esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Results and Discussion
### Feature Selection  
As mentioned in Section 4, some ground motion parameters have no linear correlation with MSI, it is important to focus on the most relevant features and eliminate the irrelevant ones. The inclusion of irrelevant features in the SVR gives bad prediction results, due to the overfitting problem in the irrelevant information. Each ground motion parameter should be checked for relevancy, and each time one ground motion parameter should be selected as the sole feature. The performance of Gaussian kernel SVR is shown in Figure 6, where the optimal model parameters are \(C=256\) and \(\gamma=1/16\), using 10-fold cross-validation on the 98-observation training dataset. It can be seen that parameters ASI, \(A_{0.3}\), \(Ic\), PGA, DI, HI, PGV, VSI, and AI all have MSEs smaller than 0.5 and accuracy percentages more than 50%. PGA gives the highest accuracy percentage, followed by \(A_{0.3}\) and HI, and \(a_{\text{RMS}}\), CAV, \(Td_{b,5}\), \(Td_{s}\), \(Td_{b,5\%}\), CF, and \(v_{\text{max}}/a_{\text{max}}\) have MSEs greater than 0.5 and accuracy percentages less than 50%.  
In this regard, the first nine ground motion parameters are considered as relevant features, and the latter seven are irrelevant features. Here, only the first nine ground mo  
Figure 5: Histogram of the macroseismic intensity dataset.  
Figure 6: Performance of SVR with a sole feature for the 16 ground motion parameters.  
for further regression study. There are \(\sum\limits_{i=1}^{9}\text{C}_{9}^{i}=511\) possible combinations of those nine features, and the best performances for SVRs having up to nine features are shown in Figure 7. It can be seen that seven features, including PGA, \(A_{0.3}\), ASI, HI, PGV, VSI, and \(Ic\), give the highest accuracy percentages, followed by six features, then by one feature (PGA). It is noted that one feature, PGA, gives almost the same level accuracy as a combination of seven features, meaning PGA is the most fundamental feature for all cases. One reason for this is that the other six ground motion parameters have relative high cross-correlation coefficients with PGA, and are partially linearly dependent on one another. When the number of features becomes larger than seven, the accuracy percentage drops below 50%, and this means more features do not necessarily give better prediction. With the development of strong ground motion observation network, many more stations will be constructed. In the future, a large number of stations will be triggered in an earthquake event. Calculating PGA, \(A_{0.3}\), ASI, HI, PGV, VSI, and \(Ic\) from ground motion requires much more time than just calculating PGA, and the time for calculating them for many stations will be even more. Since every millisecond is important in rapid seismic intensity reporting, the SVR of PGA will be more effective than the SVR of this seven ground motion parameters.
## 3 Results  
Overall, 31 orbits of OVS global images from November to December 2019 were selected as calibration samples. These images cover the low, medium, and high brightness range of the imaging sensor, and feature types cover typical scenes, such as oceans, cities, and clouds. Considering the possible nonlinear radiometric response of OVS, in order to verify the correction effect of radiometric calibration parameters more comprehensively and accurately, the DN range of the validation data must cover the three typical DN ranges, that is, low, medium, and high, of the dynamic range of OVS. Data from another five orbits were selected to validate the efficiency and accuracy of the calibration method (Figure 9). Zone 1 was used to visually verify the radiometric correction effect of each detector of the Bayer pattern under various brightness conditions. The uniform ocean data of Zones 2-5 were used to quantify the calibration accuracy.
