esa-sceva/satcom-chunk-collection huggingface dataset:
# TMVDPatch: A Trusted Multi-View Decision System for Security Patch Identification
## 2 Background and Related Work
### Multi-View Learning  
In recent years, multi-view learning has achieved information complementarity by fusing features from multiple modes, and has been widely used in practical work. Typical algorithms include co-training mechanism [18], subspace learning methods [19] and multiple kernel learning (MKL) [20].  
The traditional representative method for multi-view learning is Canonical correlation analysis (CCA). CCA seeks to maximize the correlation between views by linearly transforming the data of different views. Traditional CCA can only cope with linear dependencies between views. To solve this problem, related works have proposed nonlinear extensions of CCA, such as kernel CCA [20; 21; 22; 23] and deep CCA [24]. Kernel CCA, as a typical nonlinear CCA algorithm, mainly maps low-dimensional data to a high-dimensional kernel function space, and performs correlation analysis in the high-dimensional space through the kernel function. However, although the kernel CCA solves the nonlinear problem, the model is complex and the training cost is large, so [PERSON] et al. [24] propose the deep CCA. Specifically, deep CCA learns nonlinear correlations between different views by combining DNN(Deep Neural Networks) and CCA. Based on it, [PERSON] et al. [25] propose DCCAE(deep canonically correlated autoencoders) in combination with autoencoders, which consists of two autoencoders and optimizes the combination of canonical correlations between the learned representation and the reconstruction errors of the autoencoders.  
Although the above methods have achieved multi-view classification, they do not consider the reliability of the classification results. [PERSON] et al. [26; 27] propose a trustworthy multi-view classification method that provides interpretability for multi-view classification results. However, the proposed method does not analyze the correlation between different views, which may ignore some important information or introduce some redundant information. The correlation analysis between different views refers to evaluating the inter-relationship between the information provided by different views, such as whether there are complementarity, conflict or duplication. This can help select the most useful views or adjust their weights, thereby improving the classification performance. In this paper, we introduce Grey Relational Analysis to dynamically adjust the weights between views, which can speed up model convergence and increase accuracy. Meanwhile, after combining Grey Relational Analysis, we can quantify the weights between views and further evaluate the credibility of multi-view learning.  
Therefore, on this basis, we implement a trusted multi-view security patch identification system called TMVDPatch. In our work, we use the fused evidence to assign weights to the original evidence of different views, thereby guiding reliable and credible identification results.
## 3 Results and Discussion
### [PERSON] _et al._[8] studied conditions for SPME-GC-MS of WPC and found that extracting the sample at 40 \(\,\)C for 30 min produced better results than holding for 1 h at 40 \(\,\)C or for 2 h at 27 or 50 \(\,\)C. They also found that a 30-min equilibration period was necessary to allow the less volatile compounds to be liberated from the sample. The same conditions we used for QF--holding at 60 \(\,\)C for 10 min, extracting at that temperature for 30 min, and ramping the oven temperature at 5 \(\,\)C/min--were compared with a procedure developed by [PERSON] _et al._[24], in which the sample was equilibrated at 40 \(\,\)C for 30 min and extracted at 40 \(\,\)C for 30 min, and the oven was ramped at 10 \(\,\)C/min.  
The results using the two methods are shown in Table 6. Unlike milk and cheese, where the principal proteins are \(\alpha_{\mathrm{s1}}\)- and \(\beta\)-casein, almost all of the proteins in whey consist of \(\beta\)-lactoglobulin, \(\alpha\)-lactalbumin, and bovine serum albumin. The differences in structure (e.g., whey proteins contain cysteine and \(\alpha_{\mathrm{s1}}\)- and \(\beta\)-casein do not [25]) result in different volatile compounds being formed. The faster ramping in the Wright method [24] allowed the analysis to be completed much earlier than the QF method, the total relative abundance was higher, and eight compounds not detected using the other method were observed. However, six 8-, 9- and 10-carbon compounds not observed with the Wright method [24] were found toward the end of the analysis using the slower QF method. The amounts of hexanal and nonanal in the two methods differed substantially since nonanal is one of the compounds eluted late with the Wright method [24] and exhibited a low relative abundance. These aldehydes are  
\begin{table}
\begin{tabular}{l c c c} \hline _Ketones_ & & & \\
2-Heptanone & L & L & M \\
2-Nonanone & L & L & M \\ Nonalactone & L & M & H \\ \hline _Others_ & & & \\ Furanenol & ND & L & M \\ Methyl propionate & ND & ND & M \\ Ethyl decanoate & ND & M & VH \\ \hline \end{tabular}
\end{table}
Table 5: _Cont._important since they affect whey protein aroma and were found by [PERSON] _et al._[26] to have the highest relative abundance among non-acidic volatiles in WPC.  
