esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussions
### Data Processing and Validation
#### 3.2.2 Flame Analysis  
The image processing steps in the algorithm for flame front detection and estimation is shown by an example at TDC (15\({}^{\circ}\) CA after Ignition Timing) in Figure 8. This flame front extraction process involves the use of both automated, in-house developed algorithm and manual visual inspections of the captured images. The region bounded by the piston and the pent-roof is selected as the area of interest. First, the images are inverted and background subtraction is applied. A combination of median and Gaussian filters is then applied to eliminate artefacts due to laser scattering effects from PIV seeds. An appropriate image intensity range and results of filtering is verified by visual inspections and binary conversion using most appropriate threshold is finally applied to extract the flame front. The derived flame front is once again confirmed via visual inspections to eliminate obvious errors due  
Figure 7: Effects of the flame front on derived velocity from combustion PIV at the flame front. PIV image and schematics of the analysis (**left**) and plots of the intensity across the measurement area on one line as well as the velocity calculated from different interrogation area (**right**).  
to laser intensity fluctuations. The resulted flame front is used to derive the projected flame area, the flame centroids in the vertical plane, the equivalent flame radius R and global flame-growth speed dR/dt. Here, the flame centroids are defined as the position of the centroids of the burned region. The equivalent flame radius R is defined as the radius of a circle having the same area as the burned area. The global flame-growth speed dR/dt is calculated from the difference of equivalent flame radius between two time points. The accuracy of the burned region detection and flame front derivation has an accuracy of around \(\pm 1\) mm which correspond well to the 1.5 mm resolution of flow-field PIV data. An example of flame front boundary is found in the bottom of Figure 8. The flame front analysis will be limited to the early flame period from the first detected signal to when the flame completely fills the measurement area and pent roof.
## 2 Materials and Methods
### Conditional Generation with Electron Beam Parameters  
In this study, we focus on estimating the conditional multidimensional probability distribution of parameters that characterize the three types of particles of ionizing radiation present in the beam: photons, electrons, and positrons. In a typical phase space, approximately 99.50% of particles are photons, 0.48% are electrons, and positrons constitute the remaining 0.02%. In our phase space study, we consider only the probability distribution of photon parameters, as other particles present in the phase space contribute a very small fraction to the total dose. In particular, we have verified, in simulations performed for volumes of water phantoms or of patient bodies, that removing all electrons and positrons from the phase space and replacing them with photons sampled from the same probability density distribution as that of photons already present in the phase space has no significant effect on the spatial dose distribution. In fact, the probability distributions of parameters of electrons or positrons can be learnt independently from the probability distribution of photon parameters; thus, training GAN models for particles other than photons and obtaining a mixture of all three types of particles is a straightforward extension of the present work.  
The aforementioned multidimensional probability distribution of parameters characterizing particles of ionizing radiation can, in principle, be conditioned on four parameters of the primary electron beam used in the MC simulation of dose delivery: the average energy of electrons colliding with the target, the full width at half maximum (FWHM) of the distribution of electron energies, the size of the focal spot, and the beam divergence [7]. Of these four parameters, the FWHM of the energy spectrum of the electron beam was previously found not to be identifiable [7] against standard laboratory measurements of dose distribution profiles in water phantoms (i.e., its value bears no significant effect on the shapes of these profiles), so, in this work, the default value of FWHM = 0 MeV is assumed.  
Photons in the phase space are described by six parameters: position coordinates \(X\), \(Y\) in the plane perpendicular to the beam axis; the direction of momentum \((M_{X},M_{Y},M_{Z})\), which is a unit vector; and the energy of the photon. The GAN models considered in this study are thus trained to learn the 6D probability distribution of the above six photon parameters conditioned on a 3D vector of parameters of the primary electron beam.
## 3 Methodology
### Dual Branch Spatio-Temporal Graph Neural Network with an Attention Mechanism
#### 3.2.2 Spatial Feature Extraction Based on Spatial Graph Convolution  
The extraction of an intricate spatial correlation is a prominent concern in cellular network traffic prediction. While a traditional convolutional neural network (CNN) can acquire local spatial characteristics, it is restricted to the Euclidean space. Due to the non-two-dimensional grid format, the CNN model fails to depict the complex topology of cellular traffic interchange between regions, resulting in an inadequate capture of spatial interdependence. Lately, the transition of CNN to a graph convolutional network (GCN),  
Figure 5: Structure diagram of the temporal block.  
which is capable of handling arbitrary structured data, has garnered considerable attention. The GCN model has proven successful in various applications, including document classification, unsupervised learning, and image classification [31, 32].  
