esa-sceva/satcom-chunk-collection huggingface dataset:
# Towards a Collection of Security and Privacy Patterns
## 3 Security Patterns
### Confidentiality
#### 3.1.2 Encrypted Channel  
The Encrypted Channels pattern [14] ensures that sensitive data remain confidential during transmission across networks. Information that passes across, e.g., public networks is open to eavesdropping and can be intercepted.  
The Secure Sockets Layer (SSL)/Transport Layer Security (TLS) ([[https://tools.ietf.org/html/rfc8446](https://tools.ietf.org/html/rfc8446)]([https://tools.ietf.org/html/rfc8446](https://tools.ietf.org/html/rfc8446)) (accessed on 2 December 2020)) family of protocols is the most common implementation of the Encrypted Channels Pattern across the Internet. Web browsers, Web Servers and development platforms offer SSL/TLS capabilities. Other implementations of the pattern include protocols such as IPSec ([[https://tools.ietf.org/html/rfc4301](https://tools.ietf.org/html/rfc4301)]([https://tools.ietf.org/html/rfc4301](https://tools.ietf.org/html/rfc4301)) (accessed on 2 December 2020)) and various Virtual Private Network (VPN) applications leveraging said protocols.  
Figure 3: Descriptive overview of the Encrypted Storage pattern.  
Figure 2: Hierarchical graph of Confidentiality property and relevant patterns.  
Cryptographic information exchanged between two communicating parties (typically referred to as a \"handshake\") allows them to set up encrypted communication. A shared encryption (session) key is used for the encryption and decryption of the sensitive data. For the exchange of that key, a (typically, asymmetric encryption-based) protocol is used. Figure 4 shows the key exchange between the two communication participants.
# Bacterial Flagellar Filament: A Supramolecular Multifunctional Nanostructure
## 3 Flagellins
### Flagellin Structure  
Early biophysical characterization showed that the N- and C-termini of monomeric flagellin are mainly disordered and critical for polymerization, although they become highly structured in the filament [58; 59]. Due to the tendency of full-length flagellin to polymerize, crystallization of \(S\). Typhimurium flagellin FliC was achieved only after the removal of its terminal regions by limited proteolysis [60]. The overall shape of FliC obtained by combining X-ray crystallography and cryo-EM approaches revealed a structure in the form of a Greek letter \(\Gamma\) with four distinct domains (Figure 1a) [61; 62]. Starting from the N-terminus, the polypeptide chain makes a single helix that forms one half of an \(\alpha\)-helical domain called D0, continues with the partial formation of domains D1 and D2, and forms domain D3 prior to completing the folds of the previous three domains. Thus, domains D0-D2 have N- and C-terminal moieties.  
The N- and C-terminal helices of D0 form a coiled coil in the filament. The organization of D1 with three \(\alpha\)-helices and a \(\beta\)-hairpin resembles a four-helix bundle with an extensive hydrophobic core. D2 and D3 consist primarily of \(\beta\)-strands organized in a specific fold called \(\beta\)-foliums in which the tips of the \(\beta\)-hairpins are bent or twisted [61; 62].  
While all flagellin structures known so far exhibit a high degree of structural similarity in the D0 and D1 region, the structures of the variable regions differ extensively (Figure 1b,c). The structure of the _P. aeruginosa_ type A FliC lacking the D0 domain showed that the D1 domain is highly similar to its counterpart in \(S\). Typhimurium [63]. Conversely, the D2 domain adopts a different fold with two \(\beta\)-sheets and one \(\alpha\)-helix between them forming a less flexible cup-like structure positioned parallel to the D1 domain, instead of pointing away from it as in _Salmonella_ (Figure 1c). In the case of _Campylobacter_, the variable region of the flagellin FlaA is larger and forms three domains (D2, D3 and D4) (Figure 1c) [64]. The D2 and D3 domains of the _Campylobacter_ FlaA are structural homologs  
Figure 1: Structural characteristics of flagellins. **(a)** FliC monomer from \(S\). Typhimurium with four distinct domain (PDB: 3A5X). Domains D0, D1 and D2 have N- and C-terminal moieties, while D3 is formed by a continuous polypeptide sequence in the middle; **(b)** Structural alignment of D0 and D1 domains from various species showing high structural homology among them; **(c)** Outer domains of the _C. jejuni_ flagellin FlaA and _P. aeruginosa_ PAK type a FliC.  
of each other and of the _Pseudomonas_ FiC D2. D4 is inserted between C-terminal moieties of D2 and D1, with most of the FlaA glycans located on this domain. It is the most exposed outer domain resembling a shield for both D2 and D3 and, contrary to them, D4 does not have structural homologs.
