esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Numerical Study: Physical Understanding of Wave Propagation Mechanism
### Numerical Parameters
#### 2.1.4 Grid Convergence Study  
The convergence of the result to the exact solution in terms of wave propagation speed and wave amplitude was crosschecked by performing a grid convergence study on four different meshes. The meshes were created using the Gambit meshing software and are composed of respectively 12 k, 49 k, 110 k, and 196 k quadrilateral cells. As can be seen in Figure 4, giving detailed views of the meshes, respectively 1, 2, 3, and 4 elements were considered over the leak width. The same cell size was used over the complete mesh. The cell size \(\Delta x=\Delta y\) is defined in Figure 4 for each of the cases and had a direct influence on the accuracy of the shock arrival time and therefore on the wave propagation speed. At a duct axial distance \(1.7D_{d}\) of the leak position, this resulted in a maximum error of \(\delta v=7.8,3.9,2.6\), and 1.9 m/s going from the coarse mesh to the finest mesh. Obviously, this error on the velocity reduced with propagation distance from the leak position.  
The following graphs in Figure 5 give the amplitude of the wave and the wave velocity for an increasing number of cells. The data of both graphs were extracted for a propagation distance of flow time \(t=5.14\times 10^{-5}\) s corresponding to a \(1.7D_{d}\) propagation distance of the wave from the leak position. The same tendency was obtained at any flow time or propagation distance. The wave velocity was computed by considering the first rise in pressure as wave arrival. According to [PERSON]'s equation in 2D,  
Figure 4: Detailed views of the meshes for mesh dependency analysis.  
Figure 3: Initial and boundary conditions.  
the amplitude of the wave should equal 1.3 kPa. Refining the mesh brought the amplitude closer to the theoretical value, but a relative error of 30% was still observed.  
The main differences in the solution between the four refinements are the smoothness of the shock, resolution of the shock front, and the correct description of the flow behavior close to the leak. Clearly, the finest mesh gave a much sharper shock front, and therefore allowed locating the shock front and the wave velocity with higher accuracy. The wave velocity evolved to an asymptotic value of 372 m/s (considering a power fit) for an increasing number of cells. Consequent extra refinement would be needed to get a slight improvement of the velocity accuracy and would therefore significantly increase the computation time. These reasons explain why the finest mesh was chosen as the final grid refinement. It is important to notice that the high refinement of the duct induced high computation times. Since the length of the duct was determinant for the computational cost of the simulation, the geometry was cut into two, and a symmetry boundary condition was added on the axial centerline of the leak. This reduced the computational time by around 50% and would allow simulating longer duct lengths.
# Nonlinear Metasurface for Structured Light with Tunable Orbital Angular Momentum
## 2 Results
### Beam-Converter Metasurface Design
As illustrated in Figure 3, in the vicinity of the central wavelengths of these resonances, the phase of the transmitted light changed rapidly by 180\({}^{\circ}\). The wavelengths corresponding to the electric and magnetic resonances can be controlled by changing the side-length of the nano-blocks. As the side-length increased, the central wavelengths of both the electric and magnetic resonances increased, and at the same time they become closer to each  
Figure 2: Design of the metasurface. (**a**) Metasurface consisting of a square lattice of square-blocks made of arsenic trisulfide (As\({}_{2}\)S\({}_{3}\)) chalcogenide glass (ChG) on a glass substrate. The geometric parameters of the metasurface are: height \(h=400\) nm; lattice constant \(a=930\) nm; and the side-length of the blocks is denoted by \(l\). The refractive index of the ChG film is \(n_{0}=2.43\). The refractive index of glass is \(n_{g}=1.5\). (**b**) The transmittance of an array of ChG blocks with \(l=700\) nm. Two resonances are indicated at the wavelengths of 1464 nm and 1510 nm. (**c**) Electric and magnetic fields in the center of the unit cell (cross-section \(y=0\)) at the wavelength of 1464 nm. (**d**) Electric and magnetic fields in the center of the unit cell (cross-section \(x=0\)) at the wavelength of 1510 nm. other. Once the two resonances overlap, a nearly unitary transmission with the phase change spanning \(360^{\circ}\) can be achieved [25; 26; 37]. In contrast, when the side-length decreased, the electric and magnetic resonances were shifted to shorter wavelengths and further away from the wavelength of interest. To obtain the desired phase difference for the design shown in Figure 1a, numerical simulations were performed with the side-length of the nano-block swept from \(l=400\) nm to \(l=900\) nm. Figure 3 shows the spectra of the transmittance and the phase of the transmitted light through the metasurface with different side-length for a normally incident light. We chose two different size lengths, \(l=400\) nm and \(l=700\) nm, at the wavelength of 1550 nm, as indicated with the black crosses in Figure 3. The transmittance of both structures was higher than 70% (Figure 3a) and the phase difference between the two structures was \(90^{\circ}\) (Figure 3b).
