esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Verification  
### Double-Exposure Method Experiment  
The double-exposure holographic interferometry setup for stress detection is illustrated in Figure 8. The experiment employs a continuous-wave He-Ne laser (power: 40 mW; wavelength \(\lambda\) = 632.8 nm) and a CMOS sensor (model OV7670; frame rate: 30 fps; pixel clock: 24 MHz; photosensitive array: 640 \(\times\) 480 pixels; package size: 3.785 mm \(\times\) 4.235 mm). The tested glass has a thickness t = 4.00 mm, radius a = 17.50 mm, Poisson's ratio \(\mu\) = 0.2, and refractive index n = 1.517.  
Optical configuration:  
The following components are used: He-Ne laser, two mirrors (M1/M2), five beam expanders (L1-L5), two beam splitters (BS1/BS2), glass sample T, CMOS sensor, and computer.  
Figure 8: Optical setup for double-exposure holographic stress detection.  
The laser beam is split by BS1 into two paths:  
Reference beam: The reference beam is reflected by M2, collimated, and directed to the CMOS via BS2 and L5.  
Object beam: The object beam is reflected by M1 and collimated; then, it illuminates the glass sample T and scatters to the CMOS through BS2 and L5.  
The two beams interfere to form a Fresnel off-axis digital hologram, recorded by the CMOS and digitized for storage and processing.  
Procedure:  
First hologram: The hologram of the undeformed glass is recorded; then, digital reconstruction and phase unwrapping are performed to obtain the initial phase distribution.  
Second hologram: In-plane stress is applied to the glass while maintaining optical alignment. The second hologram is recorded and processed to extract the deformed phase distribution.  
Parameters:  
(1) Pixel size: 3.2 \(\times\) 3.2 \(\upmu\)m\({}^{2}\); resolution: 1600 \(\times\) 1200 pixels; sampling rate: 10 fps.  
(2) Object-to-CMOS distance: 0.120 m; reference beam angles: \(\alpha=pi/2.045\), \(\beta=pi/1.9655\).  
(3) Reconstructed image scaling factor: reduced to 1/3. Curvature radius of spherical reconstruction wave: \(-0.06\) (negative sign indicates converging illumination). Reconstruction image plane distance: 4 cm.  
Experimental calculation results are shown in Figure 9.  
Figure 9a: Reconstructed image of the first digital hologram. Tilted carrier fringes are superimposed with local distortion; interference fringes from two states are superimposed. Distorted regions reflect phase changes caused by deformation.  
Figure 9b,c: Pure object wave reconstruction images. Speckles originate from rough surface scattering.  
Figure 9d: High-contrast cosine fringes (fringe bending in the deformed region). For Figure 9d, the holographic interference fringes can be observed radiating outward from the center. The magnitude of fringe displacement reveals the distribution pattern of glass deformation.
# Contour Mission Flight Planning of UAV for Photogrammetric in Hillside Areas
## 5 Analysis
### Point Cloud Density and Accuracy  
Two types of flight planning methods were used to produce point cloud results. The grid-type had a point density of 16 points/m\({}^{2}\), while the contour-type had a point density of 48 points/m\({}^{2}\). Further analysis of the point cloud distribution in the valley (Figure 15) showed that the grid-type had a sparse point cloud distribution with many holes. In contrast, the contour-type had a complete representation of the terrain.  
Table 2 presents the accuracy testing results conducted after the bundle adjustment. According to Table 1, the contour-type flight plan resulted in a higher accuracy than the grid-type, with a total root mean square error (RMSE) of 11.8 cm and 16.1 cm, respectively, for the check points. This indicates that more consistent AGL height and image overlap improved reconstruction accuracy.
