esa-sceva/satcom-chunk-collection huggingface dataset:
# Mutual Authentication Protocol for D2D Communications in a Cloud-Based E-Health System
## 3 Related Work
It can accomplish a high data rate and lower energy consumption in comparison with traditional access technologies (e.g., 3 GPP LTE, according to [PERSON] and [PERSON] [15]). The literature reports some authentication and key agreement protocols for D2D communication; however, they are not designed for m-health environments. For example, [PERSON] and [PERSON] [16] developed two authentication protocols for D2D, one based on the hash message authentication code (HMAC) and the other based on identity-based signatures (IBS). [PERSON] et al. [17] proposed a group authentication protocol for D2D based on identity-based encryption (IBE) and Diffie Hellman key exchange (DHKE). In terms of standardization activities, 3 GPP has started to standardize D2D communication for its network architecture and developed technical reports and technical specifications (TS)(e.g., TS 33.303 [18], TS 23.303 [19] and TR 36.843 [20]), which describe security aspects, device discovery and, configuration for D2D communication. Table 1 shows a comparison among some studies relevant to the design of our protocol.
## 3 Software and Methodology
### Groundwater Contaminant Transport Mathematical Model
'D' signifies the diffusion coefficient, where diffusion refers to the movement of particles in the case of mass transfer and thermal conduction in the case of heat transfer. '\(v\)' represents the velocity field that arises during diffusion phenomena, and it is a function of both time and space. Given that this simulation is three-dimensional, '\(v\)' encompasses components in the x, y, and z directions, as determined by Darcy's law and the Richards equation. 'R' denotes the source term for the chemical species involved, where R \(>\) 0 indicates a chemical reaction generating more of the species, and R \(<\) 0 indicates a chemical reaction consuming the species. Freundlich adsorption isotherms are commonly used in COMSOL software to describe adsorption phenomena. The Freundlich adsorption isotherm is an empirical relationship that describes the quantity of gas adsorbed onto a solid surface as a function of gas pressure. A similar relationship holds for the concentration of a solute adsorbed onto a solid surface concerning the solute's concentration in the liquid phase. The Freundlich adsorption isotherm can be mathematically represented in various forms [25], such as the following:  
\[\frac{x}{m}=\mathrm{K}p^{\
icefrac{{1}}{{n}}}, \tag{4}\]  
\[\log\frac{x}{m}=\log\mathrm{K}+\frac{1}{n}\log p, \tag{5}\]  
\[\frac{x}{m}=\mathrm{K}c^{\
icefrac{{1}}{{n}}}, \tag{6}\]  
\[\log\frac{x}{m}=\log\mathrm{K}+\frac{1}{n}\log c, \tag{7}\]  
In the given expressions, \(x\) represents the mass of the adsorbed substance, \(m\) denotes the mass of the adsorbent, \(p\) stands for the equilibrium pressure of the adsorbate in the gas phase during experimentation, and \(c\) signifies the equilibrium concentration of the adsorbate when in contact with the dispersed solid phase (dissolved species/adsorbed species) in an aqueous solution. Both K and \(n\) are constants for the adsorbate and adsorbent at a given temperature. When the pressure is very high, i.e., when \(1/n=0\), the extent of adsorption becomes independent of pressure.
## Appendix A Simulation Setup for the IEEE 802.11 Family
### Channel Conditions for IEEE 802.11 Protocols
\begin{cases}L_{FS}(d)+X_{\sigma}&\text{if }d\leq d_{BP}\\ L_{FS}(d_{BP})+3.5 log10(d/d_{BP})+X_{\sigma}&\text{if }d>d_{BP}\end{cases}  \tag{12}\]  
where \(X_{\sigma}\) is the random variable and the value of \(\sigma\) differs before and after the break-point distance \(d_{BP}\) as described in [79]. For our analysis and propagation models, small-scale fading [84] is also considered. Small-scale fading causes signal distortion for the transmitted WLAN signal. Various physical aspects induce small-scale fading effects such as mobile speed, multipath propagation, surrounding objects motion, and the transmission bandwidth of the signal [84]. The motion of the people around the environment causes Doppler spread \(f_{d}\), defined as follows:  
\[f_{d}=\frac{\
u_{o}}{\lambda} \tag{13}\]  
where \(\
u_{o}\) is the environmental speed. For our experiment, where \(f_{c}\) is 5.25 GHz, the wavelength \(\lambda=\frac{3\times 10^{8}\,\text{m/s}}{5.25\times 10^{9}\,\text{Hz}}=0.0571\) m. \(f_{d}\) is calculated and encountered in the simulated environment for the received signal for each WLAN IEEE 802.11. The typical walking speeds for indoor environments is approximately 1.2 km/h (0.333 m/s) [79]. \[f_{d}=\frac{0.333m/s}{0.0571m}=5.832\,\text{Hz} \tag{14}\]  
The worst-case frequency shift known as Doppler spread is around 5.8 Hz for WLAN packets.
