esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### The Effect on Amidoximation on the Preparation of PAN-Oxime Fiber  
The amine of amidoxime groups generated on the prepared sorbent (i.e., PAN-oxime fiber) by the chemical reaction can act as the binding sites for anionic metals via electrostatic interaction. Here,to check the effects of the reaction times, the morphological changes of the PAN fiber were examined at different reaction times, from 0 to 30 min. As shown in Figure 1, the PAN fiber became a rigid form as the reaction time increased. The PAN-oxime fibers prepared within reaction times of 20-25 min were easily broken, and when the reaction time was 30 min, the PAN-oxime fiber was agglomerated.  
In order to identify if the amidoximation was successfully carried out, an FT-IR study of raw PAN and the prepared sorbents at different reaction times (i.e., 15 and 30 min) were performed. As shown in Figure 2, the FT-IR spectra of PAN and PAN-oxime fibers demonstrate several peaks of functional groups. The FT-IR spectrum of PAN fiber (Figure 2a) displays the most distinctive bonds of the nitrile groups (C\(\equiv\)N stretch; 2246 cm\({}^{-1}\)). After the chemical reaction of the matter, different spectral segments were observed (see Figure 2b,c). The FT-IR spectra of PAN-oxime fibers showed that the intensity of the nitrile peak (2246 cm\({}^{-1}\)) decreased as the reaction time increased, and instead the characteristic peaks of the functional groups appeared at around 1669, 1621, 1380, 923, and 3800-3000 cm\({}^{-1}\). These peaks result from the C=N stretch, N-H bend, C-N stretch, N-H bond, and overlapping of the O-H and N-H stretch, respectively. Therefore, it was confirmed that the chemical reaction was appropriately  
Figure 1: Photographs of the polyacrylonitrile (PAN)–oxime fibers prepared at different reaction times.  
(a) 0 min; (b) 5 min; (c) 10 min; (d) 15 min; (e) 20 min; (f) 25 min; and (g) 30 min.  
performed to generate functional groups for anionic metals. However, the microscopic analysis did not show significant difference between PAN and PAN-oxime fiber, as shown in Figure 3. This indicates that the chemical modification was generated on the surface of the PAN fiber, while physical structure of the PAN fiber was not affected by the chemical reaction. Thus, the diameters of the adsorbents produced at different reaction times were similar (\(\sim\)100 \(\upmu\)m). However, in the case of reaction for 30 min, the fiber could not maintain its shape, but aggregated likely due to the extensive extent of reaction.  
Figure 3: Scanning electron microscopy (SEM) images of (**a**) PAN fiber \(\times\)200 magnification; (**b**) PAN fiber \(\times\)2000 magnification; (**c**) PAN-oxime fiber \(\times\)200 magnification; and (**d**) PAN–oxime fiber \(\times\)1000 magnification. The PAN-oxime fiber was prepared with a reaction time of 15 min.  
Figure 2: Fourier-transform infrared (FT-IR) spectra of PAN fiber and PAN–oxime fiber at different reaction times: (**a**) 0 min; (**b**) 15 min; and (**c**) 30 min.
# A Tutorial on Retroreflectors and Arrays Used in Satellite and Lunar Laser Ranging
## 3 Retroreflector Far-Field Diffraction Pattern (FFDP)  
For a uniformly illuminated circular aperture, the FFDP of the reflected wave, as plotted in Figure 3, is the familiar Airy function, given by  
\[\sigma(x)=\sigma_{cc}\Big{[}\frac{2J_{1}(x)}{x}\Big{]}^{2} \tag{3}\]  
Figure 1: Two types of retroreflector/cube corner—hollow and solid.  
Figure 2: The peak cross-section in square meters as a function of the solid retroreflector diameter in inches for both Total Internal Reflection (blue curve) and aluminum back-coating (red curve) surfaces. The peak optical cross-section rises rapidly as the retroreflector diameter to the fourth power. For the popular 1.5 in (38 mm)-diameter cube with a physical cross-section of 0.001 m\({}^{2}\), the peak optical cross-section is about 5.8 \(\times\) 10\({}^{7}\) m\({}^{2}\), an increase of over ten orders of magnitude.  
\[x=\frac{\pi D}{\lambda}sin\theta \tag{4}\]  
where the frequency-doubled Nd:YAG laser wavelength, \(\lambda\) = 532 nm, is the most widely used in the SLR ground network, and \(D\) is the cube aperture diameter.  
