esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Numerical Simulation Results
### The Longitudinal Displacement Analysis of Ground Surface  
Taking small curved tunnels with a curve radius of 350 m and straight tunnels as the research object, and taking the surface settlement state of the tunnel double-line center line when the shield machine cutter head is advanced 40 m for analysis, the curve of the small radius curve and the linear tunnel double-line center line of the surface settlement, as well as the distance from the shield machine, are shown in Figure 10.  
It can be seen from Figure 10 that during the shield excavation of the small radius curve and straight tunnel, the surface in front of the shield machine cutter head slightly uplifts, and the ground surface behind the shield machine begins to settle sharply after excavation, and finally the surface settlement slows down. This is similar to the results in reference [20].  
The curve of the surface settlement of the tunnel with a small radius curve and the distance from the shield machine shows that the surface of the shield machine has a slight subsidence from 0 m to 9 m behind the cutter head of the shield machine. In the soil layer below the surface, only a small amount of subsidence occurs on the surface. The surface subsidence of 9 m to 24 m behind the cutter head of the shield machine develops the fastest. At this time, the soil layer below the surface is supported by pipe segments and grouting. The formation loss and the softening of the grout make the surface subsidence sharply expand. The surface subsidence at this stage accounts for 53.17% of the total subsidence. After 24 m behind the cutter head of the shield machine, the surface settlement rate slows down. At this time, the grout gradually hardens to support the surrounding soil, and soil consolidation becomes the dominant factor for surface settlement at this time.  
Through the above analysis, it can be found that the ground settlement caused by shield excavation can be roughly divided into three stages: in the first stage, the ground settlement is caused by ground disturbance when the shield machine passes through; in the second stage, segment assembly and slurry softening of the ground settlement is caused by  
Figure 10: Variation curve of ground settlement and distance from the shield machine.  
formation loss; and in the third stage, the ground subsidence is caused by the consolidation of the soil after the shield machine passes.  
It can be seen from Figure 9 that the second stage is the main reason for the difference in surface settlement between the straight line and small radius curved tunnels. Due to the greater stratum loss during the excavation of the small radius curve tunnel, the surface settlement develops faster in the second stage. In view of the development characteristics of the surface settlement in the second stage, accelerating the grouting speed and reducing the time of grout hardening are effective ways to reduce surface settlement.
## 4 Results and Discussion  
### Plant Model Verification  
To verify the nonlinear model of the whole plant, two cases of perturbation transients without the control system were simulated, including reactivity perturbation in the core and flow-rate perturbation in the secondary loop. The value of 'N' is 20 in this research, which is the result of a comprehensive selection considering two factors: the simulation accuracy and real-time calculation speed. The transient response results of the established nonlinear model are compared with those of the reference model described by [PERSON] [30]. In the first case, a negative reactivity of \(-\)50 pcm is introduced at t = 20 s, and the control system is not activated during the transient. The transient responses of the reactor thermal power and system temperature parameters for uncontrolled reactivity insertion are illustrated in Figure 5. In the second case, the mass flow rate is reduced by 10% in the secondary salt loop at t = 20 s, and the control system is also not activated.  
The response results of the reactor power and temperature parameters for the uncontrolled flow-rate reduction are illustrated in Figure 6. The simulation results calculated by the nonlinear model are consistent with those of the reference model, and the maximum deviation is less than 1.8% due to the effects of slightly different model assumptions. This small deviation is mainly because of the different degrees of refinement of these two models and the difference in the numerical solution algorithm. The nonlinear distributed-parameter model adopted in this paper is a one-dimensional transient equation, which is superior to the lumped-parameter model used in the reference. The comparison results show that the developed model could be accurately used for the system dynamic simulation of the MSR plant.
# Recent Advances and Applications of Semiconductor Photocatalytic Technology
## 5 Methods for Improving Photocatalytic Efficiency
### Precious Metal Depositing
Relatively speaking, Ag is one of the most popular precious metals because it is less toxic and not expensive. [PERSON] et al. [266] applied a mild in situ electrochemical method to deposit Ag nanoparticles on the surface of TiO\({}_{2}\). A photocatalytic decomposition experiment of chlorophenol was carried out using the prepared catalyst under the condition of pH = 5. After 6 h of photocatalytic decomposition, the decomposition rate of the Ag-TiO\({}_{2}\) photocatalyst was 94%, much higher than that of the other photocatalysts. Though the silver-oxygen-titanium (Ag-O-Ti) chemical bond will reduce the catalytic activity of the material to some extent, the proper content of Ag and the oxygen vacancies on the surface of the material can effectively prevent the recombination of electron-hole pairs, thereby improving the photocatalytic efficiency of the photocatalyst.
