esa-sceva/satcom-chunk-collection huggingface dataset:
# The 6G Ecosystem as Support for IoE and Private Networks: Vision, Requirements, and Challenges
###### Abstract  
The emergence of the sixth generation of cellular systems (6G) signals a transformative era and ecosystem for mobile communications, driven by demands from technologies like the internet of everything (IoE), V2X communications, and factory automation. To support this connectivity, mission-critical applications are emerging with challenging network requirements. The primary goals of 6G include providing sophisticated and high-quality services, extremely reliable and further-enhanced mobile broadband (feMBB), low-latency communication (ERLLC), long-distance and high-mobility communications (LDHMC), ultra-massive machine-type communications (tumMTC), extremely low-power communications (ELPC), holographic communications, and quality of experience (QoE), grounded in incorporating massive broad-bandwidth machine-type (mBBMT), mobile broad-bandwidth and low-latency (MBBLL), and massive low-latency machine-type (mLLMT) communications. In attaining its objectives, 6G faces challenges that demand inventory solutions, incorporating AI, softwarwarization, cloudification, virtualization, and slicing features. Technologies like network function virtualization (NFV), network slicing, and software-defined networking (SDN) play pivotal roles in this integration, which facilitates efficient resource utilization, responsive service provisioning, expanded coverage, enhanced network reliability, increased capacity, densification, heightened availability, safety, security, and reduced energy consumption. It presents innovative network infrastructure concepts, such as resource-as-a-service (RaaS) and infrastructure-as-a-service (IaaS), featuring management and service orchestration mechanisms. This includes nomadic networks, AI-aware networking strategies, and dynamic management of diverse network resources. This paper provides an in-depth survey of the wireless evolution leading to 6G networks, addressing future issues and challenges associated with 6G technology to support V2X environments considering presenting +challenges in architecture, spectrum, air interface, reliability, availability, density, flexibility, mobility, mobility, and security.  
6G; IoT; IoE; private networks; V2X +
Footnote †: journal:
## 2 C.F.R.P. Material Analysis
### Theoretical Assumptions  
The study of the mechanics of composite materials is performed with the help of specific theoretical tools due to the exhibited anisotropy. Composite structures follow the same basic principles for their analysis and incorporate specific criteria that are used to determine the failure point within their boundaries. Finally, it should be noted that such information is of high importance in order to examine the validity of a proposed design parameter set in the optimization procedure of a composite structure.  
The fabrication of race cars in formula student competitions is usually carried out through the use of composite sandwich panels, in order to shape the chassis structure. Moreover, to ensure the structural integrity of the structure, in the case of an accident, F.S.A.E. rules require that the fabricated composite chassis must possess equal or greater strength than the traditionally used steel space frames. In particular, the rulebook specifies the minimum levels of strength that must be exhibited by the manufactured structure to characterize it as safe to be allowed to race. Some examples of these specifications include perimeter shear strength, the main roll hoop attachment point strength, and the driver harness attachment point strength.  
As a result, due to the complex geometry of the structure and the number of different methods and composites that can be implemented to form it, the optimization procedure becomes very computationally intensive. For that reason, to reduce the complexity of the problem and contain its materialization within a sensible time frame, a series of assumptions that are presented in the following list were integrated into the design procedure.  
* The composite has a symmetric layup with respect to its z = 0 surface, shown in a latter chapter of the manuscript.
* Only a U.D. and a woven fiber C.F.R.P. with 0\({}^{\circ}\)/90\({}^{\circ}\) configuration are included in the analysis.
* The positioning angles of the composite prepres were selected to be only 0\({}^{\circ}\) and 90\({}^{\circ}\).
* The use of more than three UD laminates in direct succession is prohibited, so as to avoid the appearance of resin pools which induce local changes in properties in the composite and cause defects.
* The use of at least one outer protective layer of woven material is mandatory and cannot be assigned a value of zero by the optimization algorithm.
* The thickness of the honeycomb core is constant in the structure.
* A uniform lamination sequence is used throughout the structure.  
* The composite ultimately formed complies with the criteria that characterize it as a \"thin\" laminate.
Remote Sens._**2014**, \(6\), 1991-2007; doi:10.3390/rs6031991  
_remote sensing_  
**ISSN 2072-4292**  
www.mdpi.com/journal/remotesensing  
_Article_  
Evaluation of Multiple Spring Phenological Indicators of Yearly GPP and NEP at Three Canadian Forest Sites  
**[PERSON] 1,2, [PERSON] 1,*, [PERSON] 1, [PERSON] 1, [PERSON] 1 and [PERSON] 1**  
1 The State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China; E-Mails: [EMAIL_ADDRESS] (Q.W.); [EMAIL_ADDRESS] (T.W.); [EMAIL_ADDRESS] (Y.C.); [EMAIL_ADDRESS] (C.H.); [EMAIL_ADDRESS] (Q.T.)  
