esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Theoretical Foundation
### SRM-Based Nonstationary Ground Motions Simulation  
The simulation of nonstationary ground motions based on the SRM has been widely developed and applied [4]. The basic theory is briefly given as follows:  
The EPSD matrix is a Hermitian matrix with the feature of being non-negatively definite. It may be decomposed into the following product using the Cholesky decomposition at each time-frequency point:  
\[\mathbf{S}(\omega,t)=\mathbf{H}(\omega,t)\mathbf{H}^{\tau*}(\omega,t) \tag{8}\]  
where the superscript \(*\) signifies the complex conjugate and \(\mathbf{H}(\omega,t)\) is the decomposed EPSD matrix written as  
\[\mathbf{H}(\omega,t)=\begin{bmatrix}H_{11}(\omega,t)&0&\cdots&0\\ H_{21}(\omega,t)&H_{22}(\omega,t)&\cdots&0\\ \vdots&\vdots&\ddots&\vdots\\ H_{n1}(\omega,t)&H_{n2}(\omega,t)&\cdots&H_{nn}(\omega,t)\end{bmatrix} \tag{9}\]  
The diagonal elements are both real and non-negative with \(H_{jj}(\omega,t)=H_{jj}(-\omega,t)\). The off-diagonal elements are typically complex with \(H_{jk}(\omega,t)=H_{jk}^{*}(-\omega,t)\) and can be expressed as  
\[H_{jk}(\omega,t)=\Big{|}H_{jk}(\omega,t)\Big{|}e^{i\theta_{jk}(\omega)} \tag{10}\]  
where \(\theta_{jk}(\omega)\) is the phase of \(H_{jk}(\omega,t)\) and generally expressed as  
\[\theta_{jk}(\omega)=\arctan\Biggl{\{}\frac{\mathrm{Im}[H_{jk}(\omega,t)]}{ \mathrm{Re}[H_{jk}(\omega,t)]}\Biggr{\}} \tag{11}\]  
in which the letters 'Im' and 'Re', respectively, stand for imaginary and real parts. However, based on the definition of the complex phase, its value should fall into the range of \([-\tau,\pi]\), while exceeding the range of \((-\pi/2,\pi/2)\) in Equation (11). Thus, Equation (11) cannot be used to calculate the complex phase.  
Based on the spectral decomposition results, the samples of the \(j\)th element process \(x_{j}(t)\) can be generated by [4]  
\[x_{j}(t)=2\sqrt{\Delta\omega}\sum_{k=1}^{j}\sum_{l=1}^{N}\left|H_{jk}(\omega_{ l},t)\right|\cos\left[\omega_{l}t-\theta_{jk}(\omega_{l})+\phi_{kl}\right] \tag{12}\]  
\[x_{j}(t)=2\sqrt{\Delta\omega}\mathrm{Re}\Biggl{\{}\sum_{k=1}^{j}\sum_{l=1}^{N }H_{jk}(\omega_{l},t)e^{i(\omega_{l}t+\phi_{kl})}\Biggr{\}} \tag{13}\]  
where \(\Delta\omega=\omega_{u}/N\) is the frequency increment; \(\omega_{u}\) is the cutoff frequency; \(N\) is the total number of discretized frequencies; \(\omega_{l}=1\Delta\omega\); and \(\phi_{kl}(l=1,2,\ldots,N)\) are a set of independent random-phase angles uniformly distributed in the range \([0,2\pi]\).  
Obviously, the simulation's efficiency is seriously restricted by the decomposition of the EPSD matrix and the summation of trigonometric items. Although some effective schemes are available for improving the simulation's efficiency, they are still insufficient due to their limitations in their use or complexity of processing.
## 2 Methodological Evolution
### Algorithmic Innovation
#### 2.2.3 Numerical-Deep Learning Symbiosis  
Hybrid architectures bridge the gap between numerical rigor and neural flexibility. The Variational Petrov-Galerkin PINN (VPINN) embeds Legendre polynomial test spaces to reduce differential operator order by 50%, explicitly constructing variational residuals:  
\[R_{h}=\left\langle\mathcal{N}(u_{\theta}),v_{h}\right\rangle \tag{5}\]  
where \(R_{h}\) denotes the variational residual, \(N\) is the differential operator, (\(u_{\theta}\) signifies the neural network solution parameterized by weights \(\theta\), and \(v_{h}\) is the test function from the Legendre polynomial basis space. Which accelerates training efficiency by 30% for elliptic PDEs [31]. Conversely, the HFD-PINN framework integrates finite difference modules before neural outputs, collaboratively optimizing discrete and continuous representations. By constraining time steps via CFL conditions:  
\[\Delta t\leq\frac{\Delta x}{2\max|u|} \tag{6}\]where \(\Delta t\) denotes the time step size used in the numerical method, representing the discretization of time in the computational scheme, \(\Delta x\) refers to the spatial step size or grid spacing in the spatial domain, which determines the resolution of the spatial discretization. \(\max|u|\) signifies the maximum absolute value of the solution \(u\) across the domain, and it is used to control the time step size based on the stability condition of the numerical scheme. It eliminates non-physical oscillations in complex geometries, achieving 4\(\times\) faster convergence than pure data-driven models [32].  
