esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Process of Optimal Foil Width Determination and Results
### Results TP1
#### 5.1.1 Determination of the \(U_{\text{Smax}}\) Function Depending on _fw_  
Based on Figure 13, it can be seen that it is necessary to search for a descending function. Since the function is determined on the basis of three \(U_{\text{Smax}}\) values for three different _fw_ values, the function can have three parameters, which are denoted as \(a\), \(b\), and \(c\).  
The first choice is to interpolate with a second-order polynomial, written in (9).  
\[\text{F1: }U_{\text{Smax}}=a+b\cdot fw+c\cdot fw^{2} \tag{9}\]  
The second and third choices are similar to the decreasing exponential and power functions written in (10) and (11).  
\[\text{F2: }U_{\text{Smax}}=a\cdot e^{-b\cdot wf}+c \tag{10}\]  
\[\text{F3: }U_{\text{Smax}}=a\cdot b^{-wf}+c \tag{11}\]  
The fourth choice is a function that is part of the Elliot function [69], and is written in (12).  
\[\text{F4: }U_{\text{Smax}}=\frac{a}{fw+b}+c \tag{12}\]  
The last choice, which is the function that is derived from the hyperbolic functions, is written in (13).  
\[\text{F5: }U_{\text{Smax}}=\frac{a}{\cosh(b\cdot wf)}+c \tag{13}\]  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**TP1** & \multicolumn{6}{c}{_fw (m)_} \\
**2** & **3** & **4** & **5** & **6** & **7** & **8** \\ \hline \(U_{\text{Smax}}\) (V) & 2009.8 & 1377.2 & 968.4 & 797.7 & 683.6 & 575.6 & 510.7 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Dependence of \(U_{\text{Smax}}\) on _fw_ for TP1.  
Figure 13: \(U_{\text{Smax}}\) as a function of _fw_ for TP1.
## 4 Artificial Intelligence (AI)-Enabled 6G Networks
To achieve that level of efficiency, in contrast to 5G, 6G needs to be equipped with context-aware algorithms to optimize its architecture, protocols, and operations. For this purpose, 6G will infuse connected intelligence in its design in an integrating communication, computing, and storage infrastructure from the edges to the cloud and core infrastructure. Supporting a wide range of applications that are demanding in terms of low latency, high reliability, security, and execution time requires an AI-enabled optimization for 6G [19]. Traditional approaches using statistical analysis based on prior knowledge and experiences via the deployment of the Software Defined Network (SDN) [19] will not be any more effective due to the elapsed time from analysis to decision making. Consequently, ML and DL algorithms are used to solve several issues in networking, such as caching and data offloading [36]. In this section, we present AI-enabled 6G network protocols and mechanisms (Figure 3) and their employed self-learning ML/DL models.
###### Abstract  
An accurate inversion of the fraction of absorbed photosynthetically active radiation (FPAR) based on remote sensing data is particularly important for understanding global climate change. At present, there are relatively few studies focusing on the inversion of FPAR using Chinese autonomous satellites. This work intends to investigate the inversion of the FPAR obtained from the FengYun-3C (FY-3C) data of domestic satellites by using the PROSAIL model and the look-up table (LUT) algorithm for different vegetation types from various places in China. After analyzing the applicability of existing models using FY-3C data and MOD09 GA data, an inversion strategy for FY-3C data is implemented. This strategy is applied to areas with various types of vegetation, such as grasslands, croplands, shrubs, broadleaf forests, and needleleaf forests, and produces FPAR products, which are cross-validated against the FPAR products from the Moderate Resolution Imaging Spectro Radiometer (MODIS), Geoland Version 1 (GEOV1), and Global Land Surface Satellite (GLASS). Accordingly, the results show that the FPAR retrieved from the FY-3C data has good spatial and temporal consistency and correlation with the three FPAR products. However, this technique does not favor all types of vegetation equally; the FY-FPAR is relatively more suitable for the inversion of grasslands and croplands during the lush period than for others. Therefore, the inversion strategy provides the potential to generate large-area and long-term sequence FPAR products from FY-3C data.  
