esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Principles  
### Literature Review  
Recently, structural health monitoring of large-scale structures is highly in demand in order to reduce maintenance cost and enhance structural safety. Traditional methods, such as X-ray or ultrasonic C-scan technologies, are expensive and inefficient for monitoring the structural health of large-scale structures. Fiber optical sensors, including the Fiber Bragg Grating (FBG) sensors, are also being used for monitoring the health condition of various concrete structures [22; 23; 24]. However, fiber optical sensors offer only local measurements, greatly limiting their applications. Piezoelectric transducers have emerged as a new tool in the monitoring of large-scale structures due to their advantages including low-cost, small size, large bandwidth, and actuating-sensing functions. Currently, two popular methods of the piezoelectric-based health monitoring approaches have been reported to monitor the concrete structures using piezoelectric-based approach: the impedance-based method and the vibration-characteristic method.  
The impedance-based health monitoring approach is capable of monitoring the local damage of structures [25; 26; 27; 28]. The principle of this approach is based on the electromechanical coupling effect of piezoelectric materials in relative high frequency range. The impedance-based approach has been applied to RC beams [29; 30], frames [31], shear walls [15], and composite RC/ masonry walls [32],in addition to RC bridges [33]. Meanwhile, a numerical modeling study on impedance-based damage identification has also been conducted [34]. Recently, a new real-time integrated wireless structural health monitoring (SHM) system named \"WiAMS\" was developed to detect the structural damage based on the impedance signature using embedded and surface-bonded lead zirconate titanate (PZT) transducers [35; 36]. Since the impedance-based method is sensitive to the local damage where PZT sensors are placed [37], a combination of smart aggregates with surface bonded PZTs was studied to assess both the local and overall conditions of the structure [38].  
In contrast, the vibration-characteristic approach utilizes piezoelectric actuators to generate waves that propagate within the structure, and compares the structural vibration-characteristic parameters, such as the modal shape and frequency [39], active-sensing approach [40], or transfer functions [41], with those of the healthy state to detect damage. Delamination between reinforcing bars and concrete has also been studied using this approach [42; 43]. In this approach, the excitation frequency range is normally from several hundred hertz to several kilohertz. However, the operating frequency of this approach is much lower than impedance-based approach.
## 3 Methods  
### Land Use/Land Cover Classification  
Spectral angle mapper (SAM) classifier used for target discrimination compares the spectral angle between the reference spectrum vector and each target spectral vector in an n-Dimensional space [49, 51]. When the spectral angle between the pixel and the reference object is less than the given threshold value, the pixel can be considered in the same class as the reference object. The SAM combined with different vegetation distribution rules can be adopted to improve the classification accuracy of six species of forest-woodland-savannah mosaic, and compared with the maximum likelihood classification (MLC); the classification accuracy was increased by 10% in comparison to 85.2% for MLC [52]. By removing spectrum noise or making differential spectrum, SAM can be adopted to eliminate the influence of the vegetation environmental background and the phenomenon caused by the same object with different spectrums to improve the vegetation identification precision.  
For multi-vegetation classifications in urban areas, the hierarchical classification binary tree accounted for the spectral separability was designed to provide the class set and classified sequence in each node, as well as the matching thresholds. The SAM running along hierarchical tree nodes can recognise optimised classification based on the spectral separability level; therefore, Spectral Angle Mapper decision tree method was used to map 11 covers using Landsat TM images in the mega-city of Guangzhou, China.  
According to the actual land covers in Guangzhou, there are eight vegetation classes selected from the International Geosphere-Biosphere Program (IGBP) schemes in Table 1[53]. In addition, the LULC types of \"Urban and Built-up Lands\", \"Water Bodies\" and \"Barren\" are also identified with high accuracy.
