esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Classification of Articles by Paper Orientation
#### 4.1.1 Review Paper  
We found twenty review papers. Seventeen of the articles were narrative reviews, and three were systematic reviews. We classify these papers according to their focus: general/background, signal processing, classification, application, and others. We found seven general reviews, including neuroimaging techniques, emotion modeling, and applications. We also found three signal processing reviews, one classification method, seven application reviews, and two reviews that covered the training protocols, validity in the EEG-neurofeedback optimal performance field, and evidence of neurofeedback learning. Table 1 shows the review article classifications.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Focus** & **Description** & **References** \\ \hline \multirow{6}{*}{General/background} & [PERSON] et al., 2015 [7], \\  & Review recent studies that investigate the recognition of & [PERSON] et al., 2014 [8], \\  & affective states from EEG signals. Aim to present a general & [PERSON] et al., 2013 [9], \\  & discussion on one or more aspects, such as the neuroimaging & [PERSON] and [PERSON], 2008 [10], \\  & techniques, emotion representation models, physiological & [PERSON] and [PERSON], 2007 [11], \\  & signals, stimulus, feature extraction, and classification, and & [PERSON] and \\  & discuss their current achievements and applications. & [PERSON], 2007 [12], \\  & & [PERSON] et al., 2006 [3] \\ \hline \multirow{6}{*}{Signal Processing} & & Survey the recent developments in the field of EEG signal processing, including filtering and artifact processing, signal enhancement methods, feature extraction methods, and channel selection methods. & [PERSON] et al., 2015 [13], \\  & & enhancement methods, feature extraction methods, and \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Review article classifications.  
The objectives of the earliest reviews (2007 and 2008) were to understand the mirror neurons; cover the EEG correlates of emotions, brain rhythms, and functions; and, present a framework and suggestions for a research program [3; 12]. We also found two published review articles that aim mainly to highlight an emerging field, such as the neuroscience of culture in [9] and social neuroscience in [10].  
Other review articles were published in medical fields. These articles focused on EEG applications and how to use EEGs to diagnose and assess medical conditions. These articles also explored the relationships between symptoms and affective states, such as schizophrenia [17; 18], depression [20], disorders of consciousness [19], and autism [21].
# Piston Rod Coating Material Study of Reciprocating Sealing Experiment Based on Sterling Seal
## 3 Results and Discussion
### Sealing Lip Interface Performance Analysis
#### 3.2.2 Surface Wear of Seal Lips  
The field emission environment scanning electron microscope was used to measure the surface wear of the inner lip of seals. The experimental results are as follows.  
As shown in Figure 20, there are electron microscope experimental measurement results of magnification of 200 times. It can be seen from the figures that the new, unused sealing ring has neatly arranged turning marks, and the two seal ring test samples used in the reciprocating test bench experiment have no obvious turning marks after the seal lips are pressed after contact with the piston rod.  
Figures 21 and 22 show the results of electron microscopy experiments obtained by magnifying 500 times and magnifying 1000 times, respectively. It can be seen from the figures that the two seal ring experimental samples used in the reciprocating test bench experiment are worn after the piston rod is in contact with the piston rod, and the seal ring lip contact with the Cr-plated piston rod is shallow, the seal ring that is in contact with the DLC piston rod is worn deeper. The wear marks are in the same direction and are streamlined, indicating that there is abrasive grain between the seal ring and the movement of the piston rod. The abrasive grains cause the surface of the lip of the seal ring to wear and form wear marks. Since the sealing ring PTFE material is softer than the piston rod coating material, the abrasive particles are off the piston rod coating material.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Samples** & **Sa** & **Sq** & **Sz** \\ \hline \multirow{4}{*}{**Cr**} & **New** & 18.347 & 21.610 & 91.968 \\  & Cr-1 & 8.367 & 10.239 & 66.107 \\  & Cr-2 & 7.417 & 9.032 & 48.706 \\ \multirow{2}{*}{**DLC**} & DLC-1 & 7.315 & 9.390 & 59.623 \\  & DLC-2 & 6.838 & 8.283 & 45.228 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Measurement results (\(\upmu\)m).  
