esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep-Reinforcement-Learning-Based Intelligent Routing Strategy for FANETs
## 2 System Model  
Figure 1 illustrates the routing scenario in the FANET, where the blue, green, and brown circles represent the current node, one-hop neighboring nodes, and two-hop neighboring nodes, respectively, while \(\mathbf{S^{I}}\) and \(\mathbf{S^{II}}\) separately denote the state information matrix of one-hop neighboring nodes and two-hop neighboring nodes. As aforementioned, the purpose of routing in the scenario is to select a neighboring one-hop node for the current UAV node, so as to transmit the data to the final destination in a timely and complete manner.  
This paper considered the information of two-hop nodes in order to provide a rich reference for routing decision-making. We assumed that the set of one-hop neighboring nodes and two-hop neighboring nodes for the current node \(c\) are \(\mathbf{N_{c}}\) and \(\mathbf{M_{c}}\), respectively. Then, \(|\mathbf{N_{c}}|\) and \(|\mathbf{M_{c}}|\) separately represented the number of one-hop neighboring nodes and two-hop neighboring nodes. Each UAV node in the FANET was equipped with the positioning system and thus could obtain its own position, movement speed, and direction. Without losing generality, this paper set the effective communication distance of each node as \(R\). Meanwhile, it was assumed that the current node and the one-hop neighboring node could acquire or estimate the load and link quality of each of the neighboring nodes by communication interaction, e.g., by exchanging HELLO packages.  
On the basis of this information, the method proposed in this paper conducted further processing, the intelligent output of the method was obtained by relying on the reasonable design of the related parameters, and the output could then provide a recommendation for the selection among the one-hop neighboring (i.e., the candidate) nodes. It is worth noting that the current node did not require the information of all nodes within its two hops in the proposed method, and too-strict requirements may make the method infeasible due to the failure of some links. We introduce the details of the proposed decentralized and intelligent routing method in Section 4.
## 4 Results
### PLS Analysis
This is similar to the model with blindfolding redundancy \(>\) 0, indicating that the variable has a predictive relevance in the model. The construct cross redundancy Q\({}^{2}\) (=1-SSE/SSO) = 0.63 in retention was calculated, and it was a good fit in this model. Third, the R\({}^{2}\) (0.767) was higher than 0.67, indicating that it was valuable in real application. Fourth, the effect size f\({}^{2}\) (0.79) was higher than 0.35. This indicated that the outer variable (Teaching) significantly influenced the inner variable (Retention) and the R\({}^{2}\) would increase. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & **Original** & **Sample** & **Standard** & **T Statistics** & \\  & **Sample (O)** & **Mean M)** & **Deviation (STDEV)** & **(|O/STDEV)** & **\(p\) Values** \\ \hline Administration & & & & & \\ -\(>\) Retention & 0.012 & 0.017 & 0.077 & 0.150 & 0.881 \\ Counseling -\(>\) & & & & & \\ Retention & 0.093 & 0.077 & 0.097 & 0.956 & 0.339 \\ Equipment -\(>\) & & & & & \\ Retention & -0.036 & -0.029 & 0.075 & 0.482 & 0.630 \\ Teaching -\(>\) & & & & & \\ Retention & 0.822 & 0.831 & 0.055 & 14.84 & 0.000 \({}^{\star}\) \\ \hline \hline \end{tabular} Note: \({}^{\star}\)\(p\)-value is significantly smaller than 0.05.  
\end{table} 
Table 5: Path coefficients. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Convergence Validity (AVE)**} & \multicolumn{6}{c}{**Discriminate Validity**} \\ \hline  & **AVE** & **Composite** & **R Square** & **Cronback’s** & **Communality Redundancy** & **A** & **C** & **E** & **R** & **T** \\  & **R** & **R** & **Square** & **Alpha** & **Communality Redundancy** & **A** & **C** & **E** & **R** & **T** \\ \hline A & 0.714 & 0.937 & & 0.919 & 0.714 & 0.845 & 0.687 & 0.509 & 0.497 & 0.536 \\ C & 0.814 & 0.956 & & 0.943 & 0.814 & 0.687 & 0.902 & 0.525 & 0.637 & 0.675 \\ E & 0.742 & 0.920 & & 0.887 & 0.742 & 0.509 & 0.525 & 0.862 & 0.421 & 0.490 \\ R & 0.843 & 0.915 & 0.767 & 0.814 & 0.843 & 0.63 & 0.497 & 0.637 & 0.421 & 0.918 & 0.873 \\ T & 0.751 & 0.923 & & 0.889 & 0.751 & 0.536 & 0.675 & 0.49 & 0.873 & 0.867 \\ \hline \hline \end{tabular} Note: A = Administration; C = Cunningham; E = Equipment; R = Retention; T = Teaching.  
