esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Extraction and Quantification of Anthocyanins  
Peruvian purple potatoes were thoroughly washed and cut into uniform cubes measuring 2 cm \(\times\) 2 cm. These pieces were then subjected to freezing at \(-\)40 \({}^{\circ}\)C before being lyophilized under dark conditions to prevent any degradation of sensitive compounds. The freeze-dried potatoes were subsequently ground into a fine powder and sieved using a 140-mesh sieve, yielding a purple potato powder.  
To extract anthocyanins from the purple potato powder, a conventional extraction method utilizing ethanol 40% \(v/v\) was employed. This method was based on optimal conditions identified in a previous study [17]. Specifically, the extraction process was carried out at a controlled temperature of 40 \({}^{\circ}\)C, with a solvent-to-solid ratio of 5 mL of solvent per gram of purple potato powder. The extraction period lasted for 4 h, ensuring maximum yield of anthocyanins. The total anthocyanin content was determined using the differential pH method, which relies on the structural changes in anthocyanin chemical forms and their absorbance measurements at pH 1.0 and 4.5. For this analysis, 0.1 mL of the extract was diluted separately with 7.9 mL of 0.025 mol/L potassium chloride buffer (pH 1) and 9 mL of 0.4 mol/L sodium acetate buffer (pH 4.5). The total anthocyanin content (TA) was quantified and expressed as cyanidin-3-glucose equivalents, using the following Equation (1):  
\[TA\left(\frac{\mathrm{mg}}{\mathrm{L}}\right)=\frac{\mathrm{A}\times\mathrm{MW }\times\mathrm{FD}\times 1000}{\epsilon\times 1} \tag{1}\]  
where \(\mathrm{A}=\left(\mathrm{A}_{\mathrm{vis-max}}-\mathrm{A}_{\lambda}\,\gamma_{ \mathrm{00}}\right)_{\mathrm{pH}\,1}-\left(\mathrm{A}_{\mathrm{vis-max}}- \mathrm{A}_{\lambda}\,\gamma_{\mathrm{00}}\right)_{\mathrm{pH}\,4.5}\); MW is the molecular weight of cyanidin 3-O-glucose (449.2 g/mol); FD is the extract dilution factor; \(\epsilon\) is the molar extinction coefficient (26,900 L/mol-cm); 1 is the path length in cm; and 1000 is the conversion factor from gram to milligram.
# Characterization of the 2014 Indus River Flood Using Hydraulic Simulations and Satellite Images
## 4 Discussion
This paper has shown the potential of applying the HEC-RAS model in the simulation of the 2014 flood, and could be used by relevant authorities to inform risk mitigation strategies. It is cost-effective and would allow government agencies to reduce flood damage. While hydraulic models are known to be challenging to implement, several institutions in Pakistan are now familiar with the HEC-RAS model.
# Summertime Aerosol Radiative Effects and Their Dependence on Temperature over the Southeastern USA
## 3 Results and Discussion
### Radiative Impacts
Furthermore, when the seasonal AOD differences from the years 2005-2011 are compared with annual averages of tropospheric NO\({}_{2}\) column densities there is a clear linear relationship with positive correlation (\(r^{2}\) = 0.93). Therefore, it appears that anthropogenic emissions have a more dominant role than biogenic emissions in the seasonal change of AOD, although the biogenic emissions are a prerequisite for the process. [PERSON] et al. (2014) estimated that between 2001 and 2013 the summertime surface radiative effect decreased by 8.0 Wm\({}^{-2}\) in the southeastern USA. Their estimate was based on aircraft measurements and radiative transfer modelling. To see whether our datasets produced corresponding results, we did a similar calculation using the difference between the summertime averages of AATSR AOD from the years 2005 and 2011 in Equation (5). Between these years, the AOD decreased by 0.06 and the DRE decreased by 2.9 Wm\({}^{-2}\). This is only 36% of the estimate by [PERSON] et al. (2014). [PERSON] et al. (2014) had also included a time series of MISR AOD in the supplementary materials for their paper, and from this figure we estimated that the summertime AOD had decreased from 0.275 to 0.188 between 2001 and 2013. Using this