esa-sceva/satcom-chunk-collection huggingface dataset:
# Ecosystem Services Assessment for Their Integration in the Analysis of Landslide Risk
## 3 Results
### Principal Component Analysis  
Figure 4 shows the synergies and trade-offs of ecosystem services for the study area. The largest amount of independent information corresponds to ecosystem services (PCA1) synergies followed by (PCA2) trade-offs.  
Figure 4a shows that the most synergistic zones are in the northeast of the study area. These areas represent points where high values of synergies of the different ecosystem services converge and, therefore, areas of high conservation and management interest. Conversely, the least synergistic zones are in the centre and coast of the study area. Although low values of synergies show the result of concentrating few ecosystem services, they are scattered throughout the region.  
Figure 4b shows the areas of greatest trade-offs of ecosystem services in the north and northeast of the study area. These trade-offs imply that the increase in one of the services causes the detriment of another. On the other hand, it is observed that the regions with lower trade-off values are those related to the coast.  
As we can appreciate in Table 2, the two principal components related to synergies and trade-offs reach together 0.72 of explicability. The results of statistical analyses are for KMO 0.58, Bartlett analysis p value 0.0, and omega hierarchical asymptotic 0.41.  
As we can see, the value of water from the forests, organic carbon mass, and potential value of outdoor recreation reach the highest values of synergies (Table 2). This may be because the higher vegetation density produces higher organic carbon mass and a greater potential value for outdoor recreation activity. Hence, it makes greater soil retention by vegetation that prevents erosion and a higher value of water from forests.  
On the other hand, the results show that the occurrence of pollinator insects and potential soil loss obtain the highest trade-off values (Table 2). These trade-offs may be associated with areas with less vegetation, such as cultivated areas or pastures, where we can find a greater occurrence of pollinator insects linked with more potential removed soil mass. This results in less organic carbon mass, reduced non-timber products, decreased potential outdoor recreation value, and less soil retention by vegetation.
# Synthesis of Hydrogel-Based Microgels and Nanogels Toward Therapeutic and Biomedical Applications
## 4 Paradigm Shift: Bulk Hydrogels to Hydrogel Micro/Nanoparticles
### Hydrogel Microparticle Synthesis
#### 4.2.2 Spray Drying  
Spraying and drying methods rely on the atomization of a liquid-phase hydrogel precursor into fine droplets, or aerosols, followed by the removal of the solvent phase resulting in solidified microparticles. These techniques are highly practical from the perspectives of cost and scalability, and can adequately incorporate any heat-sensitive compounds. Atomization is achieved through mechanical forces or electronic forces, such as air pressure or Coulombic attraction, respectively [32]. The solidification principle also varies by the type of atomization, where mechanical spraying usually accompanied with heated air drying and electrospray utilizes electrostatic repulsion to achieve stability and to remove solvent contents. This approach can be configured almost \"chemistry-independent\" compared to other microparticle synthesis methods, but it is also applicable to restrict the secondary chemical process within the isolated aerosols by loading precursors that can be thermally crosslinked or by using additional crosslinkers in the initial solution [228].  
Electrospraying is highly compatible with charge-bearing polymeric materials, and the process requires a strong potential bias to form an electric field between the spraying nozzle and collecting platform. This method is also advantageous for encapsulating bioactive agents, by avoiding the thermal stress associated with a conventional drying process [229; 230; 231]. Though this process is already widely adapted in various particle syntheses, maintaining the uniformity and controllability of aerosols or droplets down to the nanoscale remains challenging due to the relatively high viscosity of polymer-laden solutions [99].
## 4 Diagnosis of Korea Aerospace University (KAU) Bearing Faults
After going through the discrete signal separation (lines 8-22), one obtains the optimum order optP being 318 for the AR model by maximizing the kurtosis of the residual signal. Then, the resulting residual signal becomes Figure 14 and its kurtosis is 44.0245, roughly a nine-fold increase. Demodulation band selection (lines 23-49) is then carried out using the four sets of SK made with five window lengths, which are given in Figure 15a. The proper frequency band for higher SK is selected by visual examination of this figure, which is given arbitrarily as [1.8e4 2.3e4] by the authors. The residual signal after applying band-pass filter is then obtained as shown in Figure 15b, of which the kurtosis increases to 208.849, five times greater than the previous one. Figure 14: Residual signal after the AR filtering. Figure 13: Raw vibration signals (bearing 1). After typing in the frequency band, it continues to the envelope analysis (lines 50-64), and the results are shown in Figure 16. In the figure, the four fault frequencies are plotted as vertical lines, at which the peak is examined to identify whether the fault has occurred or not. The result, however, should be interpreted with caution. It shows multitude of peaks at the several frequencies: the first group is at the shaft frequency (20 Hz = 1200 rpm/60 s) and its harmonics (black arrows), which can be caused by unbalance, misalignment, or coupling lock up. Details associated with shaft harmonics can be referred to the literature [34]. Next is at the BPFI (130 Hz) and sidebands with harmonics spaced at the interval of shaft frequency (20 Hz) around it (orange arrows). Once the peaks like these are identified around the BPFI, it indicates the inner race fault occurrence. Note that, in case of inner race fault, sidebands typically appear around the BPFI, whereas it is not so in the other faults [35]. Note also that sometimes the peaks can overlap by chance in the same frequency range, as is found at BPFO, which should not be mistaken as the fault.
