esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Machine Learning Workflow
Similarly, for SVM, the grid search was performed over different values of C (penalty parameter) and kernel types (\"linear\", \"poly\", \"rbf\", \"sigmoid\"), with C = [0.1, 1, 3, 5, 8, 10]. The grid search process involved evaluating each combination of hyperparameters using cross-validation to select the best-performing model for each algorithm. This thorough approach, including the application of undersampling, enabled us to derive meaningful insights and achieve high predictive accuracy in our complex task, showcasing the versatility and adaptability of these ML techniques. To rigorously assess the performance of our ML models, we employed a suite of comprehensive metrics. Averaged accuracy was used to represent the proportion of true results (both true positives and true negatives) among the total number of cases. Averaged precision was calculated to reflect the proportion of true positive identifications out of all positive identifications made by the model, indicating prediction reliability. Averaged recall, or sensitivity, measured the model's ability to identify all relevant instances, representing the proportion of actual positives correctly identified. The Averaged F1-score was computed as the harmonic mean of precision and recall, providing a single performance metric that considered both aspects. Additionally, we utilized the normalized confusion matrix to visualize the testing performance, showing false positives, false negatives, true positives, and true negatives, facilitating easy identification of class confusion. For model interpretability, we utilized the SHAP model, a pivotal tool in understanding ML predictions. SHAP quantifies the contribution of each feature to a model's prediction, revealing the intricate relationships within the dataset. By using SHAP, we can dissect the significance of each factor influencing the model's outcomes. Originating from game theory, SHAP provides a clear and powerful way to understand how different features impact predictions, uncovering the underlying rules of ML models. Furthermore, SHAP provides a unified framework that consistently interprets predictions across different models, ensuring that feature importance is calculated fairly and is theoretically grounded in game theory. It attributes the contribution of each feature to the model's prediction in a way that is both local (explaining individual predictions) and global (understanding overall model behavior). Unlike simpler methods, SHAP values ensure that the sum of the contributions of all features matches the model's output, offering a clear, consistent, and accurate understanding of how each feature impacts predictions. All code was implemented in Python, utilizing the Scikit-learn library ([[https://scikit-learn.org/](https://scikit-learn.org/)]([https://scikit-learn.org/](https://scikit-learn.org/)), accessed on 30 June 2024) for model development, training, and evaluation. This ensured the consistent application of ML algorithms and methods throughout this study. Figure 1 depicts the proposed ML workflow. Figure 1: Workflow of the proposed ML methodology.
## 4 Settlement Caused by Shield Tunnel Excavation
### Ground Surface Settlement Trough  
Based on the proposed model, the influence of the double-line tunnel excavations on ground surface settlement was investigated. When the double-tunnel excavation was finished, the ground surface settlement curves along the runway pavement longitudinal section are shown in Figure 11. L1 and L2 represent the sections of the runway midline and sward midline, RL1\({}^{\prime}\) and RL2\({}^{\prime}\) are the sections of runway midline and sward midline when the right-line tunnel excavation was finished and LL1\({}^{\prime}\) and LL2\({}^{\prime}\) are the sections of runway midline and sward midline when the left-line tunnel excavation was finished, respectively. The ground surface settlement curves of RL1\({}^{\prime}\), LL1\({}^{\prime}\), RL2\({}^{\prime}\) and LL2\({}^{\prime}\) were normally distributed and the Peck formula was used to fit the settlement trough. As shown in Figure 8, there was a good fit between the settlement trough and the Peck formula. The settlement of the monitoring points above the tunnel axis was maximum. The farther the  
Figure 10: Calculated settlement vs. measured settlement.  
Figure 9: Distribution layout of monitoring points: (**a**) distribution of monitoring points; (**b**) site map of settlement monitoring points; (**c**) schematic diagram of monitoring points by hole-drilling method.  
monitoring point from the shield tunnel axis, the smaller the ground surface settlement. The ground surface settlement induced by a double-line shield tunnel excavation was always larger than that induced by a single-line shield tunnel excavation for different sections.  
