esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Simulation Model
### Second Stage--Routing Path Application
**Contention resolution:** The core nodes do not employ any buffering in the data path and they do not use deflection routing. It is generally assumed that the nodes are capable of performing a full wavelength conversion. Blocking occurs only if there are no free wavelengths available to accomplish the next hop on a predetermined path to a certain destination. If scheduling fails, the burst is simply dropped and no further contention resolution method is adopted. Together with the network topology description, the OBS simulation model, which is composed of OBS capable nodes interconnected by optical fibers, is based on the modules depicted in Figure 8, whose functionality, generically described in Section 2, will be succinctly presented next from the simulation point of view.
## 5 Constructing Networks of Legal Judgments
### Analysis of the Possibility of Judgment According to Types of Privacy Invasion
#### 5.1.1 Possibility of Conviction and Innocence in Terms of Criminal Law  
To form a network of judgment on conviction and innocence according to the types of privacy invasion in terms of criminal law, conviction and innocence were set as the central nodes, and the relationship between the two was examined. To form a network, the degree centrality of types of privacy invasion were calculated according to conviction and innocent, which are presented in Tables 5 and 6 below.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Methods** & **Misclassification Rate (\%)** \\ \hline LDA & 11.82 \\ CART & 11.52 \\ Neural Network & 12.04 \\ Support Vector Machine & 11.90 \\ Random Forest & 11.72 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Misclassification Rates in 100 Simulations.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Main Keyword** & **Degree Centrality** &
\begin{tabular}{c} **Network of Privacy Invasion** \\ with Conviction \\ \end{tabular} \\ \hline \hline Adware & 0.155 & \\ Cyberattack & 0.018 & \\ Malware & 0.278 & \\ Spam & 0.266 & \\ Spyware & 0.230 & \\ Vandalism & 0.034 & \\ Virus & 0.191 & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Degree Centrality of Conviction.  
If a certain type of invasion forms a strong connection with the node of conviction through the formed network, a conviction is more likely to occur. The types of invasion that are marked with big circles or nodes in the network and are directly linked to adjudications such as conviction or innocence are likely to receive relevant verdicts. In other words, if nodes are relatively big, such as malware, and are directly connected in primary links with conviction in the network, the invasion has a high possibility to be judged illegal. Furthermore, it can be assumed that there is a slight chance of innocence, because the types of privacy invasion with the possibility of being convicted (e.g., virus, adware, spam, cyberattack, and malware) do not form a relationship with nodes focused on innocence.
# Approximate Networking for Universal Internet Access
## 1 Introduction
### Why Adopt Approximate Networking?  
#### 1.3.1 Affordable Universal Internet (GAIA)  
The right of affordable access to broadband Internet is enshringed in the 2015 sustainable development goals of the United Nations. The International Telecommunication Union (ITU) broadband \"Goal 20-20\" initiative aims at an optimistic target of universal broadband Internet speeds of 20 Mbps for $20 a month, accessible to everyone in the world by 2020 (Source: Alliance for Affordable Internet (A4 AI) Report, 2014). Such an approach, which aims at providing an \"ideal networking\" experience universally, has historically always failed (due to various socioeconomical and technical issues). An important reason is that most modern technologies (such as 3G/ 4G Long-Term Evolution (LTE) and the planned 5G) are urban focused as rural systems (being sparsely populated by definition) do not thus hold much business potential for mobile carriers [2; 3]. The Internet is also  
Figure 1: What’s new about approximate computing? (Adapted from [6; 17]).  
large unaffordable when we consider that on average the mobile broadband price and the fixed-line broadband prices are 12 and 40% of the average person's monthly income, where women and rural populations are hit the hardest [20]. Approximate networking is a particularly appealing option to reach out to the offline human population by providing an affordable, contextually \"good enough\" service.
# Neural Networks for Early Diagnosis of Postpartum PTSD in Women after Cesarean Section
## 4 Results
### Importance Values Using Random Decision Forests  
All the data from the initial set (469 \(\times\) 70) were used with the random decision forests classification which was performed using the function _randomForest_ from the library _randomForest_ version 4.6-14 in RStudio (v1.3.1093). The number of trees was 500 and the number of variables tried at each split (_mtry_) was 20. These parameters were also selected on a trial-and-error basis. As RDF classification has a stochastic feature in its operation, ten sessions were run, and the average estimated error rate was 1,13%. The average confusion matrix is shown in Table 7.  
