esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Dataset Description
### HFAVD Dataset Structure
#### 3.3.6 Eye and Mouth State  
The eye state was assessed through the utilization of a trained model. This model takes the detected face, identified via the FaceBoxes framework, as its input and generates an output that signifies whether the eyes are open or closed. Similarly, the yawning state is recognized by employing a modified version of MobileNet, as proposed by [45]. This model achieved an accuracy of 95.20%. However, a limitation of this model is that it relies on a private dataset for validation. This could potentially limit the generalizability of the findings, as the dataset is not representative of the broader population.  
The videos in the OperatorEYEVP dataset exhibit a high degree of similarity to the conditions found in the datasets used for training the models. These similarities encompass factors such as lighting conditions, skin color, and the demographic characteristics of the subjects, who were healthy and young. Consequently, the likelihood of encountering extreme or abnormal values in vital signs, particularly oxygen saturation, is minimized. However, the insufficiency of sensor data in the OperatorEYEVP dataset prevents the calculation of estimation errors. Nevertheless, considering the aforementioned conditions, it is reasonable to assume that the models exhibit good accuracy. This assumption is further supported by the alignment of the obtained results with expectations and their consistency with the existing literature.
## 2 Materials and Methods
### Equation of Motion  
Each element of the rotating system is individually modelled by the finite element method, allowing inertial coupling between translation and rotation through the mass matrix \([M]\), stiffness matrix \([K]\), gyroscopic matrix \([G]\) and proportional damping matrix \([C]\) (as showed in Equation (12)), where \(\alpha\) and \(\beta\) are proportional coefficients.  
\[[C]=\alpha[M]+\beta[K] \tag{12}\]  
The Lagrange equation is used to obtain the system's equations of motion, being its generic form shown in Equation (13). Moreover, \(q_{i}\) is the \(i\)th generalized coordinate and \(F_{i}\) the generalized force component acting in the direction of each generalized coordinate.  
\[\frac{d}{dt}\left(\frac{\partial T}{\partial\dot{q}_{i}}\right)-\frac{ \partial T}{\partial q_{i}}+\frac{\partial U}{\partial q_{i}}+\frac{\partial R }{\partial\dot{q}_{i}}=F_{i};\;\;\;\;i=1,2,\ldots,N \tag{13}\]  
Defining the energies--kinetic \((T)\), deformation \((U)\) and work performed by the nonconservative forces \((R)\)--for each element \(i\), the total energy of the system is given by the sum of the energies of each element, as follows in Equation (14).  
\[T=\sum_{i=1}^{N}T_{i};\;\;\;\;\;U=\sum_{i=1}^{N}U_{i};\;\;\;\;\;\;R=\sum_{i=1} ^{N}R_{i} \tag{14}\]  
According to [PERSON] and [PERSON] [26] the kinetic energy, deformation and the work of the global nonconservative forces are given as Equation (15), in which \(\Omega\) is the shaft rotating speed and \(I_{P}\) the polar moment of inertia.  
\[\begin{array}{c}T=\frac{1}{2}\big{\{}\dot{q}\big{\}}^{T}[M]\big{\{}\dot{q} \big{\}}-\Omega\big{\{}\dot{q}\big{\}}^{T}[G]\{q\}+\frac{1}{2}I_{P}\Omega^{2}; \\ U=\frac{1}{2}\{q\}^{T}[K]\{q\};\\ R=\frac{1}{2}\{\dot{q}\}^{T}[C]\{\dot{q}\}\end{array} \tag{15}\]Combining Equation (13) and Equation (15), the system's complete equation of motion follows as in Equation (16). The external forces vector (Equation (17)) is composed by the sum of the exciting forces \(\{f_{c}(t)\}\) (usually due to the unbalance mass force), the rotor's weight force \(\{W\}\) and the hydrodynamic forces \(\{f_{h}(t)\}\) (which can be modeled as linear (Equation (10)) or nonlinear (Equation (9))).  
