esa-sceva/satcom-chunk-collection huggingface dataset:
# Current Status and Consideration of Support/Care Robots for Stand-Up Motion
## 3 Results
### Functions and Effects
#### 3.1.1 Partial Assistance  
Twenty-four out of thirty-two cases of STS assistance robots providing partial assistance were designed for elderly people. In addition, those with lower limb motor disorders due to some problem, such as knee osteoarthritis, were the second most common target after the elderly. An example of partial support is Myosuit, which is discussed by [PERSON] et al. [18]. The robot has a function to generate extension torque of the user's hip joint and knee joint through two-joint drive using an actuator and a cable. In addition, Myosuit places ligamentous rubber bands on the anterior thigh and posterior lower leg, and at 30% elongation it exerts about 215 N, which simulates the eccentric contraction of muscle. This gives the user about 26% of the knee peak torque during the sit-to-stand transition. [PERSON] et al. [19] proposed an elbow-supporting device as an STS assistance robot with a shape that the elderly are familiar with in their daily lives. The robot is placed at the side of the user, and when the user places his or her arm against it and applies weight, the armrest moves horizontally and vertically to assist the user in STS. This applies 40-60% of the body weight to the armrest and significantly reduces the user's rectus femoris muscle activity during STS. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Year**} & **First Author** & **Assist Level** & **Function** & **Effect** & **Reference** \\ \hline \multirow{3}{*}{2011} & \multirow{3}{*}{[PERSON]} & \multirow{3}{*}{T} & Estimating STS start intention by preparatory movement. & The operating COP trajectory was controlled to 40.1\% of the & \multirow{3}{*}{[11]} \\  & & & Control COP within reference range & reference range. & \\ \hline \multirow{3}{*}{2016} & \multirow{3}{*}{[PERSON]} & \multirow{3}{*}{P} & Knee joint flexion/extension torque assistance using a linear actuator. & Knee joint work was significantly reduced. & \multirow{3}{*}{[14]} \\ \cline{1-1} \cline{3-5}  & & & Use with custom walker. & & \\ \cline{1-1} \cline{3-5}  & & & Start STS total-assist with the signal to lean forward in the trunk. & & \\ \hline \multirow{3}{*}{2017} & \multirow{3}{*}{[PERSON]} & \multirow{3}{*}{P} & Efferent/Afferent torque assistance for hip and knee joints with actuator connection cables and rubber bands. & 26\% peak torque was provided to the knee joint. & \multirow{3}{*}{[18]} \\ \cline{1-1} \cline{3-5}  & & & Use with a front wheel walker. & & \\ \cline{1-1} \cline{3-5}  & & & Attsch the exoskeleton to the ceiling rail tether to assist STS. & & \\ \hline \multirow{3}{*}{2019} & \multirow{3}{*}{[PERSON]} & \multirow{3}{*}{P/T} & STS assist with suspension harness. & ND & \multirow{3}{*}{[21]} \\ \cline{1-1} \cline{3-5}  & & & Integrated structure of exoskeleton, electric wheelchair and lift. & & \\ \cline{1-1} \cline{3-5}  & & & Weight assist during STS. & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Assist functions and effects (Wearable type).
# Design of a Compact Planar Magnetic Levitation System with Wrench-Current Decoupling Enhancement
## 5 Experimental Setup and Results
### System Setup  
Figure 11 represents the overall setup of the proposed planar maglev system with the motion-tracking system and controller. The motion-tracking system had four cameras (VICON Vantage V5, Vicon Motion Systems, Oxford, United Kingdom) to detect the reflective markers on the mover. Then, the data were transmitted over Ethernet to a software program to compute the mover position and orientation. With a proper calibration, the motion-tracking system could provide position and orientation data with a \(\pm\)10 \(\upmu\)m and \(\pm\)0.2 mrad resolution with an update frequency of 1 kHz to the controller via the UDP protocol. With the same sampling frequency of 1 kHz, the controller (NI PXI-8820, National Instruments, Austin, United States) operated with the implemented control algorithms as described in the previous section to determine the coil currents. The analog current command outputs were sent out through two analog output modules to the current amplifiers in the planar maglev system.
