esa-sceva/satcom-chunk-collection huggingface dataset:
# Eye Fatigue Detection through Machine Learning Based on Single Channel Electrooculography
## 5 Results  
After continuous testing 12 subjects a period, an EOG eye fatigue dataset is generated. Each experiment produces EOG data with 3 min length, which is downsampled 16 times, then 256 points are taken as a sample. The size of the processed dataset is shown in Table 1:  
The classification decision tree structure is determined after pruning the tree using the cross-validation method based on the processed dataset, it is shown in Figure 12:  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Relaxed Sample Size** & **Fatigue Sample Size** \\ \hline Training data set & 12,090 & 9750 \\ Test data set & 4095 & 5070 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Size of processed dataset.  
Concerning the evaluation, common classification metrics were employed to determine the anomaly detection effectiveness of the model, i.e., _Accuracy_, _Precision_, _TPR_ (Recall), _FPR_ (False Positive Rate), and _SPC_ (Specificity).  
These metrics are computed analyzing as follows:  
\[Accuracy\ =\ \frac{TP+TN}{TP+FP+FN+TN}\]  
\[Precision\ =\ \frac{TP}{TP+FP}\]  
\[TPR\ =\ \frac{TP}{TP+FN}\]  
\[FPR\ =\ \frac{FP}{FP+TN}\]  
\[SPC\ =\ \frac{TN}{FP+TN}\]  
where relax is classified as a positive category and fatigue is classified as a negative category.  
_TP_ (True Positive): Values that are actually positive and predicted as positive,  
_FP_ (False Positive): Values that are actually negative, but predicted as positive,  
_FN_ (False Negative): Values that are actually positive, but predicted to negative,  
_TN_ (True Negative): Values that are actually negative and predicted to negative.  
The _Precision_ is all the points that are declared to be positive, but what percentage of them are actually positive, calculated as _Precision_ = _TP_/_(TP_ + _FP_).  
_TPR_ (True Positive Rate) is all the points that are actually positive, but what percentage declared positive, calculated as _TPR_ = _TP_/_(TP_ + _FN_).  
_FPR_ (False Positive Rate) is all the points that are actually positive, but what percentage declared negative, calculated as _FPR_ = _FP_/_(FP_ + _TN_).  
_SPC_ (Specificity) is all the points that are actually negative, but what percentage declared negative, calculated as _SPC_ = _TN_/_(FP_ + _TN_).  
Table 2 shows the eye relax and fatigue state analysis results:  
Figure 12: The structure of the pruned classification decision tree.  
As shown, this system performs better in classifying the fatigue state than classifying the relax state which further verifies the correlation between EOG signal and eye fatigue and demonstrates that the EOG signal can be used as a test for eye fatigue.
# Overview of Tool Wear Monitoring Methods Based on Convolutional Neural Network
## 4 Information Data of Indirect Online Measurement on Tool Wear
### Acoustic Emission  
The deformation, fracture and phase change of solid materials cause the rapid release of strain energy, and acoustic emission is the stress elastic wave. Thus, acoustic emission features with a higher amplitude can be monitored when the tool is broken [64; 65; 66]. Acoustic emission is not subject to mechanical interference and propagates much higher than the characteristic frequency caused by machining. It has a better sensitivity than force and vibration features. The tool condition monitoring based on acoustic emission is recognized as the most potential monitoring technology [67; 68; 69; 70]. [PERSON] et al. [71] reported that acoustic emission features could effectively reflect changes such as tool breakage and tool chipping. [PERSON] et al. [72] collected the acoustic emission features during milling and identified the tool wear condition through a model of linear-chain conditional random field based on conditional probability. [PERSON] et al. [73] suggested that the acoustic emission sensor mounted on the top surface of the tool holder could effectively monitor the crater wear on the tool. [PERSON] et al. [74] argued that the acoustic emission feature was easy to collect and sensitive to the contact between the tool and the workpiece, suitable for tool condition monitoring during micro-milling. [PERSON] et al. [75] reconstructed the phase space of the acoustic emission feature, extracted the embedding dimension and Lyapunov exponent as the input parameters of the support vector machine. It obtained a high tool wear recognition accuracy. [PERSON] et al. [76] collected acoustic emission features during the life cycle of milling cutters and employed a random forest-based model to predict tool wear condition. The findings suggested that the accuracy of the prediction model is better than that of the artificial neural network and support vector machine. [PERSON] et al. [77] established a monitoring system of tool wear conditions based on the acoustic emission features containing four time-domain characteristic parameters.  
