esa-sceva/satcom-chunk-collection huggingface dataset:
# Failure Mode Analysis and Dynamic Response of a Coal Mine Refuge Chamber with a Gas Explosion
## The Finite Element Model
### Model Description  
The finite element model of the actual refuge chamber is established as shown in Figure 1. The refuge chamber model consists of 20 units. The intermediate section of the unit is composed of the steel plate and the edge of the unit consists of a flange. These units form a whole refuge chamber through bolting the flange and welding stiffeners together. Due to each part of the refuge chamber suffering from different blast loads, the bulkhead of the refuge chamber model is made of ordinary Q235-B steel, and the front door and stiffeners are made of Q345-B. Q460-B is used as material for other parts. In order to obtain different failure modes, different design parameters of the refuge chamber are used, as shown in Table 1.  
Figure 2 shows that the underground mine tunnel is simulated as a semi-enclosed pipe model. One end of the tunnel is closed and the other is open. The tunnel is composed of two main parts. It includes the methane/air mixture zone and an air zone as Part 1 (from A to B), and the front section of the refuge chamber to the exit of the tunnel as Part 2 (from B to C). The length of the methane/air mixture zone is 28 m. This area is full of a gas/air mixture as the explosion source. The adjacent air zone is made for explosion shockwave propagation. The explosion shockwaves pass through Part 1 and finally affect the refuge chamber. The bottom section of the actual refuge chamber is fixed to the ground. It is supposed that there is no initial velocity of the refuge chamber. In order to simplify the calculation, the tunnel wall is simulated as smooth and rigid.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Failure Mode** & **Specimen** & **Front Door** & **Front Door** & **Forehand Plate** & **Connecting** \\  & **Number** & **Stiffeners** & **Thickness (mm)** & **Thickness (mm)** & **Element** \\ \hline \hline \multirow{8}{*}{Failure mode I} & A1 & & 10 & & \\  & A2 & Yes & 15 & 20 & No \\ \cline{1-1}  & A3 & & 20 & & \\ \cline{1-1}  & B1 & & 10 & & \\ \cline{1-1}  & B2 & No & 15 & 20 & No \\ \cline{1-1}  & B3 & & 20 & & \\ \cline{1-1}  & C1 & & & 10 & \\ \cline{1-1}  & C2 & Yes & 20 & 15 & No \\ \cline{1-1}  & C3 & & & 20 & \\ \cline{1-1}  & D1 & & & 10 & \\ \cline{1-1}  & D2 & No & 20 & 15 & No \\ \cline{1-1}  & D3 & & & 20 & \\ \hline Failure mode II & E & Yes & 20 & 20 & Yes \\ \hline \hline \end{tabular} Specim A3 is the same as specimen C3 and specimen B3 is the same as Specim D3.  
\end{table}
Table 1: Design parameters used in the refuge chamber.  
Figure 1: Schematic diagram of the refuge chamber model.  
Figure 2: The cross-section and longitudinal section area of the tunnel. (**a**) Cross-section of the tunnel; (**b**) Longitudinal section area of the tunnel.
