esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussion
### Threats to Validity
#### 5.3.3 External Validity  
External threats relate to the generalization of our empirical study conclusions. Our results are based on the datasets created by several research groups, and the variations in the measurements used to collect the datasets could affect the validity of our results. To mitigate this threat, we selected datasets that cover different application domains and have different sizes and granularity. Additionally, we selected high quality datasets that have high EPV values, which has been shown to produce more stable results [42]. Nevertheless, the empirical study's findings cannot be generalized to other industrial fields. The development of the ensemble models was another threat. In our empirical study we used the scikit-learn library for data preprocessing and building the ensemble models, and coding errors might have occurred. We employed pair programming, an agile software development approach, to assure code quality, and the Google Colab environment for code exchange and execution to mitigate this threat. Moreover, we assessed the code correctness using benchmark datasets from the scikit-learn package (e.g., iris dataset).
## 3 Results
### Comparison of Different Tracking Techniques  
The total number of frames in all video clips is 91,829. As the tracking results recorded in Table 4 show, StrongSORT has the highest lost-tracking rate of 23.4%, while DeepSORT and BtyeTrack have the same rate of 8.9%. StrongSORT loses tracking in almost a quarter of all frames, which is not suitable for further alarm identification. DeepSORT and BtyeTrack with lower lost-tracking rates have no reaction delay on re-tracking a subject, while StrongSORT requires a certain number of frames to confirm the appearance of a detected subject. As for the count of ID changes, although StrongSORT has the least amount of ID changes, it could be caused by its worse performance on tracking, which means that fewer frames have been tracked, resulting in fewer scenes where ID change can occur. From the comparison of the number of ID changes in DeepSORT (2109) and ByteTrack (1697), ByteTrack demonstrates a better ability to keep tracking a specific subject. However, it is not directly relevant to the accuracy of identifying a patient's head. Therefore, a further comparison of DeepSORT and ByteTrack is conducted in the next section.
## 3 Performance Analysis  
In this paper, the authors are asserting that the network performance can be significantly improved by utilizing MAV to achieve overlapping coverage. The essential benefit of MAV is the path diversity. Multiple HAPs provide more nodes in the network for information flow than a single HAP. Therefore, path diversity can definitely improve the availability of HAPs networks.
# Landslide Susceptibility Assessment Using the Geographical-Optimal-Similarity Model
## 5 Discussion
### Outlook  
Research on landslide susceptibility spatial prediction demonstrates that GOS offers several advantages in geographic-similarity-based spatial modelling. First, GOS selects a small subset of observations with the highest similarity (1.5% of the dataset in this study) for prediction. Compared to earlier geographic similarity models that depended on all observations, GOS achieves greater efficiency and accuracy. This outcome indicates that predictions for an unknown location are based solely on a few highly similar observations rather than the entire dataset. Second, using fewer observations, GOS improves the prediction accuracy during cross-validation and outperforms the BCS model. Third, uncertainty analysis revealed that GOS predictions remain consistent across varying confidence levels,demonstrating high stability. Additionally, the GOS model demonstrates strong robustness across different evaluation metrics (RMSE and MAE). Compared to machine learning models, GOS not only enhances predictive accuracy but also maintains consistency under varying conditions, as evidenced by its lower standard deviation in multiple validation experiments. This stability is particularly crucial for landslide susceptibility assessment, where prediction reliability is essential for disaster prevention and mitigation efforts. GOS presents a robust framework for leveraging geographic similarity principles in landslide susceptibility spatial prediction.  
