esa-sceva/satcom-chunk-collection huggingface dataset:
# The Variation of Surface Shape in the Gas Jet Forming
## 8 Conclusions  
In this paper, the pressure distribution at the potential core region of the gas jet was analysed theoretically and combined with numerical simulations to draw the following conclusions: The formed surface diameter increases as the flow rate increases and decreases as the nozzle height increases. The principal curvature increases with increasing flow rate and nozzle height. According to the test results, when the nozzle's height from the liquid surface is 20-30 mm, the jet velocity is 9.5-11 m/s, the minimum value of the surface diameter formed by the gas jet is 18.5460 mm, and the maximum value is 30.1624 mm. The minimum value of the principal curvature formed by the gas jet is 0.03723 mm\({}^{-1}\), and the maximum value is 0.1159 mm\({}^{-1}\). The prediction of the surface parameters of the gas jet forming by the numerical simulation method was found to have large deviations in the experiments. The experimental results were compared with the simulation predictions, and it was found that the average prediction deviation of the diameter was 1.07 mm. The average prediction deviation for the principal curvature was 0.03665 mm\({}^{-1}\), which is challenging to correct in simulation. Therefore, we developed a dimensionless prediction model of the surface curvature and surface diameter of the mirror blanks by considering the jet parameters using experimental data. The model's average prediction error of the diameter of the formed surface was 0.3192 mm, and the average prediction error of the principal curvature of the formed surface was 0.00269 mm\({}^{-1}\)--better than the numerical simulation.  
W.F.: conceptualisation, methodology, software, investigation, writing--original draft. M.W.: validation, formal analysis, visualisation, software. X.Z.: validation, formal analysis, visualisation, investigation. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the \"Basic and Applied Basic Research Fund of Guangdong Province, grant number 2021A1515110927\", the \"Jilin Province Scientific and Technological Development Program, grant number 20200201006 JC\", \"The Open Project Program of Key Laboratory for Cross-Scale Micro and Nano Manufacturing, Ministry of Education, Changchun University of Science and Technology, grant number CMNM-KF202108\", and the \"Scientific Research Project of Education Department of Guangdong Province, grant number 2022 KCXT029\".  
\begin{table}
\begin{tabular}{c c c c c} \hline \(\mathbf{\alpha_{1}}\) & \(\mathbf{\alpha_{2}}\) & \(\mathbf{\alpha_{3}}\) & \(\mathbf{\alpha_{4}}\) & \(\mathbf{\alpha_{5}}\) \\ \hline \(-0.49287\) & 0.07184 & \(-0.00231\) & 8.28075 & 1.28306 \\ \hline \end{tabular}
\end{table}
Table 6: Coefficients of the prediction model for the principal curvature of the formed surface diameter.
## 6 Experiments and Results  
The models generate predictions based on previously predicted data. As the forecast extends further into the future, errors accumulate, leading to a decline in performance. For this reason, predictions are limited to a 24 h timeframe. The performance of each forecasting model was evaluated using the metrics described in the previous section.  
The first part is dedicated to the selection of the best hyperparameters for the forecasting of the laboratory, the second part is dedicated to the inertia tank, and the last shows the performance of both models working with each other. For both systems, the hyperparameters examined to find the best combination are the same:  
* Kernel = [\(rbf\), \(sigmoid\)]
* C = [0.1, 0.5, 1, 5, 10, 25, 50, 100]
* \(\varepsilon\) = [0.01, 0.05, 0.1, 0.5, 1, 5, 10]
* \(\gamma\) = [\(auto\), \(scale\)]  
giving a total of 224 different models analyzed for both systems.
# Audio-Visual Speech Recognition Based on Dual Cross-Modality Attentions with the Transformer Model
## 4 Experimental Results and Discussions
### Model Parameter Sensitiveness and Run-Time Complexity  
Figure 10 shows the WERs averaged over clean/noisy reverberant test data with all the experimented SNRs for various model sizes. For each model, we stacked encoders and decoders in various layers. Since the numbers of trainable model parameters and multiply-accumulate operations (MACs) are different for each model, Figure 10a,b display the WERs with the numbers of model parameters and MACs on the horizontal axis, respectively. The experimental results showed that our model yielded better recognition performance than the other compared models especially with smaller models, which indicated that our DCM structure and hybrid CTC/attention loss were efficient for fusion of audio and video.
