esa-sceva/satcom-chunk-collection huggingface dataset:
# Communication Architecture for Grid Integration of Cyber Physical Wind Energy Systems
## 5 Conclusions  
In this work, we proposed a framework for the grid integration of a cyber physical wind energy system (CPWES) that consists of four layers: a wind farm power system layer, data acquisition and monitoring layer, communication network layer, and application layer. A real WF project (Korean Southwest -phase 1) was considered as a case study. We developed a communication network model for the WF based on the OPNET Modeler. The network architecture was modeled based on IEC 61400-25 and IEC 61850 standards. Different scenarios were configured for evaluating the communication network performance at different levels, including: data transmission of condition monitoring system for a standalone wind turbine during different operation modes, communication network for a real WF consists of 20 wind turbines, data transmission of sensors and measurement devices installed at the met-mast, and communication network for protection and control devices at wind turbines, feeders, and offshore substation. The proposed architecture was evaluated with respect to network topology, link capacity, and end-to-end delay for different applications. Simulation results indicated the channel capacity of 1 Gbps satisfied the requirement of the IEEE 1646 standard for the WF system. This work contributes by providing a reference architecture for modeling, simulating, and evaluating the communication network of a WF and can be used for the design and implementation of future CPWESs.  
This work was supported by the National Research Foundation of Korea under Grant funded by the Korean government (MSIP) (No. 2015R1A2A1A10052459).  
All the authors contributed to publish this paper.  
The authors declare no conflicts of interest.
## 2 Materials and Methods
### Dataset  
For the purpose of evaluation, we utilized the BCI competition IV dataset 2a [20]. This dataset comprises EEG data measured from nine subjects while they performed four distinct types of motor imagery tasks involving the foot, tongue, left hand, and right hand. The recordings were taken using 22 EEG electrodes at a sampling frequency of 250 Hz. A total of 288 trials, including missing parts and excluded trials, were recorded for each subject for both training and testing purposes.  
As the input, an EEG signal was used for 4 s (1000 samples) for the duration of \"cue\" to \"motor imagery.\"  
When inputting the EEG time signal, it was downsampled to 62.5 Hz and then normalized to a mean of 0 and variance of 1 for each trial and electrode before being inputted in to the network.  
When inputting the power spectrum using STFT, STFT was applied with a window width of 250 samples and step width of 3 samples after normalization with a mean of 0 and variance of 1 for each trial and channel (without downsampling). A frequency range of \(0<f\leq 80\) was used as the input.
## 3 Multi-Layer Security Approach Based on Vulnerability Indices  
As mentioned in the previous section, identifying the vulnerable points of the power system based on the aforementioned formulation and studying these points would be necessary to better monitor the security control system of the network considering an appropriate security platform. Generally, the main aim of this paper is to evaluate and find the critical points of the network, which lead to widespread instability and cascading failures in the power grid under intentional and random cyber-attacks. Hence, the first and perhaps the most significant concern is to prevent the cascading failures and blackouts in the power system from the cyber-attack point of view. In such cases, cyber-hackers can intelligently get their malicious goals by targeting the vulnerable points of the grid for cyber-attacks. In other words, by exposing these sensitive points of the grid to invasion, attackers may take the many technical and financial consequences, that is, blackouts within the smart grid with regards to suffering the negligible cost of the adversary attack. To overcome this problem, there is a need to provide effective and efficient security architecture to protect the vulnerable points of the smart grid against the malicious targeting of cyber-attacks. To do so, this article proposes a multi-layer security scheme concerning the critical areas, which are specified by the vulnerability assessment of the smart grid. For more clarification, this approach is designed based on two cyber-security defense mechanisms consisting of the blockchain architecture-based public defense and the RL-based private defense within the smart grid. It is significant to say that the private defense of security is aimed at covering only some significant and sensitive areas of the smart grid to prevent cyber-unauthorized accesses. Figure 1 represents the relationship of both public and private layers to the smart grid in order to clear the proposed framework. This means that the vulnerable areas of the grid are secured not only by the public defense but also are placed under cover by the second layer named the private security defense.
