esa-sceva/satcom-chunk-collection huggingface dataset:
# Stability Analysis of Mixed Convection Flow towards a Moving Thin Needle in Nanofluid
## 3 Results and Discussion
Similar observation can be found from Table 3 as \(\varepsilon\) approaching \(\varepsilon_{c}\). \begin{table}
\begin{tabular}{c c c c c} \hline \hline \(\mathbf{\phi}\) & \(\mathbf{c}\) & \(\mathbf{\lambda}\) & **Upper Branch** & **Lower Branch** \\ \hline \(0.0\) & \(0.1\) & \(-1.612\) & \(0.0008\) & \(-0.0008\) \\  & & \(-1.610\) & \(0.0029\) & \(-0.0029\) \\  & & \(-1.60\) & \(0.0202\) & \(-0.0194\) \\  & & \(-1.58\) & \(0.0354\) & \(-0.0327\) \\  & \(0.2\) & \(-0.5998\) & \(0.0061\) & \(-0.0059\) \\  & & \(-0.599\) & \(0.0081\) & \(-0.0079\) \\  & & \(-0.594\) & \(0.0175\) & \(-0.0166\) \\  & & \(-0.58\) & \(0.0303\) & \(-0.0277\) \\ \hline \(0.1\) & \(0.1\) & \(-0.635\) & \(0.0057\) & \(-0.0056\) \\  & & \(-0.634\) & \(0.0103\) & \(-0.0100\) \\  & & \(-0.63\) & \(0.0165\) & \(-0.0159\) \\  & & \(-0.62\) & \(0.0326\) & \(-0.0301\) \\  & \(0.2\) & \(-0.1378\) & \(0.0031\) & \(-0.0031\) \\  & & \(-0.137\) & \(0.0084\) & \(-0.0082\) \\  & & \(-0.136\) & \(0.0126\) & \(-0.0121\) \\  & & \(-0.135\) & \(0.0158\) & \(-0.0149\) \\ \hline \(0.2\) & \(0.1\) & \(-0.3946\) & \(0.0024\) & \(-0.0024\) \\  & & \(-0.394\) & \(0.0071\) & \(-0.0070\) \\  & & \(-0.392\) & \(0.0164\) & \(-0.0157\) \\  & & \(-0.39\) & \(0.0171\) & \(-0.0163\) \\  & \(0.2\) & \(-0.066\) & \(0.0028\) & \(-0.0028\) \\  & & \(-0.0655\) & \(0.0090\) & \(-0.0087\) \\  & & \(-0.0650\) & \(0.0110\) & \(-0.0106\) \\  & & \(-0.064\) & \(0.0162\) & \(-0.0153\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Smallest eigenvalues \(\gamma\) for different values of nanoparticle volume fraction \(\phi\), needle size \(c\) and mixed convection parameter \(\lambda\) when \(\varepsilon=-2\) for Cu nanoparticles. \begin{table}
\begin{tabular}{c c c c} \hline \hline \(\mathbf{Nanoparticles}\) & \(\mathbf{\varepsilon}\) & **Upper Branch** & **Lower Branch** \\ \hline \(\mathrm{Cu}\) & \(-3.0674\) & \(0.0033\) & \(-0.0033\) \\  & \(-3.067\) & \(0.0045\) & \(-0.0044\) \\  & \(-3.06\) & \(0.0158\) & \(-0.0152\) \\  & \(-3.00\) & \(0.0470\) & \(-0.0420\) \\ \hline \(\mathrm{Al_{2}O_{3}}\) & \(-3.986\) & \(0.0040\) & \(-0.0040\) \\  & \(-3.984\) & \(0.0108\) & \(-0.0106\) \\  & \(-3.98\) & \(0.0152\) & \(-0.0147\) \\  & \(-3.97\) & \(0.0275\) & \(-0.0260\) \\ \hline \(\mathrm{TiO_{2}}\) & \(-3.922\) & \(0.0042\) & \(-0.0041\) \\  & \(-3.92\) & \(0.0052\) & \(-0.0052\) \\  & \(-3.90\) & \(0.0113\) & \(-0.0111\) \\  & \(-3.89\) & \(0.0401\) & \(-0.0370\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Smallest eigenvalues \(\gamma\) for different nanoparticles and for various values of velocity ratio parameter \(\varepsilon\) when \(\phi=0.1\), \(c=0.1\) and \(\lambda=-0.2\) for Cu nanoparticles.
