esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Underwater Wireless Sensor Networks (UWSNs) obtains more attention due to their wide range of applications such as underwater oil field discovery, Tsunami monitoring systems, surveillance systems, and many more. In such a resource-constrained environment, sensors are more vulnerable to malicious attacks. Node authentication and secure communication is one of the vital issues in UWSNs. In this study, a secure and lightweight key management framework for UWSNs is proposed. The proposed framework includes key generation, key distribution, revocation, and authentication mechanisms along with lightweight implementation, and scalability. We use an elliptic curve-based algorithm for key distribution, and certificate revocation list (CRL) for key revocation. We also examine the performance of the proposed framework taking into account the amount of communication overhead as well as the level of security. The simulation results show that the proposed framework provides better security with less communication overhead compared to existing frameworks. This framework can be used for secure data communication in UWSNs, which has various applications in oceanography, environmental monitoring, and military operations.  
cryptography; electric curve cryptographic; key management; underwater wireless sensor networks; computer science +
Footnote †: journal:  
Article
## 4 Experiments and Results
### Learning Ability Assessment  
As pointed out in Algorithm 2, the learning process is performed on the sets \(\mathbb{F}\) and \(\mathbb{L}\) corresponding to the set of encoded sentences in the form of high dimensional vector and their corresponding labels, respectively. From these datasets, we determine training and test datasets. On the training dataset, we performed a sampling strategy known as \(k\)-folds (with \(k=10\)) in order to find the most reliable model that minimizes the overfitting. At this point, we assessed the yield of the resulting model based on the test dataset. This assessment is defined in terms of metrics, such as _accuracy_, _precision_, _recall_, and _F1-score_. Since a single iteration of the above process does not guarantee the overall performance of the model, we repeated it 100 times by changing random seed values, obtaining a statistical approximation to the real performance values, as it is illustrated in Figure 4.  
We analyzed the variability in the performance of our method using quartile summary statistics illustrated in Table 2. From the Interquartile Range (IQR), we can see that there are not large differences in the experiments for a particular metric, and Confidence Interval (CI) allows us to quantify how we expect the average performance to be. In general, the performance of our model was mostly kept inside acceptable range in all metrics.
## 1 Introduction
In fact, carotenoid accumulation depends on the level and quality of light ([PERSON], 2017), temperature ([PERSON] et al., 2018), nutrient availability as nitrogen ([PERSON] et al., 2018; [PERSON] et al., 2018), sulfur, iron, and manganese ([PERSON] et al., 2018), phosphorus ([PERSON] et al., 2018), salinity ([PERSON] et al., 2018; [PERSON] et al., 2018), etc. Stress conditions stimulate the production of reactive oxygen species (ROS), which could play the role of the second messenger for the expression of certain transcription factors, resulting in the accumulation of antioxidant molecules by cells ([PERSON] et al., 2018). These authors have suggested that the lipids of the chloroplast membrane are the major receptors of these stimulators. However, until now, this mechanism is still not well explained. _Dunaliella_ accumulates carotenoids in plastoglobulins in the interthylakoid space of the chloroplasts ([PERSON], 2018). It was assumed that plastglobules are derived from the disintegration of the eyespot ([PERSON] et al., 2018). These lipid globules contain mainly CGP (carotene globule protein), which is a lipid-associated protein of 38 kDa containing 37 proline residues ([PERSON] et al., 2018). This protein was demonstrated to be essential for maintaining the stability of plastglobules ([PERSON] et al., 2018). In this work, we investigated how simultaneous variations of salinity (NaCl), nitrogen concentration, and light intensity affect carotenoids accumulation in _Dunaliella_ sp. and the effect of these conditions on the transcriptional response of genes involved in their production. We also attempted to understand how this stress could affect LCYB molecule by generating its 3D structure.
# 5G-MEC Testbeds for V2X Applications
## 1 Introduction
### Organization of this Paper  
The paper is organized as follows. Section 2 presents some background information on 5G aspects and features, as well as the fundamentals of MEC, essential 5G use cases such as URLLC-classified V2X applications, and a global view of the 5G-MEC testbed for vehicular communication. Section 3 discusses the existing testbeds and implementations, where the surveyed testbeds are evaluated and classified into 5G-MEC, 5G-V2X, and 5G-MEC for V2X testbeds. Section 4 describes the usage complexity of the surveyed testbeds. Section 5 provides a summary of the tools used to build the testbed. Finally, Section 6 concludes the paper and addresses the unresolved issues. Figure 1 shows a map and layout of this paper. We present the most commonly used acronyms and their definitions at the end of this paper.
