esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Tissue engineering represents a novel approach that aims to exploit the use of biomaterials composed mainly of scaffolds, cells (or grafts), and growth factors capable of restoring a specific tissue. Biomaterials represent the future of dental and oral regeneration due to their biocompatibility and affinity with the receiving site. The aim of this review was to collect results and considerations about a new type of biomaterial based on the use of micrografts in combination with different scaffolds. Micrografts are tissue particles enriched with progenitor cells (PCs), which are defined as descendants of stem cells that can differentiate into specialized cells belonging to the same tissue. PCs in the oral cavity might be extracted from various tissues such as dental pulp, periosteum, or periodontal ligament. Moreover, these cells are easy to isolate through a mechanical process that allows for the filtration of cells with a diameter of 80 \(\upmu\)m, in contrast with enzymatic procedures where reagents are used and various culture periods are needed. The aim of this review was to collect data regarding the use of micrografts, developed by a Rigenera(r) chair-side machine, in oral regeneration evaluating the clinical, histological, and radiographical outcomes. There have been encouraging results for the application of micrografts in bone and periodontal regeneration, but further randomized clinical trials are needed to validate this promising outcome.  
micrografts; oral regeneration; Rigenera; stem cells; bone graft; periodontal regeneration figure  
cells belonging to the same tissue. The process from isolation through culture is still at a crucial point. The most commonly used protocol for isolation of cells is the enzymatic process which utilizes the application of different chemical solutions to extract stem cells and requires an incubator and cell storage. Obviously, this process is not suitable in clinical practice where time and handling are crucial for clinical outcomes [11]. A recent protocol proposed by [PERSON] et al. and later by [PERSON] et al. [12; 13], using the Rigenera(r) micrografting technology, suggests the use of a new type of autologous graft (micrograft) that involves a chair-side mechanical disaggregation device and utilizes PCs derived from various sites such as the periosteum or dental pulp. Such cells can be obtained from a tissue fragment a few millimeters in length that is harvested directly during the intervention phase, even from the same surgical site, and can be used without any manipulation or cell culture. A microgrid might be compared to an autologous graft due to the presence of MSCs that are able to enhance the healing process and regenerate the damaged tissue [14; 15]. Considering the limitations of autologous biomaterials, such as their availability and quantity, and furthermore considering the osteoconductivity of xenografts, the use of micrografts leads to a greater regenerative potential. The aim of this review was to summarize the evidence regarding the use of micrografts in oral regeneration considering human studies.
## 5 Conclusions  
In this paper, a single-pixel non-scanning 3D imaging system was designed, which combines compressed sensing technology with phase method laser ranging technology. It can use a single-pixel detector to achieve 3D imaging of a target. Parametric simulation studies and actual imaging experiments prove that the Hadamard measurement matrix and the TV reconstruction algorithm produce better imaging results at the same sampling rate. In the future, we will attempt to increase the laser modulation frequency and imaging resolution to further improve the measurement accuracy and imaging resolution. This system will have even wider application prospects in the field of 3D imaging  
Figure 11: Three-dimensional image reconstructed by three reconstruction algorithms when the sampling rates are 0.8, 0.6, 0.4, and 0.2. (**a**) BP algorithm; (**b**) OMP algorithm; (**c**) TV algorithm.  
**Author Contributions:** [PERSON] conceived the method, designed the experiments, and revised the manuscript; [PERSON] performed the simulations and experiments, processed the data and wrote the original manuscript; [PERSON] provided the experimental funds and reviewed the manuscript; [PERSON] provided assistance to the experiments and proofread the manuscript. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the National Natural Science Foundation of China under Grant No. 51505113, the Zhejiang Provincial Natural Science Foundation of China under Grant No. LZ16E050001, and the State Key Laboratory of Precision Measuring Technology and Instruments Project under Grant No. PIL1601.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# The Effect of Non-Transferred Plasma Torch Electrodes on Plasma Jet: A Computational Study
### Working of RTC Non-Transferred Plasma Torch  
This study analyzes a plasma torch equipped with a rod-type cathode and an anode. In non-transferred plasma torches, both electrodes are contained within the torch, so the electric current does not flow to an external workpiece [23]. Thermal plasma is generated when the working gas interacts with the electric arc, and then the working gas ionizes. Collisions between gas molecules and electrons produce positively and negatively charged  
Figure 1: Schematic diagram of (**a**) rod-type cathode (RTC) and (**b**) well-type cathode (WTC) non-transferred plasma torches.  
particles, forming an electrically conductive plasma. These interactions also release heat, creating a sustained arc [24].  
