esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Correlation Model of Monitoring Data
### Correlation Modeling Using Partial Least Squares and Multiple Regression
#### 2.2.3 The Reconstruction Error Evaluation Index  
The average absolute error between the reconstructed value and actual value \(e_{a}\) is:  
\[e_{a}=\frac{1}{n}\underset{i=k}{\overset{n}{\sum}}|\mathbf{y}(k)-\mathbf{y}_ {0}(k)| \tag{39}\]  
The average relative error between the reconstructed value and actual value \(e_{b}\) is:  
\[e_{b}=\frac{1}{n}\underset{k=1}{\overset{n}{\sum}}\left|\frac{\mathbf{y}(k) -\mathbf{y}_{0}(k)}{\mathbf{y}_{0}(k)}\right|\times 100\% \tag{40}\]  
where \(\mathbf{y}(k)\) represents the reconstructed response of \(k^{\text{th}}\) time step of the supposed fault sensor, and \(\mathbf{y}_{0}(k)\) represents the actual response of \(k^{\text{th}}\) time step of the supposed fault sensor, respectively.  
In Equation (38), there is no exact number demanded of the response variables, which may depend on the detailed examples and the special value of the correlation coefficients. Generally speaking, the historical measurements can be used to identify the effectiveness when the different number of responses variables selected. If four response variables are chosen to build the correlation model, that is to say, \(p=4\), then:  
\[\mathbf{D}_{P}=\left\{0.6831\quad-0.5437\quad-0.5194\quad 0.4656\ \right\} \tag{41}\]  
\[\mathbf{X}_{P1} =\left\{\begin{array}{c}\left(\mathbf{x}\right)_{1}\quad\left( \mathbf{x}\right)_{2}\quad\left(\mathbf{x}\right)_{3}\quad\left(\mathbf{x} \right)_{4}\ \ \right\}\\ =\left\{\begin{array}{c}\sigma_{3-1-8}(t)\quad\sigma_{3-6-6}(t)\quad\sigma_ {3-3-2}(t)\quad\sigma_{3-2-8}(t)\end{array}\right\}\end{array}\right. \tag{42}\] \[\mathbf{Y}_{P1}=\left\{\mathbf{y}_{1}\right\} =\left\{\sigma_{3-1-4}(t)\right\} \tag{43}\]  
The correlation model of the reconstructed variable and the response variable is:  
\[\mathbf{y}_{1}(i)=1.01\left(\mathbf{x}(i)\right)_{1}+0.05\left(\mathbf{x}(i) \right)_{2}-0.09\left(\mathbf{x}(i)\right)_{3}+0.10\left(\mathbf{x}(i) \right)_{4}-20.73 \tag{44}\]  
The stress monitoring data of the stress sensor 3-6-6, 3-3-2, 3-1-8, and 3-2-8 are used to reconstruct the stress measurements of the sensor located at point 3-1-4 on 16 May 2014. The contrast curves between reconstructed values and actual values are shown in Figure 4. The reconstructed values have the same trend as the actual values, and there are small differences between them. The average absolute error is 0.89 MPa and the relative error is 2.71%.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Ranking** & **Sensor Location** & **Correlation Coefficient** & **Ranking** & **Sensor Location** & **Correlation Coefficient** \\ \hline
1 & 3-1-8 & 0.6831 & 18 & 3-1-2 & 0.2076 \\
2 & 3-6-6 & 0.5437 & 19 & 3-6-1 & 0.1975 \\
3 & 3-3-2 & 0.5194 & 20 & 3-5-1 & 0.1962 \\
4 & 3-2-8 & 0.4656 & 21
## 3 Crack Identification and Measurement System
### Crack Segmentation Based on U-Net Algorithm
#### 3.2.2 Loss Function  
The loss function is a main indicator describing the deviation between the predicted results generated from the network and the ground truth. The target of training the model is to minimize the loss value to make the final segmentation more accurate. In previous studies of crack semantic segmentation, several classic loss functions including cross-entropy loss function, dice loss function [27], focal loss function [10] and some combinations [14,17] have been widely adopted. Furthermore, data imbalance is a problem that exists in crack segmentation tasks; since the crack pixels occupy a small proportion of the total, this may cause a training weight offset.  
