esa-sceva/satcom-chunk-collection huggingface dataset:
# Aircraft Wake Recognition Based on Improved ParNet Convolutional Neural Network
## 4 Aircraft Wake Identification Results and Discussion
### Ablation Study  
In the ablation experiment, the ParNet was used as the benchmark. A ParNet with depthwise separable convolution (DSC-ParNet), a ParNet with the CBAM (CBAM-ParNet), and a ParNet with both DSC and the CBAM (DSC-CBAM-ParNet) were respectively used to complete the wake recognition. The recognition effects were compared.  
During the training in the ablation experiment, the changes in the validation set's precision and validation loss are shown in Figure 11. Compared with the ParNet, the performance of the DSC-ParNet was improved slightly, and the loss after the convergence was relatively stable. The improvement in the CBAM-ParNet's performance was more evident. The performance of the DSC-CBAM-ParNet model was significantly improved, and the loss was the lowest when it was stable. After the convolution in the ParNet model was replaced by the DSC, the reduced parameters made the convergence effect of the model obvious, and the model's performance was partially improved. The CBAM can significantly improve the performance of the model.  
The recognition effect of each model on aircraft wake is shown in Table 2. Overall, the accuracy, precision, recall rate, and F1 value of the ParNet method for aircraft wake recognition were all above 95%, with a good recognition effect. Various improved models based on the ParNet had limited improvement in the recognition effect. The DSC-CBAM-ParNet model proposed herein maximised the recognition effect of the model on aircraft wake, and the recognition accuracy, precision, recall rate, and F1 value were all above 98%. It is also seen from the MFLOPs that DSC considerably reduces the computation complexity in wake recognition. Concurrently, the CBAM, as one of the spatial attention modules, increases the accuracy of the recognition. Consequently, the improved ParNet has evident advantages over the ordinary ParNet in terms of both recognition performance and computation complexity.
## 3 Results and Discussions  
### Evaluation of Generated Images  
Before evaluating the results of the classification task, we assessed the fidelity of the generated images. The real and generated images for each class are shown in Figure 6. One can see that after around 60 iterations, the model started producing plausible artificial images. To give a fair insight into the image impression, a t-SNE method of visualization was adopted for 100 real and generated images for each class (see Figure 7). The dimension-reduction technique was used to plot the data points in a two-dimensional plot. Some outliers were identified in the t-SNE plot for Charlock. However, the synthetic Charlock images closely exhibit the shape and color features of the original images. For other classes, a similar distribution of the points corresponding to the actual and artificial images denoted that pertinent features are adequately learned and produced through the GAN. The artificial images preserved the key features of the real images and widened the coverage of the training dataset.  
Figure 6: Sample ground truth images and generated images at different epochs during GAN training. Column-wise from left to right: Charlot, Fat Hen, Shepherd’s purse, Small-flowered, Cranesbill, Maize.  
Hence, realistic images were generated with the help of GAN, which could augment the existing crop/weed dataset. The advantage of GAN-based augmentation includes a reduced annotation workload since the generated images can be associated with the same segmentation mask created earlier. Moreover, the classification model can generalize better when trained with a dataset comprising GAN-generated images, especially on shape-based features. Apart from this, GANs can also be used to enhance image clarity, which was observed in the case of some real images, especially of the maize crop. The GAN-synthesized images have the potential to replace erroneous and ill-advised real data. In addition, some real images contained irrelevant objects (such as the labels, referring to the image of Fat Hen in Figure 6) in the background, and the model was successful in the GAN-generated images.  
Figure 7: t-SNE visualization plots for real (green points) and synthetic (red) images for all five classes—(**a**) Maize, (**b**) Charlock, (**c**) Shepherd’s purse, (**d**) Fat Hen, and (**e**) Small-flowered Cranesbill.  
replacing them with the ground appearance, thus exhibiting its potential to create a variety of environmental and background conditions.  
One drawback to this GAN model is its inability to learn and reproduce textural features, though it performs exceptionally well in acquiring the shape and color attributes. Notably, in the images of [PERSON]'s purse, where the textural appearance of the weed was quite imperative, the model could not fabricate them into artificial images. This might pose a problem for classification when the crops and weeds have a similar physical appearance. For further operations, 200 images were generated for each class through the developed GAN to boost the training dataset.
## 4 Discussion
### Discussion for Explaining \(V\)  
Table 28 presents the error between the directly modeled data and data for modeling, with \(\overline{E}=mean([E_{60}\ E_{65}\ E_{75}\ E_{85}\ E_{90}\ ])\). Table 29 presents the error between the indirectly modeled data and data for validating, with \(\overline{E}=mean([E_{70}\ E_{80}])\).  
