esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
This is because, through diverse surfaces (which include articles, medical instrumentation, and equipment), contamination by pathogens can also occur within these environments. The use of O\({}_{3}\) in the development of these technologies to promote decontamination or sterilization processes can be understood because of its powerful oxidizing action. Figure 1: Temporal analysis of number of patents. (**a**) evolution per priority year; (**b**) estimated year for patent expiration. When it decomposes, this gas generates an oxidizing reaction by the release of free radicals (ROS), including generating hydroxyl radical (OH-), which has greater oxidative potential (2.83 volts) than that of O3; and both act on the inactivation of bacteria, fungi, viruses, and protozoa [50; 51; 52]. O3 is an effective and practical antibacterial agent, useful for the inactivation of bacteria of medical importance. Studies showed that a single topical application by nebulization with a low dose of O3(g) completely inhibits the growth of potentially pathogenic bacterial strains with known resistance to antimicrobial agents, such as oxacillin-resistant _Staphylococcus aureus_, vancomycin-resistant _Enterococcus faecalis_, broad spectrum beta-lactamase-producing _Klebsiella pneumoniae_, and carbapenem-resistant _Acinetobacter baumanii_[51]. [PERSON] and [PERSON] [53] demonstrated that O3(g) at 25 ppm with a short exposure of 20 min and relative humidity of 90% is a bactericide for bacterial strains that commonly cause nosocomial infections. The biocide effect of O3, according to [PERSON] et al. [54], is very similar in both gaseous and aqueous forms. In fungi, studies using O3(g) and ozonized oil were performed with genera _Epidermophyto_, _Microsporum_, and _Trichpython_. Results showed that ozonized oil was more efficacious than O3(g) is, acting through an oxidative mechanism in cell membranes and revealing their fungicidal effects causing an increase in the evasion of sugar and electrolytes, an effect upon hydrolytic enzymes (such as amylase, lipase, and alkaline phosphatase), and inhibition of sporulation [55]. Regarding viruses, the scientific literature suggested that inactivation by O3 happens mainly by lipid and protein peroxidation [56; 57; 58]. Moreover, O3 can inactivate viruses, damaging their genetic material, both DNA and RNA [56; 59]. The efficacy of O3 against a variety of simple and complex viruses was demonstrated, including those enveloped and without envelope of DNA and RNA [56; 60; 61]. Some viruses were more susceptible to the inactivation of O3(aq) in short contact time. In about 1 min, 99% of inactivation occurred [35]. In addition, O3(g) is highly effective in inactivating the SARS virus with a rate of no less than 99% [56; 62]. Table 2 shows examples of studies that evaluated the effectiveness of O3 use against microorganisms.
# Intelligent Network Service Optimization in the Context of 5G/NFV
## 4 Methodology of MAPE's Analysis Service
### Employed Metrics  
As described in Section 3.2, our study focuses on the optimization of on-site video streaming. Thus, both networking and computational measurements play an integral part in the perseverance of the visual content quality. Furthermore, the QoE concept is adapted to ensure the adequate quality of the video content, despite the ITU's original definition. Moreover, the OE, and especially the AS module, should be able to determine the overall state of the system efficiently and as accurately as possible. In this context, a combination of infrastructure (OpenStack NFVI), application-level, and QoE metrics have been employed to produce an accurate image of the system. Additionally, several metrics from collectd have been selected [48]. The collection of the utilized metrics is summarized in Table 2.  
Figure 3: Components of MAPE’s AS module.
## 5 Analysis of Numerical Calculation Results on Support Effect for Fractured Loose Rock Slopes  
### Design of Support Scheme for Excavation Slope  
According to the Specifications for the Design of Highway Landslide Stabilization (JTG/T 3334-2018) and the Code for the Design of Slopes of Hydropower Projects (NB/T 10512-2021), the principle of \"preliminary shaft, strengthening the waist and fixing the foot\" is considered in the support scheme for the excavation treatment of the cataclastic loose rock slope of the mixing plant. The use of micropiles is proposed to strengthen the toe and top of the slope, and MacMat reinforcement is implemented to protect the surface of the slope and prevent local rockfall on the surface. This procedure is conducive to the subsequent greening treatment. The lock mouth micropile has a wall thickness of 6 mm and a diameter of 10.8 cm and is composed of three \(\varphi\) 25 reinforcing bars. The micropile is poured in an M30 cement mortar. Two rows of micropiles are set on the platform in a 9/12 m plum blossom arrangement and arranged with spacing of 2 m. The row spacing is 1 m, the rock entry position is \(\geq\)2 m, and the specific support design is as shown in Figure 19.  
