esa-sceva/satcom-chunk-collection huggingface dataset:
# Comparisons of Dynamic Landslide Models on GIS Platforms
## 2 Models
### Forces
#### 2.2.2 Friction Force  
Friction is the force that resists the relative motion of solid surfaces, fluid layers, and material elements sliding against each other, which is important for the movement calculation. In this process, kinetic energy is converted to thermal energy when motion with friction occurs. There are two types of resistance: viscosity and Coulomb's friction.  
For dry granular flow, Coulomb's friction is adopted, which is  
\[\tau=\mu N \tag{14}\]  
where \(\tau\) is the unit base resistance and \(\mu\) is the friction coefficient, and \(N\) is the normal force. Generally, the basal shear forces, obtained by simple infinite landslide models, are calculated by Coulomb's friction.  
For fluid flow, viscosity is a measure of resistance to deformation at a given rate. It can be conceptualized as the internal frictional force that arises between adjacent layers of fluid that are in relative motion. There are three viscosity equations: Newtonian, Bingham, and quadratic fluids.  
(1) The Newtonian fluid is:  
\[\tau=\mu\gamma^{\prime} \tag{15}\]  
where \(\mu\) is the shear viscosity of a fluid and \(\gamma^{\prime}\) is the derivative of the velocity component.  
(2) The Bingham fluid model is  
\[\tau=\tau_{0}+\mu\gamma^{\prime} \tag{16}\]where \(\tau_{0}\) is a constant yield strength, \(\mu\) is the shear viscosity of the fluid, and \(\gamma^{\prime}\) is the derivative of the velocity component.  
(3) The quadratic fluid model is  
\[\tau=\tau_{0}+\mu\gamma^{\prime}+\xi\gamma^{\prime 2} \tag{17}\]  
The first two terms are referred to as the Bingham shear stresses. The last term represents the dispersive and turbulent shear stresses. Fluid friction can be generalized as:  
\[f=f_{\rm s}+f_{\rm v}+f_{t} \tag{18}\]  
where \(f_{\rm v}\) is the viscosity term, \(f_{\rm s}\) is the constant term, and \(f_{t}\) is a turbulent term. Based on the depth-averaged theory, shear stress is depth-integrated, and the corresponding equation is  
\[S=\frac{1}{h}\int fdz \tag{19}\]  
where \(S\) is depth-integrated shear stress. Therefore, Equation (18) can be transformed to:  
\[S_{f\rm x}=S_{\rm T}+S_{\rm y}+S_{\rm t}d. \tag{20}\]
## 3 DFIG-Based Load Flow Analysis
### Wind Speed Analysis  
For conventional wind turbines, the operating point can be obtained by an iterative process combined with the load flow analysis considering all characteristics provided by the manufacturer. To obtain all electrical variables of the operating point in steady-state for the DFIG, however, the mechanical power based on the wind speed given by the manufacturer should be estimated, for controlling the reactive power. This procedure presents the active power \(P\) and the stator voltage (\(\mathbf{V_{s}}=V_{s}\mathbf{\angle}\delta_{s}\)) as unknown variables in the model. By establishing a new equation for \(\Gamma\)7, given by: \(P-P_{m}+\left(l_{s}{}^{2}r_{s}+l_{r}{}^{2}r_{r}\right)\), the value of \(P\) is known, but \(P\) is a proposed value. This could be resolved if the process is iterative and a more accurate calculation is performed. Another way to conduct the simulation is by assuming the slip value, which depends on the control mode since the slip value is different for each wind speed. Considering the above, the slip value is known once the input value of the wind speed is available. This facilitates the calculation since the unknown in Equation (26) is  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Bus Type**} & \multicolumn{6}{c}{**Specified Variable**} & \multicolumn{6}{c}{**Variables Obtained in the Solution Process**} \\ \cline{2-13}  & \(V_{W}\) & \(P\) & \(Q\) & \(V_{s}\) & \(Q\) & \(V_{s}\) & \(\delta_{s}\) & \(V_{r}\) & \(\delta_{r}\) & \(I_{s}\) & \(\varphi_{s}\) & \(I_{r}\) & \(\varphi_{r}\) & \(s\) \\ \hline \(PQ\) bus & \(\bullet\) & \(\bullet\) & \(\bullet\) & & \(\bullet\) & \(\bullet\) & \(\bullet\) & \(\bullet\) & \(\bullet\) & \(\bullet\) & \(\bullet\) & \(\bullet\) & \(\bullet\) \\ \hline \(PV\) bus & \(\bullet\) & \(\bullet\) & \(\bullet\) & \(\bullet\) & \(\bullet\) & \(\bullet\) & \(\bullet\) & \(\bullet\) & \(\bullet\) & \(\bullet\) & \(\bullet\) & \(\bullet\) & \(\bullet\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: PQ and PV methods for Wind Turbines based on DFIG.  
eliminated and Equation (27) is a 6 \(\times\) 6 matrix. Since all DFIG variables are known, the harmonic analysis is performed as described in subsequent sections.