In order to obtain the accuracy loss values during the training produce and solve problems caused by the data imbalance, a combination of generalized dice loss (GDL) and cross-entropy loss (CE) is selected as the loss function. The GDL function can solve crack segmentation as the weight of each level is inversely proportional to class frequencies [11,34,38] and can be expressed as  
\[L_{\text{GLD}}=1\ -2\frac{\sum_{l=1}^{2}w_{l}{\sum_{n}r_{ln}p_{ln}}}{\sum_{l=1}^ {2}w_{l}{\sum_{n}r_{ln}}+p_{ln}}, \tag{33}\]  
where \(r_{ln}\) represents the ground truth of class \(l\) at \(n\) pixel position, \(p_{ln}\) is the corresponding predicted value generated by the model, and \(w_{l}\) is the weight of each class, expressed as  
\[w_{l}=\frac{1}{\sum_{l=1}^{N}r_{ln}^{2}}, \tag{34}\]  
where \(N\) is the number of training samples. The CE function is expressed as [39]  
\[L_{\text{CE}}=-{\sum_{m=1}^{N}r_{n}\log\,p_{n}}-(1-r_{n})\log\,(1-p_{n}), \tag{35}\]  
The loss function of U-net in the training process is given by  
\[L=L_{\text{GLD}}+L_{\text{CE}}, \tag{36}\]  
Applying the combination of GLD and CE can solve the problem of the excessive background ratio in order to better extract the cracks.
## 3 Results and Discussion
### Exhaustion-Regeneration Performance Equilibria  
GAC-Virgin was submitted to saturation (synthetic water, 500 mg CaCO\({}_{3}\)/L) and regeneration cycles (using 7.4 wt.% HCl), as outlined in Figure 1. The target species to be adsorbed and chemically desorbed is Ca\({}^{2+}\) since it is present as major adsorbed species on the GAC surface and thus most responsible for GAC saturation during water treatment in this process. Therefore, a synthetic water solution was used to saturate the GAC and after saturation, 7.4 wt.% HCl solution is used to regenerate the GAC. As explained in Section 2.7, the adsorption and regeneration processes were monitored by the breakthrough curves using conductivity or pH as monitoring parameters respectively.  
Figure 4 displays the different synthetic water adsorption breakthrough curves obtained for the GAC filter after each regeneration cycle. These results allow us to obtain the performance in terms of saturation time for the regenerated GAC (\(t_{sr}\)) for each regeneration cycle. The first exhaustion performance corresponds to the maximum saturation time of GAC-Virgin (\(t_{sv}\)), which will be the reference value for calculating the fractional saturation time for each subsequent regeneration. In this case, \(t_{sv}=272\) min, taking the ratio \(\mathrm{c(Ca^{2+})_{i}/c(Ca^{2+})_{0}=0.5}\) as a reference point in the breakthrough curve of GAC-Virgin, as shown in Figure 4.  
As expected, it is confirmed from Figure 4 that \(t_{sr}\) decreases with the number of regeneration cycles \(n_{i}\) due to the cumulative loss of GAC adsorption capacity after each regeneration cycle. It can be noticed that after 10 regeneration cycles, the GAC performance is significantly reduced being already saturated in around 58 min, thus representing around 21% of the saturation time performance of the virgin material.
# Review of Image Forensic Techniques Based on Deep Learning
## 2 Image Forensic Techniques
### Passive Forensics
#### 2.1.2 Performance Evalution Metrics  
The forgery detection task can be considered to be a binary classification task of pixels, i.e., whether a pixel is tampered with or not. Therefore, the evaluation metrics of the forgery detection algorithm need to use the amount of categorization of the samples, including true positive, false positive, true negative, and false negative. The commonly used evaluation metrics for forgery detection are precision \(p\), recall \(r\), and \(F_{1}\) score [3], which are expressed as Equations (1)-(3), respectively:  
\[p=\frac{T_{\mathrm{P}}}{T_{\mathrm{P}}+F_{\mathrm{P}}} \tag{1}\]  
\[r=\frac{T_{\mathrm{P}}}{T_{\mathrm{P}}+F_{\mathrm{N}}} \tag{2}\]  
\[F_{1}=\frac{2 pr}{p+r} \tag{3}\]  
where \(T_{\mathrm{P}}\) denotes the number of tampered pixels detected as tampered; \(F_{\mathrm{N}}\) denotes the number of authentic pixels detected as tampered; \(F_{\mathrm{N}}\) denotes the number of tampered pixels detected as authentic. The values of \(p\), \(r\), and \(F_{1}\) are in the range [0, 1]. The larger \(p\), \(r\), and \(F_{1}\) are, the higher the accuracy of detection results is.  
