esa-sceva/satcom-chunk-collection huggingface dataset:
# The CYGNSS Mission: On-Going Science Team Investigations
## 2 Methodology, Results, and Discussions
### Ocean Surfaces
Finally, an overview of several CYGNSS-based altimetry methods was presented, showing an accuracy on the order of several meters. Future improvements are likely to be achieved in accounting better for tides, and ionosphere and troposphere delays. Session 6: Tropical Cyclones and Tropical Convection II  
The final session of the June 2020 CYGNSS Science Team meeting focused on the use of the L2 wind speed products for improving forecasts and wind field analyses of TCs and tropical convection. CYGNSS provides wind speed data that can be used for storm surge predictions, which is a major source of destruction for communities lying in the paths of a storm. Simulations showed the storm surge from the Hurricane Harvey (2017), which were driven in part by wind speed observations from CYGNSS. Efforts were performed to diagnose the structure of TCs' wind fields using the CYGNSS wind speed observations and found that, given good quality data from CYGNSS, the size of the wind field can be determined. A methodology for creating storm wind fields that move with the storm over time was also presented (Figure 10). These datasets are available in PODAAC, alongside other CYGNSS products [68]. Apart from analysis of storm wind fields, the observations from CYGNSS are also being used to improve weather forecasts. Efforts to improve the initialization of weather model simulations with CYGNSS observations were presented, including also information about where CYGNSS improves the forecasts of TCs. Throughout the session, team members compared techniques for quality control and optimal ingestion of wind speed data from CYGNSS. CYGNSS is also playing a role in studies of tropical convection, where other observations suffer in heavy precipitation. Simulations of tropical convection with a coupled atmosphere, ocean, wave model were described, along with a comparison of simulated winds with those observed by CYGNSS. A number of on-going investigations examined CYGNSS observations near tropical oceanic thunderstorms, including an examination of the characteristics of the data in storms with and without lightning [69]. The importance of wind-driven fluxes on the Madden Julian Oscillation (MJO), using CYGNSS data was high  
Figure 9: Assimilating CYGNSS DDMs into ECMWF background winds to improve surface wind forecasts: **[top]** demonstrating the ECMWF background at 150 km resolution, [middle] analysis field with the DDM assimilation, and **[bottom]** the difference between the two. Image by [PERSON] et al. lighted [70]. Finally, it is worth highlighting that several team members are using CYGNSS data to study the processes of air-sea interaction in weather and climate science [71].
## 3 Results and Discussion
### Test Results  
Next, the results of the tests whose plan is detailed in Section 3.1 are reported and discussed in order to provide indications and examples of the ASACAS's performance and limitations.
## 5 A Hysteresis Model with Damage Accumulation of Welding Materials
### Parameter Fitting of the Model  
#### 5.5.1 The Parameter \(\lambda\) of EvolutionEquation of Damage Accumulation  
The cumulative damage variable \(D\) will be valued 1 when the specimen is destroyed, and calculate the maximum plastic strain \(\varepsilon_{m}^{p}\) and the cumulative plastic energy dissipation in the cyclic process, then the parameter \(\lambda\) can be obtained use Equation (3), so the value of \(\lambda\) can fitaccording to Equation (14) by the test data, and the fitting results are presented in Table 6.  
