esa-sceva/satcom-chunk-collection huggingface dataset:
# Chemical Composition and Biological Activities of Oregon and Lavender Essential Oils
## 3 Results
### Gas Chromatography/Mass Spectrometry (GC/MS) Analysis  
The results of GC/MS chemical analyses of the essential oils are shown in Table 1.  
In _L. dentata_ essential oil, 43 components were identified by GC/MS. The main constituents are \(\beta\)-pinene (17.3%), followed by myrtrenal (10.2%), _trans_-pinocarveol (8.7%), \(\alpha\)-thujene (7.2%), linalool (6.8%), and 1.8-cineole (6.1%). Among the identified 23 components, the analysis of _O. glandulosum_ essential oil revealed that the main compounds are thymol (33.2%), \(\gamma\)-terpinene (25.4%), p-cymene (16.1%), and carvacrol (13.0%) (Table 1).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Constituents** & **LRI** & & \\  & & & \\  & & & \\ \hline \(\alpha\)-thujene & 933 & 0.9 & 7.2 \\ \hline \(\alpha\)-pinene & 941 & 0.6 & \\ \hline camphene & 955 & & 1.0 \\ \hline thuja-2,\(A\)(10)-diene & 959 & & 0.4 \\ \hline \(1\)-octen-\(3\)-ol & 981 & 0.3 & \\ \hline \(\beta\)-pinene & 982 & 0.2 & 17.3 \\ \hline \(3\)-octanone & 987 & 0.2 & \\ \hline myrcene & 992 & 1.4 & \\ \hline \(\alpha\)-phellandrene & 1006 & 0.2 & \\ \hline \(\alpha\)-terpinene & 1020 & 2.9 & \\ \hline \(p\)-cymene & 1028 & 16.1 & 1.1 \\ \hline limonene & 1032 & 0.5 & 5.1 \\ \hline \(1\),\(8\)-cineole & 1034 & & 6.1 \\ \hline \(\gamma\)-terpinene & 1063 & 25.4 & \\ \hline \(cis\)-linaloid oxide (furanoid) & 1076 & & 0.3 \\ \hline terpinolene & 1090 & & 0.8 \\ \hline linalool & 1101 & 0.6 & 6.8 \\ \hline hotrienol & 1103 & & 0.2 \\ \hline \(endo\)-fenchol & 1112 & & 0.2 \\ \hline \(cis\)-\(p\)-menth-\(2\)-en-\(1\)-ol & 1123 & & 0.6 \\ \hline \(\alpha\)-campholenal & 1125 & & 1.2 \\ \hline \(trans\)-pinocarveol & 1141 & & 8.7 \\ \hline \(cis\)-verbenol & 1142 & & 0.4 \\ \hline \(trans\)-verbenol & 1143 & & 2.2 \\ \hline pinocarvone & 1164 & & 4.1 \\ \hline Borneol & 1167 & 0.1 & 1.7 \\ \hline \(p\)-mentha-\(1\),\(5\)-dien-\(8\)-ol & 1168 & & 0.5 \\ \hline \(4\)-terpineol & 1179 & 0.6 & 0.9 \\ \hline \(\alpha\)-terpineol & 1185 & & 3.6 \\ \hline \(\alpha\)-terpineol & 1191 & 0.4 & 1.0 \\ \hline myrtrenal & 1194 & & 10.2 \\ \hline Verbenone & 1207 & & 0.3 \\ \hline \(trans\)-carveol & 1220 & & 1.1 \\ \hline methyl thymol & 1234 & 0.1 & \\ \hline cuminaldehyde & 1241 & & 3.9 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Chemical composition of the essential oils from \(O\). _glandulosum_ and \(L\). _dentata_.
## 3 Results and Discussion  
### Concentrations of Quercetin in the Samples  
Figure 1 was to compare the HPLC chromatograms of quercetin and two samples (AW and AF) to estimate the contents of quercetin in the samples.  
Table 1 is the result of the linearity test of the standard curves in terms of the correlation coefficient (R\({}^{2}\)) from the integrated peak area of the chromatogram (\(\Upsilon\)) versus the concentrations of quercetin (X, \(\mu\)g/mL) (n = 3, five concentration points). The method was linear between 298.5 and 3825.1 \(\mu\)g/mL for the AW and 273.4 and 3589.2 \(\mu\)g/mL for the AF. The correlation coefficients were satisfactorily greater than 0.999, implying that there was strong linear relationship between peak areas and the amounts of quercetin in the sample.  