Figure 20: Seal lip magnify 200 times. (**a**) Cr-1; (**b**) DLC-1; (**c**) New.
## 3 Results  
As described in Table 1, none of the swimmers of the world championships analysed reached or improved the respective world record. All final times resulted within 101% and 112% of world records. Race times in percentage of respective word records presented no differences between the 800 and 1500 m competitions, except for males (\(p\) = 0.00), elite (\(p\) = 0.02), and 100-105% (\(p\) = 0.02) swimmers that reached times closer to the respective world records in the 1500 m race. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & \multicolumn{5}{c}{**Race Time\% Record Time**} \\ \hline  & \multicolumn{3}{c}{**1500 m**} & \multicolumn{3}{c}{**800 m**} \\ \hline
**Subjects** & **n** & **Mean \(\pm\) SD** & **Mean \(\pm\) SD** & **SE** & \(\mathbf{p}\) \\ \hline All Athletes & 96 & 105.2 \(\pm\) 2.8 & 105.5 \(\pm\) 2.1 & 0.1 & 0.07 \\ \hline Males & 48 & 104.2 \(\pm\) 0.0 & 106.0 \(\pm\) 1.4 & 0.9 & 0.00 * \\ \hline Females & 48 & 106.2 \(\pm\) 3.0 & 104.9 \(\pm\) 2.5 & 0.5 & 0.43 \\ \hline Elite & 64 & 103.7 \(\pm\) 1.6 & 104.5 \(\pm\) 1.7 & 0.4 & 0.02 * \\ \hline Junior & 32 & 108.2 \(\pm\) 2.3 & 107.5 \(\pm\) 1.3 & 0.4 & 0.90 \\ \hline Medallists & 32 & 103.9 \(\pm\) 2.1 & 104.5 \(\pm\) 1.8 & 0.3 & 0.34 \\ \hline Non-medallists & 64 & 105.9 \(\pm\) 2.9 & 106.0 \(\pm\) 2.1 & 0.0 & 0.15 \\ \hline
100–105\% of WR & 48 & 103.1 \(\pm\) 1.1 & 103.6 \(\pm\) 1.1 & 0.4 & 0.02 * \\ \hline
105–112\% of WR & 48 & 107.7 \(\pm\) 2.1 & 107.1 \(\pm\) 1.2 & 0.4 & 0.76 \\ \hline \hline \end{tabular} \(p\): differences between 1500 and 800 m freestyle; *: \(p\) < 0.05.  
\end{table} 
Table 1: Competitions times. As shown in Table 2, sequence numbers were significantly higher in the 800 m competitions for all the swimmer groups. The maximal length of sequences of the same sign (\(-\) or \(+\)) were longer in the 800 m competitions, but the difference reached a significant level only when all swimmers were analysed as a whole (\(p\) = 0.01) and for the junior (\(p\) = 0.00), medallists (\(p\) = 0.04), and 105-112% (\(p\) = 0.04) groups. As displayed in Table 3, the time-series analysis revealed that the 1500 m competitions presented a significantly higher occurrence of \"true\" time series with respect to the 800 m competitions when all swimmers' results were taken as a whole (\(p\) = 0.01) and for the male (\(p\) = 0.04) and the elite (\(p\) = 0.02) groups. The coefficient of variation in velocity along the race (CV%) was not significantly greater in the 800 m competitions for all groups. Figure 1 illustrates how the normalised velocity of the whole group of swimmers in the 1500 m with respect to the 800 m competitions resulted significantly faster from the 5 th to the 12 th split (\(p\) = 0.00). The 1500 m velocity remained close to the mean velocity (100%) from the 3 rd to the 7 th split and declined afterwards until the sp
## 2 Background
### PyVHR
showed that video compression can also influence CHROM performance, decreasing from an average error of 1.78 BPM for uncompressed to an average error of 9.31 BPM for the most compressed videos evaluated [42]. Notably, all publications discussed in this section use specialised scientific cameras [31; 41] or specific webcam models [32; 39; 40] to avoid video compression. However, hypothetically this may not be necessary in some use cases as [PERSON] et al. also showed that light video compression only influences a small decrease in average error for heart rate measurements when calculating heart rate from a one-minute window of the BVP signal [42]. Other technical features of the video signal may also hypothetically influence the performance of rPPG systems. Video frame rate is one of these factors which has been investigated, researchers have suggested a minimum frame rate of 30 FPS [41; 43]. Less research has been conducted to explore the influence of video resolution on rPPG system performance. However, [PERSON] et al. suggest that both frame rate and resolution have a minimal impact on the performance of their rPPG system at obtaining heart rate estimations [44]. In _PyVHR_, the inclusion of each sub-process is supported by its positive influence on performance as shown in the publications in which they are proposed [3]. The patches approach for skin region is of interest is of particular relevance to this project as it has has been shown to negate some of the negative influence of unstable illumination on system performance, allowing for the selection of optimal regions of interest which facilitate the extraction of a BVP signal with a higher signal to noise ratio (SNR) [32; 40; 45]. A low SNR can lead to the true heart rate frequency not being the peak value of the power spectral density function for the measurement window, hence leading to an incorrect HR estimate (see Table 1).
