esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Network Model  
In general, there are three models that can express the connection mode between nodes, which are shown in Figure 1[24]. Figure 1a illustrates the \(k\) nearest neighbor model; each node in this model has constant neighbors and can maintain the node degree by changing the communication range of each node. Figure 1b illustrates the disc model. In this model, transmission range is modeled as a disk with radius \(r\); nodes connect with the neighbors that fall into its transmission range. The disc model is more plausible in the WSNs since obtaining \(k\) neighbors is not always feasible due to the communication range limitation. In Figure 1c illustrates the Erdos-Renyi random graph that connects any two nodeswith the same probability which is not appropriate in WSNs. In this paper, we are only interested in the Disc model.  
There are two different kinds of Disc models: the Homogenous model and the Heterogeneous model, which are shown in Figure 2. The nodes in the Homogenous model have same transmission ranges while the nodes in the Heterogeneous model have different transmission ranges. Both the nodes in the Homogenous and the Heterogeneous model can change their transmission ranges from zero to maximum.  
In [5,6], the authors propose the interference model of WSNs. In this model, the network interference is defined. As shown in Figure 3, node \(u\) and node \(v\) can communicate with each other and can affect all the nodes that fall into their coverage area. The interference is the cardinality of nodes covered by node \(u\) and node \(v\) with the transmission ranges that are chosen, which is:  
\[\text{Int}(u,v)=\text{int}(u,r_{1})\cup\text{int}(v,r_{2}) \tag{1}\]  
where \(\text{Int}(u,v)\) is the interference of edge (\(u\), \(v\)), \(\text{int}(u,r)\) means the interference of node \(u\) with transmission range \(r\).  
Figure 1: Different network models: (**a**) \(k\) nearest neighbor model; (**b**) disc model; (**c**) Erdos-Renyi random graph.  
Figure 3: Interference before adjusting the transmission range.  
Figure 2: Different Disc Models: (**a**) Homogeneous Disc Model; (**b**) Heterogeneous Disc Model.  
Therefore, based on the network interference model and the conclusion of [25], the definition of the probability of interference-optimality can be found in Definition 1.  
**Definition 1**.: _The probability of interference-optimality is the probability that when the transmission ranges are adjusted, the network interference is smaller than previously._  
The notations will be used in this paper are introduced in Table 1.
###### Abstract  
The vertical foliage profile (VFP) and leaf area index (LAI) are critical descriptors in terrestrial ecosystem modeling. Although light detection and ranging (lidar) observations have been proven to have potential for deriving the VFP and LAI, existing methods depend only on the received waveform information and are sensitive to additional input parameters, such as the ratio of canopy to ground reflectance. In this study, we proposed a new method for retrieving forest VFP and LAI from Ice, Cloud and land Elevation Satellite (ICESat) Geoscience Laser Altimeter System (GLAS) data over two sites similar in their biophysical parameters. Our method utilized the information from not only the interaction between the laser and the forest but also the sensor configuration, which brought the benefit that our method was free from an empirical input parameter. Specifically, we first derived the transmitted energy profile (TEP) through the lidar 1-D radiative transfer model. Then, the obtained TEP was utilized to calculate the vertical gap distribution. Finally, the vertical gap distribution was taken as input to derive the VFP based on the Beer-Lambert law, and the LAI was calculated by integrating the VFP. Extensive validations of our method were carried out based on the discrete anisotropic radiative transfer (DART) simulation data, ground-based measurements, and the Moderate Resolution Imaging Spectroradiometer (MODIS) LAI product. The validation based on the DART simulation data showed that our method could effectively characterize the VFP and LAI under various canopy architecture scenarios, including homogeneous turbid and discrete individual-tree scenes. The ground-based validation also proved the feasibility of our method: the VFP retrieved from the GLAS data showed a similar trend with the foliage distribution density in the GLAS footprints; the GLAS LAI had a high correlation with the field measurements, with a determination coefficient (R\({}^{2}\)) of 0.79, root mean square error (RMSE) of 0.49, and bias of 0.17. Once the outliers caused by low data quality and large slope were identified and removed, the accuracy was further improved, with R\({}^{2}\) = 0.85, RMSE = 0.35, and bias = 0.10. However, the MODIS LAI product did not present a good relationship with the GLAS LAI. Relative to the GLAS LAI, the MODIS LAI showed an overestimation in the low and middle ranges of the LAI and a saturation at high values of approximately LAI = 5.5. Overall, this method has the potential to produce continental- and global-scale VFP and LAI datasets from the spaceborne lidar system.  
