esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Comparison with Actual Data
#### 3.3.2 Comparison between HHWS and REWS  
After inputting the observation values of wind resources for HHWS and REWS into WindSim, respectively, the generation amount of the Anholt offshore wind farm for two years was calculated. The wind speed values for each wind turbine simulated in WindSim and the wind speed values for each wind turbine recorded in SCADA were compared. A correlation analysis was performed between the simulated wind speed and the actual value at the location where the analysis target turbine was installed (Figure 10). On average, regardless of HHWS and REWS, the correlation between the simulated value and the actual wind speed value was 0.8 or higher. The correlation between REWS and the actual SCADA wind speed was slightly higher than that of HHWS, but there was no significant difference (Table 7). In the simulated wind speed values, REWS showed a slightly higher wind speed value than HHWS.  
Meanwhile, the wind speed difference distribution was expressed in ArcGIS using the wind resource grid values of REWS and HHWS calculated by WindSim. In the vicinity of the offshore wind farm, REWS showed a high distribution of about 0.1 m/s. Considering that the main wind is a westerly wind, it can be seen that the wavy contours are stacked on the right side of the offshore wind farm. It appears that the wake effect in the wind farm is reflected, and it is judged that as the wind passes through the wind farm, the difference between REWS and HHWS slightly increases.  
In the vicinity of the mainland located in the southwest of the analysis domain and Anholt Island located in the northeast of the analysis domain, shades of blue are shown. These indicate that the difference between REWS and HHWS is small compared to other regions, and it seems that the difference between REWS and HHWS is smaller due to the length of roughness on land being higher than at sea (Figure 11).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Sector**} & **Wind Speed** & **Wind Speed** & **Segment** & **Segment** & **Segment** & **Segment Height** \\  & **Height [m]** & **[m/s]** & **Weighting [\%]** & **Bottom Height** & **Upper Height** & **Segment Height** \\  & & & & **[m]** & **[m]** & **[m]** \\ \hline A5 & 155 & 9.68 & 10.96 & 45 & 65 & 20 \\ A4 & 135 & 9.36 & 1  
Figure 10: Correlation analysis through scatterplot distribution between simulated wind speed and actual SCADA wind speed at each wind turbine location: (**a**) A01; (**b**) A02; (**c**) A03; (**d**) B01; and (**e**) C01 wind turbine.
## 3 Experimental Results and Analysis
### Experimental Comparison and Analysis
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Scene 1** & **Tree (\%)** & **Building (\%)** & **Car (\%)** & **Accuracy (\%)** & \(F_{1}\)**-Score (\%)** \\ \hline Our method & 99.2/90.6 & 91.1/99.3 & 92.9/48.2 & 94.6 & 94.5/94.9/59.5 \\ Method 1 & 99.2/84.9 & 86.8/99.3 & 99.9/42.7 & 91.9 & 91.5/92.7/59.8 \\ Method 2 & 93.2/78.7 & 82.1/94.6 & 63.3/30.4 & 86.4 & 85.3/87.9/41.1 \\ Method 3 & 96.9/81.7 & 84.1/97.7 & 98.8/23.2 & 89.3 & 88.7/90.4/37.6 \\ Method 4 & 89.7/98.1 & 97.9/89.1 & 65.2/46.6 & 92.9 & 93.7/93.3/54.4 \\ Method 5 & 85.7/92.9 & 92.0/83.8 & 56.9/54.7 & 87.9 & 89.2/87.7/55.8 \\ Method 6 & 94.8/93.8 & 93.5/92.3 & 41.2/66.7 & 92.6 & 94.3/92.9/50.9 \\ Method 7 & 93.1/96.0 & 95.2/92.6 & 73.3/62.2 & 93.7 & 94.5/93.9/67.3 \\ \hline
**Scene 2** & **Tree (\%)** & **Building (\%)** & **Car (\%)** & **Accuracy (\%)** & \(F_{1}\)**-score (\%)** \\ \hline Our method & 92.4/94.3 & 98.5/97.9 & 73.0/68.4 & 95.8 & 93.4/98.2/70.6 \\ Method 1 & 83.2/92.9 & 98.5/92.8 & 62.6/65.7 & 92.0 & 87.8/95.6/64.1 \\ Method 2 & 77.3/94.3 & 98.3/88.9 & 71.7/60.0 & 89.6 & 85.0/93.4/65.3 \\ Method 3 & 91.3/92.6 & 96.6/96.6 & 63.2/55.5 & 93.4 & 91.9/96.6/59.1 \\ Method 4 & 86.8/91.2 & 96.8/95.5 & 44.1/34.8 & 92.2 & 88.9/96.1/38.9 \\ Method 5 & 73.9/91.2 & 93.6/88.2 & 29.5/25.4 & 87.2 & 81.6/90.8/27.3 \\ Method 6 & 90.3/93.9 & 97.6/96.5 & 49.4/42.0 & 94.1 & 92.1/97.0/45.4 \\ Method 7 & 94.7/94.5 & 98.1/97.7 & 53.9/60.5 & 95.5 & 94.6/97.9/57.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Classification results of precision/recall, accuracy and \(F_{1}\)-score.
