esa-sceva/satcom-chunk-collection huggingface dataset:
# Annual Gross Primary Production from Vegetation Indices: A Theoretically Sound Approach
###### Abstract  
A linear relationship between the annual gross primary production (GPP) and a PAR-weighted vegetation index is theoretically derived from the Monteith equation. A semi-empirical model is then proposed to estimate the annual GPP from commonly available vegetation indices images and a representative PAR, which does not require actual meteorological data. A cross validation procedure is used to calibrate and validate the model predictions against reference data. As the calibration/validation process depends on the reference GPP product, the higher the quality of the reference GPP, the better the performance of the semi-empirical model. The annual GPP has been estimated at 1-km scale from MODIS NDVI and EVI images for eight years. Two reference data sets have been used: an optimized GPP product for the study area previously obtained and the MOD17A3 product. Different statistics show a good agreement between the estimates and the reference GPP data, with correlation coefficient around 0.9 and relative RMSE around 20%. The annual GPP is overestimated in semiarid areas and slightly underestimated in dense forest areas. With the above limitations, the model provides an excellent compromise between simplicity and accuracy for the calculation of long time series of annual GPP.  
annual GPP; MODIS; NDVI; EVI; PAR; Monteith model +
Footnote †: journal: Remote Sensing Sensing
# Static and Dynamic Response of FG-CNT-Reinforced Rhombic Laminates
## 5 Numerical Results and Discussion
### Convergence and Validation of Present Formulation
#### 5.1.1 Free Vibration Analysis
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**BC** & \(V^{\bullet}_{\textbf{CNT}}\) & **Mode** & \multicolumn{3}{c}{_a/h = 10_} & \multicolumn{3}{c}{_a/h = 20_} & \multicolumn{3}{c}{_a/h = 50_} \\ \cline{3-8}  & & & **[PERSON] et al. [5]** & **Present** & **[PERSON] et al. [5]** & **Present** & **[PERSON] et al. [5]** & **Present** \\ \hline CCCC & 0.11 & 1 & 17.625 & 18.284 & 28.400 & 29.232 & 39.730 & 41.246 \\  & & 2 & 23.041 & 23.793 & 33.114 & 34.108 & 43.876 & 45.501 \\  & & 3 & 33.592 & 34.188 & 44.559 & 45.456 & 54.768 & 56.313 \\  & & 4 & 33.729 & 35.188 & 59.198 & 60.708 & 74.488 & 75.080 \\  & & 5 & 37.011 & 38.536 & 61.851 & 63.003 & 98.291 & 100.577 \\  & & 6 & 37.317 & 38.738 & 63.043 & 63.553 & 100.537 & 101.437 \\  & & 1 & 18.127 & 18.854 & 29.911 & 30.795 & 43.583 & 45.216 \\  & & 2 & 23.572 & 24.374 & 34.516 & 35.558 & 47.479 & 49.218 \\  & & 3 & 34.252 & 34.874 & 45.898 & 46.830 & 57.968 & 59.617 \\  & & 4 & 34.650 & 36.267 & 61.628 & 63.337 & 77.395 & 78.064 \\  & & 5 & 37.921 & 39.384 & 64.199 & 64.457 & 106.371 & 104.339 \\  & & 6 & 37.972 & 39.592 & 64.496 & 66.100 & 106.487 & 108.807 \\ \hline SSSS & 0.11 & 1 & 13.532 & 13.885 & 17.355 & 18.014 & 19.223 & 20.124 \\  & & 2 & 17.700 & 18.199 & 21.511 & 22.278 & 23.408 & 24.396 \\  & & 3 & 19.449 & 19.422 & 32.399 & 33.231 & 34.669 & 35.374 \\  & & 4 & 19.449 & 19.427 & 38.988 & 38.844 & 54.043 & 54.658 \\  & & 5 & 27.569 & 28.121 & 38.898 & 38.854 & 70.811 & 73.189 \\  & & 6 & 32.563 & 33.291 & 50.199 & 50.524 & 72.900 & 75.313 \\  & & 1 & 14.306 & 14.668 & 18.921 & 19.618 & 21.354 & 22.359 \\  & & 2 & 18.362 & 18.870 & 22.867 & 23.666 & 25.295 & 26.373 \\  & & 3 & 19.791 & 19.769 & 33.570 & 34.419 & 36.267 & 37.393 \\  & & 4 & 19.791 & 19.774 & 39.583 & 39.583 & 55.608 & 56.238 \\  & & 5 & 28.230 & 28.784 & 39.583 & 39.548 & 78.110 & 80.675 \\  & & 6 & 33.646 & 34.492 & 51.422 & 51.737 & 80.015 & 82.137 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison study of first six non-dimensional frequency parameter of UD CNT-reinforced composite plate.
