esa-sceva/satcom-chunk-collection huggingface dataset:
# Estimating the Human Error Probability during Lifeboat Drills
## 4 Discussion
### Limitations of the Current Research  
This research, however, is subject to several limitations. The estimated HEPs are based on expert judgement and not on empirical data, which could affect their reliability. However, as already stated, the data on lifeboat accidents are scarce, and so it was decided to use the SLIM, which relies heavily on expert judgement. This reliance may introduce biases and limit the generalisability of the findings. The study's context is limited to master mariners from one country, potentially overlooking variations in training practises, equipment, and regulatory environments in other regions. Nonetheless, it has to be mentioned that the master mariners participating in this research sail on international shipping companies and work with multinational crews. Furthermore, the experts involved are all master mariners, which, while valuable, represent a relatively homogenous group. However, the master mariners are most familiar with the problems of accomplishing the tasks for successfully performing lifeboat drills. In addition, the SLIM assumes independence among the HEPs of different tasks within the lifeboat drill. This assumption may oversimplify real-world scenarios where dependencies among tasks exist due to shared PSFs. Therefore, the authors introduced the BN-SLIM to address this issue. Also, the absence of a larger empirical dataset for validation weakens the robustness of the conclusions. This research focuses specifically on gravity lifeboats, excluding free-fall lifeboats. This limitation restricts the findings to other types of lifeboats, which might have different operational challenges and error probabilities. This study does not account for variations in ship types, crew experience levels, or environmental conditions, which could affect the generalisability of findings.
# Multi-Stage Quantum Secure Direct Communication Using Secure Shared Authentication Key
## 5 Example of the Proposed Protocol  
Let us illustrate an example of the proposed protocol and describe the three parts of secure QSDC, which is the initial authentication procedure, message sharing procedure, and security phase procedure. The implementation of proposed SSAK has been tested using a simulation developed via Python programming language. Python is chosen due to its capabilities to simulate the quantum state in superposition via mathematical form. The pseudo-code of SSAK protocol is detailed in Algorithm 1.
## 3 Statistical Methods
### Goodness-of-Fit Test
#### 3.2.5 Gamma Distribution  
The gamma distribution is primarily used to model data such as waiting times until an event occurs [41]. It is also used in high-voltage fields to analyze data, such as the Weibull distribution [24]. However, it has the disadvantage of being less useful as a life distribution due to the fact that the shape of the failure rate function is more complex than the Weibull distribution. Nonetheless, there are often cases where it fits well with various lifetime data and is applied. The gamma distribution has shape parameter and scale parameter as its main parameters. The shape parameter indicates how many times an event occurs, and a larger value models the waiting time after more events. The scale parameter represents the average interval between events and is inversely related to the average speed at which events occur. Since this paper used gamma distribution to analyze the AC-BDS, the event was considered the BDS and the time was considered the size of the AC-BDS. The pdf of a gamma distribution with two parameters represents the probability that an event will occur at a specific time, while the cdf represents the total probability that an event will occur up to a given time. The pdf and cdf of the gamma distribution are expressed as follows [41]:  
\[D_{\text{G}}(x)=\frac{x^{\beta-1}}{\alpha^{\beta}\Gamma(\beta)}\text{exp} \left(-\frac{x}{\alpha}\right)\text{ for }x>0 \tag{17}\]  
\[F_{\text{G}}(x)=\frac{\gamma\left(\beta,\frac{x}{\alpha}\right)}{\Gamma(\beta)} \tag{18}\]  
where \(x\) is a variable representing the time at which an event will occur (used here as the AC-BDS), \(\beta\) is the shape parameter (the number of times AC breakdown will occur), \(\alpha\) is the scale parameter (the average interval between AC breakdowns), \(\gamma\left(\beta,\frac{x}{\alpha}\right)\) is the incomplete gamma function (the cumulative probability of BDS occurring until a specific AC breakdown), and \(\Gamma(\beta)\) is the gamma function (the overall probability of AC breakdown occurrence). For a more detailed understanding of the gamma distribution, readers are encouraged to refer to the relevant literature [41].  
Additionally, if a deeper understanding of the five distributions introduced in this section (normal, Weibull, log-normal, exponential, and gamma) is needed, readers are referenced to the relevant literature [42; 43].
