esa-sceva/satcom-chunk-collection huggingface dataset:
# A Blockchain-Based Supervision Data Security Sharing Framework
## 4 Performance Evaluation and Analysis of Proposed Framework
### Comparative Evaluation and Analysis of Frameworks  
The supervision data-sharing framework based on consortium blockchain proposed in this article is compared with existing engineering data-sharing frameworks using a comparative analysis method from four aspects including blockchain type, user-level access control granularity, data privacy, and computing power requirements. Among them, user-level access control granularity refers to whether the framework supports user-level access control functions; data privacy refers to whether the framework protects the privacy of user information on the chain.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Test Items** & **Specific Settings Section** & **Cost/Gas** \\ \hline Supervision data & Supervision data upload & 25,236 \\ \hline \multirow{2}{*}{Metadata} & Metadata upload & 20,640 \\  & Metadata query & 16,906 \\ \hline IPFS file & IPFS file download & 24,677 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Gas Cost estimation of blockchain.  
Figure 3: Analysis of encryption and decryption performance.  
The analysis results are shown in Table 12; when using the consortium blockchain as the underlying framework of the data-sharing application, the system runtime is determined by the consensus algorithm. The PBFT consensus algorithm is used in the proposed framework, which has better performance and lower computational power requirements compared to the POW consensus algorithm used in reference [37]. Compared with reference [18], this proposed framework combines CP-ABE encryption with smart contracts to achieve privacy protection for on-chain data and user-level access control functions. Therefore, this proposed framework is more suitable for engineering data security sharing applications based on consortium blockchains compared to other frameworks.
## 3 Results and Discussion
### Modelling and Prediction  
Models of spreading the infection are created with the prediction of the occurrence and warn of possible risks. The system displays all reports in one place with a clear indication of the geographical area and time interval they covered. They make comparing the effects of control measures easier (Figure 6.). Based on these assumptions, the project team has developed themes for the current state based on existing cases, and predicted conditions based on statistics.  
Current state themes are:  
1. dispersion of cases by addresses with time presentation;
2. cumulative disease rate per municipalities (with a percentage share in total and the ratio \"per thousand inhabitants\");
3. overview of confirmed cases per settlements by annotation and choropleth map;
4. list of all tested per settlements by annotation of number of tested and choropleth maps and ratio tested in the settlement \"per thousand inhabitants\" by annotation; and
5. cumulative presentation of the ratio of cases by sex and age for the passive and active population with diagrams showing the statistics of the disease.  
Predicted conditions themes are:  
* expected total number of cases with needs for health care in relation to the entire population;
* total cumulative number of hospitalized (with mild, moderate, and severe symptoms) and isolated (supervised) and number of cases of respirator use;
* expected increase of cases for the next seven days (optimistic and pessimistic);
* epidemic curves (Epi-curves) [14; 23]; and
* instantaneous (weekly) reproductive number (Rt) to control the effects of the applied measures [14; 23].
## 3 Data Collection and Sampling  
Figure 2 shows the flow diagram of the data methodology from the data collection to the linear regression. Sentinel-2 multispectral satellite images were downloaded and processed from the JavaScript code editor Google Earth Engine (GEE) (Table 1). Sentinel-2 satellites are ideal for vegetation monitoring as it provides high-resolution images with a global 5-day revisit frequency. Most importantly, Sentinel-2 has spectral bands comparable to sensors with high spatial resolution, particularly World-View 2. For this study, S2 MSI  
Figure 1: A map of the study area.  
imagery dates were filtered to obtain multispectral images with mean reflectance between January and March.  
For data sampling, the land type of the map of Golden Gate was used as the first stratification; vegetation data were collected from February to March, the rainy growing months for South African montane grasslands. Thus, sampling sites of relatively homogenous grasses were located randomly by creating random points using the sampling tool in ArcMap 10.7.1. Subsequently, a total of 137 30 \(\times\) 30 plots of homogenous grasses were sampled; within each plot, 16 subplots of 1 \(\times\) 1 m were systematically placed along a transect at a 10 m interval, species cover and composition were recorded, and visual ariel cover estimation and species identification were performed, respectively. The dominant grass species were identified based on the highest cover from averaged subplots per plot; the shoots were then clipped and stored in a brown paper bag. The grass specimens were  
Figure 2: A flow diagram of the research methods.  
dried (80 \({}^{\circ}\)C in 24 h) and thereafter taken to the laboratory for LECO chemical analysis [26] to retrieve each species' grass nitrogen (grass N).
