esa-sceva/satcom-chunk-collection huggingface dataset:
# Privacy-Aware Access Protocols for MEC Applications in 5G +
## 3 Statement of the Problem
### System Model  
We base our system model on the scenario where a user wants to use an application APPIFY in a MEC host deployed in a network of a mobile network operator, MNO. The user does not want to reveal the contents of messages to any party other than APPIFY. For simplicity, the user is referred to as [PERSON] in this paper, and we use quotation marks, \"[PERSON]\" to point to the actual name of the user. The MEC host is also called MEC, and the application in MEC is referred to as APPIFY.  
We build an abstract model with four parties: Alice, MNO, MEC, and APPIFY. In order to use APPIFY, [PERSON] needs to send and receive messages over the networks of MNO and MEC. The visualization of the model is presented in Figure 2.  
[PERSON] is one subscriber of MNO, among other subscribers. The identifier of [PERSON] for MNO is IMSI. Due to the subscription, MNO may have learned further details about [PERSON], such as her home address, real name, social security number, and email address.  
MEC is deployed in a mobile network of an MNO, and it hosts several applications, including APPIFY. The messages between MNO and APPIFY are sent through MEC. Alice and MEC do not share any identifiers or credentials in our model.  
APPIFY is hosted in MEC along with other MEC applications. Typically, the data in the MEC application residing in the MEC host are synchronized regularly with the main server of the application (see Figure 1) to provide up-to-date service to users of the application [41]. Therefore, APPIFY is also in contact with the main server of APPIFY. However, we do not include the main server of APPIFY in our model for simplicity.  
The traffic between adjacent parties in the model, Alice-to-MNO, MNO-to-MEC, and MEC-to-APPIFY, is assumed to be secured by separate mechanisms. Therefore, the outsiders, including the other subscribers of MNO and other MEC applications in MEC, cannot learn the content of the messages. However, if there are no additional security mechanisms, MNO and MEC can see the messages between [PERSON] and APPIFY, so they would learn which application she is using, her identity for APPIFY, and the content of the messages. Still, [PERSON] does not want MNO to retain such information about her, and she also wants to stay anonymous toward MEC.
# Spatial Distribution and Monitoring of Land Subsidence Using Sentinel-1 SAR Data in Java, Indonesia
## 3 Results
### Spatial Distribution and Land Subsidence Rate  
LS, particularly in coastal regions, has a significant effect on infrastructure, water resources, and ecosystems. This phenomenon, especially along the northern coast of Java, has garnered increasing attention from researchers due to its implications for urban planning and environmental management. Several studies have highlighted the severity of LS in various regions. For example, ref. [11] conducted extensive research to identify 10 specific areas that are vulnerable to subsidence using the GNSS method. This study emphasizes the necessity of monitoring and mitigation strategies in these locations to prevent further degradation of the land and potential hazards to communities. Furthermore, ref. [13] contributed to the discourse by presenting the areas with the highest rate of LS using InSAR. Their findings serve as a crucial reference point for understanding the dynamics and scale of the issue, particularly in northern Java.  
Recent data shown in Figure 2 provide a visual representation of subsidence rates across Java, measured in millimeters per year. The color coding represents the severity of the LS area, with green to red indicating the lowest to critical LS. This mapping is vital for prioritizing interventions and allocating resources to the affected communities. Monitoring these regions is essential to assess ongoing risks and develop effective response strategies. This research has also identified previously unrecognized areas that are experiencing LS, including Serang, Cianjur, Brebes, and Tegal. This finding emphasizes the evolving nature of LS and underscores the importance of continuous research to identify its emerging trends and areas of concern. The findings in each city are discussed in the next individual subchapters.
