esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Isolation and Biodiversity of Monoterpenes from Macroalgae
### Monoterpenes Isolated from Different Macroalgae Species
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Macroalgae Species** & **Isolated Monoterpenes** & **Extraction Solvent** & **Analytical Method** & **References** \\ \hline \hline \multicolumn{5}{c}{**Genus _Plocamium_**} \\ \hline \multirow{4}{*}{_Plocamium_ _cartilageneum_} & \multirow{4}{*}{**1–8**} & Conventional extraction & \multirow{4}{*}{GC–MS, NMR} & \multirow{4}{*}{[51]} \\  & & with MeOH and EtOAc; & & \\  & & SC–CO\({}_{2}\) extraction (pure CO\({}_{2}\) and with 10 \% MeOH & & \\  & & as co-solvent) & & \\ \hline \multirow{2}{*}{_Plocamium_ _cartilageneum_} & \multirow{2}{*}{**18–25**} & CHCl\({}_{3}\) and EtOH & NMR & [57] \\ \hline \multirow{2}{*}{_Plocamium_ _cartilageneum_} & \multirow{2}{*}{**26–31**} & EtOAc–CH\({}_{2}\)Cl\({}_{2}\)-hexane & HPLC, NMR & [58] \\ \hline \multirow{2}{*}{_Plocamium_ _cartilageneum_} & \multirow{2}{*}{**32–34**} & hexane/EtOAc/CH\({}_{2}\)Cl\({}_{2}\)/MeOH & HPLC, NMR & [59] \\ \hline \multirow{2}{*}{_Plocamium_ _cartilageneum_} & \multirow{2}{*}{**35, 36**} & Et\({}_{2}\)O & NMR & [60] \\ \hline \multirow{2}{*}{_Plocamium_ _haratum_} & \multirow{2}{*}{**37–47**} & CH\({}_{2}\)Cl\({}_{2}\) & NMR & [16] \\ \hline \multirow{2}{*}{_Plocamium_ _cartilageneum_} & \multirow{2}{*}{**48–51**} & CH\({}_{2}\)Cl\({}_{2}\)/H\({}_{2}\)O & GC–MS, HPLC, NMR & [63] \\ \hline \multirow{2}{*}{_Plocamium_ _carallorhiza_} & \multirow{2}{*}{**52–57**} & MeOH and CH\({}_{2}\)Cl\({}_{2}\) & NMR & [17] \\ \hline \multirow{2}{*}{_Plocamium_ _corallorhiza_} & \multirow{2}{*}{**58–61**} & CH\({}_{2}\)Cl\({}_{2}\)–MeOH & NMR & [18] \\ \hline \multirow{2}{*}{_Plocamium_ _maxillosum_} & \multirow{2}{*}{**62, 63**} & CH\({}_{2}\)Cl\({}_{2}\)–MeOH & NMR & [64] \\ \hline \multirow{2}{*}{_Plocamium_ _anystum_} & \multirow{2}{*}{**64**} & MeOH & NMR, HPLC & [65] \\ \hline \multirow{2}{*}{_Plocamium_ _costatum_} & \multirow{2}{*}{**65**} & Hexane & NMR & [66] \\ \hline \multirow{2}{*}{_Plocamium_ _anystum_} & \multirow{2}{*}{**67, 68**} & CH\({}_{2}\)Cl\({}_{2}\)–MeOH & HPLC–NMR & [69] \\ \hline \multirow{2}{*}{_Plocamium_ _hanatum_, _Plocamium_ _costatum_} & \multirow{2}{*}{**66, 69–77**} & CH\({}_{2}\)Cl\({}_{2}\)–MeOH & HPLC–UV–MS–SPE–NMR & [67] \\ \hline \multicolumn{5}{c}{**Genus _Portieria_**} \\ \hline \multirow{2}{*}{_Portieria_ _hornemannii_} & \multirow{2}{*}{**78–82**} & MeOH:CH\({}_{2}\)Cl\({}_{2}\) & NMR & [70] \\ \hline \multirow{2}{*}{_Portieria_ _hornemannii_} & \multirow{2}{*}{**83, 84**} & CH\({}_{2}\)Cl\({}_{2}\)/MeOH & HPLC, NMR & [71] \\ \hline \multicolumn{5}{c}{**Genera _Ochtodes and Microcladia_**} \\ \hline \multirow{2}{*}{_Ochtodes crockeri_} & \multirow{2}{*}{**85–97**} & CHCl\({}_{3}\):MeOH & HPLC & [32] \\ \hline \multirow{2}{*}{_Microladia_ _culteri_, _M. borealis,_ _M. californica_} & \multirow{2}{*}{**98–101**} & CHCl\({}_{3}\), & \multirow{2}{*}{GC–MS, NMR} & [72] \\ \hline \end{tabular}
\end{table} 
Table 1: The methods of isolation of halogenated monoterpenes from different macroalgal species.
