esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experiments and Discussions
### Static Data Collection  
The real-time orbit and clock SSR products of PPP-B2b, CAS, DLR, GFZ, and WHU, the ultra-rapid products from WHU, and the broadcast ephemeris during the Days of Year (DOY) 354-357 in 2021 were collected. The distribution of the stations is illustrated in Figure 1. The IGS real-time SSR corrections were collected from the internet via the open-source software BNC Ver2.12.17 [36]. The static data from four MGEX stations (URUM, ULAB, WHU2, and JFNG) during DOYs of 354-357 of 2021 were collected.  
In the data analysis phase, (1) all of these orbit and clock products were projected to the same coordinate system and time system; (2) static test data were processed daily with dual-frequency IF PPP (B1I+B3I and L1+L2) and single-frequency PPP (B1C and L1); (3) the corresponding GPS data were also processed as a comparison; (4) the reference coordinates obtained from the MGEX weekly SINEX file. The details of the static data processing strategies are listed in Table 3. It is worth noting that the B1C observation is BDS-3 new signal, which is not used for orbit determination and clock calculation. Thus, the corresponding DCB correction should be applied to the observations.  
The available satellites and PDOP are the two critical indexes for positioning accuracy analysis; therefore, the number of visible satellites and the PDOP values for both BDS-3  
Figure 1: The location of 4 selected MGEX stations.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Item** & **Processing Strategies** \\ \hline GNSS & BDS-3 and GPS \\ Signal selection of IF PPP & BDS-3: B1I+B3I \\  & GPS: L1+L2 \\  & BDS-3: B1C \\ Signal selection of SF PPP & GPS: L1 \\ Interval & 30 s \\ Cutoff angle & 10\({}^{\circ}\) \\ Weight method & Elevation angle dependent \\ Troposphere & Estimate the wet component \\ Ionospheric & IF PPP: IF combination \\  & SF PPP: Estimated \\ PCO/PCV & IGS14.atx \\ Ambiguity & Estimated \\ Adjustment & Sequential least square \\ Satellite DCB & Corrected by DCB products \\  & IF PPP: IF combination \\ Receiver DCB & SF PPP: Estimated \\ \hline \hline \end{tabular}
\end{table}
Table 3: PPP processing strategies of static test.  
and GPS are presented in Figure 2. The number of available BDS-3 satellites on average are 8.4, 8.1, 7.8, and 8.1, with the corresponding PDOP of 2.1, 2.2, 2.9, and 2.3 for JFNG, ULAB, URUM, and WUH2 stations. For GPS, the average number of available satellites are 6.8, 7.1, 6.8, and 6.6, with the corresponding PDOP of 2.5, 2.6, 2.9, and 2.7 for JFNG, ULAB, URUM, and WUH2 stations.
## 2 DAB DC/DC Resonant Converter TPS Close Loop Control Scheme
### DAB LCC Resonant Converter TPS Control Implementation in PLECS RT Box
In Equation (40), \(\mathrm{\beta}\)s (\(\mathrm{k}-1\)) is the rectifier phase shift angle of the previous sample generated by the \"\(\mathrm{R_{ac}}\)\" proportional-integral (PI) feedback controller. \[\mathrm{R_{ac}(k)=}\frac{8}{\pi^{2}}(\sin(\pi\beta_{s}(k-1)))^{2}R_{dc}(k) \tag{40}\]  
The configuration switches are added to enable/disable the \"\(\mathrm{R_{ac}}\)\" feedback control to test DAB performance in open loop control manually \(\mathrm{\beta}\)s setting condition. In wireless charging regular operation, \(\mathrm{R_{ac}}\) feedback control is automatically enabled/disabled depending on the battery current hysteresis threshold settings as given in (41). \[ \begin{cases}\begin{aligned} if\;Io(k)<Io\_min&Disable\_Rac=0, \qquad\beta_{s\_ref}(k)=0.5\\ if\;lo\;(k)>Io\_min+\Delta Io\_min& Enable\_Rac=1,\quad\beta_{s\_ ref}(k)=\beta_{s}(PI\_out,\;k)\end{aligned}\end{cases}  \tag{41}\]  
