esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Results
### Full-Field Strain of Typical Specimens  
Full-field strains of three bench models, including DT2, DT4, and DT7 specimens, were calculated by the 3D-DIC technique. The von Mises strain was used to quantitate the dynamic deformation of the AOI.  
For DT2, the delay time between the boreholes was 27.36 \(\upmu\)s, the high-speed image resolution was 512 \(\times\) 416 pixels, and the frame rate was 30,000 fps, i.e., 33.3 \(\upmu\)s between frames. Figure 7 shows the von Mises strain contours of the DT2 specimen. As shown in Figure 7, strain concentrations were first observed at 67 \(\upmu\)s. The plots from 133 to 333 \(\upmu\)s show that there were two significant horizontal strain concentrations at the strain field. As the time increased, the strain concentration zones became wider to predict the formation of macroscopic cracks.  
The image resolution of 512 \(\times\) 392 pixels and the frame rate of 35,000 fps were set for DT4, corresponding to a time resolution of 28.6 \(\upmu\)s between frames. Figure 8 shows the surface strain field of the DT4 specimen from the 3D-DIC analysis. The plots from 0 to 171 \(\upmu\)s show that the strain concentrations first appeared on the left side of the AOI. It can be seen from the von Mises strain contours that as the time increased, the significant strain concentration was located in the left central position of the strain field. At 257 \(\upmu\)s, the significant strain concentration extended and branched in two directions. Two strain concentration zones propagated upward at 286 \(\upmu\)s, while two strain concentration zones  
Figure 7: Surface strain field of DT2.  
propagated downward. Until 257 us, a horizontal strain concentration appeared in the middle of the strain field.  
The same camera setting of DT4 was selected for DT7. The von Mises strain contours of DT7 are shown in Figure 9. It can be seen that the strain concentrations of DT7 were similar to those of DT4 until 171 us and mainly occurred on the left side of the strain field. From 143 to 286 us, the vertical strain concentration zone extended and branched in multiple directions. At 314 us, a horizontal strain concentration zone formed at the edge of the AOI. By comparing Figures 7-9, DT7 did not have a horizontal strain concentration zone at the middle position of the AOI. The strain field of the DT7 specimen was dominated by vertical strain concentration zones.  
The von Mises strain evolution process on the specimen surface can be obtained from the 3D-DIC analysis. The development of the strain concentration zone presented in the contours above is a good way to fill the observation gap before macroscopic cracks emerge. These contours indicate that the location and development of the strain concentration zone changed as the delay time increases.
# Context-Aware Enhanced Application-Specific Handover in 5G V2X Networks
###### Abstract  
The deployment of Augmented Reality (AR) is a necessity as an enabling technology for intelligent transportation systems (ITSs), with the potential to boost the implementation of Vehicle-to-Everything (V2X) networks while improving driver experience and increasing driving safety to fulfill AR functionality requirements. In this regard, V2X networks must maintain a high quality of service AR functionality, which is more challenging because of the nature of 5G V2X networks. Moreover, the execution of diverse traffic requirements with varying degrees of service quality is essential for seamless connectivity, which is accomplished by introducing efficient handover (HO) techniques. However, existing methods are still limited to basic services, including conversional, video streaming, and general traffic services. In this study, a Multiple Criteria Decision-Making (MCDM) technique is envisioned to address the handover issues posed by high-speed vehicles connected to ultra-high-density (UDN) heterogeneous networks. Compared with existing methods, the proposed HO mechanism handles high mobility in dense 5G V2X environments by performing a holistic evaluation of network conditions and addressing connection context requirements while using cutting-edge applications such as AR. The simulation results show a reduction in handover delays, failures, and ping-pong, with 84% prevention of unnecessary handovers.  
a 202  
Augmented Reality (AR) is an emerging technology that benefits from these features and unlocks their innovations. It is a technique that enhances the reality of an object in its natural context by linking it to digital technology and providing users with the ability to sense reality through computer-generated visuals. It is a subset of the Mixed Reality (MR) continuum, where Virtual Reality replaces the physical world, whereas, in contrast, AR enhances the effect of realism by importing and integrating virtual objects into the real environment [1]. Immersive technologies have led to advancements in the automotive industry. AR applications are steadily being injected into ITS to enhance the driving experience by assisting drivers with navigation and displaying road and vehicle real-time information to improve safety concerns.
