esa-sceva/satcom-chunk-collection huggingface dataset:
# An Improved Rectifier Circuit for Piezoelectric Energy Harvesting from Human Motion
## 4 Results and Discussion
### Shaker Test
#### 4.1.5 Analysis of Voltage Waveform  
To further understand the performance of H-Bridge and FBR circuits, a closer look is taken at the waveform of the rectified voltage. One typical rectified waveform (i.e., a snapshot of the oscilloscope display), at a frequency of 2 Hz with an input voltage of 2 V, is shown in Figure 19.  
The red and blue curves represent the voltage rectified by H-Bridge circuit and FBR circuit, respectively. A zoom-in view of the waveform is also presented. It is demonstrated that the voltage rectified by the H-Bridge circuit is not only higher but also more stable than the FBR circuit, as depicted in Figure 19. The use of active diodes (i.e., MOSFET) is relatively straightforward, capable of performing fast switching, having low threshold voltage, and low power consumption. Thus, the voltage rectified by the H-Bridge circuit is more stable.  
It is evident that the rectified voltage curve by the FBR circuit is lower when compared with the H-Bridge circuit. The main reasons causing the FBR circuit to yield low rectified voltage is the use of high forward voltage diodes. In fact, the conventional FBR circuit does not turn on unless the generated PD voltage is equal to or higher than the forward voltage of the diodes (i.e., when V\({}_{\text{f}}\) > 0.6 V). The key difference between MOSFET in H-Bridge  
Figure 19: Rectified voltage waveform by H-Bridge and FBR in shaker test with input voltage of 2 V.  
Figure 18: A general relationship between frequency and current.  
circuit and diode in FBR circuit is that the MOSFET output can be controlled by increasing the applied gate voltage. In other words, by increasing the gate voltage on the MOSFETs, the resistance path from D \(\leftrightarrow\) S can be reduced; while in the case of diode, the resistance path from anode to the cathode terminal cannot be reduced. Therefore, the voltage output by the FBR circuit is relatively lower than the H-Bridge circuit. As a result, the power conversion by the FBR circuit is also lower.  
It should be noted that the H-Bridge circuit consistently outperformed the FBR counterpart, in which both the rectified voltage and output power produced by H-Bridge circuit is higher than the FBR circuit for all cases. Recalling that the primary intention of this study is to improve the rectified voltage and output power, this is achieved with the use of H-Bridge circuit. In other words, conversion loss is minimised with the use of H-Bridge circuit. In addition, the power loss controlled or saved by the H-Bridge circuit over the FBR circuit is also provided Table 3.
## 3 Methodology
### Performance Metrics  
The performance metrics used in evaluating the performance of models include Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and Coefficient of Determination (\(R^{2}\)) [63; 64].  
\[MAE=\frac{1}{N}\sum_{i=1}^{N}|P_{m(i)}-P_{p(i)}| \tag{5}\]  
\[RMSE=\sqrt{\sum_{i=1}^{N}\frac{(P_{m(i)}-P_{p(i)})^{2}}{N}} \tag{6}\]  
\[MAPE=\frac{100}{N}\sum_{i=1}^{N}|\frac{P_{m(i)}-P_{p(i)}}{P_{m(i)}}| \tag{7}\]  
\[R^{2}=1-\frac{\sum_{i=1}^{N}(P_{m(i)}-P_{p(i)})^{2}}{\sum_{i=1}^{N}(P_{m(i)}- \overline{P_{p}})^{2}} \tag{8}\]  
\(P_{m}\) is the measured path loss, \(P_{p}\) is the predicted path loss, \(\overline{P_{p}}\) is the mean of predicted path loss values, and \(N\) is the number of samples.
## 3 Methods and Datasets
### Spatiotemporal Distribution of Mountain Glacier Records in the EAM Region
#### 3.2.5 Japanese Mountains  
The remains of paleoglaciers can be seen in the Hidaka Range in the southeast of Hokkaido Island, at an altitude of 1400-1500 m asl; a superimposed grouping of cirques is also traceable around Mount Tottabetsu and Mount Poroshiri. The Tottabetsu and Poroshiri glacial stages have therefore been named from two uncomformable sets of moraines on these mountains [70]. Japanese scholars used volcanic ash sequences to reconstruct the stratigraphy of the Hokkaido glacial stage; they named the Poroshiri glacial stage after a moraine near Lake Shikotsu dated to 40 \(\pm\) 5 ka (MIS 3b). The widely-accepted Tottabetsu glacial stage corresponds to the late LGP (MIS 2) and LGM [43]. However, some scientists have speculated, from analyses of moraine morphology, that there may have been even earlier glacial advances in the Esaoman Mountains. The Esaoman glacial stage has been dated to 115\(-\)112 ka U-series methods [44]. In recent years, with more in-depth analyses of the Hokkaido glacial stage, scholars have obtained two chronological datasets corresponding to 25.15 \(\pm\) 2.1-18 ka (LGM) using OSL and \({}^{14}\)C dating; they have not found any evidence of glaciation in the early LGP and Holocene glacial stage, confirming the delineation of the Tottabetsu glacial stage [45]. This differs from the mountain glaciers that developed on Honshu Island [71].