## Materials and Methods
### Evaluation Criterion  
Quantitative evaluation is important to determine the result of the change detection. When the ground truth is available, a quantitative evaluation can be performed [18; 40]. We used the false alarm (FA) rate, the total errors (TEs), the overall accuracy (OA), and the Kappa coefficient [41] to verify the accuracy of the results based on the proposed method. These indicators are calculated as follows:  
Figure 1: The flowchart of the proposed method.  
\[\left\{\begin{array}{l}FA=\frac{FP}{N_{u}}\\ N=N_{u}+N_{c}\\ TE=\frac{FP+FN}{N_{c}},OA=\frac{TP+TN}{N}\\ Kappa=\frac{OA-P_{c}}{1-P_{c}}\\ Pe=\frac{(TP+FN)(TP+FP)+(FP+TN)(FN+TN)}{N^{2}}\end{array}\right. \tag{17}\]  
where TP (true positives) means the number of changed points correctly detected; TN (true negatives) means the number of unchanged points correctly detected; FP (false positives) means the number of unchanged points incorrectly detected as changed (false alarms); FN (false negatives) means the number of changed points incorrectly detected as unchanged (missed detections). \(N_{u}\) and \(N_{c}\) are the number of unchanged points and changed points of the ground reference change map, respectively.
## 3 Data Collections
### Satellite Measurements
#### 3.1.2 Multi-Angular Observations  
Anisotropy is typically considered a source of noise when analyzing remote sensing data from mono-angular spectral instruments but constitutes a definite advantage when analyzing measurement from multi-angular instruments [45]. Over the last three decades, the potential benefits of multi-angular observations have been recognized through the operation of innovative Earth Observation (EO) instruments, such as the Along-Track Scanning Radiometer (ATSR-1 and ATSR-2), the Polarization and Directionality of the Earth's Reflectance (POLDER), and the Multi-angle Imaging SpectroRadiometer (MISR) instrument on-board National Aeronautics and Space Administration (NASA)'s Terra platform, which has been continuously operating since 24 February 2000. These instruments generate an improved characterization of the atmospheric and terrestrial environments because multi-angular measurements permit an accurate, quantitative decoupling of the distinct spectral and directional signatures of those geophysical media.  
In this context, the recent availability of land surface products at the relatively high spatial resolution of 275 m generated by the MISR-HR (for High Resolution; version 2.01-0) processing system [46], about weekly over the period 2000-2018, offers new opportunities to explore the relation between surface properties and the fluxes of matter and energy in the environment. A summarizes the specifications of the MISR instrument and outlines the products generated by the MISR-HR processing system. Specifically, the Joint Research Centre-Two-stream Inversion Package (JRC-TIP) algorithm [47; 48] derives the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) product from an analysis of albedo values obtained by integrating the Rahman-Pinty-Verstraete (RPV) model [49; 50]. FAPAR is a measure of the absorption of incoming solar radiation in the canopy, within the photosynthetically relevant spectral region (400 to 700 nm) (in [51; 52]). The JRC-TIP inversion process returns the final value of the cost function, as well as estimates of the variance, associated with each product: the former is a measure of the distance between the model and the data, while the latter measures the uncertainty associated with the retrieved product.
# Tree Internal Defected Imaging Using Model-Driven Deep Learning Network
## 2 Method
### Contrast Source Inversion (CSI) Method  
The CSI method is a model-driven algorithm which combines contrast and total field to create a new contrast source and uses the conjugate gradient method to update the contrast and contrast source to minimize the objective function and complete the electrical parameters of the target medium to achieve inversion imaging. The core point of the CSI is to use the contrast and the total field \(E_{j}^{t}\) to form the contrast induction current \(J_{j}=\chi E_{j}^{t}\). In the solution process, \(E_{j}^{t}\) is not solved directly, and the solution is solved iteratively by alternately updating the CSI \(J_{j}\) and \(\chi\) to continuously approximate the solution of the integral equation.  
The CSI approach mainly utilizes the source-type Lippmann-Schwinger equation:  
\[J(r)=\chi(r)\Big{[}E^{i}(r)+G_{D}(J)\Big{]},r\in D \tag{9}\]  
\[E^{s}(r)=G_{S}(J),r\in S \tag{10}\]  
Equation (10) is a pathological equation and requires a priori information to recover its uniqueness in the practical solution.  