## 3 ABER Performance of Transmit Aperture Selection under Jamming
### Coding Gain and Diversity Order  
To determine the coding gain and diversity order of the defined FSO system, we need to evaluate the ABER expression (44) at a very high value of \(\gamma\). At such values of \(\gamma\)the dominating terms are affiliated to the lowest exponent of \(\gamma\). We take two active and idle jammer cases, respectively, to understand the difference between the jamming and Gaussian noise.  
_Case I_: For an active jammer scenario, we put \(\gamma\to\infty\) in (39) and collect the dominating terms as  
\[\lim_{\gamma\to\infty}ABER_{\rm J_{active}} = 0.5\rho\zeta_{u}^{2}\zeta_{j}^{2}\ N\bigg{(}\frac{max(\Gamma(1- \zeta_{u}^{2}),1)}{max(\zeta_{u}^{2}-1,1)}\bigg{)}^{\!\!N}\frac{\Gamma(N(\delta+ \zeta_{u}^{2}))}{\left(\zeta_{j}^{2}+N(\delta+\zeta_{u}^{2})\right)}\bigg{(} \frac{2}{\sqrt{\rho}}\bigg{)}^{\!\!N(\delta+\zeta_{u}^{2})}. \tag{45}\]  
Comparing (45) with (12), we obtain the coding gain as  
\[G_{c} = \left[0.5\,\rho\,\zeta_{u}^{2}\,\zeta_{j}^{2}\ N\bigg{(}\frac{max( \Gamma(1-\zeta_{u}^{2}),1)}{max(\zeta_{u}^{2}-1,1)}\bigg{)}^{\!\!N}\frac{ \Gamma(N(\delta+\zeta_{u}^{2}))}{\left(\zeta_{j}^{2}+N(\delta+\zeta_{u}^{2}) \right)}\bigg{(}\frac{2}{\sqrt{\rho}}\bigg{)}^{\!\!N(\delta+\zeta_{u}^{2})} \right]^{\frac{N(\delta+\zeta_{u}^{2})}{2}}, \tag{46}\]  
and the diversity order \(G_{d}\) as \(0.5N(\delta+\zeta_{u}^{2})\). Thus, the maximum diversity order that the system can attain is \(0.5N\).  
