esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussion
### Parametric Analysis--Analysis Three  
In this analysis, a simulation is carried out, modifying one micro-parameter at a specific scale while the others are kept constant. The PSD4_S2 DEM is selected for the parametric analysis, and its calibrated micro-parameters (\(E_{LC}\), \(\bar{E_{FJ}}\), \(\overline{\sigma_{c}}\), \(\overline{\Phi}\), \(\mu\), \(k_{rat}\), and \(\bar{k}_{rat}\)) are taken from analysis one.  
The effect of each micro-parameter is presented in Figure 17. The red arrows at every plot indicate the trend going from a smaller to a larger value for the given micro-parameter. The DEM's Young's Modulus (macro-property) increments as the ELC increments, and \(k_{rat}\) decreases. There is a slight increment of the DEM's peak stress when increasing the EFJ and \(\overline{\sigma_{c}}\). In particular, \(\overline{c}\), \(\mu\), and \(\overline{\Phi}\) show a direct influence on the peak stress when incrementing their values. The \(\overline{\sigma_{c}}\), \(\overline{\Phi}\), and \(\mu\) increase the hardening section when the micro-parameter value increases. On the other hand, the \(\bar{k}_{rat}\) does not depict a clear trend when varying the initial micro-parameter value from a minor to a larger scale. It is concluded that the micro-parameters that have a greater effect on the compressive behavior of mortar are \(\overline{\sigma_{c}}\), \(\overline{\varepsilon}\), \(\mu\), \(k_{rat}\) and \(\overline{\Phi}\). In order to model masonry under compression, it is necessary  
Figure 16: Particle contact force magnitude in specimens PSD1_S2—PSD4_S2 at strain = 0.001.  
Figure 15: Analysis two, 2 micro-parameters value vs. \(D_{nt}\).  
to simulate mortar and units under compression. This research contributes to this goal. Complementary experimental results, such as tensile splitting test, are required to better calibrate the other micro-parameter of mortar (\(E_{F\!I}\), and \(\overline{k}_{rat}\)).
# Development Status and Service Performance Preliminary Analysis for BDSBAS
## 2 BDSBAS Architecture Overview
The IP facility uses preprocessing data screening from B receivers of the monitoring stations to ensure the reliability of SBAS services. The user differential range error (UDRE) monitor determines a confidence bound on the satellite ephemeris and clock corrections with the carrier smoothed iono-free pseudoranges. In parallel, the grid ionospheric vertical error (GIVE) monitor estimates the ionospheric delay confidence bounds for a set of ionospheric grid points (IGPs). If all the corrections are properly bounded, the corrections and integrity information are then arranged in accordance with RTCA MOPS and SBAS L5 DFMC standards separately and uploaded to the three GEO satellites in a sequence of messages by the message arrangement and upload facility. Both SF and DFMC SBAS messages are broadcast to the  
Figure 1: The workflow of the BDSBAS system. The red line indicates the SF service messages processing and the green line indicates the DFMC service messages processing. SBAS users with the data rate of 250 bits per second. BDSBAS SF messages are broadcast on BDSBAS-BIC signal and BDSBAS DFMC messages are broadcast on BDSBAS-B2a signal of GEO satellites.
