esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiment
### Comparison of Link Prediction Results  
In this section, the SSP-AA model is compared with other advanced models. Firstly, the sampling range is set to 1 for both the CN and AA models. Secondly, to ensure the fairness of the experiment, the number of hidden layers for other models is set to 3, with each hidden layer having a dimension of 32. The AdamW optimizer is used, and the model is trained with a batch size of 32. The initial learning rate is set to 0.0001, and the dropout is set to 0.5 to prevent overfitting. Finally, the sampling parameters \(p\) and \(q\) for the n2v model are both set to 1. The ScaLed model is set with a random walk number \(k=2\) and walk length \(h=2\). All models have trained fifty times on each of the seven datasets using five different random seeds. The average AUC of each model after five runs is extracted. The link prediction results of the various models are shown in Table 3.  
As shown in Table 3, the SSP-AA model outperforms all other GNN methods, affirming the efficacy of the adaptive attention mechanism and the skip knowledge module in the model. The USAir and Celegans datasets are smaller in scale, and the SSP-AA model can better utilize the advantage of adaptive weights to aggregate nodes with higher similarity, thereby significantly improving the model's performance. Although all the datasets are graph-structured, they come from different domains and typically have distinctive characteristics. Biological networks such as Yeast and Ecoli often possess specific topological structures and functional modules, which can impact the model's performance. Compared to other datasets, the SSP-AA model performs best on the NS dataset, accurately predicting all missing relationships in the dataset. This is because the NS dataset has a particular graph structure and feature distribution, allowing the SSP-AA model to automatically assign weights to neighboring nodes for precise aggregation of their information and more effective learning of the inter-node relationships. Simultaneously, the skip knowledge module allows the model to share information between different graph neural network layers. In the dataset, both local and global graph structure information significantly contribute to the link prediction task. By using the skip knowledge module, the SSP-AA model can better integrate information of different scales, thereby enhancing prediction performance. In summary, the SSP-AA model achieves impressive results on most datasets. The introduction of the two modules enables the SSP-AA model to more effectively learn the relationships between nodes, capture neighborhood information at different scales, and enhance the model's generalization ability. Figure 7 shows the AUC line charts of the SSP-AA model compared to other models on seven datasets. As can be seen in Figure 7, the performance advantages of different methods across multiple datasets can be intuitively observed. Overall, among a multitude of models, the SSP-AA model achieves the best results.
## 4 Analysis of Numerical Results
### Lateral Deformation Characteristics of a Caisson with Reinforcement
#### 4.2.2 The Influence of the Reinforcement Angle  
In addition to the reinforcement thickness, the effect of the reinforcement angle on the lateral performance of the caisson is further analyzed in this section. In the numerical analysis, the height of the caisson reinforcement \(h=4\) m, the thickness \(t=4\) m, and the thickness of the disturbance ring \(\delta=2\) m. The load-lateral displacement distribution curves at the loading points of the caisson for different reinforcement angles are shown in Figure 13. It can be seen from the figure that the horizontal stiffness of the caisson increased with the gradual increase in the reinforcement angle. However, when the reinforcement angle is greater than \(60^{\circ}\), continuing to increase the reinforcement angle has little effect on improving the horizontal stiffness and reducing the horizontal displacement of the open caisson, and the optimum reinforcement angle is approximately \(60^{\circ}\).  
Similarly, the variation in the horizontal displacement of the caisson with depth can be analyzed for different reinforcement angles, as shown in Figure 14. It can be seen from the figure that the caisson presents rigid rotational deformation at different reinforcement angles, and the optimum reinforcement angle is approximately \(60^{\circ}\).  
Based on the analysis above, it can be seen that the caisson presents a rigid rotating deformation under lateral jacking force acting at the bottom of the caisson. The rotating center is about 0.2 times the caisson embedment depth, which is quite different with the rotation depth (about 0.7\(\sim\)0.8 times of the caisson embedment depth) when the lateral load is acting at the pile top. In order to improve the lateral stiffness of the caisson effectively, high-pressure rotary jetting can be used to reinforce the soil outside the caisson. A preferred  
Figure 11: The load–lateral displacement curve of a caisson with different reinforcement thicknesses.  
