esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Aluminium smelter waste (ASW) is a big contributor to landfills, and its recycling has been of great interest. This study investigates the tribological properties of aluminium matrix syntactic foams manufactured using an Al 6082 alloy and ASW. Ball-on-disc tests were conducted under both dry and lubricated conditions. Under dry sliding conditions, the coefficient of friction (COF) had an initial sharp increase, followed by a gradual decrease and finally a steady state as the sliding distance increased. The wear surfaces showed the presence of adhesive, abrasive and oxidative wear, with some presence of delamination. Syntactic foams containing small ASW particles led to a decrease in surface roughness, decrease in the average COF and decrease in specific wear. Heating large ASW particles before manufacturing the syntactic foams enhanced overall wear properties because the particles are hardened due to a compositional change. The T6 treatment of the syntactic foams enhanced the wear properties due to the hardening of the Al matrix. The average COF of the ASW syntactic foams was higher than that of the E-sphere syntactic foam, which was predominantly abrasive wear. The specific wear of the ASW syntactic foams can be higher or lower than the E-sphere syntactic foam, depending on the ASW particle size. Under lubricated sliding test conditions, the wear was reduced significantly, and the type changed from predominantly adhesive to predominantly abrasive. The porous ASW particles acted as lubricant reservoirs and provided a constant supply of lubricant, further improving the lubrication effect.  
s +
Footnote †: journal: Applied  
article foam; smelter waste reuse; friction; wear
## 1 Introduction
### Historical Background for IR Sounding and Atmospheric Motion Vector Winds
MISTic Winds observations may also provide a highly effective complement to a DWL, extending the impact of the more accurate vertical profiling achievable with a LIDAR over a narrow spatial field to the broader spatial field that a mapping observation such as MISTic Winds would provide. A recent OSSE performed by NASA's Global Modeling and Assimilation Office (GMAO) [23] simulating MISTic observations of vertically resolved winds and IR hyperspectral radiances has shown significant weather forecast impact from both the assimilated wind and radiance observations. Experimental evidence of weather forecast improvement from retrieved wind observations derived using hyperspectral sounding methods has recently emerged from the AIRS Polar Winds observing system experiments [24], where these satellite-derived moisture motion-vector winds have been shown to have the largest impact of any other observation in the data-sparse polar region. These improvements were achieved, even with the disadvantages of a relatively large AIRS footprint (15 km at nadir)and the relatively long (\(\sim\)100 min) period between observations available with polar-crossing LEO sun-synchronous orbit satellites.
# Effect of Zr Modification on NH\({}_{3}\)-SCR Reaction Performance of Cu-Ce/SAPO-34 Catalysts
## 3 Results and Discussion  
### NH\({}_{3}\)-SCR Catalytic Activity  
Figure 1 shows the test results of the catalyst denitration performance. In the temperature range of 150-500 \({}^{\circ}\)C, the NO\({}_{x}\) conversion increased with increasing the temperature. In the temperature range of 200-350 \({}^{\circ}\)C, the increase in the denitration performance of the catalyst was the most obvious. 4 Cu-4 Ce/SAPO-34 showed the most excellent denitration performance in the temperature range of 200-500 \({}^{\circ}\)C, which was consistent with the conclusion that metal Zr reduced the overall denitration performance of the catalyst to a certain extent [11]. At temperatures below 200 \({}^{\circ}\)C, the denitration performance of the 4 Cu-4 Ce-4 Zr/SAPO-34 catalyst was better than that of 4 Cu-4 Ce/SAPO-34, and we inferred that an appropriate Zr doping could improve the low-temperature activity of the denitration catalyst. Among all the trimetallic catalysts, the 4 Cu-4 Ce-4 Zr/SAPO-34 catalyst had the best denitration performance. In the range of 350-500 \({}^{\circ}\)C, the denitration efficiency was above 80%. In the range of 400-500 \({}^{\circ}\)C, the denitration efficiency was more than 99%. The reason why the Zr reduced the denitration efficiency of the catalyst and the reason for the better denitration performance of the 4 Cu-4 Ce-4 Zr/SAPO-34 catalyst need to be further explored.
## 3 Results and Discussion
### Performance of Enhancement Ephemeris Monitoring
#### 3.2.2 Autonomous Orbit Determination Ephemeris  
The autonomous orbit determination message and the actual broadcast ephemeris of the medium-circular orbit satellite C27 in the BDS-3 from 2020 were selected as an example for analysis.  
