esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussions
### Effectiveness and Limitations of Blockchain, AI, and Quantum-Resilient Solutions  
An essential research area in IoE security is the integration of Blockchain, AI, and Quantum-Resilient Security to create a more effective defense architecture [20; 144; 164]. However, each technology presents distinct challenges:  
* Blockchain offers decentralized security but remains insufficiently optimized for large-scale IoE systems due to scalability and latency issues [73; 91].
* Edge AI provides efficient real-time threat detection and anomaly analysis, but remains vulnerable to adversarial attacks [25; 30; 64].
* Quantum-Resilient Security ensures data protection against quantum threats, but its practical implementation on current IoE devices remains debatable due to hardware and resource constraints [54; 180; 181].  
Table 20 provides a comparative overview of how Blockchain, AI, and Quantum-Resilient Security contribute to IoE protection, highlighting their respective strengths and current limitations in terms of scalability, attack resilience, and hardware compatibility.  
In addition to the limitations shown in Table 20, energy efficiency is a critical consideration for IoE security solutions, given the resource constraints of many edge devices. Technologies such as blockchain and AI-driven analytics, while powerful, can be computationally intensive. Future designs should incorporate lightweight consensus algorithms, hardware accelerators, and model compression techniques to minimize power consumption. Studies have shown that optimizing task allocation between edge, cloud, and possibly Non-Terrestrial Networks (NTNs) nodes can significantly improve energy profiles without sacrificing security performance. This remains an open area for innovation, especially in battery-powered or energy-harvesting IoE systems.
## 3 Analytical Results
### Influence of Joint on Responses of Tunnel  
This section investigates the influence of the joint on the responses of the tunnel built in non-homogeneous ground. The parameters introduced in Section 4.1 are also employed here. For the sake of convenience in the study, the following dimensionless variables \(\zeta_{\text{z}\text{z}\text{t}}=C_{\text{z}\text{t}\text{k}}/\kappa GA\), \(\zeta_{\text{z}\text{t}\text{k}}=C_{\text{z}\text{t}\text{k}}/EI\) are introduced, and the parameters of the joints are set as \(\zeta_{\text{z}\text{t}\text{k}}=0.05\), \(\zeta_{\text{z}\text{t}\text{k}}=0.05\), as suggested in Ref [26].  
Figure 6 shows the displacements of tunnels with and without joints along the tunnel axis. As it can be seen, the displacement difference at Joint 2 is the largest, which indicates that the sharp change of ground layers has a great influence on displacement of the tunnel, and that the flexible joint works.  
Figure 7 illustrates the internal force responses of tunnels with and without joints. The laws of the tunnel internal forces are similar with those in Section 4.2 in spite of different stiffnesses of tunnels, which verifies that the tunnel responses are mainly affected by surrounding rocks. It shows that the joints efficiently reduce bending moments and shear forces of the tunnel. For example, the maximum bending moment of the lining with a joint is 43.75% less than that without a joint, and the maximum shear force with a joint is reduced by 30.89% compared with that without joints. Moreover, the region affected by the joint is approximately 4.5 times the tunnel outer diameter on both sides of the stratum interface.  
Figure 6: Displacement along the tunnel axis.  
Figure 7: Influence of the joint on tunnel responses. (**a**) Bending moment; (**b**) shear force.
