esa-sceva/satcom-chunk-collection huggingface dataset:
# The Development of a Spaceborne SAR Based on a Reflector Antenna
## 2 Principles and Classification of Reflector Antennas
### Types and Characteristics of Reflector Antennas
#### 2.2.1 Parabolic Reflector Antennas  
Parabolic reflector antennas, or rotational parabolic antennas, operate based on the principles described in Section 2.1. As the most classic and widely used type of reflector antenna [22], its geometry ensures that parallel incoming waves converge at the focal point. In radar systems, it offers high gain, strong directivity, high efficiency, and low sidelobe levels [23].  
A key parameter is aperture gain, given by  
\[G=\frac{4\pi A}{\lambda^{2}} \tag{10}\]  
where \(A\) is the aperture area and \(\lambda\) is the wavelength. For a circular aperture with diameter \(D\), this becomes  
\[G=\left(\frac{\pi D}{\lambda}\right)^{2} \tag{11}\]  
In practice, gain is reduced by losses such as spillover, feed blockage, reflector leakage, surface deformation, and structural blockage. These factors must be considered in design and analysis.  
Parabolic reflector antennas are widely used due to their simple structure, ease of design and fabrication, and excellent electromagnetic performance. However, they inherently suffer from certain drawbacks: the feed and their supporting struts are typically positioned along the antenna's optical axis, which results in scattering and blockage of the reflected electromagnetic waves. Consequently, feed blockage leads to reduced antenna gain and increased sidelobe levels, thereby degrading overall performance.  
Offset reflector antennas address this by using an asymmetric reflector, placing the feed outside the aperture. This configuration eliminates feed blockage, thereby improving antenna gain and efficiency [24]. Offset reflector antennas significantly enhance aperture efficiency, reduce sidelobe levels, and improve cross-polarization performance. As illustrated in Figure 2, the offset reflector antenna places the feed outside the main reflector aperture to reduce feed-induced obstructions, thereby improving the antenna's overall performance [25]. Geometrically, an offset reflector is a section of a larger paraboloid, formed by intersecting a conventional parabolic reflector with a cylinder offset from the focal axis. This design removes feed blockage but, due to its asymmetry, may increase cross-polarization.  
In SAR applications, parabolic reflectors are most suited for narrow-swath, high-resolution imaging due to their high gain and well-formed main lobe. However, they suffer from feed blockage and increased sidelobe levels, especially when large apertures are needed. These limitations can reduce imaging contrast and interfere with clutter suppression.
## 2 System Model
### Radar Sensing Model  
At time slot \(t\), the received radar echo signal at BS can be expressed as  
\[\mathbf{y}_{r}(t)=\mathbf{H}_{1}^{H}\mathbf{\Phi}\mathbf{A}\mathbf{\Phi}^{H} \mathbf{H}_{1}\times(t-\tau_{k})+\mathbf{n}_{r}(t) \tag{9}\]  
where \(\mathbf{A}\in\mathbb{C}^{N\times N}\) represents the target response matrix of IRS. \(\tau_{k}\) denotes the propagation delay between the transmitter and the target. The \(\mathbf{n}_{r}(t)\) is AWGN with \(\mathbf{n}_{r}(t)\sim\mathcal{CN}\big{(}0,\sigma_{r}^{2}\mathbf{I}_{M}\big{)}\). The specific formulas are listed as\[\mathbf{A}=\sum_{k=1}^{K}\beta_{k}\mathbf{a}(v_{k},\theta_{k})\mathbf{a}^{H}(v_{k},\theta_{k}). \tag{10}\]  
The received sensing echo signal from the \(k\)-th target \(\mathbf{y}_{r,k}\in\mathbb{C}^{M\times 1}\) can be formulated as  
\[\mathbf{y}_{r,k}=\mathbf{H}_{1}^{H}\mathbf{\Phi}\mathbf{A}\mathbf{\Phi}^{H} \mathbf{H}_{1}\mathbf{w}_{k}s_{k}(t-\tau_{k})+\sum_{k^{\prime}\in\mathcal{K} \backslash\{k\}}\mathbf{H}_{1}^{H}\mathbf{\Phi}\mathbf{A}\mathbf{\Phi}^{H} \mathbf{H}_{1}\mathbf{w}_{k^{\prime}}s_{k^{\prime}}(t-\tau_{k^{\prime}})+ \mathbf{n}(t). \tag{11}\]  
We use the SINR as the sensing performance indicator [33]. Therefore, the SINR of the radar can be given by  