## 3 Experimental Results
### Experimental Results for Printed Recept Characters  
Each of the printed receipt images was preprocessed, after which horizontal projection, segmentation, and rough positioning processes were executed. The proposed YOLOv4-s model was then applied to recognize the printed characters. In Figure 19, the left panel depicts an original receipt image; the dotted red frame in the middle indicates the information to be extracted. Moreover, in Figure 19, the upper-right panel presents the positioning results for the recognized characters, the middle panel presents the YOLOv4-s model recognition results, and the right panel presents the key information extracted.  
To verify the effectiveness of the proposed YOLOv4-s model in recognizing printed receipt characters, 140 receipts were used for testing. A total of 5452 numbers and English letters were used. At the same time, YOLOv3 and YOLOv5 models are also used to compare recognition performance. As illustrated in Figure 20, the recognition accuracy of the proposed YOLOv4-s model was higher than that of the traditional YOLOv4 model by 20.57%. Compared with YOLOv3 and YOLOv5, the proposed YOLOv4-s method is also higher than 27.9% and 7.12% in recognition performance, respectively. The recognition accuracy of the proposed YOLOv4-s is 99.39%, and only 33 characters were misjudged. Although the proposed model has the lowest FPS score, it can still obtain close to 60 FPS.  
Figure 19: Recognition results of the printed receipts.  
Figure 18: CNN recognition results.  
That is to say, the proposed model has real-time recognition performance and is competitive. The definition of accuracy and error were shown as follows:  
\[Accuracy=\left(1-\frac{E_{c}}{T_{c}}\right)\times 100\% \tag{12}\]  
\[Error=\frac{E_{c}}{T_{c}}\times 100\% \tag{13}\]  
where the accuracy represents the accuracy of model identification, the \(T_{c}\) and \(E_{c}\) are the total characters and identify wrong characters in testing dataset, the error represents the error rate of model identification.
# A Reinforcement Learning Based Intercell Interference Coordination in LTE Networks
## 5 The Proposed ICIC Scheme
### User Selection Step  
Users selection step is responsible for selecting users that are eligible for RBs allocation. Users selection is done per cell.  
Depending on the number of users, the number of possible combinations to have for allocating resource blocks can be very high.  
It does not make sense to allocate resource to users if there are no packets to be sent to them. Packets should be sent within acceptable delay, otherwise they will be discarded. The selection process then considers buffer status (transmission and retransmission queues) and head of line delay so that only users having packets to receive will be selected. Users are selected in two phases to reduce the state space for the resource allocation step.  
In the first phase, users are sorted according to the buffer status. Since a resource block should be allocated to users that may effectively use it in order to not waste it, only users that have data to receive are eligible to be selected. Both transmission queues and retransmission queues are considered. Two lists are obtained. The first list contains users having data in their transmission queue and the second list contains users having data in their retransmission queue. The two lists are then combined to make one list.  
In the second step,  
```
1 Identify users with packets in their transmission buffer
2 Identify users with packets in their retransmission buffer
3 for each user having packet in retransmission bufferdo
4 for each packet in retransmission bufferdo
5 if maximum retransmission attempt is reached for given packetthen
6 remove packet from user's retransmission buffer
7
8 end if
9 ifuser is present in both liststhen
10 packets in retransmission buffer will be transmitted over allocated RBs
11 else
12 packets in transmission buffer will be transmitted over allocated RBs
13
14 end if
15
16 end for
17
18 Sort these users according to their head of line delay (HoL)
19 Select users with older HoL
```
**Algorithm 1**Users selection
## 2 Materials and Methods
### Linkage Application  
As described in the introduction section, the problem of long bellow lengths exists in bellow valves to produce the required disc motion. Our goal was to minimize the length of the bellow required by using the mechanism synthesized to generate the same output motion at the disc. Rotation of a hand wheel generated the vertical motion in the valve stem, which was then attached to the disc. For sealing, one end of the bellow was attached to the bonnet and other to the stem. When the stem or disc moved upwards or downwards, the same amount of motion was also generated in the bellow. A much longer length of bellow was required, depending on the elastic limit of the material, to produce that motion.  
