esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Evaluation of the J-Couplings Derived from the JCS
The symbols correspond to different compounds (\(\bullet\) monofluorobenzene, + 1,2 difluorobenzene, \(\times\) 1,3-difluorobenzene, \(\Diamond\) 1,4-difluorobenzene, \(\blacktriangle\) 1,2,4- trifluorobenzene, \(\blacksquare\) 1,2,4,5-tetrafluorobenzene, and \(\blacklozenge\) and \(\circ\) for pentafluorobenzene). The values on the \(y\)-axis correspond to the coupled positions on the benzene ring. from _ab initio_ calculations and compared to the relative signs determined from the JCS. The results of the _ab initio_ calculations are given in Table 2 and are in general agreement between the sign and the magnitude of the _J_-couplings. The only exceptions are in the signs of \({}^{4}\)J\({}_{\text{HH}}\) couplings in monofluuorobenzene and 1, 3-difluorobenzene, and the \({}^{5}\)J\({}_{\text{HF}}\) coupling in monofluuorobenzene. The value obtained from _ab initio_ calculations was used to linearly scale the experimental values and is the reason for the poor _R_-factors obtained using the Gaussian _J_-couplings to simulate the experimental data (Table 2) [45]. Plotting the Gaussian calculated values versus the experimental values for \({}^{4}\)J\({}_{\text{HH}}\) and \({}^{5}\)J\({}_{\text{HF}}\) (Figure 6) provides insight into the discrepancies in the relative sign change. The linear response of the calculations, with respect to the experimental values, indicates that the difference in sign is merely a scaling artifact from the Gaussian _J_-coupling values when the experimental or calculated _J_-coupling is near zero. Thus, with the appropriate scaling relationship, the relative signs between the calculated, literature, and experimental values are all in agreement.
## 3 Results and Discussion  
### Temperature Field  
#### 3.1.1 Temperature Field of Waveguide Rods with Different Materials  
Figure 4 shows the temperature field of the waveguide rod and the high-temperature specimen. From left to right, the waveguide rods are made of SS316L, aluminum, and copper, with the same dimensions. The materials and dimensions of the high-temperature specimens in the three models are also the same. Thermal analysis shows that the temperatures at the top waveguide rod are 33.6 deg C, 168.9 deg C, and 239.6 deg C, respectively.  
Figure 5 shows the temperature profile on the centerline of the waveguide rod. In order to compare the cooling effect of the three materials of the waveguide rod more visually, their curves are placed in the same graph. The left end of the temperature curve represents the highest temperature on the waveguide rod. The rightmost end of the temperature curve indicates the temperature at the top of the waveguide rod, which also is the lowest temperature. The starting temperature of the three curves is almost the same. In contrast, the temperature at the tip varies greatly, which is mainly determined by the material's physical properties. Comparing the differences of the cooling curves, we can find that the cooling effect of the waveguide rod made of SS316L is the best. When the length of the waveguide rod is 300 mm, it can meet the requirements of the experiment and ensure that  
Figure 4: Temperature clouds of variable material models. (**a**) Temperature cloud of specimen and waveguide rod made of SS316L; (**b**) Temperature cloud of specimen and waveguide rod made of aluminum; (**c**) Temperature cloud of specimen and waveguide rod made of copper.  
the ultrasonic probe is not affected by high temperature. Therefore, the waveguide rod for the experiment was made of SS316L and its length was 300 mm.
# Mapping Areal Precipitation with Fusion Data by ANN Machine Learning in Sparse Gauged Region
## 3 Results and Discussion
### Correction of TRMM Products
#### 3.2.2 The Precision of Corrected TRMM Products  
After the correction, the precision of TRMM precipitation in the Qaidam Basin on the grids with the gauge station is shown in Table 2. It can be found that the average bias is significantly reduced, from 36.51% to -0.08% (3B42 RT) and 31.63% to 0.11% (3B43), respectively. It also can be seen from the RMSD that the fusion model has a significant correction effect on TRMM products. The average RMSD value decreased from 72.25 mm and 64.24 mm to 37.51 mm and 34.22 mm after correction.  
