esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Genome Editing for Autosomal Dominant Retinitis Pigmentosa
### Rpph2  
The peripherin-2 gene, _PRPH2_, also known as retinal degeneration slow (RDS), participates in the formation of the photoreceptor outer segments. It produces a transmembrane glycoprotein, which is located at the rim of the discs and it is essential for their biogenesis and stabilization [82]. _PRPH2_ is the most clinically heterogeneous gene among all non-syndromic IRD-causing genes. More than 180 mutations have been identified, causing autosomal dominant RP, macular degeneration and cone-rod dystrophy (Figure 4). Of these mutations, at least 50 are linked to adRP and account for 5%-10% of cases [83]. Some of this variability may result from modifier genes situated at another locus, one of which is considered to be _ROM1_[84]. It has been suggested that digenic RP can arise when the p.Leu185 Pro mutation of _PRPH2_ is inherited with heterozygous mutations in the gene _ROM1_[85; 86].  
As can be expected, not all _PRPH2_ mutations have the same mode of action. Some of the mutations that cause adRP have a clear haplonsufficiency effect [82; 87]. An example is the p.Cys214 Ser (C214S) mutation, which is equivalent to a null allele, as the mutant protein is unstable and degraded. This is supported by the fact that the mouse \(Rds^{+/-}\) model, which also has one null _Prph1_ allele, presents with a disease phenotype. This phenotype was rescued by expression of a wild type _Prph2_ transgene in rods and cones [88]. Other mutations in _PRPH2_ exhibit more complicated mechanisms of action. There are cases of gain-of-function mutations that behave in a dominant-negative fashion, such as the p.Pro216 Leu (P216L) mutation. It has been shown in vivo that the amount of mutant P216L protein is only 8% of the wild type, and that it promotes degradation not only of the mutant but also of the wild type protein. The resulting low levels of protein lead to a phenotype similar to haplonsufficiency [89]. The retina is highly sensitive to the _PRPH2_ levels, which need to be approximately 60%-80% of the normal levels [88; 89].  
It has been suggested that in the case of loss-of-function mutations, such as C214S, a gene augmentation approach can be appropriate [90; 91]. However, the fact that the levels of _PRPH2_ need to be finely tuned makes this approach challenging [82]. Furthermore, gene augmentation is probably not sufficient for treating gain-of-function mutations with dominant-negative effects [82]. Instead, the safest approach would probably be targeted gene therapy by either gene invalidation or gene correction.
# Multi-Robot Formation Control Based on CVT Algorithm and Health Optimization Management
## 4 Experiment and Simulation
### Multi-Robot Formation Control Simulation Experiment
#### 4.1.3 Straight Line Formation Simulation and Results Analysis  
On the basis that the density function is a constant, by adjusting the density function to a V-shaped function, the linear formation of the robot can be completed. The V-shaped function equation is given in Equation (27). The density function distribution is shown in Figure 7a, and the resulting linear formation is shown in Figure 7b.  
In the linear formation process, the robot still needs to complete the Voronoi division under the constant density function distribution first. In the iterative process of the CVT algorithm, the controller is affected by the integral saturation to produce an overshoot. When the number of iterations \(k=30\) to \(40\), the cost function increases from \(3.45\)\(\times\)\(10^{3}\) to \(3.65\)\(\times\)\(10^{3}\). After separating the integral control process, the controller tends to a stable state again, and the cost function value decreases again. When \(k=140\), the cost function is \(3.5\)\(\times\)\(10^{3}\), and the CVT algorithm converges under the constant density function distribution. At this time, the density function was adjusted to a V-shaped function, and the optimal solution was reached again when the number of iterations \(k=180\), as shown in Figure 7d. In Figure 7c, when the number of iterations \(k=140\), the position of the center of mass changes due to the switching of the density function. It can be seen that the position error between the robot and the corresponding generating point has increased significantly. When the CVT algorithm reconverges, the position error tends to zero.  
\[\sigma(x,y)=\exp^{-\delta(ax+by+c)^{2}} \tag{27}\]  
Figure 6: Multi-robot diamond formation simulation: (**a**) density function distribution; (**b**) diamond formation; (**c**) position error; (**d**) cost function.
