esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Influence of Adhesive on Fatigue Life  
The square hole acts as a notch and results in severe stress concentrations in the vicinity of the hole. This results in the occurrence of plastic deformations in these areas due to the cyclic loading and results in crack initiation and propagation. The application of the CFRP reinforcement leads to a reduction in the stress concentrations and results in the delay of the crack initiation and slowing down after its initiation. In both cases, the influence of the notch on the crack growth is typical and described in the literature [1,2]. The observed forms of fatigue failure of steel cores for bare and reinforced samples differ in the size of necking; the largest one is observed for the bare steel sample and is getting smaller and smaller while using S&P Laminate, DP6310 NS and HY 4080 GY adhesives, respectively. What is more, for the HY 4080 GY, at least at one side, the necking does not appear. Application of the CFRP overlays evidently increases the fatigue life of the investigated samples' endurance to varying degrees [10]. The beneficial effect of the reinforcement is in taking over part of the external load and, consequently, reducing the stress concentration in the corners of the holes.  
The performed fatigue tests [10] revealed that the fatigue life of steel/CFRP adhesive joints strongly depends on the adhesive type. The fatigue life increase (\(FLI\)) was calculated as a relative change between the fatigue life of the reinforced sample \(N_{f,avg}^{reinf}\) and the fatigue life of the bare sample (without adhesive joint) \(N_{f,avg}^{bare}\) as follows:  
\[FLI(\%)=\frac{N_{f,avg}^{reinf}-N_{f,avg}^{bare}}{N_{f,avg}^{bare}}100\%. \tag{1}\]  
The highest fatigue life was obtained for samples in which HY 4080 GY cyanoacrylate/acrylic adhesive was used (Table 2). In this case, the average increase in fatigue life was equal to \(FLI\) = 890%. The application of the DP6310 NS polyurethane adhesive also leads to a considerable increment in fatigue life (\(FLI\) = 307%). The lowest fatigue life (\(FLI\) = 97%) occurred in samples in which S&P Resin 220 epoxy resin was applied. In all cases of reinforced samples, their fatigue life was at least two times greater than the fatigue life of the bare (not reinforced) samples. Additionally, stress concentration factors \(Kt\) (elastic analyses) are listed in Table 3. It can be seen that there is no relationship between \(Kt\) and the increase in the fatigue life of the reinforced structures. The lowest fatigue life was obtained for S&P Resin 220, for which the lowest \(Kt\) occurred. Therefore, the properties of adhesives are crucial for fatigue life.
## 4 Experimental Verification and Analysis
### Camera Parameter Calibration  
The internal parameter matrix and distortion coefficient of the camera are the content of the camera calibration. The knowledge of domestic and foreign scholars has been perfected. [PERSON] and [PERSON] [26] give the parameter matrix of the camera by a linear transformation; [PERSON] [27] for the first time gives a method to obtain the camera parameter matrix through the perspective transformation matrix, but the simplest and most practical is [PERSON]'s calibration method [28; 29]. This calibration method does not require high-precision calibration equipment, and the calibration accuracy is higher than the traditional calibration method. Therefore, this paper chooses [PERSON]'s calibration method as a method to obtain camera parameters. The calibration samples are shown in Figure 6 below.  
This paper uses a high-precision alumina calibration plate with a low thermal expansion coefficient. The specific parameters are shown in the following Tables 1 and 2.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Dimensions (mm)** & **Checker Side Length (mm)** & **Pattern Array** & **Accuracy (mm)** \\ \hline LGP500-300 & 500 \(\times\) 500 & 30 & 13 \(\times\) 12 & \(\pm\)0.02 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Alumina calibration plate parameters.  
Figure 6: Camera calibration sample collection.  
We used the Camera Calibrator toolbox in MATLAB to import the samples in Figure 6 and enter the parameters of the calibration plate. The output is shown in Figure 7.  
