esa-sceva/satcom-chunk-collection huggingface dataset:
# Benefits and Lessons Learned from the Sentinel-3 Tandem Phase
## 9 Geophysical Signal and Geometric Differences  
### Impact of Suell on SAR Altimetry  
The thirty-second interval between the two satellites has a generally negligible impact on the measured geophysical variables. But this is not the case for sea surface height measurements in the presence of swell. The time difference between S3A and S3B can be used to assess the impact of the swell signal on SAR altimetry measurements. As displayed in Figure 11, the spectral analyses of tandem phase data show that long ocean waves are responsible for high-frequency sea level anomaly (SLA) signals and that the spatial wavelength corresponds to the swell wavelength. It is even possible to show evidence that the phase shifts observed between the two-satellite acquisitions are consistent with the properties of the propagating waves given by the dispersion relation. Because the 20 Hz SAR mode altimeter processing does not sample fast enough, the high-frequency signals induced by swells are aliased down to lower frequency and mistaken for another signal (see Figure 12). To avoid long-wavelength errors, it is thus advocated to increase the sampling of the SAR-mode processing to provide time-series of SLA altimeter data sets of higher quality.  
Figure 10: The global map at 1\({}^{\circ}\) resolution of the differences between the standard deviation of all trend estimates from all bootstraps for an input trend of \(-\)0.5 % yr\({}^{-1}\) for (**a**) S3A-aligned and S3B, and (**b**) S3A-vicarious and S3B in each grid cell. Red indicates that the latter dataset in each pair has a higher standard deviation of trend estimates than the former dataset in each pair. Reprinted from [24].
# Augmented Reality Applications in Industry 4.0 Environment
## 4 Results
### Areas of Applications of AR  
Currently, the development of new technologies has led to the application of AR applications in various areas of the industry. A detailed analysis of the relevant publications provides insight into the primary purpose of AR in industrial environments. Developed AR applications have found implementation in six main areas: maintenance, assembly tasks, human robot collaboration, manufacturing, training and logistics (Figure 5). Nearly 13% of the selected publications refer to application in more than one category.
## 3 Results and Discussion  
In this section, the main experimental and simulation results are presented. Firstly, the two experimental data campaigns during different periods of the year 2019 are analyzed. Secondly, both the simulation and the experimental results are compared. Finally, the simulation results obtained with the calibrated model are presented and discussed, including the annual thermal demand and the potential savings under different ventilation scenarios.
# Quadratic Multilinear Discriminant Analysis for Tensorial Data Classification
## 3 Algorithm
```
Data: Training data \(\{\mathcal{X}_{i}\}_{i=1}^{m_{1}},\{\mathcal{Y}_{j}\}_{j=1}^{m_{2}}\) from two classes. Input: Tensor \(\mathcal{Z}\) to classify. Output: Class to which \(\mathcal{Z}\) belongs. 1\(\mathcal{F}=\frac{1}{m_{1}}\big{(}\sum_{i=1}^{m_{1}}\mathcal{X}_{i}\big{)}\);
2\(\tilde{\mathcal{Y}}=\frac{1}{m_{1}}\Big{(}\sum_{i=j}^{m_{2}}\mathcal{Y}_{j} \Big{)}\);
3 for\(i=1,\ldots,m_{1}\)do
4\(\mathcal{X}_{i}=\mathcal{X}_{i}-\tilde{\mathcal{X}}\);
5 for\(j=1,\ldots,m_{2}\)do
6\(\mathcal{Y}_{j}=\mathcal{Y}_{j}-\tilde{\mathcal{Y}}\);
7 Find new sets of coordinates (via Algorithm 1) \((A_{1},B_{1},C_{1})\), \((A_{2},B_{2},C_{2})\in H_{1}\times H_{2}\times H_{3}\) for each class. 8\(d_{1}=|\big{(}A_{1},B_{1},C_{1}\big{)}\cdot(\mathcal{Z}-\tilde{\mathcal{X}})|\);
9\(d_{2}=|\big{(}A_{2},B_{2},C_{2}\big{)}\cdot(\mathcal{Z}-\tilde{\mathcal{Y}})|\);
10 if\(d_{1}<d_{2}\)then
11 Assign \(\mathcal{Z}\) to class 1. 12 elseif\(d_{2}<d_{1}\)then
13 Assign \(\mathcal{Z}\) to class 2. ```  
**Algorithm 2** Classification via KNMDA. One could also use this algorithm to obtain two similarity scores \(s_{1}\) and \(s_{2}\), between 0 and 1, that represent how close \(\mathcal{Z}\) is to each class, by defining  
\[\sum_{i=1}^{N_{1}}|(A_{1},B_{1},C_{1})\cdot(\mathcal{X}_{i}-\bar{\mathcal{X}}) |^{2}\]0  
\[\sum_{i=1}^{N_{1}}|(A_{1},B_{1},C_{1})\cdot(\mathcal{X}_{i}-\bar{\mathcal{X}}) |^{2}\]1 Graphical representation of Algorithm 2 in the case of matrix data. To make an overall summary, given a training set of tensors (of any order), for each class we find the average tensor and center the data in that class. We then apply the Kempf-Ness Theorem to find optimal coordinates for a given class. Given a new tensor to be classified, we compute a distance from each class by subtracting the class average and applying the class change of coordinates. The smallest distance will identify the class to which the tensor is assigned.
