esa-sceva/satcom-chunk-collection huggingface dataset:
# Study of Copper/Aluminum Bimetallic Tube Rotary Ring Spinning Composite Forming Characteristics
## 3 Finite Element Model for Rotary Ring Spinning  
Based on large commercial ABAQUS/Explicit applications, the finite element model of bimetallic composite tube rotary ring spinning was established, as shown in Figure 2. The fixed spindle applies a thrust load to the side of the composite tube to make an axial feeding motion, while the rotary ring makes an eccentric revolution around the axis of the tube to achieve the forming process. The spindle and rotary ring are defined as rigid parts, the basic tube and cover tube are defined as deformation parts, the C3D8R solid cell is used to divide the mesh, and the thickness direction is set to a three-layer mesh to guarantee precise computation. The analysis step uses an explicit dynamic model, using the Coulomb friction condition to define the contact, with the friction factor between the rotary ring and the tube set to 0.3, the inside surface of the cover tube and the outside surface of the basic tube to 0.2, and the tube and spindle to 0.1 [11]. All contact surfaces are set to \"face-to-face contact\" to allow relative sliding between them. In the finite element simulation, the composite tube is stripped of parts that are not involved in the deformation in order to decrease the computational time. Otherwise, all geometric dimensions and loading conditions of the finite element model are identical to the experimental procedure.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Cu** & **Fe** & **Pb** & **Sb** & **Zn** \\ \hline
64.50 & 0.035 & \(<\)0.001 & \(<\)0.001 & 35.40 \\ \hline \end{tabular}
\end{table}
Table 1: Chemical composition of H65 (weight fraction (%)).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Density** & \begin{tabular}{c} **Young’s** \\ **(Kg/m3)** \\ \end{tabular} & \begin{tabular}{c} **Poisson** \\ **Modulus** \\ **(GPa)** \\ \end{tabular} & \begin{tabular}{c} **Yield** \\ **Stress** \\ **Ratio** \\ \end{tabular} & \
\begin{tabular}{c} **Tensile** \\ **Stress** \\ **(MPa)** \\ \end{tabular} &
\begin{tabular}{c} **Elongation** \\ **(\%)** \\ \end{tabular} \\ \hline
6063 & 2700 & 68.9 & 0.33 & 172 & 216.6 & 13.5 \\ H65 & 8525.4 & 105 & 0.33 & 205 & 431.5 & 28.3 \\ \hline \end{tabular}
\end{table}
Table 3: Main mechanical characteristics of materials at room temperature.  
Figure 1: Schematic of the rotary ring spinning forming process and experimental equipment: (**a**) Schematic; (**b**) equipment.
###### Abstract
A similar approach was reported in [21], which was based on a 2D model of normal and drooped breasts. The simulations included cases with different kinds of tumors located at diverse positions. The resulted thermogram data were processed by means of a genetic algorithm method to estimate the thermal and physical parameters of tumors. Comparative analysis using previously reported cases demonstrated consistency with other methodologies available at the time. An extensive review of works modelling thermal distributions associated with breast cancer was presented by [PERSON] [22]. The studies described above were based on using geometric structures mainly composed of layers as simplified configurations of breasts [10; 17; 19; 20; 23; 24; 25]. However, other analyses considered realistic models, such as the one implemented by [PERSON] et al. [13], where the 3D geometry of the breast surface was obtained from a 3D scan and the tumor shape was approximated using MR imaging. In addition to anatomic information, IR thermograms were acquired from the test subject. A parametric study varying the blood perfusion rate (w\({}_{\text{b}}\)) and metabolic heat generation rate (Q\({}_{\text{m}}\)) was conducted to obtain the solution pair (w\({}_{\text{b}}\), Q\({}_{\text{m}}\)) to approximate the surface temperature profile of IR thermograms. On the other hand, there are fewer works addressing the thermal modelling of breast cancer from a different perspective than that of IR superficial thermography. For example, in the case of [PERSON] et al. [26], breast thermal volume estimation was simulated by means of a microwave radiometer, considering a variety of tumor sizes and depths. Radiometric signals were estimated by varying the contact antenna size. A tumor of 10 mm, located within a 3 cm layer of breast, could be detected by a 0.1 K radiometer; when the tumor size decreased to 6 mm, the detection depth diminished to 1 cm. Another finding was that deeper tumor detection required incremental increases in antenna size. In the present work, we present the simulation and analysis of temperature variations associated with cases based on the initial stages of real tumors. The internal and external temperature distributions and their corresponding gradients are analyzed with the aim of establishing associations between thermal behavior and tumor parameters that can provide relevant information in practice, like tumor size or tumor malignancy.
