esa-sceva/satcom-chunk-collection huggingface dataset:
# Comparison of Genotoxicity and Pulmonary Toxicity Study of Modified SiO2 Nanomaterials
## 3 Results
### MN Test
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Group** & **Dosage** & **Frequency of** & \multicolumn{4}{c}{**Chromatic Type**} & \multicolumn{2}{c}{**Chromosome Type**} \\ \cline{3-8}  & **(mg/mL)** & **Aberration (\%)** & **Deletion** & **Intrachange** & **Interchange** & **Ring** & **Gap** \\ \hline NC 1 & 0 & 2.7 \(\pm\) 2.1 & 1.3 \(\pm\) 1.2 & 0.0 \(\pm\) 0.0 & 0.3 \(\pm\) 0.6 & 0.0 \(\pm\) 0.0 & 1.0 \(\pm\) 1.0 \\ Cyclophosphamide & 25 & 12.7 \(\pm\) 3.1 * & 13.7 \(\pm\) 4.7 * & 0.0 \(\pm\) 0.0 & 0.3 \(\pm\) 0.6 & 0.0 \(\pm\) 0.0 & 1.7 \(\pm\) 0.6 \\ SA & 1.387 & 3.3 \(\pm\) 0.6 & 2.0 \(\pm\) 0.0 & 0.0 \(\pm\) 0.0 & 0.3 \(\pm\) 0.6 & 0.3 \(\pm\) 0.6 & 0.7 \(\pm\) 0.6 \\ mSA & 0.778 & 4.7 \(\pm\) 2.1 & 2.3 \(\pm\) 2.1 & 0.0 \(\pm\) 0.0 & 0.0 \(\pm\) 0.0 & 0.7 \(\pm\) 0.6 & 2.0 \(\pm\) 1.7 \\ SB & 0.353 & 2.3 \(\pm\) 0.6 & 2.0 \(\pm\) 0.0 & 0.0 \(\pm\) 0.0 & 0.3 \(\pm\) 0.6 & 0.0 \(\pm\) 0.0 & 0.0 \(\pm\) 0.0 \\ mSB & 1.569 & 3.3 \(\pm\) 1.5 & 3.0 \(\pm\) 2.0 & 0.0 \(\pm\) 0.0 & 0.0 \(\pm\) 0.0 & 0.0 \(\pm\) 0.0 & 0.3 \(\pm\) 0.6 \\ \hline \end{tabular}  
* NC: negative control, R.D.W. 10\% _v/v_; SA: sample A, 10\% _v/v_; mSA: modified sample A, 10\% _v/v_; SB, sample B, 10\% _v/v_; mSB, modified sample B, 10\% _v/v_.
* Significant difference in comparison with the negative control and treated groups at \(p\) \(<\) 0.05.  
\end{table} 
Table 7: Frequency of chromosomal aberration with S9 metabolism of unmodified and modified nano-silica in CHO-K1 cells.
## 4 Empirical Analysis
### Fitness Modeling Simulation Based on NK Model
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Serial Number** & \(X_{1}\) & \(X_{6}\) & \(X_{12}\) & \(X_{14}\) & \(F_{d}\) & **Path** \\ \hline
1 & 0 & 0 & 0 & 0 & 0.000 & \\
2 & 1 & 0 & 0 & 0 & 0.1186 & \\
3 & 0 & 1 & 0 & 0 & 0.1372 & 1 \\
4 & 0 & 0 & 1 & 0 & 0.1247 & \\
5 & 0 & 0 & 0 & 1 & 0.1195 & \\
6 & 1 & 1 & 0 & 0 & 0.3197 & 1 \\
7 & 1 & 0 & 1 & 0 & 0.2897 & 1 \\
8 & 1 & 0 & 0 & 1 & 0.2833 & \\
9 & 0 & 1 & 1 & 0 & 0.2964 & \\
10 & 0 & 1 & 0 & 1 & 0.3098 & \\
11 & 0 & 0 & 1 & 1 & 0.2983 & \\
12 & 1 & 1 & 1 & 0 & 0.5253 & 1 \\
13 & 1 & 1 & 0 & 1 & 0.5376 & 1 \\
14 & 1 & 0 & 1 & 1 & 0.5085 & 1 \\
15 & 0 & 1 & 1 & 1 & 0.5231 & \\
16 & 1 & 1 & 1 & 1 & 0.7972 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Composite fitness values of key safety performance indicators in PBC. point of the second step. Second step: In Figure 1(a), there are six scenarios (\(F_{6}\) to \(F_{11}\)) for the pairwise combination of key indicators \(X_{1}\), \(X_{6}\), \(X_{12}\), and \(X_{14}\). Implementing the allele combination (1,1,0,0) for \(X_{1}\) and \(X_{6}\) results in the highest overall fitness value \(F_{6}=0.3197\). In Figure 1(b), this is represented by path 2, climbing from position ((0,1), (0,0), 0.1372) to ((1,1), (0,0), 0.3197). Thus, the second step should select key indicator \(X_{1}\). Therefore, (1, 1, 0, 0) is both the endpoint of the second step and the starting point of the next step. Similarly, other combination strategies for key indicators are sequentially implemented until the solution with all the key factors (1, 1, 1, 1) is found. In summary, the