esa-sceva/satcom-chunk-collection huggingface dataset:
# Finite Element Modeling of the Soil-Nailing Process in Nailed-Soil Slopes
## 5 Parametric Study: Finite Element Analysis of a Nailed-Soil Slope with Varying Soil Densities
### Horizontal Displacement of the Nailed-Soil Slope  
Figures 11 and 12 show the effect of the soil density on the horizontal displacement of the face of the slope during construction and overburden pressure stages. It can be observed from the results that, with the increase of the soil density, the slope displacements decreased, i.e., the higher soil density, the lesser the lateral displacement of the facing. This is because of interface friction of the soil and nails depending on the soil density. In dense granular soil, restrained dilatant behavior is due to an increase of normal stress on the nail level, which increases the friction between soil and the nails and increases the nail ability to resist the deformations. The increase in soil density reduces the slope face displacement by a ratio ranging from 44.01% to 72.43%. It is clear from the slope lateral displacement graphs that a greater reduction of the lateral displacement with higher soil density is found in overburden pressure stages as compared to in construction stages.  
Figure 10: Shear stresses during the construction and overburden pressure stages. (**a**) Construction stage 1. (**b**) Construction stage 3. (**c**) Construction stage 5. (**d**) Overburden pressure stage 6. (**e**) Overburden pressure stage 9.
## 4 Prediction based on LSTM  
### Prediction Solely Using Roll Data  
Based on the LSTM model, the roll motion duration data are used as the input feature to directly predict the roll motion with a different advance time. The advance forecast steps \(n\) is tested from 5 to 70. The sampling interval is 0.04 s. The advance forecast duration in the model scale is from 0.2 s to 2.8 s, which is converted to 1.4 s to 20 s in full scale.  
The roll prediction errors solely based on the roll data are shown in Table 3. The comparison between the predicted values of the advance prediction steps \(n\) of 5, 15, 40, and 70 and the sample data in the time series are shown in Figure 5. When the number of the advance prediction step is 5, the prediction accuracy is the best. The prediction results are basically matched with the sample values. When the prediction interval step is 70, the accuracy of the prediction results is poor. There is a large deviation between the prediction value and the sample value. This is because the correlation between the time series data weakens the prediction accuracy, which gradually declines with the increase in the advance forecast duration. The results show that the number of effective advance prediction steps solely utilizing the input data of roll cannot exceed 70.
# Cooperative SWIPT MIMO-NOMA for Reliable THz 6G Communications
## 6 Simulation Results  
This section includes the implementation of simulations and numerical analysis to verify the validation of data rates and OP. The simulation findings confirm the obtained closed forms of the optimized model, and they are compared to earlier research. By existing an obstruction in the link to the BS, the presented mechanism should allow the obstructed user to maintain continuous communication. The sections below explain how the proposed system and mechanism can enhance EE, SE, reliability, and system performance by addressing the distance-limited communications of THz frequencies. Table 1 shows the simulation parameters.
# Obstacle Avoidance Path Planning for the Dual-Arm Robot Based on an Improved RRT Algorithm
## 3 Algorithm Improvement
### Path Planning of Dual-Arm Based on GA_RRT
#### 3.2.2 A* Cost Function  
Although the probability-biased RRT algorithm greatly shortens the search time, there are still many redundant random points. Therefore, this paper combines the goal probability bias RRT algorithm with the A* search algorithm. The cost function was used to search for the best sampling point, so as to reduce the level of redundant computing and optimize the path. We consider the forward and backward cost of the current node in the RRT algorithm. The distance from the initial point, \(X_{init}\), to the selected node is called forward cost, which can also be called heuristic function \(H(i)\). The distance between \(X_{select}\) and \(X_{goal}\) is called backward cost \(B(i)\). The cost function of the current node is represented by \(F(i)\), shown in Equation (4).  
\[\left\{\begin{array}{l}F(i)=H(i)+B(i)\\ B(i)=\left\|X_{goal}-X_{rand}(i)\right\|\\ H(i)=\left\|X_{rand}-X_{init}(i)\right\|\end{array}\right. \tag{4}\]  
Figure 3: RRT algorithm random tree expansion process.  
