esa-sceva/satcom-chunk-collection huggingface dataset:
# FirepanIF: High Performance Host-Side Flash Cache Warm-Up Method in Cloud Computing
## 4 Evaluation
### Effect of Flash Cache Capacity  
In this section, we investigate the performance impacts of Firepan and FirepanIF with various flash cache capacities in more detail. Firepan and FirepanIF copy cache items from the source node, and the required time for copying may impact the I/O performance of the target VM. To reflect this situation, we conduct additional experiments with 5 and 20 GB capacities of flash caches. The procedure and workload of the experiment are the same as in the previous experiments.  
We provide the bandwidth results before and after the VM migration for the 5 and 20 GB flash caches in Figure 9(a),b, respectively. Please note that the scales of the x-axes differ in the figures. The results are similar to that of the previous experiment depicted in Figure 7. NoCacheMigration and Cachemior show a gradual performance increase after the VM migration. For the 20 GB flash cache, approximately more than 400 s are required to reach the highest bandwidth. Firepan and FirepanIF require 15 and 60 s, respectively, for flash cache migration, which is similar to the result in Figure 7. After these periods, the bandwidth is immediately restored to the level before the VM migration.  
Table 2 and 3 summarize the average bandwidth values during 400 and 800 s after VM migration, respectively. Firepan and FirepanIF provide better performance for every flash cache capacity. With the 20 GB flash cache, FirepanIF exhibits 59.41 MB/s as the average bandwidth for 400 s, which translates into an improvement of 21.87% over NoCacheMigration.  
Figure 10: Bandwidths before and after VM migration with various flash cache capacities.  
Figure 9: Performance of neighbor VMs on the destination host with NoCacheMigration, Cachemior, Firepan, and FirepanIF.  
The results show that the benefits of Firepan and FirepanIF increase with flash cache capacities. As the size increases, NoCacheMigration and Cachemior require a much longer time to fully warm up the increased host-side flash cache, as shown in Figure 9(b). In the case of Firepan or FirepanIF, although flash cache migration takes more time than that in the case of a small flash cache, the migration time is much smaller than the warm up times of NoCacheMigration and Cachemior. Therefore, the overall performance of Firepan and FirepanIF shows an improvement in this experiment.
# An Efficient Time-Variant Reliability Analysis Method with Mixed Uncertainties
## 6 Conclusions  
In the present article, a time-variant reliability analysis method is proposed for mixed-variable structures. The time-variant reliability problem of structures with mixed variables is transformed into a time-variant reliability problem of structures with random variables. Then the stochastic process is discretized and the static limit state functions of different periods are obtained. After the original problem is changed into an invariant reliability problem, the first-order second-moment method is applied to solve the problem. Finally, the reliability index under the design base period is obtained.  
In order to evaluate the performance of the presented approach, it is applied to three numerical and one engineering case studies. Obtained results show that in the studied cases, the performance of the presented approach is very close to that of the Monte Carlo method. Moreover, it is found that the proposed algorithm avoids multi-layer nesting optimization and greatly improves computational efficiency.  
The present method can be extended to time-variant reliability analysis problems with convex set variables and probability variables, and to multidisciplinary reliability analysis problems. When the range of interval parameters are big, the computational expenses of the proposed method cannot be justified. In order to resolve this shortcoming, improving the efficiency of time-variant hybrid reliability analyses will be discussed in the near future.  
Conceptualization, F.L.; methodology, F.L.; validation, J.R. and H.Z.; formal analysis, Y.Y.; investigation, Y.Y; data curation, H.Z.; writing--review and editing, J.R. and H.Z. All authors have read and agreed to the published version of the manuscript.  
This work was partially supported by the Science and Technology Program of Guangzhou (202102010428), National Natural Science Foundation of China (11772070,11672104,11902085), Hunan Provincial Natural Science Foundation of China (2019J40296) and the Open Fund of Key Laboratory of Bridge Engineering Safety Control by Department of Education (19 KB06).  
Not applicable.  
The authors declare no conflict of interest.
## 10 Conclusions  
Three proposals for models developed through an instrumental QoE paradigm approach were presented to measure QoE using swarms of drones connected via a flying ad hoc network to perform a mission and provide quality of experience in a natural disaster service.  
