esa-sceva/satcom-chunk-collection huggingface dataset:
# 5G Support for Industrial IoT Applications--Challenges, Solutions, and Research Gaps
## 7 Virtualization Technologies
### Identified Research Gaps  
Virtualization technologies are going through a great evolution; however, there are still numerous open questions. The combination of different virtualization technologies has not been standardized yet. The mitigation of security issues related to container based virtualization technologies is also a hot topic, however we still ensure the security of the container based architecture by running them on top of VMs. We can find open questions related to migration between different systems, as well. Furthermore, issues related to FaaS can also be found, as different FaaS implementations should be programmed in a different way, therefore a valuable effort would be the implementation of unified methods and APIs.
# A Novel Dual Authenticated Encryption Scheme Suitable for Social Networking Services
## 2 Preliminaries
### Participated Parties  
A DAE scheme consists of three participants including a sender and two designated recipients. The sender first utilizes his/her private key to create an authenticated ciphertext and transfers it to the other two participants. Then, each of the two designated recipients can run sole processes to decrypt the ciphertext and verify the corresponding signature. A DAE scheme is correct if a valid ciphertext generated by one party can only be solely decrypted and verified by the other two designated recipients in a three-party communication environment.
## 2 Related Work
### Energy Efficiency Based
In [14], some data collection methods are discussed which used minimum energy for data transmission from source to the destination. In both [14; 15], mobility management is considered, while in the [14], reliability and packet size management is not considered. Nevertheless, the papers [15; 16; 17] focus on the reliability of the network. Additionally, [15] considers both types of forwarding strategies; single-hop and multi-hop. While [14; 16; 17] only focus on single-hop from the current node. Moreover, the work in [15] considers the security issues of UWSNs. While in [14], the authors discusses the problems of getting route information. In [16], the complexity of the network is a major challenge. Additionally, paper [17] works for energy efficiency by managing the size of data packet.
# Graph Neural Networks for Routing Optimization: Challenges and Opportunities
## 7 Challenges and Opportunities
### Opportunities
#### 7.2.2 Combination with Emerging Techniques  
Another research opportunity is the combination with other emerging techniques for providing real-time routing decisions, for example, quantum computing, which has been considered for vehicle routing problems [140]. Another example is the content-centric network (CCN), which entirely changes the current end-to-end communication transmission mechanism of the traditional TCP/IP network architecture. The content is separated from the IP addresses and users can directly access the data by name. Existing routing schemes have been inapplicable in this next-generation network structure, and new routing solutions are required. Furthermore, routing and efficient content caching can be jointly considered in a content-centric network with potential GNN-based solutions.  
Future research directions for enhancing GNN performance in dynamic and distributed network environments point toward the integration of emerging techniques like federated learning, self-supervised learning, and online learning. Federated learning offers a promising avenue by enabling decentralized training of GNN models across multiple network nodes without sharing raw data, thus addressing privacy concerns and reducing communication overhead in large, distributed networks like satellite or IoT systems. This approach could allow models to learn collaboratively while preserving the autonomy of individual nodes. Self-supervised learning presents another opportunity by allowing GNNs to learn useful network representations from unlabeled data, which are abundant in network environments. By leveraging tasks like link prediction or graph reconstruction, GNNs can gain a better understanding of network structures without relying on extensive labeled data, which are often hard to acquire in real-time network scenarios. Additionally, online learning methods could significantly improve GNN adaptability in dynamic networks where topologies change frequently. These methods allow GNNs to update their models incrementally as new data arrive, facilitating quicker responses to evolving network conditions. Together, these techniques could enhance the robustness, scalability, and efficiency of GNN-based routing, making them more practical for real-world deployments in complex, large-scale networks.