# Intelligent Caching with Graph Neural Network-Based Deep Reinforcement Learning on SDN-Based ICN
## 3 System Model  
In this section, we present the system architecture of our proposed caching scheme and provide a comprehensive overview of the key components. We also define the concept of content popularity. Furthermore, we develop a user preference model based on real-world data from the MovieLens100K dataset [16]. Important notations used throughout the paper are listed in Table 1.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Notation** & **Definition** \\ \hline \(N\) & Number of network nodes \\ \(C\) & Number of content items \\ \(T\) & Number of time slots \\ \(\mathcal{N}=\{n_{1},\dots,n_{N}\}\) & Set of network nodes \\ \(\mathcal{C}=\{c_{1},\dots,c_{C}\}\) & Set of content \\ \(\mathcal{U}=\{u_{1},\dots,u_{l}\}\) & Set of users \\ \(\mathcal{T}=\{t_{0},t_{1},\dots,t_{T}\}\) & Set of time steps \\  & “Cache” or “not cache” content \(c_{i}\) at node \(n_{k}\) at time step \(t_{l}\) (i.e., \\ \(b_{t_{l}}^{c_{i},n_{k}}\) & availability of content \(c_{i}\) at node \(n_{k}\)’s cache store during the time \\  & interval between \(t_{l}\) and \(t_{l+1}\)) \\ \(\mathcal{S}_{t_{l}}=\{s_{l_{l}}^{n_{1}},\dots,s_{l_{l}}^{n_{N}}\}\) & Set of all nodes’ states at time step \(t_{l}\) \\ \(\mathcal{A}_{t_{l}}=\{a_{t_{l}}^{n_{l}},\dots,a_{t_{l}}^{n_{N}}\}\) & Set of all nodes’ caching actions at time step \(t_{l}\) \\ \(a_{t_{l}}^{n_{l}}=\{b_{t_{l}}^{c_{i},n_{k}},\dots,b_{t_{l}}^{c_{i},n_{k}}\}\) & Set of node \(n_{k}\)’s caching action at time step \(t_{l}\) \\ \(\mathcal{R}_{t_{l}}=\{r_{t_{l}}^{n_{l}},\dots,r_{t_{l}}^{n_{N}}\}\) & Set of all nodes’ rewards (i.e., cache hits) at time step \(t_{l}\) \\ \(r_{t_{l}}^{n_{l}}=\{r_{t_{l}}^{c_{i},n_{k}},\dots,r_{t_{l}}^{c_{i},n_{k}}\}\) & Set of node \(n_{k}\)’s reward for each content item at time step \(t_{l}\) \\ z & Router’s cache size \\ \(x_{c_{i}}\) & Content \(c_{i}\)’s embedding \\ \(y_{u_{i}}\) & User \(u_{i}\)’s embedding \\ \hline \hline \end{tabular}
\end{table}
Table 1: Important notations.
# A Novel PCA-Based Method for PRNU Distillation to the Benefit of Source Camera Identification
## 2 Related Work
### State of the Art  
PRNU extracted from digital images has been proven to be a unique fingerprint of digital cameras. The common extracted scheme is the wavelet-based filtering method first proposed by [PERSON] et al. [6]. The denoised wavelet coefficients are obtained using the Wiener filter. The Wiener filter is the MSE-optimal stationary linear filter for images degraded by additive noise and blurring. It separates the PRNU from the image scene information while ensuring that the noise residual contains less scene information traces and does not misinterpret the area around the edges.  
