esa-sceva/satcom-chunk-collection huggingface dataset:
# Set-Based Group Search Optimizer for Stochastic Many-Objective Optimal Power Flow
###### Abstract  
The conventional optimal power flow (OPF) is confronted with challenges in tackling more than three objectives and the stochastic characteristics due to the uncertainty and intermittence of the RESs. However, there are few methods available that simultaneously address high-dimensional objective optimization and uncertainty handling. This paper proposes a set-based group search optimizer (SetGSO) to tackle the stochastic many-objective optimal power flow (MaOPF) of power systems penetrated with renewable energy sources. The proposed SetGSO depicts the original stochastic variables by set-based individuals under the evolutionary strategy of the basic GSO, without using repeated sampling or probabilistic information. Consequently, two metrics, hyper-volume and average imprecision, are introduced to transform the stochastic MaOPF into a deterministic bi-objective OPF, guaranteeing a much superior Pareto-optimal front. Finally, our method was evaluated on three modified bus systems containing renewable energy sources, and compared with the basic GSO using Monte Carlo sampling (GSO-MC) and a set-based genetic algorithm (SetGA) in solving the stochastic MaOPF. The numerical results demonstrate a saving of 90% of the computation time in the proposed SetGSO method compared to sampling-based approaches and it achieves improvements in both the hyper-volume and average imprecision indicators, with a maximum enhancement of approximately 30% and 7% compared to SetGA.  
stochastic many-objective optimization; set-based GSO algorithm; wind power; optimal power flow +
Footnote †: journal: Applied Sciences
## 4 Results
### Application to Real Biological Data
#### 4.2.2 Comparison of the Two CORE-Clustering Algorithms  
In order to compare the two proposed CORE-clustering algorithms described (Algorithms 2 and 3) we tested them on the yeast dataset with \(\tau=30\) and \(\xi=0.75\). We indeed empirically considered that CORE-clusters containing 30 to 59 variables are reasonable to regularize the problem, and that 0.75 is a threshold above which the absolute value of the correlation between two variables reasonably shows that their behavior is similar.  
The clustering obtained using the standard algorithm, rows 1 to 19 of Algorithm 2, is shown in Figure 2. In this figure, the represented clusters 1 to 6 have a coherence **c** (see Equation (3)) equals to \((0.79,0.73,0.76,0.68,0.79,0.82)\). Only the clusters 1, 3, 5 and 6 are then considered as CORE-clusters (rows 20 to 25 of Algorithm 2) and their representative variables are RV1 = _YER1900_, RV3 = _YLL026W_, RV5 = _YDL003W_ and RV6 = _YGL120C_. Computational time for the clustering was about 160 s on an Intel(R) Core(TM) i7-6700 HQ CPU at 2.60 GHz.  
The computations were much faster using the greedy algorithm Algorithm 3. It indeed required about 3 seconds. An amount of 11 CORE-clusters was found. To interpret this result, we computed the coherence \(c\) (see Equation (2)) between the 4 representative variables obtained using Algorithm 2 and the 11 obtained using Algorithm 3. Interestingly, Algorithm 3 selected _YLL026W_ = RV3 and _YDL003W_ = RV5. Other variables very close to RV1, RV5 and RV6 (with \(c>0.83\) i.e., higher that the highest \(\epsilon\) within the CORE-clusters) were also selected: _YHR219W_, _YLR103C_, _YLR276C_ and _YLR196W_. Results equal or close to those obtained with Algorithm 2 were then obtained. The representative variables _YDR418W_, _YML119W_, _YIL038C_, _YNL283C_ and _YGR167W_ were additionally found. In this experiment, Algorithm 3 therefore selected more representative variables and has then naturally a larger score (see Equation (4)) than by using Algorithm 2. However, it also obviously captured different representative variables that would be gathered in the same CORE-cluster using Algorithm 2. The two algorithms have therefore slightly different properties but lead to coherent results.