For the C27 satellite, the differences between the position extrapolated from the autonomous orbit determination ephemeris and the position calculated from the latest navigation ephemeris in the X, Y, and Z directions are shown in Figure 16 below.  
The statistical results and Gaussian fitting of the difference between the estimated ephemeris position of the C27 satellite's autonomous orbit determination and the actual calculated position in the (a) X, (b) Y, and (c) Z directions are shown in Figure 17 below.  
The monitoring accuracy of the comparison of the uplink injection message and the autonomous orbit determination message is less than the first one. Most of the differences in the three-dimensional position are less than 15 m, but the method still works.  
The statistical characteristics of the differences in the satellite's distance over time are shown in Table 2. This analysis of the characteristics of the differences in the ephemeris are very close to the normal distribution.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**X Direction (m)**} & \multicolumn{2}{c}{**Y Direction (m)**} & \multicolumn{2}{c}{**Z Direction (m)**} \\ \hline \multirow{4}{*}{C27} & Variance & 4.65 & 4.59 & 3.34 \\ \cline{2-5}  & Skewness & 0.09 & 0.002 & 0.11 \\ \cline{2-5}  & Kurtosis & 2.27 & 2.21 & 2.56 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Statistical results of the distance difference between the reckoned ephemeris position and the actual calculated position of the C27 satellite in the X, Y, and Z directions.  
Figure 15: Statistical results and Gaussian fitting of the differences in the (**a**) X, (**b**) Y, and (c) Z directions.  
Figure 16: The differences between the estimated ephemeris position of the BDS-3 C203 satellite’s autonomous orbit determination and the actual calculated position in the (**a**) X, (**b**) Y, and (**c**) Z directions.  
Figure 17: Statistical results and Gaussian fitting of the difference between the estimated ephemeris position of the C27 satellite’s autonomous orbit determination and the actual calculated position in the (**a**) X, (**b**) Y, and (**c**) Z directions.  
Figures 18 and 19 show the differences between the extrapolated ephemeris position of the autonomous orbit determination and the actual calculated position of the C27 satellite in the X, Y, and Z directions. In the first 10 days, the results are in good agreement. As time goes by, the accuracy of the autonomous orbit determination messages decreases and the difference gradually increases. The difference in the X and Y direction gradually increases, with the largest difference appearing on Days 25 to 35, and then gradually converges to a stable value. The value of the difference increases monotonically in the Z direction. The statistical results are shown in Table 2.
# An Inference Framework of Markov Logic Network for Link Prediction in Heterogeneous Networks
## 4 Proposed Method
### VEM-Based Inference
#### 4.2.3 Inference Algorithm  
To calculate the joint distribution of fact nodes in each substructure, we use the VEM-based inference to update the parameters of GCNs in the variational E-steps and M-steps given the observed fact nodes.  
We first initialize \(\mathbf{w}\) with a normal distribution for training \(\mathbb{W}_{p}\) with \(V_{o}^{i}\) in the M-step. Then, in the variational E-step, we optimize the variational distribution \(Q(V_{u}^{i})\) with the output \(P(V_{u}^{i}|V_{o}^{i})\). In the M-step, we optimize the posterior distribution \(P(V_{u}^{i}|V_{o}^{i})\) with \(Q(V_{u}^{i})\) calculated by Equation (7). Then, we alternatively optimize \(Q(V_{u}^{i})\) and \(P(V_{u}^{i}|V_{o}^{i})\) until their KL divergence is less than the given threshold or the given maximal iteration number is reached.  
We summarize the above ideas in Algorithm 2. In the worst case, the time complexity is \(O(T_{1}\cdot|E_{\vec{s}}|\cdot\mathcal{L})\), where \(T_{1}\) is the maximal iteration number of steps 7\(\sim\)13, \(|E_{\vec{s}}|\) is the average number of edges in each substructure, and \(\mathcal{L}\) is the number of layers in GCN.  
