esa-sceva/satcom-chunk-collection huggingface dataset:
# Impact of Temperature on the Moisture Buffering Performance of Palm and Sunflower Concretes
## 2 Numerical Model
### Effective Capacitance Model  
The effective capacitance model assesses the impact of the use of hygroscopic materials on the variation in the indoor relative humidity of a room. The moisture buffering values which are obtained by experimental studies or by the numerical method will be used as input data in this model. Other parameters such as the ventilation rate, the effective exposed surface area, the source of water vapor, the exposure time and the room volume are also taken into account in the model. Note that the moisture buffering capacity of the simple wall is considered to be in equilibrium with the ambient conditions of the room [44]. The model which is established from the moisture balance for the room air can be written as follows (Equations (7) and (8)):  
\[\left(\frac{V}{R_{\varphi}T_{i}}+\frac{100 HIR*V}{P_{v,int}(T_{i})}\right)\cdot \frac{\partial P_{\overline{v}i}}{\partial t}=\left(P_{v,\varphi}-P_{v,i} \right)\cdot\frac{nV}{3600R_{v}T_{i}}+G_{v,p} \tag{7}\]  
\[HIR*=(\sum_{k}S_{k}MBV_{k})/V \tag{8}\]where \(S_{k}\) is the exposed area of wall \(k\) (m\({}^{2}\)); \(V\) is the volume of the room (m\({}^{3}\)); \(HIR*\) is the hygric inertia of the room (kg/(m3%HR)); \(R_{v}\) is the gas constant for water vapor (462 J/(kg\(\cdot\)K)); \(T_{i}\) is the indoor air temperature (K); \(P_{v,e}\) is the partial vapor pressure of the outdoor air (Pa); \(P_{v,i}\) is the partial vapor pressure of the indoor air (Pa); \(P_{v,sat}\) is the saturation vapor pressure (Pa); \(MBV_{k}\) is the moisture buffer capacity of material \(k\) (g/(m2%RH)); \(G_{v,p}\) is the indoor water vapor production (kg/s); \(n\) is the ventilation rate (1/h).  
The Simulation Problem Analysis and Research Kernel (SPARK) developed by the Lawrence Berkeley National Laboratory-USA, a simulation environment allowing for efficiently solving differential equation systems, has been used to solve this set of equations presented above [9; 45; 46; 47]. Note that the SPARK describing the model has been solved using the finite difference technique with an implicit scheme.
## 4 Error Simulation and Analysis
### Datum Transfer Error
Figure 8: The effect of a single error on transmission accuracy: (**a**) theodolite collimation error; (**b**) laser tracker collimation error; (**c**) laser tracker reflective target sphere manufacturing eccentricity error; (**d**) cubic prism coordinate system establishment error. According to the error values listed in Table 4, we generate 10,000 sets of normally distributed random numbers for simulation in each error dimension. After the calculation of (6), we get rotation \(\alpha\) around the \(X\)-axis, rotation \(\beta\) around the \(Y\)-axis, and rotation \(\gamma\) around the \(Z\)-axis, as shown in Figure 9a-i. The horizontal coordinate is the number of simulations, and the vertical coordinate is the value of the angle of rotation of each axis. SV is the angle value obtained from the simulation. The red line represents the theoretical rotation angle value, and the blue line represents the limit deviation (3\(\sigma\)). \begin{table}
\begin{tabular}{c c c} \hline \hline
**Instrument** & **Sources of Error** & \(\mu\) & **3\(\sigma\)** \\ \hline \multirow{5}{*}{Theodolite} & Collimation error & 0 & \(\Delta_{c}=\pm 1^{\circ}\) \\  & Cubic prism introduces error & 0 & \(\Delta_{cp}=\pm 4^{\circ}\) \\  & Cubic prism goniometric error & 0 & \(\Delta_{g}=\pm 3.5^{\circ}\) \\  & Mutual aiming error & 0 & - \\  & Focusing error & 0 & - \\  & Distance measurement error & 0 & \(\Delta_{d}=\pm 5.5^{\circ}\) \\ Laser tracker & Reflector manufacturing & 0 & \(\Delta_{\tau}=\pm 1.7^{\circ}\) \\  & errors & & \\ Photogrammetry & Coordinate point & 0 & \(\Delta_{\text{ph}}\leq 30\)\(\mu\)m \\  & measurement error & 0 & \(\Delta_{\text{tl}}=\pm 3^{\circ}\) \\ Coordinate system errors & system error & 0 & \(\Delta_{\text{lp}}=\pm 6^{\circ}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Datum transfer error estimation and assignment. Figure 9: _Cont._  
As can be seen in Figure 9, the simulated values of the rotation angle are distributed in a normal distribution pattern around the theoretical true value. After the transfer of (1), the \(XYZ\) coordinate transfer errors (3\(\sigma\)) are 3.75\", 7.42\", and 16.96\". After the transfer of (2), the \(XYZ\) coordinate transfer errors (3\(\sigma\)) are 9.83\", 8.72\", and 15.66\". After the transfer of (3), the \(XYZ\) coordinate transfer errors (3\(\sigma\)) are 13.50\", 13.50\", and 13.39\". Analysis can be obtained by (1)-(3), \(XYZ\) coordinate axis rotation angle error are within 17\", and different transfer on the coordinate axis transfer accuracy of different impact.
