esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Section
### RNA Extraction and Cloning of BdVgR  
Total RNA from different tissues was isolated with RNeasy Plus Mini Kit (Qiagen GmbH, Hilden, Germany). Total RNA from the whole flies and segments was extracted with TRIzol reagent (Invitrogen, Carlsbad, CA, USA) and then treated with RQ1 RNase-Free DNase (Promega, Madison, WI, USA) to eliminate genomic DNA contamination. All of the RNA samples were detected with agarose gel electrophoresis and measured with NanoVue spectrophotometer (GE Healthcare Bio-Science, Uppsala, Sweden) to ensure the integrity, quality and concentration. The PrimeScript(tm) RT reagent Kit (TaKaRa, Dalian, China) was applied to synthesize first strand cDNA with 0.5 ug total RNA. All manipulations were followed the manufacturer's protocols.  
Based on the transcriptome data of _B. dorsalis_[46], four pairs of gene-specific primers (_BdVgR_-F/R1-4, Table S1) were designed to amplify the fragments of _BdVgR_. Further, another two pairs of gene-specific primers (_BdVgR_-3R1/2, _BdVgR_-5R1/2, Table S1) were designed to determine the 5'- and 3'-flanking regions of _BdVgR_ using SMARTTer(tm) RACE cDNA Amplification Kit (Clontech, Mountain View, CA, USA). All gene-specific primers were examined by DNAMAN 5.2.2 (Lynnon, PQ, Canada) to check their self-complementarities and melting temperatures.  
The 25 \(\mu\)L PCR reaction system included: 1.0 \(\mu\)L of cDNA, 1.0 \(\mu\)L of forward and backward primer (10 \(\mu\)M), 2.0 \(\mu\)L of dNTPs (2.5 mM each), 2.5 \(\mu\)L of Mg\({}^{2+}\) (25 mM), 2.5 \(\mu\)L of 10\(\times\) PCR buffer (Mg\({}^{2+}\) free), 15.0 \(\mu\)L nuclease-free water and 0.25 \(\mu\)L of rTaq (5 U/\(\mu\)L) (Takara). All the PCR reactions were conducted as the following thermal cycles: 3 min at 94 \({}^{\circ}\)C, 33 cycles of 30 s at 94 \({}^{\circ}\)C, 30 s at 55-58 \({}^{\circ}\)C, 2 min at 72 \({}^{\circ}\)C, and final extension at 72 \({}^{\circ}\)C for 10 min. The purified PCR products were cloned into the pGEM(r)-T Easy vector (Promega, Madison, WI, USA), transformed into DH5\(a\) competent cells (Biomed, Beijing, China) and finally sequenced (Invitrogen, Shanghai, China).
## 5 Conclusions  
In this study, we demonstrated that PEF can significantly reduce the bacterial count in treated samples under the following conditions: PEF at 24 kV/cm, pulse duration 25 us, 20 pulses. We observed reductions of 2.43 and 0.9 logs in coliforms and total bacteria count, respectively. Notably, under these PEF conditions, undenatured whey protein content remained unchanged in comparison to the raw milk samples at 4.98 mg/mL. \(\beta\)-Lactoglobulin content was not affected, yielding the same results as in the raw milk samples, at 3.28 mg/mL. Regarding milk samples treated with traditional pasteurization techniques, MF yielded 4.66 and 4.55 mg/mL, UHT yielded 0.26, 0.19, and 0.51 mg/mL, and LTLT yielded 1.85 mg/mL of undenatured whey protein content. The \(\beta\)-lactoglobulin concentration results of milk samples treated with MF remained relatively high at 2.96 mg/mL and 2.41 mg/mL, LTLT yielded 0.83 mg/mL, and in samples treated with UHT, \(\beta\)-lactoglobulin was not detected. Coliforms were not observed in samples treated with traditional pasteurization techniques, while the total bacterial count in MF-treated milk yielded 3.79 and 4.35 log reductions and LTLT by 3.96 log; in UHT-treated milk, bacterial activity was not observed.  