A powerful feature of RDF classification is that an importance vector is also returned which has the Gini importance values (mean decrease in impurity, MDI) [30] of the variables used. This is very useful for having an idea of what variables contribute more to the classification process as the higher the Gini values the higher the importance of the variables. This is profound in our research as our aim was to reach a competitive level of classification using as less and more directly acquired, variables as possible.  
The Gini values for the 70 variables sorted from highest to lowest can be seen in Figure 3 and in Table 8 for more precision.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Free** & **Profile** & **PTSD** & **Class. Error** & **Sd of Class. Error** \\ \hline
**Free** & **378** & 1 & 0 & 0.002375 & 0.001498 \\
**Profile** & 1 & **31** & 2 & 0.097055 & 0.01421 \\
**PTSD** & 0 & 1 & **55** & 0.017857 & 3.66 \(\times\) 10\({}^{-18}\) \\ \hline \hline \end{tabular}
\end{table}
Table 7: The averaged confusion matrix and the classification errors from the RDF.  
Figure 3: The averaged Gini importance values of the 70 variables in descending order.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Variable** & **Gini Value** & **Variable** & **Gini Value** & **Variable** & **Gini Value** & **Variable** & **Gini Value** \\ \hline
**v38** & 28.98435 & **v45** & 1.8905019 & lec\_3 & 0.31930649 & lec\_4 & 0.137412479 \\
**v37** & 27.394505 & **v58** & 1.5943305 & **v56** & 0.30413895 & lec\_10 & 0.12593543 \\
**v39** & 17.084163 & **v49** & 1.1418203 & v11 & 0.30269473 & v12 & 0.120028475 \\ v35 & 7.1403067 & v24 & 1.00739209 & v26 & 0.29636865 & lec\_15 & 0.107513023 \\
**v44** & 6.971652 & **v51** & 0.9250898 & v6 & 0.27196558 & lec\_11 & 0.101853315 \\
**v41** & 6.3785479 & v61 & 0.81900432 & v14 & 0.26559605 & v3 & 0.101409277 \\
**v36** & 5.8372447 & v21 & 0.81722037 & v10 & 0.25888349 & lec\_17 & 0.094143941 \\
**v59** & 4.8650734 & **v60** & 0.81011046 & v15 & 0.2548879 & lec\_1 & 0.084016984 \\
**v47** & 4.3128049 & **v42** & 0.63748018 & v22 & 0.24198349 & v8 & 0.082011903 \\ v32 & 4.2619363 & v2 & 0.62349169 & v23 & 0.21767294 & lec\_16 & 0.080463707 \\ \hline \hline \end{tabular}
\end{table}
Table 8: The averaged Gini importance values of the 70 variables in descending order 1. Bolded variables are only available after six weeks of birth.