It appears that the best compromise between the two methods is simply to use both for each sample. By doing this, the entire range of volatiles can be observed.
## 3 Results
### Dataset Settings and Implementation Details  
To measure the effectiveness of the suggested methodology, the comparison experiments were performed on the three public datasets, namely the Vaihingen dataset, the Potsdam dataset, and the LoveDA dataset. Particularly for the Vaihingen and Potsdam datasets, the images were cropped to 512 \(\times\) 512 pixels. The images in the Val dataset were selected for testing. The selected samples were randomly divided into a training set and a test set at a ratio of about 1.4:1.  
The experiments were taken by using an NVIDIA GPU (11G), and the framework was created using Pytorch. The network was trained by applying the Adam optimizer, the batch size was set to eight, the initial learning rate was set as 0.001, and 100 epochs were utilized for training.
## 3 Imaging Evaluation of Therapy Response in Metastatic RCC
### Combined Treatment Using Antiangiogenic and ICIs Drugs  
As previously stated, several combination treatment regimens based on antiangiogenic and ICIs drugs have shown effectiveness in RCC, such as axitinib plus pembrolizumab as well as axitinib plus avelumab [11; 36]. In particular, the use of combined treatment protocols opens new potential imaging response criteria which are currently under investigation and require to be systematically evaluated. Of note, in such cases the imaging evaluation usually follows standard RECIST when clinical trials are performed to assess the overall survival and PFS. However, since atypical responses of these novel drugs as well as their cytostatic effect may occur, the new imaging criteria are indirectly helpful in clinical trials, even though not completely included. In detail, [PERSON] et al. reported that patients with metastatic RCC in treatment with pembrolizumab plus axinitib in clinical stable condition with unconfirmed disease progression by imaging, could continue the combined therapy until progression is confirmed by imaging at least 4 weeks later [11]. Similarly, [PERSON] et al. reported that patients with metastatic RCC in treatment with avelumab plus axitinib may continue the therapy although PD is demonstrated by imaging using RECIST, since they have no signs or symptoms of clinical progression [36]. These recent clinical experiences suggest that also combined treatment regimens in patients with metastatic RCC need not only the dimensional assessment of tumor lesions status to accurately assess cancer response (RECIST), but probably also other tumor features.
## 3 Results and Discussion
### Effect of Post-Processing on Properties
#### 3.2.1 Vacuum Pressure Impregnation of Phenolic Resin Solution and Carbonization
Therefore, in view of the fact that more than two times of vacuum pressure infiltration in phenolic resin solution has no obvious effect on the structure and properties, two times of vacuum pressure infiltration is chosen as the best scheme.
## 2 Ultra-Tightly Integrated GNSS/INS Architecture
In the ultra-tight integration, the vehicle's dynamics with the respect to the satellites are compensated by the INS aiding, and the lowest C/N0 accepted by the GNSS signal acquisition and tracking is reduced. Hence, some satellites which are considered not visible in the standard receiver can be acquired and tracked in the ultra-tightly integrated GNSS/INS receiver. The number of the GNSS satellites in view can increase in the ultra-tightly integrated GNSS/INS receiver due to the INS aid. In the following sections, the advantages of the ultra-tight integration in GNSS-challenged environments are analyzed in detail.
