esa-sceva/satcom-chunk-collection huggingface dataset:
# Fragile Watermarking for Image Authentication Using the Characteristic of SVD
## 2 Singular Value Decomposition (SVD)
\tag{4}\]  
From Equation (4), it can be observed that \(\boldsymbol{u}_{1}\boldsymbol{v}_{1}^{\text{T}}\) is only related to its matrix size (\(N\)) when the original matrix \(A\) has the same matrix elements. Generally, the local pixel values in each image block change slowly. In other words, they have similar neighboring pixels. Inspired by this, we can promote the conclusion to image processing. To verify this conjecture, the image is firstly partitioned into numerous \(N\times N\) blocks, for example \(N=4\). For each image block, SVD is applied and \(\boldsymbol{u}_{1}\boldsymbol{v}_{1}^{\text{T}}\) is calculated. Figure 1 shows the probability distribution histogram of all the values in \(\boldsymbol{u}_{1}\boldsymbol{v}_{1}^{\text{T}}\), which takes grayscale images [PERSON] and [PERSON] for example. From Figure 1, we can see that the elements in matrix \(\boldsymbol{u}_{1}\boldsymbol{v}_{1}^{\text{T}}\) satisfy the normal distribution approximately. In addition, most of the values are distributed around \(0.25\left(1/4\right)\), which is the reciprocal of the block size. This conclusion is consistent with the above analysis. For an image, there are two kinds of image blocks including smooth blocks and texture blocks. In smooth blocks, the neighboring pixels have similar pixel values. On the contrary, the probability for similar neighboring pixels in texture blocks becomes much lower than that in the smooth region. In other words, the fluctuation of pixel values in texture blocks is much more serious than the smooth blocks. Based on this characteristic and the above analysis, we can draw the conclusion that the matrix product \(\mathbf{u}_{1}\mathbf{v}_{1}^{\mathrm{T}}\) in the smooth block should be closely associated with its block size, while in the texture block like edge region, this characteristic is severely weakened. According to this conclusion, we can segment an image into the texture region and smooth region by setting an appropriate threshold \(T\). This process can be illustrated in Figure 2. Figure 3 gives the binary segmentation images of image Lena with different thresholds, when the block size \(N=4\). In the figures, the white area refers to the smooth region, while the black area represents the texture region. We can see that the texture features of an image can be well represented, especially when \(T=0.25\) as shown in Figure 3d. This characteristic provides a theoretical basis for the design of the proposed watermarking scheme. Figure 1: Probability distribution histogram of the values in \(\mathbf{u}_{1}\mathbf{v}_{1}^{\mathrm{T}}\): (**a**) Image Lena; (**b**) Image Barbara. Figure 3: \(Cont\). Figure 2: The process of image segmentation.
## 4 Analysis of Simulation Results
### Reduction in Health Risk  
In this study, long-term health effects (i.e., changes in mortality risk) were used as the metric for quantifying improvements in health risk. According to the simulation, the population density of Taiwan is 644 people/km\({}^{2}\), and the implementation of the 14 air pollution control measures in the TAPCAP will reduce the average PM\({}_{2.5}\) concentration of Taiwan by 3.7 \(\mu\)g/m\({}^{3}\), thus preventing 26 deaths every 100,000 people. The most beneficial measure is the \"retirement of Stage 1 and Stage 2 diesel trucks,\" as it is expected to prevent the deaths of 3846 (994-9415) people. The next most beneficial control measures are \"the strengthening of port area air pollution regulations\" and \"power facility regulations,\" which are expected to prevent the deaths of 994 (255-2430) and 827 (213-2022) people, respectively. The values presented in the parentheses represent the 90% confidence interval of the expected reduction in deaths.  
In the PM\({}_{2.5}\) concentration difference map (Figure 3a) and the avoided deaths map (Figure 3b), the largest changes in PM\({}_{2.5}\) concentrations are observed in central and southern Taiwan, whereas the most significant improvements in health risks are observed in the northern and southern cities of Taiwan. This implies that the effectiveness of air pollution control strategies also depends on population density, in addition to the magnitude of the changes in pollutant concentration.
# A Comprehensive Survey on Knowledge-Defined Networking
## 8 Discussion
### Challenges
#### 8.2.1 Data  
Obtaining data, in terms of both quality and quantity, is a challenge in KDN, as it relies heavily on the accuracy and completeness of the data for training machine learning models or to generate knowledge using any other model-based technique in order to self-learn and optimize network performance. In terms of quantity, for training machine learning models such as deep learning models, an enormous amount of data is required to obtain satisfactory classification or regression performance [553]. Organizations may further have difficulty collecting a large amount of data for machine learning training from real-world networks.  