# Experimental Study on Multi-Cell Counting Using an Inertial Microfluidic Device
## 2 Materials and Methods
### Experimental Variables and Setup
#### 2.5.2 Hypothesis and Expected Outcomes  
The experimental design was structured to test the hypothesis that variations in channel size, particle concentration, and flow rate would significantly affect the sorting efficiency of microparticles. As particles move through a microchannel at low Reynolds numbers, they experience inertial lift forces generated by velocity gradients and interactions with the channel walls. These forces drive particles across streamlines toward equilibrium positions. At lower flow rates, the inertial forces are weak, causing particles to either remain dispersed or exhibit inconsistent migration patterns. Increasing the flow rate enhances the lift forces, promoting lateral migration but also amplifying shear forces, which can lead to variability in particle alignment. Smaller channels intensify wall effects and shear gradients, resulting in stronger lift forces and more effective particle focusing. In contrast, larger channels reduce confinement, which can lead to less stable focusing and broader particle  
\begin{table}
\begin{tabular}{c c c} \hline
**Sr No.** & **Test Parameters** & **Description** \\ \hline
1 & Squared channel size (\(\upmu\)m) & 150, 200, 250 \\
2 & Particle concentration (8 and & \\  & 15 \(\upmu\)m, respectively) & R\({}_{1}\) = 600:1, R\({}_{2}\) = 400:1 \\
3 & Flow rates (\(\upmu\)L/min) & 1, 5, 10, 15 \\  & Inverted microscope, & \\
4 & Optical equipment & Bioimager fluorescent \\  & & microscope \\ \hline \end{tabular}
\end{table}
Table 2: Experimental Parameters.  
distributions. This led us to hypothesize that smaller channels and moderate flow rates would optimize particle alignment and improve the counting efficiency. By systematically exploring these variables, the study aimed to identify the optimal conditions for particle sorting in microfluidic devices, thereby contributing to the development of efficient cell sorting mechanisms for biomedical applications.
## 4 Results
### Signal Processing Results
#### 4.2.2 Calculation of BPM Differences  
The differences between the BPM estimates from each method and the reference are calculated as follows Equation (20):  
\[\text{DIFF}_{\text{BPM}}=\text{mean}(\text{BPM}_{\text{method}})-\text{mean}( \text{BPM}_{\text{reference}}), \tag{20}\]  
where \(\text{BPM}_{\text{method}}\) represents the BPM estimates from one of the input signals and \(\text{BPM}_{\text{reference}}\) is BPM estimated from a reference or ground truth signal (PPG).  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Conf. No.**} & \multicolumn{2}{c}{\(\Delta Z(t)_{mean}\pm\sigma(\Omega)\)} \\ \cline{2-3}  & **Perpendicular Placement** & **Distal Placement** \\ \hline A-b10 & 0.031 \(\pm\) 0.0020 & 0.089 \(\pm\) 0.0096 \\ B-b10 & 0.090 \(\pm\) 0.0039 & 0.198 \(\pm\) 0.0406 \\ C-b10 & 0.053 \(\pm\) 0.0055 & 0.163 \(\pm\) 0.0122 \\ D-b10 & 0.045 \(\pm\) 0.0021 & 0.122 \(\pm\) 0.0144 \\ A-b17 & 0.020 \(\pm\) 0.0020 & 0.147 \(\pm\) 0.0191 \\ B-b17 & 0.060 \(\pm\) 0.0017 & 0.213 \(\pm\) 0.0156 \\ C-b17 & 0.046 \(\pm\) 0.0028 & 0.194 \(\pm\) 0.0070 \\ D-b17 & 0.032 \(\pm\) 0.0033 & 0.134 \(\pm\) 0.0173 \\ E-t10 & 0.025 \(\pm\) 0.0005 & 0.037 \(\pm\) 0.0048 \\ F-t17 & 0.019 \(\pm\) 0.0010 & 0.039 \(\pm\) 0.0026 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Calculated mean values of \(\Delta Z(t)\) in the cases of the chosen electrode configurations.  
Figure 9: Calculated mean values of S in the cases of the chosen electrode configurations.
# Application of Angle Related Cost Function Optimization for Dynamic Path Planning Algorithm
## 4 The Spatial Optimized Dynamic Path Planning Algorithm
### Restrict Search Direction
#### 4.2.1 Analysis of the A* Algorithm Evaluation Method
At the same time, selecting a small angle in the search can reduce the number of nodes that need to be searched and the search range, thus further reducing the solution time of the algorithm and improving the efficiency of the algorithm.