Figure 1: Comparison of HPLC chromatogram of quercetin and two samples: (**a**) quercetin standard; (**b**) AW, hot water extraction of outer layers of leaf skin of _Aloe vera_ at 100 \({}^{\circ}\)C for 24 h; (**c**) AF, _L. plantarum_ fermentation of outer layers of leaf skin of _Aloe vera_ at 37 \({}^{\circ}\)C for 3 days and further extracted by an ultrasonicator at 40 kHz for 1 h at room temperature.  
The contents of quercetin in the samples, outer layers of aloe leaf extracts (AW) and the fermented extract (AF) were measured as 3.45 mg/g and 3.31 mg/g, respectively, and their concentrations were much higher than the value of 0.012 mg/g detected in the gels from _Aloe vera_[32], while other studies have also reported similar contents of quercetin in _Aloe vera_ ranging from 0.093 to 10.94 mg/g, depending on the parts examined [33; 34; 35]. This result would support the advantage of fermenting the plant resources to have higher elution of bioactive substances [18; 19; 20]. In particular, the outer layers of _Aloe vera_ contained much higher amounts of quercetin than the gels that are most often consumed, and a similar result was also reported in another study [36]. In the present study, the amount of quercetin in the AF was not much lower than that in the AW, although the AW was further fermented by lactic acid bacteria to make the AF. Therefore, quercetin was not substantially degraded during the fermentation process once it was extracted from the outer layers of aloe leaf. Moreover, quercetin could work together with other bioactive substances produced by lactic acid bacterial fermentation, which eventually results in greater synergistic effects of AF than AW. Based on this assumption, AF is expected to exhibit good efficacy in terms of the anti-skin aging effects since quercetin has been shown to have high efficacy in treating skin disorders, such as wound healing and wrinkling [37], possibly through synergistic effects of quercetin and other components of the AF.
# Time Series RUL Estimation of Medium Voltage Connectors to Ease Predictive Maintenance Plans
## 5 Experimental Setup and Historical Data Acquisition to Validate the Models
The loop was then disconnected from the output of the high-current transformer to cool it by forced ventilation to room temperature. Then, the next heat cycle was started. A total of 140 heat cycles were performed during 92.5 h. It is noted that recommended operating temperature of the 120 mm\({}^{2}\) aluminum stranded conductor is below 90 \({}^{\circ}\)C, and thus, the degradation process was accelerated by forcing the conductor to withstand 120 \({}^{\circ}\)C.  
During the heat cycle tests, the voltage drop, current, and temperature of the connectors were measured every 6 s to calculate their electrical resistance, thus being possible to determine the degradation trajectory of the electrical resistance, which describes the evolution of electrical resistance over time. A data acquisition (DAQ) device from National Instruments (NI USB-6210, 8 differential inputs, 88 \(\upmu\)V absolute resolution) (Austin, Texas, United States) was used to measure the voltage drop waveforms across of the seven connectors. A calibrated CWT Rogowski coil (CWT500 LFxB, 0.06 mV/A sensitivity) was used to measure the electric current, which was connected to one of the differential inputs of the DAQ. Both the current waveforms and voltage drop were sampled at a frequency of 5 kSamples/s during a few periods every 6 s. T-type thermocouples connected to an Omega Thermocouple Data Logger (USB TC-08, 8 channel, up to 10 measurements/s) were used to measure the temperature of the stranded conductor and the medium-voltage connectors, obtaining a temperature accuracy better than 1 \({}^{\circ}\)C. The accuracy in measuring the electrical resistance with this setup is better than 1 \(\upmu\Omega\).
## 3 Results  
First, Figure 3 shows the detection results--successful detections, FPs, and false negatives--by the proposed method. Figure 4 shows the free-response receiver operator characteristics (FROC) curve using the number of FPs per image, image-based sensitivity, and lesion-based sensitivity. The image-based and lesion-based detection sensitivities calculated from this curve were 0.76 (404/533) and 0.96 (90/94), respectively. Based on a previous study [18], we calculated the sensitivity by tumor size and depth, as shown in Table 2. The sizes of the neoplasms were calculated from the resected specimen (the major axis). In addition, the number of FPs per image and FPs per patient were 0.105 (127/1208) and 3.02 (127/42), respectively.  
