esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussion
### Influence of the Diameter of the New Pile  
Figures 16 and 17 represent the effect of the new pile diameter on the existing pile response, including the lateral displacement and bending moment. For this end, three different values of diameter (i.e., 0.4 m, 0.5 m and 0.6 m) are computed as examples.  
According to Figure 16, when the diameter of the pile increases, the lateral displacement of the present pile increases dramatically. At 10 m of pile penetration, when the pile diameter was enlarged from 0.4 m to 0.5 m, the maximum lateral displacement of the existing pile increased by 4.54 mm, from 8.08 mm to 12.62 mm; when the pile diameter was extended from 0.5 m to 0.6 m, the maximum value of lateral displacement rose by 5.55 mm, from 12.62 mm to 18.17 mm. At 20 m of pile penetration, when the pile diameter was expanded from 0.4 m to 0.5 m, the maximum lateral displacement of the existing pile rose by 5.46 mm, from 9.71 mm to 15.17 mm; when the pile diameter was extended from 0.5 m to 0.6 m, the maximum value of lateral displacement rose by 6.68 mm, from 15.17 mm to 21.85 mm. The Table 3 exhibits the lateral displacement variation due to diameter variation of new pile. The results suggest that lowering the pile diameter is more significantly effec  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Pile Penetration Depth (m)** & **Distance Variation (m)** & **Lateral Displacement Variation (mm)** & **Amplitude** \\ \hline
10 & 2 to 1.5 & 5.65 & 44.77\% \\
10 & 3 to 2.5 & 2.16 & 30.38\% \\
20 & 2 to 1.5 & 5.47 & 36.06\% \\
3 to 2.5 & 2.06 & 21.06\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Lateral displacement variation due to distance variation between the used and new piles.  
Figure 15: Influence of the distance between the used and new piles on bending moment of used pile.  
tive than increasing the piles' spacing in minimizing the squeezing effect, and slender piles are recommended for new piles, as shown in Table 3.  
Figure 17a,b show the bending moment of the existing pile at 10 m and 20 m of new pile penetration, respectively. The two figures show that the bending moment of the existing pile also increases as the diameter of the new pile increases. It is worth noting that  
Figure 16: Influence of the diameter of the new pile on the lateral displacement of the used pile.  
Figure 17: Influence of the diameter of the new pile on the bending moment of the used pile.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Pile Penetration** & **Diameter Variation** & \multicolumn{2}{c}{**Lateral Displacement**} & \multirow{2}{*}{**Amplitude**} \\
**Depth (m)** & **(m)** & **Variation (mm)** & \\ \hline \multirow{2}{*}{10} & 0.4 to 0.5 & 4.54 & 56.19\% \\  & 0.5 to 0.6 & 5.55 & 43.98\% \\ \multirow{2}{*}{20} & 0.4 to 0.5 & 5.46 & 56.23\% \\  & 0.5 to 0.6 & 2.06 & 44.03\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Lateral displacement variation due to diameter variation of new piles.  
the change in the diameter of the new pile has no effect on the distribution and neutral point of the bending moment of the existing pile.
# Survey of Blockchain-Based Applications for IoT
## 12 Industry-Specific Applications of Blockchains in the IoT
### Smart Homes and Cities  
Another notable application is in smart homes and cities, where blockchains can facilitate secure interactions between IoT devices. IoT devices in smart homes and cities such as smart meters, waste management systems, and traffic controllers generate massive amounts of data. Blockchains ensure the integrity and privacy of data, allowing secure interaction among devices without a centralized authority. For instance, a blockchain-enabled energy-trading platform in a smart grid allows households to trade excess energy securely. Oracles and smart contracts enhance these systems by automating payments and integrating real-world data into blockchain-based applications. Oracles fetch data, such as real-time energy prices, device utilization metrics, or environmental conditions, from external sources and feed them into the blockchain. This enables smart contracts to make informed decisions based on actual data, as in the following examples.  
* Payments for energy usage or waste collection can be automated based on real-time consumption data;
* Smart contracts can optimize resource utilization by adjusting operations in response to pricing or demand fluctuations, such as shifting energy-intensive tasks to off-peak hours to reduce costs;
* Traffic systems can dynamically reroute vehicles to improve the traffic flow and reduce fuel consumption and emissions.  
