esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Wireless Body Area Network (WBAN)
### Available Medical Sensors
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Commercial** & \multirow{2}{*}{**Structure**} & \multirow{2}{*}{**Technical Specification**} & \multirow{2}{*}{**Biological Measurement**} \\
**Name** & & & & **Technical Specification** & **Biological Measurement** \\ \hline  & & & (a) & Connectivity & \\  & & & 1. & Wi-Fi & \\  & & & & (IEEE 802.11b/g/n) & & \\  & & & 2.4 GHz and 5 GHz & & \\  & & & 2. & LTE & \\  & 1. & Optical heart rate sensor & (Licensed spectrum) & & \\  & 2. & Electric heart rate sensor & 3. & Bluetooth 5.3 & \\  & 3. & Blood oxygen sensors & (b) & Display & 1. & Records sleep hours. \\ Apple Watch & 4. & Altimeter & (b) & 2. & Measures blood \\
[34] & 5. & GPS & 1. & Retina LTPO OLED display & & oxygen levels. \\  & 6. & Compass & 2. & Brightness up to 2,000 nits & 3. & Monitors PA and HR \\  & 7. & Microphone & (c) & Power & \\  & 8. & Tactile digital crown & 1. & USB-C magnetic fast charging & \\  & & & 2. & Built-in lithium-ion & \\  & & & & rechargeable battery & \\  & & & (d) & Processor & \\  & & & & (S7 SIP Dual Core) & \\ \hline  & & & (a) & Connectivity & \\  & & & 1. & LTE & \\  & & & 2. & Bluetooth 5.2 & \\  & & & 3. & Wi-Fi & \\  & & & & (IEEE 802.11a/b/g/n) & & \\  & 1. & Bioactive sensor & & 2.4 + 5 GHz & 1. & Body composition \\  & 2. & Temperature sensor & 4. & NFC & analysis \\  & 3. & Geomagnetic sensor & (b) & Display & 2. & Monitoring blood \\  & 4. & Electrical heart sensor & (b) & Display & oxygen levels \\  & 5. & Barometer sensor & 1. & Sapphire crystal & 3. & Sleep tracking \\  & 6. & Optical heart rate sensor & 2. & 1.4” (34.6 mm) & 4. & Monitoring heart rate \\  & 7. & Light sensor & & 450 \(\times\) 450 Super AMOLED & 5. & Guided workouts \\  & 8. & Bioelectrical impedance & 3. & Full color & 6. & Irregular heartbeat \\  & 9. & Microphone & (c) & Power & \\  & 10. & GPS & 1. & Fast charging (WPC-based & 7. & Snore detection \\  & & & & wireless charging) & \\  & & & 2. & Battery-590 mAh & \\  & & & (d) & Processor & \\  & & & & (Exynos W920 Dual-Core 1.18 GHz) & & \\ \hline  & & & (a) & Connectivity & \\  & & & 1. & Bluetooth 4.2 & \\  & & & 2. & LTE & \\  & & & 3. & GLONASS, and Galileo positioning & \\  & 1. & Optical heart rate sensor & 4. & Wi-Fi & \\  & 2. & Accelerometer sensor & & (IEEE 802.11a/b/g/n) & \\  & 3. & Capacitive sensor & & 2.4 + 5 GHz & 1. & Tracks sleep \\ Huawei Watch & 4. & Ambient light sensor & 5. & NFC & 2. & Menstrual cycle \\ GT, Huawei & 5. & Gyroscope sensor & (b) & Display & & and stress \\ Band & 6. & Air pressure sensor & 1. & Type: OLED & 3. & Monitors HR 24/7 \\
[36] & 7. & Geomagnetic sensor & 2. & Size: 1.39 inches & 4. & Monitoring SpO2 \\  & 8. & Microphone & 3. & Resolution: 454 \(\times\) 454 pixels & \\  & 9. & GPS & & (\(\sim\)326 ppi density) & \\  & & & (c) & Power & \\  & & & 1. & Li-Ion 420 mAh & \\  & & & (d) & Processor & \\  & & & & ARM Cortex-M4 & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Common market available wearable healthcare watches. In the rest of this section, we introduce the most common medical sensors.