# Hybrid-Compliant System for Soft Capture of Uncooperative Space Debris
## 5 Simulation Study and Results
### Debris-Mass and Compliance Relation
#### 5.2.1 Indicative Case  
In this section, indicative plots are presented based on the simulation responses for a specific set of values in the range searched by the algorithm. Specifically, the indicative plots in this section were obtained using a simulation with \(k_{m}\) and \(b_{m}\) equal to 0.5 N/m and 0.1789 Ns/m, respectively, and debris with mass \(m_{d}\) equal to 12 kg. The positions of the point masses \(m_{s}\), \(m_{c}\) and \(m_{d}\) are shown in Figure 17. As shown in this figure, the tip of the ADR system was initially located 10 cm from the debris. For almost 10 s, the ADR system approaches the debris, moving together while in contact.  
The contact time \(t_{i}\) was calculated using the impact force shown in Figure 18. It is the time when the impact force is continuously greater than zero and, thus, is equal to 10.12 s. The impact force in Figure 18 was set to zero when the relative position of the ADR system's tip from the debris, shown in Figure 19, was negative, indicating that there is a distance between the two systems. However, when the systems are in contact, the interpenetration of the bodies, shown in Figure 19, is positive, and the impact force was calculated based on Equation (18); it is the multiplication of the spring stiffness \(k_{i}\) times the interpenetration in Figure 19.  
Figure 17: Position of masses for the indicative case.  
Figure 18: Impact force for the indicative case.
# Data-Aided Frequency Offset Estimation for CE-OFDM Broadband Satellite Systems
###### Abstract  
In recent years, the constant-envelope orthogonal frequency-division multiplexing (CE-OFDM) has been considered as a candidate waveform in broadband satellite systems as it has a 0-dB peak-to-average power ratio (PAPR). However, the carrier frequency offset (CFO) estimation scheme for CE-OFDM broadband satellite systems directly affects system performance. In this paper, we analyze the network architecture and the propagation environment of CE-OFDM broadband satellite systems, and we propose a data-aided CFO estimation strategy based on the frequency domain pilot symbols. The Cramer-Rao bound (CRB) of our CFO estimator is given by mathematical analysis, and the effect of the number of pilot symbols on the estimation performance is analyzed. The pilot symbol-based CFO estimator is composed of a phase demodulator and a discrete Fourier transform (DFT) module, and it can obtain a large estimation range under a small pilot overhead. The simulation results show that the CE-OFDM broadband satellite systems can achieve a good bit error rate (BER) performance by using the proposed strategy to estimate and compensate the CFO.  
carrier frequency offset; CE-OFDM; OFDM; broadband satellite +
Footnote †: journal: Applied Sciences
# Review of Channel Estimation for Candidate Waveforms of Next Generation Networks
## 2 Structure of Filter Bank Multi-Carrier Systems
### Overview of Next Generation Networks (5G)
#### 2.4.2 Candidate Waveforms for 5G Networks  
As briefly highlighted in Section 2.3, candidate waveforms under consideration for 5G networks include CP-OFDM, SC-FDMA, f-OFDM, filter bank based OFDM-OAM (FBMC-OQAM), UFMC and  
Figure 7: Classification of air interface design considerations for 5G networks.  
GFDM based transceivers. The features and structures of these waveforms are briefly discussed and compared in this section.
# Performance Optimization of Grounding System for Multi-Voltage Electrical Installation
## 4 Grounding Configurations and Optimization Techniques
### Grounding Using Natural Conductors  
In addition to conventional grounding methods involving rods, plates, and mesh systems, natural conductors such as metal water pipes, structural steel frameworks, and underground metallic fittings can serve as effective grounding paths. These elements, often already embedded in the infrastructure, provide an economical and practical means of achieving grounding, particularly in low-voltage or commercial installations. According to NEC Article 250.52(A) [3] and IEC 60364-5-54 (Clause 54.3), such conductors may be recognized as grounding electrodes, provided they meet specific criteria regarding their electrical continuity, corrosion resistance, and direct contact with the soil. For example, metal water pipes must be in contact with the earth for at least 3 m and must not be coated with insulating material that would inhibit conductivity. Similarly, structural steel must be securely bonded to the grounding system using approved mechanical or welded connections.  