# Handover Management in 5G Vehicular Networks
## 5 Simulation Setup and Results
### Twenty-Four-Hour Evaluation Results  
In this scenario, the efficiency of the proposed scheme was evaluated during a 24 h simulation. Specifically, the number of HOs and the respective signaling cost were studied. According to the obtained results, the discussed scheme achieved better performance than the other schemes, since it performed fewer HOs, while at the same time, its signaling cost per HO was lower than the ones observed from the other schemes for both inter  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Message Name** & **Size (Bytes)** & **Abbreviation** \\ \hline MIH\_Link\_Going\_down & 78 & \(M_{1}\) \\ MIH\_GET\_Information\_request & 1500 & \(M_{2}\) \\ MIH\_GET\_Information\_response & 1500 & \(M_{3}\) \\ MIH\_Net\_HO\_Candidate\_Query\_request & 63 + 11\(\cdot\)n + 8\(\cdot\)m\(\cdot\)n & \(M_{4}\) \\ MIH\_Net\_HO\_Candidate\_Query\_response & 77 + 101\(\cdot\)m & \(M_{5}\) \\ MIH\_N2N\_HO\_Query\_Resource\_request & 150 + 11\(\cdot\)m & \(M_{5b}\) \\ MIH\_N2N\_HO\_Query\_Resource\_response & 165 & \(M_{5c}\) \\ MIH\_N2N\_HO\_Commit\_request & 213 & \(M_{6}\) \\ MIH\_N2N\_HO\_Commit\_request (Extended) & 264 & \(M_{6c}\) \\ MIH\_N2N\_HO\_Commit\_response & 92 & \(M_{7}\) \\ MIH\_N2N\_HO\_Commit\_response (Extended) & 92 & \(M_{7e}\) \\ MIH\_Net\_HO\_Commit\_request & 122 & \(M_{8}\) \\ MIH\_Net\_HO\_Commit\_response & 103 & \(M_{9}\) \\ Handover\_Initiate & 72 & \(M_{10}\) \\ Handover\_Ack & 32 & \(M_{11}\) \\ PBU & 76 & \(M_{12,16}\) \\ PBA & 52 & \(M_{13,17}\) \\ MIH\_Link\_up & 95 & \(M_{14}\) \\ UNA & 52 & \(M_{15}\) \\ MIH\_N2N\_HO\_Complete\_request & 109 & \(M_{18}\) \\ MIH\_N2N\_HO\_Complete\_response & 112 & \(M_{19}\) \\ RS & 16 & \(M_{20}\) \\ RA & 64 & \(M_{21}\) \\ \hline \hline \end{tabular}
\end{table}
Table 7: The cost of each message exchanged during the signaling process.  
Figure 21: The signaling cost of each HO scheme.  
MAG and intra-MAG predictive and reactive HOs. Specifically, the proposed scheme performed 372,170 HOs in total. Furthermore, the VAH scheme accomplished acceptable HO counts, which were equal to 533,590, by considering the vehicle's velocity for the handover process. However, it performed the network selection considering only the strength of the perceived signal, providing worse results than the proposed scheme. The rest of the schemes accomplished HO counts equal to 805,751, since they were based on the perceived RSS per user for the HO initiation process.  
Finally, Figure 22 presents the total signaling cost observed for each scheme during the 24 h simulation by taking into consideration the number of HOs that each scheme performed, as well as the respective signaling cost. As shown, the proposed scheme achieved a lower signaling cost for both inter-MAG and intra-MAG predictive and reactive HOs.
# Effective Class-Imbalance Learning Based on SMOTE and Convolutional Neural Networks
## 3 Methodology
### Dataset Preprocessing
#### 3.1.2. Undersampling Techniques  
The simplest technique among under-sampling methods is Random Under-Sampling (RUS) which is a data-level approach. Here, the algorithm tries to reduce the number of the majority class samples to balance data. In RUS, we randomly select samples within the majority class and delete them, which makes the distribution of a class-imbalanced dataset  
Figure 3: Illustration of Random Over-Sampling technique. This algorithm randomly selects instances from the minority class, with replacement, and adds them to the training dataset.  
with a highly unbalanced ratio more balanced. RUS is a non-heuristic approach that does not behave as smart as some other algorithms. Its main drawback is the high probability of losing valuable information within a dataset [4]. More precisely, the principal issue in proposing this method is that there is no control over what information about the majority class is being thrown away. As a result, the samples that contain information and details about the decision boundary may be removed, and that valuable information is lost [23]. An overview of RUS is shown in Figure 5.