## 4 Performance Analysis
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Function ID** & **Statistics** & **GA** & **PSO** & **DE** & **IWO** & **MOA** & **WOA** & **IWOA** \\ \hline F1 & Best & \(5.9459\times 10^{-2}\) & \(9.0916\times 10^{-3}\) & \(1.5094\times 10^{-6}\) & \(2.5677\times 10^{-5}\) & \(8.091\times 10^{-13}\) & \(3.2727\times 10^{-94}\) & \(\textbf{0.0000}\times 10^{0}\) \\  & Average & \(1.7310\times 10^{-1}\) & \(1.6365\times 10^{-2}\) & \(2.9594\times 10^{-6}\) & \(3.4267\times 10^{-5}\) & \(1.674\times 10^{-10}\) & \(1.2064\times 10^{-81}\) & \(\textbf{0.0000}\times 10^{0}\) \\  & Std. & \(2.5419\times 10^{-1}\) & \(5.5216\times 10^{-2}\) & \(1.1558\times 10^{-4}\) & \(4.7093\times 10^{-3}\) & \(3.7641\times 10^{-3}\) & \(5.2016\times 10^{-81}\) & \(5.2016\times 10^{-81}\) & \(\textbf{0.0000}\times 10^{0}\) \\ F2 & Best & \(2.0635\times 10^{2}\) & \(2.6832\times 10^{-2}\) & \(2.4624\times 10^{-1}\) & \(2.5779\times 10^{-1}\) & \(3.1467\times 10^{-2}\) & \(2.6852\times 10^{-1}\) & \(\textbf{5.4799}\times 10^{-3}\) \\  & Average & \(4.009\times 10^{2}\) & \(6.3317\times 10^{-1}\) & \(5.2010\times 10^{-1}\) & \(2.6677\times 10^{-2}\) & \(2.4533\times 10^{-1}\) & \(2.7647\times 10^{-1}\) & \(\textbf{1.3783}\times 10^{-2}\) \\  & Std. & \(1.5590\times 10^{2}\) & \(3.3653\times 10^{-1}\) & \(3.6665\times 10^{-1}\) & \(0.6631\times 10^{-3}\) & \(2.9960\times 10^{-4}\) & \(0.4685\times 10^{-9}\) & \(\textbf{2.8484}\times 10^{-3}\) \\ F3 & Best & \(2.4553\times 10^{-1}\) & \(5.5778\times 10^{-1}\) & \(6.8942\times 10^{-2}\) & \(5.9710\times 10^{-3}\) & \(7.9499\times 10^{-2}\) & \(3.8572\times 10^{-3}\) & \(\textbf{5.6778}\times 10^{-4}\) \\  & Average & \(5.0547\times 10^{-1}\) & \(1.6257\times 10^{-1}\) & \(1.1957\times 10^{-1}\) & \(2.6554\times 10^{-2}\) & \(2.0486\times 10^{-2}\) & \(3.0396\times 10^{-2}\) & \(\textbf{6.1412}\times 10^{-3}\) \\  & Std. & \(1.2738\times 10^{-1}\) & \(3.8675\times 10^{-1}\) & \(3.0263\times 10^{-2}\) & \(8.8917\times 10^{-2}\) & \(7.7150\times 10^{-3}\) & \(3.9468\times 10^{-3}\) & \(\textbf{8.1179}\times 10^{-4}\) \\ F4 & Best & \(3.2840\times 10^{-1}\) & \(1.9277\times 10^{-2}\) & \(2.9891\times 10^{-3}\) & \(3.9696\times 10^{-14}\) & \(1.2840\times 10^{-1}\) & \(7.6845\times 10^{-16}\) & \(\textbf{8.8818}\times 10^{-16}\) \\  & Average & \(7.3548\times 10^{-1}\) & \(2.7495\times 10^{-6}\) & \(4.7469\times 10^{-3}\) & \(5.7495\times 10^{-4}\) & \(5.2548\times 10^{-1}\) & \(4.7962\times 10^{-15}\) & \(\textbf{8.8818}\times 10^{-16}\) \\  & Std. & \(2.1209\times 10^{-1}\) & \(5.7580\times 10^{-1}\) & \(1.0955\times 10^{-3}\) & \(8.5984\times 10^{-5}\) & \(3.1209\times 10^{-1}\) & \(2.158\times 10^{-15}\) & \(\textbf{0.0000}\times 10^{0}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of algorithms on basic benchmark functions.
# An Operations Chain Model for Automatic Assessment of Operation Procedure for Equipment Operators
## 3 Scoring by the Needleman-Wunsch Algorithm
### Penalty Score Rules  
When initializing the score matrix, it is necessary to set the corresponding penalty score rules. The formula is shown below:  
\[\sigma\big{(}S_{i},T_{j}\big{)}=\begin{cases}\text{match},&S_{i}=T_{j}\\ \text{mismatch},&S_{i}\
eq T_{j}\\ \text{indel},S_{i}=``-\"\text{ or }T_{j}=``-\"\end{cases}, \tag{1}\]  
where \"match\" means the score of two elements matching; \"mismatch\" means the score of two elements not matching; \"indel\" means the misaligning score; \(S_{i}\) is the \(i\)-th element of sequence \(S\); \(T_{j}\) is the \(j\)-th element of sequence \(T\); \(S_{i}=``-\"\text{ or }T_{j}=``-\"\) indicates a mismatch, i.e., position in the sequence needs to be filled with a space \"\(-\"\).  
