esa-sceva/satcom-chunk-collection huggingface dataset:
# Comparison of ML/DL Approaches for Detecting DDoS Attacks in SDN
## 3 Method
### Dataset (DS)  
For network security and intrusion detection, we utilized the CICIDS2017 dataset, which was developed by the Canadian Institute for Cybersecurity (CIC). This dataset comprises 79 features and covers 6 distinct attack methods. However, in this study, we only used data for one day (Tuesday) that included both normal activity and attacks, totaling 11 G of traffic. This attack can be classified as a DDoS assault, which renders the network unusable for legitimate purposes due to system overload [17]. Moreover, we employed the CIC-DDoS2019 public dataset, which contains the latest DDoS attacks and reflects actual real-world statistics. This dataset includes 84 features and encompasses 12 different attack methods, along with the outcomes of network traffic analysis. The training process was conducted using Jupyter Notebook 6.4.12 and Python 8.4.0 on a Windows 10 laptop equipped with an Intel(R) Core(TM) i5-4200U CPU @ 1.60 GHz 2.30 GHz, 12 GB RAM, and Intel HD Graphics 3000.
## 2 Materials and Methods
### Specim Preparation and Mixing Procedure  
The mix proportions of the prepared DSPB are listed in Table 2. The flowchart of the sample preparation and test method is shown in Figure 2. The mix design focuses on the effects of the proportions of HCDA and GBFS, and the dosage of PCE and the alkali activator SS. Samples with an HCDA and GBFS mass ratio of 90:10, PCE doping of 0.6%, and SS doping of 1.0% were selected as the reference group (Control). The selection of these experimental parameters was based on the results of the original pre-tests and the needs of practical applications in mining enterprises. All the excitants required for the experimental design were dissolved in water in advance to prepare a homogeneous alkaline solution. Before preparing the slurry, the mixing pot and blades were wiped with a damp cloth. First, the prepared SS solution, PCE, and water were poured into the mixing pot, then desulfurization ash and slag were added together and mixed well. The liquid-solid ratio of all the mixes was studied at a design value of 0.5. The paste was first stirred at low speed for 120 s, then the stirring was stopped and the paste was scraped from the paddles and walls into the pot during a rest period of 30 s. Finally, the paste was stirred at high speed for 120 s and at low speed for 60 s. All the samples were prepared under the same conditions of room temperature (about 23 \({}^{\circ}\)C).  
\begin{table}
\begin{tabular}{c c c c} \hline
**Item** & **HCDA to GBFS Ratio by Mass** & **PCE Addition (wt \%) *** & **SS Addition (wt \%) *** \\ \hline Control & 90:10 & 0.6\% & 1.0\% \\ \hline A1-HCDA95 & 95:5 & & \\ A2-HCDA85 & 85:15 & & \\ A3-HCDA80 & 80:20 & & \\ \hline B1-PCE0.3 & & 0.3\% & \\ B2-PCE1.2 & 90:10 & 1.2\% & 1.0\% \\ B3-PCE1.8 & & 1.8\% & \\ \hline C1-SS0 & & & / \\ C2-SS0.5 & 90:10 & 0.6\% & 0.5\% \\ C3-SS1.5 & & & 1.5\% \\ \hline \end{tabular}  
* PCE addition and activator SS addition are determined by the mass of the solid.  
\end{table}
Table 2: Component ratios of DSPB mixture preparation.  
Figure 1: XRD patterns of raw materials. (**a**) HCDA; (**b**) GBFS.
## 4 Case Study and Results
### Demand Shift  
Figures 7 and 8 show the percentage of maximum demand shift for the analysed three system configurations. The largest demand shift occurred during a period of high demand and low generation (load at 100% of peak demand and both wind generations are 0%), as shown in Figure 7. However, this scenario did not appear very often as shown in Figure 8. Results showed that a demand shift of 73.9% in the base case could be reduced to 49.3% (with the P2P configuration) and to 46.7% (with the MVDC configuration). Hence, the MVDC configuration presented the best performance. This particularly can also be observed in Figure 8, where maximum demand shift using P2P configuration was between 30%-45%, while it was reduced significantly to 25%-40%, when using the proposed MVDC configuration.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline \multirow{2}{*}{**Item**} & \multicolumn{3}{c}{**System Configurations**} \\ \cline{2-4}  & **Base Case** & **P2P** & **MVDC** \\ \hline System losses (MWh) & 484.90 & 546.22 & 494.22 \\ Demand shifted (MWh) & 2161.82 & 576.43 & 445.78 \\ Generation curtailed (MWh) & 2174.66 & 1125.83 & 620.28 \\ Operational cost (\$) & 88,611.79 & 25,425.09 & 16,658.25 \\ Total cost reduction & - & 71.3\% & 81.2\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: A comparison in minimising the operation cost.