## 3 Results and Discussion
### Molecular Docking Analysis
The side of chalcone **2d** with the A ring rests in the mostly hydrophobic zone created by Trp334, Phe331, and Phe330, while the side of the B ring points to a less hydrophobic area. Figure 4: Chalcone **2d** and its interactions with nearby residues. Figure 3: Gorge of 1 EVE depicting compounds **2d** (yellow) and **3d** (orange) position and the catalytic triad (red), central anionic site (CAS) (blue), and peripheral anionic site (PAS) (purple). The case for the best generated pose of flavone **3d** is very similar to its chalcone counterpart. Although a formal hydrogen bond is not detected by the Chimera software due to the angle between them, one oxygen of the nitro group is near the hydrogen of Tyr130, at 2.6 A, so a weak interaction among these atoms could be possible (Figure 5). The nitro group is near to Trp84, but the B ring appears to have a better interaction with this residue in form of \(\pi\)-\(\pi\) stacking at a distance of 4 A. The A ring has proximity to Tyr334, even though with the new ring formation the flavone **3d** has a more rigid position for the A ring, which now appears to have with its edge a perpendicular \(\pi\)-\(\pi\) interaction with Tyr334 at 3.6 A. The C ring is oriented to Phe330 with a distance among them of 4.4 A. As the chalcone **2d** did, the B ring with the nitro group of the flavone **3d** is oriented to the bottom of the gorge, in a less hydrophobic area, while the rest of the structure is towards the hydrophobic zone of Trp334, Phe331, and Phe330, as can be seen in Figure 6.  
Figure 5: Flavone **3d** and its interactions with nearby residues. Figure 6: Compounds **2d** (yellow) and **3d** (orange) at the gorge of TcAChE. The color of the surface of the receptor indicates hydrophilicity (blue) passing by white to red (hydrophobicity).
## 2 Materials and Methods
### Materials  
#### 2.2.1 Microalgal-Bacterial Granular Sludge  
In the study, M-BGS was pretreated with ultrasonics and then used as a substrate in AD. The M-BGS originated from the hybrid system of a photobioreactor and open raceway pond (H-PBR) with a working volume of 1.0 m\({}^{3}\). A single four-blade mechanical agitator was run at 30 rpm with a circulation flow rate of 0.5 m/s. Illumination during low-sunlight periods was provided by fluorescent lamps with narrowband phosphors. The H-PBR was capped with transparent, sunlight-permeable covers. The sunlight-permeable reactor surface (transparent covers) was approx. 2.6 m\({}^{2}\). The heating was provided by electrical heaters with a heating capacity of 1.0 kW. A liquid fraction of digested sewage sludge (LF-DSS) produced in a municipal wastewater treatment plant was used to grow the algal biomass in the H-PBR. The H-PBR was operated in a Municipal Wastewater Treatment Plant, \"Lyna\", in Olsztyn. Detailed characteristics of the construction and operation of the H-PBR, as well as the formation course of the M-BGS structure, are presented in the authors' previous publication [41].  
The M-BGS biomass was previously separated using a two-stage drum microsieve with mesh sizes of 10.0 \(\upmu\)m (first step of filtration) and 5.0 \(\upmu\)m (second step of filtration) (Hydrotech, Veolia Water Technologies, Warsaw, Poland). The thickened M-BGS biomass  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Variant** & **Sonication Time** & **Ultrasonic Energy** & **Sample Volume** & **Dry Mass** & **Volatile Solids** & **Specific Energy (E\({}_{\text{S}}\))** \\  & **[s]** & **[Wh]** & **[cm\({}^{3}\)]** & **[g]** & **[g]** & **[Wh/g VS]** \\ \hline V1 & 0 & - & & & & 0 \\ V2 & 50 & 5.55 & & & & 0.23 \\ V3 & 100 & 11.10 & 500 & 29.5 \(\pm\) 1.7 & 24.3 \(\pm\) 0.5 & 0.46 \\ V4 & 150 & 16.65 & & & & 0.69 \\ V5 & 200 & 22.20 & & & & 1.32 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The study organization and setup conditions during ultrasonic pretreatment of microalgal-bacterial granular sludge (M–BGS).  
was then pretreated using ultrasound and subsequently digested in anaerobic conditions. The characteristics of the M-BGS are presented in Table 2, while a microscopic image at x 100 magnification is shown in Figure 1.