The use of natural conductors offers several advantages, including reduced material costs, simplified installation, especially in new buildings, and potential redundancy when used alongside conventional electrodes. However, there are limitations that must be considered. These include the risk of corrosion over time, potential discontinuities due to mechanical disassembly or use of insulating joints, and challenges in verifying the integrity of buried or inaccessible connections. Consequently, natural grounding conductors are best used to supplement, rather than replace, dedicated grounding electrodes, particularly in high-fault-current environments or critical systems requiring high reliability.  
Regular testing and verification are essential to ensure the ongoing effectiveness of natural conductors in grounding applications. Their performance can vary widely depending on the type of material, the size and shape of the conductor, soil characteristics, and environmental exposure. When appropriately validated and maintained, natural conductors can play a valuable role in achieving safe and cost-effective grounding for electrical installations, particularly in residential, commercial, and light industrial settings.
## 4 Computational Experiments and Analysis
### Validation of the FDTD Algorithm  
To validate the effectiveness of the FDTD algorithm employed in this study, the composite scattering coefficients of an exponential rough surface and the target above it were computed using the FDTD method and compared with the results obtained from the method of moments (MOM). The comparison results are presented in Figure 7. In the simulation, the frequency was set to \(f=6\) GHz, and the incident angle was \(\theta_{i}=30^{\circ}\). The dielectric constant of the vehicle body was \(\varepsilon=121.6-j28.8\), and that of the wheels was \(\varepsilon=29-j8.9\). The snow layer had a depth of \(15\lambda\) and a thickness of \(\varepsilon=14.8-j0.65\), while the dielectric constant of the soil was set to \(\varepsilon_{s}=8.0-j37.7\). The rough surface scattering was simulated over \(30\) independent realizations to ensure statistical reliability. As shown in Figure 7, the angular distribution curves obtained from the two algorithms exhibit excellent agreement, which demonstrates the accuracy of the FDTD algorithm.
## 3 Results
### Morphology Characterization  
The macroscopic differences observed between the fibrous structures obtained by electrospinning and the continuous structures provided by solvent casting and spin coating is shown by the SEM images reproduced in Figure 3, which shows the three representative unloaded samples.  
In particular, electrospinning produced mats composed of a network of randomly arranged fibers, while dense films were obtained by the other techniques tested. However, spin coating provided a regular rugosity of the surface (Figure 3c), due to the support rotation, compared to casted samples (Figure 3b) where superficial random defects were noticed.  
The details of the films thickness and specific weight, as a function of the technique, are reported in Table 1. The lower specific weight of the electrospun films is due to the greater surface of the collector (25 cm \(\times\) 25 cm) compared to the plates used for the other two techniques. The fiber deposition occurs randomly due to the static collector, affecting the thickness uniformity as is evident from the standard deviations.  
Figure 3: (**a**) SEM image of the fibrous structure obtained by the unloaded electrospun mat, (**b**,_c_) SEM images of the continuous structure obtained by the unloaded casted and spin coated samples, respectively.  
Figure 2: Unloaded zein films obtained through (**a**) electrospinning, (**b**) solvent casting, and (**c**) spin coating, SCG 45% \(w/w\) loaded zein films obtained through (**d**) electrospinning, (**e**) solvent casting and (**f**) spin coating.  
Regarding the samples loaded with the extract, its presence tended to decrease the thickness of electrospun samples. This effect also influenced the micrometric and submicrometric fiber size. Indeed, as seen in Figure 4, increasing the extract loading up to 20% \(w/w\), the fiber mean size remained quite constant (1.10 \(\pm\) 0.40 \(\upmu\)m for 0% \(w/w\), 1.46 \(\pm\) 0.70 \(\upmu\)m for 15% \(w/w\), and 1.44 \(\pm\) 0.53 \(\upmu\)m for 20% \(w/w\) of extract loading), while a lower fiber mean diameter was detected at the higher loadings, i.e., 0.88 \(\pm\) 0.38 \(\upmu\)m and 0.72 \(\pm\) 0.19 \(\upmu\)m, respectively, at 35 and 45% \(w/w\).  
Furthermore, the shape of the fibers changes as the concentration of the extract increases, passing from a ribbon-like structure, typical of the reference sample, to a more filiform one.  
