esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Experimental Investigations
### Summary  
The tests described in this section showed that the curing time decreased with increasing curing temperature because high temperatures increased the crosslinking of the powder molecules. The mechanical property and appearance requirements could be satisfied when a curing temperature of 200 \({}^{\circ}\)C was maintained for 5 min. This reduced the time by half compared to the hot-air curing conditions provided by the coating manufacturer (Table 3), which have to be maintained at 200 \({}^{\circ}\)C for 10 min. All of the test results met the requirements when the curing parameters were 220 \({}^{\circ}\)C for 3 min and 230 \({}^{\circ}\)C for 2 min. Considering energy consumption, efficiency, and coating quality, 220 \({}^{\circ}\)C for 3 min and 230 \({}^{\circ}\)C for 2 min were considered the best curing parameters. The optimal curing parameters were consistent with the values from the adhesion and impact tests, and the results of the hardness test at 210 \({}^{\circ}\)C and 1 min met the requirements. Gloss requirements were satisfied at 180 \({}^{\circ}\)C and 1 min. The results for the color tests met the requirements, except for a sharp drop in L* values and a darkening of the coating at 240 \({}^{\circ}\)C. This was due to a sufficient temperature and time for the crosslinking reaction of the molecules inside the coating being used, ensuring better adhesion between the coating and substrate. The gloss and color were mainly related to the color filler in the coating composition, and the appearance of the coating deteriorated when the polyester resin, titanium dioxide, and other components in the coating aged and darkened after spending too much time in the high-temperature range. Consequently, a lower curing temperature was considered to be better for gloss and color.  
To further evaluate whether the curing parameters obtained in this study were efficient, we compared the curing parameters required for powder coatings using catalytic infrared curing with those required by other curing methods described in the literature. As shown in Table 5, the infrared heating method improves efficiency compared to the traditional hot-air heating method. The optimal curing parameters obtained in this study could reduce the time required by 80-90% compared to the hot-air curing parameters provided by the coating manufacturer.  
Figure 14: The effects of temperature on the coatings’ CIE laboratory color model values.  
The coating temperature was not maintained at a constant value when it was controlled; it fluctuated up and down. Moreover, there was an unavoidable error in the thermocouple. In addition, there were operational and instrumental errors in the subsequent coating test. Therefore, 220 \(\pm\) 5 \({}^{\circ}\)C for 3 min and 230 \(\pm\) 5 \({}^{\circ}\)C for 2 min can be considered as the optimal curing parameters for the production process.
## 4 Case Studies
### Additional Investigations on the Synthetic Dataset
#### 4.3.2 Imbalanced Dataset  
An imbalanced dataset usually refers to classification problems where the number of observations for each category is not equal [33; 34]. To investigate the impact of an imbalanced dataset on classification performance, four additional scenarios are conducted in this subsection. The training pool is randomly selected as 30% of the original dataset.  
_Scenario 1_: Training pool consists of healthy/defective samples with a ratio of 1:10  
_Scenario 2_: Training pool consists of healthy/defective samples with a ratio of 3:7  
_Scenario 3_: Training pool consists of healthy/defective samples with a ratio of 5:5  
_Scenario 4_: Training pool consists of healthy/defective samples with a ratio of 7:3  
_Scenario 5_: Training pool consists of healthy/defective samples with a ratio of 10:1  
Similarly, such random sampling is performed over 20 independent runs for each scenario in order to evaluate the average classification ability of the method. The detailed classification results under the five scenarios are listed in Figure 16, indicating their overall accuracies (_mean_\(\pm\)_std_). Additionally, the numbers on the bars stand for the overall number of selected features using the form of _mean_\(\pm\)_std_.  
Of the five scenarios, the IMCS\({}_{\text{RF}}^{\text{BPSO}}\) classifier (SMD-R1 + SMD-R2) demonstrates the optimal classification performance for both training and testing datasets. Using a balanced classification ratio of 5:5 in _Scenario 3_, marked by a black-dashed rectangular, the highest testing accuracy was obtained based on the IMCS\({}_{\text{RF}}^{\text{BPSO}}\) classifier, followed by slightly imbalanced cases (_Scenario 4_ and _Scenario 2_). _Scenario 1_, with a severe imbalanced ratio of 1:10, revealed the worst results, with an overall testing accuracy of 96.09%.  
Of additional interest is that training accuracy based on the IMCS\({}_{\text{PSO}}^{\text{BPSO}}\) classifier (SMD-R2) was gradually improved with an increase in the proportion of healthy samples. In fact, the direct cause of this phenomenon is the decrease of variation diversity of MD in MD space due to the reduction in the proportion of defective samples. However, even in this case, it does not necessarily contribute to the accuracy of the testing dataset.  
