esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results and Discussion  
The floral volatile components of the \"Sunny Bell\" _Cymbidium_ hybrid were analyzed using HS-SPME coupled with GC-MS. Each peak was identified by matching their spectra with those recorded in the National Institute of Standards and Technology (NIST) 14 mass spectral library and published data, as well as analysis of the Retention indices [RI] or Gas Chromatography the retention time [GC r.t.] data, and confirmed through analysis of the fragmentation pattern in mass spectra. Table 3 shows the 23 volatile components identified as the volatiles of the \"Sunny bell\" flowers; of which 94.05% were in the column, 97.23% in the labellum, 98.82% in the sepals, 99.80% in the petals, and 88.95% in the whole flower (new hybrid _Cymbidium_, \"Sunny Bell\"). These volatiles were grouped based on biochemical synthesis pathways [11]. Twelve volatile compounds belonging to different chemical classes, monoterenes (44.75%) and aliphatics (49.30%), were identified in the column (Figure 3A). The most abundant compounds were \(\alpha\)-pinene, 2,6-dimethylInonane, eucalypt, and 1,3-di-tert-butylbenzene, accounting for about 71% of the total GC peak area, followed by 2,4-dimethyl-1-decene (8.18%), _cis-_1,1,3,4-tetramethylcyclopentane (6.75%), and 7-methyl-1-undecene (2.52%). In the labellum, sixteen volatile compounds were identified (Figure 3B): monoterenes (48.34%), aliphatics (48.28%), and sesquiterpen (0.61%). The most abundant compounds were 4,8-dimethyl-1,3,7-nonatriene, trans-ocieme, linalool, and 1,3-di-tert-butylbenzene, accounting for about 57% of the total GC peak area, followed by \(\beta\)-myrccene (8.62%), eucalypt (6.90%), and \(\alpha\)-pinene (5.27%) (Figure 3B). The sepals yielded eleven volatile compounds (Figure 3C): monoterenes (82.86%), aliphatics (8.15%), and sesquiterpen (7.81%). The most abundant compounds were linalool, _trans-_\(\beta\)-ocieme, and 4,8-dimethyl-1,3,7-nonatriene, accounting for about 84%, followed by \(\beta\)-myrccene (3.50%), \(\alpha\)-farresene (2.93%), and (\(\pm\))-trans-nerolidol (2.25%). The relative content of linalool was significantly higher in the sepals compared to the column and labellum. In the petals, ten volatiles were identified (Figure 3D): monoterenes (91.17%), aliphatics (1.33%), and sesquiterpenens (6.60%). The major compounds were linalool, accounting for approximately 80%, followed by _trans-_\(\beta\)-ocieme (6.13%), \(\alpha\)-pinene (3.12%), and \(\beta\)-farresene (2.19%). The relative linalool content was significantly higher in the petals than in the other floral organs. The petals and sepals of the new hybrid, _Cymbidium_ \"Sunny Bell\" had floral scents composition ratio. The order of total peak areas (data not shown) was as follows: petals \(>\) sepals \(>>\) labellum \(>\) column. Figure 3: Representative total ion chromatograms for column (**A**), labellum (**B**), sepals (**C**), and petals (**D**) of the new hybrid, _Cymbidium_ \"Sunny Bell\" flower analyzed by HS-SPME-GC-MS.
# Ising-Based Kernel Clustering +
## 2 Ising-Based Combinatorial Clustering  
### Ising Model  
Equation (1) is a Hamiltonian of the Ising model. Ising machines search for the combination of variables \(\sigma_{i}\in\{-1,+1\}\) that minimizes the energy of the Ising model in Equation (1).  
\[H=\sum_{i<j}J_{ij}\sigma_{i}\sigma_{j}+\sum_{i}h_{i}\sigma_{i} \tag{1}\]  
The Ising model has the interactions between two different spins \(J_{i,j}\) and the magnetic field of its own spin \(h_{i}\).  
