esa-sceva/satcom-chunk-collection huggingface dataset:
# VDGA-Based Resistorless Mixed-Mode Universal Filter and Dual-Mode Quadrature Oscillator
## 6 Parasitic Impedance Effect Analysis  
The next nonlinear consideration is the effects of the VDGA parasitic impedances on the circuit performance. Figure 4 shows the practical behavior model of the VDGA, including corresponding terminal parasitics [30]. The real VDGA possesses high-impedance parallel parasitic elements (\(R_{p}//C_{p}\)), (\(R_{n}//C_{n}\)), (\(R_{z+}//C_{z+}\)), (\(R_{z-}//C_{z-}\)), and (\(R_{x}//C_{x}\)) at the respective ports p, n, z+, z\(-\), and x, together with a small serial parasitic resistance \(R_{w}\) at port w. Taking these parasitics into account, the nonideal values of \(\omega_{p}\) and \(Q\) for the proposed mixed-mode universal biquad filter depicted in Figure 2 are modified as follows:  
\[\omega_{p}=\sqrt{\left(\frac{g_{mA1}g_{mB1}}{C_{1}^{\prime}C_{2}^{\prime}} \right)\left(1+\frac{g_{mB2}}{g_{mA1}g_{mC1}R_{1}^{\prime}}\right)}, \tag{32}\]  
and  
\[Q=\frac{g_{mC1}}{\left[g_{mB2}+\left(\frac{g_{mC1}C_{2}^{\prime}}{g_{mB1}R_{1} ^{\prime}C_{1}^{\prime}}\right)\right]}\sqrt{\left(\frac{g_{mA1}C_{2}^{\prime }}{g_{mB1}C_{1}^{\prime}}\right)\left(1+\frac{g_{mB2}}{g_{mA1}g_{mC1}R_{1}^{ \prime}}\right)}. \tag{33}\]  
where \(R^{\prime}_{1}=(R_{p1}//R_{x1})\), \(C^{\prime}_{1}=C_{1}+C_{p1}+C_{x1}\), and \(C^{\prime}_{2}=C_{2}+C_{z2+}+C_{z2}\). Since it is known that \(R^{\prime}_{1}>>1\), the values of the external capacitors \(C_{1}\) and \(C_{2}\) should be designated as Equation (34) to avoid the parasitic effects shown in Figure 4.  
\[C_{1}>>C_{p1}+C_{x1}\ \ \ \ \ \text{and}\ \ \ \ \ C_{2}>>C_{z2+}+C_{x2}, \tag{34}\]  
Likewise, if the proposed dual-mode QO circuit in Figure 3 is evaluated in the presence of parasitics, the nonideal OC and \(\omega_{osc}\) may be derived as follows:  
\[g_{mA1}=g_{mC2}\bigg{[}1-\left(\frac{g_{mC1}}{g_{mB1}g_{mB2}}\right)\left( \frac{C_{2}^{\prime}}{R_{1}^{\prime}C_{1}^{\prime}}\right)\bigg{]}, \tag{35}\]  
and  
\[\omega_{osc}=\sqrt{\left(\frac{g_{mA1}g_{mB1}}{C_{1}^{\prime}C_{2}^{\prime}} \right)\left[1+\left(\frac{g_{mB2}}{g_{mC1}R_{1}^{\prime}}\right)\left(\frac{1}{ g_{mC2}}-\frac{1}{g_{mA1}}\right)\right]}. \tag{36}\]
## 3 Design of ACM Controller
### Fuzzy PID Control  
Fuzzy PID control mainly involves two aspects [32]. One is the hybrid structure of the fuzzy controller and the conventional PID, the other is the fuzzy self-tuning technique of the conventional PID parameters. In this section, the control block diagram of the hybrid fuzzy PID controller is shown in Figure 10, and the control block diagram of the parameter self-tuning fuzzy PID controller is shown in Figure 11.  
