esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
We developed a 3D Cu-MOF in which AZPY connects 2D Cu-Glu layers in this study. Addition of AZPY in the 3D framework may be able to contribute to improving antifungal activity of Cu-MOF because AZPY is known to produce antimicrobial effect. As demonstrated in our results, antifungal activity of **Cu(AZPY)-MOF** is highly improved (over 98 % of fungal spores inactivated with 0.25 mg/mL **Cu(AZPY)-MOF** after 1 day)  
Figure 8: Concentrations of H2O2 and NO in 1 mL of PBS treated with **Cu(AZPY)-MOF** (**a**) and free AZPY (**b**). * \(p\) < 0.05, **_p_ < 0.01.  
compared to that of MOFs constructed in our previous studies. This improvement in antifungal activity does not seem to be caused by free CuII ions or AZPY released from **Cu(AZPY)-MOF** but by intercalation of AZPY in the framework. Our data suggest that intercalated AZPY may be involved in generating ROS. However, further investigations are needed. Nevertheless, our study demonstrates that addition of antimicrobial compounds into MOF structure can enhance the efficiency of fungal inactivation by MOF.  
The following are available online at [[https://www.mdpi.com/article/10.3390/app12010260/s1](https://www.mdpi.com/article/10.3390/app12010260/s1)]([https://www.mdpi.com/article/10.3390/app12010260/s1](https://www.mdpi.com/article/10.3390/app12010260/s1)), Figure S1: Structure of **Cu(AZPY)-MOF**, Figure S2: TGA profile for **Cu(AZPY)-MOF**, Figure S3: Concentrations of CuII ions released from 1 mg of **Cu(AZPY)-MOF** in 1 mL of H2O or PBS, Table S1: Crystallographic data for **Cu(AZPY)-MOF**, Table S2: The selected bond lengths (A) and angles (\({}^{\circ}\)) for **Cu(AZPY)-MOF**.  
The original idea was conceived by Y.K. and G.P. Experiments and data analysis were performed by S.Y., M.V., N.Y., W.K. and S.K.; the crystal structure was analyzed by Y.K.; manuscript was drafted by G.P. and Y.K. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Basic Science Research Program of the National Research Foundation of Korea, which was funded by the Ministry of Education, Science and Technology (2018R1D1A1B07045327, 2020R1F1A1070942, 2021R1A6A1A03038785).  
No applicable.  
Not applicable.  
The authors declare no conflict of interest.
## 3 Transient Voltage Stability Boundary
### Safe Voltage Magnitude  
There exists a safe voltage magnitude \(U_{\text{safe}}\) for \(U_{\text{sqg}}\). During voltage sag, as long as \(U_{\text{sqg}}\) is higher than \(U_{\text{safe}}\), the induction motor will not stall, no matter how long the voltage sag lasts [2].  
In Figure 3a, curve 1 is the electromagnetic torque curve of the induction motor under rated voltage, and the induction motor operates at point A initially. When the bus voltage drops slightly, the electromagnetic torque curve becomes curve 2, and the operating point of the induction motor is changed from A to A1. Even if the voltage is not restored to the initial value, the induction motor can operate stably. However, if the bus voltage drops sharply, there is no intersection between the electromagnetic torque curve and the load torque curve, and the slip of the induction motor will increase until the induction motor is stalled, as curve 3 shows.  
With the decrease of the bus voltage, the number of intersections between the electromagnetic torque curve and the load torque curve changed from two to one, and finally to zero. When there is only one intersection between the electromagnetic torque curve and the load torque curve, the bus voltage magnitude is the safe voltage magnitude \(U_{\text{safe}}\). As long as \(U_{\text{sqg}}\) is higher than \(U_{\text{safe}}\), the motor will not stall, no matter how long the voltage sag lasts. At the safe voltage magnitude, the intersection is located at the point with the maximum electromagnetic torque, and the slip at this point is \(s_{m}\), as Equation (6) shows. Equation (7) can be obtained when the slip \(s\) equals \(s_{m}\).  
\[T_{l}(s_{m})=T_{e}\Big{(}U_{\text{safe}},s_{m}\Big{)} \tag{6}\]  
\[\frac{dT_{e}(U,s)}{ds}\bigg{|}_{s=s_{m}}=0 \tag{7}\]  
Figure 2: Simplified voltage sag waveform.  
