esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Global and local features are essential for visual-similarity texture perception. Therefore, understanding how people allocate their visual attention when viewing textures with global or local similarity is important. In this work, we investigate the influences of global and local features of a texture on eye-movement patterns and analyze the relationship between eye-movement patterns and visual-similarity selection. First, we synthesized textures by separately controlling global and local textural features through the primitive, grain, and point configuration (PGPC) texture model, a mathematical morphology-based texture model. Second, we conducted an experiment to acquire eye-movement data where participants identified the texture that was highly similar to the standard texture. Experiment data were obtained through an eye-tracker from 60 participants. The collected eye-tracking data were analyzed in terms of three metrics, including total fixation duration in each region of interest (ROI), fixation-point variance in each ROI, and fixation-transfer counts between different ROIs. Analysis results indicated the following. (1) The global and local features of a texture influenced eye-movement patterns. In particular, the texture image that was globally similar to the standard texture contained dispersed fixation points. By contrast, the texture image that was locally similar to the standard texture contained concentrated fixation points. The domination of global and local features influenced the viewers' similarity choice. (2) The final visual-similarity selection was related to the fixation-transfer count between different ROIs, but not to the fixation time in each ROI. This research also extends the applicability of the mathematical morphology-based texture model to human visual perception.  
eye-movement; visual perception; visual-similarity; synthesized texture; mathematical morphology-based texture model +
Footnote †: journal: Applied Sciences
## 4 Experimental Results
### Execution Time (Encoding/Decoding Complexity)
This reason is that the proposed scheme incorporates BCS-TV-AL3 (less complex) for initial reconstruction and the simplified process of predicting the non-key frames. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Sequence** & **Proposed (JD-TV)** & **DISCOVER** & **H.264 (Intra)** \\ \hline Hall-Monitor & 7.21 s & 1.13 s & 0.60 s \\ Mother Daughter & 7.17 s & 1.11 s & 0.59 s \\ Coast-Guard & 9.22 s & 1.27 s & 0.75 s \\ Foreman & 9.19 s & 1.22 s & 0.67 s \\ Mobile Calendar & 9.43 s & 1.27 s & 0.75 s \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Average decoding (execution) time (sec) comparison of proposed CS based JD-TV codec with conventional video codecs (DISCOVER, H.264 (intra)) for various video sequences. Figure 8: Average execution time (sec) comparison of proposed CS based JD-TV codec with other CS codecs for various video sequences.
## 2 Modelling
### The Electromechanical Coupling Model of the Akiyama Probe
#### 2.2.1 The Transformation of the Model  
With Equation (4), the cantilever part in Figure 7 can be transformed to the tuning fork part as shown in Figure 8.  
With Equation (5), the sample part in Figure 8 can be transformed to the probe as shown in Figure 9.  
Figure 8: The transformation from cantilever to the tuning fork.  
Figure 7: The electromechanical coupling model of an Akiyama probe.  
Figure 9: The transformation from sample to probe.  
With Equation (4), the mechanical part in Figure 9 can be transformed to the electrical part as shown in Figure 10.  
As the two parts of the circuit are in parallel, the tuning fork prong's electrical parameters are:  
\[R_{\text{tf}} =\frac{p^{2}}{2}b_{\text{tf}} \tag{10}\] \[L_{\text{tf}} =\frac{p^{2}}{2}m_{\text{tf}}\] \[C_{\text{tf}} =\frac{2}{p^{2}k_{\text{tf}}}\]  
where \(b_{\text{tf}}\), \(m_{\text{tf}}\), \(k_{\text{tf}}\) are damping, spring constant, mass of one of the tuning fork prongs. Factor \(p\) is the electromechanical coupling factor.  
The cantilever's electrical parameters are:  
\[R_{\text{cl}} =\frac{p^{2}}{2}b_{\text{cl}} \tag{11}\] \[L_{\text{cl}} =\frac{p^{2}}{2}m_{\text{cl}}\] \[C_{\text{cl}} =\frac{2}{p^{2}k_{\text{cl}}}\]  
The sample's electrical parameters are:  
\[R_{\text{sample}} =\frac{n^{2}p^{2}b_{\text{ha}}}{4} \tag{12}\] \[C_{\text{sample}} =\frac{4}{n^{2}p^{2}k_{\text{eff}}}\]  
The model can therefore be deemed equivalent to the circuit shown in Figure 11.  
Figure 11: The equivalent electrical model of the Akiyama probe.  
Figure 10: The transformation from mechanical parameters to electrical parameters.  
