esa-sceva/satcom-chunk-collection huggingface dataset:
# Non-Newtonian Behaviors of Crumb Rubber-Modified Bituminous Binders
## 3 Results and Discussion
### Shear-Thinning Behaviors  
The previous subsection has shown the shear-thinning behaviors of different bituminous binders at certain temperatures. The occurrence of shear-thinning in non-Newtonian fluids can be attributed to the changes in particle/molecular orientations and/or alignment in the direction of flow. Specifically, there are three types of mechanisms as shown in Figure 4 can explain the complex shear-thinning behaviors of the unmodified and modified binders [6]. For polymer solutions or melts, polymer entanglements or coils are formed in the polymer network at rest. When the polymer solution/ melts are  
Figure 3: Complex viscosity as a function of shear rate (a) at 10 \({}^{\circ}\)C; (b) at 50 \({}^{\circ}\)C.  
subjected to shear loading, the entanglements or coils will unfold and align with the applied shear force, which in turn makes the polymer network easy to deform as shown in Figure 4a. For suspensions, solid particles in the liquid are prone to agglomerate at rest. With the applied shear loading, agglomerates tend to break up and disseminate into the matrix as shown in Figure 4b. For colloidal dispersions, when particles are introduced into a liquid at rest, they are randomly oriented to reach a thermodynamic equilibrium state (disordered state). With the applied shear force, particles will try to align themselves to the shear direction as shown in Figure 4c. In all cases, materials in a disordered state at rest will shift to an orientational ordered state with applied shear, which corresponds to a shift from high viscosity to low viscosity. This means shear-thinning behavior is an entropy-decreasing process [30; 31]. Bitumen is commonly recognized as a multi-disperse colloidal system, where high molecular weight asphaltene micelles are peptidized by resins in low molecular weight maltenes. For base bitumen, the asphaltene can be regarded as particles which are randomly oriented in the maltenes and have the tendency to aggregate at rest. When the bitumen is subjected to shear loading at certain temperatures, the asphaltenes which contribute mainly to the viscosity will tend to an orientational ordered state in the maltenes. Therefore, shear-thinning happens. A similar explanation which regards wax as solid particles can be applied for the shear-thinning behavior of wax-modified bitumen. In terms of rubber modified bitumen, the potential polymer entangled network formed in the bitumen microstructure due to the rubber-bitumen interaction will unfold and stretch itself to adjust with the applied shear forces. The other two types of shear-thinning mechanism may also exist in CRMB. For the rest bituminous binders, a combination of these three types of mechanism will be capable to explain the shear-thinning behavior.
# Tropical Cyclone Winds from WindSat, AMSR2, and SMAP: Comparison with the HWRF Model
## 2 Data Sets  
### SMAP Winds  
Since 2017, RSS has been producing SMAP wind data in the form of twice-daily gridded maps (for ascending and descending swaths) on a 0.25 \(\times\) 0.25 degree Earthgrid. The original spatial resolution of the SMAP data is 40 km. These SMAP wind data are produced in near-real time (latency of ~3 h) and are freely available to download ([[http://remss.com/missions/smap/winds/](http://remss.com/missions/smap/winds/)]([http://remss.com/missions/smap/winds/](http://remss.com/missions/smap/winds/)); accessed on 10 May 2021).
# Two-Channel Information Fusion Weak Signal Detection Based on Correntropy Method +
## 2 Fundamental Theory
### Theory of Singular Value Decomposition  
First, reshape the measured signal into a matrix as preparation for SVD. For a measured signal \(\mathbf{x}=[x(1),\ x(2),\ \ldots,\ x(N)]\), form it as a Hankel matrix [21].  