The limitation of acoustic emission features is that the other features interfere with the acquisition, extraction, analysis, processing and storage of acoustic emission features during milling, and it is very sensitive to the material properties of the test object [51; 78].  
The installation diagram of an acoustic emission sensor is shown in Figure 2.  
Figure 2: An installation diagram of vibration sensor, current sensor, acoustic emission sensor and surface roughness sensor (drawn from the literature [63]).
# Toward a Comprehensive Understanding and Evaluation of the Sustainability of E-Health Solutions
## 8 Discussion
The resistance to providing funds is hindering the efficient employment of technology in healthcare and increasing the burden on healthcare practitioners, which does not achieve the digital health transformation goals. This work contributes to the body of knowledge by providing a comprehensive definition and model of the sustainability of e-health solutions that was derived empirically. In addition, it proposes an assessment method to assess the sustainability of e-health solutions. In practice, the proposed model and its assessment method could be used as a tool to support decision making regarding e-solution alternatives. Furthermore, it could be used to give an early estimate of whether an e-health solution would be sustainable or not. This work lays the foundation for many interesting research directions and industry innovations, such as automated estimation of the sustainability of products using AI and existing data. The model will be the base on which relevant data will be collected and analyzed. AI methods will replace the assessment method if sufficient data exist.
# Effects of Torso Pitch Motion on Energy Efficiency of Biped Robot Walking
###### Abstract  
Gait pattern generation has an important influence on the walking quality of biped robots. In most gait pattern generation methods, it is usually assumed that the torso keeps vertical during walking. It is very intuitive and simple. However, it may not be the most efficient. In this paper, we propose a gait pattern with torso pitch motion (TPM) during walking. We also present a gait pattern with torso keeping vertical (TKV) to study the effects of TTM on energy efficiency of biped robots. We define the cyclic gait of a five-link biped robot with several gait parameters. The gait parameters are determined by optimization. The optimization criterion is chosen to minimize the energy consumption per unit distance of the biped robot. Under this criterion, the optimal gait performances of TPM and TKV are compared over different step lengths and different gait periods. It is observed that (1) TPM saves more than 12% energy on average compared with TKV, and the main factor of energy-saving in TPM is the reduction of energy consumption of the swing knee in the double support phase and (2) the overall trend of torso motion is leaning forward in double support phase and leaning backward in single support phase, and the amplitude of the torso pitch motion increases as gait period or step length increases.  
biped robot gait pattern generation; optimal gait; walking efficiently; torso pitch motion +
Footnote †: journal: Applied  
Article
# Response of NDVI and SIF to Meteorological Drought in the Yellow River Basin from 2001 to 2020
## 2 Materials and Methods
### Datasets
#### 2.2.3 SPEI (Standardized Precipitation Evapotranspiration Index)  
SPEI (standardized precision evaporation index) is an index representing the degree of drought obtained by quantifying precipitation, potential evapotranspiration, and temperature. It has the characteristics of multiple time scales. SPEI has been extensively used in the study of vegetation response to drought, providing reliable information for drought research [15; 37]. SPEIbase v2.7 of the global SPEI database provides long-term reliable information for drought monitoring worldwide. The spatial-temporal resolution is monthly and 0.5\({}^{\circ}\), and the time range is from January 1901 to December 2020 ([[https://spei.csic.es/spei_database_2_7](https://spei.csic.es/spei_database_2_7)]([https://spei.csic.es/spei_database_2_7](https://spei.csic.es/spei_database_2_7)), accessed on 10 August 2021). SPEIbase v2.7 calculates potential evapotranspiration based on FAO-56 Penman-Monteith model, which is superior to the estimated results of Thormthwaite PET [21]. Therefore, we used the SPEI data from 2001 to 2020 to identify the spatio-temporal variation trend of drought. Here, the drought represented by SPEI is divided into five categories as shown in Table 1:
# A Survey of Target Detection and Recognition Methods in Underwater Turbid Areas
## 3 Problems in Turbid Areas  
In engineering, common turbidity areas include aquaculture farms, shallow coastal areas, deep sea areas, etc. Aquatic aquaculture farms often result in severe blurring of water bodies due to the density of aquatic organisms and insufficient light transmittance in the water body. Most shallow water receives abundant light, but the turbidity of the water body is high due to coastal sediment, aquatic organisms, and human activities. Deep sea is characterized by weak light but clear water quality. Underwater images of specific turbid waters are demonstrated in Figure 2.  
