esa-sceva/satcom-chunk-collection huggingface dataset:
# Game Theory-Based Interactive Control for Human-Machine Cooperative Driving
## 3 Driver and Vehicle Models
### BELCM-Based Driver Model
#### 3.2.1 Driver Steering Control Model  
Studies have shown that the driver's steering behavior is guided by observing a near point with the preview angle \(\theta_{n}\) and a far point with the preview angle \(\theta_{f}\) on the road, which can be represented by the two-point preview model [11]. For the detailed derivation process, one can refer to [12]. In this paper, \(e_{n},\theta_{n}\), and \(\theta_{f}\) are adopted as the external stimulus signals \(SI_{y}\) received by the thalamus for lateral control, where \(e_{n}\) represents the lateral error at the near point. \(ES_{y}\), involved in vehicle steering control in the brain's emotional learning circuit, can be formed through long-term learning. \(SI_{y}\) and \(ES_{y}\) are defined as follows:  
\[\left\{\begin{array}{l}SI_{y}=\left[\gamma_{y_{1}}e_{n},\gamma_{y_{2}}\theta_ {n},\gamma_{y_{3}}\delta_{f}\right]^{T}\\ ES_{y}=\epsilon_{y_{1}}e_{n}+\epsilon_{y_{2}}\theta_{n}+\epsilon_{y_{3}}\delta _{f}+\epsilon_{y_{4}}\delta^{h}\end{array}\right. \tag{4}\]  
where \(\epsilon_{y1}\sim\epsilon_{y4}\) and \(\gamma_{y1}\sim\gamma_{y3}\) are the weighted coefficients of \(ES_{y}\) and the weighted coefficients of \(SI_{y}\), respectively. The weight coefficients in the amygdala and prefrontal cortex can be expressed as matrices \(\mathcal{V}_{A_{y}}=\left[\mathcal{V}_{A_{y1}},\mathcal{V}_{A_{y2}},\mathcal{ V}_{A_{y3}},\mathcal{V}_{A_{h1}}\right]\) and \(\mathcal{V}_{P_{y}}=\left[\mathcal{V}_{P_{y1}},\mathcal{V}_{P_{y2}},\mathcal{ V}_{P_{y3}}\right]\), respectively. Finally, the ideal output of steering control signals in the BELCM is as follows:  
\[\delta^{h^{*}}=\mathcal{V}_{A_{y}}\left[\begin{array}{c}SI_{y}\\ A_{th}\end{array}\right]-\mathcal{V}_{P_{y}}SI_{y} \tag{5}\]  
However, there is a time delay before the arm muscles execute the steering signal transmitted by the driver's brain, and this delay characteristic can be simulated by the arm neuromuscular system (NMS) [26]. The state space equation of the NMS is as follows:  
\[\left\{\begin{array}{l}\delta^{h}_{\text{nms}}\\ \tilde{y}^{h}_{\text{mms}}\end{array}\right\} =\left[\begin{array}{cc}0&1\\ -\omega^{2}_{n}&-2\zeta\omega_{n}\end{array}\right]\left\{\begin{array}{l} \delta^{h}_{\text{nms}}\\ \delta^{h}_{\text{mms}}\end{array}\right\}+\left[\begin{array}{c}0\\ \omega^{2}_{n}\end{array}\right]\delta^{h^{*}} \tag{6}\] \[\delta^{h} =\left[\begin{array}{cc}1&0\end{array}\right]\left\{\begin{array} []{l}\delta^{h}_{\text{nms}}\\ \delta^{h}_{\text{nms}}\end{array}\right\},\]  
where \(\omega_{n}\) and \(\zeta\) represent the NMS's natural frequency and damping ratio, respectively.  
Figure 3: Framework of driver model based on BELCM.