# Initialize GroupKFold for cross-validation group_kfold = GroupKFold(n_splits = 5)
## 6 Discussion
### Limitations and Future Work  
While our study provides valuable insights, several limitations should be acknowledged. First, our real data sample was relatively small (eight networks, 72 observations), necessitating the use of synthetic networks. Although we validated key findings across both datasets, larger real-world samples would strengthen our conclusions.  
Second, our verification analysis showed challenges in model transferability, with reduced performance on the test dataset. This suggests that context-specific factors may limit generalizability, requiring model adaptation for new network environments.  
Third, our models focused primarily on static network structures, whereas real social networks evolve dynamically. Future research should incorporate network evolution into diffusion models, perhaps using the varying-weight approach suggested by [PERSON] et al. (2023) [13].  
Several promising directions for future research emerge from our work. First, exploring the causal relationship between peak_speed_time and coverage could reveal the mechanisms for early intervention in diffusion processes. Second, investigating how competing information streams affect peak diffusion timing would extend the work of [PERSON] et al. (2024) [25] on competing memes. Third, developing adaptive diffusion strategies based on early temporal patterns could improve information dissemination in time-sensitive contexts.  
Additionally, applying our comparative approach to signed networks, as studied by [PERSON] et al. (2023) [18], could reveal how trust and distrust relationships interact with different delay mechanisms. Finally, exploring threshold effects in information propagation might identify critical mass points where diffusion becomes self-sustaining.
## 4 Test Results and Analysis
### Sensibility Analysis of Factors
#### 4.2.4 Elastic Modulus  
The range analysis of elastic modulus is shown in Table 8. The range from large to small is silicone oil ratio, Portland cement/gypsum, filling material/bonding material, mica powder/standard sand. Among them, the influence of silicone oil ratio on the elastic modulus of the sample was very obvious. On the premise of keeping the proportion of raw materials of other similar materials unchanged, the elastic modulus of the sample can be effectively enhanced by increasing the silicone oil ratio. Changing the three factors of Portland cement/gypsum, filling material/bonding material, and mica powder/standard sand had little influence on the change of cohesion, and the influence degree was similar.
# Detection of Cracks in Arch Dams Using Monitoring Data and Numerical Models
## 3 Results
### Error Rates for Alternative Methodology  
The same procedure was carried out using the alternative methodology, meaning that simulations a posteriori are used instead of the interpolation equations. The results obtained are shown in Table 6 for validation reservoir levels.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & \multicolumn{2}{c}{**Level 35 m**} & \multicolumn{2}{c}{**Level 45 m**} \\ \cline{2-5}  & \multicolumn{1}{c}{**Error with 0.1**} & \multicolumn{1}{c}{**Error with 0.02**} & \multicolumn{1}{c}{**Estimated 0.1**} & \multicolumn{1}{c}{**Estimated 0.02**} \\ \hline No crack length & \(\infty\) & 0\% & 0\% & 0\% \\ No crack depth & \(\infty\) & 0\% & 0\% & 0\% \\ Crack A length & \(-100\%\) & \(-9\%\) & \(+81\%\) & \(-33\%\) \\ Crack A depth & \(-100\%\) & \(+55\%\) & \(-20\%\) & \(+40\%\) \\ Crack B length & \(-100\%\) & \(-4\%\) & \(+71\%\) & \(-50\%\) \\ Crack B depth & \(-100\%\) & \(+25\%\) & \(-4.5\%\) & \(-200\%\) \\ Crack C length & \(+140\%\) & \(+54\%\) & \(+49\%\) & \(-47\%\) \\ Crack C depth & \(-44\%\) & \(-38\%\) & \(-33\%\) & \(-36\%\) \\ Crack D length & \(-71\%\) & \(+20\%\) & \(+20\%\) & \(+14\%\) \\ Crack D depth & \(-36\%\) & \(-33\%\) & \(-18\%\) & \(-27\%\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Errors of estimation by alternative methodology with 0.1 or 0.02 accuracy. Any error in the case of length 0 is indicated by an infinite percentage error.  
