esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
We can conclude that a complex strategy is necessary to maximize communication in 5G NR sidelink C-V2X scenarios with different distances and resource constraints. First, it is important to create dynamic resource allocation techniques that can adjust to changing distances between UEs while effectively balancing connection and range. When UEs are closer, interference can be reduced by using collision avoidance techniques, such as Carrier Sense Multiple Access (CSMA). Additionally, high importance should be given to the timing of packet production, given the interaction between generation time and PRR, ensuring that packets are generated and transmitted in the most efficient manner possible. Maintaining an optimal balance between packet generation time, speed and distance is crucial in scenarios limited to a single resource block to ensure reliable communication. This balance optimizes signal strength, mitigating interference when UEs are close and preventing signal degradation at larger distances. Achieving this equilibrium is fundamental for enhancing communication reliability and managing resource constraints effectively in 5G NR sidelink C-V2X scenarios. From a practical standpoint, these strategies can be implemented in real-world vehicular communication systems by integrating adaptive resource allocation mechanisms into existing C-V2X frameworks. Dynamic scheduling algorithms can be embedded into vehicular onboard units (OBUs) to monitor network conditions and adjust resource allocation in real time, thereby optimizing collision avoidance and the PRR. Moreover, leveraging machine learning-based predictive models can further refine resource distribution by anticipating congestion and dynamically adapting allocation parameters. These approaches ensure that the proposed techniques can be seamlessly adopted in intelligent transportation systems, enhancing network efficiency and reliability for connected and autonomous vehicles.  
Conceptualization, M.T. and A.O.; Methodology, M.T.; Software, M.T.; Validation, M.T. and A.O.; Formal analysis, M.T.; Data curation, M.T.; Writing--original draft, M.T.; Writing--review & editing, A.O.; Supervision, A.O. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability StatementData are contained within the article.  
During the preparation of this manuscript, the authors used Grammarly (Michigan Technological University authorized version) to check grammar and sentence structure. The authors have reviewed and edited the output and take full responsibility for the content of this publication.  
The authors declare no conflicts of interest.
## 5 Results and Discussion
### Review Rating Prediction
#### 5.2.2 Results Based on Yelp 2013 Dataset  
Table 6 represents a comparative analysis of the Review Rating Prediction model for several performance metrics through altering training data. The analysis of TD-Spider TaylorChoA-GRU for the precision, recall and F-measure is listed below. In 70% of training data, the precision of the developed TD-Spider TaylorChoA-GRU is 0.887. The recall of TD-Spider TaylorChoA-GRU is 0.885 for 70% of training data and, considering 70% of training data, the F-measure of the developed model is 0.886.  
Figure 9 represents the accuracy and loss for the Review Rating Prediction model. When the iteration is 300, the accuracy is 0.970. When the iteration is 300, the loss is 0.030. The results show that the proposed method offers a good performance regarding review rating prediction.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Metrics** & **Data (\%)** & **DNN** & **CNN+LSTM** & **Bi-GRU** & **CNN** & **[3]** & **[34]** & **[17]** & **Proposed** \\ \hline \multirow{4}{*}{Precision} & 60\% & 0.633 & 0.665 & 0.704 & 0.799 & 0.808 & 0.841 & 0.848 & 0.855 \\  & 70\% & 0.654 & 0.696 & 0.737 & 0.824 & 0.848 & 0.865 & 0.875 & 0.887 \\  & 80\% & 0.696 & 0.733 & 0.754 & 0.865 & 0.887 & 0.895 & 0.900 & 0.905 \\  & 90\% & 0.741 & 0.765 & 0.799 & 0.895 & 0.909 & 0.925 & 0.928 & 0.933 \\ \hline \multirow{4}{*}{Recall} & 60\% & 0.654 & 0.699 & 0.741 & 0.801 & 0.826 & 0.854 & 0.859 & 0.865 \\  & 70\% & 0.675 & 0.714 & 0.754 & 0.835 & 0.859 & 0.875 & 0.879 & 0.885 \\  & 80\% & 0.714 & 0.741 & 0.785 & 0.895 & 0.900 & 0.902 & 0.908 & 0.915 \\  & 90\% & 0.769 & 0.804 & 0.841 & 0.901 & 0.917 & 0.935 & 0.939 & 0.941 \\ \hline \multirow{4}{*}{F-measure} & 60\% & 0.643 & 0.682 & 0.722 & 0.800 & 0.817 & 0.848 & 0.853 & 0.860 \\  & 70\% & 0.664 & 0.705 & 0.745 & 0.830 & 0.853 & 0.870 & 0.877 & 0.886 \\ \cline{1-1}  & 80\% & 0.705 & 0.737 & 0.769 & 0.880 & 0.893 & 0.899 & 0.904 & 0.910 \\ \cline{1-1}  & 90\% & 0.755 & 0.784 & 0.819 & 0.898 & 0.913 & 0.930 & 0.933 & 0.937 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparative analysis of Review Rating Prediction for Yelp 2013 dataset in terms of precision, recall and F-measure.  
