esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Background
### Challenges in Large-Scale File Encryption  
Advanced Encryption Standard (AES) [22] is widely used for file encryption due to its strong security and efficient symmetric-key structure. However, when applied to large-scale files in distributed or cloud systems, conventional AES-based implementations face several limitations. AES is typically deployed on a single node, where the entire file is loaded into memory and processed sequentially. As file sizes increase to tens or hundreds of gigabytes, this approach leads to I/O bottlenecks, excessive memory usage, and limited parallelism due to the lack of distributed processing [63; 64; 65].  
Figure 1: Quantum Key Distribution.  
However, AES is a block-based encryption scheme that supports subset-level processing through modes such as CTR [66] and CBC [67]. Even in these cases, several constraints arise, including key reuse management, initialization vector (IV) synchronization, and output ordering [68; 69; 70]. For example, in CBC mode, each block depends on the ciphertext of the previous block, making parallel processing infeasible. If the IV is not properly shared or is duplicated across nodes, ciphertext consistency will be compromised. While CTR mode enables parallel processing by treating block indices as nonces, strict key and IV scheduling is required to avoid offset collisions between nodes, which can limit parallelism. In addition, AES encryption is typically structured as a sequential process, where the tight coupling of key generation and data encryption limits parallelism in multi-node cloud systems [65; 71; 72; 73]. For example, when a master node generates an AES session key and distributes it to slave nodes before encryption can begin, the need to synchronize the encryption startup across all nodes introduces initialization overhead and delays, which directly affect overall system performance. To address this, offset-aware partitioning, parallel AES processing, and centralized key coordination are essential. Our proposed scheme is designed to satisfy these challenges with scalable and secure file encryption in distributed and cloud systems.
# Reinforcement Learning Based Relay Selection for Underwater Acoustic Cooperative Networks
## 3 RL Based Relay Selection Method for UWA Cooperative Networks
### Fast Reinforcement Learning Scheme  
The RL algorithms used in literature [30; 31; 32] initialize the Q-table to a full zero matrix during parameter initialization for UWA relay selection. For implementation of the proposed relay selection scheme in practical UWA networks, it is reasonable to pre-train the  
Figure 5: Flowchart of the SA-RL algorithm.  
SA-RL scheme before experimental implementation. The simulated or channel with similar settings, or the pretest data can be utilized for training. It helps to explore possible actions in certain state.  
Herein, a fast reinforcement learning scheme is proposed with the consideration of actual field implementations. It includes two stages: pre-training stage and on-line learning stage. In the parameter pre-training stage, the training data is obtained by simulation in a similar UWA channel scenario. Through the training, a pre-trained \(Q^{*}\) table is obtained. In the on-line learning stage, the Q-table matrix is initialized to \(Q^{*}\). In each iteration round, the agent will update Q-table by SA-RL algorithm. As the proposed SA-FRL scheme has explored possible actions in the first stage, it can better exploit the exploration results in the second stage for performance improvement.  
The pseudocode of the SA-FRL algorithm for UWA cooperative communication networks is shown as Algorithm 1.  
```
1. STAGE1: Pre-training stage
2. Initialize\(\sigma,\zeta,\epsilon,\lambda,K,s^{(0)},Q^{*}(s,a)=0,V^{*}(s)=0\).
3. for episode=1:Max_episode do
4. for\(t=1,2,3,\ldots,M\),do
5. Take action \(a^{(t)}\) according the latest Q-table.
6. The destination node obtain \(\gamma_{sd}^{(t)},\gamma_{nd}^{(t)},I^{(t)}\).
7. Calculate \(r^{(t)}\) via(16).
8. Calculate \(Q_{new}(s^{(t)},a^{(t)})\) via(18).
9. Calculate the difference \(V(s^{(t)})-Q_{new}(s^{(t)},a^{(t)})\).
10. if \(V(s^{(t)})<Q_{new}(s^{(t)},a^{(t)})\)
11. \(Q(s^{(t)},a^{(t)})=Q_{new}(s^{(t)},a^{(t)})\).
12. else
13. \(p=\exp(-(V(s^{(t)})-Q_{new}(s^{(t)},a^{(t)}))/K\varepsilon)\).
14. \(n=\) rand.