## 5 Discussion
### Optimization Design of Fuel Injection Speed Under Different Gear Structure Parameters
#### 5.2.1 Lubrication Analysis of Bevel Gears with Different Speeds at a Fuel Injection Speed of 15 m/s  
This study was established based on literature data obtained through searches [38], initially setting the oil injection speed of the nozzle at 15 m/s. Subsequently, numerical calculations were conducted using the VOF two-phase flow model on the volume fraction distribution of gear lubricating oil under different pitch cone angles and rotational speeds, with the results detailed in Figure 19. Longitudinal comparative analysis shows that the gear oil volume fraction progressively decreases with a constant pitch cone angle as speed increases. This phenomenon may be related to the increased pressure above the gear engagement-in area at high speeds, resulting in a relatively more minor amount of oil passing over the gear surface at the same injection speed; additionally, the centrifugal force effect induced by high speed causes gear oil to be thrown outward from the central region, further leading to a decrease in oil volume fraction. On the other hand, transverse comparative analysis indicates that with constant gear speed, an increase in pitch cone angle leads to an expanded contact area, consequently causing the oil volume fraction to exhibit an upward trend.  
To quantitatively and systematically analyze the changes in oil volume fraction under different gear operating conditions, this paper selected six monitoring points for comprehensive research. These monitoring points are distributed at different positions in the gear meshing area to ensure the comprehensiveness and representativeness of the data. By calculating the average oil volume fraction at each monitoring point, the research team can more accurately capture the dynamic changes in the oil film during gear meshing. Based on these data, the relationship between oil volume fraction and gear speed under variable pitch cone angle was further analyzed, and the results are shown in Figure 20.  
The research results indicate that at different pitch angles, an increase in gear speed leads to a gradual decrease in oil volume fraction. This phenomenon may be related to the enhanced oil film shear effect caused by the increase in gear speed, which makes the oil film more prone to rupture or be squeezed out of the contact area during high-speed operation. It is particularly noteworthy that at a pitch angle of 4\({}^{\circ}\), the oil volume fraction of the gear exhibits a relatively high value. This may be because, at a 4\({}^{\circ}\) pitch angle, the contact area and contact pressure of gear meshing are relatively small, thereby reducing the shear and extrusion effects of the oil film and keeping the oil volume fraction at a high level.
## 3 Results and Discussion
### Milk Protein Hydrolysis  
B. subtilis produces protease, which hydrolyzes proteins, resulting in the production of peptides [24]. The protease activity and tyrosine equivalent content were analyzed in the co-fermented milk. The tyrosine equivalent content increased as more peptides were generated during protein hydrolysis. As shown in Figure 5, the protease activity was greatly increased during the first day of the alkaline fermentation with _B. subtilis_ HA, resulting in a higher tyrosine equivalent content. Protease activity indicated 17.21 U/mL and the tyrosine equivalent content also increased from 0.03 mg/g to 1.40 mg/g. When MSG was added, the protease activity in the milk co-fermented for 1 day ranged from 15.49 to 17.50 U/mL. Afterward, the protease activity gradually decreased to values ranging from 3.30 to 7.12 U/mL. During the 5 days of co-fermentation, the tyrosine equivalent content was gradually increased to 1.94 mg/g. However, the protease activity decreased, indicating that in acidic conditions, the protease produced by _B. subtilis_ hydrolyzed casein into peptides. Peptides have been reported to show bioactive properties such as enhanced absorption in the gut and angiotensin-converting enzyme inhibition; thus, peptide generation by fermentation is desirable [25].  