The ground surface settlement curves at different sections induced by a double-line tunnel undercrossing were V-type settlement troughs instead of W-type. A previous study showed that the type of settlement trough induced by tunnel undercrossing is closely related to the tunnel spacing (L), depth (H) and radius (R). When L/(H + R) \(\leq\) 0.5, the settlement curves are V-type; in this study, L/(H + R) = 0.255, which was consistent with the V-type settlement trough characteristic. Among them, the maximum settlement at the L1 section was 1.96 mm, and the maximum settlement at the L2 section was 2.14 mm. This is because the runway pavement structure has a greater stiffness compared with the soil, so the runway itself had a small settlement.
# Detecting Dynamic Communities in Vehicle Movements Using Ant Colony Optimization
## 4 Discussion  
Based on the simulated and real-world experiments, we can conclude that EACO outperforms FacetNet, DYNMOGA, and RWLA in detecting dynamic communities. The primary reasons for this can be summarized as follows:  
1. EACO can well consider the temporal partition coherence at each snapshot by using the newly built pheromone initialization method. The ACO-based community searching method used by EACO makes no assumptions about the shapes of communities. The path of an ant can be arbitrary; therefore, EACO is powerful for identifying communities of different sizes and shapes. The expectation-maximization method used by FacetNet and the genetic algorithms used by DYNMOGA actually belong to partitioning clustering methods. The Leiden algorithm used by RWLA is a hierarchical clustering method. It has been proven that partitioning clustering methods and hierarchical clustering methods usually tend to find spherical-shaped communities [42].
2. EACO can evaluate the statistical significance of communities. Therefore, some weakly interacting communities can be eliminated. In contrast, FacetNet, DYNMOGA, and RWLA can identify communities in a graph, even when there is not a natural community structure in that graph. For example, these two comparison methods all identified some weakly interacting communities in the suburbs of Beijing, where OD pairs were very sparse.
3. EACO is more robust to noise because it adds a spatial contiguity constraint to the optimization process. In Section 3.1, we show that FacetNet, DYNMOGA, and RWLA cannot accurately identify predefined communities from the datasets with noise. As shown in Section 3.2, FacetNet, DYNMOGA, and RWLA identified some spatially dispersed communities, which may be spurious patterns formed by random movements [21].  
EACO can be regarded as a spatial extension of evolutionary clustering. Compared with the static communities by existing spatial community detection methods (e.g., ACOScan), the dynamic communities identified by EACO are useful for revealing the evolution of spatial interaction in a city. Spatial community detection methods can only identify the static polycentric structure of a city. In contrast, EACO can reveal how the polycentric structure of a city changes over time. Therefore, the dynamic communities identified by EACO can be used to understand the relationships between human activities and the dynamic organization of urban structure, e.g., the changes in communities caused by urban commuting activities. These dynamic communities may be helpful for understanding how human activities shape the structure of a city, and may provide useful references for human-centered urban planning [37].
## 4 Discussion
### Multiple Retrieval Sources  
Current studies regarding RAG-based LLMs predominantly address retrieval from single sources. However, scientific information is often distributed across multiple platforms, including unstructured text corpora, knowledge graphs, domain-specific repositories, and web content. By using multiple retrieval sources, RAG-based LLMs can draw from a broader, more diverse data pool to ensure that literature reviews are both exhaustive and comprehensive. Likewise, by accessing multiple sources, these models can include niche studies and emerging research that may not be available in mainstream databases. Therefore, scholars should further explore RAG-based LLMs with multiple retrieval sources in the context of literature reviews.  
While RAG-based LLMs hold potential for enhancing NLP tasks relevant to systematic literature reviews (SLRs), their application in literature reviews is still limited. Domain-specific LLMs, multimodal contexts, and multiple retrieval sources represent three promising areas for further exploration. Domain-specific models like SciBERT and ChemGPT improve relevance in specialized fields. Multimodal LLMs provide a more comprehensive analysis by integrating text with visual and multimedia data. Additionally, using multiple retrieval sources will ensure a more exhaustive and inclusive literature review process.
## 6 Conclusions  
In this paper, a method for accurate measurement of spaceborne SAR's antenna beams azimuth pointing based on ground receivers is proposed. This method is based on the fact that ground receivers can receive the complex sampling data of the pulse signals transmitted from the spaceborne SAR. These complex sampling data contain the information of the antenna pattern and the information of the changes in the positions of the satellite and receiver. These two kinds of information are in the same time coordinate, which provides the basis for high precision. The processing results of the GF-3 SAR test data show that the method effectively measures the azimuth direction of the antenna beam. For LEO satellites, the measurement accuracy reaches 0.002\({}^{\circ}\), which is equivalent to TerraSAR-X's method.  