```
0: the threshold \(\varrho\) of KL divergence, the MLN substructure \(g^{i}\), the observed fact nodes \(V_{o}^{i}\), the weights \(\mathbf{w}\), the unobserved fact nodes \(V_{u}^{i}\)
0: posterior distribution \(p(V_{u}^{i}|V_{o}^{i})\)
1: Initialize \(\mathbf{w}\) and \(\mathbb{W}_{p}\)
2: Obtain \(\mathbb{F}_{p}\) with \(V_{o}^{i}\) by Equation (12)
3: Obtain \(\mathbb{A}\) by edges of \(g^{i}\)
4: Optimize \(\mathbb{W}_{p}\) by Equation (13)
5:\(p(V_{u}^{i}|V_{o}^{i})\gets Softmax(\mathbb{A}\mathbb{F}_{p}\mathbb{W}_{p})\) // by Equation (11)
6:\(j\gets 1\)
7:while\(KL(p(V_{u}^{i}\mid\mathbb{W}_{p})\parallel Q(V_{u}^{i}\mid\mathbb{W}_{q}))\geq\varrho\)do
8: Update \(\mathbb{W}_{Q}\) in E-step by Equation (10)
9: Optimize \(Q(V_{u}^{i})\) by Equation (7)
10: Update \(\mathbb{W}_{p}\) in M-step by Equation (13)
11: Optimize \(P(V_{u}^{i}|V_{o}^{i})\) by Equation (11)
12:\(j\gets j+1\)
13:endwhile
14:\(T_{1}\gets j-1\)
15:return\(P(V_{u}^{i}|V_{o}^{i})\)
```  
**Algorithm 2** VEM-based inference  
Figure 3: Illustration of feature construction for fact nodes, where white circles and green circles represent unobserved and observed nodes, respectively.
## 3 System Models and Assumptions
### Clustering Model  
Clustering is used as a routing protocol in the network. Cluster-based routing is an efficient approach to maintain a system's scalability, lifetime, and energy efficiency within a cluster. It enables clusters to perform data aggregation in order to decrease the number of transmitted messages to the access point (AP). In our schemed, clusters are formed using the Enhanced Distributed Energy Efficient Clustering (E-DEEC) [37] protocol, where cluster head selection is made based on nodes' residual  
Figure 1: The network model.  
energy. E-DEEC uses a three-level heterogeneous network concept, where the system has three types of nodes: normal nodes, advanced nodes, and super nodes. Each type of node has its own initial energy. Normal nodes have \(E_{0}\) energy. Advanced nodes have \(a\) times more energy than normal nodes, i.e., \(E_{0}(1+a)\). Super nodes have \(b\) times more energy than the normal ones, i.e., \(E_{0}(1+b)\).  
In E-DEEC, any node can be chosen to be a cluster head. A node can be selected as a \(CH\) if it has more residual energy than the total average energy of the network. This way, we will ensure that super nodes and advanced nodes are not exhausted, and, hence, it prolongs the lifetime of the network. Cluster heads are chosen based on a probability value \(p_{i}\) that is computed differently based on the node's type. \(p_{i}\) can be computed as follows:  
\[p_{i}=\begin{cases}\frac{\mathit{P}_{opt}E_{i}(r)}{(1+m(a+m_{0}b))E(r)}&\text {if $s_{i}$ is a normal node}\\ \frac{\mathit{P}_{opt}(1+a)E_{i}(r)}{(1+m(a+m_{0}b))E(r)}&\text{if $s_{i}$ is an advanced node}\\ \frac{\mathit{P}_{opt}(1+b)E_{i}(r)}{(1+m(a+m_{0}b))E(r)}&\text{if $s_{i}$ is a super node}\end{cases} \tag{1}\]  
where \(p_{opt}\) is the probability of the optimum number of cluster heads set by the network administrator, \(E_{i}(r)\) is the remaining energy of node \(i\) of the \(r\)th round, \(m\) and \(m_{0}\) are the fractions of the number of advanced nodes and super nodes, respectively, \(a\) and \(b\) are the energy enhancement levels of advanced nodes and super nodes, respectively, and \(\overline{E}(r)\) is the average energy of the network at the \(r\)th round.
# Robust Full-Screen Wavelength Calibration Algorithm
## 3 The Proposed Calibration Method
### Functional Relation at Non-Central Pixels  
After obtaining the optimal \(\theta\) by Algorithm 1, Equation (5) can be established and the wavelengths at central pixels can be considered accurately calibrated. However, for the non-central pixels, further processing is required. Notice that, in the actual operation of the spectrometer, outliers often occur due to inherent instrument error or operation mistakes, which inevitably affect the performance of the calibration algorithm. As a result, it is necessary to make the algorithm robust to outliers while maintaining a high wavelength accuracy. As the Huber regression and Turkey regression are robust to outliers (the detailed argument is presented in Section 3.2.1 and 3.2.2), we compare the performance of Huber regression, Tukey regression, and least squares regression methods with the collected experimental data, so that a more suitable approach can be found. We also tried some other robust regression methods, such as RANSAC regression and Theil Sen regression. However, we found that they performed a little worse than Huber regression (the detailed argument is presented in Section 4.1). To simplify this paper, other robust regression algorithms are not listed.  