Second, decompose and reconstruct the signal expressed as (1):  
\[\mathbf{A}=\begin{bmatrix}x(1)&x(2)&\cdots&x(n)\\ x(2)&x(3)&\cdots&x(n+1)\\ \vdots&\vdots&\ddots&\vdots\\ x(m)&x(m+1)&\cdots&x(N)\end{bmatrix}=\mathbf{U}\mathbf{\Sigma}\mathbf{V}^{\mathrm{T}}=[ \mathbf{u}_{1},\mathbf{u}_{2},\ldots,\mathbf{u}_{m}]\begin{bmatrix}\sigma_{1}&0&\cdots&0 &0\\ 0&\sigma_{2}&\cdots&0&0\\ \vdots&0&\ddots&0&0\\ 0&0&\cdots&\sigma_{m}&0\end{bmatrix}\begin{bmatrix}\mathbf{v}_{1}^{\mathrm{T}}\\ \mathbf{v}_{2}^{\mathrm{T}}\\ \vdots\\ \mathbf{v}_{n}^{\mathrm{T}}\end{bmatrix} \tag{1}\]  
where \(m=N-n+1\). The parameter \(m\) is used to determine the number of decomposed components in SVD.  
The sub-matrix is  
\[\mathbf{A}_{i}=\sigma\mathbf{u}_{i}\mathbf{v}_{i}^{\mathrm{T}} \tag{2}\]  
Reconstruct the signal \(\mathbf{x}_{i}=[\mathbf{R}_{i,1},\mathbf{C}_{i,n}^{\mathrm{T}}]\); \(\mathbf{R}_{i,1}\in\mathbf{R}^{1\times n}\), \(\mathbf{C}_{i,n}\in\mathbf{R}^{(m-1)\times 1}\), which is achieved by extracting the first row and last column of \(\mathbf{A}_{i}\). Considering that matrix \(\mathbf{A}\) becomes a non-Hankel matrix after SVD, it is decided to obtain each element of \(\mathbf{x}_{i}\) by averaging along the anti-diagonals of \(\mathbf{A}_{i}\).  
Third, the traditional SVD denoising approach assumed that the core signal pattern is embedded in the SCs with large SV. Recent studies mostly focus on how to select a proper threshold \(k\) and then denoise the raw signal by retaining the SCs with the first \(k\) largest SV. The reconstructed signal is expressed as (3):  
\[\mathbf{\hat{A}}=[\mathbf{u}_{1},\mathbf{u}_{2},\ldots,\mathbf{u}_{m}]\begin{bmatrix}\sigma_{ 1}&0&\cdots&0&\mathbf{0}\\ 0&\sigma_{2}&\cdots&0&\mathbf{0}\\ \vdots&\vdots&\ddots&\vdots&\vdots\\ 0&0&\cdots&\sigma_{k}&\mathbf{0}\\ \mathbf{0}&\mathbf{0}&\cdots&\mathbf{0}&\mathbf{0}\\ \end{bmatrix}\begin{bmatrix}\mathbf{v}_{1}^{\mathrm{T}}\\ \mathbf{v}_{2}^{\mathrm{T}}\\ \vdots\\ \mathbf{v}_{n}^{\mathrm{T}}\end{bmatrix}=\sum_{i=1}^{k}\sigma_{i}\mathbf{u}_{i}\mathbf{v} _{i}=\sum_{i=1}^{k}\mathbf{A}_{i}\Rightarrow\mathbf{\hat{x}}=\sum_{i=1}^{k}\mathbf{x}_{i} \tag{3}\]
###### Abstract  
Laser-induced damage on fused silica optics remains a major issue that limits the promotion of energy output of large laser systems. Subsurface impurity defects inevitably introduced in the practical polishing process incur strong thermal absorption for incident lasers, seriously lowering the laser-induced damage threshold (LIDT). Here, we simulate the temperature and thermal stress distributions involved in the laser irradiation process to investigate the effect of impurity defects on laser damage resistance. Then, HF-based etchants (HF:NH4F) are applied to remove the subsurface impurity defects and the surface quality, impurity contents and laser damage resistance of etched silica surfaces are tested. The results indicate that the presence of impurity defects could induce a dramatic rise of local temperature and thermal stress. The maximum temperature and stress can reach up to 7073 K and 8739 MPa, respectively, far higher than the melting point and compressive strength of fused silica, resulting in serious laser damage. The effect of impurity defects on laser damage resistance is dependent on the species, size and spatial location of the defects, and CeO\({}_{2}\) defects play a dominant role in lowering the LIDT, followed by Fe and Al defects. CeO\({}_{2}\) defects with radius of 0.3 \(\upmu\)m, which reside 0.15 \(\upmu\)m beneath the surface, are the most dangerous defects for incurring laser damage. By HF acid etching, the negative effect of impurity defects on laser damage resistance could be effectively mitigated. It is validated that with HF acid etching, the number of dangerous CeO\({}_{2}\) defects is decreased by more than half, and the LIDT could be improved to 27.1 J/cm\({}^{2}\).  