basic idea of adaptive landscape climbing is to gradually adjust the improvement path until the solution with all the key indicators (1,1,1,1) is found, thus obtaining the optimal improvement path. In this case, the climbing sequence is ((0,0), (0,0), 0.0000), ((0,1), (0,0), 0.1372), ((1,1), (0,0), 0.3197), ((1,1), (0,1), 0.5376), ((1,1), (1,1), 0.7972), indicating that the optimal improvement path should be implemented in the sequence \(X_{6}\) (construction and implementation of safety management systems) \(\to X_{1}\) (personnel safety awareness and attitude) \(\to X_{14}\) (quality and safety status of prefabricated components) \(\to X_{12}\) (construction site working environment).
## 3 Results
### Remotely Sensed Variables and \(R_{\rm s}\)  
#### 3.3.1 Vegetation Indices  
With the exception of NDLI, none of the VIs differed between vegetation types. For NDLI, cinnamon fern (\(-\)0.0154) differed significantly from mountain laurel (\(-\)0.0144) which had the highest NDLI of all vegetation types. The annual average \(R_{\rm s}\) value, the average growing season \(R_{\rm s}\), the average non-growing season \(R_{\rm s}\), and all VIs were not significantly correlated. When we examined the correlation between the VIs and the average \(R_{\rm s}\) by date sampled, we found some statistically significant relationships (Table 4). Our \(R_{\rm s}\) under each of the sixteen plots for the sample date of August showed a significant correlation (\(p\) = 0.0214, R\({}^{2}\) = 0.324) to the NDLI. For the \(R_{\rm s}\) for the sample date of September, we found a correlation between several VIs including NDLI (\(p\) = 0.0462, R\({}^{2}\) = 0.25), NDVI (\(p\) = 0.02, R\({}^{2}\) = 0.329), PRI (\(p\) = 0.0285, R\({}^{2}\)= 0.30), and NDNI (\(p\) = 0.025, R\({}^{2}\) = 0.31). For all significant correlations, the first cinnamon fern plot acted as a high leverage data point. When removed, the relationship between August's \(R_{\rm s}\) and NDLI was stronger (R\({}^{2}\) = 0.64); however, all other significant correlations drastically decreased in strength when the high leverage value was removed. We found no reason to remove the value as the number was still an accurate representation of the site. Accounting for soil temperature and soil  
Figure 3: Soil Respiration (\(R_{\rm s}\)) in a high-elevation southern Appalachian forest (Giles County, VA, USA) as influenced by soil moisture for each vegetation type: hardwood, cinnamon fern, hemlock, and mountain laurel. Predicted lines were generated using the formulas from Table 2 while holding soil temperature at a value of 15.02 \({}^{\circ}\)C (the average soil temperature for the months of May to September).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Vegetation Type Comparisons** & **Intercept** & **A** & **B** \\ \hline Hardwood vs. Cinnamon Fern & 0.9933 & 0.1371 & 0.9466 \\ Hardwood vs. Hemlock & 0.0993 & 0.3170 & 0.0873 \\ Hardwood vs. Mountain Laurel & 0.0173 \({}^{\ast}\) & 0.2414 & 0.8176 \\ Cinnamon Fern vs. Hemlock & 0.1074 & 0.6394 & 0.0749 \\ Cinnamon Fern vs. Mountain Laurel & 0.0201 \({}^{\ast}\) & 0.7036 & 0.0149 \({}^{\ast}\) \\ Hemlock vs. Mountain Laurel & 0.2073 & 0.9108 & 0.2844 \\ \hline \hline \end{tabular} \({}^{\ast}\)Values are statistically significant at the \(\alpha\) = 0.05 level.  