The electrical parameters of the equivalent circuit can be calculated as:  
\[\begin{split} R_{\text{probe}}&=R_{\text{tf}}+R_{\text{ cl}}+R_{\text{sample}}=\frac{p^{2}}{2}b_{\text{tf}}+\frac{p^{2}}{2}b_{\text{cl}}+ \frac{p^{2}n^{2}b_{\text{sa}}}{4}\\ L_{\text{probe}}&=L_{\text{tf}}+L_{\text{cl}}=\frac{p^{2 }}{2}\left(m_{\text{tf}}+m_{\text{cl}}\right)\\ C_{\text{probe}}&=\frac{1}{\frac{1}{c_{\text{tf}}}+ \frac{1}{c_{\text{cl}}}+\frac{1}{c_{\text{sample}}}}=\frac{1}{\frac{p^{2}}{2}k _{\text{tf}}+\frac{p^{2}}{2}k_{\text{cl}}+\frac{n^{2}p^{2}}{4}k_{\text{eff}}} \end{split} \tag{13}\]  
The final RLC circuit is shown as Figure 5.  
So when \(k_{\text{eff}}\) = 0, the resonant frequency of the probe without contact is:  
\[f_{0}=\frac{1}{2\pi\sqrt{L_{\text{probe}}C_{\text{probe}}}}=\frac{1}{2\pi\sqrt{ \frac{p^{2}_{\pi}(m_{\text{tf}}+m_{\text{cl}})}{\frac{p^{2}}{2}(k_{\text{tf}} +k_{\text{cl}})}}}=\frac{1}{2\pi}\sqrt{\frac{k_{\text{tf}}+k_{\text{cl}}}{m_{ \text{tf}}+m_{\text{cl}}}}=\frac{1}{2\pi}\sqrt{\frac{k_{\text{probe}}}{m_{ \text{probe}}}} \tag{14}\]  
The resonant frequency after touching the sample is:  
\[f^{\prime}=\frac{1}{2\pi}\sqrt{\frac{k_{\text{probe}}+\frac{n^{2}}{2}k_{\text{ eff}}}{m_{\text{probe}}}} \tag{15}\]
# On End-to-End Intelligent Automation of 6G Networks
## 4 6G End-to-End Network Automation Challenges
### Private Network Support  
The fifth challenge facing 6G network automation and orchestration is how to support private networks in conjunction with the public networks. Currently, 5G supports private networks by deploying a 5G network slice completely on-premises or by offering it partially as a service [102; 103]. In this case, network management is decoupled from deployment. As a result, provider agnostic APIs have been utilized to manage these private networks. However, 6G requires a deeper level of private network automation. More specifically, these management and orchestration APIs need to be able to better communicate and coordinate to enable better public-private network orchestration. To that end, there is a need for orchestration mechanisms that are able to effectively and flexibly combine internal enterprise capabilities with external functionalities to offer new services. Moreover, these mechanisms need to also be abstract to allow private network staff members to effectively manage the network without the need for deep knowledge in network communication, management, and orchestration.
## 5 Conclusions  
The present work proposes a high-definition map construction method based on the GIS. A map construction framework including the road layer, intersection layer, and lane layer is proposed. By making use of the road edge data provided by the GIS map, the constrained Delaunay triangulation method is adopted to extract the road centerline and to further recognize the intersection to build the road layer network. Then, the geographic locations of lane-level data are generated on the basis of a road-level network using a linear reference approach to build the lane-level network. Taking Pingshan district in Shenzhen, China as an example, a sample lane-level map covering a total area of 25 km\({}^{2}\) is constructed. The detailed topology between each link and each lane is well explained in the constructed map. With this high-definition map, a lane-level trajectory matching test is further conducted to examine the feasibility of the proposed system. With the collected IMU data, GPS data, and probe vehicle lateral driving behavior, the trajectory of the vehicle is projected to the lane-level map using appropriate trajectory matching methods. The results showed that, with the help of the high-definition map, lane-level trajectory matching reaches an accuracy of over 96%. The outcomes of the present work provide a fast way of constructing lane-level road maps that could further support trajectory planning of smart vehicles and help in refining traffic management and control in smart cities. Considering the potential accuracy defects of the proposed system, further work will concentrate on validating the GIS-based map with vehicle trajectory data or other high-precision methods. Another potential direction could be the detailed expression of road networks with more complicated scenarios such as networks with roundabouts.  
Conceptualization, T.L., G.X. and X.Y.; methodology: T.L. and G.X.; data collection: T.L. and G.X.; analysis and interpretation of results: T.L., G.X. and X.Y.; draft manuscript preparation: T.L., G.X. and X.Y. All authors have read and agreed to the published version of the manuscript.  
