esa-sceva/satcom-chunk-collection huggingface dataset:
# Vibration Reduction of an Existing Glass Window through a Viscoelastic Material-Based Retrofit
## 1 Introduction
[48] found that multilayer window glass structures with polymer materials in a transient state reduced the amplitude of vibration of window glass. [PERSON] et al. [49] experimentally verified the effects of vibration and noise reduction of laminated viscoelastic materials for the windows of railway vehicles. Other reported methods of increasing window damping through laminates include References [50; 51; 52; 53]. Please note that this type of method applies to the manufacturing of new windows. 3. Improve the damping between the glass and the window frame. The viscoelastic treatment of the interface between the window glass and the window frame was also reported [54; 55]. Please note that this method only applies to new windows or the installation of new windows. Table 1 summarizes the reported technologies and solutions for the improvement of window damping. Please note that all these methods are for new windows or for installation of new windows. In addition, advances in material technology, along with newer and more efficient analytical, numerical, and experimental tools, led to damping treatments of various structures with viscoelastic materials for vibration and noise reduction [56; 57; 58; 59; 60; 61; 62]. However, all these methods are for new structures, and are not applicable to retrofitting the already installed windows for damping improvements. Other related studies [63; 64; 65] analyzed the behaviors of window glass when subjected to various excitations, including earthquakes. Window glass, which has low damping properties, experiences induced vibration and emits noise; however, retrofitting existing glass windows for vibration reduction is rarely  
studied, to the best knowledge of the authors. With the increasing popularity of light rails in cities, there is a strong need to retrofit existing glass windows in an effort to improve its damping properties, and to increase occupants' comfort. In this paper, a new method, which is simple and effective, was developed for the retrofitting of an existing glass window in an effort to improve its damping properties. The key component of the retrofit was a self-adhesive glass constrained viscoelastic strip (SaGCVS), consisting of a thin glass constraining layer, a constrained viscoelastic layer, and an adhesive layer. The SaGCVSs were taped along the edges of an existing window glass panel to improve its damping properties. To measure the vibration response of the window glass panel, lead zirconate titanate (PZT) patches were mounted onto the four corners and the center of the window glass, and the vibration damping effect of the proposed method was evaluated. The proposed method is simple and convenient to implement, and is effective in the reduction of induced vibration. In addition, the constrained viscoelastic layer requires almost no maintenance during its service.
## 2 Evolution of SDN Control Architecture for VANETs
### Hybrid Controller Architecture  
The hybrid control design is another way to deal with tending to the constraints of central and conveyed SDN controller frameworks. This hybrid framework was intended to help with the structures  
Figure 4: (**a**) Centralized controller model, (**b**) distributed controller model, (**c**) hybrid controller architecture.  
of both focal and appropriated frameworks [22]. The sensible framework utilized in the half and half model is equivalent to that utilized in the focal controller. In any case, it utilizes the information correspondence design that is utilized in the appropriated controller. The half and half SDN model supports the straightforward control of the executives example of a solitary controller, and it has the adaptability of disseminated models [23]. The crossbreed SDN-based model enables the controllers to utilize assets effectively to build the exhibition of the system. The crossbreed model additionally enables approaches to elevate the security of the whole system. Moreover, it permits the refreshing of the framework without the need to change the present system settings [22]. Figure 4C represents the hybrid controller model.
## 4 Conclusions  
Behavior of the membership function filter can be influenced mainly by two parameters:  
1. Sensitivity \(s\) of the membership function--this determines how strongly the samples near the average value are preferred, respectively, and how strongly the samples away from the average value are suppressed.
2. The number of samples \(k\) used for counting of moving weighted average-higher value of parameter \(k\) causes a longer time delay of the filter, but on the other hand if \(k\) is very low the filtered data copy more real data with noise.  
The filter with a normal distribution membership function was chosen for filtering important variables in the biomass combustion process. This function with appropriately selected parameters \(s\) and \(k\) ensures that the remote values from the average have lower weights (in opposite to Bell and Power function) and values which are close to the average have concave shape of weights (in opposite to Triangle function).  
