esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In this paper, a review has been made on the generation of OAM vortex beams in the microwave domain. In a first step, we have presented the theory of Laguerre-Gaussian beams, where it is shown that they possess a well-defined orbital angular momentum. We have also reviewed the main classical methods used to generate such beams. They mainly include structures such as phase plates, reflectors and diffraction gratings for transforming plane waves to vortex waves and antenna arrays for direct radiation of electromagnetic waves carrying OAM. More recently, metasurfaces have revolutionized the way of tailoring electromagnetic properties and functionalities through the engineering of subwavelength elements. In this context, metasurfaces, whether they operate in a reflection or transmission manner, have been applied to transform plane waves to vortex waves carrying OAM. Moreover, functionalities such as Bessel beam profile and converging lens profile have been combined with OAM phase profile to be integrated in a single metasurface in order to reduce the intrinsic divergence characteristics of OAM beams.  
In the view of the implementation of metasurfaces for the generation of vortex waves carrying orbital angular momentum, practical applications in the area of wireless communications can be considered. OAM metasurfaces can indeed offer simple and low-cost systems, along with high efficiency and other combined functionalities such as lensing. However, efforts towards integrated illuminating sources still need to be done.  
Conceptualization, K.Z. and S.N.B.; writing--original draft preparation, K.Z., Y.W., Y.Y. and S.N.B.; writing--review and editing, K.Z. and S.N.B.; supervision, K.Z. and S.N.B.; funding acquisition, K.Z. All authors have read and agreed to the published version of the manuscript.  
National Natural Science Foundation of China (61771172, 61571155, 61401122).  
The authors declare no conflicts of interest.
# A Poisson Process-Based Random Access Channel for 5G and Beyond Networks +
## 3 Existing Contention-Based Random-Access Mechanism
### RRC Connection Request (\(\mathit{UE}\to\mathit{eNB}\))  
Channel services are delegated to the UE as specified in the previous step; hence, the UE sends an RRC link request and a scheduling request to eNB. In step 3, a message is forwarded to the temporary RNTI assigned in Step 2 of the RAR message and conveys either a particular RNTI if the eNB already has one RRC-associated UE, or an initial UE identity, or a randomly chosen number. However, colliding UEs seek to retransmit RA channel requests using the same UL procedure due to a collision in phase 2; consequently, further collisions occur in the network.
## 2 Research Methods and Technical Routes
### Literature Review
#### 2.1.2 Power Method  
In order to effectively improve the accuracy of a ship emission inventory, AIS is gradually applied to the compilation of ship pollutant emission inventory. Detailed information such as the speed, time, and position of AIS is used to reflect real-time dynamic activity information such as the ship's navigation trajectory and navigation conditions. This method is also known as the \"bottom-up\" method. This method records the activities of ships in detail and selects targeted emission factors according to the actual navigation status, which can reflect the spatial and temporal distribution characteristics of pollution emissions [19]. However, this method has high requirements on the model and requires a large amount of basic ship information and activity data. These data sources are different and difficult to obtain, so improper selection may easily lead to uncertainty.  
In 2009, [PERSON] et al. [20] used AIS data for the first time in the compilation of the Baltic Ship Pollution Emission Inventory. [PERSON] et al. [21] developed a highly resolved inventory of ship emissions in China's Pearl River Delta region using precise data from AIS. [PERSON] et al. [10] conducted field measurements around Qingdao Port and used various methods to evaluate and analyze the data to determine the pollution emission inventory of ships. Combined with regional emission factors, [PERSON] et al. [12] used the \"bottom-up\" method based on AIS data to establish a Tianjin high-space-time ship emission inventory. [PERSON] et al. [13] used the emission factor method of ship activities, combined with AIS data and ship characteristic information of Lloyd's Register of Shipping, and established the Ningbo-Zhoushan Port Ship Emission Inventory. [PERSON] et al. [11] adopted the \"bottom-up\" dynamic method, based on AIS data combined with a large number of field survey information of Xiamen ports, and established the 2018 Xiamen air pollution emission inventory. [PERSON] [22] combined the basic ship information database of the Maritime Safety Administration and the dynamic information of AIS to establish a high-precision ship air pollutant emission inventory in Shanghai Port. [PERSON] et al. [23] combined AIS data and Lloyd's Register database and used the STEAM model to establish a ship emission inventory for 10 control sections in the Jiangsu section of the Yangtze River.
