esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Accuracy Metrics  
To evaluate the performance of the predictive model, the accuracy was evaluated using the coefficient of variation and the mean bias error (MBE), root mean square error (RMSE), CvRMSE. The mean bias error means the total error of the predicted value, and the coefficient of variation is a method of analyzing the error through the degree of variance. MBE is calculated according to Equation (5).  
\[MBE=\frac{\sum_{t=1}^{n}(P_{i}-M_{i})}{n}*100 \tag{5}\]  
RMSE analyze the precision of various anticipating standards and is calculated according to Equation (6).  
\[RMSE=\sqrt{\frac{\sum_{t=1}^{n}(P_{i}-M_{i})^{2}}{n}} \tag{6}\]  
CvRMSE analyze the precision of various anticipating standards and is calculated according to Equation (7).  
\[CvRMSE=\frac{RMSE}{\overline{P}}*100 \tag{7}\]  
where \(P_{i}\) is the anticipated value, \(M_{i}\) is the real value, \(n\) is the total number of estimates, \(\overline{P}\) is the average value.  
The accuracy of the prediction model was evaluated using the MBE and CvRMSE calculated according to Equations (5)-(7), which are the standards provided by ASHRAE Guideline 14, which are 10% for MBE and less than 30% for CvRMSE [23; 24].  
Figure 3: Diagram of machine-learning model. (**a**) ANN, (**b**) SVM, (**c**) RF, (**d**) KNN.
## 3 Experiments and Results
### Evaluation of Novel Fast Tunable Interference Color Filter
For the present study, a eutectic mixture of NLC compounds known to provide low \(\gamma_{1}\) and low \(U_{\text{th}}\) was used [35]. The design of the NLC with a view toward specific applications clearly bears high potential to improve the required NLC Fabry-Perot filter properties. For example, the incorporation of high birefringence NLC should lead to strongly increased operating ranges. Another potential for improvement comes from the technological side. Further decrease of the cavity thickness would result in faster switching times and increased FSR as displayed by Equations (6)-(8). Such investigations are currently underway.
## 5 Numerical Results and Discussion
### Comparative Analysis of the Ratcheting Response between the Pipe with and without Assembly Parts  
In Figure 6, the hoop ratcheting strain of the pipe without assembly parts is monotonous and becomes larger as the bending moment increases from Position 4 to Position 1 during cyclic loading. In contrast, the hoop ratcheting strain curve of the pipe with assembly parts has a valley value near the sleeve end and a peak value on the right of the sleeve end. By comparing the two cases, it is clear that the assembled sleeve changed the trend of the hoop ratcheting strain curve. The hoop ratcheting strain near the sleeve was much greater in the pipe without assembly parts, meaning that the hoop ratcheting strain near the sleeve was suppressed and the existence of the sleeve had a great effect on the ratcheting response of the pipe. That is to say, assembly contact between the sleeve and the pipe suppressed cumulative plastic deformation in the pressurized pipe near the sleeve during cyclic loading.  
Figure 5: The area marked in Figure 3 with the wireframe: (**a**) Pipe with assembly parts and (**b**) pipe without assembly parts.
# Improvement of Electro-Optical Properties of PSLC Devices by Silver Nanowire Doping
## 4 Conclusions  
Electro-optical properties of PSLC device were successfully improved by Ag nanowires doping into PSLC active layer. When doping concentration is only 0.02 wt.%, the threshold voltage V\({}_{10}\) and V\({}_{90}\) values can be reduced by 35-42% compared to undoped PSLC device. The main reason is Ag nanowire doping can effectively eliminate the ionic impurities in LC and thus reduce the conductivity of PSLC active layer. The turn on response time of PSLC device with 0.02 wt.% Ag nanowire doping is only 12.9 ms, which is 41% lower than that of undoped PSLC device. However, the change of turn off response time is negligible. This is because Ag nanowire doping can increase the polar anchoring energy and lower the ionic impurities in LC mixture, thus enhancing the dielectric response of LC molecules. Since the turn off process is an elastic response, doping has limited influence on this. The Ag nanowire doped PSLC shows similar frequency modulation performance with undoped PSLC device, with no significant improvement. All the present results imply that Ag nanowire doping can improve the performance of electro-optical devices such as adaptive optics devices, e.g. PSLC smart windows, which in general require a fast response and low threshold voltage.  