No remarkable effects as a function of the SCG extract increasing was noticed for continuous samples produced by casting and spin coating. The last process outcome was more reproducible due to the macroscopic characteristics of the film, such as the thickness of the film and the specific weight, due to the equalizing effect provided by the rotation of the support.
# UAV Formation Trajectory Planning Algorithms: A Review
## 4 Intelligent Algorithm
### Heuristic Algorithm
#### 4.1.6 Fruit Fly Optimization Algorithm  
The Fruit Fly Optimization Algorithm (FOA) is a new method for deriving global optimization based on the foraging behavior of Drosophila, which uses Drosophila to be superior to other species in sensory perception, especially in the sense of smell and vision. First, fruit flies use their sense of smell to collect the smell in the air. Then, they fly to the vicinity of the food location, where they use vision to find the location where the food and companions gather and fly in that direction, so as to realize the group iterative search of the solution space and complete the multi-UAV trajectory planning. Figure 16 is an FOA iterative evolution search diagram and a Fruit Fly Optimization Algorithm (FOA) trajectory diagram.  
Figure 16: An iterative evolution search diagram and FOA trajectory diagram. (**a**) An iterative evolution search diagram; (**b**) An FOA trajectory diagram (circles: nodes; lines: trajectory; numbers: the distance between vertices).  
[PERSON] et al. [91] proposed the multi-swarm Fruit Fly Optimization Algorithm (MSFOA), which divides the entire fruit fly group into multiple multi-task sub-swarms and introduces offspring competition strategies. They propose a collision detection method to solve the problem of slow global convergence, and local optimum of the traditional Fruit Fly Optimization Algorithm is solved.  
[PERSON] et al. [92] proposed an optimized Fruit Fly Optimization Algorithm (ORPFOA) to determine the optimal number and priority of UAVs while using a change task assignment algorithm combined with reference points and distance-cost matrices. Trajectory planning solves the problem of multi-UAV trajectory planning in a three-dimensional complex environment with online changing tasks.  
[PERSON] et al. [93] proposed an improved Fruit Fly Optimization Algorithm (NIFOA) based on Time Stamp Segmentation (TSS). The TSS model was introduced to solve the spatio-temporal coupling problem between multiple UAVs, and the multi-objective problem is transformed into a multi-constraint problem. Finally, the greedy strategy, the restart strategy and the evolutionary strategy of the optimal population are added to complete the multi-UAV trajectory planning, which solves the space-time coupling problem between multi-UAVs and the convergence speed of the traditional Fruit Fly Optimization Algorithm problems with slowness and local optima.
# A Study on Operator Allocation in Consideration of Fatigue in Cell Manufacturing System +
## 4 Computational Experiments
### Solution Procedure  
For each order, we determine the operator allocation, and the number of products allocated to each cell. Then, we update the skill index values based on the allocated tasks and number of products and determine the operator allocation and quantity of products for the following order. This is repeated until the order is completed. We use the Simulated Annealing (SA) algorithm to find a solution for each order. The Simulated Annealing (SA) algorithm is one of the effective probabilistic approximation methods for NP-hard problems, such as combinatorial optimization problems. As a feature, the variable called temperature is used. The higher the temperature, the greater the probability of accepting a worse solution, which helps prevent the algorithm from becoming stuck in a local optimum. As the temperature gradually decreases, the algorithm increasingly favors selecting better solutions. Figure 3 illustrates the solution procedure.  
First, we set the skill index values in <2>, and in <4>, we judge the terminal criterion for the number of orders t. After that, we enter the SA algorithm. After initializing the temperature \(Temp_{r}\) and the number of steps \(r\) in <6> (\(r=1\)), the operators are allocated to each cell and task in <7>. After the number of products in each cell is determined in <8>, the solution acceptance judgment is performed in <9>. The acceptance judgment is determined by Equation (14), where \(x\) is the current solution, \(x^{\prime}\) is the following solution, and \(\Delta x\) is the value of \(x-x^{\prime}\).  
\[(accept\ x)=\begin{cases}\Delta x<0&\ldots 1\\ \text{{otherwise}}&\ldots\exp\Bigl{(}-\frac{\Delta x}{Temp_{r}}\Bigr{)}\end{cases} \tag{14}\]  
If \(\Delta x<0\), we update the best solution in <11>. If \(\Delta x>0\) and \(x\) is accepted, we do not update the best solution, and we proceed to the cooling judgment in <12>. \(Temp_{r}\) represents the temperature at step \(r\). In <13>, the next temperature \(Temp_{next}\) is set using Equation (15) with cooling rate \(\mu\).  