# A Survey on Immersive Cyber Situational Awareness Systems
###### Abstract  
Cyber situational awareness systems are increasingly used for creating cyber common operating pictures for cybersecurity analysis and education. However, these systems face data occlusion and convolution issues due to the burgeoning complexity, dimensionality, and heterogeneity of cybersecurity data, which damages cyber situational awareness of end-users. Moreover, conventional forms of human-computer interactions, such as mouse and keyboard, increase the mental effort and cognitive load of cybersecurity practitioners when analyzing cyber situations of large-scale infrastructures. Therefore, immersive technologies, such as virtual reality, augmented reality, and mixed reality, are employed in the cybersecurity realm to create intuitive, engaging, and interactive cyber common operating pictures. Immersive cyber situational awareness (ICSA) systems provide several unique visualization techniques and interaction features for the perception, comprehension, and projection of cyber situational awareness. However, there has been no attempt to comprehensively investigate and classify the existing state of the art in the use of immersive technologies for cyber situational awareness. Therefore, in this paper, we have gathered, analyzed, and synthesized the existing body of knowledge on ICSA systems. In particular, our survey has identified visualization and interaction techniques, evaluation mechanisms, and different levels of cyber situational awareness (i.e., perception, comprehension, and projection) for ICSA systems. Consequently, our survey has enabled us to propose (i) a reference framework for designing and analyzing ICSA systems by mapping immersive visualization and interaction techniques to the different levels of ICSA; (ii) future research directions for advancing the state of the art on ICSA systems; and (iii) an in-depth analysis of the industrial implications of ICSA systems to enhance cybersecurity operations.  
virtual reality; augmented reality; mixed reality; extended reality; cybersecurity; cyber situational awareness; cybersecurity education; cybersecurity training +
Footnote †: journal: CyberSecur. Priv. 2025, 5, 33  
[PERSON], [PERSON]. [PERSON], Fj.  
1 School of Computer and Mathematical Sciences, The University of Adelaide, Adelaide 5005, Australia; [EMAIL_ADDRESS]  
2 College of Interdisciplinary Studies, Zayed University, Dubai 144534, United Arab Emirates 2  
3 Honeywell, Bracknell RG12 1 EB, UK; [EMAIL_ADDRESS]
###### Abstract  
Radio Frequency Interference (RFI) detection and characterization play a critical role in ensuring the security of all wireless communication networks. Advances in Machine Learning (ML) have led to the deployment of many robust techniques dealing with various types of RFI. To sidestep an unavoidable complicated feature extraction step in ML, we propose an efficient Deep Learning (DL)-based methodology using transfer learning to determine both the type of received signals and their modulation type. To this end, the scalogram of the received signals is used as the input of the pretrained convolutional neural networks (CNN), followed by a fully-connected classifier. This study considers a digital video stream as the signal of interest (SoI), transmitted in a real-time satellite-to-ground communication using DVB-S2 standards. To create the RFI dataset, the SoI is combined with three well-known jammers namely, continuous-wave interference (CWI), multi-continuous-wave interference (MCWI), and chirp interference (CI). This study investigated four well-known pretrained CNN architectures, namely, AlexNet, VGG-16, GoogleNet, and ResNet-18, for the feature extraction to recognize the visual RFI patterns directly from pixel images with minimal preprocessing. Moreover, the robustness of the proposed classifiers is evaluated by the data generated at different signal to noise ratios (SNR).  
