esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Computational Simulation Results and Discussion
### The Basins of Attraction
A synchronous response with a small orbit radius occurs in 8% (marked by the red colour), a response with a large orbit radius in 21% (marked by the green colour), and a nonsynchronous quasiperiodic response in 71% (marked by the blue colour). In summary, when the initial velocity is zero, two types of solutions can be observed, and when the initial velocity is non-zero, three types of solutions can be observed. Figure 15 shows the basins of attraction for the nondimensional speed of 9.0. In Figure 15a, the initial velocities are set to zero and the initial displacements are taken from solutions of the time-dependent quasiperiodic response. The distribution of the initial displacements is determined by the number of samples selected from the quasiperiodic response. The response was computed using the time integration for a duration of 1200 times the excitation force period. The basins of attraction are determined with the last 600 periods, containing approximately 10000 of the time samples. The non-zero initial velocities with the assumption of a circular motion were specified for the computation of the basins of attraction depicted in Figure 15b. In the vicinity of a border of the basins of attraction, two circular areas were selected for the recalculation of responses, as seen in Figure 15b. It can be assumed that the border of the basins of attraction between the different solutions is smooth, as shown in Figure 15b. The initial conditions for the assessment of the basins of attraction in Figures 16 and 17 are based on the time history of the quasiperiodic response for a nondimensional speed of 9.0. The number of initial points used differs between Figure 16a and 16b. The basins of attraction depict solutions for a small (red colour) orbit radius and a large (green colour) orbit radius, as well as a nonsynchronous quasiperiodic response (blue colour). Figure 15: The basins of attraction for the nondimensional speed of 9.0 for zero initial velocities and displacements based on a quasiperiodic response (**a**) and for the assumption of a circular motion (**b**). It can be seen that the regions of the basins of attraction can contain one solution or a combination of two solutions (as shown in Figure 17a). The border between the basins of the attraction does not have a clear structure, as seen in Figure 17b. These results are qualitatively different from results (see Figure 15b) obtained when using initial conditions that assume circular motion.
## 3 Segment Optimization for Adaptive Driving Beam  
In this section, the data collected and evaluated by object recognition were used to optimize segment sizes of adaptive driving beam (ADB) segments.  
Until now, the segments of ADB systems have generally been uniformly distributed with equal sizes over the complete light distribution, with some headlam manufacturers using wider segments at the outer edges for improved side illumination. However, the goal of this optimization is to select segment sizes that would maximize the usage of adaptive driving beam as proposed by [PERSON], [PERSON] and authors of this paper [20; 21; 22].
## 3 Results
### Front Detection and Segmentation  
Example results of the front segmentation are presented in Figure 4b. The average inference time of a single image, on a machine equipped with a quad-core processor AMD Ryzen 3 3100 supporting eight threads, was 1.19 s. Table 5 and Figure 6 show the results of the detection performed on the test set described in Table 2. The mAP value for areas with at least half overlap was 0.86, where the detection threshold of each area had to be at least 50. Increasing the minimum detection threshold causes the mAP value to decrease. This is because a larger detection threshold decreases detection sensitivity, as many detected areas are unable to exceed the detection threshold, so they are missed and detection sensitivity decreases. Increasing the minimum coverage  
It is also worth noting that for an IoU value of 0.75, the decrease in mAP values, between the detection threshold of 0.5 and 0.95, is equal to only 8.97%, while for IoU = 0.50 it is 10.47%. This means that as the minimum overlap threshold increases, the detector gets rid of low-probability detection. A similar relationship can be seen in the case of the IoU = 0.90 threshold, where the decrease in mAP values between the detection threshold of 0.5 and 0.95 is only 2.56%. Looking at the mAP@IoU = 0.75 values from Tables 4 and 5, it can be seen that the front detector does a better job of positioning the detected objects than the handle detector. The difference is due to the size of the objects to be detected and the shapes of these objects. The dimensions of the fronts are significantly larger than those of the handles, making them easier to find. Fronts also tend to have a similar rectangular shape, while the shape of handles often varies.  
