esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Mission Orbit and Relative Navigation Filter
### Relative Navigation Using Adaptive Kalman Filter  
According to the relative orbit model (11)-(17), the class of systems considered here is given as  
\[\begin{split}\mathbf{x}_{k}&=f(\mathbf{x}_{k-1}, \mathbf{u}_{k-1})+G\mathbf{w}_{k-1}\\ \mathbf{y}_{k}&=h(\mathbf{x}_{k})+\mathbf{v}_{k} \end{split} \tag{18}\]  
where \(\mathbf{x}_{k}\in\mathbf{R}^{n}\) denotes the state vector and \(\mathbf{u}_{k}\in\mathbf{R}^{m}\) denotes the control vector. The output vector is \(\mathbf{y}_{k}\in\mathbf{R}^{p}\) and the non-linear model is \(f(\mathbf{x}_{k-1},\mathbf{u}_{k-1})\). The process/measurement noise \(\mathbf{w}_{k}\) and \(\mathbf{v}_{k}\) are assumed to be independent, identically distributed Gaussian random variables with  
\[\mathbf{w}_{k}\sim\ \mathcal{N}(0,Q_{k})\,\qquad\mathbf{v}_{k}\sim\ \mathcal{N}(0,R_{k}) \tag{19}\]  
The traditional Kalman filter provides the optimal estimation of states by using a recursive scheme of propagating states and measurement update. The filter optimally blends the prior state and information update through a gain matrix which balances uncertainty in both the measurements and dynamics model.  
However, for HEO SFF missions, the real relative orbit drifted gradually away from the designed model, as in Figure 6. The orbit drift is inevitable due to the unknown process noise from perturbations. Some adaptive approaches have to be used for the on-line estimation of process noise, which will increase the accuracy of the predicted state error covariance matrix and increase the stability of the filter.  
Here, we use a recursive adaptive algorithm dealing with process noise uncertainty by minimizing a function in real time, which is determined by the difference of residual covariance computed by the filter and residual sequence generated by the filter. The adaptive approach can be summarized as follows:  
Figure 6: Formation range and elevation angle bias. The red lines represent the bias of real formation range drift from the design values (**top**) and the elevation angle drift (**bottom**). The blue lines denote the MWR equipment operation time during apogee.  
Suppose that we have the predicted state error covariance matrix \(\bar{P}(k)\), then \(\bar{P}_{y}(k)=H\bar{P}(k)H^{T}+R(k)\) is the predicted output covariance matrix. The adaptive Kalman filter for unknown process noise is used to minimize the following criterion function  
\[S_{k}=\text{tr}(\Delta\bar{P}_{y}^{2}(k))=\text{tr}[(\bar{P}_{y}(k)-\bar{P}_{y} ^{0}(k))^{2}] \tag{20}\]  
where \(\bar{P}_{y}^{0}(k)\) is the unbiased estimation of \(\bar{P}_{y}(k)\). The traditional Massachusetts Institute of Technology (MIT) rule is used here to derive the adaptive law, which is a popular method for the on-line estimation of unknown parameters in a control system [23, 24]. The solution of criterion function (20), including the process of negative gradient, discretization, and recursion for process noise update. These adaptive processes have been successfully used in HEO SFF, which are not shown here for brevity [25].
## 3 Experiments and Results
### Two-Dimensional Localization Error  
The average error based on the calculation of the 2D Euclidean distances between the matched feature locations is reported in Table 2, presenting the performance of the deep local feature approach and the SURF method, as described in Section 2.5.  
