esa-sceva/satcom-chunk-collection huggingface dataset:
# Future Industrial Applications: Exploring LPWAN-Driven IoT Protocols
## 6 Open Issue
### Integration  
Integrating LPWAN protocols using unlicensed spectrum with cellular protocols can be rewarding but a very challenging task. The key performance indicators of these protocols differ from each other. Whereas cellular protocols provide better data rates, lower latency, and better QoS, the ISM band protocols can provide more range and less power consumption at a lower cost. Depending on the specific task, an IoT use case scenario can require all of these. In such a scenario, an LPWAN-5G integration can help users in many ways. However, this integration leads to several challenges. The hybrid architecture challenge occurs due to different mechanisms and different protocols used to communicate with the server. In order to make a successfully converging core network, the routing of different protocols needs to be carefully addressed. Cellular protocols use 3 GPP security methods, whereas noncellular protocols' security is often comparatively weak. Another challenge noncellular protocols suffer from is their weak ability to handle moving UEs, which can be addressed using a hybrid core utilizing both cellular and noncellular protocols. A hybrid core can be designed using Universal Mobile Telecommunications System Subscriber Identity Module (USIM) for LoRaWAN gateway, creating evolved packet data gateway (ePDG) in 5G core, incorporating LoraWAN gateway in cellular eNB, and vice versa. More research should be conducted to evaluate the performance of an overall scenario when simultaneously utilizing two or more protocols in the same system. To tackle security integration, network-independent security solutions need to be researched for LPWAN authentication. Currently, the 5G architecture mostly uses non-standalone architecture (NSA), which utilizes NR with the evolved packet core. With the upcoming 5G standalone architecture (SA), core hybridization can lead to a reduction in complexity and cost of deployment, operation, and maintenance. Also, a unified data management system will be required for such a scenario [37; 144].
## 2 Materials and Methods
### Data and Preprocessing
#### 2.2.3. Mangrove Data  
Current mangrove distribution: According to the vegetation map of Hainan Island, the distribution range of mangroves was preliminarily determined. Then, we visually interpreted the distribution based on the \"Jilin-1\" and \"Inner Mongolia-1\" high-resolution (0.5 m spatial resolution) multi-spectral (RGB and NIR) satellite images purchased from CHANG GUAN Satellite Technology Co. Ltd. (Changchun, China) ([[http://www.jll.cn](http://www.jll.cn)]([http://www.jll.cn](http://www.jll.cn)), accessed on 15 January 2023), China's first commercial remote sensing satellite company, which were taken on 22 June 2022. Combining the results of visual interpretation with the field survey of mangrove distribution areas on Hainan Island from June to August 2022 (using drones and manual methods), the distribution of mangroves was finally confirmed. Historical mangrove distribution: The Global Mangrove Watch (GMW) dataset version 3.0 [69] provides vector data of mangrove distribution in 11 time periods from 1996 to the present and was used to obtain the historical distribution of mangroves in this study ([[https://www.globalmagnrovewatch.org](https://www.globalmagnrovewatch.org)]([https://www.globalmagnrovewatch.org](https://www.globalmagnrovewatch.org)), accessed on 15 January 2023). The estimated accuracy of GMW mangrove extent maps was 87.4%. In this study, GMW was utilized to estimate the age range of the directly impacted mangroves and then to ascertain whether they belong to mature forest. Field data collection: Field surveys were conducted on the mangrove distribution areas impacted by the chosen tropical cyclones, and three sets of 10 \(\times\) 10 m quadrats were established in the subtidal, intertidal, and supratidal zones. Data collected included diameter at breast height (DBH), canopy height (H), tree density, and so on. The surveys on the distribution of mangrove species and dominant species were manually identified and determined in the entire affected mangrove distribution area. The survey was conducted in August 2022.