The main idea of the iterative solution is to construct the objective function. The objective function is a normalized measure of the error value on both sides of the equal sign of the source-type equation. The CSI transforms the solution of the problem into a minimization objective function that approximates the exact solution [22]. Again, the objective function of the CSI is defined as:  
\[F\big{(}J_{j},\chi,\beta\big{)}=\beta\frac{\sum_{j}\|\chi E_{j}^{i}-J_{j}+\chi G _{D}J_{j}\|_{D}^{2}}{\sum_{j}\|\chi E_{j}^{i}\|_{D}^{2}}+\frac{\sum_{j}\|E_{j}^ {s}-G_{S}J_{j}\|_{S}^{2}}{\sum_{j}\|E_{j}^{s}\|_{S}^{2}} \tag{11}\]  
The update scheme for contrast \(\chi_{n}\) is:  
\[\chi_{n}=\chi_{n-1}+\alpha_{n}^{\chi}g_{n}^{\chi},n\geq 1 \tag{12}\]  
\[g_{n}^{\chi}=-\eta_{D,n-1}\frac{\sum_{j}\Big{(}\chi_{n-1}E_{j,n}^{t}-\omega_{ j,n}\Big{)}\overline{E_{j,n}^{t}}}{\sum_{j}\Big{|}E_{j,n}^{t}\Big{|}^{2}} \tag{13}\]  
where \(\eta_{D,n-1}\) is the normalization parameter. To keep the contrast consistent with the CSI updating, Equation (12) is rewritten as:  
\[\chi_{n}=\chi_{n-1}+\alpha_{n}^{\chi}d_{n},n\geq 1 \tag{14}\]  
\[d_{n}=g_{n}^{\chi}+\gamma_{n}^{\chi}d_{n-1},d_{0}=0,n\geq 1 \tag{15}\]  
where \(\gamma_{n}^{\chi}\) is:  
\[\gamma_{n}^{\chi}=\frac{Re\langle g_{n}^{\chi},g_{n}^{\chi}-g_{n-1}^{\chi} \rangle_{D}}{\langle g_{n-1}^{\chi}g_{n-1}^{\chi}\rangle_{D}} \tag{16}\]  
Replacing \(\chi_{n}\) in the second term of the objective function, we have:  
\[F_{D,n}=\frac{\sum_{j}\|\chi_{n}E_{j,n}^{t}-J_{j,n}\|_{D}^{2}}{\sum_{j}\| \chi_{n}E_{j,n}^{t}\|_{D}^{2}}=\frac{\sum_{j}\|\big{(}\chi_{n-1}+\alpha_{n}^{ \chi}d_{n}\big{)}E_{j,n}^{t}-J_{j,n}\|_{D}^{2}}{\sum_{j}\|\big{(}\chi_{n-1}+ \alpha_{n}^{\chi}d_{n}\big{)}E_{j,n}^{t}\|_{D}^{2}} \tag{17}\]  
In the minimizing Equation (17), we have:  
\[\alpha_{n}^{\chi}=\frac{-\Big{(}aC-Ac+\sqrt{(aC-Ac)^{2}-4(aB-Ab)(bC-Bc}\Big{)} }{2(aB-Ab)} \tag{18}\]
# Supervised Learning-Based Image Classification for the Detection of Late Blight in Potato Crops
## 4 Results and Discussion
### Experimental Evaluation
The CNN trained on the augmented data set obtained an AUC equal to 0.97, indicating that there is a 97% chance that this model can distinguish between a healthy leaf and a leaf infected with late blight. It is followed by the CNN trained with the original data set, which obtained an AUC equal to 0.95. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Characteristic** & **AUC** \\ \hline \multirow{2}{*}{CNN} & N/A & 0.957 \\  & Augmented & 0.973 \\ \hline \multirow{4}{*}{SVM} & Color & 0.875 \\  & Texture & 0.641 \\ \cline{1-1}  & PCA & 0.702 \\ \cline{1-1}  & Combined & 0.852 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparative matrix of results of the classifiers—ROC AUC. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{5}{c}{**Metrics**} \\ \cline{2-7}  & & **Accuracy** & **Precision** & **Sensitivity** & **Score-F** & **Specificity** \\ \hline \multirow{2}{*}{CNN} & N/A & 0.875 & 0.891 & 0.891 & 0.891 & 0.851 \\  & Augmented & 0.93 & 0.929 & 0.933 & 0.931 & 0.932 \\ \hline \multirow{4}{*}{SVM} & Color & 0.875 & 0.861 & 0.888 & 0.875 & 0.888 \\  & Texture & 0.64  
Of the SVM models, the SVM that obtained the best results in ROC curve analysis was the one trained with color features with an AUC equal to 0.87, followed by the models trained with combined PCA and texture features with AUC equal to 0.85, 0.7, and 0.64 respectively. Figure 14 shows the ROC curves obtained by validating the SVM models trained with color, texture, PCA, and combined characteristics. The line yellow represents the 0.0 point to the 1.1 point is called the reference diagonal, or non-discrimination line. Each point of the ROC curve (line blue) corresponds to a possible cut-off point of the diagnostic test, and informs us of its respective sensitivity (Y-axis) and 1-specificity (X-axis).
