esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Approach to Fuel-Cell System Analysis
### \(N_{2}\)-O\({}_{2}\) Separation  
Another critical aspect of the system is related to the process of N\({}_{2}\)-O\({}_{2}\) separation necessary to produce the O\({}_{2}\)-enriched stream. During this study, it was assumed that this process is produced on-board by means of a polymeric membrane. Two main parameters were considered for this element: the pressure ratio between the inlet and permeate streams (\(\pi_{mem}\)) and the stage cut, which represents the ratio between both mass flow rates (\(\lambda_{mem}\)). This ratio is mainly a function of the level of O\({}_{2}\) enrichment and, to a lesser extent, of the pressure and temperature conditions under which it is operated. In the present study, it was assumed as a first approximation that the operating conditions are set constant at \(\pi_{memb}=4\) and at a temperature of 80 \({}^{\circ}\)C. Thus, the ratio of mass flow rates was adjusted to the following correlation based on a complete operating map for the polymeric membrane under study (model NM C05 of UBE Corporation):  
\[\lambda_{mem}=83.287X_{\mathrm{O}_{2}}^{2}-34.504X_{\mathrm{O}_{2}}+4.635 \tag{3}\]  
This correlation presents a coefficient of determination \(R^{2}\) of 99.3%, which can be considered to reliably represent the membrane performance in the range of O\({}_{2}\) fraction used for the adjustment (from 21 to 40%). Furthermore, the outlet pressure of the retained stream (N\({}_{2}\)-rich) was assumed to be 90% of the inlet pressure, while the permeate stream was assumed to be at atmospheric pressure. Figure 2 shows the consequence of the membrane stage cut ratio by comparing the mass flow at the cathode inlet and the total air mass flow consumed as a function of the O\({}_{2}\) enrichment. During this study, the flow rates were divided by the total active area of the stack, calculated as the product of the number of cells and each cell active area, so that the actual values were independent from the PEMFC stack size. As it can be seen, applying higher levels of O\({}_{2}\) enrichment helps to reduce the mass flow arriving to the cathode, since the same excess O\({}_{2}\) ratio is assumed, but the requirements from the membrane reverse this trend. Particularly, for \(X_{\mathrm{O}_{2}}=0.25\), the total air mass flow consumed by the membrane is approximately equal to the one consumed by the PEMFC alone with \(X_{\mathrm{O}_{2}}=0.21\), where higher levels of enrichment result in an increase in the total air mass flow consumed.
## 1 Introduction  
Electromagnetic waves negatively influence biological systems in organisms, which has garnered intensive attention. Electronic products are continuously invented and produced for the sake of public welfare. For example, electromagnetic fields caused by mobile phones result in typical biological effects [1; 2; 3]. Some studies found that the radiation from mobile phones is surmised to be a factor in physiological changes [4; 5; 6]. It could also have a negative influence on the endocrine system, sense of hearing, metabolism, and the formation of tissues and organs [7; 8; 9]. However, the potentially beneficial effects that electromagnetic fields demonstrate on organisms are used in biomedical science to examine and explore new treatments [10; 11]. On the other hand, people are also thriving to decrease and solve their adverse effects on human health and electromagnetic pollution [12; 13; 14].  
Following the environmental changes, the climate also changes, and the global average temperature is rising. People thus have increasingly intense concerns about environmental protection and energy saving. As a result, the manufacturing process and materials are carefully considered in order to reduce pressure on the natural environment. Thermally insulating materials are largely used in construction in order to decrease dependence on air conditioning systems and prolong the temperature adjustment and save energy costs [15]. In order to support a minimum consumption of energy and resources, their efficient usage is needed. It will be an important measure for designs to incorporate thermally insulating materials, which facilitates energy saving and improves the thermal comfort of citizens, while enabling the construction materials to endure constantly changing outdoor weather [16].  
The textile industry is the earliest developed of all industries, encompassing apparel, household fabrics, and industrial products. In particular, nonwoven fabrics have a low cost and efficient manufacturing that does not generate harmful byproducts. Nonwoven fabrics can satisfy different requirements to have innovative functions; thus, they are functional textiles that have been continuously developed and improved for different applications [17; 18]. Noticeably, they have a great variety of applications, including clothes, domestic products, agricultural products, industrial products, high-tech electronic products, and aerospace industrial products [19; 20; 21].  
This study combines flame-retardant polyester fiber (FR fiber), far-infrared polyester fiber (FI fiber), and low-melting-point PET fiber (LM fiber) to form flame-retardant and thermally insulating matrices. Combinations of these matrices and carbon woven fabric are then processed via needle punching and circulative heat drying, thereby forming PET composite boards and PET/carbon fiber (CF) composite boards. The mechanical properties, the limiting oxygen index (LOI), the air permeability, and the thermal conductivity of these boards are then evaluated.