Figure 8: Performance analysis based on (**a**) accuracy and (**b**) loss for Review Rating Prediction using IMDB dataset.
## 2 Materials and Methods
### Measurements of the Territorial Sea Baseline  
Two measurement campaigns were performed in 2016 and 2018; their purpose was to determine the territorial sea baseline by the geodetic method with a GNSS receiver [34; 35]. This research involved an operator conducting the measurement on the submerged beach at predefined depth (Figure 3a). During the first measurement campaign on 23.06.2016, the measurements of the seafloor relief were performed at the following depths: 0 (land-water interface), 20, 40, 60, 79 (baseline), 100 cm. The bathymetric measurements of the waterbody during the second campaign were conducted in a similar manner as in the first series. The gauging station at the site in Gdynia showed 494 cm (on 23.06.2016) and 511 cm (on 19.07.2018) relative to the zero ordinate for gauging station in Kronstadt. These data were the basis for calculating the baseline depth (d\({}_{\text{TSB}}\)) from the following formula:  
\[d_{\text{TSB}}=H_{\text{CWL}}-H_{\text{LWL}}, \tag{2}\]  
where:  
H\({}_{\text{CWL}}\)--current water level in the adopted reference frame [m],  
H\({}_{\text{LWL}}\)--the lowest water level in the adopted reference frame [m].  
The baseline measurements were conducted on 40 sounding profiles planned during the 2016 measurement campaign [20; 36]. Since, during the first test series, the operator performing the baseline measurements along the 400 m long section of the beach was doing it for nearly 10 hours, it was decided that the research in the second measurement campaign could be accelerated by increasing the number of team to two members. Two types of GNSS receivers were used in the research: Leica GS15 and Trimble R10. They made it possible to carry out measurements by the Real Time Kinematic (RTK) method, which provides national GNSS geodetic networks (Leica and VRSNet.pl). Thanks to these services, the data were made with the highest possible positioning accuracy of 1-2 cm (Root Mean Square (RMS)). During the first campaign, 313 points were recorded, and 477 during the second (Figure 3b).  
At completion of the measurement, the results were processed with Trimble Business Center (TBC) commercial survey software.  
Figure 3: Territorial sea baseline measurement (**a**) and recorded measurement points (**b**) by the geodetic method during the second measurement campaign.
# Design of an Open Remote Electrocardiogram (ECG) Service
## 4 A Prototype for a Remote ECG Service
### The Display UA  
The UA on doctor's side is a web browser running on a laptop PC. The doctor opens an HTTP session with the server using an /<id> endpoint: if the device of the corresponding patient is already connected, the response contains a JavaScript application that opens a WebSocket on the /in/<id> endpoint, and displays the familiar canvas using the Chart.js library. The ECG trace is dynamically updated in _roll-mode_ using data received across the WebSocket. A hold function is available.  
Besides the graphical display, the JavaScript application performs a limited analysis of the ECG trace: it applies a simple numerical filter to remove the 50 Hz power-line noise using a moving average of five values and finds R peaks to compute heartbeat frequency. This demonstrates the possibility of additional filtering and feature extraction on doctor's side, which is compatible with end-to-end encryption.  