# Flexible Antennas for a Sub-6 GHz 5G Band:
## 4 Flexible Antennas for a Sub-6 GHz 5G Band
### Flexible MIMO Antennas for Sub-6 GHz 5G Band
#### 4.3.5 Transparent Flexible Antenna  
A transparent MIMO antenna has come to the frontline these days because of its good visual clutter. A transparent two-element circular patch antenna is proposed in [81], utilizing transparent AgHT-8 and plexiglass as the conducting and substrate materials, respectively, which utilizes a radiating structure in the form of a solid cylinder enclosed in a hollow cylinder that connects a stub. The required frequency is achieved by modifying the ground plane to a partial ground. Two MIMO configurations are proposed. Case 1 consists of a separate ground configuration having a similar orientation of the elements, whereas Case 2 consists of oppositely arranged antenna elements with a connected ground, as shown in Figure 23a. To achieve the improved impedance matching and isolation, the partial ground is made for Case 1 and a connected ground with 15 mm from the individual element is carried out for Case 2, which give an isolation of greater than 17.10 dB for Case 1 and 17.38 dB for Case 2, respectively. The impedance bandwidth ranging from 4.65 to 4.97 GHz for Case 1 and 4.67 to 4.94 GHz for Case 2 is achieved, as shown in Figure 23b,c. The peak gain of 1.83 dBi and 1.65 dBi is achieved with an efficiency of 53% and 59% for Case 1 and Case 2, respectively. A directional radiation pattern is achieved, as shown in Figure 23d. Figure 23: _Cont._In another design in [82], a wideband flexible transparent four-element MIMO is utilized for the sub-6 GHz 5G applications. The radiating part consists of a circular stub-loaded C-shaped resonator with an L-shaped partial ground embedded on either side of the substrate. The appropriate bandwidth and the reflection coefficient are produced by the L-shaped resonator integrated with a partial ground plane. To model the MIMO antenna with a connected ground plane, the conductive oxide is added to the bottom portion of the substrate which connects all of the ground planes of the four elements. Figure 24a shows the fabricated antenna prototype. The antenna provides a wide bandwidth of 2.21-6 GHz with an isolation greater than 15 dB between the elements, as shown in Figure 24b. The radiation pattern of the fabricated antenna gives a directional radiation pattern as in Figure 24c. Figure 23: Transparent MIMO antenna: (**a**) Fabricated prototype horizontal (Case 1) and a vertical (Case 2) orientations; (**b**) S-parameter of Case 1; (**c**) S-parameter of Case 2; (**d**) Radiation pattern at 4.81 GHz (Case 1). Reprinted from ref. [81]. Figure 24: _Cont._  
The maximum gain of 0.5 dBi with an efficiency of 41% is achieved. The bending analysis of the MIMO antenna is carried out using a Styrofoam cylinder of the radius with a prescribed bending angle. It is observed that the maximum gain and efficiency when the antenna is bent along the x-axis, was 0.47 dBi and 34%, respectively, and 1.07 dBi and 33%, respectively, for y-axis bending, as shown in Figure 24d. The summary of the flexible MIMO antenna for sub-6 GHz 5G band is given in Table 8.
## 1 Introduction
### UAV and Precision Agriculture
* Can the UAV operate autonomously in a closed environment and is it reliable? * Is communication loss possible during the UAV mission? * Can UAV carry loads (RGB camera, multispectral camera, etc.) for different missions? Some studies claim that problems such as speed and battery can be solved with multiple UAVs [27]. Multiple UAVs are actually a swarm, so there is a task assigned to each UAV. In this way, it is predicted that agricultural applications can be carried out quickly and without battery obstacles over a large area of agricultural land [25]. However, the number of agricultural studies using multiple UAVs is much lower than those using single UAVs. Performing a cooperative mission in open fields or a limited greenhouse environment with multiple UAVs will also pose a reliability problem. It is also a challenge to establish an effective communication network between UAVs [28]. These questions and multi-UAV applications are active research areas that still await solutions. Figure 1: The number of studies in Web of Science on UAV and UAV applications in agriculture [23].