15. if \(p<n\)
16. \(Q(s^{(t)},a^{(t)})=Q(s^{(t)},a^{(t)})\).
17. else
18. \(Q(s^{(t)},a^{(t)})=Q_{new}(s^{(t)},a^{(t)})\).
19. end if
20. end if
21. Update \(V(s^{(t)})\) via(19).
22. \(s^{(t+1)}=[\gamma_{sd}^{(t)},\gamma_{rd}^{(t)},I^{(t)}]\).
23. if \(t\geq M\)
24. go to 28.
25. else
26. \(\epsilon_{t+1}=\lambda\times\epsilon_{t}\).
27. end if
28. endfor
29. endfor
30. Save \(Q^{*}\);
31. STAGE2: On-line RL stage
32. Initialize \(Q(s,a)=Q^{*},V(s)=0\).
33. for episode=1:Max_episode do
34. for\(t=1,2,3,\ldots,T\),do
35. Select actions \(a^{(t)}\) by \(\epsilon\) strategies.
36. Repeat 7-27.
37. endfor
38. endfor
```  
**Algorithm 1**SA-FRL algorithm.
# Soy Isoflavones and PCOS: Role in Hormonal and Metabolic Mechanisms
## 5 Limitations and Future Research  
Given the prevalence of PCOS in the population of reproductive-age women and the current state of knowledge, which is often based on divergent research findings regarding the role of soy isoflav, this narrative review provides a comprehensive summary of the available literature on this topic. In addition, it has the merit of considering the interaction between the microbiota and the metabolism of soy isoflav, which is an important addition to previous studies and may point to new directions for research. However, the limitations of the present study must be taken into account. In addition to the limitations due to the heterogeneity of the available studies, the very nature of the narrative review also entails certain methodological limitations--primarily the possibility of subjective selection and interpretation of the literature, despite efforts to keep the approach as objective as possible. Although most of the scientific studies cited in this narrative review suggest that soy isoflav have a beneficial effect on endocrine, metabolic, and gut microbiota-related parameters in women with PCOS, it is worth discussing contradictory or inconclusive results for the sake of objectivity. [PERSON] et al., [PERSON] et al., and [PERSON] et al. found no statistically significant differences in hormonal profiles between groups of women taking isoflav and control groups [15; 16; 132]. In a study by [PERSON] and [PERSON], improvements in certain hormonal parameters were observed; however, these differences were not significant after statistical correction [14]. Divergent results were also reported in terms of metabolic parameters. While [PERSON] et al. showed no effect of genistein on IR or glycaemia, [PERSON] and [PERSON] observed a significant reduction in IR, a decrease in triglycerides, and an improvement in biomarkers of oxidative stress. However, they found no significant changes in total cholesterol, LDL cholesterol, HDL cholesterol, or inflammatory markers [14; 16]. Regarding the gut microbiota, [PERSON] et al. and [PERSON] et al. demonstrated in preclinical studies that soy isoflav alone had limited effects on gut microbiota and PCOS symptoms. They also showed that the therapeutic effects were
## 4 Discussion
#### Limitations and Further Studies  
A limitation of this study regards the posture of the human models which were in a standing position with arms by their side. This fact did not reflect reality as for all the three simulated scenarios, the smartphone should be handheld with the arms being bent.  
The absolute value of the local SAR is strongly dependent on the relative position of the antenna with respect to the human model, so small (millimeter) variations in the position of the antenna will result in significant changes in the SAR.  
We kept the same antenna-head relative position for all models but, due to the different head shape of the models, the angles of incidences of RF radiation on the skin were probably different. Further studies should be performed to clarify this point.  
On the other hand, since many variables determine the SAR during realistic usages, a generalization to human populations under real-world exposure conditions is very complex.