For those grids without gauges, the CR values were also evaluated. The CR of 3B42 RT and 3B43 on the grids with and without the gauge station improved up to 75%, 73% and 41%, 42% after correction  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Gauges**} & \multicolumn{6}{c}{**Corrected 3B42 RT**} & \multicolumn{3}{c}{**Corrected 3B43**} \\ \hline \multirow{2}{*}{**Name**} & \multirow{2}{*}{**X (\({}^{\circ}\))**} & \multirow{2}{*}{**Y (\({}^{\circ}\))**} & **DEM** & **MAP** & **MAP** & **Bias** & **RMSD** & **MAP** & **Bias** & **RMSD** \\  & & & **(m)** & **(mm)** & **(mm)** & **(\%)** & **(mm)** & **(mm)** & **(\%)** & **(mm)** \\ \hline LH & 93.33 & 38.75 & 2777 & 19.88 & 19.03 & \(-\)12.15 &  
(Figure 6). This means the precision of TRMM precipitation on grids with and without gauge station are also improved.
# The Impact of Terroir on the Flavour of Single Malt Whisk(e)y New Make Spirit
## 3 Results
### Aroma Active Compounds
#### 3.1.8 Key Aroma Active Compounds  
The eight most odorous aromatic volatiles of the new make spirit (Table 1) in order of importance based on MF values are as follows (all MF > 80%): (E)-2-nonenal (fried/toasted/fatty), unknown 10 (herbal/grass), 3-methyl-1-butanol (fermented/yeast/rancid), furfural (baked/toasted almond), ethyl hexanoate (pear/fruity), unknown 8 (lemon/cleaning fluid), \(\beta\)-damascenone (honey/plum), and methional (boiled potato). This is a diverse range of chemical classes incorporating aldehydes (including a furan and sulphur aldehyde), a ketone, an alcohol, an ester, and two unidentified compounds. Another fifteen aromatic volatiles with MF values (between 50 and 80%) are also likely impacting on new make spirit flavour but to a lesser extent, and they are as follows in order of importance: 2-phenylethanol (rose/floral), 1-octen-3-one (metallic/mineral/mushroom), 1,1-diethyloxy-2-methyl-propane* (pineapple/cherry), 3-methylbutyl acetate (banana/fruity/melon), heptanal (seaweed/grass/rubber), ethyl isobutyrate (fruity/sweet/cherry), unknown 7 (coffee), unknown 5 (soil/herbal), unknown 6 (cut-grass/green-bell pepper), 3-methyl butanal (butter/cheese/chocolate), 1,1-diethyloxyethane (apple/fruity), unknown 9 (grass), 2-phenylethylal acetate (floral), ethyl butyrate (fruity/apple), 2-pentyl furan (gas/bad smell), unknown 2 (coffee), ethyl 3-methylbutyrate (pineapple/apple), and unknown 1 (soil). The most prominent chemical class were esters, with aldehydes, acetals, a furan, an alcohol, and a ketone also contributing; however, six of these compounds could not be identified.  
In addition, an aromagram was created with mean FD factors of the aroma active compounds to more easily visualise their respective individual contributions (Figure 1 and Supplementary Figure S2). The highest FD factors of the odorants were determined for (E)-2-nonenal (FD = 150) and \(\beta\)-damascenone (FD = 75) followed by compounds with much lower FD factors (FD < 20), such as 3-methyl-1-butanol (FD = 15.9), furfural (FD = 15.6), ethyl hexanoate (FD = 13.4), unknown 10 (FD = 11.8), 1,1-diethyloxyethane (FD = 11.8), 2-phenylethylal acetate (FD = 9.8), and 2-phenylethanol (FD = 8.9).  
Figure 1: Aromagram of new make spirits illustrating the compounds, the mean value of flavour dilution factors (FD), and the odours of the compounds obtained by CCO analysis.  
It is noteworthy that both MF (Table 1) and FD approaches identified that (E)-2-nonenal, \(\beta\)-damascenone, 3-methyl-1-butanol, furfural, ethyl hexanoate, and unknown compound 10 as the six most impactful aroma compounds in all of these new make spirit samples. Of these six volatiles, their aroma perception was not influenced by variety or season, but (E)-2-nonenal and \(\beta\)-damascenone were impacted by environment (Table 2).
## 3 Results and Discussion
### CleanSeq Calls Variants with Satisfactory Correctness as Compared with MutScan  
Subsequently, we investigated the performance of CleanSeq for calling possible variants from decontaminated reads. As described in the Materials and Methods, simulated datasets containing reads from five different species (_P. aeruginosa, E. coli, C. freundii, B. fragilis_, and _S. pyogenes_) were employed. The results are plotted in Figure 3, showing that the overall efficiency for variant calling is above -90% in a lower coverage of 30\(\times\) and can reach 95% when the coverage is 100\(\times\). The efficiency was also correlated with the genome mutation rate (number) in the genome, especially in the case of the combination of low coverage and high genome mutation rate (i.e., 3\(\times\), 1 \(\times\) 10\({}^{-4}\), data not shown). Since it is widely accepted that depth and coverage are the key parameters in genomic analyses [42], the quality of the original data, including the read length, depth, and coverage, should be maintained within an acceptable range for bioinformatical research so that reliable conclusions can be drawn. Compared to MutScan [17], both tools are utilizable and comparable as no significant differences were observed in provided conditions.
