esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Correlations Matrix  
Pearson correlation analysis [42] was performed between all the variables. The level of significance (p \(\leq\) 0.05 and p \(\leq\) 0.01) of multi-element correlation for soil samples was determined and the results are given in Table 8. The listed r values indicated the high degree of positive correlations and significant linear relation between various pairs of metals, reflecting their simultaneous release and identical source from the DEPZ zone, transport and accumulation in soil. The inter-metallic correlation coefficients in the soil samples with p <0.05 during the dry season were: Fe-Ni, As-Pb, Cu-Zn, Cu-Cr, Cu-Cd, Zn-Pb, Zn-Cd and Cr-Cd. In the wet season the correlation trends were: Fe-As, Fe-Cr, Fe-Ni, As-Cr, Cu-Zn, Cu-Cr, Cu-Ni, Cr-Pb, Cr-Ni and Pb-Ni. The significant correlations indicate that they may have originated from common sources, presumably from other industrial (chemicals, paints) activities. The correlation of As with Cr and Fe indicate their common source from tannery industries. The strong association of Cd, Zn, and Cu indicates common sources, and these metals may have been derived from anthropogenic sources, especially the paint industry and municipal sewage system.
## 4 Discussion
### Selection of the Appropriate Techniques According to Measurement Purposes
Since the residual limb can be quantified from macroscopic and microscopic aspects, researchers or medical professionals must properly select the appropriate technique to evaluate the residual limb volume. Clinicians or researchers may employ anthropometric measurement techniques to ascertain the residual limb volume change from the macroscopic perspective, particularly in phase 2 of post-operative recovery where there are still significant residual limb volume changes. Then, more precise measurement techniques, including water displacement, contact probes, optical scanning, MRI, ultrasound, and laser scanning, can be used during the other post-operative periods (phases 3, 4, and 5). On the other hand, the bioimpedance and SXCT techniques are available to researchers that want extremely sensitive measurements, but these two techniques can only quantify the change in the residual limb without knowing the total volume of the residual limb. In terms of the application of measuring techniques in the clinical field, the clinician needs to see the availability of the measuring device in their clinical setting. Since some techniques such as anthropometric measurement and laser scanning use a portable instrument, these techniques are seen as easier to use in clinics compared to other approaches. Even MRI, ultrasound, and SXCT devices are accessible in most hospital setups, but the costs associated with these procedures are significant, and other techniques like bioimpedance, contact probes, optical scanning, and water displacement require specific experiment setups that are challenging in a clinical setting. With the right selection of measuring techniques, the researcher or clinician will be able to evaluate the residual limb volume in line with their aims and interests.
# A Review of Oil Spill Remote Sensing
## 3 Use of Active Sensors for Oil Spill Detection and Mapping
### Satellite Radar Systems  
There are now several radar satellites in orbit providing users with a choice of coverage and polarizations. Table 1 lists present and proposed radar satellites. Most radar satellites provide a selection of resolutions and polarizations. While many satellites have a lifetime of 5 years, some satellites have functioned for 17 years.  
The extensive 2010 Gulf of Mexico oil spill provided opportunity for testing and use of satellite radar data. Some of this use and other worldwide uses are shown in Figures 7-10.  
Delivery time of radar imagery is a consideration. In the past, times from the tasking of the satellite to image delivery were as long as 12 h, currently, times of 4 h are possible. A further consideration is overpass time. Many satellites will cover an area once per day. This has been improved by use of satellite constellations [43]. The larger number of SAR satellites and satellite constellations such as Cosmo (Constellation of Small Satellites for Mediterranean basin Observation) will give revisit times of a few hours compared to the present one-day.  
Figure 10: RADARSAT-2 view of a small portion of the Gulf oil spill. This is detail from another wide-field image to show the detail that radar is capable of. The white dots are ships working on well control. (Image from Canadian Space Agency website [[http://www.asc-csa.gc.ca/images](http://www.asc-csa.gc.ca/images)]([http://www.asc-csa.gc.ca/images](http://www.asc-csa.gc.ca/images))).  
Figure 9: RADARSAT-2 image of the Gulf oil spill. The red outline shows the spill. The shoreline is shown as white and the colored portions derive from a visible satellite image (Image from Canadian Space Agency website [[http://www.asc-csa.gc.ca/images](http://www.asc-csa.gc.ca/images)]([http://www.asc-csa.gc.ca/images](http://www.asc-csa.gc.ca/images))).