2 College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +86-10-6483-9450.  
_Received: 27 December 2013; in revised form: 24 February /Accepted: 24 February 2014 / Published: 5 March 2014_  
**Abstract:** Phenological shifts in events such as flowering and bud break are important indicators of ecosystem processes, and are therefore of particular significance for carbon (C) cycle research. Using long-term flux data from three contrasting plant functional type (evergreen and deciduous) boreal forest sites, we evaluated and compared the responses of annual C fluxes to multiple spring phenological indicators, including the C-uptake period onset (CUP onset), spring temperature (average value from March to May), and satellite-derived enhanced vegetation index (EVI) (average value from March to May). We found that the CUP onset was negatively correlated with annual gross primary production (GPP) for all three sites, but that its predictive strength for annual net ecosystem production (NEP) differed substantially among plant functional types. Spring temperature demonstrated particularly good potential for predicting both annual GPP and NEP for the evergreen sites, but not for the deciduous site. Spring EVI was demonstrated to have potential for predicting annual NEP for all sites. However, both plant functional types confounded the correlation of annual NEP with annual GPP. Although none of these phenological indicators provided consistent insight into annual C fluxes, using various currently available datasets our results remain potentially useful for the assessment of forest C cycling with future climate change. Previous analyses using only a single phenological metric should be considered with caution.
## 4 Simulation Experiments  
### Experimental Design  
To evaluate the effectiveness of the proposed method, a custom control program was developed using Visual Studio 2020 and VISSIM 4.3 simulation software. This allowed the creation of a customized simulation platform for vehicle-road collaboration. Various orthogonal adaptive experiments were designed, considering multiple factors, to investigate the control optimization effects. The primary objective of the experimental design  
Figure 7: Control scenario of speed guidance.  
is to investigate the adaptability of the optimization method under variations in four influencing factors: inflow traffic volume, transition segment length, desired speed, and the difference in cycle lengths between upstream and downstream intersections. The ranges and adjustment step sizes of the influencing factors of the adaptive experiments are shown in Table 1.  
The relevant simulation parameter inputs into the VISSIM software are shown in Table 2. After simulation processing, evaluation indicators such as straight-through capacity, average queue length, average vehicle delay, average travel time per vehicle, and average number of stops per vehicle were obtained for subsequent result analysis. Next, the combination of influencing factors with the best optimization effects in the adaptive experiments was selected as the experimental group. For comparison, two control groups were designed: Control Group 1, where the two intersections were optimized separately without applying the control strategy proposed in this paper, and Control Group 2, where the two intersections used the same cycle and employed the unidirectional coordination control method on the main road.  
The adaptive experiments utilize SPSS 27 data analysis software to design a four-factor, five-level orthogonal experiment. A random number of seed of 300 was set, generating a total of 25 sets of experimental combinations for each of the two transition types, resulting in a total of 50 sets of experimental combinations. Additionally, 25 control groups will be established for each of the two transition types with the same factor values but without the application of the coordinated control optimization method.
## Materials and Methods
### Data Sources and Our Processing  
The datasets used in this study include satellite imagery datasets, vegetation datasets, land use/cover datasets, meteorological datasets, topography data, and other related underlying datasets (Table 1). Figure 1: Overview of the Helong Section. (**a**) Elevation; (**b**) mean annual temperature; (**c**) mean annual precipitation; (**d**) land use/land cover types. The RAD (radiation absorbed dose) that we used is also used in the later PT-JPL model [25]. The calculation equation is as follows:  
\[R_{ns}=(R_{n}exp(-k_{R_{n}}LAI)) \tag{1}\]  
\[R_{nc}=R_{n}-R_{ns} \tag{2}\]  
\[R_{n}=R_{nshort}-R_{nlong} \tag{3}\]  
\[R_{nshort}=(1-p)I_{t} \tag{4}\]  
\[R_{nlong}=R_{ld}-R_{lu} \tag{5}\]  
\[R_{lu}=\sigma T^{4} \tag{6}\]  
In these equations, \(R_{ns}\) represents the net radiation reaching the soil surface and \(R_{nc}\) represents the net radiation intercepted by the canopy; \(k_{R_{n}}\) represents the extinction coefficient with a value of 0.6 [11; 26]; \(R_{n}\) is the net radiation, with \(R_{nshort}\) and \(R_{nlong}\) being the net shortwave radiation and net longwave radiation, respectively; \(p\) represents the surface albedo; \(I_{t}\) is the downward shortwave radiation; \(R_{ld}\) is the downward longwave radiation and \(R_{lu}\) is the upward longwave radiation; and \(\sigma\) is the Stefan-Boltzmann constant with the value of \(5.67\times 10^{-8}\) W\(\cdot\)m\({}^{-2}\). \(T\) represents the temperature (K) [27]. The units for all the radiation data used above are W\(\cdot\)m\({}^{2}\).
