esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Preparation of BCs and Their Characteristics  
Hickory sawdust (HS) and peanut shells (PS) were selected as biochar feedstock followed by our previous study [15]. Four milligrams of biomasses were taken into a covered 20 mL ceramic crucible to create an oxygen-limited environment. Subsequently, the crucible was placed in a muffle furnace and pyrolyzed it at 350 \({}^{\circ}\)C or 700 \({}^{\circ}\)C for 2 h. Then, the resulting product was ground into powder using a mortar and pestle. Because the ash had a negative impact on the SMX adsorption onto the biochar, this powder underwent a washing process with 500 mL of deionized (DI) water at least five time to eliminate it until the water was no longer turbid and was subsequently dried in an oven at 60 \({}^{\circ}\)C overnight. The resulting biochars were labeled as 350 HSBC (Hickory sawdust biochar), 700 HSBC, 350 PSBC (peanut shells biochar), and 700 PSBC, respectively.  
The biochars were characterized with a S-4800 scanning electron microscope (Hitachi, Tokyo, Japan) at 10 kV from 400 to 70 K magnification. The Raman spectrum of biochars was conducted on the LabRAM Odyssey instrument (HORIBA, Paris, France). The zeta potential was tested on the NanoBrook 90 Plus PALS (Brukers, New York, NY, USA).
## 3 Results and Discussion
### Failure Mode and Stress Concentration Factor
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Crack Dip Angle (\({}^{\circ}\))**} & \multicolumn{2}{c}{**The Nominal**} & \multicolumn{2}{c}{**The Actual**} \\  & **Maximum Principal** & **Maximum Principal** & **Stress (MPa)** & **Stress Concentration** \\ \hline
15 & 97.1 & 573.8 & 5.91 \\
30 & 99.1 & 492.5 & 4.97 \\
45 & 92.7 & 447.2 & 4.82 \\
60 & 114.1 & 456.1 & 4.00 \\
75 & 126.9 & 435.3 & 3.43 \\
90 & 136.8 & 243.2 & 1.78 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Calculation results of maximum principal stresses and stress concentration factors. The distribution of the elastic strain energy density at UCS is shown in Figure 13. The influence of the pre-existing crack on energy distribution is mainly reflected in the crack tip and near the crack surface. Due to stress concentration, there is always a small range of strain energy storage at the crack tip. The energy distribution near the crack surface is less, indicating that the crack surface lacks constraint, and energy can be transferred to the interior of the pre-existing crack through displacement in the rock and released, thus being unable to accumulate. These energy distributions are related to stress concentration, and the elastic strain energy in the focused area reaches the fracture energy earlier, making it more prone to failure. When the crack dip angle is larger, the stress concentration factor decreases, and the energy distribution becomes more uniform. In this case, the crack propagation direction is also more likely to bifurcate.
## 2 Materials and Methods
### Leaf Area Index  
This study used the 4-day composite and 500 m resolution MODIS LAI product (MCD15A3H.061) [54] to indicate leaf phenology from GEE. For each study site, quality control was conducted according to the quality flags band (i.e., \"FparLai_QC\"). Then, the 4-day MODIS LAI was linearly interpolated to daily. After that, the time series of LAI data were smoothed using the modified Savitzky-Golay filter, sgolayfilt (degree = 3, windowSize = 71), in MATLAB R2021b.