To obtain meaningful results, the match score should be higher than the mismatch score, and the \"indel\" score should be higher than the mismatch score. In this paper, when two sequence elements match, the score adds 2, when they do not match, the score subtracts 2, and when they are misaligned, the score subtracts 1, i.e.,  
\[\sigma\big{(}S_{i},T_{j}\big{)}=\begin{cases}2,&S_{i}=T_{j}\\ -2,&S_{i}\
eq T_{j}\\ -1,S_{i}=``-\"\text{ or }T_{j}=``-\"\end{cases}, \tag{2}\]  
\begin{table}
\begin{tabular}{c c} \hline
**Sequence Name** & **Sequence Content** \\ \hline \(S\) & \(1,2,3,4,5\) \\ \(T\) & \(1,2,4,3,5\) \\ \hline \end{tabular}
\end{table}
Table 2: Trainee’s operation sequence and standard operation sequence.
# Programmable Data Plane Applications in 5G and Beyond Architectures: A Systematic Review
## 3 Background in Programmable Data Plane and 5G and Beyond  
This section provides an overview of programmable data planes and their enabling technologies, such as SDN, PISA architecture, and P4 programming language. It also covers the programmable devices that support these features. Additionally, a comprehensive review of 5G and beyond architectures is provided to contextualize the implementations described in the survey. Finally, relevant paradigms that play a key role in the analyzed use cases are explained, including network function virtualization (NFV), network slicing (NS), and multi-access edge computing (MEC).
# Artificial Neural Network Assisted Error Correction for MLC NAND Flash Memory
## 2 Channel Model
### Retention  
After a cell is programmed, the number of charges in the NAND flash memory cell continually reduce over time due to trap-assisted tunneling and charge detrapping [1]. Retention noise is modeled as a Gaussian distribution, i.e., \(p_{t}(x)=\mathcal{N}(\mu_{t},\sigma_{t}^{2})=\frac{1}{\sqrt{2\pi}\sigma_{t}}e ^{-\frac{(x-\mu_{t})^{2}}{2\sigma_{t}^{2}}}\). The mean \(\mu_{t}\), and the standard deviation \(\sigma_{t}\), are given by  
\[\mu_{t}=\triangle V_{t}[A_{t}(PE)^{\alpha_{t}}+B_{t}(PE)^{\alpha_{t}}]\log(1+T), \tag{6}\]  
\[\sigma_{t}=0.3|\mu_{t}|, \tag{7}\]  
where \(\triangle V_{t}\) is the cell voltage change before and after being programmed, \(T\) donates memory retention time and \(PE\) is the number of PE cycles.  
Figure 1: Illustration of the parasitic coupling capacitances among adjacent cells.
## 3 Method
### The Overall Architecture of the SC Code Generator
#### 3.4.4 Security Checking and Deployment  
SCs are executed on the blockchain, and their security is crucial. Due to the immutable feature of blockchain, once an SC is deployed, its code cannot be manually modified, and the consequences of security vulnerabilities are extremely serious. For example, the DAO event [18] on Ethereum, which was a crowdfunding project, was subject to a re-entry attack due to a coding problem. Hackers used these two vulnerabilities to make more than 200 attacks, stealing a total of 3.6 million Ethereum, more than a third of the total amount of Ethereum raised by the project. The security vulnerabilities of SCs mainly include code reentry, integer overflow, and predictable random processing. Security checking is implemented to protect participant assets, ensure the proper execution of SCs, maintain data integrity and trustworthiness, and meet compliance requirements. Security checking can improve the ability of SCs to resist attacks; prevent fund theft, data tampering, and malicious operations; and ensure the reliability of the SC and the security of participants' rights and interests. Thus, our generator integrates security checking tools. Furthermore, to enhance its integrity, users can compile and deploy the generated code into their private chain, which they can customize for testing.  
**Security checker** is mainly responsible for performing security checks on the generated SCs to avoid security vulnerabilities. To achieve this goal, the SC security checking tool Slither [19] is integrated into our tool. Slither is a Solidity static analysis framework written in Python 3. Slither enables developers to discover vulnerabilities and enhance code comprehension. Slither's error detection exceeds that of other static analysis tools in terms of speed, robustness, and the balance of detection and false positives to find problems in SCs. The developers of Slither show that it outperforms other static analysis tools in detecting the most dangerous and common reentry vulnerabilities [19].  
Figure 10: Overall framework.  
**Blockchain Trigger** is mainly responsible for the interaction with the blockchain. We mainly use the Truffle [20] framework to integrate, compile, and deploy SCs in the system. The code can be compiled and deployed directly after being generated by the generator, greatly enhancing the system's ease of use.  
For the case of flight delay insurance mentioned in Section 1, we use the tool to generate the SC code, and part of the code is shown in Figure 11.