Hybrid fuzzy PID control combines the advantages of fuzzy control and PID control, is easy to implement, and has strong robustness. The fuzzy controller is used in parallel with the PID controller to obtain a hybrid fuzzy PID controller. The fuzzy control is the same as that in Section 3.7, and the PID control is the same as that Section 3.6. The output of the controller is the sum of fuzzy controller and PID controller.  
The basic idea of parameter self-tuning is to use fuzzy control rules to get the increment of PID control parameters, and add the increment to the initial value to realize real-time  
Figure 11: Parameter self-tuning fuzzy PID control block diagram.  
Figure 10: Hybrid fuzzy PID control block diagram.  
Figure 9: Input-output relationship of fuzzy controller.  
online adjustment of initial PID parameters. At this time, the three parameters of PID can be expressed as  
\[\left\{\begin{array}{l}K_{\mathrm{P}}=K_{\mathrm{P}}{}^{\prime}+\Delta K_{ \mathrm{P}}k_{\mathrm{pu}}\\ K_{\mathrm{i}}=K_{\mathrm{i}}{}^{\prime}+\Delta K_{\mathrm{i}}k_{\mathrm{iu}}\\ K_{\mathrm{d}}=K_{\mathrm{d}}{}^{\prime}+\Delta K_{\mathrm{d}}k_{\mathrm{du}} \end{array}\right. \tag{35}\]  
\(K_{\mathrm{P}}{}^{\prime}\), \(K_{\mathrm{i}}{}^{\prime}\) and \(K_{\mathrm{d}}{}^{\prime}\) are the values obtained by the method in Section 3.6, and \(k_{\mathrm{pu}}\), \(k_{\mathrm{iu}}\) and \(k_{\mathrm{du}}\) are the scale factors. \(\Delta K_{\mathrm{P}}\), \(\Delta K_{\mathrm{i}}\) and \(\Delta K_{\mathrm{d}}\) are the parameter increments obtained by fuzzy inference, and its fuzzy control rules as shown in Table 3.  
Parameter self-tuning fuzzy PID has the advantage of real-time adjustment of parameters, so it is chosen to represent the fuzzy controller.  
In the MATLAB/Simulink, the fuzzy toolbox and the PID control toolbox are called to get the fuzzy PID control for simulation.
## 2 Background
### Driving Technologies of Industry 4.0
#### 2.2.12 Augmented Reality (AR) and Virtual Reality (VR)  
Augmented reality and virtual reality represent variations of virtual environments that allow the overlay of digital images and objects onto the physical world using computational and graphical information [102; 103]. Augmented reality contributes to real-time data collection [101].
## 4 Experimental Results
### Individual Evaluation
#### 4.2.1 CID:IQ  
We evaluated our method on CID:IQ dataset by applying the protocol described in Section 4.1 (i.e., 5 fold cross validation). Table 3 shows the correlations for each viewing distance. The results were compared to our previous work (CNN-VD) [58], where only one CNN model with two outputs was used. As can be seen, high performances were obtained for the two viewing distances with close correlation values. Compared to CNN-VD, the correlations increased with an improvement in terms of PCC of 1.4% for the two distances.  