radio frequency interference detection; deep learning; transfer learning; pretrained convolutional neural networks +
Footnote †: journal: Applied Sciences
## 3 Low-Complexity Optimization of the Algorithm
### Reducing Matching Branches
In the orthogonal baseband MSD algorithm, for both the \(I\) and \(Q\) branches of the signal, the correlation process can be expressed as:  
\[I_{B}=I_{R}I_{L}-Q_{R}Q_{L} \tag{12}\]  
\[Q_{B}=I_{R}Q_{L}+Q_{R}I_{L} \tag{13}\]  
Figure 4: Signal phase tree schematic diagram. \[res=I_{B}+jQ_{B} \tag{14}\]  
where \(I_{R}\) and \(Q_{R}\) represent the real and imaginary parts of the received signals within the observation interval, respectively, and \(I_{L}\) and \(Q_{L}\) represent the real and imaginary parts of the local reference signals. According to Equation (5), when the middle bit \(a_{3}\) takes a value of 1 and 0, the correlation operation results between code group \(\{a_{0},a_{1},a_{2},0,a_{4},a_{5},a_{6}\}\), \(\{a_{0},a_{1},a_{2},1,a_{4},a_{5},a_{6}\}\) and the received signals are complex conjugates of each other. That is to say, the correlation value of one code group can be obtained directly from the other code group through taking the complex conjugate, instead of computing again. Therefore, in the proposed MSD algorithm, only one set of code group waveforms needs to be stored and computed to compute all correlation results. and the numbers of local reference signals can be further decreased from \(2^{N}\) to \(2^{N-1}\), which is one-eighth of the number of original branches in the traditional MSD algorithm. In the baseband MSD algorithm, as the observation interval \(N\) increases, the increase in computational complexity mainly arises from the correlation computations between the received signal waveforms within the observation interval and the local reference signal waveforms. The proposed algorithm reduces the number of matching branches significantly in the baseband MSD algorithm through the above two optimization methods, thereby decreasing both the storage resources and the computational complexity of the correlation operations.
## 3 Applying the Methodology to the Example of Dabar Power Tunnel
### Instabilities around the Contour Leading to Overbreaks
#### 3.2.2 Probabilistic Analysis
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameter** & **Mean Value (\(\mu_{\text{x}}\))** & \
\begin{tabular}{c} **Standard** \\ **Deviation (\(\sigma_{\text{x}}\))** \\ \end{tabular} &
\begin{tabular}{c} **Variation** \\ **Coefficient (cov)** \\ \end{tabular} & **Minimum Value** & **Maximum Value** \\ \hline JRC & 5.06 & 1 & 0.20 & 2.06 & 8.06 \\ JCS (MPa) & 10.81 & 2 & 0.19 & 4.81 & 16.81 \\ \(\phi_{\text{r}}\) (\({}^{\circ}\)) & 35.0 & 1 & 0.03 & 32.0 & 38.0 \\ Persistence (m) & 3 & 1 & 0.33 & 0.2 & 6 \\ Dip direction (\({}^{\circ}\)) & XX & 3 & \(X_{\text{a}}\) & XX \(-\) 9 & XX \(+\) 9 \\ Dip (\({}^{\circ}\)) & XXX & 1 & \(X_{\text{pu}}\) & XXX \(-\) 3 & XXX \(+\) 3 \\ \hline \hline \end{tabular}  
* XX indicates azimuth value in degrees; XXX indicates dip direction angle in degrees; X\({}_{\text{a}}\), X\({}_{\text{pu}}\) represent values of variation coefficient of azimuth and dip direction angle, respectively; minimum adopted value of persistence is 0.2 m.  
