esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Study Area and Data
### Hj-1A/B Overview
#### 2.2.2 Data  
The HJ-1A/B images can be obtained through the China Centre for Resources Satellite Data and Application (CRESDA) website for free, and the relevant information is available at [33] with the interface in English. The images distributed by CRESDA were Level 2 products (after systematic geometric calibration). The metadata and observation azimuth information can also be obtained  
\begin{table}
\begin{tabular}{l l l} \hline \hline \multicolumn{1}{c}{**HJ-1A/B CCD**} & **Landsat TM/ETM** \\ \hline Spatial resolution (m) & 30 & 30 \\ Spectral bands & 4 & 7 \\ Revisiting period (days) & 2 & 16 \\ Central wavelength (nm) & 475; 560; 660; 830 & 485; 565; 665; 820; 1650; 2190; 11,400 \\ Field of view (km) & 360 (700 for 2 CCD) & 180 \\ Launch date & 2008 & 1984 Landsat 5; 1999 Landsat 7 \\ \hline \end{tabular}
\end{table}
Table 1: Comparison of the characteristics between HJ-1A/B CCD and Landsat TM/ETM.  
Figure 2: Relative spectral response functions (RSF) for HJ-1A/B CCD cameras ((**a–d**) are HJ-1A CCD1, HJ-1A CCD2, HJ-1B CCD1, HJ-1B CCD1, HJ-1B CCD2, respectively), Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and MODIS.  
together with the imagery. Because of the wide coverage of HJ-1A/B image (_i.e._, 360 km), the overlapping area of adjacent orbit is very large, with an overlapping rate of nearly 87.1% at the equatorial regions [28]. The high overlap rate means that images from adjacent orbits can also be used for the image composition. In this paper, in total, 183 scenes of images from the year 2010 with the paths 14-19 and row 76 were selected for the study area.
## 3 Results and Discussion
The length of the module reveals only 16 mm, making it applicable in setups where only limited space is available. The functionality of the finalized beam shaping module is also tested while Figure 7b depicts the resulting Top-Hat profile. Hence, we have successfully demonstrated the fabrication of a beam shaping module revealing a good Top-Hat quality while different laser-based processes are used i.e., laser ablation, laser polishing, laser remote cutting and laser 3D printing.
# Multiscenario Simulation and Prediction of Land Use in Huaibei City Based on CLUE-S and PLUS Models
## 4 Discussion
Therefore, future scenarios need to be set up using local realities [7]. The current planning of Huaibei City, which is transformed by the resource city, has shown problems such as lagging strategic LU targets, ecological and farmland protection to be upgraded, and unreasonable spatial layout of the landscape. These problems are inconsistent with the fast-developing socioeconomic environment, and the planning needs to be adjusted and perfected promptly [42]. The four scenarios set in this context can provide an understanding of the evolutionary trends of LU patterns in the future. Among the four future scenarios, farmland and forestland continue to decrease with conversion to construction land under scenario NG, which does not meet the relevant planning requirements; future over-expansion of construction land will, therefore, need to be curbed [43]. The FEP scenario restrains the growth of construction land and protects ecological land and farmland. The occupation of farmland and forestland will be controlled basically in the future with the delineation of the three control lines. However, due to the high degree of LU in Huaibei City and the need to protect the demand for new urbanization land, conflicts between supply and demand for construction land may arise in a short period. For this reason, scientific and reasonable planning of the land spatial distribution is needed in future development in conjunction with the current situation to improve the level of land conservation and intensification. Shortcomings remain in this study. The PLUS model parameter settings are determined by continuous debugging and are subjective in nature. The quality and availability of data are also related to simulation accuracy. The future multiscenario prediction process mainly combines previous studies and LU planning policies to subjectively adjust the transfer probability matrix and parameters to model future LUs. Restricted areas, such as basic conservation farmland, were not included. Therefore, we should seek a more scientific and reasonable method in future studies.
