esa-sceva/satcom-chunk-collection huggingface dataset:
# Mapping the Most Susceptible Regions to Fire in Portugal
## 1 Introduction
Aiming to map the country's fire vulnerability, and, therefore, to identify the regions which are more prone to burn if local management practices remain unchanged, we apply a Principal Component Analysis (PCA) to a multivariable set concerning factors known to be directly or indirectly linked with wildfire activity, namely: fire risk; the energy released by fires; fire weather conditions, encapsulated in a Fire Weather Index (FWI); elevation; and information related to vegetation activity and productivity, i.e., gross primary productivity (GPP) and net primary productivity (NPP). These latter variables can provide information regarding vegetation dynamics, and, therefore, regarding the fuel accumulation available to burn, GPP being frequently used as a proxy of the amount of biomass produced over space and time [33]. Finally, we compare the PCA-based fire-susceptibility map, with the burned areas of 2022 in Portugal, i.e., with scars corresponding to fire events that took place during an independent year (not considered in the PCA).
# A Programmable SRv6 Processor for SFC
## 3 Protocol-Independent Pipeline Design
### Programmable SR Forwarding for Network Slice
#### 3.4.1. SRv6 Mapping in Processor
```
Input:packet Output:modified_packet
1.if(\(\mathit{loop\_limit}==0\)drop(packet);
2.elseif(\(\mathit{SRH}\)){
3.if(\(\mathit{Des\_IP}==\mathit{Local\_IP}\)){
4.if(\(\mathit{Segment\_Left}==0\)){
5.\(\mathit{Des\_IP}=\mathit{Segment\_List}\)[0];
6.Delete(\(\mathit{SRH}\));
7.\(\mathit{hop\_limit}\);
8.Submit(\(\mathit{modified\_packet}\));
9.} 10.else{
11.\(\mathit{Des\_IP}=\mathit{Segment\_List}\)[\(\mathit{Segment\_Left}\)];
12.\(\mathit{Segment\_Left}\);
13.\(\mathit{hop\_limit}\);
14.\(\mathit{out\_port}=\mathbf{Look\_up}\)(\(\mathit{FIB}\),\(\mathit{Des\_IP}\));
15.\(\mathit{Des\_MAC}=\mathbf{Look\_up}\)(\(\mathit{ADI}\),\(\mathit{Des\_IP}\));
16.Forward(\(\mathit{out\_port}\),\(\mathit{modified\_packet}\));
17.} 18 else{
19.\(\mathit{hop\_limit}\);
20.\(\mathit{out\_port}=\mathbf{Look\_up}\)(\(\mathit{FIB}\),\(\mathit{Des\_IP}\));
21.\(\mathit{Des\_MAC}=\mathbf{Look\_up}\)(\(\mathit{ADI}\),\(\mathit{Des\_IP}\));
22.Forward(\(\mathit{out\_port}\),\(\mathit{modified\_packet}\));
23.} 24.} 25 else{
26.\(\mathit{flow\_id}=\mathbf{Look\_up}\)(\(\mathit{Flow\_table}\));
27.if(\(\mathit{flow\_id}==IP\)){
28.\(\mathit{out\_port}=\mathbf{Look\_up}\)(\(\mathit{FIB}\),\(\mathit{Des\_IP}\));
29.\(\mathit{Des\_MAC}=\mathbf{Look\_up}\)(\(\mathit{ADI}\),\(\mathit{Des\_IP}\));
30 Forward(\(\mathit{out\_port}\),\(\mathit{modified\_packet}\));
31.} 32.elseif(\(\mathit{flow\_id}==\mathit{source\_SRv}\)){
33.\(\mathit{instruction}=\mathbf{Look\_up}\)(\(\mathit{Action\_table}\),\(\mathit{flow\_id}\))
34 Config(\(\mathit{PDT}\),\(\mathit{instruction}\));
35.\(\mathit{SRH}\)=\(\mathbf{Look\_up}\)(\(\mathit{Mapping\_table}\),\(\mathit{instruction}\)[index]);
36.Insert(\(\mathit{PHV}\),\(\mathit{SRH}\),\(\mathit{offset}\),\(\mathit{SRH\_length}\));
37.\(\mathit{payload\_length}=\mathit{payload\_length}+\mathit{SRH\_length}\);
38.\(\mathit{Des\_IP}=\mathit{Segment\_List}\)[\(\mathit{Segment\_Left}\)];
39.Segment_Left_ -;
40.\(\mathit{out\_port}=\mathbf{Look\_up}\)(\(\mathit{FIB}\),\(\mathit{Des\_IP}\));
41.\(\mathit{Des\_MAC}=\mathbf{Look\_up}\)(\(\mathit{ADI}\),\(\mathit{Des\_IP}\));
42.Forward(\(\mathit{out\_port}\),\(\mathit{modified\_packet}\));