# Temperature Effects in Packaged RF MEMS Switches with Optimized Gold Electroplating Process
## 5 Conclusions  
In summary, the influence of process conditions on the quality and growth rate of gold electroplating is investigated, and the optimized process parameters for the gold electroplating process are obtained. The characterization of the optimized electroplated gold layer shows that it has small surface roughness and excellent thermal stability. With this optimized gold electroplating process, the RF MEMS switches are fabricated and  
Figure 14: The RF performance variation of the switches working at different temperature environments: (**a**) The insertion loss variation of three switch samples versus temperature @ 30 GHz; (**b**) The isolation variation of three switch samples versus temperature @ 30 GHz; (**c**) The insertion loss variation at different operation frequencies versus temperature; (**d**) The isolation variation at different operation frequencies versus temperature.  
Figure 13: The insertion loss and isolation curves of the packaged switch measured at room temperature.  
hermetic packaged, and the temperature effects on the mechanical and RF performance of the packaged switch are revealed.  
Conceptualization, L.W.; Methodology, L.J.; Software, N.M.; Validation, N.M.; Formal analysis, L.J. and N.M.; Data curation, L.W.; Writing--original draft, L.J.; Writing--review & editing, L.W. and X.H.; Supervision, X.H. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Key R&D Program of China (Grant No. 2022 YFB3203600) and the National Natural Science Foundation of China (Grant No. 62074032).  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
## 3 Results and Discussion
### Comparison of Tribological Properties between Pristine and Microtextured Surfaces
Finally, the uniformly arranged microstrip texture contributes to the rapid formation of a stable lubricating water film, which in turn improves the lubrication state between the friction pairs, thus reducing the friction and wear properties of the material. A summary of the photographs, 3D topographies, and 2D profiles (perpendicular to microstrips, marked in red lines) of the Thordon and PU samples after the friction experiments is shown in Figure 10. The widths of the abrasion scar were about 8.5 mm and 6.4 mm, and the depths were about 234 um and 165 um for the Thordon samples with pristine and microstrip-textured surfaces, respectively. Microstripe-textured Thordon demonstrated a smaller abrasion width and depth than the pristine surface sample, where the width and depth of the wear scar were lower by 24.7% and 29.5%, respectively. The width and depth of the wear scar on the microstripe-textured PU reduced by approximately 5.7% and 17.4% compared to those of the pristine surface sample. The reason for this may be that the microstrip texture contributes to the formation of a stable lubricating water film, which provides a certain load-bearing capacity under dynamic pressure lubrication, reducing the load of the copper ring acting on the surface of the material. Thus, it can be  
Figure 9: Comparison of friction and wear performance between pristine surfaces and microstrip-textured surfaces fabricated on Thordon and PU. **(a)** Friction forces recorded during a 120 min ring-block friction/wear experiment. Lower friction was observed on the microstripe-textured surface than on the pristine surface for each material. **(b)** Average COF and specific wear rate of all the tested specimens. The error bar in the figure represents the standard deviation of each dataset. concluded that embedding microstripe textures can improve the tribological properties of water-lubricated materials, such as optimizing the formation of a lubricating water film, reducing adhesion, friction, and wear, thereby leading to a superior anti-wear performance compared to pristine surfaces.
## 3 Results
### Yield Stress Dependence on CF Content and Aspect Ratio  
In MMC mechanics, there are numerous mechanisms via which an increase in yield stress may take place, including load transfer, dislocation strengthening due to thermal expansion mismatch, thermal residual stress, grain refinement, and Orowan strengthening [37; 38; 55]. In this work, the assessment of the influence of the most important factor, the load transfer mechanism, was attempted. This mechanism has been described by several models, the most common of which is the shear-lag model, according to which it is presumed that there is a perfect connection between the matrix and the fiber ends as well as a single orientation. In this case, the increase in yield stress is given by:  
\[\Delta\sigma=\sigma_{M}\bigg{[}1+\frac{(L+t)A}{4L}\bigg{]}f+\sigma_{M}(1-f)- \sigma_{M}=\sigma_{M}f\frac{A+1}{4} \tag{7}\]  
where \(L\) is the fiber length in the stress direction, \(t\) the fiber size perpendicular to the stress direction, \(A\) the aspect ratio (\(L/t\)), and \(\sigma_{M}\) the alloy yield stress [38].  