In Figure 7, we show the importance of selecting an appropriate detection threshold. The detector should generate predictions as close to ideal as possible because their accuracy determines the position of the front in 3D space and the estimation of the axis of rotation or translation of the fronts. A high threshold should ensure that the number of false-positive detections will be low. However, it can generate false-negative predictions (Figure 6), as increasing the detection threshold decreases the sensitivity of detection. False-negative detections, however, have less impact on system performance than false-positive detections. Taking this into account, the default detection threshold for the front detector is set to 0.95, but even this does not guarantee that false-positive samples will not be included in the system.
# Hydrodynamic Investigation on a Land-Fixed OWC Wave Energy Device under Irregular Waves
###### Abstract  
The hydrodynamic response of a land-based oscillating water column (OWC) wave energy converter under various irregular wave conditions is investigated numerically. Based on the potential flow theory, a two-dimensional fully nonlinear numerical wave flume (NWF) is developed to model the hydrodynamic characteristics using the time-domain higher-order boundary element method (HOBEM). The inner-domain sources method and JONSWAP energy spectrum is used to generate the irregular incident waves, and a linear pneumatic model is used to determine the pneumatic pressure inside the chamber. The free surface elevations at the chamber centre and the oscillatory pneumatic pressures induced by the vertical motion of the water column are recorded. The influence of irregular waves on the hydrodynamic characteristics of the OWC device is carried out by comparison with the regular waves, and a number of significant wave heights and peak wave periods are considered. The hydrodynamic efficiency of the OWC device in irregular wave conditions is observed to be lower than that in regular waves for most wave frequencies, especially near the resonant frequency.  
wave energy; oscillating water column (OWC); irregular wave; hydrodynamic performance; HOBEM +
Footnote †: journal: Applied Sciences
# A Novel Heart Rate Robust Method for Short-Term Electrocardiogram Biometric Identification
## 3 Results
### Experimental Results  
#### 3.3.1 Experiment 1  
Figure 6 gives the comparison of different classifiers fed by features extracted using the proposed method. Related to Figure 6, the features extracted by the QRS-centered resampling strategy and PCANet could produce high heartbeat and subject identification accuracy of 83.14% and 94.72% even with the simplest classifier KNN. Meanwhile, all the other classifiers could yield heartbeat accuracy over 85% and subject identification accuracy over 95%. The obtained results demonstrated that the extracted features could reflect the difference between different subjects, and were effective for ECG identification. In our subsequent experiments, we selected Linear-SVM as the classifier not only because its performance was better compared with other classifiers, but also because it is more suitable for dealing with the extracted high-dimensional features.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Layers** & **Type** & **Number of Neurons (Output Layer)** & **Kernel Size** & **Stride** \\ \hline
0–1 & Convolution & \(16\times 16\times 6\) & \(5\times 5\) & 1 \\
1–2 & Max-pooling & \(8\times 8\times 6\) & \(2\times 2\) & 2 \\
2–3 & Convolution & \(4\times 4\times 12\) & \(5\times 5\) & 1 \\
3–4 & Max-pooling & \(2\times 2\times 12\) & \(2\times 2\) & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Detailed parameter information of traditional Convolution Neural Network (CNN) used for ECG identification.
# Joint Cell Association and User Scheduling in Carrier Aggregated Heterogeneous Networks
## 5 Conclusions  
This paper approaches the network capacity maximization problem through joint cell association and user scheduling in the HetNet. With multiple carrier aggregated across different layers for the downlink transmission, the original joint maximization problem is reformulated, in which the users could associate with BSs on more than one carrier band. The flexibility of the proposed joint maximization problem was achieved by convex optimization solutions with reasonable complexity. Numerical results were able to show the performance advantages of the proposed multi-flow solution under different carrier aggregation deployment.  
Figure 4: Network Sum Rate against Transmit Power with CA deployment. \((K=40,M=200)\).  
Figure 5: Network Sum Rate against Transmit Power with CA deployment. \((K=40,M=200)\).  
Figure 3: Network Sum Rate against Transmit Power with CA deployment. \((K=40,M=200)\).  
