esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Technical Overview
### BL Adaptation Algorithm  
The adaptation algorithm implements the integration of auditory perceptual features into the ARA mix (see Figure 8). Its input consists of the calculated \(BL\) matrices for the input signals of the real environment and the virtual one.  
The task of the \(BL\) adaptation algorithm is to estimate the \(BL\) difference between the same bands of the virtual and real components, as well as to calculate and apply the correlation coefficient of the \(BL\) difference of each band to the \(BL\) of all bands. The following functions present the methodology of the \(BL\) adaptation algorithm.  
1. Estimation of the \(BL\) difference in each n frequency bands. The algorithm estimates the difference between the same band of the real and virtual components. \[BLV_{n}=R_{n}-V_{n}\] (1)
2. Estimation of the \(BL\) environment matrix average loudness of \(n\) frequency bands of the virtual signal.  
Figure 8: This figure presents the architecture of the binaural loudness band adaptation.  
Figure 7: This figure presents the methodology of \(BL\) prediction matrix for real and virtual environments.  
\[BLVirtualBandsAVG=\frac{\sum_{l}^{n}V_{n}}{n} \tag{2}\]
3. Estimation of the correlation coefficient of the \(BL\) band according to the average \(n\)\(BL\) bands of the virtual signal. This function estimates the correlation coefficient of the \(BL\) difference of each band to the \(BL\) average energy of all frequency bands, which the model applies to each virtual band. \[BLCorrelationV_{n}=\frac{BLV_{n}}{BLVirtualBandsAVG}\] (3)
4. The estimation of the \(BL\) constant increases or decreases compared to the initial \(BL\) of the specific frequency band of the virtual signal. This procedure smooths the difference of each virtual band to the real one with respect to the average \(BL\) of all n bands. Thus, any distortions are eliminated, and the changes that follow the original virtual spectrum form without any loss in originality. \[VBLConst_{n}=BLV_{n}\times BLCorrelationV_{n}\] (4)
5. Finally, the \(BL\) constant of each band is integrated. The final coefficient for each virtual band is sent to be applied by the algorithm in the corresponding filter bank's signal. \[V_{BLn}=VBLConst_{n}+V_{n}\] (5)  
The integration of only the difference between the real and virtual energy bands in terms of \(BL\) evokes unpredictable changes in the virtual environment. To define the bounds of this difference, the correlation coefficient was inserted. Tests during the implementation process have showed that the correlation coefficient improves the performance of the \(BL\) adaptation algorithm and produces a smooth audible result without distortions.
## 3 Results
### Long-Term Stability  
A comparison of surface modifications of WTBs with PSs of different mean diameters was examined. Figure 7a shows the long-term stability concerning water repellency. Initially, the contact angle was 120 deg, 105 deg, and 93 deg for PS\({}_{30}\)-, PS\({}_{515}\)-, and PS\({}_{800}\)-coated WTBs, respectively. PS\({}_{515}\)- and PS\({}_{800}\)-coated WTBs indicated a decrease in water repellency and eventually the same level as bare WTBs (53 deg). In contrast, PS\({}_{30}\)-coated WTBs indicated a constant contact angle (120 deg). Therefore, the long-term stability depended on the mean diameter of the PS.  
Next, the effect of the hydrophobicity of the polymer on long-term stability was examined. In this trial, polymer particles with 30-50 nm of diameter were used. A coating of PS\({}_{30}\) indicated the highest value of the contact angle of polymers. The water repellency for PS- and PMMA-coated WTBs and bare WTB remained almost constant for six years. The PVA- and PLLA-coated WTBs indicated a slight reduction in water repellency (Figure 7b).  
Together with this, the survival of WTBs is monitored in Figure 7c. First, the order of decremental numbers of WTBs was bare > PLLAs > PVAs > PMMA > PS. Second, 90%, 80%, 60%, 60%, and 30% of WTBs coated with PS, PMMA, PVA and PLLA and bare WTBs survived more than six years, respectively.  
Figure 6: A comparison of the contact angle of the WTB surface with the mean diameter of the polymer particles: (**a**) PS; (**b**) PMMA; (**c**) PVA; and (**d**) PLLA. White and gray keys represent WTBs modified at \(\varphi\) = 0.020 and 0.005, respectively. (**e**) The relationship between the contact angle limit of WTBs and logP values for each monomer. Dashed curve in (**a–d**) is a curve fitted with exponential relaxation.  
