esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Anchorage Force vs. Radial Stiffness  
Using Equation (1), the radial stiffness of the tubes of PVC, Al, and steel with wall thicknesses of 5.5 and 7.5 mm used in the test could be calculated as 0.15, 1.00, 2.90, and 3.63 GPa/mm, respectively. Figure 8a,b show the relationships of the average peak anchorage force with the radial stiffness of confinement for the original and modified dextral bolting sample, respectively. Combined with Figure 2 and Equation (1), the radial stiffness of 0.85 and 2.90 GPa/mm represent the boundaries between the weak/medium-strong and medium-strong/strong surrounding rock, respectively, as indicated by the two dotted blue lines in Figure 8a,b.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Confinement**} & \multicolumn{2}{c}{**Average Anchorage Force (Standard Deviation) (kN)**} \\ \cline{2-3}  & **Rib Spacing (12 mm)** & **Rib Spacing (48 mm)** \\ \hline Steel (7.0) & 131.0 (1.0) & 164.6 (2.7) \\ Steel (5.5) & 118.2 (4.5) & 156.2 (3.3) \\ Al & 87.3 (8.0) & 100.9 (16.1) \\ PVC & 66.4 (3.5) & 82.4 (2.6) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Pull-out test results.  
Figure 6: Pull-out test arrangement.  
Figure 7: Load–displacement curves. (**a**) Bolt with rib spacing of 12 mm; (**b**) bolt with rib spacing of 48 mm.  
To study the pattern of the bolting capacity vs confinement curves, the following negative exponential relationship was proposed for peak anchorage force and the radial stiffness of the confinement in this study:  
\[F_{P}=A-Be^{-C\cdot S} \tag{4}\]  
where  
\(F_{P}\) is the anchorage load capacity (kN);  
\(S\) is the radial stiffness of confining material (GPa/mm);  
\(A\) is a constant, it is the peak anchorage force while \(S\)\(\rightarrow\)+\(\infty\);  
\(B\) is a constant, thus that (\(A\)\(-\)\(B\)) equals the peak anchorage force at S\(\rightarrow\)0; and  
\(C\) is the curvature parameter.  
Using Equation (4) to fit the experimental data of the two kinds of dextral bolt, the fitting curves of the peak anchorage force and the stiffness of the confinement for original and modified dextral bolts can be expressed as the following equations, shown in Figure 8a,b, respectively.  
\[F_{P12}=146.0-87.1e^{-\frac{5}{11}S} \tag{5}\]  
\[F_{P48}=174.8-103.3e^{-\frac{19}{17}S} \tag{6}\]  
Determinations \(R^{2}\) for Equations (5) and (6) are 0.99; thus, Equation (4) is suitable to determine the relationship between peak anchorage force and stiffness of the confinement.
# Solon: A Holistic Approach for Modelling, Managing and Mining Legal Sources
## 2 Architecture
### General Characteristics  
The main requirements for Solon, are focused on:  
* Support for automatic and manual import of unstructured documents from predefined legal sources. Legal sources, i.e., regulations, case law and administrative acts, are usually authored by government agencies and disseminated through authoritative government websites. Therefore a reliable mechanism should provide input data to the platform on a scheduled basis.
* Automatic structural analysis and semantic representation of textual data and metadata. Input legal sources are usually distributed in unstructured text, often proprietary, formats, thus, it is preferable to transform them to a suitable for modelling legal sources format, capturing the legal semantics.
* Automatic discovery and resolution of legal citations, for each respective structural unit. Typically, legal documents refer to authoritative documents and sources, thus forming a network of interconnected legal documents. A precondition for the inter-linkage of legal documents and the Legal Linked Open Data formulation is the discovery and resolution of legal citations.
* Automatic classification of legal sources based on custom rules. Since the amount of legal sources available will always outweigh the time one actually has to read them, a popular approach to manage the information overload is to assign legal sources to one or more classes or categories, either manually or algorithmically.
