esa-sceva/satcom-chunk-collection huggingface dataset:
# A Real-Time Detection of Pilot Workload Using Low-Interference Devices
## 4 Experimental Setup
### Indicator Extraction
#### 4.4.6 Workload Labels  
From the above analysis, it is evident that both ECG and EEG indicators can effectively represent the magnitude of workload in flight tasks. The EEG indicator is denoted as E, and heart rate as HR. We then draw an E-HR scatter plot, as shown in Figure 5(a), which shows a clear positive correlation between them within each participant. Principal component analysis (PCA) performed on the dataset composed of E and HR shows that the first principal component accounts for the vast majority of the variance, as seen in Figure 5(b). Calculating its loadings, the results show that both E and HR have the same loading of 0.7071 on the first principal component, indicating that the main features of the dataset are distributed along the y = x direction. Therefore, using K-means, the data points are clustered into three categories along the characteristic direction based on the values of E and HR, as illustrated in Figure 5(c). It can be observed in Figure that both E and HR increase with the increment of workload labels, as shown in Figure 5(d),e. Thus, each sample point is labeled with one of the three categories: low, medium, and high.  
Figure 5: The preprocessing of EEG data.  
Figure 6: The process of statistical analysis for a dataset composed of the indicators HR and E: (**a**) an E\(-\)HR scatter plot (only partial data are included to make the image clearer); (**b**) the PCA of the dataset; (**c**) clustering results along the main feature direction; (**d**) the distribution of HR in three types of workload labels; (**e**) the distribution of E in three types of workload labels.  
Ultimately, the dataset consists of 11 low-interference detection metrics, i.e., 10 remote eye-tracking indicators, 1 seat cushion pressure indicator, and the workload label.
## 3 Methodology  
The dataset used here for deep learning model training and evaluation included the actual data from container cranes at container terminals. It was in a proprietary format and could not be read directly. It was converted into a readable format using Python programming. It consisted of the operational data from the PLC, sensors, and other calculations. The extracted error data were Boolean, meaning the output was 0 or 1. Software systems are used to diagnose and keep PLC data for 30 days, after which it is discarded. It is a vital tool for crane operations and daily performance evaluation. It provides a dependable data source in time series, raw data directly from the sensors, and their calculations. It detects and locates periodic errors within the crane. It creates approximately 680 to 730 monthly files, storing data per hour. It holds about 23,000 PLC variables in an average crane but can go much higher in a few cranes (e.g., 31,521 PLC variables in STS CC2124). That is 518.4 billion samples per month (20,000 data at 100 ms). The data features were selected (in this case, errors and some other identifier signals), and data from those variables were converted into CSV files in Excel using Python code. The error data contained roughly 3000 variables. Only two columns were non-Boolean, including the last for timestamps/time series (of improper format). There were approximately 3000 columns and more than 16,100,000 (16 million) rows. The operational dataset analysis assessed eight cranes from different ports; finally, analysing three cranes in detail revealed that most had 2852-2878 error signals. However, only 3 to 6 signals were turning on, showing that an actual error occurred on cranes. This indicated the rarity of error events and the scarcity of relevant data to be analysed further, which posed a significant challenge in the project. A custom code in Python was developed to extract the data from the proletariat data acquired from the cranes. The resultant Excel files were then used for further data processing. The custom-built algorithm was developed using SMOTE and LSTM algorithms in Python programming, alongside customised preprocessing procedures, addressing imbalanced data and integrating evaluation metrics. Data Pre-processing: Customised preprocessing was employed to extract Boolean data. Initially, the unprocessed data, which consisted of time series and sensor readings, were loaded. Ensuring data quality was paramount so that any missing values and outliers were addressed. Subsequently, Boolean features, which consisted of binary flags or true/false values, were extracted. Addressing Imbalanced Data: In the case of an imbalanced dataset, when one class is considerably underrepresented, SMOTE can be employed. This method produces artificial samples for the underrepresented category by creating new instances through interpolating existing data points. The objective is to achieve a balanced class distribution, guaranteeing equitable representation of both classes. Next, the initial dataset is merged with the