\end{table} 
Table 1: Input parameters for probabilistic stability analysis. Figure 8: Normal distribution function of input variables (joint roughness coefficient, joint compressive strength, residual angle of shear resistance and joint persistence) and characteristic values. Figure 9: Results of probabilistic analysis at two selected sections: km 7 + 066.00 and km 8 + 514.50. (**a**,**c**) Probability of failure of different segments around tunnel contour; (**b**,**d**) probabilistic safety factor value around tunnel contour. \begin{table}
\begin{tabular}{c c c c} \hline
**Chainage** & **Probability of Failure (\%)** & **Probabilistic Safety Factor Value** & **Stability Criterion** \\ \hline \multirow{3}{*}{7 + 066} & 60–75\% crown & \textless{}1.0 crown & \textless{}7\% \\  & 23–47\% left side & \textless{}1.0 left side & \textless{}7\% \\  & 6–15\% right side & \textless{}1.0 right side & \textless{}7\% \\  & \textgreater{}99\% top heading & \textless{}1.0 top heading & \textless{}7\% \\  & 0\% left shoulder & \textgreater{}1.0 left shoulder & \textless{}7\% \\  & 3.9–7.4\% right shoulder & \textless{}1.0 right shoulder & \textless{}7\% \\ \hline \end{tabular}
\end{table} 
Table 2: Results of probabilistic analysis at CH 7 + 066 and CH 8 + 514.5. As mentioned earlier, the overbreak quantification, in terms of volume and shape, could be assessed in the deterministic analysis process by performing a calculation in the zone of each excavation face to determine the volume of unstable wedges and comparing it with the volume of measured overbreak excavation along the length of the advancing step. However, in this case, it would be necessary to perform a large number of complex analyses (cca 8100/3 = 2700), which is impractical. Therefore, as discussed before, the quantification of overbreaks was based on an upgrade of the methodology by performing statistical analysis of a large amount of available data on the mapping of the face of the tunnel excavation.
# Synthesis, Structure, and In Vitro Biological Evaluation of Semi-Synthetic Derivatives of Betulin
## 2 Materials and Methods
### Experimental Lipophilicity  
The lipophilicity of betulin derivatives was experimentally determined by reversed-phase thin-layer chromatography (RP-TLC). Aluminum plates covered with modified silica gel RP-18 with fluorescent indicator F254s (Merck, Darmstadt, Germany) were used. \(\alpha\)-Lipoic acid, betulin, derivatives **2**, **3**, **5-10**, and standard substances (presented in Table S1, Supplementary Materials) were dissolved in chloroform (concentration 1 mg/mL). In total, 2 \(\mu\)L of the obtained solutions were placed on chromatographic plates and then developed in chromatographic chambers saturated with eluent vapors. Mixtures of acetone (Merck, Darmstadt, Germany) and Tris buffer (aqueous solution of tris-hydroxymethyl)aminomethane (Merck, Darmstadt, Germany) with a concentration of 0.2 M, (pH 7.4) were used as mobile phases in volume proportions, taking into account the acetone content from 60 to 90% (every 5%). Chromatographic determinations were performed in triplicate for each compound. Chromatograms of betulin and its derivatives were sprayed with a solution of 10% sulfuric acid in ethanol, and then the plates were heated to 110 \({}^{\circ}\)C until the spots of the analyzed compounds were visible. Chromatograms with standard compounds were observed under UV light at a wavelength of \(\lambda\) = 254 nm. Spots on chromatograms with \(\alpha\)-lipoic acid were visualized in iodine vapor. For all substances in each eluent system, the distances from the starting line to the center of the spot (a) and from the starting line to the eluent front line (b) were measured, and on this basis, the retardation coefficient was calculated (R\({}_{\text{f}}\) = a/b). Then, the value of the R\({}_{\text{M}}\) coefficient was calculated based on Equation (1): \(\text{R}_{\text{M}}=\log\left[(1/\text{R}_{\text{f}})-1\right]\). The dependence of the R\({}_{\text{M}}\) parameter on the percentage of acetone in the mobile phase is described by Equation (2): R\({}_{\text{M}}=\text{R}_{\text{M0}}\) + bc, where b is the slope of the regression graph. Extrapolating the acetone content in the eluent system to zero allows us to determine chromatographic lipophilicity parameter R\({}_{\text{M0}}\). Seven standard compounds (acetanilide, prednisone, 4-bromoacetophenone, benzophenone, anthracene, dibenzyl, DDT- dichlorophenyltrichloroethane) were used to determine the calibration curve (dependence of logP\({}_{\text{lit}}\). on R\({}_{\text{M0}}\), Supplementary Materials). The literature logP\({}_{\text{lit}}\) values of these compounds are in the range of 1.21-6.38 [33]. R\({}_{\text{M0}}\) values for the references and \(\alpha\)-lipoic acid were determined analogously to those for the tested betulin derivatives (Table S1, Supplementary Materials).