###### Abstract  
The quantitative dynamic monitoring of the performance of hamstring muscles during rehabilitation and training cannot currently be undertaken using elastic resistance bands. Hip extension with a fully extended knee involves hamstring agonists, while knee flexion involves only the hamstring. The purpose of this study is to provide normative values of torque, velocity and power involving hamstring muscles opposing elastic bands. Twenty amature athletes aged \(25.7\pm 4.9\), were studied during two motor tasks--hip extension and knee flexion, both isometric & dynamic--with an elastic resistance band and DINABANG portable instrument. We compared the peak isometric torque in hip extension with agonists (2.93 Nm/kg) and without them (1.21 Nm/kg): the difference is significant. The peak angular limb velocity--starting at 50% of the maximum torque--is smaller in hip extension with agonists (215.96\({}^{\circ}\)/s) than in a knee flexion without them (452.56\({}^{\circ}\)/s). The combination of peak torque and peak velocity estimates power and there is no difference (\(p\) = 0.051) with and without agonists: 452.56\({}^{\circ}\)Nm/s.kg without agonists and 542.13\({}^{\circ}\)Nm/s.kg with them. This study opens the possibility of monitoring torque-velocity-power profiles for hamstring exercise in open chain.  
h amstring exercise; maximum isometric effort; maximum velocity +
Footnote †: journal: Applied Sciences
## 6 Experimental Results
### Speedup Analysis
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Version** & **Iterations** & **Processors** & **Time** & **Speedup** \\ \hline FPS & \(50,000\) & \(1\) & \(113.45\) & \(1\) \\ FPS A & \(50,000\) & \(2\) & \(58.73\) & \(1.93\) \\ FPS B & \(50,000\) & \(2\) & \(58.71\) & \(1.93\) \\ FPS A & \(50,000\) & \(3\) & \(43.13\) & \(2.63\) \\ FPS B & \(50,000\) & \(3\) & \(42.66\) & \(2.66\) \\ FPS A & \(50,000\) & \(4\) & \(38.69\) & \(2.93\) \\ FPS B & \(50,000\) & \(4\) & \(34.50\) & \(3.29\) \\ \hline FPS & \(200,000\) & \(1\) & \(429.17\) & \(1\) \\ FPS A & \(200,000\) & \(2\) & \(216.96\) & \(1.98\) \\ FPS B & \(200,000\) & \(2\) & \(216.30\) & \(1.98\) \\ FPS A & \(200,000\) & \(3\) & \(151.26\) & \(2.84\) \\ FPS B & \(200,000\) & \(3\) & \(150.79\) & \(2.85\) \\ FPS A & \(200,000\) & \(4\) & \(114.21\) & \(3.76\) \\ FPS B & \(200,000\) & \(4\) & \(113.56\) & \(3.78\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Times and speedups comparison. Figure 3: Speedup comparison. Let us remark that, once the skeletons were written, the programming effort needed to use them was negligible. We only needed to decide the number of iterations to be performed in parallel before each global synchronization. However, observed speedups (specially for FPS B) are satisfactory.
# A Family of ACO Routing Protocols for Mobile Ad Hoc Networks
## 2 Related Work
The differences between both versions are in the use of different mechanisms in the route reactive setup process and the route maintenance proactive process. HOPNET: The hybrid ant colony optimization routing algorithm for mobile ad hoc networks [14] is a hybrid routing algorithm based on ants hopping from one area to another. The algorithm has features extracted from the Zone Routing Protocol (ZRP) and DSR protocols, being highly scalable protocols compared with other hybrid protocols. When a forward ant reaches a destination, a return ant (backward) is sent along the path discovered. The InterRT Interzone Routing Table (InterRT) zone stores the path to a node beyond its zone. This routing table is setup on demand, and the peripheral nodes are responsible for linking the zones. When the number of nodes is small, the continuous movement of the peripheral nodes does have to discover new routes constantly, causing more delay than in other hybrid routing protocols. Table 1 shows a summary scheme of the analyzed protocols. Others related works are [15; 16; 17]. In [15], the authors propose the biologically-inspired self-organized secure autonomous routing protocol (BIOSARP) showing its architecture, implementation and experienced results outdoors. These results show that BIOSARP is ant-based energy-efficient algorithm. However, the authors claim to be investigating other experiments of sensor networks in real environments, involving the mobility factor and security autonomous mechanism based on an artificial immune system (AIS). In [16], the DDV-hop algorithm is proposed. This is an algorithm is a cluster algorithm that uses direction and velocity as the components of cluster formation. The results show that a good communication is established without system overheads. In [17], the proposed adaptive routing protocol based on QoS and vehicular density (ARP-QD) is capable of finding a fast and reliable path for end-to-end data delivery within urban VANET environments according to diverse QoS requirements of different applications. Numerical simulations showed that ARP-QD has a higher delivery ratio than Greedy Perimeter Stateless Routing (GPSR) and Receive on Most Stable Group-Path (ROMSGP), without making a large compromise on the delivery delay; however, it has the disadvantage that they do not take the real data trace into consideration to validate the ARP-QD protocol and combine the link correlations to estimate link quality.
