esa-sceva/satcom-chunk-collection huggingface dataset:
# A Statistical Procedure for Exploring a Skeletal Age-Explicative Tool for Growing Patients
###### Abstract  
_Background_: Skeletal age estimation plays a fundamental role in orthopedic treatments. Since the most reliable methods are based on ionizing radiation, this study aimed to use machine learning techniques to explore a skeletal age assessment method not based on additional radiographies. _Methods_: Patients aged between 6 and 16 years old whose clinical records included orthopontomography, radiographs of the second phalanx of the third finger, and biometric data were enrolled for the study. The radiographs were analyzed to estimate the maturation degree of the left lower first premolars, the midpalatal suture, and the second phalanx of the third finger. Both an explicative data analysis and a multivariate analysis were performed. _Results_: The sample comprised 111 subjects. The multivariate analysis revealed an explanatory role for sex (\(p<0.01\)) and chronological age (\(p<0.01\)). The ordinal tool showed how the use of height (\(p=0.02\)) and weight (\(p=0.03\)) was explicative of skeletal age against a loss of statistical significance corresponding to the use of body mass index (\(p=0.6\)). The median palatine suture (\(p=0.01\)) was explicative. _Conclusions_: The combined evaluation of weight, height, sex, chronological age, and grade of maturation of the midpalate suture provides an explicative tool for assessing skeletal age without additional radiographic exams, besides a routine orthopontomography.  
orthodontics; skeletal age assessment; pubertal growth spurt; radiographic evaluation; middle phalanx maturation Article
###### Abstract  
Sampled points on a measured surface are not evenly spaced on rectangular grids but, rather, have a more general quadrilateral geometry due to the perspective effect of the off-axis configuration, which is extensively utilized in phase measuring deflectometry (PMD). The current procedures for slope-to-wavefront integration results in reconstruction errors, which impact PMD to achieve a higher measurement accuracy. Due to the advantageous properties of the radial basis function (RBF) for irregular sampling and incomplete data, the quadrilateral geometry acquired by a CCD is first realized in this paper using the ray tracing method, which is used to obtain the actual measurement area and sampling interval in off-axis PMD instead of the area defined by the mathematical formula in the past when RBF was used. The RBF integration method is researched in this paper. The simulation and experiment show that RBF is a robust and higher accuracy algorithm for off-axis PMD. We further implement the RBF integration method to measure a crystal transparent element where an off-axis configuration is required.  
Keywords:phase measurement deflectometry; off-axis configurations; quadrilateral geometry; ray tracing +
Footnote †: journal: Applied Sciences
# Research on Effective Advertising Types in Virtual Environment
## 2 Related Work  
### Advertisement in 3D Spatial Content  
Virtual space-based content, including VR, is discussed to reflect the format of existing 3D games, and a plan to apply the in-game advertisement format to VR content is being attempted. In a 3D game space, advertisements are applied as sponsorship, branded characters (appearance), real-world analogs (billboards and posters), and product placements (real-world products, clothing, costumes, items, and buildings) [5]. As this did not have a significant impact on gameplay, users showed a favorable attitude. For example, one study applied game advertisements to 1350 male players aged 13-44 years and found that 67% of the players thought that in-game advertisements made the game more realistic, and 40% thought that this type of advertisement influenced their purchase decisions [6]. Another study showed that repeated exposure to advertisement messages in sports video games had a significant and positive effect on brand recall and awareness, brand attitude, and brand purchase intention [7].  
Advertisements applied to virtual-space-based content are mostly studied based on a metaverse platform, and the importance of ads is emphasized according to the expansion of commerce services within the content. Depending on the method of advertisement, five types were derived: use as an item, reproduction of advertisements in the real world, temporary game events, the brand's games, and exposure to advertisement banners [8]. Based on the level of participation in the metaverse content and the purpose of utilization, the virtual world type uses the brand's own virtual space, the information-providing type provides brand information, the participation-inducing type induces game participation, and the creative tool type produces a brand product in a virtual world [9]. Finally, depending on the placement method, they are classified into banner advertisements, virtual outdoor advertisements, reproductions of offline stores, and sales of branded products and services in virtual spaces [10].  
Based on these studies, we classify the types of advertisements applied to a virtual environment according to the exposure method. First, it was divided into cases of direct and indirect exposure through the background. Directly presented is when a character wears a brand costume or item and a real product is presented. Indirect presentation corresponds to presenting brand information by utilizing space elements such as wall posters or arranging the brand's building. Among these, we selected three types of advertisements that could be applied to one VR space. An avatar costume, product placement, and wall poster were selected, and the visual attention and ad effects were examined.
