esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
An experimental setup was constructed to study the acoustic extinction of jet diffusion flames burning in air. The sound wave frequency and local sound pressure varied from \(110\) Hz to \(150\) Hz and \(2\) Pa to \(16\) Pa. There was no evaporation process because the fuel used here was butane, which is gaseous at room temperature, so the quenching mechanism is different from the previous study [11].  
The results showed that the most direct cause of flame extinction was the deviation of flame away from the fuel supply outlet during oscillation. Moreover, such oscillation was driven by induced wind produced by membrane vibration instead of sound pressure. Due to the nature of periodic oscillation, during the deviation of the flame, the movement of fuel was not synchronized with the flame in time and amplitude. As a result, there was a gap between the flame and new unburned fuel, where the fuel was so scarce that the flame could not be recovered in time after the synchronous movement started and the fuel-flame cycle was consequently interrupted. After this effect was accumulated for several cycles, the flame was completely extinguished. It was the most fundamental mechanism of flame extinction.  
The frequency of induced wind was the same as that of sound waves, and there was a positive correlation between the oscillation amplitude of induced wind and sound pressure. However, the induced wind could make the flame oscillate at millimeter scale only near the membrane and decreased rapidly as the distance from the speaker increased, implying that this fire-extinguishing method is infeasible for long-distance fire extinguishing. In addition, large-scale use of acoustic fire extinguishing is not recommended, and may cause damage to hearing.  
Y.-J.Z.: conceptualization, data curation, writing--original draft, writing--review and editing. H.J.: writing--review and editing. Y.-J.W.: funding acquisition, conceptualization, writing--review and editing. Y.-J.Y.: writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
Figure 11: (**a**) Changes in the gap between the flame and the new unburned fuel; (**b**) the critical \(\overline{\delta}_{\text{ex}}\) was a constant with different sound frequencies.  
Funding: This research was funded by [the National Natural Science Foundation of China] grant number [52176128] and [the Shaanxi Provincial Key R&D Program] grant number [2019 ZDLGY15-10]. And The APC was funded by [the National Natural Science Foundation of China (Grant Nos. 52176128)]. Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: The data presented in this study are available on request from the corresponding author. Acknowledgments: The work in Xi'an Jiaotong University was supported by the National Natural Science Foundation of China (Grant No. 52176128) and the Shaanxi Provincial Key R&D Program (Grant No. 2019 ZDLGY15-10). Conflicts of Interest: The authors declare no conflict of interest. References
## 5 Model Implementation  
The RBLR models were estimated in in WINBUGS statistical software, where all model parameters were assumed to be non-informative priors following Normal distribution. The slope parameters \(\beta_{i}\) (where \(i=1,\ldots,N\)) were set to be distributed \(\beta~{}\sim~{}Normal(0,~{}0.000001)\), with the mean equal to 0 and precision equal to 0.000001 (the precision being defined as the reciprocal of the variance, so the variance is equal to 1,000,000). The intercept was set to follow normal distribution \(\beta_{0}~{}\sim~{}N(0,~{}t)\), with the mean equal to 0 and the precision parameter \(t\) having Gamma distribution \(t~{}\sim~{}Gamma(0.001,~{}0.001)\) with the mean equal to 1 and precision equal to 0.001 (i.e., the variance equal to 1000).  
The Bayesian inference was estimated using the Markov Chain Monte Carlo (MCMC) simulation. Unlike the previous crash risk analysis studies, which did not provide the initial values of the decision variables in the estimation procedure, this study used ordinary logistic regression to assign the initial values to the variables. The simulation was performed with 20,000 iterations, with the first 5000 samples considered as burn-in. The 95% Bayesian Credible Interval (BCI) was used as the significance indicator of explanatory variables [31], indicating that an explanatory variable is statistically significant if the 95% BCI for the corresponding model coefficient does not include the value of zero [32]. The Deviance Information Criterion (DIC) was used to assess the model complexity and goodness-of-fit. The Bayesian logistic model with smaller values of DIC is preferable [33]. The RBLR models of crash severity and crash likelihood in this study were estimated separately using the corresponding training datasets and both converged with the acceptable DIC. The crash likelihood model had the DIC value of 8578.47 and the crash severity model had DIC value of 4850.40, both of which were lower than the DIC of the corresponding null models, indicating that the explanatory variables improve the fit of both models to the training data. The resulting models were then applied using the corresponding testing datasets to derive the performance metrics for evaluation and comparison with other models.  
