esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Conventional data augmentation (DA) techniques, which have been used to improve the performance of predictive models with a lack of balanced training data sets, entail an effort to define the proper repeating operation (e.g., rotation and mirroring) according to the target class distribution. Although DA using generative adversarial network (GAN) has the potential to overcome the disadvantages of conventional DA, there are not enough cases where this technique has been applied to medical images, and in particular, not enough cases where quantitative evaluation was used to determine whether the generated images had enough realism and diversity to be used for DA. In this study, we synthesized 18F-Florbetaben (FBB) images using CGAN. The generated images were evaluated using various measures, and we presented the state of the images and the similarity value of quantitative measurement that can be expected to successfully augment data from generated images for DA. The method includes (1) conditional WGAN-GP to learn the axial image distribution extracted from pre-processed 3D FBB images, (2) pre-trained DenseNet121 and model-agnostic metrics for visual and quantitative measurements of generated image distribution, and (3) a machine learning model for observing improvement in generalization performance by generated dataset. The Visual Turing test showed similarity in the descriptions of typical patterns of amyloid deposition for each of the generated images. However, differences in similarity and classification performance per axial level were observed, which did not agree with the visual evaluation. Experimental results demonstrated that quantitative measurements were able to detect the similarity between two distributions and observe mode collapse better than the Visual Turing test and t-SNE.  
Alzheimer's disease; deep learning; data augmentation; generative adversarial network; positron emission tomography 10 April 2020; 10.2628; doi:10.3390/app10072628
## 4 Results
### Interpretability Analysis of Machine Learning Model Predictions  
Figure 4 presents SHAP summary plots, which show how each feature contributes to the machine learning model's predictions of K-VRQOL improvement in the (a) total, (b) physical, and (c) emotional domains. On each plot, the \(x\)-axis indicates the SHAP value, representing the degree to which a feature influences the model's output. The color gradient shows the original value of each feature (with red indicating higher values and blue indicating lower values).  
In the total domain (a), the features that most strongly influenced the model's predictions were Jitter_diff, SFF_diff, comorbidity, gender, and F0_diff. Specifically, the lower values of Jitter_diff and SFF_diff, which reflect more stable and regular vocal signals, were associated with greater predicted improvements in quality of life. This suggests that improvements in vocal stability and clarity are important factors for better overall outcomes. Additionally, being female and having comorbidities were also linked to greater predicted improvements. For the physical domain (b), the most important predictors were  
Figure 3: Scatter plots of the actual versus predicted values for the K-VRQOL score improvements in the (**a**) total, (**b**) physical, and (**c**) emotional domains. Each point represents an individual case. The red diagonal line indicates the line of perfect prediction (y = x), where predicted values would exactly match actual observed improvements. The proximity of the points to this line reflects the predictive accuracy of the model in each domain.  
Jitter_diff, gender, comorbidity, and voice training status. Among these, Jitter_diff had the greatest impact, highlighting the key role of acoustic stability in improving the physical aspects of voice-related quality of life. In contrast, the emotional domain (c) showed SHAP values that were more scattered and had a narrower range. This indicates that individual features had weaker and less consistent effects on the model's predictions in this domain. While factors such as Jitter_diff, SFF_diff, the number of voice training sessions, surgery status, and alcohol status had some influence, their overall impact was limited and more difficult to interpret. This pattern suggests that predicting emotional improvements in voice-related quality of life is more complex and may depend on factors not included in the current model. Overall, these results indicate that acoustic variables are highly important for predicting outcomes in the total and physical domains of K-VRQOL, while emotional outcomes are likely influenced by more subjective and diverse factors that are not fully captured by the present analysis.