# Performance Modeling Framework for IoT-over-Satellite Using Shared Radio Spectrum
## 3 IoT-over-Satellite Performance Modeling
### Terrestrial Interference Model
#### 3.3.2 SINR Model and Communication System Model  
The PER is commonly used to evaluate the link quality and link availability in the communication system, and it is a function of the ratio of energy per bit to the spectral noise density \((E_{b}/N_{0})\) with given noise power, interference, path loss and a modulation scheme of the link as shown in the following:  
\[\begin{split}\text{PER}&=M_{\text{MCS}}\left(E_{b}/ N_{0}\right)\\ E_{b}/N_{0}&=10\log_{10}\left(\frac{P_{\text{R}}}{P _{\text{I}}+P_{\text{N}}\times\frac{R}{R_{b}}}\right)\\ \text{SINR}&=\frac{P_{\text{R}}}{P_{\text{I}}+P_{ \text{N}}}\,\end{split} \tag{15}\]  
where \(M_{\text{MCS}}(.)\) is a function based on the modulation and coding scheme (MCS). Here, \(E_{b}/N_{0}\) is calculated by the SINR times the channel bandwidth \((B)\) divided by the bitrate of the channel (\(R_{b}\)). The linear expression of the SINR is calculated by the received signal power \((P_{\text{R}})\) divided by the interference in Equation (13) plus the system noise power \((P_{\text{N}})\). Thus, the uplink performance can be estimated by the PER of the IoT-over-Satellite communication system by given the SINR.
## 5 Simulation Experiments
### Percentage of alive nodes per established communication session (PALCS)  
Routing protocol in SD-EAR balances the load between various nodes in the network and greatly reduces the overall energy consumption. The reduction in message cost cuts down on the energy consumption in various nodes. Moreover, balancing the load prohibits overwhelming differences between the residual energy of various nodes. This increases the number of alive nodes in different communication sessions, as shown in Figures 15 and 20.
This can simplify the management of complex maritime communication networks, improve network efficiency, and reduce operational costs. This research focuses on laying the groundwork for the modernization of maritime communications in the era of the IoT. It addresses the vital need to ensure the safety of crew members, vessels, and cargo in the maritime industry, which plays a crucial role in global trade. The adoption of advanced technologies, including unmanned aerial vehicles, autonomous underwater vehicles, and the Internet of Ships, offers opportunities for enhanced communication and operational efficiency. However, this technological evolution also introduces new security and network management challenges, necessitating a comprehensive evaluation of the current state of maritime communications and the impact of SDRs and SDNs as enablers of IoT in the maritime sector. This research seeks to address these critical issues to enhance maritime operations, safety, and security. Our key contributions in this article are as follows:  
* We provide an in-depth review of the current state and various modalities of maritime communications, providing a foundational understanding of the existing landscape. * We assess the adoption and the potential impact of software-defined radios (SDRs) and software-defined networks (SDNs) in the maritime industry, focusing on their role as IoT enablers. The proposed framework leverages the flexibility of SDRs and SDNs for improved resiliency in maritime communications. * The proposed maritime communications framework seamlessly integrates with existing satellite communication (SATCOM) infrastructure, for improved communication efficiency and resiliency. The remainder of this paper is organized as follows: In Section 2, we provide the background and rationale for the proposed approach. Section 3 delves into the details of IoT, followed by discussions on security challenges in maritime networks in Section 4. Section 5 presents the proposed framework for SDN-SDR-based maritime communications, followed by conclusions and future research direction in Section 6. Figure 2: Diverse set of maritime communications platforms.
# Approximating the Temporal Neighbourhood Function of Large Temporal Graphs
## 1 Introduction
### Our Results  
In order to approximate the temporal neighborhood function, we first describe a simple dynamic programming algorithm for computing the _reverse temporal cone_ of a node \(s\), which is the set of nodes that can reach \(s\) in a specific interval \([t_{\alpha},t_{\omega}]\). Note that, if we can compute the cardinalities of the reverse temporal cones, then we can also compute the temporal neighborhood function. We then show how the _sketch_ approach, which has already been widely used in the case of non-temporal graphs [10; 11; 12; 13], can be applied to the case of temporal networks in order to approximately compute the cardinalities of the reverse temporal cones of a temporal network. More specifically, the resulting approximation algorithm, called atnf (Approximated tnf), has a relative error bounded by \(\epsilon\) with high probability, whenever the sketches have size \(k=\Theta\left(\frac{\log n}{\epsilon^{2}}\right)\). The time complexity of the algorithm is \(O(km)\).  
