esa-sceva/satcom-chunk-collection huggingface dataset:
# Microbes in Health and Disease: Human Gut Microbiota
## 1 Introduction
Under appropriate anaerobic conditions, they convert the glucose of milk lactose to lactic acid, which lowers the pH value of milk and causes the coagulation of milk protein, generating the solid curd yogurt [20]. Additionally, some cyanobacterial species (e.g., _Spirulina_) are food and dietary supplements in some regions [21]. In agriculture, nitrogen-fixing bacteria (belonging to plant growth-promoting rhizobacteria (PGPRs)) have been utilized as biof fertilizers to improve the growth of plants by increasing the bioavailability of nitrogen in the soil [4], along with other beneficial bacteria (e.g., auxin-producing PGPR, cytokin-producing PGPR, PGPR with antifungal activity, and biopesticides) [5]. Moreover, antimicrobial-producing microbes (e.g., _Streptomyces_ and _Bacillus_) have been utilized to manufacture medical antibiotics (e.g., penicillin and cephalosporin) in pharmaceutics [6]. Of note, engineered microbes have been recently constructed and utilized in clinical trials for the treatment of various non-cancer and cancer diseases (e.g., inflammatory bowel disease, diabetes, hyperammonemia, oral mucositis, human papillomavirus (HPV)-positive cancers, and prostate cancer) [22]. For example, a transgenic bacterium, _Lactococcus lactis (LL-Thy12)_, that expressed interleukin-10 showed improved outcomes for treating [PERSON]'s disease in a phase I trial [23]. A novel engineered bacterium, _Lactobacillus reuteri_ strain PRB782, that secrets high amounts of biologically active cytokine, human interleukin-22, was constructed and held promise as a delivery platform of human interleukin-22 for treating intestinal diseases [24]. Certain microbes (e.g., _Pseudomonas aeruginosa_) have been utilized as an eco-friendly bioremediation tool because of their microbial ability to degrade organic or inorganic pollutants [7; 25]. In particular, microbes inhabiting the human body have recently received increasing attention because of their tight associations with human health and disease [8]. In and on an adult human body, there are over 10,000 microbial species [26]. The collection of all these microbes is called the human microbiota. Based on the living location of microbes, the human microbiota can be further divided into the gut microbiota, skin microbiota, oral microbiota, etc. As DNA sequencing, bioinformatics, and omics technologies have rapidly developed, we have discovered the tight association of the human microbiota with the state of human health and disease at the mechanistic level. As illustrated in Figure 1B, an imbalance (or dysbiosis) in the human gut microbiota causes digestive, cardiometabolic, immune, and neural diseases, etc. [27]. Nowadays, the gut microbiota has become a focus of microbiological research in the study of human health and disease. Here, we focus on the human gut microbiota and synthesize recent advances in the exploration of the fundamentals of the human gut microbiota and their associations with human health and disease as well as therapies.
# Covert Anti-Jamming Communication Based on Gaussian Coded Modulation
## 2 Signal Design
### Codeword Generation  
A binary message vector \(\mathbf{m}\in\left\{-1,1\right\}^{L}\) is encoded into a real-valued codeword vector \(\mathbf{c}\in\mathbb{R}^{2N}\), where \(\mathbb{R}\) is the set of real numbers. Each element of the codeword, i.e., each time-frequency symbol, follows a Gaussian distribution. Here, the encoding \(\operatorname{Enc}:\left\{-1,1\right\}^{L}\mapsto\mathbb{R}^{2N}\) can be any function that includes a redundancy to the codeword. The redundancy can be exploited to estimate the jamming and correct the channel-induced errors.  
In the previous work of [PERSON] and [PERSON] [7], LBC-like encoding of the real-valued message was studied. That is, \(\operatorname{Enc}_{\text{LBC}}:\mathbb{R}^{L}\mapsto\mathbb{R}^{2N}\) was previously investigated. As it is a simple method, we can use it as a codeword generation method. We call this method Gaussian-LBC. Let us define the probability density function (PDF) for a Gaussian random variable with a mean \(\mu\) and variance \(\sigma^{2}\) as \(\mathcal{N}(\mu,\sigma^{2})\). In the Gaussian-LBC, a codeword vector becomes the product of a generator matrix and a message vector:  
\[\mathbf{c}=\mathbf{G}\mathbf{m}, \tag{1}\]  
where \(\mathbf{m}\) is the \(L\)-dimensional message vector, \(\mathbf{G}\in\mathbb{R}^{2N\times L}\) is the generator matrix of which the entries follow \(\mathcal{N}(0,1/N)\), and \(\mathbf{c}\in\mathbb{R}^{2N}\) is the codeword vector. Because the method is similar to LBC in finite fields, one can define concepts and notations corresponding to the parity-check matrix, syndromes, and error correction. However, error correction using syndromes in real-valued code is a nonlinear pattern recognition problem that has no simple solution for an arbitrary \(\mathbf{G}\)[5]. A linear programming solution to this real-valued syndrome decoding problem was described in [7]. The solution has been widely accepted for several other sparse signal recovery (SSR) problems, under the name of CS [8]. Numerous algorithms have been proposed [16; 17; 18; 19] to achieve this solution.  