Blockchains can enhance cybersecurity in IoT systems by addressing unique challenges such as data integrity and device authentication [85]. By implementing a blockchain-based architecture, smart home devices can autonomously manage data exchanges while ensuring that interactions are secure and tamper-proof [86]. This application is particularly relevant as the number of connected devices continues to grow, necessitating robust security measures to protect against hacking and data breach [87].  
Figure 17: Blockchain and IoT in supply chain.
# Ellipsoidal Path Planning for Unmanned Aerial Vehicles
###### Abstract  
The research in path planning for unmanned aerial vehicles (UAV) is an active topic nowadays. The path planning strategy highly depends on the map abstraction available. In a previous work, we presented an ellipsoidal mapping algorithm (EMA) that was designed using covariance ellipsoids and clustering algorithms. The EMA computes compact in-memory maps, but still with enough information to accurately represent the environment and to be useful for robot navigation algorithms. In this work, we develop a novel path planning algorithm based on a bio-inspired algorithm for navigation in the ellipsoidal map. Our approach overcomes the problem that there is no closed formula to calculate the distance between two ellipsoidal surfaces, so it was approximated using a trained neural network. The presented path planning algorithm takes advantage of ellipsoid entities to represent obstacles and compute paths for small UAVs regardless of the concavity of these obstacles, in a very geometrically explicit way. Furthermore, our method can also be used to plan routes in dynamical environments without adding any computational cost.  
path planning; unmanned aerial vehicles; neural networks; evolutionary algorithms +
Footnote †: journal: Applied Sciences
# An Adaptive Routing Algorithm Based on Relation Tree in DTN
## 4 Relation Tree
### Important Parameters Related to the Relation Tree  
In our algorithm, the nodes with depth 2 represent the \"friends\" of the root node, and the nodes with depth 3 represent the \"friends of friends\" of the root node. The Relation Tree is updated in real-time. The size of the Relation Tree depends on the memory cycle T and the judgment threshold M. The optimal value of these two depends on the number of nodes, the movement speed of nodes, the storage space of nodes, etc. The user can adjust the size of the Relation Tree according to the actual parameters of the network.
# Efficient Sulfate Adsorption on Modified Adsorbents Prepared from _Zea mays_ Stems
## 3 Results
### Adsorption Kinetics  
The effect of time on the adsorbent removal capacity (Figure 3) was assessed to determine the possible mechanisms involved in the adsorption process [29]. It was found  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**adsorbent** & **T (K)** & \(\Delta\)G\({}^{\circ}\) **(KJ/mol)** & \(\Delta\)H\({}^{\circ}\) **(KJ/mol\(\cdot\)K)** & \(\Delta\)S\({}^{\circ}\) **(KJ/mol)** \\ \hline \multirow{4}{*}{B 1:1} & 298 & 9.197 & \(-\)57.359 & \(-\)0.223 \\  & 303 & 10.313 & - & \\  & 308 & 11.429 & - & \\  & 313 & 12.546 & & \\  & 318 & 13.662 & & \\ \hline \multirow{4}{*}{MC} & 298 & 11.8795 & \(-\)25.5705 & \(-\)0.1256 \\  & 303 & 12.5075 & & \\ \cline{1-1}  & 308 & 13.1355 & & \\ \cline{1-1}  & 313 & 13.7636 & & \\ \cline{1-1}  & 318 & 14.3916 & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Thermodynamic parameters of sulfate adsorption.  
Figure 2: Effect of temperature on sulfate adsorption capacity using B 1:1 and MC.  
that anion uptake happened quickly in the first few minutes of the process and gradually decreased until equilibrium was reached. This phenomenon occurred due to the availability of free active sites of adsorption for sulfate retention, which are occupied as the contact time passes [1]. Equilibrium was reached at about 420 min for the two adsorbents, with better performance of B 1:1, which could be due to surface protonation as a result of its modification with sulfuric acid [30].  
The experimental data were fitted to the pseudo-first-order, pseudo-second-order and Elovich models by nonlinear regression, and the fitting parameters are summarized in Table 5.  
