esa-sceva/satcom-chunk-collection huggingface dataset:
# PMT Fluorescence Signal Denoising Processing Based on Wavelet Transform and BP Neural Network
## 4 Temperature Drift of PMT  
### Data Collection  
Firstly, verify the feasibility of the detection device. Use the square wave signal generated by flipping the pin level of the STM32 main control chip (STMicroelectronics) to drive the excitation light source. Set the working frequency of the pulse xenon lamp to 10 Hz, the number of data acquisition points to 600, and the gas flow rate to be measured to 6 L/min. Compared with ordinary electronic devices such as resistors and capacitors, photomultiplier tubes are more susceptible to the influence of environmental temperature, mainly manifested as nonlinear changes in the gain rate with temperature. Record the peak value of the fluorescence waveform as PMT Output Value, the static reference value when not irradiated as PMT Base Value, and the difference between the two as valid data, denoted as AD Value [31], as shown in Figure 9. This can, to some extent, reduce the impact of temperature changes on the detection results.  
Figure 8: Fourier spectrum of signal after wavelet denoising. The horizontal axis unit is MHz.  
The temperature sensor is selected as MLX90614 (Melexis, Shenzhen, China) with a resolution of 0.02 \({}^{\circ}\)C. Use it to monitor the temperature of PMT. Inject pure nitrogen gas into the reaction chamber, and after a period of experimentation, obtain the temperature data as shown in Figure 10.  
It can be observed that as the ambient temperature gradually increased during the detection process, the gain rate of PMT changed [32; 33; 34], manifested as a decrease in both the optical PMT Output Value and PMT Base Value, and their change rates were different. In order to mitigate the impact of temperature changes, different weights were assigned to the PMT Output Value and PMT Base Value by calculating their respective proportions of change. Finally, the following weighted comparison Figure 11 was obtained. Due to the fact that the gain rate of PMT increased as the temperature decreased, the AD Value should also increase as the temperature decreases, which is consistent with the test results. Before processing, the standard deviation of AD Value was 5843.183, and the average absolute error was 5030.428.  
Figure 10: Temperature drift of PMT.  
Figure 9: Data acquisition process.
# A New Parameter-Free Slope Unit Division Method That Integrates Terrain Factors
## 4 Results, Analysis, and Discussion
### Results and Analysis
#### 4.1.1 Qualitative Analysis  
As shown in Figure 6, the unit boundaries obtained via the hydrological method do not completely match the actual ridge and valley lines, with the distinction between horizontal planes and inclined planes often unclear. Moreover, different terrain types are merged into the same unit, and the hydrological method relies on terrain inversion to define subwatershed boundaries. These terrain depressions generated on the basis of the inversion height can be corrected by filling depressions but can lead to the division of artificially large flat areas, leading to the appearance of unrealistic parallel flow sections or pseudochannels in the river network (Figure 6b,d). The watershed boundaries defined on the basis of such river networks also tend to exhibit inaccurate parallelism among channels. Additionally, many nonflat areas are improperly smoot  
Figure 5: Shaded relief (**a**) and remote sensing imagery (**b**): flat zone (case 1) and mountainous zone (case 2).  
and their original terrain characteristics are lost. In contrast, the slope units generated by the TFPF-SU method exhibit greater uniformity and conformity to the terrain, effectively avoiding problems associated with overly large unit areas or the improper integration of multiple independent slopes. This method not only finely distinguishes between inclined and flat areas but also eliminates the phenomenon of elongated and irregular slope units through a standardized unit shape design, providing an accurate and intuitive depiction of slope terrain (Figure 6a,c). Overall, the TFPF-SU method provides significant advantages in maintaining the rationality of the slope unit structure and the authenticity of the terrain.  
This method not only overcomes the limitations of traditional hydrological subdivision but also provides a more refined analytical tool for the study of complex terrains, making it highly valuable in practical applications.
# Enhanced Scalability and Security in Blockchain-Based Transportation Systems for Mass Gatherings
## 4 Results and Findings
### Performance Metrics: CPU Usage, Memory Consumption, and Testing Time  
Some of the significant metrics used to evaluate blockchain-based transportation systems include CPU usage, memory utilization, and test time. These aspects offer insight into the system's effectiveness in processing transactions and its ability to scale with varying loads. Table 6 below presents a summary of the CPU usage, memory usage, and testing time between the proposed system and reference systems.  
