esa-sceva/satcom-chunk-collection huggingface dataset:
# Selecting Target Range with Accurate Vital Sign Using Spatial Phase Coherency of FMCW Radar
## 3 Results  
### Data Acquisition  
The radar and referenced VS signals of 20 subjects (15 men and 5 women) were recorded. The subjects sat on a chair facing the radar apart about 1 m. Depending on subjects, the distance between body parts and radar can be varied. However, the distance to the abdomen was generally 0.6\(\sim\)0.8 m and the distance to the neck was 0.9\(\sim\)1.0 m. Thus, we set the searching boundary of the target range bin to 0.5\(\sim\)1.2 m. Each subject was well informed about the entire process and purpose of recording, and agreed to the recording. All subjects had normal vital rates, and rested for least 5 min before the recording process. The recording lasted 2 min for each subject.
###### Abstract  
In this study, the biocidal activity of two plant derivatives (oregano and cloves' essential oils--EOs) was evaluated, as a potential innovative and eco-friendly cleaning method for canvas paintings. The object of the study was the oil painting on canvas entitled \"Studio di nudo\" ([PERSON], 1921), showing stains caused by microorganisms. The research focused on: (1) isolation and identification of microorganisms associated with discolorations on the obverse and reverse sides of the canvas; (2) evaluation of biocidal activity of selected EOs against fungal and bacterial collections. The phylogenetic identification was conducted with both cultivation and molecular methods. The canvas was mainly colonized by _Penicillium_, _Aspergillus_, and _Cephaloteca_ fungal genera and by bacteria of the _Bacillus_ genus. To evaluate the biocidal effect of the EOs' volatile components only, an antibiogram assay (agar disc diffusion method) and a customized assay (named the contactless test) were conducted. Tested EOs showed antimicrobial activity on fungi and bacteria. However, compared to cloves, oregano EO exhibited a better inhibition activity both in contact and contactless tests. The work is pioneering for the use of EOs' volatile compounds against oil painting biodetetetogens, and gives insights into possible extended, innovative and eco-friendly cleaning methods for painting control procedures.  
antimicrobial activity; canvas painting biodeterioration; cleaning procedure; contactless test; cultural heritage; control; plant essential oils; volatile components
# Performance Evaluation of Ground AR Anchor with WebXR Device API
## 4 Results and Discussion  
### WebXR Camera Trajectory  
In order to experiment with the camera trajectory along each axis, a comparative experiment between ground truth and WebXR was performed for each of the X, Y, and Z axes. An HMD that included a head tracker was selected for the ground truth experiment, which showed high accuracy in terms of camera trajectory because the head tracker performed the function of tracking the display device. It has been used in several studies due to its high accuracy within the sensor range [25; 26; 27].  
After loading the 3D model in the same position as the head tracker that the ground truth could recognize, the device was moved 50 cm in each direction of the X-axis direction (left, right), 50 cm in each direction of the Y-axis direction (up, down), and 50 cm in each direction of the Z-axis direction (front, back). When  
2.1609. The lowest point of ground truth was 1.8774, and at this time, the same frame value of the ground with strong reflection was 1.8199 and the same frame value of the ground with weak reflection was 1.8485.  
Figure 5c is the result of an experiment to evaluate the accuracy of the camera trajectory about the Z-axis. The experiment was conducted for about 1230 frames. The high point of the ground truth was 1.4055, and at this time, the same frame value of the ground with strong reflection was 1.5649, and the same frame value of the ground with weak reflection was 1.5525. The lowest point of ground truth was 1.1606, and at this time, the same frame value of the ground with strong reflection was 1.1614, and the same frame value of the ground with weak reflection was 1.1551. As a result of the experiment, when comparing the ground truth and WebXR for each axis, it was confirmed that there was a difference in the camera trajectory based on an anchor that was arbitrarily designated.  