# Wireless Channel Models for Over-the-Sea Communication: A Comparative Study
## 8 Conclusions  
This work focuses on the key concepts and a comparative study of existing models of channels that have been developed so far for over-the-sea wave propagation. Key concepts include basic definitions and a brief elaboration of the parameters required to understand channel models. The comparative study extensively discusses the advantages, limitations, and unique features of channel models that will be helpful for researchers and engineers to select any model from the discussed ones, based on the needs of their applications. In addition, if someone intends to improve existing channel models, a table is also presented regarding possible improvements that can be made. To the best of our knowledge, this is going to be the first comprehensive study of ocean channel models that can help interested researchers in this field by providing guidance on current trends.  
**Author Contributions:** The individual contributions of the authors are as follows. [PERSON] categorized, analyzed, and summarized channel models for over-the-sea communication. [PERSON] directed research and contributed to the review and analysis of channel models. The paper was drafted by [PERSON] and subsequently revised and approved by [PERSON].  
**Funding:** This research was supported, in part, by the Basic Science Research Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Education (NRF-2016R1D1A1A09918974). Correspondence should be addressed to [PERSON] ([EMAIL_ADDRESS]).  
**Acknowledgments:** The authors would like to thank the editor and anonymous referees for their upcoming helpful comments in improving the quality of this paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 6 Experimental Verification of the Mirror Alignment Method
### Experimental Alignment of the Plane Unit  
#### 6.2.1 Experimental Device and Process  
The experimental device for the facet alignment of the plane mirror was designed and developed to verify the effectiveness of the measurement and alignment methods presented in this paper (Figure 9). The experimental device was made up of a metal plane (Plate 1 and 2), camera (model: ORCA-Flash4.0 LT C11440-42U, sensor type: Scientific CMOS sensor FL-400, sensor size: 13.312 \(\times\) 13.312 mm; image size: 2048 \(\times\) 2048 pixels), a checkerboard target with a 20 \(\times\) 20 mm square lattice and the accuracy of 0.002 mm for calibrating the internal and external camera parameters, and circular marks. The virtual plane mirror to be aligned in this experiment was created by milling the upper surface of the metal plane using a numerically-controlled machine. The support-adjust structure (Figure 9b) is the same as the structure in Figure 1. The diameter of the connection hole (\(d_{1}\)) and bolt (\(d_{2}\)) is 14.0 and 8.0 mm, respectively, and the thickness \(L_{1}\) of the connection plate is 5.0 mm. In the experiment, the metal plane was aligned using the three-rotation and two-rotation alignment method to move the metal plane parallel to the checkerboard target. In this process, the photogrammetric measurement coordinate system O-_xyz_ was established on the checkerboard target (Figure 9). Figure 8: The numerical results of the facet alignment of the mirror unit using two-rotation and three-rotation alignment methods (from Parts 1 and 2): (**a**) error angle \(\theta\) of the axis vector in Part 1, (**b**) error distance \(L\) of the vertex in Part 1, (**c**) error angle \(\theta\) of the axis vector in Part 2, and (**d**) error distance \(L\) of the vertex in Part 2. To prepare the experiment, three circular markers made from a highly reflective material with a diameter of 16 mm were pasted on each metal plane, and the center point of each circular mark was regarded as a feature points T\({}_{1}\), T\({}_{2}\), and T\({}_{3}\) as shown in Figure 9a. Then, a number of circular markers of diameter 10 mm were attached to the virtual mirror surface of the metal plane and used to determine the plane equation of the virtual mirror surface in this experiment. Briefly, the center point coordinate of each circular marker was measured by photogrammetry, then the plane equation of the virtual mirror in the metal plane was fitted by the least squares method using these points in order to verify the accuracy of the mirror facet measurement alignment methods. In ideal conditions, the coordinate of the ball-hinge center and feature point, as well as the axis vector and position vector of the vertex in plate 1 and 2 are known (in coordinate system O-_xyz_), as shown in Table 2.  
