esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Cloud-Native Network Architecture in Future Networks
### Edge Computing  
The emerging edge computing paradigm essentially deploys decentralized cloud computing capabilities in the network infrastructures typically at the network edge [26]. The Multi-access Edge Computing (MEC) architecture developed by ETSI is a representative architectural framework for edge computing. The MEC architecture comprises two levels: the lower level consists of a set of individual MEC hosts, and the higher level forms an MEC system comprising the MEC hosts and the network connections among the hosts. Each MEC host contains an MEC platform built on top of the virtualized network-compute infrastructure, and various MEC applications are deployed on the platform. The host-level management is responsible for managing the infrastructure, platform, and MEC applications at each host. The core function of system-level management is an MEC orchestrator that maintains a global view of the entire MEC system for coordinating host management for end-to-end service provisioning [27].  
The true impact of the edge computing paradigm relies on its interaction with networking. The foundation of the MEC architecture is a virtualized network infrastructure upon which the MEC platform and applications are deployed. Most network operators want to consolidate VNFs and MEC applications on top of a shared infrastructure to the maximum possible degree for enhancing resource utilization and improving service performance. Toward this direction, ETSI MEC ISG has developed a reference architecture for deploying MEC in an NFV environment, which specifies how MEC entities can be integrated in the NFV architecture by fully leveraging NFV MANO capabilities [28]. In this MEC-in-NFV reference architecture, both the MEC platform and MEC applications can be treated by NFV MANO in the same way as VNFs being managed, which allows NFV MANO to be leveraged for unified management and orchestration of both VNFs and MEC applications.
## 2 Materials and Methods
### Data Analysis  
Raw data from the IMUs were analyzed in post-processing using MATLAB 2021a (MathWorksTM, Natick, MA, USA). Prior to all processing steps, data were filtered using a zero-lag Butterworth low-pass filter (accelerometers: 4 th order at 80 Hz; gyroscopes: 4 th order at 50 Hz) to remove noise. To separate the strides in continuous data, the vertical acceleration signal of the IMU mounted to the heel cap was zero-lag Butterworth high-pass filtered (80 Hz). The first peak in the filtered signal was defined as the initial ground contact of the foot [38; 39].  
_Biomechanical variables_  
To examine shoe rollover, the maximum angular velocity in the sagittal plane of the shoe was analyzed according to [15], and the peak angular velocity (PAV) was extracted within 200 ms after initial ground contact. The angular velocity in the frontal plane of the shoe was used to determine the peak version velocity (evVel) [40]. To determine the heel strike angle at initial ground contact time (HSA), the shoe orientation angle in the sagittal plane was calculated using data from the IMU mounted to the heel cap as described in [PERSON] et al. [41]. The maximum IMU acceleration (within 200 ms after initial ground contact) at the tibia was used to identify peak tibial acceleration (PTA).  
_Physiological variables_  
HR and \(\dot{\mathrm{V}}\mathrm{O}_{2}\) were provided by Metasoft Studio (CORTEX Biophysik GmbH, Leipzig, Germany). Relative oxygen uptake (_rel._\(\dot{V}\mathrm{O}_{2}\)) was calculated by dividing subjects' \(\dot{V}\mathrm{O}_{2}\) by their individual body mass (kg). A standard method of assessing \(RE\) is to look at the amount of \(\dot{V}\mathrm{O}_{2}\) required to run a kilometer at a predefined running speed [42]. The following Equation (1) was used to determine RE:  
\[RE=\frac{rel.\ \dot{V}\mathrm{O}_{2}}{\
u_{\ 70\%\ of\ rel.\ \dot{V}\mathrm{O}_{2} max}} \tag{1}\]  
For the exhaustion test, \(\dot{V}\mathrm{O}_{2}max\) was calculated as the mean of the highest 30 s to volitional exhaustion. For the 10 km tests, both biomechanical and physiological variables were calculated from the mean of all strides over the last 5 min of the run.
