esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Numerical Results
### Estimation Results Based on Simulation Data
#### 4.1.1 Pipe Diameter Estimation  
In this section, we evaluate the accuracy of pipe diameter estimation in terms of the major axis and minor axis. We first discuss the effect of the sensor depth error which was the main cause of the overestimation of pipe diameter. Then, the effect of pipe diameter, the secondary factor that contributed to the overestimation, is also considered. We focus on the estimation bias and the estimation error range. The estimation bias is defined as the mean of the estimation error. It evaluates if there was a bias in the estimation. The estimation error range is defined as the difference between the maximum and the minimum estimation errors. It indicates the consistency of the estimation.  
Figure 4 shows the major-axis and minor-axis estimation results with different sensor depth errors, \(\sigma_{e}\), from 0.01 to 0.05 m. The lines and circle markers represent the estimation bias while the error bar represents the estimation error range. First, C-EPD exhibited a significant overestimation of both the major and minor axes, with the estimation bias reaching up to 6.67 mm. This estimation bias was not accurate enough to estimate the thickness as well as the deformation of the pipe. It led to a wrong indication of pipe integrity.  
In contrast, RU-EPD maintained the estimation bias below 0.10 mm, demonstrating an over 66 times higher accuracy. Second, the estimation error range increased with the sensor depth error in both C-EPD and RU-EPD. In the worst case, the estimation error range could be 3.02 mm with C-EPD. In contrast, RU-EPD maintained the estimation error range below 1.12 mm, approximately 2.5 times lower than that for C-EPD. This means that RU-EPD not only eliminated the estimation bias but also ensured robust and consistent estimations, irrespective of the robot pose.  
Next, we evaluated the major axis and minor axis with different diameters from 16\({}^{\prime\prime}\) to 30\({}^{\prime\prime}\). The sensor depth error was set to 0.03 m. Figure 5 shows the major-axis and minor-axis estimation results. With C-EPD, the estimation bias was more severe with smaller pipe diameters, which agrees with our inference from (16). The estimation bias of C-EPD was up to 3.01 mm while the one of RU-EPD was as small as 0.05 mm. Additionally, the estimation error range of C-EPD also increased with smaller pipe diameters while it was almost constant and smaller in RU-EPD. In general, RU-EPD produced robust and unbiased pipe diameter estimations regardless of the sensor depth error and pipe diameter.
## 3 Materials and Methods
### Road Boundary Optimization Based on Alpha Shape Algorithm  
The road surface point cloud is generally extracted after the previous steps. However, owing to the unstructured nature of point cloud data, some of the extracted road boundary  
Figure 5: Schematic of seed supervoxel selection.  
points may be sparse and discontinuous. Additionally, occlusions caused by obstacles, such as vehicles, during data acquisition can lead to interruptions in the road boundary, making it necessary to determine continuous road edges. To achieve a complete extraction of the road surface point cloud, this study employed an edge extraction algorithm based on the characteristics of the road boundary points in highway scenarios. The goal was to extract the road contours and then generate smooth boundaries through curve fitting.  
1. After applying the region growing segmentation algorithm to the MLS point cloud, there may still be some scattered points or fragments other than road points. These points were traversed and removed based on the point threshold after clustering.
2. The Alpha Shape algorithm was used for boundary extraction because it has good shape retention and a faster calculation speed when extracting point cloud road boundaries, and can realize a flexible description and a fast extraction and analysis of point set geometry [37]. The point cloud was projected onto the _XOY_ plane, and delay triangulation was performed on the projected points. For any edge of the triangulation with endpoints \(P\) and \(Q\), two circles were drawn with \(O_{1}\) and \(O_{2}\) as the centers and radius of \(\alpha\). The coordinates of \(O_{1}\) and \(O_{2}\) are shown in Equations (5) and (6), respectively: \[X_{O_{1},O_{2}}=\frac{x_{p}+x_{q}}{2}\pm\sqrt{\alpha^{2}-\left(\frac{\|\mathbf{ a}\|^{2}}{2}\right)^{2}}\left(\frac{y_{p}-y_{p}}{2}\right)\] (5) \[Y_{O_{1},O_{2}}=\frac{y_{p}+y_{q}}{2}\pm\sqrt{\alpha^{2}-\left(\frac{\| \mathbf{a}\|^{2}}{2}\right)^{2}}\left(\frac{x_{p}-x_{p}}{2}\right)\] (6) where \(\mathbf{a}\) is the vector composed of endpoints \(P\) and \(Q\); \(\alpha\) is the radius of circle; \(x_{p}\) and \(y_{p}\) are the horizontal and vertical coordinates of the \(P\) point; and \(x_{q}\) and \(y_{q}\) are the transverse and vertical coordinates of point \(Q\). As shown in Figure 6, if at least one of the circles does not contain any points from the point set, then the endpoints of that edge are determined to be boundary points.