In general, turbid water cannot be purified, so the density of suspended particles in turbid water results in low contrast, with no inherent characteristics, blurring, and distortion. The most difficult problem encountered during the use of some detection approaches is the scattering effect of water on the light wave. Therefore, strong backscattered light, which carries impurity information, obscures the target data, and reduces the image contrast. Although typical optical image methods, such as the use of underwater cameras, offer an inherently high resolution capability, the unknown imaging conditions, including the optical water type, scene location, illumination [7], and absorption and scattering characteristics of the marine medium, severely limit the image performance. For example, the visible range in the Singapore coastline area is only 2-3 m [8]. Due to the attenuation of reflected  
Figure 1: Percentages of different application areas.  
Figure 2: Underwater images of turbid water. (**a**) An underwater photo of a fish farm. (**b**) An underwater image of shallow water. (**c**) An underwater image of deep sea.  
light, blurring caused by tiny impurities, and abnormal changes in color, the accuracy of target recognition is reduced.  
Considering the objective limitations of optical sensors in underwater scenes, most common detection methods in turbid areas are based on computer vision theory and utilize non-optical sensors, such as lidar, sonar, etc. However, as a result of the complexity and significant interference in turbid waters, these methods cannot provide satisfying results. Thus, the investigation of new approaches based on these methodologies is difficult. This work focuses on target detection methods based on deep learning, some underwater image restoration enhancement methods, and underwater image processing methods based on polarization imaging, which have demonstrated the most advanced target detection performances and meet the requirements of underwater intelligent sensing technology. The underwater image processing effect is depicted in Figure 3.
# Wind Turbine Predictive Fault Diagnostics Based on a Novel Long Short-Term Memory Model
## 4 Results
### Performance Metrics upon Individual Faults
#### 4.2.4 Pitch Fatal Faults  
In addition to the predictions on PcsTrip faults, the predictive results on pitch fatal faults are yielded with an even lower recall range (0, 0.69481) than the corresponding precision range (0, 0.8871), seen in Table 9. By excluding the poorest predictor, SVM, the MIPs and MIRs under other classifiers go beyond 0.53 and 0.22, respectively. Hence, for pitch fatal faults, MIPs are much greater than their corresponding MIRs, resulting in more FNs than FPs, so F-scores are downgraded by relatively poorer MIRs.  
The time-domain predictions prior to pitch fatal faults are seen in Figure 12. T2V-LSTM is the best fault predictor by having superior MIRs, and LSTM is just second to T2V-LSTM, whilst the other classifiers yield the MIRs below 0.5 in most cases from Figure 12b. It is noteworthy that MIRs under both LSTM models attenuate over time, obtaining lower than 0.5 after \(t-17\). By comparison, MIPs mainly surpass 0.5, except for the test cases under SVM in Figure 12a.  
Accordingly, as seen in Figure 12c, all F-scores are decreased by their lower MIRs, but both LSTM models outperform other classifiers due to their observably better recall scores in Figure 12b, and T2V-LSTM is still the best fault predictor with the best MIRs and F-scores for pitch fatal faults. However, compared with other faults, pitch fatal faults witness much lower MIRs, and the diagnosed fault cases attenuate over a longer prediction time. Particularly, since \(t-17\), the predictions on pitch fatal faults are yielded with minor reliability because of MIRs going below 50%; thus, half of the faults cannot be correctly diagnosed due to yielding more FNs than TPs.