Conceptualization, A.S. and S.S.; methodology, P.R.; software, D.U.; validation, A.S., P.R. and S.S.; formal analysis, D.U.; investigation, A.S.; resources, A.S.; data curation, M.V.; writing--original draft preparation, A.S.; writing--review and editing, A.S.; visualization, M.V.; supervision, S.S.; project administration, A.S.; funding acquisition, S.S. All authors have read and agreed to the published version of the manuscript.  
This research funded by local Vytautas Magnus University fund.  
Not applicable.  
The authors thank the Food Institute of the Kaunas University of Technology for technological support.  
The authors declare no conflict of interest.
## 3 AoI Minimization in UAV-Assisted WSN/IoT Application Architectures
### UAV Trajectory
#### 4.1.2 Trajectory and Mode Selection  
UAV data collection can occur while the UAV is hovering over the sensor nodes or CPs. Similarly, data can be transmitted to the UAV while it is flying as long as it is within the transmission range of the sensor nodes. Considering the significance of optimizing data collection time for reducing the age of collected information by the UAV, authors in [93] aimed to find the optimal flight path via a joint consideration of the node's visiting order and the data acquisition mode at each node. The authors consider three different data acquisition modes while assuming LOS links: (1) hovering mode, where the UAV hovers above the node to collect packets; (2) flying mode, where the UAV begins to collect packets, then flies to the next node at maximum speed; and (3) hybrid mode, where the UAV begins to collect data by hovering above the node and keeps collecting data while flying. They show that the age-optimal adaptive strategy outperforms that of the flight-time  
Figure 9: Illustrative architecture for UAV-assisted WSN with pre-scheduled trajectory (**a**) and UAV-assisted data dissemination (**b**) based on [84,86] respectively.  
optimal strategy with respect to the average age of delivered information. The flight-time age-optimal strategy offers a competitive solution compared with the flight-time-optimal strategy. Additionally, the age-optimal strategy's performance can be further improved by adapting a data acquisition strategy for each node. However, the impact of unreliable channel conditions, an absence of knowledge of CSI [8], and obstacles were not considered. Moreover, it was assumed that sensor nodes are of equal importance and heterogeneity with respect to the function or importance was not considered [87].
# Development of a Clock Generation and Time Distribution System for Hyper-Kamiokande +
## 3 Results  
### Clock Signal Generation  
All individual components were calibrated and characterization was cross-checked with the expert lab SYRTE. The antenna is placed on the roof of LPNHE at Jussieu campus in Paris, France.  
The clock generation part of the proposed solution has been tested at our lab. After the required cross-checks and calibrations, 6 days of data were taken with the Rubidium clock as an input to the receiver to monitor its signal with respect to the GPS time base. As mentioned before, the deterministic quadratic noise of the Rubidium clock is removed as a first step of the analysis. Common view was then performed with the French expert lab SYRTE and the remaining differences to French UTC can be seen in Figure 3. If corrections are applied on a timescale of 3 h, those would always be less than 5 ns, which gives a lot of margin for the system to stay within HK's requirements. This result might be slightly underestimated because both our lab and the reference one are in Paris (1 km away from each other only), which will not be the case in real HK conditions. The way corrections will be applied is still in development; however, it will not be sudden jumps but smooth fits. Finally, because HK will be in a valley in a mountain area, the number of satellites available is a concern. It will mostly be limited by the minimum elevation angle that allows for a clear view to the sky. A rough estimation for the worst case scenario was 50\({}^{\circ}\), and it was shown that in those circumstances in Paris, there are always at least two satellites, which is just enough.
