esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Methodology  
We used simulation to prove the performance of our solution. The simulation tool adopted was Tossim ([[http://docs.tinyos.net/tinywiki/index.php/TOSSIM](http://docs.tinyos.net/tinywiki/index.php/TOSSIM)]([http://docs.tinyos.net/tinywiki/index.php/TOSSIM](http://docs.tinyos.net/tinywiki/index.php/TOSSIM))), because we have the device kits of Crossbow ([[http://www.xbow.com](http://www.xbow.com)]([http://www.xbow.com](http://www.xbow.com))) to later perform testbeds on field and improve our solution. This kind of device supports TinyOS 2.x and Tossim is the default tool to do the simulations.  
The whole code was developed for simulation by neS C to TinyOS 2.x. They can be embedded within the sensor nodes of the Tossim simulator and also within the real sensor nodes. This ensures that the same code used to simulate the experiments is able to perform tests in real scenarios in the future.  
The simulation scenarios involve different situations of network density, data application values (gathered variables, correlated or not) and way of node deployment. Thus, we check possible real word scenarios by simulation.  
Application versions were created to check the improvement of our solution. The first version is the baseline to compare the energy consumption. The aim of this version is to measure the energy consumption without prediction and to check how much each prediction solution will waste when data reduction is used by simple or multiple linear regressions.  
The second version is a version adopted by [PERSON] _et al._[7] to perform data reduction by using simple linear regression. It is a basic prediction version in which we check the prediction errors and energy consumption. This version is based on the time variable, which is not highly correlated with the gathered variables. Therefore, we believe that prediction error tends to increase.  
The third version is a way to check if it is possible to improve prediction accuracy by changing only the independent variable. We used the temperature variable instead of the time variable, because it is more correlated with other variables. The best way to improve prediction accuracy is by decreasing prediction errors, using the same energy amount than the second version, but there is a trade-off between prediction accuracy and energy consumption.  
The last version is our solution which uses the time and temperature variables together in the prediction. The correlation between gathered variables is higher than the time variable, and then we believe that prediction error will decrease, even though it wastes more energy. Each application version has different packets length, which determines how much energy will be wasted in data communication, _i.e._, the larger the packet, the greater the energy consumption.
# Identifying Adversary Impact Using End User Verifiable Key with Permutation Framework
## 5 Conclusions  
This article presents an access and transaction adaptable PSF for mitigating the adversary impact over dense IoT services. The secure transaction sequence between the users/applications and the resources through the cloud is linearly mapped and synchronised for providing high-level access. The sessions are distinguished based on access time intervals and authenticated using RSA. In the classification process, support vectors are employed for handling linear and synchronised access between the users. The proposed framework fits the user and transaction flexibility without deviating from data collection and update. For ease of service allocation, the classifications are performed based on failing and mapping updates. This is considered by the classifier for improving the end-to-end verification checks. Based on the verification validity, the session intervals are modified,  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Metrics** & **CMAKAP** & **RBCGM** & **MPPAP** & **PSF** \\ \hline Adversary Impact & 7.11 & 5.93 & 4.11 & 2.0575 \\ \hline Service Failure (\%) & 14.4 & 10.07 & 7.28 & 4.673 \\ \hline Service Delay (s) & 0.925 & 0.795 & 0.574 & 0.2967 \\ \hline Access Rate & 84.77 & 90.08 & 93.89 & 97.311 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparative analysis summary for transactions.  
Figure 11: Service transaction analysis.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Metrics** & **CMAKAP** & **RBCGM** & **MPPAP** & **PSF** \\ \hline Adversary Impact & 7.17 & 5.94 & 4.27 & 2.0718 \\ \hline Service Failure (\%) & 14.39 & 10.11 & 7.65 & 4.547 \\ \hline Service Delay (s) & 0.931 & 0.758 & 0.564 & 0.2918 \\ \hline Access Rate & 85.68 & 90.48 & 93.62 & 97.037 \\ \hline Service Transaction & 55 & 89 & 152 & 251 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparative analysis summary for access level.  
and hence the synchronisation is retained. The proposed framework reduces adversary impact, service failure, and service delay by 10.98%, 11.82%, and 10.19%, respectively. Contrarily, it improves the access rate by 7.73% for different transactions.  
