esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
The primary objective of this study was to investigate whether the addition of CQ in Yanggaeng could enhance the physicochemical characteristics to align with consumer preferences. The secondary aim in this study was to determine the maximum concentration of CQ addition to Yanggaeng that elicited responses identical to those of the conventional Yanggaeng prototype, labeled as CON. To answer our research question, in this study, the physicochemical properties, antioxidant activity, and sensory attributes of Yanggaeng enriched with CQ powder were investigated. CQ powder addition gradually decreased the pH of Yanggaeng, resulting in acidification. Additionally, CQ powder significantly affected the \({}^{\circ}\)Brix value and WHC, thereby altering the characteristics of Yanggaeng compared with the control (CON).  
In terms of colorimetric properties, CQ powder addition reduced the \(L^{\star}\) and \(a^{\star}\) values of Yanggaeng while increasing the \(b^{\star}\) value and BI. Moreover, CQ powder addition caused proportional increases in TPC and TFC, accompanied by enhanced antioxidant activities, including DPPH radical scavenging, ABTS radical scavenging, and FRAP, indicating an increased capacity to donate electrons.  
Furthermore, CQ powder incorporation resulted in reduced hardness, gumminess, and chewiness compared with CON, affecting the textural properties of Yanggaeng. Regarding consumer preferences, CQ2 and CON received statistically similar scores across various aspects, including color, scent, flavor, sweetness, taste, chewiness, overall acceptance, and purchase intention. In conclusion, CQ2 exhibited a preference similar to that of the commercially available conventional Yanggaeng (CON). This study sets the foundation for the development of food products incorporating Yanggaeng, a traditional Korean delicacy.  
Conceptualization, I.K. and J.-H.H.; methodology, H.J. and J.L.; software, H.J., J.L., S.W., Y.K., I.K. and J.-H.H.; validation, H.J., J.L., M.D., I.K. and [PERSON]-[PERSON]; formal analysis, [PERSON], [PERSON], S.W. and Y.K.; investigation, I.K. and [PERSON]-[PERSON]; resources, [PERSON]-H.H.; data curation, [PERSON], [PERSON], [PERSON], Y.K., M.D., J.K. and [PERSON]-H.H.; writing--original draft preparation, [PERSON], [PERSON], [PERSON], Y.K., M.D., J.K. and [PERSON]-[PERSON]; writing--review and editing, [PERSON], [PERSON], [PERSON], Y.K., M.D., I.K. and [PERSON]-[PERSON]; visualization, [PERSON], [PERSON] and [PERSON]-H.H.; supervision, [PERSON]-H.H.; project administration, I.K. and [PERSON]; funding acquisition, I.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board of Dankook University (protocol code DKU-2022-03-201-001 approved on 11 May 2022).  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.
# Disjoint Tree Mergers for Large-Scale Maximum Likelihood Tree Estimation
## 4 Discussion
### Comparison between DTM Pipelines  
Our study showed that the algorithmic choices within the DTM pipelines (starting tree, maximum subset size, and how the distance matrix is computed for use with ConstrainedINC and TreeMerge) impact accuracy. In general, we found that using a highly accurate starting tree and larger maximum subset sizes, as well as computing the distance matrix using topological distances, produces better final topological accuracy when compared to the alternative variants of DTM pipelines that we tested. The first two observations (impact of starting tree and subset size) were also noted in [24], but using a different set of options for each algorithmic step. Our third observation, that using topological distances in the guide tree instead of other ways of defining pairwise distances of sequences (for use within ConstrainedINC or TreeMerge) is beneficial, has not been communicated in any prior publication in the context of gene tree estimation; however, similar findings were reported in the context of species tree estimation in [25].  
One of the surprising findings in this study is that we did not see substantial differences in accuracy in the trees that were computed using our better pipelines once everything other than the DTM method (Constrained-INC, TreeMerge, or GTM) is fixed. This is interesting since the DTM methods use very different strategies for merging disjoint subtrees, especially with respect to the degree of blending that is permitted. Specifically, GTM does not allow any blending, TreeMerge allows partial blending, and only Constrained-INC allows full blending. Given the close accuracy between the DTM pipelines, computational issues become important and, thus, pipelines using GTM are superior to the other DTM pipelines, which are slower.  
Another trend observed in this study is that DTM pipelines are impacted (even if only by a small amount) by the choice of the starting tree. The impact of a poor decomposition on GTM makes sense, since, if the subsets are not convex in the true tree (i.e., separable from each other by edge deletions), then the requirement to not blend subset trees means that the true tree cannot be constructed using GTM. Therefore, it is noteworthy that the other DTM methods, which do allow blending, are also negatively impacted by poor starting trees.