f _Article_  
Effect of Polishing-Induced Subsurface Impurity  
Defects on Laser Damage Resistance of Fused Silica  
Optics and Their Removal with HF Acid Etching
## 3 Wireless Communication Networks
### Interference  
The received signals from wireless channel can be distorted due to the presence of interference, which are caused by natural or transmission factors. Hence, techniques for mitigation, avoidance, cancelation, and management of interference play an important role. The interference caused by multiple transmissions are of interest as example.  
* Multiple access interference (MAI) occurs when multiple communications links access to channel.
* Multipath interference (MPI) is produced by different propagation paths to reach a specific receiver.
* Multiuser interference (MUI) occurs when simultaneous communication links exist in the same channel from different WDs. Two particular cases can be identified: _Exposed_ and _Hidden_ terminals.
# On Finding Optimal (Dynamic) Arborescences
## 5 Experimental Evaluation  
We implemented the original [PERSON] algorithm as described in Section 2.1 and the Tarjan algorithm as described in Section 2.2. The implementation of the Tarjan algorithm has three variants, which differ only on the heap implementation. As discussed before, we considered binary heaps, binomial heaps, and pairing heaps in our experiments. The algorithms were implemented in Java 11, and the binaries were compiled with javac 11.0.20. The experiments were performed on a computer with the following hardware: Intel(R) Xeon(R) Silver 4214 CPU at 2.20 GHz and with 16 GB of RAM.  
The aim of this experimental evaluation is to compare the performance of [PERSON] original algorithm with the Tarjan algorithm to evaluate the use of different heap implementations and to investigate the practicality of the dynamic algorithm for dense and sparse graphs. For the datasets, we used randomly generated graphs, both dense and sparse, and real phylogenetic data.
## Materials and Methods
### Elite Selection Mechanism  
The elite selection mechanism is a crucial step to improve the performance and stability of the genetic algorithm. During each generation's evolution, the elite selection mechanism effectively prevents the loss of good genes in genetic operations by retaining individuals with the highest fitness, thereby enhancing the global search ability of the algorithm.  
During each generation of the population update, all individuals in the current population are first evaluated according to the fitness function and sorted by fitness value. According to the preset number of elite individuals, the five individuals with the highest fitness values are selected as elite individuals. The number of elite individuals should not be too large, as too many elite individuals will cause the following problems:  
* Reduced population diversity:  
Too many elite individuals will reduce the number of other individuals in the population, decreasing population diversity. This makes the algorithm more likely to fall into local optima, making it difficult to escape local optimal traps, thus affecting the global search ability.
* Evolutionary stagnation:  
Too many elite individuals can cause population evolution to stagnate. New individuals may not generate enough variation, causing the evolution process to slow down or even stop. This results in the algorithm being unable to find better solutions over time.  
At each generation of the population update, the fitness values of all individuals are first calculated and sorted from highest to lowest. According to the preset number of elite individuals, the individuals with the highest fitness values are selected as elite individuals. Subsequently, these elite individuals are retained directly in the next-generation population without participating in crossover and mutation operations. This ensures that the individuals with the highest fitness values can directly enter the next generation, helping to maintain the superior genes of the population and accelerate the convergence process.  
