esa-sceva/satcom-chunk-collection huggingface dataset:
# Color Simulation of Multilayered Thin Films Using Python
## 2 Materials and Methods  
### Theoretical Background  
#### 2.1.1 Reflection from a Bulk Surface  
When light is incident on an object, it undergoes multiple absorptions, reflections, and transmissions at the surface, and the color of that object is determined by the spectrum of light reflected from its surface. Fresnel equations describe the reflected and transmitted amplitudes of light with respect to that of the incident wave, which can be derived from the boundary conditions of electric and magnetic fields at an interface. We define the plane of incidence that contains the surface normal vector and the propagation vector of incident light and determine \(p\)- or \(s\)-polarization from the direction of **E** oscillation with respect to the plane of incidence (Figure 1). The \(p\)-polarization indicates that **E** oscillates parallel to the plane of incidence (Figure 1a). Here, we first derive the Fresnel equations for the case of polarization parallel to the plane of incidence (i.e., \(p\)-polarization). Since the parallel component of **E** and the normal component of **B** with respect to the interface need to be continuous, we arrive at the following equations:  
\[E_{\text{i}}\text{cos}\,\theta_{\text{i}}-E_{\text{r}}\text{cos}\,\theta_{ \text{r}}=E_{\text{t}}\text{cos}\,\theta_{\text{t}} \tag{1}\]  
\[B_{\text{i}}+\ B_{\text{r}}=\ B_{\text{t}} \tag{2}\]  
where the subscripts i, r, and t represent the incident, reflected, and transmitted waves, respectively. Since \(B=E\cdot n/c\) (\(n\): refractive index, \(n\equiv c/v\)) where \(v\) is the speed of an electromagnetic wave in matter [23], Equation (2) indicates that  
\[n_{\text{i}}(E_{\text{i}}+E_{\text{r}})=n_{\text{t}}E_{\text{t}} \tag{3}\]  
where \(n_{\text{i}}\) and \(n_{\text{t}}\) are the refractive indices of the media before and after transmission. Given the laws of reflection (\(\theta_{\text{r}}=\theta_{\text{i}}\)) and refraction (i.e., Snell's law; \(n_{\text{i}}\text{sin}\,\theta_{\text{i}}=n_{\text{t}}\text{sin}\,\theta_{ \text{t}}\)) and by solving Equations (1) and (2), we obtain the reflection and transmission coefficients for \(p\)-polarized light:  
\[r_{\text{P}}\equiv\frac{E_{\text{r}}}{E_{\text{i}}}=\frac{n_{\text{t}}\text{ cos}\,\theta_{\text{i}}-n_{\text{i}}\text{cos}\,\theta_{\text{t}}}{n_{\text{t}} \text{cos}\,\theta_{\text{i}}+\ n_{\text{i}}\text{cos}\,\theta_{\text{t}}} \tag{4}\]  
\[t_{\text{P}}\equiv\frac{E_{\text{t}}}{E_{\text{i}}}=\frac{2n_{\text{t}}\text{ cos}\,\theta_{\text{i}}}{n_{\text{t}}\text{cos}\,\theta_{\text{i}}+n_{\text{i}} \text{cos}\,\theta_{\text{t}}}. \tag{5}\]For \(s\)-polarization (Figure 1b), the boundary conditions for \(\mathbf{E}\) and \(\mathbf{B}\) become  
\[E_{\mathrm{i}}+E_{\mathrm{r}}=E_{\mathrm{t}} \tag{6}\]  
\[-B_{\mathrm{i}}\mathrm{cos}\,\theta_{\mathrm{i}}-B_{\mathrm{r}}\mathrm{cos}\, \theta_{\mathrm{r}}=-B_{\mathrm{t}}\mathrm{cos}\,\theta_{\mathrm{t}}. \tag{7}\]  
The reflection and transmission coefficients for \(s\)-polarized light can be obtained by the same procedure.
