esa-sceva/satcom-chunk-collection huggingface dataset:
# Geant4 Simulations of a Scintillator Cosmic-Ray Detector
## 5 Conclusions  
The simulations of the proposed scintillator detector indicate that it is sensitive to almost all muons from EASs and for electrons with momenta greater than 0.03 GeV/c. Sensitivity to photons is much lower; it is \(\sim\)5% for vertical X-rays and rises up to 30% for highly inclined gamma photons with energy of 1 GeV or greater. These efficiencies were obtained for properties of the SiPM as provided by the manufacturer and under assumptions of perfect reflective coating and optical gel. The performance of real devices may be worsened by imperfections on the surface of the scintillator, the lower reflectivity of the aluminium foil, or worse transparency in optical gel. It is also not obvious if the electronics will properly process the smallest signals from the SiPM. The testing of a prototype should thus include various measurements. Efficiency for low-energy electrons and photons may be tested using radioactive sources. Measurements of cosmic-ray flux as a function of zenith angle will allow the estimation of efficiency for muons. The validity of the simulation results can be verified using measurements of secondary cosmic rays for various shieldings. Tests with beams from accelerators would also be strongly beneficial. Further simulation studies should also consider a top-bottom coincidental setup of two scintillator detectors where signals in both of them are required.  
Conceptualisation, J.P., K.W.W., L.B., K.A.C. and P.H.; methodology, J.P.; software, J.P.; validation, K.W.W. and L.B.; formal analysis, J.P.; investigation, J.P.; data curation, J.P.; writing--original draft preparation, J.P.; writing--review and editing, K.W.W., L.B., P.H., K.A.C., S.S., O.R. and O.B.; visualisation, J.P.; supervision, K.W.W. and L.B.; project administration, P.H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable  
The data presented in this study is available on request from the corresponding author. Due to its volume it is stored on a private server with limited access.  
We gratefully acknowledge Poland's high-performance Infrastructure PLGrid ACK Cyfronet AGH for providing computer facilities and support within the computational grant no PLG/2024/016954. Our sincere thanks to [PERSON] for his consultation and advice regarding detector's electronics.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
CREDO Cosmic Ray Extremely Distributed Observatory CR Cosmic ray EAS Extensive Air Shower SiPM Silicon Photomultiplier
# Core Microbiota Promotes the Development of Dental Caries
## 4 Discussion
nuclatum_ is also one of the first Gram-negative species to establish plaque biofilms [54]. It is one of the important \"bridge\" organisms in the naturally formed dental plaque [55], and has a central role in the ecology of dental plaque [56]. _F. nuclatum_ has also been studied with the ability of affecting the growth and survival of _S. mutans_. However, the effect of core microbiota on _S. mutans_ has not been previously studied. Our results showed that when the core microbiota presented, the growth of _S. mutans_ increased by about four times, suggesting that the core microbiota promoted the _S. mutans_ growth in the biofilms. In this study, we did not validate the function of these four species individually, but considered them as one factor. This is a limitation of our study, and we will further investigate the function of these individual species in our future studies. The microbial composition of mature tooth biofilm is quite stable, but the pH of the biofilm could have small fluctuations [57]. A lower pH value is favored by demineralization, while a higher pH value is good for remineralization [9]. The fine-tuned balance of the oral ecosystem is disrupted, allowing disease-promoting bacteria to over grow and cause dental caries [8; 37; 58]. Interestingly, in our experiments, we found that the core microbiota itself produced limited acid, but could promote acid production of _S. mutans_. The integrated mineral loss of the enamel block increased by about 60% when the core microbiota existed. However, the environment in the oral cavity is very complicated, and saliva washing and food intake may have an impact on dental plaque. In vitro experiments cannot fully reproduce the complexity of in situ situation [59]. We therefore established an animal model to determine the role of the core microbiota with the presence of acidogenic and aciduric bacteria _S. mutans_ and sugar. We found that rats in the Core + _S. mutans_ group had the most severe dental caries.