## 5 Discussions
### Findings and Contributions
#### 5.1.3 The Adaptive Task Scheduling for Efficiency  
This study highlights the importance of wireless transmission modules in power consumption, but it does not consider the presence of sensor modules. The energy require  
Figure 8: The power source estimation of the proposed system.  
ments of sensor modules significantly influence overall power consumption. Efficient task scheduling is crucial for reducing power consumption in IoT devices, as it minimizes idle time and optimizes task execution. In disaster monitoring systems, a balance must be achieved between real-time data availability and power consumption efficiency. Frequent transmission intervals require more energy, while extended intervals reduce power consumption and prolong battery life. The interval choice should be tailored to the specific application, with longer intervals being more appropriate during steady periods. Adaptive task scheduling dynamically adjusts transmission intervals based on the situation's urgency, ensuring the system remains responsive and energy-efficient, extending the device's lifespan. Longer transmission intervals substantially reduce power consumption, with deep sleep being the most energy-efficient solution. In disaster monitoring, extended time intervals may be deemed appropriate during stable periods when quick updates are not necessary. An adaptive scheduling system can close this disparity by modifying transmission intervals according to the monitored event's urgency level. During disasters, when real-time data are crucial, shorter intervals may be used, whereas longer intervals may be used during periods of stability to maximize energy conservation.
## 2 Architecture and Operational Analysis
### Queue-Length Models  
Based on the periodic offset characteristics, three queue-length models are presented: (1) model for the queue length of vehicles that are delayed from the previous cycle, (2) model for the queue length of the vehicles arriving ahead of the schedule in the current cycle, and (3) model for the maximum queue length in the current cycle.
## 5 Research Results
### Prediction Results of Multilayer Perceptron (MLP) and Radial Basis Function (RBF) Models
This indicates that the model performs well, providing a significant \"boost\" for a relatively small fraction of the ranked data. Bearing in mind that in terms of all model performances, the multilayered perceptron gave better prediction and classification results, Table 7 indicates the importance of the assessment of independent variables in the given formed model. This Table includes all the values of independent variables whose importance and normalized importance (NI) are ranked from the highest to the lowest. The conducted analysis indicates that the average mileage for commuting has the highest indication of all observed independent variables (NI = 100.0%). The other variables with the highest values were the frequency of using motorcycles for commuting trips (NI = 97.0%) but also for other purposes (NI = 85.3%), as well as the average mileage for trips with other purposes (NI = 83.2%). Among other things, among the influential variables, the model highlighted the respondents' attitude about the pollution of Belgrade by emissions from road vehicles (NI = 73.8%). Also, when it came to socio-demographic and economic characteristics, the age of the respondents (NI = 72.6%) and the level of education (NI = 71.7%) were highlighted as significant variables when accepting the use of electric bicycles compared to average monthly personal income (NI = 58.8%) and employment (NI = 45.5%). When it comes to the reasons for not using electric bicycles so far, the model highlighted the unfavorable type of terrain (NI = 70.6%) and the (un)safety of users when moving (NI = 69.5%) compared to the price of e-bikes (NI = 60.7%) and not by the existence of infrastructure (NI = 44.2%). Among the least significant variables that can influence the responses of e-bikes, the model highlights the frequency of walking for commuting (NI = 32.3%), the age of the user (NI = 32.6%), and the lack of a sufficient number of parking spaces for e-bikes (NI = 33.1%). Figure 6: Presentation of the correct classifications obtained by the model and measurement of its classification performance on part of the population: (**a**) lift curve of multilayer perceptron model and (**b**) lift curve of radial basis function model.
# Study on the Influence of Deep Soil Liquefaction on the Seismic Response of Subway Stations
## 2 The Numerical Modeling  
This article focuses on the seismic response of subway stations when the embedment depth of the station's base slab reaches 20 m, and yet a liquefiable soil layer still exists beneath the base slab. The main research approach is as follows:  
(1) The soil layer above the station's base slab is simplified as a non-liquefiable layer, while the layer beneath is simplified as a liquefiable layer.  
(2) The HS constitutive model is adopted for the non-liquefiable soil layer, the UBC3D-PLM constitutive model for the liquefiable layer, and an elastic constitutive model for the station structure; material parameters are selected based on the site investigation data.  
(3) Two conditions, liquefied and non-liquefied, are established. The impact of the liquefiable soil layer on the subway station's seismic response is analyzed by inputting the design earthquake (E2 action) and the high-level earthquake (E3 action).