* Support for manual curation of the automatically discovered, structured and semantically enriched content. While information extraction processes may achieve high levels of accuracy as to replace manual curation, the later is an importance step towards the completeness of the resulting legal knowledge base. Apart from that, there is also a zero tolerance for errors regarding the provision of legal documents.
* Support for multi criteria and multi faceted search using all metadata identified in documents. The aim of any information retrieval system is to deliver content that can precisely match and satisfy the users information need. With the plethora of intended users categories, ranging from novices to legal experts, distinctive information retrieval techniques, utilizing heterogeneous dimensions over the legal information sources, should be supported, so as to precisely match information requests.
* Support for structured content retrieval. A Legal Document cannot be processed as a general purpose text document; instead it exhibits strict semantics for each part of it, having a hierarchical structure of nested elements (e.g., articles that contain paragraphs, etc.) and covering multiple legal topics. Thus, a more precise approach than simply indexing and retrieving documents as single units, is needed, in order to provide more accurate and relevant results.
* Support for user-defined collections of legal resources around a topic. Integrating end users knowledge into the legal sources, is expected to assist them organize and explore legal sources in various meaningful ways.
## 3 Experiment and Analysis
### Point Cloud Registration Experiment for Complex Target Object
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Orientation**} & \multirow{2}{*}{**Algorithms**} & \multicolumn{2}{c}{**X-Direction**} & \multicolumn{2}{c}{**Y-Direction**} & \multicolumn{2}{c}{**Z-Direction**} & \multirow{2}{*}{**Average Error**} \\  & & **Rotation Angle** & **Rotation Angle** & **Rotation Angle** & **Rotation Angle** & **Distance (cm)** \\ \hline \multirow{3}{*}{1} & Traditional & \multirow{3}{*}{0.283} & \multirow{3}{*}{0.702} & \multirow{3}{*}{\(-\)0.172} & \multirow{3}{*}{0.015} \\  & algorithm & & & & \\ \cline{1-1} \cline{5-5}  & Proposed & & & & \\ \cline{1-1} \cline{5-5}  & algorithm & & & & \\ \cline{1-1} \cline{5-5}  & Traditional & \multirow{3}{*}{0.053} & \multirow{3}{*}{0.469} & \multirow{3}{*}{\(-\)0.587} & \multirow{3}{*}{0.009} \\  & algorithm & & & & \\ \cline{1-1} \cline{5-5}  & Proposed & & & & \\ \cline{1-1} \cline{5-5}  & algorithm & & & & \\ \cline{1-1} \cline{5-5}  & Proposed & & & & \\ \cline{1-1} \cline{5-5}  & algorithm & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Experimental results of different algorithms. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{3}{*}{**Group**} & **Registration** & **Registration** & **Percentage of** & **Total** & **Total** & **Percentage of** \\  & **Error of** & **Error of** & **Average** & **Registration** & **Registration** & **Total** \\  & **Traditional** & **Proposed** & **Registration** & **Time of** & **Time of** & **Registration** \\  & **Algorithm** & **Algorithm** & **Error** & **Traditional** & **Proposed** & **Time** \\  & **(cm)** & **(cm)** & **Reduction (\%)** & **Algorithm (s)** & **Algorithm (s)** & **Reduction (\%)** \\ \hline \multirow{3}{*}{1} & 0.015 & 0.011 & 36.4 & 11.680 & 11.553 & 1.1 \\  & 0.014 & 0.011 & 27.3 & 11.669 & 11.549 & 1.0 \\ \cline{1-1} \cline{5-  
* Greedy projection triangulation reflects the topological connection between data points and its neighbors, establishes local optimal correlation, narrows the search scope, and reduce unnecessary matching times. * The combination of the FPFH feature description and the greedy projection triangulation can match similar point pairs accurately and quickly, which is the key to efficient registration.
