esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 DST-LEO Satellite-Traffic Prediction Model
### Traffic Collection of Satellite Bearer Networks  
To collect traffic data of low Earth orbit (LEO) constellations from the bearer network, the following steps are generally involved. Firstly, it is necessary to determine the types of traffic data. These include traffic volume, traffic direction, protocol types (such as TCP and UDP), source and destination IP addresses, etc. Secondly, the traffic collection points need to be identified. The network control function (NCF) is responsible for network control and management and can obtain global network status information. The border router, as a network boundary device, is in charge of communicating with other networks (such as ground stations or other satellite networks) and can capture the traffic data flowing in and out of the network. The simple network management protocol (SNMP) is used to collect network status information from the NCF such as traffic volume statistics and link connection/disconnection status. The port mirroring function of the border router is configured to copy the traffic to the monitoring port, and then a packet-capturing tool is used to capture the data. Finally, the collected data are stored in a database as structured data, which facilitates subsequent data processing.
# Toward Predicting Student's Academic Performance Using Artificial Neural Networks (ANNs)
## 4 Results
### Studies Characteristics  
Remarkably, the research interest in using ANN to predict student performance has been growing over the last few years, with a substantial increase in the number of studies between 2018 and 2019, as Figure 2 depicts. However, the decline in publishing output (2019-2021) cannot be considered a trend because the database search was completed before the end of 2021. The significantly low number of publications during this timeframe (2013-2021) can be attributed to the research topic's low maturity due to its novelty. As illustrated in Figure 2a, this review period was from 2013 to 2021, with the literature sourced from peer-reviewed journals and conference proceedings through a database search. In detail, 85.7% of the literature were journal papers, and 14.3% were conference proceedings (Figure 2b).  
Figure 1: Studies selection and screening flowchart.  
More than half of the studies (57.1%, n = 12/21) were undertaken in Asia (see Figure 3), 14.3% (n = 3/21) in Europe, 14.3% (n = 3/21) in South America, 4.8% (n = 1/21) in North America, and 4.8% (n = 1/21) in Africa. Furthermore, one study was undertaken by a Turkey-based researcher, but it was unclear where the analysis was conducted. A total of 295,354 participants were involved in the selected studies. The sample sizes ranged from 150 to 162,030 participants, with a mean of 156.015, indicating that ANNs were used on large datasets.
## 6 Conclusions  
Near-space offers a significant new opportunity for radar remote sensing techniques for several reasons. Firstly, it supports uniquely effective and economical operations. Secondly, it enables a new class of especially useful intelligence data. Thirdly, it provides a crucial corridor for prompt regional strike. Inspired by the promise of near-space, this paper reviews the applications of near-space in microwave radar remote sensing. Several near-space radar configurations including passive radar and high-resolution and wide-swath SAR are described, along with the corresponding potential applications, technical challenges and possible solutions. It is shown that the novel use of cost-effective near-space radar can provide the promising solutions that were thought to be out of reach for remote sensing scientists and customers. We do not advocate eliminating satellites or airplanes; however, in many circumstances near-space platforms are indeed the best choice for providing some specific remote sensing applications. It is evident that there are large niches where near-space platforms perform much better than satellite and airplane. Once we understand that it is effects instead of the platform from which the effects are delivered, near-space makes much sense for the remote sensing applications described. Although exploring the potentials of near-space in microwave radar remote sensing applications require significant work on many fronts, we are indeed convinced the effort will be worth it.
## 3 Results and Discussion
### Repeatability Test  
To ensure the consistency and reliability of the measurements, a repeatability study was performed for sound speed measurements using the previously described experimental setup. In this study, measurements were taken 10 times by placing the transducer in the same location on one of the printed PLA specimens. Tests were performed for both types of waves, longitudinal and shear, since both of these speeds are used for calculating the elastic properties. As a result, the mean value for longitudinal speed of sound was 2044.98 m/s with a standard deviation of 1.82, which is 0.09%. For the shear speed, the mean value was 1042.81 m/s with a standard deviation of 22.61 (2.16%). An example of a collected ultrasonic signal is illustrated in Figure 6. The variation in the received signals may be caused by the slight variability in contact pressure between the sensor and the specimen, which is difficult to control and maintain at the same level. The force with which the sensor is pressed against the specimen affects the thickness of the bonding medium, which is likely to cause variations in the signal.  
Figure 5: Effects of printing parameters on density of PLA specimens. The trend for each group of specimens is shown by the dotted line.