# Triple-Band Notched Ultra-Wideband Microstrip MIMO Antenna with Bluetooth Band
## 4 Results and Discussion
### S-Parameter Characteristics for UWB-MIMO Antennas  
Figure 19 shows the simulated and the measured reflection coefficients (\(1\,S_{11}\,\), \(\mid\,S_{22}\,\), \(\mid\,S_{33}\,\)\(\mid\), and \(\mid\,S_{44}\,\)\(\mid\) in dB) of the proposed UWB-MIMO antennas. The experimental curves were obtained by using the Rohde & Schwarz VNA Model of ZVA67, Rohde & Schwarz, Columbia, MD, USA. It is depicted that the four MIMO antennas operate in the FCC-UWB spectrum with triple band notches at the WiMAX band (3.3-3.8 GHz), WLAN band (5.15-5.85 GHz), and X-band (7.9-8.4 GHz). Moreover, the proposed MIMO antennas support the Bluetooth band that extends from 2.4 to 2.483 GHz. Figure 20 shows the simulated and the measured port isolation (\(\mid\,S_{21}\,\), \(\mid\,S_{23}\,\)\(\mid\,S_{34}\,\), and \(\mid\,S_{41}\,\)\(\mid\) in dB) between the proposed UWB-MIMO antennas. It is observed that the curves of the isolation levels are below \(-20\) dB over the FCC-UWB and the Bluetooth band for the four MIMO antennas.  
Figure 19: Measured and simulated reflection coefficients for proposed UWB-MIMO antenna. (a) \(\mid\,S_{11}\,\) in dB at port (1); (b) \(\mid\,S_{22}\,\)\(\mid\) in dB at port (2); (c) \(\mid\,S_{33}\,\)\(\mid\) in dB at port (3); (d) \(\mid\,S_{44}\,\)\(\mid\) in dB at port (4).  
Figure 18: Photographs of the proposed UWB-MIMO antenna.
###### Abstract
In [41], the authors instead consider all sources of errors in the GMF inversion, and a sensitivity analysis of the wind speed uncertainty is proposed, referring to the C-band model 4 (CMOD4) [20] in particular. Ref. [42] presents a sensitivity study of wind speed retrieval with respect to the wind direction when using the X-band model 2 (XMOD2) GMFs developed at either the German Aerospace Center (DLR) or the Italian Space Agency (ASI). The study shows that an error of \(\pm\) 10\({}^{\circ}\) for the SSW direction may introduce an error greater than 10% in the retrieval of SSW speed. To the best of our knowledge, up to now no attempt has been made to provide spatial distribution maps of both SSW speed and direction uncertainty. These maps may be considered as an added value product with respect to the quality measurements provided by Copernicus. In fact, the SAR-derived wind field map available through Copernicus relies on statistical Bayesian inversion whose a priori information is obtained from NWP models [43]. The resulting map is provided with a wind quality flag based on the combination of two terms. The first one depends on the percentage of bright targets detected in the SAR image within the cell; the second one relates to the consistency between ancillary NWP model output and the NRCS used in the wind inversion scheme. The lower the minimum value of the cost function defined in the Bayesian procedure, the better the consistency and the higher the confidence level of the inverted wind vector must be [43]. However, as recalled previously, NWP models may fail to adequately retrieve SSW in complex coastal areas [10]. This may affect the quality flag provided by Copernicus. On the contrary, the definition of the SSW direction uncertainty provided by the MS LG-Mod depends only on the directional content evaluated from the NRCS in the WVC after the masking of unusable points [40], and then independently from any external NWP model reliability in such areas. As described in the paper, the SSW speed uncertainty does not depend on any external wind data source as well. The remainder of the paper is structured as follows. Section 2 reports the available datasets information, while Section 3 describes in detail the proposed methodology and the comparison procedure. Section 4 shows and critically discusses results achieved in the estimation of the SAR SSW speed uncertainty, also examining the contribution of different parameters to the uncertainty and using NWP model data for some comparison. Finally, Section 5 summarises the conclusions.