# Development of a Vibroacoustic Stochastic Finite Element Prediction Tool for a CLT Floor
## 4 Stochastic Process
### Decomposition of the Random Elastic Tensor
#### 4.2.1 Calculation of Lagrange Multipliers
In Equation (22), \(\mathbf{L}^{k}\) is an \(\mathbb{R}^{N}\)-valued random variable, which is the partial derivative of \(\Phi(\mathbf{u},\mathbf{\lambda})\) defined by the equation below. \[L^{k}_{j}\cong-\frac{\Phi\Big{(}\Delta\mathbf{U}^{k,j},\mathbf{\lambda}\Big{)}-\Phi \Big{(}\mathbf{U}^{k},\mathbf{\lambda}\Big{)}}{U^{k+1}_{j}-U^{k}_{j}}, \tag{24}\]  
with  
\[\Delta\mathbf{U}^{k,j}=\left(U^{k}_{1},\ldots,U^{k}_{j-1},U^{k}_{j}+\Delta U^{k+1 }_{j},U^{k}_{j+1},\ldots,U^{k}_{N}\right),\ \Delta U^{k+1}_{j}=U^{k+1}_{j}-U^{k}_{j}. \tag{25}\]  
Mathematical Expectation Estimation  
After the random number generator has been established and the ISDE has been discretized to obtain the random numbers, the expectation of these independent random numbers should be calculated to derive Lagrange multipliers. The mathematical expectation of the random variable \(\mathbf{B}_{\lambda}\) can be estimated by using the Monte Carlo method. The evaluation of the mathematical expectation of the random variable \(\mathbf{B}_{\lambda}\) is given by the equation below. \[E\{g(\mathbf{B}_{\lambda})\}\cong\frac{1}{n_{s}}\sum_{s=1}^{n_{s}}g(\mathbf{B}_{ \lambda}(\theta_{l})). \tag{26}\]  
After Lagrange multipliers are derived by evaluating the mathematical expectation of the random variable \(\mathbf{B}_{\lambda}\), the explicit PDFs of different elastic elements in the random elasticity matrix can be established. Depending on Equation (12), the PDF of the elastic tensor for the orthotropic symmetric class material could be defined by using the equation below. \[p_{\mathcal{C}}(\mathbf{c})=p_{\mathcal{C}_{1},\ldots,\mathcal{C}_{6}}(c_{1},\ldots, c_{6})p_{\mathcal{C}_{7}}(c_{7})p_{\mathcal{C}_{8}}(c_{8})p_{\mathcal{C}_{9}}(c_{9}), \tag{27}\]  
with  
\[p_{\mathcal{C}_{1},\ldots,\mathcal{C}_{6}}(c_{1},\ldots,c_{6})=k\;det(Mat(c_{1},\ldots,c_{6}))exp\left(-\sum_{i=1}^{6}\lambda_{i}^{(1)}c_{i}\right), \tag{28}\]  
with  
\[Mat(c_{1},\ldots,c_{6})=\left( \begin{array}{ccc}c_{1}&c_{4}&c_{6}\\ c_{4}&c_{2}&c_{5}\\ c_{6}&c_{5}&c_{3}\end{array} \right), \tag{29}\]  
and  
\[p_{\mathcal{C}_{j}}(c_{j})=k_{j}\exp\left(-\lambda_{i}^{(1)}c_{i}\right)c_{i}^ {-\lambda^{(2)}},\;j=7,8,9. \tag{30}\]  
**Remarks 1**. : _The random variables \((\mathcal{C}_{1},\ldots,\mathcal{C}_{6})\), \(\mathcal{C}_{7}\), \(\mathcal{C}_{8}\), and \(\mathcal{C}_{9}\) are mutually independent of each other. \(\mathcal{C}_{7}\), \(\mathcal{C}_{8}\), and \(\mathcal{C}_{9}\) are Gamma-distributed and the \(k\) and \(k_{j}\) are the normalization constants._
## 3 Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dependent Variables** & **Periods** & **Top-Elite** & **Elite** & \(p\)**-Value** \\ \hline Number of jumps (n) & All & 29.93 \(\pm\) 3.90 & 29.36 \(\pm\) 2.85 & 0.508 \\ \hline \multirow{3}{*}{Jump height (cm)} & T1 & 32.48 \(\pm\) 4.66 & 34.75 \(\pm\) 6.22 & 0.110 \\  & T2 & 30.46 \(\pm\) 4.74 & 30.98 \(\pm\) 5.91 & 0.702 \\  & T3 & 28.06 \(\pm\) 5.38 & 27.41 \(\pm\) 5.09 & 0.625 \\ \hline \multirow{3}{*}{Contact time (ms)} & T1 & 533.37 \(\pm\) 97.42 & 593.55 \(\pm\) 118.99 & **0.033** \\  & T2 & 533.77 \(\pm\) 106.42 & 578.91 \(\pm\) 117.99 & 0.117 \\  & T3 & 545.70 \(\pm\) 140.07 & 598.97 \(\pm\) 113.45 & 0.101 \\ \hline \multirow{3}{*}{Power (W)} & T1 & 2061.55 \(\pm\) 477.93 & 2008.21 \(\pm\) 415.99 & 0.638 \\  & T2 & 2079.09 \(\pm\) 482.78 & 1846.77 \(\pm\) 402.74 & **0.042** \\  & T3 & 1901.85 \(\pm\) 431.45 & 1621.92 \(\pm\) 344.68 & **0.006** \\ \hline \multirow{3}{*}{Force (N)} & T1 & 1795.82 \(\pm\) 654.22 & 1871.98 \(\pm\) 501.05 & 0.604 \\  & T2 & 1905.17 \(\pm\) 688.43 & 1722.68 \(\pm\) 422.01 & 0.216 \\  & T3 & 1570.86 \(\pm\) 476.38 & 1488.96 \(\pm\) 391.35 & 0.457 \\ \hline \multirow{3}{*}{Velocity (m/s)} & T1 & 2.73 \(\pm\) 0.33 & 2.85 \(\pm\) 0.36 & 0.175 \\  & T2 & 2.73 \(\pm\) 0.30 & 2.73 \(\pm\) 0.36 & 0.995 \\ \cline{1-1}  & T3 & 2.66 \(\pm\) 0.43 & 2.63 \(\pm\) 0.42 & 0.758 \\ \hline \end{tabular}
\end{table} 
Table 2: Mean values and standard deviation (M \(\pm\) SD) in jump height, contact time, power, force, and velocity, resulting from the execution of the repeated jump test, RJ30 (T1, initial; T2, 15 s; T3, 30 s) in top-elite and elite judo athletes.
