esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments
### Comparative Experiments under Noisy Conditions  
In the process of actual data acquisition by FPM, it is easy to be affected by the equipment, light source, dust, and other environments producing a certain degree of noise, and thus affecting the color image quality of the FPM acquisition and reconstruction. We added Gaussian noise with mean 0 and standard deviation \(6\times 10^{-4}\) as the main interference condition for the single-channel input data of the network to simulate the potential noise effects during the actual acquisition process, thus further verifying the effectiveness of our proposed algorithm.  
A qualitative comparison and quantitative analysis under the same noise condition are shown in Figure 7 and Table 2, respectively. With the same noise added, our method has an optimal visual performance compared to contrast experiments. Our method also performs the best on the four indicators in Table 2.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **FID\(\downarrow\)** & **LPIPS\(\downarrow\)** & **SSIM\(\uparrow\)** & **PSNR\(\uparrow\)** \\ \hline CUT [14] & 58.7077 & 0.1832 & 0.9078 & 20.1739 \\ CycleGAN [10] & 63.1189 & 0.1688 & 0.9095 & 19.4760 \\ UNIT [21] & 81.0382 & 0.1709 & 0.8884 & 19.1821 \\ GCGAN [22] & 76.1018 & 0.1649 & 0.8934 & 18.3975 \\ DCLGAN [23] & 95.7042 & 0.2131 & 0.8722 & 17.5598 \\ \hline Ours & 49.3686 & 0.1334 & 0.9241 & 20.4459 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Quantitative results of different approaches.  
Figure 6: Qualitative comparison between the best five methods.  
Different degrees of noise may be generated during the actual acquisition process, and to further verify the robustness and generalization ability of our proposed method, we conducted three sets of experiments. Gaussian noise with a mean of 0 and standard deviation of 6 \(\times\) 10\({}^{-4}\), 6 \(\times\) 10\({}^{-3}\), and 6 \(\times\) 10\({}^{-2}\) was added to the input images of the network. Figure 8 shows the visual analysis results of our method under different noise conditions, and Table 3 shows the evaluation index of stained images under three kinds of noise interference. As can be seen from the specific metrics in Table 3, the PSNR metric performs well as the Gaussian noise increases, and the FID, LPIPS, and SSIM metrics produce some degree of deterioration, but the degree of change is small and essentially flat. The visual analysis and quantitative results demonstrate the effectiveness of our proposed method against noise.
# Pulsed Thermography Dataset for Training Deep Learning Models
## 4 Experiments with Deep Models for Defect Segmentation
### Model Pre-Training  
To improve the performance of the encoder-decoder model in processing thermal images, pre-trained models such as VGG Net [2] or ResNet [3], which have been trained on optical images, can be used as encoders due to their well-designed network architecture. However, as thermal images often have many more image channels than the standard three-channel RGB images typically used in these pre-trained models, they cannot be directly applied to thermal imaging data.  
To address this issue, we modified the parameters in the first layer of the pre-trained models to adapt them to the thermal imaging data by calculating new weights using the following formula:  
Figure 6: U-Net architecture presented in [19], which is licensed under CC BY 4.0.  
\[{w_{j}}^{{}^{\prime}}=\frac{\sum_{1}^{C}w_{i}}{C^{{}^{\prime}}} \tag{8}\]  
Here, \(w_{i}\) represents the weights of the neurons in the ith channel of the network's first layer, with \(C\) being the original channel number (3 for RGB images). \(C^{{}^{\prime}}\) represents the new channel number, which in our case is 1000. The new weights, \(w_{j}^{{}^{\prime}}\), are calculated by summing the weights of the original channels and dividing by the new channel number. This allowed us to utilize pre-trained VGG11 models on the ImageNet dataset to process our thermal imaging data.
## 3 Results and Discussion
### Yield and Bulk Density of Microapsules  
Yield and bulk density are fundamental parameters during the manufacturing, packaging, and storage process of probiotic microcapsules. The yield and bulk density of encapsulation of \(L\). _plantarum_ 299v strain with five wall materials ratio and two ratios after lyophilization are shown in Figure 3. The yields varied from 49.35% to 68.02% (Figure 3A). Additionally, the samples of 1:1.5 ratio have a significantly higher yield than samples of 1:1 ratio. This is caused by the wetness of the probiotics core. With the same scale of wet probiotics, the higher amount of dry powder will result in a higher total solid amount after the drying process. Meanwhile in the case of ratio 1:1, the yield of the sample with only DWP as coating material was significantly lower than the others, whereas, in the case of ratio 1:1.5, the yield of the sample with only WP was the lowest. This may be due to under ratio 1:1 and ratio 1:1.5, WP and DWP have a poor combination of water molecules together with probiotic bacteria, hence causing more water loss during the lyophilization process.  