## 5 Results and Evaluation
After several retraining of the model, we observed that the size of utterance word embedding parsed into the memory does not affect its performance, but instead, altering the values of the epochs directly influences overall accuracy and performance of the model. As the number of epoch increases from a lower value to the max set value, the average accuracy score also increases, as well as the scores of Recall\({}_{10}\)@1, Recall\({}_{10}\)@2, Recall\({}_{10}\)@5, and vice-versa. This scenario is depicted with the plot in Figure 4. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **R\({}_{10}\)@1** & **R\({}_{10}\)@2** & **R\({}_{10}\)@5** \\ \hline DualEncoder [23] & 0.638 & 0.784 & 0.949 \\ MV-LSTM [26] & 0.653 & 0.804 & 0.946 \\ Match-LSTM [27] & 0.653 & 0.799 & 0.944 \\ Multiview [28] & 0.662 & 0.801 & 0.951 \\ DL2R [29] & 0.626 & 0.783 & 0.944 \\ SMN [2] & 0.726 & 0.847 & 0.961 \\ DAM [1] & 0.767 & 0.874 & 0.969 \\ BERT Bi-Encoder + CE [20] & 0.793 & 0.893 & 0.975 \\ \hline
**mDNA** & **0.797** & **0.891** & **0.975** \\
**mDNA\({}_{self}\)** & **0.788** & **0.885** & **0.971** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance evaluation results of the mDNA and some selected retrieval chatbot models trained on Ubuntu dataset. Figure 4: Plot showing increase in average accuracy (denoted as accuracy) scores and performance evaluation scores as the number of training epoch increases. The accuracy is averaged by R\({}_{10}\)@1, R\({}_{10}\)@2, and R\({}_{10}\)@5 at corresponding epochs.
## 4 Model Building and Training
### Model Building  
#### 4.1.1 Model Building for CatBoost  
For the algorithm based on the CatBoost method, learning rate and tree depth are selected as adjustable parameters.  
Figure 4: Parameter selection and model evaluation process using parameter grid and five-box cross-validation.  
The learning rate factor is a parameter that allows control of the amount of weight correction at each iteration. In practice, the learning rate coefficient is usually selected experimentally; its tuning allows for achieving the highest possible quality of the model.  
The second adjustable coefficient is the depth of the tree. In most cases, the optimal value is between 4 and 10, so this range of values is used in the parameter lattice. All possible combinations form a table-grid of model parameter settings, as shown in Table 3.  
As a result of five-box cross-validation, for all combinations of learning rate and tree depth, we need to train 60 models (\(3\times 4\times 5\)).  
Figure 5 shows a heatmap for the cross-validation average R\({}^{2}\) expressed as a function of two parameters: tree depth and learning rate.  
Each heatmap value corresponds to an R\({}^{2}\) value for a specific combination of parameters, where light tones correspond to a high value and dark tones to a low value. It can be seen from the graph that the implemented CatBoost algorithm is sensitive to parameter settings, so their optimization is necessary to obtain good generalization ability. Various combinations of learning rates and tree depths increase R\({}^{2}\) from 87% (learning rate 0.5, tree depth 4) to 98% (learning rate 0.1, tree depth 8).  
As a result of lattice search and cross-validation, the best parameters of the model were determined: tree depth equal to 8 and learning rate 0.1.  
Figure 5: Heat map of R\({}^{2}\) value from two parameters: tree depth and learning rate.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Depth = 4** & **Depth = 6** & **Depth = 8** & **Depth = 10** \\ \hline learning rate = 0.03 & model (depth = 4, learning rate = 0.03) & model (depth = 6, learning rate = 0.03) & model (depth = 8, learning rate = 0.03) \\ \hline learning rate = 0.1 & model (depth = 4, learning rate = 0.1) & model (depth = 6, learning rate = 0.1) & model (depth = 8, learning rate = 0.1) \\ \hline learning rate = 0.5 & model (depth = 4, learning rate = 0.5) & model (depth = 8, learning rate = 0.5) & model (depth = 10, learning rate = 0.5) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameter grid for the CatBoost model.
