esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Numerical Examples
### Comparison of Sample Behaviors  
In this section, the simulation method mentioned in this paper will be applied to generate the seismic ground motion acceleration samples using the EPSD model. The duration time used in these methods is 30 s. Figure 7 shows a sample using the classical SRM, i.e., Equation (6). The value range of frequency \(\omega_{h}\) is [0 Hz, 25 Hz], \(\Delta\omega=0.05\) Hz. Figure 8 shows the sample using the GHW method, i.e., Equation (15). The values of \(\omega\) are obtained using Equation (26). The upper values of \(m\) and \(n\) are 24 Hz and 25 Hz, respectively. The lower values are 0 Hz and 1 Hz, respectively. The corresponding upper cut-off frequency \(\omega_{U}\) is 24.5 Hz and lower cut-off frequency \(\omega_{L}\) is 0.5 Hz. It is suggested that the upper value of the time shift factor \(k_{U}=T\cdot(n-m)=30\,\mathrm{s}\cdot 1\mathrm{Hz}=30\), while the lower value \(k_{L}=0\), and \(\Delta k=1\). As shown in Equation (30), the number of components of the SGHW method is simultaneously determined by the number of frequency bands and time shift factors. The number of these two parameters is represented by \(L\) and \(D\), respectively. Shown in Figure 9 are the samples generated using the SGHW method under different component numbers.  
Figure 5: Clough–Penzien spectrum.  
Figure 6: EPSD model.  
Figure 8: Typical sample by the GHW method.  
Figure 7: Typical sample by SRM.  
Figure 9: Typical samples by the SGHW method. (**a**) Ten components (upper: \(L=5\), \(D=2\); lower: \(L=2\), \(D=5\)); (**b**) 50 components (upper: \(L=10\), \(D=5\); lower: \(L=5\), \(D=10\)); (**c**) 150 components (upper: \(L=15\), \(D=10\); lower: \(L=10\), \(D=15\)); (**d**) 500 components (upper: \(L=25\), \(D=20\); lower: \(L=20\), \(D=25\)).  
Comparing the samples in Figures 7 and 8, it can be concluded that the overall trend of the two accelerograms has similar change patterns. The average peak ground acceleration of both figures is 0.2, which is consistent with the value of average PGA in Equation (38). The similarity of time history diagrams generated by the GHW method and SRM also proves the effectiveness of the GHW method. At first glance in Figure 9, when fewer components are retained, the samples do not resemble a real earthquake ground acceleration. This situation gradually improves with the increase in component numbers. Meanwhile, comparing the samples of different scales under the same number of components, when the number of retained components is small, the accelerogram will only fluctuate at the value corresponding to the time shift factor \(k\) and hardly change at other locations owing to the property of harmonic wavelet. Figure 8 also indicates that the SGHW method can retain extremely precise probability information while retaining less components. However, the shortcoming of the SGHW method is that the obtained time-history is not as precise.
# Locating Multiple Sources of Contagion in Complex Networks under the SIR Model
## 3 Simulation and Analysis
### Comparison Methods  
To compare with the performance of PCL, we pick up some sources localization methods as benchmarks.  
Distance Centrality (DC) [10]--represents the sum of the distances from one node to all the infected nodes. The sources usually have the smallest Distance Centrality.  
Jordan Centrality (JC) [11]--denotes the maximum of the distances from one node to all the infected nodes. The sources prefer to have the least Jordan Centrality.  
Unbiased Betweenness Centrality (UBC) [13]--the betweenness of one node eliminates the effect of degree, namely unbiased betweenness. The nodes are the sources, which always have the biggest unbiased betweenness.  
Modified Label Propagation based Source Identification (LPSI) [14]: This method lets infection status iteratively propagate in the network as labels, and finally uses local peaks of the label propagation result as source nodes.
## 6 Simulation Results and Analysis  
This section presents the simulation results that evaluate the effectiveness of the proposed advanced intelligent frame generation (AIFG) algorithm under different delay constraints. The main goals of the simulation are to validate the adaptability of AIFG in varying network conditions, and to compare its performance against three benchmark algorithms: AFG + HEFG, IFG + HEFG, and FIFO + FIFO. Additionally, to strengthen the evaluation from the perspective of reinforcement learning methodologies, we introduce a Deep Q-Network-combined framing policy (DQN + HEFG) as an additional comparative baseline. This inclusion supplements the analysis with a machine learning-based benchmark, allowing for a more comprehensive performance assessment of AIFG against both traditional and contemporary intelligent algorithms. The simulation scenario emulates data traffic in an AOS, incorporating delay-sensitive and non-delay-sensitive services with dynamic arrival patterns.  