# A SiCN Thin Film Thermistor Based on DVB Modified Polymer-Derived Ceramics
## 3 Results  
### Film Morphology  
Pores and cracks are the main factors affecting PDCs conductivity [1]. Therefore, the critical thickness of the DVB-modified SiCN film (defined as the maximum thickness of monolayer deposition) was first determined. In the DIW process, the line width of the resistance grids was fixed at about 500 \(\upmu\)m, and the line thickness was successively increased. After pyrolysis, the thickness of the resistance grids and the shape of the surface profile were determined by a profilometer. As shown in Figure 2, the critical thickness of SiCN film gradually increased from 3.7 \(\upmu\)m of SiCN to 5.7 \(\upmu\)m of 10 wt% DVB-SICN, 6.0 \(\upmu\)m of 30 wt% DVB-SiCN, 6.9 \(\upmu\)m of 50 wt% DVB-SiCN. The addition of DVB provides more carbon sources for SiCN ceramics. The high critical thickness may be related to the carbon content of SiCN. The increased critical thickness reduces the risk of film cracking and peeling during DIW patterning and pyrolysis, which is beneficial for maintaining the structural integrity of the resistive grids.  
Figure 2: Surface profile of SiCN and DVB-modified SiCN lines.  
oxygen content may be related to the oxygen contamination during the DIW process, and the oxygen adsorption on the surface of the SiCN film.
# A Robust Dual-Platform GMTI Method against Nonuniform Clutter
## 2 The Dual-Platform GMTI Method against Nonuniform Environment
### Analysis of the Influence of Heterogeneous Environment in STAP
#### 2.2.1 Strong Pollution  
The strong pollution phenomenon happens under two conditions. The first one is that moving targets (including ships, cars, etc.) reside in the background used to obtain the CCM. The second one is that jam exists in the background used to obtain the CCM. These two phenomena make the STAP filter form an unexpected null at the position where the moving targets or the jam are located. Once the target to be detected is close to the null in the STAP filter, the target is canceled, and its detection performance is reduced. The target's influence CCM estimation can be expressed as:  
\[\mathbf{\hat{R}}=\mathbf{R}+\frac{1}{N_{s}}\sum_{i=1}^{N_{j}}\sigma_{i}^{2}\mathbf{s}_{i }\mathbf{s}_{i}^{H}, \tag{7}\]  
where \(\mathbf{R}\) represents the CCM without pollution, \(N_{s}\) is the number of training samples, \(\sigma_{i}^{2}\) is the energy of the ith pollution, and \(\mathbf{s}_{i}\) is the space-time steering vector of the ith pollution.  
Strong pollution will prevent the CUT and training samples zones from satisfying the IID condition. The loss of the signal-to-clutter-plus-noise ratio (SCNR) improvement factor due to the pollution target is shown as follows:  
\[Loss_{IT}=\frac{(\mathbf{s}^{H}\hat{\mathbf{R}}_{IT}^{-1}\mathbf{s})^{2}}{\mathbf{s}^{H}\hat{ \mathbf{R}}_{IT}^{-1}\mathbf{R}\hat{\mathbf{R}}_{IT}^{-1}\mathbf{s}}\cdot\frac{1}{\mathbf{s}^{H} \mathbf{R}^{-1}\mathbf{s}}, \tag{8}\]  
where \(\hat{\mathbf{R}}_{IT}\) is the CCM containing strong pollution.
# Telecommunication Systems for Small Satellites Operating at High Frequencies: A Review  
[PERSON]  
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA; [PERSON]#### Abstract  
Small Satellites and in particular CubeSats are becoming extremely popular platforms with which to perform space research. They allow for rapid prototyping with considerable cost savings with respect to traditional missions. However, as small satellite missions become more ambitious in terms of destinations to reach (from Low Earth Orbit to interplanetary) and in terms of the amount of data to transmit, new technologies need to be developed to provide adequate telecommunication support. This paper aims to review the telecommunication systems that have been developed at the Jet Propulsion Laboratory for some of the most recent CubeSat missions operating at different frequency bands: ASTERIA (S-Band), MarCO (X-Band and UHF) and ISARA (Ka-Band and UHF). For each of these missions: the telecommunication challenges and requirements are listed; the final system design is presented; the characteristics of the different hardware components are shown; and the lessons learned through operations are discussed.  