## 5 Simulation Results and Analysis
### The Performance under Different MinPts and Eps
```
Input: Sample set \(\mathbf{D}=\{\mathbf{x_{1}},\mathbf{x_{2}},\cdotscdots,\mathbf{x_{m}}\}\), neighborhood parameters (\(\mathbf{Eps},\mathbf{Minpts}\)) Workflow:  Initializing the core object set \(\mathbf{\Omega}=\varnothing\); for\(j=1,2\)......, \(m\)  Determine the number of samples \(\mathbf{N_{Eps}}\left(\mathbf{x}_{j}\right)\) in the \(\mathbf{Eps}\)-neighborhood of sample \(\mathbf{x_{j}}\) if\(\mathbf{N_{Eps}}\left(\mathbf{x}_{j}\right)\geq\mathbf{Minpts}\)  Put sample \(\mathbf{x}_{j}\) into the core sample set: \(\mathbf{\Omega}=\mathbf{\Omega}\cup\left\{\mathbf{x}_{j}\right\}\); endif endfor  Initialize the number of clusters: \(\mathbf{k}=\mathbf{0}\);  Initialize the set of unaccessed samples: \(\mathbf{\Gamma}=\mathbf{D}\); while\(\mathbf{\Omega}\
eq\varnothing\)  Sample sets not currently being accessed:\(\mathbf{\Gamma_{old}}=\mathbf{\Gamma_{i}}\);  Randomly select core objects \(\mathbf{\epsilon}\in\mathbf{\Omega}\), initializing the queue \(\mathbf{Q}=\left\langle\mathbf{c}\right\rangle\); \(\mathbf{\Gamma}=\mathbf{\Gamma}\setminus\left\{\mathbf{c}\right\}\); while\(\mathbf{Q}\
eq\varnothing\)  Take the first sample \(q\) from the queue Q  if \(\mathbf{N_{Eps}}\left(\mathbf{x}_{j}\right)\geq\mathbf{Minpts}\)  Make \(\Delta=\mathbf{N_{Eps}}(\mathbf{q})\cap\mathbf{\Gamma}\),  Put all elements of \(\Delta\) into the queue Q; \(\mathbf{\Gamma}=\mathbf{\Gamma}\setminus\Delta\); endif endwhile \(k=k+1\), generate clusters \(\mathbf{C_{k}}=\mathbf{\Gamma_{old}}\setminus\mathbf{\Gamma_{i}}\) \(\mathbf{\Omega}=\mathbf{\Omega}\setminus\mathbf{C_{k}}\) endwhile Output: cluster classification \(\mathbf{C}=\{\mathbf{C_{1}},\mathbf{C_{2}},\cdots,\mathbf{C_{k}}\}\);
```  
**Algorithm 1**DBSCAN algorithm pseudo-code
## 3 Results
### Impact of Biochar on Morpho-Physiological Attributes and Enzyme Activity of Maize Seedlings
#### 3.2.2 Physiological and Biochemical Attributes  
Data regarding RWC showed that biochar application significantly (\(p\leq 0.05\)) increased RWC, and the highest (83%) was observed with 3% biochar treatment, as compared to the control treatment (Table 3). Chlorophyll a and b contents were 74% and 43% higher than control with biochar application at the rate 1.5% and 2%, respectively (Table 3). Moreover, a significant increase in total chlorophyll (chlorophyll a + b) was also observed with biochar application over the treatment set as the control.  
Data regarding measurement of antioxidant enzyme activity showed that biochar application also increased superoxide dismutase (SOD) and catalase (CAT) activity non-significantly (\(p\geq 0.05\)), although all the treatment results were statistically significant (\(p\leq 0.05\)) at par with each other (Table 3).
## 2 Overview
### Sample Area  
The sample area, as such, is the main area where the experimenter will feel the strongest impact of the design in terms of usability. A kinematic mounting system will be installed in order to allow for an effective and speedy setup of the different experiments to be performed at SKADI. This kinematic mounting system will allow an off-instrument preparation of the experimental setup and several user groups have already developed corresponding sample environments [4; 5; 6]. This setup is designed to take up a load of up to 990 kg on the area of a EUR-pallet. It is limited to \(\leq\)1000 kg to fulfill the requirements of the machine directive, upon verification higher loads are possible. Several heights of optional spacers are also prepared, as well as appropriate moving tables in order to accurately position the sample environment. The base plate for the system will be aluminum EUR-pallets with an adapted three-point mounting integrated into the pallets.  
