esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of Baffle Board on Aerodynamic and Stealth Performance of Double S-Duct Caret Intake
## 3 Results and Analysis
### Effect of Curved Baffle Board Locations
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Polarization** & **RCS/(dBsm)** & **Model C-1** & **Model C-2** & **Model C-3** & **Model C-4** \\ \hline \multirow{3}{*}{horizontal} & average & \(-2.23\) & \(-1.11\) & \(-1.77\) & \(-1.17\) \\  & maximum & \(7.95\) & \(7.49\) & \(8.51\) & \(7.66\) \\  & value of LSA & \(39\) & \(37\) & \(37\) & \(36\) \\ \hline \multirow{3}{*}{vertical} & average & \(-1.44\) & \(-2.10\) & \(-1.43\) & \(-1.78\) \\  & maximum & \(5.31\) & \(5.38\) & \(5.25\) & \(5.54\) \\  & value of LSA & \(34\) & \(39\) & \(34\) & \(38\) \\ \hline \multirow{3}{*}{flow coefficient} & \(0.9943\) & \(0.9950\) & \(0.9860\) & \(0.9743\) \\  & TPR coefficient & \(0.8154\) & \(0.8147\) & \(0.8189\) & \(0.8126\) \\ \cline{1-1}  & distortion index DC60 & \(0.3088\) & \(0.2452\) & \(0.3472\) & \(0.2723\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: RCS and aerodynamic characteristics of intakes with different curved baffle boards.
# Investigation of Open Air Stability of CsPbBr3 Thin-Film Growth on Different Substrates
## 3 Results and Discussion
### Structural Characterization
From the insets in Figure 2b,c, it is possible to note that, in the samples obtained on ITO and soda-lime glass, the peak at a lower angle was already present in the \"as-prepared samples\", meaning that these substrates promoted the immediate partial degradation of the perovskite film. The components forming the peak seemed to have a smaller width than the sample obtained on quartz glass, indicating bigger crystallites. After the aging process, the component attributed to the degraded perovskite increased.
## 4 Experimental Results
### Regression Model Benchmarking
Moving to the second stage, we further tested and fine-tuned the parameters of the top-performing models to validate the initial results. Through this rigorous process, the final findings reinforced the earlier outcomes, solidifying our decision to proceed with the DecisionTreeRegressor algorithm for the subsequent stages of the experiment. Our decision to rely on the DecisionTreeRegressor algorithm was influenced by its desirable balance between accuracy and efficiency. This model showed promising performance in terms of predicting visual comfort, and its execution time was reasonable compared to the other models. By carefully considering these factors and conducting a thorough evaluation, we ensured the selection of a reliable regression algorithm for the progression of the experiment, establishing a solid foundation for accurate visual comfort estimation. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Model** & **Adjusted** & **R-Squared** & **RMSE** & **Time Taken** \\  & **R-Squared** & & & \\ \hline GaussianProcessRegressor & 1.00 & 1.00 & 0.00 & 0.07 \\ ExtraTreesRegressor & 1.00 & 1.00 & 0.01 & 0.10 \\ NuSVR & 1.00 & 1.00 & 0.03 & 0.06 \\ KNeighborsRegressor & 1.00 & 1.00 & 0.05 & 0.02 \\ DecisionTreeRegressor & 1.00 & 1.00 & 0.06 & 0.01 \\ XGBRegressor & 1.00 & 1.00 & 0.06 & 0.18 \\ RandomForestRegressor & 1.00 & 1.00 & 0.06 & 0.15 \\ BaggingRegressor & 1.00 & 1.00 & 0.06 & 0.02 \\ GradientBoostingRegressor & 1.00 & 1.00 & 0.07 & 0.04 \\ SVR & 1.00 & 1.00 & 0.07 & 0.02 \\ HistGradientBoostingRegressor & 1.00 & 1.00 & 0.08 & 0.17 \\ LGBMRegressor & 1.00 & 1.00 & 0.08 & 0.08 \\ ExtraTreeRegressor & 1.00 & 1.00 & 0.09 & 0.01 \\ MLPRegressor & 1.00 & 1.00 & 0.09 & 0.12 \\ AdaBoostRegressor & 0.99 & 0.99 & 0.17 & 0.10 \\ OrthogonalMatchingPursuit & 0.98 & 0.98 & 0.21 & 0.01 \\ PassiveAggressiveRegressor & 0.98 & 0.98 & 0.23 & 0.02 \\ RANSACRegressor & 0.98 & 0.98 & 0.23 & 0.02 \\ ElasticNetCV & 0.98 & 0.98 & 0.23 & 0.07 \\ LassoCV & 0.98 & 0.98 & 0.23 & 0.05 \\ SGDRegressor & 0.98 & 0.98 & 0.23 & 0.01 \\ Ridge & 0.98 & 0.98 & 0.23 & 0.01 \\ RidgeCV & 0.98 & 0.98 & 0.23 & 0.01 \\ BayesianRidge & 0.98 & 0.98 & 0.23 & 0.05 \\ Lars & 0.98 & 0.98 & 0.23 & 0.02 \\ LarsCV & 0.98 & 0.98 & 0.23 & 0.02 \\ OrthogonalMatchingPursuitCV & 0.98 & 0.98 & 0.23 & 0.02 \\ LassoLarsIC & 0.98 & 0.98 & 0.23 & 0.01 \\ LassoLarsCV & 0.98 & 0.98 & 0.23 & 0.01 \\ LinearRegression & 0.98 & 0.98 & 0.23 & 0.02 \\ TransformedTargetRegressor & 0.98 & 0.98 & 0.23 & 0.01 \\ HuberRegressor & 0.97 & 0.97 & 0.27 & 0.02 \\ LinearSVR & 0.97 & 0.97 & 0.27 & 0.02 \\ KernelRidge & 0.77 & 0.77 & 0.72 & 0.03 \\ TweedeRegressor & 0.66 & 0.66 & 0.87 & 0.01 \\ ElasticNet & 0.59 & 0.59 & 0.95 & 0.02 \\ Lasso & 0.43 & 0.43 & 1.12 & 0.01 \\ DummyRegressor & \(-\)0.30 & \(-\)0.29 & 1.69 & 0.01 \\ LassoLars & \(-\)0.30 & \(-\)0.29 & 1.69 & 0.01 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Regression algorithm comparative results.
# Angular Circle Array Multiple Input Multiple Output Underwater Optical Wireless Communications +
## 2 System Model
### Definition of Transceiver Parameters  
To better match practical application scenarios and facilitate the formula derivation for designing the angular circle array MIMO transceiver structure, we define the transceiver parameters, which are shown in Figure 2. \(\theta\), \(\beta\), and \(]\varphi\) are the scattering angle, elevation angle, and azimuth angle of the transceiver plane normal vector, respectively. \((u_{x},u_{y},u_{z})\) is the direction cosine of the normal vector of the transceiver plane. \((x,y,z)\) is the coordinate of the transceiver center position.  
Figure 1: Three-dimensional structure of transceivers in the underwater wireless optical communication system: (**a**) SISO system, (**b**) planar circle array, and (**c**) angular circle array.
## 3 A Variant Using Only Divided Differences  
For applications involving not Frechet differentiable operators, we can consider a modification of the proposed method by using divided differences. Specifically, we will consider  
\[y_{n} = x_{n}+[x_{n}-\upalpha_{n}F(x_{n}),x_{n}+\upalpha_{n}F(x_{n});F]^{-1 }F(x_{n})\] \[x_{n+1} = y_{n}-[x_{n}-\upalpha_{n}F(x_{n}),x_{n}+\upalpha_{n}F(x_{n});F]^{- 1}F(y_{n}) \tag{13}\]  
where \(\upalpha_{n}\in[0,1]\) is computed in practice to satisfy  
\[tol_{c}<<||\upalpha_{n}F(x_{n})||\leq tol_{user}\]  
Here, \(tol_{c}\) is related to the computer precision and \(tol_{user}\) is a free parameter for the user, see [20,29].  
Denoting  
\[\Upsilon_{n}=[x_{n}-\upalpha_{n}F(x_{n}),x_{n}+\upalpha_{n}F(x_{n});F]\]  
and  
\[\Psi_{n}=\Upsilon_{n}[x_{n},y_{n};F]^{-1}\Upsilon_{n}\]  
the method (3.1) can be written alternatively as  
\[x_{n+1}=x_{n}-\Psi_{n}^{-1}F(x_{n}) \tag{14}\]  
By using a similar strategy to the one in Section 2, we can derive its semilocal convergence, but in this case without assuming any differentiability of the operator.  
