esa-sceva/satcom-chunk-collection huggingface dataset:
# Effects of Aft-Body Extension Design on the Fundamental Characteristics of HWB Civil Aircraft
###### Abstract
The latter optimizes the layout of aircraft through parameters such as wing torsion distribution, cross-sectional airfoil, etc., under a fixed planform. Therefore, this kind of optimization is essentially a way of fine-tuning the aerodynamic shape based on a relatively fixed layout, and, thus, a high-order aerodynamic models can be used. Secondly, the static stability margin is a very important constraint in the MDO of HWB or BWB. Compared to the conventional TAW aircraft, the static stability margin has more influence on the resultant layout of HWB or BWB aircraft in the optimization due to the highly integrated wing-body. In this paper, since the influence of the aft-body extension design on the main characteristics of the aircraft needs to be studied, a multidisciplinary optimization design with planform variation should be chosen, and the design space should include kinds of planform parameters. As for the optimization constraint, this paper adopts the longitudinal static stability margin as the main constraint for the following reason: there is a clear correspondence between the static stability margin and the planform of the aircraft, and the configurations with different planform (including different aft-body lengths) can be obtained by setting different static stability margin constraints in the optimization design. Based on the above two premises, a physics-based MDO design planform was developed to obtain a family of configurations with different aft-body lengths of HWB by setting different static stability margins. Then, by evaluating, comparing, and analyzing the weight, basic takeoff, and landing performance, as well as flight control authority, of the aircrafts in the configuration family, we can obtain the influence of the aft-body extension design on the performance of the HWB civil aircraft, which provides advice for the layout design of the HWB civil aircraft.
# Comparing Augmented Reality-Assisted Assembly Functions--A Case Study on Dougong Structure
## 5 User Study
### Experimental Results
#### 5.3.2 Assembly Error  
The experimental result of assembly errors is summarized in Table 5. The average number of errors that occurred during the entire assembly was 4.25, 3.00, and 2.31, respectively. The ANOVA showed that only a marginally significant difference exists between the three results, F(2, 45) = 2.99, \(p~{}=~{}0.05\) (see Table 6). We then conducted the analysis on individual errors. The result showed a significant difference only in the fetching error, F(2, 45) = 4.92, \(p~{}=~{}0.012\). The post-hoc analyses using the Scheffe test showed that the paper group (M = 4.25, SD = 2.93) induced more errors than the two other systems: 3D viewer (M = 3.00, SD = 1.79), F(1, 45) = 0.439, \(p\) = 0.024 and AR (M = 2.31, SD = 1.92), F(1, 45) = 0.439, \(p\) = 0.047. The experimental result of each error is listed in Table 7. As shown in Figure 13, regarding the other three error types, although the AR-based system had consistently yielded the lowest number of errors when compared to the others, their difference was not statistically significant.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Media** & **Average** & **Standard Deviation** & **Min** & **Max** \\ \hline \multirow{3}{*}{Part recognition} & Paper & 81.75 & 40.783 & 38 & 165 \\  & 3D Viewer & 74.94 & 28.034 & 32 & 119 \\  & AR & 65.44 & 35.946 & 37 & 185 \\ \hline \multirow{3}{*}{Part fetching} & Paper & 72.81 & 37.422 & 38 & 190 \\  & 3D Viewer & 65.25 & 20.479 & 24 & 96 \\  & AR & 92.06 & 47.799 & 51 & 257 \\ \hline \multirow{3}{*}{Instructions reading} & Paper & 72.63 & 41.326 & 32 & 183 \\  & 3D Viewer & 76.19 & 38.840 & 38 & 166 \\  & AR & 74.94 & 23.561 & 46 & 133 \\ \hline \multirow{3}{*}{Assembly} & Paper & 129.94 & 40.321 & 78 & 218 \\  & 3D Viewer & 150.56 & 43.411 & 89 & 255 \\ \cline{1-1}  & AR & 213.94 & 50.301 & 145 & 347 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Experimental result of each step time in seconds.  
Figure 12: Time of each assembly step for the paper, 3D viewer, and AR assistance.
