esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Implementation and Discussion of Results
### Selecting the CNN Parameters
#### 4.4.2 Prediction of Future Confirmed, Death, and Recovered Cases  
The number of new confirmed, recovered, and death cases is determined by the disease transmission force and the investment in disease prevention resources, according to assumptions (A1)-(A3). The time-series data provide information on COVID-19 disease development and investment resource management. Such information can be used to analyze new confirmed, recovered, and mortality cases that will occur. The CNN model is used to fit COVID-19 cases from 1 April 2020 to 31 May 2021, based on the assumptions and by applying the features set utilizing the current available time-series data. We report a 14-day forecast, 30-day forecast, and 60-day forecast of the COVID-19 pandemic for each of the three cases: confirmed, recovered, and mortality. In Figures 17-19, the orange line extending until June 2021 represents the actual daily confirmed, recovered, and mortality cases and is followed by the forecasts for 3 periods: 0-2 weeks (yellow line), 2 weeks-1 month (black line), and 1-2 months (green line).  
Figure 17 indicates that the number of daily new confirmed cases (based on 60-day ahead forecasts) fluctuates between approximately 900 and 1600, with a near-constant moving average and without a clear sign of any trend, though the fluctuation in the forecasted cases has reduced a bit during the final phase of the forecasted period. Overall, the forecasting period is not large enough for a definitive conclusion about the trend.  
Figure 18 indicates that the number of daily new recovered cases (based on 60-day ahead forecasts) resides in the approximate range between 900 and 1300, indicating a lower range of fluctuation than the confirmed cases. There is no clear sign forming any trend in the forecast here as well. A forecast of mortality cases also reveals a fluctuation in the number of cases from 10 and 20. Thus, in all three cases, CNN predicts a fluctuation without a clear trend.  
Figure 17: Prediction over a 60-day forecast for confirmed cases using the CNN model.
# 1-D Convolutional Neural Network-Based Models for Cooperative Spectrum Sensing
## 5 Simulation Results and Discussion  
In this section, the detection performance and online detection time of the proposed CSS methods, which are based on a DNN model, are evaluated. To verify the effectiveness of the proposed approach, it is compared with three benchmark methods. The comparison is conducted to assess the performance of the proposed method in terms of the detection accuracy and efficiency of online detection.
# A Novel NODE Approach Combined with LSTM for Short-Term Electricity Load Forecasting
## 2 Related Work
### Statistical Method  
[PERSON] et al. [14] used several time series autoregressive integrated moving average (ARIMA) and seasonal autoregressive integrated moving average (SARIMA) models to generate forecasts of short-term electricity load demand after separating trend, seasonality, and cyclicity. Their data included electricity load in Texas recorded over the past 14 years. However, as mentioned in [14], ARIMA and SARIMA have many limitations for accurate forecasts because of the lack of ability to handle unstable volatility. In addition, due to the limitation of the size of the model, statistical methods tend to be underfitting, which increases the numerical error of forecasting. Therefore, traditional ARIMA models require high stability and small dataset. As an infinitely deep neural network with continuous layers, NODE can model huge real-life datasets and learn dynamics of series to dynamically adapt to violent data fluctuation.
## 2 Current Status of GNSS Integrity Service
### Current Status of Integrity Services of GPS, Galileo, and GLONASS
#### 2.2.1 Fundamental Integrity Services  
The system architectures of the GPS, Galileo, and GLONASS that generate the fundamental integrity services are similar to that of the BDS-3. They all rely on a network of ground monitoring stations and the SAIM technique. By the end of 2023, the GPS, Galileo, and GLONASS can all meet the performance requirements of NPAs. Tables 4 and 5 compare the integrity parameters in the broadcast messages among the GPS, GLONASS, and Galileo. Additionally, they compare the integrity performance among these three constellations.