According to Equations (2) and (7), the expression of \(s_{m}\) can be obtained as Equation (8). The value of \(s_{m}\) has nothing to do with the voltage magnitude, \(s_{m}\) remains unchanged when the bus voltage magnitude changes.  
\[s_{m}=\frac{R_{r}}{\sqrt{\left(R_{s}+R_{I}\right)^{2}+\left(X_{s}+X_{I}+X_{r} \right)^{2}}} \tag{8}\]  
In general, the excitation reactance of the induction motor is relatively large. To facilitate a theoretical derivation, the excitation can be approximated to that of an open circuit, and the constant torque load can be adopted. Then, the expression of \(U_{safe}\) can be obtained as Equation (9), according to Equations (6)-(8).  
\[U_{safe}=\sqrt{2R_{t}T_{0}+2T_{0}\sqrt{R_{t}^{2}+X_{t}^{2}}} \tag{9}\]  
Here,  
\[R_{t}=R_{I}+R_{s},\ X_{t}=X_{I}+X_{s}+X_{r}\]  
From (9), it can be concluded that the safe voltage magnitude \(U_{safe}\) has nothing to do with the rotor resistance and the rotor inertia constant. The increase of the stator impedance, the branch impedance, the rotor reactance, or the load torque will increase the safe voltage magnitude, which is detrimental to the transient voltage stability.  
Figure 3: Diagrams illustrating two crucial parameters: (**a**) safe voltage magnitude; (**b**) te safe sag duration.
# Integrated Optical Phased Arrays for Beam Forming and Steering
## 5 Material Platforms
### III-V Platforms  
As one of the III-V semiconductor material, InP is an ideal integration platform for components operating in the wavelength range of 1.3-1.6 \(\upmu\)m [128]. In addition, various monolithtically integrated active and passive components have been implemented on the InP platform [79; 128; 129]. Potentially all components necessary for beam steering can be integrated on the InP platform [79]. The platform allows for the placement of amplifiers and lasers, ultra-high-performance modulators, and wideband photodetectors in the C and L band in PICs [129], and full PICs can be tested on wafer for the integration of sources and detectors [129]. Based on the integrated semiconductor optical amplifiers on the InP platform, high optical power for large-scale OPA applications can be potentially generated [79]. InP phase shifters with current injections enable up to a gigahertz bandwidth, and the tuning time of the widely tunable laser is reduced to a few nanoseconds [79], enabling very fast beam steering with InP OPAs. Furthermore, InP is a mature platform for large-scale photonic integration. The InP platform with standardization of photonic integration processing, which enables the realization of a broad range of applications with dramatically reduced cost, is a versatile and easily accessible generic integration platform for PICs [129]. This allows a scalable solution for large-scale PICs. An OPA based on the InP platform enabled an FOV of 10\({}^{\circ}\)\(\times\) 6.5\({}^{\circ}\) and a beamwidth of 2\({}^{\circ}\)\(\times\) 0.2\({}^{\circ}\). The longitudinal beam steering efficiency of the integrated tunable sampled-grating distributed Bragg reflector laser was 0.14 \({}^{\circ}\)/nm. Very fast beam steering (>10\({}^{7\circ}\)/s) in both dimensions was achieved [79].
