esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Importance of Input Variables and Processing Methods on WUE Estimates
#### 3.3.2 Impact of Crop Physiology on Water Use Efficiency Estimates  
Naturally, alfalfa and maize have different growing patterns due to their different maturation rates, sprouting times, and since one is annual (maize) and the other is perennial (alfalfa). This impacts our ability to compare crop type WUE at small scales throughout the growing season and our ability to compare different WUE methods. For example, there were differences observed between alfalfa cuts within the season, but these differences were consistent when comparing harvested versus ecosystem WUE techniques (HWUEET was 22-29% of EWUES during each cut; Table 2). Alfalfa underwent four cuts during the growing season and as a result it did not experience a reproductive phase or stem thickening, whereas maize is an annual crop that has a stem that thickens throughout the season and undergoes biomass accumulation into reproductive components. We suggest therefore, that alfalfa biomass accumulation patterns were consistent between cuts which led to consistency in carbon use efficiency (conversion of assimilated carbon into biomass). In turn, this resulted in the differences between HWUEET and EWUES also being consistent across cuts.  
In contrast, there were differences in trends for the growth stages of maize and these differed based on the WUE method used. Maize is an annual crop with prolonged pre-sprouting period, followed by rapid growth, tasseling and ear development. In contrast to alfalfa, maize undergoes stem elongation, thickening and development of reproductive components. Additionally, maize established a root system during early growing season while alfalfa contained an established rooting system from previous years. This results in discrepancies between EWUE and HWUE since the relationship between GPP and above ground biomass accumulation is not consistent across the season. Thus, the two methods provide inconsistent results for differences between EWUE and HWUE throughout the season, when smaller timescales within a season are used. For example, during GS1, estimates of HWUEET were 80% lower than of EWUES estimates (Table 2). In subsequent growth periods, this gap decreased and during GS4, HWUEET estimate was 23% greater than EWUES. This makes it more challenging to compare WUE during different growth stages for maize when studies use different WUE methods. This is seldom an issue as most studies simply look at growth season harvesting and seldom explore growth stage; however, it was shown earlier that the maize and alfalfa experienced different discrepancies between these methods when considering the growing season as well. If HWUE methods are used for different stages of the growing season, they should be used with caution.
## 2 Materials and Methods
### Hepatic TG and TC Concentration  
The effect of fermented rice germ extracts on hepatic lipid accumulation was assessed by measuring TG and TC levels (Figure 3). Hepatic TG levels were significantly lower (decreased by 25.49%, 52.23%, and 53.33%, respectively) in FA, RG_30E, and RG_50E mice compared to the db/db controls (\(p\) < 0.05). Hepatic TC exhibited a tendency to decrease in the FA, RG_30E, and RG_50E groups compared to the db/db controls. A significant decrease was observed only in RG_50E mice, wherein TC levels were 37.05% lower (\(p\) < 0.05).
## 4 Vibration-Based Road Friction Monitoring
### High Frequency Vibrations
A novel method for estimating the tyre-road friction was presented by [PERSON] et al., whereby the sound in the tyre cavity was measured [47; 132]. The experimental setup depicted schematically in Figure 24 was employed in this work, where an acoustic sensor was attached to the wheel rim and simultaneous tyre pressure and temperature measurements were taken. Field trials comprised testing five different types of road surfaces, with a single tyre and passenger vehicle. The age of the road surfaces varied between 7-12 years. No data with respect to test speeds were given. To determine the road surface type features from the PSD of the acoustic signal were extracted. [PERSON] et al. noticed that the more the power was concentrated in the torus resonances (around 220 Hz and its many harmonics), the easier it was to find patterns on the PSD curves to distinguish a pavement from another. The features were used in a Support Vector Machine to perform the classification. However, as a distinction criterion, the power amplitude at frequencies greater than 5000 Hz was used. The classifier, after post-processing, had an average precision and recall of respectively 95.1% and 90.6% and an accuracy of 91.8%. Finally in [12; 16; 77] a vibration-based approach employing low and high frequency information was proposed. A description of this approach is provided in [77] by [PERSON] et al. In this work, the authors instrumented a tyre with accelerometers attached to the inner tread. After testing the instrumented tyre on surfaces of different friction characteristics, the authors computed the power spectrum of each acceleration signal using the [PERSON]'s average modified periodogram method. A strong correlation between the tyre circumferential acceleration and the road friction potential was identified by the authors, particularly on the tyre pre-trailing domain. [PERSON] et al. defined two frequency ranges (low-frequency band 10-500 Hz and high-frequency band 600-2500 Hz) and defined the vibration level ratio \(R\) as the ratio between the vibration level in the high-frequency band and the vibration level in the low-frequency band. The authors highlighted that the ratio \(R\) increased  
Figure 23: Block diagram of the road friction characterisation system presented in [PERSON] et al. [49]. Figure adapted by the authors from [49]. Figure 24: Experimental setup presented in [PERSON] et al. [47]. Figure adapted by the authors from [47]. and attributed this change to the higher slippage of the tyre in these circumstances. A fuzzy logic classifier was constructed taking into account these considerations and validated on smooth asphalt, regular asphalt, rough asphalt, and wet asphalt. A summary of the research works on high-frequency vibration friction estimation presented in this section is provided in Table 7.