# A Survey on IoT-Edge-Cloud Continuum Systems: Status, Challenges, Use Cases, and Open Issues
## 1 Introduction
### Contributions  
From the analysis of the previous subsection, it becomes apparent that related survey papers have dealt with a subset of the addressed challenges towards a unified IEC continuum system. For example, the works [26; 27] deal with security protection and classification of attacks, while the works [28; 30] deal with ML approaches. In the same context, the work [25] mainly emphasizes different architectural aspects of modern IEC approaches. Therefore, unlike other related surveys, the goal of this paper is to analyze all  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Survey Paper** & **Year** & **Contributions** \\ \hline
[25] & 2022 & Edge computing-driven IoT under various technological aspects \\ \hline
[26] & 2022 & Security considerations in edge-based IoT \\ \hline
[27] & 2021 & Security and privacy considerations in IEC continuum systems and classification of attacks \\ \hline
[28] & 2023 & Edge AI \\ \hline
[29] & 2022 & Analysis of different node categories in an edge-cloud-IoT environment \\ \hline
[30] & 2022 & Frameworks and simulation tools for the support of distributed intelligence in the edge to cloud environments \\ \hline
[31] & 2022 & Integration of edge computing with novel technologies \\ \hline
[32] & 2023 & Management and orchestration in edge computing IoT systems \\ \hline
[33] & 2022 & Recent advances in edge computing \\ \hline This work & - & Analysis of recent works in IEC continuum systems in the context of various challenges and key enabling technologies \\ \hline \hline \end{tabular}
\end{table}
Table 1: Indicative related survey papers.  
recent developments in the IEC continuum in the context of the addressed challenges, as described in the introductory part and also depicted in Figure 1. For this reason, the most important key enabling technologies towards an integrated IEC continuum are presented as well. This analysis is extremely important since the new era of computing systems and 6G networks will build upon a holistic integration of various cutting-edge technologies, taking into consideration, among others, the aforementioned technological challenges towards a unified access and management framework to support diverse and demanding applications. Therefore, the main contributions of our work can be summarized as follows:  
* Recent works in the IEC continuum are presented, with emphasis on the challenges they deal with, as well as on the supporting technologies.
* Basic limitations are identified, and open research directions are analyzed as well. This discussion on open issues takes into consideration the coexistence of IEC systems with next-generation broadband wireless networks.
* Indicative use cases are presented, where the synergy among IoT, edge, and cloud nodes is highlighted for optimum service deployment and user experience.
## 3 Results and Analysis
### Time-Space Coupling Characteristics of Temperature Field
For example, considering ventilation for 31 days with a thermal conductivity of 3 W/(m\(\cdot\)K), as the dimensionless radius increases from \(R=0.1\) to \(R=3\), the absolute errors of the characteristic curves are 0.042, 0.001, 0.023, and 0.012, respectively. Moreover, with an increase in dimensionless time, the absolute error of the characteristic curve initially increases and then decreases, with the maximum absolute error primarily occurring during ventilation from 31 days to 91 days. In conclusion, the fitting equation \(\Theta=\frac{-\exp\left(\text{A}\ast R^{\text{B}}\right)}{\text{O}\ast R\ast F \text{O}^{T}+\exp\left(\text{S}\ast R^{\text{O}}\right)}+1\) demonstrates a strong fitting effect regarding the spatial and temporal distribution of the surrounding rock temperature field under different thermal conductivity conditions. Furthermore, with an increase  
Figure 10: The characteristic curves of dimensionless temperature and dimensionless time at different thermal conductivity. (**a**) 0.1 \(R\); (**b**) 1 \(R\); (**c**) 2 \(R\); (**d**) 3 \(R\). in thermal conductivity, the fitting degree of the formula improves. Consequently, the equation can effectively describe the time-space coupling characteristics of the temperature field of the roadway-surrounding rock.
