esa-sceva/satcom-chunk-collection huggingface dataset:
# Super-Resolution Lensless Imaging of Cells Using Brownian Motion
## 2 Materials and Methods
### Lensless Imaging System  
To facilitate the acquisition of living cell images, we proposed a lensless imaging system that was constructed using a near coherent light source, CIS, and microfluidic chip. The overall structure of the system is shown in Figure 1.  
The near coherent light source was composed of a blue light emitting diode (LED) and a pinhole. The common 3 W blue-emitting LED was monochromatic, and the 100-\(\upmu\)m-diameter pinhole ensured that the light through the pinhole was closer to the ideal point light source. The diffractive shadow image of the cell sample irradiated by this light source can reconstruct the focus image of the cell plane using the in-line holography technique. The microfluidic chip can ensure that the diffracted shadow of the cell sample injected by the injection pump is not aliasing on the surface of the image sensor. Simultaneously, microfluidic chips keep all cells in the same plane, which is more accurate than the cell counting plate. In the polydimethylsiloxane (PDMS) microfluidic chip, the height of the observation window was 30 \(\upmu\)m, which means that the cells were close to the surface of the CIS without affecting the movement of the cell in the plane direction. A grayscale CIS (Aptina MT9P031, Micron Technology, Boise, ID, USA) was used to obtain the holography images of cells. The pixel size of the CIS was 2.2 \(\upmu\)m, the effective pixel size was 2592 H \(\times\) 1944 V (5.7 mm \(\times\) 4.2 mm), and the imaging area reached \(\sim\)24.4 mm\({}^{2}\).  
In the lensless system, we continuously collected the shadow images of the cells in the observation window. In the multi-frame image, the motion direction of each cell was different due to the Brownian motion of the cells, and the super-resolution reconstruction could not be performed on the whole image. Therefore, only local multi-frame super-resolution could be applied to each cell image. To find a suitable super-resolution algorithm, we first needed to study the Brownian motion of cells. We observed the Brownian motion of cells under light microscopy, and obtained the statistical parameters of Brownian motion, which laid the foundation for selecting the motion estimation algorithm.  
Figure 1: The structure of a lensless imaging system: (**a**) the general structure of the system and (**b**) the observation window of the microfluidic chip.
## 3 Results
### Fault Diagnosis
#### 3.2.7 Kurtogram and Filters  
With the intention of selecting the most impulsive frequency bands, a Kurtogram has been used to identify the center frequency and bandwidth on which to configure the intervention of a filter (Figure 32).  
Figure 31: Wavelets + SES applied to the signal from sensor 7 on bearing 4 (last 163 s).  
Figure 29: ALP + SES applied to the signal from sensor 7 on bearing 4 (last 163 s).  
A 10 th order band-pass FIR filter has been chosen, which is able to slightly increase the signal Kurtosis (Figures 33 and 34; nevertheless, after the computation of the SES technique (Figures 35 and 36), it appears clear that the filter brought out a frequency band in which the fault components were not predominant, probably because the impulsiveness detected by the Kurtogram was related to phenomena different from the impacts caused by faults on the surfaces or was ascribable to the signal transmission path.  
Figure 33: Signal (blue) and its envelope (brown) from sensor 6 on bearing 3 (last 49 s) before (above) and after (below) the application of Kurtogram.  
Figure 32: Kurtogram applied to the signal from sensor 6 on bearing 3 (last 49 s) (**a**) and to the signal from sensor 7 on bearing 4 (last 163 s) (**b**).  
The use of an adaptive filter applied in the very same way is not beneficial either.  
Figure 34: Signal (blue) and its envelope (brown) from sensor 7 on bearing 4 (last 163 s) before (above) and after (below) the application of Kurtogram.  
Figure 35: FIR filter + SES applied to the signal from sensor 6 on bearing 3 (last 49 s) with harmonics of the BPFI drawn in magenta.  
Figure 36: FIR filter + SES applied to the signal from sensor 7 on bearing 4 (last 163 s) with harmonics of the BPFO drawn in red.
