esa-sceva/satcom-chunk-collection huggingface dataset:
# Image-Based Dedicated Methods of Night Traffic Visibility Estimation
## 2 Backgrounds
### Multivariate Nonlinear Regression Theory  
In multivariate nonlinear regression (MNLR) analysis, observational data are modeled by a function, which is a nonlinear combination of the model parameters and depends on one or more independent variables [13]. Assume that the general behavior of a nonlinear relationship is the following:  
\[Y=\alpha_{0}\left(X_{1}^{\alpha_{1}}\right)\left(X_{2}^{\alpha_{2}}\right) \ldots\left(X_{n}^{\alpha_{n}}\right) \tag{11}\]  
\(\alpha_{0}\), \(\alpha_{1}\),..., \(\alpha_{n}\) are the parameters  
When the goodness-of-fit test is performed on the selected curve equation fitting results, the sample determination coefficient \(R^{2}\) is used as the basis for determining the goodness of fit [14]. \(R^{2}\) is defined as:  
\[R^{2}=\frac{SSR}{SST}=1-\frac{SSE}{SST}=1-\frac{\sum(y-\hat{y})^{2}}{\sum(y- \overline{y})^{2}} \tag{13}\]  
\(SSR\) is the sum of the squares of the regression. \(SSE\) is the sum of the squares of the residuals. \(SST\) is the sum of the squares of the total deviations. \(R^{2}\) is a comprehensive measure of the fitting precision to the regression equation. The larger the \(R^{2}\), the better the fitting precision. The smaller the \(R^{2}\), the worse the fitting precision. The \(R^{2}\) range is from 0 to 1. The closer the \(R^{2}\) is to 1, the higher the degree of fit will be.  
The F-test is used to test the significance of the regression equation [14]. The \(F\) statistic is the ratio of the sum of the squared regressions of the mean to the sum of the squares of the residuals of the average:  
\[F=\frac{SSR/k}{SSE/(n-k-1)}=\frac{\sum(\hat{y}-\overline{y})^{2}/k}{\sum(y- \hat{y})^{2}/(n-k-1)} \tag{14}\]  
\(SSR\) is the sum of the squares of the regression, \(SSE\) is the sum of the squares of the residuals, \(n\) is the number of samples, and \(k\) is the number of independent variables. The statistic \(F\) obeys the F distribution of the first degree of freedom k and the second degree of freedom \(n-k-1\), that is, \(F\sim(k,\,n-k-1)\). It can be concluded from the definition of \(F\) statistic that if the \(F\) value is significant, the change of the dependent variable caused by the independent variable is more significant than the influence of the random factors on the dependent variable. The more significant the \(F\) statistic, the better the fitting precision.
# Spaceborne Sparse SAR Imaging Mode Design: From Theory to Implementation
## 2 Traditional SAR Imaging Mode  
### Observation Geometry  
The imaging geometry of all kinds of SAR imaging modes is depicted in Figure 1. To further discuss the difference between all kinds of modes, we introduce a mixing degree factor:  
\[A~{}=~{}\frac{R_{\text{rot}}-R_{0}}{R_{\text{rot}}}~{}=~{}1-\frac{R_{0}k_{ \theta}}{v_{g}} \tag{1}\]  
where \(R_{\text{rot}}\) is the distance from SAR platform to the rotation center, \(R_{0}\) is the slant range, \(k_{\theta}\) is the steering angle rate, and \(v_{g}\) is the ground velocity. When \(A=0\), it indicates that the platform is working in Spotlight mode. If \(A=1\), this means that \(R_{\text{rot}}=\infty\), indicating that SAR is working in Stripmap or Scan mode. In TOPS, \(k_{\theta}\) is negative and \(A>1\), while in Sliding Spotlight and Mosaic mode, \(k_{\theta}\) is positive and \(0<A<1\). Based on the mixing degree factor, we can obtain the theoretical resolution of all kinds of modes, i.e.,  
\[\rho~{}\approx~{}\frac{L_{a}}{2}(N_{B}+1)A \tag{2}\]  
where \(L_{a}\) is the azimuth antenna size, and \(N_{B}\) is the number of subswatths. The relationship between the resolution of each mode and the steering angle rate is shown in Figure 2 (assuming that the number of subswatths is three). It is seen that with the steering angle rate increasing, Sliding Spotlight has the highest resolution, followed by Mosaic, Stripmap, and Scan, with TOPS performing the worst, and the resolution of Mosaic can be higher than that of Stripmap.