## 4 Results and Discussion
\begin{table}
\begin{tabular}{l l l} \hline \hline
**ID** & **Question** & \(\mathbf{p}\)**-Value**\({}^{a}\) \\ \hline A1 * & I believe that it is safe to ignore update warnings from computer & 0.010 \\  & software & \\ \hline A2 & I am aware of my role in keeping the school protected from & \textless{}0.001 \\  & potential cybercriminals & \\ \hline A5 * & I don’t have the right skills to be able to protect the school from & 0.016 \\  & cybercrime & \\ \hline A6 & I believe that personal information should not be revealed online, namely who I am, where I live or which school I attend & 0.002 \\ \hline A8 * & I think that reporting cybercrime is a waste of time & 0.002 \\ \hline A9 * & The Police lack the capacity to deal with cybercrime effectively & \textless{}0.001 \\ \hline A10 * & I believe that cybercriminals are more advanced than the people & 0.008 \\  & who are supposed to be protecting us & \\ \hline A11 * & I would download copyright material (images, documents, videos) & \textless{}0.001 \\ \hline A13 * & I worry that if I report a cyberattack to the Police it might damage the reputation of the school & \textless{}0.001 \\ \hline A15 & I am aware of the schools IT use policy and attempt to follow it & \textless{}0.001 \\ \hline A16 * & I would not know how to report a cyberattack if one happened & 0.008 \\ \hline A17 * & I don’t think that reporting a cyberattack launched from the school is my responsibility & 0.004 \\ \hline A18 * & I don’t pay attention to school material about the threats from cybercrime & \textless{}0.001 \\ \hline A19 & I am confident that I would be able to spot the signs of a cyberattack & 0.023 \\  & attack & \\ \hline A20 & I believe that, when inappropriate content appears online, I should ask for help from an adult & \textless{}0.001 \\ \hline A25 * & I think that I have the right to be always online, with access to & 0.008 \\  & all Internet services & \\ \hline B3 & Using the same password for multiple websites & 0.001 \\ \hline B4 & Using online storage systems to exchange and keep personal or sensitive information & 0.029 \\ \hline B5 & Entering payment information on websites that have no clear & 0.003 \\  & security information/certification & \\ \hline B9 & Disabling the anti-virus on my computer so that I can download information from websites & 0.005 \\ \hline B12 & Downloading digital media (music, films, games) from unlicensed sources & 0.017 \\ \hline B13 & Sharing my current location on social media & \textless{}0.001 \\ \hline B19 & Downloading data and material from websites on your computer without checking its authenticity & 0.034 \\ \hline \hline \end{tabular}  
* Items negatively worded were reverse-scored for further analysis;
* Mann-Whitney non-parametric test.  
\end{table} 
Table 4: Cybersecurity Attitudes and Behavior questions where significant differences were found between grades.
# A Survey on LoRaWAN Architecture, Protocol and Technologies
## 2 LoRa & LoRaWAN Technologies
### Security  
LoRaWAN has built-in security to protect network protocol and user data. Data through node-to-application and note-to-network-server are protected by AES 128 encryption by four components:  
* DevAddr: a 32 bit device identifier.
* AppEUI: an Application unique identifier which as IEEE UI64 address space.
* NwkSKey: a Network session key used to encrypt end-device to network-server communication.
* AppSKey: an Application session key (AES-128 key) used to protect application specific data.  
LoRaWAN supports two main end-device activation methods (i) Over-the-Air Activation (OTAA) and (ii) Activation by Personalization (ABP).
# A Study of Wind Shear Influences on the Aerodynamic Performances of a UAV Airfoil
## 3 Results and Discussion  
### Numerical Method Validation  
In order to verify the numerical method, numerical simulation of the NACA0012 airfoil is performed at a freestream condition with Mach number \(Ma=0.3\) and Reynolds number \(Re=4.0\times 10^{6}\). It should be noted that grid independence has been verified. The grid mesh size and distribution are carefully adjusted to ensure the numerical results are no longer changed due to the change of the grid. Because NACA0012 is a very common benchmark which has been discussed in many studies, grid independence will not be discussed in this paper. As a result, the numerical calculation results are consistent with the experimental results as shown in Figure 5. Compared with experimental results [26], the results show that the lift curve and airfoil surface pressure are in good agreement with experimental results in the literature. Therefore, the numerical simulation method and calculation grid of this paper meet the calculation requirements, the results of subsequent numerical calculations in this paper are credible and the conclusions obtained are reliable.
