esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Background
### Spectral Variability of the Plane of Substrates  
Linear mixture models [30; 31; 32] are a common way to represent multispectral and hyperspectral data as linear combinations of spectral EMs. The three most common endmembers used for these models are soil and rock substrates, green vegetation, and dark or unilluminated materials (e.g., [33]). Non-photosynthetic vegetation is also commonly added as a fourth EM.  
Of these EMs, the majority of the multispectral variability on a global scale is observed in the substrate [14; 15]. This is due to the wide range of physical, chemical, biological, and textural properties present in soil, sediment, and rock. Reflectance spectroscopy of substrates is complex. Substrate reflectance spectra can vary in at least three ways: in continuum shape, in broad absorptions (e.g., Fe), and in narrow absorptions (e.g., some clays). Broadband imagery can only be expected to capture the coarsest of these features. For a comprehensive treatment of soil reflectance (a complex subset of the plane of substrates), see the seminal work of [34], more recent analyses by [35; 36], and reviews by [37; 38].  
Early work on the hyperspectral dimensionality of the plane of substrates focused on full range Visible to SWIR (VSWIR) laboratory spectra [39] and Visible to NIR (VNIR) field spectra [40] of soils. Further laboratory VNIR characterization of a wide range of soils was performed by [41], although the dimensionality of the dataset was not discussed. The question of how the full diversity of hyperspectral VSWIR substrates (including soil, rock, and sediment) are represented in the spectral feature space, as well as how the signal is degraded when undersampled by a multispectral imager, remains open. The present study addresses this question by directly comparing substrate spectra from simultaneously acquired VSWIR hyperspectral and multispectral data, as well as by presenting a parallel analysis of 161 soil and rock laboratory spectra.  
Portions of these three questions have been investigated previously. The questions of data dimensionality and generality of the feature space have been addressed independently for hyperspectral and multispectral datasets, but not simultaneously for both. Variability of the plane of substrates has been studied by the analysis of libraries of laboratory and field spectra. However, to our knowledge, none of these three questions have been addressed using simultaneous, independently imaged multispectral and hyperspectral observations over large areas of a spectrally diverse landscape.
# Development of Knowledge Graph for Data Management Related to Flooding Disasters Using Open Data
## 4 Experimental Results and Discussion  
We performed an experiment to evaluate the proposed flooding disaster knowledge graph. The experiment aimed to show the usability and the interoperability of search results. Accordingly, the experiment compared the search results of open datasets (data.go.kr), Wikidata, and our knowledge graph according to the query types. We defined simple query types using the concept and relation of the knowledge graph. The query types also consisted of a few questions, but these questions cannot be directly queried from the experimental data. Therefore, we translated each suitable query language or retrieval method such as keyword mapping or manual searching. Table 2 shows three questions and query results. Figure 3: Principal knowledge graph with Flooding based on the open data. The result of Q1, which is a question to find a concept, showed 21 datasets as the search results when the open datasets were searched for flooding disaster data. We can find datasets such as Namdong-gu, Incheon_Status of flood risk areas, and Korea National Territory Information Corporation Flooding trace information flooding level line (annual). Wikidata returned 338 triples, whereas the flooding disaster knowledge graph returned 360 triples, including Wikidata triples. Q2 and Q3 are questions about the relationship between the data. In the case of open datasets, no query results were provided for Q2 and Q3 because no relation existed between the data. However, Wikidata showed five search results in Q2, whereas the flooding disaster knowledge graph returned more query results, including the search results of Wikidata. In the case of Q3, our knowledge graph returned more query results than did Wikidata. We proposed a model by reusing and extending the existing knowledge graph. In summary, the