## 4 Discussion
Firstly, although the planting structure is relatively complex in Xuhuai District, Jiangsu, i.e., the preceding crops of winter wheat are diversified, the possibility of applying DAS-based dynamic monitoring models and results to other scenarios under specific regional climate conditions (Figure 2) is promising. Further research will focus on exploring the performance of models incorporating short-term and long-term climate variables in different soil environments and varieties. Secondly, although there is great potential for PSRI and NDRE indices to predict winter wheat maturity, the use of remote sensing in association with other tools is suggested,such as the cumulative growing degree-day (GDD). Several studies have shown that GDD models could be used successfully to monitor wheat growth and productivity under climate change [57; 58]. For conditions in Jiangsu Province, China, all fields reached maturity around 2000 GDDs based on the impact of crop rotation. It implies that more research is needed to establish when the winter wheat cultivars reach maturity based on the GDD in Jiangsu Province, especially in a winter wheat breeding program, where breeders have been working to develop new short-cycle cultivars to be used with rice in crop rotation systems. Thirdly, it is of great scientific significance that this approach, although developed in Xuhuai District, Jiangsu, can potentially be used in different regions, not only for winter wheat but also for other staple crops such as rice, soybeans and maize. The relatively simple nature of the model input data (optical remote sensing and crop growth stage) should make it convenient and easy to develop the application devices to generate these results. Imageries from satellite platforms are valid for the modeling, while combining smartphone applications and real-time imagery data acquisition could accelerate the application of such tools in practice.
# Scaled Model Simulation and Experimental Verification of Submarine Flexible Pipeline Laying System
## 3 Similarity Verification of Experimental Scaled Model
### Dynamic Similarity Simulation Verification of Scaled Model  
For the relatively heavy pulley groups on the top of the tower and the crane on the top of the tower, the equivalent forces applied at the corresponding positions and their sizes are shown in Table 4 in the form of concentrated loads. The support of the tensioner bears the tensioner and the pipeline it holds. Therefore, in the contraction model, the load on the support of the tensioner is the combined force of the tensioner gravity and the pipeline tension.  
When laying a flexible pipeline, the laying process can be divided into three main stages: pipeline-laying preparation stage, pipeline-laying stage, and pipeline stopping laying stage. In order to ensure the rigor and accuracy of the experimental model, when verifying the mechanical similarity of the pipeline-laying system, it is necessary to verify the dynamic similarity of the pipeline-laying process, the tensioner support lifting process, and the angle adjustment mechanism adjustment process, respectively.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Device Name** & **Mass of Prototype (t)** & **Scaled Model Equivalent Mass (kg)** \\ \hline Tensioner & 400.0 & 952.0 \\ Tower-top pulley block & 20.0 & 47.7 \\ Tower crane & 36.8 & 87.7 \\ Pipeline & 325.0 & 773.0 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Equivalent force to be applied by the scaled model.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline Depth & 3000 m & Seabed stiffness & 100 kN/m/m\({}^{2}\) \\ Sea density & \(1025\,\mathrm{kg/m^{3}}\) & Seabed damping & 1.0250 \\ Sea temperature & \(15\,\mathrm{\SIUnitSymbolCelsius}\) & Surface current velocity & 1.028 m/s \\ Seabed current velocity & 0 m/s & Kinematic viscosity & \(1.35\times 10^{-6}\,\mathrm{m^{2}/s}\) \\ Air kinematic viscosity & \(15\times 10^{-6}\,\mathrm{m^{2}/s}\) & Spectral peak period & 6.1\(\sim\)16.2 s \\ Wave height & 1.25–2.5 m & Air density & 1.29 kg/m\({}^{3}\) \\ Wave type & JONSWAP & Wind speed & 8.0–10.8 m/s \\ \hline \hline \end{tabular}
\end{table}
Table 2: Marine environmental parameters.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline overall length & 204.65 m & Vertical spacing & 185.00 m \\ molded breadth & 39.20 m & Dolded depth & 14.00 m \\ structure draft & 11.00 m & Operation draft & 7–9.5 m \\ Ship displacement & 59.101T & Deadweight & 34.850 T \\ metacenter height & 8.20 m & Height center & 13.60 m \\ \hline \hline \end{tabular}
\end{table}
Table 3: Vessel parameters.