## Appendix A Examinations of Smaller-Sized Cylinders and Multiple Cylinders
### Effect of a Single Cylinder or Multiple Cylinders  
The effect of the single or multiple cylinders is considered by examining the results of the single GC and multiple GCs plotted in Figure 18. While the plots with black and blue dots indicate the results of a GC with a diameter at 2.0 mm or 0.5 mm, the plot with orange dots indicates the results of the three GCs. Comparing these, the result of the three cylinders exhibits systematic deviation in the negative velocity direction, meaning that a smaller velocity is obtained than in the single cylinder case. This is explained with the difference in the position at which PA signals are generated in each of them, as shown in Figure A1. The PA signal 1 (marked in the bottom right) generated by cylinder 1 is the closest to the UST, while the PA signal 3 generated by cylinder 3 is the farthest from the UST. In the case of the positive direction, a smaller velocity was consistently estimated compared to those of the single cylinder cases. In the case of cylinder 1 in the positive direction, the time shift between the two PA signals (red and blue) was relatively large, indicating that cylinder 1 moved closer to the UST at a relatively fast velocity (about 60 mm/s). In contrast, focusing on PA signal 3, the time shift between the two PA signals was smaller than that of the PA signal 1; cylinder 3 moved closer to the UST (about 40 mm/s). This is attributed to the fact that cylinder 1 was located closer to the UST, while cylinder 3 was far from the UST in comparison. The result is shifted in the positive direction when the cylinders are close to the UST. Accordingly, when the cylinders are far from the UST, as with cylinder 3, the result is shifted in the negative direction. In the present configuration, the PA signal from cylinder 3 is largest and the result of CC is biased towards cylinder 3. Consequently, the result of the three cylinders is biased to cylinder 3. From these results, the effect of the angle was found to be crucial for the measurement. When the size of the object is downsized or the number of objects is increased, the angle of the UST and the distance to the object need to be adjusted carefully. When the probe and objects are set far from each other, the effect of the angle change during movement is estimated to be small, but the signal intensity is reduced, resulting in a deterioration of the SNR. Consequently, it is necessary to extract only the signal of interest with relatively high intensity by minimizing the irradiation spot or by using a focused UST probe.
# Improved Visual SLAM Algorithm Based on Dynamic Scenes
###### Abstract  
This work presents a novel RGB-D dynamic simultaneous localization and mapping (SLAM) method that improves accuracy, stability, and efficiency of localization while relying on deep learning in a dynamic environment, in contrast to traditional static scene-based visual SLAM methods. Based on the classic framework of traditional visual SLAM, we propose a method that replaces the traditional feature extraction method with a convolutional neural network approach, aiming to enhance the accuracy of feature extraction and localization, as well as to improve the algorithm's ability to capture and represent the characteristics of the entire scene. Subsequently, the semantic segmentation thread was utilized in a target detection network combined with geometric methods to identify potential dynamic areas in the image and generate masks for dynamic objects. Finally, the standard deviation of the depth information of potential dynamic points was calculated to identify true dynamic feature points, to guarantee that static feature points were used for position estimation. We performed experiments based on the public datasets to validate the feasibility of the proposed algorithm. The experimental results indicate that the improved SLAM algorithm, which boasts a reduction in absolute trajectory error (ATE) by approximately 97% compared to traditional static visual SLAM and about 20% compared to traditional dynamic visual SLAM, also exhibited a 68% decrease in computation time compared to well-known dynamic visual SLAM, thereby possessing absolute advantages in both positioning accuracy and operational efficiency.  
visual SLAM; dynamic environment; feature extraction; object detection +
Footnote †: journal: Applied sciences
## The Preparation of H13 Steel for TBM Cutter and the Performance Test Close to Working Condition
###### Abstract  
The material of disc cutters is important to full-face tunnel boring machines (TBM). In recent years, disc cutters were optimized and tested by many scholars all around the world. H13(4 Cr5 MoSiV1) steel is widely used due to its excellent properties, especially in TBM disc cutters. In this paper, H13 steel with optimized composition was prepared and heat treatment. The high temperature compression of H13 steel was conducted at the temperatures ranging from 100 \({}^{\circ}\)C to 700 \({}^{\circ}\)C, with strain rate at 0.01 s\({}^{-1}\). The stress-strain curves, Rockwell hardness and microstructure of H13 steel after compression were obtained and analyzed. The results showed that the compression strength and hardness decreased as the temperature increased; and the compression strength, hardness and ductility decreased rapidly between 600 \({}^{\circ}\)C and 700 \({}^{\circ}\)C, HR\({}_{700}\) (the hardness of H13 steel at 700 \({}^{\circ}\)C) only reached 33.23 HRC. It is not recommended for TBM disc cutters to work in an environment over 600 \({}^{\circ}\)C.  
