esa-sceva/satcom-chunk-collection huggingface dataset:
# Adaptive Sparse Cyclic Coordinate Descent for Sparse Frequency Estimation
## 2 Adaptive-Sparse Coordinate Descent Algorithm
* [24] [PERSON]; [PERSON]; [PERSON] Scaled linearized Bregman iterations for fixed point implementation. In Proceedings of the 2017 IEEE International Symposium on Circuits and Systems (ISCAS), Baltimore, MD, USA, 28-31 May 2017; pp. 1-4. [CrossRef]
* [25] [PERSON] [PERSON]; [PERSON] [PERSON] Implementing Sparse Estimation: Cyclic Coordinate Descent vs. Linearized Bregman Iterations. In Proceedings of the 2019 11 th International Symposium on Image and Signal Processing and Analysis (ISPA), Dubrovnik, Croatia, 23-25 September 2019; pp. 335-340. [CrossRef]
* [26] Spectral Analysis of Signals: Resource Page. Available online: [[http://www2.ece.ohio-state.edu/~randy/SAtext/](http://www2.ece.ohio-state.edu/~randy/SAtext/)]([http://www2.ece.ohio-state.edu/~randy/SAtext/](http://www2.ece.ohio-state.edu/~randy/SAtext/)) (accessed on 1 February 2021). * [27] [PERSON]; [PERSON] A new TwIST: Two-step iterative shrinkage/thresholding algorithms for image restoration. _IEEE Trans. Image Process._**2007**, _16_, 2992-3004. [CrossRef] [PubMed]
* [28] Two-step Iterative Shrinkage/Thresholding Algorithm for Linear Inverse Problems (TwIST). Available online: [[http://www.lx.it.pt/~bioucas/TwIST/TwIST.htm](http://www.lx.it.pt/~bioucas/TwIST/TwIST.htm)]([http://www.lx.it.pt/~bioucas/TwIST/TwIST.htm](http://www.lx.it.pt/~bioucas/TwIST/TwIST.htm)) (accessed on 1 February 2021). * [29] [PERSON]; [PERSON] Multiple tone parameter estimation from discrete-time observations. _Bell Syst. Tech. J._**1976**, _55_, 1389-1410. [CrossRef]
* [30] [PERSON]; [PERSON]; [PERSON] Variable Projection for multiple frequency estimation. In Proceedings of the 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Barcelona, Spain, 4-8 May 2020; pp. 4811-4815. [CrossRef]
* [31] [PERSON]; [PERSON]; [PERSON]; [PERSON] DoA Estimation Performance and Computational Complexity of Subspace- and Compressed Sensing-based Methods. In Proceedings of the 19 th International ITG Workshop on Smart Antennas (WSA), Ilmenau, Germany, 3-5 March 2015; pp. 1-6.
## 5 Simulations and Assessments  
### Simulation Conditions  
To verify the effectiveness of the proposed DTCC-ESC method more comprehensively, six typical engine starting conditions (Case A, Case B,..., Case F), as shown in Figure 18 and Table 2, were set as simulation conditions during mode switching that included engine starting. Figure 18 shows the required ring torque converted from the acceleration pedal position (APP) and vehicle speed in EV mode. Table 2 further shows other engine-starting conditions, which include engine coolant temperature, battery temperature, and battery SOC. Note that the influence of SOC, temperature, and other factors on the dynamic response characteristics of the vehicle during engine starting can be referred to the literature [42]
# An Improved Differential Quadrature Time Element Method
## 4 Numerical Analysis
### Frequency Comparisons
#### 4.1.1 Uniform Rod  
This subsection aims to show the improved DQTEM is more accurate than DQTEM0, if the spatial DOFs used by both method are the same.  
Table 5 gives the comparisons of natural frequencies via constant strain rod element and DQ element for a fixed-free rod, in which the dimensionless frequency parameter \(\lambda~{}=~{}\omega l/c\) is used, where \(c~{}=~{}\sqrt{E/\rho}\) is the longitudinal wave propagation velocity. In Table 5, \(NE\) is the number of spatial element, and \(m\) is the DOF of each spatial element.  