## 2 Theoretical Foundation
### Fluid-Structure Interaction Solution Method  
According to the above governing equations, the fluid-structure interaction problem can be solved by giving appropriate initial conditions, boundary conditions and solution parameters. Fluid-structure interaction solution methods are generally divided into the direct coupling method and the separate coupling method. The direct coupling method solves fluid and solid control equations by integrating them into the same matrix equation, that is, the fluid and solid equations are solved simultaneously in the same solver.  
\[\begin{bmatrix}A_{ff}&A_{fs}\\ A_{sf}&A_{ss}\end{bmatrix}\begin{bmatrix}\Delta X_{f}^{k}\\ \Delta X_{s}^{k}\end{bmatrix}=\begin{bmatrix}B_{f}\\ B_{s}\end{bmatrix} \tag{8}\]  
where, \(k\) represents the number of iterations, \(A_{ff}\) represents the fluid control matrix, \(\Delta X_{f}^{k}\) represents the flow field to be solved, \(B_{f}\) represents the external force of fluid, \(A_{ss}\) represents the solid control matrix, \(\Delta X_{s}^{k}\) represents the solid region parameters to be solved, \(B_{s}\) represents the solid external force and both \(A_{sf}\) and \(A_{fs}\) represent the fluid-structure interaction matrix.  
The direct coupling method has the characteristics of no time lag, and its solving precision is very high. However, for the current computational fluid dynamics (CFD ANSYS Fluent 22) and computational solid mechanics (CSM Mechanical APDL 22) simulation software, its fluid and solid solvers often cannot be carried out at the same time, and it is difficult to directly couple fluid and solid solutions. It is very difficult to solve convergence at the same time. So, the direct coupling method can only be applied to very simple structure models, and has not played a practical role in industry. The separate coupling method separates and solves the fluid governing equation and solid governing equation by putting them in the CFD and CSM calculation modules respectively, and exchanging data through the fluid-solid interface. When the CFD calculation reaches convergence, the results will be transferred to CSM calculation modules for the solid solution, and the results will be transferred to CFD calculation modules for fluid calculation at the next moment after the solid solution converges. Such a data transfer calculation greatly improves the calculation efficiency, and the CFD software and CSM software do not need to makegreat changes to achieve fluid-structure interaction solutions, which can realize large-scale fluid-structure interaction solutions in practical projects. Almost all commercial ANSYS Workbench 2022 R1 software adopts the separation coupling method, and this simulation method is also used in this paper.
# Scheduling the Exchange of Context Information for Time-Triggered Adaptive Systems
## 4 Scheduling for Adaptive Time-Triggered Systems
### Conditional Timed Application, Platform, and Schedule Models for Combinations of Context Events  
The combination of context events results in an alteration of the platform and application models compared to the baseline models. For each combination of context event, these platform and application models can be precomputed at development time. At runtime, a certain platform and application model becomes a correct representation of the system based on two prerequisites:  
* **Conditional:** all events of the platform and application model must have occurred.
* **Time:** If an event occurs, it must do so at a predefined point in time in the system cycle. The platform and application model become a valid representation of the system at the time of the last event occurrence.  
Based on the conditional time application and platform models, a schedule can be computed. This schedule is also conditional and timed; i.e., it is only valid after the occurrence time of the last event and in the case that all events occur.
# Efficient Next-Hop Selection in Multi-Hop Routing for IoT Enabled Wireless Sensor Networks
## 4 Proposed Solution
### Time Slot Identification  
To achieve a better data collection mechanism, we utilize a tree-based model where each cluster node has some link points with other nodes for forwarding information to BS. There is a restriction on the network size \(N\) such that \(N=2^{p}\) where \(p=2,\,\ldots,\,\mathrm{m}\). The data are so well correlated that data aggregation is not important at the sensor nodes. In this scenario, the number of required time slots \(N_{TS}\log_{2}N+1\). Furthermore, the highest connection degree of \(CH\) is \(K_{max}=\log_{2}N+1\). In this case, \(N_{TS}\) demands a maximum rank or degree \(K_{max}\) to gather information from its child nodes, with \(k_{max}-1=\log_{2}N\)[28]. Next, the single-hop and multi-hop scenarios are presented. The time slots \(N_{TS}\) needed for gathering the information from cluster member (CM) nodes are obtained as \(N_{TS}\)\(D_{n}+1\), where \(D_{n}\) represents link level.  
