esa-sceva/satcom-chunk-collection huggingface dataset:
_Algorithms_**2015**, \(8\), 1129-1142; doi:10.3390/a8041129  
_algorithms_  
**ISSN 1999-4893**  
www.mdpi.com/journal/algorithms  
Efficiency Intra-Cluster Device-to-Device Relay Selection for Multicast Services Based on Combinatorial Auction  
**[PERSON] and [PERSON]** *  
Key Lab. of Broadband Wireless Communication and Sensor Network of Hubei Province, School of Information Engineering, Wuhan University of Technology, Wuhan 430070, China;  
E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +86-27-8729-0335.  
Academic Editor: [PERSON]  
_Received: 16 July 2015 / Accepted: 23 November 2015 / Published: 2 December 2015_  
**Abstract:** In Long Term Evolution-Advanced (LTE-A) networks, Device-to-device (D2D) communications can be utilized to enhance the performance of multicast services by leveraging D2D relays to serve nodes with worse channel conditions within a cluster. For traditional D2D relay schemes, D2D links with poor channel condition may be the bottleneck of system sum data rate. In this paper, to optimize the throughput of D2D communications, we introduce an iterative combinatorial auction algorithm for efficient D2D relay selection. In combinatorial auctions, the User Equipments (UEs) that fails to correctly receive multicast data from eNodeB (eNB) are viewed as bidders that compete for D2D relays, while the eNB is treated as the auctioneer. We also give properties of convergency and low-complexity and present numerical simulations to verify the efficiency of the proposed algorithm.  
**Keywords:** device-to-device communication; multicast services; D2D relay; combinatorial auction
# Radio Frequency Fingerprint Identification for 5G Mobile Devices Using DCTF and Deep Learning
## 5 Conclusions  
In this paper, the performance of RFF identification was studied for 5G mobile phones. The DCTF features were extracted from PRACH preambles. Because the PRACH preambles of formats A, B and C were generated from multiple root values, an index-based DCTF identification scheme was proposed for the case where the database of all 64 PRACH preambles was available at the gNB. The classification accuracy of this scheme reached 92.78% with a multi-channel CNN. When only a portion of the preamble sequences could be trained at the gNB, a _group_-based DCTF identification scheme was proposed, where the untrained sequences could also be identified based on the CNN of trained sequences within the same _group_. The preamble sequences generated from the same root value belonged to the same _group_. The classification accuracy of this scheme was 89.59% with a single-channel CNN. We found that the stability of the transient features within the same _group_ was not as good as that of the steady state features. To verify the performance of the proposed DCTF identification schemes, an experimental system was set up using a gNB platform and six 5G mobile phones, where the SNR of the system was 25 dB.  
**Author Contributions:** Conceptualization, methodology, validation, formal analysis and writing--original draft preparation, H.F.; methodology, software, validation, investigation, resources and writing--review and editing, H.D.; software, investigation, resources and writing--review and editing, J.Y.; resources, writing--review and editing and funding acquisition, L.P. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Key Research and Development Program of China under Grant 2022 YFB4300300, National Natural Science Foundation of China under Grants 62001106 and 62171120, Guangdong Key Research and Development Program under Grant 2020B0303010001, Jiangsu Provincial Key Laboratory of Network and Information Security (No. BM2003201) and the Purple Mountain Laboratories for Network and Communication Security.  
**Institutional Review Board Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.  
**Conflicts of Interest:** The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.  
Figure 20: Classification results of UE1 as a function of the overlap ratio.
# Matching of Energy Provisions in Multihop Wireless Infra-Structures
## 3 System Model
### Application Scenarios and Traffic  
This paper focuses on scenarios where it is necessary for BS to operate using power supplied by batteries. As specific examples, we mention night-time operation of smart sensor networks, and emergency operation of access networks. In a smart sensor network, STAs could be used by smart city monitoring devices such as sensors in smart buildings, smart power grids, and smart roads. A WMI network can be connected via the external network to services such as data centers providing storage and analysis of data collected from STAs. On the other hand, in emergency scenarios, such as in disaster areas, where local wired networks are disrupted, the WMI could be used by people to access emergency information and services in the external network.  
