esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Evaluation Results
### Optimized Conditions for Best Result  
By taking into consideration the result of the evaluation as described above, three optimized conditions (training data, statistical model, and ML classifier) to be used for the detection module were finally selected.  
1. Training data With regard to the construct of training data, it was found that its creation based on the \"summation of XDR create time and Bearer create time\" imparts the best performance.
2. Statistical model used for Feature Selection When it comes to feature selection, it was found that the chi square test when used on the statistical model imparts the highest result in terms of detection accuracy.
3. ML Classifier used for detection It was found that, out of the ML classifiers used for detection, random forest exhibits the highest degree of accuracy, with 98.4% on average which can be considered significantly high as shown in Table 11.  
It was found that, when applying the random forest technique, the detection performance differs with the hyperparameters, where the detection performance can be improved by raising n_estimator that designates the number of decision tress. For example, as shown in Table 12, with all other conditions unchanged, the degree of detection accuracy was increased up to 99.8% when n_estimator has the value 180, whereas the degree of detection  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Feature Selection** & **Number of Features** & **ML** & _Accuracy_ & **Parameters** \\ \hline XDR & Chi-square & 4 & SVM & 0.972 & C = 0.1, gamma = 0.01, kernel = rbf) \\ \hline XDR & ANOVA & 4 & RF & 0.9682 & n\_estimators = 10, max\_depth = 4, criterion = gini \\ \hline XDR & Mutual Information & 4 & NB & 0.9253 & var\_smoothing = 0.0001 \\ \hline Bearer & Chi-square & 4 & SVM & 0.9978 & C = 10, gamma = 0.001, kernel = rbf) \\ \hline Bearer & ANOVA & 4 & RF & 0.9978 & n\_estimators = 180, max\_depth = 2, criterion = gini \\ \hline Bearer & Mutual Information & 4 & NB & 0.9925 & var\_smoothing = 1 \(\times\) 10\({}^{-5}\) \\ \hline Total & Chi-square & 4 & SVM & 0.9876 & C = 1000, gamma = 0.001, kernel = rbf) \\ \hline Total & ANOVA & 4 & RF & 0.9876 & n\_estimators = 10, max\_depth = 2, criterion = gini \\ \hline Total & Mutual Information & 4 & NB & 0.8761 & var\_smoothing = 0.01 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Optimal result by tuning parameters of ML algorithm.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Random Forest (on Average)**} & \multicolumn{3}{c}{**Actual**} \\ \cline{2-4}  & **Normal** & **Abnormal** \\ \hline \multirow{2}{*}{Prediction} & Normal & 6920 & 70 (_False Negative_) \\ \cline{2-4}  & Abnormal & 242 (_False Positive_) & 12,395 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Detection accuracy on average when random forest is applied.  
accuracy is 98.7% when n_estimator has the value 10. It implies that the greater the number of decision trees in the forest, the more effective the signaling DDoS detection achieved.
## 3 Results
### Tensile Tests  
The ultimate tensile strength (UTS), elongation (A) [%] and their standard deviation, calculated for all the alloys and for all the tested conditions according to the type of cooling, are reported in Table 8.  
Figure 12: Rockwell hardness maps.  
Figure 13: Specimen surface on which Vickers hardness tests were performed.  
The UTS and elongation data obtained from the tensile tests for the different cases are reported in Figure 15 for analyzing the effect of the cooling system.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Alloy**} & \multirow{2}{*}{**UTS [MPa]**} & \
\begin{tabular}{c} **UTS Standard** \\ **Deviation [MPa]** \\ \end{tabular} & \multirow{2}{*}{**A [\%]**} &
\begin{tabular}{c} **A Standard** \\ **Deviation [\%]** \\ \end{tabular} \\ \hline \multirow{2}{*}{**AA2024**} & Air-cooled & 289.59 & 17.24 & 0.91 & 0.28 \\ \cline{2-6}  & Water-cooled & 278.23 & 22.79 & 1.11 & 0.31 \\ \hline \multirow{2}{*}{**AA6082**} & Air-cooled & 220.73 & 3.62 & 3.91 & 0.47 \\ \cline{2-6}  & Water-cooled & 217.27 & 6.06 & 2.62 & 0.21 \\ \hline \multirow{2}{*}{**AA7075**} & Air-cooled & 352.46 & 44.02 & 0.96 & 0.87 \\ \cline{2-6}  & Water-cooled & 370.33 & 40.71 & 2.16 & 1.34 \\ \hline \multirow{2}{*}{**AA5754**} & Air-cooled & 198.80 & 8.07 & 14.11 & 4.47 \\ \cline{2-6}  & Water-cooled & 201.21 & 4.39 & 17.36 & 2.01 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Average ultimate tensile strength (UTS) data, real elongation (A) and standard deviations of all the tested conditions.  