43.} ```  
**Algorithm 1**The pseudo code of SRv6.
# Quality Assessment of Photogrammetric Methods--A Workflow for Reproducible UAS Orthomosaics
## Materials and Methods  
### Multi-Temporal Flights  
Two multi-temporal UAV-based image dataset were acquired as a test sample for this study (Table 1). The first dataset is a series of six consecutive flights over a small temperate forest patch (Wolfskaute, Hesse, Germany). The surveyed area covers 7 ha of a forested hill with an adjacent meadow and ranges from 283 m to 320 m a.s.l. (above sea level). with a canopy height of up to 37 m (Figure 1a). The six flights were performed on 2020-07-07 between 11:00 and 14:00 CEST using a 3 DR Solo Quadrocopter (3D Robotics, Inc., Berkeley CA, USA) and a GoPro Hero 7 camera (GoPro Inc., San Mateo, CA, USA; Appendix B, Table A1). The flight plan was made with Qgroundcontrol and refined with a LiDAR derived digital surface model (DSM, provided by the Hessian Agency for Nature Conservation, Environment and Geology (HLNUG)) with the R-package uavRmp to achieve a uniform altitude of 50 m above the forest canopy (see Appendix A). For georeferencing and checkpoint error calculation, 13 ground control points (GCPs) were surveyed with the Real Time Kinematic (RTK) GNSS (Global Navigation Satellite System) device Geomax Zenith 35 (GeoMax AG, Widnau, Switzerland). The RTK GNSS measurements had an error of between 0.9 and 1.6 cm in the horizontal direction and between 1.9 and 3.7 cm in the vertical direction. Eight GCPs were used as controlpoints and five served as independent checkpoints to evaluate the georeferencing error (Figure 1a).  
The second dataset is an inter-annual time series of a grassland area (La Bertolina, Eastern Pyrenees, Spain). Terrain altitudes range from 1237 m to 1328 m a.s.l. The flights were performed in spring or early summer of 2013, 2015 and 2017 using an octocopter with a Pixhawk controller. Cameras and flight plans in a fixed altitude differ between the dates (Table 1), detailed camera settings in Appendix B, Table A1). The flights took place in the morning with sub-optimal illumination angles below 35 degrees [21] and partly scattered light conditions due to the presence of clouds. Five to eight GCPs were measured in each year with a conventional GPS device without RTK, from which three were used as checkpoints (Figure 1b).  
Both datasets were used to empirically determine the georeferencing accuracy and reproducibility of the photogrammetrically retrieved orthomoaics. For a better understanding of the newly introduced approaches, the following chapters first outline the general UAS image processing workflow.  
Figure 1: Overview of the two study areas and the location of ground control points. (**a**) Forested area in Wolfskaute, Hesse, Germany. (**b**) Grassland area in La Bertolina, Eastern Pyrenees, Spain. Both maps are projected in UTM but with geographic coordinates for a better overview.