For modeling \(V\), procedure 6 is the best option. Here, we have that \(\overline{E}^{6,1}<\overline{E}^{5,1}<\overline{E}^{4,1}\) and that \(\overline{E}^{6,2}<\overline{E}^{5,2}<\overline{E}^{4,2}\). Figure 6 shows that, in vectors \(\hat{C}_{sp_{1}}\) for the \(sp_{1}\) [60,65,75,90] cases, points 6 and 7 are the least accurate (i.e., dissimilar with the observed), with \(\hat{C}_{85}\) being the most accurate (\(E_{85}=0.0281\) s) and \(\hat{C}_{65}\) being the least accurate (\(E_{65}=0.0956\) s). In vector \(\hat{C}_{80}\), points 7 and 8 are the least accurate; therefore, when \(sp_{1}=80\ \mathrm{km}/\mathrm{h}\) and for \(p_{2}=70\%\) and \(p_{2}=80\%\), it was difficult to explain \(V\).  
In Figure 5, and regarding the directly modeled data, points 5 and 6 of vectors \(\hat{C}_{sp_{1}}\) for the \(sp_{1}\) {\(60,65,90\)} cases are visibly inaccurate, and points 4 to 7 of vector \(\hat{C}_{90}\) are also rather imprecise. Additionally, \(\hat{C}_{90}\) has the highest error (\(E_{90}=0.1004\) s) among \(\hat{C}_{sp_{1}}\) vectors of procedure 5, while \(\hat{C}_{75}\) along with \(\hat{C}_{85}\) present the lowest errors \(E_{75}\cong E_{85}\cong 0.03\) s. Regarding the indirectly modeled data, in vector \(\hat{C}_{70}\), points 5 to 7 are the least accurate, while in \(\hat{C}_{80}\), the least precise are points 5, 7, and 8. Hence, there are imprecisions when \(p_{2}=50\%\) and \(p_{2}=70\%\) for both of the \(sp_{1}\) [70,80] cases.  
Procedure 4 presents the higher \(\overline{E}\), as can be seen in Tables 28 and 29. Table 28 shows that vectors \(\hat{C}_{sp_{1}}\) for the \(sp_{1}\) cases [60,65,90] display \(E_{sp_{1}}\)? 0.1. Table 29 shows that vector \(\hat{C}_{80}\) is more accurate than \(\hat{C}_{70}\), because \(E_{80}<E_{70}\) (0.1070 s! 0.1569 s).  
With the results presented above, we consider procedures 5 and 6 as the best options for modeling and explaining the observed data. Besides, one important finding is that the pattern of \(V\) values, in the range \(p_{2}=50\%\) to \(p_{2}=80\%\), is not the same among \(sp_{1}\) cases. It appears that a presence of about 50% of slow vehicles is the lowest threshold from where, and up to 80% in this study, the fast vehicles' extra travel time variability follows a nonlinear pattern.
## 3 Results and Discussion
### Optimization Using GA  
To optimize the parameter levels, a trained ANN with the specifications listed in the third row of Table 4 is used as the cost function. The parameters of melt temperature, flow rate, holding pressure, and runner diameter are considered as the inputs of the GA, and the area of the blush defect is regarded as the only output. The allowable range for each of these parameters is specified in Table 3. Additionally, for the GA, values of 300 were taken into account as the initial population, 300 as the maximum number of generations, 60% as the crossover rate, and 40% as the mutation rate. During the optimization process, the value of the cost function of the GA is constantly decreasing, which indicates the proper performance of the algorithm in finding the optimal values. At the end of the process, the algorithm had reached the optimal response after approximately 170 iterations, after which the response showed little change. The algorithm introduces the values of 197.1,  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**No.** & **FEA** & **ANOVA** & **Basic ANN** & **ANN + PSO** & **ANN + GA** \\ \hline
1 & \(-\)0.161 & 0.298 & \(-\)0.161 & \(-\)0.157 & \(-\)0.119 \\
2 & 1 & 0.849 & 1 & 0.688 & 0.754 \\
3 & 0.762 & 0.83 & 0.762 & 0.7  
25.6, 84.9, and 6.4 as the optimal levels for the parameters of melt temperature, flow rate, holding pressure, and runner diameter, respectively. Figure 12 shows the optimization process by the GA. Using experimental validation, it is possible to compare the results of the experimental tests, GA, and ANOVA. This comparison can be seen in Table 5.  
