esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussions
### Results of Molecular Dynamics Simulation  
#### 3.4.1 Interaction Energy  
The interaction energy can reflect the stability of the interfacial bonding of two substances. As shown in Figure 15, the interaction energy of the PU-CaO interface is the largest,  
Figure 14: Calculation results based on surface free energy: (**a**) adhesion work; (**b**) spalling work.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Materials** & **\(\gamma\) (m/m2)** & **\(\gamma^{4}\) (m/m2)** & **\(\gamma^{5}\) (m/m2)** \\ \hline PU & 23.637 & 14.432 & 9.205 \\ HPU & 16.452 & 4.244 & 12.208 \\ Basalt & 35.647 & 26.605 & 9.042 \\ Limestone & 39.673 & 30.217 & 9.456 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Surface free energy of PU and aggregates.  
followed by the SiO\({}_{2}\) interface, and the interaction energy with the CaCO\({}_{3}\) interface is the smallest. This indicates that the PU-CaO system is the most stable, while the PU-CaCO\({}_{3}\) system is the least stable. It is worth noting that the interaction energies between H\({}_{2}\)O-SiO\({}_{2}\), H\({}_{2}\)O-CaO, H\({}_{2}\)SO\({}_{4}\)-SiO\({}_{2}\) and H\({}_{2}\)SO\({}_{4}\)-CaO are smaller than those of PU-SiO\({}_{2}\) and PU-H\({}_{2}\)SO\({}_{4}\), but the interaction energies of H\({}_{2}\)O-CaCO\({}_{3}\) and H\({}_{2}\)SO\({}_{4}\)-CaCO\({}_{3}\) are higher than those of PU-CaCO\({}_{3}\), which suggests that the hydrophilicity of limestone is higher than that of basalt. This result can be attributed to the fact that limestone surfaces contain a large number of polar groups (e.g., Ca\({}^{2+}\) and CO\({}_{3}\)\({}^{2-}\)) that attract and bind water molecules, resulting in a higher surface energy that exhibits greater hydrophilicity.
###### Abstract  
The present study examined the effect of ultrasonic pretreatment at three time the levels of 10, 20 and 30 min on some thermodynamic (effective moisture diffusivity coefficient(\(D_{\mathit{off}}\)), drying time, specific energy consumption (\(SEC\)), energy efficiency, drying efficiency, and thermal efficiency) and physical (color and shrinkage) properties of kiwifruit under hybrid hot air-infrared(\(HAI\)) dryer at different temperatures (50, 60 and 70 \({}^{\circ}\)C) and different thicknesses (4, 6 and 8 mm). A total of 11 mathematical models were applied to represent the moisture ratio (\(MR\)) during the drying of kiwifruit. The fitting of \(MR\) mathematical models to experimental data demonstrated that the logistic model can satisfactorily describe the \(MR\) curve of dried kiwifruit with a correlation coefficient (\(R^{2}\)) of 0.9997, root mean square error (RMSE) of 0.0177 and chi-square (\(\chi^{2}\)) of 0.0007. The observed \(D_{\mathit{off}}\) of dried samples ranged from 3.09 \(\times\) 10\({}^{-10}\) to 2.26 \(\times\) 10\({}^{-9}\) m\({}^{2}\)/s. The lowest \(SEC\), color changes and shrinkage were obtained as 36.57 kWh/kg, 13.29 and 25.25%, respectively. The highest drying efficiency, energy efficiency, and thermal efficiency were determined as 11.09%, 7.69% and 10.58%, respectively. The results revealed that increasing the temperature and ultrasonic pretreatment time and decreasing the sample thickness led to a significant increase (\(p<0.05\)) in drying efficiency, thermal efficiency, and energy efficiency, while drying time, \(SEC\) and shrinkage significantly decreased (\(p<0.05\)).  
