esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Model Description
### Atmospheric Stability Classification
#### 2.2.2 Wind Direction Standard Deviation (WDSD) Method  
The wind direction pulsation is a direct indicator of atmospheric turbulence. The magnitude of the wind direction pulsation angle is directly related to the diffusion parameter, so it can be used as an indicator to classify the atmospheric stability. However, the measurement of pulsation angle is easily influenced by the local influence of sampling location and instrument performance, which makes the method unrepresentative. So this method is more suitable for flat terrain. Table 2 shows the classification criteria recommended by [PERSON] [28] based on the horizontal wind direction standard deviation, \(\sigma_{\theta}\). When \(\sigma_{\theta}\) is used, the classification standard should be corrected according to the actual surface roughness. The classification standard is recommended by the United States Environmental Protection Agency (1980), and the actual area is corrected with roughness as \((z_{0}/0.15)^{0.2}\) (the corrected value is obtained by multiplying the values in Table 2).
## 3 Results and Discussion
### Measurements of Curvatures and Deformations  
For the in situ measuring of the PCB's curvature (which may not be constant across the entire surface, in contrast to the bi-material specimen) and warpage during the reflow process, back-to-back strain gauges were adhered on both the top and bottom surfaces of the PCB. The original strain data are shown in Figure 7a, which indicates that there is a slight strain difference between both surfaces during the reflow heating process. On the contrary, during the cooling process, the strain difference becomes more obvious due to the solidification of solder and the CTE mismatch between both the DIMM sockets and PCB. Then, the curvature data of the PCB was further calculated from Equations (1) and (2) and is shown in Figure 7b. It is shown that the curvature value decreases to \(-50\times 10^{-6}\) mm\({}^{-1}\) during the heating process, due to the deformation of the bare PCB. However, this value starts to build up to \(207\times 10^{-6}\) mm\({}^{-1}\) as the PCB is cooled down from \(250\)\({}^{\circ}\)C to \(30\)\({}^{\circ}\)C, resulting from the mutual constraint between the PCB and DIMM sockets after the solder's solidification.  
Figure 6: Experimentally obtained curvature variation of the Cu/core bi-material specimen with temperatures, obtained from strain gauges and shadow moire in both conventional and IR reflow ovens.  
Figure 7: (**a**) Strain data measured by one pair of strain gauges on the top and bottom surfaces of the PCB and temperature profile during reflow process, and (**b**) the obtained curvature of the PCB and temperature profile with different time.  
In addition, the out-of-plane deformations of the bare PCB and PCB assembly were also measured individually by a full-field shadow moire system. Figure 8a illustrates the out-of-plane deformation results along line a-d of both PCBs before and after the reflow process. The deformation difference, shown in Figure 8b, was obtained by subtracting the deformation before re-flow from the one after re-flow and adding the shifting and rigid-body rotation. The result indicates that the warpage change of the PCB assembly after the reflow process is about 1100 \(\upmu\)m.
# Adaptive Neural Network for a Stabilizing Shunt Active Power Filter in Distorted Weak Grids
## 4 Conclusions  
The SAPF performance in harmonics mitigation in different grid conditions was studied. The ANN-ADALINE method was used for determining the reference current for harmonic elimination. The conventional PLL was used to determine the phase angle in the balanced grid condition with the nonlinear load. Additionally, the RSF was designed to remove the harmonic components for rendering pure sinusoidal signals for the PLL in distorted unbalanced conditions with nonlinear loads. In the case of weak-grid connection and under unbalanced and nonlinear load conditions, an RSQSF PLL model was designed that revealed stable performance in determining the phase angle. For the case of the balanced voltage source and nonlinear load, the harmonic value was reduced by 86.7%, and for the unbalanced voltage source and nonlinear load under grid impedance, the harmonic value was reduced by 84%. In the case of nonlinear load and unbalanced voltage source, under weak-grid conditions, the harmonic value was reduced by 80.46%. As a result, the effectiveness of the proposed control structure in harmonic reduction can be observed. The performance of the ADALINE is questionable in fault-ride through and current limiting conditions, which is left for future works.  