In this paper, we called the GCN that dealt with spatial features as spatial graph convolution (SGC). Specifically, SGC aggregates the neighbor information and similarity of nodes by performing convolution operations on the graph. At each time step, SGC convolves the features of each node with the features of its neighbor nodes to obtain a new feature representation of the node. In this way, SGC can use the connection relationship between the nodes to capture the spatial dependence in cellular networks. In the DBSTGNN-Attt model, the input of SGC is a graph representing cellular network traffic, where the nodes represent the cellular traffic data detected by detectors in a region, the edges represent the connection relationship between regions, and the weights on the edges represent the similarity between the regions. By convoluting the graph, SGC can aggregate the features of each node with the features of its neighbor nodes to obtain a new feature representation of the node. The specific formula is shown in Equation (9).  
\[f(S,A)=\sigma\big{(}\hat{A}ReLU\big{(}\hat{A}XW_{0}\big{)}W_{1}\big{)} \tag{9}\]  
where \(S\) represents the attributed matrix, \(A\) represents the adjacency matrix, \(\hat{A}=\widetilde{D}^{-\frac{1}{2}}\widetilde{A}\widetilde{D}^{-\frac{1}{2}}\) denotes preprocessing step, \(\widetilde{A}=A+I_{N}\) is a matrix with a self-connection structure, and \(\widetilde{D}\) is the degree matrix, \(\widetilde{D}=\sum_{n}\widetilde{A}_{nm}\). \(W_{1}\) and \(W_{0}\) represent the weight matrix of each layer. \(\sigma(\cdot)\) and \(ReLU()\) represent the activation functions.
# An Effective Hybrid Routing Algorithm in WSN:
## 4 ACOHCM Routing Algorithm
### Hop Count-Classification Network Topology and Maintenance  
ACOMHC algorithm is developed considering both the ant colony optimization algorithm and the minimum hop count WSN routing algorithm. Before the ants start off, the algorithm examines the location information of each node in the network and uses the idea of hop count-classification in MHC algorithm as a reference to mark all the nodes by their hop count values. The definition for the hop count of a node is as follows.  
**Definition 1**.: _The hop count of the sink node is 0, the hop count of the other nodes is the smallest hop count among their neighbor nodes plus 1._  
After the hop count of each node is marked, the nodes in the whole network form a hop count-classification network topology as shown in Figure 1. The square at the center of the figure represents the sink node and the rest are ordinary sensor nodes. The number on the node indicates the hop count of that node. According to the definition of the hop count, any \(h\)-hop (\(h\geq 2\)) nodes cannot transmit data to the nodes whose hop counts are \(h\pm 2\) without starting power amplifier. So the best next hop nodes of a \(h\)-hop (\(h\geq 1\)) node must be \(h-1\) hop nodes without considering the energy of the nodes. Therefore, the hop count-classification topology can greatly reduce the number of alternative nodes when choosing the next hop node, which accelerates its convergence speed and reduce the time complexity of the algorithm.  
In addition, topology maintenance is responsible for the maintenance of the hop count-classification network topology. The process of topology maintenance will start when any network topology changes in the case of a sensor node failure or wireless link breakage. Any topology change will affect the nodes' hop count values dynamically. Hence, hop count values need to be updated timely. Correspondingly, the route also needs to be updated.
