esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Process and System Description
### System Description
The PI regulator is used by most flow regulators, and its transfer function is as follows:  
\[v_{i}+\frac{b_{i}}{m_{i}}v_{i}+\frac{F_{Coul,i}}{m_{i}}sign(v_{i})=\frac{k_{i }}{m_{i}}u_{i} \tag{1}\]1where \(e\) is error signal; \(K_{t}\) and \(T_{t}\) are the regulator parameter. If \(T_{v}\geq\max\{T_{a},T_{b}\}\), the mathematical model of the \"motor pump channel\" part can be merged into one link based on the model reduction theory according to Equations (6) and (7):  
\[v_{i}+\frac{b_{i}}{m_{i}}v_{i}+\frac{F_{Coul,i}}{m_{i}}sign(v_{i})=\frac{k_{i }}{m_{i}}u_{i} \tag{1}\]2  
According to the Equations (8), (9), and (10), the following can be obtained:  
\[v_{i}+\frac{b_{i}}{m_{i}}v_{i}+\frac{F_{Coul,i}}{m_{i}}sign(v_{i})=\frac{k_{i }}{m_{i}}u_{i} \tag{1}\]3  
Therefore, the open loop transfer function of the system can be simplified:  
\[v_{i}+\frac{b_{i}}{m_{i}}v_{i}+\frac{F_{Coul,i}}{m_{i}}sign(v_{i})=\frac{k_{i }}{m_{i}}u_{i} \tag{1}\]4  
Data collection was carried out on the mixing and dosing device of GM&D-A type particleboard in the laboratory. When the output of the glue flow was stable, the off-line data of the whole process were recorded with the system input of 30 Hz excitation signal. The model parameters were identified using recursive least-square algorithm based on genetic factor. The model was a four-order system with lagging terms, and the fitting degree was 93.32. The simplified model structure is as follows:  
\[v_{i}+\frac{b_{i}}{m_{i}}v_{i}+\frac{F_{Coul,i}}{m_{i}}sign(v_{i})=\frac{k_{i }}{m_{i}}u_{i} \tag{1}\]5  
where the result of parameter identification \(k=5.668e-005\), \(T_{1}=-72.1897\), \(T_{2}=3.5079\), \(T_{3}=-0.76099\), \(T_{4}=-1.3186\), \(\tau=2.6523\). The transfer function is as follows:  
\[v_{i}+\frac{b_{i}}{m_{i}}v_{i}+\frac{F_{Coul,i}}{m_{i}}sign(v_{i})=\frac{k_{i }}{m_{i}}u_{i} \tag{1}\]6  
where the simplified state equation can be given as follows:  
\[\left\{ \begin{array}{l}\dot{x}_{1}=x_{2}\\ \dot{x}_{2}=x_{3}\\ \dot{x}_{3}=-0.284x_{1}-1.587x_{2}-2.358x_{3}+0.02899u\\ y=x_{1}\end{array} \right. \tag{15}\]  
which is the nominal system of the particleboard glue mixing and dosing system. In practical engineering, the change of pressure for the glue flow and the flow rate will be affected under the intermediate frequency condition, which makes the particleboard glue mixing and dosing system suffer more external disturbances. Therefore, we changed the model represented in Equation (15) as follows:  
\[\left\{ \begin{array}{l}\dot{x}_{1}=x_{2}\\ \dot{x}_{2}=x_{3}\\ \dot{x}_{3}=f(x)+gu+D\\ y=x_{1}\end{array} \right. \tag{16}\]  
where \(f(x)=-0.284x_{1}-1.587x_{2}-2.358x_{3}\), \(u\) is control input and \(y\) is the control output, \(g=0.02899\), \(D=\Delta f+d\) is expressed as the compound disturbance, which includes the uncertainty of parameter identification \(\Delta f\) and the nonlinear external disturbances \(d\).