## 3 Results and Discussion
### Scenario 1 (Sphere)
Afterward, the shock wave reflected from the solid wall attenuates when propagating towards the bubble (see Figure 2d). The pressure peak has dropped to a value of 1075. In contrast to that, at the upper-left (\(x=-0.5\) and \(z=0.5\)), upper-right (\(x=0.5\) and \(z=0.5\)), lower-left (\(x=-0.5\) and \(z=-0.5\)) and lower-right (\(x=0.5\) and \(z=-0.5\)) boundaries of the computational domain, the initial shock wave has just arrived, forming a high-pressure region nearby. At this end stage of the simulation (\(t=0.1\)), the volume of the bubble is about eight times the initial bubble due to the expansion of the explosive gas. The time-dependent pressure curves obtained at the boundary points 1 and 2 are shown in Figure 3. It is seen that, due to the initial symmetrical shape of the bubble in this scenario, the time-pressure curves at these two points are identical. The pressure recorded at these two surface points start to  
Figure 2: The temporal evolution of the pressure field for an initially spherical bubble in slice plots (\(y=0.0\)). Note that the range of the legend in each figure is different for a better display. increase rapidly at the time of about 0.06, due to the impingement of the initial shock wave on the boundaries. The peak value of the pressure is 1550 (\(1.57\times 10^{8}\) Pa), occurring at the time \(t=0.078\) (corresponding to approximately 250 us after the initial explosion). Then, the pressure drops, at a rate of \(6.8\times 10^{5}\) Pa us\({}^{-1}\), reaching an amount less than 700 (\(0.71\times 10^{8}\) Pa) within the time of 0.04 (125 us).
# An Optimal Scheduling Model for Connected Automated Vehicles at an Unsignalized Intersection
## 4 Numerical Experiment
### Sensitive Analysis
#### 4.3.2 Safe Headway  
To analyze the impact of safe headway distance on the model solution results, the same-direction safe headway time is set to values ranging from 0.5 to 1.5 s, and the conflicting-direction safe headway time is set to values ranging from 1 to 2 s [2; 6; 13; 14], while keeping other parameters unchanged, as detailed in Table 2. Through calculations, the average vehicle delay and the reduction in average vehicle delay under different safe headway conditions are obtained, as shown in Figure 3.  
Figure 2(a) shows that the average vehicle delay gradually decreases as the safe headway distance decreases. From Figure 2(b), it is evident that when the same-direction safe headway time is fixed, the percentage reduction in the average vehicle delay gradually increases with the increase in the conflicting-direction safe headway time. This indicates that the greater the difference between the safe headway distances in the conflicting direction and the same direction, the better the results obtained using the model and algorithm proposed in this paper. The analysis shows that the model aims to find the optimal passing sequence to save the vehicles' travel time. When there is a significant difference between the safe headway distances in the conflicting direction and the same direction, the model's solution results in the same direction, adopting a platoon mode to pass through the intersection, thereby avoiding vehicles from the conflicting direction entering the conflict zone simultaneously. Therefore, the average vehicle delay can be significantly reduced compared to the FCFS  
Figure 2: Impact of optimizing time window length on average delay.  
control mode. Furthermore, from Figure 3b, it can be seen that when the safe headway times in both the conflicting direction and the same direction are equal, such as when \(\tau=\omega=1.5\) s, the results obtained using the model and algorithm proposed in this paper show no improvement compared to the FCFS control mode. The analysis reveals that the optimal passing sequence is consistent with the FCFS control mode in this case. Therefore, the percentage reduction in average vehicle delay achieved by the model and algorithm proposed in this paper is 0%.
# Reinforcement Learning-Based Routing Protocols in Flying Ad Hoc Networks (FANET): A Review
## 4 Flying Ad Hoc Networks
In this communication, GS provides the necessary services to flying nodes, and UAVs send important data to the ground station. * **Hybrid communication:** This is a combination of U2U and U2G communications and helps UAVs to send their data to GS in a single-hop or multi-hop manner using intermediate nodes [39].
