esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of Fuel Mass Flow at the End of Injection on Cavitation and Gas Ingestion in the Nozzle
## 3 Mathematical Model
### Cavitation Model  
Due to its good convergence and numerical stability and the effectiveness of bubble solving, the Schmerr-Saucer cavitation model was added [41; 42; 43]. Assuming no slip at the interface of liquid and cavitation, the bubble dynamic equation is as follows:  
\[R\frac{d^{2}R}{dt^{2}}+\frac{3}{2}(\frac{dR}{dt})^{2}=\frac{p_{b}-p}{\rho_{l} }-\frac{4\
u_{l}}{R}\frac{dR}{dt}-\frac{2\sigma}{\rho_{l}R} \tag{15}\]  
In Equation (15), \(R\) is the cavitation bubble radius; \(p_{b}\) is the pressure of cavitation bubble surface; \(\sigma\) denotes the surface tension of the cavitation bubble; \(\
u_{l}\) denotes the kinematic viscosity of the liquid.  
In this model, it was assumed that the central pressure of the control unit was the same as the local far-field pressure, and the saturated vapor pressure \(p_{sat}\) of the liquid without gas dissolved was regarded as the surface pressure of the cavitation bubble. Here, ignoring the second derivative, viscosity effect, and surface tension term, Equation (15) was simplified as follows:  
\[\frac{dR}{dt}=\sqrt{\frac{2}{3}\frac{p_{sat}-p}{\rho_{l}}} \tag{16}\]  
Combining Equations (1) and (12):  
\[\dot{m}=-\frac{\rho_{l}\rho_{v}}{\rho}\frac{d\alpha_{v}}{dt} \tag{17}\]  
The equation of the number of bubbles per unit volume of liquid \(n_{0}\), the average bubble radius, and the vapor volume fraction is:  
\[\alpha_{v}=\frac{n_{0}\cdot 4\pi R^{3}}{3+n_{0}\cdot 4\pi R^{3}} \tag{18}\]  
Substituting Equation (18) into (17):  
\[\dot{m}=-\frac{3\alpha_{l}\alpha_{v}}{R}\frac{\rho_{l}\rho_{v}}{\rho}\frac{dR }{dt} \tag{19}\]  
Substituting Equation (16) into (19):  
\[\dot{m}^{+}=\frac{3\alpha_{l}\alpha_{v}}{R}\frac{\rho_{l}\rho_{v}}{\rho}\sqrt {\frac{2}{3}\frac{p_{sat}-p}{p_{l}}}(p_{sat}>p) \tag{20}\]\[\dot{m}^{-}=\frac{3\alpha_{l}\alpha_{v}}{R}\frac{\rho_{l}\rho_{v}}{\rho}\sqrt{ \frac{2}{3}\frac{p-p_{sat}}{p_{l}}}(p_{sat}<p) \tag{21}\]  
The average bubble radius was as follows:  
\[R=(\frac{\alpha_{v}}{\alpha_{l}}\frac{3}{4\pi}\frac{1}{n_{0}})^{\frac{1}{3}} \tag{22}\]  
According to the study of [PERSON] [41], \(n_{0}=1.5\times 10^{14}\).