This was proven by implementation of the designed special filter into a control system of biomass combustion process that it is useful for signal filtering of O\({}_{2}\) concentration measured by a low-cost wide band Lambda probe and CO emissions measured by a simple CO sensor. The implemented filter based on the Gauss membership function with parameters set to values \(k=60\), \(s=4\) had a significant effect on reducing signal interferences arising in the biomass. The implemented control algorithms based on using information about the filtered tendency of nascent carbon monoxide successfully stabilized the combustion process in medium-scale biomass-based boilers, especially during the start of combustion or its interruption due to some disturbance. These advanced algorithms can also compensate for varying woodchip parameters (moisture content in wood, type and quality of wood), which is an object of intensive research at the authors' workplace [25].
## 5 Conclusions  
In summary, experimental data were used to predict the decoring behavior of various sand-binder systems. Robust models were achieved, yielding a mean MAE between 16% and 37% of the mean of the predicted feature and a mean MAE to standard deviation of 34% to 57%. The chosen forward-backward approach using machine learning models to learn about experimental data and physical relations was successful. Varying the data composition demonstrated the relative importance of various feature sets in predicting the decoring behavior. The acceleration data of the decoring process, which was suspected of having a significant influence, was not used significantly by the models for the prediction. The authors conclude that a constant experimental setup led to the reduced importance of the acceleration signal. The same explanation is used for the low importance of the aggregated composition features. One sand-binder system was used as a validation system to test whether the models could describe the decoring behavior for unknown sand-binder systems. The prediction scores are significantly worse for the validation system. However, the models indicate that some generalized correlations were learned that are also valid for the validation system. The authors suspect that the validation system variates in some parameters in ways unique to it. It is suspected that some overfitting occurred in the models that achieved the lowest mean RMSE for the test data, despite averaging over ten runs of randomized initializations. The best models for the validation system scored similar values for the test data and indicated no overfitting. This work focuses on a specific case of decoring for one sample geometry. The machine learning models calculated in this do not claim to predict the decoring behavior for other geometries. However, the behavior of the models and the identified relevant parameters can be interpreted in a generalized way. Determining which features in this dataset are particularly relevant for ML model training is, therefore, a first step towards a more generally valid ML model. This work provided helpful indications as to which data should be ascribed higher priority in future decoring experiments.  
This research received no external funding.  
Not applicable.  
**Data Availability Statement:** The data are not publicly available due to ongoing research with the data. The code presented in this study is available at [[https://github.com/FDdiss/PredicitingDecoringBehavior](https://github.com/FDdiss/PredicitingDecoringBehavior)]([https://github.com/FDdiss/PredicitingDecoringBehavior](https://github.com/FDdiss/PredicitingDecoringBehavior)) (accessed on 16 June 2023).  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Lightweight and Security-Enhanced Key Agreement Protocol Using PUF for IoD Environments
## 5 Cryptanalysis of [PERSON]'s Scheme
### Drone Impersonation Attack  
An attacker must compute the message (\(\{Auth_{D},PK_{D}^{*},T_{5}\}\)) to impersonate a legitimate drone.  
**Step 1:**: An attacker can eavesdrop on public-channel messages \(\{I_{M},PK_{M},A_{D},C_{M},H_{DM},T_{3}\}\) to obtain these values.
**Step 2:**: An attacker can compute \(D_{M}\) by performing an XOR operation on \(H_{DM}\) and the drone's public key, \(PK_{D}\).
**Step 3:**: Using the drone's identity, \(D_{M}\), and \(C_{M}\), the adversary can calculate \(SK_{D}\), allowing for the computation of the \(Auth_{D}\) value.
**Step 4:**: By eavesdropping on public-channel messages, the attacker can obtain \(PK_{M}\) and create \(PK_{AD}^{*}\) by selecting its random value.  
Therefore, [PERSON]'s protocol cannot defend against drone impersonation attacks.