# Monolitic Hybrid Transmitter-Receiver Lens for Rotary On-Axis Communications
## 2 Materials and Methods
### Hybrid Lens
#### 2.2.2 Optic Design Methods
The mapping \(\phi\) has to ensure that the infinitesimal area elements at \(\sigma_{\text{s}}\) and \(\sigma_{\text{t}}\) are passed by the same flux. This is achieved by expansion or contraction of the area elements. Mathematically spoken, the mapping has to satisfy Equation (7) for every \(\sigma_{\text{s}}\) of the source grid [42; 46]. \[\det(\
abla\phi(\sigma_{\text{s}}))\rho_{\text{t}}(\phi(\sigma_{\text{s}}))= \rho_{\text{s}}(\sigma_{\text{s}}) \tag{7}\]  
Figure 5: Energy and ray mapping. (**a**) Equi-flux grids at the source and target. Adapted from [PERSON] et al. [39]. (**b**) Mapping from non equi-flux source to anticipated target irradiance. (**c**) Input vectors \(\mathbf{v}_{i,j}\) and output \(\mathbf{o}_{i,j}\) vectors are used to construct the surface geometry by subsequently calculating points \(P_{i,j}\) and their normals \(\mathbf{u}_{i,j}\). The term \(\det(\
abla\phi(\sigma))\) represents the expansion and contraction of the area element [46]. Although many mappings \(\phi\) might satisfy Equation (7), there is only a single one that minimizes transport cost [46]. Solving Equation (7) turns out to be nontrivial [42,46]. In this article, the algorithm proposed by [PERSON] et al. [46] is used since it provides good convergence. For the RX lens part, a rotationally symmetric concentrtor lens is designed. It consists of two sections: the center is based on refraction, whereas the outer section works with total internal reflection (TIR). TIR is generally superior over refraction for large angles \(\theta_{\text{RX PD i}}\), since it limits the lens diameter and reduces Fresnel-losses [51]. Similar to the TX lens part, the surface is calculated from input and output vectors \(\mathbf{v}_{i,j}\) and \(\mathbf{o}_{i,j}\). In the first attempt, the edge ray principle [30] was applied for generating the input vectors \(\mathbf{v}_{i,j}\). Due to the TX lens part and the discontinuity between both RX lens sections, the edge ray principle is not valid. Therefore, the gain will vary over the FOV and may drop at certain alignments. In the second attempt, this issue is addressed by defining the input vectors for a range of angles of incidence rather than only for the maximum incidence angle. The output rays \(\mathbf{o}_{i,j}\) are derived from coupling angles \(\theta_{\text{RX PD i}}\) to the PD active area. Ideally, they cover the whole half-space in front of the PD. The normal vector fields \(\mathbf{N}\) of both lens parts and an initial point \(\mathbf{P}_{1,1}\) for each surface is used to calculate a finite number of surface points \(\mathbf{P}_{i,j}\). The result is only a point cloud representation for the optical surfaces. Nonuniform rational B-splines (NURBS) [52] are used for interpolation. NURBS are very flexible and they are supported in the most popular computer aided design formats, which makes them suitable for optical simulation in third-party software and for subsequent fabrication.
## 3 Results
### Unique Contribution of Implementing the AMS Checklist  
In the post-CwPAMS checklist, 10 sites provided further information in response to the open-ended questions, which indicated that the checklist had prompted them to take additional actions that were not part of the original AMS intervention plan of the CwPAMS project. The participants were invited to report up to five additional activities that they had engaged in based on the checklist. Across all sites, 29 additional AMS activities were listed. The examples included: the development of empirical guidelines; GPPS completion; GPPS training; the establishment of a multidisciplinary AMS team; a collection of baseline data on antimicrobial use; and the conduct of an AWaRe analysis of the antibiotic prescribing patterns at their hospital. The complete list is provided in Box 1.  
**Box 1.** Key interventions taken as a result of completing the pre-CwPAMS checklist (i.e., interventions not initially planned as part of the CwPAMS project).  
Cascading training to other health care providers in the hospital.  
Institution of a Hospital Antimicrobial Stewardship Team.  
Development of an adult empiric guideline based on common indications seen at the hospital. Annual guideline review.  
Engaging with Community Durbars.  
Global Point Prevalence Survey (GPPS).  
Antibiotic Guidelines.  
Radio Programmes November.  
AMS Training Manual.  
Surveillance  
Auditing.  
Dissemination of report.  
GPPS Training made a film focused on IPC and attendant behaviour.  
Increase awareness of AMS.  
Increase awareness of IPC  
It enabled us to set out the goals of our partnership clearly.  
To establish a multidisciplinary AMS team  
To gather base line data on antimicrobial use  
To conduct UPS training and studies  
Conduct an AWaRE analysis of antibiotic prescribing pattern at the hospital.  