Table 2 presents the standard deviation according to the difference between the ground truth (GT: ground truth) and the camera trajectory of WebXR. The results of the experiment showed the standard deviation of the ground with strong reflection (SR: strong reflection) on the X-axis is 0.73 cm, and the standard deviation of the ground with weak reflection (WR: weak reflection) is 0.21 cm. On the Y-axis, the standard deviation of the ground with strong reflection is 0.32 m, and the standard deviation of the ground with weak reflection is 0.38 cm. Additionally, in the Z-axis, the standard deviation of the ground with strong reflection was 0.55 cm, and the standard deviation of the ground with weak reflection was 0.51 cm.  
Figure 5: Accuracy evaluation of the camera trajectory for WebXR against the ground truth. (**a**) X-axis; (**b**) Y-axis; (**c**) Z-axis.
###### Abstract  
Gas flow mechanisms in shale reservoirs with multiscale pores and fractures are extremely complex. In this study, a dual fracture framework model was adopted to describe gas flow in multiscale shale reservoirs. Gas flow through a shale reservoir occurs through both the shale matrix and hydraulic fractures. This study considered bulk phase and adsorbed gas flow in the shale matrix. Next, a series of partial theories were combined to derive a fully coupled model simulating gas flow in multiscale shale reservoirs: (1) fractal theory was adopted to obtain the pore distribution within shale reservoirs; (2) mechanical equilibrium equations were used to investigate the stress-sensitivity of permeability and porosity; and (3) a Langmuir adsorption model was applied to describe the effects of gas adsorption/desorption. The proposed model was validated using traditional models as well as field data on gas production from Marcellus Shale, and was subsequently applied to study variations of mass flux in various flow regimes with respect to reservoir pressure. We found that mass flux in the slip flow regime decreased at first and then increased with decreasing reservoir pressure, while in the continuum regime, Knudsen diffusion and surface diffusion the mass flux decreased with decreasing reservoir pressure. Stress-sensitivity has a significant impact on bulk phase gas flow, while adsorption/desorption influence both the bulk phase gas flow and adsorbed gas flow. At high pressures, the impact of stress-sensitivity on total gas mass flux is greater than that of adsorption/desorption, while the reverse was true for low pressures. The proposed model shows promising applications for analyzing various gas flow regimes in multiscale pores/fractures, and accurately evaluating in situ apparent permeability.  
Keywords: shale reservoirs; hydraulic fractures; matrix; flow regime; stress-sensitivity; adsorption/desorption +
Footnote †: journal: Applied Physics Letters
## 2 Materials and Methods
### Evaluation Metrics  
To quantitatively analyze the performance of the seven networks trained in this study, we employed four different evaluation metrics. In the offline recognition task, we used three common classification metrics in the field of action recognition, namely accuracy (Equation (3)), precision (Equation (4)) and recall (Equation (5)). Among these, TP represents true positives, TN represents true negatives, FP represents false positives and FN represents false negatives. We calculated the precision and recall for each gesture category and then averaged these values across all categories to obtain the corresponding values for the entire test set. Accuracy indicates the average accuracy of samples across the entire test set. In the online recognition task, this paper uses frame-wise accuracy (Equation (6)). Here, TC denotes the number of correctly predicted frames, and TF represents the total number of frames in a complete video. The subtraction of 16 accounts for the input sequence length of 16 frames.  
\[Accuracy=\frac{TP+TN}{TP+FP+TN+FN} \tag{3}\]  
\[Precision=\frac{TP}{TP+FP} \tag{4}\]  
\[Recall=\frac{TP}{TP+FN} \tag{5}\]  
\[Frame-wiseaccuracy=\frac{TC}{TF-16} \tag{6}\]
# Comparison of Topologies Generated by Evolutionary Neural Architecture Search
## 2 Neural Architecture Search  
### Automated Machine Learning (AutoML)  
AutoML is a modern technique in deep learning that automates the process of designing neural network architecture [10]. Neural Architecture Search (NAS) is a subfield of AutoML that focuses specifically on the automated design of network architecture. The design of the architecture is a critical stage in deep learning since it has a significant impact on performance. However, manual architecture design is both time consuming and prone to errors [11]. As deep learning tasks have become more challenging and complex, manual design has become increasingly difficult [7].  