# Optimal Design of One-Sided Exponential Adaptive EWMA Scheme Based on Median Run Length
## 3 RL Properties of the One-Sided Exponential AEWMA Schemes
### Interpretation Problems of ARL  
The RL of the one-sided exponential AEWMA scheme is distributed skewly. Figure 1 shows the p.d.f. curves for the RL distribution of the lower-sided exponential AEWMA scheme, where \((\lambda^{-},k,LCL^{-})=(0.051,1.168,0.768)\) meets the in-control ARL (\(ARL_{0}\)) of 370, and the process mean shift \(c\in\{0.1,0.5,0.8,1\}\). From Figure 1, it is evident that the RL distributions of the lower-sided exponential AEWMA scheme are significantly skewed under both in-control and out-of-control conditions.  
Figure 1: Several p.m.f. plots of the RL distribution of the lower-sided exponential AEWMA scheme when \((\lambda^{-},k,LCL^{-})=(0.051,1.168,0.768)\), \(c\in\{0.1,0.5,0.8,1\}\).  
In the in-control state, the discrepancy between \(ARL_{0}\) and the in-control MRL (\(MRL_{0}\)) is large. However, the difference between the out-of-control ARL (\(ARL_{1}\)) and out-of-control MRL (\(MRL_{1}\)) decreases as the process mean shift increases. This suggests that the shape of the RL distribution for the lower-sided exponential AEWMA scheme evolves with the magnitude of the process mean shift, becoming increasingly symmetric as the shift grows larger.  
Therefore, in the design of the one-sided exponential AEWMA scheme, using ARL as the sole indicator of the monitoring scheme's statistical performance lacks consideration of the bias in the RL distribution. The interpretation based on ARL differs for the two cases where the RL distribution is either symmetric or highly biased. Using MRL as the statistical performance indicator for the one-sided exponential AEWMA scheme is more intuitive and reasonable.
## 3 Results and Discussion
### pH Effect  
The batch pH tests were conducted with the initial pH of the solutions ranging from 3 to 9. The solution pH strongly influences the sorption capacity of the target contaminant by determining the distribution of the species or by acting as competitor ions in the form of hydronium or hydroxide at low or high pH, respectively. Figure 6a shows the Cu(II) and phosphate removal efficiency as a function of the initial solution pH. FHO-A obtained \(\
eg 0\), 20 and 40% removal efficiency at pH values of 3, 4, and 5, respectively, while Cu(II) was not removed by FHO below pH 5, confirming that higher pH correlates with greater removal of Cu(II) with alginate on the surface of FHO-A. This is accordance  
Figure 5: Plots for the non-linear [PERSON] and Freundlich isotherm.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & \multicolumn{3}{c}{**Langmuir**} & \multicolumn{3}{c}{**Freundlich**} \\ \hline  & & **Q** & **b** & **R\({}^{2}\)** & **k\({}_{f}\)** & **n** & **R\({}^{2}\)** \\ \hline \multirow{2}{*}{Cu} & Non-linear & 487 & 0.0678 & 0.966 & 40.1 & 1.44 & 0.977 \\ \cline{2-8}  & Linear & 350 & 0.117 & 0.704 & 49.1 & 1.71 & 0.924 \\ \hline \multirow{2}{*}{Phosphate} & Non-linear & 139 & 0.3905 & 0.983 & 58.9 & 3.73 & 0.988 \\ \cline{2-8}  & Linear & 136 & 0.401 & 0.986 & 62.6 & 4.28 & 0.944 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters of the Langmuir and Freundlich isotherm with non-linear and linear equations.  
with previous studies [2] which showed that the optimized solution pH for an alginate bead was reported to be \(\sim\)5. This can be explained due to the pK\({}_{\text{a}}\) value of alginate, which is 3.38-3.65 depending on type of copolymers based on \(\beta\)-D-mannuronic acid (M) and \(\alpha\)-L-guluronic acid (G) [53]. Therefore, both deprotonation of alginate and the presence of hydronium ion compete with Cu(II) to interrupt the sorption at pH 3 (shown in Equations (8) and (9)). Note that the Cu(II) removal efficiency after pH 5 is due to precipitation.  
