esa-sceva/satcom-chunk-collection huggingface dataset:
# Bi-Directional Point Flow Estimation with Multi-Scale Attention for Deformable Lung CT Registration
## 2 Related Work  
### Deformable Lung CT Registration  
Deformable registration of lung CT scans is a fundamental task in medical imaging, supporting a wide range of clinical applications such as respiratory motion modeling for radiotherapy [6; 21; 22] and quantitative analyses of the disease progression in chronic obstructive pulmonary disease (COPD) [5; 21]. By aligning inspiratory and expiratory scans, registration enables the computation of regional ventilation maps and volumetric changes over time [23]. These applications demand a high registration accuracy, especially in anatomically critical regions such as the lung lobes, bronchial trees, and vascular structures [24].  
However, lung CT registration poses unique challenges compared to that in other organs. The lungs undergo large, non-linear deformations between breathing phases, and the sliding motion at the lung--pleura interface introduces discontinuities that are difficult to capture using smooth deformation models. Furthermore, the CT intensity values vary with air content, making the standard intensity-based metrics less reliable [25]. Techniques addressing these issues include biomechanically inspired models such as mass- or volume-preserving registration [26; 27] which incorporate constraints on the local tissue conservation. In addition, the Demons algorithm [28], free-form B-spline deformation [29], and hybrid intensity-feature methods [30] optimize a similarity metric and a smooth deformation field. Although these methods have been widely evaluated on benchmarks (e.g., EMPIRE10 [3]), their reliance on iterative optimization and the voxel intensities limits both their scalability and robustness in complex respiratory motion scenarios.
# Validation of Sentinel-2, MODIS, CGLS, SAF, GLASS and C3S Leaf Area Index Products in Maize Crops
## 3 Validation Methodology
### Validation of the Hectometric and Kilometric LAI Products  
The spatial mismatch between the reference decametric LAI and the hectometric and kilometric LAI products is the main issue for the validation [60; 61]. In order to mediate  
Figure 2: Flowchart of the multi-resolution LAI products validation. First, the SL2P-based Sentinel-2 LAI product was validated and recalibrated with ground measurements from LAINet. The recalibrated SL2P LAI was compared with an empirically based LAI resulting from up-scaling LAINet measurements with Sentinel-2 reflectance data thought the iteratively re-weighted least squares algorithm (IRLS). This resulted in reference maps. Second, hectometric and kilometric LAI products were validated by comparison with the reference maps. Third, phenological metrics from hectometric/kilometric and reference LAI time series were extracted and compared.  
geo-mismatch and footprint influence, all the LAI values covered in the 5 km \(\times\) 5 km study area were averaged, which substantially improved the robustness of the validation at expenses of reducing the number of samples. To reduce the influence of temporal mismatch, only reference-retrieved LAI pairs within 8-day temporal difference were considered.  
Three statistical metrics--coefficient of determination (\(R^{2}\), Equation (1)), the root mean square error (\(RMSE\), Equation (2)) and the mean difference of the LAI products minus the reference LAI maps (\(BIAS\), Equation (3))--were used to assess the goodness-of-fit, accuracy and systematic deviation, respectively. These metrics are computed as follows:  
\[R^{2}=1-\frac{\sum_{i=1}^{N}\left(y_{i}-{y_{i}}^{\prime}\right)^{2}}{\sum_{i= 1}^{N}\left(y_{i}-\overline{y}\right)^{2}} \tag{1}\]  
\[RMSE=\sqrt{\frac{\sum_{i=1}^{N}\left(y_{i}-{y_{i}}^{\prime}\right)^{2}}{N}} \tag{2}\]  
\[BIAS=\frac{1}{N}\sum_{i=1}^{N}\left({y_{i}}^{\prime}-y_{i}\right) \tag{3}\]  
where \(N\) is the number of observations, \(y_{i}\) is the reference LAI for the \(i\)th observation, \(y_{i}^{\prime}\) is the LAI to be validated, \(\overline{y}\) is the mean value of reference LAI. The uncertainty threshold established by GCOS, i.e., max (0.5, 20%) [62], was employed as a benchmark to check if the LAI products meet the user requirements.
