esa-sceva/satcom-chunk-collection huggingface dataset:
# Recognition of Blinks Activity Patterns during Stress Conditions Using CNN and Markovian Analysis
###### Abstract  
This paper investigates eye behaviour through blinks activity during stress conditions. Although eye blinking is a semi-voluntary action, it is considered to be affected by one's emotional states such as arousal or stress. The blinking rate provides information towards this direction, however, the analysis on the entire eye aperture timeseries and the corresponding blinking patterns provide enhanced information on eye behaviour during stress conditions. Thus, two experimental protocols were established to induce affective states (neutral, relaxed and stress) systematically through a variety of external and internal stressors. The study populations included 24 and 58 participants respectively performing 12 experimental affective trials. After the preprocessing phase, the eye aperture timeseries and the corresponding features were extracted. The behaviour of inter-blink intervals (IBI) was investigated using the Markovian Analysis to quantify incidence dynamics in sequences of blinks. Moreover, Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) network models were employed to discriminate stressed versus neutral tasks per cognitive process using the sequence of IBI. The classification accuracy reached a percentage of 81.3% which is very promising considering the unimodal analysis and the noninvasiveness modality used.  
stress; blinks; eye activity; convolutional neural networks; CNN; Markovian Analysis; Inter Blink Interval; machine learning +
Footnote †: journal: Medical and Computer Engineering  
0021  
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## 3 Results and Discussion
### Evaluation of Bubble Generation for Various Types of Electrodes
The macroscopic process of bubble generation can be viewed in Supplementary Materials Videos S1 and S2. Figure 5: The SEM image of various electrode surfaces: Plate-Pristine (PP), Plate-Micro/Nano (PMN), Mesh-Pristine (MP), and Mesh-Micro/Nano (MMN). Figure 6: The photograph of bubble generation at (**a**) MP and (**b**) MMN electrodes; (**c**) distribution of bubbles generated at each electrode. The optical evaluation of the bubbles, as shown in Figure 6c, revealed that while the mesh electrodes in the pristine configuration produced a higher rate of fine bubbles (below 100 nm) compared to the plate electrodes, the overall bubble generation rate showed no significant difference between the two. However, applying micro-nano structures to the electrode surface resulted in a more than twofold increase in bubble generation compared to the pristine configuration, with a particularly high concentration of fine bubbles in the 100-150 nm range. Consequently, the MMN electrode design was identified as the most effective configuration for enhancing EF performance.
## 4 Simulations and Results Analysis
### Security Degree  
In this section, we first present the behavior of the security degree \(g\), according to Equation (21). Then, we present the accuracy achieved by \(g\) in the simulations.
## 3 Time-Based Localization Method and Localization Performance in Numerical Examples
### Localization Performance in Numerical Examples
Notably, among all numerical examples, the maximum values of the mean and maximum relative errors (X direction) are 3.73% and 5.30%, respectively. Consequently, it proves, to some extent, the applicability of the proposed localization method in different ground and construction conditions.
## 3 Manufacturing Techniques for Wearable Antennas
### Comparison of Embroidery with Other Techniques  
Embroidery is advantageous compared to the others because embroidery machines already exist in industry; so it is easier to apply this technique for mass production of garments with integrated embroidered textile antennas. As the currents in embroidered antennas prefer to flow along threads rather than from thread to thread, embroidery may naturally lend itself to linear antennas such as dipoles or spirals. These types of structures can be very hard to fabricate using copper tape or Nora Dell cloths. The design of spirals or dipoles as opposed to patch antennas will also reduce the length of thread required and hence the cost of the antennas. Embroidery uses specialized thread which contains silver which is expensive. Note, the same is true for inkjet printing and Nora Dell. Therefore, the level of wastage of using manufacturing techniques will strongly affect the manufacturing costs.  
Embroidery allows repeatable geometries to be made via the computerized embroidery machines. As no mask is required, embroidery can also make one-off bespoke items. Repeatable structures as a Frequency Selective Surface (FSS) structure (Figure 9) can be embroidered by copying the original shape [57]. Fractal antenna technology [16] to design wearable textile antennas could result in attractive and compact designs, however, the accuracy of embroidery is limited to approximately one millimeter. This can be partially overcome by the use of computer aided embroidery. Additionally with embroidery the use of glue is not always a requirement to attach the textile layers together [58]. This can enhance the washability of the garment with the integrated antenna.