# The Effect of Exposure to an Electromagnetic Field on Entomopathogenic Fungi
## 2 Materials and Methods
### Electromagnetic Field  
The electromagnetic field was produced by a specially designed generator, emitting radio frequency electromagnetic waves (Global System for Mobile Communications: 900/1800 MHz). The 900 and 1800 MHz frequencies were chosen in this experiment because they are used in mobile communications systems and were examined in previous studies [5; 34] which proved that the electromagnetic field of these frequencies has a significant effect on living organisms (vertebrates), showing a stimulating, inhibiting, or no effect on them. However, the effect of these EMF frequencies on entomopathogenic fungi had not yet been studied.  
The EMF emitter used in the experiment allowed emissions of electromagnetic fields identical to those generated by mobile communications (Global System for Mobile Communications: 900/1800 MHz). The device consisted of a generator (maximum emission power of 330 mW) emitting electromagnetic waves, a controller, a power supply, and a cylindrical transmitting antenna (indoor 8 dBi, omni ceiling antenna bands: GSM-1800 MHz--a linear polarized antenna produced by IAPT, Tarnow, Poland). The generator was computer-controlled via a USB interface. The software made it possible to select the strength of the generated signal and generator operation time. Petri dishes with inoculated fungi were placed in an area where electric fields ranged from 4.23 V/m to 6.25 V/m (\(\pm\)0.01 V/m) and magnetic fields from 0.010 A/m to 0.014 A/m (\(\pm\)0.001 A/m). The power density fluctuated between 0.090 W/m\({}^{2}\) and 0.110 W/m\({}^{2}\) (\(\pm\)0.001 W/m\({}^{2}\)), and the frequency was 1800 MHz. The electromagnetic field parameters were measured on the surface of the Petri dishes using a Tenmars TM-195 3-axis meter (Temmas Electronics, Taipei, Taiwan), which enables, among other things, isotropic measurement of the intensity of electric fields (0.01-20.0 V/m) and the intensity of magnetic fields (0.1-532.6 mA/m), as well as power density (10.0-106.94 mW/m\({}^{2}\)) under frequencies of 50 MHz to 3.5 GHz. During the experiment, the wave generator antenna was at a constant distance of 24 cm above the dishes. The electromagnetic field was emitted continuously. The dishes were exposed to an EMF for seven days. In the control, the dishes were placed out of range of the electromagnetic field under the same growing conditions: in the dark, with a temperature of 22 \({}^{\circ}\)C. After seven days, all dishes were placed in the same conditions as the control. Before and during the experiment, the authors measured the so-called background electromagnetic radiation, which includes natural electromagnetic fields. This procedure was aimed at determining whether any disturbances in the field were present at the measurement location that could potentially influence the obtained results. Throughout the entire experiment, no significant disturbances in the background electromagnetic radiation were detected.