## 5 Establishment of 3D Geological Model
### Analysis of Variation Function
#### 5.2.1 Geological Probability Surface-Constrained Lithofacies Model
The incorporation of additional constraint conditions not only improves the accuracy of the geological model but also mitigates the uncertainty and randomness inherent in inter-well interpolation to a substantial extent. It can be seen from the lithofacies model established that, during the Chang 7 sedimentary period, the sand body was distributed in a belt from north to south, the provenance direction developed in the southwest direction and gradually diverged to the northeast direction, and large amounts of dark mudstone and sandstone were developed. The continuity is relatively good in the direction along the provenance, but it is not satisfactory in the direction perpendicular to the provenance, which is mainly caused by the mudstone obstruction formed in the depressions between divergences. From a planar perspective, the Chang 7 sand body covers a large area, exhibiting large-scale continuous distribution on the southern and northeast sides of the study area. This indicates that the lateral contact relationship of the sand body predominantly takes the form of lateral tangential substitution. In the vertical dimension, the presence of large box-shaped sand bodies in the five sublayers is indicative of the overlapping accumulation of multi  
Figure 10: Lithofacies maps of each small layer constrained by the probability surface in the Chang 7 member of the target formation. stage channel sand bodies. The intercalated mudstone layers are modestly developed, with somewhat poor continuity in the longitudinal direction. These observations are fundamentally consistent with the sedimentary microfacies discussed earlier, substantiating the success of the lithofacies modeling in accurately representing the geological conditions of the study area.
## 3 Results and Discussion
### Effect of Scan Rate on Electrografting of 4-NBD\({}^{+}\)  
The effect of the scan rate on the electrografting process of 4-NBD\({}^{+}\) onto SPCEs was evaluated by CV, monitoring the cathodic peak (peak \"1\" in Figure 3b) during the grafting step. CVs were recorded under the conditions of procedure (described in Section 2.4.1) by varying the scan rate between 10 mVs\({}^{-1}\) and 250 mVs\({}^{-1}\).  
As the scan rate increased, the cathodic peak potential shifted towards more negative values, which is a characteristic profile of irreversible systems [58]. A linear relationship was observed between the cathodic peak potential and the logarithm of the scan rate, logv, according to the following Equation (1):  
\[\mathrm{E}_{\mathrm{PC}}/\mathrm{V}=(-0.090\pm 0.002)\log\
u+(-0.022\pm 0.002); \mathrm{R}^{2}=0.998. \tag{1}\]  
The value of \(\alpha\)n was estimated from the slope (2.3 RT/\(\alpha\)nF) of the above equation and the application of [PERSON]'s equation [59; 60; 61], giving an experimental result of 0.66. Assuming \(\alpha\) = 0.5, which is typical for irreversible processes, the number of electrons involved in the electrochemical reduction of 4-NBD\({}^{+}\) to generate the aryl radical at the SPCE was determined to be 1 e\({}^{-}\)[62; 63].  
The relationship between the cathodic peak current and the scan rate showed a good linear correlation up to 100 mVs\({}^{-1}\) (R\({}^{2}\) = 0.9990) according to the following Equation (2):  
\[\mathrm{i}_{\mathrm{PC}}/\mathrm{\mu A}=(153\pm 4)\
u+(0.2\pm 0.3);\mathrm{R}^{2}=0. 9990. \tag{2}\]  
This suggests that the electrochemical process is diffusion controlled with an adsorption contribution as expected, since the generated aryl radicals are grafted on the electrode surface. At higher scan rates, a decrease in peak current was observed, which can be ascribed to the formation of multilayers during the grafting step. This is a consequence of the faster generation of radicals and the progressive dimerization and polymerization processes that take place either on the surface or in the bulk solution [55].
# Time-Domain Near-Infrared Spectroscopy and Imaging: A Review
## 3 Instruments for TD-NIRS
### Other New TD-NIRS Systems
#### 3.3.1 Monstir Ii  
In 2014, a group from the University of London developed an updated version of MONSTIR named MONSTIR II employing a supercontinuum (SC) laser for the light source with an acoustic-optic tunable filter (AOTF), which enables to select four wavelengths arbitrarily in a wavelength range from 650 nm to 950 nm [51].
## 3 Results and Discussion
### Minor Particle Position
Figure 5: Radiometric properties for particle aggregates of Mode A with different position patterns: up, down, left, right, front, and back are for the minor component particles gathering in the up, down, left, right, front, and back in the aggregate as shown is Figure 2a–c; Ran.1, Ran.2, and Ran.3 are for the minor component particles randomly positioned. Ran.1 is the average result of three different random position patterns, Ran.2 is the average result of six different random position patterns, and Ran.3 is the average result of nine different random position patterns. The refractive indices of the components in the aggregates are \(m_{1}\) = 1.6, \(m_{2}\) = 3.102 + 0.0925i, and the particle radius is 2 \(\upmu\)m. (**a**) Extinction efficiency; (**b**) absorption efficiency; (**c**) scattering efficiency; (**d**) asymmetric parameter. For the asymmetric parameter (Figure 5d), the random pattern has values that are similar to those of the other patterns, showing that the position pattern of the minor component particles has little effect on the ratio of forward scattering to total scattering.