proposed model provided more information and improved the interoperability in the flooding disaster domain. The knowledge graphs shown here were based on the data written in ASCII format. However, many more data on natural disasters have been written in binary format. Many operational weather/environment service centers in the world generate high-resolution global weather and environmental prediction data from their numerical forecasting models every day. We have an increasing number of data obtained from remote sensing instruments. Undoubtedly, the numerical forecast and satellite data from remote sensing instruments are useful, vast, and qualified for studies in the field of natural disasters. However, they are usually written in NetCDF and/or HDF5 formats, which are forms of binary format, and we did not collect any binary data for the knowledge graph development in
# A Design of a Time Synchronization Protocol Based on Dynamic Route and Forwarding Certification
## 4 Realization of DRFC-TSP on an Embedded Linux Platform  
### Development and Testing Environment  
The protocol program is developed and compiled on Ubuntu 12.04, and then implemented in the embedded system with Linux Kernel 2.6. Nodes communicate via the Wi-Fi wireless card with the IEEE 802.11 g communication protocol, which works in a 2.4 GHz public frequency band and reaches the maximum transfer rate of 20 Mbit/s or more. An ad-hoc network with the Smart210 Linux embedded platform is integrated, which communicates by Wi-Fi, the ad hoc on-demand distance vector routing (AODV) [22] WSN routing protocol, and the IP routing protocol embedded in the Linux operating system [23]. The User Datagram Protocol (UDP) communication protocol is adopted. In socket network programming, the time information in the synchronization process is acquired by calling relevant time system functions. Specifically, the data structure Timeval of the Linux kernel is used, and two functions, consisting of gettimeofday() and settimeofday(), are called to obtain and modify the system time.
# Applications of Probabilistic Forecasting in Smart Grids: A Review
## 3 Renewable Generation and Load Probabilistic Forecasting  
In general, probabilistic forecasting methods have mainly been adopted to forecast the probability distributions of renewable-based power generation and/or load in smart grids. The following sub-sections aim to review important studies that proposed parametric and non-parametric probabilistic forecasts for energy demand and/or generation in a smart grid environment. Additionally, Tables 2-4 review some selected papers that tried to develop probabilistic forecasting models for solar generation, wind generation and loads, respectively.
## 5 Conclusions  
Two metasurface designs (CSRR and circular disk) are proposed for designing a two-port MIMO antenna. The CSRR metasurface MIMO antenna design shows better reflectance and isolation than the circular disk MIMO antenna. The MIMO antenna design is optimized for variation in the inner ring diameter and outer ring diameter for the etched split ring resonator. The optimized value of the inner ring diameter is 6 mm, and the outer ring diameter is 8 mm. The ground region width also varied, and an optimized value of 8 mm width is obtained. The highest gain of 5.34 dB is obtained for the CSRR metasurface MIMO antenna design. The directivity of the CSRR metasurface MIMO antenna is higher than the other antenna. The comparison of different published methods also shows that the highest isolation of 55 dB is attained for the CSRR metasurface MIMO antenna. The performance measurement parameters were also analyzed for the proposed design in terms of _TARC_, _ECC_, _DG_, and CCL. The overall complexity of the design is also less than in other works. The design is applicable for C and X band applications.  
Conceptualization, A.A. and S.K.P.; methodology, A.A. and S.K.P.; software, S.K.P. and A.A.; validation, A.A., S.L., S.K.P. and S.Q.; formal analysis, A.A., M.A., F.A., S.Q. and K.A.; investigation, M.A. and M.G.D.; resources, A.A., S.K.P. and S.L.; writing--original draft preparation, all authors.; writing--review and editing, A.A., K.A., F.A., A.A. and S.K.P.; visualization, S.L., S.Q. and M.A.; supervision, A.A. and S.K.P. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the Deanship of Scientific Research at Jouf University under grant number DSR2022-RG-0111. The data will be made available at a reasonable request to the corresponding author.  
The authors declare no conflict of interest.