# Parametric Optimization Study of Novel Winglets for Transonic Aircraft Wings
## 4 Results and Discussion  
_Aerodynamic Analyses of Winglet Designs_  
Winglet designs are analyzed at 0.85 M and a Re of 40 \(\times\) 10\({}^{6}\) (C\({}_{\rm ref}\) = 7 m and S\({}_{\rm ref}\) = 191.845 m\({}^{2}\)) for angles of attack at 0\({}^{\circ}\), 5\({}^{\circ}\), and 10\({}^{\circ}\). Aerodynamic coefficients such as C\({}_{\rm L}\) and C\({}_{\rm D}\) are noted and compared with the baseline model, as shown in Table 2. It can be observed from Table 2 that the twisted, multi-tip, and bird-type winglets outperform other types of winglets with improved C\({}_{\rm L}\)/C\({}_{\rm D}\) for all three attack angles. From the results shown in Figure 8, it is evident that a multi-tip winglet at a zero-degree angle of attack outperforms other winglet designs, and its aerodynamic efficiency is 23.47% better than the baseline model, followed by the bird-type winglet with an improvement of 22.83%. At a 5\({}^{\circ}\) angle of attack, bird-type, twisted, and multi-tip winglets show improvement in C\({}_{\rm L}\)/C\({}_{\rm D}\) by 4.53%, 3.05%, and 1.89%, respectively. At a higher-degree angle of attack, \(\alpha\) = 10\({}^{\circ}\), the bird-type winglet performs better than the baseline model by 0.67%. The aerodynamic efficiency decreases as \(\alpha\) increases due to increased drag [39]. In particular, the multi-tip and bird-type winglets have positive twist angles, thus leading them to stall at higher \(\alpha\). An appropriate twist angle at the wingtips could resolve this issue. It is concluded from the first-stage analyses that multi-tip and bird-type winglets perform better than the other designs. Although the bird-type winglet performs marginally better than the multi-tip winglet, the parametric study is conducted on the multi-tip winglet, as it is relatively easier  
Figure 6: Validation of this present study with the AGARD’s experimental results [33].  
Figure 7: Results of grid independent test.  
Figure 8: The effect of attack angles on (**a**) drag coefficient, (**b**) lift coefficient, and (**c**) \(\rm C_{L}/C_{D}\) for different winglet designs.  
Figure 9: Contours of streamline, pressure, and vortex core for different winglets.
# A Quad-Band RF Circuit for Enhancement of Energy Harvesting
## 4 Design and Simulation
### Quad-Band Circuit Design
#### 4.2.2 Scenario 2: Quad-Band Circuit with Four Receiving Antennas  
As shown in Figure 7, each matched single-band voltage multiplier circuit had its receiving antenna. The four channels' output was connected directly via the load resistor [52;74].  
Figure 7: The proposed quad-band circuit with four receiving antennas.  
