esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Method and Theory
### DFG Power Estimation  
To estimate the expected DFG output power, we began by simulating the cavity using spatially dependent rate equations to calculate the circulating intracavity power, followed by applying nonlinear wave equations for DFG to predict the output power, initially disregarding beam overlap effects [14]. This approach allowed us to assess the impact of nonlinear conversion, which introduces additional pump loss within the cavity dubbed the nonlinear loss. After deriving an initial estimate for DFG power, we calculated a nonlinear loss factor and reran the intracavity simulation. This iterative process continued until both power levels converged to a stable value, where stable is defined as a negligible change in the magnitude of the power between iterations. Ultimately, the nonlinear loss in this experiment was negligible, as the pump intensity far exceeded both the signal and DFG intensities by orders of magnitude. However, as the signal and DFG intensities approach the pump's in magnitude, nonlinear losses from depletion would become significant.  
The cavity simulation builds on our prior research on intracavity DFG sources, utilizing a spatially dependent rate equation model taking into account the transverse wave distribution inspired by [PERSON]'s methodology [24]. This model facilitates the estimation of the intracavity 1064 nm pump power. With the input pump and signal powers established, the nonlinear wave equations governing DFG are applied to estimate the output idler power, as described in Equations (4) and (5) [25]:  
\[\frac{dA_{1}}{dz}=\frac{2 id_{eff}\omega_{1}^{2}}{k_{1}c^{2}}A_{3}A_{2}^{*}e^{ i\lambda kz} \tag{4}\]  
\[\frac{dA_{2}}{dz}=\frac{2 id_{eff}\omega_{2}^{2}}{k_{2}c^{2}}A_{3}A_{1}^{*}e^{ i\lambda kz} \tag{5}\]  
In these equations, A1 denotes the amplitude of the signal wave, A2 the amplitude of the idler wave, and A3 the amplitude of the pump wave. This approach assumes a constant cavity pump power, as the DFG conversion efficiency is low enough in this configuration to render nonlinear conversion losses negligible. This assumption remained valid even at the highest pump powers, as the DFG conversion efficiency was not large enough to significantly deplete the 1064 nm power.  
We varied the PPLN's operating temperature using the TEC to achieve optimal output power, thereby enabling phase matching across different temperatures for varying PPLN lengths and signal wavelengths. The PPLN's temperature response was modeled with the MgO-doped Sellmeier equation [26]. Through this model, we reliably predict both the DFG output power as a function of input pump power and the DFG power's temperature dependence via the Sellmeier equation. These predictions, presented alongside experimental results in the Results section, show strong alignment. Table 1 details the simulation parameters, distinguishing values measured or simulated in this study as \"This work,\" while cited values reference the existing literature.
# Signal Enhancement of Helicopter Rotor Aerodynamic Noise Based on Cyclic Wiener Filtering
## 3 Envelope Spectrum Analysis  
Envelope spectrum analysis based on the Hilbert transform is a very effective method to extract characteristic frequencies, which have been widely used in bearing fault diagnosis. However, the characteristic frequency of bearing fault is often cyclostationary [31,32]. The Hilbert transform is the output response of \(s^{\prime}(t)\), obtained by passing a continuous-time signal \(s(t)\) through a linear system with an impulse response of \(1/\pi t\). Make the original signal, \(x(t)\), the real part of the envelope signal, the Hilbert transform, \(h(t)\), of \(x(t)\) the imaginary part of the envelope signal, and then take the modulo to obtain the complex envelope signal of \(E(t)\). Then, the envelope spectrum can be obtained by the Fourier transform of \(E(t)\).  
