esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation Experiments: Security Analysis and Comparison
### Key Sensitivity Analysis  
The term key sensitivity [48] refers to the proportion of alteration in the modified ciphertext compared to the original ciphertext. The modified ciphertext is created by encrypting the plaintext using an adjusted key. A cryptographic algorithm is considered to have higher key sensitivity when the percentage of change is more significant.  
To determine the key sensitivity of the proposed scheme, modifications are made to the original keys with respect to EC equation \(y^{2}\equiv x^{3}+b\pmod{p}\), the prime \(p\), and the point \(G\) on the EC to create a modified key. The results obtained from these modifications are presented in Table 6. The result shows that even minor changes to any of the parameters \(b\), \(p\), and \(G\) result in a completely distinct modified ciphertext.
###### Abstract  
With the proliferation of 5G mobile networks within next-generation wireless communication, the design and optimization of 5G networks are progressing in the direction of improving the physical layer security (PLS) paradigm. This phenomenon is due to the fact that traditional methods for the network optimization of PLS fail to adapt new features, technologies, and resource management to diversified demand applications. To improve these methods, future 5G and beyond 5G (B5G) networks will need to rely on new enabling technologies. Therefore, approaches for PLS design and optimization that are based on artificial intelligence (AI) and machine learning (ML) have been corroborated to outperform traditional security technologies. This will allow future 5G networks to be more intelligent and robust in order to significantly improve the performance of system design over traditional security methods. With the objective of advancing future PLS research, this review paper presents an elaborate discussion on the design and optimization approaches of wireless PLS techniques. In particular, we focus on both signal processing and information-theoretic security approaches to investigate the optimization techniques and system designs of PLS strategies. The review begins with the fundamental concepts that are associated with PLS, including a discussion on conventional cryptographic techniques and wiretap channel models. We then move on to discuss the performance metrics and basic optimization schemes that are typically adopted in PLS design strategies. The research directions for secure system designs and optimization problems are then reviewed in terms of signal processing, resource allocation and node/antenna selection. Thereafter, the applications of AI and ML technologies in the optimization and design of PLS systems are discussed. In this context, the ML- and AI-based solutions that pertain to end-to-end physical layer joint optimization, secure resource allocation and signal processing methods are presented. We finally conclude with discussions on future trends and technical challenges that are related to the topics of PLS system design and the benefits of AI technologies.  
physical layer security; optimization approaches; information theory; signal processing techniques; resource allocation; AI techniques +
Footnote †: journal: 9 May 2022
## 2 Materials and Methods
### Data Analysis
#### 2.4.4 Electromyographic Data  
We used a bipolar 16-channel wireless system (FreeEMG 1000 System, BTS, Milan, Italy) to record sEMG signals at a sample rate of 1000 Hz. Surface electrodes composed of Ag/AgCl were positioned in pairs on the right limb of patients on the gluteus medius, vastus lateralis, vastus medialis, rectus femoris, tensor fascia latae, semitendinosus, biceps femoris, tibialis anterior, gastrocnemius medialis, gastrocnemius lateralis, peroneus longus, soleus, and bilaterally on trunk muscles, abdominis, and erector spinae (Figure 4B), following Atlas of Muscle Innervation Zones [47] and the European Recommendations for Surface Electromyography [48]. To compare strides of varying durations, the sEMG data were time-normalized to the duration of each cycle and reduced to 201 samples using a polynomial procedure [49; 50].
# Secure D2D Group Authentication Employing Smartphone Sensor Behavior Analysis
## 3 Preliminaries and Model Definitions
### Network Assumptions  
As shown in Figure 1, SN is assumed to have full authority to access the entire D2D communication system, where the UEs within its cellular coverage can operate communication directly with SN without additional assistance. Note that these UEs are considered the major devices in the entire D2D network, where both the cellular links and D2D links can be maintained. Moreover, the remaining UEs are beyond the effective range of SN, thus requires the major devices to forward messages to SN. An integrated D2D network is constructed involving all the participating UEs. Communication between the UEs is provided in this way. Additionally, a universal group communication channel is indispensable for message broadcasting to all users.  