Conceptualisation, M.A. and S.S.; methodology, M.A. and S.S.; software, M.A. and S.S.; validation, M.A., S.S. and Y.Y.; formal analysis, Y.Y.; resources, S.S.; data curation, H.F.G. and S.S.; writing--original draft preparation, M.A. and S.S.; writing--review and editing, H.F.G., S.S. and Y.Y.; visualisation, S.S. and Y.Y. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2023R259), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.  
Experiments are performed on simulator for real scenarios.  
The authors declare no conflict of interest.
# A New Shear Strength Model with Structural Damage for Red Clay in the Qinghai-Tibetan Plateau
## 5 Conclusions  
In this paper, the deterioration mechanism of the shear strength of QTP red clay was analyzed at multiscale through triaxial shear and NMR tests. The conclusions of the present work are summarized as follows.  
1. As the number of FTCs increases, the peak deviator stress of red clay declines significantly, and the decreasing trend can be described in three phases: rapid reduction, slow reduction, and stabilization.
2. The \(T_{2}\) spectrum curves of red clay exhibit a distinct bimodal distribution characteristic. The primary type of pores within the specimens is micropores. After FTCs, part of the micropore develops into a macropore, leading to structural damage.
3. The mechanism of the shear strength deterioration of red clay after FTCs is revealed. The phase transformation of water inside red clay causes a frost-heaving force to act on the soil skeleton and pore volume expansion. The area of macropores inside the red clay increases, leading to a looser structure and a deterioration of the shear strength of the soil.
4. An extended Mohr-Coulomb strength model considering the structural damage caused by the action of FTCs is established. The shear strength of red clay after FTCs can be well predicted by the extended strength model. In future work, more freeze-thaw cycles and experimental studies will be conducted to verify the proposed model. The research results have a reference value for exploring the disaster mechanism of freezing and thawing in the QTP.  
Conceptualization, Y.Y. and Z.Z.; methodology, Y.Y. and Z.Z.; software, Y.Y. and Z.Z.; validation, Y.Y., Z.Z. and F.D.; investigation, Y.Y.; writing--original draft preparation, Y.Y.; writing--review and editing, Z.Z., F.D. and S.B.; visualization, Y.Y., Z.Z., F.D. and S.B.; project administration, Z.Z., F.D. and S.B.; funding acquisition, Z.Z. and F.D. All authors have read and agreed to the published version of the manuscript.  
The financial support provided by the Second Tibetan Plateau Scientific Expedition and Research (STEP) Program (No. 2019 QZKK0905) is gratefully recognized.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflict of interest.  
Figure 12: Comparison between predicted and measured peak deviator stress values of the strength model.
# A Data Augmentation Method for Skeleton-Based Action Recognition with Relative Features
## 3 Method
### Feature Extraction
#### 3.3.1 Relative Coordinate  
For a skeleton sequence \(S\) in the form of 3D coordinates with \(N\) frames, where \(S=\{F_{1},F_{2},\ldots,F_{N}\}\). Assume \(J_{i,t}\) is the original coordinate of the \(i\)-th joint of the skeleton in frame \(F_{i}\). \(I_{spine,1}\) is the spine joint of the 1 st frame and is adopted as a reference point. Each joint is defined as \(J=(x,y,z)\). The relative coordinate \(R=(x,\ y,\ z)\) can be calculated as follows:  
\[R_{i,t}=J_{i,t}-I_{spine,1}\ t\in[1,N],\ i\in[1,15] \tag{2}\]  
where \(R_{i,t}\) represents the relative coordinates of the \(i\)-th joint in the \(t\)-th frame of the skeleton sequence \(S\) and \(15\) is the number of joints. Compared with the original coordinates, the relative coordinates proposed in our method are viewpoint invariant. This means that the relative data are only related to the starting position of the action and will not be affected by the position of the camera. Moreover, the relative coordinate is determined by the difference between the coordinate of the \(t\)-th frame and the spine coordinate of the first frame. So, it not only depicts the spatial relations of human skeleton joints but also provides long-term temporal information.
