subset.
## 4 Methodology
### Trip Identification  
Given that trajectory data are typically anonymized and recorded as continuous streams, effective trip segmentation is essential to decompose these trajectories into discrete trips, forming the analytical foundation for subsequent behavioral analysis. This study adopts a data-driven trip identification framework based on established methodologies from prior research [30; 31], comprising two main stages. In the first stage, truck stops are extracted from the raw trajectory data by examining key movement characteristics such as variations in travel direction and speed. This process is designed to mitigate the effects of GPS drift, which can cause inaccuracies in stop detection. By integrating these drifting points with low-speed or stationary GPS records, the method reliably identifies true stops. In the second stage, trip endpoints are determined by applying temporal thresholds informed by the empirical dwell time distribution of HDTs. These thresholds reflect typical operational rhythms across different freight sectors. To further refine the identification of meaningful trip ends, the method incorporates contextual information, including the spatial proximity of stops to freight-related points of interest (POIs) and the road network. This integration allows for the filtering of valid trip ends from all detected stops, ensuring that the identified trips are functionally relevant.  
By implementing this two-stage approach, the study successfully extracted approximately 1.67 million valid freight trips from a sample of nearly 160,000 heavy-duty trucks. We also leverage the administrative division data and aggregate all identified freight trips  
Figure 1: Spatial distribution of processed trajectories during 12–17 September 2022.  
at district level, as shown in Figure 2. This robust segmentation process captures a wide range of operational behaviors and provides a solid basis for analyzing trip-level patterns across different HDT types.
## 3 First-Passage Reliability Problem
### Parameter Calibration Using Observational Data
#### 3.2.2 Fitting the Variance of \(Y(x)\)  
With Equation (4) and a similar form of Equation (14), one can fit \(\mathbb{E}(Y\left(x\right))\) based on the discrete observed data \(y_{1},y_{2},\ldots y_{n}\) at depths \(d_{1},d_{2},\ldots d_{n}\). Furthermore, employing the concept of \"average variance\" [49], the variance of \(Y(x)\) is computed as follows,  
\[\mathbb{V}(Y(x))=\mathbb{E}\{[Y-\mathbb{E}(Y)]^{2}\}=\frac{1}{n}\sum_{i=1}^{n }(y_{i}-\widetilde{Y}(d_{i}))^{2}, \tag{15}\]  
where \(\mathbb{V}\left(\ \right)\) represents the variance of the random variable in the bracket, and \(\widetilde{Y}(d_{i})\) is the deformation response evaluated at depth \(d_{i}\) with the fitted function \(Y(x)\). The standard deviation of \(Y(x)\), \(\sigma_{Y}\), is then simply the square root of the variance.  
Figure 2: Location-dependent \(\Omega(x)\) and \(Y(x)\).
## 3 Experimental Analysis at Different Oil Pressure Levels  
During the high-speed operation of a rotor-sliding bearing, the oil film pressure inside the bearing not only supports the rotation of the rotor shaft but also dissipates heat and prevents direct contact between the journal and the shaft tile, so the oil film pressure plays an important role in the stable operation of the sliding bearing, and the influence of the oil pressure of the sliding bearing inlet pipe on the internal oil pressure distribution cannot be ignored. Most of the studies only focus on the oil film pressure distribution between the journal and the shaft tile. There is less research on the operating characteristics of the rotor system, so it is also necessary to analyze the influence of this important parameter on the operational stability.  
The sliding bearing of the rotor test bench is connected to the oil inlet pipe and the oil outlet pipe to form a self-circulating oil system. In the oil pressure circulation, it is necessary to make a good seal at the bearing support to avoid overflow under excessive oil pressure and high speed. During the experiment, the ideal oil pressure value is selected by mediating the throttle valve and observing the change of the hydraulic gauge size to achieve low pressure and low flow rate oil circulation lubrication.  
The main objective of this paper is to study the dynamic response of the sliding bearing system at different oil pressure levels. Therefore, several experimental tests are carried out under the oil pressure of 0.2-1.2 MPa with different rotational speeds. The analyses are presented according to the experimental results extracted by the acceleration sensor and eddy current sensor. In addition, the acceleration and deceleration processes of the test system are conducted at a lower oil pressure level.
## 3 Results and Discussion
### Effect of Freeze-Thaw on Interfacial Strength  
Shear and pull-off tests were performed after completing 25, 50, 75, and 100 freeze-thaw cycles to evaluate the effect of freeze-thaw on the strength. Figure 10a,b show the results of single shear test; Figure 10c,d show the results of the pull-off test.  
