esa-sceva/satcom-chunk-collection huggingface dataset:
# Small-UAV Radar Imaging System Performance with GPS and CDGPS Based Motion Compensation
###### Abstract  
The present manuscript faces the problem of performing high-resolution Unmanned Aerial Vehicle (UAV) radar imaging in sounder modality, i.e., into the vertical plane defined by the along-track and the nadir directions. Data are collected by means of a light and compact UAV radar prototype; flight trajectory information is provided by two positioning estimation techniques: standalone Global Positioning System (GPS) and Carrier based Differential Global Positioning System (CDGPS). The radar imaging is formulated as a linear inverse scattering problem and a motion compensation (MoCo) procedure, accounting for GPS or CDGPS positioning, is adopted. The implementation of the imaging scheme, which is based on the Truncated Singular Value Decomposition, is made efficient by the Shift and Zoom approach. Two independent flight tests involving different kind of targets are considered to test the imaging strategy. The results show that the CDGPS supports suitable imaging performance in all the considered test cases. On the other hand, satisfactory performance is also possible by using standalone GPS when the meter-level positioning error exhibits small variations during the radar integration time.  
radar imaging; unmanned aerial vehicle; inverse scattering; linear scattering models; global positioning systems; carrier based differential global positioning system; radar signal processing; motion compensation 2020 IEEEexample: accepted: 19 October 2020
## 3 Results and Discussion
### Apricot Fruit Headspace Volatile Analysis via SPME-GC-MS
#### 3.3.7 Ketones  
Ketones amounted for 4.7%, 4.4%, 2.9% and 10.2% of the total volatiles in apricot fruits AP-B\({}_{1}\), AP-B\({}_{2}\), AP-M3 and AP-S\({}_{2}\) respectively. Geranyl acetone (peak 15) was the most abundant ketone, is a carotenoid degradation product with sweet floral rose like odor previously isolated from apricot fruits [21]. Next to geranyl acetone, anisketone was also detected in AP-B\({}_{1}\) and AP-S\({}_{2}\) (2.7 and 3.6%, respectively) versus much lower levels (ca. 0.3% in AP-B\({}_{2}\), AP-M\({}_{3}\)). In contrast, AP-B\({}_{2}\) and AP-S\({}_{2}\) showed higher isophorone level, a ketone previously identified in apricot fruit [48] and have been found uniquely in Egyptian date fruits [51]. Various ketones that are not previously reported as anisketone, along with (\(Z\))-Jasmone and turmerone were detected in all fruit samples though at low levels.
## 3 HTP Tooth Profile Design Using IH Tooth Profile and Cycloidal Curve
### Analysis of the HTP Tooth Profile and Conventional Tooth Profiles Through Schematic Comparison
#### 3.4.2 Comparison of Tooth Profile Curve Trajectories According to Tooth Number Change  
Figure 5 illustrates the tooth profile trajectories when the number of teeth is 100 (z = 100). In this case, the harmonic IH tooth profile, cycloidal curve, exact curve, and HTP tooth profile show almost identical trajectories. This is because increasing the number of teeth reduces gear meshing errors, resulting in higher agreement with the exact curve. Notably, the HTP tooth profile nearly perfectly aligns with the exact curve, while the harmonic IH and cycloidal curves also follow similar paths. This indicates that the HTP tooth profile can achieve high precision compared to conventional tooth profiles. In other words, when the number of teeth is sufficiently large, there is minimal difference between the conventional IH and HTP tooth profiles, and all curves exhibit similar trajectories.  
Figure 4: X-Y trajectory for each tooth curve (\(x\)-axis scale adjustment).  
Figure 3: X-Y trajectory for each tooth curve.  
Figure 6 presents the tooth profile trajectories when the number of teeth is reduced to 11 (z = 11). As the number of teeth decreases, the harmonic IH tooth profile begins to diverge from the exact curve, while the HTP tooth profile still closely follows it. This demonstrates that the HTP profile is designed to maintain accurate meshing even with a small number of teeth. Additionally, under the condition where r = 1 and d = 1.2, a +offset motion is observed, as r < d. This occurs because the gear shape becomes asymmetric, leading to relative motion errors at specific contact points. In Figure 6, the influence of offset motion on the trajectory is visually evident, and the HTP tooth profile is shown to be less affected by it compared to the conventional IH tooth profile.  
