esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Analysis of Simulation Results
### Health Benefits
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \begin{tabular}{c} **Health Benefit\({}^{1}\)** \\ **Air Pollution Control Measure** \\ \end{tabular} & \begin{tabular}{c} **Percentage of** \\ **(100 Million** \\ **NTD)** \\ \end{tabular} & \
\begin{tabular}{c} **Technical Cost** \\ **Total Benefit** \\ **(5s)** \\ \end{tabular} &
\begin{tabular}{c} **Benefit/Cost** \\ **NetD)** \\ \end{tabular} \\ \hline \hline \multicolumn{6}{c}{1. Technical Measures} \\ \hline T1 & Retire 80,000 Stage 1 and Stage 2 diesel trucks & 14,019 & 54.5 & 1863 & 7.5 \\ T2 & Strengthen air pollution controls in port areas (ship speed reductions, regulations on ship fuel usage, promoting the use of shore power) & 3623 & 14.1 & 11 & 332.4 \\ T3 & Power facility regulations (stricter power sector regulations and standards) & 3014 & 11.7 & 21 & 144.9 \\ T4 & Improve control of smoke from 7000 restaurants & 751 & 2.9 & 4 & 183.1 \\ T5 & Eliminate 1 million 2-stroke motorcycles & 616 & 2.4 & 193 & 3.2 \\ T6 & Improve control of smoke from agricultural waste-burning (reduce the area of open-air burning by 90%) & 448 & 1.7 & 3 & 179.3 \\ T7 & Regulate boilers (accelerate the retirement of 5000 industrial boilers and 1000 commercial boilers) & 416 & 1.6 & 540 & 0.8 \\ T8 & Install exhaust filters in 3,000 Stage 3 diesel trucks & 317 & 1.2 & 41 & 7.8 \\ T9 & Install pollution control devices in state-owned businesses (e.g., Dragon Steel, China Steel Corporation, CPC Corporation) & 299 & 1.2 & 268 & 1.1 \\ T10 & Change fuel-burning customs and traditions (increase centralized & 98 & 0.4 & 10 & 9.8 \\  & burning to 22,000 metric tons) & 23,601 & 91.7 & 2954 & 86.9 \\ \hline \hline \multicolumn{6}{c}{2. Administrative Measures \({}^{1}\)} \\ \hline \multirow{4}{*}{M1} & Tighten emission standards for automobiles that are 10 years or older, and set up air quality maintenance zones, where the entry of highly & 758 & 2.9 & - & - \\  & polluting vehicles is restricted or forbidden & & & & \\ M2 & Control fugitive dust from riverbeds & 685 & 2.7 & - & - \\ M3 & Regulations for fugitive dust from construction sites and stockpiles of & 642 & 2.5 & - & - \\ \cline{1-1} \cline{2-6}  & Pronotion of electric vehicles (up to 2100 vehicles) for the & 18 & 0.1 & - & - \\ \cline{1-1} \cline{2-6}  & Substotal & 2103 & 8.2 & - & - \\ \hline \hline \end{tabular}  
* As the administrative measure does not involve the purchase and use of pollution control devices, they do not have an easily identifiable cost, which makes it impossible to calculate their B/C ratio.  
\end{table} 
Table 4: Health benefit of each pollution control measure under the 14 air pollution control strategies (TAPCAP). Figure 4: Health benefits of administrative pollution control measures (in billions of new Taiwan dollars (NTD)).