## 3 Overview of Rollups
### Zk-Rollups
#### 3.3.1 Zk-SNARKs  
The name zk-SNARK stays for Zero-Knowledge Succinct Non-Interactive Argument of Knowledge [15]. As suggested by the name, the proof size of zk-SNARK is very compact, regardless of the complexity of the statement. This makes such solutions widely adopted in currently available L2 projects due to their efficiency and small proof size in comparison to the length of the actual computation. Moreover, the non-interactive nature of zk-SNARKs eliminates the need for continuous communication between the prover and verifier in the proof generation process. However, zk-SNARK requires a one-time trusted setup for proof parameters generation, which can pose a security risk.  
Formally, a zk-SNARK protocol consists of three steps: Setup (S), Prove (\(P\)), and Verify (\(V\)), as defined below:  
* Setup \(S\)--The first step involves the key generation, which takes a secret parameter \(\lambda\) also known as toxic waste, to generate a proving key \(p\) and a verification key \(v\). \[(v,p)\longleftarrow S(1^{\lambda})\] (1) This phase relies on elliptic curve pairings and requires a trusted environment.
* Prove \(P\)--The prover generates a proof \(\pi\) using the proving key \(p\), the public statement \(y\) and the secret information also known as witness \(\omega\). \[(\pi)\longleftarrow P(p,y,\omega)\] (2)
* Verify \(V\)--The verifier can verify the public statement \(y\) by using proof \(\pi\) and verification key \(v\), without revealing any information about \(\omega\). Verification will return \(true\) if proof of the statement \(y\) is correct. \[V(v,y,\pi)=true|false\] (3)
# Cause Analysis and Accident Classification of Road Traffic Accidents Based on Complex Networks
## 4 Case Studies
### Comprehensive Importance Analysis of the Road Traffic Accident Cause Analysis Network
#### 4.4.2 Comparative Experiment of the Comprehensive Importance Evaluation Model  
Different node evaluation indicator inputs will result in different evaluation models. To explore the optimal performance of the model, we input various evaluation indicators for comparative experiments. The experiment comprised three groups. In Experiment A, we included the degree and clustering coefficient of nodes in the model to obtain comprehensive importance. In Experiment B, the model incorporated degree of node, intermediary centrality, and closeness centrality. Similarly, in Experiment C, the model took node degree, clustering coefficient, intermediate centrality and tight centrality as inputs. The results of the three experiments are compared in Figure 9.  
Figure 8: The comprehensive importance of nodes.  
Figure 9: Comparison of the comprehensive importance evaluation models of three complex networks for road traffic accident causation analysis.  
It can be seen from the Figure 9 that the peaks of Experiment A and Experiment C are lower than the peaks of Experiment B, and the troughs are higher than the troughs of Experiment B. It can show that Experiment B can highlight the overall characteristics of the comprehensive importance model of complex network synthesis compared with Experiment A and Experiment C. Experiment B has a higher degree of completion. When the degree of nodes, intermediate centrality, and closeness centrality are used as inputs for the comprehensive importance evaluation model, the model exhibits better performance. Therefore, Experiment B is taken as the analysis object of comprehensive important evaluation model.  
From Figures 8 and 9 we can see that 80% of the comprehensive importance of each influencing factor in Experiment B is between 0.5-1. It can be seen that most factors are closely related to the occurrence of accidents. When the comprehensive importance \(O\) value is 1, it shows that this factor must relate to the occurrence of the accidents.
# Analytical Model for the Duty Cycle in Solar-Based EH-WSN for Environmental Monitoring
## 4 Analytical Energy Consumption Model  
In this section we derive an analytical model for the energy consumed by a sensor node that is part of a data-gathering tree for environmental monitoring. We assume that this node supports the LPL implementation embedded in TinyOS. Its task consists of receiving and forwarding packets from other nodes as well as generating and transmitting own packets containing local readings. Given the lack of synchronization between any transmitting node and its corresponding receiver, the time lag between the start of their duty periods is completely random. Hence, energy consumption will also be subject to randomness, what means that we can expect a model of stochastic nature. In fact, the analysis that follows involves the derivation of complete statistical distributions for the two basic sources of energy consumption in a sensor network, namely transmission and reception. Next, the problem is addressed in detail.