Recently, scholars have embarked upon research endeavors encompassing regularization, multiscale strategies, stochastic sources, and cross-constraints [12; 13; 14], aiming to enhance the accuracy and increase the inversion speed of FWI. These endeavors have effectively promoted the practical application of FWI. Given the formidable capabilities exhibited by deep learning across a variety of domains, a series of advancements in GPR technology, based on deep learning methodologies [15], has recently been achieved. Researchers have harnessed machine learning techniques to automatically extract and identify anomalies associated with structural defects through the extraction of hyperbolic features or high-dimensional image characteristics from GPR data, accounting for differences in image and signal attributes [16; 17]. Some scholars have sought to integrate deep learning into auxiliary roles for GPR forward simulation and inversion solution processes [18] to improve efficiency and precision. Offline training and online prediction using deep learning could construct a system that maps from the model to the forward profile, considerably accelerating the forward simulation speed. Novel methodologies based on convolutional neural networks enable the rapid solution of highly nonlinear physical governing equations in less time, providing inversion practitioners with new research approaches. The diversification of objects detected by GPR has ranged from traditional realms, encompassing urban engineering, railways, water resources, mining, tunnels, and archaeology, to include diverse fields such as lunar soil investigation [19], Mars exploration [20], tree defect detection [21], soil moisture content assessment [22], and pollutant leakage detection [23]. This expansion underscores the robust vitality of GPR and its increasingly expansive prospects across a wider spectrum of applications. Conceptualization and methodology, D.F. ; investigation, B.Z. ; resources, B.Z. ; writing--original draft preparation, Y.L. ; writing--review and editing, Y.L. ; supervision, X.W. ; funding acquisition, D.F. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China (NSFC) under Grant 42074161 and Grant 42104143, and by the Natural Science Foundation of Hunan Province under Grant 2021 JJ30806 and Grant 2022 JJ40584. Thanks to all the authors and peer reviewers for their valuable contributions to the Special Issue 'Ground Penetrating Radar: Theory, Methods, and Applications'. The authors declare no conflict of interest.
# Remote Sensing Data for Digital Soil Mapping in French Research--A Review
## 1 Introduction
Comprehensive reviews of the use of remote sensing for soil and terrain mapping were made from the French research as early as 1974, albeit in French [31; 105], and major reviews were later published in English by [PERSON] et al. [106; 107], [PERSON] [108], and [PERSON] et al. [109]. The latter introduced the use of RS as primary or secondary soil data to improve DSM. As RS products have become increasingly available and diverse and also offer higher resolutions in space, time, and spectral range, updated reviews have been published by [PERSON] et al. [110] and [PERSON] et al. [111], focusing on the main soil characteristics monitored by RS and the perspectives offered by new sensors [112]. The idea of an article gathering the specific contributions of RS to DSM from French research originates from a scientific working group founded in 2015 and named Theia \"CNS for _Cartographic Numerique des Sols_\" (DSM for Digital Soil Mapping). This French working group aims to federate the efforts of French research laboratories developing digital mapping approaches for perennial soil properties [113; 114] under the supervision of _Centre National d'Etudes Spatiales_ (French Space Agency). We believe that such an approach can be useful not only on a national scale, but that it can also be considered as the first step to implement a similar approach on a global scale. The objectives of this review are as follows:  
1. Summarize the main soil properties and threats that have been studied on a large scale over the last decade using RS by the French research community;
2. Synthesize the main recent methodological advances of DSM related to the use of RS products in France or elsewhere from French research;
3. Highlight the complementarity of the new RS products and the other covariates currently used in DSM.
# A Web-Based Application for Smart City Data Analysis and Visualization
## 4 Experimental Protocol: System Implementation
### Dataset Management and Description
#### 4.1.4 Parking Data  
Parking sensors monitoring of spot usage provides insight into urban space demand and utilization:  
* Occupancy and turnover incoming data determine how many parking spots are occupied and how long vehicles remain parked, enabling analysis of space turnover and availability trends. The monitored parking layout is illustrated in Figure 4, where the system tracks specific spaces using fixed camera coverage.