## 3 Experiments
### Experimental Analysis  
#### 3.4.1 Experiment 1 Accuracy of Feature Extraction within Each Threshold  
The choice of features directly impacts the classification outcome, making feature selection a crucial aspect of intrusion detection. The objective of feature selection is accomplished by calculating the mRMR value coefficient for each feature individually. This study discarded features exhibiting no correlation or weak correlation based on the feature correlation strength correspondence table. Subsequently, classification experiments are conducted within a weak correlation strength threshold of 0.0 to 1.0, as shown in Table 12.  
In the classification experiments, the CatBoost model is utilized as an example to determine the threshold value. The results depicted in the figure indicate that the recognition rate is higher when the coefficients fall within the range of 0.2 to 0.8 compared to other values. Moreover, the effectiveness diminishes when the mRMR value is too small or too large. Consequently, this study adopts the threshold value of 0.2 to 0.8 for the classification experiment and proceeds with the feature selection process.
## 7 Results and Analysis
### Call Failure Analysis  
In this subsection, we interpret the prediction results for the KNN classifier using the Wrapper WOA feature selection algorithm, as it gives the highest prediction accuracy and best ROC performance. This enables us to get the call failures' root causes, taking into consideration the following points:  
* **Firstly**, the root causes depend on the dataset (CDRs). Therefore, applying different datasets leads to different root causes.
* **Secondly**, we had to make sure that the dataset was extracted in a normal day time without any anomaly behavior. For example, when a dataset is extracted during an  
Figure 13: ROC curve over all classifiers.  
outage inside the network, the model prediction accuracy turns out to be very high. Since specific features will lead to call failures (e.g., faulty nodes), they could be determined easily in our model, as they will have a very high weight relative to other features. Therefore, the prediction accuracy, in this case, is not reliable.
* **Thirdly,** we are tackling problems that occur due to multi-factorial reasons and are hardly detected using the traditional way (single trace analysis). In contrast, they can be detected using ML Algorithms. Based on the call failures analysis, the root causes can be illustrated as follows;
* Failures that occur for VoLTE calls that suffered from the Single Radio Voice Call Continuity (SRVCC) are more significant than the normal VoLTE calls without SRVCC.
* When SRVCC occurs through specific P-CSCF IPs, subscriber suffers from call failures with a percentage reaching 99%. This is a case where the failure is due to multifactorial reasons (P-CSCF IP and SRVCC).
* Specific 5 Tracking Area Identities (TAIs) in the network contribute to a higher percentage of call failures rather than other TAIs.
* The calls where the originating user is using VoLTE and the terminating user is using circuit switching (e.g., 3G)--or vice versa--fail more than the calls where the originating and terminating users are using VoLTE.
* As the invite response time increase, the probability of a call failure increases.
* Twenty percent of used shortcodes have a failure rate of 100%, which shows that these shortcodes are not working inside the IMS domain.
## 3 Simulation Settings
### Mesh Setup  
Once the appropriate boundary conditions have been set, the computational mesh has to be constructed in order to carry out simulation. For this purpose, it is necessary to demonstrate the mesh-independency of the obtained results, this implies applying a systematic way to construct an effective and efficient mesh not requiring a too-heavy consumption of computational resources.  
To do that, a preliminary test is performed by constructing two different meshes--referred to as _coarser_ and _finer_--for the same non-overlapping FCC structure for a certain value of porosity sufficiently close to the condition of tangent particles (\(\varepsilon\) = 0.38319), which is the most severe condition for a mesh to be generated.  
The results relative to the two meshes are reported in Table 1 in terms of tortuosity. As we can observe, the two values are equal up to the third decimal, which indicate that even the coarse mesh is effective for our calculation. However, to be conservative, in all simulations we used the _finer-mesh_ type, that is, a mesh characterized by a minimum number of four elements between the closest boundaries (e.g., Figure 8a).  
Figure 8: Mesh details: **(a)** Finer mesh and **(b)** Coarser mesh.  
Figure 7: Boundary conditions used for simulation. Visualisation example for the FCC structure.