This research was partly supported by the Guangdong Basic and Applied Basic Research Foundation (No. 2020A1515111001), Guangdong University Engineering Technology Research (Development) Center (No. 2021 GCZX002), Natural Science Foundation of Top Talent of SZTU (No. GDRC202130) and Cooperative R&D Project of SZTU (No. HKZLHT202070322).  
Not applicable.  
The data is contained within the article.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Trajectory** & **Distance** & **Trajectory Points** & **Matching Accuracy** \\ \hline
1 & 4.7 km & 9284 & 97.59\% \\
2 & 4.3 km & 5644 & 96.98\% \\ Overall & 9 km & 14,928 & 97.34\% \\ \hline \end{tabular}
\end{table}
Table 1: Trajectory matching accuracy.
## 4 Computational Experiments and Analysis
### Variation in the Composite Scattering Coefficient with Snow Layer Depth  
Figure 15 presents the computed results of composite scattering coefficient \(\sigma\) as a function of snow depth, where the snow thicknesses are set to \(10\lambda\), \(12\lambda\), and \(15\lambda\).  
As shown in Figure 15, the three angular distribution curves largely overlap, indicating that snow depth has minimal effect on \(\sigma\). This is because the rough vehicle exhibits a stronger electromagnetic scattering capability compared to a smooth target, and the vehicle's dielectric constant is significantly higher than that of the snow. Therefore, the snow depth has negligible influence on the scattering coefficient.
## 4 Discussion
### Clinical Utility of miRNA Biomarkers  
miRNAs are promising biomarkers for colorectal cancer (CRC) diagnosis, offering the potential for high accuracy in detecting the disease. The current meta-analysis, encompassing seven studies, provides a comprehensive evaluation of the miRNA diagnostic performance biomarkers in CRC.  
The pooled sensitivity of miRNA biomarkers, as illustrated in Figure 3, is 1.84 (95% CI: 1.48, 2.19), indicating a strong ability to correctly identify individuals with CRC. However, this sensitivity is accompanied by significant heterogeneity (I\({}^{2}\) = 89.16%; \(p<0.0001\)), suggest ing variability across the meta-analysis. Such variability focused on further standardization in miRNA detection methods and patient selection criteria to enhance diagnostic consistency.  
In terms of specificity, the pooled odds ratio (OR) presented in Figure 4 is 1.43 (95% CI: 1.01, 1.85), with substantial heterogeneity (I\({}^{2}\) = 94.32%; \(p\) < 0.0001). The OR indicates that miRNA biomarkers are effective in correctly identifying individuals without CRC, although the variability again highlights the challenges in achieving uniform diagnostic criteria across different study settings.  
The diagnostic odds ratio (DOR), depicted in the forest plot in Figure 5, further supports the utility of miRNA biomarkers. With a pooled OR of 0.38 (95% CI: \(-\)0.12, 0.88) and significant heterogeneity (I\({}^{2}\) = 89.51%; \(p\) < 0.0001), the data suggest that miRNA biomarkers offer a reliable generic diagnostic performance for CRC. The cumulative OR emphasizes the potential of miRNA as a robust tool for CRC diagnosis, particularly when integrated with other diagnostic modalities.  
Figure 6 shows the SROC curve summarizing diagnostic accuracy using an AUC of 0.907 (95% CI: 0.075-0.095). This high AUC underscores the excellent diagnostic potential of miRNA panels, suggesting that these biomarkers could play a crucial role in the early detection of CRC.  
In summary, miRNA biomarkers exhibit considerable promise for the accurate diagnosis of colorectal cancer. Despite the observed heterogeneity across studies, the pooled data indicate that miRNA panels offer a high level of diagnostic accuracy, particularly when used in conjunction with other diagnostic tools. Future research should focus on standardizing miRNA detection methods and addressing the sources of variability to fully realize the clinical utility of these biomarkers in CRC diagnosis.
