esa-sceva/satcom-chunk-collection huggingface dataset:
Firstly, the rough estimate of the target rotation ratio is estimated by discrete chirp Fourier transform (DCFT), and then the minimum entropy function and the conjugate gradient descent method are used to obtain the exact value of the rotational parameter. Finally, azimuth focusing is achieved by only performing MCFT once on the azimuth echo signal. Thus, this method avoids estimating and separating the sub-echo components one-by-one, greatly improving the imaging speed, and there is no cross-term effect. Simulation and experimental results show that the proposed method can realize two-dimensional, high-resolution, and fast imaging of maneuvering targets.
# Development and Biomechanical Evaluation of a Back Exoskeleton to Support Lifting Tasks
## 2 Exoskeleton Functionality and Design
### Exoskeleton Design
#### 2.2.3 Sensory  
To detect lifting motion in the sagittal plane, three inertial measurement units (IMU) are attached to the top end of the backbone and thighs. These sensors continuously track motion by measuring the angle and angular velocity, providing real-time data on body movement. The BNO055 IMU Sensor integrates accelerometer, gyroscope, and magnetometer readings and performs internal signal fusion and filtering, delivering absolute orientation data (e.g., Euler angles) without requiring additional filtering in the microcontroller. This built-in processing improves the stability and accuracy of angle measurements during dynamic activities, such as lifting. Due to variations in postures among different users, the sensors are calibrated by resetting to zero when the user is in an upright standing position, ensuring accurate angle measurements relative to the body's natural posture.  
The IMU data is processed to determine the relative angle and angular velocity between the thighs and the upper body. This information is then used as input for the control algorithm, which dynamically adjusts the torque of the motors to provide the appropriate level of support. By continuously monitoring movement patterns, the exoskeleton can adapt to different lifting techniques and user behaviors, ensuring optimal assistance while minimizing the risk of harmful overloading.
# Sizing Methodology of a Fast Charger for Public Service Electric Vehicles Based on Supercapacitors
## 4 Conclusions  
When electric vehicles integrate banks of SCs as energy storage systems, fast chargers that also use SCs present numerous advantages over other technologies. The most important, undoubtedly, is the ability to charge very quickly and handling very high power without causing distortions in the grid and without the need to use high-power converters.  
In this paper, a methodology for sizing this type of fast charger has been presented. To this end, the mathematical formulation of the electrical variables involved in the vehicle charging process has been developed: instantaneous circuit current, SC voltages, maximum charging time, peak current, energy dissipated, and charging process efficiency have been analytically calculated. From this formulation, it has been shown how the initial voltage of the charger, its capacity, as well as the capacity of each of its variable modules should be sized and selected. Special attention has also been paid to the inductance and internal resistance that the smoothing inductor must have. For this purpose, a reasonably intuitive graphical method has been developed.  
Finally, the validity and usefulness of the procedure has been checked on a real case of an electric bus equipped with SCs. Based on the characteristics provided by the manufacturer of this vehicle, the sizing of the charger has been carried out, and its behavior has been simulated with the values obtained in the design stage. Its correct operation has been demonstrated, even under limit conditions and considering the aging of the cells both in the charger and in the vehicle's accumulator. The designed fast-charging station has been dimensioned based on the values of commercial devices, thus demonstrating the possibility of its real manufacturing.  
Conceptualization, J.F.P.; methodology, J.F.P. and M.G.M.; software, M.G.M., A.S.G. and M.F.Q.; validation, M.G.M. and M.F.C.; formal analysis, J.F.P. and A.S.G.; investigation, M.G.M., J.F.P. and M.F.C.; writing--original draft preparation, J.F.P.; writing--review and editing, M.F.Q., M.F.C., G.A.O. and A.S.G.; supervision, M.F.Q. and G.A.O.; project administration, G.A.O. and M.G.M.; funding acquisition, G.A.O. and M.G.M. All authors have read and agreed to the published version of the manuscript.  
This work has been partially funded by the Principality of Asturias through the FICYT foundation under research grant AYUD/2021/51047.  
Not applicable.  
The authors declare no conflict of interest.
