esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
stated that the most likely cause of flame extinction was a blow-off. The phenomenon of sound wave extinguishing was indeed very similar to that of wind-blowing extinguishing as in previous studies; this could be explained by extinction strain-rate theory, essentially, a competitive relationship between chemical reactions driving combustion and the mixing rate of reactants [9]. Recently, [PERSON] and [PERSON] [10,11] found that flame stretch was not the cause of extinction in laminar, near-planar, and diffusion flames using acoustic waves. They argued that acoustically driven extinction was caused by the cooling effect of acoustic flow which was so great that the evaporated fuel dropped below the critical value needed to sustain a flame. They further proposed a criterion for acoustically driven extinction: whether the rate of a modified Nusselt number to the Spalding B number of the fuel was greater than the critical constant. However, for the acoustic extinction of most diffusion flames, extinction strain-rate theory was still applicable. [PERSON] et al. [12] studied the extinction mechanism for a single droplet flame in acoustic standing waves and found that extinction was caused by the temporal increase in the local rate of normal flame strain. This extinction mechanism was also supported by [PERSON] et al. [13,14,15,16]; they further modified the Damkohler number (_Da_), suitable for acoustic extinction, and the flaming firebrand. Furthermore, droplets can be extinguished when the value of _Da_ drops below 1.0. Flame displacement is the most important factor for extinction. However, the cause of flame displacement in the sound field seemed to be controversial. [PERSON] [17] thought that flame displacement was caused by the cumulative effect of acoustic mean flow and oscillatory perturbations. In his study, the gas burner flame was exposed to the sound field with a frequency of 30-50 Hz, and it is easier to extinguish the flame with lower-frequency sound waves as shown in previous works in the literature. The studies of [PERSON] et al. [18] showed that there was a good agreement between the displacements of the flame and membrane; thus, macroscopic movement of the flame was likely caused by the motion of the speaker membrane rather than the acoustic pressure. Significant progress has been made in the study of acoustic fire suppression. However, studies on the effect of sound waves on jet diffusion flames are insufficient, especially since the extinction mechanism has not been discussed in detail. In this work, an experimental investigation of acoustic extinction of a jet diffusion flame produced by a Bunsen burner with butane as fuel was performed. The study was focused on the cause of flame displacement and the extinguishing mechanism. The results are significant for understanding the acoustic fire-extinguishing mechanism, application scenarios, and the development direction of fire-extinguishing technology.
###### Abstract  
Precisely predicting photovoltaic (PV) output is crucial for reliable grid integration; so far, most models rely on site-specific sensor data or treat large meteorological datasets as black boxes. This study proposes an explainable machine-learning framework that simultaneously ranks the most informative weather parameters and reveals their physical relevance to PV generation. Starting from 27 local and plant-level variables recorded at 15 min resolution for a 1 MW array in Canakkale region, Turkive (1 August 2022-3 August 2024), we apply a three-stage feature-selection pipeline: (i) variance filtering, (ii) hierarchical correlation clustering with Ward linkage, and (iii) a meta-heuristic optimizer that maximizes a neural-network R\({}^{2}\) while penalizing poor or redundant inputs. The resulting subset, dominated by apparent temperature and diffuse, direct, global-tilted, and terrestrial irradiance, reduces dimensionality without significantly degrading accuracy. Feature importance is then quantified through two complementary aspects: (a) tree-based permutation scores extracted from a set of ensemble models and (b) information gain computed over random feature combinations. Both views converge on shortwave, direct, and global-tilted irradiance as the primary drivers of active power. Using only the selected features, the best model attains an average R\({}^{2}\cong\) 0.91 on unseen data. By utilizing transparent feature-reduction techniques and explainable importance metrics, the proposed approach delivers compact, more generalized, and reliable PV forecasts that generalize to sites lacking embedded sensor networks, and it provides actionable insights for plant siting, sensor prioritization, and grid-operation strategies.  
photovoltaic prediction; non-biased generalization; explainable machine learning; information theory; sustainability
## 5 Case Study
### Protection Method Specifications
#### 5.4.2 Improving Substation Component Redundancy  
The level of redundancy for the substation is considered to be a continuous parameter between 0 and 1, with 0 representing no redundancy and 1 representing full redundancy. The amount of direct loss from a terrorist attack is not affected by improvement in redundancy, since explosions are still expected to result in the same amount or slightly more of damage and casualties to the substation as they would if no redundant systems were provided. On the other hand, redundancy has the potential to entirely prevent indirect loss, i.e., in cases where the percentage of failed units does not exceed the level of redundancy to the component. The estimated costs of providing redundancy to the substation have been adopted from previous research on the substation by [PERSON] and [PERSON] [9].
