esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Phylogenetic Tree Analysis  
From the above results, it was determined that _Penicillium_ sp. and _Cladosporium_ sp. accounted for the largest proportions of the fungal communities, so phylogenetic tree analysis of _Penicillium_ and _Cladosporium_ fungi was carried out. We selected some strains that are often found by researchers on museum artifacts, as well as two strains of _Penicillium_ sp. preserved in our laboratory. Strain NK-NH3 (MH392741.1) was isolated from the surface of the lacquer from the Nanhai No. 1 shipwck, and strain TJM-F5 (MH171487.1) was isolated from a leather suitcase from the Tianjin Museum.  
The results showed that the ITS sequences of the four species of _Penicillium_ sp. were not highly similar, and the sequence homology reached approximately 89%. Phylogenetic tree results showed that the four fungi belonging to _Penicillium_ sp. presented relatively independent evolutionary branches, which was consistent with the results of sequence alignment. The ITS sequences of strain NKDMZ-3 and _Penicillium chrysogenum_ (TJM-F5) showed a closer genetic relationship, while the ITS sequences of strain NKDMZ-15 and NK-NH3 were highly homologous (Figure 3A). In addition to strain NKDMZ-9, the ITS sequence similarity of other _Cladosporium_ sp. was high, showing differences of only a few base pairs (Figure 3B).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Fungi** & **Closest Related Strain** & **Accession Number** & **Similarity** & **Source** \\ \hline NKDMZ-1 & _Penicillium_ & MK551157.1 & 97\% & hull \\ NKDMZ-2 & _Penicillium_ & KT310939.1 & 89\% & hull \\ NKDMZ-3 & _Penicillium_ & KT310939.1 & 89\% & hull \\ NKDMZ-4 & _Cladosporium cladosporoides_ & HQ148094.1 & 98\% & hull \\ NKDMZ-5 & _Talaromyces pinophilus_ & KY979508.1 & 99\% & hull \\ NKDMZ-6 & _Talaromyces variabilis_ & KU216713.1 & 99\% & hull \\ NKDMZ-9 & _Cladosporium cladosporoides_ & HM776418.1 & 98\% & air \\ NKDMZ-10 & _Cladosporium cladosporoides_ & KX815294.1 & 97\% & air \\ NKDMZ-11 & _Cladosporium cladosporoides_ & KY114882.1 & 98\% & air \\ NKDMZ-12 & _Cladosporium cladosporoides_ & KXS15294.1 & 96\% & air \\ NKDMZ-13 & _Cladosporium cladosporoides_ & KXS15294.1 & 96\% & air \\ NKDMZ-14 & _Cladosporium urendinicola_ & JN088229.1 & 84\% & air \\ NKDMZ-15 & _Penicillium_ & KT310939.1 & 89\% & air \\ NKDMZ-16 & _Trichoderma afroharzianum_ & KX357849.1 & 97\% & air \\ \hline \hline \end{tabular}
\end{table}
Table 1: Molecular identification of strains isolated from the pirogue and air.  
Figure 3: Neighbor-joining phylogenetic tree of _Penicillium_ sp. (**A**) and _Cladosporium_ sp. (**B**). Each of the ITS gene sequences used was 520–540 bp. The phylogenetic tree was constructed using the neighbor-joining method with 1000 bootstrap replicates. The scale bar indicates the expected number of substitutions/sites.
# Hub-and-Spoke Network Design Considering Congestion and Flow-Based Cost Function
## 4 Solution Approach
### Solving the Flow Routing Problem  
Once the hub-and-spoke network is constructed, the problem reduces to a flow routing problem. We now describe the developed heuristic algorithm. This heuristic algorithm is developed with insights from the mechanics of BD in the literature, e.g., as denoted by [PERSON] et al. (2014) and [PERSON] (2015); the neighborhood of a high-quality solution is more likely to be of similar quality. This follows the procedure in Step 3. In the following algorithm (Table 1), when we state the best solution(s), we point to the best solutions regarding the objective function.  