The evaluation focuses on three critical performance indicators: average queue delay, frame multiplexing efficiency, and throughput. To explore the robustness of AIFG, the simulations are conducted under four different delay threshold settings: 5 ms, 10 ms, 15 ms, and 20 ms.  
Beyond the standard evaluations, this section further investigates the scalability and practical applicability of AIFG. We extend the simulation environment by varying the number of network nodes (from 25 up to 200), altering the MPDU payload length (ranging from 800 to 1800 bytes), and adjusting satellite link bandwidth (from 3 Mbps to 20 Mbps). These additional experiments serve to analyze the computational overhead, convergence behavior, and performance resilience of AIFG under more realistic and heterogeneous system conditions.  
The results demonstrate that AIFG can dynamically adjust its frame generation policy based on real-time feedback from the system environment, effectively balancing delay and efficiency. The subsequent subsections provide detailed discussions of the simulation setup, algorithm configurations, and quantitative comparisons under each experimental scenario.
## Materials and Methods
### Surface Water Storage (SWS) Estimation
#### 2.4.1 SWE Retrieved from Spectral and SAR Imagery  
Two SWE datasets provided by the Joint Research Centre (JRC) and Denmark DHIGRAS Company (referred to as GRAS) over Xiaowan and Nuozhadu were chosen. The JRC Global Surface Water dataset, produced from Landsat 5, 7, and 8 optical imageries, tracks the temporal and spatial changes in SWE over 32 years (1984-2015) with a 30 m resolution globally [49]. A new algorithm for fusing optical and SAR data to map SWE dynamics has been developed to address the limitations of previous optical-only datasets (GRAS) [50], such as cloud cover and winter coverage [49; 51; 52]. The algorithm utilizes multiple indices, including VV SAR backscatter from Sentinel-1 [53; 54] and the optical Normalized Difference Vegetation Index (NDVI), and the Normalized Difference Water Index (NDWI) calculated from optical images as inputs. By integrating all available Sentinel-2, Landsat, and Sentinel-1 images from 2017-2020, the algorithm employs machine learning and logistic regression techniques, training with the Global Surface Water Dynamics database from the Global Land Analysis and Discovery (GLAD) to map surface water dynamics. GRAS fuses optical and SAR data to provide improved temporal resolution (approximately six times per month) and more accurate results.
## 4 Signal Evaluation Approach under the Local Despreading Waveform Mismatch
### Formulation and Statistical Properties of Discriminator Output
It should be noted that the result of Equation (21) is also compatible with the early minus late (E-L) discriminator, by discarding the terms after the multiplication sign, which depicts the squared loss caused by the non-coherent discriminator. Accordingly, the variance of the TOA estimation for the E-L discrimination is achieved by the first line of Equation (21). Also, the results of Equation (21) can hold for matched receiving, with the local spectrum \(G_{\delta}\) and cross-spectrum \(\mathcal{X}_{s,\delta}\) substituted with the received spectrum \(G_{s}(f)\).
# Threat Analysis for Smart Homes +
## 2 Related Work
They identified the security levels of HAN devices and data packets, and proposed a security model which aims to protect such data flows over the HAN network. [PERSON] et al. [4] proposed the use of the Operationally Critical Threat, Asset, and Vulnerability Evaluation (OCTAVE) Alegro risk assessment method to identify potential risks in the smart home environment. They focused on the cyber and the physical layer of the smart home's architecture, and they identified ten critical cyber and physical assets. Although they carried out a comprehensive risk assessment, they did not take into consideration the complexity of the smart services and devices. Additionally, [PERSON] et al. [21] proposed a structured threat analysis method for smart home scenarios by leveraging Microsoft's Security Development Lifecycle (SDL). In particular, they used a model-based method for the SDL's threat analysis and they presented an attack path Data Flow Diagram (DFD) pattern in order to explain potential intrusions in the smart home environment. A great deal of research has been conducted to analyze and validate network topology generators currently available. [PERSON] et al. [22] discussed techniques for inference, modeling, and generation of the Internet topology at both the router and administrative levels. They concluded that providing a realistic mapping of the Internet network is challenging, highlighting the importance of defining the physical interconnection of the nodes for routing and resilience purposes. [PERSON] et al. [23] comparedstructural topology generators to degree-based generators. They concluded that degree-based algorithms produce more accurate results that match the metrics used with higher fidelity than structural models. Nevertheless, they noted that choosing smaller topologies on which to run network simulations would likely provide a drastically different result. [PERSON] et al. [23] noted that a power-law distribution is almost meaningless if the number of nodes is small because, with only a few nodes, it is unlikely that the degree distribution will be able to create the implicit hierarchy necessary for modeling networks. Attack modeling techniques and analysis have also been the focus of a large number of research works on cyber threat analysis [24]. [PERSON] and [PERSON] [7] compared the performance of different malware propagation models through a spatial-temporal random process based on local interactions of nodes in networks. They concluded that a Markov model that incorporates both detailed topology information and simple spatial dependence achieves better overall results than the other models analyzed. One limitation of the model proposed by [PERSON] and [PERSON] is that it does not specifically consider the states at which the nodes could be observed. [PERSON] et al. [25] proposed a discrete-state Markov model that assumes a network node to be observable in one of the five following states: vulnerable, infected, quarantined, healthy and disseminator.
