esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Attribution of Variation in WUE
#### 3.3.2 Relative Contributions of Driving Factors  
The dominant factor underlying the WUE trends for each pixel was determined by the relative contribution of the driver to the WUE trend. Figure 5a,b show the WUE trend for the GPP-dominated area, which accounted for 79.98% of the total area. In different landscape types, the proportion of the area dominated by GPP exceeded 60%, indicating that the trend of WUE in most areas of the STC was dominated by changes in GPP.  
The relative contribution maps of the three drivers (i.e., climate change, vegetation growth, and elevated atmospheric CO\({}_{2}\) concentration) for the WUE trend during 1982- 2018 are shown in Figure 5c. Figure 5d shows the proportions of areas dominated by different driving factors. In the STC, LAI variation alone dominated the WUE trend in 58.46% of the area. Among the six major landscapes, dominant areas were also all over 50%, indicating that LAI variation is the primary factor in WUE trends across all landscapes. Ca and climate change dominated the WUE trends of 13.79% and 13.43% of the area, respectively. In cropland, the area where elevated Ca was dominant exceeded that of climate variables, indicating that Ca is a more important driver of WUE trends in cropland, while in natural landscapes (i.e., those other than cropland), the area where climate variables were dominant exceeded that of elevated Ca, indicating that climate variables are more important drivers of WUE trends in natural landscapes. Meanwhile, the single-factor categories (Cli, LAI change, or Ca) had a limited ability to control the spatiotemporal dynamics of WUE (controlling the area ratio according to the quantified dominant factor). Excluding the single dominant factors, the coupled effect of LAI change and Ca (Figure 5d) was an important driver of WUE change that dominated 3.61% of the area, while the coupled effect of Cli and LAI change was an important driver that dominated 2.92% of the area. In addition, the proportion of the balanced influence of the three factors accounted for 7.12% of the WUE trend, and the influence of the combined factors was evenly distributed among the various landscapes.  
Figure 5: Relative contributions of different driving factors to annual WUE trends based on pixel statistics. (**a**) Spatial patterns of the relative contributions of GPP and ET to WUE trends. (**b**) Controlling area ratios of GPP and ET for WUE trends. (**c**) Spatial patterns of the relative contributions of climate change (Cli), LAI, and Ca to WUE trends. (**d**) Controlling area ratios of the dominant drivers for WUE trends. The combinations of Cli, LAI, and Ca indicate that the coupled impacts of different drivers dominate WUE trends; “other” indicates the equilibrium impact of Cli, LAI, and Ca.
# Combined Improved CEEMDAN and Wavelet Transform Sea Wave Interference Suppression
## 2 Cross-Media Communication Model
### Problem Description  
There are sea waves on the sea surface, the amplitude of which is much larger than the WSAW's, and the WSAWs are superimposed on it in Figure 1. In order to ensure communication on the surface of the sea, it is necessary to separate the wave interference to realize normal communication from underwater to the water's surface.
# Pipe Routing with Topology Control for Decentralized and Autonomous UAV Networks +
###### Abstract  
This paper considers a decentralized and autonomous wireless network of low SWaP (size, weight, and power) fixed-wing UAVs (unmanned aerial vehicles) used for remote exploration and monitoring of targets in an inaccessible area lacking communication infrastructure. Here, the UAVs collaborate to find target(s) and use routing protocols to forward the sensed data of target(s) to an aerial base station (BS) in real-time through multihop communication, which can then transmit the data to a control center. However, the unpredictability of target locations and the highly dynamic nature of autonomous, decentralized UAV networks result in frequent route breaks or traffic disruptions. Traditional routing schemes cannot quickly adapt to dynamic UAV networks and can incur large control overhead and delays. In addition, their performance suffers from poor network connectivity in sparse networks with multiple objectives (exploration and monitoring of targets), which results in frequent route unavailability. To address these challenges, we propose two routing schemes: Pipe routing and TC-Pipe routing. Pipe routing is a mobility-, congestion-, and energy-aware scheme that discovers routes to the BS on-demand and proactively switches to alternate high-quality routes within a limited region around the routes (referred to as the \"pipe\") when needed. TC-Pipe routing extends this approach by incorporating a decentralized topology control mechanism to help maintain robust connectivity in the pipe region around the routes, resulting in improved route stability and availability. The proposed schemes adopt a novel approach by integrating the topology control with routing protocol and mobility model, and rely only on local information in a distributed manner. Comprehensive evaluations under diverse network and traffic conditions--including UAV density and speed, number of targets, and fault tolerance--show that the proposed schemes improve throughput by reducing flow interruptions and packet drops caused by mobility, congestion, and node failures. At the same time, the impact on coverage performance (measured in terms of coverage and coverage fairness) is minimal, even with multiple targets. Additionally, the performance of both schemes degrades gracefully as the percentage of UAV failures in the network increases. Compared to schemes that use dedicated UAVs as relay nodes to establish a route to the BS when the UAV density is low, Pipe and TC-Pipe routing offer better coverage and connectivity trade-offs, with the TC-Pipe providing the best trade-off.  
