esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
In general, these inhibitors react with the corrosion product, initially formed, resulting in a cohesive and insoluble film on the metal surface. Mixed inhibitors act by reducing both cathodic and anodic reactions. They are typically film-forming compounds that cause precipitate formation on the surface, indirectly protecting the anodic and cathodic regions, usually protecting the metal by adsorption mechanisms [2; 11; 12; 13]. Some inhibitors, however, are potentially polluting and harmful to human health, and this motivated the search for new formulations. Studies on the use of natural products, called \"green inhibitors\" or \"ecological inhibitors\" of corrosion, have been recurrent in the last 20 years [14]. This class is characterized by biocompatible, biodegradable substances from renewable sources, which are easy to acquire, low cost and nontoxic [15; 16; 17]. Inhibitors of this nature are usually based on organic compounds, generally acting as mixed-type inhibitors, as they have heteroatoms (i.e., N, S, O), electronegative groups and conjugated double bonds, characteristics present in good corrosion inhibitors as the main centers of adsorption. These inhibitors adsorb and form a covalent bond on the metal surface. Organic inhibitors cover the entire surface area of the metal with a thick film composed of several monolayers and change the structure of the electrical double layer at the metal interface, decreasing the corrosion rate. They can also act as a barrier film that blocks anodic and cathodic active sites or slows the rate of diffusion of electroactive species to the metal surface [12; 18; 19; 20]. Plant extracts stood out within this class of inhibitors. They are defined as a reservoir of naturally synthesized compounds with easy insulation processes. They have a clear advantage over the exhaustive synthesis of compounds and their toxic effects, besides being abundant in nature. Most times natural products can also be less expensive than synthetic ones. Their success as green corrosion inhibitors is attributed to the presence of complex and effective constituents in reducing corrosion rates, such as tannins, flavonoids, alkaloids, anthraquinones, polyphenols, organic acids and nitrogen-based compounds [21; 22; 23; 24]. Considering the strategic importance of the use of corrosion inhibitors and the potential of natural products for this purpose, in the present work bibliographic and patent surveys were carried out on the applicability of plant extracts as green corrosion inhibitors.
## 4 Quality Assessment Methodology
### Validation Methods
#### 4.2.3 Ground-Based Validation  
The main steps in the accuracy assessment of albedo products include the generation of blue-sky albedo [30] for a direct comparison with in-situ measurements and the test of the spatial representativeness of the in situ albedoter footprints for the satellite pixel resolution of interest according to in situ measurements standards [31; 32]. Consequently, a careful selection of ground points and the characterization of their spatial representativeness are crucial for a meaningful point-to-pixel comparison, as presented in Section 3.2. For the first step, the blue-sky albedo from a satellite is estimated from the retrieved AL-DH-BB and AL-BH-BB satellite EO product under study, and weighted by the fraction of diffuse down-welling shortwave radiation from the ground station [30]. The next step is the careful selection of the homogeneous sites, which are similar to the footprints of the satellite pixel resolution of interest. The accuracy assessment of the SA satellite products was performed against the measurements coming from the 20 selected GBOV homogeneous stations presented in Section 3.  
The study here was extended to the period from 2014 to 2018 to have the maximum concomitant samples from the GBOV dataset to PROBA-V observations. The accuracy assessment of MCD43A3 C6 for the same period and sampling was performed for benchmarking. This exercise was carried out at a resolution of 1 km, which is equivalent to one pixel in the case of PROBA-V SA v1.0, and averages of \(2\times 2\) pixels in the case of MCD43A3 C6. The 10 day frequency of C3S products was used as temporal sampling, and the average values of the daily ground data were computed to compare with satellite estimations during the corresponding temporal composite window of each product (see Table 1). This analysis was performed over the best quality pixels of C3S PROBA-V SA v1.0 and MCD43A3 C6, according to QFLAGS (Table 3).