We found that it was not necessary to use subjective tests to obtain data in the creation of the models when using the QoE instrumental paradigm approach. The proposed models make it possible to obtain a measurement of the QoE of an online video without the need to compare it with the original video, which means that the required computing power is not high. In addition, QoE can be measured directly on the video transmitted over the ad hoc network, without the need to decode it or collect information about the transmitted video at the bit level.  
With the model performance, it can be deduced that: (a) there is a close relationship between QoE and QoS, (b) the QoS parameter that most influences the QoE of a video is packet loss, (c) distortions are more visible depending on the type of video, and (d) in videos with a greater amount of movement, the distortion is more noticeable.  
With this work, the following contributions were made: (a) from the quality of the experience, a mathematical model of the quality of the service was obtained, (b) using the end-to-end NS simulator, the design of the experiment was built, (c) a methodology was developed for the mathematical and statistical analysis of the information obtained, (d) an algorithm was designed to obtain the quality of the experience for end-to-end video traffic, based on service quality metrics, and (e) a proposal for future work was presented for the data analysis in a physical environment and applied to the environmental sector.
## 3 Numerical Simulation and Experimental Analysis
### Rotating Speed  
#### 3.4.1 Numerical Analysis  
It is researched in this subsection that how the depth of transverse crack affects the propagation characteristics of the elastic waves. Crack Mode III is adopted here too. The non-uniform shaft is still the same one but with different rotating speeds as listed in Table 1. Similarly, substituting all the parameters into Equation (3), the transfer matrix is then be derived. In addition, numerical method is adopted to obtain the numerical results of the propagation constants of the elastic waves, the numerical results are shown in Figure 12.  
As can be seen in the figure, the three stopbands are affected by the rotating speeds. Different colors indicate different rotating speeds, while the real line and the dotted line of the same color are a pair of conjunct propagation constants. All the stopbands are listed in Table 7 for better comparison.  
It can be found in Table 7 that there are three stopbands for each case. The central frequencies of the second and the third stopbands will be higher and higher with the increase of the rotating speed,  
\begin{table}
\begin{tabular}{c c} \hline
**RPM** & **Stopbands (Hz)** \\ \hline
0 Hz & [0, 71, 285, 455, 743, 1181] \\
10 Hz & [0, 82, 297, 467, 758, 1196] \\
20 Hz & [0, 91, 309, 479, 772, 1210] \\
30 Hz & [8, 100, 322, 492, 787, 1225] \\
40 Hz & [23, 113, 332, 502, 808, 1243] \\
50 Hz & [33, 124, 346, 516, 826, 1261] \\ \hline \end{tabular}
\end{table}
Table 7: Numerical stopbands derived from different rotating speeds.  
Figure 12: Numerical propagation constants derived from different rotating speeds.  
while the bandwidth of which remain the same, and they are 170 Hz, 435 Hz, separately. For the first stopband, it starts with 0 Hz when the rotating speed is lower than 30, while the bandwidth will be the same for the cases 30 Hz, 40 Hz and 50 Hz. It can be thought that the central frequency and bandwidth of the first stopband vary the same manner with the other two. The rotating speed will shift up the stopbands with the increase of the rotating speed, while, without changing the bandwidths.
## 3 Method Proposal to Analyze AST n-Tools Combinations
### SAST, DAST and IAST Tools Selection  
* SAST and IAST tools are selected according with Java 2 Platform, Enterprise Edition (J2 EE), the most used technology in web applications development, the programing language used by J2 EE is Java, one of the labeled as more secure [75]. The next step is the selection of two (2) SAST tools for source code analysis that can detect vulnerabilities in web applications developed using the J2 EE specification; two (2) IAST tools for detecting vulnerabilities in runtime and finally two (2) DAST tools:
* With the premises of the above comparatives and analyzing the availability of commercial and open source tools are selected three commercial and three open source meaning tools. Selected tools:
* Fortify SCA (SAST Commercial tool by Microfocus, Newbury, United Kingdom) supports 18 distinct languages, the most known OS platforms and offers SaaS (Software as a service) and it detects more than 479 vulnerabilities.
* FindSecurityBugs. (SAST OWASP open source tool, created by [PERSON] and licensed under LGPL). It has plugins are available for SonarQube, Eclipse, IntelliJ, Android Studio and NetBeans. Command line integration is available with Ant and Maven.
