esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Plasma-Activated Water (PAW) Generation
#### 2.2.2 PAW-N Generation (Coaxial DBD System)  
PAW-N was produced using a custom-built coaxial Dielectric Barrier Discharge (DBD) system (Figure 1b), designed to maximize nitrate (NO3\({}^{-}\)) generation. The reactor consists of two concentric electrodes: an inner high-voltage electrode, which is protected by a non-conductive dielectric material and polarized by a high-voltage source (ALT0215, Inergiae, Florianopolis, SC, Brazil), and a grounded outer electrode. The setup features a gas entry at the top and an outlet near the sample, ensuring that the plasma exhaust gas is bubbled through the liquid for efficient mixing and activation. For a more detailed description of the DBD system, refer to [19; 20]. For this study, compressed air was used as the working gas. The operational frequency of the DBD system was fixed at 14 kHz. Activation was performed by exposing 150 mL of DI water to the plasma for 10 min, with the system operating at an average voltage of 5.9 kV, a peak current of 40.5 mA, and a gas flow rate of 5 L/min.
# COVID-19 Detection Model with Acoustic Features from Cough Sound and Its Application
## 4 Experiment
### Results  
The results of each study are shown in Table 5 (a) to (d) show the results using the LSTM model and the ResNet-50 model, not the model proposed in this study. (a) and (b) are the results of [PERSON] [23] using COUGHVID data, and (c) and (d) are the results using the dataset constructed in this study. (e) to (i) are the results verifying the feature set we proposed. All used a model that combined ResNet-50 and DNN. (e) is the result of study by [PERSON] et al. [22], using 13 MFCCs and a mel spectrogram as features from the COUGHVID dataset only, and the accuracy, sensitivity, and specificity was 0.89, 0.93, and 0.86, respectively. (f) is the result of [PERSON] et al. [23], using seven features (13 MFCCs, spectral centroid, spectral bandwidth, spectral contrast features, spectral flatness, spectral roll-off, and chroma), and a mel spectrogram. The accuracy, sensitivity, and specificity of [PERSON]'s study were 0.94, 0.93, and 0.94, respectively. (g) to (i) are the experimental results obtained in this study, and the model was trained using data from Cambridge, Coswara, and COUGHVID. (g) is an extension of only the database in [PERSON]'s study, with the others remaining the same. The result had an accuracy of 0.93, a sensitivity of 0.93, a specificity of 0.93, and a precision of 0.93. (h) shows the model trained with the feature set proposed by [PERSON]'s study, and the result gave an accuracy of 0.92, a sensitivity of 0.90, a specificity of 0.94, and a precision of 0.90. (i) is the method proposed in this study. The model was trained using the configured feature set, MFCC, \(\Delta\)-MFCC, \(\Delta^{2}\)-MFCC, and spectral contrast. The result had an accuracy of 0.96, a sensitivity of 0.95, a specificity of 0.96, and a precision of 0.95. This performance showed a better result than the previous studies mentioned above.  
Figure 3: Confusion matrix.  
To statistically verify the above results, a statistical analysis method was used. For the results of (e) to (i), which are experiments using the proposed model, one-way analysis of variance (ANOVA) was used to confirm whether the differences in performances are statistically significant. Table 6 shows the ANOVA results. The \(p\)-value was 0.0205, which is less than 0.05. This indicates that the difference in performance between each experiment is statistically significant.  
Thereafter, as a post hoc analysis, differences between performances were confirmed using the Bonferroni multiple comparison analysis method. It was executed using R studio, which is widely used for data analysis and statistical computing. Figure 4 shows the Bonferroni correction results. The performances of (e) to (i) are divided into groups a, ab, and b, and the difference between the performances is visualized.