## 6 Conclusions  
BFRP reinforcement are corrosion-resistant and have the potential to be used in CRCP. A 250 m test section of CRCP with BFRP bars was constructed in Zhejiang Province in China. The transverse crack spacing and crack width in the test section after one month of construction was measured prior to the placement of the HMA overlay, with the mean field-measured values of 4.85 m and 1.30 mm for the crack spacing and crack width, respectively, which are higher than the results for the conventional CRCP with steel bars. This mainly due to the lower elastic modulus (40 GPa) of BFRP bars compared to steel bars (210 GPa). An analytical model was presented to predict the crack behavior in BFRP-CRCP, with the crack spacing and width of 4.47 m and 1.78 mm, respectively, overpredicting but reasonably agreeing with the field survey results, since a period of 36 days is not enough for the transverse crack to be fully developed. The BFRP design parameters have a significant impact on the crack spacing and crack width in BFRP-CRCP. The greater the elastic modulus, reinforcement content (both BFRP spacing and diameter-related), and bond stiffness coefficient of BFRP, the lower the crack spacing and crack width. Given that the elastic modulus of BFRP increases from 40 to 80 GPa; the BFRP spacing decreases from 14 to 6 cm; the BFRP diameter ranges from 8 to 16 mm; the coefficient of the BFRP bond stiffness increases from 20 to 60 GPa/m; the crack spacing experiences a drop of 27.7%, 52.2%, 55.9%, and 39.4%, respectively; and the crack width presents a drop of 27.5%, 52.6%, 55.5%, and 39.4%, respectively. Given the same or similar reinforcement content of BFRP, a lower bar diameter with a smaller spacing is recommended in order to achieve a smaller crack spacing and crack width in the CRCP. Further field observations of the CRCP with BFRP bars are still necessary, and the load transfer efficiency between adjacent slabs with BFRP bars needs to be conducted because of the larger crack width compared to that of conventional CRCP with steel bars.  
Conceptualization: [PERSON]; validation: [PERSON] and Y.Z.; investigation: Y.Z.; methodology: Y.Z.; data curation: Y.Z.; supervision: [PERSON]; writing--original draft preparation: Y.Z.; writing--review and editing: [PERSON] and Z.H. All authors have read and agreed to the published version of the manuscript. This research was funded by the Natural Science Foundation of Jiangsu Province (BK20190604), Jiangsu Planned Projects for Postdoctoral Research Funds (2020Z327), Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University (K202002), and the Fundamental Research Funds for the Central Universities (JUSRP11937). The authors would like to acknowledge the support of [PERSON] from the University of Illinois at Urbana-Champaign. The authors declare no conflict of interest. The authors declare no conflict of interest.
###### Abstract  
This study investigates the effect of the location of an O-cell hydraulic jack along the length of a drilled shaft in a full-scale Osterberg test performed in soils containing layers of calcite. The location of the hydraulic jack with respect to calcite layers influences the measurements obtained from the Osterberg test and the subsequent interpretation of drilled shaft capacity. In this study, drilled shaft capacities were derived utilizing data from 30 Osterberg full-scale field load tests in soils containing calcite layers. The hydraulic jack was placed at the midpoint of the drilled shaft length. Additionally, the Osterberg test data was used to calibrate a numerical model by Plaxis finite element software for drilled shaft analysis. Using the calibrated model, several scenarios of hydraulic jack location were simulated. The scenarios included hydraulic jack locations at several distances above and below a calcite layer. The results of the simulations indicate that in cases where the O-cell was installed far from the calcite layer, the Osterberg tests results showed lower pile resistance capacity compared to the top-down test. However, in cases where the O-cell was installed close to the calcite layers, the Osterberg tests results showed comparable pile resistance capacity compared to the top-down tests. This study recommends installing the hydraulic jack as close as possible to the calcite layers for more reliable interpretation of the Osterberg field tests which leads to a cost-effective design approach by reducing the required shaft length.  
axially loaded drilled shafts; Osterberg test; O-Cell; calcite; pile foundations; Plaxis +
Footnote †: journal: Applied Physics Letters
## 3 Results
### Simulation System and Result Analysis
#### 3.2.2 Analysis of Experimental Results
Figure 11: (**a**) \(\log_{10}\)(BER) vs. the launching power for LE, conventional A-DBP, and proposed method with different values of \(\gamma\) with SSFM steps of 5. (**b**) \(\log_{10}\)(BER) vs. the different WDM channels at 3.5 dBm for LE, conventional A-DBP, and proposed method. (**c**) \(\log\)10(BER) vs. the different spanning cases at 3.5 dBm for LE, conventional A-DBP, and proposed method. (**d**) \(\log_{10}\)(BER) vs. the different OSNRs at 3.5 dBm for LE, conventional A-DBP, and proposed method with 4 iterations. The red arrow represents OSNR penalty when switching the algorithm from A-DBP-LTM to GDA or GDAM.