[5] reported a numerical approach to predict the photodiodes' quantum efficiency performance for different CMOS technologies and different p-n junction structures helping to select the best structure design for certain application before their microfabrication. The authors demonstrated that COMSOL is a tool that enables the numerical characterization of CMOS silicon photodiodes with relative confidence [5]. [PERSON] et al. [6] presented the main optical properties (absorbance and fluorescence) of three phytoplankton species and an identification methodology toward the development of a portable and low-cost lab-on-a-chip device suitable to detect phytoplankton taxonomic group. Furthermore, the authors presented a preliminary validation of an electronic circuit based in a lock-in amplifier suitable for chlorophyll measurements, which can be adapted for detecting the fluorescence of primary photosynthetic pigments (chlorophyll a) and accessory pigments (carotene, chlorophyll b, phycoerythrin, phycocyanin, and others) present in microalgae and cyanobacteria to quantify different phytoplankton species [6]. [PERSON] et al. [7] reported a signal noise reduction method for nanopore-based detection technology, which is an indispensable tool for monitoring ion current flow in single molecule experiments. The authors showed that the new method for noise reduction offers a better performance than the traditional Kalman filter method and can have a great impact on the analysis of biomolecular properties [7]. As Guest Editors, we hope this Special Issue can provide an opportunity to the engineering and materials science community, as well as those who are interested in the general field of novel technologies and novel materials for micro/nano devices to access and exchange novel knowledge and information. Finally, we would like to acknowledge, congratulate and thank all the authors for submitting their original manuscripts to this Special Issue, as well as all the reviewers for the time and efforts to improve the quality of the submitted papers. Although submissions for this Special Issue have been closed, more in-depth research in the field of micro/nano devices and system are expected to address the challenges of miniaturization and integration in sensitive, portable and low-cost devices. Thanks to all the authors and peer reviewers for their valuable contributions to this Special Issue 'Novel Technology and Applications of Micro/Nano Devices and System'. We would also like to express our gratitude to all the staff and people involved in this Special Issue. Finally, special thanks to [PERSON]. The authors declare no conflict of interest.
## 3 Results and Discussion
As shown in Table 9, the correlation coefficient values of all kinds of bulk tissue samples for parameters group (\(\Delta,1-b_{m}\)) are equal to 0.99, which differ from that for the group (\(\Delta,1-b\)) ranging between 0.96 and 0.98. Moreover, we can also see that the data points of the tissue samples evenly distributed along a straight line in Figure 8a,c. Besides, Figure 8b,d indicate that the MMPD depolarization parameter \(\Delta\) is approximately equal to the MMT parameters. Specifically, the parameters group (\(\Delta,1-b_{m}\)) shows no statistically significant difference with \(p>0.05\). Therefore, it is worth noting that when bulk tissue samples are measured using backscattering equipment, the parameter \(1-b_{m}\) can be used to extract the depolarization information quickly and accurately. Figure 8: Comparison of MMT and MMPD depolarization parameters of bulk tissue samples. **(a)** Depolarization MMPD parameter \(\Delta\) plotted against its counterpart MMT parameter \(1-b\) for correlation analysis with all five kinds of bulk tissue samples. **(b)** Statistical analysis bar chart of depolarization MMPD parameter \(\Delta\) and MMT parameter \(1-b\) for each kind of bulk tissue samples. **(c)** Depolarization MMPD parameter \(\Delta\) plotted against its counterpart MMT parameter \(1-b_{m}\) for correlation analysis with all five kinds of bulk tissue samples. **(d)** Statistical analysis bar chart of depolarization MMPD parameter \(\Delta\) and MMT parameter \(1-b_{m}\) for each kind of bulk tissue sample.
