esa-sceva/satcom-chunk-collection huggingface dataset:
# Grapevine Downy Mildew Warning System Based on NB-IoT and Energy Harvesting Technology
## 4 Discussion
The best results are for severe alarms: 2 out of 2, 8 out of 8, and, 4 out of 4 showing perfect 100% alignment for all locations. Secondary infection alarms are compared to iMetos(r) [13] and mechanistic model 2 [20] (M2). The analysis of Figures 16-18 shows that Winet fired 4 out of 8, 14 out of 23, and 12 out of 18 iMetos(r) light alarms for the RS, VV, and T locations, respectively. These correspond to correlations of 50%, 61%, and 67%, respectively. Moderate alarms showed similar results: 3 out of 4, 13 out of 14, and 4 out of 4 with corresponding correlations of 75%, 93%, and 100%, respectively. The best results were again obtained for severe alarms: 2 out of 2, 12 out of 12, and 3 out of 3 showing perfect 100% alignment for all locations. Comparisons between Winet and M2 show that Winet fired 5 out of 7, 15 out of 21, and 15 out of 27 M2 alarms for the RS, VV, and T locations, respectively. The corresponding correlations are 71%, 71%, and 55%. It can be concluded that Winet alarms are highly correlated with iMetos(r) for all severe alarms for primary and secondary infections. In the case of iMetos(r) moderate alarms, the correlation is also high (>90%) except for the location Rimski Sanceevi. The correlation of Winet and iMetos(r) light infection alarms are highly correlated for the locations Vrsacki Vinogradi and Trient for primary infection alarms, whereas for the secondary infection, it is between 60% and 70% for both locations. In the case of Rimski Sanceevi, both the primary and the secondary light infection correlations are approximately 50%. From comparisons with mechanistic model 2 for the secondary infection alarms, it can be concluded that the correlation is between 55% and 71%. The main limitation to our research is that we did not have access to any dataset with labeled primary/secondary infections from in vivo experiments. It could prove the exact effectiveness of the alarms of Winet (and other models). However, to the best of our knowledge, there is no such open access dataset. The obtained results demonstrate that our model could be used as a grapevine downny mildew early warning system for severe and moderate infections. On the other hand, the light infection alarms do not seem to be sensitive enough in some cases and will be investigated as part of future work. The occurrences of additional alarms (W+) also need more thorough investigation. The main drawback of the proposed system is the cost of implementation. However, since the maintenance is almost not needed due to energy harvesting, an initial, relatively high investment would be compensated in the long run.
## 3 Methodology and Results for Testing the Accuracy of Measurements  
The research involved a detailed analysis of 36 independent time series of coordinates, with the sampling interval of 10 s. The series were selected in such a way that the number (\(n\)) of measurements ranged from 160 to 360. A classic approach was adopted assuming that the average approaches the true value. In each series the following were determined:  
Figure 4: Location of the research facility (Base-station and Rover-station). Source: own calculation based on GoogleEarth. I--systematic measurement error for each coordinate by formulae, respectively:  
\[\delta^{j}_{x}=\frac{\sum_{i=1}^{i=n}\Delta x_{i}}{n},\;\delta^{j}_{y}=\frac{ \sum_{i=1}^{i=n}\Delta y_{i}}{n},\;\delta^{j}_{h}=\frac{\sum_{i=1}^{i=n}\Delta h _{i}}{n} \tag{1}\]  
where: \(\Delta x_{i}=x_{i}-x_{k}\), \(\Delta y_{i}=y_{i}-y_{k}\), \(\Delta h_{i}=h_{i}-h_{k}\)--absolute errors of the \(i\)-th measurement, for coordinates: north (\(x\)), east (\(y\)), height (\(h\)), respectively; (\(x_{i},y_{i},h_{i}\))--coordinates of the measured control point based on the \(i\)-th measurement, (\(x_{k},y_{k},h_{k}\))--reference coordinates, (true) of the checkpoint, j--number of the series, n--length of the series. To show the variability, the basic numerical characteristics of the systematic errors of measurement and the length of confidence intervals were determined for average values, estimated at a confidence level of 0.95 (Tables 9 and 10). \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**No of Series \(j\)**} & \multicolumn{2}{c}{\  