Figure 1a illustrates the internal layout of a DC non-transferred plasma torch with a rod-type cathode, showing the cathode, anode, and working gas flow. The current is applied at the tungsten cathode tip, establishing a voltage difference relative to the copper anode. This voltage drives the formation of an arc. A working gas, such as nitrogen, oxygen, carbon dioxide, or argon, is introduced near the cathode to elongate the arc and generate a high-voltage environment.  
The current in the arc induces Joule heating, raising the local temperature and ionizing the gas. The resulting plasma expands due to heating, accelerating the gas and forming a high-velocity plasma jet. Additionally, interaction between the current and its magnetic field generates a Lorentz force, which drives the jet toward the outlet.
## 2 Materials and Methods
### RR Interval Series  
In this study, RR intervals were extracted from the patients' Electrocardiogram (ECG) data sets available on PhysioNet ([[https://physionet.org/](https://physionet.org/)]([https://physionet.org/](https://physionet.org/))). These were used as the duration of consecutive heartbeats to simulate AF and normal SR [25] over a ten minute period. The distribution of RR intervals during AF can be unimodal, bimodal or multimodal [5] depending on the average heartbeat duration over a certain period [26]. Unimodal and bimodal RR interval distributions for AF were taken from a long-term AF database, as given in [27]. The data in this database was digitised at a sampling rate of 128 Hz at 16 bit resolution. The signals were band-pass filtered utilising a cut-off frequency at 1 and 50 Hz to avoid power line interference [27]. RR interval data from three different subjects was used in the simulations. The mean of the RR intervals simulating normal sinus rhythm (SR) was 0.828 s with a 0.051 s standard deviation and 0.006 coefficient of variation. The mean of the RR intervals simulating AF with a unimodal distribution was 0.512 s with a 0.106 s standard deviation and 0.207 coefficient of variation, whereas the mean RR interval duration simulating AF with a bimodal distribution was 0.884 s with a 0.260 s standard deviation and 0.294 coefficient of variation in the cardiovascular system model. The RR intervals and frequency histograms of the normal SR and AF data used in the simulations is given in Figure 1.
## 1 Introduction  
Light waves are essentially represented by complex amplitudes. Therefore, if we can display a complex amplitude hologram on a spatial light modulator (SLM), we can reconstruct perfect light waves. Complex amplitude holograms can be displayed by using two SLMs [1], or by dividing the displaying areas of a single SLM [2; 3]. However, in the former, it can be difficult to alight two SLMs precisely, and the latter sacrifices the spatial bandwidth product of an SLM.  
As there is no single spatial light modulator that can display complex amplitudes, we must convert the complex amplitude holograms to amplitude, binary, or phase-only holograms [4]. The reconstructed images from such holograms are degraded because of noises. To address this problem, deterministic methods have been proposed to encode to amplitude, binary, or phase-only holograms [5; 6; 7; 8; 9]. Although noniterative methods are computationally effective, they decrease the spatial bandwidth product of the SLM because of the use of superpixels and the spatial multiplexing of two holograms.  
In addition, iterative optimization algorithms have been proposed [10; 11; 12; 13; 14; 15]. A representative algorithm is the Gerchberg-Saxton (GS) algorithm [16]. The GS algorithm has a wide range of applications; for example, the measurement of unknown complex-valued objects from diffraction patterns or holograms; vortex beam generation; multi-spot generation; and the optimization of holograms that can display high-quality reconstructed images. We then focus on the hologram optimization for display purposes. The GS algorithm iteratively calculates the light propagation and back-propagation between the object plane and the hologram plane to optimize the hologram. During this iteration, some constraints, based on prior information, are imposed on the hologram plane and object plane. One of the GS-based algorithms uses a blank area to help the convergence of the optimization [10; 11; 12; 14]. However, the calculation time and memory usage increase.  