The rate limiting for rectifier phase shift angle is applied to smooth out the DAB converter dynamic transition caused by sudden phase shift angle change as given in (42). Figure 12: Rectifier phase shift angle \(\mathrm{\beta}\)s generation in PLECS RT Box for experimental testing purpose. \[\left\{ \begin{array}{l}if\beta_{s_{ref}}(\,k)-\beta_{s}(\,k-1)>\Delta\beta_{s}( nax),\quad then\ \beta_{s}(\,k)=\beta_{s}(\,k-1)+\Delta\beta_{s}(nax)\\ if\beta_{s_{ref}}(\,k)-\beta_{s}(\,k-1)<-\Delta\beta_{s}(nax),\quad then\beta_ {s}(\,k)=\beta_{s}(\,k-1)-\Delta\beta_{s}(nax)\\ if-\Delta\beta_{s}(nax)<\beta_{s_{ref}}(\,k)-\beta_{s}(\,k-1)<\Delta\beta_{s}( nax),\quad then\beta_{s}(\,k)=\beta_{s\_ref}(\,k)\end{array} \right. \tag{42}\]
## 2 Basic Theory of CFD Simulation
### Vortex-Induced Vibration Related Parameters  
The shape of the generation and shedding of vortices when the fluid passes through the tail of the structure is related to \(Re\), and its expression is as follows:  
\[Re=\frac{\rho UD}{\mu}=\frac{UD}{v} \tag{9}\]where \(Re\) is the Reynolds number, \(\rho\) is the fluid density whose value is 1.185 kg/m\({}^{3}\), \(U\) is the flow velocity of the fluid, \(D\) is the characteristic size of the structure, that is, the outer diameter of the pipe, and \(\mu\) is the dynamic viscosity coefficient.  
When the fluid passes through the structure, a lateral lift force \(F_{L}\) and a downstream drag force \(F_{D}\), and then the dimensionless lift coefficient \(C_{L}\) and drag force coefficient \(C_{D}\) are obtained:  
\[C_{L}=\frac{F_{L}/A}{\rho U^{2}/2},C_{D}=\frac{F_{D}/A}{\rho U^{2}/2}. \tag{10}\]  
where \(A\) is the cylinder projected area perpendicular to the direction of motion. According to the \(C_{L}\) curve, the vortex shedding frequency \(f_{s}\) can be obtained, and the Strouhal number \(St\) can be obtained by dimensionless processing of \(f_{s}\), namely:  
\[St=\frac{f_{s}D}{U}. \tag{11}\]  
According to the DNV specification, the conditions for the occurrence of transverse vortex-induced vibration [14] are:  
\[\frac{0.8}{St}<U_{R}<\frac{1.6}{St},U_{R}=\frac{U}{f_{n}D} \tag{12}\]  
where \(U_{R}\) is the reduced speed, and \(f_{n}\) is the first-order natural frequency of the pipe. The minimum wind speed at which cross-flow vortex-induced vibration occurs can be obtained as:  
\[U_{min}=\frac{0.8f_{n}D}{St}. \tag{13}\]
## 4 Experiments and Analysis
### Comparative Analysis of the Results of Different Domain Adaptation Methods
#### 4.2.3 Comparative Analysis of Diagnostic Accuracy and Change Curve  
The diagnostic accuracies of the different adaptive methods for three sets of migration tasks are shown in Table 4. Figure 8 shows the variation curve for target domain sample accuracy with the number of iterations in the three sets of tasks. In order to show the contrast effect more clearly, the change curve of the number of iterations greater than 1100 times is locally amplified.  
In Figure 8, the change curves, presented in different colors, represent different adaptive methods. It can be seen that, compared with the other four methods, the proposed method has smaller random fluctuations in the three groups of tasks and that the training effect is more stable. More specific information can be seen in Table 4. The proposed method achieves the highest diagnostic accuracy in all three tasks. The diagnostic accuracies of the proposed method in task A-C\(\rightarrow\)B, task A-B\(\rightarrow\)C, and task B-C\(\rightarrow\)A were 99.65%, 99.34%, and 97.40%, respectively. Compared with the other four methods, the diagnostic accuracy is improved by 2.03-8.06%, 0.84-1.84%, and 1.84-2.31%, respectively. This shows that,  
Figure 8: Comparison of different adaptive methods in three groups of tasks: (**a**) B-C\(\rightarrow\)A; (**b**) A-C\(\rightarrow\)B; (**c**) A-B\(\rightarrow\)C.  