# Point-Line-Aware Heterogeneous Graph Attention Network for Visual SLAM System
## 1 Introduction
We use an optimized residual block-feature pyramid network (ORB-FPN) to extract the feature map of the input image. In the point extraction branch, based on the point-aware module, the multiscale context is aggregated to obtain features with rich receptive fields. Moreover, the edge information is used for the line extraction branch to improve the accuracy of the line-segment detection. In order to make the network lightweight, a transfer-aware knowledge distillation method is proposed to compress the model for generating the point-line feature in the extraction task. * Targeting a high accuracy and efficiency, a heterogeneous attention graph neural network (HAGNN) is presented, which uses an edge-aggregated graph attention network (EAGAT) to iterate the vertices of the heterogeneous graph constructed from points and lines. To enhance the performance of the point-line matching, a cross-heterogeneous graph interaction (CHGI) is used for harmonizing heterogeneous information between graphs. * By transforming the point-line matching process into an optimal transport problem, a greedy inexact proximal point method for optimal transport, GIPOT, is proposed, which calculates the optimal feature assignment matrix to find the global optimal solution for the point-line matching problem.
## 9 Non-Symmetric Section
### Centroid Forces \(F_{x}\) and \(F_{y}\)  
These forces may be substituted separately into Equation (20a,b) or taken as a ratio \(F_{x}\)/\(F_{y}\) of zero, a positive or negative integer or a fraction, as follows:  
(1) \(F_{x}\)/\(F_{y}\) = 0, \((F_{x}\) = 0, \(F_{y}\) = \(F\))  
\(qt/F\) = 6.444(\(D_{x}/d^{3}\)) + 8.775(\(D_{y}/d^{3}\))  
Taking \(D_{x}/d^{3}\) and \(D_{y}/d^{3}\) from Table 11 provides the shear flows at median points 1, 2, 3... 7 in the absence of \(F_{x}\):  
\(q_{2}t/F\) = 0  
\(q_{2}t/F\) = (6.444 \(\times\) 0.002836) + (8.775 \(\times\) 0.0004726) = 0.02242  
\(q_{3}t/F\) = (6.444 \(\times\) 0.004861) + (8.775 \(\times\) 0.0002315) = 0.03336  
\(q_{4}t/F\) = (6.444 \(\times\) 0.009115) - (8.775 \(\times\) 0.001794) = 0.042995  
\(q_{5}t/F\) = (6.444 \(\times\) 0.006944) - (8.775 \(\times\) 0.003241) = 0.01631  
\(q_{6}t/F\) = (6.444 \(\times\) 0.005498) - (8.775 \(\times\) 0.00348) = 0.004892  
\(q_{7}t/F\) = 0  
(2) \(F_{x}\)/\(F_{y}\) = \(\infty\), \((F_{x}\) = \(F\), \(F_{y}\) = 0)  
\(qt/F\) = 8.6646(\(D_{x}/d^{3}\)) + 34.0(\(D_{y}/d^{3}\))  
Taking \(D_{x}/d^{3}\) and \(D_{y}/d^{3}\) from Table 11 provides the shear flows at median points 1, 2, 3... 7 in the absence of \(F_{y}\): \begin{table}