# A Strategy for Integrated Multi-Demands High-Performance Motion Planning Based on Nonlinear MPC
## 3 Hierarchical and Multi-Domains Strategy Based on Driving Demands
### Evaluation Functions of Driving Demands
#### 3.2.2 Collision Avoidance  
To ensure the interpretability, the collision risky area is evaluated by the risky ellipse area of OV (See Figure 6). The correspondent evaluation function is defined as:  
\[L_{colli}=1-||\textbf{R}\textbf{X}_{r}||_{F}^{2} \tag{7}\]  
\[\textbf{R}=\begin{bmatrix}cos(\theta_{i})&-sin(\theta_{i})&0&0\\ 0&0&sin(\theta_{i})&cos(\theta_{i})\end{bmatrix}\]  
\[\textbf{X}_{r}=\begin{bmatrix}\frac{X-X_{i}}{l_{a}}&\frac{Y-Y_{i}}{l_{a}}&0&0 \\ 0&0&\frac{X-X_{i}}{l_{b}}&\frac{Y-Y_{i}}{l_{b}}\end{bmatrix}^{T}\]  
where \(||*||_{F}\) is the frobenius norm operator. \(L_{dyna}\) is the evaluation function of whether the vehicle has risks of collision. \((X,Y),\ (X_{i},Y_{i})\) are the global cartesian coordinates of the SV centroid and \(i\)-th OV centroid, respectively. \(\theta_{i}\) is the yaw angle error between the OV and SV. \([l_{a},l_{b}]^{T}=\textbf{F}+\textbf{PV}\), in which \(\textbf{F},\textbf{P},\textbf{V}\) are:  
\[\textbf{P}=\begin{bmatrix}\cos(\theta_{i})&\sin(\theta_{i})&\cos(\theta_{i}) &\sin(\theta_{i})\\ -\sin(\theta_{i})&\cos(\theta_{i})&-\sin(\theta_{i})\cos(\theta_{i})\end{bmatrix}\]  
\[\textbf{F}=[\frac{\sqrt{2}}{2}L_{i}\quad\frac{\sqrt{2}}{2}W_{i}]^{T},\textbf{ V}=[(v_{x}-v_{xi})T_{r}\quad(v_{y}-v_{yi})T_{r}\quad\frac{(a_{x}-a_{yi})T_{r}^{2}}{2} \quad\frac{(a_{y}-a_{yi})T_{r}^{2}}{2}]^{T}\]where \(L_{i},W_{i}\) are the length and width of \(i\)-th OV, respectively. \(v_{xi},v_{yi}\) are the longitudinal and lateral speed of \(i\)-th OV in the vehicle coordinate of SV. The directions are shown in Figure 6. \(a_{xi},a_{yi}\) are the longitudinal and lateral acceleration of \(i\)-th OV in the vehicle coordinate of SV. \(T_{r}\) is a time constant.
## 4 Online Learning of Repeated Games  
We consider the problem of multi-channel power allocation under a repeated confrontation scenario in this section. In the actual satellite communication anti-jamming scenario, there may be many instances of confrontations between \(r\) and \(m\). Finding ways of adjusting the next allocation strategy according to the previous confrontation results is a problem worth studying. In the problem of multi-channel power allocation under a repeated confrontation scenario, the resource budgets and channel values of each game are the same as in the initial stage, and both parties allocate power resources to the selected channel at the same time. After a complete confrontation process is over, \(r\) obtains the losses of this confrontation, that is, the value (vector) of the channels successfully interfered with by \(m\). Additionally, from the loss of selected action, the loss caused by some other actions is calculated (this is the side observation, see Section 4.2 for the specific explanation). The goal of \(r\) is to achieve the smallest expected regret after multiple rounds of complete confrontation. In this case, \(r\) usually needs to continuously and dynamically learn and adjust the trade-off between exploiting the current actions and exploring the new actions.  
We aim to design an online resource allocation algorithm that minimizes the expected regret of \(r\) while maintaining good operational efficiency. For each confrontation in the repeated confrontation scenario, we first model the problem of multi-channel power allocation based on the BG model. Then, the SINR (Equation (1)) is used as the criterion to judge the outcome of the allocation strategy of the opposing parties, it is more suitable for the application requirements of satellite communication. The main problem we face is how to design an efficient algorithm that minimizes the expected regret of \(r\) after repeated games.