_Applied_  
_sciences_  
_Article_  
**Hybrid Battery/Lithium-Ion Capacitor Energy**  
**Storage System for a Pure Electric Bus for an**  
**Urban Transportation Application**  
**[PERSON] 1,2,*\(\,\)*\(\,\)**, [PERSON] 3, [PERSON] 4, [PERSON] 1,2, [PERSON] 1,2, [PERSON] 1,2 and [PERSON] 1,2**  
1 Department ETEC Mobility, Logistics and Automotive Technology Research Group (MOBI),  
Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium; [EMAIL_ADDRESS] (J.J.);  
[EMAIL_ADDRESS] (P.V.d.B.); [EMAIL_ADDRESS] (J.v.M.); [EMAIL_ADDRESS] (N.O.)  
2 Flanders Make, 3001 Heverlee, Belgium  
3 JSR Micro N.V., Technologiean 8, 3001 Leuven, Belgium; [EMAIL_ADDRESS]  
4 JM Energy Corporation, 8565 Nishi-ideo Ooizumi-cho, Hokuto City, Yamanashi 409-1501, Japan;  
[EMAIL_ADDRESS]  
* Correspondence: [EMAIL_ADDRESS]; Tel.: +32-(0)26293396  
Received: 30 May 2018; Accepted: 17 July 2018; Published: 19 July 2018  
**Featured Application: A potential application for this research work is the pure electric bus with energy recovery capability. With the hybrid energy storage system based on Lithium-ion battery and Lithium-ion Capacitor, the bus will have a longer range, a higher efficiency and a lower cost in comparison to a bus with non-hybrid energy storage system or a bus with hybrid energy storage based on battery and super-capacitors.**  
**Abstract: Public transportation based on electric vehicles has attracted significant attention in recent years due to the lower overall emissions it generates. However, there are some barriers to further development and commercialization. Fewer charging facilities in comparison to gas stations, limited battery lifetime, and extra costs associated with its replacement present some barriers to achieve better acceptance. A practical solution to improve the battery lifetime and driving range is to eliminate the large-magnitude pulse current flow from and to the battery during acceleration and deceleration. Hybrid energy storage systems which combine high-power (HP) and high-energy (HE) storage units can be used for this purpose. Lithium-ion capacitors (LiC) can be used as a HP storage unit, which is similar to a supercapacitor cell but with a higher rate capability, a higher energy density, and better cyclability. In this design, the LiC can provide the excess power required while the battery fails to do so. Moreover, hybridization enables a downsizing of the overall energy storage system and decreases the total cost as a consequence of lifetime, performance, and efficiency improvement. The aim of this paper is to investigate the effectiveness of the hybrid energy storage system in protecting the battery from damage due to the high-power rates during charging and discharging. The procedure followed and presented in this paper demonstrates the good performance of the evaluated hybrid storage system to reduce the negative consequences of the power peaks associated with urban driving cycles and its ability to improve the lifespan by 16%.**  
**Keywords: hybrid energy storage system; lithium-ion battery; lithium-ion capacitor; lifetime model; power distribution**
## 2 Theoretical Models  
### Multi-Field Coupling Theoretical Model  
#### 2.1.1 Analysis of Applied Magnetic Field Mechanism  
The simplified two-dimensional model of wire-plate ESP is shown in Figure 1. When an external magnetic field is applied to the moving charged particles in the wire-plate ESP, the particles will spiral under the action of electromagnetic fields. The charged particles in ESP have parabolic motion under the action of electric field forces and circular motion under the Lorentz force generated by magnetic fields. The coupling effect of these two makes them move in a spiral motion, prolonging the residence time in ESP and making it easier to trap in order to improve the dust-removal efficiency [29].  
