esa-sceva/satcom-chunk-collection huggingface dataset:
# Pose Tracking and Object Reconstruction Based on Occlusion Relationships in Complex Environments
## 2 Methods
### Optimization
#### 2.3.2 Neural Object Field Optimization  
At the beginning of each training cycle, the neural object field retrieves all memory frames from the memory pool and begins learning. When training converges, optimized poses are updated to the memory pool to assist subsequent online pose graph optimization. To optimize the neural object field, we adopt the loss function from BundleSDF [12]. The trainable parameters include the multi-resolution hash encoder \(\Omega\), \(\Phi\) and object pose updates within the tangent space parameterized in the Lie algebra \(\delta\xi\in R^{(|P|-1)\times 6}\), where the pose of the first memory frame is fixed as an anchor.  
The uncertain free space loss \(L_{u}\) is designed to ignore rays passing through segmentation masks or rays with uncertain depth. By over-predicting a small positive value, the model can quickly converge when reliable observations are later available. The empty space loss \(L_{e}\) trains the model to predict the truncated distance for points far from the surface, ensuring that distant points are accurately handled. The near-surface space loss \(L_{surf}\) is used to ensure that points near the surface predict the signed distance function (SDF) correctly.  
\[L_{u} =\frac{1}{|X|}\sum_{x\in X}\|\Omega(x)-\epsilon\|_{2} \tag{7}\] \[L_{e} =\frac{1}{|X|}\sum_{x\in X}|\Omega(x)-\lambda|\] (8) \[L_{surf} =\frac{1}{|X_{surf}|}\sum_{x\in X_{surf}}(\Omega+d_{x}-d_{D})^{2} \tag{9}\]  
The color loss function \(L_{c}\) is used to learn appearance and the Eikonal regularization [29] is \(L_{eik}\) over the SDF in near-surface space.  
\[L_{c}=\frac{1}{|R|}\sum_{r\in R}\|c(r)-\hat{c}(r)\|_{2} \tag{10}\] \[L_{eik}=\frac{1}{|x_{surf}|}\sum_{x\in x_{surf}}(\|\delta\Omega( x)\|_{2}-1)^{2} \tag{11}\]  
The total loss is  
\[L=w_{u}L_{u}+w_{e}L_{e}+w_{surf}L_{surf}+w_{c}L_{c}+w_{eik}L_{eik} \tag{12}\]  
and we set all w to 1 empirically.
## 5 Discussion
### Pre-MECO Phase (120-142 m): Stratified Water Column and Oligotrophic Conditions  
The pre-MECO phase corresponds to the interval preceding the warming trend as documented by the oxygen stable isotopes (Figure 2). Benthic foraminiferal assemblages are characterized by high values of P/B, C/A, and diversity, and relatively low values of bi-triserial group (Figure 3). The relatively high abundance of the epifaunal group (-35%) likely suggests a low to moderate, flux of organic matter to the seafloor. This is also supported by the highest abundance of the epifaunal and oligotrophic indicator _Nuttallides_ (i.e., _N. truempyi_ and _N. umbonifera_) species (Figure 4, Table S1). The sea floor records well oxygenated conditions as indicated by the high diversity and by the abundance of _Nuttallides truempyi_ (Figure 4, Table S1).  
Planktic foraminiferal assemblages are well diversified in this interval (Figure 5). This indicates the occurrence of a stratified upper water column with all ecological niches occupied. Mixed-layer dwellers (_Acarinina_, _Morozovelloides_, _Globigerinatheka_), thermocline dwellers (_Subbotina_, _Parasubbotina_, _Catapsydrax_, _Paragloborotalia_,) and shallow sub-surface dwellers (intermediate between mixed-layer and thermocline) _Turborotalia_ are in fact present. Planktic foraminiferal assemblages suggest oligotrophic surface-water because they contain abundant oligotrophic indices such as the symbiont-bearing large _Acarinina_ (dominant), _Morozovelloides_ and _Globigerinatheka_. A relatively expanded Oxygen Minimum Zone (OMZ) is suggested by the occurrence, though with low abundance, of _Hantkentina_. Oligotrophic conditions within the photic zone are also supported by relatively abundant _Sphenolithus_, _L. minutus_, _Z. bijugatus_, generally considered as oligotrophic indices (Figure 6).  
In Figure 7, the statistical analysis highlights prevailing oligotrophic conditions both along the water column and at the seafloor. It also suggests overall cooler conditions and the absence of carbonate dissolution.