### SSD Object Detection Model  
SSD consists of two components, a backbone model and an SSD head. The backbone is a classification model like the VGG-16 architecture without fully connected layers [22]. It is used to extract feature maps from the input image while preserving the spatial structure of the image. The head is a set of additional convolutional layers added to the backbone instead of the original fully connected layers [22]. The convolutional layers in the head are gradually reduced in size. In this way, features can be extracted at multiple scales, and the size of the input for each successive layer can be progressively reduced [22]. Figure 2 shows the SSD architecture with VGG as backbone.  
Instead of using a sliding window, SSD splits the image using a grid and lets each grid cell be responsible for detecting objects in that area of the image. Each grid cell is capable  
Figure 1: An example of the variety of images in the database.  
of outputting the position and type of the object it contains. The anchor box and receptive field are designed to detect multiple objects or multiple objects of different shapes in a grid cell [22]. Using a \(3\times 3\) convolutional layer on the output of multiple layers, each grid cell (location) is assigned multiple bounding boxes (usually four or six boxes). These bounding boxes have different sizes and aspect ratios to cover objects of different sizes. For each of the bounding boxes, scores are calculated for each class, including an additional class for no object and 4 offsets, relative to the original shape of the default bounding box (see Figure 3). Essentially, the anchor box with the highest degree of overlap with an object is responsible for predicting the class and location of that object. The last layer is the non-maximum suppression layer to avoid detecting an object several times.  
The SSD model is faster than the previous state of the art for single-stage detectors (YOLO) [27] and as accurate as Faster RCNN [22].
# A Secure and Lightweight Group Mobility Authentication Scheme for 6 LoWPAN Networks
## 4 System Models
### The Proposed Scheme (SL_GAS)  
6 LoWPAN devices generate IPv6 interface identifiers (IIDs) instead of IEEE media access control addresses using the method described in [26]. This IPv6 stateless address auto-configuration technique makes it possible to reduce the risks associated with IPv6 address tracking and address scanning, leading to a more private and secure way of generating IIDs. The 6 LMAGs are connected to the AAA via wireless communication via the LMA. A group of 6 LMns in the network must register with the AAA via a secure channel to establish a trust connection with the LMA. The use of tickets provides dynamic, time-limited, and cryptographical protection, which is suitable for scenarios requiring a high level of security. By including a Group ID (GID), a ticket can combine the advantages of both approaches. The simplicity of GIDs can be balanced with the cryptographic protection of tickets and their dynamic properties. Using secret parameters in the registration phase that are only known by AAA increases security because they can act as a unique identifier. As long as the secret parameter is changed in each session, it would enhance security by reducing the risks associated with long-term exposure. The aggregation MAC with tag ensures the mutual authenticity and integrity of the 6 LMN. SL_GAS is designed in three phases: registration, initial group authentication, and group handover authentication. The notations and descriptions are listed in Table 2 to facilitate the later description.  
\begin{table}
\begin{tabular}{c l} \hline \hline
**Notations** & **Description** \\ \hline \(A\) & AAA server \\ \hline \(GL\) & Group leader \\ \hline \(G\) & 6 LGM \\ \hline \(ID_{X}\), \(TID_{X}\) & Identity and temporary identity of \(x\) \\ \hline \(AID_{i}\) & Alias identity of the 6 LMN \\ \hline \(ID_{6G}\), \(TID_{6G}\) & The identity and temporary identity of the group \\ \hline \hline \end{tabular}
\end{table}
Table 2: Notations.