3. After extracting the road contours, it was necessary to perform a smoothing process on the road boundaries. B-spline curves are a type of curve based on polynomial equations that possess strong adaptability, where local point variations do not significantly affect the overall shape of the boundary. They can effectively preserve the local features of the road boundaries. Therefore, B-spline curves were adopted for smoothing [44].
## 3 Results and Discussions
### COVID-19 Classification Results
From Table 6, we can conclude that Hybrid EfficientNet-DOLG and DenseNet models are the best models for COVID-19 classification tasks on our customized dataset. Figures 6 and 7 demonstrate the confusion matrix of inference results on the test set and training history of the Hybrid EfficientNet-DOLG and DenseNet models. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{5}{c}{**F1-Score**} \\ \cline{2-6}  & **COVID** & **Normal** & **Pneumonia** & **Macro- Average** & **Micro- Average** \\ \hline DenseNet121 & 0.98 & **0.95** & 0.93 & 0.94 & 0.95 \\ \hline ResNet50 & 0.96 & 0.89 & 0.91 & 0.92 & 0.93 \\ \hline InceptionNet & 0.96 & 0.85 & 0.92 & 0.91 & 0.92 \\ \hline Swin Transformer & 0.82 & 0.75 & 0.90 & 0.82 & 0.82 \\ \hline Hybrid EfficientNet-DOLG & **0.99** & 0.94 & **0.94** & **0.95** & **0.96** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparing results between the five deep learning models on the COVID-19 classification task with respect to F1-score metric. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{5}{c}{**Recall**} \\ \cline{2-6}  & **COVID-19** & **Normal** & **Pneumonia** & **Macro- Average** & **Micro- Average** \\ \hline DenseNet121 & **0.98** & 0.94 & 0.92 & 0.95 & 0.95 \\ \hline ResNet50 & 0.94 & 0.96 & 0.89 & 0.93 & 0.93 \\ \hline InceptionNet & 0.93 & 0.88 & 0.94 & 0.92 & 0.92 \\ \hline Swin Transformer & 0.70 & **0.97** & 0.90 & 0.86 & 0.82 \\ \hline Hybrid EfficientNet-DOLG & 0.97 & 0.94 & **0.95** & **0.96** & **0.96** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparing results between the five deep learning models on the COVID-19 classification task with respect to recall metric.
## 4 Conclusions  
In summary, we have determined the structural, electronic, and magnetic properties of inverse Ti\({}_{2}\)CoSi-based compounds. By adjusting the concentration of titanium and cobalt, the fabricated Ti\({}_{2.25}\)Co\({}_{0.75}\)Si compound is half metallic, that is, single-channel spin polarized. The majority-spin bands are strongly metallic, while the minority-spin bands are semiconductor-like around the Fermi level. This gap has previously been reported by other authors, and is formed as a result of the strong 3d-3d hybridization between the two transition-metal atoms [42; 43; 44; 45]. It is thought that this 3d-3d interaction is essential for the formation of the gap at the Fermi level. Under the influence of isotropic hydrostatic pressure, the Ti\({}_{2.25}\)Co\({}_{0.75}\)Si alloy can preserve the half-metallic feature between a range of 5.78 A to 6.35 A. Nevertheless, it would transform to a gapless half metal upon the tetragonalization of the lattice. Tetragonal deformation could modify the crystal symmetry and vary the position of the edge of the bands, especially at some high-symmetry points in the reciprocal space. Our further calculations on Ti\({}_{2.25}\)Co\({}_{0.75}\)Si\({}_{0.5}\)B\({}_{0.5}\), Ti\({}_{2.25}\)Co\({}_{0.75}\)Si\({}_{0.5}\)P\({}_{0.5}\), and other doped compounds demonstrate that the doped foreign impurity is able to adjust the valence electron concentration of the compound and shift the Fermi level upwards or downwards, creating several different electronic structures around the sensitive Fermi level.  