###### Abstract  
In this study, a spiral groove liquid film vaporization model based on the viscosity-temperature equation, fluid internal friction, saturation temperature, and pressure relationship equation was established. Using a multiphase flow model based on the finite volume method, the influence of the change in the mass transfer coefficient on the vaporization of the liquid film was studied. Moreover, the influence law of structural parameter changes in liquid film vaporization characteristics and sealing performance was analyzed. The results indicate that, with an increase in the mass transfer coefficient, the average vapor phase volume fraction first increases and then gradually stabilizes. When calculating the average vapor phase volume fraction, it is necessary to consider the influence of the mass transfer coefficient, whereas its effect on the opening force and leakage can usually be neglected. Under the optimal mass transfer coefficient conditions, the average vapor phase volume fraction increases with an increase in the helix angle, groove-weir ratio, and groove depth. By comparison, with an increase in the groove-diameter ratio, the average vapor phase volume fraction first increases and then decreases. The opening force decreases with an increase in the helix angle, groove-to-weir ratio, and groove depth. On the other hand, it first decreases and then increases with an increase in the groove-diameter ratio. The leakage rate increases first and then stabilizes with an increase in the helix angle. Moreover, it increases continuously with an increase in the groove-diameter ratio, groove-weir ratio, and groove depth.  
vaporization; mass transfer coefficient; structural parameters; sealing performance; phase transition; numerical simulation +
Footnote †: journal: Applied Sciences
# Dose Estimation by Geant4-Based Simulations for Cone-Beam CT Applications: A Systematic Review
## 3 Results and Discussion
### RQ4: What Are the Current Research Trends and Disciplines for Prospective Research on This Topic?  
We conducted an analysis of the trending topic based on the author's keywords from the dataset. Author's keywords are generally related to such publication content and, hence, adequate to derive thematic aspects of a certain topic [133; 134]. Despite the fact that several authors' keywords have been shown in the network map of Figure 3, a further insight is given in Figure 4. A hierarchical classification of the topics is shown in this map, representing the log (frequency) of the authors' keywords over the years. In this case, we have analyzed all the authors' keywords regardless of their frequency. For instance, in 2017 and 2016, computed tomography was the most discussed topic, followed by simulation in 2017. The dosimetry, photon, cancer, and radiotherapy topics were also quite cited from 2015-2018. In 2020, the term \"validation\" appears, suggesting that the last tendency point to the development of new methods for dose estimator is dedicated to the validation and effectiveness of new simulations codes suitable for the current technologies.  
Finally, it is worthy of mention that, based on the most recent advances, new promising dose estimator algorithms involves Deep-Learning-based methods [135]. Deep Learning is a subfield of machine learning that employs algorithms inspired by the brain function and structure called artificial neural networks. The work of each neuron is entwined in a distributed manner to collectively learn from the input in order to optimize its final output [136; 137]. In particular, convolutional neural network (CNN) architectures have witnessed rapid growth in medical physics [138; 139]. Although CNNs were primarily used in the field of image pattern recognition [140], they can be also applied to accurately determine a dose distribution from an approximated simulated input dose [141; 142; 143; 144]. In this framework, [PERSON] et al. [132] proposed the use of a CNN combined with MC simulations for a CBCT equipment to generate the training data.
## 1 Introduction
The signal-to-noise-ratio (SNR) factor [34] was then proposed using the variance, which is the power of ASD, to generate an ultrasound image with an improved resolution and contrast. The dynamic CF (DCF) based on the ASD [35] has been proposed for CPWC imaging, showing overall improvements in image quality over CF. The delay-multiply-and-standard-deviation (DMASD) factor based on the delay and standard deviation (DASD) beamforming [36], which uses the ASD of echo signals, was introduced to enhance the image contrast [37]. In addition, the ASD has also been studied to reduce the dark-region artifacts introduced by ESBMV in CPWC imaging [38]. Our previous work proposed a novel adaptive beamformer named the covariance-matrix statistical beamforming (CMSB) [39], which estimates adaptive weights based on the ratio between the mean and ASD [40] of a modified covariance matrix. It has been demonstrated that CMSB obtains an improved image resolution and contrast, as well as a maintained speckle quality. However, CMSB obtains a good speckle quality at the expense of contrast degradation. One possibility to improve the contrast is to apply an adaptive weighting technique. Another possibility to further improve the speckle quality of CMSB is to adaptively determine the diagonal reducing factor for different imaging targets. Hereto, we propose enhanced CMSB methods based on LOC weighting and adaptive diagonal reducing in this study to enhance the resolution and contrast of CPWC imaging quality, as well as lesion detectability. To the best of our knowledge, LOC has not been studied as adaptive weighting for ultrasound beamforming. The goal of this paper is to enhance the noise reduction of CMSB by adaptive weighting and overcome the tradeoff between speckle statistics and contrast by adaptive diagonal reducing. The proposedmethods were validated with simulation, phantom experiments, and in vivo experiments in CPWC ultrasound imaging, and compared with DAS, DMAS, SLSC, and CMSB. We also considered the effects of the channel noise level, kernel size, and number of transmit angles on the imaging performance when applying the proposed methods. This paper is organized as follows. In Section 2, CPWC imaging, DMAS, SLSC, CMSB, and the proposed methods are introduced. Experimental setups as well as evaluating metrics are presented in Section 3. Section 4 presents the results, and Section 5 discusses the imaging performance of the proposed methods. A conclusion is drawn in Section 6.