## 4 Data Interpretation and Analysis
### Assessing the Structural Model  
The structural equation model for this study is made up of a predictive model for four exogenous latent variables and a reflective model for four endogenous latent variables before and during evacuation. To determine the validity and reliability of the model, we need to test the model. The process of testing predictive models involves four recognized criteria, including internal consistency, indicator reliability, convergent validity and discriminant validity [80]. First, the model is tested for internal consistency. The composite reliability values for the endogenous latent variables for the four reflective structures were  
Figure 3: Final PLS structural equation model. 0.936 (PAST), 0.913 (RAST), 0.908 (CPST), and 0.941 (CMST), all of which were above the internal consistency requirement of 0.7. External loads over 0.708 are acceptable for the predictive model; thus, external loads are acceptable for all endogenous variables in this study. This indicates that the reliability of the indicators of the prediction model of this study has reached a satisfactory level. Secondly, the convergent validity of the model was reviewed. The assessment of the convergent validity of this model was based on the average variance extracted (AVE) values, which were 0.879 (PAST), 0.840 (RAST), 0.889 (CMST) and 0.831 (CPST) for the four behavioural strategies, all of which were above the recommended threshold of 0.5 [80]. It is suggested that measurements of the four reflective structures of the predictive model show an extremely high level of convergent validity. Finally, the Fornell-Larcker criterion was used to assess the discriminant validity of the model. Table 3 shows the results of the assessment of the Fornell-Larcker criteria. The differential validity of the proactive, cooperative, competitive and passive strategies in the prediction model was 0.938, 0.911, 0.943 and 0.916, respectively. The square roots of the means of these discriminant validities are greater than the correlations of the other latent variables, indicating that the model has satisfactory discriminant validity in this study. Table 4, which summarizes the results of the prediction model evaluation, shows that the prediction model in this study meets all the relevant evaluation criteria and provides a foundation for the reliability and validity of predicting evacuation behaviour. In addition, the indicators of the measurement model need to be assessed for relevance and importance. The results of the co-linearity assessment of the measurement model indicators utilizing the variance inflation factor (VIF) are shown in Table 5, where the maximum
# Study on Filling Capacity of Optical Glass in a Novel Rapid Hot Embossing Process
## 3 Materials and Methods
### Characterization of the Filling Capacity  
After the hot embossing process, a microlens would be formed at the center of glass surface. Figure 5 illustrates the quantification method of filling capacity of glass. Firstly, surface topography of microlens on embossed glass is measured by the confocal laser scanning microscope (VK-250, Keyence, Osaka, Japan). The raw data of surface topography of microlens is leveled. After that, two surface profiles are extracted from two orthogonal line scans of the surface topography of microlens, both of which pass through the vertex of the microlens. The extracted surface profiles are then fitted by a step function for calculating maximum height with a fitting error. The average value of calculated maximum heights of the two surface profiles is used to quantify the filling capacity of glass in this study. Furthermore, the central part of profile 2 is fit by an arc function using least square method. The curvature radius, R2, of the best-fit profile is then used to evaluate the filling characteristics of glass.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Experiment** & **Embossing** & **Embossing** & **Soaking Time** & **Annealing Rate** \\
**Number** & **Force (N)** & **Temperature** (°C)** & **(Min)** & **(°C-Min)** \\ \hline
11 & 40 & 720 & 7 & 30 \\
12 & 40 & 720 & 9 & 30 \\
13 & 40 & 720 & 10 & 30 \\
14 & 40 & 720 & 8 & 20 \\
15 & 40 & 720 & 8 & 25 \\
16 & 40 & 720 & 8 & 35 \\
17 & 40 & 720 & 8 & 40 \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._  
Figure 5: Quantification method of filling capacity. (**a**) Surface topography of microlens on embossed glass, (**b**) surface profiles extracted from the surface topography of microlens, (**c**) fitting of profile 1, (**d**) fitting of profile 2.