The kinetics of sulfate adsorption over B 1:1 and MC is described by the pseudo-second-order model, whose correlation coefficient (R\({}^{2}\)) is closer to 1.0, and the equilibrium adsorption quantities calculated are much closer to the experimental values. This suggests that the mechanism by which adsorption occurs is chemical, in which the speed of adsorption is limited by valence forces given by the exchange of electrons between the adsorbate and the adsorbent [24], and that the limiting step is chemisorption due to physicochemical interactions between the two phases [31], as evidenced by the thermodynamic parameters (Table 2). A previous study also showed that sulfate adsorption follows the pseudo-second-order model [9].
## 5 Conclusions  
The case study set out to explore (a) the efficiency of using the Learning Management Systems' feedback delivery function in conjunction with graduated feedback informed by the principles of best practice, and (b) whether this method might be adapted to a scaled-up course offering for deployment in the developing world.  
In relation to the first part of the research question, the results of the study clearly indicate that this method works well to provide feedback that is (a) more detailed than a marker might otherwise write for each student, (b) meets (and in some cases exceeds) the students' expectations for the amount of feedback, and (c) delivered in a more timely manner because it is less labor intensive. All of these factors contribute to a positive reaction from the students to the method. The concerns of the proportion (\(<\)20%) who preferred custom-written feedback can be solved by offering one-to-one consultations in which feedback can be delivered in person. These were largely the high-achieving students who would likely benefit from a more direct mentoring-based approach.  
In relation to the second part of the research question, we conclude that a strong argument exists for the method to be used on a larger scale. What is required is for a carefully developed set of graduated feedback to be developed for each assessment item, based on [PERSON]'s method [4], and for an assessor/marker to use this to provide the feedback via an LMS. In this way, an experienced marker can process around three or four assignments in the same amount of time that a conventional marker takes to mark one assignment. This would permit the number of enrolments in a course to be scaled-up, which would be an advantage in both developed and developing world applications.  
While the participants in this study are IT students, the generic nature of the process can be generalized for a wide range of disciplines and student types, including those living in developing areas and/or people without much formal education. As long as the feedback is written in clear, simple language, there is no reason why it should not be effective.
## 5 Conclusions  
Several issues addressed in this study concern the analysis of the KH behavior for different regimes of laser welding used in several industrial applications: These processes cover deep KHs with large aspect ratios obtained at low welding speeds with multi-kilowatt lasers, and those with much smaller aspect ratios, realized at high welding speeds, with small focal spots and lasers with low incident power. One important issue is about the determination of the scaling law of the resulting aspect ratios for these very different operating conditions.  
By using a generalized piston model, it has been shown that the scaling laws obtained in the case of the \"thermal\" model applied to low welding speeds or high aspect ratios, can be formally extended to operating conditions where the laser absorption occurs mainly on the inclined absorbing KH front, which are characteristic of high welding speed processes or low aspect ratios.  
This generalized piston model has allowed us to study the behavior of several parameters of the ejected melt flow and to define the threshold conditions for KH formation, when the aspect ratio is equal to one. Moreover, it has been shown that operating parameters around this threshold should induce unstable behavior of the rear KH wall, and therefore defects in the resulting weld seam at low welding speeds. Therefore, experimental observations could be explained by this mechanism.  
As a more general result of this study, it could be proposed that a necessary condition for obtaining a stable laser welding process in the KH mode would be to generate KHs with fairly high aspect ratios, above a specified limit, this limit being dependent on the laser absorptivity of the material. It would increase as this laser absorptivity decreases. This tendency also seems to be verified when comparing laser welding of copper alloys or steels. Furthermore, it is known that weld seams obtained by the laser power bed fusion process have rather low aspect ratios, which could make them sensitive to this kind of instability as well.  
In the same vein, it can be also concluded that the use of a short laser wavelength (green or blue) which strongly increases the absorptivity of any material--and thus decreases the effect of multi-reflections within the KH--should be an efficient way to easily reduce the range of the operating parameters leading to process instability. Future experiments in this direction are of course highly expected.  
The author thanks [PERSON] for his help in carrying out the experiments presented in Section 4.3.1.  
The author declares no conflict of interest.