Auto-designing the topology of deep neural network architecture is made possible by NAS techniques. Deep neural networks have become more complex, and now have numerous layers and nodes connected by links. Typically, human experts design most neural network architectures. On the other hand, NAS can generate the architecture automatically.
# A Feature-Independent Hyper-Heuristic Approach for Solving the Knapsack Problem
## 4 Experiments and Results
### Confirmatory Experiments
#### 4.3.3 Confirmatory Experiment III  
In this final experiment, we extend our analysis to hard and larger instances. This time, we produced 30 HHs by using 60% of set \(\beta_{2}\) and tested them on the remaining 40% of the same set. With this experiment, we validate that the learning method is quite stable as it still produces competent hyper-heuristics. Despite the fact that the set comprised instances with different features, all three cases beat the best operator (MaxPW) in isolation. Additionally, setting a good training set seems to impact the efficiency of the HH model.  
Figure 5: Profit per instance obtained by the best hyper-heuristic trained on 60% of set \(\beta_{1}\), and the best and worst heuristic per instance on the remaining 40% of set \(\beta_{1}\).  
Figure 6: Frequency of the ranks obtained by MaxPW and Best-HH (trained on 60% of set \(\beta_{1}\)) the remaining 40% of set \(\beta_{1}\).  
Training done on \(\alpha_{1}\) seemed to negatively affect the results, as seen on exploratory experiment I and confirmatory experiment I. It is important to note that sets \(\alpha_{1}\) and \(\alpha_{2}\) are balanced and synthetically produced. Thus, 25% of the problem instances are designed to maximize the performance of a single low-level heuristic. This pattern is repeated for all the heuristics in this set. Conversely, hard problem instances from sets \(\beta_{1}\) and \(\beta_{2}\) are randomly sampled (without replacement) using three different seeds. This training scheme is more representative of real-life applications, where often no balanced or ideal conditions are met.  
The model's behavior is similar to what we observed in previous cases. The hyper-heuristics trained on hard and larger instances are still competitive regarding the single heuristics applied in isolation. This time, the improvement on MaxPW is smaller than in previous cases (\(<\)0.01%). Table 9 shows that, as in previous cases, there is a difference in the behavior of the heuristics and HHs in terms of ranks, but it does not necessarily represent a significant difference in terms of total profit. However, when we analyze the success rate, we observe that Best-HH obtains promising results, \((0.6542,0.0958)\). This indicates that in little more of 65% of the instances in the 40% of set \(\beta_{2}\) used for testing, Best-HH was equal in performance to the best out of the four heuristics. Besides, in almost 10% of the instances for the same set, Best-HH improved upon this result.
## 2 Metasurface Design and Simulation
Other parameters, called reflection coefficients of the LTC polarization converter (RHCP and LHCP), a polarization conversion ratio, and a polarization extinction ratio are introduced [7] to evaluate a linear-to-circular polarization conversion with a promising procedure. We established these parameters of the reflected wave from the following equations, applying the results simulated above. \[r_{RHCP-y}=\sqrt{2}\big{(}r_{xy}+ir_{yy}\big{)}/2r_{LHCP-y}=\sqrt{2}\big{(}r_{ xy}-ir_{yy}\big{)}/2 \tag{2}\]  
\[\text{PCR}=\frac{\big{|}r_{RHCP-y}\big{|}^{2}}{\big{|}r_{RHCP-y}\big{|}^{2}+ \big{|}r_{LHCP-y}\big{|}^{2}} \tag{3}\]  
\[\text{PER}=20\log\left(\frac{r_{xy}}{r_{yy}}\right) \tag{4}\]  
The reflection coefficients of the dual-frequency wideband LTC polarization converter are determined using Equation (2) based on the data simulated above. As shown in Figure 3, the calculated dual-frequency wideband \(r_{RHCP-y}\) magnitude is close to 0 dB with respect to 3 dB axial ratio, indicating that the reflected wave under the normal incidence  