# Fault Sensing Using Fractal Dimension and Wavelet
## 7 Conclusions  
The IFBD algorithm was developed to enlarge the distances between 10 classes of short circuit faults by using the improved fractal dimension feature extracted from the three-phase currents for the first stage of fault recognition. K-transform and wavelet analysis were then used to establish the relationship between the modulus value and the fault class, and the DMWC recognition algorithm was developed. The IFBD algorithm and the DMWC algorithm were then combined to produce the FS method. Finally, the test system was utilized with the LabVIEW platform. The FS method was experimentally proven to effectively recognize all 10 classes of short circuit faults. The FS method was also not influenced by the parameters of the initial angle, transient resistance, and the fault distance. Compared with the DMWC algorithm, the FS method improved the recognition accuracy from 80.3% to 96.3% in the case of varied fault resistances, and improved the recognition accuracy from 87.8% to 98.0% in the case of varied fault distances.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Initial Angle** & **Fault Resistance** & **Recognition by DMWC** & **Recognition by FS** \\ \hline
0\({}^{\circ}\) & 30 \(\Omega\) & 91\% & 97\% \\
0\({}^{\circ}\) & 150 \(\Omega\) & 86\% & 96\% \\
30\({}^{\circ}\) & 30 \(\Omega\) & 88\% & 95\% \\
30\({}^{\circ}\) & 150 \(\Omega\) & 79\% & 93\% \\
90\({}^{\circ}\) & 30 \(\Omega\) & 75\% & 98\% \\
90\({}^{\circ}\) & 150 \(\Omega\) & 72\% & 98\% \\
90\({}^{\circ}\) & 300 \(\Omega\) & 71\% & 97\% \\ Mean value & 80.3\% & 96.3\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of DMWC and FS with different fault resistances.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Initial Angle** & **Fault Distance** & **Recognition Results by DMWC** & **Recognition Results by FS** \\ \hline
0\({}^{\circ}\) & 0.8 km & 96\% & 99\% \\
0\({}^{\circ}\) & 8 km & 95\% & 99\% \\
0\({}^{\circ}\) & 30 km & 92\% & 98\% \\
90\({}^{\circ}\) & 0.8 km & 85\% & 98\% \\
90\({}^{\circ}\) & 8 km & 81\% & 97\% \\
90\({}^{\circ}\) & 30 km & 78\% & 97\% \\ Mean value & 87.8\% & 98.0\% \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparison of DMWC and FS with different fault distances.  
**Acknowledgments:** This research was sponsored by the Natural Science Foundation of China (51405381), the Key Scientific and Technological Project of Shaanxi Province (2016 GY-040), and the Science Foundation of Xi'an University of Science and Technology (104-6319900001).  
**Author Contributions:** [PERSON] conceived and designed the experiments; [PERSON] and [PERSON] performed the experiments; [PERSON] and [PERSON] and [PERSON] analyzed the data; [PERSON] and [PERSON] and [PERSON] wrote the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# A Performance Analysis of Internet of Things Networking Protocols: Evaluating MQTT, CoAP, OPC UA
## 4 Experimental Environment
### IoT Testbed  
The COPELABS IoT Testbed [31] comprises, among others, 20 Android smartphones; 2 dedicated machines to support IoT gateways and brokers; 6 Raspberry Pi devices, some of which are attached to temperature/luminosity sensors.  
Figure 2 provides a simplified illustration of the testbed, where field devices are connected via wireless to an IoT gateway device. The local testbed is connected both to the Internet and to the experimental NDN worldwide testbed, thus facilitating experimentation of IP-based approaches and of NDN solutions. Further information on the testbed can be found in [31].  
The equipment available in the testbed and which has been used in the experiments is summarized in Table 2, which describes aspects such as equipment type and model; CPU, RAM, storage and NIC details.  
Moreover, the OS considered in the testbed have been derived from an analysis on available OSs for IoT which is condensed in Table 3. The table shows aspects such as differences in memory usage (RAM and ROM); CPU processing; supported network stack; energy saving options; whether or not multi-threading is supported, and year of development.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Type** & **Model** & **CPU** & **RAM** & **Storage** & **Network** \\ \hline \multirow{4}{*}{**IoTClient1**} & Laptop Toshiba & Genuine Intel\({}^{\text{\text{\text  
The different operating systems are being adapted to support resource-constrained devices. For this case, Contiki, RIoT and TinyOS provide the best support, based on different ports. In terms of memory consumption (RAM and ROM), Contiki, RIoT, FreeRTOS, and OpenWSN can work in extremely limited devices.  