## 3 Results
### Comparisons of Performances of Machine Learning Techniques  
The main objective of this section was to predict children with ADHD using eight ML-based classifiers. The comparison of the performances of ML-based classifiers for the prediction of children with ADHD is shown in Table 5. It was noted that RF-based classifier gave the highest classification accuracy of 85.5%, sensitivity of 84.4%, and specificity of 86.4%, whereas NB provided the lowest classification accuracy of 69.8%, sensitivity of 77.3%, and specificity of 65.3%. It was also noted that DT provided 84.6% accuracy, 83.4% sensitivity, and 86.0% specificity, whereas KNN provided 84.0% accuracy, 82.6% sensitivity, and 85.6% specificity. It was also observed that RF-based classifier achieved the highest AUC of 0.94 compared to other classifiers. The corresponding ROC curve of eight ML-based classifiers is depicted in Figure 1. Therefore, the RF-based classifier performed better performance scores for the prediction of children with ADHD.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Variables** & **Overall, _n (\%)_ & **Healthy, _n (\%)_** & **ADHD, _n (\%)_** & _p-Value_1 _ \\ \hline Total & 45,779 & 40,561 (88.6) & 5218 (11.4) & \\ Child’s age & \(10.6\pm 4.4\) & \(10.4\pm 4.5\) & \(12.4\pm 3.4\) & \(<\)0.001 \\ Sex, Male & 23,901 (52.2) & 20,304 (84.9) & 3597 (15.1) & \(<\)0.001 \\ Mother’s age & \(30.0\pm 5.8\) & \(30.2\pm 5.7\) & \(28.7\pm 6.3\) & \(<\)0.001 \\ Allergies, Yes & 13,930(30.4) & 11,889 (85.4) & 2041 (14.6) & \(<\)0.001 \\ Arthritis, Yes & 182 (0.4) & 151 (83.0) & 31 (17.0) & 0.039 \\ Asthma, Yes & 6293 (13.8) & 5180 (82.3) & 1113 (17.7) & \(<\)0.001 \\ Brain injury, Yes & 2482 (5.4) & 2048 (82.5) & 434 (17.5) & \(<\)0.001 \\ Headache, Yes & 2457 (5.4) & 1931 (78.6) & 526 (21.4) & \(<\)0.001 \\ Anxiety, Yes & 5850 (12.8) & 3652 (62.4) & 2198 (37.6) & \(<\)0.001 \\ Depression, Yes & 2744 (6.0) & 1595 (58.1) & 1149 (41.9) & \(<\)0.001 \\ Insurance, Yes & 44,057 (96.2) & 39,008 (88.5) & 5049 (11.46) & 0.035 \\ Alcohol, Yes & 4728 (10.3) & 3712 (78.5) & 1016 (21.5) & \(<\)0.001 \\ Race, White & 36,235 (79.2) & 31,935 (88.1) & 4300 (11.9) & \(<\)0.001 \\ Family structure, two parent-biological/step/adopted Mother’s education, High school Very LBW, Yes & 3558 (1.2) & 451 (80.8) & 107 (19.2) & \(<\)0.001 \\
1.5 & 3837 (8.4) & 3289 (85.7) & 548 (14.3) & \(<\)0.001 \\ Premature, Yes & 5090 (11.1) & 4287 (84.2) & 803 (15.8) & \(<\)0.001 \\ Poverty, \textless{}200\% & 12,079 (26.4) & 10,426 (86.3) & 1653 (13.7) & \(<\)0.001 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Baseline and demographic characteristics of children with ADHD, 3–17 years.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variables** & **OR (95\% CD)** & **SE** & \(p\)**-Value**1  \\ \hline
**Child’s age** & 1.103 (1.096–1.110) & 0.004 & \textless{}0.001 \\
**Sex** & & & \\ Male & 2.727 (2.586–2.877) &
# Vertebral Center Points Locating and Cobb Angle Measurement Based on Deep Learning
## 1 Introduction
[12] proposed a Cobb angle measurement method which firstly determines the UEV and LEV of scoliosis segments. Then, they mark UEVEL and LEVEL on the film using the measurement program built in PACS (Picture Archiving and Communication System), and calculate Cobb angle. [PERSON] et al. [13] proposed an Adaptive Error Correction Network(AEC) to directly calculate Cobb angle. AEC first uses two methods to estimate Cobb angle: LandmarkNet, which is used to indirectly calculate the Cobb angle by regressing corner points, and