esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Random Forest Classifier  
Supervised machine learning models are inherently linked to the reference data used during training. Model generalisation capacity can be considerably influenced by the size and quality of the training dataset [18; 29; 98]. Due to our limited reference dataset relative to the large study area, we opted to apply a random forest approach. Random forests, which were introduced by [PERSON] [95], are an ensemble of decision trees trained on random subsets of input variables and training samples and are known for their robustness across both small and large datasets [10; 99; 100; 101; 102]. Previous cocoa classification studies with limited data also employed random forests and reported their good performance [43; 45; 48; 67; 103].  
Another benefit stemming from the application of random forest is the parameterisation stability, which simplifies the optimisation of the hyperparameters [18; 102; 104]. To maintain the independence of our test dataset, we fine-tuned the hyperparameters based on the validation dataset. The hyperparameter optimisation, the training and the performance assessment of the random forest were performed within the GEE environment [9]. Given that GEE lacks hyperparameter optimisation tools, such as grid search, we applied a heuristic trial-and-error method. We set the number of decision trees to 100 after examining the range of values {100, 200, 300, 500}. The minimum number of samples per leaf was determined to be 10 from the range {1, 5, 10, 15, 20, 30}. The square root of the variables per  
Figure 2: The feature importance rankings as estimated by the permutation-based measure for the vegetation indices and the GLCM textural features (**a,b**) of the raw dataset and (**c,d**) the refined dataset, respectively. The larger the mean decrease in accuracy, the more important the feature for the random forest. The prefix of the textural feature names refers to the Sentinel-2 band, and the suffix refers to the respective textural measure. For all abbreviations, the reader is referred to Tables 2 and 3.  
node split was selected, which is a commonly used optimisation method and recommended for its effectiveness [98; 102].
# Low-Power Two-Phase Clocking Adiabatic PUF Circuit
## 2 Fundamental Adiabatic PUF
### Proposed Adiabatic PUF
#### 2.3.1 Operation of the Proposed Adiabatic PUF  
To better understand the proposed adiabatic logic-based PUF, we describe the proposed circuit operation in four different adiabatic phases (Wait, Evaluate, Hold, and Recover, as shown in Figure 3b) when the challenge-bit is at logic \"0\", which enables the P1 transistor (Figure 3a) to create a path for the adiabatic operation.  
1. **Wait Phase**: During the wait phase, the power clock (Vpc) is at a constant level of 0.9 V. Therefore, the PUF cell will be in a waiting state during this phase.
2. **Evaluate Phase**: During the evaluate phase, Vpc slowly rises from 0.9 to 1.8 V (Vdd). Because Vpc is always greater than V\({}_{\text{THP}}\) in our proposed Vpc signal, both P2 and P3 are turned ON. However, owing to the effect of different threshold voltages due to imperfections in the CMOS manufacturing process, one of the transistors (P2 and P3) with a lower threshold voltage will conduct first, thereby facilitating the corresponding load capacitor to quickly become charged. This will lead to a flip in the outputs where one of the outputs leads to logic \"1\" and the other to logic \"0\". In this phase, the supply current I\({}_{\text{Vpc}}\) flows through P1 and P2 to charge the Rb node capacitor, as shown in Figure 3a.
3. **Hold Phase**: During the hold phase of the Vpc, the proposed adiabatic PUF generates a stable PUF response.
4. **Recover Phase**: During the recovery phase of the Vpc, the time ramp voltage slowly decreases from Vdd to 0.9 V. During this phase, the charge stored in the load capacitor is slowly recovered back to the power clock supply through P1 and P2 transistors. Moreover, the response bits will remain stable because the low level of Vpc is 0.9 V, which is always higher than the V\({}_{\text{TH}}\) of both NMOS and PMOS transistors. Hence, the Rb node maintains logic \"1\", which triggers transistor N3 to stay always ON and pull-down Rb- node to ground level through transistor N4 once Vpc- is higher than V\({}_{\text{THN}}\).  
When the challenge bit increases, transistor P1 turns OFF, and N1 turns ON. In this condition, the output node of the response bit Rb will be discharged from 0.9 V to ground via P2 and N1 transistors. All output nodes (Rb and Rb-) will be at logic \"0\" and flipped once the challenge bit Cb declines again to logic \"0\" for the next adiabatic operation.  