The results show that the interfacial shear strength decreases with an increase in freeze-thaw cycles. The shear strength decreased by 3.7%, 6.5%, 7.4%, and 9.9% after 25, 50, 75, and 100 freeze-thaw cycles, respectively. The pull-off strength showed a trend that was similar to shear strength. The pull-off strength decreased by 2.4%, 6.3%, 9.3%, and 13.9% after 25, 50, 75, and 100 freeze-thaw cycles, respectively. The shear strength and pull-off strength were approximately linear with the number of freeze-thaw cycles, as shown in Figure 10b,d.  
Figure 11 shows the load-displacement curves of shear test under different freeze-thaw cycles to further analyze the effect of freeze-thaw on interfacial strength. The freeze-thaw cycles led to a dual degradation of stiffness and strength. In general, a lot of voids or microcracks are present in the interface between ECC and steel plate. During the freeze-thaw cycle, water that intruded into the interface was frozen, and ice expansion and melting led to the degradation of interface bonding performance. ECC itself was also damaged by freeze-thaw, as it experiences an increase in void ratio and a decrease in stiffness due to freeze-thaw [23; 24; 25]. A larger deformation occurred in ECC under the  
Figure 8: Typical load-displacement curves: (**a**) single shear test and (**b**) pull-off test.  
Figure 9: Interfacial failure: (**a**) single shear test and (**b**) pull-off test.  
loading when the number of freeze-thaw cycles increased. Therefore, ECC and the interface between ECC and steel plate were both affected by freeze-thaw action, which led to the degradation of both strength and stiffness.
## 3 Results
### Results and Analysis
#### 3.3.2 Meta-Learning Training  
Within the meta-learning framework (as depicted in Figure 10), the meta-learning support and query sets introduced in Section 3.1 were utilized to train and evaluate the base learners corresponding to spatial, time-frequency, and Doppler features. The parameter update process of the base learner are as shown in Equation (13), and that of the meta-learner is as shown in Equation (14). The validation accuracy curves for the training processes of each feature are presented in Figure 15a-c.  
In Figure 15, batch 1600 illustrates the validation curve of the base learner's five parameter updates after completing 1600 meta-training batches. After meta-learning training, the query set test accuracy rates for spatial, time-frequency, and Doppler features are 96.8%, 90.9%, and 86.7%, respectively. The meta-learning task space captures the differences in target types and relative horizontal orientation angles, with the most pronounced distinctions observed in spatial features, followed by time-frequency features; the least pronounced distinctions were observed in Doppler features. Through meta-learning training, each feature extraction network acquired the ability to represent the features of various targets across different relative horizontal orientation angles, which constitutes the meta-knowledge.
## 6 Experimental Evaluation
### Numerical Results
#### 6.2.1 Problem Variant #1: Offset Optimization
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{3}{c}{_EA Over Real_} & \multicolumn{3}{c}{_Exact Over Real_} & \multicolumn{3}{c}{_EA vs. Exact_} \\ \hline \(\lambda\) & \(\Delta\) & _Instances_ & \(\lambda\) & \(\Delta\) & _Instances_ & \(\lambda\) & \(\Delta\) & _Instances_ \\ \hline
30 & 57.56\% & 15 & 30 & 57.96\% & 15 & 30 & \(-\)0.25\% & 15 \\
50 & 28.49\% & 15 & 50 & 28.68\% & 15 & 50 & \(-\)0.15\% & 15 \\
70 & 16.79\% & 15 & 70 & 16.80\% & 15 & 70 & \(-\)0.01\% & 15 \\
90 & 9.79\% & 15 & 90 & 9.79\% & 15 & 90 & 0.00\% & 15 \\
100 & 9.74\% & 15 & 100 & 9.74\% & 15 & 100 & 0.00\% & 15 \\ \hline \multicolumn{3}{c}{_EA Over Real_} & \multicolumn{3}{c}{_Exact Over Real_} & \multicolumn{3}{c}{_EA vs. Exact_} \\ \hline \(N\) & \(\Delta\) & _Instances_ & \(N\) & \(\Delta\) & _Instances_ & \(N\) & \(\Delta\) & _Instances_ \\ \hline
30 & 23.88\% & 25 & 30 & 23.92\% & 25 & 30 & \(-\)0.02\% & 25 \\
70 & 23.99\% & 25 & 70 & 24.15\% & 25 & 70 & \(-\)0.11\% & 25 \\
110 & 25.55\% & 25 & 110 & 25.72\% & 25 & 110 & \(-\)0.12\% & 25 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Exact and EA improvements over the baseline real solutions, grouped by tolerance and problem dimension (number of transfer zones). the optimization methods properly scale when the complexity of the problem increases, providing solutions with a better quality of service. Improvements also slightly increase for larger instance dimensions.