## 2 Nonlinear-Trajectory SAR Deception Jamming Algorithm Based on HDE
### Deceptive Jamming Based on HDE
#### 2.3.2 Fast Algorithm of the SAR System-Related Filter Based on HDE
Combined with real-time amplitude modulation and complex multiplication of jammer-related filters Equation (13), the JFR can achieve trajectory deviation construction for deceptive jamming on a pulse-by-pulse basis as follows:  
\[ \begin{split} H_{jammer,p}(x^{\prime},\eta)=&\frac {1}{w^{2}\Big{(}\frac{x^{\prime}-x_{j}}{L_{a}}\Big{)}}\exp\bigg{\{}-j\bigg{(} \eta+\frac{4\pi}{\lambda}\bigg{)}\Big{[}\delta r\big{(}x^{\prime}\big{)}-R_{f} ^{\prime}\big{(}x^{\prime},x_{j},r_{j}\big{)}\Big{]}\bigg{\}}G_{B}(\eta)\\ &\times FT_{r}\bigg{\{}\exp\bigg{[}-j\frac{4\pi}{\lambda}\psi \big{(}x^{\prime},r_{p}\big{)}\bigg{]}\cdot IFT_{\xi}\bigg{[}G_{A}(\xi,r_{p}) FT_{\chi}\bigg{(}\exp(-\frac{4\pi}{\lambda}r_{p})\sigma(x_{p},r_{p})\bigg{)} \bigg{]}\bigg{\}}.\end{split}  \tag{36}\]  
Therefore, the jammer can efficiently and flexibly implement deceptive jamming in the azimuth time-frequency hybrid domain, which is the HDE deceptive jamming algorithm.
## 4 Experimental Framework
### Data Preprocessing
#### 4.2.2 EDR Data Interpolation  
This paper used the EDR values identified for turbulence classification as a reference. To establish the training dataset, we considered data points with EDR values equal to or greater than 0.1, indicative of turbulence. The dataset was initially classified by representing turbulence (\"1\") and no turbulence (\"0\"). Additionally, since EDR values were calculated based on a series of continuous QAR data at discrete time points, they did not directly correspond to QAR data. To address this issue, linear interpolation was employed to expand the EDR dataset. Linear interpolation involves numerically filling values along a straight-line direction between two adjacent data points. This process connects two neighboring data points with a straight line and interpolates values at unknown points along that line. By assuming a linear connection between data points, linear interpolation provides a straightforward approach for interpolating values in the known interval.  
Mathematically, linear interpolation can be expressed as follows. Given two points (x\({}_{0}\), y\({}_{0}\)) and (x\({}_{1}\), y\({}_{1}\)), if x\({}_{0}<\) x \(<\) x\({}_{1}\), the interpolated value y lies on the line connecting the two points and can be calculated using the following equation:  
\[y=y_{0}+(x-x_{0})\left(\frac{y_{1}-y_{0}}{x_{1}-x_{0}}\right) \tag{9}\]  
In this equation, y represents the value to be interpolated, x corresponds to the desired input value, and subscripts 0 and 1 refer to the known data points. By substituting the values of x and the known points, the interpolated value within the desired range can be determined. The data after linear interpolation are shown in Figure 8.
# Comparative Analysis for Slope Stability by Using Machine Learning Methods
## 3 Methods and Materials  
### Analysis Method  
The stability analysis of the earth slopes, MLP, SVM, DT, and RF classifiers are utilized, which are considered benchmark algorithms in machine learning. MLP is a fully-connected feed-forward type of artificial neural network (ANN) classifier that refers to an ANN-based net consisting of multiple perceptron layers. MLP contains at least three layers of nodes as input, hidden, and output, which are supervised learning techniques [31]. MLP mainly uses fully connected activation functions such as sigmoids or rectifier linear units (ReLU) to join the nodes in different layers and supervise learning techniques. Learning in the perceptron net occurred by changing connection weights after data processing according to the error rate in the output as opposed to the expected result [34]. The SVM is a supervised model for data analysis for regression and classification objectives [35]. SVM is a prediction procedure built on statistical learning frameworks or the Vapnik-Chervonenkis theory [36]. SVM can effectively carry out a linear or non-linear classification by kernel tricks that implicitly translate their inputs into feature spaces with high dimensions [35]. An SVM constructs hyperplane(s) in a high- or infinite-dimensional space, which is utilized for prediction, classification, regression, and outlier detection [37]. A decision tree (DT) is a tool for decision support that uses a tree-like model of decisions and the potential outcomes of those decisions. An algorithm that only contains conditional control statements can be displayed in this manner. DT is frequently applied in operations, primarily in decision analysis, to assist in determining the most effective strategy for achieving a goal, but it is also a well-liked machine learning tool. DT is a flowchart-like structure whose internal nodes each represent a \"test\" on an attribute; the closely related influence diagram is used as a visual and analytical tool to help make decisions that can be linearized into decision rules [38]. Many decision trees are built during training to carry out the classification, regression, and prediction steps of the random forest (RF) algorithm, an ensemble-based learning technique [39].  
