esa-sceva/satcom-chunk-collection huggingface dataset:
# Study on Characteristic Strength and Constitutive Model of Red Sandstone under Hydraulic Coupling
## 4 Test Results and Analysis
### Evolution of Characteristic Strength  
Figure 9 shows the evolution law of the characteristic strength of red sandstone. Through linear regression of the relationship curve between the strength and confining pressure of red sandstone, and it was found that each characteristic strength and confining pressure approximately meet the linear relationship.  
Seepage pressure can accelerate the process of crack development and weaken the strength. With the increase in seepage pressure, the crack closure stress, crack initiation stress, dilatation stress, and peak stress of the rock are reduced. Confining pressure delays the process of failure and limits the formation of cracks. Confining pressure suppresses the effect of seepage pressure; the greater the confining pressure is, the weakening effect of seepage pressure on the strength decreases. This is because under the effect of seepage pressure, pore and fissure water produce expansion tension on the pore and fissure tip, which accelerates the expansion of pore and fissure.  
With the increase in seepage pressure, the strength growth rate of red sandstone under unit confining pressure increases. Taking the peak stress of red sandstone as an example, when the water pressure is 0, 1, 2, and 3 MPa, the strength growth rates under the unit confining pressure are 3.3, 3.45, 3.51, and 3.61, respectively, indicating that the greater the seepage pressure is, the better the strengthening effect of confining pressure on strength is,  
Figure 8: Schematic diagram of phase division and characteristic stress.
## 4 Case Studies
### Result Analysis for Robust Control Implementation in Automatic Load Commutation Devices
This is due to the robust control model taking the situation of load demand and fluctuations in renewable energy into account, thus sacrificing some of the operational economic efficiency of the distribution network. When the load demand and renewable energy are at their worst-case scenario, the objective function value of the robust control model is significantly lower than that of the deterministic model, which proves the necessity of considering fluctuations in load demand and renewable energy. Meanwhile, the comparison of objective function values shows that the robust control model of the automatic load commutation device does not sacrifice too much economic efficiency in the operation of the distribution network. On the contrary, it can enhance the ability of the distribution network to cope with load demand fluctuations and improve its acceptance capacity for renewable energy.
## 4 Results and Discussion
### Generation of OCT Images  
#### 4.1.1 Dataset and Experimental Environment for OCT Generation  
The fundus and OCT images used in the experiment were obtained from the Department of Glaucoma, Chang Gung Memorial Hospital. As shown in Table 1, the total number of experimental images is 441, and we randomly divide them into 200 for training and 201 for testing at 1:1.  
The fundus and OCT images were captured by the DRI OCT Triton (Swept Source OCT, Topcon, Oakland, NJ, USA). In order to ensure the correctness of labeling, all images in the experiment were judged by two glaucoma physicians of Chang Gung Memorial Hospital and labeled as glaucoma or not. Before model training, ophthalmologists assist in identifying and labeling fundus and OCT images. When testing, ophthalmologists also review the classification results of the system. The fundus image size is 2576 \(\times\) 1934, and the OCT image size is 903 \(\times\) 663.  
The hardware used in the experiment is: AMD Ryzen 7 8-core CPU, 32 GB DRAM, and NVidia GeForce RTX 2080 Ti graphics card. The software environment and tools include Microsoft Windows 10, Python, Pytorch, and OpenCV.  
In this experiment, when training the generator and the discriminator, the relevant parameters are set to 2500 training iterations, the batch size is 8, the optimizer is RMSprop, and the learning rate is 1 \(\times\) 10\({}^{-3}\).
# Swin-MRDB: Pan-Sharpening Model Based on the Swin Transformer and Multi-Scale CNN
## 3 Experiments
### Results
#### 3.4.3 Results for the Chikusei Dataset  
The Chikusei dataset contains both urban and rural areas, with arable land making up the majority. Since we have already evaluated the reconstruction of urban images on the Pavia dataset, we evaluated the reconstruction of cultivated land images by using the Swin-MRDB on the Chikusei dataset. To assess the reconstruction effect in a more objective manner, we selected the following models for comparison: PCA, GS, HIS, PNN, TFNET, MSDCNN, and SRPPNN. As shown in Figure 8, the images fused using the PCA, GS, and IHS algorithms are overall brighter, resulting in a significant loss of spectral information. All of the deep learning techniques perform well in terms of preserving spectral information; however, they all exhibit variable degrees of texture discontinuity,most notably in MSDCNN reconstructed pictures. It is clear from Figure 8 that our Swin-MRDB reconstruction results are most similar to the ground truth reference image.  
