esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
With the aim of application to on-site real-time monitoring for medical and industry workers whose extremities have potential risks of accidentally receiving high doses, the authors investigated the dose responses and thermal stabilities of a radiochromic material composed of PVA, iodine and silica nanoparticles, named \"PAISiN\". It was found that the radiation-induced coloration of PAISiN could be flexibly and simply controlled by the adjustment of temperature. We could quickly clear the color of PAISiN by heating around 40 \({}^{\circ}\)C for a few hours to reuse it; conversely, we could keep the radiation-induced color by storing it in a refrigerator for performing more comprehensive analyses later on. These findings add a practical advantage to PAISiN by highlighting its preferable features, such as high detectability, safety and cost-effectiveness.  
For the application of PAISiN to routine monitoring in a variety of workplaces, further investigations to improve its accuracy, stability and reproducibility are desirable to be performed in reference to the preceding approaches using other materials [30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44]. Those studies should include experiments to examine the UV absorbance, higher dose-response,  
Figure 6: Comparison of the time changes of the green component of PAISiN at room temperature (20 \({}^{\circ}\)C) between repeated \(\gamma\)-ray irradiations: 1 st (**left**) and 2 nd (**right**); 2 nd irradiation was made about 1 month later than the 1 st one.  
longer-term stability, changes in dosimetric properties after more repetitive irradiation and preventive measures against degradation during storage, in parallel with comprehensive theoretical analyses on the thermally dynamic kinetics of coloration/decolorization and molecular interactions with varying compositions.  
Conceptualization, H.Y. and H.M.; material synthesis, H.M.; software setting and analysis, H.Y.; validation, H.Y. and H.M.; data curation, H.Y.; writing--original draft preparation, H.Y.; writing--review and editing, H.M.; funding acquisition, H.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by JSPS KAKENHI Grant Number 18 KK0147 and the Program of the Network-type joint Usage/Research Center for Radiation Disaster Medical Science funded by the Ministry of Education, Culture, Sports, Science, and Technology (MEXT) of Japan and Hiroshima University.  
Not applicable.  
The authors declare no conflict of interest.
# A Novel Technique for Semantic Segmentation of Hyperspectral Images Using Multi-View Features
## 4 Proposed Technique
### Multi-View Feature Extraction  
CCA is a multivariate statistical technique used to explore relationships across two datasets at a time. The algorithm begins with the pre-processing step, standardizing each dataset to have zero mean and unit variance. This normalization ensures that each dataset contributes equally to the analysis, regardless of differences in scale. CCA works on two views at a time, and is referred as PCCA in our paper.  
Alongwith PCCA, we have also utilised MCCA because the former considers the information locally among two datasets at a time only. Whereas, MCCA provides simultaneous exploration of shared information across all the datasets via reduced features, thus also reducing the overload and time. In the context of MCCA, PCCA is a special case when there are only two datasets.  
The next step involves formulating covariance matrices for within-dataset and between-dataset relationships. These matrices, denoted as \(C_{ii}\) and \(C_{ij}\), capture the statistical associations within each dataset and the relationships between different datasets. Solving the generalized eigenvalue problem leads to the identification of canonical vectors \(\mathbf{a}_{1},\mathbf{a}_{2},\ldots,\mathbf{a}_{k}\) and their corresponding canonical correlations \(\lambda_{1},\lambda_{2},\ldots,\lambda_{m}\). These canonical variables represent linear combinations of the original features that maximize the associations between datasets.  
The output of CCA, including the canonical vectors and canonical correlations, can be further utilized in various applications. In this study, the exploration of hyperspectral image data involves the application of CCA to unveil intricate relationships among various views and reduce dimension of HSI. The derived canonical variables, representing consolidated information from different spectral views, serve as reduced features that encapsulate the underlying multi-view structure. These canonical variables, extracted through the synergy of MCCA, are instrumental in capturing the shared information across multiple datasets. Leveraging these reduced features, subsequent analyses, such as semantic segmentation using SVM, gained a comprehensive understanding of the inter-dataset relationships. The Canonical variables provide a compact representation that enhances the efficiency and interpretability of downstream tasks, thereby contributing to the advancement of hyperspectral image analysis techniques.  
Treating each spectral band as a different view, HSI gives us the automatic opportunity to exploit it for CCA and reduce the dimension of original HSI cube. Rather than picking every band as a view for CCA, we divided the whole dataset into subsequent groups of views. This reduced the computational overload for CCA and gave better results. For one single HSI dataset with hundreds of bands, \(K=4\) number of views were generated by combining together spectral bands from the consecutive wavelengths. This became four different datasets \(X_{1},X_{2},X_{3},X_{4}\) for CCA to act upon.