After calculating the random node, select the random point with the smallest cost function to join the random tree list. Repeat the calculation process until extended to the target node.  
We need to add two additional data groups. One is the optional random point data group, and the other is the cost function data group corresponding to the optional random point. Each time we expand the random tree, multiple random points, \(X_{rand}\), are generated and put into the optional random point group. Next, the cost function is calculated for the optional random point group. According to the cost function data group, the minimum cost node is taken.
## Materials and Methods
### Characterization and Measurement Methods
#### 2.4.5 Scanning Electron Microscopy (SEM)  
SEM images were partly acquired using a Teneo SEM from Thermo Fisher Scientific (Thermo Fischer Scientific, Waltham, MA, USA). The measurements were conducted at Enabling Technologies. Other images were acquired using a Hitachi S4800 FESEM or a FEI quanta 600 microscope. For sample preparation one droplet of the particle dispersion was placed on a silicon wafer and dried at room temperature. The samples were sputtered with gold. Particle diameters and standard deviations from SEM images were determined using MATLAB. For average diameter and standard deviation at least 200 particles per sample were measured.
## 5 Topology Design Optimization of the APTS's Bracket Structure
### Manufacturing Constraints for the Topology Optimization of the Support Structure  
The manufacturing constraints involved in this study include maximum size constraints, minimum size constraints, symmetry constraints, and mold release constraints. In particular, the minimum feature size refers to the allowed minimum scale of the region where the unit density is one in the optimization results. Applying a minimum feature size constraint can eliminate small force transmission paths in the optimization results and ensure that the minimum scale of the structure is greater than the minimum feature size. This ensures a relatively uniform material distribution, facilitates material flow in the casting process, and provides sufficient stiffness for tool machining. Generally, the minimum feature size should be greater than three times the average unit size.  
The maximum feature size constraint means that the dimensional scale of the regions where the unit density is one in the optimization results cannot exceed this size. Therefore, the maximum feature size constraint helps eliminate material accumulation in the optimization results, avoids product defects caused by the manufacturing process (such as uneven heat dissipation during casting), and provides multiple force transmission paths to improve product reliability. The maximum feature size should be greater than twice the minimum feature size. In the camera bracket optimization process described in this paper, the minimum feature size is set to 4 mm, and the maximum feature size is set to 12 mm, based on the mesh size.  
For castings or machined parts, it is necessary to consider mold release during manufacturing and the entry and exit of cutting tools. Therefore, there should be no material obstruction in either the mold release direction or the tool's entry and exit direction. In addition to specific manufacturing process constraints, mold release constraints also apply to the creation of reinforcement structures on the solid surface. In the camera bracket structure studied here, the mold release constraint is uniformly set as unidirectional, with the \(x\)-axis as the mold release direction. The symmetry constraint in OptiStruct is referred to as the 'Pattern Constraint'. Applying symmetry constraints to the design space generates symmetric designs, even for models with asymmetric meshes and boundary conditions, where the software forces results that are nearly symmetrical. In the support structure of the collection, pointing, and tracking system considered in this project, the symmetry plane is uniformly designated as the central axis-symmetric plane of the support structure when applying symmetry constraints.  
Figure 7: Schematic diagram of boundary conditions and loads for the optimization model.
# Leveraging Digital Twin Technology for Sustainable and Efficient Public Transportation
## 3 Challenges and Limitations of Digital Twins
### Security  
In the case of DTs, vast amounts of data are generated for smooth operations. With a multitude of IoT devices interconnected in the smart transportation system, it becomes complicated to properly secure data-sharing frameworks [105]. A study discusses a model that prioritizes the importance of data abstraction and control. The authors believe that security vulnerabilities in connected transportation systems can be significantly mitigated. The authors of another study also point out and classify numerous security and privacy vulnerabilities in the ITS. The risk of malicious attacks is amplified with the increase in connectivity [106]. Since most DT applications are cloud-based and often vulnerable to cyberattacks, there is a need for strict security protocols [79].  