# An Empirical Study on Software Defect Prediction Using CodeBERT Model
## 6 Experiments and Results
### RQ2: Do the Different Prediction Patterns Affect Software Defect Prediction Performance?
#### 6.3.2 RQ2b: Do the Different Prediction Patterns Affect Software Defect Prediction  
Performance in Cross-Project Defect Prediction?  
We perform cross-project defect prediction experiments on the CPPSC dataset to compare the prediction performance of CodeBERT-PS, CodeBERT-PK, CodeBERT-PT, and RANDOM models on F-measure, as illustrated in Table 6. For instance, for the JEdit-Camel project, we achieve an F-measure of 0.357, 0.357, and 0.354 on the CodeBERT-PS, CodeBERT-PK, and CodeBERT-PT model. The RANDOM model achieves an F-measure of 0.255, which is lower than the three CodeBERT-based models. Considering the average F-measure, the CodeBERT-PS model outperforms the CodeBERT-PT models by 5% in F-measure, while the CodeBERT-PK model outperforms the CodeBERT-PT model by 3% in F-measure. All three models outperform the RANDOM model, which achieves an F-measure of 0.402.  
Summary of RQ2b: The CodeBERT-PS and CodeBERT-PK models behave well, and both outperform the CodeBERT-PT model in cross-project software defect prediction. Specifically, the CodeBERT-PS model outperforms the CodeBERT-PK model.  
Summarizing RQ2a and RQ2b, we can conclude RQ2.  
Summary of RQ2: The CodeBERT-PS and CodeBERT-PK models behave well, and both outperform the CodeBERT-PT model in software defect prediction. Specifically, the CodeBERT-PS outperforms or at least is similar to the CodeBERT-PK model.
## 3 Results and Discussion
### Simulation Results of the Calibrated Model
#### 3.2.2 Analysis of the Moisture Condensation Risk  
It is important to control possible condensation problems in the surgical suite, especially during off-use periods when the intake air is allowed to vary between 15 and 30 \({}^{\circ}\)C. When the fresh air does not go through the heat pump condenser, evaporator or dehumidifier, condensation risks are higher. This is because there is not a humidity control and the ventilation system directly uses outdoor air. Such condensation can appear during different periods of the year and over surfaces with the lowest temperatures. Based on a psychometric diagram, normally during the summer the dew point temperature is higher than during the winter. In addition, during the winter season, the minimum temperature from the indoor surfaces is normally higher than the outdoor temperature and the condensation risks are in general lower if thermal bridges are avoided.  
The temperature difference between the minimum value of the inner wall surfaces and the air dew point was investigated in OR1. This analysis was accomplished in a complete year simulating ventilation during off-use periods. If this difference was a negative value, a new minimum value for the indoor air temperature had to be set. This value will depend on the outdoor air RH and the outdoor and indoor air temperatures. Figure 13 represents the difference of temperature between the minimum temperature of the inner wall surfaces from OR1 and the dew point temperature of the air inside OR1. This analysis reveals that no condensations issues will occur during a typical year considering the hygothermal conditions imposed. In addition, the most critical period is summer time when the minimum difference of temperature of 1.25 \({}^{\circ}\)C was found in the hour 6529 of the year (1:00 h on Monday 30 th September 2019). Figure 14  
appreciated that the most dangerous timeframe is summer time and during the night. Similar results were obtained for the other rooms of the suite, demonstrating that no condensation takes place in the surgical suite under the studied conditions.
