esa-sceva/satcom-chunk-collection huggingface dataset:
# Design and Calibration of a Tri-Directional Contact Force Measurement System
## 3 Calibration
### Calibration Results  
Results of calibration procedure exhibit a linear relationship between applied and measured loads, as shown in Figures 4 and 5, that refer to locknut and necked collar links respectively. As summarized in Table 1, where the average normalized calibration matrices are shown with standard deviation stated in parenthesis, the mechanical coupling between the branches of the measurement system is very low (less than 4% in the worst case). In particular, the necked collar link, more flexible in the transverse direction than the locknut, shows better performances with a lower coupling. The standard deviation for measurements with necked collar link were lower as compared with the locknut collar link. In case of a perfect decoupling between the sensors, the matrices shown in Table 1, would be diagonal with 100% values along the main diagonal.  
Figure 4: Calibration plot for system with locknut collars.
# Collaborative Unmanned Vehicles for Inspection, Maintenance, and Repairs of Offshore Wind Turbines
## 5 Conclusions
### Suggested Future Research Directions  
The authors would recommend the following future works:  
* A collaborative LiDAR-based SLAM localization algorithm can be developed to enable zero-downtime IMR operation for the OWT. This approach can minimize losses of OWT operation due to unwanted shutdown time.
* UVs in terms of a crawler robot or a UAV that can stick firmly to a WTB while in operation without suffering positioning error or odometry failure should be further explored.
* Investigation of a reliable localization and navigation system for UAV utilizing mobile UWB anchors and fixed IMU on an OWT for swaying floating OWT can be further explored.  
* Investigation into human-UVs collaboration using tele-operated manipulators for high-skilled OWT maintenance and repair tasks from an inland control station should be carried out.
* Redundancies to address potential failure modes for the collaborative UVs should be further explored and such systems should be devised to ensure the reliability and availability of collaborative UVs for the IMR OWT operations.  
Conceptualization, M.H.N. and S.S.; investigation, M.H.N.; writing--original draft preparation, M.H.N.; writing--review and editing, S.R., M.G., A.K. and R.S.; visualization, M.H.N., S.S., A.K., S.R., M.G. and R.S.; supervision, S.S. All authors have read and agreed to the published version of the manuscript.  
This research was carried out as part of the Cornwall FLOW Accelerator (Project no. 05R19P03188) and Marine-i 2 (Project no. 05R18P02816) projects, which are partly funded by the European Regional Development Fund (ERDF) as part of the European Structural and Investment Funds (ESIF) Growth Programme 2014-20.  
Not applicable.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
WT Wind Turbine  
WTB Wind Turbine Blade  
OWT Offshore Wind Turbine  
UV Unmanned Vehicle  
UAV Unmanned Air Vehicle  
UUV Unmanned Underwater Vehicle  
USV Unmanned Surface Vehicle  
ROV Remotely Operated Vehicle  
BVLOS Beyond Visual Line of Sight  
GCS Ground Control Station  
AUV Autonomous Underwater Vehicle  
NDT Non-Destructive Testing  
IMR Inspection, Maintenance, and Repair  
O&M Operation and Maintenance  
UWB Ultra-wide Band  
GNSS Global Navigation Satellite System  
SLAM Simultaneous Localization and Mapping  
LiDAR Light Detection and Ranging  
IMU Inertial Measurement Unit  
RTK Real-time Kinematics  
PP Precise Point Positioning  
PPK Post-processed Kinematics  
R-LOAM Reference LiDAR-based Odometry and Mapping  
FAME  
Failure Modes and Effects Analysis
## 2 Materials and Methods
### Observational Forcing Datasets
#### 2.1.3 Surface Climate Driving Dataset  
To estimate evapotranspiration of urban ecosystem at 10m resolution, a high-resolution surface climate forcing data including precipitation, surface air temperature, wind speed, surface pressure, specific humidity, downward shortwave and longwave radiations, etc., is needed to drive the terrestrial evapotranspiration model (PML-V2 model, see Section 2.2). In this study, we used the China Meteorological Forcing Dataset (CMFD) version 1 at 0.1\({}^{\circ}\)\(\times\) 0.1\({}^{\circ}\) and daily resolution for June 2018 as input for the PML-V2 model. The CMFD V1.0 dataset covered the period of 1979-2018 and was downscaled from station-based data, TRMM satellite-based precipitation, GEWEX-SRB shortwave radiation and the GLDAS forcing dataset [10]. The surface climate driving variables used for the Beijing Sponge City area were spatially bilinearly interpolated onto a 10m \(\times\) 10m resolution. The monthly CO\({}_{2}\) concentration observed in June 2018 is set as 407 ppm.  