## Appendix A Derivation of Equation (5)  
Equation (4) describes the relationship between the perturbation in the data and the perturbation in the model. Following [22], the inversion of the model perturbations from Equation (4) can be written as follows:  
\[\frac{\delta v}{v_{0}}+\cos^{2}\!\theta\frac{\delta\rho}{\rho_{0}}=\iiint\! \mathbf{B}(\mathbf{x}_{r};\omega;\mathbf{x}_  
In 2D, we change the variables from \((\mathbf{x}_{r},\mathbf{x}_{s},\omega)\) to \((\mathbf{k},\theta)\) and rewrite Equation (A2) as follows:  
\[\begin{array}{c}\frac{\delta\rho}{v_{0}}+\cos^{2}\!\theta\frac{\delta\rho}{ \rho_{0}}=-\iint\mathbf{B}(\mathbf{x}_{r};\omega;\mathbf{x}_{s};\mathbf{x}; \theta)\delta(\theta^{\prime}-\theta)\times\\ \int\frac{2\omega^{2}}{v(\mathbf{x}^{\prime})^{2}}\Big{(}\frac{\delta v( \mathbf{x}^{\prime})}{v(\mathbf{x}^{\prime})}+\cos^{2}\!\theta\frac{\delta p( \mathbf{x}^{\prime})}{p(\mathbf{x}^{\prime})}\Big{)}\times A(\mathbf{x}_{s}; \mathbf{x}_{r};\mathbf{x}^{\prime})e^{i\mathbf{k}(\mathbf{x}^{\prime}- \mathbf{x})}\Big{|}\frac{\partial(\mathbf{x}_{s},\mathbf{x}_{r},\omega)}{ \partial(\mathbf{k},\theta)}\Big{|}d\mathbf{x}^{\prime}d\mathbf{k}d\theta^{ \prime}\end{array} \tag{10}\]  
The inverse operator \(\mathbf{B}(\mathbf{x}_{r};\omega;\mathbf{x}_{s};\mathbf{x};\theta)\) must satisfy the following equation according to Equation (10):  
\[\frac{2\omega^{2}}{v^{2}}A(\mathbf{x}_{s};\mathbf{x}_{r};\mathbf{x})\mathbf{B }(\mathbf{x}_{r};\omega;\mathbf{x}_{s};\mathbf{x};\theta)\bigg{|}\frac{ \partial(\mathbf{x}_{s},\mathbf{x}_{r},\omega)}{\partial(\mathbf{k},\theta)} \bigg{|}=-\bigg{(}\frac{1}{2\pi}\bigg{)}^{2} \tag{11}\]  
The Jacobian is derived in Appendix B. Substituting the Jacobian in Equation (11) into Equation (11), we can obtain the following:  
\[\begin{array}{c}\mathbf{B}(\mathbf{x}_{r};\omega;\mathbf{x}_{s};\mathbf{x} ;\theta)=-\Big{(}\frac{1}{2\pi}\Big{)}^{2}\frac{v^{2}}{2\omega^{2}}\times 64 \pi^{2}|\omega||\mathbf{q}|^{2}A(\mathbf{x}_{s};\mathbf{x}_{r};\mathbf{x})\frac {\cos\beta_{s}}{v(\mathbf{x}_{s})}\frac{\cos\beta_{r}}{v(\mathbf{x}_{r})}\\ =-\frac{32\cos^{2}\!\theta}{|\omega|}A(\mathbf{x};\mathbf{x}_{s})A(\mathbf{x}_ {r};\mathbf{x})\frac{\cos\beta_{s}}{v(\mathbf{x}_{s})}\frac{\cos\beta_{r}}{v( \mathbf{x}_{r})}\end{array} \tag{12}\]  
By substituting Equation (12) into Equation (10), and by using the following relation:  
\[\frac{\delta(\rho\
u)}{\rho_{0}v_{0}}=\frac{\delta v}{v_{0}}+\frac{\delta\rho} {\rho_{0}} \tag{13}\]  
We can obtain the desired relationship in Equation (5):  
\[\sin^{2}\!\theta\frac{\delta\
u}{v_{0}}+\cos^{2}\!\theta\frac{\delta(\rho\