# Copper- and Iron-Based Nanoflowers in Cancer Theranostics
## 4 Synthesis Methods and Applications of Flower-like Copper- and Iron-Based Nanostructures
### Other Applications  
Iron- and copper-based NFs have plenty of applications both within and out of the realm of medicine. Firstly, NFs show great antibacterial properties with low values of minimum inhibitory concentration (MIC) comparable to other widely used antibiotics. [PERSON] et al., synthesized NF@FeO(OH) and studied its effect against certain bacterial strands. Their results showed that the MIC was equal to 256 mg L\({}^{-1}\) for _E. coli_, _C. parapisilosis_ and _P. aeruginosa_ and 128 mg L\({}^{-1}\) for _L. pneumoniae_, _E. hirae_, and _S. aureus_, and _C. tropicalis_. Its antimicrobial mechanism is probably linked to the changes in microbial DNA and proteins caused by ferric ions [124].  
[PERSON] used a green self-assembly method to synthesize hybrid gallic acid-Cu(II) NFs and studied their antimicrobial activity against fungus (_C. albicans_) and Gram-negative (_E. coli_, _P. aeruginosa_) and Gram-positive (_S. aureus_) bacteria. It was found that the hNFs depicted high antimicrobial efficacy at all examined microorganism concentrations [122].  
[PERSON] and [PERSON] synthesized Cu NFs and evaluated their antifungal properties against common plant pathogenic fungi, namely _F. oxysporum_, _F. moniliforme_, _F. culmorum_, _F. tricticum_ and _A. niger_. It was concluded that Cu NFs enhance the action of the commercial antifungal agent ketoconazole despite the NFs' dependence on multiple factors such as temperature, the concentration of nanoparticles, pH and the concentration of the test organism [125].
## 5 Conclusions  
This study identified and functionally characterized a novel geraniol synthase gene (_UrGES_) in the _U. rhythmchophylla_ genome. Multiple sequence alignment and phylogenetic analysis classified UrGES within the TPS-g subfamily. The enzymatic function was validated through both in vitro catalytic assays and transient expression in _N. benthamiana_. Furthermore, _UrGES_ was observed to be transcribed the most in leaves, with significant transcriptional induction by exogenous MeJA treatment correlating with increased accumulation of TIAs. Collectively, this study indicates that UrGES serves as a pivotal enzyme in the TIA biosynthetic pathway and plays a potential role in TIA accumulation. Furthermore, the identified _UrGES_ gene resource offers a potential new metabolic engineering approach to enhance iridoid and alkaloid biosynthesis in future studies.  
The following supporting information can be downloaded at [[https://www.mdpi.com/article/10.3390/plants14152273/s1](https://www.mdpi.com/article/10.3390/plants14152273/s1)]([https://www.mdpi.com/article/10.3390/plants14152273/s1](https://www.mdpi.com/article/10.3390/plants14152273/s1)): Figure S1: Total RNA extraction and _UrGES_gene cloning; Figure S2: Prediction of the transmembrane structural domains of UrGES protein using TMHMM website; Figure S3: Amino acid sequence alignment of UrGES with verified GESs in other plants; Figure S4: HPLC standard curve of TIAs.  
**Author Contributions:** [PERSON] and [PERSON] jointly developed the research, analyzed experimental results, and wrote the first draft of the paper. [PERSON] and [PERSON] initiated and coordinated the study, contributing to conception, data analysis, and manuscript revision. [PERSON], [PERSON], and [PERSON] participated in the experiment and data analysis. [PERSON] and [PERSON] participated in the experiment material preparation and data analysis. [PERSON] and [PERSON] participated in the data interpretation, refinement, and manuscript revision. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by seven projects: Hunan Agricultural University for Supporting Young Interdisciplinary Scholars (No. 2024 XKJC02), the Guangxi Major Science and Technology Project of China (GuikeAA22096021), the Hunan Provincial Key Laboratory for Synthetic Biology of Traditional Chinese Medicine (No: HCSW2023-01), the Project of Hunan Natural Science Foundation (2022 JJ30305), the Key Scientific Research Fund of Hunan Provincial Education Department (21A0138), the Horticulture's Open program of Hunan Agricultural University (2021 YYXK002), and the Postgraduate Scientific Research Innovation Project of Hunan Province (No. CX202020689).  