## 6 Performance Evaluation
### Simulation Results Analysis
#### 6.2.2 PDR with FLTEM and without FLTEM of the Network with Stationary Topology  
Based on the topology shown in Figure 13, we increase the number of MNs and compare the PDRs of the routing protocols with FLTEM and without FLTEM. Especially, the MNs can adopt an arbitrary attack method, as listed in Section 2. The experiment results are shown in Figure 15.  
From Figure 15, it can be seen that when there are no MNs in the network, the PDR reaches 0.98. As the number of MNs increases, the PDR of the network, either with or without FLTEM, decreases. However, the PDR with FLTEM is generally greater than that without FLTEM. In addition, the PDR without FLTEM decreases sharply with the increasing number of MNs, while the PDR with FLTEM decreases slowly. The experimental results show that the FLTEM greatly improves the packet delivery rate and transmission reliability of the network with stationary topology.
# NOMA-Based Rate Optimization for Multi-UAV-Assisted D2D Communication Networks
## 1 Introduction
In view of the above challenges, this paper is devoted to the study of a UAV-assisted D2D-NOMA communication model focusing on the joint optimization of channel allocation, UAV trajectory design, and power control in order to achieve the dual enhancement of spectral efficiency and system capacity. Specifically, this paper addresses the resource allocation and optimization problem of multi-UAV cooperative work in complex 3D environments, including efficient channel allocation to avoid interference, optimization of UAV trajectories, and dynamic adjustment of power control to improve the overall system throughput. In disaster recovery scenarios, the NOMA-based multi-UAV-assisted D2D communication model proposed in this paper can rapidly deploy UAVs as temporary communication base stations to ensure that the communication needs in the affected area are met in a timely manner after a natural disaster or an emergency, thus enhancing the efficiency and coordination of rescue operations. In a smart city environment, the model supports the communication needs of high-density IoT devices by optimizing the resource allocation and trajectory planning of UAVs, enhancing the communication coverage and system throughput of the city, and facilitating the construction and operation of smart cities. Through this study, we aim to provide an efficient resource optimization scheme for next-generation wireless networks as well as theoretical support and technical guidance for the deployment of practical systems.
# A Multicast Routing Scheme for the Internet: Simulation and Experimentation in Large-Scale Networks
## 6 Experimental Analysis  
### Reference Multicast Scheme  
The primary goal of these experiments is to show that GCMR can operate successfully in the context of inter-domain routing on a large-scale network testbed. In this section, we experimentally compare GCMR only against the standard PIM-SSM since the external daemon named qPIM ([[http://www.nongnu.org/qpimd/](http://www.nongnu.org/qpimd/)]([http://www.nongnu.org/qpimd/](http://www.nongnu.org/qpimd/)) accessed on 15 September 2021) is available at this moment for Quagga. In contract, the BIER protocol has not been implemented yet.  
As described previously, PIM-SSM needs two extra protocols: a unicast routing protocol and a host-router subscriber protocol. During MDT creation, the former is required to create the entries in the routing table, from which PIM-SSM generates the MRIB. For our inter-domain scenario, we used the MP-BGP Quagga daemon. For the latter, the IGMP protocol is utilised to manage multicast group subscription between hosts and routers. As a forwarding engine, qPIM makes use of the Linux kernel's ipmroute daemon to analyse the MRIB and generate the TIB entries accordingly as well as to create the add/delete instructions for the configuration of the network interfaces.
