esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Theoretical Model
### System Optimization Evaluation Indicators  
Stochastic resonance triggers resonance synchronization between nonlinear systems and input signals, transforming noise energy to low-frequency signals and amplifying weak-signal features. Stochastic resonance shifts noise energy to low-frequency signals and amplifies the weak signal characteristics by triggering resonance synchronization between the nonlinear system and the input signal. The signal-to-noise ratio (SNR) and the correlation number are widely used as evaluation indicators of the output effect of the system. [PERSON] et al. [28] pioneered the formal definition of the SNR in the context of SR, establishing it as the ratio of the power spectral density at the output signal frequency to the background noise power spectral density at the corresponding frequency within a bistable system. Mathematically, this relationship is expressed as  
\[SNR=\frac{1}{S_{N}(\Omega)}\lim_{\Delta\Omega\to 0}\int_{\Omega-\Delta \Omega}^{\Omega+\Delta\Omega}S(\Omega)d\Omega, \tag{6}\]  
where \(S(\Omega)\) is the spectral density of the periodic signal and \(S_{N}(\Omega)\) is the spectral density of the background noise. However, this SNR calculation method is inherently limited to periodic input signals, providing only local signal-to-noise ratio measurements.  
Certain scholars have adopted the residence time distribution ratio [29] and the bit error rate as metrics for assessing the quality of output signals in systems. The residence time distribution ratio is particularly apt for appraising periodic responses within the realm of coupled stochastic resonance. The bit error rate serves as a criterion for evaluating binary digital signals that have undergone processing via stochastic resonance. Given that value-leakage signals typically exhibit non-periodic characteristics, this conventional approach is inadequate for evaluating random resonance output signals. To address this limitation, this study employed correlation coefficient gain as the optimization metric for the CMSR system. The correlation coefficient, which quantifies the linear relationship between two variables, ranges from \(-1\) to \(1\). When the absolute value approaches unity, it indicates a stronger correlation and consequently better system output performance. Mathematically, the correlation coefficient is expressed as  
\[r(X,Y)=\frac{Cov(X,Y)}{\sqrt{Var[X]Var[Y]}}, \tag{7}\]  
where \(Cov(X,Y)\) is the covariance between \(X\) and \(Y\), \(Var[X]\) is the variance of \(X\), and \(Var[Y]\) is the variance of \(Y\). The cross-correlation gain is quantitatively determined by comparing two key metrics: (1) the cross-correlation between the original signal and the input signal, and (2) the cross-correlation between the original signal and the output signal. This comparative analysis can be mathematically expressed as  
\[CcG=\frac{r(X,Z)}{r(X,Y)}, \tag{8}\]  
where \(X\) is the original signal, \(Y\) is the system noise input signal, and \(Z\) is the system noise reduction output signal.
# Studies regarding the Use of Pneumatic Muscles in Precise Positioning Systems
## 3 Results
### Evaluation of Hysteresis
#### 3.1.1 Evaluation of the Hysteresis for the Case of No Load Applied  
For the situation of no load applied to the free end of the muscle, the measurement data are presented in Table 3. The measured data are plotted in Figure 4, showing the evolution of axial contraction versus applied pressure.  
For each value of applied pressure, the results show that the axial contraction of the muscle at inflation is different from the obtained value at deflation. The bottom line represents the axial contraction while the muscle is being inflated, the top line represents the displacement during deflation.  
At deflation, the values are higher than the values obtained at inflation, the maximum difference between them being 3.29 mm in the middle pressure area (3.5 bar).  
Figure 4: Evolution of the axial contraction versus pressure (no load).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Pressure**} & \multicolumn{5}{c}{**Axial Contraction [mm]**} & \multicolumn{2}{c}{**Arithmetic**} & \multicolumn{1}{c}{**Standard**} \\ \cline{2-9}
**[bar]** & **1** & **2** & **3** & **4** & **5** & **Mean** & **Deviation** \\ \hline \multirow{9}{*}{Inflation} & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\  & 0.5 & 0.4 & 0.45 & 0.35 & 0.47 & 0.38 & 0.41 & 0.04427 \\  & 1 & 0.9 & 1 & 1 & 1 & 1.1 & 1 & 0.0632  
The arithmetic mean and the standard deviation illustrate the statistical dispersion of the measured values. The values of the standard deviation are noticeably small, close to zero, and indicate a low uncertainty of the measurements.  