**Remark 2**.: _The transmit aperture selection allows the FSO system corrupted by a jammer to attain the same diversity order as an MISO-FSO system [12], while considerably saving the power requirements of \(N-1\) transmitters._  
_Case II_: For an idle jammer scenario, we put \(\gamma\to\infty\) in (43) and obtain the dominating terms as  
\[\lim_{\gamma\to\infty}ABER_{\rm J_{active}} = 0.5\,(1-\rho)\ \zeta_{u}^{2}N\bigg{(}\frac{max(\Gamma(1-\zeta_{u}^{2}),1)}{max( \zeta_{u}^{2}-1,1)}\bigg{)}^{\!\!N}\bigg{(}\frac{2\sqrt{2}}{A_{0}\sqrt{\kappa \gamma}}\bigg{)}^{\!\!N(\delta+\zeta_{u}^{2})}\frac{1}{\sqrt{\pi}N(\delta+ \zeta_{u}^{2})} \tag{47}\] \[\times\Gamma\bigg{(}\frac{N(\delta+\zeta_{u}^{2})+1}{2}\bigg{)}.\]  
Again, comparing (47) with (12), we obtain the coding gain as  
\[G_{c} = \Bigg{[}0.5(1-\rho)\zeta_{u}^{2}N\bigg{(}\frac{max(\Gamma(1- \zeta_{u}^{2}),1)}{max(\zeta_{u}^{2}-1,1)}\bigg{)}^{\!\!N}\bigg{(}\frac{2 \sqrt{2}}{A_{0}\,\kappa}\bigg{)}^{\!\!N(\delta+\zeta_{u}^{2})}\frac{1}{\sqrt{ \pi}N(\delta+\zeta_{u}^{2})} \tag{48}\] \[\times\Gamma\bigg{(}\frac{N(\delta+\zeta_{u}^{2})+1}{2}\bigg{)} \Bigg{]}^{0.5N(\delta+\zeta_{u}^{2})},\]  
and the diversity order is \(0.5N(\delta+\zeta_{u}^{2})\), which is the maximum diversity that can be achieved for any \(N\times 1\) MISO system.
# Machine Learning Models for Ecofriendly Optimum Design of Reinforced Concrete Columns
## 2 Materials and Methods
### Machine Learning Models  
After preparing the dataset with the optimum dimensions, several ML models were tested to predict \(b\), \(h\), \(A_{s}\) from \(M\) and \(N\). The ability to learn in humans is one of their most important features, and it make humans different from other living things and machines. ML is a result of the desire of machines to learn like humans and make the best decisions. The result of learning can be used for recognition, prediction, and classification [49]. With the help of ML, more accurate and valid results can be obtained by making forward-looking predictions and planning based on known data. In ML, the written algorithm makes an inference by looking at the existing inputs. A key advantage of ML is that ML methods can examine large amounts of data to find patterns that might otherwise be missed [50].  
ML has four subfields, including supervised learning, unsupervised learning, semisupervised learning, and reinforcement learning. Unsupervised learning is based on only input data without labels [51]. Supervised learning requires learning a mapping between a set of input variables and an output variable and applying this mapping to predict outputs for data whose outputs are unknown [52]. In semisupervised learning, in addition to the unlabeled data, some supervised information is provided to the algorithm, but this is not necessary for all examples [53]. In reinforcement learning, an agent is placed in an initially unknown environment and only receives evaluative feedback called reward [54].  
In this study, the regression type of supervised method was used as the dependent variables were continuous. Regression is a problem of predicting a real value. Examples of regressions include predicting stock values or variations. In regression, the penalty for an incorrect estimate typically depends on the size of the difference between the actual and predicted values, unlike the classification problem, in which there is a closeness between the various categories [55]. The dataset used in this study was a multioutput dataset (i.e., containing multiple dependent variables). For the prediction process, we have utilized Python [48] as the language, Anaconda3 [56] as the environment, and Spyder 5.2.2 as the  
Figure 4: Visualization of the correlation matrix between input and output variables.  
editor. In addition to basic Python libraries such as Numpy and Pandas, the scikit-learn library [57] developed for ML applications was used for defining ML models.  
ML algorithms used in the study will be explained under the title of foundational methods are linear regression, decision tree regression, elastic net regression, K nearest neighbors regression (KNN), and support vector regression (SVR). Other ML algorithms used in the study will be explained under the title of ensemble methods including random forest regression, gradient boosting regression, histogram-based gradient boosting regression, and voting and stacking.