## 4 Discussion
The results of this study indicated that stone casts generated using conventional impression and pouring techniques have a higher linear dimensional accuracy than 3D-printed casts, which contradicts the findings reported by recent studies [11; 21; 22]. A recent systematic review evaluated the accuracy of 3D-printed models generated by different printing technologies and reported that the dimensional variation may exceed 500 \(\upmu\)m, especially for printers that use stereolithographic technology [23]. The 3D printer used in the current study is reported to have a mean discrepancy in the range of 210-467 \(\upmu\)m, which is comparable to a few other commonly used printers [24]. Such significant variability may be acceptable for diagnostic purposes, but not for prosthodonic procedures that require high levels of precision and accuracy. Inaccuracies up to 50.0 \(\upmu\)m from the master model were considered to be clinically acceptable as they were unlikely to impede the full seating of a prosthesis [19; 25]. In the present study, alginate presented an accuracy comparable to that of PVS-2. This finding is supported by other studies [15; 26]. In contrast, another study found a high deviation pattern of full-arch alginate impressions that limited its use in dentistry [4]. Furthermore, alginate's instability across time and its surface roughness due to water loss are disadvantages, limiting its use only for diagnostic casts [15]. The type of intraoral scanner used and different scanning conditions can lead to varied results [20]. Moreover, the influence of the scanning approach and selected software on the scanning precision should be verified for each IOS model [27]. The present study is an in vitro study, which differs from the in vivo environment in patients' mouths. Factors like the presence of saliva and/or blood, the patient's and dentist's movements, accessibility in the mouth, and soft tissue stability are the common challenges faced by dentists, which can lead to potential inaccuracies. Therefore, future in vivo studies should be conducted to augment the quality of functioning of the digital workflow.
## 6 Results
### Solution Quality  
The hypervolume indicator [42] is used to measure the performance of multi-objective optimisation methods. The metric provides the volume, or area in 2-dimensional space, of the objective subspace that is dominated by the Pareto front.  
Consider a multi-objective optimisation problem with two criteria similar to the problem we are solving in this paper. Mathematically, consider that we want to minimise \(\mathbf{f}:\mathbb{R}^{n}\rightarrow\mathbb{R}^{2}\) and \(\mathbf{f}(\mathbf{x})=(f_{1}(\mathbf{x}),f_{2}(\mathbf{x}))\). The (local) optimal solutions, Pareto optima, are the minimal elements regarding a weak dominance relation. In this setting, the Pareto front is the image of the Pareto solution set under the mapping \(\mathbf{f}\). Our aim is to evaluate the set of Pareto optima using hypervolumes which measures the quality of a solution set. The hypervolume indicator, \(I_{H}\), for a solution set \(A\subset\mathbb{R}^{n}\) and a reference set \(R\in\mathbb{R}^{2}\) is defined as \(I_{H}(A):=\lambda(H(A,R))\) where \(H(A,R)=\{(z_{1},z_{2})\in\mathbb{R}^{2}|\exists\mathbf{x}\in A,\exists(r_{1}, r_{2})\in R:\forall 1\leq i\leq 2,f_{i}(\mathbf{x})\leq z_{i}\leq r_{i}\}\), is the set of points in \(\mathbb{R}^{2}\) that are enclosed by the Pareto front and the reference set and \(\lambda\) is the Lebesgue measure to calculate the volume [43]. The maximum hypervolume value is achieved if for each \(\mathbf{z}\), the solution set \(A\) contains at least a point \(\mathbf{x}\) so that \(\mathbf{f}(\mathbf{x})=\mathbf{z}\). In other words, the Pareto front is contained in the image of \(A\) under \(\mathbf{f}\)[44].  
Figure 5 provides an overview of the performance of all algorithms in terms of hypervolume values achieved over the 30 runs shown as boxplots for each problem instance. The components of Figure 5 are laid out in a way that from left to right and from top to bottom the size of problems  
is increasing. A larger hypervolume value indicates a better solution. Note that in order to compare reliability and communication overhead on the same scale, the communication overhead was normalised.  
It can be seen that for the smaller problems, ACS is more effective than BACS. Moving from top-left to the bottom-right of Figure 5, the complexity (and size) of the instances increases and finding good quality and feasible solutions is harder. We see that as the problems become more complicated, the BACS variants obtain larger hypervolumes. In particular, toward the bottom of  
Figure 5: The 30 hypervolume values of each optimisation scheme on the instances where solutions are found by all algorithms. SS = BACS (SS), Mem = BACS (Mem), Col = BASC (Col), and Com = BACS (Com).  
the Figure, BACS(Mem) shows its superiority in obtaining better quality solutions for the difficult problem instances.