## 4 Results and Analysis
### Analysis of Meteorological Factors on Historical Forest Fire Occurrences
#### 4.1.4 Impact of Average Humidity on Forest Fires  
Figure 5e displays the variation in the number of annual forest fires in Yunnan Province from 1991 to 2021 under different relative humidity conditions. It was observed that in years  
Figure 5: (**a**) Number of fires versus temperature; (**b**) affected area versus temperature; (**c**) number of fires versus precipitation; (**d**) affected area versus rainfall; (**e**) number of forest fires versus relative humidity; (**f**) affected area versus relative humidity; (**g**) number of forest fires versus wind speed; (**h**) affected area versus wind speed.  
such as 1997, 1999, 2004, and 2010, the frequency of forest fires showed a significant negative correlation with lower relative humidity, indicating that reduced humidity levels may lead to more frequent fires. Similarly, Figure 5f reflects that in the aforementioned years, the areas affected by fires expanded with the decrease in relative humidity, highlighting the impact of dry conditions on increasing the scale and intensity of fires. A comprehensive analysis indicates a significant negative correlation between the rate and severity of forest fires in Yunnan Province and relative humidity, especially in years with the most notable number of fires and affected areas. This phenomenon underscores the critical role of relative humidity in forest fire prevention and risk assessment [51]. Therefore, in forest management strategies, meteorological factors like relative humidity should be integrated to optimize fire risk assessment models, enabling the formulation of more precise and effective prevention and control measures.
# MedChain: Efficient Healthcare Data Sharing via Blockchain
## 3 MedChain Model
### Preliminaries
#### 3.2.3 Cryptographic Keys  
Cryptographic keys are applied to fulfill the security and privacy requirements of the system design. The keys to achieve confidentiality over untrusted communication channels and storage space are highlighted as follows.  
* Patient public-private key pair (\(PK_{\mathit{pat}}/SK_{\mathit{pat}}\)).
* Healthcare provider public-private key pair (\(PK_{\mathit{sp}}/SK_{\mathit{sp}}\)).
* Requester public-private key pair (\(PK_{\mathit{req}}/SK_{\mathit{req}}\)).
* Inventory secret \(S_{\mathit{data}}\): a symmetric secret key for patient inventory access, generated by a healthcare provider.
* Section secret \(K_{\mathit{sec}}\): a symmetric secret key for accessing a section of a session, generated by the patient.  
In the implementation, MedChain employs elliptic curve cryptography (ECC) [36] for public-private key pair generation. Moreover, the following notation of cryptographic functions are employed for description consistency.  
* \(E_{\mathit{key}}(m)\): encrypts message \(m\) with \(\mathit{key}\).
* \(D_{\mathit{key}}(m)\): decrypts message \(m\) with \(\mathit{key}\).
* \(H(m)\): creates a hash code/digest of content \(m\).  
For authentication and an integrity purpose, a message \(m\) sent by a user is signed with the user's private key to generate the signature appended on \(m\). Later, the signature will be verified by the public key of the user.
## 3 Modeling of the VLC System and Its Components in the Indoor Environment
### The Mathematical Model of the Behavior of a Flat Mirror  
To model a flat mirror, we must know that its behavior when it interacts with light beams adheres to specular reflection and the fundamental law of reflection. These effects can be mathematically represented as the equality between the angle of incidence and the angle of reflection. This principle forms the basis for modeling the behavior of flat mirrors in optical systems and is essential to accurately characterize signal reflections in the proposed VLC environment [20].  
\[\theta_{i}=\theta_{r}, \tag{1}\]  
where \(\theta_{i}\) is the angle of incidence of the light relative to the normal of the surface, and \(\theta_{r}\) is the angle of reflection of the light relative to the normal of the surface.  
In this sense, to control the direction of the reflected light, the IRS elements can adjust the phase of the reflected wave. The adjusted phase \(\Phi\) for an IRS element can be expressed as follows [19]:  
\[\Phi=\beta x+\gamma y+\delta, \tag{2}\]  
where \(x\) and \(y\) are the coordinates of the element on the IRS surface, \(\beta\) and \(\gamma\) are constants that depend on IRS control, and \(\delta\) is an initial phase.