## 3 Mathematical Formulation/Performance Metrics
### Communication Cost Model  
Communication Cost in NoC-based formulations is presented in Equation (2):  
\[Cost=\sum_{i,j}{[B_{t_{i},t_{j}}\times N_{i,j}]}, \tag{2}\]  
where \(B_{t_{i},t_{j}}\) is the bandwidth between tile \(t_{i}\) and \(t_{j}\) and \(N_{i,j}\) is the Manhattan distance. The NoC architecture's Manhattan distance between the source nodes \((x_{i},y_{i})\) and destination nodes \((x_{j},y_{j})\) is provided by  
\[N_{i,j}=|x_{i}-x_{j}|+|y_{i}-y_{j}|. \tag{3}\]
###### Abstract  
The freeform surface is treated as a single machining region for most traditional toolpath generation algorithms. However, due to the complexity of a freeform surface, it is impossible to produce a high-quality surface using one unique machining process. Hence, region-based methods are widely investigated for freeform surface machining to achieve an optimized toolpath. The Non-Uniform Rational B-spline Surface (NURBS) represented freeform surface is not suitable for region-based toolpath generation because of the surface gaps caused by NURBS trimming and merging operations. To solve the limitation of the NURBS, T-spline is proposed with the advantages of being gap-free, having less control points, and local refinement, which is an ideal tool for region-based toolpath generation. Thus, T-spline is introduced to represent a freeform surface for its toolpath generation in the paper. A region-based toolpath generation method for the T-spline surface is proposed based on watershed technology. Firstly, watershed-based feature recognition is presented to divide the T-spline surface into a set of sub-regions. Secondly, the concept of a PolyBoundingBox that consists of a set of minimum bounding boxes is proposed to describe the sub-regions, and Manufacturing-Suitable Regions are constructed with the help of T-spline local refinement and the PolyBoundingBox. In the end, an optimized multi-rectangles toolpath generation algorithm is applied for sub-regions. The proposed method is tested using three synthetic T-spline surfaces, and the comparison results show the advantage in toolpath length and toolpath reversing number.  
T-spline; toolpath generation; watershed; feature recognition; manufacturing-suitable region 1
## 4 Conclusions  
In this paper, HS-SPME-GC-MS and HPLC-APCI-IT-TOF-MS\({}^{n}\) were used for the first time to identify the volatile and non-volatile absorbed constituents in rabbits after intranasal administration of AR EtOAc extract and AR powder. For the first time, 46 absorbed constituents of AR containing 13 monotepenes, 10 phenylpropanoids, four benzene derivatives, two alkanes, nine _N_-alkylamides and eight lignans were found could be absorbed into the rabbit plasma, whereas 28 absorbed constituents containing 14 monoterenes, five phenylpropanoids, four benzene derivatives, two alkanes, two _N_-alkylamides and one lignan could be absorbed into the CSF. Among these absorbed constituents, 46 constituents were detected in the plasma and 28 constituents were found in the CSF after intranasal administration of AR EtOAc extract (Table 4), and 10 constituents were identified in the plasma and 6 constituents were detected in the CSF after intranasal administration of AR powder. These indicated that many types of constituents of TCM can be simultaneously absorbed at the nasal cavity into both rabbit blood and CSF. Through bibliography retrieval, the bioactivities of 33 absorbed constituents were found to be related to the pharmacological actions of AR (detailed information are listed in Table 5 and Supplemental Materials Table S1).  
