esa-sceva/satcom-chunk-collection huggingface dataset:
# Indentation Measurement in Thin Plates under Bending Using 3D Digital Image Correlation
## 4 Conclusions  
A novel full-field experimental methodology based on 3D-DIC measurements has been presented to determine the real contact law of thin plates. The adopted setup uses a perimeter-clamped squared plate and takes into account the influence of bending in the measured indentation during contact experiments on thin aluminum plates. To illustrate the proposed methodology, specific contact experiments with different specimen thicknesses have been conducted. During the experiments, images were captured using two CCD cameras to determine the full-field displacements pattern at the rear surface of the specimen.  
The main conclusions from the study are:  
* Results from the performed experiments made it possible to infer the experimental contact law for 2 mm, 3 mm, 4 mm, 5 mm and 6 mm thick specimens made of AL 1050 H14 and an energy balance was performed to quantify the amount of energy to generate contact damage and bending deformation.
* The recovered elastic energy during the unloading has been also quantified. Higher contact damage was observed as the thickness increases while bending effect is minimized when thickness increases.
* The adopted experimental methodology makes potentially possible the contact behavior evaluation at different loading rates as well as obtaining the real contact law and a complete energy balance during the test. Then, the proposed method could be used to characterize the contact behavior during impact events or creep effects.
* Additional experimentation could highlight some uses as a method to measure the contact laws and harness in complex geometries or scenarios.  
Conceptualization, F.-A.D.-G. and E.L.-A.; methodology, E.L.-A.; software, E.L.-A. and J.-A.A.-L.; validation, E.L.-A., J.-A.A.-L. and F.-A.D.-G.; formal analysis, F.-A.D.-G.; investigation, E.L.-A. and J.-A.A.-L.; writing--original draft preparation, E.L.-A. and J.-A.A.-L.; writing--review and editing, J.-A.A.-L.; visualization, L.R.-G.; supervision, L.R.-G.; funding acquisition, L.R.-G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by CTA (Corporacion Tecnologica de Andalucia) and the IDEA agency, in collaboration with Airbus Military through the contract \"_Analisis de desplazamiento y deformaciones en estructuras multifunctionales sometias a impacto emplano la tecnica DIC 3D HS_\" as part of the project \"_DESAFIO_\".  
Not applicable.  
Data Availability Statement: Data is contained within the article.  
Authors would like to acknowledge Airbus Military, CTA (Corporacion Tecnologica de Andalucia) and IDEA agency for supporting this work.  
The authors declare no conflict of interest.
## 3 System Model and Design Objectives
### Secure Data Aggregation Using Authorization and Authentication in Wireless Sensor Networks (SDAAA)  
Secure data aggregation presents the most significant security challenge for privacy in WSNs. The primary objective of secure data aggregation is to enhance the network lifetime. Another objective is to reduce the energy consumption of the sensor nodes through proper use of battery power and efficient bandwidth. However, the data aggregation process may significantly affect QoS, accuracy, and fault tolerance. A decline in QoS and accuracy leads to weak security control mechanisms, such as the limitation of authorizing legitimate sensor nodes (SNs) through authentication and encryption security. Since data aggregation enhances security by reducing redundant data, it could be affected by a compromised or malicious sensor node. Compromised or malicious sensor nodes may illegally obtain the collected data from the neighbor sensor node and report false values as aggregated data.  
In this situation, a malicious node could harm the privacy, confidentiality, and integrity of the confidential data in WSNs. The malicious node could impersonate neighboring or further aggregator nodes. In addition, the attacker may prefer to install a hostile node near the base station to compromise it. As a result, QoS parameters such as energy efficiency, reliability, and accuracy are affected. The fragile design features of WSNs may easily invite destructive cyber attacks, mainly when deployed in unpromising environments.  