# Machine Learning for Child Oral Health: A Scoping Review
###### Abstract  
Background: Machine learning (ML) has potential to assist dental professionals with diagnosing and predicting outcomes of oral health. Tooth decay in children is the most common chronic childhood disease and it can be prevented by early detection. We aim to provide a map of the current evidence on ML in child oral health and provide insight for future research. Methods: A scoping review used databases like Medline, Web of Science, EBSCO Dentistry & Oral Science Source, Cochrane Library, and Scopus. Included studies assessed ML models for diagnoses, prediction, or management of oral health in children (0-9 years). Data extraction included publication year, location, age, sample size, disease, study type, and ML algorithms. Results: a total of 29 studies were included, mainly in pediatric dentistry and dental public health. Study designs comprised cross-sectional (34.5%), cohort (31.0%), case-control (20.7%), clinical trials (10.3%), and descriptive surveys (3.5%). The majority of studies were from high-income (69.0%) and upper middle-income countries (27.6%), with a small representation from low middle-income countries (3.4%). ML focused on predicting and diagnosing oral health issues such as caries progression and risk, with neural networks predominantly tested alongside emerging techniques like random forest, regression, and gradient boosting. Conclusions: ML algorithms hold promise in improving dental diagnosis and prediction accuracy, benefiting dental professionals, including pediatric and general dentists, in enhancing proficiency and reducing clinical errors.  
machine learning; child oral health; dental diagnosis
# Compact High-Diectivity Contra-Directional Coupler
## Appendix A Derivation of Equations (14)-(17)  
From Equation (12)  
\[\Re T_{11e}=\Re T_{11e}\]  
\[\Re T_{11e}-\Re T_{11e}=0\]  
Substituting \(\Re T_{11e}\) and \(\Re T_{11e}\) in Equation (5) into the last equation,  
\[\frac{1}{4\sqrt{n}}[(n+1)A_{o}-B_{o}(b_{1o}+nb_{2o})]-\frac{1}{4\sqrt{n}}[(n+1 )A_{e}-B_{e}(b_{1e}+nb_{2e})]=0\]  
By rearranging the last equation  
\[B_{e}(b_{1e}+nb_{2e})-B_{o}(b_{1o}+nb_{2o})=(n+1)(A_{e}-A_{o}) \tag{17}\]  
From the imaginary part of Equation (12)  
\[\Im T_{11e}=\[PERSON] T_{11e}\]  
\[\Im T_{11e}-\Im T_{11e}=0\]  
Substituting \(\Im T_{11e}\) and \(\Im T_{11o}\) in Equation (6) into the last equation,  
\[\frac{1}{4}[(b_{1e}+b_{2e})A_{e}+B_{e}(1-b_{1e}b_{2e})+C_{e}]-\frac{1}{4}[(b_{ 1o}+b_{2o})A_{o}+B_{o}(1-b_{1o}b_{2o})+C_{o}]=0\]  
By rearranging the last equation,  
\[A_{e}(b_{1e}+nb_{2e})-A_{o}(b_{1o}+b_{2o})-B_{e}b_{1e}b_{2e}+B_{o}b_{1o}b_{2o }=B_{o}-B_{e}+C_{o}-C_{e} \tag{18}\]  
From Equation (13),  
\[\Re T_{12e}=\Re T_{12o}=0\]  
\[\Re T_{12e}+\Re T_{12o}=0,\Re T_{12e}-\Re T_{12o}=0\]  
