esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Proposed Method
### Feature Points Matching Strategy
#### 2.3.1 Dual-Match  
Assuming that the feature vector sets obtained in the previous feature extraction stage are V\({}_{\text{s}}\) and V\({}_{\text{o}}\), respectively, the simplest way to calculate the similarity between feature vectors is to calculate the Euclidean distance. However, in the actual matching process, due to noise and scene occlusion problems, some feature points do not have correct matching points corresponding to them, and the points with the closest Euclidean distance found in the corresponding images may be mismatched points. Therefore, [PERSON] proposed the k-nearest neighbor ratio method to eliminate the false matches [10].  
According to experiments, this method can achieve good results in most scenes. However, this one-way matching method is prone to \"one-to-many\" wrong matching phenomenon due to similar neighborhoods in different locations. A certain eigenvector in V\({}_{\text{s}}\) has multiple eigenvectors that satisfy the k-nearest neighbor ratio method in the V\({}_{\text{o}}\) set, which will cause a lot of trouble for the subsequent error matching elimination. Therefore, this paper adopts a two-way matching strategy [15] to improve the accuracy of matching point pairs.  
The specific process is as follows: (a) For each feature vector L\({}_{\text{a}}\) in V\({}_{\text{s}}\), calculate its Euclidean distance with each feature vector in V\({}_{\text{o}}\) by traversing. Then, sort and find the closest Euclidean distance Dist\({}_{\text{f1}}\) and the next closest Euclidean distance Dist\({}_{\text{f2}}\) and the corresponding vectors L\({}_{\text{b}}\) and L\({}_{\text{b}}\)\({}^{\prime}\). If Dist\({}_{\text{f1}}\)/Dist\({}_{\text{f2}}\)\(\leq\) Threshold, then L\({}_{\text{a}}\) and L\({}_{\text{b}}\) match. Traverse all the feature vectors in V\({}_{\text{s}}\) to obtain the forward matching set of M\({}_{\text{forward}}\). (b) For each feature vector L\({}_{\text{b}}\) in V\({}_{\text{o}}\), calculate its Euclidean distance with each feature vector in V\({}_{\text{s}}\) by traversing. Then, sort and find the closest Euclidean distance Dist\({}_{\text{f1}}\) and the next closest Euclidean distance Dist\({}_{\text{f2}}\) and the corresponding vectors L\({}_{\text{a}}\) and L\({}_{\text{a}}\)\({}^{\prime}\). If Dist\({}_{\text{b1}}\)/Dist\({}_{\text{b2}}\)\(\leq\) Threshold, then L\({}_{\text{a}}\) and L\({}_{\text{b}}\) match. Traverse all the feature vectors in V\({}_{\text{o}}\) to obtain the forward matching set M\({}_{\text{backward}}\). (c) Set up two loops, traversing M\({}_{\text{forward}}\) and M\({}_{\text{backward}}\)respectively. If the set of matching satisfies the relationship \(\text{Dist}_{\text{f1}}=\text{Dist}_{\text{b1}}\), then the set of matching point pairs is retained to obtain a set of bidirectional matching point pairs.
## 4 Proposed Method
### Hardware Monitoring Module
#### 4.4.1 Basic Block Check  
When the program is running, the HMM starts to work. By analyzing the binary code of each instruction in the ID stage, the program is also divided into multiple BBs. Each BB starts from the instruction in the target address after the last jump and ends with the delay slot instruction after the next jump instruction. The HMM will check whether the effective value (PC [17:2]) of the starting address of the currently executed BB has corresponding pre-stored data (BB\({}_{\text{start}}\)) in the Integrity Parameter Memory. If not, this means that the data has been maliciously attacked, affecting the normal extraction of BBs.
## 1 Introduction
Our aim in this paper is to propose an optimal algorithm by using the appropriate learning gain, and hence to improve the convergence characteristic in the ILC for discrete-time systems with the data quantization. In this paper, based on the lifted model description for ILC systems with quantized error, the optimal method of the learning gain is presented on the basis of the POILC algorithm. The optimal learning gain is a solution to the minimization problem about the tracking error energy and the learning effort amplified by an iteration-wise weighting factor. By discussing the convergence condition of the parameter optimal gain-arguable ILC with quantized error (POGAILCQe) laws, it is found that the optimal iteration time-varying learning gain is existent, and the convergence factors are related to the model parameters, the quantized density, and the weighting factor. The algorithm of POGAILCQe can improve the convergence performance of tracking errors and reduce the negative effects of data quantization for control performance from the logarithmic quantizer, and we provide a rigorous algorithm convergence analysis based on the existence of this selection of learning gains by optimization method, which can improve the active learning ability of the control mechanism and avoid the passivity of existing selective learning gains. The paper is arranged as follows. In Section 2, the quantized ILC law with quantized error is constructed, and the convergence condition is analyzed. The optimal ILC law with data quantization is constructed and the convergence conditions are given in Section 3. Section 4 exhibits the numerical simulations and the last section concludes the paper.