* Utilization patterns help detect peak usage periods and inform better management of limited parking infrastructure based on demand over time.  
Figure 4: Visual layout of the monitored parking area. These specific spots are covered by the fixed-position camera used for detecting occupancy [68].
## Materials and Methods
### Deep Convolutional Learning and Mapping  
A custom deep learning model made up of convolutional and densely connected neural network layers [78; 79] capable of effectively handling time-series of the satellite data was implemented for the supervised classification of satellite images. A default model architecture was composed of four one-dimensional convolutional layers with ReLU activations followed by three densely connected neural network layers, two with ReLU activations and a final layer with Softmax activation to collect classifications. In this research, since each prefectural region was modeled and mapped separately, the parameters and hyper-parameters of the model including number of layers, number of neurons, learning rate, number of epochs, and batch size also varied with the prefectural regions. A 10-fold cross-validation method was used for fine-tuning the input features and model parameters and hyperparameters with reference to the classification accuracy metrics such as Kappa coefficient and F1-score [80]. After confirming optimum model parameters and hyperparameters, the mapping was undertaken with well-shuffled 85% training data, whereas 15% data were still kept for testing the model performance. The fine-tuned model established in this manner for each prefecture was utilized for prediction with unseen satellite datasets to produce DGPE maps. See Supplementary Material S3 for details on optimizing the deep learning models.
## 3 Results
\[\sigma=\sigma(t,s)=\frac{u_{0}(t)\cdot s_{\rm c}}{\delta_{0}\cdot s_{\rm f}+ \frac{\delta_{1}}{2}\cdot(s_{\rm f}{}^{2}+2\cdot s_{\rm f}\cdot\widehat{s})+ \frac{\delta_{2}}{3}\cdot(s_{\rm f}{}^{3}+3\cdot s_{\rm f}{}^{2}\cdot\widehat{ s}+3\cdot s_{\rm f}\cdot\widehat{s}{}^{2})} \tag{15}\]1 The evolution of the rotation center (\(\mathrm{s_{c}}\)) in relation to the weighting coefficient (\(\sigma\)), for five values of the time (t) independent variable. 1. The elastic moment given by the equation: \(\mathrm{M_{E}}=\mathrm{u_{0}(t)}\cdot\mathrm{s_{c}(t)}\) expressed in [grf\(\cdot\)mm] or in [mN\(\cdot\)m];
2. The mechanical work in elastic regime given by the equation: \(\mathrm{L_{E}}=\mathrm{u_{0 AV}(t)}\cdot\mathrm{\Delta y^{\prime\prime}_{\alpha}(t)}\), where \(\mathrm{y^{\prime\prime}_{\alpha}(t)}=\frac{\mathrm{s_{c}(t)+\widehat{s}}}{ \mathrm{s_{c}(t)+\widehat{s}+\widehat{s}^{\prime}}}\cdot\mathrm{y^{\prime}_{ \alpha}(t)}\), \(\mathrm{y^{\prime}_{\alpha}(t)}=\frac{\mathrm{s_{c}(t)+\widehat{s}^{\prime}}}{ \mathrm{s_{c}(t)-(s_{c}+\widehat{s})}}\cdot\mathrm{y_{\beta}(t)}\) and (\(\mathrm{\Delta y^{\prime\prime}_{\alpha}(t)}\)) represent the variation of (\(\mathrm{y^{\prime\prime}_{\alpha}(t)}\)) on the considered time interval; with (\(\mathrm{y^{\prime\prime}_{\alpha}(t)}\)), the teeth's movement for \(\mathrm{s}=\widehat{s}^{\prime}\) is logged (the movement of the point from the tooth's fictional axis which belongs to the tooth's transversal section upon which the force (through the bracket) is applied; the mechanical work is expressed in [grf\(\cdot\)mm] or in [\(\mu J\)]. As notations, we mention: the submultiple \(\mathrm{m}=10^{-3}\); the submultiple \(\mathrm{\mu}=10^{-6}\); the measurement unit for force grf = grams force; the gravity acceleration \(\mathrm{g}=9.80665\frac{\mathrm{m}}{\mathrm{g}^{2}}\); the measurement unit for force, in international system, \(\mathrm{N}=\mathrm{Newton}=1\frac{\mathrm{Kg}}{\mathrm{g}}\); the measurement unit for work, in international system, \(\mathrm{J}=\mathrm{Joule}=1\mathrm{N}\cdot\mathrm{1m}\). Moreover, the simulations in this paragraph are made for the value \(\mathrm{\sigma}=0.6\) grf/mm\({}^{2}\) of the weighting coefficient associated to the tissue resistance. As was previously mentioned in the paper, this small value of the (\(\mathrm{\sigma}\)) coefficient (smaller than 1) highlights a high tissue resistance, which implicitly generates the tooth movement in translation regime. In the approached case, we have considered another tooth with other dimensions as in the case of Section 2 and we, also, have considered the (\(\mathrm{\sigma}\)) coefficient sensibly higher than in the case of the simulation from Figure 6 (implicitly a sensibly lower tissue resistance), and the value \(\mathrm{y^{\prime}_{\alpha}(t)}=1.27\) mm is obtained for the movement of the tooth's apex, in steady-state regime. In Table 4, the simulations' results, obtained for this example, are presented.