## 3 Results and Discussion
### Whether the Workplaces Would Use the Tool in the Future  
There was a wide range of responses from the participants as to how they regarded the overall utility of CHAP-Risk, and how they judged the feasibility of the future use of the tool--whether it would become a tool that the WHSCs would regularly use as part of their assessment of their workplaces' chemicals. The range of views seemed to be based on the person's level of OHS knowledge and on how comfortable they were seeking workplace-specific information from other workers. The OHS managers and the committee members who attended the training sessions were very positive about the potential for the tool's future use, including its ability for them to engage in a \"real-world\" WHMIS process:_\"This [training on the tool] is great. It will help my health and safety team be involved in the process... It has been difficult for me. Anything that I can do to make [WHMIS] more interactive and less boring will be great. I am excited to show them\"._  
After trialing the tool, some thought that CHAP-Risk would be incorporated into their system:  
_\"We could use the hazard ratings as part of our Emergency Response Plan, to ensure we have appropriate PPE to handle it, and better engineering controls on how we handle and store the product\"._  
However, others thought the CHAP-Risk tool was too complicated as a basic tool for use by shop-floor workers, and some members of the WHSC:  
_\"Using the CHAP tool is way above [the workers'] pay grade. It has information that is not relevant to them. They don't compute the hazard codes. The supervisor tells them how to contain [the chemical] and what PPE to use\"._  
When considering the barriers and facilitators for implementation of the CHAP-Risk tool by SMWs, some identified barriers which the tool can help address are assisting the workplace in better regulatory compliance, providing them an easy-to-use resource, and providing them with more information on the chemicals they are using [42]. However, workplace-specific facilitators such as the need for a 'workplace champion'/'knowledge transformation leader', support by the organization, and positive social dynamics are all required for the CHAP-Risk tool to be implemented effectively [43].
# Robust Near-Optimal Aerocapture Guidance Method Based on Saturation Function
## 3 Guidance Law Design Based on Saturation Function
### Architecture of the Numerical Predictor-Corrector Guidance  
Based on the guidance law in the two channels mentioned above, the whole predictor-corrector guidance procedure for aerocapture is shown in Figure 3. At the beginning of a guidance cycle, based on the spacecraft's current states and guidance profile, the longitudinal guidance law predicts the atmospheric exit state by numerically integrating the three-DOF dynamic equations. Then we can calculate the post-aerocapture orbit parameters and total velocity increment \(\Delta V\) required to perform orbit insertion. If the orbit parameters and \(\Delta V\) don't meet the mission requirements, we will correct the guidance parameter \(t_{m}id\) and update the guidance profile. The longitudinal guidance system repeats the above process until the mission requirements are met, then the bank-angle magnitude is determined. As for the lateral channel, the lateral guidance law determines the bank-angle symbol by judging if the post-reversal orbit inclination satisfies the mission requirement. Combining the outputs of both two channels, we can get the bank-angle command in the current guidance cycle. According to the bank-angle command, we update the spacecraft's states through measurement in actual flight or integration of the dynamics Equations (1)-(6) in simulation. Then, the first-order low-pass filter is used to estimate the dispersion effect and update the aerodynamic profile. After that, we repeat the above steps in the next guidance cycle.
## 3 Results and Discussion  
The main goal of this research is to explore a possible loose focusing HHG configuration and find a combination of input experimental parameters which enable the generation of XUV attosecond pulses at high flux. We have built up one scenario which is experimentally feasible in currently operating large attosecond science facilities, for example the ELI-ALPS. All input parameters are experimentally measurable, from which we fix all but 4 parameters, these are the scanned parameters.  
