esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
Based on our analysis results, the following conclusions can be drawn:  
1. In this study, a finite element model of RPC columns under coupled fire and explosions was proposed by ABAQUS finite element software. Based on the established model, the dynamic response analysis of the RPC column under coupled fire and explosions was realized. The results show that the fire seriously weakened the explosion  
Figure 20: Diagram of frame damage when explosion load located at the top of a beam on the first floor.  
resistance of the RPC column, and the consideration of the fire-explosion coupling effect is a non-negligible factor in the explosion-resistant design of the structure.
2. The influence laws of fire duration, section size, axial compression ratio, and reinforcement ratio on the dynamic response of RPC columns were revealed. It was found that the maximum mid-span and residual deformations of RPC columns decrease with the increase in section size and longitudinal reinforcement ratio and increase with the increase in fire time and axial compression ratio.
3. The three-story, two-span RPC frame and RC frame structures were modeled, respectively. The dynamic responses of RC frame and RPC frame structures were investigated when the explosion load was applied to the bottom corner columns, side columns, and top beams of the frame, respectively, and the two structures were compared and analyzed. It was found that under the same working conditions, the overall damage degree of the RPC frame was lower than that of RC frame. The method of using RPC materials to locally replace or reinforce the explosion resistance of the RC frame structures at high temperatures was proposed.  
Q.R.: Writing--review and editing. Formal analysis, Data curation, Investigation, Methodology. C.P.: Writing--original draft, Data curation, Investigation, Validation, Methodology. X.H.: Conceptualization, Methodology, Project administration, Writing--review and editing. Y.C.: Formal analysis, Methodology, Validation. T.F.: Investigation, Resources. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (Nos. 52278169 and 52078169), the Natural Science Foundation of Heilongjiang Province (No. LH2022E089), the China Postdoctoral Science Foundation (No. 2023M731030), and the Heilongjiang Postdoctoral Science Foundation (No. LBH-Z23031).  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflicts of interest.
## 3 Results
### Statistical Analysis
\begin{table}
\begin{tabular}{c c c c c} \hline  & **Group 1 (CS1–CS2)** & **Group 2 (CS3–CS4)** & **Group 3 (CS5–CS6)** & **Intergroup** \\ \cline{2-5}  & **T1–T0** & **T1–T0** & **T1–T0** & \(p\)**-Value** \\ \hline
**N** & 21 & 28 & 19 & \\ \hline \multicolumn{5}{c}{**III (mm)**} \\ \hline C & 3.31 \(\pm\) 2.94 & 1.61 \(\pm\) 2.02 & 2.24 \(\pm\) 1.78 & \\ \hline \(p\)_-value_ & _0.002_ & _0.003_ & \(<\)_0.001_ & 0.120 \\ \hline cent & 3.13 [2.56, 4.55] & 1.32 [0.48, 3.04] & 2.17 [1.36, 3.01] & \\ \hline \(p\)_-value_ & _0.001_ & \(<\)_0.001_ & \(<\)_0.001_ & 0.112 \\ \hline L & 2.05 \(\pm\) 2.09 & 1.42 \(\pm\) 1.48 & 2.35 \(\pm\) 1.33 & \\ \hline \(p\)_-value_ & _0.004_ & _0.001_ & \(<\)_0.001_ & 0.276 \\ \hline
**IV (mm)** & & & & \\ \hline C & 6.00 \(\pm\) 2.28 & 4.75 \(\pm\) 2.67 & 5.48 \(\pm\) 1.69 & \\ \hline cent & \(<\)_0.001_ & \(<\)_0.001_ & \(<\)_0.001_ & 0.227 \\ \hline cent & 5.30 [4.29, 6.29] & 4.16 [3.02, 5.12] & 4.90 [3.84, 6.08] & \\ \hline \(p\)_-value_ & \(<\)_0.001_ & \(<\)_0.001_ & \(<\)_0.001_ & 0.122 \\ \hline L & 3.93 [2.81, 6.05] & 3.52 [2.83, 4.50] & 4.33 [3.16, 5.39] & \\ \hline \(p\)_-value_ & \(<\)_0.001_ & \(<\)_0.001_ & \(<\)_0.001_ & 0.557 \\ \hline
**V (mm)** & & & & \\ \hline C & 6.30 \(\pm\) 1.37 & 5.43 \(\pm\) 2.12 & 5.41 \(\pm\) 2.49 & \\ \hline \(p\)_-value_ & _0.015_ & \(<\)_0.001_ & \(<\)_0.001_ & 0.809 \\ \hline cent & 5.42 \(\pm\) 1.54 & 4.73 \(\pm\) 2.01 & 4.72 \(\pm\) 2.20 & \\ \hline \(p\)_-value_ & _0.026_ & \(<\)_0.001_ & \(<\)_0.001_ & 0.855 \\ \hline L & 4.58 \(\pm\) 1.74 & 4.26 \(\pm\) 1.68 & 4.23 \(\pm\) 1.78 & \\ \hline \(p\)_-value_ & _0.045_ & \(<\)_0.001_ & \(<\)_0.001_ & 0.947 \\ \hline
**VI (mm)** & & & & \\ \hline C & 3.65 \(\pm\) 2.15 & 3.67 \(\pm\) 1.56 & 3.82 \(\pm\) 1.71 & \\ \hline \(p\)_-value_ & \(<\)_0.001_ & \(<\)_0.001_ & \(<\)_0.001_ & 0.950 \\ \hline cent & 2.79 \(\pm\) 1.87 & 2.81 \(\pm\) 1.48 & 3.02 \(\pm\) 1.76 & \\ \hline \(p\)_-value_ & \(<\)_0.001_ & \(<\)_0.001_ & \(<\)_0.001_ & 0.889 \\ \hline L & 3.46 [2.42, 4.56] & 2.90 [2.28, 3.39] & 3.25 [1.95, 4.08] & \\ \hline \(p\)_-value_ & \(<\)_0.001_ & \(<\)_0.001_ & \(<\)_0.001_ & 0.529 \\ \hline \end{tabular}
\end{table} 
Table 4: Differences over time in expansion measurements (N = 68). Results are expressed as mean \(\pm\) standard deviation or median [interquartile range]; intergroup \(p\)-value = one-way analysis of variance with group as factor or Kruskal–Wallis test \(p\)-value. Intragroup T1–T0 \(p\)-value = paired \(t\)-test \(p\)-value, or [PERSON]’s signed rank test \(p\)-value. III: canine; IV: first premolar; V: second premolar; VI first molar.
## 4 Discussion
### Isokinetic Temperature
The favourable case can be defined as the inequality: \(T_{lim}>T_{iso}\) when \(r^{2}=1\), because \(T_{iso}\to T_{low}\), which means approaching to the initial temperature \(T_{o}\). When \(T_{lim}\ll T_{iso}\), then this condition [43] characterizes isoconversional methods (\(\alpha=\text{const}\)) relative to classical methods (\(q=\text{const}\)). For simplicity, still assuming \(r^{2}=1\), this position parameter informs us about the relation between the isokinetic temperature and the range of tested temperatures in which it was determined, that is (Figure A5):  
1. \(K=0\); \(T_{iso}=T_{lim}\);
2. \(K<0(K=-1)\); \(T_{iso}<T_{low}(T_{iso}=T_{low})\);
3. \(K>0(K=+1)\); \(T_{iso}>T_{high}\big{(}T_{iso}=T_{high}\big{)}\). Case (c) can lead to excessively high isokinetic temperatures. Returning to the calculations presented in Figures A1-A4, it can be noted that, despite statistically satisfactory significance levels, none of the items meet the \(CQF\) criteria. Thus, the isokinetic temperatures are experimentally determined quantities that do not satisfy the \(CQF\) in the physicochemical sense. These considerations indicate that an important issue is the temperature range in which we observe \(KCE\), and it is advantageous that it is as wide as possible. Due to the \"quality\" of \(KCE\), when \(T_{lim}>T_{iso}\), then \(T_{iso}\) is closer to \(T_{0}\) than to the final temperature of the reaction/process, which is relatively unfavorably much larger. The criteria proposed in the works [48; 49] may make it necessary to revisit many already published works.