\(h(t)\) is obtained by Hilbert transformation of signal \(x(t)\):  
\[h(t)=\frac{1}{\pi}\cdot\int_{-\infty}^{\infty}\frac{x(\tau)}{t-\tau}d\tau=\frac {1}{\pi t}*x(t) \tag{9}\]  
where \(*\) indicates the convolution operator. The signals \(x(t)\) and \(h(t)\) form a new complex signal, \(z(t)\), as the analytical signal:  
\[z(t)=x(t)+jh(t) \tag{10}\]  
The envelope of the complex signal \(z(t)\) is defined as:  
\[E(t)=|z(t)|=|x(t)+jh(t)|=\sqrt{x^{2}(t)+h^{2}(t)} \tag{11}\]  
The envelope spectrum is obtained by spectral analysis of the envelope signal, \(E(t)\), which is sensitive to events related to impact force. The distribution of each frequency amplitude in the envelope spectrum is different from that in the spectrum. Specifically,the amplitude of fault characteristic frequency in the spectrum diagram is small, and the amplitude of fault characteristic frequency in the envelope spectrum diagram is very high. This is easy to identify. Nevertheless, the fault characteristic frequency is generally consistent with the cyclic frequency. Therefore, the envelope spectrum eliminates unnecessary frequency interference and highlights the noise signal with cyclostationarity, compared to the spectrum analysis. Figure 2 exhibits the difference between the envelope spectrum and the spectrum of the Robinson R44 helicopter.  
Figure 2 reveals that the characteristics of rotor aerodynamic noise can be observed more clearly from the envelope spectrum.
## 2 Models and Methodology  
### Characteristics of Three Kinds of IR-Reflector Coatings  
The transparent conductive oxide (TCO) film (marked as IRR-I) used in this study mainly consists of In\({}_{2}\)O\({}_{3}\):Sn, it possesses good thermal resistance and performance stability [22; 23]. The spectral selective characteristics of TCO film are exhibited in Figure 3. The average solar irradiance transmittance and the average reflectance of emissive radiation heat from the absorber tube at the absorber temperature of 600 \({}^{\circ}\)C are 0.699 and 0.639, respectively.  
The ideal IR-reflector coating with a fixed cutoff wavelength of 2.5 \(\upmu\)m (marked as IRR-II) has high transmittance (set as 0.99) and low reflectance (set as 0.01) below 2.5 \(\upmu\)m, which can allow vast majority of solar irradiance pass through. In the wavelength band above 2.5 \(\upmu\)m, opposite spectral characteristics of high reflectance and low transmittance appear as shown in Figure 2. In the case of an absorber temperature of 600 \({}^{\circ}\)C, the average transmittance of solar irradiance and average reflectance of emissive radiation heat are 0.99 and 0.607, respectively.  
The optimal cutoff wavelength (\(\lambda_{c,opt}\)) mainly depends on the values of solar irradiance and absorber temperature. In the condition of ambient temperature of 25 \({}^{\circ}\)C and wind speed of 2.5 m/s, the values of \(\lambda_{c,opt}\) under different values of _DNI_ and absorber temperature are calculated and presented in Table 1[19]. It is observed that the value of \(\lambda_{c,opt}\) lowers with the increasing absorber temperature for intercepting more heat loss, by contrast, with the increasing DNI, the value of \(\lambda_{c,opt}\) becomes higher for reducing the solar irradiance loss incurred by optimal IR-reflector coating (marked as IRR-III). In  
Figure 3: Spectral characteristics of the TCO film.  
Figure 2: The selectivity characteristics of ideal IR-reflector coating.  
the case of absorber temperature of 600 \({}^{\circ}\)C and _DNI_ of 800 W/m\({}^{2}\), the average transmittance of solar irradiance and average reflectance of emissive radiation heat are 0.929 and 0.946, respectively.
## 1 Introduction
### Methodology and Contributions  
The aim of the paper is to minimize both cost and emission simultaneously. Cost refers to the fuel cost utilized by the generators in supplying power, and emission refers to the pollutants emitted by the combustion of fossil fuels during the process of producing power. Simultaneous minimization of both cost and emission with proper balance is called combined economic emission dispatch (CEED) and there are 3 ways to evaluate CEED. All generators have maximum and minimum limits within which they operate, and this is called the inequality constraints. The sum of the power outputs of the generators should be exactly equal to the load demand every hour and this is called equality constraint. A proposed hybrid CSAOA is utilized to minimize the cost (ECD) and all the three types of CEED, which are the fitness functions of the work. An economic strategy called DSM is also implemented as a step before CEED to restructure the load demand, which helps in reducing the cost component furthermore. All the mathematical modeling of fitness functions and formation of CSAOA are explained in further sections of the manuscript.  
