esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussions
### Comparative Analysis of Difficult-to-See Objects  
The proposed segmentation mask in the CeRDE and GCRDE approaches provided significant improvements in processing time compared to the traditional PbyP and CoRDE approaches, as shown in Figure 26. Among all approaches, the bounding box in CeRDE achieves the lowest extraction time, particularly for the person and bicycle classes, demonstrating its high computational efficiency. In contrast, the bounding box and the segmentation mask in CoRDE exhibit the highest time consumption, making them less suitable for real-time applications.  
Figure 24: This comparison shows depth extraction accuracy (%) at occlusion Levels 1 for the traditional approach: PbyP (Point-by-Point) and our proposed approaches: CoRDE (Complete Region Depth Extraction), CeRDE (Central Region Depth Extraction), and GCRDE (Grid Central Region Depth Extraction).  
Figure 25: This comparison shows depth extraction RMSE (in meters) at occlusion Levels 1 for the traditional approach: PbyP (Point-by-Point) and our proposed approaches: CoRDE (Complete Region Depth Extraction), CeRDE (Central Region Depth Extraction), and GCRDE (Grid Central Region Depth Extraction).  
The PbyP and CoRDE approaches generally yield lower accuracy, especially for the car and truck categories, as shown in Figure 27. The proposed segmentation mask in the GCRDE method achieves the highest accuracy across a lot of classes, reaching over 90% for the person class and significantly outperforming others in the truck and car categories. Also, the segmentation mask in CeRDE shows strong performance, particularly for cars and trucks.  
The segmentation mask in the GCRDE approach achieves the lowest RMSE for trucks and persons, as shown in Figure 28. The segmentation mask and the bounding box in CeRDE also demonstrate improved depth precision compared to other approaches. PbyP and CoRDE exhibit the highest RMSE, particularly for larger objects such as buses and trucks, suggesting poor reliability in occlusion-heavy scenarios.  
The bounding box and segmentation mask in the CeRDE approach outperform others in all three metrics, achieving the fastest processing time, the highest depth extraction accuracy, and the lowest RMSE. Central region depth extraction strategies provide substantial advantages over traditional approaches, especially when applied to segmentation mask objects.  
Figure 27: This comparison shows depth extraction accuracy (%) at occlusion Levels 2 for the traditional approach: PbyP (Point-by-Point) and our proposed approaches: CoRDE (Complete Region Depth Extraction), CeRDE (Central Region Depth Extraction), and GCRDE (Grid Central Region Depth Extraction).  
Figure 26: This comparison shows mean depth extraction time (in milliseconds) at occlusion Levels 2 for the traditional approach: PbyP (Point-by-Point) and our proposed approaches: CoRDE (Complete Region Depth Extraction), CeRDE (Central Region Depth Extraction), and GCRDE (Grid Central Region Depth Extraction).
## 4 Implementation of the Proposed UniChain System
### Registering a Student Node  
The process of registering a student can be viewed as an example of generating a stewardship among two different nodes: one stores and manages the data for the other (i.e., a university node stores and manages the data for a student node). A student registration process is performed whenever a new student visits a university.  
The process begins by ensuring and confirming that the student node already is a registered node in the blockchain system. The DB Manager of the university node, which provides an access interface to the existing database, sends the student's Ethereum address along with its \"student\" role to the NC for verification. The NC ensures that the registration process will be accomplished for a student node that already registered in the blockchain. The NC returns a Boolean value for confirmation. Otherwise, the process of adding a new student node has to be completed first. Upon confirmation, the university node sends the student information (i.e., the student's Ethereum address, and its ID) in a transaction to its SRHC. The SRHC confirms whether the student is a new student or not.  