Experimental process:  
1. The checkerboard target and the two pieces of the metal plane were fixed in any alignment and the checkerboard target was kept fixed during the experiment. 2. The camera was used to take 32 photos from different views with the two metal planes and checkerboard target included in each photo.
## 7 Observations and Recommendations  
Two main considerations have to be taken into account when designing anomaly detection schemes for IWSN applications: (1) the resource constraints of the nodes and (2) the lack of training data [70]. The former requires the designer to consider the trade-offs between the detection accuracy and the computation requirements because as the detection accuracy increases then so too does the computational complexity. The latter requires the designer to be cognisant of the fact that supervised learning as a classification problem may not always be possible because the knowledge formulation process will not always be in their full control. The schemes discussed in this paper will now be compared taking this into consideration. It is important to note that this discussion is also applicable to cyber-physical systems as outlined in Section 5 and there will be many recurring themes here because many of the challenges are interchangeable. Table 3 shows a comparison of the anomaly detection schemes discussed in this paper.  
\begin{table}
\begin{tabular}{l l l l l l l l l l} \hline \hline
**Scheme** & **Technique** & \begin{tabular}{} **Prior** \\ **Knowledge** \\ \end{tabular} & **Complexity** & \
\begin{tabular}{} **Practical** \\ **Consideration** \\ \end{tabular} & **Accuracy** &
\begin{tabular}{} **Data** \\ **Prediction** \\ \end{tabular} & **Anomaly** & **Drawback** \\ \hline [PERSON] et al. [34] & Multivariate & Yes & Low & No & High & No & DOS & Dimensionality/PK \\ [PERSON] et al. [35] & Multivariate & Yes & Low & Yes & High & Yes & Data Loss/Modification & Affected by Routing/PK \\ [PERSON] et al. [36] & Multivariate & Yes & Low & No & High & Yes & Tamperd Data & Traffic Imbalance/PK \\ [PERSON] et al. [41] & kNN & No & Moderate & No & High & No & Generic & No Regression \\ [PERSON] et al. [42] & kNN & Yes & High & No & High & No & Generic & Dimensionality \\ [PERSON] et al. [43] & kNN & No & High & No & High & No & Mishebaving Nodes & Complexity \\ [PERSON] et al. [45] & SVM & No & High & No & High & No & Generic & Complexity \\ [PERSON] et al. [46] & SVM & Yes & High & No & High & Yes & Data Integrity & Complexity/PK \\ [PERSON] et al. [47] & SVM & Yes & High & No & High & No & Generic & Complexity/PK \\ [PERSON] [50] & ANN & No & High & No & N/A & No & DOS & Complexity \\ [PERSON] et al. [51] & ANN & No & Moderate & Yes & High & No & Generic & Detection Bias \\ [PERSON] et al. [52] & ANN & Yes & High & Yes & High & Yes & Data Integrity & Complexity/PK \\ [PERSON] et al. [53] & GA & Yes & High & No & Average & No & Mishebaving Nodes & Complexity/PK \\ [PERSON] et al. [54] & GA & No & High & Yes & High & No & Mishebaving Nodes & Complexity \\ [PERSON] et al. [55] & GA & Yes & High & No & High & No & Generic & Complexity/PK \\ [PERSON] et al. [56] & Hybrid & Yes & Moderate & Yes & High & No & DOS & PK \\ [PERSON] et al. [57] & Hybrid & Yes & High & No & High & No & Generic & Complexity/PK \\ \hline \hline \end{tabular}
\end{table}
Table 3: A comparison of the discussed anomaly detection schemes.