For future work, the joint cell association and user scheduling problem in the HetNets could be considered with physical layer multiple, or even massive, antennas involved in the transmission design, where the users may access the network non-orthogonally by multiplexing in the power domain.  
The work in this paper was supported by National Natural Science Foundation of China (Grants No. 61601269, No. 61402268 and No. 61701269), and Shandong Natural Science Foundation (Grants No. ZR2015 PF006).  
[PERSON] proposed the idea, carried out the simulations and wrote the paper; [PERSON] and [PERSON] analyzed the data; all authors have read and approved the final manuscript.  
The authors declare no conflict of interest.
# UV3D: Underwater Video Stream 3D Reconstruction Based on Efficient Global SFM
## 4 Lts-Lira
### Lts-L1 ra Algorithm
```
Input:\(R_{ij}=\left\{R_{ij1},R_{i2},\ldots,R_{ijk}\right\}\) Relative Rotations Output:\(R_{global}=\left\{R_{1},R_{2},\ldots,R_{n}\right\}\) Global Rotations Initialization: Initial guess, h = 0.75k procedure1: c-steps \(R_{new}\) = Initial guess for I in range (3): 1:\(R_{old}=R_{new}\)  2: compute errors of \(R_{old}\) in degrees:  for all \((i,j)\in R_{ij}\), \(R_{i},R_{j}\in R_{old}\) do: \(\Delta R_{ij}=R_{j}{}^{-1}R_{i}R_{ij}\)  \(\theta_{ij}=\arccos(\left(tr(\Delta R_{ij})-1\right)/2\)  end for  3: H \(\leftarrow\) sort (errors of \(R_{old}\) in degrees) [h]  4: compute \(R_{new}:=11 RA\) based on \(R_{ij}[H]\): 5: \(R_{new}\leftarrow\) LI (\(R_{ij}[H]\),\(R_{old}\)) endfor endprocedure2: LIRA on subsets of k cases
```  
**Algorithm 4** Least trimmed squares (LTS)-L1 RA  
In our method, we firstly use LIRA (\(l1-norm\) rotation averaging) based on the h-subset with the smallest errors in degrees three times due to the fast convergence character of the c-steps described above. We set the breakdown value h as 0.75k (the parameter k corresponds to the total number of samples, while h corresponds to the coverage of the total samples, which means a subset of h cases) in our method based on [39] in order to obtain a good balance between the breakdown value and statistical efficiency. In this step, we can quickly estimate the initial guess of the global rotations \(R_{new}\) based on small-scale samples with the lowest errors in degrees through 3 c-steps (step 1-step 5). After this, in procedure 2, we minimize the \(l1-norm\) based on all samples until we reach algorithm convergence and obtain the final global estimate \(R_{global}\) (step 6).
## 3 Results and Verification
### Performance of the Array Element  
Figure 3 shows the simulated S-parameters of the array element in the cases of a single element (Figure 2b) and in the array (Figure 2c). In both cases, the overlapped \(-\)10 dB impedance bandwidth covers 3 GHz (27-30 GHz). The S11 in the cases of single element and in the array are similar. Additionally, the mutual coupling between each element in the array is lower than \(-\)14 dB. Figure 4 shows the simulated radiation patterns of the array element in the cases of single element (Figure 2b) and in the array (Figure 2c) at 29 GHz. For the two cases, the simulated gain is about 5.5 dBi. The cross-polarization level is lower than \(-\)25 dB and \(-\)15 dB for the case of the single element and the case in the array, respectively.