In this part, the data were collected as described in Appendix A.2.1 of the Appendix A. We divide the data into two cases: central pixels (\(x_{c}\), \(p_{c}\)) and non-central pixels (\(x_{n}\), \(p_{n}\)). For the standard wavelength \(\lambda_{c}\), we first performed a preliminary analysis with \(\Delta x=x_{n}-x_{c}\), \(\Delta p=p_{n}-p_{c}\). The \(p_{n}\) value ranges from 1 to 2048. By exploring the scatter plot of \(\Delta x\) and \(\Delta p\), as shown in Figure 2, a linear relationship between them is demonstrated. According to this fact, we will fit a linear regression model.  
Figure 2: The scatter plot of \(\Delta x\) and \(\Delta p\).  
Suppose that the model between \(\Delta x\) and \(\Delta p\) is  
\[\Delta x=e\Delta p+f+\epsilon, \tag{8}\]  
where \(\Delta x\) and \(\Delta p\) indicate the response and independent variables, respectively. The symbols \(e\) and \(f\) are unknown regression parameters, and \(\epsilon\) is the random noise term. We denote \(\beta=[e,f]\), \(f_{\beta}(\Delta p)=\beta[\Delta p,1]^{\top}\).  
The core idea of our proposed non-central pixel algorithm is to estimate the feedback value \(x_{c}\) when the feature peak \(\lambda_{c}\) is at the non-central pixel \(p_{n}\) according to the established Equation (8), given the known values of \(p_{c}\), \(p_{n}\)and \(x_{n}\). In other words, \(x_{n}\) at \(p_{n}\) can be converted to \(x_{c}\) at \(p_{c}\) by Equation (8). Since the wavelength of the central pixel \(p_{c}\) was accurately calibrated by Equation (5), the wavelength of the non-central pixel can be calibrated by the feedback value \(x_{c}\) estimated using the above procedure. By this means, we calibrated the wavelengths of the non-central pixels.
## 4 Classification of MAC Protocols Based on Channel
### Cooperative Single-Channel MAC Protocols  
It basically deals with packet relay from the source to destination in an cooperative way. It improves the reliability of transmission and the throughput of channels.
# Design of Road-Side Barriers to Mitigate Air Pollution near Roads
## 2 Methodology
### Barrier Configuration and Location  
With the purpose of studying the effect of the barrier geometry on the dispersion of pollutants downwind the roads, three different geometric structures were considered: (i) a vertical straight barrier which is the most used shape in barrier studies, (ii) a 45\({}^{\circ}\) inclined straight barrier, which has shown a significative effect in pollutant dispersion ([PERSON], 2016), and (iii) a quarter-ellipse with zero eccentricity which is a shape found in some highway-type noise barriers. For the last two cases, the possibility that the barriers could be tilted towards and from the road was considered.  
As mentioned before, height has been found to be the most significant variable affecting pollutant dispersion. As such, a set of barrier heights (\(h\) = 1.5, 3.0, 4.5, and 6.0 m) was considered. It is important to note that most transport agencies around the world recommend noise barriers to be at least 1.5m above ground in order to achieve satisfactory noise mitigation, however, additional factors such as visual impact, public privacy, shading, air circulation, and visibility must be also considered ([PERSON], 2016; [PERSON], 2016; [PERSON], 2016). The 6.0 m case was of particular interest since the resulting pollutant concentrations can be directly comparable with the experimental measurements obtained in the Near Road Tracer Study (NRTS08) campaign ([PERSON] et al., 2018). The effects of the barrier location on pollutant dispersion were also studied. Barriers located at distances \(d\) = 0, 6, 10, 30 m downwind the road were considered.  
Figure 1: Computational domain considered in this study. Length scale is not uniform on the illustration.