Figure 12: Example of representation of the matrix of errors for the crack 2 m deep and 15 m long with 35 m.a.s.l.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & \multicolumn{2}{c}{**Level 35 m**} & \multicolumn{2}{c}{**Level 45 m**} \\ \cline{2-5}  & \multicolumn{1}{c}{**Real (m)**} & \multicolumn{1}{c}{**Error with 0.1**} & \multicolumn{1}{c}{**Error with 0.02**} & \multicolumn{1}{c}{**Error with 0.1**} & \multicolumn{1}{c}{**Error with 0.02**} \\ \hline No crack length & 0 & 0\% & 0\% & 0\% & 0\% \\ No crack depth & 0 & 0\% & 0\% & 0\% & 0\% \\ Crack A length & 15 & \(-100\%\) & \(+41\%\) & \(+1\%\) & \(-33\%\) \\ Crack A depth & 2 & \(-100\%\) & \(-25\%\  
An additional analysis examined the extent to which the results are affected by changes in the type of noise. When the noise level was altered from 0.1 to 0.02, the original methodology yielded consistent results in 65% of the cases, while the alternative methodology did so in only 45% of the cases. In contrast, modifying the noise from 0.02 to 0.01 led to the same outcomes in 85% of the cases for both the proposed and the alternative methodologies.
## 3 Cluster Routing Protocol
### CH Selection  
In the last time slot of \(T_{initialize}\), the BS broadcasts the start message: \(START\_MSG\). After receiving such a message, each node in the network randomly generates a random number \(W_{i\_r}\). \(W_{i\_r}\in\left[\begin{array}{cc}0,&W_{r}\end{array}\right]\), where \(W_{r}\) is a dynamic value that it will change dynamically during the operation of the network:  
\[W_{r}=\beta\frac{E_{r\_aver}}{E_{r\_max}}+\left(1-\beta\right)\frac{M_{r\_aver }}{M_{r\_max}} \tag{7}\]  
\[M_{r\_aver}=\frac{1}{N_{active}}\sum_{i=1}^{N_{active}}M_{i\_curr} \tag{8}\]  
\[E_{r\_aver}=\frac{1}{N_{active}}\sum_{i=1}^{N_{active}}E_{i\_curr} \tag{9}\]  
where \(0\leq\beta\leq 1,0\leq W_{r}\leq 1\). A variable data set \(G_{i}(t)\) is introduced to determine whether the node participates in the CH competition of this round. When node \(i\) is selected as the CH, \(G_{i}(t)=0\), otherwise \(G_{i}(t)=1\). Similar to the LEACH protocol, a randomly generated number \(W_{i\_r}\) is compared with a threshold; if \(W_{i\_r}\) is smaller than the threshold \(T_{i}(t)\), the node will be selected as the CH and the message \(CLUSTER\_HEAD\_MSG\) will be broadcast to the surrounding nodes. The expression for \(T_{i}(t)\) is:  
\[T_{i}(t)=\left\{\begin{array}{cc}0,&G_{i}(t)=0,\\ T^{\prime}{}_{n}(t)&\left[\beta\frac{E_{i\_curr}}{E_{r\_max}}+\left(1-\beta \right)\frac{M_{i\_curr}}{M_{r\_max}}\right]&G_{i}(t)=1,&r<R\\ 1,&G_{i}(t)=1,&r=R\end{array}\right. \tag{10}\]  
where:  
\[T^{\prime}{}_{n}(t)=\frac{k_{r}}{N_{active}-k_{r}(r\text{mod}\left[N_{active} /k_{r}\right])} \tag{11}\]  
Here, \(t\) refers to the current round of the network (viz., \(t=r\)) and \(R\) is a complete cycle \(R=N_{active}/k_{r}\), _i.e._, a minimum time unit guaranteeing that each node is eventually selected as the CH. In each cycle, \(r\) is a counter that is reset to 0 when \(r=R\). \(N_{active}\) is the number of nodes participating in the CH competition at present (also called the number of active nodes), and \(\beta\) is the weight of the regulating energy factor and data frame transmission quality. In contrast to the previous judgment threshold, we have introduced two factors that mainly guarantee the optimal overall energy consumption of the network based on optimal energy consumption and link quality.