H13 steel; TBM disc cutter; high temperature compression; hardness +
Footnote †: journal: Applied Sciences
## 2 Literature Review
### CRYSTALS-Kyber
The process involves generating a random key \(\hat{K}\) and a nonce \(r\). The Kyber CPA encryption algorithm is then applied to generate the ciphertext \((\mathbf{u},v)\) using the public key and message. The shared key \(K\) is derived from hashing \(\hat{K}\) and the hash of the ciphertext. ```
\(m\leftarrow\{0,1\}^{256}\) \((\hat{K},r):=\text{G}(\text{H}(pk),m)\) \((\mathbf{u},v):=\text{Kyber.CPA.Enc}((\mathbf{t},\rho),m;r)\) \(c:=(\mathbf{u},v)\) \(K:=\text{H}(\hat{K},\text{H}(c))\) return\((c,K)\)
```  
**Algorithm 2** Kyber.CCAKEM.Enc(pk) [35]. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Security Level** & \(n\) & \(k\) & \(q\) & \(\eta\) & \(\mathbf{d_{u}}\) & \(\mathbf{d_{v}}\) & \(\delta\) \\ \hline Kyber 512 & 256 & 2 & 3329 & 2 & 10 & 4 & \(2^{-139}\) \\ Kyber 768 & 256 & 3 & 3329 & 2 & 10 & 4 & \(2^{-164}\) \\ Kyber 1024 & 256 & 4 & 3329 & 2 & 11 & 5 & \(2^{-174}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Parameter sets for CRYSTALS-Kyber. **Key exchange verification:** In the Kyber key exchange verification process, upon receiving a ciphertext \((\mathbf{u}^{\prime},\mathbf{\
u}^{\prime})\), the decryption takes place using the secret key \(\mathbf{s}\). Subsequently, the shared key \(\hat{\ell}^{\prime}\) and nonce \(r^{\prime}\) are recalculated in a manner analogous to the encryption procedure. To ensure the integrity of the received ciphertext, the Kyber CPA encryption algorithm is once again applied, regenerating a new ciphertext \((\mathbf{u}^{\prime},\mathbf{\
u}^{\prime})\). The comparison between the regenerated and received ciphertexts is pivotal: if they match, the shared key \(K\) is computed by hashing \(\hat{\ell}^{\prime}\) and the hash of the received ciphertext; conversely, if a mismatch occurs, an alternative key is derived by hashing a predetermined placeholder value \(z\) along with the hash of the received ciphertext. This multilayered verification mechanism solidifies the security and reliability of the Kyber key exchange protocol. ```
\(m^{\prime}:=\) Kyber.CPA.Dec\((\mathbf{s},(\mathbf{u},\mathbf{\
u}))\) \((\hat{\ell}^{\prime},\mathbf{\
u}^{\prime}):=\) G\((\mathrm{H}(pk),m^{\prime})\) \((\mathbf{u}^{\prime},\mathbf{\
u}^{\prime}):=\) Kyber.CPA.Enc\((\mathbf{t},\rho),m^{\prime};r^{\prime})\) if\((\mathbf{u}^{\prime},\mathbf{\
u}^{\prime})=(\mathbf{u},\mathbf{\
u})\)then return\(K:=\mathrm{H}(\hat{K}^{\prime},\mathrm{H}(c))\) else return\(K:=\mathrm{H}(z,\mathrm{H}(c))\) endif
```  
**Algorithm 3** Crystal-Kyber key exchange verification [35].
## 5 Methodology
### Sobol Sensitivity Analysis Method  
The Sobol method [57] is a global sensitivity analysis technique that quantifies the influence of each input variable and their interactions on a model's output. Based on variance decomposition, it assigns indices that measure both individual (first-order) and combined (second-order and total) effects. First-order sensitivity indices (S1s) measure the effect of each variable individually, while keeping all other variables constant. Total-order sensitivity indices (STs) measure the total effect of each variable on the output, including both its individual effect and its interactions with other variables. In this way, higher-order effects can be obtained by calculating the difference between both indices (ST-S1). The Sobol method is particularly useful for nonlinear models or those with multiple parameters, as it provides a comprehensive understanding of the system's behaviour. Widely used in scientific simulations and engineering, it helps identify the most influential variables and can assist in reducing model complexity.
