esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
However, considering the uncertainty in the construction progress, excessive construction intensity makes it difficult to guarantee the construction schedule. For example, in schedule 3, the planned elevation during the 2018 flood season is 392.0 m, which is a fast schedule, but it increases the construction intensity. Therefore, the elevation range during the 2018 flood season is [387.6, 394.2], the progress risk is 0.67, and it is difficult to ensure the construction progress. According to the calculation results, the probability of overtopping considering the uncertainty in construction progress for schedule 3 during the 2018 flood season is 0.0072, which is higher than the probability of overtopping corresponding to the planned elevation, which is 0.0063. Therefore, the formulation of construction schedules should be in line with the average advanced level of current construction techniques in order to reduce the difficulty of construction progress control and ensure that the probability of overtopping during the construction period of the dam is not higher than the probability of overtopping corresponding to the planned elevation. 4. Comparing the calculation results of the two methods in Figure 2, the results of the improved method are not significantly different from those of the flood regulation simulation. For example, the probability calculations of the flood overtopping in 2017 for schedule 1 and schedule 2 using the improved INPR method and the flood regulation simulation method are 0.0522 and 0.0446, respectively. Although the results calculated by the two methods differ slightly, they may lead to significant deviations in the risk assessment results. For instance, assuming that the potential loss of life due to flood overtopping during the 2017 flood season was two people, and considering the dam life risk standard formulated in the \"Guidelines for the Classification and Evaluation of Dam Risks\" of China [36], it can be known that the two different probability results and the accident with a loss of two lives are of extremely high risk and high risk, respectively. This indicates that a small difference in probability results may lead to a significant difference in risk evaluation results. Therefore, a small change in the probability of flood overtopping should not be ignored. 5. This paper improves the traditional INPR method to enhance the rationality of the calculation results for the overtopping probability during the construction period of earth-rock dams. The purpose of this is twofold. Firstly, when the probability distribution of the construction progress is difficult to be accurately described, the influence of construction progress uncertainty on the overtopping probability is considered. The improved INPR method is proposed to make the calculation results more accurate and reasonable, providing a reference for subsequent risk decision-making and management. Secondly, this study expands the application scope of non-probabilistic reliability theory. It is not to replace the probabilistic reliability theory but to make it better serve as a supplement to the probabilistic reliability theory.
# A Quadrilateral Geometry Classification Method and Device for Femtocell Positioning Networks
## 5 Simulations
Both decision criteria 1 and 2 are verified by the simulation results of Table 5. Table 6 shows the GDOP values of the first three best quadrilateral shapes for the case not including FeNB\({}_{4}\) in the adjacent multi-floor buildings. It shows that the GDOP of the lozenge geometry group is smaller than the IQ geometry. The GDOP values of the first three best quadrilateral shapes for the case not including FeNB\({}_{4}\) and FeNB\({}_{8}\) in the adjacent multi-floor buildings are shown in Table 7, which demonstrates that the GDOP values are smaller when the distance between the diagonal intersection and the center of the IQ is shorter. The decision criterion 3 is verified. When four FeNBs with coordinates (0, 0, 50), (0, 180, 50), (180, 0, 50), and (180, 180, 50) are added into the buildings, the total number of four FenBs in the coverage range of the target MUE increases to \(P\) = 4845. The simulation results show that the GDOP value is calculated as 1.053 m and the classification accuracy achieved is 97.196%. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**MUE Trajectory** & **CAD** & **Lozenge (0\(\circ\) CAD)** & **Lozenge (3.1\({}^{\circ}\) CAD)** & **IQ (0 m)** \\ \hline (60, 0, 0) & 10.901 & 12.648 & 13.703 \\ (60, 10, 0) & 7.506 & 9.796 & 11.27 \\ (60, 20, 0) & 6.905 & 7.203 & 9.656 \\ (60, 30, 0) & 4.631 & 5.049 & 6.157 \\ (60, 40, 0) & 2.564 & 3.593 & 4.658 \\ (60, 50, 0) & 2.012 & 2.432 & 3.604 \\ (60, 60, 0) & 1.435 & 1.915 & 2.161 \\ (60, 70, 0) & 2.216 & 2.609 & 2.958 \\ (60, 80, 0) & 2.651 & 3.140 & 3.411 \\ (60, 90, 0) & 3.206 & 3.710 & 3.907 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: GDOP of the first three best shapes not including FeNB\({}_{4}\). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**MUE Trajectory** & **Distance** & **IQ (0 m)** & **IQ (8.75 m)** & **IQ (15.052 m)** \\ \hline (60, 0, 0) & 15.474 & 16.466 & 17.476 \\ (60, 10, 0) & 12.862 & 14.911 & 16.143 \\ (60, 20, 0) & 10.781 & 11.168 & 12.569 \\ (60, 30, 0) & 6.656 & 7.114 & 7.572 \\ (60, 40, 0) & 5.005 & 6.436 & 6.632 \\ (60, 50, 0) & 4.354 & 5.444 & 6.733 \\ (60, 60, 0) & 3.015 & 4.064 & 6.256 \\ (60, 70, 0) & 3.482 & 3.924 & 4.719 \\ (60, 80, 0) & 3.815 & 4.327 & 6.191 \\ (60, 90, 0) & 3.252 & 5.235 & 5.592 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: GDOP of the first three best shapes not including FeNB\({}_{4}\), FeNB\({}_{8}\). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**MUE Trajectory** & **CAD** & **Square (0\({}^{\circ}\) CAD)** & **Lozenge** & **Lozenge** \\  & **(Distance of MUE to Square Center)** & **(0.8\({}^{\circ}\) CAD)** & **(3.1\({}^{\circ}\) CAD)** \\ \hline (60, 0, 0) & 8.114 (67.082 m) & 10.901 & 12.648 \\ (60, 10, 0) & 6.359 (58.31 m) & 7.506 & 9.796 \\ (60, 20, 0) & 4.781 (50 m) & 6.905 & 7.203 \\ (60, 30, 0) & 3.203 (42.426 m) & 4.631 & 5.049 \\ (60, 40, 0) & 1.806
# Pro-Health Potential of Selected Uncommon Oilseed Plants
## 3 Pro-Health Potential of Selected Uncommon Oilseed Plants
### Camelina sativa L
#### 3.2.2 Antimicrobial Properties  
Camelina seeds possess significant value, not only owing to their unique fatty acid profile but also due to the presence of phenolic compounds exhibiting a wide spectrum of activities [63; 64]. One of the potential properties may be antibacterial activity. There are several scientific studies available in this area. Scientific reports include information on the antibacterial effect of methanolic and ethanolic camelina seed extracts [65]. The inhibiting effect on selected strains was also demonstrated in the case of petroleum ether, chloroform, and toluene seed extracts [66]. One of the more recent studies appeared in 2021, where researchers examined the antimicrobial properties of camelina oil and hydroalcoholic seed extracts. They tested these using three different extraction methods (using ethanol, i-propanol, and methanol) and 13 microbial strains, building on previous reports of camelina's antimicrobial effects. It has been proven that camelina oil and i-propanolic and ethanolic extracts showed medium and high antimicrobial activity in several strains of human and animal pathogens (e.g., _Bacillus cereus_, _Staphylococcus Aureus_, and _Candida albicans_). The best results were achieved in the case of _Salmonella typhimurium_ and _Enterococcus faecalis_ strains, using propanolic extracts obtained by suspending ground camelina seeds in a 90% solvent solution in a weight-to-volume ratio of 1:4 [64].
# A Framework to Determine the Utilization of Vacant Taxis on HOV Lanes with the Optimal Deployment
## 7 Numerical Examples
### Sioux Falls Network Example  
In this example we use the Sioux Falls network to demonstrate the feasibility of the model and algorithm. The topology and parameters of the Sioux Falls network follows [45] and the demand follows [46]. According to reality, the other parameters of the traffic system are set by \(\lambda_{t}=60,\lambda_{p}=40,\lambda_{ow}=50,b_{s}=3,b_{h}=3,b_{v}=1.5,b_{o}^ {0}=8,b_{v}^{1}=2,b_{o}^{2}=25\), \(\beta=0.6\), \(\sigma=5\), \(n=2,\eta Z_{r}=10\), \(\pi_{rs}=0.03,\Delta=17,N=5000,k_{rs}^{sd}=25,k_{rs}^{h}=10,k_{rs}^{o}=20\). For example, the value of time \(\lambda_{t}\) is set according to the average salary and the working time in a month. The optimal deployment of the HOV lanes is illustrated in Figure 7. The blue dashed lines represent the HOV lanes, for which we know that the optimal deployment schemes are different with/without the schemes.  