Another important metric is the area under curve (AUC), which is defined as the area under the receiver operating characteristic (ROC) curve and can reflect the classification performance of a binary classifier. Like the \(F_{1}\) score, AUC can evaluate the precision and recall together. Its value is generally between 0.5 and 1. The closer the value of AUC is to 1, the higher the performance of the algorithm. When it is equal to 0.5, the true value is the lowest and has no application value.
## 2 Materials and Methods  
### Carousel Wind Rotor Blade Profiles  
Considering the principles of the of the blade motion, presented in the introduction, the blade cross-section must have point symmetry. In this study, we compared two different blade cross-sections. The first one, known as the single coherent, is shown in Figure 5. The outline of the blade is shaped with a flat surface on the first half of the blade parallel to the blade chord, and on the second half it curves outward along an arc and turns into a convex surface. The opposite surface the blade has an identical shape, rotated by 180\({}^{\circ}\), resulting in point symmetry. The second cross-section of the blade, proposed by the authors, is defined as the dual coherent (Figure 6). The blade consists of two identical lobes placed in point symmetry relative to each other. The axis of the blade's rotation coincides with the symmetry point. The inner surface of a single lobe is parallel to a blade chord. It is also flat, while the outer surface is convex. The cross-section of the dual coherent blade is a modification of the cross-section of single coherent blade introducing a gap between the lobes.  
Figure 4: The movement of the carousel rotor blade: (**a**) positions of the single blade (\(\alpha=22.5^{\circ}\)); (**b**) forces acting on the rotor blade for subsequent angular positions.  
Comparing structural integrity, the DC blade can achieve higher stiffness due to the greater distance between supporting elements within its structure. The DC blade can also be easier in manufacturing due to the less complex cross section of the single profile. The main dimensions of the tested blades were summarized in Table 1.  
The geometric scale of the tested blade was 1:1. Therefore, it was not necessary to specify other similarity criteria. Aerodynamic tests of single coherent and dual coherent blades were carried out to determine aerodynamic forces, aerodynamic torque, and next to calculate the propelling torque of the wind turbine rotor. For this purpose, the rigid full-scale blades models were prepared as shown in Figure 7.  
\begin{table}
\begin{tabular}{c c c} \hline
**Dimension [m]** & **Single Coherent Blade** & **Dual Coherent Blade** \\ \hline Blade width & \(a_{S}=0.250\) & \(a_{T}=0.250\) \\ Blade length & \(b_{S}=0.044\) & \(b_{T}=0.076\) \\ Blade height & \(h=0.625\) & \(h=0.625\) \\ Gap between the lobes & - & \(b_{Tg}=0.016\) \\ \hline \end{tabular}
\end{table}
Table 1: Main dimensions of the tested blade models.  
Figure 5: Profiles of the single coherent blade with dimensions in mm.  
Figure 6: Profiles of the dual coherent blade with dimensions in mm.
# Current Status and Future Developments of Micromegas Detectors for Physics and Applications
## 2 Micromegas Fabrication Techniques
### Microbulk Micromegas  
In Microbulk Micromegas, the mesh, the pillars, and the readout structure are produced in one single structure. This technology was invented in 2006 by [PERSON] (CEA Saclay) and [PERSON] (CERN) [11].  