Figure 12: The variation curve of lateral displacement along the depth of a caisson with different reinforcement thicknesses.  
reinforcement thickness is roughly twice the thickness of the disturbance ring, and the reinforcement angle is about 60\({}^{\circ}\).
# Comparative Impact Analysis of Cyclone Ana in the Mozambique Channel Using Satellite Data
## 1 Introduction
### Relationship between Mangrove Ecosystems & Cyclones  
Over the past several decades, annual global mangrove loss is estimated at 1-2%, exceeding rates in many inland tropical forests [14]. Mangroves are defined as highly productive carbon rich ecosystems that receive nutrients from both sea and land. Mangroves not only sustain local ecological ecosystems, they also support local populations that rely on them for fuelwood, construction material, medicine, food from mangrove fisheries, timber, and tannins [15]. Mangrove forests exhibit pronounced zonation, which has been attributed to the species' responses to factors such as river discharge, temperature and precipitation, land surface elevation and salinity of these ecosystems [15]. In a context of sea-level rise, mangrove ecosystems also play an important role in vertical elevation gains, owing to their aerial rooting systems and linked biological processes, such as plant litter and woody debris deposition, root accumulation, sediment trapping and algal mat development on the soil surface [16].  
In Mozambique particularly, mangroves occur almost along the entire coast and act as a crucial first barrier for local populations against damaging tropical storms and sea-level rise [17]. It has one of the largest mangrove areas in Africa (with 3054 km\({}^{2}\)), only second to that of Nigeria (8573 km\({}^{2}\)) [18]. Mangroves are generally threatened by deforestation for firewood or construction and occasionally from oil spills [17]. Mangroves forests in Mozambique decreased in area between 1972 and 2004 from 408,000 ha in 1972 to 357,000 ha in 2004 [18]. This trend is believed to be matched by similar trends in degradation throughout the country. Madagascar on the other hand, displayed mangrove forested areas of 2800 km\({}^{2}\), Africa's fourth largest amount, and 2% of the global distribution. It, however, suffers from a 1-2% deforestation rate on average [19]. As these two countries in the Mozambique Channel represent some of the biggest African mangrove ecosystems combined and are increasingly threatened by sea-level rise and tropical cyclones, investigating the impacts of the latter on these ecosystems is vital.
## 5 Precoding as Cooperative and Hybrid Networks  
So far, we have focused on both cooperative and precoding schemes as a means of improving the interference management and the resilience to blocking effects. However, as the size of the VLC network increases, i.e., for a large number of optical APs and users, these schemes are subject to some drawbacks. Cooperative schemes require a large number of orthogonal resources, in order to avoid the inter-cell interference and, as the room size grows, the complexity involved by cooperating from multiple APs increases exponentially. On the other hand, precoding schemes typically assume two conditions; (i) full connectivity between all the optical APs to every user and (ii) linearly independent channel responses between transmitters and users. It can be easily checked that both conditions are affected by blocking effects, since obviously a blocked link means losing connectivity in the VLC network. However, mixing up these two ingredients, cooperative and precoding schemes, it is possible to generate a VLC network providing continuous high spectral efficiency in the whole scenario, good interference management and resilience to blocking effects.
# The Application of Spectral Entropy to P-Wave Detection in Continuous Seismogram Analysis
## 4 Discussion and Conclusions
In addition, this approach can be integrated into automated seismic monitoring systems that perform real-time data analysis. Conceptualization: A.A., S.K. and D.Z. ; methodology: A.A.; software: D.Z. and A.M.; formal analysis: S.K. ; investigation: A.S. and S.K. ; resources: M.I. and A.M.; data curation: A.S., D.Z., M.I. and A.M.; writing--original draft preparation: A.A. and S.K. ; writing--review and editing: A.A.; visualization: A.S.; supervision: M.I. and S.K. ; project administration: S.K. ; funding acquisition: M.I. and S.K. All authors have read and agreed to the published version of the manuscript. This research was funded by the Science Committee of the Ministry of Science and Higher Education of the Republic of Kazakhstan (grant no. AP23488521). Not applicable. All seismic data were downloaded through the EarthScope Consortium Web Services accessed on 15 January 2025 ([[https://www.iris.edu/app/station_monitor/](https://www.iris.edu/app/station_monitor/)]([https://www.iris.edu/app/station_monitor/](https://www.iris.edu/app/station_monitor/))). All of the data collected in this study can be provided directly by the corresponding author upon reasonable request. The facilities of EarthScope Consortium were used for access to the waveforms, related metadata, and/or derived products used in this study. These services are funded through the National Science Foundation's Seismological Facility for the Advancement of Geoscience (SAGE) Award under Cooperative Agreement EAR-1724509. The authors declare that there are no conflicts of interest regarding the publication of this article.