# Accurate Needle Localization in the Image Frames of Ultrasound Videos
###### Abstract  
Ultrasound imaging provides real-time guidance during needle interventions, but localizing the needle in ultrasound videos remains a challenging task. This paper introduces a novel machine learning-based method to localize the needle in ultrasound videos. The method comprises three phases for analyzing the image frames of the ultrasound video and localizing the needle in each image frame. The first phase aims to extract features that quantify the speckle variations associated with needle insertion, the edges that match the needle orientation, and the pixel intensity statistics of the ultrasound image. The features are analyzed using a machine learning classifier to generate a quantitative image that characterizes the pixels associated with the needle. In the second phase, the quantitative image is processed to identify the region of interest (ROI) that contains the needle. In the third phase, the ROI is processed using a custom-made Ranklet transform to accurately estimate the needle trajectory. Moreover, the needle tip is identified using a sliding window approach that analyzes the speckle variations along the needle trajectory. The performance of the proposed method was evaluated by localizing the needle in _ex vivo_ and _in vivo_ ultrasound videos. The results show that the proposed method was able to localize the needle with failure rates of 0%. The angular, axis, and tip errors computed for the _ex vivo_ ultrasound videos are within the ranges of 0.3-0.7\({}^{\circ}\), 0.2-0.7 mm, and 0.4-0.8 mm, respectively. Additionally, the angular, axis, and tip errors computed for the _in vivo_ ultrasound videos are within the ranges of 0.2-1.0\({}^{\circ}\), 0.3-1.0 mm, and 0.3-1.1 mm, respectively. A key advantage of the proposed method is the ability to achieve accurate localization of the needle without altering the clinical workflow of the intervention.  
ultrasound-guided needle interventions; healthcare quality; ultrasound imaging; needle localization; machine learning +
Footnote †: journal: applied sciences  
Article
# Bayesian Updating for Random Tensile Force Identification of Ancient Tie Rods Using Modal Data
## 3 Computational Model  
The computational model, \(\hat{\mathcal{M}}\), used for the structural identification problem of tie rods, is illustrated in Figure 1. It consists of a uniform square cross-section Euler-Bernoulli beam hinged at both sections with two rotational springs and affected by a constant axial load, \(N\). The rotational springs are used to describe the effect of the anchorage length of tie rods in the lateral walls. In addition to the tensile axial load, the input parameters governing the free transversal vibration problem in Figure 1 are the length, \(l\), (i.e., distance between the two lateral walls), the Young modulus, \(E\), the mass density, \(\rho\), the square cross-section transversal dimension, \(a\), of the tie rod, and the stiffness of the left and right rotational springs, \(k_{l}\) and \(k_{r}\). The length, \(l\), is assumed to be a known measurable parameter, while all the other parameters are assumed as uncertain.  
It is worth noting that in all the existing literature research works about this topic, the computational model used to reproduce the static and/or dynamic response of ancient tie rods is marked by several simplifications, as deeply discussed in the Introduction section. These include--but are not limited to--the use of a restricted number of unknown parameters to be estimated and the assumption of symmetry in the stiffnesses of the lateral rotational springs introduced to describe the unknown boundary conditions. In the proposed paper, these limitations are overcome and non-symmeare considered. Indeed, the computational model is distinguished by a number of governing parameters greater than those typically used in the literature to address the same problem.  
The FE numerical model used in this work is based on two nodes with two-dimensional beam elements with two Degree of Freedom (DoF) at each node (i.e., deflection and rotation). A total of 50 beam elements is used to discretize the structure and perform modal analyses.
# A Comprehensive Survey of Recent Routing Protocols for Underwater Acoustic Sensor Networks
## 3 Routing Protocols for UWSNs
### Localization-Free Routing Schemes
#### 3.2.1 Localization-Free Routing Schemes with Mobility Consideration  
This section surveys the localization-free routing schemes that consider the limited or free mobility of sensor nodes, relay nodes, and/or sink nodes.
## 4 Assumption Involved in CS-SR
### Filtration of Candidate Service in CS-SR  
The user sent the requirement and priority to the decision-making system. The D.M.S. will check the user requirements from the available service list and will return the candidate service, and will store their feedback collected from the service provider. The filtration of the required candidate services is shown in Algorithm 1.  
```
Input : \(U_{r},\;S_{j},\;P_{u}\) Ensure : candidate services \(CS_{j}\), feedback (\(t_{\text{n}}\)S\({}_{\text{n}}\));
1 : for all services in \(S_{j}\) list do
2 : if \(S_{j}\) found in a user-specified range of \(U_{r}\)
3 : add \(S_{j}\) to set of candidate services
4 : end if
5 : end for
6 : return candidate set and feedback to D.M.S.