###### Abstract  
In this paper a concept of the second order digital infinite impulse response narrow band-reject filter with reduced transient response is proposed. In order to suppress the transient response of the considered infinite impulse response (IIR) notch filter its pole radius is temporarily varied in time using the Bezier parametric curve. Computer simulations verifying the effectiveness of the proposed pole-radius-varying notch filter are presented and compared to the performance of the traditional time-invariant filter using ECG signals distorted by unwanted powerline interference.  
time-varying filters; notch filters; digital signal processing; IIR filters; Bezier curve
# Iterative Parameter Estimation Algorithms for Dual-Frequency Signal Models
## 2 The Gradient-Based Iterative Parameter Estimation Algorithm
Let \(k=1\), give a small number \(\varepsilon\) and set the initial value \(\hat{\mathbf{\theta}}_{0}=\mathbf{1}_{n}/p_{0}\), \(p_{0}\) is generally taken to be a large positive number, e.g., \(p_{0}=10^{6}\). 2. Collect the observed data \(y(t_{i})\), \(i=1,2,\cdots,L\), where \(L\) is the data length, form \(\mathbf{Y}(L)\) by Equation (10). 3. Form \(\hat{\mathbf{\varphi}}_{k}\) by Equation (13) and \(\hat{\mathbf{\Phi}}_{k}\) by Equation (11). 4. Form \(\hat{f}_{k}\) by Equation (14) and \(\hat{F}_{k}\) by Equation (12). Choose a larger \(\mu_{k}\) satisfying Equation (16), and update the estimate \(\hat{\theta}_{k}\) by Equation (15). * Compare \(\hat{\theta}_{k}\) with \(\hat{\theta}_{k-1}\), if \(\|\hat{\theta}_{k}-\hat{\theta}_{k-1}\|\leqslant\varepsilon\), then terminate this procedure and obtain the iteration time \(k\) and the estimate \(\hat{\theta}_{k}\); otherwise, increase \(k\) by \(1\) and go to Step 3.
# Interval-Based LDA Algorithm for Electrocardiograms for Individual Verification
## 2 Materials and Methods
### Feature Extraction
#### 2.2.2 Interval-Based Feature Extraction  
Figure 5 shows an example of ACPs for two different subjects (subject A and subject B). We assumed that each subject has C classes and those C classes are displayed together within the same time period \(Q\).  
From Figure 5, the intervals (a) and (c) are when the two subjects are difficult to distinguish by using only the amplitude patterns of \(\Phi_{c}(m)\), but these two subjects are more easily distinguished using the angle patterns of \(\Phi_{c}(m)\). It is obvious from the two profiles of (a) and (c) that their angle patterns are significantly different, in which the angle pattern is defined as the angles between the horizontal \(x\)-axis and the ACPlet. On the other hand, (b) and (d) are the intervals when the two subjects can be easily distinguished using the amplitude patterns of \(\Phi_{c}(m)\).  
These results lead to the idea of an interval-based feature vector. Accordingly, to facilitate the identification between different subjects, each class needs to be sliced into short-time ACP segments, i.e., the ACPlets. Therefore, in this work, the two features of amplitude and angle patterns of ACP produced for every ACPlet are used to distinguish the ECG signals.  
Figure 5: An example of a set of ACPs, displayed together within the same time period of \(Q\), for two different subjects (subject A and subject B) when C = 5. (**a**–**d**) illustrate the differences in amplitude and angle patterns of ACPlet generated when the ACPs are segmented into six ACPlets, even though the two subjects have a very similar ACP shape.