In Table 4, the correlation coefficients for each distortion, at five levels, are shown and compared to those of MSSIM and CNN QUALITY. In general, the performances were high for all distortions and viewing distances. The highest values were obtained for SGCK at 50 cm and at 100 cm, while the lowest ones were obtained for JPEG at 50 cm and JP2K for 100 cm. We also noticed that for SGCK, JPEG, GB, and DeltaE the proposed method has slightly higher coefficients for the 100 cm viewing distance compared to 50 cm, while it was the opposite for JP2K and PN. Compared to the MSSIM and CNN Quality metrics the proposed metric has good performance, giving a higher correlation value for JPEG, PN, SGCK and DeltaE for 50 cm and JPEG, SGCK and DeltaE for 100 cm. It is also noticeable that the proposed method is more stable compared to MSSIM.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{4}{c}{**50 cm (2.5\({}^{\star}\)H)**} \\ \hline
**Distortion type** & **Our method** & **MSSIM** & **CNN Quality** \\ \hline
**JP2K** & 0.819 & **0.851** & 0.826 \\ \hline
**JPEG** & **0.812** & 0.736 & 0.801 \\ \hline
**PN** & **0.836** & 0.811 & 0.792 \\ \hline
**GB** & 0.870 & 0.576 & **0.882** \\ \hline
**SGCK** & **0.913** & 0.736 & 0.814 \\ \hline
**DeltaE** & **0.919** & 0.792 & 0.837 \\ \hline
**100 cm (5\({}^{\star}\)H)** & & & \\ \hline
**Distortion type** & **Our method** & **MSSIM** & **CNN Quality** \\ \hline
**JP2K** & 0.735 & **0.825** & 0.804 \\ \hline
**JPEG** & **0.820** & 0.700 & 0.811 \\ \hline
**PN** & 0.793 & **0.838** & 0.771 \\ \hline
**GB** & 0.884 & 0.598 & **0.893** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Pearson (PCC) correlation coefficients between the predicted quality scores and the subjective ones for each distortion of the CID:IQ database.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{4}{c}{**50 cm (2.5\({}^{\star}\)H)**} & \multicolumn{2}{c}{**100 cm (5\({}^{\star}\)H)**} \\ \hline
**PCC** & **SROCC** & **PCC** & **SROCC** \\ \hline
**CNN-VD (NR)** & 0.858 & 0.855 & 0.858 & 0.826 \\ \hline
**Our method (NR)** & **0.870** & **0.867** & **0.870** & **0.846** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Pearson (PCC) and Spearman (SROCC) correlation coefficients between the predicted quality scores and the subjective ones for each viewing distance of the CID:IQ database. Highest values are in bold.
###### Abstract  
Steel bars, which are commonly used as reinforcements in concrete structures, are slender rods and are good conduits for stress wave propagation. In this paper, a lead zirconate titanate (PZT)-based steel bar corrosion monitoring approach was proposed. Two PZT transducers are surface-bonded on the two ends of a steel rod, respectively. One works as actuator to generate stress waves, and the other functions as a sensor to detect the propagated stress waves. Time reverse technology was applied in this research to monitor the corrosion of the steel bars with a high signal to noise ratio (SNR). Accelerated corrosion experiments of steel bars were conducted. The anti-corrosion performance of the protected piezoceramic transducers was tested first, and then they were used to monitor the corrosion of the steel bar using the time reversal method. The degree of corrosion in the steel bar was determined by the ratio of mass loss during the experiment. The experimental results show that the peak values of the signal that were obtained by time reversal operation are linearly related to the degree of corrosion of the steel bar, which demonstrates the feasibility of the proposed approach for monitoring the corrosion of steel bars using the time reversal method enabled by piezoceramic transducers.  
Keywords: piezoceramic transducer; steel bar; corrosion detection; time reversal method; structural health monitoring +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
### Wound Closure Assay with PEMF Stimulation  
Wound closure assay was performed to characterize FLS migration via PEMF stimulation following injury. Bovine FLS were seeded in two separate 12-well plates at 0.1 \(\times\) 10\({}^{6}\) cells/well and expanded until confluent using \(\alpha\)MEM supplemented with 10% FBS, 1% AA, and 0.5 ng/mL FGF-2. FLS were inflicted with a cruciform wound using a P20 pipette tip. Following injury, one plate was cultured within the PEMF chamber, which was active for 8 h per day, while the other plate received 0 h of electrical stimulation (sham) [37]. Resulting images of the wound were assessed at 0, 12, 24, and 48 h (Olympus IX-70 inverted microscope) following the initial cross-shaped scratch. Images were processed in ImageJ (convert to 8-bit, invert black and white) and percent closure area was quantified for both PEMF treated and sham FLS groups (\(n\) = 6 wells per treatment group).