AngleNet, which is used to directly regress Cobb angle. Then, the errors of the two estimated Cobb angles are adaptively offset by an alternative error correction network using extrapolation. [PERSON] et al. [14] first detected the C7-L5 vertebrae from an X-ray image of the whole spine, reducing the resolution from 1750 \(\times\) 3064 pixels to 768 \(\times\) 1152 pixels and cropping the area to 512 \(\times\) 512 pixels. This cropped area was then sent to a corner detection model, from which the Cobb angle was calculated according to the corner points. Although these methods have achieved specific results in the automatic detection of scoliosis, their stability and robustness could still be improved, as they are affected by the initial image quality. Moreover, the data sets used by these methods are not anteroposterior X-ray images of the entire spine. In actual use, requiring the X-ray images need to be cropped or manually boxed selected for actual use. This paper proposes a deep learning-based algorithm for detecting vertebral center points, tailored to the needs of scoliosis in practical applications. The cervical vertebrae, sacrum, and cocycx are filtered by detecting four anchor points through a parallel network. Heatmap is used to regress the desired vertebral body center points, fit the spine curve, adjust the normal slope of the spine curve, and calculate the accurate Cobb angles. This method does not require cropping or other operations on the X-ray image, and can automatically detect the Cobb angles.
## 3 Results and Discussion
### Fusion of the Prokaryotic SuSy with UGT76G1
At 50 \({}^{\circ}\)C and 7.2, using the mass ratio (stevioside: sucrose) 1:20, the RebA yield (17.0 g/L) in the reaction mixture with 15 mg/mL of the crude enzyme was 1.8 times higher than that with 5 mg/mL crude extract, and stevioside was almost completely converted after reaction of 8 h.  
Compared with UGT-SuSys systems composed of eukaryotic SuSys, the reactions using prokaryotic SuSys, such as Acm-76G1 and Acm-R3-76G1, prefer higher temperature and higher sucrose concentration. The K\({}_{\rm m}\) value of _Ac_SuSy for UDP is 7.8 mM. After mutation, a significantly reduced K\({}_{\rm m}\) value for UDP (up to 60 times) was achieved for _Ac_SuSym. However, it is still above 0.1 mM, higher than that of most plant SuSys [28]. In the UGT-SuSy reactions set up in this study, the required UDP came from the cell lysate. At an elevated temperature (50 \({}^{\circ}\)C), with the extension of reaction time, the degradation of UDP cannot be ignored. As indicated in Figure 4, 2 mM of UDP was enough to support the reactions catalyzed by Acm-76G1 and Acm-R3-76G1, while a lower efficiency was found in the control reactions that only used UDP in the cell lysate. However, Acm-R3-76G1 that was catalyzed with the fusion protein always outperformed Acm-76G1 with the free enzymes. RebA accumulation in Acm-R3-76G1 without UDP supply was even more than Acm-76G1 with 2 mM UDP. After reaction for 2 h, 13.5 g/L RebA was detected in Acm-R3-76G1 containing 2 mM UDP, which indicated the initial reaction speed was relatively fast. After that, RebA increased slowly, reaching 18.8 \(\pm\) 0.6 g/L (a yield of 78.2%) at 12 h.  
Figure 4: The influence of UDP (2 mM) supplement on RebA production catalyzed by Acm-76G1 and Acm-R3-76G1. The reaction mixture (10 mL), consisting of approximately 15 mg/mL of the crude extract, 20 g/L stevioside, 400 g/L sucrose, and a certain concentration of UDP, was incubated at pH 7.2 and 50 \({}^{\circ}\)C.  