# FlexBFT: A Flexible and Effective Optimistic Asynchronous BFT Protocol
## 4 FlexBFT Design  
This section delineates the algorithmic design of FlexBFT, a pioneering optimistic asynchronous BFT protocol. Distinguishing itself from other protocols of its kind, FlexBFT introduces a unique approach to perform persistently in back-up mode, augmented by supplementary mechanisms aimed at enhancing both its efficiency and scalability.
## 3 Design of Main Working Parts
### Seedbed Preparation Device
#### 3.1.1 Structure and Operation Principle  
As shown in Figure 3, the seedbed preparation device is mainly composed of a shaper, crushing roller, crushing roller-regulating rod, connecting rod of the shaper, adjusting rod of the shaper, etc. The structure size and spatial position of the shaper directly affect the molding of the ridge bed and the quality of collected soil in the ridge and furrow. The shaper is made of a 65 Mn steel plate with a thickness of 10 mm. The working surface is designed as a right-angle trapezoid structure. The angle between the hypotenuse and the long right angle is 75\({}^{\circ}\), which can ensure that the angle between the seedlings and the horizontal plane is 15\({}^{\circ}\). In order to ensure that the soil will not exceed the top of the shaper when scraping, the width of the shaper is set to be 330 mm and the length of the short side is 200 mm. The shaper is welded on the connecting rod of the shaper. In order to meet the requirements of different ground conditions, the operator can adjust the shaper up and down through the adjusting rod of the shaper and the adjusting bolt. The crushing roller is installed on the shaper through the adjusting rod of the crushing roller, and its axis is parallel to the bevel of the shaper. The height of the crushing roller can be adjusted through the crushing roller-adjusting rod and adjusting nut. The upper and lower height adjustment of the shaper and the crushing roller and the adjustment of the ground wheel of the operation machine jointly determine the depth of the shaper into the soil and the position of the crushing roller. The adjustable range of the shaper and the crushing roller is 0-150 mm and 0-250 mm, respectively.  
Figure 3: Structural Diagram of Seedbed Preparation Device. 1. Frame. 2. Adjustment lever of shaper. 3. Connection lever of shaper. 4. Adjusting rod of crushing roll. 5. Shaper. 6. Crushing roll. 7. Adjusting bolt. 8. Adjusting nut.  
Figure 4 shows the operation process of the seedbed preparation device. In the process of seedbed preparation, while the shaper and crushing roller move forward, the crushing roller crushes the soil and suppresses soil moisture. The seedbed and seedling soil-covering belt are formed after the soil is turned through the shaper. When the crushing roller contacts with the soil on the seedbed, it rolls and crushes the soil to overcome the friction force and cohesion between the seedbed and soil particles, compress the gravity water, attached water, and the space between soil particles in the soil layer, and discharge the air in the soil layer [13] so as to ensure the soil particles are closely arranged and the seedbed is flat, thus avoiding the instability of plant spacing, the overall deviation of the seedlings, and poor outcrop.
# Point of Interest Recognition and Tracking in Aerial Video during Live Cycling Broadcasts
## 3 Methodology
### Automatic Point of Interest Recognition and Tracking
#### 3.2.3 Object Mask Retrieval  
The heatmap generated in the previous step serves as the input for this stage. Here, the focus shifts to identifying the most salient object within the image. To achieve this, the most and least salient points are first located within the heatmap, which are then utilized to prompt a Segment Anything Model (SAM).  
SAM is a versatile model capable of generating segmentation masks for any object in an image based on various prompts, such as bounding boxes or points. Its promptable design enables zero-shot performance on object types not encountered during training. By providing the most salient point as a positive prompt and the least salient point as a negative prompt, SAM is guided to focus on the object of interest while avoiding uninteresting areas.  
The original SAM proposed by [PERSON] et al. has been improved in its second version, known as SAM 2, as detailed by [PERSON] et al. [39]. This updated version introduces multiple encoder sizes, which allows users to balance the model's size with its segmentation performance. Empirical evaluations have demonstrated that SAM 2, using the largest Hiera encoder, achieves superior results in segmenting points of interest.  
Figure 4 illustrates an example of this process. The green and red dots represent the most and least salient points, respectively. The blue mask visualizes the output of the large SAM 2, showing a well-segmented object of interest based on the provided prompt points. This demonstrates the effectiveness of the approach in accurately identifying and isolating the most salient object within the image.  