The final calibration results are shown in the table below:  
From the Table 2 calibration results, the camera imaging quality is much less affected by tangential distortion than by radial distortion. Therefore, when the accuracy requirements of the project are low, in order to improve the calculation speed, the influence of tangential distortion on the imaging quality can be ignored. The internal parameter matrix of the camera can be derived from Table 2:  
\[M=\left[\begin{array}{ccc}5010.8179&-0.6924&2768.7096\\ 0&5011.2457&1806.3417\\ 0&0&1\end{array}\right]\]
## 2 Experimental Details
### Characterization  
The Rigaku SmartLab Diffractometer (Rigaku SmartLab, Neu-Isenburg, Germany) obtained powder X-ray diffraction patterns (XRD) using Cu K\(\alpha\) = 1.5406 A (40 kV, 40 mA) radiation. The usual range for scanning was 20\({}^{\circ}\) \(<\) 20 \(<\) 80\({}^{\circ}\). The measurements were taken at a rate of 5.0985 degrees per minute in increments of 0.02. The length-limiting slit was 10 mm; the incidence slit was 0.66 degrees; and the divergence slit was 0.66 degrees (Figure 1A). A Shimadzu model ZU SSX-550 Superscan scanning electron microscope (SEM) (Shimadzu, Columbia, MD, USA) was used to analyze the sizes and shapes of the particles of ZnZrO\({}_{3}\):Mn\({}^{+3}\) phosphor at a spectral slit width of 1.5 nm to examine the morphology of the produced materials, as seen in Figure 1B. TL measurements were conducted using samples in discs that were 5 mm in diameter and 1 mm thick. Before heating, the samples were excited at a rate of 10.75 Gy per minute using a beta source from the 90 Sr isotope that was located nearby. In a nitrogen atmosphere, measurements were conducted with a Rise model TL/OSL-DA-20 luminescence reader (Riso DTU, Roskilde, Denmark). A Rise TL/OSL reader model DA-20, which features a PM tube with a bialkali photocathode (Electron Tubes, 9235 QA) that has an extended UV response with maximum detection efficiency between 200 and 800 nm, was used to perform TL and OSL measurements. Figure 1 shows a picture of this system. Detection filters prevented scattered stimulation light from reaching the PMT. Three interchangeable filter packs are included with the standard Riso reader: the Hoya U-340, Schott BG-39, and Corning 7-59 (Guide to \"The RisQ TL/OSL Reader\"). First, the heating element heated the sample, which then was raised into the measuring position. A nitrogen flow cooled the heating strip and shielded the heating system from oxidation at elevated temperatures. The Schott BG-39 filter extracted the TL glow peaks between 250 and 800 nm (seen in Figure 1C).
## 3 Results
### Corrosion Properties  
The corrosion rates of the samples were analyzed through the weight loss technique by immersing them in a cell culture medium, as shown in Figure 7a. The corresponding change in pH (as shown in Figure 7b) was also assessed to develop an insight into the reaction of the samples with the culture medium. Cell culture medium was selected for the study to inspect the real-time corrosion rate under in vitro conditions. It was observed that the successful incorporation of aloe vera into the porous matrix helped bind the substrate particles together, reducing the corrosion rates. These rates seemed to be much lower when a layer of PLA was coated over the substrate, which helped restrict the composite's rapid corrosion. The maximum corrosion rate achieved was 5.2 mm/yr with a pH value of 10.1 for the pure porous Mg sample, whereas the minimum value was 2.7 mm/yr with a pH change of 8.7 for the sample with aloe vera incorporation and PLA coating after the seventh day of degradation. Moreover, the barrier provided by the PLA resulted in reduced corrosion rates with a percentage reduction of approx. 40% as compared to the substrate material.  
In addition, the dissolution of Mg into the corrosion medium was examined through the atomic absorption spectroscopy technique, as shown in Figure 7c. A high amount of leaching of metal ions into the medium would increase the solution's pH level by releasing more hydrogen [64; 65]. Therefore, the Mg ion concentration released into the corrosion medium was analyzed, and it was observed that the release rate increased with  
Figure 7: Corrosion properties of Mg–HA based composite sample. (**a**) Corrosion rate through weight loss method under cell culture medium. (**b**) Change in pH. (**c**) Mg release rate analysis through atomic absorption spectroscopy.  