In contrast, the prokaryotic _Ac_SuSym had better performance than plant SuSys in the fusions. From the conversion results, the rigid linker (EAAAK)3 that pulls close the _C_-terminal of _Ac_SuSym and the _N_-terminal of UGT76G1 did not seem to affect enzyme activities. In addition, the increased soluble expression of Acm-R3-76G1 could be observed by SDS-PAGE (Figure S5). It is known that enzymes are the key factors that affect reaction efficiency. Soluble expression is the premise of obtaining active enzymes. As predicted in Table 5, UGT76G1 is probably insoluble when it is overexpressed in _E. coli_. However, _At_SUS1 and _Ac_SuSym are predicted to have more probability of solubility, which increases the solubility of the fusion constructs when they are fused with UGT76G1, respectively. Among 76G1-P3-At1, At1-P3-76G1, R3 (At1-R3-76G1), and Acm-R3-76G1, Acm-R3-76G1 has the maximum probability of solubility (56%). Therefore, the technology of enzyme fusion will also improve the soluble expression of fusion partners.
## 4 Experiment and Results
### Performance Evaluation
#### 4.2.2 Portfolio Performance Evaluation  
On the basis of the prediction results for each time horizon from 1% to 10% of total trading days, as shown in Table 2, a portfolio was constructed \(P_{i}\) (\(i=1,2,\ldots,10\)) by selecting the top four stocks with the highest predicted returns. For the purpose of optimizing those constructed portfolio performances, simulation modeling and optimization modeling were adopted to select ten efficient portfolios, as represented by \(P_{1,\ldots 10}\), at the final stage by allocating optimal weights. A statistical model for each portfolio was conducted to calculate key factors such as daily return, cumulative return, average daily return, and standard daily return. The cumulative return was used as an identical investment reward to evaluate the performance of each portfolio. However, a high return may come with high volatility or risk in investment. Sharpe ratio (\(SR\)) was used for calculating risk-adjusted return, which has used as the industry-standard measurement. Furthermore, active returns of optimal portfolios represented as the difference between the portfolio's actual return on a benchmark were calculated. In our work, the S&P 500 market index was selected as the benchmark. In order to optimize asset allocation for the constructed portfolios, we carried out three different techniques for portfolio optimization:  
* Equal-weighted portfolio (EQ) is a type of weighting that gives the same weight to each stock in a portfolio. In our work, we chose initial weight \(w=\left[.25,\.25,\.25,\.25\right]\).
* Monte Carlo simulation (MCS) was used to find the optimal weights of thousands of scenarios or iterations. The number of iterations is n = 50,000.
* Mean-variance optimization (MVO) was used to find an adaptive weights portfolio that adapted the stock weights using the prediction models.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Categories** & **Hyperparameters** \\ \hline Optimizer & Adam \\ The number of hidden layers & 2 \\ The number of neurons & 512 and 256 \\ Number of epochs & 4000 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experiment hyperparameters setup.
## 3 CFD Modelling as Digital Twin
### CFD Models for Filling and Emptying Processes
#### 3.3.4 Air Expulsion/Admission in Pipelines Using CFD  
Hydraulic filling and emptying operations in water pipes with trapped air require the release or admission of trapped air, respectively, so that in practice, hydraulic systems can be equipped with protective devices such as air valves [PERSON] et al. [66], [PERSON] et al. [67; 68], [PERSON] et al. [69]. These devices play an essential role in dissipating overpressure due to the compression of trapped air pockets and, in the case of emptying processes, help mitigate the effects of sub-atmospheric pressures.  
The simulation of hydraulic filling and emptying events with two-phase flow and with air release/admission orifices/valves has been a recent and ongoing contribution, where authors such as [PERSON] et al. [55; 56] have developed a methodology to represent the effects of the air valve in a 2D CFD model using a slot sized from a geometric aspect ratio that depends on the ratio of the discharge/admission orifice diameter to the internal diameter of the pipe, adjusted with a coefficient of 0.95. The authors used this geometric aspect ratio, which was subsequently used by [PERSON] et al. [54; 58] to simulate emptying processes with air intake. On the other hand, authors such as [PERSON] et al. [36] have developed three-dimensional CFD models to simulate the effects of orifices and air valves through three-dimensional simulations, making significant advances in the simulation of these hydraulic scenarios.
## 2 Materials and Methods
### Satellite Vegetation Indices (VIs) and XCO\({}_{2}\) Data  
Compared with NDVI, EVI reduced influences from the atmosphere and canopy background and improved sensitivity in high biomass areas to enhance the vegetation signal [16]. We used the 0.05\({}^{\circ}\) monthly EVI data (MOD13C2, V6), which contained quality assurance flags layers from 2007 to 2016. We only employed good quality EVI data (pixel reliability = 0) without contamination of clouds or aerosols in this study. Moreover, bidirectional reflectance distribution function (BRDF) correction for small biases was applied for EVI data.  