# Positron Emission Tomography in Coronary Heart Disease
## 3 Perfusion Imaging
### Imaging Myocardial Perfusion with 15O-Water  
Oxygen-15-labelled water is a freely diffusible and metabolically inert tracer, and is considered the best tracer for quantitative studies [41]. It has a half-life of 2.4 min and an extraction fraction in comparison to MBF of around 95% [35].  
A prospective clinical study involving 208 patients with suspected CAD who underwent CCTA, technetium 99 m/tetrofosmin-labeled SPECT, and 15O-water PET with examination of all coronary arteries by fractional flow reserve, revealed that PET exhibits the highest accuracy for diagnosis of myocardial ischemia [42]. Although not FDA approved and mainly used in research, routine clinical use of 15O-water PET with a bedside generator and infusion solution has proven to be reliable and efficient [43].
# A Efficient and Accurate UAV Detection Method Based on YOLOv5s
## 3 Materials and Methods
### Boundary Box EIoU Loss Function  
The bounding box loss function plays a critical role in target localization and recognition [56]. However, the Complete Intersection over Union (CIoU) loss function used in the YOLOv5 algorithm struggles to effectively address the severe imbalance between positive and negative samples. Additionally, it is prone to gradient vanishing issues, slowing down the convergence speed and affecting detection accuracy [4]. To mitigate these challenges, this experiment adopts the Efficient-Intersection over Union (EIoU) loss function [57], which explicitly measures differences in three geometric factors within the bounding box--overlap area, center point, and edge length--thereby resolving issues related to sample imbalance and gradient explosion.  
The CIoU loss function adds a penalty term for the aspect ratio of the bounding boxes. This addition ensures consistency in aspect ratios between the predicted and target boxes. The mathematical expression for CIoU is as follows:  
\[\text{CIoU}=\text{IoU}-\frac{\rho^{2}\left(b,b^{gt}\right)}{c^{2}}-\alpha v \tag{7}\]  
\[\alpha=\frac{v}{(1-IoU)+v} \tag{8}\]  
\[v=\frac{4}{\pi^{2}}\left(arctan\frac{v^{gt}}{b^{gt}}-arctan\frac{w}{h}\right) ^{2} \tag{9}\]  
In Equations (7)-(9), \(\alpha\) represents the balancing parameter, b denotes the predicted box, v is the parameter measuring the consistency in the width and height ratio between the predicted and ground truth boxes, \(\mathbf{b^{gt}}\) denotes the ground truth box, \(\mathbf{w^{gt}}\) and \(\mathbf{h^{gt}}\) represent the width and height of the ground truth box, while w and h represent the width and height of the predicted box.  
To address the issue of aspect ratio imbalance in CIoU, this study introduces the EIoU loss function. The EIoU loss function improves aspect ratio loss by splitting it into the differences between the width and height of the predicted box and its minimum enclosing box. This method more accurately measures the shape and positional differences between the predicted box and the target box. It not only accelerates the convergence speed of the predicted box but also significantly enhances localization accuracy, especially when  
Figure 5: The structure of the ghost convolution.  
detecting small objects and objects in complex backgrounds. The EIoU loss function is formulated as follows:  
\[\begin{split} L_{EIoU}=L_{IoU}+L_{dis}+L_{asp}\\ =1-IoU+\frac{\rho^{2}\left(b\,b^{st}\right)}{C^{2}}+\frac{\rho^{2} \left(w,w^{st}\right)}{C_{w}^{2}}+\frac{\rho^{2}\left(h,h^{st}\right)}{C_{h}^{ 2}}\end{split} \tag{10}\]  
EIoU consists primarily of three components: the overlap loss \(\mathbf{L_{IoU}}\), the center distance loss \(\mathbf{L_{dis}}\), and the width-to-height loss \(\mathbf{L_{asp}}\). In the formula, \(\mathbf{C_{w}}\) and \(\mathbf{C_{h}}\) represent the width and height of the smallest enclosing box that contains both the ground truth and predicted boxes, and \(\mathbf{\rho}^{2}\) measures the Euclidean distance between two variables. The structure of EDU-YOLO is shown in Figure 6.