## 5 Experiments
### Discussion on the States of the MMPP in Conventional IS Simulation  
We now discuss the removing state information of the MMPP in conventional IS simulation. As mentioned in Section 4.3, we can remove state \(\{i,j\}\) from Equation (18), but the proposed IS estimated value is not changed much. In this section, we want to make an experiment to show the estimated value of the rare event probability. We run IS simulation without the state with the parameters in Scenario-2. The result shows that the estimated values of conventional IS before and after the removal of the state information are equivalent. The reason is explained in Section 4.3. On the other hand, we also compare the simulation time of conventional IS without the state information with that of conventional IS. In Case 4, we will simulate to estimate the probability \(P(Q>q)\) with \(q=20[packets]\), and the number of RCs is \(10^{2}\) cycles. The result in Table 9 shows that the simulation  
\begin{table}
\begin{tabular}{c c c c} \hline  & **MC Method** & **Conventional IS Method** & **Proposed IS Method** \\ \hline RCs & \(10^{7}\) & \(10^{2}\) & \(10^{2}\) \\ Mean of estimate & \(5.58\times 10^{-6}\) & \(6.19\times 10^{-6}\) & \(5.80\times 10^{-6}\) \\ Sample variance & \(1.14\times 10^{-12}\) & \(3.09\times 10^{-13}\) & \(4.12\times 10^{-13}\) \\ Simulation time [s] & 1136.81 & 0.39 & 0.35 \\ \hline \end{tabular}
\end{table}
Table 8: The simulation time’s comparison.  
Figure 7: Convergence comparison of estimates by MC and the proposed IS (in Case 4 with q = 20 [packets]).  
time of conventional IS without state information is slightly faster than the simulation time of conventional IS (about 5% faster). The reason is that the formula of the weighting function of conventional IS without states is simpler than the weighting function formula of conventional IS (the part \(e^{-\theta_{1}^{*}(j-i)},\ (i,j=0,1)\) is removed).
# On the Use of FOPID Controllers for Maintenance Phase of General Anesthesia
## 2 Materials and Methods
### PID Controller  
The (standard) PID control scheme that is considered in this paper is shown in Figure 2. The PID controller is in ideal form; that is, its transfer function is  
\[C(s)=K_{p}\left(1+\frac{1}{T_{i}s}+\frac{T_{d}s}{\frac{T_{d}s}{N}s+1}\right), \tag{6}\]  
where \(K_{p}\) is the proportional gain, \(T_{i}\) is the integral time constant, and \(T_{d}\) is the derivative action time constant. The derivative action is filtered by using a first-order system with \(N=5\)[26]. Further, an anti-windup back-calculation strategy (where \(T_{t}=\sqrt{T_{i}T_{d}}\) is the tracking time constant) is employed [26]. The use of anti-windup is necessary to handle the actuator saturation. It is easily understandable that clinical syringe pumps cannot have a negative infusion rate, and thus, the lower saturation limit is set to 0. The upper saturation limit depends on the type of infusion pump used since the rate is related to the syringe size. In this work, the specifications of a Graseby(tm) 3500 pump were used to model the surgical equipment available in a typical surgery room. Accordingly, the maximum flow rate of 1200 (mL/h) was set, resulting in a maximum infusion rate of 6.67 (mg/s) by considering a propofol concentration of 20 (mg/mL).  
