esa-sceva/satcom-chunk-collection huggingface dataset:
# Vehicle Speed Estimation Based on 3D ConvNets and Non-Local Blocks
## 4 Experiments and Analysis
### 3D ConvNets and Non-Local Neural Networks
#### 4.3.1 Different 3D ConvNets  
Table 3 comparatively displays the performance across various 3D ConvNets on VehSpeedDataset10. The model input is a sequence of RGB images. From the table, it can be seen that IR3D50 outperforms IR3D18 and IR3D34 in terms of MAE and MSE. In short, more layers in the 3D convolution network correspond to better results. These results are analogous to those in ImageNet [12].  
For the 2D convolutional neural network, it is known that ResNet50 is better than Inception-V1 in the image classification problem. Nevertheless, we see from Table 3 that inception-v1 outperforms ResNet50 in the video-based speed estimation problem. One explanation is that 3D ConvNets are  
\begin{table}
\begin{tabular}{c c c} \hline
**3 DConvNets** & **MAE** & **MSE** \\ \hline IR3D18 & 5.04 & 44.35 \\ IR3D34 & 4.97 & 42.21 \\ IR3D50 & 4.83 & 41.14 \\ I3D(inception-v1) & 4.58 & 38.00 \\ IR(2+1)D50 & 3.76 & 25.80 \\ \hline \end{tabular}
\end{table}
Table 3: The results of different 3D ConvNets on the VehSpeedDataset10.  
much more complicated than 2D ConvNets and the 2D ConvNets convolution is inadequate for 3D ConvNets.  
Another inspection from the Table 3 is IR(2+1)D50 shows the best performance on the test set. The MAE of IR(2+1)D50 is 1.07 lower than that of IR3D50, while the MSE 15.43 lower. This striking comparison suggests that in the video-based vehicle speed estimation problem, the results can significantly improve with spatial and temporal dimensions considered separately in a 3D convolution. The rationale is that spatial domain and time domain are not symmetrical. Thus, the features in image sequences are better captured with the two domains considered separately. Furthermore, 3D ConvNets are significantly more complicated than 2D, which necessitates appropriate modifications in converting the 2D network structure to the 3D counterpart.
## 3 Results and Discussion
### Selection of the Optimal Magnetic Nanoparticles  
The addition of MNPs to the aqueous micellar two-phase system can improve the extraction and shorten the phase-forming time. Thus, four kinds of MNPs, Fe\({}_{3}\)O\({}_{4}\), Fe\({}_{3}\)O\({}_{4}\)@NH\({}_{2}\), Fe\({}_{3}\)O\({}_{4}\)@SiO\({}_{2}\), and Fe\({}_{3}\)O\({}_{4}\)@SiO\({}_{2}\)-NH\({}_{2}\), were considered. It can be seen in Figure 3 that the MNPs can increase the extraction efficiency of dyes, the order of the extraction efficiency by addition of MNPs was as follow: Fe\({}_{3}\)O\({}_{4}\)@NH\({}_{2}\)\(>\)Fe\({}_{3}\)O\({}_{4}\)@SiO\({}_{2}\)-NH\({}_{2}\)\(>\)Fe\({}_{3}\)O\({}_{4}\)@SiO\({}_{2}\)\(>\)Fe\({}_{3}\)O\({}_{4}\). The reason for interpreting this was the stronger hydrogen-bond interaction thattould be occurring between -NH\({}_{2}\) and the dyes. The reason for MNPs Fe\({}_{3}\)O\({}_{4}\)@SiO\({}_{2}\)-NH\({}_{2}\) was inefficient than Fe\({}_{3}\)O\({}_{4}\)@NH\({}_{2}\) was that less -NH\({}_{2}\) in the surface of Fe\({}_{3}\)O\({}_{4}\)@SiO\({}_{2}\)-NH\({}_{2}\) after being coated by silica. Moreover, the extraction efficiency of the three dyes follow the order CR\(>\)MV\(>\)MB using Fe\({}_{3}\)O\({}_{4}\)@NH\({}_{2}\) in system. The reason for interpreting this is that hydrogen-bond interaction can be occurring between-NH\({}_{2}\) in Fe\({}_{3}\)O\({}_{4}\)@NH\({}_{2}\) and -NH\({}_{2}\) in CR, moreover -NH\({}_{2}\) in Fe\({}_{3}\)O\({}_{4}\)@NH\({}_{2}\) and Cl\({}^{-}\) in MV (seeing the structures of dyes in Figure 1). Therefore, the MNPs Fe\({}_{3}\)O\({}_{4}\)@NH\({}_{2}\) was chosen for further studies.  