The flow of application traffic will depend on the application scenarios. For example, the traffic of the smart sensor network in a smart city may be polarized with most traffic flowing from sensors to the data centers in the external network [2; 37], as shown in Figure 1a. On the other hand, traffic due to persons communicating with each other may be more evenly distributed within the WMI. Accordingly, the traffic could be more polarized during the night, when people are less active and the traffic is dominated by sensor traffic or time shifting of Internet access [38].  
In the case of an WMI operating in an emergency scenario, as shown in Figure 1b, the majority of network traffic may be access to the external network, either by smart city elements reporting to data centers, or people communicating with emergency support centers [4; 37]. In this case, the traffic in the WMI may be highly polarized with a dominant flow between STAs and the GW. In both types of WMI scenarios, night-time operation of smart-sensor networks, and emergency scenarios, the energy consumption by nodes in the network may be highly differentiated due to the polarized traffic, depending on the position of the node and the routes to the GW.
# SiMFlex Micromanipulation Cell with Modular Structure
## 2 SiMFlex Cell Concept  
The SiMFlex micromanipulation cell is a modular construction concept, with the possibility of being reconfigured for different specific applications in micro assembly or grasping the small objects. The SiMFlex cell can work as an independent unit or with different auxiliary systems attached to it such as: sensors, additionally control system, cleaning system, etc. The compact size is a big advantage, so it can be very easily integrated in different places for manufacturing or micromanipulation purposes. The designed system (Figure 1) has three main modules: the base platform for working area, the micro positioning system, and the micromanipulation system. Each module has different design variants which can be combined for specific applications.  
The base platform that has the overall dimensions of \(136\times 136\) mm. and the possibility of vibration isolation in for the working area is presented in Figure 1.  
The micromanipulation system has three parts: compliant gripper with a piezoelectric stack actuator and jaws. This module will be discussed in the next chapter.  
The positioning system has translation stages for each axis: Ox, Oy, and Oz. The parts can be coupled in several ways in order to increase the precision along one specific axis. One example is presented in Figure 1a where the maximum displacements along Oz and Ox are 12 mm. Along the y axis, the maximum stroke value is 5 mm and a precise positioning with differential adjuster is realized.  
In Figure 1b, another variant is presented where the precise positioning part for displacement stroke is positioned along Oy axis. For the whole positioning system, with all three parts, the active workspace is \(12\times 12\times 5\) mm [14].
## 4 Description of the Proposed Transport System
### E2E Latency-Aware Path Computation (LAPC) Procedure
```
1:m, c, \(\lambda\), eCPRI line rates, link connections and distances
2:Low-latency routes from the RRHs to the BBU pool with E2E latencies
3:SelectInitialPaths()
4:CalculateE2 ELatency()
5:procedureBestPaths()
6:if(E2 ELatencyStream\({}_{\text{i}}>\) 250 \(\upmu\)s) then
7:ifLoad\({}_{\text{i}\text{ to }\text{ to }\text{ }}^{\text{FL}}\)! isEqual then
8: Shift Load\({}_{\text{LowLatencyFL}}^{\text{FL}}\leftarrow\)Load\({}_{\text{HighLatencyFLL}}^{\text{FL}}\)
9: ReselectRoute()
10: UpdateGlobalMatrix()
11: Repeatprocedure
12:elseifLoad\({}_{\text{i}\text{ to }\text{ to }\text{ }}^{\text{SL}}\)! is Equal then
13: Shift Load\({}_{\text{LowLatencySL}}^{\text{SL}}\leftarrow\)Load\({}_{\text{HighLatencySL}}^{\text{SL}}\)
14: ComputeNewRoute()
15: Calculate2 ELatency()
16:ifE2 ELatency\({}_{\text{stream}}^{\text{NewRoute}}<\)E2 ELatency\({}_{\text{stream}}^{\text{PreviousRoute}}\)then
17: SelectNewRoute()
18: UpdateGlobalMatrix()
19:else
20: RetainPreviousRoute()
21:endif
22: Repeat 10 to 20 for TLLs up to NLLs. 23:endif
24:endif
25: Repeat procedure for all streams
26:return GlobalMatrix
27:endprocedure
```  
**Algorithm 1** LAPC Algorithm
###### Abstract  
Gear failure caused by traveling wave resonance (TWR) generally occurs quite suddenly and causes catastrophic results in aero-engines. In this study, the TWR characteristics and stress distribution characteristics of a high-speed bevel gear in an aero-engine are analyzed in detail by means of experiments and simulations. Based on the acoustic waveguide system and dynamic stress test system, the TWR fatigue failure monitoring experiment of the central drive bevel gear in an aero-engine is carried out, and the TWR frequency, dangerous speed, dynamic stress and fatigue fracture characteristics of a driven bevel gear are obtained. Based on the transient dynamic analysis method and Hertz contact theory, the stress distribution characteristics of the driven bevel gear, which cannot be obtained in the test under the condition of TWR, are analyzed. The influence of the changes in the working temperature