## 2 Materials and Methods
### Datasets
#### 2.1.3 Meteorological Data  
1. Reanalysis of precipitation, temperature, soil moisture, and relative humidity products. The meteorological data used in this study are sourced from the ERA5 dataset provided by the Copernicus Climate Change Service Data Store, which is the fifth-generation global climate reanalysis produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) [58]. Integrating advanced models and dataassimilation systems with extensive historical observations, ERA5 offers various critical meteorological parameters such as precipitation, temperature, soil moisture, and relative humidity, at a spatial resolution of 0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\), providing high-precision spatiotemporal variation information [59]. Additionally, for carbon dioxide (CO\({}_{2}\)) data, this study utilizes information from the Copernicus Atmosphere Monitoring Service (CAMS). CAMS provides comprehensive data on atmospheric composition, including CO\({}_{2}\) concentrations, which are essential for understanding and analyzing climate change impacts. The integration of CO\({}_{2}\) data from CAMS complements the meteorological parameters from ERA5, offering a more holistic view of environmental conditions and their variability over space and time. The ERA5 dataset integrates advanced numerical models and data assimilation systems, combining a large volume of historical observational data to provide high-precision spatiotemporal variation information. ERA5 offers a wide range of key meteorological parameters that can comprehensively reflect changes in meteorological conditions [60]. Additionally, the long temporal coverage of ERA5 allows it to fully encompass the time period under analysis in this study, which is why this dataset was selected for the analysis.
2. Shortwave radiation. Shortwave radiation data originate from the SYN1 deg-Level 3 dataset of the CERES (Clouds and the Earth's Radiant Energy System) project within NASA's Earth Observing System, specifically utilizing the \"daily surface radiation components--downward shortwave radiation\" product, with a spatial resolution of 1\({}^{\circ}\)\(\times\) 1\({}^{\circ}\) and units measured in watts per square meter (W/m\({}^{2}\)). The CERES projects on the study of Earth's radiation energy balance, and its shortwave radiation data holds a high degree of specialization and scientific value [61; 62].
## 3 Effects of Incidence Angle and Circumferential Location of Splitter Vane
### Studied Cases  
Three cases with different blade loading and various CR were constructed for analyses of effects of incidence angle and circumferential location of splitter vane. The geometry parameters of the three cases are shown in Table 3.  
Two incidence design rules were successively used to design the SVs of all three cases. One was that SVs were simply given the same camber lines as those of PBs at the same axial position according to [PERSON] [3] and [PERSON] et al. [17]. This resulted in an automatic design of incidence for SVs by subtracting the blade inlet metal angle from the local inflow angle. The other one was that the inlet metal angle was properly set for the leading edge of SVs to adapt to the local inflow angle so that the stagnation point was exactly located at the intersection of the leading edge and the camber line. The setting was considered to form an \"impact-free entry\" condition so as to smooth the pressure distribution on both suction surface (SS) and pressure surface (PS) of SVs according to [PERSON] [11]. The smooth pressure distribution usually results in lower loss and a higher pressure ratio. The three cases using the former incidence rule for SVs have been named original designs, and the latter are \"impact-free entry\" designs.  
In addition, the percent pitch (PP) was varied to examine the effects of circumferential position of SVs on profile performances for all of the three cases designed by different incidence rules.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameters** & **Ma** & \(\beta_{1}\) & \(\beta_{2}\) & \(P_{\text{s2}}\)/\(P_{\text{s1}}\) & \(\omega\) \\ \hline Experiments & 0.62 & 47.0 & 20.2 & 1.1221 & 0.0186 \\ MISES computations & 0.62 & 47.0 & 20.9 & 1.1221 & 0.0182 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performances from experiments and MISES computations.  
Figure 5: Comparison of the MISES calculated results and experimental results: (**a**) loss characteristics and (**b**) blade surface pressure distribution.
## 2 Related Works  
Existing and related methods for orbit extrapolation mainly rely on the increased frequency of ephemeris broadcasting and complex interpolation calculations. They include three methods: analytical orbit extrapolation (AOE), polynomial fitting orbit extrapolation (PFOE), and dynamic model orbit extrapolation (DMOE).  