dry; efficiency; energy; kiwifruit; quality; ultrasound +
Footnote †: journal: Applied Sciences
## 3 Simulation Analysis of High-Energy Pulse EMAT  
In this paper, COMSOL Multiphysics was used to establish a high-energy pulse EMAT model. The effects of Lorentz force, magnetostrictive force, electromagnetic force, and lift-off distance on the Lamb wave generated by high-energy pulse EMAT were simulated. Figure 2a is the schematic diagram of the high-energy pulse EMAT excitation acoustic wave, where \(\mathbf{B_{dx}}\) and \(\mathbf{B_{dy}}\) are the horizontal and vertical components of the dynamic magnetic field, and \(\mathbf{F_{dx}}\) and \(\mathbf{F_{dy}}\) are the horizontal and vertical components of the electromagnetic force. In the low-frequency band, S mode particle vibration is mainly in-plane displacement, whereas a mode vibration is mainly out-of-plane displacement. The finite element simulation model of high-energy pulse EMAT is shown in Figure 2b. The steel plate size is 300 mm \(\times\) 1 mm, the starting coordinate of the left end of the excitation coil is (\(-63\), \(0.5\)), the point \(P\) to be measured is (107, \(-0.01\)), and the distance between the sending and receiving is 170 mm. In the high-energy pulse excitation coil, the current is obtained by the Equation (8), which is shown in Figure 3a. The spectrum analysis of the current is shown in Figure 3b. The coil line spacing should be designed as the line spacing corresponding to the frequency of about 220 kHz. The model parameters are shown in Table 1.  
Figure 2: High-energy pulse EMAT. (**a**) Excitation principle of the high-energy pulse EMAT; (**b**) High-energy pulse EMAT finite element simulation model.
## 4 Discussion
### On Radiation Limit and Radiation Exposure Level  
According to studies conducted with animals, 4 W/kg is widely recognized as the upper limit for organism exposure to radio frequency. The exposure limit of 2 W/kg for the general public, as stipulated by the ICNIRP (International Commission on Non-Ionizing Radiation Protection), leaves a large safety margin. The ICNIRP standard, formulated on the basis of a thorough analysis of all available scientific materials, can provide a good guarantee of human safety from all identified radio frequency hazards. Thus, it is well supported and received by the WHO (World Health Organization), as well as being a recommended standard of the International Telecommunication Union. To put it simply, the ICNIRP guideline established the recommended limits on the following basis: 4 W/kg is the basic limit of thermal effect when SAR is up to 4 W/kg, the human body would present with significant harmful reactions. According to the basic limit, when a ten-time coefficient of safety is applied for the occupational group, a limit value of 0.4 W/kg is established; when a 50-time coefficient of safety is applied for the general public, a limit value of 0.08 W/kg is established.  
It can be drawn from the experiment in this paper that when a wearable device was close to the skin during use, a considerable exposure of electromagnetic radiation to the surface tissue of human body was experienced, which was measured to be up to 0.961 W/kg, and far exceeded the limit value stipulated for the occupational group. Meanwhile, electromagnetic radiation attenuated along with penetration depth, electromagnetic radiation at a depth of 6 cm below the skin was measured to be 0.37 W/kg, which was equivalent to the limit value stipulated for the occupational group. Therefore, it is concluded that the SAR values in tissues and organs change greatly with varied frequencies and penetration depths. As muscle and skin are located in the outer layer of the human body, they usually absorb more radiation energy than internal organs. Moreover, some tissues and organs are more susceptible to electromagnetic radiation, while some are less susceptible. Therefore, different tissues and organs need a comprehensive evaluation based on actual conditions.