# Automatic Modulation Recognition Using Compressive Cyclic Features
## 3 CS-based AMR
### Feature Extraction  
Based on the fact that the carrier frequency offset and the symbol rate can be properly estimated with compressive measurements respectively [18], feature extraction can be implemented by solving the problem expressed by (7). [PERSON] and [PERSON] [11] proposed a rough estimator as  
\[\hat{\mathbf{\eta}}=\mathbf{A}^{H}\mathbf{c_{z}}=\mathbf{\Phi}\mathbf{\Psi}^{H}\mathbf{c_{z}}, \tag{12}\]  
which can be interpreted as taking the fast Fourier transform (FFT) of the zero-padded vector \(\mathbf{c_{z}}\) (a length-\(N\) vector containing the \(M\) entries of \(\mathbf{c_{z}}\) at the nonuniform sample locations). It is easy to find that the estimator expressed by (12) is essentially the simplified Orthogonal Matching Pursuit (OMP) algorithm without further iterations. Moreover, since \(\mathbf{A}^{H}\) satisfies the RIP, Lemmas of [19] guarantee this reasonability of the rough estimator theoretically. Thus, the feature extraction can be described as:  
\[\hat{C}_{x}^{\beta_{0}}(\mathbf{0})_{(n,q)}=\mathbf{v}\mathbf{c_{z}}, \tag{13}\]  
where \(\mathbf{v}=\{e^{-j2\pi\beta_{0}n}\}_{n=0}^{N-1}\). Although this process is not a full-scale or exact recovery, it is simple and efficient enough for an AMR application.  
It is worth mentioning that the computation of TC in CS-based AMR is implemented with \(M\) compressive measurements, instead of \(N\) uniform samples in classical AMR. Then, after zero-padding, the \(N\)-point FFT is taken, just as in the classical method, such that a significant reduction of computational burden is achieved.  
In previous literature, the CC-based AMR is often realized by comparing the Euclidean distance between the measured feature vector and its counterparts of catalog patterns. In our work, a support vector machine (SVM) classifier is proposed. In SVM, groups of feature vectors are separated by the hyperplane, derived from previously generated training sets. The SVM is more valid than the aforementioned classifier, since it can not only perform the linear separation, but also the nonlinear separation by using a variety of kernel functions [20].
## 4 Discussion
However, to our knowledge, there are no studies in the literature regarding the correlation between SMFc and metal level in toenails of workers occupationally exposed to metals. For metal biomonitoring studies based on the non-exposed population, it is relevant to obtain data on diet, especially fish, mollusk or shellfish consumption, depending on the metals of interest. However, for those studying metal exposure levels in workers with occupational metal exposure, it might not be necessary for most metals, with exceptions like arsenic and cobalt. The present study has several limitations, including its cross-sectional design with only one spot measurement, which hampers the study of causality. The second limitation was the relatively small sample size of the population and the relatively smaller toenail sample size compared to the urine sample size, although this study had a much bigger sample size than many studies reported in the literature. We consider that the robustness of the toenail data and urine data should be evaluated under an absolute approach for each one rather than relatively by comparing one to the other. With 146 subjects included in the urine analyses, the precision and power of our study is higher for urine analyses than for toenail analyses (97 subjects). However, with 97 subjects with toenails, in the worst-case scenario (lowest prevalence of exposure (i.e., any type of seafood)), with 87 unexposed (to seafood) subjects and 11 seafood consumers, we have 80% power to detect differences (increase or decrease) of 29% between the means of the two groups assuming an SD of 1/3 of the mean and a type I error of 5%, which we believe is more than acceptable. We assessed multiple metals, which led to multiple comparisons; however, our results are in agreement with the international scientific literature. Some degree of residual confounding could be present due to lack of information on the geographic origin of the fish, mollusks and seafood being collected. The results of our study are also reinforced by some methodologic strengths. A large number of metals in both urine and toenails and their relationship with fish, mollusk and seafood consumption were included in the study. Moreover, a rigorous standardized protocol for toenail/urine preparation and analysis was applied to detect chemical levels after SMFc by taking into account information on the subjects' work activity and other relevant data. Information on fish, mollusk and seafood intake in the 8 h before sample donation was directly obtained from participants.
# Adaptive Handover Decision Using Fuzzy Logic for 5G Ultra-Dense Networks
## 5 Conclusions  
In this study, we have proposed an FL-based handover scheme for 5G UDNs. The proposed scheme can dynamically adjust two handover parameters, namely HOM and TTT, by using the SINR and horizontal moving speed of UE as inputs to an FL controller. The proposed scheme is designated FLDHDT. Similar to other handover decision schemes, signal strength is considered an important factor when making handover decision in FLDHDT. A higher signal strength inevitably leads to a higher HOM. Moreover, UE velocity is an input factor governing handover decision in FLDHDT. A higher UE speed warrants a shorter TTT. The proposed FLDHDT reduces the number of unnecessary handovers and shortens the interruption gap caused by hard handovers. Moreover, only the BSs in front of the UE can be the target BS candidates to ensure more accurate selection of target BS within a relatively short time. The superiority of FLDHDT has been verified by conducting a simulation in which it substantially improved the handover performance of 5G UDNs in terms of the number of handovers, ping-pong ratio, and overall system throughput compared to a conventional handover scheme, namely Event A3, and an FL-based handover scheme with dynamic adjustment of only HOM. In the future, we intend to design a more practical FL-based handover scheme by considering different coverages of small BSs and providing additional inputs to the FL controller, such as BS loading.  