## 5 Experiments and Discussion
### Error Analysis and Calibration  
During micro-vibration testing, the noise, which is generated by the external mechanical environment and the electronic components inside the system, is the main factor affecting the accuracy of HPSOP testing. According to the distribution characteristics, the errors in micro-vibration testing could be divided into systematic errors and random errors. For systematic errors, the gray value deviation errors of the CCD pixel and the suspension apparatus (AQZSSD) are the main parts, where the errors of CCD could be eliminated by image processing algorithms like the grayscale centroid method, and the errors caused by the low-frequency disturbance peak could be calibrated by using a high-pass filter algorithm or LOS stabilization equipment. For random errors, mechanical noise generated by complex the mechanical environments and the noise generated by electronic components such as CCD are the main components. Compared with systematic errors, random errors are usually difficult to eliminate because of the characteristics of randomness and wide bandwidth. Therefore, to evaluate the random errors, it is necessary to conduct noise testing before micro-vibration testing and calculate the random errors by subtracting systematic errors from the CCD responses. For the micro-vibration test system used in this paper, as mentioned before, the systematic errors appear at 0.4 Hz, while the RMS value of systematic errors is less than the pointing accuracy of 0.1 \(\upmu\)rad, according to the static test results.
# Prediction Equation for Horizontal Ground Motion Peak Values in the Loess Plateau Region
## 2 Methods and Data
### Correlation Analysis of PGA and PGV in the Loess Plateau Region  
The relationship between PGA (unit: gal) and PGV (unit: cm/s) in the Loess Plateau region is expressed as follows:  
\[\rm PGA=6.023+18.067\times\rm PGV \tag{3}\]  
This equation represents a statistical relationship between PGA and PGV rather than a physical conversion. Equation (3) applies to the Loess Plateau region of China for earthquakes with magnitudes (\(M_{\rm S}\)) ranging from 3.0 to 6.5 and epicentral distances between 0 and 200 km.  
Figure 3 depicts the distribution of PGA and PGV concerning the epicentral distance in the region while Figure 4 illustrates the fitted relationship between PGA and PGV. In Figure 4, PGV is plotted on the horizontal axis (unit: cm/s) and PGA on the vertical axis (unit: gal, equivalent to cm/s\({}^{2}\)). The regression line in Figure 4, based on Equation (3), reveals a linear relationship between PGA and PGV, with the slope of 18.067 representing the rate at which PGA increases with PGV and the intercept of 6.023 indicating the baseline  
Figure 2: Magnitude-epicentral distance distribution of strong motion records in the Loess Plateau region.  
PGA when PGV is negligible. This relationship allows straightforward estimation of one parameter from the other, providing practical insights into seismic ground motion in the Loess Plateau region.
## 4 Review and Status of Cryogenic Laser Technology  
In the past ten years, enormous progress has been made in our fundamental understanding of cryogenic lasers. Our increased knowledge of the fundamental materials properties of laser crystals at cryogenic temperatures has enabled the demonstration of ever more powerful cryogenic laser sources. The already demonstrated compelling performance advantages of cryogenic lasers has led to the adoption of the technology by many researchers around the world, and have garnered attention in the commercial and military laser communities as well. While the acceptance of cryogenic lasers in commercial and military applications has been delayed, due in large part to the need for the acceptance of cryogens that add complexity and cost, the technology has now been fully embraced by the scientific community because of the many performance advantages that can be obtained, and the need for HAPP lasers with near-diffraction-limited beam quality.
