esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Literature Review  
Except for occlusion and material interference, intentional radio intrusions such as those caused by truck drivers' personal privacy devices (PPD) are gaining more attention [3, 4]. To obtain better telecommunication integrity, using correlated data analysis is rational [5, 6]. Hence, communication transceivers or sensors allocated and networked in complementary positions are basically prototyped, probably in a modular perspective--just like coordinated sensory mechanisms in two eyes or ears--promoting more diagnosing resilience and intelligence. Furthermore, such dual-surveillance can incorporate with allocation design concerning availability and maintainability in changing environments.  
An effective, efficient sequential order for area inspection needs to be pondered, especially if available resources are limited [7]. Regarding networking, a Hamiltonian path (cycle) is a path (cycle) linking all nodes once. Such a sequentially ordered feature is essential in communication due to confidential needs in network inspection errands. Wireless communications have peer-to-peer connectivity. Hence, different mathematical performance can be assumed and inspected to help assure network reliability, and analyze the impact of communication interference confidentially.  
On the strategy of tracing performance on different networks with the same wireless communication nodes, we show that the performance of mutually independent Hamiltonian paths (MIHP) in both GHT and SW [8] networks can be feasible approaches. Their topology can be adaptable in the dedicated short range communication (DSRC) and the cellular communication networks. Network topologies that have comprehensive information capability can be considered first.  
Consequently, hypercube derived networks, e.g., cube-connected-cycle (CCC) graphs [9], which can have similar regular-degree-3 performance, with potentially packaged merits, are schemed to offer more adaptability and mathematical traits including fault-tolerance and Hamiltonian reliability/maintainability. Packaged nodes accommodating CCC or different network topologies may be concerned for maintainability to promote the application of wireless communication in less populated areas, where wireless information and communication is essential; however, they may potentially be significantly and adversely affected due to changing weather and other unexpected conditions.
## 2 Materials and Methods
### Eddy Covariance Data  
Gross primary productivity of the forests was estimated using the eddy covariance technique. Instrumentation comprises HS-50 ultrasonic anemometers (Gill Instruments, Lymington, UK) and LI-7200 infrared gas analyzers (LI-COR Biosciences, Lincoln, NE, USA) to collect data. The instruments were installed approximately 8 m above the forest at heights of 25 m (Bliy Kriz) and 44 m (Stitna) above ground level. Measurements and data processing of the flux data follow standards established by ICOS. Processing of the flux data were performed using EddyPro version 6.2.x (LI-COR Biosciences, Lincoln, NE, USA). Net ecosystem exchange was partitioned into gross primary productivity and ecosystem respiration according to [PERSON] et al. [33] with the use of REddyproc [34]. Fluxes were aggregated at half-hour intervals using block averaging.
# Skin Absorbed Dose Coefficients for Human Legs from Beta Radiation as a Function of Height
## 3 Results and Discussion
One major factor is the selection of the cross-section libraries from the Geant4 simulator. To avoid a large difference in the results, we conducted different simulations with different cross-section libraries; the differences in the results from different cross-section libraries were negligible. The important point during the simulations was using the same cross-section library for all energy sources. Another important factor that could play a significant role in the differences in the results is the pseudo-random number generator seeds in Geant4. Even when one simulator toolkit such as Geant4 is used, results can be different if the seeds are different [54]. To avoid that, we considered the number of events to be high enough; thus, the results would be statistically significant. Also, the same pseudo-random number generator seeds were used for all energy ranges studied in this paper. Furthermore, the numerical approximations in built-in physics functions that simulate the interaction,  
Figure 8: Total skin absorbed dose coefficient for 3.0 and 4.0 MeV. the passage, and the generated secondary electrons or photons contribute to the variation in the results. To further validate the results and expand the current data used in health physics and protection guidelines, the SADC from the simulations was compared with the results from the Federal Guidance Report (FGR) 12. FGR 12 calculated the skin dose coefficients for different source energies for the 1.00 m height above the contaminated area with beta particles. The comparison graph for the SADC for phantom cells at a height of 100 cm is plotted for Geant4 and the published values from FGR 12 (Figure 10). The minimum energy with available FGR 12 data were 0.4 MeV. For sources with low energy, the difference between the FGR 12 data and the calculated data from Geant4 is larger than the sources with higher energies; even though for lower energies, the difference is still acceptable. It can be observed from Figure 10 that for the sources with energies > 1.0 MeV, the difference between the calculated data and the published data from FGR 12 is negligible. Comparing the Geant4 data with the FGR 12 data for 1 m height, the R\({}^{2}\) value is 0.972 and the correlation coefficient is 0.986. The calculated value for each cell in the leg phantom using the Geant4 simulations for different energy sources for both total deposited energy and the energy deposited from the primary electrons is provided in Table 1 in Appendix A at the end of the manuscript. Figure 9: Secondary electrons and generated photons share from the deposited energy in the phantom legs. The percentages for energy sources from 0.4 MeV to 4.0 MeV overlapping as they are close to zero.