* The Japanese Alps on Honshu The paleoglacial geomorphic landforms of the Japanese Alps in central Honshu are principally distributed in the alpine belt above 2900 m asl in the three mountain ranges of the Northern Alps, the Central Alps and the Southern Alps [72]. Using the latest dating methods, Japanese scientists delineated different glaciation sequences/glacial stages for the mountains in the Japanese Alps [47,73]: the Karasawa, the Iwato and the Yokoo glacial stages. These represent late LGP (MIS 2), middle LGP (MIS 3b) and early LGP (MIS 4) paleoglacial relics, respectively, found on Mount Hotaka, the highest peak in the Northern Japanese Alps. There were multiple glacial advances during the YD and the Holocene glacial stage (MIS 1); glacial development was more extensive in the middle LGP and the early LGP (MIS 3/4) than in the late LGP (MIS 2) and the LGM [51, 52, 53, 54]. However, some scholars have put forward opposing views. They believe that the mountains altitude and sufficient water vapor in the atmosphere over Honshu Island could have promoted the development of glaciers during the Penultimate Glaciation (MIS 6), thereby defining the Otanihara and the Yoshiwara glacial stages on Mount Kashima-yari and Mount Shirouma [52], based on volcanic ash sequence and U-series dates of 134\(-\)120 ka measured on Mount Shirouma, in the Northern Japanese Alps [44,46].
## 2 Material and Methods
### Leaf Phenology Analysis  
Growing season onset, dormancy and length were all calculated based on the LAI seasonal amplitude. In fact, LAI has been shown to have a normal distribution over the year in northern latitudes [71], so we consider the start of the growing season to be 20% of the maximum amplitude. The values of 20% was defined after different tests were conducted using different thresholds ranging between 5% and 30% of the maximum amplitude; we found that this value provided the best results.  
Overall, this method has being proven to be more stable for monthly data, compared to an approach based on sudden LAI changes [8]. It also should be noted that due to the lack of daily data for the LAI we were unable to use other methods used in previous studies based on the daily LAI variability [8].  
In order to analyse changes in the growing season, we mask out regions where there are small changes in LAI over the year (e.g., evergreen forests and mixed forest with a small deciduous component). Therefore, all grid points where the difference between the maximum and minimum LAI amplitude is less than 0.5 are ignored in this analysis.  
Considering every grid cell (x,y) we calculated a critical threshold value (\(CT^{x,y}\)) above which we assume the plants to be photosynthetically active:  
\[\mathrm{CT}^{x,y}=LAI_{\mathrm{min}}^{x,y}+0.2\times\left(LAI_{\mathrm{max}}^{ x,y}-LAI_{\mathrm{min}}^{x,y}\right) \tag{1}\]where \(LAI_{\rm min}^{x,y}\) and \(LAI_{\rm max}^{x,y}\) represent the minimum and maximum LAI over one year for the grid cell (x,y). This procedure was repeated on each grid cell and for each year for any given CMIP5 model. The length of the growing season was then calculated as the number of months with a value above this threshold; the onset is the first month above that value and the dormancy is the last. Since naturally part of the growing season might occur after the end of the year [88], we included the first three months of the next year in the calculations of the dormancy. Hence the growing season offset can occur on of the following year, having a DOY (day of year) higher than 365.  
Finally, mean length, onset and dormancy were calculated as the average over the whole time period. It should be noted that, even when calculated monthly, all results are presented as days; we retrieved the daily values from the monthly data by multiplying all monthly results by the number of days within the given month.  
The temporal changes in the mean annual LAI and GSL were estimated by the linear trend value obtained from a least squares fit line computed for period 1986-2005 of satellite and model data.  
In order to quantify the mismatch between models and data, we calculate the root mean square errors (RMSE) and the spatial correlation coefficient between each model and the satellite observations.
