esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Photocatalytic Activity Tests
#### 3.1.2 Influence of 1.08 Cu_ZnO Catalyst Dosage in Photocatalytic Hydrogen Production  
The optimization of the photocatalyst dosage was obtained by testing different amounts of 1.08 Cu_ZnO in the range of 0.75-3 g/L (Figure 3).  
Figure 3 displays the hydrogen production for the different 1.08 Cu_ZnO dosages as a function of visible light irradiation time. As expected, very low H\({}_{2}\) production was observed for the photolysis test (without the photocatalyst). In the opposite way, the photocatalytic H\({}_{2}\) production from the glycerol  
Figure 3: Hydrogen production during the irradiation time as a function of catalyst dosage. Glycerol initial concentration: 5 wt %; solution pH: 6; visible light intensity: 32 mW/cm\({}^{2}\); photocatalyst: 1.08 Cu_ZnO.  
Figure 2: Proposed reaction mechanism for H\({}_{2}\) production from a glycerol aqueous solution using Cu-doped ZnO under visible light (CB: conduction band; VB: valence band).  
solution was significantly enhanced in the presence of the 1.08 Cu_ZnO photocatalyst in the glycerol aqueous solution. At a fixed irradiation time, there was an increase of hydrogen production up to a 1.08 Cu_ZnO dosage of 1.5 g/L (4180 \(\upmu\)mol/L of H\({}_{2}\) after 4 h of visible light irradiation). Beyond this value of catalyst dosage, the photocatalytic performances worsened. In particular, at 3 g/L of catalyst dosage, the hydrogen production was 1670 \(\upmu\)mol/L lower than that obtained with 0.75 g/L of catalyst dosage. The worsening of photocatalytic activity may be explained by the increased opacity of the aqueous solution, which made light penetration through the solution increasingly difficult [53]. Therefore, the optimal catalyst dosage was 1.5 g/L and it was used to evaluate the influence of the initial glycerol concentration in aqueous solution and the effect of pH of solution.
# Mobility-Enabled Edge Server Selection for Multi-User Composite Services
## 5 Simulated Experiments and Analysis
### Baseline Approaches  
To our best knowledge, MESP is the first attempt to consider edge server selection for many mobile users in MEC for composite services. Due to the issue of user's mobility and the limited capacity of the edge server, existing approaches designed for static users cannot be directly applied to the many mobile users environment. Thus, in the experiments, the MESP algorithm was benchmarked against two baseline approaches for multi-user edge server selection, namely the _random_ and _traditional_ algorithms:_Random_: Each user will randomly select an edge server as long as the server has sufficient remaining resources to accommodate the invoking service and has the users within its coverage.
2. _Traditional_: Each user will select an edge server with the least data transmission time as long as the server has sufficient remaining resources to accommodate the invoking service and has the user within its coverage.
## 7 What beyond 5G Should Look Like  
The exchange of data between physical space and cyberspace is expected to significantly increase during this decade (2020-2030). Physical interactions in physical space will be replicated in the form of digital data in cyberspace as the integration of cyberspace and physical space (cyber-physical systems (CPS)) progresses. Using AI to analyze such vast amounts of digital data will allow us to not only grasp the current state of physical space, but also to make decisions about future actions in physical space based on that information. CPS is expected to grow in line with the introduction of 5G, which offers a number of benefits including low latency and a large number of simultaneous connections in addition to high speeds. As a result, cyberspace will not only expand the functions of physical space, but it will also create a society that is both robust and resilient, with daily lives and economic activities fully maintained within cyberspace, even if there are unexpected crises in the physical world. This is expected to enable not only further progress in resolving social challenges and generating economic growth in Japan, but also to significantly contribute to the realization of the principles outlined in the Sustainable Development Goals (SDGs) [76]. The three elements listed below can be viewed as specific goals for a vibrant and resilient society (Society 5.0) in the 2030s.  
* The first is an inclusive society in which everyone can participate actively by eliminating differences such as age and disability, as well as geographical barriers such as urban and rural areas and national borders. The realization of such a society will necessitate, for example, super tele-presence technologies that will allow people to feel as if they are physically present anywhere in the world via an avatar, robot, or other means without having to leave their own home.
* The second is a sustainable society in which society can achieve sustainable growth while remaining convenient, with no social loss, by optimizing cyberspace with real-world reproduction that can be fed back into the real world.
