esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Numerical Example of Two-Dimensional Discrete Memristor Model
### Hyperchaotic Attractor  
From Section 3.2, we know that the three models are in a hyperchaotic state with determined parameters. This section will study the hyperchaotic sequences generated by three models. Given control parameter \(\mu\), initial conditions \((x_{0},q_{0})\), and coupling coefficient \(k\) in details, respectively, the trajectories of the three models in phase space are shown in Figure 4. It can be seen that all hyperchaotic attractors are distributed in a bounded region and have complex fractal structures. Secondly, hyperchaotic sequences generated by the three models are shown in Figure 5. The three sequences are all similar to noise signals, and the magnitude of sequence values is irregular and aperiodic. Next, we will evaluate the performance of hyperchaotic sequences through Sample entropy (SE) [30], Permutation entropy (PE) [31], Correlation dimension (CorDim) [32], and Kaplan-Yorke dimension (K-YDim) [33]. Note that the length of the sequence is set to \(10^{5}\). The calculation results of the three hyperchaotic sequences are shown in Table 3. It can be seen that the hyperchaotic sequence generated by the system has excellent complexity and can be used in image encryption, chaotic secure communication, and other fields. Secondly, the discrete memristor model based on chaotic systems can enhance the complexity of a one-dimensional chaotic system.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Items** & \(\mu\) & \(k\) & \((x_{0},q_{0})\) & \(LE_{1},LE_{2}\) \\ \hline
2D-DMLM & \(0.200\) & \(1.99\) & \((0.3,0.7)\) & \(0.3028\), \(0.0731\) \\
2D-DMSM & \(-0.05\) & \(1.82\) & \((0.2,0.4)\) & \(0.3200\), \(0.0757\) \\
2D-DMTM & \(-0.40\) & \(1.58\) & \((0.1,0.6)\) & \(0.2797\), \(0.1062\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: The two Lyapunov exponents of three models.  
Figure 4: The phase space trajectory of three models: (**a**) 2D-DMLM; (**b**) 2D-DMSM; (**c**) 2D-DMTM.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Items** & **SE** & **PE** & **CorDim** & **K-YDim** \\ \hline
2D-DMLM & \(0.7268\) & \(0.8311\) & \(1.6163\) & \(2\) \\
2D-DMSM & \(0.7786\) & \(0.8010\) & \(1.6315\) & \(2\) \\
2D-DMTM & \(0.7076\) & \(0.8216\) & \(1.6295\) & \(2\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance of hyperchaotic sequences generated by three models.
## 3 Results and Discussion
### The Effect of Sulfite Addition  
Similarly to what was observed by addition of either AsA or CySH, the sigmoidal course of Qt degradation was more evident by the addition of sodium sulfite (Figure 7). At a concentration of 10 umol L-1, sulfite provoked a decrease in \(k\) by almost 15% compared to control (no sulfite addition), but at 25 umol L-1 an increase was recorded (Table 3). Thereafter, \(k\) followed a constant decline, as in the case of CySH. Likewise, although \(t_{1/2}\) decreased up to 50 umol L-1, from 100 to 200 umol L-1 an increase was seen. This outcome indicated that sulfite showed a consistent stabilizing effect from 50 umol L-1 onwards.  
Sulfite may act as oxygen scavenger and reducing agent [35]. Therefore, the protective action it could exert would be through preventing reaction of Qt (or Qt quinone) with oxygen, or reduction of the Qt quinone back to Qt. Sulfur dioxide (SO\({}_{2}\)) has been proven to prevent Qt electrochemical oxidation [36], and the authors claimed that the antioxidant protection seen was a result of quinone reduction or semiquinone radical reduction. Judging by the degradation products generated, it was also concluded that Qt oxidative degradation follows the same pathway, irrespective of the presence of SO\({}_{2}\). To test this hypothesis, as well as to shed more light onto the effect of sulfite on Qt oxidative degradation, a Qt solution treated for 50 min, at 90 \({}^{\circ}\)C, in the presence of 200 \(\upmu\)mol L\({}^{-1}\) sodium sulfite, was subjected to LC-MS/MS analysis. Apart from the typical degradation products PCA, PGCA and Qt quinone, two other peaks with \(m/z=381\) and 379 were also detected (Table 3). These compounds were encountered neither in Qt solutions, nor in Qt solutions treated with AsA and CySH. The peak with \(m/z=381\) might correspond to Qt 3-sulfite, as  
Figure 6: Implementation of the sigmoidal kinetic model to trace Qt thermal degradation in the presence of various concentrations of CySH.  
