esa-sceva/satcom-chunk-collection huggingface dataset:
# Pallet Recognition with Multi-Task Learning for Automated Guided Vehicles
## 3 Experiments
### Evaluation of the Performance of Pallet Recognition
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{3}{*}{**Methods**} & \multicolumn{8}{c}{**Architectures**} \\ \cline{2-7}  & **AlexNet** & **VGG11** & **ResNet-18** & **ResNet-50** & **ResNet-101** \\ \hline \multirow{3}{*}{Single-task} & 0.58 & 0.67 & 0.75 & 0.70 & 0.68 \\  & (0.09) & (0.08) & (0.07) & (0.08) & (0.05) \\ \cline{1-1}  & **0.71** & **0.79** & **0.81** & **0.81** & **0.77** \\ \cline{1-1}  & **(0.08)** & **(0.08)** & **(0.08)** & **(0.06)** & **(0.07)** & **(0.11)** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparisons of results using single-task models and multi-task models, in terms of exact match ratios. The best values for each architecture are in bold. The mean exact match ratio and standard deviations (in parentheses) are reported. We conducted experiments with the proposed model using additional training data. Lighted and flipped images constituted the additional data, and experiments were conducted in four cases as shown in Tables 7 and 8 to examine the effects of each image dataset. In most cases, the additional data delivered improved performances with the most improvement associated with lighted images. The best performance was achieved when both images were used. Figure 6: Illustrations of training and validation losses for ResNet-50: (a,c,e) are Task 1’s loss plots for different training data; (b,d,f) are Task 2’s loss plots for different training data. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{2}{c}{**Training Dataset**} & \multicolumn{5}{c}{**Architectures**} \\  & **Number of Samples** & **AlexNet** & **VGG11** & **ResNet-18** & **ResNet-50** & **ResNet-101** \\ \hline \multirow{5}{*}{**Multi-task (proposed)**} & Default & 0.71 & 0.79 & 0.81 & 0.81 & 0.77 \\  & 270 & (0.08) & (0.08) & (0.06) & (0.07) & (0.11) \\ \cline{1-1}  & Default + light on & 0.93 & **0.93** & 0.94 & 0.93 & 0.94 \\ \cline{1-1}  & 540 & (0.03) & **(0.04)** & (0.04) & (0.04) & (0.03) \\ \cline{1-1}  & Default + flipped & 0.72 & 0.75 & 0.86 & 0.81 & 0.81 \\ \cline{1-1}  & 540 & (0.09) & (0.06) & (0.06) & (0.06) & (0.07) \\ \cline{1-1}  & Default + light on + flipped & **0.94** & **0.93** & **0.95** & **0.95** & **0.96** \\ \cline{1-1}  & 1080 & **(0.03)** & **(0.03)** & **(0.03)** & **(0.03)** & **(0.03)** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparisons of results using the different training datasets, in terms of exact match ratio. The best values for each architecture are in bold and the best performing model among the architectures is underlined. Figure 7: Illustrations of accuracy for ResNet-50: (**a**,**c**,**e**) are Task 1’s accuracy plots for different training data; (**b**,**d**,**f**) are Task 2’s accuracy plots for different training data.
## 2 Methods
### Covariance Pooling Module
#### 2.3.1 Covariance Normalization  
Supposing that the feature tensor generated by a ReLU-activated convolutional layer has dimensions \(C\times H\times W\), adjusting this tensor to a feature matrix \(X\) with dimension d and feature number \(N=H\times W\), then matrix \(X\) can be expressed as \(Xe\mathbb{R}^{d\times N}\). Next, the covariance matrix \(F\) is established. The second-order pooling applied by \(F\) can be calculated by the following equation:  
\[F=X\overline{I}X^{T},\overline{I}=\frac{1}{N}(I-\frac{1}{N}hh^{T}) \tag{3}\]  
where \(I\) denotes an identity matrix of size \(N\times N\), \(h\) represents a column vector filled with ones, and \(T\) denotes matrix transposition. The covariance matrix's square root can be obtained via singular value decomposition (SVD) or eigen decomposition (EIG):  
\[F\rightarrow(U,\Lambda),F=U\wedge U^{T} \tag{4}\]  
where \(\Lambda=diag(\lambda_{1},\ldots,\lambda_{d})\) denotes a diagonal matrix containing eigenvalues sorted in descending order, and \(U=[u_{1},\ldots,u_{d}]\) refers to an orthogonal matrix formed by the associated eigenvectors.  