## 4 Results  
The results of the path loss measures fit for the CI and FI prediction models are illustrated in Figure 5, with red color at 850 MHz and blue color at 3.5 GHz. Similar behavior is observed in the value of the PLE \(n=2.25\) and \(\beta=2.21\) and \(n=2.41\) and \(\beta=2.37\) for the CI and FI models, respectively, at the two study frequencies 850 MHz and 3.5 GHz. In both cases, the PLE of the FI model is slightly higher.  
Figure 5: Measured path loss, predicted path loss using the FI and CI models, and mean path loss at 850 MHz and 3.5 GHz.  
Tables 2 and 3 summarize the parameters and the standard deviation of the SF term \(\sigma\) of the FI and CI models at 850 MHz and 3.5 GHz, respectively. These parameters and their corresponding 95% confidence intervals were estimated from the measured data using the MMSE approach through the cftool function of Matlab. The PLE ranges that have been obtained for both models from 2.21 to 2.41 are larger than the free space (PLE = 2). A higher loss exponent was found for FI and CI at 3.5 GHz. It is worth noting that PLE values lower than 2 have been found in an indoor corridor environment for frequencies below 6 GHz [12; 13]. For example, in [12] PLE values of 1.03 to 1.83 were measured for LOS conditions at 3.7 GHz. In [13] PLE values of 1.50 and 1.60 were measured for LOS conditions at 3.5 GHz.  
Figures 6 and 7 show the box plots for each Tx position at 850 MHz and 3.5 GHz, respectively. In each box, the central mark is the median, the edges of the box are the 25 th and 75 th percentiles and the whiskers are the most extreme values of the data points that are not outliers.  
Tables 4 and 5 summarize the statistical path loss at 850 MHz and 3.5 GHz. Note that while Figures 5 and 6 show a higher number of outlier data points for the Tx-Rx separation distances of Tx1-2 m and Tx-16 m at 850 MHz and Tx4-8 m, Tx5-10 m, Tx8-32 m, and Tx9-40 m at 3.5 GHz, the number of outliers is low compared to the total number of data points obtained for each distance. For example, for the Tx4-16 m at 850 MHz, the percentage of outliers is 6.4%.  
as the COST-231 Hata model in an urban environment. Similarly, in [16], measurement campaigns were used to obtain path loss coefficients for 2.4 GHz RF signals. In other work, ref. [17] proposed a statistical path loss characterization using measurements up to deep indoor scenarios. In [18], an algorithm called LOKO has been developed for application to current 5G (or future) deployment processes. On the other hand, work has focused on how to improve data transmission in IoT sensor networks by selecting the best cluster head node using an overlapping clustering method [19], also based on criteria such as distance or received signal strength [20] on fuzzy logic and content-based routing methods [21] or improving security algorithms [22].