Figure 16: Classification results on the testing dataset for 5 scenarios.  
The discussion of the impact of an imbalanced classification ratio indicates that the increase of an imbalanced ratio in training data sets indeed reduces testing accuracy. In particular, the classification could be worse if the imbalance weight is heavily skewed towards the defective samples.  
However, the above discussion on the classification performance of an imbalanced dataset is based on the condition that sufficient samples are available. For a sparse dataset, such as the turbine blades in Section 4.1, the imbalance is still challenging.
# Explore Long-Range Context Features for Speaker Verification
## 8 Conclusions  
In this paper, we propose three methods to capture long-range dependencies and improve performance for speaker verification. The first method replaces the \(3\times 3\) convolution with an HS-block to enlarge the local RFs of a single layer. The second method proposes the DSSA module, which integrates information from the global perspective in each channel of the feature maps. The third method is the cross-convolution kernel, which replaces the traditional \(3\times 3\) convolution kernel with a \(1\times 5\) and \(5\times 1\) convolution kernel. These methods capture long-range dependencies from different perspectives and are highly complementary. To evaluate the robustness of our methods, we conduct experiments in VoxCeleb and CnCeleb sets. By integrating them into one model, we achieve state-of-the-art performance in VoxCeleb sets and CnCeleb sets.  
Conceptualization, Z.L.; methodology, Z.L.; software, Z.L. and Z.Z.; validation, Z.L.; formal analysis, Z.L.; investigation, Z.L.; resources, Z.L.; data curation, Z.L., W.W. and P.Z.; writing--original draft preparation, Z.L.; writing--review and editing, Z.L., W.W., P.Z. and Q.Z.; visualization, Z.L.; supervision, W.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Publicly available datasets are analyzed in this study. This data can be found here: [[[https://www.robots.ox.ac.uk/vgg/data/voxceleb/index.html](https://www.robots.ox.ac.uk/vgg/data/voxceleb/index.html)]([https://www.robots.ox.ac.uk/vgg/data/voxceleb/index.html](https://www.robots.ox.ac.uk/vgg/data/voxceleb/index.html)), accessed on 1 March 2018; [[http://cnceleb.org/](http://cnceleb.org/)]([http://cnceleb.org/](http://cnceleb.org/)), accessed on 10 April 2020].  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} HS-Block & Hierarchical-split block \\ cross-conv & cross convolution kernel \\ RFs & receptive fields \\ DSSA & Depthwise Separable Self-Attention \\ SV & speaker verification \\ TDNN & Time Delay Neural Network \\ CNN & Convolutional Neural Network \\ ECAPA-TDNN & Emphasized Channel Attention, Propagation, and Aggregation in TDNN \\ LSTM & Long Short Term Memory \\ CV & Computer Vision \\ spks & speakers \\ utts & utterances \\ \end{tabular}  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Vox1-O**} & \multicolumn{2}{c}{**Vox1-E**} & \multicolumn{2}{c}{**Vox1-H**} \\  & **EER** & **minDCF** & **EER** & **minDCF** & **EER** & **minDCF** \\ \hline ECAPA-TDNN [13] & 0.87 & 0.107 & 1.12 & 0.132 & 2.12 & 0.21 \\ SimAM-ResNet34 [43] & 0.64 & 0.067 & 0.84 & 0.089 & 1.49 & 0.146 \\ MBFA-MW-ECAPA-TDNN [44] & 0.87 & 0.115 & 1.22 & 0.135 & 2.31 & 0.222 \\ HS-Net50+cross+DSSA(ours) & 0.52 & 0.039 & 0.76 & 0.081 & 1.38 & 0.142 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of EERs of our proposed model with others.
# The Evolution of Volatile Memory Forensics
## 4 Memory Analysis
### Machine Learning Approaches
#### 4.3.1 Feature Engineering Approaches  
Many of the attempts at a machine learning volatile forensic method involved executing malware in a sandbox system, acquiring a memory dump, extracting features from the dump using _Volatility_ (or another tool, such as _Rekall_). The creation of these features is often termed feature engineering in machine learning literature. The features can then be used in a classification algorithm. Such was the case with [61, 71, 72, 73].  
