esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Mesh Independence Study  
The grid independence study was conducted using four grids M1, M2, M3, and M4. The details of these four grids are described in Table 2. Nodes along the streamwise direction \(N_{U},\ N_{D}\) and \(N_{CL}\) were kept constant and equal for the grids. Meanwhile, nodes along the radial direction, i.e., \(F_{DN}\), \(N_{TE}\), and \(N_{LE}\) are increased by 50 counts and \(F_{LE}\) are increased by 25 counts of the initial numbers of nodes and therefore new mesh grid is generated using new grid data. Steady-state simulation of airfoil NREL S-821 was carried out for all four grids (M1-M4) at a wind speed of 34 m/s. Cd, Cl, CPU time per 10 iterations, and allocated memory by the solver for all the simulations were recorded and compared. Computational results shown in Table 2 have the same values of C\({}_{1}\) and C\({}_{d}\) in mesh M3 and M4. CPU time and allocated memory consumed by the mesh M3 were considerably lesser than that of mesh M4. Thus, mesh M3 was finalized based upon the consumption of CPU time for all the simulations conducted in the study. Figure 8 demonstrates the mesh topology design with its specific name in the CFD domain for grid independence study.  
Figure 7: Meshing of the computational domain.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline
**Boundary** & **Velocity of** & **Mach** & **Reynolds** & **Angle of** & **Dynamic** & **Density (\(\phi\))** & **Chord Length** & **Temperature** & **Gas Constant** & **Working** & **Pressure (P)** \\
**Condition** & **Flow (b)** & **Number (\(\Omega\))** & **Number (\(\Omega\))** & **Attack (deg)** & **Velocity (\(\phi\))** & **Density (\(\phi\))** & **Chord Length** & **Temperature** & **Gas Constant** & **Working** & **Pressure (P)** \\ \hline Units & m/s & – & – & – & – & kg/ms & kg/ms\({}^{2}\) & m & k & j/kg & – & P\({}_{x}\) \\ \hline NEIL 5-821 & 34 & 0.1 & \(0.8\times 10^{6}\) & 3 & \(1.82\times 10^{-5}\) & 1.258 & 0.34 & 228 & 207 & Air & 104,054.49 \\ RAE-822 & 238 & 0.7 & \(0.8\times 10^{6}\) & 3 & \(1.82\times 10^{-5}\) & 0.179 & 0.34 & 228 & 207 & Air & 14,684.92 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Operating parameters for an Airfoil.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline
**Mesh** & **Nodes** & **Nodes** & **
## 2 Background
### Stacking Ensemble  
Stacking [18] is a heterogeneous ensemble model that produces the final prediction model by combining several base classifiers through a meta-classifier. Different learning algorithms are used by the base classifiers and are trained using the whole training dataset. To create the final prediction model, the results of the base classifiers are fed into the meta-classifier as a training set. To resolve any overfitting, the training dataset needs to be split into two parts, one to train the base classifiers and one for the meta-classifier. The pseudo-code in Algorithm 1 summarizes the algorithm for the stacking ensemble [19]. The algorithm takes as input a training data set \(D\), which consists of \(m\) instances, and each instance \(i\) has \((x,y)\), where \(x\) represents a feature vector and \(y\) is the class label. It returns a stacking ensemble model H after three main steps:  
* Step 1--first-level classifiers: Assume that we have \(T\) base classifier. For each base classifier \(t\in T\) learn and fit the classifier using \(\mathcal{D}\).
* Step 2--construct new datasets: The outputs of the \(T\) base classifiers are used as input to the meta-classifier. To create new datasets, for each instance \((x,y)\), construct a new instance \((x^{\prime},y)\) such that (1) \(x^{\prime}\) is a vector of length \(T\) and \(x^{\prime}=\{h_{1}(x),h_{2}(x),\ldots,h_{T}(x)\}\), where \(h_{t}(x)\) is the prediction output of a base classifier \(t\) and (2) \(y\) represents the original class labels.
* Step 3--a second-level classifier: Using the dataset created in step 2, fit and teach a meta-classifier which produces a final prediction by combining the outputs of all base classifiers.  
