esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### FE Model of a Lumbar Spine with Decompression and the NIPD  
To study lumbar spines with decompression, a defect model of the lumbar spine with bilateral laminotomies and partial disccetomy at L3-L4 was created in this study, as displayed in Figure 2.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Material**} & \multirow{2}{*}{**Element Type**} & \multicolumn{2}{c}{**Young’s Modulus**} & \multirow{2}{*}{**Poisson’s Ratio**} & \multirow{2}{*}{**Area (mm\({}^{2}\))**} \\  & & **(MPa)** & & \\ \hline \multicolumn{5}{c}{C\({}_{10}\) = 0.42} & C\({}_{01}\) = 0.105 \\ \hline
**Annulus Fibers** & 2-node LINK10 & & & \\ Outermost & & 550 & - & 0.76 \\ Second & & 495 & - & 0.5928 \\ Third & & 412.5 & - & 0.4712 \\ Innermost & & 357.5 & - & 0.3572 \\ \hline
**Endplate** & 8-node SOLID185 & 24 & 0.4 & - \\ \hline
**Ligament** & 2-node LINK10 & & & \\ \hline ALL & & 7.8 & - & 24 \\ PLL & & 10 & - & 14.4 \\ TL & & 10 & - & 3.6 \\ LF & & 15 & - & 40 \\ ISL & & 10 & - & 26 \\ SSL & & 8 & - & 23 \\ CL & & 7.5 & - & 30 \\ \hline \hline \end{tabular}  
* C10, C01 indicated two parameters of Mooney–Rivlin hyperelastic formation; d, material incompressibility parameter; ALL, anterior longitudinal ligament; CL, capsular ligament; ISL, interspinous ligament; LF, ligament flavour; PLL, posterior longitudinal ligament; SSL, supraspinous ligament; TL, transverse ligament.  
\end{table}
Table 1: _Cont._The designed NIPD is U-shaped and offers high stability because it has a larger contact area than conventional DIAMs, as depicted in Figure 3. The NIPD was implanted in the motion segment of L3-L4. To simulate the surgical procedure, the interspinous ligaments were removed. Since the NIPD is tied to the spinous process by using cables during surgery, bonded contact elements were added between the NIPD and spinous process to ensure that the NIPD did not move. Three FE models were constructed: the INT model (94,162 nodes, 112,174 elements), a lumbar spine model with bilateral laminotomies and partial disectomy at L3-L4 [the defect (DEF) model; 189,824 nodes, 228,538 elements], and a lumbar spine model with bilateral laminotomies and partial disectomy with the NIPD placed at L3-L4 (NIPD model; 199,969 nodes, 296,818 elements).  
Figure 3: FE model of the lumbar spine with the NIPD.  
Figure 2: Defect model of the lumbar spine. The L3–L4 disc was partially removed from the ground substance and nucleus. The posterior element at the L3 level was also partially removed. Note: the arrow indicates the location of removed parts.  
To study spinal stability, the ROM of the lumbar spine was determined using FE analysis. In general, the lumbar spine with a spinal implant exhibited iatrogenic effects at times, especially in adjacent discs and the facet joint. To investigate the influence of the NIPD on the spinal disc and facet joint, the disc stress and facet joint contact force were analyzed in this study.
## 2 Pipeline Deformation Calculation
### Derivation of Pipeline Deformation Formula  
In calculating the pipeline deformation caused by shield tunnel excavation, the pipeline is solved by simplifying it to a Euler-Bernoulli beam placed on a Pasternak foundation [19]. The differential equations controlling the longitudinal deformation of the pipeline are as follows:  
\[kw(x)-G_{c}\frac{d^{2}w(x)}{dx^{2}}+\frac{EI}{D}\frac{d^{4}w(x)}{dx^{4}}=p(x) \tag{10}\]  
Equation (10) is a fourth-order ordinary differential equation, which can be solved by the finite-difference method. The pipeline is discretized into \(n+5\) nodal units; there are 2 virtual nodal units at each end boundary, and each section is long, as shown in Figure 4. The finite-difference expression is as follows:  
\[kw_{i}-G_{c}\frac{w_{i+1}-2w_{i}+w_{i-1}}{l^{2}}+\frac{EI}{D}\frac{6w_{i}-4(w_ {i+1}+w_{i-1})+(w_{i+2}+w_{i-2})}{l^{4}}=p_{i} \tag{11}\]  
Firstly, the boundary conditions are determined to describe a fixed boundary, then two virtual nodes are set up according to the boundary conditions and calculation accuracy requirements, and finally, the displacement relationship between the virtual nodes and the neighboring actual nodes is established to satisfy the condition that the corner is zero.  
