esa-sceva/satcom-chunk-collection huggingface dataset:
# A High-Efficiency Super-Resolution Reconstruction Method for Ultrasound Microvascular Imaging
## 4 Discussion  
In this paper, we propose a new super-resolution construction method for ultrasound imaging. Compared to the existing methods [3; 34; 35; 36; 33], the innovations of the proposed method lie in two aspects. In the microbubble localization step, we adopt a new Gaussian PSF to measure the accurate location of the microbubbles. This kind of Gaussian PSF takes the discreteness of the ultrasonic imaging coordinates into consideration and obtains more accurate positioning result. In the trajectory tracking step, we introduce the ASH method. The ASH enjoys several advantages compared with the conventional histogram method: better visual interpretation, better approximation, and nearly the same computational efficiency. According to the comparison of the methods in Results, our method has two main advantages over the other super-resolution construction methods:  
First, the proposed method acquires the best resolution. This is because our method takes into account the characteristics of ultrasonic imaging when calculating the PSF. Besides, a more accurate visualization method is adopted.  
Second, our approach has better real-time performance. Basically, the frames needed for the reconstruction are decreased in our method, which in turn increases the frame rate.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **FWHM (\(\mu\)m)** & **PSL (dB)** & **Run Time (s)** \\ \hline Gaussian PSF deconvolution + scatter plot & 7.72 & \(-\)37.22 & 14.20 \\ High-order moment + power Doppler integration & 13.44 & \(-\)26.46 & 9.27 \\ Integral form of Gaussian PSF deconvolution + ASH & 3.88 & \(-\)42.35 & 17.11 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Full-Width at Half-Maximum (FWHM), Peak Side Lobe (PSL) and the run time of different reconstruction methods.  
Figure 6: The lateral variation of different reconstruction methods for the point target at the right top corner.  
Besides, our algorithm still keeps a low amount of computation. Thus, our proposed method makes a step forward to the real-time super-resolution imaging.  
At present, our improved method has achieved good results in displaying the rabbit kidney microvessels. In order to further verify the effectiveness of the proposed method, we need to carry out more in vivo animal experiments and clinical experiments in the future.
## 3 Experiment Results and Discussion
### Experimental and CFD Simulation Comparison
However, the larger stroke angle leads to a smaller vertical acceleration component and thus leads to the small bumps for the InSim curves in Figure 10. The phenomenon is more apparent during the end of the downstroke in Case 3-5 and during the end of the upstroke in Case 1-6. From Figure 9(a), the peak value of the simulated inertial forces and the experimental inertial forces is \(-0.185\) N and \(-0.218\) N, respectively. The peak differences in Figure 9(b) (\(-0.206\) N and \(-0.187\) N) and Figure 9(c) (\(-0.196\) N and \(-0.181\) N) become smaller. When it comes to the lift results, it is observed that the variation trend of the lift forces' curves from the CFD simulation is close to that from the experiment. This difference between our CFD results and experiment results should be mainly due to the fact that the wings' deformation is neglected in our CFD model.
its performance ought to be conducted, considering the channel characteristics that will be encountered in 5G systems and beyond. The propagation scenarios in 5G systems are diverse and include vehicular and machine-to-machine communication, communication in Internet of Things (IoT) systems, remote set-ups, and urban scenarios with high-density crowds. In addition to the propagation scenario, channel characteristics are also influenced by other factors such as transmission equipment (smart antennas), transmission frequency (mm-wave transmission), and the mobility of communicating devices. Since 5G channel characteristics are so diverse and peculiar, conventional statistical distributions such as the Rayleigh and Rican methods, etc., fail to accurately characterize most channel scenarios. There is, therefore, a need to study the performance of any technology that will be used in 5G systems and beyond using other more appropriate and versatile statistical distributions, the parameters of which can be modified to accurately represent a wide range of fading scenarios [4].