```
0: Training data \(\mathcal{D}=\left\{\mathbf{x}_{i},\mathbf{y}_{i}\right\}_{i=1\dots m}(\mathbf{x}_{i }\in\mathbb{R}^{n},\mathbf{y}_{i}\in\mathrm{Y})\)
0: An ensemble classifier \(H\)
1: Step 1: Learn first-level classifiers
2:for\(t\gets 1\) to \(T\)do
3: Learn a Tree-based ensemble \(h_{t}\) based on \(\mathcal{D}\)
4:endfor
5: Step 2: Construct new data sets from \(\mathcal{D}\)
6:for\(i\gets 1\) to \(m\)do
7: Construct a new data set that contains \(\left\{\mathbf{x}_{i}^{\prime},\mathbf{y}_{i}\right\}\), where \(\mathbf{x}_{i}^{\prime}=\left\{h_{1}(\mathbf{x}_{i}),h_{2}(\mathbf{x}_{i}), \dots,h_{T}(\mathbf{x}_{i})\right\}\)
8:endfor
9: Step 3: Learn a second-level classifier
10: Learn a new classifier \(h^{\prime}\) based on the newly constructed data set
11:return\(H(\mathbf{x})=h^{\prime}(h_{1}(\mathbf{x}),h_{2}(\mathbf{x}),\dots,h_{T}( \mathbf{x}))\)
```  
**Algorithm 1**Stacking ensemble.
## 6 Conclusions  
In conclusion, we designed and developed the HYDE algorithm to estimate the geometry (size, depth, dip and strike angles) of fault planes using the hypocenter locations of earthquakes. Assuming that most earthquakes are clustered on fault planes, the algorithm identifies the principal fault using a density-driven search even in complex fault geometries.  
The effectiveness of HYDE in rapidly and accurately identifying seismogenic fault planes makes it a useful tool for seismotectonic studies when other methods are not applicable, or in a complementary manner to reduce uncertainties. The applicability of the algorithm in real cases is extremely straightforward when a dataset containing only the 3D locations of the earthquakes is available. In the L'Aquila 2019 case, which involved a seismic sequence of 70,000 events, we tested the algorithm using 1000 pivots (rotation centers) on a 16-core PC, achieving a runtime of approximately one week. We emphasize that we obtained the same result using only 200 pivots in a high-density earthquake area, with a runtime of just 2 days, on an Intel i7 16-core PC. This capability is fundamental for risk mitigation, as it enables us to accurately and rapidly define the seismogenic area and implement appropriate security, civil protection measures and mitigation strategies. The key is to have many computed hypocenters available as quickly as possible.  
Future enhancements to the algorithm could provide even greater insights into the kinematics and temporal evolution of fault planes, further broadening its scope of application.  
Conceptualization, A.M., Z.P., S.S. and S.T.; methodology, A.M. and Z.P.; software, Z.P. and S.S.; validation, A.M., Z.P. and S.S.; resources, L.V., R.N. and G.A.; data curation, L.V., R.N. and G.A.; writing--original draft preparation, A.M., Z.P., S.S., L.V., R.N., G.A. and S.T.; writing--review and editing, A.M., Z.P., S.S., L.V., R.N., G.A. and S.T.; project administration, A.M.; funding acquisition, A.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the 2021-2023 INGV-funded project Ricerca Libera \"ALFA-PARC\" and the 2020-2024 INGV-funded project \"LOVE-CF\" (\"Progetti Dipartimental\", Internal Register no. 1865 17/07/2020, [[https://progetti.ing.vit/en/love-cf](https://progetti.ing.vit/en/love-cf)]([https://progetti.ing.vit/en/love-cf](https://progetti.ing.vit/en/love-cf)) accessed on 15 October 2020).  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors would like to express their gratitude to the reviewers for their excellent work in evaluating this manuscript. Their insightful comments and constructive suggestions have significantly improved the quality of this article.  
The authors declare no conflicts of interest.
## 4 Decentralization in IoT Security
### Applications of Blockchain in IoT
This distribution network, also known as a \"smart grid\", was developed to both increase the reliability of service provided to final consumers and to reduce the wasteful use of energy during generation [117]. It is a system in which power plants and consumers are linked via a network to ensure that supply and demand are always in step [11; 117]. Similar to other IoT systems, security and privacy are also major concerns in a smart grid due to its inherent complexity and dissemination of sensitive information [4; 118]. As a result, setting up a secure, non-centralized, distributed infrastructure is essential [119]. Blockchain is able to benefit the smart grid in many aspects, e.g., both security and privacy guarantees, improved scalability, efficiency, etc. Transactions are securely stored in the blockchain due to its consensus protocol. Given the difficulty of altering or removing a block in the blockchain, the system is reliable and secure once an electricity transaction has been recorded. There is much less exposure to attacks than with a conventional, centralized data architecture because there is no single point of failure. Electricity producers and consumers alike can benefit from the increased clarity in pricing resulting from the widespread adoption of blockchain-based trading infrastructure. Smart City: The term \"smart city\" describes a community in which digital tools are used for problem solving and management [120; 121]. By using IoT, a smart city can provide better service quality with lower computational overhead. In the meantime, blockchain has been studied to enable decentralized smart city applications and integrate with cutting-edge urban technology. A connected intelligent city network generates tremendous data through IoT devices, which are then analyzed in distributed blockchain databases. Typical privacy safeguards are not easily implemented in smart cities. One of the most practical solutions in a smart city is lightweight privacy security that provides strong secrecy protection and data utility power. In blockchain-based smart city initiatives, smart contracts are viewed as a potential technological advancement [11].