The novel contributions of the work conducted in this article can be listed as follows:  
1. A unique hybrid CSAOA is suggested as the optimization technique for this study. Prior to using it to address the DEED issue, the suggested methodology is additionally tested on six multi-dimensional and varied modal benchmark functions.
2. The test system leverages a combination of conventional generating plants and combined heat and power (CHP) plants. This allows for the evaluation of various dispatch strategies, including dynamic economic dispatch, emission dispatch, and weighted economic emission dispatch, contributing to the optimization of power generation based on cost, emissions, or a balance of both.
3. A load-shifting policy called demand side management (DSM) is also considered and all the above-mentioned studies are also conducted in the presence of DSM to highlight its benefits.  
The remainder of this paper is set out as follows: in Section 2, we outline the formalization of the issue; in Section 3, we focus on how the suggested hybrid algorithm is used here; the simulation findings are described in Section 4, and this paper is wrapped up in Section 5.
## 4 Metallic MEMS Cantilevers: Nonlinear Dynamics of the Pull-In Behavior
This force is always oriented along the normal of the boundary. So, COMSOL can self-consistently handle variations with changing vector field directions and varying parameter values. The problem of displacement is treated using continuum mechanics involving the strain tensor, stress tensor, and elasticity tensor. In this paper, the stress-strain relation is considered to be linear. This a valid approximation if the displacements are small related to the total length of the cantilever. The extension of the linear stress-strain regime is strongly dependent on the metal materials: highly elastic materials such as steel show a large one, and ductile materials such as copper show a small one. Beyond the linear regime, all materials show nonlinear behavior. Rayleigh damping has been considered, keeping the coefficients constant (mass damping parameter \(\alpha_{\text{dM}}\) and stiffness damping parameter \(\beta_{\text{dK}}\)) for the dynamic studies. As mentioned by COMSOL for many applications, it is common to assume the case of damping being proportional to the stiffness, and it is sufficient in that case to consider \(\alpha_{\text{dM}}=0\) and to define damping only using the coefficient \(\beta_{\text{dK}}=\eta/2\pi\!f\)[39]. The loss factor \(\eta\) of aluminum at a given frequency \(f=80\) kHz is 0.05% [40]. This provides the two values \(\alpha_{\text{dM}}=0\) and \(\beta_{\text{dK}}=1\times 10^{-9}\) s. As already mentioned, we kept damping constant, since variable damping was not the scope of our paper. Damping has been found to have a major influence on the dynamics of MEMS devices and has been studied in detail in [41], revealing excellent insights on this crucial point. In the following, 3D electro-mechanical simulations of MEMS cantilever (as defined in Figures 4 and 5) are performed for different geometrical and material parameters compiled in Table 1. For the meshing of the model, tetrahedra, triangles, edge elements and vertex elements have been automatically built by COMSOL. Figure 5: Schematic 2D cross-section of an electrostatically actuated cantilever, displaying the electric field lines (blue) and the electrostatic force (red) in \(xz\)-space. Regarding \(E\)-\(\rho\) charts of materials [42], foams, natural materials, polymers, elastomers, technical ceramics, composites and metals can be found in characteristic \"bubbles\". For some selected metals, we depict an orange \"bubble\" in Figure 6. The red rectangles represent the metal materials used in our simulations, and the white rectangles show further metals for comparison. To identify the influence of different values for \(\rho\) and \(E\), separately and together, artificial metals not existing in nature are also considered (red points in Figure 6). These \(\rho\) and \(E\) variations are starting from aluminum (Al) as a reference and are displayed by black arrows.
