esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 EFTs Classification Scheme
### Time-Series Filtering  
Although the MODIS data are 8 or 16 day Maximum Value Composite (MVC) products [66], these still include noise that can affect the time series signal [67]. For this reason, spectral data was smoothed using a Savitzky-Golay filter to mitigate the effects of sensor noise and more efficiently reduce contamination [68]. This method is based on simple polynomial least-square calculations and has showed effectiveness in eliminating the cloudy values and exceptional data in MODIS time-series data [69]. However, instead of fitting a least-square curve to the total length of spectrum all at once, the method fits the spectral data piece-by-piece with the size equal to a user-defined value (_i.e._, filter size) using a special form of matrix calculations [65]. This requires two key parameters: the filter size and the degree of polynomial orders. The degree of the smoothing polynomial ranges from 2 to 4. A smaller value will produce a smoother result but introduce more bias; conversely, a higher value will reduce filter bias,but may \"over fit\" the data and give a noisier result [68]. In this study, it was found that a combination of a filter size of 5 and a degree of polynomial orders of 3 provided the best result in most cases.
## 3 Results and Discussion
### Study of ITZ Diffusion Through a Semipermeable Membrane Using Immersion Chambers
The average values for the tested parameter of the tested gel formulations are presented in Figure 3 and Table S2. Figure 3: Amount of active substance released as a function of time element for all gel formulations. Compared to a gel comprising solely ITZs, incorporating AuNPs enhances the diffusion rate and modulates the release profile of ITZs from hydrogel formulations. The diffusion rate of ITZ across the semi-transparent membrane is contingent upon the size of the gold nanoparticles. This corroborates the hypothesis that the larger specific surface area of the coated material may exert an influence on the diffusion process, as evidenced by the data available in the literature [49, 50]. In vitro studies have also demonstrated that nanoparticles can facilitate enhanced diffusion processes, thereby enabling more effective penetration of biological barriers by drugs [51]. The diffusion rate parameter for each gel formulation was calculated separately for each test vessel (N1-N6) and subsequently subjected to statistical analysis. Due to the considerable variability observed in the data, only the middle values were subjected to analysis for each gel, with the extreme minimum and maximum values being excluded (Table S3). An analysis of variance was conducted, with a significance coefficient of \(\alpha=0.05\). The F statistic value of 3.12 was not considered statistically significant (\(p=0.0662\)). A [PERSON]'s variance homogeneity test and a Brown-Forsete test were conducted. Both tests confirmed the homogeneity of variance, which violated the assumptions of the ANOVA analysis of variance. Consequently, the results were analyzed using the Kruskal-Wallis nonparametric ANOVA test (the statistical test values are presented in the Supplementary Materials, Tables S4-S7). Descriptive statistics cross-sections were prepared for the tested parameters in the form of mean, sample standard deviation, and standard error. The results are given in Table 6.  
Based on the results of the \(p\)-parameter for multiple comparisons, no statistical significance can be found for the compared parameter (diffusion rate) between the compared gel formulations (Table S8). Therefore, the hydrogel formulations need further optimization.
## 2 Principle
### Autoencoder-Based Symbol-Wise Optimization
The encoder learns to find the constellation of the one-hot vector and send the constellation to the neural-network-based channel model while the decoder learns to de-map and reconstruct original information, \(s_{out}\), according to the constellation received from the channel model. The output \(s_{out}\) of the decoder is a probability vector over the \(M\) possible classes, where the largest probability term is trained to have the same index as the \"1\" term in the encoder's corresponding one-hot input \(s\). As we only take the randomly generated one-hot vector as the input and label, the training of the transceiver can be regarded as an unsupervised learning process. The modified stochastic gradient descent algorithm [PERSON] (1996) is employed to train the transceiver. In the training process, [PERSON] uses the cross-entropy loss to calculate the gradient of the whole network and update the weights of the encoder and decoder. In this case, the network of the transceiver will self-optimize to find the most suitable constellation distribution for real underwater visible-light channel transmission. The loss function of this training procedure can be expressed as  
\[L=\sum_{n=1}^{M}-s_{n}\log(s_{out,n}) \tag{3}\]  
where \(s_{n}\) and \(s_{out,n}\) represent the \(n-th\) elements of \(s\) and \(s_{out}\), respectively.