* OWASP ZAP (DAST OWASP open source tool with Apache 2 license). Popular tool developed by the active OWASP Foundation community. It is a widely used tool in the field of penetration testing. It supports a great number of plugins, features and input vectors.
* Arachni (DAST tool with public source License v1.0 created by [PERSON]). Open source vulnerability scanner highly valued by the community, and which allows the assessment of vulnerabilities such as SQLi, XSS, Command Injection and other included in OWASP Top Ten project.
* Contrast Community Edition (IAST free version of the commercial tool for 30 days by Contrast Security, Los Altos, EE.UU.). It allows the analyses of the application in Java language in an interactive way, making an agent in charge of reporting the vulnerabilities to the server. It provides the complete functionality of our paid platform solutions, Contrast Assess, and Contrast Protect. Contrast Community Edition's main limitations from the paid platform are language support (Java,.NET Core framework) and only one (1) application can be onboarded.
* CxIAST (IAST commercial tool, by Chekcmarx, [PERSON], Israael). It is an IAST engine for Java language. It inspects custom code, libraries, frameworks, configuration files, and runtime data flow. It supports Java,.Net framework and Nodejs languages. It can integrate with CxSAST (SAST tool) to correlate and improve results regarding true and false positives.
# A Scalable Moving Boundary Treatment in the Lattice Boltzmann Method
## 2 D2G9 Model  
A complete lattice Boltzmann model usually consists of the discrete velocity model, equilibrium distribution function, and distribution function evolution equation. In 1992, for the two-dimensional model, [PERSON] et al. proposed the D2Q9 model [25] as the basic model of LBM. Many scholars have developed different models to solve problems in various application environments. The macroequation derived from the basic D2Q9 model is the compressible Navier-Stokes equation, so there will be an inevitable compression error when simulating incompressible flow. Since the followup research in this paper is the fluid-solid coupling problem of blood vessels, the computational fluid is blood under the assumption of incompressibility. For incompressible flow, people have developed a variety of incompressible lattice Boltzmann models [26; 27; 28]. [PERSON] [29] proposed an incompressible model of D2G9, which has the characteristic that the sum of the balanced distribution functions is equal to a constant.  
In the Cartesian coordinate system, the D2G9 model is the same as the discrete velocity model of the D2Q9 model. It can be seen from Equation (1) that the evolution equation of this incompressible model is the same as that of the classic D2Q9 model, which transforms the complex collision migration process into a relaxation process.  
\[f_{i}(x+e_{i},t+1)=(1-\tau)f_{i}(x,t)+\omega f_{i}^{eq}(x,t) \tag{1}\]  
\(f_{i}(x,t)\) is the probability density distribution function at the grid point \(x\) at time \(t\) after being discretized by the discrete velocity model. \(f_{i}^{eq}(x,t)\) is the local equilibrium distribution function under the current macroscopic quantity, and the relaxation factor \(\tau\) is related to the viscosity coefficient \(\
u\). In SRT-LBM, the expression is \(\tau=\upsilon c_{s}^{2}/\Delta t+0.5\) in which \(c_{s}\) is the speed of sound and \(\Delta t\) is the time step. Different from the D2Q9 model, the D2G9 model selects a new equilibrium distribution function, and the new equilibrium distribution function \(g_{i}^{(0)}\) is given by the equation group (2):  
\[g_{i}^{(0)}=\left\{\begin{array}{ll}-4\sigma\frac{p}{c^{2}  
function, which is obtained after collision migration. \(\sigma\), and \(\gamma\) are model parameters, which satisfy the following equations:  
\[\left\{\begin{array}{c}\lambda+\gamma=\sigma\\ \lambda+2\gamma=1/2\end{array}\right. \tag{5}\]  
The reference values that can be selected are \(\lambda=1/3,\gamma=1/12\).  
In the D2G9 model, pressure replaces the density as an independent variable, and the standard incompressible Navier-Stokes equations can be derived, effectively eliminating the compressibility error of the D2Q9 model. In the calculation Formula (4) of macroscopic pressure, it can also be seen that the calculation of pressure is unrelated to density.