All other models in this study were implemented in R statistical software using CARET package version 6.0-86 [34]. Before model tuning and validation, the data preprocessing was performed including centering and scaling of all continuous variables used in the model. The model tuning and validation was performed in all ML models using 10-fold cross-validation.
# Biosynthesis of Rubbellins in _Ramularia collo-cysni_--Genetic Basis and Pathway Proposition
## 4 Materials and Methods
### Expression Profiling of Putative Rubellin Biosynthetic Genes  
Transcript levels of candidate genes were assessed in artificially inoculated barley seedlings following the inoculation method developed by [PERSON] et al. [55] and [PERSON] et al. [56]. Plant material used to assess the transcript level of candidate rubellin biosynthetic genes was obtained from [PERSON] et al. [26]. Seedlings of the barley cultivar Century were grown for two weeks at 18 \({}^{\circ}\)C, 80% humidity, and 16 h of light (250 umol m\({}^{-2}\) s\({}^{-1}\)), then sprayed with a mycelial suspension obtained from a two-week-old _R. collo-cygni_ isolate DK05 culture grown in potato dextrose broth (PDB). Infection was promoted by ensuring maximum humidity and incubating the plants for 48 h in the dark at 18 \({}^{\circ}\)C. After incubation, previously described growing conditions were restored with the exception of humidity levels which were maintained at the maximum level for a further 72 h before returning to 80%. Disease progression was visually assessed at 5, 7, 10, 12, 15, 18, and 21 days postinoculation (dpi). At each time point, two leaves were harvested and pooled per biological replicate and immediately snap-frozen in liquid nitrogen. _R. collo-cygni_ DNA was quantified throughout the experiment and was previously reported in [PERSON] et al. [26]. RNA extraction was carried out using the RNeasy mini kit (Qiagen Ltd., Hilden, Germany) following the manufacturer's instructions, and potential genomic DNA contaminants were removed using the TURBO DNase I kit (Thermo Fisher Scientific, Austin, TX, USA) following the manufacturer's instructions. cDNA synthesis was carried out with the SuperScript III First-Strand Synthesis kit (Invitrogen, Carlsbad, CA, USA) following the manufacturer's instructions prior to being used in qRT-PCR with a SybrGreen Jump Start Taq system (Sigma, Dorset, UK). The stability of four housekeeping genes was assessed, and the two most stable ones, glyceraldehyde-6-phosphate dehydrogenase (GAPDH) and \(\alpha\)-tubulin, were used to normalise cDNA using GeNorm [57]. Using gene-specific primers (Supplementary information Table S3) with amplification efficacies ranging from 80% to 110%, transcript quantification was carried out based on the E\({}^{-\Delta\Delta\text{CI}}\) method [58]. Data presented were mean normalised expression value \(\pm\) standard error of three independent experiments.
# A Detailed Review on Foam Concrete Composites: Ingredients, Properties, and Microstructure
## 5 Mechanical Properties of Foam Concrete
### Compressive Strength
There is a linear relationship between the dry density of the prepared foam concrete and its compressive strength. As the dry density increases, the compressive strength increases [15; 21; 38]. In addition, the porosity increases with the increase in the amount of foam. Increasing porosity causes an increase in the amount of air voids, thus decreasing the compressive strength. On the contrary, when the amount of foam is reduced, the amount of cement used increases, thus increasing the compressive strength [5; 15; 21]. The type of foam used also affects the strength. Looking at the studies in the literature, protein-based foaming agents provide better compressive strength than synthetic ones. [PERSON] et al. [38] reported that foam concrete containing a protein-based foaming agent, with a w/c of 0.5, showed higher compressive strengths, and a synthetic-based foaming agent, with a w/c of 0.3, led to higher compressive strengths. He observed that protein-based foaming agents increased the compressive strength of foam concrete. The curing method applied to the samples affects the strength. Air, water and cell-phane curing methods were used in the studies. It was observed that the air curing method resulted in the lowest strength values. The reason for this is the lack of optimal conditions for the hydration of the samples [15; 38]. In contrast, the strength of standard curing and moisture-proof cured samples increased with increasing curing age [15]. The addition of fibers can help limit crack propagation, thereby helping to increase compressive strength. The use of basalt and PVA fibers increases the compressive strength [59; 60]. A 38% increase in compressive strength was achieved by using 1% of sugar cane pulp fiber. Compressive strength is adversely affected by increasing the amount of fiber. As workability decreases, the need for water increases, which negatively affects the strength [61]. Compressive strength values for different studies.
