esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Proposed Methodology
#### 3.2.2 Segmentation  
Image segmentation is a technique used in various fields, such as medical imaging, to separate an image into distinct regions or objects [1]. This project investigates the use of the U-Net architecture, renowned for its effectiveness in semantic segmentation tasks, with various backbones for image segmentation. Utilizing the Segmentation Models library [23] with user-friendly interfaces, we construct diverse segmentation models supporting various architectures and backbones.  
The proposed image segmentation model for this study employs a U-Net architecture with ResNet34 as its backbone as shown in Figure 2, pretrained on ImageNet. We chose ResNet34 for precise segmentation of structures like the optic disc and optic cup. It consistently outperformed other backbones. We utilized SoftMax activation and compiled the model with an Adam optimizer with a learning rate of 0.0001 to optimize its performance. The used loss function is a combination of dice loss and categorical focal loss, and the models are evaluated based on the mIoU metric.  
This study assessed the performance of the U-Net model with five pretrained backbones, ResNet34, ResNet50, VGG19, DenseNet121, and EfficientNetB0, aiming to identify the most effective architecture. During this phase, we conducted experiments to analyze the impact of various batch sizes on segmentation accuracy, as measured by mIoU. Additionally, we evaluated the performance of the U-Net model by considering various numbers of epochs. The method used here yields a segmentation mask that shows the retinal image's OC and OD boundaries.  
* Extraction of ROI  
This method employs the resulting segmentation mask from the previous step to isolate ROI in the retinal image and retain only the relevant pixels. The bitwise AND operation is used to keep the pixels in the original image that match the segmented regions (OC and OD) marked by non-zero pixels in the mask [24]. In this study, we applied the bitwise AND operation between the output mask and the original image to extract the ROI.  
Figure 2: The proposed image segmentation model for this study.  
Postprocessing of Segmented Images  
This stage involves cropping and resizing segmented images using bounding box coordinates of the largest contour [24], to enhance model accuracy. Prior to the classification stage, the image dimensions are modified to match the EfficientNetB0 model input size of 224 \(\times\) 224. Furthermore, under-sampling and image augmentation techniques are utilized to address class imbalance and enhance the model's generalization ability.
# Dynamic Energy Management Strategy of a Solar-and-Energy Storage-Integrated Smart Charging Station
## 2 Description
### A Real-Wold Scenario  
Illustrated by the case of a major wholesale store and its parking facility in a northern region of Taiwan, shown in Figure 1, this scenario exemplifies an abundance of unused rooftop spaces within the wholesale store premises. The outdoor parking lot presents the opportunity to install rain-shielding structures adorned with solar panels. Upon proportional assessment, both locations reveal the availability of at least 1000 square meters of space, sufficient to install approximately 450 kW of solar capacity.