# Evolutionary Approach to the Euclidean Steiner Tree Problem in n-Space
## 2 Proposed Algorithm
### Data Structure  
In the ESTP, it is necessary to determine both the number and coordinates of Steiner points. A naive approach to the ESTP would be to encode just these two pieces of information. However, this is not the best approach, as it does not utilize previous algorithmic achievements related to the ESTP, including the ability to efficiently calculate the RMT for a given FST. Such an algorithm was provided in [10], where he also proposed an efficient way of encoding information about a given FST. This method is very simple and simultaneously relates to the previously mentioned process of building an FST in the process of adding successive terminals through another Steiner point.  
Here, we assume that the order of terminal attachment follows a certain adopted sequence, which is irrelevant at this point. The initiation of the FST construction begins by taking the first three terminals and connecting them with the first Steiner point. However, attaching the fourth terminal can occur in three ways, the fifth in five, the sixth in seven, generally in \(2*(k-1)+1\) for \(k=1,...,p-2\). This leads to the proposed data structure as a vector of \(p-3\) integers \(fst=[s_{2},s_{3},...,s_{p-2}]\), where the possible value for \(s_{k}\) depends on its position, meaning \(s_{k}\) belongs to the interval \([1,2*(k-1)+1]\). Note that there is no need to encode the unique way the first Steiner point \(s_{1}\) is connected. During the random initialization of the initial population, values for each \(s_{k}\) are generated randomly from the appropriate interval for it.  
In Figure 6, the general data structure used as a chromosome is graphically presented, along with a sample chromosome for the problem involving eight terminal points.  
At this point, it is worth emphasizing that the proposed data structure is independent of the dimensionality of the Euclidean space in which the Steiner problem is being solved. Its length depends solely on the number of terminal points, and the possible values depend only on their indices. The dimensionality of the space does not affect the representation of the solution as a chromosome encoded using this data structure. It will, however, influence the method of calculating the Euclidean distance between points; i.e., higher-dimensional Euclidean space will be more computationally demanding.  
It is worth noting that adopting such a data structure to represent the solution turns the problem into a combinatorial optimization problem.  
Figure 5: General scheme of genetic algorithm.
## 2 Materials and Methods
### Entropy-Based Feature Extraction
#### 2.1.4 Fuzzy Entropy  
To deal with some of the issues with sample entropy, [PERSON] et al. proposed the use of fuzzy membership function in computing the vector similarity to replace the binary function in sample entropy algorithm [PERSON] et al. (2016), so that the entropy value is continuous and smooth. While maintaining the merits of sample entropy algorithm, the new algorithm obtains stable results for different parameters, and offers better noise resistance. It is more suitable than the sample entropy as a measure of time series complexity [PERSON] et al. (2016). The procedure for the FuzzyEn-based algorithm is described in detail as follows:  
1. Set a \(L\)-point sample sequence: \(\{v(i):1\leq i\leq L\}\);
2. The phase-space reconstruction is performed on \(v(i)\) according to the sequence order, and a set of m-dimensional vectors are obtained as \((m\leq L-2)\). The reconstructed vector can be written as: \[T_{i}^{m}=\{v(i),v(i+1),...,v(i+m-1)\}-v_{0}(i)\] (13) where \(i=1,2,...,L-m+1\), and \(v_{0}(i)\) is the average value described as the following equation: \[v_{0}(i)=\frac{1}{m}\sum_{j=0}^{m-1}v(i+j)\] (14)\(d_{ij}^{m}\), the distance between two vectors \(T_{i}^{m}\) and \(T_{j}^{m}\), is defined as the maximum difference values between the corresponding elements of two vectors: \[\begin{split} d_{ij}^{m}=d[T_{i}^{m},T_{j}^{m}]=& \sum\limits_{k\in(0,m-1)}\{|v(i+k)-v_{0}(i)-(v(j+k)-v_{0}(j))|\}\\ (i,j)=& 1\,\sim L-m,i\
eq j)\end{split}\] (15)
4. According to the fuzzy membership function \(\sigma(d_{ij}^{m},n,s)\), the similarity degree \(D_{ij}^{m}\) between two vectors \(T_{i}^{m}\) and \(T_{j}^{m}\) is defined as: \[D_{ij}^{m}=\sigma(d_{ij}^{m},n,s)=\exp(-(d_{ij}^{m})^{n}/s)\] (16) where the fuzzy membership function \(\sigma(d_{ij}^{m},n,s)\) is an exponential function, while \(n\) and \(s\) are the gradient and width of the exponential function, respectively.