\begin{tabular}{l} \hline \(D_{x1}/d^{3}\) = 0 \\ \(D_{x2}/d^{3}\) = \((t/d)(1\) – \(Y^{\prime}/d)(a/d\) –\(X^{\prime}/d)=(1/40)(7/12)(1/3 –5/36)\) = 0.002836 \\ \(D_{x3}/d^{3}\) = \((t/d)(1\) – \(Y^{\prime}/d)(a/d)=(1/40)(1-5/12)(1/3)=0.004861\) \\ \(D_{x4}/d^{3}\) = \((1/2)(t/d)(1-Y^{\prime}/d)^{2}+(t/d)(1-Y^{\prime}/d)(a/d)\) \\ \(=(1/2)(1/40)(7/12)^{2}+(1/40)(7/12)(1/3)=0.009115\) \\ \(D_{x5}/d^{3}\) = \((t/d)(1-Y^{\prime}/d-1/2)+(t/d)(1-Y^{\prime}/d)(a/d)\) \\ \(=(1/40)(7/12-1/2)+(1/40)(7/12)(1/3)=0.006944\) \\ \(D_{x6}/d^{3}\) = \(-(t/d)(Y^{\prime}/d)(X^{\prime}/d)+D_{x5}/d^{3}=-(1/40)(5/12)(5/36)+0.006944 = 0.005498\) \\ \(D_{x7}/d^{3}\) = \(-(t/d)(Y^{\prime}/d)(b/d)+D_{x5}/d^{3}=-(1/40)(5/12)(2/3)+0.006944\) = 0 (check) \\ \(D_{y1}/d^{3}\) = 0 \\ \(D_{y2}/d^{3}\) = \((1/2)(t/d)([a/d)\) – \(X^{\prime}/d]^{2}=(1/2)(1/40)(1/3-5/36)^{2}=0.0004726\) \\ \(D_{y3}/d^{3}\) = \((t/d)(a/d)([1/2)(a/d)-X^{\prime}/d]=(1/40)(1/3)(1/6-5/36)=0.0002313\) \\ \(D_{y4}/d^{3}\) = \(-(t/d)(X^{\prime}/d)(1-Y^{\prime}/d)+(t/d)(a/d)[(1/2)(a/d)-X^{\prime}/d]\) \\ \(=-(1/40)(5/36)(7/12)+(1/40)(1/3)(1/6-5/36)=-0.001794\) \\ \(D_{y5}/d^{3}\) = \(-(t/d)(X^{\prime}/d)+(t/d)(a/d)[(1/2)(a/d)-X^{\prime}/d]\) \\ \(=-(1/40)(5/36)+(1/40)(1/3)(1/6-5/36)=-0.003241\) \\ \(D_{y6}/d^{3}\) = \(-(1/2)(t/d)(X^{\prime}/d)^{2}-(t/d)(X^{\prime}/d)+(t/d)(a/d)[(1/2)(a/d)-X^{ \prime}/d]\) \\ \(=-(1/2)(1/40)(5/36)^{2}-(1/40)(5/36)+(1/40)(1/3)(1/6-5/36)=-0.00348\) \\ \(D_{y7}/d^{3}\) = \((t/d)[(1/2)(t/d)^{2}-(X^{\prime}/d)(b/d)]-(t/d)(X^{\prime}/d)+(t/d)(a/d)[(1/2)(a/ d)-X^{\prime}/d]\) \\ \(=(1/40)[(1/2)(4/9)-(5/36)(2/3)]-(1/40)(5/36)+(1/40)(1/3)(1/6-5/36)=0\) (check) \\ \hline \end{tabular}
\end{table} 
Table 11: Moments of area referred to median points for centroidal axes \(x\) and \(y\).
###### Abstract  
In this study, a new approach for the automated extraction of high-resolution time-variable water surfaces is presented. For that purpose, optical images from Landsat and Sentinel-2 are used between January 1984 and June 2018. The first part of this new approach is the extraction of land-water masks by combining five water indexes and using an automated threshold computation. In the second part of this approach, all data gaps caused by voids, clouds, cloud shadows, or snow are filled by using a long-term water probability mask. This mask is finally used in an iterative approach for filling remaining data gaps in all monthly masks which leads to a gap-less surface area time series for lakes and reservoirs. The results of this new approach are validated by comparing the surface area changes with water level time series from gauging stations. For inland waters in remote areas without in situ data water level time series from satellite altimetry are used. Overall, 32 globally distributed lakes and reservoirs of different extents up to 2482.27 km\({}^{2}\) are investigated. The average correlation coefficients between surface area time series and water levels from in situ and satellite altimetry have increased from 0.611 to 0.862 after filling the data gaps which is an improvement of about 41%. This new approach clearly demonstrates the quality improvement for the estimated land-water masks but also the strong impact of a reliable data gap-filling approach. All presented surface area time series are freely available on the Database of Hydrological Time Series of Inland (DAHITI).  
