esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
This study proposes a unit cell design with linear polarization conversion characteristics with a beam steering functionality, capable of turning to the desired direction, and using only a PIN diode to create a reconfigurable multi-function \(X\)-band transmitarray. In contrast to other studies, we verified the performance by fabricated finite transmitarray which has a complicated bias network to electrically control [16; 17], and to reduce the side effects of the bias network, the decoupling components were designed and presented. As shown in Figure 1, circular patches including two PIN diodes are located on each of the upper and bottom sides of the ground plane located in the unit cell center. It is configured in the form of a short circuit through a via hole, and multi-functionality is achieved by controlling the current path directed through the receiver unit patch on the bottom side. This paper consists of five sections. Section 2 explains the beam steering performance estimation of the reconfigurable transmitarray designed based on a PIN diode. In Section 3,  
Figure 1: Conceptual schematic view of the proposed multi-functional transmitarray. the unit cell with multi-functionality is designed, and its performance result is evaluated and presented using a numerical method and commercial EM field analysis software. Section 4 takes this design, arranges the unit cells in an 8 \(\times\) 8, and analyzes the radiation pattern characteristics using a near-field antenna measurement system. Finally, Section 5 concludes the paper.
# Convolutional Neural Networks: A Roundup and Benchmark of Their Pooling Layer Variants
## 2 Materials and Methods
### Let the Pooling Begin
#### 2.3.5 Rank-Based Pooling  
The rank-based pooling methods [18] are an alternative to Max pooling, with three variants: rank-based Average pooling (RAP), rank-based weighted pooling (RWP), and rank-based stochastic pooling (RSP). The most-important characteristics of these methods are:  
* The top features can be easily identified by their ranks.
* Ranks remain slightly unchanged from the activation values.
* Ranking can avoid scaling across value-based methods.  
Before applying any of the three methods, the ranking process takes place, where an activation function is applied to the individual elements, and they are sorted in descending order according to that function's value.  
RAP attempts to resolve the main issues of Max and Average pooling, which are the information loss of non-Max values in Max pooling and the information being downgraded due to near-zero negative activations in Average pooling. It does so by using an average of the top t important features, where t is a predefined downsizing threshold--if we want to downsize, for instance, by a factor of 2 and the kernel has a size of 2 \(\times\) 2, \(t\) will have the value of 2 as well. Then, we set weights for all the elements within the kernel, with the top \(t\) having a weight of \(1/t\), whereas all other weights are set to 0, and the output is calculated from Equation (2).  
\[s_{\text{j}}=\frac{1}{t}\sum_{i\in R_{\text{j}}\cdot r_{\text{i}}\leq t}a_{ \text{i}} \tag{2}\]  
where \(a\) is the activation function value and \(t\) is the rank threshold that determines which activation affects the averaging.  
RWP takes into consideration that each region in an image might not be equally important, thus setting rank-based weights for each activation. Thus, the pooling output now changes to Equation (3).  
\[s_{\text{j}}=\frac{1}{t}\sum_{i\in R_{\text{j}}}p_{\text{i}}a_{\text{i}} \tag{3}\]  
Figure 3: An example of Average pooling’s functionality [14].  
where \(a\) is the activation value and the probability \(p\) that is used for each weight is given by the ranking Equation (4) where \(b\) is a hyper-parameter, \(r\) is the rank of activations, and \(n\) is the size of the pooling area.  
\[p_{\text{r}}=b(1-b)^{r-1},r=1,\ldots n \tag{4}\]  
Lastly, Equation (5) is used for RSP in a very similar way to RWP.  
\[s_{\text{j}}=\alpha_{i},where\ i\sim Multinomial(p_{1},\ldots,p_{n}) \tag{5}\]  
where \(\alpha\) is the activation value for each element in the pooled region. Then, the final activation values are sampled based on probabilities \(p\) calculated by a multinomial distribution, based on Formula (4).
## 4 Results and Discussion
### Predictive Analysis  
We used five machine learning-based predictive models: decision tree (DT), random forest (RF), ridge regressor (RR), AdaBoost (AB), and gradient boosting (GB).  
Our proposed method predicts the near-optimal block size based on the structure of the matrix, which is an unknown entity until runtime. Fourteen sparse matrix features are extracted and divided into three categories: full feature set (including all fourteen features), and basic and important feature sets. Basic features have the lowest computational complexity and do not require a full scan of the matrix. We calculated the feature score of  
Figure 9: Domain wise execution time comparison (**a**–**c**) and speedup (**d**–**f**).  
all machine learning models and labeled them as important features, as shown in Figure 11. Important features are selected using Scikit-Learn built-in feature importance metric and score is calculated against each machine learning algorithm used in this study. Common features with highest feature importance score are selected as important features.  