\[\lambda=\frac{1-\frac{\varepsilon_{m}^{p}}{\varepsilon_{n}^{p}}}{\sum\limits \limits_{n_{1}}^{n}\frac{E_{n}}{E_{n}}-\frac{\varepsilon_{m}^{p}}{\varepsilon_ {n}^{p}}} \tag{14}\]  
5.2 The Parameters \(\eta\), \(\xi\), \(g\), \(h\), \(K^{\prime}\) and \(n^{\prime}\)  
(1) Damage Parameters \(\eta\), \(\xi\), \(g\), \(h\)  
The parameters can be fitted according to Equations (8) and (9) by the results of low-cycle fatigue test. Equations (8) and (9) can be transformed into Equations (15) and (16), and the curves of \(\frac{\sigma_{m}^{D(n)}}{\sigma_{m}}-D_{n}\) and \(\frac{E_{n}^{D}}{E_{0}}-D_{n}\) of WB1(2) are presented in Figure 15 (an example). Therefore, the parameters can be fitted according to Figure 15, the values of the parameters of all welding specimens are summarized in Tables 7 and 8. \[\frac{E_{n}^{D}}{E_{0}}=g-hD_{n} \tag{15}\]  
\[\frac{\sigma_{m}^{D(n)}}{\sigma_{m}}=\eta-\xi D_{n} \tag{16}\] \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Welding Material of Q235 Steel**} & \multicolumn{3}{c}{**Welding Material of Q345 Steel**} \\ \cline{2-7}
**Specimens** & **Butt Weld** & **Fillet Weld** & **Butt Weld** & **Fillet Weld** \\ \cline{2-7}  & **WA1** & **WA2** & **WA3** & **WB1** & **WB2** & **WB3** \\ \hline (1) & 0.02529 & 0.02609 & 0.02871 & 0.02311 & 0.02156 & 0.02176 \\ (2) & 0.02233 & 0.02538 & 0.02613 & 0.01905 & 0.01909 & 0.02433 \\ (3) & 0.02905 & 0.03043 & 0.03280 & 0.02071 & 0.02326 & 0.02230 \\ Model Parameters & 0.0256 & 0.0273 & 0.0294 & 0.0209 & 0.02130 & 0.0227 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Values of Parameter \(\lambda\). The Parameters \(K^{\prime}\) and \(n^{\prime}\) can be fitted through Equation (12) and the test results, which are presented in Table 9.
# Applications of Pulsed Light Decontamination Technology in Food Processing: An Overview
## 7 Pulsed Light Technology for Enhancement of Bioactive Components in Foods  
PL treatment has been used in recent studies to show enhancement of bioactive compounds like phenolic compounds, carotenoids and antioxidants [111; 115; 129; 139]. These are produced as a defense mechanism to external severities. However, light-based technologies like UV have been used for the enhancement of nutrients like vitamin D in mushrooms [155]. However, due to longer exposure time of 5-20 min, there are discolorations in UV treated mushrooms [156]. PL processing being a shorter treatment has been shown to achieve the same objective with little to no discoloration. In an earlier study by [PERSON] and [PERSON] [157], vitamin D\({}_{2}\) was enhanced up to 824% daily value (DV-400 IU/84 g being 100% DV) per 84 g brown and white button mushrooms by application of 3.5 J/cm\({}^{2}\). [PERSON] et al. [158] in a separate study, also observed Vitamin D\({}_{2}\) content enhancement in white button mushrooms (_Agaricus bisporus_) was up to 27 \(\mu\)g/g dry weight using 18 pulses of pulsed UV light of 0.791 J/cm\({}^{2}\) per pulse for 1 s treatment. In another study, [PERSON] et al. [156] treated the white button mushrooms (_Agaricus bisporus_) passed in conveyor belts (at 31.7 cm distance) and enhanced Vitamin D\({}_{2}\) by 22.5 \(\mu\)g/g dry weight using nine pulses at 1.15 J/cm\({}^{2}\) per pulse. No visible discoloration was observed by them. [PERSON] et al. [159] treated _Pleurotus_ mushrooms with nine pulses of 1.15 J/cm\({}^{2}\) per pulse and enhanced vitamin D\({}_{2}\) by up to 2.78 \(\mu\)g/g fresh weight. They also treated mushroom powder under severe conditions of 60 pulses of 1.15 J/cm\({}^{2}\) per pulse at 5 cm distance and observed a vitamin D\({}_{2}\) increment of up to 65.4 \(\mu\)g/g dry weight. Overall, the vitamin D\({}_{2}\) enhancement varies proportionally with energy delivered during PL treatment. The vitamin D\({}_{2}\) enhancement in mushrooms by PL was also patented recently [160; 161].
## 2 Characteristic Analysis of the Iridium System
### The Signal Level  
#### 2.3.1 The Fluctuation of SNR  
Variations in age, operational status, and orbital characteristics among individual Iridium satellites contribute to significant SNR fluctuations. Furthermore, Iridium's beam-switching mechanism introduces sever SNR fluctuations even among epochs originating from the same satellite. Figure 3 presents empirical SNR measurements from 100 real Iridium signal epochs. The SNR fluctuation reaches about 9 dB during the 2 min observation window and demonstrates numerous instantaneous SNR transitions. These abrupt variations necessitate that positioning algorithms explicitly account for SNR effects and implement real-time adaptive adjustments.