## 2 Materials and Methods
### Method
#### 2.2.3. Verifying the Model
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Source** & **Sum of Squares** & **df** & **Mean Square** & **F Value** & \(p\)**-Value (Prob > F)** & \\ \hline Model & 1.98 & 3 & 0.66 & 25.77 & \(<\)0.0001 & Significant \\ \(V\) & 0.93 & 1 & 0.93 & 36.18 & \(<\)0.0001 & \\ \(W_{s}\) & 0.25 & 1 & 0.25 & 9.69 & 0.0032 & \\ \(O_{W}\) & 0.80 & 1 & 0.80 & 31.43 & \(<\)0.0001 & \\ Residual & 1.18 & 46 & 0.026 & & & \\ Lack of Fit & 0.93 & 39 & 0.024 & 0.68 & 0.7966 & Not significant \\ Pure Error & 0.25 & 7 & 0.035 & & & \\ Cor Total & 3.16 & 49 & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: ANOVA test results for the sidewall penetration model. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Source** & **Sum of Squares** & **df** & **Mean Square** & **F Value** & \(p\)**-Value (Prob > F)** & \\ \hline Model & 1.05 & 7 & 0.15 & 8.59 & \(<\)0.0001 & Significant \\ \(W_{f}\) & 0.22 & 1 & 0.22 & 12.59 & 0.0010 & \\ \(V\) & 0.074 & 1 & 0.074 & 4.25 & 0.0455 & \\ \(W_{s}\) & 0.044 & 1 & 0.044 & 2.51 & 0.1203 & \\ \(O_{W}\) & 0.011 & 1 & 0.011 & 0.65 & 0.4233 & \\ \(\alpha\) & 0.37 & 1 & 0.37 & 21.30 & \(<\)0.0001 & \\ \(O_{W}\times\alpha\) & 0.19 & 1 & 0.19 & 11.00 & 0.0019 & \\ \(W_{s}^{2}\) & 0.14 & 1 & 0.14 & 7.85 & 0.0076 & \\ Residual & 0.73 & 42 & 0.017 & & & \\ Lack of Fit & 0.50 & 35 & 0.014 & 0.42 & 0.9573 & Not significant \\ Pure Error & 0.24 & 7 & 0.034 & & & \\ Cor Total & 1.78 & 49 & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: ANOVA test results for the welding penetration model. Table 7 shows the ANOVA test results for the model of the flatness degree of the weld bead \(C\). The F value and \(p\)-value indicate that the model is significant, and the Lack of Fit item reveals that the flatness degree of the weld pass \(C\) is adequate. The \(p\)-value of the model shows that the terms \(\alpha\) and \(\alpha^{2}\) have the most significant effect on the welding height. Meanwhile, the terms \(V\), \(W_{f}^{2}\), \(V^{2}\), and \(W_{s}^{2}\) have a significant effect on the flatness degree of the weld bead. The statistical model for the flatness degree of the weld-bead surface \(C\) in terms of the affecting factors can be expressed as follows:  
\[C=69.42264-3.73275W_{f}-3.67518V-0.044209W_{s}-0.040437\alpha+0.18979W_{f}^{2} +0.078645V^{2} \tag{5}\] \[+0.0000577+0.000266\alpha^{2}\]
## 2 Materials and Methods
### Near-Infrared Spectroscopy Characteristic Extraction Method  
The CARS algorithm is suitable for dealing with complex and nonlinear optimization problems. It is a feature variable extraction method combining Monte Carlo and partial least squares regression (PLSR). The dependent variable in this study was a categorical attribute, and therefore the discriminant partial least squares regression (DPLSR) algorithm was used to improve the PLSR algorithm in CARS. The regression coefficient (\(b_{i}\)) of each wavelength was obtained using the DPLSR model. Then, the proportion of wavelength points to be retained for the next modeling round was calculated according to the exponential decay function (\(R_{t}\)), and the wavelength points with larger absolute value weights (\(\omega_{i}\)) of regression coefficients were retained as a new subset to re-establish the DPLSR model. Monte Carlo cross-validation was used to select the DPLSR model with the minimum classification error, and the corresponding wavelength point of the NIR spectrum was the characteristic spectrum.  
\[R_{t}=\mu e^{-k_{t}} \tag{5}\]  
\[\mu=(\frac{n}{2})^{1/(N-1)} \tag{6}\]  
\[k=\frac{\ln(n/2)}{N-1} \tag{7}\]  
\[\omega_{i}=|b_{i}|/\sum_{i=1}^{m}|b_{i}| \tag{8}\]  
where \(n\) is the number of wavelength points in the original NIR spectrum; \(m\) is the number of retained wavelength points in each iteration; and \(N\) is the number of Monte Carlo samples.
# Multiple Thermal Parameter Inversion for Concrete Dams Using an Integrated Surrogate Model
## 3 Case Study
### Analysis and Discussion of Inversion Results
#### 3.4.2 Comparison of Inversion Results by Integrated and Individual Surrogate Models  
To further verify the effectiveness of the proposed method, the thermal parameters were obtained using the individual surrogate models, and the inversion accuracy was quantified using the validation method in Section 3.4.1. The comparison of the inversion results of different models is shown in Figure 11.  