[PERSON] et al. [71] compared sequential minimal optimization, random forest, decision tree, naive Bayes, and instance-based classifiers at the task of malware detection on volatile memory. The data used included 350 instances of malware, and 200 instances of benign executables. The classifiers were built using 130 features extracted from the VAD tree, the file mapped in memory, registry keys from the process handle table, and registry changes. Specific features were not provided by the authors. As the success of the above-listed classifiers is highly dependent upon the features used to build them, we believe a more thorough discussion of the selected features and the feature importance is needed to replicate these results.  
Similarly, [PERSON] et al. [61] built machine learning classifiers on extracted features regarding the API calls, DLL injections, registry, and network connections. These features were used to fit several classification algorithms, including naive Bayes, support vector clustering, K-nearest neighbors, logistic regression, a decision tree, random forest, and linear discriminant analysis, to create a final feature set. A recurrent neural network was then fit with the finalized feature set, and it led to >93% accuracies at detecting malicious processes.  
[PERSON] et al. [72] extracted 15 features (9 of which were ultimately used after feature selection) from 900 memory dumps. These features were used in an XGBoost classification algorithm to identify the presence of ransomware on the system. Only five families of ransomware were used in the dataset, and they were compared against only three benign processes. The algorithm was able to identify the ransomware with 89% accuracy.  
[PERSON] et al. [73] extracted 36 features (provided as a list by the authors) from memory dumps, that described the system's processes, DLLs, handles, loaded modules, code injections, connections, sockets, services and drives, and callbacks. These features were fed into randomized decision trees which achieved a 93% true positive rate and a 6.6% false positive rate. However, their 1900 samples were derived from only 7 malware families and a few benign samples.  
The promising results in these papers indicate that there is potential for malware detection based off feature engineering from volatile memory snapshots.
## 3 The Model Validation
### Addendum Modification Coefficient
#### 4.2.3 The Influence on the Load Sharing Ratio  
The relationship between the load sharing ratio and addendum modification is studied. It can be easily found that the load sharing ratio curves increase first and then decrease. This is because as the gear rotates, the gear tooth takes increasingly more of the load until it reaches the maximum value, and then the load sharing ratio decreases with the tooth exiting the engagement region. The maximum value of the load sharing ratio is less than one because there are at least two teeth in the engagement region with the contact ratio exceeding 2. For Case 1 with \(x_{1}+x_{2}=0\), the load sharing ratio curve with \(x_{1}=-x_{2}=-0.6\) is on the left side of the curve with \(x_{1}=x_{2}=0\), and the curve with \(x_{1}=-x_{2}=0.6\) is on the right side of the curve with \(x_{1}=x_{2}=0\). That is, the phase of mesh stiffness is ahead of the standard gear when the driving gear is in positive modification and the driven gear is in negative modification. For Case 2 with \(x_{1}>0\) and \(x_{2}>0\), the load sharing ratio curves are almost the same. This indicates that a positive addendum modification has a slight effect on the load sharing ratio. For Case 3 with \(x_{1}<0\) and \(x_{2}<0\), the maximum value of the load sharing ratios decreases slightly because the contact ratio increases when the negative coefficient is applied, as shown in Figure 23.
## 4 Experiment and Analysis
### Analysis of Experiments
#### 4.2.2 Validation of Theoretical Model  
The roll angle gains \(a\) and the lead time \(\tau\) were calculated as per Equation (12) from the experimental data. The results obtained from the experiments were compared with the theoretical results at speeds 3 and 5 km/h as shown in Figure 7a,b respectively. The figures show that the values of \(a\) and \(\tau\) calculated from the experiments are in the stable regions determined from the theoretical results thereby validating the theoretical model. It also confirms that the model can be used to predict the stable zone for a motorcycle at low speeds.  