## 3 Optimization Formulation  
This section proposes an optimal scheduling model of a CVPP, including RES, ESS, and flexible demand, in the DA and BE markets. This model aims to determine the CVPP's selling/buying strategy in the DA market when information on the RESs' available power output and customers' consumption is only forecast data. Also, the CVPP's reserve energy provision ability in the BE market is estimated considering the probability of the CVPP being selected to provide reserve energy. In addition, the reserve capacity contract of the CVPP in the BC market needs to be guaranteed. The operating schedule of RESs and the charging/discharging state of ESS are coordinated to maximize the total revenue of the CVPP in both the DA and BE markets.  
To address the uncertainty in RES and demand, the CVPP's optimization model is formulated as a two-stage stochastic optimization problem. The CVPP's trading strategy in the DA market is the first-stage variable that should be determined for each hour over a 24-hour time horizon and maintained even if the actual data of RES and demand differ from the forecast results. By contrast, the CVPP's upward/downward reserve energy in the BE market, as well as the operational parameters of RESs and ESS, can be treated as the second-stage variables. Because the BE market time unit is typically 15 min, the second-stage variables are determined with a quarter-hourly granularity. Unlike the first-stage variable, the operational parameters in the second stage will be adjusted to correspond to the actual value of the uncertain parameters. Thus, RES and demand data should be presented as a set of scenarios (\(P_{RES\_forecast}^{t,s}\) and \(P_{D\_forecast}^{t,s}\)), considering the probability of each scenario occurring. This ensures that optimal results are reliable in real time. In this paper, the day-ahead forecast errors of RES and demand are assumed to follow a normal distribution with zeros mean. Thus, each scenario can be represented by the sum of the forecast value and a random value generated from the distribution function of the forecast error.
# A Two-Echelon Electric Vehicle Routing Problem with Time Windows and Battery Swapping Stations
## 5 Numerical Experiments
### Computational Results and Analysis
#### 5.2.1 Comparison between the Proposed VNS and GUROBI for the Small-Sized Instances  
The results obtained by the GUROBI solver and the proposed VNS are presented in Table 3. The instance size is based on the number of satellites \((m)\), customers \((n)\), and BSSs \((l)\), as well as the number of ICVs and EVs, denoted as n (ICV) and n (EV), respectively. Each instance is operated 10 times, and \"Best\" is the best-found solution and \"Avg\" is the average optimal solution for the 10 instances. The objective value and CPU time for GUROBI and VNS are listed in the column of Obj and Time, respectively.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Value (Unit)** & **Parameter** & **Value (Unit)** \\ \hline \(c_{1}\) & 3 & \(Q_{1}\) & 400 (kg) \\ \(c_{2}\) & 5 & \(Q_{2}\) & 280 (kg) \\ \(c_{3}\) & 5 & \(v_{1}\) & 50 (km/h) \\ \(c_{f}^{1}\) & 50 & \(v_{2}\) & 40 (km/h) \\ \(c_{f}^{2}\) & 80 & \(\alpha\) & 500 \\ \(a\) & 3 (kWh) & \(\beta\) & 500 \\ \(B\) & 100 (kWh) & \(\gamma\) & 500 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameter settings for 2E-EVRPTW-BSS model.  
It is easy to find in Table 3 that the proposed VNS achieves the optimal solution for all the 10 instances whose optimum can be reached by GUROBI. However, the running time of the VNS is incredibly short compared with that of GUROBI. With the size of the instance growing, the GUROBI solver fails to produce the exact solutions for the last five instances even when the running time is up to 4500 s, while the proposed VNS algorithm can still find solutions with good performance efficiently. In fact, the proposed VNS can arrive at the solution within 5 s for most small-sized instances and consumes only 26 s even for the instances that GUROBI is hard to handle in a reasonable time.