The Quadratic Unconstrained Binary Optimization (QUBO) model is an equivalent representation of the Ising model. Equation (2) is a Hamiltonian of the QUBO model. Here, binary variables \(q_{i}\in\{0,1\}\) are decision variables of the QUBO model. The Hamiltonian of the QUBO model is transformed from the Hamiltonian of the Ising model by \(q_{i}=\frac{\sigma_{i}+1}{2}\).  
\[H=\sum_{i<j}a_{i,j}q_{i}q_{j}+\sum_{i}b_{i}q_{i}^{2} \tag{2}\]  
The QUBO model also has the interactions between two different variables \(a_{i,j}\). In contrast to the Ising model, the QUBO model does not have any magnetic field but has another type of self-interaction \(b_{i}\) because \(q_{i}=q_{i}^{2}\) holds for \(q_{i}\in\{0,1\}\). In addition, the coefficients of the Ising and QUBO models are stored in the matrix form. The matrix form is a unified interface that various Ising machines have adopted.
# Brain-Inspired Healthcare Smart System Based on Perception-Action Cycle
## 5 Healthcare Smart System for Measuring Dolphin-Assisted Therapy Efficiency
### EEG Acquisition
#### 5.1.1. Echolocation  
As part of their evolution, dolphins have developed a sophisticated sensory system called echolocation, emitting sound waves that travel through the water and return to the dolphin in the shape of echoes to be processed and interpreted by their brain.  
The sounds used by these mammals in echolocation consist of small emissions of repeated clicks at different frequencies. Low-frequency _clicks_ can travel long distances with a high penetration power; these clicks reflect the object structures, so the dolphin can obtain information from the surrounding topography of the objects. Regarding the location of their nearby dams, dolphins emit high-frequency _clicks_, inaudible to humans. For instance, bottlen dolphins emit clicks at frequencies ranging from 15 to 130 KHz, while killer whales emit clicks at an average 14 KHz-frequency. This explains why dolphins rely fully on their sonar to navigate and communicate simultaneously, as well as hunting in dark waters where vision is of little use. Also, bottlen dolphins produce clicks and sounds whose frequency ranges from 0.25 to 150 KHz, the maximum frequency of the echolocation clicks becomes 100 KHz [15].
## 2 Related Work
### Recent Works and Methods  
The study published in [13] highlighted the deficiencies found in many previous works addressing network attacks classification when they consider an outdated dataset such as KDDCup'99 for performance evaluation. Other works similar to this one usually achieve high classification performance rates, although this is mainly motivated by the flaws found in KDDCup'99 dataset. This assumption is confirmed by comparing accuracy results obtained in [15] through classical ML algorithms against the ones obtained by using the same ML methods but, in this case, considering a recent and realistic dataset: NGIDS-DS [16]. Although the same ML algorithms were used for comparison, no specific details were mentioning the methodology followed by the authors. The authors in [15] proposed an IDS architecture addressing new challenges on managing vast volumes of network traffic in real time. To evaluate the system, attack classification performance was tested by using very well known ML algorithms on the old-fashion KDDCup'99 dataset. Additionally, an interesting study about the impact of specific feature selection techniques on the classification performance is provided. Finally, a proposed FS method combining manual exploratory feature analysis and two of the considered FS techniques (FSR (Forward Selection Ranking) and BER (Backward Selection Ranking)), got the best results in terms of modelling and detection time. However, the authors did not mention anything about FE, DP and HS, making the reported results hard to reproduce and compare. A realistic network dataset comprising up-to-date attack and normal traffic patterns from an emulated network environment is introduced by [PERSON] et al. [17]. They corroborated the feasibility of the dataset in order to detect network-based attack with up to seven classical ML algorithms. To this end, they extracted several network traffic flow-based characteristics employing a specific tool. Then, they performed an FS procedure called Random Forest Regressor for relevance feature weighting depending on the attack. However, neither HS nor DP methods were described.