## 5 Discussion
### Comparison Models
#### 5.1.2 The SML Model  
[PERSON] et al. [15] proposed a TEC prediction method based on SML. The modeling approach is illustrated in Figure 7. This study trained the model using OBSs from October 1998 to March 2006. The polynomial representation of the solar activity index was used to express the correlation between TEC and the solar activity index. At the same time, trigonometric functions were employed to depict the finer periodic variations in TEC, such as annual, seasonal, and monthly changes [34]. In this model, SA\({}_{12}\) represents the 12-month smoothed average of the solar activity index involved in the modeling. \(K\) denotes the maximum number of harmonics in the trigonometric functions, and \(J\) indicates the highest power of the solar activity index. Given SA\({}_{12}\), \(J\), and \(K\), the coefficients \(\gamma_{k,j}\), and \(\beta_{k,j}\) in the model can be determined using the least squares regression method. The final model is selected based on the RMSE between the modeled values and OBSs.  
The model is trained, and when the solar activity index \(F_{12}\) is chosen and \(J=K=1\), the RMSE is minimized. Therefore, the final model is:  
\[\text{TEC}(F_{12},m)=\sum_{k=0}^{1}\sum_{j=0}^{1}\left[\gamma_{k,j}F_{12}^{j} \cdot\cos(2\pi km/12)+\beta_{k,j}F_{12}^{j}\cdot\sin(2\pi km/12)\right], \tag{8}\]  
where \(m\) represents the month corresponding to \(F_{12}\).
###### Abstract  
Accurate and real-time classification of low-latency Internet traffic is critical for applications such as video conferencing, online gaming, financial trading, and autonomous systems, where millisecond-level delays can degrade user experience. Existing methods for low-latency traffic classification, reliant on raw temporal features or static statistical analyses, fail to capture dynamic frequency patterns inherent to real-time applications. These limitations hinder accurate resource allocation in heterogeneous networks. This paper proposes a novel framework integrating wavelet transform (WT) and artificial neural networks (ANNs) to address this gap. Unlike prior works, we systematically apply WT to commonly used temporal features--such as throughput, slope, ratio, and moving averages--transforming them into frequency-domain representations. This approach reveals hidden multi-scale patterns in low-latency traffic, akin to structured noise in signal processing, which traditional time-domain analyses often overlook. These wavelet-enhanced features train a multilayer perceptron (MLP) ANN, enabling dual-domain (time-frequency) analysis. We evaluate our approach on a dataset comprising FTP, video streaming, and low-latency traffic, including mixed scenarios with up to four concurrent traffic types. Experiments demonstrate 99.56% accuracy in distinguishing low-latency traffic (e.g., video conferencing) from FTP and streaming, outperforming k-NN, CNNs, and LSTMs. Notably, our method eliminates reliance on deep packet inspection (DPI), offering ISPs a privacy-preserving and scalable solution for prioritizing time-sensitive traffic. In mixed-traffic scenarios, the model achieves 74.2-92.8% accuracy, offering ISPs a scalable solution for prioritizing time-sensitive traffic without deep packet inspection. By bridging signal processing and deep learning, this work advances efficient bandwidth allocation and enables Internet Service Providers to prioritize time-sensitive flows without deep packet inspection, improving quality of service in heterogeneous network environments.  
network traffic classification; artificial neural network; wavelet transform; feature selection; internet traffic mix; statistical features; QoS; low latency +
Footnote †: journal: _Algorithms_
## 1 Introduction
### Contribution of the Study  
* The use of the triangular membership values in this study enhances the unpredictability and complexity of the resulting encrypted image, which increases the security of the encryption process. This method enhances the randomness of the generated keys and makes it difficult for hackers to predict or crack the encryption code. This approach is an improvement over traditional encryption methods that rely on fixed values and can be easily cracked using brute force attacks.