# Detection of Periapical Lesions on Panoramic Radiographs Using Deep Learning
## 2 Materials and Methods  
### Dataset and Preprocessing  
This retrospective diagnostic cohort study was conducted after obtaining approval from the Institutional Research Board (IRB) of the University of Sao Paolo. The approved protocol was given the Ethics Committee number 3.239.265. We collected 713 panoramic radiographs (width: 2879, height: 1563 pixels) from patients treated by Dr [PERSON] and Dr [PERSON] using a panoramic imaging system (Cranex D, Soreck, Tuusula, Finland) set-up with the following parameters: 85 kVp, 10 mA, exposure time 17.6 s,CCD sensor size 48 micrometer, and focal spot size 0.5 mm. We de-identified patients' information by cropping the panoramic radiographs to size 2250, 1000 pixels) to include only the dental radiograph and exclude patient information registered on the image. We excluded images with unacceptable quality, containing severe artifacts, and radiographs of mixed dentition and edentulous patients. Three examiners ([PERSON], [PERSON], and [PERSON]) independently annotated the Periapical Root Areas (PRAs) as having Periapical Lesion (PL) or not having periapical lesion (Healthy (H)) in duplicate; a fourth examiner ([PERSON]) settled discrepancies between examiners. At the time of the conduction of the study all above-mentioned examiners had more than 15 years of clinical experience. The examiners were calibrated on 10 OPGs to address discrepancies between them, and then, recalibrated using 20 OPGs and a Kappa index for inter examiner agreement in detecting PLs of 90% was achieved. Upon labeling, only 5% of the cases need the consultation of the fourth examiner to settle discrepancies. We labelled 18,618 PRAs, which contained 1732 unhealthy PRAs (PL), and 16,886 of healthy PRA (H).  
The exact location of the PRAs and its labeling as PL or H was done by drawing a bounding box at each PRA using labelImg, an open-source annotation tool that allows annotations in xml Pascal VOC format. We converted the annotation format from xml to coco format as a compatibility requirement to use the Detectron2 framework. In all experiments, we shuffled our dataset once and followed 80-20 sampling split for training and testing datasets, respectively. We dedicated 10% of the training dataset for validation to prevent overfitting when tuning the network hyper-parameters. Table 1 summarizes the applied data preparation methods.
## 2 Materials and Methods
### Optimization of the Rotor Magnetic-Pole Topology and Different Dimension Schemes  
With the aim of improving the torque density and increasing the torque under the overload flux-weakening control, the optimized structure adopts the \"\(\
abla\)-type\" magnetic-pole rotor topology. Figure 4 draws the brief structure of the permanent magnets at each pole and gives the introduction to the dimension parameters. To reduce the production and manufacturing costs of the motor, the dimensions of the three permanent magnets at each pole are identical.  
The rotor permanent magnet's dimension and arrangement will have a significant impact on the cogging torque and torque ripple. The basic methods proposed for reducing the cogging torque are the adjustment of the magnet arc width relative to the pole pairs. The embedded depth, length and width of the three parts of the permanent magnets can be adjusted to reduce the torque ripple [25; 26]. To save analysis and calculation time, the optimization of the rotor pole topology uses the Taguchi method [27; 28] to calculate the structural parameters tagged in Figure 4. And for a better torque characteristic, the sensitivity-based optimization method is applied [29]. The optimization results of the \(\
abla\)-type topology of three different dimension schemes are displayed in Table 4. Considering the cogging torque (because the cogging torque is generated by the interaction between the  
Figure 4: \(\
abla\)-type rotor magnetic-pole topology and the dimension parameters.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Scheme I** & **Scheme II \({}^{*}\)** & **Scheme III** \\ \hline \(N_{1}\) & 17 & 18 & 19 \\ \(N_{2}\) & 18 & 16 & 14 \\ Total number of turns per phase & 312 & 312 & 312 \\ Maximum slot fill rate & 0.77 & 0.73 & 0.77 \\ Average pitch & 44.8 mm & 45.3 mm & 45.7 mm \\ Fundamental winding factor & 0.944 & 0.948 & 0.953 \\ Rated output torque & 97.7 N-m & 98.2 N-m & 98.6 N-m \\ Efficiency & 97.02\% & 97.03\% & 97.03\% \\ \hline \hline \end{tabular} \({}^{*}\)The optimization results of the stator winding adopted in this article is scheme II.  
\end{table}
Table 3: Different winding turn schemes and calculation results.  
permanent magnet and the stator core and without a winding current, the average value is 0, so the comparison value is the peak-to-peak value), rated output torque and torque ripple, scheme II has a better comprehensive performance.  