and the thickness of the spoke on the TWR characteristics and the stress distribution characteristics are discussed. The simulation and test results show that the gear has the problem of stress concentration at the root of the tooth and the back of the spoke plate under the 4 th node-diameter (ND) TWR, and the stress distribution form is consistent with the fracture form of the test gear, covering 12 teeth. The relationship between the stress at the test monitoring point and the maximum stress at the tooth root is obtained, and the generality of the relationship is verified. Based on this relationship, the maximum stress of tooth root, which is difficult to monitor in the test, is predicted to be 1271.7 MPa. An accurate and convenient means to obtain the maximum stress at the tooth root of the central transmission bevel gear under TWR is obtained so as to provide a basis for failure cause analysis and central transmission bevel gear design and lay the foundation for future research focusing on the propagation of the gear under TWR conditions.  
aero-engine; bevel gear; traveling wave resonance; stress distribution; fatigue fracture
## 3 Combined Numerical Forecast Model and Machine Learning  
In this section, we present our integrated numerical forecast model in conjunction with the ML approach. In the present study, our objective was to enhance the temporal forecasting of wind velocity and direction at a designated location within Lake Kinneret. To achieve this goal, we implemented an ML framework to refine predictions derived from the WRF atmospheric model, thereby augmenting prediction accuracy. As illustrated in Figure 2, the output of the WRF atmospheric model served as the input for the training process of the ML model, incorporating site-specific observations obtained from the IMS as the validation dataset. We begin this section by describing the research site, followed by a detailed discussion of the WRF simulation model, the training and validation dataset, and finally, our proposed ML model.
# Aerodynamic Study of the NASA's X-43A Hypersonic Aircraft
## 1 Introduction
[10], where the nonlinear longitudinal equations of motion were derived using the inverse square law gravitational model and the centripetal acceleration. [PERSON] et al. [11] studied numerically the influence of energy sources on the flow past X-43A at different angles of attack and showed that energy input in front of the bow of the aircraft led to a significant weakening of the bow shock wave and an increase in aerodynamic efficiency of the vehicle. [PERSON] et al. [12] developed a new in-house code capable of optimising the design of hypersonic propulsion systems under the consideration of the viscous stresses and the real gas effects. In [PERSON] et al. [13], the shock-wave structures appearing around the same hypersonic aircraft studied by the rest of the researchers cited in this paper were filmed using a high speed video camera and considering two different prototypes with small variations in the geometry. Visualisations of the flow at the engine entrance throat, where the shock waves reflected, were particularly interesting. The effects of thermochemical non-equilibrium in hypersonic flows were highlighted by [PERSON] et al. [14], demonstrating the importance of considering them. They studied the case of a hypersonic flow past a sphere as a test model for systems in strong chemical and thermal non-equilibrium conditions, mimicking the extreme environment experienced by objects entering a planetary atmosphere. A deep study of the main characteristics of the different hypersonic aircrafts was presented in [PERSON] et al. [15], where recommendations and required actions to be taken to design and configure the next generation of hypersonic vehicles was carefully addressed. An improvement of a basic shape aircraft geometry based on genetic algorithms was introduced in [PERSON] et al. [16]. The Mach number was maintained constant and equal to 7 throughout the study and different altitudes between 25 and 40 km were considered. In the present paper, two different models are developed in order to analyse the aerodynamics of a 2D geometry of X-43A given a certain Mach number, altitude and for different angles of attack. In the analytical model, the integral aerodynamic characteristics of the base geometry are calculated. Furthermore, these parameters are also evaluated when small modifications in the geometry of the aircraft are carried out. In the CFD model, not only are the main aerodynamic parameters presented, but also the pressure, density, temperature and Mach fields as well as the mass flow rate throughout the scramjet for different angles of attack and considering three different geometries. In order to perform a proper validation of the CFD model, an aerodynamic comparison between both models is brought at the beginning of Section 4. The present CFD results are also compared with those presented in [2; 3; 8]. Finally, a brief explanation of the nose optimisation study whose main objective is to find the configuration of the nose geometry of X-43A under which both oblique shock waves meet on the tip of the lower edge of the scramjet is introduced.