# Model-Driven Deep-Learning-Based Underwater Acoustic OTFS Channel Estimation
## 4 Numerical Results
### System Performance under Experimental UWA Channel
Comparing Figure 9 with Figure 10, the MSE performance of the DnCNN-based channel estimation method under the NOF channel is better than that under the KAU1 channel, as the KAU1 channel exhibits more complex CIR structure and significant channel variations than NOF. Figure 10: Comparison of MSE under KAU1 channel. Figure 9: Comparison of MSE under NOF channel. Figures 11 and 12 show the bit error rate (BER) of the uncoded OTFS system with various channel estimation and signal detection methods under NOF and KAU1 channels, respectively. The OTFS communication system using the DnCNN-based channel estimation method outperforms the system using the conventional OMP- and threshold-based channel estimation methods, regardless of whether zero-forcing (ZF) or linear minimum mean squared error (LMMSE) signal detection is employed. As shown in Figure 11, in the NOF channel scenario, when employing the LMMSE signal detection algorithm, the proposed DnCNN-based channel estimation method has 0.5 dB gain over the OMP-based channel estimation method and 0.7 dB SNR gain over the threshold-based channel estimation method at a BER of \(3\times 10^{-3}\). When employing ZF as the signal detection method, the DnCNN-based channel estimation method still has better performance than the conventional methods. Figure 11: Comparison of BER under NOF channel. Figure 12: Comparison of BER under KAU1 channel. As shown in Figure 12, in the KAU1 channel scenario, when employing LMMSE signal detection, the DnCNN-based channel estimation method has an SNR gain of about 1 dB and 0.5 dB compared with the OMP- and threshold-based channel estimation methods at a BER of \(3\times 10^{-3}\). In the UWA channel, the obvious channel expansion and the limited resolution of the DD grid result in fractional delay and Doppler frequency shift. The conventional OMP channel estimation algorithm, relying on compressed sensing, suffers from inaccurate estimation due to the degradation of channel information sparsity. In contrast, the proposed method incorporates a residual neural network to reconstruct and denoise the channel information, therefore enhancing the accuracy of the channel estimation and system reliability.
## 5 Discussion
### Limitations and Considerations  
The coverage of the SVI does not always include all slum areas, as many areas may not be represented in the SVI. This limitation arises because the SVI can only capture information along main roads [14], while small streets or alleys are often omitted or unavailable in Google SVI. This research utilizes IDW interpolation to generate SVI feature maps, so unified images can represent multiple locations. Slum areas are also detected in areas with alleys or small roads whose images are not available in Google SVI, but this is overcome by utilizing the 2021 Bandung City neighborhood road database. Further researchcould explore more accurate and advanced methods to address this issue of inconsistent coverage. Methods for future research could include techniques that specifically address missing data or additional data, such as street networks and city blocks that are available on OpenStreetMap to spatially improve SVI interpolation [28].  
The SVI featured map was prepared by collecting imagery through the Google Application Programming Interface (API), focusing on 15 urban villages with an area of more than 10 Ha. The area selection was based on the authority of the provincial and central governments in accordance with the applicable regulation, namely the Regulation of the Minister of Public Works and Housing Number 14/PRT/M/2018 of 2018 on Prevention and Quality Improvement of Slum Housing and Slum Settlements. The limitation of using the Google API meant that not all SVIs in Bandung City could be accessed. Therefore, programmed data collection focused on priority areas, resulting in 2904 images which were then combined with neighborhood road imagery from the Bandung City Government in 2021 as additional data.  
The obtained images and components covered only 33% of the total required SVIs, and the existing SVIs were integrated as layers in FCN-DK. Future research is expected to explore methods to generate feature maps that are able to capture more information from SVIs without being hampered by Google API access limitations. In addition, it is also expected to produce SVIs that are easier to integrate into FCN-DK without requiring many spatial layers [16].
## 3 Mobile Network Security Evolution and the Evolution of Threat Landscape
### Fifth-Generation Network Security
This is evinced by the comparative analysis illustrated in Table 3, where the evolutionary trajectory of mobile network encryption strategies across generations is elucidated. Table 3 offers an in-depth comparative analysis across mobile generations in terms of the nature of threats, typical threats encountered, role of encryption, and countermeasures adopted. In particular, the table highlights how threats have evolved from being predominantly hardware-based in 1G networks to increasingly sophisticated software-based attacks in 3G and 4G networks. Concurrently, encryption strategies have also undergone a \begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Aspects** & **1G Networks** & **2G Networks** & **3G and 4G Networks** \\ \hline \multirow{4}{*}{Nature of threats} & Predominantly hardware-based vulnerabilities, such as physical tampering and unauthorized access. & Transition from analog to digital led to software-based threats. & Sophisticated software-based threats, including IP-specific attacks, due to transition to all-IP networks. \\ \cline{2-4}  & Frequency interference, eavesdropping, and unauthorized physical access. & Data interception, message tampering, and software vulnerabilities, such as buffer overflows. & Mobile malware, IP spoofing, phishing, man-in-the-middle attacks, and application-layer vulnerabilities. \\ \hline \multirow{4}{*}{Role of encryption} & Minimal or absent; lack of standardized encryption protocols. & Introduction of encryption algorithms like A5/1 in GSM, albeit some were vulnerable. & Robust encryption standards such as AES and 3 DES; enhanced focus on data integrity and user authentication. \\ \hline \multirow{4}{*}{Countermeasures} & Physical security measures, such as locked cabinets and controlled access to network facilities. & Introduction of firewalls, intrusion detection systems (IDSs), and periodic security audits. & Multilayered security protocols, intrusion prevention systems (IPSs), real-time monitoring, and regular updates. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: 1G to 4G Security threats and encryption strategies [16; 17; 50; 76; 78; 79]. transformation: from minimal or absent standardized protocols in 1G to robust, multilayered encryption and security protocols in the latest generations. Countermeasures have similarly progressed, with a transition from rudimentary physical security approaches in 1G to real-time monitoring and multilayered security protocols in later generations.