# A Realistic, Flexible and Extendible Network Emulation Platform for Space Networks
## 4 Designs
### Network Topology
#### 4.3.1 Lower Physical Network Topology  
The lower physical network topology is the basis of the emulation system, which is constructed by the main controller, switches, and servers. For each server in the data plane, a virtual switch is implemented. Some network interfaces of the server are attached to  
Figure 10: Default queuing disciplines in TC.  
the virtual switch. To communicate with other servers, other ends of attached network interfaces are connected to a high performance SDN hardware switch in the form of Virtual Extensible LAN. In this way, a distributed server cluster is constructed in a star structure. The main controller is linked to virtual switches and SDN hardware switches, and it controls them directly. When researchers create a specific network scenario in the logical plane, the main controller, integrated with container orchestration, allocates resources and places emulation nodes at the server cluster. Emulation nodes are linked to a virtual switch at each server in a star network topology. Physical nodes and external emulation devices can also be linked to the SDN hardware switch and managed by the main controller. From top to bottom, all the switches, servers, and emulation nodes are constructed in a hierarchical star network structure. This network topology makes the platform have high scalability. If computing resources need to be expanded, more virtual node servers can be simply accessed through the SDN hardware switch.
# Malware Collusion Attack against SVM: Issues and Countermeasures
## 5 Evaluation
### Q1&Q3: Effectiveness and Performance of ColluDroid  
In order to measure the effectiveness of these methods, we first trained the classification model on the dataset mentioned above. Then, we perform three experiments to evaluate our approach. In the first experiment, we try to answer the research question one. However, due to the lack of real collusion malware, we made four malicious apps manually to make them as the ground truth. Then, we test ColluDroid (both ColluDroid-Linear SVM and ColluDroid-Sec-SVM) on our man-made apps to evaluate if it can address the collusion app set successfully. In the second and third experiments, we evaluate the performance of these four approaches under our simulated Collusion Attack and Evasion Attack. The experimental results will be detailed in the following sections.
###### Abstract
The research results indicate that, with the increase in the number of hanging sleepers, the components of the track system such as sleepers and sleeper fasteners and the subgrade parts adjacent to the hanging sleepers may be damaged prematurely and lose their bearing capacity. [PERSON] et al. [19] discovered that the variation of wheel-rail impact load is related to the size of the gap between the sleeper and the track bed. Once exceeding a critical size, the gap exerts remarkable influence on the variation of contact force. [PERSON] et al. [20] investigated the dynamic behavior of a turnout system with unsupported bearers in different locations and velocities. The results show that the load distribution of adjacent unsupported bearings is two times higher than that of the reference case in the turnout system. From the above, there have been substantial studies on the dynamic response of the unsupported track in the subgrade. However, two possible real-world scenarios of unsupported track have not been investigated, one of which is the unsupported track on the bridge, and the other is the unsupported sleepers on both sides or one side. For the three-dimensional (3-D) vehicle-track-bridge coupled dynamics, the previous studies analyzed the complex equilibrium compatibility and geometric compatibility of the wheel-rail and bridge-rail contact interface, which increases the cost of calculation and reduces the efficiency of computation [21; 22; 23; 24]. In this paper, two rails of the same track line are modelled separately, and the track line is laid on the subgrade and the bridge to solve the above issues. The 3-D train-track-bridge coupled model is established based on the virtual work principle through the relative virtual displacement of vehicle and rail, bridge and rail. The paper is organized as following:  
* In Section 2, the 3-D train-track-bridge coupled model including the support failure of the track is deduced using the virtual work principle;
* In Section 3, the computational model is validated by comparison with the analytical solution, the nonlinear contact model and the measured results;
* In Section 4, the dynamic response of the track and the bridge and the wheel-rail contact force are analyzed.