At an arbitrary incidence angle, \(\theta_{inc}\), the effective area of the cube is reduced by the factor  
\[\eta(\theta_{inc})=\frac{2}{\pi}\Big{(}sin^{-1}\mu-\sqrt{2}tan\theta_{ref} \Big{)}cos\theta_{inc} \tag{5}\]  
where \(\theta_{inc}\) is the incidence angle and \(\theta_{ref}\) is the internal refracted angle, as determined by Snell's Law, i.e.,  
\[\theta_{ref}=sin^{-1}\bigg{(}\frac{sin\theta_{inc}}{n}\bigg{)} \tag{6}\]  
where \(n\) is the cube index of refraction, and the quantity \(\mu\) is given by the formula  
\[\mu=\sqrt{1-tan^{2}\theta_{ref}} \tag{7}\]  
Thus, as the incidence angle, \(\theta_{inc}\), increases, the peak optical cross-section in the center of the reflected lobe falls off as  
\[\sigma_{eff}(\theta_{inc})=\eta^{2}(\theta_{inc})\sigma_{cc} \tag{8}\]  
and is plotted in Figure 4.  
Figure 3: The FFDP of an unspoiled circular aperture retroreflector is plotted on a linear scale and a logarithmic scale in parts (**a**,**b**) of the figure, respectively. The half-power and first null occur at \(x\) = 1.6 and 3.8, respectively. For the popular 1.5 in (38 mm)-diameter cube at 532 nm, this corresponds to \(\theta\) = 7.1 and 16.9 microradians (1.5 and 3.5 arcsec), respectively.
_Article_  
Fatigue Assessment on Suspenders under Stochastic Wind and Traffic Loads Based on In-Situ Monitoring Data  
[PERSON] 1, [PERSON] 2,*(c), [PERSON] 2,3 and [PERSON] 3(c)  
Received: 21 July 2019; Accepted: 16 August 2019; Published: 19 August 2019  
**Featured Application: The present study attempts to predict the fatigue life of the suspenders through the incorporation of in-situ monitoring data into the proposed analytical coupled traffic-bridge-wind platform. The new findings highlight the importance of both wind load and traffic load in predicting the time-dependent fatigue reliability of the suspenders of long-span suspension bridges during their life time. It also brings an in-depth insight into the stress response characteristics of both the long suspender and short suspender under individual wind load, individual traffic load, as well as combined wind and traffic loads. The proposed methodology in this paper could provide essential guidelines for decision-making regarding the maintenance and replacement of suspenders.**  
As a critical component of a suspension bridge, the integrity of the suspenders plays a critical role in the serviceability and reliability of the bridge during its life time. Despite the wide recognition of the importance of the suspenders, very few studies have been devoted to the condition evaluation of suspenders in operation. The present study performs the fatigue assessment on the suspenders accounting for the stochastic wind and traffic loads using the in-situ monitoring data. To this end, a probabilistic numerical framework is proposed to predict the time-dependent fatigue reliability of the suspenders under stochastic wind and traffic loads during the bridge's life time, based on the linear fatigue damage rule. As a demonstration, the proposed numerical framework is applied to a long-span suspension bridge located in a mountainous canyon. The results indicate that it is of paramount importance to consider both the wind and traffic load effects in the fatigue reliability evaluation of the suspenders. In addition, it was also found that among the suspenders under investigation, the short suspender at the bridge mid-span (S36) is more prone to the fatigue damage, while the long suspender at the end of the bridge girder (S2) is less prone to the fatigue damage. Finally, provided with a target reliability index of 3.0, the fatigue life of the suspenders S36 and S2, considering the life time wind and traffic load, is estimated as 53 years and 167 years, respectively. The present research could provide essential guidelines for the optimization of inspection and replacement in maintenance practices for suspenders.  
**Keywords:** suspender; fatigue reliability; stochastic wind and traffic load; suspension bridge
## 2 Finite Element Analysis of the Electromagnetic Track Launcher
### Finite Element Analysis Method
#### 2.1.2 Mesh Analysis and Discretization  
According to the armature and track divided, the boundary conditions in the computational domain can be given, which mainly consist of the armature-track contact surface condition, the track-air domain interface condition and the armature-air domain interface condition, as shown below:  
\[\left\{\begin{array}{l}\mathbf{n}_{ij}\times\left(\mathbf{E}_{i}-\mathbf{E}_{j}\right)= \mathbf{0}\\ \mathbf{n}_{ij}\times\left(\mathbf{H}_{i}-\mathbf{H}_{j}\right)=\mathbf{0}\\ \mathbf{n}_{ij}\times\left(\mathbf{D}_{i}-\mathbf{D}_{j}\right)=\mathbf{0}\\ \mathbf{n}_{ij}\times\left(\mathbf{B}_{i}-\mathbf{B}_{j}\right)=\mathbf{0}\\ \end{array}\right. \tag{4}\]  
where, \(\mathbf{n}_{ij}\) is the normal vector of the manifold. Where \(i=1,j=2\), \(\mathbf{n}_{ij}\) represents the armature-track contact surface condition, \(i=1,j=3\) represents the track-air domain interface condition and \(i=2,j=3\) represents the armature-air domain interface condition.  