## 4 Conclusions  
Yarrow essential oil that was obtained by steam distillation from plant material brought into cultivation from the spontaneous flora presented chemical profiles with much higher chamazulene (21.97%, 25.37%) and germacrene D (21.28%, 21.13%) levels in two consecutive years compared to commercial samples from two different sources. These results aligned best with data from the literature for essential oil from the western Romanian region. Taking into consideration the diversity in compositions described as well as possible synergism between components, further research work would be required for the assessment of the relationship between the chemical profile and biological activity.  
Core-shell-type microcapsules with an average particle size of 47 \(\upmu\)m and a polynucleated core containing yarrow essential oil were successfully obtained by complex coaervation technology and transformed into solid-state material by consecutive freeze-drying. SEM images of the freeze-dried form revealed a loose, sponge-like structure with interconnected lyophilized particles by solid bridges.  
The technological process was characterized by a high encapsulation efficiency value of approx. 88%.  
According to the GC-MS investigation, 33 out of 45 components were present in the microencapsulated yarrow essential oil with the same major compounds.  
FT-IR analyses indicated electrostatic interactions between shell components, and that the essential oil was successfully encapsulated and there was no significant interaction between the core and the wall materials. DSC analysis confirmed the successful shell formation surrounding the entrapped essential oil.  
Taking into account the multiple biological effects of yarrow essential oil, the authors consider that microencapsulation, due to its protective role and ability to transform material from a liquid state into solid form, is worth being further considered as a potential formulation tool, which might open new application routes for the cosmetic, food, and pharmaceutical industries. Our plans include expanding the plantation size to ensure an adequate amount of raw material for semi-industrial-scale production, which will allow the assessment of self-prepared essential oil as core material and further investigation of the stability and bioactivity of AMO-loaded microcapsules.  
Conceptualization, I.S.-S., E.S. and Z.-I.S.; methodology, I.S.-S.; formal analysis, I.S.-S.; investigation, I.S.-S., C.A., E.M.R., A.-L.G. and B.S.-S.; resources, I.S.-S., C.A., E.M.R., A.-L.G. and B.S.-S.; data curation, I.S.-S.; writing--original draft preparation, I.S.-S.; writing--review and editing, B.S.-S., Z.-I.S. and B.K.; visualization, I.S.-S.; supervision, E.S.; project administration, I.S.-S. and Z.-I.S.; funding acquisition, I.S.-S. and E.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: 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 conflicts of interest.
## 4 Experimental Simulation Analysis
### Influence of IFFT Points on System Performance  
According to the 3 GPP LTE technical report, the number of IFFT points changes and the number of subcarriers \(N_{sub}\) changes, as shown in Table 1. The number of IFFT points is set to 1024, 1536, and 2048, respectively, and the number of subcarriers in each group \(M\) is kept unchanged, so as to change the number of packets \(L\). The 2 DLogistic map is used to obtain the experimental data.  
Figure 8 shows the impact of different packet numbers on the MC\(-\)ODBR\(-\)DCSK system using a 2D-Logistic map with initial values of 0.40 and a semi\(-\)spread spectrum factor of 100. From the figure above, it can be found that changing the number of IFFT points in the experiment, that is, changing the number of groups \(L\), has little impact on the system bit error performance.
## 3 Imaging in the Follow-Up of Treated HCC
### Current and Future Perspectives in Targeting Tumor Nodules  
Considering the advent of minimally invasive procedures and liver surgery for HCC, augmented reality training is gaining interest as an instrument that can aid the interventional radiologist or surgeon [162; 163]. The technology can overlay preoperative imaging onto the surgical field and can increase the spatial awareness, reducing risks related to critical adjacent structures or anatomical variations [164; 165]. Moreover, integrating image-guided simulation systems in minimally invasive procedures has demonstrated improved patient outcomes, allowing for precise targeting of tumor nodules [166; 167].  
Looking forward, preoperative planning and additive technologies have an increasingly important role, as 3D-printed anatomical models based on patient-specific imaging can offer the opportunity to plan and practice the procedure with high accuracy [168; 169]. Innovations in digital planning as well as augmented reality and artificial intelligence can assist image-guided simulation, can be integrated with surgical robots, and will likely further improve the precision in targeting the tumor nodules while minimizing invasiveness and complications [170; 171; 172].