Figure 5: Ratio of the link flow to the capacity on the HOV links.  
Figure 6: Ratio of maximum social welfare in case 1 to that in case 2 versus the flag–fall price of taxis.  
Figure 7a shows the optimal deployment of HOV lanes in case 1, whereas Figure 7b shows the deployment in case 2 in the Sioux Falls network. From Table 4 we see that the social welfare in case 1 is lower than that in case 2, which means this scheme is invalid for increasing social welfare in this Sioux Falls network due to the paradox. The social welfare and the demand pattern vary slightly, since the optimal HOV lane deployment adjust the objective. Although the elastic demand exists, the paradox is still possible. Thus, the topology and the parameters of the network can determine whether the paradox happens. The results in the Sioux Falls network demonstrate that the models and the algorithms we proposed can be use in reality since it is the simulative network from a real case.
## 2 Research Scope and Methods
### Data Extraction  
To create a keyword map for electronic voting based on cryptographic protocols and blockchain technology, we used data from the conducted research, uploading titles and abstracts of articles to VOSviewer. This tool automatically selects keywords [28] and generates a map, taking into account the proximity and strength of the links between keywords based on the number of articles in which they occur together. Keywords are also grouped and displayed by color, and the size of the bubbles on the map reflects the frequency of keyword usage. VOSviewer uses a unified approach to keyword matching and clustering [29].  
When creating a map based on bibliographic data or text data, data cleaning is often required [30]. To ensure consistency and accuracy in the analysis, various spellings of the same concept, such as \"voting machines\", \"voting system\", \"voting systems\", and \"e-voting system\", are harmonized. This process involves recognizing different expressions conveying a similar meaning and grouping them for a unified representation in the research dataset. Standardizing terminology enhances precision in subsequent analyses and interpretations. To accomplish this, we have developed the VOSviewer thesaurus ([[https://github.com/Tolegen95/SystLiterRW-Voting](https://github.com/Tolegen95/SystLiterRW-Voting)]([https://github.com/Tolegen95/SystLiterRW-Voting](https://github.com/Tolegen95/SystLiterRW-Voting)) (accessed on 17 November 2023)), observing the following rules:  
1. Combining different spellings of the same word, for example, \"voting machines\", \"voting system\", \"voting systems\", and \"e-voting system\", etc.;  
Figure 1: PRISMA block diagram (according to [17]).  
Combining abbreviated keywords with full keywords, such as \"electronic voting systems web language\" and \"e-voting systems\";  
Combining synonyms, for example, \"consensus mechanism\" and \"consensus protocols\";  
Exclusion of common keywords such as \"management\", \"scheme\", \"system\", etc., because they do not provide enough information.  
The thesaurus includes 84 combined and excluded keywords. Finally, VOSviewer identified 3995 keywords, of which 217 occur at least five times in the titles and annotations of the selected 881 articles. The analysis of the keyword map is presented in Section 3.