## 2 Methodology and Principles
### The Principle of Singular Value Decomposition  
The principle of noise reduction using SVD relies on the separability of signal and noise energy. A noisy signal is reconstructed using a phase-space matrix constructed with a certain time delay and then subjected to SVD. By examining the correspondence between the singular values obtained from the decomposition and the signal and noise, only the singular values corresponding to the signal features are retained, thereby achieving the goal of noise reduction. In the SVD process, selecting the window length is an important step, as it divides the original signal into consecutive sub-signal segments for processing. The choice of window length determines the precision of signal decomposition and the quality of the analysis results. In this study, we adopted a simple method where the original signal length is divided by 10 and rounded up to obtain the window length N. This choice aims to preserve the characteristics of the original signal while achieving a certain level of decomposition accuracy. Depending on the application requirements and signal characteristics, the factor can be adjusted to change the window length.  
As early as 1873, [PERSON] proposed the basic idea of SVD, and since then, numerous scholars have conducted extensive research in this field [29; 30]. Compared to traditional signal processing algorithms, SVD offers several unique advantages, primarily characterized by the following three points: 1 minimal waveform distortion; 2 no phase drift; 3 a relatively high signal-to-noise ratio [31; 32]. Since the signal data is one dimensional and SVD applies to matrix data, the process of processing a signal using SVD consists of, among other things, first converting the one-dimensional signal data into two dimensions and then performing a singular value decomposition on the resulting two-dimensional matrix.  
The basic principles of SVD are as follows:  
Suppose \(X\) is a matrix consisting of \(m\times n\) data, which can be transformed mathematically accordingly if the matrix satisfies certain conditions:  
\[X=A\sum B^{T} \tag{15}\]  
In Equation (15), \(\sum\) represents a quasi-diagonal matrix, \(T\) denotes the transpose operation, and \(A\) and \(B\) are both orthogonal normalized matrices.  
\[A^{T}A=AA^{T}=I_{m} \tag{16}\]  
\[B^{T}B=BB^{T}=I_{n} \tag{17}\]  
In Equations (16) and (17), \(I\) is the unit matrix.  
If we define \(A=[a_{1},a_{2},\ldots,a_{m}]\) and \(B=[b_{1},b_{2},\ldots,b_{n}]\), Equation (1) can be rewritten as follows:  
\[X=\sum_{i=1}^{m}\lambda_{i}a_{i}b_{i}^{T} \tag{18}\]  
In Equation (18), \(a_{i}\) and \(b_{i}\) represent the left and right singular vectors, also known as eigenvectors, with dimensions \(m\times 1\) and \(n\times 1\), respectively. \(\lambda_{i}\) is the singular value. In this way, the matrix \(X\) is decomposed into m submatrices, each with the same dimension.
## 3 Results and Discussion
### Results of Calculations of Grouting Subsidence Reduction in Goaf Areas with Different Spacing
The inclination value and uneven subsidence control rate caused by uneven subsidence of the subgrade for different grouting treatment ranges are plotted in Figure 9. From the figure, it can be concluded that when the treatment width is selected within the range of subgrade width or half-width of the subgrade, the improvement effect on uneven subsidence of the subgrade is poor. When the treatment width is selected as trapezoidal and full-range grouting, the uneven subsidence of the subgrade is significantly improved. After mining, the goaf is immediately treated with full-range grouting, with a control rate of over 95% for uneven subsidence of the subgrade. Trapezoidal range grouting is used with a control rate of over 56% for uneven subsidence of the subgrade. Figure 6: Residual subsidence nephogram for different grouting ranges. In summary, when the treatment width adopts the trapezoidal range and full-range grouting, the subsidence reduction at the center of the subgrade and uneven subsidence of the subgrade are significantly improved. Through simulation calculations, when the treatment width is treated according to the trapezoidal range and the control effect meets the standard requirements, the trapezoidal range can be used for treatment. The advantage is that the trapezoidal range has a smaller width than the full-range grouting treatment, and the impact on the environment after grouting treatment is relatively small, which reduces engineering costs and shortens the construction period. If the engineering and regulatory  
Figure 8: The amount of subsidence reduction and the subsidence reduction rate of the subgrade center for different grouting range conditions. Figure 7: The surface subsidence curve for different grouting range conditions. requirements are not met, then full-range grouting can be used for prevention and control, which can achieve good subsidence control effects.