To quantify the paradigm shift enabled by PINNs, Table 3 benchmarks their performance against conventional numerical methods across computational efficiency, data requirements, and physical consistency. Notably, the VPINN framework reduces the differential operator order by 50% through Legendre polynomial projection, achieving a 30% training acceleration over classical FEM in elliptic PDEs--a feat attributed to its seamless integration of reduced-order modeling and neural optimization. Meanwhile, the HFD-PINN hybrid architecture demonstrates how embedding finite difference modules into neural networks mitigates non-physical oscillations via CFL-conditioned time stepping, outperforming pure data-driven models in complex geometry simulations. Such hybrid strategies not only inherit the interpretability of numerical methods but also leverage the mesh-free advantage of deep learning, positioning PINNs as a bridge between computational physics and modern machine learning.
# Deriving High Spatial-Resolution Coastal Topography From Sub-meter Satellite Stereo Imagery
## 4 Results  
### Comparison between RTK-GPS, Pleiades and LiDAR Topography  
Figure 2 shows the vertical difference between the three concurrent survey methods over 4138 points (i.e., the number of data points measured during the RTK-GPS survey). The differences between remote sensing methods (Pleiades and LiDAR) and RTK-GPS are normally distributed, with mean differences (BIAS) of 0.01 m and 0.03 m, and RMS errors of 0.35 m and 0.37 m for Pleiades and LiDAR respectively (both Pleiades and LiDAR elevations are slightly higher than the RTK measurements). The observed slight mean difference between the RTK-GPS and remote sensing methods are within the accuracy of the RTK-GPS, thus indicating that the different methods have similar accuracy. It is important to note that even though the LiDAR data have a larger RMS error than the Pleiades one, the error distribution is narrower and skewed for negative values. The 1:1 scatter-comparison of the surveys shown in Figure 2 indicates that the remotely-sensed beach topography is highly correlated with the RTK-GPS observations (slope = 1.01 and CC = 0.99 for both Pleiades and LiDAR). It is also observed that the correlation with Pleiades values does not vary with elevation (extending from the back of the dune to the top of the swash zone) while for LiDAR some scattering is observed in the lower part of the beach (beach face).  
A close inspection of the datasets indicates the scatter (observed in the beach face) between RTK-GPS and LiDAR topography was due to morphological changes (berm erosion--Figure 3) over the beach profile that occurred between the two surveys. During this period of the year (winter season), this coastal area is under energetic waves and significant morphological changes (> 1 m) in the beach and dune face are likely to occur [(23)]. Considering that the RTK-GPS survey and Pleiades image acquisition were days apart, small morphological changes likely occurred in the beach face, resulting in minor differences between the two datasets (Figure 3).
# Analysis of AgRED Performance in LR-WPAN Dense Ad-Hoc Networks +
###### Abstract  
IEEE defines the standard 802.15.4 for Low Data Rate Wireless Personal Area Networks (LR-WPAN) to be used with Internet of Things (IoT) sensor devices. IoT devices utilizing this standard suffer from traffic congestion on a node level in dense network scenarios in real time applications. Active queue management (AQM) schemes can optimize the queues of the nodes in order to relive nodes from congestion and improve performance. This paper investigates the impact of Aggressive Random Early Detection (AgRED) AQM in dense network configuration with larger payload and higher service rate for LR-WPAN. The findings indicate better delay, throughput and packet delivery ratio when using AgRED as compared to RED.  