As can be seen in Figure 11, the inversion accuracy of the ISM is higher than that of the individual surrogate models for all the pouring blocks except for 15#-52 and 15#-53. The average MAE for C35 and C40 concrete were calculated as shown in Table 5.  
It can be seen that the inversion accuracy of ISM is better than all the individual models, with the closest result given by the Kriging model, and the worst by the RBF model. Compared with the Kriging, SVR, and RBF model, the MAE based on ISM reduces by  
Figure 11: Comparison of MAE based on different models. (**a,b**) describe the comparison results for C35 and C40 concrete, respectively.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Models** & **Kriging** & **SVR** & **RBF** & **ISM** \\ \hline C35 & 0.35 & 0.34 & 0.41 & 0.32 \\ C40 & 0.33 & 0.40 & 0.55 & 0.31 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of average MAE based on different models (\(\lx@math@degree\)C).  
8.45%, 3.93% and 20.85%, respectively for C35 concrete, and by 6.53%, 23.82% and 44.43%, respectively for C40.  
Compared to the other surrogate models, ISM increases the time taken to calculate the weights as well. For a dataset with a sample size of 60, the time cost to determine the weights is about 50 min. Furthermore, it takes one hour to execute the finite element model in this paper on an ordinary computer, while a trained ISM only needs a few seconds. Based on the parameter setting of the Jaya algorithm in this paper, 8000 times are required to execute the finite element model by directly invoking it for inversion analysis, while the proposed method only needs only 60 + 10 times, resulting in an approximately 111.7-fold increase in computational efficiency. In summary, the method proposed in this paper still maintains a massive advantage in computational efficiency.
## 2 Data and Methods
### Data
#### 2.1.3 OMI/Aura Tropospheric and Stratospheric NO\({}_{2}\)  
Another core product of this research is the Ozone Monitoring Instrument onboard Aura satellite (OMI/Aura) QA4 ECV tropospheric (tropospheric_no2_vertical_column) and stratospheric (stratospheric_no2_vertical_column) NO\({}_{2}\) vertical column density (VCD) version 1.1 (v1.1) dataset [59]. OMI is a nadir-looking, pushbroom ultraviolet/visible (UV/VIS) solar backscatter grating spectrometer that has been flying onboard Aura satellite at a sun-synchronous polar orbit since July 2004, measuring the Earth radiance spectrum from 270 to 500 nm at a spectral resolution of around 0.5 nm [60]. Aura is part of the A-trainsatellite constellation with an equator overpass time 8 min (15 min before May 2008) after that of Aqua (13:38 LT). With its broad swath (2600 km) OMI provides almost daily global coverage at a spatial resolution of 13 km \(\times\) 24 km (at nadir).  
The QA4 ECV algorithm, which is described in detail in [PERSON] et al. [59], comprises the following three basic steps: 1) the total NO\({}_{2}\) slant column density (SCD) is calculated using the differential optical absorption spectroscopy (DOAS) method, 2) the stratospheric contribution to the SCD is calculated from the TM5-MP chemistry transport model (CTM) with data assimilation and it is subtracted from the total SCD to obtain the tropospheric SCD, and 3) the tropospheric/ stratospheric SCDs are converted to tropospheric/stratospheric VCDs by dividing by the corresponding air mass factors (AMFs).  
The QA4 ECV NO\({}_{2}\) dataset exhibits significant improvements compared to its predecessor (DOMINO version 2.0) [59; 61; 62] and similar agreement with multi-axis DOAS (MAXDOAS) and direct-sun ground measurements [63]. QA4 ECV tropospheric NO\({}_{2}\) VCDs exhibit a negative bias of the order of 1\(-\)4 \(\times\) 10\({}^{15}\) molecules/cm\({}^{2}\) and 1.8\(-\)25 \(\times\) 10\({}^{15}\) molecules/cm\({}^{2}\) (27-38%) compared to ground-based observations according to [PERSON] et al. [64] and [PERSON] et al. [63], respectively. [PERSON] et al. [64] also showed that QA4 ECV stratospheric NO\({}_{2}\) VCDs exhibit a low bias of \(\sim\)0.2 \(\times\) 10\({}^{15}\) molecules/cm\({}^{2}\) (5-10%) compared to zenith-scattered light DOAS (ZSL-DOAS) ground-based measurements.  