The controller was tuned by minimizing the worst-case integrated absolute error (IAE) for a specific set of 13 patients representative of a diverse population; see [3; 27] for the details of the patient demographics. The IAE is defined as  
\[IAE=\int_{0}^{\infty}\lvert BIS(t)-r(t)\rvert dt, \tag{7}\]  
where \(BIS(t)\) is the the value of the BIS signal of the patient at time \(t\), and \(r(t)=50\) is its reference value. The optimization problem was solved in MATLAB by using genetic algorithms with a population size of 100. A Gaussian mutation was used to add a random number taken from a Gaussian distribution with mean 0 to each entry of the parent vector, thus resulting in the random perturbation of the individuals that will belong to the future generation. Furthermore, a scattered crossover was employed to create a random binary vector. The genetic algorithm selects hereditary genes from the first parent when the random vector element is a 1 and genes from the second parent where the vector has a 0. Then, the selected genes are combined to form the child. The resulting optimal values of \(K_{p}\), \(T_{i}\), \(T_{d}\) and \(T_{t}\) are shown in Table 1.  
Figure 1: Schematic of the pharmacokinetics compartmental model.  
Figure 2: PID–based control structure.
# Defect Detection in Striped Images Using a One-Dimensional Median Filter
## 2 Materials and Methods
### Flow of Defect Detection  
The flow of defect detection is shown in Figure 3. To use the one-dimensional median filter effectively, the grayscale values of the elements in each row must be as consistent as possible. Therefore, pre-processing and image rotation are required to align the stripes in the image horizontally. Image segmentation is then performed to obtain the block images and the interval images. Defect detection can then be conducted on the blocks and the intervals separately, and finally the results can be superposed to complete the task. The process is described in detail below.
## 3 Observability of the Navigation System  
The observability of the navigation system is the prerequisite and foundation for the system to have good navigation performance, and it is also the key to optimize the navigation filter algorithms. In this section, according to the definition of 'observability in engineer' of the navigation system, we propose a new method of observability calculation for high-orbit satellite navigation system based on GNSS, including observability discrimination methods and measurement criterion, aiming at the linearized navigation model (4) and considering the difference of relative observability of each state component of the navigation system.
## Appendix A Stress-Strain Analysis in an Unbolted Tunnel
### Stress-Strain Analysis in an Unbolted Elastic Region  
Resolving the differential equations composed using Equations (1) and (7)-(10) with the boundary condition \(\sigma_{r}=p_{\sigma}\) at \(r=r_{p}\) and \(\sigma_{r}=p_{0}\) at \(r=\infty\), the stresses and displacements in an elastic region (\(r>r_{p}\)) can be calculated using Equations (12)-(13):  
\[\sigma_{r}\ =\ p_{0}-\left(p_{0}-p_{\sigma}\right)\frac{r_{p}^{2}}{r^{2}}, \tag{14}\]  
\[\sigma_{\theta}\ =\ p_{0}+\left(p_{0}-p_{\sigma}\right)\frac{r_{p}^{2}}{r^{2}}, \tag{15}\]  
\[u_{r}\ =\ \frac{1}{2G}\left(p_{0}-p_{\sigma}\right)\frac{r_{p}^{2}}{r}. \tag{16}\]  
Note that when \(p\geq p_{\sigma}\), the rock mass behaves elastically and its stresses and displacements can be calculated by replacing \(p_{\sigma}\) and \(r_{p}\) in Equations (14)-(16) with \(p\) and \(R\), respectively.  