## 3 Test Results and Analysis
### Failure Mechanism Analysis
Moreover, due to the lower bending stiffness of the outer steel tube surface, shear connectors in the pressure area will assume the formation of the ring distribution of the steel tube wall. The bending moment M will lead to a local bulge deformation at the weld of the steel tube and the connector; the shear connector will therefore produce a flexible rotation, reducing its horizontal projected area, i.e., the pressure-bearing area, and the compressive stress will no longer be balanced, as shown in Figure 6. The extrusion force generated by the lateral expansion of the core concrete will increase the friction at the interface, which is very favorable for the mechanical occlusion force and the shear resistance of the shear connectors. An increase in these resisting forces can cause the load borne by the steel tube-concrete interface to continue increasing. This mutually reinforcing mechanism is highly beneficial to the shear resistance of the interface. Therefore, at this stage, the launching load will be transferred to the steady stage after a substantial growth state until the steel tube yields, the reinforcement weld breaks, or the concrete breaks locally. For specimens under corrosion maintenance, it has been shown [33] that steel tube-concrete interfacial corrosion is an electrochemical process: \(\alpha\)-FeOOH and Fe\({}_{3}\)O\({}_{4}\) produced by Fe in a corrosive seawater environment are exfoliated, and their crystal structure becomes loose and easily falls off; this effect, in turn, makes the interfacial structure loose, thus forming a loose isolation layer, which alters the contact area between the steel tube and the core concrete and influences the bond at the interface. The contact area between the steel tube and the core concrete changes, affecting the bonding force at the interface and leading to a decrease in the bonding capacity. Figure 6: Sketch of the forces on the connection and the surrounding steel tube.
###### Abstract  
Determining the exact position of a forest inventory plot--and hence the position of the sampled trees--is often hampered by a poor Global Navigation Satellite System (GNSS) signal quality beneath the forest canopy. Inaccurate geo-references hamper the performance of models that aim to retrieve useful information from spatially high remote sensing data (e.g., species classification or timber volume estimation). This restriction is even more severe on the level of individual trees. The objective of this study was to develop a post-processing strategy to improve the positional accuracy of GNSS-measured sample-plot centers and to develop a method to automatically match trees within a terrestrial sample plot to aerial detected trees. We propose a new method which uses a random forest classifier to estimate the matching probability of each terrestrial-reference and aerial detected tree pair, which gives the opportunity to assess the reliability of the results. We investigated 133 sample plots of the Third German National Forest Inventory (BWI, 2011-2012) within the German federal state of Rhineland-Palatinate. For training and objective validation, synthetic forest stands have been modeled using the _Waldplaner_ 2.0 software. Our method has achieved an overall accuracy of 82.7% for co-registration and 89.1% for tree matching. With our method, 60% of the investigated plots could be successfully relocated. The probabilities provided by the algorithm are an objective indicator of the reliability of a specific result which could be incorporated into quantitative models to increase the performance of forest attribute estimations.  
Keywords:co-registration; individual tree detection; tree matching; point set registration; machine-learning; forest inventory +
Footnote †: journal: Remote Sensing
# A Deep Learning Approach to Estimate Soil Organic Carbon from Remote Sensing
## 5 Discussion
### Research Limitations  
While this study has produced meaningful results, it is important to acknowledge its limitations.  
The main limitation is that we restricted our training data to a subset of land cover classes available in LUCAS. While we did not perform any filtering on the dataset, our model best performed on CHLSOC, and it is also restricted to a subset of land covers by design. The majority of data points were concentrated in specific regions, such as deciduous forests, broad-leaved forests, sclerophyllous forests, and thorny forests. These areas, characterized by high-quality soil and available water, have experienced significant land use conversion, making them of particular interest to our study. On the other hand, certain regions, such as the high Andes, the Atacama Desert, and western Patagonia, are underrepresented in terms of soil data. The lack of comprehensive data in these ecologically significant regions restricts our understanding of soil organic carbon dynamics and ecosystem processes in these areas, as well as the applicability of our model.  
This is why we chose to use CORINE to filter our predictions of our model that cannot reasonably be expected to be valid. But, to create an approach able to monitor the SOC across all ecosystems, further data need to be collected and integrated into the proposed approach.  
An additional limitation of our approach is that we did not consider the temporal dynamics of SOC and that the model predicts the value based on a single satellite image. This is a limitation that we aim to address in the near future.