## 1 Introduction
\begin{table}
\begin{tabular}{l l} \hline \hline
**Abbreviation** & **Full Meaning** \\ \hline ER & Extended Reality \\ \hline FDMA & Frequency Division multiple Access \\ \hline FeMBB & Further Enhanced Mobile Broadband \\ \hline FL & Federated Learning \\ \hline GNN & Generative Neural Network \\ \hline GSM & Global System for Mobile Communication \\ \hline IRSs & Intelligent Reflecting Surfaces \\ \hline IoE & Internet of Everything \\ \hline IoNT & Internet Of Nano Things \\ \hline IoST & Internet of Space Things \\ \hline IoT & Internet of Things \\ \hline ITU-T & International Telecommunication Union-Telecommunication Standardization \\ \hline IRS & Intelligent Reflective Surfaces \\ \hline JTDPA & Joint Trajectory Design and Power Allocation \\ \hline k-NN & K-Nearest Neighbor \\ \hline KPIs & Key Performance Indicators \\ \hline LML & Lifelong Machine Learning \\ \hline LSTM & Long Short-Term Memory \\ \hline LTE & Long-Term Evolution \\ \hline MARL & Multi-Agent Reinforcement Learning \\ \hline MEC & Mobile Edge Computing \\ \hline MFRPO & Mean-field Trust Region Policy Optimization \\ \hline MIMO & Multiple-input Multiple Output \\ \hline ML & Machine Learning \\ \hline mMTC & Massive Machine-Type Communication \\ \hline mmWave & Millimeter-wave \\ \hline MR & Mixed Reality \\ \hline MTC & Machine Type Communication \\ \hline NFV & Network function virtualization \\ \hline NGMA & Next Generation Multiple Access \\ \hline NLOS & Non-Line-Of-Sight \\ \hline NOMA & Non-orthogonal Multiple Access \\ \hline OFDM & Orthogonal Frequency-division Multiplexing \\ \hline OWC & Optical Wireless Communication \\ \hline PCA & Principal Component Analysis \\ \hline PDS & Post Decision State \\ \hline PER & Prioritized Experience Replay \\ \hline PLS & Physical Layer Security \\ \hline QC & Quantum Computing \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._
## Results  
### Bioinformatics and Molecular Characteristics of OpWRKY3  
In this study, _OpWRKY3_ was cloned and functionally characterized from _O. pumila_. It owned a full-length open reading frame (ORF) of 1128 bp, encoding a protein with 375 amino acid residues, calculated molecular weight of 41.95 kDa, and isoelectric point of 5.77. BLASTp search (NCBI website) revealed that OpWRKY3 shared 55.53%, 54.85%, 53.77%, and 53.51% identity with PtWRKY30, AcWRKY41, VqWRKY52, and VvWRKY30, respectively. Based on the OpWRKY3 and other species' WRKYs, a phylogenetic tree was constructed and it showed that they could be classified into three major groups, and OpWRKY3, together with AtWRKY30 and AtWRKY41, belongs to group III (Figure 1A). Multiple sequence alignment indicted that OpWRKY3 possesses a highly conserved WRKY domain (WRKYGQK) and a C2H2-type zinc-finger structure (Figure 1B).
# Numerical Analysis of the Take-Off Performance of a Seaplane in Calm Water
## 5 Results and Discussion
### Heave  
During the takeoff process, the seaplane is lifted by the aerodynamic lift acting on the wings and the buoyancy and hydrodynamic lift acting on the fuselage. The changes of buoyancy, aerodynamic lift, and hydrodynamic lift are shown in Figure 10. The heave of seaplane is essentially determined by the displacement volume of the fuselage that reflects the contribution of buoyancy.  