## 4 Graph Theoretic Resiliency Framework
### Resiliency Estimation Framework
#### 4.2.3 Case-III: Restoration State  
In this case, we consider that the failed edge in the previous case \(e_{1-6}\in E\) starts to repair itself either by substitution or recovery at time \(t_{3}\). Upon recovery, the failed edge \(e_{1-6}\in E\) restores the path \(\gamma_{1-6}^{1}\), which increases the \(PDR\), as can be deduced from Equation (16).  
\[PDR_{6-1}^{\gamma_{1-6}^{1}}=\frac{\sum\limits_{t=t_{60}}^{t_{60}}\alpha_{t}^{ 6}=\min\left\{\sum\limits_{t=t_{20}}^{t_{20}}\alpha_{t}^{2},\sum\limits_{t=t_{ 30}}^{t_{30}}\alpha_{t}^{3},\sum\limits_{t=t_{40}}^{t_{40}}\alpha_{t}^{4},\sum \limits_{t=t_{50}}^{t_{50}}\alpha_{t}^{5}\right\}}{\sum\limits_{t=t_{10}}^{t_{ 10}}\beta_{t}^{1}} \tag{16}\]  
In this case, the \(PDR\) corresponding to the dominant path increases such that \(\max\left\{PDR_{6-1}^{\gamma_{1-6}^{1}}\right\}=0.8\). The restoration will be achieved over a certain time span, such as \(t_{3}\) to \(t_{4}\) such that, at \(t=t_{4}\), \(\sum\limits_{t=t_{3}}^{t_{4}}\alpha_{t}^{6}\approx 0.8\sum\limits_{t=t_{3}}^{t_{4}} \beta_{t}^{1}\).  
Further, with the restoration of dominant path \(\gamma_{1-6}^{1}\), the traffic from path \(\gamma_{1-6}^{2}\) will be off-loaded and diverted to dominant path \(\gamma_{1-6}^{1}\). This is because the dominant path is considered to be dedicated, whereas the \(\gamma_{1-6}^{2}\) path is shared. Hence, the \(PDR\) at the destination node corresponding to path \(\gamma_{1-6}^{2}\) becomes \(PDR_{6-1}^{\gamma_{1-6}^{2}}\leq 0.2\), such that, at \(t=t_{4}\), \(PDR_{6-1}^{\gamma_{1-6}^{2}}=0.2\). There will be some time for the re-routing of the traffic over path \(\gamma_{1-11}^{1}\). During this time span, the \(PDR\) will continue increasing.  
The overall \(PDR\) at destination \(v_{6}\) corresponding to source \(v_{1}\) can be given by  
\[PDR_{6-1}=\sum\limits_{m=-\gamma_{1-6}^{1}}^{m=\gamma_{1-6}^{L}}PDR_{6-1}^{m} \tag{17}\]  
\[PDR_{6-1}=PDR_{6-1}^{\gamma_{1-6}^{1}}+PDR_{6-1}^{\gamma_{1-6}^{2}} \tag{18}\]  
Figure 6: Resiliency curve of the SCN model for _Case-II_.  