The 2D error average per subject is calculated when three different band extraction/selection algorithms are used. Overall, a mean error of 0.39 (\(\pm\)0.08), 0.27 (\(\pm\)0.07) and 0.25 (\(\pm\)0.05) mm is found when deep features are detected using PCA, VGBS and SSBS, respectively. A mean error of 0.45 (\(\pm\)0.07), 0.42 (\(\pm\)0.07), and 0.30 (\(\pm\)0.08) mm is found when handcrafted features are detected using PCA, VGBS and SSBS methods, respectively. We observe a difference in error of slightly more than 0.10 mm when using the band selection algorithms compared to PCA for both feature extraction algorithms (SURF and DELF). It is noticed that DELF is capable of detecting skin features, which are clearly more visible when using the original bands, while the information is partially lost when PCA is used as dimensionality reduction algorithm. Overall, the trend in the results shows better performances when DELF is used in combination with the SSBS method.  
The analysis of the number of inliers has been reported in Figure 9, revealing a higher amount of matched features for the majority of subjects when DELF is used, to detect and match features after outlier removal by using RANSAC.  
Figure 8: (**a**) Speed Up Robust Features (SURF) correspondences (green) after random sample consensus (RANSAC) geometric verification for subject no. 3 when principal components analysis (PCA) is used as a band extraction method. The two images correspond to the acquired images during inhalation and exhalation for the second principal component. (**b**) Deep local features (DELF) correspondences (red) after RANSAC geometric verification for subject no. 3 when PCA is used as a band extraction method. The two images correspond to the acquired images during inhalation and exhalation for the second principal component. (**c**) SURF correspondences after RANSAC geometric verification for subject no. 3 (\(\approx\)590 nm) when the band selection algorithms Volume-Gradient-Based Band Selection (VGBS) and SSBS are used. (**d**) DELF correspondences after RANSAC geometric verification for subject no. 3 (\(\approx\)590 nm) when the band selection algorithms VGBS and SSBS are used.
# A Deep Learning Model to Forecast Solar Irradiance Using a Sky Camera
## 4 Results and Discussion
[7] also reported that the persistence model has better performance than their proposed model for overcast day conditions, with an RMSD of 110 Wm\({}^{-2}\) for 10 min ahead forecast horizon. To further analyze the variability of the solar irradiance under different sky conditions, the metric that indicates the variability of solar irradiance is presented in Figure 8. The metric was calculated using the universal variability index (UVI) to quantify the variation of solar irradiance under clear, partly cloudy, and overcast days by comparing the measured irradiance and calculated clear-sky irradiance. As expected, the small variations in clear and overcast days resulted in the values of UVI close to one. However, the UVI for partly cloudy days does not remain constant anymore due to the high variability of solar irradiance. It shows the high variability index with the value ranging from 2.68 to 61.16. Figure 8: Universal variability index (UVI) for 10 min ahead forecasting in different sky conditions: clear sky, partly cloudy, and overcast day. The performance of forecast models significantly differs by solar irradiance variability. Figure 9 shows the mean RMSD and the mean UVI of 10-min-ahead forecast results under different sky conditions. It clearly explains that the proposed model remarkably outperformed the persistence model in partly cloudy days with the mean UVI > 20. On the other hand, when the mean UVI < 5, the forecast performance of the proposed model is comparable to that of the persistence model. In overcast days, the proposed model is slightly less accurate than the persistence model. Probably, the reason is that the solar radiation model is less accurate under overcast sky conditions.
## 3 Generation of the Initial Data Set
### Data Processing  
To decrease the noise of the initial data set generated by the CSS simulation model, the normalization was conducted in the data processing processes, in which all of the values of input and output variables were transformed to the range of [0, 1] by the following Equation (2).  