## 3 Models and Algorithms
### Nonlinear Noise Mitigation  
NLI noise mitigation by optimizing the detection areas on the Rx side is addressed in this section. The proposed method computes near-optimal detection areas for the CPs and estimates the resulting pre-FEC BER. The method is based on dividing the whole coordinate IQ plane (\(A\)) into \(k\) small square areas \(a\) to create a grid. Figure 5 shows an example for a 16-QAM signal, where \(A\) is defined by corner points \([5+5i]\) and \([-5-5i]\). The detection area for CP \([3+3i]\) is shown as a set of small areas.  
Figure 5: Definition grid for 16-QAM signal constellations.  
Then, the probability of receiving a symbol originally transmitted in CP \(i\) can be defined as Equation (4), which facilitates computing \(\Phi_{A}\) as Equation (5).  
\[\varphi_{a}(i)=P\Big{(}x\subset a\Big{|}\ x\ \sim N\Big{(}\Upsilon^{i}\Big{)} \Big{)} \tag{4}\]  
\[\Phi_{A}(i)=\sum_{a\in A^{i}}\varphi_{a}(i) \tag{5}\]  
With these definitions, the proposed method consists of assigning each of the small areas \(a\) to the detection area \(A^{i}\) of the CP \(i\) with the highest probability. Algorithm 3 shows the pseudocode of the algorithm that receives features \(Y\), the coordinate plane \(A\) divided into small areas and the number \(m\) of CPs in the signal, and computes a vector of detection areas [\(A^{i}\)], where each area defines a subset of small areas of the whole coordinate plane.  
```
1:\(Y\), \(A\), \(m\)OUTPUT: [\(A^{i}\)]
2:\(A^{i}\leftarrow\mathcal{O}\), for each \(i\):\(1..m\)
3:for each \(a\) in \(A\)do
4:\(a.i\gets 0\)
5:\(a.\)maxProb\(\gets 0\)
6:for\(i\):\(1..m\)do
7:if\(\varphi_{a}(i)\)\(>\)\(a.\)maxProb then
8:\(a.\)maxProb\(\leftarrow\varphi_{a}(i)\); see Equation (4)
9:\(a.i\gets i\)
10:\(A^{a.i}\gets A^{a.i}\cup a\)
11:return[\(A^{i}\)]
```  
**Algorithm 3** Compute Detection Areas  
Vector [\(A^{i}\)] is then transformed into matrix \(D\), where every position in the matrix identifies the CP \(i\) for decoding the symbols detected in the related small area \(a\). Matrix \(D\) is sent to the Rx as part of the lightpath provisioning phase, so the Rx can now easily decode every received symbol \(x\) using Equations (6)-(8).  