Figure 3: (**a**) Proposed Adiabatic PUF circuit and (**b**) its timing chart.
# GNSS-R Observations of Marine Plastic Litter in a Water Flume: An Experimental Study
## 2 Materials and Methods
### Plastic Litter  
Multiple types of plastics are found in the oceans. In [14; 15] it was shown that the most important polymer in terms of relative abundance in all the environment compartments, including marine and freshwater systems is polyethylene (HDPE and LDPE), followed by polypropylene (PP) and polystyrene (PS). [PERSON] et al. [16] classified the ocean plastics in hard plastics, sheets and films (type H), ropes and fish nets (type N), pellets (type P), and foam (type F), and provided mass concentrations for these types of plastics where the plastic types N and H were most frequently observed in the Pacific gyre.  
Plastics of different shapes, sizes, and types were used in this field experiment, in different concentrations to assess the impact of their presence in the water surface in the GNSS-R signal. The plastics used in these tests are both clean, newly produced plastics, as well as marine plastics collected in nature. The plastics used were: water bottles, bottle caps, fishing nets, marine litter bags, Styrofoam, polyethylene and polypropylene pellets. In total, 23 different types of plastics were used, some of them are shown in Figure 6. Table 3 summarizes the experiment conditions: plastic types, concentration, surface density and size, and the rms wave height created with the mechanical paddles.  
Figure 6: Pictures of: (**a**) water bottles used, (**b**) bottle caps used, (**c**) fishing nets used, (**d**) marine litter plastic bags used, (**e**) Styrofoam used, and (**f**) polypropylene pellets used.
## 3 Results
### FWC Visualization in Mixture Samples  
Figure 7 shows representative images for increasing FWC content (0, 5, 10, 15, 20, 30, 40, 50, and 100%) in the mixture samples, which were obtained through application of the developed LS-SVM model to produce a distribution map of the FWCs of the mixture samples. Each LS-SVM image is 500 \(\times\) 500 pixels in size. With increasing FWC, more pixels gradually change from blue to red; the color change index is described in the color bar.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multirow{2}{*}{**Preprocessing**} & \multicolumn{4}{c}{**Calibration**} & \multicolumn{4}{c}{**Prediction**} \\ \cline{3-10}  & & \(R_{e}^{2}\) & **SEC (\%)** & **Factors** & **Bias** & \(R_{p}^{2}\) & **SEP (\%)** & **Bias** \\ \hline \multirow{5}{*}{\
\begin{tabular}{c} PLS \\ \end{tabular} } & Smoothing & 0.74 & 10.35 & 9 & 0.69 & 0.65 & 12.27 & 0.57 \\ \cline{2-10}  & \multirow{2}{*}{Normalization} & Mean & 0.79 & 9.47 & 6 & 0.74 & 0.76 & 10.12 & 0.10 \\  & Maximum & 0.8 & 9.22 & 8 & 0.76 & 0.71 & 11.28 & 0.78 \\  & Range & 0.77 & 9.73 & 8 & 0.73 & 0.68 & 11.7 & 0.76 \\ \cline{2-10}  & MSC & 0.77 & 9.77 & 7 & 0.73 & 0.67 & 12.03 & 5.03 \\ \cline{2-10}  & SNV & 0.75 & 10.18 & 7 & 0.71 & 0.69 & 11.53 & 0.53 \\ \cline{2-10}  & \multirow{2}{*}{SGD} & 1 st deri. & 0.75 & 10.28 & 7 & 0.68 & 0.66 & 11.94 & \(-\)0.12 \\  & & 2 nd deri. & 0.79 & 9.32 & 9 & 0.67 & 0.68 & 11.68 & \(-\)0.06 \\ \cline{2-10}  & Raw & 0.73 & 10.52 & 8 & 0.69 & 0.676 & 11.73 & \(-\)0.20 \\ \hline \multirow{5}{*}{
\begin{tabular}{c} LS-SVM \\ \end{tabular} } & Smoothing & 0.9 & 6.44 & - & 0.76 & 0.76 & 10.03 & \(-\)0.12 \\ \cline{2-10}  & \multirow{2}{*}{Normalization} & Mean & 0.85 & 7.86 & - & 0.82 & 0.82 & 8.62 & 1.17 \\  & Maximum & 0.89 & 6.81 & - & 0.81 & 0.82 & 8.74 & 0.40 \\  & Range & 0.87 & 7.44 & - & 0.83 & 0.83 & 8.46 & 0.94 \\ \cline{2-10}  & MSC & 0.9 & 6.36 & - & 0.83 & 0.83 & 8.55 & 4
