esa-sceva/satcom-chunk-collection huggingface dataset:
# Real-Time Safety Decision-Making Method for Multirotor Flight Strategies Based on TOPSIS Model
## 2 Motivation and Main Methodology
### Construction of a Data-Driven Failure Level Classification Model for Multirotor
#### 2.3.2 Construction of Failure Level Classification Model of Multirotor Based on Random Forest  
On the basis of obtaining the information source data of the condition assessment, the failure level classification model of the multirotor was constructed to classify the flight status safety level of the multirotor. The fault types include abnormal motor speed fault, propeller tension coefficient fault, accelerometer scale factor and offset, gyro random walk noise variance, and barometer white noise variance. According to the simulation effect of the fault injection platform and the change in the flight data, the five fault types can be divided into three safety levels, namely, disaster, danger, and slight. Among them, abnormal motor speed fault and propeller tension coefficient fault are disaster-level faults, accelerometer scale factor and deviation are danger-level faults, and gyroscope random walk noise variance and barometer white noise variance are slight-level faults.  
Random forest (RF) is an ensemble learning algorithm with a CART decision tree as a weak classifier [38]. The algorithm introduces random attribute selection on the basis of decision trees, and its essence is a combined classifier with multiple decision trees. The Random forest consists of several decision trees, \(\theta\) are independent random vectors, and the random forest (RF) algorithm is an ensemble learning algorithm with a CART decision tree as a weak classifier [38]. The algorithm introduces random attribute selection on the basis of decision trees, and its essence is a combined classifier with multiple decision trees. The random forest algorithm consists of several decision trees, where \(\theta\) represents, independent random vectors, and the final classification result is that the category with the most votes is the final classification of the input. Compared with a single classifier, the random forest algorithm obtains better classification results and can effectively improve the generalization ability of the learning system. Therefore, this paper constructed a failure-level classification model of the multirotor based on the random forest algorithm and input flight data of three types of safety level failures to train the model. The random forest algorithm extracts multiple sample sets using the bagging method, uses the classification regression treeas a meta-learner to generate a combined classification regression, forms a \"forest\" by establishing multiple decision trees, and further introduces random attribute selection in the training process of the decision trees, which obtains good results in stability and accuracy. The algorithm framework is shown in Figure 2.
## 4 Flow around the 30P30N Three-Element High-Lift Airfoil Configuration
### Simulation Result by Wall-Modelled Navier-Stokes Simulation
The Cartesian cut-cell simulation of the present study well estimates the surface pressure distribution as well as the boundary-fitted mesh simulation [25] and hybrid mesh simulation [26], except for the suction side of the slat and the main element suction side of \(x/c\leq 0.15\). The curvature of the pressure distribution changes around \(x/c=0.15\), and this \(x\)-coordinate coincides with the location from which the vortices of the outer layer of the turbulent boundary layer develop on the suction side of the main element. The underestimation of the lift coefficient of the present study in Table 4 is consistent with the surface pressure distribution in Figure 5:0 The positive error of the surface pressure on the suction side of the main element at \(x/c\leq 0.15\) and the suction side of the slat results in the underestimation of the lift. This error is caused by the mismatched estimation of the wall shear stress of the laminar parts of the boundary layers in these areas. The Cartesian cut-cell method experiences difficulty with resolving \begin{table}
\begin{tabular}{l c c c} \hline  & \multicolumn{2}{c}{**Total Number of Cells**} & \multicolumn{1}{c}{**Lift Coefficient**} \\ \hline \multirow{3}{*}{Present Wall-Modelled NS} & Coarse & 15,972,528 & 2.60 (7.8\%) \\  & Medium & 93,515,136 & 2.69 (4.6\%) \\ \cline{1-1}  & Fine & 612,392,768 & 2.71 (3.9\%) \\ \cline{1-1}  & Boundary-fitted mesh (SA DDES) [25] & 70,445,430 & 2.77 (1.8\%) \\ \cline{1-1}  & Hybrid mesh (SA DDES) [26] & 242,000,000 & 2.95 (4.6\%) \\ \cline{1-1}  & Experiment [23] & & 2.82 \\ \hline \end{tabular}
\end{table} 
Table 4: Lift coefficient (\(\theta=5.5^{\circ}\)) and its difference from the experiment (%); SA DDES, detached eddy simulation with Spalart–Allmaras turbulence model. Figure 11: Surface pressure distribution of the 30P30N high-lift airfoil configuration when \(\theta=5.5^{\circ}\); SA DDES, detached eddy simulation with Spalart–Allmaras turbulence model. the laminar boundary layer due to its incompatibility with the grid concentration in the wall-normal direction. This problem is expected to be solved by the future development of the wall-stress model for laminar boundary layers. The results of the present simulations showed that Cartesian cut-cell simulation with the wall-stress model by [PERSON] and [PERSON] [5] is one of the most useful schemes for the external flows of \(Re\sim 10^{6}\). The difference in the lift coefficient in the experiment in the medium grid simulation in Table 4 is the same as the hybrid mesh simulation [26]. Considering the total number of computational cells, the prediction capability of the present Cartesian cut-cell simulation with the wall-stress model is the same as the hybrid simulation [26]. Future improvements in the wall-stress model for the laminar part of boundary layer is expected to produce more accurate prediction within the same degree of accuracy as the boundary-fitted mesh simulation by [PERSON] [25].