The data used here are gridded at a 0.125\({}^{\circ}\)\(\times\) 0.125\({}^{\circ}\) spatial resolution on a monthly basis covering the period 2005-2019. While averaging, each single-pixel observation is weighted by its fractional area within the grid cell. Pixels with cloud radiance fraction greater than 50% (cloud fraction greater than about 20%) and solar zenith angles higher than 80\({}^{\circ}\) are filtered out. The monthly gridded tropospheric NO\({}_{2}\) data are available on the Tropospheric Emission Monitoring Internet Service (TEMIS; [[http://www.temis.nl](http://www.temis.nl)]([http://www.temis.nl](http://www.temis.nl)), accessed on 30 June 2021).
## 3 Results and Discussion
### Quantitative Analysis and Normalized bOCT Contrast
#### 3.2.4 Comparison of Gender Groups  
Figure 11 shows the average speckle contrast given as a function of the three conditions for different genders, with blue and red representing the male and female subjects, respectively. The average biospeckle contrast was calculated from the shallow ROI1 for both age groups of male and female participants separately, irrespective of age. * denotes the statistical significance of the difference between the male and female subjects. There was a statistically significant difference for the conditions of soon after heating and after 5 min of rest after heating. However, there was no statistically significant difference prior to heating between genders, which could be reasonable. Our results suggest that depending on the gender, the changes within the skin structure associated with the heating of the skin may be different and the difference is significant for both soon after heating and after 5 min of rest.  
A comparison of the average biospeckle contrast for males and females within the age groups is given as Supplementary Information S3.  
Figure 10: Average speckle contrast for all 10 participants in their 20s (**left**) and 10 participants in their 30s (**right**) for ROI1 (shallow region) under different conditions: before heating, soon after heating, and after 5 minutes of rest following heating. * indicates the significance with respect to before heating. There is a difference in the age groups depending on the condition. Based on Figure 9, only the shallow region was investigated.
## 4 Parameter Identification
### Electrical Load Damping of Linear Alternator  
The electromagnetic force of the linear alternator is directly applied to the PP motion as a damping force, and is analytically calculated by using the theoretical electromagnetic damping equation in Reference [24], as follows:  
\[c_{p}=\frac{K_{elec}{}^{2}(R_{elec,alt}+R_{elec,load})}{(R_{elec,alt}+R_{elec,load} )^{2}+(\omega_{alt}L_{elec,alt}-1/\omega_{alt}C_{elec,load})^{2}} \tag{10}\]  
where \(K_{elec}\) is the electromechanical coupling constant. \(R_{elec,alt}\) and \(R_{elec,load}\) represent the internal resistance of the linear alternator and load resistance, respectively. \(L_{elec,alt}\) and \(C_{elec,load}\) represent the inductance of the linear alternator and the load capacitance for compensating electric power factor, respectively. \(R_{elec,load}\) is variable to control the load damping of the engine and corresponds to the electric power output of the engine. Table 3 lists the design parameter values of the linear alternator.  
Figure 4 shows the predicted load damping coefficients and damping ratios for various load resistances from 20 \(\Omega\) to 240 \(\Omega\). The prediction reveals that lower electric load resistances yield increases in the electric current of the alternator and thus generate higher mechanical damping loads to the engine.  
The applied load resistance of 80 \(\Omega\) in the experiment cases corresponds to the damping coefficient of 160 N\(\cdot\)s/m and the damping ratio is 0.17 from the definition of \(\zeta_{load}=c_{p}/2(m_{p}k_{p})^{1/2}\)[25]. The damping coefficient is used for the direct damping coefficient of \(C_{pp}\) in Equation (7).  