# Design of a Compact 1-MV-Class Radial Closing Switch for Tesla Type Generator
## 4 Optimization Techniques
### Optimization
#### 4.2.5 Connections to Tesla and Water Load  
In addition to the four optimization techniques mentioned above, the geometry of the central HV electrode connectors with both the Tesla transformer and the water load must also be optimized, as presented in Figure 13.  
The technique employed uses a part of a spherical prolate shape, instead of the traditional spherical shaping method. By adjusting the semi-axis \"a\" and \"b\", an optimum field enhancement factor (FEF) can be obtained. For example, for the connector toward Tesla, as presented in Figure 13, if \"a1\" is set as 40 and \"b1\" is set as 80, the FEF is obtained as 1.17. As shown in Figure 13, for this geometry and using an electrostatic solver, the \(E_{max}\) is calculated as 150 kV/cm as follows: first, using the formula:  
\[E_{max}=E^{\prime}_{av}\cdot f=\frac{2U_{max}}{D_{i}\ln(D_{o}/D_{i})}f \tag{4}\]  
\(E^{\prime}_{av}\) can be estimated as 128 kV/cm, where \(D_{o}\) is 430 mm, \(D_{i}\) is 190 mm, and \(U_{max}\) is 1 MV. \(f\), the field enhancement factor (FEF), is simulated to be 1.17 for a spherical prolate shaped electrode, which is much smaller than that obtained by the simpler spherical shaping technique, for which the FEF is usually obtained with values between 1.3 and 1.5. A small value for the FEF not only reduces the breakdown probability of oil or water, but also decreases the \(E_{max}\) on the surfaces of the two corresponding insulators.  
As a summary, the following characteristics of the switch optimization were successfully obtained:  
1. For a voltage of 1 MV, the final electrode design generates an electric field up to 1010 kV/cm, which should produce the electrical breakdown of the pressurized gas;
2. For a safe and reliable operation, the electric field on the four surfaces of the gas chamber insulators is maintained at a relatively low value;
3. The two metallic connectors mounted on the central HV electrode have a relatively low FEF, which allows their safe operation under oil and water;
4. The volume of the gas chamber has been reduced to only 3.2 L.  
Figure 12: The gas chamber before (**a**) and after (**b**) optimization. The gas volume reduced from 8.3 L (**a**) to only 3.2 L (**b**).
###### Abstract  
Hilly mountainous regions are ecologically complex, featuring diverse environmental ecosystem services (ESs) and intricate interactions. However, the variability, drivers, and management of these ESs remain poorly understood, particularly in regions with significant topographical and climatic heterogeneity. This study focuses on the southern hilly mountain belt of China, examining five key ecosystem services: food production (FP), carbon storage (CS), water yield (WY), habitat quality (HQ), and soil conservation (SC). This study examines these ESs across long-term, pixel, and regional scales, exploring the interactive relationships and identifying the driving factors and cluster characteristics. The results indicate the following: (1) Over the past 23 years, although food production and carbon storage have increased, habitat quality has declined. (2) From a spatial perspective, the differences in trade-offs and synergies across the years are relatively small. However, significant differences are observed when considering continuous temporal change, and trade-off relationships are generally prevalent. Additionally, the distribution of trade-offs and synergies is also influenced by a combination of factors. (3) Climatic, vegetation, topographical, and socioeconomic factors are key factors influencing the distribution and changes in ESs. For instance, climate-vegetation interactions enhance carbon storage and soil conservation. Socioeconomic factors, though less impactful, optimize ESs through land management and policy. (4) We found that the ecological priority region covers the largest area, followed by the hilly agricultural development zone, the mountainous agricultural and forestry development zone, and the integrated ecological security zone. These findings deepen our understanding of ESs in hilly mountainous regions, providing actionable insights for enhancing conservation and sustainable management in complex landscapes.