## 2 Methods
### Scale Adaptive Wavelet Packet Transform  
#### 2.3.1 Basic Principles of Wavelet Packet Transform  
Wavelet packet transform is the improvement of wavelet transform. It can provide more detailed analysis capability for noisy signal by re-decomposing the high frequency part without subdivision in wavelet transform, so it can further distinguish the PD pulse from the noise signal. According to the double-scaling equation satisfied by wavelet function \(\psi(t)\) and orthogonal scaling function \(\phi(t)\), let \(u_{0}(t)=\phi(t)\) and \(u_{1}(t)=\psi(t)\), the equation becomes:  
\[\left\{\begin{array}{l}u_{0}(t)=\sqrt{2}\underset{k}{\sum}h(k)u_{0}(2t-k)\\ u_{1}(t)=\sqrt{2}\underset{k}{\sum}g(k)u_{0}(2t-k)\end{array}\right. \tag{13}\]  
where \(g(k)\) and \(h(k)\) are the low-pass and high-pass filter coefficients in the multi-resolution analysis, respectively.  
Then the \(u_{n}(t)\) can be deduced by recursive relation  
\[\left\{\begin{array}{l}u_{2n}(t)=\sqrt{2}\underset{k}{\sum}h(k)u_{n}(2t-k) \\ u_{2n+1}(t)=\sqrt{2}\underset{k}{\sum}g(k)u_{n}(2t-k)\end{array}\right. \tag{14}\]  
Define the function set \(\left\{u_{n}(t)\right\}_{n\in\mathbb{Z}}\) as the wavelet packet in terms of \(u_{0}(t)=\phi(t)\). From the perspective of signal filtering, wavelet packet decomposition is to filter the signal through a set of low-pass filters \(H\) and a set of high-pass filters \(G\), and obtain a set of low-frequency signals and a set of high-frequency signals after decomposition. So, the wavelet packet transform is to solve the wavelet packet coefficients, wavelet packet decomposition is to solve \(\left\{d_{l}^{j,2n}\right\}\) and \(\left\{d_{l}^{j,2n+1}\right\}\) from \(\left\{d_{l}^{j+1,n}\right\}\) by Equation (12), wavelet packet reconstruction is to solve \(\left\{d_{l}^{j+1,n}\right\}\) from \(\left\{d_{l}^{j,2n}\right\}\) and \(\left\{d_{l}^{j,2n+1}\right\}\) by Equation (13).  
\[d_{l}^{j,2n}=\underset{k}{\sum}h_{k-2l}d_{k}^{j+1,n} \tag{15}\]  
\[d_{l}^{j+1,n}=\underset{k}{\sum}\left[h_{l-2k}d_{k}^{j,2n}+g_{l-2k}d_{k}^{j,2n +1}\right] \tag{16}\]  
The process of wavelet packet decomposition is shown in Figure 2 in which the decomposition scale is \(J=3\). In the figure, F denotes an original signal, D1 denotes a high-frequency component of the first scale decomposition, A1 denotes a low-frequency component of the first-scale decomposition; DA2 denotes a high-frequency component of the second-scale decomposition, and AA2 denotes a low-frequency component of the second scale, the rest can be done in the same manner.