\end{table} 
Table 6: Reliability and validity.
# Effects of Intrinsic Factors on the Stability of Silver Nanowire-Based Flexible Electrodes
## 3 Results and Discussion
### Effects of the Surface Density of AgNWs on the Stability of AgNW Electrodes
Compared to PET, PVA shows better stability at the same silver nanowire surface density, which is also because PVA can encapsulate more silver nanowires and junctions internally rather than exposing them directly on the surface (as shown in Figure 8c,d).
# Dealing with Low Quality Images in Railway Obstacle Detection System
## 3 State-of-the-Art Techniques for Blur Detection  
The increased transmission of multimedia contents through the internet and mobile networks has made the quality monitoring of multimedia data an important topic. The evaluations obtained directly from human viewers are the most reliable for assessing the quality of multimedia data. Subjective scores or mean opinion scores are terms used to describe the findings of this evaluation. Several human viewers under controlled test conditions are necessary to obtain these scores. As a result, subjective scores are unsuitable for use in real-time applications because they imply extensive and tedious work.  
Figure 2: An example of image de-blurring: (**a**) test blurred image and graphical user interface developed in AppDesigner from MATLAB; (**b**) Image obtained by pseudo-inverse restoration.  
Another option is to use some objective metrics to automatically rate image quality. Image quality assessment (IQA) approaches can be divided into three categories based on whether the undistorted image (reference image) or information about it is available: full-reference (FR-IQA), reduced-reference (RR-IQA), and no-reference IQA (NR-IQA). FR-IQA algorithms require as input not only the distorted image, but also a pristine (clean) reference image with respect to which the quality of the distorted image should be assessed [13; 14]. However, because obtaining reference photos to measure image quality is not always possible, it is critical to establish an objective quality assessment that correlates well with human perception without the need of a reference image. As a result, more effort was put into the development of the most realistic scenario of objective-blind or NR-IQA, where image quality has to be estimated without any reference image [15; 16]. If a system possesses some information regarding the reference image, but not the original image itself, the developed algorithms belong to the RR-IQA scenario [17; 18]. Having in mind that the BD problem is of particular interest in this paper, and that we do not have a reference image, in the rest of this section, we describe state-of-the-art algorithms related to the blur assessment with no reference.  
Based on the available information about camera settings, blur image detection (BID) can be divided into single-image and multi-image detection. While in multi-image detection, it is necessary to have additional information regarding blur densities, blur type, used sensors, etc.; single-image detection can be used without any prior information about the camera settings. Although there are numerous methods for detecting blur described in the literature, the majority of existing BID approaches can be classified into three main categories: frequency-based, depth-based, and edge-based methods [19].
## 3 Results and Analysis
### SVM-Based Prediction Model of Improved Loess Permeability
#### 3.3.3 Model Prediction Results  
In this study, the experimental results of samples PL1-PL32 were selected as training data, while the experimental results of samples PL33-PL36 were used as prediction data. The results of this fitting are shown in Table 3. This fitting effect is good. It can predict the permeability coefficient of the improved loess material with high accuracy. The results can provide a reference basis for the future permeability prediction.  
Figure 13: Illustration of estimated function \(f(x)\) using SVR.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sample Name** & **Measured Values** & **Predicted Values** \\ \hline PL33 & \(3.64\times 10^{-7}\) & \(4.16\times 10^{-7}\) \\ PL34 & \(8.47\times 10^{-7}\) & \(8.09\times 10^{-7}\) \\ PL35 & \(5.00\times 10^{-7}\) & \(5.73\times 10^{-7}\) \\ PL36 & \(7.60\times 10^{-7}\) & \(9.19\times 10^{-7}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: SVM model predicted and measured values.  