# A Novel Image Encryption Algorithm Based on Voice Key and Chaotic Map
## 2 Principles of the Proposed Method  
The image encryption principle used here involves two aspects, including key extraction and encryption and calculation of the image. The key extraction adopts two methods involving time-domain and frequency-domain processing of voice information. The key information must not only reflect the user's voice characteristics, but also conform to the oscillation conditions of the chaotic maps so that the encryption result is sensitive enough to the key information.
# Estimation of Cultivated Land Quality Based on Soil Hyperspectral Data
## 3 Results
### The Optimal Algorithm for Evaluating CLQ  
The PLSR and BPNN algorithms were used to construct a relationship model between the optimal spectral variables of dry soil and CLQ. The 71 dry soil samples (black plots in Figure 2a) corresponding to the optimal spectral variables (Table 1) were used as the independent variable, and the value of CLQ was the dependent variable. A 10-fold cross-validation was used to evaluate the performance of the CLQ estimation model. Comparing the PLSR and BPNN, the BPNN model exhibited better performance in cross-validation than the PLSR, with R\({}^{2}\)(C) of 0.71 (R\({}^{2}\)CV of 0.67), CCC of 0.79, and NRMSEC of 11.20% (NRMSECV of 12.10%). The R\({}^{2}\)(C) of the PLSR model was 0.33 lower, and the NRMSEC was 6.73% higher than that of the BPNN (Table 2). The points of the BPNN model in the scatterplot were closer to the 1:1 line than that of the PLSR (Figure 6). The result indicated a nonlinear relationship between the optimal spectral variables of dry soil and the value of CLQ.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Optimal Spectral Sunke Variables** & **X1** & **X2** & **X3** & **X4** & **X5** & **X6** & **X7** \\ \hline FI & 0.0200 & 0.0590 & 0.1442 & 0.0031 & 0.0387 & 0.0447 & 0.0721 \\ VIF & 8.139 & 3.569 & 9.445 & 3.753 & 3.256 & 2.921 & 5.282 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The optimal spectral variables.  
Figure 5: The feature important of soil spectral variables: (**a**) smooth spectral variables; (**b**) first derivative (FD) spectral variables; (**c**) second derivative (SD) spectral variables.  
Figure 6: Scatterplots of measured versus estimated values of CLQ obtained two models: (**a**) PLSR model; (**b**) BPNN model.
## 3 Improved Rotational Modulation Scheme Based on Tri-Axis RINS
### Error Characteristics of Different Rotation Schemes
\begin{table}
\begin{tabular}{l l} \hline \hline
**Position** & **Oscillation Error Angles** \\ \hline Position 1–4 & (1): \([-2\delta_{yx}^{G}-2\delta_{xy}^{G}\Lambda K_{gx}\pi]^{T}\) (2): \([2\delta_{xx}^{G},\Delta K_{gy}\pi,0]^{T}\) (3): \([2\delta_{yx}^{G},-2\delta_{xy}^{G},\Delta K_{gx}\pi]^{T}\) \\  & (4): \([2\delta_{xx}^{G}-\Delta K_{gy}\pi,0]^{T}\) \\ \hline Position 5–8 & (5) \([-2\delta_{xx}^{G}-\Delta K_{gy}\pi,0]^{T}\) (6): \([-2\delta_{yx}^{G},2\delta_{gy}^{G}\Lambda K_{gx}\pi]^{T}\) (7): \([-2\delta_{xx}^{G},\Delta K_{gy}\pi,0]^{T}\) \\  & (8): \([2\delta_{yx}^{G},2\delta_{gy}^{G},\Delta K_{gx}\pi]^{T}\) \\ \hline Position 9–12 & (9): \([-2\delta_{yx}^{G},-2\delta_{xy}^{G}-\Delta K_{gx}\pi]^{T}\) (10): \([2\delta_{xx}^{G},-\Delta K_{gy}\pi,0]^{T}\) (11): \([2\delta_{yx}^{G},-2\delta_{xy}^{G},-\Delta K_{gx}\pi]^{T}\) \\  & (12): \([2\delta_{yx}^{G},\Delta K_{gy}\pi,0]^{T}\) \\ \hline Position 13–16 & (13): \([-2\delta_{xx}^{G},\Delta K_{gy}\pi,0]^{T}\) (14): \([-2\delta_{yx}^{G},2\delta_{xy}^{G},-\Delta K_{gx}\pi]^{T}\) (15): \([-2\delta_{zx}^{G},-\Delta K_{gy}\pi,0]^{T}\) \\  & (16): \([2\delta_{yx}^{G},2\delta_{xy}^{G},-\Delta K_{gy}\pi]^{T}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Error Angles in the n-frame of Scheme B.  