## 4 Discussion  
### Advantages  
In recent years, the CNN model has been significantly improved and proven to be reliable. In the present study, the same model was used to predict four water quality parameters simultaneously, and the PCWA-ResCNN model has a high accuracy in predicting the concentration of water quality parameters, namely, TUB, CODMn, NH\({}_{3}\)-N, TUB, CODMN, and NH\({}_{3}\)-N. The R\({}^{2}\) values of DO were 0.97, 0.95, 0.95, and 0.96, respectively (Figure 4), indicating that this technology has the potential of high-precision prediction in solving water environmental problems and showing strong generalization ability in the inversion of different water quality parameters, which can be applied in the water quality inversion of major and small rivers, lakes, and reservoirs. Due to the accuracy and reliability of deep learning methods, an increasing number of scholars are making use of SVR, RF, and neural network models to predict water quality parameters. For example, [PERSON] et al. [34; 35] used CNN models of different dimensions to simulate and train Chl-a concentration, and they achieved high-precision results. However, only a few water quality parameters were selected; thus, further optimization of the model and its application in the prediction of other water quality parameters still requires further research and exploration. [PERSON] et al. [15] also conducted a similar study. Compared with their study, we increased the input dimension and introduced a custom attention mechanism and residual block to achieve a higher accuracy. Compared with other models, our model has high robustness and can maintain stable performance even when faced with data changes, noise interference, and system changes.  
The authors of some studies have reported the problem of gradient disappearance [36; 37]. In order to address this challenge, we can introduce more complex attention mechanisms, which can not only effectively alleviate the problem of gradient disappearance but also enhance the model's learning ability for important features and context information. By ensuring the steady flow of gradients between the layers of the network, it is also expected to significantly improve the accuracy and generalization ability of spatio-temporal prediction models in practical applications. Therefore, we focused on designing a more robust and efficient neural network architecture to overcome the challenges brought by the gradient disappearance problem and promote the further application and development of deep learning in complex data analysis and prediction tasks. The prediction results of the PCWA-ResCNN model proposed herein (Figure 2) also demonstrate the potential of this model, which has been widely applied in the inversion of water quality in the Yellow River basin.
## 4 Results  
### InSAR Results of Muyubao Landslide  
As stated in Section 3.2, the displacement time series of the Muyubao landslide was extracted by applying the time series InSAR technique and projected onto the direction of the downward sliding direction. Figure 7 shows the average deformation speed of the Muyubao landslide. The strong deformation mainly occurred at the eastern upper portion of the landslide, with deformation rates up to 100 mm/year. In the direction perpendicular to the sliding direction, the deformation of the eastern side was slightly stronger than the western side of the Muyubao landslide.  
Some points in different parts of the Muyubao landslide were selected to further analyze the spatial deformation characteristics. The locations and cumulative displacement time series curves are shown in Figures 7 and 8, respectively. It can be seen that the cumulative displacements of PSI-04 and PSI-05 show the active part of the landslide (Figure 7). The cumulative displacement of these two points from March 2016 to September 2017 exceeded 250 mm (Figure 8d,e). The deformations of the monitoring points in the middle part were smaller, with the cumulative displacement about 150 mm (Figure 8f,h). The deformation rates were the smallest (about 50 mm/y) at the western front part of the landslide (Figures 7 and 8i-k). Compared to the linear monitoring curves at the upper portion of the landslide, the monitoring curves near the lower portion showed slight step-like characteristics (PSI-01, PSI-02, PSI-06, PSI-09 and PSI-10), which were directly affected by periodic rainfall and reservoir scheduling [34; 35]. In summary, the deformation of the Muyubao landslide seems to show obvious  
Figure 7: The spatial distribution of the Interferometric Synthetic Aperture Radar (InSAR) monitoring points, with deformation speed and the spatial distribution of the feature points. Note: Global Positioning System is abbreviated to GPS, and Persistent Scatterer InSAR is abbreviated to PSI.  
differences in space. The deformation on the eastern upper portion of the landslide was the strongest, followed by the middle part, and the western part showed the smallest deformation.
## 2 System Modeling
### Thermal Modeling of the Power Electronic Converter and Device Lifetime Estimation  
#### 2.4.1 Thermal Modeling  
Of the many methods to model the thermal response of a power electronic device under varying load conditions, equivalent circuit models such as the Foster and Cauer thermal networks are some of the simplest and most commonly found in the literature. Traditionally, thermal resistance \(\theta\) and thermal capacitance \(C\) values are required for each medium between the junction of the device and ambient environment. In this work, the thermal chain is composed of the power electronic devices, thermal interface materials (TIM) that bond the device to the heat sink, and a large heat sink that interacts with the incoming flow. However, it is assumed that the time-varying aspects of the flow and environmental temperature are sufficiently slow such that the thermal capacitance present in the thermal chain of the MOSFET can be ignored and only the thermal capacitance of the heat sink is considered.  