# Fast-RRT: A RRT-Based Optimal Path Finding Method
## 4 Simulation and Result
### Find Initial Path
Figure 11: Comparison of the operation results of (**a**) the improved RRT algorithm and (**b**) the RRT algorithm. The sampling points of the Improved RRT algorithm are more densely distributed in the area where the random tree has not reached, and its random tree is more efficient to achieve the state space. As for the RRT, the sampling points are evenly distributed throughout the space. Compared with Improved RRT, it uses more nodes to achieve coverage of the state space. Figure 13 compares the results of the memory consumption of RRT and Fast-RRT algorithms. Compared with the RRT algorithm, Fast-RRT also shows remarkable advantages in memory usage. At step sizes of 10, 20, 30, 40, and 50, the average number of nodes of the random tree is 659.6, 322.9, 207.9, 158.4, and 129.5, respectively. In contrast, the RRT algorithm has a larger number of nodes of the random tree of 1182.3, 551.9, 323.6, 244.5, and 198.8, respectively. At the same time, the variance of the number of nodes in the Fast-RRT algorithm random tree is also smaller. When the step size is set to 10, the variance of the number of nodes in the Fast-RRT random tree (113.9) is only 22.1% of the RRT algorithm (515.6).
# A Survey of Blockchain Applications for Management in Agriculture and Livestock Internet of Things
## 4 Applications of Blockchain in Agricultural and Animal Husbandry IoT
### Supply Chain Management  
In the contemporary sphere of agriculture and livestock production, the transparency and traceability of supply chains are of paramount importance. These aspects are vital for assuring product quality, fostering consumer trust, and adhering to regulatory standards. The capability to trace agricultural products' journey from farm to table is not just a requirement of informed consumers but also essential for conforming to global standards and market expectations.  
Traditional centralized supply chain management systems, while meeting regulatory and coordination demands to a certain extent, face inherent challenges in modern agriculture and livestock contexts. Centralized systems are prone to single points of failure, where disruptions in one link of the supply chain can affect the entire system's operation. Moreover, the lack of transparency and mechanisms for information sharing results in low trust among supply chain participants, potentially leading to data falsification and resource waste.  
In contrast, blockchain technology offers a novel solution through its decentralized architecture. Its immutability ensures data authenticity, its transparency enhances trust among stakeholders, and its smart contracts significantly improve operational efficiency. These features make blockchain technology particularly effective in improving the operations of traditional supply chains, especially in scenarios where information exchange and collaboration are critical, such as in the agriculture and livestock industries.  
This section delves into how technological innovations, especially the synergy of blockchain and the IoT, are ushering in transformative changes in transparency and traceability within the agricultural and livestock supply chains. The application of these technologies enables companies to offer an extensively transparent view of the product lifecycle, encompassing every phase from production and processing to distribution and final sale. This methodology is instrumental not only in combating food fraud and contamination but also in significantly enhancing the overall efficiency and sustainability of supply chain operations.
# R&D of an Innovative OFDM Communication Payload for Small-Size AUV
## 2 Systems and Methods
### Doppler Estimation and Compensation Implementation
#### 2.3.3 Agile and Low Computational Overhead Doppler Compensation Approach
Finally, the demodulated binary information is decoded by convolutional decoding and interleaving decoding to recover the transmitted data information.
# Accumulative Roll Bonding--A Review
## 4 Properties
### Thermal Stability  
Heat treatment such as annealing process may considerably improve the mechanical properties of ARBed materials if appropriate temperature and time are chosen, particularly for SPDed materials with high level of stored energy. Intensive grain growth and loosing UFG structures are the results of improper annealing.  
[PERSON] et al. [116] investigated the thermal stability of ARBed 6061 Al alloy where the grain size and microhardness were studied at different annealing temperatures. It can be seen in Figure 15 that grain size and hardness were relatively unchanged and uniformly distributed below the annealing temperature of 473 K. In contrast, for the samples annealed at temperatures above 473 K, bimodal grain size was observed including coarse grains and UFG. Considering the low activation energy of grain growth for the ARBed specimens, they emphasized that the non-equilibrium characteristics of grain boundaries of UFG alloy leads to the existence of ill-defined grain boundaries; hence, it can be postulated that the thermal stability of the ARB produced UFG materials depends mostly on the state of the SPD microstructure.  