Upon confirmation, the SRHC of the university node requests to generate a new stewardship with the student who can accept or reject that request. The SRHC of the university node will generate a new entry with the Ethereum address of the student node, its ID, \"waiting approval\" status, and last update of the status. Similarly, the SRHC of the student node will create a new entry with the Ethereum address of the university node, its ID, \"waiting approval\" status, and last update of the status. When the student accepts the request, the status field of both the university and the student SRHC will be updated with \"acknowledged student approval\" along with the last_update field. Otherwise, the process will be canceled, the status field will be updated with \"acknowledged student approval\", and a notification will be sent to the university node. After accepting the request and updating the SRHC of the university, the university's SRHC creates a new RC for the new stewardship. The RC then accordingly fills the student's Ethereum address, his/her ID in the Owner field and all the university database related information. The RC sends its address to the SRHC of the university and the student nodes to update their \"RC.add\" data field for future reference.
# Two-Stage Metaheuristic Algorithms for Order-Batching and Routing Problems
## 4 Proposed Algorithm
### Initial Batch Order
Specifically, when there are several batches to select from, the system selects the batch with the lowest remaining capacity (batch total capacity-batch present capacity) after an order is assigned to it. When a batch cannot contain the order, the order is reassigned to a new batch.
## 3 Experimental Results
### Data Acquisition  
The test site is a land reclamation area where mostly soft ground characteristics appear. The area is 391,535 m\({}^{2}\) and the soft ground depth ranges 9.7-49.2 m. The drone flight was designed considering the weather, wind speed, and construction interference, such as PBD (Plastic Board Drain) and DCM (Deep Cement Mixing) operations. The drone was operated at an 80 m flying height and 2.5 m/s. We divided the target area into three sub-targets, as shown in Figure 7, because of the limited battery capacity, but the data were acquired considering the overlapping between the sub-targets. The original point cloud went through noise removal and filtering that removes trees and construction equipment. Figure 8 depicts a cross-section profile from June 2020 (Red) to January 2021 (Green). With the banking process, the elevation change was observable. In addition, Figure 9 shows the differential DTM from February 2021 to February 2022. The blue color shows the banking part while the red color depicts the removal of the banked soil.
# Energy-Efficient and QoS-Aware Computation Offloading in GEO/LEO Hybrid Satellite Networks
## 2 Materials and Methods
### System Model
#### 2.1.3 Energy Consumption Model  
The energy consumption of GEO/LEO hybrid satellite networks during the mission offloading process is mainly divided into two aspects: on the one hand, the computational energy consumption generated during the computation processing, and, on the other hand, the transmission energy consumption during the data transmission process.  
**Computational energy consumption:** in this paper, we use \(E_{n,i}^{leo}\) and \(E_{n,i}^{j,geo}\) to denote the energy consumption of the processing mission on LEO satellite \(i\) and the processing offloaded mission on GEO satellite \(j\), respectively. In addition, we use \(p_{n,i}^{geo}\) to denote the computational power of LEO satellite \(i\) for processing mission \(T_{n,i}\). Since the processing power of the device directly affects the processing energy consumption [26], \(p_{n,i}^{leo}\) is given by  
\[p_{n,i}^{leo}=\kappa^{leo}(f_{n,i}^{leo})^{3} \tag{6}\]  
where \(\kappa^{leo}\) denotes the effective switching capacitance constant determined by the chip integration architecture [26].  
