esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Binary Mn-Based Metal Oxide Catalysts
### MnO\({}_{x}\)-TiO\({}_{2}\) Catalysts
This mechanism involves the activation and conversion of O\({}_{2}\) linked to nitro species conversion. The alteration of bidentate nitrate weakens the O-O bond in O\({}_{2}\), facilitating its breakage and NO\({}_{2}\) formation, with the rate-determining step of NO oxidation being the change in bidentate nitrate. Zeng et al. [33] prepared various MnO\({}_{x}\)-TiO\({}_{2}\) catalysts with different Mn/Ti ratios via wet impregnation. They examined the N\({}_{2}\)O formation in the process of NH\({}_{3}\)-SCR of NO\({}_{x}\) from a catalyst perspective. The results indicated that MnO\({}_{x}\) and TiO\({}_{2}\) act as the primary redox and acidic sites, respectively, in the NH\({}_{3}\)-SCR denitration reaction on the MnO\({}_{x}\)-TiO\({}_{2}\) catalyst. With an increasing Mn/Ti ratio, the MnO\({}_{x}\) species layer on the TiO\({}_{2}\) support expands, increasing redox sites while decreasing surface acid sites. These changes lead to non-selective NH\({}_{3}\) oxidation on MnO\({}_{x}\) and excessive NH\({}_{3}\) activation at the Mn-Ti interface, resulting in the formation of N\({}_{2}\)O via 2 NH\({}_{3}\) + 2O\({}_{2}\)\(\rightarrow\) N\({}_{2}\)O + 3H\({}_{2}\)O and 4 NO + 4 NH\({}_{
## 4 Overview of Advanced Ceramic Materials as Catalysts
Each of these methods offers specific advantages, allowing the fabrication of customized ceramic catalysts to meet the demands of various catalytic systems [132; 133]. Ceramic catalysts have found widespread use in industrial processes ranging from energy production to pollution control [120; 121; 122; 123; 124; 125; 126; 127; 128; 129; 130; 131; 132; 133; 134]. One of the most prominent applications is in the automotive sector, where ceramic catalysts are integral to catalytic converters that reduce harmful emissions. Their ability to withstand high temperatures and corrosive environments makes them ideal for operation within vehicle exhaust systems. In the energy sector, ceramic catalysts are utilized in hydrogen production and fuel cells, where their robustness and capacity to support metal nanoparticles enhance overall efficiency. Their role in biofuel production is also gaining momentum, as they facilitate the conversion of biomass into energy with high efficiency and lower environmental impact. Furthermore, ceramic catalysts are essential in environmental remediation efforts, particularly in treating industrial waste streams. Their chemical and thermal stability enables them to drive reactions that break down hazardous pollutants into less toxic byproducts. Additionally, ceramic materials are employed in the synthesis of fine chemicals and in petrochemical processes, where they offer a cost-effective, scalable, and efficient catalytic platform [134]. Despite their many advantages, ceramic catalysts may face limitations in terms of structural flexibility and functional group diversity. In such contexts, materials like MIPs can offer more tailored molecular recognition capabilities. A comparative analysis of ceramic catalysts and MIPs underscores the need to match material properties with the specific requirements of the catalytic process in question.
# A New One-Dimensional Compound Chaotic System and Its Application in High-Speed Image Encryption
## 2 The Proposed New One-Dimensional Chaotic Map
### Logistic Map (LM)  
The mathematical model of the logistic map (LM) system is shown in Equation (1) [10].  