# Semi-Direct Point-Line Visual Inertial Odometry for MAVs
## 3 Proposed System Implementation  
### System Framework  
The proposed system mainly includes two parts: front-end image tracking (measurements preprocessing and initialization), back-end nonlinear optimization, marginalization and loop closure detection, as shown in Figure 1. In the front end, the system extracts point and line features from the image, and carries out a motion estimation of adjacent image frames combined with IMU pre-integration. By aligning visual and inertial information, visual inertial joint initialization is performed to restore metric scale, and estimate inertial bias and gravity vector. Then, after judging whether the current frame is a keyframe according to the selection criteria, the system uses the semi-direct method to track keyframes and non-keyframes, respectively. In the back end, the system adopts a cost function to obtain the optimal state estimation by minimizing prior information, IMU residuals and visual re-projection errors, and uses improved marginalization to decompose the high-dimension matrix step-by-step, corresponding to the cost function, which optimizes the solution and improves the computational efficiency. The key processes are described in detail in the following sections.
## 7 Conclusions and Outlook  
The standard version of the decoder presented here is very much the \"vanilla\" version. There are many changes that one could imagine making in order to improve the performance of the method, beyond the known example of shortcuts. For example, rather than pairing with the first anyon found in each case, one can compare all anyons found at the same distance and make an informed choice on which to pair with. Another possibility is the Markov chain Monte Carlo techniques enhancements of [8]. All such modifications will depend on the code and error model considered. This paper only aims to demonstrate the core method, so such application specific modifications were not studied here.  
Despite its simplicity, the method is shown to behave very well as a decoder. It achieves respectable benchmarks for the standard testing ground of the planar code with independent bit and phase flip noise: the threshold is similar to other decoders and the theoretical maximum; error correction is evident for small system sizes; and the logical error rate is found to decay strongly. Good results were also found for the case of spatially correlated errors. The decoder can therefore be used to gain good results for topological codes as well as more exotic decoding problems, before more complex tailor-made methods are developed.
# A Linear-Arc Composite Beam Piezoelectric Energy Harvester Modeling and Finite Element Analysis
## 3 Finite-Element Simulation  
### Parameter Settings  
Finite element simulation is a widely used and effective numerical analysis method, especially for structural analysis with complex strain and stress. First, 3D modeling and material property settings were performed in COMSOL software. Each parameter is listed in Table 1.  
The whole frame is schematically shown in Figure 2. Figure 1(a) to Figure 1(f) show the schematic diagrams of curvature from 0 m\({}^{-1}\) to 100 m\({}^{-1}\), respectively.  
After meshing, the type of module was a free tetrahedral. Because the PVDF was thin, in order to ensure the accuracy of numerical simulation, the PVDF was subjected to grid refinement processing. The end mass was less relevant to the finite element analysis in this paper, so the mesh division was sparse and the final division result is shown in Figure 3.  
During the finite element analysis, the PVDF of the straight beam part and the arc part have different polarization directions. The working mode of the piezoelectric material PVDF in the piezoelectric energy harvester was the D31 mode, so the polarization direction of the PVDF in the arc part should have been polarized along the radial direction. Therefore, the two material coordinate systems needed to be set separately and these coordinate systems are shown in Figure 4. For the convenience of calculation, the material coordinate system was simplified to assume orthogonality.  
\begin{table}
\begin{tabular}{c c c} \hline
**Parameter** & **Value** & **Unit** \\ \hline Mass density & 7800 & kg/m\({}^{3}\) \\ Mass size & \(8\times 8\times 5\) & mm \\ PVDF density & 1780 & kg/m\({}^{3}\) \\ PVDF Piezoelectric stress & 11.5 & C/m\({}^{2}\) \\ PVDF elastic modulus & 3 & GPa \\ PVDF height & 0.11 & mm \\ PVDF overall length & 51.4 & mm \\ PVDF width & 8 & mm \\ Substrate layer density & 8300 & kg/m\({}^{3}\) \\ Substrate layer elastic & 128 & GPa \\  & 0.2 & mm \\ Substrate layer overall length & 51.4 & mm \\ Substrate layer width & 8 & mm \\ \hline \end{tabular}
\end{table}
Table 1: Material parameters for simulation.  