change of AOD in Equation (5), we calculated the corresponding decrease in DREto be 4.3 Wm\({}^{-2}\), which is closer to our AATSR estimate (2.9 Wm\({}^{-2}\)) and only half of that reported by [PERSON] et al. [24]. As the AOD-based estimates of DRE decrease are in the same range but much lower than the reported decrease of 8.0 Wm\({}^{-2}\), it may be that the reported value of [PERSON] et al. [24] is an overestimate. As the DRE estimates showed, biogenic SOA has a significant direct radiative effect on a regional scale and especially over forests. To estimate its indirect radiative effects, we used the model data to approximate the effective radiative forcing (ERF). ERF includes the rapid tropospheric adjustments (often related to humidity and clouds) to the radiative forcing. ERF was calculated from the difference of the summed net top-of-atmosphere solar and thermal radiation from the CONTROL and the noBIOSOA simulations as a function of temperature anomalies. A linear fit to the dataset produced the following function: \(\mathrm{ERF_{bio}}=-1.05(\,\pm\,0.46)\mathrm{LST_{ano}}+0.80(\,\pm\,0.53)\) (see Figure S14 for details). Thus, a one Kelvin increase in temperature corresponds to a biogenic ERF of \(-1.0\,\pm\,0.5\) W/m\({}^{2}\). [PERSON] et al. [15] reported an average cloud albedo effect (first indirect effect) for the growth season (T \(>\) 5 \({}^{\circ}\)C) at several locations across Europe to be \(-0.19\,(-0.76\)-\(0.06)\) W/m\({}^{2}\)/K. Our estimate for the southeastern USA is five times larger than the European average, but our maximum estimate is only about 60% larger than the European maximum. This indicates that biogenic SOA has also a significant indirect radiative effect in the studied region.
# A Tile-Based Multi-Core Hardware Architecture for Lossless Image Compression and Decompression
## 3 High-Efficiency Parallel Processing Architecture
Upon receiving the data, the decompression cores employ a decompression algorithm to unravel the compressed information. Once decompressed, the data are transferred to the hub module, where they are combined and prepared for final output. The capabilities of the cores are enhanced by the integration of three dynamic state machines. The control flow is managed by the ARM-based PS. Data access is ensured through DDR3 memory and its associated control logic. Compression and decompression tasks are handled by the PL side. A robust eight-core parallel design is adopted. By processing eight tiles fully in parallel, high throughput and efficient compression and decompression are achieved.
# Solar Radiation Pressure Models for BeiDou-3 I2-S Satellite: Comparison and Augmentation
## 2 Characteristics and SRP Models for BeiDou-3 I2-S Satellite
### Attitude  
The estimated yaw attitudes of the BeiDou-3 I2-S satellite demonstrated that the continuous yaw attitude has been adopted instead of orbital normal mode used by BeiDou-2 IGSO and MEO satellites when BeiDou-3 I2-S is in eclipse season [3], and that the satellite experienced midnight- or noon-turn maneuvers when the sun elevation angle above the orbital plane (\(\beta\) angle) was in the range of [\(-3^{\circ}\), \(+3^{\circ}\)] and the orbital angle was in the range of approximately [\(-6^{\circ}\), \(6^{\circ}\)] or [\(174^{\circ}\), \(186^{\circ}\)]. The midnight- and noon-turn yaw maneuver model have been established by [12], but not been used in this study. For other period, the nominal yaw attitude is used to describe the orientation of BeiDou-3 I2-S; in this case, the satellite body-fixed frame (\(\mathbf{e}_{SAT,X}\), \(\mathbf{e}_{SAT,Y}\), \(\mathbf{e}_{SAT,Z}\)) is defined by the three vectors, respectively:  
\[\mathbf{e}_{SAT,X}=\mathbf{e}_{SAT,Y}\times\mathbf{e}_{SAT,Z}\mathbf{e}_{ SAT,Y}=\frac{\mathbf{e}_{\odot}\times\mathbf{r}}{\|\mathbf{e}_{\odot}\times \mathbf{r}\|}\mathbf{e}_{SAT,Z}=-\frac{\mathbf{r}}{\|\mathbf{r}\|}, \tag{1}\]  
where \(\mathbf{r}\) is the geocentric vector of the satellite and \(\mathbf{e}_{\odot}\) is the unit vector pointing from the satellite to the Sun.
