esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Data and Methodology
### Methodology
#### 3.2.2 Visibility Analysis  
SAR data are affected by the azimuth and incident angle of the satellite, resulting in foreshortening, layover and shadow of the image [52]. In mountainous areas, if the incident angle is greater than the slope angle, foreshortening occurs on the slope facing towards the antenna, causing the target to be compressed on the SAR image. While the incident angle is less than the slope angle, the signal of the radar will reach the top of the slope first, which induces the layover and causes the top and bottom of the slope to be inverted. The shadow phenomenon occurs on a slope facing away from the satellite, of which the slope angle is greater than the incident angle of the satellite [45]. The signal of the radar cannot reach the slope, causing a black area on the image. These phenomena form the invisible area of the satellite, which reduces the quality of the SAR image and cannot provide effective deformation information, adversely affecting the monitoring accuracy. Therefore, we used ArcGIS software and the Digital Elevation Model (DEM) of the study area to determine the slope and aspect and integrated the incident angle of the satellite's ascending orbit to perform reclassification with raster calculator tools to obtain the terrain-based visibility map of the Sentinel-1A data. The processing flowchart is shown in Figure 5.  
According to the result of the aspect analysis, the angles 0\({}^{\circ}\)-22.5\({}^{\circ}\), 337.50\({}^{\circ}\)-360\({}^{\circ}\), and 157.5\({}^{\circ}\)-202.5\({}^{\circ}\) were divided into north and south aspects; 22.5\({}^{\circ}\)-157.5\({}^{\circ}\) were divided into northeast, east, and southeast aspects; and 202.5\({}^{\circ}\)-337.5\({}^{\circ}\) were divided into southwest, west, and northwest aspects. Since Sentinel-1A operates in a near-polar mode, the radar LOS is less sensitive to north-south deformation; thus, the north-south direction was divided into  
Figure 4: The time-position plot (**a**) and the time–baseline plot (**b**).  
a low sensitive area. As shown in Figure 5, the research area was classified into five groups: good visible, low sensitive, shadow, foreshortening, and layover [45]. Finally, the good visible area and low sensitive area were combined into the visible area, and the shadow, foreshortening, and layover areas were classified as the invisible area.
## 3 Materials and Methods
### Clustering Setting  
Here, the optimal setting used for k-means, FCM and sFCM clustering algorithms is reported, in terms of the following parameters: _(i)_ number of clusters; _(ii)_ maximum number of iterations; _(iii)_ minimum improvement in objective function between two consecutive iterations; _(iv)_ exponent for the fuzzy partition matrix (only for fuzzy approaches); _(v)_\(<p,q>\) parameters (only for fuzzy approaches).  
Regarding the number of clusters, the lower the partition fuzziness, the better the segmentation result: the best clustering is achieved when \(V_{pe}\) is minimal. If the partition entropy for \(C=2\) is lower than the one obtained for \(C=3\), the lesion does not include any necrotic region: if so, pixels belonging the brightest cluster are turned to 1 and the others to 0. Otherwise, the two brightest clusters are fused together and their pixels are turned to 1, allowing for the inclusion of necrotic cores into the preliminary mask. Considering that, sometimes breast lesions can include an inner necrotic region that appears darker (i.e., hypo-intense) with respect to the rest of the lesion. In those cases where a binary clustering (\(C=2\)) does not allow for properly detecting a necrosis, the number of clusters might be increased (\(C=3\)). The choice of the optimal number of clusters to be used in k-means, FCM and sFCM is automatically set by evaluating the partition entropy for both cases \(C=2\) and \(C=3\). The partition entropy \(V_{pe}\) is a cluster validity function defined as in Equation (17):  
\[V_{pe}=\frac{-\sum\limits_{j=1}^{N}\sum\limits_{i=1}^{C}u_{ij}\log u_{ij}^{2}} {N}. \tag{17}\]  
The \(\langle p,q\rangle\) parameters, dealing with FCM and sFCM clustering, control the relative importance of both functions defined in Equations (10) and (11), respectively. In particular, with \(\langle p,q\rangle=\langle 1,1\rangle\), we sFCM is equivalent to the traditional FCM. As a matter of fact, to properly weight spatial information, the values \(\langle p,q\rangle>=\langle 1,2\rangle\) were used. The choice was guided by the analysis and results obtained in [18], tackling a similar problem on DCE-MRI images. All these parameters are reported in Table 2.  