Figure 11: _Cont._  
Figure 10: Speedup of BCSR (with and without format conversion time) against CSR.  
We have drawn graphs for the actual versus predicted values (AAQAL). Actual values were obtained from the observed values in our data set. Figure 12A-E show the AAQAL versus actual observations marked as blue, while the AAQAL values are shown in red.  
Figure 11: Feature’s score (**A**) GB, (**B**) AB, (**C**) RF, (**D**) DT, and (**E**) RR.  
Figure 12A shows the mean absolute errors of different machine learning-based predictive models based on the full feature set. We calculated the mean absolute errors with basic features, important features, and all 14 sparse matrix features. Figure 13B shows the MAE comparison of different predictive models using basic features, which are calculated based on their feature importance graphs. Figure 13C shows the MEA comparison with important sparse matrix features. With the full feature set, DT outperformed the others with an error rate of 12.26. RF and GB both showed almost similar performance with error rates of 15.36 and 16.94, respectively. With the basic features set, RF performed better than all other models and showed an error rate of 14, while RR underperformed in all three cases. RF, DT, and GB show competitive performance with important features (based on the feature importance graph shown in Figure 11) and show error rates of 13.23, 14.14, and 14.98, respectively.  
Figure 12: Actual vs. Predicted Values (**A**) GB, (**B**) DT, (**C**) RF, (**D**) RR, and (**E**) AB.
## 4 Results and Discussion
### Influence of Stand-Off Distance on the Coating Removal for the Metal Wall  
The stand-off distance means the distance between the outlet of the nozzle and the target surface. Here in this section, the coating removal experiments under four levels of stand-off distances (10, 15,  
Figure 12: Predicted static pressure and shear stress distribution along the target surface under inlet pressure of 100 MPa.  
Figure 13: Influence of water pressure on the maximum static pressure and the maximum shear stress on the target surface.  
20, and 25 mm) are carried out and compared. Figure 14 shows the surface condition for the four cases with the other experimental condition as follows: the water pressure is 120 MPa, the travelling speed is 2.25 m/min and the inclined angle is 10 degree. It can be seen, for the case with stand-off distance is 25 mm, there are still obvious residual coating. Figure 15 also shows the measured coating removal rate for the four cases. It is clear that the coating removal rate decreases with the increased stand-off distance. That's because the water pressure gradually disputes during the water jet process as shown in Figure 4. When the stand-off distance is larger, the residual water pressure impacting on the target surface is lower.  
The numerical simulation calculation under various stand-off distances in a wide range (15, 30, 45, and 60 mm) were also conducted. For this study, the inlet water pressure and the inclined angle are 200 MPa and 0\({}^{\circ}\), respectively. As can be seen from Figure 16, the velocity reacting on the target surface decreases with the increased stand-off distance. In addition, the water flow becomes more divergent with the increased stand-off distance. Figure 17 also shows the predicted maximum static pressure reacting on the target surface. With the increase of the stand-off distance, the static pressure deceases. This can be explained as follows. When the high-pressure water jet is ejected from the nozzle outlet, it sucks in the surrounding air and energy loss increases with the increased stand-off distance, resulting in the decrease of the static pressure. So, considering the results shown in both Figures 16 and 17, it can be concluded that decreasing the stand-off distance can obviously improve the jet impacting effect. For this study, considering the actual experimental results, the optimal stand-off distance is 15 m/min.  
Figure 16: Predicted velocity cloud chart under various stand-off distances (**a**) 15 mm; (**b**) 30 mm; (**c**) 45 mm; (**d**) 60 mm.  
Figure 17: Influence of stand-off distance of nozzle on the static pressure on the impacting surface.