\[colIdx=\bigg{\lfloor}\frac{x^{I}}{\delta}\bigg{\rfloor}+\frac{\sqrt{k}}{2} \tag{6}\]  
\[rowIdx=\bigg{\lfloor}-\frac{x^{Q}}{\delta}\bigg{\rfloor}+\frac{\sqrt{k}}{2} \tag{7}\]  
\[\text{i}=\text{D[rowIdx][collIdx]} \tag{8}\]
# Steganalysis of Context-Aware Image Steganography Techniques Using Convolutional Neural Network
## 1 Introduction
It can be concluded from previous literature that SRM high-pass filters, residual connections, and different arrangements of layers can alleviate the detection accuracy of CNN. In this paper, several effective steps were taken to improve the network efficacy. The major steps of the suggested technique are given below:  
* The proposed technique highlights the high-frequency elements using a high-boost filter in the first non-trainable convolutional layer. It improves the detection accuracy by more than one percent. * Thirty high-pass filtered images were generated using SRM filters in the second non-trainable convolutional layer to give prominence to the noise of the stego-image effectively. * A single pooling layer in the last part of the CNN was used to sustain the complete statistical traces from each layer. * A clipped ReLU layer was introduced for customized thresholding to obtain more statistical information. * The SVM classifier was utilized instead of the softmax classifier to increase the detection performance. The SVM classifier outperforms in many applications. * Experimental results of the proposed technique were compared with SRNet, Ye-Net, Yedroudj-Net, and Zhu-Net. The experimental results are displayed for the HILL, S-UNIWARD, and WOW steganography algorithms with payloads of 0.2, 0.3, and 0.4 bits per pixel. * In the detailed experimental analysis, the proposed technique was proven to be better than the existing techniques with a higher detection accuracy. In the next section, the proposed technique is discussed. The experimental analysis is discussed in Section 3. The conclusions are presented in Section 4. Figure 2: Computer-generated cover image and difference arrays of the cover and stego-image. (**a**) Cover image. (**b**) DI of cover and HILL stego-image. (**c**) DI of S-UNIWARD stego-image. (**d**) DI of WOW stego-image.
# Retrofitting of Steel Structures with CFRP: Literature Review and Research Needs
## 2 Bond Behavior between CFRP and Steel
### Surface Preparation  
Surface preparation is one of the most crucial parameters of the bond behavior of the CFRP retrofitted elements. The bond capacity can be determined by the cohesion strength of the adhesive as well as the adhesion strength of the interface between the adhesive and the substrate [78]. Adhesion failure can occur at the CFRP/adhesive or the steel/adhesive interface. However, debonding at the steel/adhesive interface is more likely to occur [6; 79]. Therefore, the preparation of the steel substrate has received more attention. The adhesion is mainly provided by mechanical interlocking and chemical bonding between the adhesive and the adheren [80]. It is debatable regarding the agent that is responsible for strong bonds. However, it seems that chemical bonding is more important than mechanical bonding [6]. To improve the mechanical bonding, the steel substrate is roughened before the CFRP bonding process. However, applying an improper roughening technique may cause tiny crevices in which the trapped air bubbles or solvent can induce failure in a rigid adhesive by increasing the stress concentrations [81]. It can also reduce the contact between the adhesive and the adherent, which is so-called wetting [82]. Indeed, increasing the contact surface area leads to enhanced wetting and more extensive chemical bonding [83]. The most commonly used mechanical treatments are grit blasting, needle scaler, and sandpaper. In these abrasive techniques, the surface geometry of the substrate is modified and the oxide layer is removed. It is generally accepted that using the grit blasting method is effective [2; 6; 78]. It also can lead to modifying the surface chemical composition by removing the contaminants such as oil and grease. However, it can introduce grit residues or other contaminants onto the steel substrate [84]. Furthermore, the grit type should be carefully chosen to ensure chemical compatibility with the applied adhesive [78]. Improper surface preparation can lead to premature failure, thereby hindering the achievement of the full capacity of the CFRP retrofitting technique.
## 2 Methods
### Different Interpolation Methods for GIM Products
#### 2.2.1 Junkins Weighted Interpolation  
Figure 1 shows the schematic of Junkins interpolation. In Figure 1, the latitude and longitude of the interpolation point are \(b\) and \(l\), that is, \(p(b,l)\), and the values of the four GIM grid points adjacent to it are \(p_{11}(b_{1},l_{1}),p_{12}(b_{1},l_{2}),p_{21}(b_{2},l_{1})\), and \(p_{22}(b_{2},l_{2})\). It is assumed that the TEC values at \(p_{11},p_{12},p_{21}\), and \(p_{22}\) are \(TEC_{11},TEC_{12},TEC_{21}\), and \(TEC_{22}\), respectively. The VTEC of point \(p\) can be interpolatedaccording to the VTEC of the surrounding four grid points. The interpolation formula used is as follows:  
\[f(b,l)=w_{1}(x,y)\cdot TEC_{11}+w_{2}(x,y)\cdot TEC_{12}+w_{3}(x,y)\cdot TEC_{21}+w_ {4}(x,y)\cdot TEC_{22}, \tag{11}\]  
The weighted function \(w_{n}(x,y)\), \(n=1\), 2, 3, 4 is as follows:  
\[w_{1}(x,y)=\omega(1-x_{p},1-y_{p}), \tag{12}\]  
\[w_{2}(x,y)=\omega(x_{p},1-y_{p}), \tag{13}\]  
\[w_{3}(x,y)=\omega(x_{p},y_{p}), \tag{14}\]  
\[w_{4}(x,y)=\omega(1-x_{p},y_{p}), \tag{15}\]  
where  
\[\omega(A,B)=A^{2}B^{2}(9-6A-6B+4 AB), \tag{16}\]  
\[x_{p}=\frac{l-l_{1}}{l_{2}-l_{1}},\ \ \ \ \ y_{p}=\frac{b-b_{1}}{b_{2}-b_{1}}. \tag{17}\]  
We can use nearest-neighbor extrapolation when the \(p\) point only has two proximate GIM grid points.