# Dynamic Deflection Measurement on Stiff Bridges with High Piers by Preloaded Spring Method
## 5 Wind Load Effects
In Figure 19, the deflection measured by the SHM shows an irregular trend with the length of the pier height when the wind speed is 8 m/s, while the deflection of the PSM decreases gradually with the increase in the length of the stretched wire. The peak deflection of the PSM has a maximum difference of 0.06 mm from the girder, whereas the difference in the SHM is 0.2 mm. In Figure 20, as the pier height is 5 m, with the increase in the wind speed, the deflection of the SHM is more unreliable, and it has already shown the tendency of independent movement with the girder. In contrast, the PSM always vibrates in synchronization with the girder, and the deflection amplitude is the same: when the wind speed is 16 m/s, the peak deflection error with the main girder is 0.15 mm. The DLAs of the SHM and the PSM are shown in Figure 21. For a wind speed that is less than or equal to 8 m/s and a pier height that is less than or equal to 30 m, the error ranges of the DLA and deflection between the PSM and girder are from \(-2.56\%\) to \(-15.73\%\) and from \(-0.41\%\) to \(-2.53\%\), respectively; meanwhile, the error ranges of the DLA and deflection between the SHM and girder are from \(-98.74\%\) to \(50.38\%\) and from \(-15.87\%\) to \(7.31\%\), respectively. With the gradual increase in the wind speed, the errors of the DLAs as measured by the PSM tend to increase, whereas the SHM results are difficult to predict. However, the average errors of the deflection and the DLA measured by the PSM are smaller than those of the SHM due to the effect of the initial preload force that enhances the transverse stiffness of the wire.
# Analysis of the Dynamic Characteristics of Coaxial Counter-Rotating Planetary Transmission System
## 3 Dynamic Model of the Transmission System
### Derivation of the Motion Differential Equations
#### 3.3.5 Motion Differential Equation for the Planet Gear pi  
The support force of bearing acting on the planet gear _pi_ is as follows:  
\[\left\{\begin{array}{l}F_{bxpi}=k_{bxp}\delta_{xpih}+c_{bxp}\dot{\delta}_{ xpih}\\ F_{bypi}=k_{byp}\delta_{ypih}+c_{byp}\dot{\delta}_{ypih}\\ F_{bzpi}=k_{bzpi}\delta_{zpih}+c_{bzpi}\dot{\delta}_{zpih}\\ M_{\theta zpi}=k_{bgzp}\theta_{2 pih}+c_{bgzp}\dot{\theta}_{zpih}\end{array} \right., \tag{24}\]  
where \(k_{bxp}\), \(k_{byp}\), \(k_{bzp}\) and \(k_{bgzp}\) represent the support stiffnesses of the bearing in the respective directions, and \(c_{bxp}\), \(c_{bxp}\), \(c_{bzp}\) and \(c_{bgzp}\) represent the support damping of the bearing.  
The force acting on the planet gear _pi_ as a result of its mesh with the ring gear \(r\) is as follows:  
\[\left\{\begin{array}{l}F_{rpiL}=k_{rpiL}\delta_{rpiL}+c_{rpiL}\dot{\delta}_{ rpiL}\\ F_{rpiR}=k_{rpiR}\delta_{rpiR}+c_{rpiR}\delta_{rpiR}\end{array}\right., \tag{25}\]  
where \(k_{rpiL}\) and \(k_{rpiR}\) represent the mesh stiffnesses of the gear pairs between the left and right ring gears \(r\) and planet gears \(p\), and \(c_{rpiL}\) and \(c_{rpiR}\) represent the mesh dampings.  
The motion differential equation for the planet gear _pi_ is as follows:  
\[\left\{\begin{array}{l}m_{p}\big{(}\ddot{x}_{pi}-2\omega_{h}\dot{y}_{pi}- \omega_{h}^{2}x_{pi}\big{)}-\left[(F_{spiL}+F_{spiR})\sin\alpha_{spi}-(F_{rpiL} +F_{rpiR})\sin\alpha_{rpi}\right]\cos\beta_{b}+F_{bxpi}=0\\ m_{p}\big{(}\ddot{y}_{pi}+2\omega_{h}\dot{x}_{pi}-\omega_{h}^{2}y_{pi}\big{)} -\left[(F_{spiL}+F_{spiR})\cos\alpha_{spi}+(F_{rpiL}+F_{rpiR})\cos\alpha_{ rpi}\right]\cos\beta_{b}+F_{bypi}=0\\ m_{p}\ddot{z}_{pi}+(F_{spiL}-F_{spiR}-F_{rpiL}+F_{rpiR})\sin\beta_{b}+F_{bzpi}= 0\\ \int_{p}\dot{\theta}_{2 pi}+(F_{rpiL}+F_{rpiR}-F_{spiL}-F_{spiR})r_{bp}\cos\beta _{b}+M_{\theta zpi}=0\end{array}\right., \tag{26}\]
# Free Vibrations of Tapered Horseshoe Circular Arch with Constant Volume
## 3 Solution Methods and Validation  
The input parameters of the differential equations, Equations (26) and (27), are as follows: end constraint (hinged-hinged, hinged-clamped/clamped-hinged and clamped-clamped); opening angle \(\alpha\); taper ratio \(m\); and volume ratio \(\kappa\). The boundary conditions, Equations (29)-(34), were subjected to the differential equations according to the given end condition, and the frequency parameter \(C_{i}\) with the mode shape \((\delta,\lambda)_{i}\) were calculated. In order to calculate \((\delta,\lambda)_{i}\), the differential equations were numerically integrated by the Runge-Kutta method [24], which is a direct integration method, and \(C_{i}\) was calculated by the determinant search method enhanced by the Regula-Falsi method [24], which is a numerical solution method of the nonlinear equation. These kinds of numerical methods have proven useful in several studies [2; 15; 20], so detailed descriptions are omitted here.  