Figure 7: Confusion matrix: (**a**) JMMD; (**b**) CORAL; (**c**) MK-MMD; (**d**) LMMD; (**e**) MSDSA.  
compared with global domain adaptation, the improvement in the sub-domain adaptation can learn the fine-grained information of each category and can align the distribution of the sub-domains of the same category more effectively. Compared with the LMMD method, the diagnostic accuracy of the proposed method is improved by 2.03%, 0.84%, and 1.97%, respectively. It can be shown that the Ranger optimizer can train the network parameters more effectively and achieve better training results.
# Infrared Small Moving Target Detection via Saliency Histogram and Geometrical Invariability
###### Abstract  
In order to detect both bright and dark small moving targets effectively in infrared (IR) video sequences, a saliency histogram and geometrical invariability based method is presented in this paper. First, a saliency map that roughly highlights the salient regions of the original image is obtained by tuning its amplitude spectrum in the frequency domain. Then, a saliency histogram is constructed by means of averaging the accumulated saliency value of each gray level in the map, through which bins corresponding to bright target and dark target are assigned with large values in the histogram. Next, single-frame detection of candidate targets is accomplished by a binarized segmentation using an adaptive threshold, and their centroid coordinates with sub-pixel accuracy are calculated through a connected components labeling method as well as a gray-weighted criterion. Finally, considering the motion characteristics in consecutive frames, an inter-frame false alarm suppression method based on geometrical invariability is developed to improve the precision rate further. Quantitative analyses demonstrate the detecting precision of this proposed approach can be up to 97% and Receiver Operating Characteristic (ROC) curves further verify our method outperforms other state-of-the-arts methods in both detection rate and false alarm rate.  
1
# Research on the Applicability of Transformer Model in Remote-Sensing Image Segmentation
## 3 Experiment and Results
### Result
#### 3.2.5 Training Process of Different Transformers  
The whole training process of SwinUnet was relatively stable, the accuracy value shows a steady increase, and the loss value shows a steady decrease (Figure 13). There are fluctuations in both SETRnet and TransUnet; however, all the models converged after about the 25 th epoch. Among them, fluctuations occurred at the beginning of the training for SETRnet. The final accuracy and loss values of SETRnet were the worst. TransUnet showed optimal results directly after the wave. This shows that SwinUnet is more robust and less difficult to train. The training result of TransUnet is better than the SwinUet and SETRnet models, but the training difficulty is relatively high. It is necessary to set the appropriate learning rate and simultaneously adjust the training strategy, including the epoch, learning rate decay strategy, etc.
# A Web Shell Detection Method Based on Multiview Feature Fusion
## 4 Evaluation
### Evaluation Metric  
In terms of accuracy, the following evaluation matrixes are defined:  
**True positive (TP)**, the malicious web shells are accurately marked.  
**False positive (FP)**, the benign web scripts are marked as web shells.  
**True negative (TN)**, the benign web scripts are accurately marked.  
**False negative (FN)**, the malicious web shells are marked benign ones.  
In terms of classification accuracy, the following indicators are used to judge the quality of the model. The indicators are defined based on experience, which has been widely used in previous work [37; 38; 39].  