# Secure Transmission in mmWave Wiretap Channels: On Sector Guard Zone and Blockages
## 4 Performance Analysis
### Direct Transmission  
For the direct transmission, there does not exist an eavesdropper within the sector secrecy guard zone, the transmitter keeps sending the confidential information to the legitimate receiver. Substituting Equation (5) into Equation (3), the COP is derived as  
\[p_{coa} =\Pr\!\left(\gamma_{D_{A}}\!\!<\!\!2^{R_{B}}\!-\!1\right)=\!\! \Pr\!\left(\frac{PM_{S}|h_{D}|^{2}L\left(r_{D}\right)}{\sigma_{D}^{2}}\!<\!2^{R _{B}}\!-\!1\right)=\Pr\left(\left|h_{D}\right|^{2}<\frac{\left(2^{R_{B}}-1 \right)\sigma_{D}^{2}}{PM_{S}L\left(r_{D}\right)}\right)\] \[=\sum\limits_{i\in\left\{L,N\right\}}\left(\frac{\mathrm{Y}\left( N_{i},\frac{\left(2^{R_{B}}-1\right)\sigma_{D}^{2}}{PM_{S}L\left(r_{D}\right)}N_{i} \right)}{\Gamma\left(N_{i}\right)}\right)P_{i}\left(r_{D}\right), \tag{11}\]  
where \(\mathrm{Y}\left(\cdot,\cdot\right)\) is the lower incomplete gamma function [42] (Equation (8.350)). In the case of \(E\in\Phi_{1}\), substituting Equation (6) into Equation (2), the SOP is calculated as follows:  
\[p_{soa}=\Pr\left(\gamma_{E_{A}}>2^{R_{B}-R_{S}}-1\right)=\int_{2^{R_{B}-R_{S}} -1}^{\infty}f_{\gamma_{E_{A}}}\left(x\right)dx, \tag{12}\]  
where \(f_{\gamma_{E_{A}}}\left(\cdot\right)\) stands for the probability density function of \(\gamma_{E_{A}}\). By using the thinning theorem [26; 43; 44], the eavesdroppers are divided into two independent PPPs, namely LOS point process \(\Phi_{1}^{\mathrm{LOS}}\) with density function \(\lambda_{E}P_{L}\left(r_{d}\right)\), and NLOS point process \(\Phi_{1}^{\mathrm{NOS}}\) with density function \(\lambda_{E}\left(1-P_{L}\left(r_{d}\right)\right)\). Then, the cumulative distribution function of \(\gamma_{E_{A}}\) is derived as  
\[F_{\gamma_{E_{A}}}(x) =\Pr\!\left(\gamma_{E_{A}}\!<\!x\right)\ =\Pr\left\{\frac{\max\limits_{E\in\Phi_{1}}\left(PM_{S}|h_{E}|^{2}L \left(r_{E_{A}}\right)\right)}{\sigma_{E}^{2}}\!\!<\!x\right\}\] \[=\underbrace{\Pr\left\{\frac{\max\limits_{E\in\Phi_{1}^{\mathrm{ L}}}\left(PM_{S}|h_{E}|^{2}L\left(r_{E_{A}}\right)\right)}{\sigma_{E}^{2}}<x \right\}}_{Z_{1}}\times\underbrace{\Pr\left\{\frac{\max\limits_{E\in\Phi_{1}^{ \mathrm{N}}}\left(PM_{S}|h_{E}|^{2}L\left(r_{E_{A}}\right)\right)}{\sigma_{E}^ {2}}<x\right\}}_{Z_{2}}, \tag{13}\]  
where \(\Phi_{1}^{L}\) and \(\Phi_{1}^{N}\) are the set of LOS and NLOS eavesdroppers, respectively. \(Z_{1}\) and \(Z_{2}\) are calculated by Equations (14) and (15). In \(Z_{1}\), step (a) follows the probability generating functional of the PPP, and step (b) is based on [42] (Equation (8.354.1)).