## 4 Performance Evaluation
### Performance Evaluation Criteria  
The cross-validation approach is developed to enhance the efficiency, validity of performance and to verify the outcome of each database. To analyze the classification efficiency of our proposed approach several metrics are used i.e., accuracy (_Acc_), sensitivity (_Sen_), specificity (_Spe_), error rate (_Er_), matthews correlation coefficient (_Mcc_), Jaccard (_Jac_), positive predicted value (_PPV_), F1 score (_F1_), and area under receiver operating characteristic (ROC) curve also called the area under curve (AUC). These parameters are utilized as measurable elements to compare the proposed method performance with state-of-the-art algorithms. These measured values are defined as follows:  
\[Accuracy(Acc)=\frac{(\textit{TP}+\textit{TN})}{(\textit{TP}+\textit{FP}+ \textit{TN}+\textit{FN})}\times 100\%, \tag{30}\]  
\[Sensitivity(Sen)=\frac{\textit{TP}}{(\textit{TP}+\textit{FN})}\times 100\%, \tag{31}\]  
\[Specificity(Spe)=\frac{\textit{TN}}{(\textit{TN}+\textit{FP})}\times 100\%, \tag{32}\]  
\[ErrorRate(Er)=\frac{\textit{FP}+\textit{FN}}{(\textit{TN}+\textit{FP}+ \textit{FN}+\textit{TN})}\times 100\%, \tag{33}\]  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Database** & \multicolumn{2}{c}{**Total Number of Images**} & \multicolumn{2}{c}{**Training**} & \multicolumn{2}{c}{**Testing**} \\  & **Normal** & **Abnormal** & **Normal** & **Abnormal** & **Normal** & **Abnormal** \\ \hline DDSM & 519 & 981 & 311 & 589 & 208 & 392 \\ INbreast & 67 & 269 & 40 & 162 & 27 & 107 \\ MIAS & 207 & 119 & 125 & 71 & 82 & 48 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Database specifications (normal and abnormal).  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Database** & \multicolumn{2}{c}{**Total Number of Images**} & \multicolumn{2}{c}{**Training**} & \multicolumn{2}{c}{**Testing**} \\  & **Benign** & **Malignant** & **Benign** & **Malignant** & **Benign** & **Malignant** \\ \hline DDSM & 479 & 502 & 287 & 302 & 192 & 200 \\ INbreast & 220 & 49 & 132 & 29 & 88 & 20 \\ MIAS & 68 & 51 & 40 & 31 & 28 & 20 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Database specifications (benign and malignant).  
\[Mcc=\frac{TP*TN-FP*FN}{\sqrt{(TP+FP)(TP+FN)(TN+FP)(TN+FN)}}\times 100\%, \tag{34}\]  
\[Jaccard(Jac)=\frac{TP}{(TP+FP+FN)}\times 100\%, \tag{35}\]  
\[PPV=\frac{TP}{(TP+FP)}\times 100\%, \tag{36}\]  
\[F1 score(F1)=2\times\frac{Precision\times Recall}{Precision+Recall}, \tag{37}\]  
where \(TP\) represents true positive values which correctly recognized the disease cases, \(TN\) stand for true negative values which correctly identified the healthy cases, \(FP\) stand for false positive values which incorrectly identified the disease cases, and \(FN\) stand for false negative values which incorrectly identified the healthy cases.
# Low-Cost Automation for Gravity Compensation of Robotic Arm
## 5 Proposed Implementation
### Gravitational Compensation  
The use of ROS consists of the handling of components described in C++, those that make possible the communication between ROS and the youBot controller, the included libraries allow direct access through EtherCAT for the configuration of positions and velocity on the device hardware, this tool works as a wrapper for reading and writing data in addition to the transmission of messages, facilitating the development of applications through its API, it also allows the user to transparently visualize communication between nodes, through a series of simple commands.  
A new ROS node is developed that will be mainly responsible for the calibration and control of the robotic arm, for this the physical parameters required by the gravitational compensation module such as friction, mass, gravity, etc. have been previously configured.  
The main class that ROS uses is called Module Gravity Compensation. This class develops the gravity compensation of every joint of the Kuka youBot Arm. This class is designed by the other two classes that are listed below (See Figure 11) :  
* **Controller Module Class:** The Controller Node is the central component of the driver developed. It is responsible for motion execution and trajectory planning. It provides various subscribers, service servers, and action servers for different kinds of motion commands for the manipulator and the gripper.