Since then more and more researchers have conducted further studies. [PERSON] et al. [11] proposed a contextual content-adaptive interpolation (PCAI) algorithm based on eight neighbourhoods. [PERSON] et al. [12] proposed a content-adaptive filtering method based on image-guided filtering. [PERSON] et al. [13] applied the improved BM3D algorithm to PRNU fingerprint extraction. [PERSON] et al. [14] recently proposed a scheme for training Siamese neural network models for source camera identification. By training pairs of images and PRNU training samples into the Siamese neural network, the final trained model can extract camera features for source camera identification. [PERSON] et al. [15] proposed a method to solve the problem of clustering a set of images based on source devices without any a priori information. [PERSON] et al. [16] proposed an image source pipeline forensics method based on binary similarity measure of image response non-uniformity to address the identification issue of both natural images and computer-generated graphics. When applied to videos, [PERSON] et al. [17] proposed the extraction of video reference PRNU noise using partly decoded video. [PERSON] and [PERSON] [18] proposed a method to mitigate the impact of video compression on the PRNU noise in video frames.
# A Systematic Review of the Usability of Telemedicine Interface Design for Older Adults
## 3 Results
### Study Characteristics  
A summary table was developed to systematically capture relevant information from the 41 publications, including the author's name, research method, country, sample size, participant characteristics, application description, main field, and main findings (Supplementary Material S1). This is supported by [46]. Among the 41 studies, 21 focused on health management, 6 on cognitive health, 4 on medication management, 4 on electronic health records, 3 on rehabilitation, 2 on behavioral medicine, 1 on health habits, and 1 on health information management.  
In terms of research methodologies, 14 articles employed mixed methods, 13 utilized qualitative research, 10 conducted quantitative research, and 4 adopted experimental research. In terms of participant numbers, most studies included fewer than 100 participants. Specifically, 3 studies had between 4 and 7 elderly participants; in 15 studies, the sample size ranged from 10 to 20; in 14 studies, it ranged from 21 to 100; and in 9 studies, the number of participants exceeded 100.
# Provision of Energy- and Wavelength-Efficient Traffic
## 5 Conclusions  
In this paper, the energy- and wavelength-efficient traffic grooming based on the matching algorithm has been investigated in the DSCNs. Considering the limited wavelength and energy resource in the DSCNs, we, respectively, establish the energy- and wavelength- minimized models. To solve the problem, we introduce the sub-wavelength granularity and formulate the traffic grooming into a two-phase ILP problem, i.e., the first phase for TAASA and the second phase for SG. Then, we develop the TPTG_MA to resolve the ILP problem. Simulation results demonstrate that the TPTG_MA and DLG_GA outperform TPTG and DLG in the average wavelength utilization ratio, the energy consumption saving, and the blocking probability. Compared with the DLG_GA, the proposed TPTG_MA achieves at most 23% higher AWUR and 10% ECS improvement. When the AWNR is 40%, the TPTG_MA can achieve 12%, 9%, and 3% blocking probability reduction compared to the DLG, TPTG, and DLG_GA, respectively. However, the performance enhancement is at the expense of elongating the hops of a few traffic requests and increasing the number of iterations. This is worthwhile because the overall network capacity utilization is significantly improved. In the future, it may be interesting to investigate the placement of the hybrid cross-connect in the DSCNs when the traffic load changes.  
Conceptualization, C.P. and Y.H.; methodology, C.P.; software, C.P. and D.Y.; validation, H.F., Y.H. and S.Z.; formal analysis, C.P.; investigation, Y.H.; resources, Y.H.; data curation, C.P.; writing--original draft preparation, C.P.; writing--review and editing, S.Z.; visualization, D.Y.; supervision, S.Z.; project administration, H.F.; funding acquisition, Y.H. All authors have read and agreed to the published version of the manuscript.  
This work was supported by National Key research and development plan of China (No. 2019 YFB1803200).  
Not applicable.  
The authors declare no conflict of interest.
# Delegation-Based Personal Data Processing Request
## 3 Delegation-Based Personal Data Processing Request Notarization Framework
### GDPR Compliance Audit Scenario of Personal Data Processing Request  
This section provides a service scenario based on a centralized system as shown in Figure 3 to withdraw certain required features to ensure GDPR compliance of the personal data processing request.  