# Molecular Targets of Minor Cannabinoids in Breast Cancer: In Silico and In Vitro Studies
## 2 Results  
### Molecular Docking Results  
As previously stated, the therapeutic potential of almost all the 129 different phytocannabinoids from the _Cannabis sativa_ plant is still unknown. In order to investigate their possible beneficial actions on ER\({}^{+}\) breast cancer treatment, a molecular docking analysis on aromatase and ER\(\alpha\), two key targets responsible for the development of this disease [6], was performed. Our calculations revealed that 45 compounds are located in the aromatase binding site above the heme group and are stabilized by several residues, such as Arg115, Asp309, Phe221, Trp224, Met374, and Leu372 (Table 1, Supplementary Figure S1), previously reported as important for aromatase inhibition [82; 83]. In fact, most of those cannabinoids belong to **A\({}^{9}\)-THC**, **CBD**, and miscellaneous types and showed the ability to establish hydrogen bonds with some residues, particularly with Asp309, a fundamental residue for the aromatase reaction [84; 85]. According to our calculations, the compounds with the best energy score were cannabicyclol and (-)-\(\Delta^{7}\)-_trans_-(1\(R\),3\(R\),6_R_)-isotetrahydrocannabinol-C\({}_{5}\), both presenting a binding affinity of \(-9.8\) kcal/mol.
# Coupled Thermomechanical Responses of Zirconium Alloy System Claddings under Neutron Irradiation
## 3 Finite Element Modeling  
The fuel rod contained 40 pellets with a radius of 4.09 mm. The inner diameter and thickness of the cladding were 4.16 mm and 0.57 mm, respectively. The length of the simulated tube was 200 mm, as shown in Figure 1a. The initial gap distance was set at 0.07 mm, as shown in Figure 1b. The reduced integration FEs (C3D8R) were employed in ABAQUS, and the number of meshes was 10,100. Due to the axial symmetry of cladding cube, axisymmetric boundary conditions were applied, and the node along the mid-circumference was constrained at the axial end of the cladding to prevent rigid body rotation.  
The cladding material parameters of fuel rods are shown in Tables 1-4.  
In the simulation process, the left and right end faces of the cladding were fixed, and the heat source was the core area of the pellet. The pellet core was heated to 1200 \({}^{\circ}\)C by radial irradiation. The cooling coefficient of the outer surface of the fuel rod was set at 20,000 W/(m\({}^{2}\cdot\)K), which made the outer surface of the fuel rod gradually cool down. The coolant pressure was kept at a constant value of 15.5 MPa during the entire simulation process. The initial pressure of helium precharged in the pellet cladding gap was 2 MPa. The simulated power was calculated according to the average power of the nuclear power plant, which was 200 W/cm. A power increase of 1 d resulted in a rise from 0 W/cm to 200 W/cm, and the steady state operation of 200 W/cm took 1200 days. During steady state operation, the fast neutron fluence rate of the fuel rod remained constant, and its value was 9.5 \(\times\) 10\({}^{17}\) n/(mm\({}^{2}\cdot\)s). In the simulation, the input parameters were obtained from the practical working conditions of the reactor pressure vessel.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Density** & **Temperature** & **Neutron Injection** & **Young’s Modulus** & **Thermal Conductivity** & **Poisson’s** & **Specific Heat Capacity** \\
**g/cm\({}^{3}\)** & \({}^{\circ}\)C** & **Quantity n/mm\({}^{2}\)** & **GPa** & **W/ (m\({}^{\circ}\)C)** & **Ratio** & **J/(kg\({}^{\circ}\)C)** \\ \hline
6.59 & 100 & 224 & 94.4 & 18.4 & 0.33 & 272 \\ - & 200 & 899 & 89.2 & 19.1 & - & 285 \\ - & 300 & 3376 & 82.4 & 19.6 & - & 301 \\ - & 400 & 1,746,000 & 73.8 & 20.7 & - & 318 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Pure Zr parameters of fuel rod cladding.  
Figure 1: Fuel rod finite element model, (**a**) global geometry, and (**b**) the cross-section element.