Conduct regular AMS training of the staff using the Scottish Triad approach  
Establishing the MTC/  
Plans to reconstitute alcohol hand rub.  
Global Point Prevalence Study  
Use of MicroGuide app to promote guidelines  
Larger cohort of HPs trained.  
Strengthening Hospital AMS Committee. Ensuring clarity on roles and responsibilities of AMS committee members.  
Revising AMS committee membership, Developing a clear and timely action plan.  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{1}{c}{**Priority**} & **Top 5 Barriers to AMS Selected by Participants Pre- and Post-CwPAMS Intervention (Pre-AMS Intervention)** \\ \hline
1 & Lack of funding \\ \hline
2 & Insufficient microbiology lab capacity \\ \hline
3 & Qualified personnel do not have enough time to perform stewardship \\ \hline
4 & Inadequate use of the microbiology laboratory \\ \hline
5 & Lack of motivated or engaged staff \\ \hline \hline \end{tabular}
\end{table}
Table 3: Top five barriers to AMS pre- and post-AMS intervention.
# Noninvasive Suspicious Liquid Detection Using Wireless Signals
## 2 Preparatory Work  
### Data Acquisition  
The WCI represents the channel state of a communication link on Orthogonal Frequency Division Multiplexing (OFDM) technology. The WCI describes how links are transmitted from a transmitter to a receiver. It also combines the influence of scattering, fading, power attenuation, and other factors. The WCI reflects the performance of a link and the interference caused by other factors to a great extent [22]. The WCI is composed of an OFDM matrix containing 30 subcarriers. Data can be transmitted simultaneously on multiple subcarriers, greatly improving the efficiency and accuracy of the system [23]. The frequency domain of the wireless channel can be expressed as:  
\[\overset{\rightarrow}{Y}=\ H\cdot\overset{\rightarrow}{X}+\overset{ \rightarrow}{N}, \tag{1}\]  
where \(\overset{\rightarrow}{X}\) and \(\overset{\rightarrow}{Y}\) are the transmitted and received vectors while \(\overset{\rightarrow}{N}\) is Gaussian noise (AWGN) vector and \(H\) represents the frequency response of the channel.  
The receivers receive packets for each subcarrier from a channel. The packet carrying the original amplitude and random phase information complex frequency domain can be expressed as:  
\[\mathrm{H(n)}\ =\big{|}\,\mathrm{H(n)}\big{|}\,e^{\mathrm{\iota H(n)}}. \tag{2}\]  
In Equation (2), H(n) is the data for subcarrier number n, where n \(\in\)[1 to 30]. \(|\mathrm{H(n)}|\) is the raw amplitude information and \(\mathrm{\angle H(n)}\) denotes the random phase data.
# An Investigation on Base Metal Block Shear Strength of Ferritic Stainless Steel Welded Connection
## 3 Finite Element Analysis and Parametric Study
### Comparison of Experimental Results and Parametric Study  
#### 3.2.1 Ultimate Strength and Fracture Mode  
Based on the material test results and experimental results of the existing welded connections in Chapter 2, numerical analysis was performed with finite element (FE) analysis methods presented in Section 3.1, and Table 2 summarizes the FE analysis and experimental results for the fracture mode and ultimate strength. This study focused on the comparison of ultimate strength and fracture mode between test results and analysis results except from initial stiffness and deformation to verify the effectiveness of finite element analysis procedures. Figure 4 shows the load-displacement curves of three typical specimens obtained from the test and analysis results. Since the coupling part (6.0 mm thick plate) in the center and weld metal in the analysis model were assumed to be deformed, initial stiffness and deformation were not considered in the comparison. In addition, Mises stress distribution at the ultimate strength point obtained from the analysis results of FT20L20, FT30L30, and FT40L40 specimens are shown in Figure 5 to compare with the fracture modes of the experimental results in Figure 1. From the analysis results, the block shear fracture mode (a combination of tensile fracture and shear fracture) was observed in the base metal near the toe of the weld metal as in the experimental results, and namely, Mises stress distribution was concentrated first on the critical tensile area perpendicular to the direction of loading and both corner parts of the weldment, as displayed in Figure 5. Mean ultimate strength ratio (_Pua/Pue_) of analysis result against test result was 0.98 in Table 2, and it can be noted that ultimate strength and block shear fracture mode predicted by finite element analysis model presented in this paper provided a good correspondence to the test results.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Test Results**} & \multicolumn{2}{c}{**Analysis Results**} & \\ \cline{2-5}
**Specimen** & **Ultimate Strength** & **Fracture** & **Ultimate Strength** & **Fracture** & **Pua/Pue** \\  & **Pue (kN)** & **Mode** & **Pua (kN)** & **Mode** & **Pua/Pue** \\ \hline FT20L20 & 84.77 & & 80.77 & & 0.95 \\ FT20L30 & 99.86 & & 94.50 & & 0.95 \\ FT20L40 & 116.33 & BS & 107.04 & BS & 0.92 \\ FT30L30 & 113.19 & (Block shear & 113.80 & (Block shear & 1.00 \\ FT30L40 & 124.07 & fracture) & 116.54 & fracture) & 0.94 \\ FT40L30 & 117.80 & & 122.36 & & 1.04 \\ FT40L40 & 131.03 & & 134.56 & & 1.02 \\  & & Average & & & 0.98 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of test and finite element (FE) analysis results.