# Identification and Localization of Wind Turbine Blade Faults Using Deep Learning
## 4 Simulations and Results  
This study investigates the performance of the YOLO family of architectures at differing parameter sizes, along with Mask R-CNN with altered backbone networks, for the purpose of fault localization in wind turbines. Various hardware platforms were utilized to accomplish the training including the use of online cloud-based solutions such as Google Colaboratory and local machines with NVIDIA RTX 4080 Graphic Processors (Nvidia, Santa Clara, CA, USA). Additionally, PyTorch v. 2.3.1 was leveraged as the deep learning framework for both YOLO and Mask R-CNN training.
# Distributed Graph Diameter Approximation
## Preliminaries  
### Graph-Theoretic Concepts  
We introduce some notation that will be used throughout the rest of the paper. Let \(G=(V,E,w)\) be a connected undirected weighted graph with \(n\) nodes, \(m\) edges, and a function \(w\) which assigns a positive integer weight \(w(e)\) to each edge \(e\in E\). We make the reasonable assumption that the ratio \(w_{max}/w_{min}\) between the maximum (\(w_{max}\)) and the minimum (\(w_{min}\)) weight is polynomial in \(n\). The _distance_ between two nodes \(u,v\in V\), denoted by \(d(u,v)\), is defined as the weight of a minimum-weight path between \(u\) and \(v\) in \(G\). Moreover, we let \(\Phi(G)\) denote the _diameter_ of the graph defined as the maximum distance between any two nodes. The following definition introduces the concept of \(k\)-clustering in weighted graphs.  
**Definition 1**: _For any positive integer \(k\leq n\), a \(k\)-clustering of \(G\) is a partition \(\mathcal{C}=\{C_{1},C_{2}\ldots,C_{k}\}\) of \(V\) into \(k\) subsets called clusters. Each cluster \(C_{i}\) has a distinguished node \(c_{i}\in C_{i}\) called center, and a radius \(r(C_{i})=\max_{v\in C_{i}}\{d(c_{i},v)\}\). The radius of a \(k\)-clustering \(\mathcal{C}\) is \(r(\mathcal{C})=\max_{1\leq i\leq k}\{r(C_{i})\}\)._
## 3 Numerical Examples
### Numerical Example 2  
Figure 4 illustrates the cross-section for Example 2, which incorporates multiple flanges and web connections, simulating complex architectural elements. This configuration was utilized to study stress distribution and deformation under multidirectional loading, highlighting the model's capability to handle advanced structures commonly found in aerospace and architectural engineering.  
When an axial load of P = 0 kN is applied, Table 3 presents the six natural frequencies calculated for the simply supported thin-walled beam in modes \(n\) = 1, 2, and 3, as derived from Example 2. As illustrated in Figure 5, Example 2 exhibits a similar pattern to Example 1: the natural frequency decreases with an increasing axial load, ultimately reaching zero at the point of elastic buckling. Table 4 details the magnitude of the critical axial load at which buckling occurs. It is evident that the critical axial load decreases with an increase in the beam length while simultaneously increasing with higher mode numbers, a trend consistent with the observations in Example 1.  
The calculation results in Appendix D indicate that, similarly to in Example 1, the differences between the Timoshenko-Vlasov and traditional beam theories decrease as the beam length increases. However, there is a trend of increasing error with an increase in the mode number for shorter beams (\(L\) = 3 m and \(L\) = 10 m). The increasing error with the mode number for shorter beams in this comparison can be attributed to several key factors. First, shear deformation and warping effects become more pronounced in shorter beams and at higher modes. The Timoshenko-Vlasov model incorporates these effects, whereas traditional beam theories neglect them, resulting in growing discrepancies as the mode number increases. Second, geometric non-linearities are more significant in shorter beams, and these non-linear effects become increasingly influential at higher modes, further intensifying the error in traditional models. Lastly, boundary conditions and localized stress concentrations exert a more substantial influence on the dynamic response of shorter beams, particularly at higher modes. Traditional theories, which often assume simplified boundary conditions, fail to account for these localized effects, thereby contributing to an increase in difference with higher modes.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline \(L\) (m) & \(n\) = 1 & \(n\) = 2 & \(n\) = 3 \\ \hline
3 & 3998.8 kN & 15,841.5 kN & 35,553.0 kN \\
10 & 404.0 kN & 1470.7 kN & 3248.3 kN \\
50 & 62.6 kN & 105.3 kN & 176.4 kN \\ \hline \hline \end{tabular}
\end{table}
Table 2: Axial load at a natural frequency of zero for the different beam lengths and mode numbers of numerical example 1.  
Figure 4: Cross-section for numerical example 2.  