If Galileo constellation is not complete, some discontinuities in observations occur. Then the solutions can be made only in selected \"time windows\", when the number of observed satellites exceeds 4. The breaks in measurements cause that there is no continuity of observation and therefore systematic errors occur which depend mostly on the length of the breaks. However, in this paper only such measurement series were chosen for the analysis, in which there were no breaks. II--average error of a single coordinate measurement according to the formulae, respectively:  
\[m_{\Delta x}=\sqrt{\frac{\sum_{i=1}^{i=n}\Delta x_{i}{}^{2}}{n}},\ m_{\Delta y} =\sqrt{\frac{\sum_{i=1}^{i=n}\Delta y_{i}{}^{2}}{n}},\ m_{\Delta h}=\sqrt{ \frac{\sum_{i=1}^{i=n}\Delta h_{i}{}^{2}}{n}} \tag{2}\]  
or:  
\[m_{v_{x}}=\sqrt{\frac{\sum_{i=1}^{i=n}v_{x_{i}}^{2}}{n}},\ m_{v_{y}}=\sqrt{ \frac{\sum_{i=1}^{i=n}v_{y_{i}}^{2}}{n}},\ m_{v_{x}}=\sqrt{\frac{\sum_{i=1}^{i= n}v_{x_{i}}^{2}}{n}} \tag{3}\]  
where: \(\left(v_{x_{i}},v_{y_{i}},v_{h_{i}}\right):=\left(\Delta x_{i}-\delta_{x}^{j},\Delta y_{i}-\delta_{y}^{j},\Delta h_{i}-\delta_{h}^{j}\right)\)--educed errors of the \(i\)-th measurement of the corresponding coordinate. Table 11: Shows the average errors of a single measurement for each series in two variants: A--without taking into account the reduction of absolute errors, B--after taking into account the reduction of absolute errors.
## 5 Numerical Results
Moreover, as it was expected, the MOD value of the system increases for lower normalized average electrical SNR values and stronger non-zero boresight amounts of pointing mismatch. Figure 4: Total outage probability (OP) of a dual-hop relay-aided system with varying path configurations as a function of a wide range of \(\gamma/\mu\), for gamma-gamma (G-G) modeled weak turbulent channels, along with different amounts of pointing mismatch. Figure 7, highlights the evolution of MOD values per hour over a wide normalized average electrical SNR range, obtained by using (21), for the total relay-aided FSO system with \(N=3\) or 5 and \(H_{j}=2\) or 4, under weak gamma-gamma modeled turbulence and different amounts of non-zero  
Figure 5: Total outage probability (OP) of a quadruple-hop relay-aided system with varying path configurations as a function of a wide range of \(\gamma/\mu\), for gamma-gamma (G-G) modeled weak turbulent channels along with different amounts of pointing mismatch. Figure 6: Total mean outage duration (MOD) of a quadruple-hop relay-aided system with varying path configurations as a function of a wide range of \(\gamma/\mu\), for negative exponential (NE) modeled saturated turbulent channels along with different amounts of pointing mismatch. boresight pointing errors. As we can see, apart from increasing the electrical SNR value and mitigating the strength of pointing errors effects, the increase of the number of paths can lead to significant outage performance enhancements of the total system, and therefore, \(N=5\) configurations are shown to outperform \(N=3\) configuration, in terms of MOD metric. However, by increasing also the number of hops in each path, the total MOD value increases too. Additionally, the performance comparison between Figures 6 and 7 concludes that the MOD results of the latter outperform the corresponding MOD results of the former. This is due to the weaker amount of turbulence-induced fading that is addressed in the latter case. In fact, given that \(\gamma/\mu=18\) dB for \(N=5\) with \(H_{j}=4\) and strong non-zero boresight pointing errors, due to the weaker turbulence conditions we now obtain a strongly reduced MOD value almost of 1.5 ms per hour instead of the almost 2.9 s per hour we obtained before for the same system's characteristics, except for saturated turbulence conditions. Additionally, given that \(\gamma/\mu=18\) dB for \(N=5\) with \(H_{j}=4\) and weak non-zero boresight pointing errors this time, the MOD value is now shown to be almost 4.8 \(\mu\)s per hour instead of the corresponding MOD value of almost 1.6 s per hour of Figure 6.
# Numerical Analysis on Spanwise Correlation of Vortex-Induced Force of Split Double-Box Beam
## 3 Overview of Numerical Simulation of Split Double-Box Beam
### Verification of Irrelevance  
The grid independence and time-step independence were verified to find the number of grids and time steps suitable before conducting the 2D numerical simulation in this paper.  
1. Verification of irrelevance of grid quantity:  
The grids were divided into three quantities 219,812, 582,427, and 1,060,227, the time step was set as 0.001 s, and the reduced wind velocity of the incoming wind was 6.21.  
Figure 5: Boundary conditions and demarcation of grids. (**a**) Grid demarcation of 2D model of a bridge section; (**b**) Grid demarcation of 3D model of a bridge section; (**c**) Details of grid demarcation of a rigid area; (**d**) Boundary conditions.  