# Towards Lunar In-Situ Resource Utilization Based Subtractive Manufacturing
## 2 Methods and Materials
### Experimental Procedure
Moreover, Figure 9 schematically shows the execution and traversing strategy of the machining tests. After switching the water jet on, the abrasive supply is also switched on. Then, the lowest v\({}_{\text{f}}\)-level (Table 16) is applied in Section 1 of the workpiece (Figure 9). In Section 2, v\({}_{\text{f}1}\) is increased to v\({}_{\text{f}2}\) while m\({}^{\prime}\)\({}_{\text{f}1}\) is reduced to m\({}^{\prime}\)\({}_{\text{f}2}\). In Section 3, v\({}_{\text{f}2}\) is increased to v\({}_{\text{f}3}\) while m\({}^{\prime}\)\({}_{\text{f}2}\) is reduced to m\({}^{\prime}\)\({}_{\text{f}3}\). After machining, the kerfs on the jet inlet and outlet side \(b_{t}\) and \(b_{b}\) (Figure 10) are measured using a Makrolite light microscope from the company Eurovision. However, the angular error \(u\) is calculated from the two kerf widths using Formula (1) to evaluate the parallelism of the cut. \[u=\frac{|b_{t}-b_{b}|}{2}, \tag{1}\]  
If no complete cut through the material can be achieved, an evaluation of the angular error is not useful. For these cases, referred to as surface structuring, the erosion rate E is evaluated. The erosion rate indicates how much volume has been removed from the workpiece surface in relation to one gram of abrasive over a machined length of one millimeter. Furthermore, to assess the quality of the cut surfaces, the roughness Ra and Rz are measured in the lower 10% of the cut surface, as indicated in Figure 10. Nevertheless, \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Symbol** & **Value** & **Unit** \\ \hline Water jet pressure & P & 200, 300 & MPa \\  & & 45–63 & \\ Particle size fraction & SF & 63–90 & \(\upmu\)m \\  & & 90–125 & \\ Mass flow per 1 mm of machined workpiece & m\({}^{\prime}\)\({}_{\text{11-3}}\) & 0.3, 0.1, 0.05 & g/mm \\ Feed rate & v\({}_{\text{f}1-3}\) & 0.5, 1.5, 3 & mm/s \\ Abrasive material & - & LS-M100; & - \\  & LS-T100 & & \\ \hline Legend: With LS-M100 equivalent to sieved LX-M100, LS-T100 equivalent to sieved LX-T100. & & & \\ \end{tabular}
\end{table} 
Table 16: Processing parameters Al-Miloy. Figure 9: Traversing strategy of the machining tests. a Keyence VK-9700 confocal microscope (KEYENCE, Osaka, Japan) with a scanning plane height of 2 \(\upmu\)m was used to measure the kerfs and the cut surfaces. The variables to be measured and the positions of the variables to be evaluated are shown schematically in Figure 10.  
Following the test plan with the aluminum material (Table 16), individual cutting tests were carried out with the lithium silicate materials in the various states analogous to the tests described for the aluminum material. However, the parameter combinations used for the individual tests are summarized in Table 17.
## 3 SLD-GEDIR Protocol  
In VANET traffic environment, geographic- and position-based routing protocols perform better, particularly for ITS application warning systems [47]. In both types of protocols, packets are forwarded to all the vehicles within a specified geographical region. This increases forwarding overhead, wastes bandwidth, and results in looping problems. Researchers have focused their attention on solving these problems [48]. Due to the constraints of vehicular traffic environment such as high variations in vehicle speeds, unstable channel conditions, unreliable link quality, high collision probability, _etc._, the design of efficient routing protocols in VANETs has been a challenging task. The design of the SLD-GEDIR protocol is a sincere effort to address the abovementioned problems of geographic routing by closely analyzing some of these constraints. SLD-GEDIR is based on the following three main concepts:  
* Segment Vehicles
* One-Hop link quality Prediction
* Degree of connectivity of NHVThe following assumptions have been made in the design of SLD-GEDIR.  
* Hello (beacon) control messages are exchanged to identify current neighbors.
* Vehicles are equipped with GPS receiver, road/street digital map and sensors.
* Dedicated Short Range Communication (DSRC) protocol is used for vehicle to vehicle communication [12].
* No other communication infrastructure is required.
* MAC and Physical layer Protocols are considered to be functioning properly.
* Maximum Transmission range for all the vehicles is considered equal.  
The following subsections provide the mathematical formulations and details of the conceptualization of SLD-GEDIR.