Substituting \(\Re T_{12e}\) and \(\Re T_{12o}\) in Equation (7) into the last two equations,  
\[\frac{1}{4\sqrt{n}}[(n-1)A_{e}+B_{e}(b_{1e}-nb_{2e})]+\frac{1}{4\sqrt{n}}[(n- 1)A_{o}+B_{o}(b_{1o}-nb_{2o})]=0,\]  
\[\frac{1}{4\sqrt{n}}[(n-1)A_{e}+B_{e}(b_{1e}-nb_{2e})]-\frac{1}{4\sqrt{n}}[(n- 1)A_{o}+B_{o}(b_{1o}-nb_{2o})]=0\]  
By rearranging the last two equations,  
\[B_{e}(b_{1e}-nb_{2e})+B_{o}(b_{1o}-nb_{2o})=-(n-1)(A_{e}-A_{o}) \tag{19}\]  
\[B_{e}(b_{1e}-nb_{2e})-B_{o}(b_{1o}-nb_{2o})=-(n-1)(A_{e}+A_{o}) \tag{20}\]  
From the imaginary part of the first part of Equation (13),  
\[\Im T_{12e}+\Im T_{12o}=  
By (A3) + (A4):  
\[B_{e}(b_{1e}-nb_{2e})=-(n-1)A_{e}\] (A6)  
By (A3) \(-\) (A4):  
\[B_{o}(b_{1o}-nb_{2o})=-(n-1)A_{o}\] (A7)  
By (A2) + (A5):  
\[A_{e}(b_{1e}+b_{2e})-B_{e}b_{1e}b_{2e}=B_{o}-C_{e}\] (A8)  
By (A5) \(-\) (A2):  
\[A_{o}(b_{1o}+b_{2o})-B_{o}b_{1o}b_{2o}=B_{e}-C_{o}\] (A9)  
By (A1) + (A4):  
\[B_{e}b_{1e}=B_{o}b_{1o}+A_{e}-A_{o}\] (A10)  
By (A1) \(-\) (A4):  
\[B_{e}b_{2e}=B_{o}b_{2o}+A_{e}-A_{o}\] (A11)  
From (A6):  
\[b_{2e}=\frac{1}{n}b_{1e}+\frac{(n-1)A_{e}}{nB_{e}}\] (A12)  
From (A7):  
\[b_{2o}=\frac{1}{n}b_{1o}+\frac{(n-1)A_{o}}{nB_{o}}\] (A13)  
By substituting (A12) into (A8) and rearranging into a quadratic equation of \(b_{1e}\),  
\[\frac{B_{e}}{n}b_{1e}^{2}-2\frac{A_{e}}{n}b_{1e}+B_{o}-C_{e}-\frac{(n-1)A_{e}^ {2}}{nB_{e}}=0\] (A14)  
By solving the last equation,  
\[b_{1e}=\frac{2\frac{A_{e}}{n}\pm\sqrt{\left(\frac{2A_{e}}{n}\right)^{2}-4\frac {B_{e}}{n}\left(B_{o}-C_{e}-\frac{(n-1)A_{e}^{2}}{nB_{e}}\right)}}{2\frac{B_{e }}{n}}\] (A15)  
Equations (14)-(17) can be derived as below.  
From (A15) and the condition \(n=1\),  
\[b_{1e}=\frac{A_{e}+\sqrt{A_{e}^{2}-B_{e}(B_{o}-C_{e})}}{B_{e}},n=1\]  
From (A10),  
\[b_{1o}=\frac{B_{e}}{B_{o}}b_{1e}-\frac{A_{e}-A_{o}}{B_{o}}\]  
From (A12) and the condition \(n=1\),  
\[b_{2e}=b_{1e}\]  
From (A13) and the condition \(n=1\),  
\[b_{2o}=b_{1o}\]
# Memory Efficient Implementation of Modular Multiplication for 32-bit ARM Cortex-M4
## 2 Background
### SIKE Round 2
#### 2.3.2 Key Encapsulation Mechanism  
KEM consists of three parts: [PERSON]'s key generation, [PERSON]'s key encapsulation, and [PERSON]'s key decapsulation. Figure 1 describes the KEM in detail.  
Key Generation  
[PERSON] chooses an random integer \(\text{sk}_{A}\in\mathbb{Z}/2^{\epsilon_{A}}\mathbb{Z}\) and by applying an isogeny \(\phi_{A}:E_{0}\to E_{A}\) with kernel \(R_{A}:=\langle P_{A}+[\text{sk}_{A}]Q_{A}\rangle\) to the base points \(\{P_{B},Q_{B}\}\), computes her public key \(\text{pk}_{A}=[E_{A},\phi_{A}(P_{B}),\phi_{A}(Q_{B})]\). Moreover, [PERSON] generates a \(t\)-bit (the implementation parameter defines the \(t\) value) random sequence \(s\in_{R}\{0,1\}^{t}\).  