[PERSON] et al. [117] studied the thermal stability of ARBed Al-Fe-Mn-Si sheets at elevated temperatures (see Figure 16) using the hardness as a function of annealing temperatures. In all specimens, the hardness remains nearly constant at annealing temperatures of \(<\)200 \({}^{\circ}\)C, but then drops more rapidly, especially for cold-rolled specimens. The different annealing responses of ARBed and cold-rolled samples might be justified by discrepancies in stored deformation energy providing driving force for recrystallization. Furthermore, the nucleation and growth of new grains are more rapid in cold-rolled samples than that of elevated temperature ARBed ones.  
For ARBed samples roll bonded at 250 \({}^{\circ}\)C, the most pronounced reduction in hardness occurs at 250 \({}^{\circ}\)C in the specimen with six ARB cycles, whereas in the one with two cycles, a gradual hardness diminution is observed when the annealing temperature is raised within the range 200-350 \({}^{\circ}\)C. In contrast to the ARBed specimens with two cycles, the sheets roll bonded by six ARB cycles required a considerably higher temperature of 450 \({}^{\circ}\)C to achieve fully softened material. In the specimens roll bonded at 350 \({}^{\circ}\)C, the greatest drop is shifted to 350 \({}^{\circ}\)C, therefore, softening due to the annealing treatment of the ARBed samples depends on the strain induced by the ARB as well as the recovery influenced by the ARB temperature. The results of the hardness measurements thus indicate that the ARBed stacks processed at 350 \({}^{\circ}\)C or by six cycles at 250 \({}^{\circ}\)C are most thermally stable.
## 3 Results and Discussion
### Performance of the Three Hybrid Classifiers
Given what is shown in Table 7, with the use of approximately 30% of dataset 1 for training, all three hybrid algorithms achieved classification performance 100%, or close to that, on the remaining 70% of dataset 1 that was used for validation and testing, and at least 96% on the independent dataset 2 that was used for the second round of testing. These findings provide evidence that the proposed method for training set minimization does not lead to overfitting of the hybrid algorithms. On the contrary, it seems that the three proposed hybrid algorithms are capable of detecting the minimum _training core_ of the dataset, i.e., the minimum training subset that can be used for training, in order to achieve high, or even perfect, performance on the training subset. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithm** & _alpha_ & _Accuracy_ & \(S\) & **Biopsy** & **No Biopsy** \\ \hline GAKMEANS & 0.80 & 1.00000 & 222 & 50 & 172 \\ \hline GAKMEANS & 0.75 & 1.00000 & 222 & 50 & 172 \\ \hline GAKMEANS & 0.70 & 1.00000 & 222 & 50 & 172 \\ \hline GAKMEANS & 0.70 & 1.00000 & 224 & 34 & 210 \\ \hline GAKNN & 0.40 & 1.00000 & 107 & 59 & 48 \\ \hline GAKNN & 0.35 & 1.00000 & 110 & 62 & 48 \\ \hline GAKNN & 0.35 & 1.00000 & 106 & 58 & 48 \\ \hline GAKNN & 0.30 & 1.00000 & 110 & 60 & 50 \\ \hline GACLUST & 0.60 & 1.00000 & 127 & 34 & 93 \\ \hline GACLUST & 0.60 & 1.00000 & 108 & 41 & 67 \\ \hline GACLUST & 0.50 & 1.00000 & 113 & 45 & 68 \\ \hline GACLUST & 0.40 & 1.00000 & 125 & 37 & 88 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Summarized, representative results of four independent computer experiments for each one of the three hybrid algorithms regarding the optimal training set for each experiment.
