esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Validation
### Capturing Reference Points  
For the workpiece used in this paper, no CAD data were available to extract the reference point coordinates from. For this reason, a Leica LTD800 Laser Tracker was used, which offers an absolute measurement precision of less than 50 \(\upmu\)m in the range that was used in this paper [40]. This precision far exceeds the expected accuracy of the moving head and therefore will be assumed to be sufficient as a reference. This approach can likewise be applied if CAD data are available; however, for example, the workpiece is still flexible before final assembly, which is typical for large, flat workpieces such as side panels that do not possess much structural integrity independently.  
The reference points chosen were rivets in distinctive positions, such as on an intersection of a horizontal and vertical river series. As described in Section 3, the rivet heads stand proud of the surface, rendering them easy to distinguish from the surface itself. Since the light spot, even with the smallest aperture, is much larger than a rivet head, a target printed on paper was placed over the rivets to aid with centering the light spot, similar to Figure 10. The \(pan\), \(finepan\), \(tilt\) and \(finetilt\) angles of each reference point were saved and used for the calibration process (see A.2).  
It should be noted here that this procedure offers only limited accuracy since the light spot on the curved surface of the workpiece deviates distinctly from a perfect circle in most instances. On a flat surface that is not perpendicular to the light beam, the light spot would become a non-symmetric ellipse; however, on the curved surface, the resulting shape is no longer easily parametrizable. The effect is shown in Figure 9. Therefore, the \(pan^{\circ}\) and \(tilt^{\circ}\) of the reference points should only be regarded as a best-effort value, with an uncertainty in the mm range.  
Figure 9: Schematic representation of the moving head spot distortion due to the varying angle between light beam and surface normal.
## 2 Materials and Methods
### Soil-Vegetation-Atmosphere Transfer Model
#### 2.3.1 Specific Air Humidity Calculation  
Specific air humidity is defined as the mass of water vapor in a unit of air mass (kg kg\({}^{-1}\) or g kg\({}^{-1}\)) and can be calculated by using RH, air temperature, and atmospheric pressure data [35]. RH is the ratio between partial water vapor pressure (\(e\)) and water vapor saturation pressure (\(e_{s}\)). Thus,  
\[RH\;=\;\frac{e}{e_{s}}\;\times\;100 \tag{1}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Annual Rainfall Accumulated (mm)** & **Annual Rainfall Anomalies (mm)** \\ \hline
**Years** & **Cabrobo** & **Ibmirim** & **Cabrobo** & **Ibmirim** \\ \hline
2009 & 747.5 & 724.4 & 367.9 & 311.2 \\
2010 & 502.0 & 835.6 & 122.4 & 422.4 \\
2011 & 562.2 & 560.8 & 182.6 & 147.6 \\
2012 & 207.6 & 147.0 & \(-\)172.0 & \(-\)266.2 \\
2013 & 321.2 & 338.2 & \(-\)58.4 & \(-\)75.0 \\
2014 & 229.4 & 307.8 & \(-\)150.2 & \(-\)105.4 \\
2015 & 241.4 & 285.4 & \(-\)138.2 & \(-\)127.8 \\
2016 & 386.2 & 327.8 & 6.6 & \(-\)85.4 \\
2017 & 224.4 & 264.0 & \(-\)155.2 & \(-\)149.2 \\
2018 & 373.8 & 341.4 & \(-\)5.8 & \(-\)71.8 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Annual accumulated rainfall and annual anomalies to the period from 2008 to 2018 in Cabrobo and Ibmirim.  
where \(e_{s}\) is determined by the Tetens equation as follows:  
\[e_{s}\ =\ 6.1078\ \times\ 10^{\frac{7.5\ T}{207.3+T}} \tag{2}\]  
where \(T\) (\({}^{\circ}\)C) is air temperature. Then, we calculate \(e\) as follows:  
\[e\ =\ \frac{RH\ \times\ e_{s}}{100} \tag{3}\]  
and finally, specific humidity is determined by the following equation:  
\[q\ =\ \frac{0.622\ e}{P-0.378\ e} \tag{4}\]  
where \(P\) (hPa) is the atmospheric pressure directly measured at the INMET station.