# A Phased Array Antenna with New Elements Designed Using Source Transformations
## 3 Full-Wave Simulation Results and Discussion
To further verify the design, the difference between the two fields is shown in Figure 4d, which clearly shows that there is almost zero field distribution around the array after subtraction of field in Figure 4c from the field in Figure 4a. Furthermore, the far-fields of the \"reference dipole array\" and the transformed \"pinwheel\" array are simulated and illustrated in Figure 5. As shown in Figure 5, the normalized radiation patterns of the reference array and the transformed \"pinwheel\" array are almost the same when each element of the \"pinwheel\" array is enclosed by the transformed medium, but are different when the elements are not compensated with proper material parameters. Figure 5b shows the normalized radiation pattern at a scan angle \(\theta_{s}=11.25^{\circ}\). The phase difference between any two adjacent elements is set to \(45^{\circ}\), as set in the original dipole array. Figure 3: The z-component of the magnetic field of single antenna element from: (**a**) dipole antenna of length \(L=\lambda/2\) in free-space; (**b**) dipole that has undergone a ”pinwheel” rotation of \(\Delta\theta=180^{\circ}\) without any material compensation from Equation (7); (**c**) dipole that has undergone a ”pinwheel” rotation of \(\Delta\theta=180^{\circ}\) with proper material compensation from Equation (7); and (**d**) the difference between the fields (**a**,**c**). Metamaterials are foremost candidates for realizing the non-homogeneous and anisotropic material parameters from (7) to implement the proposed \"pinwheel' array. In metamaterial realizations, losses may be a restraining factor in practical implementation [16; 17; 18]. Considering the losses in practical materials, numerical simulations were performed introducing different values of loss tangents (\(\tan\delta\)) for a scan angle \(\theta_{s}=22.5^{\circ}\), as shown in Figure 6. Loss was incorporated in the simulations by replacing \(\epsilon_{yy}\) with \((\epsilon_{yy}-j|\epsilon_{yy}|tan\ \delta)\)[17; 18; 22]. Similar modifications were made to other material tensor parameters. Figure 6 shows that the antenna performance degrades with the increase of loss factor. With the loss tangent reduced to only 0.1, the effect of loss is almost unnoticeable. No significant differences were observed in the range \(tan\delta\leq 0.01\). With loss tangents of 0.1, 03, and 0.5, the lossy material is still effective, excepting for the reduction of the electric field magnitude. Figure 4: Total electric field distributions for three different array configurations for a scan angle of \(\theta_{s}=22.5^{\circ}\) for (**a**) reference/original dipole antenna linear array, (**b**) “pinwheel” antenna array without any material compensation, (**c**) material-embedded “pinwheel” shaped antenna linear array, and (**d**) difference between the electric fields in (**a**,**c**). Figure 5: Far-field radiation patterns for three different array configurations at scan angles of (**a**) \(\theta_{\rm s}=22.5^{\circ}\) and (**b**) \(\theta_{\rm s}=11.25^{\circ}\).
## 3 Materials and Methods
### Image-Based Transformation Techniques
#### 3.3.4 Gramian Angular Field  
Gramian Angular Field (GAF) is an approach that encodes time-series data into images by transforming it into polar coordinates and calculating the trigonometric sum or difference between each pair of points [39]. Specifically, GAF first scales data values into the interval \([-1,1]\)\([-1,1]\)\([-1,1]\) to convert them into angular values, representing each point's relationship over time as an image. This transformation captures both the magnitude and the directional changes from the dataset, making it especially effective for uncovering patterns that arise from fluctuations in student behavior over time. The GAF representation provides a rich visual summary of the time-series data. In this study, we use GAF to represent both the temporal evolution and magnitude differences across the student behavior data.  
In summary, the selection of Pixel Representation (PR), Sine Wave Transformation (SWT), Recurrence Plot (RP), and Gramian Angular Field (GAF) is based on their ability to capture diverse aspects of student behavior data. PR provides a simple yet effective visualization of numerical structures, SWT emphasizes cyclical and temporal trends, RP uncoverscomplex temporal dynamics and recurrence patterns, and GAF preserves both spatial and temporal dependencies. As a whole, these methods enable a thorough investigation of student learning behaviors, utilizing the advantages of different image transformation techniques to enhance classification performance.
## 4 Exploring Vocabulary-Mismatched Dysarthric Speech Recognition
### Results
#### 4.3.2 Cross-Lingual Setting  
Table 6 presents the averaged PER results across all dysarthric datasets in a cross-lingual setting. **E18** represents the result of vocabulary-informed base models trained on **C_rich_extgt**, which contain canonical speech knowledge from non-target languages. **E19** and **E20** denote models trained with cross-lingual dysarthric data **D_extgt** using different model initializations.  
Compared to model **E3** in Table 3, which utilizes rich-resource training data including the target language, **E18** exhibits lower recognition accuracy due to the lack of target language knowledge. When adapting the base models to dysarthric speech using cross-lingual dysarthric data **D_extgt** (**E19-20**), the PER is even higher compared to the results of base models (**E1** in Table 3 and **E18**). This is attributed to the significantly smaller size of **D_extgt** in both data volume and vocabulary, exacerbating overfitting to the training data and leading to poor recognition in word and phone vocabulary mismatched cases. Furthermore, employing the vocabulary-informed base model **E18**, which contains wide phone knowledge from multiple source languages, potentially mitigates overfitting as evidenced by slightly improved PER results (**E20**). Nevertheless, the retraining on **D_extgt** leads to forgetting previously acquired vocabulary knowledge, outweighing these benefits.  
