esa-sceva/satcom-chunk-collection huggingface dataset:
# From the Martian Surface to Its Low Orbit in a Reusable Single-Stage Vehicle--[PERSON]
## 3 Results
### Vehicle Subsystems
#### 3.2.6 Aerothermal  
During the reentry phase of flight, the vehicle experiences extreme heating from the Martian atmosphere at hyper-sonic velocities. With the vehicle assumed to be inclined at 50 deg during reentry, the stagnation point heat flux was calculated and assumed to be a uniform heat flux over the entire flow-facing surface. This is a reasonable assumption according to [PERSON] and [PERSON] (1988). To size the Thermal Protection System (TPS), the material is assumed to have similar properties to the Space Shuttle LI900 (Lockheed Missiles and Space Company, Sunnyvale, US) tiles (Dal  
low for repairs to still be carried even when some parts cannot be shipped from Earth fast enough.  
The crew and cargo compartments are sized. They are both located in the pressurised volume of the capsule, capable of bringing six people on board. This pressurised volume has been sized by computing the stress in a cone with cutouts for the window, docking port, and loading hatch. It is pressurised to 1 atmosphere. Such computation method has been taken from [32].  
The docking mechanism was selected to be Androgynous Peripheral Docking System (APDS) with some slight adjustments to account for the different mass of the chaser and target, as suggested by the International Docking System Standard [33].  
The main material for the oxidiser and fuel tank is IM7-977 Carbon Epoxy Composite [34]. For the tank design, a common bulkhead is used, with the fuel tank being reinforced by aluminium stiffeners. The relevant stresses in the tank structure have been computed following methods from [PERSON] [35]. Together, the tanks have a height of 8.485 m and a diameter of 6 m. The skin also has a minimum thickness of 2.9 mm.  
It was determined that four landing legs would be required for Charon to be able to sustain the loading during landing. These were designed according to a dynamic load analysis, using equations from [PERSON] and [PERSON] [36; 37]. Each leg is sized such that it would not fail under the off-nominal condition where one leg hits the ground before the rest at a vertical speed of 16 m/s, which would result in a maximum normal stress of 293.8 MPa.  
Finally, a vibrational analysis of the vehicle has been carried. Approximating the Young's modulus of the vehicle by a combination of the one of Aluminium and the Bulk modulus of Methane, and taking the mass of the vehicle into account, it is taken to be 13.87 GPa. The natural frequency in the longitudinal and lateral direction are then computed to be respectively of 78.19 and 14.81 Hz.  
Figure 13: Main capsule and tank structure.
## 2 CFD Simulations
### Numerical Details  
Figure 2 shows that the 2D axisymmetric computational domain has a length of 360 mm and width of 75 mm. We determined the geometry of the cylindrical ETDP with a flat shaped heated probe tip by referring to a previous experimental study [14]. The ETDPwith a height of 160 mm and radius of 25 mm was placed within the ice region, as shown in Figure 2. The temperature of the ice boundary, \(T_{ice}\), was set as 253 K. The top and side temperatures of ETDP, \(T_{top}\) and \(T_{side}\), were determined as 323 K. The present study changed the heated probe tip temperature, \(T_{tip}\), from 348 to 368 K to analyze the influence of the heated probe tip temperature on thin film formation near the ETDP and the corresponding ROP. A grid size of 0.5 mm was selected by the grid convergence tests, and the total grid number was 92,150. The present work used the pressure-implicit with splitting of operators (PISO) algorithm, which is widely used for solving solidification and melting problems to couple the pressure with the velocity [24; 25; 26]. The time-step was adaptively selected in the range from \(10^{-5}\) to \(10^{-3}\) s to reduce the computational time. Convergence criteria of the solution were set as the residual value of \(10^{-3}\) for continuity, \(10^{-4}\) for momentum, and \(10^{-8}\) for energy.
