esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Energy-Efficient Routing Protocols (E2 RPs) for UWSNs
### Reinforcement Learning Energy-Efficient Routing Protocols (RL-Based E2 RPs)
#### 5.4.7 Enhanced Energy-Efficient Depth-Based Routing Protocol (E3D-bRP)  
An enhanced version of existing energy efficient routing protocol E2D-bRP in [157], and an enhanced energy efficient D-bRP (E3D-bRP) is presented in [160], which explains that how to increase the lifetime of the communicating nodes in the network, which lives in the middle depth level, similar to existing E2D-bRP in [157], if a Tx anchor node have a communicating data packet, it will select a communicating node, which is very close to the network sink node for forwarding communicating data packet. A communicating node which has a high depth level value and high remaining energy, would have a chance to become a forwarder node. After receiving the communicating data packet, the network forwarding node will calculate the holding time on the basis of priority value and remaining energy. The value of priority helps the communicating nodes with dilemma, which arises in the network, when the energy level of different communicating nodes is same. In this scenario, a communicating node which have a high value of priority in the network will be selected as a repeater node. However, in contrast with the existing routing protocol E2D-bRP in [157], the E3D-bRP in [160] utilizes a technique of reactive routing to accommodate abrupt updates in the network.
## 3 Results and Discussions
### Optimization of the \(InGaP/GaAs\) DjSCs with Buffer and New Top BSF Layers
#### 3.3.1 Doping Optimization for AlGaAs Bottom Buffer Layer in an InGaP/GaAs DjSCs  
Doping concentration plays a crucial role in modeling the configuration of a solar cell, exerting a direct influence on the extent of the depletion region. To clarify the impact of doping concentration on solar cell performance based on the approach investigated in this paper, the optimal thickness of the buffer layer is kept below 50 nm.  
A comprehensive overview of solar cell parameters is available in Table 5, covering various doping concentrations in the lower buffer layer, ranging from 1 \(\times\) 10\({}^{17}\) cm\({}^{-3}\) to 1 \(\times\) 10\({}^{20}\) cm\({}^{-3}\). It can be seen that the open circuit voltage (\(V_{\rm OC}\)) increases with the increase in the doping concentration. This is due to the increased built-in potential across the junction.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Doping of Buffer Layer (cm\({}^{-3}\))** & \(J_{SC}\) **(mA/cm\({}^{2}\))** & \(V_{OC}\) **(V)** & **FF (\%)** & **\(\eta\) (\%)** \\ \hline
1 \(\times\) 10\({}^{17}\) & 16.821 & 2.302 & 84.157 & 32.582 \\
5 \(\times\) 10\({}^{17}\) & 16.823 & 2.303 & 84.684 & 32.801 \\
1 \(\times\) 10\({}^{18}\) & 16.825 & 2.304 & 85.447 & 33.125 \\
5 \(\times\) 10\({}^{18}\) & 16.826 & 2.307 & 86.159 & 33.447 \\
1 \(\times\) 10\({}^{19}\) & 16.826 & 2.308 & 86.167 & 33.451 \\
1 \(\times\) 10\({}^{20}\) & 16.826 & 2.308 & 86.167 & 33.451 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance of DjSCs solar cell with various doping concentrations of the bottom buffer layer.  
Figure 6: Simulated structure of \(InGaP/GaAs\) DjSCs solar cell.  
The optimal doping density for achieving maximum efficiency is determined to be \(1\times 10^{19}\) cm\({}^{-3}\), yielding a conversion efficiency of 33.451%. Whereas the corresponding \(V_{\text{OC}}\) and \(J_{\text{SC}}\) are 2.308 V and 16.826 mA/cm\({}^{2}\), respectively. This balance between \(V_{\text{OC}}\) improvement and the stability of \(J_{\text{SC}}\) highlights the importance of optimizing the doping concentration to ensure efficient charge carrier collection while minimizing recombination losses [38]. The resulting I-V characteristics of \(InGaP/GaAs\) DJSCs at the optimal doping concentration of the bottom buffer layer is shown in Figure 7.