* **Arm Dynamics Class:** Robot dynamics is concerned with the relationship between the forces acting on a robot arm mechanism and the accelerations they produce. Typically, the robot mechanism is modeled as a rigid-body system, in which case robot dynamics is the application of rigid-body dynamics to robots. This class makes the gravity compensation calculation.
* **youBotKinematics sub-Class:** provides tools to calculate forward kinematics, inverse kinematics, and statics of the youbot manipulator.  
When the gravitational compensation module is started, the above-mentioned parameters are loaded, simultaneously and automatically the initialized robotic arm calibration is performed, each rotary joint is defined as an EtherCAT slave, and for them several iterations are performed to find the most exact value in which the torque made by the motors is reduced, this is because the torque changes are proportional to those of current.  
When the module is activated, the current values delivered to the joints are the minimum so that it can maintain the links statically, therefore, when applying an external force in order to manipulate the robotic arm, it does not present opposition, for which the operation of the control is verified by gravitational compensation.  
Figure 10: Rqt ROS Graph.
# Single Trace Side Channel Analysis on NTRU Implementation
## 2 Background
### Algorithm of NTRU
#### Decryption  
Decryption is used to recover the message from sender. The received data is usually called as cipher-text. The cipher-text \(e\) is decrypted by computing the following equations.  
\[a=f\cdot e\pmod{q} \tag{3}\] \[m=a\cdot f_{p}^{-1}\pmod{p} \tag{4}\]  
The correctness of the decryption is confirmed by the Equations (5) and (6).  
\[\begin{array}{lcl}a&=&f\cdot e\pmod{q}\\ &=&r\cdot h\cdot f+m\cdot f\\ &=&pr\cdot g+m\cdot f\pmod{q}\end{array} \tag{5}\]\[a\cdot f_{p}^{-1} = (pr\cdot g+m\cdot f)\cdot f_{p}^{-1}\pmod{p} \tag{6}\] \[= m\cdot f\cdot f_{p}^{-1}\pmod{p}\]  
Note that by choosing the private key \(f\) as \(pF+1\) where \(F\in\mathcal{L}_{f}\), then \(f_{p}^{-1}\) is equal to 1 so that the Equation (4) can be omitted [13]. Both target of this paper (NTRUEncrypt, NTRU Open Source) use this optimization.
## 3 Proposed SPMIPv6 Protocol
### Proxy Binding Message Format of SPMIPv6  
In the proposed proxy binding update (PBU) and proxy binding acknowledgement (PBA) messages in Figure 5, we have specified a flag bit S. If this S flag is set, it indicates SMIPv6 operations. If the S bit is not set, this indicates non-SPMIPv6 operations. The meanings and descriptions of the other flags are described in different request for comments (RFC) [11,18-20]. The mobility options header plays an important role for the different types of mobility scenario. Some of the mobility header options are hand off indicators options, access technology type options, link layer identifier options, link local address options and time stamp options.
# Implementation and Evaluation of Nodal Distribution and Movement in a 5G Mobile Network
## 5 Results
### Using Okumura-Hata Propagation Loss Model  
The results obtained with the distribution of nodes are presented in the Table 4. The distance values show that the algorithm functions correctly, and the result does not fall outside the range of 100-150 m. The SINR value differs from the values obtained using the 3 GPP model. It is due to the fact that the initial positions of the subscribers are set randomly, and the distance for one pair is different. Additionally, the distribution model itself plays an important role. The total signal loss is different due to different calculation methods. While the SINR differs at the starting positions, the BLER value remains unchanged at 0.  