In conclusion, this section examined model recognition performance in scenarios with a vocabulary mismatch, where access to a sufficient training vocabulary is limited. The findings reveal that conventional training approaches result in suboptimal recognition performance in very-low-resource and cross-lingual settings, especially when a vocabulary-informed base model is unavailable for model initialization.
## 2 Materials and Methods
### Spectral and Image Feature Extraction
#### 2.3.2 Optimal Wavelength Selection  
Each of the extracted spectra consisted of 233 wavelengths and suffered from multicollinearity. Selecting a few important wavelengths for developing more robust classification models and for implementation in a multispectral imaging system for high-speed sorting applications is desirable. SPA is a forward selection method and is proposed for optimal wavelength selection. It can minimize the collinearity among wavelengths and has been used in previous research [19; 20; 21]. SPA was used for optimal wavelength selection in this study.  
Several studies [22; 23] reported that the number of selected wavelengths would affect the classification accuracy. To determine the best wavelength number for the following analysis, the selected wavelength number from 5 to 15 was changed. Then, the classification accuracies for different numbers of wavelengths were evaluated to determine the final optimal wavelength number.
# A Multi-Layer Secure Sharing Framework for Aviation Big Data Based on Blockchain
###### Abstract
(3) Lack of incentives: Most data producers are reluctant to share data voluntarily, while data consumers remain skeptical of data utility, highlighting the absence of an effective fair incentive mechanism for ATM data sharing. Blockchain, a specialized database technology, employs P2P networks for communication, cryptography for ownership and privacy assurance, and distributed consensus frameworks for consistency. Since the release of the Bitcoin whitepaper in 2008, blockchain technology has made continuous breakthroughs in data security and collaborative computing. The Bitcoin network, with its SHA-256 encryption and distributed ledger architecture, pioneered tamper-proof storage without trusted third parties [1]. In 2015, Ethereum introduced smart contracts [2], enabling auditable trust mechanisms for automated program execution. For high-throughput processing, Solana's Proof-of-History consensus in 2021 achieved 710,000 transactions per second (TPS), 46 times faster than Ethereum 1.0 [3]. These advancements have transformed blockchain from a cryptocurrency tool into a universal trust infrastructure, laying the groundwork for its integration into aviation. Blockchain's decentralized, tamper-proof, traceable, and scalable features offer novel solutions for secure data architectures, attracting widespread attention. Organizations like Air France, ICAO, and NASA have explored blockchain applications in aviation maintenance and data tracking [4]. Blockchain can deeply integrate with ATM operations, effectively addressing trust, efficiency, and collaboration challenges in distributed data sharing. Aviation operations generate vast heterogeneous data with varying sensitivity and timeliness requirements. A blockchain-based multi-layer data-sharing mechanism can cater to diverse stakeholder needs. For real-time-dependent operators (e.g., airlines), encrypted blockchain data with access controls enables rapid airspace restriction updates and trajectory adjustments. For regulatory bodies focused on trend analysis, federated learning leveraging blockchain preserves privacy while enhancing decision-making models. To meet aviation's real-time demands and storage constraints, optimized blockchain deployment and consensus mechanisms can improve scalability and processing efficiency. This holistic approach balances security, timeliness, and consistency while maximizing data utility under multiple constraints. This paper is structured as follows: Section 1 introduces the context; Section 2 covers background knowledge; Section 3 presents the mathematical model for secure data sharing; Section 4 details the multi-layer sharing framework; Section 5 analyzes security and formal verification; and Section 6 concludes this paper.
## 4 Case Study
### Simulation Analysis
#### 4.2.4 Sensitivity Analysis of Carbon Price  
In the power market, the demand response of users is often achieved through price-based incentives. The time-of-use electricity pricing is to guide users to optimize their own electricity consumption behavior by changing the price of power products in each period so as to achieve the purpose of peak cutting and valley filling. With reference to the electricity price incentive, under the background of carbon trading introduced into the electricity market, it is of great significance to take carbon price as a variable and discuss the impact of the price incentive mechanism on carbon emissions caused by the consumption of electricity. Therefore, the sensitivity analysis of carbon price is carried out. Considering that setting a carbon price too low will lose the price incentive effect and setting a carbon price too high is unrealistic, this paper refers to reference [18] and sets the carbon price as 55/t, 105/t, 205/t,30 5/t, and 45 5/t, respectively, and calculates the carbon emission of the system under the two scenarios of calculating carbon responsibility on the user side and on the power generation side, respectively, as shown in Figure 9 below.  