Precision can be estimated as true positive/(true positive + false positive); recall is true positive/(true positive + false negative); accuracy is (true positive + true negative)/(true positive + true negative + false positive + false negative); and f1-score is 2 \(\times\) (precision \(\times\) recall)/(precision + recall). The coordination of the positivity and negativity of the variables is presented in Figure 1[40]. The models learned under specific optimizing functions that improve the learning rates. The learning rate is a configurable hyperparameter utilized in the training procedures for machine learning algorithms. Table 1 provides information about the utilized hyperparameters in different applied classifiers.  
Figure 1: Basic information about the confusion matrix and evaluation criteria [40].
## 4 Results  
### Lithofacies Associations  
Borehole and outcrop analyses reveal diverse lithofacies within the target strata, including sandstone, siltstone, mudstone, coal seams, and conglomerates. Based on sedimentary structures, fourteen distinct lithofacies (Table 1) and five lithofacies associations have been identified, representing four depositional systems.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{1}{c}{**Faces/Code**} & \multicolumn{1}{c}{**Lithology**} & \multicolumn{1}{c}{**Sedimentary Structures**} & \multicolumn{1}{c}{**Description**} & \multicolumn{1}{c}{**Interpretation**} \\ \hline Gm & Massive, clast-supported & Massive & Poorly sorted, moderate & \\  & & conglomerate & & rounding, oriented in the \\  & & & long-axis direction, underlain by \\  & & & erosional surfaces & \\ \hline Sh & Fine-to-coarse sandstone & Parallel bedding & Well cemented, well sorted, & Distributary channel, \\  & & & widely developed & \\ \hline Sm & Medium-coarse sandstone & Massive & Well cemented, moderately & \\  & & & sorted, showing gray-brown & \\  & & & and gray–white colors & \\ \hline Smg & Pebbly sandstone & Massive & Graves are small, with grain & \\  & & & sizes from 0.5 to 1 cm, & \\  & & & predominantly black or & \\  & & & gray–white, and appear & \\  & & & randomly within the sandstone & \\ \hline Sp & Fine-to-coarse sandstone & Planar cross-bedding & Larger in scale, with a thickness & \\  & & & of 5–10 m, and & \\  & & & commonly developed & \\ \hline St & Medium-to-coarse & & & \\ sandstone or & & Trough cross-bedding & Common in outcrops, with & \\  & & & a larger scale & \\ \hline Fl & Mudstone to siltstone & Horizontal bedding & Interbedded thin layer & Distal bar, lacustrine, \\  & & & floodplain deposits \\ \hline Fmg & Muddy siltstone to & Massive & Containing irregular mud clasts & Delta subaqueous \\  & & fine sandstone & & channel deposits \\ \hline Fm & Mudstone to & Massive & Gray or gray-black, interbedded & \\  & muddy siltstone & Massive & thin layer & Subaqueous interdistributary \\  & & & & bay, lacustrine, \\  & & & thin layer & floodplain deposits \\ \hline Fr & Muddy siltstone & Ripple bedding & Well sorted, small & \\  & & & ripple amplitude & \\  & & & & bay, floodplain deposits \\ \hline \hline \end{tabular}
\end{table}
Table 1: Lithofacies of Lower-Middle Jurassic Ahe, Yangxia, and Kezilenuer Formations (facies codes are from Mail, 1978 [43]).
# Effect of Ti-B Grain Refiners on Wear and Corrosion of the A332 Alloy with Sr Modification
## 4 Discussion
### Primary Aluminum Phase Grain Size and Hardness  
From the grain size and hardness analysis, the use of the Al5 Ti2B refiner showed the worst results compared to the Al5 Ti1B. This may be associated with a higher boron concentration, due to the likely agglomeration of TiB\({}_{2}\) particles, which is one of the biggest challenges of the refining process. With the increase in the fraction of B, it possibly caused an increase in the size of the bunches, impairing their efficiency in reducing the grain size [36]. The work by [PERSON] et al. [37], shows that the insertion of B in values higher than 0.03 wt.% reduces the refining capacity, which is attributed to the reaction of boron with Al\({}_{3}\)Ti in a layer of TiB\({}_{2}\) and the consumption of solute Ti. Along with the above 0.03 wt.%, there is a gradual increase in refining performance, which is related to the formation of primary AlB\({}_{2}\) particles that provide additional nucleating substrates. However, as observed in this work, a greater amount of B well above to 2 wt.%, can possibly cause an agglomeration effect, which may impair its performance in grain refining.  