Figure 14: Vickers hardness maps.  
Figure 16 reports the fracture locations on the tensile test specimens for each alloy and for each cooling condition. The reported images refer to the most representative situation of each condition drawing a direct comparison between the different cooling methods. It is possible to observe that, for each studied condition, the break occurred near the welding, while the only substantial difference is represented by the water-cooled welding of the AA5754.  
Figure 16: Fracture location for each alloy and for each cooling condition.  
Figure 15: Ultimate tensile strength (UTS) versus elongation [%] data obtained from tensile tests for all the alloys and for all the tested conditions. The continuous line indicates the UTS value of the base alloy.
# Privacy-Preserving Solution for European Union Digital Vaccine Certificates
## 2 Related Work
There are also other blockchain-based privacy-preserving digital vaccine certificate solutions such as [17; 18; 19] that share similar advantages with [PERSON]'s solution, but also have several drawbacks such as robust user registration on the blockchain [17; 18] or costly Ethereum transactions [19]. [PERSON] et al. [2] surveyed numerous proposals and works focused on blockchain-based COVID certificates. Their study observes that performance results, implementation details, and deployment aspects are often not given in those proposals. For instance, [PERSON] et al. [20] proposed a blockchain-based vaccination certificate model. Nevertheless, the proposal just employs underlying techniques without more information, and it does not specify how zero-knowledge-proofs and other techniques are designed or implemented. In this paper, we present a novel solution for privacy-preserving EU Digital Vaccine Certificates (DVC). Our proposal is based on our Attribute-Based Credential (ABC) scheme, which allows us to disclose only the necessary attributes from a certificate. These attributes can represent, e.g., a photo of the certificate holder and the date of the last vaccination. Our solution addresses significant shortcomings of the current EU DVC, such as (1) insufficient identification of the person presenting the given digital certificate and (2) disclosure of all sensitive personal information about the holder and his/her vaccination status. We clarified, defined, and discussed the main security requirements for a privacy-preserving DVC. Furthermore, we provide a security analysis of our cryptographic core and system design. Finally, we provide implementation details about our proof-of-concept application and present several experimental results.
## 6 Real-Time In-Vivo and In-Vitro Corrosion Monitoring
### Electrochemical-Based Monitoring System
(**1**) Temperature instrument, (**2**) Sensor for water hardness, (**3**) Reference electrode, (**4**) Pt solution ground, (**5**) pH sensor, (**6**) H\({}_{2}\) sensor, (**7**) Working electrode, (**8**) Pt auxiliary electrode, (**9**) Reference electrode, A, B, pre amplifiers; M1, M2, MeasureNet workstations [28]. intermetallic phases, Mg\({}_{2}\)Ca and MgZn\({}_{2}\) to measure corrosion process differences present in Mg-Mg\({}_{2}\)Ca and Mg-MgZn\({}_{2}\) through current density, pH value and magnesium ions concentration. All in all, methods for corrosion monitoring that is associated with electrochemical techniques include potentiodynamic polarization, potentiostatic polarization, galvanostatic and galvanodynamic polarization, and electrochemical impedance spectroscopy. Furthermore, methods composed of localized electrochemical technique consist of scanning vibrating electrode technique, scanning electrochemical microscopy (SECM), and atomic emission spectroelectrochemistry (AESEC) [10]. The set-up that [PERSON] et al. developed [32] used CCS to perform real-time corrosion monitoring of Mg with an upgrade by creating a new generation of CCS with home-made capillary pH and Mg\({}^{2+}\) electrochemical sensors, where the main advantage of the pH microelectrode is its compatibility with the human body, making it helpful for cell biology applications or small volume experiments. The fabricated electrodes based on glass capillaries for pH and Mg\({}^{2+}\) had the ability to detect differences in pH and Mg\({}^{2+}\) during corrosion of Mg in NaCl solution. EIS was combined with the sensor to allow for real-time corrosion monitoring during immersion tests without the need for removal of the solutions for offline observation. It was observed that the capillary microsensors for pH and Mg\({}^{2+}\) were deemed successful for Mg corrosion real-time measurement [32].