At the end of the segmentation of each slice, the algorithm also verifies whether or not the contour of the detected lesion touches the boundaries of the initial rectangular bounding-box: if so, the shape of the lesion results in a cut version of the original one. For this reason, when the lesion intersects the boundaries of the original \(m\times n\) rectangular crop, the algorithm shifts and expands the bounding-box itself until the boundaries of both the lesion and the crop do not intersect each other.
# Overcoming Limitations of LoRa Physical Layer in Image Transmission
## 4 Experimental Method
### Experimental Setup
#### 4.2.1 Image Encryption Scheme for LoRa Transmission  
Data transmission from high bit rate devices, such as image sensors, over the LoRa network is very slow. Such information is inappropriate for transfer with this technology because of the bandwidth limitation and LoRa constrained to 1% duty-cycle (i.e., 36 s/h) which means only can transmit data  
Figure 8: Completed receiver node fixed to the back of a four-wheel drive vehicle to enable testing at different distances.  
Figure 9: Experimental setup for transferring images of the mangrove forest between two LoRa nodes.  
36 s each one hour of transmission time. The LoRa MAC layer is typically responsible for transferring data from nodes to gateways. However, it has a limited ability to cope with image data due to the typical image size and the encryption used in the MAC layer. Hence, to enable transmission of image data, the LoRa physical layer is used with a new encryption method instead (see Appendix A).  
With the new method the image is captured by the Adafruit TTL camera and then saved in SD memory in the JPEG file format. From there it is subsequently converted into a hexadecimal formatted file. The Arduino processor then splits the hexadecimal file into packets containing 84 hexadecimal characters, which is the largest file size that can be transferred over the LoRa bandwidth at one time. To begin serial data transfer, the LoRa radio transmission command 'radio tx' is sent to the receiver node, followed by the hexadecimal packet. Data transfer is paused until the LoRa receiver node sends an acknowledgement that the initial packet has been received successfully. Then, subsequent packets are transmitted.
## 3 RPM Path Planning Method Based on Probabilistic Roadmap and Heuristic Rule  
In Section 2, we construct the complete optimal control model of PMCU pushing path planning. In order to solve this model, this section proposes an RPM path planning method based on a probabilistic roadmap and heuristic rule. Firstly, the optimal control model was constructed based on the PMCU process kinematics model, and the RPM algorithm was used to solve the problem. Aiming at the initial value sensitivity of the RPM, the probabilistic roadmap (PRM) algorithm is used to construct the passable path, and a path pruning heuristic rule with reverse search is proposed to further optimize the passable path generated by the PRM. Thus, high-quality initial value guesses are generated and input into the RPM algorithm to improve the quality of RPM path planning.
## 3 Materials and Methods
### HS-SPME-GC-MS Analysis  
For the VOC analysis, a slightly modified method optimized by [PERSON] et al. [40; 41] was used. A total of 15 g of pericarp tissue (without seeds) were homogenized with 15 mL 1M NaCl water solution in a 50 mL plastic tube using T25 Ultra-Turrax (IKA, Konigswinter, Germany) homogenizer. The obtained puree was frozen in liquid nitrogen and stored at \(-80\)\({}^{\circ}\)C. Three replicates per treatment were analyzed for both the red ripe stage with a different level of Se accumulation in fruit.  
VOCs were identified using a gas-chromatograph (Perkin Elmer Clarus 680) coupled with a mass spectrometer (Perkin Elmer Clarus 600 S). A total of 5 g of tomato puree was transferred into a 20 mL crimp vial (Sigma Aldrich, Milano, Italy) and was thawed at 15 \({}^{\circ}\)C for 15 min. Samples were incubated for 60 min at 40 \({}^{\circ}\)C, and VOCs were extracted for 45 min at the same temperature using an SPME Fiber (50/30 \(\upmu\)m, DVB/CAR/PDMS, Sigma Aldrich, Italy).  