CubeSat; telecommunication; antenna; transceiver; operations +
Footnote †: journal: Information
## 1 Introduction
The novelty of the work lies in the use of the Byzantine general's problem, which is the principle used in blockchain in order to secure the OLSR control plane. Furthermore, we considered the performance evaluation of our solution within a scale-free network. The contributions to the work are the following:  
* Enhancement of OLSR protocol security for V2X Networks: By developing a new approach to enhance the security of the Optimized Link State Routing (OLSR) protocol in V2X (Vehicle-to-Everything) networks. * Secure MPR selection: By ensuring secure MPR selection, the performance of OLSR in V2X can be optimized, leading to efficient and reliable routing. * Trust-Based model with a decentralized verification system: By introducing a three-step model for securing the MPR selection process. * Detection and isolation of malicious nodes: The proposed model performs very well at identifying and isolating malicious nodes in the V2X network. * Implementation and performance evaluation: NS-2 was used to implement and evaluate the proposed solution in the case of a scale-free network. * Performance improvement over current OLSR implementations: The research findings show that the proposed approach outperformed the widely used UM-OLSR implementation in terms of packet delivery ratio as well as in detecting and handling malicious nodes. Figure 1 presents the workflow of the proposed approach. The remainder of the paper is structured as follows: Section 2 provides an overview of the OLSR protocol, discussing the traffic it generates and the default MPR selection process outlined in RFC 3626. Section 3 examines various attacks that exploit control traffic to disrupt routing processes and network topologies. Section 4 is dedicated to a brief presentation of works related to the performance and security improvement of V2X communications. In Section 5, we present our proposed algorithm, followed by Section 5, where we will conduct a case study analysis. InSection 7, we present simulation results and demonstrate the effectiveness of the proposed approach and the improvements it brings over the UM-OLSR implementation. The final section of this article provides a synthesis of the key contributions and conclusions.
## 2 Methodology
### Dynamics of a VBI System
\(M(x)\) can be calculated from the bridge displacement as  
\[\text{E}I\frac{d^{2}u}{dx^{2}}=M \tag{13}\]  
By incorporating Equations (11)-(13) into Equation (5b), it was deduced that  
\[\varepsilon=y\frac{\pi^{2}}{L^{2}}\sum_{n}\frac{\Delta_{stn}}{1-S_{n}^{2}} \Big{[}sin\frac{n\pi x}{L}S_{n}\text{sin}\,\omega_{b,n}t\Big{]} \tag{14}\]  
Comparing Equation (14) with Equation (5b) showed that the strain in the latter includes a constant spatial coordinate, \(y\). With strain sensors in place on the target bridge, these spatial coordinates were set. Thus, a strain-related transfer matrix was established to clarify the relationships among strains at multiple cross-sections. Assuming three strain gauges were installed at \(x_{1}\), \(x_{2}\), and \(x_{3}\), with corresponding vertical coordinates \(y_{1}\), \(y_{2}\), and \(y_{3}\), the strain-related transfer matrix was  
\[T_{strain}= \begin{bmatrix}\frac{y_{2}}{y_{1}}&0\\ 0&\frac{y_{3}}{y_{2}}\end{bmatrix}\begin{bmatrix}sin\frac{\pi x_{2}}{L}&sin \frac{2\pi x_{2}}{L}\end{bmatrix}\begin{bmatrix}sin\frac{\pi x_{1}}{L}&sin\frac {2\pi x_{1}}{L}\\ sin\frac{\pi x_{2}}{L}&sin\frac{2\pi x_{2}}{L}\end{bmatrix} ^{-1} \tag{15}\]  
Equation (15) indicates that the transfer matrix for bridge strain should also only be related to the bridge mode shape, rendering it time-independent and constant. Furthermore, while \(T\) can theoretically be determined through mode shape functions, this study leveraged a Backpropagation Artificial Neural Network (BP-ANN) to directly acquire \(T\) without the need for explicit calculation of mode shape functions. As detailed in Section 2.2, the training of the network to emulate \(T\) was treated as a regression task. This approach diverged from previous studies [10; 11; 12; 13] by eliminating the requirement for inverse calculations to derive mode shape functions. An additional benefit of the proposed method was that it circumvented the challenges associated with noise in existing techniques for calculating mode shape functions [10; 11; 12; 13]. Moreover, as indicated by Equation (10), \(T\) was shown to be time-independent. Therefore, BP-ANN was confirmed to be time-independent. Hence, the BP-ANN is a more fitting strategy than time-series forecasting methods [15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25] in this scenario. The advantage here is the avoidance of computations of time-dependent parameters during the training phase.