Figure 5 presents the third-degree polynomial regression function, which best fits the obtained results at inflation. The red line represents the evolution of the measured values, and the colored zone represents the range of confidence.  
The polynomial regression that best fits the measured values for inflation is one of a third degree, as seen in Equation (1):  
\[\Delta l_{inflation}=0.096+0.079\cdot p+0.718\cdot p^{2}-0.053\cdot p^{3} \tag{1}\]  
The parameters of the regression were found using a specialized software, the Curve Expert Professional v. 2.7, and based on the measured values. The correlation coefficient (\(r\)) is 0.999 and the coefficient of determination (\(r^{2}\)) is 0.998. Those values validate the function.  
In Figure 6, the polynomial regression is presented, which best fits the obtained results at deflation.  
The polynomial regression that best fits the measured values at deflation is, according to Curve Expert Professional v. 2.7 software:  
\[\Delta l_{deflation}=0.296+0.684\cdot p+1.005\cdot p^{2}-0.118\cdot p^{3} \tag{2}\]  
Figure 5: Polynomial regression curve: inflation.  
Figure 6: Polynomial regression: deflation.  
The correlation coefficient (\(r\)) is 0.999 and the coefficient of determination (\(r^{2}\)) is 0.998. These values confirm that this function describes the evolution of axial contraction versus pressure at deflation with high fidelity.
## 3 Results and Discussion
### The Effects of HR on Toluene Adsorption  
The effects of RH of 40%, 60% and 80% on the adsorption of toluene was studied on 0.05 g of the ZIF-8/Air/300 \({}^{\circ}\)C adsorbent at 25 \({}^{\circ}\)C with a constant feed (toluene) concentration of 1000 ppmv in N\({}_{2}\) (as the carrier gas). The breakthrough curves of toluene adsorption on ZIF-8/Air/300 \({}^{\circ}\)C sample at RH of 40%, 60% and 80% are depicted in Figure 4.  
Figure 4: Breakthrough curves of toluene adsorption on ZIF-8/Air/300 \({}^{\circ}\)C at 40%, 60%, and 80% RH.  
Figure 3: (a) XRD pattern and (b) FT-IR spectra of ZIF-8 after activation at different conditions.  
Different adsorption parameters such as breakthrough point, equilibration time, adsorption capacity, and total adsorption percentage for the adsorbent examined at different RH are summarized in Table 2. While increasing of RH from 40% to 60% did not affect the breakthrough point (e.g., 55 min), however slightly decreased the saturation pint resulting in slight reduction of adsorption capacity (i.e., from \(\sim\)411 to 359 mg\(\cdot\)g\({}^{-1}\)). Further increasing of RH of feed stream to 80% resulted in substantial shortening of breakthrough point to 5 min and adsorption capacity to 230 mg\(\cdot\)g\({}^{-1}\) (Table 2).  
In general, MOFs structure has an amphiphilic property, in which metal clusters are hydrophilic and organic linkers are lipophilic [24; 48]. Both metal sites and linkers (e.g., functional groups) act as adsorption site in MOFs structure [24]. In the ZIF-8 structure, H\({}_{2}\)O molecules are adsorbed by Zn(II) sites (hydrophilic sites) and toluene molecules are adsorbed by 2-methylimidazol linkers (lipophilic sites). Although the Zn(II) coordinates are saturated with imidazole, however, some of Zn(II) ions are not completely coordinated with the linker during the ZIF-8 synthesis. This structural defects form open metal sites (defect sites) can act as water adsorption sites [49]. In addition, at higher RH, water adsorption can be increased because of capillary condensation [50]. The decreasing trend of toluene adsorption by increasing the RH from 40% to 60% and 80% can be attributed to the hydrophilic characteristics of metal sites in ZIF-8 structure.  
FT-IR spectra of the toluene saturated ZIF-8/Air/300 \({}^{\circ}\)C (i.e., after toluene adsorption at different RH) are illustrated in Figure 5a-c. Comparing these spectra with those of fresh adsorbent (Figure 5b) reveals that the adsorption bands at 3034 cm\({}^{-1}\) can be assigned as the =C-H stretch of the toluene aromatic ring. The band at 2925 cm\({}^{-1}\) is for -C-H stretch of methylene group and the peaks at 1614 cm\({}^{-1}\), 1506 cm\({}^{-1}\), and 1450 cm\({}^{-1}\) belong to the C=C aromatic ring stretching [51].