During the calculations, the external magnetic field is added as a uniform one in the wire-plate ESP to reduce the error and facilitate the solution, and the method of controlling variables is adopted in this study. The uniform magnetic field is evenly distributed throughout the entire space and remains unaffected by fluctuations caused by the external environment. Therefore, its magnitude and direction remain constant and do not change. As a result, the solution does not involve solving [PERSON]'s equations.
## 3 Results and Discussion
### Volatile Compounds Profile
A previous study showed that alcohols such as 3-hexen-1-ol and 3,6-nonadien-1-ol detected by GC-olfactometry are responsible for the fresh and marine odors in the halophyte plant _Mertensia martiima_ L. [50]. Oven-dried _S. ramossissima_ is characterized by a green and herbal aroma, therefore the seaweed odor characteristic is less intensive compared to the fresh plant due to reduction of 3-hexen-1-ol [50]. The freeze-drying process is less aggressive than the oven-drying used due to the low temperature and oxygen content [65; 66; 67]. Results show that in the freeze-dried plant, there was an increase in the contribution of the terpen class from 19.0 to 57.7% and these compounds are usually responsible for aromas described as fresh, eucalyptus, and camphoraceous. Terpenes were also the most represented volatiles in freeze-dried sea fennel (_Crithum martiimum_ L.), specifically limonene, \(\gamma\)-terpinene, \(\alpha\)-pinene, \(p\)-cymene, sabinene, myrcene, \(\alpha\)-thujene, camphene, \(\beta\)-thujene, \(\alpha\)-terpinene, (Z)-\(\beta\)-ocimene, and terpinolene, which have also been identified in _S. ramossissima_[68].
## 4 Conclusions  
This study using ATR-FTIR technology investigated the feasibility to quantitatively and qualitatively detect the methanol in methanol gasoline. The calculated result demonstrated the accuracy and efficiency of IR spectra in quantitative and qualitative analysis of the methanol gasoline. As for the qualitative analysis, PLS-DA reached 100% and 96.88% accuracy for calibration and prediction set, respectively. When the quantitative analysis was considered, the PLSR and LS-SVM model, combined with UVE and CARS variables selection methods, were applied to establish the prediction model, and UVE-PLSR obtained the best prediction result with the RPD and ABS value of 6.420 and 0.847.  
As a primary exploration using IR spectra to qualitatively and quantitatively the methanol in gasoline, the computed results show that it is feasible to nondestructive and rapid detect the methanol content. This research indicates that IR spectra is a promising analytical method in applying in the gasoline industry and more effort should be made to speed up industrial application.  
**Author Contributions:** Data curation, Q.X. and L.M.; formal analysis, L.-m.Y. and X.C.; funding acquisition, L.-m.Y. and G.H.; investigation, Q.X.; methodology, L.-m.Y., L.M. and G.H.; software, L.M. and G.H.; supervision, L.-m.Y. and X.C.; writing--original draft, Q.X.; writing--review and editing, L.-m.Y.  
**Funding:** This study was funded by National Natural Science Foundation of China (NO.51876145, NO.61705168), Zhejiang natural science foundation of China (NO. Q20F030059) Wenzhou bureau science & technology project of China (NO.S20170003, NO.G20190024).  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Structural Modal Time Domain Identification Method Based on the Bayesian Uncertain Quantification
## 4 Numerical Examples
### Three Degrees of Freedom Spring-Mass System
#### 4.1.2 The Effect of Measured Noise  
Measured noise can degrade the data quality of the measured response signals, affecting the accuracy of the parameter identification results. Therefore, the effects of different noise levels on the parameter identification results must be discussed. In this subsection, the effect of the measured responses of nine different noise levels on the identification results of the natural frequencies and damping ratios was investigated. The noise levels from low to high levels were as follows: 6, 9, 12, 15, 18, 21, 24, 27, and 30%. The fourth setting is discussed in detail in Section 4.1.1.  
Figure 4 shows the identification results of the natural frequencies and damping ratios at different noise levels, where the MPVs of the modal parameters are represented by a dot and the error bar covers \(\pm 2\) standard deviations. The dashed lines indicate the theoretical values. As shown in the figure, the error bars cover the MPVs of the natural frequencies and damping ratios. With increasing noise levels, the MPVs gradually deviate from the theoretical values, and the extents of the error bars gradually widen (i.e., the standard deviation gradually increases). This observation aligns with our expectation that as the noise level increases, the signal-to-noise ratio of the measured data decreases, leading to greater uncertainties in the identification results.