\[[M]\{\dot{q}(t)\}+([C]+\Omega[G])\{\dot{q}(t)\}+[K]\{q(t)\}=\{f(t)\} \tag{16}\]  
\[\{f(t)\}=\{f_{c}(t)\}+\{W\}+\{f_{h}(t)\} \tag{17}\]
## 3 Results
### Multiple Linear Regression Model Results
#### 3.2.2 UVE-MLR Established by Sensitive Variables  
Using the eight sensitive variables screened by UVE as the model's independent variables, the Multiple Linear Regression model is built again, and the model results are presented in Tables 6 and 7.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Variable Names** & \
\begin{tabular}{c} **Unnormalized** \\ **Coefficient** \\ \end{tabular} & **Standard Error** &
\begin{tabular}{c} **Standardization** \\ **Coefficient** \\ \end{tabular} & _VIF_ & **Significance** \\ \hline Constant & 22.596 & 1.094 & - & - & \textless{}0.001 \\ Upper outlet temperature & 0.139 & 0.085 & 0.575 & 44.91 & 0.105 \\ Lower outlet temperature & 0.029 & 0.093 & 0.117 & 11.320 & 0.756 \\ Drying section A temperature & 0.199 & 0.099 & 0.851 & 36.012 & 0.046 \\ Drying section B temperature & \(-\)0.31 & 0.058 & \(-\)1.458 & 29.79 & \textless{}0.001 \\ Drying section C temperature & \(-\)0.381 & 0.119 & \(-\)1.56 & 74.06 & 0.002 \\ Drying section D temperature & 0.088 & 0.039 & 0.106 & 3.37 & 0.027 \\ Drying section E temperature & 0.09 & 0.017 & 0.587 & 17.73 & \textless{}0.001 \\ Upper outlet humidity & 0.041 & 0.023 & 0.389 & 21.52 & 0.078 \\ Lower outlet humidity & 0.026 & 0.036 & 0.178 & 23.17 & 0.477 \\ Humidity in drying section A & 0.128 & 0.03 & 0.591 & 29.33 & \textless{}0.001 \\ Humidity in drying section B & \(-\)0.01 & 0.01 & \(-\)0.041 & 2.939 & \textless{}0.001 \\ Humidity in drying section C & \(-\)0.093 & 0.03 & \(-\)0.438 & 30.65 & 0.003 \\ Humidity in drying section D & \(-\)0.102 & 0.033 & \(-\)0.881 & 53.16 & 0.003 \\ Humidity in drying section E & \(-\)0.062 & 0.045 & \(-\)0.076 & 4.6 & 0.172 \\ \hline \hline \end{tabular}
\end{table}
Table 4: MLR model coefficient table.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model Evaluation** & **R\({}^{2}\)** & **Adjusted R\({}^{2}\)** &
\begin{tabular}{c} **Root-Mean-** \\ **Square Error** \\ \end{tabular} & **Significance** \\ \hline value & 0.919 & 0.910 & 0.881 & \textless{}0.001 \\ \hline \hline \end{tabular}
\end{table}
Table 5: MLR model evaluation results.  
The model's adjusted R\({}^{2}\) is 0.898, and its RMSE is 0.908%. While the model-fitting effect is adequate, the error remains relatively high. Compared with the MLR model, from the _VIF_ values in Table 6, it can be seen that there is only a slight multicollinearity problem between the variable data of the model, and the coefficients corresponding to the individual variables are not significant, and the problem of model covariance is obviously solved. The R\({}^{2}\) of the model is significantly lower and the error is significantly higher, which shows that the reduction in the types of variables and the sample size of the data have an impact on the model, which may be caused by the fitting mechanism of the linear model.
# FIDO2 Facing Kleptographic Threats By-Design
## 2 Details of FIDO2
### Security of FIDO2 Authenticators--Capabilities of an Auditor  
From the security point of view of the overall FIDO2 framework, it is essential to know how much we can trust the authenticators. There are two complementary concepts relating to the security of the FIDO protocol: Certified Authentication Levels [26], and Attestation [23; 27]. By running attestation, the Relying Party does not check the implementation of an authenticator. All the information obtained is a declaration of origin that may come from a trusted party and/or be based on secure architecture mechanisms. In this way, the Relaying Party may effectively ban the devices coming from unknown or blacklisted sources. However, the most popular attestation mode, called \"Basic\", is based on a key shared by a large group of devices. Hence, it suffices to extract such a key from a single device, or to obtain it from the manufacturer (by means of espionage or coercion), to poison a supply chain with counterfeit devices enriched with malicious functionality. Moreover, attestation alone does not guard against an accepted vendor installing trapdoors. The latter problem should be addressed by certification of the devices. On the other hand, even during a more general Common Criteria certification process, the party evaluating the device cooperates with the manufacturer, receives extensive documentation, and might request an open sample of the evaluated product (cf. Section 5.3.4 of [28]). A more specific document [29] states that \"A side-channel or fault analysis on a TOE (Target of Evaluation) using ECC in the context of a higher Evaluation Assurance Level (...) is not a black-box analysis\".  