In general, as the velocity increases, the increases of hydrodynamic lift and aerodynamic lift would reduce the contribution of buoyancy leading to an increase in heave as shown in Figure 11. While, it can be seen that the seaplane experienced a slight sinkage soon after the seaplane started moving. As indicated in Figure 10, at low speed regime the weight of the seaplane was mainly supported by buoyancy acting as a displacement hull. The forward motion of the seaplane caused a low pressure field on the bottom surface, creating a negative hydrodynamic lift that sucked the fuselage deeper into the water, known as squat effect.  
Figure 12 displays the disturbed water surface and pressure distribution at the bottom at speeds of 2 m/s, 4 m/s, and 6 m/s. It can be observed that the water interacted the fuselage at the stagnation line resulting in a pressure jump at the line. And part of the water rose up and blended into a thin sheet water flowing forward and outboard along the bow surface which is the source of spray. Figure 13 demonstrates pressure distribution along the keel line changes with speed. The peak of pressure at the stagnation line increased with speed, while slight decreases of pressure were observed behind the stagnation line. For the afterbody, the water separated at step and re-attached to the afterbody causing a second stagnation line which is apparently weaker than the one of forebody. After the speed of 4 m/s, the hydrodynamic lift increased rapidly and significantly heaved the seaplane. At high speeds, the heave further increased due to the growing aerodynamic lift.  
Figure 10: Variations of lift components against speed.  
Figure 11: Heave curve against speed.  
Figure 12: Disturbed water surface and pressure distribution at the bottom at 2 m/s, 4 m/s, and 6 m/s.
# Estimation of Optimal Speed Limits for Urban Roads Using Traffic Information Big Data
## 1 Introduction  
### Background and Objectives of Study  
In South Korea, 72.1% of traffic accidents and 51.2% of total deaths occurred on urban roads during the past five years (2010-2015) [1]. The annual growth rate of accidents was 1.31% in cities, which is higher than the rate of 0.45% for the whole country. The decrease in the rate of the deaths was 0.22% in cities, lower than the decrease of 3.44% for the whole country.  
During the past 10 years (2004-2013) [2], 38% of the total number of traffic deaths in 27 countries of the EU occurred in cities. During the same period, while the number of deaths decreased by approximately 42% the ratio of deaths in cities increased slightly.  
Compared to these countries, South Korea has a high ratio of traffic deaths in cities (51.2%), while the decrease rate of deaths in cities is low. Thus, cities and provinces need to make efforts to reduce the incidence traffic accidents.  
The 8 th National Traffic Safety Basic Plan (2017-2021, Ministry of Land, Infrastructure, and Transport) presents speed control reinforcement measures as a traffic safety promotion strategy. Specifically, the ministry plans to lower the urban speed limits to 50 km/h in principle and to expand roads with 30 km/h or lower speed limits.  
Although such policy efforts for traffic safety are timely, there is a problem in that there is no method by which traffic officials in charge of speed control can set a specific speed limit in the field. Since urban roads have various functions and roadside conditions, if the officials in charge establish speed limits based on their experience, intuition, or civil petitions, it is difficult to secure the justification for speed control due to the lack of concrete grounds for it, and decreased confidence in the speed limit can contribute to low compliance rates.  
The objective of this study is to develop a model that can be used by traffic officials to easily estimate the optimal speed limit based on objective grounds when they want to establish speed limits for urban road sections using vehicle traffic information big data, as well as to estimate the traffic accident reduction effect.