Conceptualization, W.-S.H. and Y.-J.W.; methodology, W.-S.H., Y.-J.W. and M.-H.C.; software, T.-Y.C. and M.-H.C.; validation, T.-Y.C. and M.-H.C.; formal analysis, T.-Y.C.; investigation, T.-Y.C. and M.-H.C.; data curation, T.-Y.C.; writing--original draft preparation, W.-S.H.; writing--review and editing, Y.-J.W.; visualization, T.-Y.C. and M.-H.C.; supervision, W.-S.H.; project administration, W.-S.H.; funding acquisition, W.-S.H. All authors have read and agreed to the published version of the manuscript.  
This research was supported in part by Taiwan Ministry of Science and Technology under Grant No.: MOST 110-2221-E-992-029-MY2. And, the APC was supported in part by Shih Chien University Research Project with the Grant No.: USC-111-08-01008.  
The data used to support the findings of this study are available from the corresponding author upon request.  
We thank the anonymous reviewers for their constructive comments, which helped in improving the quality of this paper.  
The authors declare no conflict of interest.
# A Numerical Study on Computational Time Reversal for Structural Health Monitoring
## 3 Computational Time Reversal for Structures
### Illustrative Comparison  
We present here the results for a common setup by utilizing both approaches that arise from Sections 2.2.1 and 2.2.2. These TR approaches fulfil the partial information property [38]. We consider a simply supported beam of length L. During the forward step, excitation loading acts as a source on the \(0.65\,L\) point in the form of a Ricker pulse of Equation (19) both in the transverse and axial direction. Our selection of the monitoring quantity is that of the total energy density.  
As it can be observed in plots of Figure 2 together with the original point of source at \(0.65L\), two other smaller peaks (ghosts) are present. The ratio of the peak at the original source location to the ghosts' peaks is increased with the increasing number of receivers.  
The presence of ghosts is due to the bounded range of the domain and their location depends on the location of the receiver and boundary conditions. An in depth investigation has been presented in the literature [36] for the case of the acoustic field. In the current work, in the following section we present further results for the case of a beam and frame structures. Furthermore, in the plot of Figure 3, similar results for this specific example are presented for the switched boundary conditions' time reversal, presented in Section 2.2.2, with very satisfactory results in the absence of ghosts. However, the TR of Section 2.2.1 is a better choice since it is fully adaptive with possible unlimited SNR improvement.
# MAPSKEW: Metaheuristic Approaches for Partitioning Skew in MapReduce
## 2 Materials and Methods
### Metaheuristics
#### 2.3.1 Simulated Annealing  
In metallurgy, annealing is a process for hardening metals or glass by heating and cooling them gradually, allowing the material to reach a low-energy crystalline state [20]. Simulated Annealing (SA) can be seen as the process of making a ping-pong ball reach the bottom of an irregular surface. If we just let the ball roll, it can get stuck at a point other than the bottom, because the surface is uneven. In order to the ball to leave the current point and continue rolling, it is necessary for someone to shake the surface.  
In our problem, we want to attenuate the imbalance of the intermediate data allocated to the reducers. The solution based on SA is done as follows. An initial temperature and a cooling factor are defined. According to the intermediate data given as input, a key partitioning is created using the standard Hadoop partitioning function. We call this partitioning _current partitioning_. From this, we compare the current with a randomly generated neighbor partitioning. If the neighboring partitioning better balances the intermediate data allocated to each reducer, it becomes the current partitioning. Otherwise, the neighboring partitioning will become the current partitioning with a probability less than 1. This probability decreases proportionally to the current temperature. Finally, the current temperature is updated according to the cooling factor. This process is repeated until the temperature reaches 0. To facilitate understanding, we will show the pseudocode of this procedure.  