## 2 The Proposed Approach
### Specific Steps of the Proposed Approach
Make the judgment of each alternative. \[Pr(C|[A_{i}])\geq\alpha_{i}\quad\text{Decide}:\ A_{i}\in POS(C);\] (8)  
\[\beta_{i}<\Pr(C|[A_{i}])<\alpha_{i}\quad\text{Decide}:\ A_{i}\in BND(C); \tag{9}\]  
\[Pr(C|[A_{i}])\leq\beta_{i}\quad\text{Decide}:\ A_{i}\in NEG(C). \tag{10}\]  
\(Pr(C|[A_{i}])\) represents the conditional probability of the alternative AM machine \(A_{i}\). \(Pr(C|[A_{i}])\) can be calculated using the method in [66; 67; 68]. By comparing the conditional probability \(Pr(C|[A_{i}])\) with the comprehensive thresholds \(\alpha_{i}\) and \(\beta_{i}\) of the alternative \(A_{i}\), the judgment of the alternative \(A_{i}\) is made: acceptance (\(POS(C)\)), noncommitment (\(BND(C)\)), or rejection(\(NEG(C)\)).
# Patent Analysis Using Bayesian Data Analysis and Network Modeling
## 3 Proposed Method  
### Technical Analysis Procedure  
For drone technology analysis, we analyzed the patent document data related to drone technology using patent keyword visualization and BART modeling. The following procedure represents all steps of our drone technology analysis from collecting patent documents to performing patent data analysis.  
1. Collecting Drone Patent Documents (1-1) Searching patent documents related to drone technology from patent databases. (1-2) Removing noise from searched patent documents to select valid patents.
2. Preprocessing Valid Patent Data (2-1) Representing collection of text documents from valid patent data. (2-2) Parsing text collection to build text database (DB). (2-3) Constructing the patent-keyword matrix as structured data.
3. Visualizing Patent Keywords (3-1) Generating word cloud for finding top frequency keywords. (3-2) Making correlation networks between all keywords. (3-3) Calculating degree values of all keywords for ranking of core keywords.
4. Performing Bayesian Additive Regression Trees (BART) model (4-1) Comparing p-values of explanatory keywords using a multiple regression model. (4-2) Selecting important keywords using BART modeling. (4-3) Building technology scenarios of drone using all results.  
Our proposed method consists of four steps from searching drone patents to building technology scenarios. In Step 2, we constructed a patent-keyword matrix using text-mining techniques. The visualization of word cloud and correlation networks requires this matrix data. In addition, the multiple regression and BART models need the matrix type based on table structure of DB. Using the results of Steps 3 and 4, we selected the important keywords for developing drone technology. In step (4-3), we built technology scenarios of drones using the results of visualization and analytical modeling. The technology scenarios can be applied to technology management of drones such as R&D planning and new service development of the drone system.
# Simulation Framework for Detection and Localization in Integrated Sensing and Communication Systems
## 1 Introduction
Since there is still a lack of contributions addressing the abstraction of the physical layer in the ISAC framework, this paper aims to fill this gap with the following contributions:  
* A background on PLA, emphasizing the rationale behind the approach and revitalizing key elements from the existing literature. * A description of the proposed PLA method's essential elements, focusing on those utilized in previous technologies, including channel generation and Signal to Noise Ratio (SNR). * Two sensing services are considered, target location and detection, whose algorithms are formulated as examples of the PLA method. * An evaluation of the sensing algorithms' performance, offering quantitative insights. * The mentioned evaluation is used to abstract the performance metrics of the algorithms. As a result of this abstraction process, a set of lookup tables is generated, which can be incorporated into system-level simulations. The search tables are submitted to a repository available in [16]. _Notation:_ Matrices are denoted by bold uppercase letters, vectors are defined by bold lowercase letters, and the standard font represents scalars. The transpose conjugate is represented as (\({}^{*}\)).
# Forewarning Model for Glacial Lake Outburst in Southeast Tibet
## 2 Materials and Method
### Data Sources  
The data regarding glacial lakes in the study area were obtained from the 2008-2017 High Asia 30 m resolution annual glacial lake data established by the Institute of Aerospace Information Innovation, Chinese Academy of Sciences. This data set integrates images from Landsat and high-resolution satellites, and uses adaptive threshold methods and artificial digitization methods to provide high-resolution Asian glacial lake products of 30 m resolution at an annual scale, including the location, type (four categories), altitude, distance from the parent glacier, area, perimeter, and other information.  