Figure 7: Implementation of the sigmoidal kinetic model to trace Qt thermal degradation in the presence of various concentrations of sulfite.  
previously proposed [37; 38], while the peak with \(m/z\) = 379 could be its quinone (Figure 3). This finding raised evidence for plausible reaction(s) between Qt and sulfite.
## 4 Discussion
Under this perspective, being a member of the team can help to build trustworthiness towards automated vehicles: this means that the members have to obey the norms of logic, rationality, and ethics under pragmatic constraints, and, in case of disagreement in the team, automation may need sophisticated social identities, including empathy and reputational concerns (in order to facilitate dialogue and argumentation). From a technical point of view, artificial agents must be reliable, incorporating adaptability and redundancy, to assure trustworthiness. Concerning the future steps, this research goes in three directions:* Extend the experiments presented in this work, with a larger number of participants, a more realistic scenario with higher curvature sections, other secondary tasks, and additional metrics (e.g., secondary task fulfillment evaluation and take-over performance when automation support is unexpectedly deactivated). In particular, the take-over behavior will be a key aspect in the acceptance of the SC mode in the future, as it is expected to result in better performance in comparison to when the driver is kept out of the loop. * Extend the scenarios beyond the distracted driver, using the driving simulator again. With reference to the work of [PERSON] and colleagues [54], we will include curves and other road layouts. Moreover, two additional use-cases will be evaluated: (1) support in collision avoidance of sudden obstacles when driving in automated mode, and (2) support in an overtaking maneuver, evaluating transitions from automated to manual, initiated by the driver when the AD cannot perform this maneuver due to--for example--limitations in perception. In such a situation, the system informs the driver and asks for support. Two possibilities can be investigated: cooperation in perception (where the influence of visual HMI presented in this work will be evaluated) and real-time cooperation in action. * Integration of the HMI: while presented in this work as an initial prototype, the next step is to integrate the visual HMI with real data coming from the simulator, to be used in the experimental test to evaluate its effectiveness as an enabler of trust in decisions and actions performed by the co-pilot. * Implementation of the co-pilot enabler in a real demonstrator vehicle, using an automated Renault Twizy.
## 5 Experimental Results
### Performance Comparison between the Proposed Method and Baseline Approaches  
In this subsection, the proposed method is compared with the previously mentioned schemes. The compared results divide into two parts: maximum embedding capacity and embedding capability in different embedding capacities. The comparison results show that the proposed method has better embedding capacity, and the image qualities are still maintained well.  
Figure 6: The cover images and the stego-images (embed 10,000 bits).  
Figure 7: PSNR (dB) and embedding capacity (bits) of the proposed scheme, for image Lena.
# PK-Judge: Enhancing IP Protection of Neural Network Models Using an Asymmetric Approach
## 6 Results and Discussion
### Watermark Generation  
The ownership watermark combines BLS signatures [51] with Reed-Solomon coding [52] (Section 4):  
1. Generate 32-byte BLS private key \(sk\) using BLS12-381 curve and compute the corresponding public key \(pk\).
2. Compute message digest: \(h=\text{SHA-256}(\theta\parallel\text{id}_{\text{owner}})\) While we typically include a nonce to ensure message uniqueness (e.g., by setting \(h=\text{SHA-256}(\theta\parallel\text{id}_{\text{owner}}\parallel\text{nonce})\), this is not strictly mandatory since the subsequent challenge-response phase already enforces fresh ownership proofs.)