u
## 3 Results and Discussion
### ICEO around a Janus Micropillar Free from External Wiring
(**A–D**) A surface and streamline plot of the circulating ICEO flow field around a single Janus pillar of different radii at their respective ideal pumping frequencies: (**A**) R = 2.5 \(\upmu\)m at \(\rm f_{ideal}\) = 1000 Hz, (**B**) R = 5 \(\upmu\)m at \(\rm f_{ideal}\) = 600 Hz, and (**D**) R = 10 \(\upmu\)m at \(\rm f_{ideal}\) = 500 Hz. (**E**) Frequency-dependent pump-flow rate under varying post radii. (**F**) Ideal pumping frequency and peak pump velocity as a function of the radius of Janus pillar. (**G**) A quantitative comparison of the magnitude of tangential ICEO slipping velocity as a function of field frequency for R = 5 \(\upmu\)m. (**H**) Position-dependent ICEO slip velocity on the polarizable pillar surface as a function of the polar angle for different radii of the Janus entity. A study on ICEO streaming adjustable through the AC field-effect flow control of a Janus pillar can offer a theoretical foundation for developing high-performance active microfluidic pumps and mixers. However, at the very start of this process, we first pay attention to the regular ICEO vortex flow pattern when its metal portion is free from external wiring, i.e., \(\rm V_{G}=V_{D}/2\) (Figures 3 and 4). An appropriate parametric space is chosen for preliminary numerical calculation: \(\rm W_{C}=100~{}\mu m\), \(\rm L_{C}=100~{}mm\), and \(\rm R=5~{}\mu m\); \(\rm f=200~{}Hz\) and \(\rm V_{D}=2~{}V\); \(\rm\dot{\epsilon}_{f}=80\epsilon_{\phi}\), \(\rm\dot{\epsilon}_{d}=3\epsilon_{\phi}\), \(\rm\dot{\epsilon}_{m}=10\epsilon_{\phi}\), \(\rm\dot{\sigma}_{f}=0.001~{}S\cdot m^{-1}\), \(\rm\sigma_{d}=0~{}S\cdot m^{-1}\), and \(\rm\sigma_{m}=10^{8}~{}S\cdot m^{-1}\); and \(\rm\eta=0.001~{}Pa\cdot s\), \(\rm C_{S}=0.8~{}F\cdot m^{-2}\), \(\rm\lambda_{D}=37.6~{}nm\), and \(\rm C_{D}=0.019~{}F\cdot m^{-2}\). In the early stage, the liquid medium is assumed to possess an ionic strength of \(0.001~{}S\cdot m^{-1}\) and a real permittivity of \(80\epsilon_{\phi}\), resulting in a Debye relaxation frequency of \(f_{\rm MW,f}=\sigma_{f}/2\pi\dot{\epsilon}_{f}=225~{}kHz\) for the onset of the bulk ion screening and a double-layer thickness \(\rm\lambda_{D}=37.6~{}nm\), with \(\rm\lambda_{D}/R\)\(=0.0075<0.01\), approximately satisfying the thin-layer limit.