# Time Series Analysis of Acoustic Emissions in the Asinelli Tower during Local Seismic Activity
## 3 Results
### b-Value versus Natural Time Analyses of AE Time Series
This concept is introduced by ascribing the natural time \(\chi_{k}=k/N\) to the \(k\)-th event of energy \(Q_{k}\)[25; 26; 27]. Regarding the normalized energies \(p_{k}\equiv Q_{k}/\Sigma_{k=1}NQ_{i}\) as the probability distribution of the discrete variable \(\chi_{k}\), the variance \(\kappa_{1}\equiv\chi^{2}-\chi^{2}\), is considered a key parameter for identifying the approach to a critical state, and defined as follows:  
\[\kappa_{1}\equiv\sum_{k=1}^{N}p_{k}\chi_{k}^{2}-\left(\sum_{k=1}^{N}p_{k}\chi _{k}\right)^{2}\equiv<\chi^{2}>-<\chi^{2}> \tag{3}\]  
As \(\chi_{k}\) and \(p_{k}\) are rescaled upon the occurrence of any additional hit, \(\kappa_{1}\) results to be an evolutionary parameter. It has been successfully shown that a variety of dynamical systems (2D Ising model [47], Bak-Teng-Wiesenfeld sandpile model [12; 47], and pre-seismic electric signals [25; 26; 27]) become critical when \(\kappa_{1}\), evolving hit by hit, approaches the value 0.07. Two criteria were defined to identify the entrance of a system to true critical state [47; 48; 49; 50]:  
1. the parameter \(\kappa_{1}\) must approach the value 0.07 \"by descending from above\";
2. the entropies \(S\) and \(S_{rev}\) (entropy upon time reversal) must be lower than the entropy of uniform noise, \(S_{u}=0.0966\), when \(\kappa_{1}\) coincides to 0.07. The entropy \(S\) is defined as \[S\equiv<\chi\,\ln\chi>-<\chi>\,\ln\,<\chi>\] (4) where \(<\chi\,\ln\chi>=\sum\limits_{k=1}^{N}p_{k}\chi_{k}\ln\chi_{k}\). Here, the evolution of variance \(\kappa_{1}\) and entropies \(S\) and \(S_{rev}\) of the natural-time transformed AE time series \(\{\chi_{k}\}\) has been studied, where the event energy \(Q_{k}\) is derived from the amplitude \(A_{k}\) through the relation \(Q_{k}=cA_{k}\)[1; 5], where \(c\) is a constant of proportionality [51; 52]. Plotting all natural-time quantities as functions of the conventional time \(t\) provides a visual way to reveal the possible entrance point to \"critical stage,\" corresponding to the fulfillment of criticality Conditions (1) and (2). An entrance point to critical stage has been identified at time \(t_{crit}=492\) h (criticality initiation time, marked by a vertical dashed line in Figure 4 and by a vertical dotted line in Figure 6), i.e., 9 h before the \(b\)-value  
Figure 5: The \(b\)-value and \(lb\)-value calculated using groups of 300 numbers of signals. reaches its minimum. Remarkably, this critical point corresponds to the middle-height peak in the AE rate (highlighted by the dashed line before the highest peak in Figure 4) and to the appearance of high-amplitude AE signals. This analysis suggests that the variance \(\kappa_{1}\) of the natural-time transformed AE series can be identified as a pre-failure indicator, before the onset of non-reversible damage--supposedly revealed by the minimum \(b\)-value--within the bulk of the structural element.
# Deep Clustering of Student Code Strategies Using Multi-View Code Representation (CMVAE)
## 3 Methodology
### Pre-Training to Learn Code Representations
#### 3.3.3 Extraction of Semantic Features  
Based on the transformer architecture, CodeBERT [14] is a pre-trained model that has been trained on large-scale code and natural language corpora. It generates embedding vectors that capture both the syntactic and semantic properties of valid code. While there are various pre-trained models available for code-related tasks, the resource-intensive nature of fine-tuning these models led us to choose a more efficient approach. Instead of fine-tuning, we opt to freeze the parameters of CodeBERT, directly utilizing its pre-trained embeddings as input features.  
To begin with, the source code files submitted by students undergo preprocessing to remove unnecessary distracting information, which includes the following steps:  
1. Removing comments and documentation strings from the code while retaining the core logic.
2. Using regular expressions to eliminate redundant whitespace characters and line breaks, ensuring that the input is concise and standardized.  
After cleaning the code, CodeBERT's tokenizer is used to break the code into tokens, enabling truncation and padding as needed to ensure uniform token sequence lengths. These tokenized sequences are then passed through the CodeBERT model, which generates context embeddings for each token. The global vector representation of the code is obtained by averaging the context embeddings of all tokens, providing a semantic feature for the code. This process is described in Equation (9):  
\[X_{2}=\frac{1}{N}\sum_{i=1}^{N}e_{i}, \tag{9}\]  
where \(X_{2}\) denotes the semantic embedding vector of the code, \(N\) represents the number of tokens in the code sequence, and \(e_{i}\) represents the context embedding vector of each token.