# Numerical Study on Tree Belt Impact on Wind Shear on Agricultural Land
## 2 Methods
### Numerical Method
#### 2.2.2 Turbulence Modeling  
Turbulence was modeled using the realizable \(k\)-\(\varepsilon\) turbulence model, selected due to its proven reliability in predicting flows characterized by separation, recirculation, and strong velocity gradients, conditions often encountered downstream of porous vegetation structures, such as tree belts. Compared to the standard \(k\)-\(\varepsilon\) model, the realizable variant employed modified formulations for turbulence viscosity, significantly improving accuracy in highly sheared and turbulent regions [84; 85]. This model is widely validated and accepted in the literature for atmospheric boundary layer studies involving vegetation-induced momentum sinks [84].  
To accurately capture the near-ground velocity and turbulence profiles specific to atmospheric flows, modified wall functions adapted for neutral atmospheric boundary layers were implemented. These atmospheric wall functions incorporate the aerodynamic roughness length (\(z_{0}\)), necessary for correctly modeling wind velocity profiles near surfaces with vegetation [86]. The parameters adopted were \(z_{0}\) = 0.298 m and friction velocity \(u\) = 0.5 m/s, derived directly from the measured velocity data at the study site.  
The turbulence kinetic energy (\(k\)) and turbulence dissipation rate (\(\varepsilon\)) were set at the inlet using logarithmic vertical profiles fitted to the experimental data, ensuring realistic turbulence intensity representation within the computational domain.  
Figure 2: (**a**) Numerical mesh of the streamwise plane. (**b**) Tree belt mesh.
## 6 Experimental Verification
### Implementation of Control Logics  
LQ control logic was implemented as presented in Figure 5, and no further modifications were made. All three intelligent control logics were implemented to be as similar as possible in order to be comparable and in accordance with Figure 3. The similarity was achieved by putting the same number of neurons within three neural networks and selecting the same activation functions and initial values of weights. The number of neurons within the networks was determined empirically by trying the OENN + AOG structure with hidden layer neurons from 2-200. The best performances were achieved with 40 neurons in the hidden layer. The control problem determined the number of neurons in the input layer, and it was two for the MLS problem. The number of output neurons was 1. According to the determined structure, all three intelligent control logic were based on neural networks with the structure 2-40-1.  
Finally, the OENN+OEANFIS model and OENN + AOG structure required environmental stimuli for employing artificial glands. For that purpose, the following environmental stimuli were used. For OENN + OEANFIS, there was a single environmental stimulus used, a temperature. The temperature was measured by using an appropriate laboratory sensor. On the other hand, the point of OENN + AOG is to exploit numerous environmental stimuli in order to provide all relevant information to the control logic. To achieve that, the following stimuli were measured: magnetic field detection, temperature, humidity, vibration and the concentration of combustible gas in the air.  
The complete process of designing the OENN + AOG structure was performed using the developed algorithm in Sections 2-4 of this paper. To integrate the AOG mechanism, it was necessary to classify all determined stimuli into two categories, which was performed in the following way: the strength of magnetic field and vibration were classified as PES (two mediator glands), while temperature, humidity and the concentration of combustible gas were classified as SES (three leaf glands). Summarized, the AOG structure was in the format (3 leaf glands-2 mediator glands-1 root gland). At that moment, the classification of all introduced environmental parameters was a subjective process and was based on human evaluation of available stimuli and determination of their importance. In future work, the authors will try to establish a universal automatic approach for classifying all relevant environmental stimuli in two categories in a reliable manner.
# Human Activity Recognition Using CSI Information with Nexmon
## 8 HAR Classification and Performance Analysis
### HAR Classification Using Support Vector Machine for Experiment 1.1  
Support vector machines are well-known machine learning methods [44] for classification or regression problems. As in our case, we have multiple classes to classify, a multi-class approach is required.
## 3 Results and Discussion  
### Monitoring of Salami during Ripening Process  
Acidity in dry-cured meat products is linked to the growth of lactic acid bacteria (LAB), commonly used to start the salami-making process [28]. The acidification of meat during the first days of the ripening process is crucial to guarantee the attainment of a suitable pH, ensuring the microbiological quality of salami [29]. The pH monitoring of salami during the ripening process is shown in Figure 1.  