Information on the slope of the glacier, slope of the glacier tongue, and dam backwater slope was acquired from the National Tibetan Plateau Scientific Data Center. This data set is mainly the fourth version of the SRTM terrain data obtained by CIAT (International Center for Tropical Agriculture) using a new interpolation algorithm. The interpolation algorithm was used to fill in the data missing from SRTM90 [20].
## 5 Energy-Efficient Transmission Strategies  
### Energy Consumption  
It is assumed that broadcast packets are used and that all of the \(N_{H}\) LSAs of the HAPs and the one GEO satellite are received by the terminal in the network. Over a specified time period \(T\in\mathbb{R}^{+}\), the energy consumption of the terminal for the T-S direct link can be calculated as the integral of the transmission power over that period, which can be written as:  
\[E_{d}=\int_{0}^{T}P_{s,1}(t)dt+e_{Rx,LSA} \tag{19}\]  
where \(e_{Rx,LSA}=P_{Rx}T_{LSA}\) represents the energy consumption to process the broadcast LSA of the satellite and \(T_{LSA}\) is the total time taken for the LSA transmission in the time period \(T\). \(P_{Rx}\) denotes the necessary power to process the demodulation of the received radio-frequency (RF) signal, baseband signal and coding and decoding.  
For the T-H-S relay path, the energy consumption consists of the T-H link transmission energy at the terminal end and the H-S link transmission energy at the HAP end, which can be respectively represented as:  
\[E_{T}=\int_{0}^{T}P_{s,2}(t)dt+\left(N_{H}+1\right)e_{Rx,LSA} \tag{20}\]\[E_{H}=\int_{0}^{T}P_{s,3}(t)dt+P_{Rx}T+e_{Tx,LSA} \tag{21}\]  
where \(e_{Tx,LSA}=P_{Tx}T_{LSA}\) is the energy consumption for broadcasting packets of the LSA and \(P_{Tx}\) denotes the transmission power of the LSA from the HAP to the terminal.  
Assuming that the terminal can only select one relay HAP during one time slot \(\Delta t\), the overall energy consumption of the T-H-S relay path can be given by:  
\[\begin{split} E_{r}=E_{T}+E_{H}=&\int_{0}^{\Delta t }P_{s,2}(t)dt+\int_{0}^{\Delta t}P_{s,3}(t)dt\\ &+\left(N_{H}+1\right)e_{Rx,LSA}+P_{Rx}\Delta t+e_{Tx,LSA}\end{split} \tag{22}\]  
For the \(i\)-th terminal, the HAP should be selected that has the lowest energy consumption between \(E_{d,i}\) of the T-S link and \(E_{r,i}\) of the T-H-S path. Therefore, the optimal transmission path \(v_{i}^{\mathit{opt.}}\) in a single time slot can be represented as:  
\[v_{i}^{\mathit{opt.}}=\left\{v_{i}\leftrightarrow E_{i}=\min(E_{d,i},E_{r,i}) \right\},i\in N_{T} \tag{23}\]  
If the number of terminals is \(N_{T}\), then the energy consumption of \(N_{T}\) terminals and the energy consumption of the overall network can be respectively represented as:  
\[E_{T,tot.}=\sum_{j=1}^{T/\Delta t}\sum_{i=1}^{N_{T}}E_{T,i,j} \tag{24}\]  
\[E_{tot.}=\sum_{j=1}^{T/\Delta t}\sum_{i=1}^{N_{T}}E_{r,i,j} \tag{25}\]
# Leveraging Blockchain Technology for Secure 5G Offloading Processes +
## 4 Performance Evaluation  
The time it takes for a 5G offloading process can vary depending on several factors, such as the specific network infrastructure, the traffic load and the specific technology used for offloading. In general, the 5G offloading process is designed to be very fast and efficient, minimising latency and ensuring a smooth transition between networks. For this reason, it is important that the two new systems proposed to be included to secure the process do not degrade its performance to undesirable values.