## 4 Discussion and Conclusions  
In this work, a novel material, the Cu\({}_{2}\)TeO\({}_{6}\) monolayer, is discovered. Its dynamic and thermal stability is studied and confirmed. Importantly, an application of the Cu\({}_{2}\)TeO\({}_{6}\) monolayer is possible at various temperature conditions and geographical locations, from Nordic countries to the tropics, as its stability is confirmed at a room temperature of 300 K and at the elevated temperature of 320 K.  
It is found that the Cu\({}_{2}\)TeO\({}_{6}\) monolayer exhibits a narrow band gap of 0.2 eV and a high WF value of 7.76 eV. In addition, the Cu\({}_{2}\)TeO\({}_{6}\) monolayer has anisotropic mechanical properties with the highest Young's modulus of 91.04 N/m, shear modulus of 34.65 N/m, and Poisson's ratio of 0.37.  
These properties make the Cu\({}_{2}\)TeO\({}_{6}\) monolayer a good candidate for applications in various nanodevices. Particularly, due to its exceptionally high WF value, the Cu\({}_{2}\)TeO\({}_{6}\) monolayer is a good candidate material for photovoltaic devices [42], metal-insulator-metal capacitors [48], and high-WF anodes [49], etc. In addition, despite the Young's modulus of the Cu\({}_{2}\)TeO\({}_{6}\) monolayer being \(\sim\)3 time lower than that of graphene [50], which suggests its lower stiffness compared to graphene, the Cu\({}_{2}\)TeO\({}_{6}\) monolayer possesses higher elasticity relative to graphene [51]. This offers the application of the Cu\({}_{2}\)TeO\({}_{6}\) monolayer in straintronic nanodevices [52].  
Conceptualization, methodology, software, validation, formal analysis, investigation, data curation, visualization, A.A.K.; writing--original draft preparation, E.A.K., V.A.B. and A.A.K.; supervision, resources, project administration, funding acquisition, E.A.K. All authors have read and agreed to the published version of the manuscript.  
A.A.K. and V.A.B. are grateful for financial support to the Ministry of Science and Higher Education of the Russian Federation within the framework of the state task of the USATU (No. 075-03-2022-318/1) of the youth research laboratory <<Metals and Alloys under Extreme Impacts>>. E.A.K. acknowledges the support of Grant NSh-4320.2022.1.2 of the President of the Russian Federation for state support of young Russian scientists/candidates of sciences and doctors of sciences.  
Not applicable.  
The datasets are available from the authors on a reasonable request ([PERSON]The authors express their gratitude to the Joint Supercomputer Center of the Russian Academy of Sciences for providing access to cluster computational resources. A.A.K. grateful [PERSON] for the discussions on the possible structure of the Cu\({}_{2}\)TeO\({}_{6}\) monolayer.  
The authors declare no conflict of interest.  
Figure 5: Spatial dependencies of (**a**) the [PERSON]’s modulus, (**b**) the shear modulus, and (**c**) [PERSON]’s ratio for the Cu\({}_{2}\)TeO\({}_{6}\) monolayer.
## 4 Construction Monitoring
### Stage I--Preparation of the Object for Measurements  
At this stage, the strength cage around the gondola was covered with coded markers, constituting reference points, enabling correlations of the location of measurement points from subsequent photographs. Placed markers had the form of single points on magnetic pads or crosses with 9 coded points. On the gondola, 82 uncoded markers (measuring points) were glued, which were then recognized by the software. An example of the arrangement of reference and measurement points is shown in Figure 10a,b.  
The arrangement of the points was selected in such a way that they allowed determining changes in the characteristic dimensions of the gondola. Measuring points with a diameter of 8 mm were used for the measurements.