A cluster's rank \(k\) is based on the CM node's rank, where each member can maintain \(k-1\) data links. Each member node also maintains its rank \(k\) and can be considered as child of main node. The nodes with higher \(k\) values can be considered as parent nodes, where highest rank value is for \(CH\) as illustrated in Figure 3. The dashed arrow denotes the data connection among a pair of nodes, and the pointing direction of the sign presents the flow of data. For example, the presented system investigates with \(N=16\), where \(N\) denotes the number of nodes. In the given example, BS takes \(5\times T\) time to obtain data from the 16 nodes. Moreover, it splits the total time into several time slots of period \(T\)  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Notation** & **Description** \\ \hline \(BS\) & Base station \\ \(CH\) & Cluster head \\ \(CM\) & Cluster member \\ \(N_{TS}\) & Number of time slots \\ \(K_{max}\) & Maximum rank or degree \\ \(D_{n}\) & Connection degree \\ THV & Threshold value specified by the user \\ R.E & Remaining energy \\ \(CH_{1}\) to \(CH_{n}\) & Nodes at a distance of one-hop to n-hop from CH \\ \(b\) & Number of nodes selected from Hs \\ \(k\) & Rank of each node \\ \(Nxt_{hop}\) & Next hop to forward the message \\ \(N_{Gi}\) & Neighboring node \\ \(C_{1}-C_{3}\) & Constant values \\ \hline \hline \end{tabular}
\end{table}
Table 1: List of Notations for EMRP.  
and a specific interval of time is denoted by \(T\). The total time is divided into the \(T\) time slots where the collection process at the base node takes five time slots. We illustrated the movement of data exchanged from the rank 1 node towards \(CH\).
## 5 A Model Predicting the Impact of Vibration from Trains on Over-Track Buildings
### The Correction Item of the Type of Building Structure, \(C_{\rm S}\)  
To predict the influence of train vibration on shear wall buildings, \(C_{\rm S}\) was introduced. \(C_{\rm S}\) represents the difference between the \(VL_{\rm Zmax}\) on each floor in shear wall buildings and that in frame buildings. According to the fitting results given in Figures 10 and 11, the difference between the \(VL_{\rm Zmax}\) on the first floor in shear wall buildings and that in frame buildings can be expressed as \((k_{\rm S1}N+b_{\rm S1})-(k_{\rm N1}N+b_{\rm N1})\). Similarly, the difference between the \(\Delta VL_{\rm Zmax}\) in shear wall buildings and frame buildings can be expressed as \((k_{\rm S2}N+b_{\rm S2})-(k_{\rm N2}N+b_{\rm N2})\). Therefore, \(C_{\rm S}\) can be calculated by Equation (16).  
\[\begin{array}{ll}C_{\rm S}&=(k_{\rm S1}N+b_{\rm S1})-(k_{\rm N1}N+b_{\rm N1} )+(k_{\rm S2}N+b_{\rm S2})-(k_{\rm N2}N+b_{\rm N2})\\ &=(-0.2N+67.6)-(-0.47N+68.4)+(-0.1N+8.6)-(-0.34N+12.8)\\ &=0.51N-5.0\end{array} \tag{16}\]  
According to the model developed in this study, the maximal impact of train vibration on an over-track building can be predicted on the basis of the \(VL_{\rm Zmax}\) on the ground right under the over-track building of a metro depot.  
[PERSON] [26] presented a comprehensive approach for the simulation of train vibration in tunnels. However, the influence of building structure parameters was not considered. [PERSON] [27] presented an integrated methodology to determine the ground vibration induced by railway traffic, and a case study was carried out. However, the two models above are not suitable for directly predicting the influence of train vibrations on over-track buildings.  
Since existing models are not suitable for predicting the effect of train vibration on each floor of over-track buildings of metro depots, this model cannot be compared with previous similar research.
# Prototype to Increase Crosswalk Safety by Integrating Computer Vision with ITS-G5 Technologies
## 3 Prototype Specification
### Computer Vision Solution  
The main computer vision task aims to detect pedestrian instances and locate their positions in images and their correspondent positions on the road. Detected pedestrian positions are then analyzed to verify if they are on the crosswalk.  
The pedestrian pattern recognition task uses the OpenCV library for the global image processing functions and to interact with YOLO v3 which is an object detector using deep learning for real-time object detection. The OpenCV library has image and video processing modules, data structures, and supports various computer vision algorithms for real-time object recognition. The global functional blocks of the pedestrian detection module (PD) are shown in Figure 7.  