## 4 Results and Analyses
### Joint Opening and Bolt Stress  
Figure 13 illustrates the changes in joint openings. It reveals that the openings at joints J1 (7.5\({}^{\circ}\)), J2 (72\({}^{\circ}\)), J3 (144\({}^{\circ}\)), and J5 (288\({}^{\circ}\)) increase significantly as \(P_{0}\) rises. Notably, joints J1 and J2 exhibit similar expansion trends, as do J3 and J5. In contrast, the openings at J4 and J6 remain relatively stable, with all measurements under 0.2 mm. Through analyzing the deformation patterns of the lining structure, it can be observed that J1 (7.5\({}^{\circ}\)), J3 (144\({}^{\circ}\)), J4 (216\({}^{\circ}\)), and J6 (352.5\({}^{\circ}\)) demonstrate outer arc openings, whereas J2 (72\({}^{\circ}\)) and J5 (288\({}^{\circ}\)) show inner arc openings. This pattern indicates that the bolts at J1, J3, J4, and J6 experience tension on the outer side and compression on the inner side, while those at J2 and J5 undergo the opposite, with tension on the inner side and compression on the outer side.  
Figure 14a and b depicts the variations in bolt tensile and compressive stress, respectively, where positive values indicate tension and negative ones represent compression. Notably, J2 experiences the most significant fluctuations in both tension and compression stresses. J1 and J5 display similar stress variations, whereas J3, J4, and J6 exhibit less change. Interestingly, the maximum tensile and compressive stresses within the same bolt are nearly equal. A comparison of joint opening and bolt stress data reveals that, although J1 and J2 have similar joint openings, J2's bolt stress is higher. This suggests that larger joint openings cause bolts to exert more constraint on the inner arc surface compared to the outer arc surface. Specifically, when the joint opening on the inner arc surface reaches 4.9 mm, the bolt stress hits the yield strength of 640 MPa. Furthermore, Figure 15 illustrates the distribution of bolt stresses. Prior to ground surcharge, the maximum tensile stress on the bolts occurs at J2, and the maximum compressive stress at J6. Post ground surcharge, both the highest tensile and compressive stresses are found at J2, with the most significant stress concentrations near the nut.  
Figure 13: Variation in joint opening with \(P_{0}\).  
Figure 14: Variation in bolt stress with \(P_{0}\). (**a**) Tensile stress; (**b**) Compressive stress.  
Figure 15: _Cont._
## 6 Conclusions  
This paper proposes a DMD-JD FTN detection algorithm to solve the nonlinear aliasing problem in FTN detection of multipath channels. This algorithm can not only achieve better detection performance than traditional algorithms, but also hasower complexity and stronger robustness. The transmission quality can be guaranteed in the scenario of channel switching, making the practical application of FTN possible. The core of the algorithm is that the use of data-driven methods can better fit the aliasing of nonlinear noise withower complexity, and the use of model-driven methods can enhance the robustness and generality of the algorithm on the basis of improving the performance.  
The experimental results show that the DMD-JD algorithm performs well in FTN signal detection, and the performance is improved by nearly 1.6 dB compared with the traditional algorithm. Similar performance has been achieved in QPSK (4 QAM) modulation,aying the foundation for future work under high-order modulation. In addition, the relatively optimal network model structure parameters of FTN signal detection are obtained through simulation and comparison. Finally, we verify that the addition of equalization modules makes the DMD-JD algorithm more robust and adaptable in multipath channel transmission scenarios.  
It is worth pointing out that in order to simply verify the performance gain of the FTN detection algorithm, an ideal channel equalization method is used. In future work, we will try to use the traditional channel estimation and equalization methods from the perspectives of signaling processing and data driving to analyze the adaptability of compression factor, FTN transmission under high-order modulation, and time-frequency two-dimensional FTN signal detection.  
Conceptualization, X.D., M.L. and X.B.; Methodology, X.D. and X.B.; Validation, X.D.; Investigation, X.D.; Resources, M.L.; Writing--Original Draft Preparation, X.D.; Writing--Review and Editing, X.D., M.L. and X.B.; Supervision, X.D., M.L. and X.B.; Project Administration, M.L. and X.B.; Funding Acquisition, M.L. All authors have read and agreed to the published version of the manuscript.  
The work was supported in part by the National Key R&D Program of China (2018 YFB1802 201), in part by the Shanghai Super Postdoctoral Incentive Plan (2020000125).  
Not applicable.  
The study did not report any data.  
The authors declare no conflict of interest.