## 3 Experimental Results and Discussion
### Stage 3--Work Life
#### 3.2.1 Water--Gravimetric Analysis  
The average experimental value for the water uptake of the adhesive bulk plate samples can be seen in Figure 10.  
Figure 9: Maximum shear strength of steel RJS specimens coated by oxidation and nickel-plating, compared with reference untreated specimens. The red dotted line represents the threshold value for the RJS specimen’s minimum allowed joint strength (1.5 kN).  
Since the experimental values of water uptake did not stabilize, as can be seen by the slope of the curve, the uptake cannot be described by normal Fickian diffusion. Therefore, a dual-Fickian model was considered in order to best fit the experimental data. The rule that describes the Berens and [PERSON] model is given by [20] Equation (2).  
\[M_{t}=M_{D\infty}\Bigg{[}1-\sum_{n=0}^{\infty}\frac{8}{(2n+1)^{2}\pi^{2}}\,e^{ \frac{-D(2n+1)^{2}\pi^{2}}{4^{2}}}\Bigg{]}+\sum_{i}M_{R\infty,i}\left[1-e^{- \Omega_{i}t}\right] \tag{2}\]  
where \(t\) is the time starting from the immersion of the specimens and \(l\) is the thickness of the plates. A Matlab script was developed in order to obtain the best fitting values for the coefficient of diffusion, \(D\), infinite water uptake in the unrelaxed polymer, \(M_{D\infty}\), infinite uptake related to the relaxation process, \(M_{R\infty,i}\) and relaxation rate constant, \(\Omega_{i}\), for five relaxation processes, \(i\). The literature indicates that two relaxation terms constitute the minimum required values of \(i\) that provide a good fit to the experimental data [16]. The values determined for this analysis are given in Table 4.  
Using these values, the following curve was obtained to fit the experimental values. A perfect fit was attained, confirming that a dual-Fickian phenomenon characterises this adhesive.
# Spatial Autocorrelation Incorporated Machine Learning Model for Geotechnical Subsurface Modeling
## 2 Study Area and Methodology  
### Geotechnical Subsurface Modeling Methodology  
The methodology for subsurface modeling of geotechnical properties is summarized and visualized in the flow chart as presented in Figure 1. The first step is to prepare the soil investigation data as the machine learning dataset. The dataset is pre-processed using various methods, including data transformation. The new features named Euclidean distance field (EDF) are generated in the data pre-processing step, and will be used as the input data in the machine learning modeling process. The dataset is then divided into training-validation data and test data. Dimensionality reductions are performed for the EDF features by employing the principal component analysis (PCA) technique.  
Afterwards, the process advances to the machine learning training and validation. In this step, an optimized machine learning model is generated based on several popular machine learning algorithms (MLA). The five-fold cross-validation technique was chosen  
Figure 1: Flow chart of subsurface modeling process of geotechnical properties in this study.  
to validate the models. This step is processed iteratively until it reaches the predetermined stopping criteria. The machine learning models that best predict each of the targets are determined based on their ability to predict the target from the test data. Finally, the chosen machine learning models are used to predict subsurface geotechnical properties in the study area. The results are then visualized in three-dimensional space using MATLAB R2021a.