AgRED; RED; active queue management; LR-WPAN; WSN; IEEE 802.15.4
## 2 Failure Criteria for Components at High Temperatures  
### Reduction Factors for Yield Strength and Elastic Modulus  
Currently, research on transmission tower-line systems rarely considers fire conditions, and investigations into their high-temperature response are scarce. Transmission tower components are typically constructed from equal-leg angle steel, and their constitutive relationship undergoes significant changes at elevated temperatures compared to ambient conditions. The distance between the transmission tower's conductor (or ground wire) and the ground is generally large, making it less affected by temperature. This paper exclusively examines the response of the transmission tower-line system when a fire occurs around the target transmission tower. When a fire occurs, the temperature of the steel rises rapidly, and once it exceeds 400 \({}^{\circ}\)C, the steel's yield strength and elastic modulus decrease sharply [36]. Steel exhibits excellent tensile and compressive properties at ambient temperature, but at high temperatures, its strength gradually decreases as the temperature rises, leading to insufficient structural load-bearing capacity and even instability or collapse. According to the \"Technical Specification for Fire Protection of Steel Structures\" (GB 51249-2017) [37], the reduction factors for yield strength and elastic modulus of steel at high temperatures are calculated using Equations (1) and (2), respectively.  
\[\eta_{T=}\begin{cases}1.0&20\ ^{\circ}\text{C}\leq T_{s}\leq 300\ ^{\circ}\text{C}\\ 1.24\times 10^{-8}T_{s}^{3}-2.096\times 10^{-5}T_{s}^{2}+9.228\times 10^{-3}T_{s}- 0.2168&300\ ^{\circ}\text{C}\leq T_{s}<800\ ^{\circ}\text{C}\\ 0.5-T_{s}/2000&800\ ^{\circ}\text{C}\leq T_{s}\leq 1000\ ^{\circ}\text{C}\end{cases} \tag{1}\]  
\[\chi_{T}=\left\{\begin{array}{l}\frac{7T_{s}-4780}{6T_{s}-4760}\ 20\ ^{\circ} \text{C}\leq T_{s}<600\ ^{\circ}\text{C}\\ \frac{1000-T_{s}}{6T_{s}-2800}\ 600\ ^{\circ}\text{C}\leq T_{s}\leq 1000\ ^{ \circ}\text{C}\end{array}\right. \tag{2}\]  
In the formula, \(\eta_{T}\) is the reduction factor for the yield strength of steel at high temperatures; \(\chi_{T}\) is the reduction factor for the elastic modulus of steel at high temperatures; \(T_{s}\) is the environmental temperature. The variation of the reduction factors for the yield strength and elastic modulus of steel at high temperatures with temperature is illustrated in Figure 1. It can be observed that when the temperature is below 300 \({}^{\circ}\)C, the yield strength of steel remains unchanged with temperature, and the elastic modulus shows little variation. When the temperature reaches 800 \({}^{\circ}\)C, both \(\eta_{T}\) and \(\chi_{T}\) approach 0.1.
# Enhancing Street-Crossing Safety for Visually Impaired Pedestrians with Haptic and Visual Feedback
## 3 Study 1: Haptic and Visual Feedback for Visually Impaired Pedestrians Crossing at Traffic Signals
### Results
#### 4.2.1 Cross Rate, Decision Time, and User Behavior Data in Study
We also recorded the head movement distance along the X, Y, and Z dimensions. For the X dimension, the main effects were found for frequency (\(\chi^{2}(3)=62.92,p<0.001\)) and state (\(\chi^{2}(1)=8.57,p<0.01\)). Post hoc Bonferroni pairwise comparisons showed that the distance of frequency 0.5 Hz (4.72 cm) was significantly longer than that of frequency 1.0 Hz (3.16 cm) (\(p<0.05\)) and frequency 4.0 Hz (2.73 cm) (\(p<0.05\)). The distance of the dynamic state (3.89 cm) was significantly longer than that of the static state (2.93 cm) (\(p<0.05\)). For the Y dimension, the main effects were found for frequency (\(\chi^{2}(3)=51.14,p<0.001\)), size (\(\chi^{2}(1)=7.36,p<0.01\)), and state (\(\chi^{2}(1)=16.10,p<0.001\)). Post hoc Bonferroni pairwise comparisons showed that the distance of frequency 0.5 Hz (1.97 cm) was significantly longer than that of frequency 1.0 Hz (1.31 cm) (\(p<0.05\)), frequency 2.0 Hz (1.10 cm) (\(p<0.01\)), and frequency 4.0 Hz (1.03 cm) (\(p<0.01\)); that of frequency 1.0 Hz (1.31 cm)  
Figure 11: Mean head rotation angle and head movement distance results of Study 2. (**a**) Mean head rotation angle; (**b**) mean head movement distance. was significantly longer than that of frequency 2.0 Hz (1.10 cm) (\(p<0.05\)) and frequency 4.0 Hz (1.03 cm) (\(p<0.05\)). The distance of the small size (1.42 cm) was significantly longer than that of the medium size (1.20 cm) (\(p<0.05\)). The distance of the dynamic state (1.46 cm) was significantly longer than that of the static state (1.17 cm) (\(p<0.01\)). For the Z dimension, the main effects were found for frequency (\(\chi^{2}(3)=59.36,p<0.001\)). Post hoc Bonferroni pairwise comparisons showed that the distance of frequency 0.5 Hz (4.74 cm) was significantly longer than that of frequency 1.0 Hz (3.24 cm) (\(p<0.05\)), frequency 2.0 Hz (2.82 cm) (\(p<0.01\)), and frequency 4.0 Hz (2.71 cm) (\(p<0.01\)).