# Improving Waste Management System Efficiency and Mobility with Efficient Path MANET
## 4 Experiment Results and Discussion  
In this section, three experiments are presented. The experiments were performed on network topologies with four different sizes. The parameters for each scenario were determined based on the randomness of the network's behavior. The network sizes in these experiments were 20, 30, 40, and 50 nodes. The first experiment discovered the routes from the source node to all other nodes as destinations and evaluated the performance of the chromosome intersection method in the proposed algorithm.  
The second experiment examined the intersection GA-based routing algorithm to find the efficient path based on the multiconstraint's values from the source node to the specific destination node.  
The third experiment compared the proposed intersection GA-based routing selection and dynamic source routing (DSR). DSR is one of the common reactive routing algorithms in MANET. Thus, it is considered the best in the ad hoc network field.  
The simulation experiment was performed using ns-2 according to the simulation setup shown in Table 2.
## 6 Application
The overall evaluation results for both Sample A and Sample B are strikingly alike; however, Sample B demonstrates a higher level of proportionality across all criteria compared to Sample A. After that, by applying Equation (12), the colored sections within the improved radar chart are analyzed to determine their area and perimeter, resulting in the extraction of evaluation results for each respective section. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Main Criterion**} & \multirow{2}{*}{**Sub-Criterion**} & \multicolumn{2}{c}{**Weight**} \\ \cline{3-4}  & & \(C_{i}\) & \(C_{ij}\) \\ \hline \multirow{2}{*}{\(C_{1}\)} & & 0.2080 & \\  & \(C_{11}\) & & 0.5753 \\  & \(C_{12}\) & & 0.2740 \\  & \(C_{13}\) & & 0.1507 \\ \multirow{2}{*}{\(C_{2}\)} & & 0.2570 & \\  & \(C_{21}\) & & 0.0105 \\  & \(C_{22}\) & & 0.0210 \\  & \(C_{23}\) & & 0.0829 \\  & \(C_{24}\) & & 0.0804 \\ \multirow{2}{*}{\(C_{3}\)} & & 0.1590 \\  & \(C_{26}\) & & 0.1741 \\ \multirow{2}{*}{\(C_{27}\)} & & 0.1300 \\  & \(C_{28}\) & & 0.1043 \\ \multirow{2}{*}{\(C_{29}\)} & & 0.1013 \\  & \(C_{210}\) & & 0.0444 \\ \multirow{2}{*}{\(C_{211}\)} & & 0.0338 \\  & \(C_{212}\) & & 0.0583 \\ \multirow{2}{*}{\(C_{3}\)} & & 0.2690 & \\  & \(C_{31}\) & & 0.6000 \\ \multirow{2}{*}{\(C_{32}\)} & & 0.2000 \\  & \(C_{33}\) & & 0.2000 \\ \multirow{2}{*}{\(C_{4}\)} & & 0.2660 & \\  & \(C_{41}\) & & 0.0664 \\ \multirow{2}{*}{\(C_{42}\)} & & 0.4839 \\  & \(C_{43}\) & & 0.1768 \\ \multirow{2}{*}{\(C_{44}\)} & & 0.2730 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Weights and consistency ratios for each criterion. The order of performance ranking for the three earthquake search and rescue robots is as follows: Sample C (the evaluation result is 0.613) outperforms Sample A (the evaluation result is 0.522), which in turn outperforms Sample B (the evaluation result is 0.510), as depicted in Figure 6b. The height of a rectangle represents the weight assigned to each criterion, with weights of 0.208 for survivability, 0.257 for mobility, 0.269 for Detection perception ability, and 0.266 for Communication control ability. It is clear that Sample B surpasses in Survivability and Communication control ability but falls behind in Mobility and Detection perception ability. Sample B demonstrates lower walking performance values, including Minimum turning radius \(C_{24}\), Maximum width across trenches \(C_{25}\), Maximum height of obstacles to be overcome \(C_{26}\), Maximum angle for climbing \(C_{27}\), and Number of steps climbed per unit time \(C_{28}\), which are deemed important factors by experts. Consequently, this results in the comparatively inferior performance of sample B among the three earthquake search and rescue robots.