\begin{table}
\begin{tabular}{l l} \hline \hline Step 0 & initial pool \(\leftarrow\) Ø, the selected pool \(\leftarrow\) Ø, solution pool-2 \(\leftarrow\) Ø solution pool-3 \(\leftarrow\) \\  & sets top\(\leftarrow\) false \\ \hline  & for (i = 1:100) do \\  & Run 01, Ø2, and use to solve 03 \\ Step 1 & initial pool \(\leftarrow\) initial pool \(\cup\) solution (i) \\  & i \(\leftarrow\) i + 1 \\  & end for \\ \hline Step 2 & selected pool \(\leftarrow\) the top 25 solutions from initial pool \\ \hline  & for (i = 1:25) \\  & terminate \(\leftarrow\) false \\  & while (terminate = false) do \\  & apply 1-opt algorithm on solution (i) from selected pool \\  & if 1-opt modified solution (i)! better original then \\  & “solution pool-2” \(\leftarrow\) “solution pool-2” \(\cup\)1-opt modified solution (i) \\ Step 3 & else \\  & “solution pool-2” \(\leftarrow\) “solution pool-2” \(\cup\) solution (i) \\  & end if \\  & if there is no 1-opt modification left then \\  & terminate \(\leftarrow\) true \\  & end if \\  & end while; end for \\ \hline Step 4 & selected pool-2 \(\leftarrow\) the top 25 solutions from “solution pool-2” \\ \hline  & for (i = 1:25) \\  & terminate \(\leftarrow\) false \\  & while (terminate = false) do \\  & apply 2-opt algorithm on solution (i) from selected pool-2 \\  & if 2-opt modified solution (i)! better original then \\  & “solution pool-3” \(\leftarrow\) “solution pool-3” \(\cup\)2-opt modified solution (i) \\ Step 5 & else \\  & “solution pool-3” \(\leftarrow\) “solution pool-3” \(\cup\) solution (i) \\  & end if \\  & if there is no 2-opt modification left then \\  & terminate \(\leftarrow\) true \\  & end if \\  & end while; end for \\ \hline \hline \end{tabular}
\end{table}
Table 1: Solving the flow routing problem.  
In the heuristic algorithm, we apply solution Diversification in Steps 1, 3, 5, and 8 by implementing 1-opt/2-opt algorithms. In Steps 2, 4, 6, intensification of the solution pool is implemented by choosing only the elite solution from a pool of several solutions.  
In Step 3, \(\xi\) is a tuning parameter for the model. We arbitrarily choose \(\xi\) = 80%, but it can be further modified, with higher values of \(\xi\) resulting in tighter selection of variables, while lower values of \(\xi\) end in a more generous approach to locate facilities (as less common solutions are needed as selection criteria).
## 2 Materials and Methods
### Participants and Eligibility Criteria  
All participants underwent a medical examination to identify any controlled cardiovascular or metabolic disease that would exclude them from the exercise training.  
The results from a previous study with older adults revealed changes in the IKE strength (main outcome) with an effect size (ES) of 0.36 [29]. Assuming an alpha value of 0.05 and a power of 95%, a clinically relevant change between the pre- and post-intervention values of IKE strength was identified with 28 subjects using G*Power Software (version 3.1.9.7). Assuming a maximal loss of follow-up of 20%, 36 nursing home residents over 70 years old were recruited. However, two participants dropped out of the exercise intervention due to illness. Therefore, the total number of older adults who completed the 4-week training intervention was 34. Then, a subsample of 15 was re-evaluated after a 14-week inactive period after the training program. A flowchart diagram of the study with patient follow-up is shown in Figure 1.  
The characteristics of the sample analyzed in the exercise and inactivity periods are detailed in Table 1. The inclusion and exclusion criteria were set according to the Vivifrail exercise program and HEAL study [27]. The study, which was conducted according to the Declaration of Helsinki, was approved by the Ethics Commission of the Local University (ID: 2131/2018). All participants signed a written consent form after being informed of the purpose and experimental procedures.  