\[\text{Pmalicious}=\text{TP}/(\text{TP}+\text{FP})\]  
\[\text{Rmalicious}=\text{TP}=\text{TP}/(\text{TP}+\text{FN})\]  
\[\text{Finalicious}=2*\text{Pmalicious}*\text{Rmalicious}/(\text{Pmalicious}+ \text{Rmalicious})\]  
\[\text{Pbenign}=\text{TN}/(\text{TN}+\text{FN})\]  
\[\text{Rbenign}=\text{TN}/(\text{TN}+\text{FP})\]  
\[\text{Fbenign}=2*\text{Pbenign}*\text{Rbenign}/(\text{Pbenign}+\text{Rbenign})\]  
\[\text{FPR}=\text{FP}/(\text{TN}+\text{FP})\]\[\text{Accuracy}=\left(\text{TP}+\text{TN}\right)/\left(\text{TP}+\text{FP}+\text{ FN}+\text{TN}\right)\]  
Pmalicious, Rmalicious, and Fmalicious represent the precision, recall, and F1-score of malicious web shells, respectively. Pbenign, Rbenign, and Fbenign represent the precision, recall, and F1-score of benign web scripts, respectively. In addition, the receiver operating characteristic (ROC) curve is used to represent the overall recognition performance of our method. The ROC curve shows the relationship between the true-positive rate (TPR) and the false-positive rate (FPR) at different classification thresholds, \(\theta\). In particular, the area under the curve (AUC) is used to represent the area enclosed by the ROC curve and the coordinate axis.
## 3 Results and Discussion
### Impacts of Fire
#### 3.4.1 Impacts on Ecosystem Performance Anomalies
Maximum average EPA values occurred 30-40 years after fire (Figure 4A), a finding supported by the chronosequences developed by [25] in Alaska and that of [92] in northern Manitoba, corresponding to the peak cover of deciduous species (Figure 4B). The EPA values in recovering burns during the period 30-40 years after fire tended to be higher (with an average EPA of \(11.3\pm 3.2\)) than pre-fire conditions and compared to unburned areas, a finding consistent with that of [70]. Moreover, the EPA in deciduous stands during this period was \(32\pm 5.5\) (Figure 4A), significantly higher than the overall study area average of \(-28.7\) (Table 1). Overall, the temporal recovery of average EPA following fires was best fit by a second-order polynomial relationship (R\({}^{2}=0.80\), \(p<0.01\), \(y=-0.0050x^{2}+7.5875x-149.06\), where \(y\) equals EPA and \(x\) equals years since fire), a relationship only valid for the first 50 years after fire.
## 2 Materials and Methods  
### Mathematical Formulation  
In the quasi-static regime, the frequencies under examination take values in the sub-MHz range, which is the main frequency area of interest for planetary science space missions. In this way, close to the spacecraft area, the near-field approximation on dipole field equations is valid. The electric field produced by an electric dipole oscillating at a single frequency, located at (x, y, z), is expressed in cartesian representation (electric field components) at the measurement point (x\({}_{\mathrm{m}}\), y\({}_{\mathrm{m}}\), z\({}_{\mathrm{m}}\)), with r as the magnitude of the distance vector between the measurement point and dipole location, as in [2]:  
\[\mathrm{E_{x}}=\frac{1}{4\pi\varepsilon_{\mathrm{o}}}\left[\frac{3(\mathrm{x_{ m}}-\mathrm{x})\cdot\mathrm{C}}{\mathrm{r^{5}}}-\frac{\mathrm{p_{y}}}{\mathrm{r^{3}}}\right] \tag{1}\]  
\[\mathrm{E_{y}}=\frac{1}{4\pi\varepsilon_{\mathrm{o}}}\left[\frac{3(\mathrm{y_{ m}}-\mathrm{x})\cdot\mathrm{C}}{\mathrm{r^{5}}}-\frac{\mathrm{p_{y}}}{\mathrm{r^{3}}}\right] \tag{2}\]  
\[\mathrm{E_{z}}=\frac{1}{4\pi\varepsilon_{\mathrm{o}}}\left[\frac{3(\mathrm{z_{ m}}-\mathrm{x})\cdot\mathrm{C}}{\mathrm{r^{5}}}-\frac{\mathrm{p_{z}}}{\mathrm{r^{3}}}\right] \tag{3}\]  
where  
\[\mathrm{C}=\mathrm{p_{x}}\cdot(\mathrm{x_{m}}-\mathrm{x})+\mathrm{p_{y}}\cdot (\mathrm{y_{m}}-\mathrm{y})+\mathrm{p_{z}}\cdot(\mathrm{z_{m}}-\mathrm{z}) \tag{4}\]  
and then the total electric field magnitude for every measurement point is [2]:  
\[|\mathrm{E_{tot}}|=\sqrt{\mathrm{E_{x}}^{2}+\mathrm{E_{y}^{2}}+\mathrm{E_{z}} ^{2}} \tag{5}\]  
Equations (1)-(3) are used in data generation procedure, as described in the next section, in order to calculate the electric field components \(\mathrm{E_{X}}\), \(\mathrm{E_{y}}\), and \(\mathrm{E_{z}}\) (EDMnet inputs) in the 14 measurement points.