## 2 An Efficient Algorithm for Moment-Independent Importance Analysis
### Moment-Independent Importance Index  
Consider an input-output system represented by  
\[Y=g(\mathbf{X}) \tag{1}\]  
where \(\mathbf{X}=[X_{1},X_{2}\cdots,X_{n}]\) is the n-dimensional vector of the structural input variables with random uncertainties. \(Y\) is the output seismic demand with uncertainty propagated by \(\mathbf{X}\) through the nonlinear function \(g(\bullet)\). Therefore, if \(\mathbf{X}\) follows a certain distribution, the PDF of \(Y\) can be evaluated.  
Denote as \(f_{Y}(y)\) the unconditional PDF of \(Y\) and as \(f_{Y|X_{i}=x}(y)\) the conditional PDF with \(X_{i}\) fixed at one implementation value \(x\). The area difference between \(f_{Y}(y)\) and \(f_{Y|X_{i}}(y)\) demonstrates the entire influence of the individual input distribution, which is denoted as [15]  
\[s(X_{i})=\int\limits_{\Omega_{Y}}\Big{|}f_{Y}(y)-f_{Y|X_{i}}(y)\Big{|}dy \tag{2}\]  
The MII index \(\delta_{i}\) of \(X_{i}\) is defined by half of the expectation of \(s(X_{i})\)[7]  
\[E_{X_{i}}[s(X_{i})]=\int s(X_{i})f_{X_{i}}(x_{i})dx_{i} \tag{3}\]\[\delta_{i}=\frac{1}{2}E_{X_{i}}[s(X_{i})] \tag{4}\]  
where \(f_{X_{i}}(x_{i})\) is the marginal PDF of \(X_{i}\).  
From Equations (1)-(4), the MII index \(\delta_{i}\) can be interpreted as an average influence quantification on the PDF of output seismic demand, while the input variable is fixed over their PDF ranges.  
Reference [13] summarized the advantages of the MII index and highlighted that MII is not limited to any specific moment, unlike with the variance importance method or the [PERSON]'s indices. Thus, it can reflect the influence of the input uncertainties on the output uncertainty more comprehensively.
## 4 Materials and Methods
### Proposed DDPG Control Methodology for Attack Mitigation
#### 4.2.1. Stator Active and Reactive Power Measurement and Set Points  
The direct (d) part of the stator current is directly related to the real power of the stator, and the quadrature (q) part of the stator current is related to the reactive power of the stator [14]. Thus, by utilizing these values, the real and reactive power of the stator can be found mathematically in real time. The data points are created using a small-scale wind farm consisting of six wind turbine DFIGs, and the AI controller is trained during a live simulation. Therefore, the training should closely resemble real world parameters. The set points for the system are determined by the generators utilized in the wind farm and are considered constants under best-case scenarios. In this paper, the stator active power set point is 9 MW, and the reactive power set point is 0 MVAR.
# Compressive Sampling with Multiple Bit Spread Spectrum-Based Data Hiding
## 4 Experimental Result
### Security Analysis  
In Section 3.3, there were two parameters having an impact on the model security, i.e., \(N_{l}\) as the number of the generated integer random permutation value and \(r\) as the row and column number of the diagonal matrix after being truncated, \(\mathbf{S_{r}}\). The original Hadamard matrix is denoted as \(\mathbf{H}_{r}\), and the secured Hadamard matrix is denoted as \(\mathbf{H}_{s}\). We applied the simulation varying \(r\) and \(N_{l}\) to understand how much \(r\) and \(N_{l}\) affected the security performance. In the real situation, one can try  
Figure 5: Three performance parameters affected by \(r/M\) and computational time.  
to break the security model by using the original Hadamard matrix for detecting the watermark and reconstructing the audio due to the simplicity of the Hadamard matrix generation. With the secured Hadamard matrix in the encoder, we applied the decoding by the original Hadamard matrix to analyze the strength of the security model. If the security model worked well, the detection watermark should ideally be damaged, or the BER should be near 0.5.  