\[\rho_{r,k}=\frac{\left\|\mathbf{H}_{1}^{H}\mathbf{\Phi}\mathbf{A}\mathbf{\Phi }^{H}\mathbf{H}_{1}\mathbf{w}_{k}\right\|_{F}^{2}}{\sum_{k^{\prime}\in\mathcal{ K}\backslash\{k\}}\left\|\mathbf{H}_{1}^{H}\mathbf{\Phi}\mathbf{A}\mathbf{\Phi}^{H} \mathbf{H}_{1}\mathbf{w}_{k^{\prime}}\right\|_{F}^{2}+M\sigma_{\tau}^{2}}, \tag{12}\]
## 3 Results and Discussion
### Effects of MSW Blended Ratio \(R_{\rm M}\)  
To research the effects of the mixed ratio on co-gasification performance, several simulations were conducted by varying the MSW blended ratio \(R_{\rm M}\) ranges of 0 to 1. The other parameters are all kept constant, such as \(T=900\ ^{\circ}\)C, \(R_{\rm o}=0.2\), and \(R_{\rm c}=0.5\). These calculation results are shown in Figure 6. It can be clearly seen that the mole fraction of CO and CH\({}_{4}\) decreased with increasing \(R_{\rm M}\). The reason t  
Figure 5: Effect of \(R_{\rm c}\) on raw syngas composition and system performance (\(T=900\ ^{\circ}\)C and \(R_{\rm o}=0.2\)): (a) BC; (b) MSW; (c) \(R_{\rm M}=0.6\).  
for this trend is that the carbon content of MSW is less than that of BC. However, the mole fraction of H\({}_{2}\) and CO\({}_{2}\) increased with increasing \(R_{\rm M}\) due to the higher moisture content of MSW compared with BC, resulting in the occurrence of the steam reforming R5 reaction to promote the formation of H\({}_{2}\). In addition, the OEE of the entail co-gasification system had been in a state of decline from 0.78 to 0.14, and the degree of decline was extremely quick when the \(R_{\rm M}\) was over 0.6. Therefore, to maximize the disposal of MSW amounts and not to reduce the overall efficiency of the system too much, the \(R_{\rm M}\) ratio of 0.6 can be recommended as a suitable proportion for co-gasification process of raw MSW and BC. For the suitable ratio blends, the CGE and OEE of co-gasification system are 0.95 and 0.57, respectively.
## 1 Introduction
In order to evaluate the accuracy and robustness of the inertia estimation, a power system was implemented as an experimental setup in a Real-Time Digital Simulator (RTDS), and the results of the inertia estimation were compared against the professional software DIgSILENT PowerFactory [19]. The rest of the paper is organized as follows: in Section 2 is presented the literature review referent to the inertia estimation in power systems; in Section 3, the methodology to estimate the inertia constant of the generation units is presented; Section 4 shows the inertia estimation of the experimental validation; Section 5 presents the behavior of the inertia estimation when the generated active power is varied; Section 6 presents the results obtained with the application of the methodology in the southeast zone of the Mexican electric system; finally, Section 7 draws the main conclusions of this paper.
# Analysis of Fire Resistance of Prestressed Concrete T-Beam Based on ABAQUS Numerical Simulation
## 4 Regression Prediction Formula for Fire Resistance of Prestressed Concrete T-Beams  
The parametric analysis results show that the fire resistance of the prestressed concrete T-beam is affected by the fire mode, load ratio, flange plate thickness, ratio of the prestressing bar, concrete protective layer thickness, and concrete strength. Based on the results of the fire resistance calculated from the finite element mode analysis of the prestressed concrete T-beam, an equation for calculating the fire resistance of the prestressed concrete T-beam is proposed for the fire mode H1, as in Equation (1). \(K\) represents the fire resistance of the prestressed concrete T-beam. \(K_{Y}\) denotes the effect of flange plate thickness on fire resistance. \(K_{P}\) denotes the effect of the ratio of the prestressing bar on the fire resistance. \(K_{A}\) denotes the effect of concrete protective layer thickness on fire resistance. \(K_{C}\) denotes the effect of concrete strength on fire resistance. \(K_{n}\) denotes the influence of the load ratio on the fire resistance.  