Figure 4: Pipeline discretization.  
Figure 3: Schematic of excavation (unit: m).  
According to the linkage between the displacement, shear force, and bending moment of Euler-Bernoulli beams, the following relational equation can be obtained:  
\[\left\{\begin{array}{l}M_{0}=M_{n}=-EI\frac{d^{2}w(x)}{dx^{2}}\\ Q_{0}=Q_{n}=-EI\frac{d^{3}w(x)}{dx^{3}}\end{array}\right. \tag{12}\]  
From Equation (12), the displacement expressions for the four virtual node units on both sides of the pipeline can be obtained as follows:  
\[\left\{\begin{array}{l}w_{-2}=4w_{0}-4w_{1}+w_{2}\\ w_{-1}=2w_{0}-w_{1}\\ w_{n+1}=2w_{n}-w_{n-1}\\ w_{n+2}=4w_{n}-4w_{n-1}+w_{n-2}\end{array}\right. \tag{13}\]  
Iteration of Equations (12) and (13) shows that Equation (10) is essentially an \(n+1\) algebraic equation. It is necessary to transform the higher-order differential equations corresponding to each node unit after discretization into continuous equations and realize the solution by constructing matrix equations, which are as follows:  
\[[p]=([k]-[G]+[K])[w] \tag{14}\]  
where  
\([p]\) is the additional load matrix acting on the nodes of the pipeline;  
\([k]\) is the matrix of soil bed coefficients;  
\([G]\) is the matrix of shear layer stiffness;  
\([K]\) is the matrix of pipeline stiffness;  
\([w]\) is the matrix of the deformation of each node of the pipeline.  
In order to verify the correctness of the method developed in this paper, we will verify it through numerical simulation by setting different working conditions to ensure the applicability of the method developed in this paper. These equations will be defined, constructed, and programmed using MATLAB to solve the values of the vertical deformation of the pipeline caused by the excavation of a two-lane shield tunnel, as shown in Figure 5.
# Mapping with Pleiades--End-to-End Workflow
## 4 Methods
### Digital Surface Model Generation
#### 4.2.6 DSM Assessment  
In remote sensing, it is common sense to compare two DSMs based on their differences. This leads to a distribution defined as \(\text{DSM}_{\Delta}=\text{DSM}_{\text{ref}}-\text{DSM}_{\text{current}}\), where in our case the reference is a LiDAR DSM and the current DSM the one retrieved from Pleiades image data. For judging the quality of the differences the statistical values mean and standard deviation are used. However, this only makes sense if the underlying distribution is a normal distribution. As observed by researchers this is actually not the case (cf. References [42; 120; 121; 122]). First, both DSMs include strong outliers such that the distribution of differences contains heavy tails. Second, the distribution itself is often non-Gaussian as, for instance, gaps between buildings are not reconstructed from satellite data causing a systematic bias next to the main lobe. In addition, the mean and standard deviation measures are non-robust estimates which suffer from both aspects. Therefore, in this case, robust estimates have to be used; in particular, the median (MED) value for the center of our normal distribution and the normalized median absolute deviation (NMAD) for the standard deviation. The NMAD is defined as as  
\[\text{NMAD}(\Delta H)=1.4826\cdot\text{median}(|\Delta H-\text{median}\, \Delta H|)\,. \tag{9}\]  
Figure 13 depicts a real example of this study where only errors between \(-10\) m and \(+10\) m are plotted. In particular, we show the distributions of height differences of a stereo DSM from test site Ljubljana (blue) with the classical normal distribution fits based on mean and standard deviation (red)  
Figure 12: DSM resampling: (**a**) representation of 3D point cloud as a multi-band image and (**b**) regridding options.  
and the robust fits based on median and nmAD (green). From this plot, it is clearly visible that the standard deviation of the classical fit is far too large which is a result of the non-robust estimate due to outliers. The NMAD based normal distribution is narrower and fits the input data better. The estimated central value of the default fit is drawn to the negative side. This obviously happens with the robust fit as well, however, less distinctive. This aspect has to be kept in mind when interpreting height differences and thus, will later be discussed in Section 5.