# Suppression of Multiple Reflection Interference Signals in GPR Images Caused by Rebar Using VAE-GAN
## 3 Validation on Synthetic GPR Data
### Feature Visualization Analysis  
To visually demonstrate the feature extraction effectiveness of the CSA module, we compared the output feature maps of the last CSA module in the generation path with those from a model without the CSA module at the corresponding node. For the model without the CSA module, we used skip connections as a substitute. The final visualization results are shown in Figure 7.  
In the figure, the model with the CSA module exhibits clearer feature details and more effectively focuses on the critical void defect feature locations, making the defects more prominent. Simultaneously, this model successfully ignores unrelated rebar clutter interference. In contrast, the model without the CSA module still shows noticeable rebar clutter at the top of the image, and the void defect features are relatively less distinct. This indicates that the inclusion of the CSA module enables the network to concentrate more effectively on relevant features, assigning higher weights to these features. Consequently, the network can reconstruct cavity defects more accurately.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Models** & **RMSE** & **PSNR (dB)** & **SSIM** \\ \hline Ours & 5.0145 & 27.3702 & 0.8761 \\ The model with channel attention & 5.0776 & 26.5532 & 0.8647 \\ The model with spatial attention & 5.1816 & 25.7657 & 0.8528 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of module evaluation metrics.  
Figure 6: Comparison of rebar suppression results of different modules. (**a**) Original GPR image with rebar clutter interference. (**b**) GPR image of void defects corresponding to the original image. (**c**) Our model with CSA module. (**d**) The model with channel attention module. (**e**) The model with spatial attention module in improving performance.
# A Co-Rotational Meshfree Method for the Geometrically Nonlinear Analysis of Structures
## 2 Theoretical Formulation
### Equilibrium and Constitutive Equations  
As shown in Figure 2, the fulfilment of equilibrium of the beam segment with distributed forces and moments has to satisfy the following conditions:  
\[d\textbf{F}_{XYZ}/ds+\textbf{\emph{f}}=\textbf{0} \tag{18}\]  
\[dM_{XYZ}/ds+(d\textbf{\emph{r}}/ds)\times\textbf{\emph{F}}_{XYZ}+\textbf{ \emph{m}}=\textbf{0}. \tag{19}\]  
The co-rotational scheme means the equilibrium condition can be constructed directly with respect to the local coordinate system. Hence, Equations (21) and (22) can be given by the following transformation:  
\[d\textbf{\emph{G}}_{XYZ}/ds=d\textbf{\emph{G}}_{xyz}/ds+\mathbf{\omega}\times \textbf{\emph{G}}, \tag{20}\]  
where \(\textbf{\emph{G}}_{XYZ}\) and \(\textbf{\emph{G}}_{xyz}\) represent any vector with reference to the global coordinate and local coordinate, respectively:  
\[d\textbf{\emph{F}}_{xyz}/ds+\mathbf{\omega}\times\textbf{\emph{F}}+\textbf{\emph{ f}}=0 \tag{21}\]  
\[dM_{xyz}/ds+\mathbf{\omega}\times\textbf{\emph{M}}+\textbf{\emph{e}}_{z}\times \textbf{\emph{F}}+\textbf{\emph{m}}=\textbf{0}. \tag{22}\]  
In the present study, we established the constitutive equations with the Timoshenko beam hypothesis. Therefore, the constitutive laws between the external forces and deformation can be expressed as follows:  
\[M_{x}=E_{xx}\left(\omega_{x}-\omega_{x0}+\mathrm{d}\gamma_{y}/\mathrm{d} \mathrm{s}\right) \tag{23}\]  
\[M_{y}=E_{yy}\left(\omega_{y}-\omega_{y0}-\mathrm{d}\gamma_{x}/\mathrm{d} \mathrm{s}\right) \tag{24}\]  
\[M_{z}=GJ\left(\omega_{z}-\omega_{z0}\right) \tag{25}\]  
\[F_{x}=k_{x}GA\gamma_{x} \tag{26}\]  
\[F_{y}=k_{y}GA\gamma_{y} \tag{27}\]  
\[F_{z}=EA\varepsilon \tag{28}\]  
where \(E\) is the Young's modulus, \(A\) is the cross-sectional area, \(J\) is the torsional constant, \(G\) is the shear modulus, \(\varepsilon\) is the axial strain of beam axis, \(\gamma_{x}\) and \(\gamma_{y}\) are the average shear  
Figure 2: Free-body diagram of the beam segment with distributed forces and distributed moments.  