# Estimating Significant Wave Height from SAR with Long Integration Times
## 2 Estimating Significant Wave Height from SAR with Long Integration Times
### Scanning Distortion Calibration  
Next, the optimum refocused SAR image corresponding to each wave system is processed by the scanning distortion calibration, wavelength and propagation direction calculation of \(n\)th ocean wave and significant wave height estimation. At first, this subsection needs to initialize the \(n\)th wave system and the number \(Num\) of wave systems, where \(n=1\), and the \(Num\) is given by the number of local maximum value of \(PBR_{i}\) in the Section 2.3.  
In order to obtain the wavenumber vector \(k_{o}^{n}\) the \(n\)th wave system on the real ocean surface, it is necessary to calibrate the SAR image scanning distortion caused by the velocity bunching modulation. The optimum refocused SAR image corresponding to the \(n\)th ocean wave system is transformed into the two-dimensional wavenumber domain, and the wave number vector \(\mathbf{k_{s}^{n}}\) of the \(n\)th ocean wave system is obtained, which is expressed as  
\[\mathbf{k_{s}^{n}}=[k_{rs}^{n}\quad k_{as}^{n}] \tag{7}\]  
where \(k_{rs}^{n}\) and \(k_{as}^{n}\) are the range wavenumber and the azimuth wavenumber of the \(n\)th ocean wave system in the SAR image, respectively.  
The range wavenumber \(k_{ro}^{n}\) of the \(n\)th ocean wave system on the real ocean surface is equal to that \(k_{rs}^{n}\) of the \(n\)th ocean wave system in the SAR image. However, due to the scanning distortion, the azimuth wavenumber \(k_{ao}^{n}\) of the \(n\)th ocean wave system on the real ocean surface is typically different from that \(k_{as}^{n}\) of the \(n\)th ocean wave system in the SAR image [18; 25], which can be expressed as  
\[k_{ao}^{n}=k_{as}^{n}+\frac{\sqrt{gk_{o}^{n}}}{V} \tag{8}\]  
where \(g\) is the acceleration of gravity, \(k_{o}^{n}=\sqrt{k_{ao}^{n\ 2}+k_{ro}^{n\ 2}}\) is the wavenumber of the \(n\)th ocean wave system in the real ocean surface, and \(V\) is the platform speed. By solving Equation (8), the wavenumber vector \(k_{o}^{n}\) of the \(n\)th ocean wave system in the real ocean surface can be obtained, which is  
\[k_{o}^{n}=[k_{ro}^{n}\quad k_{ao}^{n}] \tag{9}\]
## 3 Wave Equations and Theoretical Wave Velocities
### Finite Permeability
#### 3.1.1 Compressional Wave  
The characteristic equation of the Equation (16) is:  
\[\left|K_{P}l_{P}^{2}-M\omega^{2}\right|=0 \tag{19}\]  
The dispersion equation can be obtained by unfolding Equation (19):  
\[(\lambda+2G)Q_{b}\left(\frac{l_{P}}{\omega}\right)^{4}-\left[\left(\lambda+2 G+\alpha^{2}Q_{b}\right)\rho_{m}-2\alpha Q_{b}\rho_{f}+Q_{b}\rho\right]\left(\frac{ l_{P}}{\omega}\right)^{2}+\left(\rho\rho_{m}-\rho_{f}{}^{2}\right)=0 \tag{20}\]  
The solution of Equation (20) is:  
\[\left(\frac{l_{P,1,2}}{\omega}\right)^{2}=\left(\frac{1}{C_{P1,2}}\right)^{2 }=\frac{\rho_{1,2}}{\lambda+2G} \tag{21}\]  
where,  
\[\begin{split}&\rho_{1,2}=\rho\left(b\mp\sqrt{b^{2}-ac}\right) \\ & a=\frac{\lambda+2G}{Q_{b}}\\ & b=\frac{1}{2\rho}\left(\rho-2\alpha\rho_{f}+\frac{\lambda+2G+ \alpha^{2}Q_{b}}{Q_{b}}\rho_{m}\right)\\ & c=\frac{\rho_{m}}{\rho}-\left(\frac{\rho_{f}}{\rho}\right)^{2} \end{split} \tag{22}\]  
Equation (21) is two complex solutions of Equation (20), respectively, where wave numbers \(l_{P1,2}\) and wave velocities \(C_{P1,2}\) are the theoretical wave numbers and velocities of two compressional waves in saturated soils, respectively. P1 and P2 represent the fast and low compressional waves in saturated soils, respectively.  