The engineering analogy method is selected to determine the mechanical parameters of the supporting structure. The values of the physical and mechanical parameters of the locking micropiles and reinforced MacMat are shown in Table 3.  
Figure 19: Schematic diagram of the support.  
Figure 18: Deformation rate monitoring curve.
## 7 Research Gaps and Future Opportunities
### Protection against Communication Attacks
#### 7.1.3 Lack of Context  
Blockchain-based solutions have no information about the actual supply chain process-except transaction details and related information of business parts-through which the information has been generated or exchanged. Therefore, they are unable to understand the actual context of the compromise, which is very critical in detecting modern day multi-stage attacks with cascading effects. To support automated decision making in the supply chain, knowing context of undesired incidents is a key; without a context, it will not be possible to automatically mitigate impact of such threats and recover from such incidents.
## 2 Data and Methods
### Generalized Additive Models  
Regression models known as generalized additive models (GAMs) are specifically engineered to capture complex non-linear relationships between a response variable and one or more predictor variables [22]. Different from traditional linear regression models such as generalized linear models (GLMs), these models perform better on regression tasks because they can handle more complex correlations found in the data. GAMs provide a more flexible and smoother approach than GLMs, which are limited to linear connections and impose distribution assumptions on the response variable [23]. In order to circumnavigate these restrictions, Additive models were developed. These models enable the modeling of non-linear connections by the summation of many predictor variable functions, or basis functions. A common basis function that is essentially the summation of simpler piecewise polynomials is the spline, particularly the b-spline. The ability of GAMs to handle a variety of response variable distributions without the need for predetermined assumptions is a significant feature.  
From a mathematical perspective, a GAM defines the relationship between a random variable \(Y\) and a series of predictor variables \(X_{1}\), \(X_{2},\ldots,X_{p}\) through summation:  
\[\mathcal{E}\{Y|X_{1},X_{2},\ldots,X_{p}\}=f_{0}+\sum_{j=1}^{p}f_{j}(X_{j}) \tag{1}\]  
In this equation, \(f_{j}(\cdot)\) represents smooth nonparametric functions (natural b-splines) that are standardized (penalized b-splines) so that \(\mathcal{E}\{f_{j}(X_{j})\}=0\)[22]. For regression tasks, generalized additive models essentially provide a more flexible and potent tool, particularly when working with intricate, non-linear correlations in the data. A trade-off between model complexity, smoothness, and goodness of fit usually determines the number of splines in a GAM. A model involving lesser splines may be computationally cheaper to run, but it may also be underfitting; similarly, a model that has numerous splines may be more complex and may be overfitting. When disturbances such as noise and artifacts are present, GAMs ensure that outliers are treated differently and will, for example, highlight the associated uncertainty with wide confidence intervals and at the same time not let the outliers disproportionately influence the regression outcome and lessen the effect of a predictor variable for the majority of the data [24].
# On-Orbit Polarization Calibration for Multichannel Polarimetric Camera
## 2 The Multichannel Polarimetric Camera and Main Polarization Elements
### On-Orbit Rotary Analyzer Polarization Calibration Model Based on Polarization Optics Theory
The calibration accuracy of the method is restricted to the performance of the solar diffuser, and a high control accuracy of the microrotation is required.
## 2 Model of Four-Plate Parallel DCPT Coupler
### Simulation Model of Four-Plate Parallel DCPT  
Figure 4 illustrates the four-plate parallel DCPT model analyzed in this study, with the specifications detailed in Table 1. The transmitter comprises plates P1 and P2, while the receiver consists of plates P3 and P4. Considering its design on a PCB substrate, the model uses a 0.035 mm thick copper sheet and a 1.52 mm thick dielectric substrate. In Figure 4a, the width L of both the transmitter and receiver substrates is 90 mm, the length TW of the  
Figure 3: Equivalent circuits of CPT coupler. (**a**) Basic equivalent circuit; (**b**) practical equivalent circuit; (**c**) simplified practical equivalent circuit.  
Figure 2: Topology of CPT system.  
transmitter substrate is 165 mm, and the length RW of the receiver substrate is 70 mm. The transmitter and receiver are separated by a gap of 10 mm.  