This elite selection mechanism can significantly improve the global search ability and optimization efficiency of the algorithm while ensuring population diversity. It avoids the loss of excellent individuals due to random operations and shows good stability and robustness in practical applications, effectively improving the algorithm's ability to solve complex optimization problems.
# Few-Shot Steel Defect Detection Based on a Fine-Tuned Network with Serial Multi-Scale Attention
## 3 Methodology
### Dataset Construction and Analysis
This discovery prompted an exhaustive statistical analysis to investigate the relationship between defect categories and pixel distributions. Upon examining Table 4, several key findings come to light. Firstly, scratches were dominant in the class distribution, accounting for approximately 73% of the total, which highlighted a stark imbalance in terms of defect types within the dataset. Owing to the large number of scratches, they also exhibited the highest proportion of pixel scales, which was around 80%. In contrast, oxidation scales, despite accounting for only 11.3% of the defect types, constituted 16.8% of the pixel representation, which suggested a relatively larger size for this defect type. The pits and cracks were noticeably smaller in size. Although they accounted for 12.6% and 3.48% of the total defects, respectively, they only contributed a marginal 2.39% and 0.51% to the pixel counts within the defect masks. This implied that the network might have faced challenges detecting pits and cracks due to their tiny dimensions. \begin{table}
\begin{tabular}{c c c c} \hline
**Defect Categories** & **Defect Quantity** & **Category Proportions** & **Pixel Proportions** \\ \hline Pits & 897 & 12.6\% & 2.4\% \\ Cracks & 247 & 3.5\% & 0.5\% \\ Scratches & 5150 & 72.6\% & 80.3\% \\ Iron oxide scales & 801 & 11.3\% & 16.8\% \\ \hline \end{tabular}
\end{table} 
Table 4: Types and pixel ratios of four defect categories. Figure 1: Illustration of four different defects. (**a**) Pits. (**b**) Cracks. (**c**) Scratches. (**d**) Iron oxide scales. These findings prompted an incisive analysis focusing on the pixel scales of the defects. Given the significant contrast in pixel sizes between iron oxide scales and pits, it was imperative to gather more definitive data to validate this observation. A comprehensive statistical assessment of the pixel scales for each defect type was conducted, and the results are listed in Table 5.  
It was revealed that pits exhibited the smallest pixel dimensions, with the minimum scale recorded at 163 pixels. In contrast, cracks, with an average pixel dimension of 3378.4, emerged as the category having the smallest mean pixel scale among all defect types. Scratches, on the other hand, displayed the largest pixel dimensions with the maximum recorded at 368,240 pixels. Oxidation scales had an average pixel dimension of 34,373.9, which was the largest mean value among the defects. Consequently, the analysis underscored the significant difference in pixel dimensions across the four types of defect present in the dataset.
# Transmission Efficiency of Cycloid-Pinion System Considering the Assembly Dimensional Chain
## 4 Transmission Efficiency Model
### Maximum Contact Pressure  
Theoretically, the contact between the cycloid and the pinion is a linear contact. As the reviewer mentions, the axes present misalignments. In actual process, the pin tooth will deflect due to the presence of the fit clearance. During the experimental observation, it is observed that the pin tooth moves along the axial direction, which is in fact due to the axial parting force. An ideal line contact would hardly lead to this phenomenon. The shift of the pin teeth leads to a reduction in the contact surface, which tends more towards the elliptical surface produced by the point contact. Therefore, in the course of the study, the authors employed a point contact rather than a line contact, as shown in Figure 7.  