## 2 Transmission Mode Analysis and System Modeling
### Evaluation of Indicators of Mode Switching Quality  
Output shaft speed fluctuation during mode switching is quantified by the velocity drop amplitude. There are many reasons for the increase or decrease in output shaft speed, such as energy loss caused by various frictional forces in mechanical transmission, sudden changes in load leading to speed changes, and vibrations and impacts caused by system mode switching. The velocity drop amplitude of the output shaft is a key indicator of mode switching quality. The expression is:  
\[\delta=\frac{|\overline{n}_{o}-n_{\text{omin}}|}{\overline{n}_{o}} \tag{11}\]  
where \(\overline{n}_{o}\) is the steady output speed of the output shaft, \(n_{\text{omin}}\) is the lowest output speed of the output shaft, and \(\delta\) is the speed drop amplitude of the output shaft.  
During the mode switching process, the engagement and disengagement of the clutch will cause the internal oil pressure to fluctuate dramatically. The fluctuations in the oil pressure will directly affect the transmission torque of the clutch, and then cause the torque of the output shaft to change, affecting the ride comfort and driving experience of the vehicle. To quantify the fluctuation degree of output shaft torque, the concept of dynamic load coefficient of output shaft is introduced [18]. Its expression is:  
\[\gamma=\frac{T_{\text{omax}}}{\overline{T}_{o}} \tag{12}\]  
Figure 14: Clutch hydraulic control module simulation model.  
Figure 13: Hydraulic speed control module.  
where \(T_{\rm omax}\) is the maximum torque of the output shaft, \(\overline{T}_{o}\) is the steady-state output torque of the output shaft, and \(\gamma\) is the dynamic load coefficient of the output shaft.  
The output shaft impact strength describes the change rate of acceleration of the vehicle. A smaller value indicates that the vehicle is more stable during mode switching. The Chinese standard for impact strength is \(\left|J\right|\leq 17.64\) m/s\({}^{3}\). Its expression is:  
\[J=\frac{da}{dt}=\frac{d^{2}v}{dt^{2}}=\left|\frac{r_{q}}{i_{r}}\frac{d^{2}}{dt^ {2}}(\frac{\pi n_{o}}{30})\right|_{\rm max} \tag{13}\]  
where \(r_{q}\) is the radius of the driving wheel; \(i_{r}\) is the rear axle transmission ratio; and \(J\) is the impact strength of the output shaft.  
The mode switching time is an important index that can be used to measure the efficiency of mode switching, which is defined as the time required for the output shaft speed to transition from a stable state in one mode to a stable state in another mode. To accurately measure the switching time, this paper defines mode switching completion as the point at which output shaft speed reaches 99% of the target mode's stable speed.
## 4 Seismic Response Spectra in the USA
### Deterministic Spectrum According to ASCE/SEI 7-16  
For structures of risk category III or IV, the seismic elastic design spectrum based on the deterministic procedure is recommended, using the \(MCE\) at the site of interest. Similar to the deterministic spectrum section shown in MDOC Sismo CFE 2015 in Section 3.5, the initial inputs required to perform the process are the magnitude of the selected earthquake, \(M\), the focal length, \(R\), the geological fault type, and the hypocenter depth distance.  
The process involves the application of the local seismicity and appropriate ground motion prediction equations (GMPE) according to the seismic zone to perform the spectral acceleration, \(S_{a}\), considering a log-normal distribution with 84% and a damping ratio of 5% [2].  
To consider the soil dynamic effects, site classes A, B, and C require applying factors \(F_{a}\) and \(F_{v}\) with the values shown in chapter 11 of ASCE/SEI 7-16, according to the spectral parameter values of \(S_{S}=1.5\) and \(S_{1}=0.6\). For site class D, the factors \(F_{a}=1.0\) and \(F_{v}=2.5\) are taken. Finally, for site class E and F, the values of \(F_{a}=1.0\) and \(F_{v}=4.0\) are selected [2].  