The bulk density was between 0.23 g/cm\({}^{3}\) to 0.27 g/cm\({}^{3}\) (Figure 3B), which is lower than the results (approximately 0.5 g/cm\({}^{3}\)) reported by Rajam & Anandharamakrishnan in 2015. The difference may be due to the use of coating materials and drying technology, the combined whey proteins, and fructooligosaccharide as coating materials and applied  
Figure 3: Yield (**A**) and bulk density (**B**) of microencapsulated \(L\). _plantarum_ 299v strain with different core-to-wall ratios and wall material ratios. WP: whey protein, DWP: denatured whey protein. Results are shown as the means and standard deviations. Significant differences (\(p<0.05\)) were indicated by superscript letters.  
spray-drying technology. Probably due to the high-water evaporation ability of spray-drying method, less water content remained in the microcapsules while lyophilization. Furthermore, there is no significant difference in bulk density between different formulations. However, the bulk density of samples with core-to-wall ratio 1:1 is significantly higher than that of samples with core-to-wall ratio 1:1.5. This may be explained by the physical structure state of the WP, while the DWP has no significant difference. The difference ratios of coating materials resulted in a fluffier structure and in a significantly lower bulk density of samples with ratio 1:1.5 than ratio 1:1.
## 2 Materials and Methods
### Evaluation Index  
Trend changes analyzed by the Mann-Kendall (M-K) significance test [31; 32] in Guangzhou and Shenzhen from 2016 to 2021. The M-K test uses a standardized Z-statistic to determine the monotonic trend of the data, where Z > 0 indicates that the data has an increasing trend and Z < 0 indicates that the data has a decreasing trend (Table 5).  
The coefficient of variation (CV), mean relative error (MRE), and root mean square error (RMSE) were used to calculate the evaluation index. The CV compares the dispersion degrees of different groups of data. Smaller CV values indicate smaller degrees of variation of the data; larger CV values indicate greater degrees of variation. These parameters were calculated as follows:  
\[\text{MRE}=\frac{1}{n}\frac{\left|A-A^{\prime}\right|}{A} \tag{9}\]  
\[\text{RMSE}=\sqrt{\frac{\sum_{1}^{n}\left(A-A^{\prime}\right)^{2}}{n}} \tag{10}\]  
\[\text{CV}=\frac{\text{Standard deviation}}{\text{Mean value}} \tag{11}\]  
\[\text{Standard deviation}=\sqrt{\frac{\sum_{i-1}^{n}\left(A_{i}-\overline{A} \right)^{2}}{n}} \tag{12}\]  
\[\text{Mean value}=\frac{A_{1}+\dots+A_{n}}{n}=\frac{\sum_{i=1}^{n}A_{i}}{n} \tag{13}\]  
Figure 6: The amount of valid Sentinel-2 data in Guangzhou and Shenzhen.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Z** & **\(\mathbf{\alpha}\)** & **Significance** \\ \hline \(\geq\)2.58 & \(\leq\)0.01 & Extremely significant \\ \(\geq\)1.96 & \(\leq\)0.05 & Significant \\ \(<\)1.96 & \(>\)0.05 & Not significant \\ \hline \hline \end{tabular} Note: \(\mathbf{\alpha}\) represents the significance level; Z represents the standard confidence value.  
\end{table}
Table 5: Mann–[PERSON] significance test.  
where \(A\) is the result of Landsat 8 OLI, \(A^{\prime}\) is the result of Sentinel-2, and \(n\) is the number of verification points.  
The correlation coefficient is used to explain the correlation between two parameters. Positive correlations indicate that when one variable increases/decreases, the other also increases/decreases. The Pearson correlation coefficient (PCC) represented the correlation between two variables, as follows:  
\[rho=\frac{cov(X,Y)}{\sigma_{X}\sigma_{Y}}=\frac{\sum XY-\frac{\sum XY\sum Y}{N}} {\sqrt{\left(\sum X^{2}-\frac{(\sum X)^{2}}{N}\right)\left(\sum Y^{2}-\frac{( \sum Y)^{2}}{N}\right)}} \tag{14}\]
# Pseudo-Phoneme Label Loss for Text-Independent Speaker Verification
## 5 Experimental Results
### Analyses  
Through experimental validation, it can be found that phoneme-like features can be learned using PPL loss, which is achieved by jointly computing the deep clustering loss and speaker recognition loss. As the network can make use of pseudo-phoneme labels to enable the system to assign the input feature frames to the corresponding class centers, the segment level uses the CE loss to make the learned embedding map more discriminant.  
