esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Energy Status  
EA was found to be as low as 16 kJ/kg fat free mass (FFM)/day in professional ballet dancers [27]. EA defines the remaining energy after deducting exercise EE from EI that is available to sustain physiological function [12; 13]. Optimal physiological function occurs at an EA of at least 188 kJ/kg FFM/day (45 kcal/kg FFM/day), especially in adolescents [32]. Disturbances to endocrine and metabolic parameters are observed at and below 126 kJ/kg FFM/day (30 kcal/kg FFM/day). Although only two studies fell below the threshold of LEA in pre-professional contemporary dancers (100 kJ/kg FFM/day) [24] and professional ballet dancers [27], reduced EA was observed in all dancers where reported [19; 22; 24; 27; 29]. Suboptimal performance and health consequences may be seen where EA is less than the ideal 188 kJ/kg FFM/day, thus highlighting the importance of addressing adequate EI to match EE in both professional and pre-professional dancers to prevent complications of LEA and subsequent RED-S, including amenorhea [2; 22; 25; 32; 33; 34] and low bone density [12; 19; 32; 33; 34; 35], which are pervasive in these populations [36], as well a range of other health issues such as mental health issues, impaired gastrointestinal function, and sleep disturbances [12]. Male students, while underrepresented in our sample, are also susceptible to LEA, disordered eating, and eating disorders, although are generally underdiagnosed [37]. Preliminary research suggests that male athletes can sustain a lower EA before exhibiting physiological disturbances [12]; however, LEA compromises stamina, muscular strength, and bone and joint integrity, increasing injury risk for themselves and their female dance partners. Both pre-professional ballet and contemporary dancers exhibited improved energy balance on the weekends; however, contemporary dancers had stable EE on weekends [22; 24]. Contemporary dancers may also face less pressures of aesthetic ideals than ballet dancers, as their clothing is usually looser fitting to accompany the style of the genre, and \"athletic\" bodies are believed to be more acceptable [9]. The variations of EI, EE, and EA between different genres merits further research into other dance styles to appropriately quantify energy needs to ensure nutritional adequacy, and to investigate the unique pressures experienced across genres and how they interact with EI. Investigating weekday and weekend differences in energy status parameters can assist in gaining a better understanding of the workload and adequate fueling required to support this.
## 2 System Model
### Transmission Model
Precisely, the satellite user accesses the channel according to the following rules: if the sensing result shows that the primary terrestrial network is idle, the satellite user can enhance the power to access the channel without considering the interference in the primary terrestrial network; otherwise, if the state of primary terrestrial network is evaluated to be busy, the satellite user adopts its power to constrain the interference to the terrestrial network. \(P_{s}^{(b)}\) and \(P_{s}^{(i)}\) are used to represent the transmission power of the satellite user when the sensing result is busy/idle, respectively. It is obvious that \(P_{s}^{(b)}<P_{s}^{(i)}\). As mentioned above, the capacity of the satellite network can be described as follows: if terrestrial network is idle and the sensing result is just correct, the capacity of satellite network is calculated as:  
\[C_{ii}=B\text{log}_{2}\left(1+\left(P_{s}^{(i)}G_{t}(\theta)G_{r}(\varphi)L_{ss }h_{ss}\Big{/}N_{0}\right)\right), \tag{12}\]  
where \(B\) represents the bandwidth of the channel. Otherwise, if there is a false alarm, the capacity can be presented as:  
\[C_{ib}=B\text{log}_{2}\left(1+\left(P_{s}^{(b)}G_{t}(\theta)G_{r}(\varphi)L_{ ss}h_{ss}\Big{/}N_{0}\right)\right). \tag{13}\]  
Meanwhile, in the perfect detection conditions, which means, if the terrestrial network is active and the sensing result is correct, the capacity of satellite network is calculated as follows:  
\[C_{bb}=B\text{log}_{2}\left(1+\left(P_{s}^{(b)}G_{t}(\theta)G_{r}(\varphi)L_{ ss}h_{ss}\Big{/}N_{0}\right)\right). \tag{14}\]  
However, if the sensing result is false, the capacity is represented as:  
\[C_{bi}=B\text{log}_{2}\left(1+\left(P_{s}^{(i)}G_{t}(\theta)G_{r}(\varphi)L_{ ss}h_{ss}\Big{/}N_{0}\right)\right). \tag{15}\]
## 5 Comparative Experiments and Discussion
### Experiment: Application to Reference Dataset  
In order to further verify the accuracy and engineering practicability of the Mod edge method in this paper, the simulated and actual edge image datasets are determined for MTF measurement, and the MTF measurement result comparisons with multinational research teams are conducted. The simulated and actual edge image dataset (Geospatial Quality Reference Dataset) is provided by the MTF project team of CEOS/WGCV/IVOS [1]. The four simulated edge images (labeled Sim-A, C, D, E) and two actual edge images (labeled Act-B, F) contained in this dataset are shown in Figure 20. The measurement results are shown in Figure 21, and the comparison and summary with the corresponding MTF results provided in reference [1] are shown in Table 3.  