Figure 5: The impact of axial load variations on the lowest natural vibration frequency (rad/s) of numerical example 2: (a) \(L=3\), (b) \(L=10\), and (c) \(L=50\).
## 4 Results and Analysis
### Fog Attenuation Based on the Rayleigh Approximation  
Letting \(V=30\) m, the radiation and advection fog attenuation under different temperatures are shown in Figure 3. At first, fog attenuation gradually increases with an increase in frequency. Second, radiation fog attenuation is significantly less than advection fog attenuation. For instance, in area II (0.24-1 THz), when the temperature is 30 \({}^{\circ}\)C and the frequency is increased from 0.25 THz to 1 THz, the difference between the two types of fog attenuation increases from 22.16 dB/km to 71.34 dB/km. Third, the fog attenuation of terahertz waves hardly changes in area I (0.1-0.24 THz). However, fog attenuation increases with temperature in area II, but the upward trend gradually reduces. Therefore, the temperature significantly affects fog attenuation in the high-frequency band of terahertz waves. In contrast, the temperature has little impact on fog attenuation in the low-frequency band of terahertz waves.  
Different water contents in fog under diverse visibility conditions can have an impact on fog attenuation of terahertz waves. Figure 4 illustrates the fog attenuation at a temperature of 10 \({}^{\circ}\)C, and the simulation results in [18] are represented by a black line with diamonds. There is a strong agreement between the research results in this study with the simulated results in [18]. As shown in Figure 4, attenuation gradually increases with a decrease in visibility and an increase in frequency, and visibility has a greater influence on attenuation. When visibility decreases from 100 m to 50 m, wave attenuation in 1 THz increases by 8.1 dB/km in radiation fog and 27.5 dB/km in adv  
Figure 3: Fog attenuation of terahertz waves at diverse temperatures, where area I is the frequency range of 0.1–0.24 THz, and area II is the range of 0.24–1 THz.  
decreases from 50 m to 30 m, terahertz wave attenuation increases by 24.7 dB/km in radiation fog and 47 dB/km in advection fog. Thus, the upward trend in fog attenuation is evidently larger with a decrease in visibility of the fog. Moreover, advection fog attenuation is greater than radiation fog attenuation due to the relatively high density of liquid water.
## 5 Field Testing
### Integrated Path Tracking Controller Testing
#### 5.2.2 Tracking Result
In summary, the integrated path tracking controller can ensure the vehicle path tracking at 1 m/s within reasonable accuracy. (b) Path Tracking Results at 2 m/s. The results show that both controllers can make the vehicle track the path. As shown in Figure 21, the peak lateral error is 0.1351 m, and the peak heading angle error is 0.1014 rad under traditional NMPC. For the proposed NMPC, the peak lateral error is 0.0858 m, and the peak heading angle error is 0.0740 rad. However, each peak error increases slightly compared to the reference speed of 1 m/s. In addition, the lateral error in the traditional NMPC is slightly larger than that in the proposed NMPC case at the exact coordinates during the entire path tracking process. In both cases, the vehicle velocity is well controlled at the reference speed. Both the velocity and the steering rate are smooth. The computation time of the controller is concentrated around 0.09 s, only very few times exceed 0.1 s, which had no impact on the actual control; however, the computation time increases compared with the tracking result of 1 m/s. In summary, the NMPC controller can guarantee vehicle path tracking at 2 m/s. As the velocity increases, however, the tracking error increases slightly, and the computation time rises marginally. Figure 21: Path tracking results at 2 m/s: (**a**) path comparison results; (**b**) lateral error and heading error comparison results; (**c**) computation time comparison results; (**d**) control inputs comparison results.
