esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Methods
#### 2.3.2 Processing of Satellite-Based NDVI-Time Series  
MODIS HDF products were re-projected from the sinusoidal projection to geographical projection WGS84 and converted from an HDF to a GeoTIFF format using the MODIS Reprojection Tool. Both the eight-day red band and NIR band data were then calculated to eight-day NDVI data and stacked to the original 8-day NDVI time series. After preprocessing, TIMESAT software was used to remove noise and generate the smoothed eight-day NDVI time series using a Savitzky and Golay filter [57].  
We obtained monthly NDVI values for each crop during the dry season, which are shown in Table 1 according to each study site, to use as input variables for the host-plant phenology factor for model development. The method was as follows:  
1. The buffer was drawn with a radius of 10 km around the light trap. This distance is considered appropriate in terms of the migratory behavior of BPH [58].
2. Only the rice paddy fields that are within the buffer from the land use map were extracted.
3. Only the grids within the rice paddy fields from the smoothed eight-day NDVI-time series data obtained from MODIS were selected (Figure 3). The values in the grid are the NDVI profile of the rice at that grid location from 2006 to 2016.
4. Then, the NDVI profiles from each grid were classified into ten groups using the k-mean clustering classification method, so that they have a similar NDVI profile pattern within each group. This classification method can be used to identify and monitor the same information in the same group based on Euclidean distance analysis [59].
5. The start date of season data was extracted from the smoothed eight-day NDVI-time series data using TIMESAT software.
6. One of ten groups that has a sowing date similar to the start date of season was selected based on the start date of season data.
7. The selected NDVI profile group was analyzed using median statistics in order to obtain a single NDVI profile that could represent the rice paddy field area in the buffer zone.
8. Finally, the eight-day NDVI-time series data were used to compute monthly means.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
## 1 Introduction
[38] carried out numerical and finite-element analysis (FEA) for simulating drilling in CFRP laminates and the results were compared with the experimental result. The numerical analysis was carried out using a genetic algorithm. The results indicate that the variations observed using the FEA showed 20% variation and the genetic-algorithm-based mathematical model showed a variation in the range of 10%. The numerical analysis also identified the optimised condition for the input parameters for the improved output parameters. Identification of the optimum input parameters had been a major challenge in the machining of the composites using EDM. In an EDM process, a spark between the tool and the workpiece through a dielectric medium leads to material removal from the workpiece. The process of material removal is very complex, as the phase change from a solid to plasma state of the material of the workpiece and the removal of material by the flowing dielectric also plays a vital part in the proper material removal and the surface roughness of the final component. Proper removal of the materials from the workpiece due to sparks reduces the chance of formation of a white layer on the surface of the workpiece. The re-solidification of the molten metal during the pulse-off time leads to the formation of a white layer on the surface of the workpiece. This formation of white layers on the machined surface of workpiece due to resolidification of the melted material also adds to the uncertainty of the output parameters such as MRR and surface roughness. The referred works indicate that the material property of the workpiece had a vital role in the output parameters of the machining. This work aims to predict the output parameters linking it with hardness, which is identified to be one of the major properties of a composite material [14]. This work aims to provide a prediction of the different output parameters based on the experimental values obtained by [PERSON] [16], [PERSON] [15], [PERSON] [2] and [PERSON] [1] in the machining of different combinations of Al6061 composites and to compare the mathematical models to identify and predict the output parameters based on the variation of these parameters. The mathematical model was validated by carrying out experiments on a different combination of Al6061 composite, viz. Al6061-1% SiC metal matrix composite. The objectives of the present work are:  
* To identify the optimum mathematical model for Al6061 metal matrix composite with different combinations of reinforcements, mainly SiC;
* To identify the pattern of variation of coefficients of different input parameters used in the mathematical model based on the hardness of each material;
* To create an equation for each coefficient, thereby predicting the mathematical model for a new combination once the hardness of the material is obtained;
* To verify the derived equations by carrying out experiments on Al6061-1% SiC MMC and comparing the model results with the experimental results.
