esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation
### Impact of Message Generation Rate  
In this section, we vary the data generation rate in order to evaluate the performance of the four protocols under different transmission loads. As the date generation rate varies from 0.01 to 0.1 message/s, the performance of these protocols is as shown in Figure 3.  
From Figure 3(a) we can see that all the four protocols achieve very high performance and their data delivery rates are close when the data generation rate is very low. However, as the data message generation rate goes up, the performance of the epidemic protocol decreases dramatically. This is due to MAC layer collision and rapid exhaustion of the limited network resources resulting from forwarding a tremendous amount of copies in epidemic routing. By using the fixed message replica number, which effectively limits the transmission overhead, Spray-and-Wait shows better performance than epidemic routing. In addition, we find that FAD outperforms Spray-and-Wait and the epidemic protocol as to the data delivery ratio. As the transmission load increases, its performance descends gradually since it is generating very many copies in this protocol. In general, DRADG achieves better performances than FAD, Spray-and-Wait and epidemic protocols, though its performance slowly decreases as the data generation ratio increases. The reason is that DRADG has less resource demands and can deal with high transmission loads by efficiently reducing the number of redundant data message replicas.  
\begin{table}
\begin{tabular}{l l} \hline Maximum buffer size of sensor (message) & 200 \\ Data message size (bytes) & 100 \\ Control message size (bytes) & 25 \\ Message generation ratio (message/s) & 0.01 \\ Maximum delay tolerant value (s) & 1,800 \\ Position of sink node (m) & (300, 300) \\ Timer expiration value \(\tau\) (s) & 180 \\ \(Num_{TH.}\) & 2 \\ \(\alpha\) value & 0.8 \\ \(T_{max}\) value & 10 \\ \hline \end{tabular}
\end{table}
Table 1: _Cont._  
Figure 3: Impact of message generation ratio. **(a)** Average delivery ratio; **(b)** Average delay.  
As shown in Figure 3(b), the average delivery delays of all protocols go up when the data generation rate increases. Obviously, the delivery delay of epidemic routing has the fastest speed increase among the four protocols. Because data messages with single-quota are not forwarded until a direct encounter with the respective destinations, Spray-and-Wait routing has higher delivery delay compared with the other three protocols in most cases. It can clearly be seen that our proposed DRADG routing has the shortest delivery delay among all protocols, because it efficiently cut down communication overhead as well as properly chooses next hop based on nodes' delivery probabilities.
# A Wideband Terahertz Transmissive Polarization Manipulator Based on Metasurfaces
## 3 Results and Discussion
### Simulation and Analysis of the LCPC
However, the performance criterion for linear to circular transmission conversion (axial ratio within 3 dB) is maintained. Furthermore, the \(y\) component of the transmitted wave is ahead of the \(x\) component; hence, the transmitted wave is left-handed circularly polarized (LHCP). In addition, the proposed unit cell behaves equally well for the incident vertical (\(y\) polarized) wave, resulting in a RHCP transmitted wave for 0.94 THz to 1.47 THz. Figure 5 shows the axial ratio for the incident \(x\) polarized and \(y\) polarized waves. To evaluate the performance of the transmitted polarized wave, Stokes parameters were calculated using Equations (8) to (11) [41]:  
Figure 3:0  
Figure 3:1  
Figure 3:2  
Figure 3:3  
where \(\varnothing_{d}\) is the phase angle between the \(x\) and \(y\) components of the transmitted wave. Ellipticity for the polarized wave can be calculated as Ellipticity \(=V/I\). Ideally, ellipticity as '\(-1\)' and '\(+1\)' depicts that the transmitted wave is RHCP and LHCP, respectively. Furthermore, polarization conversion efficiency (PCE) can be defined as [39]:  
Figure 3:4  
Figure 6 shows the ellipticity and PCE for the transmitted wave. It is clear from Figure 6 that from 0.94 THz to 1.4
## 3 Results
### Rabbit Brain Imaging
#### 3.2.2 Global SVD Clutter Filtering for H-Doppler  
Figure 10a shows the experimental rabbit with the craniotomy and the location of the probe. Figure 10b shows the B-mode image of the FOV with seven angles of CPW. Figure 10c,d demonstrates the comparison results between the conventional UCDI method with low- and high-order SVTs and the global H-Doppler method on the same set of in vivo rabbit brain data. According to the principles of the Marcenko-Pastur distribution, the turning point where the frequency curve hits the noise threshold was used as the pre-cutoff of the high-order SVT [16]. In this study, a cutoff value of 232, which achieved the best visualization of the detailed vasculature, was set as the high-order SVT for the conventional UCDI approach. As one can see, the image quality is improved with reduced noise by applying both low- and high-order SVTs. For H-Doppler, after low-order SVT (cutoff value is 22, the same as Figure 9), the tissue signal can be mostly removed. Next, the ratio of the mean power value of the R channel and B channel is computed and used to weigh the power Doppler result in the B channel. Lastly, noise subtraction calculations are performed.  