# Network Slicing for Beyond 5G Systems: An Overview of the Smart Port Use Case
## 5 ML for Mobility Prediction
Prior to the fulfillment of HO criteria, RAN signaling related to HO is executed by using the prediction outcome, which results in less signaling overhead and latency. A graphical representation is shown in Figure 6. Results show that intra-CBS HO latency is reduced by 26% as compared to conventional HO. In contrast, the inter-CBS HO can generate higher signaling load as compared to single HO. Though, it is not practical to use the Markov-chain model to predict the future DBS HOs. There are two kinds of stochastic variables (state variable and output variable) set by the hidden Markov model (HMM) [37]. These variables relate the realistic environment of observed object, and a Markov chain is formed by the state sequence. Sometimes these measured values cannot map to practical environments due to inaccuracy of observation. According to our observation, signalling reduction gain can be achieved up to 60% in inter-CBS HO. Smart cell controller can be introduced within a CBS footprint. In [38], authors proposed a scheme where a designated cell controller within a cluster handles a set of small cells and keeps the forwarding information within a localized mobility management entity (MME) for user equipments (UEs). Moreover, the data forwarding information is forwarded from the small cell controller to the serving gateway (SGW) by creating a data path tunnel. Although, this proposed scheme is not based on ML and CSDA architecture but results shows that signaling reduction gain has been achieved by up to 58.1%. Better results can be achieved by employing ML techniques and CSDA architecture as it has been discussed in [39].
# Chaotic Orthogonal Composite Sequence for 5G NR Time Service Signal Capture Algorithm
###### Abstract  
Establishing a national comprehensive PNT (Positioning, Navigation, and Timing) system has become a consensus among major countries worldwide. As a crucial component in completing the entire PNT system, the 5G NR (new radio) time service signal plays a vital role. This paper proposes a 5G NR time service signal that uses a spread spectrum system, shares the 5G signal frequency band, but does not occupy the bandwidth of the 5G communication signal. This timing service signal has relatively low power, making it appear \"submerged\" within the power of the 5G communication signal. The spread spectrum code for this timing signal employs the chaotic orthogonal composite sequence proposed in this paper. Compared to traditional spread spectrum sequences, this sequence offers better security than m-sequences, improved autocorrelation than Walsh sequences, and an effective suppression of the short-period characteristics exhibited when the Skew Tent-Map chaotic sequence takes special values. This paper simulates the capture of the 5G NR time service signal in an environment with a signal-to-noise ratio of 10 dB using an FFT-based parallel code phase search algorithm, successfully capturing the 5G NR time service signal and verifying the feasibility of the proposed chaotic orthogonal composite sequence as a spread spectrum code.  
PNT system; 5G; 5G NR time service signal; chaotic orthogonal composite sequence; timing signal capture +
Footnote †: journal: Electronics  
article
## Assessing Routing Strategies for Cognitive Radio Sensor Networks  
**[PERSON] 1,*, [PERSON] 1, [PERSON] 1 and [PERSON] 2**  
1 UTM-MIMOS Centre of Excellence in Telecommunication Technology,  
Faculty of Electrical Engineering, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru,  
Malaysia; E-Mails: [EMAIL_ADDRESS] (N.F.); [EMAIL_ADDRESS] (Y.S.B.)  
2 Center of Excellence in Information Assurance (CoEIA), King Saud University, 11195 Riyadh,  
Saudi Arabia; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +6-011-1618-2006; Fax: +6-07-5566272.  
_Received: 5 July 2013; in revised form: 5 September 2013 / Accepted: 5 September 2013 / Published: 26 September 2013_  
**Abstract:** Interest in the cognitive radio sensor network (CRSN) paradigm has gradually grown among researchers. This concept seeks to fuse the benefits of dynamic spectrum access into the sensor network, making it a potential player in the next generation (NextGen) network, which is characterized by ubiquity. Notwithstanding its massive potential, little research activity has been dedicated to the network layer. By contrast, we find recent research trends focusing on the physical layer, the link layer and the transport layers. The fact that the cross-layer approach is imperative, due to the resource-constrained nature of CRSNs, can make the design of unique solutions non-trivial in this respect. This paper seeks to explore possible design opportunities with wireless sensor networks (WSNs), cognitive radio _ad-hoc_ networks (CRAHNs) and cross-layer considerations for implementing viable CRSN routing solutions. Additionally, a detailed performance evaluation of WSN routing strategies in a cognitive radio environment is performed to expose research gaps. With this work, we intend to lay a foundation for developing CRSN routing solutions and to establish a basis for future work in this area.  
**Keywords:**_ad-hoc_ networks; cognitive radio; cross-layer; wireless sensor network; routing