esa-sceva/satcom-chunk-collection huggingface dataset:
# Piezoelectric Sensor with a Helical Structure on the Thread Core
## 3 Experimental Setup  
Figure 3 shows an experimental setup for applying flexural loadings to the piezoelectric sensor. One end of the piezoelectric sensor was fixed with acrylic brackets, and the opposite end was clamped to a 3D-printed bracket connected to a motor (Dynamixel MX-28, Robotis Co., Ltd., Seoul, Korea), as illustrated in Figure 3a. The free length \(L\) was 50 mm, excluding the clamped parts. In the test for the smart textile, the same setup was utilized, as shown in Figure 3b. Additionally, an aluminum pole (diameter: 12 mm) was laid underneath the sensor to perform bending of the objects by the operation of the motor. The position of the aluminum pole was selected as \(L_{p}=~{}35\) mm. To operate the motor, a controller board (OpenCM 9.04-C, Robotis Co., Ltd., Korea) was utilized. Specifically, the flexural loading had one-sided movement with a bending angle \(\Phi\), as shown in Figure 3c. Herein, we utilized sinusoidal flexural loadings with the peak-to-peak bending angle, \(\Phi_{0}\), ranging from 1.5\({}^{\circ}\) to 30\({}^{\circ}\), and the operating frequency, \(f\), ranging from 0.8 Hz to 3.0 Hz. Specifically, the experimental scope of the operating frequency was determined by considering the frequency range of human motion [39], to show the feasibility of the sensor for biometric applications. A few thousands sinusoidal flexural loadings under the experimental scope were applied to the sensor to test the durability and repeatability of the sensor. We measured the movement and performed tracking to confirm the exact loading angle from the motor. In particular, the movement was recorded at 60 fps using a digital camcorder (XC10, Canon, Japan) and processed to the bending angle using a post-processing tracking program (ProAnalyst, Xcitex). Simultaneously, the sensing outputs were measured using a data acquisition board (DAQ, NI-6211, National Instruments Corp., Austin, TX, USA) and LabVIEW 2017. A low-pass filter having  
Figure 2: Fabrication process of the sensor. (**a**) PVDF film cut in an ax shape, (**b**) FPCB electrode attached to the PVDF film, (**c**) PVDF film curled around a thread with a helical structure, (**d**) sensor coated with a PET film tape, (**e**) sensor covered by six threads, (**f**) sensor woven by a weaving kit, (**g**) schematic of the smart textile, (**h**) picture of the piezoelectric sensor, and (**i**) picture of the smart textile.  
a cutoff frequency of 60 Hz and band-stop filters between 40 Hz and 80 Hz and between 170 Hz and 190 Hz were implemented to remove 60 Hz noise, the coupling effect from the power supply, and the third-order harmonic noise term, respectively [40]. The input impedance of the DAQ board was 10 G\(\Omega\), which was 10 to 40 times larger than the internal impedance of the sensor in our test range. Therefore, we assumed that the measuring condition was close to the open-circuit condition.
## 4 Conclusions  
In summary, we have calculated and discussed the sea water wave propagation over under-sea structures with periodically arranged cylinders deployed on the sea bottom with parametric excitations. Only two dimensionless geometric parameters are required to be adjusted to enhance the wave amplitude. Numerical results show that for each lattice structure, the wave amplitude can be enhanced by changing both the orientation of the incident wave and the geometric parameters of the lattice array. Among the triangular, square and hexagonal lattice structures, triangular and square lattice types can induce larger wave amplitude than the hexagonal one. The predicted wave amplitude enhancement is clearly seen in the reduced-scale water tank experiment. It is very promising that the phenomena can be utilized to extract the sea wave energy. Therefore, we call for an actual-scale nearshore test, which may require a cross-disciplinary collaboration, but no technical difficulty is foreseen. The phenomena can be utilized to extract the sea surface wave potential energy and a real-scale nearshore test is highly desirable.  
Figure 8: (**a**) Water wave amplitude enhancement of the surface wave; (**b**) Comparison of the observed enhancement factor with theoretical results at the driving frequency 0.7 Hz.  
**Supplementary Materials:** The following are available online at [[http://www.mdpi.com/2076-3417/7/8/782/s1](http://www.mdpi.com/2076-3417/7/8/782/s1)]([http://www.mdpi.com/2076-3417/7/8/782/s1](http://www.mdpi.com/2076-3417/7/8/782/s1)). Videos S1a and S1b: experimental result (0.7 Hz); Video S2: experimental result (0.75 Hz).  
**Acknowledgments:** The authors would like to thank [PERSON] for many useful suggestions and discussions. This work was partially supported by the MOST of Taiwan, ROC (MOST 104-2221-E-002-032-MY3) and the National Taiwan University (CQSE 104-R891401).  