Future research on landslide susceptibility using the GOS method can focus on the following areas: (1) Refining the approach to selecting non-landslide points. For instance, the grey prediction method could determine factor weights in the information entropy model. Combining regional characteristics to delineate non-landslide zones would increase the representativeness of selected points. (2) Integrating GOS with spatial models grounded in spatial correlation and heterogeneity theories for improved predictions. Each spatial prediction method has unique strengths. Studies show that combining multiple methods substantially enhances prediction accuracy [37]. For example, integrating the GOS model with the geographically weighted regression (GWR) model can capture both geographical similarity and correlation characteristics of landslides. Models that could complement GOS include GWR and geographic detector models by evaluating the GOS model's performance across various sample sizes and spatial scales. (3) In addition, advancements in remote sensing and GIS technology will significantly enhance data acquisition efficiency and accuracy [38; 39]. Future studies could integrate multisource data, such as high-resolution remote sensing imagery and geological surveys, to develop more comprehensive and precise landslide susceptibility models.
# Prioritization Based Task Offloading in UAV-Assisted Edge Networks +
## 5 Task Offloading Algorithms in Our Model
### Aggressive Tx-Order Offloading Algorithm (AGG-2)  
The second greedy algorithm is similar to the first one, AGG-1. The only difference is that the task transmission completion order (Tx-Order), rather than the task assignment order as it was in AGG-1, determines the order in which tasks are processed; the one downloaded earlier is processed first. This difference corresponds to the handling of \(Event.TaskComplete\) and \(Event.TxComplete\) events in Table 1, and the related processor does not check whether the downloaded task was the first one assigned before processing, contrary to AGG-1.
# An Efficient and Reliable Algorithm for Wireless Sensor Network
## 4 Formation of Receiver Group  
The basic principle of EERASCA is the way of communication through which data are transferred between each node in the group. It should be known that the network is a collection of all the nodes within the predefined range, but the group is the collection of nodes that have a common interest. For communication in a group, the most appropriate method is multicasting, which increases the efficiency of the network by reducing overhead. On the other hand, communication in EERASCA within the group is performed through flooding, which increases the reliability but with a larger overhead.  
Two cases are used during the receiver group formation. Firstly, when a node \(n\) is willing to be a part of the current receiver group. If node \(n\) receives any request from the group member, it will transmit a join request for the membership of the receiver group. In response, a join ACK is communicated to \(n\) by the group member. It joins the group and begins sending messages that identify the group. Secondly, if no receiver group exists, then the first receiver that joins the group will declare itself as a core node and begin disseminating SD messages as a core node to notify the receivers to join the group. After joining the group, the group's receivers continuously checked the periodic updates of the SD messages, and if not received within 2\(\times\) SD interval, then it is considered that the core node failure has occurred. A receiver announces a core failure request (CFR) for the confirmation of the core node failure.  
In contrast, if the core failure does not occur, the CFR communicates a receiver that accepts a new SD message than the sequence number in the CFR. The receiver then communicates the fresher sequence number to the receiver group, which relies on a similar path to the sender that started the CFR request. If the core failure occurs in reality, then the CFR request will not reach the sender because of the disconnection of the connectivity list. Therefore, the core failure announcement (CFA) should be fixed to a definite time interval which is enough for the CFA Request to send back to the receiver which started the CFA request.
## Materials and Methods
### Data Acquisition
#### 2.2.2 Satellite Data and Atmospheric Correction Algorithm  
The GEE platform provides various Landsat 8 products, including surface reflectance, top-of-atmosphere (TOA) reflectance, and raw images. To avoid possible errors induced by an unsuitable atmospheric correction algorithm [9], raw image data were used in this study.  
ACOLITE algorithms, which were developed by the Royal Belgian Institute of Natural Sciences (RBINS) and are particularly suitable for inland and marine water bodies, provided an atmospheric correction algorithm to produce output data, such as surface reflectance (rhos), surface reflectance for water pixels (rhow), and remote-sensing reflectance  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **Dianchi Lake** & **Erhai Lake** & **Fuxian Lake** & **Yilong Lake** \\  & **(_n_ = 696)** & **(_n_ = 1186)** & **(_n_ = 504)** & **(_n_ = 358)** \\ \hline Min (m) & 0.14 & 0.53 & 3.00 & 0.08 \\ Max (m) & 1.70 & 4.90 & 9.00 & 2.00 \\ Mean (m) & 0.47 & 1.92 & 5.59 & 0.38 \\ Median (m) & 0.45 & 1.80 & 5.50 & 0.30 \\ Stdev (m) & 0.15 & 0.55 & 0.79 & 0.24 \\ CV & 0.33 & 0.29 & 0.14 & 0.65 \\ \hline \end{tabular}
\end{table}
Table 1: Statistics of the measured SD for the four lakes. CV (coefficient of variation) = Stdev/Mean.  
