esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experiments
### Performance Comparison  
The result of analyzing 95% confidence level interval with 100 \(\mathrm{Rate}_{\mathrm{ud}}\) data for each ROM size are shown in the Table 3. In the Table 3, \"Margin of Error\" = 1.96 \(\times\)\(\sigma\)/n\({}^{0.5}\). \"StdDev\" is the abbreviation of \"Standard Deviation\". \"UDR\" is the abbreviation of \"Un-Detected error Rate\". Each \"Average\", \"StdDev\" and \"Margin of Error\" value comes from the 100 trials, and each trial has 10\({}^{6}\) fault injection simulations. From the 10\({}^{6}\) fault injection simulations, the \(\mathrm{Rate}_{\mathrm{ud}}\) (UDR in the Table 3) from the Equation (1) can be obtained. Figure 8 shows the undetected error rate of each methodology versus ROM size. The point of the value in the Figure 8 is the field of \"Average\" in the Table 3 with the process of \(\mathrm{log}_{10}\). 2D-VLEDC has better performance at the ROM size 8 \(\times\) 32. With the growing of the ROM size, ECC is getting better performance than the 2D-VLEDC. However, the error detection performance has no significant difference between them. Figure 9 shows the size overhead of each methodology versus ROM size. It was calculated by the Equation (2). The overhead of 2D-parity gradually reduces and approaches the overhead size of the 1-bit parity with the growing of the ROM size. 2D-VLEDC has approximate 10 times improvement in error coverage rate over the 2D-parity, but pays for 10-12% overhead. The results show that 2D-VLEDC needs 15% redundancy while ECC needs 22% redundancy in average. The 2D-VLEDC has not only the improvement of error coverage rate that approximates the coverage of ECC but also has 30% less overhead than the ECC's. For address of \(n\), the required worst-case and best-case redundant bits are 7\(n\) and \(n\), respectively, under the 2D-VLEDC. Figure 7: The flow of experiment.
# Aerodynamic Enhancement of Vertical-Axis Wind Turbines Using Plain and Serrated Gurney Flaps
## 1 Introduction
[21] investigated slotted flaps to reduce lift and drag while improving the lift-to-drag ratio. Three-dimensional modifications to the GF configuration were found to change the two-dimensionality of the wake and reduce the drag by 12%. [PERSON] [22] investigated the use of perforated flaps, which resulted in a decrease in the drag with a limited lift decrement. These studies demonstrate ongoing research and development aimed at optimizing the performance of the Gurney flap and minimizing any potential negative impacts on drag. The serrated Gurney flap (SGF) is a combined architecture of the plain Gurney flap and a serrated or slotted trailing edge (STE). The plain Gurney flap improves the lift, but it increases drag and noise. The slotted trailing edge is prone to reducing airfoil noise but fails to maintain the lift [20; 23]. [PERSON] et al. [24] demonstrated an SGF airfoil with a delayed boundary layer separation on its upper surface. [PERSON] et al. [25] showed that, although SGFs resulted in a lower lift coefficient increment compared with PGFs, they could increase the lift/drag ratio. However, experiments conducted by [PERSON] and [PERSON] [26] in a wind tunnel on an NACA0012 airfoil equipped with PGF and SGF with a 5% chord height showed a significant lift increase from 80% to 65% in the range of 80-65%, but a 7% decrease in the maximum lift-to-drag ratio. It should be noted that the GF height used in their study was greater than that used by previous researchers [27; 28]. Additionally, [PERSON] and [PERSON] [29] reported that an SGF with 90-degree filaments was more effective in terms of the lift-drag ratio since it decreased the half-projected area normal to the local stream direction. As a new member of the Gurney flap family, the SGF seems to have all the advantages of the PGF and STE and it can obtain a better lift-to-drag ratio. However, the authors have reviewed the available literature and found that nearly all focused on the control effects on isolated airfoils with SGFs at different angles of attack. The flow around a VAWT's blade is different from the flow on the static airfoil surface. It is exposed to the varied inlet flow direction, as its rotation axis is perpendicular to the free stream flow. Therefore, it is a key issue to explore the potential effect of the SGF on a generic VAWT rotor. This study aims to explore the flow control mechanism of the SGF on an isolated airfoil and its potential effect on a three-straight-blade VAWT rotor by numerical simulations. Three flap heights are chosen to compare the impact of the PGF and SGF on the performance of an NACA0021 isolated airfoil at different angles of attack (AoAs), as well as the VAWTat various tip speed ratios (TSRs), respectively. Finally, the potential application of the SGF in vertical-axis wind turbines is assessed, highlighting its prospect in enhancing turbine performance.
