esa-sceva/satcom-chunk-collection huggingface dataset:
# Charting Past, Present, and Future Research in the Semantic Web and Interoperability
## 5 Co-Citation Clustering Analysis
### Ontology Mapping  
Ontology standards such as RDF and DAML are essential to offer semantic context-based applications to users. In light of this, the fourth cluster represents the research perspective focused on ontology mapping. For example, ref. [76; 88] study and review ontology mapping and its importance in combining distributed and heterogeneous ontologies. Developing ontology mapping facilitates semantic interoperability and improves alignments among the domain ontologies during their design and use. Due to the existence of several ontologies over many domains, ontology mapping serves to achieve semantic correspondence between common elements of different ontologies and interoperability between agents of services [89]. Moreover, matching ontologies increases interoperability between semantic web applications that use different but related ontologies. In this regard, ref. [89] develops a structured-based partitioning algorithm that classifies each ontology's entities into a collection of small clusters and blocks by assigning RDF sentences to those clusters. The authors of [90] aim to automate the discovery of ontology mapping and resolve the instance heterogeneity issue by proposing an approach called risk-minimization-based ontology mapping. Likewise, ref. [109] introduces MAFRA, an ontology mapping framework for distributed ontologies in the semantic web. The suggested framework provides the foundations for managing and executing mapping between distributed ontologies and supporting all parts of the ontology mapping lifecycle. Overall, the body of research aiming at mapping ontologies is extensive; nevertheless, reliance on interactive methods to align ontologies sometimes requires significant human intervention [110]. As a result, it is crucial to automate ontology mapping using novel methods based on artificial intelligencetechniques such as deep learning and decision-tree learning to structurally and semantically ensure valid mappings between similar concepts in two or more distinct ontologies [111]. Another future research direction is improving the existing ontology systems in terms of efficiency and availability and developing systems for large-scale mapping tasks in a linked open data environment [112]. Researchers may also examine the impact of ontological changes, adjustments, and corrections on information systems. This is important because no agency has a perfect ontology to comply with. Therefore, ontology mapping should be an ongoing service and not a one-off process [113]. More practical and innovative solutions for the readjustment of ontologies and data remarking are welcome to minimize the negative impacts of ontology changes on information systems.
## 4 Results of Roadway Tunneling Simulation
### Determination of Burst-Prone Area around the Roadway
#### 4.1.1 Displacement Analysis of Coal and Rock Mass during Roadway Tunneling  
To characterize the roadway failure during the tunneling process through displacement, data from three monitoring points on the coal and rock mass were analyzed, as illustrated in Figure 3. The displacement curves at the monitoring points reveal that the coal-and-rock mass in the roof, floor, and roadway side areas have simultaneous displacement with comparable initial changes. As the time steps increase, the displacements of the three points continue to grow. The displacement at point C on the floor reaches a peak of 0.05 m before stabilizing. In contrast, the displacements at the roof and roadway side show  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameter Group** & \(\mathbf{(\mu-3\sigma,\mu+3\sigma)}\) & **Elastic Modulus** & **Internal Friction Angle** & **Cohesion** \\ \hline a & \((-3,3,-3)\) & 1.705 & 9.9 & 1.65 \\ b & \((-3,-3,0)\) & 1.705 & 9.9 & 3.0 \\ c & \((-3,-3,3)\) & 1.705 & 9.9 & 4.35 \\ d & \((-3,0,-3)\) & 1.705 & 18 & 1.65 \\ e & \((-3,3,-3)\) & 1.705 & 26.1 & 1.65 \\ f & \((0,-3,-3)\) & 3.1 & 9.9 & 1.65 \\ g & \((3,-3,-3)\) & 4.495 & 9.9 & 1.65 \\ h & \((0,0,0)\) & 3.1 & 18 & 3.0 \\ i & \((3,3,3)\) & 4.495 & 26.1 & 4.35 \\ j & \((0,3,3)\) & 3.1 & 26.1 & 4.35 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Ten varied parameter groups of coal mass based on 3\(\sigma\) criterion.  