Figure 1: The distribution of the lakes in this study in Yunnan Province, China. (**a**) Distribution map of rivers and lakes in China. The red box indicates Yunnan Province, where the study area is located. (**b**) Distribution map of rivers and lakes in Yunnan Province. Red boxes indicate the four lakes investigated in this study. (**c**) Geographical location of Erhai Lake. (**d**) Geographical location of Dianchi Lake and Fuxian Lake. (**e**) Geographical location of Yilong Lake.  
for water pixels (Rrs) (the latest ACOLITE python source code is available from GitHub ([[https://github.com/acolite/acolite](https://github.com/acolite/acolite)]([https://github.com/acolite/acolite](https://github.com/acolite/acolite)), accessed on 21 March 2023). To implement the ACOLITE algorithm in the GEE platform, some revisions were required: (1) Screening: filtering the image collections containing sampling points according to the input time series and sampling points. (2) Atmospheric correction: Converting the unmodifiable ee.image format from the GEE to the modifiable.nc format, in which non-aqueous pixels and cloud pixels are masked, and the TOA reflectance values are converted to Rrs values. (3) Cutting: Obtain the corresponding Rrs value from the.nc file according to the coordinates of the measurement points. (4) Reorganization: collecting the Rrs values of all measuring points and storing them in a csv file. The above was executed using Google Colab, and the relevant content is shared at GitHub ([[https://github.com/Zwz0003/github.git](https://github.com/Zwz0003/github.git)]([https://github.com/Zwz0003/github.git](https://github.com/Zwz0003/github.git)), accessed on 21 March 2023). A total of 1829 satellite data points were obtained based on the measurement points and time series (January 2013-August 2022).
###### Abstract  
During the crystal growth process using the floating zone method, the uneven distribution of impurities on the surface of the melt can trigger a coupling mechanism between solutocapillary convection driven by the concentration gradient and thermocapillary convection driven by the temperature gradient, resulting in the Marangoni convection at the free surface. When the temperature and concentration gradients reach certain values, the crystal surface and interior exhibit time-dependent, periodic oscillations, leading to the formation of micrometer-scale impurity stripes within the crystal. This study focuses on the effects of temperature difference and heat loss in a liquid bridge under microgravity on the structure and interface oscillation characteristics of thermo-solutocapillary convection, aiming to explore the coupling phenomenon of this oscillation and provide valuable information for crystal growth processes. An improved level set method is employed to accurately track every displacement of the interface, while the surface tension is addressed using the CSF model. In addition, the area compensation method is used to maintain simulation quality balance. A comprehensive analysis is performed on the oscillation characteristics of thermo-solutocapillary convection at the free surface, ranging from the temperature, concentration, deformation, and velocity distributions at the upper and middle heights of the liquid bridge. The results indicate that under small temperature differences (\(\Delta T=1-3\)), the transverse velocity at the upper end exhibits a single-periodic oscillation, while the longitudinal velocity presents a double-periodic oscillation. At the intermediate height, both the transverse and longitudinal velocities display a single-periodic oscillation. Under a large temperature difference (\(\Delta T=6\)), the oscillation of velocities at the upper end and the middle position become multi-periodic. In addition, heat loss has certain regular effects on the oscillatory flow of thermo-solutocapillary convection within a certain range. The velocity, amplitude, and frequency of the upper end and the middle position at the free surface decrease gradually, and the oscillation intensity also weakens with the increase in heat loss (\(Bi=0.2-0.6\)). These new discoveries can provide a valuable reference for optimizing the crystal growth process, thereby enhancing the quality and performance of crystal materials.  
