esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results and Discussion
### Volatile Compounds in the Wines
#### 2.2.4 Norisoprenoids and Terpenes  
Norisoprenoids and terpenes are derived from grapes and have relatively low thresholds. Thus, a small variation of their concentrations can result in significant influence on the entire aroma profiles of wines [8]. One norisoprenoid (\(\beta\)-damascenone) and two terpen (citronellol and 4-terpineol) were detected in this study, with \(\beta\)-damascenone exceeding its threshold. (Table S1, Figure 2). The additions of 12 and 60 mg/L OA, LA, and ALA resulted in increased production of \(\beta\)-damascenone, but this promotion was weakened with increasing UFAs concentrations (Figure 3). The highest level of \(\beta\)-damascenone was found in LA_12 wine, followed by OA_12 and ALA_12 wine. There were no significant differences of \(\beta\)-damascenone in other treatments.  
The above results indicated that the production of volatile compounds in final wines was significantly affected by UFAs type and content in grape must, including C6 alcohols, higher alcohols, MCFAs, and esters. To highlight the differences in wines with different treatments, we carried out principle component analysis (PCA) using volatile compounds and main fermentation products (including ethanol, glycerol, acetic acid, citric acid, malic acid, and succinic acid) (Figure 4). As shown in Figure 4A, the first and second accounted for 54.9% (PC1) and 15.8% (PC2) of the total variation, respectively. The PCs clearly distinguished the wine samples that were fermented by the addition of OA, LA, with ALA. UFAs levels had stronger contribution to the variance of these compounds than UFAs types in OA and LA wines. OA_12 and LA_12 wines were located on the negative part of PC1 and they were clearly separated from other treatments. The main responsible components for this separation were MCFAs (octanoic acid and decanoic acid), acetate esters (hexyl acetate, phenethyl  
Figure 4: Principal component analysis (PCA) score plot (**A**) and loadings plot (**B**) using 36 volatile compounds and main fermentation products (including ethanol, glycerol, acetic acid, citric acid, malic acid, and succinic acid) in the Control (green circle), 12 mg/L (blue), 60 mg/L (red), 120 mg/L (yellow), and 240 mg/L mg/L (light blue) addition of oleic acid (OA, square), linoleic acid (LA, triangle), and \(\alpha\)-linolenic acid (ALA, diamond).  
acetate, and isoamyl acetate), and ethyl esters (ethyl hexanoate, ethyl butanoate, and ethyl octanoate). In comparison, the OA_240 and LA_240 wines were positioned on the positive side of PC1 by higher alcohols (2,3-butanediol, hexanol, and benzyl alcohol), as well as ethanol, glycerol, succinic acid, and acetic acid. The PC2 could discriminate the wine of ALA from the wines of OA and LA, mainly by isobutyl alcohol, isoamyl alcohol, and 2,3-butanediol, suggesting that ALA addition could produce the wines having different enological properties with OA and LA wines.
## 4 Conclusions
### Limitations of the Study  
Several limitations were identified in the study. Firstly, the resulting dimensionality reduction from using Principal Component Analysis (PCA) suggests that the first component accounts for about 49% of the overall variance. The commonly used combination with PCA relies on >70% of the variance explained. However, this certainly does not downplay our findings given that we used dimensionality reduction, i.e., PCA, only on the first component; combining the first and second components accounts for 80% of the variance. In addition, we then performed a hypothesis test by assessing each variable on all three target variables to ensure the reliability and validity of outcomes.  
Secondly, some predictor and target variables synthesised from the survey suggest 1-4% missing data, imputed using average values or mean imputation. This, in turn, slightly reduced the data variability, leading to underestimation of standard errors and overestimation of significance in predictor coefficients. That is why, in our hypothesis testing and significance analysis, we performed bootstrapping and sensitivity analysis to ensure the actual impact of predictors on individual target variables. This helped us increase the robustness and generalisability of the findings.  
Finally, the findings obtained may suffer from heterogeneity in applications. Since fluid power systems are employed in different working environments and regimes, ahydraulic subsystem (fluid power system) in an extrusion production line and a mining excavator significantly differ in their applications. Therefore, the findings may benefit from isolating the target organisations to a specific application that will offer a more homogeneous study sample.