# Distributed Satellite Relay Cooperative Communication with Optimized Signal Space Dimension
## 4 Discussion  
### Result Analysis  
Whether it is interference alignment or signal space alignment, the DOF of messages \(W^{[j,i]}\) and \(W^{[i,j]}\) transmitted by a pair of nodes \(i\) and \(j\) exchanging information is generally the same, that is, symmetrical transmission. For example, the essence of the \(K\)-node multiple pairs bidirectional relay transmission algorithm is the process of eliminating \(K/2\) rings [30]. The essence of the \(K\)-node MIMO channel SSA and PNC algorithm is the process of eliminating \(K(K-1)/2\) rings [31]. The essence of the \(K\)-node multi-directional relay successful NC algorithm is the process of eliminating one \(K\)-ring [32]. Therefore, the common typical algorithms can be reduced to a special case or subset of the method proposed in this paper. This method has more general applicability.  
According to the cut-set boundary analysis of the degrees of freedom of the multi-directional relay channel, the total degrees of freedom in the transmission system can be  
Figure 11: The proposed technology is applied to remote sensing imaging. (**a**) Traditional MIMO transmission. (**b**) The proposed scheme.  
obtained as: \(d_{total}=\sum\limits_{i=1}^{K}\sum\limits_{j\in S_{i}}d_{i,j}\). The upper bound of the degree of freedom of the transmission node \(i\) is: \(\sum\limits_{j\in S_{i}}d_{i,j}\leq d_{i}^{upper}=\min\{\min\{M,N\},\min\{(K-1)M,N\}\}\). Therefore, the degree of freedom of the whole network is \(d_{total}\leq\sum\limits_{i=1}^{K}d_{i}^{upper}=\min\{KM,KN\}\). Meanwhile, it is necessary to consider the cut sent by \(K\) nodes to the relay and the cut sent by the relay to \(K\) nodes. The upper bound of the degree of freedom of the whole network is following:  
\[d_{total}\leq\min\{KM,KN,2 KM,2N\}=\min\{KM,2N\} \tag{19}\]  
Currently, only [33; 34] analyze the maximum reachable DoF in the entire domain, and other algorithms only give piecewise local maximum reachable DoF.
## 4 Volatile Aroma Compounds Biosynthetic Pathways and Related Enzymes
### Fatty Acids Pathway  
Fatty acids are major precursors of aroma volatiles in most fruit [4]. Fatty acid-derived straight-chain alcohols, aldehydes, ketones, acids, esters and lactones ranging from C\({}_{1}\) to C\({}_{20}\) are important character-impact aroma compounds that are responsible for fresh fruit flavors with high concentrations, and are basically formed by three processes: \(a\)-oxidation, \(\beta\)-oxidation and the lipoxygenase pathway [127]. Aroma volatiles in intact fruit are formed via the \(\beta\)-oxidation biosynthetic pathway, whereas when fruit tissue is disrupted, volatiles are formed via the lipoxygenase (LOX) pathway [128]. Nevertheless, some studies suggest that increasing availability of fatty acid, along with higher membrane permeability, during fruit ripening might allow the LOX pathway to become active in intact plant tissue and to function as an alternative to \(\beta\)-oxidation [30]. Many of the aliphatic esters, alcohols, acids, and carbonyls found in fruits are derived from the oxidative degradation of linoleic and linolenic acids [16]. In addition, some of the volatile compounds derived from enzyme-catalyzed oxidative breakdown of unsaturated fatty acids may also be produce by autoxidation [129]. Autoxidation of linoleic acid produces 9,13-hydroperoxides, whereas linolenic acid also produces 12,16-hydroperoxides [27]. Hexanal and 2,4-decadienal are the primary oxidation products of linoleic acid, while autoxidation of linolenic acid produces 2,4-heptadienal as the major product. Further autoxidation of these aldehydes leads to the formation of other volatile products [129].
