esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis and Application of Selected Control Charts Suitable for Smart Manufacturing Processes
## 1 Introduction
The most various control charts are analyzed by the authors of [19]: the empirical likelihood-based control chart, classical Shewhart X-bar chart, classical EWMA chart and bootstrap chart. Another goal of this research was to analyze the performance of 6 selected NPCCs to cover various deviations from the data assumptions as much as possible. For this reason data from 7 distributions, i.e. normal distribution, distributions with greater and lower kurtosis as compared to normal distribution, asymmetrical, two bimodal distributions and autocorrelated data were incorporated into these simulations. Other authors when simulating NPCC performance used fewer distributions, distributions with different parameters or quite different distributions, such as the \(\beta\) distribution [20]; normal and exponential distributions [14]; normal, Laplace and uniform distributions [12]; normal, Laplace and Gamma distributions [13]; normal, Student, Gamma, two symmetrical bimodal and one asymmetrical bimodal distributions [19]; and normal, uniform, Student, double exponential and Cauchy distributions [16]. To assess control chart performance, an ARL (average run length) indicator is applied most frequently [12; 14; 19; 21]. However, some criticism of this indicator can be found in the literature [10; 22], based on the fact that \"_the run length is a positive integer valued random variable, so the ARL loses much of its attractiveness as a typical summary if the distributions is skewed_\" ([10], p. 304). Some authors recommend and use MRL (median run length) or quantiles \(x_{10}\) and \(x_{90}\)[13; 16]. As the quality of the performance indicators has a crucial impact on precise evaluation of NPCC performance and so on meeting the main goal of this research, the authors incorporated into the research an additional partial goal--to analyze ARL, MRL, \(x_{5}\) and \(x_{95}\) to identify the most precise indicators for simulations. To reach all goals and realize the planned output, the authors set several hypotheses that were verified during the research:  
**Hypothesis 1**. : _Some NPCCs are more suitable (they have better performance) for the particular data assumption violations than others._  
**Hypothesis 2**. : _Some NPCCs are robust (distribution-free) against most data precondition violations._  
**Hypothesis 3**. : _Performance indicators such as MRL or other quantiles of the run-length distribution are better for the evaluation of control chart performance._  
**Hypothesis 4**. : _Nonparametric control charts are resistant against outliers (which cannot be explained and for this reason cannot be removed from the data or repaired)._  
The paper consists of the following sections: Section 2 focuses on the theoretical framework and the solved topic. Section 3 describes the used methods, instruments and analyses. Section 4 is devoted to the results obtained from the previous analyses. The designed instructions for the practical use of NPCCs are applied to real data in Section 5. Finally, in Section 6, discussion of the results can be found.
# Research on the Robustness of Interdependent Networks under Localized Attack
## 3 Results and Analysis
### Effect of Coupling Preference and Coupling Strength
(**a**) WS-WS with \(N_{\text{WS1}}=N_{\text{WS2}}=1000\), \(<\)\(K_{\text{WS1}}\)\(>\) = \(<\)\(K_{\text{WS2}}\)\(>\) = 4; (**b**) WS-WS with \(N_{\text{WS1}}=N_{\text{WS2}}=2000\), \(<\)\(K_{\text{WS1}}\)\(>\) = \(<\)\(K_{\text{WS2}}\)\(>\) = 4; (**c**) WS-WS with \(N_{\text{WS1}}=N_{\text{WS2}}=2000\), \(<\)\(K_{\text{WS1}}\)\(>\) = \(<\)\(K_{\text{WS2}}\)\(>\) = 6; (**d**) BA-BA with \(N_{\text{BA1}}=N_{\text{BA2}}=1000\), \(<\)\(K_{\text{BA1}}\)\(>\) = \(<\)\(K_{\text{BA2}}\)\(>\)\(\approx\) 4; (**e**) BA-BA with \(N_{\text{BA1}}=N_{\text{BA2}}\) = 