* The third is a dependable society, a human-centered society in which trust bonds are unbreakable and everyone can work in peace by autonomously ensuring the safety and stability of communications networks as a social infrastructure.  
The progress of CPS towards a Society 5.0 era with a 5G-centered information and communication technologies (ICT) infrastructure is presented in Figure 20.  
Figure 20: Progress of cyber physical system and society in the 2030s (adapted from [77]).
# Angular Dependence of Photonic Crystal Coupled to Photovoltaic Solar Cell
## 3 Results and Discussion
### Polarization Effect  
In addition, irradiation is not only described upon its spectrum or the sun position elevation. The state of the light is thus completely defined only when polarization and its dependence are well known. Angular modification had implications on the scattering of polarized light in photonic crystals [30; 31; 32]. Thus, when a 1 DPC is designed for solar applications, design specifications must consider not only the solar spectrum at normal incidence but also angular and polarization dependence. Solar radiation does not have a preferential orientation in the electric field of the light; it is called natural polarization. When this natural polarization is partially reflected by a dielectric material, the degree of polarization of both the reflected and the transmitted light changes: reflected light gets fully polarized in the perpendicular plane of incoming radiation, while transmitted light has higher intensity and the nature of polarization is partially linear.  
Photonic crystals coupled to solar cells were located in the back side of the front glass of the device. Thus, despite solar radiation being naturally polarized, light incoming on the 1 DPC was partially polarized. As photonic crystals modified the polarization nature of the light, either by reflection or transmission, performance of the solar cell coupled to this optical component might be affected. To investigate the influence of this polarization shift, the angular dependence of the reflection of the light by 1 DPC was measured, considering natural and linear polarization (Figure 8).  
Figure 7: Losses derived from the 1 DPC and solar cell optical coupling measured into a 2-axis tracking system, to avoid the effect of irradiance spectrum modification during the day. Red circles represent data collected at the fixed structure (Section 3.1), while blue diamonds correspond to data collected from the 2-axis tracking experiment (Section 3.2).  
At low incident angles, there was a coincidence between linear and natural reflected light. This means that with the normal incident direction or low incident angles, reflectivity was independent of the polarization. However, when the incident angle increased, there was a difference in intensity between light with linear polarization reflected and natural light, but the Bragg peak position remained almost constant. At higher incidence angles, due to the periodic structure of the photonic crystal, it was possible to create spectral selective mirrors that were polarization dependent. Polarization affected the photovoltaic device in terms of the intensity of the light reflected or transmitted, but there was no dependence on the Bragg peak shift observed previously. These results were in good concordance with Equations (3)-(5). Angular reflection modified polarized light scattering in plasmonic structures, and this opens an interesting portfolio of applications like splitters.
# Non-Ionizing Radiation Measurements for Trajectory Radars
## 4 Results and Discussion
### Predictions  
The power density values for the points in the far field region (P02, P03 and P04) were predicted using Equation (4) [22], which assumes free space propagation.  
\[S=\frac{P_{t}G_{t}}{4\pi d^{2}} \tag{4}\]  
Figure 8: Radar signal spectrum and measured average power (Anritsu MS2720T).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Point** & \begin{tabular}{c} **Spectrum Analyzer** \\ **Model** \\ \end{tabular} & \
\begin{tabular}{c} **Average Power at the** \\ **Terminals of the Antenna (dBm)** \\ \end{tabular} &
\begin{tabular}{c} **Power Density (W/m\({}^{2}\))** \\ \end{tabular} \\ \hline P01 & Agilent-N9010A & 29.62 & 27.15 \\ P01 & Anritsu-MS2720T & 29.70 & 27.66 \\ P02 & Anritsu-MS2720T & 16.40 & 1.29 \\ P03 & Anritsu-MS2720T & 4.60 & 0.09 \\ P04 & Anritsu-MS2720T & 11.50 & 0.42 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Field Power Measurement and Computed Power density.  
where \(P_{t}\) is the transmitted average power, \(G_{t}\) is the transmitting antenna gain and \(d\) is the distance from the measurement point to the transmitting antenna. We assume a transmitter average power of 0.851 kW and a transmitter antenna gain of 44 dBi.  