\[Temp_{next}=\mu Temp_{r}\ \ \ \ \ (0.8\leq\mu\leq 1) \tag{15}\]  
The terminal criterion of the SA algorithm is determined by the number of steps \(r\). We return to <7> operator allocation and task assignment if the terminal criterion is not reached. After completing the step and terminating the SA algorithm, the best solution is output as the order t solution in <16>. Then, in <17>, based on the task assigned and the number of products assigned to each cell \(Q_{tcj}\), the execution time is determined, and the skill index values are updated using Equations (12) and (13). This is repeated until the value of the order count t satisfies the termination criterion in <4>.
## 2 Materials and Methods  
### Raw Materials  
#### 2.1.1 Conventional Raw Materials  
SCC was composed of CEM II/ A-L 42.5 R cement, superplasticizers, water, siliceous gravel 4/12 mm, siliceous sand 0/4 mm, and limestone sand 0/2 mm.  
* Cement was standardized as per EN 197-1 [49]. Its main characteristic was a limestone content between 6% and 20% by weight, which allowed reducing the environmental impact related to cement use [19].
* The superplasticizers were polycarboxylate-based. They had been used in previous research by the authors, in which they showed adequate performance when used to produce SCC [5; 22]. The water was provided by the municipal water supply.
* Three aggregate fractions were considered to reach adequate aggregate-particle packing, thus providing the SCC with a suitable granular skeleton [17]. Furthermore, limestone sand 0/2 mm provided the necessary fine aggregate content to achieve SCC [50]. The appearance of the aggregates is shown in Figure 1. Their chemical and physical properties are detailed in Table 1, and their gradations are depicted in Figure 2.  
Figure 1: Aggregates used in SCC production: (**a**) siliceous 4/12 mm; (**b**) siliceous 0/4 mm; (**c**) limestone 0/2 mm.
# A Road Crack Detection Method Based on Residual and Attention Mechanism
## 3 Experimental Results and Analysis
### Visualization Analysis of Experimental Results  
Table 1 presents the test results of different network models on the CrackTree260, CrackLS315, and DeepCrack datasets under the same experimental setup. The optimal values for each evaluation indicator have been bolded. From Table 1, it can be seen that the proposed network model had the best F1 score on all three test datasets. Compared with the methods mentioned in UNet and Refs. [21; 22], our model achieved an accuracy improvement of 5.7%, 1.3%, and 1.8% on the CrackTree260 dataset, respectively. Our model had improved accuracy by 6.7%, 3.5%, and 1.4% on the CrackLS315 dataset, respectively. Our model had improved accuracy by 0.5%, 0.2%, and 3.5% on the DeepCrack dataset, respectively.  
Figure 8 shows the detection results of different methods on the CrackTree260 dataset. Compared with the results of the crack detection methods shown in Figure 8c-e, the edge information of the cracks detected by our model was richer and had better continuity. Although the crack detection methods in Figure 8 can obtain relatively complete crack shapes, the crack detection methods in Figure 8c-e lost a lot of crack detail information and were more susceptible to noise compared to the model proposed in this paper. For example, there are relatively small cracks in the red and blue rectangles of the first image. Other methods can also detect cracks here, but there may be varying degrees of fracture, and the method proposed in this article can also detect small cracks more completely and continuously. In the third image, the noise is quite severe. Except for the algorithm in this article, all other algorithms are affected to varying degrees by the noise and background information in the image, resulting in missed detections and false positives of small crack details in the image.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{3}{c}{**CrackTree260**} & \multicolumn{3}{c}{**CrackLS315**} & \multicolumn{3}{c}{**DeepCrack**} \\ \cline{2-9}  & **P** & **R** & **F1** & **P** & **R** & **F1** & **P** & **R** & **F1** \\ \hline UNet & 0.729 & 0.757 & 0.743 & 0.677 & 0.712 & 0.694 & 0.746 & 0.606 & 0.669 \\ \hline Ref