Due to the inherent wireless characteristic, D2D data exchange suffers from D2D data exchange suffers from various security risks and privacy threats, especially in the D2D group communication involving large numbers of participating devices. The transmitted information may be eavesdropped, impersonated, and even altered illegally. In this case, advanced security strategies and privacy preservation techniques are vital for the proposed D2D scenario. Proper authentication mechanism should be deployed so that the identities of the requesting UEs are verified before accessing the D2D services. Subsequently, the unique group key shared between SN and all legitimate UEs should be generated and allocated, ensuring the secure broadcasting channel for emergency use.  
In addition, after being successfully verified, the continuous authentication is of great significance, where periodical validation is activated. In this case, the compromised and disabled UEs can be detected and removed accordingly, offering resistance to various insider attacks. Note that essential behavioral characteristics derived by the user's smartphone (UE) is adopted in the continuous authentication process.
# Assessment of Soybean Oil Oxidative Stability from Rapid Analysis of its Minor Component Profile
## 2 Results and Discussion  
### Characterization of the Commercial Virgin and Refined Soybean Oils  
#### 2.1.1 Composition in Main Components Determined by \({}^{1}\)H-NMR  
The samples subject of study were two commercial soybean oils from Spanish companies, one of them virgin (VSO: Virgin Soybean Oil), and the other one refined (RSO: Refined Soybean Oil), from different producers. The molar percentages of the different kinds of acyl groups in the two types of soybean oil are presented in Table 1.  
These data reveal that both types of soybean oils display very similar proportions of acyl groups, and the same can be said of their unsaturation degree, estimated from the ratio of unsaturated to saturated protons (see Section 3.3.3), which was found to be 1 to 10.03 for VSO and 1 to 9.94 in RSO. Therefore, it seems reasonable to assume that the differences in their respective oxidative stabilities will be due to their composition in minor components.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sample** & **Linolenic** & **Linoleic** & **Oleic** & **Saturated** \\ \hline VSO & \(5.3\pm 0.7\) & \(43.7\pm 0.5\) & \(31.9\pm 0.7\) & \(19.1\pm 0.5\) \\ RSO & \(4.8\pm 0.1\) & \(45.8\pm 0.8\) & \(32.2\pm 0.5\) & \(17.2\pm 1.2\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Molar percentages of the main types of acyl groups in each of the studied oils.
## 2 Results and Discussion
### Volatiles Isolated by Headspace Solid-Phase Microextraction  
Headspace solid-phase microextraction (HS-SPME) combined with GC and GC/MS enabled identification of the most volatile organic headspace compounds in the samples. This approach for honey profiling is very important for obtaining reliable composition of the most volatile organic compounds.  
The most abundant compounds in the headspace of samples I and II were terpenes: _trans_-linalo oxide (18.1%; 13.8%), _cis_-linalo oxide (10.8%; 14.0%), hotrienol (4.4%; 9.7%) and epoxyllinalool (4.4%; 2.0%). Linalool oxides and hotrienol were already found in oak honeydew [2]. Another abundant group of organic compounds were aliphatic acids: octanoic acid (8.5%; 9.4%), nonanoic acid (2.8%; 4.7%), decanoic acid (1.5%; 2.4%) and hexadecanoic acid (3.7%; 3.8%). No specific headspace marker compounds were found. Ubiquitous benzene derivatives in all honeys were also found: 2-phenylethanol (3.5%; 6.0%), phenylacetaldehyde (2.0%; 1.5%) and benzyl alcohol (2.4%; 4.8%). High concentrations of phenylacetaldehyde and 2-phenylethanol and lower quantity of benzaldehyde were found in oak honeydew from Spain [2]. Although furan derivatives were found particularly in the sample I (2-furancarboxaldehyde (0.7%), 2-furanmethanol (1.3%), 1-(2-furanyl)-ethanone (1.1%) and 5-methyl-2-furfural (8.4%)) their percentages in Table 1 are not reliable due to high polarity and low volatility.  