## 3 Results and Discussion
### Changes in pH Value and Soluble Solids Content
A decrease in pH value from 7 to even 30% as a result of VI of pepper, eggplants, and mushrooms with a lactic acid solution was also obtained by [PERSON] et al. (2018); [PERSON] et al. (2018); [PERSON] et al. (2018). These results also confirmed that VI treatments allowed improving pH reduction in samples in comparison to traditional dipping acidifying. In turn, the content of the soluble solids in the fresh apple cubes ranged from 10.9% for the Gala to 14.8% for the Red Jonaprince. These values are in a similar range (10.0%\(-\)16.8%) as other findings ([PERSON] et al., 2018; [PERSON] et al., 2018; [PERSON] et al., 2018). After VI, a significant decrease in soluble solids content was noted in the following cultivars: Boskoop, Cortland, Gala Must, Golden Delicious, Jonagold, Lobo, Red Jonaprince, and Shampion. The decrease ranged from 4.5% to 13.5%. In the case of the Gala cultivar with the lowest content of soluble solids, a slight increase was found, but it was not statistically significant. As in the case of the pH value, changes in the soluble solids content after VI were not directly affected by the initial soluble solids content in the raw material. Soluble solids of the impregnation liquid were close to isotonic. As found by [PERSON] and [PERSON] (2018) using the VI process of apples with 50% fructose corn syrup, differences in soluble solids content could lead to approximately 6% moisture loss and a 5.5% gain in soluble solids. However, the changes in these properties were just 1.5% for samples impregnated with 20% fructose corn syrup. In the VI process, the mass exchange is mainly caused by the mechanically induced pressure difference. The difference in osmotic pressure between the tissue and the solution plays a minor role in saturation compared to the mechanical properties of the tissue, such as porosity, size and shape of pores, connectivity, tortuosity, and capillary curvatures ([PERSON], 2018). However, osmosis could occur if the impregnation process was prolonged. The effect of the osmosis phenomenon on the content of soluble solids in the tissue was observed during cranberry impregnation with the relaxation time extended to 30 min ([PERSON], 2018). [PERSON] et al. (2018) studied the influence of pH and soluble solids content on the sensory characteristics of apples. They found that differences in pH values by 0.14 or soluble solids content by 1.38 could be identified as a change in sour taste. At the same time, sweet taste changes could be noticed when the difference in pH value is 0.25 and in soluble solids content is 0.99. VI of tested apples, in most cases, causes a greater difference in the pH
## 3 Methodology  
### Geophysical Corrections  
The space-borne radar altimeter is an essential tool for monitoring the oceans, but it can also be used for inland water surfaces, including lakes and rivers. The principle of altimetry can be found in [78] with application of geophysical corrections. In this study, corrections for the wet troposphere, dry troposphere, ionosphere, solid earth tide, geocentric pole tide and ocean loading tide were considered, as discussed by [PERSON] et al. (2008), and the geoid correction was applied. As the objective of the study was to directly exploit official Sentinel-3 L1B data and compare against Level-2 official products, the use of the geophysical corrections available in the Level-2 product were considered. The detailed corrections and their ranges are listed in Table 3([PERSON] et al., 2016). The Geoid model used is the Earth Gravitational Model 2008 (EGM2008)([PERSON] et al., 2016).  
To improve the altimeter range accuracy, which is related to the water level measurement accuracy, the waveform needs to be retracked precisely to determine the accurate tracking point located on the leading edge ([PERSON] et al., 2016). We tested three different retrackers, which are listed in Section 3.2, to find the tracking points precisely from the land-contaminated waveforms.