# A Real-Time Water Level and Discharge Monitoring Station: A Case Study of the Sakarya River
## 3 Results
### Validation of River Monitoring Station Data
#### 3.1.3 Comparison of Monthly Data  
Figure 15 shows the comparison of daily average discharge values between HPP and the river monitoring station for May 2023 and August 2023. During May 2023, the daily average discharge for HPP ranged between 41.02 m\({}^{3}\)/s and 118.73 m\({}^{3}\)/s, while for the river monitoring station, it varied between 38.81 m\({}^{3}\)/s and 103.08 m\({}^{3}\)/s. The correlation  
Figure 14: Comparison of Daily Discharge Variations.  
coefficient between the two datasets was calculated as 0.92, indicating a high level of agreement. In August 2023, the daily average discharge for HPP was approximately 32 m\({}^{3}\)/s, while for the river monitoring station, it was approximately 31.5 m\({}^{3}\)/s. The correlation coefficient was calculated as 0.85, showing a strong linear relationship between the datasets. Notably, significant discrepancies were observed between 16-20 August, primarily due to water retention operations and sudden releases for electricity generation at the dam. The average absolute error during this period was approximately 1.5 m\({}^{3}\)/s, and the root mean square error was 2 m\({}^{3}\)/s. The strong agreement observed during both periods reinforces the accuracy of the river monitoring station's measurements.
# NOMA-Based Rate Optimization for Multi-UAV-Assisted D2D Communication Networks
## 4 Problem Solution
### Subchannel Assignment Based on Dynamic Hypergraph Coloring
#### 4.1.2 Dynamic Hypergraph Coloring
```
1: Initialize the UAV, cellular users, and D2D clusters with position information, power allocation factors, and set all vertex candidate color sets to color full sets
2:Step 1: Dynamic hypergraph construction
3: Construct simple edges \(e_{m}(t)\) between cellular users under time slot \(t\)
4:repeat
5:if\(SIR<\lambda_{c}\) or \(SIR<\lambda_{d}\)then
6: Under time slot \(t\), if Equations (27) and (28) are satisfied, a simple edge \(e_{m}(t)\) is constructed between cellular user \(C_{i}\) and D2D cluster \(D_{j}\)
7: Under time slot \(t\), if Equations (29) and (30) are satisfied, a simple edge \(e_{m}(t)\) is constructed between D2D clusters \(D_{j}\) and \(D_{j}^{\prime}\)
8:endif
```  
**Algorithm 1** Subchannel assignment based on dynamic hypergraph coloring. Based on the above analysis, the subchannel assignment algorithm based on dynamic hypergraph coloring is shown in Algorithm 1. Figure 2: Schematic of subchannel assignment using dynamic hypergraph coloring. ```
9:until Output the simple edge \(e_{m}(t)\) between \(C_{i}\), \(D_{j}\) and \(D^{\prime}_{j}\)
10:repeat
11:if\(SIR<\lambda_{c}\) or \(SIR<\lambda_{d}\)then
12: Under time slot \(t\), if Equation (31) is satisfied, a multilateral \(e_{m}(t)\) is constructed between cellular user \(C_{i}\) and D2D cluster \(D_{j}\)
13: Under time slot \(t\), if Equation (32) is satisfied, the construction of a polygon \(e_{m}(t)\) between D2D clusters \(D_{j}\) and \(D^{\prime}_{j}\) occurs
14:until Output the multilateral \(e_{m}(t)\) between \(C_{i}\), \(D_{j}\) and \(D^{\prime}_{j}\)
15:Step 2: Dynamic Hypergraph Coloring
16: Convert the hypergraph to a directed graph under time slot \(t\) using Definition 1
17:repeat
18:for all cellular users and D2D clusters in a directed graph do
19: Coloring cellular users and D2D cluster users using Definition 2 and Definition 3
20:endfor
21:until All cellular users and D2D cluster users are colored
```  
**Algorithm 1**_Cont._
# Assessment of Six Machine Learning Methods for Predicting Gross Primary Productivity in Grassland
## 4 Results
### Key Factors and Contribution Ratio  
Based on the identification of the optimal overall performance model, we further explored the key impact factors in predicting grassland GPP. By monitoring the training process of the comprehensive optimal model (LightGBM), we counted the number of times the impact factors were determining factors, as shown in Figure 6a. They range from 7 \(\times\) 10\({}^{3}\) to 60 \(\times\) 10\({}^{3}\). Enhanced vegetation index (EVI) was the most frequently determined factor, with up to 59.4 \(\times\) 10\({}^{3}\), followed by normalized difference vegetation index (NDVI), precipitation (P), land use/land cover (LULC), with the number of them as determining factors exceeding 40 \(\times\) 10\({}^{3}\). Then, there is maximum air temperature (Tmax), potential evapotranspiration (PET), evapotranspiration (ET), land surface temperature (LST), time, digital elevation model (DEM), with the number of them as determining factors ranging from 21 \(\times\) 10\({}^{3}\) to 34 \(\times\) 10\({}^{3}\). Minimum air temperature (Tmin), the fraction of photosynthetically active radiation (FPAR), leaf area index (LAI), and land surface water index (LSWI) are the least determinant, all being less than 18 \(\times\) 10\({}^{3}\).  