The base component of this technique is a Kapton foil covered on both sides by a copper layer. One side of the foil is used for the anode and the other for the mesh. The mesh and the readout segmentation is produced by photochemical etching, inspired by the production technique of a Gas Electron Multiplier (GEM) detector [17; 18]. In the first step, circular holes are formed in one of the copper foils through standard lithography to form the mesh. Then, the Kapton is completely removed below the holes and down to the anode by chemical etching, while most of the material around the holes remains for mechanical support of the mesh. A schematic drawing of the process and a picture of a small prototype are shown in Figure 4. The anode can be segmented in strips or in pads with the addition of extra layers of Kapton and Copper in order to add 1D or 2D granularity [11].  
Typically, the hole opening on the mesh side is selected to be smaller than the gap thickness and, production-wise, is limited to \(\sim\)40 \(\upmu\)m. The thickness of the Kapton foil defines the length of the amplification gap, typically 50 \(\upmu\)m, while there have been detectors made with 25 and even 12.5 \(\upmu\)m gaps [19]. The optimum gap to obtain the maximum gain in a Micromegas detector depends on the type and pressure of the gas mixture. Light gases, like neon or argon, require gaps of 50-100 \(\upmu\)m, while heavy ones, like krypton or xenon, require smaller gaps of 12.5-25 \(\upmu\)m, especially when the detector operates under a few bars of pressure [20].  
Experiments with requirements for a low material budget, excellent energy resolution, and high radiopurity of the materials, commonly use Microbulk detectors. For example, the CAST experiment used Microbulk Micromegas to enhance the background rejection due to their energy resolution and radiopurity [21; 22]. The amplification gap of Microbulk detectors is more homogeneous compared with that of other types of Micromegas, and an energy resolution better than 12% FWHM at 5.9 keV can be achieved [23]. Microbulk  
Figure 4: Schematic layout of the production process of a single-anode Microbulk (**a**). Picture of a Microbulk prototype and a microscope view of the mesh (**b**).  
detectors also find use in neutron experiments, such as n_TOF [24], to minimise the \(\gamma\) production by the detector material.
## 5 Examples of Forward-Inverse Modeling
### Atmospheric Input & Output  
To minimize the influence of light backscattered from the water, we used a near infrared wavelength at 1036 nm, with significant water absorption, and adopted two atmospheric layers: one Rayleigh (molecular) layer (2-10 km) and one layer with aerosols and molecules homogeneously mixed (0-2 km). Based on the US standard atmosphere [104], the single-scattering albedo at 1036 nm is \(\alpha_{\text{mol}}=0.9610\) in the upper layer (molecular scattering and water vapor absorption, no aerosols) and the upper layer optical thickness is \(\tau_{\text{mol}}=0.00645\). In the lower layer, we adopted an aerosol model implemented in SeaDAS [30]. The inherent optical properties (IOPs) of aerosols and molecules were then combined to give a \"mixed\" single-scattering albedo \(\alpha_{\text{mix}}=(\beta_{\text{mol}}+\beta_{\text{aer}})/(\gamma_{\text{ mol}}+\gamma_{\text{aer}})=0.9772\), where \(\beta_{\text{mol}}\) and \(\beta_{\text{aer}}\) are scattering coefficients, and \(\gamma_{\text{mol}}\) and \(\gamma_{\text{aer}}\) are extinction coefficients for molecules and aerosols, respectively.  
The simulated atmospheric output is the radiance \(I(\tau,\mu,\phi)\) in arbitrary directions \((\mu,\phi)\). However, for comparison with measurements, we used the bidirectional reflectance factor defined as \(\text{BRF}(\tau,\mu,\phi)=\pi I(\tau,\mu,\phi)/\mu_{0}F_{0}\), where \(\mu=\cos\theta\), \(\theta\) is the zenith view angle, \(\phi\) is the azimuth angle, \(\tau\) is the optical thickness (at aircraft altitude), \(\mu_{0}=\cos\theta_{0}\), \(\theta_{0}\) being the solar zenith angle, and \(F_{0}\) is the extraterrestrial solar irradiance. Please note that this BRF is defined such that it would represent the reflected irradiance normalized by the TOA incident irradiance \(\mu_{0}F_{0}\) if the radiance were to be isotropic.