## 5 Experimental Research on the Positioning of Armored Composite Materials
### Experimental Study on Multi-Damage Imaging Localization
Figure 13: _Cont._  
Table 9 provides the coordinates and error statistics for the localization of each damage position. In the second group, the imaging localization of damage 1 yielded a center position of (148, 135), and the actual center position of the damage was (150, 140), with a horizontal error of 2 mm, vertical error of 5 mm, and radial error of 5.39 mm. For damage 2, the imaging localization yielded a center position of (99, 96), and the actual center position of the damage was (100, 100), with a horizontal error of 1 mm, vertical error of 4 mm, and radial error of 4.12 mm. The imaging and localization of damage in armored composite structures utilize the time-reversal focusing method. The response signal undergoes Shannon continuous complex wavelet transform to extract narrowband signals, and the direct wave portion of the damage scattering signal is chosen for time reversal. The time-reversed signal is delayed by the distance from the unit point in the monitoring area to the piezoelectric sensor,  
Figure 13: The imaging localization results of each group’s damage. (**a**) Damage in Group 1; (**b**) damage in Group 2; (**c**) damage in Group 3; (**d**) damage in Group 4; (**e**) damage in Group 2. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Group**} & **Actual** & **Image** & \\  & **Coordinates/mm** & **Coordinates/mm** & **Radial Error/mm** \\ \hline Group 1 & (70, 90) & (69, 86) & 4.12 \\  & (30, 50) & (34, 53) & 5 \\ Group 2 & (150, 140) & (148, 135) & 5.39 \\  & (100, 100) & (99, 96) & 4.12 \\ Group 3 & (120, 80) & (119, 82) & 2.24 \\  & (130, 30) & (134, 29) & 4.12 \\  & (70, 140) & (66, 137) & 5 \\ Group 4 & (50, 50) & (54, 52) & 4.48 \\  & (90, 90) & (89, 88) & 2.24 \\ Group 5 & (90, 50) & (90, 53) & 3 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: The coordinates and error statistics of the imaging localization for each damage position. resulting in a damage imaging map of the monitoring area. The positioning errors for single and multiple damages in this method are within 4.12 mm and 5.39 mm, respectively.
# Landslide Susceptibility Assessment Using the Geographical-Optimal-Similarity Model
## 4 Results  
### Analysis of Landslide Sample Set Construction Results  
Figure 5 depicts the correlation analysis results of each feature factor using Pearson's correlation coefficient method. The findings suggested that most absolute correlation coefficients were below 0.2, showing low correlation, with a maximum value of only 0.44. Given the weak correlations among factors and low feature redundancy, no features hadto be eliminated. Therefore, the subsequent research used all features for non-landslide point selection.  
The outcomes of multicollinearity testing for the sample set indicate that VIF values for 11 feature factors range from 1 to 3 (Table 3), with the elevation having the highest VIF value of 2.57, suggesting the strongest multicollinearity among the features. The TOL values for all feature factors ranged between 0.3 and 1, with the aspect having a value close to 1, suggesting negligible collinearity. These findings demonstrated that the VIF and TOL values for the selected 11 landslide influencing factors fulfilled the threshold requirements, showing great independence and no collinearity issues, thus making them suitable for subsequent model development.
# Energy-Efficient Wireless Hopping Sensor Relocation Based on Prediction of Terrain Conditions
## 5 Conclusions  
In the wireless sensor network, hundreds of sensors are dispatched in the desired area and sensors are continuously operating. Various previous studies were conducted, including the proper placement of sensors and the selection of cluster headers to transmit the collected data to the desired sink nodes. However, wireless sensors are inherently energy-limited, thus the appearance of the sensing hole cannot be avoidable and sensing hole recovery to maintain network operation is necessary. In particular, since the charging system of the sensor is not always available, it is essential to consider energy-efficiency from the time the sensor is relocated to recover the sensing hole.  
While there has been vigorous study of hopping mobility models suitable for rough areas recently, the majority of past studies have been based on not practical classic theory (i.e., relocation schemes based on source-based routing). In this paper, overcoming these problems, a new hopping sensor relocation protocol is proposed to suit the real distributed environment. To reflect the real situations, obstacles of a given area (stones, mud, etc.) have been considered. Since the status of obstacles around the sensing hole is unknown, the condition of the surrounding environment is predicted through the success rate of the hopping sensor relocation.  
We would strongly emphasize that the relocation protocol proposed by this research team is a protocol in a distributed environment and the successful implementation of OMNeT++ simulation in the hopping sensor relocation protocol is our unique skill in this field of research. Various obstacles were considered for performance evaluation and analysis. Finally, superior energy efficiency with delaying sensing hole appearance of the proposed scheme was demonstrated.  
Figure 10: The moments for sensing hole appearance at the middle cluster zone.  
**Author Contributions:** [PERSON] and [PERSON] are the co-first authors and contributed equally. [PERSON], [PERSON], and [PERSON] designed the protocol and the simulation process. [PERSON], [PERSON], and [PERSON] coordinated the grant funding, conducted the study, analyzed the data, and drafted the first version of the manuscript. The simulation was conducted by [PERSON] and [PERSON], and [PERSON] supervised the software development. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by Research Assistance Program (2019) in the Incheon National University and the National Research Foundation of Korea (NRF) grant funded by the Ministry of Science, ICT & Future Planning (No. NRF - 2019R1G1A1007832).  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Energy-Efficient On-Off Power Control of Femto-Cell Base Stations for Cooperative Cellular Networks
###### Abstract  
Improving energy efficiency (EE) of mobile communication systems (MCSs) has been considered a key aim in recent years, and has been the subject of intense research interest. One of the simplest yet most powerful ways to increase the EE is to turn off redundant communication entities whose operation does not greatly affect the overall performance of the MCS. In this paper, we propose a novel on-off power control scheme for femto-cell base stations (FBSs) considering cooperative transmission in which multiple FBSs collaborate on the same data transmission. In the proposed scheme, the operation of the redundant FBSs is halted in an adaptive manner. For the proper determination of redundant FBSs with low computational complexity, we propose using the level of contribution (LOC), which specifies the importance of a given FBS in the cooperative transmission. Redundant FBSs are chosen based on their LOC value, and these FBSs are turned off in order to reduce the power consumption of MCSs while minimizing the degradation of the overall throughput of cooperative transmissions. The performance of the proposed scheme is verified through extensive simulations, which shows that near-optimal performance can be achieved without excessive computations.  