Figure 19: Simulation comparison: proposed method vs. other methods for BNDE images with different tilt angle: (**a**) when edge angle is 3 deg, (**b**) when edge angle is 10 deg, (**c**) when edge angle is 25 deg, (**d**) when edge angle is 40 deg.  
From the comparison of MTF measurement results in Table 3, it can be seen that the MTF of edge images A, D, B, and F measured by the Mod edge method is slightly higher than the measurement reference value provided in reference [1], but the measurement error is kept within 0.01, which verifies the effectiveness of the Mod edge method. On the one hand, it is related to the insufficient adaptability of the existing MTF compensation model.  
Figure 21: MTF measurements of Geo spatial quality reference dataset by Mod edge method: (**a**) Simulated edge image A, (**b**) Simulated edge image C, (**c**) Simulated edge image D, (**d**) Simulated edge image E, (**e**) Actual edge image B, (**f**) Actual edge image F.  
\begin{table}
\begin{tabular}{c c c}  & **MTF** & **Mod MTF at Nyquist** & **Reference [1] MTF Average at** \\
**Edge Image** & & **Frequency** & **Nyquist Frequency** \\ \hline Simulated edge image A & 0.221 & 0.20–0.21 \\ Simulated edge image C & 0.299 & 0.29–0.30 \\ Simulated edge image D & 0.145 & 0.12–0.14 \\ Simulated edge image E & 0.097 & 0.08–0.10 \\ Actual edge image B & 0.141 & 0.13–0.14 \\ Actual edge image F & 0.134 & 0.12–0.13 \\ \end{tabular}
\end{table}
Table 3: MTF experiment result comparison between modified edge method and reference [1].  
Figure 20: Geospatial quality reference dataset provided by CEOS/WGCV/IVOS: (**a**) Simulated edge image A (200 pix:200 pix), (**b**) Simulated edge image C (50 pix:50 pix), (**c**) Simulated edge image D (200 pix:200 pix), (**d**) Simulated edge image E (160 pix:160 pix), (**e**) Actual edge image B (320 pix:311 pix), (**f**) Actual edge image F (204 pix:162 pix).  
On the other hand, it is related to the generally low MTF measurement result of the existing edge method, which mostly adopts a single large ROI.
# Deterministic Discrete Fracture Network Model and Its Application in Rock Mass Engineering
###### Abstract  
This study departs from conventional stochastic statistical approaches for rock mass structural modeling. Based on deterministic structural surface parameters, including orientation (dip and dip direction), trace length, trace center coordinates, and spacing between structural surface sets, this research investigates the relationships among volumetric density, areal density, structural surface persistence, and inter-set spacing. With a focus on model domain dimensions, positioning of the model center, and mitigation of boundary effects, the methodology systematically addresses key considerations in modeling joints, layers, and faults. A deterministic Discrete Fracture Network (DFN) modeling approach is proposed accordingly. In this framework, joints are represented by disks, whereas lithological interfaces such as layers and faults are modeled as flat planes. The proposed method was applied to the Qingdao Metro Line 15 project. Validation results demonstrate that the surrounding rock classification derived from the model is in good agreement with field geological investigation data.  
structural surface parameters; rock mass quality assessment; DFN; surrounding rock classification 2025 15 6264 1
# Predicting Road Crash Severity Using Classifier Models and Crash Hotspots
## 5 Exploratory Data Analysis
### Hyperparameter Optimization for Each Model  
Characteristics of the model, which enable their customization to perform a certain task, are called hyperparameters [82]. There is no clear way to set them for a given set of data because these hyperparameters may interact in non-linear relations. Therefore, a search algorithm is required to set the optimum set of these parameters for any data set. This process is also known as hyperparameter tuning.  