For this problem, DQTEM0 adopts 10 and 100 constant strain elements, respectively. For comparison using the same number of spatial DOFs, the improved DQTEM employs a DQ element with 11 and 101 unequally spaced points (Gauss-Lobatto-Chebyshev points), correspondingly. It can be concluded from Table 5 that the Nerr5 of DQTEM0 with \(NE=10\), \(m=2\) is about 30%, while the Nerr5 of the corresponding improved DQTEM with \(NE=1\), \(m=11\) is almost 70%, and the RelErr1 of the improved DQTEM are much smaller than DQTEM0. A similar conclusion is also apparent when \(NE=100\), \(m=2\) is used in DQTEM0 and \(NE=1\), \(m=101\) is used in the improved DQTEM. If more spatial DOFs are used, this conclusion can also be observable.  
Note that Nerr5 indicates the percent of accurate frequencies, for example, when \(M=10\) as in Table 5, the RelErr1 of the first three frequencies by DQTEM0 is less than 5%, so Merr5 is 3, then Nerr5 = Merr5\(/M\times 100\%=3/10\times 100\%=30\%\).  
To further show the superiority, the improved DQTEM is also compared with High-Order Lagrange Element Method (HOLEM) with equally spaced nodes in Table 6. It can be seen in Table 6 that the Nerr5s of the improved DQTEM and HOLEM are the same since they use the same order of  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Rod** & \multicolumn{2}{c}{**Euler Beam**} \\ \hline [PERSON]’s modulus & \(E=125\) GPa & [PERSON]’s modulus & \(E=125\) GPa \\ Density & \(\rho=8980\) kg\(/\)m\({}^{3}\) & Density & \(\rho=8980\) kg\(/\)m\({}^{3}\) \\ Length & \(l=1\) m & Length & \(l=3\) m \\ Diameter & \(d=0.1\) m & Cross section area & \(S=0.0468\) m\({}^{2}\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Material properties.  
interpolation polynomials. However, the improved DQTEM is more accurate and efficient because, in the improved DQTEM, the unequally spaced nodes are used, and the mass and stiffness matrices are only the products of the explicit weighting coefficient matrix and the coefficient matrix of Gauss-Lobatto integration, while those of HOLEM depend on shape functions.  
From the comparisons in Tables 5 and 6, one can see that the improved DQTEM is superior to DQTEM0 if the same number of spatial DOFs is used for the solution of natural frequencies, which is further validated by the results of the following beam.
# Predicting Phubbing Through Machine Learning: A Study of Internet Usage and Health Risks
## 6 Conclusions and Future Work
The dataset used in this study, which included global averages for 2014-2024, provided by the World Bank, highlights that technology use and social norms exhibit regional and cultural differences. Future studies could explore these local effects by collecting data from various geographical regions and cultural contexts, such as comparing internet usage habits in developed and developing countries and examining how these differences influence phubbing. With the proliferation of 5G/6G technologies and edge-fog-cloud infrastructures, the expected surge in digital data will necessitate the use of more complex and large datasets for the analysis of social phenomena like phubbing. In this context, the applicability of artificial intelligence and machine learning techniques in edge environments should be prioritised, integrating real-time data analytics and prediction methods, particularly with data collected from user devices like smartwatches. Real-time data analysis for stress management and behaviour prediction offers a promising approach to better understand and mitigate phubbing. Future research should focus on larger datasets, edge-fog-cloud architectures, and advanced hybrid AI models to process data in real time and predict digital behaviors more effectively. Given phubbing's potential to cause more significant negative effects than other types of addiction, as highlighted in prior research [70], its dangers are expected to increase with rising internet usage. Therefore, it is critical to develop more robust AI-based models, utilise additional datasets and independent variables, and devise innovative methods to predict and address the social impacts of phubbing. Conceptualisation, M.A.A., M.K. ([PERSON]) and T.L. ; data curation, S.K. and F.K. ; formal analysis, M.K. ([PERSON]); funding acquisition, A.Y. ; investigation, M.K. ([PERSON]), F.Y.A. and T.L. ; methodology, M.A.A. and M.K. ([PERSON]); project administration, A.Y. ; resources, A.Y., M.K. ([PERSON]) and S.K. ; software, M.K. ([PERSON]); validation, M.K. ([PERSON]), F.Y.A., T.L. and F.K. ; writing--original draft, A.Y., M.K. ([PERSON]), S.K., F.Y.A. and T.L. ; writing--review and editing, M.A.A., F.Y.A. and F.K. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The dataset is available from the authors upon reasonable request. The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of the data; in the writing of the manuscript; or in the decision to publish the results.