In the simulation, we assumed \(p=r=20\) and \(M=128\) samples. \(N_{l}\) varied from zero to \(r\). Parameter \(N_{l}\) was zero, meaning that \(\mathbf{H}_{s}=\mathbf{H}_{r}\). We used five clips for analysis by calculating the average BER after the watermark detection process. We applied the simulation in 100 iterations for each clip. The simulation result is shown in Figure 5(a). The worst detected watermark was obtained when \(N_{l}\) was half of \(r\), and the perfect watermark was detected when \(N_{l}=0\) and \(N_{l}=r=20\). We could limit the accepted minimum BER to restrict the value of \(N_{l}\). We chose BER = 0.4 as a safe minimum BER because we could still interpret the digital visualization from the detected watermark with BER < 0.3 [30]. Therefore, we chose \(N_{l}>6\) or generally \(N_{l}>0.3r\) as the minimum value of \(N_{l}\) and \(N_{l}<14\) or generally \(N_{l}<0.7r\) as the maximum value of \(N_{l}\) to keep the detected watermark uninterpretable when one tries to detect the watermark by the original Hadamard matrix.  
Figure 5(b) shows the relation between BER and \(r\) and compares the detected watermark quality using the different \(N_{l}/r\). The simulation was applied to 50 clips via 10 iterations for each clip. The range of \(r\) was \([6,30]\). The worst watermark was detected when \(N_{l}/r=0.5\). The detected watermark quality was better when \(N_{l}/r\) decreased and as the value of \(r\) increased. When \(N_{l}/r=0.3\), most of the BER values were more than 0.4. This result confirmed the restriction of \(N_{l}\) in the range \([0.3r,0.7r]\).
# Review on Active and Passive Remote Sensing Techniques for Road Extraction
## 3 Road Extraction Based on Different Data Sources
### Road Extraction Based on SAR Images
#### 3.3.2 Status and Prospects  
Automatic road extraction from SAR imagery remains solely experimental to date. For operational tasks, semiautomatic methods [172] with human intervention or manual postprocessing are required. Large-scale tests have not been conducted, and for the reported methods, remarkable variations in the results between different test sites have been observed. The studied road types include forest roads, city streets, highways, desert roads, gravel roads, paved roads, jicy roads and bridges. However, different or mixed road classes have been evaluated in only a few studies. Different methods are required for different road types and study areas. In general, the developed methods are complex and involve many steps.  
Owing to the side-looking sensor [212], SAR produces many linear features, causing many false alarms in road extraction. In addition, road detection varies with the looking direction, especially in urban areas (radar shadows). The speckle and speckle filters also affect the road extraction accuracy. In addition, the roads may appear bright because of the surrounding structures, instead of appearing as dark lines. Therefore, the road appearance differs depending on the SAR image resolution. A highly complex image is obtained with high-resolution data, showing strong geometric effects in urban areas. Therefore, the use of multiple datasets is often required. Conversely, road detection from SAR is independent of road surface material; this feature is less pronounced in the optical detection method. Thus, the fusion of SAR and optical signals can be beneficial.
## 3 Work Methods
### Impact Force  
According to the structure and working principle of the torque generator, the impact force \(F\) is related to impact hammer mass, impact hammer speed, and impact surface angle. After the impact hammer collides with the impact anvil, because of the existence of the impact-inclined plane, the impact-inclined plane will generate an impact reaction force against the impact hammer. One component of the impact reaction force will make the impact hammer move in the axial direction of the connecting shaft, so that the impact hammer will pass over the impact anvil inclined plane, and it will then continue to rotate under the drive of the transmission shaft until it meets the impact-inclined plane here, thus completing the periodic impact task. During this impact process, the motion energy of the impact hammer is transferred to the impact anvil. According to the principle of momentum conservation:  
\[F\cdot\Delta t=m\cdot V_{x} \tag{2}\]  
where \(F\) is the impact force that is generated by torque generator, N; \(\Delta t\) is the contact time between impact hammer and impact anvil during impact, s; \(m\) is the impact hammer mass, kg; and \(V_{x}\) is the linear velocity of impact hammer, m/s.  