We used a laptop with a _Core i5 3230M_ CPU (2012) using Ubuntu Linux as an operating system and Chromium as browser.
# Feedback Stabilization of First Order Neutral Delay Systems Using the Lambert W Function
## 1 Introduction
The proposed method consists of two parts: (1) sufficient conditions for the oscillation and non-oscillation of solutions of the NDDE using the geometric approach, and (2) a stabilization method using a state feedback controller. The Lambert W function is well applicable to the DDE with a single delay. Furthermore, the roots of its corresponding characteristic equation can be expressed in terms of the Lambert W function. An outline of the first part of the method is that firstly we define two functions \(f(\lambda)\) and \(g(\lambda)\), which are transcendental equations, such that the characteristic equation \(p(\lambda)\) is the difference of two functions, i.e., \(p(\lambda)=f(\lambda)-g(\lambda)\). The solution sets of \(f(\lambda)=g(\lambda)\) are then equal to the roots of the characteristic equation. Secondly, the geometric properties of the introduced functions are obtained using the Lambert W function. The solutions of the characteristic equations are the intersection points (or tangent points), of the two defined functions. This enables us to easily derive the sufficient oscillation conditions, and the sufficient conditions for the existence of non-oscillations of the characteristic equation using the Lambert W function. Therefore, the solutions of the NDDE are analytically expressed in terms of the Lambert W function. In the second part of the method, we designed a stabilizing state feedback controller for NDDS with two time-delays, where the proportional and derivative gains were analytically determined using the results from part one. This paper is organized as follows: In Section 2, the problem is formulated and a few preliminary concepts are detailed. In Section 3, we present the oscillation and non-oscillation criteria for the NDDE and design a stabilizing state feedback controller. In Section 4, some illustrative examples are given to show the effectiveness of the proposed method. This paper ends with a few concluding remarks.
# Human Pose Estimation Based on Lightweight Multi-Scale Coordinate Attention
## 4 Experiments
### Validation and Analysis
#### 4.3.2 Loss  
The original Yolo-Pose network used CIOU (complete intersection over union) [43] as the loss function. It considered the boundary box regression's overlapping area, center point distance, and aspect ratio. At the same time, it also added the scale loss of the detection box based on DIOU (distance intersection over union) [43] and added the loss of length and width to make the prediction box better conform to the true box. The V in the formula, on the other hand, reflected the disparity in aspect ratio rather than the true difference between width and height and their level of confidence, which may impede effective model optimization similarity. To address this issue, we tried to replace it with EIOU (efficient intersection over union) [44]. Unlike CIOU, EIOU separates the aspect ratio influence factors to measure the length and width of the target and anchor frames respectively. EIOU can be described as below:  
\[L_{EIOU}=L_{IOU}+L_{dis}+L_{asp}=1-IOU+\frac{\rho^{2}(b,b^{gt})}{c^{2}}+\frac{ \rho^{2}(w,w^{gt})}{C_{w}^{2}}+\frac{\rho^{2}(h,h^{gt})}{C_{h}^{2}} \tag{14}\]  
The loss function is divided into three parts: overlapping loss, center distance loss, and width height loss. The first two components are identical to those used in CIOU, but the width-height loss immediately lowers the width and height discrepancy between the target box and anchor box, allowing for faster convergence. Where \(C_{w}\) and \(C_{h}\) are the minimum bounding box's width and height, respectively, for the two boxes [45].  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Settings** \\ \hline Optimizer & SGD \\ Momentum & 0.937 \\ Batch size & 32 \\ Learning rate & \(1\times 10^{-2}\) \\ Weight decay & 0.0005 \\ Epoch & 300 \\ \hline \end{tabular}
\end{table}
Table 1: Hyperparameter Settings.