Figure 3: Example visualization of the saliency heatmap predicted by the UNISAL model.
## Data and Methods  
### LDAS-Monde System Components  
LDAS-Monde allows sequential assimilation of satellite derived land observations at a global scale. The assimilation is performed into the open-access SURFEX modelling platform of Meteo-France (SURFace Externalisee, [18]). It produces offline re-analyses of LSVs using (i) an LSM along with data assimilation techniques, (ii) observations, and (iii) atmospheric forcing. Those components of LDAS-Monde are briefly described below.
# Research on Optimization Design of Geometric Parameters of a Novel Frame-Embedded Track (NFET)
## 6 Conclusions  
The following important conclusions can be obtained, which can be used as a reference for optimizing the design of the modern tram track structure:  
1. As the width of the beam increases, the vertical displacement of the NFET structure does not change significantly. The LHP and TEB are the two most dangerous points in the whole construction process, and the tensile stress can reach more than 1.3 MPa. The former increases slowly, in the range of 185-240 mm, while the latter achieves the minimum value at 240 mm. Therefore, it is recommended that the design reference value of the beam width is 240 mm.
2. When the thickness of the lower slab increases by 20 mm, the displacement or stress of most of the measuring points of the NFET does not change much (within 10%), and the increase point and the decrease point are roughly equal in number. It is worth noting that the LHP stress will increase with the increase of the thickness of the lower slab and is close to the design value of the axial tensile strength of C35 concrete. Therefore, it can be considered that the stress state of the track structure has not been improved by increasing the thickness of the lower slab. At the same time, in order to save manufacturing costs and consider factors such as construction convenience, it is recommended that the design reference value of the lower slab thickness is 80 mm.
3. When considering the cable and the drainage facilities, the displacement increase is generally within 10%, but for stress, only three measuring points have an increase of more than 20%, and they are all safer points (the maximum is 0.976 MPa), indicating that the pipeline design is not the decisive factor of the displacement and stress of NFET. Therefore, it can be considered that the cable and the drainage facilities are not enough to destroy the original stress state of the NFET structure, and the pipeline layout in the original design scheme is feasible.
4. The stress of the rail seats is generally greater than that of the cast-in-place concrete. It is recommended that, when the NFET structure is adopted, the intensity of the cast-in-place concrete should be greater than that of the prefabricated frame structure. At the same time, in order to reduce the generation of clutch joints, it is recommended to take measures to control the shrinkage of cast-in-place concrete, such as choosing cement with low drying shrinkage and pouring in sections during construction.
## 2 Computational Model and Numerical Method
### Governing Equation  
The heat generated by the battery can be expressed as:  
\[q=\frac{I}{V}(U_{o}-U)+\frac{I}{V}(T\frac{dU_{o}}{dT}) \tag{1}\]  
in which \(q\), \(V\), \(U_{o}\), \(U\), and \(T\) denote heat production rate per unit volume, volume, open circuit potential, voltage, and temperature of the battery, respectively. The first term on the right side of Equation (1) represents the ohmic heat generated by the internal resistance of the battery and the irreversible heat generated by the polarisation reaction. The latter term represents reversible heat, which can be positive or negative, depending on the direction of the current and the sign of the entropy coefficient (\(\mathrm{d}U_{o}/\mathrm{d}T\)).  
The energy conservation equation of the battery is as follows:  
\[\frac{\partial}{\partial t}\left(\rho_{b}C_{pb}T\right)=\
abla\cdot\left(k_{b} \
abla T\right)+q \tag{2}\]  
in which \(\rho_{b}\) indicates the density of the battery, \(C_{pb}\) indicates the heat capacity of the battery, \(k_{b}\) indicates the thermal conductivity of the battery.  
The Forchheimer-Brinkman-extended Darcy model of the porous media and volume-averaged technique is used to describe the flow and heat transfer characteristics in aluminium foam [37]. The continuity equation can be expressed as:  
\[\frac{\partial\rho_{f}}{\partial t}+\
abla\cdot\left(\rho_{f}\overrightarrow{v }\right)=0 \tag{3}\]  
where \(\rho_{f}\) denotes the fluid density, \(v\) denotes the average speed vector.  