## 4 Results
### Macroporosity
At CAS, the ratio between the slopes of the \(\theta_{s}\) and \(\theta_{10}\) vs. the _ar_ relationships was equal to 1.12 for the COM, 1.40 for the VER, and 1.01 for the BIO (Table 2). Therefore, with the COM and the VER, the \(\theta_{s}\) increased more than the \(\theta_{10}\) as the _ar_ increased and consequently the \(P_{MAC}\) increased. Instead, with the BIO, the slopes of the \(\theta_{s}\) and \(\theta_{10}\) vs. the _ar_ relationships were similar, hence the \(P_{MAC}\) did not vary significantly with the _ar_. At GIN, the ratio between the slopes of the \(\theta_{s}\) and \(\theta_{10}\) vs. the _ar_ relationships was equal to 1.15 for the COM, 1.50 for the VER, and 0.96 for the BIO. Therefore, even in this case, using the COM and the VER determined an increase of the \(P_{MAC}\) with the _ar_ since \(\theta_{s}\) increased more  
Figure 3: Macroporosity, \(P_{MAC}\), against the amendment rate for the three tested amendments (COM = compost; VER = vermicompost; BIO = biochar) at (**a**) the Castellaneta and (**b**) the Ginosa field sites. For a given site, the continuous lines are the significant linear regression lines (black for COM; red for VER; blue for BIO). The dashed grey lines define the range of values inside which the soil physical quality is considered good. than \(\theta_{10}\). Instead, the \(P_{MAC}\) decreased with the BIO since the \(\theta_{10}\) increased more than the \(\theta_{s}\) as the _ar_ increased. Even for those cases in which the \(P_{MAC}\) increased with the _ar_, the macroporosity always remained well below the lower limit of the optimal range. Therefore, neither a minimum amendment rate, that is an amount of amendment making the \(P_{MAC}\) acceptable, nor obviously a maximum rate were identified for the \(P_{MAC}\) in the range of the considered _ar_ values. In summary, regardless of the soil, adding the BIO did not improve soil macroporosity, instead there was some sign that this amendment induced an additional decrease of an already low value. Using the COM and the VER favored development of some new pores with a diameter \(>\) 300 \(\mu\)m [3], but the increase in macroporosity overall was limited and therefore the \(P_{MAC}\) remained low even with high soil amendment rates.
## 3 Results
### Directional Forest Canopy Reflectance  
#### 3.3.1 Spatial Variations  
We obtained the spatial variations of shaded forest background (Figure 9a), sunlit forest background (Figure 9b), shaded overstory (Figure 9c), and sunlit overstory (Figure 9d) with the full azimuthal angle range (i.e., 0\({}^{\circ}\) to 360\({}^{\circ}\)) and limited zenith angles ranging from -60\({}^{\circ}\) to 60\({}^{\circ}\) under a fixed azimuthal (150\({}^{\circ}\)) and zenith (37\({}^{\circ}\)) angles of sun position in plot WPA-P2. The proportions of shaded forest background and shaded overstory reached the minimum value of 0% at the hotspot point, and gradually increased as the observation direction deviated away from hotspot position, shown in Figure 9a,c. Conversely, the peak values of sunlit forest background (46.0%) and sunlit overstory (54.9%) were observed in the hotspot position, shown in Figure 9b,d.  
However, compared with the forest background components, more substantial variation rates and more obviously sharp changing points for both shaded and sunlit overstory were observed near the hotspot area in their hemispherical spatial distribution, shown in Figure 9c,d.  
Figure 9: The spatial distributions of the ALS-based four forest component proportions (insets **a–d**) and forest canopy reflectance in red (inset **e**) and near-infrared (inset **f**) bands for forest plot WPA-P2 with the solar zenith and azimuth angles of 37\({}^{\circ}\) and 150\({}^{\circ}\), respectively.  
We also obtained the reflectance values of shaded and sunlit forest background and shaded and sunlit overstory as 0.02 and 0.057, 0.014 and 0.03 in the red band, and 0.275 and 0.358, 0.10 and 0.49 in the near-infrared band using the method described in Section 2.6. As shown in the Figure 9e,f, we plotted the spatial distribution of forest canopy reflectance in both red and near-infrared bands in plot WPA-P2 using Equation (4). The reflectance values of red (i.e., 0.046) and near-infrared (i.e., 0.43) bands reached the maximum at the hotspot point and gradually decreased with the observation direction deviating away from the hotspot position.