# Numerical and Experimental Investigations of Laser Metal Deposition (LMD) Using STS 316L
## 1 Introduction
Therefore, the present study aims to derive a quantitative relationship between the microstructure and the solidification parameters for each layer through a numerical model that considers the heat accumulation in the LMD multilayer deposition process. As a result, solidification parameter guidelines for the design/control of the process parameters are suggested. For this purpose, the temperature profile at a fixed point on the deposit was measured in real time by using a pyrometer during deposition with STS 316L powder. A numerical model was developed to quantify G and R of the deposit based on the measured temperature profile. Then, the relationship between the calculated G and R values from the numerical model and microstructure was analyzed. Based on this analysis, practical guidelines for the change in microstructure morphology due to G/R during the deposition process were suggested. The rest of this paper is organized as follows. Sections 2 and 3 describe the experimental procedures and numerical models, respectively. Section 4 presents the experimental results. Finally, Section 5 presents the conclusions of this study.
# Review of Photodetectors for Space Lidars
## 4 Discussion  
Photodetectors play a critical role in space lidars where the signal is weak, and the operating environment is severe. It is difficult and expensive to develop space-qualified detectors that meet both the performance and reliability requirements. There have been only a few types of photodetectors used in the past and present space lidars. Ruggedized PMTs have been used in UV and visible wavelengths. Actively quenched Geiger mode silicon APD SPCM from Excelitas Canada have been used in atmospheric lidars at visible wavelengths. The SPADs from the Czech Technical University have been used in a planetary lidar and space laser ranging instrument. The infrared QE-enhanced silicon APDs in analog mode from Excelitas Canada have been used for 1.064 \(\upmu\)m wavelength in almost all space lidars in the past 30 years.  
HgCdTe APD arrays have emerged recently to fill the gap in single photon detectors from near- to mid-infrared. The HgCdTe APD arrays from Leonardo DRS are the most mature at present in terms of performance and packaging, but devices from other manufacturers are rapidly improving. The technologies for small cryo-coolers in space are also mature. IDCAs with HgCdTe APD arrays have been successfully built and tested. The 4 \(\times\) 4 pixel HgCdTe APD arrays developed by Leonardo DRS for NASA have been successfully used in several airborne atmospheric lidars. A 2 \(\times\) 8 pixel LMPC HgCdTe APD array has been integrated into prototype surface elevation lidars for small airless planetary bodies. Larger size arrays with more pixels have been fabricated and tested in the laboratory. Space radiation effects on HgCdTe APDs have been investigated, and an effective annealing technique for radiation damage has been found.  
HgCdTe APD arrays have extended lidar measurement capabilities from near- to mid-infrared for active spectroscopy measurements. They bring the receive sensitivity to near the quantum limit to allow the use of low pulse energy lasers for long-distancemeasurements. They can be made into large, multipixel arrays to map surface topography with wider spatial coverage at finer resolution. They give linear waveform outputs at a sub-photon noise level to measure the vertical profile of vegetation and atmospheric backscattering. HgCdTe APD arrays have been successfully integrated with tactical coolers and integrated into prototype lidar instruments that are close to being space-qualified. The near-quantum limited performance and extended spectral response from the HgCdTe APDs will likely have a major impact on space lidar deployment for future Earth science and planetary science missions.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
I would like to thank all the people who worked to improve the performance and reliabilities of the HgCdTe APD arrays for space lidar applications. I also thank [PERSON] of NASA GSFC for his long-time support and encouragement in the development of space lidar detectors over the years  
The author declares no conflicts of interest.
# Nonlinear Nonsingular Fast Terminal Sliding Mode Control Using Deep Deterministic Policy Gradient
###### Abstract  
Background: As a control strategy of industrial robots, sliding mode control has the advantages of fast response and simple physical implementation, but it still has the problems of chattering and low tracking accuracy caused by chattering. This paper proposes a new sliding mode control strategy for the application of industrial robot control, which effectively solves these problems. Methods: In this paper, a deep deterministic policy gradient-nonlinear nonsingular fast terminal sliding mode control (DDPG-NNFTSMC) strategy is proposed for industrial robot control. In order to improve the tracking control accuracy and anti-interference ability, DDPG is used to approach the uncertainties of the system in real time, which ensures the robustness of the system in various uncertain environments. Lyapunov function is used to prove the stability and finite time convergence of the system. Compared with the nonsingular terminal sliding mode control (NTSMC), the time to reach the equilibrium point is shorter. With the help of MATLAB/Simulink, the tracking accuracy and control effects are compared with traditional terminal sliding mode control (TSMC), NTSMC and radial basis function-sliding mode control (RBF-SMC), the results showed that it had the advantages of nonsingularity, finite time convergence, small tracking error. The motion accuracy and anti-interference ability of the uncertain manipulator system was further improved, and the chattering problem of the system in the motion process is effectively eliminated.  