Furthermore, we have done several pharmacological experiments on AR EtOAc extract and its absorbed constituents. We find that AR EtOAc extract intranasally administrated exerts its analgesic effect more quickly and more potent than that of oral administration, and the four major absorbed constituents of AR (3,5-dimethoxyytoluene, 2,3,5-trimethoxytoluene, 3,4,5-trimethoxytoluene, methyleugenol) and their combinations have significant analgesic effect and anti-inflammatory effect. We are applying for a Chinese patent based on these findings and for this reason the pharmacological results will be published elsewhere in the future.  
These findings will enhance our understanding of the effective substances of AR, and this paper can be regarded as a paradigm for the systematic screening and identification of absorbed constituents of TCMs used in nasal therapy. Based on our results and literature retrieval, we make a hypothesis on the mechanism of additive effect of multiple constituents of TCMs, which is very worthy of further study and verification in the future.  
Supplementary materials can be accessed at: [[http://www.mdpi.com/1420-3049/19/4/4857/s1](http://www.mdpi.com/1420-3049/19/4/4857/s1)]([http://www.mdpi.com/1420-3049/19/4/4857/s1](http://www.mdpi.com/1420-3049/19/4/4857/s1)).
### Notation  
The notation used is fairly standard. Matrices and vectors are represented in bold upper and bold lower case, respectively. Scalars are represented in italic lower case. **I** is an identity matrix and \(\mathbf{0}\) is a null matrix. The dimensions of the matrices and vectors can generally be determined trivially by the context. The symbol \(\
abla\) stands for the gradient and is considered as a row vector. The symbol \({}^{\mathrm{T}}\) denotes transposition.  
The \(\mathrm{vec}(\cdot)\) is a operator that stacks the columns of the matrix one underneath the other. The Kronecker product of the two matrices \(\mathbf{A}\ (m\times n)\) and \(\mathbf{B}\ (p\times q)\), denoted by \(\mathbf{A}\otimes\mathbf{B}\) is a \(mp\times nq\) matrix defined by \(\mathbf{A}\otimes\mathbf{B}=(a_{ij}\mathbf{B})_{ij}\). The definitions of matrix differentials calculus and the algebras related to Kronecker products can be found in [19; 20].  
The Euclidean norm of the vector is defined as \(\|\mathbf{x}\|=\sqrt{\mathbf{x}^{\mathrm{T}}\mathbf{x}}\). \(\mathcal{L}_{2}(I,\mathbb{R}^{n})\) stands for the standard Lebesgue spaces of vector valued square-integrable and essentially bounded functions mapping an interval \(I\subset\mathbb{R}\) to \(\mathbb{R}^{n}\). This space is equipped with an \(\mathcal{L}_{2}\) norm defined by \(\|\cdot\|_{\mathcal{L}_{2}}=\sqrt{\int_{t_{0}}^{t_{f}}\|\cdot\|^{2}dt}\). We avoid explicitly showing the dependence of the variables from the time when not needed.
## 3 Methodology
### ML-Based RSRP and RSRQ Prediction Models
The reason for choosing the logarithmic model is that, based on the data distribution, a logistic function can provide a good approximate model, since it has the property of being fit with the changes in the dataset. In addition, it is consistent with the well-known conventional logarithmic models such as Okumura-Hata, COST231-Hata, and ITU-R P.1546. Therefore, the nonlinear models have been fitted based on the following equations:\[\text{RSRP \emph{or} RSRQ}=\varphi_{1}+\varphi_{2}\times\log_{10}(d_{2D})+\varphi_{3} \times\log_{10}(\beta), \tag{8}\]  
where \(\varphi_{1},\ \varphi_{2}\), and \(\varphi_{3}\) are the coefficients that can be calculated and optimized by the _curve_fit_ function from the _optimize_ module of the _SciPy_ library. The dataset was randomly split into train and test sets with a portion of 80% and 20%, respectively. The train set was used to train the model, and the test set was used to evaluate the accuracy of the prediction model. The \"_metrics_\" module of _Scikit-learn_ has been used to measure the performance of regression models. In this study, metrics such as RMSE, MAPE, and median absolute error (MedAE) were used to evaluate the models' performances. RMSE is the standard deviation of the prediction errors and residuals. Residuals are a measure of how far from the regression line data points are and can be estimated over \(n\) samples as:  
\[\text{RMSE}=\sqrt{\frac{1}{n}\sum_{j=1}^{n}(y_{j}-\hat{y}_{j})^{2}}, \tag{9}\]  
