esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation of Global Precipitation Products over Wabi Shebelle River Basin, Ethiopia
## 4 Result and Discussion
### Comparison of Global Precipitation Products at Temporal Scale
#### 4.1.1 Daily Comparison
A relatively high coefficient (\(R^{2}\) = 0.039) was obtained by TAMSTAv3 and TAMSAT_D, and whereas CHIRPSv2 scored the lowest value. PERSIANN_CDR (downscaled and raw) showed reasonable agreement with the ground reference (\(R^{2}\) = 0.03). Furthermore, all products were comparatively symmetric to a 45\({}^{\circ}\) inclination. According to the CDFs (Figure 5b), all products were not comparatively denser for a 45\({}^{\circ}\) inclination. Furthermore, TAMSAT_D and PERSIANN_CDR_D revealed the worst correspondence with the station CDFs. This shows that these products underestimated the distribution for rainfall \(\leq\) 10 mm/day, whereas CHIRPSv2 and CHIRPS_D overestimated the distribution for rainfall \(\leq\) 10 mm/day.
# PBR Clutter Suppression Algorithm Based on Dilation Morphology of Non-Uniform Grid
## 4 Experiment result and Analysis
### Testing by Simulated Data
#### 4.1.2 The Clutter Suppression Performance Analysis  
For grid construction, the standard deviation of range is set as 300 m according to the bandwidth of the illuminator. The standard deviation of angle is set as 0.3 according to the number of beams simultaneously covering the surveillance range. The range is from 20 km to 120 km. The angle scope is from 60\({}^{\circ}\) to 170\({}^{\circ}\). The baseline range is 400 km. For frame operation, we categorize the simulated data into frame data by every 0.5 seconds. The structural element size is \(3\times 3\). The half number of the reference frames is set as 3. Set SCR as \(-1.26\) dB. Figure 9 demonstrates the contrast before and after the suppression. In addition Table 3 shows three performance indexes of the suppression algorithm. The detection accuracy rate is the ratio of the number of correct target points extracted to the whole number of the correct target points. The false alarm decline rate is the ratio of the number of the false points extracted to the whole number of the clutter points set before. The miss detection rate is the ratio of the number of missing target points to the whole number of correct target points. From Table 3 and Figure 9, we can conclude that near 90% of clutter points are suppressed, while 97.45% of target points retain. To illustrate the algorithm performance comprehensively, change the SCR of the simulation scenario from \(-3\) dB to 1 dB. In each SCR scenario, do Mont-Carlo experiment for 50 times and calculate the mean value of the performance indexes. We obtain following results, as Figure 10 shows. Red line stands for the correct detection rate. Blue line stands for the CFAR decline rate. Green line stands for the missing rate. Over 90% of target points can be extracted correctly in this algorithm. In addition when SCR is up to \(-2\) dB, the number of false alarm points can be decline to the 30% of the original number. When SCR is up to \(-1\) dB, over 90% of clutter points can be suppressed.
## 2 Experimental Methods
### Surface Characterization via Scanning Electron Microscopy (SEM)  
SEM imaging was performed to investigate the surface of the 3D-printed copper porous structure at the microscale (Figure 6). A JEOL JCM-7000 scanning electron microscope (JEOL, Tokyo, Japan) was used. The image was acquired with a secondary electron detector (SED) at an accelerating voltage of 5.0 kV, and a working distance (WD) of 61.8 mm was maintained for both 50 \(\upmu\)m and 20 \(\upmu\)m scale images. High vacuum conditions were used with a vacuum reading of 400 for the 50 \(\upmu\)m and 800 for the 20 \(\upmu\)m image. Figure 6 shows a SEM image area corresponding to a plain surface fabricated through BJ3 DP. The surface is rough and irregular, with finer details such as cavities created during manufacturing due to the sintered copper particles visible at this scale. These cavities are known  
\begin{table}
\begin{tabular}{c c c} \hline
**Line** & **\(R_{\text{a}}\) (\(\upmu\)m)** & **\(R_{\text{Z}}\) (\(\upmu\)m)** \\ \hline
1 & 9.77 & 61.5 \\
2 & 9.95 & 74.5 \\
3 & 9.26 & 75.3 \\
4 & 9.07 & 79.8 \\
5 & 9.71 & 74.5 \\
6 & 9.46 & 67.7 \\
7 & 7.77 & 62.5 \\
8 & 9.46 & 74.1 \\
9 & 10.13 & 62.2 \\
10 & 8.89 & 77.8 \\ \hline \end{tabular}
\end{table}
Table 1: Surface roughness measurements.  