The results of the movement algorithm are presented in the Table 5. The model works correctly, and the subscriber moves away from the base station steady, in common with the experiment with 3 GPP. The results show that the rate of degradation of a signal quality according to distance is higher as contrasted with the 3 GPP propagation model. It is associated with the calculation principle of loss in Okumura-Hata. Due to such a sharp drop at a distance of 208.33 m, an increase in BLER is already observed. The number of lost packets becomes more than 60% upon reaching 234.4 m. The BLER value goes to 1 as the distance increases beyond 243.96 m, which indicates the complete packet loss.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**BS Index** & **User Index** & **Distance** & **SINR** & **BLER** \\ \hline
1 & 1 & 129.24 & 33.482 & 0 \\
1 & 2 & 137.68 & 31.921 & 0 \\
1 & 3 & 126.53 & 34.481 & 0 \\
1 & 4 & 131.73 & 34.883 & 0 \\
1 & 5 & 144.84 & 37.48 & 0 \\
2 & 6 & 130.81 & 30.751 & 0 \\
2 & 7 & 125.19 & 37.416 & 0 \\
2 & 8 & 146.3 & 35.166 & 0 \\
2 & 9 & 132.69 & 37.24 & 0 \\
2 & 10 & 105.02 & 42.076 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Numerical result of the distribution.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Time** & **Distance** & **SINR** & **BLER** \\ \hline
540 & 252.85 & 24.785 & 0 \\
726 & 409.23 & 20.641 & 0.004 \\
810 & 487.42 & 20.109 & 0.19 \\
852 & 522.17 & 19.557 & 0.837 \\
1045 & 687.24 & 19.21 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Subscriber movement results.
### Image Decomposition in Deep Learning Frameworks  
Image decomposition, which is also known as layer separation in other fields, has been steadily used for image restoration [26], image enhancement [27], and image fusion [28]. Image decomposition is an approach for separating an input image into two or more layers with different gradient and illumination characteristics. Traditional image decomposition has been realized based on image transformations (e.g., wavelets) [29] and image pyramids [30] to achieve multiple resolutions. In addition, sparse representation [31], the Gaussian mixture model [32], and adaptive filtering methods such as bilateral [33] and guided-image filtering [34] have been used for two-layer separation, i.e., base and detail layers. In this study, the base layer corresponds to a layer whose brightness changes smoothly, resembling a low-pass-filtered image, whereas the detail layer refers to a high-pass filtered image whose brightness changes rapidly. The definition of the base and detail layers may vary based on the application field.  
Recently, image decomposition approaches have been incorporated into deep learning frameworks. U-net [35], Laplacian-net [36], residual networks (RNs) [37; 38], and progressive residual networks (PRNs) [23; 25] are representative deep learning models that apply the concept of image decomposition. U-net and Laplacian-net primarily aim to realize multiple resolutions, whereas RNs and PRNs focus on predicting residual layers. In particular, the key factor for improving image quality and accelerating convergence in an RN is that the brightness range of the residual layer should be narrow. In other words, by narrowing the output range in which the solution exists, RNs can obtain the optimal solution more easily. Therefore, it is critical to design a residual layer with a narrow brightness range.
# Advancing C-V2X for Level 5 Autonomous Driving from the Perspective of 3 GPP Standards
## 6 Network Data Analytics Function (NWDAF)
### Challenges of Federated Learning
#### 6.2.2 Achieving Reliable and Dynamically Configurable Communication Bit-Pipes  
FL involves three major steps: Step 1--clients download the global model; Step 2--clients train the model with their local datasets; and Step 3--clients upload a global model with their locally trained models. In the classical FL, all the clients need to communicate their full gradient (e.g., in GBs) update for each epoch. These steps are looped until the convergence of a global model is achieved. Such inter-client server interaction asks for QoS-guaranteed communication bit-pipes. The issue gets massive in situations where the clients are highly mobile, e.g., autonomous driving, mobile networks, etc. To capture the communication link requirements, we consider the utility function \(U(.)\) that captures the type of service, type of user, and communication-specific parameters. This function is adapted from the author-proposed user satisfaction function in [35]. Let \(u_{j,k,c}(n)\) represents the satisfaction function of user \(j\), the proposed utility function takes care of service relevant requirements, and is given by:  
\[\begin{split} u_{j,k,c}(n):=\bar{u}_{j}\bigg{(}\frac{b_{k}^{c}}{n _{k}}\bigg{)}\prod_{l\in L}(v_{jl}(k,c,n))^{w_{jl}}\\ +\sum_{l^{\prime}\in L^{\prime}}\omega_{l^{\prime}}v_{jl^{\prime }}(k,c,n),\end{split} \tag{1}\]  
We decompose the user utility function into four components:  
* The term \(\bar{u}_{j}\bigg{(}\frac{b_{k}^{c}}{n_{k}}\bigg{)}\) is the function of network state \(n=(n_{k}^{c})_{k,c}\) and the offered bandwidth \(b_{k}^{c}\). The collection \(n\) is the vector that represents the total number of users who request the service of a specific class.