# Sediment Erosion Characteristics and Mechanism on Guide Vane End-Clearance of Hydro Turbine
## 2 Sediment Erosion Status of Dongshuixia Hydropower Station  
The Dongshuixia hydropower station is a water diversion and electric generating project with a low-clamp long tunnel, and is located at the main stream canyon segment of the Taolai river in Sunan County of Gansu Province (see Figure 3). The average annual generated energy is up to 2.43 billion kW-h. This project was completed and put into operation in July 2012, which was of great significance to improve the local network structure, palliate the power shortage, and promote the development of the local economy.  
This hydropower station is a run-off hydroelectric station without storage capacity, which fails to discharge sand effectively in service, resulting in a high concentration of sediment. After operation through two flood seasons with a sediment-laden river, no obvious sediment erosion was found when the distributor was under a heavy load. Whereas in the dry season, the sediment erosion of the distributor was very serious. Through the numerical simulation of the three-dimensional flow field of the turbine, the velocity and pressure in the head cover was uniformly distributed, and there was no negative pressure zone and no flow separation. Therefore, under normal circumstances, the hydrodynamic cavitation does not take place in the guide vane, head cover region, and bottom  
Figure 3: Dongshuixia hydropower station.  
ring. Therefore, sediment erosion is the main reason for the erosion of the distributor flow-passage walls. The sediment erosion regions mostly focus on the guide vane, head cover, bottom ring, upper and lower fixed wearing ring, guide vane sleeve, etc. There were abrasion pits of 5-15 mm on the upper and lower end surfaces behind the guide vane shaft, the erosion depth of the end clearance of guide vane was 10-20 mm, and sediment erosion of the guide vane sleeve was more severe.  
Sediment samples from three different positions at the Dongshuixia hydropower station forebay were collected by a suspended sediment sampler. The weight of each sample was not less than 50 g, and the distance between the sampling points was about 12 km. After the clarifying, pouring out the clear water, and drying steps, a laser granularity analyzer was used to analyze the particle size in this study. The median particle size d\({}_{50}\) of the sand particles entering the turbine was 45 \(\mu\)m, and its average density was 2650 kg/m\({}^{3}\).
# Six Sigma-Based Frequency Response Analysis for Power Transformer Winding Deformation
## 2 Materials and Methods
### Proposed FRA6\(\sigma\) Approach--Define Phase  
Traditional FRA techniques rely on expert interpretation, often leading to inconsistencies. In integrating FRA with Six Sigma, this study introduces a structured statistical method for improving fault diagnosis reliability. In this subsection, the methodology of the proposed FRA6\(\sigma\) approach is presented as demonstrated in Figure 2 using the DMAICV (define, measure, analyze, improve, control, verify) lifecycle. The FRA technique detailed in the previous section is integrated, for the first time, with the Six Sigma (6\(\sigma\)) technique. Six Sigma is a class of methods and tools for process improvement and was presented by [PERSON] during his time with Motorola in 1986 [43; 44]. Six Sigma methods aim to enhance exceptional workmanship by determining and eliminating the factors associated with defects and reducing irregularity in manufacturing and industry processes. This is developed by utilizing empirical and statistical quality management techniques, where each 6\(\sigma\) project conforms to an outlined process and has specific set point values, for instance lowering pollution or increasing client satisfaction.  
In Figure 3, the statistical conditions of 6\(\sigma\) underlined by the normal distribution curve are presented. At 0, \(\mu\) (mu) indicates the mean alongside the horizontal axis demonstrating the distance from the mean, expressed in units of standard deviation (characterized as \(\sigma\) or sigma). The higher the standard deviation, the greater the wingspan of values; for the green function, \(\mu=0\) and \(\sigma=1\)[45]. The upper and lower specification limits (USL and LSL) are at a measure of 6\(\sigma\) from the mean [45]. A normal distribution implies that values much further from the mean are highly unlikely, i.e., roughly one in a billion ultra-low, likewise too high. Even though the mean ought to move left or right near 1.5 standard deviations (i.e., 1.5\(\sigma\) shift--red and blue coloured), there is still a safety mat.  
\begin{table}
\begin{tabular}{c c c} \hline
**Excitation Voltage (V)** & **Method** & **Application** \\ \hline
0.2–2 V & FRA & Common for standard FRA testing to avoid saturation and maintain linearity. \\
1 V RMS & FRA Setup & Most used voltage for FRA across a wide frequency range (20 Hz–2 MHz). \\
5–20 V & Impulse FRA (IFRA) & Higher voltage is used in some impulse-based FRA methods but is less \\  & & common in industry practice. \\ \hline \end{tabular}
\end{table}
Table 2: Typical FRA excitation voltages.  