Figure 5: Location of surface roughness measurement.  
to promote the formation of boiling nucleation sites, and a higher density of such cavities is desirable to increase the number of active nucleation sites, thereby enhancing boiling heat transfer performance. This characteristic unevenness is consistent with a porous layer formed by sintered powder. The size and distribution of these features are influenced by the size and morphology of the initial powder particles and the sintering parameters. Hence, the resultant porous layer formed by sintered particles leads to a surface rich in cavities, which act as nucleation sites, promoting bubble formation and ultimately enhancing the efficiency of boiling heat transfer. Accordingly, such cavity structures on the test pieces used in this study are expected to enhance the heat transfer efficiency during boiling.
###### Abstract  
The aim was the validation of a platform for internal dosimetry, named MCID, based on patient-specific images and direct Monte Carlo (MC) simulations, for radioembolization of liver tumors with \({}^{90}\)Y-labeled microspheres. CT of real patients were used to create voxelized phantoms with different density and activity maps. SPECT acquisitions were simulated by the SIMIND MC code. Input macros for the GATE/Geant4 code were generated by MCID, loading coregistered morphological and functional images and performing image segmentation. The dosimetric results obtained from the direct MC simulations and from conventional MIRD approach at both organ and voxel level, in condition of homogeneous tissues, were compared, obtaining differences of about 0.3% and within 3%, respectively, whereas differences increased (up to 14%) introducing tissue heterogeneities in phantoms. Mean absorbed dose for spherical regions of different sizes (10 mm \(\leq\) r \(\leq\) 30 mm) from MC code and from OLINDA/EXM were also compared obtaining differences varying in the range 7-69%, which decreased to 2-9% after correcting for partial volume effects (PVEs) from imaging, confirming that differences were mostly due to PVEs, even though a still high difference for the smallest sphere suggested possible source description mismatching. This study validated the MCID platform, which allows the fast implementation of a patient-specific GATE simulation, avoiding complex and time-consuming manual coding. It also points out the relevance of personalized dosimetry, accounting for inhomogeneities, in order to avoid absorbed dose misestimations.  
radioembolization; internal dosimetry; Monte Carlo-based dosimetry
## 2 Model Test of the XCC Pile-Raft Foundation for a Ballastless High-Speed Railway  
### Model Tests System  
The XCC pile-raft foundation for a ballast-less railway was constructed in a reinforced concrete box with dimensions of 5 m \(\times\) 4 m \(\times\)7 m (length \(\times\) width \(\times\) height), as shown in Figure 1. The track structure was composed of double rails, fasteners, a track slab, and a concrete base. Rails of CHN60 type (similar to UIC60-type rails) were fixed to the track slab with WJ7-type fasteners. The physical model was scaled to 1/5. The concrete base was constructed in situ with steel reinforcement. The substructure consists of embankment, concrete raft, gravel cushion, subsoil, XCC piles, and a supporting layer. The embankment was a layer of 0.54-m-thick AB granular below the concrete base. A concrete raft with the dimensions 5 m \(\times\) 3.34 m \(\times\) 0.12 m was constructed with steel reinforcements below the embankment. The gravel cushion was layered 0.06 m thick between the raft and the pile. The pile was 4.3 m (21.5 in full scale) in scaled size. The pile distance was 0.6 m. The underlying ground was composed of silly soil. The 1-m-thick supporting layer was composed of medium-density sand. The geometric parameters of the pile head are 2 R, 2a, and \(\theta\), where 2R is the diameter of the surrounding circle, 2a is the length of the flat sides, and \(\theta\) is the angle between tangents drawn from the center of two adjacent sides. In this study, the geometric parameters 2R, 2a, and \(\theta\) were 0.157 m, 0.039 m, and 90\({}^{\circ}\). The concrete strength grades of the XCC pile, raft, concrete base, and track slab are C25, C35, C40, and C55, respectively. The composite geo-membrane and asphalt material were used around the inner wall of the concrete box, to reduce the influence of dynamic wave reflection and friction force of the inner wall.  
Figure 1: Physical model of ballastless track embankment and XCC pile–raft foundation.  
The schematic arrangement of the velocity and soil pressure sensors used to record the vibration velocity and soil pressure is shown in Figure 2a,b. Velocity sensors (V1-V12) were placed in various locations along the horizontal and vertical directions.  