\end{table}
Table 3: \(p\)-values for the comparison of parameter estimates of the vegetation-type specific models.  
moisture variation, adding our vegetation indices individually into our base model did not explain any additional variation of \(\mathrm{R_{s}}\).
# Sulfuric Acid Resistance of CNT-Cementitious Composites
## 3 Test Results and Discussion
### Pore Distribution Characteristics  
The pore size distribution and cumulative pore volume of cement mortar incorporating CNTs before and after damage by 5% sulfuric acid solution are shown in Figures 12 and 13, respectively. As the incorporated amount of CNTs increased, relatively large pores with sizes ranging between 370 and 80 \(\upmu\)m occurred (see Figure 11a). It was observed that the pore size was greater in the specimen incorporating SWCNT than MWCNT. These results  
Figure 11: Electrical resistance of CNT cementitious composites before and after deterioration by sulfuric acid: (**a**) 5% and (**b**)10%.  
Figure 10: Strength of carbon nanotube (CNT) cementitious composites: (**a**) compressive strength and (**b**) flexural strength.  
indicate that although the porosity was reduced due to the filling effect in the incorporation of CNTs with a nano-sized diameter of 1-100 nm, the hydrophobic CNTs were not uniformly dispersed in cementitious mixtures but exist in a bundled form. However, in the plain specimen, most of the pores with sizes ranging from 370 to 30 \(\upmu\)m were distributed, and micro-pores (less than 1 \(\upmu\)m) was not observed. These pores were originally filled with water remaining after mixing. As the water evaporated, these pores were created. Figure 12b depicts the pore distribution characteristics of the specimen after immersion in a sulfuric acid solution. The maximum pore diameter increased to 500 \(\upmu\)m, and the distribution of larger pores than before was observed because the cement hydrate, which is vulnerable to chemical erosion, was dissolved in the sulfuric acid solution. This denotes that CNT cementitious composites had high resistance to chemical erosion, meaning that they helped to strengthen the cement hydrate structures. The pore distribution results in Figures 12 and 13 imply that as CNTs were incorporated, large pores were formed and could serve as easy paths for the sulfuric acid solution to penetrate. However, CNTs with excellent chemical resistance were attached to cement particles and prevented the penetration of ions such as cement hydrate, sulfate (SO4\({}^{2-}\)), and hydrogen (H\({}^{+}\)), thereby improving the weight loss and strength reduction of the composites in Figure 6.  
Figure 12: Pore size distribution of CNT cementitious composites before and after immersion in 5% sulfuric solution: (a) before damage and (b) after damage.