## 3 Testing Results and Analysis
### One-Dimension Consolidation Test  
The one-dimension consolidation test was based on ASTM D2435. The loaded weights were 25, 50, 100, 200, 400, 800, and 1600 kPa, and the loading time was 24 h. Seven sets of tests were conducted to examine untreated soil specimens, treated specimens with 4% and 8% cement content, and three dry density levels. The test results are presented in Figure 18. Figure 18a indicates that the compaction level of mudstone soil was the highest. Specimens with 4% cement content exhibited flat straight lines of compaction when dry density was increased, suggesting low compaction levels. Figure 18b displays a similar trend. The results were used to calculate the compression index C\({}_{\text{c}}\) and swelling index C\({}_{\text{s}}\). Figure 19a indicates that for specimens with C = 4% and dry density levels of 17.5, 18.5, and 19.5 kN/m\({}^{3}\), their corresponding C\({}_{\text{c}}\) values were 0.034, 0.031, and 0.022, respectively. For specimens with C = 8% and dry density levels of 17.5, 18.5, and 19.5 kN/m\({}^{3}\), their corresponding C\({}_{\text{c}}\) values were 0.028, 0.025, and 0.016, respectively. The C\({}_{\text{c}}\) value of the soil specimen without cement content was 0.142. The results suggested that an increase in cement content and dry density reduced compression indexes. Thus, the addition of cement was effective in inhibiting soil consolidation settlement. Figure 19b indicates similar results for the swelling index C\({}_{\text{s}}\). In 2015, [PERSON] et al. [50] proposed that clay's initial moisture content and LL considerably affect remolded clay specimens' compression indexes. The present study used specimens with low moisture content and a LL of 26.8%; thus, the C\({}_{\text{c}}\) value obtained in this study was not high. After cement paste was added, the compression index further decreased.  
Figure 19: A comparison of compression index C\({}_{\text{c}}\) and swelling index C\({}_{\text{s}}\) versus testing soils dry density levels: (a) C\({}_{\text{c}}\) versus \(\gamma_{\text{d}}\); (b) C\({}_{\text{s}}\) versus \(\gamma_{\text{d}}\).  
Figure 18: Compression curves of untreated and treated samples based on a soil consolidated test: (a) mudstone and mudstone–cement with C = 4%; (b) mudstone and untreated mudstone–cement with C = 8%.
## 3 Results and Discussion
### Modulation Length and Depth Analysis of Laser-Structured Surfaces
Finally, since the ablated line spatially extends as track energy and fluence increases,the periodicity of the ablated lines (\(\Lambda_{ablation}\)) also increases according to the readjustment of the scanning distance between ablated lines (see Section 2).The modulation depths reveals a similar behavior for all three types of periodic structuring. With increasing track energy, i.e., increasing local energy deposition, \(d_{LSFL}\), \(d_{u\text{-}ripple}\), and \(d_{ablation}\) increase, e.g., for \(d_{LSFL}\), from about 160 to 260 nm, for \(d_{u\text{-}ripple}\), from about 200 to 350 nm, and for \(d_{ablation}\), from 0.6 to 2.1 um, respectively. With increasing fluence, however, the modulation depth remains almost constant on the respective level determined by the track energy. Figure 2: A schematic surface model of the laser-generated hierarchic periodic nano-micro structures showing both laser-ablated trenches with superimposed u-ripples and LIPSS perpendicular to the trenches and ripples. Please note the different scale for the LIPSS which appear on a nano-micro scale whereas ablation trenches and u-ripples are on a micro-scale.
## 4 Remote Base Unit
### Hardware Description of the Remote Base Unit  
The main function of the BTU consists of decoding the data acquisition requests originating in the central server and perform the corresponding queries to each one of the RTUs within the wireless network. Thus, the RBU acts as an entry/exit point to the network, in other words, joins together two networks that use different base protocols, in one side a WSN (the one constituted by the RTUs) transmitting in a 900 MHz frequency using the band ISM (intended for Industrial, Scientific and Medical applications) and on the other side the a WAN interconnected by satellite networks. In Figure 5, the main blocks of a Remote Base Unit are illustrated.  