# A Systematic Survey on 5G and 6G Security Considerations, Challenges, Trends, and Research Areas
## 3 5G Security Architecture: A Systematic Approach
### Core Network
#### 3.1.4. Vendor Trust
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{2}{c}{**Backhaul Network Security Considerations**[37]} \\ \hline
**Security Consideration** & **Explanation** \\ \hline  & Cryptography is a necessity in 5G networks to allow for more confidentiality and integrity in communications. Network availability can be affected negatively if these algorithms are not efficient and lightweight, however. The use of ASICs (Application-Specific Integrated Circuits) for these algorithms can improve the reliability and availability of the network [43]. Two principal concerns with using an ASIC are forward compatibility and equipment cost. Thus, FPGAs (Field-Programmable Gate Arrays) have emerged as a preferred intermediate solution for hardware-specific designs, since they can be reprogrammed when future standard updates are released. \\ \hline  & The inner workings of a network need to be tamper-proof and protected against \\  & leaking of sensitive information. This requires that no backdoors are \\  & incorporated into the network architecture during its design or implementation \\  & and that any devices storing secret keys are well guarded and cannot be \\  & easily accessed. \\ \hline  & The network must be able to reject forged requests to prevent illegitimate users \\  & from gaining access. Security Mode Control (SMC) and Radio Resource Control (RRC) replay attacks are common replay attacks that accomplish this. In SMC attacks, a NAS-SMC packet is replayed to impersonate a UE and reduce the security of a UE’s security context [44]. RRC replay attacks utilize a false base station, which receives UE RRC packets, informs the core network of a \\  & successful connection, and subsequently disconnects the UE, forcing it to \\  & attempt to re-acquire the RRC connection continually. If implemented \\  & successfully, this can deny the UE access to the 5G network [45]. For these \\  & attacks, the 5G Core needs to be able to identify and prevent these attacks to \\  & mitigate false users. \\ \hline  & A PUF is a physical identifier hardware algorithm built inside of a \\  & semiconductor material [46]. PUFs output a non-reversible random key that can \\  & be used for cryptography algorithms, authentication, and signatures. [47] \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._  
Figure 6: Network path through untrusted core.
## 5 Conclusions  
The high-frequency continuous monitoring of glacial lake dynamics is crucial for the understanding of their distribution, evolution, and the driving mechanism of rapid expansions. However, we currently have limited information of glacial lake dynamics at high frequency due to the low availability of data and lack of advanced techniques. Using time series of Sentinel-1A/1B SAR intensity data with a six-day interval, the new method introduced in this paper contributes toward improving the accuracy of glacial lake mapping and change detection at a high temporal resolution for various environmental studies and applications. This method uses a simple and systematic median composite technique to find the backscattering coefficient for enhancing the water separability, and meanwhile, to remove part of the shadow and dark surface noises, which are often the major causes of misclassification in the mapping of glacial lakes. The median composite values were used as the regularization term in a level-set function to smooth the segmentation boundaries and prevent the occurrence of small, isolated regions in the classification of an individual image. The manually digitized glacial lake outlines based on the GF-2 PMS imagery served as reference data to validate the glacial lake mapping results. Comparing with the SVM and ISODATA classification approach, the new method was shown to extract glacial lakes with the highest overall accuracy of 96.54%, particularly in mountainous areas where there were complex terrain conditions. This method was also shown to be effective for large glaciated areas in the southeastern Tibetan Plateau.  
[PERSON], [PERSON], and [PERSON] contributed the main idea and designed the experiments; [PERSON] performed the experiments and analyzed the SAR data; [PERSON] wrote the manuscript, which was then improved upon through the contribution of [PERSON], [PERSON], [PERSON], [PERSON] All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Key R&D Program of China (Grant No. 2017 YFE0100800), the International Partnership Program of the Chinese Academy of Sciences (Grant No. 131551 KYSB20160002/131211 KYSB20170046), and the APC was funded by the National Natural Science Foundation of China (41701481).  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Advances in Toughening Modification Methods for Epoxy Resins: A Comprehensive Review
## 6 Nanoparticle Toughening Method
### Hybrid Epoxy Nanocomposite
#### 6.3.4 Block Copolymer-Based Hybrid Epoxy Nanocomposites  
[PERSON] et al. [112] incorporated graphene-oxide-grafted triblock copolymer (GO-g-TBCP, particle size range: 6-11 nm) into epoxy resin and analyzed the mechanical properties of the composite. The results demonstrated that, for the 0.5 wt% GO-g-TBCP composite, the tensile strength and KIC increased by 32.7% and 397.9%, respectively, compared to pure epoxy resin. Additionally, the glass transition temperature (Tg) rose from 168 \({}^{\circ}\)C to over 185 \({}^{\circ}\)C.  