## 4 Truncation Error Estimation
Next, we only give conclusions of the other cases. The truncation error of (11) is  
\[t_{i}^{1}=\sum_{j=0}^{6}k_{j}^{1}y_{i}^{(j)}+O(h^{7}),\]8  
where \(k_{0}^{3}=k_{1}^{3}=k_{2}^{3}=k_{3}^{3}=0;k_{4}^{3}=\frac{h^{4}}{24^{i}};k_{5}^{ 3}=0;k_{6}^{3}=-\frac{h^{6}}{5760}\). The truncation error of (12) is  
\[t_{i}^{1}=\sum_{j=0}^{6}k_{j}^{1}y_{i}^{(j)}+O(h^{7}),\]9where  
\[k_{0}^{1}=0;k_{1}^{1}=h(\lambda_{i-1}A_{i}-\lambda_{i}A_{i-1})+ \frac{h}{2}(\lambda_{i-1}A_{i}B_{i-1}-\lambda_{i}A_{i-1}B_{i});\]0 \[k_{0}^{1}=0;k_{1}^{1}=h(\lambda_{i-1}A_{i}-\lambda_{i}A_{i-1})+ \frac{h}{2}(\lambda_{i-1}A_{i}B_{i-1}-\lambda_{i}A_{i-1}B_{i});\]1  
The truncation error of (13) is  
\[k_{0}^{1}=0;k_{1}^{1}=h(\lambda_{i-1}A_{i}-\lambda_{i}A_{i-1})+ \frac{h}{2}(\lambda_{i-1}A_{i}B_{i-1}-\lambda_{i}A_{i-1}B_{i});\]2  
where \(k_{0}^{5}=0;k_{1}^{5}=\frac{h^{4}}{6}\lambda_{i-1}^{3}+\frac{h^{6}}{40} \lambda_{i-1}^{5}+O(h^{7});k_{2}^{5}=-\frac{h^{5}}{24}\lambda_{i-1}^{3}+O(h^{ 7});k_{3}^{5}=-\frac{11t^{6}}{144}\lambda_{i-1}^{3}+O(h^{7});k_{4}^{5}=\frac{h ^{5}}{12}\lambda_{i-1}+O(h^{7});k_{5}^{5}=k_{6}^{5}=0+O(h^{7})\). The truncation error of (14) is  
\[k_{0}^{1}=0;k_{1}^{1}=h(\lambda_{i-1}A_{i}-\lambda_{i}A_{i-1})+ \frac{h}{2}(\lambda_{i-1}A_{i}B_{i-1}-\lambda_{i}A_{i-1}B_{i});\]3  
where  
\[k_{0}^{1}=0;k_{1}^{1}=h(\lambda_{i-1}A_{i}-\lambda_{i}A_{i-1})+ \frac{h}{2}(\lambda_{i-1}A_{i}B_{i-1}-\lambda_{i}A_{i-1}B_{i});\]4 \[k_{0}^{1}=0;k_{1}^{1}=h(\lambda_{i-1}A_{i}-\lambda_{i}A_{i-1})+ \frac{h}{2}(\lambda_{i-1}A_{i}B_{i-1}-\lambda_{i}A_{i-1}B_{i});\]5  
The truncation error of (15) is  
\[k_{0}^{1}=0;k_{1}^{1}=h(\lambda_{i-1}A_{i}-\lambda_{i}A_{i-1})+ \frac{h}{2}(\lambda_{i-1}A_{i}B_{i-1}-\lambda_{i}A_{i-1}B_{i});\]6  
where \(k_{0}^{7}=0;k_{1}^{7}=-\frac{h^{4}}{12}\lambda_{i-1}^{3}+\frac{h^{6}}{80} \lambda_{i-1}^{5}+O(h^{7});k_{2}^{7}=\frac{h^{5}}{48}\lambda_{i-1}^{3}+O(h^{7 });k_{3}^{7}=\frac{h^{6}}{288}\lambda_{i-1}^{3}+O(h^{7});k_{4}^{7}=\frac{h^{5} }{24}\lambda_{i-1}+O(h^{7});k_{5}^{7}=k_{6}^{7}=0+O(h^{7})\). The truncation error of (16) is  
\[k_{0}^{1}=0;k_{1}^{1}=h(\lambda_{i-1}A_{i}-\lambda_{i}A_{i-1})+ \frac{h}{2}(\lambda_{i-1}A_{i}B_{i-1}-\lambda_{i}A_{i-1}B_{i});\]7  
where \(k_{0}^{8}=0;k_{1}^{8}=-\frac{h^{4}}{6}\lambda_{i}^{3}-\frac{h^{6}}{40}\lambda_ {i}^{5}+O(h^{7});k_{2}^{8}=-\frac{h^{5}}{24}\lambda_{i}^{3}+O(h^{7});k_{3}^{8}= \frac{h^{4}}{18}\lambda_{i}^{3}-\frac{h^{6}}{16}\lambda_{i}^{3}+\frac{h^{6}}{ 360}\lambda_{i}^{5}+O(h^{7});k_{4}^{8}=\frac{h^{5}}{12}\lambda_{i}+O(h^{7});k _{5}^{8}=k_{6}^{8}=0+O(h^{7})\). The truncation error of (17) is  
\[k_{0}^{1}=0;k_{1}^{1}=h(\lambda_{i-1}A_{i}-\lambda_{i}A_{i-1})+ \frac{h}{2}(\lambda_{i-1}A_{i}B_{i-1}-\lambda_{i}A_{i-1}B_{i});\]8  
where \(k_{0}^{9}=0;k_{1}^{9}=\frac{h^{4}}{12}\lambda_{i}^{3}-\frac{h^{6}}{80}\lambda_ {i}^{5}+O(h^{7});k_{2}^{9}=\frac{h^{5}}{48}\lambda_{i}^{3}+O(h^{7});k_{3}^{9}= \frac{h^{6}}{288}\lambda_{i}^{3}+O(h^{7});k_{4}^{9}=\frac{h^{5}}{24}\lambda_{i }+O(h^{7});k_{5}^{9}=k_{6}^{9}=0+O(h^{7})\). To summarize, the local truncation errors of the proposed method is \(O(h^{4})\).