## 3 Results
### Cloudy Versus Clear Sky Days  
From each 1024 \(\times\) 768 fused raw image, the algorithm extracts a 128 \(\times\) 128 pixel subimage centered on the sun. Using the _transform.resize_ function from Skimage, this is resized to 8 \(\times\) 8 pixels to achieve dimensionality reduction. This idea is critical to successful machine learning: each example in the dataset is a vector in a high-dimensional space and there are many examples. Principal Component Analysis and Linear Discriminant Analysis are other techniques for reduction, but our approach is simple and has proved to be effective.  
In the following case study ten days of Skylmager data acquired at NREL were used to synthesize two datasets. Five moderately cloudy days comprised the first dataset, October 16, 17, 18, 19, 20 in 2015. Five clear sky days of data from November 10, 11, 12, 13, 14 of 2015 made up the second dataset. The neural networks were fed 32 \(\times\) 32 pixel resized images and 4 ML models from Scikit-Learn were compared: Generalized Linear Regression Model (GLM), Multi-Layer Perceptron (MLP), Random Forest Regressor (RFR), and Gradient Boosted Trees (GBT). Table 4 and Figure 20 show the results. GLM and GBT have much shorter runtimes in both cases, while MLP and RFR achieve higher \(R^{2}\) values. Maximum accuracies are achieved with MLP but at a cost of increased runtimes. Extreme accuracy in \(R^{2}\) values for clear sky days (0.97, 1.0, 1.0, 0.99) indicates the networks are learning the analytic form of the [PERSON] clear-sky GHI model well.  
Figure 19: Time series of measured and predicted GHI for all testing days.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Hid.** & **Nodes per H.** & **\# Epochs** & **Run Time** & **MAE** & **MAPE** & **nRMSE** & \(R^{2}\) \\
**Layer** & **Layer** & **\# Epochs** & **Run Time** & **MAE** & **MAPE** & **nRMSE** & \(R^{2}\) \\ \hline
2 & 50,50 & 10 & 1:55 min & 65.807 & 32.73\% & 25.8\% & 0.815 \\ \hline
2 & 195,195 & 10 & 7:10 min & 66.872 & 40.76\% & 25.6\% & 0.824 \\ \hline
3 & 195,195,195 & 500 & 43:52 min & 50.992 & 27.13\% & 21.6\% & 0.871 \\ \hline
5 & 195,195,97,195,195 & 100 & 67:42 min & 48.519 & 26.09\% & 21.6\% & 0.871 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of 4 Deep Learning Models.  
Using still finer sampling for the resized sub-images should yield better results at the cost of larger data files and runtimes. At some point however, statistics suggests diminishing returns. In addition to detailed descriptions of ML models and software our article [9] presents another case study. It uses only one moderately cloudy day (17 October 2015) of observations and runs the four ML models with 8 \(\times\) 8, 32 \(\times\) 32, and 64 \(\times\) 64-pixel sub-images. The size of the CSV input data file increases quickly: 3 Megabytes, 111 Mb, and 442 Mb, as do runtimes 139 s, 444 s, and 1309 s. Accuracies improve, but not beyond a certain point.
# Investigation of the Effect of Capillary Barrier on Water-Oil Movement in Water Flooding
## 3 Capillary Barrier Phenomenon
### Conceptual Physical Model of Capillary Barrier  
In view of the aforementioned capillary barrier phenomenon, a cylindrical tube model is presented in Figure 3. The proposed capillary barrier model with circular cross-section is analogous to a regular cylindrical tube. The length of the inlet section, the throat section, and the outlet section of the model are \(L\) and the throat radius is \(r\). The unit of length \(L\) is m. \(\theta\) is the contact angle of the water phase and \(\beta\) is the opening angle, as shown in Figure 3. The unit of the angle is a degree. The opening angles in the inlet section and the outlet section are identical. The liquid will flow through the throat section from the inlet section and then flow out of the channel from the outlet section. The origin of the coordinate is set at the center of the cross-section of the inlet section.  