## 5 Random Linear Network Coding for 5G New Radio
### Related Studies on Mobile Video Delivery in 5G NR  
Without the goal of being exhaustive, we finalize this review paper with an overview of related work in the domain of mobile video delivery in 5G. Along with our discussion above, these papers could help the reader to identify possible research avenues in the domain of 5G mobile video delivery.  
Starting with the general papers on 5G architecture and services, the METIS project vision presents eMBB service that will support future UHD, multi-view or 360-degree video services [75]. For the  
Figure 8: The concept of Coded PDCP Duplication in 5G NR.  
core network, mobile edge computing (MEC) and edge content caching are identified as promising NG-RAN architectures that will greatly improve massive unicast OTT video delivery services such as ABR. In [76], the combination of network function virtualization (NFV) and MEC is explored for deployment of context-aware virtualized adaptive prefetching agents at the mobile edge that will provide QoS-guaranteed UHD video services. The work in [77] focuses on QoS provisioning in 5G mobile networks with emphasis on mobile video delivery services. In ultra-dense 5G network scenario, the work presented in [78] advocates local caching of video in small cells in combination with device-to-device (D2D) communication, while in [79], the authors consider network-aware ABR video content caching at the network edge combined with end-to-end video streaming resource allocation optimization. Edge caching is jointly optimized with multicast transmissions in what authors term as multicast-aware edge caching for 5G video delivery of popular content in [80]. We also point to the multi-server video streaming architecture for optimized ABR video delivery in 5G, relying on a combination of cloud RAN (C-RAN) and MEC concepts, as detailed in [81].  
In RAN domain, we emphasize several studies related to the work presented here. The work in [82] presents a study on high-quality high-throughput video streaming with enhanced reliability, which is achieved using a combination of end-to-end RLNC and 5G multi-connectivity via legacy LTE and 5G mmWave radio links. The combination of mmWave radio links and video caching at network edge is investigated as suitable solution for 5G mobile video in [83]. Our discussion on coded PDCP duplication draws inspiration from the recent work on optimized interface diversity for 5G ultra-reliable and low-latency services (URLLC) [84]. Both studies point out to the potential of using multi-connectivity (e.g., via carrier aggregation or dual-connectivity) for reliable and low-delay video delivery in 5G. Finally, we also emphasize the role of resource management in NG-RAN, as recently explored in 5G mobile vehicular video delivery in [85]. Resource management in NR-RAN will require novel ideas for optimized cross-layer video optimization exploiting both SDAP-based allocation of QoS flows to DRBs and MAC-based dynamic scheduling of radio resources to UE terminals.
# Digital Systems in Smart City and Infrastructure: Digital as a Service
## 6 Digital Transmission
### Local and Wide Area Area Network  
Local Area Networks [63] are restricted to a physical location enabling the Ethernet OSI Layer 2 wired connectivity to sensors that are normally fixed, such as CCTV Cameras or Wireless Access Points. Local Area Networks consist on copper Category (CAT) twisted pair or fibre data cables to connect devices and Ethernet Switches to transmit frames (Table 1). In addition, Power over Ethernet (PoE) allows the same cable to transmit data and power, reducing additional cabling requirements and space.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Name** & **Code** & **Standard** & **Data** & **Distance** & **Cable** \\ \hline Ethernet & 10 BASE-T & 802.3i-1990 & 10 Mbits & 100 m & Copper \\ Fast Ethernet & 100 BASE-T & 802.3u-1995 & 100 Mbits & 100 m & Copper \\ Fast Ethernet & 100 BASE-SX & 802.3u-1995 & 100 Mbits & 2000 m & Fibre \\ Giga Ethernet & 1000 BASE-T & 802.3 ab-1999 & 1000 Mbits & 100 m & Copper \\ Giga Ethernet & 1000 BASE-LX & 802.3z-1998 & 1000 Mbits & 5 km & Fibre \\
10 Gigabit Ethernet & 10 GBASE-T & 802.3 an-2006 & 10 Gbits & 100 m & Copper \\
10 Gigabit Ethernet & 10 GBASE-LR & 802.3 ae-2002 & 10 Gbits & 10 km & Fibre \\
100 Gigabit Ethernet & 100 GBASE-LR4 & 802.3 ba-2010 & 100 Gbits & 10 km & Fibre \\ Terabit Ethernet & 400 GBASE-LR8 & 802.3 bs-2017 & 400 Gbits & 10 km & Fibre \\ \hline \hline \end{tabular}
\end{table}
Table 1: Local Area Network (LAN) Specifications.  