**Data Availability Statement:** The primary data generated during this research is available within the published article and its Supplementary Materials.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 2 Classification of Phase Change Materials (PCMs)
### Eutectic PCMs
#### 2.3.1 Theoretical Method  
Theoretical calculations determined the mass ratio of a eutectic mixture were based on Schroder's equation. It is derived from the phase equilibrium theory and the second law of thermodynamic to describe the relationship between the physical properties of different compounds of the eutectic mixture (molar fraction, melting temperature, enthalpy) and the melting temperature of their mixtures (Equations (1) and (2) [67]).  
\[\mathrm{T}=\frac{1}{\mathrm{T}_{m,A}}-\frac{\mathrm{R}\ln\mathrm{x}_{A}}{ \mathrm{A}\mathrm{H}_{m,A}} \tag{1}\]  
\[\mathrm{T}=\frac{1}{\mathrm{T}_{m,B}}-\frac{\mathrm{R}\ln\mathrm{x}_{B}}{ \mathrm{A}\mathrm{H}_{m,B}} \tag{2}\]  
where \(\mathrm{T}\) is the onset melting point of the eutectic mixture, and \(\mathrm{x}_{A}\) and \(\mathrm{x}_{B}\) are the mole fractions of the components A and B in the mixture (\(\mathrm{x}_{A}+\mathrm{x}_{B}=1\)), respectively. \(\mathrm{\Delta H}_{m,A}\), \(\mathrm{\Delta H}_{m,B}\), \(\mathrm{T}_{m,A}\), and \(\mathrm{T}_{m,B}\) are the components A and B's heat enthalpies and melting temperatures. R is the gas constant (8.314 J\(\cdot\)K\({}^{-1}\cdot\)mol\({}^{-1}\)).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Fatty Acids**} & **Melting** & **Melting** & \multirow{2}{*}{\begin{tabular}{c} **Density** \\ **Latent Heat** \\ **(\({}^{\circ}\)C)** \\ \end{tabular} } & **\begin{tabular}{c} **Specific Heat** \\ **(J\(\cdot\)g\({}^{-1}\))** \\ \end{tabular} & \begin{tabular}{c} **Thermal Conductivity** \\ **Liquid** \\ **Solid** \\ \end{tabular} & **Ref.** \\ \hline \multirow{2}{*}{\begin{tabular}{c} Stearic acid (SA) \\ CH\({}_{3}\)(CH\({}_{2}\))\({}_{16}\)COOH \\ \end{tabular} } & 69 & 202 & 965 & 848 & 1.6 & 2.2 & 0.172 & [56] \\  & 60 & 178 & - & - & - & - & [57] \\  & 69 & 223 & - & - & - & - & - & [58] \\ \hline \multicolumn{8}{l}{Palmitic acid (PA)} & 64 & 185 & 989 & 850 & 1.9 & 2.8 & 0.162 & [56] \\  & 60 & 233 & - & - & - & - & - & [59] \\ \hline \multirow{4}{*}{\begin{tabular}{c} Myristic acid (MA) \\ CH\({}_{3}\)(CH\({}_{2}\))\({}_{12}\)COOH \\ \end{tabular} } & 58 & 187 & 990 & 861 & 1.7 & 2.4 & 0.150 & [56] \\  & 54 & 187 & - & - & - & - & - & [58] \\  & 52 & 211 & - & - & - & - & - & [60] \\  & 52 & 178 & - & - & - & - & - & [61] \\  & 52 & 205 & - & - & - & - & - & [61] \\ \hline \multirow{2}{*}{\
\begin{tabular}{c} Lauric acid (LA) \\ CH\({}_{3}\)(CH\({}_{2}\))\({}_{10}\)COOH \\ \end{tabular} } & 44 & 177 & 1007 & 862 & 1.7 & 2.3 & 0.147 & [56] \\  & 42 & 190 & - & - & - & - & - & [59] \\  & 44 & 184 & - & - & - & - & - & [62] \\ \hline \multirow{2}{*}{
\begin{tabular}{c} Capric acid (CA) \\ CH\({}_{3}\)(CH\({}_{2}\))\({}_{8}\)COOH \\ \end{tabular} } & 32 & 153 & 1004 & 878 & 1.9 & 2.1 & 0.153 & [56] \\  & 30 & 140 & - & - & - & - & - & [63] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Thermal properties of some fatty acids.  