WSNs comprise resource-restricted sensor nodes with insufficient storage, limited power resources, and lower computational capability. Hence, it is crucial to examine traditional security algorithms in secure data aggregation in WSNs such as SD, EEHA, HAS, IIF, and RHC protocols, as these f
# Image Processing of UAV Imagery for River Feature Recognition of Kerian River, Malaysia
## 4 Discussion
### UAV Imagery in Landscape of Kerian River and other Malaysian Rivers  
The tropical climate of Malaysia and the year-round rainfall makes it difficult to analyze the river characteristics using low-cost satellite images because of heavy cloud covers [48]. UAV technology has a lead over satellite imagery because of its low operating cost. The Kerian River, for most of its upstream length, flows in an isolated region. The Kerian and other river basins of Malaysian rivers are the habitats of crocodiles, which makes it threatening for field visits. UAV imagery proves to be safe considering the climate and threats. The present study focuses only on flow area and vegetation growth using UAVs. We will get more features of the Kerian River using UAVs as inputs to the Ministry of Environment and Water, Malaysia in the future for river management. The sediment concentration by collecting sediments using samplers [49] and its validation using image processing are some gaps in this research topic. Apparently, for the Kerian River, there is limited river surveillance camera and flood does not occur frequently in this region. Therefore, direct use of a convolutional neural network (CNN) may not be a viable option for the river state classification. However, for future consideration, if enough surveillance cameras are installed at various cross-sections of the Kerian River, then we can develop a CNN model, as there will be a sufficient dataset to train the network. We may plan further work to use spectral cameras in UAVs to find the sediment concentration and composition using reflectance.
## 2 Materials and Methods
### Methods
#### 2.2.5 Accuracy Assessment  
To verify the accuracy of the abandoned farmland extraction results, random points located on the farmland in the study area were generated using ArcGIS software. Then, high-resolution remote-sensing images (GF-2, GF-6, and Google Earth images) were used to determine whether each random point location was abandoned or not using visual interpretation in order of the image date. A point with no crop planted in the imagery for each date represented abandonment; otherwise, it did not. The obtained validation sampling points were overlain with the extraction results. The different types of validation sampling points (abandoned points and cultivated points), falling inside and outside the abandoned farmland extraction results, were counted separately in order to construct the confusion matrix. Then, the overall accuracy and Kappa coefficient were calculated to evaluate the extraction results based on the confusion matrix, which was then used to measure the precision and consistency of the extraction results. The overall accuracy and Kappa coefficient were calculated as follows:  
\[Overall\ Accuracy=P_{0}=\frac{TP+TN}{TP+FP+TN+FN} \tag{9}\]  
\[P_{e}=\frac{\left(TP+FN\right)*\left(TP+FP\right)+\left(FN+TN\right)*\left(FP+ TN\right)}{\left(TP+FP+FN+TN\right)^{2}} \tag{10}\]  
\[Kappa\ coefficient=\frac{P_{0}-P_{e}}{1-P_{e}} \tag{11}\]  
where _TP_ represents true positive, _TN_ represents true negative, _FP_ represents false positive, and _FN_ represents false negative.  
In order to verify that the STARFM-fused dataset could effectively improve the accuracy of the extracted abandoned farmland data using an NDVI time series analysis, this study additionally extracted the results using only GF-6 and Landsat 8 data, which were then used for a comparison of the accuracy of the results. In addition, Support Vector Machine (SVM), a supervised classification algorithm based on statistical learning theory, was utilized. In this approach, a hyperplane between different categories was obtained based on the edge sample and was used for sample classification and abandoned farmland data extraction using a multidimensional NDVI time series dataset classification, obtaining good results. The SVM classified the data from the NDVI in different dimensions of the dataset, ignoring the changes in the NDVI between two adjacent time periods. Therefore, the SVM was additionally used for abandoned farmland data extraction based on the STARFM-fused NDVI time series dataset to verify the accuracy of the results based on the NDVI time series analysis.
## 3 Results and Discussion
### Energy Balance
power was recorded after 15 min. of operating. Later, it begins to decrease as the electric arc length decreases, dependent on the growth of the melt layer. In the areas where the arc contacted the PT electrodes, the heat flux reached high values through the arc spot. Local heating in this area is very intensive; for conventional PT, only the use of graphite may protect the anode from melting. When an electric arc is used for material processing, very intensive heat fluxes may destroy the material melting. Therefore, the temperature of the process must be regulated. It is important to note that part of the energy is transmitted by electrons to heavy particles and is included in the convective heat flux. The distribution of arc radiation depends on the degree of the arc blowing from under the electrode towards the walls and the ratio of the electrode diameter and the arc length. According to the experiments, the radiation heat transfer Q\({}_{R}\) for \(I<\) 100 A was negligible. According to the experimental data, the heat transfer in the air arc in the range of \(I\) = 50-70 A varied by 2-5%. Part of the convective heat flux in the energy balance at the anode side depends on the arc length. Radially directed electromagnetic forces compress the electric arc during the current passage. In the arc of a variable cross-sectional area (for example, near the cathode), a longitudinal pressure gradient arises because of the action of electromagnetic forces, which increases the generation of a plasma stream from the sites of the constricted area. In the present case, a plasma jet flowed from the cathode toward the anode.