Figure 8: The spin densities of (**a**) Ti\({}_{2.25}\)Co\({}_{0.75}\)Si\({}_{0.5}\)B\({}_{0.5}\) and (**b**) Ti\({}_{2.25}\)Co\({}_{0.75}\)Si\({}_{0.5}\)P\({}_{0.5}\) at the equilibrium lattice parameters.  
Figure 7: The total density of the states (DOS) of (**a**) Ti\({}_{2.25}\)Co\({}_{0.75}\)Si\({}_{0.5}\)B\({}_{0.5}\) and (**b**) Ti\({}_{2.25}\)Co\({}_{0.75}\)Si\({}_{0.5}\)P\({}_{0.5}\) at the equilibrium lattice parameters. The partial DOS of (**c**) Ti\({}_{2.25}\)Co\({}_{0.75}\)Si\({}_{0.5}\)B\({}_{0.5}\) and (**d**) Ti\({}_{2.25}\)Co\({}_{0.75}\)Si\({}_{0.5}\)P\({}_{0.5}\) at the equilibrium lattice parameters. The black, red, blue, and orange solid lines indicate the Ti-d, Co-d, Si-p, and B (P)-p states, respectively.  
**Author Contributions:** [PERSON] and [PERSON] designed the project. [PERSON] and [PERSON] performed the calculations and prepared the manuscript. All of the authors analyzed the data and discussed the results. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Natural Science Foundation of China (grant no's. 61804010, 11204209, 60876035, and 61874004), the Natural Science Foundation of Tianjin City (grant no. 17 JCYBJC16200), and the Project of Guangdong Baiyun University (grant no. 2017 BYKY29).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results
### Correlation between GPP and mTVWI  
Partial correlation coefficients between GPP and mTVWI were significant (\(p<0.05\)) in most of the European tree species (Table 1). In general, GPP and mTVWI correlated positively in conifers and broad-leaf evergreen species and negatively in broad-leaf deciduous species. We found positive correlation coefficients in _L. decidua, P. pinaster, P. nigra, B. pendula,_ and _Q. ilx_ ranging from 0.22 to 0.56 (\(p<0.05\)). By contrast, _P. sylvestris, A. pseudoplatanus, A. glutinosa, C. betulus, F. excelsior, P. tremula, Q. pubescens_, and _Q. robur_ showed negative coefficients varying from \(-0.24\) to \(-0.56\) (\(p<0.05\)). Other species such as _P. abies, P. halepensis, B. pubescens, C. sativa, F. sylvalica_, and _Q. petraea_ did not show significant relationships between GPP and mTVWI. Mediterranean species such as _P. pinaster_ and _Q. ilex_ showed the highest positive correlation coefficients between GPP and mTVWI (0.36-0.50 and 0.24-0.33, respectively). Conversely, species growing in wet conditions like _P. sylvestris_ and _P. tremula_ showed negative correlation coefficients between GPP and mTVWI. Figure 3: mean annual values of mTVWI (\(\pm\)SE) over the time period 2000–2010. Figure 2: Spatial distribution of the mTVWI values over the time period 2000–2010. The points show ICP Forests level I sites.
## 3 The Obtained Data and Their Discussion  
### Results of Microstructure Examination  
Figure 2 shows the photomicrographs of the field emission scanning electron microscope (FESEM) for the AA6061 aluminum matrix and the fabricated nanocomposites Al/B4C and Al/TiC with different percents of B4C and TiC. The photomicrographs show that B4C and TiC nanoparticles are diffused at the grain boundaries between the B4C, TiC and Al matrix. Owing to the thermal reaction and diffusion mechanism created in the sintering process, strong bindings have been created at the grain boundaries between the ceramic nanoparticles B4C, TiC and Al matrix. It can be revealed that B4C and TiC nanoparticles may be wetted by aluminum matrix through the sintering process. Owing to the aggregation in a specific region, a homogeneity of B4C and TiC in the Al matrix cannot be created in some specimens. Also, the difference in thermal properties between the Al matrix and B4C and TiC nanoparticles plays a great role in the dispersion of B4C and TiC nanoparticles into the Al matrix and then affects the wetting between the B4C and TiC nanoparticles and the Al matrix.
