esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Distribution Coefficient  
The Japan Atomic Energy Agency (JAEA) provides the JAEA sorption database (JAEA-SDB) for utilization in the disposal safety evaluation modeling of radioactive waste [9]. The distribution coefficient (Kd) of radionuclide is a major variable used to predict the migration and retardation behavior of radionuclides in the groundwater system. The Kd values used in this study were selected from the JAEA-SDB considering the geological structure model of the Wolsong NPP site that was assumed above and the mean pH value of the groundwater around the Wolsong NPP site. The pH values of groundwater were obtained from the National Groundwater Information Center ([[https://www.gims.go.kr](https://www.gims.go.kr)]([https://www.gims.go.kr](https://www.gims.go.kr)); accessed on 31 August 2021) which provides comprehensive groundwater information on South Korea. Consequently, the pH of the natural groundwater of Gyeongju City, which is near the Wolsong NPP site, 5.83-8.40, was used to derive the Kd values. Table 2 shows the Kd values for 14 target radionuclides derived from the JAEA-SDB. Since the JAEA-SDB does not provide the Kd value of Ag for granitic rock that satisfies \begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{1}{c}{**Radionuclide**} & **Relative Fraction of Radioactivity (\%)** & **Decay Constant (6\({}^{-1}\))** \\ \hline \({}^{94}\)Nb & 70.6 & \(1.08\times 10^{-12}\) \\ \({}^{63}\)Ni & 1.2 & \(2.20\times 10^{-10}\) \\ \({}^{125}\)Sb & 0.3 & \(7.97\times 10^{-9}\) \\ \({}^{121m}\)Sn & \(1.2\times 10^{-2}\) & \(5.01\times 10^{-10}\) \\ \({}^{90}\)Sr & \(2.0\times 10^{-2}\) & \(7.63\times 10^{-10}\) \\ \({}^{93}\)Zr & 2.5 & \(1.44\times 10^{-14}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._  
Figure 1: Geological structure model of the Wolsong NPP site. the above pH condition, the present work replaced the Kd value of Ag with that of Cs for granitic rock by referring to the literature [16]. Further, there is a lack of data regarding Fe and C for granitic rock in the JAEA-SDB. Thus, it was assumed that the Kd values of Fe for unsaturated zone and granitic rock were identical to each other. Whereas for C, the Kd value suggested in the literature [16], was selectively applied; however, a deterministic single value was input, instead of a distribution function.
## 3 Materials and Methods
### Vulnerability and Threat Analysis
#### 3.3.1 Operating Systems and Vulnerabilities  
Satellite networks consist of multiple segments, each comprising various nodes that operate on distinct operating systems (Table 1). In the ground segment, the _FW_, _RT_, _GCS_, _GW\({}_{1}\)_, and _GW\({}_{2}\)_ all run on Red Hat Enterprise Linux (RHEL). The space segment, consisting of _MEO_ and _LEO_ satellites, operates on VxWorks, an RTOS designed for high-reliability aerospace and mission-critical applications. VxWorks is particularly well suited for satellite networks because of its lightweight architecture, enabling efficient utilization of limited system resources while ensuring reliable orbit control and data transmission. In the user segment, the _GNSS_ operates on Red Hat Enterprise Linux, leveraging its proven stability in satellite ground systems and aerospace applications. _UAVs_ run on PX4, while the _IoT_ operates on the Contiki OS, which is optimized for low-power and resource-constrained environments.  
The security vulnerabilities in each operating system are summarized in Table 2. The vulnerability analysis for Linux-based operating systems in the ground and user segments was conducted using Nessus [47] and Nmap, while the VxWorks RTOS was analyzed with Wind River Security Tools [48]. Additionally, the PX4 operating system used in _UAVs_ was systematically evaluated at both the network and application levels using Metasploit [49] and Nessus. The vulnerability analysis was performed based on CVE and CVSS data, and the key vulnerabilities for each node are summarized in Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Segment** & **Host** & **Operating System** \\ \hline \multirow{4}{*}{Ground Segment} & _FW_ & Red Hat Enterprise Linux \\  & _RT_ & Red Hat Enterprise Linux \\  & _GCS_ & Red Hat Enterprise Linux \\  & _GW\({}_{1}\)_ & Red Hat Enterprise Linux \\  & _GW\({}_{2}\)_ & Red Hat Enterprise Linux \\ \hline \multirow{2}{*}{Space Segment} & _LEO_ & VxWorks \\  & _MEO_ & VxWorks \\ \hline \multirow{2}{*}{User Segment} & _GNSS_ & Red Hat Enterprise Linux \\  & _UAV_ & PX4 \\  & _IoT_ & Contiki OS \\ \hline \hline \end{tabular}
\end{table}
Table 1: Operating systems in satellite network hosts.