However, we require another approach with Gaussain-LBC. This is because \(\operatorname{Enc}_{\text{LBC}}\) cannot fully exploit the characteristics of the digital message; thus, one cannot obtain an  
Figure 3: Time-domain waveform of (**a**) proposed signal, (**b**) noise, and (**c**) signal with noise under additive white Gaussian noise (AWGN) channel with a signal-to-noise ratio (SNR) of 0 dB.  
adequate coding gain. In the following Section 2.2., we discuss an alternate design of \(\text{Enc}:\{-1,1\}^{L}\mapsto\mathbb{R}^{2N}\) to obtain the coding gain.
## 3 Results
### Quantitative Assessment of Biodegradation Processes
#### 3.2.2 Mann-Kendall Test  
The zero hypothesis \(H_{0}\) to time series arrays (\(x_{1}\),..., \(x_{n}\)) is \(n\) independent and the random variable distribution of the same sample, and alternative hypothesis \(H_{1}\) is a bilateral examination. For the related \(k,j\leq n\) and \(k\
eq j\), and \(x_{k}\) and \(x_{j}\) distribution is not the same; inspection of the statistical variable \(S\) is based on the following formula:  
\[S=\sum_{i=1}^{n-1}\sum_{k=i+1}^{n}sgn(x_{k}-x_{i}), \tag{2}\]  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **September 2009** & **October 2009** & **November 2009** & **January 2010** & **March 2010** \\ \hline TPH (mg/L) & 3.7–15.1 & 2.8–12.0 & 4.3–6.6 & 3.4–6.8 & 3.2–5.6 \\ pH & 6.8–7.6 & 6.8–8.3 & 7.9–8.6 & 7.3–9.7 & 7.2–7.9 \\ ORP & \(-\)163.0–36.4 & \(-\)137.6–45.7 & \(-\)130.2—45.4 & \(-\)160.9—77.4 & \(-\)126.5—21.5 \\ NO\({}_{3}\)\({}^{-}\) (mg/L) & 1.2–14.0 & 1.2–3.0 & - & 0.6–2.0 & 0.6–4.0 \\ NH\({}_{4}\)\({}^{+}\)(mg/L) & 0.1–0.3 & 0.2–1.3 & 0.56–1.86 & - & - \\ Mn\({}^{2+}\) (mg/L) & 0.68–2.98 & 0.05–2.27 & 0.90–4.89 & 0.85–5.23 & 0.36–3.52 \\ Total Fe (mg/L) & 1.7–27.8 & 0.3–20.9 & 0.1–10.9 & 2.0–13.3 & 0.8–47.1 \\ Fe\({}^{3+}\) (mg/L) & 0.1–1.1 & 0.1–0.3 & 0.1–0.6 & 0.1–0.5 & 0.2–0.8 \\ SO\({}_{4}\)\({}^{2-}\) (mg/L) & 0.2–142.7 & 0.2–104.7 & 0.3–70.8 & 4.8–48.0 & 7.9–42.5 \\ \hline \end{tabular}
\end{table}
Table 1: Summary of water quality data intervals for monitoring wells in September 2009 to March 2010.  