This section also shows the pan-sharpening effect of our model on the Chikusei dataset using objective metrics. As can be seen in Table 5, our Swin-MRDB method achieves the best results for CC, SAM, RMSE, and ERGAS, indicating that our method retains more spatial detail and spectral information. Among them, ERGAS showed significant improvement, with 12.6%, 6.3%, 28%, and 3.2% improvement compared to PNN, TFNet, MSDCNN, and SRPPNN, respectively. The performance on SAM is also outstanding, with 20%, 1.4%, 28%, and 15.2% improvement compared to PNN, TFNet, MSDCNN, and SRPPNN, respectively. For the RMSE, compared to PNN, TFNet, MSDCNN, and SRPPNN, the improvement is 19.6%, 0.1%, 28%, and 5.3%, respectively. For the CC, compared to PNN, TFNet, MSDCNN, and SRPPNN, the improvement is 1.1%, 0.3%, 1.7%, and 0.4%, respectively.
# The Extreme Function Theory for Damage Detection: An Application to Civil and Aerospace Structures
## 2 Gaussian Processes and Extreme Function Theory  
In the specific case of interest, the problem can be stated as follows. Let \(i=1,\ldots,n\) be the index of \(n\) examples taken from a population of functional data. Each \(i\)-th example is an identified mode shape \(\mathbf{\varphi}_{i}\), i.e., a vector of \(j=1,\ldots,n_{i}\) modal coordinates \(\varphi_{i,j}\) observed at a finite number of corresponding spatial coordinates \(\mathrm{x}_{i,j}\). From a theoretical standpoint, there is no requirement that the \(n\) mode shapes have the same number of coordinates nor the same location. This is the first strong point of EFT in comparison to EVT, as the functions can be collected from different sensor arrangements--even if, for practical concerns, it is more reliable to consider a fixed number and position of the output channels. Importantly, \(\mathrm{x}_{i,j}\) can be a scalar for a 1-dimensional beam element, or a pair \(\mathrm{x}_{i,j}=\left\{\xi_{i,j},\eta_{i,j}\right\}\) for a 2-dimensional plate-like structure (and so on). For simplicity's sake, all the rest of this discussion will focus on the 1-dimensional case but the formulation for higher dimensionalities can be straightly derived.  
There are two subsequent aims. The first one is to build the \"normality\" model \(\mathcal{M}\) from the pairs of training data \(\left\{\mathbf{x}_{i},\mathbf{\varphi}_{i}\right\}\), collected from a given structure excited in the current conditions (assumed as a baseline for outlier detection). Once the model is defined, the second step is to discern if a test set \(\left\{\mathbf{x}_{i}^{*},\mathbf{\varphi}_{i}^{*}\right\}\) (in this case, \(i=1,\ldots,n^{*}\)) belongs or not to the tails of the distribution \(\mathcal{M}\). The first part can be achieved with the classic GP Regression, which is here briefly recalled for completeness, while the EFT comes in the second part.
## 4 Performance Evaluation
### BER Performances of the Proposed Hexagonal GFDM  
In this section, the proposed hexagonal GFDM model is evaluated in terms of the BER values on different SNR regions. A single antenna for the receiver and a single antenna for the transmitter is used for the implementation. QPSK modulated symbols are used for each subsymbol and subcarrier grid.  
We implement the proposed hexagonal GFDM model in Figures 5 and 6 for 16 subcarriers, \(K\), and 6 subsymbols, \(M\). The bit error rate (BER) performances of the proposed waveform are compared over additive white Gaussian noise (AWGN) and Rayleigh Fading channel. We consider that the Rayleigh channel is modeled as a simple flat fading. The effects of frequency selectivity on performances of the traditional GFDM and hexagonal GFDM schemes are eliminated with the complex impulse response. Moreover, we state that the effects of the proposed model on error performance more explicitly. The proper channel estimation and equalization algorithms can also be considered for the other type of channel models [24; 30]. The channel adds zero mean AWGN noise with variance \(\sigma^{2}\), i.e., \(N(0,\sigma_{n}^{2})\). Consequently, the simulated BER results are in satisfactory limits under the theoretical BER results and GFDM performances [31]. The performance results are close to each other; the performance gains are approximately the same. Both traditional GFDM and hexagonal GFDM can be used in these conditions interchangeably depending on the requirements of the users.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Time-Frequency Allocation** & **Frame Duration** & **The Number of the Symbols for 1 ms** \\ \hline Rectangular allocation on LTE standard [29] & 1 ms & 14 or 12 symbols \\ Hexagonal allocation & 0.8 ms & 16 or 14 symbols \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of optimum frame duration of rectangular and hexagonal lattice structure based on the LTE frame structure.  