In our case, an auditor examining an authenticator for malicious implementation cannot fully trust the manufactured devices. In particular, the auditor cannot always guarantee that the samples received from the manufacturer during the certification process contain precisely the same implementation as all devices delivered to the market. Consequently, to some extent, the auditor needs to use the black-box approach, which is much more difficult, especially for authenticators certified for Level 3+.  
We may assume that the auditor has the following capabilities:  
1. The auditor may run the authenticator with the input data of their choice and may confront its output with the collected history of interactions with the device.
2. The auditor may observe the input and output of the authenticator for real input data coming from the original services during real authentication sessions.  
However, repeating the investigation of the internals of an authenticator for different copies taken from the market may be beyond the auditor's capabilities--the high level of protection of the devices may hinder deeper investigation.
## 4 Experimental Datasets and Results  
For the purpose of experimental design, we used five-fold-cross-validation to evaluate the performance of our proposed pipeline. From the results, one can conclude that our proposed approach significantly enhances the classification performance with comparatively less computational time.  
This section enlists the experimental results obtained by several sample selection methods with various types of classifiers. These classifiers include Support Vector Machine (SVM), k-Nearest Neighbours (KNN), Ensemble Learning (EL) and Extreme Learning Machine (ELM). The important parameters for all the experimental classifiers mentioned above are carefully tuned and to avoid bias, all the experiments are carried out under the same settings that maximize performance.  
KNN classifier is tested with Euclidean distance function \(K\) = [2:1:20]. The SVM classifier is tested with the RBF kernel function. EL i.e., both Logistic (LB) and Gentle boost (GB) is trained using tree template with 100 trees and leaf size \"number of rows in training set/10\". ELM classifier is tuned with [1:1:500] hidden neurons with a sigmoid as an activation function. Here our main aim is to compare the performance of different classifiers with MLR-LORSAL.  
The experiments described below show the \(\kappa\) evaluation for the above-discussed classifiers with a different number of training samples in each iteration for several HIS datasets. The number of selected training samples is set as shown in the following figures i.e., initially, 50 samples are used to train the model and in each iteration, 100 new non-random samples are selected using the proposed strategy. Based on the experimental results shown in the following figures, MLR-LORSAL classifier performs better than other state-of-the-art classifiers. From the following results, one can conclude that with a different number of training samples, there is a significant improvement in classification accuracy.
# Numerical Simulation and Design of a High-Temperature, High-Pressure Fluid Transport Pipe
## 3 Results and Discussion  
### Simulation of Heat Transfer, Thermal Stress, and Mechanical Stress  
With regards to a single shot, the barrel experienced heating and cooling for approximately 1 s. The results of the heat-transfer analysis (Figure 8a) showed that the temperature of the barrel and the barrel housing was 37 \({}^{\circ}\)C or less. This was a very low temperature compared with a high-temperature gas of 2.800 \({}^{\circ}\)C (3.000 K). The reason was that the heating time for the barrel was very short--0.5 ms. Thus, there was no danger of low-temperature burns, because the surface temperature of the barrel housing was below 37 \({}^{\circ}\)C. A low-temperature burn was caused by continuous exposure at the 40 \({}^{\circ}\)C level, which was more dangerous than expected, because people did not immediately feel symptoms or pain. In the case of products that had continuous body contact (such as hand calibers), designers must consider the surface temperature for this reason.  
As the thermal stress increased when the barrel and the barrel housing were constrained to each other, and the temperature difference was greater, the maximum temperatures of the barrel and the barrel housing were examined over time. When t = 0.3 s, the temperature difference was the highest at 9 \({}^{\circ}\)C. At t = 0.3 and 1 s, the thermal stress in the barrel was maximum--17 and 21 MPa, respectively. Figure 8b shows the maximum mechanical stress generated by the dynamic pressure.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Name** & **Velocity at the Muzzle [m/s]** & **Travel Time [ms]** \\ \hline Ballistic Data & 347 & 0.31 \\ Fluid\_1 & 335 & 0.318 \\ Fluid\_2 & 341 & 0.316 \\ Fluid\_3 & 345 & 0.314 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of internal ballistics code data with computational fluid dynamics (CFD) simulation results.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(\rho\) & \(\mathrm{M_{w}}\) & \(\mathrm{c_{p}}\) & \(\mathrm{k}\) & **Molecular Viscosity** \\  & [g/mol] & [J/gK] & [W/mK] & [kg/m-s] \\ \hline Equation (18) & 39.5 & 1870 & 0.0242 & \(1.7894\times 10^{-5}\) \\ \hline \hline \end{tabular}
\end{table}
Table 7: Fluid properties of the propellant.  