Further, the relationship between the steering angle and roll parameters defined by Equation (13) in the Section 3 was examined. Linear MRA between the steering angle as a dependent variable; and, the roll angle and the roll rate as independent variables was performed from the experimental data using the following equation:  
\[\delta=a_{1}\phi+a_{2}\phi, \tag{19}\]  
where \(a_{1}\) and \(a_{2}\) are the regression coefficients of the roll angle and the roll rate respectively. The regression coefficient \(a_{2}\) was compared with the corresponding coefficient (\(-a\tau\)) of roll rate derived from the experimental data as per the Equation (13). The correlation coefficients between the coefficients \(a_{2}\) and (\(-a\tau\)) were strong and more than 0.7. Figure 8 shows that both the coefficients match closely which shows the strong relationship between the theoretical model and experimental results.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Target Speed**} & \multicolumn{4}{c}{**3 km/h**} & \multicolumn{4}{c}{**5 km/h**} & \multicolumn{4}{c}{**10 km/h**} \\ \hline  & & \multicolumn{3}{c}{**Speed**} & \multicolumn{3}{c}{**RA Gain**} & \multicolumn{3}{c}{**Speed**} & \multicolumn{3}{c}{**RA Gain**} & \multicolumn{3}{c}{**Speed**} & \multicolumn{3}{c}{**RA Gain**} \\ \hline
**Rider** & **Set** & **avg** & **SD** & **avg** & **SD** & **avg** & \(\mathrm{SD}\) & **avg** & **SD** & **avg** & **SD** & **avg** & **SD** \\ \hline
1 & 1 & 2.46 & 0.32 & 14.49 & 4.26 & 5.54 & 0.44 & 6.21 & 1.34 & 10.16 & 0.34 & 2.59 & 0.67 \\
1 & 2 & 2.56 & 0.48 & 14.18 & 4.20 & 5.49 & 0.63 & 6.55 & 1.72 & 10.32 & 0.35 & 2.42 & 0.66 \\
2 & 1 & 3.12 & 0.34 & 16.31 & 4.11 & 5.69 & 0.71 & 7.00 & 1.73 & 10.62 & 0.52
## 5 Discussion  
### Why Hold 3 Digits for Informative Variable D?  
As mentioned in Section 3.3, the rank value of informative variable D on features of a sample have a great effect on estimating how informative the sample is, moreover, the precision of variable D affect the rank value directly. Therefore, it is necessary to discuss a proper value of this parameter (precision for variable D). The aim of this discussion is to present the effects of different precision of D on the performance of proposed LIMCR. Considering the space limitation, here, we randomly select the result of three datasets with different sample size (sample sizes of synapse-1.0, PC4, prop2 are 157, 1270, 23014, respectively) and the average result of 29 datasets introduced in Section 3.1. We choose GNB as classifier and evaluate the performance with balancedscore, precision, recall, and G-mean. The value of the precision of D is varied from 0 to 5 with increment of 1. The experimental result are presented as shown in Figure 6.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Datasets** & **ILMCR+GNB** & **ILMCR+DTC** & **ILMCR+ABC** & **IHT+GNB** & **IHT+DTC** & **IHT+ABC** \\ \hline JM1 & 0.562 & 0.515 & 0.522 & 0.615 & 0.64 & 0.612 \\ KC1 & 0.631 & 0.634 & 0.617 & 0.628 & 0.62 & 0.647 \\ MC1 & 0.655 & 0.536 & 0.57 & 0.697 & 0.563 & 0.617 \\ PC3 & 0.486 & 0.53 & 0.538 & 0.718 & 0.755 & 0.741 \\ PC4 & 0.577 & 0.717 & 0.759 & 0.65 & 0.826 & 0.799 \\ PC5 & 0.554 & 0.556 & 0.595 & 0.623 & 0.679 & 0.669 \\ prop-1 & 0.612 & 0.652 & 0.626 & 0.63 & 0.69 & 0.692 \\ prop-2 & 0.593 & 0.539 & 0.53 & 0.595 & 0.573 & 0.7 \\ prop-3 & 0.539 & 0.589 & 0.542 & 0.58 & 0.64 & 0.665 \\ prop-4 & 0.606 & 0.607 & 0.597 & 0.632 & 0.691 & 0.704 \\ prop-5 & 0.544 & 0.56 & 0.551 & 0.605 & 0.635 & 0.681 \\ ec-2.0 & 0.694 & 0.624 & 0.683 & 0.674 & 0.686 & 0.685 \\ ec-2.1 & 0.643 & 0.628 & 0.651 & 0.683 & 0.677 & 0.659 \\ ec-3.0 & 0.679 & 0.638 & 0.648 & 0.672 & 0.65 & 0.651 \\ \hline Average & 0.598 & 0.595 & 0.602 & 0.643 & 0.666 & **0.68** \\ Rank & 4.286 & 4.643 & 4.714 & 3 & 2.429 & **1.929** \\ \hline \hline \end{tabular}
\end{table}
Table 17: Balancedscore of matching of classifiers with LIMCR and IHT on large datasets.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Datasets** & **ILMCR+GNB** & **ILMCR+DTC** & **ILMCR+ABC** & **IHT+GNB** & **IHT+DTC** & **IHT+ABC** \\ \hline JM1 & 0.398 & 0.187 & 0.231 & 0.559 & 0.628 & 0.596 \\ KC1 & 0.583 & 0.569 & 0.534 & 0.619 & 0.609 & 0.634 \\ MC1 & 0.654 & 0.277 & 0.389 & 0.668 & 0.375 & 0.521 \\ PC3 & 0.207 & 0.27  
From the figures we notice that when this parameter equals to 3, the result of LIMCR performs stable and better than most of other values on average result. The metrics excepting precision have an increasing trend with the increase of this parameter value. Inversely, the metric precision decreases with the parameter value increasing. In order to obtaining the global optimum performance, we choose 3 as the generic value of this parameter.