# Impact of Texture Information on Crop Classification with Machine Learning and UAV Images
## Materials and Methods
### Classification Procedures  
The entire procedure for crop classification with UAV images is presented in Figure 2. For each classifier, optimal parameters were first sought during a training phase. To investigate the impacts of both the number of input images and texture features, we tested eight combination cases for each classifier: UAV images (two cases: with the August image and with six multi-temporal images), and texture features (four cases: with texture features from three different kernel sizes (GK3, GK15, and GK31), and without texture features). These combinations were considered for comparison purposes since the main objective of this study was to evaluate the effectiveness of using texture information when a single-date UAV image is used for crop classification. The classification accuracy was assessed using quantitative measures based on a confusion matrix such as overall accuracy (OA), producer's accuracy (PA), and user's accuracy (UA).
# Review of the Research Progress in Soft Robots
## 5 Problems of Current Research  
### Problems of 3D Printing in Soft Robot Manufacturing  
Soft robots are mainly manufactured using soft elastic materials, and the design structure, size, mechanical properties, and other factors put forward high requirements for printing technology and equipment. Currently, in FDM technology, with a low cost and high popularity, similar to SLS, the printable materials are limited to thermoplastic materials, and the material properties find it difficult to meet the high elasticity needs of soft robots. SLA technology has high molding accuracy and printing speed, but the equipment is more expensive and more difficult to achieve the multi-material 3D printing method. Ink-based DIW and IP are currently the most widely used printing technologies in the field of soft robots. However, IP is more complex and less flexible than DIW technology in terms of ink configuration. DIW is similar to extrusion printing technologies such as FDM, where resolution and speed are traded off against each other, and is less accurate and faster than IP [139]. Traditional 3D printing technologies have their advantages and disadvantages, and new printing technologies that can take into account these issues still need to be explored.  
The shortage of 3D printing soft materials, and efficient and effective manufacturing, are the current problems of 3D printing. Current 3D printing technologies for soft materials still face various limitations. The 3D printing technologies based on the heating-melting-cooling-curing principle, such as FDM and SLS, are limited to the use of thermoplastic materials such as polyurethane, making it difficult to meet multifunctional needs. Although SLA and IP technologies can achieve efficient manufacturing, the current printable materials are limited to photopolymer. For the manufacturing of multi-functional soft robots, since various 3D printing methods have their own advantages and disadvantages, such as the balance between the printing speed and printing accuracy of DIW, the use of a single 3D printing technology may not meet the requirements for efficient and effective manufacturing.  
Figure 11: (**a**) The prototyped soft and lightweight robotic hand assistive device [22]. (**b**) CRUX: a soft, lightweight (1.3 kg), robotic eXosuit [137]. (**c**) CRUX: a soft, lightweight (1.3 kg), robotic eXosuit [138].
# Comparisons of Dynamic Landslide Models on GIS Platforms
## 4 Results and Discussion
### Reason for Differences  
#### 4.1.1 Differences Caused by Models  
Different models can produce different results due to assumptions. We used a 2-D bilateral dam break simulation to analyze the applicability of the traditional CFD model (two-phase model), the depth-averaged model, and the SPH model (Figure 3). In these simulations, we set the initial state to 1 m at [0, 1]. Under the action of gravity, the fluid moves downward. In this case, we applied the interfoam solver of OpenFOAM(r) to calculate the two-phase model [64], PySPH to calculate the SPH model [65], and the Lax-Friedrichs scheme to calculate the depth-averaged model. In OpenFOAM(r), we select the area in the grid with water content greater than 0.6 to obtain the profile. In the SPH model, we set the ball to have a diameter of 0.03 m.  