Figure 1: The nonlinear relationship between LAI and NDVI. (**a**) Frequency distribution of NDVI-LAI. Higher values of the scatter number in (**a**) indicate stronger relation between LAI and NDVI. (**b**) Regression between LAI and NDVI. The brown curve was fitted using Equation (2) from the NDVI-LAI values (blue stars) under the maximum probability density.
## 5 Limitations
### Limitations on Wind Speed  
In real walking competitions, the human body's aerodynamic drag is influenced by the constantly changing wind speed, direction, and varied walking routes. The chosen windward side's constant wind speed of 2 m/s in this study may not precisely replicate actual competition conditions. Drag coefficient and drag reduction rate outcomes are predominantly determined by the Reynolds number and the human body shape. During race walking competitions, the wind speed varies relatively little (0-2 m/s), leading to minimal fluctuations in the Reynolds number. Furthermore, the human model used in the CFD simulation remained constant. Therefore, the selected speeds and normal wind speeds have a marginal impact on the drag coefficient and drag reduction rate results, with no effect on the analysis of the drafting drag reduction mechanism.  
To confirm the limited effect of wind speed on drag coefficient and drag reduction rate, we conducted a comparison between two wind speed conditions: 0 m/s (\(Re=1.9\times 10^{5}\)) and 2 m/s (\(Re=2.8\times 10^{5}\)). The results demonstrate that at 0 m/s wind speed, the drag coefficient for an individual walking is 0.69, a mere 1% reduction compared to 0.70 at 2 m/s wind speed. Furthermore, at 0 m/s wind speed, the optimal double drafting formation achieves a drag reduction rate of 65.4%, representing a 0.6% improvement over the 65% rate observed at 2 m/s wind speed. It is evident that varying wind velocities have minimal influence on both drag coefficient and drag reduction rate, ensuring that the analysis of drag reduction effects and mechanisms remains unaffected.
# A Digital Twin for Intelligent Transportation Systems in Interurban Scenarios
## 3 Digital Twin Architecture Proposal for Interurban Intelligent Transportation Systems
### Congestion Use Case  
In the congestion use case, the study focuses on a traffic scenario where there is an incident that creates congestion on the highway. For this use case, the input data has been provided by the road operator of the highway. It has the necessary infrastructure to obtain information about the number of vehicles on the highway through cameras and sensors in its entrances and exits.  
Possible strategies to reduce congestion consist of giving speed recommendations, re-routing vehicles off the highway, or opening additional lanes on the highway. Reducing speed limits in a particular situation, also referred to as Variable Speed Limits (VSL), is a common technique used to reduce congestion [29]. Reducing speed limits when there is a heavy traffic situation increases road capacity. Moreover, slower speed limits result in more comfortable driving, so vehicles are closer together, increasing road capacity and minimizing stop-and-go situations. Consequently, travel times are reduced because there is less congestion. In addition, on congested highways, variable speed limits have proven to be safer and reduce accidents. Tests performed in Germany [30] show that travel times are reduced by 5-15% and collisions by 30% when this technique is applied.  
On the other hand, in incident situations, where one or more lanes have been blocked, the capacity of the highway is reduced, so the only option to reduce congestion is for some vehicles to exit the highway. Alternative routes may be offered to decrease the number of vehicles on the highway.  
Another viable solution is to increase road capacity by opening additional lanes. This can be done by either converting the road's shoulder into an additional lane or using a lane from the opposite direction of the highway. However, there are some limitations when executing this strategy in real time. Opening additional lanes must be a premeditated decision, done when congestion is expected, for example, during the holiday season when many vehicles leave the city. In such cases, the road has to be prepared and signalized, and safety must be ensured, so it cannot be implemented in real time according to spontaneous congestions or incidents on the highway.  
As a result, the traffic strategies that we have applied in this work consist of the following: (1) speed recommendations; and (2) re-routing of vehicles. Lower speed recommendations can be applied to the section of the highway where the incident is located, or to a larger part of the highway by also limiting previous sections. Both speed recommendation strategies have been implemented. In addition, when re-routing vehicles, this strategy is considered as a percentage of vehicles exiting the highway.