The finite element calculation includes spatial discretization and time discretization. Firstly, Equation (2) is spatially discretized, and the solution region is dissected into grids, the result of grid segmentation is shown in Figure 2.  
Then, the discretization of the dissected cells is substituted into the governing equations, and the final form of the matrix equation is obtained as [17]:  
\[\mathbf{KI}+\mathbf{M}\frac{\partial\mathbf{U}}{\partial t}=F \tag{5}\]  
where \(\mathbf{K},\mathbf{M}\) and \(\mathbf{F}\) are the overall stiffness matrix, overall mass matrix and overall load vector, respectively. \(\mathbf{U}\) is the overall unknown function vector.  
Then, the time discretization of the above equation is obtained by using the Euler difference equation.  
\[\mathbf{KI}^{t+\Delta t}+\frac{\mathbf{M}\big{(}\mathbf{U}^{t+\Delta t}-\mathbf{U}^{t}\big{)} }{\Delta t}=\mathbf{F}^{t+\Delta t} \tag{6}\]  
where, \(\Delta t\) is the time step.  
Figure 2: Grid segmentation of electromagnetic track launcher. (**a**) Armature, inner and outer tracks of the launcher; (**b**) armature center and the contact of the armature tail and track.  
By solving the system of algebraic equations formed by the above discretization, the distribution of the current, magnetic and temperature fields in the solution region of the launcher can be obtained.  
The magnetic induction intensity and electric field intensities are:  
\[\left\{\begin{array}{l}\mathbf{B}=\
abla\times\mathbf{A}\\ \mathbf{E}=-\Big{(}\frac{\partial\mathbf{A}}{\partial t}+\
abla\varphi\Big{)}\end{array}\right. \tag{7}\]  
The armature electromagnetic thrust density is:  
\[f_{e}=\mathbf{J}\times\mathbf{B} \tag{8}\]
## 4 Results
### Experimental Configuration  
The experimental platform adopted Ubuntu 18.04 LTS operating system, PyTorch deep learning framework version 1.9.0 with Compute Unified Device Architecture (CUDA) 11.1, and RTX3090 24 GB graphics card. The dataset was divided into training (554 images), test (79 images), and validation (159 images) sets following a 7:1:2 ratio to ensure balanced category representation. The training set was used for model parameter optimization, the validation set for hyperparameter tuning and early stopping strategy, and the test set for final performance evaluation. Adaptive Moment Estimation with Weight Decay (AdamW) optimizer was selected with learning rate set to 0.001, batch size of 8, total training epochs of 300, network input image size of 640 \(\times\) 640, and no pre-trained weights loaded. Model performance evaluation adopted three metrics: Precision (P), Recall (R), and mean Average Precision (mAP) [18]. These metrics comprehensively evaluate detection performance, with Precision addressing false positive control crucial for industrial applications, Recall ensuring complete detection coverage, and mAP providing integrated assessment across all confidence thresholds and categories. All comparative experiments were conducted under identical hardware, software, and parameter configurations to ensure fair comparison across different methods.
## 4 Welding Multi-Source Sensing Systems and Their Information Processing and Knowledge Modeling
### Time-Domain Feature Analysis and Acquisition of Welding Process Parameters  
The core parameters for quality control in the welding process are the welding process parameters, including welding current and arc voltage. However, in the actual production process, we often find discrepancies between the set values and the actual values. In this paper, we have collected the actual current values for different weld quality problems, such as porosity, incomplete penetration, burn-through, incompletely filled groove, satisfactory weld, and weld misalignment, respectively. Their actual current values are denoted as 01, 02, 03, 04, 05, and 06.  