2000, \(<\)\(K_{\text{BA1}}\)\(>\) = \(<\)\(K_{\text{BA2}}\)\(>\)\(\approx\) 6; and (**f**) BA-BA with \(N_{\text{BA1}}=N_{\text{BA2}}=2000\), \(<\)\(K_{\text{BA1}}\)\(>\) = \(<\)\(K_{\text{BA2}}\)\(>\)\(\approx\) 6. Figure 7: Cascading failures of interdependent networks with different coupling preference. The results of TA, RA, and TLA are also plotted for comparison. Every point is averaged over 100 independent realizations. (**a**) Robustness of WS-WS interdependent networks with \(N_{\text{WS1}}=N_{\text{WS2}}=2000\), \(<\)\(K_{\text{WS1}}\)\(>\) = \(<\)\(K_{\text{WS2}}\)\(>\) = 6, and rewiring probability \(p_{\text{re}}=0.2\); and (**b**) robustness of interdependent BA-BA networks with \(N_{\text{BA1}}=N_{\text{BA2}}=2000\), \(<\)\(K_{\text{BA1}}\)\(>\) = \(<\)\(K_{\text{BA2}}\)\(>\)\(\approx\) 6. In addition, by comparing Figure 8b,c and Figure 8e,f, we find that when the average degree of the subnetwork increases from four to six, the robustness of interdependent networks under different coupling strengths is very similar. It is suggested that the robustness of interdependent networks with different coupling strengths under PDLA is related to the average degree of the subnetworks. When the average degree is small, larger coupling strength makes interdependent networks more vulnerable; but when the average degree is large, the coupling strength has little effect on the robustness of interdependent networks. This is an interesting phenomenon worth further research.
## 5 Copper Nanoparticles and Their Antibacterial Role
### Comparison between the Antibacterial Activity of Metallic Nanoparticles  
Research has focused on several metal nanoparticles that have antimicrobial activity, such as silver, copper and gold nanoparticles. Studies have shown these nanoparticles have antimicrobial activity against both Gram-positive and -negative bacteria such as Staphylococcus aureus and Escherichia coli, respectively [67; 68].  
Figure 2: Gram-negative and positive bacteria [64].  
Besides their antimicrobial activity on the aforementioned bacteria, these metals also influence Bacillus subtilis and are frequently used in medicine, dental materials, water treatment and even sunscreen lotions.  
Most often, it is copper and silver nanoparticles that are compared because of their low cost, their stability from a physical and chemical standpoint and easiness of fusing with polymers [69].  
While comparing silver and copper's effect from a bactericidal effect on various microbial strains, three types of bacteria stood out--_E. coli_, _B. subtilis_ and _S. aureus_. Their antimicrobial effect was measured considering the inhibition zone by establishing the minimum growth inhibitory concentrations (MIC) and minimum bactericidal concentration (MBC) (Table 6).  
All the _S. aureus_ strains presented the same level of sensitivity to silver and copper nanoparticles and no difference in strains was observed. However, while comparing _E. coli_ with _S. aureus_ it was observed the difference of sensitivity is less for silver as opposed to copper.  
Similar results were found by [PERSON] et al. who reported their findings on the antibacterial effects of silver and copper nanoparticles against _E. coli_ and _B. subtilis_, where in that case too, the copper materials proved to have superior antibacterial effect compared to the silver alloys [70].  
Other studies have also shown the effects of copper and zinc nanoparticles as bactericidal agents. With this in mind, [PERSON] et al. discovered a high antibacterial effect against _S. mutans_ of AgNP at a lower concentration than Au or Zn, for example, which would allow obtaining great results at a lower toxicity rate [71].  
Nonetheless, copper is one of the least studied materials in terms of its antibacterial properties due to its increased instability in terms of oxidation and that it often forms complexes in relation with water molecules in aqueous environment [72].