## 4 Proposed CPCROK Scheme
### Overview of the CPCROK Scheme  
As depicted in Figure 3, the CPCROK approach comprises three modules: the IKF-based coarse-grained trajectory-generation module (IKF-CGTGM), the RNN-based fine-grained trajectory-generation module (RNN-FGTGM), and the fake beacon generation module (FBG). Each vehicle's trajectory \(V_{i}\) before entering a mix zone is represented by a series of beacons (\(x(t_{1})\), \(x(t_{2})\), etc.). Leveraging the steering angle \(\theta\) in the mix zone, the IKF-CGTGM provides a coarse trajectory prediction \(P_{i}\) within the mix zone for each vehicle, along with corresponding timestamps (\(t_{1}\), \(t_{2}\), etc.) in beacons. The RNN-FGTGM then trains an RNN model using \(P_{i}\) to generate outputs that closely match the observed trajectories \(V^{{}^{\prime}}\) within the mix zone (\(z_{t+1}\), \(z_{t+2}\), etc.). Once the training phase is complete, the inference phase begins, where the IKF-CGTGM follows the same process for each new vehicle \(V\), and the coarse prediction \(P\) is adjusted by the well-trained RNN model in the RNN-FGTGM. Finally, the FBG generates extended fake beacons based on the estimated states from the RNN-FGTGM to form the whole fake trajectory \(V_{f}\). By working together, these modules distribute highly realistic fake beacons using a single fake trajectory.
# A Novel Fault Diagnosis Method for TE Process Based on Optimal Extreme Learning Machine
## 2 Materials and Methods
### Experiment
#### 2.4.2 Tennessee Eastman Process  
The Tennessee Eastman (TE) process is a platform for chemical simulation experiments based on the actual chemical reaction process. [PERSON] and [PERSON] [55] proposed the use of this method for evaluating process monitoring and monitoring methodologies. The TE process is a classic example in the chemical process. As a result, numerous academics have researched the process and have utilized it to drive process monitoring and defect identification. Figure 4 depicts the Tennessee Eastman process's approximate schematic, which includes five primary units: reactor, condenser, compressor, stripper, and separator. The TE process uses four reactants, A, C, D, and E, as well as two products, G and H. Inertia component B and by-product F are also present.  
There are 12 controlled variables and 41 measured variables in the TE process. The response rate, on the other hand, is always omitted from the variable, and the other 52 variables are used to represent the process rate in its entirety. The first 41 variables are measured variables, followed by 11 controlled variables. There are 16 known faults and 5 unknown faults in the TE process. Every defect has a train set and a test set, for a total of 22 training sets, including normal conditions. The fault training datasets were collected during a 24 h fault simulation. The test datasets were generated using a 48 h running simulation, and the problem was introduced at the 8 h mark. Three minutes was chosen as the sampling time.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Fault** & **Feature** & **Fault** & **Feature** & **Fault** & **Feature** \\ \hline
1 & 1, 20, 22, 44, 46 & 8 & 16, 29, 38, 40, 41 & 15 & 16, 19, 20, 39, 40 \\
2 & 10, 34, 39, 46, 47 & 9 & 19, 25, 31, 38, 50 & 16 & 18, 19, 38, 46, 50 \\
3 & 18, 20, 37, 40, 41 & 10 & 18, 19, 31, 38, 50 & 17 & 21, 38, 46, 50, 51 \\
4 & 19, 38, 47, 50, 51 & 11 & 7, 9, 13, 38, 51 & 18 & 16, 19, 22, 41, 50 \\
5 & 17, 18, 38, 50, 52 & 12 & 4, 11, 18, 19, 35 & 19 & 5, 13, 20, 46, 50 \\
6 & 1, 20, 37, 44, 46 & 13 & 7, 18, 19, 39, 50 & 20 & 19, 39, 41, 46, 50 \\
7 & 19, 38, 45, 46, 50 & 14 & 9, 11, 21, 38, 50 & 21 & 7, 16, 19, 45, 50 \\ \hline \end{tabular}
\end{table}
Table 2: Selected features for each conditionFigure 4: The TE process diagram.
## 6 Simulation Results and Discussion
### Optimization Results
#### 6.1.5 PV/Diesel/Battery System
The yearly power contributions of the PV and diesel generator, and the state of charge of the battery for the PV/diesel/battery hybrid system were depicted in Figure 14. As shown in Figure 14a during, the daytime, especially from 8:00 to 16:00, the PV system contributed a relatively high power share, whereas the remaining time and the peak load conditions were handled by the diesel generator and the battery system, as depicted in Figure 14b,c.