## 4 Conclusions  
This paper presents the synthesis of four novel complexes of 6-methyl-2-thiouracil and 6-propyl-2-thiouracil. The structures of the new complexes are discussed based on melting point analysis (MP-AES) for Cu and Pd, UV-Vis, IR, ATR, solution and solid-state NMR, and Raman spectroscopy. Based on the spectral data, we proposed the coordination  
\begin{table}
\begin{tabular}{l c c c} \hline \hline \multirow{2}{*}{**Test Microorganisms**} & \multicolumn{3}{c}{**Complexes**} \\ \cline{2-4}  & **6-Propyl-2-Thiouarcil** & **CuL2** & **PdL2** \\ \cline{2-4}  & \multicolumn{3}{c}{**Inhibition Zone, mm**} \\ \hline _Staphylococcus aureus_ ATCC 25923 & - & 8 & 11/16 \({}^{*}\) \\ \hline _Escherichia coli_ ATCC 8739 & - & 10 \({}^{*}\) & - \\ \hline _Eterococcus faecalis_ ATCC 19433 & - & 12 & 15 \\ \hline _Salmonella enterica_ ssp. _enterica_ ser. & 8 & 12 & 15 \\ Enetritidis ATCC 13076 & 8 & 12 & 14 \\ \hline _Pseudomonas aeruginosa_ ATCC 9027 & 8 & 12 & 14 \\ \hline _Proteus vulgaris_ G & 10 & 9 \({}^{*}\) & - \\ \hline _Bacillus subtilis_ ATCC 6633 & 8 & 12 \({}^{*}\) & 12 \\ \hline _Bacillus cereus_ ATCC 11778 & 10 \({}^{*}\) & 8 & 11/15 \({}^{*}\) \\ \hline _Listeria monocytogenes_ ATCC 8787 & 8 & 9 & 14 \\ \hline _Klebsiella pneumoniae_ ATCC 13883 & 11 \({}^{*}\) & 12 \({}^{*}\) & 12 \({}^{*}\) \\ \hline _Candidia albicans_ ATCC 10231 & 12 & 11 & 11 \\ \hline _Saccharomyces cerevisiae_ & 11 \({}^{*}\) & 9 & 8 \\ \hline \hline \end{tabular}
\end{table}
Table 13: Antimicrobial activity of 6-propyl-2-thiouracil and its complexes.  
binding site of the ligands. We assume that polymer complexes are formed, as shown by their low solubility in different polarity organic solvents.  
The antimicrobial activity of the ligands and their complexes against Gram-positive and Gram-negative bacteria and yeasts was investigated. In general, addition of metal ions improved the antimicrobial activity of both 6-methyl-2-thiouracil and 6-propyl-2-thiouracil. The complex of Cu(II) with 6-methyl-2-thiouracil and Pd(II) with 6-propyl-2-thiouracil demonstrated the highest activity against the test microorganisms.  
Conceptualization, P.M. and P.P.; methodology, P.M. and M.H.; formal analysis, P.P., N.B., S.T. and E.V.; investigation, S.T., N.B., D.B., A.S. and E.V.; resources, N.B.; data curation, P.M.; writing--original draft preparation, P.M., N.B., D.B., A.S. and P.P.; writing--review and editing, P.M., P.P. and N.B.; supervision, P.M.; project administration, S.T.; funding acquisition, P.P. All authors have read and agreed to the published version of the manuscript.  
We acknowledge the financial support from the Fund for Scientific Research of the Plovdiv University, project CII 23-X4-006.  
Not applicable.  
Data Availability Statement: Data is contained within the article.  
The authors declare no conflict of interest.
## 6 Discussion
### Future Work  
In terms of future work, addressing these limitations is paramount. First, expanding the dataset size is essential. Incorporating a larger and more diverse sample pool of bladder cancer patients would help mitigate the biases and enhance the generalizability of our model. This expansion would also provide a more robust foundation for validating the feature importance and network model thresholds across various types of bladder cancer data. Moreover, an exciting direction for future work is the integration of multi-omics data with artificial intelligence. Multi-omics approaches, which encompass genomics, proteomics, metabolomics, and more, offer a holistic view of the biological mechanisms underlying bladder cancer [63; 64]. Leveraging such comprehensive data can uncover deeper insights into bladder cancer biology and potentially lead to more accurate and personalized treatment strategies. The combination of multi-omics data and AI has the potential to significantly advance our understanding of bladder cancer, enabling more effective and tailored interventions [65].  