The four channels are connected in parallel with the load resistance. If one channel produces an output voltage higher than the others, the remaining three channels would be off since the diodes will be in reverse bias. A simple solution is proposed here. To investigate this point, the output voltage of each channel was found separately. One channel was connected, and the remaining three channels were disconnected. This step was repeated to find the output voltage of each channel.  
Therefore, to ensure that the four channels are active a resistor was added between each channel's output and the load resistor to avoid the negative effect if one channel of the four channels produces an output voltage higher than the others. The values of these resistors are equal. This modification will enable the output voltage of all channels to be equal, i.e., the output voltage is the same across all branches. The equivalent circuit of the adopted method is shown in Figure 8, whereas the improved circuit of the second scenario is presented in Figure 9. The resistors have a small value of less than 1 \(\Omega\), which will not significantly affect the RF-DC conversion efficiency or system performance.  
Figure 8: Equivalent quad-band circuit based on [PERSON]’s theorem.
## 2 Modified Boost Converter (MBC)
### Converter Analysis
#### 2.1.2 Mode-II  
In this time interval, \(L_{X}\) is demagnetizing on capacitor \(C_{1}\) through \(D_{1}\) and \(D_{2}\) is in OFF mode. Additionally, inductor \(L_{Y}\) is demagnetizing through diode \(D_{0}\) on the output capacitor and load. In this  
Figure 3: State of the proposed structure (**a**) when the power switch is ON and (**b**) when the power switch is OFF.  
time interval, as above-mentioned, the voltage values on capacitors \(C_{1}\) and \(C_{2}\) will increase through \(L_{X}\) and \(L_{Y}\), respectively. Figure 4 illustrates all of the components' conductions in both ON and OFF modes of power Metal Oxide Semiconductor Field Effect Transistor (MOSFET). Small signal analysis for this state was also analyzed and written below. Inductor \(L_{X}\) can be calculated as:  
\[L_{X}\frac{di_{LX}}{dt}=(V_{in}-V_{C1})(1-d)\Rightarrow\frac{di_{LX}}{dt}= \frac{V_{in}-V_{C1}}{L_{X}}(1-d) \tag{6}\]  
Additionally, the current waveform of inductor \(L_{Y}\) is equal with:  
\[L_{Y}\frac{di_{LY}}{dt}=(V_{C1}-V_{0})(1-d)\Rightarrow\frac{di_{LY}}{dt}= \frac{V_{C1}-V_{0}}{L_{Y}}(1-d) \tag{7}\]  
The voltage waveform for capacitor \(C_{1}\) and \(C_{0}\) are:  
\[C_{1}\frac{dv_{C1}}{dt}=(i_{LX}-i_{LY})(1-d)\Rightarrow\frac{dv_{C1}}{dt}= \frac{i_{LX}-i_{LY}}{C_{1}}(1-d) \tag{8}\]  
\[C_{0}\frac{dv_{0}}{dt}=(i_{LY}-\frac{V_{0}}{R})(1-d)\Rightarrow\frac{dv_{0}}{ dt}=\frac{i_{LY}-\frac{V_{0}}{R}}{C_{0}}(1-d) \tag{9}\]  
Figure 4: Mode of operation of the proposed structure.  
