esa-sceva/satcom-chunk-collection huggingface dataset:
# DDTMS: Dirichlet-Distribution-Based Trust Management Scheme in Internet of Things
## 3 Dirichlet-Distribution-Based Trust Management Scheme (DDTMS)
### Trust ValueunderDirect Observation  
Different from the categoriesof cooperation and non-cooperation of behavior types in Beta distribution and Gaussian distribution, in DDTMS, the interactive behaviors of sensor nodes are divided into three types: success, failure, and uncertainty. The uncertain type of behavior is caused by uncontrollable factors, which involve the occasional network noise, the instantaneous transmission delay, and so on. Here, we use \(DT_{i,j}\) to represent the trust value by the direct observation of node \(j\) to node \(i\), and use \(<\)\(S_{ij}\),\(f_{ij}\), \(u_{ij}\)\(>\) to representthe observation of node \(i\)to node \(j\), which including the success, failure, and uncertainty type behavior, respectively.  
In order to satisfy the principle of trust value, that is, \"hard to get, easy to lose\", first of all, the corresponding behaviors will be weighted \(<\)\(w_{s}\), \(w_{f}\), \(w_{u}\)\(>\) to limit the measurement of each behavior by custom. Secondly, the variation of the failed interaction in the historical interaction behavior is used as one of reference for the evaluation of the current trust value. Finally, the trust value of the node \(j\) is  
\[DT_{i,j}=\frac{\left(w_{s}s_{ij}+1\right)\cdot\left(1-\mu_{history}\right)}{w _{s}s_{ij}+w_{f}f_{ij}+w_{u}u_{ij}+3} \tag{8}\]  
where  
\[\mu_{history}=\frac{\sum\limits_{k=1}^{n}\left(f_{ij}\right)_{k}-\sum \limits_{k=1}^{n-1}\left(f_{ij}\right)_{k}}{s_{ij}+f_{ij}+u_{ij}} \tag{9}\]  
where, \(\mu_{history}\) is the ratio of the failure number to the total interactions number in the current round process. We take this parameter into the calculation of \(DT_{i,j}\). It can increase the impact of the failure number on the reduction of the trust value. This can better agree with the principle of trust value, that is, 'hard to get'. Meanwhile, the setting of various types of parameters in \(DT_{i,j}\) intensifiesthe impact of the failure behavior on the reduction of the trust value, while the successful behavior needs to be accumulated to a certain magnitude to enhance the trust value. For example, the \(<\)\(w_{s}\), \(w_{f}\), \(w_{u}\)\(>\) weights are set to \(<\)1, 1.5, 0.3\(>\), and then the increase in the number of failures can directly lead \(DT_{i,j}\) to a rapid decline. Therefore, compared with the increasing node trust value of normal interaction, the occurrence of failure behavior is more likely to impact on the change of trust value.
## 3 Numerical Results
### Deformation Characteristics under Different Compression Rates
#### 3.3.1 Deformation Characteristics of Node \(\mathrm{A_{e}}\)  
Figure 6 shows the equivalent stress and radial displacement of \(\mathrm{A_{e}}\) nodes under different compression ratios (10-15%) at a line speed of 320 mm/s. The majority of the partial layout in Figure 6b is a stacked Y-axis offset line map with dimensionless and physically meaningless vertical coordinates, only showing the differences between different curves.  
The radial displacement trend of the \(\mathrm{A_{e}}\) node at each compression rate was the same. As it approached the loading plate, it produced a radial displacement far from the roller axis and gradually increased in a stepped shape, contrary to the negative Poisson's ratio effect of the honeycomb structure in the in-plane uniaxial compression experiment. This may be due to the fact that the tensile expansion effect of the negative Poisson's ratio structure is more pronounced under quasi-static compression compared to dynamic load [45]. After  
Figure 5: Radial displacement of each node in cell column I at 320 mm/s and 15% conditions.  