# A Deep Learning-Based Crop Disease Diagnosis Method Using Multimodal Mixup Augmentation
## 2 Related Works
### Mixup Augmentation  
Conventional deep learning models are trained using the ERM (Empirical Risk Minimization) method, which aims to minimize the occurrence of mispredictions. However, the ERM method often exhibits overconfidence in predicting a specific class, even for data points that are located in the vicinity of the distribution boundaries. To address the limitation of ERM, mixup--a data augmentation technique--is introduced [24]. It involves creating augmented samples by applying linear interpolation to pairs of images and labels. Specifically, two samples with different labels are randomly selected from the training dataset, and their images and labels are linearly interpolated to generate new samples. This process results in the creation of new data points that lie on the line connecting the original data points in the feature space. By doing so, mixup allows the model to leverage the distribution information present in the neighborhood of the training data. As a result, the model becomes more robust and capable of better generalizing better to unseen data, as it learns from a wider range of data points and distributions. Mixup effectively regularizes the model, preventing it from memorizing the training samples and improving its generalization ability. We note that mixup augmentation applications have primarily focused on unimodal data augmentation, such as video or audio data [25].  
In this paper, we propose a multimodal mixup augmentation method that extends the unimodal mixup augmentation approach. In multimodal mixup, we can combine images with environmental data, leading to more comprehensive and robust models that can handle diverse data types and improve performance across different domains.
## 2 Materials and Methods
### LIBS Calibration Model  
For LIBS plasma under the local thermal equilibrium (LTE) condition, the intensity of an emitted line of a given element \(I_{i}\) can be expressed by the Lomakin-Scherbe formula [22; 23]  
\[I=KC_{i}A_{nm}hv\beta e^{-\frac{E}{kT}} \tag{1}\]  
We can simplify the Lomakin-Scherbe formula  
\[\mathbf{I_{i}}=KC_{i}LM_{pl}e^{-\frac{E}{kT}} \tag{2}\]  
Also, we can simplify the formula  
\[I_{i}=KC_{i}LM_{pl} \tag{3}\]  
where K is a constant factor affected by the chosen spectral line and the collection efficiency of the device, \(C_{i}\) represents the content of element \(i\) in the plasma, which is the same as that in the sample to be analyzed, \(A_{\text{mn}}\) is the transition probability, \(hv\) represents the energy of a photon, \(M_{pl}\) represents the total mass of laser ablation, and \(e^{-\frac{E}{kT}}\) represents the excitation temperature under the assumption that the plasma is in the local thermal equilibrium (\(k\) is Boltzmann's constant). We can draw the conclusion that the discrepancy of the ablative mass and the plasma temperature of different substrates are the main cause of the matrix effect in LIBS. Hence, the matrix effect can be eliminated if the plasma temperature and ablative mass can be measured simultaneously. However, both of these parameters cannot be accurately and easily obtained. The magnitude of the ablative mass is usually at the nanogram level, thus, it is almost impossible for traditional weighting methods to obtain it. Meanwhile, the plasma temperature cannot be directly obtained. The plasma temperature is usually estimated by the Boltzmann method [24], Stark broadening, or two-line ratio techniques, which are widely applied in laser-induced plasmas. However, those methods require the selection of spectral lines and cannot satisfy the needs of online detection. In this work, the ablative radius can characterize the plasma, and the ablative mass can be obtained by the 3D construction to eliminate the matrix effect. Laser-induced breakdown spectroscopy is a non-destructive testing method. During the testing process, the ablation mass is generally very small and cannot be measured by weight, as Figure 4a shows. Therefore, it is necessary to find a new way to characterize the ablation amount. We can obtain its microscopic three-dimensional morphology by scanning with a microscopic three-dimensional scanner, calculate its ablation volume, and obtain its ablation amount based on the density, as shown in the formula  
\[z=f(x,y)->(f(x,y,z)=0) \tag{4}\]  
\[z=f(Rsin\theta,Rcos\theta) \tag{5}\]  
\[M=\rho*\int f(x,y)dxdy \tag{6}\]  
where \(z\) is the height of the ablation morphology, \(x\) and \(y\) are the horizontal and vertical coordinates of the morphology, \(R\) is the radius of the surface ablation morphology, \(f\) is the three-dimensional function of the ablation morphology, \(\rho\) is the density of the ablated material, \(M\) is the ablation mass, and \(\theta\) is the angle converted to the polar coordinate system.