Figure 2: Geometry and instrument layout of the physical model test. (**a**) cross-sectional view, (**b**) plane view.  
The grain-size distributions of gravel, granular soil, sand, and silty soil are plotted in Figure 3, and the physical properties and parameters of these geomaterials are summarized in Table 1.  
The embankment was compacted by a tamping machine to the designed densities to bear the static and dynamic loadings induced by the train. The compaction coefficient \(K\), modulus of subgrade reaction \(k_{30}\), and dynamic deformation modulus \(E_{vd}\) were tested and are illustrated in Table 2. Although the physical model was reduced to the scale of \(1/5\), it still preserves the physical and mechanical properties of the natural materials. The scale factors of the dynamic model test were calculated by the Bockingham \(\pi\) theorem and are summarized in Table 3.
# 3D Point Cloud Generation Based on Multi-Sensor Fusion
## 4 Results
### Accuracy of Point Cloud Restoration  
To verify the accuracy of the point cloud restoration, the restored point cloud target and the control point target were first compared, and then several combined navigation solution point clouds and multi-sensor fusion generated point cloud data were selected for comparison with the same name feature points. Analysis showed that the root mean square error of the feature points with the same name was 0.0214 m. Figure 22 shows the point cloud extraction target and target measurement position. Table 5 is the Point cloud extraction target and target measurement error. Figure 23 shows the multi-sensor fusion generated point cloud and integrated navigation generated point cloud with the same name point selection.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Mileage Location(m)** & \multicolumn{2}{c}{**Deviation before Filtering Correction**} & \multicolumn{2}{c}{**Deviation after Filtering Correction**} \\  & **Lateral Deviation(m)** & **Vertical Deviation(m)** & **Lateral Deviation(m)** & **Vertical Deviation(m)** \\ \hline
5 & 0.0347 & 0.0307 & 0.0347 & 0.0307 \\
30 & \(-\)0.0145 & \(-\)0.0089 & 0.0052 & \(-\)0.0009 \\
55 & \  
As shown in Table 6, comparing the three-dimensional point cloud data generated by multi-sensor fusion and the three-dimensional point cloud data generated by integrated navigation, the overall point error could be controlled within 0.04 m. The maximum deviation of points in the horizontal direction was 0.036 m, and the average deviation was 0.0226 m; the maximum deviation in the elevation direction was 0.0391 m, and the average deviation was 0.0136 m.  
Figure 23: Multi-sensor fusion generated point cloud and integrated navigation generated point cloud with the same name point selection.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Target** & **North** & **East** & **Height** & **Horizontal** & **Elevation** \\  & **Error(m)** & **Error(m)** & **Error(m)** & **Deviation(m)** & **Deviation(m)** \\ \hline tag1 & 0.0107 & 0.0000 & 0.0000 & 0.0107 & 0.0000 \\ tag2 & \(-\)0.0081 & 0.0008 & 0.0003 & 0.0081 & 0.0003 \\ tag3 & \(-\)0.0107 & 0.0038 & 0.0005 & 0.0113 & 0.0005 \\ tag4 & 0.0276 & 0.0129 & 0.0012 & 0.0305 & 0.0012 \\ \hline Average & 0.0143 & 0.0044 & 0.0005 & 0.0152 & 0.0005 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Point cloud extraction target and target measurement error.  
Figure 22: Point cloud extraction target and target measurement position.
# Energy Efficient Routing Protocol in Novel Schemes for Performance Evaluation
## 1 Introduction
### Related Work  
The ever-growing energy consumption has facilitated the development of novel, efficient routing algorithms for improving the performance of the wireless sensor system. Now, we present the related works that are the closest to our research. For simplicity purposes, we have categorized the related work into three categories. The first category is the work carried out for selection of the CH and relay node with the fitness functions. The second category is the works that are related to the selection of CH with multi-objective parameters for optimal routing. The third category is the related works, regarding energy balancing for effective energy consumption. Table 1 shows about the Conventional methods by different authors commencing at Cluster head and relay node selection based on Fuzzy based, LEACH for consideration of network life and optimal routing and Table 2 shows the energy routing scheme for efficient energy directions of different boundaries with battery life, less no of nodes usage and multi objective cluster selection head.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Authors and Reference** & **Category** & **Brief Description** & **Limitations** \\ \hline [PERSON] [28] & Article survey & The performance of the LEACH protocol was improved, and the network lifetime was increased, according to a study on the distance-based construction of cluster approach. & The cluster head and relay node selection with fitness function for optimal routing was not covered in this paper because it only focused on the distance-based CH approach. & The cluster head and relay node selection with fitness function for optimal routing was not covered in this paper because it only focused on the distance-based CH selection. \\ \hline [PERSON] [29] & Article survey & An investigation was made on the methods used by fuzzy-based energy-aware and delay-intelligent routing to select effective routes. & Higher percentage of inactive nodes. There are fewer communications in the WSN as a result of these inactive nodes. \\ \hline [PERSON] [30] & Article survey & To solve the shortcomings of the LEACH (low energy adaptive clustering hierarchy) protocol, a survey was carried out. Cluster heads should be selected using EN-LEACH approach, taking into account their proximity to the BS and degree of expertise. & This idea did not encompass multi-hop routing and not reacted to the relay of nodes. \\ \hline \end{tabular}
\end{table}
Table 1: Conventional methods for CH and relay node selection.