# Traffic Congestion Detection System through Connected Vehicles and Big Data
## 5 Results
### Time and CO\({}_{2}\) Reductions Achieved by the System  
The results obtained are shown in Figures 17 and 18 show a reduction in the time required to reach a destination in vehicles using the OBU module (Nodes 4, 5 and 7). Data also shows a significant reduction in carbon dioxide emissions of vehicles receiving traffic congestion alerts by changing their route. By increasing the average vehicle speed and reducing congestion, CO\({}_{2}\) emissions are significantly reduced when compared to the emissions of congested cars idling that do not use the OBU module (nodes 1-3). The formula used to calculate the CO\({}_{2}\) generated by vehicles is provided by [75].  
Figure 16: Records contained in the OnRoad_Cluster_Data model.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Qty. Vehicles** & **Scheduled Traffic** & **Traffic Alerts** & **False Traffic** & **Traffic Alerts** & **Percentage** \\
**with OBU.** & **Congestions** & **Generated** & **Alerts Generated** & **not Generated** & **of Precision** \\ \hline
24 (10.1\%) & 64 & 63 & 1 & 2 & 93.7\% \\
118 (50\%) & 64 & 63 & 0 & 1 & 98.4\% \\ \hline \end{tabular}
\end{table}
Table 9: Results of precision of the traffic detecting algorithm.  
Figure 17: Comparison of the simulation results: (**a**) Comparison of CO\({}_{2}\) emissions; (**b**) Comparison the distance traveled.  
Figure 18: Comparison of the simulation results: (**a**) Comparison of the time traveled; (**b**) Comparison of the average speed of vehicles.
# Flight Level Assignment Using Graph Coloring
## 2 Flight-Level Assignment as Spectrum Coloring Problem with Hard Constraints  
In this section we describe the model we have developed for the problem in this work. We start by stating the utilities for the different actors in the model, including the individual utilities (each flight operator wants to minimize fuel consumption) and the social utility (keep crossing airways separate enough to ensure certain levels of safety). Next, we model the flight level assignment as a factorized optimization problem. Finally, we propose to approximate the solution to the problem as an instance of spectrum graph coloring.
## 2 Simulation Methodology
### FEM-SPH Adaptive Algorithm
#### 2.2.4 Calculation Process  
The computation process of the FEM-SPH adaptive coupling algorithm, as presented in Figure 3, is divided into the following steps: (1) calculate the minimum time step of finite elements based on the minimum element size of the FEM model; (2) compare the time steps of the FEM and the SPH and take the smaller one as the time step of the FEM-SPH coupling algorithm; (3) update the displacement of finite element nodes, and recalculate the time step of finite elements based on the current minimum size of the FEM model; (4) determine whether the finite elements fail according to the strain failure criteria and convert them into SPH particles if they fail; (5) apply the relevant physical quantities to the SPH particles with Equations (17)-(19) and update the time step of the SPH; (6) judge whether the finite element nodes are in contact with the SPH particles and calculate the contact force; (7) substitute the contact force into the kinetic equation of the FEM method and the momentum equation of the SPH method; (8) solve and display the kinetic equation with the FEM method and solve the Navier-Stokes equation with the SPH method; (9) update the physical quantities of finite elements and SPH particles, and the time step is finally determined.
## 1 Introduction
### Notations  
We will use boldface upper case letters to denote matrices and boldface lowercase letters to indicate column vectors. Throughout the paper, \(log\) indicates \(log\) to the base of two. The norm, \(\parallel\mathbf{x}\parallel\), denotes the \(l^{2}\)-norm of \(\mathbf{x}\) given by \(\parallel\mathbf{x}\parallel\stackrel{{\Delta}}{{=}}\sqrt{\sum_{ i=1}^{L}\mid x[i]\mid^{2}}\). The absolute value of \(x\) is given by \(\mid x\mid\). Furthermore, a diagonal matrix is denoted by \(diag(\mathbf{x})\) with the diagonal entries indicated by vector, \(\mathbf{x}\).