strain, \(k_{x}\) and \(k_{y}\) are the shear modificatory factors, and \(I_{xx}\) and \(I_{yy}\) are the moments of inertia about the \(x\) and \(y\) axes, respectively.
# Research on Defect Detection Method for Composite Materials Based on Deep Learning Networks
## 2 Related Works
### Attention Mechanisms  
The attention mechanism is a prominent feature in the feature extraction stage of the model, particularly within the convolutional component. The function of the model allows it to prioritize regions that contain a significant amount of semantic information, thereby enhancing classification accuracy. Attention mechanisms can be broadly classified into spatial attention mechanisms and channel attention mechanisms, each adhering to distinct principles.  
The channel attention mechanism enhances the attention weights of the model at the channel level, assigning different weights to each channel in the feature map data. Channels deemed crucial for achieving accurate results carry greater significance. Notable channel attention mechanisms include the SE attention mechanism [21] and the enhanced ECA mechanism [22]. The SE attention mechanism consists of two crucial steps: the S (squeeze) part and the E (excitation) part. The squeeze operation manipulates the global receptive field of the feature map, while the excitation operation computes weight values for each channel. The ECA mechanism simplifies the process of assigning channel weights from SE by replacing the two FC with a one-dimensional convolution (Conv1d). This results in weight data of the same size. The key distinction lies in the ECA mechanism, which utilizes a one-dimensional convolution and produces channel weights using the sigmoid activation function.  
The spatial attention mechanism, in contrast, operates on the target's location information within the feature map. It assigns varying weights to data at each position in the feature map, enhancing the model's ability to identify the target and placing greater emphasis on the target object's location within the image. This, in turn, improves the accuracy of recognition. A well-known spatial attention mechanism is the Spatial Attention Module (SAM), which is integrated as part of the Convolutional Block Attention Module [23].
# Multi-Source Data Fusion for Vehicle Maintenance Project Prediction
## 3 Method
### Model Optimization  
We train the MsDFN model to predict the last maintenance project for each vehicle, with a binary cross-entropy loss function for the global objective function, as shown in Equation (10):  
\[loss=-\sum_{i=1}^{|P|}\Bigl{(}y_{i}^{T}\log(\widehat{y_{i}})+(1-y_{i})^{T} \log(1-\widehat{y_{i}})\Bigr{)} \tag{10}\]  
where \(\widehat{y_{i}}\) represents the predicted results of the maintenance project \(p_{i}\) and \(y_{i}\) represents the true label of the maintenance project \(p_{i}\).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Base Information** & **Notation** & **Representation Function** & **Representation Result** \\ \hline Location & \(B_{1}\) & \(Em_{2}\) & \(S_{1}\) \\ Manufacturer & \(B_{2}\) & \(Em_{1}\) & \(S_{2}\) \\ Type & \(B_{3}\) & \(Em_{1}\) & \(S_{3}\) \\ Engine Type & \(B_{4}\) & \(Em_{1}\) & \(S_{4}\) \\ Engine & \(B_{5}\) & \(Em_{1}\) & \(S_{5}\) \\ Displacement & & & \\ Model Year & \(B_{6}\) & \(Em_{2}\) & \(S_{6}\) \\ Production Year & \(B_{7}\) & \(Em_{2}\) & \(S_{7}\) \\ Assembly Plant & \(B_{8}\) & \(Em_{1}\) & \(S_{8}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Base information and representation.  