# Late Fusion-Based Video Transformer for Facial Micro-Expression Recognition
###### Abstract  
In this article, we propose a novel model for facial micro-expression (FME) recognition. The proposed model basically comprises a transformer, which is recently used for computer vision and has never been used for FME recognition. A transformer requires a huge amount of data compared to a convolution neural network. Then, we use motion features, such as optical flow and late fusion to complement the lack of FME dataset. The proposed method was verified and evaluated using the SMIC and CASME II datasets. Our approach achieved state-of-the-art (SOTA) performance of 0.7447 and 73.17% in SMIC in terms of unweighted F1 score (UF1) and accuracy (Acc.), respectively, which are 0.31 and 1.8% higher than previous SOTA. Furthermore, UF1 of 0.7106 and Acc. of 70.68% were shown in the CASME II experiment, which are comparable with SOTA.  
deep learning; image processing; facial micro-expression; emotion recognition; vision transformer +
Footnote †: _sciences_
Since FME datasets were captured by a high-speed camera, we thought the influence of the optical flow in FME recognition would be different from general video recognition. Therefore, in ablation we conducted various experiments about that influence and empirically found the proper way to use the optical flow. As a result, our proposed model achieves the SOTA in the SMIC [1] and comparable performance in the CASME II [2] (see Table 2).
## 4 The Novel Circularly Polarized Folded Transmitarray Antenna Design  
### Design of NCPFT TA  
Generally, it's a challenge to design wideband cell of transmitarray for CPFTA, since the transmitarray cell should possess four characteristics:  
1. High transmission ratio: the energy of the feed can be radiated through the transmitarray and the impedance match is acceptable.
2. The manipulation of transmission phase: compensating the phase of the spatial path difference from feed antenna.
3. Polarized conversion: linearly polarized incident waves are converted into circularly polarized transmission waves.
4. Selectively polarized transmission: _x_-polarized waves can be transmitted while the y-polarized waves will be totally reflected.  
To simplify the requirements for the cell of transmitarray, sequential rotation technique [26, 27] is applied to design CPFTA for the first time. It is worth noting that the only difference between the proposed GCPFTA and NCPFTA is different arrangements of transmitarray. The novel design method can simplify the design difficulty significantly without adding any additional structure.  
\(2\times 2\) sub-array second-order sequential rotation is adopted [28], and the anisotropic patches of the transmitarray are arranged as shown in the Figure 14a. In order to realize radiation beam focusing, both the compensation of spatial path difference and sequential rotation phase are considered simultaneously. Based on Formula (4), the phase compensation can be expressed as:  
\[\phi(m,n)=\frac{2\pi f_{0}}{c_{0}}\cdot(F_{mn}-F)+rotation_{mn} \tag{6}\]  
The _rotation\({}_{mn}\)_ is the feeding phase of sequential rotation in unit (\(m,n\)). The bottom E-shaped patches of transmitarray are used for 1-bit phase compensation. The arrangement of E-shaped patch is shown in Figure 14b. Based on the above descriptions, NCPFTA is fabricated, assembled and tested, as illustrated in Figure 15.  
Figure 14: (a) The arrangement of \(24\times 24\) anisotropic patches with size of \(240\) mm \(\times\)\(240\) mm. (b) The arrangement of \(24\times 24\) E-shaped patches with size of \(240\) mm \(\times\)\(240\) mm.
# Simulation and Experimental Verification of the Thermal Behaviour of Self-Written Waveguides
###### Abstract  
In this work, we investigated the optical response of a self-written waveguide (SWW) in detail by heating the structure from room temperature up to 60 \({}^{\circ}\)C. Previous results indicated a decrease in the optical transmission with increasing temperature for certain waveguide parameters. Based on new experimental measurements, we have identified material parameters resulting in opposite behaviour. An experimental setup was conceived to verify these results. Hereby, we were able to show that we can adjust material parameters such as refractive index and the corresponding density of the material by adapting the curing time applied during the fabrication of the waveguides. This, in turn, affects the material's response during the heating process. We showed that a limitation of the external curing time changes the internal conditions of the SWW and the cladding in a manner that the numerical aperture increases with the temperature, which subsequently also results in an increase in the optical transmission. In this study, we explain this unexpected behavior of the SWW and point towards possible future applications.  
optical interconnects; self-written waveguides; optical simulation; refractive index measurements; thermal simulation; optical sensor +
Footnote †: journal: Applied Sciences
## 2 Methods
### Theory Analysis of Combiner Branch
#### 2.1.2 The Bandwidth Analysis  
Based on the preceding analysis, the intermediate section of the microstrip line and parallel-connected capacitor is illustrated below in Figure 2.  