Figure 9: Chart of comparative metrics for security analysis [33; 34; 35; 36; 37; 38; 39; 40; 46; 47].  
1. CPU Usage One of the primary measures used to assess a system's working capabilities is the utilization of the CPU. In the offered system, the base setup operates within the 15-18 percent range of CPU utilization, which can be considered the optimal decision for 800 transactions per second (TPS). This relatively low CPU consumption means that the system can comfortably handle a moderate transaction load without overloading its computational resources. When scalability solutions, such as State Channels, are implemented, however, they can consume up to 25% of CPU resources. This is caused by the complexity brought by off-chain transactions that must be processed, verified, and finalized on-chain. The most resource-intensive type of scalability, Rollups, increases the CPU usage by 35%, as the system must process batches of off-chain transactions and aggregate them before sending them to the primary blockchain.  
The CPU usage is significantly higher in comparison to the reference systems. In other words, the baseline base in reference [17] uses about 20 percent or 25 percent of the cognitive assets of the computer. According to [33], State Channels involve increased usage of CPU, and go up to 22-28 percent, and Rollup involves higher usage as compared to State Channels, and is pegged at 30-35 percent. These findings are also supported by other sources [34; 35]; there are baseline systems in which the CPU consumption amounts to approximately 22-26 percent. In such systems, State Channels raise the utilization to 24-30 percent, whereas Rollups can reach 40 percent. These findings point to the fact that the suggested system is more efficient in regard to CPU consumption, notably in the case when the Rollups and State Channels are used.
# Parameter Extraction of Photovoltaic Cells and Panels Using a PID-Based Metaheuristic Algorithm
## 2 Materials and Methods
### PV Cell Models  
PV systems are characterized by their ability to convert sunlight into electrical energy. As a result, knowing the behavior of the photovoltaic cell is critical for assessing its performance. Several models have been utilized to acquire the extracted parameters. The maximum power, short-circuit current, and open-circuit voltage can be obtained using their current-voltage characteristics. The single-diode model (SDM) and double-diode model (DDM) are presented since they are the most well-known models utilized in the challenge of extracting parameters, with the SDM being simpler and requiring fewer parameters to be extracted than the DDM [36, 37]. The PSA is developed for optimizing the SDM and DDM parameters for three photovoltaic cells and three photovoltaic panels.
## 6 Key Issues and Challenges
### Solar Cell Technology  
The efficiency of the solar PV monitoring system depends on the type of solar cell technology. Further, the monitoring capabilities of the sensors attached depend on the data extracted from the solar cell in terms of irradiance, temperature, current, and voltage which are linked to solar cell efficiency. In recent times, very few works have monitored the efficiency of various solar cell technologies such as mono crystalline, third-generation PV cells, and perovskite solar cells [145; 146; 147]. The authors in [148] compared the efficiency of various solar cell technologies such as poly-crystalline silicon (pcSi), mono-crystalline silicon (mc-Si), thin-film copper indium disulfide (CIS), amorphous silicon (a-Si), and heterojunction incorporating thin film (HIT). The lowest efficiency was recorded for CIS while the highest efficiency was reported for pCSi technology. Hence, it is necessary to select a suitable solar cell technology to achieve optimal efficiency.
## 5 Response of FOWT-SFFC Driven by Environmental Loads
### Environmental Conditions and Load Cases  
The northwest South China Sea is a potential region for FOWT-SFFC to be commissioned. The location is E111.5\({}^{\circ}\), N19\({}^{\circ}\) with a water depth of 150 m, southeast of Hainan Island, as depicted in Figure 7.  
The significant wave heights, mean wave periods, and 10-min mean wind speeds at 10 m used in this study are downloaded from the public dataset ERA-Interim [48] which is a re-analysis project of European Centre for Medium-Range Weather Forecasts. The temporal range of ERA is from 00:00 1 January 1979 to 18:00 31 December 2017 with time resolution of 6 h and the number of data points is  
Figure 6: Pitch response amplitude operators (RAOs) of three concepts of offshore wind turbines.  
56,980. It should be noted that the downloaded data only contains mean wave periods T\({}_{1}\) rather than zero-up-crossing periods Tz or peak wave periods Tp. In this paper Tp is derived according to the relationship with T\({}_{1}\)[46].  