the immersion time. Nonetheless, the release of Mg ions tended to reduce when aloe vera solution was added to the porous substrate. This was seen to decline more after coating with PLA. The maximum release of 42 mg/L in the pure substrate was observed on the third day of immersion, followed by the aloe vera-loaded sample with 31.5 mg/L and the PLA-coated sample with a 20.7 mg/L Mg ions release rate. Afterward, this release rate seemed to reduce due to the oxide layer developed across the substrate, which restricted the Mg ions release into the immersion medium [66; 67]. In the case of the aloe vera loaded sample, a possible reason for an initial burst of Mg ions can be the surface abrasion of the substrate when it was in contact with the corrosion medium. Once the surface layer was abraded, the aloe vera solution, which was infused inside the substrate, resisted the release of Mg ions into the medium. However, when PLA was coated over the substrate, the release rate was constant, showing lower Mg ions release due to effective coating development.
## 4 Results and Discussion
### Results
#### 4.1.6 XGBoost  
For the CIC-MalMem-2022 dataset, this model achieved an accuracy rate of 99.96%. According to Figure 42, the model correctly predicted 5806 malicious and 5803 benign instances. However, one malicious instance was incorrectly predicted as benign, and three benign instances were incorrectly predicted as malicious. The accuracy result for the CIC-IDS-2018 dataset was 99.73%, and the confusion matrix in Figure 43 indicates that the model correctly predicted 2076 malicious and 4432 benign instances. However, only 1 malicious instance was incorrectly identified as benign, while 16 benign instances were incorrectly identified as malicious. In addition, on the CIC-IDS-2017 dataset, the model achieved 99.84% accuracy, with the confusion matrix shown in Figure 44 showing that the model correctly predicted 6288 malicious and 7044 benign instances. On the other hand, the model incorrectly predicted 7 malicious samples as benign and 13 benign samples as malicious. Figure 45 shows that the training score is 100% at every cycle, while the cross-validation score is between 99.83 and 99.89% accurate. As shown in Figure 46, the CIC-IDS-2018 learning curve shows a high  
Figure 41: Hyperparameter tuning for RF on CIC-IDS-2017.  
Figure 39: Hyperparameter tuning for RF on CIC-MalMem-2022.  
training score and a high cross-validation score, and the training score does not plateau, indicating that the model does not overfit the data during the training phase. According to Figure 47, the training score for the CIC-IDS-2017 dataset oscillates between 100% and 99.99%, in contrast to the cross-validation score, which begins at 99.54% and then increases, reaching 99.90%. For the XGBoost model, the learning rate is a very important parameter. As shown in Figure 48, the best parameter for the CIC-MalMem-2022 dataset is 0.7, with a mean test score of 99.92%. According to Figure 49, the best parameter value for the CIC-IDS-2018 dataset is 0.5, with a mean test score of 99.50%. In addition, the best parameter value for the CIC-IDS-2017 dataset is 0.7, with a mean test score of 99.49%, as displayed in Figure 50.  
Figure 46: Learning curve for XGBoost on CIC-IDS-2018 dataset.  
Figure 47: Learning curve for XGBoost on CIC-IDS-2017 dataset.  
Figure 45: Learning curve for XGBoost on CIC-MalMem-2022 dataset.
## 4 Stretchable MSP Antenna-Based Strain Sensor
### Dimension Reduction of Coordinates of the Patch  
In Section 4.2, the DR was employed to analyze the antenna's mechanical behavior to obtain the coordinates of the geometry points of the deformed patch as the outputs. Then, these geometry points are used as the inputs for the electrical analysis of the deformed antenna to calculate the R\({}_{\ell}\) value as the output. On the basis of the inputs, HFSS software was used to check the R\({}_{\ell}\) behavior of the distorted antenna. As output data, 121 resonance frequencies were taken into account.  