## 3 Results and Discussion  
### Caroenoid Composition of the Wine Grape Varieties  
Nineteen compounds including carotenoids (15), chlorophylls (2), and pheoplytins (2) were detected by HPLC-DAD-MS in the wine grape extracts (Figure 1).  
It is worth noting that consistency in carotenoid attribution is very complex due to their structural diversity. In particular, the double bond-conjugated system is susceptible to heat, light, oxygen, and acids, giving rise to unwanted _cis-trans_ isomerization. Using antioxidants (i.e., BHA), an ice bath, and minimizing the light exposition of the samples during the extraction steps are mandatory to prevent the formation of _cis_-isomers as artifact compounds [26]. Therefore, alongside the main trans carotenoids identified, \(\beta\)-carotene and (all\(E\))-lutein, accounting for 60% and 70% of total carotenoids in mature Primitivo and Negro Amaro grapes [7], we were also confident that the _cis_-isomers (assigned as 9Z-lutein and 9Z-\(\beta\)-carotene) were present in the grape tissue (Table 1).  
Analogously, an acidic medium could cause the isomerization of 5,6-epoxyxanthophylls (Figure 2). Indeed, treating an acetone/hexane solution of violaxanthin and (9'Z)-neoxanthin (3 ug/mL) with 500 uL of tartaric acid (15 g/L) at a concentration similar to that of green berries, hypysochromic shifts of their absorption maxima (\(\lambda\)max) of 20 nm (corresponding to the generation of luteoxanthin and neochrome) and 40 nm (corresponding to the generation of auroxanthin) were observed within 30 min (Figure 3). Thanks to the addition of (MgCO\({}_{3}\))4 Mg(OH)\({}_{2}\) 5H\({}_{2}\)O, which neutralized the extraction solution, we could exclude that the recognized 5,8-epoxyxanthophylls, (8'R)-neochrome (peak 3),(8\({}^{\prime}\)S)-neochrome (peak 5), Iuteoxanthin (peak 7) and auroxanthin (peak 10), were derived from the isomerization of violaxanthin (peak 2) and (9\({}^{\prime}\)Z)-neoxanthin (peak 4), respectively, during the extraction (Table 1).  
Furthermore, the absence of fluxoxanthin and chrysantemaxanthin, originating from 5,6-epoxylutein (Figure 2), also confirmed this speculation. Although the lack of these two xanthophylls appeared to disagree with our past report [4], it is worth noting that the carotenoid composition is often influenced by climate and viticulture practices, such as trellis systems [27].
# MFHE: Multi-View Fusion-Based Heterogeneous Information Network Embedding
## 5 Experiment
### Comparative Experiment
#### 5.3.3 Node Embedding Dimension Experiment  
To select the node dimension with the best performance for MFHE, we aggregate the node representation dimensions on 16, 32, 64, 128, and 256, respectively. The extracted HIN embedding results are assessed by NMI. Figure 8 demonstrates that 64 dimensions have the best performance on MFHE.
## 2 System Model  
In this section, we discuss the effect of the PAPR in OFDM modulated signals, and how it affects the communication system and the BER and degrades performance. The lower-case \(x\) is used for time-domain values, and for frequency-domain values, we use \(X\) upper-case values. Furthermore, the complex conjugate of \(x\) is denoted by \(x^{*}\). The vectors are represented as boldface or sequence \(x[n]\), e.g., \(x[n]=[x_{0},\ldots x_{N-1}]\). The frequency is denoted as \(k\), and index n stands for time. In order to better understand the PAPR, the OFDM-modulated symbols were analyzed more deeply. We consider the quadrature amplitude modulation (QAM) symbols \([X(0),\ldots,X(N-1)]\); these are modulated over subcarriers \(0..n\). Each of the QAM has maximum amplitude as \(a_{n}=\pm a\), separated by a bandwidth B of Hz or a duration of \(T=\frac{1}{8}\) seconds. Each OFDM symbol comprises of N, equally spaced QAM symbols. It can be written mathematically as  
\[x^{p}(t)=\frac{1}{\sqrt{N}}\sum_{K=\frac{-N}{2}}^{\frac{-N}{2}-1}X_{k}^{p}e \frac{j2\pi kt}{T} \tag{1}\]  
The OFDM symbol index is denoted by \(p\); \(X_{k}^{p}\) represents the QAM value of \(k^{th}\) subsymbol. To avoid the intersymbol interference (ISI) and make channel flat fading in each subcarrier, the CP cyclic prefix is added; the length of the CP is greater than the delay spread of the channel. Some part of the signal \(x_{n}(t)\) is copied from the end, and we add it to the front of the transmit signal. Hence, the signal transmitted with a cyclic prefix can be represented by Equation (2).  