Consider a system wherein each service provider stores various data in its own database, which is related to the personal data processing request trusted by the DS to provide various services. In this situation, GDPR SA conducts a GDPR compliance audit based on the evidence data submitted by the service providers. The scenario where network participants perform their own actions and the SA conducts a GDPR compliance audit related to the processing of SPs is as follows:  
* The DS must consent to the processing of personal data to utilize the services of an SP and subscribe to services on the system. The SP is the DC of personal data.
* SPs collect and manage the personal data of DS adhering to GDPR regulations.
* In accordance with GDPR regulations, the DS requests the SP to view, correct, delete, stop, and transmit his/her personal data at any time if needed.
* The SP accepts the request and performs all processes without any delay. After executing the process, the results of the processing are notified to the DS. Particularly, if the DS uses their request on an electronic method, it responds to the request through an electronic method. The SP must record the processing logs and establish legal evidence data to prove GDPR compliance.
* The SA manages SP to ensure compliance with GDPR regulations and performs a GDPR compliance audit by analyzing evidence data presented by the SP to certify them.
* DSs may request a GDPR compliance audit of the SP from the SA in the processing of their personal data based on a specific situation.  
Figure 3: GDPR compliance audit scheme in a conventional centralized environment.  
* SA investigates the operation status of SPs considering the requested GDPR compliance audit and takes appropriate measures based on results obtained from the investigation.
# Experimental and Numerical Studies on Ground Shock Generated by Large Equivalent Surface Explosions
## 4 Finite Element Analysis
### Grid Validation  
Many studies have demonstrated that grid size has a significant influence on the accuracy of the numerical explosion simulations [28]. Therefore, it is necessary to check the accuracy of the newly constructed finite element model prior to in-depth analysis. A uniform grid was used in the numerical model, and trials were performed with grid sizes of 4, 2, 1, and 0.5 cm. The velocity-time curve was computed for a point at a distance of 2.1 m from the blast site and at a burial depth of 0.05 m in each of these trials, and the computed velocity-time curves were compared to the experimental velocity-time curve. Because the numerical model was scaled down using a model ratio of \(C_{L-1}\)t = 10, the spatial and temporal dimensions of the computational result were magnified by a factor of 10 prior to this comparison. The results of the comparison are shown in Figure 11.  
In Figure 11, it is shown that a decrease in the grid size led to corresponding increases in maximum velocity and decreases in the rise time of the velocity-time curve. However, it can be observed that the magnitude of the increase in the maximum velocity and the magnitude of decrease in the rise time reduced gradually with each successive decrease in grid size, which indicates that the computations were converging. When the grid size decreased from 1 to 0.5 cm, the maximum velocity increased by less than 5%, while no significant change was observed in the rise time and duration of the velocity-time curve. With a grid size of 1 cm, the numerical model contained 960,000 elements and 1,112,030 nodes. Although further decreases in grid size may improve the precision, computational efficiency will suffer greatly. Furthermore, the computational results were already in good agreement with the previously derived theoretical and experimental results with a grid size of 1 cm. Therefore, it was determined that the results of the numerical computations are sufficiently accurate and admissible for further discussion when they are performed with a grid size of 1 cm.
## 1 Introduction
In this paper, we present a review of recent advancements in satellite instrumentation technology, remote sensing applications, and their role in developing near-real-time weather prediction models for a range of civilian and military sectors.
## 3 Doppler Frequency Evaluation in the Absence of Noise
### Test of Validity  
The formulas in the previous sections show that, in the absence of noise and other nuisances, the proposed equations are able to evaluate the true Doppler frequency, hence reducing the estimation error range from the traditional \((-f_{sp}/2,f_{sp}/2)\) to theoretically zero. A possible residual error can arise due to the fact that the method is based on an approximate formulation of the test statistic Equation (19). Therefore to test its validity, we set up a simulated acquisition campaign, where the Doppler frequency estimation error due to the traditional acquisition method is compared with the residual error introduced by the algorithms R-3, R-\(\overline{n}\), C-3, and C-\(\overline{n}\). In the simulations, the GNSS signals are generated by using the signal simulator N-FUELS [20], and several instances of a Galileo E1-b signal are obtained. Firstly we tested the methods in an ideal scenario (_i.e._, noiseless), with parameters \(f_{IF}=4\) MHz, \(f_{s}=17\) MHz, \(\tau=0.11\) ms.  