The hydrolyzed pattern of casein by SDS-PAGE analysis is shown in Figure 6. Casein as a major milk protein and \(\beta\)-lactoglobulin were hydrolyzed during the first day of the alkaline fermentation, resulting in the production of peptides with low molecular weight (<10 kDa). Both \(\alpha\)-casein and \(\beta\)-casein were completely hydrolyzed because of effective enzymatic digestion due to their fibrous structure. However, \(\beta\)-lactoglobulin (a globular protein), remained as a faint fraction after digestion. The most thoroughly digested protein fraction could not be detected in SDS-gel because of its low molecular weight. Therefore, alkaline fermentation of milk with _B. subtilis_ could be a novel means to fortify fermented products with peptides and \(\gamma\)-PGA.  
Figure 5: Protease activity and tyrosine content of milk co-fermented by _B. subtilis_ HA and _L. plantarum_ EJ2014. (\({}^{1}\)) 1: 0% MSG, (\({}^{2}\)) 2: 1.5% MSG, (\({}^{3}\)) 3: 3% MSG, (\({}^{4}\)) LAB: Lactic acid bacteria. Different letters indicate significant differences at \(p\) < 0.05 by [PERSON]’s multiple range test.
# Efficient Sleep-Wake Cycle Staging via Phase-Amplitude Coupling Pattern Classification
## 2 Material and Methods
### PAC Computation by Modulation Index and Comodulograms  
The phase-amplitude coupling (PAC) was quantified using the modulation index (MI), which essentially measures the amount in which the amplitude of a higher-frequency oscillation is modulated by the phase of a slower oscillation, following a methodology detailed elsewhere [29]. Briefly, the LFPs were subjected to finite impulse response (FIR) bandpass filtering in both the forward and reverse directions using the \"eegfit\" function in Matlab(r) 2023b to separate the LFPs into the low- and high-frequency bands of interest while also ensuring the elimination of phase distortion. Subsequently, the instantaneous phase of the low-frequency component was obtained, and the amplitude of the high-frequency oscillation was computed using the Hilbert transform. Afterward, a histogram of amplitudes of the high-frequency oscillation falling within the phase bins (\(N\) = 18, each spanning 20\({}^{\circ}\)) of the low-frequency oscillation was built. The resulting histogram/distribution \(P\) was then compared against a uniform distribution \(U\), by means of applying the Kullback-Leibler distance (\(D_{KL}\)), defined in general form by:  
\[D_{kL}(P,Q)=\sum_{j=1}^{N}P(j)log\left[\frac{P(j)}{Q(\hat{j})}\right]\]  
where \(Q\) is any distribution. The distance was then normalized to obtain \(MI\) by:  
\[MI=\frac{D_{k_{L}}(P,U)}{log(N)}\]  
Multiple values of the \(MI\) using many combinations of low- and high-frequency pairs were then computed to create the comodulograms. Twenty frequency bands for the phase (1-20 Hz, 1 Hz increments, and 1 Hz bandwidth) and forty frequency bands for the amplitude (5-200 Hz, 5 Hz increments, and 10 Hz bandwidth), amounting to 800 values, were considered for the different forms of input data construction (Section 2.4). These discrete frequency vectors were deliberately adopted to ascertain an optimal balance between capturing potential PAC patterns and mitigating unnecessary computational burden. By focusing on this targeted frequency range, we were able to discern the salient features pertaining to the PAC while minimizing the impact of irrelevant oscillatory components that did not add significant informational value. A comodulogram was computed for each one of the 480 available consecutive 30 s segments (a total of 240 min), without temporal overlap, of the LFP spanning the entire 4 h long recording period.