A possible theorem should be:  
**Theorem 2**.: _Let \(X,Y\) be two Banach spaces. Let \(B\) be a convex open subset of \(X\), and suppose that there exists a first order divided difference of the operator \(F:B\subset X\to Y\) satisfying_  
\[||[x,y;F]-[v,w;F]||\leq\upomega(||x-v||,||y-w||),\qquad x,y,v,w\in B \tag{15}\]  
_and_  
\[||[x,y;F]-[x_{0},x_{0};F]||\leq\upomega_{0}(||x-x_{0}||,||y-x_{0}||),\qquad x,y\in B \tag{16}\]  
_where \(\upomega:\mathbb{R}_{+}\times\mathbb{R}_{+}\longrightarrow\mathbb{R}_{+}\)\(\upomega_{0}:\mathbb{R}_{+}\times\mathbb{R}_{+}\longrightarrow\mathbb{R}_{+}\) are continuous functions, nondecreasing in both variables, such that \(\upomega(0,x)=\upomega(x,0)=\frac{1}{2}\upomega(x,x)\) and \(\upomega_{0}(0,x)=\upomega_{0}(x,0)=\frac{1}{2}\upomega_{0}(x,x)\). By definition \(\upomega_{0}(x,y)\leq\upomega(x,y)\)._  
_Let \(x_{0}\in B\). Assume that_  
(1) \(||\Upsilon_{0}^{-1}||\leq\upbeta\)_._  
(2) \(\max(||\Upsilon_{0}^{-1}F(x_{0})||,||\Psi_{0}^{-1}F(x_{0})||)\leq\upeta\)_._  
(3) _The equation_  
\[t(1-\frac{m}{1-\upbeta(\upomega(t+2 tol_{user},t+2 tol_{user})+\upomega_{0}(t+2 tol_{user},t+2 tol_{user}))})-\upeta=0 \tag{17}\]  
_has a smallest positive root \(R\), where \(m=\upbeta\upomega_{0}(\upeta+tol_{user},\upeta+tol_{user})\)._  
_If \(\upbeta(\upomega(R+2 tol_{user},R+2 tol_{user})+2\upomega_{0}(R+2 tol_{user},R+2 tol_{user}))<1\) and \(\overline{B(x_{0},R)}\subset B\), then \(M:=\frac{m}{1-\upbeta(\upomega(R+2 tol_{user},R+2 tol_{user})+\upomega_{0}(R+2 tol _{user},R+2 tol_{user}))}\in(0,1)\) and the method (14) is well defined, it remains in \(B(x_{0},R)\) and converges to the unique solution of \(F(x)=0\) in \(\overline{B(x_{0},R)}\)._
## 4 Experiments and Results
### Evaluation Indexes  
We used weighted precision, weighted recall, and weighted F1 as our evaluation metrics; the calculation formulas are as shown in Equations (10)-(12):  
\[\text{weighted\_precision}=\frac{\sum_{i=1}^{N}\left(\frac{\text{TP}_{i}}{\text{ TP}_{i}+\text{FP}_{i}}\times n_{i}\right)}{\sum_{i=1}^{N}n_{i}} \tag{10}\]  
\[\text{weighted\_recall}=\frac{\sum_{i=1}^{N}\left(\frac{\text{TP}_{i}}{\text{ TP}_{i}+\text{FN}_{i}}\times n_{i}\right)}{\sum_{i=1}^{N}n_{i}} \tag{11}\]  
\[\text{weighted\_F1}=\frac{2\times\text{weighted\_precision}\times\text{ weighted\_recall}}{\text{weighted\_precision}+\text{weighted\_recall}} \tag{12}\]  
Weighted precision is a measure of a classifier's ability to correctly identify samples. It indicates the proportion of correctly predicted samples among the samples identified as positive samples. Weighted recall indicates the proportion of all predicted positive samples that could be correctly predicted. The weighted F1 score can be regarded as the harmonic average of the model's precision and recall. The larger the F1 value, the more accurate the model is in identifying true positive samples when predicting positive samples while also identifying as many positive samples as possible.