Figure 4: Prediction of the load damping coefficient and average damping ratio for cases 1–5 at increasing load resistances.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Electromechanical coupling coefficient, \(K_{elec}\) & 119.0 N/A \\ Internal resistance of the linear alternator, \(R_{elec,alt}\) & 1.62 \(\Omega\) \\ Load capacitance, \(C_{elec,load}\) & 90 \(\mu\)F \\ Inductance of the linear alternator, \(L_{elec,alt}\) & 0.273 H \\ Linear alternator design frequency, \(\omega_{alt}\) & 30 Hz \\ \hline \hline \end{tabular}
\end{table}
Table 3: Design parameters of the linear alternator.
# Machine Learning Algorithm for Delay Prediction in IoT and Tactile Internet
## 1 Introduction
* Finally, the results of the simulation-based tests demonstrate that:
* The model trained with the Trainbr training algorithm outperforms its competitors and is the best predictive model in both 1-step prediction and 15-step prediction. * Moreover, the model trained with the algorithm Traingf outperforms its competitors for the 10-step prediction case. * On the other hand, the model trained with the algorithm Trainrp has poor prediction accuracy, compared to its competitors. The outline of the article is structured accordingly: the related literature review is presented in Section 2; machine learning for time series prediction is presented in Section 3; the problem formulation and system model are presented in Section 4; the performance evaluation is presented in Section 5; the simulation results are shown in Section 6; and the conclusions and directions for future work are presented in Section 7.
# Effect of Zr Modification on NH\({}_{3}\)-SCR Reaction Performance of Cu-Ce/SAPO-34 Catalysts
## 2 Materials and Methods
### Experimental Conditions and Equipment  
#### 2.2.1 Catalytic Activity Test  
The denitration performance of the catalyst samples was tested using a VDRT-200 SCR catalyst evaluation device (Quzhou Ward Instrument Co., Ltd., Quzhou, China). The test required 200 mg of the catalyst. The simulated flue gas consisted of 0.1% NH\({}_{3}\), 0.1% NO, and 1% O\({}_{2}\). N\({}_{2}\) was used as a balance gas. The evaluation device was equipped with a mass flow meter, which could accurately control the gas flow. The simulated flue gas flow was 200 mL/min, the space velocity was 22,000 h\({}^{-1}\), and the temperature range was 100-500 \({}^{\circ}\)C. The temperature was increased by 50 \({}^{\circ}\)C within 5 min, kept constant for 30 min, then increased by 50 \({}^{\circ}\)C within 5 min, and this cycle was repeated. The volume concentration of NO\({}_{x}\) after the reaction was recorded. The NO\({}_{x}\) concentration in the reaction tail gas was detected using a German Testo 350 Pro flue gas analyzer. The following formula was used to calculate the denitration efficiency \(\eta\):  
\[\eta=(\text{NO}_{x,in}-\text{NO}_{x,out})/\text{NO}_{x,in}\times 100\%\]  
where NO\({}_{x,in}\) represents the volume concentration of NO\({}_{x}\) before the reaction and NO\({}_{x,out}\) is the volume concentration of NO\({}_{x}\) after the reaction.
## 5 Implementation and Validation  
A YANG to OPC UA model design generator is developed as a plugin on top of existing open-source software called pyang [22]. The logic of the OPC UA pyang plugin for the generation of the OPC UA model design file is based on the YANG to OPC UA mapping explained in previous sections. The generated XML output conforms to the schema defined in the UA Model Design.XSD and then passed to the existing OPC foundation model compiler to generate the OPC UA NodeSet2 XML files and different-language SDK files, as depicted in Figure 7. The authors published the opc-ua pyang plugin and its software implementation, the sample YANG and OPC UA files, and the documentation on how to use the plugin to generate OPC UA NodeSet2 files in their GitHub repository [23].  