Regarding the segmentation accuracy of the invasive region of gastric cancer, the histogram of the Dice index is shown in Figure 5. Based on this histogram, the average Dice index was 72.0%. As a result of extracting the gastric cancer region using U-Net, the average Dice and Jaccard coefficients were 57% and 43%, respectively.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Depth of Tumor**} & \multicolumn{4}{c}{**Tumor Size (mm)**} \\ \cline{2-5}  & **\textless{= 5}** & **6–10** & **11–20** & **>= 21** \\ \hline T1a & 6/6 (100) & 14/16 (87.5) & 23/23 (100) & 23/23 (100) \\ T1b & 1/1 (100) & 2/2 (100) & 9/10 (90) & 9/10 (90) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Sensitivity based on tumor size and depth (sensitivity, %).  
Figure 3: Detection results. (**a–c**) Successful gastric cancer detection, (**d**) false positives (FP) in healthy subjects, and (**e**) false negative in the abnormal case.  
The detection speed was 0.1 sec per image using AMD Ryzen 9 3900X (12 core) as the central processing unit (CPU) and NVIDIA TITAN RTX as the graphics processing unit (GPU).
## 6 Application to Geriatric Assessment Survey
### Functional Activity Questions
#### 6.1.3 Alternative Orderings  
The function level questions for the historical surveys are ordered from highest to lowest with respect to physical capabilities. Because the questions can be ordered in terms of function, it would not be natural to consider all 10! permutations as equally viable orderings. However, two alternative permutations that would be natural to consider are reverse order, where the questions are ordered from lowest functional level to highest, and inside out order where consecutive questions are separated.  
Would ordering the questions in either of these alternative ways yield a larger overall benefit compared to the original ordering? The inputted survey data is an \(N\times\ q\) matrix of 0/1 responses. To explore the effect of alternative orderings of the questions, all we have to do is permute the \(q\) columns in the data matrix appropriately, and then proceed with the analysis as before.  
First, consider reverse order. For a budget of 70 allowable changed answers, 9 CQs shown in Table S2 of the supplementary materials were identified for elimination for a total reduction of 2396 CQs. Corresponding branching rules are shown in Table S3 of the supplementary materials. The reverse order analysis leads to more branching rules, though not more questions saved.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Question** & **Previous Answers** & **Presumed Answer** \\ \hline
2 & 0 & 0 \\
3 & 00 & 0 \\
3 & 10 & 0 \\
4 & 000 & 0 \\
5 & 0000 & 0 \\
5 & 1000 & 0 \\
6 & 00000 & 0 \\
8 & 000000 & 0 \\
8 & 1000000 & 0 \\
9 & 00000000 & 0 \\
9 & 10000000 & 0 \\
10 & 10000000 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Function level CQs to eliminate (Budget is 1%).  
Next, consider the inside out ordering which changes the sequence of the questions numbers to 1, 3, 5, 7, 9, 10, 8, 6, 4, and 2. The intuition here is that consecutive questions are highly correlated, so separating them in space may lead to more efficient branching rules. For a budget of 70 allowable changed answers, the 15 CQs shown in Table S4 of the supplementary materials were identified for elimination for a total reduction of 3255 CQs. The inside out ordering leads to an increase 330 saved questions. Corresponding branching rules are shown in Table S5 of the supplementary materials. The inside our order analysis leads to one more branching, and increases the percentage of questions saved from 41.6% (initial analysis) to 46.2%.