## 1 Introduction  
The world of telecommunication is evolving from closed legacy networks towards open IP-based networks. The control plane of these networks mainly relies on text-based protocols, such as the Session Initiation Protocol (SIP) [1]. This protocol is also increasingly used to embed conversational services into numerous new applications for end users _across_ multiple mobile and fixed access networks. Therefore,the risk is also increasing that attackers exploit vulnerabilities in SIP processing network elements for gaining control over them or performing fraud attacks.  
If an attacker gains control of a network device or somehow disturbs network operation, this quickly results in loss of revenue for the service provider, damages the provider's image, and leads to customer dissatisfaction. For the protection of SIP-based networks, a first approach is deploying firewalls at the edge that operate on a reactive principle using a database containing all known attack patterns. However, attacks for which no patterns are contained in the database, so-called zero day exploits, cannot be detected. A proactive capability to detect previously unknown attack variants can be provided by machine learning algorithms, which capture characteristics of observed normal and/or incorrect messages to classify anomalies as malicious events.  
[PERSON] _et al._[2] proposed Support Vector Machines (SVM) to detect anomalies in SIP signaling traffic. SVMs are a relatively new approach for classification in high dimensional feature spaces. Instead of reducing the dimensions of the feature space directly, SVMs extend the dimensions even further and separate the classes with a suitable yet simple description of a border in between, called a hyperplane [3]. In contrast to other classification algorithms, the hyperplane does not need to fully separate the classes from each other. Additional support vectors are used to describe exceptions from the hyperplane classes. The hyperplane and support vectors are calculated in the training phase and constitute the classification model. The computational complexity during classification, and thus the achievable throughput, depends on the formulation of this model. While [PERSON] _et al._ proposed to use _traffic_ characteristics as features for the classification, the approach by [PERSON] _et al._[4] is based on \(n\)-grams that are generated from the SIP _messages_.  
As the SIP protocol incorporates many extensions, the false positive rate (FPR) of currently known algorithms is likely to exceed a threshold which endangers the applicability in products. We propose to add protocol knowledge to the feature extraction step to achieve better classification results and herewith lower FPR. We detail our approach in the next section, present an evaluation in Section 3, and summarize the results in Section 4.
## 3 Results and Discussion  
### Surface Topography  
Figure 5 shows the surface macro-morphologies of different L-PBF samples. The results show that, in terms of scanning strategy combination, the CHESS scanning strategy combination had the smoothest surface, a continuous melt channel, and good overlap, as shown in Figure 5a-c. One the other hand, the sample's surface combined with the LINE scanning strategy was uneven, and the melt channel had large fluctuations, as shown in Figure 5g-i. The surface flatness of the S0, S90, and S45 samples was lower than that of the C0, C45, and C90 samples. There were some collapsed areas on the surface, as shown in Figure 5d-f. The samples with poor surface flatness were the L0-L45 sample, which had intermittent melt channels and poor overlap between the melt channels, and the collapse area was further expanded.  
Figure 4: L-PBF forming tensile specimen. (**a**) L-PBF forming sample; (**b**) the actual L-PBF tensile specimen after high-speed wire cutting.  
Figure 5: Macroscopic topography of different L-PBF samples. (**a–o**) Corresponding to the surface topography of C0, C90, C45, S0, S90, S45, 0, L90, L45, 0\({}^{\circ}\), 90\({}^{\circ}\), 45\({}^{\circ}\), C, S, and L samples, respectively.  
From the offset angle analysis, the L-PBF specimen's surface with 0\({}^{\circ}\) and 45\({}^{\circ}\) angle offset was flatter and less collapsed than that of the 90\({}^{\circ}\) specimen. Because the distance of each scanning line was shorter, the mutual influence between each melt channel was larger. The laser interacted violently with the metal powder bed, resulting in more splashes when the offset angle was 90\({}^{\circ}\), as shown in Figure 5b,e. The distance of each scan line increased, the molten metal powder could be fully flowed and cooled, the spatter was less, and the surface became smoother when the offset angle was 0\({}^{\circ}\) and 45\({}^{\circ}\), as shown in Figure 5a,c,d,f.  