3. Sign digest: \(\sigma_{\text{BLS}}\leftarrow\text{Sign}(h,sk)\in\{0,1\}^{512}\)
4. Error Correction Code (RS) encode: \(\psi=\text{RS-Enc}(\sigma_{\text{BLS}},t=32)\in\{0,1\}^{768}\)  
When using DeepSigns as the host framework, we partitioned the watermark \(\psi\) mirroring the number of classes that will carry it as described in Section 5.3.  
Embedding and Verification in Real Architectures. PK-Judge seamlessly integrates with larger models (e.g., ResNet101, DenseNet-201) by embedding the encoded BLS signature into selected layers. Ownership is verified by (1) extracting and ECC-decoding the watermark to recover the BLS signature; (2) validating it with the public key; and (3) prompting a fresh challenge-response, ensuring the claimant indeed holds the private key. This process securely confirms ownership without ever exposing the secret key. Unlike one-time watermarks, this scheme provides persistent, publicly verifiable ownership through PKI, leveraging digital signatures' properties to preserve integrity.  
PK-Judge has been implemented via blspy [70] and reedsolo [71] libraries, added 32 bytes (256 bits) of redundancy to the 512 bits signature, resulting in a 768-bit (96-byte) watermark. A detailed evaluation of PK-Judge's performance across various requirements follows in subsequent sections.
## 3 Simulations and Data Analysis  
Fluent 2021R1, known as a prominent computational fluid dynamics (CFD) software, excels in geometric construction, numerical computation, and data post-processing with its high-precision capabilities. Using FLUENT, we calculated the air density distribution around the fairing at varying velocities and altitudes.  
The geometric shape of the laser communication equipment studied in this paper is shown in Figure 1. There are slight differences in the shapes of the different optical turrets. To maintain generality, the computational model is chosen as the fairing outside the electro-optical gondola. The fairing consists of a cylindrical base and a hemispherical surface, in which the height of the cylindrical base is denoted as \(L_{\text{TU}}=114\) mm, and both the radius of the cylindrical base and the hemispherical working surface are \(R_{\text{TU}}=152\) mm, while the aperture of the optical window is \(D_{\text{R}}=\pi\times\left(0.9\times R_{\text{TU}}\right)^{2}=0.0059\) m\({}^{2}\).  
The geometric structure and grid division of the flow field were established using Ansys-ICEM (Integrated computer engineering & manufacturing) 2021R1 software. The computational domain is rectangular, with a length of 4180 mm, and both the width and height are 912 mm. The flow field is divided using a hexahedral grid, resulting in a total of 361,0720 hexahedral elements within the computational domain.The velocity is set to 0.4 MA-0.9 Ma, and the altitude to 0 km-10 km above sea level. The FLUENT solver type is a three-dimensional transient velocity-pressure solver with single precision. It is tailored for subsonic motion, ensuring precise computations for compressible fluids and swift convergence. Boundary conditions for the flow field include ambient temperature and static pressure, which are determined by the flight altitude. Specific parameters are provided in Table 1. The boundary condition for the flow field is set as a pressure far field to simulate free boundary conditions under infinite inflow conditions. The wall is configured as non-adiabatic, with a thermal flux density of zero.  
Aerodynamic-optical numerical simulation methods mainly consist of Reynolds-averaged Navier-Stokes (RANS) equations and large eddy simulations (LESs). This study adopts the large eddy simulation approach, with a time step of 0.001 s and a maximum of 20 iterations per time step.