nonlinear nonsingular fast terminal sliding mode control; deep deterministic policy gradient; dynamic uncertainty; disturbance; manipulator +
Footnote †: journal: Applied Sciences
## 3 Results
### Dissolved Chlorinated Hydrocarbon Analysis Results
#### 3.2.1 Ethyl Lactate Test Cell cVOC Results  
The proportions of PCE, TCE, DCE isomers, VC, and their sum (total cVOC) at HSE-11 are summarized in Figure 5. A comparison of VOC analysis results prior to and following the addition of EL showed that the dissolved-phase concentration of total cVOCs decreased from a baseline of 9.88 micromode (uM) to a low of 6.59 uM. Following the supplemental EVO addition, the concentration increased to 14.87 uM before decreasing to 9.39 uM on 8 January 2008. PCE concentrations steadily dropped from a baseline average of 1410 ug/L to non-detectable (<1.0 ug/L) on 19 November 2007, but increased to 2.7 ug/L on 8 January 2008. TCE increased from a baseline average of 307 ug/L to a high of 790 ug/L on 31 July 2007 before steadily decreasing, and was non-detectable (<1.0 ug/L) after 19 November 2007. Cis-DCE increased from a baseline average of 90 ug/L to a high of 1800 ug/L on 19 November 2007 and decreased to 900 ug/L as of 8 January 2008. Trans DCE increased from being non-detectable (<5.0 ug/L) to a concentration of 2.4 ug/L on 8 January 2008. Vinyl chloride was non-detectable (<1.0 ug/L) until the supplemental addition of EVO, after which a peak concentration of 32 ug/L was reached on 19 November 2007 and decreased to 4.1 ug/L by 8 January 2008. It should be noted that an anticipated result of ERD is increased dissolved-phase concentrations in the initial phases of remediation due to the increased solubility of PCE-daughter products (e.g., VC is approximately 18 times more soluble in water than PCE), the desorption of adsorbed-phase contaminants from the addition of amendments, and the increased flow induced by recirculation.
## 2 Materials and Methods
### Statistical Analysis  
Statistical analysis is a crucial step in evaluating the accuracy and performance of a model.  
The relative error, mean squared error, and coefficient of determination metrics are utilized to assess the model's performance. Relative error serves as a metric to assess the accuracy of a prediction or estimation relative to the actual or expected value. Usually expressed as a percentage, it is calculated by taking the absolute difference between the predicted value and the actual value, dividing it by the actual value, and then multiplying by 100. This measurement provides valuable insights into the level of accuracy in various predictions or estimations, aiding in evaluating the performance of models and methodologies in different fields. The following equation represents the formula of \(RE\) (7):  
\[RE=[(y_{i}-\hat{y})/y_{i}]*100 \tag{7}\]  
The quadratic error, also referred to as the mean squared error (MSE), is a widely employed metric that quantifies the average squared disparity between the predicted values and the actual values. By considering both the magnitude and direction of the differences, the MSE offers a means to quantify the overall deviation or error between the forecasted and real values. The following equation represents the formula of \(MSE\) (8):  
\[MSE=\ \frac{1}{N}\sum_{i=1}^{N}(y_{i}-\hat{y})^{2} \tag{8}\]  
The coefficient of determination, often represented as R-squared (R\({}^{2}\)), is a statistical metric indicating the portion of the variance between the variable being predicted and the variables used for prediction in a regression model. A high R\({}^{2}\) value suggests that the regression model is a good fit for the data and
# A High-Resolution Magnetic Field Imaging System Based on the Unpackaged Hall Element Array
## 2 Materials and Methods
### Design of the Magnetic Field Imaging System  
Figure 4 illustrates the system block diagram of the magnetic field imaging system. This system consists of 256 Hall elements, 32 channel multiplexers, switch modules, programmable amplifiers, a data acquisition card, an X-Y axis motorized rail, etc. The 256 Hall elements grouped into 16 subarrays, each 1 \(\times\) 16, with the switching modules controlling one or two subarrays of supplies simultaneously. The output signals of each subarray are transmitted to 32 channel multiplexers with a 23 ns transition time. The amplifiers are designed as programmable linear amplifiers convert differential Hall signals to single-ended signals, allowing for repetitive gain adjustments to ensure suitability for different magnetic field environments. The microcontroller controls the switching modules activating the power terminals of the subarrays sequentially to reduce signal interference, while the selected and amplified Hall signals are captured by the DAQ system.  