As this is a new analysis after the mechanical analysis, it was inevitable to conduct the DR for the electrical system behavior as well. The ECC value, \(e\), was calculated as 0.442 for the deformed antenna inputs, which are the coordinates of the patch geometry points. This value was less than 0.5, indicating that FS should be used as the DR method in the electrical analysis step. The FS method used in this study was the IFT method and unnecessary coordinates were eliminated based on the significance value of 2. As a result of the IFT method, 28 meaningful coordinates were selected, as shown in Figure 6. Although the \(R_{e}\) value for the initial coordinates was calculated as 8.382, it was calculated as 5.623 for the deformed coordinates. Thus, it indicates that IFT precisely achieved the \(R_{e}\) reduction of 32.11% and the correlation of the original coordinates was changed to the independent and uncorrelated ones. In Figure 6, the white points represent the 28 points identified by the IFT method. It is seen that the number of input geometry points for the electrical analysis was reduced from 57 to 28. Although the total number of these selected points is still high, those are the necessary ones to accurately represent the geometry.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & \multicolumn{2}{c}{**Redundancy Prediction**} & \multicolumn{4}{c}{**Coordinates Prediction Error**} \\ \hline  & & By \(PCA\) & By \(AE\) & By \(ANN\) & By \(PCA/ANN\) & By \(AE/ANN\) \\  & No \(DR\) & (Redundancy & (Redundancy & (X coord,/Y & (X coord,/Y & (X coord,/Y \\  & Reduction (\%)) & Reduction (\%)) & coord.) & coord.) \\ _Thickness_ & 11.053 & 5.503 & 6.127 & 7.03\%/7.62 \% & 3.70\%/5.41\% & 4.88\%/7.09\% \\ _Coord._ & & (50.21\%) & (44.57\%) & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Redundancy prediction and estimation error of the thickness coordinates. DR, dimension reduction; PCA, principal component analysis; AE, auto-encoder; ANN, artificial neural network.
## 3 Methodology
### Hierarchical and Optimized Clustering for Superpixels
#### 3.3.1 Definition of Dissimilarity between Superpixels  
The superpixel segmentation is a process to assign a superpixel label for each pixel, while the clustering of superpixels refers to reassign a new label for each superpixel, i.e., superpixels with similar characteristics would be divided into the same group. In this paper, regional dissimilarity is defined as the criterion of superpixels clustering.  
Suppose the image \(I\) is segmented into \(n\) superpixels, which is represented as the following set \(S\):  
\[S=\{S_{1},S_{2},\cdots,S_{n}\}, \tag{25}\]  
For the \(i\)th superpixel \(S_{i}\)\((i=1,\cdots,n)\), the corresponding set of pixels is denoted as Equation (26).  
\[S_{i}=\Big{\{}p_{1}^{(i)},p_{2}^{(i)},\cdots,p_{m}^{(i)}\Big{\}}, \tag{26}\]  
where \(m\) means the number of pixels in the \(i\)th superpixel; \(p_{k}^{(i)}\) denotes the \(k\)th pixel in the \(i\)th superpixel, \(1\leq k\leq m\).  
The regional dissimilarity relation between superpixels \(S_{i}\) and \(S_{j}\) could be described as Equation (27).  
\[\big{(}S_{i},S_{j}\big{)}=1-\big{(}S_{i}\cap S_{j}/S_{i}\cap S_{j}\big{)}, \tag{27}\]  
where the range of value is \([0,1]\), and the more similar the two superpixels, the closer the value and 0, else, the closer the value and 1.  
In order to describe the regional dissimilarity in a global sense, Equation (27) should be combined with Equation (28).  
\[\text{sgn}(S)=\left\{\begin{array}{ll}1,&S_{i}\text{ is next to }S_{j}\\ +\infty,&else\end{array}\right., \tag{28}\]  
Figure 8: The flowchart of hierarchical and optimized clustering for superpixels.  
Suppose the dip angle and dip sets of pixels in superpixel \(S_{i}\) are denoted as Equation (29), and the corresponding histograms could be obtained.  
\[\begin{cases}\theta_{1}^{(j)},\theta_{2}^{(j)},\cdots,\theta_{m}^{(j)}\\ \alpha_{1}^{(i)},\alpha_{2}^{(i)},\cdots,\alpha_{m}^{(i)}\end{cases}, \tag{29}\]  
Denote the attitude of superpixel \(S_{i}\) as \((\theta_{i},\alpha_{i})\), then values of which are respectively equal to peaks of above histograms. The regional dissimilarity function between superpixels \(S_{i}\) and \(S_{j}\) is defined as Equation (30).  