Similarly, the computational power of the GEO satellite \(j\) for processing mission \(T_{n,i}\) can be expressed as  
\[p_{n,i}^{geo}=\kappa^{geo}(f_{n,i}^{j,geo})^{3} \tag{7}\]  
Thus, the computational energy consumption of processing mission \(T_{n,i}\) on LEO satellite \(i\) can be obtained by  
\[E_{n,i}^{leo} =p_{n,i}^{leo}D_{n,i}^{leo} \tag{8}\] \[=\kappa^{leo}(f_{n,i}^{j,geo})^{2}(1-\lambda_{n,i})B_{n,i}m_{n,i}\]  
The computational energy consumption of processing the offloaded mission on GEO satellite \(j\) can be expressed as  
\[E_{n,i}^{j,geo} =p_{n,i}^{geo}D_{n,i}^{j,geo} \tag{9}\] \[=\kappa^{geo}(f_{n,i}^{j,geo})^{2}\lambda_{n,i}B_{n,i}m_{n,i}\]  
**Transmission energy consumption:** in this paper, we use \(E_{n,i}^{j,tra}\) to denote the energy consumption of communication resources generated by the LEO satellite \(i\) uploading \(T_{n,i}\) to the GEO satellite \(j\). Thus, the transmission energy consumption can be expressed as  
\[\begin{split} E_{n,i}^{j,tra}&=p_{i}D_{n,i}^{j,tra}\\ &=\frac{p_{i}\lambda_{n,i}B_{n,i}}{\omega_{n,i}^{j}log_{2}(1+ \frac{p_{i}\lambda_{n,i}B_{n,i}}{\sum_{w\in\mathcal{L}}p_{n}h_{w,i}+N_{0}})} \end{split} \tag{10}\]
## 4 Imaging of Bragg-Dip Profiles and Evaluation of Detailed Inner-Grain Information
### Measured Neutron Transmission Spectra of Three Evaluated Grains
#### 4.3.1 Mosaic Block Detection through the Observation of Bragg-Dip Broadening  
Firstly, the widths of Bragg-dip at entire Grain 3 and Mini Region A were compared. The FWHM of each Bragg-dip, shown in Figure 7d,e was evaluated. The FWHM of (101) Bragg-dip was  
Figure 7: **(a)** Imaging of FWHM of (101) Bragg-dip in Grain 3. **(b)** Imaging of FWHM of (110) Bragg-dip in Grain 3. **(c)** Comparison of neutron transmission spectra at Mini Regions A, B and entire Grain 3. **(d)** Comparison of (101) Bragg-dip profiles at Mini Regions A, B and entire Grain 3. **(e)** Comparison of (110) Bragg-dip profiles at Mini Regions A, B and entire Grain 3.  
Bragg-dip at entire Grain 3 was 0.0021 nm. The FWHM of (101) Bragg-dip at Mini Region A was broader, 0.0033 nm. On the other hand, the FWHM of (110) Bragg-dip at entire Grain 3 was 0.0017 nm. The FWHM of (110) Bragg-dip at Mini Region A was also 0.0017 nm. The Bragg-dip pattern at entire Grain 3 corresponded to that of Mini Region A. However, at Mini Region A, the width of only (101) Bragg-dip was broad. In contrast, the width of (110) Bragg-dip did not change.  
This suggests the possibility of the existence of another mosaic block with slightly different crystal orientation. This is because around the crystal orientation of [100 74 26], which is the crystal orientation of entire Grain 3, (110) Bragg-dip wavelength did not change so much depending on the change in the crystal orientation, according to the database presented in the previous study [19]. At entire Grain 3, which has the crystal orientation of [100 74 26], (101) and (110) Bragg-dips appeared at the wavelength of 0.2842 and 0.3925 nm, respectively. On the other hand, in the case of the crystal orientation of [100 74 27], (101) and (110) Bragg-dips appeared at the wavelength of 0.2860 and 0.3918 nm. The difference between the (101) Bragg-dip wavelengths was 0.0018 nm, and the difference between (110) Bragg-dip wavelengths was 0.0007 nm. Thus, the difference in (110) Bragg-dip wavelengths was smaller than that of (101) Bragg-dip wavelengths. Therefore, at Mini Region A, it is considered that the change in the width of (110) Bragg-dip was not observed, and the change in the width of (101) Bragg-dip was observed. This can be caused by another mosaic block with slightly different crystal orientation.
# Optimal Path Routing Protocol for Warning Messages Dissemination for Highway VANET
## 5 Performance Evaluation
### End-to-End Delay  
End-to-end delays can be computed as:  
\[E_{r}=\frac{\Sigma_{i=1}^{R_{p}}\Sigma_{d_{i}}}{R_{p}}, \tag{10}\]  
where the terms \(Er\) and \(E_{di}\) and \(Rp\) stand for the end-to-end delay ratio, the individual packet delay, and the total number of packets received. Due to the increased number of hops in DABFS and ID-LAR, as a result of the path reconstruction process, as detailed in Section 4.2.1, the total number of send and receive operations also increases. The efficiency of both DABFS and ID-LAR is negatively impacted by the growing number of transmitting and receive operations because they are time-consuming. However, by choosing the next hops traveling in the target node's direction, OPRP reduces this latency. The end-to-end delay decreases as the density increases because nodes are closer. Hence, the proposed protocol with clustering and the compared protocols without clustering become clearer in the results. Figure 6a-e reveals Scenario 5.  