In addition, the simplifier form can be written as below, by substituting Equation (14) into Equation (13)  
\[(\lambda+2G)\
abla^{2}\varphi_{s}-\omega^{2}\Big{(}\alpha\rho_{f}-\rho\Big{)} \varphi_{s}-\omega^{2}\Big{(}\alpha\rho_{m}-\rho_{f}\Big{)}\varphi_{f}=0 \tag{23}\]  
The potential functions of the solid and fluid displacements are, respectively, expressed by the potential functions of the P1 and P2 waves [34]  
\[\left\{\begin{array}{l}\varphi_{s}=\varphi_{P1}+\varphi_{P2}\\ \varphi_{f}=\eta_{1}\varphi_{P1}+\eta_{2}\varphi_{P2}\end{array}\right. \tag{24}\]  
where,  
\[\eta_{1,2}=-\frac{\alpha Q_{b}-\rho_{f}C_{P1,2}^{2}}{Q_{b}-\rho_{m}C_{P1,2}^{2}} \tag{25}\]  
Substituting Equation (24) into Equation (23) and using Equation (20), we can obtain the following equations:  
\[\left\{\begin{array}{l}C_{P1}^{2}\
abla^{2}\varphi_{P1}+\omega^{2}\varphi_{ P1}=0\\ C_{P2}^{2}\
abla^{2}\varphi_{P2}+\omega^{2}\varphi_{P2}=0\end{array}\right. \tag{26}\]  
Equation (26) is obtained as follows using the Fourier inverse transform:  
\[\left\{\begin{array}{l}C_{P1}^{2}\
abla^{2}\varphi_{P1}=\ddot{\varphi}_{P1 }\\ C_{P2}^{2}\
abla^{2}\varphi_{P2}=\ddot{\varphi}_{P2}\end{array}\right. \tag{27}\]  
In Equation (27), the equations of the potential functions of the P1 and P2 waves have the forms of wave equations.
## 2 Methodology
### Experimental System and Conditions  
As illustrated in Figure 4, the unit equipment is connected with the compression type load cell through the platform. The gas and liquid materials inside the unit equipment are provided by the low-pressure steam generation system and the thermostatic water tank, respectively, and are transported into the vessel through the piping. When the gas phase and the liquid phase contact directly, the mass transfer of the gas phase from the high pressure phase to the low partial pressure phase occurs, so that the gas phase condenses on the liquid surface. Therefore, the total amount of liquid accumulated in the container will be greater than the actual liquid inflow, and this difference is the condensation amount of steam. The real-time material mass inside the equipment is obtained through signal acquisition.  
Considering the steam generator is much heavier than the unit equipment, the pipe and the steam generator can be considered as the fixed end constraint. Pressure is regulated by a vacuum pump. The vacuum degree can be up to 98 KPa. The liquid phase flows into the unit equipment from the top of the vessel. Liquid flow rate can be accurately measured by a peristaltic pump. The liquid inlet pipe adopts a flexible slender hose and is connected to the container vertically. It is considered that there is no constraint reaction force. The measurement range of the resistance strain gauge load cell is 0-1000 g, the resolution is 0.01 g, the accuracy is 0.02%, and the compensation temperature range is \(-\)10-40 \({}^{\circ}\)C.  