* The system for image encryption employs the Henon map and a triangular membership function to generate a sequence of random numbers that can be used as a key. The Henon map provides a source of randomness, while the membership function adds variability to the sequence, making it harder to predict. The resulting key is therefore more secure and suitable for encryption purposes. By incorporating fuzzy logic, the system introduces an extra layer of complexity, making it even more difficult for attackers to decode.
* To further enhance the security of the encryption system, the scheme combines the logistic sine map with the fuzzy Henon map to generate secret keys. The use of multiple mathematical concepts increases the space of possible secret keys, making the system more robust against sensitivity attacks. This innovative approach to encryption has the potential to revolutionize the field of data security and can be applied in various contexts where secure communication is essential. With its ability to generate complex and unpredictable keys, the system provides an effective solution to the challenge of secure image encryption.  
In summary, this study's structure consists of several sections. The second section offers an overview of the essential concepts and background information required to understand the proposed image encryption method. The third section provides a detailed description and illustration of the encryption process and its implementation. The fourth section showcases the simulation results, analysis, and evaluation of the proposed encryption method, as well as a comparison with other studies. Finally, the study concludes with a summary of the findings in the last section.
# Privacy-Preserving Byzantine-Resilient Swarm Learning for E-Healthcare
## 7 Evaluation
### Experimental Setup  
#### 7.1.1 Experimental Environment  
**Implementation Settings:** In order to evaluate the integrated performance, we implement the PBSL scheme on a private Ethereum blockchain setup with real medical datasets. In our experiment, smart contract performs secure a global gradient aggregation algorithm instead of the centralized server.  
* **A blockchain system** uses Ethereum, a blockchain-based smart contracts platform [56; 57; 58]. Smart contracts developed in the Solidity programming language are deployed to private blockchains using Truffle v5.10 And Ethereum is deployed on a desktop computer with 3.3 GHz Intel(R) Xeon(R) CPU, 16 GB memory, and Windows 10.
* **CKKS Scheme** is deployed using the TenSEAL library, which is a library for carrying out homomorphic encryption operations on tensors, and its code is available on Github ([[https://github.com/OpenMined/TenSEAL](https://github.com/OpenMined/TenSEAL)]([https://github.com/OpenMined/TenSEAL](https://github.com/OpenMined/TenSEAL)), accessed on 9 June 2024).  
**Dataset:** Our PBSL scheme was evaluated using two widely used datasets, the MNIST dataset and the FashionMNIST dataset.  
* **MNIST dataset** consists of handwritten digital pictures of 250 different people. Every sample is a black and white image with \(28\times 28\) pixels. The dataset is divided into a training set with 60,000 samples and a test set with 10,000 samples. We distributed the entire dataset evenly across 10 **MCs**.
* **FashionMNIST dataset** constitutes images in 10 categories. The whole dataset contains 60,000 samples (50,000 for training and 10,000 for testing). The dataset was also evenly distributed to 10 **MCs**.
# Dynamic Workload Management System in the Public Sector: A Comparative Analysis
## 5 Discussion
### Comparative Analysis and Practical Recommendations for Algorithm Selection  
The results of this study highlight the relative strengths and limitations of the selected machine learning algorithms in the context of workload distribution and predictive modeling. Linear Regression served as a baseline model, demonstrating its inability to effectively capture non-linear dependencies within the dataset. In contrast, Artificial Neural Networks (ANNs) significantly improved predictive accuracy by modeling complex patterns through their layered structure and adaptive learning capabilities.  
Among the tested models, the Adaptive Neuro-Fuzzy Inference System (ANFIS) emerged as the most effective, achieving superior accuracy and robustness. Its hybrid architecture, which integrates fuzzy logic with neural networks, enables efficient handling of uncertainty and non-linearity, making it particularly well suited for workload optimization.  