Figure 3: Effect of the MNPs added to the aqueous micellar two-phase system (AMTPS) on the extraction efficiency of dyes. (Each system contained 10 mL dyes solution (1 mg/mL Congo red (CR), methyl blue (MB) and 0.125 mg/mL methyl violet (MV)).10 mg MNPs, 4 wt. % TX-114, 40 min vibration time, and incubated at temperature 40 \({}^{\circ}\)C for 10 min). Different letters in the same series indicate significant difference at \(p<0.05\) level.  
Figure 2: The Fourier-Transform Infrared Spectroscopy (FT-IR) for the magnetic nanoparticles (MNPs).
# Fragility Analysis of Transmission Towers Subjected to Downburst Winds
## 2 Fragility Analysis Framework
### Uncertain Structural Model of Transmission Towers  
It is well known that the deterministic model cannot be used to conduct an uncertainty analysis. In order to estimate the strength capacity and identify the failure modes of transmission towers more accurately, the uncertain collapse analysis and fragility analysis of transmission towers are required. An important task of these two kinds of analyses is to establish the uncertain structural model of transmission towers. For a transmission tower, there are many probable uncertain variables. For example, the geometric dimensions, material properties, subjected loads, initial conditions, and boundary conditions [32]. Among these random variables, the material properties are an important factor that is required to be carefully considered. To describe the uncertainties of a structural model, a probability distribution of a random variable is generally prescribed. During this process, the selection of an appropriate probability distribution seems to be very important. After the probability distribution has been determined, the Latin hypercube sampling (LHS) technique can be used to generate the random sample of the uncertain model [33], which will be introduced in detail in Section 3. Latin hypercube sampling is a stratified sampling method, which allows to achieve the same results with fewer sampling times as with many random samples.
# Calibration of Sliding Friction Coefficient in DEM between Different Particles by Experiment
## 2 Experimental Methods
### Determination of the Sliding Friction Coefficient  
Based on the methodology of [PERSON] [41], an improved experimental method for measuring the sliding friction coefficient is proposed. This method can reduce the deviation that may be caused during the experiment and is more convenient than the original method and can determine the sliding friction coefficient between particles and the sliding friction coefficient between particles and boundaries.  
The main steps in determining the sliding friction coefficient between particles were carried out as follows: (1) After the test bench was leveled, the peaks of the two plates were placed on the test bench so that they were in contact with each other, and a lifting platform was placed on the rear side of the test bench. The height of the lifting platform was measured at the point of minimal contact with the test bench, recorded as \(h_{1}\), as shown in Figure 2a; (2) The distance from the front of the test bench to the rear of it was measured, recorded as \(s\). The height of the rear side of the lifting platform was slowly raised, so that the test bench was gradually tilted, as shown in Figure 2b; (3) When the sliding phenomenon occurred in the upper sample, the rising of the lifting platform rising stopped, as shown in Figure 2c. The rising height \(h_{2}\) of the lifting platform was measured at this time, and the sliding friction coefficient under different contact forms was calculated. The sliding friction coefficient can be calculated as follows:  
\[\mu_{s}=\frac{h_{2}-h_{1}}{\sqrt{s^{2}-\left(h_{2}-h_{1}\right)^{2}}} \tag{1}\]  
The test was repeated five times, each value of experimental data was recorded, and the average value of the sliding friction coefficient was calculated. The experimental results are shown in Table 2.  