## 3 Review of the Design Codes
### Raff Foundation Design
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Notation** & **Description** & **Value** & **Notes** \\ \hline \(\gamma_{\mathrm{c1}}\) & \multirow{2}{*}{Partial factors on operational conditions} & 1.25 & Taken according to Table 4 of SP RK \\ \cline{3-4} \(\gamma_{\mathrm{c2}}\) & & 1 & 5.01-102-2013 \\ \hline \multirow{2}{*}{k} & \multirow{2}{*}{Dimensionless coefficient} & \multirow{2}{*}{1} & Depends on the method of determination of strength characteristics of soil (\(\mathrm{\varphi}\) and \(\mathrm{c}\)) \\ \cline{3-4}  & & & \\ \cline{3-4}  & & & \\ \cline{3-4}  & & & \\ \cline{3-4}  & & & \\ \hline \multirow{2}{*}{M\({}_{\gamma}\)} & Dimensionless coefficients representing a & & & \\ \cline{3-4}  & function of frictional angle, load inclination, and & & \\ \cline{3-4}  & shape of the foundation & & & \\ \hline \multirow{2}{*}{M\({}_{\mathrm{q}}\)} & \multirow{2}{*}{3.44} & \multirow{2}{*}{3.44} & \multirow{2}{*}{3.44 b} & \multirow{2}{*}{3.4 b} \\ \cline{3-4}  & & & \\ \cline{3-4}  & & & & \\ \hline \multirow{2}{*}{k\({}_{\mathrm{z}}\)} & Dimensionless coefficient & 1 & \multirow{2}{*}{4 b} & \multirow{2}{*}{4 b} \\ \cline{3-4}  & & & \\ \hline \multirow{2}{*}{b} & Width of the foundation base (m) & 10 & & \\ \cline{3-4}  & & & \\ \hline \multirow{2}{*}{\(\gamma_{\mathrm{\Pi}}\)} & Average design values of the specific weight of & & \\ \cline{3-4}  & soil under the foundation base (kN/m\({}^{3}\)) & & \\ \hline \multirow{2}{*}{\(\gamma^{\prime}_{\mathrm{\Pi}}\)} & Average design values of the specific weight of & & \\ \cline{3-4}  & soil above the foundation base (kN/m\({}^{3}\)) & & \\ \hline \multirow{2}{*}{c\({}_{\mathrm{\Pi}}\)} & Design value of the specific cohesion of soil & & \\ \cline{3-4}  & below the foundation base (kPa) & & \\ \hline \multirow{2}{*}{d\({}_{\mathrm{\Pi}}\)} & Depth of the foundation (m) & 1.5 & & \\ \hline \multirow{2}{*}{d\({}_{\mathrm{b}}\)} & Depth of the basement (m) & 0 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Description of parameters in SP RK 5.01-102-2013—Foundations of buildings and structures. Eurocode 7 applies the adjusted elasticity method for the determination of the elastic settlement of a raft foundation in accordance with Equation (13):  
\[\mathrm{s}=\mathrm{p}\mathrm{B}\mathrm{f}/\mathrm{E}_{\mathrm{m}} \tag{13}\]  
where \(\mathrm{p}\) is the net bearing pressure linearly distributed on the foundation base (kPa); \(\mathrm{f}\) is the settlement coefficient; and \(\mathrm{E}_{\mathrm{m}}\) is the design modulus of deformation of the soil (kPa). When adhering to EC 7, the values of partial factors on permanent and variable loads from Table 3 for different design approaches are considered for the calculation of the vertical load action on the raft foundation.