So, in this condition, the output voltage of the structure can be gained by Equation (16):  
\[Y=\hat{V}_{0}=\left[\begin{array}{ccc}0&0&0&1\end{array}\right]\left[\begin{array} []{c}\begin{array}{c}\wedge\\ i_{LX}\\ \wedge\\ i_{LY}\\ V_{C1}\\ \hat{V}_{0}\end{array}\right] \tag{16}\]  
The voltage gain of the converter can be found through Equation (17):  
\[\frac{V_{0}}{V_{in}}=-\frac{\frac{\left(1-d\right)^{2}}{C_{1}C_{0}L_{X}L_{Y}}} {S^{4}+S^{3}\Big{(}\frac{1}{RC_{2}}\Big{)}+S^{2}\Big{(}\frac{\left(1-d\right)^ {2}\left(C_{1}L_{X}+C_{0}L_{Y}\right)+L_{X}C_{0}}{C_{1}C_{0}L_{X}L_{Y}}\Big{)}+S \Big{(}\frac{L_{Y}\left(1-d\right)^{2}+L_{X}}{C_{1}C_{0}L_{X}L_{Y}}\Big{)}+ \frac{\left(1-d\right)^{4}}{C_{1}C_{0}L_{X}L_{Y}}} \tag{17}\]
## 2 Dynamic Model  
The dynamic model of the OES can be derived from the [PERSON]'s second law as:  
\[\left\{\begin{array}{c}K_{\rm t}\cdot i-M_{\rm f}=J\cdot\frac{{\rm d}\omega _{\rm m}}{{\rm d}t}\\ u_{\rm m}=L_{\rm m}\cdot\frac{{\rm d}i}{{\rm d}t}+R\cdot i+K_{\rm e}\cdot\omega _{\rm m}\\ M_{\rm f}=B\cdot\omega_{\rm m}+T_{\rm c}\\ u_{\rm m}=K_{\rm c}\cdot K_{\rm g}\cdot(u-K_{\rm a}\cdot i)\\ v=K_{\rm r}\cdot\omega_{\rm m}\\ \theta=\int v\cdot{\rm d}t,\end{array}\right. \tag{1}\]  
where \(K_{\rm t}\) (N\(\cdot\)m/A) is the torque coefficient of the motor, \(i\) (A) is the current of the motor, \(J\) (kg\(\cdot\)m\({}^{2}\)) is the equivalent moment of inertia on the motor side, \(\omega_{\rm m}\) (rad/s) is the motor speed, \(u_{\rm m}\) (V) is the input voltage of the motor, \(L_{\rm m}\) (H) is the inductance of the armature, and \(R\) (\(\Omega\)) is the armature resistance. \(K_{\rm e}\) (V/rad\(\cdot\)s\({}^{-1}\)) is the back-electromotive force(back-EMF) coefficient, \(M_{\rm f}\) (N\(\cdot\)m) is the friction torque, \(T_{\rm c}\) (N\(\cdot\) m) is the Coulomb friction torque, \(B\) (Nm/rad\(\cdot\)s\({}^{-1}\)) is the viscous friction coefficient, \(K_{\rm c}\) is the gain of the current loop controller, \(u\) (V) is the reference input of the current loop, and \(K_{\rm a}\) (V/A) is the current feedback coefficient. \(v\) (rad/s) is the speed on the load side, \(K_{\rm r}\) is the transmission ratio, and \(\theta\) (rad) is the angular position of the load side.  
According to Equation (1), a block diagram representing the speed open-loop OES can be constructed, as shown in Figure 2a, and the transfer function is:  
\[U(s)=\frac{L_{\rm m}s+K_{\rm a}K_{\rm c}+R}{K_{\rm c}K_{\rm t}}T_{\rm c}+\frac {L_{\rm m}Js^{2}+(K_{\rm a}K_{\rm c}J+RJ+BL_{\rm m})s+K_{\rm a}K_{\rm c}B+RB+K_ {\rm e}K_{\rm t}}{K_{\rm c}K_{\rm t}}V(s) \tag{2}\]  
where \(V\) (\(s\)) is the s-domain representation of \(v\).  
Considering that the cut-off frequency of the current loop is much larger than the working frequency of the OES, the current loop of model (2) can be equivalent to a proportional component. Then, convert the Coulomb friction torque \(T_{c}\) to the input portion, i.e., \(d=\frac{K_{\mathrm{d}}}{K_{\mathrm{t}}}T_{c}\), and a simplified speed open-loop dynamic model can be obtained, as is shown in Figure 2b. The corresponding transfer function is:  
\[G_{\mathrm{u}\emptyset}(s)=\frac{\Theta(s)}{U(s)}=\frac{K}{(p+s)\cdot s} \tag{3}\]  
\[G_{\mathrm{u}\mathrm{v}}(s)=\frac{V(s)}{U(s)}=\frac{K}{p+s} \tag{4}\]  
where \(p=\frac{\beta}{f}\), \(K=\frac{K_{\mathrm{d}}K_{\mathrm{t}}}{K_{\mathrm{d}}s}\), \(\Theta(s)\), \(U(s)\) are the \(s\)-domain representations of \(\theta\) and \(u\) respectively.