We then experimentally evaluate the quality of the approximation performed by atnf by comparing the approximate value of the temporal neighborhood function with the exact one (computed by making use of the scan-based exact algorithm described in the previous section) on a data-set containing several medium-size temporal networks. As a matter of fact (and as expected), the obtained approximation is much better than the one guaranteed in theory, even when the size of the sketches is significantly smaller than the required size. By making use of the approximation algorithm, we hence show how we can accurately approximate, in a few minutes, the temporal neighborhood function (and, hence, the distance distribution) of two large temporal networks: the IMDB collaboration network (which is undirected) and the Twitter re-tweets network (which is directed). The first network contains more than half a million nodes and more than three million edges, while the second network contains more than two million nodes and more than sixteen million edges.  
Finally, we apply atnf in order to analyze and compare the behavior of twenty-five public transportation temporal networks [14]. In particular, we analyze the reachability properties of these networks, by computing the values of the temporal neighborhood function in different intervals during a day. As a matter of fact, we observe that there are cities which perform significantly better than others with respect to these reachability properties, and that this result cannot always be deduced by simply looking at the density of the temporal network. Moreover, we show that the quality of the approximation is preserved even with small values of the sketch sizes: this allowed us to perform the entire experimental evaluation for all the cities in less than one hour (while the exact algorithm would have roughly required almost four days).
## 4 Further Analysis with Discussions and Implications
### Angular Analysis and the Inferences of Fact
#### 4.2.2 Results from Using Angular Similarity as the Classification Basis  
In the aforementioned analysis, the market and industrial orientations of the countries are classified using the quadrants. However, they can also be classified according to how close the arrow directions are between each pair of vectors in the circle. The smaller the angle between a pair is, the more correlated the two vectors are, which in turn means that the two economies in question should be more similar in terms of their high-tech industries.  
Using this logic for further analysis, it is observed that the top nine technological power countries can be re-classified into three groups: (i) a main group consisting of the USA, CHN and EU; (ii) another main group consisting of JPN, GER and KOR; and (iii) an 'outliers group' consisting of CAN, TWN and GBR. The fact that CAN, TWN and GBR are centrifuged from the horizontal axis of the circle in the figure means that they contribute less to the first principal component (i.e., they have smaller cosine values for the first component but larger sine values for the second one). In other words, these countries are relatively unique in terms of their roles in the global high-tech market or industry, and the technological focus of each is very independent, as indicated by their diverse arrow directions. At the same time, this does not imply that the technological strength of these countries is weak. Rather, their strengths are measured by the magnitudes of the vectors (see the length of each arrow), and the lengths of all the arrows are roughly similar. This reflects the claim made in Section 4.1 that 'no single country may dominate the others'. Therefore, within the patent databases of these 'outlier' countries, some niche IPs should be found. This is true because, in the real world, it is generally acknowledged, for example, that the patent database of GBR includes a considerable number of IPs regarding financial transactions, which are uncommon in other countries' patent databases.
# Novel Sparse-Coded Ambient Backscatter Communication for Massive IoT Connectivity
### System Model
Since RF tags are powered by ambient energy harvesting, circuit-power constraint that harvested power with efficiency \(\eta\)[51] is greater than circuit power for entire \(\widetilde{K}\) sampling period should be satisfied. If we assume a linear power consumption model with respect to energy per symbol \(\sigma_{c}^{2}\) and backscatter symbol rate \(R_{s}\)[8; 9], the circuit-power constraint can be formulated as  
\[\underbrace{D(1-\sigma_{b}^{2})\eta E_{n}}_{\text{active state}}+\underbrace{(1-D)\eta E_{n}}_{\text{ idle state}}\geq P_{c}, \tag{6}\]where \(D=\frac{K_{1}}{K}\), \(P_{c}=R_{s}\sigma_{c}^{2}\) and \(R_{s}=\frac{1}{LT}\). The average backscatter symbol power proportional to \(\alpha\) is denoted by \(\sigma_{b}^{2}\) and will be discussed in Section 3. For convenient analysis of EH, we define the normalized energy per backscatter symbol as  
Figure 2.0  
Under these settings, energy harvesting (EH) constraint or the circuit-power constraint can be reformulated as [37]  
Figure 2.1  
From Equation (8), the state of tag \(\mathbf{a}=[a_{1},\cdots,a_{N}]\) (0: idle, 1: active) with EH threshold \(\theta\) can be obtained by  
Figure 2.2  
where \(\mathbb{I}\{x\}\) denotes the indicator function of the event \(x\). If the EH constraint is satisfied, tag can encode sparse codewords using simple modulator circuit. In the following sections, we describe AmBC modulator in Section 3 and detector in Section 4.