## 4 Simulation Results and Discussion
### Case Studies
#### 4.1.3 Case Study 3 (16 \(\Omega\) Shunt Resistor)  
In this case, the shunt resistor was set to 16 \(\Omega\). The fault current and corresponding voltage profile of the single line-to-ground fault are depicted in Figure 5. The r-SFCL with the 16 \(\Omega\) shunt resistor was able to limit the fault current to 2.44 and 2.27 kA for the first and second peaks, respectively, representing a reduction of 60.96% for the first peak compared to the case without the r-SFCL. The voltage dropped to about 14.49 kV\({}_{\text{rms}}\) in this case.  
Figure 5: Fault response without and with r-SFCL using 16 \(\Omega\) shunt resistor: (**a**) fault current; (**b**) voltage drop; (**c**) r-SFCL temperature; (**d**) current analysis.  
The temperature of the r-SFCL is shown in Figure 5c; it exhibited a peak temperature of 384 K in this case study. The r-SFCL completely recovered about 9.88 s after the fault occurred, meaning that it returned to the expected coolant boiling temperature of 77 K at T = 11.88 s. Note that the recovery time in this case was significantly longer than in the previous cases using the 4 and 8 \(\Omega\) shunt resistors. Before the fault occurred, all current passed through the r-SFCL while the shunt resistor's current was zero. After the fault occurred, the temperature increased, which in turn increased the r-SFCL's resistance, resulting in a significant portion of the current passing through the shunt resistor while the remainder still passed through the r-SFCL. Both observations can be validated by examining the current distribution shown in Figure 5d.
# Endophytic Colonization of Pepper (_Capsicum annum_) Controls Aphids (_Myzus persicae_ Sulzer)
## 3 Results  
### Re-Isolation of Endophytic Entomopathogenic Fungi from Pepper Leaves on SDA Substrate  
To prove endophytic colonization of pepper leaves by _M. anisopliae_, _B. bassiana_ and _I. fumosorosea_, leaves of same age plants were sprayed with conidial suspensions at 10\({}^{8}\) conidia/mL. Seven days post inoculation, leaves were cut and their surface sterilized. Subsequent growth of endophytic fungi on dextrose agar was monitored. Mycelium began to appear 6 days later and had developed completely after 11 days at 25 \({}^{\circ}\)C + 2 and 95% humidity. Successful re-isolations of the three fungi were obtained from both leaves and stems of plants of the corresponding treatments (Figures 1 and 2). Decline of colonization was observed after 14 days, in leaves of plants treated with endophytes (F = 2.156, df = 2, 70, \(p\) = 0.133).  
Figure 1: Mean (\(\pm\)sd; \(n=24\)) surface colonization of same age _C. annum_ leaf parts by _B. bassiana_, _M. anisopliae_ and _I. fumosorsea_ at 7 days, 14 days, and 21 days after exposure. Mean \(\pm\) sd values with the same superscript letter are not different in a significant way (Bonferroni’s test: \(p<0.05\)).  
Figure 2: Detection of endophytic entomopathogenic fungi on treated and untreated pepper plants. The fungus was fully developed after 11 days, on Sabouraud dextrose agar. (**a**) _B. bassiana_, (**b**) _M. anisopliae_, (**c**) _I. fumosorsea_, and (**d**) Control.
## 2 Data and Methodology  
### Study Area  
The Haihe basin is located in North China (35\({}^{\circ}\) N-3\({}^{\circ}\) N, 112\({}^{\circ}\) E-120\({}^{\circ}\) E), with an area of 318,000 km\({}^{2}\)[34]. It meets the needs of around 10% of China's population and contributes about 8% of cultivated land in China. Corn and wheat are widely cultivated in the basin. The agricultural production of corn and wheat accounts for 36% and 27% of the total panting area [35], respectively. The study area is located in the middle of the Haihe basin and covers an area of 1728 km\({}^{2}\) (Figure 1). It has a sub-humid monsoon climate with an elevation of 14-52 m. The annual average temperature is 11.6 \({}^{\circ}\)C, and the annual average precipitation is 556 mm. The rainfall in July, August, and September accounts for 60-70% of the annual total precipitation. The soil types in the study area are mostly loam and sandy loam, and the main crops are winter wheat and summer corn. The growth period of winter wheat is from March to May, and summer corn is grown from July to September.  
