esa-sceva/satcom-chunk-collection huggingface dataset:
# Coronary Centerline Extraction from CCTA Using 3D-UNet
## 2 Related Research
### Machine Learning-Based Centerline Extraction  
A single voxel wide centerline extraction was implemented in [16]. It employs a fully convolutional neural network (FCN), which generates distance maps and detects branch endpoints. A distance map is a volume with each voxel representing the probability that a voxel belongs to the centerline, proportional to the distance from the true centerline. Using this information, a minimal path extractor given a root point can extract the centerline tree.  
A state-of-the-art convolutional neural network (CNN) for automatic centerline extraction using an orientation classifier was implemented in [17]. Here, the authors feed the network small patches of size 19 \(\times\) 19 \(\times\) 19 (which is a rather small context). The architecture of the network compensates for the small input size by using dilation convolution kernels. The output of the network is a quantized direction of the centerline of the vessel in the patch and also the radius of the vessel. Generating a full centerline relies on an initial point placed anywhere inside the vessel. Using this, a tracking algorithm iteratively feeds patches in the network and constructs the centerline. The tracking stops when the confidence of the output direction is below a given threshold. Due to the nature of the tracking algorithm, it would seem that a CNN with recurrent layers [18] was a missed opportunity. This work was extended in [19] by improving bifurcation detection. The bifurcation angle is an important feature in CAD classification [6].  
Machine learning was combined in a hybrid approach with global geometry learning in [20] in order to improve the connectivity of the result of a CNN. Geometry-aware grouping was also added to improve the continuity of the vessel tree further.
# Dynamic Damage Mechanism and Seismic Fragility Analysis of an Aqueduct Structure
## Dynamic Response Analysis
### Case Model  
This paper takes an aqueduct of the SNWDP in China as an example. The body of the aqueduct is rectangular with a length of 60 m, with a bottom width of 15 m and a height of 8.95 m. The pier cap is 17.10 m long, 5.20 m wide, and 2.50 m high. The pier shaft is 5.50 m in height; and the pile cap is 16.10 m in length, 10.20 m in width, and 3.50 m in height. There were 71,172 three-dimensional solid elements used to build the body of the aqueduct and the foundation model. The model mainly uses the eight-node hexagonal Solid65 element, and the pull beam is simulated by the Beam 3 element (Figure 2). The main materials are shown in Table 1. In the dynamic calculation, an artificial viscous-spring boundary is applied to the foundation to simulate the seismic wave propagation process and reflect the energy dispersion at the boundary. The far boundary of the foundation is at a distance from the aqueduct of approximately 1.5-2 times the aqueduct in all directions. The dynamic response is analyzed by the commercial finite element software ABAQUS. In this study, a dynamic implicit analysis is performed.  
For the seismic analysis of the aqueduct structure, natural frequencies and modes are usually used to study its vibration characteristics. In the solutions to most seismic problems, the first sloshing mode of a structure makes a dominant contribution to all the other modes [2]. The natural frequency of the aqueduct selected in this paper is 1.162 Hz when the design water level is 7.50 m. Table 2 shows the first eight vibration modes of the aqueduct structure. Compared to the natural frequencies of other aqueduc of similar height, width, and length under wet mode conditions, this aqueduct's natural frequency and mode of vibration are similar to other aqueduc [33; 34]. For example, the first natural frequency of an aqueduct (50 m \(\times\) 14 m \(\times\) 7 m) is 1.199 Hz [35]. We refer to the results of a doctoral dissertation. The aqueduct structure is similar to it in this paper (single 30 m \(\times\) 20 m \(\times\) 8 m) [35]. In the dynamic model experiment, the first frequency of modal identification is about 0.9-1.2 Hz, and the finite element calculation result is 0.973 Hz. Therefore, the natural frequency results of the aqueduct structure in this paper can be used to validate the accuracy of the finite element model. In this study, Rayleigh damping is used for the analysis, and the damping ratio corresponding to the structural frequency is taken as 5%, \(\alpha=0.366\), and \(\beta=0.007\).
