esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Simulation of Liquid Film Characteristics during Atomization of Aluminum Alloy Powder
## 3 Simulation Results and Discussion
### Comparative Analysis of Velocity and Pressure Distribution on Different Disk Surfaces
The maximum liquid film velocity is approximately 13.0 m/s, the maximum radial velocity is approximately 5.7 m/s, and the maximum tangential velocity is approximately 11.5 m/s. The velocity distribution of the liquid film on the surface of the spherical disk is shown in Figure 4:0 The velocity distribution of the liquid film, including the radial and tangential velocities, on the spherical disk increases with the increase in the diameter. This means that the velocity of the inner circle is relatively small, that of the outer circle gradually increases, and the velocity reaches the maximum at the outer edge of the disk. The maximum liquid film velocity is approximately 15.5 m/s, the maximum radial velocity is approximately 6.7 m/s, and the maximum tangential velocity is approximately 13.5 m/s. Compared with the flat disk, the liquid film velocity, including the radial and tangential velocity, of the spherical disk is increased by approximately 17-20%. Due to the different forces acting on the fluid film on the surface of the spherical disk, the spherical arc will lead to greater positive pressure between the molten metal and the disk surface, and the liquid film will be subjected to greater friction and relatively less slip, and will have a greater speed. At the same rotational speed, the liquid film on the spherical disk will obtain more energy and achieve a higher energy utilization rate. Figure 4:1 Velocity distribution cloud diagram of liquid film on the surface of the flat disk. (**a**) Liquid film velocity distribution; (**b**) radial velocity distribution; (**c**) tangential velocity distribution. Figure 4:2 The distribution of liquid film on the surface of different disk actual atomization processes. (**a**) Flat disk; (**b**) spherical disk.
# Integrated Optical Phased Arrays for Beam Forming and Steering
## 8 Applications & Potential Alternatives
### Applications
#### 8.1.3 OPAs for Imaging  
OPAs hold great potential in desirable high-precision imaging at short wavelengths [150]. Chip-scale autostereoscopic displays [53], ghost imaging [54], 3D hologram in the near field [150], and incoherent beam steering and imaging [151] have been demonstrated by OPAs.  
An image projection system using an array of integrated OPAs as chip-scale autostereoscopic displays was presented [53]. Each OPA illuminated a desired virtual object from a different angle, and this recreated a virtual object in space with continuous parallax observability. In total, 16 Si\({}_{3}\)N\({}_{4}\) OPAs were used to generate a static image with a viewing angle of 5\({}^{\circ}\) at a wavelength of 635 nm. Each OPA was composed of 32 \(\times\) 32 WGAs with different passively encoded phases [53].  
Unlike conventional OPA beam scanning, the ghost imaging (GI) scheme enabled calibration-free robust imaging [54]. An integrated Si OPA chip presented in Figure 15a was demonstrated for GI applications. A slit pattern with over 90 resolvable points in one dimension was retrieved by driving 128 phase shifters using rapid changing random electrical signals. 2D imaging can be demonstrated by additional wavelength tuning [54].  
Previous imaging-oriented OPAs have been implemented in the far field with a single focal plane for 2D imaging. In contrast, multiple patterns focusing on different projection distances in the near field created a framework for generating a 3D hologram. A 128 \(\times\) 128 WGA-based OPA with an aperture of 1.2 \(\times\) 1.2 mm\({}^{2}\) generated flexible 3D holograms in the near field [150]. The near-field vertical distance of each desired plane was different. Back propagations of desired image planes superimposed to a common plane. Flexible 3D holograms in the near field could be obtained by required amplitude and phase distributions utilizing Fresnel backpropagation [150].  
While recent advances in Si photonics have enabled large-scale OPAs for coherent imaging, some applications require incoherent and/or broadband light [151]. A 64 \(\times\) 64 WGA-based OPA using a planar, fractal, path-length-matched \"H-tree\" structure enabled incoherent beam steering and natural light imaging. A resolution of 5 mm at 18 m apart, which roughly matched a diffraction-limited resolution of 240 \(\upmu\)rad, was achieved by the individually controlled OPA [151].