## 3 Challenges for Routing Protocols in FANETs
### Communication Standards and Various Links  
Multi-UAV networks can be used for most civilian and disaster-monitoring applications [86]. Different types of communication links may exist in multi-UAV systems, such as UAV-to-UAV, UAV-to-satellite, and UAV-to-ground. The IEEE 802.11 standard technology is commonly utilized in FANETs for ad hoc communication. Because it can manage larger bandwidth with fast data rates and long-range coverage, IEEE 802.11 can be utilized for UAV-to-ground communications. The IEEE 802.15.4 standard can be used for UAV-to-UAV communications with lower bandwidth requirements. IEEE 802.15.4 allows for a low-power, simple implementation with a lower data rate [87]. Moreover, TR 22.829 of the Third Generation Partnership Projects (3 GPP), issued in 2019, identifies a number of UAV-enabled applications and use cases provided by 5G networks and the required communications and networking performance enhancements [88]. UAVs can communicate with each other ad hoc to avoid transmission range limits. For a variety of applications, this wireless network is utilized to send data between nodes in multi-hop communications.
## 5 Simulation Results
### QoS Based Capacity Maximization  
In this section, the Quality of Service (QoS) based system capacity maximization are investigated by the proposed RPSO. The QoS based multi-object function can be written as:  
\[f_{\alpha}\ =\ \alpha\cdot f_{\mathrm{C}}+(1-\alpha)f_{\mathrm{R}}\,, \tag{19}\]  
where \(\alpha\) is the weighting factor for the balance between capacity and throughput, while the object function for system throughput is defined by:  
\[f_{\mathrm{R}}\ =\ \sum_{n\ =\ 1}^{N}x_{u_{l}}^{(n)}R_{u_{l}}^{(n)} \tag{20}\]  
where \(x_{u_{l}}\ =\ 1\) or \(0\). Each UE can only use one RB, where \(x_{u_{l}}\ =\ 1\) indicates one RB and \(R_{u_{l}}^{(n)}\) is the throughput for the \(n\)th RB used by the user \(u_{l}\).  
Figure 7 shows the simulated results with different weight values for \(U\!E=18\), which respectively represent the transmission capacity and system capacity of each UE. In Figure 7a, during the period of about the first 10 generations of this period, the rate of increase in the average throughput is similar regardless of the numerical value, indicating that the weight value has little effect. However, after the number of generations exceeds 10 generations, the weighting starts to have an effect. A smaller weight value results in a larger amount of transmission. Figure 7b shows that after more than 10 generations, a larger weight results in a larger system capacity. This result indicates that there is a conflict between maximizing the system capacity and the individual throughput of UEs.  
Figure 6: System capacity for various population sizes with \(K=18\).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(K\) & 12 & 18 & 24 \\ GA & 12 & 17.96 & 21.8 \\ RPSO & 12 & 17.86 & 20.41 \\ Rand & 12 & 15.08 & 16.27 \\ \hline \hline \end{tabular}
\end{table}
Table 5: System capacity comparisons of RPSO, GA and Rand methods with \(M=100\) and \(G=200\).  
Figure 8 shows the comparison between the system capacity and the throughput obtained for \(K=12\), 18 and 24. In Figure 8a, if more UEs are found, there is a greater difference in the performance, indicating that the transmission capacity and system capacity are increasing. After the complex situation is raised to a certain value, the mutual influence is greater and the difference is the largest at 0.5-0.6.  
In this study, the system capacity is taken as the higher priority requirement and the weighting is set to 0.6 according to Figure 8. Under this weighting parameter, the system capacity can reach close to 16 UEs when \(K=18\). Moreover, Figure 8b shows the comparison of the throughput of each UE under the condition of \(\alpha=0.2\)-0.8. When \(\alpha=0.6\) and \(K=18\), the throughput can still reach 1300 Kpps and the system capacity can reach 16 UEs. Therefore, with the proposed multi-objective function, there is a throughput of approved transmission and guaranteed system capacity.  
Figure 7: System performance comparisons in 200 generations for (**a**) throughput and (**b**) system capacity with \(K=18\).