\[\text{diff}\big{(}S_{i},S_{j}\big{)}=\text{diff}\big{(}\theta_{i},\theta_{j} \big{)}+\text{diff}\big{(}\alpha_{i},\alpha_{j}\big{)}, \tag{30}\]
# Quantum Computing for Healthcare: A Review
## 7 Open Issues and Future Research Directions
### Quantum Security Applications  
Cyberspace has been under the constant threat of an increasing number of attackers [204; 205]. Necessary security frameworks have been developed to protect cyberspace against these attacks. However, this process becomes daunting for classical computing systems. Quantum computing using ML helps develop security schemes for traditional computing systems. Quantum computing supports quantum cryptography, which provides efficient solutions to protect data against privacy-breaching attacks. However, the unprecedented computing power of quantum computing also raises security risks and undermines traditional encryption schemes. This motivates the need for quantum-resisting encryption techniques to mitigate the threats of quantum computing. The National Institute of Standards and Technology (NIST) is developing such a solution to cope with encryption problems. Encryption techniques should be carefully developed to ensure that they are quantum-ready. Moreover, traditional password management schemes could become insufficient in the quantum environment. For example, passwords that may require extended time for decryption can be guessed in a shorter period using quantum computing applications. Personal health information (PHI) is particularly vulnerable to weaknesses in postquantum encryption because it is long-lived. Consider an encrypted medical record that states that a person had bone surgery due to a sports injury at school. This record remains valid for the person till their death and does not expire. Such records, if stolen today, can be kept around by hackers and decrypted and exposed by attacks once quantum computing becomes a reality. Therefore, novel techniques need to be developed to enforce strong encryption schemes to protect sophisticated data. Quantum services are also currently being offered via the cloud; it is important to acknowledge and mitigate the various security risks that emerge from using cloud services, especially when quantum machine learning services are being offered via the cloud [206].
# Review on System Identification and Mathematical Modeling of Flapping Wing Micro-Aerial Vehicles
## 3 System Identification and Mathematical Modeling of Flapping Wing Micro Air Vehicles (FWMAVs)
### Tailless Flapping Wing Micro Air Vehicles
#### 3.2.2 System Identification via Frequency Approach of _Drosphilla melanogaster_
The system identification strategy via the frequency-based analysis is considered to be more powerful [142] than the analytical linearization and computational based techniques because by coherence (\(\gamma(\omega)\)), one may easily determine the model over certain frequencies where the linear and nonlinear model closely match. \[\gamma(\omega)=\frac{\left|G_{IO}\right|^{2}}{G_{I}(\omega)G_{O}(\omega)} \tag{29}\]  
Figure 5: Definition of the different control inputs in longitudinal motions [143]. The stability and control derivatives, in the forthcoming general state space model, were determined at low frequency regions (0-20 Hz) by maximizing the coherence. In addition, the small periodic high frequency motions were neglected in this range of frequencies. The resulting mathematical model for longitudinal dynamics is shown by Equation (30). \[\left[ \begin{array}{c}\Delta\dot{u}\\ \Delta\dot{v}\\ \Delta\dot{q}\\ \Delta\dot{\theta}\end{array} \right]=\left[ \begin{array}{cccc}X_{u}&0&0&-g \\ 0&X_{w}&0&0\\ M_{u}&0&M_{q}&0\\ 0&0&1&0\end{array} \right]\left[ \begin{array}{c}\Delta{\rm u}\\ \Delta w\\ \Delta\mathrm{q}\\ \Delta\dot{\theta}\end{array} \right]+\left[ \begin{array}{cccc}0&X_{\beta}& X_{\Phi_{lf}}&X_{a_{ud}}\\ Z_{\Phi}&0&0&0\\ 0&M_{u}&M_{u}&M_{u}\\ 0&0&0&0\end{array} \right]\left[ \begin{array}{c}\Phi_{c}\\ \beta_{c}\\ \vartheta_{0 ff}\\ \alpha_{ud}\end{array} \right] \tag{30}\]  
The typical determined values are displayed in Table 2 where the standard deviations were measured by following the procedure of [149]. In the state space model, an additional control input \(\phi_{c}\) was introduced, which physically indicates the stroke amplitude and which has a direct effect on the heave dynamic without affecting the other system states. The discussed frequency-based study has far appeal with fruitful results. In the subsequent subsection, we highlight the individual efforts for the explicit identification of either the system's stability derivatives or system's stability derivatives and system's control derivatives.