To delve deeper into these issues, we extracted eight data features from the actual current values, including standard deviation \(I_{std}\), mean \(I_{mean}\), root mean square \(I_{rms}\), peak-to-peak \(I_{p2p}\), peak factor \(I_{pf}\), shape factor \(I_{sf}\), skewness \(I_{sk}\), and kurtosis \(I_{ku}\). We then compared these characteristics with the actual current values of a normal (good) weld. As shown in Figures 8 and 9, we found that when there are problems such as porosity, under-welding, burn-through, under-welding, weld deviation, etc., their time-domain features are somewhat different compared to the eigenvalues of the ideal weld, which can be used as classifying features for defect identification.  
Figure 8: Comparison of statistical characteristics of actual current for different weld qualities. (**a**) standard deviation; (**b**) mean; (**c**) root mean square; (**d**) peak to peak; (**e**) peak factor; (**f**) shape factor; (**g**) skewness; (**h**) kurtosis.  
Figure 7: Schematic diagram of CNN feature extraction process.
## 4 Application of the \(EDNN\) in Denoising  
The research on signal denoising with \(EDNN\) was conducted. Firstly, a single-input, single-output, single-hidden-layer \(EDNN\) was realized. Then the \(EDNN\) is applied in the denoising of a second-order damped free oscillation signal and an internal combustion engine cylinder pressure trace signal.
# Channel and Bit Adaptive Power Control Strategy for Uplink NOMA VLC Systems
## 5 Conclusions  
In this paper, intended for an equivalent uplink NOMA VLC system, we have developed a channel and bit adaptive power control strategy to enhance the system's energy-efficiency by jointly considering the channel state information and the transmission bit rate of each LED transmitter. Thanks  
Figure 4: **(a)** Two users’ BER performance comparisons of our proposed APC and TDMA for different photodiode (PD) receiver’s location; **(b)** Two users’ average BER performance comparisons of our proposed APC, gain ratio power allocation (GRPA), fixed power allocation (FPA), and TDMA for different \((K_{1},K_{2})\).  
Figure 5: **(a)** Three users’ average BER performance comparisons of our proposed APC, GRPA, FPA and TDMA for different \((K_{1},K_{2},K_{3})\); **(b)** Four users’ average BER performance comparisons of our proposed APC, GRPA, FPA, and TDMA for different \((K_{1},K_{2},K_{3},K_{4})\).  
to the nice structure of the received constellation under our proposed APC strategy, we have proposed a low-complexity ML demodulation and decoding algorithm. Comprehensive computer simulation results have demonstrated that our proposed APC strategy significant outperforms GRPA, FPA, and TDMA schemes.  
[PERSON] has contributed in the conception and design of the study. [PERSON], [PERSON] hace designed the experiments. [PERSON] has written the paper. [PERSON] has critically reviewed the paper. All authors have read and approved the final manuscript.  
This work was supported in part by National Natural Science Foundation of China under Grant 61701536, in part by Guangdong Province Science and Technology Planning Project of China under Grant 2017B010114002 and in part by Dongguan Xinda Institute of Integrated Innovation.  
The authors declare no conflict of interest.
# Measuring the Centrality of DNS Infrastructure in the Wild
## 2 Related Work  
### DNS Measurement  
A large body of work exists on measuring and analyzing the client-side DNS infrastructure. Several researchers measured the population of open resolvers using an Internet-wideactive measurement method over the past decades and discussed the malicious behavior of open resolvers and the structure of the client-side DNS infrastructure [11; 12; 13; 14; 15; 16; 17]. For example, [PERSON] et al. [14] proposed the LDNS by presenting a large-scale measurement of hosts sharing the same local DNS servers. Nevertheless, the author measured resolver pools utilizing a _CNAME_ chain-based approach, which is ineffective for current DNS, as we prove later in this paper. Some researchers measured the client-side DNS infrastructure using passive data such as log data collected from specific DNS servers (and web servers in some cases) [9; 18; 19; 20]. For example, [PERSON] et al. [20] characterized and classified resolver pools using log data from the authoritative DNS of a mainstream CDN provider. Though DNS traffic collected from different sources provides a feasible resolution, passive measurement results may be biased or limited by its traffic and network location, and thereby this would influence the understanding of the client-side DNS infrastructure. We complement existing studies by quantifying the degree of centralization of the client-side DNS infrastructure using a novel active measurement approach.  
Another research branch in DNS measurement focuses on measuring and analyzing the server-side DNS infrastructure, authoritative nameservers (ADNS), specifically. [PERSON] et al. [21] presented a large-scale measurement of the ADNS deployment patterns of modern web services and examined the characteristics of different deployment patterns, such as performance, life-cycle of servers, and availability. [PERSON] et al. [22] explored the sacrificial nameserver renaming practices and quantified its scope and scale. We extend existing studies in terms of involving dataset and analysis perspectives by analyzing the supporting infrastructure of 210,446,494 domain names from 1138 gTLD zone files from various dimensions.