\[V_{x}=\omega\cdot r=\frac{V_{z}}{60}\cdot 2\pi\cdot\frac{r}{1000} \tag{3}\]  
where \(\omega\) is the angular velocity, rad/s; \(V_{z}\) is the rotation speed of the transmission shaft, rot/min; and \(r\) is the rotary radius of impact hammer, mm.  
In addition, when the impact hammer acts with the impact anvil, it comes into contact with an inclined plane (i.e., there is an impact angle \(\alpha\), 0\({}^{\circ}\) \(<\) \(\alpha\) \(<\) 90\({}^{\circ}\)); in other words, part of the force that is generated in the impact process is in the radial direction of the impact anvil, and this part of force cannot work on the impact anvil. Only the force in the tangential direction of the impact anvil can work on the impact anvil. Therefore, the impact force f acting on the torsional impact that is generated by the torsional impactor is:  
\[F=\frac{sin\alpha\cdot mv_{x}}{\Delta t}=\frac{sin\alpha\cdot m\cdot\frac{V_{ z}}{60}\cdot 2\pi\cdot\frac{r}{1000}}{\Delta t} \tag{4}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **Displacement of** & **Rotation Speed** & **Torque** & **Recommended Hole** \\  & **Pump (L/s)** & **(rot/min)** & **(N\(\cdot\)m)** & **Diameter (mm)** \\ \hline
165 & 25 & 490–600 & 2800–3900 & 200.0–215.9 \\
175 & 25 & 465–560 & 3000–4300 & 212.7–215.9 \\
195 & 30 & 430–560 & 3200–5600 & 212.7–250.8 \\
240 & 35 & 400–500 & 3360–6200 & 250.8–311.0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Basic parameters of different types of turbodrill (drilling fluid density is 1.2 g/cm\({}^{3}\)).  
where \(F\) is the impact force, N; \(\Delta t\) is the impact contact time of two parts, s; and \(\alpha\) is the impact angle, \({}^{\circ}\).
## 3 Results and Discussion
### H\({}_{2}\)-TPR Characterization  
Additional information about the redox properties of the catalysts was obtained by H\({}_{2}\)-TPR. The TCD profiles obtained in H\({}_{2}\)-TPR experiments are included in Figure 7. Three peaks are observed for all catalysts. The H\({}_{2}\) consumed above 700 \({}^{\circ}\)C is assigned to bulk ceria reduction, that is, to the reduction of Ce (IV) cations placed within the oxide particles. The lowest-temperature peak at around 100 \({}^{\circ}\)C is attributed to both rhodium oxide reduction and noble metal-catalyzed surface reduction of ceria [30]. The TCD signal appearing between 200 and 400 \({}^{\circ}\)C can be attributed to different events: the reduction of surface ceria which is not in close contact with rhodium, the decomposition of surface carbonates (occluded within the CeO\({}_{2}\) structure) and/or the reduction of hydroxyl and peroxide/superoxide surface groups [8; 31; 32; 33].  
A detailed analysis of the lowest temperature reduction peak shows a slightly lower reducibility of the catalyst with the lowest activity (Rh25 Ce25, see lowest area in the Figure 7) in comparison to the remaining catalysts. This observation is in agreement with the conclusions of previous studies, where a relationship between surface reducibility and N\({}_{2}\)O decomposition capacity was obtained for a set of  
Figure 7: H\({}_{2}\)-TPR profiles of fresh catalysts after an _in situ_ pretreatment with 5% O\({}_{2}\)/He at 500 \({}^{\circ}\)C.  
