esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Quantification of Fecal SCFAs  
We first calibrated our fecal SCFA quantification method using a pool of five fecal samples. Fecal samples were homogenized and diluted with distilled-deionized water in a ratio 1:1. An aliquot of 1 g was spiked with a combined standard solution of SCFAs diluted in water (organic acid kit ref. 47264, Supelco (Bellefonte, PA): Acetic acid--ref. R475165; propionic acid--ref. R412368; butyric acid--ref. R420040; isobutyric acid--ref. R412415) to obtain curves in the range 25-750 ng/mL (6 concentrations; 9 replicates). We prepared the standard solution of SCFAs on the day of analysis. Linearity (homoscedasticity test, analysis of residuals), precision (RSD 3.0 for the detection of each analyte), and accuracy (3-way test of concentration for each analyte and 3 replicates; value = G\({}_{\text{table}}\) (a = 0.05; k = 3; n = 3) = 0.871 (G\({}_{\text{exp}}\) < G\({}_{\text{table}}\) acceptance)) were considered in the evaluation of the analytic method.  
We next quantified SCFAs in all fecal samples. For this, approximately 1 g of each sample was weighed into a vial of 20 mL with a magnetic thread cap and PTFE septum. The volatiles were sampled using a CTC Combipal 3 autosampler in HS/SPME mode equipped with a gray fiber (Carboxen/DVB/PDMS--ref. SU57329U, Supelco). The autosampler was programmed as follows: Fiber-conditioning module, 5 min at 250 \({}^{\circ}\)C; sample equilibration, 30 min at 80 \({}^{\circ}\)C; extraction of analytes, 25 min at 80 \({}^{\circ}\)C; desorption of the fiber, 1 min at 250 \({}^{\circ}\)C. The analytes were injected in the splitless mode into an Agilent 7890 gas chromatography (GC) system (Wilmington, DE, USA) equipped with a 5975C mass spectrometer (MS) detector and an Agilent J & W DB-WAX column (30 m, 0.25 mm,0.250 \(\upmu\)m). The oven temperature was programmed to start at 80 \({}^{\circ}\)C (5 min), to increase to 100 \({}^{\circ}\)C for 1 min at 3 \({}^{\circ}\)C/min, and then to increase to 250 \({}^{\circ}\)C for 1 min at 6 \({}^{\circ}\)C/min. The MS was tuned during all experiments; the signal acquisition for quantification was done in the single-ion monitoring (SIM) mode (Table S1). The temperature of the ionization source and the quadrupole were 230 \({}^{\circ}\)C and 150 \({}^{\circ}\)C, respectively. The electron-impact ionization energy was 70 eV. The chromatographic peaks were checked for homogeneity using the extracted ions of the characteristic fragments to optimize the resolution and peak symmetry. We performed data analysis using a MassHunter WorkStation (Agilent; Santa Clara, CA, USA). Concentrations of acetate, propionate, butyrate, and isobutyrate were expressed in \(\upmu\)mol/g of feces. Total SCFAs were the sum of acetate, propionate, and butyrate.
## 5 Numerical Example
### Result Analysis
#### 5.2.1 Algorithm Validity Test  
Utilizing the aforementioned case study framework, a comprehensive comparative analysis was conducted between the whale optimization algorithm (WOA) and the improved whale optimization algorithm (IWOA) to evaluate algorithmic performance metrics. Both algorithms were systematically configured with identical population sizes (N = 100) and maximum iteration thresholds (T = 100) to ensure experimental parity. Through systematic validation involving 100 experimental trials, critical performance indicators, including convergence behavior and computational efficiency, were rigorously assessed. Experimental results (Figure 12) demonstrate that IWOA achieves solution convergence with an average of 8 iterations (standard error = 0.95), significantly outperforming WOA's average convergence at 16 iterations (standard error = 1.77). Figure 13 illustrates a representative iteration process, revealing that IWOA's initial solutions exhibit superior proximity to optimal values while demonstrating enhanced convergence velocity compared to WOA's gradual convergence pattern. Notably, both algorithms successfully terminate within 50 iterations, with IWOA displaying accelerated convergence dynamics and improved solution stability. These empirical findings substantiate the efficacy of the proposed algorithmic enhancements in optimizing global search capabilities while significantly improving convergence efficiency and robustness in multimodal transportation optimization scenarios.