# Modeling of Vessel Traffic Flow for Waterway Design-Port of Swinoujscie Case Study
## 3 Results
### Regression Analysis of Traffic Flow Parameters
#### 3.3.2 Model Including Width B  
Testing the correlation between mean, standard deviation, and width of the ships showed that a regression model could only be constructed for the mean width relation  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dependent** & **Ships** & **Coefficient** & **Coefficient** & **\(R^{2}\)** & \(F\)** & \(p\) \\
**Variable** & **Type** & **Intercept** & **T[m]** & & & \\ \hline Mean & GC & 1037.7 * & \(-\)53.8 * & 0.7993 * & 208.7330 * & 0.0000 * \\ \hline Mean & OPT & 1340.6 * & \(-\)100.1 * & 0.6801 * & 72.2755 * & 0.0000 * \\ \hline Mean & PAS & 1367.1 * & \(-\)136.8 & 0.2667 & 3.5840 & 0.0849 \\ \hline Std.Dev. & GC & 523.9 * & \(-\)18.1 * & 0.2265 * & 14.3461 * & 0.0004 * \\ \hline Std.Dev. & OPT & 372.4 * & \(-\)1.2 & 0.0003 & 0.0093 & 0.9238 \\ \hline Std.Dev. & PAS & \(-\)663.1 & 224.1 * & 0.6101 * & 17.2121 * & 0.0016 * \\ \hline \hline \end{tabular} Legend: * statistical significance to regression parameter.  
\end{table}
Table 1: The parameter of the simple regression T-model.  
Figure 8: (a) The mean of the ship’s distance from the centre; (b) standard deviation of ship distance from the centre to the ship’s draught.  
for GC and OPT ships (for gates N1N6). The wider the ship, the closer to the middle of the track the navigator sails, as shown in Figure 9. \(R^{2}\) for the GC was 0.37, which means that the model explained 37% of the variability of the response data around its mean, as shown in Table 2. For OPT vessels, the model explained 45%. Although the coefficient of determination was not high, the statistic \(p\) was equal to 0.00. For both models, the significant \(p\)-value indicates that we can reject the null hypothesis that the coefficient is equal to zero (which still indicates a fundamental relationship between the significant predictors and the response variable). The coefficient of determination may be low because the model predicts the behaviour of the navigator. People are more complex, thus less predictable than, for example, physical processes.
## 2 Method
### Conceptual Process
In principle, either an alkaline, a polymer electrolyte membrane, or even a solid oxide electrolyser (a less mature technology) could be used. The principle of operation of a solid oxide electrolysis cell (SOEC) device can be considered to be the reverse of that of a solid oxide fuel cell (SOFC). SOEC systems require a supply of steam, electrical power, and absorbed heat to carry out the electrolysis process. Since the system operates at high temperatures, the electrical power required is less than that of electrolysis operating at lower temperatures, due to the fact that the variation in free energy between reactant and products is smaller, as is the overvoltage during operation [14]. The amount of electrical energy required could be 35% less than that which is required by alkaline electrolysers for the same amount of H\({}_{2}\) produced. The amount of heat absorbed by the process is a little higher than that of low-temperature processes, again for thermodynamic reasons. However, when integrated with a biorefinery plant, it would be possible to recover and use both the heat and the steam released by the gasification process as a feed for the high-temperature electrolyser. In addition, the decreased intensity of heat released into the environment would be a positive factor. The heat generated by the Joule effect within the electrolysis cells is also used for the endothermic reaction. The all-solid phase constitution of SOECs ensures that liquid corrosion problems in alkaline electrolysers are avoided, in addition to problems of degradation of membranes in polymer electrolyte membrane (PEM) electrolysers. Moreover, the SOEC high operating temperature means that the reaction kinetics are faster, thus avoiding the need for expensive catalysts such as platinum that is used in PEM electrolysers. In addition, the lifespan of solid oxide stacks is known to be longer than that of polymer membrane stacks. For these reasons, for the purpose of this article, we selected the solid oxide electrolyser as a well-suited technology.