**Keywords:** VFP; LAI; GLAS; transmitted energy profile (TEP); lidar; radiative transfer theory
## 5 Lattice Boltzmann Methods (LBMs)
### Application to Fluid Flows by Advection/Diffusion with Phase Change in Porous Media
#### 5.4.1 Forced Convection Melting of a PCM in a Latent Heat Thermal Energy System (LHTES)  
In what follows, some results of recent numerical studies on laminar forced convection with melting in porous media are presented to illustrate our points on the ability of the REV-LBM approach to deal with this type of problem and many others. These findings provide insight into the applications of REV-LBM to a variety of transport phenomena in porous media saturated with phase change materials.  
For the values of the relevant parameters considered, the reader can consult the Refs. cited herein.  
The open-ended straight channel incorporating a porous medium (_here copper foam_) and filled with a PCM (_paraffin_) is here considered as it may depict an LHTES template [118, 119, 166].  
Figure 18 exhibits the temperature contours of the fluid and porous medium (\(\Theta_{f}\) and \(\Theta_{s}\)) for Reynolds numbers of 200 and 400 during the charging process without viscous dissipation (\(Ec=0\)) characterized by the key dimensionless parameters, viz the numbers of Prandtl (_Pr_), Biot (_Bi_), Darcy (_Da_), Eckert (_Ec_), thermal conductivities ratio (_Kr_), heat capacities ratio (\(R_{c}\)), and the medium's porosity (\(\epsilon\)). It can be seen that the heat propagates very slowly (from left to right) for fluid phase temperature (\(\Theta_{f}\)) compared to that of the solid phase temperature (\(\Theta_{f}\)). This gives rise to the forced convection in the channel, which speeds up the heat transfer toward its exit (_right_).  
Figure 19 portrays the melt front evolution vs. time(s) during charging process for Re = 400 with \(\Pr=50,Bi=0.1,Ste=1,Da=10^{-3},Kr=10^{3},Rc=1,Ec=5\). It appears that the melting speed improves when the porosity decreases (_see from right to left_).  
Figure 20 highlights the effect of pore density (PPI) on _U_-shaped contours during the charging process at the porosities \(\epsilon=0.8\) and \(0.9\), respectively. It can be observed that the PPI obviously influences the streamwise velocity (_U_) during charging and discharging processes, even though the latter is deliberately left out here.  
Figure 20: Pore density effect on \(U\)-contours during charging process at \(\varepsilon=0.8\) and \(0.9\)[119].  
4.2 REV-LBM Simulation of Unsteady Flow and Heat Transfer around and through a Confined Diamond-Shaped Porous Block  
Porous body arrangement could arise when designing porous heat sinks, pin-fin arrangements, etc. Recently, [PERSON] et al. [167] have numerically investigated the hydrodynamic and thermal behavior of a diamond-shaped porous block confined in a laminar unsteady uniform flow via the SRT-LBM approach (see Figure 21). They demonstrated the effect of permeability on flow and heat transfer for different Darcy numbers (\(10^{-6}\leq Da\leq 10^{-2}\)) and Reynolds numbers (\(50\leq Re\leq 150\)). They pointed out that the higher the permeability, the greater the thermal plume downstream of the block.