In this study, we propose the reduction of computational complexity and memory usage for iterative optimization using scaled diffraction that can calculate light propagation with different sampling pitches on a hologram plane and object plane. The scaled diffraction can introduce a virtual blank area without using physical memory. We demonstrate that the proposed method can reconstruct a better complex amplitude from an optimized hologram. We further propose an algorithm that combines scaled diffraction-based optimization with conventional methods. The combination algorithm improves the quality of the reconstructed complex amplitude while accelerating iteration optimization.
## 3 Results
### Identification Results of Different Methods  
Table 6 presents the classification results compared with SVM, KNN, DeepLabV3+ [33], and Unet++ [34], in which the distance of KNN was the Euclidean distance. The kernel function of SVM is the Gaussian kernel, and the optimal values of the penalty parameter and \(\gamma\) were obtained using the network-searching algorithm and the 10-fold cross validation method, which were 32 and 0.005. The learning rate in the training process of DeepLabV3+ and Unet++ was 0.0001, the value of the batch_size was 8, the value of momentum was 0.9, and the number of iterations was 300. Compared with the other methods, the identification results of our method in terms of identifying fruit cracking and rust spot disease were the highest, with an average OA of 92.06% and 88.98%, respectively. Compared with deep learning methods, the average time costs of SVM, KNN, and our method are obviously smaller, and the three are similar. Though the identification results of DeepLabV3+ and Unet++ are also relatively high, their average time costs are much higher at 126.46\(\sim\)265.65 s. As the datasets in this study were relatively small, they are not suitable for deep learning models. Therefore, the insufficient number of samples also affects the deep learning results. The experimental results show that the method proposed in this paper can effectively identify disease from the nectarine images and that the reconstructed fruit cracking images are better than those of rust spot disease.
## 4 Experimental Results
### In Vivo _Experiments_  
Figure 20 shows the system mounted on a rat for _in vivo_ signal acquisition. It consists of two parts, a wireless neural recording headstage and a backpack. To limit disturbances collected from the environment, a small board (\(3\times 3\) cm\({}^{2}\) and \(4.5\) g of weight) is directly connected to a \(16\) channel microelectrode array (Tucker-Davis), with an impedance in the \(20\)-\(60\) k\(\Omega\) range at \(1\) kHz, implanted into the somatosensory cortex of an adult rat. The board includes the packaged chip and \(10\) external small surface-mount components (\(0805\) or \(0603\) footprint): the VCO inductor, the power-amplifier resonant filter components (\(2\) capacitors and \(1\) inductor), the \(20\) MHz quartz, the PLL loop-filter (\(1\) resistor and \(2\) capacitors) and \(2\) decoupling capacitors between power supplies. The antenna is a quarter-wavelength whip antenna: it consists of a piece of wire about \(17\) cm long, easily placed along the back of the rat. The backpack has a weight of \(40\) g and includes two AAA batteries with \(1000\) mA/h capacity that may allow neural activity recordings for more than \(100\) hours. The two batteries are connected via three \(10\) cm long wires to the headstage. At the beginning of the _in vivo_ experiment, the quality of the signals from the implanted electrodes was analyzed using a commercial acquisition system by recording the raw signals and noise from the same \(16\) electrode channels. The recorded traces featured a noise of about \(10\)\(\mu\)V\({}_{rms}\) on all channels. The threshold of the digital peak processor was set to \"\(x1000000\)\" and the gain of the overall amplifying chain to the maximum value (\(83\) dB) since the peak-to-peak spike amplitude was always lower than \(100\)\(\mu\)V. Thus, the digital threshold corresponds to an input-referred amplitude of about \(\pm 30\)\(\mu\)V, _i.e._, \(\pm 3\) times the rms noise. The high-pass cut-off frequency of the front-end amplifiers was set to about \(300\) Hz to properly reject the low-frequency signals and the power-line noise, enabling correct spike detection. The receiver dipole antenna has been placed at two meters from the rat while neural activity was recorded. Figure 21 shows a single trace recorded during the experiment and two spike waveforms extracted from the data stream, while in Figure 22 a large number of spikes recorded on the same channel are shown aligned in time.  