# Fundus Image Registration Technique Based on Local Feature of Retinal Vessels
## 5 Results
### Registration Performance
The difference in vessel thickness between fixed and moving images leads to different descriptors being computed for local features at the same part of the vessels. As a result, these local features were unable to establish a correspondence, resulting in low registration accuracy. In the event of increased tortuosity, the corresponding local features were appropriately established. However, the tortuosity causes the vessels to bend and alters the actual physical position of the vessels on the eyeball. Consequently, the registration was performed between local features on the same part of the vessels but at different physical positions, which resulted in high TRE. For existing feature-based RIR techniques, the invariant features utilized in their works were extracted throughout the image. Thus, minimize the impact of vessel thinning and tortuosity compared to our work. Examples of registered image pairs for CURVE-SIFT in each application are depicted in Figure 15. \begin{table}
\begin{tabular}{c c c c c} \hline
**Feature-Based** & \multicolumn{2}{c}{**Overlapping Area**} & \multicolumn{2}{c}{**Rotation**} \\ \cline{2-5}
**RIR Technique** & \(\mathbf{r_{s}}\) & \(\mathbf{p}\)**-Value** & \(\mathbf{r_{s}}\) & \(\mathbf{p}\)**-Value** \\ \hline GDB-ICP & 0.443 & \textless{}0.001 ** & \(-\)0.380 & \textless{}0.001 ** \\ Harris-PIIFD & 0.732 & \textless{}0.001 ** & \(-\)0.723 & \textless{}0.001 ** \\ Ghassabi’s-SIFT & 0.795 & \textless{}0.001 ** & \(-\)0.766 & \textless{}0.001 ** \\ H-M 16 & 0.785 & \textless{}0.001 ** & \(-\)0.763 & \textless{}0.001 ** \\ H-M 17 & 0.773 & \textless{}0.001 ** & \(-\)0.765 & \textless{}0.001 ** \\ D-Saddle-HOG & 0.769 & \textless{}0.001 ** & \(-\)0.745 & \textless{}0.001 ** \\ CURVE-SIFT & 0.415 & \textless{}0.001 ** & \(-\)0.382 & \textless{}0.001 ** \\ \hline \end{tabular} _rs: Spearman’s rho. **: Correlation is significant at the 0.01 level (2-tailed)._  
\end{table} 
Table 7: Correlation between success rate and factors.
# Mobile LiDAR for Scalable Monitoring of Mechanically Stabilized Earth Walls with Smooth Panels
## 4 Methodology
### MSE Wall Facade Partitioning and Panel Identification  
A semi-automated procedure is developed in this study to isolate the faces and the panels of the MSE wall. This is done by partitioning the MSE wall facade into individual faces that can be deemed planar (Figure 11). The sectioning process is manually accomplished while fitting a plane to the constituent points to evaluate the planarity of the derived partitions. A sectioning is considered acceptable if the RMSE values of the normal distances of the points from the best-fitting plane through the different sections are below a threshold, which is defined according to the noise level in the data. If the derived RMSE values are larger than the inherent noise level in the point cloud data (which is in the order of 1.5 cm), the partitioning process is revised. The \(LF_{cs}\) is defined, as discussed in Section 4.1, once the individual faces are finalized. A region-growing segmentation technique is then applied  
Figure 10: Registration outcome of TLS scans colored by RGB and MLS point clouds from different drive runs in different colors.  
Figure 9: (**a**) source to reference point cloud transformation for a given point, and (**b**) condition for accepting point-to-patch correspondence for a transformed point ([PERSON] et al. [29]).  
for each face to cluster points comprising the individual panels, as described by [PERSON] and [PERSON] [30]. The similarity criteria for the region growing process includes the local point spacing (LPS) and normal distance (\(n_{d}\)) between the points and the fitted plane through the face. More specifically, a point is augmented into a segmented region if it is within a multiplication factor of the LPS and a normal distance threshold. For the involved point clouds in this work, the multiplication factor is in the range of 2.0-2.5 and the normal distance threshold is in the range of 0.01-0.03 m. The segmentation result for one face is shown in Figure 12. Following the panel segmentation technique, the \(P_{cs}\) for a given panel is simply established by deriving the MBR enclosing that panel, as proposed by [PERSON] et al. [5]. More specifically, the \(x\)-axis of the \(P_{cs}\) is oriented along the normal to the best-fitting plane through the points covering that panel. The \(y\)- and \(z\)-axes of the \(P_{cs}\) are aligned along the bottom and left sides, respectively, of the MBR enclosing the panel in question, as can be seen in Figure 8. Having defined the \(LF_{cs}\) and \(P_{cs}\), the different serviceability measures can be established.
## 3 Case Study
### Comparison and Analysis of the Gross Error Removal Effect
which can accurately identify the outliers in the dam displacement monitoring data and eliminate them; meanwhile, the parameters \(\omega\) and b in the ELM are randomly generated, which overcomes the time complexity by avoiding repeated adjustment parameters and significantly improves the identification efficiency of gross errors.