## 3 Results  
### Power Measurements  
The power output of the emitter, shown in Figure 3a, was measured for frequencies between 50 GHz and 1000 GHz using both a pyroelectric detector (Sensor- und Lasertechnik THZ 10) and a Golay cell (Tydex GC-1T). THz radiation from the emitter was collected and re-focused by two parabolic mirrors, as shown in Figure 3b. The pyroelectric detector was calibrated by the national institute for metrology of Germany (PTB) and was used to measure the emitter power at frequencies between 50 GHz and 500 GHz. Frequencies between 200 GHz and 1000 GHz were measured using the more sensitive Golay cell, and the overlapping measurements between 200 GHz and 500 GHz were used to calibrate the Golay cell responsivity. Measurements were taken in 10 GHz steps.  
Figure 1: (**a**) Photograph of the fibre-coupled PIN-PD emitter module. Optical fibre (blue cable) and SMB cable (black cable) for the electrical bias can be seen. (**b**) The H- and E-plane orientations are marked on the housing of the module. (**c**) Schematic structure of the PIN-PD emitter chip. The waveguide integrated PD is connected to an extended bow-tie antenna with a bracket-like electronic structure (inset). H- and E-field orientations are highlighted.  
Figure 2: Experimental set-up for mapping angular beam profiles. The source, mounted on a rotary stage, is rotated between \(-45^{\circ}\) and \(45^{\circ}\) with respect to the detector. The detector is placed on a linear translation stage to allow variation of the distance between the emitter and detector.
## 4 Calculation Example
### Analysis Results of the Construction Technology Application
#### 4.2.1 Analysis of Cable Net Traction Lifting
Comparisons and Effects  
Figure 30 shows an envelope diagram of CR radial deformation before and after adjusting. It indicates that the radial deformation of the CR reduced from between \(-161\) mm and \(149.6\) mm to between \(21.5\) mm and \(99\) mm, which means that the CR deformation was greatly improved and there was no internal shrinkage. The maximum equivalent stress of CR before and after adjusting is plotted in Figure 31, indicating that it was reduced to \(46\) MPa. Figure 31: Maximum equivalent stress of CR before and after adjusting. Figure 30: Envelope diagram of radial deformation of CR before and after adjusting. Figure 32 shows the internal forces of the axis 1 and axis 15 CR external supports under various calculation conditions before and after adjusting. During the traction lifting process, the internal force of the external support near the long and short axes, which was zero before adjusting, turned to negative through adjusting; this means that all CR brackets are in compression and local torsion can be avoided. Figure 33 shows that the traction cable forces are almost the same before and after adjusting, and there are no higher requirements for the traction cable due to the adjustment.
## 1 Introduction
In this context, the present study aimed to evaluate the influence of different substrates (dentin, zirconia, and CoCr alloy) and cements (neutral and light) on the final color of pre-shaded zirconia ceramics from two different commercial brands. Unlike previous research, which often examined these factors individually in standardized specimens, this study innovates by analyzing the combined interaction between the restorative material, the substrate, and the cement, providing a more comprehensive understanding of the elements affecting the final esthetic outcome of ceramic crowns. Additionally, by incorporating two distinct commercial brands of pre-shaded zirconia, this study enables a comparison between different industrial formulations, offering valuable insights for optimizing currently available materials. The use of quantitative color analysis through electronic measuring devices adds objectivity to the research, minimizing the subjectivity of traditional visual evaluations. Ultimately, by investigating new approaches to enhance the predictability of esthetic outcomes, the findings of this study may contribute to the development of more effective clinical protocols and color prediction algorithms [14], aiding dentists and dental technicians in selecting the most suitable materials for each clinical case. The null hypotheses were that neither the crown brand nor the substrate nor the cement has an influence on the final color of pre-shaded zirconia restorations. In addition, for helping in the clinical decision of what cement to use with specific brand/substrate combinations, an additional null hypothesis was added that stated that the type of cement has no effect on the color of each substrate/brand group.
## 2 Background and Related Work
### Model Checking
#### 2.1.2 Probabilistic Computation Tree Logic  
PCTL is an extension of Computation Tree Logic that introduces probabilistic operators, allowing for the specification of properties regarding the likelihood of events occurring in systems modeled by DTMC. The syntax of PCTL consists of state formulas and path formulas.  