The optimization process includes the definition of a search space of n-dimensions, where 'n' is the number of parameters to be optimized and the size of each dimension is the possible range of values of alternatives that may be assigned to that hyperparameter. The outcome of the optimization process is a vector of hyperparameters which enables the best model performance.  
Among the many algorithms available for optimization, random and grid search are considered the common and most robust procedures [83]. During the random search process, hyperparameter values are chosen randomly/arbitrarily, whereas in the grid  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Crash Reasons** & **Frequency of Crash Reasons** \\ \hline Sudden lane changes & 48.00\% \\ \hline Speeding & 18.00\% \\ \hline Not giving way & 15.00\% \\ \hline Insufficient safe distance & 6.40\% \\ \hline Driver distraction & 5.75\% \\ \hline Other & 2.35\% \\ \hline Faulty tires & 1.15\% \\ \hline Not using pedestrian crossing & 1.00\% \\ \hline Illegal overtaking & 0.55\% \\ \hline Red light violation & 0.30\% \\ \hline Driving opposite to traffic & 0.25\% \\ \hline Not stopping at STOP sign & 0.20\% \\ \hline Drifting & 0.10\% \\ \hline Falling asleep & 0.05\% \\ \hline Getting out vehicle before stopping & 0.05\% \\ \hline Hanging on the outside of vehicle & 0.05\% \\ \hline Unsafe road works & 0.05\% \\ \hline Exhaustion & 0.02\% \\ \hline Violating pedestrian sign & 0.02\% \\ \hline Downhill & 0.02\% \\ \hline No warning signs & 0.02\% \\ \hline Faulty breaks & 0.02\% \\ \hline Faulty electrics & 0.02\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Frequency of crash reasons.  
search, the space is divided into a grid network and values are evaluated in a systematic manner covering all cells of the grid. This study used the randomized search method to obtain the optimized hyperparameters that would produce the best accuracy for a specific model. With the random search, the user can set up a grid of hyperparameter values to select random combinations for training and evaluating the model. Using random search, we were able to explicitly control the number of parameter combinations. Table 3 shows the parameters, ranges, and optimized values.
## 2 Methodology
### LiDAR Measurement  
LiDAR determines angles and ranges to a target with a laser by measuring the time for the reflected light to return to the receiver, as shown in Figure 2. The spectral band for LiDAR ranges from 500-1500 nm. The position and the attitude of the sensor are determined using a GNSS receiver and INS or IMU (Inertial Measurement Unit). The onboard GNSS information is processed with ground control GNSS station information for high accuracy. The two pieces of GNSS information are processed with the DGPS (Differential GPS) method--more specifically, double differencing. This was possible because Korea has a dense CORS (Continuously Operating Reference Station) in order to support accurate positioning.  
Figure 1: Flowchart of the study.  
Figure 2: Aerial LiDAR measurement principle.  
Finally, the 3D coordinates of every target point are computed using the distance and the angle measurement with the help of the GNSS and IMU, as shown in Equation (1). The resulting original data consists of the GNSS time, 3D coordinates of a target, and the intensity of the reflection, stored in a standard LAS (LASer) format.  
\[\begin{bmatrix}X\\ Y\\ Z\end{bmatrix}=\begin{bmatrix}X_{0}\\ Y_{0}\\ Z_{0}\end{bmatrix}+\begin{bmatrix}X_{G}\\ Y_{G}\\ Z_{G}\end{bmatrix}+\begin{bmatrix}R_{IMU}R_{IMU}^{LS}\\ I_{Y}\\ I_{Z}\end{bmatrix} \tag{1}\]  
where \(X,Y,Z\) are the ground coordinates; \(X_{G},Y_{G},Z_{G}\) are the GNSS coordinates; \(X_{0},Y_{0},Z_{0}\) are relative LiDAR position from the GNSS receiver; \(R_{IMU}\) is the rotation matrix between the coordinates' system and the IMU; \(R_{IMU}^{LS}\) is the rotation matrix between the IMU and the LiDAR; and \(I_{X},I_{Y},I_{Z}\) are the LiDAR vector.