## 4 DC Fault Current Analysis of Hybrid MMC Considering FRT Control
### Current Control Mode
Enhanced PI parameter magnitudes yield attenuated fault current amplitudes (Figure 7), attributable to amplified error magnitude between \(I_{dcref}\) and instantaneous fault current. This elevates the \(M_{dc}\) attenuation rate, thereby inhibiting fault current escalation. Contrarily, excessive proportional gain diminishes the fault current decay rate (Figure 7a), prolonging fault clearance duration. Consequently, proportional gain selection necessitates a design compromise between peak current suppression and transient duration minimization. Prior to FRT activation, under active power-regulated outer-loop operation, \(I_{dcref}\) is mathematically defined as:  
\[I_{dcref}=k_{pv}\Big{(}P_{ref}-P_{pu}\Big{)}+k_{iv}\int\Big{(}P_{ref}-P_{pu} \Big{)}dt \tag{16}\]  
Figure 6: DC fault currents under different current reference values (underdamped characteristic case). Figure 7: DC fault currents under parametric variation of (**a**) proportional and (**b**) integral gain in the PI controller with \(I_{dcref}=0\). Power transfer controllability between the hybrid MMC and AC system persists transiently post-fault [32]. Consequently, \(I_{dcrf}\) maintains quasi-stationary characteristics prior to FRT activation. The fault current solution is derived from (11)-(15) with \(I_{dcrf}\) normalized to unity. Subsequent analysis addresses the DC voltage regulation scenario.
## 4 Model Calibration and Square Lattice Surrogate Models
### Diffusivities for the Square Lattice Surrogate Models
#### 4.2.1 Equally Spaced Obstacles Model  
Consider a 2D rectangular lattice with horizontal spacing \(h(=\Delta x)\) and vertical spacing \(k(=\Delta y)\). Assuming a fixed volume and a uniform particle mass, let \(u(x,y,t)\) be the density of particles and let \(q\) be the probability that a particle stays in its current subdomain; \((1-q)\) is the probability of transitioning to a neighboring lattice point. The resulting particle density is given by  
\[u(x,y,t+\Delta t) =qu(x,y,t)\] \[+\frac{1}{4}(1-q)\big{[}u(x+h,y,t)+u(x-h,y,t)+u(x,y+k,t)+u(x,y-k,t )\big{]}. \tag{19}\]  
Consider the Taylor series expansions about the point \(P(x,y,t)\),  
\[u(x,y,t+\Delta t)=u+u_{t}(\Delta t)+\frac{1}{2}u_{tt}(\Delta t)^{2}+\frac{1}{ 6}u_{ttt}(\Delta t)^{3}+...\, \tag{20}\]  
\[u(x\pm h,y,t)=u\pm u_{x}(\Delta x)+\frac{1}{2}u_{xx}(\Delta x)^{2}\pm\frac{1}{ 6}u_{xxx}(\Delta x)^{3}+...\, \tag{21}\]  
\[u(x,y\pm k,t)=u\pm u_{y}(\Delta y)+\frac{1}{2}u_{yy}(\Delta y)^{2}\pm\frac{1}{ 6}u_{yyy}(\Delta y)^{3}+.... \tag{22}\]  
Using these series expansions, (19) becomes  
\[u_{t}(\Delta t)+\mathcal{O}((\Delta t)^{2})=\frac{(1-q)}{4}\Big{[}u_{xx}( \Delta x)^{2}+u_{yy}(\Delta y)^{2}+\mathcal{O}((\Delta y)^{4})+\mathcal{O}(( \Delta x)^{4})\Big{]}. \tag{23}\]  
Assuming a 2D square lattice (\(\Delta x=\Delta y\)) and dividing by \(\Delta t\), we can rewrite (23) as  
\[u_{t}+\mathcal{O}(\Delta t)=\frac{(1-q)}{4}\bigg{(}u_{xx}\frac{(\Delta x)^{2} }{(\Delta t)}+u_{yy}\frac{(\Delta x)^{2}}{(\Delta t)}+\mathcal{O}\bigg{(} \frac{(\Delta x)^{4}}{(\Delta t)}\bigg{)}\bigg{)}, \tag{24}\]  
resulting in the approximate equation (assuming \((1-q)(\Delta x)^{4}\ll\Delta t\)),  
\[u_{t}\approx\frac{(1-q)(\Delta x)^{2}}{4(\Delta t)}\big{(}u_{xx}+u_{yy}\big{)}. \tag{25}\]  
Recognizing (25) as the 2D diffusion equation, the theoretical diffusivity for the equal obstacles model (\(D\)) is given in terms of square lattice model properties by  
\[D=\frac{(1-q)(\Delta x)^{2}}{4(\Delta t)}. \tag{26}\]
## 3 System Overview
### Problem Description
#### 3.1.1 Definition of QoE  
In this paper, QoE is modeled as a linear combination of three factors: perceptual quality, smoothness, and rebuffering time of a video block:  
\[Q_{i}=q_{i}(r_{i}[k_{i}])-\alpha|q_{i}(r_{i}[k_{i}])-q_{i}(r_{i}[k_{i}-1])|- \beta re_{i}(k_{i}) \tag{1}\]  