surface area time series; spatial data gaps; lakes; reservoirs; Landsat; Sentinel-2; MNDWI; NWI; AWEI\({}_{\text{sh}}\); AWEI\({}_{\text{nsh}}\); TC\({}_{\text{wet}}\); satellite altimetry; DAHITI; AWAX
# NPP-VIIRS DNB Daily Data in Natural Disaster Assessment: Evidence from Selected Case Studies
## 2 Materials and Methods
### Methods
#### 2.3.3 Statistical Significance  
The Mann-Whitney U-test was used to test the statistical significance of the DNB changes at the pixel level. The Mann-Whitney U-test is a nonparametric test that tests for differences between two independent groups with no specific distribution [72]. As the DNB values of the study areas were not normally distributed, the Mann-Whitney U-test was suitable for comparisons between them. The Mann-Whitney U-test has been used to compare the nighttime light before and after a war [43]. The null hypothesis (H0) stipulates that the two groups of data are homogeneous and have the same distribution, whereas the alternative hypothesis (H1) stipulates that the distributions of the two groups are different [72]. This study tested the statistical significance of DNB changes using a 95% confidence interval. So, if the \(p\)-value was smaller than 0.05, the H0 was rejected, meaning that the difference between the pre-and post-disaster DNB images was statistically significant.
# Comparing PlanetScope and Sentinel-2 Imagery for Mapping Mountain Pines in the Sarntal Alps, Italy
## 1 Introduction
The combination of multi- and hyperspectral datasets with features derived from airborne light detection and ranging (LiDAR) information (e.g., digital terrain model (DTM) and canopy height model (CHM)) improved the mapping of treeline vegetation [26; 27; 28; 29]. Additionally, image-derived features increased class separability and classification accuracy, such as textural properties based on a gray level co-occurrence matrix (GLCM) [22; 24; 25; 28; 30] or phenological information derived from spectral indices [6; 12; 20; 23; 24] or multi-temporal imagery [8; 10; 20; 23; 24; 28]. In studies classifying alpine vegetation, the usage of additional features resulted in an increased accuracy on the classification of mountain pines [10; 23; 24; 25; 28]. However, the mountain pine never was of a main interest in any of the above-mentioned studies, potentially as their distribution is comparably small to other subalpine tree species [10; 28; 29], and research focused on different tree species [22; 24; 27] or methodological approaches [23; 25; 30]. Considering the lack of knowledge about the distribution of mountain pines in the European Alps and the missing studies focusing on the classification of mountain pines, our overall research goal is developing an approach to map mountain pines from spaceborne optical remote sensing imagery. For this, we chose the Sarntal Alps, Italy, where large areasof mountain pine are present and which have been harvested intensively in recent years. The objectives of our study are:  
1. To create an accurate map of the overall coverage and spatial distribution of mountain pines in the Sarntal Alps,
2. To quantify how the spatial resolutions of open-access Sentinel-2 (10 m) and commercial PlanetScope (3 m) data influence the mapping accuracy of mountain pines,
3. To analyze which of the features previously used for alpine vegetation classification are the most useful for the delineation of mountain pines. Our study will improve the overall understanding of how mountain pines can be accurately mapped using satellite observations by assessing the choice of satellite data and classification features. The derived mountain pine map for our study region is expected to support local forest management to monitor and assess recent and ongoing anthropogenic and climatic changes at the treeline.
# The Small of Synthetic Biology: Engineering Strategies for Aroma Compound Production in Yeast
## 4 Biosensing Aroma Compounds in Yeast  
Rational engineering strategies to redistribute carbon flux towards the production of a specific metabolite or the introduction of novel synthetic biosynthesis pathways, have been successfully employed in the past to produce the desired molecule of interest [68; 69]. Aroma compound production has also benefitted from these methodologies when ample precursor molecules are available [70]. However, the inherent volatile nature of some precursor molecules have excluded them from many directed evolution endeavours, as these are mostly limited to growth-selectable phenotypes, with limited high-throughput possibilities for the rapid screening of mutant libraries [71].  
A promising, more recent addition to the synthetic biologist's toolbox is biosensors [72]. These genetic circuits translate a metabolic 'input' into a measurable 'output' signal like fluorescence; decoupling metabolite production from cellular growth. Biosensor designs are becoming increasingly more complex, with higher order circuits combining multiple interacting components and logic gate arrays to allow enhanced pathway regulation and output sensitivity. Table 1 shows some recent examples of biosensors developed for the direct or indirect detection of aroma compounds produced in yeast.  