# Stability-Aware Geographic Routing in Energy Harvesting Wireless Sensor Networks
## 5 Performance Evaluations
### The Impact of Number of Nodes on PDR, Hop Count, and Energy Consumption between EHWA and SAGREH  
Figure 7 shows the performance of both the EHWA and SAGREH in terms of the \(PDR\), average hop count, and average energy consumption as functions of the radio range, which was 90 m. The number of nodes varied from 50 to 100.  
Moreover, SAGREH provides better \(PDR\) compared to EHWA. When the number of nodes increased, the \(PDR\) of SAGREH increased. While the PDR of the EHWA slightly decreased because the \(PRR\) of a route heavily depends on the hop count.  
Figure 7: (**a**) The PDR, (**b**) average hop count, and (**c**) average energy consumption as a functions of the number of nodes.  
Figure 7b shows an interesting result; when the number of nodes increased, the \(\overline{HC_{i}}\) of the EHWA also increased, but the \(\overline{HC_{i}}\) of SAGREH slightly decreased because the EHWA tended to select longer routes to avoid nodes with low energy levels. In the worse cases, it even selected spiral routes. The SAGREH selected shorter routes to maximize the \(PRR\) of routing path from source node to sink node. With a given radio range, the SAGREH provided more stability for routing than EHWA did. Specifically, the average hop count of the SAGREH did not depend on the number of nodes. For instance, with a \(\beta\) = 4 and the number of nodes varying from 50 to 100, the average hop count varied from 3.73 to 3.46, while the average hop count of EHWA changed significantly according to the number of nodes. For example, with a \(\beta\) = 4 and the number of nodes changing from 50 to 100, the average hop count change was from 4.52 to 5.23.  
Because the EHWA selected longer paths with a higher probability of packet loss, it consumed much more energy than the SAGREH protocol. Figure 7c shows that the \(\overline{CE_{i}}\) of SAGREH was unchanged, while the \(\overline{CE_{i}}\) of the EHWA increased. This shows the remarkable advantages of the proposed protocol compared to the EHWA in terms of energy consumption.
## 4 Results and Discussion
### Flow Characteristics with Different Flow Rate Input  
The input flow rate directly determines the flow velocity and pressure of drilling fluid in boreholes. As the flow rate increases, the ability to remove rock debris from the borehole bottom and the effect of bit cooling will be enhanced. Timely removal of rock debris from the borehole bottom significantly affects the drilling process and overall drilling efficiency, as repeated rock crushing and useless energy consumption will be avoided. Meanwhile, circulating flow resistance will also increase correspondingly as flow rate increases, thus increasing the burden of the mud pumps. Furthermore, if the flow rate is too large, it will obviously erode the cores and affect the core recovery rate. Consequently, reasonably optimizing and selecting the drilling fluid input flow rate are very important and necessary in the drilling process. To study the flow characteristics of drilling fluid flowing through drill bits with different input flow rates, a group of simulation analyses was carried out. The inlet flow rates were set to 300, 400, 500, and 600 L/min; the pressure and flow velocity results when drilling fluid flowed through the drill bit are displayed in Figure 10. From these two curves, we find that the pressure and velocity in the coring bit and non-coring bit show similar patterns of change. According to the results, when the input flow rate increases, drilling fluid velocity at the bit outlet increases linearly, and the pressure at the bit inlet increases parabolically. That is to say, increasing drilling fluid flow rate will cause increases in both velocity and pressure, and the pressure rises faster than the velocity. It is recommended to select a small flow rate so that the drilling fluid can carry rock cuttings effectively. For wireline coring drilling, the velocity of the drilling fluids flowing upward from the borehole should reach at least 0.5 m/s to 1.5 m/s to carry rock powder. In the analysis for the coring bit, as Figure 9 shows, the velocity can reach up to 0.706 m/s with the lowest pressure of 18,760 Pa occurring when the inlet flow rate is 300 L/min, which can completely meet the needs of most diamond drilling. For the non-coring bit, when inputting drilling fluid with flow rate of 300 L/min, the drilling fluid's upward velocity can reach up to 0.706 m/s and pressure at the bit inlet can reach 8842 Pa. As there are more rock cuttings produced in non-coring drilling when a borehole is drilled with the same diameter as the coring drilling, a large flow rate needs to be provided to remove rock cuttings effectively. From the perspective of drilling fluid flow characteristics, there needs to be sufficient flow velocity and low pressure loss when drilling fluid flows out of a drill bit. According to these two groups of analyses, it can be concluded that when inputting at the same flow rate, similar velocities and lower pressure loss can be obtained in non-coring drilling compared with the using the coring bit.  