Figure 4b depicts the circuit components, including the ports and matching circuits for the transmitter and receiver shown in (a). The transmission line width is 1 mm, and the positions of passive components reflect their actual sizes. On the left is a top view of the transmitter circuit, while on the right is a bottom-up view of the receiver circuit. The hole at the edge of the substrate is a support-fastening part used to secure the coupler during manufacturing. To accommodate practical design considerations, additional soldering pads for securing SMA connectors are included.  
The simulation results of this model are used to determine the self-capacitance and mutual capacitance, which are parameters of the equivalent circuit model. The mutual capacitance and transmission characteristics are observed as the receiver moves along the x-axis. Observations are made from \(-\)30 mm to 120 mm, covering the range where the receiver fully moves out of the area corresponding to the transmitter's metal plates. Additionally, to observe the efficiency under misalignment, the transmission characteristics are observed in the range of \(-\)50 mm to 50 mm along the y-axis. The component values of the matching circuit, along with the self-capacitance and mutual capacitance calculation results, are described in conjunction with the equivalent circuit model.
## 2 Project Example Overview
### Monitoring Scheme and Actual Measurement  
Two kinds of monitoring methods (i.e., GB-RAR and precision leveling) were used to monitor the settlement deformation and evaluate the safety of Wuhan Metro Line 11 as it crossed the ELH bridge. The GB-RAR deformation monitoring adopted the IBIS-S system, and the interferometric radar was installed at a stable position near the No. 5 pier (Figure 1). During the monitoring period, the interferometric radar adopted the real-time monitoring mode, the maximum monitoring distance was set to 200 m, and the sampling frequency was set to 20 Hz. The main configuration parameters for IBIS-S system monitoring are shown in Table 1. Figure 2 shows the IBIS-S interferometric radar monitoring scene and the monitoring scene in the direction of radar line-of-sight taken from the installation position of the IBIS-S system.  
The second-order leveling was adopted to monitor the settlement of the bridge. Figure 3 shows the leveling monitoring site and the layout position of the leveling monitoring points on No. 7 pier. We arranged four leveling monitoring points on each pier. The bar code of precise invar leveling rod for leveling is directly pasted at the monitoring point on the pier to reduce the measurement error as much as possible and protect the pier from damage (Figure 3b). The monitoring time of leveling is the same as that of radar monitoring, but the time interval of leveling was 2 h.  
Figure 1: ELH Bridge. (**a**) aerial view of the east side of the bridge, the black dotted line denotes the Wuhan Metro Line 11, the red rectangle indicates the bridge to be monitored, and the black five-pointed star represents the installation position of radar; (**b**) ground view of the bridge, the red rectangular frames are the monitored piers.
## 3 Methodology
### Determine the Effective Master Frequency Band  
According to the estimation method of non-linear curve fitting parameters based on whale swarm optimization algorithm described in this paper, the peak signal of acceleration spectrum can be fitted. In order to ensure that the information of the effective frequency band is not lost, the effective main frequency band should be delimited first. The upper and lower frequencies of the effective main frequency band should be defined as follows:  
\[\left\{\begin{aligned} c\cdot\max(\bar{A}_{k})&=\max( \bar{A}_{k})\cdot\exp\left[-\left(\frac{-\left(\bar{f}-X\right)}{\sigma_{k}} \right)^{\lambda_{k}}\right]&\quad X<\bar{f}\\ c\cdot\max(\bar{A}_{k})&=\max(\bar{A}_{k})\cdot \exp\left[-\left(\frac{X-\bar{f}}{\sigma_{k}}\right)^{\lambda_{k}}\right]& \quad X\geq\bar{f}\end{aligned}\right. \tag{16}\]  
where \(c\) is the frequency band division coefficient. Generally, it will be selected in the range of 0.05-0.5% according to the main frequency signal shape. The upper and lower limit frequency of the effective main frequency band are as follows:  
\[\left\{\begin{aligned} \bar{f}_{l}&=\xi(c;\lambda_{k}, \sigma_{l_{k}})&\quad X<\bar{f}\\ \bar{f}_{r}&=-\xi(c;\lambda_{k},\sigma_{r_{k}})& \quad X\geq\bar{f}\end{aligned}\right. \tag{17}\]  
where \(\xi(\bullet)\) is the monotonically decreasing function of the frequency band division coefficient, including parameters \(\lambda_{k}\), \(\sigma_{l_{k}}\) and \(\sigma_{r_{k}}\). The actual integrated frequency band is as follows:  
\[\left[\underline{\underline{f}_{k}}+\frac{1}{2}(\overline{\overline{f}_{k}}- \underline{\underline{f}_{k}})\cdot(1-\bar{f})),\overline{\overline{f}_{k}}+ \frac{1}{2}(\overline{\overline{f}_{k}}-\underline{\underline{f}_{k}})\cdot( \bar{f}-1))\right] \tag{18}\]  
where \(\overline{\overline{f}_{k}}\) is the upper limit frequency of the peak region, and \(\underline{\underline{f}_{k}}\) is the lower limit frequency of the peak region.  