[PERSON] et al. [113] investigated the effects of incorporating a triblock copolymer (SBM) and carbon nanotubes (CNTs) into epoxy resin to enhance its fracture toughness. The results revealed that the composites exhibited significantly improved fracture toughness and energy absorption capabilities compared to the pure epoxy resin. Specifically, the addition of 5 phr SBM increased the fracture toughness by approximately 115%, while the composite containing only 0.25 phr CNTs showed a moderate enhancement of 14.5%. Interestingly, no significant synergistic effect was observed when SBM and CNTs were added simultaneously, which may be attributed to the increased plastic cavity size induced by CNTs, thereby diminishing the toughening contribution from SBM.
## 1 Introduction
Deep learning, with its ability to learn complex relationships, is particularly suited for flood inundation mapping due to the complexity and widespread availability of satellite data. However, deep learning models are often considered \"black boxes\", posing challenges regarding transparency and potential ethical biases, which can be addressed through the use of Explainable Artificial Intelligence (XAI). This study is focused on the application of XAI to flood inundation mapping, an area that is largely unexplored as far as the authors are aware, with only two existing works [9; 10]. This approach offers enhanced insight into the behavior of the proposed deep learning model, as well as how this is impacted by varying input-data types [11].
## 4 Results and Discussion
### Prediction Capability
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Part** & **Method** & **R** & \begin{tabular}{c} **RMSE** \\ **(kN)** \\ \end{tabular} & \begin{tabular}{c} **MAE** \\ **(kN)** \\ \end{tabular} & \begin{tabular}{c} **m\({}_{\text{error}}\) \\ **(kN)** \\ \end{tabular} & \begin{tabular}{c} **StD\({}_{\text{error}}\)** \\ **(kN)** \\ \end{tabular} & \
\begin{tabular}{c} **Slope** \\ **(kN)** \\ \end{tabular} &
\begin{tabular}{c} **Slope** \\ **Angle (\({}^{\circ}\))** \\ \end{tabular} & **Intercept** \\ \hline \multirow{3}{*}{Training} & ANFIS & 0.943 & 68.235 & 58.722 & 0.000 & 69.105 & 0.889 & 41.652 & 140.790 \\  & ANFIS-SA & 0.969 & 52.552 & 40.793 & 1.819 & 53.190 & 0.872 & 41.086 & 164.965 \\  & ANFIS-BBO & 0.926 & 77.692 & 60.437 & 4.441 & 78.553 & 0.837 & 39.944 & 211.516 \\ \hline \multirow{3}{*}{Testing} & ANFIS & 0.896 & 111.830 & 82.407 & \(-\)9.651 & 114.842 & 0.939 & 43.202 & 62.530 \\  & ANFIS-SA & 0.941 & 81.990 & 65.594 & 16.445 & 82.796 & 0.897 & 41.883 & 138.937 \\ \cline{1-1}  & ANFIS-BBO & 0.960 & 66.558 & 50.723 & 4.363 & 68.459 & 0.916 & 42.482 & 104.257 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary of prediction capability. For comparison purposes, the performance of the ANFIS-SA and ANFIS-BBO models compared with the initial ANFIS is also reported. Table 4 indicates the comparison of prediction models with respect to R, RMSE, MAE, and StDerror, respectively. The increase of performance index, denoted as _Inc_, was calculated as  
\[Inc=\frac{\left|W^{Hybrid}-W^{ANFIS}\right|}{W^{ANFIS}}\times 100, \tag{14}\]  
where \(W^{Hybrid}\) and \(W^{ANFIS}\) were the considered criterions using hybrid and ANFIS models, respectively. It was shown that the prediction capability was increased: by 5.03% and 7.15% in terms of R; by 26.68% and 40.48% in terms of RMSE; by 20.40% and 38.45% in terms of MAE; and by 27.90% and 40.39% in terms of StDerror when using the SA and BBO optimization techniques, respectively. The SA and BBO algorithms were thus potential for improving the prediction performance of the initial ANFIS model. However, the BBO technique yielded better results. As a conclusion, from the statistical and error analysis of the prediction capability, ANFIS-BBO was the best model for the prediction of the critical buckling load of Y-section columns under compression when accounting for initial geometric imperfections.