# Closed-Form Expressions of Upper Bound for Polarization-MDCSK System
## 3 Closed Expression over Multipath Rayleigh Fading Channels
The general form of the bound is written as  
\[g(z)=\tau e^{-\theta z^{2}}\leq Q(z),\quad for\quad z\geq 0. \tag{14}\]  
The function of \(g(z)\) is a lower bound if the \(\tau\) and \(\theta\) are established in [21] as  
\[\theta\geq 0,\quad and\quad 0<\tau\leq\sqrt{\frac{2e(\theta-1)}{\pi\theta^{2}}}. \tag{15}\]  
In order to simplify the calculation, the right side of the formula (7) is expressed as  
\[P_{t}=P_{signal}+P_{polarization}+P_{joint}, \tag{16}\]  
Thus, (13) can be separated as  
\[ \begin{split}& P_{multi}=\int\limits_{0}^{\infty}P_{t}f(\gamma_{s}) \mathrm{d}\gamma_{s},\\ & P_{ms}=\int\limits_{0}^{\infty}P_{signal}f(\gamma_{s}) \mathrm{d}\gamma_{s},\\ & P_{mp}=\int\limits_{0}^{\infty}P_{polarization}f(\gamma_{s}) \mathrm{d}\gamma_{s},\\ & P_{mj}=\int\limits_{0}^{\infty}P_{joint}f(\gamma_{s}) \mathrm{d}\gamma_{s}.\end{split}  \tag{17}\]  
By applying the bound to the given BER formula over the multipath Rayleigh fading channel, the lower bound of \(P_{t}=P_{signal}+P_{polarization}+P_{joint}\) is calculated separately, and the lower bound of \(P_{signal}\) becomes  
\[\mathrm{LB_{s}}(\gamma_{s})=F\int\limits_{0}^{\infty}e^{-\theta\gamma_{s}}e^ {-\gamma_{t}}\mathrm{d}\gamma_{s}\leq P_{ms}, \tag{18}\]  
where \(F\) is expressed as  
\[F=\frac{2}{n_{b}}\sqrt{\frac{2e(\theta-1)}{\pi\theta^{2}}}\sum\limits_{l=1}^{ L}\frac{\rho_{l}}{\gamma_{c}}\mathrm{exp}\bigg{(}-\frac{1}{\hat{\gamma}_{c}} \bigg{)}M_{s}, \tag{19}\]where \(M_{s}=\frac{n_{b}}{b+n_{b}}\). Moreover, the integral in (18) is not analytically integrable. Thus, the exponential function is upper-bounded by [19]  
\[ \begin{split}& e^{-z}\leq\frac{1}{1+z},\\ & e^{-\theta\left(\frac{\pi\pi}{M}\right)^{2}}=e^{-\theta\left( \frac{\pi^{2}}{M^{2}}\frac{4z^{2}}{2\pi+\beta}\right)}\\ &\leq\frac{M^{2}(2z+\beta)}{M^{2}(2z+\beta)+4z\theta\pi^{2}}, \end{split}  \tag{20}\]  
where \(M=2^{n_{b}}\). The expression of the lower bound \(X\) can be derived by taking the sum of the items in (18) and replacing them with the upper bounds, which is expressed as  
\[X(z)=\frac{M^{2}(2z+\beta)}{(1+z)(M^{2}(2z+\beta)+4z\theta\pi^{2})}. \tag{21}\]  
Thus, the integral of the closed expression of \(P_{ms}\) over multipath Rayleigh fading channels can be written as  
\[ \begin{split}&\text{LB}_{s}(z)=F\int X(z)\text{d}z=M^{2}*[-\\ &\frac{(M^{2}(2+\beta)-2M\beta-4\pi^{2}\beta\theta)\arctan h \left(\frac{M^{2}+4\pi^{2}\beta}{\sqrt{M(M^{2}-4\pi^{2}\beta\theta)}}\right) }{\sqrt{M(2M^{2}-M\beta-4\theta\pi^{2})}\sqrt{M^{3}-4\pi^{2}\beta\theta}}\\ &+\frac{(2-\beta)\ln(1+z)}{2M^{2}-M\beta-4\theta\pi^{2}}+\frac{( \beta-2)\ln(2M^{2}z+M\beta+4\theta z^{2}\pi^{2})}{4M^{2}-2M\beta-8\theta\pi^{ 2}}].\end{split}  \tag{22}\]  
Similarly, the closed expressions of \(P_{mp}\) and \(P_{mj}\) can also be derived by the same derivation process which is given in Appendix A. Finally, the total closed expression of \(P_{t}\) can be expressed as \(\text{LB}(\text{z})=\text{LB}(\text{z})+\text{LB}(\text{z})+\text{LB}(\text{ z})\).