State formulas are expressed as \(\Phi::=\text{true}\mid a\mid\
eg\Phi\mid\Phi_{1}\wedge\Phi_{2}\mid P_{\sim p }[\varphi]\), where \(a\) represents an atomic proposition, \(\Phi_{1}\) and \(\Phi_{2}\) are state formulas, and \(P_{\sim p}[\varphi]\) expresses the probability of path formula \(\varphi\) holding according to the operator \(\sim\) (e.g., \(\geq,\leq\)) and probability value \(p\).  
Path formulas are defined as \(\varphi::=X\Phi\mid\Phi_{1}U\Phi_{2}\), where \(X\) (next) asserts that \(\Phi\) holds at the next time step, and \(U\) (until) asserts that \(\Phi_{2}\) will eventually hold, and \(\Phi_{1}\) will hold continuously until that point. Additionally, F operator can be defined as \(F\Phi\equiv\text{true}\ U\,\Phi\), indicating that \(\Phi\) will eventually hold at some point in the future.  
PRISM enhances the capabilities of model checking by providing extended functionalities, particularly through the integration of reward structures within DTMC models [13]. This allows for the inclusion of quantitative assessments alongside probabilistic values, adding more depth to the analysis. By incorporating a reward structure, PRISM can assign numerical values to states or transitions, reflecting metrics such as costs or benefits associated with specific events. In this study, we extend the standard state formulas with the addition of a reward function, denoted as \(\rho\), to capture these quantitative aspects. The function \(\rho\) is applied to a state reward structure, where \(C\leq k\) represents the cumulative reward up to step \(k\), and \(F\,\Phi\) expresses the expected value until a particular condition is met.  
The state reward function is as follows:  
\[\rho:S\rightarrow\mathbb{R}_{\geq 0}\]  
And the state formula with reward is  
\[\Phi::=R_{=?}[C\leq k]\mid R_{=?}[F\,\Phi]\]  
In addition to this, PRISM allows for the use of filter operators, which provide flexibility in addressing the limitation that an initial state must be specified. Filter operators enable us to calculate averages or evaluate values that satisfy particular conditions. Specifically, the operator selects states that meet a given condition, _cond_, and then computes the average or value of the result of applying the formula \(\Phi\) to those states. The syntax used for this operation in our study is to calculate the average:  
\[filter(avg,\Phi,cond)\]  
And to return the value of a single state that satisfies the condition:  
\[filter(state,\Phi,cond)\]
## 2 End-to-End Speech Recognition
### Attention-Based Encoder-Decoder  
Compared with the CTC approach above, the Attention-based Encoder-Decoder model does not make any conditional independence assumptions and directly estimates the posterior. It employs two distinct networks: an RNN encoder network that transforms the input feature \(\mathbf{x}\) into hidden vectors \(\mathbf{h}\) and an RNN decoder network that transforms \(\mathbf{h}\) into output labels \(\mathbf{y}\). Using these, the posterior probability is:  
\[p(\mathbf{y}|\mathbf{x})=\prod_{u=1}^{u}p(\mathbf{y}_{u}|\mathbf{y_{1:u-1}},\mathbf{c_{u}}), \tag{3}\]  
where \(\mathbf{c_{u}}\) is the context vector that is a function of \(\mathbf{x}\) at time \(u\). \(\mathbf{U}\) is the length of output sequence that is allowed to differ from the input length \(T\). The \(p(\mathbf{y}_{u}|\mathbf{y_{1:u-1}},\mathbf{c_{u}})\) is obtained as :  
\[\mathbf{h_{t}}=Encoder(\mathbf{x}), \tag{4}\]  
\[a_{ut}=Attend(\mathbf{s_{u-1}},\mathbf{a_{u-1}},\mathbf{h_{t}}), \tag{5}\]  
\[\mathbf{c_{u}}=\sum_{t=1}^{T}a_{ut}\mathbf{h_{t}}, \tag{6}\]  
\[p(\mathbf{y_{u}}|\mathbf{y_{1:u-1}},\mathbf{c_{u}})=Decoder(\mathbf{y_{u-1}},\mathbf{s_{u-1}},\mathbf{c_ {u}}), \tag{7}\]  
where \(Encoder(\cdot)\) and \(Decoder(\cdot)\) are RNN networks. \(\mathbf{s}\) is the hidden state of Decoder. \(\mathbf{h}\) is the hidden vector generated by an Encoder. \(a_{ut}\) is an attention weight. \(Attend(\cdot)\) computes the attention weight \(a_{ut}\) using a single layer feedforward network:  
\[e_{ut}=Score(\mathbf{s_{u-1}},\mathbf{a_{u-1}},\mathbf{h_{t}}), \tag{8}\]  
\[a_{ut}=\frac{exp(e_{ut})}{\sum_{t^{\prime}=1}^{T}exp(e_{ut^{\prime}})}, \tag{9}\]  
where \(Score(\cdot)\) can either be content-based attention or location-aware attention.  