\(x_{k}\), \(x_{i}\) are particular values in a sequence of data; \(n\) is the length of data; and  
\[sgn(\theta)=\left\{\begin{array}{c}1,\theta>0\\ 0,\theta=0\\ -1,\theta<0\end{array}\right., \tag{3}\]  
\(sgn(\theta)\) is the cumulative amount of time series. The Mann-Kendall test requires at least four independent samples of the time [52]. The normal statistical distribution of \(S\) is  
\[Z_{C}=\left\{\begin{array}{c}\frac{S-1}{\sqrt{\text{var}(s)}},S>0\\ 0,S=0\\ \frac{S+1}{\sqrt{\text{var}(s)}},S<0\end{array}\right., \tag{4}\]  
Variance:  
\[\text{var}[s]=\frac{[n(n-1)(2n+5)-\frac{t}{t}t(t-1)(2t+5)]}{18}, \tag{5}\]  
For the trend in the bilateral inspection, at a given confidence level \(\alpha\), if \(|Z|\geq Z_{1-\alpha/2}\), then the original assumption is unacceptable. That means time-series data versus an obvious is upward or downward trend at a confidence level. Statistical variables can determine the trend of the plume, and based on the corresponding numerical credibility trends, have complete the plume assessment. For statistical variables \(Z\), if greater than 0, it is and upward trend; if less than 0, it is a downward trend. The absolute values of \(Z\) were greater than or equal to 1.28, 1.64, and 2.32 at the time, showing that \(Z\) passed the reliability of the 90%, 95%, and 99% significance tests, respectively.  
In this test, the plume stability is represented as decreasing, probably decreasing, stable, no trend, probably increasing, and increasing at given confidence levels.
###### Abstract  
The green synthesis of silica has been extensively explored over the last few decades, as silica compounds found in commercial products can cause negative effects on human health. This calls for alternative ways to produce silica that are safer, cheaper and more environmentally friendly. Some of the agricultural wastes proven to contain silica include rice husk, sugarcane bagasse, coconut shells and coconut husk. This paper describes the synthesis of silica from coconut husk waste, and its physical and optical properties for potential utilization in optical applications. Coconut husk was subjected to fire at 500-700 \({}^{\circ}\)C so as to form coconut husk ash (CHA), and was then treated with sulfuric acid to extract silica from the ash. Most of the weight degradation subsequently occurred at temperatures from 221 to 360 \({}^{\circ}\)C. X-ray fluorescence (XRF) analysis proved that 91.76% of the silica was obtained, while major peaks on the X-ray diffraction (XRD) spectrum were observed after the acid treatment. Chemical bonds such as Si-O-Si, CH\({}_{2}\), -OH and Si-OH were found in the spectrum of the Fourier transform infrared spectroscopy (FTIR). Furthermore, the particles displayed rod-like shapes and irregular sizes, but the particle with sizes ranging from 200-750 nm decreased after the acid treatment. The relationship between the absorption coefficient and photon energy was obtained by finding the optical energy gap, which was found to be 4.3 eV. These data points provide critical information when used in optical applications. The overall studies show that synthesized silica has great potential for use in optical field applications.  
Keywords:silica; coconut husk ash; green synthesis; agriculture waste; XRD +
Footnote †: journal: Applied Physics Letters
## 2 Ground Fault Protection of Ungrounded System
### IMD (Insulation Monitoring Device)  
IMD is a device that monitors the insulation status of connected devices or power lines. When the insulation resistance is measured to be under the threshold value set by the user, an alarm is output or a relay signal for switching is generated.  
Internal components of an IMD include an auxiliary signal source, which injects signals into the system, a coupling circuit which protects devices from high voltage levels of the system and injects signals without distortion, and a measuring circuit, which measures returned signals due to the insulation resistance between the power line and the ground.  
An example of an IMD connected to two power lines is shown in Figure 1. \(U_{p}\) is the voltage of an auxiliary signal source. \(R_{m}\) is the resistance of measuring circuit, and the voltage induced across it is \(U_{m}\). \(R_{c}\) is the coupling resistance, and \(R_{e}\) is the equivalent insulation resistance that exists between the power line and the ground as the subject of IMD monitoring. \(C_{e}\) represents the distributed capacitance in the system. The equivalent insulation resistance of a system is calculated using the voltage of the measuring circuit, and transients appear in the measured voltage due to the capacitance component between the system and the line-to-ground. IMD can be largely categorized into two groups depending on the injected wave. The measurements and calculations by IMD are conducted by injecting either a sine wave or a square wave. In this paper, the universally utilized square wave injection type IMD is used, and the insulation impedance of the system and the simplified equivalent circuit of the IMD are depicted in Figure 2.
## 3 Performance Comparison
### Block Fuel Comparison
#### 3.1.3 Powertrain Efficiency  
The average powertrain efficiency \(\eta_{\rm pr}\) during the mission can be defined according to Equation (14):  
\[\eta_{\rm pr}=\frac{E_{p}}{E_{s}} \tag{14}\]  
where \(E_{s}\) is the amount of energy supplied during the operating mission by battery and fuel, and \(E_{p}\) is the energy effectively supplied to the propeller considering the powertrain losses \(E_{l}\), as sketched in Figure 15.  