In conclusion, our study introduces a new precision medicine approach for bladder cancer treatment, combining multiview knowledge graphs with graph convolutional neural networks. This methodology has been thoroughly tested on the GEO dataset usinga five-fold cross-validation, demonstrating excellent performance with an AUC-ROC of \(0.8724\pm 0.0511\), accuracy of \(0.7789\pm 0.068\), F1 score of \(0.8529\pm 0.0338\), recall of \(0.9231\pm 0.0719\), and precision of \(0.8038\pm 0.0955\). These results outperform most metrics of other methods and underscore the benefits of integrating graph convolutional neural networks with biological knowledge in predicting treatment outcomes. Furthermore, the alignment of selected genes with clinical insights confirms the reliability of our approach. Although these findings are promising for the prediction of neoadjuvant therapy efficacy in bladder cancer, the need for further research is evident. Future studies involving larger and more diverse datasets, as well as the exploration of multi-omics data in conjunction with AI, are crucial. These steps, challenging as they may be and requiring interdisciplinary collaboration and funding, are essential to enhance the reliability and applicability of our approach and to achieve significant advancements in personalized medicine for bladder cancer treatment.
## 2 Ultrasonic Fault Detection Methodology
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Features** & **Details** & **Definition** \\ \hline \multirow{3}{*}{Mean} & Mean & Average of the data. \\ \cline{2-3}  & Median & The middle value of the signal. \\ \cline{2-3}  & RMS (Root Mean Square) & The square root of the mean of the squares of the signal values. \\ \hline \multirow{3}{*}{Variance} & Standard Deviation & The square root of the variance. \\ \cline{2-3}  & Range & The difference between the maximum and minimum values of the signal. \\ \cline{2-3}  & Interquartile Range (IQR) & The difference between the 75 th and 25 th percentiles of the signal. \\ \hline \multirow{3}{*}{Zero Crossing} & Number of Peaks & The total count of peaks in the signal. \\ \cline{2-3}  & Number of Valleys & The total count of valleys in the signal. \\ \cline{2-3}  & Peak-to-Peak Distance & The average distance between consecutive peaks. \\ \hline \multirow{3}{*}{Envelope} & Envelope Mean & The mean value of the signal envelope. \\ \cline{2-3}  & Envelope Variance & The variance of the signal envelope. \\ \cline{2-3}  & Envelope Energy & The sum of the squared values of the signal envelope. \\ \hline \multirow{3}{*}{Crest Factor} & Form Factor & The ratio of the RMS value to the mean absolute value. \\ \cline{2-3}  & Peak-to-RMS Ratio & The ratio of the maximum value to the RMS value of the signal. \\ \cline{2-3}  & Margin Factor & The ratio of the maximum value to the RMS value. \\ \hline \multirow{3}{*}{Shape Factor} & Normalized Energy & The energy normalized by the length of the signal. \\ \cline{2-3}  & Energy Entropy & The logarithm of the energy of the signal. \\ \cline{2-3}  & Impulse Factor & The ratio of the maximum value to the mean of the absolute values of \\ \hline \multirow{3}{*}{Number of Peaks} & Number of Positive Peaks & The count of positive peaks in the signal. \\ \cline{2-3}  & Number of Negative Peaks & The count of negative peaks in the signal. \\ \cline{2-3}  & Peak Amplitude & The amplitude of the highest peak in the signal. \\ \hline \multirow{3}{*}{Time of Peak} & Time of First Peak & The time at which the first peak occurs. \\ \cline{2-3}  & Time of Last Peak & The time at which the last peak occurs. \\ \cline{2-3}  & Time of Median & The time index of the median value. \\ \hline \multirow{3}{*}{Skewness} & Absolute Skewness & Skewness calculated on the absolute values of the signal. \\ \cline{2-3}  & Skewness of Positive Values & Skewness calculated only for the positive values of the signal. \\ \cline{2-3}  & Skewness of Negative Values & Skewness calculated only for the negative values of the signal. \\ \hline \multirow{3}{*}{Kurtosis} & Absolute Kurtosis & Kurtosis calculated on the absolute values of the signal. \\ \cline{2-3}  & Kurtosis of Positive Values & Kurtosis calculated only for the positive values of the signal. \\ \cline{2-3}  & Kurtosis of Negative Values & Kurtosis calculated only for the negative values of the signal. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Feature definitions and their details.
## 3 Results
### Fracture Toughness and Resistance Curves According to ASTM E1820  
Experimental determination of fracture toughness regarding the criteria set by the standard was valid on three test specimens, two of which are produced from the drawn rod, and one via selective laser melting. The values obtained via the experiments are presented in Table 3.  