###### Abstract  
Carbon fiber reinforced plastic composites (CFRPs) possess superior elastic mechanical properties. However, CFRPs lack sufficient viscoelastic performance, such as damping and creep resistance. In an effort to improve these properties, in this study, hybrid multiscale composites with various combinations of zinc oxide nanorods (ZnO) and carbon nanotubes (CNTs) were deposited at the interface of carbon fiber laminae. The viscoelastic properties of the corresponding composites were characterized via dynamic mechanical analysis (DMA) during both temperature and frequency sweeps. The creep activation energy for each composite configuration was also calculated. The DMA temperature sweep analysis reported that the composite incorporating both ZnO and CNTs exhibited the highest improvements in all viscoelastic properties. This composite also attained better creep resistance, evident by the highest activation energy. The DMA frequency sweep analysis revealed that composites incorporating a single nanofiller improves the viscoelastic properties more than the combined nanofiller composite. Despite these improvements in the viscoelastic properties, the non-uniform dispersion and agglomerations of the nanofillers affected some of the elastic properties negatively, such as the storage modulus.  
Zinc Oxide (ZnO) nanorods; Carbon Nanotubes (CNTs); damping; thermal activation energy (\(\Delta H\)); scanning electron microscopy (SEM); glass transition temperature (\(T_{g}\))
###### Abstract
[PERSON] et al. [13] reviewed the application of nanofluids in various types of solar systems including solar collectors. [PERSON] et al. [14] considered a dish concentrator using different nanofluids (Cu/oil, Al\({}_{2}\)O\({}_{3}\)/oil, TiO\({}_{2}\)/oil and SiO\({}_{2}\)/oil). They also concluded that the Cu/oil nanofluid had the best thermal performance. Furthermore, [PERSON] et al. [15] experimentally investigated a solar dish concentrator with multi-walled carbon nanotubes (MWCNT/oil) as the solar working fluid. It was found that the application of the nanofluid improved the thermal performance of the investigated dish system. [PERSON] et al. [16] numerically investigated the effect of different nanofluids in a solar pond. They concluded that single-walled carbon nanotubes (SWCNT/water) showed the best performance among the investigated nanofluids. Finally, the organic Rankine cycle (ORC) system is a good candidate for converting thermal energy into electricity or mechanical power. The ORC heat source can be solar, geothermal, biomass, and waste heat energy sources. The application of solar energy as the ORC's heat source has attracted much attention for efficient power generation. The studies on ORC firstly started in the 1970s, and advanced research works are still ongoing. A cavity receiver was optimized by [PERSON] et al. [17; 18]. It was concluded that the investigated system's thermal performance can be improved by decreasing the inlet temperature and increasing the mass flow rate of the working fluid. [PERSON] et al. [19] modeled a combined cooling, heat and power (CCHP) system, using solar energy and an ORC. They concluded that the environmental conditions, including solar irradiance and ambient temperature, had significant effects on the performance. [PERSON] [20] investigated an integrated solar combined cycle (ISCC) using two bottoming cycles, including a steam Rankine cycle and an organic Rankine cycle. The cycle with R1234 ze (Z) revealed an increase in the output power of the investigated cycle. Furthermore, [PERSON] et al. [21] considered the dynamic behavior of a solar ORC with a compound parabolic collector (CPC) as heat source. Considering the aforementioned literature review, nanofluid utilization in a dish collector is a growing research field. In the current research, the performance of an ORC cycle with three cavity receivers are considered and compared together, as a novel research. The three investigated cavity receivers include the cubical, cylindrical and hemispherical cavity receivers. As a novel idea in the current study, the thermodynamic analysis of the investigated solar ORC is examined using different types of nanofluids: Al\({}_{2}\)O\({}_{3}\)oil, CuO/oil, and SiO\({}_{2}\)oil. The ORC cycle analysis is performed at the evaporator pressure of 2.5 MPa and condenser temperature of 38 \({}^{\circ}\)C; also, methanol is employed as the working fluid.