## 4 Terminal Load and Communication  
The FL-AHNEAP method is improved based on federated learning, so the process of model training is performed on the terminal. However, the computing power of the terminal is limited. Thus, we need to consider whether the terminal is capable of undertaking the model training of the FL-AHNEAP method and whether the communication overhead between the terminal and the cloud will impose a burden on users. Next, we analyze the terminal load from the spatiotemporal complexity of the FL-AHNEAP method, and analyze the communication overhead of the terminal.
# IoT and UAV Integration in 5G Hybrid Terrestrial-Satellite Networks
## 5 IoT, UAVs, and Satellites in the Emerging 5G Technology
### IoT, UAV/Satellite, and 5G Integration
Gateway** & **Pros** & **Cons** \\ \hline  & & & & simpler and less resource-consuming UAVs and satellites, which can be based on non-5G technologies; 5G-gNB & more complex and resource-consuming IoT devices equipped to get direct access to the 5G-RAN. \\ \hline  & & & & simpler and less resource-consuming UAVs; regenerative satellites able to deploy 5G cells, increasing the reliability and availability of the terrestrial 5G access network. & more complex and resource-consuming IoT devices equipped to get direct access to the 5G-RAN. \\ \hline  & & & &
## 3 Analysis of the Results  
It was difficult to analyze the deformation of the entire tunnel because of the huge amount of data. To simplify the analysis process, a simple analysis unit that could represent the behavior of the entire tunnel is needed. Figure 4 shows a schematic diagram of the analysis unit. Under the homogeneous geological condition, every part of the tunnel between every two supports could be considered to be stressed equally and deformed in the same way, thus the behavior of this part was sufficient to represent the entire tunnel. The two cross sections were selected to evaluate the tunnel stability. One cross section was located at the support anchorage, which is where the best support effect works, termed as the support face. The other cross section was located at the middle of the two support faces, namely middle face. Assuming that the axial deformation of the tunnel was continuous, these two faces could typically characterize the axial deformation of the entire tunnel. The selected support face and middle face were located at Y = \(-15\) and Y = \(-16\), respectively.  
Theoretically, in this research, the floor deformation was not directly correlated to the support action due to the nonexistence of the bolt on the floor, and the deformation of the left wall was equal to that of the right wall due to the symmetry of the model. Therefore, in the following analysis, the analysis of the floor deformation was not given, and the lateral deformation (is equal to the 50% of the sum of the left and right wall deformations) was given to express the deformations of the left and right wall. Finally, to eliminate the calculation error, the displacement reduction was used to evaluate the support effect instead of the displacement.
# XLNet-Based Prediction Model for CVSS Metric Values
## 4 Experiments and Results
### Analysis of Results  
In this study, the experimental results of the XLNet model were compared with several pre-trained models, several traditional machine learning algorithms, and similar studies. Figures 6 and 7 present the essential data in two bar charts to simplify analysis. It can be seen that the XLNet model is superior to other models in assessment metrics, as shown by the aforementioned experimental findings. Comparing this study's methodology to previous studies demonstrates that it likewise reaches superior performance levels. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Metric** & **Model** & **AV** & **AC** & **PR** & **UI** & **S** & **C** & **I** & **A** \\ \hline \multirow{4}{*}{Accuracy} & Our method & **0.9625** & 0.9562 & **0.9161** & **0.9440** & **0.9642** & **0.9303** & **0.9358** & **0.9423** \\  & [PERSON] [21] & 0.9115 & **0.9607** & 0.8379 & 0.9321 & 0.9545 & 0.8704 & 0.8735 & 0.8894 \\  & [PERSON] [23] & 0.9141 & 0.9520 & 0.8642 & 0.9333 & 0.9640 & 0.8671 & 0.8761 & 0.8881 \\ \hline \multirow{4}{*}{Precision} & Our method & 0.8931 & 0.8613 & **0.8777** & **0.9405** & 0.9486 & **0.9132** & **0.9253** & **0.8927** \\  & [PERSON] & **0.9090** & **0.9570** & 0.8392 & 0.9318 & **0.9553** & 0.8714 & 0.8736 & 0.8868 \\  & [PERSON] & / & / & / & / & / & / & / \\ \hline \multirow{4}{*}{Recall} & Our method & 0.8685 & 0.7791 & **0.8568** & **0.9386** & 0.9236 & **0.9030** & **0.9179** & 0.8336 \\  & [PERSON] & **0.9115** & **0.9607** & 0.8379 & 0.9321 & **0.9545** & 0.8704 & 0.8735 & **0.8894** \\  & [PERSON] & / & / & / & / & / & / & / \\ \hline \multirow{4}{*}{F1} & Our method & 0.8800 & 0.8139 & **0.8662** & **0.9395** & 0.9355 & **0.9078** & **0.9214** & 0.8521 \\  & [PERSON] & **0.9089** & **0.9574** & 0.8378 & 0.9319 & **0.9548** & 0.8681 & 0.8731 & **0.8863** \\  & [PERSON] & / & / & / & / & / & / & / & / \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison of related methods on the CVSS v3.1 dataset.