## 2 Principles and Methods
### Hierarchical Frequency Fusion
#### 2.2.2 Fusion Criterion  
The amplitude of surface waves decreases exponentially with depth during propagation. When the depth is approximately equal to the wavelength, its energy decays rapidly, and its penetration depth is about a wavelength range. According to the half-wavelength theory, the surface wave velocity measured using microtremor survey can be considered as the average elastic medium reflection of the medium within half a wavelength depth, and the surface wave survey depth is about half-wavelength [29]. So, the detection depth can be written as follows:  
\[h_{\text{max}}=\frac{\lambda_{\text{max}}}{2}=\frac{V(f_{\text{min}})}{2f_{ \text{min}}} \tag{7}\]  
where \(h_{\text{max}}\) is the maximum detection depth, \(f_{\text{min}}\) refers to maximum reliable low frequency of the dispersion curve, \(\lambda_{\text{max}}\) and \(V(f_{\text{min}})\) are the wavelength and the surface wave phase velocity corresponding to the maximum reliable low frequency, respectively.  
The ideal exploration depth for the SPAC is 3-5 times the station spacing [30], based on the theoretical agreement between the SPAC and SI; in this paper, 5 times the distance is taken as the maximum ideal detection depth, so the maximum wavelength that can be detected by a single station pair can be expressed as follows:  
\[\lambda_{\text{max}}=\frac{V(f_{\text{min}})}{f_{\text{min}}}\leq 10d \tag{8}\]  
where \(d\) is distance of two stations. Equation (8) shows the mathematical model for maximum reliable low frequency and station pair distance. Figure 4 shows the principle of maximum reliable low-frequency calculation. When one calculates the \(V-f\) curve at 10 times the station distance according to Formula (8), the intersection point with the dispersion curve is the maximum reliable low frequency of the dispersion curve of the station pair.  
Figure 4: Principle of maximum reliable low-frequency calculation.  
Calculating maximum reliable low frequencies of the dispersion curves for each distance and then using these frequencies as the fusion boundary to perform hierarchical frequency fusion, the final fused dispersion curve can be written as follows:  
\[V(f)=\left\{\begin{array}{ll}V(f,d_{1})&f\geq f_{\text{min}}(d_{1})\\ V(f,d_{2})&f_{\text{min}}(d_{1})<f\leq f_{\text{min}}(d_{2})\\ \dots\dots\dots\\ V(f,d_{n})&f_{\text{min}}(d_{n-1})<f\leq f_{\text{min}}(d_{n})\end{array}\right. \tag{9}\]  
The final dispersion curve consists of a fusion of reliable segments of the dispersion curve for each distance, each segment reflecting the relative accurate phase velocity structure at its corresponding reliable depth. The hierarchical frequency fusion schematic is shown in Figure 5.
## 3 Results and Discussion
### Volatiles Evaluation
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Compound** & **Identification 1** & **RI** & \multicolumn{2}{c}{**Content (ug/g) 2**} & **Reference** \\ \hline  & & & **0 h** & **48 h** & **1 st Week** & **4 th Week** & \\ \hline  & & & Ketones & & & \\ \hline
2-Heptanone & B & 1178 & 0.2 \(\pm\) 0.04 b  & 0.1 \(\pm\) 0.02 c  & 0.5 \(\pm\) 0.05 a  & 0.3 \(\pm\) 0.10 b  & [48] \\
4-Methyl-2-heptanone & B & 1203 & 1.3 \(\pm\) 0.17 b  & 0.6 \(\pm\) 0.13 c  & 1.7 \(\pm\) 0.11 a  & \\
1-Hydrov2-2-propanone & B & 1294 & tr & tr & 0.1 \(\pm\) 0.03 a  & 0.1 \(\pm\) 0.04 a  \\
5-Methyl-3-hexen-2-one & C & 1230 & 0.1 \(\pm\) 0.03 a  & tr & 0.1 \(\pm\) 0.03 a  & 0.1 \(\pm\) 0.02 a  \\