# Effects of the 12 May 2021 Geomagnetic Storm on Georeferencing Precision
###### Abstract  
In this work, we present the positioning error analysis of the 12 May 2021 moderate geomagnetic storm. The storm happened during spring in the northern hemisphere (fall in the south). We selected 868 GNSS stations around the globe to study the ionospheric and the apparent position variations. We compared the day of the storm with the three previous days. The analysis shows the global impact of the storm. In the quiet days, 93% of the stations had 3D errors less than 10 cm, while during the storm, only 41% kept this level of accuracy. The higher impact was over the Up component. Although the stations have algorithms to correct ionospheric disturbances, the inaccuracies lasted for nine hours. The most severe effects on the positioning errors were noticed in the South American sector. More than 60% of the perturbed stations were located in this region. We also studied the effects produced by two other similar geomagnetic storms that occurred on 27 March 2017 and on 5 August 2019. The comparison of the storms shows that the effects on position inaccuracies are not directly deductible neither from the characteristics of geomagnetic storms nor from enhancement and/or variations of the ionospheric plasma.  
Global navigation satellite system; geomagnetic storms; global positioning system; precise point positioning; total electron content; rate of change of the tec index
# Image Noise Reduction by Means of Bootstrapping-Based Fuzzy Numbers
## 2 Preliminaries  
In this section, we introduce the concepts that we use in the following sections.  
One of the definitions that is used in the fuzzy metric space is triangular norms (t-norms for short), which was first defined by [PERSON] and [PERSON] [16].  
**Definition 1** ([17]).: _A t-norm is a binary operation \(*:[0,\ 1]\times[0,\ 1]\rightarrow[0,\ 1]\), such that, for all \(a,\ b,\ c,\ d\in[0,\ 1]\), the following four conditions must be met:_  
* \(a*1=a\)_;_
* \(a*b\leq c*d\) _whenever_ \(a\leq c\) _and_ \(b\leq d\)_;_
* \(a*b=b*a\)_;_
* \(a*(b*c)=(a*b)*c\)_._  
_If the binary operation_ \(*\) _is a continuous function on_ \([0,\ 1]\times[0,\ 1]\)_, then the t-norm is said to be continuous._  
A fuzzy set of \(\mathbb{R}^{p},p\geq 1\) is a function of \(u:\mathbb{R}^{p}\rightarrow[0,1]\). Here, \(\mathrm{supp}\ u\) is the support of \(u\), where \(\{x\in\mathbb{R}^{p}\mid u(x)>0\}\). Furthermore, the \(\alpha\)-level set for each fuzzy set \(u\) is defined by  
\[L_{\alpha}u=\{x\in\mathbb{R}^{p}\mid u(x)\geq\alpha\},0<\alpha\leq 1.\]  
Suppose \(\mathcal{F}(\mathbb{R}^{p})\) is the collection of those fuzzy sets on \(\mathbb{R}^{p}\). Two fuzzy sets \(u\) and \(v\) are equal, written as \(u=v\), if and only if \(u(x)=v(x)\) for all \(x\) in \(\mathbb{R}^{p}\)[18].  
We denote \(d(u,v)\) as a metric in \(\mathcal{F}(\mathbb{R}^{p})\), i.e., for \(u,v,z\in\mathcal{F}(\mathbb{R}^{p})\):  
* \(d(u,v)\geq 0\);
* \(d(u,v)=0\) if and only if \(u=v\);
* \(d(u,z)\leq d(u,v)+d(v,z)\).  