# Sentinel-1 and -2 Based near Real Time Inland Excess Water Mapping for Optimized Water Management
## 2 Materials and Methods
### Optical Data Based Classification
Integration  
The binary maps produced by the three processing workflows covered different spatial areas and were from different dates during the week that was being processed. Data from different dates were aggregated into one weekly inland excess water map because data from single date do not provide a reliable result [10]. To integrate the data and to determine the reliability of the final weekly inland excess water map, all individual partial result maps were extended to the total study area. Pixels where no information was available on whether there was water were designated \(-100\). Pixels can have a \(-100\) value if they are clouds, shadows, outside the original image or for any other reason undetermined. The result is a large set of maps with three values: 0 (no water), 1 (water), and \(-100\) (undetermined) covering the total study area. In the next step, a map was created storing the number of times that it was determined if in a pixel there was water or no water. In another map, for each pixel, it was determined how many times water was found. By dividing the second map by the first map, a frequency map was created specifying the relative number of times water was found in a pixel compared to the number of times data were available. If the relative frequency of water was above the manually specified threshold (e.g., 0.3 or 30%), the pixel was determined to be water. This map was then filtered to delete separate water pixels surrounded by no water pixels and to fill up individual no water pixels surrounded by water pixels. In this way, more continuous inundations were generated. In the final step, all known permanent water bodies were masked out based on the masked file (see Section 2.2.3), and a binary raster inland excess water map was created. The raster map was vectorized, and using zonal statistics the amount of inland excess water per hectare was calculated for different administrative units.
## 3 Results
### Yearly Evaluation of GPP  
As illustrated in Figure 9, the study work examined the multi-year mean tower-based GPP observations (GPP\({}_{\text{EC}}\)) and three different satellite-based GPP products including the GPP\({}_{\text{SIF}}\), GPP\({}_{\text{MOD}}\) and GPP\({}_{\text{BESS}}\) at the two subtropical forest flux sites. At the evergreen needle-leaf forest site, multi-year mean tower-measured GPP was 1799 \(\pm\) 95 g C m\({}^{-2}\) y\({}^{-1}\), which was over twice of the estimate by GPP\({}_{\text{MOD}}\) (812 \(\pm\) 39 g C m\({}^{-2}\) y\({}^{-1}\)), and close to the estimates by GPP\({}_{\text{SIF}}\) (1800 \(\pm\) 70 g C m\({}^{-2}\) y\({}^{-1}\)) and GPP\({}_{\text{BESS}}\) (1795 \(\pm\) 83 g C m\({}^{-2}\) y\({}^{-1}\)). At the evergreen broadleaf forest site, multi-year mean tower-measured GPP was 1411 \(\pm\) 89 g C m\({}^{-2}\) y\({}^{-1}\), which was quite close to the estimate by GPP\({}_{\text{SIF}}\) (1415 \(\pm\) 26 g C m\({}^{-2}\) y\({}^{-1}\)), whereas GPP\({}_{\text{MOD}}\) and GPP\({}_{\text{BESS}}\) exhibited a larger overestimation by about 22.8% and 55.0%, respectively. All analysis again confirmed the good performance by the SIF-based GPP estimates.
# New Insights on Volatile Components of _Vanilla planifolia_ Cultivated in Taiwan
## 2 Results
### Analysis of the Volatile Components of Taiwan-Cultivated Vanilla Pods Using HS-SPME
#### 2.2.1 Different Harvest Maturity  
The vanilla pods can be harvested approximately 38 weeks after flower pollination and then further processed. To ensure the quality of vanilla pods, we compared the volatile components of vanilla pods possessing different harvest maturities that were harvested at 34, 38, and 42 weeks after pollination.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Compounds A** & **RI B** & **Relative Percentage C (\%)** \\ \hline _Alcohols_ & & \\ benzyl alcohol & 999 & 0.77 \(\pm\) 0.14 \\