# Tolerance Modeling and Analysis Considering Form Defects for Spaceborne Array Antenna
## Appendix A  
The performance of a phased array antenna is described by the far field pattern [26], which is defined by Equation (A1).  
\[F(\theta,\varphi)=\sum_{i=1}^{Nx}\sum_{j=1}^{Ny}a_{ij}\exp\left\{J.\frac{2\pi} {\lambda}.\left[(u-u_{0})\cdot ex_{ij}+(v-v_{0})\cdot ey_{ij}+\cos\theta\cdot ez _{ij}\right]\right\}\] (A1)  
where function \(F\) is the far-field pattern; coefficient \(a_{ij}\), the element weight (amplitude and phase); \(J=\sqrt{-1}\), the imaginary unit; and \(\lambda\), the wavelength of the electromagnetic wave.  
The position of the element with geometric error is described by coordinates \(ex_{ij}\), \(ey_{ij}\), and \(ez_{ij}\), and the far field point is located at \((r,\theta,\varphi)\), as shown in Figure A1. The scanning angle is set to \((\theta_{0},\varphi_{0})=(0,0)\), which leads to \(u=\cos\varphi\sin\theta,v=\sin\varphi\sin\theta\), and \(u_{0}=0,v_{0}=0\).  
The maximum power density \(P_{max}\) is one of the most important performance parameters, and it is determined by the z-directional position of the elements [27]. Here we ignored the beam direction angle error since it is very small.  
\[P_{max}=P(0,0)=F(0,0)\cdot F^{*}(0,0) \tag{10}\]  
\[F(0,0)=\sum_{i=1}^{Nx}\sum_{j=1}^{Ny}a_{ij}\exp\left\{J\frac{2\pi}{\lambda}\cdot ez _{ij}\right\} \tag{11}\]
## 4 Simulation Results
### Test Parameters  
The test cases and the associated results are presented below. If not otherwise stated, the following configuration is used:  
* Transmission lines: \(y_{0}\) = 0.25 m, \(I\) = 700 A
* Two sensors aligned with \(L_{u}\). Their positions in \(L_{u}\) are: \[\vec{p}_{1}=(0.05m,0.2m,-0.05)^{T},\quad\vec{p}_{2}=(0.05m,-0.2m,-0.05)^{T}.\]
* Two sensors rotated by \(90^{\circ}\). Their positions in \(L_{u}\) are: \[\vec{p}_{3}=(0.05m,0.2m,-0.25)^{T},\quad\vec{p}_{4}=(0.05m,-0.2m,-0.25)^{T}.\]  
In the previous publication [25], it was shown that the electric motor of a small 1:5 scale electric toy car can produce stray magnetic fields of several uT; especially when the vehicle accelerates. The reason for this is that inadequate magnetic shielding measures are often taken in technical installations. The supply lines of electronic components with high power requirements can therefore emit strong magnetic interference fields. Since the UAV was also equipped with several electric motors, the test cases were performed with different standard deviations \(\sigma\) of the white noise. A value of \(1\,\upmu T\) was assumed for the case of good shielding and \(8\,\upmu T\) for poorly shielded electric motors. These were realistic values based on the measurements in [25] and took into account that the UAV may have multiple electric motors.
## 4 Parallel Performance and Application in Actual Scenarios
### Experiment on the Application of LICOM in a Real Scenario  
Moreover, we conducted a set of application tests. LICOM was used with 10 km \(\times\) 10 km as the resolution. CORE-II was employed as a forcing field. The simulation period was from 1993 to 2007. The output included temperature, salinity, sea surface height, and the 3D current field. Figure 12 shows the abnormal sea surface height value (difference from the average value) on 31 December 2007. As can be seen, an eddy was apparent. Additionally, there was a special difference. The middle image in Figure 12 shows the average number of eddies on every grid point. West and east boundary eddies often occurred in the southern ocean. There were at least 50 eddies that occurred in some areas. Therewas a considerable difference between the structures of the eddies of anti-cyclones and cyclones, as shown in the bottom image in Figure 12. An anticyclone eddy  
Figure 9: Speedup of LICOM on Tianhe II.  
had a sunken center with a temperature increment of 2\({}^{\circ}\), while a cyclone eddy had a raised center with a temperature decline of 2\({}^{\circ}\).  