## 3 Results and Discussion
### AFB1 Detection  
AFB1 samples with unknown concentrations were extracted from maize by the Vietnam National Institute for Food Controls. AFB1 (S1, S2, S3, and S4) and CdSe/ZnS QDs were mixed with a volume ratio 50:50 in 2 mL methanol. Fluorescence spectra of the mixtures were recorded by Cary Eclipse fluorescence spectrometer. No 435 nm emission peaks was observed, whereas a shoulder at 435 nm as a peak emission of AFB1 in three samples (S1, S2, and S4) besides the emission peak of the CdSe/ZnS QDs have been obtained by a fluorescence spectrometer on the basis of TCSPC under 405 nm excitation. Among four samples, one (S3) does not have this AFB1 emission peak. This is the control sample without AFB1 (Figure 5).  
Similar to what was done with the first batch of samples with known AFB1 concentrations, the time-resolved fluorescence spectra for all AFB1 and CdSe/ZnS QDs mixtures have been measured at the peak wavelength of 435 nm under 405 nm excitation. From time-resolved fluorescence decay curves of AFB1 and CdSe/ZnS QDs mixtures (S1, S2, and S4), the fluorescence lifetime of these samples have been determined. These are 1.07, 0.6, and 0.57 ns for samples S1, S2, and S4, respectively. Using the calibration curve in Figure 4, the AFB1 concentrations extrapolated for samples S1, S2, and S4 are 15, 10, and 9 ppm, respectively. These values can be compared with the concentration determined using the HPLC method at Vietnam National Institute for Food Control. Using HPLC method, the AFB1 Concentrations S1, S2, and S4 are 43, 25, and 11 ppm, respectively.  
Figure 4: Standard reagent AFB1 concentration by fluorescence lifetime.  
Figure 5: Fluorescence spectra of AFB1 extracted from maize–CdSe/ZnS mixture sample. S1, S2, and S4 shows a shoulder at 435 nm chartered AFB1, S3 does not have a shoulder (without AFB1 in sample).
###### Abstract  
Continuous low thrust is widely used in orbit transfer maneuvers. If the unknown maneuvers are not correctly compensated, the orbiting accuracy will be seriously affected. We propose a rapid method for pre-identifying thrust acceleration based on single-arc orbit determination in order to determine the orbit of non-cooperative continuous low-thrust maneuvering spacecraft. The single-arc orbit determination results of two ground-based radar observations with a certain time interval are used to inversely determine the direction and magnitude of acceleration of the spacecraft under continuous thrust based on their relationship with satellite orbit parameters. The solution error is relatively small when using this method, even over a short period of time when data are sparse. The results can then be applied to the orbital adjustment of a satellite. The results show that when the satellite climbs with maximum tangential acceleration, the interval between the two radar observations is greater than 7 h, and the proposed method can rapidly pre-identify tangential thrust acceleration with a solution error of less than 5%. When the satellite adjusts the orbital plane with the maximum normal acceleration, the average relative measurement error of the normal acceleration is about 20% when the time interval between two observations is 24 h. The longer the observation interval and the greater the thrust acceleration, the smaller the relative error. The calculation results can be used as the initial value for precision orbit determination of continuous low-thrust maneuvering spacecraft.  
continuous low thrust; tangential acceleration; orbit determination; radar observation Article
## 2 Materials and Methods
### Antibacterial Activity by Agar Disc Diffusion  
The agar disk diffusion method [28] was used to determine the antibacterial effect of garlic extract against _E. coli_, _S. mutans_, _P. gingivalis_, and _S. aureus_ sub. _aureus_. Prepared agar plates of different nutrient agar media were inoculated with 0.1 mL of a broth culture of tested bacteria. Using a sterile L-shape spreader; the inoculums were spread over the agar surface of the plates and kept aside. Sterile paper disks of 10 mm of diameter were saturated with 0.1 mL of different concentrations (100,80,60,40,20,10, and 5 mg/mL) of ultrasonicated garlic extract or 40 mg/mL of garlic extract sonicated without centrifugation, sonicated supernatant, or without sonication to be laid onto the seeded plates. Another disk was impregnated with 25 mg/mL of streptomycin (standard) and used as a positive control. Petri dishes with disks (saturated with garlic extract and control) were incubated overnight at 37 degrees Celsius in an inverted position. After incubation, the diameters of the zone of inhibition for each respective bacteria for every prepared concentration was measured around each disk using a ruler in millimeters [29]. Each assay was repeated in triplicate.