## 3 Results
### Results of TGA Characterization  
TGA was used in the present work to evaluate the thermal stability of materials and the effect of the presence of GHPs in the thermal decomposition of 3D-printed composites. In the TGA thermogram (Figure 3), it is possible to observe that the addition of a low content of GHPs did not have a significant effect on the thermal stability of the pristine PP matrix because the 3D-printed PP-1 GHP composite showed a similar decomposition temperature to that of pristine PP, but the 3D-printed PP-3 GHP and PP-5 GHP composites showed an increase in the decomposition temperature, which could indicate an improvement in the thermal stability of the 3D-printed composites. This behavior can be attributed to the residue from char formation in the material from the GHPs, which acted as a barrier that inhibited free heat flow to slow the thermal decomposition of the material. A similar behavior was reported with other kinds of lignocellulosic materials in 3D-printed PP composites [38].  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Composite Filament** & \(\Delta H_{c}\) [J/g] & \(T_{c}\) [\({}^{\circ}\)C] & \(\Delta H_{m}\) [J/g] & \(T_{m}\) [\({}^{\circ}\)C] & \(X_{c}\) [\%] \\ \hline PP & 92.785 & 115 & 64.109 & 170 & 30.97 \\ PP-1 GHP & 91.109 & 112 & 68.631 & 162 & 33.15 \\ PP-3 GHP & 100.105 & 113 & 79.47 & 166 & 38.39 \\ PP-5 GHP & 90.573 & 114 & 65.56 & 166 & 31.67 \\ \hline \end{tabular}
\end{table}
Table 2: DSC results for 3D-printed PP-GHP composites.  
On the other hand, Figure 4 shows a derivative curve from the TGA, where the peak represents the medium decomposition temperature of the material, which is associated with the decomposition susceptibility or the speed of variation of weight with respect to the temperature of the material. It is possible to observe that the temperature peaks in the DTG curves were similar for the 3D-printed PP and PP-GHP composites. The main difference was that the 3D-printed PP-3 GHP composite showed a higher quotient value at its peak, which indicated that this material presented the highest susceptibility to weight loss (it decomposed faster) in comparison with the 3D-printed PP and other PP-GHP composites. Previous works reported the opposite behavior, with the presence of lignocellulosic materials such as bamboo fibers in a 3D-printed PP matrix resulting in a lower decomposition temperature than that of 3D-printed lignocellulosic composites [24].  
Figure 4: DTGA thermogram curves for 3D-printed PP and PP-GHP composites.  
Figure 3: TGA thermogram for 3D-printed PP and PP-GHP composites.
# A Novel Machine-Learning Approach to Predict Stress-Responsive Genes in Arabidopsis
## 4 Conclusions  
In conclusion, this study utilized a hybrid gene selection approach to identify predictive genes involved in stress tolerance in Arabidopsis, which could potentially be used to improve crop production systems and address food security challenges. Through the use of various feature selection tools and machine learning algorithms, the study identified three common genes (AT5G44050, AT2G47180, and AT1G70700) that could serve as biomarkers for tolerant crops. The XGBoost and random forest algorithms demonstrated superior performance in classifying stress and control conditions, indicating their potential utility in crop breeding programs. However, further experimental research is needed to validate the identified genes and explore their potential for developing stress-tolerant crop varieties. Overall, this study provides valuable insights into the mechanisms underlying stress responses in plants and highlights the potential of gene selection and machine learning approaches for improving crop resilience.  
**Supplementary Materials:** The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/a16090407/s1](https://www.mdpi.com/article/10.3390/a16090407/s1)]([https://www.mdpi.com/article/10.3390/a16090407/s1](https://www.mdpi.com/article/10.3390/a16090407/s1)), Supplementary Table S1: Differentially Expressed Genes (DEGs) based on empirical Bayes statistics and \(p\)-value \(\leq\) 0.01 as well as the ranking of the top 20 DEGs for Arabidopsis under control and various stress condition; Supplementary Table S2: The top genes identified by differentially expressed genes (DEGs) analysis and different feature selection methods.  