From the results, one can see that the global H-Doppler method can significantly enhance the contrast of the image at the cost of the loss of small blood vessel signals in deep tissue and retain more blood flow information in the superficial areas of the image. Note that the power value given by global H-Doppler would be reduced to a smaller range (0-44 dB) due to the subtraction operation, but the color range of H-Doppler was intentionally set to 0-50 dB for comparison purposes. To quantify the image quality, the green dashed region was selected as the noise area to calculate \(\sigma_{\text{noise}}\), and the white dashed and yellow  
Figure 10: (**a**) Example of in vivo experiment on rabbit brain blood flow imaging. (**b**) B-mode image of the Field of View (FOV) with a dynamic range of 60 dB. (**c**) Conventional ultrafast compound Doppler imaging (UCDI) results using global SVD filtering with low- and high-order singular value threshidings (SVTs). (**d**) H-Doppler results using global SVD filtering with only low-order SVT. Both (**c**) and (**d**) are displayed under a 50-dB dynamic range for comparison purposes.  
dashed regions containing fine blood vessels were selected as the blood vessel areas to calculate \(\overline{S}_{\text{blood}}\), respectively. The CNR of the global low- and high-order SVT filtering method was 15.5 dB (white) and 10.1 dB (yellow), and the CNR of the global H-Doppler method was upgraded to 19.6 dB (white) and 16.7 dB (yellow). These results indicate that the global H-Doppler method is superior to the traditional method in improving the image contrast, but the global noise subtraction processing inevitably results in the loss of small blood flow signals in deep tissue. To solve this problem, the block-wise local SVD (BWL-SVD) filtering method is introduced into H-Doppler by dividing the global plane wave data into overlapped local spatial segments [15].
## 2 Literature Review of the Methods for Solving Plate Bending Problems
The obtained results are in perfect agreement with known analytical and numerical solutions. Thin rectangular cantilever plate [28] and irregular thin plate on Winkler foundation [36] were also solved with this method. Finite element method was used [64] to solve the anisotropic elastic plates with holes and cracks. Method of boundary equations is applied to bending problem of the thin plate [65]. Plate is supported on the columns. Substitute forces at the edges and concentrated forces in the corners are not introduced in the given formulation. The problem was solved by boundary integral equations method. Collocation points were placed outside of the plate. This approach allowed to eliminate singular integrals in calculation. Bending analysis of rectangular moderately thick plates with spline finite element method is subject of the paper [39]. Analysis of deflection of regular and irregular plates with matrix method is fulfilled in the paper [43]. Boundary element method was used to solve stiffened plates [37]. Elastic bending problem of such plates were investigated with boundary element method [66]. Fracture mechanics problems for plane stress and plane strain state solved with extended finite element method is considered in the paper [51]. With help of this method Reissner-Mindlin cracked plate was solved [3]. In article [67] bending by concentrated force of a cantilever strip with a through-thickness crack perpendicular to its axis was solved using linear conjunction method. The new analytical-numerical method to analysis of symmetric Kirchhoff plate is presented in this paper. The method combines the advantages of an analytical ([PERSON] and Levy) as well as numerical (Trefftz collocation and finite element) methods. Equation of equilibrium is performed exactly in whole area within the plate. Kinematic and static boundary conditions are satisfied only at the separate nodes at the plate edges with the high accuracy. The suggested method is close to Trefftz collocation method and meshless ones. It was tested on examples of cantilever plate [68], isotropic irregular plate [69], orthotropic plate resting on Winkler's foundation [70,71], double-connected isotropic rectangular plates [71] and plate connected with truss [72]. Its improved version is given in the presented paper. Polynomial which was introduced in our previous publications for improvement of calculation results has been removed here as unnecessary.