# Post-Earthquake Fire Resistance in Structures: A Review of Current Research and Future Directions
## 7 Influence of Different Structural Systems
### Concrete-Filled Steel Tube (CFST) Columns  
Concrete-filled steel tube (CFST) columns are widely recognized for their strength, seismic performance, and ease of construction. However, their load-bearing capacity can be significantly reduced when subjected to earthquakes followed by subsequent fires (PEF). Below are key insights into the behavior of CFST columns under PEF conditions. Researchers often utilize quasi-static tests and fire tests to examine CFST columns under PEF conditions. In quasi-static tests, columns are subjected to a constant compressive force and cyclic horizontal force to simulate earthquake-induced damage. This is followed by fire tests, where the pre-damaged columns are exposed to high temperatures while maintaining a constant axial load. These tests allow researchers to observe fire resistance time, failure modes, and temperature distribution within the columns. CFST columns exposed to PEF typically exhibit local buckling at the base, along with softening and buckling at mid-height. Additionally, concrete crushing and cracking may occur [77]. Several factors affect CFST column performance under PEF:  
* Thickness of Steel Tube: Thicker steel tubes enhance a CFST column's load-bearing capacity, generally leading to increased fire resistance under normal conditions. However, in PEF scenarios, thicker tubes can result in more rapid concrete core failure. The deteriorated steel tube transfers more load to the concrete core, causing it to fail sooner, a pattern that persists regardless of the degree of pre-earthquake damage [77].
* Yield Strength of Steel Tube: Higher yield strength in steel tubes improves the initial load-bearing capacity of CFST columns. However, the fire resistance time decreases with increased yield strength. This is because high-strength steel, when exposed to PEF, undergoes accelerated material property degradation, which has a more pronounced impact on the column's overall behavior [77].
* Axial Compression Ratio: A higher axial compression ratio reduces the fire resistance time of a CFST column. The increased axial load diminishes the steel tube's ability to confine the concrete core, making the concrete more vulnerable to crushing under high temperatures [77].
* Height and Wall Thickness of the Column: The height and wall thickness of the CFST column significantly affect its fire resistance. Columns with insufficient wall thickness are more prone to local buckling, particularly at the top and bottom. To enhance fire resistance, it is recommended to maintain a wall thickness of 8.5-10.5 mm and a column height of 3250-3550 mm, which can help optimize steel content and mitigate excessive bending moments [78].  
Understanding the behavior of CFST columns under PEF conditions is essential for developing robust design strategies that enhance their resilience. Future research should focus on these factors to improve the performance and safety of CFST columns in seismic regions.
## 5 Engineering Case Study
### Application to Paiyashan Tunnel  
To validate the proposed method, images of the tunnel face were collected from station YK3 + 229 to YK3 + 237 at the entrance of the right tunnel. All tunnel face images were captured using the same device (Nikon D750 DSLR, manufactured by Nikon Corporation, Tokyo, Japan; purchased via Taobao, China) at a fixed resolution of 6016 \(\times\) 4016 pixels. Illumination was provided by portable LED panels, and the camera-to-face distance was maintained at approximately 2.5 m to ensure geometric consistency. As shown in Figure 14, structural plane traces were extracted from the tunnel face images for 3D reconstruction.  