\[x_{transmit}(t)=x(N-L+1)\ldots\ldots x(N-2)x(N-1)x(0)x(1)\ldots\ldots x(N-2)x(N-1) \tag{2}\]  
where the last L symbols are added together in a series at the front of the OFDM symbol block. The IFFT is used to generate \(x_{transmit}(t)\). Before the IFFT operation, the signal is converted from serial to parallel, then it is upsampled by a factor L. Upsampling can be regarded as one way of pulse shaping in an OFDM. After these two operations of upsampling and IFFT, the transmitted digital signal can be represented as  
\[x^{p}[\frac{n}{L}]=\frac{1}{\sqrt{N}}\sum\limits_{k=\frac{N-1}{2}}^{\frac{N}{ N}-1}X_{k}^{p}e^{\frac{2\pi\mathrm{i}n}{NL}} \tag{3}\]  
\[\sum\limits_{k=0}^{\frac{N-1}{2}}X_{k}^{p}e^{\frac{2\pi\mathrm{i}n}{NL}}+\sum \limits_{k=NL-\frac{N}{2}}^{NL-1}X_{k-N(L-1)}^{p}e^{\frac{2\pi\mathrm{i}n}{NL}} \tag{4}\]  
\[=\mathit{IFFT}([X_{0}^{p}\ldots\ldots X_{\frac{N}{2}-1}0,\ldots\ldots,0X_{N(L- 1)},0X_{\frac{Np}{2}}\ldots\ldots X_{N-1}^{p}]) \tag{5}\]  
where \(X_{L}^{p}\) is L times the oversampled QAM vector. Hence, the transmitted symbols are \([x^{p}(0),x^{p}(1),x^{(p)}(2)\ldots\ldots x^{p}(N-1)]\), which are the IFFT of the upsampled QAM symbols \([X_{0}^{p}\ldots\ldots X_{\frac{N}{2}-1}0,\ldots\ldots,0X_{\frac{N}{2}}p\ldots \ldots X_{N-1}^{p}]\).
## 3 Experimental Results
### Dimensionality Reduction Preprocessing of Time Domain and Frequency Domain Feature Parameters
Since the input variables of the DBN are mainly calculated based on the failure rate and repair rate of the equipment, the sensitivity of each piece of equipment is obtained by setting the uncertainty of the failure rate of a single piece of equipment to 10%. The specific process is as follows: keep the failure rate of other equipment unchanged, adjust the failure rate of a single device to 110% and 90% of the original failure rate, and obtain the instantaneous availability value of the system after the change in the failure rate of the device, as shown in Figure 11. With the increase in the equipment failure rate, the reliability of the system decreases. As the failure rate of equipment decreases, the reliability of the system increases. The order of the influence of the failure rate of each piece of equipment on the instantaneous availability of the system is EOP > TOP > GSOC > ORV > SPRV > HFT > PUOC, and the weakest equipment affecting the reliability of the system are the Main Pump, Heat Exchanger, and Priming Pump. According to the three axioms of a DBN, the established DBN model is verified and analyzed. The failure probability of the root node TOP1 is set to 0.5, and after 106 days of model operation, the transient availability of the marine oil system without maintenance during navigation decreases from 0.811 to 0.690. On this basis, the initial failure probability of the root node PUOC1 is set to 0.5, and after 106 days of system operation, the transient availability of the marine oil system without maintenance during navigation is reduced to 0.628. The analysis results are consistent with the three axioms of a DBN, and the  
Figure 9: Reliability curves of GUB1 and PUB1. availability of the DBN model in this paper is verified. A reliability block diagram and Monte Carlo simulation are widely used for reliability analysis of two-state or multi-state systems. In this paper, this method is used to solve the instantaneous availability of the system, and it is compared with the instantaneous availability calculated by the DBN method to verify its effectiveness. The results are shown in Figure 12. The reliability block diagram and the availability curve under the Monte Carlo method and the DBN method are basically consistent, which verifies the effectiveness of the method. Figure 11: Comparison of reliability with and without maintenance. Figure 7:0 The availability curves of Monte Carlo and DBN methods. Figure 7:1 Reliability curve of main oil system under regular maintenance condition.