## 4 Discussion
### The Sensitivity of RSM and PRE to Drought  
Drought stress effects on vegetation's CO\({}_{2}\) photosynthetic uptake have been identified as a major source of uncertainty in forecasting future terrestrial carbon sequestration and climate change dynamics [63]. However, terrestrial ecosystem models typically employ only empirical plant-functional-type functions to capture drought stress [64]. This study examined the sensitivity of GPP to the RSM availability index (SAI), uncovering inconsistent patterns between GPP drought sensitivity and climatic gradients. Specifically, GPP sensitivity to the \(SAI\) displayed varying regional trends, with extreme arid regions exhibiting a progressive decline in GPP variability as climatic humidity increased (Figure 8c). Although GPP values in these extreme arid zones were relatively low, their variation patterns were notably distinct, reflecting the heightened sensitivity of these ecosystems to changes in climatic humidity [18]. Increases in RSM or PRE (or other factors) led to climatic moistening, consequently driving GPP variations. Arid and semi-arid ecosystems served as primary drivers of global terrestrial CO\({}_{2}\) flux variations, where RSM's drought response time and recovery rate proved critical in these regions [60], demonstrating RSM's decisive role in regulating vegetation productivity across drylands. As shown in Figure 8a, the values reached their maximum in extreme arid zones and decreased with reducing drought intensity, indicating that RSM exerted greater influence on GPP in drier regions. Furthermore, given the global declining trend of RSM [10] and projected expansion of drylands [65], RSM's impact on the global carbon cycle is expected to intensify in the future.
## 2 Related Work  
### Receptive Field Enhancement  
In industrial defect detection, expanding the feature receptive field is critical for improving small target recognition capabilities. Traditional methods rely on stacking convolutional layers or downsampling operations to enlarge the receptive field, but the resulting resolution reduction and detail loss severely constrain small target localization accuracy, particularly under dense background interference. Dilated convolution offers a breakthrough solution. By inserting zero values to expand kernel coverage, it captures multi-scale contextual information and preserves both parameter efficiency and feature map resolution, significantly enhancing the semantic understanding of minute defects [24]. However, standard dilated convolutions still face challenges due to high-frequency noise interference and local feature dilution in complex industrial scenarios. To address this, a 3 \(\times\) 3 convolutional layer before dilated convolution is embedded for local context fusion, effectively suppressing pixel-level noise at small target edges and preventing detail attenuation caused by large receptive fields. Simultaneously, the ConvNeXtBlock is introduced to further expand the receptive field and strengthen global semantic capture capability.
## 3 Results and Discussion
### In Silico Analysis of Bacillus safensis CN12 Genome Sequence
In this consortium, the genes _sMO_, _mPH_ (it is responsible for monohydroxplating the carbon atom adjacent to the pre-existing hydroxyl group, producing alcohols and catechols in short- and/or medium-chain alkylphenols), and _C12O_ were detected, whereas _alk_ and _C23O_ were not identified [29]. According to the literature reported previously, various sequences of the C23O gene (Accession numbers: WP_095432302.1, WP_048623272.1, and NP_388705.2) and nonyphenol monooxygenase (Accession number: BAI43694.1) retrieved from databases (NCBI) were compared with the complete genome of _B. safensis_ CN12. However, the results did not show the presence of these genes in its genome. Hence, they would not be responsible for NP degradation in this strain. Only [PERSON] et al. [59] and [PERSON] et al. [60] have described genes responsible for 4-n-NP degradation in species of the _Bacillus_ genus (_cotA_ and _mhqO_); for this reason, they were studied in the genome of our bacteria of interest. The surface-expressed multicopper oxidase, CotA, and the ring-cleaving dioxygenase, MhqO, were identified with a BLAST search [61]. A 99% identity score was achieved when aligning the sequence of the CotA protein (Accession number: AEX93437.1) described for _Bacillus pumilus_ strain A56 and the assembled genome of strain CN12, confirming its presence in the genetic material of the study bacteria. [PERSON] et al. [59] described the CotA enzyme in the _Bacillus_ genus as responsible for 4-n-NP degradation and mineralization. This oxidation process happens on the cell surface, facilitating 4-n-NP immobilization on the bacterial cells and increasing the reaction speed. Moreover, the in silico study of the genomic annotation of _B. safensis_ CN12 demonstrated the presence of a gene annotated as encoding the ring-cleaving dioxygenase MhqO protein. This protein showed 64% identity with the MhqO protein (Accession number: AGE62380.1) described for _Bacillus subtilis_ XF-1 by [PERSON] et al. [60]. Thus, it could play an important role in the degradation and/or mineralization pathway of 4-n-NP by _B. safensis_ CN12. MhqO with dioxygenase function can oxidize a substrate, transferring an atom of oxygen from molecular oxygen (O\({}_{2}\)) to the substrate, and according to [PERSON] et al. [55], dioxygenase enzymes have a relevant role in 4-n-NP biodegradation, as they can break the aromatic ring of the molecule, leading to its degradation into simpler compounds or complete mineralization. These authors were indeed the first to report genetic evidence of a possible aromatic ring-cleaving pathway of 4-n-NP in an aquatic environment. Based on the biodegradation results and the genetic information provided by the whole genome sequencing results, we can conclude that strain CN12 is a potential candidate for the degradation of aromatic compounds and even for their mineralization, including 4-n-NP.