# Calibrating the Attack to Sensitivity in Differentially Private Mechanisms
## 3 Adversarial Classification in Laplace Mechanisms
### Two-Sided Test
Let us then consider the first possible case of \(\mu_{0}<\mu_{1}\). In this case, we have either \(k_{2}<\mu_{0}<k_{1}<\mu_{1}\) or \(k_{2}<\mu_{0}<\mu_{1}<k_{1}\). On the contrary for \(\mu_{1}<\mu_{0}\), we have for the thresholds either of the cases \(\mu_{1}<k_{2}<\mu_{0}<k_{1}\) or \(k_{2}<\mu_{1}<\mu_{0}<k_{1}\). These different cases can be used for deriving an interval to include \(\Delta\mu\) as a function of the error probabilities, privacy budget and the sensitivity. **Corollary 1**. : _The absolute bias \(|\Delta\mu|=|\mu_{1}-\mu_{0}|\) induced by the adversary is confined in the following interval so that the defender detects \(X_{a}\) and preserves \((\epsilon,0)\)- DP_  
\[\frac{s}{\epsilon}\log\!\left(\alpha\tilde{\beta}^{\theta}\right)<\Delta\mu< \frac{s}{\epsilon}\log\!\left(\frac{1}{\alpha\tilde{\beta}^{\theta}}\right) \tag{40}\]  
_for \(\theta\geq 1\) where \(\alpha\) and \(\tilde{\beta}\) respectively are the significance level and the power of the test of (35)._Proof. : We begin with deriving the power of the two-sided test (35) as a function of the thresholds of the critical region. The probability of correctly detecting the attacker is as follows. \[\bar{\beta} =\int_{-\infty}^{k_{2}}\frac{\epsilon}{2\theta s}\exp\left\{\frac{ \epsilon(z-\mu_{1})}{\theta s}\right\}dz+\int_{k_{1}}^{\infty}\frac{\epsilon} {2\theta s}\exp\left\{-\frac{\epsilon(z-\mu_{1})}{\theta s}\right\}dz \tag{41}\] \[=\frac{1}{2}\exp\left\{\frac{\epsilon(k_{2}-\mu_{1})}{\theta s} \right\}+\frac{1}{2}\exp\left\{-\frac{\epsilon(k_{1}-\mu_{1})}{\theta s}\right\} \tag{42}\]  
Each addend in (41) corresponds to \(\bar{\beta}/2\) and can be rewritten for the thresholds as functions of the power of the test as \(k_{1}=\mu_{1}-\frac{s}{\epsilon}\log(\bar{\beta})^{\theta}\) and \(k_{2}=\mu_{1}+\frac{s}{\epsilon}\log(\bar{\beta})^{\theta}\). Combining this with \(k_{2}<k_{1}\) for the case \(\mu_{0}<\mu_{1}\), the bias is lower bounded as follows  
\[\mu_{0}+\frac{s}{\epsilon}\log\alpha <\mu_{1}-\frac{\theta s}{\epsilon}\log\bar{\beta} \tag{43}\] \[H_{1} :\text{ at least one of the equalities does not hold} \tag{35}\]0  
As for the upper bound we have  
\[H_{1} :\text{ at least one of the equalities does not hold} \tag{35}\]1 \[H_{1} :\text{ at least one of the equalities does not hold} \tag{35}\]2  
By analogy, we get the swapped upper and lower bound for \(-\Delta\mu\) for the second case of \(\mu_{1}<\mu_{0}\). Finally, we get the interval for the absolute bias as given by (40). This concludes the proof of the corollary.
# Real-Time Rain Rate Evaluation via Satellite Downlink Signal Attenuation Measurement
## 4 Direct Method for the Estimation of the Rain Rate  
### Limitations of the Statistical Models  
Though adequate for the prediction of the rain fading attenuation when dimensioning an operational margin of a satellite link, the above models definitely reveal themselves as not suitable for the inverse task; i.e., the derivation of an accurate rain rate estimate starting from signal attenuation measurements. Actually, customary statistical models only yield a relationship between the CCDF of the rain rate and that of the signal attenuation, i.e., \(\Pr(R>\overline{R})=p\rightarrow\Pr(A>\overline{A})=p\), while the relationship \(\Psi(\cdot)\) needed for the proposed NEFOCAST rain rate sensing system should be of the type \(\hat{R}=\Psi(\hat{A})\), where \(\hat{A}\) is a measured attenuation and \(\hat{R}\) is the corresponding estimated rain rate.