Conceptualization, X.Y. and W.L.; methodology, X.Y.; data curation, X.Y. and Y.Z.; writing--original draft preparation, X.Y. and D.Y.; writing--review and editing, W.Z. and X.H.; and supervision, D.Y. and G.Z.  
This research was funded by the National Natural Science Foundation of China (Nos. 21711530647 and 51561135014), Science and technology project of Guangdong Province (Nos. 2018A050501012 and 2017B020240002), and Guangdong Innovative Research Team Program (No. 2013C102).  
This work was also supported by the National Center for International Research on Green Optoelectronics, MOE International Laboratory for Optical Information Technologies, Guangdong Provincial Key Laboratory of Optical Information Materials and Technology, the 111 Project and Yunnan expert workstation (No. 2017 IC011).  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
### Wavelet Transformation
#### 1.1.1 Wavelets  
There are various wavelet families, including Morlet, Haar, Daubechies, Symlets, Gaussian, Mexican Hat, Mayer, and Biorthogonal wavelets. The wavelet chosen depends on the signal being processed. For ECG signal processing, Daubechies and Symlet wavelets are often used due to their similarity to the ECG waveform QRS complex [22; 23; 24; 25; 26]. The Daubechies family has 20 types (e.g., db2, db3, and db4), and the Symlet family has 19 types (e.g., sym3, sym4, and sym6). Figure 1 shows the db4 and sym4 wavelets used for ECG analysis.
## 3 Dataset Construction
### Dataset Splitting  
For this work, a total of 325 channels were collected. In this dataset, 280 channels were used to create an eye diagram dataset, referred to as Dataset A, and 215 channels were used to create a dataset for the COMs and predicate the parameters of the equalizers, referred to as Dataset B. Due to the impact of the FOM, the COM approach uses different equalizer parameter optimization methods for stripline and microstrip systems, which results in  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Symbol** & **Setting** & **Unit** \\ \hline Symbol rate & \(f_{b}\) & 20 & GBd \\ \hline Number of signal levels & \(L\) & 4 & \\ \hline Samples per UI & \(M\) & 32 & \\ \hline Target detector error ratio & \(DER_{0}\) & \(10^{-8}\) & \\ \hline Transmitter output voltage, victim & \(A_{v}\) & 0.4 & V \\ \hline CTLE DC gain & \(gDC\) & [\(-\)12:1:0] & dB \\ \hline CTLE DC gain2 & \(gDC\_HP\) & [\(-\)6:1:0] & dB \\ \hline CTLE HP pole & \(f_{HP}\_pZ\) & 0.25 & GHz \\ \hline CTLE zero & \(f_{Z}\) & \(f_{b}\)/2.5 & GHz \\ \hline CTLE pole1 & \(f_{P1}\) & \(f_{b}\)/2.5 & GHz \\ \hline CTLE pole2 & \(f_{P2}\) & \(f_{b}\) & \\ \hline FFE main cursor & \(c\) (0) & 0.62 & \\ \hline FFE pre-cursor & \(c\) (\(-\)1) & [\(-\)0.18:0.02:0] & \\ \hline FFE post-cursor & \(c\) (1) & [\(-\)0.38:0.02:0] & \\ \hline DFE length & \(N_{b}\) & 12 & UI \\ \hline \multirow{2}{*}{DFE magnitude limit} & \(b_{\max}(1)\) & 0.75 & \\ \cline{2-4}  & \(b_{\max}(2\sim N_{b})\) & 0.2 & \\ \hline COM pass threshold & \(th\) & 3 & dB \\ \hline \hline \end{tabular}
\end{table}
Table 2: Configuration of COM parameters.  
a weakened fitting ability of the ML model. Consequently, we standardized Dataset B using strip-line channels. Among the 215 channels in Dataset B, 170 channels are stripline channels in Dataset A, while 45 channels are newly created stripline channels. According to standard methodology, both datasets are partitioned into training, validation, and test sets. Part of the test set in Dataset B is extracted from the validation set. The split settings are detailed in Table 3.