In contrast to the c-vector, our structure just needs speaker labels and no other annotated data to train the ASR model. The pseudo-phoneme labels are obtained by clustering frame-level features, which greatly reduces the annotation data requirements. At the same time, because the two loss calculations share the front-end frame-level feature extraction layer, only the deep clustering loss needs to add a clustering process, which does not increase the complexity of the network. Additionally, because the two kinds of losses are computed in parallel, they do not affect the inference speed of the network either.
## 5 Simulation Results and Discussions  
### Displacements  
Figure 7a,b plot the simulation results of the time-displacement curves of the track beam, joint of the down chord, joint of the upper chord, and the arch rib in the vertical and transverse direction of the bridge, respectively, under one train running through the bridge at a speed of 60 km/h. The horizontal axis represents the distance along the bridge, which is measured from one end of the bridge. Both the vertical and transverse displacements reach their maximum values when the train arrives at a distance of 530 m on the bridge. Figure 7a shows that the vertical deflection of the track beam is the largest, followed by the deflection of the joint of the down and upper chords. Figure 7b shows that the transverse displacement of the arch rib is larger than that of the girder, which suggests that the transverse vibration of the arch rib is greater due to the smaller out-plane stiffness.  
Figure 6: The comparison of test and numerical results with one train running through the bridge at a speed of 60 km/h: (**a**) the track beam in middle span; (**b**) the track beam in middle span; (**c**) the vault; (**d**) the skewback.  
Figure 7: The time–history curves of the displacements of the bridge at the mid-span with one train running through the bridge at a speed of 60 km/h: (**a**) the vertical direction; (**b**) the transverse direction.  
The peak values of the displacements in Figure 7a,b represent the vibration amplitudes, which are plotted in Figure 8a,b. Both the vertical and transverse amplitudes do not change significantly with the train's speed when the speed is no more than 30 km/h. When the speed is increased to 40 km/h, the amplitude of the vertical displacement is abruptly increased and a peak appears in Figure 8a. The peak is likely due to the resonance between the bridge and the train moving on it at the speed of 40 km/h. However, the transverse amplitude is insensitive to the change of the train speed. When the loading case is changed from a single line to a double line, the vertical amplitude of the bridge is increased while the transverse amplitude of the bridge is reduced. The transverse amplitude of the bridge approaches zero as the two trains run on the double line, while the transverse amplitude of the bridge increases obviously as one train runs on a single line. The transverse vibration of the bridge is mainly caused by the eccentric train loading.
# Security Awareness in Software-Defined Multi-Domain 5G Networks
## 3 Framework for Security Monitoring and Multi-Domain Trust Management
### The Framework  
The monitoring framework, illustrated in Figure 3, is a collection of enablers and features (software components for extracting security awareness from 5G networks) and information sharing mechanisms for tying these enablers together. The framework consists of the following enablers:1. The micro-segmentation enabler facilitates creation and control of slices. It organizes and isolates network traffic flows. The enabler is a software component that uses a virtualization platform, access control functions, and an SDN controller to create slices and manage and adapt traffic flows.
2. Monitoring brokers distribute security event information. Brokers combine event flows from parts of the network within the end-to-end slice and make them available for different security inferencing functions.
3. The inferencing platform and functions generate security awareness from monitored data. The platform provides libraries for correlating and analysing large amount of streaming data flows.
4. Security adaptation mechanisms change the behaviour of 5G networks and security control mechanisms based on inferred knowledge on risks and trust levels.
5. The Trust Level Agreement (TLA) mechanism and Trust Metric Enabler facilitate knowledge exchange across administrative domains. The TLA enables the orchestration of end-to-end trustworthy slices.
## 2 Finite Element Modeling
### Finite Element Mesh and Boundary Condition  
A typical geometry and mesh of the finite element model, along with the respective notations, are illustrated in Figure 2. Due to symmetry, only half of the monopile-soil system is considered in the analysis. The monopile is modeled as an open-ended steel half-pipe with wall thickness (t). To minimize the influence of boundary conditions, the model extends vertically to twice the embedded length of the monopile (2L) and horizontally to 6 times the diameter of the monopile (6D) along the loading direction and 4D perpendicular to it, measured from the monopile centerline [40]. Fixed boundary conditions are applied at the model's bottom, while normally fixed conditions are applied along its sides. In PLAXIS 3D, the soil and structural plate are discretized using 10-node tetrahedral elements and 6-node triangular elements, respectively. An automated mesh generation procedure in PLAXIS 3D, which considers soil stratigraphy, structural bodies, loads, and boundary conditions, was used to discretize the model.  