The experimental study was carried out in two parts: static experiment and dynamic experiment. The static experiment was used to assess the effect of the piping and to determine the optimal parameter range. Meanwhile, the weight of materials inside the unit equipment was measured under the condition of atmospheric pressure and no flowing materials. In the process of the experiment, the material weight was calibrated repeatedly with F1 standard weight under the positive and negative stroke respectively and independently. The dynamic experiment was carried out at different pressures and liquid flows to evaluate the influence of operating conditions on weighing results. Then, the weight calibration equations were obtained by fitting the average value of the measurement results. The experimental conditions are listed in Table 1.  
Figure 4: Schematic diagram of the weighing system.
## 4 Results and Discussion
### False Track Rejection with Contaminated Data
If the chi-squared value is below a certain threshold, the track is labeled as correct; otherwise, it is labeled as false. The labeled tracks are stored in a list called \(T\). The final output of the algorithm is the labeled tracks \(T\). Overall, the algorithm is a pipeline for track reconstruction, starting with raw hit data and generating labeled tracks using a neural network model and a thresholding procedure. The results presented in Table 4 demonstrate that the proposed approach is capable of detecting correct tracks with high levels of recall and precision. Recall is calculated as the ratio of the number of real tracks detected by the model to the number of real tracks in the dataset. Precision is calculated as the ratio of the number of real tracks detected by the model to the total number of reconstructed tracks. The testing was performed on a DGX cluster equipped with A100 GPU with 40 Gb and 1 Tb memory, as well as a Dual 64-Core AMD CPU, using a dataset of 10,000 events. The training process took around 25.6 h to complete, with 500 epochs for training on a dataset comprising 100 noisy points and slightly more for a dataset comprising 1000 fake points. In [PERSON] et al. [9], the authors present a combined approach based on TrackNetv3 and a GNN (graph neural network). The algorithm presented here in this paper describes a different approach to track elimination, where track candidates generated by the model are fed to a parallel track elimination algorithm with the same aim to reduce a false positive rate inherent to the TrackNetv2 model. We present a novel procedure with a clustering of track candidates based on chi-squared criteria. This approach demonstrated to be highly \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Recall** & **Precision** &
\begin{tabular}{c} **Calculation Time for** \\ **1 Event (s)** \\ \end{tabular} \\ \hline With fake hits & 90.2 & 92.2 & 0.00154 \\ (100 points) & 89.6 & 91.5 & 0.00211 \\ Without fake hits & 93.5 & 94.5 & 0.00127 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Recall and precision with fake hits (100 and 1000 hits per station) and without fake hits. effective to reduce the false positive rate, as can be witnessed by the presented results. Comparing these two approaches, we can conclude that in terms of efficiency (recall and precision) both algorithms show similar results. Moreover, our algorithm displays a fast inference time (calculation time for 1 event) due to the parallel nature of the computation and less computational workload.