On the whole, the surface of the L-PBF specimen after the combination of scanning strategies was generally better than that of the single scanning strategy, as shown in Figure 5a-c,j-o. The C (CHESS) scanning strategy can reduce the heat accumulation of L-PBF forming and generate less residual tensile stress, and the forming layers are bonded well [33], which explains why the surface of the C0-C45 samples and the CHESS specimen was flat. The L (LINE) scanning strategy was more similar to the scanning path of the angular offset layer, and thus it may cause overlap between the scanning lines, increase the heat accumulation, hinder the cooling of the melt, and result in a rough surface in the sample. The poor bonding between the forming layers resulted in localized collapsed areas on the surface, which also explained the worst surface of the 0\({}^{\circ}\)-45\({}^{\circ}\) specimen, as shown in Figure 5j-l.
# Design Optimization of Truss Structures Using a Graph Neural Network-Based Surrogate Model
## 3 Materials and Methods
### GNN-Based Optimization
#### 3.2.1 Data Preparation  
For training the GNN and validating its effectiveness in approximating the nodal displacements of various designs during iterations in the PSO algorithm, a comprehensive dataset must be generated to encapsulate the structural responses of different trusses within the full design space, \(\textbf{A}^{L}\leq\textbf{A}\leq\textbf{A}^{U}\). First, a random sample set of member cross-sectional areas, **A**, is generated and assigned to trusses of the same configuration, topology, and boundary conditions as the truss structure to be optimized. Second, finite element analyses of these trusses are performed to determine their nodal displacements, \(\delta_{node}\), under the same set of forces applied at the joint. The trusses are then transformed into graph representations as input to the GNN. Both the nodes and edges of the graphs are typically associated with input/output feature vectors. In this study, the cross-sectional areas of the truss members serve as the edge input feature, while the truss joint coordinates, joint external forces, and support conditions (0 for restrained directions and 1 otherwise) are the node input features. Thus, in graph representations of a three-dimensional (space) truss and two-dimensional (planar) truss, the nodal input feature vector consists of nine and six variables, respectively. The nodal output feature vector for the space and planar trusses consists, respectively, of the three and two nodal displacement components obtained from finite element analyses. Accordingly, the generated dataset used for training and validation of the GNN consists of the graphs and the input and output feature vectors of their nodes and edges.
# Product Image Generation Method Based on Morphological Optimization and Image Style Transfer
## 5 Case Study
### Automotive Front-Face Style Transfer Experiment Design  
#### 5.2.1 Experimental Setup and Dataset  
This experiment was conducted on the Linux platform, utilizing an SSH remote connection to access a cloud computing server. The server's configuration comprises a GPU processor, namely the NVIDIA GeForce RTX 3080 with 10 GB of VRAM, a CPU processor, specifically the Intel(R) Xeon(R) Platinum 8255C CPU @ 2.50 GHz, and a 50 GB SSD. The deep learning framework employed was PyTorch 1.11.0, running on Python 3.8 (Ubuntu 20.04) within a Cuda 11.3 environment. Furthermore, commonly utilized third-party Python libraries, including NumPy 1.24.3, OpenCV 4.5.2, Matplotlib 3.8.0 and Tqdm 4.65.0, were installed.  
The experimental dataset comprises 2891 original images of car front faces, sourced from \"The Car Connection\" and \"Autohome\" websites. The contour dataset of car front faces was generated using the Canny edge detection algorithm. These images possess a resolution of 512 \(\times\) 384 pixels. For the training phase, the model employs the Adam optimizer, configured with a learning rate of 0.0002, to optimize its parameters. The loss function incorporates weight parameters \(\lambda\)1, \(\lambda\)2, \(\lambda\)3, and \(\lambda\)4, which are assigned values of 1.0, 100.0, 0.5, and 1000.0, respectively. The batch size is set to 4, and the model undergoes training for 800 epochs.
# STN-Homography: Direct Estimation of Homography Parameters for Image Pairs
## 5 Sequence STN-Homography
### Training, Results and Comparison with Other Approaches  
For simplicity, when training the sequence STN-Homography model, we used similar training parameters as those used for training single STN-Homography model and the loss weights of all losses of the sequence STN-Homography model are set to 1.0. The only difference is that when the two-stage sequence STN-Homography model was trained, we used 150,000 training steps, and when the three-stage sequence STN-Homography model was trained, we use 130,000 training steps and a smaller initial learning rate of 0.01 instead of 0.05. Figure 7 shows the training loss and validation loss during the training of three-stage sequence STN-Homography model.  
Figure 6: Architecture of the three-stage sequence STN-homography model.  