liquid bridge; level set method; thermo-solutocapillary convection; oscillation; heat loss Article
## 4 Simulation Analysis of CRPDC
### Coil Currents under Different Power Voltage  
The influence of power voltage on the coil current is investigated as well, as shown in Figure 11. The frequencies and duty cycles of the input PWM signals are set to be 4 kHz and 50%, respectively. The energy-storage capacitances are all set to be 10 uF. The power voltages are set to be 16 V, 20 V, 24 V, and 28 V, respectively. As can be observed from Figure 11, the resulting average currents at different power voltages are calculated to be 1.014 A, 1.288 A, 1.575 A, and 1.862 A (results from the Multisim model in Figure 4a). The resulting current ripples at different power voltages are determined to be 24.44 mA, 30.41 mA, 36.06 mA, and 42.10 mA (peak-to-peak values, results from the Multisim model). As can be seen from Figure 12, with the increase in the power voltage, the average current and the current ripple of the proportional valve coil both increase in an almost linear way. Hence, the coil-current control characteristics of the proposed CRPDC can be regulated by changing the power voltage. Small power voltage means a small current ripple and low drive capability.  
Figure 11: _Cont._  
Figure 10: The current characteristics of the CRPDC under the different energy-storage capacitances (results from Multisim model): (**a**) Average current; (**b**) current ripple (peak-to-peak value).  
Figure 9: The resulting coil currents of CRPDC under different energy-storage capacitances: (**a**) 2 uF; (**b**) 4 uF; (**c**) 6 uF; (**d**) 8 uF.
## 3 Calculation Methods
### CFD-DEM Coupling Principle  
The coupling between particles and fluids occurs through the exchange of interaction forces in Equations (2) and (3). The interaction forces between particles and fluids generally include drag, buoyancy, and lift forces. The drag force is generally considered as a function of the drag coefficient. According to [PERSON] [35] and [PERSON] [36],  
\[F_{d}=\frac{\pi d^{2}}{8}C_{d}\rho_{f}\left(\overset{\rightarrow}{\overline{v}} -\overset{\rightarrow}{\overline{u}}\right)\left|\overset{\rightarrow}{ \overline{v}}-\overset{\rightarrow}{\overline{u}}\right| \tag{15}\]  
\[C_{d}=\left(0.63+\frac{4.8}{\sqrt{Re_{p}}}\right)^{2} \tag{16}\]  
\[Re_{p}=\frac{d\rho_{f}\left|\overset{\rightarrow}{\overline{v}}-\overset{ \rightarrow}{\overline{u}}\right|}{\mu_{f}} \tag{17}\]  
where \(F_{d}\), \(C_{d}\), \(d\), \(\rho_{f}\), \(\overset{\rightarrow}{\overline{v}}\), \(Re_{p}\), and \(\mu_{f}\) represent the drag force, drag coefficient, particle diameter, fluid density, fluid velocity, particle velocity, Reynolds number of the particles, and dynamic viscosity of the fluids, respectively.  
The buoyancy force can be calculated using Equation (18) [37]:  
\[F_{b}=\frac{1}{6}\pi\rho_{f}d^{3}g \tag{18}\]  
where \(F_{b}\) represents the buoyancy force and g is the acceleration due to gravity.  
The lift force is calculated using Equation (19):  
\[F_{l}=\frac{1}{8}\pi\rho_{f}d^{2}\left(\frac{1}{2}\
abla\overset{\rightarrow} {\overline{v}}-\overset{\rightarrow}{\overline{\omega}}\right)\left( \overset{\rightarrow}{\overline{v}}-\overset{\rightarrow}{\overline{u}}\right) \tag{19}\]  
where \(\overset{\rightarrow}{\overline{\omega}}\) denotes the angular velocity of particles.