# Thermal Fatigue Life Prediction of Thermal Barrier Coat on Nozzle Guide Vane via Master-Slave Model
## 3 Life Modeling Process
As seen in Figure 7a, the change in thermal stress basically agrees with the temperature curve in Figure 5, and the thermal stress increased with the increasing TGO thickness. The maximum thermal stress occurred in 50 s to 160 s in the first cycle. For the strain contour at the thickness of 2 \(\upmu\)m, the shear strain and axial strain are shown in Figure 7b. The results in Figure 7b show that the maximum shear strain and axial strain are near by the bottom of the valley (i.e., point I and II, respectively). The cracks caused by shear strain were perpendicular to the mixed layer of the TGO, while the cracks induced by axial strain were tangent to the valley center (\(z\) direction in Figure 6). The value of the shear strain was higher than that of the axial strain, thus the grid node of shear strain was regarded as the risk point for the simulation. The axial and shear strain ranges at the risk point in one thermal shock cycle were imported into Equation (2), and the relationship equations of TBC thickness and strain under the \(i\)th operation condition were fitted as  
\[\left\{ \begin{array}{l}\Delta\gamma_{i}=0.00856-4.649\cdot\delta+1613\cdot \delta^{2}-183023\cdot\delta^{3}+7.58\times 10^{6}\cdot\delta^{4}\\ \Delta\varepsilon_{i}=0.0058-4.213\cdot\delta+1308\cdot\delta^{2}-160674\cdot \delta^{3}+6.71\times 10^{6}\cdot\delta^{4}\end{array} \right. \tag{12}\]  
According to Equation (11), Equation (1) and the cyclic data in Table 5, the coefficients of Equation (2) can be obtained (i.e., \(a\) = 54, \(b\) = 3.45). In the same condition, more thermal fatigue test data are provided by Geng [37], and applied to verify the accuracy of the TFL model. The verification results are illustrated in Figure 8. As revealed in Figure 8, all of the TFL points were almost distributed in triple dispersion zone, which supports the validity and feasibility of the fitted phenomenological life prediction model. Figure 6: Finite element (FE) model and boundary conditions of the ceramic metal tube.
# C-V2X Centralized Resource Allocation with Spectrum Re-Partitioning in Highway Scenario
## 2 Assumptions and Problem Formulation
### Broadcast Collision Zone  
The overlapping coverage of two broadcast signals using the same resource allocation in different RSUs can cause packet collision for the receivers in this zone. The broadcast collision zone (BCZ) can arise when the frequency reuse distance between two broadcasters is lower than the minimum frequency reuse distance, i.e., \(h<h_{\text{reuse}}\). Furthermore, BCZ can be decreased when the \(h\) distance increases, or vice-versa when it decreases, as shown in Figure 2. As shown in this figure, the two broadcasting vehicles A and B are located in the coverage areas of different RSUs. Both vehicles are broadcasting CAMs with the same resource location in time and frequency domains. Thus, vehicle C is located in BCZ of the overlapped coverage area of vehicles A and B. This area can be expressed as follows:  
\[BCZ_{area}=\begin{cases}0&h\geq h_{\text{reuse}}\\ A_{A}\cap A_{B}&h<h_{\text{reuse}}\end{cases} \tag{18}\]  
where \(A_{A}\) and \(A_{B}\) are the coverage areas of transmitters A and B, respectively. The vehicles in BCZ (e.g., vehicle C in Figure 2) receive signals with the signal-to-interference plus noise ratio \(\gamma\) lower than \(\gamma_{\min}\) due to interference. Thus, the area of BCZ depends on the values of \(D_{max}\) and \(h\) and it can be calculated as [35]:  
\[BCZ_{area}=D_{\max}^{2}(\theta(D_{\max},h)-\sin\theta(D_{\max},h)), \tag{19}\]  
where \(\theta(.)\) can easily be expressed in terms of \(D_{\max}\) and \(h_{\text{reuse}}\) through the Carnot theorem as below:  
\[\theta(D_{\max},h)=2\arccos\biggl{(}\frac{h}{2D_{\max}}\biggr{)}. \tag{20}\]In this work, we focused on the distribution of resources among vehicles in the network to reduce or eliminate the BCZ of broadcasting vehicles, assuming the broadcasting power is fixed and the same for all vehicles.