\[x_{n+1}=\text{f}(x_{n})=bx_{n}(1-x_{n})\,,b\in(0,4]. \tag{1}\]  
In Equation (1), \(x_{n}\) represents the state values of the system at the \(n\)-th discrete time point (\(n=0\), \(1\),... ), \(x_{0}\) represents the initial state value of the system. \(b\) is the control parameter of the system. When \(b\in[3.57,4]\), the function \(\text{f}(x_{n})\) is a map: \(x_{n}\in(0,1)\to x_{n+1}\in(0,1)\), and the iterative sequence \(\{x_{0},\,x_{1},\,x_{2},\,\dots\,\,\}\) generated by the initial state value of \(x_{0}\in(0,1)\) is aperiodic (or the period is infinite and the state value is not repeated) and is seemingly an irregular (random) sequence. However, the state values in the sequence are bounded (that is, the value of \(x_{0}\in(0,1)\), \(x_{n}\) will not run out of the range of (0, 1), which is called the attractor). Figure 1a is a graph of the Lyapunov exponent of the logistic map changing with parameter \(b\). Among them, the formula for calculating the Lyapunov exponent \(LE\) of 1D maps is shown in formula (2).  
\[LE=\lim_{N\to 0}\frac{1}{N}(\sum_{n=0}^{N-1}\log_{c}|\text{f}(x_{n})\,|) \tag{2}\]In formula (2), \(\mathsf{f}^{\prime}(x_{n})\) is the value of the derivative function of the mapping function at \(x_{n}\). In actual calculation if the value of \(N\) is large enough, the Lyapunov exponent will converge to a fixed constant. As long as it is found that the Lyapunov exponent starts to stabilize (no longer changes with the increase of \(N\)), then \(N\) can be regarded as satisfying the condition of \(N\rightarrow\infty\). A positive Lyapunov exponent means chaos. Figure 1 shows the variation of the Lyapunov exponent and bifurcation diagram of the system with parameter \(b\). As can be seen from Figure 1a, when the parameter \(b\) satisfies \(b\in[3.57,4]\), the system goes into a chaotic state. When \(b=4\), the maximum Lyapunov exponent occurs, which is \(LE_{\max}=0.6931\). Figure 1b is the bifurcation diagram of Logistic map changing with parameter \(b\), which is consistent with the phenomenon revealed in Figure 1a; that is, when the parameter \(b\) satisfies \(b\in[3.57,4]\), the value number of the system state is infinite, and with the increase of \(b\), the value of the system state gradually fills the whole range of \((0,1)\).
# Study of Urban Unmanned Aerial Vehicle Separation in Free Flight Based on Track Prediction
## 6 Conclusions  
1. The research problem in this paper is a new problem brought by new things. At present, at home and abroad, including ICAO and JARUS, the operating separation standard for urban logistics UAVs has not been formulated. Based on the JARUS requirements for UAV operation aiming at the autonomous separation maintenance ability of UAVs, a method to calculate the required separation between UAVs is proposed.
2. As the basis of autonomous separation maintenance ability of UAVs, a two-stage track prediction method is proposed, which breaks through existing track prediction methods. By using K-means, track classification and identification are realized, serving as macro-level track classification. For similar tracks, the Adam-LSTM model is employed to achieve refined track prediction, serving as micro-level track prediction. Compared to the traditional model, the method proposed in this paper can more accurately achieve track prediction.
3. There is uncertainty in the calculation of the conflict frequency. The calculation of the conflict frequency in the model is directly related to traffic density. As the flow within a unit airspace increases, the value will also increase, thereby ultimately affecting the required separation. The selection of the TLS also has a significant impact on the calculation of separation. The relatively strict TLS provided by JARUS is adopted in this paper. With the accumulation of UAVs' operational experience and the development of the industry, the TLS will be larger; that is, the required separation value will gradually decrease.  
Conceptualization, Y.Z. and J.Z.; methodology, F.L.; software, Z.L.; validation, X.L., J.Z., F.L. and Z.L.; formal analysis, J.Z.; investigation, Y.Z.; resources, F.L.; data curation, Z.L.; writing--original draft preparation, J.Z.; writing--review and editing, X.L.; visualization, Z.L.; supervision, J.Z.; project administration, J.Z.; funding acquisition, J.Z. and F.L. All authors have read and agreed to the published version of the manuscript.  
This research is funded by the National Key Research and Development Plan Project (2022 YFB4300904).  
Not applicable.  
The original data in the study is not easy to provide because it involves the privacy of the provider.  