# A Decision Support System for Crop Recommendation Using Machine Learning Classification Algorithms
## 4 Results and Discussion
### Solution to Research Question RQ3:  
The scores of different algorithms are generated and compared here. Figure 12 shows a box plot that compares the changes observed in the performance of algorithms. It is seen that the SGDC has good score compared to others after the SMOTE.  
The research question can be solved using the SMOTE to enhance the performance of crop recommendation classifiers. Our objective was to determine whether the oversampling technique contributes to enhanced model discrimination and overall classification performance in imbalanced datasets related to crop recommendation. Figure 13 shows the ROC AUC curve for comparison. It seems that it continuously improved model discrimination and overall classification performance in the context of imbalanced datasets related to crop recommendation.  
Figure 11: Bar chart and box plot for analyzing the classifiers using the accuracy rate.  
Figure 12: Comparison using a box plot before and after the SMOTE.
## 2 Second configuration: The Improved ADRC (IADRC)  
The IADRC consists of the following units:  
1. Conventional TD is given by Equation (43).
2. _fall_-based control law given by Equation (44).
3. A novel NHOESO, proposed as: \[\left\{\begin{array}{c}\dot{\xi}_{i,1}=\dot{\xi}_{i,2}+a_{i,1}\omega_{o,i}^ {1}\,\
ot\!\sigma_{i}\big{(}y_{i}-\dot{\xi}_{i,1}\big{)},\\ \dot{\xi}_{i,2}=\dot{\xi}_{i,3}+u_{i}^{*}+a_{i,2}\omega_{o,i}^{2}\,\
ot\!\sigma _{i}\big{(}y_{i}-\dot{\xi}_{i,1}\big{)},\\ \dot{\xi}_{i,3}=\dot{\xi}_{i,4}+a_{i,3}\omega_{o,i}^{3}\,\
ot\!\sigma_{i}\big{(} y_{i}-\dot{\xi}_{i,1}\big{)},\\ \dot{\xi}_{i,4}=a_{i,4}\omega_{o,i}^{4}\,\
ot\!\sigma_{i}\big{(}y_{i}-\dot{ \xi}_{i,1}\big{)},\ i\in\{1,2\}\end{array}\right.\] (46) where the vectors \(\big{(}\dot{\xi}_{i,1},\dot{\xi}_{i,2}\big{)}^{T}\) and \(i\in\{1,2\}\) are the observed model states, \(\dot{\xi}_{i,3}\) and \(i\in\{1,2\}\) are the estimated generalized disturbances, \(a_{i,s}\), \(i\in\{1,2\}\) and \(s\in\{1,\ 2,\ 3,\ 4\}\) are design parameters, and \(\omega_{o,i}\) is the bandwidth of the NHOESO of the \(i\)-th subsystem. The nonlinear function \(\
ot\!\sigma_{i}:\mathbb{R}\rightarrow\mathbb{R}\) is designed as in Reference [63]: \[\
ot\!\sigma_{i}(\epsilon_{i})=K_{i,\alpha}|\epsilon_{i}|^{a_{i}}sign(e_{i})+K_ {i,\beta}|\epsilon_{i}|^{b_{i}}e_{i}\] (47) with \(i\in\{1,2\}\), where \(K_{i,\alpha},K_{i,\beta},\alpha_{i}\), and \(\beta_{i}\) are positive design parameters and \(e_{i}\) is the estimation error.