# A Calculation Model of Grout Migration Height for Post-Grouting Technology
## 4 Conclusions  
Studying the reinforcement scope of post-grouting is of great significance. It contributes to create a bridge to connect the pile bearing capacity with grouting parameters, which makes post-grouting design more reasonable and economic. A theoretical analysis on migration height of grouting slurry is conducted in this paper. The rising process of slurry is simplified as the flow of Bingham fluid in an axial annular space. After that, a useful formula is developed to estimate the migration height value. The parameter influence analyses show that the investigated migration height is closely related to the pile embedment depth, pile diameter, and grouting pressure.  
In order to verify the proposed calculation model, five bored piles treated with post-grouting technology were selected and excavated after treatment to obtain the actual migration height. The comparison results indicated that the calculated height from proposed model is slightly smaller than the actual values, while it is fully acceptable in engineering design. It is noteworthy that the pressure loss and the resulted permeation during grouting process also have important influence on the slurry migration, which is recommended to be further investigated in future.  
Conceptualization, validation, and writing--review and editing, S.-G.H.; methodology, T.Z.; writing--review and editing, H.C. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
Thank you for the helps provided from the editor and the reviewers to improve this paper.  
The authors declare no conflict of interest.
## 3 Methodology
### Autocorrelation Coefficient (AC) and Partial Autocorrelation Coefficient (PAC)  
The successive series of observations may be correlated with each other. Thus, the index for determining the linear relationship between the lagged values of the time series includes the autocorrelation coefficient and the partial autocorrelation coefficient. The autocorrelation coefficient is an index of the correlation between the data, \(y_{t}\) and \(y_{t+k}\) for a lag time t. The coefficient calculation formula is as follows [32]:  
\[r_{k}=\frac{\sum_{t=1}^{n-k}(y_{t}-\overline{y})(y_{t+k}-\overline{y})}{\sum_{ t=1}^{n}(y_{t}-\overline{y})^{2}} \tag{18}\]  
where \(k\) is the number of autocorrelations in the function, \(n\) is the number of observations in the series and \(\overline{y}\) is the mean value.  
The partial autocorrelation coefficient is an index of the correlation between data, \(y_{t}\) and \(y_{t+k}\) which excludes influence from observations with any lag time other than time \(t\). The coefficient calculation formula is as follows:  
\[p_{k,k}=\frac{r_{k}-\sum_{j=1}^{k-1}p_{k-1,j}r_{k-j}}{1-\sum_{j=1}^{k-1}p_{k-1,j}r_{j}} \tag{19}\]  
where \(p_{k,j}=p_{k-1,j}-p_{k,k}p_{k-1,k-j}\), \(k\) is the number of autocorrelations in the function.  
In general, if the autocorrelation coefficients are persistently large. This indicates that the time series is probably non-stationary. Therefore, the concept of a 95% confidence interval which assumes that it is not different from zero within a 95% confidence interval, was used in autocorrelation and partial autocorrelation. The 95% confidence interval is calculated as follows:  
\[95\%\ CI=\pm 1.96\frac{1}{\sqrt{n}} \tag{20}\]  
where CI is the confidence interval and the value of 1.96 represents the area under the normal curve.  
The autocorrelation coefficient (AC) and partial autocorrelation coefficient (PAC) are widely used in statistics and econometrics, and in time series analysis by using the ARIMA model. For example, there are analyses of the Computer I/O pattern, forecasting of water quality (water temperature and concentration of dissolved oxygen), forecasting of supply and demand about electricity, etc. [33; 34; 35].
## 4 Results
### Evaluation of Conditioning Factors
#### 4.2.2 Application of Advanced Algorithms  
Actually, conditioning factors have different influences on the occurrence of landslides while the bivariate methods fail to recognize the difference. Gini importance (GI) is defined as the total reduction in average nodal impurities across all trees. GI was also applied to analyze the relative importance of different factors, exploring the factor's contribution to landslide occurring.  
The bigger the GI express greater the importance of factors of landslides occurring [44]. Table 5 showed the rank of the main conditioning factors of this study. The result showed that DTF was the most important factor responsible for landslides as the GI reached 5.36. Moreover, the factor MED, DTS, elevation, plan curvature, and slope were also pivotal since the GI were all greater than zero. While the other factors as TWI, rainfall, profile curvature, NDVI, and DTR showed little contribution to landslides in the study area (Figure 7).