Figure 2: _Cont._
## 2 Materials and Methods
### Technical Approach
#### 2.2.1 Smudge Mark Removal  
We observe that the \(I^{inv}(x,y)\) image has a pattern of smudge marks, which makes it difficult to accurately perceive the beads and cells present in the image. These smudge marks arise from an unclean camera lens and/or spillage of oil from the oil-immersion objective on the workspace container, which can be either a glass microscope slide or a glass-bottomed Petri dish. Such smudge marks are, however, not observed in the other images, \(I^{sw}(x,y)\), \(I^{sm}(x,y)\) and \(I^{cm}(x,y)\). If such marks are present, we use an algorithm [40], which has been successful for outdoor robotics and computer vision applications, to remove the marks. The algorithm uses the following equation:  
\[I_{r}(x,y)=\frac{I_{o}(x,y)-cb(x,y)}{a(x,y)}, \tag{1}\]  
where \(I_{r}(x,y)\) denotes the smudge-free image; \(I_{o}(x,y)\in I^{inv}(x,y)\) represents the original image; \(a(x,y)\) is the attenuation map that models the reduction in illumination due to absorption by lens dirt; \(b(x,y)\) is the intensification map that captures the scattering effect of lens dirt; and \(c\) is the aggregate of scene-dependent, outside illumination intensity. \(a(x,y)\), \(b(x,y)\) and \(c\) are then given as  
Figure 2: Example images of cells and microspheres in different fluid media: (**a**) amoeba cell and silica beads in water, \(I^{inv}_{j}(x,y)\); (**b**) human endothelial cells and silica beads in water, \(I^{sw}_{j}(x,y)\); (**c**) human endothelial cells and silica beads in Matrigel, \(I^{sm}_{j}(x,y)\); (**d**) clustered silica beads in polymerizing Matrigel, \(I^{cm}_{j}(x,y)\).  
\[a(x,y)=\frac{Avg(|\
abla I|)}{Avg(|\
abla I_{\text{test}}|)}, \tag{2}\]  
\[b(x,y)=Avg(I)-Avg(I_{\text{test}})a(x,y), \tag{3}\]  
\[c=\operatorname*{arg\,min}_{c}||\
abla I_{r}(x,y)||_{1}. \tag{4}\]  
Here, \(Avg(.)\) denotes an averaging operation and \(I\) represents a set of training or calibration images of the same workspace using the same camera system but with some objects' movements across the different image frames. \(Avg(|\
abla I|)\) is the averaged magnitude of the image gradient over the calibration set, and \(Avg(I_{\text{test}})\) and \(Avg(|\
abla I_{\text{test}}|)\) are estimated from \(Avg(I)\) and \(Avg(|\
abla I|)\), respectively, using least squares fitting with a bivariate polynomial \(\sum_{3}^{i=0}\sum_{3}^{j=0}a_{ij}x^{i}y^{j}\), where \(x\) and \(y\) are normalized pixel coordinates. \(a(x,y)\) and \(b(x,y)\) are pre-computed from \(I\), whereas \(c\) is calculated online for the first few images and the average of all the \(c\) values is used for the rest of the images as outside illumination remains the same. An example of a smudge-free image, \(I_{r}^{inv}(x,y)\), is shown in Figure 3.