Figure 2: Schematic locations of monitoring points.  
an approximately linear increase until about 25,000 steps. Subsequently, the displacement at point B suddenly drops, while at point A, the displacement continues to increase. After that sudden drop at point B, the displacement increases again, although at a reduced rate, until the end of the model solution. Additionally, point A exhibits minor fluctuations towards the final stage of the model equilibrium.  
By comparing these three displacement curves, the likelihood of rock burst at those points can be ranked as B > A > C. The patterns of the three curves indicate that the roof is prone to collapsing under stress disturbances caused by tunneling, while the floor is less likely to experience block displacement. The coal mass at the roadway side shows the most significant displacement changes under stress disturbances.
# Subtitling 3D VR Content with Limited 6 DoF: Presentation Modes and Guiding Methods
## 1 Introduction
By leveraging insights from earlier work on VR360 content (e.g., [9; 13]), this paper proposes novel presentation modes and guiding methods to not only provide the freedom to explore the omnidirectional 360\({}^{\circ}\) scenes, but also address the additional specificities of 3D VR compared to VR360 content: depth, 6 Degrees of Freedom (6 DoF), content blocking, and viewing perspectives. In particular, the paper aims to provide insights into two key Research Questions (**RQ**) related to the implementation of subtitling, as the most widespread access service, in 3D VR environments:  
**(RQ1)** What are the most appropriate subtitling presentation modes in 3D VR environments? **(RQ2)** Do visual indicators, such as arrows, provide benefits for guiding the users toward the target speakers in 3D VR environments? By implementing state-of-the-art and novel alternatives, applying these to professionally created 3D VR test sequences, and running user tests with hearing users (but without audio, such that subtitles must be relied on to understand the story), the obtained results prove that always-visible subtitles and a novel proposed comic-style presentation mode are significantly more appropriate than state-of-the-art fixed-positioned subtitles, particularly in terms of immersion, ease and comfort of reading, and identification of speakers. Similarly, the obtained results show that the use of indicators (arrows), as a guiding method, is well received and provides benefits. In the adopted test sequences, the key actions happened with delimited spaces and no major displacement of speakers, and without requiring large 6 DoF by the users. However, the evaluation of these test sequences is nonetheless valuable because they are applicable to, and representative of, a wide variety of use cases, such as e-learning, content watching, and sports/cultural events. In conjunction, the contributions of this paper are expected to become a valuable resource for the audience interested in this field, including: (1) users with accessibility needs and frequent users of subtitles, to know about potential options and the options with greater acceptance; (2) the development community, content producers, and service providers, in order to improve their solutions; and (3) the standardization bodies and research community, in order to have an overall view of solved and critical missing aspects, and/or to assess the extent to which the existing requirements and guidelines are met. The remainder of this paper is structured as follows. Section 2 briefly reviews the state-of-the-art approaches. Next, Section 3 describes the proposed and implemented solutions, and the adopted 3D VR content as stimuli for the tests. Section 4 presents the methodology for the user tests and the obtained results. Section 5 provides a discussion about the obtained results and insights. Finally, Section 6 provides the conclusions and ideas for future work.
# Gradient-Guided and Multi-Scale Feature Network for Image Super-Resolution
## 3 Proposed Network  
There exist some problems for most CNN-based SR methods, for example, the structure information of the image cannot effectively be exploited, a single-scale convolutional kernel is utilized solely to separate features, there is no viable fusion mechanism between adjacent feature extraction modules, and each channel feature is dealt with similarly. To tackle the above issues, we propose a gradient-guided and multi-scale feature network for image super-resolution (GFSR) to reconstruct as much detailed information as possible.  
In this section, we first present the overall framework of the network, and afterward describe the gradient branch, multi-scale convolution unit, residual feature fusion block, and the adaptive channel attention block used in detail.