The test process for determining the sliding friction coefficient between particles and the pads was the same as the experimental method described above. The only difference was the replacement of the sample plate placed on the test bench with a pad. The test was repeated five times under the same contact condition, each value of experimental data was recorded, and the average value of the sliding friction coefficient was calculated. The experimental results are shown in Table 3.  
Figure 2: Measurement method of the sliding friction coefficient between particles. (**a**) **Initial state. (**b**) **Lifting state. (**c**) **Final state.**
## 3 Preliminaries
### Attribute Group Key Distribution [41]  
Consider \(\mathcal{T}\) as a complete binary tree where each leaf node is assigned to a user from the set \(\mathcal{U}=\{u_{1},\ldots,u_{8}\}\). As shown in Figure 1, the tree will be used to distribute attribute group keys to users in \(\mathcal{U}\). Each user securely receives a path key from a leaf node to the root node of the tree. Given the set of revoked users \(L=\{u_{5},u_{8}\}\), we mark the nodes in \(path(u_{5})=\{v_{1},v_{3},v_{6},v_{12}\}\) and \(path(u_{8})=\{v_{1},v_{3},v_{7},v_{15}\}\). \(Cover(L)\) is defined as \(\{v_{2},v_{13},v_{14}\}\). Unrevoked users are represented by the leaf nodes covered in \(cover(L)\).  
An example of the key revocation mechanism for attribute groups is as follows: if \(u_{1},u_{2},u_{3}\) are associated with \(\{x_{1},x_{2},x_{3}\}\), \(\{x_{2},x_{3}\}\), and \(\{x_{1},x_{3}\}\) (\(x_{i}\in S_{A}\)), respectively, the authority gives \(G_{y_{1}}=\{u_{1},u_{3}\}\), \(G_{y_{2}}=\{u_{1},u_{2}\}\), and \(G_{y_{3}}=\{u_{1},u_{2},u_{3}\}\) to the data service manager.
# Analysis of Channel Potential Recovery According to the Back Pattern in 3D NAND Flash Memory
## 3 DCP Recovery Time
Because the gradient of the hole concentration of the P1 pattern was higher than that of the other patterns, the hole diffusion flux increased, which was similar to when the hole concentration was at 0 s, as shown in Figure 5d. The channel potential can be recovered quickly when a hole diffusion occurs to a great extent toward to the floating channel. Furthermore, the hole concentration of the adjacent WLs region varies owing to electrons trapped in the nitride layer. The force between the electron and the hole can affect the hole concentration of the adjacent WLs region and the hole diffusion into the floating channel. The process of hole diffusion, including the electron-hole force and EHP recombination in the floating channel, is shown in Figure 6[32]. The electrons trapped in the nitride layer change according to the programmed state of the adjacent WLs. The trapped electrons affected the movement of the holes owing to the forces that attracted each other. Because the force was weak in the adjacent WLs programmed to P1, it did not significantly interfere with the movement of the hole, such as by causing diffusion. If the force of the trapped electron of the nitride becomes strong, such as the case of the adjacent WLs programmed to P2 and P3, it hinders the movement of the hole. Therefore, the P1 pattern recovered quickly at the beginning of the channel potential recovery because of the relatively higher occurrence of EHP recombination and hole diffusion compared to those of the other patterns.