At first, the 0.2% offset yield stress values of the composites were calculated from the corresponding curves of Figure 9, equal to 428, 451, 473, and 496 MPa for the 0%, 4%, 8%, and 12% MMCs, respectively. The results are displayed in Figure 11, compared with the ones predicted by Equation (7), assuming that \(\sigma_{M}\) is equal to 428 MPa. There is a deviation from the model's predictions, which is reasonable due to the random arrangement of CFs. The deviation increases with an increase in the volume fraction of CFs.  
The next step was to examine whether the \(A\) ratio affects the yield stress of the composites, again according to the same model, and whether this can be observed through the framework. Thus, by performing tensile simulations on specimens of the same volume fraction (\(f=8\%\)), same fiber diameter (\(t=7\) um), and different fiber lengths (\(L=15\), 30, and 45, respectively), the results are plotted in Figure 12a. As can be seen, these curves show no noticeable difference, but only a minimal difference in the slope of the strain hardening curves. Zooming in on the yield stress region for \(\varepsilon=0.2\%\) (Figure 12b), it is observed that the yield stress increases slightly with increasing \(A\). The increasing trend is expected based on the shear-lag model; however, the values expected from the model differ significantly from the ones calculated, a reasonable difference due to the random arrangement of CF in the matrix. Figure 13 shows the values of the shear stress as found from the Abaqus FEA analysis and the corresponding values as predicted by the shear-lag model. An increase in the difference with increasing CF volume fraction is observed, as in the previous case.  
Figure 11: Yield stress of AA7075/CF MMCs with different volume fractions compared with shear-lag model.
# Optimal View Estimation Algorithm and Evaluation with Deviation Angle Analysis
## 3 Methods  
Although our goal is to develop an image-based viewpoint estimation method, this issue also involves obtaining an image dataset with accurate viewpoint information and establishing a reasonable evaluation metric. Therefore, the proposed method consists of four modules: obtaining viewpoints, generating an image dataset with viewpoint annotations, estimating a viewpoint using a CNN, and evaluating the accuracy of the estimated viewpoint. The flow chart of the proposed method is shown in Figure 2.  
In our flowchart, the first two modules, the obtaining viewpoints and the generating the image dataset with viewpoint annotations modules, are the same steps as the existing CNN-based methods listed in Table 2. The difference is that our method analyzes the selection of viewpoints, but these listed methods overlooked this point. The third module is the estimating viewpoint using a CNN without a rule-based module, given that the effectiveness of rule-based methods is trained in a class-dependent manner [26], with a comparison in Table 2. In the last module, viewpoint evaluation, an accuracy evaluation method with deviation angle tolerance is proposed.
## 3 Results and Discussion
### Optimization of Experimental Conditions
#### 3.3.2 Optimization of Fluent and Elution Time  
Whether the template elution is clean and the cavity structure on the molecular surface is complete are the pivotal elements that affect the success of the sensor preparation. In order to effectively remove the template ions, the elution efficiency of desorbing UO\({}_{2}\)\({}^{2+}\) from MIIP/Fe\({}_{3}\)O\({}_{4}\)@SiO\({}_{2}\)/MCPE electrode with inorganic acid eluent was investigated, as shown in Figure S6. The effects of the properties of inorganic acids on the desorption of UO\({}_{2}\)\({}^{2+}\) were examined by using 5.0 mL of 1 mol L\({}^{-1}\) HCl, 1 mol L\({}^{-1}\) HNO\({}_{3}\), 1 mol L\({}^{-1}\) H\({}_{2}\)SO\({}_{4}\) and 1 mol L\({}^{-1}\) CH\({}_{3}\)COOH. Desorption rate was obtained by measuring the potential valuesbefore and after elution. As demonstrated by the experimental results, desorption rates were 98%, 91%, 50%, and 76%, respectively. Therefore, 1 mol L\({}^{-1}\) HCl was selected as the leaching agent over several other inorganic acids. For the sake of investigating the optimal concentration of the leaching solution, several hydrochloric acid solutions with different concentrations (0.1, 0.2, 0.5, and 1.0 mol L\({}^{-1}\), respectively) were utilized to extract UO\({}_{2}\)\({}^{2+}\) ions from imprinted sites of polymer matrix during quantitative desorption. The results showed that the desorption of UO\({}_{2}\)\({}^{2+}\) ions increased with the increase of hydrochloric acid concentration which is most likely owing to the increased protonation of ligand heteroatoms in the polymer networks. Hence, 0.5 mol L\({}^{-1}\) was chosen as the optimum eluent in the voltammetry of uranyl ions. As well, the effect of the elution time on the peak current was analyzed, and the optimum elution time was 15 min.