Equation (5) [22; 23], which evaluates the maximum power density expected for the near field, was used for point P01, located in this region.  
\[S_{m}=\frac{4P_{t}}{A} \tag{5}\]  
where \(S_{m}\) is the maximum power density (W/m\({}^{2}\)) and \(A\) is the area of the transmitting antenna (m\({}^{2}\)). Equation (5) provides the maximum power density that can exist on the axis of the antenna beam that is focused at infinity, with no reflections. However, if the calculated prediction reveals a power density value equal or greater than the maximum permissible exposure (MPE), we must assume that this value may exist at any point in the near field region [22]. The predicted and measured values for all four points are shown in Table 4.
## 4 Simulation Results and Discussion
### Case 2: Sensitivity Analysis with Criterion Variation Within \(\pm 10\%\)
#### 4.3.5 Scenario 4: Variation in Upload Speed, Download Speed, Ping, and Packet Loss  
In this last scenario, we randomly vary the initial packet loss values, in addition to the previous variation in upload speed, download speed, and ping. Table 20 shows the random variation rates applied to the initial packet loss values of each satellite. The resulting decision matrix is shown in Table 21.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Satellite ID** & 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 & 9 & 10 \\ \hline
**Variation rate (\%)** & \(-10\) & 5 & 6 & 1 & \(-2\) & \(-3\) & \(-10\) & \(10\) & \(-1\) & \(-2\) \\ \hline \hline \end{tabular}
\end{table}
Table 18: Random variation rates applied to ping, for criterion variation within \(\pm 10\%\).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Satellite ID** & **Upload Speed** & **Download Speed** & **Ping [ms]** & **Packet Loss** [\%] \\ \hline
5 & 14.94 & 222.93 & 47.31 & 0.01 \\ \hline
6 & 13.97 & 233.57 & 57.13 & 3.52 \\ \hline
7 & 15.66 & 192.05 & 60.84 & 1.28 \\ \hline
8 & 12.05 & 203.59 & 53.19 & 13.28 \\ \hline
9 & 17.02 & 181.23 & 65.85 & 0.48 \\ \hline
10 & 15.66 & 213.01 & 68.96 & 19.04 \\ \hline \hline \end{tabular}
\end{table}
Table 20: Random variation rates applied to packet loss, for criterion variation within \(\pm 10\%\).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Satellite ID** & **Upload Speed** & **Download Speed** & **Ping [ms]** & **Packet Loss** [\%] \\ \hline
**Variation rate (\%)** & \(-10\) & 5 & 6 & 1 & \(-2\) & \(-3\) & \(-10\) & \(10\) & \(-1\) & \(-2\) \\ \hline \hline \end{tabular}
\end{table}
Table 19: Decision matrix of Scenario 3, for criterion variation within \(\pm 10\%\).
## 2 Fabrication Process and Waveguide Performances
### Fabrication Processes
* Si\({}_{3}\)N\({}_{4}\) for wideband comb generation To control strain and to prevent cracks from appearing, the silicon nitride layer is deposited via low-pressure chemical vapor deposition (LPCVD) in two steps of 370-nm-thick layers each [32]. The deposition is carried out with a tailored ultra-low deposition rate (<2 nm/min) to produce a very high-quality film. Furthermore, between the two deposition stages, the wafer is rotated by 45\({}^{\circ}\) in order to distribute the uniaxial strain along the overall film thickness, thus avoiding film cracks upon subsequent subtractive patterning. Each deposition run is carried out at 780 \({}^{\circ}\)C with post-deposition cooling to around 630 \({}^{\circ}\)C for 20 min. The resulting film is close to stoichiometry, and its optical index is n\({}_{\text{Si3N4}}\) = 2 (more details will be given in the nonlinear Section 4). * SiN for near infrared LIDAR For LIDAR applications in the near infrared, both types of SiN can be used indifferently; however, stoichiometric Si\({}_{3}\)N\({}_{4}\) shows slightly lower losses at a wavelength of \(\sim\)900 nm. For this application we therefore chose a 300 nm thick Si\({}_{3}\)N\({}_{4}\). Finally, a 10/100 nm Ti/TiN metal layer separated by a certain distance from the SiN layer was incorporated. This layer can be used to form resistive wires (heater in Figure 1a), allowing for phase shifts in the SiN waveguides using the thermo-optic effect. Electrical contact to the heater was achieved using vias and AlCu contact pads. All types of silicon nitride waveguides are patterned using 248 nm Deep Ultra Violet (DUV) lithography and CF\({}_{4}\)-CH\({}_{2}\)F\({}_{2}\)-O\({}_{2}\) chemistry-based reactive-ion etching. The produced structures range from 200 nm tips, spaces and lines to hundreds of micrometers. The selective etching provides a low edge roughness and a vertical sidewall. Figure 1b shows a FIB cross-section of the final Si-SiN stack with a 200 nm SiO\({}_{2}\) interlayer thickness. SiN structures are encapsulated by 1 \(\upmu\)m SiO\({}_{2}\) before optical characterization.