Figure 14: The change trend of the required separation.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders have 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.
## 3 Materials and Methods  
### Cultural Heritage Sites: Wat Maha That, Thailand  
The Wat Maha That site, including the site of the studied pagoda, is located at Ayutthaya, which was historically one of Thailand's capitals and is now a UNESCO World Heritage site. The city is located 85 km directly north of Bangkok. A historical park forms an important part of the city and is home to several culturally significant ancient temples. The historical park was granted UNESCO World Heritage status in 1991. This was justified under criteria III, which held that the site could be deemed an excellent witness to a period in which a true national form of Thai art was developed [29].  
As shown in Figure 1, Wat Maha That is situated east of the Ayutthaya Island. It was a temple that held royal status and was considered the most sacred during the Ayutthaya period. The main Mahathat Chedi (bell-shaped pagoda) and Mahathat Prang (towering corn-cob pagoda) pagodas (Figure 2) contain relics from [PERSON]. At one point in history, these were temples where the Supreme Patriarch (head of the order of Buddhist monks in Thailand) resided.  
The Ayutthaya Chronicle [29] reported that the construction of the Mahathat Chedi commenced in 1374 during the reign of [PERSON] and was completed under the reign of [PERSON] [PERSON]. The foundation of the pagoda collapsed during the reign of King [PERSON], but was later reconstructed under King [PERSON]'s reign. Finally, the temple was destroyed and burned during the Burmese invasion in 1767 and has since been left in ruins.  
Figure 1: Aerial overview of the study area, Wat Maha That, Ayutthaya Island, Ayutthaya Province.
###### Abstract  
Photosynthesis and its sensitivity to the changing environment in alpine regions are of great significance to the understanding of vegetation-environment interactions and other global ecological processes in the context of global change, while their variations along the elevation gradient remain unclear. Using solar-induced chlorophyll fluorescence (\(SIF\)) derived from satellite observations, we discovered an increase in solar-induced fluorescence yield (\(SIF_{yield}\)) with rising elevation in Tibetan meadows in the summer, related to the altitudinal variation in temperature sensitivity at both seasonal and interannual scales. Results of the altitudinal patterns of \(SIF_{yield}\) demonstrated higher temperature sensitivity at high altitudes, and the sensitivity at the interannual scale even exceeds that at seasonal scale when the elevation reaches above 4700 m. This high-temperature sensitivity of \(SIF_{yield}\) at high altitudes implies potential adaptation of alpine plants and also indicates that changes in photosynthesis-related physiological functions at high altitudes should receive more attention in climate change research. The altitudinal \(SIF_{yield}\) patterns revealed in this study also highlight that variations in temperature sensitivity should be considered in models, otherwise the increasing trend of \(SIF_{yield}\) observations can never be discovered in empirical simulations.  
solar-induced chlorophyll fluorescence; \(SIF_{yield}\); alpine vegetation; altitudinal patterns 2021
## 2 Syntchitic Aperture Radar
### Lfm-Cw Sar Raw Signal Modeling  
Modeling synthetic aperture radar signals with continuous wave linear frequency modulation is similar to the case of a pulsed SAR system; however, one way to quickly obtain the SAR signal is by multiplying the transmitted signal by the conjugate of the backscattered signal, as described in Equation (20), taking into account that \(s_{tc}(t)\) is the signal transmitted by the LFM-CW SAR system and the backscattered signal received by the antennas of said system is \(s_{rc}(t)\)[14; 19; 22].  
\[s_{tc}(t)=exp\left(j\Big{(}2\pi f_{0}t+\pi k_{r}t^{2}\Big{)}\right) \tag{18}\]  
\[s_{rc}(t)=exp\left(j\Big{(}2\pi f_{0}(t-\Delta t)+\pi k_{r}(t-\Delta t)^{2} \Big{)}\right) \tag{19}\]  
Figure 4: Tx and Rx in SAR system [19].  