Thus, at \(t=t_{4}\), : \(\cdot\): \(PDR_{6-1}\approx 1\). Therefore, the following inequality holds:  
\[PDR_{th}\leq PDR_{6-1}\leq 1 \tag{19}\]  
The corresponding resiliency curve is shown in Figure 7, where the restoration phase is shown through the interval \(t_{3}\) to \(t_{4}\). Nevertheless, as \(PDR_{6-1}\geq PDR_{th}\), the network is considered to be functional in this case.
# Monitoring the Joint Area of Composite Membrane Materials
## 3 Results and Discussion
### Embroidery and HF Welding with Conductive Sewing Thread
#### 3.3.2 Sensor Positioning Based on Simulation Results  
After validation of the FEA model, it was used as basis for the appropriate positioning of the sensors. The strain sensors were arranged in the middle of the welding zone as displayed in Figure 5 (see Section 2.2.6). According to simulation results (see Figure 13), this might not appear conclusive as the peak stress values occur outside the weld seams, which is in line with practical experience. The most critical area in terms of failure is the boundary region between the weld seam and the undisturbed membrane surface. However, since the simulation models predicted strain in the seam area as well, measurements can be taken in this area. According to FEA models, the mechanical load affecting the membrane outside the seam areas is greater than that within the seam, so that a  
Figure 11: FEA models of welded textile membranes under shearing load: (**a**) Shear test and (**b**) peeling test.  
Figure 12: Comparison of experimental and simulation results for welded membrane tests: (**a**) Shear load and (**b**) peeling load (with correlation coefficient r).  
corresponding conversion had to be carried out. Based on this conversion, the actual hazard potential for the membrane outside the weld seams originating from the internal strains of the seam can be estimated. The required correlations can be obtained from the simulation models.  
There are several reasons to perform this indirect method via strain measurement in the weld seam. The two decisive ones are explained in the following:  
1. The future positioning of weld seams within a membrane web can be determined relatively precisely on the basis of the cutting pattern even prior to the manufacturing of the membrane. The correct sensor arrangement can be predicted during membrane production. The exact prediction of local load peaks in the installed state of a membrane structure prior to its final positioning and over an extended time period (settlement effects, local false loads due to external influences, etc.) is impossible.
2. Even if the seam position is defined prior to membrane manufacturing, as described in paragraph 1, this poses great challenges for manufacturers. Sensors positioned in welding seams allow for alternative approaches, such as sewing of membrane blanks with a sensor thread (embroidery and sewing processes are technologically closely related, see Section 2.2.3) with subsequent sealing of the seam by welding on a membrane cover strip (butt joint) or a butt joint that is welded directly to a cover strip with integrated textile sensor. Both facilitate a high degree of integration of the textile sensors into the ready-made membrane constructions without affecting established membrane manufacturing processes.
## 4 Simulation
### Antenna System  
In this paper, the presented antenna system is the 10-tile cylinder arrangement structure. Therefore, to estimate the counterpart's location, each tile must be sequentially operated, and the operating tile must be determined based on the synthesized received signal strength determined via Equation (21). Afterwards, the counterpart's location can be estimated based on the improved monopulse ratio curve of the determined tile. Based on the presented antenna, a system-based simulation experiment was performed to analyze the accuracy of the estimation of the counterpart location. Figure 17 shows the counterpart location (star marker) set using a random function and presented antenna system configuration. The counterpart location based on the azimuth (\(\phi\)) is 198.450\({}^{\circ}\), and the counterpart location based on the elevation (\(\theta\)) is 87.7191\({}^{\circ}\).  
Figure 18 shows the received signal strength and the location estimation results based on sequential tile operation. As a result of the simulation, the rece  
Figure 16: Simulation results for single tile: (**a**) counterpart location; (**b**) estimation error.  
Figure 17: Antenna system configuration and counterpart location.  
the highest in tile #6. The counterpart location can be calculated based on the location of tile #6 on the antenna system and the corresponding location estimate value. As a result of this analysis, the azimuth-based counterpart's location, as confirmed via the proposed technique, was found to be 198.478\({}^{\circ}\). Accordingly, the azimuth-based location estimation error was confirmed to be approximately 0.028\({}^{\circ}\).  
To confirm the performance of the proposed technique, 100 simulation experiments were conducted for the estimation of the counterpart's location based on the presented antenna system.  
Figure 19 shows the counterpart's location set based on random estimation, and it shows the azimuth-based estimation error estimated using the proposed method. The analysis results show that for 100 simulations, the average and maximum azimuth-based estimation errors are 0.021\({}^{\circ}\) and 0.042\({}^{\circ}\), respectively.