esa-sceva/satcom-chunk-collection huggingface dataset:
# Analyzing Brain Waves of Table Tennis Players with Machine Learning for Stress Classification
## 3 Results and Discussion
### Evaluation Metrics  
A Confusion matrix is a matrix for statistical work on the correctness or incorrectness of classification. In the case of two-level classification, there are four types of results, namely, True Positive (TP): correctly predicting a positive sample as positive, True Negative (TN): correctly predicting a negative sample as negative, False Positive (FP): incorrectly predicting a negative sample as positive, and False Negative (FN): incorrectly predicting a positive sample as negative.  
The commonly used assessing indicators are described next. Accuracy (Acc), which is the proportion of correctly predicted samples to the total predicted samples, as Formula (13) shows. Precision (Pre), which is the proportion of correctly predicted positive samples to the total samples predicted as positive, as Formula (14) shows. Recall (Rec), which is the proportion of correctly predicted positive samples to the total actual positive samples,also called the true positive rate (TPR), as Formula (15) shows. The F1-score (F1), which is the harmonized means of precision and recall, as Formula (16) shows. The Matthews correlation coefficient (MCC), which consists of all four types of the Confusion matrix (TP, TN, FP, and FN) and, thus, is a more balanced indicator for all types, as Formula (17) shows. In multi-level classifications, only the indicators of two-level classification that can be used would differ according to the way of the calculations; the method of this study is to determine, straightforwardly, the means of assessing indicators of different types added together.  
\[\text{Acc}=\frac{(\text{TP}+\text{TN})}{(\text{TP}+\text{TN}+\text{FP}+\text{ FN})} \tag{13}\]  
\[\text{Pre}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{14}\]  
\[\text{Rec}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{15}\]  
\[\text{F1}=\frac{2\text{Pre}\times\text{Rec}}{\text{Pre}+\text{Rec}} \tag{16}\]  
\[\text{MCC}=\frac{\text{TP}\times\text{TN}\ -\ \text{FP}\times\text{FN}}{ \sqrt{(\text{TP}+\text{FP})(\text{TP}+\text{FN})(\text{TN}+\text{FP})(\text {TN}+\text{FN})}} \tag{17}\]  
The 10-fold cross-validation is a sampling of many times technique capable of minimizing the bias and variance of datasets and avoiding the overfitting problem. In 10-fold cross-validation, a dataset is randomly divided into ten subsets, of which one is used as a test set and the rest as a training set; when all subsets have been run once, their evaluation indicators are determined by a predicted value and real value. The mean measures are determined to obtain the final validation of the models [44].  
In order to assess the results of the performance validation of the models, the 10-fold cross-validation was used in this study, along with a variety of model assessing indicators, which were five in the two-level classification, including accuracy, precision, recall, F1, and MCC, or four in the three-level classification, including accuracy, precision, recall, and F1.
## 2 Theoretical Framework
### Tourist Flow Analysis and Social Media Analytics
It also allows the analysis of multi-modal data such as User Generated Content (including text, images, and even videos), and geotagged information [68, 32]. However, studies based on geotagged photos and other social media like Flickr [38, 69], Foursquare [60], or Instagram [70] have also proliferated showing tourists' flows, movements, and behaviors in destinations. It has also been shown that analyzing different social media sources or platforms is useful because they provide complementary information and enrich the knowledge of different tourists' movements [35]. In this line, [PERSON] et al. [71] analyzed tourists' movements at destinations through three social media (Twitter, Foursquare, and Flickr) and identified different types of trips according to the origin of the tourists. A study by [PERSON] et al. [72] combined tracking technologies and surveys to study the relationship between visitor mobility and urban spatial structures. [PERSON] et al. [35] analyzed the digital footprint of urban tourists through photos, check-ins, and tweets from three social media (Panoramio, Foursquare, and Twitter). In addition, they used a clustering methodology to identify certain areas of the destinations according to the tourist activities that visitors carried out in them. However, they did not cluster or segment tourists to know their different preferences, movements, and behaviors. Many studies on GSMD have focused on tourists' mobility patterns [73, 74, 59, 75]. However, the difference in mobility patterns between sub-groups or clusters of tourists has not been fully researched [37].