## 2 Data and Methodology
### IBI Selection Criterion
The total number of IBI events is 831 divided into 97/super storm, 36/intense  
Figure 1: A typical example of an EPB/IBI event observed by Swarm B on March 17, 2015. Black solid line, red dashed line, and blue solid line correspond to the IBI index, the IBI probability, and the \(n_{e}\) (cm\({}^{-3}\)) profile, respectively. storm, and 698/moderate storm. According to Table 2, both the two super storms observed in the current study demonstrate a large number of IBIs (97 events). Also, some of the moderate storms showed a larger occurrence rate of IBIs than during super storms. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Start Date** & & **Duration** & **Storm** & **Dst** & \\ \hline
**Year** & **Month** & **Day** & **(Days)** & **Type** & **(nT)** & **Number of IBIs** & **Storm Driver** \\ \hline
2015 & 3 & 17 & 9 & Super & \(-\)223 & 64 & CME \\
2015 & 6 & 21 & 11 & Super & \(-\)204 & 33 & CME \\
2014 & 2 & 18 & 5 & Intense & \(-\)119 & 1 & Weak CME \\
2015 & 12 & 19 & 5 & Intense & \(-\)155 & 15 & CME \\
2015 & 12 & 31 & 5 & Intense & \(-\)108 & 0 & CME \\
2016 & 10 & 12 & 8 & Intense & \(-\)104 & 19 & CME \\
2017 & 5 & 27 & 4 & Intense & \(-\)125 & 1 & CME \\
2017 & 9 & 7 & 5 & Intense & \(-\)142 & 10 & CME \\
2018 & 8 & 25 & 6 & Intense & \(-\)174 & 0 & CME \\
2014 & 2 & 23 & 4 & Moderate & \(-\)55 & 0 & CME \\
2014 & 2 & 26 & 4 & Moderate & \(-\)97 & 2 & CME \\
2014 & 4 & 29 & 6 & Moderate & \(-\)67 & 4 & Solar flare \\
2014 & 8 & 26 & 10 & Moderate & \(-\)79 & 8 & CME \\
2014 & 10 & 8 & 5 & Moderate & \(-\)51 & 101 & Negative IMF Bz \\
2014 & 11 & 9 & 19 & Moderate & \(-\)65 & 83 & CME \\
2014 & 12 & 23 & 2 & Moderate & \(-\)57 & 0 & CME \\
2014 & 4 & 11 & 5 & Moderate & \(-\)87 & 5 & Negative IMF Bz \\
2014 & 9 & 12 & 6 & Moderate & \(-\)88 & 23 & Double CME \\
2015 & 2 & 16 & 7 & Moderate & \(-\)64 & 107 & Solar wind streamwind \\
2015 & 4 & 15 & 6 & Moderate & \(-\)79 & 0 & CME \\
2015 & 7 & 10 & 6 & Moderate & \(-\)61 & 26 & Solar wind stream \\
2015 & 7 & 22 & 8 & Moderate & \(-\)63 & 4 & CME \\
2015 & 8 & 22 & 9 & Moderate & \(-\)92 & 5 & Solar wind stream of solar wind \\
2015 & 9 & 20 & 6 & Moderate & \(-\)75 & 1 & CME \\
2015 & 9 & 7 & 14 & Moderate & \(-\)98 & 1 & CME \\
2015 & 11 & 2 & 5 & Moderate & \(-\)60 & 26 & Solar wind stream \\
2015 & 11 & 6 & 10 & Moderate & \(-\)89 & 92 & CME \\
2015 & 1 & 7 & 9 & Moderate & \(-\)99 & 7 & Negative IMF Bz \\
2015 & 4 & 9 & 5 & Moderate & \(-\)75 & 7 & CME \\
2015 & 6 & 7 & 6 & Moderate & \(-\)73 & 6 & CIR \\
2015 & 7 & 4 & 7 & Moderate & \(-\)67 & 20 & CME \\
2015 & 8 & 15 & 7 & Moderate & \(-\)84 & 7 & CME \\
2016 & 2 & 2 & 3 & Moderate & \(-\)53 & 1 & CIR \\
2016 & 2 & 15 &
# Low-Carbon Logistics Distribution Vehicle Routing Optimization Based on INNC-GA
## 1 Introduction
[18] integrated the adaptive large neighborhood search algorithm with a genetic algorithm to create an Adaptive Large Neighborhood Search Genetic Algorithm (ALNSGA) for vehicle routing models in low-carbon cold chain logistics. [PERSON] [19] and [PERSON] [20] combined a genetic algorithm with a large-scale neighborhood search algorithm, introducing the concept of destruction and repair to prevent genetic algorithms from getting trapped in local optima easily. They successfully solved vehicle routing models for low-carbon logistics distribution and cold chain logistics distribution, respectively. Additionally, [PERSON] et al. [21] developed an improved genetic algorithm and formulated a dynamic vehicle routing model that considers the impact of carbon emissions, demonstrating the algorithm's effectiveness through simulation testing. These studies demonstrate the wide application of genetic algorithms in the field of vehicle path optimization, solving complex vehicle path optimization problems and providing useful references and insights for environmentally sustainable development and improving transport efficiency. In summary, although genetic