## 4 Review and Comparative Evaluation of VQA Models  
In this section, first, the related works on VQA models are reviewed, intending to highlight their strengths and limitations from the perspective of HFR video quality evaluation. The models under consideration include traditional metrics such as PSNR (Peak Signal-to-Noise Ratio), SSIM (Structural Similarity Index) [15], MS-SSIM (Multi-Scale SSIM) [16], and FSIM (Feature Similarity Index) [17], as well as advanced approaches like ST-RRED (Spatio-Temporal Reduced Reference Entropy Difference) [18], SpEED (Spatio-Temporal Entropic Differencing) [19], FRQM (Frame Rate Quality Metric) [20], DeepVQA [21], GSTI (Gradient-based Spatio-Temporal Index) [22], several variants of AVQBits [23] (AVQBits | M3, AVQBits | M1, AVQBits | M0, AVQBits | H0 | s, AVQBits | H0 | f), VMAF (Video Multi-method Assessment Fusion) [5, 24], and the recently developed FLAME-VQA [6] and BH-VQA (Blind High Frame Rate Video Quality Assessment) [2]. Second, all models were evaluated on the LIVE-YT-HFR video database, and their performances were compared against the current model using standard metrics (namely RMSE, PCC, and SROCC). The test results for the models were taken directly from the experiments reported in [3], except BH-VQA, FLAME-VQA, and AVQBits, whose results were taken from [2, 6, 23], respectively.
## 3 Numerical Method
### Grid Independence Validation  
In the validation of grid independence, ICEM 14.5 is used to divide the computational domain for Geometry 2. The computational domain is divided into hexahedron structured grids by the O-type multi-block method. At the same time, the wall grids are refined to accurately reflect the change of flow parameters near the wall. The first grid height of rotor outer surface and stator inner surface is 5.0 \(\times\) 10\({}^{-6}\) m and the grid growth rate is 1.1. The numerical calculation uses three sets of grids of 1.66 million, 3.27 million and 6.40 million, respectively. The Reynolds number is 2.91 \(\times\) 10\({}^{7}\). Figure 6 shows the grid and grid independence validation results of the computational domain.  
Figure 5: Turbulence model validation [3].  
Figure 6: _Cont._The grid independence is verified according to the Celik [23] method. This method evaluates the relative error of the numerical calculation by calculating the grid convergence index. When calculating the grid convergence index, three or more sets of grids with different numbers are needed. The denser the grid, the higher the accuracy of the discrete solution to a certain extent, the smaller the corresponding relative error and the smaller the grid convergence index.  
Figure 6b is the circumferential velocity distribution at the radius ratio \(R/R_{0}=0.5\) with different grid numbers. The flow and velocity distribution in this paper are absolute velocity. The local order of accuracy p ranges from 1.7 to 6.2 with an average value of pave = 3.1. In Figure 6b, the 19 points in the center of the rotating fluid outside the boundary layers exhibited oscillatory convergence. Discretization error bars are shown in Figure 6c along with the fine-grid solution. According to the results, the average approximate relative error \(e_{a}\)[32] is 2.56% and the average extrapolated relative error \(e_{ext}\)[32] is 0.04%. The average fine-grid convergence index \(\mathrm{GCl}_{\mathrm{fine}}\)[32] is 0.05% indicating that the grid refinement is effective and the discrete solution is close to the exact solution. In addition, when the number of grids increases from 3.27 million to 6.4 million, the torque of the rotor decreases from 257.2 N-m to 256.9 N-m and the difference between the two is only 0.11%. It can be considered that when the number of grids reaches 3.27 million, it no longer affects the calculation results. Therefore, a grid of 3.27 million was selected for subsequent related research. All cases in this paper are verified repeatedly and ensure that the \(y+\) value of the rotor wall meets the requirements.
## 4 Data Description and Processing
### Data Reduction and Extraction
After data reduction and extraction, the four derivative parameters of acceleration and jerk were calculated as measurements of classifying and analyzing driving styles of heavy-duty truck drivers. After this step, a cluster method was conducted to classify the driving styles of the heavy truck drivers at intersections, which is discussed in the next section.