## 3 Results and Discussion
### Physical Properties of Linsed Oil Powders
#### 3.2.2 Powder Morphology  
Scanning electron microscopy (SEM) images of the powders obtained from emulsions showed that particle shapes and surface regularities were quite similar regardless of SDF I or SDF N presence (Figure 2). The particles had a generally highly porous structure and varied sizes. Some differences can be observed in samples with the lowest OSA-starch concentration (samples Nos. 3 and 6), where particle deformation appears to be lower. Furthermore, the powders containing SDF N (samples Nos. 1-3) were characterised by a higher number of fine particles than those with SDF I (samples Nos. 4-6). This morphological characterization of oil powders differs significantly from that presented for powders composed of linseed oil, maltodextrin, milk protein, and guar gum, where a regular and globular particle shape is observed [39]. Globular particle shapes were also presented by [PERSON] et al. [42], encapsulating an emulsion containing avocado oil and a mixture of OSA-starch and maltodextrin as wall materials. In turn, [PERSON] et al. [43] reported that polysaccharide spray-dried powders (e.g., OSA-starch) are characterised by spherical particles of several sizes with outer surfaces free of pores, but more dents are observed on their surface. Also, [PERSON] et al. [44] encapsulated conjugated linoleic acid with OSA-starch and obtained microcapsules whose surface morphology was lacking in cracks. They explained this phenomenon by the strong viscoelastic properties of the wall material during expansion at the final stage of spray drying. Such properties of wall material to prevent capsule breakage can probably be linked to the low surface fat content of the powders, and thus the high encapsulation efficiency of the linseed oil (Table 3). The cited authors also observed wrinkles on the surface of most microcapsules, which is consistent with our study.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sample** & **Surface Oil [\%]** & **Encapsulation Efficiency [\%]** \\ \hline
1 & 0.41 \(\pm\) 0.03  & 98.76 \(\pm\) 1.8 4  \\
2 & 0.42 \(\pm\) 0.01  & 98.73 \(\pm\) 1.3 4  \\
3 & 0.54 \(\pm\) 0.06  & 98.36 \(\pm\) 1.5 5  \\
4 & 0.55 \(\pm\) 0.03  & 98.33 \(\pm\) 1.0 5  \\
5 & 0.44 \(\pm\) 0.02  & 98.67 \(\pm\) 2.1 5  \\
6 & 0.57 \(\pm\) 0.02  & 98.27 \(\pm\) 2.0 5  \\ \hline \hline \end{tabular}
\end{table}
Table 3: Surface oil and encapsulation efficiency of linseed oil powders.
## 4 Results and Discussion  
### Statistics of SAR-Derived Parameters  
Before reporting the results of the SSW speed uncertainty analysis from the above-described methodology applied to the available S-1 dataset, the SAR NRCS, incidence angle and SSW relative direction (the latter derived through the MS LG-Mod [40]) are analysed in terms of the statistical indicators of mean and standard deviation (or marginal error [40], in the case of the wind direction). These indicators are evaluated for all the ROIs enclosed within 65 km by 65 km sparse regions in the Atlantic European northwest shelf, where wind rows are clearly visible.  
In order to investigate the effects of the considered spatial resolution of cells on the uncertainty in the SAR SSW speed estimation, different sizes of ROIs are considered. These ROIs are characterised by different square dimensions of about 5 km, 10 km and 15 km, providing a total number of useful cells (i.e., N_ROI) of 11,902, 2490 and 936, respectively.
## 3 Grids and CFD Set-Up  
In the present study, rectangular domains around the hydrofoil NACA0015 were used to simulate the cavitating flow. Figure 1 shows the boundary conditions and shape of the computational domain that was used for the NACA0015. For NACA0015 hydrofoil, the inlet boundary was placed three chord lengths ahead of the leading edge, while the outlet boundary was placed 5.5 chord lengths behind the trailing edge, and the span length was \(0.3c\). The top and bottom boundaries were placed 2.5 chord lengths from the hydrofoil interface. The chord length \(c\) of the NACA0015 hydrofoil is 0.07 m.  