## 2 Access to Android Raw GNSS Measurements and GNSS Observation Generation
### Doppler Observation Generation  
The Doppler shift causing from the satellite movement can be obtained as follows:  
\[dopplershift=-PseudorangeRatemetersperSecond/\lambda, \tag{4}\]  
where \(PseudorangeRatemetersperSecond\) is the pseudorange rate at the timestamp in m/s and can be found as one of the variables in GNSSMeasurement class.  
Computing the carrier-phase and Doppler observations are straightforward and we do not face any numerical problems while converting them. However, we might have some numerical issues/errors while generating the pseudorange observations. This is due to the fact that each logging App implements its own GNSS observation conversion algorithm and uses different parameter settings, thresholds and float computing accuracies. Such an issue will affect the quality of the generated observations saved into the RINEX file. In this contribution, we have also developed our in-house convertor in C++, namely UofC CSV2 RINEX, to convert a CSV file into a RINEX file. In Section 4, three RINEX files coming from GnssLogger App, Geo++ RINEX logger and our converted RINEX using UofC CSV2 RINEX are thoroughly investigated and compared from different aspects.
# FEA-Based Ultrasonic Focusing Method in Anisotropic Media for Phased Array Systems
###### Abstract  
Traditional ultrasonic imaging methods have a low accuracy in the localization of defects in austenitic welds because the anisotropy and inhomogeneity of the welds cause distortion of the ultrasonic wave propagation paths in anisotropic media. The distribution of the grain orientation in the welds influences the ultrasonic wave velocity and ultrasonic wave propagation paths. To overcome this issue, a finite element analysis (FEA)-based ultrasonic imaging methodology for austenitic welds is proposed in this study. The proposed ultrasonic imaging method uses a wave propagation database to synthetically focus the inter-element signal recorded with a phased array system using a delay-and-sum strategy. The wave propagation database was constructed using FEA considering the grain orientation distribution and the anisotropic elastic constants in the welds. The grain orientation was extracted from a macrograph obtained from a dissimilar metal weld specimen, after which the elastic constants were optimized using FEA with grain orientation information. FEA was performed to calculate a full matrix of time-domain signals for all combinations of the transmitting and receiving elements in the phased array system. The proposed approach was assessed for an FEA-based simulated model embedded in a defect. The simulation results proved that the newly proposed ultrasonic imaging method can be used for defect localization in austenitic welds.  
a 2021 21 September 2  
required. As a result, many studies have been conducted to determine the distribution of grain orientation [1; 3; 5] and the elastic constants [6; 7; 8] in austenitic welds. In addition, several studies have applied ultrasonic array data to NDEs for austenitic welds [3; 6; 9; 10; 11; 12; 13; 14; 15; 16; 17]. However, there is no reliable and practical ultrasonic imaging method that can be applied to defects in austenitic welds.  
In a previous study, the authors proposed a grain orientation prediction methodology in a nondestructive manner [5]. In the present study, a finite element analysis (FEA)-based ultrasonic imaging methodology for austenitic welds is proposed for practical applications of ultrasonic imaging. The proposed ultrasonic imaging method uses the total focusing method (TFM) and a wave propagation database, which was constructed using FEA that considered the grain orientation and anisotropic material properties of the welds. The grain orientation was extracted from a macrograph obtained from a dissimilar metal weld specimen, and the anisotropic elastic constant was iteratively optimized by minimizing the ultrasonic wave propagation velocity difference between the test and simulation results. A full matrix of time-domain signals for all combinations of transmitting and receiving elements in the phased array system was calculated through a series of finite element analyses. Subsequently, the TFM was applied to build a defect image in the finite element (FE) model.