# Comparison of Manual and Automated Capillary Morphometry Measurements in Oral Mucosa: A Pilot Study
## 2 Materials and Methods  
### Study Design and Data Source  
This pilot study utilized pre-existing videocapillaroscopy data from healthy subjects, which had been collected as part of previous research conducted at the Clinical Odon-tostomatology Department of the University of Palermo. The data included high-resolution videocapillaroscopy recordings of the oral mucosa from 10 healthy participants, with measurements taken at four distinct anatomical sites: the right buccal mucosa, left buccal mucosa, lower labial mucosa, and upper labial mucosa. The original studies were approved by the Ethics Committee of the University of Palermo (Protocol No. 01/2021), and all participants had provided written informed consent for the use of their data in future research. The use of these pre-existing data allowed for a robust comparison between the automated and manual methods without the need for additional recruitment or invasive procedures.
## 4 Conclusions  
In summary, a concise and efficient synthesis of the proposed structure of aaptoline B was accomplished. Key features of the synthesis involve Batcho-Leimgruber indole synthesis and subsequent AgSbF\({}_{6}\)-catalyzed cycloisomerization for the construction of the unique pyrrol [2,3-h]quinoline skeleton of **1**. The spectral data of synthesized **1** did not agree with those in a previous report. However, the structure of the synthesized ethyl 7_H_-pyrrolol [2,3-h]quinoline-5-carboxylate (**1**), originally proposed as aaptoline B, was confirmed using combined two-dimensional NMR analysis. Our findings from a biological study revealed that **1** could protect _C. elegans_ from MPP\({}^{+}\)-induced dopaminergic neuronal damage and restore dopamine-related behavioral functions, suggesting its therapeutic potential in PD. Further studies, such as pharmacophore identification and structure modification of **1**, were currently underway.  
The following are available online at [[https://www.mdpi.com/article/10.3390/app11199125/s1](https://www.mdpi.com/article/10.3390/app11199125/s1)]([https://www.mdpi.com/article/10.3390/app11199125/s1](https://www.mdpi.com/article/10.3390/app11199125/s1)), Table S1: \({}^{1}\)H- and \({}^{13}\)C-NMR assignment of aaptoline B.  
Y.-T.H. and D.-S.C. conceived and designed the experiments, and wrote the paper; S.K. performed the synthesis and chemical analysis; W.Y. performed biological studies. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Research Foundation of Korea (NRF-2020R1F1A1058295).  
Not applicable.  
The authors gratefully acknowledge Center for Bio-Medical Engineering Core Facility for providing analytical equipment of Dankook University.  
The authors declare no conflict of interest.  
Figure 4: Neuroprotective effects of synthesized **1** on the DA neurodegeneration in the MPP\({}^{+}\)-exposed _C. elegans_. **(A)** GFP fluorescence signals of CEP (arrowhead) and ADE (arrow) in the Pdat-1::GFP nematode were photographed at 400\(\times\) magnification. **(B)** The viability of all eight DA neurons was scored by inspecting the GFP expressions of Pdat-1::GFP nematode. **(C)** The velocity of wild-type nematodes was observed in the presence or absence of food (_E. coli_ OP50) and the basal slowing response was calculated. **(D)** The travel distance of wild-type nematodes was recorded for 20 s under a dissecting microscope. Statistical significance was determined by one-way ANOVA; \({}^{***}p<0.001\) compared with vehicle alone; \({}^{***}p<0.001\) compared with MPP\({}^{+}\)-treated control.