# Experimental Study of a Piezoelectric De-Icing System Implemented to Rotorcraft Blades
## 5 Discussion on the Small-Scale Blade Results
### Numerical Model Stress Prediction  
Experimental validation of the numerical model could not be performed due to the need for developing a proper measurement system to scan the vibration level over the surface of the blade when iced in the wind tunnel, which is a complex task on its own. The numerical model developed for this task allowed us to draw very useful conclusions to show that the behavioral trends of the model are accurate and that the model can be used for comparative study. Table 7 shows the shear stress calculated using the numerical model. The numerical model prediction for delamination was between 0.20 and 0.35 MPa, which was in the 0.20 to 0.90 MPa range obtained previously [19], meaning that the numerical model stress predictions are in accordance with what was obtained using the flat plate setup. No cracking, only delamination, of the ice layer was predicted by the numerical model and no cracking, only delamination was obtained experimentally.  
For the experimental tests, including blade rotation, the calculated stress is presented in Table 8. The shear stresses predicted by the numerical model were higher than for the static tests. Those values estimated the shear stress limit for de-icing and shedding of the ice from the surface and not simply delamination. To de-ice the blade structure in rotation, the model predicted a shear stress range of 2.60 to 2.84 MPa.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Mode** & **Excitation** & **Frequency** & **Vpp** \\ \hline
5 & 1–3–4–6/2–5 & & 2\% \\
1–2–3–4–5–6 & 8\% & 2\% \\
1–2–3–4–5–6 in & 10\% & 3\% \\  & rotation & & \\ \hline
6 & 1–3–5/2–4–6 & & 3\% \\  & 1–2–3/4–5–6 & 2\% & N/A \\ \hline \hline \end{tabular}
\end{table}
Table 6: Percentage of variation for static and rotating experimental testing for modes 5 and 6.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Mode** & **S12** \\
**(Repetition)** & **(MPa)** \\ \hline
5 p * (1) & 0.27 \\
5 p * (2) & 0.30 \\
5 (1) & 0.26 \\
5 (2) & 0.35 \\ \hline
6 p * (1) & 0.33 \\
6 p * (2) & 0.32 \\
6 p * (3) & 0.22 \\
6 (1) & 0.20 \\ \hline \hline \end{tabular} \({}^{*}\)with optimally-phased actuators.  
\end{table}
Table 7: Shear stresses calculated by the numerical model in static.  
However, the preliminary investigation demonstrated the methods' robustness on the flat plate setup and the successful implementation in a more complex setup, even if it highlighted that more work is required to optimize the testing setup and improve the numerical models. The numerical model was useful to design and implement the system to the structure, to properly excite the actuators and to predict resonant frequencies, modal shapes and delamination. For the shear stress prediction, the numerical model of the blade was consistent with the flat plate model in predicting delamination. In rotation, the calculated shear stress corresponded to the stress for de-icing and shedding of the ice layer from the blade. The value predicted by the model was 3 to 8 times higher than for simple delamination.
# Synchronizing TSN Devices via 802.1 AS over 5G Networks
## 4 Simulation Setup for TSN Synchronization over 5G Scenarios  
5G Release 16 supports TSN time synchronization by enabling the 5 GS as a TSN bridge. The entire end-to-end 5 GS is considered a Time-Aware System, meaning that the 5G system is able to provide time-critical services to TSN. Only the edges of the 5 GS must support IEEE 802.1 AS operations, while inside 5 GS, a separate synchronization mechanism is used for the 5 GS specific elements. As a Time-Aware System, 5 GS calculates and adds the measured residence time between its network edges to the correction field of the synchronization messages.  
3 GPP Release 17 adds a Time Translator (TT) functionality on the edges of 5 GS--the so-called Network-Side TT (NW-TT) and Device-Side TT (DS-TT). NW-TT and DS-TT update the rateRatio fields of the passing though synchronization messages. RateRatio is the ratio of clock oscillator frequencies of the master and the slave node. It is used to update the gathered timestamps to the common time base. To compute the residence time, the 5 GS ingress side (i.e., the 5G component which receives the sync messages which are to be forwarded over the 5 GS) adds an ingress timestamp to the sync messages. When the message is leaving the 5 GS, the TT at the egress side adds the egress timestamp in the message. Upon reception of the message at a TSN device, the egress and ingress timestamps are used to calculate the time that the message spent within the 5 GS. This residence time is used in the correction field calculations during the synchronization process. Although the procedures for TSN time synchronization over a 5G system are standardized, the propagation delay measurement still assumes symmetric uplink and downlink, which could introduce errors and reduce the synchronization accuracy significantly. A way to cater to the asymmetry could be using the averaged downlink timestamps from the previous synchronization cycles for the synchronization in downlink. A similar process can be performed for synchronization in uplink.  