# An Improved Image Filtering Algorithm for Mixed Noise
## 2 The Filtering Algorithm for Mixed Noise with Salt and Pepper + Gaussian Noise
### The Improved Median Filtering Algorithm
#### 2.1.1 First-Level Detection of Noise Point  
Salt and pepper noise points generally represent the maximum or minimum value of the local area of the image, the positions of the contaminated pixels are randomly distributed, and the probability of positive and negative noise points is usually equal. Image noise points are often extreme points in local areas, but extreme points in local areas are not necessarily noise points. As shown in Figure 1a, the central pixel is the maximum value of the 3 \(\times\) 3 neighborhood. It differs greatly from the surrounding pixel value and can be considered as a noise point, while the central pixel value in Figure 1b is still the maximum value. It has little difference from the surrounding pixel values and should be considered as a signal point.  
Figure 1: 3 \(\times\) 3 neighborhood pixel value: (**a**) The center of the neighborhood is a noise point; (**b**) The center of neighborhood is a signal point.  
Set \(f_{i,j}\) is the gray value of the center pixel of the 3 \(\times\) 3 window and \(w^{3}\), \(w^{3}_{max}\) and \(w^{3}_{min}\) represent, respectively, the maximum gray value and the minimum gray value of all pixels in window \(w^{3}\). If \(f_{i,j}=w^{3}_{max}\) or \(f_{i,j}=w^{3}_{min}\), \(f_{i,j}\) is marked as the possible noise point; otherwise, \(f_{i,j}\) is marked as the signal point. The following formula is constructed:  
\[flag(i,j)=\left\{\begin{array}{ll}1,&\quad f_{i,j}=w^{3}_{max}\text{ or }f_{i,j}=w^{3}_{min}\\ 0,&\quad f_{i,j}\
eq w^{3}_{max}\text{ and }f_{i,j}\
eq w^{3}_{min}\end{array}\right. \tag{1}\]
###### Abstract
The evaluation results show that the proposed method is effective in terms of packet transmission reliability, redundancy, and the average number of paths utilized to enable the target reliability compared to methods utilizing a fixed number of paths.
## 4 Joint Task Offloading, Resource Allocation, and Security Assurance Algorithm
### Potential Applications  
In the present decade, the potential applications of MEC-enabled UAV-assisted VANETs are under discussion in commercial settings. Several researchers emphasized that the potential of MEC-enabled autonomous vehicles to significantly reduce the traffic and logistics-related challenges in complex urban ITS environments. For example, the benefits of using MEC-enabled UAV-assisted VANETs in urban ITS environments or commercial settings include, but are not limited to, increased safety, reduced congestion, reduced fuel consumption and emissions, and transporting disabled or older people [14]. Specifically, UAVs can keep the services and applications of urban ITS environments stable and active. The MEC architecture can effectively complete the task distribution and migration scheduling, thus alleviating task aggregation and improving UAV-assisted VANETs QoS.
## Nomenclature  
\begin{tabular}{l l l l} a\({}_{0}\) & Half initial crack length & \(\alpha_{0}\) & Normalized initial crack length \\ a\({}_{1}\) & Half final crack length & \(\alpha_{1}\) & Normalized final crack length \\ B & Specim thickness & \(\alpha_{\rm B}\) & Normalized specimen thickness \\ R\({}_{\rm S}\) & Saw radius & \(\alpha_{\rm S}\) & Normalized saw diameter \\ a & Half crack length & \(\alpha\) & Normalized crack length \\ R & Specim radius & D & Specim diameter \\ P\({}_{\rm Max}\) & Maximum load & N\({}_{\rm