## 3 Results
### The Results of the Single-Factor Test for Sandstones with Different Pore Structures
#### 3.1.2 Influence of Water Pressure on Permeability Evolution  
The sole influence of water pressure on pore permeability was studied under three levels of confining pressure: 20 MPa, 30 MPa and 40 MPa. Under each level of confining pressure, the variation in sandstone permeability with different water pressures was calculated according to the seepage flow rate of the four subcategories of sandstone measured by the corresponding pore seepage test. The test results are shown in Figure 11.  
Figure 9: Permeability variation of the four subcategories of sandstone with different confining pressures: (**a**) subcategory I\({}_{1}\) sandstone; (**b**) subcategory II\({}_{2}\) sandstone; (**c**) subcategory II\({}_{1}\) sandstone; (**d**) subcategory II\({}_{2}\) sandstone.  
Figure 11: Permeability variation of the four subcategories of sandstone with different water pressure: (**a**) subcategory I\({}_{1}\) sandstone; (**b**) subcategory I\({}_{2}\) sandstone; (**c**) subcategory II\({}_{1}\) sandstone; (**d**) subcategory II\({}_{2}\) sandstone. The water pressure value of the horizontal axis refers to the inlet value, and the outlet value is fixed at 1 MPa.  
Figure 10: The variation in the permeability ratio (k\({}^{\prime}\)/K) of different sandstones with confining pressure.  
The test results show that under the three levels of confining pressure, the permeabilities of the four subcategories of sandstone vary with the water pressure difference, but the variation trends are different. The difference is shown in the following two aspects:  
1. The permeabilities of subcategory \(\mathrm{I_{1}}\), \(\mathrm{I_{2}}\) and \(\mathrm{II_{1}}\) sandstones increase linearly with increasing water pressure (see Figure 11a-c), but the rate of increase in the permeability gradually slows (the slope of the straight line gradually decreases) from subcategory \(\mathrm{I_{1}}\) to \(\mathrm{I_{2}}\) to \(\mathrm{II_{1}}\) (see Table 5);
2. Under different confining pressure levels, the variations in the permeability of subcategory \(\mathrm{II_{2}}\) sandstone are different, and the permeability varies greatly and decreases as a power function under a low confining pressure (20 MPa), but the variation is small or basically stable under a high confining pressure (over 30 MPa) (see Figure 11d and Table 5).