## 2 Results
### Correlation Analysis of the Metabolome and Transcriptome Data  
The enrichment results of the differential metabolites and differential genes were analyzed using KEGG pathway analysis. Then, a histogram was drawn to show the enrichment degrees of both. Finally, by comparing the DAMs and DEGs of each group, many DAMs and DEGs were found to be enriched for the same KEGG pathway. For example, compared with CK, the pathways in the DF14 group mainly included the metabolic pathways of 2-oxocarboxylic acid, ABC transporters, carbon, tyrosin, and inositol phosphate, as well as the secondary metabolites, amino acids, flavonoid, isoflavenoid, isoquinoline alkaloid, and the flavone and flavonoid biosynthesis pathways (Figure 8). A total of 33 DAMs and 212 DEGs were found to be enriched for the same metabolic pathway. A total of 16 DAMs and 137 DEGs were found to be enriched for the biosynthesis of secondary metabolites (Supplementary Table S6). A correlation analysis between the DAMs and DEGs of each  
Figure 7: qRT-PCR analysis of differentially expressed genes in the terpenoid and alkaloid biosynthesis pathways. The _X_-axis represents the average change in the expression value, the _Y_-axis represents different expression patterns at six time points, and the FPKM values represents the average transcriptome sequencing value. The FPKM values are shown in red, and the relative expression (RE) levels are shown in cyan. A red asterisk indicates that the FPKM value is significant; a black asterisk indicates that the RE is significant; \({}^{\star}\), \({}^{\star\star}\) and \({}^{\star\star\star}\) represent significant _p_-values \(\leq\) 0.05, 0.01, 0.001, respectively.  
group was carried out to further analyze the DAM regulation network. The correlation analysis showed that these metabolites were positively and/or negatively simultaneously regulated by multiple genes (Figure 9). Among them, a total of 492 genes were consistent with the expression pattern of the metabolites, and the changes in the metabolites may be positively or negatively regulated by these genes. The combined transcriptome and metabolome analysis showed that five DEGs were significantly correlated with four DAMs (Figure S8).  
Figure 8: A KEGG enrichment analysis was performed for the DEGs (red column) and DAMs (green column) enriched by the same pathway. In the vertical coordinate, red and green represent the enrichment values of differential genes and differential metabolites, respectively, which are represented by the \(-\)log(\(p\)-value), where the higher the enrichment degree, the higher the ordinate.
## 4 Results and Discussion
### The Herculane Dam Data
In time, this can lead to corrosion of the rebars. These areas might pose a long-term risk in deterioration at the surface layer of the crest. The anomalies marked as BM (Figure 13) represent metallic elements due to their very high intensity and reverberation on the vertical; they are most probably the benchmarks used in the topographic leveling surveys which are positioned in the central part of the dam. These elements appear in the first data set as well, but significantly reduced in intensity (Figure 12). As the metallic elements are really just on the surface, these would appear significantly reduced, probably due to the combination of a high stacking number (which indicates the reduced dimension of the metallic elements) and the negative gain function applied at the top of the profile. The first data set, S1 (Figure 12), may not provide much valuable information in the upper section, but it successfully identifies areas of interest at greater depths compared to the second data set, S2 (Figure 13), where the data becomes less distinct beyond 90 nanoseconds. The area marked as AOI2 (Figure 12), between 90 and 180 ns, represents a less homogeneous area compared to the surrounding environment, which might represent deterioration inside that area. However, due to the fact that this appears on only one profile, the interpretation of the highlighted anomaly has a high degree of ambiguity. If this anomaly truly exists in reality and is not due to different human errors in data collection or parameters used, it would be located in the blocks 7 and 8 (Figure 4). So far, there is no clear correlation between this behavior and any vertical displacements appearing in the topographic leveling data (non-publicly available), and more profiles and high precision positioning are needed in order to find the relationship between the two. The surveys may benefit from a more precise positioning system, which has proven challenging here, but this is unlikely to significantly affect the data in this situation.