# Magneto-Acoustic Imaging in Biology
## 3 Mat-Mi  
### [PERSON] and [PERSON]'s Invention of Magneto-Acoustic Tomography with Magnetic Induction  
In 2005, [PERSON] directed the Biomedical Functional Imaging and Neuroengineering Laboratory at the University of Minnesota, where [PERSON] was a postdoc. There, [PERSON] and [PERSON] published their first article on \"Magneto-Acoustic Tomography with Magnetic Induction,\" which they abbreviated as \"MAT-MI\" [10]. The novel feature of MAT-MI that distinguished it from [PERSON]'s magneto-acoustic imaging of current and from [PERSON]'s reverse method for conductivity imaging was that [PERSON] and [PERSON] introduced current into the tissue by electromagnetic induction instead of injecting it through electrodes (Figure 4). The advantage of induction is that the induced current is more uniform. For example, suppose you were measuring the conductivity distribution in the brain. If you passed the current through electrodes on the head, most of the current would shunt through the high conductivity scalp with relatively little passing through the low conductivity skull to enter the brain. This shielding effect is minimized during induction, because the magnetic permeability of the skull, scalp, and brain are all about the same, nearly equal to the permeability of air or a vacuum.  
[PERSON] and [PERSON]'s technique required two magnetic fields: a strong (1 T) static field and a time-varying field. The varying field was applied in brief pulses with a rise time of about a microsecond. This changing magnetic field induced a pulse of eddy current in the  
Figure 4: Magneto-acoustic tomography with magnetic induction.  
sample that interacted with the static field to create a Lorentz force. This force acted as the source of an acoustic wave that was detected by ultrasonic transducers at various locations surrounding the sample. The arrival time of the acoustic signal indicated the distance of the source from the transducer.  
Their image reconstruction algorithm consisted of first determining the source of the signal (the dot product between the static magnetic field and the curl of the eddy current density), and then determining the conductivity by dividing the source by the dot product of the static and time-varying magnetic fields [10]. Again, the signal originated primarily from locations where there was a conductivity gradient. As a proof-of-concept, they used MAT-MI to image a coil of wire.
## 3 Results and Discussion
### Vessel Segmentation  
Figure 5 exhibits the MRA and MRV along with the filtered images. The MRA has high intensity in the large vessels, but the small vessels have similar intensity to the grey matter. After filtering, all vessels appear brighter which facilitates automatic vessel segmentation. The MRV imaging protocol was designed to remove non-vascular tissues inherently but the small veins are still difficult to detect. After filtering, all veins have high intensity and the filtered image is used for segmentation. The MRA imaging protocol was designed to enhance intensity for high flow areas which also includes signals from the superior sagittal sinus. The contamination from the vein was removed by excluding common voxels between MRA and MRV. After applying our vessel filter, it is shown that the non-vascular tissues were suppressed and small vessels are enhanced. The filter images then served as input for the centerline and diameter extraction. The vessel mesh was reconstructed using the diameter and centerline information shown in Figure 4.