## 5 Results
### Parameter Analysis
#### 5.2.1 Reduction Dimensionality
\begin{tabular}{c c c c c c c} \hline \hline
**CNN Model and Layer** & **Number of Features** & **LSVM** & **QSVM** & **CSVM** & **MGSVM** & **CGSVM** \\ \hline  & 10 & 96.8 & 97.8 & 97.7 & 98.1 & 96.1 \\  & 20 & 97.7 & 97.9 & 97.6 & 97.8 & 97.5 \\  & 30 & 97.9 & 97.7 & 97.5 & 97.9 & 97.7 \\
**EfficientNetB0** & 40 & 97.8 & 98.0 & 97.9 & 97.8 & 98.0 \\
**Layer 1 Deep** & 50 & 97.8 & 97.9 & 97.8 & 98.0 & 97.8 \\
**Features** & 60 & 98.1 & 97.9 & 97.7 & 98.0 & 98.0 \\  & 70 & 97.6 & 97.9 & 97.8 & 98.0 & 98.0 \\  & 80 & 98.1 & 98.1 & 97.9 & 97.4 & 97.8 \\  & 90 & 97.9 & 98.0 & 97.5 & 97.5 & 98.0 \\  & 100 & 97.3 & 97.2 & 97.2 & 97.2 & 97.6 \\ \hline \hline \end{tabular} \begin{tabular}{c c c c c c} \hline \hline
**MobileNet Layer 1** & 50 & 97.7 & 98.1 & 97.7 & 97.4 & 97.7 \\
**Deep Features** & 60 & 97.8 & 98.0 & 98.0 & 97.9 & 97.9 \\  & 70 & 97.8 & 97.6 & 97.7 & 97.6 & 97.6 \\  & 80 & 97.8 & 97.6 & 97.7 & 97.6 & 97.6 \\  & 90 & 98.0 & 98.0 & 98.0 & 97.4 & 97.9 \\  & 100 & 97.7 & 97.8 & 97.7 & 97.7 & 97.7 \\ \hline \hline \end{tabular} 
\begin{tabular}{c c c c c c} \hline \hline
**ShuffleNet Layer 1** & 50 & 95.2 & 96.2 & 95.6 & 96.2 & 93.4 \\
**Deep Features** & 60 & 95.2 & 95.8 & 95.5 & 96.0 & 94.2 \\
**70** & 96.3 & 96.4 & 96.1 & 96.3 & 94.5 \\
**80** & 96.0 & 96.2 & 96.5 & 96.5 & 94.2 \\
**90** & 95.0 & 96.6 & 96.0 & 96.6 & 94.5 \\
**100** & 95.4 & 94.7 & 94.6 & 95.7 & 93 Table 4 emphasizes Layer 2 features, demonstrating that enhanced performance among all CNNs highlights the significance of intermediate-layer features in classification tasks. The highest accuracy of 99.2% for EfficientNetB0 was attained with LSVM employing 90 features, rendering it the most precise configuration in the stage. The consistently elevated accuracies across various classifiers and feature set lengths for EfficientNetB0 Layer 2 demonstrate its capacity to identify intricate mid-level features that balance spatial and semantic information. MobileNet's Layer 2 features achieved a peak accuracy of 97.6% with both LSVM and QSVM, deploying 100 features. This indicates a modest enhancement compared to Layer 1, yet the improvements were not as significant as those observed with EfficientNetB0, implying that MobileNet's intermediate variables are less extensive. ShuffleNet exhibited superior performance with Layer 2 features, attaining a maximum accuracy of 97.2% using LSVM and 100 features. This enhancement relative to Layer 1 suggests that mid-level features in ShuffleNet substantially enhance classification accuracy, presumably owing to their capacity to encapsulate more intricate representations. Table 4 demonstrates that Layer 2 features consistently surpass Layer 1 features across all CNNs. This trend supports the hypothesis that intermediate layers in CNNs frequently entertain the most distinctive characteristics for classification tasks. EfficientNetB0 proved to be the best-performing construction, with its Layer 2 features yielding the highest overall accuracy, demonstrating the network's capacity to effectively acquire significant features.
## 2 System Model
### The Multi-Domain Channel
(3) Delay-Doppler domain  
The DD domain channel is obtained by applying fast Fourier transform (FFT) to the time domain on the basis of the time-delay domain channel. Hence, the relationships between the DD domain and the time-delay domain are as follows:  
\[\mathbf{y}=(\mathbf{I}_{M}\otimes\mathbf{F}_{N})\cdot\widetilde{\mathbf{y}} \tag{15}\]  
\[\mathbf{x}=(\mathbf{I}_{M}\otimes\mathbf{F}_{N})\cdot\widetilde{\mathbf{x}} \tag{16}\]  
where \(\mathbf{I}_{M}\in\mathbb{C}^{M\times M}\) is the identity matrix. In addition, \(\mathbf{F}_{N}\in\mathbb{C}^{N\times N}\) is Fourier matrix, which consists of the following:  
\[F(n,k)=\frac{1}{\sqrt{N}}\exp(-j2\pi nk/N) \tag{17}\]  
According to Equations (14)-(16), the input-output relation of the DD domain can be obtained as follows:  
\[ \begin{array}{ll}\mathbf{y}&=(\mathbf{I}_{M}\otimes\mathbf{F}_{N})\cdot \widetilde{\mathbf{H}}\cdot(\mathbf{I}_{M}\otimes\mathbf{F}_{N}^{\text{H}}) \mathbf{x}+\mathbf{w}\\ &=\mathbf{H}\mathbf{x}+\mathbf{w}\end{array}  \tag{18}\]  
In (18), \(\mathbf{H}=(\mathbf{I}_{M}\otimes\mathbf{F}_{N})\cdot\widetilde{\mathbf{H}} \cdot(\mathbf{I}_{M}\otimes\mathbf{F}_{N}^{\text{H}})\in\mathbb{C}^{MN\times MN}\) is the DD domain channel matrix.
