esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep Learning Semantic Segmentation for Water Level Estimation Using Surveillance Camera
## 4 Discussion
Besides estimating the water level, the proposed method can also provide information in terms of water level thresholds (normal level, alert level, warning level, or danger level) simultaneously, which can act as an added value that aids the authorities to quickly understand the current situation and detect any changes in water level visually to avoid uncertainty in decision making. There were several problems detected when analyzing the proposed method, such as some segmentation errors which may be the result of the lack of a training dataset or poor choice of hyperparameters during the training stage. In terms of water level estimation, providing more elevation points within the water level thresholds may reduce the estimated errors and increase the efficiency of the proposed framework. The current results show that there is plenty of room for improvement regarding the proposed method. Compared to the current approach of measuring water levels in rivers using water level sensors such as pressure transducer, radar, ultrasonic, and air bubbler, the proposed water level framework has a high potential to provide water level information at a low cost. In general, the installation of water level sensors can cost around RM13,000 (roughly around $3100), not including the maintenance cost. The pressure sensor needs to be submerged in the water, which results in high maintenance costs due to river sedimentation or damage by flood. Installation of radar and ultrasonic is limited as they need to be set up on structures, such as bridges, footbridges, and jibs. The air bubble is suitable to be installed in the river with deep slope elevation and the sensor is not in direct contact with the water. The only part of the sensor that is in contact with the water is the bubble tube but it often requires frequent maintenance. In contrast, this work proposed to use a surveillance camera, which is much cheaper in terms of cost and easy to maintain. The installation cost of one surveillance camera is between RM1500 and RM2000 (around RMS360-5600), depending on the features offered, with straightforward installation. Using the surveillance images, information regarding the flood extent was extracted using deep learning techniques, which DeepLabv3+ demonstrated better performance compared to SegNet, and then the segmentation results were further analyzed using water level framework (Section 2.5.2) to estimate water levels and their category. Besides, the proposed approach can observe the water fluctuation more clearly, which can be easily understood by the authorized personnel, and help in response planning.
## 2 Materials and Methods
### Analysis of Volatile Compounds Lising GC-MS  
Solid-phase extraction of volatile aroma compounds was performed as previously described [13]. Briefly, cartridges containing 200 mg of LiChroult EN sorbent (200 mg/3 mL, Merck, Darmstadt, Germany) were preconditioned using successive washing with 3 mL of dichloromethane (UHPLC gradient grade J.T. [PERSON], Deventer, The Netherlands), methanol (UHPLC gradient grade J.T. [PERSON], Deventer, The Netherlands), and a 13% aqueous ethanol solution (LiChrosolv, Merck, Darmstadt, Germany). After loading the 50 mL sample onto the column, residual sugars and other polar compounds were washed out with 3 mL of water. The column was dried in a vacuum. The analytes were recovered using elution with 1 mL of dichloromethane. For a quality control, 50 mL of water was injected into the SPE column instead of the sample. Identification and quantification of targeted aromatic compounds in the wines was performed as previously described [14; 15]. Gas chromatography-mass spectrometry analysis was performed using an Agilent 6890 system equipped with an Agilent 6890 automatic liquid sampler and a ZB_WAX capillary column (60 m \(\times\) 0.32 mm i.d., with 0.5 \(\upmu\)m film thickness, Phenomenex, Torrance, CA, USA) coupled to a 5973 N mass-selective detector. The injection was performed in splitless mode with volume of 2 \(\upmu\)L, while the injector was held at 250 \({}^{\circ}\)C. The temperature gradient was as follows: initial oven temperature of 40 \({}^{\circ}\)C for 15 min, then increasing to 250 \({}^{\circ}\)C in steps of 2 \({}^{\circ}\)C/min and held at 250 \({}^{\circ}\)C for 15 min. The ion source and MS transfer line were at 250 \({}^{\circ}\)C. The carrier gas flow was 1.0 mL/min 5.0 grade helium (Messer, Zapresic, Croatia) in constant flow mode. The mass detector was operated in electron ionization (EI) mode at an ionization energy of 70 eV with total ion current monitoring. The mass detector ion source and quadrupole were maintained at 250 \({}^{\circ}\)C. Compounds were identified by comparing the retention times and mass spectra with those of commercial standards. Six-point calibration was performed for each standard as reported in [14]. Concentration ranges for alcohols, esters, and acids were 5 ug L\({}^{-1}\) to 5000 ug L\({}^{-1}\); and for terpen, C\({}_{13}\) norisoprenoids, and lactones: 1 ug L\({}^{-1}\) to 300 ug L\({}^{-1}\). Standard calibration curves were generated using EnhancedChemStation software (Agilent Technologies, Santa Clara, CA, USA) to quantify the compounds. A list of used standards, linear retention indices, and other identification and quantification parameters for the GC-MS are presented in [14]. The results were expressed in \(\upmu\)g L\({}^{-1}\) as the concentration of the compounds calculated from the peak area of each compound compared to that of the internal standard. In the eluate, 10 \(\upmu\)L of internal standard (50 mg L\({}^{-1}\), 2-octanol) was added.