## 3 Results and Discussions
### Overall Mass-Transfer Coefficient  
The effects of \(T\), pH, \(C_{L}\), and \(Q_{g}\) on \(K_{G}a\) can be found in Table 4. However, the effective correlation equation for \(K_{G}a\) is not prone to being obtain. Here, the mass transfer coefficient can be correlated to the liquid molar flux, gas molar flux, and temperature [39; 40]. In order to obtain the empirical equation, the gas and liquid flow rates are needed to translate into the molar flux, that is, \(G_{x}\) (mol/s\(\cdot\)m\({}^{2}\)) and \(G_{y}\) (mol/s\(\cdot\)m\({}^{2}\)); the values are in the range of 0.9469-2.8407 mol/s\(\cdot\)m\({}^{2}\) and 0.2321-1.7936 mol/s\(\cdot\)m\({}^{2}\) for \(G_{y}\) and \(G_{x}\), respectively. Using the linear regression, a total of sixty data sets for \(K_{G}a\), as listed in Table 4, were used. The correlation results became the following:  
\[K_{G}a(1/\text{s})=1.2525\exp(-\frac{661.062}{T(\text{K})})[G_{x}(\text{mols}^ {-1}\text{m}^{-2})]^{0.6069}[G_{y}(\text{mols}^{-1}\text{m}^{-2})]^{0.08109} \tag{20}\]  
Figure 7: A plot of the calculated value versus measured values for \(\alpha\) showing the regression confidence. MEA—monoethanolamine.  
In order to understand the confidence of regression, a plot of \((K_{G}a)_{\text{cal}}\) versus \((K_{G}a)_{\text{mea}}\) is shown in Figure 8, where it is found that the most data are within a 15% error and are close to the solid line, indicating that regression is reasonable. In order to verify the optimum experiments and the uncontrolled experiments, all of the data were plotted in the figure. It was found that all of the data are close to the solid line, thus, Equation (20) is reliable. In addition, from an operation range analysis, the parameter importance was found to be \(G_{x}>T>G_{y}\). Because of this, controlling \(G_{x}\) can effectively improve the \(K_{G}a\). This could be done by increasing the pH of the solution. In addition, the \(K_{G}a\) values obtained in the two solvents, MEA [35] and aqueous ammonia [36], which were used in the bubble-column scrubbers for CO\({}_{2}\) absorption, were shown in the same figure for comparison. It was found that the reported data for the two solvents were close to that obtained in this work. This indicated that the \(K_{G}a\) of a sodium glycinate solution is comparable to that of the MEA and aqueous ammonia solvents. The range in \(K_{G}a\) for the three solvents was 0.04-0.4 s\({}^{-1}\), depending on the operating conditions.