###### Abstract  
Monitoring and predicting land surface phenology (LSP) are essential for understanding ecosystem dynamics, climate change impacts, and forest and agricultural productivity. Satellite Earth observation (EO) missions have played a crucial role in the advancement of LSP research, enabling global and continuous monitoring of vegetation cycles. This review provides a brief overview of key EO satellite missions, including the advanced very-high resolution radiometer (AVHRR), moderate resolution imaging spectroradiometer (MODIS), and the Landsat program, which have played an important role in capturing LSP dynamics at various spatial and temporal scales. Recent advancements in machine learning techniques have further enhanced LSP prediction capabilities, offering promising approaches for short-term prediction of vegetation phenology and cropland suitability assessment. Data cubes, which organize multidimensional EO data, provide an innovative framework for enhancing LSP analyses by integrating diverse data sources and simplifying data access and processing. This brief review highlights the potential of satellite-based monitoring, machine learning models, and data cube infrastructure for advancing LSP research and provides insights into current trends, challenges, and future directions.  
emote sensing; land surface phenology; monitoring; prediction; Earth observations; data cubes +
Footnote †: journal: Applied sciences  
**Citation:** [PERSON]; [PERSON]; [PERSON]; [PERSON] Monitoring and Prediction of Land Surface Phenology Using Satellite Earth Observations--A Brief Review. _Appl. Sci._**2024**, _14_, 12020. [[https://doi.org/10.3390/app142412020](https://doi.org/10.3390/app142412020)]([https://doi.org/10.3390/app142412020](https://doi.org/10.3390/app142412020))
## 4 Conclusions  
The following conclusions were drawn in the present study:  
1. The Cu and 85 Zn-15 Al coatings deposited through a twin-wire arc thermal spray process exhibited severe defect formation and porosity, while Ti showed a dense morphology with 1.69% porosity;
2. Cu and Ti coatings were partially oxidized owing to their high melting points, as confirmed by XRD results;
3. The Cu coating showed the lowest bond adhesion, attributed to the defects and porosity, which led to a reduction in the anchoring properties of the substrate. By contrast, the Ti coating exhibited 11 times higher bond adhesion than the Cu coating;
4. The Cu coating was susceptible to the NaCl solution, as delamination was observed on the concrete surface after 28 days of immersion, whereas the 85 Zn-15 Al coating formed a layer of white rust. However, NaCl did not affect the properties of the Ti coating;
5. The Ti coating exhibited around 1.5-2 times lesser carbonation penetration depth after four and eight weeks of exposure than those of the Cu and 85 Zn-15 Al coatings;
6. Ti could be an alternative to cement-based repair materials, polymer-modified materials, etc., as Ti-based repair materials deposited through twin-wire arc thermal spray processes extend the service life of buildings and infrastructure.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \multirow{2}{*}{**Coatings**} & \multicolumn{8}{c}{**V\({}_{\text{f}}\) (\%)**} \\ \cline{2-10}  & **CuO** & **Cu\({}_{2}\)O** & **Zn** & **Al** & **TiO** & **Ti\({}_{3}\)O** & **NaCl** & **CuCl\({}_{2}\)(H\({}_{2}\)O)\({}_{2}\)** & **Zn\({}_{5}\)(OH)\({}_{8}\)Cl\({}_{2}\)\(\cdot\)H\({}_{2}\)O** \\ \hline Cu & 18.60 & 20.98 & - & - & - & - & 11.50 & 48.92 & - \\ \hline Ti & - & - & - & - & 78.54 & 21.46 & - & - & - \\ \hline
85 Zn-15 Al & - & - & 45.31 & 12.95 & - & - & 10.65 & - & 31.09 \\ \hline \end{tabular}
\end{table}
Table 6: V\({}_{\text{f}}\) (%) of the corrosion products.  
Figure 8: XRD of the corrosion products formed on the coating surface after immersion in 5% NaCl solution for 28 days.  
**Author Contributions:** S.Y.K., methodology, data curation, investigation, formal analysis, writing--original draft preparation, and writing--review and editing. H.-S.L., formal analysis, supervision, funding, writing--original draft preparation, and writing--review and editing. J.-H.P., methodology, data curation, conceptualization, supervision, funding, writing--original draft preparation, and writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Technology Development Program (S3224226), funded by the Ministry of SMEs and Startups (MSS, Korea).  
Not applicable.  
The authors declare no conflict of interest.
## 3 Results  
### Comparison of the DFT and DCT Basis Matrices  
The DFT and DCT basis matrices for converting the original signal to a sparse form were chosen because they both contain the frequency information that is necessary to make the proposed framework viable. To identify the preferred option, we conducted a comparison of the performances of both matrices, based on the resulting sparsity after each was applied to the original signal. Here, the original signal is a random ultrasound pulse, 6 mm long in the axial direction, that was transformed by the DFT and DCT. The transformation coefficients for both matrices were sorted in descending order and normalized with respect to their maximum. Coefficients larger the 1% of maximum relative weight (Figure 4) were counted, and sparsity was subsequently compared based on the number of significant coefficients. From Figure 4, it can be observed that significant information was encoded in 128 coefficients with the DCT, compared to the 211 required with the DFT. This indicates that the DCT exhibits better sparsity than the DFT in contrasting inclusion images, suggesting that DCT basis matrix can achieve better spectral estimation performances under the same compression ratio. Note that although the transformation coefficients of the DCT do not strictly match the physical frequency components of the original signal, following averaging, this should not affect the accuracy of most spectral analysis processes.