# Ultra-Low Interfacial Tension Foam System for Enhanced Oil Recovery
## 1 Introduction  
Compared to conventional liquids, foam fluid has several unique properties, such as, a high apparent viscosity, a high system energy, and a blocking effect. Foam fluid has been utilized in oil recovery as a tertiary oil recovery technology [1; 2]. Foam flooding has been well studied for several decades and has been used in oil recovery worldwide [3]. The properties of foam fluids related to oil recovery can be modified to better adapt to the geological conditions; this is usually accomplished by changing the composition, preparation method, and/or preparation conditions of the foam system [4; 5]. Most improvements have focused on improving the stability, salt resistance, temperature resistance and oil resistance of the foam [6; 7]. Bubbles in the foam fluid can lose their shape over the course of the flooding process, as the foam system is in a state of foaming-flowing-defoaming dynamic equilibrium in the porous media encountered. Thus, all components of the foam system (the liquid, gas, and bubbles) play an important role in the flooding process [8; 9].  
Studies have shown that the interfacial tension (IFT) of the displacement phase strongly influences flooding effects [10; 11]. A low IFT system refers to a system with an IFT between \(10^{-2}\) and \(1\) mN/m; in ultra-low IFT systems, the IFT is below \(10^{-2}\) mN/m. A low IFT system is better able to strip crude oil from rock surfaces by improving the fluidity of the crude oil [12; 13]. Therefore, reducing the IFT of the displacement phase is an effective method to improve the flooding effect [14]. In addition, stability is an important indicator of the foam's performance [15; 16]; an ideal foam system maintains high stability while adjustments are made to its IFT.  
In this study, several surfactants [hexadecyl trimethyl ammonium bromide (CTAB), sodium oleate (SO), sodium dodecyl sulfate (SDS) and dodecyl hydroxypropyl phosphate betaine (DHPB)] and polymers [anion- polyacrylamide (APAM) and xanthan gum (XC)] were selected as additives, and dehydrated crude oil and simulated formation water representing that from the Dagang Oilfield were also used in this study. A foam system with an ultralow IFT liquid phase was generated by changing the chemical composition of the foam and the stirring method. To further investigate the foam's stability, a 1000 mL measuring cylinder was used to test the foaming capacity. This study systematically investigated the effect of surfactants on the stability and oil-liquid IFT of the foam, to gain insight into the means to improve foam flooding applications for enhanced oil recovery (EOR).
## 5 Results and Discussion
### Evaluation of Clustering Model  
The clustering performance has been evaluated in accordance with the DBI score, utilizing an unseen dataset. To demonstrate the superiority of the proposed method for failed TCs clustering, the DBI has been calculated for deep-learning-based clustering and standalone k-means clustering. As illustrated in Table 4, a comparative analysis in terms of DBI and accuracy of the proposed method has been conducted with LSTM-AE + k-means, AE + k-means, and standalone k-means. The testing results demonstrate that the proposed method exhibits both high feature extraction performance and high clustering accuracy. This fact has been illustrated by analyzing the impact of 1 dCNN-AE on clustering performance, as demonstrated by a low DBI score of 0.51 and an accuracy score of 0.9434%. In instances where the data are particularly complex, the standalone k-means algorithm exhibits poor performance, as demonstrated by a high DBI score of 1.20 and a clustering accuracy of 59.49%. The AE + k-means method exhibited relatively high DBI and low accuracy with 1.07 and 62.26%, respectively. Nevertheless, the results indicate that integrating AE with k-means can enhance the clustering performance in comparison to a standalone method. In contrast, the application of the extracted features from LSTM-AE with k-means yielded effective clustering performance, as indicated by a DBI score of 1.06 and an accuracy score of 66.03%. It can thus be concluded that the use of 1 dCNN-AE  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**MSE** & **Standard AE** & **LSTM-Based AE** & **1 dCNN-Based AE** \\ \hline Training & 0.004107 & 0.004823 & 0.005843 \\ Validation & 0.008547 & 0.009017 & 0.006623 \\ Testing & 0.009311 & 0.010697 & 0.006386 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Reconstruction error of the proposed model compared to other AE variants.  