## 6 The Multilayer Plate Concept in the Numerical Calculus by FEM of FGPs
### Calculus of the Free Vibration Frequencies
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Power** & **Freq.** & \(\alpha_{i}\) & \(D^{\star}\) & \(\rho^{\star}\) & \(\omega^{\star}\) & \(f^{\star}\) \\
**coef.** & **No.** & **-** & **[Nm]** & **[kg/m\({}^{3}\)]** & **[rad/s]** & **[Hz]** \\ \hline \multirow{4}{*}{\(k=0.05\)} & 1 & 10.217 & \multirow{4}{*}{2115.80} & \multirow{4}{*}{3900.84} & 47,589.57 & 7574.12 \\  & 2 & 21.22 & 2115.80 & & 98,840.24 & 15,730.93 \\  & 3 & 34.84 & & & 162,280.58 & 25,827.78 \\ \hline \multirow{4}{*}{\(k=0.50\)} & 1 & 10.217 & \multirow{4}{*}{1449.26} & \multirow{4}{*}{3539.99} & 41,345.24 & 6580.30 \\  & 2 & 21.22 & 2149.26 & & 85,871.20 & 13,666.84 \\  & 3 & 34.84 & & & 140,987.39 & 22,438.86 \\ \hline \multirow{4}{*}{\(k=1.00\)} & 1 & 10.217 & \multirow{4}{*}{3330.00} & 37,326.03 & 5940.63 \\  & 2 & 21.22 & 1111.12 & & 77,523.57 & 12,338.27 \\  & 3 & 34.84 & & & 127,281.86 & 20,257.55 \\ \hline \multirow{4}{*}{\(k=2.00\)} & 1 & 10.217 & \multirow{4}{*}{865.58} & \multirow{4}{*}{3120.00} & 34,035.31 & 5416.89 \\  & 2 & 21.22 & 2865.58 & & 70,688.98 & 11,250.51 \\  & 3 & 34.84 & & & 116,060.50 & 18,471.62 \\ \hline \multirow{4}{*}{\(k=4.00\)} & 1 & 10.217 & \multirow{4}{*}{751.98} & \multirow{4}{*}{2952.00} & 32,613.57 & 51,90.62 \\  & 2 & 21.22 & 2751.98 & & 67,736.12 & 10,780.55 \\  & 3 & 34.84 & & & 111,212.37 & 17,700.01 \\ \hline \multirow{4}{*}{\(k=6.00\)} & 1 & 10.217 & \multirow{4}{*}{713.71} & \multirow{4}{*}{2880.00} & 32,167.55 & 5119.63 \\  & 2 & 21.22 & 2713.71 & & 66,809.78 & 10,633.12 \\  & 3 & 34.84 & & & 109,691.45 & 17,457.95 \\ \hline \multirow{4}{*}{\(k=8.00\)} & 1 & 10.217 & \multirow{4}{*}{686.24} & \multirow{4}{*}{2839.99} & 31,763.84 & 5055.38 \\  & 2 & 21.22 & 2839.99 & & 65,971.29 & 10,499.67 \\  & 3 & 34.84 & & & 108,314.79 & 17,238.85 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Some results of natural frequencies by analytical calculus. Figure 22: Natural frequencies versus power coefficient \(k\). The analysis of the results in Table 12 and the graphs in Figure 23 shows that Model_I (ordinary finite element) lead to the results closest to the analytical calculus regarding the first three frequencies. The coincidence of the three curves is the best for the first frequency. The shapes of the free vibration frequencies for those two models used (Model_I, Model_II) are presented in Figures 24-26.
# Infrared Ship Target Detection Based on Dual Channel Segmentation Combined with Multiple Features
## 4 Experimental Results and Analysis  
### Introduction of Data Sets and Comparison Algorithms  
The proposed algorithm in this paper was tested using six sets of video sequences. Figure 12 shows typical images from each set, and Table 1 provides specific information about each set.  
In this paper, the algorithms proposed in references [20; 28; 29] were selected as comparative methods.  
Ref. [20] proposes a self-adaptive segmentation algorithm for infrared ship targets against strong sunlight reflection background (SRISTAS). After applying mean-shift filtering to the image, it calculates the mean of the vertical gradient of each row, and compares the difference between the mean gradients of two rows separated by ten rows to locate rows with large gradients. Regions are then obtained by expanding from the identified rows, and a self-adaptive segmentation based on mean values in rows and columns is performed, respectively. Finally, the algorithm imposes restrictions on aspect ratio and area for the final results.  
Figure 11: The dual channel segmentation process.  
Ref. [28] proposes an algorithm named infrared target detection in backlighting maritime environment based on the visual attention model (ITDBE), which enhances the attention model. After preprocessing the image with Gaussian difference, the algorithm selects the layer with the most prominent target saliency from the central-surround differences of each layer and performs segmentation using the maximum between-class variance method (OTSU). A regional growth method is used to preserve the size of the target as much as possible.  
[PERSON]. [29] introduces an algorithm called infrared small ship detection in sea clutter based on morphological reconstruction and multi-feature analysis (ISSD). It preprocesses the image using grayscale morphological reconstruction and obtains a saliency map by multiplying the brightness-contrast map and the intensity foreground saliency map. An adaptive segmentation method is then applied, followed by the incorporation of geometric feature quantities to eliminate false alarms.