## 2 Materials and Methods
### Data  
#### 2.2.1 Landsat Archive  
The Landsat mission provides the longest, continuous space-based record of Earth's land surface [55]. For the present analysis, all available images covering the Vietnamese coastal zone of the Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), and Landsat 8 Operational Land Imager (OLI) instruments were used. The United States Geological Service (USGS) provides the datasets already pre-processed to surface reflectance (SR) [56]. The TM and ETM+ sensors capture electromagnetic radiation in seven bands with four bands in the visible (VIS) and NIR spectrum, two short-wave infrared (SWIR) bands, and one thermal infrared (TIR) band. The visible and near-infrared (VNIR), as well as the SWIR, bands are acquired with a spatial resolution of 30 m [55]. Each of the near polar-orbiting Landsat satellites has a revisit time of 16 days. For Vietnam's coastal zone, a total of 26,109 Landsat acquisitions were analyzed. The spatio-temporal availability of acquisitions is shown in Figure 2, depicting low availability in the 1980s and 2012, while most acquisitions for Vietnam are available from 2014 until 2021 after the launch of Landsat 8. Spatially, central Vietnam encompasses the lowest availability at the southern tip of the country with a total of 400 observations.  
Figure 1: Study area: Vietnam.
# Deep Learning for Computational Mode Decomposition in Optical Fibers
## 2 Methodology  
### Digital Holography-Based Modal Decomposition  
The approach to perform an MD using DH is based on the linearity of the Maxwell equations [19]. Arbitrary electromagnetic field distributions \(\underline{E}_{\text{arb}}\), with amplitude \(A_{\text{arb}}\) and phase \(\Phi_{\text{arb}}\), can be decomposed into a linear series of base functions \(\underline{E}_{i}\) with the individual complex-valued weights \(a_{i}\):  
\[\underline{E}_{\text{arb}}=A_{\text{arb}}e^{i\Phi_{\text{arb}}}=\sum a_{i} \underline{E}_{i}=a_{0}\underline{E}_{0}+a_{1}\underline{E}_{1}+.... \tag{1}\]  
However, the light field exiting a step-index MMF can be represented by a distribution that is a superposition of \(N\) LP-modes, assuming that the MMF is weakly guiding [24]. The number of modes \(N\) which can be guided in an MMF can be estimated by  
\[N\approx\frac{1}{2}\cdot\left(r\cdot\frac{2\pi}{\lambda_{0}}\cdot\text{NA} \right)^{2}, \tag{2}\]  
with \(r\) being the core radius, NA being the numerical aperture, and \(\lambda_{0}\) being the wavelength of the utilized laser light. Now, if the light field emerging from an  
orthogonality of the LP modes, each of the \(N\) complex-valued weights representing the individual field distributions can be determined as follows:  
\[a_{i}=\rho_{i}e^{\phi_{i}}=\frac{1}{K_{i}}\sum_{ROI}\underline{E}_{\text{MMF}} \underline{E}_{i}^{*}, \tag{4}\]  
where \(\rho\) and \(\phi\) are the magnitude and the phase value of the respective mode weight, \(K_{i}=\sum_{ROI}\underline{E}_{i}\underline{E}_{i}^{*}\), ROI is the whole 2D region of interest (in most cases, the MMF's core area), and \(\underline{E}_{i}^{*}\) is the complex conjugated field distribution of an LP mode. To perform the MD, holograms are recorded with a CMOS camera and reconstructed numerically using the Angular Spectrum method [25]. In recent work, this approach was used for inverse precoding within a novel MMF communication system, enabling mode division multiplexing, and exploiting physical layer security [18]. The drawback of digital holography is the need for an interferometric setup, which leads to optical systems with higher complexity.
# Parameter Estimation of the Bishop Photovoltaic Model Using a Genetic Algorithm
## 3 Mathematical Formulation of the Optimization Problem  
The parameter estimation of the Bishop model was performed by means of a GA, which is a metaheuristic optimization technique. The optimization process was based on the objective function given in (5), which corresponds to the root mean square error (RMSE) between the current measured in the experimental tests (\(I_{cell_{m}}\)) and the estimated value  
Figure 1: The Bishop model of a photovoltaic cell.  