Linkage was synthesized using type synthesis after modifications in the Stephenson III six-bar linkage. The linkage created formed three slider joints at points D, G, and F, allowing straight line motion only. When input motion was applied at point D, it moved along in the Y direction, causing the link DGH to rotate at point G and also to move joint G along the X direction. This movement will also create the same kind of motion in link HF, causing it to rotate and slide at joint F in X direction, thus producing a magnified motion at point F. The magnitude of the motion produced at joint F can be increased by adding more links between link DGH and link HF with the constraints to produce the same motion as of link DGH. The synthesized linkage is shown in Figure 2.  
For this study, a commercially available bellow globe valve of size DN 25 was used. Bellow length in the valve was 305 mm, which made the height of the valve 380 mm long. To fully open and close the valve, the disc had to cover the displacement of 25 mm. Using the linkage synthesized above, a mechanism was designed for the valve that reduced the input displacement required by keeping the output constant. Reduced input will in turn reduce the length of bellow required for the valve. For this purpose, Autodesk Inventor 2017 was used to model the mechanism for opening and closing the valve.  
Figure 3 shows the assembly of the designed mechanism in a globe valve. The output link of the mechanism was attached to the disc and the input link was attached to the bellow. By rotating the handle wheel, the rotary motion of the hand wheel was converted into the translatory motion of a rod by screw threads. The other end of this rod was attached to the mechanism and acted as the input link. The translatory motion produced in this rod was converted into larger displacement at the disc through the mechanism. A bellow was attached to the valve with one end connected to the rod and  
Figure 2: Synthesized linkage.  
the other to the valve bonnet to provide sealing. The attached bellow compressed with the translatory motion of the valve while the valve was closing, and came back to its natural position while opening. Due to the small displacement of the input rod, the length of the bellow was significantly reduced.
## 4 Numerical Simulation of Tunnel Excavation
### Influence of the Anisotropy Parameters
For the considered horizontally foliated rock mass \(\beta=0^{\circ}\), due to the transversely isotropic linear elastic material behavior, the magnitude of the principal normal stresses has the most unfavorable effect in the horizontal and vertical planes. For this reason, at the top, bottom and sidewalls of the tunnel the damage variable and the displacement magnitude exhibit maximum values. In the present case of a two-dimensional analysis, \(\delta\) represents a scale factor of the shear strength \(f_{\rm sh}^{(12)}\) in planes perpendicular to the foliation planes. Thus, by comparison of the results for \(\delta=0.75\) (cf. Figure 11C,D) and \(\delta=1.25\) (cf. Figure 11A,B), it can be seen that due to the higher shear strength predicted for \(\delta=0.75\) compared to \(\delta=1.25\), the highly damaged zones become smaller and, hence, the displacement magnitude is reduced significantly by a factor of \(0.37\). Figure 10: Distribution of the damage variable \(\omega\) (**left—A,C**) and the displacement magnitude (**right—B,D**) (displacement scale factor 3) in the final stage of the tunnel simulation using the **transversely isotropic** RDP model with values of \(\gamma=1.25\) (**top—A,B**) and \(0.75\) (**bottom—C,D**) and a constant value \(\delta=1\). By comparing the results obtained by the isotropic RDP model, shown in Figure 9, and by the TI-RDP model, shown in Figures 10 and 11, the influence of the anisotropy parameters can be summarized as follows: For a horizontally layered rock mass, depending on the ratio between the uniaxial compressive strength parallel and perpendicular to the foliation planes, failure at either the side walls or at the tunnel crown and invert is predicted. This is in contrast to the simulations with the isotropic RDP model, where several highly damaged zones are distributed around the tunnel perimeter. Furthermore, it was shown that a smaller shear strength influences the mechanical behavior significantly in the highly damaged zones due to larger shear deformations as well as in zones where the predominant loading direction is inclined to the foliation planes.