Encapsulation  
Bob generates a \(t\)-bit random message \(\text{m}\in_{R}\{0,1\}^{t}\). Afterward, [PERSON] concatenates the message with her public key \(\text{pk}_{A}\). [PERSON] generates an \(\epsilon_{B}\)-bit hash result \(r\) with \(\text{cSHAKE256}\) hash function \(H_{1}\) and input (\(m\parallel\text{pk}_{A}\)). With \(r\), [PERSON] executes a secret isogeny \(\phi_{B}:E0\to EB\) to base points \(\{P_{A},Q_{A}\}\). Then, [PERSON] forms his public key \(\text{pk}_{B}(r)=[E_{B},\phi_{B}(P_{A}),\phi_{B}(Q_{A})]\). [PERSON] also performs the common \(j\)-invariant of curve \(E_{BA}\) by using another isogeny \(\phi_{B}^{\prime}:EA\to E_{BA}\) with [PERSON]'s public key. Finally [PERSON] generates a ciphertext \(c=(c_{0},c_{1})\), such that:  
\[c=(c_{0},c_{1})=(\text{pk}_{B}(r),H_{2}(j(E_{BA}))\oplus m),\]  
where \(H_{2}\) is generated with a \(\text{cSHAKE256}\) hash function. This generates an arbitrary-length of output with a defined initialization parameter. Lastly, [PERSON] generates the shared secret \(K=H_{3}(m\parallel c)\). Afterward, [PERSON] sends the ciphertext (\(c\)) to [PERSON].  
Decapsulation  
With the ciphertext (\(c\)), [PERSON] performs the common \(j\)-invariant of \(E_{AB}\) by using her secret isogeny to \(E_{B}\). [PERSON] executes \(m^{\prime}=c_{1}\oplus H_{2}(j(E_{AB}))\) and \(r^{\prime}=H_{1}(m\parallel\text{pk}_{A})\). Lastly, [PERSON] validates [PERSON]'s public key by performing \(\text{pk}_{B}(r^{\prime})\) and comparing the value with \(c_{0}\). [PERSON] generates the secret shared value \(K=H_{3}(m^{\prime}\parallel c)\) when the public key is correct. Otherwise, [PERSON] generates a random value \(K=H_{3}(c\parallel s)\) to be secure against active attacks.  
Figure 1: SIKE mechanism.  
In 2019, the candidates for the second round of post-quantum cryptography competition is selected. The round 2 of SIKE protocol [28] is different from the first version. These changes are outlined as follows:  
* Two new parameter sets for NIST security levels 1 and 3 have been added (i.e., SIKEp434 and SIKEp610).
* One parameter set (i.e., SIKEp964) has been removed.
* The security categories for parameter sets have been adjusted upward (i.e., the NIST security levels of SIKEp503 and SIKEp751 are changed to 2 and 5, respectively.).
* The starting curve has been changed.
* A public key compression has been implemented.
## 2 Materials and Methods
### Wine Spirit Samples and Experimental Design  
The same wine spirit obtained by column distillation in Lourinha region (Portugal), at a Portuguese winery (Adega Cooperativa da Lourinha) was used to fill several vessels corresponding to five modalities with different ageing conditions:  
* CB--250 L wooden barrel of chestnut wood;
* CO15--50 L glass demijohns with chestnut staves and micro-oxygenation with a flow rate of 2 mL/L/month during the first 15 days followed by 0.6 mL/L/month until 365 days;
* CO30--50 L demijohns with chestnut staves and submitted to a micro-oxygenation with a flow rate of 2 mL/L/month during the first 30 days followed by 0.6 mL/L/month until 365 days;
* CO60--50 L glass demijohns with chestnut staves and submitted to a micro-oxygenation with a flow rate of 2 mL/L/month during the first 60 days followed by 0.6 mL/L/month until 365 days;
* CN--50 L demijohns with chestnut staves and nitrogen application with a flow rate of 20 mL/L/month (nitrogen modality).  