Figure 6: Equivalent stress and radial displacement of node \(\mathrm{A_{e}}\) under different compression ratios at a line speed of 320 mm/s: (**a**) equivalent stress and (**b**) radial displacement.  
making contact with the loading plate, the radial displacement of node \(\rm A_{e}\) was limited. When the radius of the roller at node \(\rm A_{e}\) was perpendicular to the loading plate, the radial displacement of node \(\rm A_{e}\) reached its minimum value, which is the difference between the outer contour radius of the roller and the compression amount. After leaving the loading plate at node \(\rm A_{e}\), the radial displacement returned to its initial value and remained unchanged. In addition to the minimum radial displacement affected by compression, the difference between the curves is mainly reflected in the stepped increase stage of the radial displacement value before the \(\rm A_{e}\) node makes contact with the loading plate. Compared with Figure 4a, it can be seen that the above stage is the stage where the \(\rm A_{e}\) node produces the maximum equivalent stress peak. From the locally enlarged image in Figure 6b, it can be seen that the radial displacement curve was constantly in frequent small oscillations, and within the experimental range, the amplitude of small oscillations gradually decreased as the compression rate increased.  
The compression rate had little effect on the stress variation characteristics of the \(\rm A_{e}\) node. The \(\rm A_{e}\) node's stress was positively correlated with the compression rate during the entire roller rotation process. Due to curve oscillation, there was no strict correlation between the stresses at \(\rm A_{e}\) nodes in the \(\rm A_{e}\)-C to \(\rm B_{e}\)-R range. As the compression rate increased, the third stress curve peak value gradually decreased in the \(\rm\alpha\)-C to \(\rm\beta\)-R range.
## 3 Theory and Methods  
### System Constructions  
The crystal structure (PDB entry: 4 EFL) of the GppNHp-bound WT HRAS, obtained from the Protein Data Bank (PDB) [73], was utilized as a template to replace the GTP analog GppNHp with authentic GTP, resulting in the generation of the GTP-bound WT HRAS. Meanwhile, A59 and K117 were substituted with E59 and R117, respectively, forming the GTP-bound A59E and GTP-bound K117R. The initial coordinates of the GDP-bound A59E HRAS were taken from the PDB, and its entry was 7 JI [33]. The E59 in 7 JI was mutated to A59 to form the GDP-bound WT HRAS. Subsequently, the residue K117 was replaced by R117 to derive the GDP-bound K117R HRAS. A magnesium ion Mg\({}^{2+}\) was located at the same site in both crystallographic structures and remained in the constructed systems. The protonated states were checked and assigned to each residue with the program H++ 3.0 [78]. The missing hydrogen atoms in the crystal structures were added using the Leap module in the Amber 20 package [79; 80]. The ff195B force field [81] was employed to produce the force field parameters of the WT and mutated HRAS. The GDP and GTP force field parameters were obtained using the general Amber force field (GAFF2) [82; 83]. The Austin Model 1 method with bond charge correction (AM1-BCC) [84] was adopted to derive the atomic charges of two ligands, GDP and GTP, through the Antechamber tool in Amber [85]. All systems were immersed in an octahedral, periodic box of water with a buffer of 10.0 A to reflect the solvent environment, in which the TIP3P model [86] was utilized to yield the force field parameters of water molecules. The appropriate number of sodium (Na\({}^{+}\)) and chloride (Cl\({}^{-}\)) ions were added to the water box in a 0.15 M NaCl salt concentration to neutralize the simulation systems, and the parameters of the Mg\({}^{2+}\), Na\({}^{+}\), and Cl\({}^{-}\) ions were assigned using the parameters from the works of [PERSON] et al. [87; 88].
## Materials, Methods and Results
### Materials  
The granular materials are referred to by location or name of the source quarry. Ayreshire was selected as the standard (or reference) material and was used as the benchmark against which the properties of the marginal and improved marginal materials were measured. Bluerock, Weipa and Stonemaster (Figure 1) were selected as marginal materials. All three marginal materials are produced commercially as select fills for infrastructure construction but were considered marginal for the purpose of FBB production. The gradation of each material is shown against the local specification requirements (MRTS 07-C) in Figure 2, and Table 1 summarises fine material plasticity properties, also against the local specification requirements.  
The reference material (Ayreshire) had a smooth gradation, well inside the specification limits and all material properties were within the limits, meaning it was not marginal. The Bluerock gradation was marginally coarser than required, but the aggregate particle properties were acceptable. In contrast, the Weipa material met the gradation requirements, although the fines content was high and the fines were reactive and plastic. The Weipa material is a natural gravel with rounded particles, making it typical of the very marginal materials commonly found in regional Australia. Finally, the Stonemaster material is sourced from the scalps from quarry blasting and includes a combination of crushed aggregate and naturally occurring fine material. The gradation is close to the fine limits of the specification above the 1 mm sized sieve, indicating that it generally lacks coarse aggregate particles. However, it is considered marginal because the fines are reactive.  