# Rock Breaking Performance of TBM Disc Cutter Assisted by High-Pressure Water Jet
## 3 Setup for Numerical Simulation
### Numerical Model  
The rock is set as an explicit solid 3D element considering its deformation and failure criterion under the multiple forces of the TBM disc cutter and the high-pressure water jet. The linear equation of state is chosen to describe the properties of rock deformation, which can be expressed as Equation (1).  
\[P_{r}=K\mu=K\frac{\rho_{r}}{\rho_{0}-1} \tag{1}\]  
where \(P_{r}\) is the pressure within the rock, K is the rock bulk elastic modulus, \(\rho_{r}\) is the rock real density, and \(\rho_{0}\) is the reference density.  
Failure plays a significant role in the process of rock being broken by the disc cutter and the water jet, which is determined by the maximum principal stress criterion. As shown in Equation (2), rock will be broken if the maximum principal stress exceeds the rock tensile strength, for rock is a brittle material whose tensile strength is rather less than the compression strength.  
\[\sigma_{1}(\sigma_{2})\geq\sigma \tag{2}\]  
Figure 2: Schematic diagram of rock being broken by a disc cutter coupled with a high-pressure water jet.  
where \(\sigma_{1}\) (\(\sigma_{2}\)) is the maximum principal stress, and \(\sigma\) is the rock tensile strength. The details of the rock physical properties are shown in Table 1.  
The disc cutter is also set as an explicit solid 3D element but ignoring its deformation and failure criterion, for whom rigid is chosen as the material model. The details of the disc cutter physical properties are shown in Table 1.  
The water jet is treated as a smooth particle flow, and the NULL material type was adopted to it with the Mie-Gruneisen equation of state, which can be expressed as the following [32].  
\[P=\frac{\rho_{0}C^{2}\mu[1+(1-\frac{\gamma_{0}}{2})\mu-\frac{\alpha}{2}\mu^{2}] }{1-(S_{1}-1)\mu-S_{2}\frac{\mu^{2}}{\mu+1}-S_{3}\frac{\mu^{3}}{{(\mu+1)}^{2} }}+(\gamma_{0}+\alpha\mu)E_{0}\alpha \tag{3}\]  
where \(E_{0}\) is the internal energy per unit volume, \(\mu\) is the volumetric strain, \(\rho_{0}\) is the initial density, \(\gamma_{0}\) is the Mie-Gruneisen constant, and \(\alpha\) is the first order volume correction. \(C\) is the intercept of the \(V_{\text{s}}\)-\(V_{\text{p}}\) curve, while \(S_{1}\), \(S_{2}\), and \(S_{3}\) are the slope coefficients of the \(V_{\text{s}}\)-\(V_{\text{p}}\) curve. The above parameters of the jet fluid are shown in Table 2.
## 2 Materials and Methods
### Determination of the Parameters of the SIR Model  
Good methods alone never resolve any problem, but bad or unsuitable ones can make matters worse. The essential part of using epidemic mathematical models is the use of proper parameter values. The parameter values affect not only the unknown calculated values of the model but also the mathematical properties of the model itself [15; 17]. To obtain suitable and accurate values of the parameters, our treatment focuses on the model (4). Adding the first two equations of the model (4) and integrating the resultant equation along fixed time period \([t_{m},t_{n}]\), one obtains:  
\[\int_{t_{m}}^{t_{n}}(S(t)+I(t))^{\prime}dt=-\int_{t_{m}}^{t_{n}}\alpha_{c}Idt, \tag{6}\]  
\[S(t_{n})+I(t_{n})-S(t_{m})-I(t_{m})=-\int_{t_{m}}^{t_{n}}\alpha_{c}Idt. \tag{7}\]  
Using the assumption, \(S(t)+I(t)+R(t)=N\), one finds  
\[R(t_{n})-R(t_{m})=\alpha_{c}\int_{t_{m}}^{t_{n}}I(t)dt, \tag{8}\]  
which is compatible with the third equation, integrating the first equation of the system 4 over the time period \([t_{m},t_{n}]\).  