In one, early biosensor study [73], it was shown how biosensors can be employed to develop a high-throughput screen for yeast strains producing high concentrations of \(\beta\)-phenylethanol. This indirect method used the flux through the Ehrlich pathway as an indicator of high end-product concentrations. Endogenous biosensors have previously been employed to increase the flux toward the production of precursors of aroma compounds in yeast [74; 75; 76; 77]. Synthetic biosensors have been constructed to allow feedback-regulated evolution of high IPP-producing strains [74].  
There is much more scope for the development of aroma biosensors for yeast when compared to the diversity of available bacterial sensors, with _E. coli_ sensors allowing for the detection of benzaldehyde, cinnamaldehyde, salicaldehyde, syringaldehyde and vanillin [78; 79]. Attempts have been made to translate some of these concepts in an endeavour to sense aroma compounds produced by bioengineered yeast, using encapsulated _p_-coumaric acid-sensing _E. coli_ to screen for yeast cells producing _p_-coumaric acid [80].
###### Abstract  
In next-generation Low-Earth-Orbit (LEO) satellite networks, securing inter-satellite communication links (ISLs) through strong authentication is essential due to the network's dynamic and distributed structure. Traditional authentication systems often struggle in these environments, leading to the adoption of Zero-Trust Security (ZTS) models. However, current ZTS protocols typically introduce high computational overhead, especially as the number of satellite nodes grows, which can impact both security and network performance. To overcome these challenges, a new bio-inspired ZTS framework called Manta Ray Foraging Cost-Optimized Zero-Trust Security (MRFCO-ZTS) has been introduced. This approach uses data-driven learning methods to enhance security across satellite communications. It continuously evaluates access requests by applying a cost function that accounts for risk level, likelihood of attack, and computational delay. The Manta Ray Foraging Optimization (MRFO) algorithm is used to minimize this cost, enabling effective classification of nodes as either trusted or malicious based on historical authentication records and real-time behavior. MRFOO-ZTS improves the accuracy of attacker detection while maintaining secure data exchange between authenticated satellites. Its effectiveness has been tested through numerical simulations under different satellite traffic conditions, with performance measured in terms of security accuracy, latency, and operational efficiency.  
ISL; LEO networks; attacker; detector; continuous authentication; zero trust security +
Footnote †: journal:  
Article
## 2 Materials and Methods  
### Animal Model and Sample Collection  
Rats (male Wistar \(200\pm 25\) g), aged 6 weeks, were collected from King Saud University, Saudi Arabia and were acclimatized for one week. The animal house was suitably ventilated with a 12-h cycle of day as well as night light conditions and the temperature was maintained at around 25 \({}^{\circ}\)C. The animals were fed a standard rodent pellet diet and had ad libitum access to water. All protocols concerning current study were in compliance with the ethical guidelines of the institute. The animals were categorized into four groups (a total number of eight rats in each group): Group 1: control; group 2: DEN (50 mg/kg bw in vehicle solution, oral gavage); group 3: DEN + EGCG (40 mg/kg bw in vehicle solution, oral gavage); and group 4: EGCG only, Table 1. All animals were sacrificed after 10 weeks of treatment and samples, such as blood and tissue, were collected for further analysis to achieve the objectives of the study. The serum was collected from the blood by centrifugation for 10 min at 1500\(\times\)\(g\) and stored at a low temperature of \(-80\)\({}^{\circ}\)C until further biochemical analysis. The aim of the study was achieved through the measurement of liver enzyme levels, levels of pro-inflammatory markers, expression of Phosphatase and tensin homolog (PTEN) protein through immunohistochemistry, and identification of apoptosis by Terminal deoxynucleotideyl transferase dUTP nick end labeling (TUNEL) staining. To provide greater insight into the subject, flow cytometry and transmission electron microscopy were done to evaluate the cell cycle and ultra-structural changes of the tissue. The research methodologies were directed towards the following landmark in a stepwise manner.  
Determination of Liver Function (ALT, ALP, and AST) Enzymes, SOD, CAT, and GPx Antioxidant Enzymes and Total Antioxidant Capacity  
Aspartate transaminase (AST), alanine transaminase (ALT), and alkaline phosphatase (ALP) levels were measured in serum. Total antioxidant capacity (TAC), superoxide dismutase (SOD), Glutathione peroxidase (GPx) and catalase (CAT) were assayed to measure the levels of hepatic injury, according to the instructions provided by the manufacturer (Abcam, UK).  