\begin{table}
\begin{tabular}{l l c c c} \hline  & & & \multicolumn{3}{c}{**Area percentage (\%)**} \\ \cline{4-5}
**No.** & **Compound** & **RI** & & \\  & & & **sample I** & **sample II** \\ \hline
1. & Pentan-1-ol\({}^{\text{a}}\) & \(<900\) & 0.5 & 0.9 \\
2. & 2-Methylbutan-1-ol & \(<900\) & 0.7 & - \\
3. & 2-Furancarboxaldehyde & \(<900\) & 2.0 & - \\
4. & 3-Methylbutanoic acid (Isovaleric acid) & \(<900\) & 0.8 & - \\
5. & 2-Furanmethanol & \(<900\) & 1.3 & 5.2 \\
6. & 1,3-Dimethylbenzene\({}^{**}\) & \(<900\) & 0.4 & - \\
7. & 1-(2-Furanyl)-ethanone & 914 & 1.1 & - \\
8. & 5-Methylfurfural & 969 & 8.4 & 3.5 \\
9. & Hexanoic acid\({}^{\text{a}}\) & 974 & 0.7 & 2.2 \\
10. & 2-Ethyl-1,3-dimethylbenzene\({}^{\text{a}}\) & 1032 & 0.7 & - \\
11. & 2-Ethylhexan-1-ol\({}^{\text{a}}\) & 1035 & 1.1 & - \\
12. & Benzyl alcohol\({}^{\text{a}}\) & 1037 & 2.4 & 4.8 \\ \hline \end{tabular}
\end{table}
Table 1: Oak honeydew organic headspace volatiles composition isolated by HS-SPME.
# Parameter Design for Inter-Satellite Laser Link Acquisition Under Weak Light Conditions
## 2 Acquisition System Influencing Factors
### Beam Power  
As the laser beam scans the UC, the acquisition camera carried by the receiving SC accumulates enough energy to meet the accuracy required for the laser spot center positioning method. Several factors influence the energy accumulation process, including the far-field distribution of the scanning beam, the distance between the receiving SC and the center of the scanning beam, the speed of the scanning beam, and the performance of the camera.  
The laser power received by the telescope can be estimated via the following [15]:  
\[P_{rcx}=\frac{\pi^{2}R_{tel}^{4}}{\lambda^{2}L^{2}}\tau P_{out}, \tag{3}\]  
where \(\tau\) is the transmittance in space and \(P_{out}\) is the power of the outgoing beam at the transmitting telescope. For the Taiji program, the laser has a wavelength of \(\lambda=1064\) nm and output power will be approximately 1 W. Assuming \(\tau\approx 1\), the estimated receiving power of the receiving SC's telescope, \(P_{rec}\), is about 1.5 \(\mathrm{nW}\). However, only less than  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value of the Taiji** \\ \hline Satellite separation (\(L\)) & \(3\times 10^{6}\)  km \\ Laser wavelength (\(\lambda\)) & 1064 nm \\ Telescope aperture radius (\(R_{tel}\)) & 0.2 m \\ Laser beam waist radius (\(w_{0}\)) & 0.16 m \\ Divergence half-angle (\(\theta_{div}\)) & 1.06 \(\mathrm{\SIUnitSymbolMicro rad}\) \\ Standard deviation of receiving SC (\(\sigma_{uc}\)) & 7.967 \(\mathrm{\SIUnitSymbolMicro rad}\) \\ Acquisition Camera FOV & \(\pm 150\) \(\mathrm{\SIUnitSymbolMicro rad}\) \\ QPD FOV & \(\pm 1\) \(\mathrm{\SIUnitSymbolMicro rad}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of the Taiji program. The half-cone angle of UC \(\theta_{uc}=3.44\sigma_{uc}\).  
10% of the \(P_{rec}\) received by the acquisition camera for laser spot location, approximately \(P_{0}\approx 100\) pW, here \(P_{0}\), represents the power before the acquisition camera when the optical axes of the telescopes on the two satellites are perfectly aligned with each other.  