# Policy Compression for Intelligent Continuous Control on Low-Power Edge Devices
## 4 Methodology
### Distilling the Mean Action and Its Standard Deviation  
Some teachers perform better when actions are sampled stochastically, in which case the student should also learn the value of \(\sigma\) to perform optimally. By learning when precise action is required and when actions can be taken more stochastically, the agent can also build a deeper understanding of its environment. This could be seen as a different form of 'dark' knowledge, similar to the distribution of alternative actions in policy distillation for discrete action spaces ([PERSON] et al., 2015). Learning \(\sigma\) is even more important when using student-driven policy distillation, as this allows the student to explore more of the state space to learn multiple viable ways to obtain a high return, further enhancing its representation of the  
Figure 4: An illustration of the proposed distillation losses.  
task dynamics. In effect, it enables the exploration-exploitation trade-off to apply in a distillation context, where the mean action would focus purely on exploitation. This has the potential to increase generalization and robustness against changes in the environment. In turn, this enables the student to recover more gracefully from mistakes caused by the remaining distribution shift, leading to increased task performance, as reflected by a higher return. It can also help prevent the student from becoming stuck in local minima, where the teacher is less knowledgeable and provides inaccurate behavior. By encouraging the student to deviate more from this suboptimal strategy, it can move towards a region in the state space where the teacher's guidance is more effective.  
If \(\sigma\) is state-independent, this vector can simply be copied from the teacher to the student while continuing to train using Equation (2). Note that in this case, it would likely be beneficial to train the teacher using a state-dependent standard deviation instead. Otherwise, we include an additional term for distilling \(\sigma\), with \(\lambda\) a scaling factor to ensure that the loss for \(\sigma\) does not dominate over the one for \(\mu\) or the other way around:  
\[L(D,\theta_{S})=\sum_{i=1}^{|D|}\sum_{a\in A}\text{Huber}(\mu_{i,a}^{S},\mu_{i, a}^{T},1)+\lambda\cdot\text{Huber}(\sigma_{i,a}^{S},\sigma_{i,a}^{T},1) \tag{4}\]  
Since we have access to both \(\mu\) and \(\sigma\), these can be used to once again define a probability distribution. This also allows the student to sample actions based on N(\(\mu\), \(\sigma\)), which was not possible when trained using Equation (2). The Huber loss is still not optimally suited for defining a distance metric between two probability distributions however; it simply defines a distance between the two values separately, without any context of how they are used together.
## 3 Security of the Protocol
[19] show that once the density matrix of the qubit string is fixed, the probabilities \(p_{i}\) of [PERSON] accepting a revelation of the string of \(|\varphi_{i}\rangle_{\text{B}}\) satisfy \(p_{0}+p_{1}<1+\delta\)where \(\delta\) can be made arbitrarily small by choosing the security parameters appropriately, therefore [PERSON] has no successful strategy for such cheating. Second, in the case that [PERSON] does not follow the honest strategy at point \(O\), we can suppose that some operations are carried out at this point, but they leave her significantly uncommitted and retain as much freedom as possible in her choice of which qubit to unveil. We then further suppose that in the causal future of \(O\), all the strategies that the agent on the line segment \((O,Q_{i}]\) can follow form a set \(\{S_{iq}\}\) and the probabilities of successfully unveiling the qubit \(|\psi_{k}\rangle_{\text{X}}\) when the agent on the line segment \((O,Q_{0}]\) performs the strategy \(S_{0q}\) while the other agent on the line segment \((O,Q_{1}]\) performs \(S_{1j}\) is \(T_{qjk}(S_{0q},S_{1j},|\psi_{k}\rangle_{\text{X}})\). Similar to the discussion in the case that [PERSON] follows the honest strategy at point \(O\), by the results of Ref. [19] again, when the two agents follow \(S_{0q}\) and \(S_{1j}\) strategies, once the qubits B collapse to the states corresponding to the committed qubit \(|\psi_{k}\rangle_{\text{X}}\), there should exist no cheating strategy for [PERSON] to successfully unveil any other qubit unless the probability of passing the unveil phase is lower than a certain threshold \(\delta\) which can be made arbitrarily small by increasing the security parameters, therefore the sum of the probabilities of successfully unveiling all the qubits of the public fixed qubit set except \(|\psi_{k}\rangle_{\text{X}}\) when the two agents follow \(S_{0q}\) and \(S_{1j}\) strategies must be less than \(1-T_{qjk}+\delta\), resulting in \(\sum_{k}T_{qjk}<1+\delta\). Moreover, by Minkowksi causality, any operations carried out on the half-open line segment \((O,Q_{0}]\) cannot affect the operations carried out on the half-open line segment \((O,Q_{1}]\), and vice versa. The agent at \(Q_{1}\) can only guess the strategy \(S_{0q}\) that the agent at \(Q_{0}\) follows with probability \(t_{q}\), which must be agreed by the two agents at or before \(O\) or decided by the agent at \(Q_{1}\) alone, hence satisfies \(\sum_{q}t_{q}=1\). Therefore, the probability of successfully unveiling the qubit \(|\psi_{k}\rangle_{\text{X}}\) in the unveil phase is \(p_{k}=\sum_{qj}t_{q}r_{qj}T_{qjk}\), where \(r_{qj}\) is the probability of the agent at \(Q_{1}\) following \(S_{1j}\) when the agent at \(Q_{0}\) follows \(S_{0q}\), which must satisfy \(\sum_{j}r_{qj}=1\). Combining the three conditions \(\sum_{k}T_{qjk}<1+\delta\), \(\sum_{q}t_{q}=1\), and \(\sum_{j}r_{qj}=1\), we have \(\sum_{k}p_{k}=\sum_{qjk}t_{q}r_{qj}T_{qjk}<1+\delta\). This is consistent with our above mathematical definition of unconditionally secure of a quantum qubit commitment.