In addition to those verifications, the shielding of the sample cave has been designed to avoid magnetic disturbances of a \({}^{3}\)He-analyzer for polarized neutrons. In order to achieve this, the magnetic permittivity of all walls is limited to \(\mu_{r}\leq 1.2\), on average, and never exceeds \(\mu_{r}=1.8\).  
Additionally, the \(3\times 3\) m\({}^{2}\) large sample area will feature a false floor, below which cabling and hoses for experimental connections can be routed. The defining sample slit of the collimation will be mounted on an extendable nose in order to minimize the free flight path of the neutrons in air.  
Figure 8 details the setup. Please note that the routing of hoses and cables from top as well as from the side is also foreseen, in order to route supply media and controls to the sample environment with a closed shielding. Access from the top is intended to facilitate working with cold fingers in cryostats or other, similar top-heavy, sample equipment. For a safe handling of those sample environments also, a crane is foreseen.  
The top of the generic sample environment is a breadboard of the same size as the EUR-pallet. There, all equipment can be mounted on the threaded grid.  
The achievable flux at sample position is 7.7\(\times\) 10\({}^{8}\) neutrons s\({}^{-1}\)cm\({}^{-2}\) with 4 m collimation length. This is based on the McStas simulation of the instrument, as described in this manuscript.  
Figure 8: (**a**) CAD cut of the sample area, neutron beam is indicated in red. (**b**) Photograph of the sample stage and false floor in a mockup of the sample area, as seen from the detector tube. Test load on table is 732 kg, not affecting the positioning accuracy of 0.1 mm.
## 5 Performance of TVMS-BPSO  
To evaluate the capabilities of TVMS-BPSO presented in Section 3.2 for day-ahead BESS scheduling, the conditions of Case I (low wind speed with fully charged battery) previously described in Section 4.1 have been considered. This case has been chosen because the number of optimization variables to be determined is the highest. Regarding the number of agents and iterations, these have been set equal to the population size and generations of GA previously implemented in Section 4 (75 agents and 100 iterations), and this guarantees a fair comparison between both methods. Other parameters of BPSO have been adjusted as follows; \(C_{PSO}^{a}\) = 2.05, \(C_{PSO}^{b}\) = 2.05, \(\sigma_{min}\) = 0.1, and \(\sigma_{max}\) = 1.  
Figures 31-33 show the comparison between GA and TVMS-BPSO for different wind speed profiles, whereas Table 13 shows the value of the objective function. As can be observed, TVMS-BPSO employs global exploration during the first iterations, analyzing solutions with high objective function value. As the algorithm evolves, exploitation has the relevant role of guiding the algorithm to a high quality solution, comparable to that obtained from GA implementation, according to Table 13.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(S_{W}^{b}\) & **GA** & **BPSO** & **Difference (\%)** \\ \hline
0 & \(-\)24,680.21 & \(-\)24,679.41 & 0.00326 \\
0.1 & \(-\)24,528.93 & \(-\)24,520.42 & 0.03469 \\
0.2 & \(-\)24,348.18 & \(-\)24,331.04 & 0.07039 \\
0.3 & \(-\)24,134.06 & \(-\)24,116.79 & 0.07154 \\
0.4 & \(-\)23,877.54 & \(-\)23,877.54 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 13: Comparison of objective function values.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{\(t\)/\(S_{W}^{b}\)} & \multicolumn{6}{c}{**Without BESS**} & \multicolumn{6}{c}{**With BESS**} \\ \cline{2-10}  & **0** & **0.1** & **0.2** & **0.3** & **0.4** & **0** & **0.1** & **0.2** & **0.3** & **0.4** \\ \hline  
\hline \hline \end{tabular}
\end{table}
Table 12: Diesel power generation (Case III).