where \(\hat{y}_{j}\) is the predicted value of the _j_th sample and \(y_{j}\) is the corresponding true value. MAPE, also known as mean absolute percentage deviation, is a measure of prediction accuracy of a statistical forecasting model and expresses the accuracy as a percentage, which can be calculated as:  
\[\text{MAPE}=\frac{1}{n}\sum_{j=1}^{n}\frac{\left|y_{j}-\hat{y}_{j}\right|}{ \max(\epsilon,\left|y_{j}\right|)}, \tag{10}\]  
where \(\epsilon\) is an arbitrarily small positive number to avoid undefined results when \(y_{j}\) is zero. MedAE is a robust measure of the variability of a univariate sample of quantitative data, and it is particularly interesting because it is robust to outliers. The loss is calculated by taking the median of all absolute differences between \(y_{j}\) and \(\hat{y}_{j}\), as:  
\[\text{MedAE}=\emph{median}(\left|y_{1}-\hat{y}_{1}\right|,\ \ldots,\left|y_{n}-\hat{y}_{n}\right|). \tag{11}\]
## 2 UAV-Enabled ITS Framework
### Third Layer: Data Collection and Processing
#### 2.3.2 Data Processing
The experimental results showed that the adaptive Kalman filter approach could provide good traffic prediction with associated intervals and was highly adaptable in volatile traffic situations.
## 4 Discussion
### Relationship between Wettability and Icephobicity  
In many reports in the literature, hydrophobic and icephobic properties are related [33; 34; 35; 36; 37]. Figure 10 shows the relationship between ice adhesion and the hydrophobic parameters water contact angle and roll-off angle. The values of contact angle hysteresis were similar for all chemical modifications; so, they will not be collated in the following discussion.  
Analyzing the relationship between IA and WCA, it can be concluded that as the value of WCA increased, the value of IA decreased. This was related to the change in surface free energy. In previous works [26; 38], it was proven that this relationship has a strong influence on the icephobic properties. Only the MOD2-modified sample showed a deviation from this relationship, but it also recorded a significant decrease in the IA values compared to the reference sample. This sample obtained a very similar IA value as the MOD1-modified sample, despite obtaining a higher WCA value. This was likely influenced by the surface roughness. The MOD2-modified sample recorded twice the value of the Ra parameter, which may have offset the effect of the increased water contact angle, i.e., reduced surface energy. It was proven in many works that an increased roughness can significantly affect ice adhesion through the formation of a mechanical connection between the emerging ice and the rough substrate (ice anchoring) [37; 38; 39]. The second relationship between the icephobic and hydrophobic parameters was that as the value of RoA decreased, the value of IA also decreased.  
The lowest possible values of IA and RoA and the highest value of WCA are key features that coatings intended for photovoltaic panels, among others, must present. The respective values of these parameters guarantee the surface's icephobicity. In further studies, the authors of this work will focus on surface texturization to obtain higher values of the WCA and lower values of the RoA parameters to achieve self-cleaning properties for the investigated coatings.
# Satellite-Based Estimation of Carbon Dioxide Budget in Tropical Peatland Ecosystems
## 3 Results
### Fire Emissions  
The monthly average FE from 2002 to 2018 was 1.90 Mt C/month in the study area. The FE from SOM that represented the C emissions from burning soil organic biomass was 1.6 Mt C/month on average, which accounted for 84.17% of the total FE including AGB and SOM. FE from AGB was 0.3 Mt C/month and accounted for 15.83% of the total FE. In this study area, FE from SOM was the main contributor to the total FE. Monthly time series of FE and GFED were compared (Figure 10a). The \(R^{2}\) between both datasets was 0.74, the RMSE was 1.67 Mt C/month, and the slope of linear fitting function was 0.80 (Figure 10b). The spatial distributions of the average of yearly FE are shown in  
Figure 8: Time series of the gross primary production (GPP) from observations (flux tower derived GPP), the MODIS product (MOD17A2H GPP), and modified MODIS products (this study) (modified GPP robust) at a flux tower (drained forest; \(DF\)) in Palangkaraya.  