# A Sensitivity Analysis of a Computer Model-Based Leak Detection System for Oil Pipelines
## 3 Results and Discussion
### Uncertainty in \(R\) Factor
#### 3.1.2 Bulk Modulus Uncertainty
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Data Type**} & \multirow{2}{*}{**Variable**} & \multirow{2}{*}{**Flow Condition**} & \multirow{2}{*}{**Level of Uncertainty**} & \multicolumn{2}{c}{**Baseline Detection Time**} & \multicolumn{2}{c}{**Change in Detection Time (Min)**} \\ \cline{5-6}  & & & \(R=0.49\) & \(R=2.20\) & \(R=0.49\) & \(R=2.20\) \\ \hline \multirow{8}{*}{Perfect Data} & \multirow{2}{*}{Flow Decrease} & \(+10\%\) & \multirow{2}{*}{24.2} & \multirow{2}{*}{24.9} & 0.7 & 0.0 \\  & & \(-10\%\) & & & 1.7 & 4.9 \\ \cline{3-6}  & Bulk & \multirow{2}{*}{Flow Increase} & \(+10\%\) & \multirow{2}{*}{24.5} & 0.5 & 0.0 \\  & & \(-10\%\) & & & 1.4 & 7.0 \\ \cline{3-6}  & & Steady State & \(+10\%\) & \multirow{2}{*}{24.4} & 0.5 & 0.05 & 0.2 \\  & & \(-10\%\) & & & \(-0.05\) & \(-0.2\) \\ \cline{3-6}  & & Flow Decrease & \(10\) s & 24.2 & 24.9 & 2.5-3.9 & 0.6-3.1 \\  & & Flow Increase & \(10\) s & 24.5 & 26.1 & 0.8–2.2 & 0.5-3.3 \\  & & Steady State & \(10\) s & 24.4 & 25.5 & 0.0 & 0.0 \\ \hline \multirow{8}{*}{Noisy Data} & \multirow{2}{*}{Flow Decrease} & \(-20\%f\) & \multirow{2}{*}{31.1} & \multirow{2}{*}{33.1} & \multirow{2}{*}{1.8} & \multirow{2}{*}{-4.5} \\  & & \(+20\%f\) & & & 0.2 & 0.5 \\ \cline{3-6}  & & Flow Increase & \(-20\%f\) & & & \(-22.6\) & \(-34.2\) \\ \cline{3-6}  & & \(+20\%f\) & & & 1.3 & 0.1 \\ \cline{3-6}  & & Steady State & \(-20\%f\) & & & \(-8.7\) & \(-15.2\) \\ \cline{3-6}  & & \(+20\%f\) & & & 0.5 & \(-0.3\) \\ \hline \multirow{8}{*}{Noisy Data} & \multirow{2}{*}{Flow Decrease} & \(+10\%\) & \multirow{2}{*}{31.1} & \multirow{2}{*}{33.1} & \multirow{2}{*}{0.1} & \multirow{2}{*}{-1.0} \\  & & \(-10\%\) & & & 0.4 & 4.9 \\ \cline{3-6}  & & Flow Increase & \(+10\%\) & & & 0.0 & \(-4.6\) \\ \cline{3-6}  & & Flow Increase & \(-10\%\) & & & 1.3 & 7.1 \\ \cline{3-6}  & & Steady State & \(+10\%\) & & & 0.05 & 0.2 \\  & & \(-10\%\) & & & \(-0.02\) & \(-0.3\) \\ \hline \multirow{8}{*}{Time Skew} & \multirow{2}{*}{Flow Decrease} & \(10\) s & \multirow{2}{*}{31.1} & \multirow{2}{*}{33.1} & \multirow{2}{*}{28.0} & \multirow{2}{*}{35.0} \\  & & Flow Increase & \(10\) s & & 55.5 & 61.7 & Undetected \\ \cline{1-1} \cline{3-6}  & & Steady State & \(10\) s & & 41.7 & Undetected & Undetected \\ \hline \hline \end{tabular}  
* Viscosity error was introduced to obtain \(\pm 20\%\) error in the friction factor \(f\).  
\end{table} 
Table 5: Change in detection time due to uncertainties in systems with low and high \(R\) factors. Figure 6: Effect of bulk modulus error on leak detectability with exact and excessive \(BMER\) in a system with \(R=2.20\) with perfect data. Figure 7: Effect of bulk modulus error on leak detectability in a system with \(R=2.20\) with \(1\%\) Gaussian noise: (**a**) flow decrease; and (**b**) flow increase.