# Course Recommendation Based on Enhancement of Meta-Path Embedding in Heterogeneous Graph
## 4 Proposed Approach for Course Recommendation
### Embedding Weight Aggregation Layer  
#### 4.2.1 Aggregation of Meta-Path Embedding Based on Attention Mechanism  
In order to aggregate the neighbor information of each node, EWAL adopts a GAT mechanism to update the embedding representation of each node to form a new node embedding vector. For each meta-path embedding, including the original meta-path embedding and the simulated meta-path embedding, Equation (4) can be used to generate a new node embedding for each node under each meta-path:  
\[t_{v,j}^{(l)}=LeakyReLU(\eta^{(l)T}\cdot[W^{(l)}e_{v}^{(l)}||W^{(l)}e_{j}^{(l) }]), \tag{4}\]  
where \(e_{v}^{(l)}\) represents the embedding of node \(v\) under the \(l\)-th meta-path, \(t_{v,j}^{(l)}\) represents the contribution of features of neighbor \(j\) to node \(v\) under the \(l\)-th meta-path, \(\eta^{(l)T}\) represents the attention parameter vector under the \(l\)th meta-path, \(W^{(l)}\) represents the trainable weight matrix, and \(||\) represents the splicing operation between vectors. Then, we can use Equation (5) for normalization:  
\[a_{i,j}^{(l)}=\frac{exp(t_{vj}^{(l)})}{\sum_{k=1}^{k\in N_{v}}exp(t_{vk}^{(l) })} \tag{5}\]  
Figure 3: Schematic of GCN simulated embedding.  
After the weighted average of the neighborhood features of node \(v\) is obtained according to contribution \(a_{i,j}^{(l)}\), a nonlinear conversion function \(\sigma\) is added to obtain the new features of node \(v\), i.e., \(\tilde{e}_{v}^{(l)}\). Equation (6) shows this process:  
\[\tilde{e}_{v}^{(l)}=\sigma(\sum_{j\in N_{v}}a_{vj}^{(l)}W^{(l)}e_{j}^{(l)}) \tag{6}\]  
In order to better learn the correlation between nodes and neighbors, the module uses the multi-header attention mechanism to perform \(K\) times attention operations, then splices the \(K\) times of the results, as shown in Equation (7):  
\[\tilde{e}_{v}^{(l)}(K)=\underset{k=1}{|}\sigma(\sum_{j\in N_{v}}[a_{vj}^{(l)} ]^{k}[W^{(l)}]^{k}e_{j}^{(l)}), \tag{7}\]  
where \([a_{vj}^{(l)}]^{k}\) represents the normalized neighbor contribution weight to node \(v\) on the \(k\) attention header under the \(l\) meta-path, and \([W^{(l)}]^{k}\) represents the trainable weight matrix on the \(k\) attention header under the \(l\) meta-path.  
In order to retain the embedded information before and after updating the node, the embedded information before and after the update is spliced, meaning that the final embedded dimension after splicing is twice the previously embedded dimension. Both the user node and the project node need to undergo graph attention network aggregation embedding.  
The framework of GAT Embedding is shown in Figure 4.
# Toward a Standard Approach for Echo Chamber Detection: Reddit Case Study
## 2 Related Work
### Echo Chamber Detection
For example, [PERSON] [17] leverages the Modularity function (see Definition 2) to discover several cohesive clusters across Facebook pages. In Table 1, we give a summary of related works based on the two categorizations proposed above. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Paper** & **Network** & **Content** & **Micro-Scale EC** & **Meso-Scale EC** & **Macro-Scale EC** \\ \hline
[14,16] & ✗ & ✓ & ✓ & ✗ & ✗ \\
[13] & ✗ & ✓ & ✗ & ✗ & ✓ \\
[17] & ✓ & ✗ & ✗ & ✓ & ✗ \\
[22] & ✓ & ✓ & ✗ & ✗ & ✓ \\
[18] & ✓ & ✓ & ✗ & ✗ & ✓ \\
[19, 20, 21] & ✓ & ✓ & ✗ & ✗ & ✓ \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of related works for echo chamber detection on online social networks. Summing up, according to [PERSON] and [PERSON] [15], there are two key methodological issues with how echo chamber work has been conducted. First, the adoption of so many different approaches for EC detection has led to conflicting results over the years. Second, most of works leveraged platform-specific features or resources to identify ECs, limiting their generalizability, e.g., _mention_ and _retweet_[18; 22], _follow_[19; 21], _friend_[13], users' clicks [14], pre-annotated datasets [14; 22]. Moving from the literature, in this paper we propose a framework to identify _meso-scale ECs_ built on top of features commonly shared by most OSNs. In doing so, we leverage both the content and network dimension of the phenomenon. In the following, we provide a brief overview of techniques both for ideology and community detection used in this work.