Numerical simulations were carried out for the three numbers of grids to verify the grid independence, and the results are shown in Table 2.  
It was found that the drag coefficient, Storaha number (St), and the root means square (RMS) value of lift coefficient were basically close to each other under three conditions with different grid quantities when the time step was set as 0.001 s, and the difference was not significant. Therefore, the influence of grid quantity on the calculation results is small and can be neglected.  
2. Verification of irrelevance of time step:  
The time steps were taken as 0.0005 s, 0.001 s, and 0.01 s to verify the irrelevance of the time step when the number of grids was 582,427, and the results are shown in Table 3.  
It was found that the root means square of lift coefficient, resistance coefficient, and ST number do not differ much at time steps of 0.001 s and 0.0005 s. However, the results at the time step of 0.01 s differ significantly, probably because the smaller the time step for the same number of iterative steps in the calculation process, the more accurate the resultant values are. At the same time, if the time step is too small, the calculation period will increase significantly; if the time step is too large, the accuracy of the calculation results will be affected.  
Therefore, considering the accuracy of the calculation results and the efficiency of the calculation process, the number of grids was chosen as 582,427 and the time step was chosen as 0.001 s to save the calculation time and improve the calculation efficiency.
# Shake Table Testing of Standard Cold-Formed Steel Storage Rack
## 2 Experimental Program
### Testing Protocol
#### 2.3.2 Phase Two and Three: Shake Table Tests  
The objective of these two phases was to capture the seismic response of the standard rack system under various intensity ground motions and different time-domain scales. The excitation intensity was therefore applied in an incremental fashion. The loading schedule in phase two and phase three of the experiment was as follows:  
* In phase two, the tests started at a Peak Ground Acceleration (PGA) of 20% of the 1940 El-Centro earthquake record, and then the earthquake record was increased by steps of 10% or 20% (increasing 10-20% of the PGA for shake table excitation in each step) up to 80% intensity of the El-Centro earthquake. The shake table tests were conducted without scaling time domain.
* In phase three, the shake table testing program was carried out to subject the rack frame under a range of intensities between 40% and 220% of the El-Centro earthquake record. The increment increase was continued until a significant drop in the fundamental period of the system was observed, which indicates the formation of local plastic deformation in the structure. In this phase, the time domain was scaled down by a factor of 2 to better simulate the sensitivity of taller racks with elongated periods.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Test Phase** & **Time Scaling Factor \({}^{*}\)** & **Earthquake Record** & **Intensities (\%)** \\ \hline \multirow{3}{*}{One} & \multirow{3}{*}{1} & Northridge (1994) & 10 \\ \cline{3-3}  & & & 20 \\ \hline Two & & & 20, 40, 60, 70, 80 \\ \hline \multirow{3}{*}{Three} & \multirow{3}{*}{2} & \multirow{3}{*}{140, 160, 180, 200, 220} \\ \cline{3-3}  & & & 40, 60, 80, 100, 120, \\ \cline{3-3}  & & & 140, 160, 180, 200, 220 \\ \hline \hline \end{tabular} \({}^{*}\)The earthquake record’s scaling factor in the time domain.  
\end{table}
Table 2: Summary of the experimental tests.  
As stated earlier, sine sweep tests with a low intensity (0.02 g) were also conducted after every seismic excitation (each increment) to evaluate the damping ratio as well as monitoring possible period elongation due to damage in the system.
## 3 Anomaly Detection Process for Battery Cells
### Consistency Assessment for Battery Cells  
In the singular value matrix \(\Sigma\) obtained through the RPCA, the size of the singular values reflects the weight of every component in the data matrix \(D\). The larger the singular values are, the more corresponding components contribute when reconstructing the original data. Usually, the first singular value is the largest. Moreover, the ranking of the singular values also determines the nuclear norm of the low-rank matrix (the sum of the singular values), which is used in optimization problems to measure low rankness.  
The first singular value \(\sigma_{1}\) in the singular value matrix \(\Sigma\) represents the most significant component in the data matrix \(D\). Only \(\sigma_{1}\) is retained, and all other singular values are set to 0, as shown in Equation (9).  
\[\Sigma^{{}^{\prime}}=\begin{bmatrix}\sigma_{1}&0&\cdots&0\\ 0&0&\cdots&0\\ 0&0&\ddots&0\\ 0&0&\cdots&0\end{bmatrix} \tag{9}\]  
Then, the original data matrix is reconstructed through Equation (10), and the matrix \(D^{{}^{\prime}}\) can be obtained. At this point, the rank of matrix \(D^{{}^{\prime}}\) is 1, which is equivalent to extreme denoising on the original data matrix.  
\[D^{{}^{\prime}}=U\Sigma^{{}^{\prime}}V^{T} \tag{10}\]  
Here, \(\Sigma^{{}^{\prime}},D^{{}^{\prime}}\in\mathbb{R}^{m\times n}\).  