It can be seen that the SVM predictions are closest to the 'actual' points, with higher correlation coefficients and lower RMSE values, indicating that the SVM has a better prediction capability. In the poorer-performing models, the multiple linear regression has a more significant standard deviation and a lower correlation coefficient, resulting in a larger root mean square error in the prediction. This shows that the SVM model has the highest prediction accuracy and is the closest to the measured values. Multiple linear regression and neural network regression predictions deviated from the measured values by a large margin.
We produced daily 100 m ET and GPP data using the revised SEBS model and the MODIS GPP product algorithm by fusing multiple sources of remote sensing data for irrigated areas midstream of the HRB. The objectives of this paper are to (i) estimate and validate daily ET, GPP, and WUE at 100 m scale by using a multi-source remote sensing data fusion method; (ii) analyze the spatiotemporal variations of ET, GPP, and WUE; and (iii) assess and discuss cropland WUE in an irrigated oasis area using remote sensing estimations of ET and GPP. Our results can help to better understand the adaptability of typical irrigated agroecosystems and provide support for water resource management practices in other agricultural areas.
# A Novel Anomaly Behavior Detection Scheme for Mobile Ad Hoc Networks
## 2 Background
### Dynamic Source Routing (DSR)  
Dynamic source routing is an on-demand/reactive protocol. In DSR, the route information is stored in the header packet, which travels from source to destination. To discover the path, the header packet is broadcast to all nodes, and in response, the destination sends  
Figure 1: RREQ and RREP in AODV [7].  
the reply. The route between source and destination is established with the intermediary nodes when required; this results in a reduction of overhead and collision. The route request (RREQ) and route reply (RREP) primitives are employed to establish the path from source to destination. There can be multiple paths established; however, the final path setup is based on the least hop count. Thus, to keep track of the latest route, the routing table has to be updated regularly, along with the information in the header packet. This involves more energy consumption and causes node mobility delays in the larger networks.
## 3 Analytical Solutions for Beams with One, Two, and Three Equal Spans
### Influence of the Arrival of a Uniformly Distributed Mass  
The frequency \(\overline{\omega}_{r}\) for this additional mass case can be obtained using Equations (24) and (25), where introducing the shift \(x_{0}\) = \(\lambda\) and rearranging with a translation of \(x_{0}\) by \(\lambda/2\) can be represented as follows:  
\[G_{r}=\frac{\mu_{q}}{M_{r}}\int_{0}^{x_{0}}{V_{r}}^{2}(x)dx=\frac{\mu_{q}}{M_{ r}}\int_{0}^{x_{0}}{sin^{2}\Big{(}\frac{r\tau x}{l}\Big{)}dx}=\frac{\mu_{q}}{M_{ r}}\cdot\frac{l}{2}\cdot\left(\frac{x_{0}}{l}-\frac{1}{2r\tau\pi}sin\frac{2r\tau x _{0}}{l}\right), \tag{26}\]  
\[\overline{\omega}_{r}=\frac{\omega_{r}}{\sqrt{1+G_{r}}}=\omega_{r}\bigg{[}1+ \frac{\mu_{q}}{M_{r}}\frac{l}{2}\cdot\left(\frac{x_{0}}{l}-\frac{1}{2r\pi}sin \frac{2r\pi x_{0}}{l}\right)\bigg{]}^{-\frac{1}{2}}, \tag{27}\]  
\[\overline{\omega}_{r}=\omega_{r}\bigg{[}1+\frac{\mu_{q}}{M_{r}}\frac{l}{2} \cdot\Phi_{b}(x_{0})\bigg{]}^{-\frac{1}{2}}, \tag{28}\]  
where:  
\[\Phi_{b}(x_{0})=\bigg{(}\frac{x_{0}}{l}-\frac{1}{2r\pi}sin\frac{2r\pi x_{0}}{l }\bigg{)}. \tag{29}\]  
Relation (28) is relevant for beams with one, two, or three equal spans. For the analysis of these beams, the generalized mass \(M_{r}\) should be calculated according to Relation (21). Detailed expressions for the frequencies of such systems are listed in Table 2.  
Changes in fundamental frequency due to arrival of uniformly distributed mass \(\mu_{q}\) defined based on Table 2 and compared with the circular frequencies are presented in Figure 4 for bridges with one, two, and three equal spans. It is evident that the largest effect  
Figure 3: Variation of the fundamental frequency of a bridge with additional mass \(\mu_{q}\) along length \(\lambda\) = 0.2\(l\): (**a**) single-span beam \(L=l\); (**b**) two-span beam \(L=2l\); (**c**) three-span beam \(L=3l\).