## 3 Results
### Hole Cylindricity and Perpendicularity  
Figure 4 shows the average values of hole cylindricity for AA2024, AA6061, and AA5083. The results showed that the lowest hole cylindricity error was possible when drilling AA2024, followed by AA6061 and then AA5083. The result showed that the lowest cylindricity error of 0.0240 mm was obtained during the drilling of AA2024, which was possible at the \(n\) of 1007 rpm and \(f\) of 0.04 mm/rev. For AA6061, the minimum cylindricity error was 0.0389 mm and resulted at the \(n\) of 3025 rpm and \(f\) of 0.08 mm/rev. The minimum hole cylindricity of 0.0403 mm for AA5083 was possible at the \(n\) of 1007 rpm and the \(f\) of 0.04 mm/rev. These results agreed with the results obtained in the circularity error, where AA6061 showed the highest cylindricity error at low \(n\). At the same time, AA5083 had the maximum cylindricity error at the highest \(n\) of 3025 rpm, while a higher \(n\) resulted in less circularity error for AA6061. Table 3 shows that the \(n\) was somewhat influential on the cylindricity error with a percentage contribution of 11.35%; however, the impact of feed  
Figure 3: Effect of cutting parameters on hole circularity.  
and other interactions was insignificant. The most dominant factor in regard to cylindricity was the alloy type, with a percentage contribution of 34.46%.  
Similarly, the perpendicularity error for alloys selected in this study during multi-spindle simultaneous drilling is illustrated in Figure 5. Interestingly, the holes produced in AA2024 also gave the lowest perpendicularity, ranging between 0.0080 and 0.0178 mm. AA5083 followed with minimum and maximum perpendicularity errors of 0.0222 and 0.0694, respectively. In contrast, AA6061 showed the maximum perpendicularity error, which ranged between 0.0331 and 0.0579. The minimum value of the perpendicularity error for AA2024 was obtained at the \(n\) of 3025 rpm and \(f\) of 0.08 mm/rev. In contrast, the same \(f\) of 0.04 mm/rev and spindle speeds of 1007 rpm and 2017 rpm caused the minimum perpendicularity for AA5083 and AA6061, respectively. However, like the cylindricity error, it was also evident from the ANOVA result that the only parameter that had the highest influence on the perpendicularity error was the alloy type, with a percentage contribution of 73.04%, while none of the other parameters showed a significant impact on the perpendicularity error.
# Adaptive Linearization for the Sub-Nyquist Photonic Receiver Based on Deep Learning
## 2 Materials and Methods
\begin{aligned} x,x\geq 0\\ x/\alpha,x<0,(\alpha=0.03)\end{aligned} \right. \tag{5}\]  
During the training process, we conducted the supervised learning scheme of the deep neural network by minimizing the Mean Absolute Error (\(MAE\)) loss function between the rectified data (output) and raw data (input). The loss function is shown in Equation (6). In detail, as shown in Figure 2, the input of the deep Resnet was the raw time-domain signal waveform, and the training target was the reference time-domain signal waveform. In the testing process, the untrained raw data was fed into the trained model, while the output of the model was the linearized time-domain signal waveform data. \[Loss_{MAE}=abs\Big{(}y_{ref}-y_{pred}\Big{)} \tag{6}\]  
The hyperparameter settings are shown in Table 1. The length of the input signal data was intercepted to 49,900 before being fed into the Resnet. Furthermore, [PERSON] was adopted as the optimizer, and the initial learning rate was set to 0.001. To ensure adequate learning capacity, the initial quantities of epochs were set to 10,000. Meanwhile, the early stopping mechanism was introduced to prevent overfitting, in which the training was stopped when the loss function did not change after 150 epochs. In addition, we applied the adaptive learning rate reduction mechanism (ReduceLROnPlateau), in which the learning rate was reduced by 1% when the loss function of the validation set was no longer reduced after 50 epochs.
# Bayesian Compressive Sensing Based Optimized Node Selection Scheme in Underwater Sensor Networks
###### Abstract  
Information acquisition in underwater sensor networks is usually limited by energy and bandwidth. Fortunately, the received signal can be represented sparsely on some basis. Therefore, a compressed sensing method can be used to collect the information by selecting a subset of the total sensor nodes. The conventional compressed sensing scheme is to select some sensor nodes randomly. The network lifetime and the correlation of sensor nodes are not considered. Therefore, it is significant to adjust the sensor node selection scheme according to these factors for the superior performance. In this paper, an optimized sensor node selection scheme is given based on Bayesian estimation theory. The advantage of Bayesian estimation is to give the closed-form expression of posterior density function and error covariance matrix. The proposed optimization problem first aims at minimizing the mean square error (MSE) of Bayesian estimation based on a given error covariance matrix. Then, the non-convex optimization problem is transformed as a convex semidefinite programming problem by relaxing the constraints. Finally, the residual energy of each sensor node is taken into account as a constraint in the optimization problem. Simulation results demonstrate that the proposed scheme has better performance than a conventional compressed sensing scheme.  