Data curation, S.C. and Y.A.; formal analysis, Y.A., S.C., M.M. and M.E.; investigation, M.E. and Y.A.; methodology, Y.A., M.S.T. and S.C.; software, Y.A. and S.C. and M.E.; supervision, M.E. and M.H.M.; validation, M.M. and M.E.; visualization, Y.A. and M.M.; writing--original draft, Y.A., S.C. and M.M.; Writing--review and editing, M.M., Y.A., M.E. and M.H.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.  
Figure 10: The simulation results of proposed algorithm in Section 3.3: (**a**) Unbalanced and non-sinusoidal 3-phase voltage source; (**b**) 3-phase source current before mitigation; (**c**) 3-phase source current after mitigation; (**d**) power factor; (**e**) DC-link voltage; (**f**) THD for load current; (**g**) THD for line current.
## 4 Experimental Studies
### Simulation Results and Discussion
Even if MO-MFEA and NSGA-II share the same evolutionary search operator, with the help of the implicit cross-task genetic transfer, in general, MOMFEA will show faster convergence than NSGA-II on multiobjective tasks. However, experimental results show the opposite, with NSGA-II having a faster convergence rate, which also suggests that negative transfer occurs even with the higher similarity between tasks. The average HV values over 20 independent runs on task\({}_{2}\) under all conditions are shown in Figure 5. It can be observed that for the EMT-EGT algorithm, inductive bias from  
Figure 4: Average HV numerical curves of NSGA-II, MO-MFEA, EMT-EGT and Tr-EGT over 20 independent runs on task\({}_{1}\). another task fails to relieve premature convergence, resulting in deteriorated performance. Like Figure 4, the MO-MFEA also falls into a local optimal. Table 8 shows the mean computational times of the four algorithms for tasks under conditions 1, 7, and 8. Among them, MO-MFEA, EMT-EGT, and EMT-EGT, as multitasking algorithms, are able to optimize tasks T1 and T2 at the same time, with values in the table being the sum of the time of both tasks. The Tr-EGT algorithm takes more time than EMT-EGT, NSGA-II and MO-MFEA. There are two main reasons for this. First, in the EMT-EGT algorithm, the MMD distance between the solution set of one task and the environment of another needs to be calculated to generate the initialized population pool for the iteration process of the next task, thus leading to accelerate the convergence speed of the algorithm. Secondly, we set a small value for inter-task knowledge transfer frequency G relative to Tr-EGT, which means an increase in the frequency of computing MMD distance. However, the additional computational time added in the EMT-EGT algorithm is acceptable for the optimization process in the carbon fiber industry. Figure 5: Average HV numerical curves of NSGA-II, MO-MFEA, EMT-EGT and Tr-EGT over 20 independent runs on task\({}_{2}\). Figure 6 shows the average approximation PFs among 20 runs obtained by the MO-MFEA, EMT-EGT, and Tr-EGT in the objective space under conditions 1, 7, and 8. It can be observed that the solutions got by the Tr-EGT can be distributed to a large area. In other words, under these conditions, the Tr-EGT algorithm can find a high production capacity utilization with an acceptable yield. With this information, the Tr-EGT can achieve better diversity and convergence than the traditional multitasking framework, thus helping the engineers/technicians to make informed decisions.
## 4 Genetic Algorithm Configuration
### GA Variables  
The optimization process considers eight variables, divided equally for the two inclination groups. The variables are as follows:  
* **Inclination (deg)**: The orbital inclination of each satellite group, which is critical for meeting the revisit time and data transmission requirements and directly affects economic efficiency;  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Algorithm** & **Final Fitness \({}^{\ddagger}\)** & **Avg. conv. iter.** & **Main Source** \\ \hline Genetic Algorithm (GA) & 1.00 & 50–80 & [36; 38] \\ Particle Swarm Optimization (PSO) & 1.08–1.15 & 40–60 & [35; 36] \\ Simulated Annealing (SA) & 1.03–1.05 & \(>\)150 & [36] \\ Artificial Bee Colony (ABC) & 1.01–1.02 & 60–100 & [36] \\ \hline \hline \end{tabular} \({}^{\ddagger}\) Ratio of the best fitness to the GA value within the same study.  