## 1 Introduction  
The temperature gradient is in the nature of every solidification process [1; 2; 3]. For alloys with a certain solidification range, it is inevitable to generate a mushy zone, which is characterized by partial melting during cooling down. Furthermore, solidification microstructures (i.e., the shape [4; 5; 6], size [7; 8; 9], composition [10; 11; 12]) formed in the mushy zone determine the final performance of the material to a great extent [13; 14]. Accordingly, it is significant to study the characteristics of the mushy zone during the solidification process.  
High-pressure solidification is featured by application of high pressure during the melting and solidification process. Numerous works have proven that application of high pressure can greatly influence the solidification parameters, for example, inhibition of solute diffusion [15] and decrease in interfacial solidification velocity [16]. The temperature distribution in a cylindrical furnace for a high-pressure experiment has also been researched [17; 18], and the results demonstrated that the temperature distribution is uneven and the temperature gradient is normally relatively low at the solidification interfaces. Hence, it provides the possibility to study the microstructure evolution resulting from the uneven distribution of temperature during high-pressure solidification.  
Al-Ni intermetallic compounds have excellent corrosion resistance, high temperature stability, and strong corrosion resistance, which cannot be replaced by other alloys. Therefore, Al-Ni intermetallic compounds are regarded as the research objects of national defense and the military industry. For the Al-Ni alloy phase diagram [19], there are eutectic reactions at the Al-rich and Ni-rich ends, which are \(\mathrm{L}\rightarrow\alpha\)-Al + Al\({}_{3}\)Ni and \(\mathrm{L}\rightarrow\alpha\)-Ni + AlNi\({}_{3}\), respectively. A series of peritectic reactions exist in the middle part. There are also abundant intermetallic compounds with different solid solubility ranges in the alloy system, such as Al-Ni, Al\({}_{3}\)Ni, AlNi\({}_{3}\), Al\({}_{3}\)Ni\({}_{5}\) and Al\({}_{3}\)Ni\({}_{2}\). According to their solid solubility, the above intermetallic compounds can be simply divided into two categories: intermetallic compounds with large solid solubility and low melting entropy, such as Al-Ni, and intermetallic compounds with small/nil solid solubility and high melting entropy, such as Al\({}_{3}\)Ni. The liquid-solid correlation, phase structure, and rapid solidification kinetics of Al-Ni alloys have been extensively studied [20; 21; 22], which provide a theoretical basis for the study of microstructure evolution and phase growth of Al-Ni alloy under high pressure.  
In order to explore the mushy zone solidification behavior of Al-Ni alloy under high pressure, the structure of a graphite heater for high-pressure synthesis was redesigned to produce a temperature gradient along the radial direction of the sample, thus forming a mushy zone. In addition, the migration behavior of the liquid pools and channels of the mushy zone with the effect of high pressure was studied.
# Detecting Dynamic Communities in Vehicle Movements Using Ant Colony Optimization
## 5 Conclusions  
In this study, we developed an evolutionary ant colony optimization method for detecting dynamic communities in vehicle movements. A multilayer snapshot network was constructed from vehicle movements, with road segments as nodes and OD pairs as weighted edges. With the help of a newly built pheromone initialization strategy, an ACO-based spatial scan statistic was upgraded to identify irregular-shaped and statistically significant dynamic communities in the multilayer snapshot network. Therefore, the identification of communities at each snapshot can balance partition quality and temporal partition coherence. Different rules defined based on the Jaccard coefficient were used to identify the evolution of spatial communities. Compared with the static spatial communities identified in vehicle movements, these dynamic communities can be used to reveal the dynamic organization of urban structures. The effectiveness and superiority of the proposed method were tested using both simulated and real-world vehicle movement datasets. The dynamic communities identified by the proposed method may aid in human-centered urban planning.  
In this study, we only used taxi trajectory data as an example to explore the dynamic organization of urban structures. Therefore, the results may be biased. In the future,multisource human mobility data (e.g., mobile phone logs, or private and public traffic travel data) should be fused to identify dynamic communities. In addition, it is time-consuming to evaluate the statistical significance of the communities using the Monte Carlo simulation method. A parallel computation method should be further developed to improve the efficiency of the proposed method.  