## Materials and Methods
### Methods
#### 2.2.5 Characterization Methods of MG-Loaded Mesoporous Silica  
The nitrogen sorption isotherms were recorded on a Quantachrome Nova 2200 Instrument and Pore Size Surface Area Analyzer (Quantachrome Instruments, Odelzhausen, Germany) at \(-\)196 \({}^{\circ}\)C. Before measurements, the samples were outgassed under high vacuum at room temperature for 12 h. The Brunauer-Emmett-Teller (BET) specific surface area (\(S_{BET}\) (m\({}^{2}\)/g)) was calculated from the linear part of the BET plot. Micropore area (\(S_{\mu}\) (m\({}^{2}\)/g)) was determined by a \(t\)-plot method. The average pore diameter (\(D_{p}\) (nm)) was estimated using the desorption branch of the isotherm and the Barrett-Joyner-Halenda (BJH) model [29; 41]. The volume of liquid nitrogen adsorbed \(P/P_{0}\) = 0.95 was used to assess the total pore volume (\(TPV\) (cm\({}^{3}\)/g)).  
The Scanning Electron Microscopy (SEM) investigations were performed using a Vega Tescan electron microscope (Tescan Orsay Holding, Brno, Czech Republic).  
Fourier Transform Infrared Spectroscopy (FTIR)  
The FTIR spectra were collected on a Vertex 70 (Bruker Co., Billerica, MA, USA) using a KBr pellet technique. The KBr pellets were prepared by mixing 3 mg of silica sample with 500 mg spectroscopy grade KBr. Spectra were recorded on all formulations as well as on raw materials (C, C-MG, Cf, Cf-MG, MG). The spectra obtained were compared with those reported in the literature.
# Laser Beam Positioning by Using a Broken-Down Optical Vortex Marker
## 6 Experimental Results
The intensity recorded with an additional beam (Figure 7): (**a**) intensity, (**b**) magnified intensity area, (**c**) phase profile. The green circles represent the locations of the vortex points. Figure 12 shows an example of the experimental results. There are three vortex points marked in the figure. In further calculations, the center of gravity of the figure formed by the constellation of optical vortices was determined. In this case, it was a triangle. The center of gravity is an appropriate point for beam positioning because communication often uses information carried by the optical vortices themselves. If the disturbances do not affect the finding of the intensity minima, this method can be used for beam positioning. In experimental conditions, the intensities of the light beams are often distorted, which significantly affects the detection of a single beam. The pseudo-phase method gives more reliable results in non-laboratory conditions. Figure 11: Experimental results with a separated optical vortex with the topological charge of \(m=3\). Measurements made without an additional beam (Figure 8). (**a**) Intensity, (**b**) magnified intensity area, (**c**) pseudo-phase profile. The green circles represent the locations of the vortex points. Figure 12: Experimental results with a separated optical vortex with the topological charge of \(m=3\). (**a**) Pseudo-phase profile, (**b–d**) enlarged fragments for three vortex points. The yellow “x” indicates a point found by the procedure using artificial neural networks, while the blue circle indicates the minimum intensity found on the basis of the procedure described in Section 3. As an example, the results of tracking the centroid of a constellation of three optical vortices using the intensities recorded in two different settings of the measurement system (Figures 7 and 8) are also shown (Figure 13). Figure 13 shows the movement of an optical beam propagating through the air in free space. Both the pseudo-phase and interferogram methods allow the determination of the position of the center of gravity of the constellation of optical vortices. Figure 13 shows the location of approximately 500 points. The scatter of the results is due to atmospheric disturbances and ambient vibrations. The algorithms allowed us to track the position of the beam. Based on the information from the presented methods, it is possible to position the optical beam.