# Reverse-Net: Few-Shot Learning with Reverse Teaching for Deformable Medical Image Registration
## 6 Discussion  
For supervised registration, the number of labels limits the registration performance. Unsupervised registration is easily disturbed by outliers and task-irrelevant areas due to the constraint of missing labels, which can lead to eventual misalignment and distortion. To solve these problems, we propose a few-shot deformable image registration method based on reverse learning, which can improve registration performance effectively. For few-shot learning, the reverse teaching network utilizes the deformation field generated by the pixel alignment network to generate additional training data from different unlabeled images and teaches the border enhancement network rich structural knowledge for more accurate enhancement of T1 image ROI boundaries. Therefore, the above operation can provide high-quality data for the subsequent pixel alignment network and improve the registration performance of the model. The registration results in Figure 6 also show the ability of the border enhancement network to enhance the ROI boundaries,our experiments on brain MRI images show that our network has better registration performance and effective texture preservation. Compared with Cof-net, we achieved 3.5% improvement in DSC while reducing the processing time per slice, which indicates that our framework has better potential for future clinical applications.  
Reverse-net has improved deformable medical image registration with only a few segmentation labels, but the number of segmented labels becomes a challenge. In order to fully utilize the segmentation labels, we introduced the reverse teaching network and redesigned the loss function. Experiments in which different numbers of segmentation labels are used are conducted on the MRbrain dataset. It was shown that competitive registration accuracy could be obtained by using only five segmentation labels.  
Although methods based on few-shot learning are efficient, the smoothness of the deformation field is insufficient. In order to enhance the smoothness, various joint loss functions were designed and introduced, and suitable hyperparameters were selected after extensive experiments. As can be seen in Figure 10, the smoothness of the deformation field is better with our method.  
By redesigning the network structure and loss function, our model shows better performance on brain datasets. However, MR-CT multimodal registration based on few-shot learning is still a challenging task in medical image deformation registration, so future research applying our reverse-net for MR-CT multimodal registration tasks is significant.
That is, at a fixed position or calibration zone, in the same temperature condition (value), the Raman anti-Stokes data should be the same for any measurement. If the output power of the pulse laser changes or the gain factor of the APD fluctuates by multiplying a coefficient, the newly sampled data can be calibrated entirely in theory. Hence, a temperature calibration zone is set in front of the FUT, and corresponding temperature information and the Raman anti-stokes scattering values are recorded for reference. Then, based on the relationship between temperature and Raman anti-Stokes scattering power, the temperature demodulation formula is obtained. Compared with the traditional double channel-based scheme, the proposed distributed optical fiber temperature sensing system is much simpler in structure, and it also saves one signal channel, which benefits the integration of hybrid distributed optical fiber sensors for multi-parameter monitoring with Rayleigh and Raman scattering spectra. More importantly, the proposed system avoids the walk-off effect of the traditional scheme, so the data processing algorithm is much easier and the temperature measurement results should be more reliable.