## 3 Experimental Results
### Experimental Results
#### 3.2.5 Experiment 5: Experiment on Vibration Data from Finite Element Models  
To further verify the performances of the proposed method on different structures, experiments on finite element models are conducted. In the experiment, three structures are used, namely a plate, a simply supported beam and a spatial truss structure. Figure 10 gives the sketches of the three models.  
The material of the three models is set to be the same of the GARTEUR model, which is the 2024-T3 aluminum. The plate is modeled using the quad4 unit, with a size of 1000 mm \(\times\) 1000 mm \(\times\) 10 mm. The simply supported beam is modeled using the beam unit. The length of the beam is 2000 mm and the cross-sectional size of the beam is 10 mm \(\times\) 10 mm. For the spatial truss structure, the size is 1000 mm \(\times\) 1000 mm \(\times\) 1000 mm, the nodes are located at the middle-point of bars, the spatial truss structure is modeled using bar unit. In Figure 10, the arrow indicates the position of the excitation and the circles indicate the positions of accelerometers. The load is set to be the same with the random loads in Experiment 1 for the asymmetrical sensor distribution. For the plate and the simply supported beam, the load is applied along the direction that perpendicular to the plate and the simply supported beam, respectively. For the spatial truss structure, the load is applied along the axis of the bar.  
Based on the data collected from the above finite element models, the performances of the proposed method are further evaluated. We also compare the proposed method with the time domain data-driven method in [25]. Table 8 gives the quantitative comparisons.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Structure** & **Method** & **RMSE** & **CC** \\ \hline plate & 1D-att-CNN & 2.0326 & 0.8303 \\  & ours & 0.8993 & 0.9762 \\ \hline simply supported beam & 1D-att-CNN & 2.1473 & 0.7965 \\  & ours & 0.6768 & 0.9864 \\ \hline spatial truss structure & 1D-att-CNN & 1.7968 & 0.8492 \\  & ours & 1.2056 & 0.9620 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Quantitative comparisons on the performances based on vibration data from the finite element models.  
Figure 10: The three model used in the experiment, which are the plate, the simply supported beam and the spatial truss structure. In the figure, the arrow shows the position of the applied random loads, while the circles show the positions of the accelerometers. (**a**) The plate model used in the experiment. (**b**) The simply supported beam model used in the experiment. (**c**) The spatial truss structure model used in the experiment.
## 3 Watermarking Approaches
### Watermarking Methods
#### 3.2.3 Watermark Embedding Through Full LDM Optimization
Although this deviates somewhat from the full LDM optimization approach discussed in this section, this methodology, which creatively utilizes the LDM structure, is worth mentioning. Optimizing watermark embedding across the entire latent diffusion model, though it requires higher training costs, allows for the creation of extremely resilient and robust watermarks. This method ensures excellent protection against tampering and enhances the ability to recover the watermark, providing stronger security and traceability for the generated images.