## 6 Toughness-Based Classification of Fine-Grained Soils  
Soil classification using the Casagrande plasticity chart is not entirely accurate, being inefficient in discenning toughness, e.g., some soils with high toughness (typical of _clay_) can be incorrectly classified as _silt_, and vice versa [50]. This observation supports a toughness-based soil classification to augment (or be used instead of) the Casagrande plasticity chart in obtaining more reliable soil classification for some hitherto challenging fine-grained soil types.  
Based on fundamental experimental observations described in the previous section and directly connecting plasticity with toughness, [PERSON] and [PERSON] [11] proposed an updated soil plasticity classification method in which the determining factor is toughness. For measured \(w_{\rm L-FC}\) and \(I_{\rm P}\), the soil is non-plastic for \(I_{\rm P}/w_{\rm L-FC}\)\(<\) 0.2 and low plastic for 0.2 \(\leq\)\(I_{\rm P}/w_{\rm L-FC}\)\(\leq\) 0.33 (i.e., silt, organic soil, sandy soil, etc., with computed \(T_{\rm max}\) of \(\sim\) 0 kJ/m\({}^{3}\)/100 r). Soil with a low or moderate influence of clay minerals (i.e., clayey silt, sandy clay, etc., whose \(T_{\rm max}\)\(<\) 20 kJ/m\({}^{3}\)/100 r) has 0.33 \(<\)\(I_{\rm P}/w_{\rm L-FC}\)\(<\) 0.5, while _clay_ (i.e., plastic soil with \(T_{\rm max}\)\(\geq\) 20 kJ/m\({}^{3}\)/100 r) has \(I_{\rm P}/w_{\rm L-FC}\)\(\geq\) 0.5 [11]. The developed soil classification chart of Moreno-Maroto and Alonso-Azcarate [11] is presented in Figure 7a. [PERSON] et al. [50] investigated the correctness of soil classifications assigned to 31 different inorganic fine-grained soils by applying six of the main plasticity-based soil classification proposals (charts), including the Moreno-Maroto and Alonso-Azcarate [11] and Casagrande plasticity charts, with their research results ranking the Moreno-Maroto and Alonso-Azcarate [11] chart (Figure 7a) as having the strongest predictive capacity among the examined proposals.  
An original contribution of the present paper is the chart presented in Figure 7b, which provides a simple and expedient way of estimating the value of \(T_{\rm max}\) from the measured consistency limits values. Taking into account Equation (3), Figure 7b was produced by calculating the \(T_{\rm max}\) isolines (of 0 to 80 kJ/m\({}^{3}\)/100 r) and then plotted them in the typical plasticity chart (of \(I_{\rm P}\) vs. \(w_{\rm L-FC}\)).
# Analysis of Ground Subsidence Vulnerability in Urban Areas Using Spatial Regression Analysis
## 2 Flow and Data of the Study  
### Flow of the Study  
In this study, an urban area in South Korea was selected as the target area. To develop a prediction model of ground subsidence in the target area, the target area was divided into cells with a size of 100 m \(\times\) 100 m using GIS to extract the ground subsidence history information and the attribute information of underground utilities (water pipelines, sewer pipelines, communication pipe, power cables, heating pipelines, and gas pipes). After removing the missing data and outliers from the extracted data, a correlation analysis between the attribute information of underground utilities and the history information of ground subsidence was performed. Missing values in the data are values that were left blank because the survey was not fully completed. Outliers are error values, such as negative (-) or 0 values for the buried depth and age of the pipes. Then, the study singled out influencing factors that exhibited a significant correlation with ground subsidence through the correlation analysis and performed OLS analysis using the selected factors. Furthermore, after verifying multicollinearity using the variance inflation factor (VIF) and spatial autocorrelation through the OLS analysis results, SEM and SLM analyses were performed to verify the results. Finally, the study selected an appropriate prediction model of ground subsidence vulnerability by comparing the OLS, SEM, and SLM results, and verified the attribute information of underground utilities that were significantly correlated with ground subsidence through the selected model. A map of ground subsidence vulnerability in the target area was also created. Figure 1 shows the flow chart of this study.