Determination of C-Reactive Protein, Interleukin-6 (IL-6), and Tumor necrosis factor-\(\alpha\) (TNF-\(\alpha\))  
Serological analysis was performed to measure the serum levels of C-reactive protein (CRP), IL-6, and TNF-\(\alpha\). Specific rat ELISA kits (Abcam, UK) were used for the measurement of serum C- reactive protein (CRP), IL-6, and TNF-\(\alpha\) according to the instructions provided by the manufacturer.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
\begin{tabular}{c} EGCG with vehicle solution \\ \end{tabular} & 08 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Animal grouping.
## 5 Conclusions  
Continuous system evaluation and autonomous damage assessment under seismic excitations are critical to structural safety evaluation. Generally, the recursive algorithms are exploited to achieve real-time or online processing for SHM. In this study, RSI implementation using the bona fide recursive algorithm is introduced to identify the time-varying dynamic characteristics under the modal domain. To demonstrate and verify the effectiveness for seismically excited structures, both numerical simulation and experimental investigation have been presented and the results are discussed.  
In the numerical simulation, the performance of the proposed method is clarified through an 8-story shear-type frame. The only user-defined parameter, \(i\), is suggested to account for the data number rather than the row number of the Hankel matrices. The extension coefficient, \(C_{E}\), brings a reasonable and effective selection for the practical implementation of RSI. For the spurious modes, MAC is taken between the column spaces instead of the stability diagram. The sampling rate shall be reduced for a real-time or online  
Figure 13: The hysteresis loop of the 10-story RC frame over time. (**a**) 9 to 12.5 s; (**b**) 11 to 15.5 s; (**c**) 14 to 21.5 s; (**d**) 20 to 31.5 s; (**e**) 30 to 41.5 s; (**f**) 40 to 51.5 s.  
application, but it needs to be larger enough to see those significant frequencies. In the experimental investigation, the capability of the proposed method is verified through two full-scale frames. In the test of the 10-story RC frame, the results show that RSI can track the slow time-varying parameter changes and provide the equivalent modal parameters for the nonlinear behaviors. Next, in the test of the 3-story steel frame, the RSI is capable to detect this kind of sudden damage and estimate the event time by leveraging the fixed window shown in the proposed method.  
Overall, the bona fide recursive algorithm is demonstrated and verified in this study. Future works will be focused on the implementation of field application and the long-term performance under the effect of the environment, such as temperature, moisture, and so on.  
Conceptualization, resources, writing, supervision, project administration, and funding acquisition, S.-K.H.; methodology, software, validation, original draft preparation, F.-C.C.; formal analysis, investigation, and data curation, Y.-T.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Ministry of Science and Technology, Republic of China (Taiwan), grant number MOST 109-2222-E-005-004-MY2.  
Not applicable.  
The experiments were conducted at the National Center for Research on Earthquake Engineering (NCREE) in Taiwan and the National Research Institute for Earth Science and Disaster Resilience (NIED) in Hyogo, Japan. The authors would like to thank all the technical support from NCREE and NIED.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
# A Unified Speech Enhancement System Based on Neural Beamforming With Parabolic Reflector
## 1 Introduction
The microphone array speech enhancement framework is extended by integrating the speech enhancement component from the parabolic reflector into the mask-based beamformer. The performances of the two different enhancement approaches with two different types of mask application are evaluated. The performances of a traditional Delay and Sum Beamformer (DSB) are evaluated too. The performance of the proposed system is also compared to a Complex Ideal Ratio Mask (CIRM) estimation method using a DNN network, presented in [18]. The performance of the proposed system for different noise types is also analyzed. The rest of the paper is organized as follows. In Section 2, the design of the proposed system is described, including the signal model, the acoustic focus performance of the paraboloid, as well as masking and beamforming-based enhancement approaches. In Section 3, the experimental setups are presented. In Section 4, results and analyses are reported. Section 5 concludes the paper.