## 4 Conclusions  
1. Analysis was made of the energy characteristics of the linear capacitor and supercapacitor when charged from a lithium-ion battery, taking into account the condition that the capacitance of the linear capacitor \(C_{W}=846.84\) F is equivalent to the capacitance of the supercapacitor, provided they have accumulated energy \(W_{\text{LC}}=W_{\text{SC}}=2196\) (\(\eta\)) at \(U_{\text{LC}}=U_{\text{SC}}=0.99U_{\text{n}}\)). The study was performed in the Matlab application package.
2. It was established that at charge of completely discharged SC and LC (provided that their internal resistances \(R_{\text{LC}}=R_{1}=0.0025\) Ohm) the energy transfer coefficient increases nonlinearly. In order to reduce energy losses, the charge of SC and LC to voltages higher than \(0.9U_{\text{n}}\) with the energy transfer coefficient \(\eta_{\text{SC}}\geq 53.9\) and \(\eta_{\text{LC}}\geq 45\%\), is optimal.
3. When charging a fully discharged supercapacitor to a voltage of \(0.99U_{\text{n}}\), its energy transfer coefficient is \(\eta_{\text{SC}}=59.7\), which is \(1.2\) times higher than the energy transfer coefficient of a linear capacitor. When the LC and SC are charged to a voltage of \(0.99U_{\text{n}}\), the doses of accumulated energies in them is the same (according to the selected initial conditions) \(W_{\text{LC}}=W_{\text{SC}}=2196\) (\(\eta\)), and the energy of losses in the LC charge circuit are \(51\%\) higher than the SC charge circuit losses. In such modes of operation, the supercapacitor is more energy efficient.
4. Comparison of research results for different ranges of voltage change during LC and SC charge was performed. It was established that in all studied ranges of voltage change, the energy transfer coefficient at the SC charge exceeds the energy transfer coefficient at the LC charge by \(0.5\) to \(0.8\%\). Their values, depending on the range of voltage changes are \(37.5\) to \(95\%\) (with increasing voltage, the energy efficiency of the charge increases). When the SC and LC charge in the range \((0.9\) to \(0.99)U_{\text{n}}\), the energy transfer coefficients in the SC and LC increase to \(95\%\). The advantage of an SC is that it accumulates \(30\%\) more energy than an LC.
5. With the advent of new serial supercapacitors, it will be necessary to improve substitution schemes, taking into account the material properties of the electrodes of the SC. When using an equivalent substitution scheme for the analysis of longer energy processes in the SC (lasting more than \(30\) min), additional parallel branches in the scheme with large time constants will need to be entered.  
Conceptualization, methodology, project administration by V.K.; software, validation, formal analysis, investigation by O.B.; resources, review and editing, funding acquisition by N.V.; data curation, visualization, writing, original draft preparation by A.S. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. Not applicable. Not applicable. The authors declare no conflict of interest.