# Few-Shot Knowledge Graph Completion Model Based on Relation Learning
## 2 Related Work
### Few-Shot Learning  
Traditional knowledge graph completion methods require a large number of training instances to improve model accuracy. However, in the real world, there are numerous new facts, and knowledge graphs are constantly changing dynamically. When knowledge graphs cannot provide sufficient training instances for these new facts, the model's training process is greatly limited. Therefore, researchers have begun to explore knowledge graph completion tasks with only a small number of instances. In metric-learning-based methods, GMatching [16] was the first to propose the few-shot knowledge graph completion problem, obtaining embedding representations from one-hop neighbor structures in the neighborhood and using LSTM networks to match embeddings with targets, yielding similarity scores to measure the similarity between query triples and the reference set. However, GMatching does not distinguish between neighboring information in the neighborhood. FSRL [17] can effectively capture knowledge from heterogeneous graph structures, aggregate representations of a small number of samples, and assign different weights to neighborhood information using a heterogeneous neighbor decoder. Compared to the first two models, FAAN [18] considers the dynamic attributes of entities and relations and captures dynamic features that change in different tasks through attention mechanisms, thereby improving their fine-grained semantic representations. MetaR [19], in contrast to the above methods, which utilize neighborhood information to enhance entity embeddings, adopts a meta learning framework including gradient meta and relation meta for few-shot knowledge graph completion tasks, effectively improving the model's learning ability. GANA [20] improves upon MetaR, using a global local framework to accurately filter out noise information in the neighborhood and addressing the complex relation problems of one-to-many (1-N), many-to-one (N-1), and many-to-many (N-N) in knowledge graphs.
## 5 Test-Result Analysis
### Static-Load-Test Results
#### 5.1.2 Support Plate N1  
The stress value of the measuring points at the weld between the outer side of the N1 plate and the beam web was large in the middle and small on both sides, and the compressive stress at most of the measuring points was larger than the tensile stress. The upper part of the supporting plate N1 was mainly subjected to compressive stress, while the lower part was mainly subjected to tension. The maximum tensile stress, which was 25.88 MPa, occurred at the measuring points N1-2; the maximum compressive stress, which was \(-\)60.12 MPa, occurred at the measuring points N1-8, located at the weld connected to the pressure plate N7. The test value of the support plate N1 is shown in Figure 14.  
Figure 13: Stress-test-value diagram of inner web of main girder F1 (MPa).
## 5 Parametric Studies
### Real-Scale Modelling  
Finally, the transformation of debris flows into turbidity currents on the field scale is considered here. A computational domain of 50 km \(\times\) 1 km is generated to perform the two-dimensional simulation. A uniform grid with an element size of 15 m \(\times\) 15 m is used for meshing, and the entire domain of interest is meshed with 200,049 quadrilateral elements. Through trial calculations, the mesh is determined to be sufficiently fine. The initial length and thickness of the slurry are set as 3000 m and 200 m, respectively. The slurry, with an initially rectangular shape, starts to accelerate due to gravity on a slope with an angle of 1\({}^{\circ}\). After 30 km, the slope angle suddenly decreases to zero. The mixture model is used as the multi-phase flow model, while the laminar model is adopted as the viscosity model. The density and the yield stress of the slurry are considered to be 1.6 \(\times\) 10\({}^{3}\) kg/m\({}^{3}\) and 500 Pa, respectively. The viscosity of the water is 0.001 Pa\(\cdot\)s. Pressure inlet and outlet conditions are imposed on the left wall and the right wall, respectively, while a no-slip condition is applied to the base.  
The velocity and the run-out distance at the toe of the slurry front are presented in Figure 13. In the run-out process, the maximum velocity reaches 25 m/s in 4 min at the toe of the slurry front. Then, the velocity decreases due to frictional resistance along the interface between the slurry and the bed. The corresponding final run-out distance is 42.5 km. In the transportation process, the variation in the transformation ratio T with time throughout the whole process is presented in Figure 14. The debris flow is fully transformed into a turbidity current in 17.5 s, which is in accordance with the field observations. This indicates that the turbidity current can move over a longer distance than the parent debris flow.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Case** & **Slope Angle 0 (\(\cdot\))** & **Initial Thickness H (m)** & **Yield Strength s\({}_{\infty}\) (Pa)** & **Transformation Rate E (m/s)** & **Representative Velocity \(v\) (m/s)** \\ \hline  
\hline \end{tabular}
\end{table}
Table 1: Transformation rates for different cases at initial moments.