Figure 9: Velocities and pressure with different layouts of nozzles on the non-coring bit.
# PPP and PPP-AR Kinematic Post-Processed Performance of GPS-Only, Galileo-Only and Multi-GNSS
## 2 Materials and Methods
 \tag{9}\]  
where:  
* \(P_{r,\text{IF}}^{s}\) is the ionosphere-free code measurement at receiver \(r\) from satellite \(s\) (m)
* \(E_{r,\text{IF}}^{s}\) is the ionosphere-free code measurement error at receiver \(r\) from satellite \(s\) (m)
* \(L_{r,\text{IF}}^{s}\) is the ionosphere-free carrier measurement at receiver \(r\) from satellite \(s\) (m)
* \(\varepsilon_{r,\text{IF}}^{s}\) is the ionosphere-free carrier measurement error at receiver \(r\) from satellite \(s\) (m)
* \(\lambda_{nl}\) (\(\lambda_{nl}=c/\Big{(}f_{i}+f_{j}\Big{)}=\ \lambda_{i}\lambda_{j}/\Big{(} \lambda_{i}+\lambda_{j}\Big{)}\)) is the narrow-lane (NL) wavelength (m)  
The following Table 1 gives the wide-lane and the narrow-lane wavelengths for GPS and Galileo frequencies used in this article. The term \(W_{r}^{s}\) is calculated by using the models proposed by [PERSON] [20]. All equations are then gathered to form another LSE processing. The system of equations for the ionosphere-free code and phase measurements are modelled according to the models given in Table 2 and used to estimate all the parameters: stations position, tropospheric parameters, receiver clocks and inter system bias in case of multi-GNSS measurements. For the PPP case the \(N_{r,i}^{s}\) are solved with the other parameters as real values. For the PPP-AR case they are fixed to integer numbers within a bootstrap processing [8; 13]. Once the integer \(N_{r,i}^{s}\) is determined, the final PPP-AR solution is performed. \begin{table}
\begin{tabular}{c c c} \hline
**GNSS Frequency** & \(\lambda_{wl}\)**(m)** & \(\lambda_{nl}\)**(m)** \\ \hline GPS (\(L1\), \(L2\)) & 0.862 & 0.107 \\ Galileo (\(E1\), \(E5a\)) & 0.751 & 0.109 \\ \hline \end{tabular}
\end{table} 
Table 1: Values for wide-lane and narrow-lane wavelength. Figure 1 is giving a graphic overview of the PPP and PPP-AR method used. In the beginning, the satellite \(\mu^{s}\) are needed together with the station RINEX file. The pre-processing phase uses the \(MW^{s}_{r}\) observations formed from the individual RINEX observations and the GPS and Galileo biases \(\mu^{s}\) to solve the \(N^{s}_{\
u l,r}\) for integers. Then the \(N^{s}_{\
u l,r}\) and satellite orbit and clock products are used as inputs to form the ionosphere-free combination measurements (code and carrier phase) for the first processing. The system of equations is solved for the PPP solution. In the case of PPP-AR processing the proceedure continues further after having fixed the \(N^{s}_{r,i}\) to integers; in this second processing to give the PPP-AR solution mostly ionosphere-free ambiguity-free carrier phase measurements are used for GPS and/or Galileo.
## 6 Complexity Analysis
### Impact of High-Order Modulation Format Levels on Computational Complexity  
In this section, we investigate the impact of high-order modulations formats (up to 128-QAM) on the computational complexity of the most widely-adopted machine learning, ANN. We also provide a comparison in Table 3 between full-step DBP, IVSTF and ANN, for two systems with different total lengths and different numbers of constellation points. Since the ANN is independent of the link parameters, the ANN complexity is the same for both systems. From Table 3, it is evident that DBP is the technique with the highest computational complexity compared to ANN for both low- and high-modulation format levels. However, when comparing IVSTF with ANN for large constellation point numbers, it can be appreciated that for the System A (transmission at 2000 km) IVSTF has lower complexity than ANN for \(M=64\) and above, while for System B (transmission at 3200 km) IVSTF has lower complexity than ANN only for \(M=128\). We note that we made the calculations assuming 40 steps-per-span for the full-step DBP. With regard to the subcarrier numbers, we considered 512 for IVSTF and DBP (as this is used to calculate the total occupied band) and 210 for the ANN (because subcarriers carrying data are only processed by the NLEs).