Thus, the coating of WTBs with polymer particles gave an impact on not only water repellency but also the stability of the WTBs. The water repellency depended on the diameter of the polymer particles and the logP value of the monomers.
## 2 Design of eVTOL  
The aircraft independently designed by our team is an eVTOL aircraft with a tandem wing layout and folding function, which has the advantages of a small floor area requirement and high flight efficiency. The structure is shown in Figures 1 and 2.  
Figure 1: Structure diagram of the aircraft.  
For low-speed aircraft, the main tail layouts are '+-shaped' and 'T-shaped' layouts. Compared to the +-shaped tail, the T-shaped tail has higher control efficiency and better heading stability when the wing stalls [8]. After comprehensively considering the interference between the wing and tail when the aircraft is folded and the maneuverability at low speed, the T-shaped tail is used in this paper.  
A lightweight structure was used in the fuselage to ensure smaller take-off and landing space. The folding and unfolding device is driven by servos. The dynamic layout is a tandem wing layout, the lift-drag characteristics of which change with the relative position and shape of the front and posterior wings [9]. Different layout schemes have different advantages and disadvantages, as shown in Table 1.  
The thrust power plant and lift power plant are electric power systems that combine batteries, motors, propellers or ducted fans, and control systems [10]. The thrust power unit uses the forward-pulling layout of the fuselage so that the lift coefficient is larger at a high angle of attack, which is beneficial in providing larger lift at low speed [11]. Vertical lift devices mainly include multi-rotor and ducted fans to reduce the noise and size of the aircraft and improve its efficiency. To make the wing structure more compact, ducted fans were placed at the wing tips to achieve vertical take-off and landing.  
In this paper, the components used to install ducted fans at the wing tips were studied. The study aims to reduce the weight of this component with minimal impact to its original shape and strength. Meanwhile, it was also expected that the weight loss process would be simple, fast, and easy to operate. Two commonly used lightweight schemes were formulated: manual setting honeycomb structure weight reduction and topology optimization calculation.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Layout Scheme** & **Advantages** & **Disadvantages** \\ \hline Both wings up & Large lift–drag ratio and good stability & The drag difference between the front and posterior wings is large and the efficiency is low \\ Both wings down & Low lift–drag ratio fluctuation & Small lift–drag ratio \\ One wing up and one wing down & Maximum lift–drag ratio & Relatively poor stability \\ \hline \hline \end{tabular}
\end{table}
Table 1: Advantages and disadvantages of different layout schemes.  
Figure 2: Photos of the aircraft.
# Forced Convection Heat Transfer for Stratospheric Airship Involved Flight State
## 3 Results and Discussions
### Formula Fitting  
#### 3.2.1 Horizontal State  
The horizontal state is the main state in a flight course. Thus, investigating the forced convection heat transfer around an ellipsoidal airship under horizontal state is important. Assume \(\alpha=0^{\circ}\), the values of \(Re\) and \(e\) are determined according to Table 2. The average heat transfer in terms of the \(Nu\) around an ellipsoidal airship can be written as a power law equation  
\[Nu=c_{1}Re^{c_{2}}e^{c_{3}} \tag{5}\]  
where \(c_{1}\), \(c_{2}\) and \(c_{3}\) are constant coefficients.  
Based on the data obtained from the aforementioned simulation results, a new correlation of Nu with a determination coefficient (r\({}^{2}\)) of 0.9996 and a root mean squared error (RMSE) of 2054 is proposed via MATLAB R2018a. The fitting points and fitting surface are shown in Figure 6.  
The values of the fitting parameters are listed in Table 3. The value of c\({}_{1}\sim\) c\({}_{3}\) is introduced into Formula (5) to obtain Formula (6)  
\[Nu=0.0161 Re^{0.8543}e^{-0.0454} \tag{6}\]  
where \(Re\in[1\times 10^{7},2.0\times 10^{8}]\) and \(e\in[2,6]\).  