5. Define the function \(\gamma^{m}(n,s)\): \[\gamma^{m}(n,s)=\frac{1}{L-m}\sum\limits_{i=1}^{L-m}\frac{1}{L-m-1}\sum \limits_{j=1,j\
eq 1}^{L-m}D_{ij}^{m}]\] (17)
6. Repeat the steps from (1) to (4) in the same manner, a set of \((m+1)\)-dimensional vectors can be reconstructed according to the order of sequence. Define the function: \[\gamma^{m+1}(n,s)=\frac{1}{L-m}\sum\limits_{i=1}^{L-m}\frac{1}{L-m-1}\sum \limits_{j=1,j\
eq 1}^{L-m}D_{ij}^{m+1}]\] (18)
7. The fuzzy entropy can be expressed as: \[FuzzyEn(m,s,L)=\ln\gamma^{m}(n,s)-\ln\gamma^{m+1}(n,s)\] (19)  
In these four entropies, \(m\) and \(s\) are the dimensions of phase space and similarity tolerance, respectively. Generally, a too-large similarity tolerance will lead to a loss of useful information. The larger the similarity tolerance, the more information may be missed. However, if the similarity tolerance is underestimated, the sensitivity to noise will be increased significantly. In the present study, \(m=2\), \(n=4\) while \(s=0.2\)\({}^{*}\)SD, where SD denotes the standard deviation of the time series.
## 4 Metallic MEMS Cantilevers: Nonlinear Dynamics of the Pull-In Behavior
### Simulation of Resonance Frequencies  
First, we will recall and demonstrate that the actuation speed of MEMS cantilevers is related to the mechanical oscillation modes and their corresponding resonance frequencies \(f_{\text{n}}\) (mechanical Eigenfrequencies or natural frequencies). Each characteristic dynamic movement of the cantilever can be attributed to one or a superposition of several modes (There are different notations in the literature for the resonance frequencies (eigenfrequencies, natural frequencies). In some cases in the literature, the notation is as follows: fundamental mode, 1 st order mode, 2 nd order mode, 3 rd order mode.To make it as simple as possible for the reader, we use the following notation instead: 1 st order mode, 2 nd order mode, 3 rd order mode, 4 th order mode. These modes are also related to mechanical eigenfunctions, and the corresponding mechanical resonance frequencies are also called eigenvalues). The cantilever movement which has been considered up to now is simple and is representing the fundamental mode (here, we call it the 1 st order mode, which is characterized by the resonance frequency \(f_{1}\)). The higher\(f_{1}\), the faster the response speed (e.g., shorter actuation time until the cantilever tip has reached the lower electrode). In general, the higher\(f_{\text{n}}\), the faster the response speed of motions in that characteristic way.  
For the Al cantilever with \(l\) = 100 \(\upmu\)m, \(h\) = 1 \(\upmu\)m, \(b\) = 20 \(\upmu\)m, \(E\) = 70 GPa and \(\rho\) = 2700 kg/m\({}^{3}\), the 1 st order, 2 nd order, 3 rd order, and 4 th order eigenfrequencies as  
Figure 8: Displacement of the cantilever tip as a function of voltage for different MEMS cantilevers, keeping \(\rho\) = 2700 kg/m\({}^{3}\) constant. These colors correspond to the colors in Table 2.  
well as the mode shapes (Eigenfunctions) are shown in Figure 9. The 1 st order mode shape is the fundamental bending oscillation with a frequency of 83.87 kHz. Here, the tip oscillates the strongest. The 2 nd order mode shape has one node line and is oscillating in the middle and oscillating in the opposite direction at the free-end (tip), and the eigenfrequency equals 525 kHz. The 3 rd order mode shape is the torsion motion, and the eigenfrequency is 810 kHz. The 4 th order mode shape has three oscillation ranges and two node lines, and it comes with an eigenfrequency of 1470 kHz.  
The operating modus of the MEMS cantilever considered here is related to the 1 st order mode having no node outside of the fixed end. At the tip (free-end), the cantilever is always most displaced, as already mentioned. Among the lowest four modes, only the first mode shape contributes to the pull-in process, so this mode is normally used to actuate the cantilever and to study its time response.