###### Abstract  
Accurate pre-harvest estimation of avocado (_Persea americana_ cv. [PERSON]) yield offers a range of benefits to industry and growers. Currently there is no commercial yield monitor available for avocado tree crops and the manual count method used for yield forecasting can be highly inaccurate. Remote sensing using satellite imagery offers a potential means to achieve accurate pre-harvest yield forecasting. This study evaluated the accuracies of high resolution WorldView (WV) 2 and 3 satellite imagery and targeted field sampling for the pre-harvest prediction of total fruit weight (kg-tree\({}^{-1}\)) and average fruit size (g) and for mapping the spatial distribution of these yield parameters across the orchard block. WV 2 satellite imagery was acquired over two avocado orchards during 2014, and WV3 imagery was acquired in 2016 and 2017 over these same two orchards plus an additional three orchards. Sample trees representing high, medium and low vigour zones were selected from normalised difference vegetation index (NDVI) derived from the WV images and sampled for total fruit weight (kg-tree\({}^{-1}\)) and average fruit size (g) per tree. For each sample tree, spectral reflectance data was extracted from the eight band multispectral WV imagery and 18 vegetation indices (VIs) derived. Principal component analysis (PCA) and non-linear regression analysis was applied to each of the derived VIs to determine the index with the strongest relationship to the measured total fruit weight and average fruit size. For all trees measured over the three year period (2014, 2016, and 2017) a consistent positive relationship was identified between the VI using near infrared band one and the red edge band (RENDVI1) to both total fruit weight (kg-tree\({}^{-1}\)) (R\({}^{2}\) = 0.45, 0.28, and 0.29 respectively) and average fruit size (g) (R\({}^{2}\) = 0.56, 0.37, and 0.29 respectively) across all orchard blocks. Separate analysis of each orchard block produced higher R\({}^{2}\) values as well as identifying different optimal VIs for each orchard block and year. This suggests orchard location and growing season are influencing the relationship of spectral reflectance to total fruit weight and average fruit size. Classified maps of avocado yield (kg-tree\({}^{-1}\)) and average fruit size per tree (g) were produced using the relationships developed for each orchard block. Using the relationships derived between the measured yield parameters and the optimal VIs, total fruit yield (kg) was calculated for each of the five sampled blocks for the 2016 and 2017 seasons and compared to actual yield at time of harvest and pre-season grower estimates. Prediction accuracies achieved for each block far exceeded those provided by the grower estimates.  
avocado (_Persea americana_); remote sensing; WorldView; yield; fruit size +
Footnote †: journal: Remote Sensing
# A Sector-Matching Probability Hypothesis Density Filter for Radar Multiple Target Tracking
## 4 Simulations
### Example 1: CV Motion Model
This is mainly caused by the additional measurement error caused by the sampling time diversity mentioned earlier. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & **SM-LGM-** & **LGM-** & **SM-GM-** & **Basic GM-** & **Joint-** \\  & **PHD Filter** & **PHD Filter** & **PHD Filter** & **PHD Filter** & **GLMB Filter** \\ \hline CT (\(T_{s}=1\) s) & 0.0164 s & 0.0180 s & 0.0130 s & 0.0069 s & 0.1125 s \\ CT (\(T_{s}=2\) s) & 0.0097 s & 0.0090 s & 0.0078 s & 0.0058 s & 0.1009 s \\ OSPA (\(T_{s}=1\) s) & 25.66 m & 28.71 m & 30.60 m & 33.18 m & 12.51 m \\ OSPA (\(T_{s}=2\) s) & 16.62 m & 21.61 m & 18.20 m & 28.17 m & 14.80 m \\ \hline \hline \end{tabular}
\end{table} 
Table 2: List of the average CT and average OSPA of each filter in example 1 (CV model). Figure 9: The average OSPA of different filters in example 1 (CV motion model) for 100 sample runs when \(T_{s}=1\) s (\(T_{i}=2\) ms, \(\lambda=104\), \(p_{D,k}=0.8\)). (**a**) Average OSPA distances of different filters. (**b**) OSPA locations of different filters. (**c**) OSPA cardinalities of different filters. Comparing the results in Figures 9 and 10, it can be found that the accuracy of multi-target state estimation is consistent with what we expected. The estimation accuracy of the filter deteriorates as the scan period increases and also deteriorates with the increase in target velocity.
## 5 Experiments and Results  
### Implementation Steps  
We use TensorFlow to build, train, and test the network. All the experiments were implemented in the workshop with Intel i7-8700 K CPU, NVIDIA GTX1080 ti GPU, and 32 GB RAM. Our network is based on Faster R-CNN, in which the feature extraction network is VGG16. The parameters from the first layer to the full connection layer were pre-trained on the ImageNet dataset. Other layers were randomly initialized by the Xavier method. We used datasets provided by FILR for training and testing. Additionally, the number of official training images was 8862. However, in our experiment, only person, car, and bicycle were selected for use in the experiment; there were 7856 pictures left after deleting the pictures without the above categories. We merged truck, van, and tram in other vehicle into car, and person sitting into person. The number of test sets was 1360. All images were subtracted by mean values (102.9801, 115.9465, 122.7717) before training and testing. We trained the RPN and Fast R-CNN jointly. For all the networks involved in this paper, we trained for 8 epochs. The learning rate of the first three epochs was 0.001, and that of the last five epochs was 0.0001. Using the stochastic gradient descent (SGD) training method, the momentum was 0.9 and the weight decay weight was 0.0001. The batch size was \(1\). We resized the shortest edge of each image to 600 during training and testing.