## 3 Research on BPL Decoding Algorithm for Polar Codes Based on Information Geometry
### Path Selection Criterion Based on Soft Information Centroid
#### 3.2.1 Soft Information Centroid  
Definition: For \(L\) points \(\mathbf{\lambda_{1}},\mathbf{\lambda_{2}},\cdots,\mathbf{\lambda_{j}},\cdots,\mathbf{\lambda_{L}}\) corresponding to soft information, the expression  
\[\overline{\lambda}=\frac{\sum\limits_{j}w_{j}\wedge_{j}}{\sum\limits_{j}w_{j}} \tag{16}\]  
is defined as the soft information centroid. Here, \(w_{j}\) represents the weight corresponding to each soft information centroid.
# Interferometric DEM-Assisted High Precision Imaging Method for ArcSAR
## 5 Accuracy Analysis
### The DEM Accuracy Analysis  
#### 5.1.1 Phase Error Analysis as the Decrease of the SNR Caused by Image Defocusing  
Defocusing causes the decrease of the SAR image's SNR, which results in larger phase error. We analyze this type of phase error in this section. The SNR loss of the imaging result is defined as \(S_{\text{loss}}\). According to the geometric model in Figure 7, we apply the numerical analysis method to obtain the \(h\)-\(S_{\text{loss}}\) curve of the imaging result P\({}_{0}\). The relationship between \(h\) and \(S_{\text{loss}}\) can be expressed as:  
\[S_{\text{loss}}=10\text{lg}\left(\frac{\sum\limits_{i=-\frac{\theta}{2}}^{i= \frac{\theta}{2}}\text{exp}\left(j\frac{4\pi f}{c}\Delta R(i,h)\right)}{\theta} \right)\right)^{2} \tag{31}\]  
The necessary parameters are given in Table 1. The \(h\)-\(S_{\text{loss}}\) curve is shown in Figure 11.  
It can be seen in Figure 11 that, as \(h\) increases, the SNR loss gradually becomes severe. We assume that the SNR of the imaging result is 20 dB when \(h\) = 0 m. The SNR will decrease to 9.03 dB when h = 100 m, which means that the imaging result is seriously defocused.  
Figure 11: \(h\)–\(S_{\text{loss}}\) curve.  
According to the authors of [24], the root mean square error \(\sigma_{\text{PS}}\) of the phase error due to the decrease of the SNR can be expressed as:  
\[\sigma_{\text{PS}}=\frac{1}{\sqrt{2 NL}}\cdot\frac{\sqrt{1-\rho^{2}}}{\rho} \tag{32}\]  
where \(NL\) stands for the number of looks. \(\rho\) represents the coherence of the SAR image pair. It is related to the SNR, which is [24]:  
\[\rho=\frac{1}{1+SNR^{-1}} \tag{33}\]  
With reference to Equations (31) and (33), the \(h\)-\(\rho\) curve can be obtained, as shown in Figure 12.  
It can be seen in Figure 12 that as \(h\) increases, \(\rho\) gradually decreases. When \(h\) = 100 m, \(\rho\) decreases to 0.89. We use the \(3\times 3\) filter window for phase filtering, thus \(NL\) = 9. According to Equation (32) and Figure 12, the \(h\)-\(\sigma_{\text{PS}}\) curve can be acquired, as shown in Figure 13.  
As shown in Figure 13, as \(h\) increases, \(\sigma_{\text{PS}}\) gradually rises. When \(h\) = 100 m, the \(\sigma_{\text{PS}}\) rises to 6.96.  
Figure 12: \(h\)–\(\rho\) curve.
## 3 Results
### Power Optimization of System under Inertial Load
#### 3.1.1 Resonant Solution  
Figure 4 shows the values for \(\gamma_{opt\_R}\), as a function of \(k\) and \(\xi_{E}\), at \(\eta=\{5,\ 1,\ 0.1\}\). The general behavior is that for \(k\gg 1\), \(\gamma_{opt\_R}\approx 1\), and the dependance on \(\xi_{E}\) becomes negligible in the given range. For \(k\ll 1\), \(\gamma_{opt\_R}\) tends to be below unity, inversely proportional to \(\eta\), but approaches unity for \(\xi_{E}\gg 1\). For \(k\approx 1\), we see the largest dependance between \(\gamma_{opt\_R}\) and \(\xi_{E}\), primarily in the region of \(\xi_{E}\ll 1\). For \(k=1\) and \(\xi_{E}=0\), we find that \(\gamma_{opt\_R}=\sqrt{1+1/Q_{crit}}\), which is also derived in [3].  