## 2 Materials and Methods
### Sampling and Data Collection  
The survey was conducted in June and September 2018, respectively. The total number of sampling sites was fifteen, and encompassed the extensive area from the Seomijn River estuary to the outer Gwangyang Bay (Figure 1). The water samples were collected vertically from sediments to surface (depth: 10-50 m). For marine plankton sampling, a 200 m mesh-sized net was used. The corresponding water volume (ca. 7560 L; 7.56 m\({}^{3}\)) was calculated by a flow-meter equipped in front of the net inlet. Zooplankton samples were identified and counted under a dissecting microscope (SV11, Zeiss and SZ60, Olympus, Tokyo, Japan), according to [PERSON] and [PERSON] [30]. Water temperature and salinity were measured on site using a portable probe (Professional Plus, YSI, Yellow Springs, OH, USA). Nutrient and chlorophyll \(a\) concentrations (Chl-_a_) were analyzed in the lab using the collected water samples. Specifically for the measurement of phosphorus, nitrogen, and Chl-_a_, automatic water quality analyzer (AutoAnalyzer 3 HR, Seal Analytical Inc., Mequon, WI, USA) was used, and we adapted the standard analytical methods proposed by the Korea Ministry of Oceans and Fisheries (downloadable from [[http://www.mof.go.kr/jfile/readDownloadFile.do?fileId=MOF_ARTICLE_5689&fileSeq=1](http://www.mof.go.kr/jfile/readDownloadFile.do?fileId=MOF_ARTICLE_5689&fileSeq=1)]([http://www.mof.go.kr/jfile/readDownloadFile.do?fileId=MOF_ARTICLE_5689&fileSeq=1](http://www.mof.go.kr/jfile/readDownloadFile.do?fileId=MOF_ARTICLE_5689&fileSeq=1))). For Chl-_a_ measurement and eDNA metabarcoding, the water samples (1 L per sample) were immediately filtered in the lab, using a 0.45 m pore-size membrane (MFS membrane filter, Advantec, Irvine, CA, USA). The membrane for Chl-_a_ was then, homogenized after acetone extraction prior to the spectrophotometry. The membrane for eDNA was preserved at \(-80\) degC. Organic and inorganic carbon concentrations were measured using a carbon analyzer (vario TOC cub, Elemetar, Langenselbold, Germany) on the basis of 850 degC combustion catalytic oxidation methods.
# A Computer Simulation Research of Two Types of Cardiac Physiological Pacing
## 3 Results
### LBBB Correction with LBBB  
In this case, although LBBB-2 occurred in the left main bundle branch, it still yielded a similar synthetic ECG as that in Figure 4c. With the existence of a normal beat from His-bundle, we performed LBBB at different moments. The synthetic ECGs under LBBB varied morphologically. Given that the variations on the QRS complex were more obvious than the variations on the T wave, we used the QRS complex for analysis. Figure 5 indicates respective QRS complexes and the QRS duration for each specific moment, compared with the referenced synthetic ECG of the normal state. The duration of the QRS complexes of these simulations were 21%, 16%, 13%, 5%, 5%, 8%, 47%, 58%, 63% longer than that of the synthetic ECG of the normal state, respectively. The synthetic ECGs of LBBP starting at 18 ms, 22 ms, and 26 ms had RBBB morphologies, and those of LBBP starting at 30 ms, 34 ms, and 38 ms appeared to be basically synchronous, while those of LBBP starting at 42 ms, 46 ms, and 50 ms possessed LBBB morphologies. The best pacing results were obtained when LBBP started at 34 ms. Morphologically, the synthetic ECG of this situation was extremely consistent with the normal synthetic ECG.  