## 1 Introduction
They showed that the vibration of the structure can be effectively isolated by using a piezoelectric actuator. [PERSON] et al. [16] studied the vibration reduction for an input applied using an accumulator and when converting the mechanical vibration energy into electrical energy and feeding it to the input of a piezoelectric actuator. They showed effective vibration reduction ability. [PERSON] et al. [17] made an automotive engine mount actuator using PZT (Lead zirconate titanate) with low power and quick reaction characteristics, which demonstrated vibration reduction. [PERSON] et al. [18; 19] proposed a new type of engine mount that isolates engine vibration. This was designed to withstand the dynamic forces of the structure, thus reducing power requirements by using small actuators. [PERSON] et al. [20] conducted studies to quantify active path interactions owing to passive system dynamic characteristics in a source-path-receiver system and vibration reduction for structures with path-coupled piezoelectric stack actuators and rubber mounts. Based on the source-path-receiver structure, a model with six degrees of freedom (6-DOF) motion equations was simulated and experimented to demonstrate the reduction of vibration. Three mounts support an actual vehicle, and the 6-DOF, introduced in [20], expresses all the three paths but is limited to a two-dimensional motion. However, as the engine vibrates, the movement occurs not only on the front but also on the sides. Hence, the model is expanded to three paths to model conditions similar to those encountered by an actual automotive engine. This study discusses a case of typical single-frequency excitation in the mid-frequency bands occurring in engines of electric and hybrid vehicles. The overall structure is based on the source-path-receiver structure and is modeled on the basis of the schematic diagram shown in Figure 1. The source is the vehicle engine, the path is the active path, and the receiver is the sub-frame. It is assumed here that the active path consists of a combination of a piezo stack actuator and a rubber mount with no mass. This study discusses the following three points: (1) mathematical 3D modeling of structures with three active paths combining piezo stack actuators and rubber mounts;(2) numerical calculation of the amplitude and phase of each actuator input of the active path based on the mathematical modeling and the investigation of vibration reduction; (3) application of the NLMS algorithm to improve the inconvenience of the numerical method and discussion of the effect of vibration reduction on the source and receiver.
## 5 Result Discussion
### Comparison of Acoustic Emission Signal and Strain Value  
Figure 11 depicts the relationship between mid-span strain and load variation based on the strain data obtained from the beam in Section 3. From Figure 11a, when the P1 beam is loaded to 25 kN, the readings of strain gauges 4 and 5 at the mid-span disappear first. This indicates that these gauges have been pulled apart, and the bottom concrete of the mid-span has cracked. At this point, there is a significant increase in the acoustic emission (AE) signals, as shown in Figure 8c. When the load reaches 50 kN, strain gauge 1 at the top of the P1 beam loses its reading, indicating that the top concrete has also cracked, and the beam is completely damaged. During this stage, there is another increase in AE signals. This increase shows a good agreement between the strain data during the loading process of the P1 beam and the acoustic emission signals.  
Figure 11: Mid-span load-strain curve. (**a**) P1: load-strain curve; (**b**) P2: load-strain curve; (**c**) P3: load-strain curve.  
From Figure 11b, it can be observed that when the P2 beam is loaded to around 60 kN, strain gauges 3, 4, and 5 are pulled apart, indicating the formation of through-cracks in the beam. This is accompanied by peak values in the acoustic emission (AE) signals, as shown in Figure 9a. Similarly, Figure 11c reveals that when the P3 beam is loaded to 70 kN, strain gauge 5 at the bottom of the mid-span suddenly shows no reading. This indicates that it has been pulled apart, and the bottom of the mid-span has cracked. As the load is increased from 70 kN to 75 kN, and strain gauges 3 and 4 also lose their readings. This indicates that they have been pulled apart, resulting in the formation of a through-crack at the mid-span. This is accompanied by a significant increase in the AE signals. The variation of strain with load in the P2 and P3 test beams is found to be in good agreement with the results obtained from the previous acoustic emission signal analysis.
# Kinematic Analysis of Daily Activity of Touching Lateral Shoulder for Normal Subjects
## 3 Results
### Elbow Joint Peak Velocity for Whole Groups  
A graph of tangential velocity is shown in Figure 3. During the lifting phase, the touching-the-contralateral-shoulder movement produced only one peak. The peak value for the whole group was 0.47 m/s or the equivalent to 1.7 km/h. During a maximum of  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Kinematic Variable** & **Mean** & **SD** & **95\% Confidence Interval** \\ \hline
**Movement times (s)** & & & \\ Lifting & 0.8 & 0.2 & 0.7–0.9 \\ Resting & 0.6 & 0.3 & 0.4–0.7 \\ Returning & 0.7 & 0.2 & 0.7–0.8 \\ Total movement times & 2.1 & 0.5 & 1.9–2.3 \\ \hline
**Joint angle (\({}^{\circ}\))** & & & \\ _Sloulder_ & & & \\ Lifting (horizontal adduction) & 39 & 1.4 & 36–42 \\ Resting (cross-body adduction) & 47 & 1.9 & 43–51 \\ Returning (abduction neutral) & 32 & 1.2 & 30–35 \\ _Elbow_ & & & \\ Lifting (minimum flexion) & 70 & 1.9 & 66–74 \\ Resting (flexion) & 55 & 1.6 & 52–59 \\ Returning (extension increase) & 75 & 2.5 & 70–80 \\ _Wrist_ & & & \\ Lifting & 157 & 0.8 & 155–158 \\ Resting & 164 & 0.6 & 163–166 \\ Returning & 155 & 0.8 & 153–156 \\ \hline
**Peak velocity (PV) (m/s)** & & & \\ _Elbow_ & & & \\ PV for lifting & 0.47 & 0.03 & 0.47–0.48 \\ PV for returning & 0.40 & 0.12 & 0.37–0.43 \\ Time to PV in lifting (s) & 0.27 & 0.08 & 0.11–0.48 \\ Time to PV in lifting (\%) & 36.4 & 8.82 & 14.15–55.81 \\ \hline
**Joints Coordination** & & & \\ _Pearson Correlation Coefficients_ & & & \\ Lifting & 0.985 ** & & \\ Returning & 0.989 ** & & \\ \hline \hline \end{tabular}  
* _Correlation is significant at the 0.01 level (2-tailed)._  
\end{table}
Table 2: Kinematic variables for all subjects.  