The comparisons between those schemes are based on the analysis of the motor's overall performance, which includes the average torque, torque ripple, anti-demagnetization ability, mechanical stress and motor temperature rise. After that, considering the material quantity and production cost assessment, scheme II is determined as the optimal scheme.
# Tensor Implementation of Monte-Carlo Tree Search for Model-Based Reinforcement Learning
## 2 Related Work
### Mcts and Reinforcement Learning
In board games, rewards usually mean results of games (1 for a win, 0 for a draw and \(-1\) for a loss). In domain of Atari games, rewards are obtained either during or at the end of the game. Rest of Equation (1) forms the exploration part. \(P(s_{t},a|\theta_{p})\) represents predicted probability of action \(a\) by the prediction function, \(c_{1}\) and \(c_{2}\) are exploration constants. * Phase of expansion--After selection of unexplored combination of state \(s_{t}\) and \(a_{t}\), new node (with state \(s_{t+1}\)) is added to the tree structure. State \(s_{t+1}\) is obtained by using dynamic function with selected combination \(s_{t}\) and \(a_{t}\) as an input. * Phase of simulation--during the phase of simulation, DNN is called again, specifically prediction function to obtain probability distribution (used in PUCT equation) and state value (used to compute q values). These variables are assigned to new node. * Phase of backpropagation--q value computed from state value is back propagated through trajectory composed of each traversed node during the phase of selection. Each traversed node's number of visits \(N(s_{t},a_{t})\) is increased by one. \[a_{t}=\underset{a}{\operatorname{argmax}}Q(s_{t},a)+P(s_{t},a|\theta_{p}) \cdot\frac{\sqrt{\sum_{b}N(s_{t},b)}}{1+N(s_{t},a)} \tag{1}\] \[\cdot\left(c_{1}+\log\left(\frac{\sum_{b}N(s_{t},b)+c_{2}+1}{c_{2} }\right)\right)\]  
\[Q(s_{t},a_{t})=\mathbb{E}\big{[}r_{t}+\gamma\cdot V(s_{t+1}|\theta_{p})\big{]} \tag{2}\]  
Unlike the original MCTS, MuZero creates new tree of specific size in each step of the game to avoid a cumulative error of predicted states. The size of the tree depends on the number of simulations \(C_{S}\) (e.g., 800 simulations per state in board games or 50 simulations in the domain of Atari games [2]). Each MCTS node stores following values:  
* State \(s\)
* Predicted probability distribution of actions \(P(s,\cdot|\theta_{p})\)
* Vector \(N(s,\cdot)\) representing the number of visits of each action
* Vector \(Q(s,\cdot)\) of q values for each action
* Vector \(R(s,\cdot)\) of rewards for each action
* Set of children nodes  
In the theory, most of RL papers explain MCTS as a single tree. However, in practice it is appropriate to play many games in multiple processes in order to collect data as soon as possible. As we mentioned before, in case of MuZero, new tree is build in each step of game. Therefore, multiple trees are build at the same time in many processes separately. Also each process uses DNN with the same weights.
# Design Parameters Affecting Rill Swell Events for Block Caving Applications
## 5 Analysis  
Figure 10 summarizes the magnitudes of all RS events modeled, including the drawpoint width of 3 m (dashed line). Here, the magnitudes increase according to the amount of fine material over the hang-up; hence, there was a maximum expected RS event for each drawpoint geometry. Considering drawpoints of 5 m in width, the maximum expected RS events would be 500 t, 350 t, and 150 t for brow beams of 1 m, 2 m, and 3 m high, respectively. Also, regarding drawpoints of 3 m in width and a brow beam of 2 m in height (effective drawpoint height of 2.5 m), the maximum expected RS event would be around 250 t.  
On the other hand, the presence of large rocks in the production drift was observed to increase with a smaller brow beam and/or a larger amount of fine material over the hang-up. The observations in this study suggest an opportunity to define operational guidelines for mining sectors that have been classified as at high risk for RS events. The geometric characteristics of the drawpoints allow the maximum expected events to be estimated. Additionally--as a mitigation measure--results suggest the brow beam could be modified as caving progresses and finer fragmentation arrives at drawpoints.  