## 5 Conclusions  
1. The simulation testing conducted on a set of scenarios with variable conditions allowed for an assessment of the effectiveness of the forecast of the risk of loss of support. In typical cases, the system provided detection of the approach to critical phases with an accuracy of up to 87%, which is confirmed by the nature of the dynamics of innovations and the fusion coefficient \(\alpha(t)\), shown in Figure 7. Full quantitative assessment of the forecast accuracy is included in a separate series of experiments, the results of which will be presented in detail in subsequent works;The algorithm for adaptive weighting between the Kalman filter and GERT forecast estimates was developed. The simulations demonstrated that changing the weight coefficient \(\alpha(t)\) in accordance with the level of interference reduces the mean square error of support from 0.975 to 0.724 (a decrease of 25.7%) compared to the classical Kalman filter. This is achieved by dynamically shifting the confidence in the estimate, which better reflects the real state of the signal (see Table 3, Figure 9);
3. The model for assessing the reliability of measurements was created based on an LSTM network that works on the derivative features of filtering. In test scenarios, the accuracy of the forecast regarding the reliability of measurements reached 91.2%, which made it possible to adapt the filtering parameters in advance and ensure a response delay of only 1.25 cycles after a drop in signal quality (see Figures 6 and 7). This is explained by the ability of LSTM networks to detect time dependencies in changes in innovation indicators and measurement losses;
4. The final mechanism for forming an estimate of the target position was developed, taking into account weighted fusion and the adaptive operating mode. The method reduced the probability of the loss of tracking from 0.2 to 0.1, and reduced the forecast error by 25% (RMSE). The implementation of switching between standard, robust and predictive modes ensured the stability of tracking, even with a loss of up to 30% of the measurements (see Figure 10, Table 3). This was made possible by using adaptive control logic that takes into account the predicted reliability and context of the dynamics.  
Conceptualization, S.S.; methodology, S.S. and O.W.; software, J.G. and A.J.; validation, S.S., V.V. and M.K.-K.; formal analysis, Y.T., S.L. and V.B.; investigation, S.S. and M.K.-K.; data curation, Y.T., S.L. and V.B.; writing--original draft preparation, S.S.; writing--review and editing, S.S.; visualization, V.V. and A.M.; supervision, S.S. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. Not applicable. Not applicable. Data Availability Statement: The raw data supporting the conclusions of this article will be made available by the authors on request. The authors declare no conflicts of interest.
## 5 Discussion  
### Most Informative Feature  
After analyzing the results of SVM-RFE to choose the most critical feature for each landscape, it was revealed that NDVI, VV, and B12 bands were selected as prominent features in all six landscapes, which could confirm their importance in LC classification [16]. In contrast, NDBI, which consider as an essential index for extracting built-up areas [23], was only introduced as a critical feature in three landscapes (namely plain, semi-arid, and coastal). NDWI also had the same situation; it was only selected as the main feature for the coastal landscape. This selection is potentially related to the fact that separation of the water bodies is much simpler than the other classes, which makes the NDWI unnecessary in most cases. The SAR data were selected as informative features in all of the landscapes, which is plausible due to the excellent applicability of SAR data in LC mapping for broad land covers. Such data is already applied for different broad land cover classes such as forest [34], cropland [9], and built-up areas [35]. Among the texture information, only three features were selected in all experiments, except coastal sites, including mean, variance, and homogeneity. The results illustrated that the texture information was relatively unimportant in comparison to other features.