## 2 LFC Modeling in the Hybrid AC/DC System with Virtual Inertia
### LFC System Model in the State-Space Form
We omit the detail of the output matrix \(\bar{C}\) in (13) as its formulation is straightforward considering that the output vector \(\bar{y}\) corresponds to wide-area measurements of frequency in each area and power lows on both AC and HVDC transmission lines. In the end, the parameters of the two-area system and also associated control loops, i.e., the parameters appeared in Equations (1)-(12) for LFC purpose, are referred to Table 1, based on the work in [27]. As mentioned, it is easy to observe that the open-loop LFC models for the normal AC system and the AC/DC system, but without inertia emulation functionalities, can also be derived in the form of (13). For instance, for the conventional LFC structure in a normal AC system, there would be no such state variables of \(\Delta P_{DC_{1,2}}\), \(\Delta P_{ESS_{1}}\) and \(\Delta P_{ESS_{2}}\). Besides, the variable \(\Delta P_{DC_{ref}}\) related to the control input of DC link is not included in \(u\), and there is no wide-area measurement for the DC power flow in the output vector \(\bar{y}\). Before looking into the effects of cyber attacks, we first validate the LFC system models. To do that, as a common approach, we launch a step load change for the input of the system. The load change happens in Load 1 of Area 1 at \(t=5\) s with an increase of 0.03 p.u. Figure 2 provides the results of frequency deviations in both areas. It is easy to observe that the expansion of the interconnected system using HVDC link and especially the strategy of inertia emulation can help in improving the LFC system dynamics. The improvements are significant in damping frequency oscillations in a load change, which indicates a good performance when the overall LFC system model is equipped with HVDC link by SPMC control and also ESS for virtual inertia emulation. \begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & \multicolumn{2}{c}{**Area 1**} & \multicolumn{2}{c}{**Area 2**} \\  & **GEN 1** & **GEN 2** & **GEN 3** & **GEN 4** \\ \hline \(T_{ch_{i,g}}\) (s) & 0.38 & 0.38 & 0.36 & 0.39 \\ \(R_{i,g}\) (Hz/p.u.) & 2.4 & 2.5 & 2.5 & 2.7 \\ \(\phi_{i,g}\) & 0.5 & 0.5 & 0.5 & 0.5 \\ \(K_{p_{i}}\) (p.u./Hz) & 102 & & 102 & \\ \(T_{p_{i}}\) (s) & 20 & & 25 & \\ \(\beta_{i}\) (p.u./Hz) & 0.425 & & 0.396 & \\ \(K_{l}\) & 0.7 & & 0.7 & \\ \(T_{ESS_{i}}\) (s) & 0.026 & & 0.026 & \\ \(T_{AC_{i_{j}}}\) (s) & & 0.245 & & \\ \(K_{1}\) & & 0.3 & & \\ \(K_{2}\) & & 0.1 & & \\ \(K_{AC}\) & & 4.7 & & \\ \(J_{em_{1}}\) & & 0.87 & & \\ \(J_{em_{2}}\) & & 0.093 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parameters of the two-area system and associated control loops.
# Towards a Collection of Security and Privacy Patterns
## 3 Security Patterns
### Confidentiality
#### 3.1.3 Encrypted Processing  
Although encryption during transfer and storage does offer protection of sensitive data, it is typically decrypted while being processed, leaving it exposed to unauthorized disclosure if the processing host is compromised. The Encrypted Processing pattern aims to address this concern. The most significant advantages from the adoption of this approach include [31] strengthening of data security; considerable savings in computer time, and savings in the costs of the handling part of the security problems of the operating system.  
Practical implementations of the Encrypted Processing pattern are mainly based on the use of homomorphic functions [31; 32] and secure multi-party computation (SMC) [33; 34]. The latter takes advantage of additive secret sharing, which refers to the division of a secret and its distribution among a group of independent, willing participants. As an example, let us consider a scenario where three employees want to calculate their average salary without revealing each person's own salary. Figure 5 depicts how the salary of each employee is randomly split into three pieces (secret shares) and distributed to the three employees. Therefore, the sums of the corresponding pieces allow the calculation of the total sum and the average salary, without disclosing any useful information about each employee's salary.
## 2 Virtual Power Plant (VPP) Communication Architecture
### VPP Communication Architecture
The main components of local communication can be divided into three types. They are demand side resources, terminal equipment, and aggregation control devices. The demand side resources include DERs, various loads, and energy storage. The terminal equipment consists of smart meters, data collectors, and inverters. The aggregation control devices include telecontrol devices, concentrators, distribution automation terminals, and substation integration terminals. These end devices commu  
Figure 1: VPP communication architecture. nicate with the aggregation control gateways through various communication protocols such as RS-485, Mod-bus, HPLC (high-speed power line communication), IP, and LoRA (Long-range RAdio). The aggregation control gateways are connected to the VPP control platforms through remote wireline or wireless communication networking. How to design the converged wireline and wireless remote network to guarantee the required performance of the critical traffic generated by the VPP systems is the main focus of this paper.
# Spacecraft and Asteroid Thermal Image Generation for Proximity Navigation and Detection Scenarios
## 4 Applications
### Detection  
The tool is used also for far-range detection, where the advantages of the thermal image are clearer. The object analyzed in this case is the VESPA (VESa Secondary Payload Adapter) debris at 100 m distance. The object is pictured in Figure 25, and the interested reader can refer to [11; 18] for more information.  
The context of the research work in which the method is applied is a Phase B mission funded by ESA, named e.Inspector, whose main goal is to perform proximity orbits around the non-cooperative object to demonstrate the navigation capability of dedicated algorithms.  