Figure 3: Removal of the \(\frac{1}{2}cos(4\pi f_{0}t+\varnothing(t))\) and \(-\frac{1}{2}sin(4\pi f_{0}t+\varnothing(t))\) signal located at the frequency \(2w_{0}\) using the low-pass filter (LPF) [19].  
The equation of the SAR raw signal with continuous wave linear frequency modulation is shown in Equation (21), similar to that in [14; 19; 22]. This equation is used in SAR digital signal processing to focus images.  
\[s_{dc}(t)=s_{tc}(t)s_{rc}^{*}(t) \tag{20}\]  
\[s_{dc}(t)=exp\left(j\left(2\pi f_{0}\Delta t+2\pi k_{r}t\Delta t-\pi k_{r} \Delta t^{2}\right)\right) \tag{21}\]  
In an LFM-CW SAR system, two antennas are used, one to transmit \((T_{x})\) and the other to receive \((R_{x})\) chirp signals, whose transmitted signal and the backscattered signal are shown in Figure 5. On the other hand, the LFM-CW SAR systems can use two or more transmitting and receiving antennas, and this depends on the application. Figure 6 shows the sampling of the backscattered signal by the slow time frequency also called pulse repetition frequency \((PRF=1/PRI)\).
# A Novel Cell-Based Adaptive Cartesian Grid Approach for Complex Flow Simulations
## 3 Numerical Methods  
### Governing Equations  
In compressible RANS simulations, Favre-averaged Navier-Stokes equations are used:  
\[\frac{\partial\rho}{\partial t}+\frac{\partial}{\partial x_{j}}(\rho u_{j})=0 \tag{10}\]  
\[\frac{\partial}{\partial t}(\rho u_{i})+\frac{\partial}{\partial x_{j}}(\rho u _{i}u_{j})=-\frac{\partial p}{\partial x_{i}}+\frac{\partial\tau_{ij}}{ \partial x_{j}} \tag{11}\]  
\[\frac{\partial}{\partial t}(\rho E)+\frac{\partial}{\partial x_{j}}\big{[}( \rho E+p)u_{j}\big{]}=\frac{\partial}{\partial x_{i}}(u_{i}\tau_{ij}-q_{j}) \tag{12}\]  
where \(u_{i}\), \(\rho\), \(p\), and \(E\) are the mean velocity components, the density, the pressure, and the specific internal energy respectively. The Reynolds stress tensor is derived using the Boussinesq approximation:  
\[\tau_{ij}=2(\mu+\mu_{t})\big{[}S_{ij}-S_{kk}\delta_{ij}\big{]},\,\,S_{ij}= \frac{1}{2}(\frac{\partial u_{i}}{\partial x_{j}}+\frac{\partial u_{j}}{ \partial x_{i}}) \tag{13}\]  
where \(\mu\) and \(\mu_{t}\) are the molecular dynamic viscosity and the eddy viscosity, respectively. Furthermore, the heat flux is defined as follows:  
\[q_{j}=-c_{p}(\frac{\mu}{P_{T}}+\frac{\mu_{t}}{P_{t}})\frac{\partial T}{ \partial x_{j}} \tag{14}\]  
Figure 5: Newly generated grid assignment.  
where \(c_{p}\) is the specific heat at constant pressure and \(T\) is the temperature. The laminar Prandtl number \(Pr\) is 0.72 and the turbulent Prandtl number \(Pr_{t}\) is 0.9.  
The solver presented in this paper is founded on the finite difference method, with the inviscid terms being discretized through the employment of the Roe approximate Riemann solver in conjunction with a third-order accurate MUSCL scheme. The discretization of viscous terms is achieved via a second-order central difference scheme. For time discretization, an explicit third-order Runge-Kutta scheme is utilized for unsteady problems, whereas steady problems employ the lower-upper symmetric Gauss-Seidel implicit advancement scheme.
## 2 Methodology
### Study Cases  
Electrical disturbances can result from environmental factors, external events, or internal operational failures. These disruptions manifest mainly through voltage variations, such as sags, surges, or complete interruptions, which compromise system stability, accelerate equipment wear, and reduce power quality.