Figure 9: Spatial distributions of the annual averaged gross primary production (GPP) from 2002 to 2018 based on the calibrated MOD17A2H created by using the drained forest (\(DF\)), flux tower-derived GPP in this study.  
Figure 11. As a result, the FE values were higher in Sumatra and Kalimantan of Indonesia and Borneo of Malaysia than the other regions.
# SafeNet: SwArm for Earthquake Perturbations Identification Using Deep Learning Networks
## 4 Results
### Considering Various Spatial Windows  
SafeNet worked effectively for a circular region centered at the epicenter with a Dobrovolsky radius (DataSet 03). To further investigate the impact of various spatial windows on the performance of the model, satellite datasets with spatial windows of 3\({}^{\circ}\) (DataSet 07), 5\({}^{\circ}\) (DataSet 08), 7\({}^{\circ}\) (DataSet 09), and 10\({}^{\circ}\) (DataSet 10) were created (Table 1).  
Table 2 details the SafeNet's performance on the five datasets, and Figure 8 illustrates the ROC and MCC curves, F1 score, and accuracy bar plot curves. SafeNet performed best when the dataset was used with the spatial window radius given by [PERSON]'s formula (DataSet 03), with an F1 score of 0.846 and an MCC of 0.717. Comparing the results from Figure 8D and Table 2 shows that an improvement in the overall performance was not achieved with larger spatial windows. This tendency is also evident in the ROC curves in Figure 8A-C, where DataSet 03 had the highest AUC value of the datasets. Though the cause for these findings is unclear, it could be that a disturbance moving upward from the Earth's surface alters the ionosphere's properties geometrically, and the radius of the affected area matches the radius calculated using [PERSON]'s formula. In addition, among the cases with different sizes of the preparation area (DataSet 07-10), it is the smaller ones that show the best performance. This could be due to the fact that there are more earthquakes with a smaller magnitude (i.e., 4.8-5.0) than those with a larger magnitude.
## 5 Experiments
### Experiment of Interference Monitoring
#### 5.2.1 Pre-Correlation  
The power spectral density is utilized as the pre-correlation indicator. With the broadband and narrowband interference monitoring tests, the monitoring effect of the SVM is verified.  
The testing parameters are set as follows:  
Standard SVM: penalty parameter \(C=~{}10\), kernel function parameter \(\sigma=1\);  
LSTWSVM: penalty parameter \(c_{1}=c_{2}=~{}10\), kernel function parameter \(\sigma=1\).  
The monitoring results are shown in Tables 1 and 2 respectively.  
Tables 1 and 2 show the comparison of classification accuracy and speed between LSTWSVM and the standard SVM for the broadband and narrowband interference monitoring. The results indicate that both the LSTWSVM and the standard SVM are of high classification accuracy for the broadband and narrowband interferences, but the training speed of LSTWSVM is much faster than the standard SVM. Taking the broadband interference as an example, the training speed of LSTWSVM is 80 times  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Training Algorithm** & **Interference Strength (dB)** & **Training Time (ms)** & **Monitoring Time (μs)** & **Monitoring Precision (\%)** \\ \hline \multirow{4}{*}{Standard SVM} & \(I/S<5\) & & & 100 \\  & \(5\leqslant I/S<15\) & & & 100 \\  & \(15\leqslant I/S<25\) & & & 100 \\  & \(I/S\geqslant 25\) & & & 100 \\ \hline \multirow{4}{*}{LS-TWSVM} & \(I/S<5\) & & & 100 \\  & \(5\leqslant I/S<15\) & & & 100 \\  & \(15\leqslant I/S<25\) & & & 100 \\  & \(I/S\geqslant 25\) & & & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Broadband interference monitoring result.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Training Algorithm** & **Interference Strength (dB)** & **Training Time (ms)** & **Monitoring Time (μs)** & **Monitoring Precision (\%)** \\ \hline \multirow{4}{*}{Standard SVM} & \(I/S<5\) & & & 100 \\  & \(5\leqslant I/S<15\) & & & 100 \\  & \(15\leqslant I/S<25\) & & & 100 \\  & \(15\leqslant I/S<25\) & & & 100 \\ \hline \multirow{4}{*}{LS-TWSVM} & \(I/S<5\) & & & 100 \\  & \(5\leqslant I/S<15\) & & & 100 \\ \cline{1-1}  & \(15\leqslant I/S<25\) & & & 100 \\ \cline{1-1}  & \(1/S\geqslant 25\) & & & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Narrowband interference monitoring result.  