# Signal Source Localization of Multiple Robots Using an Event-Triggered Communication Scheme
## 4 Simulation Results
### Case 2: The Variance of Noise \(R=8\)  
For Case 2, the noise variance error \(R=8\) is set in order to evaluate the noise influence on the proposed decision-control approach. The movement trajectories of three robots in one run are illustrated in Figure 5. From this figure, one can see that the three robots can coordinate their behaviors and locate the signal source. Correspondingly, Figure 6 shows the localization errors \(LE\), where one can see that the localization errors \(LE\) quickly become small such that the signal source is found when the search time approaches 30 s. Finally, we conduct 30 runs and obtain the statistical results for communication frequencies \(fre_{i}\) and localization errors \(LE_{i}\), shown in Table 4. From this table, one can see that the communication frequencies and the localization errors are small, which means that the communication burden is lightened and the proposed decision-control approach can predict the position of signal source under big noise well.  
Figure 4: The curves for the localization errors for Case 1.  
Figure 3: Movement trajectories of three robots for Case 1 where the red points denote the initial positions, the black lines are the trajectories of three robots, the yellow small stars are the current positions and the red big star refers to the signal source. The colors of the background represent the signal strength and are also labeled by the numbers. The signal strength increases with the decrease of the distance from the source. (**a**) \(t=0\) s; (**b**) \(t=5\) s; (**c**) \(t=10\) s; (**d**) \(t=15\) s; (**e**) \(t=20\) s; (**f**) \(t=30\) s.  
Figure 5: Movement trajectories of three robots for Case 2 where the red points denote the initial positions, the black lines are the trajectories of three robots, the yellow small stars are the current positions and the red big star refers to the signal source. The colors of the background represent the signal strength and are also labeled by the numbers. The signal strength increases with the decrease of the distance from the source. (**a**) \(t=0\) s; (**b**) \(t=5\) s; (**c**) \(t=10\) s; (**d**) \(t=15\) s; (**e**) \(t=20\) s; (**f**) \(t=30\) s.