To obtain the seismic elastic design spectrum for an \(MCE\) with a deterministic procedure, the following equation is applied:  
\[S_{a}(g)=\frac{2}{3}S_{aM} \tag{39}\]  
where \(S_{aM}\) is the response spectral acceleration for a previously calculated \(MCE\) with the factors \(F_{a}\) and \(F_{v}\) applied. Finally, the spectral acceleration values obtained with the previous equation must not be less than the limits values established in the following equation:  
\[S_{a}(g)=\left\{\begin{array}{cc}0.6F_{a}&T=0\\ 1.5F_{a}&T_{0}\leq T\leq T_{S}\\ \frac{0.6F_{v}}{T}&T_{S}<T\leq T_{L}\\ \frac{0.6F_{v}T_{L}}{T^{2}}&T>T_{L}\end{array}\right. \tag{40}\]  
where \(T_{0}=0.08F_{v}/F_{a}\) and \(T_{S}=0.4F_{v}/F_{a}\). Figure 18 shows the shape of the spectral acceleration graph with the lower limit values according to Equation (40).  
Figure 18: Lower limit of deterministic response spectrum for risk category III and IV.
# Factory Logistics Improvement: A Case Study Analysis of Companies in Northern Thailand, 2022-2024
## 4 Results and Case Study
### Warehouse and Inventory Management
#### 4.2.4 Automation in Material Handling  
This case study examines a large beverage company with a daily production capacity of 1 million water bottles, employing an automated production process encompassing water sourcing, purification, bottle preparation, filling, capping and sealing, labeling, and packaging. Despite the high level of automation in production, the quality control process remains manual. The current procedure involves human operators collecting sample bottles from the production line at 30 min intervals and transporting them to a laboratory located 100 m away. At the laboratory, tests are conducted for pH, conductivity, and turbidity, along with microbiological analysis, chemical analysis, and physical tests. This manual sampling and delivery process necessitates a dedicated employee to perform these tasks consistently. Recognizing the potential for improvement, the company is exploring automation options for this quality control process. A proposed solution involves the implementation of an Autonomous Mobile Robot (AMR) equipped with a robotic arm, capable of replicating the human operator's functions in sample collection and delivery. Financial analysis indicates a breakeven point of one year for this automation investment, based on a 24 h production schedule.
# Periodontal Condition and Subgingival Microbiota Characterization in Subjects with Down Syndrome
## 2 Materials and Methods  
### Study Population  
A cross-sectional observational study was designed in a Spanish Caucasian population diagnosed of DS. This investigation followed the Strengthening the Reporting of Observational studies in Epidemiology (STROBE) criteria for reporting [24] and was approved by the Research Ethics Committee (EC) of Santiago-Lugo, Spain (Registration Code: 2018/510). All subjects and, when appropriate, their legal guardians, confirmed in writing their agreement to participate and signed the EC-approved informed consent, once they were informed on the objectives and processes associated with this investigation.  
Screening among DS subjects regularly attending seven educational or occupational therapy centers in the region of Galicia, in the North-West of Spain, was carried out between September 2017 and November 2018.  
At this screening visit, information on their age, gender, presence of co-morbidities and use of current medications was collected. Study subjects were consecutively included if DS was genetically confirmed and did not have any of the following exclusion criteria: under 18 years of age, comorbidities that could influence the periodontal condition (e.g., diabetes), presence of harmful habits (e.g., smoking), having received antimicrobial therapy in the previous month (e.g., systemic antimicrobials and/or oral antiseptics), insufficient degree of collaboration for performing clinical assessment and microbiological sampling.  
Upon inclusion in the study, subjects had an intraoral examination where the following periodontal variables were recorded in four sites at the six reference teeth [25] or when absent, at the adjacent tooth: plaque index (PII) [26], probing depth (PD) (using a PCP UNC15 periodontal probe, applying a force of 20-25 g), bleeding on probing (BOP) [27], gingival recession (REC) (distance from the gingival margin to the cemento-enamel junction) and clinical attachment level (CAL). All measurements were recorded by a trained and calibrated clinical investigator (N.L.). Calibration was achieved during several sessions of repeated measurements, until the clinical investigator demonstrated an intra-class correlation coefficient (ICC) higher than 0.75 in all the periodontal variables evaluated. The intra-class correlation coefficient values for intra-examiner calibration were 0.87 (95% confidence interval = 0.75-0.98). Clinical assessments were not conducted in dental clinics/chairs so, together with the specific condition of the subjects, neither full-mouth clinical evaluations or radiographical assessments were feasible.  
Following a modification of the case definitions proposed by the Classification of Periodontal and Peri-Implant Diseases and Conditions [17], the study group was categorized as:  
* Healthy periodontal condition: PD < 4 mm and BOP detected in no more than two sites (less than 10% of the total) [28],
* Gingivitis: PD < 4 mm and BOP detected in more than two sites (more than 10% of the total) [28],
* Periodontitis: PD \(\geq\) 4 mm in, at least, one site [29].