# Design and Experiments of Autonomous Path Tracking Based on Dead Reckoning
## 3 Experiments and Results
### Tracking Accuracy Analysis in Different Conditions
#### 3.3.3 Start Position and Angle  
As the method in this paper uses the dead reckoning method for localization, the initial position and angle have a degree of influence on path tracking accuracy. The position has a small influence on the entire trajectory, but if there is a deviation, the angle will lead to the rotation of the whole path, resulting in a larger deviation, as is shown in Figure 15. Therefore, a comparison experiment was conducted, with one group of data having a certain starting angle deviation, with the other two groups having a very small angle deviation. The results of this are shown in Figure 15.  
Figure 14: Tracking error with different look-ahead distances.  
Figure 13: Path tracking with different look-ahead distances.  
The red curve in Figure 16 is the result of a certain starting angle deviation, and the error is obviously larger than the other two groups, demonstrating a certain regularity. The specific deviation angle is estimated by taking two sets of data at the trajectory of the linear segment, such as points A and B in the figure, and the estimated error angle can be calculated using the following formula:  
\[\varnothing=\arctan\left(\frac{y_{A}-y_{B}}{x_{A}-x_{B}}\right) \tag{19}\]  
Based on the coordinate values of points A and B on the curve, the calculated angle estimate was 0.931\({}^{\circ}\). In order to verify whether the estimated value was correct, the original trajectory coordinate data from Test2 was rotated by angles of 0.690\({}^{\circ}\), 0.931\({}^{\circ}\), and 1.146\({}^{\circ}\), and the respective tracking errors were then calculated, as is shown in Figure 17. The results show that the curves after rotating 0.690\({}^{\circ}\) and 0.931\({}^{\circ}\) were closer to the original Test1 and Test3, while rotating 1.146\({}^{\circ}\) produced a larger error in the opposite direction. Therefore, the estimated angles can be assumed to be roughly correct.
## 3 Orthogonal Matrix-Autoencoder-Based Encoding Method for Unordered Multi-Categorical Variables
### Code Dimensionality Reduction  
An autoencoder (AE) [28] is an unsupervised neural network model, which mainly consists of two parts, an encoder and decoder, and has a symmetric structure. Through neural network training, an autoencoder can achieve the tasks of data denoising, feature learning, or data dimensionality reduction [29].  
An example of the structure of the autoencoder and the working principle of the autoencoder is shown in Figure 2. The autoencoder includes an input layer, a hidden layer, latent space, and an output layer, each with a different number of nodes. In this case, the structure from the input layer to the latent space is referred to as the encoder, and the structure from the latent space to the output layer is referred to as the decoder. The number of nodes in the input layer is the size of the original dimension of the data, the number of nodes in the latent space is the dimension in which the data needs to be compressed, and the number of nodes in the hidden layer is in between the original dimension and the compressed dimension.  
The working principle of the autoencoder is as follows: the encoder compresses the input data into a predefined low-dimensional encoding through a series of transformations \(f\), and the decoder tries to reconstruct the low-dimensional encoding into the original input data through a series of transformations \(g\). Iterative training of the autoencoder is carried out by minimizing the reconstruction error between the input and the output, and ultimately, it is hoped that the autoencoder learns the abstract feature representation of the samples \(\mathbf{z}\).  
The working principle of the autoencoder to carry out data dimensionality reduction is shown in Equation (16).  
\[\begin{array}{l}f:\mathbb{R}\rightarrow\mathbb{Z}\\ g:\mathbb{Z}\rightarrow\mathbb{R}\\ \mathbf{z}=f(\mathbf{W_{1}x+b_{1}})\\ \mathbf{\hat{x}}=g(\mathbf{W_{2}z+b_{2}})\\ f,g=arg\,\,\min\limits_{f,g}\left\|\mathbf{x}-g(f(\mathbf{x}))\right\|_{2}\end{array} \tag{16}\]  
Figure 2: The structure and working principle of the autoencoder.  
In this paper, multiple symmetric fully connected layers are designed in the encoder and decoder of the autoencoder to increase the complexity of the model and feature extraction capability by stacking multiple connected layers.  