# Imaging Top of Volcanic Mounds Using Seismic Time- and Depth-Domain Data Processing
## 3 Results of Seismic Data Processing
### Depth-Domain Processing Using the MVA Technique
Therefore, we confirmed that not only the velocity but also the subsurface structure was corrected after applying the depth-domain data processing using the MVA technique. Figure 6: (**a**) Initial depth-domain interval velocity for MVA obtained by converting the time-domain RMS velocity. (**b**) Final updated depth-domain interval velocity after MVA at the 3 rd iteration. (**c**) Difference between (**a**) and (**b**). Figure 8: Velocity error curve at a depth of 960 m in the 2705 th CRP gather. Figure 7: (**a**) CRP gather before MVA and (**b**) CRP gather after MVA at the third iteration. Figure 9: (**a**) Depth-domain stack section obtained by converting the time-domain result (Figure 5b). (**b**) Depth-domain stack section after applying MVA at the 3 rd iteration. The red and green lines represent the top of the basalt before and after applying MVA, respectively. The difference in depth between the two basalt tops ranges from 32.5 to 60 m.
## Materials and Methods
### RNA-Sequencing and qRT-PCR  
Total RNA was isolated from plant tissues (leaves) using the Trizol reagent (Ambion, Life Technologies, California, USA), and RNA-seq libraries were prepared according to the manufacturer's protocol (Illumina Inc. San Diego, CA, USA). The libraries were then sequenced on an Illumina Hiseq 2500 instrument (Illumina, San Diego, CA) to produce 150 bp paired-end reads. qRT-PCR assays were conducted according to our previously described method [(22)]. Primers were designed using the IDT Primer Quest web tool ([[http://www.idtdna.com/primerquest/Home/Index](http://www.idtdna.com/primerquest/Home/Index)]([http://www.idtdna.com/primerquest/Home/Index](http://www.idtdna.com/primerquest/Home/Index))). The cDNA was synthesized by Prime Script RT Reagent Kit (Perfect Real Time) (TaKaRa Bio Inc. Dalian, China). The qRT-PCR was performed using GoTaq 2-Step qRT-PCR System (Promega, Madison, WI, USA) and on the ABI Prism1 7500 (Applied Biosystems, Carlsbad, CA, USA). qRT-PCR was performed with the follow PCRcycling condition: 95 \({}^{\circ}\)C for 10 s, followed by 40 cycles of 95 \({}^{\circ}\)C for 30 s and 60 \({}^{\circ}\)C for 1 min. Two housekeeping genes, actin (TRINITY_DN78316_c0_gl) and Alpha tubulin (TRINITY_DN106409_c0_gl), were selected to use as reference genes for normalization of gene expression in the qRT-PCR analysis. The relative expression levels of each gene were evaluated by the comparative2\({}^{\Delta\Delta\Delta\Gamma}\).
# A High-Efficiency K-band MMIC Linear Amplifier Using Diode Compensation
## 3 Circuit Design  
The K-band MMIC linear amplifier is composed of three-stage FETs fabricated with a 0.15 \(\upmu\)m gate length AlGaAs/GaAs pHEMT technology. The process exhibits a gate-drain breakdown voltage of 16.5 V and a cutoff frequency (\(f_{T}\)) of 90 GHz. The FETs for the power stage are \(8\times 100\)\(\upmu\)m. Source-pull and load-pull simulations using a large signal model at a center frequency were taken to  
Figure 8: The influence of the diode size on the stability factor of the network.  
determine the optimal input and output impedances that lead to a higher output power and efficiency. The output matching network combining two FETs matches the fundamental load impedance based on the load-pull simulation. The interstage and input matching networks are designed to match the conjugated impedance and were optimized for low loss. The circuit of the amplifier configuration is shown in Figure 9. The simulation result of the amplifier is shown in Figures 10 and 11.  
Figures 10 and 11 show the improvement of the amplifier linearity performance generated by the parallel diode circuit design.  
Figure 10: (**a**) Simulated output power and PAE versus frequency at a 3 dBm input power; (**b**) simulated S-parameter versus frequency; (**c**) simulated output power, gain, and PAE versus input power at a 21 GHz frequency; (**d**) simulated IMD3 and PAE versus output power.  
Figure 9: Schematic of the MMIC linear amplifier.
## 3 Experiment
### Quantitative Metrics and User Study  
#### 3.8.1 Quantitative Evaluation of Generation Quality  
To comprehensively evaluate the quality and diversity of generated layouts, we introduce two widely used metrics for generative models:  
* Frechet Inception Distance (FID): This measures the similarity between the distribution of generated samples and real data. A lower FID indicates better generation quality. We use a pre-trained Inception-v3 model to extract feature vectors from layout images and compute the Frechet distance between their means and covariances.