Figure 7 analyzes the trajectories under the condition z = 11, with the gear radius and displacement coefficient equal (r = d = 1). Here, the HTP tooth profile and the exact curve show perfectly matched trajectories. The harmonic IH tooth profile and cycloidal curve also exhibit similar shapes. In particular, the initial contact point is identical, indicating consistent and uniform gear meshing.  
Figure 5: X-Y trajectory for each tooth curve (z = 100).  
Figure 6: X-Y trajectory for each tooth curve (z = 11, r = 1, and d = 1.2).  
Figure 8 examines the case where z = 11, and the gear radius is greater than the displacement coefficient (r = 1; d = 0.9), resulting in -offset motion. This occurs due to contact point shifts and relative displacement differences during meshing. Under this condition, the conventional IH and harmonic tooth profiles deviate from the exact curve, whereas the HTP tooth profile continues to align with it. This confirms that the HTP design maintains precise meshing even under varied conditions. In other words, the HTP profile structure is optimized to minimize the effects of offset motion compared to the conventional IH and harmonic profiles, offering improved meshing accuracy.
# Removal of Cesium from Radioactive Waste Liquids Using Geomaterials
###### Abstract  
In this study, we investigated the removal of Cs from aqueous solutions using geomaterials. Adsorption was chosen as an effective method to develop for the removal of Cs from radioactive waste liquids. Geomaterials, including fly ash and slag as raw materials, were prepared as adsorbents using an alkali activator. The materials were characterized by X-ray diffraction (XRD); scanning electron microscopy with energy dispersive spectrometer (SEM-EDS); and BET surface area, pore volume, and pore size analysis. The effects of various parameters, such as pH, contact time, and adsorbent dosage on the adsorption of the Cs were studied. The partition coefficient (PC) as well as the adsorption capacity were evaluated to assess the true performance of the adsorbent in this work. The fly ash-based geomaterials showed a maximum Cs adsorption capacity of 89.32 mg\(\cdot\)g\({}^{-1}\) and a high PC of 31.02 mg\(\cdot\)g\({}^{-1}\)-mM\({}^{-1}\) for the Cs under our experimental conditions. From this work, this method can be regarded being practical for use as a potential adsorbent for treating Cs in wastewater. Furthermore, the immobilization of Cs in geomaterials was explored from a chemical perspective. In conclusion, fly ash-based geomaterials may be a promising option for the treatment and disposal of nuclear-contaminated waste.  
cesium (Cs); radioactive waste liquid; fly ash; geomaterials; adsorption capacity; partition coefficient +
Footnote †: journal: Applied Sciences
# SCMC: An Efficient Scheme for Minimizing Energy in WSNs Using a Set Cover Approach
## 2 Related Works
### Clustering  
The authors in [25] proposed to use the Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol [26] with the Fuzzy Inference System (FIS) based on [PERSON]'s method [27] to select cluster heads and to extend the lifetime of WSNs. FIS consists of a fuzzifier, an inference engine, and a defuzzifier. The proposed model uses three parameters: distance to coordinator, energy, and density. IF-THEN rules are used to calculate the chance for a sensor node to become a cluster head. The chance value is obtained by defuzzification, which aggregates the results of each rule. Then, the node chooses the maximum of the chance values and compares it to a pre-defined threshold value. If it is found to be less than the threshold then it becomes the cluster head. Experimental studies conducted with LEACH and LEACH-Fuzzy Logic (LEACH-FL) showed that using LEACH-FL increased the network's lifetime twice more than when using LEACH.
## 3 Control System  
### Perturb and Observe-Based Voltage Controller (POVC)  
To ensure the DC voltage provided to the MLI by the boost converter was constant, POVC was deployed. This was necessary to ensure that the voltage generated by the inverter did not deviate from the specified value. Figure 4 shows a flowchart of the POVC operation.  