In all cases, the bitumen used was C170 supplied to the Australian standard (AS 2008) and is an unmodified bitumen similar to 100-150 penetration grade bitumen used in other jurisdictions. Because bitumen is treated to reduce the risk of foaming during transportation, 0.5% (by mass of bitumen) of a commercially available foaming agent, known as Interfoam, was added prior to foamed bitumen production. When Interfoam was added, the bitumen met the Australian requirements for foamability, including the expansion ratio and half-life [1].  
Figure 1: Reference and marginal granular materials.  
Figure 2: Granular material gradations.
## 2 Methodology
### Turbulence Model  
According to [PERSON] et. al [28], the thermal air flows in data centers are generally complex, with a Reynolds number, Re, based on the air velocity at the inlet air vent openings at around 1 m\(\cdot\)s\({}^{-1}\) with server racks with a height of around 2.4 m, a Re = 10\({}^{5}\) is obtained, which indicates that the air flow regime is turbulent. When the studies performed in CFD in data centers are analyzed, it can be observed that the model that provides more accurate forecasts makes use of the Reynolds Averaged Navier-Stokes (RANS) turbulence model, as was the case in [39].  
In this study, the turbulence \(k\)-\(\varepsilon\) standard model is based on the conservation equations of continuity, momentum and energy in conjunction with the turbulence \(k\)-\(\varepsilon\) standard model, where \(k\) and \(\varepsilon\) are the variables kinetic turbulent energy and turbulent dissipation, respectively. According to the authors of [39], this model is more suited to turbulent flow due to the way it calculates turbulent viscosity and conductivity. It is also the most widely validated and frequently used in commercial CFD codes. In addition, according to [PERSON] et al. [40], previous studies have shown that the \(k\)-\(\varepsilon\) turbulent model displays a better performance and results when compared to the SST, \(k\)-\(\omega\), RSM and RNG \(k\)-\(\varepsilon\) models.  
The mathematical expressions that characterize the model proposed and used in this study are based on the ones presented in [41] and are given as follows:  
\[\frac{\partial\rho k}{\partial t}+div(\rho Uk)-div\left[\left(\mu+\frac{\mu_{T }}{\sigma_{k}}\right)grad(k)\right]=P_{t}-\rho\varepsilon \tag{7}\]  
\[\frac{\partial\rho\varepsilon}{\partial t}+div(\rho Uk)-div\left[\left(\mu+ \frac{\mu_{T}}{\sigma_{\varepsilon}}\right)grad(\varepsilon)\right]=C_{1 \varepsilon}\frac{\varepsilon}{k}P_{t}-C_{2\varepsilon}\frac{\varepsilon^{2} }{k} \tag{8}\]  
where \(k\) is kinetic turbulent energy m\({}^{2}\)-s\({}^{-2}\), \(U\) represents the velocity vector in m\(\cdot\)s\({}^{-1}\), \(\mu_{T}\) represents the turbulent viscosity in kg\(\cdot\)m\({}^{-1}\)-s\({}^{-1}\), \(\sigma_{k}\) gives the turbulent Prandtl number for turbulent kinetic energy, \(P_{t}\) characterizes the turbulent energy production in kg\(\cdot\)m\({}^{-1}\)-s\({}^{-3}\), \(\varepsilon\) gives the turbulent kinetic energy dissipation in m\({}^{-2}\)-s\({}^{-3}\), the Prandtl number for turbulent energy dissipation is given by \(\sigma_{\varepsilon}\), and finally \(C_{1\varepsilon}\) and \(C_{2\varepsilon}\) are the constants of the production term and dissipation term, respectively. The abovementioned constants \(\sigma_{k}\), \(\sigma_{\varepsilon}\), \(C_{1\varepsilon}\), and \(C_{2\varepsilon}\) assume the values of 1, 1.3, 1.44, and 1.92, respectively, as in the work of [PERSON] et al. [41].  