In the subsequent subsections, a structured five-step approach based on the proposed method is detailed.
## 4 Results and Discussion  
### Cutoff of Infinite Series Solutions  
As EIT analytical solutions have the form of an infinite series, it is necessary to determine the number of terms required for computations. To the end, the configuration shown in Figure 5 was chosen, and the sum of voltage V1 and V2 voltages was calculated to determine the point where the difference in voltage did not change significantly as the number of terms increased. As shown in Figure 6, the voltage change stabilized after adding the 50 th term of the series (\(n=50\)). Therefore, the analytical solutions of the infinite series were changed to a finite series that added up to the 50 th term. However, it is noteworthy that the cutoff of infinite series depends on the size and the composition of the samples.  
\[\text{Case 1}:u(x,y)=\sum_{n=1}^{\infty}\Bigl{(}A_{n}\cdot\cosh \Bigl{(}\frac{n\pi x}{b}\Bigr{)}\cdot\cos\Bigl{(}\frac{n\pi y}{b}\Bigr{)} \Bigr{)}\simeq\sum_{n=1}^{50}\Bigl{(}A_{n}\cdot\cosh\Bigl{(}\frac{n\pi x}{b} \Bigr{)}\cdot\cos\Bigl{(}\frac{n\pi y}{b}\Bigr{)}\Bigr{)} \tag{23}\] \[\text{Case 2}:u(x,y)=\sum_{n=1}^{\infty}\bigl{[}\bigl{\{}A_{n}\cosh \bigl{(}\frac{n\pi}{b}x\bigr{)}+B_{n}\text{sinh}\bigl{(}\frac{n\pi}{b}x\bigr{)} \bigr{\}}\cos\bigl{(}\frac{n\pi}{b}y\bigr{)}\bigr{]}+a_{0}x+b_{0}\] (24) \[\simeq\sum_{n=1}^{50}\bigl{[}\bigl{\{}A_{n}\cosh\bigl{(}\frac{n\pi }{b}x\bigr{)}+B_{n}\text{sinh}\bigl{(}\frac{n\pi}{b}x\bigr{)}\bigr{\}}\cos \bigl{(}\frac{n\pi}{b}y\bigr{)}\bigr{]}+a_{0}x+b_{0}\]  
Figure 4: (**a**) Experimental EIT setup of the samples, (**b**) Sciospec EIT device.  
Figure 5: Theoretical setup for determining the cutoff of infinite series solutions (V1 and V2 were measured simultaneously).
## 3 Results and Discussion  
This study considered two power system models, the modified two-area four-machine power system and the IEEE 68 bus power system, for implementing the D-LSE. All three level results are presented under topology changes and with bad data in the measurements for the modified two-area four-machine power system. For the IEEE 68 bus power system, the area-level and network-level results are presented. In the results tables, magnitude quantities are in per unit (pu) and indicated by \"M (pu)\". Angle quantities are in degrees and indicated by \"\(\angle\)\". It is important to note that all the test results presented except for Sections 3.1.1 and 3.2.1 have considered the fully connected topology of the networks.  
The PMUs implemented in the RTDS simulation provided noise-free measurements, which are defined as the \(True\ Value\). The PMU measurement errors were regulated by the  
Figure 5: Flow diagram of the network level. LSE procedure flow path E, measurement communication path Z, and H-TNTP-PMU topology path O are referred to in Figure 3.  
total vector error (TVE), which is the difference between the true phasor and the measured phasor. The standard maximum permissibility of the TVE is 1% [32]. To mimic the reality of the PMU measurements, a Gaussian white noise (GWN) was added to all _True Values_ at the simulator, thus defined as the _Measurement_. The GWN is a zero mean, user-defined level of variance noise addition to the signal using (10). To analyze the performance of the D-LSE, 5% of the noise level (variance) was considered in all experiments.  
\[Measurement=True\ Value+n\text{; where }n\sim N(0,\sigma^{2}) \tag{10}\]