# Opportunities and Challenges of Smartglass-Assisted Interactive Telementoring
## 2 Related Work
### Interaction Styles of Telementoring
#### 2.3.2 Voice-Based User Interface  
A voice-based UI is another common style of interaction found in telementoring systems. Voice-based UIs are used between mentors and mentees to provide timely feedback and verbally communicate using conversations to request appropriate guidance. Unlike touch-based UIs, voice-based UIs can be implemented entirely hands-free, avoiding hygienic concerns for medical and surgical telementoring. The use of naturally spoken language is intuitive and highly expressive. Moreover, the performance of speech recognizers is greatly improved recently with artificial intelligence. Nonetheless, situations and environments involving many speakers, noisy workplaces, microphone quality, and the requirements of large vocabulary pose implementation and deployment issues. Often, a microphone is integrated with the device used for telementoring (i.e., smartglasses and notebooks). Previous telementoring studies using smartglasses have provided variations of voice-based UIs [13; 14; 16; 17; 18; 19; 23; 31; 42]. Figure 2 illustrates hands-free and voice-based interaction styles.  
Figure 1: Illustrations of touch-based interaction styles.
## 3 Damage Detection and Localization Scheme
iForest efficiently identifies anomalies in high-dimensional datasets by isolating data points, though it may struggle with complex local structures. Finally, OCSVM provides a robust, data-distribution-independent decision boundary, but its performance depends on kernel selection and parameter tuning. After detecting damage, data from various locations within the structural system must be integrated to facilitate localization through relative comparison. To achieve this, local TF information at each frequency is analyzed. More specifically, the local TF with the largest difference is identified at each frequency and the count of occurrences are updated until the entire frequency range is covered. The localization decision is based on the maximum number of occurrences over the frequency range of interest. The sensor pair that is recorded most frequently with maximum occurrence (\(n_{max}\)) is isolated, and the region between the respective sensors is marked as a potential damage location. The method of counting occurrences allows for emphasizing the shape-wise shift in the local TF due to damage, enhancing the reliability of damage localization. The localization index (\(DI\)) is defined as a normalized count by dividing this value by the total number of spectral lines. The normalization of the counts with the number of frequencies in the band of interest facilitates a clearer interpretation of the results. This process bounds the index to a maximum of 1, which occurs when the same local TF exhibits the highest difference between the tested and intact states across all frequencies in the range. Table 1 displays the steps of the methodology employed for damage identification methodology at each phase. The initial training phase includes collecting repeated measurements at the baseline state of the structure to build a training dataset for the selected DSFs. Using this dataset, OCC models are developed. The monitoring phase begins with data collected from an unknown state of the system. DSF vectors extracted from this state are tested against the developed models for damage detection. Finally, the localization stage employs a data fusion approach integrating sensor information deployed throughout the structure to identify the potentially damaged region.
## 4 Case Study
### Analysis of the Prediction Results
#### 4.2.2 Prediction Error Analysis
Based on the maximum absolute error value, the overall error of the tamping maintenance cycle prediction model for the 2171 track segments was controlled within three months. Combining the MAE with the percentiles of absolute error, it is evident that the prediction error for most sections is far less than the maximum error of 89 days. For long-term (1-5 years) tamping maintenance planning of heavy machinery, the planning cycle is typically divided into multiple fixed decision-making periods, during which the segments requiring tamping maintenance in each period are determined. The length of these decision-making periods is generally set to three or six months [31], allowing tamping maintenance operations to be scheduled within the available maintenance windows in each decision-making period. This provides flexibility for opportunistic maintenance strategies and reduces the track occupation costs. Considering the experience in setting the decision-making period, the range of prediction errors for this model is acceptable and can support long-term tamping maintenance planning decisions. Figure 14: Posterior distribution of parameters for the track segment \(k=1986\) via adaptive MCMC sampling: (**a**) parameter \(\beta_{k}\), (**b**) parameter \(\sigma_{k}\), and (**c**) parameter \(\theta_{k}\).