I}\) & Auxiliary variable \\ \(\gamma_{\rm min}^{*}\) & Minimum normalized stress intensity factor & KIC & Mode I fracture toughness \\ CCNBD & Cracked chevron notched Brazilian disc & L & Specim length \\ ISRM & International Society for Rock Mechanics & \(\rho\)Sat & Electrical resistivity \\ A & Cross section area & n & Porosity \\ \(\rho\) & Dry density & V\({}_{\rm P}\) & P-Wave velocity \\ W.A. & Water Absorption & V\({}_{\rm S}\) & S-Wave velocity \\ V\({}_{\rm P-Dry}\) & P-Wave velocity under dry state & RN & Schmidt rebound number \\ V\({}_{\rm P-Sat}\) & P-Wave velocity under saturated state & R\({}^{2}\) & Correlation coefficient \\ XRD & X-ray diffraction & VIF & Variance inflation factor \\ EDS & Energy dispersive spectroscopy & & \\ RMSE & Root mean square error & & \\ R\({}^{2}\) Adjust & Adjusted correlation coefficient & & \\ \(p\)-Value & Probability value & & \\ \end{tabular}
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Competition** & **Stepwise Iteration** & **R\({}^{2}\)** & **F** & \(p\) & **Cook’s** \\ \hline \multicolumn{6}{c}{MODEL 1} \\ \hline Competitions 2021–2023 & 5 & 0.999 & 540.069 & 0.000 & 0.545 \\ Olympic Games 2020 (2021) & 2 & 0.598 & 19.628 & 0.000 & 0.676 \\
19 th Word Cup 2022 & 5 & 0.953 & 150.791 & 0.000 & 0.161 \\
26 th AfroBasket 2023 & 1 & 0.592 & 8.843 & 0.000 & 0.368 \\
17 th AmeriCup 2023 & 1 & 0.154 & 1.821 & 0.143 & 0.737 \\
30 th Asia Cup 2023 & 1 & 0.794 & 25.466 & 0.000 & 0.242 \\
39 th EuroBasket 2023 & 4 & 0.745 & 28.721 & 0.000 & 0.291 \\ \hline \multicolumn{6}{c}{MODEL 2} \\ \hline Competitions 2021–2023 & 6 & 0.907 & 0.907 & 309.733 & 0.000 \\ Olympic Games 2020 (2021) & 2 & 0.384 & 0.384 & 8.801 & 0.001 \\
19 th Word Cup 2022 & 5 & 0.960 & 0.960 & 176.610 & 0.000 \\
26 th AfroBasket 2023 & 3 & 0.784 & 0.784 & 33.574 & 0.000 \\
17 th AmeriCup 2023 & 4 & 0.902 & 0.902 & 62.901 & 0.000 \\
30 th Asia Cup 2023 & 3 & 0.723 & 0.723 & 33.993 & 0.000 \\
39 th EuroBasket 2023 & 7 & 0.910 & 0.910 & 77.817 & 0.000 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Number of iterations, value of adjusted coefficient of multiple determination, analysis of variance result (F-test and accompanying \(p\)-value), and maximum Cook’s distance between Model 1 and Model 2 in the observed competitions. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{3}{c}{**MODEL 1**} \\ \hline \multirow{4}{*}{**Variable**} & \multicolumn{3}{c}{**Analysis of the Presence**} & \multicolumn{3}{c}{**Analysis of the Presenceof**} \\  & \multicolumn{3}{c}{**of Multicollinearity**} & **Variable** & \multicolumn{3}{c}{**Multicollinearity**} \\ \cline{3-6}  & \multicolumn{2}{c}{**Tolerance**} & **VIF** & **Tolerance** & **VIF** \\ \hline \multirow{4}{*}{**Variance**} & \(\Delta\)M2 & 0.916 & 5.733 & \(\Delta\)2\% & 0.972 & 1.029 \\  & \(\Delta\)M3 & 0.856 & 3.765 & \(\Delta\)3\% & 0.905 & 1.105 \\  & \(\Delta\)MFT & 0.902 & 7.120 & \(\Delta\)DR\% & 0.962 & 1.039 \\  & \(\Delta\)TO\% & 0.973 & 1.028 & \(\Delta\)TO\% & 0.973 & 1.028 \\ \hline \multirow{4}{*}{**Variance**} & \(\Delta\)AS & 0.983 & 1.017 & \(\Delta\)2\% & 0.989 & 1.011 \\  & \(\Delta\)MFT & 0.983 & 1.017 & \(\Delta\)OR\% & 0.989 & 1.011 \\ \hline \multirow{4}{*}{**Variance**} & \(\Delta\)DR & 0.632 & 1.583 & \(\Delta\)2\% & 0.899 & 1.112 \\  & \(\Delta\)TO & 0.923 & 1.084 & \(\Delta\)3\% & 0.813 & 1.230 \\ \cline{1-1}  & \(\Delta\)M3 & 0.424 & 2.360 & \(\Delta\)OR\% & 0.944 & 1.060 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Analysis of multicollinearity in the observed competitions in Models 1 and 2.