The detection of structural planes was performed using the YOLOv5 model, which accurately located the target areas. These regions were then processed with the DeepLabV3+ model to conduct pixel-level semantic segmentation and extract precise structural features. Using OpenCV image processing techniques, the centerlines of the structural planes were extracted and plotted within a defined coordinate system (Figure 15). The extracted coordinates were then used to fit curves in 3D space (Figure 16), followed by B-spline  
Figure 14: Excavation face views at different mileage stations of the Paiyashan Tunnel.
# Hybrid Trust Model for Node-Centric Misbehavior Detection in Dynamic Behavior-Homogeneous Clusters
## 4 Segmentation of Dynamic Behavior-Homogeneous Vehicular Cluster
### Clustering Method  
In the adaptive clustering method, the cluster head vehicle clusters are based on three parameters: arrival angle, RSS, and inter-vehicle distance. This section focuses on dynamic vehicle cluster heads, transforming the clustering rules based on transmission angle into N equidistant transmission angles \((A_{1},A_{2},A_{3},\ldots,A_{n})\), with each transmission angle denoted as \(360^{\circ}/N\). The cluster head vehicle can establish N directional groups by assigning each transmission angle to a unique cluster member vehicle, as shown in Figure 3. Therefore, the characteristic parameters of different dynamic behavior-homogeneous vehicle clusters defined by the transmission angle and RSS are as follows:  
\[\left\{\begin{aligned} \theta_{\mathrm{x}}-\theta_{y}\leq\frac{360^{ \circ}}{N}\\ S_{N}=(\text{Cos}\theta_{N},\text{Sin}\theta_{N})\\ RSS_{X}-RSS_{y}\leq 1-e^{-\frac{N-V_{\mathrm{T}}-1}{4}}\\ \Delta RSS\leq 1-e^{-\frac{\Delta V}{4}}\\ d\leq\text{T}_{\mathrm{r}}\cdot(1-e)\end{aligned}\right. \tag{1}\]  
where, apart from \(\theta_{x}\) and \(\theta_{y}\) representing the arrival angles of the vehicle, \(S_{N}\) is the directional vector of any vehicle \(N\).
## 4 Model Validation
### FEM Model Setup  
Based on the finite-element method (FEM), the numerical models of the single-well pumping test (model A in in Figure 6) and dewatering with a suspended waterproof curtain (model B in Figure 7) were established. Both models have a dimension of 400 m in diameter and 30 m in thickness, and the thickness of the aquifer is 26 m. The initial groundwater level is 4 m below the ground surface, the radius of the pumping well is 0.1 m, the filter length of pumping well is 9.5 m, pumping time is 4 h, and the permeability coefficient is  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Name** & **Value** \\ \hline Groundwater type & \multicolumn{2}{c}{Phreatic aquifer} \\ Radius of pumping well & 0.1 m \\ Length of pumping well & 9.5 m \\ Pumping time & 8 h \\ \(r_{1}\) & 5 m \\ \(r_{2}\) & 10 m \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters of pumping test.  
Figure 5: Results of pumping tests: evolution of groundwater drawdown in the pumping well and the observation wells; (**a**) first groundwater drawdown is 1.21 m, (**b**) second groundwater drawdown is 2.65 m, (**c**) third groundwater drawdown is 3.93 m.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Test****Statum** & \multicolumn{2}{c}{**Groundwater Drawdown**} & \multicolumn{2}{c}{**Water Inflow**} & \multicolumn{2}{c}{**Calculated Value**} \\  & \(S_{w}\) **(m)** & \(Q\) **(m\({}^{3}\)/d)** & \(K\) **(m/d)** \\ \hline Coarse sand \(\backslash\)1-4 & 1.21 & 176.8 & 52.4 \\ Gravully sand & 2.65 & 360.3 & 62.1 \\ \(\backslash\)1-5 & 3.93 & 471.4 & 57.8 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of pumping test.  
57.8 m/d during the pumping test. The applied constitutive model of soil is the Hardening Soil model with small-strain stiffness; the parameters of the constitutive model are shown in Table 4. The radius of foundation pit is 10 m during dewatering with the suspended waterproof curtain, and other parameters are shown as above. Key features of the two models are summarized as follows:  
Model A. The numerical model was established to simulate the single-well pumping test. The model A activates the well structure to simulate the process of pumping at groundwater drawdowns of 1.21 m, 2.65 m, and 3.93 m.  
Model B. The groundwater drawdown needs to be lowered by 12 m when the foundation pit is excavated. Therefore, the groundwater drawdown of model B lowers to \(-\)16 m. The groundwater seepage process was simulated by activating the well structure, plate structure and interface element.