### Stress-Strain Analysis in an Unbolted Elastic Region  
As shown in Figure 11, a plastic region is discretized into \(m\) annuluses with a uniform thickness \(\Delta r\) starting from the elastic-plastic interface. The \(j\)th annulus is bounded by two circles of \(r=r_{j-1}\) and \(r=r_{j}\). According to Equations (14)-(16), the stresses and displacement on the outer boundary of the plastic region (\(r=r_{(0)}=r_{p}\)) are:  
\[\sigma_{r(0)}\ =\ p_{\sigma r}, \tag{17}\]  
\[\sigma_{\theta(0)}\ =\ 2p_{0}-p_{\sigma r}, \tag{18}\]  
\[u_{r(0)}\ =\ \frac{1}{2G}\left(p_{0}-p_{\sigma r}\right)r_{p}. \tag{19}\]  
The number of the rock mass annuluses \(m\) and inner radius \(r_{(j)}\) of each annulus can be expressed as:  
\[m\ =\ \left(r_{p}-R\right)/\Delta r, \tag{20}\]  
\[r_{(j)}\ =\ r_{(j-1)}-\Delta r. \tag{21}\]  
Incorporating Equations (4) and (5) into Equation (1), the stresses at \(r=r_{(j)}\) can be obtained. For the M-C rock mass, the stresses at \(r=r_{(j)}\) can be expressed in an incremental form by using the finite difference method, as follows:  
\[\sigma_{r(j)}\ =\ \frac{r_{(j)}\sigma_{r(j-1)}+\Delta rY_{r}}{r_{(j)}-(N_{r}-1 )\Delta r}, \tag{22}\]  
\[\sigma_{\theta(j)}\ =\ N_{r}\sigma_{r(j)}+Y_{r}. \tag{23}\]  
For the H-B rock mass, the stresses at \(r=r_{(j)}\) are:  
\[r_{(j)}\left(\sigma_{r(j)}-\sigma_{r(j-1)}\right)\ =\ \Delta r\sigma_{cir}\left(m_{b}\frac{ \sigma_{r(j)}}{\sigma_{cir}}+s_{r}\right)^{a_{r}}, \tag{24}\]  
\[\sigma_{\theta(j)}\ =\ \sigma_{r(j)}+\sigma_{cir}\left(m_{b}\frac{\sigma_{r(j)}}{ \sigma_{cir}}+s_{r}\right)^{a_{r}}. \tag{25}\]Incorporating Equations (4) and (5) into Equation (17), the radial displacement at \(r=r_{(i)}\) is:  
\[u_{r(i)}\ =\ \frac{\Delta rr_{(j)}\Big{(}C_{1}\sigma_{r(j)}+C_{2}\sigma_{\theta(j )}-C_{3}p_{0}\Big{)}+2 Gr_{(j)}u_{r(j-1)}}{2G\Big{(}r_{(j)}+K_{\psi}\Delta r\Big{)}}. \tag{17}\]  
Equation (15) can be solved numerically using the Newton-Raphson method. By repeating Equations (8)-(17) until \(j=m\), the stresses and displacements in a plastic region can be obtained.
# A Compact Triple Band Antenna Based on Multiple Split-Ring Resonators for Wireless Applications
## 5 Comparative Study  
Table 9 compares the proposed multi-SRR-based antenna with reported compact multiband antennas on FR4 substrate. As can be seen in this table, although the proposed antenna achieves gains of 1.5, 2 and 3.1 dBi over the respective frequency bands, which are comparable to the gains reported in previous studies, it is important to note that this performance is achieved despite a significant size reduction of 87.77%. In addition, the proposed antenna has a relatively wide operating bandwidth in three different frequency bands and outperforms most of its competitors in this respect. Thus, this study achieves an efficient balance between the dimensions, bandwidth and gain, which makes it very suitable for modern communication systems.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Band** & **Bandwidth** & **Bandwidth** & **Covered Bands** \\
**No.** & **(Simulation)** & **(Measurement)** & **WLAN, RFID, Wi-Fi 6 (802.11 ax) \\
1 & 2.4–2.80 GHz (14.55\%) & 2.25–2.85 GHz (23.5\%) & (2.4–2.48 GHz), 5G Sub-6 GHz Mid-band (2.5–2.6 GHz), WiMAX (2.3–2.5 GHz), Wi-Fi 7 \\ \hline
2 & 3.25–3.75 GHz (14.28\%) & 3.35–4.05 GHz (18.9\%) & 5G Sub-6 GHz Mid-band (3.3–3.8 GHz), WiMAX (3.3–3.8 GHz) \\ \hline
3 & 4.5–7.84 GHz (54.13\%) & 5.7–7 GHz (20.47\%) & (5.92–7.12 GHz), WLAN (5.15–5.35 GHz), 5G Ghz /5.725–5.825 GHz), WiMAX (5.725–5.850 GHz), 5G Sub-6 GHz Mid-band (4.5–5 GHz) \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of the simulated and measured S11 bandwidths and the corresponding wireless standards.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Device Type** & **Active SRRs** & **Optimized For** & **Application** \\ \hline Basic IoT Device & SRR1 & 2.4 GHz & \
\begin{tabular}{c} Wi-Fi 4/Blue- \\ tooth/Zigbee/IoT \\ \end{tabular} \\ \hline Wi-Fi 7 Router &
\begin{tabular}{c} SRR1 + SRR3 + \\ SRR4 \\ \end{tabular} & 2.4/5/6 GHz & Wi-Fi 4/5/6/6E/7 \\ \hline Enhanced Wi-Fi \\ Device & SRR1 + SRR3 & 2.4/5.8 & Wi-Fi 4/5 \\ \hline
5G NR Device & SRR2 + SRR3 & 3.5/5.8 &
\begin{tabular}{c} 5G NR mid-band \\ (n77/n78), Wi-Fi 5 \\ (5.8 GHz) \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 8: Application-specific configuration of the proposed multiband antenna.  