## 1 Introduction
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**S/N** & **Location** & **Source Name** & **Quantity (Tonnes)** & **Date** \\ \hline
1. & Kuwait & 700 oil wells & 71,428,571 & 10 March 1991 \\
2. & Kuwait & Min al Ahmadi Terminal & 857,143 & 20 January 1991 \\
3. & Russia & Oil wells & 700,000 & 3 August 2000 \\
4. & United States & Deepwater Horizon & 686,000 & 20 April 2010 \({}^{\star}\) \\
5. & Mexico & Iztoc I well & 476,190 & 3 June 1979 \\
6. & Iraq & Bahra oil fields & 377,537 & 1 February 1991 \\
7. & Uzbekistan & Oil well & 299,320 & 2 March 1990 \\
8. & Trinidad/Tobago & TN Atlantic Express & 286,354 & 19 July 1979 \\
9. & Russia & Kharyaga-Usink Pipeline & 285,714 & 25 October 1994 \\
10. & Iran & No. 3 Well (Nowrruz) & 272,109 & 4 February 1983 \\
11. & South Africa & TN Castillo de Bellver & 267,007 & 6 August 1983 \\
12. & France & TN Amoco Cadiz & 233,565 & 16 March 1978 \\
13. & Canada & TN Odyssey & 146,599 & 10 November 1988 \\
14. & Italy & TN Haven & 144,000 & 11 April 1991 \\
15. & Libya & D-103 concession well & 142,857 & 1 August 1980 \\
16. & Nigeria & Pipeline & 142,857 & 6 January 2001 \\
17. & Kuwait & TN Al Qadasiyah & 139,690 & 19 January 1991 \\
18. & Kuwait & TN Hileen & 139,690 & 19 January 1991 \\
19. & United Kingdom & TN Torrey Canyon & 129,857 & 18 March 1967 \\
20. & Oman & TN Sea Star & 128,891 & 19 December 1972 \\ \hline \hline \end{tabular}  
* From [PERSON] et al. (2018) as well as [PERSON] and [PERSON] (2018); others adapted from [PERSON] (2018).  
\end{table} 
Table 1: The top 20 largest oil spills (\(>\)125,000 tonnes) in world’s history.
## 3 Results and Discussion
### Pressure Drops and Friction Factor Analysis
Figure 16: Pressure gradient trends at different Reynolds numbers for sample S1. Applying the [PERSON] theorem:  
\[L_{DA}=\sqrt{{L_{DA}^{s}}^{2}+\left(\frac{w_{p}}{2}\right)^{2}} \tag{14}\]  
The quantity \(U_{DA}\) is calculated as the arithmetic average of the magnitude associated with the central region and that corresponding to the minimum passage area at the open port, approximated as \(\pi r_{p}h\) to simplify the procedure. The maximum velocity is thus computed using the continuity equation. When comparing the results from this approach and CFD data for the contributions of the distribution areas alone, very low absolute errors (<1%) were found for samples S1 and S2, both in terms of their extent and contributions to the global pressure drop, provided that an enlargement factor for the closed port diameter is introduced. The reason lies in the flow being guided by the brazed joint on that side, which is larger than the nominal diameter, thus altering the actual direction of the flow path based on the scheme in Figure 17.  
For the mentioned geometries, when adding the \(\Delta P_{C}\) component and accounting for the deviations in the friction factor, with the hydraulic diameter as specified in Equation (4), the difference in the pressure drops across the various tested Reynolds numbers remains below \(\pm\)2.5%. In contrast, model S3 exhibits higher errors of approximately +7%. This discrepancy arises from a significant portion (~17%) of the fluid mass coming from the central part and navigating through the available passage around the closed port. Indeed, in describing the method and deriving \(U_{DA}\), the initial hypothesis was that the entire flow rate deviates through the imaginary duct constructed along the tangent line. This holds true for S1, where the distance between the brazed wall on the blocked side and the plate edge is minimal. For S2, although the situation is somewhat similar to S3, the HP structure ensures that the passage near the port closely aligns with the one considered in the methodology. Finally, in S3, the flow enters the port in a 360\({}^{\circ}\) manner, with higher frontal velocities corresponding to the majority (~83%) of the channel mass flow originating from the middle region. These results should be viewed as a preliminary attempt to weigh the distribution zones, emphasizing the potential for improving the friction factor correlation by adjusting only the length and velocity, along with refining the specific definition of the hydraulic diameter. Future analyses will be required to develop an even more accurate formulation by incorporating the illustrated flow phenomena near the closed ports. Figure 17: Distribution area: (**a**) stream tube concept; (**b**) main geometrical parameters.