\[\int_{t_{m}}^{t_{n}}\bigg{(}\frac{S^{{}^{\prime}}(t)}{S(t)}\bigg{)}dt=-\int_{t _{m}}^{t_{n}}\beta_{c}Idt, \tag{9}\]  
\[\ln\bigg{(}\frac{S(t_{m})}{S(t_{n})}\bigg{)}=\beta_{c}\int_{t_{m}}^{t_{n}}I(t)dt. \tag{10}\]  
According to formulas (8) and (10), one can determine the local values \(\alpha_{c}\), the local recovery coefficient and \(\beta_{c}\), the local transmission coefficient corresponding to the considered period from the daily data on active cases published by the WHO Dashboard [18], and represented in Figure 2 if a good approximation of the integral \(\int_{t_{m}}^{t_{n}}I(t)dt\), appears in the right hand side of both (8) and (10) is calculated. The data on active cases from day 1 up to day 11, are used, for each month to calculate a good approximation of the integral \(\int_{t_{1}}^{t_{11}}I(t)dt\), (\(m=1\) and \(n=11\)) many numerical techniques can be used, the simplest form is the trapezoidal rule [4].  
\[\int_{t_{1}}^{t_{11}}I(t)dt=\frac{1}{2}\{(I_{1}+I_{11})+2(I_{2}+I_{3}+...+I_{ 10})\}, \tag{11}\]  
where \(t_{i}\) denotes the day \(i\) of the month under consideration. Therefore, the parameter values calculated according to this approach for the Saudi Arabia region are given in Table 1. From formula (8) the value of \(\alpha_{c}\) depends only on the actual recorded data \(I(t)\) and \(R(t)\) not on the selected value \(N\).  
The standard recovery time for corona virus COVID-19 [16], during this period is approximately \(1/\alpha=14\), which was achieved in December 2020 as shown in Table 1.
## 5 Discussion
### Definition of \(G_{\rm max}\)
For instance, when \(G_{\rm max}\) is calculated by \(1/a\), the \(G/G_{\rm max}\) determined by the autocorrelation function method is typically 30-40% lower than that of the hysteresis loop method in the strain range of \(10^{-3}\)\(\sim\)\(10^{-2}\) at \(\sigma_{\rm c}=50\) kPa (Figure 15b) while the percentage change is 20-25% for the \(G_{\gamma=10}^{-6}\) case, as depicted in Figure 12. From Figure 15b, the decreased relative difference with increasing effective confining pressure can be observed. Additionally, the relative difference will further increase when both the uncertainties associated  
Figure 13: Comparison of \(G_{\rm max}\) between \(1/a\) and \(G_{\gamma=10}^{-6}\). with the \(G\) determination methods and definitions of \(G_{\max}\) are considered. For instance, for \(\sigma_{\rm c}=900\) kPa, the relative difference of \(G/G_{\max}\) between the autocorrelation function method with \(G_{\max}=1/a\) (i.e., green dash line, Figure 15a) and the hysteresis loop method with \(G_{\max}=G_{\gamma=10}{}^{-6}\) (i.e., black solid line, Figure 15a) can be up to around 50% at \(\gamma=10^{-2}\). The discussion presented in this section further highlights that the adverse influence of the random choice of determination methods for both \(G\) and \(G_{\max}\) on the \(G/G_{\max}\). Different \(G/G_{\max}\) results are obtained and some of them differ significantly. Therefore, a unified determination method that accurately represents the irregularity of the hysteresis loop at large strains is warranted. Thus, the uncertainties associated with the \(G/G_{\max}\) measurements, to some extent, can be reduced. This would be highly beneficial for engineering and practical because more accurate empirical models could be developed if the uncertainties in \(G/G_{\max}\) are substantially eliminated [2; 5; 6; 23; 38]. Figure 14: Comparison of \(G/G_{\max}\) between \(1/a\) and \(G_{\gamma=10}{}^{-6}\) for various \(\sigma_{\rm c}\) (**a**) Hysteresis loop method, (**b**) Least square method, (**c**) Autocorrelation function method, (**d**) Arithmetical average method. Figure 15: Comparison of (**a**) \(G/G_{\max}\) between \(1/a\) and \(G_{\gamma=10}{}^{-6}\) for three determination methods and (**b**) the corresponding percentage changes (\(\sigma_{\rm c}=50,900\) kPa).