## 3 Mechanical-Electromagnetic Performance Analysis
### Mechanical and Rotor Dynamics Performance Analysis
#### 3.2.2. Rotor Dynamic Performance  
For HSPMSM design, rotor dynamic performance analysis is very important; when operation speed reaches the critical speed, the rotor wound resonates at its nature frequency and is then damaged. Nature speed is determined by axial length, rotor diameter, and its bearing stiffness; thus, changing bearing stiffness can change the rotor's critical speed. In modal analysis, 3D FEM rotor dynamic analysis is performed, and nature frequency and mode shape are calculated. Figure 6 shows the relationship between critical speed and bearing stiffness with the same stack length. The separation margin between bending forward whirling critical speed and operation speed should be as large as possible. When bearing stiffness is less than 1 \(\times\) 10\({}^{6}\) or exceeds 1 \(\times\) 10\({}^{9}\) N/m, it does not impact the first critical speed much. Critical speed is determined by bearing stiffness when bearing stiffness is between 1 \(\times\) 10\({}^{6}\) and 1 \(\times\) 10\({}^{9}\) N/m. In this paper, the airfoil bearing was selected due to its advantages in high-temperature and HS conditions. Generally, the commercial gas dynamic bearings can achieve rotational speed of up to 600 krpm. The bearing stiffness of airfoil bearing is 5 \(\times\) 10\({}^{7}\) N/m in this paper. All HSPMSMs could safely operate at 160 krpm. Figure 7 shows the Campbell diagram of HSPMSMs with different rotors. The first critical speed and separated margin of M1 were 311.3 krpm and 90%, respectively, which was the best dynamics performance. M3 and M4 presented worse dynamics performance than that of M1 and M2 due to their sleeveless rotor topology. However, all these rotors could safely operate at the rated speed.  
Figure 6: Influence of the critical speed on bearing stiffness.
## 3 Applications
### Geoacoustic Parameter Inversion  
#### 3.4.1 Inversion of Sound Speed and Thickness of Sediment by Grid Search Method  
We can see that Equation (16) was not sensitive to \(\rho_{\text{s}}\), \(c_{\text{b}}\), and \(\rho_{\text{b}}\) by the sensitivity analysis. Thus, \(c_{\text{b}}\) was assumed as a reasonable constant and the density was obtained to reduce the inversion dimension by [PERSON]'s empirical formula, and then the sensitive geoacoustic parameters were inverted by the grid search method. We selected the 7 th, 9 th, and 11 th order fringes in the fringe extraction results in Figure 9d as the measured data, and inverted \(c_{\text{s}}\) and \(d\) by Equation (16). Considering the limited range in the practical towing process, we only selected the data points after the distance of 9 km. The density was obtained by combining Equation (21) with the sound speed. The \(c_{\text{b}}\) was assumed to be 2000 m/s, which means \(\rho_{\text{b}}\) was 2.28 g/cm\({}^{3}\).  
[PERSON]'s empirical formula is as follows [27]:  
\[c=2330.4-1257.0\rho+487.7\rho^{2} \tag{21}\]  
\(c_{\text{s}}\), \(d\), and \(n\) were inverted. The parameter range was the same as Table 2, but the truevalue of \(n\) was [7; 9; 11]. Figure 11 shows the inversion results of the grid search method.  
Figure 10: The RMSE of each order fringe.  
Figure 11a shows that \(n\) could be accurately calculated by the grid search method. The white \"+\" in Figure 11b shows the inversion results of sound speed and thickness of seabed sediment, where \(c_{\rm s}\) was 1601.7 m/s, and \(d\) was 20.5 m (the grid was 0.1 m/s and 0.1 m). The inversion error was 0.106% and 2.5%, respectively.  
Judging from the concentration of the inversion results, the two-dimensional inversion results in Figure 7a were better than those in Figure 11b. This is due to the errors caused by the extraction method and the assumptions made when reducing the inversion dimension, which slightly increased the uncertainty of the inversion results of \(c_{\rm s}\) and \(d\). However, compared with Figure 7b, the inversion results in Figure 11b still had advantages. The inversion error was relatively small in terms of error. Thus, the error of the extraction method is acceptable. At the same time, if we focused on the inversion of \(c_{\rm s}\) and \(d\), we could achieve fast and comparatively accurate linear inversion based on the basement sound speed assumption and the density acquisition by the empirical formula.