## 1 Introduction  
The problem of clustering can be defined as follows. The given \(N\) data points in a \(D\)-dimensional space should be organized into \(K\) clusters. Data points from one cluster should have more similarities than those from different clusters. Clustering algorithms can be classified as partition algorithms and hierarchical ones [1]. Partition algorithms are fast and require small memory. K-mean clustering is an example of a partition algorithm [2, 3]. Hierarchical algorithms combine agglomerative and divisive algorithms. Generally, hierarchical algorithms are quite demonstrative. Agglomerative algorithms are deterministic: identical cluster trees are generated for randomly sorted datasets. Agglomerative clustering works bottom-up, collecting compounds and clusters to form larger clusters [4, 5]. Divisive clustering works top-down, splitting clusters into smaller ones down to individual structures [6, 7]. Hierarchical methods typically yield binary trees (Figure 1), which usually represent the results. Clustering algorithms are widely discussed in literature [8, 9, 10].  
Clustering of chemical databases requires much time and resources. Modern large chemical databases can be of 10\({}^{7}\)-10\({}^{8}\) records in size and, even after filtering, the number of compounds of interest may be more than 10\({}^{6}\). Therefore, the basic trend in literature on new algorithms development lies in finding new ways of clustering large databases. The NIPALSTREE system with hierarchical k-mean algorithms was suggested for clustering a 400K records database in less than 40 minutes [11, 12]. The divisive k-mean algorithm was used for clustering a 1.1M records database [7]. A dataset of 5.1M chemical structures was clustered in 24 hours using a fingerprint sorting algorithm [13]. Random sorting of initial dataset has shown the used algorithm to be deterministic. The algorithm [13] is not hierarchical. This article describes a new method for divisive hierarchical clustering of chemical structures based on the topological information only. Model databases of up to 2M records were used to evaluate algorithm performance and clusters validity. Methods for obtaining deterministic clusters are also discussed.
###### Abstract  
Traceability is a critical issue in the manufacturing of aerospace components. However, extracting understandable information from huge amounts of data from manufacturing processes may become a very difficult task. In this paper, a novel proposal for geometrical defect detection in the manufacturing of fir-tree slots for disk turbines using wire electrical discharge machining is presented. Useful data about the wire Electrical Discharge Machining (WEDM) process are collected every 5 ms and each single discharge is classified as a function of ignition delay time. Information from this large amount of data is extracted by using a deep neural network, which includes two hidden dense layers, each with 64 units and Relu activation, and it ends with a single unit with no activation. The average of the per-epoch absolute error (MAE) scores has been used to decide the optimum training situation for the deep learning network. Validation of the method has been carried out by machining a high-precision fir-tree slot for a disk turbine under industrial conditions. Results show that even though a strict tolerance band of \(\pm\)5 \(\upmu\)m has been applied, as many as 80% of the predictions from the network match the results of the conventional measuring method (coordinate measuring machine).  
defect detection; traceability; aerospace; wire electrical discharge machining; artificial intelligence; deep learning Article
## 2 Experimental Section
### Description of the \"COMMODORE\" Solution
#### 2.1.2 Hardware Electronics  
All the hardware components are protected from the surrounding harsh environment (inside concrete) by a 3D-printed housing. The housing material is polyamide 11 (PA11) and is printed using the SAF (Selective Absorption Fusion) 3D-printing technology. In addition, three lithium AA batteries are placed inside the housing to provide the required power for operation of all the units.  
The electronics PCB acts as the hub for impedance measurements through the designed sensor board. This function is orchestrated by a Cortex-Arm processor, the central processing unit of this module. By seamlessly integrating with the connectivity module (Qucetel BG95-M6), wireless communication capabilities are enabled. This module is based on Low-Power Wide-Area (LPWA) technology, ensuring energy-efficient, long-range wireless connectivity. The PCB includes a SIM card slot that connects to the connectivity module. This combination of the SIM card and connectivity module harnesses the potential of Narrowband Internet of Things (NB-IoT) and LTE Machine-Type (LTE-M) communications, as standardized by 3 GPP. NB-IoT emphasizes extended battery life, cost-effectiveness, and high connection density. In contrast, LTE-M offers greater speed, optimizing communication duration.  