Additionally, the length of navigation time noted as \(t\), and the changes in device error values with the environment are important factors of uncertainty. Generally, a gyroscope with a smaller zero bias error will have a smaller temperature error term and a more stable scale factor. However, when comparing FOG and laser gyro with the same zero bias stability, FOG's scale factor and installation error temperature terms are more evident than those of laser gyro. In such cases, the advantage of Scheme C becomes more significant for RINS using FOG. It is worth noting that Scheme C does not completely eliminate the effect of z-gyro zero bias error, so it is not recommended for use in navigation applications with excessively long navigation times.
# Modeling Wicking Fabric Inhibition Effect on Frost Heave
## 3 Frost Heave Test of the Soil Column
### Testing Method  
#### 3.3.1 Soil Column Preparation  
The selected soil sample was obtained from Tomakomai, Hokkaido, where the soil is usually used as a subgrade layer. In addition, a density test (JIS A 1202:2009) [49], a sieve analysis (JIS A 1204:2009) [50], and a compaction test (JIS A 1210:2009) [51] were conducted. The density (\(\rho_{s}\)) and the maximum dry density (\(\rho_{dmax}\)) of the soil were 2.64 g/cm\({}^{3}\) and 1.27 g/cm\({}^{3}\), respectively. The uniformity coefficient (_Llc_) was 153.8. This soil can be classified as SFG (sand fine particle and gravel) according to JGS 0051 [52]. The optimum water content of the Tomakomai soil is 32.0%. After the in situ soil was dried, it was sprayed with water until the water content reached 90% and sealed with plastic bags to prevent the evaporation of moisture and obtain a uniform water content distribution.  
The test apparatus is shown in Figure 4a. The size of soil columns for both the NWF model and the WWF model was 250 mm in height, 150 mm in width, and 100 mm in depth. This geometry size was selected to ensure the sensor readings are precise, and to ensure  
Figure 4: Frost heave model test. (**a**) Schematic of the test apparatus. (**b**) Picture of the test setup.  
that the model is able to observe the drainage scope of the buried WF. In the NWF model experiment, the wet soil samples were placed into the mold in 5 layers and compacted by layers from bottom to top. Each layer was compacted using a 2.5 kg rammer in order to achieve compaction of 95.0% (\(\rho_{d}\) = 1.21 g/cm\({}^{3}\)) and saturation of 90%. In order to shorten the saturation time before freezing, the saturation of the soil samples was prepared to 90%. To prevent stratification of the soil, the surface of layers 1-4 was scraped after compaction. Before filling in the next layer of soil samples, a T-type thermocouple and a moisture meter were placed. The first layer of soil samples was 2.5 cm deep, and the second to fifth layers were each 5 cm thick. On the other hand, in the WWF model experiment, a dry WF (100 mm wide by 300 mm long) was laid into the soil after the soil sample was filled to a depth of 150 mm, and its end was exposed from the preset slide at a length of 150 mm. The grease was used to fill the space between the mold and the WF during the installation of the WF. Afterward, the 4 th and 5 th layers of the soil were placed on the top.