Figure 16 shows that, for the same MTOW, the BW configurations exhibit a higher propulsive efficiency. This is due to the concatenation of two aspects; first, the lower OEW at the MTOW allows the BW configurations to embark on a larger number of batteries, as discussed in Section 3.1. Consequently, this aspect favors a higher utilization of the electrical power chain of the hybrid powertrain, which exhibits a higher efficiency than the thermal one, resulting in higher overall efficiency, as discussed extensively in the authors' recent  
Figure 14: Wetted surface of lifting systems of BW and TW configurations.  
Figure 13: Statistical output for the \(\left(b^{BW}/b^{TW}\right)^{2}\) parameter.  
Figure 15: Simplified sketch of energy transmission chain for parallel hybrid-electric powertrain.  
work [21]. The higher utilization of the electrical power chain thus reflects in two distinct effects: first, for the same MTOW, the supplied thermal power fractions are lower for the BWs, or, the other way around, for the same supplied thermal power fractions, correspond in a lower MTOW as shown in Figure 17; second, the higher propulsive efficiency introduces an additional contribution to the reduction in fuel consumption.
# Vehicle Target Tracking Algorithm Based on Improved Strong Tracking Unscented Kalman Filter
## 2 Related Work
Furthermore, the adaptive adjustment ability for inaccurate system models and noise is still insufficient. In summary, current methods struggle to handle uncertainties in multidimensional variables and noise and lack sufficient adaptability. This research gap indicates that existing methods still have limited capabilities in handling noise in dynamic traffic environments, necessitating the development of more effective adaptive adjustment mechanisms for multidimensional variables. Therefore, this paper proposes a new method aimed at improving the adaptability to multidimensional variables and the ability to handle noise.
## 3 Results
### Realization and Evaluation of the Pre-Test Phase
#### 3.1.2 Results Obtained in the II Stage of the Pre-Test Phase  
This stage of the pre-test phase was intended to analyse the possibility of reducing the time requirements for the measurement by reducing the number of DOFs. Since the aim of the authors was to compare the modal parameters of all three specimens with different infill patterns, estimated from a larger amount of repeated measurements, reducing the DOF number to, e.g., 50% (i.e., 24) would lead to considerable time-saving. On the other hand, if the results of analyses with a larger DOF number (i.e., 96) would lead to significant changes in the mode shapes or the analysed modal parameters (natural frequencies and damping ratios), the authors would already have the possibility of making the required adjustments to increase the accuracy of the analysis at this pre-test phase.  
When comparing the mean courses of the CMIFs (Figure 9) obtained from the three measurements realized in both the Iref and II stages, the plots were found to be vertically shifted compared to each other. In addition, significant differences in the heights of the individual CMIF peaks can be seen. The authors consider that the above differences in the plots are related to the fact that the accuracy of the singular value decomposition depends on employing a sufficient number of DOFs to capture the responses of all structure modes.  
The abovementioned statement can also be proved through Figure 10, which shows the mode shapes of the model with 24 DOFs. Although most of the mode shapes correspond to those obtained in the Iref stage, crucial differences can be observed, especially in modes 5 and 6, where the lack of DOFs (too large a distance between excitation points) resulted in an incorrect estimation of the mode shapes.  
The mode shapes depicted in Figure 11 were obtained from the EMA carried out using the model with 96 DOFs, and it is evident that they look smoother than those shown in Figure 8 (obtained for the model with 48 DOFs). On the other hand, it should be noted that the increase in the DOF number increased the time requirements for the realization of the experiments. Based on the comparison of the obtained mode shapes (having the same character as those obtained in the Iref stage) and the analysed modal parameters (presented in Section 3.2), this process did not cause significant changes and, thus, the authors consider it necessary to perform an increase in the number of DOFs only in cases when the most accurate mode shape, as the output of the EMA, is required (e.g., for damage identification processes [40]).  
Figure 10: Mode shapes of the clamped specimen with honeycomb infill pattern estimated in the II\({}^{\text{A}}\) stage (model with 24 DOFs) of the pre-test phase.  
Figure 9: Comparison of mean singular curves of CMIF plots obtained from the three experimental modal analyses carried out in the Iref, II\({}^{\text{A}}\), and II\({}^{\text{B}}\) stages of the pre-test phase.