# Enhancing Image Dehazing with a Multi-DCP Approach with Adaptive Airlight and Gamma Correction
## 5 Evaluations
### Discussion  
Our approach excels in images with light-to-moderate haze, ensuring that object features remain clearly visible. However, as demonstrated in row #4 of Figure 9l, when haze becomes too dense, there is a heightened risk of losing structural details, leading to incomplete haze removal. This limitation is particularly noticeable with distant structures heavily obscured by thick haze, where the fine details become challenging to recover, making it difficult to remove the haze effectively while preserving the clarity of objects. Furthermore, as shown in row #7 of Figure 9l, using a guided filter can occasionally result in a stair-step effect in sky regions with sharp color transitions.  
Thus, the most suitable images for this method are those with moderate haze, where the objects remain distinguishable. Achieving effective dehazing without compromising object visibility becomes increasingly challenging when the haze becomes too thick.  
Figure 8: The result of object detection using pretrained Faster RCNN [20]. (**a**) Hazy image \(I(x)\). (**b**) [PERSON]’s methods and (**c**) Our methods with a \(1\times 1\) patch and a guided filter. (**d**) Proposed method.  
Figure 7: (**a**) uses \(t^{th}(x)\) with \(15\times 15\) patches. (**b**) uses \(t^{ch}(x)\) with \(1\times 1\) patches and a guided filter. (**c,d**) are zoomed-in versions of (**a,b**), respectively.  
Figure 9: (**a**) Original image, (**b**) [PERSON] [2], (**c**) [PERSON] et al. [3], (**d**) [PERSON] et al. [4], (**e**) [PERSON] [5], (**f**) [PERSON] et al. [6], (**g**) [PERSON] et al. [7], (**h**) [PERSON] et al. [8], (**i**) [PERSON] et al. [13], (**j**) [PERSON] [11], (**k**) [PERSON] [12], and (**l**) our proposed method. The DHQI value for each dehazed image is given.
## 4 Soundscape Evaluations and Influencing Factors
### Influencing Factors  
Although sound identification could reliably describe the soundscape, it was still beneficial to investigate the relationships between the acoustic factors and the soundscape evaluations based on annoyance and acoustic comfort. Using Pearson correlations, the correlations between various factors and the subjective evaluations were analyzed, as shown in Table 5.  
As shown in Table 5, although many factors were not significantly correlated with the annoyance and acoustic comfort evaluations, the most significant correlations involved the sound levels and annoyance evaluations in some blocks. It can be seen that in the block with an artificial soundscape (BLK-II), the percentage of natural sound showed a significant positive correlation with acoustic comfort. However, in the block with a natural soundscape (BLK-IV), higher sound levels could contribute significantly to higher annoyance perceptions. In addition, it was noted that negative correlations were broadly observed between many factors and the subjective evaluations of both annoyance and acoustic comfort. Nevertheless, the correlations between these factors and the soundscape evaluations were different in the different blocks. Therefore, a comparison of the soundscape evaluations and influencing factors was made among the four blocks.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Variables** & **BLK-I** & **BLK-II** & **BLK-III** & **BLK-IV** & **ANOVA** \\ \hline
**Identified Soundscape** & **Artificial** & **Neutral–Natural** & **Neutral–Artificial** & **Natural** & **F** & \(\mathbf{p}\) \\ \hline Annoyance evaluation & 3.46 & 3.62 & 3.42 & 3.80 & 1.831 & 0.14 \\ Acoustic comfort evaluation & 3.78 & 3.64 & 3.54 & 3.92 & 2.316 & 0.08 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Soundscape identification and evaluation of the four blocks.  