The data collected for training the knowledge generation models must be complete, which means that they should represent every possible state. Thus, when training, data from multiple networks under multiple network conditions must be obtained to train for every possible instance of the network. In instances where complete data cannot be obtained from a real network, the network administrators can alternatively generate synthetic data from existing data to represent unknown/new data. For instance, consider data related to DDoS attack samples, which only represent data related to a limited set of attack patterns. In order to represent unknown or new attack data, attack samples can be generated by using generative adversarial networks, and ML models can be trained on a combination of original and synthetic data representing a complete security dataset in order to have better performance. A collection of data that is incomplete, inconsistent, or biased can lead to incorrect or unreliable results, which is problematic when optimizing a complex system such as a network. Furthermore, these data must also be highly relevant to the learning task, and additional features may need to be extracted before learning. For example, in order to obtain a better classification performance for a network intrusion detection system using machine learning, specific data related to network flows, event logs, security events, etc. may need to be collected, and more pre-processing in terms of feature selection may need to be carried out to increase classification accuracy. Another requirement of the data is that they should not be outdated. However, due to the fact that networks are dynamic, data can become outdated easily. When making predictions, they should be based on the latest data that represents the state of the network. Predicting from outdated data can lead to sub-optimal performance, as the outdated data do not represent the current state of the network.  
Therefore, gathering a large quantity of high-quality (relevant, complete, unbiased, consistent, and non-outdated) data is challenging, as it requires the careful attention of experts.
## 4 Experiments and Results
### Comparison with Other Methods
#### 4.2.1 In Terms of a Reconstruction Effect
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Dataset** & **Scale** & **Bicubic** & **Aplus** & **SRCNN** & **VDSR** & **DRCN** & **FGLRL** \\ \hline \multirow{3}{*}{Set5} & \(\times\)2 & 33.66/0.9299 & 36.55/0.9341 & 36.34/0.9542 & 37.53/0.9587 & 37.63/0.9588 & **37.75**/0.9633 \\  & \(\times\)3 & 30.39/0.8682 & 32.59/0.9270 & 32.39/0.9090 & 33.66/0.9213 & 33.82/0.9262 & 32.95/0.9105 \\  & \(\times\)4 & 28.42/0.8104 & 30.28/0.8603 & 30.09/0.8628 & 31.35/0.8888 & 31.53/0.8854 & 31.44/0.8846 \\  & \(\times\)2 & 30.24/0.8688 & 32.28/0.8986 & 32.45/0.9067 & 33.03/0.9124 & 33.04/0.9118 & 33.11/0.9133 \\ Set14 & \(\times\)3 & 27.55/0.7742 & 29.13/0.8311 & 29.00/0.8215 & 29.77/0.8314 & 29.76/0.8311 & 29.51/0.8325 \\  & \(\times\)4 & 26.00/0.7027 & 27.33/0.7491 & 27.50/0.7513 & 28.01/0.7674 & 28.02/0.7670 & 28.05/0.7688 \\  & \(\times\)2 & 26.88/0.8403 & 28.58/0.8744 & 29.50/0.8946 & 30.76/0.9140 & 30.75/0.9133 & 29.99/0.9102 \\ Urban100 & \(\times\)3 & 24.46/0.7349 & 26.11/0.7869 & 26.24/0.7989 & 27.14/0.8297 & 27.15/0.8276 & 27.30/0.8280 \\  & \(\times\)4 & 23.14/0.6577 & 24.32/0.7183 & 24.52/0.7221 & 25.18/0.7524 & 25.14/0.7510 & 25.13/0.7501 \\  & \(\times\)2 & 29.56/0.8431 & 30.78/0.8766 & 31.36/0.8897 & 31.90/0.8606 & 31.85/0.8942 & 32.30/0.8842 \\ BSD100 & \(\times\)3 & 27.21/0.7385 & 28.18/0.7644 & 28.41/0.7863 & 28.82/0.7976 & 28.80/0.7963 & 28.66/0.7870 \\  & \(\times\)4 & 25.96/0.6675 & 26.77/0.7087 & 26.90/0.7101 & 27.29/0.7251 & 27.23/0.7233 & 27.26/0.7244 \\ \hline \hline \end{tabular} Comment: Red color indicates the best performance of all methods and blue color indicates the second-best performance of all methods.  