## 3 Test Arrangement and Procedure
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Specimen**} & \multirow{2}{*}{**Soil Type (USCS)** [58]} & \(\epsilon_{0}\) & \(PL\) & \(LL\) & \(PI\) & **Fines** & **Testing Conditions** \\ \cline{3-8}  & & [/] & [\%] & [\%] & [\%] & [\%] & \(\sigma^{\prime}_{1}\)=\(\sigma^{\prime}_{2\text{-}3}\) or \(\sigma^{\prime}_{1}\)/\(\sigma^{\prime}_{2\text{-}3}\) [kPa] \\ \hline RCBE\_1 & CIM & 0.835 & 21.1 & 43.6 & 23 & 97 & 50, 65, 50/100, 100 \\ \hline RC\_2 & CIM & 0.644 & 16.6 & 35.4 & 19 & 72 & 55, 75, 55/110, 110 \\ \hline RC\_3 & CIM & 0.827 & 20.1 & 46.0 & 26 & 95 & 55, 75, 55/110, 110, 220, 440 \\ \hline RCBE\_4 & SiH & 0.784 & 33.0 & 60.6 & 28 & 98 & 60, 80, 60/120, 120 \\ \hline RCBE\_5 & CIM & 0.848 & 23.0 & 49.7 & 27 & 99 & 65, 85, 65/130, 130 \\ \hline RCBE\_6 & CIH & 0.772 & 24.0 & 56.6 & 33 & 97 & 70, 95, 70/140, 140 \\ \hline RCBE\_7 & CIL & 0.718 & 20.5 & 33.2 & 13 & 80 & 75, 95, 75/150, 150 \\ \hline RC\_8 & CIH & 1.107 & 24.0 & 57.4 & 33 & 98 & 80, 105, 80/160, 160 \\ \hline RCBE\_9 & CIH & 0.905 & 23.7 & 55.3 & 35 & 97 & 80, 105, 80/160, 160, 320, 640 \\ \hline RCBE\_10 & CIM & 0.828 & 19.2 & 42.3 & 23 & 98 & 90, 120, 90/180, 180 \\ \hline RCBE\_11 & CIH & 0.814 & 25.8 & 55.7 & 30 & 92 & 90, 120, 90/180, 180 \\ \hline RC\_12 & CIM & 0.905 & 21.5 & 46.2 & 25 & 99 & 95, 125, 95/190, 190 \\ \hline RC\_13 & SiH & 1.066 & 30.0 & 54.4 & 24 & 96 & 115, 155, 115/230, 230 \\ \hline RC\_14 & CIH & 0.799 & 23.5 & 52.8 & 29 & 92 & 120, 160, 120/240, 240 \\ \hline RCBE\_15 & CIH & 0.620 & 20.2 & 52.8 & 33 & 81 & 120, 160, 120/240, 240 \\ \hline RC\_16 & CIL & 0.626 & 14.4 & 29.3 & 15 & 61 & 125, 165, 125/250, 250, 500, 750 \\ \hline RC\_17 & CIM & 0.873 & 17.3 & 39.6 & 22 & 85 & 125, 165, 125/250, 250, 500, 750 \\ \hline RCBE\_18 & CIM & 0.624 & 17.4 & 37.8 & 20 & 74 & 130, 175, 130/260, 260 \\ \hline RCBE\_19 & CIM & 0.616 & 17.2 & 38.6 & 21 & 91 & 140, 185, 140/280, 280 \\ \hline RC\_20 & CIL & 0.521 & 17.3 & 28.0 & 11 & 78 & 145, 195, 145/290, 290 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Properties of soil and testing conditions.
## 4 Results of Experiment and Simulation
### Description of the Coupling Effect  
In this section, the amplitude of the elevation gimbal is 0 degrees in the first 47.9 s (Stage A); the gimbal is assigned to perform a sinusoidal motion \(0.5\times\sin(0.334\times\pi\times t)\) from 47.9 s to 89.8 s (Stage B); the gimbal is assigned to perform a sinusoidal motion \(1.0\times\sin(0.1336\times\pi\times t)\) from 89.8 s to 200 s (Stage C), as shown in Figure 6. In order to describe the coupling effect caused by the elevation and vehicle movement, there is no control and no input applied to the azimuth gimbal. The vehicle is assigned to move with speed of \(0.5^{\circ}/\mathrm{s}\). Simulation and experiment results are demonstrated in Figure 7.  