# Fast Frequency Response of a Doubly-Fed Induction Generator for System Inertia Support
###### Abstract  
Converter-interfaced doubly-fed induction generators (DFIGs) can provide short-term frequency support (STFS) capability by releasing rotating kinetic energy. After arresting the frequency decrease, the rotor speed should return to its initial operating condition. During the rotor speed recovery process, special attention should paid to the performance of the rotor speed restoration duration and size of the second frequency drop (SFD). This paper suggests an enhanced STFS method of DFIGs to preserve better performance of the frequency nadir with less released rotating kinetic energy and accelerate the rotor speed restoration. To this end, a rotor speed-varying incremental power is proposed and is added to the maximum power tracking (MPT) operation reference during STFS, thereby releasing less rotating kinetic energy from DFIGs; afterward, the power reference smoothly decreases to the reference for MPT operation during the preset period. Test results clearly demonstrate that since even less rotating kinetic energy is utilized, the proposed method can preserve better performance of heightening the frequency nadir; furthermore, the proposed method accelerates the rotor speed restoration when the proposed strategy produces the same SFD as the conventional method, thereby improving the power grid resilience.  
wind energy; fast frequency response; short-term frequency support; DFIG +
Footnote †: journal: Applied Sciences
## Appendix A  
The Rayleigh-Ritz procedure is a numerical technique used by many solvers to extract eigenvalue approximations from an associated to these eigenvalues subspace. The eigenvalues are extracted by projecting the matrix pencil onto the constructed subspace. In particular, given a subspace associated to \(p\) eigenvalues of \(s\mathbf{I}_{r}-\mathbf{A}\), the Rayleigh-Ritz procedure for the LEP consists of the following steps:  
1. Construct an orthonormal basis \(\mathbf{Q}_{p},\mathbf{Q}_{p}\in\mathbb{C}^{r\times p}\), which represents the subspace \(\mathcal{Q}_{p}\) associated to \(p\) eigenvalues.
2. Compute \(\tilde{\mathbf{A}}=\mathbf{Q}_{p}^{\mathrm{H}}\mathbf{A}\mathbf{Q}_{p}\). Matrix \(\tilde{\mathbf{A}}\) is the projection of \(\mathbf{A}\) onto the subspace \(\mathcal{Q}_{p}\).
3. Compute the eigenvalues \(\lambda_{1},\lambda_{2},\ldots\lambda_{p}\) and eigenvectors \(\mathbf{v}_{1},\mathbf{v}_{2},\ldots\mathbf{v}_{p}\), of \(\tilde{\mathbf{A}}\). The eigenvalues are called Ritz values and are also eigenvalues of \(\mathbf{A}\).
4. Form the vector \(\mathbf{u}_{i}=\mathbf{Q}_{p}\mathbf{v}_{i}\), for \(i=1,2,\ldots,p\). Each vector \(\mathbf{u}_{i}\) is called Ritz vector and corresponds to the eigenvalue \(\lambda_{i}\).  
In case that a GEP is to be solved, then the Rayleigh-Ritz procedure consists in finding the \(p\) eigenvalues of \(s\mathbf{E}-\mathbf{A}\). Then, steps 2 and 3 have to be modified as follows:  
2. Compute \(\tilde{\mathbf{A}}=\mathbf{Q}_{p}^{\mathrm{H}}\mathbf{A}\mathbf{Q}_{p}\) and \(\tilde{\mathbf{E}}=\mathbf{Q}_{p}^{\mathrm{H}}\mathbf{E}\mathbf{Q}_{p}\).
3. Compute the eigenvalues \(\lambda_{1},\lambda_{2},\ldots,\lambda_{p}\) and eigenvectors \(\mathbf{v}_{1},\mathbf{v}_{2},\ldots\mathbf{v}_{p}\), of \(s\tilde{\mathbf{E}}-\tilde{\mathbf{A}}\). The \(p\) eigenvalues found are called Ritz values and are also eigenvalues of \(s\mathbf{E}-\mathbf{A}\).
# Antarctic Surface Ice Velocity Retrieval from MODIS-Based Mosaic of Antarctica (MOA)
## Data
### Landsat-8 Rock Outcrop  
An updated Landsat-8-based rock outcrop dataset of Antarctic was released in 2016 [54], which had become part of the Antarctic Digital Database (ADD). It used Landsat-8 multispectral data, including thermal bands, to generate a new automated methodology for delineation of rock outcrop in Antarctica. Overcoming the limitations of previous techniques, it enjoys several improvements, such as calibration overestimate areas and more consistent georeferencing. In areas south of 82 deg40 degS, where Landsat-8 does not cover, or islands lacking suitable cloud-free images, the outcrops are derived from the existing ADD rock outcrop dataset to supplement. Thanks to the higher accuracies than previously achieved (proven as high as \(85\pm 8\%\)), here we utilize it as a stationary area mask to evaluate our velocity result's residual.  