Content-based attention mechanism is represented as follows:  
\[e_{ut}=\mathbf{v^{T}}tanh(\mathbf{Ks_{u-1}}+Wh_{t}), \tag{10}\]where \(\mathbf{v}\) is a learnable vector parameter. \(tanh(\cdot)\) is a hyperbolic tangent activation function. \(\mathbf{K}\) and \(\mathbf{W}\) are learnable matrix parameters of the linear layers.  
Corresponding to the content-based attention, location-aware attention encodes both content factor \(\mathbf{s_{u-1}}\) and location information \(\mathbf{a_{u-1}}\). Thus, \(\mathbf{Score}(\cdot)\) is computed as follows:  
\[e_{ut}=\mathbf{v}^{\mathbf{T}}tanh(\mathbf{Ks_{u-1}}+\mathbf{Q}(\mathbf{F*a_{u-1}})+\mathbf{Wh_{t}}). \tag{11}\]  
where attention parameters \(\mathbf{W,v}\) have the same meaning as above. The operation \(\mathbf{*}\) denotes convolution. Meanwhile, \(\mathbf{F}\) and \(\mathbf{Q}\) are the convolution and linear layer parameters, respectively.
## 3 Results and Discussions
### Combined Non-Sinusoidal Pitching and Sinusoidal Plunging Motion
At the same time, \(C_{l}\) in an oscillation cycle at \(K_{\theta}=2\) is higher than that at \(K_{\theta}=1\) in general. Thus, from Figures 10 and 11, the case with \(K_{\theta}=2\) extracts more energy. To analyze the difference of the \(C_{l}\) curve at different non-sinusoidal pitching motions in Figure 11, we discuss the evolution of the vortex field around parallel foils in the cases of \(K_{\theta}=1\) and \(K_{\theta}=2\) during \(t/T=0\)-0.5. Comparing the vortex field in Figure 12a,b, it is found that the vortex structures at \(K_{\theta}=2\) are bigger than those at \(K_{\theta}=1\) during every moment. The bigger low-pressure area is formed because of the bigger vortex structure, increasing the lift force. So \(C_{l}\) at \(K_{\theta}=2\) is higher than that at \(K_{\theta}=1\) in Figure 11. The \(C_{l}\) curve in Figure 11b has a significant fluctuation near \(t/T=0.05\), combining with Figure 12b. It is concluded that a big trailing edge vortex sheds at this time, causing a sharp change of \(C_{l}\). Meanwhile, the leading edge vortex is formed earlier at \(K_{\theta}=2\). Figure 11: \(C_{l}\) curves of upper foil under different \(K_{\theta}\) at \(k=0.8\): (**a**) \(K_{\theta}=1\); (**b**) \(K_{\theta}=2\).