## 3 Proposed Scheme
### Iteration Method Review
#### 3.1.3 Chebyshev Acceleration  
Chebyshev acceleration [31] can be regarded as the promotion of extrapolation acceleration. Consider \(\mathbf{x}^{(0)},\mathbf{x}^{(1)},...,\mathbf{x}^{(k)}\), if \(\varepsilon_{k}=\mathbf{x}^{(k)}-\mathbf{x}^{*}\) is defined as the error of the \(k\)th iteration solution, in which \(\mathbf{x}^{*}\) is a target signal and \(\mathbf{x}^{(k)}\) is \(k\)th-time iteration output  
where \(\Lambda\) is a diagonal matrix and \(Q\) is an orthogonal matrix; therefore,  
\[\begin{split}\min_{p_{k}\in R_{k},p_{k}(I)=I}\left\|p_{k}(G)\right\| _{2}&=\min_{p_{k}\in R_{k},p_{k}(I)=I}\left\|p_{k}(\Lambda)\right\| _{2}\\ &=\min_{p_{k}\in R_{k},p_{k}(I)=I}\max_{1\leq i\leq n}\left\{\left| p_{k}(\lambda_{i})\right|\right\}\\ &=\min_{p_{k}\in P_{k},p_{k}(I)=I}\max_{\lambda\in[\lambda_{n}, \lambda_{1}]}\left\{\left|p_{k}(\lambda)\right|\right\},\end{split} \tag{36}\]  
where \(\lambda_{n}\) and \(\lambda_{1}\) are the maximum and the minimum eigenvalue, respectively. Next, we consider the iteration equation, Equation (10), in which the eigenvalues of the iteration matrix \(G\) are all real numbers, the iterative matrix spectral radius \(\rho=\rho(G)<1\) and \(\lambda\in[-\rho,\rho]\subset(-1,1)\). Thus, Equation (36) can be converted into  
\[\min_{p_{k}\in P_{k},p_{k}(I)=I}\max_{\lambda\in[-\rho,\rho]}\{\left|p_{k}( \lambda)\right|\}. \tag{37}\]  
According to the above result, we can follow the Chebyshev polynomials' properties [31] to solve Equation (37) as follows:  
\[p_{k}(t)=\frac{T_{k}(t/\rho)}{T_{k}(1/\rho)}, \tag{38}\]  
where \(T_{k}(t)\) is the k-degree Chebyshev polynomials. In fact, we can use the recurrence relation of the Chebyshev polynomials as follows:  
\[T_{k}(t)=2 tT_{k-1}(t)-T_{k-2}(t),\qquad k=2,3,..., \tag{39}\]  
in addition, let \(\mu_{k}=\frac{1}{T_{k}(1/\rho)}\) or (\(T_{k}(1/\rho)=\frac{1}{\mu_{k}}\)), and we can obtain a new recurrence relation equation as  
\[\frac{1}{\mu_{k}}=\frac{2}{\rho}\cdot\frac{1}{\mu_{k-1}}-\frac{1}{\mu_{k-2}}. \tag{40}\]  
Substituting Equation (40) into Equation (32), we can obtain  
\[\hat{\mathbf{x}}^{(k)}=\frac{2\mu_{k}}{\mu_{(k-1)}}\cdot\frac{G}{\rho}\hat{ \mathbf{x}}^{(k-1)}-\frac{\mu_{k}}{\mu_{(k-2)}}\hat{\mathbf{x}}^{(k-2)}+\frac {2\mu_{k}}{\mu_{(k-1)}\rho}\mathbf{d}. \tag{41}\]
## 3 Proposed Analog RF Beamforming  
This section deals with the derivation of phase-only precoder at source, the analog RF beamforming matrices at relay nodes and the RF combiner at the destination.
# A New Method of Ski Tracks Extraction Based on Laser Intensity Information
## 3 Methodology
### The Second Segmentation with The Cloth Method  
A cloth simulation filtering (CSF) algorithm is based on a simple physical process simulation [30; 31; 32; 33]. The algorithm assumes that a virtual cloth falls by gravity on the terrain surface. If the cloth is soft enough, it will be attached to the terrain, and the shape of the fabric is the \(DSM\). When the terrain is turned over, the fabric shape that falls on the surface is \(DEM\), as shown in Figure 4a. The algorithm uses a cloth simulation to extract the ground points of laser point cloud data.  