## 4 Results and Discussion
### Comparison of Performance with Various Working Gases  
The simulation results of the volume flow rate and the power consumption of the Roots pump with various working gases are presented and compared in Figure 8. The suction temperature for both the air and helium is 30 \({}^{\circ}\)C and that for all cases with hydrogen is 70 \({}^{\circ}\)C. The pressure differences for all the cases are 25 kPa, and the suction pressures are all 200 kPa, except for the suction pressure for air, which is equal to the ambient pressure. For a given speed, it can be seen from Figure 8 that the volume flow rate of the Roots pump increases with the increase of the contents of nitrogen. Generally speaking, the performance of the Roots pump operating under the typical working conditions of the PEM fuel cell system is between the performance of the pump working with air and helium. As can be seen from the Figure 8a, the absolute volume flowrate with the mixture containing a nitrogen fraction of 15% is 158 L/min less than with air, but is 118 L/min more than with helium. This discrepancy is almost the same under the different speed conditions. This is because the absolute leakage is almost the same under different speeds. This implies that the performance of the Roots pump can be evaluated on the test rig with air or helium, and the testing results can be used to represent the performance of the hydrogen pump integrated in the PEM fuel cell system.
# An Auto-Adjustable Semi-Supervised Self-Training Algorithm
## 4 Experimental Results
### Sensitivity of AAST to the Value of Parameter \(k\)  
In the sequel, we focus our interest on the experimental analysis for the best value of parameter \(k\); hence, we have tested values of \(k\) ranging from 3 to 8 in steps of 1. Figure 1 presents the performance profiles for various values of parameter \(k\), relative to the used ratio of labeled data. Clearly, AAST exhibits better classification performance as the value of parameter \(k\) increases, revealing its sensitivity. More specifically, using 10% as labeled ratio, AAST with \(k=3,4,5,6,7\) and 8 classifies 22.5%, 25%, 25%, 35%, 47.5% and 80% of the test problems with the highest accuracy, respectively. Furthermore, AAST with \(k=3,4,5,6,7\) and 8 classifies 20%, 27.5%, 30%, 40%, 52.5% and 80% of the test problems with the highest accuracy, respectively for 20% labeled ratio as well as 17.5%, 17.5%, 22.5%, 35%, 57.5% and 80%, respectively for 30% labeled ratio.  
Figure 1: Log\({}_{10}\) scaled performance profiles for AAST using various values of parameter \(k\).
# Seismic Performance Evaluation of Highway Bridges under Scour and Chloride Ion Corrosion
## 3 Effect of Cl\({}^{-}\) Corrosion on the Seismic Performance of Bridge Column
### Comparison of Quasi-Static Test Results of Columns before and after Cl\({}^{-}\) Corrosion
#### 3.3.3 Displacement Ductility Coefficient of Specimen  
Displacement ductility coefficient is one of the important indicators to measure ductility performance of bridge columns. The displacement ductility coefficient of specimens MME and MHE declined by 41.56% and 46.85% before and after corrosion, respectively, indicating that Cl\({}^{-}\) corrosion weakens the ductility properties of bridge columns; in contrast, the displacement ductility coefficient of specimen LLE raised by 3.91% compared with specimen LL, indicating that Cl\({}^{-}\) corrosion had little effect on ductility performance of this specimen with a low height and low longitudinal reinforcement ratio. However, by comparison with specimen LL, the hysteretic loop area of specimen LLE reduced by 49.71%, which indicates that there is a limitation in judging seismic performance of columns only by displacement ductility coefficient. It revealed the necessity of the synthetic evaluation of seismic performance from the perspective of comprehensive indicators such as the level of energy dissipation.