Figure 5 shows \(\xi_{C\_opt\_R}\) as a function of \(k\) and \(\xi_{E}\). For \(\xi_{E}\gg 1\), \(\xi_{C\_opt\_R}\) is a linear function \(Q\) with a slope proportional to \(\eta\) and an offset which is a linear function of \(\xi_{E}\). For \(\xi_{E}\ll 1\), the relation between \(Q\) and \(\xi_{C\_opt\_R}\) is nonlinear in the region close to \(k=1\). Figure 6 shows the optimal load coefficient as a function of \(k\) for \(\eta=\{5,\ 0.1\}\) and \(\xi_{E}=\{0,\ 0.1\}\). For \(k=1\), \(\xi_{E}=0\) and \(\gamma_{opt\_R}=\sqrt{1+1/Q_{crit}}\), we have \(\xi_{C\_opt\_R}=(Q_{crit}+1)/Q_{crit}\).  
Figure 7 shows the power at resonance with optimal load as a function \(k\) and \(\xi_{E}\). As determined in the previous literature, the maximum power over the load resistance is a factor with \(1/8\) of the reference power, for \(\xi_{E}=0\). Any increase in \(\xi_{E}\) naturally leads to a reduction in the maximum power, which can be generated over the load, for a given value of \(Q\). This effect is mitigated in systems with a large \(Q\) value.  
Figure 4: Normalized angular excitation frequency at resonance, under the condition of power optimal load. \(\eta=\{5,\ 1,\ 0.1\}\), from left to right. The color scale is linear and independent for each plot. Y axis is identical for each plot but only specified in the left plot.  
Figure 5: Power-optimum normalized load resistance, at resonant excitation frequency. \(\eta=\{5,\ 1,\ 0.1\}\), from left to right. The color scale is logarithmic and independent for each plot. Y axis is identical for each plot but only specified in the left plot.
## 4 Results and Discussion
### DL Model Performance on Proposed Dataset  
For our proposed offensive language detection dataset, the DL (deep learning) model results are summarized in Table 3, where CNN (Convolutional Neural Network) and Bi-LSTM (Bidirectional Long Short-Term Memory) models are applied over our dataset. The above shows the four key performance metrics that we can calculate, which include precision, recall, F1-score, and accuracy, all measured in percentage form. These metrics give a general indication of the effectiveness of the model in detecting offensive language and reducing false positives and negatives.  
Table 3 indicates that the performance of the CNN model is superior to Bi-LSTM in all indices. We are testing the data on a CNN which has a precision of 97.25%, capable of  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Model** & **Precision** & **Recall** & **F1-Score** & **Accuracy** \\ \hline CNN & 97.25 & 94.67 & 95.43 & 95.19 \\ Bi-LSTM & 92.38 & 91.74 & 92.19 & 92.31 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of DL models on the proposed dataset.  
Figure 2: F-measure values of various word-level n-grams across different ML and DL models.  
accurately classifying between offensive and non-offensive labels with some false positives. Its recall is 94.67%, indicating that it is neglecting the majority of the legitimate attack instances in the dataset. The F1-score at 95.43% thus shows good precision and recall leads to information that in turn propagates CNN accuracy for offensive language detection. Further, the CNN achieves an accuracy of 95.19%, showing the ability to generalize through all samples of the dataset.  