**Author Contributions:** [PERSON] conceived and designed the simulations and experiments; all authors performed the study and analyzed the data; [PERSON] wrote the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Biphasic and Linear Fractional Poroelastic Models  
The assumptions at the basis of both the poroelasticity and biphasic theories are the following:  
* The solid phase is incompressible, linear elastic, subject to infinitesimal strain, homogeneous, isotropic (i.e., the material parameters of the solid phase do not depend on the orientation nor the position in the sample) and non dissipative.
* The fluid phase is incompressible, it flows slowly through the pores, it is homogeneous, isotropic and non dissipative, and there is no fluid source.
* The only dissipation comes from the frictional drag due to the relative velocities of the two phases.
* The absence of external body forces (other than those explicitly mentioned for the confined compression tests).
* The isotropy and homogeneity of the permeability tensor, which is then reduced to the scalar \(k\) representing the averaged intrinsic permeability of the sample.
* The permeability \(k\) is a constant parameter (time-independent) in the biphasic model and it is a time-dependent quantity in the fractional poroelastic model (due to the time-fractional Darcy's law described in Equation (1)).
## 2 Demand-Side Management Classifications and Their Roles in Cost Minimization
### Classification of DSM
#### 2.1.2 Load Scheduling
For example, the authors in [68] proposed a DSM technique based on game theory to schedule energy consumption by using a smart pricing scheme. This technique benefits the consumer in terms of providing a price reduction and a reliable energy supply. Moreover, the authors also maximized the social welfare. One of the DSM schemes used by the authors in [86] is the RTP management scheme which has the objective of reducing the consumers' bills and managing the demanded loads of the consumers. The authors presented a possible demand curve of the appliances (PDCA) of different consumers by using the proposed algorithms. Furthermore, the authors implemented the scheme for two different case studies and concluded that the proposed scheme reduces the consumers electricity bills by 8% to 22% in summer. Figure 5: Optimization types and various methods for their solution. In [121], the authors investigated a dynamic energy management scheme based on the use of dynamic prices to reduce the consumers' demands during peak hours and improve the system's efficiency. The authors considered the use of a flexible load, e.g., the heat ventilation and air conditioning (HVAC) and heating systems of consumers, controlled through a proposed control strategy. The control strategy shifts this equipment into off-peak hours based on a dynamic pricing method. The retail price is updated every 15 min from the supplier end. The control strategy compares the retail price with the threshold set by the consumers, and this control strategy is operated through a dynamic demand response controller. The proposed scheme reduces the annual energy costs by up to 9%. DSM Based on Energy Consumption Scheduling (ECS)  
DSM can be carried out through energy consumption scheduling (ECS). Various home energy management technique based on ECS are available in the literature. An optimization based residential load control scheme based on simple linear programming (LP) using the concepts of ECS was proposed in [82; 83]. The authors proposed a mathematical model to represent the residential load control. The proposed scheme works on RTP which requires a price predictor. The price predictor collects information about the prices from yesterday, the day before yesterday, and for the same day in the previous week, and on the basis of this information, it predicts the price per kWh in real-time. The price predictor and ECS device of [82] are shown in Figure 6. The scheme in [82; 83] significantly reduces the peak load which results in the reduction of the PAPR and the demanded load for different scenarios.
# Structural Assessment of Endodontic Files via Finite Element Analysis
## 2 Materials and Methods
### Finite Element Modeling of Physical Tests  
Physical tests described in the previous section are modeled via FEM. In the finite element model development, high-definition representation of the test configuration is emphasized. The computed deformations, stress, and dissipated energy are compared to the available data reported in the literature, as presented in the following sections. In addition, idealized versions of the models are also developed to reduce computational time and improve convergence behavior. Simulations are performed in Abaqus [17]. Since exactly the same methodology is used for simulations involving F1 and F2 endodontic files, the details of EEA model development are only reported for F2 endodontic file.  
Material properties of conventional endodontic files (those without special heat treatment) are considered for the simulations. In particular, properties measured by [PERSON] et al. [12] from tensile tests of 1 mm Nitinol wires are used (Table 1).  
The built-in shape memory alloy (SMA) material implementation in Abaqus is based on [PERSON]'s formulation which defines the austenite, martensite, and transformation phases [17]. This implementation also accounts for the effect of temperature [18; 19], represents the inner hysteretic loop effect and accurately captures the maximum change in tensile strain, which is essential for accurate prediction of the fatigue life of a shape memory alloy [20].