# Space-Based Displacement Monitoring of Coastal Urban Areas: The Case of Limassol's Coastal Front
## 3 Results
### PSI Classification and Trends of PSI Results
#### 3.1.2. PSI Results Analysis of Tall Buildings on Limassol Coastal Front  
Olympic Residence, The Oval, iHome, and Arc Ship Tower are included in the tall buildings category (Table 7). PSI geospatial analysis of the tall buildings reveals that the Olympic Residence building, which follows a two-building complex construction, has an average displacement rate of \(-1.7\) mm in the vertical component. Equally notable is that the specific buildings analyzed had completed construction in the year 2012. The lowest observed minimum value of \(-4.17\) mm among all tall buildings corresponded to Olympic Residence. Concerning the Oval and Arc Ship building, the average displacements of height are equal to \(-0.41\) and \(-0.42\) mm, respectively.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Map of PSI Analysis—Skyscrapers**} & \multicolumn{3}{c}{**Statistics (mm)**} \\ \cline{2-4}  & **Mean/Average** & **Maximum** & **Minimum** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Cont.
# FTGM: Fast Transformer-Based Global Matching for Particle Image Velocimetry
## 5 Limitation and Discussion
FTGM-PIV has a large error in the estimation of the horizontal component, while the estimation result of the vertical component is consistent with that of RAFT256-PIV. This is mainly because the displacement of the vertical component is small (the background color is light), while the displacement of the horizontal component is large (the background color is dark). This can be seen from the prediction results. In the estimation results of the horizontal component, near the wavy channel, the estimation results of FTGM-PIV are close to those of RAFT256-PIV. However, in areas with larger displacements, the estimation results of FTGM-PIV become unreliable. This is mainly because FTGM-PIV only uses 1/8 resolution features for matching, and such coarse resolution features are insufficient when dealing with images of high-density particles with large displacements. After introducing 1/4 resolution features for refinement, the prediction results have been greatly improved. Given that there have been numerous performance assessments and generalization studies on deep recurrent optical flow methods in PIV estimation, and our FTGM method has demonstrated the potential of global matching methods in PIV estimation, more research is still needed for further development and improvement. Therefore, we believe that in future research, it is crucial to comprehend the sensitivity of the algorithm to inputs such as image attributes such as particle diameters, densities, particle displacements (Reynolds numbers, Re), and PIV hyperparameters. To enhance the estimation and generalization ability of FTGM for low-matching areas, it is a natural choice to consider adopting the iterative refinement structure from RAFT. However, determining how to use the enhanced features to construct a structure similar to a 4D cost volume while avoiding the parameter redundancy brought by the context network and ultimately achieving the goal of preserving the expressive ability of the iterative refinement structure while reducing the risk of introducing unexpected fine structures is a promising option.