# Neural Network-Informed Lotka-Volterra Dynamics for Cryptocurrency Market Analysis
###### Abstract  
Mathematical modeling plays a crucial role in supporting decision-making across a wide range of scientific disciplines. These models often involve multiple parameters, the estimation of which is critical to assessing their reliability and predictive power. Recent advancements in artificial intelligence have made it possible to efficiently estimate such parameters with high accuracy. In this study, we focus on modeling the dynamics of cryptocurrency market shares by employing a Lotka-Volterra system. We introduce a methodology based on a deep neural network (DNN) to estimate the parameters of the Lotka-Volterra model, which are subsequently used to numerically solve the system using a fourth-order Runge-Kutta method. The proposed approach, when applied to real-world market share data for Bitcoin, Ethereum, and alternative cryptocurrencies, demonstrates excellent alignment with empirical observations. Our method achieves RMSEs of 0.0687 (BTC), 0.0268 (ETH), and 0.0558 (ALTs)--an over 50% reduction in error relative to ARIMA(2,1,2) and over 25% relative to a standard NN-ODE model--thereby underscoring its effectiveness for cryptocurrency-market forecasting. The entire framework, including neural network training and Runge-Kutta integration, was implemented in MATLAB R2024a (version 24.1).  
Lotka-Volterra; deep neural network; MATLAB; Runge-Kutta method; cryptocurrency; forecasting
## 2 Related Work
### Feature Extraction and Processing  
#### 2.2.1 Local Feature-Based  
Since image descriptors represent image information such as textures and shapes, the repeated patterns in an image can be viewed as a set of sparse repeated features whose location information is consistent with each other. The repetition structure is extracted by hypothesizing links between the repeated feature points [18; 20; 21; 22]. Some algorithms use SIFT [7] as feature descriptors [5; 23; 24]. However, these methods are only applicable to images with distinct corner or edge features and simple content. Furthermore, some feature descriptors are handcrafted and designed based on professional knowledge in specific fields [25]. Therefore, even though local feature-based approaches are widely used in two-dimensional images, they are complex in feature descriptors' design and hard to extend to other domains of images. Additionally, many algorithms require image rectification as a pre-processing step to ensure accuracy and better applicability [26].
## 6 Conclusions  
In this paper, the SSDAE-DRN model is proposed for achieving modulation spectrum denoising, enhancement, and ship shaft frequency extraction. Meanwhile, the structure and activation function of the traditional SSDAE network is improved and combined with the depth regression network to improve the accuracy of ship shaft frequency extraction.  
A mathematical model of a ship radiation noise modulation spectrum based on Gaussian pulse sequences is established, and the simulation data set is generated based on the mathematical model and ship-related parameters, which provides data support for model training. The SSDAE-DRN is trained on simulation data and fine-tuned on simulation and real data, which improves the accuracy of shaft frequency extraction and network generalization performance.  
The experimental results show that the SSDAE-DRN model proposed in this paper has a good denoising and enhancement effect on the modulation spectrum of ship radiated noise, and can effectively improve the accuracy of ship shaft frequency extraction. The average error rate on the real ship radiated noise data in the Changjiang River is 9.7%, which is 16.7% lower than that of the sequence matching method as a comparison. In  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type of Ship** & **Method of Sequence Matching** & **Method in This Paper** \\ \hline \hline \begin{tabular}{c} Cargo Ship \\ Tanker \\ \end{tabular} & 1.18 & 0.63 \\ \begin{tabular}{c} Law Enforcement Vessel \\ Large Junk \\ \end{tabular} & 1.94 & 0.71 \\ \
\begin{tabular}{c} Law Enforcement Vessel \\ Large Junk \\ \end{tabular} & 1.75 & 0.62 \\
\begin{tabular}{c} Large Junk \\ \end{tabular} & 1.73 & 0.50 \\ \hline \hline \end{tabular}
\end{table}
Table 4: MAE of shaft frequency extraction on real data.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type of Ship** & **Method of Sequence Matching/\%** & **Method in This Paper** \\ \hline \hline \begin{tabular}{c} Cargo Ship \\ Tanker \\ \end{tabular} & 27.6 & 11.2 \\ \begin{tabular}{c} Tanker \\ Law Enforcement Vessel \\ Large Junk \\ \end{tabular} & 35.3 & 11.8 \\ \
\begin{tabular}{c} Law Enforcement Vessel \\ Large Junk \\ \end{tabular} & 21.6 & 8.3 \\
\begin{tabular}{c} Large Junk \\ \end{tabular} & 20.9 & 7.5 \\ \hline \hline \end{tabular}
\end{table}
Table 5: MRE of shaft frequency extraction on real data.  
particular, this method has high accuracy and stability in the case of line spectrum missing and noise interference and also has good robustness under different spindle speeds.  