# Bilinear CNN Model for Fine-Grained Classification Based on Subcategory-Similarity Measurement
## 4 Results
### Model-Configuration Study
#### 4.2.2 Softmax Effectiveness  
In order to further verify the effectiveness of our deep-learning method, we obtain deep features before feeding them to the last softmax layer after the weakly supervised localization, and combine them with some traditional classifiers, including k-Nearest Neighbor (k-NN) [38], Centroid Displacement-Based k-Nearest Neighbors (CDNN) [39], and Support Vector Machine (SVM) [40]. Depicted in Figure 4, the size of the pooled bilinear feature is \(512\times 512\); then, we concatenate it to a fixed dimensional feature vector. These fixed length feature vectors are utilized as the input to train these classifiers. Popular library LIBLINEAR was used in our experiments for SVM. Since bilinear features were \(\ell_{2}\) normalized, and the hyperparameter Csvm was independent of the dataset, so we set Csvm to 1 to train SVM using the entire training data.  
k-NN is a classical instance-based machine-learning algorithm that has been applied to classification issues for a long time. Because of the class-determination criteria, the majority vote can be a problem if distances between the test instance and its nearest neighbors widely vary, so we also used the CDNN, which uses a new class-determination criterion compared with K-NN, to verify the effectiveness of our deep-learning method. If a small k value is chosen in the above two methods, the whole model becomes complex and overfitting. When we select a large k, it is equivalent to using training data in a larger neighborhood to predict and training examples far from the input examples also play a role in the prediction, leading to prediction errors. In addition, we used PCA to reduce the dimension of bilinear features because of its high-dimension. Because there are too many pictures in the whole dataset, we select a subset to carry out the following experiments and efficiently run our code. We tried different k values \(from\) 3 to 10 in k-NN and CDNN with the validation set, and then selected the values giving the best performance on the validation set. When k = 8, k-NN performs best in this particular dataset during our experiments. Following the authors' implementation of CDNN, we obtained the best accuracy of 78.34% and 85.45% on CUB-200-2011 and Cars-196, respectively, when k = 7. The results in Table 3 show that the best classification accuracy was achieved using the  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **CUB-200-2011** & **Cars-196** \\ \hline WSDL & 46.05\% & 56.60\% \\ MEAN & 44.93\% & 55.79\% \\ Unsupervised object discovery & 69.37\% & **93.05\%** \\ SCDA & 76.79\% & 90.96\% \\ Ours & **77.31\%** & 91.02\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Accuracy of weakly supervised localization.  
SVM classifier. However, we still selected the softmax method due to its convenience in computing probability and measuring similarity between subclasses.
# Analyzing Zone-Based Registration under 2-Step Paging in Mobile Communication Network
## 5 Conclusions  
In this study, we have considered zone-based registration under 2-step paging. 2 ZR (ZBR with two zones) is known to outperform 1 ZR (ZBR with one zone) in most cases. However, in studies on comparison of 1 ZR and 2 ZR, the paging method of 1 ZR was simultaneous paging and that of 2 ZR was 2-step paging.  
Moreover, we have analyzed the performance of 1 ZR when 2-step paging (2 SP) is applied in the same manner as 2 ZR for exact performance comparison under similar conditions. To analyze the performance of 1 ZR under 2-step paging, we have considered the zone that is composed of n cells and presented the mobility model for movement between the cells in a zone. The steady-state probability of each cell was obtained by using the Markov chain model, and the total signaling cost was finally calculated. Through various numerical results, it could be seen that when 1 ZR adopts 2-step paging such as 2 ZR, 1 ZR behaves in a manner superior to 2 ZR in most cases, which is attributed to the reduction in the paging cost.  
In conclusion, when 2-step paging can be applied to both 1 ZR and 2 ZR, it is possible to reduce the paging cost and finally reduce the total signaling cost by selecting and operating the better 1 ZR and 2 ZR while considering the change in the traffic environment. In this study, we adopted a rather simple inter-cell mobility model and assumed that the time spent in a cell follows an exponential distribution for its simplicity. In the future, studies on diverse inter-cell mobility models and various types of distributions on time spent in a cell or a zone will be performed for a more sophisticated comparison between 1 ZR and 2 ZR.  
Modeling and data analysis, Z.T. and J.H.B.; writing--original draft, J.H.B.; writing--review and editing Z.T. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Research Base Construction Fund Support Program funded by Jeonbuk National University in 2022, and supported by the National Research Foundation of Korea (NRF) grant funded by the Ministry of Science and ICT, Republic of Korea (2022R1F1A1074141).  
The authors declare no conflict of interest.  
Figure 14: Effect of 2-step paging (\(\lambda_{m}=3\), \(\lambda_{c}=1\)).