# Sound Field Modeling Method and Key Imaging Technology of an Ultrasonic Phased Array: A Review
## 3 Imaging Technology
### Total Focusing Method
[62] reported that the synthetic aperture total focusing method (SATFM) can achieve higher resolution, but the imaging detection efficiency and real-time performance were not satisfactory. Later, the synthetic aperture method of Virtual Source Aperture (VSA) was proposed. The VSA not only increases the processing speed exponentially, but also maintains good lateral resolution and signal-to-noise ratio by comparing the processing speed parameters of the FMC/TFM, the SATFM [63; 64], and the VSA method [65]. The artificial defects of complex welding materials were detected and evaluated successfully using TFM [66; 67; 68], and the influence of thickness and material attenuation on volumetric inspection was researched using conventional and advanced TFM-based ultrasonic arrays [69]. Currently, The Eddyfi M2M Gekko ultrasonic flaw detector is a field-proven flaw detector that offers conventional UT, standards.PAUT, TOFD, and real-time TFM. [70]. With the improvement of industrial inspection requirements, 2-D imaging has been fully developed into 3-D imaging; the 3-D method has gradually become the current research hot spot of ultrasonic arrays [71]. [PERSON] et al. [72] used matrix transducers to conduct 1 TFM imaging of the internal defects of structural parts, which can realize 3-D imaging of internal defects without moving the transducer. Given the high price of the 2-D transducer, some researchers [73; 74] also use a 1-D linear array transducer to achieve 3-D TFM. TFM is at a critical stage of development; with the further increase in computer speed and acceleration algorithms, the advantages of the FMC data algorithm need to be fully exploited to construct detection and evaluation systems with fast and efficient simulation tools, high IO throughput acquisition units and a large-scale database of theoretical scattering coefficient matrices.
# Calibration of Sliding Friction Coefficient in DEM between Different Particles by Experiment
## 2 Experimental Methods
### Particles and Pad Materials  
In this study, spheres, ellipsoids, and polyhedron particles were made in cement, and embedded into cement plates as samples [46; 47]. The cement mortar samples were made according to a mass ratio of cement:water:lime paste:sand = 1:0.68:0.29:3.3. All particles were divided into nine groups according to different sizes and shapes. The group name, average size, and number of particles of each group are listed in Table 1. Under Table 1's group label heading, S, E, and P represent sphere, ellipsoid, and polyhedron, respectively; subscripts indicate the diameter of the spheres; maj and min subscripts indicate ellipsoid contacts with the major axis, and the ellipsoid contacts with the minor axis, respectively; the subscripts P, e, and f represent the point contact, edge contact, and face contact of polyhedron, respectively. The same naming method is used henceforth. Individual groups of particles were divided into two parts on average, the parts were then embedded into two cement plates, and the peaks of the outside part were kept horizontal, as shown in Figure 1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Group Label** & **Average Size (mm)** & **Number of Particles** \\ \hline S\({}_{3}\) & 29.62 & 32 \\ S\({}_{3,5}\) & 34.69 & 32 \\ S\({}_{4}\) & 40.08 & 32 \\ S\({}_{4,5}\) & 45.02 & 32 \\ S\({}_{5}\) & 49.72 & 32 \\ S\({}_{5,5}\) & 55.26 & 32 \\ S\({}_{6}\) & 60.12 & 32 \\ E\({}_{\text{maj}}\) & 60.30 \(\times\) 43.32 \(\times\) 43.32 & 32 \\ E\({}_{\text{min}}\) & 60.30 \(\times\) 43.32 \(\times\) 43.32 & 32 \\ P\({}_{\text{p}}\) & 40.24 & 32 \\ P\({}_{\text{e}}\) & 40.24 & 32 \\ P\({}_{\text{f}}\) & 40.24 & 32 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Average sizes, and number of particles for twelve particle groups.  
Figure 1: (**a**) Photograph of a block with embedded sphere particles; (**b**) Photograph of a block with embedded ellipsoid particles; (**c**) Photograph of a block with embedded cube particles.  
In order to measure the sliding friction coefficient between the particles and boundaries, four kinds of pad materials were applied, including wood board, glass board, crocus paper, and flint paper.