where \(q_{i}(r_{i}[k_{i}])\) is used to represent the perceived quality that can be obtained by viewing the \(k_{i}\)-\(th\) block. It supports any index that can predict the perceived quality of users according to the video content. In order to distinguish the difference in viewing quality caused by different video contents, FBAC uses VMAF [11] to estimate the perceived quality of video blocks; \(q_{i}(r_{i}[k_{i}])-q_{i}(r_{i}[k_{i}-1])\) represents the change of perceived quality between adjacent blocks, that is, the smoothness of video; \(\alpha\) represents the penalty coefficient of smoothness. The larger \(\alpha\) is, the less the users can accept the changes of video quality; \(re_{i}(k_{i})\) represents the re-buffering time experienced while viewing the \(k_{i}\)-\(th\) block; \(\beta\) represents the penalty coefficient of the re-buffered event.
# A Privacy-Preserving Routing Protocol Using Mix Networks in Opportunistic Networks
## 4 Evaluation
### Performance Metrics  
One of the main concerns when using not only mixnets, but any means to provide anonymous communications in a network, is the penalty that such solutions could worsen overall network performance. This is especially relevant in OppNets, where we already do not usually have good performance in message delivery. The use of mix nodes introduces latency in message delivery and decreases the delivery ratio. The goal is to see whether these penalties are tolerable. To that end, we measured message latency and delivery ratio in several simulations for the three proposed scenarios, and using different setups and parameterizations.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Scenarios**} \\ \cline{2-4}  & **Cambridge** & **Info5** & **Taxis** \\ \hline Number of nodes & 51 & 41 & 304 \\ Connections per minute & 0.9979 & 8.035 & 15.13 \\ Total connection time (s) & 1,383,082 & 2,145,974 & 6,189,716 \\ Max node degree & 12 & 14 & 12 \\ Average node degree & 0.2996 & 0.6433 & 0.4713 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristics of 3 scenarios.  
We mainly focused our experiments to observe:  
* Message latency: the average time that it takes for a message to reach its destination.
* Delivery ratio: ratio of successfully delivered messages.
* Overhead ratio: the factor of number of relayed messages minus delivered messages, divided by the number of delivered messages. The overhead ratio shows the number of network resources used in the process of delivering a message to its receiver.
* Average routing-step path length: the average number of hops that one packet needs to traverse in each routing step using epidemic routing (see Section 3.2). That is, the number of nodes used to route a message from the source node to the first mix node, from the first mix node to the second, and so on.  
There are two main parameters that influence how our proposal is deployed in a given scenario. One is the percentage of mix hosts, i.e., the percentage of nodes that can be chosen as mix hosts or the size of \(\mathcal{M}\) with respect to \(V\). These are 20%, 40%, 60%, 80%, and 100%.  
The other parameter is the number of mix nodes used in cascade. That is, the number of mix nodes that each message gets through. In our experiment, we set the number of mix nodes from 0 to 6. Here, the value set to 0 means using the general broadcast method. We used simple broadcast to transmit our message from one mix to another. To simulate a real OppNet environment, the TTL (time-to-live) of each message was set to a relatively large value of 5.5 h (20,000 s). We assumed that each node had enough buffer to store the messages. During the simulation, every 100 s, a randomly selected node sent a 1 byte message to a randomly chosen node. This was performed in all three scenarios. In our simulation, we set parameter \(k_{i}\) to 10 for each mix node \(i\) mentioned in previous sections.