# Autonomous Pointing Control of a Large Satellite Antenna Subject to Parametric Uncertainty
## 3 Autonomous Controller Design  
The pointing angles of the LSA are measured by an appropriate sensor, and this measured value can be used as feedback to correct pointing errors, considered as the noncollocated control. As can be seen in Equation (6), there exists coupling among satellite attitude angles, antenna pointing angles and control torques. A commonly-used method in practice is to diagonalize \(I\) and \(B_{roti}\) through satellite structure design, then the satellite with large antenna is designed as three-axis decoupling. The autonomous pointing control of LSA pitch-axis, which is the most representative, is therefore proposed in this paper; others are similar with respect to pitch axis and are thus omitted here.  
The design process of the autonomous pointing controller is addressed below. PD feedback plus a structural filter to perform pointing maneuvers and suppress antenna vibration are firstly presented in this section. Based on the controller, a modified adaptive controller is proposed subject to parametric uncertainty and pointing errors. Furthermore, the frequency-domain analysis technique is employed to design the control system, which is more practical in engineering problems. The proposed control approach has a simple structure, low orders and clear physical significance, which therefore avoids excessive complexity and provides a possible solution for engineering projects.
## 5 Major Equipment and Technologies
### Agricultural Communication
#### 5.2.2 Bluetooth  
Bluetooth is an essential wireless communication mode that links small-sized devices over short distances. There are many IoT agriculture devices that are Bluetooth enabled, such as Farm note Air gateway and Color sensor work with Bluetooth-enabled sensors. Bluetooth has an ubiquitous nature, due to which it is studied as suitable equipment for multi-tier farming functions [105].
## 3 Experimental Results
### Target Landing  
In order to determine the performance for targeted landing, an experiment was conducted indoors where the marker was placed along a certain distance away from the MAV and with a constant angle, \(\alpha\), as shown in Figure 8a. Both the target location and the MAV were placed on the same initial height and a signal was transmitted to the MAV to take off and hover above ground at a height of 30 cm. By using the measurement pad (Figure 8b), we could compute the exact landing place of the MAV and compare it with the desired one. For this purpose, we performed 10 trials while keeping the same posture in each trial. The experimental results are shown in Table 11 and Figure 9. From the results, we can conclude that there are some inconsistencies in terms of both precision and accuracy. The MAV is able to land in relatively close proximity to the center of the measurement pad with a standard deviation of about \(\sigma\approx\)5.32 cm in both x- and y-direction.  
Figure 7: Experimental results: (**a**) x-direction controller; (**b**) yaw angle controller.  
Figure 8: (**a**) The micro aerial vehicle (MAV) is located 1.5 m away from the target location and a rotation of 30 degrees; (**b**) image of the selected ArUco marker and measurement pad for target landing.  
Figure 9: Each point, P\({}_{i}\), on the diagram corresponds to the landing point for each attempt.
## 4 Design and Development
### Deployment Configuration and Execution Scripts
#### 4.3.5. Script Flow Overview  
The simulation execution flow is summarized as in Figure 6:  
For complete YAML configurations, CLI command sequences, and reproduction instructions, refer to Appendix A.  
In summary, the architecture follows a modular and layered design that integrates Hyperledger Fabric smart contracts with an ABAC policy engine. The structured use of data formats and automated scripting ensures scalability, reproducibility, and integration readiness for performance testing and analytics workflows.
## 3 Field Case Study of Pumping Test
### Pumping Test  
Figure 4 shows the water pumping histories from Well-3 and Well-4, the change of water level measured at Well-b, and the strain estimated from the fiber optic sensing at Well-a. Pumping started simultaneously at Well-3 and Well-4 at 10:00 on 2 November 2015, at a pumping rate of 480 L/min. Several hours after starting pumping, warm colors appeared in the upper layer of Chonan formation. The yellow and red colors indicate compressive deformation of the reservoir rocks. Variations in the magnitude and the times of compressive strain with depth in the Chonan formation suggested the different responses of the sandstone-mudstone alternation.  
Figure 4: Pumping history and vertical strain observed by the fiber sensor. The spatial and temporal sampling intervals are 5 cm and 5 min, respectively.  
To examine the difference in hydraulic communication between Well-a (fiber sensor well) and the two pumping wells, pumping at Well-4 was paused at 14:00 on 3 November. Then pumping was restarted at 10:00 on 4 November concurrently with stopping pumping at Well-3. Compressive strain increased to 40 \(\mu\)e at the depth from 150 to 220 m when pumping at Well-4 continued from 10:00 on 4 November through 11:00 on 11 November. The total volumes of water pumped out from Well-3 and Well-4 were 1,382.4 and 4,233.6 m\({}^{2}\), respectively. After the termination of pumping at Well-4 the compressive strain decreased gradually in the measurement time period. Later in 2015, additional pumping tests were conducted separately at Well-3 and other wells to investigate the heterogeneity of the Chonan Formation.