## 4 Discussion  
This systematic review demonstrated significantly lower ADC values in MIBC and high-grade disease than in NMIBC and low-grade disease, respectively. The median AUCs for detecting MIBC and high-grade disease were 0.762 and 0.902, respectively. These findings suggest the key role of ADC values in the detection of clinically aggressive bladder cancer. To the best of our knowledge, this study is the first systematic review of the role of ADC values in detecting MIBC and high-grade bladder cancer. The ADC is considered a biomarker that reflects the aggressiveness of bladder cancer. Malignant tumors are characterized by increases in cellularity, microvascular circulation, and tortuosity of the extracellular space, which limits the diffusion of water molecules, leading to low ADC values [30]. These features in the tumor microenvironment are prominent in highly aggressive cancers. Bladder cancer is classified into two major pathological phenotypes in terms of clinical aggressiveness: low-grade papillary non-invasive tumor (pTa) and high-grade invasive disease (T1 or greater). The former has a low tendency to develop disease progression with favorable prognosis, whereas the latter frequently leads to muscle invasion or incurable metastatic disease with an adverse prognosis [31]. Our results suggest that lower ADC values reflect the presence of a disease with an aggressive phenotype. Interestingly, the ADC values were inversely correlated with the KI-67 labeling index of MIBC, an indicator of cell proliferation [14]. Incorporation of ADC values into conventional preoperative T staging may help reduce understaging by performing appropriate diagnostic TURBT, contributing to the development of appropriate treatment strategies based on accurate histopathological diagnosis. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Author** & **NMIBC** & **MIBC** & **\(p\) Value** & **Cutoff** & **Sensitivity (\%)** & **Specificity (\%)** & **AUC** \\
**[Reference No.]** & **(Mean \(\pm\) SD)** & **(Mean \(\pm\) SD)** & **\(p\) Value** & **Cutoff** & **Sensitivity (\%)** & **Specificity (\%)** & **AUC** \\ \hline [PERSON] [26] & 1.30 \(\pm\) 0.31 & 1.06 \(\pm\) 0.24 & 0.003 & NA & 72.0 & 66.7 & 0.730 \\ [PERSON] [16] & 1.04 \(\pm\) 0.25 & 0.85 \(\pm\) 0.24 & 0.006 & 0.87 & 75.0 & 81.0 & 0.765 \\ [PERSON] [20] & 1.08 (0.16-2.18) & 0.72 (0.35-1.82) & \(<\)0.001 & 0.89 & 68.8 & 84.4 & 0.758 \\ [PERSON] [17] & 1.12 (1.02-1.22)\({}^{\star}\) & 0.76 (0.65-0.87)\({}^{\star}\) & \(<\)0.001 & 0.92 & 90.0 & 78.8 & 0.884 \\ [PERSON] [18] & 1.31 \(\pm\) 0.27 & 1.00 \(\pm\) 0.21 & \(<\)0.001 & NA & NA & NA & NA \\ Tab. & T1 & \(\geq\)T2 & & & & & \\ [PERSON] [14] & 1.00 (0.92-11.14) \(\dagger\) & 0.83 (0.75-0.92) \(\dagger\) & 0.74 (0.62-0.85) \(\dagger\) & \(<\)0.001 & NA & NA & NA \\ \hline \hline \end{tabular}  
* mean (95%CI), \(\dagger\) median (IQR). AUC, area under curve; MIBC, muscle-invasive bladder cancer; NMIBC, non-muscle-invasive bladder cancer; SD, standard deviation.  
\end{table} 
Table 2: Diagnostic performance of ADC values to differentiate MIBC from NMIBC.
## 6 Papain Use as an Exfoliant Agent in Cosmetic Applications
### Chemical Properties and Structure of Papain
#### 6.3.2 Stability and Activity Under Different pH and Temperature Conditions  
Papain's stability and activity are significantly influenced by both temperature and pH, with optimal conditions varying based on the context. Generally, papain exhibits stability over a broad temperature range, with some studies indicating activity from 10 to 90 \({}^{\circ}\)C [101], while others suggest an optimal temperature between 40 and 80 \({}^{\circ}\)C [107]. However, the optimal temperature for papain activity is often reported at around 60 \({}^{\circ}\)C [91; 97; 107], with the enzyme retaining its activity at higher temperatures, but experiencing decreased stability over time [91]. Papain retains activity at higher temperatures, but its stability decreases over time [101]. For example, at 80 \({}^{\circ}\)C, the enzyme functions properly, but may rapidly lose its activity within an hour at 90 \({}^{\circ}\)C. Papain's stability is reportedly enhanced when immobilized in different chitosan- or alginate-based membranes or graft copolymers [97].  