The dissolved oxygen content in each modality was controlled over the time, as described previously [24] and the values are shown in Figure 1.  
Two replicates of each modality were carried out and the chestnut wooden barrel and staves were submitted to medium plus toasting at the cooperage ([PERSON], Palacoulo, Portugal). The number of staves (50 cm length \(\times\) 5 cm width \(\times\) 1.8 cm thickness) introduced into the demijohns was planned in order to mimic the surface area to volume ratio of a 250 L barrel (85 cm\({}^{2}\)/L).  
The supply of pure oxygen (X50S Food, Gasin, Perafita, Portugal) in the 50 L demijohns was done through a multiple diffuser micro-oxygenator (VISIO 6, Vivelys, Vileneuve-les-Maguelone, France) with ceramic diffusers, at different flow rates according to the ageing modality (O). Pure nitrogen (N) (X50S Food, Gasin, Perafita, Portugal) was supplied during the ageing experiment through a specific apparatus (Gasin, Perafita, Portugal).  
All the ten experimental units, located in the cellar of Adega Cooperativa da Lourinha in similar environmental conditions, were sampled at a middle height and over specific time periods (at 8, 60, 180, 270 and 365 days of ageing) in order to monitor the target volatile compounds: acetaldehyde, linalool, acetic acid and syringol.  
Figure 1: Average dissolved oxygen content in each ageing modality, during the ageing period (adapted from [24]).  
At the experiment's conclusion (365 days), the samples were taken for odorant profile screening and sensory evaluation of the wine spirits.  
At the beginning of the experiment, a sample of the unaged wine spirit was also taken, and the target and odorant compounds as well as the sensory characteristics were determined. This sample was coded as a control spirit.
# A Set of Active Disturbance Rejection Controllers Based on Integrator Plus Dead-Time Models +
## 4 Real-Time Experiments and Analysis
### Evaluation of ADRC2  
In evaluation of ADRC\({}_{2}\)(\(\overline{T}_{d}\)) controllers with the ESO delay \(\overline{T}_{d}=T_{d}\) we will focus on choice of the ESO parameters \(T_{c}=T_{d}\) and \(T_{c}=T_{d}/2\) and analyze, how these values can be combined with the tuning parameter \(T_{1}\).  
The results corresponding to responses in Figure 7 show (see Figure 8) that acceleration of transients by decreasing \(T_{c}\) increases the effect of noise. The excessive control effort is, however, generally lower than in case of ADRC\({}_{1}\) control.  
The influence of the parameter \(T_{1}\) is different for setpoint and disturbance steps and depends to a large extent on the selected value \(T_{c}\). For \(T_{c}=T_{d}\), it affects the value of \(IAE_{s}\) only slightly, while the value of \(IAE_{d}\) decreases with increasing value of \(T_{1}\). For \(T_{c}=T_{d}/2\), we get the smallest values of \(IAE_{d}\), which are only slightly dependent on \(T_{1}\). However, for this \(T_{c}\), \(T_{1}\) has a strong impact on the excessive control effort \(TV_{1}(u_{d})\).
# Effect of Plate Curvature on Heat Source Distribution in Induction Line Heating for Plate Forming
## 2 Model and Method  
In order to investigate the influence of plate curvature, curved plates were modeled to simulate the deformed plate. We used the FEM to calculate the entire processing of line heating. The authors divided the induction heating process, which is a complex multi-physical problem, into three parts: electromagnetic calculation, thermal calculation, and mechanical calculation. The three calculations were coupled in sequence. The result of the previous calculation is the input of the latter (see Figure 1).  