# Print and Try Technique: 3D-Printing of Teeth with Complex Anatomy a Novel Endodontic Approach
###### Abstract  
The purpose of this report is to evaluate the applicability of a novel Print and Try technique in the presence of aberrant endodontic anatomies and to achieve a predictable treatment with improved outcome. According to guidelines, cone beam computed tomography (CBCT) is recommended in the presence of complex anatomies. The volumes were utilized to produce a stereo lithographic (STL) file, from which a 3D-plastic tooth model including a hollow root canal system was obtained. The 3D-patient-specific model facilitates the treatment planning and the trial of therapeutic approaches. Using a transparent material, all the treatment steps could be directly visualized. Subsequently, endodontic therapy could be performed in vivo with reduced operating time and with a better overview. Clinicians benefit from the Print and Try technique when facing a complicated root canal system with reduced stress and higher chances of success. In aberrant endodontic anatomies, the use of a transparent 3D plastic model, derived from the CBCT dicom, provides an exact preview of clinical challenges that will be met intra-operatively. A patient centered tailored approach to shaping, cleaning, and filling strategies can be applied.  
cone beam computed tomography; pre-operative planning; root canal therapy; three-dimensional printing; tooth abnormalities; tooth anatomy +
Footnote †: journal: Applied Sciences
## 4 Input/Reactions from Participants
### Speech Recognition  
In some studies, speech recognition is employed to enable a participant to answer questions or engage in a program via predefined voice commands. At this stage, we have yet to see work that has incorporated unscripted conversations.  
In [11], a participant can interact with a robot by a number of predefined voice commands, such as move left/right, follow me, stop, and goodbye. The voice commands were captured using the Asus Xtion PRO device, which is equipped with two microphones. The Microsoft Speech Platform Software Development Kit was used to perform speech recognition. In [105], the robot Lucy is said to have speech recognition capability. However, the paper did not provide any technical detail and the use scenarios of the capability in their study.
# Millimetre-Wave Backhaul for 5G Networks: Challenges and Solutions
## 3 System Overview and Problem Statement  
### System Overview  
Compared to traditional cellular networks, small cells in HetNets have smaller coverage, but higher capacity and less interference, all of which make mm-wave techniques naturally applicable in such scenarios. Typically, there are two options of system architecture for 5G backhaul. One is a centric solution, and the other a distributed solution [4]. In a centric solution, a macrocell base station (BS) is situated in the center, with small cell BSs (SBSs) uniformly distributed around. Direct links between SBSs are not allowed. Instead, they are required to get access to the core network through the center macrocell BS, which is connected to the gateway by fibre links. In a distributed solution, all backhaul data are relayed to a single specific wired SBS instead of the macrocell BS. Backhaul data are allowed to transmit between the established mm-wave links of adjacent SBSs and finally are collected by the designated wired SBS to the core network through fibre links. System-level simulations have shown that the distributed solution achieves higher energy efficiency and throughput gain, mainly due to sharing cooperative traffic among multiple wireless SBSs [4].  
In this article, a more general system architecture is adopted, which is an extension of the distributed solution. An example of the system containing five small cells is illustrated in the right part of Figure 1. In this model, more than one SBSs (SBS1 and SBS4 in this example) is connected to the gateway by fibre links. As usual, backhaul data can be transmitted between SBSs through mm-wave links. Since a mm-wave link can be established between any two SBSs, a backhaul mesh network is formed by all of the SBSs. Moreover, there are two ways of backhaul data transfer: wired and wireless ways. For example, if SBS2 wants to transmit data to SBS4, it can either choose pure mm-wave links (both the direct link and relayed links are allowed) or sends the data to a wired SBS, _i.e._, SBS1, which then forwards the data through the fibre link to SBS4. Since mm-wave communication suffers from severe penetration loss, which makes blockage a serious and common problem in backhaul networks, such a multi-way data transfer architecture makes the backhaul network more reliable. Two types of blockage are depicted in this architecture. On the one hand, the link between SBS4 and SBS5 is affected by a temporary blockage, e.g., a moving vehicle. It may require the SBSs to adopt an effective beam tracking method to switch to an alternative link after detecting link interruption and to switch back when the blockage disappears. On the other hand, the blockage between SBS3 and SBS4 is a permanent one, e.g., a building. although such situations may be mitigated to some extent by the NLOS path through reflection, they result in the largely reduced channel capacity. In this case, transmitting data to adjacent SBSs as relays may be a more suitable solution.