\[X^{\prime}=\frac{X-X_{\text{min}}}{X_{\text{max}}-X_{\text{min}}} \tag{2}\]  
where the \(X^{\prime}\) and \(X\) are the processed and initial values of the input and output variables, respectively. The \(X_{\text{max}}\) and \(X_{\text{min}}\) are the minimum and maximum data points. It is noted that the predicted COP by the developed PA-LM model needs to be rescaled to the original scale using the same parameters used for the normalization.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Geological and Operational Parameters** & _PCC_ & **Order** \\ \hline Reservoir porosity & 0.0171 & 4 \\ Reservoir permeability (MD) & 0.0217 & 3 \\ Oil saturation & 0.0557 & 2 \\ Well spacing (m) & 0.0160 & 5 \\ Well distance (m) & 0.0147 & 6 \\ Initial reservoir temperature (\({}^{\circ}\)C) & 0.0104 & 8 \\ Steam temperature (\({}^{\circ}\)C) & 0.0111 & 7 \\ Steam quality & 0.0022 & 11 \\ Steam injection volume (m\({}^{3}\)) & 0.0045 & 10 \\ Steam injection rate (m\({}^{3}\)/day) & 0.0051 & 9 \\ Liquid production rate (m\({}^{3}\)/day) & 0.6321 & 1 \\ Soaking time (day) & 0.0018 & 12 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The calculated _PCC_ values and the order of the parameters.
# Optimizing Geospatial Data for ML/CV Applications:
## 4 Results  
For a better depiction of the impact of the optimization process on the file size, a comparative study was conducted. A sample of 30 parcels was selected for examination from each set of image resolutions: 5, 10, and 25 cm per pixel as well as for each category, roads and highways. The fetching process was repeated three times:  
* for the full-size image
* for images divided into tiles, where just the tiles intersecting with the parcel geometry were included  
Figure 11: A tiled road before (**left**) and after (**right**) applying the parcel geometry mask.  
Figure 10: A road divided into tiles intersecting with the parcel geometry.  
* for images with the parcel geometry mask and where the trimming process was applied.  
The optimization process significantly reduced the file sizes of orthoimagery images across all resolutions and road types. Table 1 presents the results of the optimization of the highway image category.  
Table 2 demonstrates the optimization impact on images from the road category.  
Figure 12 presents a comparison of the percentage of file size reduction for different resolutions and types.  
The greatest difference in file size between a full-sized image and an optimized image was 207.75 MB. The original image had 218.16 MB and after removing the area outside the parcel the size was reduced by 95% to 10.41 MB.  
Figure 13 presents the difference between a file without optimization and after applying the optimization algorithm.  
\begin{table}
\begin{tabular}{l c c c} \hline
**Resolution** & **5 cm per Pixel** & **10 cm per Pixel** & **25 cm per Pixel** \\ \hline Average file size without optimization & 13.67 MB & 2.60 MB & 0.91 MB \\ Average file size of tiled images & 6.50 MB & 0.91 MB & 0.48 MB \\ Average file size after optimization & 4.94 MB & 0.55 MB & 0.40 MB \\ Total reduction
## 5 Simulation
### Simulation Experiment with Initial State Disturbance  
To further demonstrate the effectiveness and robustness of our method, the simulation is operated when the initial state of the vehicle is disturbed with the same target point and the no-fly zones. The target point and no-fly zones are set as the same as Case 2 in Section 5.3, the initial altitude \(\Delta h_{0}\)= +2 km, initial speed \(\Delta v_{0}=-100\) m/s, initial flight path angle \(\Delta\theta_{0}\)= +0.5\({}^{\circ}\) and initial flight heading angle \(\Delta\psi_{0}=-10^{\circ}\) are, respectively, simulated. The optimization effect is shown in Figure 10 and Table 3. It can be seen from  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Case** & \(\mathbf{E_{l}}\) **(km)** & \(\mathbf{E_{h}}\) **(km)** & **Waste Time (s)** & **Iteration** \\ \hline
1 & 0.001 & \(-6.43\times 10^{-4}\) & 22.93 & 5 \\
2 & 0.041 & 0.005 & 27.20 & 5 \\
3 & 0.016 & \(-0.001\) & 110.67 & 19 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Generation effect with different cases.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Case** & \(\mathbf{\phi_{f}}\) (\({}^{\circ}\)) & \(\mathbf{\varphi_{f}}\) (\({}^{\circ}\)) & \(\mathbf{E_{l}}\) **(km)** & **\(\mathbf{E_{h}}\) (km)** & **Waste Time (s)** & **Iteration** \\ \hline
1 & 28 & 23 & 0.167 & 0.009 & 135.84 & 23 \\
2 & 30 & 20 & 0.082 & \(-0.002\) & 38.63 & 7 \\
3 & 30 & 10 & 0.079 & \(-0.009\) & 34.87 & 6 \\
4 & 27 & 27 & 0.139 & \(-0.019\) & 122.70 & 21 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Generation effect with different target points.  