Figure 5: The BER (bit error rate) performance of hexagonal GFDM model on AWGN channel: The simulated and theoretical results of the proposed method are similar.
## 3 Algorithms for Managing Complex Traffic Scenarios and Violations
### Types of Complex Traffic Scenarios and Violations
#### 3.1.4 Blind Spot Challenges and Multiple Moving Objects  
Managing blind spots and tracking multiple moving objects simultaneously is crucial for safe autonomous driving. [PERSON] et al. (2018) highlighted the complexity of monitoring blind spots and predicting the trajectories of multiple objects in dynamic traffic environments.  
[PERSON] et al. (2018) developed a multi-object tracking and segmentation network that can simultaneously detect, segment, and track multiple objects in real-time, demonstrating the potential of deep learning in addressing this complex task.
## 2 Numerical Analysis  
### Finite Element Mesh and Boundary  
Three-dimensional conditions were used to model the tunnel-type anchorage and the surrounding rock. A commercial FE analysis software package, PLAXIS 3D [25], was used for this study. The analysis was conducted on Ulsan Grand Bridge, the only tunnel-type anchorage constructed in Korea. Figure 2 shows the typical 3D FE model used in this study. The mountainous area where the tunnel-type anchorage was constructed and the anchorage itself were modeled in the same shape using the design drawings (Figure 2a). The tunnel-type anchorage and soil were modeled with finite elements, enabling rigorous treatment of the soil-structure interactions. The soil and tunnel-type anchorage elements were 10-node wedge elements and 10-node tetrahedrons in the vertical direction. The outside of the anchorage and rock joint was modeled through interface elements. The interfaces were comprised of 10-node interface elements constituting of eight pairs of nodes, compatible with the 10-node-tetrahedrons side of the soil element. The interface  
Figure 1: Typical types of anchorages: (**a**) gravity-type and (**b**) tunnel-type.  
elements had a 3 \(\times\) 3 point Gaussian integration, which allowed differential displacements between the node pairs (slipping and gapping).  
Mesh convergence studies were initially performed to determine the optimal mesh size required for analytical accuracy and computing efficiency. Figure 3 shows the load-displacement curves of the tunnel-type anchorages and the mesh information for different mesh densities. Finer mesh density was employed near the rock and anchorage interface zone, whereas coarser mesh was used closer to the boundary. The minimum size of all the meshes was the same 0.06093 m. The maximum size of all the meshes ranged from 27.51 m to 61.87 m. The mesh size reduced from Mesh 1 to Mesh 5; conversely, mesh density rose from Mesh 1 to Mesh 5. Meshes 4 and 5 had the same ultimate resistance, as illustrated in Figure 3, implying that the size of Mesh 4 had reached mesh convergence. As a result, Mesh 4 was employed for all studies.
## 4 Invariant Multi-Mode Vectors Pushover Analysis
### The Modified Modal Pushover Analysis  
[PERSON] et al. [18] developed the modified modal pushover analysis (MMPA) to estimate seismic demands in frame buildings, with a key simplification that assumes higher vibration modes are elastic. This reduces computational effort compared to the traditional modal pushover analysis (MPA). The study found that MMPA generally yields larger and potentially more accurate demand estimates, especially in cases where MPA underestimates demands. However, for cases where MPA already overestimates demand, MMPA may increase conservatism. MMPA's main limitation is in lightly damped systems (with damping less than 5%), where its linear elastic assumption for higher modes may not accurately represent inelastic behavior, potentially leading to an overestimation of demand. The accuracy of the MMPA procedure is also found to be sensitive to specific building and ground motion characteristics. The following phases in the modified modal pushover analysis (MMPA) approach to calculate the peak inelastic response of a multistory structure with a plan symmetric about two orthogonal axes subjected to earthquake ground motion along an axis of symmetry are summarized below:  
1. Modal pushover analysis application: implement Steps 1-4 like those outlined in the standard modal pushover analysis.
2. Determine peak deformation \(D1\): For the primary mode's inelastic single degree of freedom (SDF) system, compute \(D1\) using parameters \(T1\) and \(\zeta 1\)[20]. This can be achieved via nonlinear response history analysis (RHA), an inelastic design spectrum, or specific empirical formulas.