The first block uses OpenCV functions for image acquisition and image resize to minimise the computing demands of the YOLO v3 object detection.  
The second block uses the YOLO v3 with the pre-trained model generated from the common objects in context (COCO) dataset with adjusted settings to output only the person class. The output of this block is the YOLO v3 return bounding box coordinates in the form of _centerX_, _centerY_, _width_ and _height_.  
The third block uses OpenCV functions to implement the region of interest (ROI) classification and is responsible for analyzing the bounding box coordinates to check if they correspond to the crosswalk ROI. If the pedestrians are inside the ROI, the DENM messages are trigged.  
Several tests have been conducted with different typical crosswalk scenarios to access their geometric variations, the pedestrian's sizes and if the YOLO v3 accuracy was suitable for this prototype.
# Optimization of Submodularity and BBO-Based Routing Protocol for Wireless Sensor Deployment
## 2 Wireless Sensor Deployment Optimization
### IHACA-COpSPIEL Deployment Method
#### 2.2.3 IHACA-COpSPIEL Algorithm  
(1) Clustering  
Using the local parameter \(\alpha r\) chaotic sequence pair of Equation (9) randomly divides the position set \(V\) into small clusters of diameter \(\alpha r\), where \(\alpha\in(0,1]\). The nodes near their cluster boundaries are stripped, so the clusters are well separated. The locality of F makes the clusters almost independent and provides a wealth of information [13]. (2) Establishing module approximation  
In the \(i\)th cluster (\(C_{i}\)), a greedy algorithm is used to obtain the ranks of \(g_{i,1},g_{i,2},\ldots,g_{i,n_{i}}\) on the \(i\)th cluster's nodes (\(n_{i}\)), and the nodes are connected in this order to form a chain of clusters. A module approximation graph \(G^{\prime}\) is created from \(G\) through these chains. A modular directional arithmetic algorithm is used on \(G^{\prime}\) to solve the corresponding objective function, the selected path in \(G^{\prime}\) is extended according to the corresponding shortest path in \(G\), and the solution set \(A^{\prime}\) is output. (3) Select the next position  
The initial node of \(A^{\prime}\) is used as the initial value of the Improved Heuristic Ant Colony Algorithm (IHACA). The IHACA algorithm selects the next position from the first node according to Equation (14), and adds the selected position to the taboo table or \(tabu_{k}\) of ant \(k\). \(\eta_{ij}^{\beta}\) is calculated by Equation (10), and \(\tau_{ij}\) is calculated by Equation (15). \[P_{ij}^{k}=\frac{\tau_{ij}^{\alpha}\eta_{ij}^{\beta}}{\sum\limits_{j\in A}\tau_{ ij}^{\alpha}\eta_{ij}^{\beta}} \tag{14}\]  
\[\tau_{ij}=F(C_{i}\cup k_{j})-F(C_{i}). \tag{15}\]  
In Equation (14), \(\alpha\) is the weight of the path, \(\beta\) is the weight of the heuristic information, and \(\tau_{ij}\) represents the pheromone intensity of the path from the cluster \(C_{i}\) to the \(k_{j}\) sensor. (4) Pheromone update  
After the next position is determined, the pheromone traversed by the ant \((i,j)\) is updated according to Equation (11). When all ants reach the endpoint, the global pheromone is updated according to Equation (12), and the tabu list is cleared. The pseudo-code of the IHACA-COpSPIEL algorithm is as follows. Line 2 calls clustering with complexity \(O(m_{c\max})\). Lines 3-6 form a chain with complexity \(O(NlogN)\). Line 7 calls the block-oriented algorithm with complexity \(O(nlogN)\) where \(n\) is the number of connectable edges of graph nodes (\(n\leq N\)). Lines 8-10 select nodes for \(A^{{}^{\prime\prime}}\) of the greedy algorithm with complexity \(O(log(N-n_{i}))\). Lines 13-17 reach the given maximum mutual information with complexity \(O(kN^{2})\) where \(k\) is iteration times. Line 22 calls updating global pheromone with complexity \(O(N^{2})\). The computational complexity of Algorithm 1 is \(O(kN^{2})\) approximately.