Another significant aspect to consider is that in Al alloys with Si content above 4 wt.%, there is a reduction in the performance of grain reduction of the refined alloys [38]. The Ti present in the refiner alloys reacts with Si to form titanium silicates that decrease the Ti content in the molten bath, thus reducing the efficiency of grain refinement [39].  
In addition, the use of refiners (AlTiB), caused an increase in the size and a decrease in the thickness of the needles of the Al\({}_{5}\)FeSi phase, as demonstrated in the analysis in Figure 6. This phenomenon also influences the increase in microhardness, as observed by [PERSON] et al. [7], who explained that the needles of the Al\({}_{5}\)FeSi present in the microstructures of alloy A332 may be responsible for an increase in the brittleness and hardness of the alloy.
## 2 Problem Formulation  
Figure 1 shows the schematic diagram of a loudspeaker line array with its broadside differential beampattern. A loudspeaker line array composed of \(L\) (\(L=2L_{0}+1\), \(L_{0}\geq 1\)) equally spaced loudspeakers with a spacing of \(\delta\) lies on the \(x\) axis. The array is centred at the origin of the polar coordinate system. Assuming that each loudspeaker of the array is a monopole source, the far-field sound pressure at a listener position \((r,\theta)\) is  
\[p(k,r,\theta)\approx\frac{e^{ikr}}{4\pi r}\sum_{l=-L_{0}}^{L_{0}}w_{l}^{*}(k)e^ {-ikx_{l}\cos\theta} \tag{1}\]where \(k\) is the wave number, the superscript \((\cdot)^{*}\) stands for the complex-conjugate operator, \(w_{l}(k)\) represents the weight of \(l\)-th loudspeaker at \((x_{l},0)\), where \(x_{l}=l\delta\) is the loudspeaker index \(l\in[-L_{0},L_{0}]\).  
For the directivity of radiation, we define the normalized far-field radiation pattern as  
\[B(k,\theta)=p(k,r,\theta)/(e^{ikr}/4\pi r)=\sum_{l=-L_{0}}^{L_{0}}w_{l}^{*}(k)e^ {-ikx_{l}\cos\theta}, \tag{2}\]  
which can be expressed in a vector form  
\[B(k,\theta)=\mathbf{w}^{\mathrm{H}}\mathbf{g}(k,\theta) \tag{3}\]  
where the superscript \((\cdot)^{\mathrm{H}}\) is the conjugate-transpose operator  
\[\mathbf{w}=[w_{-L_{0}}(k),\dots,w_{L_{0}}(k)]^{T} \tag{4}\]  
\[\mathrm{g}(k,\theta)=[e^{-ikx_{-L_{0}}\cos\theta},\cdots,e^{-ikx_{L_{0}}\cos \theta}]^{T} \tag{5}\]  
The superscript \((\cdot)^{\mathrm{T}}\) is the transpose operator and \(\mathbf{w}\) is the weighting vector to be determined.  
The white noise gain (WNG) can represent the radiation efficiency of a loudspeaker array and is often used as a robustness measure.  
\[\mathrm{WNG}=\frac{\left|B(k,\pi/2)\right|^{2}}{\mathbf{w}^{\mathrm{H}} \mathbf{w}} \tag{6}\]  
The directivity factor (DF) quantifies the directional property. The 2D-DF can be defined as the ratio between the power radiated in the broadside direction and the spatial average of the radiated intensity over the half plane where the loudspeaker array is located.  
\[\mathrm{DF}=\frac{\pi|B(k,\pi/2)|^{2}}{\int_{0}^{\pi}|B(k,\theta)|^{2}d\theta }=\frac{\left|B(k,\pi/2)\right|^{2}}{\mathbf{w}^{\mathrm{H}}\mathbf{\Gamma w}} \tag{7}\]  
where  
\[\mathbf{\Gamma}=\frac{1}{\pi}\int_{0}^{\pi}\mathbf{g}^{H}(k,\theta)\mathbf{g }(k,\theta)d\theta \tag{8}\]  
is a square matrix, whose elements are \(\left[\Gamma\right]_{ij}=J_{0}(k(i-j)\delta)\). \(J_{0}(\cdot)\) is the zero-order Bessel function of the first kind.  
The directivity index (DI) represents the directivity factor (DF) on a log scale.  
\[\mathrm{DI}=10\log_{10}(\mathrm{DF}) \tag{9}\]  
Figure 1: Broadside beamforming with a differential loudspeaker line array.