The model of the non-fit points is numerically simulated to validate the correctness of Formula (6). Table 4 compares the values of \(Nu\) calculated by Formula (6) with those obtained from the simulation results. The calculated results agree well with those of the simulation results.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(e\) & \(Re\) &
\begin{tabular}{c} **Calculated Results by** \\ **Formula (6)** \\ \end{tabular} & **Numerical Results by CFD** & **Error/(\%)** \\ \hline
2.2 & \(1\times 10^{7}\) & 14,838 & 14,989 & 1.01 \\
3.75 & \(5.75\times 10^{7}\) & 64,543 & 63,685 & 1.33 \\
4.25 & \(5.75\times 10^{7}\) & 64,177 & 63,202 & 1.52 \\
5.3 & \(2.0\times 10^{8}\) & 184,295 & 18,4979 & 0.37 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Validation of Formula (6).  
Figure 6: Fitting points and fitting surface.
###### Abstract  
The TH-2 satellite system, including the TH-2A and TH-2B, is the first distributed interferometric synthetic aperture radar (InSAR) satellite system in China. During the in-orbit operation, the TH-2A satellite should perform three maneuvers per day to keep the formation flying geometry. We estimate those maneuvers in the precise orbit determination (POD) by the GPS and BDS2 measurements on board, respectively. The residuals of the POD show that the effects caused by orbital maneuvers can be well eliminated for both the GPS and BDS2 data. The precision of the BDS2-based POD is better than 8.0 cm in the three-dimensional direction (3D) compared with the orbit derived from the GPS observations. Such a precision level of the satellite orbit satisfies the InSAR mission requirement of the TH-2. In addition, the relative error of velocity changes is employed to evaluate the maneuver estimations by the POD using the regional navigation system of BDS2. The results show that the relative error of velocity changes between the GPS- and BDS2-based POD is less than 7.0%, which indicates that the maneuver performance extracted from the regional BDS2 data is as good as that extracted from the global GPS data. In the GNSS fused processing, we found that the independent receiver clock offsets should be taken into account, since the time tag corrections for the GPS and BDS2 observations collected on the TH-2 spaceborne receivers were different. The precision of the GPS and BDS2 (GC) combined single point positioning (SPP) can be improved by 12-14% compared with the GPS-only solution when the position dilution of precision (PDOP) of GPS exceeds three. The overlap comparisons of the GC combined orbits show that the internal orbit precision of the TH-2 satellites is better than 0.7 cm. However, the improvement of the GC combined POD result is only 3-4% with respect to the GPS-only solution, which is limited to the precision of the precise orbit and clock products of BDS2 at the present stage.  
Keywords:TH-2; precise orbit determination; maneuver assessment; BDS2; GPS +
Footnote †: journal:  
_Article_  
Precise Orbit Determination and Maneuver Assessment for TH-2 Satellites Using Spaceborne GPS and BDS2 Observations
## 1 Introduction
### Possible Role of Canada Goose in HPAI Transmission  
Large populations of Canada geese live across North America, but populations have specifically rebounded in Iowa over the past five decades. Millions of Canada geese reside in Iowa year-round, but they can intermingle with migrating waterfowl, given their instinct to seek out inland water [16]. Approximately 2.6 million Canada geese are harvested annually in North America, but they still have consistent population growth [12].  
Adult Canada geese have been observed to be generally resistant to H5N1 [17]. This might indicate that they are efficient vectors of H5N1 as they can sustain infection long enough to transmit the virus. A 2022 study live-caught wild North American geese from 2008-2018, and collected oral-pharyngeal, cloacal, and blood samples that were then compared with global reference sequences for the three HPAI subtypes (H16, H13, H5). The results indicated that Canada geese could be resilient hosts and vectors of H5 subtypes, as the phylodynamic analysis of H5N1 during the reference period indicated goose-associated mutations were present when duck samples were analyzed. While these results did not identify Canada geese as the specific species to seed H5 infections, the study did report that the species is abundant and attracted to farms where co-grazing with livestock can influence HPAI transmission [18].  
The current consensus is that _Anas platyhynchos_ (mallard ducks) are most likely to be primary reservoirs and vectors of HPAI due to their abundance and ability to continue migrating when infected. A recent study that evaluated mallard ducks' ability to migrate while infected compared the movement patterns of infected and uninfected birds. During the study's reference period (winter 2022), infected mallard ducks migrated slightly earlier but still migrated similar distances as uninfected birds. This suggests they had the potential to be effective dispersal agents for H5N1 during its initial introduction to North America [19]. While identifying a specific species of waterfowl impacting H5N1 transmission during this epizootic has yet to occur, there is enough evidence to state that waterfowl abundance is a current factor worth evaluating.