We used the following GC temperature program: the initial temperature was 40 \({}^{\circ}\)C, which was maintained for 1 min; the temperature was then increased to 250 \({}^{\circ}\)C at a rate of 4 \({}^{\circ}\)C min\({}^{-1}\) and held for 1 min; finally, the temperature was increased to 280 \({}^{\circ}\)C at a rate of 15 \({}^{\circ}\)C min\({}^{-1}\) and held for 2 min.  
Desorption was performed in spitless mode using a PSSI injector at 260 \({}^{\circ}\)C. A fused silica 30 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m film thickness SLB-5 MS column was used for the analysis, and helium was used as a carrier gas at a constant flow of 1 mL min\({}^{-1}\). Chromataps were analyzed using AMDIS (National Institute of Standards and Technology, Gaithersburg, MD, United States), and compound identification was carried out by comparing detected spectra with NIST v. 2 (National Institute of Standards and Technology, United States). Only compounds with 80%, or higher, levels of matching were considered. The retention index (RI) information was used to increase the identification efficiency. VOCs were also compared with molecules in the literature on tomato fruit [35; 42]. In order to reducevariability due to the SPME fiber decay, the raw peak area was normalized on the sum of the areas for each specific chromatogram.
## 2 Materials and Methods
### Ground Truthing  
Agricultural field polygon data were obtained from the Ministry of Agriculture, Forestry and Fisheries, Japan (available at: [[https://www.maff.go.jp/j/tokei/porigon/](https://www.maff.go.jp/j/tokei/porigon/)]([https://www.maff.go.jp/j/tokei/porigon/](https://www.maff.go.jp/j/tokei/porigon/))) and used to draw the shape of each parcel of agricultural land on satellite images (Figure 1). To gather reference data (i.e., training and validation data) for image classification, we conducted field surveys in the study area. Crop types and land cover conditions were recorded in each field and period (November 2018 and February, May, July, and October 2019). Main crop types and practices were recorded to the agricultural field polygon data. The following classes were used: (1) Sugarane (ratoon), (2) sugarcane (spring planting), (3) sugarcane (summer planting in 2018), (4) sugarcane (summer planting in 2019), (5) pasture, (6) purple yam, (7) pineapple, (8) agricultural facility (e.g., vinyl greenhouse).  
The use of available agricultural field boundary data improved the accuracy of classification because it allowed us to eliminate the effect of spectral variability within the agricultural field and the mixed-pixel effect ([PERSON], 2018). Therefore, we used the agricultural field polygon data and extracted pure pixels corresponding only to the agricultural land. Sample sizes of reference data sets for each class were: (1) 36,902, (2) 12,671, (3) 17,743, (4) 2897, (5) 55,335, (6) 6628, (7) 874, and (8) 4281 pixels.
# Scalability of Water Property Measurements in Space and Time on a Brackish Archipelago Coast
## 2 Materials and Methods  
### Study Area  
The analyses were based on a pre-existing dataset from the SW Finnish archipelago [9]. The study covered an area of c. 2500 km\({}^{2}\), and it was c. 50 km and 70 km wide in the west-east and south-north directions, respectively. The study area was located in the northern limits of the temperate zone at 60\({}^{\circ}\) north, within the semi-enclosed Baltic Sea basin. The summer peak in primary production takes place typically in July and August.  
The area provided a good test case for the analysis, since it is very complex: it is shallow (0-100 m), has c. 25,000 islands (counting those with an area over 500 m\({}^{2}\)), is geologically and bathymrically varying and exhibits a strong land-sea transition. The  
Figure 1: Graphical workflow of the aims and approach of this study.  
area is further characterised by brackish water (salinity varying between 0 and 7% in this area along the land-sea-continuum [13, 28]), and seasonal sea ice cover (up to 100 days annually [29]). The SW Finnish archipelago has a clear seasonal cycle in physicochemical processes: a stratification develops in the summer and in the winter and is interrupted by vertical autumn and spring circulations. Moreover, there are very high concentrations of organic dissolved matter [30], and the archipelago is therefore highly eutrophic [31, 32].