Figure 3: Schematic representation of the conceptual physical model of the capillary barrier.
## 3 SPS: Grid-Based Single Phantom Node Source Location Privacy Protection Scheme
### The Routing Phase
#### 3.3.1 Step 1: The Source Node Sends Data Packets to The Phantom Node  
In the process of sending data packets to the PN, the SoN only knows the ID of the PN and the grid number it is in. How to send data packets to the PN via multi-hop routing when no positioning capability is available is a question worth exploring. In this paper, we design the following strategies to solve this problem. Firstly, determine which grid will be passed through during the transmission process, and then find the node in the grid to forward the data packets by searching the neighbor table.  
Taking the SoN as an example, the specific process is as follows:  
(1) The SoN randomly selects a PN from the PNCS and removes the PN ID from the PNCS, which means that a PN can only be used once.  
(2) Once selecting the PN, the SoN can simulate the grid diagram, as shown in Figure 4, and determine their positions in the grids according to the grid numbers of itself and the PN. The SoN simplifies Figure 4 to Figure 5 and builds a coordinate system. As shown in Figure 5, the SoN is in grid \(\text{G}_{i\times j}\), and the PN is in grid \(\text{G}_{m\times n}\).  
(3) According to its grid number \(\text{G}_{i\times j}\) and the PN's grid number \(\text{G}_{m\times n}\), the SoN obtains the coordinates of the center point of the grids are \((i,j)\) and \((m,n)\), respectively. Then it calculates two-point straight line \(l\), as shown in Equation (4),  
\[\frac{x-i}{m-i}=\frac{y-j}{n-j}. \tag{4}\]  
(4) The SoN determines whether the slope of the line \(l\) is 0. If the slope is 0, that is, the grids where the SoN and the PN belong to are in the same row, the SoN searches its neighbor table for the set of  
Figure 4: Grid simulation.  
neighbor nodes in the direction of the target grid, and randomly selects a node as the next hop node. The subsequent nodes do the same operation until the data packet reaches the PN.  
(5) If the slope of the line \(l\) is not 0, the SoN calculates \(x=i\pm 1,\ y=j\pm 1\), so that the distance from \((x,y)\) to line \(l\) does not exceed \(\sqrt{2}*r\). For example, as shown in Figure 5, if there are two possibilities (right grid and bottom right grid), the left and the right grids are determined first. The SoN makes \(x=i+1,\ y=j\), that is to say, the SoN determines the next grid through which the data packet passes is the right grid. Then it searches its neighbor table to find the neighbor node set in the right grid, and randomly selects one node as the next hop node.  
The subsequent nodes repeat steps (4) and (5) until the data packet is transmitted to the PN.
## 4 Overview of the Type of Classifiers Used for MR
### Complexity Analysis for Classifiers  
Some studies discussed the complexity analysis of the classifier used in MR in different approaches. For the ANN classifier in [38], the rough sets used for the selected features with the NN reduced the network complexity. A combination of the rough sets and neural network has smaller training time. This method can remove the redundant features from the training samples, and simplifies the structure of the ANN classifier. In [72; 94], the features subset selection using principal component analysis (PCA) is used to reduce the complexity of the used neural network through the selection of the best features.  
Similarly, in SVM classifiers, some researchers were also interested in reducing the complexity. In [29], the number of features extracted from the dataset includes 27 different features, so the complexity of the computation and runtime will be increased. Therefore, a feature subset selection process is needed to reduce the SVM complexity through the use of PSO. In [30], the hierarchical SVM classifier was used and has considerably low complexity compared to the structure of the OAO approach. This is because the number of the required SVMs in the proposed tree scheme for recognition of seven different modulation types is equal to six, while the OAO approach needs 7(7\(-\)1)/2 = 21 SVMs. In [34], the author reduced the computation complexity of the SVM classifier through using the technique of the rough set. This technique works by selecting the best features from the original set. Four features were chosen from 25 features, which eliminates redundant information in the original features and reduces the complexity of the training process.  