## 2 Data and Methods
### Methods  
#### 2.2.1 Identification Method of Upwelling  
To determine the specific time and frequency of an upwelling occurrence in the central and western South China Sea, we utilized an automatic upwelling identification method based on the monthly mean sea surface temperature map of the study area. This approach allowed us to accurately determine the life cycle of upwelling events in the central and western South China Sea. Specifically, we extracted the latitudinal mean sea surface temperature, \(SST_{mean}\), of the upwelling region and the mean sea surface temperature, \(SST_{open\_mean}\), of the adjacent open sea area. The difference between the mean sea surface temperature of the two sea areas was then calculated.  
\[SST_{open\_mean}-SST_{mean}\geq SST_{ref} \tag{1}\]  
\(SST_{ref}\) served as the reference temperature threshold, and the detection algorithm returned a True value if the temperature difference exceeds \(SST_{ref}\), indicating the presence of upwelling at that latitude. The coordinates of the upwelling center were determined as the latitude and longitude of the lowest temperature value among all latitudes where the algorithm returned True. This method enabled an accurate determination of the specific time and frequency of upwelling events in the central and western South China Sea.  
Figure 1: Division of upwelling areas.
## 4 Results and Discussion
### Simulation and Experimental Results Analysis  
The experimental and simulated temperature profiles were compared, and the data were found to be consistent, thus verifying the authenticity of the simulation. In the existing literature [34], it has been reported that distributor-type water inlet structures exhibit superior stratification characteristics in water tanks. In this study, we aimed to compare and analyze the experimental outcomes with the simulation results for both elbow and water distributor-type tanks. This study investigated the temperature distributions of two types of tank inlet and outlet structures at t = 200 s, 700 s, 1200 s, and 1700 s to provide a comprehensive visualization of the thermal behavior within the tanks. The cloud diagrams of the temperature distributions at different times for different types of thermal storage tanks are presented in Figures 10 and 11. As shown in Figures 10b and 11b, the temperature stratification effect of the two types was more obvious and consistent at  
intervals demonstrated the concordance between the experimental observations and simulation outcomes. This alignment not only validates the precision of our experimental setup but also attests to the accuracy of our simulation model, indicating the accuracy of the simulation and experiment. The temperature curves at each measurement point are shown in Figure 12. Figure 12 shows the temperature curves captured at each measurement point, further corroborating the reliability of our findings.  
Figure 13 shows the temperature profiles recorded at each measurement point in the simulation of the water distributor- and elbow-type inlet temperature profiles. Under the initial conditions, the energy in the water tank remained constant. The exothermic conditions, comparison of different structures, the same measurement point, and the tank inlet were in constant contact with cold water, which ensured that the lower measurement point temperature remained consistent. According to the conservation of energy and specific heat capacity formula, a structure with a higher temperature at the measurement point results in better temperature stratification of the tank.  
Figure 12: Comparison of experimental and simulation case temperatures at different height measurement points.  
Figure 13: Comparison of temperatures at different heights for different inlet simulation results.  