The matrix power of the covariance matrix \(F\) is transformed into the power of eigenvalue by EIG decomposition. The detailed calculation process is as follows:  
\[(U,\Lambda)\to Z,Z=F^{\alpha}=U(\Lambda)U^{T} \tag{5}\]  
where \(\phi(\Lambda)=diag(\lambda_{1}^{\alpha},\ldots,\lambda_{d}^{\alpha})\) and \(\alpha\) is a positive real number. The normalization performance of the covariance matrix is optimal when \(\alpha=0.5\).  
Figure 3: Structural details of the second-order pooling block. The process begins with covariance pooling to capture second-order feature statistics from convolutional feature maps. The resulting covariance matrix is then pre-normalized by its trace, followed by iterative matrix square root approximation using the Newton-Schulz method. Post-compensation restores the scale of the features, and the output is fed into a softmax classifier.
# A Novel Network Protocol Syntax Extracting Method for Grammar-Based Fuzzing
## 4 Design and Implementation
### Main-Parsing Module
#### 4.2.2 Normalizing  
The parsing process will normalize the code structure of each dissector's main parsing function and save the results to a standardized file. The purpose of this step is to optimize the code structure so that all protocol dissector codes comply with the same standards, thereby avoiding errors caused by format issues during the parsing process. The normalizing rules are as follows:  
* Delete all comments.
* Merge statements that are displayed on multiple lines.
* All \"if/else if/else\" statements must have a complete structure. For structures lacking else statements, add an empty else statement to it.
* All conditional and loop statements must be enclosed with a pair of curly braces.
* All sub-function call statements are replaced with the sub-function body.
* The offset and length-related variables are replaced with their real values.
## 2 Methodology  
During tunneling, there are many factors affecting the attitude of the shield, and there are complicated relations among them, which means that the conventional mathematical model can not accurately predict the attitude of the shield tunneling. By using the support vector regression (SVR) algorithm in machine learning, a mathematical model can be built to accurately predict the attitude of shield tunneling. Figure 1 shows the flow of the shield-attitude-prediction method based on principal component analysis (PCA) and support vector regression algorithm. In our method, the initial stage focuses on data preparation, where the site data are systematically categorized into geological parameters, operational parameters, and geometric parameters. These parameters, combined with the geological exploration report, shield-machine driving system details, and the shield design specifications, provide a comprehensive description of the shield-machine state. The selection of these parameters was rigorously based on their direct impact on tunneling efficiency, stability, and safety, especially under complex geological conditions. The second stage is data preprocessing and feature extraction, which mainly includes eliminating stopping data and filtering noise to improve the accuracy of prediction model, and using principal component analysis to reduce the dimension of data. The third stage is model establishment and execution; that is, a support vector regression (PCA-SVR) hybrid model based on principal component analysis is established, and the model data is divided into training sets and test sets. After model training, the trajectory of the shield's driving attitude is predicted based on the input parameters after noise reduction.  
Figure 1: Attitude-prediction process.
## 5 Experiment
### Quantitative Analysis  
We compared the effectiveness of DeepChaos+ on test sets of 20%, 40%, 60%, and 80%, as shown in Figure 4 (left). For all test sets, DeepChaos+ was able to generate data and learn until achieving near 100% accuracy on the 40%, 60%, and 80% sets. However, on the 20% set, the model only achieved about 84% accuracy due to the initial lack of data, which was insufficient for effective inference and generation of samples. The remaining competing methods showed significantly lower effectiveness, as they could not infer features like DeepChaos+, especially on the 40% dataset, where DeepChaos+ achieved an accuracy of nearly 95%. Methods like BiLSTM only reached around 67%, and GRU-D reached 72%. The traditional approach also achieved accuracy similar to that of DeepChaos+ across all four datasets. However, when it comes to BER (discussed in the following section), this method yields a lower performance compared to DeepChaos+.  
Regarding the BER of the test sets (Figure 4, right), we only show the results for the 60% and 80% sets since DeepChaos+ has not yet reached 100% accuracy on the other two sets, resulting in higher BER. DeepChaos+ clearly has the best performance, as seen in the 60% set where its BER falls within the range of \(2.3\times 10^{-4}\). The remaining methods have BER ranging from approximately \(1.9\times 10^{-3}\) to \(3\times 10^{-3}\). For the 80% set, DeepChaos+ demonstrates superior performance, with a BER of around \(1.5\times 10^{-4}\). This can be explained by the fact that, as DeepChaos+ has more datasets, the interpolation for generating data becomes more accurate. However, when reaching a certain threshold, the other methods also start to generalize and narrow the gap slightly compared to DeepChaos+.