The accumulation time of the acquisition stage is set to the minimum period (4 ms), assuming that no data transition occurs in this period, the Doppler frequency step is  
\[f_{sp}=\frac{2}{3 LT_{s}}=167\ \mathrm{Hz} \tag{33}\]  
and 67 different values of \(f_{d}\) are randomly chosen in the range \((-4,500,4,500)\) Hz. This makes the original errors uniformly distributed in \((-f_{sp}/2,f_{sp}/2)\), as also proved in Figure 7, where the cumulative distribution of the Doppler frequency estimation errors is shown. After the refinements are obtained by using R-3 or C-3, the error range decreases to nearly \((-0.8,0.8)\) Hz, which is a residual numerical error due to the approximations introduced in Equations (18) and (19).  
Therefore, we can conclude that in the absence of noise, in experiments, the new methods eliminate the error in the evaluation of the Doppler frequency due to the discretization of the search space, just using three cells selected in the main lobe of the frequency-domain CAF. The only constraint is that the Doppler frequency step has to be as given in Equation (28), with \(\overline{n}=3\). Moreover the obtained results show that the method is not affected by the code delay error \(\Delta\tau\).  
The practical situation in which the noise is unavoidable is discussed in the next section.
# An Enhanced Lightweight IoT-based Authentication Scheme in Cloud Computing Circumstances
## 4 Security Analysis and Performance Evaluation
### Computational-cost Comparison
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multicolumn{2}{c}{} & \multicolumn{2}{c}{**[PERSON] et al.**} & \multicolumn{2}{c}{**[PERSON] et al.**} & \multicolumn{2}{c}{**[PERSON] et al.**} & \multicolumn{2}{c}{**Our**} \\ \hline  & **Op** & **Case 1** & **Case 2** & **Op** & **Case 1** & **Case 2** & **Op** & **Case 1** & **Case 2** & **Op** & **Case 1** & **Case 2** \\ \hline \multirow{4}{*}{R} & \(U_{i}\) & 3 Th & 0.0151 & 0.0000894 & 3 Th & 0.01551 & 0.0000894 & 3 TH & 0.01551 & 0.0000894 & 2 Th & 0.01034 & 0.00000565 \\  & \(S_{j}\) & 0 Th & 0 & 0 & 0 Th & 0 & 0 & 0 TH & 0 & 0 & 1 Th & 0.05157 & 0.0000328 \\  & \(CS\) & 4 Th & 0.0208 & 0.0001321 & 4 Th & 0.0208 & 0.0001321 & 4 TH & 0.0208 & 0.0001312 & 12 TH & 0.02040 & 0.000396 \\  & \(U_{i}\) & 6 TH & 0.03102 & 0.001968 & 6 Th & 0.03102 & 0.0001968 & 6 Th & 0.03102 & 0.0001968 & 3 Th & 0.01551 & 0.0000984 \\  & \(S_{j}\) & 3 Th & 0.01551 & 0.0000894 & 1 Th & 0.00517 & 0.0000328 & 3 Th & 0.01551 & 0.0000984 & 3 Th & 0.01551 & 0.0000984 \\  & \(CS\) & 0 Th & 0 & 0 & 0 Th & 0 & 0 Th & 0 & 0 Th & 0 & 0 & 0 \\  & \(U_{i}\) & 3 Th & 0.01551 & 0.0000894 & 3 Th & 0.01551 & 0.0000894 & 4 Th & 0.02068 & 0.0001312 & 4 Th + 3 Ts & 0.08512 & 0.0644467 \\  & \(S_{j}\) & 3 Th & 0.01551 & 0.0000894 & 3 Th & 0.01551 & 0.0000894 & 4 Th & 0.02068 & 0.0001312 & 2 TH + 3 Ts & 0.07478 & 0.0643811 \\  & \(CS\) & 14 TH & 0.07288 & 0.000492 & 10 Th & 0.0517 & 0.000328 & 19 Th & 0.08263 & 0.000232 & 21 TH + 2 Ts & 0.151353 & 0.043568 \\ Total & 36 TH & 0.18612 & 0.011808 & 30 TH & 0.1551 & 0.0000894 & 3 TH & 0.22231 & 0.0014104 & 48 Th + 5 Ts & 0.420000 & 0.173082 \\ \hline \hline \end{tabular}  
* \({}^{\mathbf{*}}\)R = Registration phase, L = Login phase, A = Authentication phase, Op = Number of operations.  