\begin{tabular}{c c c c c c c c} \hline \hline Original & Bicubic & Aphs & SRCNN & VDSR & DRCN & FGLRL \\ (PSNR/SSIM) 21.33/0.6851 & 22.07/0.7414 & 22.19/0.7375 & 23.31/0.8014 & 23.07/0.7955 & 25.35/0.8625 \\ \hline \hline Original & Bicubic & Aphs & SRCNN & VDSR & DRCN & FGLRL \\ (PSNR/SSIM) 22.43/0.5882 & 23.19/0.6506 & 23.23/0.6445 & 24.02/0.6961 & 23.71/0.6865 & 25.71/0.7843 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) values for various methods with a scale of \(\times\)2, \(\times\)3, \(\times\)4 on Set5, Set14, Urban100, BSD100. Figure 4: Qualitative comparison. The first row shows image “img_096” (Urban100 with scale factor \(\times\)3). The second row shows image “img_099” (Urban100 with scale factor \(\times\)3). The third row shows image “ppt3” (Set14 with scale factor \(\times\)4).
# An Experimental Performance Evaluation of Cloud-API-Based Applications
## 4 Experimental Results and Analysis
### Results for Different Cloud-API-Based Applications on the Amazon AWS EC2 Platform  
We investigated the four measured parameters for the selected applications on the AWS EC2 platform. Figure 1(a) shows the response time of both cloud-API-based applications. We can see from the figure that the response time of each application was stable for the different numbers of tested users. The number of users never affected the system's performance or its ability to handle the requests. However, we can observe that Sebawayh had a faster response time than the TaqManTak application. The reason behind this is the size of the data transmitted in each application. A larger number of request and response messages of the applications increased the response time parameter on the tested cloud platform. TaqManK requires transferring more parameters between the two end nodes compared to Sebawayh. The black interval lines in all performance evaluation figures illustrate the variation of the response times of the different pages requested for each application to provide accurate results.  
The latency parameter of these APIs on the Amazon AWS EC2 platform is illustrated in Figure 1(b). It was approximately the same for both applications for the different investigated numbers of users. However, the latency of the two applications was different. The latency of Sebawayh was less than that of TaqManTak. This was due to the size of the transmitted data and the complexity of the investigated applications.  
Moreover, Figure 1(c) shows the processing time of these two applications. We can observe that Sebawayh required more processing time than the TaqManTak service application. Sebawayh's algorithm depends on image processing and the different database transactions of the users' levels, grades, and exams. Under a high processing time, the response time of this application became lower, as seen in Figure 1(c), since most of the work was processed on the servers and did not involve the network. Another observation from Figure 1(c) is that the variation of the processing time results of TaqManTak was greater than the variation results of Sebawayh. This was affected by the required services of the different pages in these applications.  
Finally, Figure 1(d) shows the throughput of the network for these applications in the Amazon AWS EC2 environment. We can observe that both of these applications obtained a 100% throughput in all tested scenarios, which means that no packets were dropped and no requests were delayed or ignored.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Platform** & **Scenario1** & **Scenario2** & **Scenario3** & **Scenario4** \\ \hline Amazon AWS EC2 & Sebawayh & TaqManTak & Both APIs & - \\ \hline Microsoft Azure & Sebawayh & TaqManTak & - & Both APIs \\ \hline \hline \end{tabular}
\end{table}
Table 5: The experimental scenarios.
## 4 Results and Analysis  
### Remotely Sensed Phenology  
The spatial patterns of mean annual SOS, EOS, and LOS extracted from the NDVI time series data were provided in Figure 3. As depicted, the SOS showed a decreasing trend from west to east, consistent with the hydrothermal gradient of the TP. The EOS showed a large spatial heterogeneity across the TP and roughly exhibited a decreasing trend from south to north. Meanwhile, the LOS generally showed a decreasing trend from southeast to northwest, which is also consistent with the hydrothermal gradient on the TP. In addition, according to the statistical results, the SOS was mainly concentrated between the 100 th and 150 th days of the year, accounting for 95.4% of the alpine grassland area. The EOS mainly occurred between the 290 th and 320 th days of the year, accounting for 94.7% of the alpine grassland area. Furthermore, the LOS was mainly concentrated between 170 and 210 days, accounting for 85.3% of the alpine grassland area.