\[\text{At pH}>\text{pka},\,\text{(R-COO)-H}^{+}+\text{Cu}^{2+}\rightarrow\text{(R-COO)-Cu}^{2+}+ \text{H}^{+}\,\text{(reaction)} \tag{8}\]  
\[\text{pH}<\text{pka},\,\text{(R-COO)-H}^{+}+\text{Cu}^{2+}\rightarrow\text{(R- COO)-H}^{+}+\text{Cu}^{2+}\,\text{(no reaction)} \tag{9}\]  
In terms of phosphate removal, the trend is opposite to Cu(II) where efficiency clearly decreases with increasing pH, indicating lower pH favors phosphate sorption. In general, phosphate speciation (pK\({}_{\text{a}}\) values: 2.12, 7.21, and 12.67) and the surface charge of the adsorbent was determined by the solution pH presented by the point of zero charge (PZC), which has been previously reported to be 6.7 to 8.6 [54]. In the experimentally tested pH range, phosphate species (Figure 6b) was negatively charged, while the surface of the FHO was generally positively charged. This indicates that electrostatic interactions are possible. Consequently, it is proposed that the deprotonated carboxyl group on alginate led to the interaction with Cu(II), and the surface charge of FHO participated in phosphate removal.
## 5 Adiabatic DKS Generation  
After confirming the stability of a single DKS generation, it is important to check if these paths can create a single DKS using an adiabatic trajectory. To validate those paths, we propose conducting the DKS generation using the same paths shown in Figure 4, extending the simulation time to 1 ms. Figure 8 presents the results for the proposed adiabatic paths. It is noteworthy that for all four configurations, we successfully achieved a single DKS, indicating the solutions' stability and ability to maintain their shape over time.  
Figure 7: (**a**) Single DKS Stability region for \((a_{1},b_{1})\). (**b**) DKS Stability region for \((a_{2},b_{2})\), (**c**) DKS Stability region for \((a_{3},b_{3})\). (**d**) DKS Stability region for \((a_{4},b_{4})\).
# Chaotic Image Encryption: State-of-the-Art, Ecosystem, and Future Roadmap
## 4 Ecosystem
### Application Areas
#### 4.2.2 Medical Systems  
Protecting medical records (including images) is not just a privacy concern, but a legal requirement [169]. This is especially true when transferring said medical images over open or public networks [170; 171].  
Many chaos-based approaches for encryption have been proposed. These approaches often involve chaos maps [169], especially the Arnold cat map [172] and logistic map [171]. Approaches using DNA-based algorithms have also been proposed [173], including a rather novel one by [PERSON] and [PERSON] [171], where chaotic maps were used to decrease the computational complexity of DNA-rule-based sequences.  
The use of key streams is also a common technique. [PERSON] et al. [174] proposed a method for synchronizing chaotic systems at both the transmission and receiver ends so that keys can be obtained from the streams and no keys need to be directly shared. Another novel proposal was given by [PERSON] et al. [175], where a logistic map was used to generate a sequence from which a Hermite neural network was trained. This trained network was then used to generate key streams for use in encryption.
## 5 Conclusions  
This paper studied the voids structure distribution inside the materials. The digital camera processing was adopted to obtain the vertical and horizontal section profiles of the materials and software such as MATLAB and Photoshop were used to process the images. Given that the voids inside the materials are irregular and complicated, equivalent aperture was proposed in this study to simplify the voids. The conclusions are summarized as following:  
* In both vertical and horizontal sections, voids with the equivalent aperture ranging from 0-4 mm account for a large proportion, and when the equivalent aperture is beyond 4 mm, the total number of voids would decrease as equivalent aperture increases.
* In both vertical and horizontal sections, the number of voids with the equivalent aperture ranging from 0-2 mm has a great impact on the total voids number. However, large amounts of small voids do not significantly affect the overall air voids content. In addition, the number of voids with different equivalent apertures do not have a good correlation with the air voids content, no matter in vertical or horizontal sections. The air voids content would not be affected significantly by a single or several sizes of voids.