\end{table}
Table 4: Performance ratios of heuristic optimizers as reported in the literature (GA = 1.00). Absolute numbers vary by study; only relative trends are compared here.  
* **Number of planes**: The number of orbital planes for a given inclination, influencing the spatial distribution of satellites;
* **Satellites per plane**: The number of satellites within each plane; fewer satellites, while still meeting performance targets, imply higher economic efficiency;
* **Phase difference (deg)**: The phase offset between satellites in the same plane, which affects the consistency of revisit times.
## 4 Results and Discussion
### Availability of BDS-3/BDS-2 Constellations
The average PDOP values of BDS-3/BDS-2 on a global scale fall between 1.2 and 3.3, while the corresponding fluctuation range is 1.2-1.8 and 1.9-3.3 in the BDS-2 service regions, and the 0\({}^{\circ}\)-150\({}^{\circ}\) W longitude and 60\({}^{\circ}\) S-60\({}^{\circ}\) N latitude areas, respectively. As to the GPS, the PDOP values change from 2.1 to 15.8, from 1.2 to 1.6, and from 1.7 to 2.2 for the maximum, minimum and average statistics, respectively.
# Experimental Verification of the CFD Model of the Squeeze Film Lifting Effect
###### Abstract  
The presented study shows the results of the research into the squeeze film levitation phenomena. The system introduced in the investigation is composed of a vibrating surface, air squeeze film, and the surface of the body freely suspended over the film. The use of the CFD (Computational Fluid Dynamics) model used in the system allows us to determine the steady state, periodic behavior of the air film (described by Navier-Stokes, continuity equations, and ideal gas law), and the lifted object dynamics. The model allows us to determine multiple factors, among others, mean film thickness and pressure distribution inside the fluid film. The influence of factors, such as vibration amplitude, frequency, and load on the lifting conditions, was presented. A series of calculations show the levitations height in the range of 5.61 up to 58.12 microns, obtained for masses of samples between 5-20 g, vibration frequency of 5-25 kHz, and the motions amplitude of 0.5-1.5 \(\upmu\)m. A series of CFD multivariable calculations for a standing wave inducer were not previously published. The CFD model was validated with the use of experiments on a specially developed test rig. The authors experimentally obtained the height of levitation up to 200 microns.  
CFD calculations; acoustic levitation; near-field acoustic levitation (NFAL); squeeze film acoustic levitation (SFAL); squeeze film effect +
Footnote †: journal: Applied Sciences  
Acticle  
Experimental Verification of the CFD Model of the Squeeze Film Lifting Effect
## 4 Incentive Mechanism for Contracts of Vehicles  
As mentioned earlier, we have introduced an incentive stage to boost the enthusiasm of VUs to respond to task publishers and finalize the amount of data that different types of VUs need to contribute during training. Due to the information asymmetry between the task publishers and VUs, the UAVs should design specific contracts for different types of VUs with different data orders of magnitude to improve their profits. The UAVs provide different reward packages from publishers to VUs based on the VU type to reward them for providing local data.  
For VUs with a different amount of data, the UAV provides a contract \((R_{j},q_{j})\) to VU \(c_{j}\), where \(q_{j}\) refers to the amount of data contributed by j-VU, \(q_{j}\leq D_{j}\), and \(R_{j}\) refers to the corresponding reward for that VU. Assume that \(\rho_{j}\) denotes the proportion of type-j VU, which satisfies \(\sum_{j=1}^{J}\rho_{j}=1\), and \(x\) denotes the number of participating VUs. Focusing on a task publisher, the utility function (i.e., publisher's revenue from achieving models) can be modeled as follows:  
\[U_{TP}=\rho_{j}x\Sigma_{j=1}^{J}\omega\log{(1+bq_{j})}-R \tag{1}\]  
where \(\omega\) is the transformation parameter from model performance to revenue, and \(b\) is the dynamical parameter. The log function captures the relationship between the data quantity and the performance of the model.  