It is reported that the calculated results could be affected by the presence of impurities that influence the data obtained by differential scanning calorimetry (DSC) measurements.
# Identification of NLOS and Multi-Path Conditions in UWB Localization Using Machine Learning Methods
## Abbreviations  
The following abbreviations are used in this manuscript:  
AltDS-TWR Alternative double-sided two-way ranging  
BDT Boosted decision tree  
CIR Channel impulse response  
CNN Convolutional neural network  
FP First-path  
GP Gaussian process  
HSI High speed internal (clock)  
IoT Internet of Things  
KNN K-nearest neighbor  
LOS Line-of-sight  
MCU Microcontroller unit  
ML Machine learning  
MLP Multi-layer perceptron  
MP Multi-path  
NLOS Non-line-of-sight  
PATD Preamble accumulation time delay  
PUB Publications at Bielefeld University  
RBF Radial basis function  
RF Random forest  
RSS Received signal strength  
RX Received or receiver  
SNR Signal-to-noise ratio  
SVM Support vector machine  
TOF Time-of-flight  
UWB Ultra-wideband
# Development of a Hardware-in-the-Loop Platform for a Teleoperation Flexibility Robotic System
## 6 Conclusions  
In this study, a control method for a CDHR robot in a teleoperation system was proposed using the HIL simulation technique. Because the teleoperation system, consisting of a haptic device, called a delta robot, and a CDHR robot, was a large system, an HIL simulation system was developed to evaluate the safety of the teleoperation control algorithm and system before implementing it at the experimental scale. The CDHR had many degrees of freedom, and, therefore, its inverse kinematics were complex. For this reason, the Jacobian method was used to solve the inverse kinematics. Additionally, in the teleoperation system, the workspaces of the haptic device and CDHR robot were different in terms of size and coordinate systems. Therefore, the position mapping method was applied to utilize these workspaces. The parameters of this mapping method were selected using trial and error to increase the size of the CDHR robot's workspace. The other challenges in teleoperation systems are the singularity area resulting from differences between workspaces. Therefore, a haptic algorithm was integrated into the proposed system to provide feedback to the operator for preventing the CDHR from moving into the singularity area. A few simulations were conducted using the proposed HIL simulation scheme in the MATLAB Simscape environment to evaluate the teleoperation control method developed herein. The results indicated that the challenges related to the inverse kinematics, workspace differences, and singularity of the workspace were solved. Furthermore, the size of the workspace was increased considerably while maintaining the system stability and safety.  
Conceptualization, D.T.T., T.D.N. and M.K.T.; methodology, D.T.T., T.D.N. and M.K.T.; software, T.D.N. and M.K.T.; validation, T.D.N. and M.K.T.; formal analysis, T.D.N. and M.K.T.; investigation, D.T.T., T.D.N. and M.K.T.; resources, D.T.T., T.D.N. and M.K.T.; data curation,  
Figure 17: Interaction of forces along the X-, Y-, and Z-axes.  
T.D.N. and M.K.T.; writing--original draft preparation, T.D.N. and M.K.T.; writing--review and editing, D.T.T. and K.K.A.; visualization, D.T.T., T.D.N. and M.K.T.; supervision, D.T.T. and K.K.A.; project administration, K.K.A.; funding acquisition, K.K.A. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Korea Hydro & Nuclear Power Co and Ulsan (2023) and \"Regional Innovation Strategy (RIS)\" through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (MOE) (2021 RIS-003).  
Not applicable.  
Data Availability Statement: Data is contained within the article.  
Thank you for the support from Ho Chi Minh City University Technology and Education, Vietnam, and University of Ulsan, South of Korea.  
The authors declare no conflicts of interest.
## 5 Bit- and Power-Loaded DMT for Highest Bit Rates  
In this section, bit- and power-loaded DMT transmission is used to maximize the data throughput for any of the three bandwidth limited sources. We proceed in two steps. We again first study the impact of the cavity length on the RSOA-FCL and the achievable data throughput in more detail. Only when we know the optimum operation conditions of the RSOA-FCL, we investigate and compare the maximum capacity of all three schemes.  
Figure 9: Comparison of the measured SNR for the three colorless transmitter schemes: RSOA ASE spectrum-sliced (SS), self-seeded RSOA-FCL and externally seeded (ES) RSOA. (**a**) Shows the BtB (back-to-back) SNR measurement. The externally seeded RSOA (green line) outperforms the RSOA-FCL (red and brown) and the ASE spectrum-sliced scheme (blue) by \(\sim\)10 and \(\sim\)5 dB SNR across the whole frequency range, respectively; (**b**) Shows the SNR after 25 km NZDSF transmission. The SNR bandwidth is reduced due to dispersion of the transmission fiber. In general, the RSOA-FCL offers performance midway between an ASE spectrum sliced and an externally seeded RSOA.