# Lossless Compression of Sensor Signals Using an Untrained Multi-Channel Recurrent Neural Predictor
## 4 Experiments
### Results of Different Recurrent Units
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Signal** & **LSTM** & **GRU** & **MCRU** \\ \hline BLOND-1 & 1.714 & 1.717 & 1.708 \\ BLOND-2 & 1.754 & 1.755 & 1.748 \\ BLOND-3 & 1.802 & 1.796 & 1.634 \\ BLOND-4 & 1.787 & 1.791 & 1.783 \\ BLOND-5 & 2.230 & 2.232 & 2.232 \\ \hline ESC-0 & 2.689 & 2.644 & 2.392 \\ ESC-1 & 2.490 & 2.471 & 2.191 \\ ESC-2 & 3.804 & 3.764 & 3.483 \\ ESC-3 & 3.572 & 3.575 & 3.198 \\ ESC-4 & 4.036 & 4.041 & 3.696 \\ \hline MPACT-DJI & 3.554 & 3.546 & 3.347 \\ MPACT-FlySky & 3.261 & 3.273 & 3.134 \\ MPACT-Futaba & 3.275 & 3.271 & 3.120 \\ MPACT-Graupner & 3.241 & 3.272 & 3.125 \\ MPACT-HobbyK & 3.450 & 3.432 & 3.271 \\ \hline DJI-14 bits & 4.381 & 4.387 & 3.827 \\ FlySky-14 bits & 4.131 & 4.139 & 3.606 \\ Futaba-14 bits & 4.073 & 4.097 & 3.581 \\ Graupner-14 bits & 4.105 & 4.080 & 3.587 \\ HobbyK-14 bits & 4.391 & 4.370 & 3.710 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The median BPC over 10 runs for each recurrent unit on each signal.
###### Abstract  
To address the issue of GNSS receivers being unable to function properly in tunnels due to the loss of Global Navigation Satellite System (GNSS) signals, this paper proposes a two-dimensional differential positioning system for tunnel environments based on dual leaky coaxial (LCX) cables with GNSS signal frequency relay forwarding. The system receives mixed GNSS signals from open environments and utilizes the frequency selection capabilities of the MAX2769E chip to separate and generate radio frequency signals at different frequencies corresponding to GPS, BDS, and GLONASS. These signals are then used to drive three ports of the LCX cables, which are laid in parallel within the tunnel. By leveraging the uniform radiation characteristics of the LCX cables, stable GNSS signal coverage is achieved throughout the tunnel. On the receiving end, the GNSS receiver achieves two-dimensional positioning by utilizing inter-satellite pseudorange differences and reference point error correction. The simulation results indicate that the dual T-shaped radiating LCX cables configuration offers excellent positioning accuracy and noise resistance, achieving meter-level positioning accuracy in tunnel environments.  
two-dimensional positioning; tunnels; satellite navigation; frequency division forwarding; leaky coaxial cables; differential positioning +
Footnote †: journal: Applied sciences  
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Footnote †: journal: Applied sciences  
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Footnote 0: Copyright** © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
## 3 Results
### Direct PCA and PDR  
Conventional PCA and PDR were applied to evaluate overall class separations without considering any confounding factors. Figure 2D-F shows the first two largest scores of the UV-Vis spectra using conventional PCA. The score plots are marked by EVOO origin, adulteration level, and adulteration type, so that the impact of these three factors on adulteration spectrum can be easily observed. Figure 3 shows the PDR mapping obtained through conventional PDR processing. The overall degree of discrimination can also be estimated by the average PDRs. Generally, in PDR mapping, each block corresponds to PDRs from any pair of classes, while these classes can be defined as any factors or outputs. The degrees of separations of the entire dataset can be observed in an intuitive way. As a result, the internal separation of the three factors of EVOO origin, adulteration level, and adulteration type are respectively displayed.  
As shown in Figure 2E, samples from different EVOO origins were observed to be significantly separated, indicating that the EVOO origin dominated the total variance in the model. Due to the influence of the EVOO origin, there is no significant aggregation between any samples of the same adulteration level or adulteration type, indicating relatively little influences on the UV-Vis spectra. It can be observed from Figure 3 that most PDRs of the three factors are less than 1.5, indicating a poor separation. To sum up, the differences between the producing origin of EVOO significantly affect the UV-Vis spectra. The differences between the adulteration level and the adulteration type remained undetectable, possibly due to the dominating affect by the origin. The conventional PCA and PDR cannot distinguish each factor directly. Therefore, further data treatments by ANOVA to remove the cofounding effects arising from multiple factors are necessary.
# OTFS: A Potential Waveform for Space-Air-Ground Integrated Networks in 6G and Beyond
### Contribution  
This research evaluates and compares the performance of OFDM and OTFS modulation schemes in extreme environments characterized by multiple propagation paths and high mobility, as expected in SAGINs for 6G and beyond. Unlike previous studies that primarily analyze OTFS and OFDM in terrestrial or low-mobility scenarios, this work extends the analysis to highly dynamic SAGIN environments, covering speeds ranging from 20 km/h (V2X, cars) to 2000 km/h (jets and UAVs). Static channels without Doppler effects were also considered.  