###### Abstract  
A novel flat beam steering prototype based on the specific arrangement of a cylindrical unit cell is designed, manufactured and tested. The wideband and broad scanning capability is demonstrated at the Ku-band. We have considered two configurations, first with circular rings (CR) of the defined permittivity values for respective radial distance from the center of the aperture, second with cylindrical rods that shall be placed on a respective CR, which mimics the defined permittivity. The structure is generated from Vero CMYK full color wax, which utilizes the Multijet 3D printing method. The proposed prototype is designed in the operating frequency of 12 GHz (\(\lambda\) = 25 mm) and separation distance between the two parallel plates are maintained at 12.5 mm (0.5\(\lambda\)) for the TE\({}_{10}\) mode of operation. The diameter of the two parallel plates and the proposed structure is of 100 mm (4\(\lambda\)) where the radius of cylinders varies from 0.5 to 3.5 mm and corresponding relative permittivity varies from 0.6687 to 2.4395. The overall height of the structure is 12.5 mm and is placed between two parallel plates. The minimum separation distance is maintained between the proposed structure and the feed WR-75 waveguide. Irises effect is performed to obtain impedance matching in the operating frequency bands. Beam steering of the radiated waves is observed for relative rotation angles of 0\({}^{\circ}\), 30\({}^{\circ}\), 45\({}^{\circ}\), 75\({}^{\circ}\) and 90\({}^{\circ}\) of the WR-75 waveguide along the edge of parallel plates. The overall proposed system weighs 179 g, which signifies the light weight characteristics. Moreover, the proposed structure shows low return loss over 10 GHz to 15 GHz operating frequency band.  
parallel plate; wide band steering; microwave communication; 3D printing +
Footnote †: journal: Applied Sciences
# Performance Analysis of a Vertical FSO Link with Energy Harvesting Strategy
## 2 System Model
### Composite Channel Model  
Once the three factors included in Equation (3) have been individually discussed, the statistical characterization of the composite channel \(h=h_{a}h_{l}h_{p}\) can be achieved. Thus, from [45],  
\[f_{h}(h)=\frac{2\gamma^{2}(\alpha\beta)^{\frac{\alpha+\beta}{2}}}{(A_{a}h_{l}) \gamma^{2}\Gamma(\alpha)\Gamma(\beta)}h^{\gamma^{2}-1}\int_{\frac{h}{A_{0}h_{ l}}}^{\infty}h_{a}^{\frac{\alpha+\beta}{2}-1-\gamma^{2}}K_{\alpha-\beta}(2\sqrt{ \alpha\beta h_{a}})\,dh_{a}. \tag{12}\]  
where \(\alpha\) and \(\beta\) parameters include the information on the strength of the turbulence, with \(\gamma\) containing the severity of the pointing error, where \(A_{o}\) denotes the geometric spread attenuation and with \(h_{l}\) being the path loss.