A free-slip wall condition was used on the solid surface of the top and bottom, while the no-slip condition was used on the surface of hydrofoil. For the outer boundary, static pressure was set. When the static pressure \(P_{\text{ref}}=31\),325 Pa, it belongs to the sheet cavitation condition. When the static pressure \(P_{\text{ref}}=20\),325 Pa, it belongs to the cloud cavitation condition. On the inlet boundary, the values of the free-stream velocity components and turbulence quantities were fixed. The water volume fraction was set equal to one and the vapor volume fraction was set to zero on the inlet boundary. The free-stream velocity \(U_{\infty}\) was set equal to 7.2 m/s. Moreover, in the computation, the water and vapor density were kept constant, and they were set as follows: \(\rho_{\text{t}}=998\) kg/m\({}^{3}\), \(\rho_{\text{v}}=0.023\) kg/m\({}^{3}\). Different cavitating flow regimes were related to the cavitation number \(\sigma\), which was defined as follows:  
\[\sigma=\frac{P_{\text{ref}}-P_{\text{v}}}{\frac{1}{2}\rho_{\text{t}}U_{\infty }^{2}} \tag{8}\]  
where \(P_{\text{v}}\) represents the saturation pressure. In this research, \(\sigma\) is equal to 1.075 when the value of saturation pressure \(P_{\text{v}}=31\),325 Pa, i.e. a sheet cavitation condition, and \(\sigma\) is equal to 0.065 when the value of saturation pressure \(P_{\text{v}}=20\),325 Pa, i.e. a cloud cavitation condition. In the numerical simulation, the pressure coefficient \(C_{\text{p}}\) was used and defined as follows:  
\[-C_{\text{p}}=\frac{P_{\text{ref}}-P}{\frac{1}{2}\rho_{\text{t}}U_{\infty}^{2}} \tag{9}\]  
where \(P\) is the local absolute pressure.  
Mesh was hexa-structured and generated using the commercial software ANSYS-ICEM meshing tool. Local mesh refinement was performed in the surrounding area of the hydrofoil wall, which made the computational results more accurate. Figure 2 shows the mesh around the NACA0015 hydrofoil and the close view of the mesh along the leading trailing edge.  
The simulations were conducted by using ANSYS CFX 13.0. The time-dependent governing equations were discretized both in space and time. The velocity was assigned at the domain inlet, and the static pressure was assigned at the outlet according to the cavitation number.
## 3 Proposed Method
### PQD Feature Heuristic Selection  
The original feature vectors in high dimensions contain not only useful information, but also redundant messages. As a result, the original high dimensional feature vectors would lead to the waste of computer resources and a decrease in accuracy. In this paper, a two-stage heuristic feature selection algorithm is adopted to filter relevant features for the classification of PQDs. When considering the function of the VMD method, some decomposed IMFs may be the separated noise, which contains little useful information. As a result, it is obligatory to remove these feature vectors extracted from the useless IMFs. Further, permutation entropy is imported by estimating the time-series complexity of the obtained IMF to select IMFs. In this work, a hard threshold of permutation entropy is computed to select valid IMFs, which is set as 0.6. In other words, if the permutation entropy of the IMF is larger than 0.6, the decomposed IMF is treated as chaotic with less useful information [30].  
In the second stage, the Fisher score is adopted to filter the relevant features. As described above, if the feature is highly relevant, the corresponding Fisher score is relatively large. Hence, the classification accuracy would achieve perfection. On the contrary, if the feature shows irrelevance for the classification result, the Fisher score is near zero. In this circumstance, the classifier would have poor performance. In addition, one point should be noted: the threshold selection of the Fisher's score is very important. In this study, the threshold value of the Fisher score is set as 0.25 and Section 4.3 discusses the reason of the threshold selection.
## 3 Numerical Simulations
### Grid Independence and Validation  
In order to verify the validity of the current model, it was necessary to test the grid sensitivity of the whole fluid region. In this work, three mesh cases were constructed, which were composed of 321,489, 928,746 and 3,356,160 elements, respectively. The grid sensitivity test was carried out with the aluminium foam 10 PPI and porosity of 0.918 at 7 gs\({}^{-1}\) cooling air. The results are shown in Table 4, it can be seen that all the results were very close. Finally, mesh 2 was selected for this work.  
The numerical results were compared with [PERSON] et al. [35], which are shown in Tables 5 and 6. Under the forced air-cooling condition without/with foam aluminium, the predicted numerical results agreed well with [PERSON]'s results, the maximum error between the present study and [PERSON] was no more than 7%. Through these tests, the validity of the current model was verified.