The cloth is a grid made up of a large number of interrelated nodes, as shown in Figure 4b, which is called the particle spring model [34]. In this model, the cloth points are connected by a virtual spring, and the interaction between the points follows the law of elasticity. In other words, the cloth point will produce displacement after being stressed. To simulate the shape of the cloth at a certain time, the position of the cloth points in the \(3D\) space needs to be calculated. Combined with [PERSON]'s second law, the relationship between the position of the distribution point and the interaction force follows the following formula:  
\[X(t+\Delta t)=2X(t)-X(t-\Delta t)+\frac{G}{m}\Delta t^{2} \tag{3}\]  
where \(m\) represents the mass of the distribution points and is set as constant \(1\); \(X\) represents the node position at a certain time; \(\Delta t\) represents the time step; \(G\) represents the gravitational constant. Given the time step and the initial position of the node, the position of the current point will be obtained.  
In the process of cloth simulation, the position of the cloth point after the displacement caused by gravity is calculated by Formula (3). In addition, the blank area of the overturned surface is usually micro-terrain such as buildings or deep pits. Therefore, to limit the movement of distribution points, it is necessary to calculate the elevation difference between adjacent distribution points to correct the position of distribution points moved by the force between adjacent nodes. If two adjacent nodes are movable points and have different elevation values, they move the same distance in the opposite direction in the vertical direction. If there is only one immovable point between them, the other point moves. If both are at the same elevation, neither is moved. The corrected displacement of each cloth point is calculated as follows:  
\[d=\frac{1}{2}b(p_{i}-p_{0})\cdot n \tag{4}\]  
where \(d\) is the movement vector of the node; \(p_{0}\) is the current position of the node to be moved; \(p_{i}\) is the adjacent node position of \(p_{0}\); \(n\) is the standard vector in the vertical direction, \(n=(0,0,1)^{T}\); \(b\) is the parameter used to determine whether the node moves. When the node is a movable point, set \(b=1\), otherwise \(b=0\).
## 1 Introduction
The authors have developed theoretical equations and represented the pressure drop characteristic of sintered metal porous media in terms of experimentally determined property coefficients ([PERSON] et al., 2012; [PERSON] and [PERSON], 2013; [PERSON] and [PERSON], 2013). However, the measurements are conducted under steady flow condition and this inevitably lengthens the test time and consumes much energy. [PERSON] et al. (2013) proposed a method using an isothermal chamber to obtain the flow rate characteristics of solenoid valves by the ISO 6358 equation. The equation depicts the flow rate characteristics with two independent parameters: sonic conductance \(C\) and critical pressure ratio \(b\), which is determined in the choked flow state and subsonic flow state, respectively (the equation is detailed in the Appendix). The isothermal chamber is stuffed with steel or copper wool to enlarge the heat transfer area so that the air state change can be considered isothermal. Therefore, instantaneous flow rate charged into or discharged from the chamber can be indirectly calculated by differentiating the pressure in the chamber. If this approach can be applied to the test of porous media, the property coefficients can be ascertained more conveniently and effectively. To date, the discharge method has been used by other researchers for determining the flow rate characteristics of some pneumatic components (valves, etc.) in terms of the ISO 6358 equation. However, the porous media have very different properties to the common pneumatic components, and therefore the operating procedure needs to be reconstructed to extend its application to porous media. In this study, air is employed as the working fluid in order to investigate the flow rate characteristics of the porous media for some pneumatic applications (e.g., air bearing). The objective of the study is to use a simple and convenient method to measure and evaluate the permeability and inertial coefficient to represent the flow rate characteristics. Accordingly, a discharge method based on an isothermal chamber is proposed to conveniently ascertain the coefficients. This method accomplishes the test within the discharge process (only a few seconds), and of course reduces the air consumption. Three groups of sintered metal porous media made by different class of powder were tested. The validity of the discharge method was verified because the flow rate characteristics can be well represented with the determined coefficients.