on-off power control; BS Switching-off; femto-cell BS; cooperative transmission; optimization problem +
Footnote †: journal: Applied Sciences
# Non-Contact Heart Rate Detection Based on Hand Vein Transillumination Imaging
## 2 Materials and Methods  
### Acquisition of Vein Transmission Images  
The transmission image acquisition device used to obtain the pulse wave of a hand is shown in Figure 1. It consists of an infrared light-emitting tube, an 850 nm narrowband filter (manufacturer: Hua Shang Laser, model: HS-850 LGP-F4.5), a 150\({}^{\circ}\) fisheye lens camera (model: 3200_720P), a computer (model: HP Pavilion Gaming Desktop 690-05 xx), and MATLAB software (MATLAB version: R2018a) which is employed to further process images. The infrared light-emitting tube light source used in the experiment has an emission angle of 45\({}^{\circ}\), a voltage of 1.3-1.6 V, a current of 20-30 mA, and a peak wavelength of 850 nm. The center wavelength of the narrow-band filter is 850 \(\pm\) 10 nm, the half-band is 30 nm, the peak transmittance is > 86%, and the ring depth is <1%. The camera frame rate is 20 fps, the output resolution is set to 1280 * 720, and the output format is set to JPG. In the process of data collection, the light source directly illuminates the hand, and the light passing through the hand is received by the camera and then transferred to the PC via USB for storage and post-processing. The entirety of the above-mentioned experiment was carried out in a closed environment to avoid interference from ambient light.  
This experiment was performed on 10 subjects, and 10 sets of data were collected for each participant. Each set of data includes a heart rate measured by a blood pressure meter, which is treated as the true heart rate. Before data collection, the subjects were instructed to relax for a period of time while maintaining steady breathing and a stable emotional state. During data collection, the subjects held the near-infrared light source and tried to keep their hands steady. The data were collected by camera, and the measurement time of each subject in the experiment was 20 s. Image acquisition was performed on the basis of the experimental device to obtain the original vein transmission image (a set of data is used as an example for illustration below).  
Infrared light serves as the experimental light source. The light source was held in the palm and the camera was located on the other side. The light source was turned on and video images of the light transmitted through the hand were recorded. A 20 s video was obtained, from which 400 image frames were extracted. Since the data obtained in the initial stage of acquisition may be unstable, 300 frames of the intermediate acquisition stage are selected for experimental data processing and analysis, one of which is shown in Figure 2a. The region of interest (ROI) was determined, and the corresponding interception for each frame was performed to obtain the image corresponding to the ROI, which is expressed as \(I_{\mathrm{i}}\) (\(i\) = 1, 2... \(m\), \(m\) = 300), as shown in Figure 2b. A gray-scale operation was performed to obtain a single-channel image corresponding to the ROI, denoted as \(I_{gray}\), one of which is shown in Figure 2c.  
Figure 1: The diagram of the experimental device.
# Design of Universal Code Generator for Multi-Constellation Multi-Frequency GNSS Receiver
## 3 Code Generators
### Universal Code Generators Based on LFSR
#### 3.1.1 Universal Ranging Code Generator  
The Universal Ranging Code Generator, as presented in reference [2] and depicted in Figure 8, comprises two 14-bit linear feedback shift registers, namely SR 1 and SR 2. The initial state of each shift register is stored in their respective CODE STATE 1 and CODE STATE 2 registers. The bitmasks stored in the CODE BITMASK 1 and CODE BITMASK 2 registers control the feedback switches. Similarly, the bitmasks stored in the CODE OUT BITMASK 1 and CODE OUT BITMASK 2 registers determine the output switches.  
To maximize the utilization of resources within the universal code generator structure, the structure employs two operational modes.  
1. Two separate 14-bit registers (as shown in Figure 8);  
2. The two registers are combined to form a 28-bit shift register (as shown in Figure 9).  
Figure 7: IRNSS IZ4 Code Generator.  