Figure 9: The generation effects of different target points: (**a**) represents the lateral trajectories; (**b**) represents the longitudinal trajectories; (**c**) represents the control angles.  
Figure 10 that with certain disturbance of the initial state, the vehicle can still effectively avoid the no-fly zone and reach the target points with high accuracy in both the lateral and longitudinal directions. The control angle obtained by changing the height, speed and the flight angle is similar, which has a certain impact on the longitudinal trajectory. When the initial flight heading angle is changed, the optimization trajectory changes obviously, and the number of iterations in this case increases significantly. This is because the adjusted initial flight heading angle is larger than the LOS angle. In general, the proposed method is robust to initial state disturbances.
## 3 Numerical Simulations
### Parameter Identification with Different Integration Methods  
In EKF, realizing the state prediction (Equation (9)) is a critical issue for accurately identifying the structural parameters, especially the damping. In this paper, three integral methods, such as the rectangular integration, the trapezoidal integration and the fourth-order Runge-Kutta integration, are introduced to identify the stiffness and damping of the structure. Their corresponding estimation results will then be compared and discussed.  
Figure 2: The Hanshin earthquake wave.  
The \(f(\mathbf{X},\mathbf{F})\) can be regarded as an invariant constant if the \(\Delta t=t_{k+1}-t_{k}\) is small enough. Then, the rectangular integral can be introduced to the state prediction, and Equation (9) can be approximated as:  
\[\widetilde{\mathbf{X}}_{k+1}=\mathbf{\hat{X}}_{k}+\mathbf{f}\big{(}\mathbf{\hat{X}}_{k},F_{k} \big{)}\cdot\Delta t \tag{19}\]  
A trapezoidal approach to approximating each area can be used to improve the accuracy of the approximate value, known as the improved Euler integration or trapezoidal integration. The \(f(\mathbf{X},\mathbf{F})\) can be approximately regarded as linear in the interval \([t_{k},t_{k+1}]\), and Equation (9) can be expressed by:  
\[\widetilde{\mathbf{X}}_{k+1}=\mathbf{\hat{X}}_{k}+\frac{1}{2}\big{[}f\big{(}\mathbf{\hat{X} }_{k},F_{k}\big{)}\cdot\Delta t+\mathbf{f}\big{(}\mathbf{\hat{X}}_{k+1},F_{k+1}\big{)} \cdot\Delta t\big{]} \tag{20}\]  
The \(n^{\text{th}}\)-order Runge-Kutta integration can approximately calculate \(n\) values in each step. Actually, the rectangular integration and the trapezoidal integration can be also regarded as the first-order and second-order Runge-Kutta integration, respectively. Here, the fourth-order Runge-Kutta integration was introduced to solve the state prediction problem, and can be expressed as:  
\[\widetilde{\mathbf{X}}_{k+1}=\mathbf{\hat{X}}_{k}+\frac{1}{6}(\Delta\mathbf{f}_{1}+2\Delta \mathbf{f}_{2}+2\Delta\mathbf{f}_{3}+\Delta\mathbf{f}_{4}) \tag{21}\]  
\[\Delta\mathbf{f}_{1}=\mathbf{f}\big{(}\mathbf{\hat{X}}_{k},F_{k}\big{)}\cdot\Delta t\]  
\[\Delta\mathbf{f}_{2}=\mathbf{f}\big{(}\mathbf{\hat{X}}_{k}+\Delta\mathbf{f}_{1}/2,F_{k+1/2} \big{)}\cdot\Delta t\]  
\[\Delta\mathbf{f}_{3}=\mathbf{f}\big{(}\mathbf{\hat{X}}_{k}+\Delta\mathbf{f}_{2}/2,F_{k+1/2} \big{)}\cdot\Delta t\]  
\[\Delta\mathbf{f}_{4}=\mathbf{f}\big{(}\mathbf{\hat{X}}_{k}+\Delta\mathbf{f}_{3},F_{k+1}\big{)} \cdot\Delta t\]  
where \(F_{k+1/2}=F(t_{k+1/2})=F(t_{k}+1/2\cdot\Delta t)\), and the linearity is also assumed in the interval \([t_{k},t_{k+1}]\).