Papain's activity is also highly pH-dependent, with optimal performance often reported between pH 6 and 7, although a range from 3.0 to 9.0 is also cited, depending on the substrate [91; 94]. Some studies specify optimal pH ranges around 5.0-5.5 [74; 98], while others report it to be around 7.5 [113]. Optimal pH values may shift to more alkaline ones when the enzyme is immobilized. For instance, immobilization can shift the optimal pH values from 7 to 8 [97]. Papain's stability significantly decreases under strong acidic conditions and extreme pH levels, with better stability at neutral pH values [98]. Immobilized papain can retain more activity at pH 12 than free papain [97].
## 2 Experimental
### Characterization and Testing  
#### 2.3.1 Structural Characterization  
1. Chemical structure  
Fourier-transform infrared spectroscopy (FTIR) spectra were recorded on a Vector-22 spectrometer (Bruker, Karlsruhe, Germany) in the range of 400 to 4000 cm\({}^{-1}\) with a resolution of 2 cm\({}^{-1}\). The samples were pressed with KBr into pellets before FTIR measurement. X-ray diffraction (XRD) was carried out using a D/max 2200 PC X-ray diffractometer (Rigaku, Tokyo, Japan) with Cu K\(\alpha\) radiation (\(\lambda\) = 0.15418 nm) at a scan rate of 5\({}^{\circ}\cdot\)min\({}^{-1}\). X-ray photoelectron spectroscopy (XPS) was carried out using an AXIS Supra XPS spectrometer (Shimadzu, Kyoto, Japan) with an Al K\(\alpha\) (1487 eV) X-ray source. Zeta potential was acquired with a Zetasizer Nano-ZS90 (Malvern, Malvern, UK).  
2. Micro structure  
Scanning electron microscopy (SEM) images were obtained using an EVO 18 electron microscope (Zeiss, Jena, Germany) at 20 kV. The sample was sliced and transmission electron microscope (TEM) images were collected using a JEM-2010 (JEOL, Tokyo, Japan) at 200 kV.  
3. Pore structure  
Porosity was calculated according to Equation (1):  
\[\varepsilon=\frac{W_{1}-W_{2}}{A\times l\times\rho_{W}}, \tag{1}\]  
where \(\varepsilon\) is the membrane porosity (%), \(W_{1}\) is the dry membrane weight (g), \(W_{2}\) is the wet membrane weight (g), \(A\) is the membrane area (m\({}^{2}\)), \(l\) is the membrane thickness (m) and \(\rho_{W}\) is water density (0.998 g\(\cdot\)cm\({}^{-3}\)).  
The average pore size of the composite ultrafiltration membrane was calculated by pure water flux, thickness, effective area and so on. The calculation formula is the Guerout-Elford-Ferry Equation (Equation (2)) [28]:  
\[r_{M}=\sqrt{\frac{(2.9-1.7\varepsilon)\times 8\eta lQ}{\varepsilon\times A\times \Delta P}}, \tag{2}\]  
where \(r_{M}\) is the average membrane pore size (m), \(\varepsilon\) is the membrane porosity (%), \(\eta\) is water viscosity (8.9 \(\times\) 10\({}^{-4}\) Pa\(\cdot\)s), \(l\) is the membrane thickness (m), \(Q\) is the water flux per  
Figure 1: Diagram illustrating process of preparing CGO/PVDF composite ultrafiltration membrane.  
unit time (m\({}^{3}\cdot\)s\({}^{-1}\)), \(A\) is the membrane effective area (m\({}^{2}\)) and \(\Delta P\) is the operating pressure (0.1 MPa).