(\(I_{cell_{t}}\)). For this purpose, both data vectors should be taken from the same voltage vector and have the same size (\(n\)).  
\[min(O.F.)=min\left(\sqrt{\frac{\sum_{i=1}^{n}(I_{cell_{t}}(i)-I_{cell_{m}}(i))^{2} }{n}}\right) \tag{5}\]  
The constraints of the optimization problem correspond to the search ranges of the parameters to be estimated, which are defined in Equations (6)-(13). Those search ranges should be respected to ensure a correct estimation of the current using Equation (1).  
\[\eta_{min}\leq\eta\leq\eta_{max} \tag{6}\] \[R_{smin}\leq R_{s}\leq R_{smax}\] (7) \[R_{shmin}\leq R_{sh}\leq R_{shmax}\] (8) \[I_{omin}\leq I_{o}\leq I_{0 max}\] (9) \[I_{plmin}\leq I_{pl}\leq I_{plmax}\] (10) \[a_{min}\leq a\leq a_{max}\] (11) \[m_{min}\leq m\leq m_{max}\] (12) \[V_{brmin}\leq V_{br}\leq V_{brmax} \tag{13}\]  
Regarding the parameters associated with the SDM (\(I_{ph}\), \(R_{s}\), \(R_{sh}\), \(I_{o}\) and \(\eta\)), it is possible to find defined ranges for different cell technologies in the literature. However, the parameters' ranges that affect the second quadrant behavior (\(a\), \(V_{br}\) and \(m\)) have not been extensively studied; therefore, those ranges are described in Section 4.2.2.
## 4 Discussion
### Uncertainty Analysis  
This study conducted a systematic examination of the effects of RSM and PRE on summer GPP, although several issues remain unresolved. The hydrological cycle within the research area should encompass processes such as E, RUM dynamics, plant transpiration, and variations in terrestrial water storage. Although the data binning approach constrained variations in E and RUM, it did not adequately account for other influential factors. Changes in surface runoff have a direct impact on vegetation growth [66], and permafrost thawing similarly influences GPP [67]. Despite agricultural land constituting a small proportion of the study region, artificial irrigation contributes to GPP variability [68]. Natural grasslands predominate in the area; however, grazing and anthropogenic activities modify grassland patterns, thereby affecting GPP [69]. During the study period, vegetation-type changes in China's arid and semi-arid regions were minimal, yet the potential effects of vegetation-type variation on the findings were not considered [29]. Future research should incorporate datasets related to water cycle components to elucidate the hydrological mechanisms within the study area, while also comprehensively addressing the impacts of vegetation type and land use changes to resolve existing uncertainties.
# Photonic Crystal Cavity with a Thin Low-Index Layer for Silicon-Compatible Nanolight Source
## 1 Introduction  
Based on well-established complementary metal-oxide semiconductor (CMOS) fabrication technologies, silicon nanophotonic devices have been developed for optical waveguides [1], demultiplexers [2], optical switches, and optical modulators [3; 4] because of CMOS compatibility. However, because of the indirect bandgap of silicon, the realization of an efficient light source remains a challenging issue in silicon photonics. Recently, in order to develop a light source for a silicon photonic circuit, several designs, including a III-V semiconductor nanolaser coupled to silicon waveguides [5; 6], nanolasers based on active nanomaterials such as nanowires [7], two-dimensional (2D) materials [8], and nanoparticles [9], have been demonstrated by placing other active materials on the silicon photonic circuits. However, in such systems, the coupling efficiencies between the gain materials and the silicon photonic structure become a bottleneck for the efficient device. In contrast, rare-earth atoms, such as erbium (Er) and ytterbium (Yb), and doped silicon oxide or silicon nitride have been reported to have bright telecommunication wavelength emissions [10; 11]. Very recently, microdisk cavities of several tens of micrometers demonstrated the enhancement of the emission in the rare-earth doped silicon-compatible materials. However, some papers report a further miniaturized light source with a size of a diffraction limit mode volume, where the small mode size enables ultrafast light modulation [12].  
In contrast, photonic crystals, which are periodic structures consisting of two different refractive index materials with a period of a wavelength, are attracting considerable attention in applications ofnanophotonic devices owing to their ability to control light, confine photons in a wavelength-sized cavity, guide light along the waveguide, and enhance/suppress light emission from emitters in photonic crystals. In particular, the easier fabrication and less-suppressed cavity designs enable the realization of wavelength-sized low-threshold lasers [13], ultralow power consumption optical switches [14], and ultrafast light sources [15]. However, low-index material photonic crystal cavities for rare-earth doped silicon (Si)-based light sources can suffer low \(Q\) factors owing to weak total internal reflection (TIR) or large mode volume [16].  