Correspondingly, the utility function \(U_{j}\) for the VU of type-j is as follows:  
\[U_{j}=\varepsilon_{j}R_{j}-cq_{j} \tag{2}\]  
where \(\varepsilon_{j}\) is the willingness of type-j VU to participate in training, \(\varepsilon_{1}<\cdots<\varepsilon_{j}<\cdots<\varepsilon_{J}\), and \(c\) is VU's unit cost required for the training of data.  
As such, the optimization problem can be expressed as follows:  
\[\max_{\left(R_{j},q_{j}\right)}U_{TP}=\rho_{j}x\Sigma_{j=1}^{J} \omega\log{(1+bq_{j})}-R\] \[s.t\] \[\varepsilon_{1}R_{1}-cq_{1}\geq 0\] \[\varepsilon_{j}R_{j}-cq_{j}\geq\varepsilon_{j}R_{j-1}-cq_{j-1}\] \[\sum_{j=1}^{J}\rho_{j}xq_{j}\leq q_{max} \tag{3}\]  
where \(q_{max}\) is the total amount of the data contributed by VUs.
## 5 Limitations of the Study  
The current study focuses on analyzing OSM data based on intrinsic quality indicators. However, only line type features have been assessed, whereas these models can be easily applied to other types of features (e.g., ways and relations), to assess their development, completeness and their fitness-of-use for various GI domains. Further, we have compared the OSM dataset of Punjab between two points in time, but the models can be easily adapted to assess and compare the urban and rural developments by preparing appropriate spatial datasets. Furthermore, the data were prepared using open source tools, and their weakness certainly would be reflected in data used for the study, e.g., [PERSON] et al. (2014) have reported that the OSM history importer carries a few bugs, and sometimes, the deleted ways and polygons are imported as if they had not been deleted. Another limitation of the current study is that the shortest routes are compared for the district-to-district level. More low-level origin-destination points data, e.g., village-to-village, may have revealed a clearer picture of the completeness of data. In addition to this, the routing algorithm computes the shortest path based on the availability of the route rather than other attributes, e.g., the maxspeed and traffic situation. Hence, the heuristic metrics defined for analyzing the completeness of data is a proxy measure, and longitudinal studies are required for the generalization of such heuristic measures.
## 3 Results
### Effect of Si Foliar Biostimulant Application on Elemental Concentration in Olive Leaf  
The effects of Si treatment on elemental leaf concentration were not as straightforward as for phenolic concentrations and significant interaction effects between silicon application and cultivar. Elements significantly affected by Si-foliar application were copper, zinc, and silicon (Table 3).  
The increase in Si content was the most straightforward. Si concentrations in all treatments were higher than in the control, and the Leccino cultivar had significantly more Si. Due to the significance of the interaction effect, it can be said that Si-foliar application affected Si content differently in these two cultivars (Figure 3).  
Leccino showed a constant increase with the increase in Si dosage, while in Istarska Bjelica, Si content decreased in Si2 treatment before increasing again at the final Si concentration. In addition, Si content was not decreased in Si2 treatment (Si2: 259.35 \(\pm\) 32.15, Si1: 287.11 \(\pm\) 39.01), as shown in Table 3. The copper and zinc response to the foliar application of Si, regarding it as a main effect, was similar for both elements and did not have a clear trend. Differences between the two cultivars were significant, with Istarska Bjelica having higher values for both, whereas the opposite was recorded for Ca. Since the interaction between the two main effects was significant, they are presented in Figure 4.  