[PERSON] proposed the processing method of this paper, and discussed with [PERSON], [PERSON] and [PERSON] wrote a data processing program. [PERSON] processed the data and screen shots. [PERSON] and [PERSON] collected ground receiver data. [PERSON] wrote the manuscript. All authors read and approved the final version of the manuscript.  
This work was funded by the project of Gaofen-3 SAR calibration processing system under Grant No.Y5H2980402.  
**Acknowledgments:** The authors would like to thank the GF-3 SAR Satellite Group for the satellite and ground system development work, the staff of China Center for Resources Satellite Data and Application for their help, and the members of the calibration team for their efforts in deploying equipment at the Erdos calibration site in Inner Mongolia, China.  
Figure 11: The comparison of the pulse compression results before and after interpolation.
# Resilience Quantification of Smart Distribution Networks--A Bird's Eye View Perspective
## 3 Taxonomy of Resilience Evaluation Methods
### Performance Calculation
#### 3.2.3 Surrogate Model Method  
A relatively new approach to performance evaluation in smart grids is the introduction of surrogate models, borrowed from the evolutionary computation community [109]. Surrogate models aim to reduce runtime and complexity of analytical and simulation-based models while maintaining a high degree of fidelity. The idea is to bypass conventional system modeling (where the name \"surrogate\" or \"meta-model\") using techniques such as neural networks [110; 111], and kriging methods [112]. A simple example is a machine learning (ML) agent taking as input system topology parameters, hazard characteristics, area climate, and topography; and outputs performance measures. The system model is replaced by an implicit non-linear multi-variate function implemented by the ML agent. The biggest challenge is to choose the right inputs (predictors). A Polynomial Chaos Expansion-based method is proposed in [113] to conduct risk analysis for rare events, which is projected by the authors to have an extension to resilience assessment.
## 3 Calculated Results for Mitigation Measures at the Track
### The Reduction of the Dynamic Axle Loads  
Usually, the mitigation due to a special track is analysed for the dynamic effects. The dynamic loads are generated by the passage over irregularities of the train or the track. The dynamic interaction between the track and the vehicle yield a wheelset-track eigenfrequency and a force reduction for higher frequencies. Elastic track elements such as rail pads, sleeper pads and ballast mats shift this vehicle-track eigenfrequency to lower frequencies so that the frequency range of reduction is widened to lower frequencies. The effects for the force acting on the ground are presented for typical stiffness values of the elastic track elements in Figure 2; see the standard parameters in Table 2. The minimum stiffness per sleeper is quite similar for all elastic elements due to safety regulations. The lower the elastic element placed in the track, the more mass is activated. Therefore, the lowest eigenfrequencies and the best reduction effects can be found for the under-ballast mats where eigenfrequencies of 20 Hz can be established (Figure 2c). The second-best reduction can be achieved by under-sleeper pads with eigenfrequencies starting from 25 Hz and maximum reductions below 0.1 at 100 Hz (Figure 2b). The eigenfrequencies that can be established with elastic rail pads are normally quite high (between 40 and 80 Hz) so that train-induced ground vibrations up to 100 Hz are hardly affected (Figure 2a). A reduction of one-third is the best that can be reached by the softest rail pads with a stiffness of \(k_{R}=20\) kN/mm. The dynamic reduction effects of elastic track elements lie generally at high frequencies, mainly above 50 Hz, maybe above 30 Hz.  
The bending stiffness of an under-ballast plate has no influence on the reduction of the dynamic axle loads, as can be seen by Equation (14): the bending stiffness \(El\xi^{4}\) for \(\zeta=0\) is zero. The influence on the dynamic track stiffness (15) and the force transfer of the vehicle-track interaction (16) is weak, as well as the influence of the mass of the under-ballast plate.  