2-ethyl-1-hexanol & 1007 & \textless{}0.01 \\ phenethyl alcohol & 1080 & 0.29 \(\pm\) 0.04 \\ anise alcohol & 1250 & 0.39 \(\pm\) 0.02 \\ cinnamic alcohol & 1268 & 0.06 \(\pm\) 0.00 \\ _Aldehydes_ & & \\ furfurral & 799 & 0.56 \(\pm\) 0.12 \\ benzaldehyde & 928 & 0.25 \(\pm\) 0.05 \\ salicaldehyde & 1006 & 0.22 \(\pm\) 0.07 \\ nonanal & 1074 & 0.12 \(\pm\) 0.02 \\ safranal & 1172 & 0.08 \(\pm\) 0.02 \\ anisaldehyde & 1212 & 0.16 \(\pm\) 0.01 \\ \(p\)-hydroxybenzaldehyde & 1335 & 1.32 \(\pm\) 0.15 \\ vanillin & 1373 & 75.80 \(\pm\) 9.64 \\ methylvanillin & 1428 & 0.20 \(\pm\) 0.06 \\ _Esters_ & & \\ benzyl acetate & 1125 & 0.13 \(\pm\) 0.01 \\ methyl salicate & 1166 & 0.31 \(\pm\) 0.09 \\ phenethyl acetate & 1221 & 0.06 \(\pm\) 0.00 \\ _Ketones_ & & \\ acetophenone & 1030 & \textless{}0.01 \\ acetovanillone & 1446 & 0.12 \(\pm\) 0.00 \\ _Monotreprene_ & & \\ limonene & 1016 & 0.14 \(\pm\) 0.03 \\ _Phenols_ & & \\ \(p\)-cresol & 1043 & 0.69 \(\pm\) 0.12 \\ guaiacol & 1056 & 1.78 \(\pm\) 0.30 \\ _p_-creosol & 1161 & 0.46 \(\pm\) 0.06 \\ _p_-vinylgluaiacol & 1280 & 0.14 \(\pm\) 0.01 \\ _Sesquiterpenenes_ & & \\ \(\alpha\)-copene & 1380 & 0.17 \(\pm\) 0.03 \\ \(\alpha\)-dependanotene & 1441 & 0.49 \(\pm\) 0.04 \\ \(\alpha\)-amorphene & 1474 & 0.08 \(\pm\) 0.00 \\ \(\alpha\)-muurolene & 1498 & 0.12 \(\pm\) 0.05 \\ \(\delta\)-cadinene & 1517 & 0.07 \(\pm\) 0.01 \\ _Others_ & & \\
3,5-ocatadien-2-one & 1035 & 0.08 \(\pm\) 0.04 \\ tetramethylpyrazine & 1061 & 0.06 \(\pm\) 0.02 \\
**unknown** & & 13.36 \\
**total** & & 85.17 \\ \hline \hline \end{tabular}  
* Tentatively identification of components based on GC-MS library (Wiley 7n). B Retention indices, using paraffin (C\({}_{5}\)-C\({}_{25}\)) as references. C Relative percentages from GC-FID, values are means \(\pm\) SD of triplicates.  
\end{table}
Table 1: Analysis of volatile compounds and relative percentage from Taiwan cultivated vanilla using HS-SPME.
# Scanning Electron Microscopy Techniques in the Analysis of Gunshot Residues: A Literature Review
## 4 Discussion
### Portable SEM Devices: Revolutionizing On-Site Forensic Analysis  
The development of portable SEM devices represents a groundbreaking innovation in forensic science, offering the possibility of performing high-resolution microscopy and elemental analysis directly at crime scenes. Traditional SEM instruments are large, stationary, and require highly controlled environments to operate effectively. Portable SEM systems, by contrast, are compact and designed for field use, bringing the power of electron microscopy to forensic investigators in real time.  
Portable SEM devices can be used to quickly screen suspects, vehicles, and other surfaces for GSRs, providing immediate preliminary results that can guide the direction of the investigation. These systems retain many of the core capabilities of laboratory-based SEMs, including high-resolution imaging and EDS analysis, although with some limitations in resolution and analytical depth due to their smaller size and simplified design [52; 53; 54; 55].  
The main challenges associated with portable SEM systems are related to sample preparation and environmental control. Maintaining vacuum conditions and ensuring accurate calibration in field settings can be complex. Nevertheless, ongoing improvements in design and software are addressing these limitations, making portable SEM an increasingly viable option for rapid, in situ forensic analysis.
## 4 Experimental and Simulation Results and Discussion
For the underwater parallel CPT coupler, the size of the cross-coupling capacitance and the main coupling capacitance are significantly different,  
Figure 9: Coupler equivalent component variations (at \(d=10\) mm to 200 mm). and water can positively affect the increase of \(C_{m}\). On the other hand, for the underwater stacked CPT coupler, the size of the cross-coupling capacitance and the size of the main coupling capacitance are similar, so water does not have a significant effect on the increase of Cm, or rather, it can have a lower \(C_{m}\) than the air transmission environment. The enhancement of cross-coupling capacitance is a fatal disadvantage, and this aspect can cause the \(C_{m}\) of the underwater stacked CPT coupler to be lower than that of the stacked CPT coupler in air. Figure 10 shows the maximum \(S_{21}\) and \(C_{m}\) values according to distance when the system is optimally matched. Despite that optimal matching is performed, the reason why \(S_{21}\) does not reach 1 is due to the high resistance of \(R_{w}\) connected in series with \(C_{1}\) and \(C_{2}\). The maximum value of \(S_{21}\) is 0.79, and it gradually decreases as the distance increases up to 100 mm. As mentioned earlier, \(C_{m}\) exponentially decreases as \(d\) increases. However, the reason why \(S_{21}\) does not decrease exponentially like the Cm value is because the value of \(R_{w}\) decreases sharply from 0 mm to 30 mm, as shown in Figure 9. Since the maximum value of the \(S_{21}\) parameter tends to increase as Cm increases and \(R_{w}\) decreases, it seems that the effects of the two parameters cancel out at a distance of 0 to 30 mm, and S21 decreases gradually. At distances greater than 100 mm, the transmitter and receiver lose almost all coupling, and \(S_{21}\) also reaches zero.