For space-based GW detection, after propagating over a long distance, the far-field spot size is significantly larger than the aperture diameter of the transceiver telescope. Therefore, we can assume that the light intensity entering the telescope is uniform. When the distance between the receiving satellite and the center of the scanning beam is \(r\) and assuming that the far-field beam intensity follows a Gaussian distribution with a waist \(w_{0}\), the power before the acquisition camera is provided by the following:  
\[P(r)=P_{0}e^{-\frac{-\frac{2r^{2}}{w_{0}^{2}}}{w_{0}^{2}}}. \tag{4}\]  
When the scanning beam hits the receiving SC, the laser spot is truncated by the telescope. The number of photoelectrons generated is integrated over the acquisition camera's integration time. If the number of photoelectrons meets the detection threshold, the spot is successfully detected. This requires establishing the threshold criterion for successful spot detection.
# Hub-and-Spoke Network Design Considering Congestion and Flow-Based Cost Function
## 2 Literature Review
### Contribution of This Work  
Airports' congestion is expected to be restricted by government policies in the post-pandemic era, to address health and safety concerns as a consequence of the outbreak of COVID-19 in 2019-2020. Highly congested ports and airports are not favorable, due to the high rate of delayed flights reported in congested airports ([PERSON] et al., 2019; [PERSON] et al., 2020). Congestion is handled in two different ways: either by limiting the flow, as studied by [PERSON] et al. (2019) and [PERSON] and [PERSON] (2020), or by considering the monetary cost of congestion, which would impact the objective function. This was first modeled by ([PERSON] et al., 2019), who proposed a nonlinear cost penalty term for the congestion of a hub facility. [PERSON] et al. (2020) expand this model to study the effect of considering congestion in a multiple allocation hub location problem in the airline industry. [PERSON] and [PERSON] (2020) incorporate flow-dependent economies of scale and the effects of congestion, utilizing piece-wise linearization for the nonlinear components.  
In this work, we aim to bridge this gap and propose a mathematical model for multiple allocation hub-and-spoke location/transportation problems, considering the congestion balance of facilities, as well as flow-based hub transportation cost functions. To the best of our knowledge, no research work considers the utility of facilities in a transportation network, considering the hub-and-spoke location/transportation problem, which considers transportation problems as well as hub congestion.
## 5 Modeling and Analysis in Ansys
### Meshing Strategies  
Meshing methods are crucial for generating a discretized representation of the geometry used for FEA. The choice of meshing method can significantly impact the accuracy and efficiency of simulations. The three most famous meshing methods in ANSYS are tetrahedral meshing, hexahedral meshing, and sweep meshing.  
Tetrahedral meshing is widely used due to its ability to handle complex and irregular geometries, making it a go-to option for unstructured domains [38]. Hexahedral meshing, known for its high accuracy and better convergence, is preferred for structured geometries and simulations requiring precise results, such as CFD or stress analysis [39]. Sweep meshing is ideal for prismatic or axisymmetric geometries, creating structured, high-quality meshes by sweeping a 2D mesh along a path [40]. These methods are versatile and widely adopted across various engineering applications.  
Automatic meshing in ANSYS simplifies the process by automatically selecting the best meshing method based on the geometry and analysis type. It is ideal for quick setups, offering efficient and reliable results without manual intervention, making it popular for general-purpose simulations. This method has been used in the simulation part. The meshing of the different parts was performed in ANSYS to ensure accurate division of the geometry for the simulation, as shown in Figure 5.  
A finer mesh with a body sizing of 0.25 mm was applied to improve the accuracy of the analysis in ANSYS. This finer discretization ensures a more precise representation of the geometry, allowing for a better resolution of the solution gradients and capturing subtle variations in the simulation, which leads to more reliable results.  