In the Algorithm 1, we aim to reduce the imbalance, which is the load of the reducer with the highest volume of data allocated. That is, in order to balance the load, we must partition the keys between the reducers so that the allocated load of the machine with the highest allocation is minimal.  
Our implementation consists of integrating Simulated Annealing to Hadoop, using it as the partitioning function. The partitioning phase works as follows: as the dataset is processed and the intermediate data is generated, Hadoop accumulates a portion of that data and the SA algorithm is executed to find an allocation that minimizes the unbalance of the reducers. This process will repeat itself until the end of the map phase.  
```
1:procedurePartitioner(\(D\),\(T\),\(C\),\(R\))
2:\(current\leftarrow\) partitioning of the keys of \(D\) using \(Hash(HashCode(D)\ mod\mid R\mid)\).
3:\(t\gets T\)
4:\(c\gets C\)
5:while\(T\
eq 0\)do
6:\(neighbor\leftarrow\)generateNeighbor(\(current\))
7:\(b_{1}\gets unbalance(current)\)
8:\(b_{2}\gets unbalance(neighbor)\)
9:if\(b_{2}<b_{1}\)then
10:\(current\leftarrow neighbor\)
11:elseif\(random(0,1)<e^{\frac{b_{1}-b_{2}}{t}}\)then
12:\(current\leftarrow neighbor\)
13:\(t=t-c\)
14:return\(current\)
```  
**Algorithm 1** Simulated Annealing
# Diode Laser Welding/Brazing of Aluminum Alloy to Steel Using a Nickel Coating
## 3 Results
### Mechanical Properties  
Figure 6 shows the microhardness profile of the interfacial region in the Al/Ni-coated steel joint and Al/bare steel joint. At least three measurements were conducted to obtain relatively accurate hardness results. For the Al/Ni-coated steel joint, the average microhardness of the interfacial layer at the direct irradiation zone was 318 HV, which was harder than FZ (\(\sim\)120 HV) and the Ni coating (\(\sim\)255 HV). With regard to the FZ root, the average microhardness of the interfacial layer was 278 HV. For the Al/bare steel joint, a similar microhardness of the interfacial layer was obtained at the direct irradiation zone and the FZ root, which ranged from 512 to 526 HV. It was apparently much harder than that of the Al/Ni-coated steel joint. It was reported that Fe\({}_{2}\)Al\({}_{5}\) was the hardest phase among all of the Fe-Al IMCs, which was responsible for the higher microhardness in the Al/bare steel joint. Besides, it was well established that a higher microhardness indicated a more brittle microstructure [8; 9]. That was to say, the interfacial reaction layer with the Al/Ni-coated steel joint could be much less brittle than that of the Al/bare steel joint.  
Tensile-shear testing results showed that the fracture load of the laser Al/Ni-coated steel joint reached 932 \(\pm\) 92 N, while the fracture load of the laser Al/bare steel joint was only 743 \(\pm\) 65 N.  
Figure 6: Hardness distribution across the interface of the Al/bare steel joint and Al/Ni-coated steel joint.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Point** & **Al** & **Fe** & **Zn** & **Ni** & **Possible Phases** \\ \hline P1 & 6.1 \(\pm\) 0.7 & - & 74.4 \(\pm\) 0.3 & 19.4 \(\pm\) 0.5 & Ni\({}_{5}\)Zn\({}_{21}\) \\ P2 & 68.9 \(\pm\) 0.9 & - & 3.5 \(\pm\) 0.3 & 27.6 \(\pm\) 0.4 & Al\({}_{3}\)Ni \\ P3 & 69.5 \(\pm\) 5.1 & - & 7.2 \(\pm\) 0.9 & 23.2 \(\pm\) 4.5 & Al\({}_{3}\)Ni \\ P4 & 71.6 \(\pm\) 0.5 & 23.8 \(\pm\) 0.1 & 4.6 \(\pm\) 0.5 & - & Fe\({}_{2}\)Al\({}_{5}\) \\ P5 & 10.8 \(\pm\) 3.6 & 9.7 \(\pm\) 1.8 & 79.5 \(\pm\) 3.1 & - & FeZn\({}_{10}\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Energy-dispersive X-ray spectrometer (EDS) analysis of the marked points in Figures 4 and 5 in at.%.  
It was concluded that the joint fracture load increased by about 26% after inserting the Ni coating. The improvement on the fracture load will be discussed later.