As shown in Figure 13, at 1500 s, the temperature at measurement point 1 of the water distributor inlet was 28.2 \({}^{\circ}\)C, while the temperature at measurement point 1 of the curved tube inlet was 34.59 \({}^{\circ}\)C. The temperatures at measurement point 6 were identical for all the structures. Given identical inlet conditions and all other initial parameters being equal, it is evident that the elbow-type inlet exhibits superior temperature stratification compared with the water distributor-type inlet.
## 5 Conclusions  
In this paper, the design and development of the onboard TLE observation system for the CubeSat VisionCube is presented. The onboard TLE observation system consists of dedicated hardware and software so as to detect occurrence of TLEs and obtain corresponding images from the low Earth orbit. Both hardware and software are designed and developed in-house, as the main hardware components are the MaPMT and the CMOS image sensor while the software including the low-level hardware logics and data manipulation algorithms are implemented on a SoC device. This device allows both hardware and software to be integrated seamlessly into a compact form factor. In order to verify the functionality of the developed hardware and the validity of the software algorithm of the onboard TLE observation system, the experimental validation has been conducted on the ground simulation facility. The ground simulation facility was designed to imitate a TLE on the ground using a set of ultraviolet LEDs. By the similarity justification on the output of the MaPMT compared to the actual TLE, the UV LED was chosen to emulate a random flash light of TLE. Finally, the experimental results using the onboard TLE observation system on the ground simulation facility reveals that the hardware and the software algorithm can effectively detect TLEs and obtain the digital images, demonstrating the capability of the onboard TLE observation system for the in-orbit operation. The TLE observation system will be integrated into the VisionCube and will go through space environmental tests, as it is currently scheduled to be launched in the second half of 2018.  
Conceptualization, D.J.; Methodology, D.J.; Software, S.K., T.N.; Validation, S.K., T.N.; Formal Analysis, D.J., S.K.; Investigation, D.J.; Resources, S.K.; Data Curation, D.J.; Writing-Original Draft Preparation, S.K., T.N.; Writing-Review & Editing, D.J.; Visualization, S.K.; Supervision, D.J.; Project Administration, D.J.; Funding Acquisition, D.J. This research was supported by the Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (No. NRF-2017M1A3A3A02016572, Research and Development of Variable Speed Control Moment Gyroscope for Nanosatellites Attitude Control). The authors declare no conflict of interest.
# A Phase Correction Model for Fourier Transform Spectroscopy
## 4 All-Pass Filter and PCM-APF Model
### The PCM-APF Model  
The correction results of the sub-pixel phase correlation method still contains residual nonlinear phase errors. To effectively eliminate these residual errors, the use of an all-pass filter is well suited. The residual error is still calculated using the arctan function:  
\[\theta_{res}(\omega)=\arctan\frac{Im(B^{\prime}(\omega))}{Re(B^{\prime}( \omega))} \tag{8}\]  
where \(\theta_{res}\) represents the nonlinear residual phase error, and \(B^{\prime}(\omega)\) is the spectrum corrected after the sub-pixel phase correlation correction step. Here, \(\omega\) is not the wavenumber but represents the normalized frequency, which ranges from 0 to \(\pi\) radians/s, where \(\pi\) radians/s corresponds to the Nyquist frequency \(v_{max}\). The relationship between normalized frequency \(\omega\) and wavenumber \(\
u\) is as follows:  
\[\omega=\pi\frac{\
u}{v_{max}} \tag{9}\]  
Since a stable all-pass filter only generates negative phase responses, so the interferogram needs to pass through a delay line before applying the filter. This introduces a linear phase component, ensuring that the residual phase error curve values are all positive [14]. Thus, the final output error phase is  
\[\theta_{out}(\omega)=\theta_{res}(\omega)+N_{shift}\omega-N\omega-2\arg Q\Big{(} e^{i\omega}\Big{)} \tag{10}\]  
The value of the order \(N\) of the all-pass filter should depend on the smoothness of the error phase curve. \(N\) also depends on the magnitude of the residual phase error we expect for the corrected spectra. For a 3-10 degree error and a typical interferogram resulting from an absorption experiment on a rapid-scan FTIR, \(N=9\) will suffice [12]. In the proposed PCM-APF model, the value of \(N\) will be smaller because the interferogram is \"pre-processed\" by the phase correlation method before passing through the all-pass filter, in which most of the linear phase errors are removed.  