Figure 11: Regression analysis of the basic ANN model.  
Figure 12: Optimization process by GA.  
From the results obtained from Table 9, it can be seen that, on account of optimization using GA, the amount of blush defect area has been reduced by 81.7%. Additionally, the optimization performed using the CCD method has reduced the defect area by 74.0%. Figure 13 shows a visual comparison between the blush area, before and after optimization in the injection molding simulation, alongside the image of the defect in the bushing produced with the optimized parameters.
## 3 Modeling Approach
### Rotation Forest  
Rotation forest (RF) is a popular aggregation technique proposed by [PERSON] et al. [82]. RF is an effective technique for improving weak classifiers [83]. It uses principal component analysis (PCA),a multivariate technique used for analyzing large multivariate datasets, to reduce its dimensions [84]. In this method, features are extracted from the learning (training) dataset and a base classifier is used to generate learning sub training dataset [82].  
For the use of RF: randomly divide the training dataset into D subsets, where D is the parameter of the algorithm, and construct the rotated sparse matrix by performing feature extraction for each subset. The classifier is based on the feature of a repeated matrix projection, and the result is obtained by combining the output of multiple classifiers [84]. RF can be used with any basic classifier, and the feature extraction of each classifier retains all the features that promote variability [84].  
In the RF algorithm, \(x=(x_{1},x_{2},\cdots,x_{n})\) is the training sample set, and Y is the corresponding class label, that is used to consider landslides and non-landslides; \(D_{1},D_{2},\cdots,D_{L}\) are the classifier in the set frame; and P is the set of landslide condition factors. The coefficients of the rotation matrix \(R_{i}{}^{a}\) are obtained by transformation and base classifier. Obtain \(R_{i}{}^{a}\) by rearranging \(R_{i}\) matrix [84]:  
\[R_{i}=\left[\begin{array}{cccc}b_{i,1}{}^{(1)},\cdots,b_{i,1}{}^{(M_{1})}&0& \cdots&0\\ 0&b_{i,2}{}^{(1)},\cdots,b_{i,2}{}^{(M_{2})}&\cdots&0\\ \vdots&\vdots&\ddots&\vdots\\ 0&0&\cdots&b_{i,K}{}^{(1)},\cdots,b_{i,K}{}^{(M_{K})}\end{array}\right] \tag{5}\]  
For each sub training dataset extracted by the rotation matrix \(R_{i}{}^{a}\), average grouping method is adopted to obtain the coefficients of each class in a given test sample [85]:  
\[\mu_{j}{}^{(x)}=\frac{1}{L}{\sum_{i=1}^{L}d_{ij}(xR_{i}{}^{a})},\ j=1,\ldots,c. \tag{6}\]  
where \(\mu_{j}{}^{(x)}\) is the maximum confidence specified on the class, classifier probability allocation \(D_{i}\), and the \(d_{ij}\) regression \(d_{ij}(xR_{i}{}^{a})\)[85].
## 3 Results
### Results of Breakdown Strength Measurements
While a decrease of 35.82% occurred in the ASA material, this rate was calculated as 45.79% in the PETG material. Among the tested material types, the material most affected by the shape change was PC/ABS. While the circular PC/ABS material has a value of 30.14 kV/mm, the square PC/ABS is broken down at a value of 8.29 kV/mm. The change rate is determined as 72.49%. As a result of the breakdown strength measurements made on circular and square-shaped samples, it was observed that square-shaped solid insulator samples were punctured at lower voltages.
# Multi-Source Data Fusion for Vehicle Maintenance Project Prediction
## 3 Method
### Maintenance Project Correlation Representation  
During the process of vehicle maintenance, each maintenance session frequently entails the execution of multiple distinct maintenance projects simultaneously. For example, within a vehicle's maintenance record, activities such as an oil change and air filter replacement occur simultaneously, indicating an interdependence among various maintenance projects. To fully utilize the correlations among maintenance projects, we propose a correlation representation module for their correlation based on the co-occurrence matrix.  