# Development of IoT-Based Real-Time Fire Detection System Using Raspberry Pi and Fisheye Camera
## 3 Methods  
In this study, Raspberry Pi, a small and low-cost computer, was used to develop an effective fire detection system. A fisheye camera with a 220-degree angle of view was installed for the omnidirectional capture, and the system was controlled through a laptop computer using the remote control function of Raspberry Pi. By connecting the laptop and Raspberry Pi to the same Wi-Fi, the Raspberry Pi was remotely controlled by the laptop, reducing the size and cost of the product and minimizing power consumption. By writing the Python code for the operation of the Raspberry Pi, images were continuously taken and image data could be transmitted to the Firebase database through the Wi-Fi communication function. To analyze continuously transmitted images in real-time and quickly detect forest fires, a desktop computer accesses images in the database and uses deep learning-based object detection technology to detect forest fires. In addition, by using the relative coordinates of forest fires detected in polar images and DSM, the location of a forest fire can be specified on a map. Forest fires typically travel upwind toward the top of a mountain and begin to spread in the direction of the wind. Therefore, it is necessary to install a fire detection system at a low altitude in a basin terrain to monitor a large area of a mountain slope. In this study, the system was installed in a place with a wide visible area by applying GIS-based viewshed analysis.
# Vibration Parameters Identification of Rotating Blades Based on Blade Tip-Timing Sensor Waveforms
## 4 Identification Accuracy Analysis of Waveform Mapping Method
### Comparison of Identification Accuracy between Waveform Mapping Method and Other Methods  
The identification accuracy of blade synchronous vibration parameters is compared between the waveform mapping method and the classical blade tip-timing method. According to the classical blade tip-timing theory, at least four blade tip-timing sensors are needed to identify the blade synchronous vibration parameters (amplitude, excitation order, initial phase and constant offset). When the SNR is 30 db, the classical blade tip-timing method (four sensors) and waveform mapping method (four sensors, three sensors and two sensors) are used to identify the synchronous vibration parameters of the blade. The accuracy of classical blade tip-timing method and waveform mapping method in identifying excitation order is shown in Figure 13. The number of identifications is 100 times. As can be seen from Figure 13, the accuracy of waveform mapping method (four sensors) in identifying excitation order is higher than that of classical blade tip-timing method. With the decrease of the number of sensors, the accuracy of the waveform mapping method for identifying the excitation order decreases, but it is still higher than that of the classical blade tip-timing  
Figure 11: Relative error of constant offset identification by waveform method under different SNR: (**a**) single-sensor; (**b**) double-sensor; (**c**) three-sensor.  
Figure 14: Average relative error of vibration amplitude.  
Figure 13: Identification accuracy of excitation order.  
Figure 15: Average relative error of initial phase.  
The identification accuracy of excitation order and vibration frequency of the waveform mapping method and waveform method in Ref. [29] are compared, as shown in Table 1. According to Table 1, when the SNR is infinity (no error), the relative error of vibration frequency identification of waveform method in Ref. [29] is 14.10%, the excitation order identification accuracy of waveform mapping method is 100% and the relative error of vibration frequency identification is 0%. The vibration frequency identification accuracy of the waveform mapping method is higher than that of the waveform method in Ref. [29]. With the decrease of SNR, the identification accuracy of excitation order of waveform mapping method decreases. However, as long as the excitation order identification is accurate, the relative error of vibration frequency identification is 0%.