## 4 Case Study
### Model Construction and Model Testing
#### 4.4.2 \(t\)-Test  
The F-test yielded a significant result. However, it does not imply that each explanatory variable has a significant effect on the explained variable. As a result, the significance of each variable's coefficient must be tested using the \(t\)-test, as indicated in Table 7:  
The model coefficients other than \(b_{0}\) and \(b_{3}\) are highly significant when the significance level is 0.05, indicating that headway and spacing in the preparation stage are not significant, whereas speed, acceleration, and duration time are significantly related to the longitudinal driving distance of lane-changing behavior.
# Automated Monitoring of Bluefin Tuna Growth in Cages Using a Cohort-Based Approach
## 2 Materials and Methods
### Computer Vision Algorithms for Fish Width and Length Estimation  
The estimation of tuna sizes using computer vision techniques is based on fitting a deformable model of the fish's ventral silhouette. The algorithms involved in the process of fish sizing are summarised in Figure 3. Image segmentation is implemented using local thresholding [19], a region-based technique for extracting compact regions (blobs) on each video frame, coupled with morphological operations. The segmented blobs are geometrically characterised and sifted using shape (aspect ratio), pixel density, and dimensional filters. An edge detection algorithm is then applied, and a minimisation algorithm is used to fit a deformable tuna model. To evaluate the goodness of the fit, a Fitting Error Index (FEI) is computed. The FEI, based on the quadratic distance between the model points and target edges points, ranges from 0 to 10, where FEI = 0 denotes a perfect fit between the segmented blob and the geometric model. Fittings with high values (FEI > 3) are discarded. The results from left and right videos, obtained separately, are merged to calculate straight fork length (SFL) and the maximum width of the fish. The image plane information is then transformed into 3D measurements using the calibration parameters of the stereoscopic vision system and 3D triangulation. Samples are excluded if stereo correspondence is not met for the first and last model vertebrae, that is, if the distance from the points to the epipolar lines exceeds a defined threshold. As fish are deformable due to swimming motion, measurements from a single frame may lack reliability [8]. Two common approaches are used in the literature to mitigate the effect of swimming motion on length measurement: (i) taking measurements in all frames and deducing straight body length from a sinusoid-like pattern [8]; (ii) accounting for body bending by adding contiguous linear segments [20]. In this study, the swimming length problem was addressed by considering the tuna model bending angle \(\theta\). Valid samples were identified as those where vertebral points formed a straight line, and others were discarded. The challenge of not observing the tail fork from the ventral perspective was tackled by excluding the caudal fin from the deformable tuna model. The relationship \(\text{SFL}=1.0312\text{ ML}+0.065641\), deduced from experimental samples in [13], where ML is the model length, was then applied. The system automatically provides length and maximum width measurements of tuna, contributing to a more accurate estimate of their weights. See Refs. [13; 21] for further details of the computer vision algorithms.
# An Adaptable Train-to-Ground Communication Architecture Based on the 5G Technological Enabler SDN
## 3 Proposed ACS Architecture
### ACS Functions
#### 3.3.2 SDN Path-Computing Application  
In the control plane, the SDN controller runs the Disjoint Path-Computing (DisPaC) application, which is responsible for the forwarding in the core network. DisPaC is based on the path computation element (PCE) described in [13]. The PCE calculates disjoint paths between two switches--edge switches in Figure 3--and the service manager installs the correct flow rules in the OF switches. In this case, the application has been customized to install flow rules corresponding to precomputed backup paths in order to be installed when a failure occurs.  
Five parameters are necessary to configure each service: source edge switch/port, destination edge switch/port and VLAN ID (to differentiate services in case of having more than one).  
* If there are two disjoint paths, one path is set to active--dark color in Figure 3--and the other one to backup--light color in Figure 3. However, the flow rules corresponding to the backup path are also installed, so the traffic is also forwarded through this path. Edge switches are responsible for discarding the traffic from the backup path in order to avoid duplicated packets to be delivered out of the network. If the active path fails, the application can detect it and activate the backup path, that is, change a flow rule on each edge switch. When the active path recovers, the traffic is again handed off to it. If the backup path crashes while the active one is down, the ongoing service ends.
* If there are two different paths, but they are not fully disjoint, the application establishes them and works exactly as in the previous case.  
Figure 3: Data flow of the proposed ACS architecture; MPTCP—multipath TCP.  
* Finally, if there is a single path, it is established and used. When any link in the path crashes, the service stops.  