This research employs 100 GHz carrier frequency and 480 kHz subcarrier spacing, to model THz communication in 6G. By evaluating bit error rate (BER) performance under varying SNR, modulation orders (BPSK, QPSK, and 8-PSK), Doppler shifts, and mobility conditions, this study provides insights into the resilience of OTFS against severe channel impairments. The results highlight OTFS's ability to mitigate Doppler effects and maintain signal integrity, making it a strong candidate for high-mobility 6G applications in SAGINs. These findings contribute valuable insights into the suitability of these modulation techniques for dynamic, high-mobility environments in SAGINs to develop more resilient communication systems for 6G and beyond.  
The rest of this research paper is organized as follows. Section 2 comprehensively reviews the related literature on OFDM, OTFS, and SAGIN. Section 3 describes the system model employed in this research. The received signal and channel model are described, along with the principles of OFDM and OTFS modulation and demodulation. Section 4 presents the results and discussion, while Section 5 provides a conclusion and outlines future work.
# Light Transmission in Fog: The Influence of Wavelength on the Extinction Coefficient
## Theory and Definitions: Light and Fog
### Light Attenuation Due to Fog
This moderate resolution atmospheric transmission model solves the radiative transfer equation [14] by computing line-of-sight (LOS) atmospheric spectral transmittances and radiances for participating media (molecular and clouds):  
Figure 1:1  
where \(L_{\lambda}(r,u)\) is the spectral radiance for the wavelength \(\lambda\) at point \(r\) and in direction \(u\), expressed in W\(\cdot\)m\({}^{-2}\cdot\)sr\({}^{-1}\cdot\)m\({}^{-1}\), and \(d\Omega_{i}\) is the elementary solid angle. The terms \(\beta(\lambda)\) and \(k(\lambda)\) are respectively the scattering coefficient and the extinction coefficient given by the Mie theory. The function \(\phi\) is the scattering phase function making it possible to take into account energy gain by in-scattering [14]. In the MODTRAN model, the medium is modeled in one dimension: it is assumed to be homogeneous in the other two dimensions. The software computes the spectral radiance received by an observer from the black body radiance emitted by a source. Users set the source temperature, the Mie coefficients of the participating media (including the scattering phase function), and the distance between the source  
Figure 1:2 Extinction efficiency factor of spherical particles according to Mie theory. (**a**) As a function of wavelength; (**b**) as a function of the diameter. and the observer. In this work, we used the black body source, whose spectrum is plotted in Figure 4, and a molecular composition of a typical continental atmosphere at ground level. It is important to note that when we omit the second term of the right-hand side of the Equation (10), it goes back to our Mie model. This first section presented the most common modeling of the propagation of light in fog. It takes into account DSD and wavelength to estimate an extinction coefficient. It is also important to look at direct measurements of this extinction coefficient.
## 3 Results and Discussion
### Kinetic Measurements
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Extracts of Lyophilized** & & & & & \\
**Sea Buckthorn (\% \(w/w\))** & **pH Initial** &
\begin{tabular}{c} **V\({}_{\text{max}}\)** \\ **(10\({}^{-3}\) upH/min)** \\ \end{tabular} & **pHV\({}_{\text{max}}\)** & **T\({}_{\text{pH5}}\) (min)** & **T\({}_{\text{pH4.6}}\) (min)** \\ \hline
0 & 6.6 \(\pm\) 0.21 \({}^{\text{a}}\) & 8.9 \(\pm\) 1.17 \({}^{\text{b}}\) & 6.15 \(\pm\) 0.15 \({}^{\text{a}}\) & 308.00 \(\pm\) 50.48 \({}^{\text{a}}\) & 421.00 \(\pm\) 1.22 \({}^{\text{a}}\) \\
0.5 & 6.29 \(\pm\) 0.18 \({}^{\text{b}}\) & 11.22 \(\pm\) 0.39 \({}^{\text{a}}\) & 5.90 \(\pm\) 0.17 \({}^{\text{a}}\) & 227.00 \(\pm\) 12.29 \({}^{\text{b}}\) & 337.22 \(\pm\) 2.56 \({}^{\text{b}}\) \\
1 & 6.02 \(\pm\) 0.11 \({}^{\text{c}}\) & 6.6 \(\pm\) 2.01 \({}^{\text{c}}\) & 5.64 \(\pm\) 0.25 \({}^{\text{b}}\) & 266.00 \(\pm\) 21.07 \({}^{\text{b}}\) & 338.04 \(\pm\) 1.67 \({}^{\text{b}}\) \\
2 & 5.82 \(\pm\) 0.09 \({}^{\text{d}}\) & 8.00 \(\pm\) 0.33 \({}^{\text{b}}\) & 5.23 \(\pm\) 0.02 \({}^{\text{c}}\) & 175.00 \(\pm\) 13.88 \({}^{\text{c}}\) & 243.00 \(\pm\) 2.08 \({}^{\text{c}}\) \\
3 & 5.58 \(\pm\) 0.13 \({}^{\text{e}}\) & 7.00 \(\pm\) 0.00 \({}^{\text{b}}\) & 5.13 \(\pm\) 0.14 \({}^{\text{c}}\) & 165.00 \(\pm\) 3.22 \({}^{\text{c}}\) & 241.39 \(\pm\) 1.59 \({}^{\text{c}}\) \\ \hline \end{tabular}  
* \({}^{*}\) values with different superscript letters indicate statistical differences among treatments (\(p<\) 0.05). All measurements were conducted in triplicate.  