\begin{table}
\begin{tabular}{l l l} \hline
**Command** & **Description** & **Module** \\ \hline T,0 \(\times\)0d,0 & Temperature & Analogical 0 \\ H,0 \(\times\)0d,0 & Relative Humidity & Analogical 1 \\ P,0 \(\times\)0d,0 & Barometric pressure & Analogical 2 \\ S,0 \(\times\)0d,0 & Wind speed & Module CCP \\ R,0 \(\times\)0d,0 & Solar Radiation & Analogical 4 \\ L,0 \(\times\)0d,0 & Rain & External Interruption \\ D,0 \(\times\)0d,0 & Wind direction & Analogical 5 \\ Z,0 \(\times\)0d,0 & Restarts remotely the MCU & N.A. \\ C,0 \(\times\)0d,0 & Dew point & N.A. \\ V,0 \(\times\)0d,0 & Battery voltage & Analogical 10 \\ A,0 \(\times\)0d,0 & Set up the real time clock & Module I\({}^{2}\)C \\ \hline \end{tabular}
\end{table}
Table 1: List of commands decoded by the Remote Terminal Units.  
Figure 5: Main components of the Remote Base Unit.  
The satellite module is constituted by a Skystar 360E modem, which receives frequencies between the ranges from 11.7 to 12.7 GHz, decode and send them through a 10 Mbps Ethernet link. By using a category 5 UTP cable and RJ45 connectors, these signals are transmitted to an embedded RTL8019S network controller of the HTTP server which decodes the Manchester line code used by Ethernet and send them by using a SPI bus to the PIC18F6722 microcontroller (MCU). The MCU then, examines these queries and sends the corresponding commands to the RF Xtend module via RS232. Once the RTUs acquire data, they send the information back to the base which will retransmit it to the CS. In Figure 6, a photograph of the Embedded HTTP server is shown.
## 3 Results  
### GP_GAIN Configurations  
Table 5 summarizes the classification performance of the GP_GAIN method using different configurations. Noticeably, configurations with fewer shallow trees generally achieved higher classification performances. The best configuration used two trees and three depth levels, achieving the highest AUC of 0.69 and accuracy of 0.65. It also maintained an adequate balance between precision and recall. Thus, we selected this configuration for the internal and external validation experiments.  
Figure 4 shows an example of two features constructed by the best GP_GAIN configuration obtained from one of the 5-fold cross-validation experiments. The first tree with two levels of depth constructs a new feature from three texture features; the second tree with three levels of depth combines shape and histogram features to construct a new feature. These two constructed features thus retain information from all three radiomic classes, i.e., shape, histogram-based, and texture features.
## 3 Results and Discussion
### Extraction and Evaluation of Antioxidant Power
#### 3.1.2 1-Diphenyl-2-picrylhydrazyl (DPPH) Assay  
DPPH radical scavenging activity is one of the available tests used for estimating the antioxidant activity of natural products [42; 51]. The results (Figure 3) showed that ethanol 80% extracts have the highest antioxidant activity. Among the different species analysed, CF and CA are the two species that show the highest antioxidant activity displaying the lowest values of IC\({}_{50}\), followed by CI and CM.  
N-hexane, which is the least polar solvent, has a low extraction yield and moderate activity as a percentage of DPPH inhibition, while ethanol 80%, which was slightly more polar, was the best extraction solvent. In this case the more polar solvents, such as water, did not demonstrate high antioxidant activity.  
Figure 3: DPPH radical scavenging activity (IC 50, mg/mL) determined in extracts obtained from dried leaves of different _Calendula_ species (CM: _C. maritima_; CF: _C. fulgida_; CI: _C. hybrid_ (_maritima_\(\times\)_fulgida_); CA: _C. arvensis_), with different solvents (Results of the statistical analysis are shown in Supplementary Table S3).  
Figure 2: Total phenolic content (mg GAE/g) in extracts obtained from dried leaves of four Calendula taxa (CM: _C. maritima_; CF: _C. fulgida_; CI: _C. hybrid_ (_maritima_\(\times\)_fulgida_); CA: _C. arvensis_), with different solvents (Results of the statistical analysis are shown in Supplementary Table S2).  
Extracts obtained with SFE (Figure 2) showed high antioxidant activity, in particular CM extracted with SFE showed better antioxidant activity with IC\({}_{50}\) value (\(2.84\pm 0.37\)) significantly lower than the IC\({}_{50}\) of the ethanol 80% extract (\(3.81\pm 0.16\)).