# Optimal Placement of Multiple Feeder Terminal Units Using Intelligent Algorithms
## 2 The Reliability Algorithm
### Fault Isolation Time Analysis  
If a fault occurs in segment EF, the CB S1 operates and de-energizes all downstream load points (LD1-LD7). The FTUs on the type B and type C switches upstream to the fault (S1-S3) send the data of fault current to the master station, and the FTUs on the type B and type C switches downstream to the fault (S7) do not send any data about fault current. The minimum range where the fault occurred can be identified as segment CG by the operators in the master station according to the remote data provided by the FTUs of the switches S1, S2, S3, and S7. In this paper, the segment CG is regarded as the remotely non-visual segment (RNVS) which may require further manual inspection. That is to say, RNVS is defined as the feeder segment between the upstream type B switch/type C switch/CB closest to the fault and the downstream type B switch/type C switch/tie switch closest to the fault.  
Maintenance operators are dispatched to the RNVS (segment CG) to check all the FTUs installed on the type A switches in the RNVS (S4 and S6). Similar to the switches S1-S3, the switch S4's FTU indicates the fault current by turning on the indicator light, while the switch S6's FTU does not. Then the minimum range where the fault occurred can be reduced to segment DF. In this paper, the segment DF is regarded as the minimum patrol segment (MPS) which may require manual patrol to find out the exact fault location. More precisely, MPS is defined as the feeder segment between the upstream  
Figure 2: A simple feeder with a fault on Section EF and the process of fault management.  
switch or CB with any type of FTU closest to the fault and the downstream switch or CB with any type of FTU closest to the fault.  
With the inspection for the MPS (segment DF), the minimum fault segment (MFS) can be found out, which is segment EF in this case. It is easy to understand that the MFS is between the upstream and downstream switches or CBs adjacent to the fault.  
The time for locating the fault \(t_{22}\) consists of the time for checking the display of the FTUs installed on the type A switches (defined as \(t_{221}\)) and the time for patrolling the feeder (defined as \(t_{222}\)), which can be calculated as:  
\[t_{22}=t_{221}+t_{222} \tag{2}\]  
\[t_{221}=n_{A-RNVS}t_{A} \tag{3}\]  
\[t_{222}=l_{MPS}t_{p} \tag{4}\]  
where \(n_{A-RNVS}\) is the number of type A switches in the RNVS, \(t_{A}\) is the time for check one FTU of a type A switches, \(l_{MPS}\) is the length of MPS, \(t_{p}\) is the patrol time for per unit length of feeder.  
After the fault location is completed, if the first and last switching elements of the MFS cannot be remotely controlled, they need to be operated on the spot manually to isolate the fault. The formula for calculating this time \(t_{23}\) is calculated as follows:  
\[t_{23}=n_{m-MFS}t_{m} \tag{5}\]  
where \(n_{m-MFS}\) is the number of switching elements without remote control conditions in the MFS, \(t_{m}\) is the time for manual operation of a single switch on site.
## 4 Methodology
### The Seismic Sequences Stratigraphy  
The synthetic seismogram is a computer-generated seismic response computed from well data and is most frequently used to correlate geologic information from well logs with seismic data. Seismic data mainly provides time values, while synthetics provide time and depth values that are used to verify reflection events. The components of the generated synthetics produced in this research are time-depth charts, reflection coefficient, seismic trace, the new generated synthetic picked horizons, and the new finding of top formation which are all shown in Figure 3.  