```  
**Algorithm 1**Filtration of Candidate Services  
The above Algorithm 1 shows that every available service is checked to find if the service matches the user requirement, then it is shown in lines 1 and 2. In case if the available service falls in the required range, it is included in the candidate services set shown in line 3. Lastly, the candidate services with respective feedback are stored in D.M.S. for further action.
## 4 Results of the Real-Time Atmospheric Turbulence Measurement  
### Indoor Experiment  
The measurement system was deployed at the GLAO system of the NVST in October 2022. Before measuring natural atmospheric turbulence, we experimented with the measurement system using indoor laser point sources. A soldering iron was placed in the optical path to create heat-air turbulence. The position of hot air turbulence roughly corresponds to an optical altitude of about 2500 m. The results of the open-loop and closed-loop data are shown in Figure 9.  
The results indicate that the measurement results of the open and closed-loop data are highly similar. On this basis, it can be inferred that the system is capable of effectively measuring the stratified conditions of atmospheric turbulence under closed-loop conditions.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**J/\%**} & **1248** & **2496** & **3743** & **4991** & **6239** & **7487** & **8734** & **9982** \\ \hline \multirow{3}{*}{SLODAR} & Open & 95.6 & 93.0 & 93.6 & 93.9 & 95.7 & 94.0 & 91.2 & 88.5 \\  & Close & 96.8 & 92.5 & 92.9 & 93.8 & 97.9 & 93.3 & 92.2 & 81.1 \\ \multirow{3}{*}{S-DIMM+} & Open & 94.4 & 89.7 & 75.5 & 90.8 & 78.6 & 87.1 & 89.7 & 95.1 \\  & Close & 94.8 & 90.1 & 81.4 & 89.8 & 85.2 & 88.2 & 87.6 & 96.5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Quantification of measurement results using two methods.  
Figure 9: Measurement results of open-loop and closed-loop data under heat–air turbulence conditions: the upper graph shows the results of open data, the lower shows the results of closed data.  
Due to the width of the turbulent hot air, the measurement results are not distributed on a thin layer. Due to the folding of the optical paths in the GLAO system [4], some weak turbulence is detected at higher altitudes.
## 2 DBSCAN Algorithm and Sparrow Search Algorithm  
### DBSCAN Algorithm  
The DBSCAN algorithm is a density-based clustering algorithm introduced by [PERSON] et al. [5] in 1996. The core idea is to use the density connectivity to divide regions that are density-connected into a single cluster, while marking noise points as those that do not belong to any cluster. Therefore, this algorithm can discover clusters of arbitrary shape and is robust to outliers.  
The DBSCAN algorithm begins by randomly selecting an unvisited data point. It then examines whether enough points lie within the neighborhood of this point, defined by a radius Eps. Specifically, it checks if there are at least MinPts points within that area. When this condition is met, it marks the point as the core point, creates a new cluster, and recursively expands it: adding all unvisited points in the neighborhood of the core point to the candidate set. If the candidate points also meet the core point conditions, neighborhood is expanded to form clusters with direct density, reachable density, or connected density. If there are not enough neighboring points, the point is temporarily classified as noise and may be absorbed as a boundary point by clusters of neighboring core points in the future. All unclassified points are ultimately considered noise, while boundary points are assigned based on the cluster of core points they belong to. Upon completion of processing all points within a cluster, the algorithm moves on to the next unvisited point. This cycle continues until every data point is assigned to a cluster or marked as noise. Through this process, DBSCAN effectively identifies clusters of varying shapes and demonstrates some resilience to noise. The detailed steps of the DBSCAN algorithm are as follows:  
Step 1: Input the dataset, neighborhood distance value Eps, and neighborhood density threshold MinPts.  
Step 2: Traverse all sample points. Search for core sample points based on Eps and MinPts and add the core sample points to the core sample set.  
Step 3: Select a core sample point and find its density-connected sample points to form a cluster.  
Step 4: Find new core sample points and repeat Step 3.  
Step 5: Mark any sample points not included in any cluster as noise points.