## 3 Results and Discussion  
### Electrical and Optical Properties of InGaN-uLEDs  
We studied the electrical and optical properties of the fabricated InGaN-uLEDs before they were embedded into the system. The relationship between current density and bias voltage (J-V) and that between light output power and injection current (L-I) of circular uLEDs with different diameters are illustrated in Figure 2. The figure indicates a dependent relationship between output performance and device scale. That is, the smaller the uLEDs, the higher the current density that the uLEDs could sustain, and a lower light output that the uLEDs could produce. These results were in accordance with the J-V and L-I characteristics of the InGaN-uLEDs with square pixels, as shown in Figure S1. Therefore,  
Figure 1: (**a**) The schematic diagram of the typical fabrication process and (**b**) the epitaxial structure of square InGaN-uLED. (**c**) The 3D schematic diagram of square InGaN-uLED arrays with different sizes. (**d**) Schematic diagram of WL-VLC system based on the white-light GaN-based uLED with PQDs as color converters.  
there was a trade-off between modulation bandwidth and light-output power, which will be discussed in the following section.  
Figure 3 demonstrates the normalized electroluminescence (EL) spectra of the circular InGaN-\(\mu\)LEDs, taking 80 \(\upmu\)m\(\mu\)LED as an example. In Figure 3a, with increasing injection current, the peak wavelength of the EL spectrum moved to the shorter wavelength direction and the full width at half maximum (FWHM) increased, then both kept saturation. Both the movement of peak wavelength and the increase of FWHM were on a small scale, ensuring good monochromaticity for SSL. Figure 3b shows the peak wavelength as a function of the injection current, which was extracted from Figure 3a. The blueshift of the emission wavelength observed is attributed to the carrier-screening effect of the quantum-confined Stark effect and/or the band-filling effect [44], which degrades the performance of \(\upmu\)LEDs (e.g., aging, reliability, and modulation bandwidth) [45; 46]. As a result, to obtain optimal modulation bandwidth and light-output power, the appropriate working current was chosen to drive the \(\upmu\)LEDs. For InGaN-\(\upmu\)LEDs with square pixels, the EL spectra were similar to those of InGaN-\(\upmu\)LEDs with circular pixels, as shown in Figure S2.
# DARAL: A Dynamic and Adaptive Routing Algorithm for Wireless Sensor Networks
## 4 Simulation
### Energy Consumption during the Set-up Phase  
In Figure 14, Small shows a high energy use of _O-RPL_ (R) in small-size scenarios under low-density conditions, but with a major reduction under medium- and large-density conditions. _AODV_ (A) shows an equilibrated performance, showing a lower performance than _DARAL_ (D), which, on the other hand, presents the best results as Table 5 shows, requiring around 527-574 mWs depending on the conditions. _DARAL_ also presents the lowest maximum and minimum, and a low dispersion, regardless of the scenario conditions.  
Finally, in Figure 14, Large shows a low performance for _O-RPL_ in large-size scenarios under low-density conditions. _AODV_ also improves, but the performance is worse than _O-RPL_ and _DARAL_ under medium- and high-density conditions. _O-RPL_ presents a notable improvement between low-, medium- and high-density conditions, even improving _DARAL_ in this last case. _DARAL_ shows again an equilibrated performance under different density conditions, using 850-896 mWs, which is a tight range. The results from _O-RPL_ are interesting, but according to the deviation values showed in Table 5, they are not significant enough, whereas _DARAL_ results are much more robust.  