4-Dimethyl-2-heptanone & C & 1241 & 0.1 \(\pm\) 0.04 b  & 0.1 \(\pm\) 0.02 b  & 0.2 \(\pm\) 0.03 a  & 0.2 \(\pm\) 0.03 a  & \\
6-Methyl-5-hepten-2-one & C & 1335 & 0.4 \(\pm\) 0.11 a  & 0.1 \(\pm\) 0.09 b  & tr & tr & [46] \\
2,7-Ctanclione & C & 1342 & 0.1 \(\pm\) 0.02 b  & tr & 0.2 \(\pm\) 0.05 a  & 0.2 \(\pm\) 0.05 a [FOOTNOTE:a]Footnote a: footnotemark:  
For instance, it has been reported that lower aliphatic aldehydes have a strong irritating odor, providing rancid flavors that can have significant effects on fermented liquid products due to their very low threshold value [52]. The decrease of aldehydes' concentration observed during fermentation at 4 \({}^{\circ}\)C of fermented chokeberry juice can be considered as a desirable outcome, improving the final aromatic profile of the juice. Terpenes detected in the chokeberry juice can contribute positively to the final aromatic profile, providing pleasant aromatic notes. More specifically, terpenes detected in fermented juice such as D-limonene, eucalypt, menthol, linalool, terpenen-4-ol, and \(\alpha\)-terpineol may contribute to the aromatic profile of fermented juices even at low or trace concentrations due to their very low threshold values. Terpenes were identified in fermented chokeberry juice during the entire storage period, without significant differences in their concentration. As reported in previous studies terpenes can enhance the shelf life of products acting as an antimicrobial [53; 54] and antioxidant [55]. Moreover, some terpenes have been identified as having significant health benefits for the consumer, for instance a-terpineol can act against _Helicobacter pylori_ or even provide anti-Leishmana activity [56]. The most important outcome at this point is the beneficial effect that terpenes can pose, which were provided constantly as their content did not alter significantly during storage, highlighting the good quality of fermented chokeberry juice.
# Feature-Based Image Watermarking Algorithm Using SVD and APBT for Copyright Protection
## 3 The Proposed Algorithm
### Preparation before Embedding  
Although a large number of feature points are obtained by SIFT, some feature points are still not suitable for watermark information embedding and extraction in terms of scale and orientation. Therefore, feature points should be screened and improved before embedding operation.  
In this section, the Airplane image is chosen to embed the watermark for representation. Figure 2 represents the obtained information of feature points after SIFT. The length of the blue arrow in the host image represents the scale, pointing to the direction of feature points, and 2050 feature points are picked out by SIFT, in total.  
Since the scale range of initially obtained feature points are 0-50, points with larger or smaller scales may not be easily detected or successfully matched. Therefore, a certain scale range of points should be selected as candidate feature points, and points whose scale is between 2.3 and 8 are selected as feature points. Then, the 8 \(\times\) 8 size of the neighborhood is chosen for each feature point to define feature regions for following operations. Additionally, feature points which are beyond the image boundary and have overlapping neighborhoods with others should be eliminated. Finally, feature points for embedding watermarks are obtained, as shown in Figure 3, where feature points are located in the center of the rectangles.  
Figure 3: Ultimate feature points for watermark embedding.  
Figure 2: Feature points of the host image by the SIFT algorithm.