## 5 Analysis of Simulation Experiments  
This section is an experimental analysis presenting the efficiency of the mechanism implemented in the multi-stage system. A small portion of the experiments designed for the study's purposes are presented in Table 2. In this table, one can find the average rates constituting events occurred in the examined production system. The mathematical notations of the rates used in Table 2 are as follows. \(\mu^{\pi}\), \(\mu^{p}\) and \(\lambda^{a_{o}}\) are order, production, and failure rates; \(\lambda^{m_{o}}\) and \(\mu^{e}\) refer to rates related to maintenance and repair operations; and finally, \(\mu^{r}\) and \(\mu^{p}\) denote the frequency of remanufacturing and refurbishing activities. Considering the maintenance and failure frequencies, it is considered that \(\lambda^{m_{0}}>\lambda^{m_{1}}>\ldots>\lambda^{m_{o}}\) and \(\lambda^{a_{0}}<\lambda^{a_{1}}<\ldots<\lambda^{a_{o}}\). By defining these rates, the two-stage manufacturing system is evaluated by mimicking and simulating real-world-like circumstances, e.g., unforeseen machine failures, which have great effects on it. The scope of the experiments is to study the performance of the presented methodology under the simulated stochastic manufacturing system. For this reason, the rates are specifically formulated to be pertinent to the frequency of customer orders, production operations, equipment failures, maintenance, repair, remanufacturing, and refurbishing activities. In this respect, let us examine the ninth experimental scenario presented in Table 2 to demonstrate the concept of the performed experiments. According to this scenario, the system moderately initiates new production, remanufacturing, and refurbishing activities, while accepting a fair number of orders from incoming customers. Within this context, the system degradation failures are occasional. These could affect moderately the \"well-being\" of the manufacturing machines. To this end, the intention was
## 3 Proposed Architecture: Multi-Model IDS Based on Traffic Load  
A new centralized network architecture that utilizes a multi-model IDS based on traffic load has been proposed to ensure security and privacy, as shown in Figure 1. The proposed architecture provides two-layer security. The first layer contains two ML-based IDS models and is activated according to the amount of incoming network traffic. The first model takes less time, so it is faster but less accurate than the second model. The second model has a single model and is more accurate than the first but is less speedy and takes more time. The proposed  
\begin{table}
\begin{tabular}{l l l l l l l l} \hline
architecture allows organizations to monitor incoming traffic and encrypt sensitive data while restricting access to authorized users only. The IDS models used in this study incorporate both signature-based detection and behavior-based detection, which reduces the false alarm rate and increases accuracy. First, signature-based detection is applied to the traffic; if no signatures are found, behavior-based detection is applied. By combining signature detection and behavior detection, both sensitivity and specificity can be maintained for network traffic. Signature detection enhances specificity, thereby reducing false alarm rates, and behavior detection enhances sensitivity, increasing accuracy.
## 3 Applying Predictive Models to an eLearning Messaging System
### The Implementation Phase
#### 3.2.1 The Current Database  
Each new course offering brings new students. The _current_ database is populated with information about them. A current database differs from the historical database in several ways.  
* The current database does not contain a field for the dependent variable, since the purpose of the system is to estimate those unknown values for each current student; _i.e._, there is no column of numbers for the dependent variable in the current database in Figure 3.
* The current database almost certainly contains fewer input variables than is found in the historical database because model derivation usually means variable elimination. The current database will include fields only for the variables used in the prediction formula or algorithm.
* The current database contains different records--those characterizing the current students.  
The current database need not be static. Information might be added to the current database as the course unfolds. For example, if a prediction formula includes information about performance on the second exam, that field can only be populated once students complete the exam.
## 7 Conclusions  
This study focused on evaluating the performance of a DBS network at mmWave frequencies as the envisioned architecture for a 6G network. The evaluation encompassed reducing Non-Ionizing Radiation (NIR) levels and assessing the network performance in terms of coverage probability and data throughput.  
As the demand for wide bandwidth at millimeter-wave frequencies increases, it is necessary to consider any potential impact on the organic and biological systems of humans. This becomes crucial with massive MIMO and beamforming techniques in next-generation networks, which provide substantial gains in signal power to improve the network performance. To assess the potential radiation exposure in the 6G network, this study employed the IEEE C95.1-2005 standard, aiming to achieve a clean and environmentally friendly mobile network. In this context, a DBS network with low transmit power is suggested as a way to reduce the NIR exposure to nearby users. The performance of both mMIMO and DBS networks is evaluated in terms of radiation, spectral efficiency, and data throughput.  