Figure 11: Logarithmic plot of da/dN versus dK relationship representing fatigue crack growth curve for (**a**) test specimen produced from drawn Ti-6 Al-7 Nb rod and (**b**) test specimen produced by SLM process out of Ti-6 Al-7 Nb powder.  
The drawn Ti-6 Al-7 Nb shows significantly higher fracture toughness than the SLM-produced specimen. This large difference highlights the superior resistance to crack propagation in the drawn material, while the lower value for SLM is likely due to internal defects and microstructural inhomogeneity typical of additive manufacturing.  
For the drawn test specimens, the resistance curves J-R and CTOD-R are constructed according to the same standard. These curves are nearly identical for both of the drawn test specimens used in the experiment. The J-R resistance curve is presented in Figure 12, while the CTOD-R resistance curve is presented in Figure 13.  
The J-R and CTOD-R curves for the drawn specimens exhibit nearly identical rising trends, confirming predictable ductile fracture behavior and stable crack growth under  
Figure 12: J-R resistance curve for Ti-6 Al-7 Nb produced by drawing.  
Figure 13: CTOD-R resistance curve for Ti-6 Al-7 Nb produced by drawing.  
\begin{table}
\begin{tabular}{c c} \hline
**Production Process** & \(K_{\mathbf{c}}\)**MPam\({}^{\mathbf{0.5}}\)** \\ \hline Drawing (Specimen 1) & 84 \\ Drawing (Specimen 2) & 83.5 \\ Selective Laser Melting & 21.9 \\ \hline \end{tabular}
\end{table}
Table 3: Fracture toughness values determined according to ASTM E1820 [23].  
increasing \(\Delta K\). Their overlapping trajectories validate the consistent mechanical response of Ti-6 Al-7 Nb produced via conventional production processes. The absence of curve plateaus suggests no sudden fracture instability, making the material suitable for applications requiring damage-tolerant design. However, recent work demonstrates that the use of advanced, physics-based damage laws further improves predictive reliability at the design stage. In particular, the SC11-TNT damage law developed by [PERSON] et al. [27] is shown to accurately represent the ductile fracture and evolution of porosity in titanium alloys, enabling precise simulation of failure under complex loading conditions. Incorporating such numerical models in future work would allow the insights observed here to be integrated into the design and safety assessment of components.
## 3 Experiment and Results
### Properties of Scanning Probes  
The approaching curves of the two probes are obtained to calculate the measurement sensitivity (Figure 10). Approach movement is performed by moving the stage toward the microsphere. The approaching curve shows the relationship between the output signal and the displacement of the probe. In the test, the probe vibrates freely at its resonant frequency at the start position, and the probe, together with the micro stage, moves toward the microsphere with a step distance of 5 nm. When the probe taps the microsphere, the vibration amplitude of the probe decreases sharply. The probe, together with the micro stage, stops moving until the amplitude drops to a setpoint value, which is often set as 80% of the free vibration amplitude.  
Figure 9: Principle of the visual alignment error.  
The output voltage of the probe is a linear function of its vibration amplitude. In Figure 10, the slope rate of the decreased part is the sensitivity of the probe, that is, the slope rates of probe-1 and probe-2 are about 7.3 and 8.1 V/\(\upmu\)m in the vertical direction, respectively.  
When the probes are moving toward the microsphere, the noise curves are about 3 mV for each probe (Figure 11). Combined with the obtained sensitivity, the vertical spatial resolutions for probe-1 and probe-2 can be calculated as 0.41 and 0.37 nm, respectively.  
The resolution is calculated in a theoretical way by the sensitivity and the dynamic noise in the vertical direction. During the measurement, the probe is driven by the micro stage, the driving resolution remains on the nanometer scale, which is much smaller than the value of the roundness and sphericity of the microsphere, and the measurement accuracy can be guaranteed.
## 5 Results and Discussion
### Axial-Flow Gallery Ventilation Scheme
#### 5.2.2 CO Concentration Field  
Figure 13 displays the CO concentration distribution with time under the axial-flow gallery ventilation scheme. With an increase in ventilation time, the CO gas distributed near the middle bench moves continuously towards the connecting roadway. After 20 min of ventilation, it is transported to the vicinity of the connecting roadway and gradually discharged out of the cavern through the construction roadway. It can be seen that the overall CO concentration of the cavern has dropped below the safety standard value of 30 mg/m\({}^{3}\) after 25 min of ventilation. The velocity of pollutant discharge in the construction roadway is greater than the main cavern due to its smaller section area and the airflow collection of four caverns, thus, the CO gas can be quickly discharged outside.  