###### Abstract  
Accurately estimating the modulation parameters of pseudorandom binary code-pulse amplitude modulation (PRBC-PAM) signals damaged by strong noise poses a significant challenge in emitter identification and countermeasure. Traditionally, weak signal detection methods based on chaos theory can handle situations with low signal-to-noise ratio, but most of them are developed for simple sin/cos waveform and cannot face PRBC-PAM signals commonly used in ultra-low altitude performance equipment. To address the issue, this article proposes a novel adaptive detection and estimation method utilizing the in-depth analysis of the Duffing oscillator's behaviour and output characteristics. Firstly, the short-time Fourier transform (STFT) is used for chaotic state identification and ternary processing. Then, two novel approaches are proposed, including the adjusting zero value (AZV) method and the chaotic state ratio (CSR) method. The proposed weak signal detection system exhibits unique capability to adaptively modify its internal periodic driving force frequency, thus altering the difference frequency to estimate the signal parameters effectively. Furthermore, the accuracy of the proposed method is substantiated in carrier frequency estimation under varying SNR conditions through extensive experiments, demonstrating that the method maintains high precision in carrier frequency estimation and a low bit error rate in both the pseudorandom sequence and carrier frequency, even at an SNR of \(-30\) dB.  
a pacs: date = +
Footnote †: journal: _Mathematics_  
**Clication:** [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Adaptive Measurement and Parameter Estimation for Low-SNR PRBC-PAM Signal Based on Adjusting Zero Value and Chaotic State Ratio. _Mathematics_  
**MSC:** 93E11; 60G35
# Determining Effective Threshold Range of Image Pixel Values for Municipal Waste-Contaminated Clay
## 3 Results
### Morphology of Microstructure of Contaminated Clay  
The control and seeping municipal waste-contaminated clay samples (obtained at column depths of 3.1, 17.3, 36.8, 48.5, 56.3, and 75.8 cm) and uncontaminated clay and soaking municipal waste-contaminated clay samples (with concentrations of 0.2, 0.4, and 0.6 mol/L CaCO\({}_{3}\) and 0.2, 0.4, and 0.6 mol/L CH\({}_{3}\)COOH) were scanned at different magnifications (1200\(\times\), 1500\(\times\), 1800\(\times\), and 2100\(\times\)). The research results of [PERSON] et al. [41] were incorporated; notably, the observation effect did not necessarily improve with increasing magnification, and the best magnification scale occurred within the 1500 \(\pm\) 300 range. Therefore, SEM images magnified by 1500 were selected for the control and seeping municipal waste-contaminated clay samples, and SEM images magnified by 1200 were selected for the uncontaminated clay and soaking municipal waste-contaminated clay samples. The sample table was cleaned by a cleaning instrument before the observation. Then, the samples were covered with a small amount of conductive adhesive involving a wooden stick firmly pressed against the clay surface. SEM images of the control and seeping municipal waste-contaminated clay samples collected at depths of 17.3, 56.3, and 75.8 cm and corresponding binary images after treatment are shown in Figure 3. SEM images of the uncontaminated clay and CaCO\({}_{3}\)- and CH\({}_{3}\)COOH-contaminated clay samples with concentrations of 0.2, 0.4, and 0.6 mol/L and corresponding binary images after treatment are shown in Figure 4.  
Figure 3: SEM images and binary images of the control sample and contaminated clay with different seepage depths at 1500 magnification.  
Figure 4: SEM images and binary images of uncontaminated soil and contaminated clay with different concentrations at 1200 magnification.