## 4 Results and Discussion
### Case Study 1
#### Application of RTO  
As discussed in Section 2, to apply RTO to this problem, the objective function was first converted into an unconstrained optimization problem by integrating the relay constraint into the objective function. The constraints pertaining to the MOT of the relay have the ability to alarm the lower bounds of the RTO. The TMS values of all the relays varies from 0.025 to 1.2. The constraint owing to the MOT of the relay was taken care of defining the lower bounds of the variable in RTO. The lower and upper limit of all TMS of the relay is considered as 0.025 to 1.2. The constraints pertaining to coordination of the relay depicted by Equations (28)-(35) were incorporated in the objective function by means of a penalty scheme. In order to justify the RTO, a population size of 50 (i.e., candidate solutions, eight designed variables x\({}_{1}\) to x\({}_{8}\), and 200 iterations as meeting criterion) has been assigned to the coordination problem. The population was conveyed to the fitness function and the proceeding values of the objective function are perceived. As the objective function is of the minimization type, the lowest fitness value of the objective function is considered is the best solution among the population size. After running 200 iterations, the optimum values are shown in Table 3. Table 3 shows the optimal TMS values achieved by the proposed RTO method for this case and has been compared with the literature. Figure 6 shows the objective function values achieved during the course of the simulation for the best candidate solution in each iteration, and this figure shows that the convergence rate is faster and achieves satisfactory results in less iterations compared to other techniques cited in the literature. This figure also shows that \(R_{n}\) = 0.4, \(R_{r}\) = 0.3, and \(R_{c}\) = 0.3 are the feasible values for the RTO technique. These parameter values are used for the rest of the case studies presented in this paper as it gives an optimal solution compared with other adjustable parameters of the RTO algorithm. Figure 7 shows the graphical representation of the optimized total operating time, _Top_ (_z_), and demonstrates that the total operating time is minimized up to the optimum value. The optimal value of objective is found to be 26.681 s by considering a CTI of 0.6 s, which is obtained in a fewer number of simulations.  
The values depicted in Table 3 illustrate that the RTO technique gives an ideal and best-streamlined aggregate working time (i.e., total operating time) up to the ideal optimum value. The optimum values guarantee that the relay will operate in the minimum time to detect a fault at any location. All the optimal values obtained by RTO satisfy all the coordination constraints.
# Identification of Mode Shapes Based on Ambient Signals and the IA-VMD Method
## 2 Framework of VMD-Based LFO Identification  
### Broadband Ambient Model  
Broadband oscillatory signals are constructed as follows. The original broadband signal \(S_{0}(t)\) contains several oscillatory modal signals of different frequency bands, ambient signals of system operating parameters, and noise signals in the process of measurement and transmission. The original measurement signal \(S_{0}(t)\) is expressed as  
\[S_{0}(t)=\sum_{i=1}^{N}S_{i}(t)+R(t)+N_{0}(t) \tag{1}\]  
In the formula, \(S_{0}(t)\) represents the measured signal collected by the system, \(S_{i}(t)\) (\(i\) = 1,2,..., \(N\)) represents the oscillation signal in the \(i\)-th frequency band, \(R(t)\) represents the residual modal component containing the trend component, and \(N_{0}(t)\) represents the persis tent environmental noise and transmission noise in the system. The oscillation morphology of oscillation modal signal \(S_{i}(t)\) in each frequency band is decomposed as follows:  
\[S_{i}(t)=\sum_{k=1}^{K}IMF_{k}(t) \tag{2}\]  
In the formula, \(S_{i}(t)\) represents oscillation signals of different frequency bands, and each frequency band oscillation signal consists of multiple inherent modes. \(IMF_{k}\) (\(t\)) (\(k\) = 1,2,..., \(N\)) represents the \(k\)-th different inherent oscillation mode signal and its expression is shown as follows:  
\[IMF_{k}(t)=U_{k}(t)e^{-D_{k}t}\cos(2\pi f_{k}) \tag{3}\]  
In the formula, \(U_{k}(t)\) represents the amplitude of the \(k\)-th intrinsic modal component varying with time, \(f_{k}\) represents the frequency of the \(k\)-th intrinsic modal component, and \(D_{k}\) represents the damping ratio of the \(k\)-th intrinsic modal component.