# Accounting for Training Data Error in Machine Learning Applied to Earth Observations
## Guidelines and Recommendations
### Step 2: Minimize Design-Related Errors
#### 4.2.3 Legend Design  
For thematic maps, legend design merits special consideration as it relates to TD, particularly for multi-temporal and/or large area projects that rely on multiple image datasets [61]. As discussed in Section 2 above, objects of interest, including land-cover patches (i.e., the MMU), should be at least twice as large as the pixel resolution of the imagery used in the classification algorithm, assuming a requirement for spectrally pure pixels [136; 168; 208]. When image spatial resolution is too coarse relative to the scene elements of interest, image interpretation errors are likely due to mixed pixels [137; 138; 127]. This implies that in designing a legend, researchers should select classes with an MMU that is large enough to be effectively captured by the coarsest resolution imagery to be incorporated in the model, which will help avoid the problem of collecting training samples with mixed pixels [55]. This consideration is particularly relevant since HR/VHR imagery is often used to create TD and map reference data, while the mapping algorithm is applied to moderate- or coarse-resolution imagery, e.g., [55; 120; 209; 210]. Alternatively, researchers may opt to select a classification workflow which explicitly incorporates mixed pixels, e.g., [98; 165; 173].  
Spatial representativeness should be considered as a limiting factor for legend design [53], and to the extent possible, researchers should attempt to use categories that are supported by both the spatial resolution of the model data and the field sampling protocols to be used; we recommend that researchers consult the extensive literature on legend design [25; 144; 145; 146; 147; 211; 212; 213].
# Retro-Reflective-Marker-Aided Target Pose Estimation in a Safety-Critical Environment
## 2 Methods
### Marker Detection and Identification
We use the pose estimate provided by the direct linear transform (DLT) homography estimation method [36; 48] as an initial estimate to start an iterative approach that minimizes the re-projection error as a non-linear least squares problem. The minimization of the re-projection error is solved using the Levenberg-Marquardt algorithm [49; 50] and can be written for the single-camera case in the following manner:  
\[\operatorname*{arg\,min}_{r,t}\sum_{i=1}^{N}\left[m_{i}-P(M_{i},K,r,t)\right]^ {2}, \tag{8}\]  
where \(P\) describes the projection of world points \((X,Y,Z)\) into image points \((x,y)\):  
\[ \begin{bmatrix}x\\ y\\ 1\end{bmatrix} =\lambda K\big{[} \begin{array}{cc}R&t\\ \end{array} \big{]} \begin{bmatrix}X\\ Y\\ Z\\ 1\end{bmatrix} . \tag{9}\]  
Figure 7: Workflow of marker ordering. \(M_{i}\) and \(m_{i}\) represent world and image point coordinates, respectively. \(\lambda\) represents a scaling factor. The three-dimensional vector \(r\) represents the axis-angle representation of the rotation matrix, \(R\), obtained using the Rodriguez formula [51]. The intrinsic matrix, \(K\), describing the internal parameters of the camera is computed at the calibration stage and is written as function of the camera focal length (\(f_{x},f_{y}\)) and principle point (\(c_{x},c_{y}\)) as follows:  
\[K=\left[ \begin{array}{ccc}f_{x}&0&c_{x}\\ 0&f_{y}&c_{y}\\ 0&0&1\end{array} \right]. \tag{10}\]  
We solve the stereo camera case in a similar manner. Here, the homography estimation and decomposition approach is used to compute an estimate of the transformation between each camera and the world reference frame (\(T_{\text{C1}}^{K},T_{\text{C2}}^{K}\)). The known extrinsic camera parameters describing the rigid body transformation between the auxiliary and reference camera (\(T_{\text{C1}}^{\text{C2}}\)) are used to relate the estimate provided by the auxiliary camera to the frame of the reference camera:  
\[T_{\text{C1}}^{K}=T_{\text{C1}}^{\text{C2}}\,T_{\text{C2}}^{K}\,. \tag{11}\]  
The average value of the estimate provided by each camera is taken as the initial estimate, which is minimized in the following manner:  
\[\operatorname*{arg\,min}_{r,t}\sum_{i=1}^{N}\left[m_{i}^{I}-P(M_{i}^{I},K^{I}, r,t)\right]^{2}+\left[m_{i}^{r}-P(M_{i}^{r},K^{r},r,t)\right]^{2}. \tag{12}\]