u) }{\rho_{0}v_{0}}=-\iint\iint\frac{32\!\cos^{2}\!\theta^{\prime}}{|\omega|} \frac{\cos\beta_{r}}{v(\mathbf{x}_{r})}\frac{\cos\beta_{s}}{v(\mathbf{x}_{s}) }A(\mathbf{x};\mathbf{x}_{s})A(\mathbf{x}_{r};\mathbf{x}) \tag{14}\]
## 2 Operating Principle and Structure  
### Operating Principle  
PVDF is a piezoelectric plastic material that generates equal and opposite charges on both sides of the film when it is mechanically deformed [16]. Figure 1 is schematic picture of PVDF film. In our proposed sensor, there is no external electric field in the PVDF film. The piezoelectric equation is below [17; 18]:  
\[\left[\begin{array}{c}P_{1}\\ P_{2}\\ P_{3}\end{array}\right]=\left[\begin{array}{cccccc}0&0&0&0&d_{15}&0\\ 0&0&0&d_{24}&0&0\\ d_{31}&d_{32}&d_{33}&0&0&0\end{array}\right]\left[\begin{array}{c}T_{1}\\ T_{2}\\ T_{3}\\ T_{4}\\ T_{5}\\ T_{6}\end{array}\right] \tag{1}\]  
Figure 1: Schematic of polyvinylidene fluoride (PVDF) film.  
where \(P_{1}\), \(P_{2}\), and \(P_{3}\) are electric displacements in directions 1, 2, and 3; \(d_{31}\), \(d_{32}\), and \(d_{33}\) are the piezoelectric strain coefficient in directions 1, 2, and 3; \(d_{24}\) and \(d_{15}\) are the piezoelectric shearing strain coefficients of the film sides in directions 1 and 2; \(T_{1}\), \(T_{2}\), and \(T_{3}\) are the tensile stress in directions 1, 2, and 3; and \(T_{4}\), \(T_{5}\), and \(T_{6}\) are the shear stress in directions 1, 2, and 3. In our proposed sensor, the piezoelectric shearing strain coefficients are much smaller than the piezoelectric strain coefficients. The output charge in the electroded area of the PVDF film is below:  
\[Q\ =\ (d_{31}T_{1}\ +\ d_{32}T_{2}\ +\ d_{33}T_{3})A \tag{2}\]  
where \(Q\) is the output charge, and \(A\) is the electrode area.  
The sensor mentioned in this paper detected motion signals by using the PVDF film. With silicone rubber as the backing material of the sensor, it can be considered that the PVDF is deformed only in the embedded direction, while the deformation in thickness is 0, which means that \(T_{3}\) is 0. Normally, \(d_{31}\) is much larger than \(d_{32}\), which can be ignored. Therefore, Equation (2) can be transformed into the equation below [19]:  
\[Q\ =\ d_{31}T_{1}A \tag{3}\]  
Since the deformation of elastic material is directly proportional to stress, the quantity of charges \(Q\) that is generated by the sensor is directly proportional to the external force \(F\), while the PVDF film and the backing of silicone rubber are all made of elastic materials. The output response of the sensor can be expressed as the equation below:  
\[Q\ =\ SF \tag{4}\]  
where the slope \(S\) is defined as the sensitivity of the sensor, and \(F\) is the external force.