# Predictive Distillation Method of Anchor-Free Object Detection Model for Continual Learning
## 4 Experiment
### Experimental Results
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**20 Classes** & **20 Classes** & **10 Classes** & **Fine Tuning** & **LwF** [6] & **SID [8]** & **SID [8]** & **SID [8]** & **SDR** [15] & **SDR** [15] & **Proposed** \\  & & & & **k = 2** & **k = 3** & **k = 4** & **KD1** & **KD2** & \\ \hline aeroplane & 73.7 & 73.5 & 0 & 0 & 33 & 60 & 59 & 33.2 & 0.1 & 56.2 \\ bicycle & 79.1 & 78.4 & 0 & 0 & 58.1 & 70.2 & 68 & 8.3 & 0.2 & 67.8 \\ bird & 70.6 & 68.5 & 0 & 0 & 20.6 & 56.2 & 58.5 & 12.2 & 2.3 & 37.2 \\ boat & 62.6 & 62.5 & 0 & 0 & 15.3 & 44.3 & 39.1 & 30.5 & 4.6 & 38.8 \\ bottle & 56.9 & 54.5 & 0 & 0 & 6.1 & 35 & 33.2 & 5.7 & 0 & \(\underline{42}\) \\ bus & 79.1 & 74.7 & 0 & 0 & 56.9 & 63 & 63.1 & 24.4 & 0.1 & 30.9 \\ car & 83.7 & 84.9 & 0 & 0 & 69 & \(\underline{79}\) & 76.5 & 56.3 & 0.2 & 75.1 \\ cat & 81.2 & 70.7 & 0 & 0 & 46.5 & 38.7 & 61.4 & 45.9 & 18.1 & 34.8 \\ chair & 58.4 & 56.7 & 0 & 0 & 29.2 & 33 & 42.5 & 11.6 & 0.1 & \(\underline{48.6}\) \\ cow & 78.8 & 57.3 & 0 & 0 & 55.2 & 31.6 & 29.8 & 19.3 & 27.3 & 40.8 \\ dingtable & 70.5 & - & 50.1 & 35.7 & 38.3 & 38.6 & 26.4 & 32.9 & 49.6 & 43.1 \\ dog & 79.3 & - & 47.9 & 35.8 & 39.5 & 42.3 & 27.4 & 40.6 & 46.1 & 47 \\ horse & 81.6 & - & 68.7 & 60.5 & 62.8 & 61.1 & 51 & 63.4 & \(\underline{68.8}\) & 67.5 \\ motorbike & 79.3 & - & 55 & 44.7 & 55.1 & 44.3 & 36.9 & 46.2 & 53.6 & \(\underline{60.9}\) \\ person & 78.7 & - & 75.1 & 67.1 & 72.6 & 70.3 & 64.1 & 66.3 & 74.4 & 71.8 \\ pottedplant & 47.7 & - & 41.4 & 29.3 & 36.3 & 33 & 20.5 & 26.6 & \(\underline{41.9}\) & 37.5 \\ sheep & 77.9 & - & 66.5 & 44.9 & 58.5 & 58.5 & 38.2 & 59.4 & 65.9 & 63.5 \\ sofa & 70.1 & - & 55.4 & 49.1 & 46.1 & 48.2 & 37.8 & 48.7 & \(\underline{55.6}\) & 55.4 \\ train & 79.1 & - & 38 & 31.2 & 29.7 & 40.8 & 30 & 28.2 & 36.5 & \(\underline{44.1}\) \\ tv/monitor & 73.4 & - & 62.2 & 51.8 & 61.3 & 58.5 & 46.9 & 49.5 & 57.8 & 61.6 \\ mAP & 73.1 & 68.2 & 28 & 22.5 & 44.5 & 50.3 & 45.5 & 35.5 & 30.1 & \(\underline{51.2}\) \\
1-15 mAP & - & 68.2 & 0 & 0 & 39 & 51 & \(\underline{53.1}\) & 24.7 & 5.3 & 47.2 \\
16-20 mAP & - & - & 56 & 45 & 50 & 49.6 & 37.9 & 46.2 & 55 & 55.2 \\ \(F1^{i}\) & - & - & 0 & 0 & 43.8 & 50.3 & 44.2 & 32.2 & 9.6 & \(\underline{50.9}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of 10 + 10 Scenario (%).