# Numerical Study of Pressure Attenuation Effect on Tunnel Structures Subjected to Blast Loads
###### Abstract  
This study used experimental and numerical simulation methods to discuss the attenuation mechanism of a blast inside a tunnel for different forms of a tunnel pressure reduction module under the condition of a tunnel near-field explosion. In terms of the experiment, a small-scale model was used for the explosion experiments of a tunnel pressure reduction module (expansion chamber, one-pressure relief orifice plate, double-pressure relief orifice plate). In the numerical simulation, the pressure transfer effect was evaluated using the ALE fluid-solid coupling and mapping technique. The findings showed that the pressure attenuation model changed the tunnel section to diffuse, reduce, or detour the pressure transfer, indicating the blast attenuation effect. In terms of the effect of blast attenuation, the double-pressure relief orifice plate was better than the one-pressure relief orifice plate, and the single-pressure relief orifice plate was better than the expansion chamber. The expansion chamber attenuated the blast by 30%, the one-pressure relief orifice plate attenuated the blast by 51%, and the double-pressure relief orifice plate attenuated the blast by 82%. The blast attenuation trend of the numerical simulation result generally matched that of the experimental result. The results of this study can provide a reference for future protective designs and reinforce the U.S. Force regulations.  
blast; tunnel; pressure reduction module; LS-DYNA +
Footnote †: journal: Applied Sciences
# Combining Subgoal Graphs with Reinforcement Learning to Build a Rational Pathfinder
## 4 Results
### Performance of the SG-RL Method  
SG-RL is a method used to find rational paths from a start position to a goal position, which decomposes complex path-planning problems into two phases: the first for the global abstract path planning and the second for the feasible trajectory planning.  
In the first phase, we used the SSG method to generate subgoal sequences. The time to find subgoal sequences, called H-time, is a key assessment indicator, which can evaluate the ability to eliminate first-move lags. SSG has constrained heading changes, which cannot generate smooth paths for agents moving in continuous environments. By contrast, the proposed hierarchical method, SG-RL, has free heading changes. We evaluated its ability to deal with continuous environments by comparing its path lengths of it with those of SSG.  
In the second phase, we used a feasible trajectory planner based on LSPI to find safe paths between subgoals, and all trajectory segments between subgoals could be linked to obtain complete feasible trajectories from the start position to the goal position. In the LSPI method, some parameter values came from Reference [18] and others came from trials. The discounted factor \(\gamma\) was 0.9. Basis function \(\varphi\) was polynomial. In OA MDP, the order of basis function \(\varphi\) was 3, and in SA MDP, the order was 4. The subgoal tolerance was 1.5 and the final goal tolerance was 0.5. The subgoal tolerance was bigger since subgoals only play auxiliary roles in the trajectory planner. The time interval of action choice was 0.5 s. The way of collecting samples was to choose actions randomly per second. If robots collided with obstacles or boundaries, this sampling epoch ended and robots started from random positions.  
Action-switching frequency is a significant factor which can evaluate the performance of planned trajectories. Its definition is the ratio of the sum of the current action that is different from the last action to all actions. For robots, it cannot be high because of limitations of hardware systems and network latency. Realizing fast and frequent responses requires more precise chassis gears and more energy. Meanwhile, in areas with signal interference, network latency is relatively high.
# A Dynamic Model Updating Method with Thermal Effects Based on Improved Support Vector Regression
## 5 The Numerical Examples
### The Thermophysical Parameters Identification of Wing Structure  
In this numerical example, a wing structure dynamical modal updating problem with temperature dependence is presented to verify the method proposed in this paper; the finite element model is shown in Figure 7.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Node Number** & **Real Values (\({}^{\circ}\)C)** & **Parametric (\({}^{\circ}\)C)** & **Deviation** & **Functional (\({}^{\circ}\)C)** & **Deviation** \\ \hline
32 & 288.45 & 288.53 & 0.028\% & 288.41 & 0.013\% \\
86 & 258.61 & 258.70 & 0.035\% & 258.52 & 0.035\% \\
151 & 220.74 & 220.53 & 0.095\% & 220.67 & 0.032\% \\
216 & 180.32 & 179.72 & 0.333\% & 180.37 & 0.028\% \\
281 & 136.89 & 136.34 & 0.402\% & 136.96 & 0.051\% \\
346 & 90.78 & 90.74 & 0.044\% & 90.97 & 0.21\% \\
411 & 43.79 & 43.83 & 0.091\% & 43.80 & 0.23\% \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparison of temperature distribution based on the two methods.  