Figure 1: Satellite data of the study area: (**a**) relative location of the study area; (**b**) S2 image (in false-color composite: R = NIR, G = red, B = green), sample area from the study area; (**c**) NDVI calculated from an S2 image, sample area from the study area; (**d**) NDWI calculated from an S2 image, sample area from the study area; (**e**) MCARI calculated from an S2 image, sample area from the study area.
# An Advanced SAR Image Despeckling Method by Bernoulli-Sampling-Based Self-Supervised Deep Learning
## 3 Experimental Results and Analysis
### Experimental Setup  
#### 3.1.1 Compared Methods  
The following state-of-the-art despeckling methods were compared to our proposed SSD-SAR-BS: PPB [16], SAR-BM3D [17], ID-CNN [28], SAR-DRN [29], and SAR-RDCP [34]. The first two are traditional despeckling methods, and the last three are deep-learning-based despeckling methods. Specifically, PPB is based on patch matching, and SAR-BM3D is based on 3D patch matching and wavelet domain filtering. ID-CNN, SAR-DRN, and SAR-RDCP all adopt the deep CNN model to learn the mapping relationships between speckled inputs and clean targets. SAR-RDCP combines the traditional variational model and the deep CNN model. It is worth noting that ID-CNN, SAR-DRN, and SAR-RDCP can only train the despeckling network on the synthetic speckled data rather than the real SAR data, due to them requiring clean targets to achieve supervised learning. For all the compared methods, the algorithm parameters were set as suggested in their corresponding papers. Furthermore, to make a fair comparison, SAR-CNN [26] and some recent works (e.g., NR-SAR-DL [38]) employing multitemporal data were not used, due to only single speckled SAR images being required in our proposed SSD-SAR-BS. Meanwhile, their training datasets are not publicly available.
## 3 Power Line Communication Systems  
Smart metering systems commonly utilize two types of communication protocols: G3-PLC and the PRIME protocol. Both systems use the Orthogonal Frequency Division Multiplexing (OFDM) scheme for sending the data. The procedure taken to achieve the OFDM technique for both PRIME and G3-PLC systems is the same despite the techniques settings being different [27].  
As the PLC signal propagates, the signal attenuates with the increase in the line length. In addition, the presence of the noises interferes with the OFDM signal. There are several ways to evaluate the PLC channel performance. One way is to calculate the maximum allowable channel capacity data transmission rate in the presence of noises using the Shannon-Hartley equation. This method gives an accurate and similar rate as those achieved in practice by communication systems utilizing sophisticated channel codes such as the FEC codes adopted in G3-PLC and PRIME [28]. The capacity of the PLC channel is calculated from the Shannon-Hartley equation:  
\[C_{\text{G3}}=\int_{D_{\min}}^{D_{\max}}\log_{2}\!\left(1+\frac{S_{PLC}(f)}{N (f)}\right)\,df, \tag{6}\]  
Figure 2: Sketch for the output spectrum in the case of using various modulating signal profiles: (**a**) Random PAM, (**b**) sawtooth, and (**c**) sine wave.  
In this context, \(D_{min}\) and \(D_{max}\) represent the frequency boundaries of the PLC bandwidth channel. The term \(S_{PLC}(f)\) refers to the power spectral density (p.s.d) of the PLC signal, while \(N(f)\) denotes the total noise power spectral density. For this study, the total channel noise is defined as  
\[N(f)=N_{0}+S_{ss}, \tag{7}\]  
\(N_{0}\) represents the background noise p.s.d, modeled as Additive White Gaussian Noise (AWGN), and \(S_{ss}\) corresponds to the power spectral density of the electromagnetic interference (EMI). Based on this, the percentage of channel capacity loss can be calculated as  
\[C_{Loss}=\frac{C_{0}-C_{G3}}{C_{0}}\times 100\% \tag{8}\]  
where \(C_{0}\) represents the theoretical capacity of the PLC channel under ideal conditions where only AWGN is present. This value serves as the baseline for comparing the channel capacity when EMI is introduced under different spread spectrum (SS) parameter configurations.