Identifying vulnerabilities through digital twin simulations can help in planning effective countermeasures against potential cyberattacks. Secure frameworks are necessary to manage data generated by IoT devices within smart ecosystems [59; 79; 105; 106]. The integration of digital twins in transportation systems can help authorities identify and address potential problems, like security threats, before they occur in the physical world. For instance, the maritime transportation sector has already recognized the importance of securing IoT-enabled DTs, which can also be inferred for bus transportation systems. The insights from maritime transportation apply to bus transit systems, as both use the IoT for monitoring, route optimization, and maintenance. Like maritime DTs, bus systems need strong cybersecurity to prevent potential threats such as data breaches, unauthorized access, and cyberattacks [28; 107]. Hence, robust security measures need to be prioritized for both the digital and physical realms of transportation systems.  
To ensure data security for passenger and bus operational data, the authors propose that organizations can adopt several strategies like encryption, access controls, and threat detection mechanisms [46]. The combination of blockchain technology with DTs has also been proposed as a possible solution to enhance data security in smart transportation systems. By securely storing data related to traffic events and user interactions such as traffic events and user reputation, blockchain can mitigate risks associated with data breaches and unauthorized access. The authors of one study developed a software application using technologies such as libp2p (peer-to-peer networking), which contained functionalities for creating transactions and blocks to verify data integrity and displaying the blockchain. The aim was to make processes accessible, regardless of the number of layers required to be passed, ensuring the security of the new connection effortlessly [47].
# Post-Quantum Digital Signature: Verkle-Based HORST
## 3. Lamport-Diffie One-Time Signature Scheme
Verification involves checking if the values in the signature that are disclosed correspond to the values that are anticipated in the public key. Considering the signature: \(=\left(\text{sig}_{1},\text{sig}_{2},\ldots,\text{sig}_{256}\right)\), the verifier calculates \(\text{H}(\text{sig}_{i})\) for each component and checks whether it matches to the correct entry in the public key. If \(\text{msg}_{i}=0\), then  
\[\text{H}(\text{sig}_{i})=\text{pk}\text{O}_{i} \tag{6}\]  
otherwise, if \(\text{msg}_{i}=1\), then the verifier checks the following:  
\[\text{H}(\text{sig}_{i})=\text{pk}1_{i} \tag{7}\]  
If all 256 checks pass, the signature is considered valid, and the message is authenticated. The Lamport-Diffie one-time signature secure hash-based signature system has a number of drawbacks. Because it requires 512 hash outputs for the public key and 256 random values per signature, its key size is greater than that of other signature schemes, increasing the cost of transmission and storage. It is also a one-time signature system, which means that a private key cannot be used again without jeopardizing security after it has been used. Reusing a secret key compromises security, as attackers can forge signatures from multiple observations. Because of this, it is susceptible to quantum assaults. In order to get around this, [PERSON]'s signature technique was developed, which effectively authenticates multiple one-time public keys using a Merkle tree. Even with its drawbacks, Lamport-Diffie OTS is still a crucial starting point for research on post-quantum cryptography and hash-based signatures. Advanced techniques such as Merkle Tree-Based Authentication, which increases scalability, and Winternitz OTS (WOTS), which decreases key sizes, have been influenced by it. These developments overcome the inefficiencies of L-DOTS while maintaining quantum resistance.
## 3 Results and Discussion
### Effect on Larger Pieces  
The horizontal bars deposited in an open environment, using argon as shielding gas as the only mean to protect them from oxidation and interstitial diffusion, presented visible cracks on the external surface, as reported in Figure 9a. These features were consistent throughout the whole sample on the top surface (Figure 9b) and also on the side (Figure 9c). The analysis of the cross-section proved that the cracks penetrated inside the sample (Figure 9d) and were linked to undesired phenomena, such as layer debonding.  
Figure 9: Horizontal bars built using shielding gas. Top (**a**,**b**), side (**c**) view and cross-section (**d**).  
Contrarily, bars built in a sealed environment (glovebox-like) filled with argon gas, keeping the oxygen level below 0.2% throughout the whole process, did not show visible cracks on the external surface (Figure 10a) nor in the cross-section (Figure 10b). These bars were built in CB and HB conditions, in the same way as the cubic samples were deposited. In Figure 10, the bar built using platform preheating is shown as a representative example.  