# Data Fusion in Agriculture: Resolving Ambiguities and Closing Data Gaps
## 3 Discussion
### Comparison of the Results Yielded by Fused and Individual Sources of Data
The use of data fusion for the estimation of soil variables in agricultural areas is more complex, due to the significant differences between the variables that are normally considered. In these cases, data fusion can be effective at improving the estimation of some variables [30; 33; 35; 36], but may fail to produce any improvement for others [31; 33; 35; 95], mostly because those variables do not correlate well with any of the current soil sensors. Some studies also remark that no single data fusion approach works for all soil variables of interest, so a comprehensive variable estimation may require the use of multiple techniques [35; 92]. Conversely, the usefulness of data fusion for determining homogeneous zones and producing soil maps, which usually employ soil property measurements, can also be highly dependent on the sensed data, as the inclusion of certain variables can actually decrease accuracy due to the weak link with the properties being used to determine homogeneous areas [90]. This is not a trivial task, and although some studies report encouraging results [28; 95], in many cases, further research is needed to better understand how to properly explore the data collected by the sensors [25; 27; 76]. This has led some authors to remark that the choice of data is more important than the data fusion method employed [90]. Another application in which the use of data fusion has some particular characteristics is the positioning and navigation of autonomous vehicles to be deployed in agricultural areas. In all studies considered in this article, fusing different sensors led to decreased errors, as long as the data fusion methods were calibrated correctly [11; 12; 13; 14; 16; 17; 98]. It is worth noting that a variety of different vehicles were considered in those studies, and the error requirements varied from case-to-case. As a result, the level of success of the data fusion process is relative; that is, the same error levels could be considered a success or a failure depending on the vehicle and environment where it will be used. Some of the studies considered in this article use types of data that are more particular and do not fit a general category, normally in combination with some of the more commonly used variables [24; 44; 64; 67; 93]. In general, the performance of the data fusion reported on in those cases compares favorably with single data sources, although some difficulties related with data compatibility have also been reported [82; 129].
## 2 Materials and Methods
### Volatile Metabolite Extraction and GC-MS Analysis  
Frozen samples (~0.2 g) were ground in liquid nitrogen and transferred into 20 mL headspace vials. Each vial was supplemented with 0.2 g NaCl and 20 \(\upmu\)L of internal standard solution (10 \(\upmu\)g/mL 2-octanol-d17). Volatile compounds were extracted using headspace solid-phase microextraction (HS-SPME) with a 120 \(\upmu\)m DVB/CWR/PDMS SPME Arrow fiber at 60 \({}^{\circ}\)C for 15 min, followed by thermal desorption at 250 \({}^{\circ}\)C for 5 min. GC-MS analysis was performed using a DB-5 MS capillary column (30 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m, Agilent) with helium as the carrier gas (1.2 mL/min). The oven temperature was programmed from 40 \({}^{\circ}\)C (holding for 3.5 min) to 280 \({}^{\circ}\)C with ramping steps of 10, 7, and 25 \({}^{\circ}\)C/min. The mass spectrometer operated in EI mode (70 eV). The MS transfer line temperature was set at 280 \({}^{\circ}\)C, the ion source temperature at 230 \({}^{\circ}\)C, and the quadrupole temperature at 150 \({}^{\circ}\)C. The ionization voltage was 70 eV, scan speed was 2.5 scans/s, and the solvent delay was set to 5.0 min. Data were acquired in selected ion monitoring (SIM) mode. Metabolites were identified based on retention time and characteristic ions using an in-house reference library. Only compounds with a spectral similarity (cosine match) \(\geq\)80% and a retention index (RI) deviation \(\leq\)30 units were considered reliably annotated.  
Quantification was performed using the peak area of selected quantifier ions under selected ion monitoring (SIM) mode, with internal standard correction using 2-octanol-d17. The workflow followed the semi-quantitative protocol described by [PERSON] et al. (2022), in which selected ions were extracted under SIM mode and normalized using an internal standard for each sample [23]. Extraction, detection, and primary data processing were performed by Metware Biotechnology Co., Ltd. (Wuhan, China). To address potential issues arising from co-eluting isomers, metabolite identification was further supported by manual verification of diagnostic fragment ions and retention behavior. In cases in which stereoisomers could not be confidently resolved due to overlapping ion fragments or retention times, compounds were annotated as combined isomeric forms (e.g., \"\(\beta\)-ocimene isomer\") following conservative reporting standards.
## 2 Formula for the Spring Constant \(K_{x}\) (Horizontal Motion)  
In this chapter, based on the assumptions given by [PERSON] [9], the formula for the spring constant \(K_{x}\) and dimensionless spring coefficient \(\beta_{x}\) are derived. The theoretical foundations used to derive these formulas are also given. The final form of the formula for \(K_{x}\) can be found, among others, in papers [10; 11; 13; 14; 16]. However, these works do not provide either the derivation of these relations or the precise theoretical assumptions.  