## 5 Dynamic Analysis
### Simulation Results
#### 5.3.5 Test Case 5--3-Steps Burr Profile \"High-Low-High\"  
A 3-steps burr profile \"high-low-high\" (see Figure 14 (top)) is used as an input for the dynamic simulation. In the first and third step the burr height is sufficiently high to make the mechanism work in the compliant mode of operation, whereas in the second step the burr height is sufficiently low to generate a cutting force projection below the elastic force threshold. The main aim of this test case is to verify that, when the burr height decreases and generates a cutting force projection below the elastic force threshold, the mechanism returns in the initial configuration, i.e., with the grinding wheel support in contact with the mechanical stop. The simulation results are presented in Figure 14 (middle and bottom). It can be noticed that, during the first step, the grinding wheel moves backwards thanks to the compliance of the mechanism, and an equilibrium condition is achieved with a certain height of the residual burr. In the second step, the compliant mechanism moves forward and rapidly returns towards the TDC (Condition 3 of Section 5.3 is satisfied). Since the cutting force projection is below the elastic force threshold, the mechanism would tend to go to the TDC, but due the mechanical stop this is not possible. Indeed, an impact between the grinding wheel support and the mechanical stop takes place, as it can be noticed in Figure 14, in which small bounces are reported after \(t=1.5\) s. Of course, the amplitude and damping of these bounces depend on the dynamic parameters of the mechanism (masses, inertias, springs stiffness, and damping) and on the characteristics (stiffness and damping) of the mechanical stop. After the bounces, the mechanism remains in the initial configuration (\(x\) is about 0.3 mm, as it can be noticed in Figure 14), up to the beginning of the third step. Finally, in the third step, a new equilibrium condition is obtained, with a height of residual burr higher than in the first step (as expected, since the burr height is higher).  
Figure 13: Three-steps burr profile (“high–highest–high” (**top**)); position (**middle**) and velocity (**bottom**) of the grinding wheel support.
## 3 Results and Discussion  
### Room Temperature Raman Spectra of a Typical Coal Sample  
Figure 1(a) shows a typical room temperature Raman spectrum obtained from one of the tested coals (Coal C1). Due to the low transmission efficiency of the ultraviolet light used in the spectrometer at 355 nm, a strong fluorescence interference, as evidenced by the sloping background with the Raman shift, was observed. To avoid the interference from the background, the background was subtracted using the baseline method. The background-subtracted spectrum is shown in Figure 1(b).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sample ID** & **N (\%)** & **C (\%)** & **H (\%)** & **S (\%)** & **O (\%)** \\ \hline C1 & 1.84 & 79.69 & 4.67 & 0.27 & 11.50 \\ C2 & 1.92 & 77.84 & 4.72 & 0.41 & 7.23 \\ C3 & 1.81 & 78.56 & 4.50 & 0.58 & 11.00 \\ C4 & 1.88 & 78.54 & 4.26 & 0.75 & 10.70 \\ C5 & 2.12 & 76.63 & 5.36 & 0.64 & 9.76 \\ C6 & 1.84 & 77.02 & 4.62 & 0.44 & 11.80 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Elemental composition of coal samples.  
Figure 1: In-situ Raman spectra of coal samples in Ar atmosphere by using a 532 nm excitation laser.  