DT techniques are simple to design and implement, and do not need training of the classifier as in PR techniques. In [48; 63], the researchers used two and three features, respectively, based on the DT algorithm and hence reduced the computational complexity. In [40], a hybrid approach was used that implements both the FB and LB modulation classification for the efficient classification of multiple linear and nonlinear modulation formats. This hybrid classification approach is helpful in both reducing time and reducing computational complexity.  
Reducing the complexity in KNN techniques was the aim of some researchers. In [54], the GP generated super features from the dataset based on a single-stage strategy that leads to the reduction of the computational time. In [90; 108], the authors compared the KNN classifier to other classifiers, such as SVM and ANN, and found that the KNN has less computational complexity than SVM and ANN.  
Clustering, which is another classifier, has drawn attention of researchers trying to reduce the computational complexity. In [84], the subtractive clustering algorithm, in which the number of cluster centers is adaptive, was used. This is greatly reduces the computational complexity. In [85], the K-means clustering method, which needs to calculate the number of clustering center points for each hypothesis, was used, leading to an increase of computational complexity.
# Additively Manufactured Inconel 718 Low-Cycle Fatigue Performance
## 3 Results  
### SN Curves  
During the low-cycle fatigue testing of the L-PBF Inconel 718, cycles were counted until failure occurred. Each specimen's fatigue life (N\({}_{\text{f}}\)) and associated alternating pseudo-stress values, \(\Delta\sigma\) = E \(\times\)\(\Delta\varepsilon\), were plotted on the fatigue life curves shown in Figure 6. The fatigue curves indicate that room-temperature specimens exhibited minimal differences in fatigue life between the fully machined and as-printed conditions. However, at 538 \({}^{\circ}\)C (1000 \({}^{\circ}\)F), a more significant difference was observed, with fully machined specimens demonstrating better fatigue performance compared to as-printed specimens. Additionally, it was noted that the fatigue performance at 24 \({}^{\circ}\)C (75 \({}^{\circ}\)F) was roughly 200 MPa higher than the fatigue performance at 538 \({}^{\circ}\)C (1000 \({}^{\circ}\)F).  
Table 3 identifies the average fatigue life and standard deviation of the fatigue life for each specimen type and test condition. It is noted that the standard deviation was less for the as-printed specimen condition when compared to the fully machined specimen condition regardless of test temperature. Additionally, the knockdown due to surface roughness can be calculated for each test temperature. The 538 \({}^{\circ}\)C (1000 \({}^{\circ}\)F) as-printed specimens had an average knockdown of 62.8%, while the 24 \({}^{\circ}\)C (75 \({}^{\circ}\)F) as-printed specimens showed an average knockdown of 8.5%. To better understand the mechanisms that drive this difference in surface roughness-induced fatigue life knockdown, eight specimens were selected for tractography analysis based on their fatigue life. The highest and lowest fatigue life specimens for each test and specimen configuration were selected and are shown in Figure 6 and Table 4.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Temperature** & **Surface Condition** & **\(\Delta\varepsilon\) (\%)** & **Average Nf (Cycles)** & **Nf Standard Deviation (Cycles)** \\ \hline
24 \({}^{\circ}\)C (75 \({}^{\circ}\)F) & Fully Machined & 0.479 & 4305 & 739 \\
24 \({}^{\circ}\)C (75 \({}^{\circ}\)F) & As-Printed & 0.479 & 3939 & 87 \\
538 \({}^{\circ}\)C (1000 \({}^{\circ}\)F) & Fully Machined & 0.445 & 7723 & 2203 \\
538 \({}^{\circ}\)C (1000 \({}^{\circ}\)F) & As-Printed & 0.445 & 2867 & 661 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Average and standard deviation for the fatigue data for each test condition.  