# Active Sonar Target Classification Method Based on Fisher's Dictionary Learning
## 5 Conclusions  
This paper proposes an active sonar target classification method based on [PERSON]'s dictionary learning to solve the similar target classification problem of active sonar in reverberant environments. In order to improve the reverberation suppression ability, we used fractional Fourier optimal-order domain features. In order to enhance the recognition of the learning dictionary, we added the Fisher restriction criterion to restrict the sparse coefficient on the basis of dictionary learning. The experimental results showed that the accuracy of 200 test data types at signal-to-reverberation ratios of \(-5\) dB, \(-3\) dB, \(0\) dB, \(3\) dB, and \(5\) dB were 94%, 95.5%, 96%, 96.5%, and 97%, respectively; the proposed method exhibited better classification performance than the existing classification methods. Moreover, this method has the features of anti-reverberation and similar target identification, and can effectively address the problem of active sonar target classification under reverberation conditions, especially low signal-to-reverberation ratio conditions. This provides strong support for the improvement of target detection, positioning, and recognition in a complex marine environment. In future work, two aspects will be further studied: one is the fusion of multi-order fractional Fourier domain features; the other is the classification of observed target data at sea.  
Conceptualization, Methodology, Writing--Original Draft Preparation, T.S. and J.J.; Validation, T.L.; Writing--Review and Editing, Funding Acquisition, T.S. and J.Z.; Formal Analysis, T.S. and J.J. All authors have read and agreed to the published version of the manuscript. This research was funded by the Extension Fund from the Underwater Test and Control Technology Key Laboratory, Grant No. YS24071802, and the Open Fund from Zhejiang Electronic information Products Inspection and Research Institute, Grant No. KF201901. Not applicable. Not applicable. Not applicable. Not applicable. Data Availability Statement: Dalian Test and Control Institute (China) provided the experimental data support. This article used experimental data collected at Dalian Test and Control Institute (China), and we gratefully acknowledge our colleagues for their experimental skills. Thanks are given for the support of the open funding from Zhejiang Province Information Security Key Laboratory. Thanks are given for the support of the funding from the Extension Fund from Underwater Test and Control Technology Key Laboratory. The authors declare no conflict of interest.
## 2 Materials and Methods
### OH-PAHs Analysis  
For preparation of the airborne particle-bound OH-PAHs, a previously established method was modified [21]. Briefly, the filters were cut into pieces and then placed in glass centrifuge tubes. An internal standard solution was added to each tube. Ethanol/pentane (1:1, _v/v_) was added for extraction, and the samples were sonicated. The supernatant was collected after centrifugation (652\(\times\) _g_). The extraction was repeated three times. The extracts were concentrated with N\({}_{2}\) gas to approximately 1 mL. Next, the concentrated samples were loaded onto Bond Elut NH\({}_{2}\) (Agilent, Santa Clara, CA, USA) that had been prewashed and conditioned with ethanol. The target compounds were eluted with ethanol/water (3:1, _v/v_). Each elute was filtered through a 0.21 \(\upmu\)m pore size syringe filter. For sample analysis, we used a slight modification of an established method [35]. The target analytes were separated by high-performance liquid chromatography using a Kinetex F5 column (100 \(\times\) 2.1 mm, 5 \(\upmu\)m; Phenomenex, Torrance, CA, USA) and detected using tandem-mass spectrometry (LC/MS; Shimadzu Inc.).