\end{table} 
Table 4: Execution-time by participant. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Phase** & & **[PERSON] et al. ** & **[PERSON] et al. ** & **[PERSON] et al. ** & **Our Scheme** \\ \hline \multirow{4}{*}{Registration} & \(U_{i}\) & \(3T_{h}\) & \(3T_{h}\) & \(3T_{h}\) & \(2T_{h}\) \\  & \(S_{j}\) & \(0T_{h}\) & \(0T_{h}\) & \(0T_{h}\) & \(1T_{h}\) \\  & \(CS\) & \(4T_{h}\) & \(4T_{h}\) & \(4T_{h}\) & \(12T_{h}\) \\  & \(U_{i}\) & \(
## 4 Main Security and Privacy Challenges
### Major Attacks and Countermeasures
We found that the main target of gaining access to sensitive data is threats, seizures, or vulnerabilities of the examined paradigms, whether joint or apart. Importantly, we found that these vulnerabilities can be properly discovered with the right tools and approaches. Despite the constant search for vulnerabilities in systems by attackers (hackers/cybercriminals), there are up-to-date, sophisticated countermeasures to mitigate such threats, internal or external. Most essentially, each vulnerability has a specific \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**FNs**} & \multirow{2}{*}{**Brief Description**} & \multirow{2}{*}{**Attack**} & \multicolumn{2}{c}{**Specifics of Paradigm/Main Proposed Countermeasures**} \\ \cline{3-4}  & & & **Cloud** & **Edge** & **Fog** \\ \hline \multirow{6}{*}{\begin{tabular}{c} **FNs** \\ \end{tabular} } & \multirow{6}{*}{DoS attack} & Intrusion Detection System (IDS) [134], Access Security & Network Authentication mechanisms & Deploy routing security and observing the behaviour of nodes [135]. \\ \cline{2-4}  & & & & \\ \cline{2-4}  & & & & \\ \cline{2-4}  & & & & \\ \cline{2-4}  & & & & \\ \cline{2-4}  & & & & \\ \cline{2-4}  & & & & \\ \hline \multirow{6}{*}{\
\begin{tabular}{c} **FNs** \\ \end{tabular} } & \multirow{6}{*}{MITM} & \multirow{6}{*}{Data Encryption [118].} & Time stamps, encryption algorithm [121]. & Use of Authentication schemes [114]. \\ \cline{2-4}  & & & & \\ \cline{2-4}  & & & & \\ \cline{2-4}  & & & & \\ \cline{2-4}  & & & & \\ \cline{2-4}  & & & & \\ \cline{2-4}  & & & & \\ \hline \multirow{6}{*}{
\begin{tabular}{c} **FNs** \\ \end{tabular} } & \multirow{6}{*}{Spoofing attacks} & Identity Authentication [118]. & Secure trust schemes [39]. & Secured identification and Strong authentication [39]. \\ \cline{2-4}  & & & & \\ \cline{2-4}  & & & & \\ \cline{2-4}  & & & & \\ \cline{2-4}  & & & & \\ \cline{2-4}  & & & & \\ \cline{2-4}  & & & & \\ \cline{2-4}  & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._mechanism to counter its threats and attacks. Moreover, another important aspect is that the vulnerabilities turn to undermine the security and privacy of the related paradigms, exposing them (data) to potential security attacks and privacy leakages.