## 3 Material and Methods
### Experimental Dataset Formation and Preprocessing
The simulation results for determining the optimal cluster structure are depicted in Figure 2. The findings confirmed the possible inconsistency between the internal and external clustering criteria in the process of creating the optimal cluster structure. The minimum value of the internal criterion corresponds to a five-cluster structure in subset A of gene expression profiles and a seven-cluster structure in subset B, as shown in Figure 2a,b. However, it should be emphasized that the difference between the values of the internal criteria within the range of the number of clusters from 4 to 7 is negligible. This finding indicates the complexity of selecting an optimal cluster structure based on the analysis of internal clustering quality criteria. The best structure in terms of reproducibility error is the three-cluster structure, since it has the minimum value of the external criterion, but the four- and six-cluster structures are also acceptable, according to the external criterion. The analysis of the balance criterion values, which include both internal and external clustering quality criteria components, suggests that the optimal structures are the four- and six-cluster structures, which correspond to the maximum value of the balance criterion. The obtained results were validated by performing an object classification procedure using gene expression values and examining the three-, four-, five-, and six-cluster structures. Figure 3 depicts the distribution of gene expression profiles across the formed clusters. Figure 3 reveals that the four- and six-cluster structures demonstrated an approximately uniform distribution of gene expression profiles in the formed clusters. This observation suggests a higher quality of distribution of the gene expression profiles in the clusters based on the criteria employed in the proposed model. Figure 3: Distribution pattern of gene expression profiles in clusters using the inductive spectral clustering algorithm. Figure 2: Simulation results for determining the optimal cluster structure using the inductive algorithm of spectral clustering: (**a**,**b**) internal clustering quality criteria values depending on the number of clusters calculated on equivalent subsets A and B, respectively; (**c**) external clustering quality criterion; (**d**) balance criterion. In the next stage, a convolutional neural network (CNN) was employed to analyze the gene expression data in the formed clusters. To correctly apply the CNN, the expression matrices were augmented with gene expression profiles containing zero values. The number of additional profiles was adjusted to make the total number of profiles a multiple of fifty, which is one of the parameters of the CNN filter.
# Enhanced Scalability and Security in Blockchain-Based Transportation Systems for Mass Gatherings
## 3 Methodology
### Strengthen Security and Trust Governance  
Implementing blockchain in transportation systems for mass events requires careful regulation and technological solutions to ensure safety and reliability. Key concerns involve sustainable governance principles, especially when controls are decentralized. A multistakeholder governance model involving transport authorities, blockchain developers,  
Figure 4: Flowchart of blockchain in mass event transportation systems.  
emergency responders, and other participants is essential. Decisions affecting blockchain or smart contracts are subject to public scrutiny and transparent voting. A precise conflict-resolution mechanism and regular independent audits should ensure system integrity. A blockchain security framework for mass gathering transportation is presented in Figure 5, which addresses vulnerabilities through data integrity, access control, real-time monitoring, and tamper-proof security of the transactions.  
By applying BFT methods, such as Tendermint or Practical BFT, blockchain uptime is increased, and communication overhead is reduced, making transaction irreversible possible for ticketing and emergency notifications [4; 9; 14]. However, it should be protected from future quantum threats in post-quantum cryptography (PQC), among which is lattice-based cryptography. Short framing times also provide security against fraud and economic sanctions, such as staking. BFT, PQC, and anti-fraud measures, when combined, create a secure platform for protecting transportation data.  
For example, most countries are adopting public-private partnerships (PPPs) to share the costs and expertise, such as Sweden's blockchain-based land registry. Infrastructure is funded by governments, while it receives blockchain expertise from private companies [17]. Moreover, a phased adoption of blockchain technology is necessary to enhance security, efficiency, and trust in large-scale mobility systems. Initially, it may be centered on ticketing and emergency management, and this may later be expanded to real-time tracking. This practice will enable a decrease in the number of entries as well as an increase in general security.