algorithms are relatively well-established in the research for solving such problems, they still suffer from the problem of slow convergence. In addition, the existing research results on green vehicle path problems rarely have vehicle path objective models that consider time window constraints, vehicle loading constraints, and low carbon constraints in an integrated manner. Therefore, the purpose of this paper is to study the vehicle path model with the objective of minimizing the total cost of the vehicle fixed cost, transportation costs, and carbon emission costs, and to consider the effects of vehicle loading constraints and time window constraints on the total distribution cost to establish a low-carbon logistics and distribution model that considers the time window constraints. A further objective is to perform simulation operations on the model by designing a genetic algorithm that improves the minimum-cost-nearest-neighbor method, hoping to provide new ideas and new solutions for research in the fields of sustainable development and environmental protection, while providing logistics companies with more optimized and environmentally friendly distribution solutions, thus promoting the sustainable development of low-carbon logistics.
## 2 Current Research of Cyber-Physical Power System (CPPS) and Co-Simulation Platforms  
### Characteristics of CPPS  
In addition to traditional power system devices, a large number of distributed energy resources (DER) and diverse smart clients are accessed by the CPPS [16]. Moreover, various sensors and communication networks with high information sensitivity have been added to meet the new service requirements. As shown in Figure 1, the key units of CPPS (i.e., physical power grid, sensors,computing units, and control systems) are closely integrated through communication systems. Therefore, based on the data-driven principle, the analysis and handling of the original status information has gone through four stages: state awareness, real-time computation, scientific decision, and precise execution.  
The characteristics that differentiate CPPS from traditional power systems can be summarized as follows: (1) the large scope of information collection; (2) a wide combination of wired communication and wireless communication networks; (3) a large number of distributed computing equipment; and (4) the equipment directly connected and controlled by the control center.  
The main application of a CPPS is to make the best of strong computing ability and wide information through co-simulation service requirements mapped into the simulation network. All kinds of information can be collected and utilized on a larger scale to promote optimal decision-making. Therefore, the safety, reliability, and efficiency of the power system operation are improved [17]. The next two subsections will discuss current co-simulation methods in detail.
## 5 Conclusions  
This article proposes a spoofing-detection method based on angle comparison applied to a single rotating antenna. Based on MLE, the estimated value of IA-DOA is calculated using the CNR and CPSD of multiple epochs. Detection variables are established based on the predicted and estimated values of the IA-DOA. The influence of the antenna's parameters on the detection performance was analyzed through simulation. Finally, on-site experiments were conducted to validate and evaluate the proposed method. The results indicate that the method proposed in this paper can effectively achieve real-time detection of multi-agent spoofing. This method can be applied to spoofing detection for fixed station receivers.  
Compared with other spoofing-detection methods based on signal spatial correlation, this method only requires a single rotating antenna to achieve multi-agent spoofing detection. Compared with other detection methods based on a single rotating antenna, this method not only has higher detection performance but also achieves the detection of multi-agent spoofing.  