Figure 7: Throughput during the dissemination of WM. (**a**) Scenario 1: \(H\)\((S,D)\) = 1, \(S\) is rear, and \(D\) is front. (**b**) Scenario 2: \(H\)\((S,D)\) = 1, \(S\) is front, and \(D\) is rear. (**c**) Scenario 3: \(H\)\((S,D)\) = 0, \(S\) and \(D\) are moving toward each other. (**d**) Scenario 4: \(H\)\((S,D)\) = 0, \(S\) and \(D\) are moving away from each other. (**e**) Scenario 5: \(H\)\((S,D)\) = 0, \(S\) and \(D\) are moving toward each other in opposite directions.
# The Kinematic Analysis of a Wind Turbine Climbing Robot Mechanism
## 2 Mechanical Design
### Robot Dimension  
The entire system of a climbing robot is made up of two body frames that have a winding mechanism, dimensions as reckoned with both top view and side view are shown in Figure 4.  
The robot has two body frames with a height of 45 mm, a length of 500 mm as shown in Figure 4, and the total robot weight of around 8 kg; it is made of aluminum alloy 6061 material which is lightweight and economical. The mecanum wheels have a diameter of 75 mm with 8 roller wheels. The roller wheels have a point of contact with the wind turbine tower which will constantly change as the robot moves along the tower surface. The tapering wind turbine tower is made of steel material with an average base diameter of  
Figure 4: Climbing Robot Dimensions: (**a**) top view; (**b**) side view.  
Figure 3: Mecanum wheel design: (**a**) Left mecanum wheel; (**b**) Right mecanum wheel.  
400 mm and the top diameter is of 330 mm. If the robot is at the lower segment of the tower, the diameter distance is increased, and as it moves upward, the diameter distance decreases. This change in diameter leads to a variation in length and forces. The winding adhesion provides the ideal traction force of the robot to move along the tower in different movement states: the up-down motion, rotational motion, and spiral motion. The mechanical design is carried out with the aid of SketchUp. The assumed velocity was carried out in MATLAB to calculate the speed of the mecanum wheels as the robot is in different movement states on a circular truncated cone tower, and holding onto the tower surface. The robot weight, wheel torque, traction force, and the coefficient of friction between the mecanum rubber roller wheel and tower surface were taken into consideration. The robot weight directly affects its ability to climb due to the gravitational force. Thus, this robot manifests a lightweight material that allows for a better efficiency of energy. The wheel torque must be at the equilibrium state where the robot moves with constant speed to attain the synchronization of body frame as it moves along the tower. The traction force must guarantee that the robot will adhere to the tower surface, ensuring that during the climbing process in the different movement states the robot will not fall. The coefficient of friction between the mecanum rubber roller wheel and metal tower surface are important to keep the robot moving safely where the wheels lock on the tower surface. Then, the robot's weight, wheel torques, forces, and the inclination angle can be managed to meet the climbing scenario needed.
## 2 Materials and Methods
### The Proposed System
#### 2.2.4 Learning Parameters  
In order to achieve promising results, it is key to train a good model with many important parameters, which can influence performance of entire structure. During the process of training, the network weight parameters are learnt using mini-batch stochastic gradient descent with momentum (set to 0.9). Each 10 video batch is sent to the network with weight decay of 0.0005. The base learning rate is \(10^{-3}\) and the value is further dropped when the loss stops changing.
# Analysis of Fatigue Performance of Spot-Welded Steel T-Profiles Under Cyclic Torsional Loading
## 4 Conclusions  
This study examined the fatigue failure of the T-profile assembly under torsional loading by considering several key parameters that included the magnitude of torque applied, thickness of assembly components, and spot-weld diameter. Crack propagation was simulated using the XFEM modeling approach, with the model's accuracy confirmed against experimental data from previous studies. The following conclusions were drawn.  