Fixed input data are: pulse duration 10 fs, central wavelength 800 nm, focal length 21 m, radius (1/\(e^{2}\) intensity) of the beam at the focusing element is 30 mm, total length of the interaction region is 20 cm, gas medium is argon. The scanned parameters with the values chosen for the full 3D calculations are the following: input laser pulse energy \(E\) (0.77; 3; 6; 9; 11.6) mJ; argon pressure \(p\) (0.1; 0.38; 1.41; 5.31; 20) mbar; cell entrance position relative to the nominal focus \(z_{i}\) (\(-\)50; \(-\)25; \(-\)20; \(-\)10; 0; 10; 25; 50) cm, medium length \(L_{med}\) (4; 8; 12; 16; 20) cm. Negative values for \(z_{i}\) stand for gas cell beginning before the focus position. Although the parameters are very sparsely spaced, scanning along all these parameters and performing complete 3D simulations, while also to individually analyze and interpret the results means an enormous data pool and working time and effort, which would be very inefficient. In Figure 3 we represent in a very schematic manner the general configuration (Figure 3a) and the cases for which the complete 3D simulations have been performed, checked, validated and analyzed (Figure 3b).  
The unprecedented power of the ANN comes in exactly at this stage of the research. (1) We set the ranges of all the moving parameters; (2) we perform--in a carefully chosen way--a sufficient number of complete simulations, such as to have representative points from every part of the multi-dimensional parameter space; (3) use these simulation results as inputs for the ANN; (4) train, test and validate the ANN as it was presented in Section 2.2. The expected results are the estimated high-harmonic yields in the whole multi-dimensional parameter space with arbitrarily fine resolution along each parameter. We emphasize that the ANN method in the present stage of implementation is not yet a parameter optimization method, but an efficient prediction method. However, the trained ANN is also suitable to search for optimum, and the maximum yield within this 4-dimensional parameter space can be found by the user.  
The results obtained with the complete 3D code are discussed in detail elsewhere [43]. There the main result was that the highest harmonic yield around 40 eV can be obtained with the parameter combination: \(E\) = 11.6 mJ; \(p\) = 5.31 mbar; \(z_{i}\) = \(-\)25 cm; \(L_{med}\) = 16 cm. In this study we also test this result, and present other parameter combinations which guarantee high yield comparable to the one found with the multi-parameter scan. We also propose experimentally feasible configurations for existing attosecond science laboratories.
## 2 Materials and Methods
### Morphological Parameters of the Sole  
Eight parameters describing the morphology of the sole of the foot, particularly those related to the classification of the foot types based on the medial longitudinal arch morphology, were considered in the current study, and were obtained from the 3D surface of the sole reconstructed using the SIBPAIR method with the FAST system, namely FL, FW, HVA, AI, SAI, CSI, AH, and NAH (Figure 2). The FL was defined as the horizontal distance from the most anterior point of the foot to the most posterior point of the foot, projected onto the longitudinal axis of the foot, defined as the line joining the tip of the second distal phalange and the most posterior projection of the calcaneus. The FW was defined as the horizontal distance perpendicular to the longitudinal axis between the most medial prominence of the head of the first metatarsal and the most lateral prominence of the fifth metatarsal. The HVA was defined as the angle formed by the vertical planes best fitting the medial surfaces of the hallux and the foot (Figure 2). Three parameters for quantifying the medial longitudinal arch based on the 2D footprint were considered in the current study. They were obtained from the horizontal projection of the measured 3D surface model of the sole. The AI was calculated as the ratio of the areas of the horizontal projections of the mid foot (part B) and the foot (parts A + B + C) (Figure 2) [11]; SAI was the ratio of the widths of the midfoot and heel (\(b/a\), Figure 2) [12]; and the CSI was the ratio of the widths of the midfoot and the metataralsals (\(b/c\), Figure 2) [13]. In contrast to the above parameters in the horizontal plane (or, footprint), the AH and NAH were obtained from the vertical projection of the measured 3D surface model of the sole. The AH was the maximum height of the longitudinal arch on the vertical plane passing through the medial borders of the calcaneus and the first metataralsal, which further defined the NAH when divided by the truncated foot length (TFL) (Figure 2) [17]  
Figure 2: Schematic illustration of the definitions of the foot parameters based on the measured 3D sole surface: (**A**) foot length (FL); (**B**) foot width (FW); (**C**) hallux valgus angle (HVA); (**D**) arch height (AH) and normalized arch height (NAH). (**E**) Arch index (AI), Staheli’s arch index (SAI), and Chippaux-Smirak index (CSI). (This figure is best viewed in color).