## 3 Simulation Results
### Further Analysis of True Total DC Estimation  
This section presents results of estimating true total DC with the alternative method of summing three Gauss-Markov processes with the same or different correlation distance values for the different error sources. Similar results as presented in the previous section, are achieved in case the correlation distance is assumed the same for satellite ephemeris, ionosphere and troposphere error correlation functions. The focus herein is the case where the true total DC is simulated as the sum of three Gauss-Markov processes with different correlation distance values:  
\[\xi[n]=\sum_{l=-\infty}^{\infty}\sum_{i=1}^{3}h_{i}[l]w_{i}[n-l]=\sum_{l=- \infty}^{\infty}\sum_{i=1}^{3}a_{i}b_{i}^{l}w_{i}[n-l] \tag{8}\]  
with \(a_{i}=\dfrac{d\sqrt{2\alpha_{i}}}{1+\alpha_{i}d}\), \(b_{i}=\dfrac{1}{1+\alpha_{i}d}\), and \(\alpha_{i}=1/D_{i}\). In this case, the true total DC covariance matrix is the sum of three covariance matrices with different correlation distance values as in Equation (4). As concluded from the previous section, the higher the correlation distance for a fixed value of the distance separating RSs, the lower is the impact of the modeling errors. Therefore, at a first thought, only the variance and correlation distance relative to the most narrow correlation function that is characterized by the lowest correlation distance ought to be taken into consideration. However, the ionosphere error is characterized by the largest variance as compared to the other error sources and so determines the correlation value in Equation (3). Therefore, only the variance and correlation distance of the ionosphere error correlation matrix ought to be considered herein. It is thus safe to say that this analysis is based on varying two parameters instead of the six aforementioned parameters. The estimation error variance plotted in Figures 9 and 10, is computed numerically as described in the previous section. As expected, Figures 9 and 10 are very similar to Figures 7 and 8 because the impact of the troposphere, clock and ephemeris error correlation matrices on the total DC estimation is marginal with respect to the ionosphere error correlation matrix. The same conclusion holds, mainly that as the correlation distance and variance modeling errors increase, the performance of the LMMSE algorithm equals or is better than the true error variance for higher values of the correlation distance.
## 4 Chemical Methods for Synthesis of Magnetic Ferrite Nanoparticles
### Microemulsion Synthesis  
Microemulsion synthesis is an efficient technique for producing ferrite nanoparticles with precise control over size and morphology (Figure 10). This method utilizes a microemulsion system, where tiny water droplets containing metal salts are dispersed in an oil phase, stabilized by surfactants. The oil-to-water ratio and the amount of surfactant are crucial, as precise ratios ensure stability. Reaction temperature plays a significant role in the formation and stability of the microemulsion, while the pH is adjusted from slightly acidic to neutral (around 5-7) to maintain stability and proper formation. Proper mixing is essential to ensure uniformity and stability of the microemulsion. Sodium hydroxide or ammonia is used as a precipitating agent to initiate nucleation and growth of the nanoparticles. Sometimes, salts or other additives are included to modify the properties of the microemulsion. By controlling these conditions, the microemulsion synthesis method can  
Figure 9: Schematic illustration of thermal decomposition method for the synthesis of magnetic ferrite nanoparticles. Reproduced with permission from Ref. [212]. produce ferrite nanoparticles with uniform size distribution and desired properties for various applications [213; 214; 215]. These droplets act as nanoreactors, providing confined spaces for the synthesis of ferrite nanoparticles. Introducing a precipitating agent, such as sodium hydroxide or ammonia, converts the metal salts within the water droplets into metal hydroxide, initiating nucleation and growth of the nanoparticles. The size of the nanoparticles is determined by the size of the droplets, allowing for fine control over particle uniformity [216; 217]. After the reaction, the nanoparticles are separated, purified, and dried. This method offers several advantages, including the ability to produce monodispersed nanoparticles with controlled properties, making it suitable for applications in magnetic data storage, catalysis, and biomedical fields [217; 218; 219; 220; 221]. Magnetic ferrite nanoparticles can be synthesized using various chemical methods, including coprecipitation, sol-gel, hydrothermal, solvothermal, microemulsion, and thermal decomposition. These methods allow precise control over particle size, shape, and composition, which are crucial for tailoring their magnetic properties. Coprecipitation is one of the most common techniques due to its simplicity and efficiency, while sol-gel offers better control over the material's purity and homogeneity. Each method has its advantages and is chosen based on the desired application and properties of the nanoparticles. The chemical synthesis methods for magnetic ferrite nanoparticles are comprehensively referred to in Table 1.  
Figure 10: The schematic illustration of the synthesis of magnetic ferrite nanoparticles via microemulsion process. Reproduced with permission from Ref. [212].
# Enhanced Intrusion Detection Systems Performance with UNSW-NB15 Data Analysis
## 5 Results and Discussion
### Models with Important Features
Data imbalance introduced additional complexities, influencing precision, recall, and false alarm rates differently. The Decision Tree with Features and Random Forest with Feature selection underscored the impact of specific feature handling on overall performance. Lastly, algorithm sensitivity, with SVM excelling in handling specific patterns in the dataset, further contributed to the observed variations.