Figure 11: Speedup of LICOM on Tianhe III.  
Figure 10: Elapsed time and speed of LICOM on Tianhe III.  
In addition, we compared the results produced by the original code and the fully optimized code to see whether there were any differences. Figure 13 shows the results of the sea surface temperature and the differences between the two editions.  
Figure 12: Output of the LICOM application.
## 3 Regulatory Mechanisms Controlling AHL Synthesis in _Sinorhizobium meliloti_
### Transcriptional Mechanisms
#### 3.1.3 Modulation of Quorum Sensing by Cyclic Diguanylate  
QS and cyclic diguanylate (cdG)-dependent regulation are interconnected in several bacterial species. The most common mechanism is regulation of cdG biosynthesis or degradation by QS lowering cdG levels in the QS state [55]. In _S. meliloti_, QS does not seem to influence the total cdG content. In contrast, both native and elevated levels of cdG negatively affect expression of the AHL synthase gene _sint_ and accumulation of AHLs in the growth medium [56]. The factor mediating cdG-dependent negative regulation of the synthase gene is unknown. Since the cdG content was higher in exponential than in stationary-phase _S. meliloti_ cells [56], negative regulation of AHL biosynthesis by cdG may serve as a fine-tuning mechanism attenuating AHL accumulation in rapidly growing cells.
# Numerical Study of Endwall Modification with Micro-Scale Ribs in a Turbine Cascade
## 1 Introduction
### Mesh Generation and Grid Independence Analysis  
Due to the rib, the grid of the ribbed endwall needs local mesh encryption to simulate the near-wall flow. The detailed mesh topology is shown in Figure 5. The grid was generated using Pointwise. To meet the requirement of turbulence model, the first layer height of boundary layer was 0.001 mm and thus y\({}^{+}\) was defined as less than 1, which is suitable for SST \(k\)-\(\omega\).  
The sensitivity analysis of grid was conducted using the GCI [22]. Three types of grids (coarse with 23,989,236 cells, moderate with 35,531,716 cells, and fine with 66,834,042 cells) were adopted. Details of GCI are presented in Table 2. The area-averaged \(\eta\) was selected as target variable. After the calculation, the extrapolation value was 0.155815, which presented the deviation values of 0.14% and 1.93% for fine grid and moderate grid, respectively. The GCI value of fine and moderate grid was 0.18% and 3.17%, respectively, indicating that these two grids could be used for calculation. However, the number of grids in the ribbed endwalls was relatively high. Therefore, to balance the computational accuracy and the time costs, the moderate grid was adopted for the following calculation, which was suitable.
## 3 Performance Optimization Using Genetic Algorithm
### Performance Comparison  
This section describes a comparison between the optimal solutions under the above three different base excitations, which would prove the effectiveness of the performance optimization of the BI-DSVI. The iteration curves and the vibration transmissibility comparison before and after optimization for the three optimization cases are presented in Figures 3-5. The design parameters before optimization are taken as \(l_{d}=0.2\) m, \(k_{d}=6000\) N/m, \(\varphi_{d0}=\pi/4,a=0.25\) m, \(b=0.2\) m.  
Figure 3: (**a**) The iteration curve and (**b**) the vibration transmissibility under optimal design parameters and basic design parameters, when \(\phi_{01}=\phi_{02}=\phi_{03}=\phi_{04}=0\).  
Figure 2: The flowchart of the GA optimization method.  