## 3 Results
### Aroma-Active Compounds in Strong-Aroma Baijiu
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multicolumn{2}{c}{**Aroma**} & \multicolumn{2}{c}{**Odor**} & \multirow{2}{*}{**Fraction**} & \multicolumn{2}{c}{**RI**} & \multicolumn{2}{c}{**Intensity**} & \multicolumn{2}{c}{**Identification**} \\  & \multicolumn{2}{c}{**Compounds**} & \multicolumn{2}{c}{**Description**} & & \multicolumn{1}{c}{**(DB-WAQ)**} & \multicolumn{1}{c}{**LRI**} & \multicolumn{1}{c}{**} & \multicolumn{1}{c}{**GJ**} & \multicolumn{1}{c}{**TZL**} & \multicolumn{1}{c}{**WLC**} & \multicolumn{1}{c}{**WLY**} & \multicolumn{1}{c}{**Methods**} \\ \hline
1 & Acetaldehyde & Pungent & NBF & 714 & 728 & 2.5 & 2.3 & 1.3 & 1 & RL, Arroma, MS, Std \\
2 & Isobutyraldehyde & Pungent, Malt, & NBF & 821 & 826 & 2.7 & 2.5 & 2.7 & 2 & RL, Arroma, MS, Std \\  & Green & & & & & & & & & \\
3 & Ethyl acetate & Pineapple & NBF/AF & 888 & 890 & 2.5 & 3.2 & 3.5 & 3 & RL, Arroma, MS, Std \\
4 & Ethyl propionate & Fruity & NBF/AF & 951 & 943 & 2 & 2 & 1.5 & 2.5 & RL, Arroma, MS, Std \\
5 & Ethyl isobutyrate & Fruity, Sweet, & & & & & & & & \\
6 & 2.3-Butanedione & Butter & AF & 975 & 981 & 1.5 & 1.5 & 1 & 3 & RL, Arroma, MS, Std \\
7 & Isobutyral acetate & Fruity, Apple, & & & & & & & & \\  & Baana & & & & & & & & & \\  & Gasoline, & & & & & & & & & \\
8 & 2-Pentanone & & & & & & & & & \\  & Rubber & & & & & & & & & \\
9 & 2-Butanol & Wine, Fruity & NBF & 1024 & 1022 & 1 & 1.3 & 1.3 & 1.3 & RL, Arroma, MS, Std \\
10 & Ethyl butyrate & Apple & NBF/AF & 1035 & 1028 & 3.3 & 3.2 & 3.7 & 3.3 & RL, Arroma, MS, Std \\
11 & 1-Propanol & Alcohol, Fruity, & & & & & & & & \\  & Ethyl & & & & & & & & & \\  & Ethyl & Apple & NBF & 1050 & 1053 & 2.8 & 1.7 & 2.1 & 1.5 & RL, Arroma, MS, Std \\
13 & Ethyl isovalerate & Fruity & NBF/AF & 1068 & 1070 & 2 & 2 & 1 & 1.5 & RL, Arroma, MS, Std \\
14 & Hexanal & Grass & NBF & 1078 & 1083 & - & 1 & 1 & 1.5 & RL, Arroma, MS, Std \\
15 & 2-Methyl-1- & & & & & & & & & \\  & proposed & & & & & & & & & \\
16 & 2-Pentanol & Green, Apple & NBF & 1118 & 1116 & 1 & 2 & 1 & 1.8 & RL, Arroma, MS, Std \\
17 & Isoamyl acetate & Banana & NBF & 1124 & 1125 & 2.8 & 2.5 & 2 & 2.5 & RL, Arroma, MS, Std \\
18 & Ethyl valerate & Fruity & NBF & 1134 & 1135 & 3 & 3.3 & 3.5 & 3.7 & RL, Arroma, MS, Std \\
19 & 1-Butanol & Fruity, Sweet & NBF/AF & 1142 & 1141 & 2.3 & 2.7 & 2.5 & 1 & RL, Arroma, MS, Std \\
20 & Amy acetate & Fruity, Banana & NBF & 1175 & 1178 & - & - & 1 & 1 & RL, Arroma, MS, Std \\  & Fruity, & & & & & & & & & \\  & Fragrance & NBF & 1184 & 1184 & 1.5 & 1.8 & 2.3 & 2 & RL, Arroma, MS, Std \\
22 & 2-Heptanone & Pungent & NBF & 1182 & 1190 & - & - & 1 & 1 & RL, Arroma, MS, Std \\
23 & Ethyl 4- & Fruity & NBF & 1190 & 1192 & - & 1.5 & 1.5 & 2.5 & RL, Arroma, MS \\  & methylpentanoate & & & & & & & & & \\
24 & 3-Methyl-1-butanol & Pungent & NBF/AF & 1209 & 1202 & 3.8 & 3.5 & 3.8 & 2.8 & RL, Arroma, MS, Std \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The aroma compounds detected in representative strong-aroma baijiu by GC-O.