If a horizontal force is applied to the foundation at the level of the contact zone between the foundation base and the ground (see Figure 1a), the foundation will move in the direction of this force. As engineering experience shows, under conditions that exactly correspond to those established for soil compression, it can be assumed that there is a linear relationship between the foundation sliding and the average sliding shearing stress generated along the plane of contact with the foundation base [9], i.e.,  
\[\tau_{av}=c_{\tau}S_{e}^{\prime}, \tag{4}\]  
where  
\(\tau_{av}\) [Pa]--the average shearing stress in the soil at the plane of contact with the foundation; \(S_{e}^{\prime}\) [m]--the elastic part of the total horizontal sliding of the foundation base under the action of \(\tau_{av}\).  
[PERSON] [9] assumed \(S_{e}^{\prime}=S_{av}^{\prime}\) and stated that the average value of the horizontal sliding along the surface area \(2a\) by \(2b\) equals  
\[\begin{array}{l}S_{av}^{\prime}=\frac{4a}{\pi}\frac{1-\
u^{2}}{E}\Big{(} \frac{1}{a}\mathrm{sinh}^{-1}\alpha+\mathrm{sinh}^{-1}\frac{1}{\alpha}-\frac{1} {3}\Big{[}\frac{1}{a^{2}}\Big{(}\sqrt{1+\alpha^{2}}-1\Big{)}+\sqrt{1+\alpha^{ 2}}-\alpha\Big{]}+\\ \frac{\
u}{1+\
u}\Big{\{}\frac{1}{a}\mathrm{sinh}^{-1}\alpha+\frac{1}{3}\Big{[} \sqrt{1+\alpha^{2}}-\alpha-\frac{2}{\alpha^{2}}\Big{(}\sqrt{1+\alpha^{2}}-1 \Big{)}\Big{]}\Big{\}}\Big{)}\tau_{av},\end{array} \tag{5}\]  
where  
\(\alpha=\frac{\alpha}{b}\) [-]; \(E\) [Pa]--[PERSON]'s modulus of soil; \(\
u\) [-]--[PERSON]'s ratio.  
By substituting \(\tau_{av}=\frac{H}{A}\left[\frac{\text{N}}{\text{m}^{2}}\right]\) and \(a=\frac{1}{2}\sqrt{A\cdot\alpha}\) [m] (knowing that \(A=2a\cdot 2b\) [m\({}^{2}\)]) into Equation (5), and by making minor transformations, the following is obtained:  
\[\frac{H}{S_{av}^{\prime}}=\frac{E}{1-v^{2}}\sqrt{A}\cdot k_{\tau}, \tag{6}\]  
where  
\[k_{\tau}=\frac{\pi}{2\sqrt{\left(\frac{1}{2}\text{sinh}^{-1}a+\text{sinh}^{-1} \frac{1}{2}-\frac{1}{3}\left[\frac{1}{2}\left(\sqrt{1+\pi^{2}}-1\right)+\sqrt{1 +\pi^{2}}-a\right]+\frac{1}{\text{m}^{2}}\left[\frac{1}{2}\text{sinh}^{-1}a+ \frac{1}{2}\left[\sqrt{1+\pi^{2}}-a-\frac{1}{2}\left(\sqrt{1+\pi^{2}}-1\right) \right]\right]\right)}}. \tag{7}\]  
By substituting \(E=2G(1+v)\) into Equation (6), \(K_{x}\) is obtained  
\[K_{x}=\frac{H}{S_{av}^{\prime}}=2G(1+v)\sqrt{A}\cdot\beta_{x}, \tag{8}\]  
where  
\[\beta_{x}=\frac{1}{1-v^{2}}\cdot k_{\tau}. \tag{9}\]  
The spring constant according to Formula (8) has the dimension [N/m], whereas the spring coefficient (9) is dimensionless. \(G\) [Pa] is the shear modulus of soil.