## 4 Response Analysis of EMP Coupling on RF Front-End Circuits-Based Norms
### Response Analysis with Norm Theory
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Norms**} & **HPEM Type** & \begin{tabular}{c} **Antenna Response vs.** \\ **Electric Field** \\ \end{tabular} & \begin{tabular}{c} **Ratio of Response after** \\ **BPF vs. Electric Field** \\ \end{tabular} \\ \cline{2-5}  & \begin{tabular}{c} **Ratio** \\ \end{tabular} & \begin{tabular}{c} **Risk Rank** \\ \end{tabular} & \
\begin{tabular}{c} **Ratio** \\ \end{tabular} &
\begin{tabular}{c} **Risk Rank** \\ \end{tabular} \\ \hline \multirow{3}{*}{\(\left|f(\text{t})\right|_{\text{max}}\)} & HEMP & \(1.68\times 10^{-5}\) & III & \(9.80\times 10^{-6}\) & III \\ \cline{2-5}  & UWB & \(9.25\times 10^{-4}\) & II & \(6.32\times 10^{-4}\) & II \\ \cline{2-5}  & HPM & \(2.86\times 10^{-3}\) & I & \(8.93\times 10^{-4}\) & I \\ \hline \multirow{3}{*}{\(\left|\frac{\partial f(\text{t})}{\text{dt}}\right|_{\text{max}}\)} & HEMP & \(3.98\times 10^{-4}\) & III & \(3.29\times 10^{-4}\) & III \\ \cline{2-5}  & UWB & \(1.16\times 10^{-3}\) & II & \(9.02\times 10^{-4}\) & II \\ \cline{1-1} \cline{2-5}  & HPM & \(2.97\times 10^{-3}\) & I & \(9.77\times 10^{-4}\) & I \\ \cline{1-1} \cline{2-5}  & HEMP & \(4.72\times 10^{-6}\) & III & \(2.12\times 10^{-6}\) & III \\ \cline{1-1} \cline{2-5}  & UWB & \(1.74\times 10^{-3}\) & II & \(1.45\times 10^{-3}\) & II \\ \cline{1-1} \cline{2-5}  & HPM & \(2.79\times 10^{-3}\) & I & \(8.65\times 10^{-4}\) & I \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Risk rank results of the HPEMs for the linear RF channel. Besides, from the point view of EM sensitivity of electronic modules connected to the RF channel, we can use expert knowledge to assign different weights to these norms based on the EM sensitivity of the electronic module, thus obtaining EM risk analysis result that are more consistent with the target system. Finally, we propose a methodology of EM risk assessment based on the previous analysis. The basic flowchart is illustrated in Figure 18. After the wave responses of the interested electronic modules are derived by SPICE modeling, we can configure the weights of the waveform characteristics with norm theory for EM assessment, where the configuration information can be from expert knowledge or previous test data about the physical effect mechanism.
## 4 Simulation Results
The tabulated results emphasize the above-presented results. It can be noted that the proposed and its peers from deep learning-based CSEs outperformed the traditional LS and MMSE estimators when using fewer pilots (4 and 8). Figure 8:2 Comparison of SNR performance of the suggested estimator, LSTM, and GRU when 64 pilots are used. Figure 8:3 Comparing the loss functions of 4 pilots and different optimization algorithms for DL peephole LSTM-based CSE training. Figure 8:4 Comparison of SNR performance of the suggested estimator, LSTM, and GRU when 4 pilots are used. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Number of Pilots** & **64** & **8** & **4** \\ \hline Peephole LSTM & 99.95\% & 99.41\% & 99.65\% \\ \hline LSTM & 99.78\% & 99.55\% & 99.75\% \\ \hline GRU & 99.88\% & 87.78\% & 99.97\% \\ \hline LS & 99.98\% & 91.54\% & 0.08\% \\ \hline MMSE & 100\% & 91.25\% & 0.20\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The accuracy percentage for the suggested estimator vs. the other estimators. Figure 20: Comparison of SNR performance of the suggested estimator, LSTM, and GRU when 8 pilots are used.