# An Advanced DEM-FEM Method for Herringbone Gear in Shot Peening
## 4 Results and Discussion
### Effect of Shot Process Parameters on Surface Integrity Parameters  
#### 4.3.1 Effect of Shot Diameter  
The surface topography at the pitch circle for different shot diameters is shown in Figure 23, with an initial shot velocity of 60 m/s. The study investigated the surface integrity parameters under three types of shots: S110, S170, and S230, with diameters of 0.36 mm, 0.50 mm, and 0.70 mm, respectively. As the shot diameter increased from 0.36 mm to 0.70 mm, the initial grinding topography was completely covered, and the pit size gradually increased. Consequently, the surface roughness Sa also increased with the shot diameter. After the impacts of S110, S170, and S230 shots, the surface roughness Sa was 0.523 \(\upmu\)m, 0.721 \(\upmu\)m, and 0.874 \(\upmu\)m, respectively. This indicates that as the shot diameter increases, the surface quality continues to deteriorate.  
Figure 23: Effect of shot diameter on surface topography. (**a**) S110, (**b**) S170, (**c**) S230.  
Figure 22: Residual stress of different regions of the tooth surface.  
Figure 24 shows the influence of shot diameter on residual stress. As the shot diameter increased from 0.36 mm to 0.50 mm, the surface residual stress on the gear decreased from \(-\)850.41 MPa to \(-\)759.24 MPa. Further increasing the shot diameter from 0.50 mm to 0.70 mm resulted in a further decrease in surface residual stress from \(-\)759.24 MPa to \(-\)573.21 MPa. Thus, the surface residual stress decreased with an increase in shot diameter. When the shot diameter increased from 0.36 mm to 0.50 mm, the maximum residual stress decreased from \(-\)1350 MPa to \(-\)1307.36 MPa. However, as the shot diameter increased from 0.50 mm to 0.70 mm, the surface residual stress increased from \(-\)1307.36 MPa to \(-\)1317.16 MPa, showing no clear trend in the maximum residual compressive stress with increasing shot diameter. The depths at which the maximum residual compressive stresses occurred were 32 \(\upmu\)m, 45 \(\upmu\)m, and 60 \(\upmu\)m for shot diameters of 0.36 mm, 0.50 mm, and 0.70 mm, respectively, indicating a significant impact of shot diameter on the location of the maximum residual compressive stress. As the shot diameter increased from 0.36 mm to 0.50 mm, the effective depth of residual compressive stress influence increased from 70 \(\upmu\)m to 100 \(\upmu\)m, an increase of 42.9%. Further increasing the shot diameter from 0.50 mm to 0.70 mm resulted in an increase in the effective depth of residual stress influence from 100 \(\upmu\)m to 160 \(\upmu\)m, an increase of 60%. This suggests that an increase in shot diameter significantly enhances the depth of residual stress influence.
# Advancing C-V2X for Level 5 Autonomous Driving from the Perspective of 3 GPP Standards
## 4 4G and NSA-Based 5G V2X Services
### System Support
#### 4.1.4 Security Aspects Support
Furthermore, the specification lists many key issues and their potential solutions. Some of the important key issues are security for eV2X unicast and broadcast messages over PC5, privacy protection for unicast, broadcast, and groupcast messages over PC5, security for setting up groupcast, security of the UE service authorization and revocation, etc. Apart from the solutions provided for the above-mentioned key issues, some other important solutions are against V2X UE tracking based on PC5 identifiers, solution for the activation of user plane security in NR PC5 unicast, PC5 layer key derivation using the 5G network keys, solution on minimizing the impact of privacy protection mechanism in the application layer communication, etc. Each of the solutions mentioned here and from the technical specification (_TS 33.836 Rel-16_) contains complete solution details and evaluations. Obviously, when the security aspects for aV2X services based on 4G & 5G technologies will purely follow the 5G architectures, a number of issues are expected.
# RIS-Assisted Network Slicing Resource Optimization Algorithm for Coexistence of eMBB and URLLC
## 4 Problem Conversion and Solution  
### Solution of eMBB Allocation Problem  
Since there are three optimization variables in problem \(P1\), i.e., \(P\), \(\Phi\), and \(x_{eb}\), the optimal global solution cannot be found directly. A two-stage alternating iterative eMBB allocation algorithm is proposed.