## 3 Simulation Results
### Clutter Edge Environment  
In a clutter edge environment, the clutter power level in the reference window will suddenly change, which will lead to a sharp increase in the false alarm probability and the formation of the false alarm peak. Generally, the lower the false alarm peak, the better the false alarm control ability of the detector. When the background power of the weak clutter region is 1 dB, and the background power of the strong clutter region is 10 dB, set \(P_{FA}=10^{-4}\), and each detector undergoes \(10^{6}\) Monte-Carlo experiments, respectively. The \(P_{fit}\) of VIHCEMOS-CFAR and KLQVI-CFAR in a clutter edge environment is shown in Figure 9.  
Figure 8: False alarm control capability of each detector in a multiple-target environment.  
Figure 7: ROC in a multiple-target environment.  
In Figure 9, NC indicates the location of the clutter edge. When the clutter edge appears at the 18 th reference cell, the false alarm rate of the proposed detector is reduced by approximately 89.64% compared to VIHCEMOS-CFAR. Thus it can be seen that the false alarm control capability of the FOSTA detection algorithm used in KLQVI-CFAR is significantly stronger than that of the HCE detection algorithm used in VIHCEMOS-CFAR. From the experimental results, it can be seen that KLQVI-CFAR has better false alarm control ability than VIHCEMOS-CFAR.
## 2 Methods
### Task Scheduling Model
#### 2.1.2 Multi-Objective Optimization Model  
Multi-objective optimization, also known as multi-criteria optimization, whose aim is to optimize multiple sub-objectives at the same time, is considered to be a hot research topic in the field of mathematics and multi-criteria analysis. When a sub-objective is being optimized by multiple objectives, it may affect the target performance of other sub-objectives. Therefore, there is a nonlinear relationship among multiple optimization sub-objectives, which makes it difficult to optimize multiple sub-objectives at the same time.  
According to the Pareto optimal solution principle, when there are multiple optimization objectives in a problem at the same time, there are conflicts and incomparable phenomena between standards for the multiple sub-objectives. The solution of the problem may be optimal for one goal, but not for other goals. When solving the objective function of this type of problem, it will inevitably weaken the solution for other objectives. Therefore, this principle is called the non-dominated solution or Pareto solution. The solution to this kind of problem is to convert the corresponding mathematical model to a problem with multi-objective function solutions. The maximal problem with an m-dimensional decision vector and n optimization objectives is usually defined as:  
\[Min\{F(x)=[f_{1}(x),\ f_{2}(x),\ \ldots,\ f_{n}(x)]\} \tag{1}\]  
in which there is a decision space \(Q\) and a target space \(R\), and there are m-dimensional decision variables \(x=(x_{1},\ x_{2},\ldots,x_{m})\in Q\) and an n-dimensional target vector \((f_{1},\ f_{2},\ldots,f_{n})\in R\). The solution process is to map the decision space through the objective function to project into the target space, that is, \(Q_{m}\to R_{n}\). For the multi-objective task scheduling based on the time limit, energy consumption, and cost in this work, the problem is transformed into an optimization problem for these three mutually influencing objectives. The solution to this problem is a set of Pareto optimal solutions, and a set consisting of each solution represents a task scheduling scheme.
## 3 Results and Discussion
### Limitations and Future Work  
The GPP modeling and fusion were implemented at the selected sites across four forest types without considering the impact of climate footprints which may not match exactly with corresponding MODIS pixels at specific sites. The land surface conditions and height of the eddy covariance towers greatly affect the GPP footprints [73,74]; therefore, the proposed machine learning methods in this study may need more validation by considering the influence of site footprints before being applied at larger scales. Although we used the special software package supplied by ORNL DAAC to identify MODIS pixels based on the geographical coordinates of each FLUXNET2015 sites, the specific site may not be located at the center of a remote sensing pixel; meanwhile, errors may also be introduced in the spatial heterogeneity which may result in mixed pixels in MODIS products of 500 m resolution.  
Key factors in LUE models are \(\epsilon_{0}\), Tmin, Tmax, and Topt in Table 2. In this study, we adopted a specific look-up table to provide uniform parameters for all LUE models, and we could conduct more research on LUE model parameters optimization to leverage the advantages of each model in the future.  