# Factors That Affect Liquefaction-Induced Lateral Spreading in Large Subduction Earthquakes
## 2 Background of Lateral Spreading in Subduction Earthquakes
### Liquefaction and Lateral Spreading  
The word liquefaction was first used after the 1964 Niigata Mw7.6 earthquake ([PERSON] et al. 2018 [11]). This phenomenon is defined as a change of the soil phase from a solid to a liquid state due to pore water pressure increment, and the corresponding loss of effective stress, during an earthquake (Figure 1). As [PERSON] (2018) [12] indicated, when an earthquake occurs, waves propagate through the soil, shear strains increase, pore water pressure goes up, and the intergranular forces get reduced. As pore water pressures reach a critical level, and the intergranular stresses approach zero, the soil behavior goes from a solid to a viscous liquid state.  
Liquefaction is a relevant soil phenomenon for geotechnical design as it may cause local or global failures of foundations and even the collapse of complete structures ([PERSON] (2017) [13]). Among the potential consequences of soil liquefaction, one of the most dangerous ones is lateral spreading. [PERSON] (2018) [12] defined this phenomenon as the horizontal displacement of a soil layer riding on liquefied soil either down a gentle slope or toward a free face like a river channel (Figure 2). When the underlying soil layer liquefies, the non-liquefied upper soil crust continues moving down until it reaches a new equilibrium position. Figure 3 shows two recent examples of liquefaction-induced lateral spreading: (1) west levee of the Westside Main Canal in the 2010 Sierra El Mayor Mw 7.1 earthquake, where a cumulative horizontal displacement of more than 1 m was observed (Figure 3a), and (2) the Muzoi Bridge in the 2005 Nias Island Mw 8.6 earthquake, where a lateral movement of more than 4 m towards the river on both sides of the bridge was reported (Figure 3b).  
Figure 1: Liquefaction mechanism: soil particles floating due to the increment of pore water pressure.  
Figure 2: Schematic representation of lateral spreading ([PERSON] (2018) [12]).  
Prediction of lateral spreading is essential because it can cause damage to the overlying and subsurface infrastructure, and the amount of displacement may influence the design of the infrastructure concerning the decision to, for instance, perform soil improvement in the area affected by this phenomenon ([PERSON] et al. (2017) [10]).
## 2 Related Works
### Large-Scale Field Testing  
Deploying many vehicles on the road is an unacceptable solution; thus, OBU devices are often considered as substitutes for vehicles. Since OBUs lack the mobility of vehicles, traffic simulators are used to provide OBUs with vehicle motion status and trajectory information. The approach outlined in [18] employs this idea to conduct congestion control testing for large-scale LTE-V2X in real-world straight road and intersection scenarios.  
However, several aspects of this solution could be improved.  
The first issues are the deployment cost and the flexibility related to the number and placement of OBU devices.  
This solution deployed 198 OBUs, with six OBUs per group, installed on flatbed vehicles placed statically on a closed road segment. A 320 m straight road required 33 flattened vehicles.  
This problem can be improved using the method proposed in [19], which generates traffic directly at the physical layer using the Software-Defined Ratio (SDR), bypassingcommunication protocol limitations and enabling the simulation of a large number of communication nodes with fewer physical nodes. Our proposed TS also adopts this method.  
Another limitation is the lack of application testing.  
The difference between application testing and communication testing in terms of vehicle movement information lies in the involvement of key vehicles.  
Our previous work [20] achieved the extraction of critical scenarios for typical V2X applications, such as Forward Collision Warning (FCR), Lane Change Warning (LCR), and Intersection Collision Warning (ICR).  
Our proposed architecture combines the SDR-based dense node array with a scenario generator for critical scenario extraction. It integrates communication scenarios with application scenarios to enable the testing and analysis of application response characteristics in actual communication scenarios.