# Seismic Multi-Parameter Full-Waveform Inversion Based on Rock Physical Constraints
## 3 Results
### Numerical Experiments of the Layered Model
#### 3.2.4 Numerical Experiment of Standard Geological Model
To better analyze the impact of prior information on the inversion results, a velocity-density scatter plot was drawn, as shown in Figure 16. It can be seen that after adding the prior information of the petrophysical relationship, the inversion objective function incorporates a penalty term based on the petrophysical relationship. As a result, the inversion results converge towards the direction of the prior petrophysical relationship. In the scatter plot, this is manifested as the inversion data points being closer to the fitted line than the data points without constraints. Table 4 shows the comparison of the objective functions and model parameter reconstruction errors for the 1994 BP model between unconstrained inversion and petrophysical relationship constrained inversion. White circles represent the true model results, blue circles represent the inversion results under the unconstrained strategy, red circles represent the inversion results under petrophysical relationship constraints, and the green line is the fitted line of the true model. From the data in the table, it can be seen that the value of the inversion objective function, velocity reconstruction error, and density reconstruction error decrease by approximately 82%, 12%, and 25%, respectively. The value of the inversion objective function, and the inversion accuracies of the velocity and density, were all improved. In summary, introducing petrophysical relationships among different physical property parameters into the inversion objective function can reduce the degrees of freedom of the inversion problem, mitigate the ill-posed nature of the inversion, enhance the stability and accuracy of the inversion, and obtain a reconstructed model that is more consistent with  
Figure 15: Parameter sections of the FWI results in the 1994 BP model based on different constrained strategies. (**a**) Section of velocity at \(z=250\) m. (**b**) Section of density at \(x=800\) m. (**c**) Section of density at \(z=250\) m. (**d**) Section of density at \(x=800\) m.  
geological prior information. This constrained inversion method achieved good application results in both simple models and standard geological models.
# Secure and Efficient Authentication Scheme in IoT Environments
## 4 Cryptoanalysis of the Proposed Protocol
### Formal Security Validation Using the AVISPA Tool
The section summary on the CL-AtSe and OFMC backend indicates whether the protocol is SAFE, UNSAFE, or INCONCLUSIVE against all active and passive attacks [13; 25]. As depicted in Figure 7, our output summary section shows the safe terminology, meaning that the proposed protocol is safe against significant attacks. Furthermore, the section details specify that the condition under which the proposed protocol is safe or has been used for an attack, and the details also specify why the analysis was inconclusive. Moreover, the protocol section specifies the name of the protocol. The goal section indicates the main objective of the analysis. The backend section represents the name of the backend used. The statistics section specifies why the analysis was inconclusive. The attack-trace section specifies whether an attack is found; the trace of the attack is printed in the standard seq (i.e., Alice-Bob) format. It also represents how the attack has been performed in the protocol. Therefore, on the basis of analyzing the simulation result from Figure 7, we conclude that the proposed protocol is safe. * Replay attack: For the replay-attack check, the CL-AtSe and OFMC backend confirms whether the genuine agents can execute the specified protocol by inquiring about an inactive intruder. This backend provides the interloper with the information of some normal sessions among the genuine agents. The outcomes in Figure 7 show that our scheme is secure against replay attacks. * Active and passive attack check: The outline result for the CL-AtSe and OFMC backend shows that the proposed scheme is SAFE, meaning that the proposed scheme is secure against all active and passive attacks. * Dolev-Yao model check: For performing the Dolev-Yao model check, the CL-AtSe and OFMC backend additionally confirms whether there is a conceivable man-in-the-middle attack by an intruder. The outcomes show that our scheme satisfies the design properties and is also secure under this backend. Figure 6: Role specification for IoT devices. Figure 7: Role specification for server.
## 2 Materials and Methods
### Electric Potential Recording  
Hydrogenic tomato plants at the four-leaf stage were used for electrical potential recording. Borosilicate glass capillaries with an outside diameter of 1.5 mm and an inner diameter of 0.86 mm (Sutter Instrument Company, America) were fashioned into glass microelectrodes with a tip diameter of around 0.5 mm for intracellular cytoplasmic potential measurements. An APE-2 vertical micropipette puller (Narishige, Tokyo, Japan) was used to pull capillaries after heating. Microelectrodes were filled with 1 M KCl and inserted into the mesophyll cells of a leaf. The leaf was submerged with the basic solution (composed of 5 mM MES, 0.5 mM CaSO\({}_{4}\), 0.05 mM KCl, and pH 6.0) [13], which was performed as shown in Supplement Figure S1. Each electrode was connected to a differential amplifier. Two dual-channel amplifiers (FD223 and Duo/73, World Precision Instruments) were used to detect the signals. The hydroponic solution was replaced by the same solution added 2 mM Glu or 2 mM Gly, using the infusion tube, when the value of measured membrane potential was stable. Amplitudes are potential differences relative to the baseline before the changes.