## 7 Summary and Perspective
### Generalization Issues  
Most published data report an acceptable accuracy rate for classifying defects and regression analysis using different sensors and different ML algorithms. However, these results were obtained from different geometries and different materials using different machines. Therefore, generalized ML algorithms need to be developed to demonstrate the feasibility of knowledge transfer across different platforms using different machines. Minimal research has discussed the generalization of the developed ML models to monitor L-PBF by changing the material type. For example, [PERSON] et al. [74] used a VAE approach to generalize the developed machine learning model to classify defects in two different materials, H13 tool steels and 316L stainless steels. [PERSON] et al. [76] attempted to test the same ML model on different part geometries; the model accuracy was not high when generalized. It was found that training a separate model for complicated geometry was necessary. An approach to generalize an ML model was suggested by [PERSON] et al. [163]. Previously published hardness data was used to train an ML model to predict the part's porosity. More generalized ML models can help reduce the training time and enhance ML algorithms' transferability of ML algorithms, not only across different materials but across different L-PBF platforms. Generalization is usually assessed by testing the ML model with new samples that were not used in training.
# An Overview of FIR Filter Design in Future Multicarrier Communication Systems
## 3 FIR Prototype Filter Design
### Frequency Sampling Methods
#### 3.1.5 Salcedo-Sanz's Method  
In [124], the authors propose the variable limits evolutionary programming (VLEP) based on the evolutionary programming (EP) to design the prototype filter, including variable limits classical evolutionary programming (VLCEP) and variable limits fast evolutionary programming (VLFEP). The final optimum solutions are sensitive to the initial values of \(f(k)\) in [120], while the algorithm in [124] can solve this problem.  
Three design methods in [120; 123; 124] are analyzed in terms of amplitude distortion peak, aliasing error and minimum stopband attenuation, as presented in Table 1. The number of subcarriers is 128 and the length of the prototype filter is 2049. We select the VLFEP algorithm in [124]. Generally, all of the three methods can obtain nearly perfect reconstruction (NPR) filter banks. By comparison, the method in [123] achieves the best filter design performance. To summarize, the essence, pros and cons of aforementioned frequency sampling methods are presented in Table 2.
## 4 Comparison of Results  
We first compare the experimental and simulation results corresponding to the transmitted laser light due to the onset of RSIT. These are plotted as a function of target thickness in Figure 5a. The two sets of results are in good agreement regarding the onset of transparency in the thinnest targets, although the measured percentage of laser light transmitted is higher than that obtained in the simulations. The temporal evolution of the peak electron density normalized to the critical density, the electron Lorentz factor and the transmitted fraction of the laser light are plotted in Figure 5b,c, for \(l=10\) nm and \(l=40\) nm, respectively. In the former case, the peak electron density normalized to the critical density drops below the electron Lorentz factor at \(\sim\)56 fs, after which a small fraction of the laser light is transmitted through the foil. In the latter case this threshold condition is not satisfied over the duration of the laser pulse and thus RSIT does not occur.  
We next compare the maximum PCDS velocity obtained experimentally with the PIC simulation results and model calculations, as plotted in Figure 6, as a function of target thickness. These results illustrate the following three distinct regimes of laser-foil interaction: (1) for \(l>150\) nm, a HB-dominant mode of interaction in which the PCDS velocity is essentially independent of target thickness; (2) for \(40\) nm \(<l<150\) nm, a LS-dominant mode in which the velocity increases exponentially with decreasing \(l\), with a scaling very similar to that predicted by the LS model (taking account of the Gaussian temporal profile of the laser pulse); and (3) for \(l<40\) nm, a RSIT-onset mode in which the PCDS velocity drops below that expected from the LS model scaling, due to the reduction in radiation pressure when light is transmitted through the target.  