## 5 Conclusions
Nevertheless, the characteristic time associated to phase changes is much longer than the time associated to the propagation of precursor and primary waves along the whole system, which is the main phenomenon considered in this article. The earliest effect of a phase change that may affect the first failure-induced waves is the apparition of small bubbles at the beginning of the boiling stage. This would result in a slight decrease in pressure wave velocity but would not significantly change the aspect of the wavefront. Moreover, coolant fluid vaporization does not occur at every failure. For the simulations presented in this article, a uniform temperature of 280 \({}^{\circ}\)C and a single-phase coolant fluid were considered. A sensitivity analysis should be carried out to accurately assess effects of the temperature field and of possible changes in pressure wave velocities caused by steam bubbles in the coolant fluid. Furthermore, the numerical model still needs to be validated by comparisons with experimental results. To this aim, an experimental mockup was designed and is currently being used at the Cadarache center of the French Commission for Atomic and alternative energies (CEA) [23]. In this mockup, the failure of fake fuel rods is reproduced, and the resulting fluid pressure waves and vibrations are recorded at several positions of the system. Thus, the fluid pressure profile evolution along the system will be compared to the numerical results to validate the model presented in the current article. Measurements related to elastic waves in the structure, carried out with AE sensors, strain gauges, and accelerometers, will be compared to the numerical results in order to estimate the bias induced by the response of those sensors. After this validation, the numerical model can be used according to the presented methodology for the interpretation of experimental results from tests in a real reactor. In such a case, it will be necessary to adjust some parameters, such as the failure length and position, to make the simulation representative of the specific test being analyzed. **Author Contributions:** Conceptualization, all authors; methodology, T.J. and V.F. ; software, V.F., validation, V.F., L.P., G.R. and E.S. ; formal analysis, T.J.; investigation, T.J.; resources, V.F. ; data curation, T.J. and V.F. ; writing--original draft preparation, T.J.; writing--review and editing, all authors; visualization, T.J.; supervision, L.P. and G.R. ; project administration, T.J., L.P. and G.R. All authors have read and agreed to the published version of the manuscript. **Funding:** This research received no external funding. **Acknowledgments:** This work was developed within the framework of the MISTRAL joint research laboratory between Aix-Marseille University, CNRS, Centrale Marseille and CEA. **Conflicts of Interest:** The authors declare no conflict of interest. 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.
## 3 Results
### Baseline Characteristics of the Study  
Table 1 demonstrates the baseline characteristics of the overall cohort as well as those of the patients with sPAP \(<\) 40 mmHg and sPAP \(\geq\) 40 mmHg. The sPAP groups were compared for significance, and the corresponding \(p\)-values were documented. The overall cohort had a mean age of 82.34 \(\pm\) 5.41 with a gender distribution male:female ratio of 50.60% vs. 48.40%. Regarding cardiovascular risk factors and concomitant diseases, 24.70% presented with diabetes mellitus, 77.60% with arterial hypertension, 9.40% with pAVK, 8.20% with COPD, and 61.20% with coronary artery disease. Echocardiographic left ventricular ejection fraction (LVEF) averaged 53.42 \(\pm\) 9.99%, and sPAP was 46.36 \(\pm\) 17.49 mmHg. Patients with an sPAP \(\geq\) 40 mmHg showed significantly higher STS scores (3.29 \(\pm\) 1.51 vs. 2.56 \(\pm\) 1.29; \(p\) = 0.025), significantly higher percentages of atrial fibrillation (45.30% vs. 12.50%; \(p\) = 0.002), significantly lower tricuspid annular plane systolic excursion (TAPSE) (20.95 \(\pm\) 4.06 mm vs. 23.12 \(\pm\) 3.12 mm; \(p\) = 0.017), and significantly higher percent distributions with respect to tricuspid valve insufficiency (39.60% vs. 12.50%; \(p\) = 0.007) than did those with an sPAP \(<\) 40 mmHg. For general laboratory chemistry data, creatinine (1.10 \(\pm\) 0.50 mg/dL vs. 0.95 \(\pm\) 0.30 mg/dL), BNP (3162.00 \(\pm\) 4440.00 pg/mL vs. 1122.50 \(\pm\) 1017.53 pg/mL), and cTnI (31.00 \(\pm\) 41.00 pg/mL vs. 16.00 \(\pm\) 15.50 pg/mL) were significantly higher, and Hkt (37.40 \(\pm\) 8.50% vs. 41.20 \(\pm\) 575%) and Hb (12.30 \(\pm\) 3.40 g/dL vs. 13.05 \(\pm\) 1.78 g/dL) were significantly lower in the sPAP \(\geq\) 40 mmHg cohort than they were in the sPAP \(<\) 40 mmHg cohort.
## 3 Computational Modeling Method  
### Pipe Whip Model  
The pipe is modelled with a coupled 1D/3D modeling approach with three-dimensional solid elements created at the localized region of interest (elbow) of the pipe, and the rest of the horizontal length of the pipe is modelled with one-dimensional elements (Figure 1). These simplified models are then compared with a fully 3D model for simplified model verification. The design is based on the Aquitaine II pipe model layout as experimentally tested in the CEA tests [5]. The dimensions of the pipe model are taken from the standard Schedule 80 3\({}^{\prime\prime}\) pipe made of equivalent material to A106 grade B carbon steel.  