It is worth noting that another Antarctic rock outcrop dataset was published recently; the authors improved the detection techniques in the shadow area and achieved greater accuracy [55]. We compared the two datasets in the experimental phase and found that [PERSON]'s result is featured with finer detail. The rock polygons smaller than the final velocity pixel with postings of 750 m are commonly found. Meanwhile, the spatial scale of the ADD dataset is suitable for following medium resolution analysis.
# Automatic Selective Encryption of DICOM Images
## 1 Introduction and Related Work  
Several frameworks have been introduced to facilitate medical data processing, archiving, and exchanging, such as Picture Archiving and Communication System (PACS). PACS is recognized in medical organizations and institutes. Alongside PACS, there are workstations, imaging devices, networks, and many other hardware devices, which are utilized in medical organizations. This creates the need to integrate, exchange, secure, and archive medical data from all these devices, from different manufacturers with minimum cost. To this end, Digital Imaging and Communications in Medicine (DICOM) is the most common standard which is adopted by most of the medical institutes and manufacturers who define methods to achieve all required functionalities [1, 2].  
The security mechanisms were missing in the initial version of the DICOM standard [1], which means that sensitive medical data were exchanged as plain text. As a result of the exponential growth of using the Internet, more strict security requirements arose, especially in the medical sector. Accordingly, the DICOM standard introduces some security improvements which are integrated into DICOM objects and their related applications.  
Nonetheless, most of these mechanisms are not enough to ensure a robust security solution, especially for multimedia data. Moreover, these recommendations do not include the pixel data of the DICOM object. Accordingly, many research works have proposed security mechanisms for pixel data to extend the DICOM standard.  
Security requirements for medical images are derived from strong ethics and legal regulations for privacy, and they need to fulfill the confidentiality and reliability requirements, to prevent tampering that can occur during the transmission time through a malicious individual that leads to an incorrect diagnosis. Such acts might cause severe problems as well as patient death. Security needs for medical data (text, audio, image, and video) can be classified into three categories [3]: confidentiality, reliability, and availability. Patients' health data privacy is one of the most critical issues to be considered among individuals' information [4]. Therefore, medical data confidentiality is considered a crucial security requirement. Encryption is a technique used to provide data confidentiality by ensuring the incomprehensibility of data to unauthorized parties, and several encryption algorithms have been proposed to encrypt the entire pixel data and/or header data. However, traditional encryption algorithms, such as RSA, are not suitable for medical images, due to their pixels' redundancy and complexity [5]. Therefore, the DICOM image size and the complexity of encryption algorithms directly affect the delay time for viewing and exchanging DICOM images amongst distant medical entities and radiologists, in which, the encryption algorithm processing time is proportional to the DICOM image size [6; 7].
## 4 Flow Field Analysis of Corner-Filled Goaf Area Using ANSYS Fluent
### Simulation Analysis of Different Filling Interval Distances at the Corners of the Working Face
However, as the interval distance is further increased to 70 m, the measured width of the oxidation zone in the goat exceeds that observed at 10 m, 30 m, and 50 m distances. In fact, in the central section of the goat, the width of the oxidation zone even surpasses that of the primary filling condition. This indicates that excessively large interval distances between filling bodies have limited effects on reducing the width of the oxidation zone in the goat. Therefore, it is important to establish a reasonable interval distance for filling to effectively control the width of the oxidation zone in the goat. Figure 14: Distribution of pressure field in the goat with different interval distances of filling bodies. Figure 16: Width of the oxidation zone in the goat at different interval distances of filling bodies. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Scheme** & **Location within the Goaf** & **Spontaneous Combustion Area** & **Width of the Oxidation Zone** \\ \hline \multirow{3}{*}{Spacing distance of 10 m} & Inlet air side & 154.19–218.77 m & 64.58 m \\  & Middle & 113.14–187.73 m & 74.59 m \\  & Return air side & 25.03–143.68 m & 118.65 m \\ \hline \multirow{3}{*}{Spacing distance of 30 m} & Inlet air side & 153.19–216.77 m & 63.58 m \\  & Middle & 112.64–185.73 m & 73.09 m \\  & Return air side & 30.04–140.68 m & 110.64 m \\ \hline \multirow{3}{*}{Spacing distance of 50 m} & Inlet air side & 153.69–217.27 m & 63.58 m \\  & Middle & 113.14–186.73 m & 73.59 m \\  & Return air side & 34.54–142.18 m & 107.64 m \\ \hline \multirow{3}{*}{Spacing distance of 70 m} & Inlet air side & 153.19–229.79 m & 76.6 m \\  & Middle & 113.14–203.25 m & 90.11 m \\ \cline{1-1}  & Return air side & 37.55–179.72 m & 142.17 m \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Distribution of the oxidation zone in the goat with different interval distances of filling bodies at different corners of the working face. Figure 15: Distribution of the oxidation zone in the goat with different interval distances of filling bodies.