Our proposed design prioritizes a compact footprint, low cost and multiband operation, which inherently limits the achievable gain. Nevertheless, we fully recognize the importance of gain optimization, especially for emerging high-frequency wireless applications such as 5G. To this end, we are actively exploring several strategies to enhance gain in future work, including the development of compact MIMO configurations. Transitioning from a single-element to a MIMO architecture not only improves gain and diversity performance but also enhances robustness for 5G and IoT scenarios. This direction is particularly promising for achieving both wideband operation and higher gain within a compact form factor.
## 4 Experimental Results
### Interdependencies Impact on Predictive Results  
Figure 5 shows a comparison of predictive results with respect to the interdependencies effect between components. The results show that the interdependencies effect on the prediction of component 1 is statistically significant. The difference in model prediction is estimated using chi-square with a \(p<0.01\). Moreover, the effect of interdependencies is noticeable in component 3. In component 4, the interdependencies are not obvious in predictive results. However, the variance increases when interdependencies are not taken into account. Consequently, component 2 shows no obvious effect concerning interdependencies.  
In general, results highlight two important aspects. First, the interdependencies are not found at every component within the system. Although, in these cases, neglecting this aspect could lead to poorer predictive results, e.g., component 4. Therefore, it is important to clearly identify components subject to interdependencies and consider this information accordingly. Second, modeling interdependencies on the identified components is crucial to improve predictive results. In our use case, components 1 and 3 demonstrate the significant effect of interdependencies on predictive results. In general, identifying interdependencies is crucial to achieving the best results while building predictive models.
## 5 Discussion
### A Second Transponder in Gavdos  
The European Space Agency FRM for the Sentinel-6 (FRM4S6) project is developing a prototype transponder to be installed and operated as part of the PFAC at Gavdos, called GVD1 Cal/Val, to ensure redundancy (FRM standard requirement) in transponder calibrations for Sentinel-6. This transponder is specifically responding to the need of Copernicus Sentinel-6. The second transponder is to calibrate not only the range but also the backscatter coefficient (sigma-naught, \(\sigma^{0}\)) of radar altimetry.  
The backscatter coefficient (sigma-naught, \(\sigma^{0}\)) is a parameter that is useful for all Earth observing radars. It controls the efficiency of imaging radars when land deformations are observed at the mm level. It is used in radar scatterometers to determine wind speed and direction on the ocean surface and moisture determination in soil. It is also applied to radar altimeters to provide accurate measurements of the sea level, wave heights and wind speed over the ocean. Some ocean and meteorological models make use of satellite altimeter-derived wind speed and significant wave height observations. Indeed, the longest and most complete record of global ocean sea state is derived from satellite altimetry.  
This transponder will be established at the GVD1 Cal/Val site under a triple crossover of Sentinel-6 (descending Pass No. 18, and ascending No. 109) and Sentinel-3A (descending Pass No.335) (Figure 30).  
The future research direction of the PFAC is to provide two alternative, independent techniques to perform range and sigma-naught calibration primarily for the upcoming Sentinel-6 [34]. Transponder and sea-surface Cal/Val is already carried at the PFAC, whereas the GVD1 transponder Cal/Val is to operate in late 2020 (Figure 31).  
Figure 30: The location of the GVD1 transponder Cal/Val site in Gavdos island is under a crossover of the Sentinel-6 descending Pass No. 18, its ascending Pass No. 109 and the descending pass of Sentinel-3A Pass No. 335. The land of GVD1 is property of the Technical University of Crete. A permanent GNSS station has been operating there since 2004 (GVD0) whereas a Doppler Orbitography and Radiopositioning Integrated by Satellite (DORIS) beacon (GAVB) also operated for almost a decade (start 27-September-2003, end 27-March-2014).