# Diurnal Cycle of Passive Microwave Brightness Temperatures over Land at a Global Scale
## 3 Computation of Diurnal Cycle of PMW TBs
Figure 4: Diurnal variations of interpolated TB at 37 GHz vertical polarization for (**a**) 17.9\({}^{\circ}\)S and 36.4\({}^{\circ}\)E with wooded and non-wooded grassland land cover for June 16, 2016; (**b**) 8.1\({}^{\circ}\)S and 26.6\({}^{\circ}\)E with deciduous woodland land cover for 1 October 2016; (**c**) 17.4\({}^{\circ}\)N and 28.1\({}^{\circ}\)E with non-vegetated desert land cover for 9 February 2016; and (**d**) 27.4\({}^{\circ}\)N and 115.1\({}^{\circ}\)E with evergreen broad-leaved and needle-leaved forest land cover for 27 December 2016. Time in each subplot is in local solar hours. Finally, the spline fit was applied to the smoothed AMSR2, SSMIS F16, SSMIS F17, and SSMIS F18 data to estimate the half-hourly TB diurnal cycle in polar regions. Final TB diurnal interpolations at 18.7 GHz (H) for four sample locations (three in Northern and one in Southern Hemisphere) having distinct land cover types are shown in Figure 6. As expected, the magnitude and timing of maximum TBs are notably different for distinct land cover types. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & **Jan.** & **Feb.** & **Mar. ** & **Apr. ** & **May** & **Jun. ** & **Jul. ** & **Aug.** & **Sep.** & **Oct.** & **Nov.** & **Dec.** \\ \hline LC01 & 1.56 & 0.81 & 0.93 & 1.17 & 1.08 & 0.72 & 1.07 & 1.24 & 1.34 & 0.60 & 1.21 & 1.10 \\ \hline LC02 & 2.07 & 3.
# Study on Multi-Objective Optimization-Based Climate Responsive Design of Residential Building
## 3 Multi-Objective Optimization Platform Set-Up
### Design Parameter Settings of Typical Buildings
\begin{table}
\begin{tabular}{c c c} \hline \hline
**N** & **Design Variable** & **Parameter** \\ \hline
1 & Building orientation (\({}^{\circ}\)) & \(-45\); \(0\); \(45\) \\
2 & Solar absorbance of external wall (-) & \(0.1\); \(0.25\); \(0.4\); \(0.5\); \(0.6\); \(0.7\); \(0.8\); \(0.9\) \\
3 & Solar absorbance of roof (-) & \(0.1\); \(0.2\); \(0.3\); \(0.4\); \(0.5\); \(0.6\); \(0.7\); \(0.9\) \\
4 & Insulation thickness of external wall (m) & \(0\); \(0.03\); \(0.04\); \(0.05\); \(0.06\); \(0.08\); \(0.10\); \(0.12\) \\
5 & Insulation thickness of roof (m) & \(0\); \(0.03\); \(0.04\); \(0.05\); \(0.06\); \(0.08\); \(0.10\); \(0.12\) \\
6 & Insulation thickness of ground floor (m) & \(0\); \(0.03\); \(0.04\); \(0.05\); \(0.06\); \(0.08\); \(0.10\); \(0.12\) \\
7 & Block thickness of external wall (m) & \(0.25\); \(0.30\); \(0.35\); \(0.40\) \\
8 & Block thermal conductivity for the external wall (W/mK) \({}^{\star}\) & \(0.25\); \(0.30\); \(0.36\); \(0.43\); \(0.50\); \(0.59\); \(0.72\); \(0.90\) \\
8 & Block density for the external wall (Kg/m) \({}^{\star}\) & \(600\); \(800\); \(1000\); \(1200\); \(1400\); \(1600\); \(1800\); \(2000\) \\
9 & Block thickness for roof (m) & \(0.25\); \(0.30\); \(0.35\); \(0.40\) \\
10 & Block thermal conductivity for the roof (W/mK) \({}^{\star}\) & \(0.25\); \(0.30\); \(0.36\); \(0.43\); \(0.50\); \(0.59\); \(0.72\); \(0.90\) \\
11 & Block thickness for ground floor (m) & \(0.25\); \(0.30\); \(0.35\); \(0.40\) \\
12 & Block thermal conductivity for the ground floor (W/mK) \({}^{\star}\) & \(0.25\); \(0.30\); \(0.36\); \(0.43\); \(0.50\); \(0.59\); \(0.72\); \(0.90\) \\
12 & Block density for the ground floor (Kg/m) \({}^{\star}\) & \(0.25\); \(0.30\); \(0.36\); \(0.43\); \(0.50\); \(0.59\); \(0.72\); \(0.90\) \\
13 & Types of window (see Table 6) & \(1\); \(2\); \(3\); \(4\); \(5\); \(6\); \(7\) (see Table 6) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Calculation method of initial investment cost. With the help of integrated parametric software, various parameters used for building energy modeling can be collected and analyzed on the same platform. The research uses the plug-in Octopus of Grasshopper to search the target function value. Octopus applies the evolutionary principle of SPEA-2 to the process of parametric design, and produces a series of trade-off solutions between the extreme values of multiple targets. The operation flow of Grasshopper is shown in Figure 12.