## 2 AnFIS
### Rule Creation Method  
You divide all dimensions of the input space consisting of input variables into separate areas when you deduce a fuzzy law and organize them into segmentation and conquest methods that allow the resulting values of the inference in those areas to be determined. In other words, the premise of a fuzzy rule splits the input space into several regions, and the product of inference from each of those areas is the conclusion of the fuzzy rule. The creation of these fuzzy rules is closely connected to how the input space is separated. ANFIS has a method of grid-based rule development and a method of clustering-based rule creation, mainly based on generating rules consisting of input variables at all stages. Figure 1 shows how to create grid-based rules and how to create clustering-based rules based on ANFIS. Grid partitioning [36] is a method of dividing space into the same structure as the grid so that there is no overlap in the input space. Generally, the application of the grid partitioning method produces uniformly specific partitioning areas, that is, areas with fuzzy rules, which facilitate the analysis of fuzzy rules. When the number of input variables is minimal, that is, when the input space dimension is low, grid partitioning is used. If there are 10 input variables, for example, each input variable is split into two member functions, or \(2^{7}=128\) specific areas. In other terms, for each particular region, one rule is made, and the total number of rules is 128, which is a very complex structure. Therefore, where the number of input variables is limited, the grid partitioning approach is mainly used. By improving the C-means clustering approach suggested by [PERSON] [37; 38], the fuzzy C-means (FCM) clustering method is based on a fuzzy set and the least square method. By listing the values belonging to the data in a cluster according to the degree of belonging of each data object in a cluster, the FCM clustering system distinguishes particular subdivided regions. The methods of FCM clustering include the m vector \(x_{i},\ i=1,\ 2,\ \ldots,\ m,\) set in c fuzzy clusters and locate the center in each cluster as it minimizes the objective function of the non-similar calculation. In standard methods of clustering, every data point belongs to a cluster with a membership of 0 or 1. However, there is a gap in the degree of membership of the arbitrary date between 0 and 1 in the FCM clustering process, and it belongs to \(n\) clusters. The number of clusters is fixed by the user. The number of clusters here is the number of laws that are fuzzy. Next, we explain the procedure for FCM clustering methods. Step 1: To have some value between 0 and 1 that satisfies the parameter and membership matrix, initialize  
\[u_{ij}=\left[\sum_{k=1}^{cx}\frac{\|x_{j}-v_{i}\|^{\frac{2}{n-1}}}{\|x_{j}-v_{ i}\|}\right]^{-1} \tag{1}\]  
Figure 1: Adaptive neuro-fuzzy inference system (ANFIS) rule creation methods: (a) grid-based rule creation method and (b) clustering-based rule creation method.
## 4 Results
### Natural Frequencies  
Figure 1 shows that a great variety of values for \(M_{i},J_{i}\), and \(d_{i}\), are possible. In this section, we show how we calculated the nondimensional frequency parameter \(\beta\) for the first five natural frequencies for different end masses and rotary inertias. The acquired numerical results are listed in Tables 1 and 2. The values of the nondimensional frequency parameter obtained from the frequency equation for some standard cases in the literature are shown in Table 1, and these values were the same as the corresponding precise frequency equations of free-free, free-clamped, free-pinned, pinned-pinned, clamped-clamped, and clamped-pinned beams [6]. The case of \(\Omega_{1}=\Omega_{2}=\alpha_{1}=\alpha_{2}=0\) corresponds to a free-free beam with no tip mass; the case of \(\Omega_{1}=\Omega_{2}\rightarrow\infty\), the beam behavior as a pinned-pinned beam. Additionally, \(\Omega_{1}=\Omega_{2}=\alpha_{1}=\alpha_{2}\rightarrow\infty\) corresponds to a clamped-clamped beam, while \(\Omega_{1}=\alpha_{1}\rightarrow\infty\); \(\Omega_{2}=\alpha_{2}=0\) or \(\Omega_{2}=\alpha_{2}\rightarrow\infty\); \(\Omega_{1}=\alpha_{1}=0\) corresponds to a clamped-free or a free-clamped beam, respectively. Additionally, a vibrating beam-mass system with \(\Omega_{2}\rightarrow\infty\); \(\Omega_{1}=\varepsilon_{1}=\varepsilon_{2}=0\) or \(\Omega_{1}\rightarrow\infty\); \(\Omega_{2}=\varepsilon_{1}=\varepsilon_{2}=0\) represents a free-pinned beam or pinned-free beam. In order to represent infinity, a sufficiently large number should be chosen. We arrived at \(10^{10}\) in place of \(\infty\) to represent infinity and to make it possible for numerical computation. The first five nondimensional frequency parameters were computed by solving the characteristic equation. As can be seen from Table 2, an increase in \(\Omega_{1}\) or \(\Omega_{2}\) decreased the frequency values as expected. In addition, while the \(\Omega_{i}\) value was constant, the increase in the rotary inertia of tip masses decreased the natural frequency values. Provided that the mass of the end mass remained constant, an increase in the rotary inertia should result in an increase in the distance between the end-mass center of gravity and the beam attachment point. In this case, the thickness of the end mass should decrease. Since the rotational inertia is directly related to the \(\varepsilon_{i}\) value, this shows that increasing the value of \(\varepsilon_{i}\) decreases the natural frequencies of the beam.