The 3D elements are created in the region of the elbow where the pipe is expected to impact on the neighboring concrete slab kept at a distance of 200 mm from the longitudinal axis of the pipe. The solid elbow region of the pipe is modelled for two different pipe lengths of 550 mm and 150 mm to evaluate the structural response to the same failure conditions for predicting and comparing the computational time for the change in the number of elements. This research is dedicated to understanding the structural behaviour of simplified and localized 1D/3D coupled models under dynamic loading conditions through a fluid-structure interaction method.  
The resultant values of pressure and velocity from the CFD simulations will serve as the input parameters of the structural study and, hence, the CFD results are required to be accurate. A grid independence test is performed to justify the use of the selected element size of 4 mm for the fluid domain inside the 3 m pipe model by considering the flow conditions with the change in element size. The grid test is performed in the parametric design study of the Ansys Workbench module by taking into consideration the outlet velocity and pressure. The 3D fluid domain of the 3 m pipe is first discretized into an element size of 8 mm using the multizone meshing algorithm to produce hexahedral mesh throughout the fluid domain. Five layers of inflated mesh with a growth-rate of 1.2 are created along the wall boundary to facilitate further accuracy, and a face meshing method was applied to the cross-sectional surface to prevent unstructured mesh (Figure 2). The inflation algorithm is applied to the model due to the significance of the wall boundary which affects the flow conditions. Increasing the number of finite elements leads to an increased accuracy in the solution but can be inadequate in situations where the geometry is significantly large. Hence, an efficient meshing method and procedure is necessary to provide a balance between the computational time and accuracy [31; 32]. The CFD analysis is solved for the operational conditions of the pipe whip test, and the parametric grid test is analyzed for the change in element size. The results of the parametric grid study are shown in Figure 3. The element size is reduced from 8 mm to 1 mm (See Figure 4) for the grid test and the corresponding values of pressure and velocity outlet are plotted where the linear
# A Numerical Study on Computational Time Reversal for Structural Health Monitoring
## 4 Applications with Numerical Examples
### Imaging in Frequency Domain  
Imaging techniques traditionally take place in the frequency domain. By this, one avoids the need to establish the appropriate time step of a refocusing snapshot. The imaging functional for our case is given as [33]:  
\[I_{m}=\frac{1}{2\pi}\sum_{\omega}\sum_{r=1}^{N_{r}}\overline{\hat{h}_{rs}( \omega)\widehat{f}_{s}(\omega)}\hat{h}_{rm}(\omega)=\frac{1}{2\pi}\sum_{\omega }\sum_{r=1}^{N_{r}}\overline{\hat{u}_{r}(\omega)}\ \widehat{h}_{rm}(\omega) \tag{21}\]  
which associates a value at the \(m^{\text{th}}\) DOF by back propagating the recordings (data) reversed in time, \(\overline{\hat{u}_{r}(\omega)}\), using all receivers and all available frequencies. For problems of damage localization in the frequency domain, the scattered field at the receivers are considered and the imaging functional is defined as (mention here that a typo originally existed in the respective equation of chapter [33] has been corrected here in Equation (22)):  
\[I_{m}=\sum_{\omega}\sum_{r=1}^{N_{r}}\overline{\hat{u}_{rs}^{\text{sc}}(\omega )}\,\widehat{h}_{rm}(\omega)\widehat{h}_{ms}(\omega), \tag{22}\]  
where \(\widehat{u}_{rs}^{\text{sc}}(\omega)\) is the scattered field recorded at \(r\)th DOF due to an excitation at the \(s^{\text{th}}\) DOF.  
A numerical example based on the same setup as that of Section 3.4 is presented for the general case of source localization based on Equation (21). To the best of the authors' knowledge, such an implementation for the case of beam bending has not been published in the literature to date. Similar implementation was first presented in [40] for the case of an 1D acoustic field or for the axial deformation of a rod. It has been assumed that there exist five equidistant stations on the beam that may operate as the source and/or receiver points to emit signals in the form of Ricker pulses and/or record the response data of beams' transverse displacements. The experiment has been conducted numerically, using 480 nodes with a respective number of finite elements. For the spatial locations where values for imaging are to be defined, 103 points have been considered including the two boundaries. The results for imaging where source localization is performed using Equation (21) are depicted in plots of Figure 13 for several source locations, each time using the same number of receivers.  
Figure 13: _Cont._