# Characterisation of Holographic Recording in Environmentally Stable Photopolymerisable Glass
## 2 Materials and Methods  
### Preparation of PHSG  
Detailed description of the PHSG development was reported in [14]. Briefly, the PHSG was synthesised by employing a four-step sol-gel process using two hybrid precursors: 3-trimethoxysillypropyl methacrylate (MAPTMS, purchased from Safic-Alcan) and a zirconium complex (ZCO) prepared from the chelation of zirconium (IV) propoxide (ZPO, purchased from Merck) and methacrylate acid (MAA, purchased from Merck). The first step involved the formation of the hybrid silicate matrix and the complexation of the transition metal. Therefore, MAPTMS (6.04 \(\times\) 10\({}^{-2}\) mol) was mixed with HNO\({}_{3}\) (4.44 \(\times\) 10\({}^{-2}\) mol) for 45 min. Simultaneously, in a separate container, ZPO (2.218 \(\times\) 10\({}^{-3}\) mol) was complexed by MAA (1.567 \(\times\) 10\({}^{-3}\) mol) and enabled to react for 45 min to form the ZCO complex. In the second step, the two hybrid components (hybrid silicate and ZCO solutions) were mixed together and left to stir for 10 min to enable the formation of a transparent and homogeneous sol. The third step included a second hydrolysis performed with deionized water (6.04 \(\times\) 10\({}^{-2}\) mol) leading to an optimum total hydrolysis rate of 55% (against the total alkoxide groups). The sol was then left to stir at ambient temperature for 24 h to enable the completion of the sol-gel reactions. At the fourth step, in order to increase the condensation ability of the hybrid nanoparticles, modification of the sol-gel was carried out by adding (3-aminopropyl)triethoxysilane (APTES, purchased from Merck) in a content of 3.608 \(\times\) 10\({}^{-3}\) mol. Along with APTES, a photoinitiator, Bis(eta.5-2,4-cyclopentation-1-yl) -bis(2,6-difluoro-3-(1H-pyrrol-1-yl)-phenyl) titanium (Irgacure 784, purchased from Ciba) was added in a content of 1.81 \(\times\) 10\({}^{-4}\) mol in order to make the material photosensitive. After another 10 h of stirring, the full reaction of APTES with the hybrid sol-gel matrix was completed. UV-Vis absorption spectrum of the sol dissolved in isopropanol showed a wide absorption band with the peak absorbance at 480 nm [14].
# Comprehensive Review of Traffic Modeling: Towards Autonomous Vehicles
## 6 Simulation Platforms for AV Traffic
### SUMO (Simulation of Urban MObility)
Their study proposed a technical concept to bridge the gap between current capabilities and future needs, underscoring the ongoing development and adaptation of SUMO to meet emerging challenges in urban mobility [218]. [PERSON] et al. explored the use of SUMO-generated mobility data in the context of Multi-access Edge Computing (MEC) systems. Their research developed and evaluated MEC simulation scenarios, providing insights into how mobility management strategies can be tested and optimized using SUMO [229]. [PERSON] et al. extended the application of SUMO into the realm of Urban Air Mobility (UAM), presenting an approach that includes modeling passenger demands, vehicle allocations, route planning, and vertidrome scheduling. This work represents a forward-looking application of SUMO, as it adapts to new transportation paradigms such as UAM [230]. In summary, SUMO is a powerful and adaptable tool that has been effectively applied across a wide range of urban mobility studies. The diverse case studies and research applications demonstrate SUMO's capability to evolve alongside emerging transportation technologies and needs. The relationships between these studies illustrate this tool's versatility and the continuous efforts to enhance its accuracy and applicability in simulating complex urban environments.