As shown in Figure 7, the azimuth gimbal has a large angular output in both the simulation and experiment. The azimuth gradually increases due to the motion of the elevation gimbal. Apparently, the results are not what the controller engineer expected. Therefore, a decoupling controller is required.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Parameters** & **Values** & **Unit** & **Parameters** & **Values** & **Unit** & **Parameters** & **Values** & **Unit** \\ \hline J\({}_{\mathrm{ex}}\) & 0.51 & kg\(\cdot\)m\({}^{2}\) & J\({}_{\mathrm{ey}}\) & 0.54 & kg\(\cdot\)m\({}^{2}\) & J\({}_{\mathrm{ez}}\) & 0.59 & kg\(\cdot\)m\({}^{2}\) \\ J\({}_{\mathrm{ax}}\) & 1.49 & kg\(\cdot\)m\({}^{2}\) & J\({}_{\mathrm{ay}}\) & 1.56 & kg\(\cdot\)m\({}^{2}\) & J\({}_{\mathrm{az}}\) & 1.42 & kg\(\cdot\)m\({}^{2}\) \\ J\({}_{\mathrm{ad}}\) & 0.96 & kg\(\cdot\)m\({}^{2}\) & J\({}_{\mathrm{af}}\) & 1.37 & kg\(\cdot\)m\({}^{2}\) & N\({}_{\mathrm{a}}\) & 2.8 & N\(\cdot\)m/A \\ k\({}_{\mathrm{at}}\) & 0.42 & & k\({}_{\mathrm{ab}}\) & 0.42 & & R\({}_{\mathrm{aa}}\) & 1.23 & \(\Omega\) \\ f\({}_{\mathrm{ad}}\) & 0.3 & & f\({}_{\mathrm{af}}\) & 0.5 & & & & \\ \hline \end{tabular}
\end{table}
Table 1: Parameters of MSA system.  
Figure 5: Experimental two axis MSA system.
# Human-Like Walking with Heel Off and Toe Support for Biped Robot
## 3 Results  
### Walking Pattern Generation  
The role of the walking pattern generator is to output the desired trajectories of the CoM of the torso and the swing foot. In this paper, the cart-cable model shown in Figure 5 is adopted, with the cart representing the CoM of the robot, and the foot of the table representing the stance foot of the robot [32; 33]. It is assumed that the mass of the biped robot is lumped with the CoM of the robot, and the CoM is kept at a constant height. Based on the model, the relationship between the trajectories of the CoM and the reference ZMP can be described as [34]  
\[\ddot{x}_{\text{COM}}=\frac{g}{z_{\text{COM}}}(x_{\text{COM}}-x_{\text{ZMP}}) \tag{6}\]  
where \(x_{\text{COM}}\) is the horizontal position of the CoM, \(x_{\text{ZMP}}\) is the horizontal position of the ZMP, \(z_{\text{COM}}\) is the height of the CoM, and \(g\) is the gravity acceleration.  
The reference ZMP trajectory of the biped robot needs to be predefined according to the walking phases described previously. In the heel support phase (from time \(T_{k}\) to \(T_{k}+T_{ls}\)), the ZMP moves from the heel to the toe of the stance foot. In the toe support phase (from time \(T_{k}+T_{ls}\) to \(T_{k+1}\)), the ZMP is kept constant at the tiptoe. In the impact phase, the ZMP transfers from the tiptoe of the stance foot to the heel of the swing foot. Figure 6 shows the generated reference ZMP trajectory in the horizontal plane. Using Equation (6), the corresponding CoM trajectory is deduced. The trajectory of the CoM of the torso link can then be obtained by adding an offset to the reference CoM trajectory.  
The trajectory of the swing foot is planned using polynomial interpolation that satisfies certain constraints in a complete walking cycle. There are four constraints on the curve, including two positional constraints and two constraints on the gradient. Thus, a polynomial of degree four is adopted to describe the trajectory. The trajectory consists of two sections related to the heel support phase and the toe support phase. The coefficients are obtained respecting the continuity of position and velocity. Moreover, in order to preserve a smooth landing of the swing foot, the velocity before the impact moment is considered to be zero.  