Figure 5: Mesh representation of the IGBT parts generated in ANSYS.  
\begin{table}
\begin{tabular}{c c c c} \hline  & \(\rho\) (kg\(\cdot\)m\({}^{-3}\)) & **E (GPa)** & \(\
u\) \\ \hline Cu [28] & 8960 & 120 & 0.33 \\ \hline SAC305 [29] & 7380 & 26.2 & 0.42 \\ \hline Al\({}_{2}\)O\({}_{3}\)[30] & 4000 & 400 & 0.23 \\ \hline Si [31] & 2330 & 130 & 0.28 \\ \hline Al [32] & 2710 & 70 & 0.33 \\ \hline \end{tabular}
\end{table}
Table 3: Material properties of FP50R12 KT4 IGBT module.
# Internet-of-Things and Information Fusion: Trust Perspective Survey
## 5 Trust-Based Information Fusion
Information fusion techniques can utilize trust models either in the selection [21; 45; 57; 58; 59; 21; 46] or in the information fusion phase [49; 52; 53; 60]. That is, the decision maker can select the information providers based on trust. On the other hand, the decision maker can integrate trust during the fusion process. Another classification dimension is to identify the target node for which trust is computed. Some information fusion techniques compute trust of the information sources because their objective is to filter recommenders [21; 45; 53; 57; 51; 52; 53; 54; 55; 56; 57; 58; 59]. Other information fusion techniques compute trust of the aggregator node(s) [60]. Once reports are collected by the decision maker, these reports can be customized by a decaying or discounting function [21; 45; 51; 60; 46] before fusing them using various fusion operators.
## 3 Detector Design
### AE-GLRT Detector
```
0:\(\hat{\gamma}_{l}^{0,(0)},\ l=1,2,\cdots,L,\ \hat{\gamma}_{l}^{1,(0)},\ l=1,2, \cdots,L\) and \(t_{\max}\)
0:\(\hat{\gamma}_{l}^{0,(t_{\max})},\ l=1,2,\cdots,L,\ \hat{\gamma}_{l}^{1,(t_{\max})},\ l=1,2, \cdots,L\) and \(\hat{\xi}^{(t_{\max})}\)
1: Let the initial value of \(\gamma_{l}\) under hypothesis H\({}_{1}\) be \(\hat{\gamma}_{l}^{1,(0)}=\mathbf{z}_{l}^{\mathrm{H}}\big{(}\mathbf{z}\mathbf{z} ^{\mathrm{H}}\big{)}^{-}\mathbf{z}_{l},\ l=1,2,\cdots,L\);
2: Substituting \(\hat{\gamma}_{l}^{1,(0)},\ l=1,2,\cdots,L\) into (A4) yields the value of \(\hat{\xi}^{(1)}\);
3: Substituting \(\hat{\xi}^{(1)}\) into (A8) results in \(\hat{\gamma}_{1}^{1,(1)},\hat{\gamma}_{2}^{1,(1)},\cdots,\hat{\gamma}_{L}^{1, (1)}\);
4: Substituting \(\hat{\gamma}_{1}^{1,(1)},\hat{\gamma}_{2}^{1,(1)},\cdots,\hat{\gamma}_{L}^{1, (1)}\) into (A4) results in \(\hat{\xi}^{(2)}\);
5: Substituting \(\hat{\xi}^{(2)}\) into (A8) results in \(\hat{\gamma}_{1}^{1,(2)},\hat{\gamma}_{2}^{1,(2)},\cdots,\hat{\gamma}_{L}^{1, (2)}\). Terminating the process when the number of iterations reaches \(t_{\max}\). Finally, we can obtain \(\hat{\gamma}_{1}^{1,(t_{\max})},\hat{\gamma}_{2}^{1,(t_{\max})},\cdots,\hat{ \gamma}_{L}^{1,(t_{\max})}\), and \(\hat{\xi}^{(t_{\max})}\);
6: Setting \(\hat{\xi}=\mathbf{0}\) under hypothesis H\({}_{0}\), and then repeating the above steps to obtain \(\hat{\gamma}_{1}^{0,(t_{\max})},\hat{\gamma}_{2}^{0,(t_{\max})},\cdots,\hat{ \gamma}_{L}^{0,(t_{\max})}\). ```  
**Algorithm 1** The algorithm of alternate estimation. When obtaining the estimations of \(\xi\) and \(\gamma_{l},\ l=1,2,\cdots,L\) from Appendix A, we can substitute (6), (7), (A4), (A8), and (A10) into (5), resulting in the AE-GLRT as  
\[t_{\text{AE-GLRT}}=\frac{\left[\frac{\prod_{l=1}^{L}\gamma_{l}^{1,(t_{\max})} \left[\prod_{l=1}^{L}\gamma_{l}^{0,(t_{\max})}\right]^{\frac{N}{L+1}}}{\left| \mathbf{z}\mathbf{z}^{\mathrm{H}}+\sum\limits_{l=1}^{L}\frac{\mathbf{z}_{l} \mathbf{z}_{l}^{\mathrm{H}}}{\hat{\gamma}_{l}^{0,(t_{\max})}}\right|}}{\left[ \left(\mathbf{z}-\mathbf{H}\hat{\xi}^{(t_{\max})}\right)\left(\mathbf{z}- \mathbf{H}\hat{\xi}^{(t_{\max})}\right)^{\mathrm{H}}+\sum\limits_{l=1}^{L} \frac{\mathbf{z}_{l}\mathbf{z}_{l}^{\mathrm{H}}}{\hat{\gamma}_{l}^{0,(t_{\max })}}\right]} \tag{10}\]