## 4 Results and Discussion
### Potential Effects of the WEC on Sediment Transport and Coastal Protection
Skewness (\(S_{k}\)) is related to a horizontal asymmetry in the velocity shape, where the onshore directed orbital velocities are greater and of a shorter duration than the offshore directed orbital velocities, which are milder and of a longer duration ('Stokes wave', e.g., Figure 10c for \(x=2\)). This behaviour of the orbital velocities has been associated with prevailing onshore sediment transport [11; 43; 54]. Onshore transport is also linked to vertical asymmetry (\(A_{s}\)), where waves are pitched  
Figure 10: Surface elevation records for the test with \(H=1.0\) m, \(T=8\) s, and \(R=0.25\) m for virtual gauges at symmetrical distances from the buoy: (**a**) 2, (**b**) 4, and (**c**) 8 m to each side. Negative values to the left of the buoy and positive to the right. forward with a saw-tooth shape (e.g., Figure 10a for \(x=8\) shows an inverse saw-tooth shape). Positive values of \(A_{s}\), as defined by Equation (15), are associated with an onshore sediment transport contribution. The values of skewness and asymmetry, as calculated using Equations (14) and (15), are shown in Figure 11. For both wave periods, the values of \(S_{k}\) tended to increase considerably towards the buoy's position, reaching a maximum value just after passing the buoy. From that point, \(S_{k}\) values decreased considerably to values close to 0.1, then increased again towards the end of the NWT. In the case of \(T=8\) s waves, after passing the buoy, the increase in \(S_{k}\) was less prominent and attained values that were very similar to those reached at the buoy's position. This tendency can be observed for all the buoys' radii, implying that the increased onshore sediment transport would alleviate coastal erosion and improve beach accretion. The values of skewness were rather low, since the initial wave conditions only included monochromatic first-order Stokes waves. The behavior of asymmetry (\(A_{s}\)) is presented in Figure 11c,d. For this variable, similar results can be seen, with an increase in \(A_{s}\) close to the buoy. However, there are very important differences between \(T=4\) s and \(T=8\) s. With the former, the magnitude of the changes in \(A_{s}\) are very mild (increments of only 0.05) and the evolution of \(A_{s}\) is fairly flat, whereas with \(T=8\) s, increments in \(A_{s}\) are greater (0.15) and have a fairly linear increment towards the end of the NWT. A comparison between \(S_{k}\) and \(A_{s}\) suggest that the changes in \(S_{k}\) would be more likely to transport sediment onshore than those in \(A_{s}\). Figure 11: Skewness (horizontal asymmetry **(a,b)** and asymmetry velocity (**c,d)**) evaluated with five buoys’ radii for \(T=4\) s and \(T=8\) s. The different colors represent different buoys’ sizes, as shown in the bottom panels. The vertical, dashed lines show the position of the buoy, with the different colors representing different buoys’ radii.