## 4 Discussion
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Zone** & **Period** & \begin{tabular}{c} **Total Burned** \\ **Surface (km\({}^{2}\))** \\ \end{tabular} & \
\begin{tabular}{c} **Average Yearly** \\ **Burned Surface (km\({}^{2}\))** \\ \end{tabular} &
\begin{tabular}{c} **Increase of Total** \\ **Burned Surface (\%)** \\ \end{tabular} \\ \hline \multirow{3}{*}{Zone 1} & 2000–2014 & 4.77 & 0.32 & - \\  & 2071–2100 RCP4.5 & 60.99 & 2.03 & 534 \\  & 2071–2100 RCP8.5 & 85.95 & 2.86 & 259 \\ \hline \multirow{3}{*}{Zone 2} & 2000–2014 & 8.81 & 0.59 & - \\  & 2071–2100 RCP4.5 & 40.66 & 1.36 & 131 \\  & 2071–2100 RCP8.5 & 54.08 & 1.80 & 205 \\ \hline \multirow{3}{*}{Zone 3} & 2000–2014 & 1.70 & 0.11 & - \\  & 2071–2100 RCP4.5 & 37.01 & 1.23 & 1018 \\  & 2071–2100 RCP8.5 & 53.46 & 1.78 & 1518 \\ \hline \multirow{3}{*}{Zone 4} & 2000–2014 & 15.75 & 1.05 & - \\  & 2071–2100 RCP4.5 & 118.23 & 3.94 & 275 \\ \cline{1-1}  & 2071–2100 RCP8.5 & 154.05 & 5.14 & 390 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Comparison of historical (2000–2014) and predicted (2071–2100, both RCP4.5 and RCP8.5 scenarios) burned surface in wildfires larger of 30,000 m\({}^{2}\). Increase of total burned surface is related to baseline period 2000–2014. in all zones reaching percentages between 60% and 70%. Likewise, 10-fold cross-validation results were in line with previous goodness-of-fit tests, leading to an overall performance of the classifying method around 65% in the region. Burned surface prediction by ANNs show a significantly increase of burned area under both climate change scenarios. In fact, the straightforward view of a warming climate affecting fire regimes is supported by scientific literature [96], finding wide agreement of projections of increased area burned [97] of up to two- to five-fold increases in annual area burned under a moderate warming scenario [98]. In the region under study, yearly average predicted burned surface depends both on the climate scenario and on the zone, and it ranges from four times (Zone 4) to 12 times (Zone 3) higher than datasets recorded for the period 2000-2014. Statistics indicate that the error in burned surface prediction is below 40%, and it is generally underestimated around 20%, which may lead to worse consequences than expected. Nevertheless, the highest risk season is shortened to late spring and summer, and an overall reduction in fire cycles is predicted, as suggested by [PERSON] et al. [99].
## 4 Verification through Prototyping and Experimentation  
### Prototype of Optimal FCPM Model  
To verify the performance improvement of the optimal FCPM model, a prototype was produced, and an experiment was conducted. Figure 11 shows the prototype of the proposed, optimal FCPM model. Figure 11a shows the stator assembly and Figure 11b shows the rotor assembly. Also Figure 11c shows the PM arrangement inside the rotor assembly. As shown in Figure 11c, the polarity inside the rotor is composed of a CP-type (four N poles). Figure 12 shows the experimental setup.  
Figure 10: Rated load analysis result after optimization: (**a**) rated torque waveforms, (**b**) flux density of basic FCPM model, (**c**) flux density of optimal FCPM model.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Item** & **Unit** & \begin{tabular}{c} **Basic** \\ **FCPM Model** \\ \end{tabular} & \
\begin{tabular}{c} **Optimal** \\ **FCPM Model** \\ \end{tabular} &
\begin{tabular}{c} **Relative** \\ **Difference** \\ \end{tabular} \\ \hline Rated speed & rpm & 7500 & 7500 & - \\ Rated torque & Nm & 1.30 & 1.29 & - \\ Torque ripple & Nm & 1.91 & 0.50 & 73.8\% \\ (peak to peak) & & 15.3 & 12.8 & - \\ Rated current & Apeak & 93.8 & 65.6 & 30.1\% \\ Copper loss & W & 28.7 & 26.5 & 77.7\% \\ Eddy-current & W & 7.1 & 6.8 & 4.2\% \\ Output & W & 1019.2 & 1010.3 & - \\ Efficiency & \% & 88.7 & 91.1 & 42.4\% \\ \hline \hline \end{tabular}
\end{table}
Table 8: Analysis results at the rated load condition.  
Figure 13 shows a rotor assembly of the prototype. As shown in Figure 13, a guide made of a non-magnetic material is inserted inside. This prevents the magnetic flux of the permanent magnet from circulating inside the rotor. The end plate is assembled on the upper and lower parts of the rotor and combines the structure of the rotor. It prevents the permanent magnet from scattering at high speed. Figure 14 shows the surface gauss value of the optimal FCPM rotor assembly. It can be seen that eight poles are formed inside the rotor.  