Figure 12: Air velocity distribution in the central axis of the caverns.  
In order to inspect the variation in CO with ventilation time in the working area (about 300 m to 400 m away from the excavation face), four cross-sections with a spacing of 100 m in the middle bench were monitored in the numerical calculation, as shown in Figure 14a. The CO concentration in the working area was decreased below the safety standard value of 30 mg/m\({}^{3}\) after 15 min of ventilation, which meets the requirements for safe and rapid construction. Figure 14b presents the CO concentration distribution at breathing height (1.6m from the ground) with ventilation time. It can be seen that the high concentration of CO in the cavern continues to migrate toward the connecting roadway with the increase of ventilation time, and the maximum CO concentration decreases gradually. The CO concentration of the whole cavern decreases below the safety standard value after 30 min of the axial-flow gallery ventilation scheme, which is much better than the effect of forced ventilation.  
Figure 13: CO concentration distribution with time in caverns under the optimized ventilation scheme: (**a**) Ventilation for 5 min; (**b**) Ventilation for 10 min; (**c**) Ventilation for 15 min; (**d**) Ventilation for 20 min; (**e**) Ventilation for 25 min; (**f**) Ventilation for 30 min.
# Nondestructive Evaluation of Aluminium Foam Panels Subjected to Impact Loading
## 3 Results and Discussion  
### Low-Velocity Impact Tests  
The load-displacement curves for all the investigated panels at different impact energies are shown in Figure 4.  
Figure 4: _Cont_.  
The load-displacement curves for each type of the investigated panels at different impact energies are presented in Figure 5.  
Figure 4: Load–displacement curves at different impact energies.  
Figure 5: _Cont_.  
Peak Crush Force (\(F_{MAX}\)) and depth (\(w\)) are defined as the highest values of force magnitude and displacement observed in the force-displacement response, respectively. The specific absorbed energy (\(SAE\)) was calculated by dividing the amount of energy absorbed during the impact event, equal to the area under the force-displacement curve, by the density (\(\rho\)) of each specimen, as described in Equation (1) below.  
\[SAE=\frac{\int_{0}^{w}Fds}{\rho} \tag{1}\]  
The specific weight absorbed energy (\(SAE_{w}\)) was defined by dividing the amount of energy absorbed during impact event (\(E_{a}\)) for the weight of each specimen. Table 1 reports the specimens' thickness (\(t_{tot}\)), impact energy (\(E_{i}\)) and the results of the experimental tests in terms of peak force (\(F_{MAX}\)), depth (\(w\)) and specific absorbed energy respective to density (\(SAE\)) and weight (\(SAE_{w}\)). The specimens with 44 mm thickness were the double-layer panels; the core densities of each layer are reported in Table 1.  
According to the analysis of the results shown in Figure 4 and Table 1, the responses of the different AFS panels with skins (Types III, IV, V and VI) were similar at the same impact energies; they showed similar values of \(w\), \(F_{MAX}\) and \(SAE\). Different behaviours were observed for the AF panels without skins (Types I and II), with lower values of \(F_{MAX}\) and higher values of \(w\) and \(SAE\). Generally, a lower \(F_{MAX}\) is desirable for crashworthiness designed to minimise any sudden deceleration due to the impact. Conversely to \(SAE\), the lower the \(F_{MAX}\), the better the energy absorption structure.  
According to the analysis of the results shown in Table 2, the maximum \(SAE\) producing the full panel perforation showed a linear trend with respect to the total thickness of the panels (Figure 6). Moreover, the AFS panels exhibited, obviously, higher values of maximum \(SAE\) compared to the values for the AF panels without skins and similar values respect to the values for AHS panels with 3 mm cell diameter.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Specimen** & **Type** & \begin{tabular}{c} **E\({}_{1}\)** \\ **[J]** \\ \end{tabular} & \begin{tabular}{c} **\(\rho_{\text{core}}\)** \\ **[kg/m\({}^{3}\)]** \\ \end{tabular} & \begin{tabular}{c} **t\({}_{\text{tot}}\)** \\ **[mm]** \\ \end{tabular} & **Skins** & \begin{tabular}{c} **w** \\ **[mm]** \\ \end{tabular} & **\begin{tabular}{c} **F\({}_{\text{MAX}}\)** \\ **[N]** \\ \end{tabular} & \
\begin{tabular}{c} **SAE\({}_{\text{w}}\)** \\ **[J/kg]** \\ \end{tabular} &
\begin{tabular}{c} **SAE\({}_{\text{w}}\)** \\ **[J/m\