Increasing the spot weld diameter from 4.5 mm to 6.5 mm significantly enhanced the assembly's torsional performance. The number of cycles \(N_{f}\) increased 3.23 times, while the crack propagation rate \(da/dN\) decreased by approximately 5.7 times. Regardless of weld size, cracks formed at the same location but propagated faster with smaller diameters due to inadequate assembly. Lower strains and stresses from larger diameters improved fatigue performance and decreased the chance of fracture.  
When the thicknesses of the parts were changed from 1.5 mm to 1.0 mm, the \(N_{f}\) decreased by more than 3.8 times, while the average \(da/dN\) increased more than 3 times. For \(t=2.0\) mm, an infinite fatigue life was achieved.  
When the \(T\) was changed from \(\pm 360\) into \(\pm 600\) Nm, the \(N_{f}\) decreased by 4.28 times, while the average crack propagation rate increased 5.33 times. It was found that one of the assembly's supporting components greatly enhanced the structure's fatigue performance, with the \(N_{f}\) rising by more than 14 times.  
Conceptualization, M.D.; Methodology, M.D. and F.K.; Validation, M.D.; Formal analysis, M.D.; Investigation, M.D.; Writing--original draft, M.D.; Writing--review & editing. F.K. All authors have read and agreed to the published version of the manuscript.  
The APC was funded by the American University of the Middle East, Kuwait.  
Not applicable.  
Data Availability Statement: Data sets generated during the current study are available from the corresponding author on reasonable request.  
The authors of this manuscript declare that there are no conflicts of interest regarding the publication of this paper. All research was conducted without any financial or personal relationships that could inappropriately influence or bias the work presented. We affirm that the results and conclusions of this study are solely based on objective scientific inquiry.
## 8 Discussion
### Hardware-Related Considerations
#### 8.2.1 The Role of Node Calibration  
It is a well-known fact that different CC2420-based devices can show variation in the nominal response of the RSSI curve. Since the core of the proposed method is based on RSSI sampling and threshold-based features, it is of primary importance to analyze if these variations can hamper the performance of the classifier. In their work, [PERSON] et al. [41] showed that these variations are due to two different phenomena: a non-linearity in the CC2420 RSSI response curve and the presence of a node-dependent offset. While the first phenomenon is of minor relevance, since the non-linear and non-injective regions do not influence significantly the RSSI curve (which remains mostly linear), a consistent offset of \(\pm 6\) dB is reported among different nodes.  
We have tested several different TeloSB nodes, sampling IEEE 802.15.4 Channel 26 in a radio-controlled environment to determine both the amplitude distribution and the mean of the collected RSSI traces for different nodes. As shown in Figure 14, we have observed a maximum RSSI offset of \(\pm 5\) dB.  
We carried out an analysis of the influence of the RSSI offset existing between the network device deployed for channel sensing and the device used for preliminary training set collection. In Figure 15, we show the impact of RSSI offset on the classification accuracy of the three targeted interference classes.  
As can be observed, performing the channel sensing using a node with a consistent RSSI offset can greatly hamper the performance of the classifier, also considering that the offset between two nodes could theoretically span up to 12 dB. Even an offset of 5 dB, such as the one reported in our node set, can decrease the performance of both IEEE 802.11 andMWO classification up 15-20%, rendering a node calibration process a relevant step from the perspective of safeguarding the performance of the proposed approach. Fortunately, this process is straightforward, since as shown in [41], only a simple noise-floor-based RSSI offset calculation and compensation is needed. In the proposed approach,  
Figure 13: Detection rate for microwave oven at Location C for different sampling window lengths. Some curves have been removed for clarity.  
for example, once the RSSI offset is acquired, the offset compensation can be simply implemented by employing a software-based adaptation of the energy-threshold used for the feature-extraction task.  
Figure 14: The RSSI profiling process for TelosB motes. (**a**) Amplitude distribution of RSSI traces from background noise sensing for different CC2420 nodes. Some curves have been removed for clarity. (**b**) Mean of recorded RSSI sample traces.  
Figure 15: Detection accuracy with respect to RSSI offset between the node used for collecting training data and the actual sensing node.