## Appendix A Equivalent Beam Model  
We describe a homogenization procedure to define a continuum with a mechanical response equivalent to the piezoelectric-beam composite employed in this work (see Figure 1). We formulate a suitable objective function \(Obj\), whose constrained minimization yields the set of parameters of the equivalent continuum. Denoting by \(E_{0},\rho_{0},h_{0}\), respectively, the Young's modulus, density and thickness of the steel support layer of the beam, and by \(E,\rho,h\) the corresponding parameters of the homogeneous equivalent beam (see Table 1 for the values), the objective function \(Obj\) to be minimized is defined as:  
\[Obj(E,\rho,h)\equiv\left(\frac{E-E_{0}}{E_{0}}\right)^{2}+\left(\frac{\rho- \rho_{0}}{\rho_{0}}\right)^{2}+\left(\frac{h-h_{0}}{h_{0}}\right)^{2}. \tag{1}\]  
The first natural angular frequency is measured \(\omega_{exp}=157\) rad/s by analysing a free oscillation of the composite beam. Moreover, a static experiment is carried out by placing a point load \(p_{L}=0.565\) N at the free end of the cantilever and measuring its displacement \(w_{exp}=7.7\) mm. The minimization procedure is then constrained as follows:  
1. The first natural angular frequency \(\omega_{10}\) of the equivalent beam must match \(\omega_{exp}\), namely: \[\omega_{10}=a\left(1.875/L\right)^{2}=\omega_{exp},\] (2) where \(a(E,\rho,h)\equiv\sqrt{E/(\rho\,b\,h)}\) and \(b\) the width of the beam's section;
2. The tip deflection must match the measured one, namely: \[w_{L}=p_{L}L^{3}/(3\,E\,J)=w_{exp};\] (3)  
Employing a set of two Lagrange multipliers, \(\lambda_{1}\) and \(\lambda_{2}\), we find stationary points of the Lagrangian \(\Lambda\):  
\[\Lambda\equiv Obj(E,\rho,h)+\lambda_{1}\Big{(}\omega_{exp}-a(E,\rho,h)(1.875 /L)^{2}\Big{)}+\lambda_{2}\Big{(}w_{exp}-p_{L}\,L^{3}/(3\,E\,J(h))\Big{)}, \tag{4}\]  
considered as a function of \(E,\rho,h,\lambda_{1},\lambda_{2}\).  
The set of equivalent parameters are listed in Table 1.
# Statistical Power Analysis in Reliability Demonstration Testing: The Probability of Test Success
###### Abstract  
Statistical power analyses are used in the design of experiments to determine the required number of specimens, and thus the expenditure, of a test. Commonly, when analyzing and planning life tests of technical products, only the confidence level is taken into account for assessing uncertainty. However, due to the sampling error, the confidence interval estimation varies from test to test; therefore, the number of specimens needed to yield a successful reliability demonstration cannot be derived by this. In this paper, a procedure is presented that facilitates the integration of statistical power analysis into reliability demonstration test planning. The Probability of Test Success is introduced as a metric in order to place the statistical power in the context of life test planning of technical products. It contains the information concerning the probability that a life test is capable of demonstrating a required lifetime, reliability, and confidence. In turn, it enables the assessment and comparison of various life test types, such as success run, non-censored, and censored life tests. The main results are four calculation methods for the Probability of Test Success for various test scenarios: a general method which is capable of dealing with all possible scenarios, a calculation method mimicking the actual test procedure, and two analytic approaches for failure-free and failure-based tests which make use of the central limit theorem and asymptotic properties of several statistics, and therefore simplify the effort involved in planning life tests. The calculation methods are compared and their respective advantages and disadvantages worked out; furthermore, the scenarios in which each method is to be preferred are illustrated. The applicability of the developed procedure for planning reliability demonstration tests using the Probability of Test Success is additionally illustrated by a case study.  
testing; reliability demonstration; probability of test success; statistical power analysis Article  
Statistical Power Analysis in Reliability Demonstration Testing: The Probability of Test Success  
1
# Gaze Tracking Using an Unmodified Web Camera and Convolutional Neural Network
## 3 Materials and Methods
### Data Collection Methodology  
This section presents the data acquisition method, which plays a significant role in deep learning methods. The quality of the acquired data, with the help of which the neural network is trained, has a paramount influence on the process itself because any image that deviates from the others can negatively affect the entire process of training the neural network.  