Finally, the turbulent viscosity is defined by Equation (9) [41]:  
\[\mu_{T}=\rho C_{\mu}\frac{k^{2}}{\varepsilon} \tag{9}\]  
where \(C_{\mu}\) is the constant of the turbulent viscosity term. In this study, \(C_{\mu}=0.09\).
## 5 Results and Discussion
### Experimental Verification of Reliability Allocation
#### 5.1.2 Solved Using a SMA-DE Hybrid Algorithm

Table 11: Unit reliability allocation values based on PSO-DE hybrid algorithm. By observing Table 13, it can be found that when the parameter values change, the average calculation cost of the SMA-DE hybrid optimization algorithm remains at a low level, and the magnitude of the change is relatively small. From this, it can be concluded that the proposed SMA-DE hybrid optimization algorithm has strong robustness, and its optimization effect will not undergo drastic changes with changes in parameter values.
# A Survey of Precision Formation Relative State Measurement Technology for Distributed Spacecraft
## 3 Analysis of the Characteristics of Relative State Measurement Technology  
Accurate relative state information, such as relative position, relative pointing, and relative velocity, is the basis for the realization of the precise formation coordination control of spacecraft. The traditional non-autonomous method is to measure and locate a single spacecraft via a ground station. The disadvantage is that it is restricted by the ground station in the visible arc, resulting in limited measurement time, poor real-time performance, and bidirectional accuracy errors. It cannot meet the precise formation flying requirements of distributed spacecraft. Therefore, high-precision real-time state measurement technology emerges as the demand increases. The relative state measurement methods proposed include the GNSS relative measurement technology, inter-satellite radio measurement, high-precision radar measurement, optical-based autonomous measurement, and composite measurement. Autonomous measurement and non-autonomous measurement techniques are classified according to whether they need to rely on ground station equipment to complete the state measurements.  
The mission objectives of different formation spacecraft projects mean that the formation shape, orbital height, and measurement accuracy have different characteristics. Figure 14 shows the navigation techniques used by the formation spacecraft at different altitudes in space orbits. After years of development, there have been many mature devices that provide inter-satellite measurement and time synchronization, such as the autonomous formation-flying sensor (AFF) [28] in the DS-3 plan, the Star Ranger star rangefinder from AeroAstro, the Stanford Pseudolite transceiver crosslink (SPTC) developed by Stanford University, the Cross Link transceiver (CLT) of the Johns Hopkins University Laboratory, and the low-power transceiver(LPT) developed by NASA.
## 4 Test Results and Analysis  
### Analysis of Results under Ambient Excitation  
Prior to the initial displacement release test, the building is evaluated for environmental excitation. Acceleration sensors are used to capture signals generated by random vibrations. The sampling frequency is 200 Hz and the sampling time is 60 s, which meets the requirement for data smoothness [28; 29]. Figure 18 illustrates the acceleration time-history curve in the Y direction and the corresponding spectrum.  
DHMA modal analysis software is used to conduct modal analysis on acceleration signals in order to determine the structure's inherent vibration characteristics. Figure 19 illustrates the structure's first three vibration modes. We compared the first three natural vibration periods, damping ratios, and modes to the theoretical values for seismic structures under the same conditions. Table 3 summarizes the findings.  
As illustrated in Table 3, the isolation structure's self-vibration characteristics are similar to those of the seismic structure subjected to ambient excitation. While the natural vibration periods of the two structures are different, they both indicate the structure's high rigidity. The isolation structure's damping ratio is less than 5% of the seismic structure's theoretical damping ratio (Chinese Code [15]). The isolation structure's vibration modes  
Figure 19: Vibration modes of structures of different order: (**a**) first order mode; (**b**) second order mode; (**c**) third order mode.  
\begin{table}
\begin{tabular}{c} horizontal \\ movement \\ torsional movement \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 3: Analysis results of natural vibration characteristics.  
Figure 18: (**a**)Acceleration time-history curve (Y direction); (**b**) The corresponding spectrum.  
are identical to those of the seismic structure, with the first and second modes involving horizontal movement and the third mode involving torsional movement. It can be deduced that the isolation structure is sufficiently stable in its serviceable (wind load and micro vibration). Additionally, it demonstrates that the seismic structure model's design value is credible.