The study sample analyzed at baseline.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Outcome** & \
\begin{tabular}{c} **Exercise Group** \\ **(n = 34)** \\ \end{tabular} &
\begin{tabular}{c} **Inactive Subgroup** \\ **(n = 15)** \\ \end{tabular} \\ \hline Age (_years_) & 84.4 (9.7) & 86.0 (7.6) \\ Women, \(n\) (\%) & 15 (44\%) & 6 (4\%) \\ BMI & 27.7 (4.8) & 28.8 (3.8) \\ MMSE (_score_) & 25.6 (6.2) & 25.6 (5.8) \\ Barthel (_score_) & 76.9 (25.7) & 68.1 (28.3) \\ Lawton (_score_) & 3.8 (2.5) & 2.3(1.3) \\ SARC-F (_score_) & 3.9 (2.1) & 4.0 (2.1) \\ FES-I (_score_) & 11.1 (3.3) & 12.0 (3.1) \\ SPPB (_score_) & 5.6 (2.8) & 7.1 (3.6) \\ Dominant IHS (_kg_/_kg_) & 0.24 (0.1) & 0.23 (0.1) \\ Non-dominant IHS (_kg_/_kg_) & 0.23 (0.1) & 0.24 (0.1) \\ Dominant IKE (N/_kg_) & 2.6 (0.9) & 2.6 (0.9) \\ Non-dominant IKE (N/_kg_) & 2.5(0.9) & 2.6 (0.8) \\
5 per STS, (s) & 19.7 (6.9) & 12.5 (2.9) \\
4-m gait speed (s) & 8.8 (6.6) & 6.3 (3.6) \\ Timed up and go (s) & 22.5 (13.5) & 19.0 (9.7) \\ \hline \hline \end{tabular} Data are presented as mean (standard deviation) unless otherwise stated. BMI, body mass index; FES-I, Falls Efficacy Scale—International; IHS, isometric handgrip strength; IKE, isometric knee extension; MMSE, minimal state evaluation; SARC; simple questionnaire to rapidly diagnose sarcopenia; SPPB, short physical performance battery; STS, sit-to-stand.  
\end{table}
Table 1: Characteristics of the study sample analyzed at baseline.  
Figure 1: Flowchart of the study with participants.
## 3 Problem Formulation
### Mathematical Formulation
* Constraint \(C_{9}\) restricts the offloading ratio \(\eta_{l,u}(t)\) to the range [0,1], where \(\eta_{l,u}(t)=0\) represents full local execution, and \(\eta_{l,u}(t)=1\) represents full offloading to the UAV edge server. * Constraint \(C_{10}\) guarantees that the total computational task volume \(D_{max}\) is completed by the end of the service period. This formulation provides a comprehensive framework for balancing task processing efficiency and energy consumption in multi-UAV MEC systems, ensuring optimal resource utilization and system performance.
## 3 Results
### Medical and Admission Background  
The patients' medical background and details of their admission at the time were also gathered. The median admission period was four days, with an IQR of 8.25 days. Half of the patients had at least one chronic disease. The majority of the patients lived with other family members at home. However, only half of them were accompanied all the time while in the ward. More than 80 percent of them did not receive any visitors while in hospital. For almost 40 percent of the patients, this was the first time in their lives that they had ever been admitted to the ward. About half of the patients were dependent on others to move, practice self-care, or perform activities of daily living. About 60 percent of the patients experienced pain or discomfort. However, more than half reported no anxiety or depression issues. Table 2 summarizes all the findings.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Characteristic** & _n (\%)_ & **Mean (SD)** & **Median (IQR)** \\ \hline Day(s) of admission & & & 4 (8.25) \\ Distance from home (km) & & 19 (24) \\ Self-rated health status score & & 74.6 (15.5) & \\ Chronic illness & & \\ None & 62 (44.9) & & \\ One disease & 38 (27.5) & & \\ More than one & 38 (27.5) & & \\ Self-rated health & & \\ Healthy & 117 (84.8) & \\ Not satisfactory & 21 (15.2) & & \\ Not alone at home & 132 (95.7) & & \\ Lives alone & 6 (4.3) & & \\ Companion in ward & & & \\ All the time & 75 (54.3) & & \\ Occasionally & 18 (13.0) & & \\ None & 45 (32.6) & & \\ Visitors while in ward & & & \\ Everyday & 5 (3.6) & & \\ Occasionally & 17 (12.3) & & \\ None & 116 (84.1) & & \\ First hospital admission & & & \\ First hospitalization & 54 (39.1) & & \\ Repeated hospitalization & 84 (60.9) & & \\ Reason for admission & & & \\ Emergency & 83 (60.1) & & \\ Electro & 55 (39.8) & & \\ Ability to move & & & \\ No problem & 73 (52.9) & & \\ Some problems & 52 (37.7) & & \\ Bedbound & 13 (9.4) & & \\ Ability to practice self-care & & & \\ No problem & 80 (58.0) & & \\ Some problems & 43 (31.2) & & \\ Unable & 15 (10.9) & & \\ Activities of daily living & & & \\ No problem & 56 (40.6) & & \\ Some problems & 56 (40.6) & & \\ Unable & 26 (18.8) & & \\ Pain or discomfort & & & \\ None & 53 (38.4) & & \\ Some & 77 (55.8) & & \\ A lot & 8 (5.8) & & \\ Anxiety or depression & & & \\ None & 93 (67.4) & & \\ Some & 39 (28.3) & & \\ Severe & 6 (4.3) & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Medical and admission background (_n_ = 138).