# Effective Surface Roughness Impact in Polarimetric GNSS-R Soil Moisture Retrievals
## 4 Results
### Roughness Uncertainty on Soil Moisture Retrievals  
Both \(\sigma_{h}\) and \(s\) have been shown to be correlated to DEMSTD and DEMSLPSTD. However, the non-negligible RMSD of this parameter could prevent the implementation of Fresnel reflection coefficient inversion algorithms to estimate surface soil moisture [21].  
In this section, we will use the model with \(\sigma_{h}\), estimated via Equation (24) with an RMSD of 0.80 cm, to quantify the soil moisture estimation uncertainty. We now introduce the error metric due to the roughness uncertainty as:  
\[\varepsilon_{\sigma_{h}}=10\text{log}_{10}\Big{(}e^{-4\lambda^{2}\sigma_{h}^{ 2}\cos^{2}(\theta)}\Big{)}-10\text{log}_{10}\Big{(}e^{-4\lambda^{2}(\sigma_{h }+\varepsilon)^{2}\cos^{2}(\theta)}\Big{)}, \tag{29}\]  
where \(\varepsilon\) is the error estimating the surface rms height parameter.  
The impact of different values of \(\varepsilon\) is shown in Figure 5 assuming an average value of \(\sigma_{h}=5.2\) cm, its average value for L-polarization, and \(\theta=40^{\circ}\).  
Figure 4: Scatter plot maps between the DEM rms height, DEM slope, retrieved surface rms height (top), and squared mean square slope or slope rms (s).  
A surface roughness uncertainty of 0.8 cm (the standard deviation of the fit from Equation (28)) produces an error of \(\varepsilon_{\sigma_{b}}=5.0\) dB, producing an error in the soil moisture retrieval of \(\sim\)0.12 cm\({}^{3}\)/cm\({}^{3}\) for \(\Gamma_{V}\), and even larger for other polarimetric components as the \(\Gamma_{H}\) or \(\Gamma_{L}\), with errors higher than 0.2 cm\({}^{3}\)/cm\({}^{3}\) for the LHCP component. These large differences in single-polarization approaches suggest that by only using single-polarization measurements, soil moisture retrievals might not be feasible for single-pass retrievals if the surface roughness is not accurately modeled. In other words, because the correlation coefficient is very low with respect to the actual GNSS-R reflectivity measurements, retrievals using the Tau-Omega model, using VOD, and estimating surface roughness from DEM models, will not produce an accurate soil moisture product. As indicated in ([42], p. 423) an acceptable DEM base product to produce the small-scale surface roughness that is required in the coherent model needs to be comparable to \(\lambda\), which is not feasible nowadays for a global coverage approach.
## 4 Results and Discussion
### Correlations  
Based on experimental results, the following correlations are fitted for the shell-side Nusselt number, friction factor and Euler number of non-Newtonian nanofluids flowing in the shell side of elliptic tube heat exchanger with helical baffles.  
\[Nu_{o}=aRe_{o}{}^{b}Pr_{o}{}^{1/3}(1+\varphi^{c}) \tag{15}\]  
\[f_{o}=aRe_{o}{}^{b}(1+\varphi^{c}) \tag{16}\]  
\[Eu_{o}=aRe_{o}{}^{b}(1+\varphi^{c}) \tag{17}\]  
The predicted values and maximum deviations of aforementioned correlations are listed in Table 3. Good agreements exists between the correlations and experimental data.  
Equations (15)-(17) are suggested for the ranges of \(Re_{o}\), \(Pr_{o}\) and \(\varphi\) as following:  
\[370\leq Re_{o}\leq 760;\ 90\leq Pr_{o}\leq 110;\ 0\leq\varphi\leq 1.0\%\]