Very few tools are available to simulate 5G networks and Time-Sensitive Networks together. While NS-3 [8], Riverbed [9] and OMNeT++ can be used to simulate TSN functionality, and NYUSIM [10], Vienna LTE Simulator [11; 12], WiSE [13], etc. can be used to simulate 5G functionality [14], only OMNeT++ offers both TSN and (at least partially) 5 GS simulation capabilities.
# Proving Fixed-Point Theorems Employing Fuzzy \((\sigma,\mathcal{Z})\)-Contractive-Type Mappings
## 2 Mathematical Preliminaries
A mapping \(T\) is said to be \(\sigma\)-admissible if_  
\[\forall\ \alpha,\beta\in\mathcal{K},t>0,\ \ \ [\sigma(\alpha,\beta,t)\leq 1 \implies\ \ \sigma(T\alpha,T\beta,t)\leq 1]. \tag{1}\]  
For examples of the \(\sigma\)-admissible mapping of fuzzy metric spaces, we refer the reader to [27; 32]. Now, we add another two examples of \(\sigma\)-admissible mappings. **Example 2**. : _Consider a mapping \(T:\mathcal{K}\to\mathcal{K}\), where \(\mathcal{K}=[0,\infty)\) and \(\sigma:\mathcal{K}\times\mathcal{K}\times(0,\infty)\to(0,\infty)\) defined by_  
\[T\alpha=\sqrt{\alpha},\ \ \forall\ \alpha\in\mathcal{K}\qquad\text{and} \qquad\sigma(\alpha,\beta,t)=\left\{ \begin{array}{cc}e^{\frac{\beta-\alpha }{T}}&\alpha\geq\beta,\\ 2&\alpha<\beta.\end{array} \right.\]  
_Then, \(T\) is a \(\sigma\)-admissible mapping._  
**Example 3**. : _Assume that \(T:\mathcal{K}\to\mathcal{K}\), where \(\mathcal{K}=[0,\infty)\) and \(\sigma:\mathcal{K}\times\mathcal{K}\times(0,\infty)\to(0,\infty)\) defined by_  
\[T\alpha=\ln\alpha,\ \ \forall\ \alpha\in\mathcal{K}\qquad\text{and} \qquad\sigma(\alpha,\beta,t)=\left\{ \begin{array}{cc}\frac{1}{t}&\alpha \geq\beta,\\ 2&\text{otherwise.}\end{array} \right.\]  
_Then, \(T\) is a \(\sigma\)-admissible mapping._  
Based on the above two definitions, the authors in [27] proved the following theorem:  
**Theorem 2** ([27]). : _Let \((\mathcal{K},\mathcal{M},*)\) be an \(\mathcal{M}\)-complete FMS and \(\sigma:\mathcal{K}\times\mathcal{K}\times(0,\infty)\to[0,\infty)\). Assume that \(T:\mathcal{K}\to\mathcal{K}\) is a \(\sigma\)-\(\psi\)-fuzzy contractive mapping satisfying the following assumptions:_  
\((i)\): \(T\) _is_ \(\sigma\)_-admissible;_ \((ii)\): _There exists_ \(\alpha_{0}\in\mathcal{K}\) _with_ \(\sigma(\alpha_{0},T\alpha_{0},t)\leq 1\)_, for each_ \(t>0\)_;_ \((iii)\): _For each sequence_ \(\{\alpha_{n}\}\) _of_ \(\mathcal{K}\) _with the property that_ \(\sigma(\alpha_{n},\alpha_{n+1},t)\leq 1\)_, for each_ \(t>0\)_, there exists_ \(k_{0}\in\mathbb{N}\) _such that_ \(\sigma(\alpha_{n},\alpha_{m},t)\leq 1\)_, for each_ \(m,n\in\mathbb{N}\) _with_ \(m>n\geq k_{0}\)_,_ \(t>0\)_;_ \((iv)\): _If_ \(\{\alpha_{n}\}\) _is a sequence in_ \(\mathcal{K}\) _such that_ \(\lim_{n\to\infty}\alpha_{n}=\alpha\in\mathcal{K}\) _and_ \(\sigma(\alpha_{n},\alpha_{n+1},t)\leq 1\)_, for each_ \(n\in\mathbb{N}\) _and_ \(t>0\)_, then_ \(\sigma(\alpha_{n},\alpha,t)\leq 1\)_._ _Then,_ \(T\) _admits a fixed point._
# Fine Characterization Method of Concrete Internal Cracks Based on Borehole Optical Imaging
## 3 Morphological Characteristics Analysis of Cracks
### Analysis of Crack Orientation
\(A_{1}\) and \(A_{2}\) are two points on the crack at the same depth in the borehole wall image, respectively. \(x_{1}\) and \(x_{2}\) are the distances from \(A_{1}\) and \(A_{2}\) to the leftmost side (0\({}^{\circ}\)) of the borehole wall image, respectively. \(L\) is the perimeter of the borehole. There was little difference in the orientation of cracks at the orifice. The orientation of cracks at the orifice was measured in boreholes zk15 + 025, zk14 + 896, zk14 + 858, and z14 + 903.5, which were 151\({}^{\circ}\), 143\({}^{\circ}\), 132\({}^{\circ}\), and 132\({}^{\circ}\), respectively, and the maximum difference was only 19\({}^{\circ}\). The orientation change of cracks at different depths in the same borehole was relatively small, and, basically, the deviation did not exceed 30\({}^{\circ}\). However, after passing through