# Joint Hybrid Beamforming Design for Millimeter Wave Amplify-and-Forward Relay Communication Systems
## 4 Hybrid Beamforming Design for FD AF Relay Systems
### Proposed HBF-MO-FD-R Algorithm
8: Update \(\gamma_{2,k+1}\), \(\mathbf{G}_{\text{U}_{2},k+1}\) and \(\mathbf{\overline{G}}_{2,k+1}\) according to (49) and (51). 9: Calculate \(\
abla\)J3(\(\mathbf{W}_{\text{RF},k}\)) according to (66) and compute \(\mathbf{W}_{\text{RF},k+1}\) by using the MO method. 10: Update \(\mathbf{W}_{\text{B},k+1}\) and \(\mathbf{W}_{k+1}\) according to (62). 11:\(k\gets k+1\). 12:endwhile
13: Compute \(\mathbf{G}_{2}^{\text{SIC}}\) by using the SIC-NP algorithm and update \(\mathbf{G}\) according to (60). 14:Power normalization. ```  
**Algorithm 3** HBF-MO-FD-R Algorithm.
# Security Challenges for Light Emitting Systems
## 4 Results and Discussion
If L is equal to the current error number, the gap during the iteration process becomes zero before n becomes larger than or equal to 2L. Or, the algorithm will update the value of L and g(x), decrease L, and put \"m = 1\". \"L = (n + 1 - L)\" indicates the number of valid syndromes to calculate and subsequently correct errors, and also handles the case where L decreases by more than 1 [14]. The change made is to add a variable k that is used to decode the message sent via the light signal. This variable is selected by the user from the odd numbers and must be different from 1. Figure 6 below shows the simulation results of the Berlekamp-Massey algorithm and its modified variant; the images represent the noise resistance during communication over a Li-Fi network. We notice that the existence of noise is quite regular for the modified BM, unlike for the noise in (Figure 6b). This represents an advantage to adapt the modified BM algorithm. Figure 7 represents an example of the RS code flow for error correction: (15,11), (31,27), (63,59), and (127,123). The above graph shows that the error-correcting Reed-Solomon codes become more efficient as the code size grows, since the noise impact decreases with a larger code size. Furthermore, for bigger code words, the noise duration must represent a relatively small proportion, and the received noise must be averaged over a lengthy period of time. For large block sizes, the Reed-Solomon codes are favored. This indicates that the ideal big size code word represents a step forward in terms of performance. On the other side, a very high size code word will complicate transmission implementation. Figure 6: Noise measurement in a Li-Fi network. (**a**) according to modified BM, (**b**) according to BM.
## 3 Result and Discussion  
### Batch Experiments  
The results of the FSA batch test in the adsorption of copper and phenanthrene are described in Figures 2 and 3. The maximum adsorptions with FSA were approximately 28 mg/g of copper and 890 mg/g of phenanthrene, respectively, indicating the greater adsorption capacity of FSA for phenanthrene, which was approximately 30 times higher than the adsorption of copper. Previous copper adsorption isotherms are described with a semi-log in Figure 2b as well as in Figure 3b. As a result of linearizing the adsorption isotherm models using the Langmuir and Freundlich models in Figures 2a and 3a, the Langmuir model predicted the adsorption of the heavy metals and the organic compounds better than Freundlich model. Similarly, the semi-log scale (e.g., Figures 2b and 3b) also shows a better fit in the Langmuir model.  
However, the undistinguishable parts in the adsorption isotherm graphs confirm initial part on basic graphs and the Langmuir and Freundlich models. Accordingly, the proper comparison including these undistinguishable parts was not successful in the adsorption isotherm, but it can be confirm through the semi-log scale.