Two bands are discernible at approximately 1360 and 1590 cm\({}^{-1}\). These bands correspond to the D and G bands for graphite in Figure 2. Due to a disordered and defective diamond band or an sp\({}^{3}\) hybridization carbon, the D band was assigned to the A\({}_{\text{1g}}\) mode, which relates to the average graphite domain dimension, while the G band was assigned to the graphite E\({}_{\text{2g}}\) mode with D\({}^{4}\)\({}_{\text{6h}}\) crystal symmetry and resulted from a graphitic, tangential band or sp\({}^{2}\) hybridization [22]. However, unlike what observed for graphite, the bands were very diffuse and therefore overlapped, most likely due to the fluorescent background. In order to determine the wavenumber position, the relative peak intensity, and the width or full width at half maximum (FWHM), the overlapped Raman bands were further deconvoluted into two well-defined peaks using origin 8.0. (Lorentz function), as shown in Figure 2b. The deconvoluted peaks were then discussed to reveal the structural difference and structural evolution of the coals during pyrolysis. A typical room temperature Raman spectrum of the graphite is shown in Figure 3. In comparison to Figure 3, the coal in Figure 2b showed that both D and G bands had shifted slightly towards the red spectral range.  
Furthermore, the difference in the band position of P\({}_{\text{G}}\), P\({}_{\text{D}}\), W\({}_{\text{D}}\), and W\({}_{\text{G}}\) and the area ratio (I\({}_{\text{D}}\)/I\({}_{\text{G}}\)) of the G and D bands were calculated.
## 3 Results and Discussion
### Adsorption Isotherms  
In this study, the models studied are the Langmuir model, [PERSON] model, Temkin model, and Dubinin-Radushkevich model. The most frequently established model for the study from the adsorption isotherms is the Langmuir model (Figure 17a-d); the results obtained are shown in Table 2.  
Figure 14: Effect of contact time on the adsorption of the two dyes X-PR and MGC by BS-HVL (**a**) and BC-HVL (**b**) (T = 21 \(\pm\) 1 \({}^{\circ}\)C; mad = 50 mg; Co = 50 mg L\({}^{-1}\); stirring speed 150 rpm and pH = 7 \(\pm\) 0.3).  
Figure 13: Effect of the initial dye concentration on the adsorption of X-PR and MGC by BS-HVL (**a**) and BC-HVL (**b**) (t = 90 min; T = 21 \(\pm\) 1 \({}^{\circ}\)C; mad = 50 mg; stirring speed 150 rpm and pH = 7 \(\pm\)0.3).  
Figure 16: Kinetic models of the adsorption of the dyes X-PR and MGC by BC-HVL (T = 21 \(\pm\) 1 \({}^{\circ}\)C); mad = 50 mg; C\({}_{0}\) = 50 mg-L\({}^{-1}\); stirring speed 150 rpm and pH = 7 \(\pm\) 0.3).  
Figure 15: Kinetic models of the adsorption of the dyes X\(-\)PR and MGC by BS-HVL (T = 21 \(\pm\) 1 \({}^{\circ}\)C); mad = 50 mg; Co = 50 mg L\({}^{-1}\); stirring speed 150 rpm and pH = 7 \(\pm\) 0.3).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{2}{c}{**BS-HVL**} & \multicolumn{2}{c}{**BC-HVL**} \\ \cline{3-6}
**Models** & & **X-PR** & **MGC** & **X-PR** & **MGC** \\ \hline \multirow{3}{*}{**Pseudo-first-order**} & R\({}^{2}\) & 0.9179 & 0.9546 & 0.9127 & 0.9333 \\  & K\({}_{1}\) (min\({}^{-1}\)) & 0.045 & 0.0565 & 0.0401 & 0.0551 \\  & q\({}_{\rm e}\) (mg g\({}^{-1}\)) & 44.7817 & 72.3633 & 40.8007 & 66.4201 \\ \hline \multirow{3}{*}{**Pseudo-second-order**} & R\({}^{2}\) & 0.9379 & 0.9969 & 0.9647 & 0.9692 \\  & K\({}_{2}\) (g mg\({}^{-1}\) min\({}^{-1}\)) & 0.0005 & 0.0011 & 0.0006 & 0.0007 \\  & q\({}_{\rm e}\) (mg g\({}^{-1}\)) & 61.7283 & 54.0540 & 41.6667 & 41.8410 \\ \hline \multirow{3}{*}{**Elovich**} & R\({}^{2}\) & 0.9101 & 0.9742 & 0.9429 & 0.9341 \\  & \(\alpha\) (mg g\({}^{-1}\) min\({}^{-1}\)) & 3.6528 & 7.8653 & 2.1251 & 2.6571 \\  & \(\beta\) (g mg\({}^{-1}\)) & 0.0674 & 0.0913 & 0.1071 & 0.1073 \\ \hline \multirow{3}{*}{**Intraparticle diffusion**} & R\({}^{2}\) & 0.7969 & 0.9191 & 0.9519 & 0.9306 \\  & K\({}_{i}\) (mg g\({}^{-1}\) min\({}^{0.5}\)) & 4.2488 & 3.2554 & 2.8709 & 3.3419 \\ \cline{1-1}  & C (mg g\({}^{-1}\)) & 5.3228 & 14.733 & 1.0959 & 3.3419 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Adsorption kinetic constants of X-PR and MGC by BS-HVL (T = 21 \(\pm\) 1 \({}^{\circ}\)C; m\({}_{\rm ad}\) = 50 mg; C\({}_{0}\) = 50 mg L\({}^{-1}\); stirring speed 150 rpm and pH = 7 \(\pm\) 0.3).  