## 3 Application
### Probabilistic Seismic Demand Model and Correlation Analysis for Bridge Structure  
The probabilistic seismic demand model describes the relationship between structural seismic demand parameters and the ground motion intensity index. Based on the time history analysis of 10 random samples of isolated bridge ground motion, the relationship between the maximum response of dimensionless components and the ratio of required capacity \(\mu\) under each ultimate failure state and the seismic intensity PGA was obtained. Furthermore, the fitting result obtained by logarithmic regression analysis (that is, the least squares method used for regression analysis of seismic demand and ground motion parameters of structural components) was the probabilistic seismic demand model of the structure. The relationship between the demand capacity ratio \(\mu\) of each component of the bridge structure under different failure states and the peak seismic acceleration PGA can be expressed as follows:  
\[\ln(\mu)=A\ln(PGA)+B \tag{15}\]  
where, \(\mu\) is the requirement capacity ratio of the structure; \(A\) and \(B\) are regression coefficients.  
According to Equation (15), the regression analysis of each component of the bridge system under various failure states could be carried out, and the correlation analysis of the seismic demands of each component could be carried out according to the analysis results of nonlinear dynamic time history, and the relevant parameters of the seismic demands of the structural components of the bridge were obtained, as shown in Table 4.  
It can be seen from Table 4 that the correlation between pier and abutment collision was relatively weak, but the minimum correlation coefficient was still greater than 0.75. The results showed that the seismic requirements of each component of the bridge structure were significantly correlated.
# Integrated Model Selection and Scalability in Functional Data Analysis Through Bayesian Learning
## Appendix F Experiments
### Benchmark Simulation
We followed the simulation benchmark in [21] to select the number of components, focusing on Scenario 1 with 50 measurements per function (the densest data). Because LFRM does not estimate a mean function, we omitted the mean from the simulation run here. **The following hyperparameters of LFRM need to be determined:**  
* Gamma prior for white noise and correlated noise;
* Length scale;
* Number of basis functions;
* Number of iterations. LFRM, with its default white noise prior, correctly identified the white noise variance (true value 0.2) in all tests. We thus retained this default. We tested different Gamma priors for correlated noise: the default prior, a noninformative-like (vagor) prior (same mean but 100 times the variance) and a low-noise prior (same variance but 100 times the mean). We maintained the number of locations for basis functions at 10, which is the default setting. For the length scale in LFRM, we first used the best estimate from our cross-validation (CV). We then tried all 10 CV-selected length scales, producing 100 basis functions in total. However, this required substantial time, so we performed only two repeated runs for this setting. We kept LFRM's default of 5000 burn-in iterations (25,000 total) with thinning at intervals of 5, verifying convergence through trace plots in line with [18]. Meanwhile, BSFDA was run 200 times as in Section 5.1, LFRM (10 length scales) two times, and all other settings 10 times. Across repeated trials, LFRM consistently overestimated the true number of components (which was 3). Specifically,  
* Standard LFRM estimated 10-14 components;
* LFRM with 10 length scales estimated 6-8 components;
* LFRM with a low-correlated-noise prior estimated 8-15 components;
* LFRM with a noninformative-like correlated-noise prior estimated 10-14 components. In contrast, our method BSFDA produced a clear gap in the distribution of the precision parameters, effectively separating effective dimensions from redundant ones. Several factors may explain LFRM's performance. * **Correlated noise interference:** The correlated noise can obscure the true signal. * **Prior specification:** LFRM's precision parameter priors are potentially less noninformative and not as sparse as those sparse Bayesian learning priors [30] in BSFDA. * **Element-wise vs. column-wise precision:** The element-wise precision parameters in LFRM might compensate in a way that reduces the overall sparsity.