\[K=K_{Y}\cdot K_{P}\cdot K_{A}\cdot K_{C}\cdot K_{n} \tag{1}\]  
where \(K_{Y}=-2.47271(Y/100)^{2}+8.61385(Y/100)-0.57829\)  
\(K_{P}=-6.32065P^{2}+4.42725P+1.21516\)  
\(K_{a}=15.78616(a/100)+1.74918\)  
\(K_{C}=9.50256(C/100)^{2}-5.76757(C/100)+1.74918\)  
\(K_{n}=0.29974n^{2}-2.41873n+2.88604\)  
Then \(Y\) and \(a\) are in mm.  
Figure 15 shows the comparison between the calculated value of the fire resistance formula and the simulated value of the finite element software. Among them, the correlation coefficient between the calculated value of the fire resistance formula and the finite element simulation value is 0.94635, which indicates that the accuracy of the fire resistance formula is good and shows good consistency.
## 2 Methodology
### Step 3: Validation and Visualization of Results  
The accuracy of RF trained models was determined using the testing set (more detailed in phases 2 and 3 of Figure 2). This technique is used to ensure that the predictions obtained through the data mining process are independent of the partition between training and test data, was also implemented. Statistical indicators used to evaluate the prediction performance of the models: mean square error (RMSE), coefficient of determination (\(R^{2}\)) and normal quantile-quantile (Q-Q) plot. \(R^{2}\) is the most common statistical coefficient used to explain the percentage of the total variance explained by the model [41]. RMSE indicates the variation between the actual and predicted values, and has been widely used as a standard statistical measure of model performance in several fields [42; 43; 44], being more appropriated than mean absolute error (MAE) when errors follow a normal distribution [45]. A Q-Q plot is a commonly used graphical method to compare empirical and theoretical values of the same model (residuals). This graph provides a fast visualization of trends within the data distribution and can be used to limit the data ranges for which the model can be applied based on residuals deviations [46; 47].
## 4 Danger Theory (DT) Based Approach
### Second Level Detection
#### 4.2.1 Battery Check  
In [28], the authors discussed the various categories of batteries frequently utilized in the mobile nodes, their voltage rate for initial start-up and ending interval. Mobile nodes will encounter the information based on the mobile node ID, battery level and key during the meeting with CH node in a precise time limit [29]. The CH will establish a list for specific intervals in memory and will also build the connection to broadcast the message. Accordingly, the presence of two or multiple mobile nodes with identical ID directs that the clone node exists in MWSN. This conclusion is derived from the assumption that to establish, control or screen the entire system, an adversary requires maximum capacity of voltage and the factor that the clone is deployed after the real node too. Hence, we assume that the clone has a greater value in contrast to real one. Battery types, for example Zinc Manganese Dioxide and LeClanche, are not advised to be used in sensor networks because of their lower capacity and huge charging time as discharging occurs by itself in the storage and it discharges quickly. Some batteries have low cost specification like Alkaline with a high capacity and low discharging, hence it is highly suggested in MWSN. It is much difficult to charge a huge number of mobile nodes in MWSN,which is why the battery used in the network is not rechargeable. The battery-based comparison is established by screening the voltages in the network operation. Furthermore, the battery level rapidly goes down because of discharging abilities and due to rapid change in the discharge rate capacity that varies as well. The author in [26] explained the method to measure the MICA2 sensor node.  
**Lemma 1**.: _Comparison based on battery: Consider \(B_{i}\) is a Battery level of a real mobile node which is deployed at the initial time (i.e., \(t\) = 0) and \(B_{j}\) is the battery level of a clone which is deployed after time \(t\) in the MWSN (i.e., \(t+t^{\prime}\)). \(B_{j}\) is deployed later at time \(t+t^{\prime}\) while \(B_{i}\) at \(t=0\), hence \(B_{j}\) has a greater value. Furthermore, to control and screen the information advisory requires a huge voltage level, else will fail the aim to produce clone. Besides, if \(B_{i}\
eq B_{j}\)_  
_At \(t=0\); \(B_{i}\approx B_{max}\)_  
_At \(t=t+t^{\prime}\); \(B_{i}\approx B_{max}-B_{consumed}\)_  
_\(B_{j}\approx B_{max}\)_  
\(B_{i}<B_{j}\)__  
This demonstrates the expression for mobile node deployment with specific time and clone comes after \(t+t^{\prime}\) of real mobile node, hence has a maximum battery capacity. With this consideration, it has a probability of high false positive and low true positive rate. The proposed DT based method is computed for maximum efficiency in terms of true positive and false positive rate by contrasting the battery level.