Figure 5: (**a**) Measured percentage of laser energy transmitted through the target foil as a function of target thickness (open triangles), plotted together with values from the PIC simulations at corresponding thicknesses (filled squares); (**b**) PIC simulation results for the peak electron density normalized to the critical density along the \(Z=0\) axis (blue), electron Lorentz factor (red) and transmitted laser light (green), as a function of time, for \(l=10\) nm. The incident laser pulse temporal profile is shown by the dashed line; (**c**) Same for \(l=40\) nm.  
We note that although the experiment and simulation results are in very good agreement over most of the target thickness range explored, the measured velocity is lower than in the simulation results for \(l\) = 10 nm and \(l\) = 20 nm. This difference is explained by the higher measured laser energy transmission compared to the simulations, as shown in Figure 5a. The PCDS velocity is also overestimated in the simulations, in particular for the LS regime, due to the fact that the perfect mirror approximation (reflection coefficient \(R\) = 1) is assumed, whereas in reality \(R\
eq 1\).
# Enhancing 3D Lung Infection Segmentation with 2D U-Shaped Deep Learning Variants
###### Abstract
Our approach not only addresses the critical challenge of accurately representing the complex spatial relationships within these scans but also presents a unique amalgamation of cutting-edge methodologies. By seamlessly integrating the power of U-shaped deep learning architectures, encompassing UNet, LinkNet, Attention UNet, UNET 3+, and TransUNet, we transcend the limitations of 2D images and propel the field forward. Furthermore, we bridge the computational efficiency of 2D models with the precision of 3D projection through innovative post-processing strategies, offering a compelling balance between analytical speed and comprehensive visualization. Our work not only enriches the arsenal of medical imaging tools but also facilitates rapid and accurate lung diseases' diagnosis, making significant strides toward personalized treatment and improved patient care. The primary contributions of the research are as follows:  
1. Our study will contribute to the advancement of medical image analysis by exploring the potential of 2D architectures in reconstructing 3D lung projections. By evaluating the performance of UNet, LinkNet, Attention UNet, UNET 3+, and TransUNet in this specific context, we aim to enhance the understanding of how 2D architectures can effectively capture lung structures and infection areas, enabling accurate diagnosis and treatment planning for lung diseases, specifically COVID-19. 2. To contribute to the academic literature, our study carries practical implications for healthcare professionals and researchers working on lung disease diagnosis and treatment. By identifying the most suitable 2D architecture for reconstructing 3D lung projections, we can provide valuable guidance on selecting appropriate techniques and tools for accurate assessment of disease severity and monitoring disease progression. This can lead to the development of more efficient and reliable clinical protocols for lung diseases, particularly COVID-19 cases. 3. In addition, this study provides a comprehensive comparison of UNet, LinkNet, Attention UNet, UNET 3+, and TransUNet for visualizing the 3D lung construction of CT-scans, specifically focusing on the effectiveness of 2D architectures in reconstructing 3D projections. This aspect has not been extensively studied in the literature, and our findings will shed light on the advantages and limitations of using 2D architectures for this task. The remainder of this paper is structured as follows: Section 2 delves into a comprehensive review of various research papers pertaining to the utilization of UNet, LinkNet, Attention UNet, UNET 3+, and TransUNet in their respective studies. Section 3 provides an explanation of the dataset, data preprocessing, model setting, metrics evaluation, proposed architecture, and postprocessing steps utilized in this study. In Section 4, the results of every architecture are explained and analyzed.
# A Comparative Study of On-Body Radio-Frequency Links in the 420 MHz-2.4 GHz Range
## 2 Materials and Methods
### RF Technologies  
The studied RF technologies are described in the following subsections.