## 4 Results and Discussion  
At the beginning of the result analysis, it is necessary to briefly discuss the physical and mechanical properties of the prepared MMs (see Table 2). The requirement was that the SCC strength class be at least C 50/60. Additionally, the MMs were required not to be worse than the vibro press compacted concrete from the straight section of the pipes. The strength class requirement must be verified on concrete; however, this only makes sense for those MMs and corresponding concretes that have successfully passed the durability test. It is already evident that concretes derived from mortars with a compressive strength  
Figure 8: Principles of the TBTC construction.  
higher than around 90 N/mm\({}^{2}\) will certainly meet the strength condition. There are five such mortars in the experimental setup. Only reference mortars M1, M2 and M3 did not achieve higher compressive strength than 90 N/mm\({}^{2}\) after 28 days of aging. The worst of them is mortar M3, which is M2 (model mortar for standard SCC) modified with the crystallization admixture Xypex Admix C-1000. This crystallization admixture was the biggest disappointment of the entire tested MM set. The biggest issue of the Xypex Admix C-1000 admixture occurred already during the preparation of the MM, when M3 required a 45% increase in the plasticizer dosage to achieve the same consistency as the initial M2. In absolute terms, this is an increase of 5 kg/m\({}^{3}\), which means, among other things, an increase of the amount of water by 3-4 L/m\({}^{3}\). As a result, M3 was by a few % worse in all indicators than the original M2. In terms of compressive strength, on the other hand, the majority of the other MMs can be evaluated positively as almost all have achieved compressive strengths higher than 100 N/mm\({}^{2}\). An excellent result has been achieved with the MM with GBPS--the average compressive strength after 28 days was 134.4 N/mm\({}^{2}\). This constituted a promising precondition for improved resistance in aggressive sulfate environments.  
The following part includes a graphical representation of the parameters of selected corrosion indicators of all the tested model mortars placed in the environment of 5% sodium sulfate, as well as in the water bath. In all the graphs, each plotted point represents an arithmetic mean of the measurements on four test specimens and the error bars represent the sample standard deviation.
# CrowdPower: A Novel Crowdsensing-as-a-Service Platform for Real-Time Incident Reporting
## 3 System Architecture and Design
### CrowdPower's Web Service Interfaces  
CrowdPower uses RESTful APIs to reveal its functionality as services. The advantage of using RESTful APIs is that they are lightweight and provide a standardized communication format that is understood by various HTTP-based client applications regardless of  
Figure 5: Collaboration diagram: producer uploads sensor data for an incident.  
Figure 6: Collaboration diagram: Creation of voluntary sensing request.  
the system framework. Moreover, RESTful APIs ensure scalability, which is essential when new services must be introduced [38]. Table 3, provides some examples of RESTful APIs, from the user management, community management, and incident management categories with respective actions.  
To interact with the APIs, the application authenticates the user. The authentication is handled through JWTs (JSON Web Tokens). Any authenticated request presents the JWT in the authorization header of the HTTP request. All the API end points are not necessarily authenticated. The application must provide valid credentials (email and password) during login to obtain an API token. The application handles invalid/error states, such as losing Internet connection in the middle of sensor upload or invalid user credentials. Standard HTTP errors, such as 404-Not Found and 500-Internal Server Error are used. Standard HTTP verbs such as POST, GET, PUT, PATCH, and DELETE are used for initiating actions. Since PATCH is not idempotent, failed requests are not automatically re-attempted on the network. In addition, if a PATCH request is made to a non-existent URL, it simply fails without creating a new resource, unlike PUT, which would create a new resource using the payload.
## 5 Case Studies
### Inertia Response Analysis of Wind Farms  
In order to assess the efficacy of the proposed methodology in accurately determining the inertia of the wind farm across different control modes and wind speeds, as well as to evaluate the influence of various inertia control parameters on the equivalent virtual inertia of the DFIG wind farm, four distinct operating conditions are examined. The analysis prioritizes the effects of inertia control parameters while maintaining other variables at constant levels. Taking a wind farm with integrated inertia control as a case study, the specific conditions under consideration are as follows:  
1. The average wind speeds for the doubly fed wind farm are set to 9 m/s and 10 m/s, with the control parameters defined as follows:\(k_{p1}=6\), \(k_{i1}=2\), \(k_{p}=15\), \(k_{d2}=10\), \(T_{f}=0.5\);
2. The average wind speeds for the doubly fed wind farm are set to 9 m/s, with the control parameters defined as follows:\(k_{p1}=6\), \(k_{i1}=2\), \(k_{p}=20\), \(k_{d2}=10\), \(T_{f}=0.5\);
3. The average wind speeds for the doubly fed wind farm are set to 9 m/s, with the control parameters defined as follows:\(k_{p1}=6\), \(k_{i1}=2\), \(k_{p}=15\), \(k_{d2}=15\), \(T_{f}=0.5\);
4. The average wind speeds for the doubly fed wind farm are set to 9 m/s, with the control parameters defined as follows:\(k_{p1}=6\), \(k_{i1}=2\), \(k_{p}=15\), \(k_{d2}=10\), \(T_{f}=1\).  