# Band-Pass Sampling in High-Order BOC Signal Acquisition
## 2 Dual-Sideband Model of BOC Signals  
The BOC(\(m\),\(n\)) signal can be expressed as:  
\[s(t)=Ad(t)c(t)sc(t)\cos(2\pi f_{0}t+\theta_{0}) \tag{1}\]  
where \(A\) is the signal amplitude; \(d(t)\) is the navigation data; \(c(t)\) is the pseudorandom noise (PN) code with code rate \(f_{c}=m\times 1.023\) MHz; \(sc(t)\) is the squared subcarrier with frequency \(f_{sc}=n\times 1.023\) MHz; \(f_{0}\) and \(\theta_{0}\) are the carrier frequency and phase, respectively. Generally, the integration period is shorter than the bit duration. So we will ignore the navigation data, that is, \(d(t)\equiv 1\).  
The squared-wave subcarrier can be expanded in Fourier series [19] (for sine-phased BOC signals):  
\[sc(t)=\sum_{i=1}^{\infty}a_{i}\sin(2\pi if_{sc}t) \tag{2}\]  
where \(a_{i}=\frac{1}{T_{sc}}\int_{0}^{T_{sc}}sc(t)\cdot\sin(2\pi if_{sc}t)dt\) is the Fourier coefficients; \(T_{sc}=1/f_{sc}\) is the subcarrier period.  
Equation (2) indicates that the squared-wave subcarrier can be expressed as the sum of a series of sine waves. In practice, both the transmitter and the receiver are band-limited, which means that the high-frequency component of the signals will be filtered out. So in band-limited cases, the squared-wave subcarrier can be approximated by:  
\[sc(t)\approx\sum_{i=1}^{N}a_{i}\sin(2\pi if_{sc}t) \tag{3}\]  
where \(N\) is the number of terms preserved by front-end filtering.  
For high-order BOC signals, the front-end filter may remove all the harmonic waves. Then the subcarrier can be approximated by a sine wave:  
\[sc(t)\approx a_{1}\sin(2\pi if_{sc}t) \tag{4}\]  
As a result, the BOC(\(m\),\(n\)) signal can be approximated by  
\[\begin{array}{ll}s(t)&\approx Aa_{1}c(t)\sin(2\pi f_{sc}t)\cos(2\pi f_{0}t+ \theta_{0})\\ &=\frac{Aa_{1}}{2}c(t)\sin(2\pi(f_{0}+f_{sc})t+\theta_{0})-\frac{Aa_{1}}{2}c(t )\sin(2\pi(f_{0}-f_{sc})t+\theta_{0})\end{array} \tag{5}\]  
Equation (5) indicates that in band-limited cases, high-order BOC signals can be treated as a dual-sideband signal.
# Mono Camera-Based Optical Vehicular Communication for an Advanced Driver Assistance System
## 2 System Overview  
The proposed OCC based vehicle localization technique is shown in Figure 1. The taillights of every vehicle broadcast the vehicle ID and other information to other FLVs. A high-speed camera is mounted on the front side of every vehicle as a receiver to capture the taillights of the FWVs, which are detected using deep learning among the numerous interfering light sources in the road. Both localization and necessary information can be received from the blinking patterns of the LEDs. The modulation frequency is maintained at over 100 Hz to make the blinking pattern indistinguishable from that of human eyes [22].  
The IS of the FLVs can receive data from multiple vehicles concurrently owing to its spatial analysis capability. Additionally, up-to-date advancements in image processing techniques can efficiently eliminate the effect of noises from sunlight and streetlights. Therefore, a highly reliable LOS link can be established. In real-world scenarios, numerous vehicles will be on the road, so identification of every vehicle is necessary. Thus, OCC-integrated vehicles transmit a low-rate stream as their ID apart from a high-rate communication data stream so that they can be distinguished from other vehicles on the basis of their unique ID. This technique reduces the extremely low-rate OCC link setup time in a high-mobility environment. As the position of the FWVs and FLVs changes quickly as a result of their high-mobility, the camera of the latter is considered the reference point of the coordinate system for the distance estimation between the FWVs and FLVs.  
Three general on-road scenarios are considered here, i.e., the vertical distance between FWVs and FLVs can increase or decrease, the FWVs can move laterally, and the FWVs can turn either left or right. In the first two cases, both rear LED sizes remain equal, but in the last case, they become unequal. The size of the LEDs in the captured image decreases with an increase in distance, whereas the coordinates of the LEDs varies with the lateral movement of the FWVs, FLVs, or both, as shown in Figure 2a-c. This distance can be calculated with the variation in LED size; after a certain distance, the LEDs become too small to be detectable. This system will work provided that the projected LED image on the IS covers a minimum of a one-unit pixel area. This system can also be integrated with an RF system in such a way that when the LED size falls below the threshold level, it will switch to the RF network. However, vehicles at a large distance impose a smaller threat than closer vehicles do. Thus, a hybrid system for large distance estimation is considered superfluous.