We used electromagnetic FEM to obtain the Joule heat produced by the inductor on the plate with different initial curvatures. We used the commercial software ANSYS 15.0 to perform the electromagnetic calculation, as the ANSYS Parametric Design Language (APDL) can effectively modify the model and perform the batch calculation. We compared the results of the heat distribution with different initial curvatures. The heat distribution obtained was used as a surface heat source to perform the heat transfer calculation of the line heating. The result of the temperature field is the input for the mechanical analysis. Then, the elastic-plastic mechanical analysis was performed to get the plastic strain and displacement. We used the commercial software ABAQUS 6.14 to perform the thermal and mechanical calculations. In the ABAQUS, the subroutine can easily apply surface heat source, and the temperature field can transfer from the thermal calculation to the mechanical calculation.
## 3 Experiment and Discussion
### Evaluation of the Improved Image Quilting
The parameters are shown in Table 2.  
Four categories of images selected from the real scene were taken as a reference, and the quality of synthesized images was evaluated by RSME, PSNR, and SSIM, as the index of objective evaluation, PSNR, and RMSE are the most widely used [45]. Assume that the size of image was \(H\times W\), then RMSE and PSNR were defined by:  
\[RMSE=\left(\frac{1}{H\times W}\sum_{i=1}^{H}\sum_{j=1}^{W}(X(i,j)-Y(i,j))^{2} \right)^{1/2} \tag{24}\]  
\[PSNR=20\lg\frac{(2^{n}-1)}{RMSE} \tag{25}\]  
where \(X\) and \(Y\) were the reference and generated image, respectively, and \(n=8\) was the number of sample bits of the image. The average RMSE and PSNR of four categories of images were shown in Figure 9a,b, respectively. Since the size of images was 360 \(\times\) 360 pixels and the average value of RMSE was less than 35, and PSNR was in the range [30; 35], it meant that the values of RMSE and PSNR were within a reasonable range, indicating that the pixels of the generated images share some significant similarity with that of the images in real scene. Nevertheless, in (24) and (25), it can be seen that RMSE and PSNR can evaluate pixel similarity well regardless of structure information. However, the structure is not taken into account. SSIM is an objective evaluation index that is based on structure features, wherein the brightness, contrast, and structure similarity are taken into consideration. The result of SSIM is shown in Figure 9c. The values \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Image Category** & \(\mathbf{N}_{I}\) & \(\boldsymbol{L}_{th}\) & \(\boldsymbol{H}_{th}\) & \(\boldsymbol{\gamma}\) & \(\boldsymbol{L}_{s}\) & \(\boldsymbol{c}\) \\ \hline I & No wear, no dirt, no shadows & 2 & 130 & 120 & 0.3 & 20 & 5 \\ II & Light wear, dirt, no shadows & 2 & 97 & 159 & 0.5 & 20 & 5 \\ III & Middle wear, dirt, shadows & 2 & 10 & 215 & 0.6 & 20 & 75 \\ IV & Severe wear, no dirt, shadows & 2 & 10 & 215 & 0.6 & 20 & 82 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Road parameters. Figure 9: Evaluation results of label images. The three are the results of Root Mean Square Error (RMSE), Peak Signal to Noise Ratio (PSNR), and Structural Similarity (SSIM). Further, the I, II, III, and IV are four types of images. were all greater than 0.65. Therefore, it can be concluded that the structure of label images was consistent with the structure of the images in the real scene. From the experimental results that are mentioned above, we may draw the conclusion that the pixel and structure of the label images share a significant similarity with that of the images of the real scene, and the generated label images can meet the requirement of model training.
## 3 Results and Discussion
### Comparative Study with the Available Published Results  
The P-FGM is reduced to a homogeneous isotropic plate when \(k=0\). As a result, in Figure 3, the sound radiation fields computed by the aforementioned present approach are compared to those calculated by the method used by [PERSON] et al. [7]. The assumption used in this study is that the size of the element is smaller than half of the structural and acoustic wavelength, leading to constant normal velocity over each elemental radiator. The constant velocity eases solving the Rayleigh integral by elemental radiator approach with radiation resistance matrix. [PERSON] et al. [7] used the direct integration approach with the assumption that the plate is significantly thin (a/h = 40). An excellent agreement is observed in Figure 3.  