**Author Contributions:** Conceptualization, L.N. and V.G.; methodology, L.N. and M.N.; software, L.N.; validation, L.N., V.G., M.N. and J.P.; formal analysis, L.N.; investigation, L.N. and M.N.; resources, L.N.; data curation, L.N.; writing--original draft preparation, L.N., V.G. and M.N.; writing--review and editing, L.N., V.G., M.N. and J.P.; visualization, L.N. and M.N.; supervision, L.N. and J.P.; project administration, L.N. M.N. and J.P. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Data Availability Statement:** The data presented in this study are openly available in Gene Expression Omnibus (GEO) ([[http://www.ncbi.nlm.nih.gov/geo/](http://www.ncbi.nlm.nih.gov/geo/)]([http://www.ncbi.nlm.nih.gov/geo/](http://www.ncbi.nlm.nih.gov/geo/)) (accessed on 25 June 2022)), reference number GSE41935.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Coverage Area Decision Model by Using Unmanned Aerial Vehicles Base Stations for Ad Hoc Networks
## 3 Proposed UAVs-BS Network Coverage Model
The coverage area provision is still a challenge, especially in congested networks. Allocation of a UAV-BS needs to be set with maximum and minimum vertical attitude. This section presents a UAVs-BS coverage area model for cellular networks. The complete design and development phases of the proposed model are presented in the next sub-sections. Figure 1 shows a UAV-BS coverage area example where the UAV-BS provides coverage to the cells of the cellular network. To design the proposed UAVs-BS network coverage model, this paper considered a few assumptions, which are as follows:  
* Security is not considered in designing the coverage model. * The GPS services are available to find the locations and positions of the nodes. * Mobility and number of nodes are set according to simulation settings. * 3D deployment of UAVs is not considered in this study.
## 4 Orbit Determination Results
### Analysis of Experimental Results
It can be seen that the \(\Omega\) of each satellite was different and it would cause errors in the inter-satellite distance, which would indirectly lead to orbital RERR. Compared with the algorithm for independent satellite constraints, the algorithm for global satellite constraints achieved a smaller improvement in the tangential and normal directions, but it constrained the RERR well. This result illustrates that the algorithm for independent satellite constraints did not destroy the configuration of autonomous orbit determination. Therefore, it would not lead to an increasing orbit RERR. The following conclusions were summarized through this study: (1) Under the premise of no external constraints, the accuracy of autonomous orbit determination deteriorates with time. (2) Both algorithms for rotation correction can weaken the influence of the overall constellation rotation. (3) The algorithm for independent satellite constraints will affect the integrity of the constellation configuration as a rigid body to a certain extent, which may lead to an increasing RERR. However, the algorithm for global satellite constraints will not destroy the constellation configuration. (4) Among the three cases, the 30-day accuracy in autonomous orbit determination using the algorithm for global satellite constraints was the best.
# DMRVR: Dynamic Milk-Run Vehicle Routing Solution Using Fog-Based Vehicular Ad Hoc Networks
## 3 DMRVR Solution
### Initial Route Calculation  
DMRVR adopts a scheme similar to [11], where the pickup vehicle route is optimized with a 3-optimal tour (3-opt) algorithm [52]. DMRVR uses GA to calculate the initial pickup route, where a chromosome represents the supplier's route. The fitness function, used to evaluate the route, minimizes the pickup vehicle's total distance traveled. It considers the distances between the logistics operator and the suppliers, as well as the distance to return to the OEM company. Equation (6) gives this function:  
\[f=min\left(t_{1}^{(tv)}+\sum_{j=1}^{q-1}t_{j+1}^{(tv)}+t_{q+1}^{(tv)}\right) \tag{6}\]  
where \(q\) is the number of pickup locations; \(t_{1}^{(tv)}\) is the vehicle displacement time from the logistics operator to the first supplier; \(\sum_{j=1}^{q-1}t_{j+1}^{(tv)}\) is the sum of the displacement between a \(j\) supplier until its successor \(j+1\); and \(t_{q+1}^{(tv)}\) is the return time to the OEM company.  
The GA evolutionary process adopts the crossover and the mutation genetic operator. For the crossover operation, our solution applies a cycle crossover (CX). DMRVR adopts a simple random permutation of two elements of the chromosome for the mutation. Once the GA process is completed, the solution sorts the new population through the fitness function and executes the 3-OPT algorithm. The GA process was defined with an initial population of 10 chromosomes, a mutation rate of 15%, and 50 replications. The optimal route converged to a minimum distance of 21.75 km in all cases.  