## 3 Results  
Aerodynamic forces obtained from experimental and numerical studies were used to determine the aerodynamic coefficients for the two girder models. The formulas for determining the drag coefficient \(C_{x}\) and the lateral force coefficient \(C_{y}\) are as follows:  
\[C_{x}=\frac{2F_{x}}{\rho{V_{ref}}^{2}\cdot A_{ref}},\ C_{y}=\frac{2F_{y}}{\rho {V_{ref}}^{2}\cdot A_{ref}} \tag{3}\]  
where \(A_{ref}\) is the effective area, which is \(A_{refB}\) = 0.112 m\({}^{2}\) for box girder model and \(A_{refT}\) = 0.028 m\({}^{2}\) for truss girder model.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Cell Size (mm)** & **Faces (Box Girder)** & **Nodes** & **Cells** & \(F_{x}\) **(N)** & \(F_{y}\) **(N)** \\ \hline
1.5 & 14,251,914 (186,649) & 1,698,020 & 6,655,980 & 38.605 & 20.094 \\
2.5 & 5,857,993 (71,680) & 689,675 & 2,743,302 & 38.491 & 20.035 \\
3.5 & 4,923,579 (35,813) & 571,922 & 2,312,836 & 37.077 & 18.521 \\ \hline \end{tabular}
\end{table}
Table 2: Aerodynamic forces for different mesh.  
Figure 12: The mesh created for (**a**) a sectional model of the box girder (length of the element edge \(l_{c}\) = 2.5 mm) and (**b**) a sectional model of the truss girder (length of the element edge \(l_{c}\) = 2 mm).
## 2 Review and Evaluation of Solar Irradiance Forecasting Sensors
### Persistence Model and Error Metrics
As demonstrated by the skill score (\(SS\)) equation, the performance of a forecasting model can be evaluated using various error metrics regardless of which persistence model is used for comparison. Among these error metrics, the most used ones (\(\varepsilon\)) are Mean Bias Error (\(MBE\)), Mean Absolute Error (\(MAE\)), Root Mean Square Error (\(RMSE\)), and Standard Deviation (\(STD\)). These metrics were also employed in the studies reviewed above, where \(p_{i}\) is the predicted value, \(o_{i}\) is the observed or measured one, and N is the number of measured instants. Relative values are calculated for individual days and the observed mean daily measurement \(o_{m}\) is established as a reference. * Mean Bias Error (\(MBE\)) is a metric appropriate for evaluating forecast bias by reflecting the difference between the average value and the actual value of measured magnitude. It is expected to be as small as possible. \[MBE=\frac{1}{N}\cdot\sum_{i=1}^{N}p_{i}-o_{i}\] (2) \[Relative\;MBE=\frac{100\%}{o_{m}}\cdot MBE\] (3)
* Mean Absolute Error (\(MAE\)) is a linear score which means that all individual differences are weighted equally in the average. It is a metric less sensitive to outliers than the widely used \(RMSE\) and is appropriate for estimating uniform prediction errors. \[MAE=\frac{1}{N}\cdot\sum_{i=1}^{N}\lvert p_{i}-o_{i}\rvert\] (4) \[Relative\;MAE=\frac{100\%}{o_{m}}\cdot MAE\] (5)
* Mean Absolute Percentage Error (\(MAPE\)) is used to evaluate uniform prediction errors such as \(MAE\). \[MAPE=\frac{100\%}{N}\cdot\sum_{i=1}^{N}\left(\frac{p_{i}-o_{i}}{o_{i}}\right)\] (6)
* Root Mean Square Error (\(RMSE\)) measures the global error over the entire forecasting period. \[RMSE=\sqrt{\sum_{i=1}^{N}\frac{\left(p_{i}-o_{i}\right)^{2}}{N}} \tag{7}\] \[Relative\ RMSE=\frac{100\%}{o_{m}}\cdot RMSE \tag{8}\]
* Standard Deviation (_std_ or _SDE_) is a relative measure of average dispersion that gives an idea of the magnitude outliers mentioned above. \[std=\sqrt{\frac{1}{N-1}\cdot\sum_{i=1}^{N}(\left(p_{i}-o_{i}\right)-MBE)^{2}}\] (9) \[Relative\ std=\frac{100\%}{o_{m}}\cdot std\] (10)
* Coefficient of determination (\(R^{2}\)) is one of the most common statistical metrics for characterizing model quality. It compares the error variance to the variance of the modeled data. \[MBE=\frac{1}{N}\cdot\sum_{i=1}^{N}p_{i}-o_{i}\]0 (11)
* _KSI_ aims to quantify the model's ability to reproduce observed statistical distributions. \[MBE=\frac{1}{N}\cdot\sum_{i=1}^{N}p_{i}-o_{i}\]1 (12)  
where \(D_{n}\) is the difference between two cumulative distributions. Regarding the above concepts, when the skill score is applied, such as in the case of _SS-MAE_, the performance of a model is compared to that of persistence in terms of Mean Absolute Error (_MAE_).