The results show that the range of movement observed in all models is similar at 0.5 s, spanning [\(-\)2, 3]. In the 2-D simulation, different models will have different results in terms of details. The depth-averaged model can obtain a smoother result than the other models. Additionally, the results of the two-phase model and the SPH model include more details in the \(z\)-directions than the depth-averaged model. Based on the above analysis, the model selection depends on the relevant requirements. When detailed information is required, we must select a complex model to calculate the results. When we focus on the range, we can use the depth-averaged model.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Software** & **Description** & **Scheme** & **Format** & **Platform** \\ \hline ravaflow [14] & Eulerian & NOC & All raster format & GRASS \\ RAMMS [55] & Eulerian & 1 st/2 nd order & ASCII, Geoff & \\ Massflow [12] & Eulerian & TVD–MacCormack & ASCII & \\ Massmov2D [56] & Eulerian & 2-step scheme & PCRaster & PCRaster \\ LA and DA [31; 57] & Eulerian–Lagrangian & PIC-like & GeGTIFF & \\ Titan2D [15] & Eulerian & AMR & All raster format & GRASS \\ IMEX\_SfloW2D [58] & Eulerian & Semi-discrete central scheme & ASCII & \\ Geo-Claw [59] & Eulerian & AMR & ASCII, NetCDF & \\ FLO-2D [13] & Eulerian & 1 st order & All raster format & QGIS \\ DAN3D [60] & Lagrangian & SPH & ASCII & \\ SPHERA [61] & Lagrangian & SPH & All raster format & QGIS \\ \hline \hline \end{tabular}
\end{table}
Table 2: Flow-like programs on GIS platforms.
## 3 Numerical Framework
### Implicit Time Integration
The system (19) can be solved by means of an iterative Newton-Krylov method whose \(\mathrm{k}\)-th nonlinear iteration, with \(\mathrm{k}\geq 0\), requires to find the solution \(\Delta\mathbf{W}_{\mathrm{k}}^{\mathrm{i}}\) of the linear system\[\left[\mathbf{I}+a_{\mathrm{ii}}\Delta t^{n}\mathbf{M}_{P}^{-1}\mathbf{J}_{P} \right]_{\mathrm{k}}^{\mathrm{i}}\Delta\mathbf{W}_{\mathrm{k}}^{\mathrm{i}}=- \left[\mathbf{W}_{\mathrm{k}}^{\mathrm{i}}-\mathbf{W}^{n}+\sum_{\mathrm{j=1}}^{ \mathrm{i-1}}a_{\mathrm{ij}}\Delta t^{n}\left(\mathbf{M}_{P}^{-1}\mathbf{R} \right)^{\mathrm{j}}\right]-a_{\mathrm{ii}}\Delta t^{n}\left(\mathbf{M}_{P}^{- 1}\mathbf{R}\right)_{\mathrm{k}}^{\mathrm{i}}. \tag{20}\]  
In (20), the following definitions are introduced  
\[\mathbf{W}_{\mathrm{k+1}}^{\mathrm{i}}=\mathbf{W}_{\mathrm{k}}^{\mathrm{i}}+ \Delta\mathbf{W}_{\mathrm{k}}^{\mathrm{i}}\qquad\text{and}\qquad\mathbf{J}_{P} =\mathbf{M}_{P}\frac{\mathrm{d}}{\mathrm{d}\mathbf{W}}\left(\mathbf{M}_{P}^{- 1}\mathbf{R}\right)=\mathbf{J}-\frac{\mathrm{d}\mathbf{M}_{P}}{\mathrm{d} \mathbf{W}}\mathbf{M}_{P}^{-1}\mathbf{R},\]  
with  
\[\mathbf{J}=\frac{\mathrm{d}\mathbf{R}}{\mathrm{d}\mathbf{W}}\qquad\text{and} \qquad\mathbf{M}_{P}\frac{\mathrm{d}\mathbf{M}_{P}^{-1}}{\mathrm{d}\mathbf{W}} =-\frac{\mathrm{d}\mathbf{M}_{P}}{\mathrm{d}\mathbf{W}}\mathbf{M}_{P}^{-1}.\]  
Unfortunately, the linear system (20) is not solvable in the present state. Note that the matrix  
\[\mathbf{M}_{P}^{-1}\Big{|}_{\mathrm{K}}=\lim_{\gamma\to 0}\left[ \begin{matrix} \mathbf{B}_{\gamma}^{-1}&\mathbf{0}&\mathbf{0}&\mathbf{0}\\ \mathbf{0}&\mathbf{B}_{\rho}^{-1}&\mathbf{0}&-\mathbf{B}_{\rho}^{-1}\mathbf{B }_{\rho\bar{\rho}^{\mathrm{i}}\mu_{1}}\mathbf{B}_{\rho\bar{\rho}^{-1}}^{-1}\\ \mathbf{0}&\mathbf{0}&\mathbf{B}_{\rho}^{-1}&-\mathbf{B}_{\rho}^{-1}\mathbf{B }_{\rho\bar{\rho}^{\mathrm{i}}\mu_{2}}\mathbf{B}_{\rho\bar{\rho}^{-1}}^{-1}\\ \mathbf{0}&\mathbf{0}&\mathbf{0}&\mathbf{B}_{\rho\bar{\rho}}^{-1}\end{matrix}  \right],\]  