\begin{table}
\begin{tabular}{l l} \hline Wheelset & \\ Mass & 1500 kg \\ Rail & \\ Bending stiffness & 6.4 \(\times\) 10\({}^{6}\) Nm\({}^{2}\) \\ Mass per length & 60 kg/m \\ Sleeper & 2.6 m \\ Distance & 0.6 m \\ Mass & 338 kg \\ Ballast & \\ Longitudinal wave velocity & 400 m/s \\ Static stiffness & 5 \(\times\) 10\({}^{8}\) N/m \\ Height & 0.35 m \\ Damping ratio & 10\% \\ Ballast plate & \\ Young’s modulus & 3 \(\times\) 10\({}^{10}\) N/m\({}^{2}\) \\ Mass densitiy & 2500 kg/m\({}^{3}\) \\ Width & 3 m \\ Height & 0.3 m \({}^{*}\) \\ Soil & \\ Shear wave velocity & 200 m/s \({}^{*}\) \\ Mass density & 2000 kg/m\({}^{3}\) \\ Poisson’s ratio & 0.33 \\ Damping ratio & 5\% \\ \hline \end{tabular}
\end{table}
Table 2: Standard parameters of the vehicle–track–soil systems (parameters with * varied).
# An Automatic Navigation System for Unmanned Surface Vehicles in Realistic Sea Environments
## 3 The Static Environment Modeling and Path Planning
### The Fast Marching Method
When the FMM is being executed, grid points are categorized into three different groups as:  
* Dead (marked as red): indicates that the arrival time value \((T)\) of the grid point has been calculated and determined;
* Open (marked as blue): indicates that the arrival time value \((T)\) of the grid point is an estimated value and may be changed;
* Far (marked as gray): indicates that the arrival time value \((T)\) of the grid point is unknown. The arrival time output of one wave source in \(50\times 50\) grid map is shown in Figure 4a. If we select \(T\) as the z axis of the third dimension, the result of the wave expansion is shown in Figure 4b. In the case of the four wave sources expanding, the same process is applicable. When the waves from different sources reach each other, the propagation continues as if they were only one wave, as shown in Figure 4c,d. With one single source, there is only one minima at the source. For more than one source, there is a minima at each source with \(T=0\). Figure 3: The updating process when using the fast marching method (FMM). (**a**) Initial state; (**b**) Iteration 1; (**c**) Iteration 5; (**d**) Iteration 13; (**e**) Iteration 25; (**f**) Iteration 37; (**g**) Iteration 45; (**h**) Iteration 49. Suppose Figure 2b, which is a binary map, represents the planning space \((M)\). Its arrival time matrix \(T\) (shown in Figure 5a) from the end point is calculated by FMM. Then, on the base of the \(T\), by applying the gradient descent method the optimal path is finally generated and shown as the magenta line in Figure 5b,c.
# The Total Phenolic Content and Antioxidant Activity of Nine Monofloral Honey Types
## 2 Materials and Methods
### Ferric Reducing Power (FRAP) Assay  
The total antioxidant activity of the samples was determined according to the Benzie and Strain method, in order to measure the Fe\({}^{3+}\)/Fe\({}^{2+}\) couple reducing ability of a complex matrix [25]. Specifically, the honey samples were diluted with distilled water (1:5) (_w/v_) and after stirring for 20 min, 3 mL from the FRAP solution were added to the 100 \(\mu\)L of the honey solution. The FRAP working solution comprised of three components: (1) 300 mM CH\({}_{3}\)COONa (\(\geq\)99%, Chem-Lab, Zedelgem, Belgium)--CH\({}_{3}\)COOH (\(\geq\)99%, Chem-Lab, Belgium) buffer (pH = 3.6), (2) 10 mM TPTZ (2,4,6-tripyrid-ly-st-triazine) solution (\(\geq\)98.0%, Merck, Germany) in 40 mM HCl (3 M, Chem-Lab, Belgium), and (3) aquatic solution of 20 mM FeCl\({}_{3}\)\(\times\) 6H\({}_{2}\)O (>99%, Chem-Lab, Belgium), in a ratio of 10:1:1. After stirring, The samples were transferred to a water bath at 37 \({}^{\circ}\)C for 4 min. Absorbance was then measured at 593 nm, using water (100 \(\mu\)L) with 3 mL of FRAP as the reference solution. The analysis was performed in duplicate. For the quantification of the total antioxidant activity, calibration curves were created using standard solutions of ascorbic acid (>99.7%, Chem-Lab, Belgium) (concentrations: 50-2000 \(\mu\)M). The results were expressed as mg ascorbic acid equivalents (AAE)/100 g honey