## 3 Results and Discussion
### Comments on Formalism of Polarization Field Determination
The thicknesses of the undoped region, depletion region, the well width, the barrier width, and the number of quantum wells are represented as \(d_{u}\), \(d_{d}\), \(L_{w}\), \(L_{b}\), and \(N\), respectively. Solving Equation (38) with respect to the internal field  
\[\overset{\rightarrow}{E}_{w}+\overset{\rightarrow}{P}_{w}=\overset{ \rightarrow}{E}_{b}+\overset{\rightarrow}{P}_{b} \tag{23}\]8  
and inserting Equation (39) in Equation (37) results in Equation (40), which coincides with Equation (35):  
\[\overset{\rightarrow}{E}_{w}+\overset{\rightarrow}{P}_{w}=\overset{ \rightarrow}{E}_{b}+\overset{\rightarrow}{P}_{b} \tag{23}\]9  
The internal field \(\overset{\rightarrow}{E}_{i}\) is the sum of the fields due to the piezoelectric effect and the spontaneous polarization. The background field written as the total voltage drop divided by the distance, over which that drop occurs, has led to the replacement of the MQW-width \(L=n(L_{w}+L_{b})\) in Equation (35)by \((d_{u}+d_{d})\) in Equation (40). Apparently, the width of the intrinsic region \(d_{u}\) is given by the sum of the unintentionally doped multiple well and barrier widths:  
Figure 9:0  
The depletion width \(d_{d}\) is the sum of the p- and n-type depletion widths and varies with the applied bias. The depletion width \(d_{d}\), in a p-i-n structure, can be approximated by [99]:  
Figure 9:1  
Figure 9:2  
where \(d_{QW}\) is the sum of the multiple QW widths and \(N_{A}\), \(N_{D}\) are the acceptor and donor doping density, respectively.
Feature Attention Cycle Generative Adversarial Network: A Multi-Scene Image Dehazing Method Based on Feature Attention  
[PERSON]  
1 College of Computer Science and Technology, Xi'an University of Science and Technology, Xi'an 710054, China; [EMAIL_ADDRESS] (N.L.); [EMAIL_ADDRESS] (Y.D.); [EMAIL_ADDRESS] (Y.C.)  
[PERSON]  
No.  
as image segmentation [1] and object detection in hazy weather [2]. Therefore, research on image dehazing technology has important practical significance and application value.  
Due to the powerful learning ability of neural networks [3], researchers have proposed a large number of methods for image dehazing. Most current approaches achieve dehazing in a supervised manner [4,5], relying on a large number of mapping pairs of haze-free images for model training. While these methods achieve promising results on specific benchmark datasets, they face several critical challenges in real-world applications. First, obtaining large-scale paired datasets is extremely difficult and costly. As a result, most methods rely on synthetic datasets generated using physical models. However, the domain gap between synthetic and real-world hazy images often leads to overfitting issues. Second, existing methods are often limited to specific types of scenes (e.g., land scenes) or haze concentrations, making them less effective under diverse real-world conditions involving complex terrain, variable lighting, and a range of haze densities. Third, many methods overlook the inherent depth variation and atmospheric light in the scene, which are crucial for realistic dehazing. These shortcomings often result in degraded visual quality, including color distortion and the loss of fine details.  
To address the abovementioned issues within a unified framework, we propose FA-CycleGAN, an improved cycle-consistent generative adversarial network integrated with a feature fusion attention mechanism. Our approach is designed with three core motivations: (1) to eliminate the need for paired training data by employing an unpaired training strategy via CycleGAN, (2) to enhance physical interpretability by incorporating an atmospheric scattering model that guides the generator in simulating realistic haze removal, and (3) to preserve more discriminative features by introducing a feature fusion block with coordinate attention (FBCA) that adaptively fuses multi-scale features and enhances important spatial and channel-wise information. This unified framework allows our model to simultaneously address dataset limitations, multi-scene application issues, and feature loss problems, resulting in improved performance across diverse dehazing scenarios.