# A Calibration Method for Time Dimension and Space Dimension of Streak Tube Imaging [PERSON]
## 3 Calibration Method
### Time Calibration  
#### 3.1.1 Time Calibration Experiment  
For the convenience of later expression, the time calibration experiment is referred to as Experiment I.  
The effect of time calibration directly affects the range accuracy of the lidar. This paper proposes a method of gradually changing the delay of sweeping voltage to calibrate the time information corresponding to the position of the signal on the CCD. The process of the time calibration experiment is shown in Figure 4: (1) we chose a suitable experimental target, like a tall building with a height greater than the size of the light spot; the building has a flat part without windows to obtain a smooth streak signal on the CCD that is not affected by changes in the target distance; (2) we turned on the lidar to start working and adjusted the delay time of the sweeping voltage so that the streak signal would be on one edge of the image; and (3) we adjusted the delay time of the sweeping voltage step by step to move the streak to the other edge of the image. Every time a step was changed, 2000 images were recorded.  
In this experiment, one step of the delay was 10 ns; more intensive detection was also possible, but overly sparse detection was not feasible. Later on, we aimed to achieve a calibration resolution of 0.1 ns through interpolation, but sparse data would affect the accuracy of interpolation results. Thus, 2000 images were recorded with the same delay to take the average and reduce the impact of the jitters from the detection system, time-sequence control unit, atmospheric disturbances, etc. After data processing, we could obtain the corresponding time information of the signal based on its position on the image.  
Figure 3: The streak image of STIL under the influence of diffusion.  
Because the streak image of the building was smooth, except for the known angles at both ends of the stripe image, the absence of angle information at other positions is uncertain. Based solely on this experiment, we were still unable to achieve angle calibration.
## 2 Materials and Methods
### Methods
#### 2.3.4 Urban Aggregation Patterns and Hotspot Analysis  
Patterns of ecological quality within the context of urbanization exhibit significant spatial heterogeneity, with a discernible spatial dependence among pixels organized into grids, leading to either spatially aggregated or dispersed distribution patterns [83]. In order to obtain richer spatial details and more specific clustering hotspots, we converted the EQI grids into very small patches based on five EQI grades, rather than relying solely on administrative regions. After all, the division of administrative regions may hinder the understanding of spatial characteristics of some geographical elements [84]. Based on this setting, we adopted Exploratory Spatial Data Analysis (ESDA) to explore the spatial autocorrelation and detect the clustering patterns.  
Employing Exploratory Spatial Data Analysis (ESDA) has proven instrumental in assisting researchers in detecting spatial mechanisms, visualizing spatial aggregation, and identifying anomalies in the EQI [85]. The global Moran's I was used to gauge spatial autocorrelation and determine the presence of aggregation patterns or outliers in space [86]. However, global indices like global Moran's I obscure the spatial connections between local pixels and cannot signify cold/hot spots or spatial correlations. The adoption of Local Indicators of Spatial Association (LISA) metrics addresses this limitation, revealing aggregated and discrete spatial patterns [85]. We conducted the research in our settings by creating the queen contiguity weight matrix rather than inverse distance weighting method, which may cause some unpredictable and inexplicable problems in our settings when detecting hotspots and determining distance threshold [87]. The specific calculation formulas for the two indices are appended in the Appendix A. According to the LISA results, five clustering patterns were identified in the EQI assessment: High-High (hot spots), Low-Low (cold spots), Low-High, High-Low, and \"Not significant\".