## 3 Results and Discussion
### Fracture Toughness and Critical Stress Intensity Factor
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Tested** & **Crack Length, a** & **Load, P\({}_{\rm{m}}\)** & **a/W** & **Y(a/W)** & **K\({}_{\rm{lc}}\) [MPa** \\
**Specimen** & **[mm]** & **[kN]** & **a/W** & **Y(a/W)** & **m\({}^{1/2}\)]** \\ \hline SBS\_1 & 9.45 & 4.87 & 0.54 & 25.38 & 78.23 \\ SBS\_2 & 10.22 & 4.46 & 0.59 & 25.92 & 73.03 \\ SBS\_3 & 11.14 & 4.20 & 0.64 & 27.36 & 72.59 \\ \hline \end{tabular}
\end{table} 
Table 3: Fracture tests data and results obtained with the SBS specimens. \[\mathrm{K}=\mathrm{Y_{A}}\frac{\mathrm{F}}{\mathrm{\pi R}^{2}}\sqrt{\mathrm{\pi a}} \tag{3}\]  
\[\mathrm{Y_{A}}=0.67-0.33\xi,+5.73\zeta-0.29\xi^{2}-2.943\xi,\zeta-22.692\zeta^{2 }+2.41\xi^{2}\zeta+10.684\xi,\zeta^{2}\] \[+49.34\zeta^{3}-8.82\xi^{2}\zeta^{2}-10.16\xi\zeta^{3}-21.43\zeta^{4} \tag{4}\]  
The crack dimensions a and c used in Equations (3) and (4) are those of the fatigue crack fronts (Figure 5b), as optically measured on the SEM images of fractured surfaces of the tested specimen. The stress intensity factors given in Table 4 present mean and scatter values quantitatively comparable to the fracture toughness given in Table 3. The average value of the critical stress intensity factors thus computed, of 81 MPa m\({}^{1/2}\), is only 8% higher than the fracture toughness found from the tested short bar specimens, while the standard deviation of these seven values is 12% of the mean value. This indicates that the local condition ahead of the deepest point of the crack front is linear-elastic and controls failure, even though, as shown in Figure 5a, the global condition of specimens is elasto-plastic. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Tested** & **Crack Depth, a** & **Max. Load, F** & **Y\({}_{\mathbf{A}}\)** & **K\({}_{\mathbf{c}}\) [MPa m\({}^{1/2}\)]** \\
**Specimen** & **[mm]** & **[kN]** & **[k]** & **[k]** \\ \hline FC1 & 2.08 & 14.89 & 1.400 & 83.64 \\ FC & 1.9 & 15.67 & 1.260 & 76.96 \\ FC3 & 1.55 & 16.93 & 1.100 & 66.3 \\ FC4 & 2.86 & 8.77 & 2.160 & 86.08 \\ FC5 & 2.64 & 9.63 & 2.170 & 97.73 \\ FC6 & 1.87 & 16.05 & 1.180 & 73.66 \\ FC7 & 1.94 & 15.76 & 1.310 & 81.93 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Stress intensity factors at fracture of fatigue cracked cylindrical specimens (F\({}_{0}\) = 22.34 kN). Figure 5: (**a**) Load-CMOD plots obtained from post-fatigue fracture testing of notched cylindrical specimens; (**b**) SEM images illustrating the fracture surfaces of FC1–FC7 specimens.
# Development of Cybersecurity Technology and Algorithm Based on Quantum Computing
## 4 Transforming the Classical AES Algorithm into a Quantum-Gate-Based AES Algorithm
### Quantum Gate to Classical Logic Gate Association
#### 4.2.2 Classical OR Gate  
The classical OR gate can be made from a combination of NOT and classical AND gates. This is represented in Figure 9.  
We described the classical AND gate earlier; hence, we will only describe the classical NOT gate here.  
The classical NOT gate is shown in Figure 10. The classical NOT gate can be replaced by the Pauli-X gate.  
The Pauli-X gate is shown in Figure 11. The Pauli-X gate rotates the qubit to the \(X\)-axis and changes it to \(\mid\)0\(>\) if the qubit is \(\mid\)1\(>\). It changes the qubit to \(\mid\)1\(>\) if it is \(\mid\)0\(>\).  
Table 2 compares the classical NOT gate with the Pauli-X gate.
# Analyzing the Performance of the S3 Object Storage API for HPC Workloads
## 2 Materials and Methods  
We aim to analyze the performance of the S3 interface of different vendors in an HPC environment to assess the performance potential of the S3 API. To achieve this, a five step procedure is implemented by (1) identifying suitable benchmarks; (2) modifying the benchmark to support S3; (3) defining an HPC environment supporting the S3 API to run them; (4) determining a measurement protocol that allows us to identify the main factors influencing the performance of S3 for HPC workloads; and (5) providing alternative implementations for S3 to estimate the best performance.