Furthermore, the norm of \(u\) is defined \(\|u\|=d(u,I_{[0]})\), which \(I_{[0]}\) is the indicator function (the fuzzy set taking a value of one at \(0\) and zero for all \(x\
eq 0\)).  
Let \(\mathcal{F}_{c}(\mathbb{R}^{p})\) be the collection of those fuzzy sets \(u:\mathbb{R}^{p}\rightarrow[0,1]\) with the following properties:  
* \(L_{\alpha}u\) is compact for all \(0<\alpha\leq 1\);
* \(\mathrm{supp}\ u\) is compact;
* \(\{x\in\mathbb{R}^{p}|u(x)=1\}\
eq\emptyset\).
# Matheuristics for the Design of a Multi-Step, Multi-Product Supply Chain with Multimodal Transport
## 3 Problem Description
The objective of the supply-chain design is to minimize the sum of the fixed cost by opening factories and warehouses, the total purchasing costs, the total manufacturing costs, and the total transportation costs. This supply-chain design can be applied to global networks of manufacturing and distribution in industries such as pharmaceutics, automotive, and electronics. For example, in the production of drugs, many ingredients can be blended, purchased from suppliers in different countries, and distributed to customers in other continents using alternative transportation modes. A potential example is to have suppliers in Asia, shipping materials to plants in Europe to manufacture products, to be transported to markets in the US, Canada, and Brazil. Similar cases can be devised to manufacture vehicles, tablets, cell phones, computers, or vaccines. Some assumptions of the model that may represent limitations to its applicability are:  
1. It is a single-period problem that usually is applied to long-term planning. 2. The model is deterministic and is not considering variability, although being fast to solve is useful for what-if analysis. 3. The model is not incorporating other operational and strategic elements such as routing, sustainability, inventory, service level, etc.
# A Two-Archive Many-Objective Optimization Algorithm Based on D-Domination and Decomposition
## 2 Materials and Methods
### Two Archives Strategy  
The two-archive algorithm is a multi-objective evolutionary algorithm for balancing the convergence and diversity using the diversity archive (\(DA\)) and convergence archive (\(CA\)). The non-dominated solution and its descendants are added to \(CA\), while the dominated solution and its descendants are added to \(DA\). Solutions that exceed the \(CA\) and DA thresholds will be deleted. However, as the objective dimension increases, the size of \(CA\) increases enormously, leaving little space for \(DA\). When the total size of both archives' overflows, the removal strategy should be applied.  
First, the solutions in \(CA\) are classified by a set of weight vectors \((\gamma_{1},\gamma_{2},\dots,\gamma_{N})\) generated by the initial population. The purpose of this is to obtain a uniformly distributed solution set to maintain its diversity. The following formula is used to classify the solutions in \(CA\).  
\[CA^{i}=\bigg{\{}x\Big{|}x\in CA,\Delta\Big{(}F(x),\gamma^{i}\Big{)}=\max_{1\leq j \leq N}\Big{\{}\Delta\Big{(}F(x),\gamma^{j}\Big{)}\Big{\}}\bigg{\}} \tag{6}\]  
Figure 2: D-dominance example diagram.  
\[\Delta\Big{(}F(x),\gamma^{i}\Big{)}=\frac{\gamma^{i}*(F(x)-Z)^{T}}{||\gamma^{i}||*||F (x)-Z||},i=1,2,\ldots,N \tag{7}\]  
where \(Z=(Z_{1},Z_{2},\ldots,Z_{m})\) is a reference point, and \(\Delta\big{(}F(x),\gamma^{i}\big{)}\) is cosine of the angle between \(F(x)-Z\) and \(\gamma^{i}\).  
In the \(DA\) archive, to maintain the diversity, select the solution with the largest difference between any two distances in the population. The distance value of any solution is calculated by Formula (8).  
\[d(x)=\min\{||F(x)-F(y)||_{2}*||F(x)-F(y)||_{\infty}|y\in POP\cap y\
eq x\,\} \tag{8}\]  
where \(||F(x)-F(y)||_{2}\) and \(||F(x)-F(y)||_{\infty}\) represent the distance and difference of the two solutions individually.