We utilized the Greenhouse gases Observing SATellite (GOSAT) short-wave infrared Fourier transform spectrometer (FTS) data from April 2009 to December 2016, the v201 version of the L1B calibrated spectral radiance. We detrended the GOSAT XCO\({}_{2}\) data by correcting for the year-to-year average global XCO\({}_{2}\) trend. The Orbiting Carbon Observatory-2 (OCO-2) observed the CO\({}_{2}\) bands at wavelength in 1.61 and 2.06 \(\upmu\)m and aimed to measure the column-averaged CO\({}_{2}\) dry-air mole fraction, XCO\({}_{2}\), to identify and characterize sources and sinks of CO\({}_{2}\) on regional scales [45]. [PERSON] et al. [46] applied the Atmospheric CO\({}_{2}\) Observations from Space algorithm to retrieve the XCO\({}_{2}\). In addition, the OCO-2 SIF at 757 nm was a serendipitous but important supplementary by-product.
# Design and Performance Analysis of Navigation Signal Based on OFDM
## 5 Performance Evaluation of OFDM Navigation Signals
### Capture Time  
In the integrated receiver, the navigation processing module is generally in two states: the acquisition state and tracking state. The acquisition state provides the initial Doppler shift and pseudo-code phase difference for the tracking state. The size of the indefinite interval in a two-dimensional search for a signal basically determines the time required to complete the search for the signal. The average capture time A of the signal can be calculated by using the following formula:  
\[T_{\text{acq}}=\frac{1}{2}\frac{f_{\text{unc}}t_{\text{unc}}T_{\text{dwell}}} {f_{\text{bin}}f_{\text{bin}}}, \tag{24}\]  
In the formula, \(f_{\text{unc}}\) and \(t_{\text{unc}}\) represent the Doppler indeterminate interval and the code phase indefinite interval, respectively. \(f_{\text{bin}}\) and \(t_{\text{bin}}\) represent the Doppler search frequency band and code phase bandwidth, respectively. The dwell time \(T_{\text{dwell}}\) is the average search time for a unit. The traditional BOC modulation algorithm can capture the OFDM navigation signal with only minor changes. Because the ACF sub-peak-to-peak value of the OFDM navigation signal is lower, the false acquisition probability of the OFDM navigation signal is lower than that of the BOC signal.
# Evaluation of _Aspergillus flavus_ Growth on Weathered HDPE Plastics Contaminated with Diesel Fuel
## 3 Results
### Fungal Growth Dynamics on Plastic Substrates
#### 3.3.7 Influence of Temperature and Substrate Type on Fungal Growth: ANOVA Results  
At 30 \({}^{\circ}\)C, the analysis of variance (ANOVA) revealed statistically significant differences in the colony growth area of _Aspergillus flavus_ among the different types of HDPE evaluated (contaminated with commercial diesel, weathered diesel, and clean control). The obtained \(p\)-value was 0.0018, well below the conventional threshold of 0.05, indicating that the observed differences are unlikely to be due to random variation. Furthermore, the F-statistic of 8.09 supports the presence of a substantial effect of substrate type on fungal development under elevated thermal conditions. These results suggest that, at 30 \({}^{\circ}\)C, the nature of the contaminant present on the HDPE surface plays a decisive role in the fungus's colonization capacity.  
As temperature increased from 20 \({}^{\circ}\)C to 25 \({}^{\circ}\)C and subsequently to 30 \({}^{\circ}\)C, the differences in colony growth area of _Aspergillus flavus_ among the various HDPE types became progressively more statistically significant. At 20 \({}^{\circ}\)C, no significant differences were detected between the substrate conditions. However, at 25 \({}^{\circ}\)C, a near-significant trend emerged, and by 30 \({}^{\circ}\)C, the differences were clearly significant, indicating a temperature-dependent enhancement of the fungus's discriminatory growth response to the physicochemical characteristics of the HDPE surfaces.