\begin{table}
\begin{tabular}{l l l} \hline
**Parameter** & **Unit** & **Value** \\ \hline \(\gamma^{\prime}\) & kN/m\({}^{3}\) & 8.5 \\ \(K_{0}\) & - & 1 \\ \(S_{0}^{A}\) (\(Z=0\)) & kPa & 5.2 \\ \(S_{0}^{A}\)\({}_{inc}\) & kPa/m & 3.1 \\ \(G_{ur}\) & MPa & Detailed in Table 3 \\ \(S_{0}^{C}\)/\(S_{0}^{A}\) & - & 0.99 (default value) \\ \(S_{0}^{P}\)/\(S_{0}^{A}\) & - & 0.5 \\ \(S_{0}^{DSS}\)/\(S_{0}^{A}\) & - & 0.75 \\ \(\tau_{0}\)/\(S_{0}^{A}\) & - & 0 \\ \(\gamma_{f}^{C}\) (\%) & \% & \(60\cdot S_{0}^{A}\)/\(G_{ur}\cdot 100\) (\(K_{f}=60\)) \\ \(\gamma_{f}^{E}\) (\%) & \% & \(2\cdot\gamma_{f}^{C}\) \\ \(\gamma_{f}^{DSS}\) (\%) & \% & \(1.5\cdot\gamma_{f}^{C}\) \\ \hline \end{tabular}
\end{table}
Table 4: Summary of NGI-ADP constitutive model for the idealized soil profile.  
Figure 2: Typical mesh configuration used for the 3D finite element modeling of monopile.
## 2 LNA Design Methodology
### Device Selection
#### 2.2.2 Contact Poly Pitch (CPP)  
As the intrinsic gain of an amplifier drops with frequency, using transistors with high \(f_{T}\)/\(f_{MAX}\) in LNA design is essential at mm-Wave frequencies. As high \(f_{T}\) is usually associated with lower device \(NF\), we have biased several N-MOSFET to reach \(f_{T}\) above \(\sim\)340 GHz to study their noise performance, as shown in Table 1. However, unlike the device's \(NF_{min}\)/\(f_{T}\) is known to be less sensitive to the transistor's layout parasitics resistance and capacitance. Similar to \(NF_{min}\)/\(f_{MAX}\) is highly dependent on the layout of the transistor, and since GF's 22 FDX offers options to vary the contact poly pitch (CPP, see Figure 2), the effects of CPP on \(NF_{min}\) and \(f_{MAX}\) were explored in pre-PEX simulation [14]. As shown in Table 1, transistors with pitches that are 208 nm (CPP2x) have higher \(f_{MAX}\) than the narrower pitched 104 nm (CPP1x) devices, and their differences become more significant as the total width is increased. Similarly, \(NF_{min}\) at 28 GHz is consistently better for the CPP2x devices as they are lower than the CPP1x devices. This is because a wide pitch in CPP2x devices can fit more drain and source contacts, and due to the increased contact areas, the drain and source resistance is effectively reduced and, therefore, both \(NF_{min}\) and \(f_{MAX}\) are improved [14]. Consequently, a CPP of 208 nm is selected for all NMOS devices used in this design.  
Figure 1: NMOS’s minimum noise figure as a function of frequency at various finger widths (total width = 19.2 \(\upmu\)m).  
Figure 2: NMOS contact poly pitch (CPP).
# Public Key-Based Authentication and En-Route Filtering Scheme in Wireless Sensor Networks
## 4 The Proposed Scheme
### Scheme Overview
#### 4.1.3 Design of Mechanism for Resisting Selective Forwarding Attacks  
The multi-path routing protocol in [6] incurs high communication and computation overhead, while the report forwarding method in LEDS [11] incurs high computation overhead. To overcome these drawbacks, the authors have designed an effective report forwarding protocol that can defend against selective forwarding attacks with less communication and computation overhead. The main idea is that the sender first selects one upstream node to forward the report and, if the selectedupstream node does not forward the report, the sender will inform another upstream node to verify and forward the report. Unless all the upstream nodes of the sender are compromised, the legitimate report ultimately will be forwarded to the BS. Compared with the methods in [6; 11], the current scheme can reduce computation and communication overhead by reducing the number of nodes that verify and forward the report at each hop.