In this paper, we propose a transverse magnetic (TM) polarized two-dimensional (2D) photonic crystal thin silica (SiO\({}_{2}\)) layered cavity with a high \(Q\) factor, small mode volume (\(V_{\text{m}}\)), and high confinement factor (\(\Gamma\)) in the thin SiO\({}_{2}\) layer on a square lattice of silicon rods. The proposed cavity is a possible best candidate for the realization of a silicon-compatible nanolight source.
## 3 Proposed Methodology
### Key Features of the Proposed DDBG Technique  
* The selection of the IDS node is dynamic, after considering that the highly mobile environment of MANETs as the deployment of IDS nodes on fixed locations is not a practical approach in MANETs. To enhance the network performance and minimize the routing overhead, the proposed technique provides an optimized IDS node-based solution in MANETs.
* In MANETs, the mobility of the nodes is obvious, therefore, the topology of the network changes from time to time. The proposed technique manages the position of all of the IDS nodes according to the current network topology to cope with the dynamic environment of MANETs.
* The proposed technique provides the quick detection of black hole and gray hole nodes because if any node in the IDS set range is not responding or is sending false information, it is declared to be a malicious node for a short time.
# Study on Thermal Radiation Characteristics and the Multi-Point Source Model of Hydrogen Jet Fire
## 4 Results and Discussion
### Numerical Simulation
#### 4.4.1 Effect of Leakage Pressure  
For the leakage diameter of 10 mm, the thermal radiation data in the \(y\)-direction and \(x\)-direction (\(y=2\) m plane) were extracted at the leakage pressures of 4 MPa, 8 MPa, and 12 MPa, as shown in Figure 9.  
Figure 9a shows that at the flame height of 2 m, as the leakage pressure increases, the maximum thermal radiation also increases. This increase is more pronounced when the pressure increases from 8 MPa to 12 MPa. Figure 9b shows that in the \(y\)-direction, the thermal radiation also increases with the increase in the leakage pressure, and similar behavior can be observed in that the increment is more significant at a higher pressure of 12 MPa. Additionally, along the flame axis, the thermal radiation increases first and then decreases. The position of the peak thermal radiation along the flame axis shifts to a higher flame height with increasing leakage pressure, consistent with the experimental observations.  
Figure 10 shows the temperature distribution along the flame axis in the \(y\)-direction for the three pressure conditions. It is seen that the hydrogen jet flames have close values of the maximum temperatures under different pressures, indicating that the maximum flame temperature is independent of the pipeline pressure. As the pipeline pressure increases, the flame axis position corresponding to the highest temperature also increases. This is influenced by the hydrogen leakage rate. That is, when the hydrogen leakage rate increases,  
more oxygen is required for the reaction, leading to a longer flame length. Comparing the temperature and thermal radiation data in the \(y\)-direction, it is found that the high-temperature and high-radiation heat flux density regions tend to overlap, indicating a significant influence of temperature on thermal radiation release.
## 5 Experiments and Analysis of Results
### Experimental Results and Analysis  
#### 5.3.1 Results and Analysis of Ablation Experiments  
In this paper, two ablation schemes were set. One is the base scheme, and the other is the scheme with asynchrony. Table 2 shows the time overhead of the ablation experimentin this paper, and Table 3 shows the results of different schemes in the three indicators of average transmission delay, acceleration ratio, and throughput.  
By analyzing the data in Table 3, the following conclusions can be drawn:  
Firstly, in terms of average transmission delay, the scheme Base+Asy+Pre has the best performance, and its average transmission delay is only 0.00098 s/KB, which is 83.8% lower than that of the scheme Base (0.00606 s/KB). Compared with Base+Asy (0.00340 s/KB), the transmission delay is also reduced by 71.1%. This indicates that the scheme Base+Asy+Pre has a significant advantage in reducing the transmission delay.  
Secondly, from the perspective of throughput, the throughput of Base+Asy+Pre reaches 1017.50 KB/s, which is significantly higher than that of Base (164.96 KB/s) and Base+Asy (294.05 KB/s). Compared with the scheme Base, the throughput is increased by 516.5%, and compared with the scheme Base+Asy, the throughput is increased by 246.0%. This shows that the scheme Base+Asy+Pre not only improves the transmission speed, but also makes more efficient use of network resources.  
Finally, from the analysis of the acceleration ratio index, the acceleration ratio of the scheme Base+Asy+Pre is 6.17, which is much higher than that of the scheme Base+Asy (1.78) and the scheme Base (baseline, 1.00). The acceleration ratio measures the degree of performance improvement, and the high acceleration ratio of the scheme Base+Asy+Pre means that its performance is 517% higher than that of the baseline scheme Base, which is also significantly improved compared to the scheme Base+Asy.  