Another limitation is the insufficient ground observation stations for training data. Considering the data availability in long time series, the selected sites were mostly distributed in Europe and North America, as shown in Figure 1, which may make the proposed methods not applicable to each continent. Future research should pay more attention to other datasets which include eddy covariance sites across South America, Australia, Asia, and Africa.  
The selection and combination of input models using machine learning contribute to the performance of model fusion [75]. Here, we chose five popular LUE models as the inputs of machine learning models; however, there are many other models to simulate GPP at different spatial scales [7]. How to select and combine these models to obtain reliable and accurate GPP estimation needs to be further studied in the future.
An alternative approach is to use high-performance computing (HPC) or a Cloud platform such as Google Cloud or Amazon Web Services (AWS) to improve the efficiency of imaging modalities. HPC has been used for quantitative imaging such as tomography [31; 32], where the data obtained for human head imaging was wirelessly transferred to a remote HPC for postprocessing. Big data frameworks such as Hadoop and Spark have recently gained attention to make the most of these platforms. Therefore, these frameworks need to be considered as accelerators for medical imaging algorithms [33]. In our previous work [34], a parallel microwave image reconstruction algorithm (PMIR) was proposed based on the Spark framework. The proposed algorithm was tested on a high-performance computing (HPC) cluster and Google Cloud platform. The results demonstrated that the parallel is about 128 % faster than its sequential version. The objective of this work is to investigate the use of big data frameworks, especially Spark, to improve the efficiency of the DMAS algorithm. The parallel version of the DMAS algorithm is compared with the sequential version as well as Python built-in multiprocessing library. The increase in processing speed is evaluated on both a standalone PC and on HPC. The main contributions of this paper are as follows. * We examine the potential gains in performance and scalability of DMAS using big data framework. * We use parallel implementation of the DMAS algorithm using Pyspark and Python multiprocessing library. * We evaluate the sequential and parallel versions of DMAS on a standalone PC and on an HPC cluster for efficiency. The proposed parallel version of the DMAS algorithm is implemented in Python programming language and deployed using PySpark API for Apache Spark. The algorithm is deployed on both the standalone and cluster modes. The computational results demonstrate that the parallel DMAS fully inherits the parallelism of the DMAS algorithm, resulting in an efficient reconstruction of images.
## 2 Concepts and Methods
### SAR Received Backscattered Signal Modeling
With the help of Equation (18) and interchange of the order of the integration, Equation (19) can be rewritten as Equation (20):  
\[\Psi(\tau_{r},x)=\Psi^{in}(\tau_{r},x)+\Psi^{sc}(\tau_{r},x) \tag{13}\]1  
where:  
\[\Psi(\tau_{r},x)=\Psi^{in}(\tau_{r},x)+\Psi^{sc}(\tau_{r},x) \tag{13}\]2This represents the point spread function (PSF) of one observation of the SAR sensor, which results in an image. The key concept of the SAR sensor is to improve this PSF with the help of summing over n. Hence, by combining information from multiple observations or simply multiple looks:  
\[\Psi(\tau_{r},x)=\Psi^{in}(\tau_{r},x)+\Psi^{sc}(\tau_{r},x) \tag{13}\]3  
where the PSF is calculated as:  
\[\Psi(\tau_{r},x)=\Psi^{in}(\tau_{r},x)+\Psi^{sc}(\tau_{r},x) \tag{13}\]4  
This point spread function is called the SAR ambiguity function (AF) of the transmitted waveform.