# ICN-Oriented Mobility Support Method for Dynamic Allocation of Mobile Data Flows
## 2 Related Work
### Mobility Support in ICN
#### 2.2.2 Anchorless Proposals
The flooding of packets consumes a large amount of wireless resources, which is not conducive to network scalability. Multipoint registration can also bring consistency issues. MobilityFirst [26] uses a globally unique identifier (GUID) to identify the identity information of the device or data, and a network address (NA) represents the current location information of the device or content. The mapping of GUID to NA is completed through the global name-resolution system (GNRS). When the NA is unreachable after the device or content is moved, the router caches the data and continues to forward the data after querying the GNRS to obtain a valid NA so as to achieve hop-by-hop forwarding path optimization. MobilityFirst uses universal storage-aware routing to effectively sense the network's status and ensures the reliable transmission of data by using the hop-by-hop confirmation transmission mechanism. MobilityFirst's routing mechanism can play a very good role in interstellar communication or disaster relief communication; however, it is not suitable for improving user experiences in modern life, such as in scenarios where low latency is required. [PERSON] et al. [27] proposed an enhanced programmable data plane supporting ICN mobility. Combined with SDN, by offloading mobility-related control plane functions from the controller to the data plane, the data plane can handle mobility signaling locally without interacting with the controller. By improving the flow table matching algorithm, mobile signaling processing capability is enhanced and the delay is reduced. This proposal is similar to MobilityFirst; when the NA is unreachable, a valid NA is obtained by querying the Name Resolution System (NRS). However, the proposal does not focus on mobile route optimization.
## 3 Stability Analysis Based on Lyapunov Exponential Method
### MATLAB Simulation Analysis
#### 3.3.2 Hovering Stage  
When the tethered UAV is in the hovering phase, the Liapunov exponential spectrum variation of the tethered UAV can be obtained by simply varying the tether cable density \(\rho\), as shown in Figure 9.  
Figure 9: Lyapunov exponential spectra for different cable densities during the hovering stage.  
Further, the results of Figure 9 are statistically analyzed, and the numerical quantification of the Lyapunov exponential spectrum is shown in Table 3.  
As seen from Table 3, since the change in the final value of the Lyapunov exponent is considered, the Lyapunov exponent of the tethered UAV does not change much during the hovering phase. Therefore, the change in state quantities in the hovering phase is similar to the takeoff phase. The overall stability of the tethered UAV decreased by 32.49% under tethered cable density \(\rho\) = 1.38 g/cm\({}^{3}\) than \(\rho\) = 0.92 g/cm\({}^{3}\). The overall stability of the tethered UAV decreased by 61.12% in the condition of tether cable density \(\rho\) = 1.84 g/cm\({}^{3}\) over \(\rho\) = 0.92 g/cm\({}^{3}\).
## 6 Experimental Results
### Experiment 1: Feature Importance Using RF  
In this experiment, the RF feature selection method is applied to the eight ML algorithms: LR, LDA, KNN, DT, NB, SVM, RF, and XGB. For each conducted ML algorithm, the experiment starts with the first \(k=100\) features where the model is evaluated and the score accuracy is recorded. Based on the EFF-SA algorithm, the experiment is continued by increasing an additional \(k+1=100\) to the previous \(k\) fold to obtain \(k=200\) as an accumulative feature and then the model is executed again to find the highest accuracy. As explained in Figure 4, the highest accuracy is recorded on the accumulative feature \(k=2400\) with the NB algorithm that achieved an accuracy of 84.4%. The second highest accuracy is recorded on the accumulative feature \(k=2300\) with the same algorithm of NB that achieved an accuracy of 84.17%.  
As stated, the four ML algorithms, NB, LR, SVM, and LDA, start the experiments with a linear increase but the results of the LDA algorithm start decreasing at the accumulative feature \(k=1800\). The NB, LR, and SVM score the best results when compared to the remaining ML algorithms where the highest scored accuracy is recorded with NB algorithm.  
Figure 4: Accuracy of RF feature selection method with ML models.