# Investigation of Cavitation Noise in Cavitating Flows around an NACA0015 Hydrofoil
## 2 Mathematical Model
### FBM Model  
Cavitating flow is a fully developed turbulent phenomenon which has a mass transfer between two phases. The standard \(k\)-\(\varepsilon\) model is favored because of its low requirements for computing resources and stable calculating result. However, the widely used \(k\)-\(\varepsilon\) turbulence model is mainly based on a steady average flow, where the viscosity coefficient is calculated by turbulent kinetic energy and turbulent dissipation rate. This indicates that the turbulence scale calculated by the \(k\)-\(\varepsilon\) model is very large and the excessive forecast of viscous coefficient affects the accuracy. So, this article adopted the filter-based model (FBM) to optimize the \(k\)-\(\varepsilon\) model. The viscosity coefficient of FBM is defined as:  
\[\mu_{\mathrm{TPBM}}=\frac{C_{\mathrm{\mu}}\rho_{\mathrm{m}}k^{2}}{\varepsilon} F_{\mathrm{FBM}} \tag{3}\]  
\[F_{\mathrm{FBM}}=\min\left(1,C_{3}\frac{\lambda\cdot\varepsilon}{k^{\frac{3}{2} }}\right) \tag{4}\]  
where \(F_{\mathrm{FBM}}\) represents the filter function and \(\lambda\) represents the filter scale. This article takes \(C_{\mathrm{\mu}}\) to be 0.09 and \(C_{3}\) to be 1.0.
## 2 Methodology
### LV Model  
Once the propellant pair is entered, Chemical Propellant Equilibrium Program (CPropEP), an external software based on NASA calculations [27], is used to calculate propellant thermodynamic properties. Detailed information on CPropEP is given in [28; 29]. This software calculates a range of output variables, from which the characteristic velocity (\(c^{*}\)), thrust coefficient (\(C_{F}\)), combustion chamber temperature (\(T_{c}\)), isentropic expansion factor (\(\gamma\)), molar mass (M) and the expansion ratio (\(\epsilon\)) are required for further calculations in this research.  
As \(p_{c}\) is not fixed and as optimum \(p_{c}\) is required, the above variables would have to be recalculated in every iteration during the optimization process. In order to reduce computing times, an additional code was written to calculate the optimum Mixture Ratio (MR) for the propellant combination at different values of \(p_{c}\). The data were fitted using non-linear curve-fitting. The equations generated are then used to recalculate the thermodynamic variables in each iteration during optimization instead of repeatedly running CPropEP again. Therefore, the advantages of this additional step are as follows:  
* Time saved during the optimization process as CPropEP is not repeatedly run for each iteration. Instead, it only runs once to generate equations for each of the required variables.
* The optimum MR for each \(p_{c}\) is calculated to achieve the maximum \(c^{*}\) at that specific \(p_{c}\). Maximizing \(c^{*}\) is important as it indicates a higher combustion efficiency and an increase in engine performance.  
The thermodynamic properties are then used in the engine model developed for the purpose of this research.  
This method also minimizes the optimization processing time as otherwise CPropEP would load and run for each value of \(p_{c}\). MR is calculated via the equation below:  
\[MR=a+b\times p_{c}^{0.125}+\frac{c}{p_{c}} \tag{1}\]  
Figure 1: Optimization and modelling flow chart.  
The output variables \(c^{*}\), \(C_{F}\), \(T_{c}\), M and \(\gamma\) are calculated in succession using the following equation:  
\[y=a+b\times p_{c}^{0.125}-\frac{c}{p_{c}} \tag{2}\]  
where y alternately represents \(c^{*}\), \(C_{F}\), \(T_{c}\), M and \(\gamma\).  
Equation (3) is used to calculate \(\epsilon\).  
\[\epsilon=a+b\times p_{c}^{0.769} \tag{3}\]  
These equations and the values of the variables \(a\), \(b\) and \(c\) are unique to the combination of a given propellant pair at a given exit pressure. If the performance of an LPRE cycle needs to be analyzed with a different propellant pair or the performance of the propellant pair needs to be evaluated at a different value of \(p_{e}\), data need to be generated from the start to obtain the required values for each equation.