We aim for a smaller value of \(\theta_{out}\). So for an \(N\)th-order all-pass filter, we establish an error function concerning the filter parameters and the delay order \(N_{shift}\), with the expectation that the error function reaches its minimum value:  
\[E\Big{(}N_{shift},a_{1},a_{2},\cdots,a_{N}\Big{)}=\max_{\omega_{a}\leq\omega \leq\omega_{b}}(\theta_{out}(\omega)) \tag{11}\]  
where \(\omega_{a}\) and \(\omega_{b}\) represent the upper and lower limits of the normalized frequencies.  
So the whole PCM-APF model for phase correction is as shown in Figure 4.
## 3 Results and Discussion
### Machine Learning Results and Discussion
#### 3.2.1 Comparison of Machine Learning Models
Figure 17: Comparison of actual and predicted targets for \(\gamma_{max}\) models; (**A**) DR; (**B**) RFR; (**C**) GBR; (**D**) XGBoost; (**E**) SVR; (**F**) ANN. ANN and XGBoost Models clearly aligned closely with the ideal line, demonstrating their accurate predictions across a range of stress levels. GBR models followed this trend with minimal deviations from the reference line, although there were some exceptions. RFR also showed good alignment overall, with occasional deviations. In contrast, DTR and SVR exhibited more noticeable differences from the ideal line, indicating variations between actual and predicted. Furthermore, determining an acceptable error margin is critical in determining the training dataset's adequacy. Figures 16 and 17 show a 20% error margin for \(\sigma_{min}\) models and a 1% error margin for \(\gamma_{max}\) models on each cross plot to demonstrate this. The prediction of \(\gamma_{max}\) requires increased precision, with the smallest possible margin of error due to the significant influence even minor changes can have on its behavior, as shown in Figure 17, where most projected values fall within the 1% error margin boundaries. Both the \(\sigma_{min}\) and \(\gamma_{max}\) models performed well, with most predictions falling within the error margin lines during the training, validation, and testing phases. This demonstrates that the training dataset was enough to support the \(\sigma_{min}\) and \(\gamma_{max}\) predictive models using the ANN and XGBoost algorithm proposed in this study.
## 1 Introduction  
Optical fiber Bragg grating (FBG) sensing technology has become one of the most attractive sensing technologies in a variety of fields, such as the optical fiber smart structures [1; 2; 3; 4]. FBG offers many advantages, such as wavelength encoded nature, wavelength-division multiplexing, immunity to electric-magnetic interference, and small size, etc. FBG is fragile, thus, is required to be protected before its practical use. Previous studies have demonstrated the protective packages can function as parameter compensations [5; 6; 7; 8]. During the FBGs actual use period, the birefringence is induced when FBGs are bent, clamped or twisted [9; 10; 11; 12]. As a result, the corresponding two Bragg spectra are shown in the reflection spectrum. This might reduce the accuracy of measuring the principal strain and, therefore, be considered as a noise signal. But on the other hand, the dual-peak resonance will provide an opportunity to develop the sensor with the capability of measuring both strain and temperature simultaneously using one optical fiber Bragg grating. Other studies of simultaneous measurement of strain and temperature using birefringence FBGs have been conducted [13; 14; 15].  
Metal coatings can protect FBG and also enhance their temperature sensitivity and have attracted considerable interest [16; 17; 18]. However, there have been few reports about the metallized FBGs'dual-peak resonance restructured by thermal strains induced by stepped-metal coating on the same Bragg length.  
This paper describes the experimental evaluation of the temperature sensing of the single-mode FBG with the Ni/Cu stepped-metal coating by electroless-plating and electroplating. The simulation models analyze the dual-peak resonance restructured by thermal strains and demonstrate the influence of the coating parameters on the two peaks. This kind of optical fiber Bragg grating can be used for a dual-parameter sensor at one location which can measure two physical parameters simultaneously.