In the research on AOE methods, [PERSON] et al. (2023) proposed a nonlinear programming model for orbit prediction based on the two-line element (TLE) set, showing that this model achieves better orbit prediction accuracy compared to the standard Keplerian and simplified general perturbation 4 (SGP4) models [17]. [PERSON] et al. (2024) exploited the Chebyshev extrapolation to maintain the continuity of clock accuracy in the real-time global navigation satellite system (GNSS), which could reduce the root mean square error (RMSE) of the along-track, cross-track, and radial errors in real-time LEO orbits from 5.40, 4.70, and 7.33 cm to 5.18, 4.55, and 5.99 cm, respectively [18].  
In the research on PFOE methods, [PERSON] et al. (2023) obtained real-time LEO satellite orbits based on batch least squares and short-term prediction. Using real data from the GRACE C satellite at 500 km and the Sentinel-3B satellite at 810 km, the orbit error range was obtained as 2-4 cm [19]. They employed a set of known satellite orbit data to solve the polynomial coefficients, which fit the polynomials via methods such as Chebyshev curve fitting, Lagrange interpolation, and least squares.  
In the research on DMOE methods, [PERSON] et al. (2023) employed a broadcast ephemeris and GRACE D data for LEO's 10-day real-time dynamic orbit determination, achieving a three-dimensional root mean square (3D-RMS) of 72.8 cm for GRACE D over 10 days [20]. This work was based on known satellite trajectory data and used dynamic analysis to calculate the satellite's initial position, velocity, and other dynamic parameters for orbit prediction.  
In summary, the primary format for a satellite-transmitted ephemeris is the two-line element (TLE), which records the six Keplerian orbital elements (SKOEs) of the satellite's motion. The PFOE method is greatly influenced by the fitting time and the selection of the polynomial order, making it difficult to balance the fitting time and accuracy. The extrapolation accuracy of the DMOE method is highly dependent on the initial state parameters and the chosen dynamic model; if the initial state parameters are not well selected, the prediction accuracy cannot meet the requirements. On the other hand, the AOE method has the advantages of simplicity, a low computational load, and the ability to predict orbits without numerical integration.  
Due to the Earth's gravitational effects, the gravitational effects of other celestial bodies, satellite attitude deviations, and ephemeris transmission errors, large errors occur in satellite orbit prediction when using the existing methods. To better eliminate the errors, the LEO-SIN orbit extrapolation method based on MSEC and MS-FARIMA decomposes the extrapolation error into RE, TE, and PE, enabling significant improvements in accuracy and efficiency.
## 3 Methodology
### Model Construction
#### 3.3.2 ANFIS Background and Modeling  
The adaptive neuro-fuzzy inference system (ANFIS) is an innovative analytical technique that successfully simulates human behavior by combining neural networks and fuzzy logic systems for prediction. Despite their inability to learn on their own, fuzzy inference systems offer robust knowledge representation when expert information is available. Based on the Takagi-Sugeno fuzzy inference system, the ANFIS successfully combines the advantages of neural networks and fuzzy logic to tackle difficult issues. It uses fuzzy techniques to mix linguistic and numerical data and makes use of artificial neural networks' abilities to recognize patterns and classify data. The ANFIS is a rule-based fuzzy modeling framework that uses a set of data to create fuzzy rules during the training stage. Building a fuzzy inference system (FIS) with membership function parameters derived from training data is what it is used for [109].  
Figure 2 shows the general architecture of the ANFIS. Figure 3 displays the two-dimensional input space where \(x_{1}\) and \(x_{2}\) are separated into four symmetric triangular fuzzy sets.  
Fuzzy sets are only present in the premise portion of [PERSON] and [PERSON]'s fuzzy if-then rule. There are fuzzy if-then control rules available as  
\[R_{r}:IF\ x_{1}\ is\ S_{r}^{(1)},\ x_{2}\ is\ S_{r}^{(2)},\ldots,\ x_{n}\ is\ S_{r}^{(n)}\]  
\[THEN\ y_{r}=f_{r}(x_{1},x_{2},\ldots,x_{n}),\]  
where \(S_{r}^{(i)}\) is a fuzzy set that corresponds to a partitioned domain of the input variable \(x_{j}\) in the \(r\)-th if-then rule, \(y_{r}\) is the output of the \(r\)-th if-then inference rules \(R_{r}\), \(n\) is the number of input variables, and \(f_{r}\) (.) is a function of the \(n\) input variables.  