# Clinical Applications of Optical Coherence Angiography Imaging in Ocular Vascular Diseases
## 4 Retinal Artery Occlusion  
Central retinal artery occlusion (CRAO) is an ophthalmic emergency and the majority of cases are caused by atherosclerosis-related thrombosis or occlusion of the retinal arteries by emboli [16]. The incidence of CRAO is linked to older age, hypertension, hyperlipidemia, carotid artery disease, diabetes, and smoking [62]. CRAO often foreshadows cerebrovascular or cardiovascular events [16; 62].  
In the acute stage, the compromised capillary blood flow leads to axoplasmic stasis, intracellular edema, and ischemic necrosis of the inner retinal layers [63]. Examination in CRAO reveals retinal edema and pallor over the affected area, a cherry red spot, cattle trucking, arterial attenuation, and optic disc edema [16]. After the acute stage, resolution of retinal edema is observed with the development of residual inner retinal atrophy and attenuation of vessels [63]. Clinical findings in chronic CRAOs are optic atrophy, retinal arterial attenuation, clioretinal collaterals, and macular retinal pigment epithelium changes [62; 63; 64]. A long-term complication of CRAO is the risk of neovascularization and in some cases subsequent secondary glaucoma [62; 65]. However, the etiology is controversial as some have found no cause-and-effect relationship between CRAO and neovascular glaucoma [66]. In general, the visual prognosis in CRAO is poor with 61% of patients having a final visual acuity of 20/400 or worse [16].  
Figure 6: A case of central retinal vein occlusion. (**A**): Multi-color fundus photograph depicting tortuous venous segments, dot, blot, and flame hemorrhages and cotton wool spots. (**B**): FA depicting dilated and tortuous retina veins with patchy areas blocked by retina hemorrhage. (**C**): color-coded depth-resolved en face OCTA, which is a fusion image of the superficial (red), deep (green), and avascular (blue) en face OCTA slabs. An enlarged fovea avascular zone (blue circle) and temporal loss of flow (blue arrow), which is not depicted on FA, is seen on OCTA.
## 2 Adaptive PCNN Combined with GWO and BEMD
### Noise Reduction Mechanism of PCNN
```
Boundary treatment:  Generate edge-symmetric extension, make the original image \((m,n)\) change into \((m+6,n+6)\); Initialize the PCNN parameters: Initialize \(\alpha_{T}\), \(\beta_{t}\), \(\
u_{T}\) and \(W_{ijkl}\) the number of pass \(N\), step length \(\Delta_{t}\);  Keep each pixel in an unignited condition; Contaminated points detection and treatment:  Calculate every neuron \(L_{ij}[c]\) values in \(3\times 3\) neighboring region using Equation (7); adjust the threshold \(L_{ij}[c]\) using Equation (10); calculate the neuron internal modulation signal \(L_{ij}[c]\) using Equation (9); if a neuron \((i,j)\) is ignited in \(3\times 3\) neighbor region and more than 4 adjacent neurons are unignited, the \((i,j)\) pixels brightness will decline an \(\Delta_{t}\)end; if a neuron \((i,j)\) is unignited in \(3\times 3\) neighbor region and more than 4 adjacent neurons are ignited, the \((i,j)\) pixels brightness will increase an \(\Delta_{t}\)end; Otherwise the pixels brightness values of the \((i,j)\) will not change end; Compare the \(U_{ij}[c]\) values and \(E_{ij}[c]\) values, record the neuron output, ignited or unignited; \(N=N-1\), if return to contaminated points detection and treatment end;
```  
**Algorithm 1**PCNN Noise Reduction Algorithm  
The time decay parameter \(\alpha_{T}\) affects the operation efficiency directly and determines the number of iterations required in a cycle; \(V_{T}\) determines the conditions that must be issued only once in the iterative calculations. \(\beta_{T}\) has a major impact on restoration of the image. Therefore, we propose use of the GWO intelligent optimization algorithm to solve this complex nonlinear problem.