Conservation of momentum can be expressed as:  
\[\frac{\partial}{\partial t}(\rho_{f}\overrightarrow{v})+\
abla\cdot\left( \rho_{f}\overrightarrow{v}\right)=-\
abla p+\
abla\cdot\tau+\rho\overrightarrow{g }+S \tag{4}\]  
where \(p\) represents the static pressure, \(S\) represents the source term that can be expressed as follows [38]:  
\[S=-(\sum C_{1}\mu\overrightarrow{v}+\sum C_{2}\frac{1}{2}\rho|v|\overrightarrow {v}) \tag{5}\]  
in which \(\mu\) is the fluid viscosity coefficient \(C_{1}\) and \(C_{2}\) are viscous resistance and inertial resistance, respectively [38], and can be expressed as:  
\[C_{1}=\frac{150\left(1-\epsilon^{2}\right)}{d_{p}^{2}\epsilon^{3}} \tag{6}\]  
\[C_{2}=\frac{3.5(1-\epsilon)}{d_{p}\epsilon^{2}} \tag{7}\]  
where \(\epsilon\) and \(d_{p}\) are the porosity and pore size, respectively.  
Energy conservation equation:  
\[\frac{\partial}{\partial t}(\epsilon\rho_{f}E_{f}+(1-\epsilon)\rho_{s}E_{s}) +\
abla\cdot\left(\overrightarrow{v}(\rho_{f}E_{f}+p)\right)=S_{f}^{h}+\
abla \cdot\left(k_{eff}\
abla T-(\sum h_{i}I_{i})+(\tau\cdot\overrightarrow{v})\right) \tag{8}\]  
where \(E_{f}\), \(E_{s}\), \(h\), \(j\), \(\tau\) and \(S_{f}^{h}\) are the total fluid energy, total solid medium energy, sensible enthalpy, diffusion flux, stress tensor, and fluid enthalpy source term, respectively. The effective thermal conductivity \(k_{eff}\) of the aluminium foam region can be calculated as the volume average of the air thermal conductivity and the thermal conductivity of aluminium [38].  
\[k_{eff}=\epsilon k_{f}+(1-\epsilon)k_{s} \tag{9}\]
## 2 Six-Hole Aerostatic Rectangular Thrust Bearing Model  
As illustrated in Figure 1, the dashed area represents the solid domain of the porous air static rectangular thrust bearing, while the white section corresponds to the fluid domain. To explore its operational features, a model for the porous aerostatic rectangular thrust bearing was established, featuring dimensions such as the length (\(L\)) and width (\(S\)) of the gas outlet, the diameter (\(d\)) of the supply holes, their depth (\(H\)), the gap for the gas film (denoted as \(h\)), and the supply gas pressure (\(Ps\)). The configuration of the hole plate array is rectangular, with length \(a\) and its width at half the length. The principle of operation for the bearing involves a gas supply mechanism injecting pressurized gas into the bearing through the supply hole. This gas flows between the thrust plate and the bearing seat, creating a thin film of gas that supports the load. Finally, the gas is discharged at the bearing's edge into the surroundings, thus enabling the functionality of the gas static pressure direct thrust bearing [24].  
In order to set the structural parameters and working condition parameters of the porous gas static pressure rectangular thrust bearing, it can be seen from refs. [25; 26; 27] that in order to avoid the air film gap being too small, resulting in slipping of the air film gap, or the air film gap being too large, resulting in the bearing capacity being too low, the gas film thickness range needs to be 8 \(\upmu\)m-20 \(\upmu\)m. Too small of a diameter of the throttle hole will increase the difficulty of processing, and too large of a throttle hole will produce gas accumulation, so the diameter of the throttle hole is selected to range from 0.2 mm to 1.0 mm. Under normal circumstances, the gas supply pressure of the aerostatic thrust bearing is between 0.2 MPa-0.6 MPa. Bearing length and width are determined based on the structural parameters of the aerostatic guide rail, and the throttle holes are uniformly distributed across the bearing. Therefore, the basic parameters of the bearing are shown in Table 1.  
Figure 1: Physical model of a six-hole aerostatic rectangular thrust bearing.