###### Abstract  
Nowadays growing attention is paid to the control of fluorine content in samples of biological origin as it is present in the form of various biologically active organic compounds. Due to the chemically-rich matrix of biological tissues, the determination of fluorine becomes a very difficult task. Furthermore, a required complex sample preparation procedure makes the determination of the low contents of F by ion chromatography UV-Vis or ion-selective electrodes not possible. High-resolution continuum source graphite furnace molecular absorption spectrometry (HR-CS GF MAS) seems to be the best option for this purpose due to its high robustness to matrix interferences, especially in the presence of carefully selected modifiers. In this work the possibility of quantitative F determination in water and animal tissues was examined by measuring the molecular absorption of gallium monofluoride (GaF) at 211.248 nm with the use of a commercially available HR-CS GF MAS system. Experimental conditions for the sensitive and precise determination of fluorine were optimized, including the time/temperature program as well as addition of gallium and modifier mixture in combined mode. Under these conditions the fluoride present in the sample was stabilized up to 600 \({}^{\circ}\)C, and the optimum vaporization temperature for GaF was 1540 \({}^{\circ}\)C. Palladium and zirconium deposited onto the graphite surface served as solid modifiers; sodium acetate and ruthenium modifiers were added directly to the sample. The limit of detection and the characteristic mass of the method were 0.43 \(\mathrm{\SIUnitSymbolMicro g}\)/L and 8.7 \(\mathrm{\SIUnitSymbolMicro g}\), respectively. The proposed procedure was validated by the use of certified reference materials (CRMs) of lake water and animal tissue; the acceptable recovery was obtained, proving that it can be applied for samples with a similar matrix.  
high-resolution continuum source graphite furnace molecular absorption spectrometry; fluorine determination; gallium monofluoride +
Footnote †: journal: Applied Sciences
# A Channel Measurement-Based Listen-Before-Talk Algorithm for LTE-LAA and WLAN Coexistence
## 3 Proposed CM-LBT Scheme
### Throughput Estimation of LTE-LAA and WLAN Systems
When the type of slot is considered to be independent and identically distributed, \(q\) is given by \(q\ =\ \sum P[\text{packet becomes available}|\text{slot type}]P[\text{slot type}]\)[9]. Thus, it can be obtained as follows:  
\[ \begin{split} q&=(1-P_{tr})(1-e^{\lambda_{p}^{(u)} \sigma})+P_{u,s}(1-e^{\lambda_{p}^{(u)}T_{u,s}})+P_{w,s}(1-e^{\lambda_{p}^{(u) }T_{w,s}})\\ +& P_{u,c}(1-e^{\lambda_{p}^{(u)}max\{T_{u,c}T_{w, c}\}})+P_{w,c}(1-e^{\lambda_{p}^{(u)}T_{w,c}}).\end{split}  \tag{22}\]  
Both LTE-LAA and the WLAN adapt their modulation and coding schemes to the instantaneous SINRs. The LTE-LAA rate function \(R_{U}(\cdot)\) and WLAN rate function \(R_{W}(\cdot)\) are dependent on their modulation and coding schemes. Then, given the contention window size, \(W\), and the CCA threshold, \(P_{cca}\), the throughput of the WLAN system can be obtained as follows:  
\[S_{w}(W,P_{cca})=\frac{P_{w,s}T_{w,s}R_{W}}{E_{s}(P_{cca})}. \tag{23}\]  
where the average rate of the WLAN system, \(R_{W}\), is determined by \(R_{W}(\cdot)\) as defined in the 802.11 standard [22]. Let \(I_{u}\) and \(\varepsilon_{u}\) denote the set of LTE-LAA devices associated with LTE-LAA eNB and the ratio of transmissions for LTE-LAA device \(u\) to all LTE-LAA transmissions, respectively. Then, given \(W\) and \(P_{cca}\), the throughput of the LTE-LAA system can be obtained using Equation (16) as follows:  
\[S_{u}(W,P_{cca})=\sum_{u\in I_{u}}\frac{\varepsilon_{u}P_{u,s}T_{u,s}R_{U}( \gamma(R_{cca}))}{E_{s}(P_{cca})}. \tag{24}\]
# Blade Crack Diagnosis Based on Blade Tip Timing and Convolution Neural Networks
## 2 Method
### Numerical Model
System parameter deviations \(y_{\rm err}\) include structural deviation \(y_{\rm err}^{\rm b}\), blade tip deformation under centrifugal force \(\overline{y}_{\rm err}^{\rm b}(f_{\rm o}(t))\), and sensor positioning deviation \(\Delta y_{\rm err}^{\rm s}\), as in Equation (3):  
\[y_{\rm err}=y_{\rm err}^{\rm b}+\overline{y}_{\rm err}^{\rm b}(f_{\rm o}(t))+ \Delta y_{\rm err}^{\rm s} \tag{3}\]  