# A Joint Detection and Decoding Scheme for PC-SCMA System Based on Pruning Iteration
## 3 Proposed Algorithm for Joint Detection and Decoding
### Joint Detection and Decoding Scheme Based on Pruning Iteration PI-JDD
#### 3.1.2 SCAN Decoding  
The message passing process of the polar decoder is performed on the factor graph shown in Figure 4. Each node on the factor graph of the SCAN algorithm propagates two types of LLR information \(\text{L}_{\text{s},\text{t}}\) and \(\text{R}_{\text{s},\text{t}}\). \(\text{L}_{\text{s},\text{t}}\) and \(\text{R}_{\text{s},\text{t}}\) represent the LLR message passed to the left and right of the factor graph by node (s, t), respectively, where s = 0,1,... n, n = \(\log_{2}(N)\), denotes the column index and \(t\) = 0,1,... \(N\)-1, represents the row index, as shown the unit factor graph in Figure 5.  
The LLR message of user \(J\) is initialized once it is input into the SCAN decoder. The left message is initialized as \(L^{B}_{a,\text{polar}}\), and the initialization of the right message \(R^{j}_{n,t}\) is defined as follows:  
\[R^{j}_{n,t}=\left\{\begin{array}{cc}0,&t\in I\\ \infty,&t\in I^{C}\end{array}\right. \tag{11}\]  
where \(I\) denotes the set of information bits, \(I^{C}\) denotes the set of frozen bits.  
The left and right messages are transmitted on the factor graph, and the message updating formula is shown as the following equation:  
\[\begin{array}{l}L^{j}_{s+1,l_{2}}=f\Big{(}R^{j}_{s+1,l_{3}}+L^{j}_{s,l_{1}},L ^{j}_{s,l_{0}}\Big{)}\\ L^{j}_{s+1,l_{3}}=f\Big{(}R^{j}_{s+1,l_{2}},L_{s,l_{0}}\Big{)}+L^{j}_{s,l_{1}} \\ R^{j}_{s,l_{0}}=f\Big{(}R^{j}_{s+1,l_{2}},R_{s+1,l_{3}}+L^{j}_{s,l_{1}}\Big{)} \\ R^{j}_{s,l_{0}}=R^{j}_{s+1,2}+f\Big{(}R_{s+1,l_{2}},L^{j}_{s,l_{0}}\Big{)} \end{array} \tag{12}\]  
where \(f(a,b)\approx sign(a\ )\times sign(b)\times min(a\ |\ |b|)\).  
Figure 4: Factor graph of SCAN decoding with \(N=8\).  
Figure 5: Unit factor graph of SCAN.  
After the SCAN factor graph has been updated one iteration, the messages are interleaved and then input to the SCMA detector, denoted as:  
\[\begin{array}{ll}L_{a,SCMA}^{B}\Big{(}c_{j,(l-1)Q+m}\Big{)}&=\prod\left(L_{e, polar}^{B}\Big{(}c_{j,(l-1)Q+m}\Big{)}\right)\\ &=\prod\left(R_{0,\ell_{j,(l-1)Q+m}}+a\frac{\overline{R_{0,\ell_{j,(l-1)Q+m}}} }{L_{0,\ell_{j,(l-1)Q+m}}}L_{0,\ell_{j,(l-1)Q+m}}\right)\end{array} \tag{13}\]  
where \(\overline{R_{0,\ell_{j,(l-1)Q+m}}}\) denotes the average value of \(R_{0,\ell_{j,(l-1)Q+m}}\) and the meaning of \(\overline{L_{0,\ell_{j,(l-1)Q+m}}}\) is similar. \(a\) is the weight factor, whose value depends on the code rate [17].  
The SCMA detector transforms the LLR message into symbol probability for the next iteration, which is expressed as:  
\[P_{p,SCMA}^{B}\Big{(}c_{j,(l-1)Q+m}=q_{j,m}\Big{)}=\frac{\left[L_{a,SCMA}^{B} \Big{(}c_{j,(l-1)Q+m}\Big{)}\right]^{1-q_{j,m}}}{1+L_{a,SCMA}^{B}\Big{(}c_{j, (l-1)Q+m}\Big{)}} \tag{14}\]  
and  
\[\begin{array}{l}\mathrm{P}\Big{(}x_{j}^{l}\Big{)}=\prod_{m=1}^{Q}P_{p,SCMA}^ {B}\Big{(}c_{j,(l-1)Q+m}=q_{j,m}\Big{)}\end{array} \tag{15}\]  
where \(q_{j,m}\in\{0,1\}\).