Figure 24: From left to right: visible spectrum, thermal infrared spectrum, fused image with IFEVIP method, and fused image with ADF method [2].  
Figure 25: VESPA adapter (**left**) and thermal model (**right**).  
The generated synthetic thermal image is compared with the respective visual one in Figure 26 (picture sizes are different as the visible camera and TIR camera have different pixel array sizes). As stated in the introduction, the visible image captures all the star lights, making it harder to detect the target compared to the thermal infrared image, where it is a bright light point, which means the image processing algorithms might take more iterations, and thus more time, to detect and isolate the object in the image. The same goes for long-exposure images in Figure 27, (credits to [PERSON] for the images in the visible spectrum [18]). Even though it is possible to remove the stars from the images by knowing the camera pose and using star catalogs, it is a time-consuming operation and it increases the overhead of the image processing in a real-time navigation scenario, thus leaving the thermal image the faster way to detect the target for this case.  
Of course, in a real scenario as reported in [3], the camera noise level and response function must be modeled as presented in Section 2 to clearly asses the detection range of the object. The tool is able to take these data as input together with the noise models or noise maps once the full camera characterization is complete. Nonetheless, if the response function and noise data are not available, the tool is able to simulate and provide the team with a realistic input that can be used to test on ground the navigation and detection chain, something that is fundamental to access the next phases of the mission.  
Figure 26: Visible image with stars (**left**) and thermal image (**right**).  
Figure 27: Inertial pointing visible image with stars (**left**) and thermal image (**right**).
## 2 Challenges and Routing in 6G Networks  
In 6G networks, different routing scenarios are used to control and enhance data flow. These scenarios include unicast routing, broadcast routing, multicast routing, and anycast routing. Different forms of routing scenarios have unique traits, obstacles, and prerequisites. Unicast routing is the most common type of routing scenario in which a source delivers data to a specific destination. Unicast routing is used for applications such as video streaming, file transfer, and online gaming. The main obstacle in unicast routing is to identify the optimal path from sender to receiver, aiming to reduce transmission lag and dropped packets. However, broadcast routing is used to transmit data from one origin to every node in the network. Broadcast routing is used for applications such as critical warnings, software upgrades, and network announcements. The main challenge in broadcast routing is to make sure that information reaches its destination to all nodes in the network while minimizing transmission lag and dropped packets. Multicast routing is employed to send data from a single source to multiple destinations. Multicast routing is used for use cases like video conferencing, internet gaming, and live streaming. The main obstacle in multicast routing is to determine the best tree layout that links the source to all destinations while minimizing transmission lag and dropped packets. Anycast routing is used to transmit communication initiated by a single source towards the closest endpoint in a group of destinations. Anycast routing is used for applications such as content distribution networks and distributed databases. The main obstacle in anycast routing is to find the nearest endpoint that can provide the best quality of service (QoS) while minimizing transmission lag and dropped packets [33, 34].
## 3 Results and Discussion
### Function of Microbial Communities
Other bacteria involved in the sulfur cycle, such as _Desulfomicobium_, _Desulfoplanes_, _Defluiosulfativacter_, and members of _Thermovira_, were important bacterial genera in PW_L1, while _Defluiosulfovibrio_, _Sphaerochaeta_, and _Defluvitataleaceae_ UCG-011 were important bacterial genera in PW_L2. _Desulfomicobium_ members of the order _Desulfovibrionales_ were found by many researchers in various oil reservoirs as putative agents of microbial corrosion [5; 78]. _Thermovira_ members are on the list of fermentative bacteria isolated from various oilfield environments [78; 79]. Although the produced waters have been studied as enrichments of sulfate-reducing bacteria as the primary factor of MIC, many other anaerobic species have been identified as important taxa for corrosion monitoring, which is consistent with the conclusions of researchers that the oil environments are niches with broad microbial ecology [5; 12]. Finally, taxonomic analysis of produced waters from various oil wells and deposits as the objects of this study allowed the identification of several microbial taxa putatively involved in biocorrosion, namely metal-reducing, acid-producing bacteria, and sulfidogenic taxa. The concentration of sulfates in analyzed samples confirms the possibility of the biological formation of sulfides. In addition, the produced waters were \"rich\" in aggressive salts, which also contributed to corrosion.