## 2 Materials  
### Satellite Dataset  
All the S2 MSI data, acquired during the year 2017 from both Sentinel-2A and Sentinel-2B satellites, were collected for the identification of burned areas affected by the 2017 wildfires in the Italian territory. S2 L2A data atmospherically corrected using the MACCS-ATCOR Joint Algorithm (MAJA) [45; 46; 47; 48] and distributed by Theia in MUSCATE format were downloaded and used for the analysis. A total of 62 S2 granules, corresponding to 5795 images (about 7.8 Tb), were processed. MAJA has been shown to achieve better performances with respect to other S2 atmospheric correction algorithms, especially at low reflectance values [49]. It also takes advantage of the use of aerosol derived from the Copernicus Atmosphere Monitoring Service (CAMS) operational analysis, which has shown a significant improvement in aerosol optical thickness estimates when compared to the use of a constant aerosol type, commonly used by the atmospheric correction algorithms [50].
# A Review on the Thermal Hazards of the Lithium-Ion Battery and the Corresponding Countermeasures
## 3 The Causes of Thermal Hazards Associated with Lithium-Ion Batteries
### Physical Factor
Moreover, traditional electrode materials, such as silicon and transition metal oxides, may result in extreme volume changes during operation and further result in fracturing, electrical conductivity loss and mechanical integrity [65].
## 4 Computational Efficiency Assessment with a 2D Office Problem
### Results and Discussion
We tested the convergence property of the iterative solver at four pure tones, 3 kHz, 4 kHz, 5 kHz, and 6 kHz, for a mesh with an element size of 0.005 m \(\times\) 0.005 m, having a spatial resolution of 11.5 elements per wavelength. The resulting mesh has 1,599,840 DOF. Table 1 lists the iteration number and CPU time \(t_{\text{cpu}}\) for FEMs based on the lossy and lossless Helmholtz equations. The reduction rates of iteration number and CPU time are also shown as \(R_{\text{iter}}\) and \(R_{\text{cpu}}\). Results showed fewer iteration numbers for the FEM based on the lossy Helmholtz equation. Therefore, faster convergence still holds for the higher spatial resolution mesh. The iteration number and CPU time reductions, respectively, reached 27.7-38.8% and 27.5-39.2%. To show how efficiency changes with different spatial  
Figure 11: Comparison of convergence history of the iterative solver at 4704 Hz in FEMs based on lossy and lossless Helmholtz equations. Results are shown for atmospheric conditions (a) with the GW32K absorber. \[D=\frac{1}{n_{r}}\sum_{i=1}^{n_{r}}[\overline{L}_{\text{lossless}}(\mathbf{r}_{i},\omega_{c})-\overline{L}_{\text{loss}}(\mathbf{r}_{i},\omega_{c})]\ [\text{dB}], \tag{12}\]0 Comparison of the 1/3 octave band averaged reduction rate on the FEM based on the lossy Helmholtz equation approach at three atmospheric conditions for cases with (a) GW24K, (b) GW32K, and (c) GW96K sound absorbers. The error bars represents the standard deviation values. resolution meshes, Table 2 presents the ratio of iteration number over DOF denoted as \(n_{\text{loss}}/\text{DOF}\times 100\,[\%]\) and the iteration number increase rate denoted as \(n_{\text{loss}}^{\text{Mesh2}}/n_{\text{loss}}^{\text{Mesh1}}\) for the four frequencies. Here, Mesh1 and Mesh2 represent, respectively, the baseline mesh with 400,720 DOF and the two-times-higher resolution mesh with 1,599,840 DOF. Additionally, \(n_{\text{loss}}^{\text{Mesh1}}\) and \(n_{\text{loss}}^{\text{Mesh2}}\), respectively, present \(n_{\text{loss}}\)s for Mesh1 and Mesh2. It can be found that Mesh2 with a higher spatial resolution shows fewer iteration numbers over DOF at the four frequencies. The \(n_{\text{loss}}\)s are only 1.0-1.8% against the DOE, which is about 1/10 of Mesh1. The iteration number increase rate at the four frequencies is frequency-independent and shows an almost constant value of 1.5-1.8. This indicates that the convergence trend of the lossy Helmholtz equation-based FEM does not change even with the use of a higher resolution mesh, but the iteration number increase is smaller than the increase of DOF. As a reference, the memory requirement of FEMs based on the lossy and lossless Helmholtz equations are the same. They are 151 MByte and 600 MByte for Mesh1 and Mesh2, respectively. Therefore, the lossy Helmholtz equation-based FEM does not require additional computational cost to include the air absorption effect.