# Disturbances Generated by Lighting Systems with LED Lamps and the Reduction in Their Impacts
###### Abstract  
The paper deals with electromagnetic disturbances in the form of current higher harmonics, which are generated by LED lighting elements. It presents the problems related to the formation and impact of higher harmonics in the electrical systems of commercial facilities. The results of tests and analyses of current distortions for two different LED lamps are included, and these are in reference to the parameters set out in the normal applicable standards. A system was then proposed to improve the quality of energy in the mains of commercial facilities in the form of a two-stage power supply. Tests of the systems with the aforementioned LED lamps were conducted and commented upon after introduction of the two-stage power supply. The final part of the paper contains a summary of the obtained test results.  
LED lamp; lighting system; current and voltage distortion; higher harmonics; total harmonic distortion (THD); electromagnetic compatibility; power factor correction (PFC); two-stage power supply
# An Implementation of the HDBSCAN* Clustering Algorithm
## 2 Background and Literature Review
### Hdbscan
#### 2.2.2 The HDBSCAN* Algorithm
The prominent clusters identified using the simple dataset are shown in Figure 5. Three clusters are identified, and the five yellow points are determined to be outliers. Another important artifact, that can be obtained from the execution of the HDBSCAN* algorithm, are the outlier scores for each data point. The literature [8] names this as a point's GLOSH which stands for Global-Local Outlier Score from Hierarchies. Fortunately, computing a data point's GLOSH is not too complex, but requires that some additional bookkeeping was done during the construction of the HDBSCAN* hierarchy. Earlier in this section, it was mentioned that the weight value of the edge being removed is used to denote the current hierarchical level. This weight value, and the last cluster label must be noted for every point, at the moment the point is marked as noise, or assigned the noise label, to use the same wording as before. A data point, \(x\), has the values \(\epsilon\) and \(\epsilon_{max}\), which are: the weight value of the point right before it was marked as noise, and the lowest propagated child death level from its last labeled cluster, respectively. The value of the outlier score for a point \(x\) can be obtained using Equation (2). \[GLOSH(x)=1-\frac{\epsilon_{max}(x)}{\epsilon(x)} \tag{2}\]  
Figure 5: The clustering result produced by HDBSCAN* using the simple dataset. This section is summarized by Algorithm 1 which shows the main steps of the HDBSCAN* algorithm. ```
Compute the core distance for the k nearest neighbors for all points in the dataset;  Compute the extended minimum spanning tree from a weighted graph, where the mutual reachability distances are the edges;  Build the HDBSCAN* hierarchy from the extended minimum spanning tree;  Find the prominent clusters from the hierarchy;  Calculate the outlier scores;
```  
**Algorithm 1**HDBSCAN* main steps.
## 3 Results and Discussion
### Content of Ascorbic Acid  
The highest content of ascorbic acid was detected in the PI flowers with 11.41 mg (AA)/g DW, whereas the rest of the flowers presented minimum concentrations (Table 2). The results in this study for CA, XU and XC flowers were lower compared to those reported in nasturtium flowers (_Tropaeolum majus_ L.) (_[PERSON] et al._, 2019), agave (_Agave salmiana_), aloe (_Aloe vera_), coral tree (_Erythrina americana_) and garambullo (_Myrtillocatus geometrizans_) (_[PERSON] et al._, 2019). Similarly, higher ascorbic acid contents were reported in other flowers including wild bergamot (_Monarda didyma_ L.), dragonfly (_Antirithinum majus_ L.), Chinese carnation (_Diantthus chinensis_ L.) and morning lily (_Hemercallis_\(\times\)_hybrida_) (_[PERSON] et al._, 2019). However, PI stands out for its high content of ascorbic acid compared to the data reported in the literature on edible flowers. In this regard, ascorbic acid is considered a natural organic compound, with powerful antioxidant properties (_[PERSON] et al._, 2019).