# Autonomous Underwater Vehicle Localization Using Sound Measurements of Passing Ships
## 4 MATLAB Simulations
### IMU-Only Localization  
For comparison, we consider the case where the AUV localizes itself by integrating propeller and IMU. Note that no ships are used in this IMU-only localization. The movement of the AUV is identical to that in Figure 1. Similarly to Equation (24), the AUV can locate itself using  
\[\mathbf{X}_{k+1}=\mathbf{F}\mathbf{X}_{k}+\mathbf{A}_{k}+\mathbf{w}_{k}^{p}. \tag{50}\]  
Figure 3: The AUV starts from the origin. The starting point of each ship is marked with a diamond. At every minute, the trajectory of every ship is plotted with red asterisks. At every minute, the trajectory of the AUV is depicted with blue circles (three ships scenario).  
Figure 2: Consider the case where the proposed localization method is utilized. We plot \(meanE_{k}\), \(maxE_{k}\), and \(minE_{k}\) with respect to sample-index \(k\). The AUV maneuvers at 750 and 2250 s. At these moments, the estimation error decreases sharply. This is similar to the case where the maneuver of an observer makes the Bearing-Only Target Motion Analysis (BOTMA) system observable [36].  
Here, \(\mathbf{A}_{k}\) is in Equation (26), and \(\mathbf{w}_{k}^{p}\) is in Equation (27). \(\mathbf{A}_{k}+\mathbf{w}_{k}^{p}\) in (50) is assumed to be derived by integrating a propeller and IMU [26; 27]. The error in the IMU integration is modeled by adding Gaussian noise \(\mathbf{w}_{k}^{p}\) in Equation (50). As we increase the variance in \(\mathbf{w}_{k}^{p}\), the error in the IMU-only localization increases. Recall that in Equation (27), we utilize \(\sigma_{a_{x}}=\sigma_{a_{y}}=2\ (m/s^{2})\).  
Recall that at time step 0, the IMU-only localization estimates the range between the 1-st ship and the AUV as \(\Delta=1500\) m. Hence, similarly to Equation (41), \(\mathbf{X}_{0}\) in Equation (50) is initialized using  
\[\mathbf{X}_{0}=\left(\begin{array}{c}\Delta*c(b_{0}^{n_{1}})\\ \Delta*s(b_{0}^{n_{1}})\\ \mathbf{0}_{2,1}\end{array}\right). \tag{51}\]  
Figure 4: Consider the case where the proposed localization method is utilized. We plot \(meanE_{k}\), \(maxE_{k}\), and \(minE_{k}\) with respect to sample-index \(k\). See that the estimation error decreased considerably, compared to Figure 2. This is due to the fact that the AUV localization uses three bearing lines emitted from three ships, instead of one bearing line emitted from one ship. (three ships scenario).  
Figure 5: The enlarged figure of Figure 4. We plot \(meanE_{k}\), \(maxE_{k}\), and \(minE_{k}\) with respect to sample-index \(k\). Since we utilize the bearing measurements of three ships, the estimation error is small from the beginning of the scenario (three ships scenario).  
Consider the case where the IMU-only localization is used. In Figure 6, we plot \(meanE_{k}\), \(maxE_{k}\), and \(minE_{k}\) with respect to sample-index \(k\). Since no ship is used to fix long-term drift, the estimation error does not decrease as time goes on. Compared to Figures 2 and 4, the estimation error increases. To run one Monte-Carlo simulation takes only 0.6 seconds utilizing MATLAB simulations.
# Risk Assessment of Compound Dynamic Disaster Based on AHP-EWM
###### Abstract  
The coal mine in deep mining can easily form a compound dynamic disaster with the characteristics of rock burst and gas outburst. In this paper, the analytic hierarchy process (AHP) and the entropy weight method (EWM) are combined, and the fuzzy comprehensive evaluation (FCE) secondary evaluation model of compound dynamic disaster is proposed to evaluate the risk of compound dynamic disaster, which avoids the problems of the imperfect evaluation index system and strong subjectivity of index weight. Based on the statistical analysis of typical compound dynamic disaster cases in China, three first-level indicators were established, and sixteen second-level indicators were developed. The verification results show that the accuracy and weight are better than the traditional evaluation methods. Combined with geological and mining data, the compound dynamic disaster risk assessment was carried out on the second mining area of mine B, in the Pingdingshan mining area, and the result was grade II (weak risk). Corresponding prevention measures and parameters were implemented, and no compound dynamic disaster occurred during the working face excavation.  
rock burst; coal and gas outburst; compound dynamic disaster; analytic hierarchy process; entropy weight method; risk assessment Article