# TYCOS: A Specialized Dataset for Typical Components of Satellites
## 4 Evaluation  
To establish a general benchmark for the key satellite component detection, an analysis of recent popular models and representative classic models was conducted. Subsequently, the detection results and initial detection benchmarks were analyzed, followed by a visual analysis of the detection results of satellites under different illuminations, motion states, and occlusions.
## 1 Introduction
[6] employed several ML models to predict the compressive strength of SFRC exposed to high-temperature environments, and comprehensively analyzed and discussed the application of each model and performance to obtain the optimal algorithm adapted to their specific dataset. [PERSON] et al. [7] established nine different ML models to predict the compressive strength of concrete and found that the nonlinear model showed better performance than the linear model. [PERSON] et al. [8] employed two integrated methods (SVR AdaBoost and SVR Bagging) and Support Vector Regression (SVR) independent algorithms to predict the 28-day compressive strength of SFRC. Although these studies have achieved some good results in concrete strength prediction, ML is usually regarded as \"black box\" that is unable to provide sufficient justification for the results obtained by the model [9]. That is to say, the physical significance of such models is not clear, and they generally fail to clarify the relationship between input featuresand output results based on theoretical knowledge, which reduces the credibility and application of ML. Based on the aforementioned limitations, the Shapley Additive exPlanations (SHAP) interpretable method and deep learning (DL) provide better solutions. [PERSON] et al. [10] proposed the SHAP method for interpreting machine learning models based on game theory, which introduces the concept of additive interpretation to quantify the calculation of the contribution of each feature to the prediction result. The SHAP interpretability method not only provides accurate local explanations by clearly illustrating the direction and magnitude of each feature's contribution in individual predictions, but also delivers global explanations by ranking the overall importance of input features. DL, as a kind of deep-level machine learning, can automatically perform high-dimensional abstract learning on the data through multilayer neural network architectures [11], which not only avoids the traditional ML model's feature screening and dimensionality reduction on the original data before training, reduces the composition of the feature engineering, and saves time, but also has more advantages in the processing of the problem with a larger volume of data and a high degree of nonlinearity between the target variable and the input variable. The current application in the field of concrete mechanical property prediction is still relatively small. Therefore, based on a large amount of published relevant experimental data, this study establishes a prediction model using a deep learning algorithm to predict the compressive strength of SFRC, and uses the SHAP method to reveal the potential mechanism behind the model prediction results. By visualizing the SHAP values of feature parameters, the importance and influence of the input features on the compressive strength results were directly analyzed, and the influence of the feature parameters on the compressive strength of SFRC was provided as a basis and reference for the engineering design of SFRC materials.
## 4 Materials and Methods
### Data Processing and VOC Identification  
Deconvolution and spectral alignment of chromatographic peaks across samples was based on mass-to-charge ratio (m/z) and retention time similarities and performed in MassHunter Profinder. Silanes and siloxanes, degradation products of both the GC column and SPME fiber, were removed from the matrix, as these are regarded as instrumental and method artifacts. Features appearing in less than 50% of either control or cancer weeks 1-3 sample classes were excluded from further analyses. Data normalization was performed through MS Total Useful Signal (MSTUS). Finally, MSTUS values were autoscaled (z-scored) to generate a matrix with a similar signal range. Upon completion of data screening, VOCs were identified through Agilent MassHunter Profinder and MassHunter Unknowns Analysis coupled with the NIST17 mass spectral library. Upon comparison of the features in Unknowns Analysis with the NIST17 library, VOCs with a match factor >70, along with similar non-polar retention index (NPRI), were tentatively identified. Experimental NPRI was determined through a previously implemented instrument-specific calibration curve [16; 17].