# Efficiency of Pulsating Base Bleeding to Control Trailing Edge Flow Configurations
## 3 Results and Discussion
### Trailing Edge Pulsating Actuation
#### 3.2.4 Efficiency Gain  
A last analysis is performed herein, evaluating the energy requirements for each bleeding strategy. For a particular pulsating base bleed frequency, \(f\), the total amount of mass flow injected per actuating period yields \(m=mD_{c}/f\). Hence, we may express the total energy needed to inject an amount of mass, \(m\), at a certain flow speed, \(V\), during a time period, \(T\), as:  
\[E_{Injection}=\frac{1}{2}mV^{2}=\frac{1}{2}m\frac{D_{c}}{f}V^{2}. \tag{4}\]  
The injection power, \(P_{Injection}\), defined as the energy required per unit of time, here expressed as a certain number of injection periods, \(N_{periods}\), with respect to an actuation time, \(t\), is then enunciated as:  
\[P_{Injection}=\frac{E_{Injection}N_{periods}}{t}=\frac{1}{2}mD_{c}V^{2}. \tag{5}\]  
An important conclusion drawn from the aforementioned formulation is that, in the analytical approach performed, the injection power for a given base bleed rate is independent of the injection frequency and only depends on the _duty cycle_. The injection power for steady base bleed configurations can be recovered when \(M=T\), while the injection power required for pulsating base bleed results from taking \(D_{c}=M/T=0.5\).  
Figure 10: (**a**) Absolute value of the jet angle, \(|\beta|\), (continuous lines) and averaged lift coefficient, \(\overline{C_{l}}\), against non-dimensional base bleed rate (dashed lines). Results for constant (\(\square\)-) [19] and pulsating base bleeds at 100 Hz (-\(\triangle\)-), 150 Hz (-\(\times\)-), and 200 Hz (-\(\bigtriangledown\)-). (**b**) Snapshot of base region velocity contours, illustrating base bleed jet angle variations.  
Given the two-dimensional nature of the analyses, the dissipated power per unit depth, \(P_{Dissipated}\), can be defined as the product of the dimensional aerodynamic drag per unit depth and the inflow velocity:  
\[P_{Dissipated}=q_{\infty}U_{\infty}\overline{C_{d}}, \tag{6}\]  
with the corresponding values of for the dynamic pressure, \(q_{\infty}\), and the free stream velocity, \(U_{\infty}\), calculated from the inlet conditions described in Section 2.  
The dissipated power is depicted in Figure 11, where the benefits of pulsed injection are clearly visible. It is interesting to note that, despite the variation on the ejected total mass flow and the subsequent variations on the base pressure, the gains in the averaged pressure drag of the most efficient configuration (\(C_{b}\approx 0.13\)) are nearly the same as those obtained for a steady trailing edge actuation. Increasing the injection intensity, a pulsed injection results in a more efficient use of the energy compared to constant bleeding, this effect being more accused for higher injection powers.
## 1 Introduction
Therefore, this study aimed to use a supervised classification approach and remote sensing data to analyze the patterns and dynamics of Zanzibar's mangrove forests in two major mangrove-protected bays, i.e., Chwaka and Menai in Unguja, over 47 years, from 1973 to 2020. With 10 different species of mangroves and a broad variety of plants and animals, Chwaka Bay is home to the biggest single area of mangrove forests in Zanzibar [9]. The Menai Bay Conservation Area (MISCA), established in August 1997, is the largest marine-protected area, at 470 km\({}^{2}\)[9]. The ecosystems in the two bays have supported and modeled the local economy, culture, and social well-being of the local people [9]. Mangrovergrowing stock in Unguja has diminished with time, compared to that of Pemba's [9].