Pyang is a YANG validator, transformer, and code generator command line tool written in Python, XSLT, and shell scripts. It can be used to validate YANG modules for correctness and to transform YANG modules into other formats using custom plugins. The plugin can be invoked using the \"-f\" option in the command line by specifying the plugin name. The OPC UA plugin developed as part of this article is named \"opc-ua\". The command line syntax used to execute the plugin is as follows:  
$ pyang -f opc-ua -input yang file> -o output <opc ua model output file>  
The plugin name \"opc-ua\" must be specified using the option \"-f\" followed by the input YANG file, and then the \"-o\" option must be used to specify the OPC UA model design XML file name. The OPC foundation model compiler can generate the OPC UA NodeSet2 XML files and other language source files from the generated OPC UA model design XML file, and the command syntax for the same is shown below:  
Figure 7: YANG schema to OPC UA model generation.  
$ Opc.Ua.ModelCompiler -d2 <opc ua model design xml file> -cg <types to identifier mapping> -o2 <output directory for generated files>  
Option \"-d2\" specifies the path to the model design file generated using the pyang opc-ua plugin. Option \"-cg\" is used to specify the path to the CSV file, which contains the unique identifiers for the types defined in the UA information model. Option \"-o2\" specifies the output directory for the generated files.
# A Dual-Band Modified Franklin mm-Wave Antenna for 5G Wireless Applications
## 3 Parametric Analysis
### Effect of the Width of the Second Type of Ground Cut \(W_{\rm C}\)  
The effect of the width of the second type of ground cut on the antenna is analyzed. The width is varied to observe its impact. The variation in the width causes a significant variation in both the return loss and frequency at the first resonant frequency. In contrast, at the second frequency, the return loss and frequency changes are small. The variation of the width of the second type of ground cut \(W_{\rm C}\) is shown in Figure 10.
# Impacts of Diversity Technique Application in Order to Reduce the Fading in IEEE 802.15.4 Networks
## 3 BER in the Case of an SC Combiner
### BER in the Case of SC Combiners for Nakagami-\(m\) Fading Channels
\tag{24}\]  
After substituting the integral solutions (21), (22), (23) and (24) in (20), the final expression for the BER of the transceiver of the IEEE 802.15.4 network operating in an environment with AWGN and interference, impacted by Rayleigh/Nakagami-\(m\) fading, with an SC combiner with two branches, is obtained as:  
\[BER_{Zigber,SC,Naka}=\frac{2m^{m}(m-1)! }{\Gamma^{2}(m)\gamma_{0}^{m }}\left[\frac{1}{2\sqrt{\pi}T^{m}}G_{2,2}^{1,2}\left(\left.\frac{m}{T\gamma_{0} }\right| \begin{array}{cc}1-m,&\frac{1}{2}-m\\ 0,&-m\end{array} \right)\right. \tag{25}\] \[\left.-\frac{1}{8\pi T^{m}}\sum\limits_{n=0}^{\infty}\frac{1}{n!} \left(-\frac{m}{T\gamma_{0}}\right)^{n}G_{3,3}^{2,2}\left(1\left| \begin{array} []{cc}1-n-m,&\frac{1}{2}-n-m,1\\ 0,&\frac{1}{2},\ -n-m\end{array} \right.\right)\] \[\left.-\frac{1}{2\sqrt{\pi T^{m}}}\sum\limits_{k=0}^{m-1}\frac{1} {k! }\left(\frac{m}{T\gamma_{0}}\right)^{k}G_{2,2}^{1,2}\left(\left.\frac{2m}{ \gamma_{0}}\right| \begin{array}{cc}1-k-m,&\frac{1}{2}-k-m\\ 0,&-k-m\end{array} \right)\right.\] \[\left.-\frac{1}{8\pi T^{m}}\sum\limits_{n=0}^{\infty}\sum\limits_ {k=0}^{m-1}\frac{(-2)^{n}}{n!k! }\left(\frac{m}{T\gamma_{0}}\right)^{n+k}G_{3,3}^ {2,2}\left(1\left| \begin{array}{cc}1-n-k-m,\frac{1}{2}-n-k-m,1\\ 0,&\frac{1}{2},\ -n-k-m\end{array} \right.\right)\right].\]