## 4 The New Hybrid Optimization Algorithm  
### L-M Algorithm  
The iterative method stands out as a fundamental approach for resolving nonlinear systems of equations. Among these methods, the Gauss-Newton (G-N) algorithm emerges as one of the most classical and straightforward techniques in optimization. An advancement upon the G-N algorithm is the L-M algorithm, which can be regarded as a synthesis of the G-N algorithm and gradient descent.  
The L-M algorithm differs slightly from the G-N algorithm in terms of the iterative step size:  
\[\Delta x=-\left(\mathbf{J}(x)^{T}\mathbf{J}(x)+\mu I\right)^{-1}\mathbf{J}(x) ^{T}f(x) \tag{7}\]  
where \(\mu\) represents the damping parameter. For all \(\mu>0\), the coefficient matrix is positive, and this ensures that \(\Delta x\) is a descent direction. If \(\mu\) is large, the main diagonal elements dominate, and the function approximates first-order gradient descent. When \(\mu\) decreases, it approaches the G-N algorithm.  
The L-M algorithm has defined indicators to evaluate the approximate effect of the model:  
\[\rho=\frac{f(x+\Delta x)-f(x)}{\mathbf{J}(x)^{T}\Delta x} \tag{8}\]  
The denominator represents the descent in the approximate model, while the numerator represents the descent in the actual function. When \(\rho\) approaches 1, the approximation is considered good. Conversely, if \(\rho\) is too small, the approximation is deemed poor. If \(\rho\) is relatively large, the penalty factor \(\mu\) can be reduced, allowing the algorithm to become closer to the G-N algorithm in the next iteration. On the other hand, if \(\rho\) is very small or negative, indicating a poor approximation, the penalty factor needs to be increased and the step size reduced, making the algorithm resemble the steepest descent method.  
The flowchart for solving the nonlinear least squares problem in Section 3 using the L-M algorithm is shown in Figure 5, where \(\varepsilon_{1}\) is the threshold for the incremental equation and \(\varepsilon_{2}\) is the minimum step size.  
The L-M algorithm has demonstrated an ability to attain the desired iterative outcomes with relatively swift convergence. Nevertheless, it is important to note that the L-M algorithm is commonly regarded as a local optimization technique, leaving it susceptible to converging to a local optimum. This tendency is particularly pronounced when dealing with substantial noise in the data or when the iterative process confronts local minima in the function. In such scenarios, the L-M algorithm may struggle to converge to the global optimum.
# Deformation Analysis of Large Diameter Monopiles of Offshore Wind Turbines under Scour
## 3 Consideration of Local Scour
### Scour Characteristics
#### 3.2.2 Pattern of Scour  
(1) erosion in front and deposition behind  
This pattern is typical for large diameter monopile. The morphology of local erosion and deposition around monopiles represents the majority in this survey, instead of regular scour pits. As shown in Figure 10a-d, the topography around the pile can be obviously divided into three parts: erosion area in front of the pile, deposition area behind, and symmetrical development area on the side.  
Flow field changes because of monopile, and the velocity difference between the near bottom and the upper layer occurs; meanwhile, the water particles' movement produces a vortex system, which leads to erosion in front of the pile. Because monopile's diameter is much larger than the wavelength, the shield effect and diffraction will occur in the area behind the pile. The sediment carrying capacity of water flow here will be weakened, resulting in sediment deposition behind the pile. In the process of sediment transport on both sides of the pile foundation, affected by hydraulic and topographical conditions, whether it is erosion or deposition on the pile side may be unclear, but they have good regularity and generally develop symmetrically.  
(2) annular scour with scour pit  
A small number of monopiles showed another pattern, and the representative morphology is shown in Figure 10e,f. The shape of the scour pit is relatively regular, which is an inverted cone, and the lateral range of erosion and deposition is distributed in a circular manner. This pattern often occurs in the local scour around the long slender pile rather than monopile, and the topography differences may contribute to this result.  
The results show that, except for a few obvious scouring pits around large diameter monopiles, most monopiles do not show regular scour pits, but rather local erosion and deposition.  
Figure 9: Normal probability plot. (**a**) Distribution of scour depth; (**b**) distribution of scour volume.