# Dynamic Allocation of SDN Controllers in NFV-Based MEC for the Internet of Vehicles
## 5 Experimental Analysis
### Management Complexity  
As more controllers are required, the more complex the environment management becomes. For example, considering the scenario with 15,000 RSUs, a huge amount of flows will require synchronization among controllers, which may cause packet flooding and, consequently, the degradation of applications and network performance. Therefore, the \(\beta\) parameter plays a crucial role in avoiding an unnecessary allocation of SDN controllers and an increase of such complexity and synchronization overhead.  
It is worth mentioning that the increased number of controllers does not necessarily related to an increase in wasted resources as evidenced by the result presented by the model for 150 RSUs, where there was a reduction in the number of controllers compared to the experiment with 100 RSUs. This behavior is the result of the number of templates providing different capacities to the controllers as shown in Figure 7.  
For the high-density scenario evaluation shown in Figure 8, the number of controllers changed significantly with the scale increase. However, the heuristic is more efficient, kept the percentage of wasted resources low, adopting templates with higher capacities, and higher values for \(\beta\), consequently, the number of controllers will reduce.
## 3 Results and Discussion
### Morphology and Structure of Anaerobic Biofilm Development  
The biofilm in an anaerobic bioreactor comprises a complex microbial community. To observe the morphology and structures of the anaerobic biofilm, representative anaerobic biofilm samples (15, 90, 180, 270 and 340 days) were taken from the system for SEM analysis. Images of SEM revealed that the wool felt carrier had a highly porous and rough surface structure that played important roles in the anaerobic biofilm formation (Figure 7). Various types of microbes initially began to develop on the surface of the wool felt carrier primarily by Van der Waals and electrostatic forces (Figure 7a) [38; 39]. From the anaerobic biofilm, cocci, bacillus, and filamentous bacteria were observed on the wool felt carrier. The anaerobic biofilm gradually became thicker (Figure 7c,e,g). A predominance of bacillus and filamentous bacteria deep into the biofilm matrix could be noticed. In addition, the filament-shaped _Methanosaeta-like_ structures, long rod-shaped _Methanobacterium-like_ structures, and _Methanolinea-like_ structures were observed by SEM (Figure 7b,d,f,h,j), which were also observed in previous studies [31; 40]. SEM observations were further confirmed by Illumina MiSeq sequencing analysis.  
Figure 7: SEM of biofilm on the wool felt in the reactor: (**a**,**b**): start-up on day 15; (**c**,**d**): spring on day 90; (**e**,**f**): summer on day 180; (**g**,**h**): autumn on day 270; (**i**,**j**): winter on day 340.
# Numerical Algorithm for Dynamic Impedance of Bridge Pile-Group Foundation and Its Validation
## 2 Project-Oriented Numerical Algorithm for Impedance of Pile-Group Foundation
### Boundary Conditions  
To establish the FE model of the cap-pile group-surrounding soil system, it is necessary to extract a finite domain from the unbounded soil medium. In order to accurately model the originally continuous medium after extraction, the propagation characteristics of the soil should be kept unchanged, which necessitates an artificial boundary capable of absorbing the energy of the scattering waves [35]. For this purpose, the modified viscous-spring artificial boundary proposed by [PERSON] et al. [36; 37; 38] was adopted, since it not only satisfies the above criterion, but also has the advantages of high accuracy, adequate stability, and convenient application. The three-dimensional sketch of the adopted artificial boundary is shown in Figure 2.  
Figure 1: FE model of the cap–pile group–surrounding soil system.  
The parameters of the springs and dampers that constitute the artificial boundary can be obtained by the following equations:  
\[\text{in normal direction }K_{\text{N}}=\frac{1}{1+\alpha}\cdot\frac{ \lambda+2G}{r}C_{\text{N}}=\beta\rho c_{\text{p}} \tag{6}\] \[\text{in tangential direction }K_{\text{T}}=\frac{1}{1+\alpha}\cdot \frac{G}{r}C_{\text{T}}=\beta c_{\text{s}} \tag{7}\]  
where \(G\), \(\rho\), and \(\lambda\) are the shear modulus, mass density, and Lame constant of the soil, respectively; \(c_{\text{p}}\) and \(c_{\text{s}}\) are the velocity of P-wave and S-wave, respectively; \(r\) is the distance between the point load and the boundary; and \(\alpha\) and \(\beta\) are coefficients, the recommended empirical values of which are 0.8 and 1.1, respectively.  
To simulate the springs and dampers of the artificial boundary, the combin14 element with one end fixed is used in the ANSYS model; then, the FE model used for calculating the impedance functions of pile-group foundations is completed.