## 3 CPN Models
### Basic Models
#### 3.1.2 Selection Statements  
The selection statements _if-then-else_ and _switch_ are represented using the same CPN model as they have branches, and only one of them runs (see Section 2.2.1). As these statements have branches, it is necessary to associate the probability of each branch to be true. The application developer is responsible for manually associating this probability through a C comment. For example, in the following nesC code, the probabilities associated by the application developer to each branch are: 80% (E_SUCCESS), 15% (E_ERROR) and 5% (otherwise).  
switch( e ){  case E_SUCCESS: //@0.80  value = v; break;  case E_ERROR: //@0.15  value = -1; break;  default: //@0.05  value = 0; }  
Figure 4 shows the CPN model for representing the statements _if-the-else_ and _switch_. The transition _c1_ decides the branch to be executed by generating a random value between 0 and 1 following the uniform distribution. The token value is the decided value by this transition and this value will be used by the subsequent transition.  
Figure 3: Generic CPN model of the nesC operators.  
The places _el_x_ (e.g., _el_1_ and _el_2_) and _body_y_ (e.g., _body_1_, _body_2_ and _body_3_) have power consumption calculations related to the each branch. The place _el_x_ represents the controlling expression and _body_x_ represent the body.  
As mentioned in Section 2.2.1, a selection statement executes the branch when its controlling expression returns true. In other words, the controlling expression of a branch consumes energy before deciding if it executes the body (when return true) or passes the control to the next branch (when return false). The transition _e_x_ (e.g., \(e\)1_ and \(e\)2_) represents this behaviour in the CPN model. This transition checks if it runs the body or passes to the next branch. For example, the place _el_1_ verifies the token value and move the token to place _body_1_ (when the value is equal to 0) or to place _el_2_ (when the value is nonzero). The transition \(e\)2_ has a similar behaviour.  
This model has similar properties to the previous model when all branches have a probability greater then zero. On the other hand, in the case a branch has associated a probability equal to zero (//@0.0), it means that its body will never be executed. Consequently, the transition associated to this branch (e.g., \(b\)1_, \(b\)2_ and \(b\)3_ in Figure 4) becomes a dead transition.
# Design and Experimental Study of a Down-Drive Piezoelectric High-Frequency Fatigue Testing Machine
## 3 System Dynamics Modelling and Simulation Analysis
### Structural Design
\begin{bmatrix}Z_{11}(w)&Z_{12}(w)&Z_{13}(w)\\ Z_{21}(w)&Z_{22}(w)&Z_{23}(w)\\ Z_{31}(w)&Z_{32}(w)&Z_{33}(w)\end{bmatrix}\begin{cases}X_{1}\\ X_{2}\\ X_{3}\end{cases} = \begin{cases}0\\ F\end{cases} , \tag{4}\]  
where  