Figure 9 shows the flow chart of the denoising algorithm of spectral shape fitting of asymmetric Gaussian mixture model curves. The algorithm can automatically identify and divide the non-noise frequency band according to the curve fitting of the main frequency signal of the observed acceleration spectrum, thereby suppressing interference from measurement noise outside the main frequency.  
Figure 8: Fitting effect of the proposed algorithm.
## 5 Improvements to the Original Shan-Chen Model  
### Realistic Equation of State  
Based on Yuan and Schaefer [42], to model a higher density ratio and reduce the spurious current at the same time, a different equation of state (EOS) is utilized. Here, we use the Carnahan-Starling (C-S) EOS since it is stable, easy to implement, and generates a lower spurious current compared to other EOS [42].  
No matter what EOS is used, the effective mass is expressed as:  
\[\psi=\sqrt{\frac{\left(\mathrm{p}-\rho\mathrm{c}_{\mathrm{s}}^{2}\right)}{ \mathrm{G}_{1}\mathrm{c}_{\mathrm{s}}^{2}}} \tag{15}\]  
The C-S EOS is expressed as:  
\[\mathrm{p}=\rho\mathrm{RT}\frac{1+\frac{\mathrm{b}\mathrm{p}}{4}+\left(\frac{ \mathrm{b}\mathrm{p}}{4}\right)^{2}-\left(\frac{\mathrm{b}\mathrm{p}}{4} \right)^{3}}{\left(1-\frac{\mathrm{b}\mathrm{p}}{4}\right)^{3}}-\mathrm{a} \mathrm{p}^{2} \tag{16}\]  
where \(\mathrm{a}=\frac{0.4963\left(\mathrm{RT}_{\mathrm{cr}}\right)^{2}}{\mathrm{p} _{\mathrm{cr}}}\), \(\mathrm{b}=0.1873\mathrm{RT}_{\mathrm{cr}}/\mathrm{p}_{\mathrm{cr}}\). \(\mathrm{P}_{\mathrm{cr}}\) and \(\mathrm{T}_{\mathrm{cr}}\) are the critical pressure and temperature, respectively. Without losing the generality of the model, we assume \(\mathrm{a}=1\), \(\mathrm{b}=4\), \(\mathrm{R}=1\) and by reducing the temperature in this equation, a higher density ratio is achieved [28; 29].
# A Survey of Electromagnetic Techniques Applied to Cultural Heritage Conservation
## 4 Deterioration of CH Artifacts: Causes and Process
### Organic Materials
#### 4.2.3. Leather and Parchment  
Leather and parchment are produced from the skin of animals; hence, they are strongly biodegradable. The part of the skin used to create leather goods is the dermis, because only this layer of the skin provides the good mechanical properties of the final product [14]. The leather manufacturing process is not an easy task; it involves different steps, among which tanning is a particularly critical one, and it is crucial for determining the lifetime of the manufactured leather. Tanning implies chemical bonds between tanning agents and collagen, while inhibiting hydrophilic chemical groups [14; 94]. This reduces the amount of water retention to a level of 15%, which significantly decreases the possibility of microorganism development, although it is still more likely to occur in vegetable-tanned leather.  
Aging factors accelerate the biological deterioration process, since they modify the water intake capacity of leather. The principal aging factors are listed in Table 7.
## 4 Conclusions  
From the current investigation, the following conclusions can be made:  
1. Free vibration of a sandwich composite plate which embeds SMA wires in the surface sheets and functionally gradient distribution in the core has been developed. The constitutive equation considered thermal effect and pre-strain, the Reddy shear deformation theory, and Hamilton principle were utilized to derive the governing equations and the frequency equation.