Similar to the previous case, the stress was maximized when the projectile traveled 6 mm, and its value was 770 MPa. As the dynamic pressure decreased as the projectile advanced, the mechanical stress gradually decreased.  
The maximum value of thermo-mechanical stress--the sum of the thermal and mechanical stress--was approximately 791 MPa. As the yield stress of the barrel was about 1 GPa, there was little risk of failure. When comparing the thermal and mechanical stresses, the thermal stress was only 3% of the thermo-mechanical stress and 2% of the mechanical stress. It could be seen that the effect of the thermal stress on the strength of the hand caliber was small during a single shot.
## 3 Image Edge Detection and Segmentation for Pipe Defects
### Improved Edge Detection with Sobel Operator  
The conventional Sobel operator first performs a weighted average process for each pixel using a convolutional template (shown in Figure 6) and then acquires the gradient values in the \(X\) and \(Y\) directions by performing a difference process. It is challenging for the algorithm to achieve the desired detection results and the localization accuracy is not satisfactory, because the prevalent Sobel algorithm is just sensitive to the \(X\) directions and \(Y\) directions and can only assess the edges in both \(X\) directions and \(Y\) directions [24]. Various shapes and depths of the PE gas pipeline defects lead to negligible regional variations in the grayscale of the deficiency images; the collected images of PE gas pipeline defects contain a lot of interference data because of compression and real-time transmission. It is ineffective and very likely to result in missing edges, relying solely on two directional templates to identify the edges of pipe defects. In order to detect edge pixels in images more accurately, the Sobel algorithm was improved to eight directions [31] (as in Figure 7), which not only detects image edges more effectively but also increases edge detection accuracy and lowers the likelihood of incorrect edges.  
For the pipe defect filtered image obtained above (Figure 8A1-A3), the improved Sobel algorithm (Figure 8B1-B3), the Sobel edge detection algorithm (Figure 8C1-C3) and the Prewitt algorithm (Figure 8D1-D3) are used for edge detection of the image, respectively. Figure 8 displays the outcomes of the three defects' edge detection. According to the comparison study, the improved Sobel algorithm extracts defect edges with more continuity and integrity and can completely display the defect shape characteristics. Therefore, the improved Sobel algorithm was used for edge detection in this paper.  
Figure 6: Sobel operator template.
## 3 Analysis and Results
### Qualitative Analysis
#### 3.2.4 Usage of BIM Software  
Researchers categorized the software used in experimental on-site work and examined its proportion throughout the entire process. The top three applications used in Project 1 were Revit (45.12%), Navisworks (26.76%), and Excel (12.30%) (Figure 5). These applications were utilized for additional detailed modeling, construction feasibility reviews, process management, cost management, and on-site management tasks.  
In Project 2, the predominant BIM software was Revit, accounting for 53.45%, followed by Navisworks (13.78%) and AutoCAD (13.59%). Overall, Revit emerged as the most commonly used BIM application in construction projects, likely due to its primary association with modeling tasks in the field of architectural engineering.
## 6 Simulation Results and Analysis
It is obvious that as m increases, the fading quality of the interference link improves, the rate limit at the receiver end of the eavesdropper is enhanced, and the ASC rises. Therefore, an appropriate increase in the fading severity parameter \(m\) allows the RIS-aided mixed RF/FSO system to achieve better security performance. Figure 8: Simulation diagram of ASC under different numbers of RIS reflecting elements N in the RIS-aided mixed RF/FSO system.