The microstrip line has a characteristic impedance Z0, the electrical length is \(\beta\)l\({}_{2}\), the input impedance is Zin1, and the output impedance is Zin2. By treating this two-port network as a parallel configuration, the admittance matrices of the capacitor and microstrip line are calculated separately. Using the Kuroda identity transformation [15], the total admittance matrix is synthesized as follows:  
\[Y_{total}=\begin{bmatrix}-jY_{0}\cos\beta l_{2}+j\omega C&jY_{0}\sin\beta l_{2 }-j\omega C\\ jY_{0}\csc\beta l_{2}-j\omega C&-jY_{0}\cot\beta l_{2}+j\omega C\end{bmatrix} \tag{6}\]  
The  
The bandwidth \(\Delta f\) is defined as the frequency range where \(|\Gamma|<\Gamma_{\max}\). Assuming a small electrical length \(\beta\mathbb{I}_{2}\ll 1\) wavelength, the approximate bandwidth formula is as follows:  
\[\Delta f\approx\frac{C}{2\pi\beta\mathbb{I}_{2}\sqrt{\varepsilon_{eff}}}\cdot \frac{Z_{0}}{R_{e}(Z_{in2})+\frac{Z_{0}^{2}}{\omega_{0}\mathbb{C}}} \tag{9}\]  
where \(\varepsilon_{eff}\) is the equivalent dielectric constant, Re(\(Z_{in2}\)) is the real part of the load impedance, and \(\omega_{0}\) is the center angular frequency. Thus, the characteristic impedance \(Z_{0}\) is proportional to the bandwidth, while C and \(\beta\mathbb{I}_{2}\) are directly and inversely proportional to the bandwidth.
# Threat Analysis and Risk Assessment of a Driver Monitoring System
## 6 Risk Analysis
### Risk Evaluation and Interpretation
**Attack Feasibility Evaluation:** The feasibility of each attack was assessed based on access level, complexity of tools, and the attacker's expertise. Feasibility was categorized into four levels:  
* High (network level): Attacks requiring no physical access and are executable remotely, such as exploitation of network vulnerabilities (value 10);
* Medium (adjacent level): Attacks requiring logical or physical proximity but not direct access (value of 7);
* Low (local level): Attacks requiring direct access to the system (value of 4);
* Very Low (physical level): Attacks requiring specialized tools and physical interaction with system components (value 1). **Risk Score Calculation:** Each threat's risk score (\(R_{val}\)) was calculated as the product of its probability (\(P\)), derived from feasibility and its severity (\(S_{val}\)):  
\[R_{val}=P\cdot S_{val} \tag{3}\]  
This scoring mechanism provided a quantitative assessment, categorizing threats into risk levels (negligible, low, medium, high, or critical). The methodology clearly defined the thresholds for each level, ensuring consistency across evaluations. **Results and Risk Prioritization:** The analysis identified 115 threats, with the following breakdown:* Low Risk: 82.6%: These threats had minimal impact or low feasibility and primarily required minimal attention;
* Medium Risk: 13.9%: These threats targeted communication channels and processes, often involving Tampering or Spoofing;
* High Risk: 3.5%: These involved critical scenarios, such as Elevation of Privilege or DoS attacks, which required immediate mitigation. Table 2 provides an overview of the threats and their distribution across STRIDE categories, highlighting the predominance of Tampering, DoS, and Information Disclosure as key risks. **Risk Treatment:** Based on the risk assessment, specific mitigation strategies were employed:  
* Risk Reduction: Introducing or strengthening security controls to minimize residual risks (e.g., encryption and multi-factor authentication);
* Risk Retention: Retaining low-risk threats that pose a negligible impact on system functionality or safety;
* Risk Avoidance: Removing system features or functions that introduce unacceptable risks;
* Risk Transfer: Sharing risks with external entities, such as vendors or insurers, who could, for example, offer product liability coverage. This structured and weighted evaluation framework ensured a balanced approach to risk management, prioritizing safety and operational reliability while addressing potential financial and privacy concerns.