the concrete interface and entering another different type of concrete, the orientation of cracks changed significantly. The change in crack orientation inside the concrete was relatively large. For example, in boreholes zk14 + 819 and zk14 + 903.5, the change in crack direction reached 59\({}^{\circ}\) and 43\({}^{\circ}\), respectively, in a short extension depth. The crack extension in the concrete is affected by the coarse aggregate structure. The crack extends along the interface between coarse aggregate and mortar, and rarely directly penetrates the coarse aggregate, which affects the orientation of the crack to a certain extent, as shown in Figure 10. However, in this paper, the influence of coarse aggregate on the orientation of cracks that extended from the orifice was limited. After bypassing the coarse aggregate, the crack soon continues to extend along the previous orientation. Figure 9: Statistical analysis of crack orientation. (**a**) Boreholes zk15 + 025, zk14 + 896, and zk14 + 931. (**b**) Boreholes zk15 + 018, zk14 + 994, and zk14 + 819. (**c**) Boreholes zk14 + 858, zk14 + 870, and zk14 + 903.5. Although the electronic compass inside the optical probe has a measurement error of 0.1\({}^{\circ}\), the error can be ignored relative to the influence of coarse aggregate on the orientation. In addition, the orientation of the borehole also causes measurement errors, so the verticality of the borehole should be guaranteed as much as possible. If the borehole is not vertical, the orientation of the borehole axis and the included angle with the horizontal direction are necessary to correct the error.
## 3 Results and Discussion
### Recommended Array Layout  
By comparing the results of Sections 3.1 and 3.2, it can be concluded that the lateral layout and compact layout are beneficial for improving the average power coefficient of the entire wind turbine group. Based on this concept, several array layouts are proposed, as shown in Figure 14. The conventional rectangular symmetrical layout is shown in (a). In this layout, the downstream wind turbines will be affected by the wake interference of the upstream wind turbines, which is disadvantageous for the wind energy capture of the downstream wind turbines and severely reduces the efficiency of the entire group. Based on the research results of Section 3.1 on wind direction, two layouts (b) and (c) were proposed, and an interleaved arrangement was designed according to the wind direction to reduce the wake interference of the upstream wind turbines. If farm conditions allow for it, a fully lateral layout can be designed, which would significantly increase the average power coefficient, as shown in (c). Based on the research results of Section 3.2 on spacing, three layouts, (d), (e), and (f), were proposed. They mainly propose a compact VAWT layout, a compact layout of dual and triple wind turbines, which should also be arranged in an interleaved manner to reduce the interference of upstream wind turbines. Regarding the wind turbine rotation direction studied in Section 3.3, all the different rotation directions can improve efficiency, and the differences in efficiency among the three modes are not significant. Therefore, they are not considered further. Overall, wind direction has no effect on standalone VANTs, which is an advantage of these wind turbines. However, wind  
Figure 12: Maximum average power coefficient corresponding to different rotation directions.  
direction does have a significant impact on the entire wind turbine group. The spacing research indicates that a compact layout is recommended, which distinguishes it from HAWT groups. This is a significant advantage of VAWT groups, as it can significantly increase the power density of the entire wind farm [33].  
Figure 14: Different array layouts.  
Figure 13: Velocity wake contour for different rotation directions: (**a**) counter up rotating, (**b**) counter down rotating, (**c**) co-rotating.