* Inception Score (IS): This evaluates the diversity and semantic consistency of generated samples. A higher IS indicates more reasonable results. This score is calculated based on the entropy of the class prediction distribution from Inception-v3.  
Table 3 shows the comparison results of the TGAN with existing methods. The experiments use the same test set (1452 samples), and each method generates 1000 layouts for evaluation. The TGAN achieves the lowest FID (18.3) and the highest IS (10.1), demonstrating that its generated layouts significantly outperform existing methods in terms of visual quality, distribution matching, and semantic coherence. Specifically, the TGAN reduces FID by 16.1% compared to the PLAY model and improves IS by 5.2%, validating the effectiveness of the self-attention mechanism in modeling complex layout relationships.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Samples**} & \multicolumn{2}{c}{**Subjective Score**} & \multicolumn{2}{c}{**Objective Score**} \\ \cline{3-6}  & & **Mean** & **Variance** & **Mean** & **Variance** \\ \hline GAN & 200 & 7.992 & 0.503 & 0.078 & 0.0032 \\ GAN & 400 & 7.876 & 0.521 & 0.065 & 0.0035 \\ GAN & 600 & 8.105 & 0.417 & 0.053 & 0.0029 \\ \hline LayoutTransformer & 200 & 7.983 & 0.497 & 0.076 & 0.0034 \\ LayoutTransformer & 600 & 8.173 & 0.421 & 0.057 & 0.0031 \\ \hline VTN & 600 & 8.123 & 0.412 & 0.060 & 0.0032 \\ \hline L-CGAN & 600 & 7.674 & 0.501 & 0.064 & 0.0031 \\ \hline PLAY & 600 & 8.207 & 0.445 & 0.059 & 0.0030 \\ \hline TGAN (Ours) & 200 & 8.45 & 0.38 & 0.048 & 0.0023 \\ TGAN (Ours) & 400 & 8.52 & 0.36 & 0.045 & 0.0020 \\ TGAN (Ours) & 600 & **8.60** & **0.35** & **0.042** & **0.0018** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparative evaluation of layout generation methods (bold represents the best result).  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Method** & **FID** & **IS** \\ \hline LayoutGAN & 32.7 & 8.2 \\ \hline Deep Layout & 28.5 & 8.9 \\ \hline VTN & 25.1 & 9.3 \\ \hline PLAY & 21.8 & 9.6 \\ \hline TGAN (Ours) & **18.3** & **10.1** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of FID and IS Scores (lower/higher values indicate better FID/IS, respectively; bold represents the best result).
## 2 Theoretical Background
### Functional Mock-Up Interface  
A functional mock-up unit (FMU) is a standard component designed to facilitate the integration of heterogeneous models according to the Functional Mock-up Interface (FMI). FMUs are modular and reusable components that implement standardized interfaces for the simulation of numerical models, facilitating interoperability between different modeling tools. Developed as part of the European Modelica project and formalized under the FMI standard, FMUs are employed in a wide range of applications [43; 44; 45; 46], including automotive, aerospace, energy, and robotics. An FMU is a self-contained functional unit that includes both a mathematical model and a communication interface. It is distributed as a zip file containing an XML description of the model, a binary executable file (which implements the mathematical model), and configuration data. There are two main types of FMUs:  
* Model Exchange (ME): Allows the exchange of models described as systems of differential or algebraic equations. This type of FMU is integrated directly into the host simulator.
* Co-Simulation (CS): Includes an internal solver, enabling the FMU to run the simulation autonomously while communicating with other models or simulations through standardized interfaces.  
FMUs can be generated to make ROMs reusable. In our approach, we encapsulate the ROM and the global results into FMUs. We adopt three types of FMUs to manage the geometrical model, the field results, and the global results. FMUs are intended to manipulate the sets of scalar quantities, and in our implementation, we receive the new state \(x\) to be represented in the parametric space as input (each individual parameter as a scalar CAD input), and the state of the system in the reduced coordinate system \(\
u_{i}(x)\) as output, as in (7). The evaluation happens in real time and allows to calculate the full state from the corresponding FMUs: geometry, field results, and global results. As far as the portability of the modes (large fields of data) is concerned, instead of accessing the data from the FMUs directly, we have the ability to write the full field (i.e., a complete span of the modes) on file. This is not efficient for interactivity, and so we adopt the solution to dump the modes on file from the FMU and then load them on the device (i.e., the GPU of the VR or the one of the laptop).