Based on the above results, this study employed a linear regression method to develop a regression model of the influencing factors and soundscape evaluations, as shown in Table S2. The model verified the findings of the previous correlation analyses, indicating that excessive sound levels in natural soundscapes may lead to increases in annoyance. However, the model can only explain nearly 10 percent of the total variance.
## 2 Materials and Methods
### CO\({}_{2}\) Consumption  
The first step in estimating the CO\({}_{2}\) emissions related to robot-assisted and laparoscopic procedures was to categorize the contributing components into emission scopes, following the GHG protocol. Scope 1 emissions include direct emissions from sources, such as CO\({}_{2}\) used during surgical procedures for insufflation. Under normal conditions, CO\({}_{2}\) escapes into the atmosphere directly through port site leaks, decompression at the end of surgery, or inadvertently. Indirectly, CO\({}_{2}\) absorbed by intra-abdominal organs during surgery is later eliminated through the respiratory system. Scope 2 emissions encompass indirect emissions resulting from electricity generation, the disposal of single-use devices, and energy required for the heating, cooling, production, and sterilization of instruments. To contextualize, a typical CO\({}_{2}\) cylinder used in our ORs contains compressed gas. Using the ideal gas law, where 1 mol of any gas occupies 22.4 L at 1 atm and weighs 44 g, each cylinder contains approximately 1.5 \(\times\) 10\({}^{-4}\) metric tons of CO\({}_{2}\). We estimated the operative time and the corresponding CO\({}_{2}\) consumption for robot-assisted and laparoscopic procedures by calculating the duration of surgeries and CO\({}_{2}\) use per cylinder.
## 3 Automatic Demagnetization Fault Diagnosis System Based on Three PNNs  
### The Process of Demagnetization Fault Diagnosis Using Three PNNs  
The method for the automatic diagnosis of demagnetization faults in PMSMs using three PNNs is proposed, which can be roughly divided into a data collection and DFIS calculation module and a detection and location identification of fault module, as shown in Figure 10.  
The specific steps of the proposed method are as follows:  
(1) Measure the voltage of TYC1, TYC2, and TYC3.  
(2) Calculate the residual voltage of TYC1, TYC2, and TYC3, and then, calculate \(SI_{\rm m}\), \(SI_{a1}\), and \(SI_{a2}\).  
(3) If the peak value of \(SI_{\rm m}\) is less than the set threshold (thd), the machine is healthy; otherwise, the machine is faulty.  
(4) Partition \(SI_{\rm m}\), \(SI_{a1}\), and \(SI_{a2}\) by electrical period.  
(5) Input the first electric period signal of \(SI_{\rm m}\) to the first PNN; then, the fault mode of the first pairs of permanent magnets is identified.  
(6) If the output of the first PNN is not Mode 6 (Mode 6, Mode 7, and Mode 8 are identified as Mode 6), output the identified fault mode; otherwise, input the first electric period signal of \(SI_{a1}\) to the second PNN, and then the fault mode of the first pairs of permanent magnets is further identified.  
Figure 9: \(SI_{\rm m}\) under various speeds of the machine.  
(7) If the output of the second PNN is not Mode 8H or Mode 7D, output the identified fault mode; otherwise, input the first electric period signal of \(\text{SI}_{\text{a2}}\) to the second PNN, and then the fault mode of the first pairs of permanent magnets is finally identified.  
(8) Determine the location of demagnetized permanent magnets in the first pairs of permanent magnets through the identified fault mode, Tables 3 and 5.  
(9) Input the DFISs from the second electric cycle to the p-th electric cycle into the PNN in turn, and repeat steps 5 to 8 to diagnose the condition of each pair of permanent magnets. Determine the location of all demagnetized permanent magnets in the machine.