## 4 Example Verification
### Comparison of Calculation Results  
The calculated time history of positive critical acceleration and horizontal acceleration is shown in Figure 13. The numerical simulation adopts EI-Centrol seismic wave with horizontal peak acceleration of 0.2 g and vertical peak acceleration of 0.2 g, and the duration is 30 s. The positive and negative critical acceleration time histories of sliding soil are calculated by Equations (7) and (10), respectively, and the average values of positive and negative critical acceleration are 0.185 g and \(-5.214\) g, respectively. The permanent displacement calculated in this paper is compared with that calculated by PLAXIS 3D software and Geo-Studio software, and the comparison results are shown in Table 4.  
Figure 13: The horizontal acceleration and positive critical acceleration of the calculation model.  
It can be seen from Figure 13 that the calculated value of negative critical acceleration is much larger than the peak value of horizontal acceleration, so it can be concluded that the negative critical acceleration has no effect on permanent displacement. As can be seen from Table 4, the permanent displacement calculated by this paper is larger than that calculated by PLAXIS 3D and smaller than that calculated by GEO-Studio, which is closer to that calculated by GEO-Studio. This is because the Geo-Studio software is a two-dimensional calculation software and is relatively simplified in the calculation process, and the derivation process of this paper does not consider the influence of the lateral deformation of soil on the calculation of permanent displacement. To a certain extent, the calculation method in this paper is reliable.
## 3 Results
### Modeling \(R_{\rm s}\) Seasonal Variations
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{1}{c}{**Models and Equations**} & \multicolumn{1}{c}{\(\mathbf{R^{2}}\) & **RSS** & **RMSE** & **AIC** \\ \hline \(R_{\rm s}=f(T_{\rm s})\) & (1) \(R_{\rm s}=aT_{\rm s}+b\) & 0.803 ** & 56.286 & 0.985 & 2.260 \\  & (2) \(R_{\rm s}=aT^{a}\) & 0.842 ** & 4.742 & 0.286 & \(-\)141.231 \\ \(R_{\rm s}=f(M_{\rm s}T_{\rm s})\) & (1) \(R_{\rm s}=aM_{\rm s}+bM_{\rm s}+cT_{\rm s}+d\) & 0.816 ** & 52.61 & 0.952 & 0.343 \\ \((2)\) & \(R_{\rm s}=aexp(M_{\rm s}+cM_{\rm s}^{2}+aT_{\rm s})\) & 0.850 ** & 4.499 & 0.279 & \(-\)142.282 \\ \(R_{\rm s}=f(\rm LST)\) & (1) \(R_{\rm s}=aST+b\) & 0.695 ** & 87.135 & 1.226 & 27.607 \\ \((2)\) & \(R_{\rm s}=a^{\mu\rm LST}\) & 0.762 ** & 7.135 & 0.351 & \(-\)117.535 \\ \((1)\) & \(R_{\rm s}=a^{\mu\rm SDVI}+b\) & 0.568 ** & 123.701 & 1.460 & 47.931 \\ \((2)\) & \(R_{\rm s}=a^{\mu\rm NDVI}\) & 0.605 ** & 11.823 & 0.451 & \(-\)88.243 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Regression analysis relating soil respiration (\(R_{\rm s}\)) to soil temperature (\(T_{\rm s}\)), soil moisture (\(M_{\rm s}\)), land surface temperature (LST), and the normalized difference vegetation index (NDVI). There was a small gap in the explanation capacity between the LST exponential model and the \(T_{\rm s}\) exponential model, and the Pearson correlation coefficient was 0.902 between \(T_{\rm s}\) and LST data (Figure 4). A univariate exponential model based on \(T_{\rm s}\) and LST was then constructed for each year (Table 3). The \(t\)-test analysis of the three model performance indicators of two univariate exponential models for each year produced no significant differences. Therefore, the LST exponential model based on complete remote sensing data could be used as an empirical fitting model to modify seasonal variations in \(R_{\rm s}\). The modeling accuracy of the \(R_{\rm s}\) predictive models based on the \(T_{\rm s}\) exponential equation and LST exponential equation is presented in Figure 5 (a for \(T_{\rm s}\); b for LST). Pearson's correlation coefficient was 0.862** between the measured \(R_{\rm s}\) and predicted \(R_{\rm s}\) based on \(T_{\rm s}\) and 0.807** between the measured \(R_{\rm s}\) and predicted \(R_{\rm s}\) based on the LST, verifying that the \(T_{\rm s}\) and LST data were both effective and reliable for prediction of the temporal variations in \(R_{\rm s}\) in the study site.