# Metabolite Profiling of _Christia__vevertilionis_ Leaf Metabolome via Molecular Network Approach
## 3 Results and Discussion
### C-glycosflavone Derivatives
#### 3.1.3 Benzyltetrahydroisoquinoline Alkaloids  
Previously, alkaloids were reported to be present in _C. vespertilionis_[4]. The presence of alkaloids in this plant could be closely related to its nitrogen-fixing ability [28], a property which seemed to be characteristic of Fabaceae species, especially for members of Papilionoideae and Mimosoideae [29]. In this present study, a detailed analysis of the MS/MS spectral data for the precursor ions [M + H]\({}^{+}\) at \(m/z\) 370.170 and 340.160, mapped in Cluster F (Figure 7), led to their tentative identification as new derivatives of benzyltetrahydroisoquinoline-type alkaloids, **23** and **29**, respectively. The protonated molecular ion of both compounds, [M + H]\({}^{+}\), was mainly characterized based on neutral loss of an NHR\({}_{1}\)R\({}_{2}\) moiety, R\({}_{1}\) and R\({}_{2}\) being the substituent groups on the nitrogen atom, which is a typical fragmentation pattern observed for isoquinoline-type alkaloids [30]. Based on the formation of the [(M+H)-NHHC\({}_{4}\)H\({}_{7}\)]\({}^{+}\) fragment ion at \(m/z\) 299 for compound **23** and 269 for compound **29**, the substituents, R\({}_{1}\) and R\({}_{2}\), of both compounds were thus suggested to be H and a butenyl group (C\({}_{4}\)H\({}_{7}\), 55 Da). Further detailed inspection of the MS/MS spectral data for compound **23** showed that it exhibited fragment ions at \(m/z\) 145 for [(M+H)-NHHC\({}_{4}\)H\({}_{7}\)-C\({}_{8}\)H\({}_{10}\)O\({}_{3}\)]\({}^{+}\) (base peak), and 127 for [(M+H)-NHHC\({}_{4}\)H\({}_{7}\)-C\({}_{8}\)H\({}_{10}\)O\({}_{3}\)-H\({}_{2}\)O]\({}^{+}\), suggesting a benzyltetrahydroisoquinoline structure with a monohydroylated ring-A and trisubstituted ring-C (the substituents being a hydroxyl and two methoxyl groups). A similar MS/MS fragmentation pattern was also observed in the MS/MS spectrum of compound **29**, which exhibited key fragment ions at \(m/z\) 145 for [(M+H)-NHHC\({}_{4}\)H\({}_{7}\)-C\({}_{6}\)H\({}_{6}\)O-CH\({}_{2}\)O]\({}^{+}\) (base peak) and 127 for [(M+H)-NHHC\({}_{4}\)H\({}_{7}\)-C\({}_{6}\)H\({}_{6}\)O-CH\({}_{2}\)O]\({}^{+}\). These fragments and additional ions at \(m/z\) 175 for [(M+H)-NHHC\({}_{4}\)H\({}_{7}\)-C\({}_{6}\)H\({}_{6}\)O]\({}^{+}\) and 119 for [(M+H)-C\({}_{13}\)H\({}_{10}\)O\({}_{2}\)N]\({}^{+}\) supported a disubstituted ring-A (one hydroxyl and two methoxyl groups) and a monohydroylated ring-C for compound **29**. The proposed fragmentation pathways for compounds **23** and **29** are presented in Figures S12 and S13, respectively.
## 4 Seismic Response CFST Structure with BRBs under Earthquake Sequences
### Nonlinear Response Analysis under Sequence-Type Ground Motion  
As discussed in Section 3.3, the BRBs can significantly reduce the seismic responses of the CFST frame structure. Thus, in the following analysis, this paper focuses on the CFST frame with BRBs and only the influence of the earthquake sequence on CFST-B is presented due to space limitations. It is  
Figure 11: Comparison of the roof displacement and ISDRs between CFST-A and CFST-B (Taft PGA = 1.0 g). noted that both the CFST frame with and without BRBs yielded similar results regarding the effect of the earthquake sequence. In this case, the seismic response analysis of CFST-B is performed with consideration of the strain rate effect using 30 sets of earthquake sequences. The seismic responses of the structure under the single mainshock and under different earthquake sequences (\(\delta\) = 0.4, 0.6, and 0.8) are compared to analyze the effect of the earthquake sequence on the seismic response of the structure. The comparison results from the structural dynamic response analyses are listed in Tables 3 and 4. Figure 12 compares the response curves of the CFST frame structure with BRBs under ground motion with an ID of A-CAS (PGA = 0.332 g). It is shown that the maximum roof displacements of the structure generally occur during the mainshock segments. Under the same earthquake sequence, with increasing \(\delta\), the maximum roof displacement of the structure gradually increases. The foreshock leads to certain cumulative damage of the structure. As \(\delta\) increases, the level of cumulative damage increases. Figure 13 compares the ISDRs of CFST-B under ground motion A-CAS (PGA = 0.332 g). The ISDRs under the mainshock and under earthquake sequences with \(\delta\) = 0.4, 0.6, and 0.8 are compared, respectively. It is observed that, under the single mainshock and under earthquake sequences, the ISDR of the third story is generally larger than those of the other stories. Therefore, the third story is the weakest story of the structure. With increasing \(\delta\), the maximum ISDR of the structure caused by the earthquake sequence gradually increases. To further comparatively analyze the seismic responses of the structure subjected to single mainshocks and earthquake sequences, Tables 3 and 4 compare the maximum roof displacement and the maximum ISDRs under different ground motions.