Figure 3 shows the comparison of the mean corner error of our sequence STN-Homography model with other reported approaches. The two-stage sequence STN-Homography model achieves a mean corner error of 1.66 pixels, which is smaller than the previously proposed two-stage hierarchical STN-Homography model of 2.6 pixels and all other reported CNN-based methods [22,24]. The three-stage sequence STN-Homography model achieves a mean corner error of 1.21 pixels, which is smaller than the three-stage hierarchical STN-Homography model of 1.57 pixels. We found that the performance of the sequence STN-Homography model was better than that of the hierarchical STN-Homography model. The main reason is attributed to the fact that the sequence STN-Homography model can be trained in an end-to-end manner.
# Adaptive Cruise Control Based on Model Predictive Control with Constraints Softening
## 3 Mathematical Model
### Car-Following State Space Model
\begin{array}{l}\delta_{\mathrm{ref}}(k+i)=\delta(k)+[\delta_{\mathrm{ target}}(k)-\delta(k)](1-\varphi_{1})=\varphi_{1}^{i}\delta(k)\\ v_{\mathrm{rel\_ref}}(k+i)=v_{\mathrm{rel}}(k)+[v_{\mathrm{rel\_des}}(k)-v_{ \mathrm{rel}}(k)](1-\varphi_{2})=\varphi_{2}^{i}v_{\mathrm{rel}}(k)\\ a_{\mathrm{ref}}(k+i)=a(k)+[a_{\mathrm{des}}(k)-a(k)](1-\varphi_{3})=\varphi_{ 3}^{i}a(k)\\ j_{\mathrm{ref}}(k+i)=j(k)+[j_{\mathrm{des}}(k)-j(k)](1-\varphi_{4})=\varphi_{ 4}^{i}j(k)\\ \end{array}  \tag{10}\]  
where \(\delta_{\mathrm{ref}}(k+i)\), \(v_{\mathrm{rel\_ref}}(k+i)\), \(a_{\mathrm{ref}}(k+i)\), and \(j_{\mathrm{ref}}(k+i)\) are the reference trajectories at step \(i\) in the predictive time domain for the inter-distance error, relative velocity, acceleration, and jerk, respectively; \(\delta_{\mathrm{target}}(k)\), \(v_{\mathrm{rel\_des}}(k)\), \(a_{\mathrm{des}}(k)\), and \(j_{\mathrm{des}}(k)\) are the desired inter-distance error, desired relative velocity, desired acceleration, and the desired jerk, respectively; and \(\varphi_{1}\), \(\varphi_{2}\), \(\varphi_{3}\), and \(\varphi_{4}\) are the damping exponents of reference trajectory for the inter-distance error, relative velocity, acceleration, and jerk, respectively. The ACC system needs to satisfy the basic requirements for safety car following. Meanwhile, riding comfort and fuel economy are also important evaluation indexes. Therefore, this paper chooses the inter-distance error \(\delta(k)\), relative velocity \(v_{\text{rel}}(k)\), the acceleration \(a(k)\), and jerk of the host vehicle \(j(k)\) as the output variables of the prediction model, that is \(y(k)=[\delta(k),v_{\text{rel}}(k),a(k),\) and \(j(k)]^{\text{T}}\). The car-following output equation is  
\[y(k)=Cx(k)-Z \tag{11}\]  
where  
\[C=\left[ \begin{array}{ccccc}1&-t_{\text{h}}&0&0&0\\ 0&0&1&0&0\\ 0&0&0&1&0\\ 0&0&0&0&1\end{array} \right]Z=\left[ \begin{array}{c}d_{\text{c}}\\ 0\\ 0\\ 0\end{array} \right].\]
## 4 Radiometric Processing Workflow  
### General Procedure for Generating Reflectance Maps from UAVs  
Radiometric processing transforms the readings of a sensor into useful data. First, sensor-related radiometric and spectral calibration needs to be carried out. Second, transformation to top-of-canopy reflectance based on radiometric reference panels and/or an empirical line method (ELM), secondary reference devices, or atmospheric modeling needs to be carried out. Third, influences of the object reflectance anisotropy (bidirectional reflectance distribution function, BRDF) effects, and shadows can be normalized. [PERSON] [116] calls this entire multi-step process the image chain approach. The different calibration schemes are outlined in Figure 5. These steps can be carried out sequentially as independent steps, which has been the typical approach in the classical approaches used for the airborne and spaceborne applications, for example, as implemented in the ATmospheric CORrection (ATCOR) [102]. UAVs also provide some novel aspects to be considered.  