# Computer-Aided Surgical Simulation for Correcting Complex Limb Deformities in Children
## 1 Introduction  
Computer-aided surgical simulation (CASS) is becoming an increasingly important means of improving skills acquisition, optimizing clinical outcomes, and promoting patient safety in orthopaedics and traumatology [1]. The combination of computer-aided design (CAD), three-dimensional (3D) printing technology, and mixed (virtual/augmented) reality is emerging as the cutting edge, opening new horizons in surgery. While there have been great strides in other fields of surgery [2], CASS is in its relative infancy within paediatric orthopaedics (PO). PO often deals with the surgical treatment of congenital or acquired limbs and spine deformities during infancy. PO surgery aims to restorefunction, improve aesthetics, and ensure proper residual growth of limbs and spine, using osteotomies, bone grafts, age-specific or custom-made hardware and implants. Despite the fact that several bone deformities are simple and their treatment is straightforward, complex rare deformities are frequently encountered in PO. Complex deformity is defined as occurring in at least two planes (e.g., shortening, angular, and rotational deformity), sometimes involving two or more bones (i.e., forearm deformities that involve both una and radius). This type of surgery usually requires extensive preoperative planning, because of the complex 3D anatomy of the axial and appendicular skeleton. Growing evidence has proven that CASS may increase performance, precision, and efficiency of surgeons, thus, decreasing complications and adverse outcomes, and driving down costs for both patients/families and health care system [3; 4; 5; 6; 7]. However, CASS is often considered as costly, time consuming, and the prerogative of a few specialized centers that have facilities and trained staff for it, thus, discouraging its use among surgeons. These limitations have recently motivated the use of open-source software and user-friendly low-cost technology [8].  
This work summarizes the preliminary results of a CASS process that was recently implemented at a tertiary referral center for paediatric orthopaedics and traumatology, through a close collaboration with a university department of industrial engineering. We aimed to assess the feasibility and effectiveness of an in-house low-cost CASS process in paediatric orthopaedic surgery.
# Quantum Computing Approaches for Mission Covering Optimization
## 1 Introduction
### Quantum Solutions to MCO
#### 1.1.1 Quantum Annealing (QA)  
Quantum Annealing is a quantum optimization methodology performed on a specific implementation of quantum computers such as D-Wave quantum annealing machines. It is an adiabatic quantum computing technique that capitalizes on a unique feature of quantum mechanics, e.g., quantum tunneling, which is the capability to surf and dive through an energy landscape until it hits a minimum energy level [14]. This property of quantum mechanics is engineered to solve Quadratic Unconstrained Binary Optimization problems [15]. QUBO is a type of optimization problem where the solution is a binary vector that minimizes an objective function described with terms up to a quadratic degree. These problems are unconstrained, but there are methods to incorporate the behavior of constraints [16] into the cost function. To encode an MCO onto a quantum annealing machine, we must translate the objective function into a QUBO.
## 4 Experiments
### Performance Comparison
#### 4.4.1 Comparative Experiments on the BCDD Dataset  
We display the quantitative experimental results of various algorithms on the BCDD dataset in Table 4. The results in the table reveal that our algorithm achieved 93.84%, 91.59%, 92.70%, 86.40%, and 99.32% for the precision, recall, F1 score, IoU, and OA, respectively, which were higher than all the other methods and 3.3%, 5.56%, and 0.2% over the second-best method on the main metrics of the F1 score, IoU, and OA, respectively. The highest precision and recall also indicate that our model is more robust compared to the other methods. The above results proved the method in this article surpasses these comparative methods. Figure 7 illustrates the visualization results of the comparative experiments performed on the BCDD dataset. For easier observation, the \(TP\), \(TN\), \(FP\), and \(FN\) are marked in the figure with white, black, red, and green, respectively. It is obvious that our model can avoid the \(FP\) and \(FN\) more effectively than other methods. The first and third rows show that the comparison methods had poorer detection accuracy for the change edges, leading to boundary misses or misdetections, thus making the boundary more blurry, while our method detected clearer boundaries, probably due to our TA module, which can efficiently extract the spatial relationships between bitemporal features. As viewed from the second and fourth rows, the influence of the land cover and color around the building in the bitemporal images caused the comparison methods to easily detect the non-changingareas as changing areas. By contrast, the proposed method in this paper circumvented this drawback. This is mainly due to the MFA module. By increasing the receptive field, the MFA module can obtain more global feature relationships and enhance the extraction of semantic information, thus reducing the influence of the pseudo-changes on the CD results. Moreover, due to the use of the dilated convolution and strip convolution, our method is superior to the second-best model in terms of the number of parameters.