The simulation data collected under diverse wind speed conditions and various control parameters were input into the identification models of the individual control modules to facilitate iterative optimization. A portion of the operational data is presented in Figure 7. The identification outcomes for the critical control parameters are compiled in Table 5.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **Actual Values** & **Identified Values** & **Error/\%** \\ \hline \(H_{wind}\) & 5.04 & 4.95 & 1.79 \\ \hline \(k_{p1}\) & 6 & 6.144 & 2.4 \\ \hline \(k_{i1}\) & 2 & 2.073 & 3.65 \\ \hline \(k_{p}\) & 15/20 & 14.268/18.986 & 4.88/5.07 \\ \hline \(k_{d2}\) & 10/15 & 10.587/15.738 & 5.87/4.92 \\ \hline \(T_{f}\) & 0.5/1 & 0.516/0.954 & 3.20/4.60 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Partial parameter identification results at 9 m/s.
## 3 Results
### Screening for Changes in Serum miR's Associated with PDAC Progression in Mice and Patients
The PFS of the patients did not correlate with the resection margin: patients with R0 resection had a median PFS of 11.8 months (3.1-20.6 months 95% CI), whereas patients with R1 resection had a median PFS of 10.3 months (8.2-12.5 months 95% CI). We compared the miR levels upon tumor resection in the total patient cohort (N = 28), divided in three groups: \(n\) = 9 with short PFS (\(<\)8 months); \(n\) = 13 with median PFS (8-16 months); and \(n\) = 6 with long PFS (\(>\)16 months). The average expression levels of miR-122-5p, miR-99a-5p, and miR-125b-5p before and after surgery are shown in Figure 2. Although the differential changes in expression of miR-122-5p, miR-99a-5p, and miR-125b-5p between the three groups did not reach statistical significance, the serum expression of these three miR show a trend of upregulation after tumor resection in patients with short PFS, and a trend of downregulation after tumor resection in patients who have a long PFS of \(>\)16 months. On the other hand, when analyzing the association of the serum miR levels with PFS by using a Cox regression model, two serum miR after resection (miR-99a-5p and miR-125b-5p) were identified as significantly associated with shorter PFS (HR 1.73 and HR 1.34 respectively), shown in Table 2. In summary, we found that miR-125b-5p and miR-99a-5p are circulating prognostic indicators in KPC mice and patients with pancreatic cancer. Figure 2: Pre- and post-resection expression levels of three serum miRs in PDAC patients with different PFS. Average expression levels of serum miRs 122-5p; 99a-5p or 125b-5p relative to the expression of two reference miRs, before and after surgery in three groups of patients. Short PFS in red = 0–8 months; median PFS in gray = 8–16 months; long PFS in green \(>\)16 months. Note the log scale of the \(y\)-axis. Error bars are SEM, \(P\)-values by one-way ANOVA comparing the three patient groups.
# Optimizing Latent Factors and Collaborative Filtering for Students' Performance Prediction
## 2 Literature Review  
Many DM and ML technologies have been applied to tackle the analysis of students' behavior in academic environments such as online campuses [9; 10]. From the point of view of a learning process, there are many potential applications of the prediction techniques. For example, identifying the students' performance at earlier stages [11]; predicting their success in the current semester based on data from the previous semester and the scores of internal examinations [12]; early warning systems as a means of identifying students at risk [13]; identifying students at risk of dropping out in early stages [14]; predicting the difficulties that students will face in order to inform about digital course design sessions [15]; determining optimal choices of courses for the following term [16]; predicting if students can be warned about their potentially poor performance by looking into both cognitive and non-cognitive features [17]; using chatbots for supporting students on some courses [18]; etc.  
As regards the DM and ML algorithms considered in these learning contexts, they have been applied with success according to different approaches. Next, we cite a selection of these applications: CF [19]; Recursive Clustering (RC) [11]; Bayesian Knowledge Tracing (BKT) [20]; Bayesian Additive Regressive Trees (BART), Random Forests (RF), Principal Components Regression (PCR), Neural Networks (NN), and Support Vector Machine (SVM) [13; 15; 21]; Deep Learning (DL) [22]; survival analysis approaches [14]; decision trees and meta classifiers [23]; Markov Decision Process (MDP) [16]; K-means and hierarchical clustering algorithms [24]; etc. Specifically, there are some approaches to PSP and other similar problems that utilize RS and MF, for different [19; 25; 26; 27; 28], specific purposes in the online learning context.  