## 4 Experiments and Analysis
### Measurement and Evaluation of Parameters of Leading and Trailing Edges
\tag{13}\]  
(3) Uncertainty of blade twist:  
The mathematical models of combined standard uncertainties of parameters \(\overline{\psi}\) and \(\eta\) can be derived respectively from Equations (1)-(3):  
\[u_{C}(\overline{\psi})=\frac{1}{2}[u_{C}{}^{2}(\beta_{1})+u_{C}{}^{2}(\theta_ {1})+u_{C}{}^{2}(\beta_{2})+u_{C}{}^{2}(\theta_{2})]^{\frac{1}{2}} \tag{14}\]  
\[u_{C}(\eta)=[u_{C}{}^{2}(\beta_{1})+u_{C}{}^{2}(\theta_{1})+u_{C}{}^{2}(\beta_ {2})+u_{C}{}^{2}(\theta_{2})]^{\frac{1}{2}} \tag{15}\]  
The uncertainty analysis results of parameters \(\beta\) and \(\theta\) are brought into Equations (14) and (15), then the uncertainties of parameters \(\overline{\psi}\) and \(\eta\) are obtained as follows:  
\[\left\{ \begin{array}{l}u_{C}(\overline{\psi})=0.004^{\circ}\\ u_{C}(\eta)=0.008^{\circ}\end{array} \right. \tag{16}\]  
The experimental results show that the absolute twist angular position \(\overline{\psi}\) of blade relative to the engine axis is \(-14.375^{\circ}\) and its uncertainty of measurement is \(0.004^{\circ}\), the relative twist angle \(\eta\) of blade itself is \(14.737^{\circ}\) and the measurement uncertainty is \(0.008^{\circ}\). The measurement method of blade twist presented in this study is effective and accurate, the problems of measurement and evaluation of the blade twist are solved commendably using technologies of calibration, a priori planning and geometric analysis.
## 3 Proposed Method
### The Embedding Algorithm  
The pseudocode for the embedding algorithm is as follows:  
In step 1.a.iii, since the number of blocks in the inner part and the outer part may differ, it is considered sufficient to pair at least 90% of the inner blocks with the outer blocks. Since pairing is performed using a secret key, the risk is not high for not pairing at most 10% of the inner blocks. Since the inner blocks are previously watermarked with a robust watermark, this presents no problem for the inner blocks. There might be a risk that, if someone knows the algorithm, they may try to find outer blocks that are not paired with an inner block and try to change the block contents. Firstly, since the pairing depends on a secret key, it is not possible to determine non-paired blocks without the key. Secondly, the non-paired outer blocks are not contiguously located--they are scattered along the exterior part of the image. Changing a 24 \(\times\) 24 block may not yield a desired malicious change in the image.  
In step 4, \(PSNR\_length\) takes into consideration the block size \(BS\_O\) of the outer blocks. \(BS\_O*BS\_O\) is the total number of pixel values of an outer block. In 6.b, while converting the LL1 value to a binary value, a threshold value is calculated to make the number of bits value 1 equal to the number of 0's in the whole of the inner blocks. As shown in step 6.c, the DCT of the outer block is obtained. After that, a zigzag scan of the DCT values is obtained. \(V\_exclude\) is the number of values that are not used from the beginning and from the end of the zigzag values. By doing this, we are not changing the DC component and the most precious low-frequency values as well as the high-frequency values. We try to preserve fidelity by not using high-rank, low-frequency components. By doing this, we try to make our watermarking method robust to Lossy compression, which destroys high-frequency components the most. In Figure 3, it is seen how an image is considered as the inner and outer parts and how the inner and outer parts are divided into different block sizes.