# Mesh-Free MLS-Based Error-Recovery Technique for Finite Element Incompressible Elastic Computations
## 2 Finite-Element Incompressible Elastic Formulation  
The mixed formulation is used for incompressible elastic problem analyses [35]. The basic governing equations for incompressible elastic formulation can be written as:  
\[\text{Equilibrium equation}\ \
abla\sigma+f=0\ in\ \Omega \tag{1}\]  
\[\text{Boundary conditions}\ \sigma n=\overline{t}\ on\ \Gamma_{t}\text{, and}\ u=\overline{u}on\Gamma_{u}\text{,} \tag{2}\]where \(\Omega\) is a problem domain, and \(\overline{t}\) and \(\overline{u}\) are given natural and essential boundary conditions on \(\Gamma_{t}\), \(\Gamma_{u}\) boundaries respectively.  
The stress (\(\sigma\))- and strain (\(\varepsilon\))-related equation for mixed formulations can be written as:  
\[\text{Constitutive equation}\ \sigma(u)=2\mu\varepsilon(u)+\lambda tr[\varepsilon(u)] \textbf{I},\ \text{or} \tag{3}\]  
\[\sigma(u,p)=2\mu\varepsilon+p\textbf{I},\ \text{or} \tag{4}\]  
\[\frac{p}{\lambda}=div(u), \tag{5}\]  
where \(u\) = displacement, \(p\) = pressure.  
Lame parameters:  
\[\mu=E/2(1+\
u)]\lambda=E.\
u/[(1-2\
u)(1+\
u)], \tag{6}\]  
The strain-displacement equation and the approximation of displacements (u) and pressure (p) in terms of nodal values (\(\overline{d}\), \(\overline{p}\)) of element using interpolation function (N) can be written as [36]:  
\[\varepsilon=Lu,\mu=\text{N}_{u}\overline{d},p=\text{N}_{p}\ \overline{p}, \tag{7}\]  
The system of equations obtained by applying the [PERSON] method are given as:  
\[\begin{bmatrix}A&B\\ B^{T}&0\end{bmatrix}\begin{B}\begin{B}\overline{d}\\ \overline{p}\end{B}=\begin{bmatrix}f_{1}\\ 0\end{bmatrix} \tag{8}\]  
where \(\textbf{B}=\int_{\boldsymbol{\Omega}}\textbf{G}^{T}\textbf{IN}_{p}d\boldsymbol {\Omega},\textbf{A}=\int_{\boldsymbol{\Omega}}\textbf{G}^{T}2\mu\textbf{G}d \boldsymbol{\Omega},\textbf{f}_{1}=\int_{\boldsymbol{\Gamma}_{1}}\textbf{N}_{ \textbf{u}}^{\textbf{T}}\textbf{t}_{\textbf{i}}d\Gamma+\int_{\boldsymbol{ \Omega}}\textbf{N}_{\textbf{u}}^{\textbf{T}}\textbf{f}d\boldsymbol{\Omega}, \textbf{G}=\textbf{LN}_{\textbf{u}}\),
## 3 Experimental Analysis
### Data Description and Preprocessing  
In this paper, hourly air quality concentration and meteorological data from twelve national ambient air pollutant monitoring stations during the period 1 March 2013 to 28 February 2017 were taken from the website of the University of California, Irvine ([[https://archive.ics.uci.edu/ml/datasets/Beijing+Multi-Site+Air-Quality+Data](https://archive.ics.uci.edu/ml/datasets/Beijing+Multi-Site+Air-Quality+Data)]([https://archive.ics.uci.edu/ml/datasets/Beijing+Multi-Site+Air-Quality+Data](https://archive.ics.uci.edu/ml/datasets/Beijing+Multi-Site+Air-Quality+Data)), accessed on 21 July 2022). The obtained air pollutant data (Table 1) include hourly PM\({}_{2.5}\) and PM\({}_{10}\). Hourly meteorological data obtained include temperature and pressure. The numerical changes of factors in Figure 3 are in the temporal dimension from 1 December 2016 to 31 January 2017.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Kind** & **Var.** & **Unit** & **Range** \\ \hline \multirow{6}{*}{Air Pollutant Data} & PM\({}_{2.5}\) & \(\upmu\)g/m\({}^{3}\) & [2, 999] \\  & PM\({}_{10}\) & \(\upmu\)g/m\({}^{3}\) & [2, 999] \\  & SO\({}_{2}\) & \(\upmu\)g/m\({}^{3}\) & [0.2856, 500] \\  & NO\({}_{2}\) & \(\upmu\)g/m\({}^{3}\) & [1.0265, 290] \\  & CO & \(\upmu\)g/m\({}^{3}\) & [100, 10,000] \\  & O\({}_{3}\) & \(\upmu\)g/m\({}^{3}\) & [0.2142, 1071] \\ \hline \multirow{6}{*}{Meteorological Data} & Temperature & \({}^{\circ}\)C & [\(-19.9,41.6\)] \\  & Pressure & hPa & [982.4, 1042.8] \\ \cline{1-1}  & Dew Point & \({}^{\circ}\)C & [\(-43.4,29.1\)] \\ \cline{1-1}  & Precipitation & mm & [\(0,72.5\)] \\ \cline{1-1}  & Wind Direction & & [N, ESE] \\ \cline{1-1}  & Wind Speed & m/s & [\(0,13.2\)] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Input variables for PM\({}_{2.5}\) concentration forecasting models.  