Figure 8: The power purchasing strategy of user 2.  
As shown in Figure 9, system carbon emissions under both scenarios show a downward trend and when the carbon price is set at $45 the system carbon emissions are all reduced by more than 30% compared with $5. This is attributed to the reason that when the price of carbon rises, the competitiveness of high-carbon emission-generating units decreases while the competitiveness of new energy-generating units rises due to their zero-carbon intensity advantage. Uniquely, according to the comparison of the two cases, it can be seen that the carbon emissions of the system are less when the carbon emissions cost is calculated on the user side. Under the double incentive of \"electricity-carbon\", the change in carbon price has a direct incentive effect on the user which is different from the case of calculating the carbon emission cost on the power generation side which indirectly affects the user's power consumption strategy in the form of electricity price through the quoting strategy of the power producer.  
In [18], a ladder-type carbon-trading mechanism of source-load is adopted to conduct a demand response. The results show that with the increase in carbon price, the system achieves cost reduction by reducing the mobilization of high-carbon emission units which further verifies the impact of carbon price on the system emission reduction.
# Forecasting Ionospheric foF2 Based on Deep Learning Method
## 1 Introduction
This paper aims to compare and evaluate the predictability and performance of the deep learning LSTM model with two neural network models for the forecasting of ionospheric foF2 time series. In Section 2, the structure of two neural network models and a deep learning network model is introduced and specificized to compare and analyze, the data sets and the model inputs and output are described too. Section 3 gives the forecasting results from the LSTM model, the BPNN model, the GABP model, and the IRI2016 model, together with the error analysis and discussion, followed by conclusive remarks in Section 4.
## 3 Research Method
### Heat Balance Model
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{1}{c}{**Component**} & **Heat Balance Equation** & **Description** \\ \hline \multirow{2}{*}{AC} & \multirow{2}{*}{\(W_{AC}=m_{1}(h_{2}-h_{1})\)} & Load equation of air compressor \\ \cline{3-3}  & & \(\eta_{AC}=\left(h_{2,s}-h_{1}\right)/\left(h_{2}-h_{1}\right)\) & Efficiency equation of air compressor \\ \hline \multirow{2}{*}{AP} & \multirow{2}{*}{\(Q_{AP}=m_{2}(h_{3}-h_{2})=\)} & Heat load equation of air preheater \\ \cline{3-3}  & & \(m_{4}h_{4}=m_{3}h_{3}+m_{8}h_{8}\) & Conservation equation of combustion chamber \\ \hline \multirow{2}{*}{GT} & \multirow{2}{*}{gas} & \(W_{GT}=m_{4}(h_{4}-h_{5})\) & Power equation of gas turbine \\ \cline{3-3}  & & \(\eta_{GT}=\left(h_{4}-h_{5}\right)/\left(h_{4}-h_{5,s}\right)\) & Efficiency equation of gas turbine \\ \hline \multirow{2}{*}{H} & \multirow{2}{*}{\(Q_{H}=m_{6}(h_{6}-h_{7})=\)} & Heat load equation of heat exchanger \\ \cline{3-3}  & & \(W_{ST1}=m_{9}(h_{9}-h_{10})\) & Power equation of S-CO\({}_{2}\) turbine 1 \\ \cline{3-3}  & & \(\eta_{ST1}=\left(h_{9}-h_{10}\right)/\left(h_{9}-h_{10,s}\right)\) & Efficiency equation of S-CO\({}_{2}\) turbine 1 \\ \hline \multirow{2}{*}{ST2} & \multirow{2}{*}{\(Q_{C}\), \(\eta_{ST2}=m_{19}(h_{19}-h_{20})\)} & Power equation of S-CO\({}_{2}\) turbine 2 \\ \cline{3-3}  & & \(\eta_{ST2}=\left(h_{19}-h_{20}\right)/\left(h_{19}-h_{20,s}\right)\) & Efficiency equation of S-CO\({}_{2}\) turbine 2 \\ \hline \multirow{2}{*}{HTR} & \multirow{2}{*}{\(Q_{HTR}=m_{10}(h_{10}-h_{11})=\)} & Heat load equation of \\ \cline{3-3}  & & \(m_{18}(h_{19}-h_{18})\) & high-temperature recuperator \\ \hline \multirow{2}{*}{LTR} & \multirow{2}{*}{\(Q_{LTR}=m_{12}(h_{12}-h_{13})=\)} & Heat load equation of \\ \cline{3-3}  & & \(m_{17}(h_{18}-h_{17})\) & low-temperature recuperator \\ \hline \multirow{2}{*}{SC} & \multirow{2}{*}{\(W_{SC}=m_{14}(h_{15}-h_{14})\)} & Load equation of S-CO\({}_{2}\) compressor \\ \cline{3-3}  & & \(\eta_{SC}=\left(h_{15,s}-h_{14}\right)/\left(h_{15}-h_{14}\right)\) & Efficiency equation of S-CO\({}_{2}\) compressor \\ \hline \multirow{2}{*}{CL} & \multirow{2}{*}{\(Q_{CL}=m_{13}(h_{13}-h_{14})\)} & Heat load equation of cooler \\ \cline{3-3}  & & \(\eta_{SC}=\left(h_{15,s}-h_{14}\right)/\left(h_{15}-h_{14}\right)\) & Efficiency equation of S-CO\({}_{2}\) compressor \\ \hline \multirow{2}{*}{CL} & \multirow{2}{*}{\(Q_{CL}=m_{13}(h_{13}-h_{14})\)} & Heat load equation of cooler \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Heat balance equations of each component.
## 2 Seawater Channel Modelling Elements
### Scattering Phase Function Formats
#### 2.7.4 Haltrin Empirical Scattering Phase Function  
The Haltrin Empirical Scattering (HE) SPF [78] is another widely used model in oceanic studies to describe the angular distribution of light scattering, \(\widetilde{\beta}(\theta_{s},g,\ \lambda)\), by particles suspended in seawater. This empirical phase function was developed by [PERSON] [78] and is another widely used model in oceanic studies to describe the angular distribution of light scattering, \(\widetilde{\beta}(\theta_{s},g,\ \lambda)\), by particles suspended in seawater. This empirical phase function is based on extensive measurements and observations of light scattering in natural waters. The HE-SPF is a simple yet effective parameterisation of the phase function and is commonly used in radiative transfer models for underwater light propagation simulations. It does not rely on theoretical assumptions but empirical fitting to observed data. The team of [66] used substantial regression to build an empirical match for the analytical phase function for natural waters for all of [PERSON]'s results. The phase function may be generated from this model by utilising the scattering coefficient of a single-scattering albedo as the only input.  
\[\widetilde{\beta}(\theta,\ \psi)=exp\left\{\psi\left(1+\sum_{n=1}^{5}\ (-1)^{n}\hat{\kappa}_{n}\theta^{(\frac{n}{2})}\right)\right\} \tag{35}\]  
\[\mathrm{p}(\theta,\psi)=\frac{4\pi}{b}exp\left\{\psi\left(1+\sum_{n=1}^{5}\ (-1)^{n}\hat{\kappa}_{n}\theta^{(\frac{n}{2})}\right)\right\} \tag{36}\]  
where the definition of \(\Psi\) and \(\hat{\kappa}_{n}\) are expressed in Table 4. However, we can write Equation (35) in a general-form HE-SPF [10; 78; 79; 80; 81] as follows:  
\[\widetilde{\beta}(\theta_{s})=a_{0}+a_{1}\mathrm{cos}\ \theta_{s}+a_{2} cos^{2}\theta_{s} \tag{37}\]  
where \(\theta_{s}\) is the scattering angle. The \(a_{0}\), \(a_{1}\), \(a_{2}\) empirical coefficients are determined by fitting them into experimental data. We would typically fit these coefficients to empirical data from measurements in the specific water body of interest to use the HE-SPF in the target model. Once the coefficients are determined, we can use the HE-SPF to simulate light scattering underwater.  
[PERSON] used strong regression to build an empirical match for the analytical phase function for natural waters for all of [PERSON]'s results. The HE-SPF may be generated from this model by utilising the scattering coefficient of a single-scattering albedo as the only input.  
We should note that because of the low \(\theta s\) that lead photons to propagate in straight lines, the empirical phase function vastly overrates the channel bandwidth. Figure 6 shows a family of curves for HE-SPF compared to other SPFs. It is clear that the HE-SPF has a nearly similar profile to the FF-SPF over small values of \(\theta_{s}\).