\end{table} 
Table 1: Measurement of the initial pH, V\({}_{\text{max}}\): the maximum acidification rate; pHV\({}_{\text{max}}\) the pH at the maximum acidification rate; T\({}_{\text{pH5}}\), the time when pH value is equal to 5; T\({}_{\text{pH4.6}}\), the time when pH value is equal to 4.6, in yogurts enriched with extracts of lyophilized sea buckthorn (0%, 0.5%, 1%, 2%, 3% \(w/w\)). with 2% and 3% \(w/w\) extracts of LSB were the fastest ones with T\({}_{\text{pH4.6}}\) 243.00 \(\pm\) 2.08 and 241.39 \(\pm\) 1.59 min, respectively. As mentioned above, the initial pH of samples at fermentation time T = 0 min was decreased as the concentration of LSB extracts increased. This could be explaining the smallest needed fermentation time for the yogurts with 2% and 3% \(w/w\) LSB extracts. Lactic acid bacteria are also influenced by the presence of extracts of LSB in milk as this extract could provide bacteria with many carbon sources such as phytosterols, phenolic acids, glucose, etc. [29], necessary for the acceleration of fermentation time due to the increase in lactic acid bacterial growth. Other researchers have found analogous results when they incorporated red ginseng extract in yogurt [30], and green tea powder [28]. Also, this research work agrees with our previous study where guarana aqua extracts reduced the fermentation time and influenced starter bacteria [16].
###### Abstract  
The rising demand for natural and safe products has increased interest in essential oils (EOs) as alternatives to synthetic preservatives. EOs could be encapsulated in active packaging or incorporated in nano-emulsion systems and help extend food shelf life by inhibiting the growth of pathogens. _H. officinalis_ and _Agastache foeniculum_ (_Lamiaceae_) are widely used in food and beverages. This review aims to explores their potential food applications, focusing on their antimicrobial activities, chemical compositions, and toxicity. _H. officinalis_ EO mainly consists of oxygenated monoterpenes (27.32-92.25%), with 1,8-cineole, isopinocamphone, and pincomamphone as key compounds. It also contains monoterpene hydrocarbons (3.84-67.24%), including \(\beta\)-pinene, \(\beta\)-phellandrene, and \(\beta\)-ocimene. _A. foeniculum_ EO is rich in phenylpropanoids (22.39-84.67%), primarily estragole (3.2-94.89%) and methyl eugenol, along with oxygenated monoterpenes (0.08-54.51%), mainly mentone (31.58-34.3%). _H. officinalis_ EO exhibited antimicrobial activity
# Mission-Oriented Real-Time Health Assessments of Microsatellite Swarm Orbits
## 4 Simulation and Analysis  
To verify the effectiveness of the evaluation algorithm, this paper analyzes the health status of the swarm in the context of the task for stitching the main mirror of a space telescope by a swarm of stars in orbit, and the task scenario is shown in Figure 3. The parent platform releases modular microsatellites in the target orbit, and the swarm performs the task of stitching the primary mirror, which eventually changes into a space telescope from the initial disorderly distribution. This process can be divided into three stages: the initial stage, the stitching procedure and the completion of the stitching. The dashed box indicates the relationship between the single satellite, the intersatellite communication topology, and the swarm effectiveness.
## 4 Cryptography and Wireless Security
The advantage of using threshold cryptosystems in MANET is that no single node needs to take the responsibility of storing the master secret and being available and reachable all the time. The distribution of secrets among a number of nodes effectively _decentralized_ the trust management without compromising the security. We refer readers to the literature for more discussion on applying threshold cryptography to securing decentralized wireless systems [57, 58, 59, 60],