## 3 Approach
### Data Preprocessing  
Before proceeding to feature extraction, a number of pre-processing steps were performed on the datasets, as shown in Figure 2. Recordings were first downsampled to 16 kHz and converted to mono. As can be seen in Table 3, most of the sound clips in the datasets are 5 s in length or less. A few are longer than this. Therefore, for our experiments, we extended any shorter clips to 5 s by adding silence to the end. Conversely, any longer clips were cut off in order to make them exactly 5 s long.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Aspect** & **ASED** & **RAVDESS** & **EMO-DB** & **URDU** \\ \hline Language & Amharic & English & German & Urdu \\ \hline Recordings & 2474 & 1440 & 535 & 400 \\ \hline Sentences & 27 & 2 & 10 & - \\ \hline Participants & 65 & 24 & 10 & 38 \\ \hline Emotions & 5 & 8 & 7 & 4 \\ \hline Positive valence & Neutral, Happy & Neutral, Happy, Calm, Surprise & Neutral, Happiness & Neutral, Happy \\ \hline Negative valence & Fear, Sadness, Angry & Fear, Sadness, Angry, Disgust & Anger, Sadness, Boredom & Angry, Sad \\ \hline References & [10] & [27] & [29] & [17] \\ \hline \end{tabular}
\end{table}
Table 2: Datasets used in the experiments. The table also shows the mapping from the emotion labels in each dataset into just two valence labels which can be used across them all: positive and negative.  
Figure 2: Data preprocessing.
# Publish/Subscribe-Middleware-Based Intelligent Transportation Systems: Applications and Challenges
## 4 Application of Publish/Subscribe Middleware in ITS
### Communication Technologies  
Effective communication in ITS is crucial for coordinating network operations, ensuring data flow accuracy, and enhancing overall system efficiency and reliability. We found that selected papers use WSN/Vehicular Sensor Networks (VSNs), VANET, fog computing/vehicular fog computing (VFC), P2P communication, Information Centric Network (ICN), and mobile networks (e.g., 4G and 5G).
## 8 Conclusions  
In this work, we comprehensively examined whether temporal modulation cues in the TAE of speech contribute to the perception of urgency. To this end, we performed experiments in which noise-vocoded stimuli containing TAEs identical to those of the original stimuli were compared with those in which the components were changed by low-pass or high-pass filtering. We derived the degrees of urgency from a paired comparison of the results and then used them as a basis for clarifying the relationship between the temporal modulation components and urgency perception. Our key findings are as follows.  
1. The perceived degrees of urgency of noise-vocoded stimuli are similar to those of the original stimuli.
2. Significant cues for urgency perception are temporal modulation components of the noise-vocoded stimuli higher than the modulation frequency of Hz.
3. Additional significant cues for urgency perception are temporal modulation components of the noise-vocoded stimuli lower than the modulation frequency of Hz.
4. A TAE of time-reversal speech is not likely to contain important cues for the perception of urgency.  
In other words, the instantaneous modulation frequency (or asymmetry of the time-varying pattern of modulation frequency, etc.) is presumably more important than the modulation frequency in the long-time average sense.  
Overall, our findings demonstrate that temporal modulation cues in the TAE significantly contribute to the perception of urgency.  
Conceptualization: M.U.; methodology: M.K. ([PERSON]), M.K. ([PERSON]) and S.K.; experiments: M.K. ([PERSON]); data analysis: M.K. ([PERSON]), M.K. ([PERSON]) and S.K.; data curation: all authors; general discussion: all authors; writing--original draft preparation: M.U. and S.K.; writing--review and editing: M.U., S.K. and J.L.; visualization: M.K. ([PERSON]); supervision: [PERSON]. [PERSON] and [PERSON]. [PERSON]; funding acquisition: M.U. and M.A. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the SCOPE Program of the Ministry of Internal Affairs and Communications (Grant No. 201605002) and JSPS-NSFC Bilateral Programs (Grant number: JSJSBP120197416). This research was also supported by a Grant-in-Aid for Innovative Areas (Grant Nos. 16H01669, 18H05004, and 21H03463), a Grant-in-Aid for Scientific Research (B) (21H03463), and a Fund for the Promotion of Joint International Research (Fostering Joint International Research (B)) (20 KK0233), from MEXT.  
Not applicable.  
Informed Consent Statement: Informed consent was obtained from all participants involved in the study.  
Not applicable.  
The authors declare no conflict of interest.