Taking the average curve of 238 battery cell charging curves obtained through extreme denoising using RPCA assesses the consistency of the charging state of battery cells. A battery cell is considered normal when its charging curve remains approximately parallel to the average curve, whereas a battery cell is considered abnormal when its charging curve has a clear deviation from the average curve.  
Next, a method of screening and identifying abnormal battery cells will be introduced by setting the threshold value of the charging voltage for battery cells.
# Simultaneous Correlative Interferometer Technique for Direction Finding of Signal Sources
## 1 Introduction
The rest of the paper is organized as follows:  
* In Section 2, the system model and assumptions we consider in this paper are described. * In Sections 3 and 4, we review the conventional CI-based DOA estimation technique and propose our simultaneous CI-based DOA estimation technique in detail, respectively. * In Section 6, we compare the conventional MUSIC and CI methods with a normalized DOA spectrum to clearly explain where the performance difference between them comes from. The main contributions of this paper are summarized as follows:  
* We propose a novel simultaneous CI technique exploiting multiple time samples to improve DOA estimation performance, which can be regarded as a generalized algorithm of the conventional CI method. * We show that the proposed technique with various array structures significantly outperforms the conventional DOA estimation methods, especially in harsh estimation environments, through extensive computer simulations.
## 2 Materials and Methods
### CFD Methods
#### 2.2.3 VOF Model  
The two-phase flow of gas and liquid adopts the Volume of Fluid (VOF) model, and its governing equations are shown in Equations (5)-(8). The mass conservation equation is given by:  
\[\frac{\partial}{\partial t}\int_{V}a_{i}dV+\oint_{A}a_{i}v\cdot d\mathbf{\alpha}= \int_{V}\bigg{(}S_{a_{i}}-\frac{a_{i}}{\rho_{i}}\frac{D\rho_{i}}{Dt}\bigg{)}dV- \int_{V}\frac{1}{\rho_{i}}\
abla(a_{i}\rho_{i}v_{d,i})dV \tag{5}\]  
where \(\mathbf{\alpha}\) is the surface area vector, \(\
u\) is the mixture (mass-averaged) velocity, \(\
u_{d,i}\) is the diffusion velocity, \(S_{ai}\) represents the custom source term for phase \(i\), and \(D_{\rho i}/D_{t}\) denotes the Lagrangian derivative with respect to phase density \(\rho_{i}\).  
The total mass conservation equation for all phases is given by Equation (6):  
\[\begin{array}{ll}\frac{\partial}{\partial t}\int_{V}\rho vdV+\oint_{A}\rho v \otimes v\cdot d\mathbf{\alpha}&=-\oint_{A}pId\mathbf{\alpha}+\oint_{A}T\cdot d\mathbf{ \alpha}+\int_{V}\rho gdV\\ &+\int_{V}f_{b}dV-\sum_{i}\int_{V}a_{i}\rho_{i}\rho_{i}\partial_{i}\partial_{i }\partial_{i}\partial_{i}\partial_{i}\partial_{i}\partial_{i}\partial_{i} \partial_{i}\partial_{i}\partial_{i}\partial_{i}\partial_{i}\partial_{i}\partial_{ i}\partial_{iEquation (7) represents the energy equation:  
\[\begin{array}{ll}\frac{\partial}{\partial t}\int_{V}\rho EdV+\oint_{A}\left[ \rho Hv+\sum\limits_{i}\int_{V}a_{i}\rho_{i}v_{d,i}H_{i}v_{d,i}\right]d\mathbf{\alpha}& =-\oint_{A}\hat{q}^{\prime\prime}\cdot d\mathbf{\alpha}+\oint_{A}T\cdot vd\mathbf{\alpha} \\ &+\int_{V}S_{E}dV+\int_{V}f_{b}\cdot vdV\end{array} \tag{7}\]  
where \(E\) is the total energy, \(H\) is the total enthalpy, \(q^{\prime\prime}\) is the heat flux vector, and \(S_{E}\) represents the custom energy source term.