# An Overview of the Applications of Earth Observation Satellite Data: Impacts and Future Trends
## 4 Discussion
### Big Earth Data Cloud Processing Platforms  
Big Earth Data, which includes datasets acquired by EO satellites, involves substantial and ever-increasing data volumes, is a necessary support for mankind to promote the monitoring and understanding of the Earth and its environment [98]. However, it is a challenge to process, analyze, and manage big geospatial data effectively [66; 98]. Cloud computing has emerged as a significant tool to process Big Earth Data in recent years [64; 99]. The advantages of cloud computing, easy operation, convenient access and processing of geospatial big data, and substantial computational capabilities, promote the analysis and application of Big Earth Data. The currently popular Big Earth Data cloud processing platforms include GEE, Amazon Web Services (AWS), Microsoft Azure, NASA Earth Exchange (NEX), Sentinel Hub (SH), Open Data Cube (ODC), etc. These platforms have transformed the user community and application of geospatial big data. GEE is a free access platform with substantial computational capabilities and a large amount of geospatial data as well as supports JavaScript, Python, and REST Application Programming Interfaces (APIs). AWS and Microsoft Azure, as pay-as-you-go platforms, support more APIs and provide advanced machine learning services, but do not have a significant advantage in the volume of geospatial big data and the applicability of algorithms. [PERSON], et al. [14] believe that ODC is currently the most ideal solution for geospatial big data. ODC has great potential to manage and analyze multi-dimension data, and has advantages over GEE in open governance, infrastructure replicability, data access interoperability, and extendibility [14]. However, GEE is currently the most significant Big Earth Data cloud processing platform for the remote sensing community due to its ease of use and maturity [100].
# Local Community Detection in Dynamic Graphs Using Personalized Centrality +
## 2 Background  
### Definitions  
Let \(G=(V,E)\) be a graph, where \(V\) is the set of \(n\) vertices and \(E\) the set of \(m\) edges. Denote the \(n\times n\) adjacency matrix \(A\) of \(G\) as  
\[A(i,j)=\begin{cases}1,&\text{if }(i,j)\in E,\\ 0,&\text{otherwise}.\end{cases}\]  
We use undirected, unweighted graphs so \(\forall i,j,A(i,j)=A(j,i)\) and all edge weights are 1. Algorithms can apply the linear operator in \(A\) by traversing the graph \(G\) without constructing a matrix. Much real data today is constantly changing, requiring support for dynamic graphs. We can think of a dynamic graph representation as a series of snapshots of the graph \(G\) taken at different points in time \(t\). We denote the current snapshot of the dynamic graph \(G\) and corresponding adjacency matrix \(A\) at time \(t\) by \(G_{t}=(V_{t},E_{t})\) and \(A_{t}\), respectively. In this work, the vertex set is constant over time so \(\forall t,V_{t}=V\). Our algorithms handle both edge insertions and deletions. Given edge updates to the graph, we can write the new adjacency matrix at time \(t+1\) as \(A_{t+1}=A_{t}+\Delta A\), where \(\Delta A\) represents the new edges being added to (or removed from) the graph.  
To reduce subscripts slightly, each change prefixed by \(\Delta\) is associated with time \(t\).  
We represent a community \(C\) in the graph as a group of vertices \(\{v_{1},v_{2},\cdots,v_{|C|}\}\).
# MobiDabet: Mobile Telemonitoring System for Patients with Type 2 Diabetes Mellitus (T2 DM)
## 2 Materials and Methods
### Component Diagram  
The architecture and technologies used in the proposed system are presented through a component diagram; each element of this diagram is an essential part of the system, see Figure 8. In this regard, we rely on the model-view-controller (MVC) pattern, which separates the business logic from the user interface as follows:  
* Model: This component is exclusively responsible for managing the application data and storing the information that users add.
* View: This represents the model state, without being directly linked to this component; its goal is to show the information to the user.
* Controller: This component represents the link between the model and the view. It is responsible for notifying the model when the user manipulates the view and is responsible for managing the changes that the user makes.  
The main advantages of the MVC pattern include the scalability of the application; it favors the adaptation and growth of the application and the maintainability, because it is composed of independent modules, and changes in modules would cause a minor impact on parts that could be associated [14].  
Figure 8: Diagram of system components.  
As the component diagram shows (Figure 8), the technologies adopted for the View were HTML and Angular, which makes requests to the Spring MVC controller. These requests are executed through Web services (RESTful) connected through objects to the Spring entities, which apply Data Access Objects (DAO) connection entities. DAOs are connected to the database model in PostgreSQL by means of the Java Persistence API (JPA). The description of the components is as follows:  
* Web mobile application: Handles elements present in the graphical user interface.