[PERSON] and [PERSON] wrote the main manuscript text. [PERSON] prepared Figures 1-9. [PERSON] and [PERSON] prepared Figures 10-14. [PERSON] prepared Tables 1-3. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The datasets generated during this study are available from the corresponding author upon request.  
The authors thank the Hunan matrix-sirc company for providing equipment and a venue for the experiment.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l l l} GNSS & Global Navigation Satellite System \\ IA-DOA & intersection angle between two directions of arrival \\ CNR & carrier-to-noise ratio \\ CPSD & carrier phase single difference \\ GLRT & generalized likelihood ratio testing \\ \end{tabular}
## 3 Sensor Data Security Estimator
### Proposed Security Metrics
#### 3.2.3 Legitimacy Probability of the Intrusion Detection System
There are four rates to assess the effectiveness of the evaluations performed by an individual neighbor, one for each of the four possible cases mentioned, namely:  
* True positive rate (\(\mathbf{P_{tp}}\)): This represents the fraction of malicious nodes for which this neighbor has generated an alarm. This rate also represents the probability that this neighbor generates an alarm (\(a+\)) for a malicious node (\(M+\)), _i.e._, probability \(P(a+|M+)=\frac{TP}{TP+FN}\);
* False positive rate (\(\mathbf{P_{fp}}\)): This represents the fraction of legitimate nodes for which this neighbor has generated an alarm. This rate also represents the probability that this neighbor generates an alarm (\(a+\)) for a legitimate node (\(M-\)), _i.e._, probability \(P(a+|M-)=\frac{FP}{TN+FP}\);
* False negative rate (\(\mathbf{P_{fn}}\)): This represents the fraction of malicious nodes for which this neighbor has not generated an alarm. This rate also represents the probability that this neighbor does not generate an alarm (\(a-\)) for a malicious node (\(M+\)), _i.e._, probability \(P(a-|M+)=\frac{FN}{TP+FN}\);* True negative rate (\(\mathbf{P_{tn}}\)): This represents the fraction of legitimate nodes for which this neighbor has not generated an alarm. This rate also represents the probability that this neighbor does not generate an alarm (\(a-\)) for a legitimate node (\(M-\)), _i.e._, probability \(P(a-|M-)=\frac{TN}{TN+FP}\). Notice that the true positive rate is the complement of the false negative rate (\(P_{tp}=P_{fn}^{C}\)). Likewise, the false positive rate is the complement of the true negative rate (\(P_{fp}=P_{tn}^{C}\)), that is:  
\[P_{tp}=1-P_{fn}\quad\text{e}\quad P_{fp}=1-P_{tn} \tag{12}\]  
Since the result of a global evaluation performed by the IDS is given by the combination of the results of individual evaluations performed by the neighbors of a suspected node, the hit and miss rates of a global evaluation depend on the amount of neighbors (\(N\)) and the consensus parameter (\(m\)). Thus, in a real WSN, one can estimate the hit and miss rates of global evaluations from the hit and miss rates of the individual neighbors, which were obtained by means of experiments in a controlled environment. Therefore, considering \(i\) as the number of neighbors that have generated an alarm for a suspected node, in order for a global alarm (\(A+\)) to be generated for a given node, it is necessary that at least \(m\) neighbors have generated an alarm for this node, _i.e._, \(m\leq i\leq N\). In this case, the \(N\) neighbors can be combined into generators and non-generators of an alarm in \(\binom{N}{i}\) different ways. Hence, if the suspected node is actually malicious (\(M+\)), then the \(i\) neighbors that have generated an alarm have produced a true positive (TP) outcome, and the \(N-i\) neighbors that have not generated an alarm have produced a false negative (FN) outcome.