# Wavelet-Based, Blur-Aware Decoupled Network for Video Deblurring
## 5 Ablation Study
### Effectiveness of Components in the MSPF Module  
We evaluated the effectiveness of the MSPF module design. This module utilizes redundant structural information across multiple frames through a progressive fusion strategy and structure feature fusion block (SFFB). The experimental results reported in Table 6 show that removing the progressive fusion strategy and SFFB leads to PSNR decreases of 1.72 dB and 2.61 dB, respectively.  
The visual comparison in Figure 8 demonstrates the effectiveness of two key components in the MSPF module. Without the progressive fusion strategy, the vehicle's overall outline appears noticeably blurred. This highlights the importance of this strategy in structure reconstruction. When removing the SFFB module, the vehicle's contours show motion trailing effects. This indicates that efficient multi-frame temporal information fusion is crucial for achieving ideal deblurring results. With both components working together, the reconstruction results achieve the best visual quality. The output closely matches the reference image. These results validate the effectiveness of our MSPF module design.
# Implanting Intelligence in 5G Mobile Networks--A Practical Approach
## 5 Dynamic Network Slicing--A Dilemma for Highly Changing Environment  
Now that the reader is equipped with the necessary background and platforms, in this section, we highlight a few crucial challenges of network slicing that this study addresses:  
* _Challenge 1--Dynamically adapting application service quality requirements:_ As the world transits to adopting new solutions in different verticals e.g., remote surgery in eHealth, autonomous driving in transportation, smart homes, smart farms, etc, the service quality for network relevant service are subject to frequently changing owing to highly dynamic environments. Hence, a network slice for a longer period may not fall under optimal solutions.
* _Challenge 2--Unpredictable Resource and Service Quality Demands:_ The classical method of resource allocation, bearer formation (e.g., in LTE), and network slicing are driven by demand estimation, which has been relatively deterministic owing to the type of services being used so far. However, services of the future are expected to ask for dynamically changing network slices on shorter time quanta, which is usually the consequence of a fast-changing environment. The obvious challenge here is the accurate demand estimation.  
To address the above challenges, this study proposes the concept of a data-driven proactive network slicing mechanism detailed below.  
This research aims to build a predictive model to predict the optimal network slice for a requested service resultantly assisting in allocating enough resources to that slice based on the traffic prediction. The big picture of data-driven network slice prediction is shown in Figure 4. The proposed methodology is comprised of the following steps; (i) the network slice data are downloaded, provided by [PERSON] et al. [38]; (ii) the data are pre-processed and cleaned to build models; (iii) the cleaned dataset is then, divided into training and testing sets; (iv) the predictive models are developed for three machine learning algorithms to predict the network slice type; (v) testing data are then given to the model to test the model performance; (vi) finally, the results of all the algorithms are evaluated and compared.  
Figure 4: Big picture of Data-Driven Network Slice Prediction.
# Performance Evaluation of Ground AR Anchor with WebXR Device API
## 2 Background Theory
### Using Anchor for Ground Detection  
To provide AR to the desired location, holding an accurate anchor on space is crucial. An anchor is a reference point appearing at a position to float a virtual object in a plane that can be easily indicated in a spatial coordinate system. When an object needs to be directional in AR implementation, it is a concept that includes x, y, and z coordinates in space and the rotation value of each axis. The methods for holding an anchor are largely separated into four types. The first is Image Anchor, which recognizes the image as a marker. The next is Ground Anchor, which recognizes the floor surface and implements it. The next one is point Cloud Anchor, which is implemented based on the characteristics of the point cloud. The last one is Object Anchor, which is generated by object recognition. Figure 2 shows a flow chart of how to recognize ground in a virtual environment. When the camera is looking at the real environment, if the camera is moved, point cloud information about the real environment is extracted using a Simultaneous Localization And Mapping (SLAM) algorithm. After that, the point cloud extracted by the RANdom SAmple Consensus (RANSAC) algorithm is sampled. A mesh is generated for the sampled point cloud. Thereafter, a normal vector orthogonal to the mesh is calculated, and a normal vector is a dot product for an adjacent mesh to check whether it has a constant normal vector. Grounding is checked by specifying a threshold for the dot product normal vectors. Finally, the values identified as the ground are projected onto the screen using 2D to 3D projection [11; 12; 13; 14; 15; 16; 17].