To enhance the experimentation process, we proceed to randomly partition the dataset into training, validation, and testing sets. Specifically, these sets consist of 18,000, 3831, and 5000 vehicles, respectively. In our approach, we designate the last maintenance project as the label, while utilizing the remaining maintenance projects, mileage, and vehicle base information as input features. The global project co-occurrence graph \(G\) is constructed based on the maintenance project within the training set.
# Advanced SDR-Based Custom OFDM Protocol for Improved Data Rates in HF-NVIS Links
## 3 Results and Discussion
### Laboratory Conditions
This comparison highlights the importance of selecting appropriate modulation schemes for different channels to optimize performance in non-contiguous OFDM aggregation, and demonstrates the trade-off between data rate and robustness.
## 3 Materials and Methods
### Ray-Launching Simulation Technique
Moreover, all the material properties for all the elements within the scenario can also be considered, given the conductivity and relative permittivity of all the obstacles at the frequency range of the system operation under analysis. Figure 2 presents a schematic overview of the 3D-RL principle in the considered scenario, where reflection, refraction and diffraction are depicted in the vehicular scenario. The detail of the different sizes of vehicles can also be observed. As presented in the literature, the principle of GO/UTD foresees precisely wireless communication propagation when a complex 3D scenario is considered [60], with the principal disadvantage of a high computational cost. In this sense, in order to overcome this drawback, different hybrid techniques have been proposed combining the 3D-RL algorithm with different approaches, such as Neural Networks (NN) [61], Diffusion Equation (DE) [62] or Collaborative Filtering (CF) [63]. These hybrid techniques achieve accurate results while reducing the computational cost for complex scenarios. The 3D-RL tool is based on a modular structure, where different libraries can be integrated. Thus, to consider the frequency band of 28 GHz, the 3D-RL tool has been updated with the mmWave module. Figure 3 presents the schematic view of the in-house developed simulation algorithm for propagation prediction at micro-wave and mmWave frequency bands, integrated with the different hybrid techniques to reduce the computational load for complex scenarios. In the implementation of the mmWave module, the different electric conductivity and relative permittivity models for the different materials within the scenario for the 1-100 GHz frequency range, obtained by the Recommendation ITU-R P2040-1 [64], have been added. In addition, the capability of using beamforming for the transceivers has been included, to emulate real V2X scenarios for these frequency bands. The analysis in different beam directions can be generalizable for all the possible vehicular situations in the in-house 3D-RL proposed simulation tool, as well as different angular characteristics, as presented in [65]. Moreover, the proposed methodology enables the characterization of different conditions and new case studies, such as indoor/outdoor transitions or variable height link usage, which can be given for vehicle considerations or the use of diverse urban infrastructure to allocate transceivers. Finally, the atmosphere absorption phenomena have also been considered, which is more relevant at higher frequencies [66]. Figure 2: Schematic overview of the operation principle of the 3D-RL software.
# Controller-Pilot Data Link Communication Security
###### Abstract  
The increased utilization of the new types of cockpit communications, including controller-pilot data link communications (CPDLC), puts the airplane at higher risk of hacking or interference than ever before. We review the technological characteristics and properties of the CPDLC and construct the corresponding threat model. Based on the limitations imposed by the system parameters, we propose several solutions for the improved security of the data messaging communication used in air traffic management (ATM). We discuss the applicability of elliptical curve cryptography (ECC), protected aircraft communications addressing and reporting systems (PACARs) and the Host Identity Protocol (HIP) as possible countermeasures to the identified security threats. In addition, we consider identity-defined networking (IDN) as an example of a genuine security solution which implies global changes in the whole air traffic communication system.  