The POVC was capable of producing constant DC voltages under varying solar irradiance, surrounding temperatures, and load levels. Its operation was principally similar to the original P&O MPPT algorithm. However, instead of tracking for maximum power point (MPP), the POVC would track for the targeted load power, \(\mathrm{P_{Lref}}\), by the addition of two duty cycle changing loops. The targeted load power \(\mathrm{P_{Lref}}\) was related to the reference voltage, \(\mathrm{V_{ref}}\), and load value, \(\mathrm{X_{L}}\), according to the following relationship:  
\[\mathrm{P_{Lref}}=\frac{\mathrm{V_{ref}}^{2}}{\mathrm{X_{L}}} \tag{15}\]  
Power tracking, as in (15), was achieved by the algorithm of Figure 4 through the two added loops where \(\mathrm{V_{err}}\) was checked under different conditions of \(\mathrm{P_{PV}}\). Thus, at full-load, \(\mathrm{V_{ref}}\) would be equal to MPP voltage, \(\mathrm{V_{MPP}}\) at most, and \(\mathrm{P_{Lref}}\) would be equal to MPP power, \(\mathrm{P_{MPP}}\). With POVC, the PV operating point would be between \(\mathrm{V_{PV}}=0\) and \(\mathrm{V_{PV}}=\mathrm{V_{MPP}}\), which is the regulating range of the algorithm. This means that if the power required by any selected load fell within the range, the POVC would be able to maintain the desired  
Figure 4: Flowchart of POVC.  
voltage. However, the operation was also dependent on the converter gain range according to the boost converter equation as follows:  
\[\mathrm{V_{B}}=\frac{\mathrm{V_{PV}}}{1-\mathrm{D}}\text{ for }\mathrm{V_{PV}}=0 \text{ to }\mathrm{V_{MPP}} \tag{16}\]  
Referring to Figure 4, \(\mathrm{V_{PV}}\) is the PV voltage that determines the range of \(\mathrm{V_{B}}\) in (16). In the POVC operation, its minimum and maximum value were 0 and \(\mathrm{V_{MPP}}\), respectively. It is best to note that \(\mathrm{V_{MPP}}\) values vary based on solar irradiance and temperature. Thus, the range of \(\mathrm{V_{B}}\) in (16) is not fixed. A lower range could be expected at lower solar irradiances and vice versa. Based on (16), the range of \(\mathrm{V_{B}}\) was also affected by the duty cycle. An example of the voltage regulating range calculation is given in the Results. Figure 5 depicts the POVC regulating range and \(\mathrm{P_{Lref}}\) tracking operation. First, the POVC operated as a standard P&O MPPT algorithm to track for \(\mathrm{P_{MPP}}\) (P1) and continued operating at MPP (P2) until the boost converter voltage, \(\mathrm{V_{B}}\), equalled \(\mathrm{V_{ref}}\). As \(\mathrm{V_{DC}}\) was no longer increasing and fixed at a constant value, the POVC would then track \(\mathrm{P_{Lref}}\) (P3). Thus, the PV power should be maintained (by increasing PV current) to keep the PV operation at \(\mathrm{P_{Lref}}\) (P4).
## 2 Material and Methods
### Merging MODIS and AMSR2 Sea-Ice Concentration  
After combining the gridded MODIS granules to one overflight, we select the corresponding AMSR2 overflight. As both platforms, Aqua and GCOM-W1, are part of the A-Train, the time lag is on the order of minutes. Our central assumption for the merging is that the MODIS SIC capture the variability at 1 km resolution correctly, but tend to underestimate the SIC due to the influence of thin ice thickness. Furthermore, they are limited by the presence of clouds. The AMSR2 SIC, on the other hand, are less sensitive to thin ice than the MODIS SIC, and thus retrieve the right magnitude at a spatial scale of 5 km and are available independently of cloud coverage. We thus tune the mean of the MODIS SIC in an area of 5 km by 5 km to match that of the AMSR2 data. This is done in the following way. First, the AMSR2 data are resampled/interpolated to a grid cell size of 1 km. Then, the difference between the mean MODIS SIC and the mean AMSR2 SIC in an area of 5 km by 5 km (i.e., 5 by 5 km-by-1 km pixels on the interpolated grid, not the original AMSR2 grid) is calculated:  
\[\Delta_{SIC,5 km}=SIC_{MODIS,5 km}-SIC_{AMSR2,5 km}, \tag{3}\]  
where \(SIC_{MODIS,5 km}\) and \(SIC_{AMSR2,5 km}\) are the mean MODIS and AMSR2 SIC in the respective 5 km by 5 km cell and \(\Delta_{SIC,5 km}\) is their difference. This difference is now added to each MODIS pixel in this box such that it matches the mean of the AMSR2 pixels. In the final step, the cloud gaps are filled by the respective AMSR2 pixels:  
\[SIC_{mergal_{xy}}=\begin{cases}SIC_{MODIS,5 km,_{xy}}+\Delta_{SIC,5 km}&\text{ if }SIC_{MODIS,5 km,_{xy}}\text{ available}\\ SIC_{AMSR2,5 km,_{xy}}&\text{if }SIC_{MODIS,5 km,_{xy}}\text{ not available}\end{cases} \tag{4}\]  
Here, \(SIC_{merged,_{xy}}\) is the merged SIC at the pixel with the coordinates \(x,y\); \(SIC_{MODIS,5 km,_{xy}}\) and \(SIC_{AMSR2,5 km,_{xy}}\) are the MODIS and AMSR2 SIC at this pixel, respectively, and \(\Delta_{SIC,5 km}\) is defined as in Equation (4). A similar approach has been used in [16]. Different to them, we shift the 5 km by 5 km box by 1 pixel before the calculation is repeated for each pixel in the box. The mean is selected as merged SIC value. This mitigates the inaccuracy of choosing arbitrary starting positions for the merging, analogously to the approach which we chose for the \(tp_{ice}\) retrieval in Section 2.4.  