Figure 3: The interaction effects of Si foliar treatment and cultivar on Si content in olive leaves. Different lowercase letters represent statistically significant differences between mean values at \(p\leq\) 0.05 obtained by a two-way ANOVA and Tukey’s test.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Treatment**} & \multicolumn{3}{c}{**Cultivar**} & \multicolumn{3}{c}{**T** x Crv.} \\ \hline
**Microelements** & \multicolumn{1}{c}{**C**} & \multicolumn{1}{c}{**Si1**} & \multicolumn{1}{c}{**Si2**} & \multicolumn{1}{c}{**Si3**} & \multicolumn{1}{c}{\(P\)} & \multicolumn{1}{c}{**IB**} & \multicolumn{1}{c}{**L**} & \multicolumn{1}{c}{\(P\)} & \multicolumn{1}{c}{\(P\)} \\ \multicolumn{1}{c}{**(mg kg\({}^{-1}\) DW)**} & \multicolumn{1}{c}{**C**} & \multicolumn{1}{c}{**Si1**} & \multicolumn{1}{c}{**Si2**} & \multicolumn{1}{c}{**Si3**} & \multicolumn{1}{c}{\(P\)} & \multicolumn{1}{c}{**IB**} & \multicolumn{1}{c}{**L**} & \multicolumn{1}{c}{\(P\)} & \multicolumn{1}{c}{\(P\)} \\ \hline
**Ca** & 6.63 \(\pm\) 0.53 a & 5.96 \(\pm\) 0.29 ab  
Generally, the Leccino cultivar showed stability in Cu and Zn contents across increasing Si levels, and Istarska Bjelica showed a slight decrease in Si2 treatment. The same can be applied to Ca concentrations. For iodine content, the only difference was between cultivars, where Leccino had a significantly higher iodine content. The highest positive correlation with PC1 was recorded for I, Si, and Ca, while the highest negative correlation was recorded for Cu and Zn (Table 2). For mineral content, the only correlation higher than 0.5 for PC2 was recorded for Zn.
# Computational Load Analysis of a Galileo OSNMA-Read Receiver for ARM-Based Embedded Platforms +
## 4 Software Profiling Analysis of the OSNMA Functions
### Simulation Set-Up
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Parameter** & **Description** & \multicolumn{5}{c}{**Sets of Values**} \\ \cline{3-8}  & **SS** & **11**[17] & **S2** & **S3** & **S4** \\ \hline \multirow{8}{*}{**S1**} & **Number of Statellites with different keys per** & & & & 36 & & \\  & **N8, JKKOOT** & **Number of 10-bit blocks of the DSM-KRSOOT** & 7 & & & 10 & 13 \\  & **NMAK** & **Number of MACK blocks within a submarine** & & & 2 & & \\  & **HF** & **Hash Function used for the TESLA chain** & & & SHA-256 & & \\  & **generation** & & & & & \\  & **MF** & **MAC Function used to authenticate the** & & & & & \\  & **navigation data** & & & & & \\  & **KS** & **Key Size** & & & & \\  & **MS** & **MACs size** & & & & \\  & **MO** & **MACs Office** & & & & \\  & **AUhentication Data and Key Delay defining the** & & & & & & \\  & **ADKD** & **pieers of information in the navigation message** & & & & & \\  & **to be authenticated and the key to be used for it.** & & & & & \\  & **NB, JPKR** & **Number of 10-bit blocks of the DSM-KRK** & 13 & & & & \\  & **NRT** & **New Public Key Type** & **EDSA P224/SHA-224** & **EDSA P256/SHA-256** & **EDSA P284/SHA-384** & **EDSA P251/SHA-512** \\  & **DMR Sequence** & **Observation sequence of the DSMs** & & & & [**DSM-KRSOOT**, DSM-KRSOOT**] & \\  & **D, KROOT** & **Key root distance from the simulation start time** & & & & \\  & **Number of Galileo Stacellites** & **Number of generated Galileo Stacellites** & & & & \\  & ** Galileo PRNs** & **Generated Galileo PRNs** & & & & \\  & **NawMg Length** & **Length of the generated nav. message** & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Navigation message generation setup. © [2020] IEEE. Reprinted, with permission, from [16].