Figure 2: Distribution of PM\({}_{2.5}\) concentration monitoring stations.  
In data preprocessing, firstly, as shown in Table 1, since wind direction is provided as non-numerical data, the wind direction type must be converted to numerical data for calculation using category coding. Secondly, the missing data for each site is less than 5%, thus preserving the data for all sites [26]. Missing values in the individual site data were estimated using linear interpolation from the previous and subsequent data points. Finally, to remove the influence of excessive differences in values on the accuracy of the model, all data are processed by the Min-Max function.
## 6 Conclusions  
In this paper, we investigated a proactive eavesdropping and jamming scenario which include four cases for \(UAV_{L}\) to fulfil surveillance tasks. In such a surveillance paradigm, we formulated a power-efficient eavesdropping and jamming problem which has acceptable computing complexity and can be solved. Then, we proposed a selection policy for \(UAV_{L}\) to allocate eavesdropping and jamming power efficiently. Particularly, \(UAV_{L}\) selects the most efficient case for eavesdropping and jamming suspicious UAVs according to the selection policy in each time slot. With such policy, \(UAV_{L}\) can eavesdrop more data by consuming less power. Simulation results showed that selection policy outperformed No-Jamming and Constant-Jamming schemes in both power consumption and data reception. Moreover, we applied selection policy into four typical fading channels to validate the performance, results showed that selection policy performs better in Weibull fading channels in terms of the package received rate (PRR). For future works, we plan to study the problems about jamming and eavesdropping towards suspicious UAV groups, which is a challenge for eavesdropping and jamming policy selection.  
**Author Contributions:** Methodology, X.W.; software, X.Z.; validation, S.S.K., K.L. and E.T.; formal analysis, C.G.; writing--review and editing, X.W.; supervision, D.L.  
**Funding:** This work is supported by Shanghai Science and Technology Committee under Grant No. 18 DZ1200500; the NSF of China under Grant No. 71171045, No. 61772130 and No. 61301118; the Innovation Program of Shanghai Municipal Education Commission under Grant No. 14 YZ130; and the International S&T Cooperation Program of Shanghai Science and Technology Commission under Grant No. 15220710600.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 9: Eavesdropped packets by \(UAV_{L}\) in different Ds under different fading channels.
## 4 Conclusions  
In contrast to available studies, this study focused on the fabric-cooling process more than the fabric-drying process. The fabric-cooling process, which is commonly applied in the textile industry, was simulated by the CFD method while considering geometric and operating parameters.  
* Industrial data obtained by experimental measurements validate the mesh quality, the fabric model supposed as a porous medium, fabric movement, heat and airflow mechanisms in the fabric cooling process in the stenter machine, and the turbulence model. In the prediction of industrial data by the CFD, the Realizable k-\(\epsilon\) turbulence model provides good agreement.
* If the injector channel has a low porosity, it will perform better in a low-airflow supply, whereas if the injector channel has a high porosity, it will perform better in a high-airflow supply.
* In order to minimize vortices near corners and contact fabric and air directly, a horizontal position (\(\alpha=0^{\circ}\)) will provide the best cooling performance.
* It is obviously seen that the current airflow velocity (V\({}_{\text{a}}\) = 0.5 m/s) is too low and should be increased.