ReLU is used as the activation function; ReLU is simpler to compute and has relatively stable performance. ReLU can avoid the gradient disappearing during model training, increase the nonlinear fitting ability of the model, and have good performance on multi-class tasks and datasets. The ReLU formula is \(f(\mathbf{x})=max(0,\mathbf{x})\).  
Taking the autoencoder model containing four hidden layers and one-dimensional data compression dimension as an example in Figure 3, \(\mathbf{X}_{N\times N}\) denotes the orthogonal numerical code matrix, and \(\hat{\mathbf{X}}_{N\times N}\) denotes the numerical matrix after the reconstruction of the autoencoder. Let \(\mathbf{X}^{(1)}\in\mathbb{R}^{N\times a},\mathbf{X}^{(2)}\in\
# Vertical Force Monitoring of Racing Tires: A Novel Deep Neural Network-Based Estimation Method
## 2 Related Works
### Analysis of Methodological Constraints in Related Works  
#### 2.3.1 Peak Radial Displacement-Based Approaches  
In [2,15], peak radial displacement (\(D_{\text{peak}}\)) is calculated as the radial displacement derived from the double integration of radial acceleration (\(a_{r}(t)\)) over the contact patch traversal time (\(\Delta t\)). The formula is expressed as follows:\[D_{\text{peak}}=\int_{0}^{\Delta t}\int_{0}^{\Delta t}a_{r}(\tau)\,d\tau\,d\tau \tag{1}\]  
where:  
* \(a_{r}(t)\): radial acceleration signal measured during tire-ground interactions;
* \(\Delta t\): the time required for the tire to traverse the contact patch, which serves as the integration limit.  
The contact patch traversal time (\(\Delta t\)) is calculated as the time difference between the local extremum pointsin the radial acceleration signal, corresponding to the leading and trailing edges of the contact patch. However, the identification of these extremum points is sensitive to noise in the acceleration signal, which can distort or obscure the actual extrema, leading to errors in the estimation of \(\Delta t\). While filtering or smoothing techniques can be used to reduce noise, they may inadvertently remove meaningful portions of the signal, further affecting the accuracy of the calculations. In addition, in real-time applications such as motorsport, double integration introduces additional computational load during preprocessing. Additionally, the calculation of the integration results in an area that can be affected by vehicle and road conditions. While these conditions may not necessarily impact the amplitude of the vertical force, they can influence the time, leading to an increase or decrease in the area.  
In [15], \(D_{\text{peak}}\) is related to the vertical load (\(F_{z}\)) using the following polynomial equation:  
\[D_{\text{peak}}=p_{00}+p_{10}\cdot F_{z}+p_{01}\cdot P+p_{11}\cdot F_{z}\cdot P +p_{02}\cdot P^{2} \tag{2}\]  
where:  
* \(F_{z}\): vertical load;
* \(P\): tire inflation pressure;
* \(p_{ij}\): experimentally derived coefficients.  
The accurate tuning of these coefficients (\(p_{ij}\)) is essential for the model's performance. They must be calibrated carefully using experimental data to account for differences in tire materials, inflation pressures, and other operating conditions. Poorly tuned coefficients can significantly reduce the precision of load estimations.  
A notable strength of this approach is its independence from tire geometry. Unlike methods that rely on parameters such as contact patch length or tire radius, this model is unaffected by tire wear or dimensional changes, ensuring consistent performance as tire characteristics evolve over time. However, the need for precise coefficient tuning and the sensitivity to noise in \(\Delta t\) calculations highlight the challenges that must be addressed to further improve its accuracy.
## 4 Methodology
### Digital Image Processing  
For remote-sensing images, using multiple spectral enhancement techniques helps to contrast the range of lithology or highlight the boundaries of lithological units. In this paper, we combined seven spectral enhancement techniques, including optimal index analysis, decorrelation stretch, band ratio, independent component analysis, principal component analysis, minimum noise fraction, and false color composite.