During communication sessions, the Qucetel module assesses both NB-IoT and LTE-M networks, automatically selecting the one with superior connectivity. In scenarios where network access is unavailable, measurements are temporarily stored for subsequent transmission. Following each data exchange, the information is securely stored within the Unitron cloud (uCloud), allowing for comprehensive data analysis.
# Nonlinear Trajectory Tracking Controller for Underwater Vehicles with Shifted Center of Mass Model
## 2 Description of Problem
### Preliminaries and Assumptions  
The desired trajectory is defined as \(\eta_{d}=[x_{d},y_{d},z_{d}]^{T}\) (smooth and continuous functions), whereas linear position errors are defined as \([x_{e}^{E},y_{e}^{E},z_{e}^{E}]^{T}=[x-x_{d},y-y_{d},z-z_{d}]^{T}\) (in the Earth frame). Moreover, the desired attitude angles are calculated from the relationship  
\[\theta_{d}=-\text{arctan2}\Bigg{(}\frac{\dot{z}_{d}}{\sqrt{\dot{z}_{d}^{2}+ \dot{y}_{d}^{2}}}\Bigg{)},\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\, \,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\, \,\  
**Assumption 1**.: _The conditions specified for application of the five DOF model specified, e.g., in [30; 34] are met._  
_Comment_. In this work, CG lies in x-z plane, which means that \((x_{g},0,z_{g})\) instead of \((x_{g},0,0)\). This change in assumption is crucial to the proposed control algorithm.  
**Assumption 2**.: _The bounded perturbations of the model parameter are defined as \(|m_{ij}-\hat{m}_{ij}|\leq\widetilde{m}_{ij}\), \(|d_{ij}-\hat{d}_{ij}|\leq\widetilde{d}_{ij}\), \(|\mathbb{N}_{i}-\hat{\mathbb{N}}_{i}|\leq\widehat{N}_{i}\), in which \(i,j=1,\cdots,5\). In inequalities, the symbols represent the differences between the actual and nominal values of the parameters, which must be at most equal to the expected upper limit. Furthermore, the desired trajectories can be designed as continuous and differentiable, are bounded, and can be applied to the controller._  
_Comment_. This type of assumption is known from, e.g., [6; 35].  
**Assumption 3**.: _The functions \(f_{u},f_{v},f_{w},f_{q},f_{r}\), as well as first time derivatives, are unknown but limited._  
_Comment_. Such an assumption appears in [6; 35; 36; 37].  
**Assumption 4**.: _The lumped uncertainty is limited [35; 36]. Moreover, the thrust saturation effect is not dominant or persistent; therefore, \(\tau_{u}\), \(\tau_{q}\), and \(\tau_{r}\) are considered to be bounded._  
**Assumption 5**.: _The approximation of the discontinuous signum function by the hyperbolic tangent function is allowed, as in, e.g., [38; 39]._
# Random Noise Suppression Method of Micro-Seismic Data Based on CEEMDAN-FE-TFPF
## 2 Materials and Methods
### Discriminant Factor  
In order to solve the drawback of TFPF, CEEMDAN is used to decompose the original data into several approximate stationary IMFs. Then, the window lengths for TFPF are determined by each IMF. The CEEMDAN-TFPF method will deal with each IMF. However, the low-frequency IMFs contain mostly effective signals, which are hardly affected by  
Figure 1: Original signal and denoising results with different window lengths.  
Figure 2: Local enlarged view of sampling point (**a**) 200–220 and (**b**) 770–920.  
random noise. Denoising these IMFs will damage the waveform of the effective signals. Therefore, a discriminant factor should be used to judge whether the IMFs need to be denoised. The entropy of the signal is the typical discriminant factor [20]. The entropy is an index reflecting the confusion degree of a signal. The higher the entropy is, the richer the frequency components of the signal contains. The lower the entropy is, the simpler the frequency components of the signal contains. Therefore, the entropy can be used as the discriminant factor to screen the IMFs with random noise. This paper will discuss four different signal entropies: Approximate Entropy (ApEn), Sample Entropy (SampEn), Fuzzy Entropy (FE), and Permutation Entropy (PE).