We present the simulated activation sequences of three typical situations (LBBP starting at 18 ms, 34 ms, 50 ms), as shown in Figure 6. Figure 6a shows the results of the situation in which the LBBP stimulus started at 18 ms, i.e., at 12 ms earlier than the proximal His-bundle excitation. The right ventricle was excited after the left ventricle and resulted in an BBB. Regarding the best pacing situation, the pacing was 4 ms later than the proximal His-bundle excitation, as shown in Figure 6b. The entire conduction process of the wavefront was very similar to that in the normal state, which indicated that LBBP corrected LBBB successfully and led to a physiological-like rhythm. Figure 6c shows the results of the situation in which the LBBP stimulus started at 50 ms, i.e., at 20 ms later than the proximal His-bundle excitation. The left ventricle was excited after the right ventricle. This caused an LBBB. Therefore, LBBP failed to correct the LBBB in this situation.  
Figure 5: The QRS complex and duration of the synthetic ECG (blue line) under LBBP correcting LBBB at nine different moments spaced 4 ms apart. The synthetic ECG results in the normal state were referenced (red line). LBBP: left bundle branch pacing. LBBB: left bundle branch block.
## 3 Results
### Eccentric Block and Eccentric Block Support Shaft Modal Analysis and Stress Analysis Results  
The results of the total deformation and equivalent stress analysis are shown in Figure 15, and the data are shown in Table 6.  
Based on the modal analysis conducted on the eccentric block and the eccentric block support shaft, the objective is to prevent the excitation frequency of the eccentric block and the eccentric shaft from coinciding with their own modal frequency. This is done to avoid resonance during operation and ensure the normal functioning of the vibrating crab apple picker. To achieve this, the sixth-order mode is designed, taking into consideration the modal frequencies of the eccentric block and the eccentric shaft. By ensuring that the excitation frequency does not match the modal frequency, resonance can be prevented. The analysis process for the modal analysis of the eccentric block and the eccentric block support shaft is similar to the previous analysis. The results of this analysis can be found in Table 7.  
Figure 14: Frequency line graphs of (**a**) 150 mm harmonic response analysis; (**b**) 180 mm harmonic response analysis; (**c**) 210 mm harmonic response analysis; (**d**) 240 mm harmonic response analysis; (**e**) 270 mm harmonic response analysis.  
Based on the information provided, it is stated that the vibration frequency of the eccentric block and the eccentric block support shaft is 12.7 Hz at 750 RPM. It is mentioned that this frequency is much smaller than the intrinsic frequency of the eccentric block and the eccentric block support shaft. Based on this comparison, it is concluded that no resonance will occur during the normal picking work, as the vibration frequency is significantly lower than the intrinsic frequency of the components.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Part Name**} & \multicolumn{2}{c}{**Total Deformation \(\delta\)\(l\)\(\times\)10\({}^{-4}\)**} & \multicolumn{2}{c}{**Constant Force \(\sigma\)/MPa**} \\ \cline{2-5}  & **Minimum Value** & **Maximum Values** & **Minimum Value** & **Maximum Values** \\ \hline Biasing block & 0 & 777.94 & \(5.80\times 10^{-2}\) & \(3.13\times 10^{2}\) \\ \hline Eccentric block support shaft & 0 & 1.15 & \(1.40\times 10^{-2}\) & \(9.05\times 10^{-1}\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Main parameters of the cycloidal hydraulic motor.  
Figure 15: (**a**) Eccentric block deformation diagram; (**b**) eccentric block support shaft deformation diagram; (**c**) equivalent stress diagram of eccentric block; (**d**) eccentric block support axis equivalent effect diagram.
## Abbreviations  
The following abbreviations are used in this manuscript:  
ADCS Attitude Determination and Control System AI Artificial Intelligence ANN Artificial Neural Network CNN Convolutional Neural Network FC Fully Connected FDIR Fault Detection, Isolation, and Recovery GNC Guidance, Navigation, and Control MEKF Multiplicative Extended Kalman Filter ML Machine Learning SVM Support Vector Machine