The following is an expression of the TS fuzzy inference system's general algorithm [104].  
Figure 3: A fuzzy subdivision in two dimensions.  
Figure 2: The input–output system’s ANFIS architecture.  
1. The function \(f_{i}\) in the consequence portion calculates \(y_{i}\) for each implication \(R_{i}\): \[y_{r}=f_{r}(x_{1},x_{2},\ldots,x_{n})=c_{r}(0)+c_{r}(1)x_{1}+\ldots+c_{r}(n)x_{n}\]
2. The weights are computed as follows: \[r_{r}=(m_{1}^{r}\wedge m_{2}^{r}\wedge\ldots\wedge m_{n}^{r})P^{r},\] where \(m_{1}\) represents the (membership functions) MFs' cuts as determined by the \(r\)th rule's input values, \(P^{r}\) indicates the likelihood of an event occurring, and \(L\) is the minimum or production operation. For simplicity's sake, \(P^{r}\) is assumed to be equal to 1.
3. Given the weighted average of every \(y_{r}\), the final output \(y\) deduced from \(k\) implications is given with the weights \(r_{r}\) as \[y=\frac{\sum_{r=1}^{n}r_{r}y_{r}}{\sum_{i=1}^{n}r_{r}}\]
# Comprehensive Survey on VLC in E-Healthcare: Channel Coding Schemes and Modulation Techniques
## 4 Channel Coding Schemes for Error Correction in VLC in E-Healthcare Scenarios  
The implementation of channel coding in VLC systems applied in e-healthcare scenarios plays a critical role in ensuring reliable data transmission by detecting and correcting errors. As VLC systems are often subject to noise, interference, and signal degradation--particularly in indoor environments with multiple sources of ambient light or reflections--error correction mechanisms are essential for maintaining the integrity of transmitted data. Various coding schemes, such as Reed-Solomon (RS) codes, convolutional codes, low-density parity check (LDPC) codes, and polar codes, have been explored for their effectiveness in error correction and their trade-offs in complexity and performance.  
In the general framework of error control coding, redundant bits are added to the data stream to help identify and correct errors. A typical encoder divides the input message into blocks of size 'k' bits and replaces each block with a codeword of length 'n' bits, thus introducing 'n-k' redundant bits. This redundancy enables the detection of transmission errors and, in some cases, allows error correction at the receiver, improving the BER of the system [66; 67; 68].
## 4 Classification of Routing Protocols
### QoS-Aware Routing Protocols
#### 4.1.8 Comparative Study of QoS-Aware Routing Protocols
We argue that the next-hop selection may not optimal in terms of residual energy, as energy consideration is not being made if the geographic information results in a unique solution. Furthermore, in localized hop-by-hop approach the source node depends only on a single node's delay and/or reliability information. DMQoS uses large scale networks. EPR [37] is the first peer-routing protocol based on both centralized and distributed approaches. It performance is better to reduce the energy consumption and network traffic as compared to DMQoS. QPRD [38] aims to improve EPR by categorizing the patient's vital signs information into ordinary packets and delay sensitive packets. It performs well to decrease the packet delivery delay as compared to DMQoS at high network throughput. Another QoS-aware routing protocol to improve EPR is QPRR [39], which classifies the patients' data packets into ordinary packets and reliability sensitive packets. It improves the packet delivery ratio at high network throughput than DMQoS. All the three protocols are designed to display the patient's data inside a hospital context. As compared to DMQoS, they use very small network size. Next, these protocols are suitable only for in-hospital environments. Furthermore, if the NSC is not accessible then the BANC will try to connect with nearest MDC, where the privacy of the patients and the communication control may be at risk. By comparing the QoS-aware routing protocols in terms of energy efficiency, packet delivery delay and reliable data deliver, we conclude the following: in terms of energy consumption, EPR [37], QPRD [38] and QPRR [39] perform better as compared to others and all of them use the same module to reduce the energy consumption. On the other hand, both Routing Service Framework [32] and RL-QRP [33] do not consider the energy consumption. For large scale networks and with very low network throughput, DMQoS [26] performs better to reduce delay for delay sensitive data and improve reliability for reliability sensitive data. For small scale networks and with high network throughput, QPRD [38] results into less packet delivery delay while QPRR [39] improves the reliability of data delivery.