* Data Access Object: Maps the information that is stored in the database. This mapping is performed to manipulate the information from code before appearing at the graphical interface or database.
* Spring MVC controller: Controls the application logic and responds to requests from the graphical interface or some other process that perform requests.
* Database: Manages the system's database.  
Figure 8 shows the proposed technologies to implement the components of the system. A description of these technologies is as follows:  
* PostgreSQL is an open-source relational database system.
* Angular is a web application development framework made in JavaScript by Google. Angular is flexible to work with different patterns.
* Java is a multiplatform programming language that can be used to create different applications for personal computers, mobile devices, or the web.
* Spring MVC is an open-source framework that works as a framework for the Java platform.
## 4 Results
### Ease-of-Use Perceptions  
First, a contingency table with the frequencies of ease-of-use questions per sex and condition is provided (see Tables 5 and 6). Chi-square tests were carried out to evaluate the possible differences of sex and condition in the answers regarding the factors of adequate use and comfort.  
Regarding the adequate use of the software tool, only one subject (a male in the non-gamified condition) answered 'No' to that question. Therefore, no significant differences were found across gender and condition in general or in particular for any combination of factors.  
As for subjects' perceptions of comfort, in the gamified version, the relation between gender and comfort was significant (\(\chi^{2}\) (1, N = 32) = 12.09, \(p\) < 0.001, \(\varphi_{\rm c}\) = 0.62), meaning that males felt more comfortable than females in the gamified version. However, there was no significant association between sex and comfort in the non-gamified condition (\(\chi^{2}\) (1, N = 30) = 0.87, \(p\) = 0.351). When considering both conditions together, males also felt  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Condition**} & & \multicolumn{2}{c}{**No**} & \multicolumn{2}{c}{**Yes**} & \multicolumn{2}{c}{**Total**} \\ \cline{3-8} \multicolumn{2}{c}{} & & **N** & **\%** & **N** & **\%** & **N** & **\%** \\ \hline \multirow{3}{*}{Not Gamified} & \multirow{3}{*}{Sex} & Female & 0 & 0.00\% & 16 & 55.20\% & 16 & 53.30\% \\  & & Male & 1 & 100.00\% & 13 & 44.80\% & 14 & 46.70\% \\ \cline{2-8}  & \multicolumn{2}{c}{Total} & 1 & 100.00\% & 29 & 100.00\% & 30 & 100.00\% \\ \hline \multirow{3}{*}{Gamified} & \multirow{3}{*}{Sex} & Female & 10 & 55.60\% & 5 & 35.70\% & 15 & 46.90\% \\  & & Male & 8 & 44.40\% & 9 & 64.30\% & 17 & 53.10\% \\ \cline{2-8}  & \multicolumn{2}{c}{Total} & 18 & 100.00\% & 14 & 100.00\% & 32 & 100.00\% \\ \hline \multirow{3}{*}{Total} & \multirow{3}{*}{Sex} & Female & 10 & 52.60\% & 21 & 48.80\% & 31 & 50.00\% \\  & & Male & 9 & 47.40\% & 22 & 51.20\% & 31 & 50.00\% \\ \cline{1-1} \cline{2-8}  & \multicolumn{2}{c}{Total} & 19 & 100.00\% & 43 & 100.00\% & 62 & 100.00\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Contingency table of question: Were you satisfied with your results using the tool?comfortable in a higher proportion than females (\(\chi^{2}\) (1, N = 62) = 10.33, \(p\) = 0.001, \(\varphi_{\text{c}}\) = 0.41). In addition, the analysis of the condition (gamified/non-gamified) by gender showed that there were no significant differences in males (\(\chi^{2}\) (1, N = 31) = 1.26, \(p\) = 0.263) and overall (\(\chi^{2}\) (1, N = 62) = 1.35, \(p\) = 0.245). However, females in the non-gamified condition presented a significantly higher proportion of comfort than the females in the gamified version (\(\chi^{2}\) (1, N = 31) = 4.04, \(p\) = 0.044, \(\varphi_{\text{c}}\) = 0.36).