# Direct Measurement of Heat Produced during Drilling for Implant Site Preparation
## 1 Introduction  
Medical and dental implants involve placing a fixture in the space prepared by osteotomy. The space is then replenished by osseointegration, which is a direct functional and structural connection between the bone and the implant [1]. Drilling is undeniably the most essential process during implant site preparation, as success of osseointegration is determined at this stage. Mechanical and thermal damage to the bone is inevitable during drilling [2, 3]. More than one minute of drilling at a temperature higher than 47 \({}^{\circ}\)C causes local osteonecrosis and impairs osseointegration [3, 4]. The bone tissue is absorbed and degenerates into fat cells that are replaced by less differentiated cells [5, 6]. These impaired bone cells lack the ability of proper osseous remodeling [7].  
Previous studies have attempted to measure the temperature of the heat generated by the friction between the bone and the instrument during drilling in clinically relevant situations [8]. They used artificial bones and human cadaver bones to evaluate drilling methods with less excessive thermal damage [9, 10]. However, most of the studies did not measure the heat directly from the interface. The measurements were taken with thermocouples inserted into the bone located >0.5 mm away from the drilling interface [11, 12]. A few studies measured the heat from the drill instead of from the boneto avoid potential errors associated with the different thermal conductivities between bones and the stainless steel of the drill [13; 14; 15]. However, the potential measurement errors associated with (1) the friction between the drill and thermosensor located in the middle of the drill and (2) the air thermal insulation of the gap between the thermocouple, were not considered in the previous works [14; 15].  
Therefore, a more clinically relevant thermal setup is essential to measure the actual heat generated by the implant drilling related to the drilling depth, speed, time, and pressure without the thermal insulation effect of bone and frictional heat between the drill and the thermosensor [16; 17]. In this study, the heat of the drilling was measured by the rotating thermosensor that is directly connected to the drill. The temperature measured directly on the drill was compared with previous methods that measured the temperature in the bone and in the drill. In addition, the heat produced at the moment of drilling was evaluated as a function of drilling speeds and depths, and the surface temperature of the drill was monitored using infrared analysis. The null hypothesis of this study was that there is no significant difference in temperature among different drilling speeds and depths. This study provides more sophisticated and clinically relevant heat measurements for more successful implant procedures.
# Investigation of the EWT-PSO-SVM Model for Runoff Forecasting in the Karst Area
## 2 Methodology
### Support Vector Machine (SVM)  
Support vector machines are supervised models that use statistical learning theory to improve the generalization ability of machine learning algorithms by pursuing minimum structural risk. The SVM model is a nonlinear mapping, which adjusts the feature vectors of different dimensions to the same dimension so that the feature vectors of the runoff time series complete the mapping from low to high dimensional space., so as to realize the model of separating the runoff time series into the maximum interval hyperplane [29]. From the Ref. [12], the support vector machine problems can be reconverted as  
\[\left\{\begin{array}{l}\min\sum\limits_{i=1}^{N}\sum\limits_{j=1}^{n}y_{i}y_ {j}q_{i}a_{j}\exp\bigl{(}-g\|x_{i}-x_{j}\|\bigr{)}^{2}-\sum\limits_{i=1}^{n}a_ {i}\\ s.t\ \ \sum\limits_{i=1}^{N}y_{i}a_{i}=0,0\leq a_{i}\leq c\end{array}\right.. \tag{8}\]  
The optimization effect of SVM classification problem is mainly determined by the parameters of the penalty factor \(c\) and the kernel function parameter \(g\). The parameter \(c\) represents the tolerance to sample misclassification, the higher \(c\) is intolerant to misclassification and prone to overfit. The parameter \(g\) defines the influence ripple range of a single sample, and the larger \(g\) is, the easier the model is to overfit. Therefore, selecting the appropriate parameter optimization algorithm to determine \(c\) and \(g\) is a challenge to improve the forecasting performance of SVM.