Figure 3: The calculated results of the proposed rectangular-slot converter under \(y\)-polarized incidence. (**a**) Axial ratio (dB) and (**b**) \(r_{RHCP-y}\) and \(r_{LHCP-y}\) (dB). Figure 2: The simulated results of the proposed converter. (**a**) The magnitudes (\(r_{yy}\) and \(r_{xy}\)) and (**b**) phase difference (\(\Delta\varphi_{yx}\)). \(y\)-polarized wave has converted to an RHCP and LHCP wave. Furthermore, the obtained dual-frequency wideband polarization conversion ratio, illustrated in Figure 4a, thoroughly shows that the linear-to-circular polarization conversion ratio can be accomplished at more than 99% over the frequency ranges of 17-23.07 GHz and 27.01-33.70 GHz, respectively, conquering 35.23% and 26.62% of the 3 dB axial ratio band. The effectiveness of circular polarization is determined by the polarization extinction ratio (PER) between \(r_{RHCP-y}\) and \(r_{LHCP-y}\) waves. The polarization extinction ratio should be greater than \(-\)20 dB (LHC) or +20 dB (RHCP) to establish the equal 90\({}^{\circ}\) phase difference and magnitude succeeding in a circularly polarized wave. According to Figure 4b the frequency bands between 16.01-23.95 GHz and 24.85-37.0 GHz meet the polarization extinction ratio requirement. The efficiency of the proposed polarization converter is significantly higher with the polarization conversion ratio and polarization extinction ratio frequency bands, which hold 35.23% and 26.62% of the 3 dB axial ratio over the dual-frequency bands of 16.49-23.54 GHz and 26.44-34.65 GHz, respectively. In summation, a normal \(y\)-polarized incident wave can be converted into a circularly polarized wave using the proposed converter, which has an exceptionally wide band and is highly efficient.
## 2 Numerical Model
### Big-Data Generation under Various Environmental Conditions and Failure Scenarios  
Big data were generated and collected by using the developed time-domain simulation program under various wave conditions and intact/failure scenarios, as summarized in Tables 2 and 3. As shown in Figure 1c, we considered accelerometers as sensors. However, we collected displacement signals directly assuming that real-time double integration is feasible with an appropriate bandwidth filter. Then, we conducted a systematic sensitivity test with respect to the arrangements of accelerometers to discover effective sensor combinations. Note that sensors #1 and #9 were not used for the analysis due to the fixed-fixed boundary conditions. As presented in Table 2, 21 intact/failure cases were simulated for 1800 sec with a time interval of 0.2 sec at each environmental condition. The failure cases were simulated by disconnecting the target mooring lines. The wave heading is assumed to be normal to the longitudinal direction of SFT. Based on symmetry, failure scenarios on the half domain were considered. For the random wave-elevation generation, a JONSWAP wave spectrum was utilized, and 100 regular-wave components were superposed to generate the random wave signals. Signal repetition in time histories was avoided through the equal energy method in which each regular wave component has equal spectral energy. Ten wave conditions were considered, as shown in Table 3. The enhancement parameter (\(\gamma\)) of the wave spectrum was fixed at 2.14. The total data points and time were, therefore, 210 (21 scenarios times 10 environmental conditions) and 378,000 sec (210 simulations times 1800 s). As given in Table 3, we employed 80% of data collected from eight environmental conditions for training, and thus the total data points for training were 134 while the number of data points for testing was 76. Data from the first eight environmental conditions wereutilized for studies of optimization and failure-detection performance with different arrangements of the sensors. Later, we further tested the feasibility of the algorithm by employing data from the last two environmental conditions not used for training.
## 3 Model and Methodology
### Solution Procedure
#### 3.2.2 Mode Selection Rules  
One of the complexities of the MRCPSP and its variations is the multiple modes available to execute every activity. Practitioners seldom have sufficient time to simulate or even consider all of the possible outcomes of selecting different execution modes for every single activity. Instead, they focus on critical activities and deal with non-critical activities based on either intuition, experience or simple rules of thumb. In this study, five different Mode Selection Rules (MSR) are employed to determine the best execution mode for every activity based on different metrics, e.g., duration, resource consumption and combinations of these two parameters. Table 2 presents the mode selection rules used in the proposed solution procedure. The Shortest Feasible Mode (SFM) and least resources proportion were taken from [49]; the remaining three rules were taken from [50]. The mode selection rule to be used is chosen randomly, and based on this selection, the information of the mode is used to determine the activity list, resource consumption and, ultimately, the makespan of the schedule.  