# Definition and Determination of Fin Substitution Factors Accelerating Thermal Simulations
## 2 Materials and Methods
### CFD Simulation Modeling  
Only steady-state cases were considered. The dimensions of the fluid domain depend on the dimensions of the finned array, as shown in Figure 1(a). Concerning the simplifications and the types of boundary conditions, the authors followed the modeling strategies of [PERSON] et al. [1] and [PERSON] et al. [3]. Surfaces 1, 2, 3, 5, and 6 of the fluid domain had the boundary condition pressure outlet, with a backflow total temperature corresponding to the ambient temperature. Surface 4 was defined as an adiabatic wall. The constant fin base temperature was specified on the back of the fin base plate.  
To model the heat transfer close to the wall with sufficient accuracy, the turbulence model and meshing had to be coordinated. Based on the results of [PERSON] et al. [23] and [PERSON] et al. [24] the k-\(\omega\) SST turbulence model was used. The computational domains were partitioned to create structured grids, see Figure 1(b). In principle, the grid depends on the dimensions of the finned array under consideration. The meshing was controlled edge-specifically via the number of elements. The elements grew towards the flow domain starting from the fin surface, see Figure 1(c). Depending on the case under consideration, the growth rate was between 5 and 8. For all used discretization, it was checked that the dimensionless wall distance \(y^{+}\) at the solid-fluid interface was near 1 [12].  
The fluid behavior was approximated by Boussinesq approximation and specified by the thermal expansion coefficient and the air density in each case at ambient temperature. Based on all simulations, a maximum fin base temperature of 473 K and a minimum ambient temperature of 293 K with a corresponding thermal expansion coefficient of 0.003421 1/K occurred. These values satisfy the following condition for using the Boussinesq approximation [12].  
\[\beta\left(T_{fb}-T_{\infty}\right)=0.616\ll 1 \tag{27}\]  
The maximum number of iterations was 500 for each simulation. Further simulation settings are summarized in Table 2.
# Reinforcement Learning-Based Algorithm to Avoid Obstacles by the Anthropomorphic Robotic Arm
## 2 Materials and Methods
### Methodology of the Research
#### 2.3.4 Reward Functions  
Agent#1, which is trained to avoid an obstacle, uses a reward function that takes into account the achieved position \(\left(a_{x},\ a_{y},\ a_{z}\right)\), and the goal position \(\left(g_{x},\ g_{y},\ g_{z}\right)\). The reward is calculated based on the Euclidean distance between point \(\left(a_{x},\ a_{y},\ a_{z}\right)\) and point \(g_{x},\ g_{y},\ g_{z}\). Additionally, the reward calculation takes into account a parameter \(R_{obs}\), which is a clipped logarithmic distance of the TCP of the robot from the obstacle \(d_{obs}\) multiplied by weight obstacle coefficient \(w_{obs}\), which in the investigations is equal to 0.5. To avoid infinite reward during training, while \(d_{obs}\) is equal to 0, an additional small number is added in the logarithmic function. Distance \(d_{obs}\) is a fixed number that is equal to 0.115 m. It is the distance of the TCP of the robot to the obstacle, from which the \(R_{obs}\) is taken into account to reward \(R_{dobs}\). The reward \(R_{dobs}\) is derived using the formula:  
\[\begin{array}{l}R_{dobs}=-\sqrt{\left(a_{x}-g_{x}\right)^{2}+\left(a_{y}-g_ {y}\right)^{2}+\left(a_{z}-g_{z}\right)^{2}}\\ +\left\{\begin{array}{ll}if\ d_{obs}>d_{obs}&\quad R_{obs}=0\\ if\ d_{obs}\leq d_{obs}&\quad R_{obs}=\ \mathrm{clip}\left(\log(d_{obs}+0.00001) \cdot w_{obs},-\infty,0\right)\end{array}\right.\end{array} \tag{9}\]  
Agent#2 is trained in order to achieve the goal position as fast as possible; therefore, it takes into account the optimal trajectory and the trajectory on which the agent moves. The reward is calculated using the equation:  
\[R_{dtr}=-\sqrt{\left(a_{x}-g_{x}\right)^{2}+\left(a_{y}-g_{y}\right)^{2}+ \left(a_{z}-g_{z}\right)^{2}}-d_{tr}\cdot w_{dtr} \tag{10}\]  
Figure 7: Electronic diagram of the head.  