The effect of rock density was quantified, and a sensitivity analysis of the density was performed. The density varied by \(+/-25\%\) in simulating the case of a 2 m brow beam, 7 m of fine material height, and a drawpoint width of 5 m. The density was evaluated because it is known to influence fine migration [7; 19]. Figure 11 shows the simulations for different rock densities. The red particles are the fine material, while the white ones are the coarse material present in the hang-up. For the lowest density (Figure 11a), coarse particles were not dragged to the production drift.  
Table 8 shows the effect of particle density on RS events. It can be observed that at the lowest density the tonnage decreases by 28.5%, while at higher densities it increases by as much as 33%. It is possible that a lower density decreases the percolation and potential energy available to generate an RS event. Therefore, density is a variable that can have an important effect when this type of risk exists.  
Figure 10: RS magnitude for different heights of fines over the hang-up and different drawpoint geometries.  
Being able to model rill swell of fine material events in mining contributes to a better understanding these phenomena. Although there is still much work to be done to achieve a thorough understanding in this area, the variables and results presented here enhance the comprehension of these critical events. However, one of the main variables associated with these phenomena is the uncertainty of the fine material accumulated at the drawpoints. It is advisable to use techniques to quantify this uncertainty [51; 52] and thus minimize the risk through better estimates of the fine material generation and gravity flow.
## 1 Introduction  
Metasurfaces (MTSs) are composed of a dense layer of subwavelength metallic elements, also called pixels [1; 2; 3], printed on top of a grounded substrate. Bounded slow surface waves (SWs) can be guided on these structures with engineered dispersion properties. This is the case for the near-field plates, planar lenses and cloaking structures found in the literature [4; 5; 6; 7]. Based on the transverse resonance condition, such structures have been characterized by means of scalar or tensor surface impedances [1]. In general, scalar impedance surfaces can support the propagation of either TM or TE SWs. For simplicity, efforts have been focused on guiding of the single TM mode. In fact, several studies have been carried out to analytically characterize the dispersion properties of the TM mode within circular, elliptical and slotted patches printed on top of a grounded slab [8; 9; 10; 11; 12].  
However, one of the most interesting application of MTS-based structures is the realization of low-profile lightweight antennas for space applications [13; 14; 15; 16; 17; 18]. By modulating the characteristic surface impedance, a transition from a cylindrical SW excited form the center to a leaky-wave (LW) is obtained using a MTS-based structure. By means of the modulation of the scalar or tensor impedance characteristics of the isotropic or anisotropic surfaces, beam pointing, shape and radiated field polarization can be tailored for different applications [13; 14; 15; 16; 17; 18]. Most of the configurations found in the literature work in single-mode operation. In fact, a single TM SW is supported in such structures, and consequently, single circular polarization (CP) performance is obtained. In order to obtain dual circularly polarized broadside radiation, two modes of TM and TE nature need to be supported by the MTS [19; 20]. In these solutions, both modes must have decoupled propagation, be balanced in amplitude and phase synchronized. When these modes interact with a properly modulated surface impedance tensor, a LW radiates outside the structure towards the broadside with the right-hand (RH) or left-hand (LH) CP, depending on the feed phase. The main goal of this work is to discuss a practical pixel geometry supporting the propagation of TM and TE modes balanced in phase that can be employed to implement dual circularly polarized MTS antennas.  
The paper is structured as follows. The theoretical principle for phase-matched dual-mode operation is summarized in Section 2. A pixel geometry that meets this condition is presented in Section 3, where a full-wavefrequency and spatial-dispersion analysis is carried out. Additionally, a phase-balanced single-layer guiding MTS is implemented with these pixels. In Section 4, the simulation results are shown. Finally, conclusions are drawn in Section 5.