# Dual-Channel Mapping-Gas Column Concentration Inversion Method Based on Multispectral Imaging
## 4 Error Analysis
### Dual-Channel Mapping Error Analysis Experiment  
In the inversion process, dual-channel background radiation mapping is affected by many factors, such as calibration error, atmospheric change and detector noise. All of these factors become the main sources of error in the inversion process, which far outweigh the influence of other steps. Therefore, the accuracy of the background gray image obtained by the dual-channel background radiation mapping reflects the accuracy of the gas column concentration inversion to a certain extent. By using the gas column concentration inversion experimental platform, the background gray image taken when the target gas was not released was compared with the mapped background gray image to determine the accuracy of the dual-channel background radiation mapping. The comparison results are shown in the following figure.  
Figure 16a is the background grayscale image taken when the target gas is not released, Figure 16b is the background grayscale image obtained through the mapping relationship and Figure 16c is the grayscale value difference between the two. By comparing these images, it can be seen that the gray level difference of the two images is within \(\pm\)10, which proves that the gray level of the background grayscale image of the target spectral channel under the condition of no gas release is similar to that obtained through the mapping relationship, and the error is small. Further, a gas column concentration distribution image was obtained by calculating the background grayscale image of the target spectral channel without gas release and the background gray image obtained through the mapping relationship, respectively, as shown in Figure 17.  
Figure 15: (**a**) Gas release experimental scene; (**b**) gas release image of target spectral channel; (**c**) gas column concentration inversion image.  
Figure 17a is a column concentration image calculated from the background grayscale image of the target spectral channel without gas release, and Figure 17b is a column concentration image calculated from the background grayscale image obtained from the mapping relationship. The gas column concentration image has a high consistency, the column concentration of the background part without gas is close to 0, and the gas shape and concentration distribution in the figure are consistent. Figure 17c shows the ratio between Figure 17a,b. In order to directly reflect the accuracy of column concentration inversion, a column concentration ratio image was obtained by dividing the two column concentration distribution images. When the ratio of the two is closer to 1, the similarity between the two is higher and the error of the inversion method is smaller. From the ratio images, it can be seen that the ratio of the background part without gas is far greater or less than 1 because the column concentration is approaching 0, while the ratio of the part with gas is within the range of 0.9-1.1. Therefore, it can be inferred that the relative error of column concentration inversion is within \(\pm\)10%.
# Automatic Methodology for Forest Fire Mapping with SuperDove Imagery
## 2 Materials and Methods  
This section details the materials and methods used in this study. It includes a description of the images of burned areas and the ancillary data required for the analysis. Additionally, the vegetation indices tested on the SuperDove images are presented. Finally, the methodology employed to synthesize both burned-area and severity maps is outlined.
## 4 Results
### Clustering Validation Analysis
This falls into Scenario 0, and, therefore, it is not necessary to validate the results by means of the invasive relative majority decision fusion method. The second-largest group (9 CVIs) voted for two clusters, while only 2 CVIs voted for one cluster to be the \"true\" one. To summarize, the clustering algorithm identified three clusters, and the majority-based decision fusion method confirmed and justified this number of clusters. Moreover, the research confirms the crucial and significant differences between traditional and active learning modes. However, a moderate difference between STEM and STEAM approaches could be observed. Figure 5: A scatter plot of a three-dimensional clustered data set (379 data points). \begin{table}
\begin{tabular}{l l c c c} \hline \hline  & \multicolumn{5}{c}{**The Best Number of Clusters**} \\  & \multicolumn{5}{c}{**Based on the Critical Values of the CVIs**} \\ \hline
**No** & **Name of the CVI** & **Three Clusters** & **Two Clusters** & **One Cluster** \\ \hline
1. & Calinski–Harabasz & 10.18 & & \\
2. & Krzanowski and Lai & 689.74 & & \\
3. & Hatigan & 323.57 & & \\
4. & Cubic Clustering Criterion & & 24.54 & \\
5. & Scott & 562.30 & & \\
6. & Marriot & \(1.25\times 10^{15}\) & & \\
7. & TrCovW & & & \(8.6\times 10^{8}\) \\
8. & TraceW & 134,135.90 & & \\
9. & Friedman & 23.72 & & \\
10. & Rubin & \(-8.91\) & & \\
11. & C index & & & 0.17 \\
12. & Davies and Bouldin & & & 0.63 & \\
13. & Silhouette & & & 0.60 & \\
14. & [PERSON] & 0.79 & & \\
15. & PseudoT2 & 38.61 & & \\
16. & [PERSON] & & & 2.01 & \\
17. & [PERSON] & & & 0.53 & \\
18. & [PERSON] & 102,570.40 & & \\
19. & PtBiserial & & & 0.81 & \\
20. & [PERSON] & & & 2.77 & \\
21. & [PERSON] & & & 0.23 & \\
22. & [PERSON] & & & 0.12 & \\
23. & SDindex & 0.08 & & \\
24. & SDbw & 0.29 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The validation results of the 24 CVIs engaged in the voting process using the majority-based decision fusion methods. The presence of arts in STEAM pedagogy enhances and boosts students' general sensor-based knowledge, intrinsic relevance, and creativity more than a STEM-based approach in extracurricular learning-by-doing engineering courses in secondary school settings.