## 5 Discussion and Conclusions
### Comparison with Other Prediction Methods
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & _RMSE_ & _MAE_ & _MAPE_ \\ \hline ARIMA & 20.77 & 16.27 & 8.41\% \\ \hline Seasonal ARIMA & 13.35 & 10.44 & 7.48\% \\ \hline SVR & 13.26 & 10.16 & 6.94\% \\ \hline LightGBM & 12.11 & 10.21 & 6.60\% \\ \hline LSTM & 11.81 & 9.03 & 5.87\% \\ \hline GRU & 11.19 & 8.76 & 5.74\% \\ \hline
**GRU-LightGBM** & **8.01** & **5.41** & **4.11\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Prediction performance evaluation. Figure 10: Comparison of sales prediction results by different algorithms. the _MAPE_ value is 1.63% less than for the GRU model and 2.49% less than for the LightGBM model. This means that the stacking architecture of GRU-LightGBM assists in improving the overall prediction accuracy and stability. The above indicators support Hypothesis 1.
## 3 Case Study
### FEM Creation and Updating
#### 3.3.2 Update Model Parameters through Particle Swarm Optimisation (PSO) Algorithm
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**No.** & **Uncertain Parameters** & **Initial Value** & **Upper Bound** & **Lower Bound** \\ \hline \multirow{3}{*}{1} & Young’s modulus & & & \\  & —_Steel_\(E_{s}\) (GPa) & 200 & 210 & 190 \\  & Weight density & & & \\  & —_Steel_\(\rho_{s}\) (kg/m\({}^{3}\)) & 7850 & 8000 & 7800 \\  & Masses of non-structural & & & \\  & —_m\({}_{b}\)_ (kg/m) & 3000 & 3000 & 5000 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Uncertain parameters. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No.** & **Uncertain Parameters** & **Initial Value** & **Updated Value** \\ \hline \multirow{3}{*}{1} & [PERSON]’s modulus & & & \\  & —_Steel_\(E_{s}\) (GPa) & 200 & 205.54 \\  & Weight density & & & \\  & —_Steel_\(\rho_{s}\) (kg/m\({}^{3}\)) & 7850 & 7956.5 \\  & Masses of non-structural & & & \\  & —_m\({}_{b}\)_ (kg/m) & 3000 & 3600 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Updated parameters. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Mode** & **f-Simulation (Hz)** & **f-Measurement (Hz)** & **Error (\%)** & **MAC** & **Type** \\ \hline
1 & 1.79 & 1.79 & 0.00 \(\downarrow\) & 0.99 \(\uparrow\) & 1 st vertical bending \\
2 & 2.24 & 2.25 & 0.44 \(\downarrow\) & 0.95 \(\uparrow\) & 1 st lateral \\
3 & 3.58 & 3.57 & 0.28 \(\downarrow\) & 0.96 \(\uparrow\) & 2 nd torsion \\
4 & 4.33 & 4.30 & 0.69 \(\downarrow\) & 0.94 \(\uparrow\) & 2 nd vertical bending \\
5 & 4.61 & 4.60 & 0.21 \(\downarrow\) & 0.94 \(\uparrow\) & lateral movement \\
6 & 5.05 & 5.03 & 0.39 \(\downarrow\) & 0.94 \(\uparrow\) & 2 nd lateral bending \\
7 & 8.15 & 8.09 & 0.74 \(\downarrow\) & 0.94 \(\uparrow\) & 3 rd vertical bending \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Summary of natural frequency and mode shapes of the plate structure, after being updated. Figure 14 shows that before updating the model, the calculation results and natural frequency measurement results had high errors. After updating the model, the error of the natural frequency between calculation and measurement decreased significantly, and the model became highly accurate and reliable. The calculation results show that, after about 30 iterations performed using PSO, the model parameters began to converge and give good results, minimising the process of testing parameters in the modelling. After being updated, the numerical model was accurate, being almost the same as the actual object. This model was employed for creating data to train the ANN.