## 3 Results
### Method Validation
#### 3.2.5 Stability  
ERDA stability under different conditions of processing and storage was evaluated using plasma samples spiked at two different ERDA concentrations, LQC (15 ng/mL) and HQC (750 ng/mL). Table 4 represents the calculated recovery values for ERDA, which were more than 82.82% with CV % less than 12.30%, indicating that the analy  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Nominal Conc.** (ng/mL)} & \multicolumn{3}{c}{**Inter-Day**} & \multicolumn{3}{c}{**Intra-Day**} \\ \cline{2-7}  & **Actual Conc.** & **Precision** & **Accuracy** & **Actual Conc.** & **Precision** & **Accuracy** \\  & **(Mean \(\pm\) SD)** & **(CV \%)** & **(\%)** & **(Mean \(\pm\) SD)** & **(CV \%)** & **(\%)** \\ \hline
0.5 & 0.41 \(\pm\) 0.04 & 9.76 & 82.00 & 0.41 \(\pm\)0.046 & 11.22 & 82.00 \\ \hline
1.5 & 1.32 \(\pm\) 0.18 & 13.50 & 88.00 & 1.30 \(\pm\) 0.11 & 8.46 & 86.66 \\ \hline
150 & 130.75 \(\pm\)15.62 & 11.95 & 87.16 & 130.23 \(\pm\) 6.87 & 5.28 & 86.82 \\ \hline
750 & 644.15 \(\pm\)37.10 & 5.76 & 85.88 & 643.21 \(\pm\) 77.17 & 11.99 & 85.76 \\ \hline \end{tabular}
\end{table}
Table 2: Inter- and intra-day precision and accuracy of ERDA in plasma.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Compound**} & \multirow{2}{*}{**Nominal Conc.** (ng/mL)} & \multicolumn{3}{c}{**Extraction Recovery**} & \multicolumn{3}{c}{**Matrix Effects**} \\ \cline{3-6}  & & **Mean (\%)** & **RSD (\%)** & **Mean (\%)** & **RSD (\%)** \\ \hline \multirow{3}{*}{**ERDA**} & 1.5 & 86.34 & 6.56 & 91.6 & 7.81 \\ \cline{2-6}  & 150 & 86.93 & 10.39 & 90.03 & 5.16 \\ \cline{2-6}  & 750 & 85.06 & 2.90 & 89.89 & 4.69 \\ \hline
**IS** & 100 & 86.25 & 6.50 & 89.49 & 5.61 \\ \hline \end{tabular}
\end{table}
Table 3: Recovery % and matrix effects ERDA and IS in plasma.
# Identification of Location and Camera Parameters for Public Live Streaming Web Cameras
## 4 Formulation of the Problem and Its Solution Algorithm
### Solution Algorithm
#### 4.1.1 Obtain the Intrinsic Parameters (Matrix \(A\))  
Note that from \(v^{u}=c_{v}\) and (7)-(9) it follows \(yc/zc=0\) for the point \(P^{u}_{im}=(u^{u},v^{u})\) (because \(1+k_{1}r^{2}+k_{2}r^{4}=0\) leads to \(u^{u}=c_{u}\) and \(P^{u}_{im}=C\)). So, the point \(P^{u}_{im}\) stays on the central horizontal line of the image for any values \(k_{1}\) and \(k_{2}\). By analogy, for \(P^{u}_{im}=(u^{v},v^{v})\) take place \(x_{c}/z_{c}=0\), and the point stays on the central vertical line for any values \(k_{1}\) and \(k_{2}\)Evaluate the angles between the optical axis \(OG\) and vectors \(OP_{emu}^{u}\) and \(OP_{emu}^{v}\) (Figure 2, Figure 1):  
\[\begin{array}{l}\theta_{x}=\arccos\left(OG\cdot O_{emu}^{pu}/(|OG||OP_{emu}^{ pu}|)\right)\\ \theta_{y}=\arccos\left(OG\cdot O_{emu}^{pv}/(|OG||OP_{emu}^{pv}|)\right).\end{array} \tag{15}\]  
It follows from (5) that if the effect of radial distortion on the values \((u^{u},v^{u})\) and \((u^{v},v^{v})\) can be ignored (camera radial distortion is moderate, and the points are not too far from C), then  
\[\begin{array}{l}f_{u}=(u^{u}-c_{u})/\tan\theta_{x}\\ f_{v}=(v^{v}-c_{v})/\tan\theta_{y}.\end{array} \tag{16}\]  
Equation (16) gives the initial approximation of coefficients \(f_{u}\) and \(f_{v}\) and an evaluation of matrix \(A\).