## 4 Conclusions  
Using the SPH numerical simulation approach, a study of reactive fragments striking Q345S or 304 SS plates at 1.5-4.5 km/s was carried out in this research. The results yielded the following findings in particular:  
1. The results of the numerical simulation and the experimental results were in good agreement, demonstrating the accuracy of the SPH simulation method in simulating the properties of steel plates under impact. The Z-A strength model and parameters chosen in this study can better reflect the physical change process of Q345S or 304 SS plates under the impact of reactive fragments.
2. The damage failure characteristics of the steel plate were significantly influenced by the shape of the reactive fragments, the impact velocity, and the type of steel plate. The quick prediction of the perforation diameter of a 2.5 mm thick steel plate affected by reactive fragments at varied velocities was made possible by the fitting equation of the perforation diameter of the first layer of the steel plate based on the method of quantitative analysis.
3. The impedance matching approach was used to examine the pressure variation trend in the steel plates and their behavior under various velocity impact situations. The pressure-added value produced by the impact-induced chemical reaction of reactive fragments rose as the impact velocity rose.  
Figure 13: Detonation wave pressure generated by different reaction extents.  
According to the research findings presented in this article, further investigation is required to examine the effects of large-sized spherical and cylindrical reactive fragments striking 304 SS or Q345S plates at varying velocities.  
Conceptualization, H.S. and Y.M.; methodology, H.S.; software, H.S.; validation, H.S., Y.M. and Q.C.; formal analysis, H.S.; investigation, H.S.; resources, H.S.; data curation, H.S.; writing--original draft preparation, H.S.; writing--review and editing, H.S.; visualization, H.S.; supervision, Y.M. and Q.C.; project administration, Y.M. and Q.C.; funding acquisition, Y.M. and Q.C.; All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China grant number 51931012 and Hunan Provincial Natural Science Foundation of China grant number 2023 JJ10067.  
Not applicable.  
The data used and analyzed in this study are available from the corresponding author upon reasonable request. The data are not publicly available due to privacy restrictions.  
The authors declare no potential conflict of interest with respect to the research, authorship, and publication of this article. This article does not address ethical issues. The authors confirm that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
## 4 Discussion and Conclusions  
The aim of the research was the complex analysis of a thick-walled automotive inner door handle from production to load applied during use. The production technology and the mechanical properties of the short-GF-reinforced composite part were modelled by integrating finite element software and were also experimentally investigated. By validating the simulations, it was proved that they could be applied within an acceptable margin of error. In the case of a multi-stage simulation, the reliability was higher than 85%, which contained the error of injection moulding and finite element simulation. We state that, by combining the programmes into a process, a complex mechanical analysis of injection-moulded products with a thick-walled geometry is possible, saving on the time- and money-consuming processes of conventional iteration methods.  
The following supporting information can be downloaded at [[https://www.mdpi.com/article/10.3390/app12178415/s1](https://www.mdpi.com/article/10.3390/app12178415/s1)]([https://www.mdpi.com/article/10.3390/app12178415/s1](https://www.mdpi.com/article/10.3390/app12178415/s1)). Figure S1. The pVT curves of Schulamid 6 GF 30. Figure S2. The viscosity-shear rate curves of Schulamid 6 GF 30. Figure S3. The simulated filling time. Figure S4. The simulated injection pressure. Table S1. The meaning of the used variables. Table S2. The relative errors of calculated and measured fiber orientation tensor components.  