To investigate the performance of FOWT-SFFC under wave and wind, three load cases (LC) are analyzed (Table 7). LC1 is a steady wind field with calm water, used to investigate the resisting overturning ability of the system. Considering that the wind thrust reaches the maximum value at the rated wind speed, the rated wind velocity V = 11.4 m/s at hub height is utilized in this load case. LC2 and LC3 are extreme load cases. It is conservatively assumed that in LC2 the extreme 10-min mean wind speed with 1-year recurrence period occurs during the sea state of 1-year extreme significant wave height. In LC3, the combination of 50-year extreme 10-min wind speed and the 50-year extreme significant wave height is employed [49]. All extrema are derived from the Gumbel model [46] with standard deviation \(\sigma=1.283A\) and mean \(\mu=U+0.55A\) where \(U\) and \(A\) are fitted Gumbel parameters of the downloaded site data. The irregular ocean waves are assumed to follow a Pierson-Moskowitz spectrum [46]. The mean wave periods corresponding to the 1-year and 50-year extreme sea states are determined by the conditional distribution model [50]. The Pierson-Moskowitz spectra used in LC2 and LC3 are shown in Figure 8. The linear random wave elevation and kinematics are generated by FAST.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Load Case**} & \multirow{2}{*}{**Wind Speed (U10, m/s)**} & \multirow{2}{*}{**Wind Property**} & \multirow{2}{*}{**Elevation**} & \multicolumn{3}{c}{**Wave Parameters**} \\ \cline{5-6}  & & & & **Hs (m)** & **Tp (s)** & \(f_{\mathbf{p}}\) (Hz) \\ \hline LC1 & 11.4 & Steady & Hub & — & — \\ LC2 & 15.2 & Random & Z = 10 m & 4.71 & 11.86 & 0.0843 \\ LC3 & 22.3 & Random & Z = 10 m & 6.94 & 13.81 & 0.0724 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Load cases.  
Figure 7: A presumed offshore area in South China Sea for FOWT-SFFC.
# Predicting Shot Accuracy in Badminton Using Quiet Eye Metrics and Neural Networks
## 5 Results
### Effect of Shot Type on Quiet Eye Behavior  
A two-way ANOVA (Skill Level x Shot Type) on QE duration revealed:  
* A main effect of Shot Type (F(2,54) = 11.23, \(p\) < 0.001).
* An interaction effect between Skill Level and Shot Type (F(4,54) = 3.76, \(p\) = 0.009).  
Table 10 shows how QE duration varies across different shot types and skill levels, supporting the discussion of the two-way ANOVA results.  
Figure 7: Scatter plot of QE duration vs. shot accuracy.  
Post-hoc analyses indicate a key difference: elite players kept their Quiet Eye durations steady across various shots, while novices and intermediates showed more variability. This consistency appears to be a hallmark of advanced skill.  
These results provide a comprehensive overview of the relationship between Quiet Eye, skill level, and shot accuracy in badminton. The integration of quantitative analyses, including traditional statistics and machine learning approaches, with qualitative insights offers a nuanced understanding of visual attention strategies in this fast-paced sport. The findings highlight the potential of Quiet Eye as a marker of expertise and a predictor of performance in badminton.
## 1 Introduction
[6] also showed that a linear model based on NDVI (1097 nm, 980 nm) had a better predictive performance in the estimation of winter wheat biomass (R\({}^{2}\) = 0.760, RMSE = 0.279 kg/m\({}^{2}\)). However, the major problem of using the NDVI was that it became saturated for vegetation with moderate to high density [29]. [PERSON] et al. [30] and [PERSON] et al. [31] developed the red edge chlorophyll index (CIred edge) and green chlorophyll index (CIgreen) to overcome the saturation phenomenon, which could estimate the wide range of biomass and chlorophyll of green leaf accurately. Besides, soil adjusted vegetation index (SAVI), including a background adjustment factor L, was calculated to reduce the influence of soil background. [PERSON] et al. [32] estimated the plantation biomass that is based on the SAVI and ratio vegetation index (RVI) by regression analysis (R\({}^{2}\) = 0.813). It was difficult to extract features from multidimensional hyperspectral data. [PERSON] [33] proposed a refined methodology (Partial Least Squares Regression, PLSR) that could obtain important signals and develop reliable regression models, various studies have demonstrated that PLSR was a powerful statistical technique in extracting sensitive wavelengths and reducing the influence of external environmental disturbance for a higher estimation accuracy of plant biophysical characteristics [17; 34; 35; 36]. WOR is very different from rice, wheat, and soybean, as it is a broadleaf plant with an obvious distinctive change of canopy structure during its entire growth period [37], and yellow flowers and green pods seriously affect the canopy hyperspectral data of WOR [38; 39]. To date, few studies take into account the growth stages for spectroscopic WOR AGBestimation, and it is unclear which method can effectively extract the feature wavelengths in reducing the influence of flower and pod. Thus, the substantial objectives of this study were to (1) evaluate the ability of commonly used hyperspectral VIs for WOR AGB estimation at different stages, and prove whether VIs could overcome the saturation phenomenon; (2) analyze the effects of flowers and pods on canopy hyperspectral data, extract the feature wavelengths by correlation analysis and PLSR, and then construct new VIs for WOR AGB estimation accurately.