## 6 Conclusions  
We show how transmission capacities of low-cost low-quality sources can be optimized for maximum capacities by means of bit- and power-loaded DMT transmission. Record high transmission data rates for an ASE spectrum-sliced, self-seeded RSOA-FCL and externally seeded RSOA of 6.25, 20 and 30.7 Gbit/s in BtB and 4.2, 12.4 and 24.5 Gbit/s over 25 km NZDSF transmission are reported. To the best of our knowledge, these are the highest capacities reported for such inexpensive and colorless sources. Our experiments show that even lowest cost sources such as spectral sliced ASE sources or RSOA-FCLs can offer quite some capacity if properly operated, for example, for O-band upstream transmission. More precisely, ASE sources can provide capacities in the range of 6 to 4 Gbit/s depending on the distance. Fabry-Perot sources such as the RSOA-FCL may offer capacities in the order of 20 to 10 Gbit/s for distances up to 25 km. Finally, more costly externally seeded colorless sources may provide capacities in the range of 30 to 25 Gbit/s if operated with an optimized bit- and power loaded DMT transmission format without offset filtering.  
This work was supported by the EU FP7 projects ERMES (grant agreement no. 288542) and Fox-C (grant agreement No. 318415). Sterlite Technologies Ltd. is acknowledged for providing the fiber.  
[PERSON] conceived the concept, designed and performed the experiments, carried out the signal processing and analysis and wrote the paper. [PERSON] contributed in the concept development, experiment and manuscript writing. [PERSON] designed and fabricated the RSOAs. [PERSON] organized the experiment and wrote the paper. [PERSON] conceived and developed the concept, designed the experiment, wrote the paper and supervised the work.  
The authors declare no conflict of interest.
# ECG Arrhythmia Classification using High Order Spectrum and 2D Graph Fourier Transform
## 3 Experiment Setup  
### MIT-BIH Arrhythmia Database  
The MIT-BIH Arrhythmia Database [26] is an internationally recognized database to evaluate the effectiveness and feasibility of proposed algorithms in the ECG signal processing field. The database consists of 48 real ECG recordings, including normal and abnormal beats. Each recording taken by two leads lasts for 30 min with a sampling rate of 360 Hz. Only the modified limb lead II (ML II) for each recording is used for the classification task. The recordings used in this paper can be found in the Supplementary Materials. Table 1 lists the detailed heartbeat types.  
From the annotations of the database, five types of heart beats are included in the classification table. Four records (No.102, No.104, No.107 and No.217) were excluded because they contain paced heartbeats. We randomly selected 33 recordings from the remaining 44 records. Q class (unclassified and paced heartbeats) is discarded since this class is marginally represented in MIT-BIH database. Finally, the classification is only realized on other four classes (N, S, V and F) in our study. In our experiment, the cross-validation method and inter-patient scheme are used to measure classification performance.  
In the cross-validation test, group of 75,604 heartbeats was randomly partitioned into 5 equal sized subsets (5-fold). From the 5 subsets, one is considered as the test-set for validating the trained model. The training set is constructed with the rest of subsets. This training and validation process is repeated 5 times performing the validation each time with a different subset.  
In inter-patient scheme, all records are split into two groups (Training set & Testing set) with a proportion of the whole dataset. Grouping assignment is listed in Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**AAMI class** & **MIT-BIH Class** & **MIT-BIH Heartbeat** \\ \hline \multirow{6}{*}{Normal beats (N)} & N & Normal beat \\ \cline{2-3}  & L & Left bundle branch block beat \\ \cline{2-3}  & R & Right bundle branch block beat \\ \cline{2-3}  & e & Atrial escape beat \\ \cline{2-3}  & j & Nodal escape \\ \hline \multirow{6}{*}{Supraventricular ectopic beats (SVEB)} & A & Atrial premature beat \\ \cline{2-3}  & a & Aberrated atrial premature beat \\ \cline{2-3}  & J & Nodal (juntional) premature beat \\ \cline{2-3}  & S & Supraventricular premature beat \\ \hline \multirow{3}{*}{Ventricular ectopic beat (VEB)} & V & Premature ventricular contraction \\ \cline{2-3}  & E & Ventricular escape beat \\ \hline Fusion beats (F) & F & Fusion of ventricular and normal beat \\ \hline \multirow{6}{*}{Unknown beats (Q)} & / & Paced beat \\ \cline{2-3}  & f & Fusion of paced and normal beat \\ \cline{1-1} \cline{2-3}  & Q & Unclassified beat \\ \hline \hline \end{tabular}
\end{table}
Table 1: ECG class description using AAMI standard.