## 5 Conclusions  
Overall, GPS has the best global performance of the three single systems, while Galileo and BDS-3 have better performance only at high latitudes. In general, the three single systems' RMS and convergence times are better in high latitude zones than in low latitude zones.  
For the dual-system combinations, the combination of systems presents a definite advantage over Galileo and BDS-3, and this advantage is more pronounced for kinematic PPP. For the dual-system combination based on Galileo, combined with GPS and BDS-3, the RMS and convergence time can be improved by 50% compared with the single system. For the dual-system combination based on BDS-3 combined with GPS and Galileo, the RMS can be improved by 50% and the convergence time can be improved by 60% compared with the single system. For GPS, the combination with Galileo and BDS-3 has little improvement in positioning performance.  
The precise products used in PPP have a great impact on positioning performance. Among the five precise products studied in this paper, the grg products have the best positioning performance for GPS, and the cod products have the best positioning performance for Galileo and BDS-3. The difference in RMS and convergence times between 2 cm and 15 min can be caused by different precise product selections.  
Conceptualization, Z.H.; methodology, Z.H.; software, F.Z. and Z.H.; validation, F.Z.; formal analysis, Z.H.; investigation, F.Z. and Z.H.; writing--original draft preparation, F.Z. and Z.H.; writing--review and editing, F.Z. and Z.H.; visualization, Z.H.; supervision, F.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Laoshan Laboratory (LSKJ202205104, LSKJ202205104_01) and the China Postdoctoral Science Foundation (2020M673669).  
The GNSS raw observation data was available at ftps://gdc.cddis.esodis.nasa.gov/pub/gnss/data/daily/ (accessed on 6 May 2022). The precise orbit and clock data was available at ftps://gdc.cddis.esodis.nasa.gov/pub/gnss/products/mgex/ (accessed on 6 May 2022).  
The authors declare no conflict of interest.
## 6 Space-Segment Optimization Aspects
### Applicable Optimization Methods for Space Segment
#### 6.8.4 Theoretical Modeling and ML-Based Approaches
As with the other complex cases, these additional constraints need to be taken into account when an implementation decision is made to use ML-based methods in practice. Figure 6: An example of a simple NN, with 1 hidden layer consisting of 3 neurons.
# Suitability of Graph Database Technology for the Analysis of Spatio-Temporal Data
## 5 Implementation
### Schema Design
#### 5.2.3 Graph Design  
The JanusGraph design can be seen in Figures 13 and 14, whereas that of TigerGraph is given in Figures 15 and 16. The motivation for the difference in the two designs is mainly due to the databases handling spatial data differently. JanusGraph indexes attributes on nodes and edges using either composite indexes [27], which index native data types on equality conditions, or mixed indexes which leverages an indexing backend on more complex data types or for complex search predicates, for example, fuzzy search on strings [28]. Figures 13 and 14 show which attributes are indexed using composite indexes and which use mixed indexes--making use of the indexing backend. Analogously to the relational design, _location_, _origin_, and _destination_ is indexed using R-trees with ElasticSearch's geo-search predicates. _Date_ is indexed using ElasticSearch for equality conditions using the java.time.Instant class--this uses a Bkd-tree indexing implementation [29]. Select temporal attributes in the medical response dataset use composite indexes as they are represented as floating point values and not as dates.  
TigerGraph puts less of a focus on indexing and more on writing efficient and fast queries. Notable differences between the structure in the JanusGraph implementation and that of TigerGraph (seeFigure 14) is that there are no indexes and the extension to spatial attributes such as the Geo-suffixed edges and Geo_Grid vertices in TigerGraph, cf. similar designs in Refs. [30; 31].