For two values of the power-law index, \(k=1\) and \(5\), the sound power level is computed using the present approach and cross-compared against results in [PERSON] et al. [28] and illustrated in Figure 4. In addition to above assumption mentioned in Section 3.1, the thermal effects are ignored. Furthermore, the plate is assumed to be placed in an infinite rigid baffle. In general, a good agreement is obtained in Figure 4. A small discrepancy is found at higher frequencies for \(k=1\) because [PERSON] et al. [28] used first-order shear deformation theory with subspace method, while in the present paper, classical plate theory with elemental radiator approach is used. [PERSON] et al. [28] used a frequency resolution of \(1\) Hz for a frequency range of \(0\) to \(2000\) Hz, while in the current paper, the frequency resolution is \(3\) Hz.  
Figure 5 compares the sound power level radiated from the vibrating P-FGM plate computed using the present approach and finite element package, Ansys. Al\(\backslash\)/Al_2\(0\)3 P-FGM is the material composition used for the clamped FGM plate. In Ansys, SOLID185 and Contact174 elements are used for structural modeling, while FLUID221 elements are used for acoustic medium modeling. The results show a high level of agreement.  
Figure 4: P-FGM plate’s SPL for \(k=\) (**a**) 1 and (**b**) 5, in comparison with [PERSON] et al. [28].  
Figure 3: (**a**) Sound power level (SPL) and (**b**) radiation efficiency for \(k=0\) comparison with [PERSON] et al. [7].
## 3 Discussion
We speculated that the genomic heterozygosity of _P. grandiflora_ is too high or repetitive sequences may be folded during the assembly process, leading to assembly errors and affecting the gene cloning [35; 41; 42]. Based on the sequence similarity of PgCYP716s and the gene expression changes under MeJA treatment and different saponin accumulation periods, the function of _PgCYP716A264_, _PgCYP716A391_, _PgCYP716A291_, and _PgCYP716 BWv3_ genes were further studied. Similar to the majority of CYP450 proteins, subcellular localization analysis revealed that the four PgCYP716 genes in _P. grandiflora_ encoded membrane proteins (Figure 6A) [36; 43]. Yeast heterologous expression revealed that PgCYP716A264, PgCYP716A391, PgCYP716A291, and PgCYP716 BWv3 oxidize \(\beta\)-amyrin to produce oleanolic acid (Figure 7). This indicated that the CYP716 BWv subfamily may also play an important role in catalyzing the production of oleanolic acid from \(\beta\)-amyrin [44]. Through hairy root transformation, transgenic and positive hairy roots of four genes were successfully obtained (Figure 8A; Supplementary Figure S1). Overexpression in hairy roots showed a notable enhancement in \(\beta\)-amyrin and total saponin levels in comparison to the wild-type (WT). Gene expression analysis further confirmed that the genes involved in the saponin biosynthesis pathway were up-regulated in the overexpressed hairy roots (Figure 8B-I). However, overexpression of the _PgCYP716 BWv3_ gene resulted in decreased oleanolic acid content (Figure 8E). This suggested that PgCYP716 BWv3 has a continuous catalytic function, catalyzing the formation of other downstream substances after producing oleanolic acid and leading to its reduced content [23]. Platydcoin D, platycodin D3, and platycoside E are the main saponin monomers in the _P. grandiflora_ root [1]. Though the contents of platycodin D and platycoside E increased in overexpressing hairy roots compared with WT, the content of platycodin D3 decreased (Figure 8B-E). This indicated that enzymes involved in later modifications (e.g., PgCYP450 and PgUGT) may contribute to the differences in the content of different saponin monomers.