# A Network Analysis Approach to Detecting Social Issues with Web-Based Data
## 3 Proposed Approach  
In this study, an approach to detecting social issues that considers the definition and characteristics of the issues is proposed. Figure 1 shows the overall process of the proposed approach. First, web-based data is collected, and valid keywords are selected within the data through pre-processing. Second, by extracting co-occurrence information between the selected valid keywords, keyword co-occurrence networks are constructed by time  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Approach** & **Data** & **Methods** & **Reference** \\ \hline \multirow{3}{*}{Feature-based approach} & Twitter data & Mention anomaly, Network analysis & [6] \\ \cline{2-4}  & Twitter data & Text mining, Keyword entropy & [7] \\ \cline{2-4}  & Twitter data & Text mining, Soft frequent pattern mining & [27] \\ \hline \multirow{3}{*}{Topic-modeling-based approach} & Twitter data, Academic paper data & Evolutionary clustering, LSTM model & [29] \\ \cline{2-4}  & Twitter data & Topic model, Dynamic parameter update strategy & [30] \\ \cline{2-4}  & Weibo data & Topic model, Parameter estimating process & [31] \\ \hline \multirow{3}{*}{Incremental approach} & Twitter data & Matrix decomposition method, Incremental clustering & [5] \\ \cline{2-4}  & News data & Word embedding, Online clustering & [10] \\ \cline{2-4}  & Twitter data & Text mining, Incremental clustering & [11] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of literature review for social issue detection using web-based data.  
periods. Third, a network structure entropy is used to monitor the structural changes of the networks, which ultimately leads to the identification of a time period in which social issues occurred. Finally, social issue candidates are defined for the time period identified in the previous step, and the most urgent social issue is detected based on the priority of the social issue candidates.
# Experimental Study on the Heat Transfer Performance of Pump-Assisted Capillary Phase-Change Loop
## 3 Results
### Steady-State Heat Transfer Characteristics
#### 3.2.1 Baseplate Temperature  
The variation in the copper baseplate temperature \(T_{\text{eva}}\) in response to changes in liquid-vapor pressure and heating power under steady-state conditions are shown in Figure 4. It can be seen that as long as the liquid-vapor pressure difference was 0 and 2 kPa, a small change in heating power might lead to a rapid rise in \(T_{\text{eva}}\). However, when the liquid-vapor pressure difference was 4 kPa, there were two obvious turning points in the variation curve of \(T_{\text{eva}}\) as the heating power increased. After exceeding the first turning point, \(T_{\rm eva}\) changed slightly with the heating power, and the power-temperature curve became steep. When the heating power continued to increase, a second turning point occurred, and \(T_{\rm eva}\) began to increase rapidly again. The first turning point occurred after the heat transfer process was significantly enhanced by the heat transfer mode switching from partially flooded to thin-film evaporation. The second turning point occurred after the heat transfer mode switched from thin-film evaporation to overheating. When the liquid-vapor pressure difference exceeded 4 kPa, the power-temperature curve became steep after a turning point. Additionally, with the increase in liquid-vapor pressure difference, the heating power at the turning point also increased gradually.  
However, a high liquid-vapor pressure difference is not always recommended in engineering applications. For example, under current experimental conditions, efficient heat dissipation could be achieved simply by maintaining a liquid-vapor pressure difference lower than 8 kPa. However, if the liquid-vapor pressure difference exceeded 8 kPa, unnecessary pumping power was the result.
## 4 Proposal for an Ontology Building Process
### Implementation Stage
#### 4.2.1 Existing Ontology Alignment  
The main concepts related to existing ontologies related to the domain in analysis must be captured and aligned. The concept of alignment is related to the correlation process among similar concepts with different identifiers in different ontologies. This process ensures a cohesive integration of terminologies across the ontologies, facilitating a unified understanding of the domain and minimizing potential ambiguities. The identification and correlation of these concepts are essential for the consistent representation and interpretation of data within the domain under analysis.  
The ontologies for alignment, as well as the concepts to be aligned, depend specifically on the domain being modeled and, for this reason, must be selected by the team of specialists from among the existing public ontologies. Once selected, the common concepts should be aligned following the generic model presented in Table 2, where ONTO-1, ONTO-2, and ONTO-3 represent the ontologies selected by the specialists, and \"Concept Definition\" describes the concepts to be aligned between the selected ontologies. Each ontology maps classes related to the defined concept. It is important to emphasize that only concepts related to the domain under analysis should be listed.  
In addition to aligning concepts, the associated properties must be studied to map the relationships. This analysis aimed to capture the intricate connections between ideas, ensuring that the ontology accurately reflects the interactions within the domain. By mapping these relationships, a more comprehensive framework is provided that supports identifying concepts and their interdependencies, further enhancing the ontology's utility. An example of alignment of relationships is presented in Table 3.