## 6 Results and Discussions
### Network Performance Behaviour in Normal Operations  
We first analyse the network performance under normal operations using periodical and random UDP and TCP traffic depicted in Figure 6, in which various types of requests happen under the same network, respectively. It is noted that TCP traffic tries to utilise the maximum bandwidth of the network, which leads to a higher packet loss ratio in the UDP traffic transmitting at the same time, because the tunnel deteriorates for UDP traffic.  
Even for TCP traffic itself, if a random TCP traffic comes with a periodical TCP traffic, they will have to share the bandwidth. This phenomenon is obvious in the first time slot of traffic which can be seen in Figure 6(c),d with DDoS off, as both of the TCP traffic begin to transmit in the first second. As the random TCP traffic already utilises over 80 Mbps resource, the periodic TCP traffic has to compromise and occupy much less bandwidth than normal. Meanwhile, the random UDP traffic also suffers from the lack of bandwidth resource. However, the network behaves normal, and the packet loss and bandwidth are as expected. 100% and 99% of TCP and UDP requests have been successfully transmitted to the destination, respectively. This is mainly due to the sufficient bandwidth and the small-sized IoT traffic that we emulated. We also run Wireshark, which is a network protocol analyser, on the RPis to monitor packet delivery rate. The packet delivery rate is the ratio of received packets to the total number of sent packets. This average rate of TCP packets is around 97%, and for UDP is 99%. A TCP or UDP request usually consists of multiple packets; we consider a request to be successfully delivered only if all the relevant packets are received. Although TCP packets have a lower delivery rate than UDP in the test, the TCP request is guaranteed to be sent to the destination, thanks to its re-attempt feature.
## 2 Literature Review
### Addressing Limitations in Crash Analysis Models  
Traditional statistical models like the Poisson and negative binomial models have served as the foundation of crash frequency analysis due to their simplicity and capacity to model count data. However, as the literature reveals, these models struggle with data complexities that are commonly encountered in crash datasets, such as overdispersion and zero-inflation. This is particularly problematic in datasets with high proportions of zero-crash observations, where traditional count models may yield biased or inaccurate estimates. Although various advanced statistical approaches, such as the negative binomial-Lindley, zero-inflated, and finite mixture models, have attempted to address these limitations, they often require intensive parameterization and complex estimation methods that can hinder interpretability and scalability.  
Machine learning models have been explored in recent studies to overcome some of these challenges by offering greater flexibility in capturing non-linear relationships and accommodating high-dimensional data. However, a key limitation of many machine learning approaches is the \"black box\" nature of their predictions, which can obscure insights into variables' importance and interactions, limiting their interpretability and, ultimately, their practical utility in policymaking and intervention planning.  
To address these gaps, this study introduces a machine learning-based hurdle model using CatBoost, a gradient-boosting algorithm that excels in handling categorical data and managing overfitting through ordered boosting techniques. This approach not only provides the flexibility required to model the complex relationships inherent in zero-inflated crash frequency data but also leverages SHAP to improve interpretability. This combination offers a novel, interpretable machine learning framework that builds upon the existing literature by enhancing accuracy in the prediction of crash frequencies while offering stakeholders detailed insights into variable contributions, which are essential for targeted road safety interventions.
## 3 IVR-Based SDO Streaming Distance Education System
### IVR-SDO Streaming Education System Architecture
#### 3.1.4 SDO Data Specification  
The SDO data, transmitted through the network in Figure 3, consist of SDO images, voices, and motion data of the avatar. The detailed data specifications are listed in Table 1. Figure 4 shows examples of 10 types of SDO atmospheric imaging assembly (AIA) ultraviolet wavelength images occupying most of the network bandwidth, and two types of illuminance/magnetism measurement images of the Helioseismic and Magnetic Imager (HMI).