## 2 Multi-Dimensional User Importance Indicator System and Ranking Model
### User Importance Ranking Based on the Improved AHP-EW Method  
The analytic hierarchy process (AHP) method relies on the assessment and analysis of indicators of experts, which is subjective and does not necessarily reflect the actual objective situation [21]. The entropy weight (EW) method has strong objectivity, but the weights of the indicators obtained by the entropy weight method ignore the correlation and specific meaning of the indicators, which has limitations [22]. Therefore, this paper fully exploits the advantages of subjectivity and objectivity by combining the hierarchical analysis method with the entropy weight method to obtain an accurate user importance ranking model.
## 2 Chang'E-5T Onboard GPS Data Analysis
### C1 Code Errors  
For onboard GPS signals of Chang'E-5T, most of them are ionospheric-free and tropospheric-free. It should be mentioned that only 0.2% of all the signals are affected by the atmosphere which originated from GPS satellites on the opposite part of the Earth, and excluded for computing code multipath and noise. Hence the differences between the L1 and C1 measurements used in this section include only L1 ambiguities, L1 multipath errors and noise, as well as C1 multipath errors and noise. After removing the constant ambiguities by subtracting the mean value of the differences between the L1 and C1 measurements, the residual series are dominated by the code multipath and noises since the carrier phase multipath and carrier phase noises are much smaller in magnitude. In the following the residual series is referred as C1 code errors and is used to assess the precision of C1 code observations.  
The C1 code errors for all GPS satellites are calculated and shown in Figure 6. In the figure the C1 code errors are shifted by 20 m from one to another. The code errors are mainly between \(\pm\)10 m and show consistent variations during the entire 2 h. In order to show more details of onboard GPS code measurement errors, we also plot the times series of C1 code errors for G32 satellite in Figure 7. The mean value and standard deviation of G32 code errors are 0 m and 3.92 m, respectively.  
Figure 6: The times series of C1 code errors for all GPS satellites (unit: meter). The values of each satellite have been offset 20 m for clarity. The different colors represent different satellites, and the colors are the same as Figure 1.  
Figure 7: The times series of C1 code errors for G32 satellite (unit: meter).  
In addition, to analyze the relationship between code errors and C/N0 values, the Root Mean Squares (RMS) values of C1 code errors as a function of C/N0 are shown in Figure 8. We can see that the precision of C1 observations is better with C/N0 values increasing. The C1 code errors reach to as large as 10 m when C/N0 gets close to the lower limit of the receiver sensitivity. For C/N0 larger than 30 dB-Hz, the C1 observation errors are better than 5 m. The overall precision of C1 code errors is 4.15 m.
## 4 Conclusions  
To conclude, quantum well-dots offer some advantages of both quantum wells and quantum dots and do show a combination of unique properties, such as high optical gain, the possibility of stacking a large number of layers in the active region, suppressed lateral transport of charge carriers, good temperature stability, applicability in the micro-resonators. In addition to previously achieved improvement of the performance of light converters, this allowed demonstrating high-performance edge-emitting lasers and microdisk lasers capable of error-free 10 Gbit/s data transmission at 30 \({}^{\circ}\)C without using an external optical amplifier and temperature stabilization. We expect that QWDs will found further applications in various optoelectronic devices where high optical gain and a large number of active layers are required, for instance, in vertical-cavity surface-emitting lasers, optical amplifiers, super-luminescent light-emitting diodes. Small carrier diffusion lengths are advantages for compact photonic devices where etching is done through the active area. Suppressed carrier lateral transport can reduce non-radiative recombination at facets of high-power lasers and thus decrease catastrophic optical mirror damage.  
Conceptualization, M.V.M. and A.E.Z.; growth-related part, S.A.M. and N.A.K.; microscopy, V.N.N. and S.S.R.; optical properties and visualization, A.M.N.; edge-emitting lasers, N.Y.G., Y.M.S. and A.S.P.; microdisk lasers, N.V.K. and E.I.M.; data transmission and high frequency analysis, A.E.Z. and F.I.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Russian Science Foundation (Grant No 16-12-10269).  
The authors declare no conflict of interest.