## 4 Conclusions  
The paper studied the effect of the circumferential distribution of coronary plaques on the further development of atherosclerosis. Based on coronary CT data, four three-dimensional coronary models with the same degree of stenosis but different plaque circumferential positions were established. Considering the pulsatile action of blood and the compliance of the vessel wall, the fluid-structure interaction method was used for the numerical simulation. The results of the study showed that the circumferential location of the plaque hardly changed the risk of plaque rupture. Plaques with different circumferential positions changed the direction of blood flow and caused eddies behind the plaques, which in turn altered the velocity of blood entering each branch and the flow of blood in the branches. Since each branch plays a different role in the heart, over time, this difference in flow distribution may have varying degrees of impact on heart disease. In the early stage of the cardiac cycle, the WSS did not change significantly due to the circumferential distribution of plaques; however, in the late cardiac cycle, when the blood flow velocity increased, the size and position of the vortex region after the plaque was affected by the circumferential distribution of the plaque, resulting in very different WSSs. Combined with the area of the TAWSS \(<\) 0.4 Pa and the OSI value, it is concluded that under the same degree of stenosis, when the plaque is located on the side away from the circumflex branch, the risk of further development of coronary atherosclerosis is the greatest, followed by the plaque located on the side away from the heart and the plaque located on the side of the circumflex artery. On the side closest to the heart, the least risky plaque is on the side of the circumflex artery. This conclusion can provide help for clinicians to make decisions during PCI treatment.  
Conceptualization, Y.D. and L.Z.; software, Y.D.; data curation, Z.H.; writing--original draft preparation, Y.D.; writing--review and editing, J.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China grant number No.61801256 and the National Natural Science Foundation of Shandong Province grant number ZR2019 ME051.  
Not applicable.  
The authors declare no conflict of interest.
## 3 Results
### Feature Variable Selection and Influencing Factor Analysis  
We first conducted experimental tests by developing a Shanghai population estimation model using XGBoost and interpretable SHAP methods, with 28-dimensional feature variables derived from data processing as inputs. Through feature importance ranking, we identified eight low-impact features with SHAP values below 3100 (maximum: 61,328 and mean: 9543), including the three-dimensional features derived from land use, slope, slope direction, and terrain undulation from the DEM, along with the radiation brightness values and binary processing results from nighttime light data. To optimize computational efficiency, we excluded these features and finalized 20 key variables: the kernel density of 18 POI categories, NTL DN values, and DEM elevation data. The refined XGBoost model with SHAP interpretation generated Shanghai's population estimates and feature importance rankings, as shown in Figure 1. In the figure, the x-axis shows the SHAP values, reflecting the contribution of all samples at different locations and indicating the positive and negative influences of the features on population estimation. The color axis (as shown by the red-to-blue gradient) represents the SHAP values of features, in which higher SHAP values denote a stronger predictive influence on the population estimation. Overall, the top 10 influential features for Shanghai's population estimation are as follows: address information, access facilities, dwellings, government agencies, public facilities, traffic services, enterprises, relaxation services, catering services, and DN value, with most exhibiting a positive impact on population distribution patterns.  
To analyze the distribution of influencing factors on population estimation across different districts of Shanghai, we conducted a comparative analysis of the SHAP values for the top three feature variables: address information, access facilities, and dwellings. The analysis revealed that these SHAP values consistently ranked among the top three in all districts (Figure 2). Overall, the total SHAP values for these three types of POIs were higher than those of other feature variables, and this pattern held true across different regions. Additionally, the combined SHAP values for these three types in the suburbs exceeded those in urban centers, indicating their more pronounced influence on population spatial estimation in suburban areas. With the exception of the Huangpu and Hongkou districts in urban centers, the SHAP values for dwellings in other districts were lower than those for address information and access facilities. In urban centers, the SHAP value for access facilities was higher than that for address information, while in the suburbs, the SHAP value of address information was higher, demonstrating regional variations in the influence of these features.  
Figure 1: SHAP diagram ranking the importance of feature variables.