Ensemble-based models, including Gradient Boosting Machine (GBM) and Bagged Decision Trees, also exhibited strong predictive performance, while XGBoost proved less effective. By leveraging multiple weak learners, these methods enhanced generalization capabilities and delivered competitive accuracy. In addition, they demonstrated a balance between predictive power and computational efficiency, making them viable alternatives for large-scale workload management applications.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Algorithm** & **CPU Time (ms)** & **Memory Usage (MB)** \\ \hline Linear Regression & 10–30 & 5–15 \\ \hline Artificial Neural Networks (ANNs) & 120–180 & 30–50 \\ \hline Adaptive Neuro-Fuzzy Inference System (ANFIS) & 180–220 & 40–60 \\ \hline Gradient Boosting Machine (GBM) & 150–200 & 40–50 \\ \hline Bagged Decision Trees & 140–180 & 30–40 \\ \hline Support Vector Machine (SVM) & 200–260 & 50–60 \\ \hline XGBoost & 200–240 & 40–55 \\ \hline \hline \end{tabular}
\end{table}
Table 14: Comparison of CPU time and memory usage for algorithms.  
The Support Vector Machine (SVM) provided a versatile approach although managed non-linear relationships, thus proving effective in high-dimensional spaces. However, its computational demands were significantly higher compared to other models, requiring careful consideration of trade-offs between accuracy and resource efficiency. Similarly, GBM, while offering strong performance, required extensive computational resources, underscoring the need to balance precision with operational feasibility.  
This analysis underscores the importance of selecting machine learning models based on the specific demands of a given task. Factors such as interpretability, scalability, and computational constraints should guide the choice of algorithm to ensure optimal decision-making processes in dynamic environments. By systematically evaluating these models, this study provides insights that can inform both future research and real-world applications, fostering more efficient and adaptive workload distribution strategies.
## 3 Cooperative Communication Algorithms
### Implementation of KCN Algorithm  
1. The selection of multiple cooperative nodes of node \(m\) is shown in Algorithm 1.
2. The algorithm flow of selecting all cooperative nodes is shown in Figure 6.  
Figure 5: The diagram of T_DATA and T_PROB.  
Figure 6: Algorithmic flow chart of multi-hop cooperative nodes.  
```
Input:\(n_{m}\) denotes the total number of neighbor nodes of \(m\); \(v_{m}\) denotes the array of neighbor nodes of \(m\); \(t\) is the Sink node; \(R_{KCN}\) denotes the cooperative nodes search region of \(m\); \(Q\) is array of priority for every neighbor node of \(m\); Output:\(C_{m}\) (\(C_{m}\) is array of cooperative nodes of \(m\);)
1. For \(i=0\) to K-1 do
2. \(Q_{Max}\gets 0\);
3. \(idx\leftarrow-1\);
4. for \(j=1\) to \(n_{m}\) do
5. if \(V_{m}[j]\) is \(t\) then
6. break;
7. end if
8. if (\(V_{m}[j]\) in \(C_{m}\)) or (\(V_{m}[j]\) not in \(R_{KCN}\)) then
9. continue;
10. end if
11. if \(Q[j]>Q_{Max}\) then
12. \(Q_{Max}\gets Q[j]\);
13. \(idx\gets j\);
14. end if
15. end for
16. \(C_{m}^{i}\gets V_{m}[idx]\);
17. if \(C_{m}^{i}\) is \(t\) then
18. break;
19. end if
20. return \(C_{m}\)
```  
**Algorithm 1**Choose CN (\(n_{m}\),\(v_{m}\),\(t\),\(R_{KCN}\),\(Q\)).
## 3 Proposed Status-Byte-Assisted RDMA Transmission Mechanism
### Design of the Status-Byte-Assisted RDMA Transmission Mechanism
#### 3.2.2 Transmission Mode Selection  
The sliding window transmission mechanism uses RDMA Write With Immediate to transmit data, utilizing immediate data to notify the receiver of the data's arrival. Although this method is a one-sided RDMA transmission, it still requires the receiver to issue a receive request, which generates a Completion Queue Entry (CQE). A CQE is a data structure used in RDMA to indicate the completion of an operation. The location of the transmitted data is determined by the immediate data in the CQE, which requires interaction with the receiver. To address this issue, we propose to utilize two RDMA Writes for data transmission: the first RDMA Write transmits the valid data and the second RDMA Write transmits the status byte of the corresponding memory block to the third status byte memory block on the receiving end. These two requests are sent using a linked list to minimize context switching. The first request does not generate a CQE, while the second request uses inline data. This approach effectively reduces the exchange between the RNIC and memory. Evidently, this approach achieves true one-sided transmission without interacting with the receiver, thereby enhancing transmission efficiency and reducing resource consumption.