# Compact Quad-Port MIMO Antenna with Ultra-Wideband and High Isolation
## 3 Quad-Port UWB MIMO Antenna Design
### S-Parameters Analysis  
Without sacrificing the size of the MIMO antenna, the isolation of the antenna is substantially improved, and the effectiveness of the decoupling can be verified by the transmission coefficient between the four ports of the antenna. One unit of the four-cell UWB MIMO antenna in Figure 8 is excited individually, and the remaining ports are matched with 50 \(\Omega\) load to check the reflection coefficient and transmission coefficient of the four ports, respectively.  
From Figure 9a,b, it can be seen that there is a small shift in the reflection coefficient for different end ports of the antenna at lower and higher frequencies, while the other regions can be well matched. Four ports are in the same position, so Figure 9a shows the port reflection coefficient: \(\mathrm{S}_{11}\approx\mathrm{S}_{22}\approx\mathrm{S}_{33}\approx\mathrm{S}_ {44}\), Figure 9b shows the transmission coefficient between adjacent ports: \(\mathrm{S}_{12}\approx\mathrm{S}_{23}\approx\mathrm{S}_{34}\approx\mathrm{S}_ {41}\), the transmission coefficient between two ports spaced by 2: \(\mathrm{S}_{13}\approx\mathrm{S}_{24}\), these can be observed in Figure 9.  
Figure 9: S-parameters of the four ports of the UWB MIMO antenna: (**a**) reflection coefficient with strips; (**b**) transmission coefficient with strips; (**c**) reflection coefficient without strips; (**d**) transmission coefficient without strips.  
To illustrate the role of introducing strips in reducing the coupling, the S-parameter simulation results without introducing strips are given in Figure 9c,d. The green line in Figure 9 represents S\({}_{11}=-10\) dB. Comparing Figure 9a,c, it can be seen that the reflection coefficients at each port of the MIMO antenna have better similarity when the strips are not introduced, and the return loss depth of the antenna at low frequencies is deepened and the impedance bandwidth is expanded to some extent after introducing strips. Comparing Figure 9b,d, it can be seen that the isolation of the antenna at both lower and higher frequencies is improved after introducing strips, especially at \(<\)4 GHz and \(>\)14 GHz. These are both observed in Figure 9, indicating that the coupling can indeed be reduced by introducing strips.
# A Comprehensive Survey of Masked Faces: Recognition, Detection, and Unmasking
## 5 Masked Face Methods
### Face Mask Recognition Approaches
#### 5.1.2 Multi-Stage Detection  
Multi-stage detection is a category of object detection algorithms where the detection process is divided into several sequential steps. In a typical multi-stage detector, such as RCNN, the first step involves identifying a set of potential regions of interest within an image, often through a technique like selective search. Subsequently, each region is individually processed to extract CNN feature vectors, which are then used to classify the presence and type of objects within those regions. This approach contrasts with single-stage detectors that perform detection in a single pass without a separate region proposal phase, trading some accuracy for increased processing speed.  
[PERSON] et al. [61] introduce a novel real-time face mask recognition technique. Combining one-stage and two-stage detectors, it accurately identifies individuals not wearing masks in public settings, supporting mask mandate enforcement. Utilizing ResNet50 as a baseline, transfer learning enhances feature integration across levels, and a new bounding box transformation improves localization accuracy. Experiments with ResNet50, AlexNet, and MobileNet optimize the model's performance, achieving 98.2% accuracy.  
Also, [PERSON] et al. [62] develop a two-stage architecture combines RetinaFace face detection with a CNN classifier. Faces detected by RetinaFace are processed to determine mask presence. The classifier, trained using MobileNetV2, DenseNet121, and NASNet, ensures efficient real-time performance in CCTV systems. [PERSON] et al. [63] develop a new dataset called RILFD, consisting of real images annotated with labels indicating mask usage. Unlike simulated datasets, RILFD provides a more accurate representation for training face mask recognition systems. The researchers evaluate machine learning models, including YOLOv3 and Faster R-CNN, adapting them specifically for detecting mask-wearing individuals in surveillance footage. Enhancing these models with a custom  
Figure 6: Illustration of the FMR-Net architecture for face mask recognition, depicting two-subtask scenarios: 2-class (with and without mask) and 3-class (with, incorrect, and without mask).  
CNN and a four-step image processing technique, they achieved an impressive 97.5% accuracy on the RILFD dataset, as well as on two other publicly available datasets (MAFA and MOXA).