* The best cooling process and fabric temperature distribution are achieved when the fabric velocity is 0.3 m/s under certain constant parameters.  
In future work, the detailed solid model with entire channels and centrifugal fan and thermal CFD analyses could be investigated to optimize operating and geometric parameters.  
A.E.: project administration, conceptualization, investigation, formal analysis, visualization, validation, writing--original draft, writing--review and editing. M.T.S.: conceptualization, investigation, formal analysis, visualization, validation, project administration, writing--original draft, writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the Inonu University Research Fund (project number: FKP-2021-2425) and supported by the ILSAN Textile Company.  
Not applicable.  
Data are unavailable due to privacy.  
Author [PERSON] was employed by the company ILSAN Textile Company. All authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# Evaluation and Improvement of Lidar Performance Based on Temporal and Spatial Variance Calculation
## 4 Optimization of Data Acquisition Parameters and Calibration of Lidar Data
### Evaluation of the Linear Working Range of the PMT  
With a power supply providing high voltage up to kilovolts, the PMT can work and convert the light pulse to the electrical signal. Meanwhile, the high voltage must be set in a linear working range, which results in a gain in the linear transform region. However, in lidar applications, the intensities of the lidar returns are inversely proportional to the range squared, which may result in a strong detection capability in far range but a saturation signal in near range. In Raman lidar or high-spectral-resolution lidar with multi-channels, the intensities of the separated spectral radiant flux in different channels are different, and the PMT may be set with different high voltages to achieve the optimal detection performance. Therefore, to make the received lidar returns normalized in the procedure of data retrieval, the evaluation of the linear working range of the PMT is necessary in the construction of the lidar system.  
In order to explore the linear working range of the PMT, a programmable high voltage system of PMT is designed to realize fast data acquisition of real-time laser echo signals with different high voltages under the clear atmospheric conditions. Figure 9 shows the spatial variance distribution of the PC data with different high voltages of PMT settings in clear atmospheric conditions. It can be seen that the detection capability of the ultraviolet elastic scanning lidar changes significantly with the settings of the PMT high voltages. Although the phenomenon of the pile-up effect in PC data is lessened with the decrease of the high voltages, the maximum value of PC data with a high voltage of 900 V was more than ten times of the PC data value with a high voltage of 600 V.  
Figure 10 shows the statistics results of the non-zero PC data with different high voltages of the PMT settings under clear atmospheric conditions. It can be seen that when the absolute values of high voltage were less than 500 V, the number of non-zero PC data were less than 3000 and remained constant. However, when the absolute values of high voltage were larger than 500 V, the number of non-zero PC data linearly increased with the high voltage settings, which indicates that the linear  
Figure 8: The number statistics of non-zero data points in a probing profile with different preset discriminator levels of the Licel transient recorder TR40-160 in the ultraviolet elastic scanning lidar.  
working range of the PMT is between \(-500\) V and \(-1000\) V and corresponds to the gain curve of the PMT.
## 4 Simulation and Verification  
### Simulation  
To verify the effectiveness of the upper error compensation algorithm, the step signal and impulse signal simulating dimensional errors is respectively introduced into the error compensation module. The desired value can be any value in the simulation. Here, it is defined as 9.435 mm, which is consistent with the desired dimension of experiment. Since the error acceptance of A level indexable insert is 0.005 mm, in order to increase dimensional precision, the error limit E\({}_{\text{lim}}\) is set to 0.003 mm for the simulation. Moreover, in order to operate steadily, the maximum compensation value C\({}_{\text{max}}\) is chosen as 0.015 mm. For the compensation module, the initial value of updated index \(\varphi_{1}=1\). Values of weighting factors are defined according to the established conditions of Equations (14), (17), (18) and historical data analyzing, which have great influence on simulation results. Here, values of weighting factors are: \(\rho=1,\ \mu=0.5,\ \beta=10,\ \alpha=10\).  
The step error is 0.01 mm, which is imposed on the 15 th and subsequent workpieces. The simulation result is shown in Figure 5. It is evident that the compensation method works effectively. The dimension recovers to the desired value only in two operation cycles. That is, the dimensions from the 16 th workpiece are all converged to the desired value. For the second simulation, the desired dimension and the impulse error are the same to the first simulation. The impulse error is imposed on the 15 th workpiece. The simulation result is shown in Figure 6. The dimension returns to the desired value in three operation cycles. The simulation results show that this algorithm can quickly converge to the desired value.  
Figure 4: Flowchart of dimensional error compensation method.