## 1 Introduction
constructed a two-dimensional axisymmetric surface charge accumulation model to investigate the dynamic characteristics of surface charge and electric field distribution along the GIL basin insulator surfaces under polarity-reversal voltage [20]. [PERSON] et al. investigated the time-varying characteristics of insulator surface charge distribution, and the results verified that bulk conductivity can be greatly affected by temperature, which leads to an increase in insulator surface charge density [21]. [PERSON] et al. investigated insulator surface charge and electric field distribution under DC voltage through a three-dimensional electro-thermal coupling model for DC GIL, concluding that temperature distribution further aggravates electric field distortion along the surface [22; 23]. [PERSON] et al. [24] examined the dynamics of spatial charge and electric field distributions in HVDC cables under temperature gradients and polarity-reversal conditions. However, within the existing literature, few scholars have addressed the effect of voltage polarity reversal on insulator surface charge and surface electric field distribution under multi-physics field coupling. It should be noted that in numerical simulations, differences between insulator body conductance and surface conductance parameters significantly impact calculation results [19, 25]. Moreover, insulators are typically composed of epoxy resin matrix filled with metal oxide particles, particularly alumina. Different manufacturers employ varying material selection, filling ratios, and casting processes for filler particles, which directly influence the conductivity performance of insulators [26]. Therefore, the precise determination of insulator conductivity parameters through experimental methodologies is crucial for elucidating the charge accumulation and electric field distribution characteristics of DC GIL insulator surfaces under multi-physical field coupling, thereby significantly enhancing the reliability of subsequent investigations. Research on surface charge accumulation in DC GIL basin-type insulators under multi-physics coupling conditions remains limited. Existing studies often lack experimental data on insulator material conductivity, and further exploration is needed to understand surface charge accumulation and electric field distribution during voltage polarity reversal under multi-physics interactions. This study addresses these gaps by experimentally measuring the bulk and surface conductivity of insulators across a range of temperatures and electric field strengths, deriving corresponding fitting equations. Additionally, a three-dimensional thermal-fluid-electrical multi-physics coupled model for DC GIL is developed to systematically analyze surface charge accumulation and electric field distribution under DC voltage and polarity-reversal conditions. These findings provide a robust theoretical framework and technical guidance for optimizing the design and enhancing the performance of DC GIL insulators.
## 2 Method
### The Magnetic Dipole Model for Tracking System
#### 2.2.2 The Attenuation Effect  
Since the ELF electromagnetic signal has a good penetration effect in the soil, air, and oil or natural gas, correspondingly, due to the high magnetic permeability and electrical conductivity, the metal pipe is the main factor for the power loss of the ELF signal [26]. The electromagnetic signal attenuation caused by the classical 10 mm pipe wall thickness is two orders of magnitude larger than the attenuation caused by the classical 2-3 m buried depth in soil [30]. Therefore, the attenuation effect of the ELF electromagnetic signal in the soil, air, and oil or natural gas can be ignored. According to the magnetic field propagation  
Figure 2: The magnetic dipole model for tracking system. The circle part is one turn of the transmitting coil and represents a magnetic dipole.  
model in the lossy medium [26], the attenuation effect of the pipeline can be expressed as (2):  
\[H=H_{0}e^{-\alpha h}e^{-\mathrm{i}\beta h}, \tag{2}\]  
where \(H_{0}\) and \(H\) are the amplitudes of the magnetic field intensity before and after attenuation, respectively. \(\alpha\) is the loss factor and meets \(\alpha=\sqrt{\pi f\mu\sigma}\), while \(\beta\) is the phase constant. \(h\) is the thickness of the pipe wall; \(\sigma\) is the conductivity of the pipe; \(\mu\) is the permeability of the pipe, which follows the BH curve. Furthermore, the nomenclature of the variables in the equations is listed in the Appendix A.