## 3 Results and Discussion  
### Morphological and Structural Characterization  
Figure 2a shows an optical photograph of the macroscopic appearance of one of the produced spheres when illuminated with a UV hand lamp (\(\lambda_{\text{exc}}\) = 365 nm), exhibiting the strong red luminescence typical of ruby crystals. Although spherical in shape, some irregularities are present at the surface, which can be seen in detail in the SEM micrograph of Figure 2b. The spherical shape probably results from the solidification process that follows melting induced by the local heating of the Gaussian-profile laser beam. This behavior explains the bubble-shaped irregularities at the sphere surface. At a higher magnification (Figure 2c) it is possible to observe a small roughness that comes from the polycrystalline nature of the outer sphere. The microcrystals at the surface exhibit polyhedral shapes dominated by a cubic geometry with dimensions ranging from 5 to 30 \(\upmu\)m. EDS analysis revealed an average of 0.47 at.% and 1.19 wt.% for chromium, showing an effective doping with high homogeneity, with a maximum variation between the three probed points of 0.17 wt.%. XRD analysis (Figure 3a) of the powdered sample puts in evidence its monophasic constitution, corresponding to 100% of the \(\alpha\)-Al\({}_{2}\)O\({}_{3}\) crystalline phase, according to ICDD Ref. 04-005-4505 data. The Raman characterization (Figure 3b) corroborates the XRD results. \(\alpha\)-Al\({}_{2}\)O\({}_{3}\) possesses a rhombohedral crystal structure, with two Al\({}_{2}\)O\({}_{3}\) units per unit cell, belonging to the D\({}_{3\text{d}}\) space group. The optical modes of such structure can be described by the irreducible representation \(\Gamma\) = 2A\({}_{\text{1g}}\) + 2A\({}_{\text{1u}}\) + 3A\({}_{\text{2g}}\) + 2A\({}_{\text{2u}}\) + 5E\({}_{\text{g}}\) + 4E\({}_{\text{u}}\), among which the A\({}_{\text{1g}}\) and E\({}_{\text{g}}\) modes are Raman active. Hence, seven modes were predicted in the Raman spectrum of the prepared crystal [43; 44]. Indeed, Figure 3b evidences the presence of the typical main set of peaks assigned to the vibration modes of \(\alpha\)-Al\({}_{2}\)O\({}_{3}\). In fact, as shown in Figure 3b, the expected Raman features were all observed in spectra taken from the samples' surface, namely five modes with E\({}_{\text{g}}\) symmetry at 380, 432, 450, 578, and 752 cm\({}^{-1}\) and two others with A\({}_{\text{1g}}\) symmetry at 418 and 646 cm\({}^{-1}\)[43].
# Modified Kapok Fibers (_Ceiba pentandra_ ([PERSON]) for Oil Spill Remediation
## 3 Results and Discussion
### Discussions and Recommendations
Thus, in a real application situation, the roughness of a sorbent material will have a greater contribution at the moment in which the material is removed from the oil-contaminated medium and transported to another destination, intending to minimize leaching and secondary contamination. Thus, when treating a naturally oleophilic material such as kapok fibers to improve its sorbent character, the practical effect will be to reduce its oleophilicity due to the loss of functional groups. In other words, in practice, the material will become less sorbent. However, the increase in roughness will ensure greater stability of the captured oil, balancing the effect of functional loss in small degrees. Thus, depending on the use given to the material and the environment in which the saturated sorbents are to be transported after use, it may be a reasonable strategy to change its roughness to improve the safety of the route, minimizing additional contamination, or we can choose \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Sorption (g/g)**} & \multicolumn{2}{c}{**Roughness (nm)**} & \multicolumn{2}{c}{**Functionalization**} \\ \hline
**Treatment** & **Low Limit** & **High Limit** & **Low Limit** & **High Limit** & **Low Limit** & **High Limit** \\ \hline Raw & 58.5 [21] & & 4.757 & & C–C, CHx, C=O [21] \\ \hline Thermal & 50.29 & 57.8 & 5.092 & 7.267 & & \begin{tabular}{c} Slight CHx \\ reduction \\ \end{tabular} & \begin{tabular}{c} C–O \\ increased \\ \end{tabular} \\ \hline Acidic & 50.2 & 50.96 & 4.470 & 9.735 & \begin{tabular}{c} Almost \\ constant \\ \end{tabular} & \begin{tabular}{c} C–O \\ increased \\ \end{tabular} \\ \hline Alkali & 38.56 & 56.21 & 4.404 & 12.35 & \
\begin{tabular}{c} Almost \\ constant \\ \end{tabular} &
\begin{tabular}{c} OH and C–O \\ increased and \\ C–H, C=O \\ reduced \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Relationship between sorption and roughness of kapok fibers in activation processes. not to treat the natural sorbent, if it already has some hydrophobic character and good sorption capacity.