Figure 17: Langmuir isotherms for the adsorption of **(a)** X-PR and **(b)** MGC by BS-HVL (t = 90 min; T = 21 \(\pm\) 1 \({}^{\circ}\)C; mad = 50 mg; stirring speed 150 rpm and pH = 7 \(\pm\) 0.3). Langmuir isotherms for the adsorption of **(c)** X-PR and **(d)** MGC by BC-HVL (t = 90 min; T = 21 \(\pm\) 1 \({}^{\circ}\)C; mad = 50 mg; stirring speed 150 rpm and pH = 7 \(\pm\) 0.3).
## 3 Results
### Sensor Comparisons  
To further evaluate the MODIS and SPOT sensors for monitoring rangeland production, the NDVI from the different satellites for different months over both years were compared to the field-measured NDVI (NDVI\({}_{\text{G}}\)) as a reference (Figure 9). For this comparison, the ground-measured NDVI collected from each 1-ha grid (Figure 2C) was recalculated to simulate each satellite sensor by convolving the spectral reflectance from field measurements against the satellite bands used for NDVI. When compared to these simulated NDVI values from ground measurements, NDVI from both MODIS platforms (NDVI\({}_{\text{A}}\) and NDVI\({}_{\text{T}}\)) provided similar offsets from the 1:1 line. The difference between NDVI from the Terra and Aqua platforms and the ground-simulated NDVI was greatest at the lower values, a pattern consistent with Figure 5 (where early-season MODIS values were notably higher than NDVI\({}_{\text{G}}\)). Relative to the MODIS NDVI values, the SPOT NDVI (NDVI\({}_{\text{S}}\)) values yielded a different offset pattern, showing higher values at low NDVI and lower values at high NDVI (Figure 9C). The slope of the regression between the ground-simulated NDVI and NDVI\({}_{\text{S}}\) was significantly different than from the MODIS platforms, indicating clear differences between sensors (Terra vs. Aqua \(p\) = 0.645, Terra vs. SPOT \(p\) = 0.0004, Aqua vs. SPOT \(p\) = 0.0002). The strongest correlation between simulated NDVI and satellite-derived NDVI (and the lowest RMSE) was from the Terra platform (\(R^{2}\) = 0.94, RMSE = 0.0289), followed by Aqua (\(R^{2}\) = 0.9, RMSE = 0.0336; Figure 9). The SPOT satellite provided the weakest correlation (\(R^{2}\) = 0.81) with simulated NDVI and the highest RMSE (RMSE = 0.0409, Figure 9).  
Figure 9: Ground-simulated NDVI vs. satellite NDVI for MODIS Terra (**A**); MODIS Aqua (**B**); and SPOT (**C**). Data from 2009 and 2010 are distinguished by different symbols, as indicated on each panel. Ground-simulated NDVI was derived using MODIS or SPOT band passes, as described in Section 2. Also shown are the 1:1 lines (dotted lines) and the best-fit regression lines (solid lines) for each comparison, along with regression equations, relevant statistics, and RMSE values. Error bars indicate the standard error of the mean.