Conceptualisation, G.D.; injection moulding simulation, G.D.; FEA simulation, T.M.; methodology, F.R.; validation, F.R.; writing--review and editing, G.D.; supervision, F.R. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Deflection** & \multicolumn{2}{c}{**Reaction Force**} & \multicolumn{2}{c}{**Flexural Rigidity**} & \multicolumn{2}{c}{**Ratio**} & \multicolumn{2}{c}{**Relative Error**} \\ \hline
**f** & **F\({}_{\text{m}}\)** & **F\({}_{\text{c}}\)** & **EI\({}_{\text{m}}\)** & **EI\({}_{\text{c}}\)** & **EI\({}_{\text{m}}\)/EI\({}_{\text{c}}\)** & \\
**(mm)** & ** & **(N)** & & **(MPa\_mm\({}^{4}\))** & **(-)** & **(\%)** & **(\%)** \\ \hline  & & & & & & 13.39 \\
2 & 45.7 & 51.9 & 1.27 & 1.44 & 0.88 & 5.93 \\  & & & & & & 12.24 \\
4 & 97.4 & 91.4 & 1.35 & 1.27 & 1.07 & 5.93 \\
6 & 159.0 & 139.5 & 1.47 & 1.29 & 1.14 & 12.24 \\
8 & 223.8 & 195.9 & 1.55 & 1.36 & 1.14 & 12.26 \\ \hline  & & & & & Mean & **10.95** \\  & & & & & Standard deviation & **3.39** \\ \hline \hline \end{tabular}
\end{table}
Table 1: The ratio of measured (subscript “m”) and calculated (subscript “c”) flexural rigidity.
# Kinematic Modeling of a Trepanation Surgical Robot System
## 2 Methods
### Inverse Kinematics of the Tripod Effector  
In this section, we describe the inverse kinematics of the tripod effector, which is the latter part of the kinematic chain of the proposed robot system. The tripod effector diagram is shown in Figure 4. The base frame of the effector is marked as \(\{B\}\), the frame associated with the platform is \(\{C\}\), and the end-effector frame is \(\{E\}\). The platform is actuated through three prismatic joints with displacements denoted as \(q_{1-3}\). The circles represent spherical joints.  
Let us consider the positions of points \(C_{1}\), \(C_{2}\), and \(C_{3}\) when the platform is positioned in pose \({}^{B}T_{C}\):  
\[{}^{B}C_{i}=\frac{B}{C}T\cdot{}^{C}C_{i},\qquad i=1,...,3 \tag{14}\]  
Since the kinematic chain structure of the tripod effector only allows for the movement in the \({}^{B}\hat{z}\) direction and rotation about the \({}^{B}\hat{x}\) and \({}^{B}\hat{y}\) axes, the pose of the \(\{C\}\) frame can be constrained to:  
\[{}^{B}T_{C}=\text{Trans}(0,0,z)\cdot\text{Rot}([k_{x},k_{y},0],\theta) \tag{15}\]  
Once the positions of points \(C_{2}\) and \(C_{3}\) are known, it is possible to find the positions of points \(B_{2}^{\prime}\) and \(B_{3}^{\prime}\), representing the ends of the prismatic effectors \(q_{2}\) and \(q_{3}\). Since points \(B_{2,3}\) are connected with points \(C_{2,3}\) with spherical joints, the positions of \(B_{2}^{\prime}\) and \(B_{3}^{\prime}\) can be found considering the intersection of spheres with radius \(r\) placed at \(C_{2,3}\) and vertical lines placed at \(B_{2,3}\).  
The intersection of a sphere with radius \(r\) with the center at position \((x_{0},y_{0},z_{0})\) and a vertical line going through a point \((x_{1},y_{1})\) is given as:  
\[\begin{cases}x=x_{1}\\ y=y_{1}\\ (x-x_{0})^{2}+(y-y_{0})^{2}+(z-z_{0})^{2}=r^{2}\end{cases} \tag{16}\]  
The solution of this system of equations is:  
\[\begin{cases}x&=x_{1}\\ y&=y_{1}\\ z&=\sqrt{r^{2}-(x_{1}-x_{0})^{2}-(y_{1}-y_{0})^{2}}-z_{0}\end{cases} \tag{17}\]  
Therefore, the equations describing the positions of points \(B_{i}^{\prime}\) in the [B] frame of reference can be written as:  
Figure 4: Diagram of the tripod effector.  
\[{}^{B}z_{B1^{\prime}} = {}^{B}z_{C1} \tag{18}\] \[{}^{B}z_{B2^{\prime}} = \sqrt{r^{2}-(^{B}x_{B2}-^{B}x_{C2})^{2}-(^{B}y_{B2}-^{B}y_{C2})^{2} }-^{B}z_{C2}\] (19) \[{}^{B}z_{B3^{\prime}} = \sqrt{r^{2}-(^{B}x_{B3}-^{B}x_{C3})^{2}-(^{B}y_{B3}-^{B}y_{C3})^{2 }}-^{B}z_{C3} \tag{20}\]  
Once the positions of points \(B_{i}^{\prime}\) are known, it is straightforward to find the joint displacements \(q_{1-3}\):  
\[q_{i}={}^{B}z_{Bi^{\prime}}-{}^{B}z_{Bi},\qquad i=1,...,3 \tag{21}\]