# Numerical Study of Wave Effect on Aircraft Water-Landing Performance
## 3 Validation of Numerical Model
### Mesh and Time Step Size Convergence Studies  
The validation cases for the ditching problem have been achieved in our previous studies, including the high-speed flat plate ditching [28], wedge water-entry [40], aerospace capsule water-landing [41] and regional aircraft ditching [42]. The comparison between the numerical results and experimental data in these publications shows that the numerical framework employed in this study can provide results with satisfactory accuracy for the ditching problems.  
Further, in order to verify the correct implementation of the numerical model and to show that the selected mesh and time scale are suitable for a proper analysis, the mesh and time step convergence studies are conducted for the calm water landing event. As shown in Table 1, three different grid resolutions are adopted in the region of the air-water interface and the aircraft model. Figure 5 shows the results of the vertical and horizontal accelerations obtained by these three meshes with the same time step \(\Delta t=2\times 10^{-4}\) s. It can be seen that the solution converges asymptotically as cell number increases, and the overall time history and the peaks are very close by comparing the results of medium and fine meshes. Similarly, as shown in Table 2, the time step refinement study is carried out by varying time step size with the same medium mesh. Figure 6 presents the time-history results of the vertical and horizontal accelerations of the aircraft with different time step. It shows that the solution convergence is achieved as the time step decreases from \(2\times 10^{-4}\) s to \(1\times 10^{-4}\) s, especially in the peak region (zoom-in view) where the values tend consistent. Therefore, based on the consideration of high efficiency and accuracy of the simulation,  
Moreover, for the aircraft wave-landing simulation case, the grid resolution at the air-water interface is also guaranteed based on the results of numerical wave and mesh convergence validations. Figure 7 shows the computational global and zoom-in grid of the medium mesh. As can be seen in the second zoom-in view, the grid size in the \(x\) and \(z\) directions is about \(\Delta x=0.5\) m and \(\Delta z=0.05\) m. Accordingly, the quantified grid resolution is \(L/\Delta x=80\) and \(H/\Delta z=40\). It is comparable with the grid resolution in the section of numerical wave validation. Thus, the grid resolution adopted in the numerical model of the aircraft wave-landing case is able to predict accurate wave-landing characteristics.
## 4 Case Study for Signal Processing of Pulsed Thermography
### Advances of PPT  
Combining the characteristics and benefits of PT and LIT, PPT is a common NDT technique in various industries for inspection, including metal, non-metal, composite, concrete, and other materials. As an algorithm representing thermography with a long history, PPT has been applied as a signal processing technique for PT in almost all fields where TNDT is used. Applications of optical-infrared thermography are numerous and in different fields. Some typical applications include: detection of the subsurface defect and surface crack; evaluation of coating thickness; detection of inclusion and delamination in materials; evaluation of dissimilar metal joint, such as adhesively bonded and weld joints; detection of steel corrosion; internal defects NDT&E of aircraft components; evaluation of materials such as metals, non-metal and composite; inspection of the nuclear power plant, solar cells, and electrical installation control; and assessment of cultural heritage objects.  
Recently, in recent literature, studies have been conducted to detect and evaluate defects by modifying the PPT algorithm. In [56], two modified algorithms for PPT were defined. A first algorithm is defined using a frequency-dependent Gaussian window function. Furthermore, a second algorithm is defined using a rectangular window function. The algorithms were tested on synthetic signals modeling a subsurface cylindrical defect in a plate and experimentally by PT on steel and a polymer sample. As a result, a significantly enhanced contrast-to-noise ratio (CNR) from the defects was obtained for higher analysis frequencies.  
Thermal imaging cameras are very sensitive to noise and need to new processing methods in PPT algorithms. [PERSON] et al., inspired by [PERSON]'s work, investigated a feature-based approach based on single-signal reconstruction for defect detection. Although this approach was very sensitive to noise, promising results were obtained. In [57], a phase and time-domain tomography for defecting defects in composite materials was proposed. In general, PPT requires the evaluation of multiple-phase images at different frequencies to obtain an in-depth evaluation of the sample. However, the selection and subsequent evaluation of these images is cumbered by some and not always straightforward. In order to improve these shortcomings, [58] introduced an Adaptive Spectral Band Integration (ASBI) processing. The technique is more powerful than the PPT and offers a better SNR even for barely visible defects. It also returns a single index map of the defects for a given sequence, making it easier to interpret.