In regards to the supported network stacks, Contiki comes with the uIP TCP/IP stack, as well as with Rime, a set of lightweight networking protocols designed for low-power wireless networks ([[http://contiki.sourceforge.net/docs/2.6/a01793.html](http://contiki.sourceforge.net/docs/2.6/a01793.html)]([http://contiki.sourceforge.net/docs/2.6/a01793.html](http://contiki.sourceforge.net/docs/2.6/a01793.html)) (accessed on 15 May 2021)).  
RIoT supports different network stacks, such as 6 LowPAN and the _Generic Network Stack (GNRC)_. More importantly, RIoT supports an ICN stack, CCN-lite thus providing good support to explore new networking paradigms. FreeRTOS integrates a specific network stack, FreeRTOS+TCP which is scalable, open source and thread-safe. TinyOS provides support for 6 LowPAN/RPL IPv6, while Raspbian provides support for 6 LowPAN, IPv4/v6, and CCN-lite. In terms of energy saving options, RIoT and Raspbian already integrate options to support better energetic efficiency. As also shown, only RIoT provides support for real-time applications. Multi-threading is supported in all OSs. Out of the several OSes, based on established requirements and also on the features of each OS, the choice of OS went for Raspbian, and RIoT.
# Investigation of Phishing Susceptibility with Explainable Artificial Intelligence
## 2 Related Literature
### ML and XAI in Phishing Study
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Authors** & **Year** & **Method** & **Description** \\ \hline [PERSON] et al. (2019) & 2016 & K-mean clustering & Using clustering method to identify user segments with high susceptibility, focusing on perceptions, demographics, and website traversal behavior. \\ \hline [PERSON] et al. (2019) & 2022 & Logistic regression, boosting, and support vector machine & Using various supervised learning methods to identify the relation between demographics, personality, knowledge experience, security behavior, cognitive processes, and susceptibility. \\ \hline [PERSON] et al. (2019) & 2022 & Hybrid LSTM and LightGBM & Combining static and dynamic features, together with a hybrid algorithm to predict phishing susceptibility. \\ \hline [PERSON] et al. (2019) & 2022 & C-GAN & Investigate the relationship between phishing susceptibility and personality traits using C-GAN \\ \hline [PERSON] et al. (2019) & 2022 & Multi-armed bandit & Using a restless multi-armed bandit framework to strategically target users for intervention in phishing email detection. \\ \hline [PERSON] and [PERSON] (2020) & 2020 & Max margin object detection & Employing a histogram of oriented gradients and a max-margin object detector to localize and classify brand logos in phishing web page screenshots. \\ \hline [PERSON] et al. (2019) & 2015 & SVM & Utilizing SVM for logo extraction and Google image search for identity verification. \\ \hline [PERSON] et al. (2020) & 2022 & Random forest & Developed a logo-based phishing detection mechanism using hue value distribution as a feature. \\ \hline [PERSON] et al. (2020) & 2022 & CNN and RNN & Developed three multi-scale semantic deep fusion networks using URLs to identify phishing websites. \\ \hline [PERSON] et al. (2020) & 2021 & Extreme learning machine & Proposed a non-inverse matrix extreme learning machine for phishing website detection, together with denoising autoencoder and adaptive synthetic sampling. \\ \hline [PERSON] et al. (2019) & 2019 & Random forest & Developed a real-time anti-phishing system using a random forest classifier with NLP-based features. \\ \hline [PERSON] et al. (2019) & 2014 & Random forest & Using random forest to classify phishing attacks with hand-crafted URL-based features. \\ \hline [PERSON] et al. (2020) & 2020 & GAN & Utilizing a generative model to generate URL-based phishing examples that can deceive Blackbox detection models \\ \hline [PERSON] et al. (2020) & 2020 & CNN & Developed a 1D CNN-based detection model using website URLs as features. \\ \hline [PERSON] et al. (2020) & 2019 & Recurrent CNN & Developed a phishing email detection model based on recurrent CNN with multilevel vectors and attention mechanisms. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of the ML and XAI-based phishing studies.