## 4 Materials and Methods
### Statistical Analysis  
The Shapiro-Wilk test was used to determine the nature of the distribution of the parameters extracted using all the decomposition methods, namely, EMD, DWT, and WPD. The parameters having a \(p\)-value of \(\leq\)0.05 were confirmed to have non-Gaussian distribution. On the other hand, the parameters having a \(p\)-value of \(\geq\)0.05 were considered to follow the Gaussian distribution. The parameters having non-Gaussian distribution were tested for their significance using the Mann-Whitney U test, also regarded as the Wilcoxon rank-sum test, using IBM SPSS Statistics software (ver. 24, IBM Corporation, Armonk, NY, USA) [64]. This test does not require the normal distribution of the parameters due to its nonparametric nature [65]. For parameters with Gaussian distribution, the \(t\)-test was used for statistical analysis. The Mann-Whitney U test/\(t\)-test was performed on the parameters of the different groups in the population under investigation, namely Category-B and Category-C. Here, Category-B refers to the bhang-consuming population and Category-C indicates the control group. There were 100 samples in each of the groups; therefore, each of the groups had 100 samples in the Mann-Whitney U test/\(t\)-test.
## 2 Design and Fabrication
### Design and Circuit Analysis
#### 2.1.3 Embedded Capacitor  
The three components of the capacitor \(C_{C}\), the resistor \(R_{C}\), and the inductor \(L_{C}\) are introduced to construct the model of the embedded center dendritic capacitor, taking the inductive effect and ohmic loss into consideration. However, the complex structure of the center capacitor results in difficulties in calculating the above parameters. Therefore, the simulated S-parameters and Y-parameters are introduced to calculate the capacitance, resistance, and inductance as given in Equations (9)-(11) [21]. In addition, the capacitance of the center capacitor can be tuned to match the design target by optimizing the size. The frequency-dependent simulation results of the capacitance, resistance, and inductance of the optimized center capacitor are shown in Figure 3.  
\[C(pF)=\frac{1\times 10^{12}\times imag[Y(1,1)]}{2\pi f}\, \tag{9}\]  
\[L(nH)=\frac{1\times 10^{9}}{2\pi f\times imag[Y(1,1)]}\, \tag{10}\]  
\[R(\Omega)=\textit{real}[\frac{1}{Y(1,1)}]. \tag{11}\]  
Figure 3: \(Cont.\)
## 2 Preliminary Studies on Federated Learning and Deep Reinforcement Learning
### Federated Learning for Privacy-Preserving Assurance
#### 2.1.2 Federated Optimization  
A converged concept of FL and federated analytics (FA) introduces federated optimization. By aiming to protect the user's personalization, this distributed scheme seeks to ensure a collaborative statistical report or model learning from decentralized data without raw sharing. By successfully deploying this paradigm, the efficiencies in backhaul communications, IoT data complexities, and differential privacy levels are delivered to the service providers [50]. Each aspect is tackled individually before the convergence procedure. FA, a statistical analysis, or data-science-based application requires collaborative querying data from local users, executes the local computation on clients, and generates the output aggregation on cloud tier. These executions give major benefits to data exploration and analysis visualization for specific application strategies. Since FL focuses on constructing deep models, FA can use it for serving data-science-based services.  
Figure 3: Transition procedures between participants, edge, and parameter server in edge FL.  
In a massive IoT environment, federated optimization must consider the robustness, fairness, customization, and privacy level of multi-service prioritization. Secure computation and transmission are significant objectives to obtain in FA and FL. Malicious clients must be detected and secured from the participant selection process, which was studied in [51]. The authors proposed a secure aggregation procedure that aimed to support numerous clients to achieve better communication and computation performances. Furthermore, [52] presented a system design in high-level architecture in which multiple applications were described, including next-word prediction, content suggestions, and item ranking. The beneficial factors and implementation guidelines offer interesting knowledge for further field expansion of research and development.  
From a communication perspective, to fully support the applicability of federated optimization, there are several modifications that need to be made in network architecture from infrastructure to the application layer. The operations on quick data queries and model building require zero-touch management to handle heavy-congestion states and future massive IoT participants.
## 5 Experimental Evaluation
### A3c-L4s Evaluation  
We have established an additional test environment using virtual machines specifically for evaluating A3C-L4S. Figure 24 shows this setup, which now includes two routers instead of one. This network configuration involves a source host, destination host, and two intermediary routers.  
To gather more data, we have two iperf3 experiments going on simultaneously in each path in different directions. We now have four different data streams from which we collect data to feed our A3C model.  
We utilize the normalized L4S data from the kernel logs generated from experimentation and testing to pass into the DDPG agent's observation space for training. Our A3C model runs the training session for a certain number of steps. Each episode resets and starts from a new data point. We trained our A3C model for 100 episodes. We plot the average reward in each episode against the episode index to observe how the rate and utility improve over each training session as the A3C learns more.