# Lightning Protection Methods for Wind Turbine Blades: An Alternative Approach
## 4 ANSYS Workbench Implementation
### Results from Conventional Case Studies
#### 5.1.3 Case Study C: 200 mm\({}^{2}\) Conductor Cross-Section Area  
For this case, the cross-section of the lightning conductor was further increased to 200 mm\({}^{2}\). As was expected, the resulting values further decreased as the cross-section increased (Table 4).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**First Strokes** & **LPL 0** & **LPL I** & **LPL II** & **LPL III-IV** \\  & **300 kA** & **200 kA** & **150 kA** & **100 kA** \\ \hline Voltage at striking point (V) & 4360.3 & 2969.9 & 2180.2 & 1453.4 \\ Maximum joule heating (MW/m\({}^{3}\)) & 191,420 & 85,070 & 47,855 & 21,269 \\ Current density at striking point (kA/m\({}^{2}\)) & 823,240 & 548,830 & 411,620 & 274,410 \\ Maximum temperature (\({}^{\circ}\)C) & 608.98 & 282.88 & 168.74 & 87.22 \\ Total deformation (mm) & 47.725 & 2.1232 & 1.196 & 5.337 \\ \hline
**Subsequent Strokes** & **LPL 0** & **LPL I** & **LPL II** & **LPL III-IV** \\  & **75 kA** & **50 kA** & **37.5 kA** & **25 kA** \\ \hline Voltage at striking point (V) & 1090.1 & 726.72 & 545.04 & 363.36 \\ Maximum joule heating (MW/m\({}^{3}\)) & 11,964 & 5317.2 & 2991 & 1329.3 \\ Current density at striking point (kA/m\({}^{2}\)) & 205,810 & 137,210 & 102,910 & 68,604 \\ Maximum temperature (\({}^{\circ}\)C) & 58.686 & 38.305 & 31.172 & 26.076 \\ Total deformation (mm) & 0.3019 & 0.13632 & 0.078379 & 0.037002 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results obtained from the first and subsequent return strokes for **Case Study A.**  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**First Strokes** & **LPL 0** & **LPL I** & **LPL II** & **LPL III-IV** \\  & **300 kA** & **200 kA** & **150 kA** & **100 kA** \\ \hline Voltage at striking point (V) & 1937.9 & 1292 & 968.97 & 645.98 \\ Maximum joule heating (MW/m\({}^{3}\)) & 39,689 & 17,639 & 9922.1 & 4409.8 \\ Current density at striking point (kA/m\({}^{2}\)) & 407,620 & 271,700 & 203,810 & 135,870 \\ Maximum temperature (\({}^{\circ}\)C) & 149.31 & 78.583 & 53.828 & 36.146 \\ Total deformation (mm) & 0.79158 & 0.35234 & 0.19862 & 0.08883 \\ \hline
**Subsequent Strokes** & **LPL 0** & **LPL I** & **LPL II** & **LPL III-IV** \\  & **75 kA** & **50 kA** & **37.5 kA** & **25 kA** \\ \hline Voltage at striking point (V) & 484.49 & 322.99 & 242.24 & 161.5 \\ Maximum joule heating (MW/m\({}^{3}\)) & 2480.5 & 1102.5 & 620.13 & 276.1 \\ Current density at striking point (kA/m\({}^{2}\)) & 101,900 & 68,936 & 50,952 & 33,968 \\ Maximum temperature (\({}^{\circ}\)C) & 29.957 & 25,536 & 23,989 & 23,081 \\ Total deformation (mm) & 0.0504 & 0.02311 & 0.01364 & 0.00714 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results obtained from the first and subsequent return for **Case Study B.**