## Abbreviations  
### Abbreviation  
\begin{tabular}{l l}
**ATF** & Accident-tolerant fuel \\
**DOE-NE** & Department of Energy's Nuclear Energy Office \\
**FGR** & Fission gas release \\
**LOCA** & Loss of coolant accident \\
**LWR** & Light water reactor \\
**NPP** & Nuclear power plant \\
**PCMI** & Pelle cladding mechanical interaction \\
**PHWR** & Pressurized heavy water reactor \\
**PWR** & Pressurized water reactor \\
**RIA** & Reactivity-initiated accident \\
**VVER** & Water-water power reactor \\
**Nomenclature and units** \\ \(a\) & Grain radius, m. \\ \(b\) & Probability of the intergranular atoms re-entering the solution, 1/s. \\ \(b^{\prime}\) & Probability of atoms being resolved by fission spikes, 1/s. \\ \(B_{1}\) & Parameter of the incubation threshold calculation, MWd/tM. \\ \(B_{2}\) & Parameter of the incubation threshold calculation, K\({}^{-1}\). \\ \(B_{3}\) & Parameter of the incubation threshold calculation, K. \\ \(B_{\text{MH}}\)N & Parameter of the incubation threshold calculation, MWd/tM. \\ \(B_{\text{H}}\) & Burnup, MWd/tM. \\ \(c\) & Local concentration (number of gas atoms per unit volume), 1/m\({}^{3}\). \\ \(c_{0}\) & Initial concentration (number of gas atoms per unit volume), 1/m\({}^{3}\). \\ \(c_{\delta}\) & Mean concentration at the grain boundaries, 1/m\({}^{3}\). \\ \(D\) & Diffusion coefficient, m\({}^{2}\)/s. \\ \(D_{01}\) & Diffusion coefficient constant, m\({}^{2}\)/s. \\ \(D_{02}\) & Diffusion coefficient constant, m\({}^{2}\)/s. \\ \(D_{03}\) & Diffusion coefficient constant, m\({}^{2}\)/s. \\ \(D_{1+2+3}\) & Sum of intrinsic, thermal, and athermal diffusion coefficients, m\({}^{2}\)/s. \\ \(D_{1}\) & Intrinsic diffusion coefficient, m\({}^{2}\)/s. \\ \(D_{2}\) & Thermal diffusion coefficient, m\({}^{2}\)/s. \\ \(D_{3}\) & Athermal diffusion coefficient, m\({}^{2}\)/s. \\ \(f(\theta)\) & Geometry factor of the grain-edge bubble to the sphere. \\ \(F_{0}\) & Fission gas release fraction without resolution, \%. \\ \(F_{B}\) & Fraction coverage of bubbles. \\ \(F(t)\) & Fission gas release fraction, \%. \\ \(g\) & Probability of fission spikes trapping atoms within intragranular \\ \(h_{\text{B}}\) & bubbles, 1/s. [27] \\ \(J\) & Flux exit of the grain, 1/(m\({}^{2}\)-s). \\ \(J_{ns}\) & Resolution flux, 1/(m\({}^{2}\)-s). \\ \(J_{0}\) & Flux without resolution, 1/(m\({}^{2}\)-s). \\ \(k\) & Boltzmann constant, 1.3806 \(\times\) 10\({}^{-23}\) J/K. \\ \(L\) & Mean free-path between two bubbles, m. \\ \(n\) & Number of atoms in a bubble. \\ \(n_{B}\) & Bubble density, 1/m\({}^{2}\). \\ \end{tabular}
# Assessment of a Mid-Fidelity Numerical Approach for the Investigation of Tiltrotor Aerodynamics
## 3 Numerical Models
### Proprotor Numerical Model
Figure 2a shows the time histories of the single proprotor thrust coefficient \(C_{T}\) in hover throughout ten rotor revolutions for some of the selected blade pitch angles (\(\theta\)) that were considered by the DUST simulations. The curves' behaviour shows that after three rotor revolutions, the computed thrust coefficients reach a steady value for all the considered blade collective pitch angles. This result confirms that a number of ten rotor revolutions can be considered enough to reproduce a fully developed wake of the proprotor and obtain converged values for the proprotor aerodynamic performance. \begin{table}
\begin{tabular}{c c} \hline Number of Blades & 3 \\ Radius [m] & 3.81 \\ Disc Area [m\({}^{2}\)] & 45.6 \\ Solidity & 0.089 \\ Precone angle [\({}^{\circ}\)] & 2.5 \\ Overall twist [\({}^{\circ}\)] & \(-40.9\) \\ Hover RPM & 589 \\ \hline \end{tabular}
\end{table} 
Table 1: XV-15 full-scale proprotor characteristics. \begin{table}
\begin{tabular}{c c} \hline \hline
**r/R** & **Airfoil** \\ \hline
0.09 & NACA 64-935 a = 0.3 \\
0.17 & NACA 64-528 \\
0.51 & NACA 64-118 \\
0.80 & NACA 64-(1.5)12 \\
1.00 & NACA 64-208 a = 0.3 \\ \hline \end{tabular}
\end{table} 
Table 2: Blade airfoils distribution for the XV-15 full-scale proprotor [39]. Figure 2b,c show the comparison of the DUST simulation results with the experimental data from [39]. The results that were reported for the DUST simulations were obtained by averaging the \(C_{T}\) and \(C_{P}\) over the last two rotor revolutions. A quite good agreement between the DUST simulation results and the experiments was found for both the thrust and power coefficients in the whole range of collective pitch angles considered. This comparison confirms that the parameters used to build the numerical model of the XV-15 proprotor are suitable to reproduce the aerodynamic performance accurately. Therefore, the numerical model parameters identified in this preliminary study were used for the following simulations. Figure 2: Comparison of the results obtained for the XV-15 full-scale single proprotor in hover. (**a**) Time histories of the thrust coefficient \(C_{T}\) calculated by DUST as a function of the number of rotor revolutions \(N_{rev}\); (**b**,**c**) comparison of the DUST simulations results with experimental data from [39].