# Privacy Preserving Face Recognition in Cloud Robotics: A Comparative Study
## 3 Overview of the Encryption Algorithms under Study
### RSA Algorithm  
[PERSON], [PERSON], and [PERSON] (RSA) created this algorithm back in 1977. The RSA algorithm is used to encrypt and decrypt the information. The public-key cryptosystems include the RSA cryptosystem. RSA is a cryptographic algorithm that is commonly used to transmit safe information. The RSA technique is used to encrypt the images, with two huge prime integers and a supplementary value chosen as the public key. The prime numbers are kept hidden from the public. The public key is used for image encryption, while the private key is used to decrypt them. It is not only used for image but also for text encryption. Consider two large prime numbers \(r\) and \(s\) as public and private keys respectively for encryption of images. Take two integers \(f\) and \(e\) in such a way that \(f\times e\ mod\,\phi(m)=1\). With these four selected integers, images are encrypted with the simple formula \(D=q^{f}\ mod\,\phi(m)\), where \(q\) is an original input image, \(f\) is a publicly available key, and \(\phi(m)\) is a product of (\(r\)-1) and (\(s\)-1) ensures that \(gcd(f,\phi(m))=1\) i.e., \(f\) and \(\phi(m)\) are co-primes. \(D\) is the encrypted image after encryption through RSA. To get back the original image, use \(R=D^{e}\ mod\,\phi(m)\), where e is a key available only for private people [25] (Show in Algorithm 3).  
```
1. Initially access the original gray-scale image of any size (\(R\))
2. Now, select two different large prime numbers \(r\) and \(s\).
3. Measure the m value which is equal to \(m=rs\).
4. Now, calculate \(\Phi(m)=\Phi(r)\Phi(s)=(r-1)(s-1)=m-(r+s-1)\), where

function \(\Phi\) is [PERSON]'s totient function.
5. Select another integer \(f\) (public key) in such a way that \(1<f<\Phi(m)\) ;

and ;

\(gcd(f,\ \Phi(m))=1\), in which \(fand\Phi(m)\) are co-primes.
6. Calculate e as \(e=(f-1)\ mod\,\Phi(m)\); i.e., \(e\) is the multiplicative modular

inverse of \(f\ (modulo\,\Phi(m))\).
7. Get the encrypted image, \(D=Sf\ mod\,\Phi(m)\).
8. For gray-scale images, perform \(D=D\ mod\ 256\), since image contains the pixel

intensity levels between 0 to 255.
9. To decrypt the encrypted image, perform, \(S=De\ mod\ 256\).
10. Then, the original input image \(R=S\ mod\ \Phi(n)\).
```  
**Algorithm 3**Image encryption using RSA.
## 5 Discussion
### Outlook for SWOT  
The Surface Water and Ocean Topography (SWOT) mission is planned to be launched in 2021. In addition to a classical nadir-looking Ku-band altimeter, the mission will carry a Ka-band radar interferometer called KaRin. This instrument will provide water level height information in two 50 km wide swaths with a 20 km nadir gap (which is partly covered by the nadir altimeter) (K  
reservoirs; thus, it performs its own target detection. This also enables the determination of improved storage information from the simultaneously measured area and height information.  
Thus, the future prospects are excellent thanks to SWOT. However, long-term studies still dependent on the older, incomplete data from classical missions. Moreover, careful calibration and combination concepts are necessary in order to ensure a consistent linkage of SWOT with historical datasets and with data from current nadir altimeters.