## 3 Methodology
### Graph Convolutional Network (GCN)  
GCN is introduced to process graph-structured data, for example, a social network connection, road/public traffic network, etc. [43]. Graph structures consist of vertices and edges. We can abstract the passenger flows among bike-sharing stations into a graph to demonstrate the relationship among stations. In our study, stations are defined as vertices, and dependencies among stations are defined as edges. Therefore, a graph structure is constructed to demonstrate the passenger flows among stations.  
Defining a graph, \(G=(\mathcal{V},x,\varepsilon)\), where \(\mathcal{V}\) represents a set of vertices seized \(N\); \(x\in\mathbb{R}^{N}\) is a vector for every vertex, which contains one or more attributes about the vertex; \(\varepsilon\) represents a set of edges; and \(A\in\mathbb{R}^{N\times N}\) represents the adjacency matrix, where \(A_{ij}\) records the connection between the vertices.  
We can define a computational efficiency formula to calculate the graph convolution:  
\[g\bullet x\approx D^{-\frac{1}{2}}AD^{-\frac{1}{2}}x\theta \tag{9}\]  
where \(D\in\mathbb{R}^{N\times N}\) represents a diagonal degree matrix, with \(D_{ii}=\sum_{j}A_{ij}\); \(\theta\in\mathbb{R}\); and the adjacency matrix \(A\) does not contain the connection of the vertex itself. Therefore, the identity matrix \(I_{N}\) will be summed to the adjacency matrix, \(A=A+I_{N}\); \(D_{ii}=\sum_{j}A_{ij}\). More details about deriving the graph convolution can be found in [PERSON] et al. [44].  
Generalizing the convolution calculation on the whole graph is represented with the symbol \(X\in\mathbb{R}^{N\times C}\), where vertex \(v_{i}\) has a feature vector \(X_{i}\) and length of \(C\).  
\[Z=D^{-\frac{1}{2}}AD^{-\frac{1}{2}}X\Theta \tag{10}\]  
where \(\Theta\in\mathbb{R}^{C\times F}\) is a parameter kernel that will be trained during the training process, \(Z\in\mathbb{R}^{N\times F}\) is the convolved kernel, and \(F\) is the number of hidden units in the neural network.  
The adjacency matrix needs to be predefined before the training. Different predefinitions of adjacency will produce different results. The adjacency matrix can only contain 1 and 0 and is used to define the global adjacency relationship when it is predefined manually. In this case, it cannot well demonstrate the time-varied relations among the stations. To extract more precise relations, we use another learnable parameter \(\hat{A}\) to replace \(D^{-\frac{1}{2}}AD^{-\frac{1}{2}}\). This makes it easier to build a model than the adjacency matrix and improves the performance of the model. This replacement has been proved to be effective by [PERSON] et al. [3]. The equation after the parameter replacement of Equation (10) is given as follows:  
\[Z=\hat{A}X\Theta \tag{11}\]  
Figure 1: Structure of the LSTM unit.  
where \(\hat{A}\) is the learnable adjacency matrix. It is a symmetric matrix that consists of trainable kernel parameters, \(\hat{A}\in\mathbb{R}^{N\times N}\).