The analog input resolution of the DAQ system is 16-bit, introducing 50 \(\upmu\)V\({}_{\text{RMS}}\) of random noise, allowing for continuous signal acquisition. The Hall voltage values captured by the DAQ system are displayed in real-time as waveforms on the computer. The pulse-type controller with a 32-bit ARM processor is connected to the computer using an RJ45 network cable. The controller has an output frequency of 200 KHz for each axis and an integrated pulse counting function, which enables precise control of the position and speed of drive motors. This allows the X-Y axis motorized rail with a 10 \(\upmu\)m movement accuracy, the route of which is determined by the program written. According to the movement route of the X-Y axis motorized rail, the interactive interface is designed using MATLAB R2023a, which arranges the data into a corresponding 2D matrix and maps it into a high-resolution image.  
Figure 3: The physical drawing of an unpackaged Hall linear array board.
# A Wi-Fi FTM-Based Indoor Positioning Method with LOS/NLOS Identification
## 1 Introduction
Based on the newest Wi-Fi technology, this method is suitable for future Wi-Fi devices. Moreover, the identification model built by this method is more suitable for complex indoor environments. The contributions of this work are summarized as follows:  
1. In order to discard the NLOS error, an identification model is proposed. Differing from other Gaussian-based models, which have established two models for LOS and NLOS, the model presented here only needs to establish one model in order to adapt to the complex indoor environment. 2. To solve the difficulty of setting parameters, we have researched and fit the relation between RSSI and ranging results, achieving adaptive settings. The remainder of this paper is organized as follows: Section 2 introduces the theoretical framework, including the round-trip time (RTT)-based ranging model and the propagation and attenuation of Wi-Fi signals. Section 3 introduces the proposed method, including the construction of the Gaussian model for identification and the iterative least squares algorithm. Section 4 describes the experimental results by the proposed method. Section 5 presents a conclusion and points out areas for future work.
## 3 Results  
### Spatiotemporal Pattern of Carbon Fluxes  
#### 3.1.1 Interannual Variations in Carbon Fluxes  
The carbon fluxes of alpine grassland in the TRSR increased consistently. The GPP was decomposed into four IMF components with average periods of 3, 6, 15, and 34 years (Figure 3, Table 3). The trend had the largest variance contribution, accounting for 77.43%, followed by IMF1 (26.62%). The other three IMF variance contributions were less than 10%, which indicated that the long-term upward trend of GPP in the TRSR was dominant. The EEMD decomposition of NEP showed that although the NEP was dominated by a long-term increasing trend, there were also short-term and interdecadal fluctuations. The variance contributions for the trend, IMF1, and IMF3 were 44.57%, 35.26%, and 31.33%, respectively.  