# Numerical Simulation and Analysis of Heart-Aorta Fluid-Structure Interaction Based on S-ALE Method
## 2 Materials and Methods
### Establishment of Heart-Aorta FSI Model
Figure 3: Establishment of FE model for fluid domain. (**a**) Structured orthogonal three-dimensional mesh; (**b**) the generated blood model. The relevant literature has indicated that blood flows at high speeds in the aorta, its viscosity tends to stabilize, and the rheological behavior can be expressed as a Newtonian fluid [25; 32]. Blood was described as an incompressible fluid with a bulk modulus of 2500 MPa [23]. The blood density was 1050 kg/m\({}^{3}\) and the dynamic viscosity coefficient was 0.0035 Pa-s [13; 23]. In LS-DYNA, the fluid model requires a *MAT_NULL and Equation of State (EOS) to be jointly defined, which is suggested by LSTC (2021) [34]. The deviatoric viscous stress of the *MAT_NULL can be expressed as follows:  
\[\sigma^{\prime}_{ij}=2\mu\dot{\epsilon}^{\prime}_{ij} \tag{8}\]  
where, \(\sigma^{\prime}_{ij}\) is deviatoric viscous stress, \(\mu\) is the dynamic viscosity, and \(\dot{\epsilon}^{\prime}_{ij}\) is the deviatoric strain rate. The EOS of Gruneisen can effectively express the flow characteristics of blood, accurately describing the relationship between pressure and density of blood under dynamic loads. \[p=\frac{\rho_{0}C^{2}\zeta\big{[}1+\big{(}1-\frac{\gamma_{0}}{2}\big{)}\zeta- \frac{a}{2}\zeta^{2}\big{]}}{\bigg{[}1-\big{(}S_{1}-1\big{)}\zeta-S_{2}\frac{ \zeta^{2}}{\zeta+1}-S_{3}\frac{\zeta^{3}}{(\zeta+1)^{2}}\bigg{]}^{2}}+(\gamma _{0}+a\zeta)E \tag{9}\]  
\[\zeta=1/V-1 \tag{10}\]  
where, \(p\) is the pressure, \(\rho_{0}\) is the blood density, \(C\) is the speed of sound in the water, \(S_{1}\), \(S_{2}\), and \(S_{3}\) are the unitless coefficients, \(\gamma_{0}\) is the unitless Gruneisen gamma, a is the first-order volume correction to \(\gamma_{0}\), \(E\) is the internal energy, and \(V\) is the relative volume.
## 3 Geodesic Paths on Surface Mesh
```
Input: Point \(\mathbf{p}_{0}\), starting Direction \(\mathbf{t}_{0}\), geodesic path length \(\mathrm{L}\), surface mesh \(\mathrm{M}\) Output: Geodesic path sequence \(SEQ\) (sequence of line segments), endpoint \(\mathbf{p}_{e}\), endpoint normal vector \(\mathbf{n}_{e}\), endpoint tangent vector \(\mathbf{t}_{e}\) \(\mathbf{p}\leftarrow\mathbf{p}_{0}\) \(\mathbf{t}\leftarrow\mathbf{t}_{0}\) \(l\gets L\) // initialization of p, t vectors and the path length remaining \(SEQ\leftarrow\emptyset\)// Geodesic Path Sequence while\(l>0\)do // Find set S of all triangles that contain p \(\mathrm{S}\leftarrow\emptyset\) forall triangles \(T\in M\)do ifisPointInTriangle(\(\mathbf{p}\), \(T\))then \(S\gets S+T\) // adding T to the set S // Calculation of normal vector and also change tangent vector accordingly \(\mathbf{n}\leftarrow\mathit{calculateNormal}(S)\) \(\mathbf{b}\leftarrow(\mathbf{n}\times\mathbf{t})/\|\mathbf{n}\times\mathbf{t}\|\) \(\mathbf{t}\leftarrow(\mathbf{b}\times\mathbf{n})/\|\mathbf{b}\times\mathbf{n}\|\) // Intersection triangle Create a triangle I with vertices \(\mathbf{p},\mathbf{p}+l*\mathbf{t}+l*\mathbf{t}\tan\theta_{s}*\mathbf{n}, \mathbf{p}+l*\mathbf{t}-l*\tan\theta_{s}*\mathbf{n}\) \(ls\leftarrow\) intersection of triangle I with all triangles in S (there will only be one intersection line, or none) ifno Intersection foundthen // Boundary has been reached, compute last segment \(\mathbf{p}_{1}=\mathbf{p}+l*\mathbf{t}\) \(\mathbf{t}_{1}=\mathbf{t}\) \(ls\leftarrow\) Line segment \(\mathbf{p}\mathbf{p}_{1}\) \(SEQ\gets SEQ+ls\) // Adding line segment ls to SEQ \(l=0\) else // Place line segment ls into the sequence \(\mathbf{p}_{1}\leftarrow\) other point of ls (\(\mathbf{p}\) is one point) \(\mathbf{t}_{1}=(\mathbf{p}_{1}-\mathbf{p})/\|\mathbf{p}_{1}-\mathbf{p}\|\) \(SEQ\gets SEQ+ls\) // Adding line segment ls to SEQ \(l=l-\|\mathbf{p}\mathbf{p}_{1}\|\) // Update p and t vectors for next loop \(\mathbf{p}\leftarrow\mathbf{p}_{1}\) \(\mathbf{t}\leftarrow\mathbf{t}_{1}\) // After the loop ends, assign the outputs \(\mathbf{p}_{e}\leftarrow\mathbf{p}\) \(\mathbf{t}_{e}\leftarrow\mathbf{t}\) \(\mathbf{n}_{e}\leftarrow\mathbf{n}\) // SEQ has already been updated with all the lines segments return \(SEQ\), \(\mathbf{p}_{e}\), \(\mathbf{n}_{e}\), \(\mathbf{t}_{e}\)
```  
**Algorithm 3**Generating Geodesic path sequence given input mesh, starting point, direction and desired path length
We note that the curve follows the surface, and it either completes within the mesh or upon reaching the boundary a last straight line is drawn off from it in the direction of the last direction vector up to the required length. In these cases, the end point of the geodesic path is outside the mesh.