At the start of the fermentation process (dripping phase), the pH value for both salamis (SM and SR) was approximately 5.70. Subsequently, during the initial days of drying, a significant decrease in the pH values was observed in both salamis, reaching the lowest pH values of 4.80. This trend could be attributed to the activity of lactic acid bacteria (LAB), which play a crucial role in the first step of acidifying the meat and enhancing microbial safety through the predominant production of lactic acid [30]. The choice of different strains as a starter culture, selected for their ability to induce acidification, may influence the acidification rate during fermentation [28]. In fact, the use of a rapid starter culture in SR resulted in the swift attainment of a pH value of 4.80 within three days, contrasting with SM [31]. This pH value remained stable for the next 17 days. On the other hand, SM salami showed a slower and gradual pH decrease, taking five days to reach the same pH value of 4.80 as SR. However, after drying, the pH value did not remain constant but gradually increased, yet still staying below the threshold of 5.2, conventionally considered sufficient for achieving stable meat-based products [32]. The gradual increase in pH values observed in SM compared to SR may be explained by considering that during the ripening process, the casing surface of SM salami exhibited an even distribution of white mold (Figure 2b). The hydrolysis of proteins and lipids due to the presence of molds [33] contribute to the gradual increase in pH value in SM salami. On the contrary, SR displayed a non-uniform mold distribution on the casing surface (Figure 2a), probably due to the pH value that affected the diffusion of water from the inside of the surface of the salami [34]. At the end of the process, both types of salami experienced an increase in pH according to [PERSON] et al., 2018 [32], reaching a final value of 5.10 in SM and 4.90 in SR. This rise during the second half of the ripening process could be linked to basic compounds resulting from proteolysis, most likely influenced by both indigenous proteinases and the aminopeptidase and deaminase activities of the starter culture added for technological purposes [35]. At the end of the drying and ripening process, there were no significant differences in weight loss between the two types of salami (SM and SR). In particular, the weight losses of SM and SR at the end of the drying phase were 22.6% and 22.8%, respectively. At the end of the ripening process, the measured weight loss was 34.8% for SM and 35% for SR, aligning with the standards for fermented meat products [36].
## 5 Application of the Richards Model  
It can be seen from Figures 6 and 7 that the characteristics of the cumulative settlement deformation time history curve of each monitoring point were basically similar, and the settlement deformation rate also showed an obvious trend, that being that the settlement deformation had the basic characteristics of slow growth in the early stage, rapid growth in the middle stage, and basically being stable at a fixed value in the later stage. It reflects the characteristics of foundation settlement with the addition of additional load.  
According to the previous analysis, the Richards growth model has the characteristics of slow-fast-slow change. The initial value is small and gradually increases with time before finally stabilizing at a saturation value. The curve characteristics were highly consistent with the time history curve of surface settlement deformation of the fill foundation.  
The monitoring points CP20 and CP38 in this study were representative because they were located at the bottom of the gully at the original site, with a large filling thickness and continuous and complete monitoring data. The monitoring data were selected to draw the cumulative settlement deformation and time scatter plot, as shown in Figure 8. We used the monitoring results of these two monitoring points to estimate the parameters of Equation (3). The results are shown in Table 4 and Figure 8.  
Using the monitoring results of 25 monitoring points located in the filling area in this study, the average settlement deformation rate of each phase was calculated. A scatter plot of the settling rate over time is shown in Figure 9. We also drew a settlement deformation acceleration time scatter plot, shown in Figure 10.  
It can be seen from Figures 8-10 that the time history curves of the measured cumulative settlement, settlement deformation rate, and acceleration were highly consistent with the Richards model curve. The Richards growth model can be used to describe the time history characteristics of the settlement deformation of a loess-filled foundation and predict its settlement deformation.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \(a\) & \(b\) & \(c\) & \(d\) & **Std. Deviation** & **Correlation Coefficient** \\ \hline
21.0 & 1.0 & 0.015 & 0.25 & 0.671 & 0.995 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Parameter estimation results.  
Figure 8: Regression time history curve of cumulative settlement.  
Figure 11: Comparison chart of forecast results of different models.  
It can be seen from Table 5 and Figures 11 and 12 that, compared with the modified exponential model and hyperbolic model, the predicted values of the Richards model were more consistent with the measured results, and the standard error was only 0.87. Its curve was S-shaped, which is very consistent with the measured settlement time history curve, and it can reflect the development law of settlement deformation of a fill foundation well.