# Performance and Parameter Sensitivity Analysis of the PEMFC Flow Channel with Porous Baffles
###### Abstract
[14] designed the geometry of two three-dimensional flow channels and verified the numerical model through the experiments. One structure is a waveform channel that promoted oxygen convection, increased the oxygen supply, and generated vortices, where the vortices discharged the water accumulated in the GDL through inertia. The other structure was a wavy channel with gradient depth which increased the fluid velocity along the channel, overcame severe hypoxia, flooded the downstream area, and made the current distribution more uniform. [PERSON] et al. [15] et al. established a three-dimensional non-isothermal PEMFC model using COMSOL 5.3, and studied the influence of a squid finlike flow channel on the performance of the PEMFC. The results show that the new channel significantly reduces the pressure drop and increases the diffusion rate of reactants. Inspired by human lungs and leaf veins, [PERSON] et al. [16] established an interdigital bionic flow channel. The experimental results show that the new blade channel had the advantages of a three-channel serpentine channel. With higher power density, the new interdigital channel has a higher power density than the new blade channel. In order to make the channel design simpler and increase the performance of PEMFCs, a channel with porous baffles was proposed. In this study, a 3D model of a PEMFC was established in COMSOL 5.5, and the porous media baffle was used in the flow channel, as shown in Figure 1. The effects of this new design on the performance of PEMFCs were studied, including the average current density and voltage. The results were comparedwith those of a basic straight channel. In addition, the parameter sensitivity of the channel was analyzed.
# European Geostationary Navigation Overlay Service Performance Prediction +
## 5 Machine Learning-Based Performance Prediction for EGNOS
### Performance Evaluation  
The performance of our LightGBM models was evaluated using k-folding cross-validation using 30% of the training data. LightGBM's performance can be compared to that of the macro models provided by ESA by computing confusion matrices that show to what extent actual (observed) GIVEI and UDREI values match the predicted values for the validation data. Ideally, only the diagonals of the matrices would be populated, see Figures 3 and 4. The figures clearly show that our model outperforms the macro model significantly and the outcomes are clustered significantly more around the diagonal. The percentual improvements on the correct estimations are also provided in Tables 1 and 2.
## 5 Results and Discussion
### Surface Roughness  
Figure 15 shows the average surface roughness of a sapphire substrate at different diameters after being polished for 4 h. It can be seen from the figure that the sapphire substrate polished by DEPP has lower roughness compared to that polished by the traditional CMP method. Due to high efficiency in the material removal rate of DEPP compared to that of CMP, it is expected that after the same amount of polishing time, a substrate polished using DEPP will have a better roughness value compared to that of CMP. On the other hand, the surface roughness decreases faster at larger diameter location when using traditional CMP. The reason for this can be explained by the \"edge collapse\" effect in flat polishing [22]. The polishing pad is usually made of soft materials, and under the load action, deformation will occur, resulting in an uneven distribution of pressure on the sapphire substrate surface [23]. The pressure at  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Substance** & **Thickness (mm)** & **Hardness (shore)** & **Roughness (\(\mu\)m)** \\ \hline Polyurethane & 2 & 75 & 15 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters of the polishing pad.  
Figure 14: MRR comparison between traditional CMP and DEPP.  
the edge of the sapphire substrate surface is greater than that at the center and stress concentration is more prominent at the edge. These factors lead to smaller roughness at the edge [24]. However, edge surface roughness of the sapphire substrate is slightly lower than that at the center after DEPP and, therefore, no clear trend can be observed for roughness changing from high to low while moving from the center to the edge of the substrate. This is due to the fact that the DEP force slows down the polishing slurry thrown out in center area by centrifugal force, and increases the material removal at the center region of the sapphire substrate. Therefore, the roughness is relatively uniform at different diameter locations. Figure 16 shows the sapphire substrates after polishing for 5 h with two polishing methods. Figure 16a is sapphire substrate polished by the DEPP method, and the sapphire substrate in Figure 16b was polished using the traditional CMP method. It can be seen from the figure that the transparency (surface roughness) after using the DEPP method to process the sapphire substrates is obviously better than that using the traditional CMP method. After processing for 5 h by DEPP, the surface roughness of sapphire at different diameters are all less than 1 nm. Figure 17 shows the final surface micrograph, roughness and morphology of the sapphire substrate surface; the surface damage has been eliminated, and the surface roughness is \(R_{\text{a}}\) 0.87 nm.  
Figure 16: Sapphire substrates after polishing for 5 h with two polishing methods: (**a**) polished by DEPP and (**b**) polished by traditional CMP.  
Figure 15: Average surface roughness at different positions after polishing for 4 h.