## 3 Numerical Simulation
### Reservoir Numerical Simulation  
Prediction of H\({}_{2}\)S Production  
There is a heavy oil reservoir in Bohai Bay, China, at depths of 800 to. 1000 m, with porosities of 12-45%, average permeabilities of 2450 mD, and initial oil saturation of 70-82%. In the reservoir model, to consider the reasonable running time, the total number of cells in the three-dimensional (3D) model was equal to 20,000. The oil layer was vertically subdivided into 10 zones. The reference depth of the reservoir simulation model was set equal to 800 m with pressure at 8000 kPa. Basic reservoir properties and operational parameters for the 3D reservoir model are shown in Table 5. The relative permeability curves and kinetic parameters obtained from the history matching of the one-dimensional CSS experiment are directly applied to the reservoir simulation.  
Figure 8: Matching of measured and simulated results: (**a**) cumulative oil production; (**b**) H\({}_{2}\)S production.  
Based on the established reservoir model, the H\({}_{2}\)S production during the CSS process of offshore reservoirs was predicted, ranging from 228 m\({}^{3}\) to 1490 m\({}^{3}\) (Figure 9). However, it is noted that the H\({}_{2}\)S production decreases with the increase of cycles after two cycles. This is attributed to the fact that the continuous production of heavy oil near-wellbore leads to a decrease in the amount of S-containing organic compounds, resulting in a weakened aquathermolysis reaction.
## 4 Simulations Results
Figure 4: The clustering performance of FCM and PSO-FCM; 4 QAM with SNR = 0 dB, 8 QAM with SNR = 0 dB, 16 QAM with SNR = 2 dB, 32 QAM with SNR = 4 dB, 64 QAM with SNR = 6 dB. The studies used the PSO-FCM and FCM algorithms to find modulation in 4 QAM, 8 QAM, 16 QAM, 32 QAM, and 64 QAM signals. Figure 5 illustrates the modulation recognition rates for MQAM signals, showing that PSO-FCM generally achieves a higher modulation identification rate compared to FCM. Moreover, upon analyzing the results depicted in Figure 6, it becomes apparent that the FCM algorithm converges more rapidly. PSO-FCM converges more slowly than FCM due to the simultaneous optimization of particle positions and membership values in the PSO-FCM hybrid. PSO introduces additional complexity in the PSO-FCM compared to the traditional FCM algorithm due to the augmented computational load per iteration. In PSO-FCM, there is an inclusion of supplementary complexity multiplication and addition operations. Specifically, each iteration in PSO necessitates 4 QC complex multiplications and 6 QC complex additions, where \(Q\) denotes the number of data points and \(C\) represents the  
Figure 5: Modulation recognition rate of FCM and PSO-FCM across varied SNRs for multiple QAM signals. \begin{table}
\begin{tabular}{c c c c} \hline
**Modulation Type** & **No. Classification** & **Method** & **No. Local Optima** \\ \hline
8 QAM & 8 & FCM & 10 (6.7\%) \\  & 8 & PSO-FCM & 5 (3.3\%) \\
16 QAM & 16 & FCM & 21 (14.0\%) \\  & & PSO-FCM & 9 (6.0\%) \\
32 QAM & 32 & FCM & 82 (54.7\%) \\  & & PSO-FCM & 73 (48.7\%) \\
64 QAM & 64 & FCM & 127 (84.7\%) \\  & & PSO-FCM & 121 (80.7\%) \\ \hline \end{tabular}
\end{table} 
Table 3: Clustering results of two clustering algorithms with various constellation datasets. Figure 6: Iterations vs. objective function value; 16 QAM, SNR = 2 dB. number of dimensions. These extra computational operations in PSO-FCM potentially contribute to slower convergence compared to FCM. The objective function value of the PSO-FCM algorithm gradually diminishes and eventually falls below that of the FCM algorithm, indicating superior optimization performance to some extent. Consequently, the choice between algorithms requires consideration of the trade-off between additional complexity and optimization effectiveness.