Data were collected using the _EyeTrackerDataCollector_ application, which is a desktop application registering images from a camera. No specific hardware requirements were made. The only hardware requirement was to have a webcam, preferably built into the top of the laptop screen (see Figure 1). This assumption allowed for the acquisition of images under real-world conditions and not typical laboratory conditions. These measures were designed to answer the question of whether anyone owning standard webcam equipment could use a classifier capable of determining where on the screen a person is focusing their attention. Test subjects were asked to pay attention to the lighting during image acquisition. A problem with the lack of robustness to this factor was noted at the outset. Failure to adjust the lighting resulted in very low-quality, dark even black, or completely overexposed images. The subject sat centrally facing the screen with the built-in camera at the height of the horizontal axis of the eyes at a distance of about 35 to 50 cm from the monitor. During every session, the subject was required to click 20 points displayed subsequently on screen in different evenly distributed locations (in a five by four grid). After each click, a series of three camera images was taken and stored together with information about the point's location.
## 3 Results
### Results and Evaluation of FTIR Spectra
It is worth noting that apart from the TAC from the apple juice, all the other TAC and TLC extracts showed significantly stronger anti-PAF effects (lower IC50 values) against platelet aggregation induced by PAF compared to those induced by the ADP pathway, which suggests that the components present in the TAC extracts of the apple pomace seem to have higher specificity against the PAF pathway, possibly through an inhibitory effect on the binding of PAF to its receptor, thus enhancing their stronger anti-PAF effect. Overall, the results drawn from this analysis in the TAC and TLC extracts of the juice and pomace of Starking apples are in agreement with previous studies in other apple varieties [9; 10; 11] and indicate that the TAC extracts of apple products and by-products possess strong anti-inflammatory potential against the thrombo-inflammatory PAF pathway and antiplatelet effects against the thrombotic ADP-pathway, with the TAC of apple pomace exhibiting more potent anti-inflammatory effects and higher specificity against  
Figure 2: Anti-platelet effects of total amphiphilic compounds (TACs) and total lipophilic compounds (TICs) from apple juice and its by-products (pomace) against the standard platelet agonist ADP. The expression of results is in IC50 values (half maximum inhibitory concentration), which means the mass of the compounds-extract in \(\upmu\)g present in the aggregometer cuvette containing 250 \(\upmu\)L of human plasma rich platelets (hPRP) that is capable to cause 50% of inhibition of the ADP-induced thrombotic activation and aggregation of hPRP (an extract’s anti-inflammatory efficacy increases as the IC50 value decreases). * Shows that TACs and TLCs are statistically significantly different (_p_ < 0.05) in both the juice and its by-products (pomace). PAF. Thus, apart from the well-established antioxidant capacity, the TAC extracts of the apple pomace from this apple variety also shows promising potential to be valorized as bioactive ingredients in several functional products (functional foods, supplements, nutraceuticalals, nutticynetics, cosmeceuticals, cosmetics or even pharmaceuticals-drugs) with anti-inflammatory and antithrombotic health-promoting benefits against inflammation-related chronic disorders. Fatty Acid Composition and Proposed Structures of Detected Bioactive PL in TAC Extracts of Apple Juice and By-Products  
The fatty acid composition (FA) of TAC apple juice and by-products extracts was investigated by Liquid Chromatography-Mass Spectrometry (LC-MS) analysis. The obtained results (Table 8) refer not only to the non-esterified free fatty acids (FFA) detected in the TAC extracts, which is what usually studied in food-derived samples, but mainly to those found esterified in the polar lipids (PL) of the TAC extracts for both the apple juice and apple pomace. The FFA of the TAC extracts from apple juice and by-products were rich in saturated fatty acids (SFAs) and poor in unsaturated fatty acids (UFAs). More specifically, UFAs are