where \(d_{tr}\) is the distance between the achieved position \(a_{x},\ a_{y},\ a_{z}\) and the point that lies on the ideal trajectory (usually a line) based on the start position \((s_{x},\ s_{y},\ s_{z})\) and target position \((g_{x},\ g_{y},\ g_{z})\). The distance \(d_{tr}\) is calculated from the formula:  
\[d_{tr}=\frac{\left|s_{xyz}\overset{\rightarrow}{g}s_{xyz}\times s_{xyz}^{ \rightarrow}a_{xyz}\right|}{\left|s_{xyz}\overset{\rightarrow}{g}s_{xyz} \right|} \tag{11}\]  
where \(s_{xyz}\) is the start position, \(g_{xyz}\) is the goal position, and \(a_{xyz}\) is the achieved position. Coefficient \(w_{dtr}\) is a weight coefficient, which represents the extent to which the distance \(d_{tr}\) is relevant for calculating the reward \(R_{dtr}\). In the investigations, the value of this coefficient \(w_{dtr}\) is equal to 20.  
Combining these two rewards into one equation results in a situation in which the agent is unable to achieve two goals simultaneously (avoiding obstacles and achieving the goal position as fast as possible when an obstacle is not in the workspace) compared to the case of the separate rewards and multi-agent system.
# Towards Low-Cost Pavement Condition Health Monitoring and Analysis Using Deep Learning
## 5 Discussions  
### Performance of Models  
Based on the outputs of each model, complete evaluations were done on the final graph. As the base models used for the development of the new model were generated on the 'COCO dataset' it  
Figure 14: SSD with Inceptionv2 model predictions on test data.  
Figure 15: SSD with mobilenetv2 model predictions on test data.  
was, therefore, instructive to utilize the 'COCO metrics' for evaluating the performance of the model. These metrics involve the use of Precision, denoted as Average Precision (AP) and recall, denoted as Average Recall (AR) with regards to the bounding boxes within test cases. Precision can be classified as the results within a test that are relevant to the classification problem whilst Recall refers to the percentage of relevant results which are in turn correctly identified by the model. In the evaluations, the Intersection Over Union threshold was set to 0.5. The two parameters are determined by the following equations:  
\[\text{{Precision }}=\frac{\text{{True Positive }}}{\text{{True Positive }}+\text{{ False Positive }}} \tag{1}\]  
\[\text{{Recall }}=\frac{\text{{True Positive }}}{\text{{True Positive }}+\text{{ False Negative }}} \tag{2}\]  
These values are obtained by running the eval.py scripts on the model using the test dataset for the study. These results for all the models are shown in Table 3 below.  
Given the numbers showcased within this table, it can be surmised that all 3 models are capable of solving the problem at hand with the model utilizing the Faster R-CNN model being the most effective of the three. For the purposes of this study, however, what is critical is not only the accuracy of the model but also the simple capacity to recognize that the type of distress and its associated distress category are present within the image and subsequently within the network or road section under analysis. This creates a tool that allows for low-cost hot spot analyses of points on a given road network where there are structural defects and as a result can provide an overall condition estimation of the road network being assessed.  
Once a survey is carried out using the model it would also provide a baseline which can then be utilized for future continuous overall condition monitoring of the pavement structure by simply rerunning the model and comparing the defects between the different surveys. This would be without the need for expensive equipment or elaborate survey mechanisms. This is key for road managers and allows for integration within the PMS system. The road authority would be able to determine the location of the specific types of distresses covered by the model, its severity (and thereby an idea of whether an intervention is necessary) and the location and frequency of specific and important types of distresses. This can also then be integrated with techniques that can provide the specific or more detailed measurements required for further actions.
## 2 Proposed Inverse Solution for Risley Prisms  
Given the generic Risley configuration system shown in Figure 3, the two prisms \(\pi_{1}\) and \(\pi_{2}\) are rotated independently about the Z axis. The surfaces of the first prism have been designated N11 and N12, and the surfaces of the second prism N21 and N22. The refractive index of the prisms is denoted \(n_{1}\), and \(n_{2}\) is and the wedge angle of the prisms, \(\alpha_{1}\) and \(\alpha_{2}\). The orientations of the prisms are specified by the respective rotation angles \(\theta_{1}\) and \(\theta_{2}\). The distance between the two prisms is \(D1\), and the distance between the emergent surface of prism \(\pi_{2}\) and the receiving screen is \(D2\).  
Assuming the prisms are identical and have the same characteristics:  
\[\begin{split} n_{1}&=n_{2}=n\\ \alpha_{1}&=\alpha_{2}=\alpha\end{split} \tag{2}\]