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Antioxidant Compounds**} & \multicolumn{4}{c}{**Flowers**} \\ \cline{2-5}  & **Cardón** & **X. ‘Ulapa’** & **X. ‘Cuaresmenño’** & **Pitaya** \\  & **(_C. rosea_)** & **_O. oligacantha_** & **_O. matudane_** & **(_E. cinarescens_)** \\ \hline Carotenoids mg/g DW & & & & \\ Red carotenoids & 0.26 \(\pm\) 0.02 a & 0.16 \(\pm\) 0.01 c & 0.20 \(\pm\) 0.02 b & 0.24 \(\pm\) 0.01 ab \\ Yellow carotenoids & ND & ND & 0.02 \(\pm\) 0.01 & ND \\ Total carotenoids & 0.26 \(\pm\) 0.02 a & 0.16 \(\pm\) 0.01 c & 0.22 \(\pm\) 0.01 b & 0.24 \(\pm\) 0.01 ab \\ Chlorophyll mg/g DW & & & & \\ Chlorophyll a & 29.03 \(\pm\) 1.06 a & 16.43 \(\pm\) 0.96 d & 20.09 \(\pm\) 1.19 c & 25.52 \(\pm\) 0.89 b \\ Chlorophyll b & 36.99 \(\pm\) 2.15 a & 21.52 \(\pm\) 2.25 c & 29.26 \(\pm\) 2.35 b & 32.92 \(\pm\) 1.97 ab \\ Total chlorophyll & 66.02 \(\pm\) 3.21 a & 37.95 \(\pm\) 3.21 d & 49.35 \(\pm\) 2.27 c & 58.43 \(\pm\) 2.75 b \\ Ascorbic acid mg AA/g DW & 0.23 \(\pm\) 0.06 b & 0.12 \(\pm\) 0.02 b & 0.08 \(\pm\) 0.02 b & 14.41 \(\pm\) 0.10 a \\ Total phenols mg GAE/g DW & 5.29 \(\pm\) 0.02 d & 7.01 \(\pm\) 0.05 c & 9.43 \(\pm\) 0.1 b & 44.63 \(\pm\) 0.50 a \\ Total flavonoids mg QE/g DW & 9.93 \(\pm\) 0.08 d & 11.54 \(\pm\) 0.04 c & 12.96 \(\pm\) 0.10 b & 40.61 \(\pm\) 0.64 a \\ \hline \hline \end{tabular}  
* Cardón (_Cylindropuntia rosen_), Xoconostle var. Ulapa (_Opuntia olgismuth_), Xoconostle var. Cuaresmenño rosado (_Opuntia matudane_), Pitaya (_Echinecreens_). AA, ascorbic acid; GAE, gallic acid equivalents; QE, quercetin equivalents. Values represent the mean \(\pm\) standard deviation (\(n=3\)). Values with different letters in the same row indicate statistically significant differences (\(p\leq 0.05\)) according to [PERSON]’s multiple comparison test of means. ND = not detected.  
\end{table}
Table 2: Antioxidant compounds in cactus flowers from Hidalgo, Mexico.
# Telemedicine: A Survey of Telecommunication Technologies, Developments, and Challenges
## 8 Research Challenges and Future Directions
### Classification of Research Challenges in Telemedicine
#### 8.1.10 Energy Efficiency  
Most wearable devices demand enhanced power for measuring and communicating user data. Because wearable devices are often required to be small, the batteries are expected to be in miniature form, and need to be able to sustain the energy required for the task as well. The size and capacity of batteries is an important factor in the design of wearable telemedicine devices. For energy efficiency, devices should have low power demands and be capable of saving energy when not in operation.
# Accurate Characterization of the Properties of the Rare-Earth-Doped Crystal for Laser Cooling
## 1 Introduction
Here we adopt the LITMoS method at room temperature to accurately characterize a sample material in its external quantum efficiency \(\eta_{ext}\) and background absorption coefficient \(\alpha_{b}\), two crucial laser-cooling parameters for assessing its corresponding cooling grade. The accuracy of the characterization depends on the precise measurement in situ of the laser-induced temperature change of the sample. A well-calibrated thermal camera is adopted to contactlessly measure the temperature of the sample in the LITMoS test. The calibration of the thermal camera is described in detail with a temperature resolution better than 0.1 K. The cooling window of a Czochralski-grown 5% Yb\({}^{3+}\):LuLiF\({}_{4}\) crystal of size 2 \(\times\) 2 \(\times\) 5 mm\({}^{3}\) is deduced from the LITMoS-measured cooling parameters. Corresponding results indicate that this crystal sample can be potentially cooled to about 110 K.