One critical factor to take into account when selecting modes is that given that cycles are allowed in the MRCPSP/max, the fact that the modes selected create cycles of positive length needs to be considered. A cycle of positive length is that in which the sum of the arc weights of the activities in the cycle is >0. Cycles of positive length violate the constraint in Equation (5); so, to deal with this issue, the above-mentioned MSR are used when solving the Best Mode Assignment Problem (BMAP) introduced in [47], and the approach of using a lower bound determined by the critical path is followed. Here, the authors view the MRCPSP/max as a mode assignment problem and propose to look for the best mode for each activity. The key decision variables in this model are the modes of the activities, and feasible solutions are the mode vectors that are resource and time feasible.  
Figure 5: Graphical representation of swap and insert operators.
## 2 Materials and Methods
### Electroflotation Tests  
For the electroflotation experiments, three different synthetic saline slurries were prepared: 1 g/L of kaolinite, 1 g/L of montmorillonite, and 1 g/L of the mixture 1:1 of both clays, considering concentrations of 0.1, 0.3, and 0.5 M of NaCl. The particle sizes of kaolinite and montmorillonite were equivalent to a \(-\)635 mesh Tyler with a d\({}_{80}\) of 5 \(\upmu\)m. All electroflotation experiments were carried out at room temperature of 19 \(\pm\) 0.5 \({}^{\circ}\)C and atmospheric pressure. The power supply was connected to the anode and cathode, operating at fixed cell voltages (E\({}_{\text{cell}}\)) of 10, 15, and 20 V, with electroflotation times of 10, 15, and 20 min.  
Colloidal suspension preconditioning was performed in an agitator tank for 30 min at 500 rpm at a fixed pH of 8. Subsequently, for electroflotation, NaCl solutions were homogenized for 15 min at 700 rpm using a magnetic stirrer. The suspension was promptly transferred to the Hallimond cell to perform electroflotation studies. Upon completion of the electroflotation experiment, the floated product was thoroughly extracted using a peristaltic pump and then collected in a beaker. The collected product was then filtered using a vacuum pump connected to a Buchner funnel and dried in a universal oven at 105 \({}^{\circ}\)C for 24 h. Finally, the mass of dried clay particles was weighted on an analytical scale. The electroflotation efficiency \(R_{MF}\) was determined using the following equation:  
\[R_{MF}=\frac{m_{f}}{m_{i}}\cdot 100\% \tag{1}\]  
where \(m_{i}\) is the initial mass of clay fed to the modified Hallimond cell, and \(m_{f}\) is the total mass recovered after the electroflotation experiments, including Ti flocs and clays.
# Numerical Simulation of Hot Jet Detonation with Different Ignition Positions
###### Abstract  
Ignition position is an important factor affecting flame propagation and deflagration-to-detonation transition (DDT). In this study, 2D reactive Navier-Stokes numerical studies have been performed to investigate the effects of ignition position on hot jet detonation initiation. Through the stages of hot jet formation, vortex-flame interaction and detonation wave formation, the mechanism of the hot jet detonation initiation is analyzed in detail. The results indicate that the vortexes formed by hot jet entrain flame to increase the flame area rapidly, thus accelerating energy release and the formation of the detonation wave. With changing the ignition position from top to wall inside the hot jet tube, the faster velocity of hot jet will promote the vortex to entrain jet flame earlier, and the DDT time and distance will decrease. In addition, the effect of different wall ignition positions (from 0 mm to 150 mm away from top of hot jet tube) on DDT is also studied. When the ignition source is 30 mm away from the top of hot jet tube, the distance to initiate detonation wave is the shortest due to the highest jet intensity, the DDT time and distance are about 41.45% and 30.77% less than the top ignition.  
hot jet detonation initiation technique; flame acceleration; detonation combustion; vortex; ignition position +
Footnote †: journal: Applied Sciences