_Applied_  
_sciences_  
_Article_  
**Inhibitory Effects of 4-Hexylresorcinol on Root**  
**Resorption Induced by Orthodontic Tooth Movement**  
**[PERSON] 1, [PERSON] 2, [PERSON] 3,*(c) and [PERSON] 1,*  
1 Department of Orthodontics, School of Dentistry, Seoul National University, Seoul 3080, Korea; [EMAIL_ADDRESS]  
2 Department of Oral Biochemistry, College of Dentistry, Gangneung-Wonju National University, Gangneung 28644, Korea; [EMAIL_ADDRESS]  
3 Department of Oral and Maxillofacial Surgery, College of Dentistry, Gangneung-Wonju National University, Gangneung 28644, Korea  
* Correspondence: [EMAIL_ADDRESS] (S-G.K.); [EMAIL_ADDRESS] (T.-W.K.); Tel.:+82-33-640-2468 (S-G.K.); +82-2072-2678 (T-W.K.)  
Received: 22 August 2020; Accepted: 8 September 2020; Published: 10 September 2020  
**Featured Application: The administration of 4-hexylresorcinol reduces root resorption during orthodontic tooth movement.**  
**Abstract:** Root resorption during orthodontic tooth movement (OTM) is caused by an imbalance between the bone turnover rate and applied mechanical stress. The administration of 4-hexylresorcinol (4 HR) increases the bone turnover rate and factors associated with bone formation. Thus, 4 HR may show protective activity against root resorption during orthodontic tooth movement (OTM). A total of 40 rats (male: 20; female: 20) were included in this study, and the mandibular first molar was subjected to excessive orthodontic force. The experimental group (n = 20) received 12.8 mg/kg of 4 HR every 2 weeks. The controls (n = 20) received a solvent without 4 HR. Both groups had the same sex distribution. On Day 28 after the initiation of OTM, all the animals were sacrificed for micro-computed tomography analysis, Western blot analysis, and immunohistochemistry. The ratios of the root length and root volume to the total volume were significantly higher in the experimental group compared to those in the control group (\(p<0.05\)). The expression levels of OPG, RANKL, alkaline phosphatase, and Runx2 in the experimental group according to Western blotting were significantly higher in the experimental group compared to those in the control group (\(p<0.05\)). Their expression was mainly found in the periodontal ligament area. In conclusion, the administration of 4 HR decreased the root resorption caused by OTM and increased the expression levels of OPG, RANKL, alkaline phosphatase, and Runx2.  
**Keywords:** 4-hexylresorcinol; orthodontic tooth movement; root resorption
# Advanced Progress of Optical Wireless Technologies for Power Industry: An Overview
## 2 Classification and Progress of Power Industry Optical Wireless Communications
### Modulation Methods of Hybrid Optical Wireless Communications  
Orthogonal frequency division multiplexing (OFDM) is a kind of modulation technology with high spectral efficiency, which can provide a simple solution for channel dispersion and can be implemented in the digital domain. OFDM can divide the available bandwidth of the channel into several sub-channels, and realize frequency division multiplexing through the orthogonality among sub-channels. It can also realize signal transmission and adapt to the adjustment conditions of the channel by using the allocation of bit and power on the subcarrier. It reduces the subcarrier transmission rate and prolongs the period of the code element, so it has excellent anti-multipath effect performance and realizes the high-speed transmission rate under the condition of limited bandwidth. In addition, combined with multi-input and multi-output (MIMO) technology, it is required that the receiver can receive the optical signal from the LED lamp array in order to resolve multiple independent communication channels. The parallel transmission of multiple transmitting and receiving units could improve the performance of IWOC and make different users occupy non-overlapping subcarrier sets, therefore realizing downlink multi-user transmission.
## 2 Research Methodology
### Cluster Classification and Feature Matching
#### 2.2.1 Category 1: One-to-One  
This category represents the simplest form of feature matching, where a cluster contains exactly one feature in set X\({}_{Q}\) and one feature in set X\({}_{P}\). These features are considered a direct match, as they are isolated within their own localized region, and therefore, their correspondence is strong without requiring any further processing.