# Accuracy of Assessing Weight Status in Adults by Structured Observation
## 3 Results
### Validity of Estimating BMI by Trained Observers
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline  & \multicolumn{4}{c}{**Obesity**} & \multicolumn{4}{c}{**Overweight/Obesity**} \\ \hline  & & \multicolumn{4}{c}{**Likelihood Ratio**} & \multicolumn{4}{c}{**Likelihood Ratio**} \\ \cline{2-9}  & **Sensitivity** & **Specific** & **Positive** & **Negative** & **Sensitivity** & **Specificity** & **Positive** & **Negative** \\  & & & **(LR+)** & **(LR\(-\))** & & & **(LR+)** & **(LR\(-\))** \\ \hline Gender of Observer & & & & & & & & \\ Female & 56.8\% & 95.0\% & 11.4 & 0.45 & 76.6\% & 76.6\% & 3.3 & 0.31 \\ Male & 14.3\% & 98.7\% & 11.0 & 0.87 & 66.7\% & 81.5\% & 3.6 & 0.41 \\  & \(p\) = 0.002 & \(p\) = 0.184 & & & \(p\) = 0.146 & \(p\) = 0.623 & & \\ Gender of Participant & & & & & & & & \\ Female & 50.0\% & 92.9\% & 7.0 & 0.54 & 65.6\% & 100\% & - & 0.34 \\ Male & 38.1\% & 100\% & - & 0.62 & 80.0\% & 68.0\% & 2.5 & 0.29 \\  & \(p\) = 0.411 & \(p\) = 0.014 & & & \(p\) = 0.029 & \(p\) = 0.002 & & \\ Age of Participant (years) & & & & & & & & \\
18–34 & 20.0\% & 100\% & - & 0.80 & 79.2\% & 91.7\% & 9.5 & 0.23 \\
35–54 & 37.5\% & 97.6\% & 15.6 & 0.64 & 66.2\% & 78.1\% & 3.0 & 0.43 \\ \(\geq\)55 & 54.2\% & 93.4\% & 8.2 & 0.49 & 76.8\% & 61.1\% & 2.0 & 0.38 \\  & \(p\) = 0.161 & \(p\) = 0.206 & & & \(p\) = 0.249 & \(p\) = 0.059 & & \\ \(<\)50 or \(\geq\)50 years & & & & & & & \\ \(<\)50 & 33.3\% & 100\% & - & 0.67 & 69.2\% & 85.4\% & 4.7 & 0.36 \\ \(\geq\)50 & 50.0\% & 93.0\% & 7.1 & 0.54 & 75.5\% & 65.4\% & 2.2 & 0.37 \\  & \(p\) = 0.198 & \(p\) = 0.014 & & & \(p\) = 0.350 & \(p\) = 0.046 & & \\ \hline \end{tabular}
\end{table} 
Table 3: Sensitivity, specificity, and likelihood ratios of estimated obesity and overweight/obesity by observation, according to gender of observer, gender of participant, and age of participant (n = 254 observations).
## 4 Renewable Carbon Sources for the Biosynthesis of 3-HP
### Microbial Production of 3-HP from Other Carbon Sources  
Malonyl-CoA can be produced directly from malonic acid by malonyl-CoA synthase (MatB); subsequently, malonyl-CoA is converted to 3-HP by MCR. The biosynthetic pathway from malonyl acid to 3-HP is shorter compared to other carbon sources [37]. However, the engineered _E. coli_ produced only 1.20 g/L of 3-HP from malonic acid in shake flasks [37]. In addition, 3-hydroxypropionitrile has also been developed as a substrate for the production of 3-HP. It was reported that _Myerozyma guillermondii_ containing nitrile hydrolase completely hydrolyzed 3-HPN into 3 HP with a titer of 19.48 g/L [97]. To mitigate global warming, third-generation (3G) biorefineries aim to produce high-value chemicals and biofuels from greenhouse gases or CO\({}_{2}\)-derived syngas. [PERSON] et al. [53] designed a type II methanotrophic cell factory based on _Methylosinus trichosporium_ OB3b to produce 3-HP from CO\({}_{2}\) and CH\({}_{4}\) via the malonyl-CoA pathway, but the 3-HP titer reached only 60.59 mg/L in a bioreactor. The feasibility of using microalgae to convert CO\({}_{2}\) to 3-HP was demonstrated by constructing engineered _Synechococcus elongatus_ PCC 7942 with a malonyl-CoA pathway and \(\beta\)-alanine pathway. The strain produced 665 mg/L of 3-HP via the malonyl-CoA pathway and 186 mg/L of 3-HP via the \(\beta\)-alanine pathway [98]. The ability of microorganisms to produce high-value-added chemicals from one-carbon substrates is limited by factors such as carbon fixation efficiency and metabolic properties, and the production capacity of 3G biorefineries for synthesizing 3-HP remains low.
## 5 Open Research Issues
* **Federated learning (FL)**: FL refers to a secured ML technique in a distributed environment comprising scattered edge devices or servers while ensuring the user data do not leave the source premises [286]. The research for fullproof privacy and attack mitigation techniques remains a focus of FL. In addition to data security challenges, the communication overhead of FL is comparable to the computational overhead. The two significant attacks against FL are poisoning attacks and byzantine attacks. The poisoning attack includes the act of tampering, destroying, or corrupting the edge data used in local training or model generation [287]. Poisoning attacks are relevant to a single edge node or a server, while byzantine attacks are prevalent in the collusion of multi-users distributed learning environment [288].