## Results
### Daily Drivers of Solar Induced Fluorescence  
Correlations between daily measurements of SIF, photosynthesis and PAR were examined separately for avocado and orange jasmine using regressions of means for each time point (Table 1). Correlations with YSIF, where R2 values were found to be very low (R2 < 0.01) and figures for correlations where R2 < 0.1 are not shown in text (see supplementary material; Table S2 and Figures S4-S6). For raw SIF retrievals, significant correlations were found between SIF and all photosynthetic measurements (ETR, YYSII, YNPQ and YNO), both global and leaf PARs and PRI and NDVI, for both avocado and orange jasmine (\(p\) < 0.005); with the exception of NDVI (\(p\) = 0.20) and PRI for SIF\({}_{\text{FR}}\) (\(p\) = 0.91) in avocado leaves and global PAR (\(p\) = 0.32) and NDVI (\(p\) = 0.61) for SIF\({}_{\text{FR}}\) and SIF\({}_{\text{red}}\) respectively in orange jasmine leaves (Figure 3). In avocado, positive correlations were found between both SIF\({}_{\text{FR}}\) and SIF\({}_{\text{red}}\) and leaf PAR, ETR and YNPQ, while strong negative correlations were identified between both SIF\({}_{\text{FR}}\) and SIF\({}_{\text{red}}\) and YPSII and YNO. The same correlations were identified for orange jasmine. However, in these cases the R2 values were found to be lower (Figure 3), with the exception of the relationship between SIF\({}_{\text{red}}\) and YNPQ. Multiple regression models, run with SIF\({}_{\text{FR}}\) as a response variable and all other parameters as predictors, consistently identified leaf PAR as the main significant predictor of SIF\({}_{\text{FR}}\) (\(p\) < 0.001) for both species (R2 = 0.70, avocado; and 0.45, orange jasmine). ETR (\(p\) < 0.001), NDVI (\(p\) < 0.001) and PRI (\(p\) < 0.001; avocado only) were also identified as significant predictors, but the contribution of other predictors to overall model improvement was negligible (improvement in R2 = 0.05, avocado; and 0.09, orange jasmine). Leaf PAR was also identified as the main predictor of SIF\({}_{\text{red}}\) for both species (\(p\) < 0.001; R2 = 0.64, avocado and \(p\) = 0.004; R2 = 0.43, orange jasmine). Additionally, YPSII (\(p\) = 0.009), global PAR (\(p\) < 0.001), ETR (\(p\) < 0.001), YNPQ (\(p\) = 0.019), YNO (\(p\) = 0.009) and NDVI (\(p\) < 0.001) were identified as significant SIF\({}_{\text{red}}\) predictors for avocado, while NDVI (\(p\) < 0.001) was the only other significant SIF\({}_{\text{red}}\) predictor for orange jasmine. However, unlike for SIF\({}_{\text{FR}}\), leaf PAR and NDVI as well as leaf PAR and global PAR were found to contribute substantially to avocado and orange jasmine SIF\({}_{\text{red}}\) models respectively (improvement in R2 = 0.05, avocado; and 0.09, orange jasmine). All other predictors contributed negligibly to model improvement. R2 values were found to be low for all models run with SIF yields as response variables for both avocado (R2 = 0.11, YSIF\({}_{\text{FR}}\); and 0.04, YSIF\({}_{\text{red}}\)) and orange jasmine (R2 = 0.14, YSIF\({}_{\text{FR}}\); and 0.04, YSIF\({}_{\text{red}}\)).
# A One-Class-Based Supervision System to Detect Unexpected Events in Wastewater Treatment Plants
## 2 Materials and Methods
### One-Class Techniques
#### 2.3.1 Autoencoder  
This method utilizes an Autoencoder which relies on an Artificial Neural Network (ANN) to detect anomalies by determining their characteristics. Typically, the ANN setup includes an input layer, one or more hidden layers, and an output layer interconnected by weighted links [37]. The fundamental concept behind this approach involves reconstructing the input data (\(d\)) into output data (\(d_{rec}\)) through nonlinear dimensional reduction within the hidden layer, as depicted in Figure 4. Consequently, the number of neurons in the hidden layer is smaller than the input dimensions.  
After the network undergoes training exclusively on the training dataset, data exhibiting distinct behaviors are expected to exhibit notable distinctions within the hidden layer space. Consequently, a test data point (\(q\)) is anticipated to demonstrate a substantial reconstruction error, quantified as \(|d-d_{rec}|\). This metric serves as the criterion for categorizing the data as anomalous [38].
# Condition Monitoring of Wind Turbine Blades Using Active and Passive Thermography
## 5 Results and Discussion
### Passive Thermography
The thermoplastic foam near the leading edge was detected in the amplitude results and is highlighted by the red box in Figure 14b. This method not only increased the quality of the thermal images and improved the detectability of  
Figure 13: (a) Thermographic results of the experiment around 9 a.m., (b) noon and (c) 6 p.m. (sunrise and sunset were around 5.53 a.m. and 9.30 p.m., respectively). The vertical arrows and dashed lines indicate the shear webs. thermography but also eliminated the false indications associated with environmental reflections, dirt, and dust on the surface.