## 3 Results and Discussion
### Strain Sensor  
We demonstrated the proposed electrode as a strain sensor. Achieving a high stretchable sensing range and high gauge factor in a strain sensor is challenging [43]. Although conventional strain sensors exhibit high gauge factor values for sensitivity, they have limited stretchable sensing ranges [44,45]. In contrast, while strain sensors exhibit relatively high stretchability ranging up to 70%, they exhibit low sensitivity due to a low gauge factor [46,47]. Figure 4a shows the design rule for determining the Au thickness of the proposed strain sensor. As the maximum gauge factor increases, the Au thickness increases,  
Figure 3: Optical images of the Au nanomembranes. (**a**) Pristine, (**b**) 100% stretched by a motorized X-translation stage, and (**c**) released after stretching (scale bar: 20 \(\upmu\)m).  
Figure 2: (**a**) Fabrication process of the Au–SEBS electrode. (**b**) Sheet resistance of the Au–SEBS electrodes varied by different Au thicknesses. (**c**) Photograph of the stretchable Au-SEBS electrode.  
and the electrical fracture strain decreases. To obtain a highly stretchable sensing range, we adopted a 30-nm-thick nanomembrane. For a higher gauge factor in a relatively narrow stretchable sensing range, a thicker nanomembrane can be considered. Figure 4b shows the design rule for determining the width of the nanomembrane. If the gold nanomembrane becomes thicker, its initial resistance value would be lower than before. However, the initial resistance value was saturated for a width of 3 mm, and no significant differences were observed even as the width increased. The gauge factor, compared to the same amount of strain, increased with a decrease in the initial resistance value. Therefore, we can conclude that the gauge factor value was the highest for the width of 3 mm. Figure 4c shows the design rule for determining the stretchable sensing range of the strain sensor. Although a 3-mm-wide nanomembrane can be stretched up to 400%, the linearity of the relative resistance decreases with an increase in the stretchable range. Linearity is a measure of the sensor performance. A low linearity indicates the need for sensor calibration, making the manufacturing process difficult [43]. Therefore, we adopted a 300% stretchable sensing range with a high gauge factor and moderate linearity (~0.67). Figure 4d shows the relative resistance-strain curves of the strain sensor. When the strain extended 300%, the gauge factor value was approximately 134, and an initial strain of 17% indicated residual strain. However, this value was negligible compared to the overall strain and resistance values. Figure 4e shows relative resistance-strain curve depicting the hysteresis of the selected strain sensor under a double sweep. There was not much difference in the relative resistance value within the loading-unloading cycle. Figure 4f shows the relative resistance-strain curve of the selected strain sensor from 100% to 300% during the loading-unloading cycle.
# Silica Mesoporous Structures: Effective Nanocarriers in Drug Delivery and Nanocatalysts
###### Abstract  
The application of silica mesoporous structures in drug delivery and the removal of pollutants and organic compounds through catalytic reactions is increasing due to their unique characteristics, including high loading capacities, tunable pores, large surface areas, sustainability, and so on. This review focuses on very well-studied class of different construction mesoporous silica nano(particles), such as MCM-41, SBA-15, and SBA-16. We discuss the essential parameters involved in the synthesis of these materials with providing a diverse set of examples. In addition, the recent advances in silica mesoporous structures for drug delivery and catalytic applications are presented to fill the existing gap in the literature with providing some promising examples on this topic for the scientists in both industry and academia active in the field. Regarding the catalytic applications, mesoporous silica particles have shown some promises to remove the organic pollutants and to synthesize final products with high yields due to the ease with which their surfaces can be modified with various ligands to create appropriate interactions with target molecules. In the drug delivery process, as nanocarriers, they have also shown very good performance thanks to the easy surface functionalization but also adjustability of their porosities to providing in-vivo and in-vitro cargo delivery at the target site with appropriate rate.  
silica; mesoporous; drug delivery; nanocarrier; nanomaterial; nanocatalysts +
Footnote †: journal: Applied Physics Letters  
_Applied_  
_sciences_  
_Accepted: 2020; _10_, 7533; doi:10.3390/app10217533