# Metrology of Nanostructures by Tomographic Mueller-Matrix Scatterometry
## 4 Results and Discussion  
### Sample Description  
The sample is a photoresist array on a bottom anti-reflective coating (BARC) layer deposited on a Si substrate. Figure 4 presents a cross-sectional SEM image of the photoresist grating structure. Nominal dimensions of the grating structure are as follows: grating pitch = 400 nm, top critical dimension \(x_{1}\) = 200 nm, grating height \(x_{2}\) = 311 nm, and sidewall angle \(x_{3}\) = 90\({}^{\circ}\). The SEM image indicates that the top corner rounding could not be ignored, while the bottom corner rounding is unnoticeable. Therefore, the top corner rounding denoted by parameter \(x_{4}\) in Figure 4 is also taken into consideration. In data analysis, we fixed the grating pitch and let the parameters \(x_{1}\)\(\sim\)\(x_{4}\) vary. As well, optical constants are necessary, and for the Si substrate, it was taken from literature [28]. The optical properties of the BARC layer were modeled using a Tauc-Lorentz model [29], the parameters of which were taken as \(A\) = 6.4378 eV, \(C\) = 0.609 eV, \(E_{0}\) = 5.851 eV, \(E_{\text{g}}\) = 4.914 eV, and a Gaussian model [30], parameters of which were taken as \(A\) = 0.8567 eV, \(\omega_{0}\) = 0.3174 eV, \(\sigma\) = 6.429 eV, respectively. The optical properties of the resist layer were modeled using a Cody-Lorentz model [31], the parameters of which were taken as \(A\) = 2.261 eV, \(\Gamma\) = 0.750 eV, \(E_{0}\) = 6.189 eV, \(E_{\text{g}}\) = 4.867 eV, \(E_{\text{t}}\) = 0.292 eV, \(E_{u}\) = 2.000 eV, and three Lorentz models [32], the parameters of which were taken as \(A\) = 0.7089 eV, 0.1628 eV, 0.1755 eV, \(\Gamma\) = 0.3315 eV, 0.2055 eV, 0.3886 eV, \(E_{\text{n}}\) = 5.414 eV, 4.404 eV, 4.786 eV, respectively. Both the BARC and photoresist layers were predetermined by a commercial MME (ME-L, Epotics Technology Co., Wuhan, China), which can provide full 4 \(\times\) 4 Mueller matrices in the spectral range of 200-1000 nm.
## 3 Results and Discussion
### Antimicrobial Properties of the Films
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & & & \\ \hline \hline \multicolumn{1}{c}{**Inhibition Zone against Selected Bacteria (mm)**} \\ \hline \hline \multicolumn{1}{c}{**Sample**} & _S. aureus_ & _E. coli_ & _S. typhimurium_ \\ \hline \hline \multicolumn{1}{c}{Starch-PVA 40:60 (SP)} & - & - & - \\ \hline \hline \multicolumn{1}{c}{SP+1\% CNPs} & 6.37 \(\pm\) 0.79 \({}^{\text{c}}\) & - & - \\ \hline \hline \multicolumn{1}{c}{SP+3\% CNPs} & 9.35 \(\pm\) 0.92 \({}^{\text{b}}\) & 7.45 \(\pm\) 0.63 \({}^{\text{b}}\) & 7.02 \(\pm\) 0.36 \({}^{\text{b}}\) \\ \hline \hline \multicolumn{1}{c}{SP+5\% CNPs} & 11.78 \(\pm\) 1.06 \({}^{\text{a}}\) & 9.40 \(\pm\) 0.81 \({}^{\text{a}}\) & 8.90 \(\pm\) 0.74 \({}^{\text{a}}\) \\ \hline \hline \end{tabular} - No inhibitory effect. Starch-PVA 40:60 (or SP as control film sample), starch-PVA loaded with 1% CNPs (SP+1% CNPs), starch-PVA film loaded with 3% CNPs (SP+3% CNPs), and starch-PVA film loaded with 5% CNPs (SP+5% CNPs). Results are presented as the mean \(\pm\) standard deviation. Different letters in each column indicate statistically significant differences at \(p\) \(<\) 0.05.  
\end{table} 
Table 3: Antibacterial properties of starch-PVA-based films. have a greater capability to form electrostatic bonds between their cellular wall and CNPs functional groups, mainly due to the thick peptidoglycan layer of Gram-positive bacteria formed from teichoic acids. Phosphate groups with a negative charge can form electrostatic bonds with cationic particles such as chitosan or their derivatives. CNPs can modify the cellular membrane due to their negative charge, are able to inactivate the cell membrane permeability by connecting to the microorganism DNA, and can cause the cellular death by disrupting the cell reproduction [43; 44]. These higher antimicrobial properties of CNPs against Gram-positive microorganisms were also reported by [PERSON] et al. [29] and [PERSON] et al. [45] (against _S. aureus_ and coliform bacteria for 2-4% \(w/v\) CNPs amounts), as well as by [PERSON] et al. [46] (against _L. monocytogenes_, _S. aureus_ in the case of whey protein isolate-HPMC films containing 5% CNPs).