## 2 Theoretical Approach Establishment
### Influence-Line Identification Methods
#### 2.2.2 Rejection of Axle Effects  
To effectively eliminate the vehicle multi-axis effect in the quasi-static response of the bridge, Tikhonov regularization is introduced to establish the influence-line identification model and error \(\epsilon\) is introduced to establish the influence-line identification model after the E-VMD decomposition to process the time-range response **Rs**, where **Rs** is the quasi-static response of the bridge, **L** is the vehicle-information matrix, and \(\Phi\) is the influence-line coefficient of the bridge node.  
\[\textbf{R}_{\textbf{s}}=\textbf{L}\Phi+\textbf{e} \tag{12}\]  
The Tikhonov regularization method can effectively solve the pathologicalization of the mathematical equations of the loud line-identification model due to the error term by restricting the least squares expression through the L2 paradigm as a penalty function. The regularization equation is shown in Equation (13), where \(\lambda\) is the regularization coefficient.  
\[\min_{\Phi\in\mathbb{R}}[\|\textbf{R}_{\textbf{s}}-\textbf{L}\Phi\|_{2}^{2}+ \lambda\|\textbf{T}\Phi\|_{2}^{2}] \tag{13}\]  
Figure 2: E-VMD method flow.  
The regularized empirical matrix is:  
\[\mathbf{T}=\begin{pmatrix}1&-2&1\\ &1&-2&1\\ &&\ddots&\ddots&\ddots\\ &&&1&-2&1\end{pmatrix}_{(n-2)\times n} \tag{14}\]  
Following the establishment of the axle information matrix, the vehicle-information matrix is determined by sampling frequency, vehicle axle weight, number of axles, axle distance, vehicle speed, and other parameters. Vehicle speed and sampling frequency determine the number of columns of the vehicle-information matrix, and the sensor sampling frequency determines the number of its rows. Take \(k\)-axle vehicles as an example. For the vehicle front axle on the bridge and the last axle out of the bridge at the start and end of the timing, the vehicle-information matrix expression is shown in Equation (15), where \(A_{i}\) is the vehicle axle weight.  
\[\mathbf{L}^{\text{T}}=\begin{pmatrix}A_{1}&0&\cdots&A_{2}&0&\cdots&A_{k}&0& \cdots&\cdots&0\\ 0&A_{1}&\ddots&\ddots&A_{2}&\ddots&\ddots&A_{k}&\ddots&\ddots&\vdots\\ \vdots&\ddots&\ddots&\ddots&\ddots&\ddots&\ddots&\ddots&\ddots&\ddots&\vdots\\ \vdots&\ddots&\ddots&\ddots&\ddots&\ddots&\ddots&\ddots&\ddots&\ddots&0\\ 0&\cdots&\cdots&0&A_{1}&0&\cdots&A_{2}&0&\cdots&A_{k}\end{pmatrix}_{n\times m} \tag{15}\]  
Substituting the regularized empirical matrix into the regularized equation and solving \(\mathbf{\Phi}\) with the inverse of the first derivation, then the expression of the influence line is as follows:  
\[\mathbf{\Phi}=\left(\mathbf{L}^{\text{T}}\mathbf{L}+\lambda^{2}\mathbf{T}^{ \text{T}}\mathbf{T}\right)^{-1}\mathbf{L}^{\text{T}}\mathbf{R}_{\text{s}} \tag{16}\]
## 3 Simulation Analysis
### Simulation Results
#### 3.3.1 Two-Layer Model
The points 3, 11, 12 and 13 of the \(Z\) direction are examined, and their vibration intensities are compared as shown in Figure 17, demonstrating that the vibration of the edge is stronger than that of the center. This is because the stability of the intermediate structure is higher, and the vibration will be transmitted to the edge. Figure 16: Monitoring data of six monitoring points of the \(X\) direction in the front of the house. Figure 15: Adjusted response spectrum. Figure 11:0 Displacement nephogram in two-layer model when the transformer is at position #2. Figure 11:1 Displacement nephogram in two-layer model when the transformer is at position #4. Figure 11:2 Displacement nephogram in two-layer model when the transformer is at position #2. When the position of the transformer changes, the parameters measured at the monitoring points will change accordingly. Choose three typical monitoring points for analysis. Point 1 is located at the front left side of the house; point 12 is located at the middle of the house; point 20 is located on the diagonal line with point 1 and in simulation, is always the farthest point away from the vibration source. From Figure 23, the vibration intensity of point 1 reaches the maximum when the transformer is set at position #1, even larger than that when transformer is at position #4. Besides, when the transformer is at positions #2 and #5, the vibration intensity of the high-frequency component cannot be neglected compared with the other cases. Further examined vibration data within 150 Hz are shown in Figure 11:3  
Figure 11:4 Vibration data of the point 1 when the transformer is at different positions. Figure 11:5 Displacement nephogram in two-layer model when the transformer is at position #6. As is shown in Figures 23-26, the placement of the transformer has great impact on the vibration propagation. When the transformer is located at the central location, it impacts most on the structure that is right above it and less on the surrounding structure, but still cannot be ignored. For example, when the transformer is at positions #2 and #3, the vibration intensity of the monitoring point 12 is the strongest. When the transformer is located at one side edge of the house, it impacts more on all surrounding structures, but much less on the central position. For example, when the transformer is at position #1, the vibration intensity of the monitoring points 1 and 20 all reach the maximum, while that of the point 12 is smaller. It is also the reason that the central location is structurally stable and can transfer most of the energy produced by the vibration directly to the edge location.