Figure 6: Comparison of identification temperature deviations based on the two methods.  
Figure 7: The finite element model of wing structure.  
The wing chord length is 0.595 m, half span chord ratio is 2.81, wing thickness ratio is 0.12. There are 5446 quadrilateral shell elements in the finite element model of wing structure, and wing root is restrained by means of fixed restraints. According to the principle of hierarchical model updating, the temperature field is first applied to the finite element model to generate temperature stresses. Then, the temperature stresses are added to the model as pre-stresses for modal frequency analysis.  
This numerical example is only used to verify the accuracy of the identification method, and some assumptions are made to complete the calculation.  
1. The wing structure material is aluminum alloy with a density of, 2800 kg/m\({}^{3}\), Poisson's ratio is 0.33, heat transfer coefficient is shown in the Table 1 and temperature-dependent elastic modulus is shown in Table 9.
2. The finite element model has been simplified, and the influences of rivets, bolts and other parts of the thermal contact with temperature-dependent to temperature distribution have been ignored.
# Underwater Radiated Noise Prediction Method of Cabin Structures under Hybrid Excitation
## 4 Experiments
### Analysis of Experimental Results  
In this study, the \"acoustic/vibration-acoustic\" transmission network inside and outside the cabin structure of the cabin model was numerically modeled by using the numerical calculation software COMSOL 6.0. Figure 17 shows the real and predicted values of radiation noise at four far-field radiation noise detection points, while Table 2 shows the  
Figure 16: Schematic diagram of the locations of the monitoring points and investigation points: (**a**) Cabin inner monitoring point (main view). (**b**) Cabin inner monitoring point (side view). (**c**) Far-field investigation point.  
Figure 15: Tested cabin and its finite element model: (**a**) Cabin model. (**b**) Finite element model.  
radiation noise prediction error table based on two monitoring methods at each observation point. The frequency points are the excitation frequencies of the sound source and the force source, which were selected according to one-third octave points in the 100 Hz-1 kHz frequency band. It can be seen that the underwater radiated noise prediction method proposed in this paper can accurately predict the radiated noise of the cabin model. Under the force-acoustic hybrid excitation, the prediction accuracy of the model for underwater radiated noise is higher when using the vibration-acoustic synchronous monitoring scheme. Moreover, the forecast error tendency increases with the increase in frequency, which is essentially consistent with the simulation results. The prediction error of the model for underwater radiated noise based on vibration and acoustic monitoring is less than 5 dB in the frequency band below 1 kHz. Investigation point 3 and investigation point 4 are in a symmetric position relative to the structure, and it can be seen that the prediction accuracy of radiation noise is similar in the overall trend.
# A Survey on Node Clustering in Cognitive Radio Wireless Sensor Networks
## 7 Issues and Challenges of Node Clustering in CR-WSNs
### Optimal Number of Clusters  
One of the challenges in cluster-based CR-WSNs is to determine the optimal number of clusters. A large number of clusters leads to establishing long routes, which naturally incur higher delay. On the other hand, a small number of clusters exhausts the energy of the CH, and incurs inefficient spectrum sharing.  
The cluster establishment procedure and its complexity should be independent of the network scale, and should remain constant. A fixed cluster size has load-balancing and energy-consumption issues. In particular, CHs that are closer to the BS need to forward more inter-cluster packets, and hence, will deplete their battery power sooner than the CHs that are farther away from the BS. Forming relatively small clusters near the BS distributes the workload among multiple CHs.  
When CHs are specialized CR-WS nodes (under CR-WSN-few), there should be a CR-WSN with fewer CHs, because the CHs tend to be more expensive and bigger in size than the CMs. These nodes can be easily visible and detectable if deployed for critical application fields, such as battlefields, border security, infrastructure security, etc.