## 2 Principle  
### Principle of PDM-DSB FASCD  
Figure 1 shows the schematic diagram of the PDM-DSB FASCD. In the transmitter, two independent OFDM signals form two pairs of symmetrical DSB signals corresponding to X-polarization and Y-polarization, respectively. As shown in Figure 1, the signal of the X-polarization and the Y-polarization are frequency overlapped to increase the SE. To ensure the carrier can be successfully extracted using an OBPF at the receiver, a guard band is set between the carrier and the signal. Note that in the Y-polarization, no carrier is present, and additionally, a spectral gap identical to that of the X-polarization is reserved. This is attributed to the requirement for signal equalization processing at the receiver end, where complicated channel estimation methods must be employed for the Y-polarization band at the same spectrum as the X-polarization guard band. Here, a part of the spectral  
efficiency is sacrificed in order to reduce the complexity of the algorithm. The generated optical signal is then fed into the single-mode fiber (SMF) for transmission, where the transmission signal can be expressed as \(\left(C_{x}+S_{x}\right)_{x-pol}\) and \(\left(S_{y}\right)_{y-pol}\), where \(S_{x}\) and \(S_{y}\) are the independent complex signals with equal power modulated on the X- and Y-polarization, respectively. The carrier-to-signal power ratio (CSPR) significantly impacts the performance of self-coherent systems. The CSPR is defined as the power ratio between the carriers and the PDM signals of X polarizations:  
\[CSPR(dB)=10\bullet\log_{10}\frac{P_{x-carrier}}{P_{x-signal}} \tag{1}\]  
At the receiver, due to the PMD in the fiber, both the signal and the carrier undergo random rotations in their SOP. The use of a PBS can lead to polarization fading, which degrades the quality of signal transmission traditionally. The signal is split into two paths by OC: one path is directly injected into the hybrid, while the other path utilizes an OBPF to extract the carrier, as shown in Figure 1. Subsequently, a pair of orthogonal carriers can be constructed using FRM. The FRM transforms any input polarization state into an orthogonal output polarization state [27]. During transmission, the SOP of the signal undergoes random rotations; however, even if the polarization state is unknown, the orthogonality between the two signals remains unchanged throughout the transmission process. The polarization states of the signal and the carrier consistently remain aligned. As a result, the orthogonal optical carriers, when demodulated through a receiver (as shown in Figure 1), enable the recovery of the received signal and the restoration of the optical field information.
## 2 System Model and Problem Formulations
The first term is the sum rate of V2I links, the second term is the sum rate of V2V links and the third term is the total interference of priority links. Meanwhile, to reflect the advantages of high-priority links without affecting the performance of other links, we introduce some constraints. Therefore, the overall optimize problem is:  
Maximize : \[\lambda_{1}\underset{i}{\sum}C_{i}^{c}[i]+\lambda_{2}\underset{j}{ \sum}C_{j}^{d}[j]-\lambda_{3}I_{f},\] (6) Subject to : \[I_{f}\leq I_{\max}\] \[ \begin{array}{c}C_{i}^{c}>C_{RA}^{c}\\ C_{j}^{d}>C_{RA}^{d}\end{array} \]  
where \(\lambda_{1}\),\(\lambda_{2}\) and \(\lambda_{3}\) are the weight constant used to define the priority between the three targets, \(I_{f}\) refers to the total interference received by a priority link, \(I_{\max}\) represents the maximum total interference that we set. \(C_{RA}^{c}\) and \(C_{RA}^{d}\), respectively represent the rates of V2I link and V2V link when the random spectrum allocation scheme is used.
###### Abstract  
In this paper, we propose an integrated approach to robot vision: a key frame-based skeleton feature estimation and action recognition network (KFSENet) that incorporates action recognition with face and emotion recognition to enable social robots to engage in more personal interactions. Instead of extracting the human skeleton features from the entire video, we propose a key frame-based approach for their extraction using pose estimation models. We select the key frames using the gradient of a proposed total motion metric that is computed using dense optical flow. We use the extracted human skeleton features from the selected key frames to train a deep neural network (i.e., the double-feature double-motion network (DDNet)) for action recognition. The proposed KFSENet utilizes a simpler model to learn and differentiate between the different action classes, is computationally simpler and yields better action recognition performance when compared with existing methods. The use of key frames allows the proposed method to eliminate unnecessary and redundant information, which improves its classification accuracy and decreases its computational cost. The proposed method is tested on both publicly available standard benchmark datasets and self-collected datasets. The performance of the proposed method is compared to existing state-of-the-art methods. Our results indicate that the proposed method yields better performance compared with existing methods. Moreover, our proposed framework integrates face and emotion recognition to enable social robots to engage in more personal interaction with humans.  
Keywords:robots; vision; key frame; deep neural network; optical flow; face recognition; action recognition; skeleton features +
Footnote †: journal: Applied Sciences