# Beyond Classical AI: Detecting Fake News with Hybrid Quantum Neural Networks
## 3 Results
### Comparison with Classical Models
As illustrated in Figure 7, for example, the Linear SVM model correctly identified 73.81% of the fake news instances (recall), but misclassified a relatively large number of true news items as fake (299 FPs). As a result, its precision dropped to approximately 63.80%, reflecting a moderate bias toward the positive class. The relatively high rates of both FP and FN indicate that the model has limited capacity to distinguish clearly between the two classes. The confusion matrix of the Logistic Regression model exhibits a structure quite similar to that of the Linear SVM as illustrated in Figure 8. The model achieves a recall of 74.09% for the positive class, with 185 false negatives recorded. The number of false positives is also considerably high (297), indicating a tendency to misclassify some true news items as fake. Despite relying on a linear decision boundary, Logistic Regression provides a relatively balanced precision-recall trade-off overall. Although the MLP model achieved the highest accuracy (63.38%) and precision (66.45%) among the compared models, as shown in Figure 9, its ability to correctly identify fake news--as reflected by a recall of 70.73%--was lower than that of Logistic Regression and the Linear SVM. This performance drop can be attributed to a higher number of false negatives (209 FN). These results suggest that MLP adopts a more conservative prediction  
Figure 8: The confusion matrix of the Logistic Regression model on the test set. Figure 7: The confusion matrix of the Linear SVM model on the test set. strategy toward the positive class, leading to a greater likelihood of missing fake news compared to the other models. In all three classical models, the number of false positive predictions is notably high, which in turn suppresses their precision scores. However, compared to both the HQDNN variants, these models exhibit significantly higher false negative (FN) rates, indicating a greater tendency to miss instances of the positive class (i.e., fake news). This pattern underscores the limited recall performance of the traditional approaches and highlights the superior sensitivity of the HQDNN models--particularly the Transformer-enhanced version--in detecting fake news comprehensively. The DistilBERT with Quantum HQDNN model achieves the lowest false negative count (40) among all the models, reinforcing its strength in minimizing undetected fake news. Although this comes at the expense of an increased number of false positives (492), the model's recall-oriented design aligns well with applications where failing to identify harmful content is more critical than occasional false alarms. Thus, both the HQDNN configurations, and especially the Transformer-based version, represent effective alternatives to the classical models in domains where superior recall is a strategic priority.
# Thermal Environment Prediction for Metro Stations Based on an RVFL Neural Network
## 3 Measurement Process
### Thermal Environment Model Based on an RVFLNN for a Metro Station  
#### 3.3.1 Model Input and Output  
In practice, there are many parameters that will influence the climate, such as the air velocity, the relative humidity, diffused and direct solar radiation, and the latent thermal load due to the presence of people. Because of the limitations of our measuring instruments, only the parameters of temperature and relative humidity could be measured at S1, S2, S3, and S4. The measured data should be integrated with acquired air velocity and diffused/direct solar radiation data, which are very important for the evaluation of the local comfort [21; 22; 23]. In later analysis, we found that the relative humidity did not fluctuate greatly and remained within a certain range; hence, the relative humidity and temperature were not strongly correlated. The relative humidity was therefore not used to predict the temperature. In addition, the airflow velocity in the subway station is related to the frequency of the subway trains, and the radiation is related to the number of passengers. Therefore, our temperature prediction was finally based on the temperatures in different places, passenger flow, and metro arrival frequency.  
Because of the structure of the entire metro station, the air from S1 can provide cool air to the downstairs area and directly affect the temperature of S2, S3, and S4. At the same time, whenever the metro train arrives, the air flow change and the passenger flow all influence the temperature of S2, S3, and S4. The temperature of S1 affects those of S2, S3, and S4, but S2, S3, and S4 also influence each other. The whole space in the metro station was divided into three subspaces--S2, S3, and S4. In this paper, the thermal models for S2, S3, and S4 in the metro station were built separately. The parameters for the input layer of subspace S2 were determined to be \(T_{1}\), \(T_{3}\), \(P_{flow}\), and \(F_{train}\). For subspace S3, the parameters of the input layer were determined as \(T_{1}\), \(T_{2}\), \(T_{4}\), \(P_{flow}\), and \(F_{train}\). For subspace S4, the parameters of the input layer were determined as \(T_{1}\), \(T_{3}\), \(P_{flow}\), and \(F_{train}\). Therefore, \(T_{1}\), \(P_{flow}\), and \(F_{train}\) are the common training input variables for S2, S3, and S4. The final structures of the traditional RVFL for the temperature models of \(T_{2}\), \(T_{3}\), and \(T_{4}\) are shown in Figure 6. The temperature prediction accuracy of S2 through S4 can be improved by establishing different neural network models.  
Because the data were measured over three days--Sunday, Monday, and Tuesday--we see that the passenger flow and the metro arrival frequency are very different between weekdays and weekends. In order to obtain better prediction performance, the data from the first two days were used for the training. The data from the last day were used as the test data.