# WaveConv-sLSTM-KET: A Novel Framework for the Multi-Task Analysis of Oil Spill Fluorescence Spectra
## 3 Results and Discussion
### Model Performance Evaluation
#### 3.3.2 Regression Performance
Additionally, the lightweight design of the WaveConv and KET block allows the framework to achieve high predictive performance without relying on fully connected layers. With only 4.57 M parameters, the framework is ideally suited for deployment on resource-constrained, miniaturized hardware systems. Although the proposed Hybrid framework achieves simultaneous predictions for oil spill classification and regression tasks, it has certain limitations. The current validation primarily focused on distinguishing oil types, while other organic materials commonly coexisting in marine environments (e.g., natural dissolved organic matter, microplastics, and biological substances) were not systematically incorporated into the spectral dataset. This omission may challenge the framework's discrimination accuracy in real-world scenarios, where complex material mixtures exist, as evidenced by recent LIF-based studies demonstrating the necessity of multi-material comparisons for robust identification [46]. Additionally, the model's performance depends critically on the representativeness of input spectral data. Due to experimental constraints, training and validation were conducted using laboratory-collected data rather than field measurements from dynamic marine environments, potentially limiting practical applicability. Observed performance fluctuations during preprocessing stages further suggest that the model's stability may be compromised when encountering severe noise interference or unexpected spectral artifacts in real-time detection. Future work will focus on expanding the spectral database to encompass diverse marine organic materials and validating the model's online analysis capability using more real-world data to enhance its practicality. Transfer learning strategies will be explored to improve the model's adaptability, enabling broader applications in more complex and diverse spectral analysis tasks. Integrating the framework into an end-to-end online spectral detection system presents a promising direction for future research, with the potential to significantly enhance its applicability in industrial and field environments.
# Characterization of GNSS Signals Tracked by the iGMAS Network Considering Recent BDS-3 Satellites
## 3 Methodology and Results
### Pseudo-range Noise and Multipath
Figure 4: RMS of the noise and multipath errors as a function of satellite elevation for all the available signals of GPS and Galileo (**a–c**) and BDS-2 and BDS-3 (**d–f**) at station HMNS equipped with GNSS_GGR receiver (**a,d**); station PETH equipped with CCTC-54-GMR-4011 receiver (**b,e**); and station LHA1 equipped with CETC-54-GMR-4016 receiver (**c,f**), respectively. Figure 5: RMS of the noise and multipath errors as a function of satellite elevation for the signals with overlapping frequencies of BDS-3, GPS, and Galileo at HMNS station. The signals include BIC/L1/E1 (**a**); B2a/L5/E5a (**b**); and B2b/E5b (**c**).
# Mitigating Class Imbalance in Network Intrusion Detection with Feature-Regularized GANs
## 4 Experimental Results
### Cse-Cic-IDS2018 Results
#### 4.4.2 Overall Accuracy and Macro-F1  
Table 7 presents the overall classification accuracy and macro-F1 scores on the CSE-CIC-IDS2018 dataset. All methods achieve consistently high accuracy (\(\geq\)98.7%), with minimal variation across both traditional and GAN-based augmentation strategies. The inclusion of SNNL does not adversely affect overall accuracy.  
Macro-F1 offers a more balanced perspective by averaging performance across all classes equally, making it particularly useful under imbalanced distributions. As shown in Table 7, models enhanced with SNNL tend to outperform their baseline counterparts in macro-F1. Notably, WGAN-GP+SNNL achieves the highest macro-F1 score of 84.6%, followed by WGAN+SNNL and CWGAN+SNNL. These results show that SNNL contributes to improved minority-class detection without sacrificing majority-class accuracy or overall performance.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**Infiltration**} & \multicolumn{3}{c}{**Web Attack**} \\  & **Precision** & **Recall** & **F1-Score** & **Precision** & **Recall** & **F1-Score** \\ \hline SMOTE & 0.063 & 0.547 & 0.113 & 0.163 & **0.810** & 0.263 \\ ADASYN & 0.060 & **0.557** & 0.110 & 0.133 & 0.797 & 0.220 \\ ROS & 0.087 & 0.480 & **0.137** & 0.273 & 0.737 & 0.357 \\ WGAN & 0.362 & 0.075 & 0.113 & 0.912 & 0.583 & 0.692 \\ WGAN+SNNL & 0.363 & 0.077 & 0.115 & 0.933 & 0.656 & 0.765 \\ CWGAN & 0.357 & 0.072 & 0.110 & 0.940 & 0.543 & 0.653 \\ CWGAN+SNNL & 0.356 & 0.073 & 0.110 & 0.935 & 0.628 & 0.743 \\ WGAN-GP & **0.52** & 0.05 & 0.087 & **0.983** & 0.564 & 0.705 \\ WGAM-GP+SNNL & 0.493 & 0.057 & 0.100 & 0.953 & 0.74 & **0.833** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Minority-class performance comparison on CSE-CIC-IDS2018 dataset.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Method** & **Accuracy (\%)** & **Macro-F1 (\%)** \\ \hline SMOTE & 98.7 & 75.9 \\ ADASYN & 98.7 & 75.0 \\ ROS & 97.7 & 77.6 \\ WGAN & 98.7 & 82.8 \\ WGAN+SNNL & 98.7 & 83.8 \\ CWGAN & 98.7 & 82.2 \\ CWGAN+SNNL & 98.7 & 83.5 \\ WGAN-GP & 98.8 & 82.6 \\ WGAN-GP+SNNL & 98.8 & 84.6 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Overall accuracy and macro-F1 comparison on CSE-CIC-IDS2018 dataset.