In this study, it is assumed that the blade structure's structural deviation \(y_{\rm err}^{\rm b}\) and sensor positioning deviation \(\Delta y_{\rm err}^{\rm s}\) are random variables in the range of [\(-0.5\) mm, 0.5 mm]; the blade tip deformation \(\overline{y}_{\rm err}^{\rm b}(f_{\rm o}(t))=f_{\rm o}(t)^{2}/10^{5}\), \(f_{\rm o}(t)\) is the rotation frequency. The accurate extraction of the time of arrival from sensor signals is a fundamental requirement for performing research on BTT vibration measurement. The fixed voltage threshold method and the constant fraction crossing method are the two most commonly used methods. Due to the noise impact, these two methods often extract the incorrect BTT signal [26]. In order to obtain the signal accurately, this study proposes a BTT arrival time extraction method based on the linear fitting of the rising edge interval of the pulse voltage signal. As shown in Figure 3, the signal's rising edge curve in the absence of noise is theoretically a continuous process with an approximately constant slope. This method selects a truncated voltage interval within an interval \(\rm{Ua}\sim\rm{Ub}\) containing the threshold voltage along the rising edge of the voltage signal curve, uses linear least squares fitting to obtain the rising curve, and then intersects with the threshold voltage level to solve for the intersection point corresponding to the time of arrival (TOA). The blade shape in Figure 3 is a schematic diagram of a generalized blade to illustrate how to obtain the blade tip arrival time and is not the blade shape used in the experiment. Figure 3: Method for obtaining blade tip arrival time.
## 2 Materials and Methods  
**Participants:** For the current study, a total of 215 adolescents--114 females (53.1%) and 101 males (46.9%), aged 12 (99 subjects, 46.1%) and 15 (116 subjects, 53.9%) years--were included. Four schools were selected to visit the subjects. A questionnaire was designed for acquiring a detailed history of their oral health status. One-hundred subjects from Karen Academy, Banana area; 74 subjects from Ruiru School, Ruiru area; 24 subjects from Kiambaa School, Kiambaa area; and 17 subjects from Nazareth Hospital School, Kimuga area participated in this study and completed the questionnaire. The study was conducted in accordance with the guidelines of the Declaration of Helsinki (2008 version), and informed written consent was obtained from the parent/guardian of each participant.  
**Data Collection and Analysis:** Two examiners with previous experience in epidemiological surveys participated in the study. As detailed elsewhere [12; 13], the calibration process was performed at the \"World Health Organization (WHO) Center of Collaboration for Epidemiology and Community Dentistry\" (University of Milano, Milano, Italy). Mean kappa values of 0.95 were obtained for the WHO diagnostic criteria. The DMFT index (Decay, Missing, and Filled Teeth), CPI (Community Periodontal Index), and questionnaire data concerning oral and food hygiene habits, parents' educational levels, and socioeconomic levels were recorded in accordance with the WHO criteria. The DMFT index represents the sum of the number of decayed teeth, teeth missing due to caries, and filled teeth among the permanent teeth; the mean DMFT index is the sum of the individual DMFT values divided by the sum of the population [14].  
The Thylstrup-Fejerskov (TF) index was also utilized to assess dental fluorosis [15]. The TF index assesses the dental fluorosis on all surfaces of every tooth, which is classified according to a 0-9 scale; according to this index, all erupted teeth are scored for fluorosis, and the highest score of a patient is described as their overall TF score. In the present study (Figure 1), when determining the severity of fluorosis, a TF score of 2-3 was considered mild, a TF score of 4-5 was considered moderate, and scores above 5 were considered severe.  
**Statistical analysis:** The odds ratios (ORs) of being diagnosed with dental caries lesions and co-presence of risk factors (i.e., age, gender, parents' work and educational level, number of subjects in the household, eating times, eating sweets during or between meals, drinking soft drinks during or between meals, and dental care and oral hygiene status) were calculated using multivariable models with logistic regression. The association between outcomes and explanatory variables was evaluated by setting the significance level to a \(p\)-value of less than or equal to 0.05.