Process atmosphere was the only relevant difference between the deposition scenarios that lead to the formation of fully dense samples and the case in which the bars were completely cracked. The brittle behaviour of the ArS samples, consequence of oxygen and nitrogen pick-up, would then be considered the cause of cracking. Even if Ti-6 Al-4V has a low tendency to crack during solidification, property that makes these alloys well suitable for AM [35], in the ArS case the interstitial concentration is too excessive and prevents the possibility to build bigger components. Although, cracking phenomena resulted critical only for samples bigger in size (bars). Smaller samples (cubes) instead were crack-free in all the building conditions, proving that, from a process perspective, controlling the atmosphere is critical only for samples larger in size. Even though, it must be taken into consideration that process environment variations influence the final properties of the sample. Hence, even if a small piece must be deposited (e.g., repairing applications), process environment must be thoroughly investigated and taken into consideration, as it impacts on the quality of the specimen itself.
# The Influence of Bearing Clearance on the Load Capacity of Gas Polymer Bearings
## 4 Discussion
### Decreasing Friction Torque after Lift-Off  
As mentioned before, the measurement results of the friction torque have to be discussed due to the further decrease in the friction torque after lift-off. Common aerodynamic bearings with a rigid inner wall have a minimum friction torque after leaving a dry friction/mixed friction state. This occurs when the rotor becomes airborne. From that point on, the shear stress in the air film increases with the rotational speed and a minimum exists in the transition phase. When the bearing wall is elastic, the characteristics can be different due to different effects. The first point that should be discussed is the temperature. Thermal material expansion can influence the bearing characteristics by increasing or decreasing the bearing clearance. In this specific study, this should not be relevant, due to the short test durations and the long cooling times. Another temperature-based effect that can lead to a decrease in the friction torque is the thermal softening of the polymer. The authors consider this effect as relevant in general for gas polymer bearings, but not in the context of this study because of the short test durations. The authors see relaxation processes within the elastic structure as the most plausible reason for the observed effect. This can either be due to the gradual reduction in friction (slippage) in the contact areas of the structural components or due to material relaxation, which is a typical characteristic of polymers. In [PERSON] et al. [19], a friction torque curve measured with a bump-type bearing with the same effect of decreasing friction torque was published. For steel foils, the material relaxation is not the driving factor but the friction relaxation is also a suitable explanation here. Nevertheless, this effect should be further investigated in future studies.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Adj. Foil** & **0\({}^{\circ}\)–180\({}^{\circ}\)** & **30\({}^{\circ}\)–210\({}^{\circ}\)** & **60\
# Matched-Filter Thermography
## 1 Introduction  
Active thermography is a rapidly growing non-destructive testing (NDT) technique, which, since the 1980s [1], has been widely employed in research settings for detection of defects in a broad range of materials. The principle behind active thermography is quite straightforward: the sample is exposed to some form of excitation to create a thermal-wave field inside sample while the surface temperature is constantly registered by an infrared camera. The presence of defects causes perturbation in the thermal-wave field; such perturbations are used as indications of the presence of defects. Depending on the waveform shape of excitation (pulse vs. intensity-modulated) and the signal processing scheme used, active thermography branches into two major streams of Pulsed Thermography (PT) and Lock-In Thermography (LIT). Both techniques are specifically popular for inspection of industrial samples. For instance, they have been widely used for detection of damage in Carbon Fiber Reinforced Plastic (CFRP) materials [2], inspection of airplane parts [3] and detection of leakages in integrated circuits [4]. More recently, active thermography has been utilized in biomedical applications such as detection of malignancies in human soft [5; 6] and hard tissues [7; 8; 9; 10; 11] as well as sensitive interpretation of lateral flow immunoassays [12]. Specifically, detection of dental caries at very early stages of formation has proven to be a key application of thermography in biomedicine by providing diagnostic contrast based on enhanced absorption of light at caries [7; 8; 9], as opposed to other emerging technologies such as Raman spectroscopy [13] and optical coherence tomography (OCT) [14; 15], which rely on changes in scattering of light at caries. Appealing, if not unique, characteristics of thermography include:being no-contact, having the ability to inspect opaque [4; 16; 17; 18; 19; 20] and turbid materials [7; 8; 9], and being scalable [3; 4]. Moreover, depending on the application, different types of external excitation, such as optical [7], magnetic [17], mechanical waves [19], electrical [4; 16] or even cyclic stress/strain [18], can be utilized to induce the thermal wave field inside the sample. A comprehensive review of conventional thermography techniques such as PT and LIT can be found in another paper of this special issue on \"Novel Ideas for Infrared Thermography also Applied in Integrated Approaches\".