## 2 Materials and Methods
### Regression Analysis and Modeling
#### 2.5.1 STICKRATIO Modeling  
After the statistical regression analysis, the effect of the factors was verified, where the coefficients for the STICKRATIO model presenting the linear terms X\({}_{1}\) (RPM), X\({}_{2}\) (FLOW), and X\({}_{3}\) (WOB), the quadratic terms (X\({}_{1}\)\({}^{2}\), X\({}_{2}\)\({}^{2}\), and X\({}_{3}\)\({}^{2}\)) and the second order interaction terms (X\({}_{1}\)\(\cdot\)X\({}_{2}\), X\({}_{1}\)\(\cdot\)X\({}_{3}\), and X\({}_{2}\)\(\cdot\)X\({}_{3}\)) are detailed in Table 7. It can be observed that all coefficients found in the quadratic function are statistically significant, presenting a confidence level (\(p\)-value) greater than 5 [%].  
\begin{table}
\begin{tabular}{c c c} \hline \multirow{2}{*}{**Regression Statistics**} & **STICKRATIO** & **MSE** \\  & **[-]** & **[-]** \\ \hline Multiple R & 0.9040 & 0.9509 \\ R-Square & 0.8172 & 0.9041 \\ Adjusted R-Square & 0.8161 & 0.9034 \\ Standard Error & 0.1639 & 89.8656 \\ Samples observed & 2249 & 2249 \\ \hline \end{tabular}
\end{table}
Table 6: Dataset encoded for STICKRATIO and MSE regression analysis.  
\begin{table}
\begin{tabular}{c c c} \hline \multirow{2}{*}{**Variables**} & **Coefficients** & **Standard Error** & \(p\)**-Value** \\ \hline X\({}_{1}\) & \(-\)0.3435 & 0.0180 & 0.0000 \\ X\({}_{2}\) & \(-\)0.1613 & 0.0196 & 0.0000 \\ X\({}_{3}\) & 0.2564 & 0.0196 & 0.0000 \\ X\({}_{1}\)\({}^{2}\) & 0.7089 & 0.0221 & 0.0000 \\ X\({}_{2}\)\({}^{2}\) & 0.3449 & 0.0387 & 0.0000 \\ X\({}_{3}\)\({}^{2}\) & 0.2772 & 0.0312 & 0.0000 \\ X\({}_{1}\)\(\cdot\)X\({}_{2}\) & 0.4890 & 0.0459 & 0.0000 \\ X\({}_{1}\)\(\cdot\)X\({}_{3}\) & 0.4704 & 0.0515 & 0.0000 \\ X\({}_{2}\)\(\cdot\)X\({}_{3}\) & 0.5251 & 0.0580 & 0.0000 \\ \hline \end{tabular}
\end{table}
Table 7: Effect of factors on STICKRATIO.  
\begin{table}
\begin{tabular}{c c c} \hline \multirow{2}{*}{**Predictor**} & **Lower (\(-\)1)** & **Upper (+1)** \\ \hline RPM [rev/min] & 98.69 & 180.57 \\ FLOW [L/min] & 1996.22 & 4009.09 \\ WOB [tf] & 1.03 & 21.49 \\ \hline \end{tabular}
\end{table}
Table 5: Standardization of continuous predictors with levels coded as \(-1\) and \(+1\).  
After obtaining the already detailed coefficients, the model can be completed. Equation (3) represents the model developed, denoting STICKRATIO severity (Y\({}_{1}\)) as a function of RPM [rev/min] (X\({}_{1}\)), FLOW [L/min] (X\({}_{2}\)), and WOB [tf] (X\({}_{3}\)).  
\[\begin{split} Y_{1}=-0.3435\text{X}_{1}-0.1613\text{X}_{2}+0.2564 \text{X}_{3}+0.7089\text{X}_{1}^{2}+0.3449\text{X}_{2}^{2}+0.2772\text{X}_{3}^{ 2}\\ +0.4890\text{X}_{1}\text{X}_{2}+0.4704\text{X}_{1}\text{X}_{3}+0.525 1\text{X}_{2}\text{X}_{3}\end{split} \tag{3}\]  
To confirm the STICKRATIO curves (real vs. modeled) fitting, Figure 2 plots the real versus the modeled one. As can be seen, they have proportion magnitudes and aligned trends which fit well with each other. Overall, also considering the prediction accuracy of 81.61 [%], according to the R\({}^{2}\), the model presents itself as satisfactory.