## 4 The Proposed Framework
### The Methodological Framework
#### 4.1.1 Data Acquisition  
The initial step involves acquiring high-resolution geospatial data. Landsat 8 imagery is selected due to its 30 m \(\times\) 30 m spatial resolution, which balances detailed urban analysis with manageable computational requirements. Two critical datasets are derived from this imagery:  
* Land surface temperature (LST): LST data are extracted from the Thermal Infrared Sensor (TIRS) bands of Landsat 8. These bands capture thermal radiation, which is then converted into surface temperature values using the split-window algorithm. LST serves as the primary indicator of urban heat intensity.
* Normalized difference vegetation index (NDVI): The NDVI is calculated from the near infrared (NIR) and red bands of Landsat 8. This index measures vegetation density,  
Figure 1: Flowchart of the model.  
providing insight into the cooling effect of green spaces in urban areas. NDVI is essential for understanding the interplay between vegetation cover and heat distribution.  
The acquisition process also includes quality checks to filter out poor-quality imagery, such as those affected by cloud cover or extreme atmospheric conditions, ensuring reliable inputs for subsequent analyses. The selection of satellite images for the heatwave period is carried out by analyzing heatwave periods according to the bulletin of the Italian Ministry of Health.
## 5 Numerical Applications  
The current section applies the software developed for both pre-and post-earthquake seismic damage assessment of RC buildings. Two different cases of data availability are considered, namely full data availability for RC buildings used for the pre-earthquake assessment and limited data availability for the case of post-earthquake assessment. The latter is frequently the case in real-time applications, where the need for rapid estimation of the seismic damage level is evident, along with the difficulties for structural data collection. In this case, the parametric investigation considering several scenarios regarding the values of the required structural parameters of the examined RC buildings that are not known is performed (Section 4.4). The ability of the developed software to instantly provide the classification of an RC building to the pre-defined DS, based on the use of the embedded MFPNN, makes the parametric investigation possible, even in real-time analyses.
## 1 Introduction
In contrast, while dealing with the machine learning models, the advantages are they do not need statistical assumptions before analysis and they can deal with data of various types, i.e., categorical and continuous [13]. In this regard, the ensemble models can resolve nonlinear difficulties and complexities [24]. In recent decades, numerous studies were conducted using the ensemble model approach which has shown greater effectiveness in the spatial assessment of various environmental hazards viz. gully erosion susceptibility assessment [25,26], groundwater contamination risk assessment [27], groundwater potential mapping [28], flood risk assessment [29], and landslide vulnerability assessment [30,31]. The main purpose of the predictive models is to recognize the LSS extents depending upon the nature of spatial relationship between causative factors and previous landslides. In this work,we used landslide causative factors using remote sensing data and applied several machine learning models to produce landslide susceptibility maps (LSMs) of Rudraprayag district in Uttarakhand. Subsequently, LSMs were ensemble to attain better accuracy that can be achieved from a single model. The ensemble of the models in the present analysis was implemented in four-stages, whereby in the first stage, individual models were calibrated and then a two-model combination approach was performed, i.e., artificial neural network-random forest (ANN-RF), ANN-support vector machine (ANN-SVM), SVM-RF, SVM-logistic regression (SVM-LR), LR-RF, and LR-ANN. In the second stage, LSS model calibration was performed compiling three model ensemble approaches, i.e., ANN-LR-RF, ANN-RF-SVM, ANN-SVM-LR, RF-SVM-LR. Finally, all four models were combined.
## 4 Results
### Categorization Accuracy  
To investigate which morphological analyzer is effective for topic categorization, the categorization accuracy for the entire dataset for each morphological analyzer was calculated (Figure 4). To calculate categorization accuracy for each morphological analyzer, the feature vectors containing each token and its probability extracted by TF-IDF (described in Section 3.3) were used as input LGBM with parameters (maximum number of leaves: 31; number of boosted trees: 100). The total dataset was divided by 10; eight were used for training the LGBM, and two were used for calculating test accuracies with the trained LGBM. Also, the baseline categorization accuracy was computed without applying a morphological analyzer, to investigate the effects of each morphological analyzer. Categorization accuracies and F1 score for the morphological analyzers and baseline are tabulated in Table 3.  
Most morphological analyzers perform well, with more than 80% accuracy for categorizing news topics. Hannanum has 73.26% accuracy, which is the lowest. Hannanum is sensitive to the input format, such as the spacing of the words and abbreviations. The news articles contain several abbreviations; thus, the morphological analysis might not work well. In contrast, the Kkma morphological analyzer achieved the highest performance, which may come from its detailed POS tagging process. These results indicate that Hannanum is not a good candidate and that Kkma could be an ideal analyzer for news article topic categorization.  