# Hybrid Artificial Protozoa-Based JADE for Attack Detection
## 5 Case Study: Application of APO-JADE for Attack Detection
### Experimental Procedure and Resultant Insights
#### 5.9.3 Evaluative Insights for the DS2 OS Dataset  
For our analysis focusing on the DS2 OS dataset, we implemented a 70:30 train-test split. The pivotal hyperparameters were meticulously calibrated using the hybrid PSO-SQP algorithm. Our intrusion detection model was trained on 10 salient features and iteratively refined over 100 epochs.  
In the binary classification analysis, we employed a 5-fold cross-validation mechanism. Here, the training dataset was divided into quintiles based on sample volume with a detailed breakdown of results corresponding to each fold. Notably, the APO-JADE paradigm reached its peak attack detection accuracy during the fifth fold, which had the highest sample volume, achieving accuracies of 97.42% in training and 97.51% in testing. In contrast, the first fold, with fewer samples, recorded the lowest accuracies of 95.21% (training) and 94.22% (testing). The subsequent folds marked accuracies of 96.47%, 96.42%, and 97.33%. Additionally, the fifth fold demonstrated the superiority of APO-JADE through auxiliary metrics like precision, recall, and the F1 score.  
In multiclass classification as shown in Table 5, the APO-JADE architecture excelled in distinguishing seven distinct classes. The model showed remarkable precision in identifying categories like 'Denial of Service (DoS)', 'Scan', and 'Wrong setup', with accuracies of 96.544%, 95.24%, and 92.68%, respectively. Other attack vectors such as 'Malicious operation', 'Spying', 'Malicious control', and 'Data type probing' were identified with accuracies of 97.52%, 94.74%, 96.87%, and 93.81%, respectively. The 'Normal' class was accurately categorized with a 90.65% success rate with a minor 1.46% being erroneously flagged as malicious.
# Superconducting Electronic-Photonic Platform for HEB-Based Terahertz Spectrometers
## 3 Results and Discussion  
In this section, we provide details on a superconducting electronic-photonic platform suitable for the implementation of a 2.7 THz HEB mixer. Developed THz components are evaluated by prototyping at enlarged and actual scales. The evaluation includes (a) beam pattern measurements of THz signal and LO input lines at 51 GHz and (b) analysis of the superconducting properties of a full-size HEB mixer. We also evaluate fabrication tolerances for cleanroom processes and discuss perspectives.
## 2 Overview of Random Linear Network Coding
### Introduction to RLNC
The corresponding probability density function (pdf) is \(p_{d}(n)=P_{d}(n)-P_{d}(n-1)\). Figure 1: Generic RLNC sub-layers at the transmitter and the receiver side connected via packet erasure channel. It is often useful to take the transmitter perspective and introduce a time reference assuming that each coded packet transmission takes a unit-time slot. If we fix the number of transmitted coded packets to \(N\geq K\), we can (re)define the decoding probability after \(N\) coded packets are transmitted:  
\[P_{d}(N)=\sum_{n=K}^{N}\binom{N}{n}e^{N-n}(1-e)^{n}P_{d}(n). \tag{2}\]  
Several interesting performance measures immediately follow. _Outage probability_\(P_{o}(N)\) is a probability the receiver will not recover the source block after \(N\) transmitted coded packets: \(P_{o}(N)=1-P_{d}(N)\). Average number of coded packet transmissions \(\bar{N}\) required for successful source block decoding, also referred to as _average decoding delay_, can be investigated for fixed \(N\), and for the case \(N\rightarrow\infty\)[28]. For the former case, closed-form expressions for average decoding delay are available, while for the latter case, the upper bounds have been derived, both for the systematic and non-systematic RLNC [29].