# A Feature Selection Algorithm Performance Metric for Comparative Analysis
## 3 Fitness Function
The penalty function used in this study is an exponential function with respect to the number of features selected for a solution. The exponential scale penalty function is chosen since the feature selection search space also expands exponentially as the number of features increases. The range of the penalty function is desired to be the same as that of the classifier performance measure to be able to prioritise the feature selection objectives equally. To match the range of the \(F\)-measure (\([0,1]\)), the penalty function is designed to produce the value \(1\) when all the features are selected and the value \(0\) when one feature is selected and to grow exponentially, so that higher numbers of selected features are increasingly penalised. The penalty function is formulated as  
\[f_{p}(s)=\frac{\gamma^{\frac{|s|-1}{N-1}}-1}{\gamma-1}, \tag{3}\]  
where \(|s|\) is the number of features selected and \(N\) is the total number of features available. The scaling constant (\(\gamma\)) controls how aggressive the penalty function is, without changing the range of the function. Higher values of \(\gamma\) will provide relatively less aggressive penalties for lower numbers of features selected, and relatively more aggressive penalties for higher numbers of features selected. The value of \(\gamma\) is arbitrarily chosen as \(10\) for use in this study since it provides a reasonable trade off for the penalty severity between a low number of features and a high number of features. The penalty behaviour for \(N=100\) and different values of \(\gamma\) is visualised in Figure 2. Figure 2 also shows the effect of applying a scaling constant of \(k_{p}=0.25\) and \(\gamma=10\).
# Impact of Natural and Human Factors on Dryland Vegetation in Eurasia from 2003 to 2022
## 3 Results
### Spatiotemporal Patterns of Natural Factors in Eurasian Drylands
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Variables**} & **MAT** & **MAP** & **SM** & **VPD** & **TWS** \\  & **(\({}^{\circ}\)C yr\({}^{-1}\))** & **(mm yr\({}^{-2}\))** & **(m\({}^{3}\) m\({}^{-3}\) yr\({}^{-1}\))** & **(K Pa yr\({}^{-1}\))** & **(mm yr\({}^{-1}\))** \\ \hline WCE & 0.07 * & \(-\)1.49 & \(-\)0.0012 * & 0.005 * & \(-\)1.05 * \\ EEU & 0.05 * & \(-\)0.79 & \(-\)0.0014 * & 0.006 * & \(-\)0.67 * \\ MED & 0.06 * & \(-\)0.79 & \(-\)0.0004 & 0.005 * & \(-\)0.52 * \\ WSB & 0.03 & 0.47 & \(-\)0.0002 & 0.003 * & 0.12 \\ ESB & 0.04 & 4.82 * & 0.0008 * & 0.001 & 0.19 * \\ WCA & 0.05 * & \(-\)1.47 & \(-\)0.0004 & 0.006 * & \(-\)0.88 * \\ ECA & 0.03 & 0.76 & \(-\)0.0003 & 0.002 * & \(-\)0.46 * \\ TIB & 0.01 & \(-\)3.88 & 0.0002 & \(-\)0.000 & \(-\)0.16 * \\ EAS & 0.04 * & 3.37 & \(-\)0.0006 & 0.001 & \(-\)0.84 * \\ ARP & 0.06 * & 0.44 & \(-\)0.0002 & 0.004 * & \(-\)0.37 * \\ SAS & 0.01 & 6.97 * & 0.0008 * & 0.001 & \(-\)0.55 * \\ \hline \end{tabular}
\end{table} 
Table 4: The linear regression coefficients for mean annual temperature (MAT), mean annual precipitation (MAP), soil moisture (SM), vapor pressure deficit (VPD) and terrestrial water storage (TWS) during 2003-2022 in WCE (West and Central Europe), EEU (E. Europe), MED (Mediterranean), WSB (W. Siberia), ESB (E. Siberia), WCA (W. C. Asia), ECA (E. C. Asia), TIB (Tibet Plateau), EAS (E. Asia), ARP (Arabian Peninsula) and SAS (S. Asia). The symbol “*” indicates that the linear regression coefficient has passed the significance test with \(p<0.05\) (the Student’s \(t\)-test). Figure 5: Spatial distribution of the linear trends in mean annual temperature (MAT, (**a**)), mean annual precipitation (MAP, **(b)**), soil moisture (SM, **(c)**), vapor pressure deficit (VPD, (**d**)) and terrestrial water storage (TWS, **(e)**) from 2003 to 2022.