## 4 Reliability Analysis
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**State Number** & **Condition** & **Engine** & **Clutch** & **Gearbox** & **System** \\ \hline
1 & Healthy system & 1 & 1 & 1 & 1 \\ \hline
2 & & 1 & 1 & 0 & 0 \\
3 & Single fault & 1 & 0 & 1 & 0 \\
4 & & 0 & 1 & 1 & 0 \\ \hline
5 & & 1 & 0 & 0 & 0 \\
6 & Dual fault & 0 & 1 & 0 & 0 \\
7 & & 0 & 0 & 1 & 0 \\ \hline
8 & Three faults & 0 & 0 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: States of the conventional system. Additionally, the reliability of the system in conventional, series, and parallel configurations has been calculated over a time horizon of t = 1000 h, according to the following equations:  
\[\mathrm{R}=\mathrm{e}^{\mathrm{\lambda t}}\;(\mathrm{component\; reliability}) \tag{5}\]  
\[\mathrm{R}_{\mathrm{series}}=\mathrm{R}_{1}\cdot\ldots\cdot\mathrm{R}_{\mathrm{n}}\;( \mathrm{reliability\;of\;a\;system\;made\;of\;n\;components\;in\;series}) \tag{6}\]  
\[\mathrm{R}_{\mathrm{parallel}}=1-\prod_{i=1}^{n}(1-\mathrm{R}_{\mathrm{i}}) \;(\mathrm{reliability\;of\;a\;system\;made\;of\;n\;components\;in\;parallel}) \tag{7}\]  
The results are given in Table 7, which reaffirms the advantage of hybridization. Both the power assist and series configurations increase the reliability by 33% with respect to the conventional power system. Note that the reliability analysis performed in this investigation does not take into account the replacement of the piston engine with a rotary one, because the data on the reliability of a Wankel engine in the scientific literature are limited and controversial. On the one hand, the Wankel engine has a very limited number of moving parts with respect to a piston configuration, and the study of [PERSON] et al. [38] demonstrated that this kind of engine is able to complete endurance testing. On the other hand, this kind of engine experiences trouble with the sealing of the trochoidal rotor and stress in the eccentric shaft [39].
# A Maturity Model for Trustworthy AI Software Development
## 3 A Maturity Model for Trustworthy AI Software
### Analysis of Assessment Results
#### 4.1.2 Correlation of Project Characteristics and Maturity Levels  
The relation between the overall maturity levels and project development periods is measured by Pearson correlation analysis. The relation between the overall maturity levels and the number of developers is also measured. Both measured coefficient values are under 0.2 (R = 0.107 for periods, 0.125 for developers), indicating that the development periods and the number of developers are not much related to the maturity levels (Table 8, Figure 4).  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Process**} & \multicolumn{2}{c}{**Assessment Result (Capability Level)**} \\ \cline{2-3}  & **Developers** & **Assessors** \\ \hline System safety evaluation & 2.3 & 1.8 \\ \hline System safety preparedness & 2.1 & 2.0 \\ \hline AI infrastructure & 2.3 & 1.4 \\ \hline AI model operation management & 2.4 & 1.6 \\ \hline \hline \end{tabular}
\end{table}
Table 7: _Cont._  
Figure 3: Developers’ self-assessments vs. assessors’ assessments.  
We also check the correlations of the development periods and the number of developers with the maturity levels with respect to each process. As shown in Table 9, all processes have a Pearson coefficient of 0.444 or less, which means there is no special correlation.  
Figure 4: Maturity levels, development periods, and number of developers of 13 AI projects (Graph). Pearson coefficient R = 0.125 (Developers), R = 0.107 (Periods).  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Project**} & \multirow{2}{*}{**Pr1**} & \multirow{2}{*}{**Pr2**} & \multirow{2}{*}{**Pr3**} & \multirow{2}{*}{**Pr4**} & \multirow{2}{*}{**Pr5**} & \multirow{2}{*}{**Pr6**} & \multirow{2}{*}{**Pr7**} & \multirow{2}{*}{**Pr8**} & \multirow{2}{*}{**Pr9**  
To evaluate the correlation among 13 AI projects, t-distributed stochastic neighbor embedding (t-SNE) [35] is used. This compresses and visualizes the assessment results of 13 processes on the projects into two dimensions, as shown in Figure 5. The 13 AI projects can be divided into three groups:  
* Group 1 (Pr1, Pr8, Pr12, Pr13): These projects are research (pilot) projects for system modules rather than entire systems.
* Group 2 (Pr4, Pr5, Pr6, Pr10): These projects are all vision domain projects developed by overseas teams.
* Group 3 (Pr2, Pr3, Pr7, Pr9, Pr11): These projects have all been in commercial service for more than two years.
* We also confirm that commercial projects (Group 3, average maturity level 2.34) tend to have higher maturity levels than research projects (Group 1, average maturity level 1.35).