## 4 Performance Evaluation
### Computational Complexity of the Proposed DPC Scheme  
The proposed DPC scheme takes a long time to train. However, when it is used in real time, it can be faster than the conventional iterative schemes, such as WMMSE. Figure 17 depicts the computational time to obtain the transmit powers in the WMMSE and proposed DPC schemes according to the number of transceiver pairs, \(N\). In the figure, as \(N\) increases, the computational time of the WMMSE scheme goes up, but that of the proposed DPC scheme increases slightly.  
Figure 16: Average spectral efficiency of the proposed scheme for \(\alpha=3.8\) in Nakagami-\(m\) fading channels with \(m=3\) when the cellular users are out of the D2D communication area.  
Figure 15: Average spectral efficiency of the proposed scheme for \(\alpha=5\) in Rayleigh fading channels when the cellular users are out of the D2D communication area.
# A Wideband Terahertz Transmissive Polarization Manipulator Based on Metasurfaces
## 3 Results and Discussion
### Performance Analysis of the LCPC
These multiple transmitted waves interfere with each other constructively and destructively, producing two polarized waves, one x-polarized and the other y-polarized. These waves generate circularly polarized waves when they have comparable magnitudes and difference in phase angles around 90\({}^{\circ}\). Varying substrate thickness changes the phase angles and hence affects the axial ratio for the proposed converter.
# Performance Evaluation of CF-MMIMO Wireless Systems Using Dynamic Mode Decomposition
## 3 Channel Estimation
### Channel Hardening and Favorable Propagation  
The concepts of channel hardening and _favorable propagation_ are crucial in MIMO systems [5]. We believe that a graphical representation of the behavior of the matrix **R** in the context of CF-MIMO is highly beneficial for a deeper understanding of these phenomena. In the Results section, the same experiments are qualitatively presented; however, this section provides a graphical intuition that illustrates these phenomena. Figures 13 and 14 show the effect of the number of antennas and number of pilots, respectively, on **R**. Channel hardening is a phenomenon in which the channel becomes more predictable as the number of antennas increases. On the other hand, favorable propagation leads to improved channel orthogonality, also due to the large number of antennas. Consequently, the number of pilots significantly impacts the formation of the **R** matrix.  
Figure 12: Structure of the R correlation matrix after sequentially applying DMD and mpeDMD to empirical data. mpeDMD is able to detect the Toeppliz form of the R matrix.
# A Study on Generating Webtoons Using Multilingual Text-to-Image Models
###### Abstract  
Text-to-image technology enables computers to create images from text by simulating the human process of forming mental images. GAN-based text-to-image technology involves extracting features from input text; subsequently, they are combined with noise and used as input to a GAN, which generates images similar to the original images via competition between the generator and discriminator. Although images have been extensively generated from English text, text-to-image technology based on multilingual, such as Korean, is in its developmental stage. Webtoons are digital comic formats for viewing comics online. The webtoon creation process involves story planning, content/sketching, coloring, and background drawing, all of which require human intervention, thus being time-consuming and expensive. Therefore, this study proposes a multilingual text-to-image model capable of generating webtoon images when presented with multilingual input text. The proposed model employs multilingual BERT to extract feature vectors for multiple languages and trains a DCGAN in conjunction with the images. The experimental results demonstrate that the model can generate images similar to the original images when presented with multilingual input text after training. The evaluation metrics further support these findings, as the generated images achieved an Inception score of 4.99 and an FID score of 22.21.  
multilingual BERT; text-to-image; DCGAN; webtoon; GAN +
Footnote †: journal: Applied Sciences