# Channel Measurement and Feasibility Test for Wireless Avionics Intra-Communications
## 3 Experimental Setup
### Measurement Environment
To resolve the interference issue during data transmissions, we used conventional TDMA (time-division multiple access) as in [8].
## 3 Results
### Automated Segmentation of Layer Thickness
The differences in the mean values for the clear coat are negligible. Therefore, we conclude that the segmentation algorithm gives repeatable (within the technique's measured errors) measurements independent of the OCT instrument. It should be noted that for optical techniques, the precision and accuracy of the measurement of an interface is highly dependent on the sample. For an interface that gives a significant signal, profilometry techniques are able to measure it with high accuracy. However, in this case the signal from the interface is negligible, with the image contrast being dominated by volume scattering; as such, profilometry methods would not work, and image segmentation is the preferred technique. The alternative would be to correct the B-Scan image by rotation for any tilt, and then average the A-Scans. However, this would eliminate lateral spatial information that may be present. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multirow{2}{*}{**Method**} & **Base Coat Layer Thickness** & **Clear Coat Layer Thickness** \\  & & **(nG: \(\upmu\)m)** & **(nG: \(\upmu\)m)** \\ \hline Indus Silver & FF-OCT & 27.88 \(\pm\) 6.11 & 65.64 \(\pm\) 1.34 \\ Indus Silver & LF-OCT & 24.95 \(\pm\) 3.85 & 64.98 \(\pm\) 2.44 \\ Mauritius Blue & FF-OCT & 25.83 \(\pm\) 4.62 & 80.75 \(\pm\) 1.44 \\ Mauritius Blue & LF-OCT & 22.69 \(\pm\) 5.47 & 80.68 \(\pm\) 2.20 \\ Barlo Black & LF-OCT & 16.61 \(\pm\) 4.48 & 68.96 \(\pm\) 2.01 \\ Santorini Black & LF-OCT & 8.13 \(\pm\) 3.24 & 54.50 \(\pm\) 2.30 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Thickness measurements (mean \(\pm\) standard deviation) of the base coat and clear coat layers of the paint samples scanned using full field time domain (FF) and line field spectral domain (LF) OCT.  
Figure 6: Final segmentation results of the silver (**a**) and blue (**c**) paint samples. The respective thickness profiles of each sample (**b,d**) as demonstrated by the brown arrows. Although they do look non-smooth, it should be noted that the thickness profile figures are presented at an aspect ratio of approximately 1:3.
## 4 Starlink as Case Study
### Stage 1: Acquisition of Orbital Data from FCC  
In 2018, SpaceX proposed orbit modification for their 1600 operating satellites from 1150 km to 550 km, and as part of their proposal request, an atmospheric demise report using NASA's Debris Assessment software was also attached for Starlink satellites [36]. The 1600 satellites closely match the 1665, V1 model. Hence, we assume that V1 was used in the analysis of debris assessment. To begin with, V1 version that has a total mass of 260 kg was taken. As of today, four versions of Starlink satellites have been launched. The mass value of each version is shown in Table 3. And the data regarding the number of satellites that have been launched into space are taken from [59].  
In this paper, Starlink version 1 mass budget is estimated based on the average dry mass budget table in Table 1. Table 1 cannot be explicitly implemented because the equipment list in the ESA LCI database differs for two subsystems, power, and propulsion.