# RM-Line: A Ray-Model-Based Straight-Line Extraction Method for the Grid Map of Mobile Robot
## 1 Introduction
### Perceptual Grouping Approach  
The perceptual grouping approach groups roughly collinear local features into initial line segments by analyzing pixel information, such as gradient, proximity, and connectivity, and then evaluates the initial line segments using appropriate criteria.  
[PERSON] et al. proposed a hierarchical heuristic framework [23], on this basis, [PERSON] et al. proposed the slice sampling weighted mean shift (SSWMS) algorithm [24], which iteratively selects and grows pixels according to the gradient structure. The biologically inspired method [25] first uses \"simple units\" to detect local structures, then integrates these responses with \"complex units\", and finally detects endpoints with \"hyper-complex\" mechanisms.  
[PERSON] et al. proposed a fast line segment detector (LSD) with a false detection control [26], which sorts the pixels according to the gradient, then extracts line segments according to the gradient direction of the pixels in the local area. They used the Helmholtz principle to judge the extraction results, which transfers the threshold to a quantity that is much easier to set rationally. They improved the algorithm in a subsequent article [27], aiming to process any digital image without parameter tuning. Compared with HT, LSD has certain advantages in operation speed and precision. However, LSD cannot guarantee the accuracy of the results when the local region consists of pixels with similar gradient magnitudes. To this end, [PERSON] et al. proposed edge drawing line segments detection (EDLines) [28], which first performs edge detection and constructs pixel chains, and then obtains initial line segment through the least-squares method in a local range. EDLine improves the extraction effect in local regions with similar gradient magnitudes; however, in edge detection, the threshold of the operator is difficult to select and needs to be corrected according to the specific image [29]. [PERSON] et al. proposed CannyLines [30]. They designed a parameter-free Canny operator named CannyPF to robustly extract edge maps from input images by adaptively setting the low and high thresholds of the Canny operator. Then, clusters of collinear points are collected from edges using edge linking and splitting techniques. Finally, the method based on least squares is used to fit the initial line segment. CannyLines improves edge detection. Although simpler, these methods are not robust to gaps or intersections in edge maps [31].
## 4 Results and Discussion
### Reduction to the North Magnetic Pole Map  
Due to the inclination of the Earth's magnetic field, most magnetic anomalies have both a positive and negative effect. These minima and maxima are frequently offset from the producing body along the magnetic meridian. When the inclination is 90 degrees, the magnetic anomaly is located right above the source body's centre. To remove these effects, the reduction to the pole (RTP) approach is utilised, which makes the data look as if they were taken at the pole, where the magnetic field is practically vertical. The magnetic maxima, in turn, occurs directly over the magnetised objects, notably when remnant magnetization is absent [22]. According to [PERSON] [23], the field reduced to the pole at a fixed point above the measurement plane in frequency domain is given by  
\[\text{L}\;(\theta)=1/[\sin\;(\Omega\alpha)-\text{i}\;\text{cos}\;(\text{I}) \;\text{cos}\;(\text{D}+\theta)]\;^{2}\text{, if }(\Omega<\text{I})\text{, }\Omega=\text{I}\]  
where I = geomagnetic inclination, I\(\alpha\) = inclination for amplitude correction, D = geomagnetic declination, \(\theta\) = wavenumber direction, i = imaginary component.  
Figure 4: Total-airborne magnetic-field anomaly (TMA) map of Homrit Waggat area [21].  
Figure 5: Reduced to the north magnetic pole (RTP) map of Homrit Waggat area.  
The RTP field (Figure 5) illustrates a large positive magnetic zone of anomaly with different amplitudes, ranging between 0 nT and 120 nT, trending towards the NE and NW directions. It is located in different points of the study area. This anomaly represents the highest amplitude within the study area, reflecting the granodiorite-tonalite associations. Meanwhile, the central and northeastern parts of the map are occupied by a NW-SE magnetic belt. This belt illustrates an intermediate and low magnetic anomaly with relative amplitudes, ranging between 0 nT and \(-\)110 nT. This is probably associated with the metagabbro-diorite complex, syenogranite and abitized granites.
# Adaptive Bat Algorithm Optimization Strategy for Observation Matrix
## 4 Experimental Simulation
### Signal Reconstruction Experiment
#### 4.2.1 Evaluation Index Design  
Reconstruction error (\(RE\)) is used to evaluate the quality of reconstructed signals, and Equation (12) is used to calculate. The smaller the \(RE\) value is, the smaller the reconstruction error is and the better the reconstruction accuracy is.  
\[RE=\frac{\|X-X^{*}\|_{2}}{\|X\|_{2}} \tag{12}\]  
where \(X\) represents the original signal, \(X^{*}\) represents the reconstructed signal.