PROSAIL model; look-up table (LUT); cross-validation; FengYun-3C (FY-3C) data; fraction of absorbed photosynthetically active radiation (FPAR) +
Footnote †: journal: Remote Sensing, 2020, 12, 67
## 2 Simulation Analysis
### Analysis of Simulation Results  
To explore the influence of the outlet layout on the flow field distribution in the machining gap, a geometric model is established and the boundary conditions are set according to Section 2.1, and then the simulation calculation and post-processing are carried out. Figure 4 shows the distribution of the electrolyte flow rate in the machining gap at different times when tool A is used. From the simulation results, the distribution of the electrolyte flow rate is directly related to the layout of the tool cathode outlet. At the time of 0.06 s (180\({}^{\circ}\) rotation) and 0.12 s (360\({}^{\circ}\) rotation), the high flow rate region of the electrolyte is concentrated in the location of the liquid outlet. The flow field of the liquid outlet at the center of the tool cathode is relatively concentrated. Because the liquid outlet is at the center of the circle, even if the tool cathode rotates at different angles, the high-flow-rate area always acts on the center of the tool cathode [24]. In electrochemical machining, the flow field has a significant impact on the conductivity. The high-velocity flow zone can enhance product transport, decrease stray corrosion, and improve the machined surface quality. Further, electrolytic products accumulate at low flow rates, resulting in a significant change in the composition and concentration of the local electrolyte in the machining area, thus reducing the processing reaction rate [25]. Under the action of a non-uniform flow field for a long time, the difference between the high and low flow rates will lead to different _MRRs_. In long-period processing, the center of the groove corresponding to the center of the tool may be particularly concave.  
Figure 5 shows the distribution of the electrolyte flow rate in the machining gap at different times when tool B is used. The figures reveal that because of the influence of the layout of the tool cathode outlet, the high flow rate area in the machining gap is no longer always concentrated in the center of the tool cathode but is more dispersed around the center of the tool cathode as the tool cathode rotates. At the times of 0.06 s (180\({}^{\circ}\) rotation) and 0.12 s (360\({}^{\circ}\) rotation), the position of the liquid outlet changes, and correspondingly the position of the high velocity region is quite different. During the rotation of the tool cathode, the position of the electrolyte outlet facing the center of the tool cathode constantly changed, and the high flow velocity area was always concentrated near this position. Under different rotation angles of the tool cathode, the area suffering from high-speed electrolyte flow is enlarged to avoid the concentration of the high-velocity area in a small range. The flow field simulation shows that the flow field uniformity in the machining gap can be  
Figure 4: Electrolyte velocity distribution in the machining gap at different times using tool A.  
improved in the center of the tool cathode during the rotation of tool B. Therefore, tool B was thus more suitable for performing the ECM testing study.
## 4 Experiments and Results
### Model Parameters  
The random forest, decision tree, extra trees, and SVR models were parameterized using the default settings from the scikit-learn library without additional hyperparameter tuning. For the XGBoost (XGB) and LightGBM (LGBM) models, their specific configurations are provided in Table 4. These models were used as benchmarks to compare with the transformer-based approach.  
Regarding the transformer model, we explored various configurations for key hyperparameters, balancing performance and computational efficiency. Table 5 details the tested values for each parameter. Specifically, we evaluated different head sizes, number of heads, and filter dimensions to analyze their impact on the model's performance. The transformer block was set to 1 due to computational constraints while maintaining effective learning. Additionally, a single dense layer of size 32 was included, and dropout (0.25) was applied to mitigate overfitting. We used a batch size of 32, the Adam optimizer with a learning rate of 0.001, and the \(MSE\) loss function given the regression nature of our task. These configurations were explicitly described to ensure the clarity and reproducibility of the transformer model's setup.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **XGB** & **LGBM** \\ \hline Estimators & 100 & 100 \\ Max depth & 6 & - \\ Learning rate & 0.1 & 0.05 \\ Subsample & 0.8 & - \\ Column sample by tree & 0.8 & - \\ Random state & 42 & 42 \\ Boosting type & - & GBDT \\ Number of leaves & - & 31 \\ \hline \hline \end{tabular}
\end{table}
Table 4: XGB and LGBM parameters.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** & **Mean** \\ \hline \(v\) & 3 km/h & Velocity \\ \(\Delta t\) & 5 s & Time period \\ \(B_{W}\) & 10 MHz & Bandwidth of each \(N_{B}\) band \\ \(N_{Bp}\) & 1 to 3 & Number of consecutive bands that the PU can use \\ \(P\) & 23 dBm & Power transmitted by the PU \\ \(\alpha\) & 3.8 & Path-loss exponent \\ \(\beta\) & \(10^{3.453}\) & Path-loss constant \\ \(\sigma\) & 7.9 dB & Standard deviation \\ \(N_{0}\) & \(-114\) to \(-174\) dBm/Hz & Noise power density \\ \(\eta\) & 10 dBm & Leaked power to adjacent bands \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters and values for data generation.