Figure 7: The hardware setup for the experiments.  
faster than the standard SVM. The monitoring time of the two algorithms are the same (15 us for both), which satisfies the requirement of the real-time interference monitoring for GNSS signals.
## 6 Methods
### Segmentation Model  
We have experimented with two types of segmentation architectures: 3D U-Net and 3D V-Net. They are often used to segment tissues from medical images.  
The 3D U-Net architecture on which we conducted the experiments is shown in Figure 8. Each layer of the network has a contracting encoding path and an expanding decoding path with a skip connection between them. The encoding path extracts hidden features from the input data, which are then reconstructed by the decoding path, to provide the prediction. The skip connection is used to retain more semantic information, and thus adds the initial feature map to the final feature map. The encoder downsamples the data using strided convolutions of the residual unit in the spatial dimensions by a factor of 2, followed by a Batch Normalization (BN), and a Parametric Rectified Linear Unit (PReLU) as an activation function. During backpropagation, the PReLu learns a small slope parameter, which multiplies with the negative input to better adapt to the other parameters (such as weights and biases). The decoder upsamples using \(2\times 2\times 2\) up-convolution (transpose convolutions) with stride 2. These convolutions occur at the beginning of each block.  
The 3D V-Net implementation is based on [PERSON]'s [13] design of the 3D V-Net. The 3D V-Net is intended for 3D medical image segmentation and is shown in Figure 9. The network has encoding and decoding blocks, as well as an additional convolutional block in between, similar to the 3D U-Net. The encoder and decoder are also coupled by  
Figure 6: Workflow of fusing vessel enhanced outputs after the segmentation (SegAdded), where \(X\), \(Y\) = [He or Ro, Fr, Me, Sa].  
Figure 7: Workflow of linear blending of original and RORPO enhanced image.  
skip connections, but instead of \(2\times 2\times 2\) max-pooling, down-convolutions with stride \(2\times 2\times 2\) and kernel-size \(2\times 2\times 2\) are used for encoding. The strided deconvolution is used for decoding. The top-level blocks have one convolutional layer, the second-level blocks have two, and the remaining blocks have three convolutional layers. The number of channels in each block is the same as in the U-Net configuration, but all convolutions extract spatial features using \(5\times 5\times 5\) kernels.  
In the case of hepatic vessel segmentation, the network performs better if the image data are preprocessed (see Section 6.2.1).
## 5 Methodology
### Performance Evaluation Execution  
As these evaluations are done sequentially, they loop through CNI plugins, TCP/UDP bandwidth, and latency measurements across different MTU and package sizes. The fundamentals of this process are as follows:  
1. Looping Process: The test environment cycles through different configurations iteratively. For each CNI plugin, the tests are run multiple times to ensure accuracy and consistency. Each run combines a specific CNI, packet size, frame size configuration, and bandwidth/latency evaluations to provide comprehensive performance data.
2. Test Duration and Customization: The default test configuration is designed for five TCP and two UDP runs. However, these parameters can be modified for specific experiments by adjusting the corresponding variables in the Ansible scripts.  
This approach offers a high level of customization for any environment without spending months learning how to configure Kubernetes and Kubernetes CNIs.