Wide Area Networks (WAN) [64] connect the different Networks that cover the entire Smart Infrastructure (Table 2). WANs provide the OSI Layer IP 3 Connectivity to transmit IP Packets. Wide Area Networks can prioritize traffic with QoS. Routers are normally interconnected using public networks by ISP with several protocols and technologies such as Integrated Services Digital Network (ISDN), Multiprotocol Label Switching (MPLS), asynchronous transfer mode (ATM) and Frame Relay (FR). In addition, a Wide Area Network can be provided privately by the Smart Infrastructure by dedicated Routers with Routing Information Protocols (RIP), Interior Gateway Protocol (IGRP), Open Shortest Path First (OSPF) or Enhanced interior gateway routing protocol (EIGRP).
## 2 Real-Time City-Scale Nonlinear Time-History Analysis
### High-Performance Computing for Post-Earthquake Emergency Response  
High-performance computing (HPC) is incorporated to implement the real-time city-scale THA for the post-earthquake emergency response. The OpenMP library [30] is used to parallelize the code of the city-scale nonlinear time-history analysis. Only 136 s are required to complete the computation of a ground motion record (with an Intel Xeon E5 2630 @2.40 GHz CPU and 64 GB of RAM), which satisfies the requirement for the post-earthquake emergency response.  
Furthermore, cloud computing is introduced to perform coarse-grain parallel analysis. Specifically, a number of virtual computers are quickly established (in minutes) on the cloud computing platform (e.g., Aliyun Cloud or Tencent Cloud) after an earthquake. Subsequently, the analyses of different ground motion records are assigned to different virtual computers. Each virtual computer has nearly the same performance as the local computing environment. Thus, regardless of how many ground motions are to be computed, the time consumption is almost the same as that of a single ground motion. Meanwhile, the computational cost is less than one US dollar for each virtual computer. Such a flexible cloud computing platform makes the real-time analysis of a number of ground motions technologically and economically feasible.
## 3 Uncertainty Quantification of Macro Elastic Parameters Based on Non-Probabilistic Thought
\begin{array}{ll}x_{i}\cdot\cos\theta_{k}+x_{j}\cdot\sin\theta_{k} \leq K\cdot G\cdot\frac{\Delta_{\text{max}}^{ij}(\theta_{k})}{N}+x_{i}^{c} \cdot\cos\theta_{k}+x_{j}^{c}\cdot\sin\theta_{k}&i=1,2,\cdots,n-1\\ -x_{i}\cdot\cos\theta_{k}-x_{j}\cdot\sin\theta_{k}\leq K\cdot G\cdot\frac{ \Delta_{\text{max}}^{ij}(\theta_{k})}{N}-x_{i}^{c}\cdot\cos\theta_{k}-x_{j}^{ c}\cdot\sin\theta_{k}&j=i+1,\cdots,n\end{array} \right. \tag{23}\]  
As a result, it can be easily seen that the Formula (23) is a set of linear inequalities, which can be expressed as a convex polyhedral model. \[\mathbf{Ax}\leq\mathbf{B} \tag{24}\]  
where \(\mathbf{A}=\left(a_{ij}\right)_{M\times n}\) is the \(M\times n\) dimensional coefficient matrix. \(M\) is the number of linear inequalities constraints and \(\mathbf{x}=\left(x_{i}\right)_{n\times 1}\) is the \(n\)-dimensional macro uncertain mechanical parameter vector of composites. That is to say,  
\[\mathbf{A}\in\mathbf{R}^{M\times n},\mathbf{x}\in\mathbf{R}^{n},\mathbf{B}\in\mathbf{R}^{M} \tag{25}\]  
Furthermore, the uncertain-but-bounded macro elastic mechanical parameters \(\mathbf{x}\) can be represented as:  