## 3 Discussion Results at the Hemispheric Scale
### Deposition of Mineral Dust in March 2014
Generally, the highest dust loads are located and diluted in the FT allowing an efficient long-range transport as they are less influenced by deposition and submitted to high velocity winds. Above the 45\({}^{\circ}\) N latitude, more than 80% of the dust load is in the free troposphere. Around the 40\({}^{\circ}\) N latitude areas, the transpacific transport of dust from Asia to the USA used the free troposphere routes while this transport seems more balanced between Africa and the Caribbean area. High dust loads in the BL are simulated over the emission source areas partly because of elevated height of BL in these regions. Western Europe is clearly affected by dust transport in the FT. In Figures S7-S10 in Supplementary Material, we can clearly see the evolution of the dust emitted in Africa and the Middle East at the beginning of March, and blown up into the FT and transported over East Asia during mid-March. At the end of March, a large plume emitted in Africa flies over Europe and reaches the Arctic, participating to the accumulation of dust in this region as previously described. An overview of the aerosol load can be observed from space using the equipment on board Terra and Aqua satellites. The MIRS instrument as previously described allows to generate monthly maps of AOD at 555 nm such as the one presented in Figure 9 (top panel). Unfortunately, the cloud screening does not allow a full overview of the aerosol load across the world particularly at the highest latitudes (where a lack of sunlight at certain time of year is also responsible for a poor retrieval at the highest latitudes). In the bottom panel of Figure 9, the AOD computed by CHIMERE provides the AOD 555 nm without cloud screening. The model underestimates the AOD in the lowest latitude mainly due to the missing fire emissions that are not included in this simulation, however the pattern of dust from Africa to the Caribbean zone can be identified. Over the North Atlantic and Pacific Ocean, the high AOD simulated is mainly due to sea salts over the Atlantic Ocean (due to a high absorption of water by sea salts in ISORROPIA calculations) and a mix with anthropogenic aerosols and natural dust over the Pacific Ocean; this pattern can be observed by the MISR instrument but with a lower intensity due to the cloud screening. Elsewhere at lower latitude, in India for instance, as March is a month with clear sky conditions (pre-monsoon season in many regions), a comparison between CHIMERE and MISR data can be performed and, generally, the same pattern can be observed with an underestimation of CHIMERE AOD. The Chinese plume simulated by CHIMERE cannot be caught by the instrument due to the presence of clouds but a small fraction can be observed over the Beijing area.
## 5 Bench-Marking Models
### Support Vector Machine  
Support Vector Machines (SVM) are supervised machine learning models used for both classification and regression tasks. In this paper, SVM with a Radial Basis Function (RBF) kernel is used for the regression task. This kind of SVM model utilizes the RBF kernel to transform the input space into a higher-dimensional feature space, enabling the SVM to learn nonlinear decision boundaries. The formulation of the RBF kernel is defined as Equation (11).  
\[K(\mathbf{x},\mathbf{x}^{\prime})=exp(-\gamma\times||\mathbf{x}-\mathbf{x}^{ \prime}||^{2}) \tag{11}\]  
where \(\mathbf{x},\mathbf{x}^{\prime}\) are input data points, \(||.||\) denotes the Euclidean distance between them, and \(\gamma\) is a parameter that controls the width of the Gaussian curve. Higher values of gamma result  
Figure 8: Overview of the Bi-LSTM model.  
in more localized and complex decision boundaries. Furthermore, the decision function in SVM with RBF kernel can be represented as Equation (12).  
\[f(\textbf{x})=b+\sum_{i=1}^{D}\alpha_{i}\times y_{i}\times K(\textbf{x},\textbf{x }_{i}) \tag{12}\]  
where, **x** is the input data point, \(b\) is the bias term, \(\alpha_{i}\) is the Lagrange multiplier associated with the \(i\)th support vector, \(y_{i}\) is the corresponding class label, \(K(\textbf{x},\textbf{x}_{i})\) is the RBF kernel function, and the summation is performed over all support vectors.  
The SVM with RBF kernel formulation aims to find the optimal hyperplane that maximizes the margin between the classes while allowing some misclassifications. The RBF kernel enables the SVM to capture complex, nonlinear patterns in the data by mapping the data to a higher-dimensional feature space. The model is trained by solving the quadratic programming problem to find the Lagrange multipliers ( \(\alpha_{i}\) ) and bias term \(b\) that define the decision function.
## 3 Results and Analysis
### GF2-FC road dataset  
All images are from the GF2 satellite with a spatial resolution of 0.8 m. Compared with the existing dataset, 3.2 m multispectral images were added, covering the cities of Taiyuan, Shanghai, and Dalian. The already prepared road dataset is then preprocessed to generate more training samples by flipping, cropping, and performing other operations on the dataset, which in turn enriches the diversity of the training data so that the model can better adapt to diverse scenes and changes. Finally, 6350 images are selected as the experimental objects, and the image size is 400 \(\times\) 400. They are divided into the training set and the test set according to an 8:2 ratio.