Before the numerical analysis, convergence analysis was performed to ensure the accuracy of the numerical solutions. Since the accuracy of numerical solution is influenced by step size \(\Delta\theta\) in the Runge-Kutta method [24], convergence analysis of \(C_{i}\) was performed for the number of partitions \(n_{d}\) of the arch axis shown below.  
\[n_{d}=\frac{\alpha}{\Delta\theta} \tag{35}\]  
The input arch parameters used for convergence analysis are hinged-clamped, square cross-section, \(\alpha=1.5\pi\), \(m=1.5\) and \(\kappa=0.25\). The convergence analysis for the lowest four \(C_{i=1,2,3,4}\) were conducted and the results are shown in Figure 5. The convergence of \(C_{i}\) with increasing \(n_{d}\) is very stable, and the reliability of the numerical solutions of this study can be confirmed. The solutions \(C_{i}\) converged from \(n_{d}=50\) to \(n_{d}=200\) within the order of three significant figures (see \(C_{3}\)). Then, \(n_{d}=100\) was used to calculate \(C_{i}\) in this study.  
To verify the numerical analysis, Table 1 compares the angular frequency \(\omega_{i}\) in rad/s of this study and the finite element solution ADINA. The radius of the arch is 1 m and the arch material is steel with \(E=210\) GPa and \(\rho=7850\)kg/m\({}^{3}\). The depth \(D_{a}\) and \(D_{c}\) with the input dimensionless parameters are listed in the table. As shown in this table, \(\omega_{i}\) of the two results is very close, implying that the feasibility of this study could be verified with a maximum error of less than 2.70% and an average error of 1.20%.
## 6 Experimental Evaluation
### Experimental Setup  
#### 6.1.1 Datasets  
We evaluate our approach using several image classification datasets that vary in complexity. For each dataset, different model architectures are used to reflect the characteristics of the dataset and optimize the performance. The choice of MNIST, EMNIST, Fashion-MNIST, and CIFAR-10 is deliberate, as each presents distinct challenges that are valuable for assessing the robustness and scalability of our proposed RBPS method. MNIST provides a simple, well-established benchmark for basic image recognition tasks, making it ideal for testing the foundational aspects of our model. EMNIST introduces the complexity of handwritten letters, expanding the task to include both digits and characters, which requires a more nuanced approach. Fashion-MNIST further increases the challenge with more abstract patterns and item categorization, demanding higher-level feature extraction capabilities. CIFAR-10, with its diverse set of color images across 10 different classes, offers a significantly more complex task, requiring advanced models capable of handling a wide variety of objects and features. By selecting these datasets, we ensure that our method is tested across a range of tasks that vary in difficulty and data distribution. This progression from simpler to more complex datasets allows us to evaluate the adaptability of our approach in real-world scenarios, where data complexity can vary widely. Additionally, the use of these standard datasets aligns with FL benchmarks, providing a consistent basis for comparison with existing methods in the field. Testing our approach on these datasets also enables us to assess how well our client selection strategy, based on GT, adapts to diverse data distributions, which is crucial for ensuring the efficiency, scalability, and privacy of our solution in federated settings.  
* MNIST: the MNIST dataset consists of 60,000 grayscale images of handwritten digits (0-9) for training and 10,000 images for testing. Each image is \(28\times 28\) pixels.
* EMNIST: the EMNIST dataset is an extension of MNIST, including both digits and letters (uppercase and lowercase). It contains 814,255 characters for training and 141,292 characters for testing.
* Fashion-MNIST: Fashion-MNIST contains 60,000 28 \(\times\) 28 grayscale images of 10 fashion categories, with 10,000 test images. This dataset is a more challenging alternative to MNIST.
* CIFAR-10: The CIFAR-10 dataset consists of 60,000 32 \(\times\) 32 color images categorized into 10 different classes, with 6000 images per class.