## 3 Results and Discussion
### Regression Modeling and Statistical Analysis
#### 3.2.2 Lipids Model  
The second response was the lipid content, and the polynomial fitted model was presented in Table 2, with a distinctive linear regression equation suitable for the data and no significant lack of fit. \(\mathrm{R_{adj}}^{2}\) of 0.3 indicates that the experimental data fit the linear model very adequately. Only statistically significant terms with \(p\)-values less than 0.05 were selected for model calibration. The influence of N, P + K, S, PP, and LI on lipids was empirically characterized using regression coefficients. The lipid content was governed by the combination of multiple abiotic factors estimates, each of which has a favorable overall impact on the lipid content. The 3D response surface plot with the contour  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Response** & **Model Term** & **Coefficient Estimated** & **df** & **Standard Error** & **Mean Square** & **F-Value** & \(p\)**-Value** \\ \hline  & BE & 0.3345 & 1 & 0.7284 & 0.4477 & 0.2109 & 0.6505 \\  & CD & \(-\)1.44 & 1 & 1.17 & 3.24 & 1.53 & 0.2296 \\  & CE & \(-\)0.1864 & 1 & 0.7284 & 0.1389 & 0.0655 & 0.8004 \\  & DE & 0.5540 & 1 & 1.17 & 0.4796 & 0.2260 & 0.6392 \\  & A\({}^{2}\) & 0.1134 & 1 & 0.5758 & 0.0823 & 0.0388 & 0.8457 \\  & B\({}^{2}\) & 0.6203 & 1 & 0.5758 & 2.46 & 1.16 & 0.2930 \\  & C\({}^{2}\) & 0.1341 & 1 & 0.5758 & 0.1151 & 0.0542 & 0.8180 \\  & D\({}^{2}\) & \(-\)0.2904 & 1 & 1.47 & 0.0823 & 0.0388 & 0.8457 \\  & E\({}^{2}\) & \(-\)3.43 & 1 & 0.5758 & 75.40 & 35.53 & \(<\)0.0001 \\ Chlorophyll b yield & Intercepts & 3.38 & 1 & 0.4599 & 20.92 & 4.53 & 0.0026 \\ (ug-mL-1) & A & 1.60 & 1 & 0.5375 & 41.11 & 8.89 & 0.0050 \\  & C & \(-\)0.3356 & 1 & 0.5375 & 1.80 & 0.3898 & 0.5362 \\  & E & 0.0567 & 1 & 0.5375 & 0.0514 & 0.0111 & 0.9166 \\  & B & 0.9018 & 1 & 0.8601 & 5.08 & 1.10 & 0.3012 \\  & D & 1.88 & 1 & 0.5375 & 56.56 & 12.23 & 0.0012 \\ Total Carotenoid & Intercept & 4.87 & 1 & 0.4612 & 10.62 & 2.28 & 0.0299 \\ yield (ug-mL-1) & A & \(-\)0.8053 & 1 & 0.8419 & 4.25 & 0.9148 & 0.3473 \\  & B & \(-\)0.4848 & 1 & 0.8419 & 1.54 & 0.3316 & 0.5695 \\  & C & \(-\)0.1982 & 1 & 0.8419 & 0.2575 & 0.0554 & 0.8157 \\  & D & 0.2919 & 1 & 0.8624 & 0.5324 & 0.1145 & 0.7376 \\  & E & 1.94 & 1 & 0.8419 & 24.57 & 5.29 & 0.0295 \\  & AB & 0.2719 & 1 & 1.08 & 0.2957 & 0.0636 & 0.8028 \\  & AC & 0.1836 & 1 & 1.08 & 0.1349 & 0.0290 & 0.8660 \\  & AD & 1.91 & 1 & 1.72 & 5.70 & 1.23 & 0.2779 \\  & AE & 3.14 & 1 & 1.08 & 39.36 & 8.47 & 0.0072 \\  & BC & 0.6001 & 1 & 1.08 & 1.44 & 0.3100 & 0.5823 \\  & BD & 2.82 & 1 & 1.72 & 12.42 & 2.67 & 0.1138 \\  & BE & \(-\)0.1718 & 1 & 1.08 & 0.1180 & 0.0254 & 0.8746 \\  & CD & \(-\)1.97 & 1 & 1.72 & 6.09 & 1.31 & 0.2624 \\  & CE & 1.69 & 1 & 1.08 & 11.41 & 2.46 & 0.1288 \\  & DE & 0.0983 & 1 & 1.72 & 0.0151 & 0.0032 & 0.9550 \\ \hline \hline \end{tabular}
\end{table}
Table 3: _Cont._map shown in Figure S2 provides significant information that confirms whether an optimum abiotic condition boosts the lipids and enables them to attain their highest prevalence.