[PERSON] gave academic guidance to this research work. The manuscript has been modified by [PERSON], in the content and structure of the article. [PERSON] and [PERSON] put forward feasible suggestions for the research content. [PERSON] designed the core method proposed in this paper, wrote the program, carried out relevant experimental verification, and drafted the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Free exploration project of Institute of acoustics, Chinese Academy of Sciences.  
Not applicable.  
The authors declare no conflict of interest.
# Controllable Friction of an Epoxy Composite via Thermal Treatment
## 2 Materials and Methods
### Characterization  
The chemical compositions of the samples were characterized using X-ray photoelectron spectroscopy (XPS, ESCALAB 250 xi spectrometer, Thermo Scientific, Waltham, MA, USA). The binding energy of C 1s (284.8 eV) was used as the reference. The thermal stabilization of PHHD and the encapsulation capacity of PHHD@MS were measured with a thermogravimetric analyzer (TGA, NETZSCH STA 449 F5/F3 Jupiter) from 30 to 600 \({}^{\circ}\)C in a nitrogen atmosphere. The phase-change feature of PHHD was determined using differential scanning calorimetry (DSC) with temperatures ranging from 20 to 80 \({}^{\circ}\)C at a heating rate of 1 \({}^{\circ}\)C per minute in a nitrogen atmosphere. Infrared spectroscopic measurements were performed with a Fourier-transform infrared spectrometer (FTIR, Bruker TENSOR27, KBr disks, Billerica, MA, USA). 1H NMR spectrometry was performed at 400 Hz. By dissolving PHHD in d6-DMSO, the molecular structure of PHHD was characterized by the proton nuclear magnetic resonance (1H NMR). As shown in Figure 1, six kinds of protons were found in PHHD, with an atom ratio of a:b:c:d:e:f = 2:2:2:2:24:3. In the 1H NMR spectrum, the atomic ratio of protons in the different chemical environment was 2.18:2:2.19:2.31:24.13:3.17, which was almost the same as the atomic ratio in the molecular formula. The NMR information of the product is as follows. 1H NMR (600 MHz, d6-DMSO, TMS) \(\delta\) (ppm): 0.85 (s, 3H), 1.23 (s, 12H), 1.51 (s, 2H), 2.32 (s, 2H), 2.68 (s, 2H), and 4.32 (s, 2H). In addition, the strong peaks at 2.5 ppm and 3.3 ppm were attributed to the solvent and residual water, respectively.  
Figure 1: Chemical characteristics of PHHD: 1H NMR of PHHD.  
The particle size distribution was analyzed using a nanoparticle size and zeta potential analyzer (Zetasizer Nano ZS, Malvern, Malvern, UK). The microcapsule surface morphology and size were observed using field-emission scanning electron microscopy (SEM, QUANTA 200 FEG, FEI, Hillsboro, OR, USA). TEM (JEM-2100F, JEOL, Tokyo, Japan) was utilized to observe the microstructures of the PHHD@MS samples. The compressive and tensile strengths were determined using an electronic universal testing machine (WDW-100/E, Kason Group, Jinan, Shandong, China) with a rate of 2 mm/min at ambient temperature. A shore hardness tester (SLX-D, Aili, Hefei, Anhui, China) was used to determine the surface hardness.
# Mobile Augmented Reality Application to Evaluate the River Flooding Impact in Coimbra
## 4 Materials and Methods
### Mapping of the Real Coordinates in the 3D Scene  
The rectangle method was used to show the real coordinates on the AR map. It consists of having the two corners of the real-sized model from Unity, the left down and the right up. It can be compared with the two coordinates (latitude and longitude) from BLosm that are necessary to be selected in Blender for the 3D model extraction.  
By knowing the corners in Unity and their real-world coordinates, the process first determines the real position of the mobile device. These coordinates can then be transformed into \(x\) and \(y\) positions, as the map is relatively small and can be approximated as a flat plane. The conversion between the real geographical point and the Unity coordinates' point is achieved with a linear proportion.  
Once the conversion coefficient is obtained, the operation involves calculating the difference between the location point and the low reference point (lower-left corner), multiplying this difference by the coefficient, and finally adding the lower-left corner position in Unity.