_Algorithms_**2015**, \(8\), 144-156; doi:10.3390/a8020144  
_algorithms_  
**ISSN 1999-4893**  
www.mdpi.com/journal/algorithms  
The Auxiliary Problem Principle with Self-Adaptive Penalty Parameter for Multi-Area Economic Dispatch Problem  
**[PERSON] * and [PERSON]**  
Key Laboratory of Measurement and Control of Complex Systems of Engineering, Ministry of Education, Southeast University, Nanjing, Jiangsu 210096, China; E-Mails: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: 86-25-8379-2719.  
Academic Editor: [PERSON]  
_Received: 28 January 2015 / Accepted: 13 April 2015 / Published: 22 April 2015_  
**Abstract:** The auxiliary problem principle is a powerful tool for solving multi-area economic dispatch problem. One of the main drawbacks of the auxiliary problem principle method is that the convergence performance depends on the selection of penalty parameter. In this paper, we propose a self-adaptive strategy to adjust penalty parameter based on the iterative information, the proposed approach is verified by two given test systems. The corresponding simulation results demonstrate that the proposed self-adaptive auxiliary problem principle iterative scheme is robust in terms of the selection of penalty parameter and has better convergence rate compared with the traditional auxiliary problem principle method.  
**Keywords:** multi-area economic dispatch; auxiliary problem principle; self-adaptive
# Research on a Rail Defect Location Method Based on a Single Mode Extraction Algorithm
## 4 Defect Location
### Simulation Analysis of Defect Location  
According to the results in Section 4.1, the rail with a defect in the railhead was stimulated. As shown in Figure 13, the rail length was 25 m; \(T\) represents the excitation point, which was located at 12 m along the rail. \(X\) represents the defect position, and the distance between the excitation point and \(X\) was \(l\) = 8 m. The simulation condition used was the same as the high-frequency excitation presented in Section 2.3.2. Table 2 shows the results of the modal identification.  
The spacings \(d\) between the sampling points and the excitation point were 1.5 m, 4.5, m and 6 m, and the collected signal waveforms are shown in Figure 14a  
Figure 12: Modal identification results under three excitation conditions (60 kHz).  
Figure 13: Schematic diagram of rail defect location.  
As a result of the complex propagation modes of guided waves in rails, only the first direct wave packet could be distinguished from the waveform. It was impossible to pick out which wave packet in the red circle was the reflected wave packet, and it was also impossible to know for certain which mode or modes each wave packet corresponded to; therefore, it was difficult to give the specific propagation time and the accurate mode group velocity. For this reason, the defect could not be precisely located.  
According to the SMEA, the reflected mode waveform of mode No. 7 at 4.5 m from excitation point was calculated in the frequency range 42-78 kHz. As shown in Figure 15b, the peak time \(t_{1}\) of the wave packet was 3.966 \(\times\) 10\({}^{-3}\) s. With the peak time \(t_{0}\) of the excitation wave packet (8.333 \(\times\) 10\({}^{-5}\) s) and the group velocity \(v_{0}\) of mode No. 7 (3148.2 m/s), it was possible to calculate the distance between the defect and the excitation point using Equation (11).  
\[l=\frac{(t_{1}-t_{0})*v_{0}+d}{2} \tag{11}\]  
The actual interval was 8 m and the calculation result was 8.36 m, giving an error of only 0.36 m.  
The reflection mode waveforms of mode No. 7 at 1.5 m and 6 m away from the excitation point were extracted, as shown in Figure 15a,c. The distances between the calculated defect and the excitation point were 8.33 m and 8.39 m. Therefore, the errors were 0.33 m and 0.39 m, respectively, both less than 0.5 m, and thus meeting the positioning accuracy requirements.