## 6 Security Analysis
### Informal Analysis
#### 6.3.7 Mutual Authentication  
After receiving message the \(\{Auth_{1},M_{1},HID_{i},B_{i},RA_{j},Auth_{2}\}\) from \(v_{i}\) and \(RSU_{j}\), \(TA\) checks whether \(Auth_{1}^{{}^{\prime}}\stackrel{{?}}{{=}}Auth_{1}\). If it is equal, \(TA\) also checks \(Auth_{2}^{{}^{\prime}}\stackrel{{?}}{{=}}Auth_{2}\). Subsequently, \(TA\) sends \(\{C_{i},D_{i},Auth_{3},Auth_{4}\}\) to the \(v_{i}\) and \(RSU_{j}\) for authenticating. \(RSU_{j}\) checks \(Auth_{3}^{{}^{\prime}}\stackrel{{?}}{{=}}Auth_{3}\) and \(v_{i}\) checks also \(Auth_{4}^{{}^{\prime}}\stackrel{{?}}{{=}}Auth_{4}\). If they are valid, \(v_{i}\), \(RSU_{j}\), and \(TA\) successfully authenticate each other. Previous sections have shown that \(\mathcal{A}\) cannot generate valid messages. Furthermore, all of the transmitted messages are refreshed for every session with secret random numbers. Therefore, our proposed protocol successfully ensures secure mutual authentication and achieves session key agreement.
# Characterization of the Key Aroma Compounds of Shandong Matcha Using HS-SPME-GC/MS and SAFE-GC/MS
## 3 Results
### Multivariate Statistical Analysis of Shandong Matcha and Tencha
#### 3.3.2 OPLS-DA  
In order to identify the differential compounds of different grades of tencha and matcha, the OPLS-DA model was used to analyze the data. OPLS-DA can decompose the X matrix information into two types of information related to Y and irrelevant differences and filter the differential variables by removing the latter. In the analysis of the two extraction methods, the distribution of tencha and Shandong matcha tea was the same (Figure S1). The total variance (R\({}^{2}\)) explained by the OPLS-DA model between different grades of matcha was 71.1-86.5%, and the predicted total variance (Q\({}^{2}\)) was 89.8-99.2%. The total variance (R\({}^{2}\)) explained by the OPLS-DA model between different grades of tencha was 75.0-96.4%, and the predicted total variance (Q\({}^{2}\)) was 91.3-99.6% (Table 2). On the whole, compared with the difference between M-G1 and M-G2, the difference of M-GS vs. M-G1 and M-GS vs. M-G2 was larger, and this result was also well illustrated in the tencha. Based on the results of the OPLS-DA model, a combination of fold change (FC \(\geq\) 2 and FC \(\leq\) 0.5) and VIP value (VIP \(\geq\) 1) was used to screen the key aroma compounds in Shandong matcha.
## 4 Result
### Quantitative Evaluation of Generated Images  
#### 4.4.1 Image Evaluation Indicators  
Since subjective qualitative methods alone are sometimes insufficient to assess image quality, quantitative evaluation methods are also needed for a more comprehensive analysis. Firstly, two image evaluation metrics, FID and SSIM compare the generated and real images based on defect types, and the average value is then obtained to measure the clarity and variety of the generated images. FID evaluates image quality, diversity, and is often used to  
Figure 8: Defect images of gas PE pipes generated by each sub-model.  
Figure 9: Comparison of trends in losses.  
compare the performance of GAN and other generation models. It evaluates the quality of an image by measuring the feature difference between the generated and real samples; a lower FID score indicates better image quality and diversity, with the ideal case being 0. SSIM is a metric that compares the brightness, contrast, and structure between two images. Unlike traditional disparity measures, SSIM aligns more closely with human visual perception and is closer to actual perception. SSIM has a value between 0 and 1. Values closer to 1 indicate better generated image quality. Table 4 presents the FID and SSIM evaluation scores for each model. The results in Table 4 indicate that the improved DCGAN model achieves superior performance across both metrics.  
The scores of SA-TTUR, SN-TTUR and MD-TTUR in FID and SSIM have advantages and disadvantages, because FID can reflect the diversity of generated images to a certain extent, that is, SA-TTUR and MD-TTUR have better performance in generating image diversity. SN-TTUR produces better image quality. It also shows that the improved DCGAN model makes full use of the capabilities of each module to enhance the model performance.