## Results and Discussion
### Biometric and Physicochemical Characterization of Sweet Cherries and Cultivar Effect Evaluation
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & \multicolumn{5}{c}{**Cherry Cultivar**} \\ \cline{2-5}  & **Durona** & **Lapins** & **Summit** & **Van** & **\(p\)-Value 1** \\ \hline \multicolumn{5}{c}{**Biometric**} \\ \hline Fruit mass (g) & 6.25 \(\pm\) 0.96 b  & 6.78 \(\pm\) 1.38 a  & 7.15 \(\pm\) 1.06 a  & 6.79 \(\pm\) 1.08 a  & \(<\)0.0001 \\ Length (mm) & 19.1 \(\pm\) 1.2 d  & 20.6 \(\pm\) 1.7 b  & 21.4 \(\pm\) 1.4 a  & 19.7 \(\pm\) 1.6 c  & \(<\)0.0001 \\ Maximum diameter (mm) & 22.6 \(\pm\) 1.6 d  & 23.2 \(\pm\) 1.2 c  & 24.8 \(\pm\) 1.4 a  & 24.2 \(\pm\) 1.7 b  & \(<\)0.0001 \\ Minimum diameter (mm) & 20.4 \(\pm\) 1.3 & 20.5 \(\pm\) 1.5 & 20.7 \(\pm\) 1.2 & 20.6 \(\pm\) 1.4 & 0.2000 \\ Stone mass (g) & 0.36 \(\pm\) 0.06 a  & 0.37 \(\pm\) 0.06 a  & 0.35 \(\pm\) 0.04 b  & 0.31 \(\pm\) 0.04 c  & \(<\)0.0001 \\ Pulp/stone ratio & 16.8 \(\pm\) 3.8 b  & 17.8 \(\pm\) 4.2 b  & 19.7 \(\pm\) 3.0 a  & 20.8 \(\pm\) 3.4 a  & \(<\)0.0001 \\ \hline \multicolumn{5}{c}{**Physicochemical**} \\ \hline Force (N) & 2.58 \(\pm\) 0.46 b  & 2.91 \(\pm\) 0.60 a  & 2.50 \(\pm\) 0.34 b  & 3.01 \(\pm\) 0.51 a  & \(<\)0.0001 \\ L* (CIELAB scale) & 32.3 \(\pm\) 3.1 c  & 35.6 \(\pm\) 4.7 a  & 37.2 \(\pm\) 4.1 a  & 35.0 \(\pm\) 4.6 b  & \(<\)0.0001 \\ a* (CIELAB scale) & 27.8 \(\pm\) 5.6 c  & 33.8 \(\pm\) 6.8 a  & 34.7 \(\pm\) 4.6 a  & 31.6 \(\pm\) 6.2 b  & \(<\)0.0001 \\ b* (CIELAB scale) & 10.4 \(\pm\) 3.6 c  & 14.8 \(\pm\) 5.4 a  & 16.3 \(\pm\) 4.0 a  & 13.5 \(\pm\) 4.6 b  & \(<\)0.0001 \\ Titralable acidity (TA, g gallic acid/100 g fw) & 0.17 \(\pm\) 0.01 & 0.17 \(\pm\) 0.04 & 0.21 \(\pm\) 0.01 & 0.21 \(\pm\) 0.04 & 0.0312 \\ Total soluble solids (TSSs, \({}^{\circ}\)Brix) & 14.1 \(\pm\) 1.5 b  & 14.1 \(\pm\) 2.3 b  & 12.6 \(\pm\) 1.5 c  & 15.5 \(\pm\) 3.0 a  & \(<\)0.0001 \\ \hline \hline \end{tabular}  
* For each line, a \(p\)-value \(<\) 0.05 means that, for each cherry cultivar, the mean value of the evaluated parameter of at least one of the four cultivars studied differs from the others, according to the one-way ANOVA results (in this case, multiple-comparison tests were performed). Different letters mean significant statistical differences of the parameter under evaluation at a 5% significance level (\(p\)-value \(<\) 0.05), according to a multiple comparison [PERSON]’s HSD test.  