Conceptualization, Q.L. and S.Z.; data curation, S.Z. and M.C.; methodology, Q.L., S.Z. and M.C.; visualization, S.Z. and M.C.; writing--original draft, M.C. and W.L.; writing--review and editing, Q.L. and M.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded through support from the National Natural Science Foundation of China (NSFC) (No. 41971353 and 41730105), the Natural Science Foundation of Hunan Province (No. 2021J)20058 and 2020J4695), and the Water Conservancy Science and Technology Project of Guizhou, China (No. KT202110 and KT202002).  
Not applicable.  
The authors gratefully acknowledge the comments from the reviewers.  
The authors declare no conflict of interest.
## 4 Optimization Mechanism Design
### General Design of DDAR  
The service requirement for data packets generated by each sensor is fixed, the service requirement of data can be directly mapped to the sensor. In DDAR, aggregators determine their service tag according to the number of service requirements of the nearby sensors. The value of \(N_{t}\) and \(T_{t}\) are calculated based on that tag. Then sensors select an aggregator as its data packets' destination. Meanwhile, each aggregator establishes a next-hop route in accordance with the tag. DDAR can be expressed as follows:  
* Aggregators identify their service tag.
* Aggregators configure \(N_{t}\) and \(T_{t}\).  
* Sensors determine the destination of their data.
* Aggregators choose an aggregator with the same service tag as their next hop.
* Service tags rotate.
# Design and Analysis of a Novel Floating Docking Mechanism for On-Orbit Refueling
## 4 Analysis and Discussion of Simulations and Experiments
### Dynamics Analysis of the Docking Mechanism
The results of the driving force are shown in Figure 24. The working conditions for driving force verification are the typical coupling conditions of (\(X\)\(-\)2 mm, \(Z\)\(-\)2 mm, Roll \(-\)1\({}^{\circ}\), Yaw \(-\)1\({}^{\circ}\), Pitch \(-\)1\({}^{\circ}\)). The results show that both the envelope and the maximum value of driving force of the mechanism with the elliptical-helical pipe are optimal, where the elliptical-helical pipe with center loading has 40% reduction compared with the circular-helical pipe with center loading, which verifies the superiority of the elliptical-helical refueling pipe for on-orbit berth  
Figure 22: Key positions of the docking: (**a**) Position A—Initial, (**b**) Position B—Contact and alignment I, (**c**) Position C—Probe insertion and alignment II, (**d**) Position D—Pin insertion and alignment III, (**e**) Position E—Accomplishing. (The explanation of position A—E can be found in Section 2.2.) Figure 23: Results of contact forces by simulation and experiment: **(a)** (X \(-\)2 mm), **(b)** (Roll \(-\)1\({}^{\circ}\)), **(c)** (Yaw \(-\)1\({}^{\circ}\)), **(d)** (X \(-\)2 mm, Roll \(-\)1\({}^{\circ}\), Yaw \(-\)1\({}^{\circ}\)). The three subplots of each condition are the curve of component forces of contact force, \(Fx\), \(Fy\), and \(Fz\) and time, from left to right; the points B, C, and D represent the corresponding key positions of docking in Figure 22.
## 2 Data and Methods
### Methodology
#### 2.3.2 Empirical Correlations  
The previous studies conducted by the geomechanics and geophysics researchers have led to extraction of different empirical correlations to estimate the \(V_{s}\) using other geological parameters. Each empirical correlation was proposed for a particular reservoir rock. In this research, the type of the reservoir rock is limestone; hence, only the well-known  
Figure 4: The correlations between the \(V_{s}\) and other well logging parameters obtained from the different logs.  
empirical correlations for limestone rocks have been used to estimate the \(V_{s}\) in the Asmari formation (Table 2). Those empirical correlations included those of [PERSON] et al., [PERSON], [PERSON] et al., [PERSON], and Anselmetti and Eberli. It is noteworthy to mention that only the Anselmetti and [PERSON] correlation estimates the \(V_{s}\) values using the rock density while the rest apply the compressional wave velocity (\(V_{p}\)) for \(V_{s}\) estimation [14; 15; 18; 23; 27].