# Bio-Sourced and Biodegradable Membranes
## 3 Biodegradable Antifouling Membrane Properties
### Bio-Based Materials  
In recent decades, bio-based materials have been widely used to enhance the antifouling property of the membranes [118; 119; 120; 121; 122]. According to [PERSON] et al. [54], the collagen-based nanofibrous membranes incorporated with carbon nano-tubes (CNT) were capable of separating the viscous emulsions (up to 200.0 mPa.s) by more than 99%, whereas the con  
Figure 10: Schematic of membranes’ antifouling properties.  
Figure 9: Schematic of membrane fouling mechanisms.  
ventional membranes, such as polytetrafluoroethylene (PTFE) and polyvinylidene fluoride (PVDF), failed to separate these types of emulsions. The fabricated membranes exhibited a high separation flux of 1582 Lm\({}^{-2}\)h\({}^{-1}\) bar\({}^{-1}\) and robust reusability after 10 filtration cycles, which prove their antifouling properties. Due to the presence of the mixture of -NH\({}_{2}\) and -COOH functional groups in collagen/CNT nanofibrous membranes, these materials show amphiprotic properties and act as demulsifiers. They separate the emulsions by selectively ionizing, targeting, and capturing oppositely charged molecules. Biomimetic membranes are also among the potential candidates for wastewater treatment and seawater desalination, owing to their excellent antifouling, water permeability, and selectivity characteristics [111, 117, 123, 124, 125]. Generally, biological surfaces contain particular functional groups (like zwitterionic groups on lipid head groups or glycocalyx oligosaccharideschamide chains) that inhibit the foulant adhesion. Zwitterionics prevent the protein adhesion to the exterior surface of the cells by causing electrostatic hydration and strong binding to water molecules. Glycocalyx chains also hinder foulant adhesion by electrostatic and entropic forces [117]. Biological cells also contain protein water channels called aquaporins. Aquaporins have excellent water permeability and high solute rejection [126]. Water flux and permeate flux recovery, which show the antifouling property, are improved by incorporating these water channels in membranes. The antifouling properties of cell membranes inspired the fabrication of biomimetic membranes for separation processes [127, 128, 129, 130]. A summary of the antifouling properties of common biodegradable membranes from several studies is provided in the following.
# Numerical Study on Hydrodynamic Performance of a New Semi-Submersible Aquaculture Platform
## 3 Numerical Model
### Mooring System
#### 3.3.1 Composition of the Mooring System  
Previous studies [23; 24; 25] have predominantly focused on single-point mooring systems in the context of vessel-shaped fish farms. While the single-point mooring system provides good environment direction-following capability, it requires a large sea area due to extensive platform movements. In order to reduce the range of motion, this paper adopts a multi-point mooring system for the presented semi-submersible platform. As Figure 8 shows, the system arranges eight mooring lines symmetrically into four groups, with two lines in each group. At the project site, the angle between the geographical north direction, and the position direction of the \(X\)-axis is \(135^{\circ}\). Line 1 makes an angle of \(45^{\circ}\) with the positive \(X\)-axis direction, while line 2 makes an angle of \(37.5^{\circ}\). The horizontal distance between the fairlead and the anchor measures 240 m.  
Figure 8: The mooring system layout.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Equivalent** & **Equivalent** & **Equivalent** & **Equivalent** \\  & **Number** & **Diameter (m)** & **Drag Coefficient** & **Inertia Coefficient** \\ \hline Vertical direction & 95 & 0.049 & 14.864 & 1 \\ Horizontal direction & 60 & 0.039 & 11.813 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Equivalent coefficients of nets.  
Eight identical mooring lines are utilized. The normal drag, axial force, normal inertia, and axial inertia coefficients of each line are 2.6, 1.4, 1.0, and 0.5, respectively. Other detailed parameters are presented in Table 6.  
Each mooring line connects one end to the fairlead on the platform and the other end to the anchor on the seabed. The coordinates of fairleads and anchors are provided in Table 7. The fairlead coordinates are based on the local Cartesian coordinate system, as described in Section 2.1. The anchor coordinates are based on the global Cartesian coordinate system, with the origin located at the mean water level. The \(X\)-axis points forward to the bow of the platform, the \(Y\)-axis points toward the port of the platform, and the \(Z\)-axis points vertically upward.