## 2 Materials and Methods
### Experimental Procedure
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**2-CV**} & \multicolumn{2}{c}{**5-CV**} \\ \hline
**Variables** & **Coef.** & \(p\)**-Values** & **Coef.** & \(p\)**-Values** \\ \hline
**(Intercept)** & **0.4090** & **1.52 \(\times\) 10\({}^{-39}\)** & **0.4063** & **1.745 \(\times\) 10\({}^{-62}\)** \\
**P1** & **0.2744** & **2.23 \(\times\) 10\({}^{-7}\)** & **0.2821** & **6.774 \(\times\) 10\({}^{-16}\)** \\ P2 & \(-0.0724\) & 8.83 \(\times\) 10\({}^{-2}\) & \(-0.0318\) & 3.997 \(\times\) 10\({}^{-1}\) \\
**P3** & **-11.7520** & **3.26 \(\times\) 10\({}^{-47}\)** & **-1.2157** & **2.437 \(\times\) 10\({}^{-79}\)** \\
**P4** & **0.3043** & **3.64 \(\times\) 10\({}^{-8}\)** & **0.3293** & **5.211 \(\times\) 10\({}^{-17}\)** \\
**P5** & **-0.1958** & **8.15 \(\times\) 10\({}^{-3}\)** & **-0.2192** & **9.524 \(\times\) 10\({}^{-11}\)** \\ P1P2 & \(-0.0209\) & 3.14 \(\times\) 10\({}^{-1}\) & \(-0.0379\) & 4.248 \(\times\) 10\({}^{-2}\) \\
**P1P3** & **-0.1476** & **3.26 \(\times\) 10\({}^{-5}\)** & **-0.1188** & **2.531 \(\times\) 10\({}^{-10}\)** \\ P1P4 & 0.0610 & 9.92 \(\times\) 10\({}^{-3}\) & 0.0262 & 1.722 \(\times\) 10\({}^{-1}\) \\ P1P5 & \(-0.0627\) & 3.87 \(\times\) 10\({}^{-3}\) & \(-0.0489\) & 8.091 \(\times\) 10\({}^{-3}\) \\ P2P3 & 0.0667 & 2.05 \(\times\) 10\({}^{-3}\) & 0.0724 & 5.528 \(\times\) 10\({}^{-5}\) \\ P2P4 & \(-0.0235\) & 2.67 \(\times\) 10\({}^{-1}\) & \(-0.0272\) & 1.588 \(\times\) 10\({}^{-1}\) \\ P2P5 & 0.0300 & 1.47 \(\times\) 10\({}^{-1}\) & 0.0166 & 4.256 \(\times\) 10\({}^{-1}\) \\
**P3P4** & **-0.2304** & **1.09 \(\times\) 10\({}^{-13}\)** & **-0.2320** & **3.256 \(\times\) 10\({}^{-28}\)** \\
**P3P5** & **0.2764** & **3.84 \(\times\) 10\({}^{-18}\)** & **0.2640** & **1.387 \(\times\) 10\({}^{-31}\)** \\
**P4P5** & **-0.1003** & **1.13 \(\times\) 10\({}^{-1}\)** & **-0.0926** & **2.914 \(\times\) 10\({}^{-6}\)** \\
**P12** & **-0.1127** & **4.01 \(\times\) 10\({}^{-4}\)** & **-0.1237** & **3.658 \(\times\) 10\({}^{-6}\)** \\ P22 & 0.0166 & 6.28 \(\times\) 10\({}^{-1}\) & \(-0.0048\) & 7.040 \(\times\) 10\({}^{-1}\) \\
**P32** & **0.7405** & **1.46 \(\times\) 10\({}^{-35}\)** & **0.7779** & **7.078 \(\times\) 10\({}^{-63}\)** \\ P42 & \(-0.0195\) & 5.65 \(\times\) 10\({}^{-1}\) & \(-0.0386\) & 2.334 \(\times\) 10\({}^{-1}\) \\ P52 & \(-0.0192\) & 5.43 \(\times\) 10\({}^{-1}\) & 0.0009 & 7.572 \(\times\) 10\({}^{-1}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Mean \(p\)-value relevance in multiple regression using a cross-validation procedure. In bold type the most relevant ones are highlighted. Figure 6: Prediction comparison: FEM data vs. multiple regression models. Quadratic multiple regression results are the best fitted to FEM simulations.