has one block of non-real entries, indeed \(\mathbf{B}_{\gamma}^{-1}=\mathbf{I}/\gamma\). A feasible approach to circumvent the issue consists of multiplying (20) by \(\left(\mathbf{M}_{P}\right)_{\mathrm{k}}^{\mathrm{i}}\), obtaining  
\[\left[\frac{\mathbf{M}_{P}}{a_{\mathrm{ii}}\Delta t^{n}}+\mathbf{J}_{P} \right]_{\mathrm{k}}^{\mathrm{i}}\Delta\mathbf{W}_{\mathrm{k}}^{\mathrm{i}}=- \frac{\left(\mathbf{M}_{P}\right)_{\mathrm{k}}^{\mathrm{i}}}{a_{\mathrm{ii}} \Delta t^{n}}\left[\mathbf{W}_{\mathrm{k}}^{\mathrm{i}}-\mathbf{W}^{n}+\sum_{ \mathrm{j=1}}^{\mathrm{i-1}}a_{\mathrm{ij}}\Delta t^{n}\left(\mathbf{M}_{P}^{- 1}\mathbf{R}\right)^{\mathrm{j}}\right]-\mathbf{R}_{\mathrm{k}}^{\mathrm{i}}, \tag{21}\]  
where it was considered that \(\left(\mathbf{M}_{P}\right)_{\mathrm{k}}^{\mathrm{i}}(\mathbf{M}_{P}^{-1})_{ \mathrm{k}}^{\mathrm{i}}=\mathbf{I}\).
## 3 Results and Discussion
### CT Tomography Assessment
#### 3.1.3 Assessment of Surface and Volume of Test Specimens  
As part of the basic analysis, the volume and surfaces of test specimens were assessed and we calculated the percentage change, in comparison to a theoretical value obtained from the CAD model, based on Formula (1):  
\[\Delta=\frac{(V_{R}-V_{T})}{V_{T}}\cdot 100\ [\%]. \tag{1}\]  
The results show that all test specimens have a smaller volume of material than in the theoretical one. The analysis does not account for the material pores; it only reflects the material itself. In the lattice structure, the difference is approximately two times compared to the gyroid structure, while the gray specimens have a volume change percentage of approximately half compared to the rest (Table 4).  
Figure 11: Assessment of lattice structures’ porosity: (**a**) red sample (FDM method); (**b**) gray sample (SLS method).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Sample** & **Porosity [\%]** \\ \hline Gray lattice & 7.550 \\ \hline Red lattice & 4.090 \\ \hline Black lattice & 4.290 \\ \hline Gray gyroid & 4.570 \\ \hline Red gyroid & 0.900 \\ \hline Black gyroid & 0.540 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of porosity assessment.  
Table 5 shows the values of the theoretical and obtained surface and the percentage surface change compared to a theoretical value from Formula (2):  
\[\Delta_{A}=\frac{(A_{R}-A_{T})}{A_{T}}.100\ [\%]. \tag{2}\]  
The surface in all specimens is larger than the theoretical one. In specimens manufactured by FDM technology, this is due to visible material layering. In Section 3.1, it was concluded that the black specimens were manufactured better, as also confirmed by the surface value, which, compared with the red specimens, was approximately halved in both the lattice and gyroid structures. The surfaces of gray specimens are affected by the unremoved material and higher surface porosity. In the case of gray specimens, the percentage surface change is 60%, which is six times the percentage surface change in the lattice structure and three times that in the gyroid structure when compared with the black specimens.