## 3 Results and Discussion
### Discussion of the PXRD and DSC-TG/MS Results
The values of the activation energy E\({}_{\rm A}\) for the carbonation reaction ranged from 44 to 64 kJ\(\cdot\)mol\({}^{-1}\) (Table 6) depending on \(\Delta\)T, with the latter being in a good agreement with the results reported in [PERSON] [39] (\(\sim\)72 kJ\(\cdot\)mol\({}^{-1}\)). The carbonation rate of wollastonite at different temperatures (when \(\sim\)10% of wollastonite were still present) showed a non-linear behavior (exponential decrease) of reaction time (Figure 14). The p-T conditions in the steel reactor were thermodynamically not suitable [40] for the formation of aragonite. Although metastable aragonite formation can be stabilized in presence of Sr [41], the content of Sr in the material was very low (0.01 wt %). Despite in preliminary performed experiments no aragonite was formed when the carbonate was previously removed from the raw material. Therefore, it was assumed that the small content of aragonite in the raw material functioned as a seed crystal for further metastable aragonite growth. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Activation Energies** & **Rate Constants** & **kJ/mol** \\ \hline E\({}_{\rm A}\) (323–296 K) & \(-3.79\times 10^{-6}\) \(\pm\) 2.36\(\times\) 10\({}^{-7}\) & 63.88 \(\pm\) 0.2 \\ E\({}_{\rm A}\) (333–296 K) & \(-3.32\times 10^{-5}\) \(\pm\) 1.87\(\times\) 10\({}^{-6}\) & 58.88 \(\pm\) 0.6 \\ E\({}_{\rm A}\) (333–323 K) & \(-5.42\times 10^{-5}\) \(\pm\) 1.95\(\times\) 10\({}^{-6}\) & 43.70 \(\pm\) 2.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Rate constants k and activation energies E\({}_{\rm A}\) of CaSiO\({}_{3}\)/CaCO\({}_{3}\) conversion. Figure 14: Carbonation rate of wollastonite at different temperatures at varying reaction times. Higher temperature resulted in a faster carbonation rate. The reaction time showed a non-linear behavior (data points were fitted with an exponential function).
# Botnet Detection Technology Based on DNS
## 2 Botnet Evading Technology  
A botnet is distinct from other malware, because the bot-herder can remote control and coordinate all the infected machines through a C and C server. In essence, this means that the control center of the botnet exists in the C and C server. This could be its Achilles heel. Hence, botnets put large amounts of resources into concealing the C and C server.  
The DNS system is one of the most important elements of the Internet; it translates a domain name into an IP address, or vice versa. Quite notably, DNS helps Internet users locate various online resources, such as web servers and mail servers. Unfortunately, in view of its basic functions, the DNS service is frequently involved in various malicious activities one way or another. This situation is not surprising. Except for being used for well-known benign applications, domain names are widely used for malicious purposes. For instance, DNS are playing a more and more important role in the location of botnet C and C servers, scam servers, URLs of spam mails, malicious code downloading sites, and phishing pages from unsuspected victims. Hence, it seems useful to monitor the application of the DNS system because it indicates a certain domain serving as a part of malicious activities.  
In order to avoid detection and mitigation [11], attackers have developed a series of more complicated technology to change C and C servers' IP addresses dynamically, such as DGA and domain fluxing.
## 3 Results and Discussion
### Normalization of Experimental Results from the Literature and Model Analysis
#### 3.1.2 Plastic Soils
As indicated in the normalized graph, the increase in PWP was consistent with the WP generation mode of plastic soils. The curves increased sharply in the initial stage and leveled off until initial liquefaction, and the curves corresponding to higher PI values shifted more toward the upper left corner; that is, the curve corresponding to the specimen with a PI of 40 increased more sharply in the initial excitation stage. After the WP generation range of plastic soils was obtained, the model developed by [PERSON] et al. [42] and the experimental results from the literature presented in Figure 8 were analyzed. The adjusted \(\beta\) values were compared with the range obtained from the experimental results. When \(\beta\) was 250, the curve produced by the model was closest to the  
Figure 8: PWP ratios and normalized N/N\({}_{\text{L}}\) values from previous works are shown and redrawn. (**a**) [PERSON] (2010) [54], (**b**) [PERSON] et al. (2008) [55], (**c**) [PERSON] and [PERSON] (2015) [31]. (**d**) R\({}_{\text{u}}\) and normalized N/N\({}_{\text{L}}\) values are plotted between two color lines for plastic soils. experimental curve at the upper limit; however, when \(\beta\) was 20, the curve produced by the model was closest to the experimental curve at the lower limit. Therefore, when testing soil specimens containing plastic fines, the \(\beta\) value in the model developed by [PERSON] et al. [42] should be between 20 and 250 (Figure 9).