## 2 Materials and Methods
### Data Overview  
The micrometeorological data, including air temperature, humidity, friction velocity, wind direction and the standard deviation of the crosswind velocity, were acquired at a 39.6 m height from the ground level flux tower belonging to ChinaFLUX [49]. The raw data observed by the EC system was collected and saved by the data loggers. According to the local conditions, data were downloaded through the cable for subsequent quality control, standard processing and product formation [50]. The standard protocols for processing the raw data of EC measurements were proposed by ChinaFLUX, which included raw data analysis [51], sonic temperature correction [52], coordinate rotation [53], WPL correction [54], frequency loss correction [55], canopy storage estimation [56], steady-state test and turbulent characteristics analysis [57], friction velocity threshold filtering [58], outlier removal [59], and energy balance closure evaluation.  
The land cover map of 2010 was obtained from a 30 m spatial resolution Global Land Cover product (GlobeLand30, [[http://globaland30.org/](http://globaland30.org/)]([http://globaland30.org/](http://globaland30.org/))) from the National Geomatics Center of China [60]. To characterize the seasonal changes of the vegetational flux measurement over the source areas, twelve month-by-month cloud-free Landsat-5 Thematic Mapper (TM) scenes, from 2003 to 2010, with a 30 m spatial resolution were selected from the U.S. Geological Survey ([[http://earthexplorer.usgs.gov](http://earthexplorer.usgs.gov)]([http://earthexplorer.usgs.gov](http://earthexplorer.usgs.gov))). Since no cloud-free image can be found for June at a given period, an image from 28 May 2004 was used as a replacement.  
The enhanced vegetation index (\(EVI\)) is closely related to the leaf area index (LAI), canopy architecture, photosynthesis and transpiration, which were selected as a surrogate of the CO\({}_{2}\) and H\({}_{2}\)O fluxes [61], which was calculated as:  
\[EVI=2.5\times\frac{(\rho_{NIR}-\rho_{RED})}{(\rho_{NIR}+6\times\rho_{RED}-7.5 \times\rho_{BLUE}+1)} \tag{1}\]  
where \(\rho_{NIR}\), \(\rho_{RED}\), and \(\rho_{BLUE}\) are the surface reflectance of the near-infrared, red and blue bands of the Landsat series image, respectively.
# An Efficient Single-Anchor Localization Method Using Ultra-Wide Bandwidth Systems
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l l} SAL & single-anchor localization \\ GNSS & Global Navigation Satellite System \\ MPC & multipath component \\ NLOS & non-line-of-sight \\ RF & radio frequency \\ RFID & radio-frequency identification \\ RSSI & received signal strength indicator \\ LF & location fingerprints \\ AOA & angle-of-arrival \\ \end{tabular}  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & \(\mathbf{N_{a}=1}\) & \(\mathbf{N_{a}=2}\) & \(\mathbf{N_{a}=3}\) \\ \hline SAL-Normal & 3.4 ms & 6.8 ms & 10.2 ms \\ Proposed SAL method & 3.4 ms & 3.4 ms & 3.4 ms \\ \hline \hline \end{tabular}
\end{table}
Table 3: Elapsed time comparison.  
UWB ultra-wideband CIR channel impulse response ToF time-of-flight TDOA time-difference-of-arrival TOA time-of-arrival PDOA phase-difference-of-arrival ML maximum likelihood LOS line-of-sight RMSE root mean squared error CDF cumulative distribution function
## 4 Results
### Spatial Distribution Characteristics of Public Hospital Accessibility in Shenzhen
The Lorenz curve and the Gini coefficient are not only classical tools commonly used in economics but are also widely employed in evaluating public service facilities to assess the degree of imbalance in the spatial distribution of resources. The Gini coefficient ranges from 0 to 1, where 0 denotes perfect equality and 1 denotes perfect inequality. In this paper, based on the accessibility of public hospitals and the distribution of street populations, the Lorenz curves for Shenzhen and its metropolitan core areas, both inside and outside the city, are plotted, and the Gini coefficients for 10 districts are calculated, as shown in Figure 14 and Table 4.  