The findings suggest that, at the mmWave band, the DBS architecture offers the most favorable option for next-generation mobile networks. This is due to the notable reduction in radiation levels compared to the high associated power of massive MIMO. Additionally, the LPN with DBS architecture demonstrates that the reduction in radiation exposure can be accomplished without compromising the QoS provision in the network. In summary, this study advocates for the adoption of a DBS network with an LPN as a more suitable solution for 6G deployment at the mmWave band. The implementation of such an architecture can effectively minimize radiation exposure while ensuring a robust and efficient 6G network performance, meeting the growing demands of modern wireless communication systems.  
Conceptualization, N.A.-F. and O.Y.A.; Methodology, N.A.-F. and O.Y.A.; Validation, N.A.-F. and O.Y.A.; Investigation, N.A.-F.; Data curation, N.A.-F.; Writing--review & editing, O.Y.A.; Supervision, O.Y.A. All authors have read and agreed to the published version of the manuscript.  
The authors would like to express their gratitude to The University of Salford for sponsoring and funding this research.  
Data Availability StatementData are contained within the article.  
The authors have no conflict of interest to declare.
# Implementation of a Demand-Side Management Solution for South Korea's Demand Response Program
## 3 Results  
Table 6 shows the DR benefit and reduction rate results for three contracted customers after participating in the DR program. The results show the money-saving and energy conservation using the DSMS. The data used in this paper was collected in two years (from December 2016 to November 2018) from selected customers in South Korea. The N company case is selected to demonstrate the result. In the case of N company, the contracted capacity is 10 MW, participating in the 20 h DR dispatch during the two years. As a result, the DR delivery rate which is an average 108.03% and the benefit from the DR participation which is 854,900,394 KRW (basic settlement benefit is 844,014,870 KRW and actual settlement money is 10,885,524 KRW) are occurring, and the savings in electricity cost over the two years is about 2,160,560 KRW \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Name**} & \multicolumn{3}{c}{**N Company**} \\ \hline \multirow{2}{*}{**Month**} & **Reduction** & **Reduction** & **Basic-Settlement** & **Performance-Settlement** & **DR Delivery** \\  & **Order** & **Duration Time** & **Benefit [KRW]** & **Benefit [KRW]** & **Rate [\%]** \\ \hline
2016.12 & O & 2 h & 51,862,200 & 2,224,080 & 121.65\% \\ \hline
2017.01 & X & - & 49,946,800 & - & - \\ \hline
2017.02 & X & - & 44,754,500 & - & - \\ \hline
2017.03 & O & 1 h & 37,678,400 & 1,129,080 & 121.44\% \\ \hline
2017.04 & X & - & 14,622,400 & - & - \\ \hline
2017.05 & X & - & 13,358,600 & - & - \\ \hline
2017.06 & X & - & 38,586,300 & - & - \\ \hline
2017.07 & O & 3 h & 53,221,870 & 2,429,646 & 95.30\% \\ \hline
2017.08 & X & - & 49,305,600 & - & - \\ \hline
2017.09 & X & - & 36,944,400 & - & - \\ \hline
2017.10 & X & - & 11,421,800 & - & - \\ \hline
2017.11 & O & 1 h & 23,884,000 & 878,970 & 105.14\% \\ \hline
2017.12 & X & - & 46,971,400 & - & - \\ \hline
2018.01 & O & 9 h & 57,631,200 & 1,056,280 & 108.17\% \\ \hline
2018.02 & O & 1 h & 42,493,600 & 1,094,716 & 114.69\% \\ \hline
2018.03 & X & - & 35,199,300 & - & - \\ \hline
2018.04 & X & - & 12,454,600 & - & - \\ \hline
2018.05 & X & - & 10,364,600 & - & - \\ \hline
2018.06 & O & 2 h & 33,007,200 & 993,782 & 106.06\% \\ \hline
2018.07 & X & - & 58,036,400 & - & - \\ \hline
2018.08 & X & - & 53,548,600 & - & - \\ \hline
2018.09 & O & 1 h & 31,508,300 & 1,078,970 & 104.44\% \\ \hline
2018.10 & X & - & 10,034,800 & - & - \\ \hline
2018.11 & X & - & 27,178,000 & - & - \\ \hline  & Total & & 844,014,870 & 10,885,524 & - \\ \hline  & **Name** & & & **Provincial Government** \\ \hline
2016.12 & O & 2 h & 5,186,220 & 211,700 & 114.32\% \\ \hline
2017.01 & X & - & 4,994,680 & - & - \\ \hline
2017.02 & X & - & 4,475,450 & - & - \\ \hline
2017.03 & O & 1 h & 3,767,840 & 99,475 & 105.72\% \\ \hline
2017.04 & X & - & 1,462,240 & - & - \\ \hline
2017.05 & X & - & 1,335,860 & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Participation result of annual demand response market.