# A Review: Machine Learning for Combinatorial Optimization Problems in Energy Areas
## 3 Learning to Solve COPs
### Improvement RL  
The previous works usually use NNs to generate COP solutions in one shot. Despite the efficiency, there is always a certain gap from the optimal in such methods. Another type of work learns the improvement heuristic, which improves an initial solution iteratively.  
[PERSON] et al. [97] proposed a method of making incremental improvement until convergence by locally rewriting feasible strategies, called NeuRewriter. [PERSON] et al. modeled the rewriting process as MDP, where the state is the partial solution, the action is the desired local region and the associated rewriting rule. They trained the policy with the advantaged actor-critic algorithm and applied NeuRewriter to three problems--expression simplification, online job scheduling, and vehicle routing problems (VRP)--where NeuRewriter performs better than strong heuristics.  
[PERSON] et al. [248] proposed to use deep RL to automatically improve an initial solution based on neighborhood search. The architecture is based on self-attention, where the action is a node pair for pairwise local operators. Learning with the actor-critic algorithm, the model generates a probability matrix for each pair.  
[PERSON] et al. [249] proposed a new framework to improve heuristic methods with reinforcement learning for capacitated vehicle routing problems (CVRP). They divided heuristic operators into two classes, improvement operators and perturbation operators. According to each state, a certain class of operators is selected first, and then a specific operator is selected within the class. Specifically, given recent solutions, the method for selecting a class is a threshold-based rule. If a local minimum is reached, the perturbation operator is activated to generate a new solution, otherwise, the improvement operator is activated. They used reinforcement learning to select the specific improvement operator, where the problem, solution, and running history are used as the features of the state, and different operators are used as actions. [PERSON] et al. designed two kinds of rewards: one is +1 if the solution is improved, otherwise \(-1\), and the other is to take the total distance achieved by the problem instance during the first improvement iteration as a baseline.
# Enhancing 3D-Printed Clay Models for Heritage Restoration Through 3D Scanning
## 2 Materials and Methods
### Digital Twin  
For the creation of digital models of the ornamental heritage elements of the Plaza de Espana, a 3D scanner model EinScan Pro HD, manufactured by Shining 3D, is used. This device is characterized by its rapid scanning capability and high precision, achieving a resolution of up to 0.045 mm and a scanning accuracy of up to 0.04 mm. It employs structured light technology to capture fine details and is capable of generating a dense point cloud, making it particularly suitable for digitizing objects with complex geometries and textures.  
The process begins with calibrating the scanner to ensure optimal accuracy, achieving a calibration with an error margin of less than 0.1 mm. This is done following the manufacturer's standard procedures and using the EXScan Pro_v4.0.0.7 software. Once the device is calibrated, the scanning is performed through a rapid manual scan, capturing a dense point cloud of the selected objects.  
Subsequently, the obtained point cloud is optimized through a process of filtering and removal of redundant data, thus improving the quality and accuracy of the digital model. This optimized point cloud is then converted into a polygonal mesh, which underwent a cleaning process to remove potential artefacts and defects resulting from the scan. The mesh is carefully adjusted and prepared for its subsequent use in the 3D printing process (Figure 4). The mesh sizes of the case studies in this research have exceeded 200,000 polygons. This represents a high number compared to other mesh models in the literature, which often feature lower polygon counts depending on the level of detail and the scale of the objects being scanned. In many cases, the literature focuses on larger structures, such as entire buildings, where lower polygon counts may suffice. The finer details and smaller scale of the objects in this study necessitated a higher resolution to capture the required precision [27,62]. In this process, the level of detail was adjusted according to the specific requirements of each piece based on its location. It was noted that the difference in resolution compared to the original becomes noticeable at a viewing distance of 1 m. This approach allowed for the optimization of detail where necessary while ensuring an effective balance between resolution and overall accuracy for each individual element.  
Figure 4: Digital twin creation process.  
The result is a set of highly detailed and accurate digital models, representative of the original ornamental elements, serving as the basis for their reproduction using additive manufacturing technologies.