# 40 W All-Fiber Er/Yb MOPA System Using Self-Fabricated High-Power Passive Fiber Components
## 1 Introduction
It is important to achieve a low transmission loss of the pump radiation, but most of all, it is essential to achieve a very low transmission loss of the signal radiation, which is more expensive than pump radiation (according to price per W). It is very difficult to achieve high transmission of the signal in the case of (N + 1) \(\times\) 1 configuration, especially when high taper ratio (TR) is needed [31; 36]. Thus, research teams applied thermally expanded core (TEC) techniques [29], internal MFAs [30], or by using a fiber with a clad with a reduced diameter to decrease the required TR [36]. In this work, we demonstrated a three-stage MOPA system performed in the all-fiber construction, providing more than 40 W of CW output power at the wavelengths of 1555 nm and 1563 nm. In our MOPA setup, we used self-fabricated high-power passive fiber components: a pump and signal power combiner in a configuration (6 + 1) \(\times\) 1 for high-power operation (using LMA-type signal fibers) described in Reference [31], a pump and signal power combiner in a configuration (5 + 1) \(\times\) 1 for medium-power operation (using SM-type signal fibers) described in Reference [36] and an MFA for connection of SM-type fiber with LMA-type fiber (e.g., for connection of the medium- and high-power amplifier stage). All the components were fabricated using Large Diameter Splicing System (LDS): three-electrode advanced fiber splicer which enabled tapering of single fibers or fiber bundles, splicing and cleaving fibers with diameters from 80 \(\upmu\)m up to 2 mm [34]. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Ref.** & **Gain Medium** & **Output Power** & **Pump Power** & **Slope Efficiency** & **System Type** \\ \hline \multicolumn{5}{c}{Construction with bulk optics} \\ [7] & LMA E3* & 67 W @ 1570 nm & 225 W @ 976 nm & 30\% & MOPA \\ [12] & DC LMA Er3+/Yb3+ & 297 W @ 1567 nm & 1.2 kW @ 975 nm & 40\% \(\rightarrow\) 19\% & Laser \\ [13] & DC LMA Er3+/Yb3+ & 151 W @ 1563 nm & 470 W @ 975 nm & 35\% \(\rightarrow\) 29\% & MOPA \\ \hline \multicolumn{5}{c}{All-fiber amplifier construction + free-space seed signal coupling} \\ \multicolumn{5}{c}{} \\ [14] & DC LMA Er3+/Yb3+ & 110 W @ 1556 nm & 250 W @ 940 nm & 46\% & MOPA \\ \hline \multicolumn{5}{c}{} \\ [23] & PM DC LMA Er3+/Yb3+ & 100 W @ 1566.5 nm & 360 W @ 965 nm & 29\% & MOPA \\ \multicolumn{5}{c}{} \\ [24] & PM DC LMA Er3+/Yb3+ & 56.4 W @ 1550 nm & 150 W @ 976 nm & 37\% & MOPA \\ \multicolumn{5}{c}{} \\ [25] & DC LMA Er3+ & 103 W @ 1585 nm & 275 W @ 976 nm & 37\% & MOPA \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Most important parameters of mentioned MOPA (master oscillator power amplifier)/laser systems.