# Application of Reflectance Indices for Remote Sensing of Plants and Revealing Actions of Stressors
## 2 Optical Methods and Platforms of Remote Sensing of Plants
### Main Optical Methods of Remote Sensing
The measurement of the sunlight-induced chlorophyll fluorescence is based on an analysis of the Fraunhofer  
Figure 2: Spatial (from leaf to ecosystem(s)) and temporal (from seconds to years) scales of main optical methods of plant remote sensing. Two types of measurements of the chlorophyll fluorescence are shown: a passive measurement (without fluorescence induction by special measuring light) and an active measurement (with this induction). hydrogen absorption (656.4 nm) and telluric absorption of oxygen (687 and 760 nm); the lines are used for the discrimination between the chlorophyll fluorescence and reflected light (which can be more intensive) [40]. The development of methods of measurement and analysis of the sunlight-induced fluorescence is an important task of plant remote sensing; however, the interpretation of the results of the measurements of this fluorescence remains a very difficult problem [40]. The temperature can also be informative for plant remote sensing. The temperature changes can be measured in the 7500-14,000 nm range [23] (Figure 1a). The thermal imaging is mainly used for the analysis of transpiration in plants [23; 41]. The transpiration is regulated by the opening/closing of stomates, which prevent overheating of the plant under increased temperatures [41] and control water loss in drought conditions [42]; i.e., the influence of these factors on plants can be analyzed on the basis of this thermal imaging. Additionally, changes in the temperature can be caused by plant damage during the development of diseases that influence transpiration [43]. However, the spatial resolution of the thermal imaging is lower in comparison to the RGB imaging (small details cannot be revealed), and the efficiency of this method is strongly dependent on the ambient temperature [23]. Finally, the spectral analysis of the reflected light on the basis of hyper- and multispectral imaging is a very promising method of plant remote sensing [33; 44]. It is based on strong relations between the spectra of the reflected light and growth, physiological, and biochemical parameters of plants [7; 12; 13]. Visible, NIR, and short-wavelength infrared (SWIR) light ranges are used for the spectral imaging of the reflected light [7]. As a result, this imaging can be effectively used for plant remote sensing; the development of models of prediction and management of the plant productivity in agro- and natural ecosystems can also be based on the results of the spectral imaging of the reflected light [10]. Thus, methods of the hyper- and multispectral imaging were analyzed in our review; at that, we focused on the analysis of reflectance indices because measurements on the basis of these indices are a widely used method of plant remote sensing. There are numerous systems (from handheld devices to satellites) that were developed for measurements of the reflectance indices.
# Grid-Based Vessel Deviation from Route Identification with Unsupervised Learning
## 3 Module Design
### Training Pipeline  
In this section, we provide an overview of the model training pipeline, depicted in Figure 3. The system receives raw AIS data, corresponding to the area of interest on the sea and runs a linear training process consisting of data preparation, detailed in Section 3.2.1, slicing the space into grids adapted to the vessel in question and discrediting the vessel location according to its grid, detailed in Section 1. Finally, the leap rate matrices are calculated per vessel (Section 3.2.3).
# A Review on LiFi Network Research: Open Issues, Applications and Future Directions
## 4 Applications of the LiFi Network
### Location Detection  
Location detection, which is used to process data position to a very comprehensive and precise degree is another application of LiFi. LiFi technology offers indoor (10 cm high-point) navigation information and guidance, which is useful for recognizing the users' faces [42]. Therefore, LiFi may be used to locate the user's position [82; 154]. For example, positioning data are obtained by LiFi because the user is directed through the details of the position. At that point, the AP sends the device position information to the central server. Subsequently, the Central Server utilizes the user position details to create a user location map (location information) [155].