## 4 Discussion  
On a global scale, the derived GNSS-R reflectivity sensitivity is 8.75 dB/100% using SMOS L3 SM data after applying all corrections (vegetation, surface roughness, topography flag etc.). In the three regions analyzed by using the same SMOS L3 SM values, the sensitivities are significantly higher: 18.64 dB/100% in Yanco/NSW/Australia; 9.37 dB/100% in Cemadem/Brazil; and 12.15 dB/100% over the Iberian Peninsula. They decrease when using SMOS L4 SM data: 11.18 dB/100% in Yanco/NSW/Australia; 6.62 dB/100% in Cemadem/Brazil; and \(-1.1\) dB/100% in the Iberian Peninsula, although this last result is probably not significant because of the limited amount of data, and limited range of SM values. The sensitivities derived in the Brazilian semi-arid region from point measurements are \(\sim\) 7.39 dB/100%, halfway between the values obtained using SMOS L4 and L3 SM values. This variability suggests that the average sensitivity of GNSS-R reflectivity to SM is likely to be close to \(\sim\) 9 dB/100%, but that large uncertainties can be expected regionally, which precludes the need to develop regional soil moisture retrieval algorithms from GNSS-R data.  
The sensitivity of GNSS-R reflectivity to subsurface soil moisture has also been analyzed using the LISFLOOD and MUSA models, but the values obtained (12.2 and 49.2 dB/100%) exhibit too low a correlation and are not reliable.  
Figure 17: Scatter plot TDS-1 GNSS-R reflectivity (all corrections included) and (**a**) 50 cm depth LISFLOOD soil moisture [m\({}^{3}\)/m\({}^{3}\)] for Andarai station, and (**b**) 1 m depth MUSA soil moisture for 2015.
## 2 Methods
### Forest Succession  
We evaluated the magnitude of disturbance from fire, and the subsequent recovery of EPA, using Alaska Fire Service fire perimeters from 1950 to 2002 available for the Alaskan portion of the study area. We employed a space-for-time design, or chronosequence, to study the pattern of succession following fire, specifically its influence on EPA and land cover proportions. This design assumes that observing the conditions at stands of varying ages (through space) would provide the same result if site(s) were observed throughout the entire post-disturbance recovery (through time). This assumption can be problematic at times [90]. However, this assumption was determined to be valid in boreal forest [53, 91] in which chronosequences have often been used [25, 92].  
NLCD 2001 classes were sorted into four land cover groups; deciduous forest, coniferous forest, mid-succession (shrub and mixed forest), and early-succession (barren, grassland, sedge, and moss). Random points (\(n=5000\)) were placed within fires over 200 ha in size occurring between 1950 and 2002. We believed this number of samples was sufficient to effectively quantify the population. These points were used to calculate the average EPA of each land cover group and the proportion of fire scar area in each for years before and after fire. Areas that burned more than once during the 1950-2002 period were excluded in an effort to clarify successional patterns following fires and to reduce fire-related changes to NLCD land cover. Burn severity (_i.e._, dNBR) was not considered in random point placement; consequently, our random points represent the entire range of burn severity within burned areas. We did not consider dNBR because (1) its accuracy has often been challenged in Alaskan boreal forest [93]; (2) it is unavailable for older fires (pre-1984); and (3) we wanted to represent the entire range of fire severity. Data from 2003 to 2008 were not used because the accuracy of NLCD 2001 likely diminished as later fires and succession continued to alter land cover. Only fires that occurred from 2000 to 2002 had observations of pre-fire and year of burn EPA. These data were used to generate scatterplots of average EPA and proportion of fire scar area and group by year since fire. The low sample size in some periods (e.g., 40-45 years since fire) caused greater variability in EPA.