As shown in Figure 3b, the BI-DSVI system exhibits two resonance peaks in the frequency range of 2-12 Hz. Through the optimized design, the resonance peaks are significantly reduced and the resonance frequencies are shifted to the left, which greatly broadens the vibration isolation bandwidth. The other two cases show consistent patterns. However, Figures 4b and 5b present another resonance peak between 2 and 4 Hz without optimization, which is probably due to the rotational motion of the base. Through parameter optimization, the extra resonance peak is suppressed below 0 dB, which means that complete vibration isolation is realized. In the three cases, the maximum transmissibility \(T\) without optimization is 30.97 dB, 22.10 dB, and 16.50 dB. At the same frequency locations, the transmissibility with optimization is \(-3.16\) dB, \(-36.16\) dB, and \(-46.96\) dB. In other words, the vibrations are attenuated by 34.13 dB, 58.26 dB, 63.46 dB, respectively. Thus, the vibration isolation performance of the BI-DSVI can be greatly improved by multi-parameter optimization.  
Table 2 presents the optimal design parameter values of the three different base excitations. For the three cases, the optimal structural parameters of the bio-inspired isolators are the same, and only the placement position parameters are different. Hence, there are optimal structural parameters of the bio-inspired isolator in a reasonable range (for instance, the stiffness \(k_{d}\) has a lower bound to ensure the basic loading capacity), which achieves the best vibration isolation performance. In other words, for different base  
excitations, it is only necessary to optimize the placement positions of the isolators to obtain the optimal vibration transmissibility distribution in a frequency band.
_Appl. Sci._**2015**, \(5\), 1196-1210; doi:10.3390/app5041196  
_applied sciences_  
ISSN 2076-3417  
www.mdpi.com/journal/applsci  
_Article_  
**Polyppropylene/Graphene and Polypropylene/Carbon Fiber**  
**Conductive Composites: Mechanical, Crystallization and Electromagnetic Properties**  
**[PERSON] 1, [PERSON] 2, [PERSON] 3, [PERSON] 4, [PERSON] 5 and [PERSON] 5,6,7,8**  
1 Department of Fiber and Composite Materials, Feng Chia University, Taichung City 40724, Taiwan; E-Mail: [EMAIL_ADDRESS]  
2 Institute of Biomedical Engineering and Materials Science, Central Taiwan University of Science and Technology, Taichung City 40601, Taiwan; E-Mail: [EMAIL_ADDRESS]  
3 Office of Physical Education and Sports Affairs, Feng Chia University, Taichung 407, Taiwan; E-Mail: [EMAIL_ADDRESS]  
4 Department of Aerospace and Systems Engineering, Feng Chia University, Taichung City 40724, Taiwan; E-Mail: [EMAIL_ADDRESS]  
5 Laboratory of Fiber Application and Manufacturing, Department of Fiber and Composite Materials, Feng Chia University, Taichung City 40724, Taiwan; E-Mail: [EMAIL_ADDRESS]  
6 School of Chinese Medicine, China Medical University, Taichung City 40402, Taiwan  
7 Department of Fashion Design, Asia University, Taichung City 41354, Taiwan  
8 Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +886-4-2451-7250 (ext. 3405); Fax: +886-4-2451-0871.  
Academic Editor: [PERSON]  
_Received: 23 September 2015 / Accepted: 30 October 2015 / Published: 13 November 2015_  
This study aims to examine the properties of composites that different carbon materials with different measurements can reinforce. Using a melt compounding method, this study combines polypropylene (PP) and graphene nano-sheets (GNs) or carbon fiber (CF) to make PP/GNs and PP/CF conductive composites, respectively. The DSC results and optical microscopic observation show that both GNs and CF enable PP to crystalize at a high temperature. The tensile modulus of PP/GNs and PP/CF conductive composites remarkably increases as a result of the increasing content of conductive fillers. The tensile strength of the PP/GNs conductive composites is inversely proportional to the loading level of GNs. Containing 20 wt% of GNs, the PP/GNs conductive composites have an optimal conductivity of 0.36 S/m and an optimal EMI SE of 13 dB. PP/CF conductive composites have an optimal conductivity of 10\({}^{-6}\) S/m when composed of no less than 3 wt% of CF, and an optimal EMI SE of 25 dB when composed of 20 wt% of CF.  
keywords: polypropylene; graphene nano-sheets; carbon fibers; conducting composites; EMIs +
Footnote †: journal: Journal of Applied Physics