## 1 Introduction
[PERSON] and [PERSON] [29] noted the similarities in control between free-floating space robots and ground fixed-base robots. Since then, many control algorithms designed for ground fixed-base robots have been studied and applied in space robots. Among them, the PID control method, which has been widely studied in ground industrial robots, has also been used in space robots [36]. However, this method has the problem of parameter tuning difficulty when it is applied to control an in-orbit service space robot with complex disturbances. Sliding mode control (SMC) is widely applied in the field of nonlinear systems because of its high precision and fast response [37; 38]. [PERSON] et al. [39] first used SMC to design the controller of a two-DOF manipulator, and then the researchers conducted much research on the SMC of robots, representing a typical nonlinear system [40; 41]. In recent years, many novel SMC methods have been proposed for the in-orbit service of space robots [42; 43]. Model predictive control (MPC) is an excellent control method based on a system model to obtain a control signal by minimizing the objective function [44]. [PERSON] et al. [45; 46] improved MPC for the control problem of nonlinear systems and obtained better control performance. In the field of space robots, compared with other control methods, there is relatively less research on MPC. When studying the control problem of dual-arm space robots, [PERSON] [47] found that MPC can achieve higher-precision control performance than SMC when there is no system disturbance, while SMC shows better robustness than MPC when there is system disturbance. [PERSON] [44] discussed the possibility of applying a nonlinear model predictive controller (NMPC) to a free-floating space robot. [PERSON] [11] proposed a new concept for a free-floating space robot control system, in which the NMPC controller introduced a trajectory planning module, but the paper only carried out a numerical simulation on the planar case of the two-DOF manipulator. In this paper, the dynamic model of a free-floating dual-arm space robot with stylization and high computational efficiency is established by using the concept of DEM and the explicit multi-rigid-body dynamic modeling theory [22; 23], and a novel control method is proposed. This method can be effectively applied to the terminal trajectory tracking and base attitude stability control of free-floating space robots under the condition of parameter perturbation and external disturbance. The remainder of this paper is organized as follows: in Section 2, a free-floating dual-arm space robot is modeled and analyzed by using explicit multi-rigid-body dynamic modeling theory and DEM. In Section 3, an improved NMPC controller is designed and its stability is analyzed based on Lyapunov theory. In Section 4, an in-orbit service task is designed and a numerical simulation is performed to verify the proposed controller. In Section 5, the dynamic modeling and control methods for the free-floating space robot are discussed, while the conclusions are provided in Section 6.