Figure 5: The cart-table model with the cart representing the center of mass (CoM) of the biped robot, and the foot of the table representing the stance foot of the robot.
# Validation of Sentinel-2, MODIS, CGLS, SAF, GLASS and C3S Leaf Area Index Products in Maize Crops
## 2 Study Area and Data
### Leaf Area Index Satellite Products
#### 2.2.1 Sentinel-2 LAI  
The Sentinel-2 mission consists of a constellation of two satellites: Sentinel-2A and Sentinel-2B which both carry the Multi Spectral Instrument (MSI). The two satellites allow a repeat cycle of 5 days and take images in 13 spectral bands at spatial resolutions of 10 m (blue, green, red, near infrared (NIR) bands), 20 m (three vegetation red edge bands, narrow NIR band, two short wave infrared (SWIR) bands) and 60 m (coastal aerosol, water vapour, SWIR-Cirrus bands). We collected all the available Sentinel-2 L1C (Top-of-Atmosphere, TOA, reflectance) data (22 scenes) from 1 April 2019 to 31 October 2019 from the Sentinel data access hub (Available online: [[https://schub.copernicus.eu/](https://schub.copernicus.eu/)]([https://schub.copernicus.eu/](https://schub.copernicus.eu/))(accessed on 4 November 2021)). Only images without cloud cover in the study area were selected. The L1C data were corrected atmospherically to L2A data using the Sen2 Cor processor (Version 2.8) ([PERSON] et al., 2019). Then the Sentinel-2 L2A 10 m spatial resolution bands were resampled to 20 m spatial resolution using the nearest neighbor method. The Simplified Level 2 Product Prototype  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**LAI Products** & **Algorithm** & **Sensor/ Platform** & **Spatial Resolution** & **Temporal Resolution** & **Reference** \\ \hline Sentinel-2 & Neural networks & MSI/ Sentinel-2 & 20 m & 5-day & [39] \\ \hline MODIS V6 & Look-up-table & MODIS/ Terra + Aqua & 500 m & 4-day & [40] \\ \hline GEOV2: CGLS 1 km V2.0 & Neural networks & PROBA-V/ PROBA-V & 1 km & 10-day & [41] \\ \hline GEOV3: CGLS 300 m & Neural networks & PROBA-V/ PROBA-V & 300 m & 10-day & [10; 42] \\ \hline SAF EPS V1.0 & Gaussian process regression & AVHRR/ MetOp & 1.1 km & 10-day & [11] \\ \hline GLASS V5 & Neural networks & MODIS/ Terra & 500 m & 8-day & [32] \\ \hline C3S V2 & Look-up-table & PROBA-V/ PROBA-V & 1 km & 10-day & [13] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Multi-resolution LAI products investigated in the study.  
Processor (SL2P) algorithm [39] integrated in the Sentinel Application Platform (SNAP) was applied to derive Sentinel-2 LAI from Sentinel-2 L2A images. SL2P is a hybrid retrieval algorithm based on back-propagation Neural Networks (NNs) trained over a globally representative set of simulations from PROSAIL model [44]. The L2A reflectances at nine spectral bands (green, red, red edge, narrow NIR and two SWIR bands) were used as input of the NNs. In addition to effective LAI, the SL2P algorithm was here used to estimate the canopy chlorophyll content (cf. Section 4.2).
## 3 Results and Discussion
### Real-Life Applications  
In building and infrastructure construction, a theodolite is used to measure angles between designated visible points in the vertical and horizontal planes. In special tasks, however, such as long-range detection in the morning, the imaging quality is greatly reduced by atmospheric turbulence. In our project, a customized theodolite equipped with a high-speed camera was used to capture photo sequences (in gray scale) of targets over 1.5 kilometers away in the open air of a military base. The photographs were taken early in the morning on a sunny day in autumn. The focal length of the camera was 2000 mm; the exposure time was 4 ms; and the frequency was 50 frames per second. Some examples of the targets captured over long distances are shown in this paper. The deblurring results obtained using this method are shown in Figures 10-12. In Figure 10a, the numeric character on the watchtower is dim and illegible, and the stairs are unclear. In Figures 11a and 12a, the edges of the chimney and the signal pole are indistinct. Figure 10f clearly shows that the character on the watchtower and the outlines of the stairs are more detailed. The area around the edges of the chimney and pole are clearer than in the images obtained using other methods, as shown in Figures 11f and 12f.  