## 3 Results and Discussion
### Analysis of PET Degradation
#### 3.2.1 Fourier Transform Infrared Spectroscopy Results
An analysis of the results of this study showed a growing trend in the carbonyl index compared to the control sample in all phases of the applied treatments, with the most severe treatment (T12) having the highest percentage increase at 8.33%. Notable changes were observed in the evolution of the carbonyl index after applying the treatments. The influence of temperature on the increase in the carbonyl index occurred from the first phase of the treatments (T7), in which the carbonyl index began to grow more evidently. Generally, an increase in the carbonyl index compared to the control sample was observed in all phases of the applied treatments for PET O04 and PET O1. On this occasion, the carbonyl index was higher in the initial phases of the treatments in PET O1, while \begin{table}
\begin{tabular}{l c c} \hline \hline
**Group** & **O04** & **O1** \\ \hline T0a & 2.14 \(\pm\) 0.08 & 2.14 \(\pm\) 0.07 \\ T1 & 2.22 \(\pm\) 0.05 & 2.16 \(\pm\) 0.05 \\ T2 & 2.23 \(\pm\) 0.04 & 2.16 \(\pm\) 0.07 \\ T3 & 2.17 \(\pm\) 0.08 & 2.16 \(\pm\) 0.03 \\ T4 & 2.16 \(\pm\) 0.06 & 2.17 \(\pm\) 0.36 \\ T5 & 2.15 \(\pm\) 0.05 & 2.15 \(\pm\) 0.04 \\ T6 & 2.27 \(\pm\) 0.15 & 2.20 \(\pm\) 0.09 \\ t-Student (\(\alpha\) = 0.05) & 0.797 & 0.520 \\ \hline T0b & 2.48 \(\pm\) 0.21 & 2.49 \(\pm\) 0.15 \\ T7 & 2.52 \(\pm\) 0.26 & 2.54 \(\pm\) 0.23 \\ T8 & 2.56 \(\pm\) 0.26 & 2.57 \(\pm\) 0.19 \\ T9 & 2.63 \(\pm\) 0.16 & 2.59 \(\pm\) 0.18 \\ T10 & 2.65 \(\pm\) 0.17 & 2.64 \(\pm\) 0.26 \\ T11 & 2.67 \(\pm\) 0.25 & 2.63 \(\pm\) 0.27 \\ T12 & 2.69 \(\pm\) 0.27 & 2.67 \(\pm\) 0.22 \\ t-Student (\(\alpha\) = 0.05) & 0.249 & 0.250 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Carbonyl index of PET containers of mild olive oil (O04) and intense olive oil (O1). PET O04 increased the carbonyl index in the later phases. This fact indicates that the plastic used in the O1 samples is more resistant to high temperatures, making it a higher-quality material. Finally, comparing the obtained results in A and B, the storage period was key to increasing this index, being higher in both PET O04 and PET O1 in B.
## 2 Materials and Methods
### Data Extraction  
Initially, two authors (I.K. and C.K.) independently screened the titles, keywords, and abstracts of all 41 retrieved publications. In cases of disagreement during the selection process, a third independent reviewer was consulted to resolve conflicts and establish consensus. For each included article, the following information was extracted: author, year of publication, methodology, e.g., ML, robotics, VR, or other, sample characteristics, e.g., number of participants, therapy sessions, or case studies, type of data used, e.g., sensor-based, motion tracking, or other, therapeutic context, and key outcomes.