In Figure 14, Medium shows the energy consumption comparison in medium-size scenarios, where _O-RPL_ newly presents a low performance under low-density conditions. For the other density  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**ND** & **Algorithm** & **Small-** & **Avg. \(\pm\) dev Medium-** & **Avg. \(\pm\) dev Large-Scenario** \\ \hline
5 & O-RPL & 1010 \(\pm\) 120.48 & 2056.1 \(\pm\) 217.99 & 5343.2 \(\pm\) 472.64 \\
5 & AODV & 1555.27 \(\pm\) 9689.08 & 1674.66 \(\pm\) 6413.87 & 1739.48 \(\pm\) 10750.61 \\
5 & DARAL & 201.42 \(\pm\) 55.55 & 168.96 \(\pm\) 37.30 & 112.59 \(\pm\) 27.67 \\
10 & O-RPL & 407.82 \(\pm\) 161.11 & 815.12 \(\pm\) 206.99 & 1722.5 \(\pm\) 473.59 \\
10 & AODV & 1674.25 \(\pm\) 10418.46 & 1678.56 \(\pm\) 9386.83 & 1694.73 \(\pm\) 11821.05 \\
10 & DARAL & 211.75 \(\pm\) 32.03 & 149.90 \(\pm\) 25.98 & 47.29 \(\pm\) 9.7 \\
15 & O-RPL & 245.85 \(\pm\) 31.81 & 496.63 \(\pm\) 85.01 & 1055.2 \(\pm\) 81.77 \\
15 & AODV & 1739.48 \(\pm\) 9723.29 & 1793.78 \(\pm\) 866.69 & 1777.7 \(\pm\) 14901.79 \\
15 & DARAL & 201.39 \(\pm\) 39.8 & 186.53 \(\pm\) 29.27 & 96.04 \(\pm\) 33.36 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Average number of messages sent during the set-up phase in small-size scenarios.  
Figure 13: Average number of messages to set-up the network for small-, medium- and large-size scenarios considering _AODV_.  
conditions analysed, \(O\)-_RPL_ presents a slight improvement with respect to \(AODV\). \(DARAL\) improves both algorithms, requiring 702-716 mWs, far less than \(O\)-_RPL_, which needs \(3.78e+03\) mWs, as Table 5 shows. In terms of lowest maximum and minimum and dispersion, \(DARAL\) presents the best results.
## 3 Excitation Assumption
The vertical vibration amplitude of the CoM \(\Delta z\) can be obtained by:  
\[\Delta z(t)=L_{0}-z(t)=\Delta z_{g}+A_{z}\sin\frac{\pi t}{T_{s}},\ (0\leq t<T_{s}) \tag{29}\]  
Vertical velocity of the CoM can be obtained after a derivative operation to Equation (30) as:  
\[\dot{z}(t)=-\frac{\pi A_{z}}{T_{s}}\cos\frac{\pi t}{T_{s}},\ (0\leq t<T_{s}) \tag{30}\]  
Substituting Equations (26)-(30) into Equations (23)-(25), the damping ratio, frequency and excitation of the structure induced by the crowd can be expressed as:  
\[\widetilde{\xi}\simeq\xi_{1}\frac{f_{1}}{\widetilde{f}}+\left(\frac{1}{3}- \frac{\pi^{2}}{15}+\frac{\pi^{4}}{252}\right)\frac{\chi\pi c_{leg}}{4M_{1} \widetilde{f}}\bigg{(}1-\frac{\Delta z_{g}+A_{z}\sin\pi f_{s}t}{L_{0}}\bigg{)} \tag{31}\]  
\[\widetilde{\xi}\simeq\zeta_{1}\frac{f_{1}}{\widetilde{f}}+\left(\frac{1}{3}- \frac{\pi^{2}}{15}+\frac{\pi^{4}}{252}\right)\frac{\chi\pi c_{leg}}{4M_{1} \widetilde{f}}\frac{z}{L_{0}} \tag{23}\]0  
\[\widetilde{F}=\left(1-\frac{\pi^{2}}{12}\right)\frac{\pi\chi}{2M_{1}}\left[ \begin{array}{c}\frac{c_{leg}}{L_{g}}(L_{g}f_{s}-A_{vx}\cos 2\pi f_{s}t)(L_{0}- \Delta z_{g}-A_{z}\sin\pi f_{s}t)\big{(}2L_{s}f_{s}-\frac{A_{z}\pi T_{s}}{ \pi}\sin 2\pi f_{s}t-L_{s}\big{)}\\ -\frac{\pi f_{s}A_{z}c_{leg}\cos(\pi f_{s}t)(L_{0}-\Delta z_{g}-A_{z}\sin\pi f _{s}t)}{L_{0}}-k_{leg}(\Delta z_{g}+A_{z}\sin\pi f_{s}t)\end{array} \right] \tag{33}\]  
where \(f_{s}=1/T_{s}\) is step frequency. The damping ratio of Equation (31) shows that crowd size has a positive effect on structural damping. The larger crowd size \(\chi\) induces the larger damping of the structure. However, the effect of crowd size on structural frequency is the opposite, as shown in Equation (32). Increase of crowd size \(\chi\)causes a decrease of structural frequency. In addition, structural modal characteristics are also related to the movement of pedestrians. The term \(A_{z}\sin\pi f_{s}t\) can disturb the effect of crowds on structural dynamic properties.