(4) Shenzhen exhibits overall inequality, with significant differences in equity levels within the metropolitan core and more consistency within the districts. The highest Gini coefficient is for Shenzhen as a whole, at 0.369, followed by Bao'an and Dapeng Districts, with Gini coefficients of 0.307 and 0.308, respectively. The level of equity in the other districts is more uniform. It is worth noting that a low Gini coefficient only indicates that the difference in the level of accessibility within the district is not significant, rather than high. Combined with the analysis in Figure 13, it can be seen that Bao'an District has a high level of accessibility to public hospitals but a low level of internal equity, suggesting that relatively localized adjustments to hospital resources would be sufficient. Dapeng District has the lowest level of accessibility and insufficient internal equity, requiring both global inputs and localized adjustments. Fotian, Luohu, and Nanshan Districts have high levels of accessibility and good internal equity, while Longhua, Guangming, Pingshan, and Yantian Districts require more overall resource allocation. Figure 13: Accessibility statistics of Shenzhen districts.
## 2 Results and Discussion
### Polyphenic Compounds  
The accumulation of polyphenolics in seeds during germination was significantly affected by the assessed temperatures (Table 1). The accumulation of total phenolic content (TPC) was greatly influenced by the temperature, which peaked in leaves at 30 \({}^{\circ}\)C (940.73 mg/100 g DW) and stimulated the early emergence of seed radiics (Table 1). Seed phenolic compounds accumulated for seeds at both 25 \({}^{\circ}\)C (185.23 mg/100 g DW) and 35 \({}^{\circ}\)C (165.28 mg/100 g DW). The highest TPC was found at 30 \({}^{\circ}\)C in leaves and then in roots. Similar results were found for flavonoids and \(o\)-hydroxy phenols. The increase in these compounds could also be linked to a plant's capability to adapt to different temperature regimes, which might result in biochemical compositions that have beneficial synergistic effects. The polyphenols, flavonoids, MDA, and antioxidant capacity in morringa seed at 4 \({}^{\circ}\)C, 20 \({}^{\circ}\)C, and 30 \({}^{\circ}\)C showed that maximum phenolic compounds were observed at 20 \({}^{\circ}\)C and 30 \({}^{\circ}\)C [33]. [PERSON] et al. [34] investigated the phenolic composition of different organs of _Morringa oleifera_, such as leaves, hypocotyls, and roots, and reported values ranging between 2.185 \(\pm\) 0.089 mg GAE/g and 3.805 \(\pm\) 0.304 mg GAE/g. Similar results were seen in faba beans, where polyphenolics were synthesized and accumulated at approximately the same time as seed priming throughout the germination process [35]. The authors also proposed that mung bean seeds may germinate, affecting phenolic composition, seed bitterness, astringency changes, and seed quality [36]. The bitterness and astringency of  
Figure 2: Staining of morringa seed kernels with **(a)** different concentrations of tetrazolium (0.1%, 0.5%, and 1%) under different temperatures; **(b)** stained seed kernels; and **(c)** a close-up showing the unicellular epidermal layer of the inner integument.  
plants are influenced by phenolic biosynthesis. During seed germination, the increase in the polyphenic content of quinoa seeds and their antioxidant activity may confer several health benefits [37]. Likewise, [PERSON] et al. [38] demonstrated that temperature affects the germination of _Vitis californica_ seeds, which have substantial levels of total phenolics and phenolic composition profiles demonstrating high diversity. A recent study on moringa leaves stated that the TPC values obtained for the leaf extracts ranged between 21.7 \(\pm\) 1.6 mg GAE/g and 39.1 \(\pm\) 3.3 mg GAE/g, regardless of the extraction conditions used [39]. Besides the already cited factors affecting the polyphenols in moringa, the developmental stage of the different organs analyzed seems to have a crucial effect.
# Underwater Electromagnetic Sensor Networks--Part I: Link Characterization +
## 3 Results  
### Seabed Influence in Simulations  
The results from simulation of antennas in sea water and simulations of antennas placed on seabed at several distances (d) are compared in Figure 7. As it can be seen, sea water attenuation is dramatically increased as frequencies are higher, which is coherent with theoretical graphs in literature [8]. Moreover, there are clearly two distinguishable areas, A and B, separated by a black dashed line in the figure. Side A is defined by frequency bands where attenuation is not significantly influenced by seabed layer, whereas B comprises the spectrum area where attenuation is greatly affected by the seabed. It can be inferred that, in this case, energy is travelling through the sea-to-seabed interface, as the effect of the seabed layer is to decrease the attenuation at all frequencies. However, for low frequencies and distances, or for very low frequencies, attenuation seems to be independent from the medium as eddy currents are not very important.  
Figure 7: Influence of seabed layer inclusion in simulations.  
Another interesting feature that can be extracted from this graph is the displacement of minimum attenuation with the distance. The longer the distance between nodes, the lower frequency for minimum attenuation values. This effect is studied in more detail in Section 3.5.