## 4 The Flow Characteristics of Two Inlet Configurations under High Maneuver Condition
### Analysis of Steady Flow Characteristics
In the large sideslip flight state, compared with the conventional submerged inlet configuration, \(\sigma\) at the outlet of the distributed submerged inlet configuration is increased by 44.2%, and \(D_{1}\) is reduced by 66.3%. In order to better reveal the formation and development mechanism of flow in the submerged inlet under large sideslip condition, and Figure 12 lists the main flow characteristics in the inlet under typical sideslip angles. It can be seen from Figure 12a that when \(\beta=-21^{\circ}\), the side edges on both sides of the inlet of the conventional submerged inlet can generate entrainment vortices. However, due to the different angles of the airflow passing through the side edges on both sides, the intensity of the entrainment vortices generated by the side edges with the same direction as the incoming flow is weak, and part of the airflow is not drawn into the inlet. With the further increase of \(\beta\), the intensity of the entrainment vortices generated by the side edges with the same direction of the incoming flow gradually weakens. It can be seen from the Figure 12b-d that when the absolute value of \(\beta\) is greater than \(24^{\circ}\), the entrainment vortices beneficial to the intake cannot be generated, and at the same time, there is obvious air separation. An obvious low total pressure area can be observed at the outlet of inlet. The area of the low total pressure area accounts for about one third of the total area. The same problem is also encountered at the side edges of the main inlet of the distributed inlet configuration. However, since the auxiliary inlets on both sides have a certain windward surface during sideslip maneuver flight, which can provide high energy airflow for the intake system to make up for the deficiencies of the inlet during large sideslip flight, no obvious low-pressure area is observed at the outlet of the inlet, and \(\sigma\) is above 0.8.
## 4 Seismic Responses Analysis of the Rock-Socketed Pile under the Influence of Multiple Kast Caves
### Analysis of the Rock-Socket under the Conditions of the Beaded Karst Cave and Underlying Karst Cave  
In the actual engineering situation, karst caves are always likely to appear at the bottom of the pile string. In this section, karst caves with a height of 4 m and a width of 8 m were selected, which were divided into pile passing karst caves and underlying karst caves. The through pile karst cave was located at the base elevation with a buried depth of 98 m, while the underlying karst cave was located at the buried depth of 106 m, 108 m, 110 m, and 114 m, respectively. In order to study the pile foundation problem under the coexistence of different karst caves, this section introduces a distance coefficient, which is defined as the ratio of the height of the underlying karst cave roof to the bottom plate of the pile passing karst cave to the height of the karst cave to characterize the mutual position relationship of different karst caves. In this section, the distance between the top plate of the underlying karst cave and the bottom plate of the pile passing karst cave was 4 m, 6 m, 8 m, and 12 m, while the distance coefficients (DC) relative to the height of the pile passing karst cave were 1, 1.5, 2, and 3, respectively. The influence of the existence of the underlying karst cave on the strain distribution law of the rock-socketed pile was further explored. The schematic diagram of the finite element model is shown in Figure 9. The distribution law of peak strain of pile shaft with buried depth under different working conditions is shown in Figure 10.  
By analyzing the strain peak and curve strain law of the finite element model of the underlying karst cave at different distances, it can be seen that under the action of vertical load and seismic load, the existence of the underlying karst cave reduced the restraint effect of the bedrock at the bottom, resulting in the increase of the peak strain of pile until a certain extent. The distance between the underlying karst cave and the pile is an important parameter that affects the strain of the pile. When the DC of the underlying karst cave is 1, the influence of pile strain decreased with the increase of the relative DC. When the DC of the relative through pile karst cave exceeded 2, the existence of the underlying karst cave had little effect on the pile strain. Under the condition that the distance between the karst cave under the joint influence of the pile passing and the underlying karst cave is small, the existence of the karst cave limited the restraint effect of the bedrock at the bottom. Therefore, it is recommended to strengthen the bedrock between the pile foundation and the karst cave by grouting.