# A Time-Varying Filter for Doppler Compensation Applied to Underwater Acoustic OFDM
## 1 Introduction
Similarly, in [13], the authors proposed a simple time-offset estimator that utilizes the cross-correlation of the cyclic prefix and its replica at the end of each OFDM symbol to estimate the Doppler scaling factor. In contrast to the work described in [1; 13], this paper attempts to compensate the Doppler effect due to large velocity variations that occurs during the transmission of the frames. For the techniques described in [1; 12; 13], the time-scaling factor is assumed to be common to all paths. In [14], the authors demonstrated the optimal time-scaling factor in a multipath channel in which each path is subject to a different time-scaling factor. The algorithm proposed in [14] is borrowed in this work and a wideband channel model is developed to represent realistic conditions. Note that, in [14], the authors assumed that the time-scaling factor over the period of a packet remains constant and the estimate of this factor is measured only at the start of each packet. Instead, as shown in this work, a new estimate of the time-scaling factor can be obtained using pilot tones in each OFDM symbol. Moreover, to maintain link reliability throughout the transmission of the frame, the time-scaling factor is interpolated on a sample by sample basis to further improve the Doppler shift compensation. To demonstrate the algorithm accuracy, the receiver performance was evaluated in controlled conditions, as well as with realistic data taken for two extreme deployment scenarios: long range low-bit rate transmission, and short-range high-bit rate transmission. For the long-range application, Doppler compensation was demonstrated for a variation in relative speed between \(\pm 1\) knots. The probability of error obtained after Doppler compensation was on the order of \(10^{-2}\), for a data transmission rate of 16 bps, and a code rate of 1/16. To further improve the performance, multiple receiver elements can be utilized. As shown below, the Doppler compensation allows recovering a similar bit error rate as that obtained when the platform is fixed. In addition, for a short-range telemetry application with mobile nodes, a design in the 125 kHz frequency range was deployed to establish a 2 kbps link, and the performance measured was shown to be very close to the performance obtained using a model of the environment. As shown below, for Config. #2, the measured bit error rate at a 5.5-dB was equal to 0.035, for a coded bit rate of 776 bps while the vessel was moving at 0.9 m/s over a distance on the order of 100 m. Simulations also predicted that the reliability asymptotically improves with SNR, and a bit error rate on the order of \(10^{-3}\) could be achieved. The paper is organized as follows. In Section 2, a mobile acoustic propagation model is proposed. In Section 3, the receiver implementation to mitigate the effect of Doppler shift is proposed. In Section 4, application to two extreme scenarios measured in real conditions are presented. Finally, in Section 5, the work is summarized.
# A Frequency-Selective Reconfigurable Antenna for Wireless Applications in the S and C Bands
## 5 Conclusions  
In this paper, a novel reconfigurable antenna operating in the 2, 4, and 6 GHz bands is presented. Reconfiguration is achieved using a single PIN diode. The antenna was simulated and implemented. The fabricated antenna exhibits three modes of operation with resonances at 2.07, 4.63, and 6.22 GHz. The three modes were defined by different values of the forward biasing voltage applied to the diode, in the range of just zero to \(900\,\mathrm{mV}\). The return loss is well below 40 dB for the primary resonance in every mode, and the VSWR is minimal, 1.01-1.02. The experimental results of the radiation patterns suggest that the gain is maximum at the bottom side of the antenna and equal to 5.48 dBi when operating in the 2 GHz band, 3.97 dBi on the bottom side when operating in the 4 GHz band, and 12.79 dBi in front of the antenna when operating in the 6 GHz band. If the antenna works for transmission or reception solely in the forward direction, it should be rotated toward the front by an elevation angle between \(12^{\circ}\) and \(25^{\circ}\) for maximum gain. In this case, depending on the rotation angle, the maximum gain which can be attained in the 2, 4, and 6 GHz band resonances is 3.81, 0.36, and 12.79 dBi, respectively. Finally, the fabricated antenna demonstrated better functionality in the 4 GHz band than suggested by the simulation, while the measured gain was much higher in all cases. Overall, the deviation between the simulation and the experimental results was negligible.  