## 4 Cooling System Design and Cooling Simulation Based on UG and Moldflow
### Cooling Process Simulation and Analysis
#### 4.3.1 Parameter Setting  
According to the requirements of the recommended molding conditions of the plastic parts, the injection process parameters were set as shown in Table 1. During the injection molding process, common processing parameters include temperature, pressure, injection time, holding pressure time, and cooling time. Temperature adjustment is crucial to ensure the melting and flowability of plastic materials, involving control of the melt temperature and mold temperature. Pressure plays a significant role in pushing the molten plastic material into the mold. Adjusting the injection time and holding pressure time is essential for complete filling, solidification, and stability of the plastic material. Cooling time refers to the time the product remains in the mold for cooling after the injection molding process. Proper cooling time ensures proper solidification and strength development of the product while minimizing deformation and warpage. The adjustment of these processing parameters is critical for the success and optimization of the injection molding process.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Injection Molding Process Parameter** & **Value** \\ \hline Injection Machine Clamping Force & 150 ton \\ Injection Material & PA66 \\ Cycle Time & 52 s \\ Mold Open and Ejecting Time & 6 s \\ Mold Close Time & 4 s \\ Injection Unit Forward & 4 s \\ Filling Time & 2 s \\ Holding Time & 3 s \\ Injection Unit Backward & 3 s \\ Cooling Time & 34 s \\ Injection Pressure & 50 kg/cm\({}^{2}\) \\ Holding Pressure & 60 kg/cm\({}^{2}\) \\ Material Temperature at Injection & 260 \({}^{\circ}\)C \\ Material Surface Temperature at Ejection & 65 \({}^{\circ}\)C \\ The temperature of Coolant & 20 \({}^{\circ}\)C \\ Water Volume & Tot. 16 L, Each 8 L/min \\ Mold Surface Temperature at Ejection & 40 \({}^{\circ}\)C \\ Mold Material-Conventional & P20 steel \\ Mold Material-Conformal & Fe-Cu Alloy (50 W/mK) \\ Coolant Pressure & 0.2 Mpa \\ \hline \hline \end{tabular}
\end{table}
Table 1: Injection molding process parameter settings.
## 2 Driver Visual Attention Network
### Multi-Tasking Output Module (MOM)  
MOM performs localization, classification, and predictions the next attention point P\({}^{t+1}\), according to the final output H\({}_{t}^{t}\) of IPM mapping.  
Localization and classification: The geometric parameters of the localization target border are O\({}^{t}=\left[O_{yx}^{t},O_{hw}^{t},O_{s}^{t}\right]\), where O\({}_{yx}^{t}\) represents the coordinates of the upper left point, O\({}_{hw}^{t}\) represents the size of the target, and O\({}_{s}^{t}\) represents the score of the target. They are obtained by regression of H\({}_{t}^{t}\) through the full connection layer with parameters W\({}_{O}^{D}\) and b\({}_{O}^{D}\). Furthermore, in order to ensure the output within the appropriate range, the truncation operation S\({}_{J}\) with parameter \(\varnothing_{d}\) is added after the full connection layer. The target classification probability C\({}^{t}\) is obtained from H\({}_{t}^{I}\) via a full connection layer with parameters W\({}_{C}^{D}\), b\({}_{C}^{D}\) and a Softmax operation S\({}_{f}\). The specific process of location and classification is as follows.  
\[O^{t}=S_{J}(W_{O}^{D}H+b_{O}^{D},\varnothing_{d}) \tag{8}\]  
\[C^{t}=S_{f}(W_{C}^{D}H+b_{C}^{D}) \tag{9}\]  
Predict the next attention point P\({}^{t+1}\): The mechanism of human eye fixation can be divided into bottom-up and top-down strategies [30]. Our method adopts a framework for attention-based task-driven visual processing with neural networks [26] because of its strong task orientation in driving scenes [31]. In each time cycle, DVAN predicts the next point of attention based on context information and task requirements. The input for predicting the next attention point is the final mapping H\({}_{t}^{t}\) of IPM. The next attention point is sampled from a Gaussian distribution with a mean value of u\({}^{t+1}\) and a standard deviation of \(\sigma\). The mean value \(\mu\) is estimated by a full connection layer and Sigmoid activation function operation (S\({}_{g}\)). The above process can be expressed, as follows.  
\[P^{t+1}\sim N\left(u^{t+1},\sigma^{2}\right) \tag{10}\]  
\[u=2S_{g}\left(W_{P}^{D}H+b_{P}^{D}\right)-1 \tag{11}\]  
where N \(\left(u,\sigma^{2}\right)\) represents a Gaussian distribution with a mean value of \(\mu\) and a standard deviation of \(\sigma\), and \(W_{P}^{D}H\), b\({}_{P}^{D}\) are parameters of the full connection layer.