# The 2014 Effusive Eruption at Stromboli: New Insights from In Situ and Remote-Sensing Measurements
## 2 Materials and Methods
### Topographic Data and Co-Registration
Co-registration decreased the RMS error from the initial 3.37 m to the final 1.27 m.  
The differences between the co-registered DEMs were used to detect and outline the extent of the areas affected by topographic changes in the considered time spam, and to calculate the volume and thickness variations inside them. Moreover, the perimeters of observed changes were double-checked using the ortho-rectified PLEIADES-1 images. Field surveys also strengthened data interpretation. Volume (V) emplaced or lost between two acquisitions was calculated from the DEM difference according to the equation:  
\[V=\sum_{i}\Delta x^{2}\Delta z_{i}, \tag{1}\]  
(see [42] and references therein), where \(x\) is the grid step and \(z_{i}\) is the height variation within the grid cell \(i\). These values were then summed up for all the cells in the selected areas in which we want to calculate volume changes. The residual volume errors were calculated as:  
\[\text{Err}\ (\text{V,high})=\text{A}\sigma_{\Delta x}, \tag{2}\]  
where A is the investigated area and \(\sigma_{\Delta x}\) is the co-registration residual RMS error between the two DEMs. Following [42], this formula assumes that the errors were completely correlated, and thus the volume error calculated as above is overestimated.
# Towards the Unmanned Aerial Vehicles (UAVs): A Comprehensive Review
## 8 Future Research Directions
### Aerial Blockchain  
Blockchain technology, as a decentralized solution, is projected to usher in a new era of secure and adaptable privacy protection for blockchain-based UAV systems. Aerial blockchain can prevent UAV communication privacy leaks and secure the integrity of data acquired by UAVs [123; 124]. Furthermore, a block-chain-enabled UAV softwarization may be utilized to provide communication services over the UAV network with dynamic, flexible, and on-the-fly decision capabilities [125]. While there have been various study attempts on blockchain technology in UAV networks, researchers have yet to look into block-chain-enabled softwarization for UAV networks.
## 4 Towards Enhanced Global-Scale Local-Relevant Monitoring and Forecasting
### Anthropogenic Surface Modifications  
NWP and climate models need lower boundary conditions to calculate the evolution of dynamic processes in the atmosphere and to produce a usable weather forecast, such as skin temperature, surface fluxes of heat, moisture and momentum. To compute them accurately, detailed plant functional maps are required. Previously, ecosystem maps were produced by collecting regional geodesic information at the national level and then merging the data in global maps. The main issue was that those maps were rapidly outdated because of changes in land use. Nowadays, human activity influences the Earth surface very rapidly by changing the landscape from forest to crop fields, breeding cattle in more profitable locations, providing water for arid places, or building homes for an expanding population.
## 3 The Train-to-Ground Wireless Communication Needs
### The Needs for Operational Services
Data collection could be related to the air tightness conditions, the infrastructure and vactrain aging, the power consumption, and the maintenance. In addition, the continuous monitoring with immediate processing of data is needed to observe real-time profile of train for instance. This implies small-sized packets (less than 500 bytes), thus less upload bandwidth could be needed, but high QoS criteria should be guaranteed to ensure the data real-time, completeness and accuracy. The upload bandwidth of TOSM systems could be set to 1 Mbps (up/down), with the required packet error rate in the order of 10\({}^{-3}\) and transmission delay less than 300 ms.
6. _Passenger information service:_ PIS systems allow passengers to obtain the train arrival/departure time, weather conditions, and other convenient information. The HD digital video streaming with H.264 format and various text information are transferred from the RTCC to the train. Thus, a large capacity for the downlink is required in PIS systems, reaching up to 8 Mbps.
# 2 CA-R2: A Hybrid MAC Protocol for Machine-Type Communications
## 6 Simulation Experiments and Results
### Experiments with Poisson Traffic
This situation prevailed even when the number of devices was increased to large values. The average fraction of CRIs starting with two or more devices was relatively small, and the access conflict was efficiently handled by the MAC protocol so that their impact on the average access delay was negligible. Additionally, in [18], it was shown that the algorithm is robust in the presence of estimation errors in the number of colliding stations. As a consequence, in the case of abrupt changes in the number of stations between consecutive CRIs, a significant performance degradation is not expected.