In consequence, as the backup path is precomputed and ready to work, when there is a failure, the switching time between active and backup path is reduced to the time that it takes to install one flow rule, i.e., send an OF Flow-Mod message and modify the flow table. This means that the failover response time decreases and the reconfiguration is faster. Moreover, the use of disjoint paths provides path diversity, which is reflected in the resiliency of the network.
## 2 Materials and Methods
### Data Sources
#### 2.1.1 Urban Morphology Data Sources  
Urban morphology describes the physical structure of human settlements, and several parameters, such as wind corridors, altitude, green spaces, and hydromorphology, are considered. The data sources include:  
* OpenStreetMap (OSM): Provides street-level data on roads, buildings, and urban infrastructure, accessed via the QuickOSM plugin (Section 3).
* Cadastral Data: Offers detailed land use and property boundary information. Data for this study was sourced from the General Directorate of the Spanish Cadastre.
* Digital Terrain and Surface Models (DTM/DSM): all data was sourced from the Spanish Geographic Institute (IGN), aligned with the INSPIRE Directive. The following specific data were used: 1. DTM at 2 m resolution, providing elevation data, crucial for computing street altitudes. 2. DSM at 2 m resolution, used to calculate the SVF. 3. Building height model at 2.5 m resolution that helps estimate wind corridors, influencing UHI formation.
* Coastline and Municipality Boundaries: used to determine the distance of a city to the sea and to classify cities as coastal or non-coastal. These data were sourced from the IGN.  
Figure 1: Flow chart of the methodology proposed to assess the UHI characterization based on different morphological and environmental urban parameters: WC (Wind Corridor), BBS (Building on Both Sides), SVF (Sky View Factor), NDVI (Normalized Difference Vegetation Index), NDWI (Normalized Difference Water Index), DtS (Distance to the Sea), and SCS (Summer Climate Severity).
## 6 Research and Analysis of the Obtained Results
### Test No. 3  
First Passage  
Figure 25: Graph of the reference position distance from the position estimated by EKF and DR methods and determined by the USBL system (hundred passages).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Positioning Method** & \begin{tabular}{c} **Minimum Distance to the** \\ **Reference Position (m)** \\ \end{tabular} & \
\begin{tabular}{c} **Maximum Distance to the** \\ **Reference Position (m)** \\ \end{tabular} &
\begin{tabular}{c} **Average Distance to the** \\ **Reference Position (m)** \\ \end{tabular} \\ \hline EKF & 0.7 & 4.4 & 2.9 \\ DR & 0.7 & 19.1 & 10.8 \\ USBL & 1.3 & 55.9 & 22.7 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Statistical parameters of positioning errors using EKF, DR methods and the USBL system (single passage).  
Figure 26: Histogram of the reference position distance from the position estimated by EKF and DR methods and determined by the USBL system (one hundred passages).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Positioning Method** & \begin{tabular}{c} **Minimum Distance** \\ **from the Reference Position (m)** \\ \end{tabular} & \
\begin{tabular}{c} **Maximum Distance** \\ **from the Reference Position (m)** \\ \end{tabular} &
\begin{tabular}{c} **Maximum Distance from the** \\ **Reference Position (m)** \\ \end{tabular} \\ \hline EKF & 0.1 & 11.6 & 4.0 \\ DR & 0.3 & 33.8 & 22.0 \\ USBL & 3.4 & 37.5 & 18.7 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Statistical parameters of positioning errors using EKF, DR methods and the USBL system (single passage).  
Figure 30 presents determined routs during one hundred passages using different methods of positioning. Statistical parameters of positioning errors using EKF, DR methods and the USBL system for these passages are presented in Table 12.  
The generalized results of the Test No. 3, clearly show that the estimation of position coordinates using the EKF method gives the best results. In Figure 31, a marked improvement in the accuracy of the position can be seen when calculating additional measurements from the USBL system (at 100 s). The histogram in Figure 32 confirms the exact estimated position coordinates by the EKF method can be most frequently expected.  
Figure 31: Graph of the reference position distance from the position estimated by EKF and DR methods and determined by the USBL system (one hundred passages).  
Figure 30: Determining the route (one hundred passages).  
Figure 29: Graph of the reference position distance from the position estimated by EKF and DR methods and determined by the USBL system (single passage).