## 2 Principles and Methods  
### Conventional SST (CSST)  
If complex numbers \(G(t,f)\) denote the time-frequency spectra of the inputted signal \(g(t)\), then SST results can be expressed as  
\[S_{1}(t,v)=\int G(t,f)df,if\quad V_{1}(t,f)\in[v-\Delta v,v+\Delta v] \tag{1}\]  
and  
\[V_{1}(t,f)=\text{Re}\bigg{[}\frac{1}{2\pi i}\frac{1}{G(t,f)}\frac{\partial G( t,f)}{\partial t}\bigg{]} \tag{2}\]  
where \(v\) and \(f\) are both frequencies; \(\Delta v\) is the frequency increment; \(\text{Re}[\,]\) takes the real part of a complex number; \(V_{1}(t,f)\) denote the instantaneous frequencies of the time-frequency spectra \(G(t,f)\), which are referred to as time-frequency instantaneous frequencies; \(i=\sqrt{-1}\). If  
\[\left\{\begin{array}{l}x(t,f)=\text{Re}[G(t,f)]\\ y(t,f)=\text{Im}[G(t,f)]\\ G(t,f)=x(t,f)+iy(t,f)\end{array}\right. \tag{3}\]  
where \(\text{Im}[\,]\) takes an imaginary part of a complex number, respectively, then  
\[V_{1}(t,f)=\frac{1}{2\pi}\frac{x(t,f)\frac{\partial y(t,f)}{\partial t}-y(t,f )\frac{\partial x(t,f)}{\partial t}}{x^{2}(t,f)+y^{2}(t,f)}. \tag{4}\]  
For actual discrete signals, Equation (4) should be modified as (see Appendix A)  
\[V_{2}(t,f)=\frac{1}{2\pi\Delta t}\frac{x(t-\Delta t,f)y(t,f)-y(t-\Delta t,f)x (t,f)}{[x^{2}(t,f)+y^{2}(t,f)]} \tag{5}\]  
where \(\Delta t\) is the time-sample interval of the inputted signal \(g(t)\); and \(V_{2}(t,f)\) can be considered the estimated time-frequency instantaneous frequencies of \(G(t,f)\); correspondingly, Equation (1) can be rewritten as  
\[S_{2}(t,v)=\int G(t,f)df,if\quad V_{2}(t,f)\in[v-\Delta v,v+\Delta v] \tag{6}\]  
which is considered conventional SST (CSST).  
Precisions of \(V_{2}(t,f)\) are inversely proportional to the time-sample interval, this usually leads to inaccurate SST results [21]. To minimize the impact of the time-sample interval, [PERSON] et al. proposed ASST [21], which can minimize the impact of the time-sample interval to obtain high-precision and high-resolution SST results, but at the expense of processing efficiency.  