Figure 6: R\(\varepsilon\) = 0.0 fatigue data for fully machined L-PBF Inconel 718 and as-printed L-PBF Inconel 718 at 24 \({}^{\circ}\)C (75 \({}^{\circ}\)F) and 538 \({}^{\circ}\)C (1000 \({}^{\circ}\)F).
## 4 Simulation Results
### Energy Distribution of Sensing Area  
In order to determine whether the proposed GB-PEDAP can disperse the energy consumption, the remaining energy distribution of the sensors is simulated when the first sensor dies. The number of sensors is 200 and the initial energy of sensors is 0.25 J. Figure 7 shows that the average remaining energy of GB-PEDAP, TBEEP and GSTEB is between 0.112 and 0.118, and that of PEDAP is between 0.1 and 0.123. The average remaining energy of GB-PEDAP is slightly better than TBEEP and GSTEB, and much better than PEDAP. The uniformity of energy distribution of GB-PEDAP is the best, and the uniformity of energy  
Figure 5: Number of rounds when the first node dies under different numbers of nodes: (**a**) initial energy is 0.25 J; (**b**) initial energy is 0.5 J.  
Figure 6: Total consumed energy under different numbers of rounds.  
distribution of PEDA P is the worst. The proposed GB-PEDAP can effectively achieve uniform remaining energy distribution compared to TBEEP, GSTEB, and PEDAP.
## 2 Image Preprocessing
### Bicubic Interpolation Process
A jagged contour has a significant effect on the unidirectional chord inclination angles, which was studied using the contour at a tangent point, as shown in Figure 8.  
The front and back chord inclination angles at the tangent point in Figure 8 are listed in Table 3.  
Figure 8: Effect of contour serration on chord dip angle. Figure 4:0 Diagram of the front and back chord inclination angles. The effect of a jagged contour can be suppressed by complementing the front and back chord inclination angles. The contour points had a uniform distribution of the jagged contour from \(P_{i+5}\) (the blue point in Figure 8) to \(P_{i+13}\) (the purple point in Figure 8). As can be seen in Table 3, the anterior chord inclination and posterior chord inclination of all contour points in this section always fluctuated between \(0^{\circ}\) and \(21.8^{\circ}\), with a value (21.8) that was always the same between \((\theta_{i+5^{+}},\theta_{i+5^{-}})\) and all \((\theta_{i+n^{+}},\theta_{i+n^{-}})\), \(n=5,6\cdots 13\). Once there was a point where the jaggedness distribution was clearly uneven (near the tangent point: (the red point \(P_{i+14}\) in Figure 8), there was a chord inclination angle (30.96) different from \(0^{\circ}\) and \(21.8^{\circ}\), which could be considered a segmentation point. Benefits of this property include:  
1. This property makes it possible to find the contour segment with the same chord inclination angle and the longest length and to consider the two endpoints as the segmentation points to be selected to suppress the pseudo-segmentation points caused by the jagged contour. 2. The method of complementary front and back chord inclination angles is sensitive to the tangent point that can effectively respond to the tangent point and precisely locate the tangent point. Experimental validation using the pending inspection image could effectively verify the detection effect of the algorithm on graphic elements of different scales and different types of segmentation points. The segmentation points initially screened for their contours by adopting the method of complementary front and back chord inclination are shown in Figure 4:1  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Index** & **Front Chord** & **Back Chord** & **Index** & **Front Chord** & **Back Chord** \\  & **Inclination Angle** & **Inclination Angle** & **Inclination Angle** & **Inclination Angle** \\ \hline \(P_{i}\) & 21.8 & & \(P_{i+11}\) & 21.8 & 21.8 \\ \(P_{i+1}\) & 0 & & \(P_{i+12}\) & 21.8 & 21.8 \\ \(P_{i+2}\) & 0 & & \(P_{i+13}\) & 21.8 & 0 \\ \(P_{i+3}\) & 21.8 & & \(P_{i+14}\) & 30.96 & 0 \\ \(P_{i