3. Ascertain peak roof displacement \(ur1\): using the equation \(ur1=\Gamma 1\Phi r1D1\), calculate \(ur1\) linked to the primary mode's inelastic SDF system.
4. Retrieve relevant responses: from the pushover data (from Step 2), extract response values \(r1+g\) for the combined effects of gravity and lateral forces at a displacement of \(ur1+urg\).
5. Calculate primary mode dynamic response \(r1\): determine \(r1\) using the relation \(r1=r1+g-rg\), where \(rg\) represents the contribution from gravity loads.
6. Evaluate higher-mode dynamic responses: For modes beyond the first, assume the system retains its elasticity and compute the dynamic responses. This mirrors the traditional modal analysis for a linear multi-degree-of-freedom (MDF) system, eliminating the need for extra pushover analyses and thus saving computational time. The number of modes to consider for accurate results varies based on the building's height.
7. Compute overall response (demand): integrate the gravity response with the peak'modal' responses using the square root of the sum of squares (SRSS) rule: \(r\)_smax\(\{rg\pm\Sigma rnn2\}\)_, where \(r1\) is derived from Step 5 and \(rn(n>1)\) from Step 6.
## 2 Lens Antenna: Reference and Reduced Height Designs  
### Reference Lens Antenna Design (Model 1)  
Throughout the work in this paper, a microstrip circular patch antenna is used as a radiator. As a dielectric lens structure is to be mounted on top of the circular patch, the size of the patch is chosen in consideration of the dielectric constant of the lens. The diameter of the circular patch is 18.6 mm on top of the 30 mm \(\times\) 30 mm ground. It is fed with a coaxial feeding, 3 mm away from the center, as shown in Figure 1a. RT/duroid 5880 (\(\varepsilon_{r}\) = 2.2) with a 0.79 mm thickness is used as a substrate. The simulated results of the stand-alone patch antenna without a lens are shown in Figure 1c,d with solid lines. The antenna resonates at 6.12 GHz, with a directivity of 7.73 dBi.  
For the lens design, we choose a dielectric constant (\(\varepsilon_{r}\)) of 2.5, the relative permittivity of resin commonly used for stereolithography apparatus (SLA) 3D printing. The high-resolution SLA 3D printing method provides a smoother and less porous surface, as it forms a structure by focusing an ultraviolet laser onto a vat of photopolymer resin, while fused deposition modeling (FDM) uses fine powder from many kinds of materials. Although resin might have a higher dielectric loss than ABS, we use resin to take advantage of its smoother and less porous surface printing characteristics. The designed reference lens is divided into three sections. The first and second sections are cylinders with widths and heights of 30 mm and 25 mm, and 70 mm and 22 mm, respectively. The third section is a hemisphere with a radius of 35 mm. The first section is designed to deliver waves consistently to the second section while providing a proper impedance matching characteristic, as shown in Figure 1c. The second section configures a larger width for beam focusing, and the third section transforms a spherical wave into a plane wave for a directive beam. Through this configuration, a mushroom-shaped lens is formed, and its total height is 82 mm, or 1.585 \(\lambda\) at 5.8 GHz. The ratio of the height to the diameter is 1.14, as a means of increasing the aperture efficiency of the lens [35]. The simulated results are plotted with dashed lines in Figure 1c,d. As shown in Figure 1c, the reference lens antenna operates at 5.8 GHz. The simulated radiation pattern at 5.8 GHz in the elevation plane (i.e., \(\theta\) is swept when \(\phi=0^{\circ}\)) is shown in Figure 1d. The directivity of the reference dielectric lens antenna is 14.6 dBi, which is 6.9 dB higher than that of the circular patch antenna. The next step is to reduce the lens height while maintaining its radiation performance.