We create a global co-occurrence graph \(G\) for all maintenance projects with weighted edges, where each node serves as a representation of a maintenance project \(\{p_{i}\}_{i=1,2,\ldots,|P|}\) sourced from the set \(P\). If a code pair \((p_{i},p_{j})\) co-occurs in a vehicle's maintenance record, two equal weights \(\overline{(i,j)}\) and \(\overline{(j,i)}\) are integrated into \(G\). Then, we count the total co-occurrence frequency \(t_{ij}\) of \((p_{i},p_{j})\) in all vehicles' maintenance records for further calculation of edge weights. In addition, we want to detect important and common project pairs. Therefore, we define a threshold \(\delta\) to filter out combinations with low frequency and obtain a qualified set \(\Delta_{i}=\left\{p_{i}\mid\frac{t_{ij}}{\sum_{n=1}^{|P|}t_{ij}}\geq\delta\right\}\) for \(p_{i}\). Let \(q_{i}=\sum_{p_{j}\in\Delta_{i}}t_{ij}\) be the total frequency of qualified projects co-occurring with \(p_{i}\). We use an adjacency matrix \(A\in\mathbb{R}^{|P|\times|P|}\) to represent \(G\) with the definition in Equation (1).  
\[A_{ij}=\left\{\begin{array}{l}0\text{ if }i=j\text{ or }\frac{t_{ij}}{\sum_{j=1}^{|P|}t_{ij}}< \delta\\ \frac{t_{ij}}{\overline{q_{i}}}\text{ otherwise}\end{array}\right. \tag{1}\]  
Note that \(A\) is designed to be symmetric to represent the influence of two maintenance projects. As a static matrix, \(A\) quantifies the frequency at which global maintenance projects co-occur. However, the appearance and disappearance of different maintenance projects occur at varying stages. Even if a specific maintenance project is absent from the current maintenance records, a related maintenance project may arise in the future due to the correlations of different projects. Consequently, in order to account for the correlations among maintenance projects, the correlation represent results \(\{R_{t}\}_{t=1,2,\ldots,T}\) of each maintenance project are derived for each vehicle's historical maintenance projects \(\{V_{t}\}_{t=1,2,\ldots,T}\), utilizing \(F_{1}(V_{t},A)\) individually. The definition of \(F_{1}(V_{t},A)\) is presented in Equation (2).  
\[\begin{split}& F_{1}(V_{t},A)=f\Big{(}A^{T}\times D_{t}\Big{)}\\ & D_{t}=\text{diag}(V_{t}[1],V_{t}[2],\ldots,V_{t}[n])\end{split} \tag{2}\]  
where \(n=|P|\) and function \(f\) serves to filter out the row vectors of the matrix that are all zeros.
## 4 Wind-Induced Fragility Analysis of Corroded Transmission Tower
### Wind Load Simulation and Analysis of Wind Load Effects  
The transmission tower was divided into six simulated segments from bottom to top, and the wind blocking area of each segment was calculated separately. The wind speed at the vertical midpoint of each segment was used as the simulated wind speed of the corresponding segment, and the simulated segments were numbered in increasing order from bottom to top, i.e., segment 1 to segment 6, as shown in Figure 18. According to the wind climate assessment results, the extreme wind speed under the most unfavorable wind direction (D2) in the Ningbo area was selected as the basic wind speed, the ground roughness category was set to class B, the wind profile index was set to 0.16, the Kaimal spectrum was used to simulate the wind speed spectrum, the harmonic superposition method was used to simulate and generate the wind speed series in the downwind direction and the wind load series was generated according to the technical code: DL/T5154-2012 [34].  
In the wind load effect analysis of this paper, the incremental dynamic analysis method was used to set the basic wind speed from 30 m/s and increase 5 m/s each time, setting nine groups of basic wind speeds (i.e., 30 m/s, 35 m/s, 40 m/s, 45 m/s, 50 m/s, 55 m/s, 60 m/s, 65 m/s and 70 m/s). Considering the randomness of the wind loads, 10 sets of wind load series were generated at the same basic wind speed, and 10 sets of structural dynamic responses were estimated by performing linear elastic analyses in the time domain in ANSYS, respectively, and then, the analytical functions of the wind speed and the index of the load effects, i.e., maximum displacement at the top of the tower, were established by the regression analysis.  
The relationship between \(m_{D}\) and the basic wind speed \(v\) generally obeys the power exponential relationship [10], and its logarithmic expression is:  
\[\ln m_{D}=a+b\ln(v) \tag{6}\]  
where the values of the coefficients \(a\) and \(b\) can be determined by a regression analysis. Figure 19 shows the wind load effect functions of the transmission tower under different corrosion ages. The corresponding analytical functions are also presented in the figure. It can be seen that the relationship between the maximum displacement at the top of the tower and the basic wind speed conform to the power exponential relationship.  
Figure 18: Schematic diagram of the simulated wind loads in sections (unit: mm).