2. The gradient distribution and volume fraction of graphite in the core layer of the sandwich composite plate have a significant effect on natural frequencies. The natural frequency and the critical buckling temperature of a positive gradient distribution of graphite in the core of the sandwich composite plate are higher than those of non and negative gradient distribution. Moreover, the increase of graphite volume fraction increases the natural frequency but decreases the critical buckling temperature. Therefore, the graphite in the core layer can be designed by the form of distribution and volume fraction, thereby controlling the natural frequency of sandwich plate.
3. Temperature-rising SMA transforms from the martensitic phase to the austenitic phase, which results in an increase in modulus and recovery stress, thus increasing the influence of the volume fraction and pre-strain on the natural frequency. Both volume fraction increments and pre-strain increments of SMA embedded in the surface sheets of the sandwich plate can increase the natural frequency and critical buckling temperature.  
Conceptualization, Y.H. and K.M.; methodology, Y.H. and Z.X.; characterization Y.H. and C.L.; writing--original draft preparation, Y.H. and L.L.; writing review and editing, Y.H. and K.M. All authors have read and agreed to the published version of the manuscript.  
This work was financially supported by the National Natural Science Foundation of China (NSFC) under Grant Nos 51575201.  
The authors appreciate the advice of Professor [PERSON] (Huazhong University of Science and Technology, China).  
The authors declare no conflict of interest.
# A Survey of Wi-Fi 6: Technologies, Advances, and Challenges
## 8 Modulation Techniques
### Dual Carrier Modulation (DCM)  
Dual Carrier Modulation (DCM) was first used in IEEE 802.11 ad and aimed to improve long-distance transmissions [174]. This modulation duplicates the same information on two different subcarriers at two different channel frequencies separated far apart in terms of frequency. The modulation in both subcarriers is similar. Therefore, it brings frequency diversity for OFDM. On the receiver side, demodulation is performed by a Log Likelihood Ratio (LLR) demapper that receives the two DCM symbols. It increases signal resiliency and deals with narrow-band interference in Wi-Fi 6. Therefore, DCM is recommended for MCS 0-4 [24].
## 4 Map Spectrum-Based Spatiotemporal Clustering  
With space-time trends, most studies have focused on the extraction of patterns in a spatial or temporal context [13]. This study proposed an improved k-means clustering method to extract these spatiotemporal patterns. Unlike the traditional k-means clustering method, in which clusters can only be formed in the space dimension or time dimension, the improved k-means algorithm can perform spatiotemporal clustering due to the proposed map spectrum representation model.
## 3 Methods
### Target Classification Scheme  
As shown in Figure 2, after obtaining the class-specific dictionaries of various classes, we collect them to build a dictionary set for classification. The reconstruction error and discriminant information of the coefficient (also referred to as the intra-class difference in this paper) are used during class-specific dictionary learning, and these two items are also used comprehensively for classification during testing. Specifically, it is used to solve the sparse representation coefficient of the sample \(y\) under dictionary \(D_{i}\), and the solution method can refer to Equation (10).  
\[\hat{x}=\arg\min_{\hat{x}}\left\{\left\|y-D_{i}x\right\|_{2}+\gamma\cdot\frac{ \left\|x-m_{i}\right\|_{2}^{2}}{\left\|x\right\|_{2}}\right\} \tag{10}\]  
In Equation (10), \(m_{i}\) is the mean vector of sparse representation coefficients obtained by training samples of the \(i-th\) class during training. \(\gamma\) is the weight factor. In general, the least square method is usually used to solve \(\hat{x}\), which could significantly reduce the computation complexity.  
On the basis of \(D_{i}\), \(m_{i}\) and \(\hat{x}\), we calculate the measure \(e_{i}\) for classification. As shown in Equation (11), it includes two parts: the reconstruction error term (\(\left\|y-D_{i}\hat{x}\right\|_{2}\)) and the coefficient discriminant term (\(\left\|\hat{x}-m_{i}\right\|_{2}/\left\|\hat{x}\right\|_{2}\)). \(\gamma\) is the weight factor to balance them, which is the same as that in Equation (10).  
\[e_{i}=\left\|y-D_{i}\hat{x}\right\|_{2}^{2}+\gamma\cdot\frac{\left\|\hat{x}-m_ {i}\right\|_{2}^{2}}{\left\|\hat{x}\right\|_{2}} \tag{11}\]  
Finally, the measures of test sample \(y\) under various class-specific dictionaries are calculated. Then, as shown in Equation (12), the test sample \(y\) belongs to the category with minimum \(e_{i}\).  
\[identity(y)=\arg\min_{i=1,2,..,k}\{e_{i}\} \tag{12}\]