* The accumulated number of voids is close to the total voids number when the equivalent aperture reaches 10 mm in both vertical and horizontal cases, and voids with equivalent aperture ranging from 0-10 mm comprise most of the voids area.
* As the increase in the equivalent aperture, the speed to accumulate voids area would firstly increase then decrease, the trend could be found in both vertical and horizontal cases. The inflection point would occur when the equivalent aperture is around 8 mm. For the vertical section, the speed to accumulate voids area has a high fluctuation when the equivalent aperture range is from 6-12 mm, while for horizontal sections, the range is from 4-14 mm.
* The contribution of the voids to total voids area increases as the equivalent aperture size increases, and it would approach the peak when the equivalent aperture reaches about 8 mm.
* Due to the limitation of the technique used in this study, the air voids discussed including both connected and closed voids, which are difficult to differentiate and need further research. Additionally, the equivalent aperture method proposed in this study also needs further validation.  
Figure 13: The relationship between the accumulated voids areas and the equivalent aperture along horizontal sections, (**a**) OGFC sample I, (**b**) OGFC sample II, (**c**) Average value of OGFC samples.  
**Author Contributions:** Conceptualization, G.X. and X.H.; methodology, X.H. and T.M.; validation, G.X. and W.Z.; investigation, W.Z and X.C.; writing--original draft preparation, X.C.; writing--review and editing, G.X and T.M.  
**Funding:** This research was funded by the National Natural Science Foundation of China, grant number 51808116 and Natural Science Foundation of Jiangsu Province, grant number BK20180404.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Cutting-Edge Online Fault Diagnosis Techniques for Al-Caps
### Signal Processing Module (SPM)  
The signal processing module (SPM) is responsible for extracting the primary features of the raw data (Figure 4).  
Figure 3: Buck converter schematics.  
The primary features correspond to the DC and AC components of the raw signals. The DC components are extracted using a moving average filter (AVG Filter) with a window size (WS) of 1000 samples and a hope size (HS) of 500 samples. The AC components are extracted in two stages: first, the raw signals pass through a fifth-order bandpass filter (BDF) that isolates the signal component around the converter's switching frequency (20 kHz); then, an RMS filter with a WS of 1000 samples and a HS of 500 samples is applied to compute the AC features.  
Figure 4 shows that two new raw variables, the input power (\(\rm p_{in}\)) and output power (\(\rm p_{out}\)), were generated through a simple multiplication of the original raw variables (\(\rm v_{in}\), \(\rm i_{in}\), \(\rm v_{out}\), and \(\rm i_{out}\)).  
Through the aforementioned transformations, 10 primary features were generated. In addition, a new feature (R) was created using two of these primary features, and the duty cycle was included, which requires no additional sensors as it is provided by the PWM. This resulted in a total of 12 primary features, which are summarized below:  
* \(\rm P_{inAC}\)--represents the AC component of \(\rm p_{in}\);
* \(\rm P_{inDC}\)--represents the DC component of \(\rm p_{in}\);
* \(\rm P_{outAC}\)--represents the AC component of \(\rm p_{out}\);
* \(\rm P_{outDC}\)--represents the DC component of \(\rm p_{out}\);
* \(\rm i_{inAC}\)--represents the AC component of \(\rm i_{in}\);
* \(\rm i_{inDC}\)--represents the DC component of \(\rm i_{in}\);
* \(\rm i_{outAC}\)--represents the AC component of \(\rm i_{out}\);
* \(\rm i_{outDC}\)--represents the DC component of \(\rm i_{out}\);
* \(\rm v_{outAC}\)--represents the AC component of \(\rm v_{out}\);
* \(\rm R\)--represents the load resistance, calculated as \(\rm v_{outDC}\) divided by \(\rm i_{outDC}\);
* \(\rm D\)--represents the duty cycle provided by the PWM.