## 3 Sensors Configuration Analysis  
In the analysis presented in the paper, several assumptions were made, such as forcing the vertical component of the \(E\) field in the form of a constant value (Figure 3). Analyzed sensors were placed between upper and down electrodes. The electric potential of the upper equalizing layer (upper electrode) is \(V_{s}\) = 1000 V (this value was used during static analysis). The down electrode was ground with electric potential \(V_{gnd}\) = 0 V. Distance between them was defined as value \(h_{c}\) = 10 m. It is associated with a typical mid-latitude fair weather atmospheric electric field value of 100 V/m. This electric field strength value is related to electric field strength on a fair weather day.  
The inductive component representing the displacement current was determined by the rate of change of the electric flux relative to the plane perpendicular to the sensor. The sensors with different geometries adopted for the analysis were placed flush with the ground surface, and each time, they were directly grounded in an axisymmetrical point. The following three sensor geometries were taken into account:  
* Flat plate sensor;
* The sensor in the shape of a grater;
* Ring-shaped sensor.  
Figure 3: Exemplary view on flat plate model assumptions.  
The models of the sensors described above were prepared with the ANSYS/Maxwell 3D simulation environment. The main observation parameters for each configuration were the electric field force lines and the sensor collection surface of the electric charge. These results were verified using the example of the charge accumulated between the upper and down electrodes. It has been described in the preliminary version of this research presented in the paper [18]. The latter parameter is necessary to determine the current density in the atmosphere based on measuring the current in the external circuit. The sensor was a homogeneous metal surface placed horizontally in most measurement applications.  
The primary issue to consider was current in the external circuit if the sensor is oriented vertically, e.g., to maintain the symmetry of a ring shape, or will contain horizontal and vertical elements, e.g., a grater. Similar issues have been described in the literature, e.g., [17] in the past. Electric field distribution \(E\) (or potential \(V\)) parameters were considered in each case. The surface charge \(\tau\) induced on the conductor (sensor) is equal to:  
\[\tau=-\varepsilon_{0}\frac{dV}{dn} \tag{6}\]  
where \(\frac{dV}{dn}\) is the normal derivative of potential \(V\) at the surface. Analyses were performed for the electric simulation type, as excitation was defined as time-dependent changes.
# Input Shaping Control of a Flexible Structure for Rest-to-Rest and Non-Rest-to-Rest Maneuvers
## 8 The Optimized ZVDD Input Shaper
Symbolic expressions for the final values of the states, \(\theta_{1}(t_{f}+\epsilon_{1}+\epsilon_{2}+\epsilon_{3})\), \(\dot{\theta}_{1}(t_{f}+\epsilon_{1}+\epsilon_{2}+\epsilon_{3})\), \(\dot{\theta}_{2}(t_{f}+\epsilon_{1}+\epsilon_{2}+\epsilon_{3})\), and \(\dot{\theta}_{2}(t_{f}+\epsilon_{1}+\epsilon_{2}+\epsilon_{3})\), are given in Appendix C. The value of \((t_{f}+\epsilon_{1}+\epsilon_{2}+\epsilon_{3})\) can be predefined to any value \(\beta\) (greater than or equal to the minimum-time optimal control time value). Additionally, \(x_{4}\), \(x_{5}\), \(x_{6}\), \(x_{7}\) and \(y_{4}\), \(y_{5}\), \(y_{6}\), and \(y_{7}\) are upper and lower bounds for different time intervals. The term \(U_{coeff}\) refers to the parameters or factors which are multiplied by the actual values of the torque (see Table 2). For example, from V11 to V10, the factor is \(A_{1}\). Likewise, from V6 to V5, the factor becomes \((1-2A_{1}-2A_{2})\); by limiting the values of these factors within \(\pm 1\), we ensure that the shaped torque never exceeds the maximum or minimum allowable torque limits. It is possible to obtain a near-zero value for \(\theta_{1}\) (at the end) using the conventional ZVDD shaper for the rest-to-rest problem, but the optimized ZVDD shaper offers three other additional benefits besides ensuring a final rest condition. These advantages are the following: (a) the optimized ZVDD shaper can be used to significantly reduce vibration for rest-to-rest problems, (b) complete the maneuver in a predefined fixed time, and (c) customize one or more optimization parameters (e.g., to define the impulse amplitudes or the duration between impulses). For the two example problems defined in Section 2, the Multi-Start algorithm is used to solve the optimal control problem defined in Problem (46) with \(N=1000\) uniformly distributed starting points and a numerical tolerance value of \(1\times 10^{-04}\).