## 3 Results
### Results of the Literature Assessment
The reason for this could be suggested in the new source of freely available information at a moderate spatial resolution of 10 m terrain pixels, i.e., Sentinel-2 data, which are available for consultation through various platforms (e.g., ESA's ones or those affiliated with them, such as EO Browser or the Google Earth engine). Figure 7: Percentage of articles using Sentinel data across the absolute number of papers relying on satellite RS data processing in the from period 2000 to 2022. Looking closer into the employment of machine learning (Figure 9), we can noticed that, with respect to the total of articles tagged with the satellite \"Satellite RS\", such phenomena are still contained but present. Such an indication at the moment probably represents a niche that could still be explored in the years to come. The last inspection of Step 2 considered an analysis of other geomatics technology disciplines used simultaneously with satellite imagery. Out of the 295 papers studied, 140 papers describe the use of some kind of processing conducted on satellite remote sensing data (\"Satellite RS\"), while 35 articles refer to the visual inspection of resources using satellite imagery (\"Engine\"). With regard to \"Satellite RS\" and the use of geomatics  
Figure 8: Number of scientific articles relying on satellite data processing (series “satellite RS”) compared to the number of articles relying on the visual interpretation of satellite imagery (series “Engine”). Figure 9: The number of articles using machine learning across the number of those relying on general satellite RS data processing in the period from 2000 to 2022.  
technologies, the absolute main technology macro-category is \"Geospatial information Environment\" (26%), followed by several categories with almost equal distribution, namely, \"Ground sensors\" (9%), \"Aerial (including UAV) photography\" (9%), and \"Aerial (UAV) photogrammetry\" (7%; Figure 10a). For the \"Engine\" category, the \"Geospatial information Environment\" also stays predominant for 40% of cases, followed only by \"Aerial (including UAV) photography\" in 29% of cases. Other, more technically specific, technologies such as \"Ground sensors\" and Aerial (UAV) photogrammetry account for lower percentages at 6% and 9%, respectively, that is to say, two and three articles, respectively (Figure 10b).
# Autonomous Mutual Authentication Protocol in the Edge Networks
## 3 Methodology
### Initialization Phase  
The purpose of the initialization phase is to define the structural setup required for protocol functioning. All the citizens interested in securing their personal devices can download the autonomous protocol setup by registering on the government's national website to utilize this service. The distributed network connecting every state/region's government server will also keep the record logs for the public keys with active and migrated registered devices. Therefore, the registered user's device information will be kept on both national and regional servers to allow for ease of interoperability.  
Figure 1: Autonomous device connectivity.
## 3 Materials and Methods
### Experimental Settings and Evaluation Metrics
#### 3.4.2 Evaluation Metrics  
In this study, the experimental evaluation of target detection was based on the verification subset of each dataset, and the indicators were the precision, recall, mean average precision (mAP), and giga floating-point operations per second (GFLOPS) to measure the complexity of the neural network calculation model. The variables _TP_, _TN_, _FP_, and _FN_ represent the quantities of true positive, false positive, true negative, and false negative bounding boxes, respectively, in the detection model's identification of chicken tibias.  