# A Layer-Wise Strategy for Indoor As-Built Modeling Using Point Clouds
## 3 Methodology
### Point Cloud Partition
#### 3.3.1 Boundary Point Extraction  
In this section, we extract the feature points to facilitate the representative layers selection. We utilize the 3D boundary as feature points because of the following reasons: (1) The boundary of planar primitives retains most wall information (i.e., position, orientation, and height), (2) the boundary points can avoid the repetitive structures which share the same geometry on the vertical direction (e.g., as shown in Figure 8, the bottom three layers share the same geometry on the vertical direction). A convex hull-based method [23] has been employed to extract boundaries from detected planar primitives. To be specific, the points on planar primitives can be projected onto the RANSAC fitted planes and the convex hull (see Figure 9a) can be determined by these projected points. Subsequently, we determined the boundary points as when the distances from the projected points to the key points on convex polygons (see Figure 9b) are less than a given threshold \(d_{b}\) (e.g., 0.05 m).
## 4 Simulation Results
### Results of Temperature Variations  
In this section, the effect of temperature changes on the characteristic curves of PV cells (Figure 16) as well as MPPT solving based on the ROA to achieve maximum power at different temperatures (Figure 17) are evaluated for pattern 4. As can be seen, with increasing temperature values (25, 50, and 75 \({}^{\circ}\)C), the power of the PV module decreases and vice versa. Additionally, with increasing temperature, PV module voltage decreases and the current increases. As shown in Figure 17, with increasing temperature, the amount of maximum power extracted decreases.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Parameter/Method** & **ROA** & **MFRO** & **PSO** \\ \hline Global power (W) & 80.62 & 80.62 & 80.62 \\ Extracted power (W) & 80.37 & 80.25 & 79.68 \\ Tracking efficiency (\%) & 99.68 & 99.54 & 98.83 \\ Tracking rate (S) & 0.70 & 0.91 & 1.22 \\ \hline \end{tabular}
\end{table}
Table 7: Numerical results of the ROA, MRFO, and PSO methods in pattern 4.  
Figure 14: Characteristics of the PV panel for pattern 4: (**a**) P-V, (**b**) I-V.  
Figure 15: (**a**) PV power, (**b**) PV voltage, (**c**) PV current, (**d**) Duty cycle in pattern 4 using different methods.
## 3 Results
### Cell Viability Test  
In terms of cell viability after 24 h, GaMBN1 (\(84.0\pm 10.5\%\)), GaMBN3 (\(97.9\pm 14.6\) %), and GaMBN5 (\(115.2\pm 6.7\) %) did significantly change compared to GaMBN0 (\(100.0\) %). After 48 h, the cell viability of the resins still did not exhibit significant differences: GaMBN1 (\(92.5\pm 4.3\) %), GaMBN3 (\(97.1\pm 11.9\) %), and GaMBN 5 (\(99.6\pm 11.5\) %) (Figure 10).  
Figure 10: Cell viability of human dental pulp stem cells (hDPSCs) on GaMBN-resin disks after 24 and 48 h. Based on one-way ANOVA (p \(>0.01\)), the results were not significantly different at 48 h. Error bars represent the standard error (n = 7).  
Figure 9: Adhesive remnant index (ARI) scores of four GaMBN resins used as orthodontic bonding adhesives. Based on the Kruskal–Wallis test at \(\alpha\) = 0.05, the ARI scores were not significantly different (n = 8).
# A Simulation of the Densification Process of NdFeB Bulks by a Modified Drucker-Prager Cap Model
## 4 Discussion  
1. As evidently demonstrated in the analysis of the sintering temperature field, at the preliminary stage of rising temperatures, the temperature of the outer layer of the powder's compaction is much higher than that of the interior, while the densification rate of the outer powder's compaction is much faster than the interior. Variations in sintering temperature exert a negligible influence on the trend in the temperature field.  
Figure 14: Microstructure of the fracture at corners at sintering temperatures of (**a**) 780 \({}^{\circ}\)C, (**b**) 850 \({}^{\circ}\)C, and (**c**) 940 \({}^{\circ}\)C.  
Figure 13: Cross-sectional microstructure and temperature cloud charts of NdFeB bulks at sintering temperatures of (**a**) 780 \({}^{\circ}\)C, (**b**) 850 \({}^{\circ}\)C, and (**c**) 940 \({}^{\circ}\)C.  
2. The analysis of stress-field cloud charts at three sintering temperatures revealed that longitudinal compressive stress at the cross-sectional edge transitions to tensile stress. This transformation stems from grain growth and constrained internal contraction on account of the hard surface shell, ultimately giving rise to a stress-state transition from compression to tension at the surface.
3. The simulated results show good agreement with the experimental data, which not only validates the model's accuracy but also addresses the challenge of non-visualizable temperature and stress fields. Aside from this, the model effectively forecasts crack-susceptible regions, providing a data-driven strategy to mitigate rupture in green bodies during processing.