# Quality Assessment of DSMs Produced from UAV Flights Georeferenced with On-Board RTK Positioning
## Materials and Methods
### Reference Network  
Points to be used as ground control or as checkpoints for block orientation quality were established and measured, at least three times each, with dual-frequency geodetic receivers and different GNSS techniques: by Stop&Go and RTK from a local master station, and by NRTK with respect to the CORS Network to determine the local master station. On average, each point was occupied four times; in cases of less than four occupations, the mean position was taken. Otherwise, a trimmed mean position was computed. The average standard deviation turned out to be 7 mm for the horizontal coordinates, and 11 mm for the elevations. Finally, in order to assess DSM accuracy, more than 1600 points were measured with a GNSS NRTK survey over the area on hard surfaces (asphalt and concrete) and on meadows; the expected accuracy in elevation of these measurements, which was executed with the same GNSS receiver but under varying GNSS constellations and without repeated site occupation, can be assumed to be around 1-1.5 cm. The positions of the GCPs and of both types of checkpoints are shown in Figure 2.
# Research on Innovative Apple Grading Technology Driven by Intelligent Vision and Machine Learning
## 3 Experiment and Result Analysis
### Fruit Stem Detection Experiments
#### 3.2.5 Model Comparative Experiments
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model Name** & **P/\%** & **R/\%** & **[EMAIL_ADDRESS]/\%** & **[EMAIL_ADDRESS]:0.95/\%** & **GFLOPs** & **Size/MB** \\ \hline YOLO Series & & & & & & \\ YOLOv3-t & 94.9 & 89.2 & 95.6 & 70.0 & 18.9 & 24.4 \\ YOLOv5-n & 95.9 & 89.9 & 95.2 & 68.6 & 4.1 & 3.7 \\ YOLOv5-s & 93.0 & 91.9 & 96.1 & 70.1 & 15.8 & 13.8 \\ YOLOv6-n & 95.0 & 91.0 & 94.7 & 69.8 & 11.8 & 8.3 \\ YOLOv6-s & 96.1 & 92.2 & 96.0 & 70.4 & 44 & 32.8 \\ YOLOv8-n & 92.1 & 87.3 & 95.8 & 68.9 & 8.1 & 6.2 \\ YOLOv9-t & 95.1 & 89.9 & 95.9 & 68.3 & 6.4 & 4.0 \\ YOLOv10-n & 92.2 & 86.2 & 94.1 & 65.1 & 8.2 & 5.5 \\ YOLOv11-n & 95.9 & 91.0 & 94.8 & 67.4 & 6.3 & 5.2 \\ \hline Non-YOLO Series & & & & & & \\ FasterRCNN & 96.0 & 90.2 & 96.0 & 69.9 & 134 & 315.1 \\ GFL & 95.6 & 89.2 & 96.6 & 68.2 & 128 & 245.8 \\ RTMDet & 95.4 & 88.8 & 95.3 & 68.3 & 8.1 & 79.5 \\ YOLOx & 95.9 & 88.2 & 95.5 & 67.4 & 7.6 & 61.5 \\ TOOD & 94.8 & 91.4 & 96.2 & 67.6 & 123 & 243.9 \\ \hline Ours (FDNet) & 95.6 & 91.0 & 96.9 & 69.8 & 13.6 & 14.2 \\ Ours (FDNet-p) & 96.3 & 89.4 & 96.6 & 67.9 & 3.4 & 2.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparative experiments on different detection models. By presenting detection effect images from real-world scenarios, this study further demonstrates the strong robustness and high detection accuracy of the proposed FDNet-p. For fairness in evaluating detection performance, lightweight detection models from the YOLO series (v5, v6, v8, v9, v10, and v11) were also used for comparison. The specific detection result comparisons are illustrated in Figure 13.  
Figure 12: Performance comparison of lightweight detection models. Figure 13: Comparative effectiveness of different detection algorithms. (**a**) FDNet-p. (**b**) YOLOv11-n. (**c**) YOLOv10-n. (**d**) YOLOv9-n. (**e**) YOLOv8-n. (**f**) YOLOv6-n.  
Four different scenarios were selected for analysis: (I) Multi-target with complex background (Sample 1): FDNet-p achieved the highest detection accuracy across the three different apples, while other models demonstrated lower accuracy. (II) Single target with complex background (Sample 2): The YOLOv9-n model mistakenly identified the background as a stem. (III) Simple background with sepal interference (Sample 3): Other models misidentified sepals as stems to varying degrees, while FDNet-p accurately identified the stems. (IV) Overhead view angle (Sample 4): FDNet-p exhibited the best detection performance.