If the AMSR2 SIC are 100% in the entire scene, we allow SIC above 100% in some grid cells in order to resolve leads with reduced MODIS SIC which the AMSR2 did not show because they are overfrozen. By doing this the requirement of the merging procedure to keep the AMSR2 SIC mean (i.e., 100%) is fulfilled. In the end, however, the merged SIC is capped at 100%, which means that the means of the merged and the AMSR2 SIC are not entirely consistent in such cases. We accept this as a compromise for the enhanced potential to resolve leads. The above 100% SIC values before the truncation will be provided as additional information in our dataset.
# Unsupervised Low-Light Image Enhancement in the Fourier Transform Domain
## 4 Experiments
### Denoiser Experiment  
Our methods use 3 \(\times\) 3 convolution for de-noising, but some methods also use low-pass filters to deal with noise. The phase noise in the Fourier transform primarily arises due to phase discontinuity or instability. The low-pass filter operates on the principle of reducing the energy of the high-frequency portion of the spectrum by suppressing high-frequency signal components. This approach aims to achieve signal smoothing and denoising. When it comes to phase noise denoising, the low-pass filter is effective in removing high-frequency noise components while preserving the low-frequency information in the signal, thereby mitigating the impact of phase noise. Therefore, we conducted an experimental comparison between convolution and low-pass filtering for denoising. Under the same training conditions, the results of the test dataset are shown in Table 4.  
Figure 5: Enhancement results of adding Gaussian noise with a variance of 20 for each method. (**a**) Noisy image. (**b**) LLNet [14]. (**c**) RetinexNet [15]. (**d**) Zero-DCE [24]. (**e**) Zero-DCE++ [25]. (**f**) SCI [26]. (**g**) EnlightenGAN [18]. (**h**) RUAS [17]. (**i**) Ours. (**j**) “GT” is the ground truth.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **PSNR** & **SSIM** & **LPIPS** \\ \hline LLNet [14] & 28.06767111 & 0.5964 & 0.5720 \\ RetinexNet [15] & 27.92532647 & 0.0720 & 1.1575 \\ Zero-DCE [24] & 27.91220942 & 0.0859 & 1.1218 \\ Zero-DCE++ [25] & 27.88815927 & 0.0759 & 1.1510 \\ SCI [26] & 27.92670618 & 0.0741 & 1.1769 \\ EnlightenGAN [18] & 27.87609919 & 0.1411 & 0.9781 \\ RUAS [17] & 27.92985406 & 0.0621 & 1.2399 \\ Ours & **28.11758516** & **0.6818** & **0.4379** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Quantitative results of adding Gaussian noise with a variance of 20 for each method. The best results are in bold.  
When the phase noise is mainly concentrated in the high frequency part, the phase noise can be effectively removed, and the low frequency information in the signal can be preserved by selecting a suitable low-pass filter and cutoff frequency. However, the results of the experiment may be affected by the different parameter settings of our model, such as the required learning rate and step size, etc. Perhaps we can find better denoising results of a low-pass filter in subsequent experiments.