## 3 Materials and Methods
### Video Upscaling  
Video resizing is the most commonly used operation to change the resolution of a video to match the requirements of the input layer of a convolutional neural network. There are various algorithms that can be used to perform the operation of video upscaling, and the most common are nearest neighbor interpolation, bilinear interpolation, bicubic interpolation, and Lanczos interpolation [55]. Among these methods, nearest neighbor is the fastest, and Lanczos is the slowest and most complex. Their upscaling performance is similarly related, but we used bicubic interpolation in our implementation due to its acceptable performance in terms of speed and upscaling quality. In order to perform bicubic interpolation for video scaling, we the used Libswscale library from the FFmpeg 4.2.1 package. The Libswscale library, which is developed in C and is part of the FFmpeg multimedia framework, includes highly optimized functions for scaling, colorspace conversion, and pixel format transformations.
## 4 Proposed Approach
### Dataset Overview and Collection Process  
Choosing the right and appropriate dataset is essential for creating an effective security solution in IoT-6G environments. As network attacks continue to evolve, relying on outdated datasets can compromise the accuracy and relevance of detection and analysis. To address this challenge, our proposed framework utilizes the most recent CICIoT2023 dataset from the University of New Brunswick [22], which has been generated in a real-time IoT environment. This extensive dataset comprises 46,686,579 samples across 46 attributes and includes one benign class alongside 33 distinct attack types. These attacks are grouped into seven major categories: distributed denial of service (DDoS), denial of service (DoS), reconnaissance, web-based, brute-force, spoofing, and the Mirai botnet. The dataset has 169 individual CSV files, each containing a mix of benign and malicious network traffic. This provides a robust foundation for developing and evaluating comprehensive security solutions to address a wide range of threats effectively.  
The 6G specifications are scheduled to evolve and get ratified by 2030. Since there are no 6G cellular implementations yet, actual operational data are not available. We have examined the ITU-R IMT-2030 framework document M.2160 [6] and believe that the CICIoT dataset reflects the environment that is expected to continue to exist in 6G deployments. It includes devices and network behaviors that are expected to be widespread in 6G, allowing us to address security challenges ahead of the full rollout of the technology. In the authors' view, it serves as an appropriate surrogate for the environment in the upcoming cellular networks. By leveraging this dataset, we can ensure that the security measures we develop are not only relevant and scalable but also adaptable to the evolving standards of 6G, thereby laying a solid foundation for a secure 6G ecosystem.  
Furthermore, as outlined in the ITU-R framework, 6G is intended to enhance the capabilities of 5G while integrating innovative features, as depicted in Figure 4. Since many of the technologies and features from 5G will be carried over to 6G deployments, the devices used in this dataset--such as smart home systems, cameras, sensors, smart plugs, and microcontrollers--are expected to play a key role in 6G. While these devices currently operate within 5G environments, their advanced capabilities will make them integral to the infrastructure of future 6G networks.  
In this regard, the CICIoT2023 dataset serves as an essential resource for testing and validating advanced security frameworks aimed at tackling emerging threats in 6G networks. Its comprehensive structure, which aligns with the anticipated characteristics of 6G, makes it an invaluable tool for researchers and developers working to create resilient security solutions for these complex and evolving network environments [6].  
Figure 4: The capabilities of IMT-2030 (source: ITU-R M.2160 [6]).
# STGAT: Spatial-Temporal Graph Attention Neural Network for Stock Prediction
## 2 Related Works
### Graph Attention Mechanism and Stock Price Prediction  
With the introduction of the graph attention mechanism, nodes can now attend to the features of their neighbors by stacking layers that assign different weights to each node in a neighborhood. This is achieved without the need for expensive matrix operations (like inversion) or prior knowledge of the graph structure [49]. Building on the ability of GAT to dynamically adjust attention weights, many scholars have applied it to the field of stock price prediction, leveraging the model's capacity to capture complex dependencies and temporal relationships between different stock features. [PERSON] et al. [12] combined a Time-aware Relational Attention Network (TRAN) that integrates historical sequences, company description documents, and a stock relation graph to rank stocks by return ratios, achieving superior investment return ratios. [PERSON] et al. [6] proposed an attention-based adaptive spatial-temporal hypergraph convolutional network to dynamically filter out invalid associations from traditional spatial attention. [PERSON] et al. [50] proposed DR-GAT to track the evolution of intercorporate relationships over time for stock recommendation. Inspired by the above studies, our work designed a unique GAT module that excels in effectively capturing correlations between different stock features while also leveraging residual connections to capture additional, more detailed features.