# Photovoltaic Roundabouts for Enhancement of Self-Sufficiency and Resiliency
## 2 Materials and Methods
### Calculation of Installable Modules  
Si-wafer and thin film PV technologies are available in the market. Although the efficiency of CdTe (thin film) modules has increased sharply from 9% to 19% over the past 10 years, the efficiency of average commercial wafer-based silicon modules has increased from about 15% to 20%. Si-wafer technology accounted for about 95% of total production in 2020. The share of monocrystalline technology in total c-Si production is now about 84% (compared with 66% in 2019) [29]. Therefore, this is the reason for choosing Si-wafer-based PV technology in this study.  
The number of kilowatts that a photovoltaic system can generate thanks to RAPS was determined by multiplying the available area inside the central islands of the considered roundabouts by the values of the net area occupied by the modules, estimated using the following relationships [29]:  
* A total of 6.2-7.7 m\({}^{2}\) per installed kWp, at 0\({}^{\circ}\) tilt angle, for the monocrystalline silicon modules;
* A total of 6.6-10 m\({}^{2}\) per installed kWp, at 0\({}^{\circ}\) tilt angle, for the polycrystalline silicon modules.
# Modeling of Key Specifications for RF Amplifiers Using the Extreme Learning Machine
## 2 Designed Circuits and Experiments  
To achieve the goals set in this paper, we chose two RF amplifiers for measurement and modeling. As shown in Figure 1, one is a 50-450 MHz complementary metal oxide semiconductor (CMOS) low-noise amplifier (LNA), and the other is a 0.5-2.1 GHz gallium nitride (GaN) class-AB power amplifier (PA).  
Gold wire bonding fixed the LNA to the printed circuit board (PCB). The matching stage of the LNA uses a standard source cascade amplifier with shunt feedback, which is mainly used to improve the noise figure (NF) [23]. The LNA is simulated with frequencies (50-450 MHz, step-size 20 MHz) and temperatures (\(-\)40\(\sim\)490 \({}^{\circ}\)C, step size 5 \({}^{\circ}\)C). The input power and supply voltage of the LNA is set to be \(-\)40 dBm and 1.8 V, respectively.  
The 0.5\(-\)2.1 GHz GaN Class AB power amplifier consists of a GaN High Electron Mobility Transistor (HEMT) (Cree's CGH40006P, Durham, NC, USA) fabricated on a Rogers 5880 substrate. The PA was simulated in with a set of frequencies (500\(-\)2100 MHz, step-size 20 MHz) and temperatures (\(-\)40\(-\)+90 \({}^{\circ}\)C, step-size 5 \({}^{\circ}\)C). Additionally, the input power of the PA is set to be \(-\)40 dBm. Besides, the PA's output power (\(P_{out}\)) at 1.22 GHz is simulated with a set of input powers (0\(-\)23 dBm, step-size 1 dBm).  
As shown in Figure 2, the temperature characterization of the RF amplifier was performed in an environmental chamber (SC\({}^{3}\) 1000 MHG from Votsch Industrietechnik (Gie&szlig, Germany)) with a measured temperature range of \(-\)40 \({}^{\circ}\)C to +90 \({}^{\circ}\)C.  
The \(S\)-parameters of PA and LNA were measured by Vector Network Analyzer (VNA, Rohde & Schwarz, Munich, Germany), and the output power (\(P_{out}\)) was measured by the spectrum analyzer (FSV30, Rohde & Schwarz, Munich, Germany) and Rohde & Schwarz Vector Network Analyzer (ZVA24, Rohde & Schwarz, Munich, Germany). In addition, the output power is measured using a drive amplifier ZVA-183-S+ (MiniCircuits, New York, NY, USA) and a 30 dB attenuator. Finally, Agilent Noise Figure Analyzer (NFA) N8975A (Agilent, Santa Clara, CA, USA) meas  
Figure 1: The photograph of the: (**a**) Complementary Metal Oxide Semiconductor low-noise amplifier (CMOS LNA); (**b**) Gallium Nitride class-AB power amplifier (GaN Class-AB PA).  
Figure 2: Measurement environment and setup: (**a**) S-parameters and noise figure (NF); (**b**) output power.  
The input power, temperature, and frequency ranges are consistent with the simulation settings. Each temperature testing point is stabilized for 30 min before starting the measure to ensure that the temperature of the environmental test chamber has stabilized.