Figure 9: Model performance during the training and validation process in terms of MSE. (**a**) Training and validation results of 1 dCNN-AE. (**b**) Training and validation results of LSTM-AE. (**c**) Training and validation results of standard AE.  
for comprehensive feature extraction has a discernible positive impact on the clustering process, as demonstrated by improvements in accuracy and a reduction in DBI.  
Figure 10 illustrates the k-means clustering process of the extracted features from the 1 dCNN-AE, which have been grouped into four clusters. As illustrated in the aforementioned figures, the textual logs have been classified into four clusters, which collectively encompass four distinct types of failures: software, hardware, sensor, and network failure.
# Simulative Investigation of the Radar Cross Section of Wind Turbines
## 2 Method
### Wind Turbines
#### 2.2.1 Dielectric Material Properties of FRP  
Material measurements of fiber-reinforced plastic samples were performed in the laboratory on a sample taken from a glider wing spar in order to determine the FRP's dielectric permittivity as an input parameter for the simulations. The measurements were performed with a sample fitted in an RF waveguide, using the methods described in [23; 24] in the frequency range of 8.25-12.4 GHz. The measured value of the relative dielectric permittivity yielded \(\epsilon_{\mathrm{r}}=4.35\) and a low loss tangent \(\tan\delta=0.05\). Literature on measurements of FRP [25; 26], which were performed at much lower frequencies and considered general theory on permittivity [27], suggest that the measured values can be used for lower frequencies down to about 1 GHz.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Assembly** & **Feature** & **Value in 2015** & **Value in 2019** \\ \hline Tower & Height of Nacelle & \(\approx\)123 m & \(\approx\)132 m \\ Nacelle & Shape & specific to product \\ \multirow{2}{*}{Rotor} & Aerodynamic Profile & specific to product \\  & Rotor Diameter & \(\approx\)105 m & \(\approx\)118 m \\  & Rotor Blade Length & \(\approx\)50 m & (\(\approx\)57 m) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Average sizes of on-shore wind turbines installed in Germany [21; 22].
## 3 Modeling and Simulation of the Electrical Contacts Using Numerical Methods
### The Step-by-Step Simulation Setup for Comsol
There are two accepted types of methods of refinement for the mesh: the H-based method, where the object is meshed using simple first-order or quadratic elements and the convergence is improved by increasing the elements number, and the P-based method, where the number of elements is kept minimal and the convergence is achieved by increasing the order of the elements. The first case was applied in the present study. The finite element mesh was defined as the user-controlled mesh type, for which the finite elements size depends on their position in relation to the model, implemented by using the size expression feature offered by the software. Free Tetrahedral type elements were used with the complete mesh consisting of 23,628 tetrahedra, 5208 triangles, 663 edge, and 60 vertex elements. The element volume ratio is 6.388 \(\times\) 10\({}^{-3}\) with an average element quality of 0.6535. The element size as predefined in the general physics setting can take nine values ranging from Extremely Coarse to Extremely Fine. For this simulation, based on the sensitivity test performed, it was determined that the size \"Fine\" produced consistent and accurate results in a relatively short computing time. The resulting model after meshing based on the data presented is shown in Figure 15.  