###### Abstract  
The advantageous performance in terms of energy conversion for the flow-induced motion (FIM) of T-section prisms has been experimentally reported recently. In order to further understand the oscillation and energy conversion of multiple T-section prisms, a series of tests of two T-section prisms arranged in tandem with five different spacing ratios (\(3\leq\) 1/D \(\leq\) 15) and seven load resistances (\(8\ \Omega\leq R_{L}\leq 41\ \Omega\)) were conducted. The effects of the spacing ratio and load resistances on energy conversion were discussed. The main conclusions can be summarized as follows. For most tests, the amplitudes of the upstream T-section prism (UTP) and downstream T-section prism (DTP) were both lower than the amplitude of the single T-section prism (STP) due to the mutual interference of the two prisms. Because of the mutual interference, the active powers of UTP and DTP were both less than that of STP, but at some special spacing ratios or load resistances, the mutual interference benefited the energy converted by the two prisms. In the presented tests, the total optimal active power of the upstream T-section prism and downstream T-section prism (UTP + DTP) was 30.12 W, which was 1.5 times that of STP (20.12 W).  
T-section prism; tandem; flow induced motion; spacing ratio +
Footnote †: journal: Applied Sciences
# Feasibility Study on Grouting Compactness Detection in Sleeves Using Piezoelectric Transducers
## 2 Detection Method of Piezoceramic Transducer
### Wavelet Packet Energy Method  
For small cracks, the stress waves may be diffracted and even transmitted directly. As the increase of crack sizes and widths, as well as the number of cracks, the scattering effect becomes apparent, and the energy of the stress wave signal is attenuated. According to the definition of the WPE, the original signal \(S\) received by the PZT sensor is decomposed by the \(N\)-layer -wavelet packet to obtain \(2^{N}\) sub-signals [21; 22]. \(S\) can be expressed as follows:  
\[S=s_{1}+s_{2}+s_{3}+\cdots+s_{2^{N}-1}+s_{2^{N}} \tag{1}\]  
Figure 1: Piezoelectric effect: (**a**) conversion of pressure into voltage difference; (**b**) conversion of voltage into deformation.  
where \(s_{1}-s_{2N}\) are the wavelet coefficient bands in the final stage.  
The variable \(s\) can be expressed as  
\[s=[x_{1},x_{2},x_{3}\cdots x_{m}], \tag{2}\]  
where \(x\) is the amplitude of each band, and \(m\) is the number of sampling points.  
The energy of each frequency band \(e\) is defined as  
\[e=x_{1}^{2}+x_{2}^{2}+x_{3}^{2}+\cdots+x_{m}^{2}. \tag{3}\]  
The total energy \(E\) is expressed as  
\[E=\sum e_{i}, \tag{4}\]  
where \(i\) is the number of frequency bands (\(i=1\dots 2^{N}\)).  
Equation (4) can be used to analyze the grouting compactness in the time domain.
# A Wide-Angle Scanning Leaky-Wave Antenna Based on a Composite Right/Left-Handed Transmission Line
## 4 Conclusions  
This paper proposes a frequency-independent wide-angle scanning LWA based on a CRLH TL. It operates at a frequency of3.0 GHz with a fractal bandwidth of 13%. ACPWG structure is used for the frequency-independent LWA. The phase constant of the fundamental mode in the LWA is controlled by changing the applied DC voltage of the periodically loaded varactors. A scanning range of 128\({}^{\circ}\) (from \(-66^{\circ}\) to \(62^{\circ}\)) is achieved with a low SLL and a low gain fluctuation by the designed 20-unit cell LWA. The peak gain is 9.9 dBi and the lowest radiation efficiency is about 50% during beam-scanning. The experimental verification is completed by fabricating and measuring a prototype--a 10-unit cascaded LWA.  
Antenna design, investigation and data curation, S.X. ([PERSON]) and J.L.; Formal analysis, J.L.; Funding acquisition and project administration, G.D.; Methodology, simulation and writing, J.F. and S.X. ([PERSON]); All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Natural Science Foundation of China under Grant 61731005, and in part by the Pre-research Foundation of National Defense of China under Grant 170441413086.  
The authors declare no conflict of interest.