## 3 Results and Discussion
### Surface Tensions of Common PR and B-Containing PR Solutions  
The surface tensions of common PR and B-containing PR solutions were measured, which strongly depended on the concentrations of the PR solution and tested temperatures; the results are presented in Table 1. Two concentrations of common PR and B-containing PR solutions were compounded with absolute ethanol, and three temperatures selected. For common PR solution with concentration of 0.40 g/mL, surface tensions at 25-45 \({}^{\circ}\)C are about 32.10-25.06 mN/m, and at 25-45 \({}^{\circ}\)C are about 29.45-23.12 mN/m when the concentration of common PR solution is 0.8 g/mL. As for 0.40 g/mL B-containing PR solution, the surface tensions at 25-45 \({}^{\circ}\)C are about 28.45-22.71 mN/m, and at 25-45 \({}^{\circ}\)C are about 25.62-20.01 mN/m when the concentration of the B-containing PR solution is 0.8 g/mL. From the above results, it is reasonable to believe that concentration of the PR solution and the tested temperature seriously affect the surface tension of the solution. This surface tension decreases with increase in concentration of the PR solution. Low concentration means a high content of absolute ethanol, while the surface tension of absolute ethanol is larger than that of PR, leading to a high surface tension, compared with that of high concentration of PR solution [32; 33; 34]. In general, the surface tension of PR solution increases as the temperature increases, due to the curing of PR and evaporation of the solvent. However, when the temperature is relatively lower than the cured temperature of PR, increasing the temperature is found to be favorable for the mixing of PR and solvent, resulting in a lower value of surface tension of PR solution, similar to results revealed in Table 1 [32; 33; 34]. In addition, the value of surface tension of the B-containing PR solution is lower than that of common PR solution under the same conditions, demonstrating that the introduction of B element could decrease surface tension of the PR solution, which is beneficial for infiltration into the fiber braid. The reason for surface tension of the B-containing PR solution being lower than that of the common PR solution is that incorporation of the B element improves electrical conductivity of the system and decreases whole surface energy, resulting in the reduction of surface tension [35; 36].
## 4 Discussion
Although the authors of [70] observed an enhancement of the EPBs with the activity level within the post-sunset period (18:00-24:00 LT) during equinoxes, our division of post-sunset LTs into sunset (17:00-21:00 LT) and pre-midnight (21:00-24:00) periods shows deeper details regarding the exact LT enhancement of EPBs with respect to the storm type as shown in Figure 8.  
IBI events observed within the 22-25 June 2015 super storm are marked by a vertical red line as shown in Figure 11c. The IBI event on June 23, appeared within the post-midnight (\(\sim\)01:00 LT) period after the occurrence of an intense geomagnetic substorm as shown in Figure 11b. The occurrence of a geomagnetic substorm is clearly indicated by the strong negative variation in the AL index (\(-\)1500 nT) at 05:00 UT on June 23. This substorm is also associated with large negative IMF B\({}_{\rm z}\) (\(-\)40 nT) and positive electric field variation in red solid and black dotted lines, respectively, as shown in Figure 11a, which means a strong injection into the magnetosphere. The depletion of the \(n_{e}\) is associated with the IBI event observed a few hours later at 07:00 UT on the same day. Therefore, the occurrence of IBI could be interpreted in terms of the DDEF which develops a few hours after a geomagnetic substorm. This eastward nightside DDEF has the capability to raise plasma to higher altitudes and subsequently depletes plasma in lower altitudinal ionospheric regions. This is in agreement with the authors of [20] who suggested the DDEF as the main controller of the low latitude ionospheric instabilities which appear a few hours after the start of the storm main phase. In addition, the authors of [63] stated that the DDEF remains for several days during the storm recovery phase, which is clearly inferred from the IBIs occurring during the recovery phase of the moderate geomagnetic storm shown in Figure 10. Also, the authors of [53] stated that after the onset, the DDEF is the main driver of the equatorial plasma depletion because it appeared 5 h after the IMF Bz turned southward during the recovery phase. Similarly, the IBI event observed on June 24 at 11:00 UT, occurred within the pre-midnight (~22:20 LT) period a few hours after the occurrence of the geomagnetic substorm occurred at 09:30 UT. This IBI event was observed during the insignificant variations in both the IMF Bz and electric field, which means the PPEF has no role in creating such an  
Figure 11: The solar wind and geomagnetic indices associated with the super storm observed from 22 June 2015 to 24 June 2015. (**a**) The IMF Bz and solar wind electric field, (**b**) The AL index, and (**c**) The SYMH index. IBI event. However, both the IMF Bz and the electric field have small negative and positive variations respectively, the hnr2 peak of this event rose to higher altitudes (~400 km), which confirms our suggestion about DDEF.