The maximum contact pressure \(P_{h}\) is  
\[P_{h}=3P/2\pi ab \tag{15}\]  
where \(a\) and \(b\) denote the major and minor semi-axes of the contact ellipse, respectively. When calculating the shape parameters of the contact area, two parameters \(k_{a}\) and \(k_{b}\) are obtained by the second type of complete elliptic integration.  
\[\left\{\begin{array}{l}a=k_{a}\Big{[}\frac{3w}{2E(A+B)}\Big{]}^{1/3},b=k_{b} \Big{[}\frac{3w}{2E(A+B)}\Big{]}^{1/3}\\ k_{a}=\Big{[}\frac{2E(e)}{\pi(1-\psi^{2})}\Big{]}^{1/3},k_{b}=k_{a}\sqrt{1- \psi^{2}}\end{array}\right. \tag{16}\]  
where \(\psi\) is ellipticity, \(\psi=\sqrt{1-(b/a)^{2}}\), and \(0<\psi<1\).  
The complete elliptic integrals of the first \(K(e)\) and second \(E(e)\) type are given below.  
\[K(e):=\int_{0}^{\frac{\pi}{2}}\frac{d\theta}{\sqrt{1-\psi^{2}\sin^{2}\theta}} \tag{17}\]  
Figure 7: Point contact model of cycloid wheel and pin teeth.  
\[E(e):=\int_{0}^{\frac{\pi}{2}}\sqrt{1-\psi^{2}\sin^{2}\theta}d\theta \tag{18}\]  
The distance between two contact surfaces is generally expressed as \(s=Ax^{2}+By^{2}\). The following relationship Equation (19) exists between \(A\) and \(B\) and the two kinds of complete elliptic integrals.  
\[\frac{|A-B|}{A+B}=\frac{2\big{(}1-\psi^{2}\big{)}}{\psi^{2}}\frac{E(e)-K(e)}{K( e)}+1=\cos\varphi \tag{19}\]  
Integrating Equations (16)-(19) and employing the dichotomy method, the solutions are obtained for the contact ellipse parameters a and b.
# Double-Scale Adaptive Transmission in Time-Varying Channel for Underwater Acoustic Sensor Networks
###### Abstract
However, the state space would be enlarged with a large channel fluctuation amplitude, which may increase the training burden and computational complexity. Optimization [17; 18] schemes have also been investigated for adaptive transmission, but fine-grained optimization will lead to greater computational complexity. To provide energy-efficient transmission for UASNs, we propose a double-scale adaptive transmission mechanism in this paper. The historical channel state series will first be decomposed into large-scale and small-scale series; then, real-time channel state can be predicted according to different fluctuation features with each time scale. In order to solve the problem of long-term transmission configuration with modulation and coding mode  
Figure 1: Keweenaw Waterway experiment: Average signal-to-noise rates (SNR) at the receiver. selection, an energy-efficient transmission algorithm is designed on the basis of channel state prediction. The contributions of this paper can be summarized as follows:  
* To improve the energy efficiency and reliability of data transmission, we propose a double-scale adaptive transmission mechanism for UASNs. Specifically, the historical channel state series is used for channel state prediction, and then the transmission mode is determined adaptively. * To balance the accuracy and computational complexity of channel states prediction, we propose to decompose the channel state series with two different time scales. For the large-scale channel state, a \(k\)-nearest neighbor algorithm with sliding window is designed to predict the fluctuation tendency, and then a small-scale channel state prediction algorithm is developed to enhance the accuracy. * To determine the specific configuration of data communication in UASNs, we design an energy-efficient transmission algorithm. In particular, the long-term modulation and coding problem is formulated and optimized with the constraint of limited energy cost. The paper is organized as follows. In Section 2, the related works are presented. Section 3 illustrated the framework of our proposed transmission method, and then the double-scale channel prediction and transmission scheduling are given in Sections 4 and 5, respectively. Quantitative performance analysis and computational complexity are presented in Section 6. Simulation results are analyzed in Section 7, and, finally, the paper is concluded in Section 8.
# An Efficient SS-MAC Protocol for IEEE 802.15.4-Based WSNs of Cluster Tree Topology
## 3 The Proposed SS-MAC Protocol
### Steady State Phase (SSP)  
After DCP, the sink broadcasts a Session_ST beacon to synchronize all CHs in the network, and the network enters SSP, which is divided into several sessions of equal duration and repetition. During each session, the network must complete data collection tasks from CHs to sink and from member nodes to their CHs.