## Materials and Methods  
### Experimental Setup  
#### 2.1.1 Mass Spectrometric Systems  
In this study, two different REMPI-TOFMS systems were used. The first REMPI-TOFMS (PITOF 1) was described by [PERSON] et al. [32] and was custom-built by Stefan Kaesdorf GmbH, Munich, Germany. To minimize the influence of photoelectrons on the ionization process, a delayed extraction with a delay time of 100 ns was utilized. Two 8-Bit converters with different amplifications (DP210, Acqiris, Plan-les-Quates, Switzerland) were used to extend the dynamic range of the MS. In this setup, a tunable UV laser beam was generated from 1064 nm of a Nd:YAG (Continuum Surelite II, Amplitude Laser Group, San Jose, USA; pulse duration = 5 ns; repetition rate = 10 Hz) using an optic parametric oscillator (VISIR 2, GWU-Lasertechnik, Erftstadt, Germany). For the purpose of this study, 266 nm (0.29 mJ/pulse) and 248 nm (0.28 mJ/pulse) were used. The laser intensity was varied by a variable neutral density attenuator (NDM2/M, Thorlabs Inc.; Newton, USA) placed in front of the ion source. A focusing lens (focal length = 350 mm) was used to increase the power density of the irradiating laser beam. Additionally, by varying the position of the lens relative to the ion source, the examinable intensity range was extended. The focal point inside the ion source was determined by monitoring the total ion current (TIC) as a function of the lens location. The determination of relPICS were performed at three different lens positions (3.5 cm, 2.5 cm, and 0 cm relative to the focal point). The power density of the focused laser beam was calculated with the assumption of a Gaussian beam shape. By monitoring the pulse energy behind the ion source, the power density was recalculated with an attenuation factor of 10% (energy loss of the UV window).  
The second mass spectrometric system (PITOF 2) was a compact TOFMS custom-built by Stefan Kaesdorf GmbH, Munich, Germany. Mass spectra were recorded by two 8-Bit converter cards (AP240, Acqiris, Plan-les-Quates, Switzerland). UV radiation of 266 nm wavelength was produced by the fourth harmonic generation of a Nd:YAG laser (Big Sky Ultra, Quantel, Les Ulis, France; repetition rate = 20 Hz; pulse duration = 10 ns). In contrast to PITOF 1, an unfocused Gaussian laser beam was expanded to an estimated diameter of 3 mm. The averaged power density was calculated from the pulse energy behind the ion source (attenuation factor = 7%).
## 4 Generation Scheme
### Phase Rotation Generation  
The scheme of phase rotation generation is shown in Figure 5. According to the value of the signal components, frequency-hopping pattern, and time index, \(\Theta(t)\), as the input of the \(A\cos[\Theta(t)]\) and \(A\sin[\Theta(t)]\) generator, can be obtained from the phase LUT. Then, the in-phase output I(\(t\)) and quadrature-phase output Q(\(t\)) are modulated on the cosine-phased and the sine-phased radio-frequency carriers, respectively. Finally, they are combined to obtain \(s_{RF}(t)\).  
The example signal ACE-FHBOC(10:1:1,1), shown in Figure 1, is generated via the direct generation scheme with decomposed subcarriers. The simulated and theoretical normalized ACF results are compared in the top panel of Figure 6, and the simulated and theoretical PSD results are compared in the bottom panel.  
Figure 4: Direct generation with decomposed subcarriers.  
Figure 5: Phase rotation generation.
# Axial and Bending Bearing Capacity of Double-Steel-Concrete Composite Shear Walls
## 4 Derivation of the Axial and Bending Bearing Capacity
### Failure Modes  
Based on the above assumptions and simplification, the ultimate equilibrium method is applied to derive the axial and bending bearing capacity formula of the SC wall. The balanced failure of the SC wall is defined so that the steel plates in tension reach the yield strength simultaneously when the concrete in the compressive zone reaches the ultimate compressive strain. The relative height of the concrete compressive zone is \(\xi_{b}\), which can be calculated by Equation (7) referring to [36]. When \(\xi\leq\xi_{b}\), the steel plates in tension could reach the yield strength when the wall breaks, and the failure mode of the SC wall is called large eccentric compression failure. When \(\xi>\xi_{b}\), the steel plates in tension cannot reach the yield strength when the wall breaks; the failure mode of the SC wall is called small eccentric compression failure (as shown in Figure 7).  
\[\xi_{b}=\frac{\beta_{1}}{1+\frac{f_{y}}{\varepsilon_{\mathrm{d}}\xi_{s}}} \tag{7}\]  
Figure 6: Simplification of the cross section for SC walls. (**a**) The general section structure; (**b**) Cross section simplification.