In this work, we apply matrix factorization for solving PSP by improving the prediction accuracy so as to determine, among other questions, the best number of latent factors. Previous studies have analyzed the latent relationship among students, activities, and performances for particular cases; e.g., finding latent structures from questionnaires [29]; modeling latent constructs in order to obtain data that inform research and practice on the measurement of teachers' performance [30]; latent profile analysis of students with common characteristics, based on teacher ratings of their behavior [31; 32]; identifying latent correlations between aggregated student perceptions of the teaching ability of the teachers themselves [33]; etc. Nevertheless, fewer works have focused on identifying the number of latent factors, even for generic environments, which is the main purpose of this paper. To cite but one of the few examples of previous studies in the relevant literature, an empirical approach based on statistical techniques has been applied to the investigation of hidden factors underlying the quality of student-teacher interactions [34].
# Event Coverage Detection and Event Source Determination in Underwater Wireless Sensor Networks
## 1 Introduction
Generally, an event source can be identified as a barycenter in the graph of sensor nodes, whose sensory data deviate the most in value with respect to a normal sensing range. * Extensive simulations have been conducted to evaluate the effectiveness and efficiency of our event coverage detection and event source determination mechanisms. The results show that our technique is more energy efficient, especially when the network topology is relatively steady. The rest of this paper is organized as follows. Section 2 briefly introduces the network model and our routing tree construction and maintenance strategy. Section 3 presents our event detection method and sensory data aggregation mechanism. Section 4 detects the event coverage and determines event sources. Section 5 presents the experimental evaluation of our technique. Section 6 reviews and discusses relevant techniques, and finally, Section 7 concludes this work.
## 4 Multihoming and Interworking
### Multipath Support  
When an edge network is multihomed, its nodes have multiple paths to destinations in other domains, but only a single path can be used in the current Internet. However, networking could benefit from using all available paths [38, 39]. For example, a node could balance traffic over multiple paths to maximize its throughput or it could improve fault tolerance [40, 41]. The Stream Control Transmission Protocol (SCTP) [42] takes advantage of that. Multipath support requires that hosts can determine through which  
Figure 9: Reflection of local traffic: classic IPv6-GLI-node 4 communicates with GLI-node 3 via global addresses. The gateway of their GLI-domain reflects the traffic between the hosts to ensure addressing symmetry.  
gateway their traffic should be carried. If both the source and the destination network are multihomed, multipath routing could enforce specific gateways both in the source and destination domain. With GLI-Split this can be achieved as follows. A GLI-node queries the global mapping system for the set of its own global GLI-addresses and the one of its corresponding node. Each combination of global source and destination GLI-addresses represents a different path. These paths are not necessarily entirely disjointed, but possibly so on the last mile between the customer and the provider network, which is often the slowest and most error-prone part of the path. To send traffic over a specific path, a GLI-node selects the appropriate GLI-gateway for its outgoing traffic (see Section 4.2.1.) and uses the appropriate global GLI-address for the destination node to select a specific GLI-gateway in the destination domain.
# Research on the SSUKF Integrated Navigation Algorithm Based on Adaptive Factors
## 5 Conclusions  
In response to the issue of the standard UKF algorithm requiring precisely known prior statistical information about measurement noise, this paper conducts an in-depth analysis of the limitations of traditional algorithms and proposes a UKF algorithm based on an adaptive factor. This algorithm introduces an adaptive mechanism that can estimate changes in measurement noise in real time and dynamically adjust the noise covariance matrix. It ensures accurate and real-time estimation of measurement noise changes while maintaining efficiency, avoiding the issues of filter divergence or accuracy degradation caused by inaccurate noise estimation. The simulation results show that ASSUKF improves accuracy by 18.10% in the latitude direction and 20.6% in the longitude direction. In terms of attitude errors, ASSUKF improves the pitch angle error by 27.6% compared to UKF and by 27.1% compared to SSUKF. The roll angle error is improved by 29.9% compared to UKF and by 20.1% compared to SSUKF. The heading angle error is improved by 24.3% compared to SSUKF. These results demonstrate the excellent filtering performance of ASSUKF. Although ASSUKF performs excellently in simulations, it lacks validation with real-world data. Its performance in practical applications may be influenced by various factors. In the future, it would be beneficial to collect data from real-world environments to verify the stability and robustness of the proposed algorithm in complex settings.  
Conceptualization, Z.D. and Y.Z.; methodology, Z.D. and Y.Z.; software, Z.D.; validation, Z.D., Y.Z. and Y.G.; formal analysis, Z.D.; investigation, Y.Z.; resources, Z.D.; data curation, Y.G.; writing--original draft preparation, Z.D.; writing--review and editing, Y.Z.; visualization, Y.Z.; supervision, Z.D.; project administration, Z.D.; funding acquisition, Z.D. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in the study are included in the article, and further inquiries can be directed to the corresponding authors.  
We thank the Research Centre for the Convergence of Wireless Network Communication and Navigation for their support of this research.  
The authors declare no conflicts of interest.