## 4 Solution Approach  
In order to clearly explain the approach, we introduce the related logic diagram (Figure 2). As shown in the figure, there are nine steps on the whole algorithm. Starting from \"Definition of Basic Production Line\", the initial information is prepared and defined. \"Initial Population Creation\" is the step used to randomly create the initial population that follows the given population parameters. The algorithm has a main loop whose ending policy is to check whether it has reached the maximum number of iteration times of the generations. During the main loop of the algorithm, individuals of the population in the current generation are decoded into feasible solutions through heuristic principles, and the two objectives (namely total investment cost and production rate) are then calculated for each solution. The total investment cost consists of machine tool and buffer capacity costs, while the production rate of each solution is estimated in a simulation environment. Hence, all the evaluated individuals in the current generation are sorted and ranked according to Pareto optimizing in NSGA-II [31]. Afterwards, some individuals from the worst solution set are eliminated to obtain the same population size and to form the final population of the current generation. Whenever the ending policy is not satisfied, the two NSGA-II operators (Crossover and Mutation) are introduced to generate the new population, which combines the current population with newly generated individuals. Therefore, the new population returns to the decoding step again, forming the main loop. After the ending policy is satisfied, the optimal results of the best solution set are finally presented.  
The heuristic approach in this paper is more focused on the two core steps highlighted in grey in Figure 2, which are \"Feasible Solutions by Decoding\" and \"Multi-objectives Calculation\". Therefore, this section is organized as follows. Firstly, Section 4.1 presents the new logic diagram to show the complete logic of the proposed approach. Then, Sections 4.2-4.4 describe in detail the three main steps of the logic diagram (new heuristic principles, pre-screening strategy, and simulation strategy), respectively. Finally, in order to ensure a better performance of the algorithm, all the parameters are fine tuned in Section 4.5.
## 3 Results and Discussion
### Effect of Crack Dip Angle on UCS and Energy Density  
Figure 8 presents the energy density and UCS of the cracked specimens with different crack dip angles. As the crack dip angle increases from 15\({}^{\circ}\) to 45\({}^{\circ}\), the UCS of the cracked specimens gradually decreases. The cracked specimen with a crack dip angle of 45\({}^{\circ}\) exhibits the lowest UCS, measuring only 113.44 MPa. When the crack dip angle exceeds 45\({}^{\circ}\), the UCS of the cracked specimens increases with the increase in the crack dip angle. When the crack dip angle is 90\({}^{\circ}\), the maximum UCS reaches 161.74 MPa. The cracked specimens can withstand greater loads when the UCS is higher, thus having a higher energy storage  
capacity. Therefore, the variation trend of the elastic strain energy density with the crack dip angle is the same as that of UCS.  
Due to the influence of cracks, stress concentration mainly occurs near the tips of a pre-existing crack, which is also where new microcracks are generated. The influence of the pre-existing crack on the UCS is reflected in two ways: (1) the cracks with different dip angles result in varying degrees of stress concentration, leading to the generation of different numbers of microcracks; (2) the initiation and closure of microcracks affect the magnitude of dissipated strain energy, thereby altering the ability of the cracked specimen to accumulate elastic strain energy.