To investigate how each morphological analyzer performs for each topic category, the classification accuracy for each topic for each morphological analyzer was calculated (Figure 5). In terms of the average across the topics, the classification accuracy of Sports was the highest because the topic includes several keywords, such as \"basketball\" and \"soccer\".  
Figure 3: Computational time of each morphological analyzer in the dataset.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Accuracy** & **F1 Score** \\ \hline Baseline & 69.1\% & 0.667 \\ \hline Okt & 81.7\% & 0.812 \\ \hline Mecab & 82.5\% & 0.824 \\ \hline KKma & 82.1\% & 0.817 \\ \hline Hannanum & 73.1\% & 0.724 \\ \hline Komoran & 81.8\% & 0.809 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Averaged categorization accuracy and F1 score for the baseline and morphological analyzers.  
The topics with the second highest accuracy were Politics and World, which also contain technical terms. In contrast, for Economy and Society, there tended to be few keywords; thus, the accuracy was low for these topics for all morphological analyzers. By inspecting each morphological analyzer, it was found that Hannanum had significantly low accuracy for all topics except for Society. This outcome might be due to the mentioned problem with the input format of abbreviations or the spacing of words. These findings indicate that Hannanum poorly categorizes all news topics and that the sports topic is well categorized by most morphological analyzers.
# Research on the Path Planning of Unmanned Sweepers Based on a Fusion Algorithm
## 3 Method  
In order to solve the problems of many redundant points, uneven paths and low search efficiency in the path planning of A* algorithm, this paper screened and extracted the nodes in the path of A* algorithm, deleted the redundant points in the path, and improved the smoothness of the path. At the same time, heap sort was used to improve the storage structure of algorithm nodes to improve the search efficiency. Unlike a road, a large campus such as a park has a large number of buildings and traffic off-ramps, which results in a large number of turning points. The traditional A* algorithm has the defect of unsmoothed path, which leads to the unmanned vehicle not being silky smooth when driving and turning according to the path planning. At the same time, A* algorithm has the problem of many redundant points in planning, which leads to more time spent in path planning. Therefore, in order to solve these problems, this paper filtered and extracted the nodes in the path of A* algorithm, removed the redundant points in the path, and improved the smoothness of the path using minimum snap. At the same time, heap sorting was used to improve the storage structure of the algorithm nodes and to improve the search efficiency.
## 3 Results: Prediction of Wear Characteristics in a Mining Pump
### Wear Predictions for the Rated-Flow Condition
#### 3.4.2 Wear Rates of the Flow-Passing Components  
Figure 8 illustrates the average wear rates of the first-stage impeller, first-stage guide vane, second-stage impeller, and second-stage guide vane in the rated-flow condition for three different particle sizes.  
Figure 8: Effects of particle size on the wear rates in the rated-flow condition.  
Figure 7: Particle motion trajectories and solids concentrations in the rated-flow condition.  
As shown, the dependence of wear intensity on particle size in the rated-flow condition is similar to that in the low-flow condition. With an increase in particle size, the wear of the first-stage impeller increased; however, the wear at the three other flow-passing components decreased. Therefore, wear was positively correlated with particle size at the first-stage impeller but negatively correlated with other flow-passing components. Further, in the rated-flow condition, the greatest and lowest wear rates occurred at the first-stage guide vane when the particle size was 1 mm (11.7 \(\times\) 10\({}^{-8}\) kg/s\(\cdot\)m\({}^{2}\)) and the second-stage guide vane when the particle size was 5 mm (5.8 \(\times\) 10\({}^{-8}\) kg/s\(\cdot\)m\({}^{2}\)), respectively.
## 2 Study Sites and Methods
### Model Approach  
Exemplary model simulations were carried out for Rostock harbor covering the entire Warnow Estuary including the coastal parts of the western Baltic Sea. The model simulations covered two Hanse Sail events, because the Hanse Sail is most likely the largest emission event of macro-litter into the Rostock city harbor. The aim was to gain insight into whether sinking litter is deposited locally at the sea bottom near the emission site or is transported over longer distances.  
The simulations involved two steps: first, a 3D hydrodynamic model calculated hindcast simulations for the years 2009 and 2010, and second, the results were used for an offline Lagrangian particle-tracking approach. For the former, the General Estuarine Transport Model (GETM) [39; 40] with the General Ocean Turbulence Model (GOTM) [41] as the turbulence closure model was used. The study area is numerically discretized on a structured grid with a horizontal resolution of 20 m and a vertical resolution of 25 terrain-following sigma layers. The meteorological forcing is calculated from the output of the reanalysis product of the German Weather Service (COSMO-REA6) with a temporal resolution of one hour and a spatial resolution of 6 km. Discharge data of the river  
Figure 2: Details of the harbors in Rostock and Kiel, Germany, including name, investigated sea-bottom area (m\({}^{2}\)) and concrete sampling locations within the harbors (numbered red areas). Satellite images by GoogleMaps.  