# A Novel Approach to Predict the Asian Exchange Stock Market Index Using Artificial Intelligence
## 3 Dataset
### Results and Discussion
While the difference between hybrid models may not be statistically significant, it has been discovered that using them produces better results in predicting the stock exchange close price than the traditional approach of deep learning models. Despite no significant and statistically meaningful difference between the hybrid models, this phenomenon continues. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Root Mean Squared Error 1**} \\ \cline{2-7}  & \multicolumn{2}{c}{**Conv. BLSTM 2**} & \multicolumn{2}{c}{**Conv. BGRU 3**} & \multicolumn{2}{c}{**Conv. BRNN 4**} \\ \cline{2-7}
**Index** & **Training** & **Test** & **Training** & **Test** & **Training** & **Test** \\ \hline \multicolumn{7}{c}{Mean Absolute Error 6} \\ \hline BSE & 0.0093 & 0.0112 & 0.0191 & 0.0203 & 0.0132 & 0.0146 \\ HSI & 0.0226 & 0.0236 & 0.0227 & 0.0222 & 0.0156 & 0.0159 \\ TWII & 0.0146 & 0.0202 & 0.0200 & 0.0201 & 0.0135 & 0.0166 \\ JKSE & 0.0205 & 0.0219 & 0.0198 & 0.0209 & 0.0294 & 0.0309 \\ JPXGY & 0.0124 & 0.0127 & 0.0231 & 0.0225 & 0.0223 & 0.0261 \\ KS11 & 0.0327 & 0.0341 & 0.0317 & 0.0314 & 0.0211 & 0.0231 \\ \hline \hline \end{tabular}  
* An indicator of how far off a regression model’s predictions are from reality is the Root Mean Squared Error (RMSE). 2 Bidirectional Long Short-Term Memory (BiLSTM) layer in a sequence model. To process data both forward and backward, the model employs two LSTM layers. 3 Two Gated Recurrent Units (GRUs) in a BiGRU process sequential data. Two receptors receive information backwards and forward. 4 In Bi-directional Recurrent Neural Networks (BRNNs), two hidden layers produce opposite outputs. Generative deep learning allows the output layer to learn from past and future data. 5 Forecasting system accuracy is measured by MAPE. To ascertain the accuracy, divide real values by each period’s average absolute percentage error. 6 The disparity is the gap between the actual and measured values.  
\end{table} 
Table 4: _Cont._ \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Root Mean Squared Error**} \\ \cline{2-7}
**Ref.** & **BSE** & **HSI** & **TWII** & **JKSE** & **JPXGY** & **KS11** \\ \hline Proposed Framework & 0.0122 & 0.0131 & 0.0125 & 0.0133 & 0.0156 & 0.0191 \\ [35] & 0.0143 & 0.0152 & 0.0152 & 0.0163 & 0.0184 & 0.0202 \\ [31] & 0.0139 & 0.0177 & 0.0192 & 0.0158 & 0.0213 & 0.0199 \\ [5] & 0.0147 & 0.0163 & 0.0179 & 0.0163 & 0.0198 & 0.0238 \\ \hline \multicolumn{7}{c}{Mean Absolute Percentage Error} \\ \hline Proposed Framework & 1.31\% & 1.54\% & 1.28\% & 1.66\% & 1.59\% & 1.48\% \\ [35] & 1.38\% & 1.41\% & 1.34\% & 1.78\% & 2.13\% & 1.68\% \\ [31] & 1.34\% & 1.42\% & 1.23\% & 1.79\% & 1.64\% & 1.56\% \\ [5] & 1.35\% & 1.49\% & 1.29\% & 1.82\% & 1.83\% & 1.60\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Result comparison of the proposed framework on six Asian Stock Indices and also with recently developed frameworks by other researchers.
## 3 Experiment Results and Analyses
### Dynamic Strain of Specim  
Dynamic strain is the deformation of an object, which not only changes when it is subjected to an external force, but also changes over time. Under the action of axial compression, the evolution in dynamic strain of the prefabricated fractured rock samples vs. the mean strain rate is illustrated in Figure 10.  
Figure 10: The evolution in dynamic strain of prefabricated fractured rock samples vs. average strain rate.  
Figure 9: Dynamic compressive strength changes in prefabricated fractured sandstone samples with mean strain rate.  
In Figure 10, the dynamic strain of the rock samples increases with the growth in the mean strain rate, and the dynamic strain increases approximately linearly with the mean strain rate. A clear strain rate effect is demonstrated.  
The dynamic strain of the prefabricated fractured sandstone samples changes linearly with the mean strain rate, and the fitting relationship is shown in Formula (3):  
\[\varepsilon=2.123+0.029\dot{\varepsilon}\quad(R^{2}=0.9815) \tag{3}\]  
where \(\varepsilon\) is the dynamic strain of the prefabricated fractured rock samples.