Once the initial pickup route is defined, _LogisticsService_ sends a _NotifyRoute_ message to the vehicles using the vehicular network. After the pickup of goods by the vehicle, the _VehicleService_ application sends a _NotifyPickup_ message to the _LogisticsService_. The _VehicleService_ sends a _NotifyPosition_ message to the _LogisticsService_ app to inform its geographic position every 30 s. Figure 4 shows the messages exchanged between the operator and the vehicles during a normal route.
## 2 Materials and Methods
### Light Shelf Configurations  
In this study, 11 internal-external light shelf configurations were tested using daylight simulation, as shown in Table 2. The combined internal and external light shelf (LS) configurations have been assessed using four main phases:  
Figure 3: Schematic diagram for the simulation method.  
* First phase: three horizontal light shelves of different dimensions, placed at varying heights as defined by previous studies [30; 31; 32]. The Internal LS depth is equal to the height of the clerestory window above it while the external LS depth is 1.5 times the height of the clerestory window. \[\text{d}_{\text{int light shelf}}=\text{h}_{\text{ clerestory}}\] \[\text{d}_{\text{extlight shelf, max}}\leq 1.5\times\text{h}_{\text{ clerestory}}\] The three configurations in the first phase are LS1H1, LS2H2, and LS3H3, and they have fixed heights of 1.6 m, 1.3 m, and 1.0 m, respectively.  
\begin{table}
\begin{tabular}{c|c|c|c|c} \hline  & **Reference Model** & **LS1H1** & **LS2H2** & **LS3H3** \\ \hline  & & & & \\  & & & & \\  & & & & \\  & & & & \\ \end{tabular}
\end{table}
Table 2: Light shelf configurations used in simulation for each phase.  
* Second Phase: once the appropriate height of the LS is chosen, a reflector material is added on the top of the window to enhance the illumination of the daylit area at the back and then compared with the reference model.
* Third phase: three downward-curved internal LS (having angles of 10\({}^{\circ}\), 15\({}^{\circ}\) and 25\({}^{\circ}\)) were examined and compared with horizontal internal LSs with the reflector material specified in the second phase.
* Fourth Phase: four different PV coverage and tilt angles are integrated with the external LS. LSPV1, LSPV3, LSPV5, and LSPV7 are designed to have external horizontal light shelves with 100%, 75%, 50%, and 25% coverage, respectively. The remaining configurations of LSPVs have the same coverage but are tilted at 30\({}^{\circ}\) to reflect maximum energy and prevent the occurrence of glare.
# Sample-Wise Aiding in GPS/INS Ultra-Tight Integration for High-Dynamic, High-Precision Tracking
## 3 Sample-Wise Doppler-Aided PLL
### Effects of Interpolation Techniques
In addition, before reaching the lower threshold, there is a middle threshold \(T_{m}\left(\omega,R_{s}\right)\) of \(T_{a}\), such that for the \(T_{m}\left(\omega,R_{s}\right)<T_{a}<T_{u}\left(\omega\right)\) region, the improvement effect of sample-wisely aided PLL to unaided PLL is only determined by \(T_{a}\) and the interpolation method, independent of \(R_{s}\), and can be improved by employing other more advanced interpolation techniques. It is interesting to divide each sample-wise aiding curve into the following four regions by using three thresholds of \(T_{a}\) as shown in Table 1.  
By contrast, there are only two regions for the traditional aiding: (1) \(T_{a}<T_{u}\left(\omega\right)\): \(C_{a}\left(\omega T_{a}\right)\to 0.5\,\omega T_{a}\); (2) \(T_{a}\geq T_{u}\left(\omega\right)\): \(C_{a}\left(\omega T_{a}\right)\to 1\). It is also seen that the spline interpolation extends both the lower threshold \(T_{I}\left(\omega,R_{s}\right)\) and the middle threshold \(T_{m}\left(\omega,R_{s}\right)\) towards longer \(T_{a}\) than the linear interpolation. Therefore, the pressure of high INS data updating rate can be relieved by employing advanced interpolation techniques to achieve the same performance as the linear interpolation. Additionally, higher dynamic frequency \(\omega\) demands shorter \(T_{a}\) thresholds. Yet the quantitative relationships of the \(T_{a}\) thresholds \(T_{I}\left(\omega,R_{s}\right)\) and \(T_{m}\left(\omega,R_{s}\right)\) with the signal dynamic frequency \(\omega\) and the sampling rate \(R_{s}\) with different interpolation techniques are quite complicated and still under investigation.