\[\left\{ \begin{array}{l}Z_{11}(w)=-w^{2}m_{1}+iw(c_{1}+c_{s}(t))+k_{1}+k_{s} (t)\\ Z_{12}(w)=Z_{21}(w)=iw(-c_{s}(t))-k_{s}(t)\\ Z_{13}(w)=Z_{31}(w)=0\\ Z_{22}(w)=-w^{2}m_{2}+iw(c_{s}(t)+2c_{2}+c_{3})+k_{s}(t)+2k_{2}+k_{3}\\ Z_{23}(w)=Z_{32}(w)=iw(-c_{3})-k_{3}\\ Z_{33}(w)=-w^{2}m_{3}+iw(-c_{3})+k_{3}\end{array} \right. \tag{5}\]  
If,  
\[Z(w)= \begin{bmatrix}Z_{11}(w)&Z_{12}(w)&Z_{13}(w)\\ Z_{21}(w)&Z_{22}(w)&Z_{23}(w)\\ Z_{31}(w)&Z_{32}(w)&Z_{33}(w)\end{bmatrix} , \tag{6}\]  
then Equation (4) can be expressed as:  
\[[Z(w)]\{X\}=\{F\}, \tag{7}\]  
where \([Z(w)]\) is the impedance matrix, \(\{X\}\) is the displacement column vector, and \(\{F\}\) is the excitation amplitude column vector. \[\{X\}=[Z(w)]^{-1}\{F\}, \tag{8}\]  
where  
\[\left\{ \begin{array}{l}D=Z_{11}(w)Z_{22}(w)Z_{33}(w)-Z_{11}(w)Z_{23}^{2}(w)- Z_{33}(w)Z_{12}^{2}(w)\\ a_{11}=Z_{22}(w)Z_{33}(w)-Z_{23}^{2}(w)\\ a_{12}=a_{21}=-Z_{12}(w)Z_{33}(w)\\ a_{13}=a_{31}=Z_{12}(w)Z_{23}(w)\\ a_{22}=Z_{11}(w)Z_{33}(w)\\ a_{23}=a_{32}=-Z_{11}(w)Z_{23}(w)\\ a_{33}=Z_{11}(w)Z_{22}(w)-Z_{12}^{2}(w)\end{array} \right., \tag{10}\]Thus, the steady-state response amplitude of the system can be obtained as a function of the excitation frequency \(w\):  
\[\left\{ \begin{array}{ll}X_{1}(w)=&\frac{Z_{12}(w)Z_{31}(w)F}{Z_{11}(w)Z_{22}(w) Z_{33}(w)-Z_{11}(w)Z_{23}^{2}(w)-Z_{33}(w)Z_{12}^{2}(w)}\\ X_{2}(w)=&\frac{-Z_{11}(w)Z_{23}(w)F}{Z_{11}(w)Z_{22}(w)Z_{33}(w)-Z_{11}(w)Z_{23}^ {2}(w)-Z_{33}(w)Z_{12}^{2}(w)}\\ X_{3}(w)=&\frac{(Z_{11}(w)Z_{22}(w)-Z_{12}^{2}(w))F}{Z_{11}(w)Z_{22}(w)Z_{33}(w)-Z _{11}(w)Z_{23}^{2}(w)-Z_{33}(w)Z_{12}^{2}(w)}\end{array} \right., \tag{11}\]  
When the system damping is neglected, the steady-state response of the system can be found with respect to the excitation frequency \(w\):  
\[\left\{ \begin{array}{ll}x_{1}(t)=\frac{k_{s}(t)k_{3}\sin wt}{D_{n}}\\ x_{2}(t)=\frac{k_{3}\sin wt}{D_{n}}(-w^{2}m_{1}+k_{1}+k_{s}(t))\\ x_{3}(t)=\frac{\sin wt}{D_{n}}\big{[}\big{(}-w^{2}m_{1}+k_{1}+k_{s}(t)\big{)} \big{(}-w^{2}m_{2}+k_{s}(t)+2k_{2}+k_{3}\big{)}-k_{s}^{2}(t)\big{]}\end{array}  \right., \tag{12}\]  
where  
\[ \begin{array}{ll}D_{n}=\big{(}-w^{2}m_{1}+k_{1}+k_{s}(t)\big{)}\big{(}-w^{2} m_{2}+k_{s}(t)+2k_{2}+k_{3}\big{)}\big{(}-w^{2}m_{3}+k_{3}\big{)}\\ -\big{(}-w^{2}m_{1}+k_{1}+k_{s}(t)\big{)}k_{3}^{2}-\big{(}-w^{2}m_{3}+k_{3} \big{)}k_{s}^{2}(t)\end{array}  \tag{13}\]
## 5 Discussion
### Effect of the Coal Seam Dip Angle  
As shown in Figure 13, the coal seam dip angle made a meaningful contribution to gas extraction time of completion. The required extraction time decreased slowly as the coal seam dip angle increase. When the directional distance changed from 0\({}^{\circ}\) to 28\({}^{\circ}\), the extraction time was reduced from 300 days to 280 days. The reason was that different coal seam dip angle could affect the cleat permeability because of the deferent stress distribution within the seam, which increased the gas flow and reduced the extraction time.