## 6 Conclusions  
The low-pressure environments, characteristic of aeronautical applications, favor the inception of electrical discharges in electric and electronic circuits placed in unpressurized areas. However, in their early state, due to the low energy level involved, they are undetected by on-board protections until a major failure develops. This paper has developed an optical method to detect, locate, and quantify the severity of the discharges at the earliest stage. The proposed method is based on detecting the near-UV and visible light emitted by the discharges using a high-resolution and low-cost image sensor. A discharge severity indicator based on the intensity of digital images of the discharges has been proposed and tested, demonstrating its accuracy and ability to quantify the magnitude of the discharges in all phases, from the early stage to the development of disruptive discharges. The behavior of the method and fault indicator proposed in this paper has been validated through experimental data acquired in a low-pressure chamber at different pressures in the range of 10-100 kPa, characteristic of aircraft applications, using a needle-plane air gap geometry and a high-resolution low-cost image sensor. Finally, a criterion for quantifying the magnitude of the discharges based on the discharge severity indicator has been proposed, which allows detecting and quantifying of anomalous conditions, which can be very useful to apply predictive maintenance strategies. The results presented in this work allow the fault to be detected at an early stage, long before irreversible damage occurs, so they can complement the on-board electrical protections, which are currently triggered when the fault is well developed. Further work will include testing the approach proposed in this paper in wire bundles to identify and quantify surface discharges and arc tracking activity at a very early stage. Although the method proposed in this paper has been tested in a simulated aircraft environment, a similar approach can be implemented in other applications where electrical discharges are an issue.  
Conceptualization, P.B.-C. and J.-R.R.; methodology, J.-R.R.; software, J.-R.R., P.B.-C. and B.O.; validation, P.B.-C., B.O., Y.A.Q. and M.P.; formal analysis, J.-R.R.; investigation, P.B.-C. and J.-R.R.; resources, J.-R.R.; writing--original draft preparation, [PERSON]-[PERSON] and [PERSON]-[PERSON]; writing--review and editing, J.-R.R.; supervision, B.O., Y.A.Q. and M.P. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Ministerio de Ciencia e Innovacion de Espana, grant number PID2020-114240 RB-I00, and by the Generalitat de Catalunya, grant number 2017 SGR 967.
# COVID-19 Diagnosis in Chest X-rays Using Deep Learning and Majority Voting
## 2 Materials & Methods
### Training of the Selected Algorithms
#### 2.2.7 EfficientNet  
EfficientNets [28] are a list of classifiers introduced recently in 2019 and based on AutoML and Compound Scaling. AutoML is used to develop a mobile-size baseline network (EfficientNet-B0). Then, the compound scaling method is used to scale up this baseline to obtain EfficientNet-B1 to B7. The Compound Scaling method scales uniformly all dimensions of depth, width, and resolution using a simple yet highly effective compound coefficient. The depth of layers should increase 20%, the width 10%, and the image resolution 15% to keep things as efficient as possible, while expanding the implementation and improving the accuracy. Alpha, beta, and gamma are the scaling multipliers for depth, width, and resolution, respectively. They are obtained using a grid search. Phi is a user-specific coefficient. It is a real number that controls resources. Below are the equations of depth, weight, and resolution based on Phi :  
\[\textbf{Depth}:d=\alpha^{\phi}, \tag{1}\]  
\[\textbf{Width}:w=\beta^{\phi}, \tag{2}\]  
\[\textbf{Resolution}:r=\gamma^{\phi}, \tag{3}\]  
\[while:\ \alpha.\beta^{2}.\gamma^{2}\approx 2;\ \ \alpha\geq 1,\beta\geq 1 \ and\ \gamma\geq 1. \tag{4}\]  
EfficientNet-B0 architecture is a mobile sized architecture having 11M trainable parameters. Its architecture is described in Figure 9, where every row is a separate stage \(i\) in the network. Every stage \(i\) is characterized by a number of layers \(\hat{L}_{i}\), an input resolution size \(<\hat{H}_{i},\hat{W}_{i}>\) and an output channels size \(\hat{C}_{i}\).  