## 3 Inventory Location Identification Framework in MSR  
This study aims to provide a comprehensive inventory location identification framework utilizing passive RFID in terms of MSR for low-cost WMSs in SMEs. As shown in Figure 1, the proposed framework consists of two steps, the configuration of the tag information and the identification of the inventory location, which form the backbone of the framework. Step 1 attaches the reference tag to the center of each MSR zone, registers the mapping information between the reference tag and its physical MSR location, and sets up the recognition preferences for improving the accuracy of the inventory location identification (Section 3.1). Step 2 then scans the stock in the MSR, temporarily stores the recognized information in a database, and identifies the inventory location based on the proposed algorithm (Section 3.2).
# Optimizing Flight Delay Predictions with Scorecard Systems
## 5 Results
### Variable Descriptions and Reports
#### 5.2.4 The Size of Destination Airport  
This analysis explores how the size of the destination airport (Dest_size) influences flight performance, offering insights into operational efficiency and potential congestion impacts. The detailed report on the variable category is presented in Table 9.  
In general, small delays can be observed at small airports, but additional information can be obtained from the evolution over time, presented in Figure 7.  
We can observe that for the biggest airports--partial score 297--the delayed rate has been decreasing over the last 20 years; this can be explained by the more advanced quality of services at big airports.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Score** & **Condition** & **DR [\%]** & **Share [\%]** & **Total Flights** & **Delayed** & **On Time** \\ \hline
288 & 7,953,718.5 \(<\) Origin\_size \(<\) & 6.5 & 9.4 & 115,626 & 7506 & 108,120 \\
294 & 2,039,177.5 \(\leq\) Origin\_size \(<\) & 5.2 & 50.5 & 623,175 & 32,107 & 591,068 \\
294 & 10,663,810.5 \(\leq\) Origin\_size & 5.1 & 5.4 & 66,207 & 3389 & 62,818 \\
299 & Origin\_size \(<\) 2,039,177.5 & 4.2 & 34.7 & 428,485 & 18,168 & 410,317 \\ \hline  & & 100.0 & 1,233,493 & 61,170 & 1,172,323 \\ \hline \end{tabular}
\end{table}
Table 8: The size of the origin airport (Origin_size).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Score** & **Condition** & **DR [\%]** & **Share [\%]** & **Total Flights** & **Delayed** & **On Time** \\ \hline
288 & 3,667,356.0 \(\leq\) Dest\_size \(<\) & 7.3 & 7.5 & 92,189 & 6774 & 85,415 \\
297 & 9,717,124.5 \(\leq\) Dest\_size \(<\) & 5.8 & 10.4 & 128,239 & 7484 & 120,755 \\
305 & 4,300,530.5 \(\leq\) Dest\_size \(<\) & 4.7 & 27.4 & 338,067 & 15,955 & 322,112 \\
306 & Dest\_size \(<\) 3,667,356.0 & 4.6 & 54.7 & 674,998 & 30,957 & 644,041 \\ \hline  & & 100.0 & 1,233,493 & 61,170 & 1,172,323 \\ \hline \end{tabular}
\end{table}
Table 9: The detailed variable category report.  
Figure 7: Delayed rate as a function of time for variable of the size of the destination airport. Source: Authors’ own elaboration.
# Shape Effect and Accuracy Analysis of Rock Tensile Strength Test
## 2 Analysis of Shape Effects in Splitting Tests  
### 2.1. Similitude Simulation Test  
To (rigorously) analyze the shape effects caused by the disc diameter in splitting tests, this study first considers the complexities of core sampling in real engineering practices. During field core drilling, due to the inherent heterogeneity of rock, even different parts of the same rock layer or block may exhibit significant variations in mechanical proper ties, particularly in rock strength. This heterogeneity can interfere with the accuracy and reliability of subsequent experimental data. Therefore, to minimize the impact of uncontrollable factors and ensure the comparability of experimental data, this study employs similar simulation materials. Cement, river sand, and water are mixed in a ratio of 1:3:0.7 to prepare rock-like materials, enabling shape effect analysis under controlled laboratory conditions, thereby overcoming the inherent inconsistencies of natural rock samples.  