# Space-Based Displacement Monitoring of Coastal Urban Areas: The Case of Limassol's Coastal Front
## 2 Materials and Methods
### Data Processing
#### 2.2.2 SNAP-STAMPS PSI Processing
At the same time, a time series graph was created, which calculated and visualized the mean velocity of all points that were within the buffer zone and plotted them for each of the forty-six interferograms (Figure 4b). Figure 5 is an excerpt of PS distribution in Google Earth Pro. Figure 4: Example of (**a**) PS distribution and quantity in a 1 km buffer zone of a specific point (* defined as the center point) and (**b**) time series graph shows the mean velocity of the point computed by the different interferograms from 2017 to 2021. Figure 3: Vertical displacements of PSs estimated for period 2017–2021 generated by SNAP and STAMPS software using Sentinel-1 data.
# Special Issue \"Fundamentals and Recent Advances in Epitaxial Graphene on SiC\"
## 3 Epitaxial Graphene as a Host for Material Deposition  
Epitaxial graphene on SiC is of great interest because it not only has extraordinary intrinsic properties but also can be used as an atomically flat robust support for non-hybridized growth of different materials, especially metals and metal oxides. Such an integration may expand the functionality of epitaxial graphene and boost the development of innovative technologies in conceptually new fields, like catalysis, plasmonics, and spintronics. Thus, research efforts to contribute to this field and to enrich the existing knowledge capital are in high demand. In response to this demand, the group at Linkoping University [4] launched a systematic study of metal electrodeposition on epitaxial graphene on SiC, choosing copper as a model metal at the first stage. This work sheds light on fundamental aspects of copper electrochemistry on epitaxial graphene and shows that copper electroreduction occurs via two subsequent single-electron transfer steps. The instantaneous nucleation mechanism was identified as a dominating mechanism during copper electrodeposition. The present results provide a deep understanding of the nature of copper-epitaxial graphene interaction, thereby facilitating the design of novel copper-graphene nanohybrid materials.  
At the same time, [PERSON] et al. [5] in their work gave an overview of the recent results on the growth of high-k insulators on epitaxial graphene on SiC, focusing on atomic layer deposition of Al\({}_{2}\)O\({}_{3}\) thin layers, which are important for fabrication of epitaxial graphene-based devices. It was argued that the monolayer epitaxial graphene uniformity is a key factor to achieve a homogeneous Al\({}_{2}\)O\({}_{3}\) coverage via direct deposition, the latter has not been successful before in other studies. The role of different seeding layers and surface pre-functionalization in atomic layer deposition processes on epitaxial graphene is critically discussed. Finally, the authors explained the effect of pre-treatment and grown layers on the quality and electronic properties of epitaxial graphene. As was discussed in [6], this issue requires careful consideration, since the interaction between graphene and deposited layers may significantly affect the electron transport in graphene.
## 3 Phase Contrast Imaging Techniques on Conventional Systems
### Analyzer-Based Imaging  
The first phase gradient sensitive technique, analyzer-based imaging (ABI), is also known as diffraction enhanced imaging, and it is based on filtering the transmitted beam through highly selective analyzer crystals.  
Its experimental configuration consists of a monochromator upstream of the sample and an analyzer crystal positioned according to the Bragg geometry between the sample and the detector, as shown in Figure 4.  
This analyzer crystal acts as an angular filter for the radiation coming from the sample. When the X-rays are refracted by an object, the angle of incidence on the analyzer crystal is changed. When the X-rays reach the analyzer crystal, the Bragg diffraction condition is satisfied only for a small range of angles. Thus, when the scattered or refracted X-rays have incident angles outside this range, they are not reflected and do not contribute to the signal. By adjusting the tilt angle of the analyzer crystal, the refraction angle can be extracted.  
This phase contrast experimental method proved to be the most sensitive [35] and feasible technique with low dose deposition to the sample, as demonstrated in [3].  
Figure 4: Analyzer-based imaging set-up. A monochromatic beam traverses the sample. Then it arrives on a Laue crystal that only reflects the beam arriving with a precise incident angle toward the detector. The crystal can be rotated to acquire several images with various reflection angles to retrieve the refraction from the sample.  
Figure 3: Holography imaging set-up. A coherent beam traverses the sample and propagates a certain distance before reaching the detector. Interference fringes due to wave perturbation in the sample appear on the detector plane and vary according to the distance of propagation. Several images with various distances of propagation or only one position can be taken.  
The method has been successfully implemented with laboratory sources at the cost of extremely long exposure times due to the low flux after monochromatization [36]. Moreover, the beam stability is difficult to maintain, even at synchrotrons. These characteristics do not make this technique a good candidate for transfer to conventional sources, and very few works have been done in this direction.