# Few-Shot Wideband Tympanometry Classification in
## 4 Discussion
In conclusion, the proposed algorithm with the Gaussian processes to explicitly perform the domain adaptation appears to improve the performance of WBT classification in otosclerosis significantly at the cost of a higher computational load during the training phase. Since the training is offline and the inference time is not affected, the algorithm can still be effectively applied for a real-world WBT-based otosclerosis diagnostic task. The approach also potentially provides a performant solution for the minimally invasive diagnosis of other middle and inner ear diseases. Moreover, other performant distance metrics or novel optimization strategies in domain adaptation are worthy of further exploration and research for few-shot WBT-based classification in the future work. Conceptualization, F.M., A.B.G. and C.L. ; methodology, L.N. ; software, L.N. and C.L. ; validation, L.N. and C.L. ; formal analysis, A.B.G. and L.N. ; data curation, A.B.G. ; writing--original draft preparation, L.N. ; writing--review and editing, C.L., A.B.G. and F.M. ; visualization, C.L. ; supervision, A.B.G. and F.M. All authors have read and agreed to the published version of the manuscript. This work was supported in part by China Scholarship Council, Chinese Academy of Sciences, and National Natural Science Foundation of China (No. 62171440). The open-source dataset was adopted, and the involved codes in this work will be available upon request. The authors have no conflicts of interest to declare that are relevant to the content of this article. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results. The following abbreviations are used in this manuscript:  
ANOVA Analysis of Variance  
AUC Area Under the Receiver Operating Characteristic Curve  
CE Cross Entropy  
CNN Convolutional Neural Network  
DAN Deep Adaptation Networks  
FT Fine-Tuning  
GP Gaussian Processes  
GPGDA Gaussian Processes-Guided Domain Adaptation  
MMD Maximum Mean Discrepancy  
RBF Radial Basis Function  
WBT Wideband Tympanometry
###### Abstract  
The goal of this study was to analyse the effect of sonoreactor dimensions on the effectiveness of the ultrasound-assisted extraction (UAE) of bioactive substances from rowan (_Sorbus aucuparia_ L). Sonication was carried out with a VC750 Sonics processor at the following amplitudes of ultrasound: 12, 24, and 36 \(\upmu\)m. The frequency of the ultrasound was 20 kHz. Extraction was conducted in a 2 s on-4 s off pulse system. The total phenolic content and antioxidant activity were determined using a spectrophotometric method. The pH value of the extracts was measured using a combined pH metric electrode, type EPS-1 (Elmetron). Response surface methodology (RSM) was used to optimise the investigated variables. On the basis of the developed model, the following variable values were obtained: TPC--12.48 gallic acid equivalent (GAE)/g, 2,2'-azino-bis-(3-ethylbenzothazoline-6-sulfonic acid) (ABTS) values--126.54 \(\upmu\)mol Trolox (TE)/g, 2,2-diphenyl-1-picryl-hydrazyl-hydrate (DPPH) values--79.58 \(\upmu\)mol TE/g, ferric reducing antioxidant power assay (FRAP) values--120.65 \(\upmu\)mol TE/g for an extraction vessel with a diameter of 35 mm and TPC--11.68 mg GAE/g, ABTS values--120.49 \(\upmu\)mol TE/g, DPPH values--75.90 \(\upmu\)mol TE/g, FRAP values--155.54 \(\upmu\)mol TE/g for an extraction vessel with a diameter of 25 mm. Significant influences of time, ultrasound amplitude, and alcohol concentration on the course of the extraction process in the tested extraction vessels were found. The optimum extraction conditions for an extraction vessel with a diameter of 35 mm were obtained for an ethyl alcohol concentration of about 60%, and for an extraction vessel with a diameter of 25 mm the optimum conditions were for an alcohol concentration in the range of 30-50%. A very strong positive linear correlation was found between the concentration of ethyl alcohol and the pH value of the obtained extracts for both extraction reactors. The developed models of pulsed ultrasound-assisted extraction were characterised by a good predictive capacity (77.49-91.39%) and can be used for obtaining polyphenols from rowan.  
pulsed ultrasound-assisted extraction; extraction vessel; rowan; polyphenols; DPPH; FRAP; ABTS; pH; modelling; Box-Behnken design Article
## 7 Results and Discussion
### Evaluation Metrics
#### 7.1.2 Residual Energy  
Residual energy is the average energy consumed, \(ENG_{(cons)}\) at each round by a node divided by the available total energy \(ENG_{(available)}\). Equation (27) shows a numerical form of the residual energy.  