## 1 Introduction
The current study aims to investigate the moderation role of experience and personal innovativeness on the acceptance of smart metering technology by evaluating smart meters users who have had experience in using it. Many studies have found that previous experience with a particular technology is a key factor in evaluating technology acceptance [18; 19]. This demonstrates that user's acceptance of technology is affected by user's experience with it. Therefore, familiarity with the intended or related technology will predict technology acceptance, whereby a user's experience will determine if the technology is useful and simple to use. On the other hand, users with low personal innovativeness, knowledge, and involvement with the new technology do not pay attention to the effect of the new technology in achieving a task. The work of [20] found that individuals who are more innovative are likely to adopt innovation more readily than others. This confirms the fact that an innovative individual is more capable of cultivating positive attitudes towards the perceptions of using the innovation compared to a less innovative individual. The findings of this extended model will result in a greater understanding of the acceptance of smart meters from the perspective of the users. In addition, the results of this study emphasize the experience and personal innovativeness related to the use of smart meters. The remaining of this paper is organized as the following. Section 2 explains the theoretical concept and choices behind this study, followed by Section 3, which reviews the related work, and Section 4 evaluates the research models and hypothesis. The research methodology is assessed in Section 5. The findings of the analysis are summarized in Section 6, followed by Section 7, where the results of the analysis are discussed. The implication of the findings is summarized in Section 8, and Section 9 concludes the article. Here, the study's shortcomings are discussed, and future directions for work are proposed.
## 5 Open Loop Processing Architecture
### Transition Samples  
In order to be able to coherently accumulate the code periods, the carrier removal must be very effective. The Doppler frequency shift affecting the signal can indeed be derived from A-GNSS information, combining the information on the satellites' position and velocities and the known user position. Therefore, the residual carrier \(f_{i}\) can be ideally wiped-off. Similarly, the navigation message can be perfectly removed, by exploiting A-GNSS aiding. However, the Doppler effect does not only change the carrier frequency, but also the code rate, causing it to stretch and compress. Supposing \(R_{c}\) to be the nominal chip rate, \(f_{L1}\) the transmission frequency, it can be found that the actual chip rate at time \(t\), \(\tilde{R}_{c}\left(t\right)\) is:  
\[\tilde{R}_{c}\left(t\right)=R_{c}\left(1+\frac{f_{D}\left(t\right)}{f_{L1}} \right), \tag{7}\]  
where \(f_{D}\left(t\right)\) is Doppler frequency shift on the carrier at time \(t\).  
Given a fixed sampling frequency \(f_{s}\), the time varying code rate induces a change in the number of samples per code chip from period to period. Furthermore, \(f_{s}\) and \(R_{c}\) are not likely to be multiples of each other. The result is that the same chip, in subsequent PRN code periods, might not be represented by the same number of samples. In other words, subsequent sampled chips might not be perfectly aligned, thus threatening the effects of the coherent accumulation of the code periods.  
Ideally, after the accumulation process, an exact representation of the spreading code would be obtained, as depicted by perfect rectangular wave plotted in red in Figure 6. However, in a realistic situation, as shown in blue in Figure 6, the non-perfect alignment of the samples leads to \"transition samples\" at the boundaries of the chips that are wrongly reconstructed by the quantization performed by the sign function.  
Transition samples cannot be avoided, unless a very low value of \(L\) is used, which could, in turn, prevent a correct extraction of the code from the noise. The effect of transition samples over the overall statistic of samples is depicted in Figure 7, where histograms of \(w[n]\) with and without transition samples are reported. Figure 7a clearly shows a tail in the distribution towards zero, due to the transition samples.  
If transition samples are not removed from \(w\left[n\right]\), a residual modulation remains in \(y\left[n\right]\) after the code wipe-off. Their presence can indeed alter the statistic analysis performed to extract scintillation metrics. For this reason, a simple algorithm to discard transition samples has been implemented, discarding both the sample before and after a sign transition. This strategy avoids the definition of thresholds of a multilevel non-uniform quantizer for the removal of the transition samples, which could introduce further artifacts in the signal processing chain.