## 1 Introduction
This paper takes the 1121 working face of Hongliu coal mine as the research object, conducts a physically similar model experiment, and carries out research on the design of the opening cut support parameters of Hongliu coal mine around the stability of the surrounding rock of the large-section opening cut under a large mining height. As it is the initial mining face, the surrounding rock destructive characteristics of the working face have not been completely grasped. To ensure the safe, efficient, and early production of the initial mining face and to guide better support for the next working face, it is necessary to start from the combined anchoring rod and anchor cable support technology. Suboptimal parameters and operational inefficiencies persist in the support systems of large-section open off-cuts within high-cutting-height longwall faces. In this study, through numerical simulations and theoretical analyses, the critical parameters influencing support effectiveness in large-section open off-cuts were systematically identified. The feasibility of the proposed optimized support parameter scheme was verified by comparative analysis between field monitoring data and simulation results. This investigation provides scientific guidance for support design in large-mining-height working faces. This study advances prior research by conducting multi-parameter optimization through integrated analysis of similar material simulation experiments and field monitoring data, developing a comprehensive methodology for support parameter refinement. This approach incorporates systematic considerations of material selection and parametric configuration to evaluate the impact of support structure layouts on operational effectiveness, achieving synergistic optimization of critical parameters. Validation was ultimately conducted through practical engineering applications. This involved verifying, designing, and optimizing existing support schemes and proposing a set of both safe and cost-effective optimized support parameters based on on-site mining pressure observations. The results of the research can also serve as a reference for the comprehensive control of the support of large-section cuts in similar large-height working faces. This work significantly enhances the stability and safety of support systems through improved parameter optimization.
# An Affinity Propagation-Based Self-Adaptive Clustering Method for Wireless Sensor Networks
## 6 Discussion  
The initial cluster centers are obtained by iteration using the AP algorithm. With the scale of the network increasing, the time used for calculation of initial cluster centers will also increase rapidly. Therefore, one drawback of the presented clustering method is that it is not suitable for WSNs on a large scale. Additionally, the presented APSA can be improved by adjusting the value of AP preference. The value of the AP preference is obtained by experience and it has a significant influence on the performance of the AP; -6000 is just a suitable value for AP preference and we cannot ensure it is the optimal value. Therefore, our future work will focus on optimizing the parameter P.  
The simulator used in this paper is MATLAB and it can only simulate the real world. However, in real applications, many other problems need to be solved. For example, in the simulation environment, it is assumed that the transmissions between sensors are always successful; while in the real environment, transmission may fail due to the harsh environment or the busy communication channel. Therefore, the presented algorithm still needs to be improved to adapt to the real environment.  
Our future work will mainly focus on the improvement of expandability of the method. We will also combine popular mobile sink technology as well as data fusion technology with our clustering method to further improve performance.
# European Geostationary Navigation Overlay Service Performance Prediction +
## 5 Machine Learning-Based Performance Prediction for EGNOS  
Supervised machine learning is exploited to build three separate models: one to predict the UDREI, and two to predict the GIVEI models under nominal and active ionospheric conditions, respectively, as in the latter case the ionospheric corrections are known to be less accurate and higher GIVEI values can be expected. The use of two different GIVEI models allows for assessment of the expected performance of EGNOS under different ionospheric conditions. It is left up to the users to use one model or the other, depending on their assessment of the likelihood and potential impact of ionospheric storms. In the remainder of this paper, only the nominal model is considered. The nominal model is also expected to provide valid results in case of strong ionosphere since the machine learning modules have been trained during active ionosphere periods, and features related to the ionosphere activity are known by the machine learning modules.
## 3 Experimental Results
### Effect of Vibration on 1C Discharge Capacity
#### 3.1.4 Comparison for Capacity Distribution of Samples before and after Vibration  
According to the analysis results above, the probability density curves of the capacity for samples before and after vibration are superimposed in Figure 9.  
From Figure 9, it can be seen that the probability density curve after vibration exhibits a shift to the left and is flat as well as wide compared to that before vibration. This shows that the capacity mean of samples exhibited a reduction by 68.8 mAh and the standard variation increased by 5.1463 mAh after vibration.