## 2 Methodology of Research
### Solution Method Under Limited Available Data
#### 2.2.2 Bayesian Optimization Problem
Given observed data points \(\left(x_{i},y_{i}\right)_{i=1}^{n}\) in which \(y_{i}=f(x_{i})\), the GP posterior predictive distribution at any new candidate point \(x\) is explicitly updated as follows:  
\[f(x)\mid\mathcal{D}_{n}\sim\mathcal{N}\big{(}\mu_{n}(x),\sigma_{n}^{2}(x) \big{)} \tag{21}\]  
with the posterior mean \(\mu_{n}(x)\) and variance \(\sigma_{n}^{2}(x)\) computed as follows:  
\[\mu_{n}(x)=m(x)+k(x,X_{n})[K(X_{n},X_{n})+\sigma_{e}^{2}I]^{-1}(y_{n}-m(X_{n})), \tag{22}\]  
\[\sigma_{n}^{2}(x)=k(x,x)-k(x,X_{n})[K(X_{n},X_{n})+\sigma_{e}^{2}I]^{-1}k(X_{n },x), \tag{23}\]  
where \(X_{n}\) denotes the set of observed points, \(y_{n}\) denotes the corresponding observations, \(K(X_{n},X_{n})\) is the covariance matrix of the observed data, \(k(x,X_{n})\) represents the covariance between new and observed points, and \(\sigma_{e}^{2}\) stands for the observation noise. This model includes an acquisition function that needs to be maximized in order to find the next point. In our study, we consider the Lower Confidence Bound (LCB) for this purpose, as follows:  
\[\alpha_{LCB}(\mathbf{x})=\mu_{n}(\mathbf{x})-\kappa\sigma_{n}(\mathbf{x}) \tag{24}\]  
where \(\kappa\) is a parameter for balancing exploration and exploitation. A theoretically justified selection for this value is as follows:  
\[\kappa=\sqrt{2\log\bigg{(}\frac{\pi^{2}n^{2}}{6\delta}\bigg{)}}. \tag{25}\]  
The next evaluation point is determined by solving the following optimization problem:  
\[\mathbf{x}_{n+1}=\operatorname*{argmin}_{\mathbf{x}\in A}\alpha_{LCB}(\mathbf{ x}) \tag{26}\]where the feasible domain \(A\) incorporates all the operational and physical constraints, such as allowable temperature limits and inlet-outlet temperature relationships. As seen above, this approach provides significant advantages over conventional optimization methods, particularly through the use of Gaussian Processes and the LCB acquisition function. Specifically, it requires fewer function evaluations, making it highly suitable for expensive-to-evaluate functions, can naturally manage uncertainty and noisy observations, and explicitly balances exploration and exploitation to efficiently explore the solution space. Algorithm 2 clearly summarizes this iterative optimization process. ```
Step 1: Precede \(f\) with a Gaussian process Step 2: Observe \(f\) at \(n_{0}\) points based on the initial space-filling design. Step 3: Iterative process while\(n\leq N\):do  Estimation of the posterior probability distribution on \(f\) considering the available information Calculation of the acquisition function based on the current posterior probability distribution  Estimation of \(f(\mathbf{X})\) \(n=n+1\) endwhile Final Prediction: the best observed \(\mathbf{X}^{*}\) is taken as the optimal schedule \(\mathbf{T}^{*}_{out}=\text{argmin}\ f(\mathbf{x})\)
```  
**Algorithm 2** Steps of Bayesian Optimization
# A Comprehensive Survey on Knowledge-Defined Networking
## 7 Application Plane
### Application Scenarios
#### 7.3.7 Data Center Networking  
Data center networking is the practice of connecting servers, storage devices, and other computer resources within a data center. Data centers should be designed to provide resources based on application requirements to provide services with less latency, intelligent resource utilization, and improved efficiency. A Highly Efficient Switch Migration (HESM) for load-balancing in an optical data center network, where switches are selected by minimizing migration costs, has been studied in [540]. Applications define policies for HESM, use multiple load metrics to measure the load of controllers, and select the optimal target controller with the largest remaining resource. An application with a controller-proxy method where the controller can dynamically delegate portions of event processing back to data plane switches to reduce the workload of the controller has been proposed for large-scale data center networks [541]. A congestion control technique for large data center networks that has an application policy to allocate more bandwidth to burst flows and reduce the bandwidth of background flows when switch congestion is detected has been studied in [542]. An effective energy management application by switch-on-off policy and an effective rerouting policy in a data center network that has achieved higher energy efficiency is presented in [543].