Bayesian estimation; compressed sensing; network lifetime; underwater sensor network +
Footnote †: journal: Sensors
## 4 Results
### Quality Assessment of the Sea Ice Motion Measurement  
The consistency between the sea ice drifts measured from sequential image pairs and the time-interpolated ITP80 track records indicated the quality of the satellite image-derived sea ice motion measurement. Three parameters including distances, directions and velocities were investigated for both high- and mid-resolution datasets (Figure 9). The image-derived motion vectors  
Figure 8: The results from sea ice motion measurement using the same image pair as Figure 5, resampled to the spatial resolutions of 15 m: (**a**) magnitudes of the sea ice motion vectors; (**b**) directions (headings) of the sea ice motion vectors, measured counterclockwise from east; (**c**) cross-correlation coefficient of each image block; (**d**) sea ice coverage of each image block in the prior image; and (**e**) entropy of each image block in the prior image, plotted only for the image blocks after applying the cross-correlation coefficient threshold without additional outlier removal.  
of which the center as closest to the center of two time-interpolated locations in each image pair were selected to compare with the motions connecting the time-interpolated buoy locations.  
Figure 9: Comparison between sea ice drifts measured from sequential satellite image pairs and ITP80 track records: (**a**) displacements; (**b**) directions; and (**c**) velocities.  
Differences of the displacements, directions and velocities exist between both datasets. The displacements of the spatial resolution of 4 m varied from 409.6-2839.5 m, while the distances of the spatial resolution resampled to 15 m varied from 415.0-2823.4 m (Figure 9a). The velocities of the spatial resolution of 4 m, calculated from the distance divided by the time gap between images within a pair, varied 96.7-296.7 m\(\cdot\)h\({}^{-1}\), equivalent to 2.7-8.2 cm\(\cdot\)s\({}^{-1}\), while the velocities of the spatial resolution resampled to 15 m varied 96.5-298.0 m\(\cdot\)h\({}^{-1}\), equivalent to 2.7-8.3 cm\(\cdot\)s\({}^{-1}\) (Figure 9b). The directions of the spatial resolution of 4 m varied 192.0-287.5\({}^{\circ}\), while the directions of the spatial resolution resampled to 15 m varied 192.8-284.1\({}^{\circ}\) (Figure 9c). Root mean square errors (RMSEs) and biases were calculated from the parameters (Table 5).
# Superpixel-Based Singular Spectrum Analysis for Effective Spatial-Spectral Feature Extraction
## 2 The Proposed Methodology
### 2d-Ssa  
SSA is capable of decomposing a series into multiple independent components or subseries, where each extracted eigenvalue represents an individual component of the  
Figure 1: Flowchart of the proposed method.  
original series. The SSA can be applied to the respective spectral bands of the hypercube, thereby, decomposing the 2-D scene, and then reconstructing it using the respective main components while removing the noise contribution. As a data cube is decomposed in this way, the local structure and main spatial trends are typically found in the first component. Hence, when all images within the hyperspectral cube are decomposed and only the first components are selected to individually reconstruct each of them, a resulting cube with minimum noise is generated. The SSA can be implemented using the following four steps:
## 4 Discussion and Outlooks  
The quench voltage of the INS coil used in the experiment is 1.3 mV based on 1 \(\mu\)V/cm because the total length of the superconducting tape wound on the INS coil was 13 m as shown in Table 2[18, 19, 20]. In Figure 12a, when the INS coil is in the superconducting state, the standard deviation is 0.8 mV, which is 0.5 mV less than the quench voltage. Also, in Figure 12a, the number of points is 600 and 153 in the superconducting and normal state, respectively. Table 3 shows the confusion matrix based on the results in Figure 12a.  