## 5 Algorithms and Standard Products for Inferring Crop-Biophysical Characteristics
### Fraction of Absorbed Photosynthetically Active Radiation (_f_APARgreen)  
The estimation of fraction of radiation absorbed by green photosynthetically active vegetation (_f_APARgreen) was accomplished by using either green or red edge Normalized Difference Vegetation Index (NDVI) in the following forms [19]:  
\[Green\ NDVI=\big{(}\rho_{NIR}-\rho_{green}\big{)}/\big{(}\rho_{NIR}+\rho_{ green}\big{)} \tag{6}\]  
\[Red\ Edge\ NDVI=\big{(}\rho_{NIR}-\rho_{red\ edge}\big{)}/\big{(}\rho_{NIR}+ \rho_{red\ edge}\big{)} \tag{7}\]  
where \(\rho_{green}\) is in the range 540-560 nm, \(\rho_{red\ edge}\) in the range 700-710 nm, and \(\rho_{NIR}\) in the range 840-870 nm. We have also used the Wide Dynamic Range Vegetation Index [20], in the form:  
\[WDRVI=(a\rho_{NIR}-\rho_{red})/(a\rho_{NIR}+\rho_{red}) \tag{8}\]  
with the weighting coefficient \(a=0.2\).  
These indices were related to _f_APARgreen [18] with a coefficient of determination greater than 0.91, and were able to estimate _f_APARgreen in the range 0 through 0.95 with root mean square errors less than 0.072 (Green NDVI), 0.071 (WDRVI), and 0.057 (Red Edge NDVI, Figure 7) [21].
## 4 Results and Discussion  
### Analysis of PVDF-g-PHEA Copolymer  
FT-IR spectra were measured to identify functional groups of the graft copolymer. Figure 2 shows the FT-IR spectra of graft copolymer which was synthesized for 72 h. The graft copolymer displayed wide transmittance bands at 3391 cm\({}^{-1}\), pointing to hydroxyl group (_i.e._, -OH) of 2-hydroxyethyl acrylate. The strong transmittance bands also indicate that the graft copolymer has C=O (1668 cm\({}^{-1}\)), and C-O (1233 cm\({}^{-1}\)) structures of 2-hydroxyethyl acrylate [19].  
1H-NMR was measured by using 600 Hz NMR spectrometer with DMSO-\(d_{6}\) (Deuterated Dimethyl Sulfoxide, 99.9%, Sigma-Aldrich, Yongin, Gyeonggi-do, Korea) solution. Figure 3 indicates that the graft copolymer was synthesized successfully. The two high peaks indicate DMSO and water at 2.5 and 3.2 ppm, respectively. Two strong peaks indicate head-to-tail and head-to-head bonding of vinylidene fluoride at 2.9 and 2.3 ppm, respectively. Three strong peaks are protons connected to PHEA at 4.2, 3.5, and 3.3 ppm [19]. The grafting ratio can be calculated using the following equation: [20]  
\[f_{\text{PVDF}}=\frac{l_{a}\times 64/2}{l_{a}\times 64/2+l_{b}\times 116/2} \tag{4}\]  
Figure 2: FT-IR spectra of PVDF-g-PHEA.  
_Appl. Sci._**2015**, \(5\)  
\[f_{\text{PHEA}}=\frac{I_{b}\times 116/2}{I_{a}\times 64/2+I_{b}\times 116/2} \tag{5}\]  
where \(I_{a}\) and \(I_{b}\) are integral of PVDF and integral of PHEA, respectively. 64 and 116 represent the molar mass of PVDF and PHEA, respectively. As an evaluation result, the grafting ratio of PHEA in PVDF-g-PHEA was 22.6% in a mass basis.  
Figure 4 indicates TGA graphs of PVDF and PVDF-g-PHEA graft copolymer. PVDF started declining at approximately 446.4 \({}^{\circ}\)C. On the contrary, PVDF-g-PHEA graft copolymer showed decomposition step at 380.7 \({}^{\circ}\)C, indicating the decomposition of 2-hydroxyethyl acrylate, and moisture  
Figure 4: TGA graphs of **(1)** PVDF and **(2)** PVDF-g-PHEA.  
Figure 3: \(1\)H-NMR spectra of PVDF-g-PHEA copolymer.  
for PVDF-g-PHEA was evaporated from about 30 to 100 \({}^{\circ}\)C. This result confirmed that PVDF-g-PHEA graft copolymer had smaller thermal stability than PVDF. However, PVDF-g-PHEA graft copolymer can thermally stand up to 380.7 \({}^{\circ}\)C [17].