## 2 Materials and Methods
### Chemical Analyses
#### 2.4.2 Higher Alcohols, Acetates and Esters Quantification  
Major and minor fermentation volatile compounds of wine aroma were analyzed by GC-MS using the Head-Space Solid-Phase Micro-Extraction (HS-SPME) procedure which was recently adapted and described in [7]. Twenty-five micrograms of the internal standard, namely 3-octanol (1 g/L), were added to 25 mL of wine which were placed in a 40 mL vial, and then supplemented with 3 g of NaCl, a magnetic stir bar, and then, sealed using a screw-top cap with a silicon septum. The vial was placed on a heating stir plate, and the samples were equilibrated by stirring them at 750 rpm for 10 min at 40 \({}^{\circ}\)C. The SPME needle was inserted automatically (HTA3000A S.R.L., Brescia, Italy) through the septum and the DVB/CAR/PDMS 75 \(\upmu\)m fiber was used to absorb the volatile compounds of the head-space for 30 min at 40 \({}^{\circ}\)C. The SPME needle was removed from the vial and inserted into the injector of GC for thermal desorption for 10 min. The analysis was performed using an Agilent 7890A GC that was equipped with an Agilent 5873C MS detector. The column that was used was an DBWAX capillary column (30 m \(\times\) 0.25 mm i.d., 0.25 \(\upmu\)m film thickness) and the gas carrier was helium with a flow rate of 1.2 mL/min. The injector and MS-transfer line were maintained at 250 \({}^{\circ}\)C and 260 \({}^{\circ}\)C, respectively. The oven temperature was held at 30 \({}^{\circ}\)C for 5 min and then, it was raised to 220 \({}^{\circ}\)C at 4 \({}^{\circ}\)C/min and held at this temperature for 20 min. The selective ion monitoring (SIM) mode was applied, and the quantifier ions that were used were: ethyl isobutyrate (\(m/z\) 88), ethyl butyrate (\(m/z\) 71), ethyl 2 methyl butyrate(\(m/z\) 102), isoamyl acetate (\(m/z\) 87), isobutyl acetate (\(m/z\) 116), 3-octanol (\(m/z\) 59), ethyl hexanoate (\(m/z\) 88), ethyl octanoate (\(m/z\) 88), ethyl decanoate (\(m/z\) 157), 2 phenyl ethyl acetate (\(m/z\) 104), hexyl acetate, (\(m/z\) 69), isoamyl alcohol (\(m/z\) 87), 2-methyl-1-propanol (\(m/z\) 87), hexanol-1 (\(m/z\) 84), 2-phenylethanol (\(m/z\) 122) and 3-(methylthio)-1-propanol (\(m/z\) 96).
## 4 Materials and Methods
### Quantitative Real-Time PCR (qRT-PCR)  
The quantity and quality of RNA were measured using a Nanodrop spectrophotometer (ND-1000, Thermo Fisher Scientific, Waltham, MA, USA), and approximately 500 ng of RNA was reverse transcribed to cDNA using an iScript(tm) cDNA Synthesis kit obtained from Bio-Rad, Singapore. Expression levels of enzyme genes along the trichomes of five cultivars and the stem of the HB cultivar were analyzed using qRT-PCR. Primers for qRT-PCR were designed by exploiting the cDNA sequences obtained from the RNA-seq data. The qRT-PCR reactions were performed in a 384-well PCR plate using KAPA SYBR fast master mix (Roche, Singapore) and an ABI PRISM 900 HT real-time PCR system. For a total PCR reaction of 5 \(\mu\)L, 0.3 \(\mu\)L of cDNA was used and the cycling profile was set at 50 \({}^{\circ}\)C for 2 min, 95 \({}^{\circ}\)C for 10 min, 40 cycles of 95 \({}^{\circ}\)C for 15 s, and 60 \({}^{\circ}\)C for 60 s. After thermal cycles, the dissociation analysis (melting curve) was carried out to confirm the specific amplifications of the PCR reaction by adding a profile of 95 \({}^{\circ}\)C for 15 s, 60 \({}^{\circ}\)C for 15 s, and 95 \({}^{\circ}\)C for 15 s. In the current study, _CsGAPDH_ was used as an internal reference, due to its similar expression across the five cultivars. A non-template reference was included for each gene to eliminate the possibility of random genomic DNA contamination and primer dimer formation. SDS 2.4 software (Applied Biosystems) was used to analyze the obtained results. The threshold cycle (Ct) value of a gene is the cycle number required for the SYBR Green fluorescence signal to reach the threshold level during the exponential phase for detecting the amount of accumulated nucleic acid [69]. Comparative delta Ct values of target genes to _CsGAPDH_ were taken as relative expressions among different tissues. The amounts of target genes, normalized to the _CsGAPDH_ gene, were calculated by \(2^{-(\text{Cl}[\text{target gene}]\text{Cl}[\text{GAPDH}])}\). Error bars represent means \(\pm\) SDs. All primers used in this study were designed manually and are listed in Table S1.