## 2 Proposed Integrated Mobile VLCP Scheme
### Proposed Scheme for MISO Configuration
To enhance positioning accuracy, we can select different groups of \(\hat{D}_{i}^{n}\) to obtain multiple solutions, i.e., (\(\hat{x}_{r1}^{q}\), \(\hat{y}_{r1}^{q}\)) (\(q=1\), \(2\), \(3\), \(4\)), and then take their average value ((\(\hat{x}_{r1}^{1}+\hat{x}_{r1}^{2}+\hat{x}_{r1}^{3}+\hat{x}_{r1}^{4})/4\), (\(\hat{y}_{r1}^{1}+\hat{y}_{r1}^{2}+\hat{y}_{r1}^{3}+\hat{y}_{r1}^{4})/4\)) as the final estimated horizontal coordinate of the receiver. It is worth noting that Equation (4) assumes equal weight coefficients for the VLCP data blocks within the observation block. When the receiver is static, maintaining the same weight coefficient does not affect CSI accuracy. However, for a mobile receiver, where the channel varies over time, the estimated channel from different VLCP data blocks varies as well. Specifically, the closer the VLCP data blocks, the greater their impact on the channel estimation of the current block. Therefore, optimizing the weight coefficients can lead to better performance. Generally, closer blocks are assigned higher weights, with the neighboring block potentially having the highest weight. By considering the unequal weight coefficients, the CSI used for subsequent channel estimation in the next VLCP data block can be optimized as follows:  
\[\mathbf{\hat{H}}^{n+1}(k)=\sum_{p=n-L+1}^{n}\frac{\alpha^{n-p}(1-\alpha)}{1- \alpha^{L}}\mathbf{Y}^{p}(k)\big{[}\hat{\mathbf{X}}^{p}(k)\big{]}^{-1}, \tag{9}\]  
where \(\alpha\) represents a forgotten factor (\(0<\alpha<1\)).
# Diffusion-Denoising Process with Gated U-Net for High-Quality Document Binarization
## 5 Conclusions  
In this paper, we introduce a novel document binarization model rooted in LDMs that offers intricate text-stroke extraction and robustness against unanticipated degradation. For the first time, we use the diffusion-denoising process within the latent space to produce high-quality, clean, binarized document images. Furthermore, our denoising network takes the form of a gated U-Net, incorporating a fully gated convolution to safeguard text strokes in significantly degraded document images. Within this framework, we introduce a filter to update the gating values, ensuring the preservation of text regions. Using a pixel-level loss to dynamically update the filter for the gating values, we effectively safeguard text strokes, even in highly degraded images. Our extensive experiments on challenging benchmark datasets validate the effectiveness of our proposed model. Quantitative experimental results affirm the remarkable performance enhancements achieved by the proposed model in text-stroke preservation and extraction, particularly when compared to existing methods on the H-DISCO datasets. Through diverse qualitative evaluations, we demonstrate how our model effectively overcomes the limitations of existing approaches and adeptly accomplishes text and background classification tasks. However, as illustrated in Figure 9,  
Figure 11: Binarization results of a sample image from DIBCO 2016. (**a**) Degraded image, (**b**) ground-truth image, (**c**) baseline, (**d**) our model w/o \(L_{pix}\), (**e**) our model w/o gated U-Net, and (**f**) our model.  
Figure 12: Binarization results of a sample image to confirm the effect of preserving the text region from DIBCO 2016. (**a**) Degraded image, (**b**) ground-truth image, (**c**) baseline, (**d**) our model w/o \(L_{pix}\), (**e**) our model w/o gated U-Net, and (**f**) our model.  
we acknowledge that our model cannot achieve flawless binarization performance in scenarios featuring untrained noise. In future work, we anticipate that research focusing on data generalization will address these challenges, ultimately rendering our model adaptable to various tasks that rely on binary masks.  
Conceptualization, S.H. and J.R.; methodology S.H.; software, S.H.; validation, S.H. and S.J.; investigation, S.H.; writing--original draft preparation, S.H. and S.J.; writing--review and editing, S.H. and S.J.; visualization, S.H. and S.J.; supervision, J.R.; project administration, J.R.; funding acquisition, J.R. All authors have read and agreed to the published version of the manuscript.  
This research was financially supported by the Ministry of Trade, Industry, and Energy (MOTIE) and the Korea Institute for the Advancement of Technology (KIAT) through the International Cooperative R&D program (Project No. P0016096).  
Not applicable.  
The authors declare no conflict of interest.