_Precision:_ The percentage of correct predictions in the predicted sample.  
\[Precision=\frac{TP}{TP+FP} \tag{5}\]  
_Recall:_ The number of correct predictions as a proportion of the total number of positive samples.  
\[Recall=\frac{TP}{TP+FN} \tag{6}\]  
Intersection over union (_loU_): The ratio of the intersection to the union between a predicted bounding box and a ground-truth bounding box; this is used to quantify their degree of overlap.  
\[IoU=\frac{Area\ of\ Intersection}{Area\ of\ Union} \tag{7}\]  
Mean average precision (_mAP_): This metric was utilized to evaluate the detection performance of tibia-containing facets of the model. To ensure a comprehensive assessment, it was not appropriate to rely on a single confidence threshold for AP verification. Hence, we employed a range of intersection-over-union (_loU_) thresholds to calculate multiple AP values and subsequently averaged them out. Given that this task involved detecting a single category, \(N\) = 1.  
\[mAP=\frac{1}{N}\sum_{i=1}^{N}\int_{0}^{1}P_{i}(R_{i})d(R_{i}) \tag{8}\]  
\(mAP_{0.5}\) represents the _AP_ at an _loU_ threshold of 0.5 and \(mAP_{0.50.95}\) represents the average AP achieved across various _loU_ thresholds ranging from 0.5 to 0.95 in increments of 0.05.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Training Parameter** & **Value** \\ \hline Input size & 640 \(\times\) 640 \\ Final learning rate & 0.01 \\ Optimizer & SGD \\ Momentum & 0.937 \\ Weight decay & 0.0005 \\ Batch size & 16 \\ Epoch & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental parameter settings.  
Mean Absolute Error (_MAE_): The average of the absolute differences between the true and predicted tibia length values. Used to measure the average of the residuals in the dataset.  
\[MAE=\frac{1}{N}\sum_{i=1}^{N}\lvert\hat{y}_{i}-y_{i}\rvert \tag{9}\]  
Mean Squared Error (_MSE_): Represents the mean of the absolute difference between the true tibia length value and the predicted tibia length value in the regression model.  
\[MSE=\frac{1}{N}\sum_{i=1}^{N}(\hat{y}_{i}-y_{i})^{2} \tag{10}\]  
Root Mean Square Error (_RMSE_): Refers to the square root of the mean square error (_MSE_), which is used to measure the standard deviation of the residuals.  
\[RMSE=\sqrt{\frac{1}{N}\sum_{i=1}^{N}(\hat{y}_{i}-y_{i})^{2}} \tag{11}\]
# Distributed Reputation for Accurate Vehicle Misbehavior Reporting (DRAMBR)
## 2 Background and Foundations
### V2V Attack in Disconnected Areas  
In V2V, a malicious vehicle is an attacker that behaves differently from other normal vehicles in the vehicular network. The attacker can misbehave by launching a wide range of attacks to gain unauthorized access to the system for their own interest. The misbehavior becomes dangerous during emergencies in disconnected areas where there is no way to perform the verification. In such a situation, sensitive information (such as collision avoidance) is expected to be shared among vehicles. Tempering such communication with false messages prohibits legitimate vehicles from receiving the correct information on time, which results in a severe impact on the network. This activity, as explained by [15], is known as a False Alert Attack, where an attacker misleads a vehicle or group of vehicles by broadcasting a false message indicating the opposite of the correct message. For example, the attacker alters the correct message that indicates a serious road condition eg an accident, or creates a false message to disrupt the normal operation of the traffic. These types of false alerts lead to undesirable consciences in the network such as jamming and collisions.  
Algorithm 1 presents the high-level pseudocode which can be seen that whenever a message arrives, the attacker checks the message, and does the following: Forwards the false message, ignore the correct message, or create a false message if the received message is correct. Then, the attacker broadcasts the false message which is then received by the legitimate vehicles in its vicinity.  
```
0: Received Message \(M\)
0: Attacked Message \(M_{F}\)
1:if received message \(==M\)then
2:if content of \(M==\) \"correct data\" then
3: Choose action:
4: 1. Ignore the correct message \(M\)
5: 2. Generate and broadcast a new \(M_{F}\):
6: Create False Message \(M_{F}\)
7: Broadcast \(M_{F}\)
8:else
9: Forward the incorrect message \(M\)
10:endif
11:else if received message \(!=M\)then
12: Generate and broadcast a new \(M_{F}\):
13: Create False Message \(M_{F}\)
14: Broadcast \(M_{F}\)
15:endif
```  
**Algorithm 1** False Alert Attack