It uses seven inverted residual blocks. Squeeze and excitation blocks are used along with the swish activation function. EfficientNet uses 7 MBConv blocks. Every MBConv block takes two inputs. The first is data, and the second is block arguments. The data is received from the last layer. The block arguments are a collection of attributes to be used inside an MBConv block, like input filters, output filters, expansion ratio, squeeze ratio, etc. The expansion phase aims to expand the layer to make it wide. The depth-wise convolution phase applies a depth-wise convolution using the kernel size mentioned in the block arguments. The Squeeze and excitation phase extracts the global features using the global average pooling. Then, it squeezes the numbers of channels using the squeeze  
Figure 9: EfficientNet-B0 architecture [28].  
ratio. The Output phase applies convolution operation using the output filters mentioned in the block arguments.
# Methods for Mid-Term Forecasting of Crop Export and Production
## 3 Materials and Methods
### Modeling of Wheat Export
#### 3.2.2 Stationary Term  
Many approaches, such as ARIMA, represent the time series as a combination of stationary and non-stationary processes. We believe that would help generalize the series better, separating annual oscillations from long-term trends (Figure 3).  
Therefore, we enhance our models with a stationary term (1).  
\[Y_{t}=f(X_{1},\ X_{2},..,X_{t})+\mathcal{E}(X_{1},\ X_{2},..,X_{t}) \tag{1}\]  
Figure 3: Annual global wheat import and its global trend (UN Comtrade data).  
where \(X_{1},\ X_{2},..,X_{t}\) is a time series, \(f(X_{1},\ X_{2},..,X_{t})\) is a non-linear part (regression models a, b, c from the previous subsection), and \(\mathcal{E}(X_{1},\ X_{2},..,X_{t})\) is a stationary normally-distributed part. It is worth noting that we do not make any assumptions regarding the distribution of \(f(X_{1},\ X_{2},..,X_{t})\). We use a series of linear layers to estimate the E term. The idea is to train the model and find the layer weights \(w_{1}\),...,\(w_{d}\) to generate stationary weighted differences between various feature values in the time series (2).  
\[\mathcal{E}(X_{1},\ X_{2},..,X_{t})={w_{d}}^{T}\Big{(}\ldots\Big{(}{w_{1}}^{T} (X_{1}X_{2}..X_{t})\Big{)}\Big{)} \tag{2}\]  
The variance of \(\mathcal{E}\) is unknown; therefore we add a tanh activation to keep the term in an interval \([-1..1]\) (3). That normalized term \(\mathcal{E}_{N}\) can already be used to design an additive regularizer for the model loss. Eventually, we utilize Kullback-Leibler divergence \(KL(\mathbf{P}(\mathcal{E}_{N})||\aleph(0,1))\) between the distribution of \(\mathcal{E}_{N}\) in the time-series and normal distribution \(\aleph(0,1)\) as the regularizer (4).  
\[\mathcal{E}_{N}=\tan\h(\mathcal{E}) \tag{3}\]  
\[l(X_{1},\ X_{2},..,X_{t},Y_{t})=MSE(Y_{t},Y_{t}{}^{*})+\alpha KL(\mathbf{P}( \mathcal{E}_{N})||\aleph(0,1)) \tag{4}\]  
where the hyperparameter \(\alpha\) is the weight of the regularization. This way, we force the models to generate the normally distributed term \(\mathcal{E}\), which can be added to the smooth trend provided by \(f(X_{1},\ X_{2},..,X_{t})\). In the experiments we used fairly high weight for \(\alpha\), which is \(10^{5}\) to overpower the primary loss.