Figure 11: Prototype of the optimal FCPM model: (**a**) stator assembly, (**b**) rotor assembly, (**c**) the PM arrangement inside the rotor assembly.  
Figure 12: Experiment setup (test bench).  
Figure 13: Rotor assembly of the prototype.  
As shown in Table 9, the experimental results of the optimal FCPM model are similar to the analysis results. This confirms the performance improvement of the proposed model. In particular, the use of PMs was halved, compared with the basic FCPM model, the efficiency of the optimal FCPM prototype was improved by 2.9% and the torque ripple was significantly reduced by 74.9%.
## 3 Performance Analysis
### Mutual Approximation Algorithm Based on the PDF and MGF Equations
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(\sigma_{\text{D}}\) & \(s_{1}\) & \(s_{2}\) & \(m_{\text{D}}\) & \(\beta_{\text{D}}\) & **Objective Function** \\ \hline
2 & 3.2051 & 23.6809 & 4.2187 & 0.1997 & 2.0254 \\
2.5 & 2.8908 & 24.6753 & 3.4932 & 0.2339 & 1.0062 \\
2.8 & 2.8617 & 22.8037 & 3.1355 & 0.2561 & 0.6931 \\
3 & 3.0924 & 17.5526 & 2.8639 & 0.2776 & 0.6951 \\
3.3 & 3.2179 & 15.1707 & 2.5934 & 0.3015 & 0.7413 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Optimal solution for different power line fading parameters \(\sigma_{\text{D}}\). Figure 4: Comparison of PDF for instantaneous SNR with the LogN and approximated Gamma distributions using the best \(s\) value optimization method. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(m_{\text{R}}\) & \(s_{1}\) & \(s_{2}\) & \(\mu_{R}\) & \(\sigma_{R}\) & **Objective Function** \\ \hline
2 & 3.7589 & 14.7629 & \(-\)0.0852 & 0.4554 & 0.8255 \\
2.5 & 4.1872 & 21.2461 & \(-\)0.0472 & 0.4197 & 0.6104 \\
2.9 & 2.4379 & 15.7817 & \(-\)0.0416 & 0.4005 & 0.5272 \\
3.2 & 2.3628 & 20.4712 & \(-\)0.0340 & 0.3854 & 0.5281 \\
3.5 & 2.9697 & 20.3867 & \(-\)0.0178 & 0.3700 & 0.7707 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Optimal solution for different wireless line fading parameters \(m_{\text{R}}\). Similarly, in the approximation process of the Gamma distribution to the LogN distribution of Figure 5, according to \(\gamma_{\rm GR}=H_{\rm GR}^{2}\Delta_{\rm GR}\), the key parameters after the variable \(\gamma_{\rm GR}\) is approximated to \(\rm LogN(\overline{\mu_{R}},\overline{\sigma_{R}^{2}}^{\prime})\) are \(\overline{\sigma_{R}^{2}}=\sigma_{R}^{2}\) and \(\overline{\mu_{R}}=\ln\Delta_{\rm GR}+\mu_{R}\).
## 3 System Model
(Backward Iteration) Calculate the interference-plus-noise covariance matrices \(\mathbf{\bar{J}}_{k,l}\) in the reciprocal channel as: \[\mathbf{\bar{J}}_{k,l}=\sum_{j=1,j\
eq k}^{K}\mathbf{H}_{jk}^{\mathrm{H}} \mathbf{U}_{j}\mathbf{U}_{j}^{\mathrm{H}}\mathbf{H}_{jk}+\sum_{r=1,r\
eq l}^{ d}\mathbf{H}_{ik}^{\mathrm{H}}\mathbf{U}_{k,r}\mathbf{U}_{k,r}^{\mathrm{H}} \mathbf{H}_{ik}+\sigma^{2}\mathbf{I}\] Formulate the decoding matrix \(\mathbf{V}_{k}\), the \(l\)-th column of which can be represented as \[\mathbf{V}_{k,l}=\mathbf{\bar{J}}_{k,l}^{-1}\mathbf{H}_{ik}^{\mathrm{H}} \mathbf{U}_{k,l}/\parallel\mathbf{\bar{J}}_{k,l}^{-1}\mathbf{H}_{ik}^{\mathrm{ H}}\mathbf{U}_{k,l}\parallel\]
4. Repeat step (2) and (3) until the algorithm converges or the maximal iterations number is reached. ```  
**Algorithm 1** Max-SINR Algorithm
# Analysis of Dynamic Response of a Two Degrees of Freedom (2-DOF) Ball Bearing Nonlinear Model
## 2 Mathematical Model of the Rolling Element Bearing
### Nonlinear Effect--Shape Errors on Rolling Surfaces  
As a result of the grinding process, the shape errors (waviness) occur on the rolling surfaces of ball bearings in form of smaller and bigger undulations over the bearing's circumference. By the fact of rolling over the manufacturing imperfections, additional frequency peaks are induced in a wide range of operational frequencies. The amplitude of frequencies related to the waviness depends on the number of undulations and the value of waves [20; 51]. In Figure 3, the graphics showing the visual waviness profile applied in the mathematical model taking into account imperfections on the inner and outer ring. The waviness of balls is omitted as they characterize by smaller values of undulations, the fact is related to the bigger hardness of balls than rings and a longer and more precise manufacturing process (super-finish). Despite the fact that values of respective waves are much smaller than the value of clearance, they should be taken into account during the calculation of nonlinear contact in bearings. As a result of the long-term bearing operation, the depth of each wave propagates and leads to damage. The mathematical description of the raceway waviness for the inner and outer ring is the following:  
\[\left(U_{inner}\right)_{i}=U_{inner}\sin(N_{inner}\psi_{0}), \tag{4}\]  
\[\left(U_{outer}\right)_{i}=U_{outer}\sin(N_{outer}\psi_{0}), \tag{5}\]  
where \(U_{inner}\) is the amplitude of the inner raceway surface waviness, \(N_{inner}\) is the number of undulations on the inner raceway, \(U_{outer}\) is the amplitude of the outer raceway surface waviness, \(N_{outer}\) is the number of undulations on the outer raceway.
# Seismic Behavior of RC Beam Column Joints with 600 MPa High Strength Steel Bars
## 3 Experimental Results and Discussion
### Stiffness Degradation  
In order to evaluate the stiffness reduction level of the specimen, the secant stiffness (\(K\)) is defined, which is calculated by [30]:  
\[K_{i}=\left(\left|+F_{i}\right|+\left|-F_{i}\right|\right)/\left(\left|+X_{i} \right|+\left|-X_{i}\right|\right), \tag{1}\]  
where \(+F_{i}\) and \(-F_{i}\) are the positive and negative maximum loads, respectively, and \(+X_{i}\) and \(-X_{i}\) are the corresponding displacements.  
Since an asymmetrical reinforcement was used in this test, the extreme loads and displacements of each loading stage are different for upward and downward loading. Therefore, the secant stiffness in both positive and negative directions are adopted for the analysis. It is noted that the stiffness decreases continuously as the displacement levels increase, which is determined by the increasing cumulative damage in the tested specimens. The specimens experience severe stiffness degradation at the end of all tests. Figure 8 illustrates the trend of the stiffness degradation of six specimens, which show to be similar. In the elastic stage, the stiffness degradation is fast, and after yielding, it becomes smaller with the increase of displacement. The stiffness curves of QD1 and QD2, and BD1 and BD2 are very similar, and the degradation trend is slower than the one of QD3 and BD3, respectively. This phenomenon indicates that the specimen with higher reinforcement ratios has lower rates of stiffness degradation. Moreover, there is little relevance between the axial compression ratios and stiffness degradation.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Specimen** & **Loading** & **Yield Load \(f_{y}\)** & **Ultimate Load** & **Failure Load \(f_{l}\)** & \\  & **Direction** & **(kN)** & \(f_{u}\)**(kN)** & **(kN)** & \(f_{u}\)/\(f_{y}\)** \\ \hline QD1 & up & 129.82 & 147.54 & 123.90 & 1.14 \\  & down & 182.73 & 212.28 & 180.00 & 1.16 \\ QD2 & up & 120.80 & 145.84 & 124.00 & 1.20 \\  & down & 182.79 & 202.95 & 171.70 & 1.11 \\ QD3 & up & 87.00 & 108.60 & 92.30 & 1.25 \\  & down & 133.00 & 147.72 & 125.60 & 1.11 \\ BD1 & up & 182.28 & 217.54 & 184.90 & 1.19 \\  & down & 237.37 & 252.28 & 214.40 & 1.06 \\  & up & 186.00 & 205.96 & 175.10 & 1.11 \\ BD2 & down & 229.85 & 242.46 & 206.10 & 1.05 \\  & up & 74.00 & 113.20 & 96.20 & 1.53 \\ BD3 & down & 158.00 & 187.50 & 159.40 & 1.19 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Strength of six specimens in two loading directions.  
Figure 8: Stiffness degradation of the six specimens in two directions. (**a**) upward in direction; (**b**) downward in direction.