As shown in Table 3, false positive rate is a ratio that is mistakenly determined as quench when the INS coil is in a superconducting state. Also, true positive rate means the ratio of determination as quench when the INS coil is in a normal state. Therefore, this results mean that the percentage of false alarm is 4.5% when the INS coil is in the superconducting state, and the percentage of detecting quench when it is in the normal state is 100%. However, when the quench voltage is set to three times,  
Figure 12: FFT analysis results of the electromagnetic force (EMF) signal (@70 Hz): (**a**) N = 100, (**b**) N = 500, and (**c**) N = 1000.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Condition Positive** & **Condition Negative** \\ \hline \multirow{2}{*}{Predicted Condition Positive} & True Positive (=153) & False Positive (=27) \\  & True Positive Rate (=1) & False Positive Rate (=0.045) \\ \hline \multirow{2}{*}{Predicted Condition Negative} & False Negative (=0) & True negative (=573) \\  & False Negative Rate (=0) & True negative Rate (=0.955) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Confusion matrix of quench detection.  
the percentage of false alarm converges to 0% [21]. Also the sampling rate for data acquisition was 1 kHz, so resolution was 10 when the window size is 100. Here, the window size is a minimum value in which the spectral leakage does not occur. In this case, the quench detection can be performed every 0.1 s [22; 23]. However, the quench detection time can be performed faster depending on the resonance frequency, sampling rate and window size. Therefore, the effect of applying the proposed system to the INS coil has been confirmed. However, it is necessary to examine further the applicability of this method to the metal clad superconducting coil and the LTS coil [24; 25].  
It was confirmed through experiments that the quench detection method proposed in this paper can increase the reliability of quench detection compared to a system that detects abnormal signals in time domain using a conventional voltage tap. We also confirmed the advantage of reducing detection time, which is one of the most important factors to consider in designing the quench detection system. However, in order to apply this method, a capacitor having a capacity that can be tuned to the resonance frequency of the system is required. In addition, when this method is applied to a system other than HTS rotating machinery, an additional AC source is required to satisfy the resonance condition.
###### Abstract  
Purpose: To assess the performance of 2D T2-weighted (w) Turbo Spin Echo (TSE) MRI reconstructed with a deep learning (DL)-powered super resolution reconstruction (SRR) algorithm combining compressed sensing (CS) denoising and resolution upscaling for high-resolution hippocampal imaging in patients with (epileptic) seizures and suspected hippocampal pathology. Methods: A 2D T2w TSE coronal hippocampal sequence with compressed sense (CS) factor 1 (scan time 2/70 s) and a CS-accelerated sequence with a CS factor of 3 (scan time 103 s) were acquired in 28 patients. Reconstructions using the SRR algorithm (CS 1-SSR-s and CS 3-SSR-s) were additionally obtained in real time. Two readers graded the images twice, based on several metrics (image quality; artifacts; visualization of anatomical details of the internal hippocampal architecture (HIA); visibility of dentate gyrus/pes hippocampi/fornix/mammillary bodies; delineation of gray and white matter). Results: Inter-readout agreement was almost perfect ([PERSON]'s alpha coefficient = 0.933). Compared to the CS 1 sequence, the CS 3 sequence significantly underperformed in all 11 metrics (_p_ < 0.001-_p_ = 0.04), while the CS 1-SRR-s sequence outperformed in terms of overall image quality and visualization of the left HIA and right ppes hippocampi (_p_ < 0.001-_p_ < 0.04) but underperformed in terms of presence of artifacts (_p_ < 0.01). Lastly, relative to the CS 1 sequence, the CS 3-SRR-s sequence was graded worse in terms of presence of artifacts (_p_ < 0.003) but with improved visualization of the right pes hippocampi (_p_ = 0.02). Conclusion: DL-powered SSR demonstrates its capacity to enhance imaging performance by introducing flexibility in T2w hippocampal imaging; it either improves image quality for non-accelerated imaging or preserves acceptable quality in accelerated imaging, with the additional benefit of a reduced scan time.  
magnetic resonance imaging; artificial intelligence; hippocampus; compressed sensing; super resolution reconstruction