## 2 Materials and Methods
### Characterization  
The in-plane electrical conductivity of the composites was measured according to the ASTM C611 method using the conventional four-probe technique at a constant current supply [2]. The tensile strength of the composites was measured according to the ASTM D3039 standard using a universal testing machine (Wew 300C, TIME Group Inc, Tianjin China). Thermogravimetric analysis (TGA) and differential scanning calorimetry (DSC) were carried out using a Mettler Toledo brand machine (SDTA 851e type, Grinfensee, Switzerland) in the temperature range of 30 to 600 \({}^{\circ}\)C at a heating rate of 20 \({}^{\circ}\)C/min in a nitrogen environment. Scanning electron microscopy (Hitachi Type S-3400 N, Hitachi High-Tech, Kawasaki, Japan) was used for microstructural observations of gold-coated samples.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Epoxy/G** & **G150 (wt.\%)** & **G75 (wt.\%)** & **G44 (wt.\%)** & **Epoxy (wt.\%)** \\ \hline \multirow{3}{*}{Epoxy/G150} & 65 & 0 & 0 & 35 \\  & 70 & 0 & 0 & 30 \\  & 75 & 0 & 0 & 25 \\  & 80 & 0 & 0 & 20 \\ \hline \multirow{3}{*}{Epoxy/G150/G75} & 77.5 & 2.5 & 0 & 20 \\  & 75 & 5 & 0 & 20 \\  & 72.5 & 7.5 & 0 & 20 \\  & 70 & 10 & 0 & 20 \\ \hline \multirow{3}{*}{Epoxy/G150/G44} & 77.5 & 0 & 2.5 & 20 \\  & 75 & 0 & 5 & 20 \\ \cline{1-1}  & 72.5 & 0 & 7.5 & 20 \\ \cline{1-1}  & 70 & 0 & 10 & 20 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Composition of different epoxy/G composites.  
Figure 1: Graphite powders with different particle sizes.
## 3 Results and Discussion
### Properties of the Nanocomposite Films  
#### 3.2.1 Optical Properties  
The appearance of the neat gelatin and its nanocomposite films is shown in Figure 2a. The neat gelatin film is colorless and has high transparency, but the gelatin film with CuSNP added has a green color depending on the concentration of the nanofiller. The color properties of the films are presented in Table 2. When CuSNP was included in the gelatin film, the \(L\)- and \(a\)-values of the film were reduced, and the \(b\)-value increased, demonstrating that the brightness of the film was reduced, and the greenness and yellowish of the film improved. The total color difference (\(\Delta E\)) also increased noticeably compared to the control film, mainly due to the green color of CuSNP. A similar color alteration was detected for the agar-based film to which CuSNP was added [40].  
Figure 1: TEM image of CuSNP.  
Figure 2: Appearance (**a**) and UV-vis transmittance spectra (**b**) of Gel/CuSNP films.  
The UV-visible light transmittance spectra of the films are shown in Figure 2b. The control gelatin film presented high UV and visible light transmittance. In contrast, the transmittance of gelatin/CuSNP composite films reduced reliance on the CuSNP content due to the obstruction of the light passageway via light opaque nanoparticles. An increase in the UV blocking properties of gelatin/CuSNP films was primarily due to CuSNP's UV absorption capacity [40]. The UV-barrier and transparency properties of the film are also provided in Table 2. The control gelatin film was extremely transparent with some UV blocking properties with 89.5% of T\({}_{660}\) and 18.2% of T\({}_{280}\). The control film's UV protection properties were due to the UV absorption ability of the aromatic amino acids of gelatin, such as tyrosine and phenylalanine [7]. The addition of CuSNP significantly reduced the T\({}_{660}\) and T\({}_{280}\) of the film depending on CuSNP concentration. As the concentration of CuSNP increased, the reduction in the transmittance for visible and ultraviolet light was different; namely, the T\({}_{660}\) decreased linearly with increasing CuSNP concentration (C) such that T\({}_{660}\) = \(-25.371\)C + 89.3 (R\({}^{2}\) = 0.9929), while the T\({}_{280}\) decreased exponentially such that T\({}_{280}\) = 19.535e\({}^{-1.494\)C (R\({}^{2}\) = 0.9901). In other words, the clarity of the film decreased linearly, while the UV-light blocking property of the film decreased exponentially with CuSNP concentration. These results imply that the UV-blocking phenomena of the film improved significantly with less sacrificing visibility of the film. At 2 wt % of CuSNP added gelatin film, ultraviolet rays were almost completely blocked. However, the transparency of the film was still high enough to see through the film with a T\({}_{660}\) of 39.5%, which is a useful property in UV-barrier packaging applications. A similar conclusion of CuSNP on the optical properties of agar and alginate-based films has recently been described [40,44].