Considering that the heat sink directly interacts with the flow of the water in this system, the thermal resistance of the heat sink would have some dependence on \(u_{0}\) through convective heat transfer. However, the changes in thermal resistance relative to the total equivalent thermal resistance are negligible and have been assumed to be constant in this work. It should also be noted that the thermal parameters and losses have been assumed to be equal for each device in the converter.  
The simplified version of the Cauer network can be observed in Figure 4, where \(P_{dev}\) is defined as the per-device losses calculated using (12), \(\theta_{jc}\) represents the device junction-case thermal resistance, \(\theta_{TIM}\) represents the thermal resistance of the TIM, \(C_{HS}\) represents the thermal capacitance of the heat sink common to each device, \(\theta_{HS}\) represents the thermal resistance of the heat sink, \(T_{amb}\) represents the temperature of the ambient temperature of the environment, and \(T_{j}\) represents the junction temperature of the device.
## 4 Experiments and Results
### Fout-Times Downsampling (\(1^{\circ}\times 1^{\circ}\) LR Data)  
To investigate the model's performance in SR reconstruction at different scales, we performed a 4\(\times\) downsampling operation on the original data to obtain LR data with a resolution of \(1^{\circ}\times 1^{\circ}\); the experimental flow is shown in Figure 11. As shown in Figure 12, compared to the 0.5\({}^{\circ}\times\) 0.5\({}^{\circ}\) dataset, this dataset converged more slowly, taking approximately 85 epochs. Like the previous set of experiments, Figures 13 and 14 show the SR reconstruction results for the same moment. Whether comparing results at a single moment or average moments, the differences between SR data and LR data, as well as between true data and LR data, exhibit a certain degree of similarity. This indicates that our model still possesses the capability to perform SR reconstruction work even after a 4\(\times\) downsampling. However, it must be acknowledged that as the downsampling factor increases, the model's performance declines. The specific manifestation is that the data reconstructed from the \(1^{\circ}\times 1^{\circ}\) dataset appears significantly smoother, indicating that more detailed information is lost during the SR reconstruction process. Additionally, the differences between SR data and LR data (Figure 13e) show an overall trend of overestimation, with an increase in the magnitude of deviation.  
Figure 10: SR results for the average moment at a resolution of 0.5\({}^{\circ}\times\) 0.5\({}^{\circ}\): (**a**) LR temperature data; (**b**) SR temperature data; (**c**) true surface temperature data; (**d**) comparison between true data and LR data; (**e**) comparison between true data and SR data; (**f**) comparison between SR data and LR data.  
Figure 11: (**a**) ResNet-Subpixel (4\(\times\)) structure and experimental flow, containing some residual blocks. (**b**) Residual block containing 2 convolutional layers, 2 BatchNorm layers, and an activation function. Relu. (**c**) Upscaling Block.  
Figure 12: Model training results at \(1^{\circ}\times 1^{\circ}\) resolution.  
Figure 13: SR results for a single moment at a resolution of \(1^{\circ}\times 1^{\circ}\): (**a**) LR temperature data; (**b**) SR temperature data; (**c**) true surface temperature data; (**d**) comparison between true data and LR data; (**e**) comparison between true data and SR data; (**f**) comparison between SR data and LR data.  
Table 1 presents the performance of two types of SR tasks (single or average moment) using different downsampling methods (2\(\times\) and 4\(\times\)) in the experiment. Comparative analysis shows that the 4\(\times\) model achieves lower scores than the 2\(\times\) model on all performance metrics. It is evident that the average results over a long period are superior to the SR reconstruction at a single moment, and this holds true for both 2\(\times\) and 4\(\times\) downsampling. The SR results exhibit a strong correlation with the actual data, regardless of whether the downsampling is by a factor of 2 or 4.