In the first structure, the PRN code generator comprises two m-sequence generators. Each generator corresponds to a specific PRN number, resulting in distinct initial states and feedback points. During each code cycle, the feedback value is obtained through modulo-2 operations on the bits specified by CODE BITMASK 1 and CODE BITMASK 2 registers. Subsequently, Registers SR 1 and SR 2 undergo left shifts at each cycle, followed by writing the obtained feedback values into the lowest-order bits of SR 1 and SR 2, respectively.  
In the second mode, the two shift registers are combined into a single 28-bit shift register, enabling the generation of spread spectrum codes for signals that utilize 27-bit registers (as depicted in Figure 9). During each operational cycle, Shift Registers SR 1 and SR 2 undergo left shifts, with the highest-order bit of SR 1 being written into the lowest-order bit of SR 2. The feedback value is calculated through modulo-2 summation using the bits specified by the CODE BITMASK 1 and CODE BITMASK 2 registers, and the resulting feedback value is written into the lowest-order bit of SR 1.  
In each mode, the output sequence is formed by taking the modulo-2 sum of the bits specified by the bit masks stored in the CODE OUT BITMASK 1 and CODE OUT BITMASK 2 registers.
## 4 Game Theoretic Client Selection Model
### Client Selection Algorithm  
As shown in Algorithm 1, at each round, all clients decide simultaneously. Participation is based on whether the reward exceeds a threshold.  
```
0: Accuracy \(A_{i}\), Battery \(B_{i}\), Privacy \(P_{i}\) for each client \(i\); threshold \(\tau\)
0: Selected set of clients \(\mathcal{S}_{t}\)
1:for each client \(i\in\mathcal{C}\)do
2: Assign \(\omega_{A},\omega_{B},\omega_{P}\) based on device type
3:\(R_{i}\leftarrow\omega_{A}A_{i}+\omega_{B}B_{i}+\omega_{P}P_{i}\) reward (payoff) from accuracy, battery, privacy
4:if\(R_{i}\geq\tau\)then
5:\(a_{i}\gets 1\) participate
6:else
7:\(a_{i}\gets 0\) skip
8:endif
9:endfor
10:\(\mathcal{S}_{t}\leftarrow\{\,i\in\mathcal{C}\mid a_{i}=1\}\)
11:return\(\mathcal{S}_{t}\)
```  
**Algorithm 1** Nash-Based Client Participation Decision
## 5 Results and Discussion
### Subjective Evaluations
For every pair of samples, listeners were asked to listen to utterances presented in a random order and decide which of the two samples was better in terms of speech quality. The obtained results of the AB test on naturalness and similarity are respectively presented in Figure 5 along with the average of the scores with 95% confidence interval. Overall, we can see in Figure 5b,d that the proposed method outperforms the baseline S2 and S4 in terms of naturalness and speaker similarity of the converted speech. In Figure 5a, comparing the baseline S1 to our proposed system on naturalness, we see that 50.34% of the listeners prefer the proposed method, 44.66% prefer the baseline method, and the remaining 5% show no significant preference between the two converted samples. On the other hand, when comparing the baseline S3 to our proposed system on similarity, Figure 5c shows that listeners preferred S3, with a preference rate of 50%. In Figure 5d, we see the preference of our system in both naturalness and similarity, with an average preference rate of 60.67% compared to 11.67% for baseline S4. This indicates that our proposed system outperforms the S2 and S4 baseline systems in terms of naturalness and similarity scores while showing comparable performance to the S3 baseline system. Therefore, based on the above test results, it can be concluded that our non-parallel VC system significantly outperforms the baseline non-parallel methods and can achieve a level of conversion performance comparable to that of parallel VC methods, both in terms of speech quality and similarity to the target speaker. Figure 4: ABX results with 95% confidence intervals on similarity obtained from Female-to-Female and Male-to-Male conversion pairs.
## 3 Data and Methodology
### Storm Surge Analysis  
The Sea, Lake, and Overland Surges from Hurricanes (SLOSH) model is a two-dimensional operational storm surge model developed by the National Weather Service [34]. The SLOSH model uses pressure, size, forward speed, and tracking data to calculate the storm surge height of a tropical cyclone to create a wind field model that pushes water around [34]. The composite approach within the SLOSH uses hypothetical hurricanes to run thousands of synthetic hurricane scenarios [34]. This method produces two products, including the maximum envelope of water (MEOWs) and the maximum of MEOWs (MOMs). The MEOW product is the maximum height of the storm surge reached at each point in the model domain when thousands of synthetic storms with the same storm parameters but different parallel paths are modeled [35]. The MOM product is the maximum storm surge height obtained after merging all MEOW surge results from Saffir-Simpson storms of the same category [35]. In this study, the inundation maps of hurricane categories 1 and 2 were created for 889 sub-basins in NYC through GIS.