## 2 UAV in Wireless Communication Networks
### Channel Characteristics
#### 2.1.2 Air-to-Air Channel  
The LoS link dominates an air-to-air channel characteristic, which reduces the impact of multipath fading. A small-scale fading can be neglected, hence a pathloss-dependent large-scale fading with LoS probability can be considered to model an air-to-air channel. Such a channel characteristic allows for the mmWave protocol, which is a new radio protocol in 5G networks [5], to be applied in UAV wireless networks, which achieves a higher capacity of the UAV-to-UAV wireless link, thereby easily co-operating in 5G networks. However, further studies are needed to properly model an air-to-air link and select suitable communication protocols for a given air-to-air channel characteristic.
## 5 Numerical Experiments
### Illustrative Cases
Then, we set up Scenarios IV-VI to test the ability of the proposed algorithm to handle some concentrated tasks. From the experimental results, it is concluded that the proposed algorithm is able to obtain better solutions under capacity constraints when dealing with high-density task sets. It is worth noting that no task in the task transmission route scheme is transmitted three times by nodes with the same satellite attribute (the experimental congestion threshold is 3), demonstrating the ability of the algorithm to avoid packet loss due to congestion. The specific experimental results are present in Table 9. From the planning results of the six scenarios, we find that the gap between the upper and lower bounds of the solutions is small in each scenario. This phenomenon does not indicate that the proposed algorithm is invalid, but proves that our proposed method for the construction of feasible solutions is very effective and is able to obtain an excellent upper bound value at the early stage of algorithm planning, which provides more information for the subsequent algorithm convergence process. Figure 6: Experimental results for illustrative cases. (**a**) Scenario I. (**b**) Scenario II. (**c**) Scenario III. (**d**) Scenario IV. (**e**) Scenario V. (**f**) Scenario VI.
## 3 Results and Discussion
### Rotary Tillage Cycle
The working point distribution of EM_T in the SMPS under the rotary tillage cycle is shown in Figure 17. It can be seen that more working points of EM_T were located in the low-efficiency region than in the motors in the DICPS, which resulted in the overall efficiency of the SMPS being lower than in the motors in the DICPS. The higher the efficiency of the SMPS being lower than in the motors in the DICPS, which resulted in the overall efficiency of the SMPS being lower than in the motors in the DICPS. Figure 16: Working points of power take-off (PTO) motor with different PDR. (**a**) PDR = 0.8 or 0.2; (**b**) PDR = 0.65 or 0.35; (**c**) PDR = 0.5.  
that of the DICPS. The overall efficiency of SMPS under the whole cycle was 76.3%, which was 2.9% lower than the worst configuration of the DICPS. From the simulation of two typical working conditions, we found that different parameter configurations of DICPS led to changes in overall efficiency and the maximum difference in the overall efficiency was 6.6% (under rotary tillage cycle), which illustrated that the design and optimization of the powertrain parameters had a great impact on the economic performance of the DMET. According to the simulation results, the optimal parameters of DICPS were PDR = 0.5, \(k\) = 1.6, with the PTO shaft connected to the EM_S through the reduction gear. The DICPS had a much higher overall efficiency than the SMPS, demonstrating that dual-input coupling powertrains can effectively improve the energy efficiency of electric tractors.