# A Relay Optimization Method for NOMA-Based Power Line Communication Systems
## 3 Optimization Process and Performance Analysis
### Limitations and Future Work
The UMass Smart dataset [18] offers channel gain and noise measurements from residential powerline networks with 50+ nodes, enabling scalability testing for multi-user NOMA-PLC deployments. Furthermore, it collaborates with utility providers (e.g., IEEE PES Test Feeder Systems [18]) to deploy the proposed algorithm in microgrids with dynamic loads, where PLC relays can coexist with VLC-based IoT sensors for real-time fault detection. The application of these practical scenarios will be focused on in future research. In the end, in the Internet of Things based on PLC, security is the content that has to be considered. Therefore, it is discussed in the last part. In general, data integrity, eavesdropping, interference attacks, and replay attacks are common ways to threaten security, as detailed in Table 7. In view of these security threats, a security mechanism defense scheme is proposed. For physical layer security enhancement, NOMA security power allocation can introduce security capacity constraints to maximize the SNR gap between legitimate users and eavesdropping users. It can also complete the anti-interference OFDM symbol design, insert an encrypted pilot in the subcarrier, dynamically verify the channel integrity, and detect interference through CP matching degree at the receiving end, triggering 5A re-optimization. For data integrity protection, it is a feasible method to attach HMAC tag before LSTM input data and use PLC channel feature to generate key. For replay attacks, autoencoders (AEs) can be deployed on edge devices to monitor reconstruction errors in CSI sequences, update LSTM models when thresholds are exceeded, and isolate contaminated nodes. Under the proposed method, limited preliminary experiments have been carried out, and under the attack intensity conforming to the IEC 62443-3-3 standard, the eavesdropping capacity is reduced by using safe power distribution, and the legitimate user capacity is lost by 7% [30]. Future work will begin to implement these approaches, with the hope that by integrating physical layer security design, encryption, and real-time anomaly detection, the system will be more resistant to attack while the computational overhead is manageable. Finally, it is hoped that many of the limitations mentioned above can be improved in future work.
# A Review of Machine Learning-Based Routing Protocols for Wireless Sensor Network Lifetime +
## 3 Routing on Wireless Sensor Networks
### Routing Approach  
It was discovered that such a strategy falls into two distinct methodological camps, i.e., the following:  
Classical-based routingBy selecting nodes at random using a timed function, traditional routing provides an unstable flow of traffic between the many nodes in the core cluster, which in turn causes problems with scalability, load balancing, connection, and coverage.  
Optimized-based routingThis demonstrates a critical function in prolonging the WSN network's usefulness. The key problems with this system are its intolerance, poor energy efficiency, poor connectivity, rigidity, and inability to scale.
# Cluster-Based Transmission Diversity Optimization in Ultra Reliable Low Latency Communication
###### Abstract
URLLC ensures a packet transmission reliability of 99.999% while maintaining a transmission latency of \(\leq\)1 ms. In this paper, a Cluster-based Orthogonal Frequency Subcarrier-based Multiple Access (C-OFSMA) communication mechanism is proposed for intra-vehicular sensor-based communication ensuring the standard of URLLC. The total communication area was composed of a finite number of clusters following K-means clustering [10]. A fixed number of orthogonal subcarrier channels were assigned to each cluster to transmit the data packets from the sensor to the receiver end. The different clusters of the network also increased the transmission diversity resulting in the increase in the reliability of the transmission. The proposed C-OFSMA was used to ensure a 99.999% transmission reliability. To achieve this, a short payload was considered with the slotted-Aloha protocol and the packet diversity principle to enhance the transmission reliability [11, 12]. Moreover, we also  
Figure 1: A vehicle installed with a finite number of sensors for wireless backbone connectivity. assumed that, in each Transmission Time Interval (TTI), no self-packet collision occurred among the duplicate packets generated from the same sensor. The rest of the paper is organized as follows: Section 2 briefly discusses the related works. Section 3 presents the system architecture along with the packet transmission and collision scenario and the path loss model of the proposed C-OFSMA system. Section 4 outlines our experiment and discusses the results, and finally, Section 5 concludes the paper with some future directions.