## 4 Results and Discussion
### Emissions of CO, NOx and SO\({}_{2}\)
(2020), NOx emissions depend on a number of factors including the temperature, combustion time, volatile matter and burner configuration. The authors report that NOx emissions were over 700 ppm when coal was burned. Beech wood generated significantly less NOx in comparison to coal. On the other hand, the smallest values in the range of 251-269 ppm were obtained for torrefied beech wood. The authors noted a similar trend for the emissions of SO\({}_{2}\), which amounted, respectively, to 593, 13.47 and 1.07-1.98 ppm for coal, beech wood and beech biochar. The authors correlate this relationship with the lower content of siraka in biomass that has undergone torrefaction [77]. [PERSON] et al. (2017) found similar results in their study; they indicate the emissions of SO\({}_{2}\) during the combustion of coal and torrefied willow at 1184 and 2 ppm, respectively [78]. Numerous authors indicate, in turn, that burning mixtures of \"raw\" fuels such as biomass or coal with torrefied biomass can significantly reduce CO\({}_{2}\), SOx and NOx emissions into the atmosphere [79; 80; 81; 82]. Figure 4: _Cont._  
The obtained results allowed for the comparison of the energy potential of a wide group of production residues from the edible oil production sector. Additionally, the parameters of the pyrolysis process of waste biomass generated from six different types of oilseeds were optimized. The results achieved complement the existing knowledge on the impact of the pyrolysis process on gas emissions during the combustion of a wide group of biochars generated from oil cakes.
## 2 Materials and Methods
### Data Sources
#### 2.4.2 Spatiotemporal Data  
Incorporating real-time sensor data is an important aspect of the application's functionality, particularly for the purpose of visualizing hydrological data. To enable this, various REST APIs were integrated, facilitating the display of real-time data from third-party providers while accurately geolocating it within the application's terrain. The application relies not only on data fetched from external APIs but also on information available through the map provider.  
**USGS Discharge and Weather API:** One of the key components of the hydrological data integration is the utilization of the United States Geological Survey (USGS) REST API for water bodies. This API serves as a vital resource for accessing real-time data concerning water bodies across the United States, precisely pinpointed to their geographic locations. When a user loads a specific location within the application, the USGS API is invoked to retrieve pertinent information about the water bodies encompassed within a square area of 10 square kilometers. Subsequently, the application instantiates markers that are positioned at the exact geographic coordinates of these water bodies. These markers actively display dynamic data, including critical parameters such as discharge rate and flow velocity.  
In addition to hydrological data, real-time weather information plays a crucial role in enhancing the application's functionality. To this end, a weather API was thoughtfully integrated, offering continuous updates on meteorological conditions. Users can readily access this real-time weather data through a dedicated weather panel within the application. The weather panel is designed with user-friendliness in mind, presenting an array of weather-related information, including temperature and current weather conditions. Thesedetails are presented with an intuitive and visually engaging interface, featuring distinct icons that correspond to various weather conditions.  
**Incorporating Data Provided by Mapbox:** Within the application, we leverage Mapbox's diverse dataset, offering users the choice between terrain views, including both street and satellite perspectives, depending on their preferences. This versatility extends to the representation of urban landscapes, with a rich assortment of detailed building data that further enhances the user experience. To ensure users have an enhanced understanding of urban conditions, we integrate live traffic data from Mapbox. This dynamic feature displays traffic information on roadways, categorizing congestion levels into three distinct tiers: severe, moderate, and low. This aids users in planning their hydrological exploration routes effectively and efficiently. Additionally, we do not limit our geographical scope to land; the application also incorporates bathymetry data, extending our visualization capabilities to underwater landscapes, further broadening our understanding of hydrological systems and the interconnectedness of water bodies.