## 8 Applications of Two-Dimensional Nanoscale SnS
have also been utilised for hydrogen evolution reactions, with a current density of 10 mA cm\({}^{-2}\), a turnover frequency of 0.23 s\({}^{-1}\), and a \(-\)125 mV overpotential, comparable to non-noble metal catalysts such as nickel phosphide [196]. The Faraday yield was 99% for 10 min of electrolysis [197]. SnS\({}_{2}\)/SnS heterojunction nanosheets have been prepared by [PERSON] et al. for hydrogen evolution catalysis. Sn nanoparticle preparation was performed via DC arc plasma technology followed by a one-step sulfurisation method. The nanosheets produced had dimensions of 1-3 um diameter and 50-100 nm thickness with a band gap of 2 eV, between SnS (1.1 eV) and SnS\({}_{2}\) (2.15 eV). The SnS/SnS\({}_{2}\) junction nanosheets exhibited 10- and 6-fold improvements in photocurrent density under visible light (\(-\)4.7 mA cm\({}^{-2}\)) compared to pure SnS\({}_{2}\) (\(-\)0.6 V) and SnS (\(-\)0.54 V), respectively, due to the transfer of carriers from SnS\({}_{2}\) to SnS [198]. The standard hydrogen evolution catalyst is platinum, which has a current density of 30 mA cm\({}^{-2}\)[199], an overpotential of 19.5 mV, and a turnover frequency of 1.25 s\({}^{-1}\)[200], however platinum has the considerable disadvantage of being less abundant and more expensive compared to SnS. A 2D 2-3 nm SnS layer deposited on gold needles via atomic layer deposition followed by reduction has also been used for the formation of formate from CO\({}_{2}\) by [PERSON] et al., with the potential for carbon-based fuel synthesis. DFT was also used to probe the energetics of pathways towards formate formation, with S content influencing the morphology and structure. The SnS/Au catalyst was able to run continuously for 40 h with under a 2% drop in current, indicating minimal CO poisoning [201]. A range of catalysts utilising SnS have been created with a variety of morphologies including nanoparticles/quantum dots and 1D nanorods, among others. Some examples of 0D SnS include: SnS nanocrystal films used for hydrogen evolution [202], indium-doped SnS nanoparticles [203], and sonochemically synthesised SnS nanoparticles [204] used for the degradation of methylene blue. SnS nanoparticle/Bi\({}_{2}\)WO\({}_{6}\) nanostructures have been used for the degradation of rhodamine B [205]. Nanowire-based SnS catalysts include: ZnS/SnS/A-FA catalysts for red dye and bacteria treatment in wastewater [206], with SnS microtubes used for the degradation of dyes [207]. Other catalysts include SnS nanoflowers for nitrogen reduction [208], carbon/SnS\({}_{2}\)/SnS nanospheres [209], and Mo-doped SnS nanoflowers [210] for hydrogen evolution and hollow P-doped g-C\({}_{3}\)N\({}_{4}\)/SnS composites for Cr(VI) degradation [211].
# Comparison of Damage Indexes for Assessing Seismic Fragility of Bearings in an Offshore Bridge
## 1 Introduction
There was a lack of attention paid to the comparative analysis of the differences between various methods of quantifying damage indexes. See details in Table 1. Additionally, there is no systematic approach among the different quantification methods. Therefore, this study proposes a new method for quantifying damage indexes of bearings based on incremental dynamic analysis and fragility analysis. The proposed method is validated using a numerical model of an offshore bridge. The results show that the proposed method is able to accurately predict the damage state of bearings under seismic loading. This paper is structured in the following manner: Section 2 presents the theoretical model, which includes the time-varying durability analysis for materials, the effect of time-dependent bond-slip, the quantification of different damage indexes of the bearing, and the fragility analysis method of the bearing. Section 3 introduces the nonlinear finite element model for the bridge. Section 4 discusses the comparison of damage indexes for assessing the seismic fragility of bearings in an offshore bridge. Finally, Section 5 presents the overall discussions and main findings of this study. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Theme** & **Year** & **Main Contribution** \\ \hline \multirow{6}{*}{Definitions of damage state} & 2001 & [PERSON], [PERSON] [2] defined a damage index for a neoprene bearing pad. \\  & 2005 & [PERSON] [3] updated damage index limits for movable and fixed steel bearings. \\  & & [PERSON] and [PERSON] [4] proposed classification criteria for different damage states using the shear strain, displacement angle, and displacement. \\  & & [PERSON], [PERSON] [1] proposed a damage index using a displacement ductility ratio and displacement for a plate rubber bearing and polytetrafluoroethylene (PTFE) sliding plate bearing. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Literature review of damage indexes for the bearings.