## 3 Results and Discussion
### Transport of Small Molecules through PCNT  
Next, we chose CO\({}_{2}\) and urea on behalf of small hydrophobic and hydrophilic molecules to tentatively explore the transport of small molecules through CNT hollow channels. For simplicity, the PCNT was position-restrained in the bilayer center vertically. The simulation results are presented in Figure 7. As a control run, we also investigated the distribution of CO\({}_{2}\) and urea in the lipid bilayer without CNTs. Interestingly, it was found that although CO\({}_{2}\) molecules can readily enter the membrane, they prefer to aggregate in the PCNT hollow channel. Figure 6C shows the number of water and CO\({}_{2}\) molecules in the PCNT as a function of simulation time. Since the PCNT interior was hydrophobic, water molecules were gradually excluded from the CNT, and CO\({}_{2}\) molecules flow in. Correspondingly, the number of water declined from 11 to 0, while that of CO\({}_{2}\) increased from 0 to 9. Different from CO\({}_{2}\), hydrophilic urea molecules could not reach the membrane center spontaneously, but similarly could flow into the PCNT interior. Surprisingly, the PCNT was filled with a mixture of water and urea molecules. As shown in Figure 7D, there were approximately 4 urea and 13 water molecules in the PCNT hollow channel. These results were independent of whether the starting structures of the PCNT were empty or filled with CO\({}_{2}\)/urea (see Supplementary Material, Figure S2)  
Figure 6: The effects of CNTs on membrane protein. (**A**,**C**) The initial configurations, where water molecules are not shown for clarity. (**B**,**D**) The structures of CNT-KcsA complexes. (**D**) root-mean-square-deviations (RMSDs) of KcsA in the lipid bilayer with or without CNTs.
## 3 Methodology
### Testing and Relaxing the Classical Assumptions
#### 3.3.4 Diagnostics of the Nullity Assumption  
The validity of the nullity assumption (that the expected value of the error variable, \(\varepsilon\), is zero) is rarely tested or discussed after the construction of a MLR model. There are two reasons for that unfortunate situation. The first one is that, according to (16), the sample mean of the resubstitution OLS residuals is zero. Often, that fact is incorrectly interpreted that the nullity assumption holds for any OLS-estimates of the model coefficients. The second reason is that there is no obvious method to relax the nullity assumption in case it does not hold. There is nothing wrong with the second reason, but the nullity assumption should always be tested as an additional confirmation of the validity of the MLR model.  
The predicted residuals (21) form a sample of \(n\) independent unbiased variates from the r.v. \(\varepsilon\). Therefore, the sample mean of the predicted residuals will be an unbiased estimate for the expected value, \(E[\varepsilon]\), of the error variable, \(\varepsilon\). We can test the null hypothesis \(H_{0}^{null}\) (that the expected value of the error variable is zero) against the alternative hypothesis \(H_{1}^{null}\) (that the expected value of the error variable is not zero):  
\[H_{0}^{null}\colon E[\varepsilon]=0\text{ against }H_{1}^{null}\colon E[ \varepsilon]\
eq 0 \tag{31}\]  
The \(t\)-test solves the formulated hypothesis test using the test statistics \(t_{stats,null}\) where the denominator is the sample error of the sample mean, shown in the numerator:  
\[t_{stat,null}=\frac{\frac{\frac{1}{n}\sum\limits_{i=1}^{n}\varepsilon_{i,HO}} {\frac{1}{\sqrt{n}}\sqrt{\frac{1}{n-1}\sum\limits_{i=1}^{n}\left(\varepsilon _{i,HO}-\frac{1}{n}\sum\limits_{i=1}^{n}\varepsilon_{i,HO}\right)^{2}}}} \tag{32}\]  
If \(CDF_{t,n}(.)\) is the cumulative distribution function of the \(t\)-distribution with \(n\) degrees of freedom, then the \(p\)-value of the test (31) is:  
\[p-value_{null}=2 CDF_{t,n}\big{(}-\big{|}t_{stat,null}\big{|}\big{)} \tag{33}\]  
The test (31) will almost certainly produce \(p\)-value greater than the predefined significance level, \(\alpha\), which will reflect the absence of evidence for rejecting the nullity assumption. However, when the test (31) rejects the nullity assumption it will indicate that maybe the data contains outliers that need to be removed.