\[RSD_{(ENG)}=\frac{ENG_{(cons)}}{ENG_{(available)}}\times 100. \tag{27}\]
## 3 Results and Discussion
### Example for the Paganin Method  
To demonstrate the functioning of our in-house cone-beam tomographic reconstruction package for laboratory phase-contrast imaging using the Paganin phase-retrieval technique, we will use as an example the propagation-based micro-CT of a horse fly. The scan was performed on an EasyTom XL Ultra 230-160 micro/nano-CT scanner (RxSolutions SAS, Chavanod, France). The scanner features a Hamamatsu reflection-type micro-focus X-ray tube. The scan was performed using a MH110 XC-KK-FA, XiRay detector from XIMEA that features a 11 MPixel CCD chip coupled directly via an optical fibre bundle to a 20 \(\upmu\)m-thick GadOx scintillator and having a native pixel size of 9 \(\upmu\)m and 14 bits of dynamic range. The tube was operated at 65 kVp and a current of 120 \(\upmu\)A, i.e., at slightly lower than 8 W tube power rendering an emission spot size of around 4 \(\upmu\)m. The geometrical magnification was 1.16 and the voxel size of the CT scan was 7.8 \(\upmu\)m. Thus, the setup represents a so-called inverse geometry with relatively short propagation distance and a high resolution detector as opposed to the traditional large propagation-distance setup with a large pixel size detector. The images were acquired with 4.0 s exposure time per projection. 1600 projections were taken over 360 degree sample rotation. Results are given in Figure 1 showing the cross-sectional CT slices at two regions: one where the flight muscles are located (see orange line in Figure 4a) and one at the level of the compound eyes (see blue line in Figure 4a). The latter clearly resolves the individual ommatidia. From the images and the corresponding gray-scale histograms, it is clear that the Paganin method strongly increases the CNR of the images and correspondingly the histograms are becoming much more structured, showing several peaks compared to the attenuation case. This means that the different structures corresponding to the different gray-scale peaks can be better resolved in the phase image. This is a well-known property of the Paganin method and the corresponding filter [34; 35]. The CNR values for the different methods and samples are summarized in Table 1. A \(\
u\) value of 10.0 was used in Equation (6) to obtain the images. For a projection image of the horse fly see Figure 4a.
## 3 Experiments and Discussions
### FIL Simulation Experiments
#### 3.1.3 External Disturbance  
In normal use, the jittering of the electrode needle can cause fluctuations in the temperature data, thus affecting the temperature control effect. To evaluate the stability performance of the two controllers, a \(-\)5 \({}^{\circ}\)C fluctuation was added to simulate the jittering phenomenon of the electrode needle in a time range of 30-40 s. The results are shown in Figure 6 and Table 3.  
The error and recovery time analyses presented in Figure 6 and Table 3 show that the ANFIS-PID controller has a larger disturbance error (4.58 \({}^{\circ}\)C) compared to the PID controller when subjected to an external disturbance. However, after the disturbance disappears, the ANFIS-PID controller shows superior a control performance to the PID  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Target (\({}^{\circ}\)C)** & \multicolumn{5}{c}{**15**} \\ \hline  & **Overshoot** & **Undershoot** & **Rise Time (s)** & **Settling Time (s)** & **Steady-State Error** \\  & **(\%)** & **(\%)** & **(0–0.95)** & **(0.97–1.03)** & **(\({}^{\circ}\)C)** \\ \hline PID & 0 & 8.58 & 50 & 4.39 & 1.09 \\ ANFIS-PID & 1.56 & 3.03 & 5.15 & 5.53 & 0.18 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The simulation results when the target temperature changes.  
Figure 6: The temperature rise curves of the two controllers with added disturbance.  
controller, with the ANFIS-PID having a smaller steady-state error (0.42 \({}^{\circ}\)C) and a shorter recovery time (7.79 s) from the disturbance state to a steady state.
## 3 Number of Dominant Rays: Simulation Results  
Multi-path in an indoor environment contains rays reflected from the walls, ceiling, floor, as well as from obstacles. However, each reflection of a ray from a surface results in an attenuation in accordance with the reflection coefficient given by [PERSON]'s Equation (5). The more it is reflected, the less power it maintains, so effectively, there is a finite number of reflections that need to be taken into consideration in the summation Equation (1). To analyze it properly, the discussion should be divided into two types of transmissions; omni-directional and directive links.