The desired output is usually reflectance. However, in typical situations, the output is strictly speaking the hemispherical conical reflectance factor (HCRF; [117; 118]), because the IFOVs of each pixel captures a conical beam. The pixels of imaging spectrometers have a relatively small IFOV; therefore, their measurements can be considered an approximation of hemispherical directional reflectance factors (HDRF; [42; 119]). Finally, multi-angular measurements across large parts of the hemisphere can be used to approximate the BRDF of a surface (e.g., [24]). Besides, if measurements from the hemisphere are averaged, the bihemispherical reflectance, which is also called albedo (blue sky albedo in the MODIS product suite; [118]), can be approximated. Consequently, the albedo is also approximated if the information of pixels with a wide range of different viewing geometries(e.g., from multiple images) are averaged, as is often done during orthomosaic generation from 2D images [42]. In every case, the data needs to be calibrated. In the following subsections, the sensor calibration (Section 4.2) and the image data calibration (Section 4.3) processes are described in detail.
## 2 Materials and Methods
### Model Architecture
#### 2.2.5 Pseudo-Labeling  
By introducing a semi-supervised learning strategy based on the idea of pseudo-labeling, the performance of the model can be significantly improved. The specific approach is to use the existing model to pre-segment the unlabeled data, and then use the pre-segmentation results as pseudo-labels for further training of the model. This method not only increases the size of the training dataset but also reduces the dependence on a large amount of manual annotation. In this process, the quality of the pseudo-labels is assessed through the following mechanism: if multiple models have a consistency indicator in the calculation of porosity for the segmentation results of the same image that is below a given consistency threshold, it indicates that this image is not suitable to be used as a pseudo-label.  
Equations (5)-(7) are for calculating the consistency metrics of multiple models. Here, \(M\) represents the total number of models, and \(w_{m}\) is the weight of model \(m\). For a given sample \(x\), collect the porosity values \(\{p_{m}(x)|m\in\{1,\dots,M\}\) calculated by all the models for the pre-segmentation results, compute the weighted average \(\mu_{x}\) of these porosity values, then calculate the weighted standard deviation \(\sigma_{x}\) of these porosity values, and finally, calculate the reciprocal of the coefficient of variation \(C_{x}\) as the consistency score. The higher the score, the higher the consistency.  
\[\mu_{x}=\frac{1}{M}\sum_{m=1}^{M}w_{m}p_{m}(x) \tag{5}\]  
\[\sigma_{x}=\sqrt{\frac{1}{M}\sum_{m=1}^{M}w_{m}(p_{m}(x)-\mu_{x})^{2}} \tag{6}\]  
\[C_{x}=\frac{\mu_{x}}{\sigma_{x}} \tag{7}\]  
Therefore, only pseudo-labels with high consistency are retained for training. The entire process is iteratively executed, with the termination condition being that the consistency metrics of the segmentation results in porosity calculation for the sampled unlabeled data by multiple models remain consistently low, or the maximum number of iterations is reached. The training process is shown in Algorithm 1 below:
## 2 Materials and Methods
### Data
#### 2.2.2 MODIS Products  
MODIS products, including EVI, fraction of photosynthetic active radiation (fPAR), photosynthetic active radiation (PAR), gross primary productivity (GPP), land surface temperature (LST), and evaporation (ET), were used in this work. The algorithms of NDVI and EVI are similar, but EVI reduces the influence of atmospheric and canopy background and enhances the vegetation signal [66]. EVI is an upgraded algorithm of NDVI [67]. We selected MODIS EVI as a traditional vegetation index in this study. The 16-day MODIS EVI product (MOD13C1 collection 6) is the Terra MODIS level 3 vegetation index product with 0.05\({}^{\circ}\) spatial resolution, and contains reliability and QA layers. MODIS EVI data were quality filtered by excluding pixels contaminated by clouds or aerosols using quality flags.  
MODIS fPAR and PAR products were used to further interpret the dynamics of SIF. The MODIS fPAR is the standard 1 km spatial resolution product with a temporal resolution of 8 days [68]. The MODIS-based PAR dataset from GLASS products was used, and is available from [[http://www.glass.umd.edu/PAR/](http://www.glass.umd.edu/PAR/)]([http://www.glass.umd.edu/PAR/](http://www.glass.umd.edu/PAR/)) (accessed on 22 November 2021). In addition, the GPP dataset derived from FLUXNET was used. This dataset provides global daily estimates of GPP and uncertainties at 0.05-degree resolution from 2000-03 to near recent. The 16-day MODIS LST product (MOD11C3) and ET were used to analyze possible changes in thermal dynamics under drought conditions. MODIS products are mainly available from [[https://lpdaac.usgs.gov/](https://lpdaac.usgs.gov/)]([https://lpdaac.usgs.gov/](https://lpdaac.usgs.gov/)) (accessed on 13 February 2022).