According to the \"Standard of Engineering Rock Mass Testing Methods\", this experiment selected disc specimens with a thickness-to-diameter ratio of 0.5. To systematically investigate the impact of shape effects on splitting test results, the disc diameters in the numerical model were set to 30, 40, 50, 70, 90, and 110 mm. These specimens cover a range of diameters from small to large, which facilitates a thorough investigation of the influence of disc diameter on the splitting test results. The thickness-to-diameter ratio of the specimens was uniformly set to 0.5 [18]. Eight specimens of each diameter were produced, yielding a total of forty-eight specimens. This large sample design enhances the representativeness and statistical reliability of the experiment. After production, all specimens underwent a 28-day standard curing process to ensure that the strength of the similar materials stabilized. The splitting tests were then performed on these disc specimens, as shown in Figure 1, to quantitatively analyze the impact of disc diameter on shape effects.
###### Abstract  
Rock-filled concrete (RFC) technology is a new type of mass concrete construction technology, which consists of two basic components: the force transfer frame formed by large-size rock accumulation and the matrix formed by self-compacting concrete (SCC) filling. Its unique construction method also distinguishes RFC from ordinary concrete in terms of its force characteristics. In this paper, RFC is considered as a composite material consisting of aggregate and SCC; based on the realistic failure process analysis (RFPA) method, the effects of specimen size and aggregate size on the compressive strength of RFC were studied. Firstly, RFC cube specimens were prepared and uniaxial compression tests were conducted. During the preparation process, in order to eliminate the influence of factors such as shape, spatial distribution state, and volume share of aggregates on the compressive strength, aggregates of different sizes were set as spheres and arranged in simple cubic stacking; then a numerical model of RFC with different specimen sizes and different aggregate sizes was established for uniaxial compression numerical simulation experiments to analyze the variation law and failure pattern of the RFC compressive strength. The results indicate that the compressive strength of RFC exhibits a significant size effect and follows a negative exponential function distribution law; with the same volume fraction of aggregate, the smaller the aggregate size, the higher the compressive strength of the RFC will be, and this increasing trend gradually levels off. Based on the findings of this study, it is recommended that the size effect and the reduction of aggregate size on dam strength be taken into account in the design of RFC dams.  
rocked-filled concrete; compressive strength; aggregate size; size effect +
Footnote †: journal: Applied Sciences
## 3 Application of Drive-by Methodologies on the Bridge Subsystem
### Bridge Modal Identification
#### 3.1.3. Damping Ratio Evaluation  
Besides being less widespread, some studies are found to involve damping estimation using the drive-by concept. [PERSON] et al. [28] were one of the pioneers in this type of development. They proposed the damping estimation by reverse modeling to derive the track irregularities profile under each of the two wheels of the 2D model from acceleration at the vehicle's axles. The process is performed by a series of assumed damping ratios. The bridge damping ratio is taken as the one which provides the lower squared error between the estimated profiles under each wheel since, in theory, they should be the same as the wheels cross the same path. The method was validated numerically and performed very well for a wide range of vehicle velocities (10 to 30 m/s) and track irregularities. However, some difficulties were found in dealing with short-span bridges since its higher stiffness results in lower excitation levels. [PERSON] et al. [29] proposed the use of the same correction function applied to extract the bridge's mode shapes, as mentioned in Section 3.1.2, to estimate the bridge's damping. The method proposed by the authors involves finding for the correction function the damping value which provides the better mode shape estimative, that is, the highest MAC value with the actual mode shape. However, one of the disadvantages of this methodology is that it requires previous knowledge of the true mode shape.
## 3 Results and Discussions
### Critical Dynamic Stress
#### 3.3.2 Impact of Temperature and Moisture Content
It shows that under the long-term reciprocating cyclic loading, when the moisture content is low, the proportion of ice particles increases and the ice cementation increases insignificantly, which does not play a controlling role in resisting deformation, and the CDS decreases slowly, but when the moisture content is high, the proportion of ice particles increases greatly and the cementation between ice particles is enhanced, which increases the ability of resisting deformation significantly, and thus the CDS is increasing.