# Remote Sensing and Bio-Geochemical Modeling of Forest Carbon Storage in Spain
## 3 Study Area and Data  
### Peninsular Spain  
Peninsular Spain, located between \(-10^{\circ}\) and \(3^{\circ}\) longitude and between \(36^{\circ}\) and \(40^{\circ}\) latitude, is a heterogeneous and large region of Southwestern Europe. The climate ranges from Atlantic to semiarid following a NW\(-\)SE gradient. Annual precipitation ranges from more than 2000 mm to less than 200 mm following the same gradient. The elevation ranges from sea level to 3479 m. These features favor the existence of a great range of different ecosystems [35; 36].  
Human activity has also had an impact in the study area [37], resulting in a great heterogeneity in Spanish forest types and characteristics. Of the total area, 55% is covered by wooded lands, while 37% is covered by tree species [38]. Spanish forests can be grouped into five main types, i.e., evergreen broadleaved forest (EBF), low-altitude deciduous broadleaved forest (LDBF), high-altitude deciduous broadleaved forest (HDBF), low-altitude evergreen needleleaved forest (LENF), and high-altitude evergreen needleleaved forest (HENF) [19]. _Quercus _ilex_ is the most widespread broadleaved tree species (total broadleaves occupy 46% of the tree-covered area), while _Pinus halepensis_ is the most widespread needleleaved one (total needleleaves occupy 34% of the tree-covered area).
# Analytical Method and Analysis of Cold-Joint Interface
## 5 Numerical Parametric Interface Analysis
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{\(\varphi\) = 10\({}^{\circ}\)} & \multicolumn{2}{c}{\(\varphi\) = 20\({}^{\circ}\)} & \multicolumn{2}{c}{\(\varphi\) = 30\({}^{\circ}\)} & \multicolumn{2}{c}{\(\varphi\) = 40\({}^{\circ}\)} \\ \hline
**Point** & \(\tau\)**, MPa** & **s, mm** & \(\tau\)**, MPa** & **s, mm** & \(\tau\)**, MPa** & **s, mm** & \(\tau\)**, MPa** & **s, mm** \\ \hline A & 2.39 & 0.18 & 3.15 & 0.36 & 4.36 & 0.49 & 5.39 & 0.59 \\ B & 1.41 & 6.00 & 2.58 & 6.00 & 4.59 & 6.00 & 6.25 & 1.30 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Shear stress and slip values of different friction angle interfaces. Figure 12: Shear stress–layer slip relationship depending on the connector bar diameter. The same kind of comparison, which was used for Figure 11, was performed for Figure 12. Table 4 shows the values of shear stress and slippage. At point A, curve \(\mathcal{O}=8\) mm reached a shear stress value which was 42.3% higher than that for \(\mathcal{O}=6\) mm. For \(\mathcal{O}=10\) mm, this value was 92.1% higher and for \(\mathcal{O}=12\) mm, it was 145.4% higher than the lowest value. The slip values were 50.0%, 88.5% and 134.6% larger than the lowest value for \(\mathcal{O}=6\) mm, respectively. At the end of both tests (point B), the shear stress values for \(\mathcal{O}=6\) mm and \(\mathcal{O}=8\) mm were, respectively, 18.2% and 1.9% lower than the values at point A. For \(\mathcal{O}=10\) mm and \(\mathcal{O}=12\) mm, they were, respectively, 5.0% and 9.0% larger than the values at the previous point. Comparing the shear stress values at the end of the test with the lowest value of \(\mathcal{O}=6\) mm, the values were 65.1%, 138.5% and 216.1% higher. Comparing the behaviours of the interfaces, the curve for \(\mathcal{O}=12\) mm is prominent. At the end of the test, the interface failed. This occurred when the interface's concrete was crushed by a large connector diameter. A parametric analysis with a variation in the interface connector diameter led to some conclusions. First, by increasing the interface connector diameter, a higher interface shear resistance was achieved. Additionally, with a larger connector diameter, a larger layer slip was needed for the interface to fully crack. Lastly, with an increase in connector diameter, a higher shear stress was achieved in the further stages of interface performance, after the interface cracked and the cohesive bond was lost.