## 4 Model Validation  
In this section, the pile-soil-cap mathematical model is degraded to compare with the existing solutions in saturated soil. A corresponding model test was also carried out to demonstrate the validity of the theoretical model. In the calculation, the parameters are listed as follows unless otherwise specified. The dimensionless thicknesses of the freeze-thaw layers were \(\theta_{2}=h_{2}/a=3.75\), \(7.5\) and \(11.25\). The porosity of soil was \(n=0.45\). The density of soil grain was \(\rho_{s}=2650\) kg/m\({}^{3}\), the density of water was \(\rho_{w}=1000\) kg/m\({}^{3}\), and the ice density was \(\rho_{i}=920\) kg/m\({}^{3}\). The bulk modulus of the grain, water and ice were 38.7 GPa, 2.25 GPa and 8.58 GPa, respectively. The shear moduli of the grain and ice were 36.0 GPa and 3.7 GPa, respectively. The bulk modulus and the shear modulus of the dry skeleton of soil were 14.4 GPa and 42.92 MPa. The permeabilities of the soil and ice were 1.07 \(\times\) 10\({}^{-13}\) m\({}^{2}\) and 5 \(\times\) 10\({}^{-4}\) m\({}^{2}\), and the viscosity of water was 1.798 \(\times\) 10\({}^{-3}\) Pa\(\cdot\)s. The length of pile was 8 m and the radius was 0.4 m. The density of pile was \(\rho_{b}=2500\) kg/m\({}^{3}\), and the elastic modulus was \(E_{b}=36.1\) GPa. The mass of the pile cap was \(M_{\rm g}=1000\) kg. The bearing of the pile cap was simplified by \(k_{ms}=M_{\rm g}\times 2500\pi^{2}\) N/(m/s) and \(c_{ms}=1\times 104\) N/m. The dimensionless bearing parameters between the soil layers were \(k_{sb2}=0.1\) and \(k_{sb1}=1\). The bearing of pile toe was \(k_{pb1}=0.03\). The stiffness between the pile and frozen soil were \(k_{s}=0.05\) and \(k_{i}=0.05\), respectively.  
As shown in Figure 1, the lower soil layer was taken as the first soil layer, and the bottom layer was simplified by a Winkler foundation, with \(k_{sb1}\) as the bearing coefficient of the soil layer. The continuous condition between layers was also simulated by a simplified Winkler foundation, with \(k_{\text{sb2}}\) as the elastic bearing coefficient between layers. A recently published paper by the present authors analyzed the impact of this simplification and verified the rationality of a single pile in the double-layered model with perfect contact [61]. To clarify the validation of the present model of a pile cap foundation with the slippage between the pile and soil, two aspects of further verifications will be given in the following subsections.
## 4 Discussion
### Implementation Issues  
Expected reference-disturbance pairs can be generated in several different ways. For example, for an aircraft autopilot application, the commands of a human pilot can be saved for several real flights as expected references and, for realistic disturbances, a neural network-based disturbance observer can be used to estimate the real disturbances during real flights. Similarly, for drone applications, references of a human pilot can be saved for realistic references and again a neural network-based disturbance observer can be used to estimate the real disturbances to occur in real flights under various air conditions. Information from real experts can also be incorporated into realistic reference-disturbance pair generation. Realistic simulations with complex realistic mathematical models can also be used for realistic reference-disturbance pair estimation.  
Note that, for simulation experiments, it is very important to limit the outputs of the controller and actuator in order to have realistic results. This is ensured and shown in this study and, without limiting the controller's output, any theoretical or simulation analysis will be unrealistic and impossible to implement. The important parts of the simulation code are given in Appendix A.  
Physical implementation of the proposed approach is feasible with the current sophisticated digital systems. Once the neural network weights are learned with a high-performance computer, the feedforward calculations are tractable and easy to implement with either microcontrollers or even FPGAs.