In summary, the scheme Base+Asy+Pre performs best in various key performance indicators such as average transmission delay, throughput and acceleration ratio, demonstrating its excellent ability to improve transmission efficiency and system performance. This also shows that the improved scheme after adding Asynchronous Optimization (Asy) and the Preprocessing (Pre) mechanism plays an important role in improving the system performance.
# Numerical Simulation of Non-Linear Models of Reaction--Diffusion for a DGT Sensor
## 2 The Model
### Initial and Boundary Conditions  
When the DGT is deployed in the solution, at time \(t=0\) the concentration of each of the species \(i\) inside the spatial domain \((0,r+g)\) is zero, that is \(c_{i}(x,0)=0\), except for the free places inside the resin, whose initial concentration is \(c_{\text{R}}(x,0)=c_{\text{TR}}\) (the total concentration of resin sites).  
Resin sites are considered immobile, but for species with \(D_{i}\
eq 0\), it must be considered one boundary condition at \(x=r+g\), i.e., at the interface between the diffusive gel and the solution:  
\[c_{i}(r+g,t)=c_{i}^{*}\,\]  
where \(c_{i}^{*}\) stands for the bulk concentration of species \(i\).  
At the DGT bottom, i.e., at \(x=0\), there should be no flow of any species,  
\[\left.\frac{\partial c_{i}}{\partial x}\right|_{x=0}=0\.\]  
Finally, at the resin-gel interface (\(x=r\)), there must be continuity in the concentration profiles and flows of each species,  
\[c_{i}(r^{-},t)=c_{i}(r^{+},t)\ ;\quad D_{i}^{\rm R}\left.\frac{\partial c_{i}}{ \partial x}\right|_{x=r^{-}}=D_{i}\left.\frac{\partial c_{i}}{\partial x} \right|_{x=r^{+}}\.\]
# A High Latitude Model for the E Layer Dominated Ionosphere
###### Abstract  
Under certain conditions, the ionization of the E layer can dominate over that of the F2 layer. This phenomenon is called the E layer dominated ionosphere (ELDI) and occurs mainly in the auroral regions. In the present work, we model the variation of the ELDI for the Northern and Southern Hemispheres. Our proposed Neustrelitz ELDI Event Model (NEEM) is an empirical, climatological model that describes ELDI characteristics by means of four submodels for selected model observables, considering the dependencies on appropriate model drivers. The observables include the occurrence probability of ELDI events and typical E layer parameters that are important to describe the propagation medium for High Frequency (HF) radio waves. The model drivers are the geomagnetic latitude, local time, day of year, solar activity and the convection electric field. During our investigation, we found clear trends for the model observables depending on the drivers, which can be well represented by parametric functions. In this regard, the submodel NEEM-N characterizes the peak electron density \(NmE\) of the E layer, while the submodels NEEM-H and NEEM-W describe the corresponding peak height \(hmE\) and the vertical width \(wE\) of the E layer electron density profile, respectively. Furthermore, the submodel NEEM-P specifies the ELDI occurrence probability \(\%ELDI\). The dataset underlying our studies contains more than two million vertical electron density profiles covering a period of almost 13 years. These profiles were derived from ionospheric GPS radio occultation observations on board the six COSMIC/FORMOSAT-3 satellites (Constellation Observing System for Meteorology, Ionosphere and Climate/Formosa Satellite Mission 3). We divided the dataset into a modeling dataset for determining the model coefficients and a test dataset for subsequent model validation. The normalized root mean square deviation (NRMS) between the original and the predicted model observables yields similar values across both datasets and both hemispheres. For NEEM-N, we obtain an NRMS varying between 36.1% and 47.1% and for NEEM-H, between 6.1% and 6.3%. In the case of NEEM-W, the NRMS varies between 38.5% and 41.1%, while it varies between 56.5% and 60.3% for NEEM-P. In summary, the proposed NEEM utilizes primary relationships with geophysical and solar wind observables, which are useful for describing ELDI occurrences and the associated changes of the E layer properties. In this manner, the NEEM paves the way for future prediction of the ELDI and of its characteristics in technical applications, especially from the fields of telecommunications and navigation.  
Keywords:ionosphere modeling; E layer; polar ionosphere; radio science; GPS radio occultation; space weather; particle precipitation +
Footnote †: journal: remote sensing