# Exploring Data Model Relations in OpenStreetMap
## 4 Experimental Analysis
### Relation Analysis  
To consider a consistent approach to the analysis of the relations, we pay particular attention to the five most popular types of relations, as outlined in Section 3.1. The area in km2 extracted for each city is also provided in the third row of the table. Table 1, summarises the five most popular types of relations (based on the type tag key) in all of our case-study cities. These are presented as percentages of the total number of relations in each city. The results in this table are reasonably consistent across each of the four cities. There are some notable differences in particular involving Zurich. Zurich appears to have a smaller proportion of type=multipolygon relations than the other three cities. Vienna has the lowest percentage of type=restriction for turn restrictions. The use of the type=route relation is high in all cities with low percentages observed for type=public_transport in Rome and Madrid. We find that type=route_master has the low representation in all of the four cities. This could be related to the fact that type=route_master represents a public transport service and therefore probably correlates to the number of public transport service lines or routes in a given city.
## 3 Methodologies
### Discrete Fourier Transformation  
Discrete Fourier Transform is a transformation that converts a finite sequence of equally spaced samples of a function into a same-length sequence of equally spaced samples of the discrete-time Fourier Transform. In image processing, the samples are pixel values of an image. The watermarking is mostly applied in the middle-frequency bands since the high frequencies represent the main information causing any change to be visually noticed in the spatial domain and the low frequencies are surpassed by the compression techniques. The application of watermarking in middle frequencies makes it robust and imperceptible at the same time [35; 36].  
The DFT transform can be obtained as per:  
\[F(u,\
u)=\frac{1}{MN}\sum_{x=0}^{M-1}\sum_{y=0}^{N-1}f(x,y)e^{-2\pi(\frac{HK}{ M}+\frac{\
u y}{N})} \tag{10}\]  
where _f(x,y)_ represents the \(M\times N\) grayscale original image and _F(u,v)_ is a DFT coefficient. \(e\)-\(j2\pi\) represents the basis function of Fourier Transform.  
The inverse Fourier Transform is given as:  
\[f(x,y)=\frac{1}{MN}\sum_{u=0}^{M-1}\sum_{v=0}^{N-1}F(u,\
u)e^{-j2\pi(\frac{HK}{ M}+\frac{\
u y}{N})} \tag{11}\]  
The output of Fourier Transform has both real and imaginary components distributing the information in both magnitude and phase. Since the magnitude contains less information than the phase, it is recommended that the watermarking be embedded by altering the magnitude.
## 3 Proposed Detection Mechanism
### Traffic Classification
Various simulations are made to verify the performance of the system and are also compared with some existing techniques. Our experimental and simulation results show that the proposed detection system can differentiate DDoS attack traffic from FC with more than 93% accuracy, with 5.2 FPR and 6.3 FNR based on the two real datasets (1998 FIFA World-Cup and CAIDA DDoS attack 2007). In the initial tests with the low level of attack intensity, the proposed work does not perform well. With 10% attack intensity, it gives 14.12% FPR and 14.9%3 FNR, which are not good among the competitors. This is because it operates on the naive-based probabilistic model with multilevel probabilities. Such types of models require an adequate amount of data so that optimal and more accurate results are achieved. However, for attack intensities of 40% to 90%, the proposed mechanism outperforms all the mechanisms. For the highest attack intensity, DDoDF gives around 93% detection rate, 5% FPR, and 6% FNR, which are the most optimal values in the experiments. The results are encouraging and prove that the proposed mechanism performs well in the target domain. In the future, the detection accuracy can be enhanced by combining some additional parameters, such as payload patterns and hop count information, with the existing parameters to build a strong detection system. The proposed system can also be simulated on multiple datasets, i.e., HTTP logs, NLANR, and MIT Lincoln, with additional parameters for evaluation. This work can be implemented on other ad hoc networks with some alterations. This work only detects and discriminates DDoS attack traffic from FC, and it does not prevent the network from DDoS attacks. The prevention module can also be added to the detection module to prevent all the attacker nodes in the network. **Author Contributions:** This work was carried out in collaboration between all authors. [PERSON] performed a detailed study and analysis. [PERSON] and [PERSON] managed the literature searches. [PERSON] formatted the document and prepared the tables and figures. [PERSON] wrote the first draft of the manuscript in consultation with [PERSON], N.C. finalized the document. All authors have read and agreed to the published version of the manuscript. **Funding:** This research received no external funding. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** Not applicable. **Acknowledgments:** The authors would like to acknowledge Prince Sultan University (PSU) and Smart Systems Engineering Lab for their valuable support and provision of research facilities essential for completing this work. Furthermore, the authors would like to acknowledge the support of PSU for paying the Article Processing Charges (APC) of this publication. **Conflicts of Interest:** The authors declare no conflict of interest.