Further, we quantitatively evaluate the contribution ratio of the 14 factors to the prediction accuracy by the recursive feature elimination method (Figure 6b). It can be found that the contribution ratio of a factor has a significant positive correlation with the number of times it is a determining factor (Pearson's r = 0.934, P = 1 \(\times\) 10\({}^{-6}\)). The contribution ratios of all factors are greater than 0, showing that the addition of each factor can have a positive impact on the accuracy of the model. Among them, EVI and NDVI have the highest contribution ratios, which are 26.62% and 24.36%, respectively. Followed by P, and LULC, the contribution ratio is 17.94 and 14.52, respectively, followed by Tmax, PET, and ET, with contribution ratios of 6.15%, 3.91%, and 1.52%, respectively. The contribution ratio of other factors is relatively low, less than 1%.  
Based on the number of times the impact factors were determining factors and their contribution ratio to accuracy, this study identified EVI, NDVI, P, LULC, Tmax, PET, and ET as the most important factors for predicting GPP. By selecting these factors as inputs, model accuracy of more than 95% can be achieved.
# Realistic Simulation for Body Area and Body-To-Body Networks
## 4 Performance Evaluation
### Results
To summarize, it is important to carefully select the transmission power level; apparently, most of the radio transceivers have nearly flat power profiles after \(-10\) dBm, and it does not help to improve the performance. On the contrary, it enhances the delay and reduces the energy efficiency.
## 6 Numerical Study and Comparison
### Loading and Boundary Conditions  
The PCCSW is prevented from translational movements in some directions, while the rotational displacements are excluded in all directions. The translational restraints of the \(AD\) and \(BC\) edges are under the restrained state except in the shear load direction. All the longitudinal and inclined panels of the \(AB\) and \(CD\) edges are subjected to radial restraints, and the boundaries of \(AB\) and \(CD\) are also subjected to longitudinal restraints at the midpoint of all inclined panels. The shear load is uniformly distributed along edge \(BC\). The considered simply supported boundary conditions, representing the lower bound conditions in real bridge girders, are shown in Table 2. It is worth pointing out that these boundary conditions have been verified by the co-author [32] by comparing the critical buckling stresses of flat webs with the theoretical predictions [29].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Boundary** & **Symbols** & **AB, CD** & **AB, CD (Midpoint of Inclined Panel)** & **AD** & **BC** \\ \hline \multirow{3}{*}{Translation} & \(\delta_{x}\) & F & R & R & R \\  & \(\delta_{y}\) & F & F & R & F \\  & \(\delta_{z}\) & R & F & R & R \\ \hline \multirow{3}{*}{Rotation} & \(\theta_{x}\) & F & F & F & F \\  & \(\theta_{y}\) & F & F & F & F \\ \cline{1-1}  & \(\theta_{z}\) & F & F & F & F \\ \hline \hline \end{tabular}
\end{table}
Table 2: Boundary conditions of the PCCSW (Note: R: Restrained, F: Free).  
Figure 4: Finite element model of plane curved corrugated steel web (PCCSW).  
A uniform nodal force is applied along the boundary \(BC\). The sum of all nodal forces on the edge is the unit force. Thus, each nodal force is \(1/n\), where \(n\) is the node number. Figure 5 represents the positive first order global elastic shear buckling mode of the PCCSW, which is the most conservative mode in the structural buckling calculation [17]. The elastic buckling eigenvalue calculated by FEM and the critical buckling value of the finite element can be calculated by:  
\[\tau_{cr,F}=\frac{eigenvalue}{tH} \tag{38}\]  
where buckling eigenvalue extraction uses the block Lanczos method, and the middle surface value of the shell is extracted by finite element results.