**Figure 20.** Arrangement for _in vivo_ measurements. Note the headstage, the battery backpack and the whip antenna placed along the back of the animal.
## 6 Conclusions  
This study approaches the problem of efficient channel modeling for the adverse and erratic environment of underground coal mines. It reveals that different areas of an underground mine need to be characterized separately for electromagnetic signals. It is very important to efficiently model the entire environment of a mine for the development of a stable wireless network. The areas of a mine are classified into main gallery and sub-galleries, and a hybrid model scheme is used for channel modeling of complex mines. The EM wave propagation characteristics are defined by the multimode model, and field intensities are estimated with the aid of the GO model. The received power and various evaluating parameters are computed to examine the effectiveness of the suggested model. The complex segments and branches of the mine are classified by defining the line-of-sight, non-line-of-sight, and partial-line-of-sight zones for managing the optimized signal strength in every zone of an underground mine. The environment of the mine rapidly changes due to mining activities, and the materials that occupied the mine are a big source of deterioration for wireless communication. A detailed evaluation of these degrading elements is conducted in this research, which elaborates that the irregular structure of a mine, the roughness of its boundary walls, the dielectric medium of a mine, and equipment used for mining applications cause major disruption for signal propagation and network decay. Operating frequency is the most important parameter in a wireless network and, after a detailed examination of 2 GHz to 5 GHz band frequencies in different zones of the underground mine, it can be concluded that a lower-frequency signal in the UHF band attains low path loss, but captures high refraction loss and roughness loss. The overall performance of the 2.4 GHz frequency is much better than the 5 GHz frequency in the harsh environment of underground coal mines.  
Conceptualization, F.J.; Data curation, M.U.S. and I.A.; Formal analysis, A.W., M.U.S. and A.H.; Investigation, M.U.S.; Methodology, F.J., A.H. and I.A.; Project administration, A.W.; Software, A.H.; Supervision, A.W.; Writing--original draft, F.J.; Writing--review and editing, I.A. All authors have read and agreed to the published version of the manuscript.  
This research is funded by Natural Science Foundation of China, 'Theory and method of marine communication channel identification and model optimization', Project No. U19B2015.  
Not applicable.  
Figure 11: Cumulative distribution function of shadow fading.
# Variable Structure PID Controller for Satellite Attitude Control Considering Actuator Failure
## 4 Discussion
### Comparing Group  
In order to demonstrate the performance of the proposed controller, the following standard PD controller was compared.  
\[\mathbf{u}=-k_{d}\mathbf{\omega}-k_{p}\mathbf{q}_{v} \tag{41}\] \[k_{d}=10,k_{p}=5\]  
The simulation results are given as follows.  
Based on the simulation results, it could be found that the system is stable under a standard PD controller. Based on Figures 1 and 2, system convergence time is about 120 s, and steady accuracy is about \(6\times 10^{-4}\) rad/s of angular velocity and \(1.5\times 10^{-3}\) of attitude quaternion. (Generally in a satellite attitude control issue, when the system state approaches its equilibrium point, i.e., \(\varphi\approx 0\), we have \(\|q_{v}\|\approx\|\sin\varphi/2\|\approx\varphi/2\). Hence, in this condition, the steady accuracy of the Euler angle is about \(3\times 10^{-3}\) rad, i.e., 0.17 degree, and in later text, this derivation will be omitted.)  
Figure 1: Curve of angular velocity under a standard PID controller.  
The finite time controller proposed from [20] was also compared. The simulation results of [20] are given as follows.  
Based on Figures 3 and 4, it could be found that the system convergence rate is about 80 s, which is largely improved compared to a standard PD controller. Steady accuracy is about \(6\times 10^{-6}\) rad/s of angular velocity, \(1\times 10^{-7}\) of attitude quaternion, and \(10^{-5}\) degree of the Euler angle. It could also be found that the system has a high frequency issue near the system equilibrium point. The advantages of this method could be concluded as a fast convergence rate and a robustness to model uncertainty, and these were claimed in reference [20].  