\[\mathbf{x}=\sum\limits_{i=1}^{L}\alpha_{i}\mathbf{X}_{i},\sum\limits_{i=1}^{L}\alpha_ {i}=1,\alpha_{i}\geq 0,i=1,2,\cdots,L \tag{26}\]  
where \(\mathbf{X}_{1},\mathbf{X}_{2},\cdots,\mathbf{X}_{L}\) are the extreme points of the convex polyhedral model and \(L\) is the number of all extreme points. \(\alpha_{i}\geq 0,i=1,2,\cdots,L\) is the corresponding coefficient. Further, the correlation coefficient between any two elastic parameters can be evaluated as [39]:  
\[\rho(x_{i},x_{j})=\frac{u(x_{i},x_{j})}{u(x_{i})\cdot u(x_{j})} \tag{27}\]  
Here, \(u(x_{i})\) and \(u(x_{j})\) are auto uncertainties of uncertain elastic parameters \(x_{i}\) and \(x_{j}\), respectively. \(u(x_{i},x_{j})\) is the mutual uncertainty between them. Subsequently, the quantified macro elastic mechanical parameters of composites can be used for structural response analysis and reliability evaluations of fiber-reinforced composite laminates in further work. The flowchart of the proposed method is shown in Figure 3. The whole process of the proposed method includes three parts; namely, the micro- and macro-mechanical, non-probabilistic interval propagation by MCS and the prediction of macro-mechanical parameters.
## 4 Results and Discussion

Table 5: Maximum achieved range for different modes and wavelengths @ BER limit for ground-to-underwater communication under moderate scintillation. Table 7 shows measured results in terms of gain, noise figure (NF), input-output signal, SNR, and OSNR for the LG[0,0] mode in the weak scintillation scenario within pure sea underwater conditions. Furthermore, Table 8 illustrates the superiority of the proposed design over others. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Range** **(m)** & **Gain** **(dB)** & **NF** **(dB)** & **Input** **(dB)** & **Input** **(dB)** & **Input** **(Noise)** & **Input** **(dB)** & **Output** **(dB)** & **Output** **(dB)** & **Output** **(dB)** \\ \hline
5 & \(-75.02\) & \(75.02\) & \(-3.30\) & \(-81.17\) & \(77.87\) & \(79.91\) & \(-78.32\) & \(21.67\) & \(21.67\) \\
10 & \(-75.30\) & \(75.30\) & \(-3.31\) & \(-81.24\) & \(77.92\) & \(79.96\) & \(-78.62\) & \(21.37\) & \(21.37\) \\
15 & \(-75.64\) & \(75.64\) & \(-3.32\) & \(-81.26\) & \(77.94\) & \(79.98\) & \(-78.97\) & \(21.02\) & \(21.02\) \\
20 & \(-75.95\) & \(75.95\) & \(-3.30\) & \(-81.20\) & \(77.90\) & \(79.94\) & \(-79.25\) & \(20.74\) & \(20.74\) \\
25 & \(-76.22\) & \(76.22\) & \(-3.31\) & \(-81.23\) & \(77.91\) & \(79.95\) & \(-79.54\) & \(20.45\) & \(20.45\) \\
30 & \(-76.44\) & \(76.44\) & \(-3.30\) & \(-81.19\) & \(77.88\) & \(79.92\) & \(-79.75\) & \(20.24\) & \(20.24\) \\
35 & \(-76.77\) & \(76.77\) & \(-3.32\) & \(-81.28\) & \(77.95\) & \(79.99\) & \(-80.10\) & \(19.89\) & \(19.89\) \\
40 & \(-76.99\) & \(76.99\) & \(-3.31\) & \(-81.31\) & \(77.99\) & \(80.03\) & \(-80.31\) & \(19.68\) & \(19.68\) \\
45 & \(-77.29\) & \(77.29\) & \(-3.31\) & \(-81.15\) & \(77.83\) & \(79.87\) & \(-80.60\) & \(19.39\) & \(19.39\) \\
50 & \(-77.53\) & \(77.53\) & \(-3.31\) & \(-81.27\) & \(77.96\) & \(80.00\) & \(-80.84\) & \(19.15\) & \(19.15\) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Obtained results for LG[0,0] in the weak scintillation scenario. Figure 7: Eye diagrams at (a) 5 m, (b) 10 m, (c) 15 m, (d) 20 m, (e) 25 m, and (f) 30 m ground-to-underwater communication for pure sea.