* \(\prod_{l\in L}(v_{jl}(k,c,n))^{w_{jl}}\) is the weighted multiplicative approach for _bandwidth-dependent_ associated QoE attributes, i.e. delay, packet loss, etc.
* \(\sum\limits_{l^{\prime}\in L^{\prime}}\omega_{l^{\prime}}v_{jl^{\prime}}(k,c,n)\) is the weighted sum of different _independent_ QoE attributes.  
For more details on the user satisfaction function, readers are encouraged to refer to [36; 37]. We further assume that the process of handover incurs costs, which we capture in a similar fashion as proposed in our earlier work. More on this may be found in our work in [38]. As mobile users' channel conditions vary, the service offers of network technologies (i.e., offered bandwidth with associated QoS parameters) need to meet the users' required service parameters. We represent such parameters by a utility function \(u_{j}\) for host \(j\) as proposed in our earlier work [36]. The proposed utility function is a quasi-concave function that realistically captures user satisfaction on offered bandwidth, QoS indices (e.g., delay, jitter, packet loss) of offered network service, and service costs.
## 4 Experiments
### Quantitative Evaluation
#### 4.4.1 Comparison with State-of-the-Art  
As is shown in Table 1, we compare our PUB-SalNet model to nine other state-of-the-art unsupervised methods. We demonstrate through experimental results that our proposed PUB-SalNet outperforms all existing unsupervised methods with a great margin (such as 46.51% for E-Measure on \(SNR=1.5\) and 41.88% on \(SNR=0.5\)) and achieves new state-of-the-art performance.
## 4 Results and Discussion
### Simulation for ESD Protective Fabric  
The second textile tested was a polyester fabric with a conductive grid. The thickness of the material was assumed to be 0.35 mm, and the conductive grid was 5 mm \(\times\) 5 mm. The conductive fiber was assumed to have a square cross-section of 0.02 mm \(\times\) 0.02 mm. This model is much more complex than the model considered in the previous section, and the analysis time is also much longer. Figure 14a shows the model's meshing effect, and Figure 14b shows an enlarged part of the mesh. Simulations were performed for a base-material conductivity of \(\sigma_{1}=1\times 10^{-13}\) S/m (a typical conductance for polyester) and conductive-fiber conductivities of \(\sigma_{2}=100\) S/m (a typical conductance for carbon threads) and \(\sigma_{2}=0.001\) S/m.  
Figure 15 shows the charge decay plots for the assumed conductivities. The time at \(t_{50}\) is below 0.01 s. Thus, the textiles meet the EN 1149-3 standard. Figures 16 and 17 show slice plots for several time points in the \(x-y\) and \(y-z\) planes. Analysis of the figures suggests that the fabric effectively shields against electric fields.  
Figure 14: Meshing result of the polyester fabric model. (**a**) Full model; (**b**) an enlarged part of the model.  
Figure 15: Charge decay plots for a textile thickness of 0.35 mm and \(\sigma=1\times 10^{-13}\) S/m (result of simulations); (**a**) the full scale; (**b**) the zoomed initial part of the plot.  
Figure 17: Slice plots in the \(x-y\) plane for a textile thickness of 0.35 mm and parameters of \(\sigma_{1}=1\times 10^{-13}\) S/m and \(\sigma_{2}=100\) S/m at two time points and three distances (\(d\)) from the bottom surface of the specimen.