Figure 3: Curve of angular velocity under a finite time controller from [20].  
Figure 2: Curve of attitude quaternion under a standard PID controller.
## 5 Experiment
### Experimental Results  
In the experiments, the 3-DOF hydraulic manipulator was used to verify the proposed algorithm. The system parameters were supposed to be well known and are presented in Table 1. The desired trajectory is described by the dot-line. The dash-dot line, the solid line, and the dash-line represent the tracking performances of the PD controller, SMC controller, and the NNSMC controller, respectively. The parameters of the PD and SMC controller were manually set and fine-tuned such that the best performance could be achieved, whereas the parameters of the NNSMC are initialized with small values to verify their adaptability. The desired trajectory was designed as:  
\[\left\{\begin{array}{ll}q_{1d}=30\sin(^{2 nt}/5)&(\mathrm{degree})\\ q_{2d}=20+20\sin(^{2 nt}/5+\
icefrac{{\pi}}{{2}})&(\mathrm{degree})\\ q_{3d}=40+20\sin(^{2 nt}/5)&(\mathrm{degree})\\ \end{array}\right.. \tag{45}\]  
Figure 11: The hardware setup for the 3-DOF manipulator.  
The comparison in tracking performance and its error of the proposed algorithm with the conventional controllers are depicted in Figures 12 and 13, respectively. The frequency was set at 0.2 Hz to verify the workability of the proposed algorithm. As can be seen in Figure 13, as with the simulation results, the tracking error from the NNSMC was the smallest. However, the slope of the sliding surface of each element reached larger values compared with the simulations due to existing modeling errors inside the actuators. In addition, as opposed to the conventional SMC, the control signals of the NNSMC were modified by adding an offset term \(K_{o}\) to compensate the variations or uncertainties of the system. However, this parameter could not be set to a large value and its learning rate had to be kept at a small value because the large value could affect and degrade the performance of the system.  
Compared to the simulation results, the robust gains depicted in Figure 14 changed very quickly at the beginning and tended to be a large value due to the uncertainties. The slope of the sliding surface was changed by tuning the weighting of the two variables \(e\) and \(\dot{e}\). The first value of the state trajectory could be anywhere depending on the initial value of the system. Then adjusting the slope of the sliding surface at the same time with tuning robust term could drive the system error convergence to zero (ideal case) or a boundary quickly. The adaptive slopes of the three joints are presented in Figure 15. The profiles of the adjusted parameters in the experiment result were quite similar to the simulation due to uncertainties or disturbance existing in the real mechanical structure.  
Figure 12: Comparison of the tracking performance of the three joints: (**a**) first joint, (**b**) second joint, (**c**) third joint.  
Figure 14: The adaptive gains: (**a**) Robust gain K, (**b**) \(\alpha\), (**c**) offset gains of the \(i\)th joint, where \(i=1,2,3\) with respect to the first joint, the second joint, and the third joint.  
Figure 13: Comparison of the tracking error of the three joints: (**a**) first joint, (**b**) second joint, (**c**) third joint.
## 2 Related Work
### Approaches Based on Polynomial Fitting  
[PERSON] [21] performs a least-squares fit of a quadratic polynomial to a low-pass version of the input image. The approach obtains saddle points from the polynomial coefficients and is invariant to affine transformations. [PERSON] [22] computes intermediate values in the Harris-corner-like detection phase to obtain a second-order Taylor expansion of input image, saddle points are found based on a corner model restricted to orthogonal corners. [PERSON] [23] proposes an edge-based nonlinear corner localizer. The localizer performs a least-squares fit of a parametric edge model to an edge version of input image. [PERSON] [24] develops a modified strategy inspired by the method mentioned in [21]. A corner is refined to sub-pixel accuracy by filtering the adjacent region around it using a 2-D cone filter for an intensity surface amenable to fitting a quadratic polynomial. On the premise of selecting a reasonably small neighborhood, these approaches can yield a suitable approximation in the presence of nonlinear distortions and projective transformations, due to the affine invariance [25]. However, they require a filtered version of the input image for polynomial fitting, because their corner models are not accurate enough for direct processing, which still need to be optimized for improving reliability and efficiency of sub-pixel localization.