Figure 2: Layout shows the adopted methodology in this study.  
The first step in this study has been carried out by tying the seismic data to the well data. The sequence boundary determination, seismic facies, stacking patterns, para sequences and system tract analysis were respectively delineated. The usual way to tie well information to seismic data in depth is to convert them to time and reconvert them back to depth [37]. In the well tie technique, the synthetic seismogram was generated by integrating density and sonic logs to estimate the seismic impedance (Z) of each subsurface layer. The reflection coefficient (RC) at boundaries between these layers was then determined with the Zoepretz equation (R = (Z2 \(-\) Z1)/(Z2 \(+\) Z1)). The series of reflection coefficients were then convolved with a Ricker wavelet of 28 Hz frequency to produce the synthetic seismogram using Witiora-1 well. Reflection coefficient curve, seismic traces near the well and formation tops in-depth domain have been displayed side by side for comparison as displayed in Figure 3. A good match between the formation tops from Witiora-1 well and the generated synthetic seismogram.  
Figure 3: Generated synthetic seismograms that tied the well to the seismic data.  
Ten 2D seismic profiles covering an area of 217 km\({}^{2}\) and logs from Witiora-1 well exploration were used in this analysis. The sequence boundaries were interpreted based on reflection configuration, reflection termination, and well log information, this interpretation was confirmed by a synthetic seismogram. The seismic facies were identified based on the internal seismic reflection patterns and reflection terminations.
# Influence of Yeast Strain on Odor-Active Compounds in Fiano Wine
## 2 Materials and Methods
### Extraction and Analysis of Volatile Compounds  
The volatile compounds of the wine were determined, using an extraction procedure previously reported by [PERSON] et al. [27]. A total of 200 mL of wine, obtained after mixing three equal bottles, was submitted to continuous liquid-liquid extraction for 3 h with 20 mL of di-chloromethane. As the internal standard, 2-methyl-1-pentanol at a final concentration of 1 mg L\({}^{-1}\) was added. The organic layer was recovered in a separating funnel. Residual water was removed by means of the addition of Na\({}_{2}\)SO\({}_{4}\), and the solvent was concentrated first in a Kuderna-Danish concentrator to 1 mL and finally under a low stream of nitrogen (1.5 L min\({}^{-1}\)) to 500 \(\upmu\)L. Extraction of each sample was performed in triplicate.  
GC/MS was performed using an Agilent 6890 chromatograph equipped with a split/splitless injector (Agilent Technologies, Folsom, CA, USA), a J&W DB-Wax column (30 m length \(\times\) 0.25 i.d. \(\times\) 0.25 film thickness; J&W Scientific, Folsom, CA), and 5973 Network series quadrupole mass spectrometric detector (Agilent Technologies, Folsom, CA, U.S.A.). The temperature program used was 40 \({}^{\circ}\)C for 3 min, raised at 4 \({}^{\circ}\)C min\({}^{-1}\) to 220 \({}^{\circ}\)C, and held for 20 min at maximum temperature. The carrier gas (He) velocity was 37 cm/s. The injector port and the ion source were maintained at 250 and 230 \({}^{\circ}\)C, respectively. Electron impact mass spectra were recorded with an ion source energy of 70 eV. A 1 \(\upmu\)L aliquot of each concentrated extract was injected in splitless mode.  
Volatile compound identification was performed by comparing retention times and mass spectra obtained by analyzing pure reference compounds under the same conditions. The identification was further confirmed by comparing mass spectra with those of the NIST database. Compounds for which pure reference standards were not available were tentatively identified only based on mass spectra comparison.  
GC/O analysis was performed on extracted volatile compounds, using a 5890 Hewlett-Packard gas-chromatograph equipped with a same column of GC/MS analysis and connected with a Hewlett-Packard \"Y splitter deactivated\", allowing the effluent to be split between the sniffing port and flame ionization detector (FID). Dilutions of Aroma Extract Dilution Analysis (AEDA) were done sequentially by volume (1:5) [28]. A 1.5 \(\upmu\)L splitless injection of extract was made. The gas chromatographic conditions were the same as those described for GC/MS analysis. Two experienced judges operated independently for the assessment.