## 4 Results and Discussion
### Phase Contrast Trends  
The phase contrast was calculated to analyze the quantitative change of the phase angle according to the lock-in modulation frequency, and the contrast analysis is defined as Equation (11) [34].  
\[|\Delta\mathrm{C}|=\mathrm{C_{d}}-\mathrm{C_{s}} \tag{11}\]  
where \(\mathrm{C_{d}}\) is the average value of phase angle in the defective area, and \(\mathrm{C_{s}}\) is the average value of phase angle in the sound area. As shown in Figure 12, to determine the optimum frequency, two ROIs of \(5\times 5\) pixels (one at the center of defective area and another in the adjacent sound area) were considered. For analysis, cropping was performed except for unnecessary parts of the image. In addition, the relatively large and deep A6, A5, and A4 defects that were detected in the phase images were selected.  
Figures 13 and 14 show the phase contrast of A6, A5, and A4 defects according to the HA and FFT algorithms. It can be seen that the phase contrast increases from 0.01 Hz to  
Figure 11: Processed images with HA and FFT algorithms for detecting defects in group 4 of samples at different modulation frequencies: (**a**) 1 Hz, (**b**) 0.2 Hz, (**c**) 0.1 Hz, (**d**) 0.05 Hz, (**e**) 0.03 Hz, and (**f**) 0.01 Hz.  
Figure 12: Representation of selected areas for phase contrast and signal to noise ratio (SNR) computation.  
0.05 Hz as the modulation frequency increases. Then, above 0.05 Hz, the phase contrast begins to decrease and approaches 0 (blind frequency) as it approaches 1 Hz. Therefore, phase detection can be estimated as the optimal frequency at 0.05 Hz. The maximum phase contrast of defect A6 in modulation frequency 0.05 Hz, HA was 0.8794 rad, FFT was 0.6208 rad.  
Table 4 shows the phase contrast results according to each algorithm for all samples and defects. In general, it can be seen that the size (length, width) of a defect is large, and the closer the defect is to the surface, the greater the phase contrast, so it is easy to detect the defect. In addition, it can be seen that defects having a phase contrast of about 0.2 rad or more quantitatively according to the modulation frequency are clearly distinguished from the sound area. For example, in the 0.05 Hz result of the HA algorithm, all group 3 defects were clearly detected. The phase contrast value of all group 3 defects in 0.05 Hz:C1 was 0.2275 rad, C2 was 0.2615 rad, C3 was 0.3714 rad, C4 was 0.3748 rad, C5 was 0.4372 rad,  
Figure 14: Phase contrast trend according to modulation frequency by FFT.  
Figure 13: Phase contrast trend according to modulation frequency by HA.  
and C6 was 0.4702 rad. On the other hand, when the phase contrast is less than 0.1 rad, a defect is detected, but the boundary between the sound area and the defect area is not cleared. For example, in the 0.2 Hz result of the FFT algorithm, all group 2 defects were clearly detected. The phase contrast value of all group 2 defects in 0.2 Hz:B1 was 0.0651 rad, B2 was 0.0501 rad, B3 was 0.0525 rad, B4 was 0.0478 rad, B5 was 0.0404 rad, and B6 was 0.05 rad.
###### Abstract  
This study used thermal impedance spectroscopy to measure a 46 Ah high-power lithium-ion pouch cell, introducing a testing setup for automotive-sized cells to extract the relevant thermal parameters, reducing the time for thermal characterisation in the complete operational range. The results are validated by measuring the heat capacity using an easy-to-implement calorimetric measurement method. For the investigated cell at 50% state of charge and an ambient temperature of 25 \({}^{\circ}\)C, values for the specific heat capacity of 1.25 J/(gK) and the cross-plane thermal conductivity of 0.47 W/(mK) are obtained. For further understanding, the values were measured at different states of charge and at different ambient temperatures, showing a notable dependency only on the thermal conductivity from the temperature of \(-\)0.37%/K. Also, a comparison of the cell with a similar-sized 60 Ah high-energy cell is investigated, comparing the influence of the cell structure to the thermal behaviour of commercial cells. This observation shows about 15% higher values in heat capacity and cross-plane thermal conductivity for the high-energy cell. Consequently, the presented setup is a straightforward implementation to accurately obtain the required model parameters, which could be used prospectively for module characterisation and investigating thermal propagation through the cells.  
Keywords: lithium-ion battery; thermal characterisation; thermal impedance spectroscopy; heat capacity; thermal conductivity +
Footnote †: journal: Applied Sciences