# Forecasting Ionospheric foF2 Based on Deep Learning Method
###### Abstract  
In this paper, a deep learning long-short-term memory (LSTM) method is applied to the forecasting of the critical frequency of the ionosphere F2 layer (foF2). Hourly values of foF2 from 10 ionospheric stations in China and Australia (based on availability) from 2006 to 2019 are used for training and verifying. While 2015 and 2019 are exclusive for verifying the forecasting accuracy. The inputs of the LSTM model are sequential data for the previous values, which include local time (LT), day number, solar zenith angle, the sunspot number (SSN), the daily F10.7 solar flux, geomagnetic the Ap and Kp indices, geographic coordinates, neutral winds, and the observed value of foF2 at the previous moment. To evaluate the forecasting ability of the deep learning LSTM model, two different neural network forecasting models: a back-propagation neural network (BPNN) and a genetic algorithm optimized backpropagation neural network (GABP) were established for comparative analysis. The foF2 parameters were forecasted under geomagnetic quiet and geomagnetic disturbed conditions during solar activity maximum (2015) and minimum (2019), respectively. The forecasting results of these models are compared with those of the international reference ionosphere model (IRI2016) and the measurements. The diurnal and seasonal variations of foF2 for the 4 models were compared and analyzed from 8 selected verification stations. The forecasting results reveal that the deep learning LSTM model presents the optimal performance of all models in forecasting the time series of foF2, while the IRI2016 model has the poorest forecasting performance, and the BPNN model and GABP model are between two of them.  
ionospheric foF2; neural network; long short-term memory; forecasting model; deep learning Article  
Forecasting Ionospheric foF2 Based on Deep Learning Method  
[PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], Y. Forecasting Ionospheric foF2 Based on Deep Learning Method. _Remote Sens._**2021**, _13_, 3849. [[https://doi.org/10.3390/rs13193849](https://doi.org/10.3390/rs13193849)]([https://doi.org/10.3390/rs13193849](https://doi.org/10.3390/rs13193849))
# Photonic Stopband Filters Based on Graphene-Pair Arrays
## 1 Introduction
Furthermore, in the graphene-based photonic crystals, the widths of photonic stopbands can be flexibly regulated by changing the chemical potential of graphene. Compared with the single-layer graphene photonic crystals, the regulation on photonic bandgap in the graphene-pair complex structure is further enhanced [17]. Therefore, it would be desirable to investigate the photonic bandgap properties and their optical tunability in graphene-pair photonic crystals. The photonic crystals based on graphene pairs are constructed here. Graphene pairs are inserted in a bulk dielectric to form periodic graphene-pair arrays. The photonic bands in transmission and reflection spectra of light are investigated, and the optical tunabilities of stopbands by the chemical potential graphene are discussed as well. Then, we demonstrate the linear transmittance varying with the spatial period of graphene-pair arrays. The dependency of transmission bandgaps on the phenomenological relaxation time of electrons in graphene is further illustrated. Finally, we also show the full passbands in this graphene-pair photonic crystal by changing the incident angle and light frequency simultaneously. This work could be utilized for multiple stopbands photonic filters.
## 4 Computational Experiments and Analysis
### Variation in the Composite Scattering Coefficient with Snow Layer Moisture Content  
The snow moisture contents are set to \(V_{1}=10\%\), \(V_{2}=30\%\), and \(V_{3}=50\%\), with corresponding relative permittivities of \(\varepsilon_{s1}=5.8-j0.14\), \(\varepsilon_{s2}=14.8-j0.65\), and \(\varepsilon_{s3}=27.9-j1.4\), respectively. The numerical simulation results are shown in Figure 14.  
As shown in Figure 14, \(\sigma\) exhibits oscillatory variations with the scattering angle, with an enhanced scattering effect observed in the specular reflection direction and a peak occurring at \(\theta_{s}=-50^{\circ}\) (this phenomenon is consistently observed throughout this study and will not be repeated hereafter). Overall, the scattering coefficient increases with increasing snow moisture content. The scattering coefficients corresponding to \(V_{2}=30\%\) and \(V_{3}=50\%\) are relatively close, while there is a significant difference compared to \(V_{1}=10\%\). This is because an increase in snow moisture content leads to a higher dielectric  
Figure 12: Variation in the composite scattering coefficient with the orientation of the rough-surfaced vehicle.  
Figure 13: Variation in the composite scattering coefficient with vehicle surface height fluctuations.  
constant, thereby enhancing the snow's scattering capability and resulting in an increase in \(\sigma\). Similarly, \(\varepsilon_{s3}\) and \(\varepsilon_{s2}\) yield comparable scattering coefficients, which differ considerably from that of \(\varepsilon_{s1}\).