## 2 Results and Discussion
### Vapor-Phase Minimum Inhibitory Concentration (VP-MIC)  
Natural antimicrobial compounds have a wide range of applications, particularly in food and biomedical applications [40]. Essential oils' vapor phase antimicrobial potential has numerous applications in food preservation [41]. As a result, several research studies have been conducted to examine the components and antibacterial activities of essential oils in the vapor phase, owing to the advantages of VP antibacterial assays over direct-contact assays (diffusion or dilution methods) [42]. As an example, EO components are partitioned across the agar in diffusion assays, while in dilution methods, low water solubility must be overcome by the use of emulsifiers or solvents, which may change the action [42]. The vapor-phase of the four mixtures as well as their major components (thymme (Th), sumac (Su), oriander (Cr), chickpeas (Cp)) showed varied antibacterial activity against the highly virulent and resistant tested bacterial strains (Table 3/Figure 1). Interestingly, the vapor-phase of the tested mixtures recorded antibacterial activity against both Gram-positive and Gram-negative pathogens, justifying the use of these herbal blends in food preservation. The tested individuals showed differences in antimicrobial effects against the selected bacterial strains. Coriander was the most potent with VP-MIC ranging from 1.2 \(\pm\) 0 to 2.4 \(\pm\) 2 mg/mL (Table 3). Regarding the mixtures, EDN recorded the highest antibacterial activity with the least VP-MIC value against tested pathogens (0.8 \(\pm\) 0.3 mg/mL) (Table 3; Figure 1). Mixture EDN was the only tested mixture with a vapor-phase that showed antibacterial activity against _Enterococcus faecalis_ ATCC19433, with a VP-MIC value of 3.1 \(\pm\) 1.4 mg/mL. It is worth mentioning that the antimicrobial potential of the EDN exceeded that of individuals against the tested bacterial strains, with the exception of oriander, which showed the lowest VP-MIC (1.2 \(\pm\) 0 mg/mL) against _Klebsiella pneumoniae_ (Table 3). The vapor-phase of mixture SZS recorded the lowest antibacterial activity, with no detected activity against _Enterococcus faecalis_ ATCC19433 or _Enterobacter cloacea_. The essential oils of the four herbal blends had effective antibacterial effects in the vapor phase, which was attributed to volatile component dispersion. The presence of carvone [29], furfural, and furfural derivatives [43] could explain EDN's potential antimicrobial activity. Together with estragole [44] and the other minor constituents, these components could be, at least in part, responsible for the antibacterial activities of EDN. While thyme and coriander have previously been reported to prevent foodborne diseases [45], the synergistic effect between them and the other components of the herbal blends shown in EDN and the other tested mixtures may be a viable option to improve their antimicrobial potential, suggesting the use of Egyptian dukkah and Syrian za'atar in food preservation.
## 5 Results
### Total Number of Satellites and Economic Efficiency Analysis  
Figure 21 shows that, under identical communication specifications, the VLEO (300 km) constellation requires the fewest satellites (52) and achieves the lowest GA fitness value (279.74). The GA value is a composite metric derived from the fitness function, which was designed to minimize the average revisit time while maximizing data transmission efficiency. In essence, a lower GA value indicates a more optimal configuration that balances performance and cost effectively.  
Figure 19: Average revisit times of the VLEO (300 km) constellation by latitude.  
Figure 20: Average revisit times of the VLEO (200 km) constellation by latitude.  
Specifically, the LoRa and Mioty constellations in both the VLEO (300 km) and VLEO (200 km) configurations demonstrate superior performance in terms of satellite count and overall efficiency compared to the LEO (600 km) configuration. In particular, the VLEO (200 km)-LoRa configuration deploys 51 satellites--one fewer than the VLEO (300 km) configuration--and achieves a competitive GA fitness value of 299.82, although its overall performance is slightly inferior to that of the 300 km configuration. While the launch costs at 200 km may be marginally lower, the higher commercial demand for the 300 km orbit likely minimizes this cost difference.  
These results, derived from a balanced design approach that considers both data transmission efficiency and revisit time, suggest that VLEO configurations--particularly at 300 km--offer superior economic efficiency and performance compared to LEO setups.