The Bi-LSTM model also yields similar performance, with precision of 92.38%, recall of 91.74%, and F1-score of 92.19%. Nonetheless, these metrics are relatively lower compared to the CNN metrics but still show good performance overall. Comfortably trailing on the accuracy scale behind the CNN, the Bi-LSTM scores a reasonable 92.31%. This gap in performance can be explained by the more potent ability of the CNN to find spatial features in short text data, in particular, YouTube comments.
# Analysis and Visualization of Production Bottlenecks as Part of a Digital Twin in Industrial IoT
## 3 Bottleneck Detection Method by the Active Period Method
### Boundaries of Active Period Method  
To obtain the mandatory information for calculating the Active Period Method, the time series of operating states from the different machines of the overall production system or line is necessary. These data then have to be converted into active periods. Preprocessing the raw time series data is crucial; here, as in smart manufacturing a lot of data are gathered, but the available data are typically not ready for further processing. Assuming that most of the industrial companies use some kind of ERP or PPS software, the following aspects are important in order to apply the Active Period Method:  
* **Correct interpretation of the operating states.** Often, operating states are not recorded correctly. As an example, concrete failure states are often not detected by the system automatically but entered manually at the end of a shift. These errors have to be corrected during preprocessing, which is a time-consuming task.
* **Usage of the job schedule.** In case of a task change, it is important to set the operating state to free capacity in case the next task does not start immediately. Unsubstantiated downtimes need to be investigated.
* **Understanding of the production process.** Without any understanding of the product profile, the production layout and structure or special features of the production process, the results of the Active Period Method can hardly be understood.  
The Active Period Method works with almost any time series data, but as stated above, sufficient knowledge on the production process is important, in order to avoid the misinterpretation of results. In general, the Active Period Method tells which machines are likely to be a bottleneck, not which machines are guaranteed to cause a problem and, thus, false positive results are an issue.
## 3 Methodology
### Proposed Scheme  
#### 3.2.1 Estimation Model  
We used a DNN as a estimation model to acquire residuals between the estimated values and measured values. Moreover, we used three separate models for AC power, current, and voltage. The weather information (irradiance \(D_{t}\), temperature \(T_{t}\), wind speed \(W_{t}\), humidity \(H_{t}\)) was input to the estimation models, and the outputs were estimated values, such as \(\hat{P}_{t}\), \(\hat{I}_{t}\) and \(\hat{V}_{t}\). Each model consisted of 4 layers, where the input layer had 4 nodes, two hidden layers had 128 nodes, and the output layer had 1 node, and the rectified linear unit (ReLU) function was used as an activation function for the hidden layers. We used Adam optimizer as a learning algorithm and we set a fixed learning rate at 0.0001.  
\[\hat{P}_{t}=f_{\text{P}}(D_{t},T_{t},W_{t},H_{t}), \tag{2}\]  
\[\hat{I}_{t}=f_{\text{I}}(D_{t},T_{t},W_{t},H_{t}), \tag{3}\]  
\[\hat{V}_{t}=f_{\text{C}}(D_{t},T_{t},W_{t},H_{t}) \tag{4}\]  
We split the NIST PV canopy dataset into training, validation, and test datasets for model learning. The canopy PV dataset of 2015 was used for training and validation, where we selected the last day of every week as a validation set, (i.e., training and validation ratio was 6:1). The dataset of 2016 was used for testing. When we trained and evaluated the estimation model, the time period when irradiance somewhat existed was considered, from 9:00 a.m. to 16:00 p.m.  
We utilized mean absolute error (MAE) and symmetric mean absolute percentage error (SMAPE) as metrics for the estimation performance evaluation.  
\[\text{MAE}=\frac{1}{N}\sum_{t=1}^{N}\lvert\hat{X}_{t}-X_{t}\rvert \tag{5}\]  
\[\text{SMAPE}=\frac{100}{N}\sum_{t=1}^{N}\frac{\lvert\hat{X}_{t}-X_{t}\rvert}{( \lvert X_{t}\rvert+\lvert\hat{X}_{t}\rvert)/2} \tag{6}\]