As stated at the start of Section 3.2, the simulation is carried out in two stages. In the first stage, only solid mechanics phenomena are simulated using the Solid Mechanics (solids) solver, and in the second stage, the Joule effect and heat transfer phenomena are simulated using the Electric Currents and Heat Transfer in Solids solvers. The two stages of the simulation address the need to first solve the effect of the contact pressure on the characteristics of the electrical contact elements material, and next the Joule effect of the contacts. The simulation of temperature variation when the current flows through the current path of the contact is performed considering an initial temperature of contact elements \(T\) = 293.15 K (20 \({}^{\circ}\)C). The base values of the parameters for the contact elements were set up prior to the simulation. Thus, in the Solid Mechanics module of Contact Pair 1, the contact pressure was set to \(T_{n}\) = 1 \(\times\) 10\({}^{7}\) N/m\({}^{2}\). In the Electric Currents > Pair Electrical Contact module, for the same Contact Pair 1, the Microhardness parameter had an assigned value of \(H_{c}\) = 0.1 GPa. In the Heat Transfer in Solids module, an identical value of the microhardness was set for the Thermal Contact pair. Figure 14: (**a**) Contact element with zero voltage (ground). (**b**) Contact element with 1 mV voltage (electric potential).
# Towards Improved Inertial Navigation by Reducing Errors Using Deep Learning Methodology
## 2 Methods
### Strapdown INS Mechanization  
The mechanization process is the step of converting the IMU outputs into navigation information which includes velocity, position, and attitude. In this project, position information is calculated and compared with the ground truth measured by a tape ruler. The navigation information is computed by using the following pseudo steps [1,61].  
Figure 3: Sampled IMU outputs filtered by Deep Learning implementation.  
Figure 2: Three network configurations.  
First, the dynamic transformation matrix is calculated by the equation given as:  
\[\dot{R_{b}^{l}}=R_{b}^{l}\Omega_{b}^{l}, \tag{1}\]  
where \(\Omega_{b}^{l}\) is the angular velocity matrix measured by the gyroscope and \(R_{b}^{l}\) is the transformation matrix from the last state. The latest transformation matrix is updated by the dynamic \(\dot{R_{b}^{l}}\).  
Next, the specific force \(f^{b}\) measured from an accelerometer is transferred into \(f^{i}\) where \(f^{b}\) is the specific force on the body frame and \(f^{l}\) is on the local frame. The computation equation is given as:  
\[\dot{v^{l}}=f^{l}=R_{b}^{l}f^{b}-\left(2\Omega_{ie}^{l}+\Omega_{el}^{l}\right)v ^{l}+g^{l}, \tag{2}\]  
where \(\dot{v^{l}}\) is the dynamic velocity information in the local frame. \(g^{l}\) is the earth's gravity field. \(\Omega_{ie}^{l}\) and \(\Omega_{el}^{l}\) are the skew-symmetric matrices corresponding to \(w_{ie}^{l}\) and \(w_{el}^{l}\), where \(w_{ie}^{l}\) is the rotation of the Earth in the local-level frame and \(w_{el}^{l}\) is the transportation rate in terms of the position information from the Earth frame to the local frame. Then, the velocity information is updated by the dynamic \(\dot{v^{l}}\).  
After the velocity information is updated, the position is updated in terms of the displacements along the three axes. The equations are given as:  
\[\dot{R^{l}}=v^{l}, \tag{3}\]  
\[R=\sqrt{R_{N}+R_{E}}, \tag{4}\]  
where \(R_{N}\) is the displacement on the north axis and \(R_{E}\) is the displacement on the east axis. \(R\) is the trajectory result of the motion, as shown in Figure 4.
## 2 Materials and Methods
### Inductively Coupled Plasma Atomic Emission Spectroscopy Analysis  
Inductively coupled plasma atomic emission spectroscopy (ICP-AES; Perkin Elmer, Optima 4300 DV) was used for composition analysis. The samples were initially placed in a 25 mL conical flask and dissolved with 2 mL of aqua regia while heated on a heating plate. After the samples were gradually dissolved at room temperature and complete dissolution was verified, the solution was transferred to a volumetric flask to obtain 50 g of dissolved sample. The solution was then diluted with a 1000 ppm BDH Spectrosol solution, and 1 mL of aqua regia was added to match a sample matrix. The results were quantified by obtaining the average value of three analyses for various elements, namely copper (Cu), tin (Sn), lead (Pb), arsenic (As), silver (Ag), cobalt (Co), iron (Fe), nickel (Ni), antimony (Sb), and zinc (Zn).