## 2 Description of the Environment to Monitor
### Description of the Research Facilities in Do nana  
Early sensor deployments in Do nana integrated different types of sensors to gather information about underground water levels and vegetation stress, among other subjects. However, these initial installations were run in an _ad-hoc_ manner, without integration or communication between the different networks. Moreover, the facilities were based on wired networks.  
To allow for the integration of sensors and to facilitate the retrieval of information, some remote communication towers have been currently deployed in the Do nana Biological Reserve (Figure 2). Thus far, there are six remote communication towers that are spread all over the park. These communication towers use a WiMAX connection to allow communication with a central server.  
Figure 1: Natural Park of Do nana.  
Each communication tower provides a local wired network, Wi-Fi, and a wired datalogger, which allow it to deploy sensors in the surroundings of these towers. Moreover, these towers provide power energy to wired sensors by solar panels.  
The Wi-Fi is mainly used for communication with remote motorized cameras. In the current typical installations, most of the remaining sensors are connected to the datalogger by wires. However, the use of wired communication sensors has drawbacks, such as:  
* If aerial cables are used, then they are exposed to animals that continuously break the lines.
* The use of underground wires increases considerably the cost of installation and could affect the vegetation of the zone.
* The maximum distance between the sensors and the communication tower is limited by the wired technology that is used (4-20 mA sensors). For this reason, there are some areas of the natural park that do not have coverage for monitoring.  
Furthermore, wired installations have an important drawback in their lack of flexibility: after the installation is performed, it is difficult and expensive to add new sensors or to modify their locations, especially in harsh environments such as Do nana. There are important areas of the park that have very difficult access to vehicles, which complicates the transportation of the hardware that is required to modify the wired installations.
## 3 Results and Discussion
### EPR Analysis  
To further confirm the XPS results, EPR measurements were performed for two samples. Figure 5 shows the EPR spectra of different ZnO NPs, recorded at room temperature. One can see that the--EPR signal (g = 1.9568) intensity is the same in both samples.  
However, a significant EPR signal with a g factor of 2.0035 was detected in Sample B. This is commonly attributed to an unpaired electron trapped in an oxygen vacancy site [41; 42; 43]. Hence, a stronger g-2.0035 EPR signal corresponds to the higher concentration of oxygen vacancies in ZnO. The surface of Sample B has more oxygen vacancies than the surface of Sample A.
# Development of Fast Protection System with Xilinx ZYNQ SoC for RAON Heavy-Ion Accelerator
## 2 RAON Central Control System  
The _RAON_ central control system has a middleware to combine multiple experimental devices into one united control system. It uses _EPICS_ middleware to integrate local control systems into _RAON_ central control systems. The _RAON_ central control system has a three-tier architecture. Figure 1 shows a simplified architecture of the _RAON_ central control system. The _EPICS_ middleware framework is used to integrate various systems into one unified system. The unified central control system, installed in a central control room, supervises every component of the accelerator by monitoring all signals from distributed local devices. Basically, by integrating different systems into one, the central control system classifies all possible derived systems by its functionality and standardizes them. By doing so, it assures safety, reliability, scalability, and availability.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameters** & **Description** \\ \hline \(T_{Diag}\) & Beam loss diagnosis time (within 25 \(\upmu\)s) \\ \hline \(T_{FPS}\) & _FPS_ process time (within 20 \(\upmu\)s) \\ \hline \(T_{MD}\) & Mitigation device process time (within 5 \(\upmu\)s) \\ \hline \(T_{BPM}\) & Beam position monitor fault logic \\ \hline \(T_{BLM}\) & Beam loss monitor fault logic \\ \hline \(T_{BCM}\) & Beam current monitor fault logic \\ \hline \(T_{DFN}\) & Fault signal transition time \\ \hline \(T_{L_{n}N_{n}}\) & Link node processing time \\ \hline \(T_{N_{n}N_{n-1}}\) & Transmission time between nodes \\ \hline \(T_{MN}\) & Mitigation node processing time \\ \hline \end{tabular}
\end{table}
Table 1: Parameters used to calculate beam shut-off latency.