# Improving End-To-End Latency Fairness Using a Reinforcement-Learning-Based Network Scheduler
## 2 Materials and Methods
### Reinforcement Learning Algorithms
Consequently, the expected cumulative future reward changes minimally, making it less prone to falling into local optimization. To use the Q-network learned so far for future Q-value calculations, \(\theta^{-}\) is periodically updated by copying the current \begin{table}
\begin{tabular}{c c} \hline \hline
**Symbol** & **Meaning** \\ \hline Q & Q-table or deep learning network \\ S, s & State \\ A, a & Action \\ R, r & Reward \\ T & Time \\ \(\alpha\) & Learning rate \\ \(\gamma\) & Discount factor \\ \(\theta\) & Deep learning network’s weights \\ Y & Target Q-value \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Symbols used in formulas. \(\theta\) parameter. The policy determines the action to be taken by obtaining the maximum Q-value using the Q-network with \(\theta^{-}\). DDQN showed more stable training than DQN by effectively reducing the overestimate problem, and therefore exhibited better performance than DQN [27]. Deep RL, an integration of RL algorithm and deep learning, requires a buffer to hold experiences. In the present learning method, experiences were randomly chosen from the buffer, and trained the deep RL afterwards. Prioritized experience replay (PER) is an experience replay buffer designed to learn by the choice of novel experiences rather than randomly chosen ones from the buffer [29]. PER obtains the TD-error for each sample and sets the priority for each sample. Equations (3) and (4) are used for calculating TD-error and priority in the DDQN model, respectively:  
\[\text{TD error}=|Y_{t}-Q(S_{t},A_{t},\theta)|, \tag{3}\]  
\[\text{priority}=(TD\;error+\epsilon)^{\beta} \tag{4}\]  
where \(\beta\) is a variable between 0-1 that determines the level at which priority is determined by _TD error_, and \(\epsilon\) is a very small constant added so that priority does not become 0. Deep RLs using PER showed better performance than cases where PER was not used because it selects high-priority samples to proceed with learning [29]. Practical domains that can be encountered in real environments, such as autonomous driving, robots, and games, are environments where multiple agents influence each other. RL in which multiple agents compete or cooperate to solve problems in such an environment is called multi-agent RL (MARL). Conversely, RL with one agent is called single-agent RL (SARL). The simplest MARL is independent Q-learning (IQL): a set of multiple Q-learning agents in the environment where each agent acts on its own policy independently based on its observed states [30]. IQL does not perform well when there are many agents or when there are various types of agents, but it works well in a simple environment.
## 4 Results Analysis
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Hypothesis** & **Path** & **Estimate** & **Post SD** & **(95\%) CI** & **Rhat** & **Result** \\ \hline
**H1** & LF\(\rightarrow\)PEU & 1.158 & 0.054 & [1.056, 1.267] & 1.005 & Supported \\
**H2** & LF\(\rightarrow\)WTCTU & 0.017 & 0.119 & [\(-\)0.217, 0.266] & 1.008 & Not Supported \\
**H3** & PF\(\rightarrow\)LF & 1.110 & 0.074 & [0.966, 1.254] & 1.002 & Supported \\
**H4** & EF\(\rightarrow\)PF & 1.238 & 0.143 & [0.993, 1.551] & 1.006 & Supported \\
**H5** & PEU\(\rightarrow\)PU & 1.037 & 0.037 & [0.967, 1.113] & 0.999 & Supported \\
**H6** & PEU\(\rightarrow\)WTCTU & 0.355 & 0.236 & [\(-\)0.128, 0.817] & 1.023 & Not Supported \\
**H7** & PEU\(\rightarrow\)SA & 1.072 & 0.037 & [1.001, 1.148] & 1.001 & Supported \\
**H8** & PU\(\rightarrow\)WTCTU & 0.271 & 0.091 & [0.085, 0.447] & 1.014 & Supported \\
**H9** & PE\(\rightarrow\)WTCTU & 0.223 & 0.056 & [0.116, 0.341] & 1.001 & Supported \\
**H10** & SA\(\rightarrow\)WTCTU & 0.530 & 0.112 & [0.314, 0.741] & 1.018 & Supported \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Results of Bayesian SEM path coefficient hypothesis testing. rect promoting effect on the willingness to continue using (WTCTU); (2) Environmental factors (EF) mainly indirectly affect WTCTU through the chain mediating path of \"EF\(\rightarrow\)PF\(\rightarrow\)LF\(\rightarrow\)PEU\(\rightarrow\)SA/PU\(\rightarrow\)WTCTU\", among which the path effect mediated by satisfaction (SA) is the strongest. (3) The effects of some intermediate paths (such as EF\(\rightarrow\)PF\(\rightarrow\)LF\(\rightarrow\)WTCTU) are not significant, indicating that the influence of environmental factors on WTCTU mainly depends on the complete intermediate chain rather than a single link. It is suggested that subsequent studies can deeply explore the boundary conditions of different mediating pathways, as well as the interaction between environmental factors and individual traits.