# An Efficient and Secure Fog Based Routing Mechanism in IoT Network
## 4 Results and Discussion
### Overall Threshold Comparison between ESRM and TSRP  
The comparison of the overall threshold values between the suggested and the base research is shown in Figure 15. These threshold values indicate how many data packets are transferred in a given amount of time. The proposed technique is showing results that are 16% better than the TSRP as the number of nodes grows.  
Figure 14: Transmission Rate of Nodes for Two Way Transmission and DT.  
Figure 13: Energy Consumption Comparison between Proposed and Base Study.  
Figure 15: Transmission Rate of Nodes for Two Way Transmission and DT.  
The comprehensive trust value of each node is evaluated and compared against a predefined threshold to determine whether it should be categorized as a rogue node or a suitable normal node. To enhance data security during transmission, an advanced RSA algorithm is employed, safeguarding data as it moves from the Cluster Head (CH) to fog nodes, from fog nodes to the cloud server, and even directly from CH to the cloud server. Additionally, the protocol utilizes a clustering technique to ensure efficient Cluster Head selection. Extensive simulations demonstrate that the proposed Enhanced Secure Fog-based Routing Mechanism (ESFRM) surpasses the performance of the Trust-Based Secure Routing Protocol (TSRP) by achieving a 16.44% higher malicious nodes detection rate. Furthermore, ESFRM exhibits a remarkable energy efficiency advantage, consuming approximately 13.67% less energy compared to the Secure Energy-efficient Fog-based Routing (SEFR) protocol.
## 4 Results
### Comparison of LDS U-Net with Other Contemporary Models
#### 4.2.2 LDS U-Net Gives the Best Identification of Sony Features  
The detection of exact locations of lateral bony features (thoracic and lumbar) is very crucial for UCA calculation. However, the associated speckle noise makes the identification process more challenging because it suppresses many important features. Four sets of images are shown in Figure 13. Each set consists of an input image, a truth mask, and the segmentation results using U-Net, Attention U-Net, and MultiResUNet and the proposed LDS U-Net model.  
From the set shown in Figure 13a, it is clear that the right side upper TBFs of the spine image are missing in the U-Net segmentation. For Attention U-Net, these are just visible, and for MultiResUNet, and LDS U-Net, they are more prominent. In the case of LBFs, identification is not as straightforward. The T12 region and six LBFs are visible in the truth mask. U-Net can identify the T12 region and five LBFs. The Attention U-Net and MultiResUNet could identify only four of the LBFs. The proposed LDS U-Net could identify all LBFs and the T12 region, which indicates that this model is capable of segmenting LBFs correctly.  
From Figure 13b, it is clear that U-Net is not capable of segmenting all the bony features in the six different segments of LBFs. Attention U-Net and MultiResUNet were somehow able to distinguish the six features, but some features were unclear and provided misleading information. On the other hand, LDS U-Net provided a clearer identification of the T12 region and six LBFs, although the last two features were still conjoined.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Method**} & **Avg. Jaccard Index** & **Avg. Dice Score** & **Avg. F1 Score** & **Avg. Accuracy** \\  & **(Std Dev)** & **(Std Dev)** & **(Std Dev)** & **(Std Dev)** \\ \hline U-Net & 0.7015 (\(\pm\)0.035) & 0.8133 (\(\pm\)0.039) & 0.8327 (\(\pm\)0.0296) & 0.8919 (\(\pm\)0.0271) \\ Attention U-Net & 0.7189 (\(\pm\)0.033) & 0.8297 (\(\pm\)0.037) & 0.8401 (\(\pm\)0.0296) & 0.9195 (\(\pm\)0.0269) \\ MultiResUNet & 0.7264 (\(\pm\)0.032) & 0.8458 (\(\pm\)0.033) & 0.8658 (\(\pm\)0.0289) & 0.9398 (\(\pm\)0.0258) \\
**Proposed LDS U-Net** & **0.7415 (\(\pm\)0.03)** & **0.8694 (\(\pm\)0.0285)** & **0.8885 (\(\pm\)0.0256)** & **0.9592 (\(\pm\)0.020)** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Quantitative performance evaluation of various architectures.  
Similar observations about TBFs and LBFs can be made in Figure 13c. In U-Net, the two uppermost LBFs were not segmented correctly, but appeared to be fragmented, while in Attention U-Net and MultiResUNet, they appeared to be elongated. LDS U-Net clearly distinguished all the thoracic and lumbar bony features.  
Figure 13d shows that U-Net and MultiResUNet were unable to segment the T12 region and all LBFs from the ultrasound spine image. On the other hand, Attention U-Net could identify all six LBFs. However, in both cases, the T12 region was conjoined with the uppermost lumber bony feature. The proposed LDS U-Net could segment the T12 region and all the lumbar bony features consistently.