Figure 1a-c show time-frequency instantaneous amplitudes (modulus of time-frequency spectra) of a 50 Hz harmonic signal obtained by MSTFT, CSST, and ASST with \(\Delta t\) = 2 milliseconds (ms), respectively; Figure 1d-f show time-frequency instantaneous amplitudes of the 50 Hz harmonic signal obtained by MSTFT, CSST, and ASST with \(\Delta t\) = 4 ms, respectively; Table 1 shows the time consuming of MSTFT, CSST, and ASST for the 50 Hz harmonic signal with 4 s length. The peak frequencies in Figure 1a-f are 50, 48, 50,
## 1 Introduction
[17] conducted a numerical simulation with SOLIDWORKS Flow Simulation software (ver.2015) to analyze the flow and heat transfer characteristics of the sewage flow in diaphragms with different structures. The results showed that the same shell-side mass flow rate, heat transfer coefficient, pressure drop and heat transfer rate are found to reach their maximum with single-segmental baffles. By applying the CFD numerical simulation methodology, [PERSON] et al. [18] simulated the flow and heat transfer characteristics of a corrugated heat exchanger within the Re ranges of 15,000-40,000 for a Pr of 2.9 and 4.3, and the influence of spiral corrugation on the heat exchange performance and the friction coefficient were investigated. These results are highly consistent with the results reported by [PERSON] et al. in 2004 [19]. Above all, it can be obviously concluded that numerical simulation is an effective tool to reveal the heat transfer process of the present heat exchanger. In order to figure out the weak links of the heat exchange process, as well as optimize the heat exchange performance of the heat exchanger in an industrial corn drying system, the numerical simulation and exergy analysis methodologies were adopted to analyze the heat transfer performance of the heat exchanger. The velocity and pressure nephograms of the flue gas with different velocities (\(v_{fg}\)) of 3, 5 and 7m/s, as well as the ambient air with different relative humidities (\(RH_{0}\)) of 80%, 85% and 90%, were simulated. Moreover, the variations in the fluid dynamic indexes, including the pressure drop (\(\Delta p\)), Nusselt number (\(Nu\)), Reynolds number (Re) and heat exchange coefficient (\(h_{s}\)), as well as the exergetic indexes including the exergy efficiency (\(\eta_{HE}\)), sustainable index (\(SI\)) and improvement potential (\(IP\)) with time, were investigated in the present work with the hope of figuring out the weak links of the heat exchange process related to the heat exchanger and to provide some guidelines for optimizing the structure of the heat exchanger.
# Human Pose Detection for Robotic-Assisted and Rehabilitation Environments
## 4 Experimental Setup
### Ground Truth  
The ground truth of the pose estimation was calculated using the skeletons provided by the two Kinect cameras. As previously mentioned, these cameras were located at approximately \(90^{\circ}\) from each other to obtain accurate data on rehabilitation exercises that focus on the upper limb where one camera does not give fully reliable estimations. Figure 5 shows four examples of 3D human pose estimations obtained by the Kinect 2 (blue color), and the skeleton fusions (red color) during an elbow side flexion exercise. The skeleton fusion was calculated with a simple average of the Kinect 2 skeleton and the projected skeleton from the Kinect 1, which was calculated using the information obtained in the calibration phase and using Equation (3). When performing this projection, a difference is expected between the coordinates of the Kinect 2 (main camera, front view) and the Kinect 1 (auxiliary camera, side view). This difference is due to the viewing angle of each Kinect and the volume of the joints of the human.  
Figure 4: Rehabilitation exercises: (**a**) elbow side flexion, (**b**) elbow flexion, (**c**) shoulder extension, and (**d**) shoulder abduction.  
Figure 6 shows the X, Y, and Z positions of the left and right wrist of the two Kinects (Kinect 1 and Kinect 2) during a shoulder extension exercise and the projected points (Projected 1). As stated before, an error was expected in the calculation of the fused skeleton, and the results show how even with the fully calibrated system, we obtained some errors. For the left wrist, we measured MAEs (mean absolute errors) of 7.35, 2.16, and 3.71 cm for the coordinates X, Y, and Z, respectively. For the right wrist, we measured MAEs of 7.70, 2.90, and 3.25 cm for the coordinates X, Y, Z, respectively.
# Leveraging Machine-Learning for D2D Communications in 5G/Beyond 5G Networks
## 4 Applications of ML in D2D Communications
### ML for D2D Security and Commercial Availability  
There are still ongoing discussions on D2D commercial pricing and network security, where ML can handle such problems [1]. ML can help in addressing new D2D security challenges and threats related to device and user authentication to prohibit unauthorized access and attacks on the complete network. As an example of such usage, a radio frequency (RF) fingerprint based identification method of D2D device is proposed in [30]. Firstly, Hilbert transform (HT) and PCA are utilized to create the RF fingerprint of D2D device. Then, cross-validation (CV)-SVM is used as the classifier. Moreover the development of ML solution will make the issues of related commercial products close to appear in the market by large mobile companies like Huawei and Samsung with reasonable prices.