# Dynamic Round Robin CPU Scheduling Algorithm Based on K-Means Clustering Technique
## 5 Conclusions and Future Work  
In this paper, a dynamic SRR-based CPU scheduling algorithm has been proposed that could be used in timesharing systems in which it is important to reduce the time cost of the scheduling. Unlike the SRR algorithm which uses fixed time slice for the processes in the ready queue in all rounds, the proposed algorithm uses dynamic time slice. The proposed algorithm benefits from clustering technique in grouping processes that resemble each other in their features (i.e., burst times, weights, and NCS). Each cluster is assigned a time slice proportional to its weight, and every process in this cluster receives this amount of time for its execution in the current round. The features will be updated in the subsequent rounds. In addition, the proposed algorithm gives processes that are close to completion a chance to complete execution and leave; this in turn helps in reducing number of processes in the ready queue and reducing NCS. The proposed algorithm was compared with five common algorithms from the point of view of average waiting, turnaround times, and NCS. The proposed algorithm outperformed all the others; however, it behaves somewhat similar to ADRR in NCS.  
In cloud-computing systems, achieving optimality in scheduling processes over computing nodes is an important aim for all researchers interested in both cloud and scheduling, where the available resources must be scheduled using an efficient CPU scheduler to be assigned to clients. The relation between CSP (cloud service providers) and CSC (cloud service consumers) is formalized through service level agreement (SLA). CSP must achieve best performance through minimizing time cost. Because of the superiority of the proposed algorithm over the compared algorithms in all the datasets, the proposed algorithm in this work is promising for cloud computing systems.  
Conceptualization, formal analysis, methodology, and writing--original draft, S.M.M. The research work was carried out under the supervision of H.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors thank the anonymous reviewers for their careful reading of our paper and their many insightful comments and suggestions.  
The authors declare no conflict of interest.  
Figure 6: Improvement percentage of the proposed algorithm over the compared algorithms.
Additionally, the article explores the latest studies on optimizing different components of energy harvesting systems, including rectifiers and power management units, to enhance overall system efficiency [18]. This review aims to provide a thorough understanding of the current state of energy harvesting antenna technology, highlighting key advancements, ongoing challenges, and future directions. By doing so, we hope to contribute to the advancement of sustainable energy solutions that can support the growing demand for low-power electronic devices and autonomous systems. The article is organized as follows. Section 2 presents a prior review of studies that address related topics, describing their main findings and limitations in order to establish this article's research contribution. In Section 3, the PRISMA-based methodology is described, starting from the formulation of research questions, keywords, and search equation, as well as the inclusion and exclusion criteria used. Subsequently, Section 4 answers the research questions, presenting the results clearly, combining graphical visualizations with narratives describing the most important contributions of each study. The analysis has been divided into three sections: antenna design (Section 4.1), substrate material of rectennas (Section 4.2), and rectifier systems in RF energy harvesting (Section 4.3). Finally, Section 5 summarizes the main conclusions with regard to the most relevant findings and some perspectives from the authors as a starting point for generating new ideas in this area.
## 3 System Model
### Link Quality
#### 3.2.3 SNR Calculation  
According to the above considerations, the path loss and Doppler shift were considered as the primary factors of the SNR. However, shadowing fading was also considered in the channel state information. The shadowing effect is caused by obstacles between the transmitter and the receiver, i.e., non-LOS (NLOS) transmissions, which degrade the signal power through absorption, reflection, scattering, and diffraction; in severe cases, it can even block the signal entirely. The SNR between node \(i\) and its neighboring node \(j\) was expressed as follows:  
\[SNR_{i,j}=\frac{P_{i}\big{|}h_{ij}\big{|}^{2}}{N_{0}\cdot P_{L_{i,j}}\cdot DF_ {i,j}} \tag{6}\]  
where \(P_{i}\) is the hello transmission power of node \(i\), and \(h_{i,j}\) is the fading coefficient of link \(L_{i,j}\) following the Nakagami-m channel, which included the effect of shadowing fading. The expression \(N_{0}\sim N(0,\sigma^{2})\) is the power of an additive white Gaussian noise (AWGN) with a noise variance \(\sigma^{2}\), \(PL_{i,j}\) is the absolute form of the path loss for link \(L_{i,j}\), and \(DF_{i,j}\) is the absolute form of the loss value of the Doppler shift.