# A Context-Aware Edge-Based VANET Communication Scheme for ITS +
## 4 Simulation Results
### Connection-Dependent Unicast Flows  
The TCP throughput under various source-destination distances and various numbers of flows are shown in Figures 11 and 12, respectively. The best performance is attained by the proposed  
Figure 10: End-to-end packet delivery ratio for various numbers of flows (the case of 400 m source-destination distance).  
Figure 9: End-to-end packet delivery ratio for various source-destination distances (the case of 2 broadcast flows).  
scheme. This is because by using different types of beacon messages, the proposed scheme can reduce the number of hops as compared with \"Edge (BeaconSz = 1024 B)\" and select better forwarder nodes that have better qualities as compared with \"Edge (BeaconSz = 56 B)\". For an end-to-end TCP flow, the proposed scheme is able to select different routes for data packets and ACK packets, which can significantly shorten the round trip time as compared with the conventional approaches. The advantage over other approaches for a large number of traffic flows comes from the edge-based forwarding scheme which reduces the number of contender nodes for the same wireless resources.
# Ground Based Hyperspectral Imaging to Characterize Canopy-Level Photosynthetic Activities
## Results
### Correlation between Traits  
High correlations (r = 0.92 to 0.95) were achieved for sETR values calculated using the PAM and HSI methods, further indicating the validity of using the developed method for canopy-level  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Trait** & **Cultivar** & **Treatment** & **Interaction between Cultivar and Treatment** \\ \hline CGR & 0.1568 & 0.0001 & 0.8406 \\ NAR & 0.1229 & 0.0002 & 0.3513 \\ RGR & 0.0128 & 0 & 0.2899 \\ \(\Delta LAI\) & 0.1801 & 0.0018 & 0.3906 \\ \(\Delta LMF\) & 0.9069 & 0.0008 & 0.006 \\ \(sETR_{PAM}\) & 0.0792 & 0 & 0.65 \\ \(sETR_{HSIL}\) & 0.2661 & 0 & 0.0237 \\ \(sETR_{HSIQ}\) & 0.0864 & 0 & 0.0411 \\ \(sETR_{HSIA}\) & 0.1663 & 0 & 0.0241 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The \(p\)-values of ANOVA tests on the growth traits, and standardized ETR values estimated using four approaches.  
Figure 7: ANOVA tests on the growth traits and standardized ETR values estimated using four approaches. Asterisks indicate statistical differences between two treatments at different significance levels: * \(<\) 0.05, ** \(<\) 0.01, and *** \(<\) 0.001. CGR, NAR, RGR, and sETR values are in the units of g/m\({}^{2}\)/d, g/m\({}^{2}\)/d, g/g/d, and \(\mu\)mol/m\({}^{2}\)/s, respectively.  
photosynthetic efficiency quantification (Figure 8). sETR, irrespective of calculation method, also showed moderate correlations (r = 0.46 to 0.67) with growth traits, indicating the potential of using calculated sETR for crop growth prediction. Among the three HSI methods, the HSI linear method provided the lowest correlation, whereas the HSI quadratic and average methods showed relatively higher correlations. This demonstrates that the estimation of maximal fluorescence affects the capability of using sETR for growth prediction as well.  
Compared with sETR, sPRI showed poorer correlations with other traits, especially for Diuron-treated plots. A possible reason was that PRI tends to reach a plateau value at a relatively low PAR. When the PAR value of 1500 \(\mu\) mol/m\({}^{2}\)/s was used, most sPRI fell into a small value range irrespective of plant growth status, resulting in a poor correlation with growth traits. This observation agreed with a previous study [59]. This suggests that the SIF-based characterization of photosynthetic activity might be broadly applicable under a range of environmental conditions.  
Figure 8: Pearson correlation values between growth traits and standardized ETR values estimated using four approaches. Values in the upper triangle are correlation values between traits indicated by a row and a column. Subplots along the main diagonal are the value distribution of individual traits, whereas subplots in the lower triangle are scattering plots between traits indicated by a row and a column. Significant (\(p\)-value < 0.05) correlation values are rendered by red color, and values are otherwise black color.