Figure 8: Statistics related to the average peak signal-to-noise ratios (PSNRs) for the comparison results of all the test images.  
Figure 9: Statistics related to the average structural similarity indexes (SSIMs) for the comparison results of all the test images.  
To evaluate the deblurring methods for images without ground truth, we used the Blind Image Quality Index (BIQI) and the Spatial-Spectral Entropy-based Quality (SSEQ) index as the non-reference image quality assessments (NRIQA). Both of these constitute a scoring mechanism: a 2-D image is inputted and then a score is outputted (100 represents the worst quality while 0 represents the best). The BIQI was proposed based on distorted image statistics (DIS), and the proposers found that the distortions of natural images have unique characteristics using DIS, and the images can be classified into distortion categories via these characteristics; details can be obtained from [40]. The SSEQ index utilizes down-sampled responses as inputs, then a local entropy feature vector of twelve dimensions can be extracted, and the image quality scores are generated from these features via a learning-based method [41]. Table 3 shows the original NRIQA data of a comparative analysis performed on the examples.
## 3 Case Study
### Prediction Model of SMA Parameters
#### 3.3.2 Model Building and Training  
According to the prediction framework introduced in Figure 3, we constructed eight prediction models for SMA parameters. The characteristics of SMA at time \(t\) can be expressed as:  
\[Y_{t}^{i}=f(x_{t-1}^{i},x_{t-2}^{i},\ldots,x_{t-nr}^{i},Y_{t-1}^{j},\ldots,Y_{t- n}^{j}), \tag{14}\]  
where \(i\) and \(j\) are the input and output characteristic parameters, respectively; \(n\) is the time window, which determines the time span of backtracking data. In this paper, the range of \(i\) was \(1\) to \(14\), representing the fourteen input parameters; the range of \(j\) was \(1\) to \(4\), representing the four SMA parameters. The time window was taken as \(5\), i.e., the SMA parameters at drilling cycle \(t\) were predicted in advance based on the information of five historical drilling cycles. To fix the size of the time window, we used the shift function in Pandas to shift the entire column. Since the order of tunneling data is important in our analysis, the random (that is, shuffle) of all algorithms was set to \"False\".  
After determining the basic parameters of the AI algorithms, it was also necessary to determine other hyperparameters, such as the number of trees in RF algorithm, the hidden layer nodes in BPNN, CNN, LSTM, and GRU algorithms, etc. For these hyperparameters, we used grid search to determine specific values [13; 61]. First, on the training set, we provided a series of priori candidate values of various model parameters, then all parameter combinations were searched through the grid. The \(5\)-fold cross-validation was applied to optimize the robustness of the model during the whole training process. Finally, we obtained the parameters' combinations that make the algorithm perform optimally. The hyperparameters used in AI algorithms are shown in Table 3. It is noteworthy that the input-output settings of the ML algorithms differ from that of the DL algorithms. For more physical meanings of algorithms hyperparameters and specific settings of input-output formats, please refer to [PERSON] [62] and [PERSON] [63].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameters** & **Average** & **Min** & **Max** & **Unit** \\ \hline _Time_ & \(876.51\) & \(193.55\) & \(2244.28\) & s \\ \(N\) & \(1.75\) & \(1.07\) & \(2.50\) & r/min \\ \(V\) & \(40.89\) & \(9.07\) & \(64.24\) & mm/min \\ \(N_{s}  
We trained four models for each algorithm, and each model was used to predict one of the four output variables. The ML algorithms were implemented using Scikit-learn 0.24.2, and the DL algorithms were implemented on Keras with TensorFlow 2.1.0 as the backend. All models were trained and optimized on the computer equipped with Windows 64-bit operating system, Intel Core i7-7700k 4.20 GHz, 8-core CPU with 32 GB RAM.