The increasing rates of carbon fluxes were measured in the TRSR and its sub-park regions (Figure 4, Table 4). Generally, carbon fluxes in the TRSR increased significantly, with increasing rates of GPP and NEP of 1.31 and 0.40 g C/m\({}^{2}\)/yr, respectively. The LRSP had the greatest GPP and NEP of the three sub-park regions, with average values of 128.91 g C/m\({}^{2}\)/yr and 7.05 g C/m\({}^{2}\)/yr, respectively, followed by the HRSP, which had average GPP and NEP values of 89.87 g C/m\({}^{2}\)/yr and 6.57 g C/m\({}^{2}\)/yr, respectively. With average values of 49.52 g C/m\({}^{2}\)/yr and 4.06 g C/m\({}^{2}\)/yr, respectively, the YRSP had the lowest GPP and NEP. The GPP of all three parks exhibited a very strong increasing trend (\(p<0.001\)). The NEP of the alpine ecosystem in the park also increased. The NEP of the  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Carbon** & **Variables** & **IMF1** & **IMF2** & **IMF3** & **IMF4** & **Trend** \\ \hline \multirow{3}{*}{GPP} & Period (yr) & 3 & 6 & 17 & 34 & - \\  & Variance & 24.41 & 9.04 & 5.57 & 0.00 & 77.43 \\  & Contribution (\%) & 3 & 6 & 17 & 34 & - \\ \multirow{2}{*}{NEP} & Variance & \multirow{2}{*}{35.26} & \multirow{2}{*}{11.18} & \multirow{2}{*}{31.33} & \multirow{2}{*}{0.00} & \multirow{2}{*}{44.57} \\  & Contribution (\%) & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Average periods and their variance contributions to carbon fluxes.  
Figure 3: EEMD analysis of the average GPP (**a**) and NEP (**b**) changes during 1985–2018. IMF1–IMF 4 and Trend represent variations on different time scales and long-term trends, respectively.  
HRSP and YRSP increased significantly at rates of 0.40 g C/m\({}^{2}\)/yr and 0.22 g C/m\({}^{2}\)/yr, respectively. Nevertheless, the increasing trend of NEP for LRSP did not achieve a statistically significant level (\(p\) = 0.16).
# Evaluation and Prediction of Higher Education System Based on AHP-TOPSIS and LSTM Neural Network
## 5 Performance Evaluation
### Some Defects of Our Model  
We try our best to ensure the comprehensiveness of the indicators, but some more complex factors are omitted, such as age structure of a country, youth population and influence of various extreme conditions. These factors are likely to have a great impact on a country's higher education system, which is also where our model needs to be improved.  
As a matter of fact, for two years, the worldwide COVID-19 epidemic has brought disruptive changes to higher education systems in many countries. For instance, at the beginning of the epidemic, China was severely impacted. At the same time, the Chinese government formulated strict epidemic prevention policies, such as blocking transportation in many large cities, limiting residents at home in the epidemic areas or even mandating individual isolation. Such measures effectively controlled the spread of the epidemic but obviously also had a great impact on all walks of life in China. In 2020, the growth rate of China's GDP per capita was 2.2%, the lowest in recent years. Some industries have been greatly impacted by the epidemic, while others have had better developmental opportunities. In addition, it is difficult for students to learn efficiently and cultivate and enhance their skills greatly when limited indoors. Further, the epidemic leads to many international travel limitations and thus less opportunities for them to study abroad. In some countries, even the demographics and number of school-age populations are changed due to severe epidemics. These all have implications for the evaluation of higher education.  
Additionally, we ignore interactions between certain indicators in the model. For example, an increase in unemployment may lead to an increase in the gross enrollment ratio, as laid-off workers seek to improve their future employment prospects to continue their studies. On the one hand, politically stable and economically prosperous countries may also bring many beneficial factors to the development of higher education, such as higher investment in education and more output of scientific research, and the attraction to overseas talents may also be enhanced. On the other hand, scientific research also has a positive effect on national development. Some of the research results in papers and patents are applied in practice and provide convenience for production and living. It will effectively help improve the country's economy and, in turn, provide more expenditures in education and scientific research.