Keywords:cybersecurity; air traffic communication; controller-pilot data link; privacy; threat model +
Footnote †: journal: Sensors
# DScanNet: Packaging Defect Detection Algorithm Based on Selective State Space Models
## 4 Dataset Preparation and Experimental Environment  
### Dataset Preparation  
This section verifies the effectiveness of the proposed method in the task of target defect detection as well as in solves problems in real production. In this experiment, carton samples with different types of defects were collected from a company for image acquisition to make a dataset, which we call the BIGC-LP defect dataset. The dataset contains three main types of defects, i.e., tears, scratches, and deformations, totaling 1056 sheets, and the defect images are shown in Figure 5. These logistics packaging defects exhibit significant scale diversity. For example, some defects appear as elongated scratches or indentations, while others exhibit localized depressions or large stains. The significant differences in morphology and size of these defects pose a great challenge to conventional inspection models, which often struggle to accurately recognize these defect types simultaneously.  
Figure 5: Defect types.  
In addition, in order to enhance the diversity of the dataset and improve the generalization ability and robustness of the model, while avoiding problems such as overfitting, we used data enhancement techniques to extend the dataset, including rotation, scaling, horizontal and vertical flipping, and brightness adjustment, and a total of 3270 defect images were obtained after processing. All images were uniformly sized at 640 \(\times\) 640 and all defect images were labeled using labeling software with the smallest possible labeling box and the results were stored as a tkt file for recording the defect locations. We divided the dataset according to the ratio of 8:1:1, and finally obtained a training set of 2616 images, a validation set of 327 images and a test set of 327 images. Such a division ensures that the model can learn on sufficient training data and also perform performance evaluation on independent validation and test sets.  
Figure 6 shows the location and size distribution of defective target centroids, from which it can be seen that the location of defective samples is more centralized, there is cross-overlap between the labeled boxes, and the defects are mostly small target samples with small distances from each other, which is difficult to be detected and identified, so they are well suited for the research design of the paper's original intention.
# Detectron2 for Lesion Detection in Diabetic Retinopathy
## 2 Related Work
### Previous Studies: A Summary of Techniques, Focus, and Challenges  
The reviewed literature explored a range of ML and DL techniques for DR detection and summarized the different methods of the models in Table 1. Approximately 70% of the previous studies were focused on detecting the stages of DR or classifying images as DR or non-DR [15], as depicted in Figure 2. In contrast, only 27% of the studies aimed at classifying NPDR and PDR, and a mere 6% aimed at detecting and classifying lesions in fundus images into NPDR or PDR. Despite the various techniques and focus areas, the current challenge in the field remains to create a reliable system that can detect both DR and DME. This challenge has been the emphasis of recent studies and is a crucial area of ongoing research in the field of DR detection.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Field** & **Ref** & **Method** & **Database** \\ \hline \multirow{4}{*}{Classical Machine Learning} & [7] & Integrated SVM, PCA, and Moth-Flame Optimization & Own dataset \\ \cline{2-4}  & [8] & Ensemble of Random Forest, Decision Tree, Adaboost, K-NN, & \\  & Logistic Regression & & DR dataset \\ \cline{2-4}  & [9] & LBP16-ri vectors and SVM with PCA and BoW representations & - \\ \hline \multirow{4}{*}{DL methods for DR grading} & [6] & DeepDR & Single ethnic dataset \\ \cline{2-4}  & [5] & DLS & Single ethnic dataset \\ \cline{2-4}  & [10] & CNNs (VGG-16 and VGG-19) & Fundus images \\ \hline \multirow{4}{*}{DL methods for DME detection} & [12] & Deep Convolutional Neural Network & Own dataset \\ \cline{2-4}  & [13] & Convolutional Neural Network & Own dataset \\ \cline{1-1} \cline{2-4}  & [14] & Transfer Learning-based Convolutional Neural Network & Own dataset \\ \hline \hline \end{tabular}
\end{table}
Table 1: Table summarizing classical machine learning and deep learning methods for diabetic retinopathy and diabetic macular edema detection and grading