\end{table} 
Table 1: Fruit biometric and physicochemical parameters (mean value \(\pm\) standard deviation) of the four sweet cherry cultivars studied (Durona, Lapins, Summit, and Van) and statistical differences (one-way ANOVA).
## 1 Introduction  
A trial fibrillation (AF) is a common cardiac arrhythmia that can cause severe stroke and heart failure and is associated with premature mortality [1]. AF is characterized by a progressive atrial remodeling, which results in electrical dissociation and local conduction heterogeneities, favoring re-entry and perpetuation of the arrhythmia, with irregular and often abnormally fast heart rate [2]. Multiple risk factors have been implicated for AF,including valvular heart disease, diabetes mellitus, coronary artery disease, chronic kidney disease, hypertension, obesity, and smoking [3]. However, AF is also known to occur in the absence of these conditions [4]. Family-based studies indicate that AF has a clear genetic predisposition [5; 6]: the risk to develop AF is -40% larger in first-degree relatives of an AF case [6]. Genome-wide association studies (GWAS) have identified >160 gene loci for AF [7]. Usually, common variants identified in GWAS occur in intergenic regions or in introns and are presumed to contain regulatory sequences that influence gene expression [7]. However, numerous rare coding variants have been identified in families and in early onset cases, predominantly in genes encoding cardiac ion channels, myocardial structural components, and cardiac transcription factors [7]. Cardiac excitation-contraction coupling and ion channel/pump integrity require oxygen and nutrients and are closely linked to cellular metabolic conditions. Recent studies describing the metabolite profiles of atrial tissues [8] and blood samples from AF patients [9; 10; 11] indicate a possible involvement of metabolic alterations in AF pathophysiology, but further efforts are necessary to fully explain heritability and metabolic processes in AF.  
In this work, we describe a systematic scan for genetic and metabolic determinants of AF. We conducted a GWAS of 19,057,004 markers in 10,518 individuals, familial aggregation analysis in the same set of individuals, and a screening of 175 metabolites in 5688 of the 10,518 individuals from the same general population study, where participants were unselected for any disease or condition.
## 3 Preliminary  
### 5G Mobile Communication Technology  
5G refers to the fifth generation of mobile communication technology, characterized by large bandwidth, low latency, and high reliability [13]. Figure 2 illustrates the comparison of performance metrics between 5G and 4G networks. Compared to the previous generation of technology, the network architecture is flatter and more efficient, envisioning the future of interconnected everything right from the design phase, supporting a connection density of over one million devices per square kilometer. At the same time, 5G introduces the concept of network slicing based on virtualization technology, creating multiple logically independent networks on the same physical network resources through logical partitioning, providing differentiated network support for various smart devices. Furthermore, 5G adopts a more open architecture, incorporating Software-Defined Networking (SDN) and Network Functions Virtualization (NFV) technologies, enabling users to efficiently deploy network services according to scenario requirements and perform personalized configurations. Overall, the 5G network can well meet the needs of the target system.  
Figure 1: The 5G and mobile edge computing convergence architecture [13; 14].  
Figure 2: Comparison of performance metrics between 5G and 4G networks.