## 1 Introduction
It is difficult to exactly determine the equations that define the geometric shape described by the beam. The greater the accuracy of the equations, the greater the precision of the solution. In this research, the geometric shape described by the beam has been approximated by the equations of a circumference and an ellipse. The first approximation is fast but less accurate, and the second approximation is slow but more accurate. Figure 2: Representation of the different geometric shapes that the beam describes depending on the wedge angle of the prisms. The organization of the paper is as follows: Section 2 presents the method that offers an inverse solution for Risley prisms. Section 3 shows the results obtained from a time and accuracy analysis of the developed method and compares the two approximations of the method with each other and with other methods developed by other authors. Conclusions are drawn in Section 4.
## 3 Flow-based optical detection
### Spectrophotometry
This method employed the sample/reagent intermixing by combining binary sampling with flow reversal. The homogenization was improved even with highly viscous solutions. Recently, the solenoid micro-pump controlled by microprocessor has been introduced as the flow propelling system and used to replace the peristaltic pump. [PERSON] et. al. [91] have proposed the method using a multicommutated flow system for the spectrophotometric determination of paracetamol in pharmaceutical formulations. They used the pinch solenoid value which functioned simpler than the conventional three-way solenoid valve and two solenoid micro-pumps to employ the multicommutation system resulting in the reduction of reagent consumption and waste generation. This method had a linear range of 5.0 to 125.0 mg L-1 with a detection limit of 0.4 mg L-1. Multisyringe FIA was interfaced with in-line gas-diffusion to determine volatile analytes such as sulfides [92]. They used the gas-diffusion method for the separation of volatile and gas evolving species from the solid and liquid matrices in conjugation with a multisyringe flow injection system. The method based on the analyte releases hydrogen sulfide from the donor channel of the gas-diffusion module and reacted with N,N-dimethyl-p-phenylene diamine in the presence of Fe(III) or Fischer's reagent to form methylene blue against the oxidative coupling. This species was downstream to the detector. The implementation of MSFIA was accompanied with spectrophotometric detection for the determination of iron and boron in soil extracts [93]. The sample was directly introduced into the flow system where the pH was controlled. Both analytes were determined based on the formation of ferroin complex (for iron) and azomethine H-reation (for boron) in the same configuration but by changing the reagent in the syringes. complex formation of 2-5(-bromo-2-pyridylazo)-5-[N-n-propyl-N-(3-sulfopropyl)amino]aniline (5-Br-PSAA) with copper(II) and/or iron(II) at pH 4.6. Copper reacts with 5-Br-PSAA to form a complex while the iron was only complexed with 5-Br-PSAA in the presence of reducing agent. The large amount reagent blank was suppressed using spacer, which contained 5-Br-PSAA. In the system, there are two determination zone from holding coil 1 give the first peak and holding coil 2 give the second peak. Manifold of the SIA system is shown in figure 4.
## 5 Discussion
### Limitations and Future Work  
There are still limitations to the current work. On the one hand, it relies on only three sets of experimental data to extract ionospheric scintillation errors and perform SAR imaging. However, the ionospheric scintillation is related to local time, season, solar activity cycle, geomagnetic activity, and other factors. The study of its mechanism is a very complex research subject, which is beyond the scope of this paper. Based on the characteristics of dual-channel receiving, this paper focused on the compensation of the Doppler phase error introduced by ionospheric scintillation in the GNSS-BSA system. Therefore, more experiments and mathematical statistical analysis need to be carried out in the future, which may provide helpful references for the study of ionospheric effects. On the other hand, the employed BP algorithm has high computational complexity. Speeding up the data processing efficiency for large-scale imaging is also an issue that will continue to be studied in the future. Finally, it is worth mentioning that Figure 20 demonstrates the use of the incoherent accumulation method for preliminary image fusion. The results illustrate the natural potential of GNSS-BSA in multi-source fusion, which is also our possible future research direction.  
Figure 21: Incoherent accumulation results of three SAR images (after phase error compensation).  
Figure 22: Overlapped result of the focused SAR image and the optical image.
### Types of Antenna Systems Used in Underground Mining
#### 1.2.1 Reconfigurable Antennas  
Reconfigurable antennas are those that can be adjusted in real time to maximize performance in response to changing communication requirements. These antennas may adjust their frequency, radiation pattern, polarization, and other factors, making them extremely versatile and adaptable to many communication settings [26; 27; 28]. [PERSON] et al. [29] investi  
Figure 1: Radiation pattern of different antenna systems, where denser colors represent higher radiation intensity and lighter colors indicate lower radiation intensity.  
gated the performance of pattern re-configurability in a rich multipath environment using a reconfigurable and dipole antenna in a hard rock underground mine. The results suggest that re-configurability achieves between a 20% and 34% improvement in path loss and delay spread compared to fixed-beam antennas in NLOS propagation.