# One-Shot Distributed Generalized Eigenvalue Problem (DGEP): Concept, Algorithm and Experiments +
## 3 Approximation Error Analysis of Distributed GEP
Then:\[\|\Sigma_{1}-\Sigma_{2}\| =\left\|\mathrm{M}_{0}-\sum_{i=1}^{\mathrm{N}}\mathrm{M}_{i} \right\|=\left\|\left(\sum_{i=1}^{\mathrm{N}}\mathrm{B}_{i}\right)^{-\frac{1}{ 2}}\sum_{i=1}^{N}A_{i}\left(\sum_{i=1}^{N}B_{i}\right)^{-\frac{1}{2}}-\sum_{i=1 }^{\mathrm{N}}\mathrm{B}_{i}^{-\frac{1}{2}}\ A_{i}\mathrm{B}_{i}^{-\frac{1}{2 }}\right\|\] \[\leq\left\|\left(\sum_{i=1}^{\mathrm{N}}\mathrm{B}_{i}\right)^{- \frac{1}{2}}\sum_{i=1}^{N}A_{i}\left(\sum_{i=1}^{N}B_{i}\right)^{-\frac{1}{2}} \right\|+\left\|\sum_{i=1}^{N}B_{i}^{-\frac{1}{2}}A_{i}B_{i}^{-\frac{1}{2}}\right\|\] \[\leq\mathrm{N}^{-3}\sum_{i=1}^{N}\|B_{i}\|^{-1}\sum_{i=1}^{N}\|A_ {i}\|+\sum_{i=1}^{N}\Bigl{(}\left\|B_{i}\right\|\bigr{)}^{-1}\|A_{i}\|\Bigr{)}\] \[\leq\frac{\max_{i}a_{i}}{N^{3}\max_{j}b_{j}}+N\max_{k}\frac{a_{k} }{b_{k}},\]  
where \(a_{i},b_{i}\) are the maximum spectral radii of \(A_{i}\), \(B_{i}\), respectively, for \(i=1,\ldots,N\). Hence, Theorem 1 is obtained. The upper bound of the approximation error of Algorithm 1 is concerned with the eigenvalues of the empirical data covariance, and the number of local servers. This accounts for the divergence of data covariance in one-shot communication. For the special case, such as distributed EP, \(B_{i}=I\) for any local server, \(b_{j}=1\) for any eigenvalue, and \(\Sigma_{1}=\Sigma_{2}\). When the number of local servers \(N\to\infty\) and the eigengap is \(\Delta\to\infty\), the right side of (6) approaches zero, satisfying the known conclusion.
# IoT IP Overlay Network Security Performance Analysis with Open Source Infrastructure Deployment
## 3 Components of Overlay Architecture
Typically employed in virtualization, MACVLAN interfaces allow containers or VMs to obtain DHCP addresses directly, easing integration into existing networks. MACVLAN offers four types, with MACVLAN bridge being common, enabling local communication without external routing. External connectivity utilizes the underlay network, as illustrated by two containers communicating via the MACVLAN bridge. * _MACSec_: MACsec operates at Layer 2, ensuring protection (integrity and/or encryption) transparently within the network. Unlike IPsec, which can pose performance challenges, MACsec is designed to run at line rate, typically in hardware's ASIC, though not universally supported across hardware. The protected MACsec frame utilizes an Ethertype of 0x88e5. IPValan is akin to MACVLAN but with a key distinction: the endpoints share the same MAC address. Supporting both L2 and L3 modes, IPValan offers flexibility in networking configurations. In L2 mode, each endpoint retains the same MAC address but receives a different IP address. Conversely, L3 mode facilitates packet routing between endpoints, enhancing scalability. While the Ethernet Header and SecTag are sent in plaintext, they are always integrity-protected by ICV. The default cryptographic algorithm is AES-GCM-128. Additionally, MACsec supports optional replay protection with a configurable replay window. Figure 1 illustrates the generic concept of stacking and encapsulation, fundamental to understanding the functioning of Overlay Networks, as well as VPNs. They are at the forefront of current research in the sector and serve as a foundation for new developments. These protocols support commercial platforms and open-source solutions. In the preceding section, we outline the protocols for implementing Overlay Networks on Linux operating systems, covering proprietary and open hardware platforms.