# Effects of Atmospheric Turbulence on Lensless Ghost Imaging with Partially Coherent Light
## 4 Experimental Measurement of the Ghost Image through Monostatic and Bistatic Turbulence in the Lab
the measured quantity of the image in the experiment is:  
\[g_{n}^{(k)}=\frac{g^{(k)}(u_{2},z)-1}{\left[g^{(k)}(u_{2},z)\right]_{\max}},(k=m, b). \tag{33}\]  
We see from Figure 7a1-d1 that the turbulence almost has no effect on the quality of the image in the monostatic case when the temperature of the hot plate is from 0-160 \({}^{\circ}\)C. As the distance \(l\) increases, the image is gradually blurred by the turbulence-induced degradation as the strength of the turbulence increases (see Figure 7a2-d2). When \(l~{}=~{}0.9\) cm, the turbulence in two paths develop into the bistatic case, and no image information can be obtained for the temperatures \(T~{}=~{}160\)\({}^{\circ}\)C and (see Figure 7a3-d3). The distance \(l\) is closely related to the types of the turbulence. Our experimental results are consistent with the theoretical analysis in Section 2. Next, we investigate the effects of the initial beam width on the quality of the image through the turbulence. Figure 8 shows the ghost image of the double slit at propagation distance \(z~{}=~{}0.5\) m for different hot plates' temperatures with the initial beam width being \(\sigma_{I}~{}=~{}4\) mm (first row) and \(\sigma_{I}~{}=~{}1\) mm (second row). The coherence width of the source is the same as that in Figure 6. One sees that the image quality has not much difference under the same strength of turbulence, while the image seems smoother in the second row than that in the first row. As we know, the beam size and coherence width increase on propagation due to diffraction. The smaller the initial beam width is, the faster the increase of the transverse coherence width on propagation is. Thus, the coherence width for \(\sigma_{I}~{}=~{}1\) mm is larger than that for \(\sigma_{I}~{}=~{}4\) mm in the receiver plane, implying that the speckle size in a single realization of the intensity profile for \(\sigma_{I}~{}=~{}1\) mm is relatively larger, which leads to a smoother image. Because our experiment was carried out indoors with the help of the optical table and the hot plane, whose sizes are limited, we were not able to show more experimental results about the influence of the propagation distance z on the quality and visibility of the ghost image although the propagation distances in Figures 7 and 8 are different. We plan to study the influence of the propagation distance on the ghost image in outdoor real turbulence in the future.
# Estimation and Adjustment of Interface Stiffnesses for Machine-Tool
## 2 Theoretical Foundation  
### Fast Fourier Transform  
In the real signal measurement and analysis, the signal is measured in the time domain first, and the Fourier transform is conducted for the measured time signal subsequently to facilitate the further analysis and interpretation. Because the measured vibration signal cannot fully express the vibration-related information in the time domain, it is necessary to use the data analysis tools to process the measured time domain signal, that is, to perform the fast Fourier transform. Switching from the time domain to the frequency domain can not only highlight the periodic characteristics that cannot be observed in the time domain, but it can also display the frequency components composed of the signal. The time domain signal is simple and direct, while the frequency domain signal is easier to understand with regards to the physical meaning within it. In addition, if the time domain and the frequency domain are regarded as a three-dimensional coordinate system, the observer actually observes the signal from different perspectives, while the time domain emphasizes \"when it happens\", and the frequency domain focuses on \"how often it happens\".  
As mentioned above, the vibration signals acquired in the time domain sometimes cannot perfectly present the vibration-related information, and the fast Fourier transform is often used in signal processing to convert the time domain signals into frequency spectra. The basic theory is to assume that the record of the acquisition signal is an infinite and continuous periodic signal, and its fast Fourier transform (FFT) mathematical form is as follows:  
\[F(\omega)=\int_{-\infty}^{\infty}f(t)\cdot e^{-j\omega t}dt \tag{1}\]  
\[f(t)=\frac{1}{2\pi}\int_{-\infty}^{\infty}F(\omega)\cdot e^{j\omega t}d\omega \tag{2}\]  
where \(f(t)\) represents the original time domain signal, t represents time, and \(\omega\) represents angular speed related to frequency domain.  
It can be seen from Equation (1) that the time interval of Fourier transform is from \(\infty\) to \(\infty\), so different frequency signals appear at any time, and after conversion, they can appear on the spectrum at the same time. The physical characteristics are easily observed as a time domain signal \(f(t)\) is converted to a frequency domain signal \(F(\omega)\), as shown in Figures 1 and 2. However, the above two conversions belong to the overall conversion, and the spectrum can only be obtained from the time domain representation of the overall signal, or the time domain of the signal can be obtained from the frequency domain representation of the overall signal. In addition, after Fourier transform, the frequency domain signal \(F(\omega)\) displays the amplitude and phase of the individual frequency components in the time domain signal \(f(t)\). It cannot display the change of each frequency component of the signal over time and, thus, it lacks its time information.  
Figure 1: Signal exhibited in time domain.