Conceptualization, A.S. and V.C.; methodology, A.S., V.C. and S.K.C.; investigation, A.S., V.O. and G.B.; validation, V.O., G.M., G.T. and V.T.; data curation, A.S. and G.T.; writing--original draft, A.S. and V.C.; writing--review and editing, A.S., V.C., V.T. and S.K.C.; supervision, V.C., G.T. and S.K.C.; project administration, V.C. and S.K.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data that support the findings of this study are available from the corresponding authors, upon reasonable request.  
The authors declare no conflict of interest.
## 3 The PPBR Key Management Scheme
### Joining Into of the New Nodes  
When a new node \(S_{i}\) joins the network, the unique identifier \(ID_{i}\), \(N\) polynomials and \(n\) keys which are randomly selected and assigned from the polynomials pool \(F\) and the keys pool \(K\) by the server respectively, and the hash function \(H\) will all be pre-loaded. When the new node is within the communication radius of the MS node, the MS will launch the identity authentication for the new node. After confirming the node's legality, it will establish a session key by using the method mentioned above.
## 2 Materials and Methods
### Determination of ACE Inhibitory Activity and Measurement of IC\({}_{50}\) Value  
The potential peptides were analyzed for their ACE inhibitory activity using [PERSON]'s method [23] with slight modifications. In this protocol, captopril and hippuryl-L-histidyl-L-leucine (HHL) were utilized as the positive control and substrate, respectively, for the ACE inhibitory assay. The mixture for each assay that contained 10 \(\upmu\)L of a sample (at a tested concentration) and 30 \(\upmu\)L of 2.5 mM HHL was maintained at 37 \({}^{\circ}\)C for 5 min. For the blank and positive control, borate buffer (200 mM borate buffer; 300 mM NaCl, pH 8.3) and captopril were used to replace the sample under the same condition, re spectively. Thereafter, 20 uL of ACE (0.05 mU/uL) in borate buffer was added to the pre-incubated mixture and kept at 37 \({}^{\circ}\)C for 1 h. For the first 30 min, stationary incubation was employed, followed by the use of a shaker incubator (speed up 200 rpm) for the remaining half hour. Afterward, 60 uL of 1 M HCl was used to quench the reaction. The amount of HA, a product released from HHL hydrolysis, was then detected using HPLC (Hitachi Chromaster, Japan) with a NUCLEODUR C18 HTC column (4.6 mm \(\times\) 250 mm; 5 um, C18; Macherey-Nagel, Duren, Germany) and a UV detector at 228 nm. The product of the reaction was monitored under an isocratic elution with 82% mobile phase A (5% ACN and 0.1% TFA in ddH\({}_{2}\)O) and 18% mobile phase B (95% ACN and 0.1% TFA in ddH\({}_{2}\)O) at a constant flow rate of 1 mL/min and was maintained for 20 min. The ACE inhibitory activity (%) was calculated using the following formula:  
\[\text{ACEI activity (\%)}=[1-(\text{A}_{\text{inhibitor}}/\text{A}_{\text{blank }})]\times 100\% \tag{1}\]  
The HPLC peak areas of HA formed in the ACE-mediated reaction with and without the inhibitor are represented by A\({}_{\text{inhibitor}}\) and A\({}_{\text{blank}}\), respectively. The IC\({}_{50}\) value was determined as the inhibitor concentration that was required to inhibit 50% of the ACE activity. Nonlinear regression using Graphpad Prism 5.0 (GraphPad Software, Inc., La Jolla, CA, USA) was performed to calculate the IC\({}_{50}\) value with various concentrations of the inhibitor.