# A Communication-Efficient Secure Routing Protocol for IoT Networks +
## 4 Proposed Method
### Route Request Phase  
Based on the network model illustrated in Figure 1, we describe the RREQ phase below:  
1. The source node \(\mathsf{S}\) generates an RREQ with its own signature (\(\mathsf{SigS}\)) and broadcasts it to the neighboring nodes. If relay node \(\mathsf{N}_{1}\) receives this packet, this phase can be expressed as \[\mathsf{S}\rightarrow\mathsf{N}_{1}:(\mathrm{RREQ})_{\mathsf{SigS}}\] (1) For this RREQ, the packet valid time (LT) and its own location information (\(\mathrm{LI}_{\mathsf{S}}\)) are added to the RREQ of the original AODV (\(\mathrm{RREQ}_{\mathrm{AODV}}\)). However, information on hop counts is _not_ included, as the digital signature-aided routing prohibits any nodes from updating the hop count. Based on \(\mathrm{LI}_{\mathsf{S}}\), the intermediate nodes estimate the distance from the source node, and can stop the routing if far from the source. Thus, the contents of the RREQ in this method are expressed as \[\mathrm{RREQ}=(\mathrm{RREQ}_{\mathrm{AODV}},\,\mathrm{LT},\,\mathrm{LI}_{ \mathsf{S}})\] (2)
2. Node \(\mathsf{N}_{1}\) verifies the signature of node \(\mathsf{S}\) in the received packet, establishes a path in the direction of \(\mathsf{S}\), and stores the routing information and sent RREQ in its own table. Subsequently, \(\mathsf{N}_{1}\) adds its own signature and address (\(\mathrm{ID}_{\mathsf{N}_{1}}\)) to the RREQ and broadcasts it. When this packet reaches relay node \(\mathsf{N}_{2}\), this phase can be written as \[\mathsf{N}_{1}\rightarrow\mathsf{N}_{2}:((\mathrm{RREQ})_{\mathsf{SigS}},\, \mathrm{ID}_{\mathsf{N}_{1}})_{\mathsf{SigN}_{1}}\] (3)
3. Upon receiving the RREQ from \(\mathsf{N}_{1}\), \(\mathsf{N}_{2}\) verifies the signatures of \(\mathsf{S}\) and \(\mathsf{N}_{1}\), establishes a path in the direction of \(\mathsf{S}\), and stores the RREQ from \(\mathsf{N}_{1}\). The signature and address of \(\mathsf{N}_{1}\) are replaced with the signature and address of \(\mathsf{N}_{2}\).
4. The storing and broadcasting processes are repeated until this RREQ reaches \(\mathsf{D}\) or an intermediate node that knows a valid route to the destination node \(\mathsf{D}\).  
Intermediate nodes do not relay the RREQ when the received time exceeds LT or the distance between their own location and \(\mathrm{LI}_{\mathsf{S}}\) exceeds a certain distance (e.g., 1 km).
# The Polar Code Construction Method in Free Space Optical Communication
## 2 FSO Communication System  
The free-space optical communication system model mainly consists of a signal transmitter, atmospheric channel, and receiver, and its model is shown in Figure 1. (1) At the transmitter, the random bit sequence is first generated by MATLAB, then the STOP construction method proposed in this paper is used to construct the polarization code. The polarization code sequence is OOK modulated, and the modulated signal is electro-optically converted to obtain the optical signal, and finally, the 1550 nm continuous laser source is used as the optical source to transmit the optical signal into the free-space optical communication channel. (2) At the receiver, the optical signal is received by an optical collimator, and the corresponding OOK signal is collected by a photodiode, then the polarization code is decoded by the SC decoding algorithm after threshold processing.  
Figure 1: FSO System Model of STOP-Polar Codes.