RhOx/CeO\({}_{2}\) catalysts prepared with different ceria carriers (either pure and doped with La or Pr) [17]. Moreover, the rate limiting step of the RhOx/CeO\({}_{2}\) catalyzed N\(\mathrm{\SIUnitSymbolO}\) decomposition mechanism was reported to be the reduction of the catalysts sites by N\(\mathrm{\SIUnitSymbolO}\). The redox properties of the support play an important role in the stabilization of cationic rhodium species under reaction conditions, and best Rh/Ceria catalysts where those with a highly reducible CeO\({}_{2}\) surface. The current results suggest that flash calcination of rhodium nitrate (starting heating either at 250 or 350 \(\mathrm{\SIUnitSymbolO}\)) allows obtaining a much better noble metal-support interaction than the conventional ramp calcinations (starting heating at 25 \(\mathrm{\SIUnitSymbolO}\)) [18,29], which was confirmed by TEM analysis.
# Review of Structural Health Monitoring Techniques in Pipeline and Wind Turbine Industries
## 2 Health Monitoring Techniques in Pipeline Industry
### In Pipeline Industry
#### 2.1.5 Ultrasonic Technique
The advantage of ultrasonic guided waves includes their assessment ability of concealed structures--which are present underwater as well as in concrete, coatings, and insulations--with magnificent sensitivity. Due to its simplicity and speed, it is considered a cost-effective solution for structural health monitoring [38]. The application of ultrasonic guided waves includes various structures such as tear straps, landing gears, lap splice joints, and so on. Its aircraft applications are also presented in the references [38; 39; 40; 41; 42]. Ultrasonic guided waves can also be used to perform surgery by the harmonic scalpel. In this case, the energy used for activation is sent to the tip of the rod, which is deployed for surgical use [43]. These waves are also used in inspecting rails in the railroad industry [44; 45; 46]. Moreover, guided waves could also show their impact in some adhesive bonding and joining applications [47; 48; 49]. At the interface between materials, these waves can generate both longitudinal and shear waves energy, which is especially useful in the adhesive bond inspection. The result of the dispersion of a sample guided wave is illustrated in \[C_{1}=\sqrt{\frac{\lambda+2u}{\rho}}=\sqrt{\frac{E(1-V)}{(1+V)(1-2V)\rho}} \tag{14}\]1 Fourier transform was applied to obtain this profile, and then, the result obtained is compared with the theoretical value in which the dominant modes of the structure are highlighted [50].
## 2 Materials and Methods
### Methods
#### 2.2.4 Image Fusion
The approximation sub-band from MRA-DWT (\(A\)) consisted of only slow change components, so we combined it with the Difference of Gaussian (\(L\)), which had details of distinctness of edges. The detail sub-bands from MRA-DWT (\(H\), \(V\), and \(D\)) represented small and fast changes. We combined them with a Gaussian-filtered image (G1_1), where the result also showed slow changes from the original image. In addition, to support our idea, we tried to perform an experiment combining the components of the same frequencies, but the results were unsatisfactory. Figure 6 displays the results of the proposed image fusion. The fusion rules in our proposed method were as follows (Figure 2):  
* Fused \(A\) (a low-frequency component) with \(L\) (a high-frequency component), resulting in \(AL\);
* Fused \(H\) (a high-frequency component) with \(G1\_1\) (a low-frequency component), resulting in \(HG\);
* Fused \(V\) (a high-frequency component) with \(G1\_1\) (a low-frequency component), resulting in \(VG\);
* Fused \(D\) (a high-frequency component) with \(G1\_1\) (a low-frequency component), resulting in \(DG\). The image fusion rules were as follows:* LP-IF: averaging for the base low-pass and highest absolute value for the fused high-pass coefficient;
* DWT/IDWT-IF: max-min, min-max, and mean-mean rules with variations in wavelet family and level of decomposition.