Warnow were included as daily mean values. [PERSON] et al. [42] provide details on boundary conditions and model validation.  
The main output parameters were staggered horizontal current velocities on an Arakawa C-grid, horizontal eddy viscosity calculated using a Smagorinsky parameterization, and bottom shear-stress based on a quadratic drag, stored with 5 min resolution each. These were used as forcing inputs for the particle-tracking model, Ocean Parcels [43]. Diffusion was considered using the Milstein scheme (1 st order), and a critical stress condition for near-bottom particles was implemented (see below). Particles were allowed to beach in coastal sections characterized by reeds.
## 6 Challenges
### Dependence on Environmental Conditions and Load Resistance Sensitivity  
The solar component of many hybrid systems depends heavily on weather, causing a significant drop in power output when skies are cloudy or during rainfall [15]. Although the piezoelectric aspect can help maintain some level of energy production, it is not entirely reliable. Nevertheless, continuous usage is often noted as a weak point in these designs. In the context of this study, however, Al-Haram remains busy around the clock--though there is typically lower foot traffic between midnight and 4 a.m.  
Additionally, the system introduced by [PERSON] et al. [4] relies on finding the optimal load resistance, making it susceptible to variations in the connected electrical circuitry. Even small changes in resistance can reduce the energy harvested. Likewise, the design described in [22] requires a specific number of continuous walking hours, which can be challenging to achieve unless the setting inherently provides steady foot traffic.
# An Improved Wavelet Modulus Algorithm Based on Fusion of Light Intensity and Degree of Polarization
## 3 Experimental Results and Analysis
In the graph, 1357 corresponds to the original glass image and the glass images with 1%, 5%, and 10% noise, and 2468 corresponds to the initial liquid image and the liquid images with 1%, 5%, and 10% noise. It can be seen from the figure that only the improved wavelet modulus maxima algorithm and the improved wavelet modulus maxima algorithm based on the fusion of light intensity and degree of polarization were stable in the middle between the maximum and the minimum, indicating that their edge extraction effect was good. The \(H\) value of the improved wavelet modulus maxima algorithm based on the fusion of light intensity and degree of polarization was higher than that of the improved wavelet modulus maxima algorithm, which shows that the improved wavelet modulus maxima algorithm based on the fusion of light intensity and degree of polarization had a large amount of information, and the edge extraction effect was the best. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Sobel Algorithm** & **Canny Algorithm** & \begin{tabular}{c} **Traditional Wavelet** \\ **Modulus Maxima** \\ **Algorithm** \\ \end{tabular}  & \ \begin{tabular}{c} **Improved Wavelet** \\ **Modulus Maxima** \\ **Algorithm** \\ \end{tabular}  & \begin{tabular}{c} **Algorithm in** \\ **This Article** \\ \end{tabular}  \\ \hline Entropy of glass & 0.2079 & 0.1643 & 0.1042 & 0.1196 & 0.1498 \\ \hline Entropy of liquid & 0.3082 & 0.2241 & 0.1792 & 0.1906 & 0.2508 \\ \hline Entropy of glass & 0.2091 & 0.1480 & 0.1126 & 0.1220 & 0.1474 \\ with 1\% noise & 0.2757 & 0.2087 & 0.1791 & 0.1905 & 0.2222 \\
## 4 Recent Advancements in Applications
### Natural Resource Monitoring and Management  
Natural resource monitoring (NRM) covers observing a broad definition of entities such as forests, lakes, sea shorelines, and rivers using technologies integrated in MMSs. There is usually a constant requirement to observe natural entities in short periods of time or regularly to ensure biological and environmental parameters. Therefore, it is essential for environmental observatory organizations and individual NRM parties to access MMSs to collect timely, high-quality, and accurate geospatial data. A large number of studies cover different aspects of NRM using MMSs. Here, we briefly cover some of the most important  
Figure 5: **(Left)**: RIEGL VMX-2 HA. Image from courtesy of REIGL; (right): Trimble MX50.  
applications that have the widest attention. Otherwise, compiling a comprehensive list requires separate research, which is outside the scope of this work.  
The literature about NRM discussed in this section is divided into a selected set of observational applications on forests, agricultural fields, water bodies such as lakes, and green areas such as parks and gardens. Therefore, it only covers a portion of NRM applications.