tangential velocity, the peak velocity of the elbow joint was obtained. The velocity profile was bell-shaped and smooth with a major characteristic peak. For each phase of lifting and returning, there were two peaks observed during the entire movement. The velocity profiles of both the lifting and returning phases were found to be similar.
# A Multi-Objective Hybrid BESSA Optimization Scheme for Parameter Extraction from PV Modules
## 6 Parameter-Setting of the Optimization Technique
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Function** & **Range** & \(f_{min}\) \\ \hline \(f_{8}(p)=\sum_{i=1}^{n}p\sin\left(\sqrt{p_{1}}\right)\) & \([-5 multimodal functions (\(f_{14}(p)-f_{23}(p)\)), the proposed Hybrid BESSA technique and the BES were both able to find global optima for functions \(f_{16}(p)-f_{19}(p)\). For functions \(f_{14}(p)\), \(f_{15}(p)\), \(f_{17}(p)\), \(f_{19}(p)\), and \(f_{20}(p)\), the Hybrid BESSA produced better results than the BES, CamWOA, and all of the other mentioned 10 optimization techniques [9]. For functions \(f_{16}(p)\) and \(f_{18}(p)\), the proposed BESSA was able to find global optima. For functions \(f_{21}(p)\), \(f_{22}(p)\), and \(f_{23}(p)\), CamWOA provided better results. From the above analysis, it is evident that the Hybrid BESSA has a better exploration capability when compared to the BES, CamWOA, and other techniques. Examining all of the above analysis for the unimodal functions, multimodal functions, and fixed dimensional multimodal functions, the proposed Hybrid BESSA produced better results for 19 out of the 23 functions. Hence, the proposed BESSA technique was proved to be a more efficient technique compared to the BES [17], CaMWOA [9], ABC [23], CS [24], GSA [27], PSOGSA [25], WCA [28], MFO [26], DA [29], and WCMFO [22] as detailed in [9]. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Function**} & \multicolumn{2}{c}{**BESSA**} & \multicolumn{2}{c}{**BES**} & \multicolumn{2}{c}{**CamWOA**} & \multicolumn{2}{c}{**WOA**} \\ \cline{2-9}  & **Av.** & **St. Dev** & **Av.** & **St. Dev** & **Av.** & **St. Dev** & **Av.** & **St. Dev** \\ \hline \(f_{u}(p)\) & 3.70141 & 4.3351 & 3.27674 & 4.1862 & 0.9980 & 3.86634 & 0.9980 & 0.60541 \\ \(f_{35}(p)\) & 0.000402 & 0.00031 & 0.000423 & 0.000321 & \(4.765\times 10^{-4}\) & 0.0003808 & 0.00055 & 0.0003073 \\ \(f_{36}(p)\) & -1.03164 & 0 & \(-1.03162\) & 0 & \(-1.0316\) & \(7.228\times 10^{-5}\) & \(-1.03163\) & \(4.67\times 10^{-3}\) \\ \(f_{37}(p)\) & 0.3979 & 0.0 & 0.3979 & 0.0 & 0.3978 & \(0.010685\) & 0.3978 & \(1.05\times 10^{-5}\) \\ \(f_{38}(p)\) & 3.0 & 0.3 & 0.0 & 0.3 & \(3.377\times 10^{-5}\) & 3 & \(2.51\times 10^{-7}\) \\ \(f_{39}(p)\) & \(-3.8628\) & 0.0 & 3.8628 & 0.0 & \(-3.8587\) & \(0.003693\) & \(-3.8626\) & 0.00021 \\ \(f_{32}(p)\) & \(-3.2765\) & 0.05993 & 3.26252 & 0.0605 & \(-3.273\) & \(0.102893\) & \(-3.2\) & 0.068717 \\ \(f_{21}(p)\) & \(-7.43431\) & 2.58685 & \(-7.94413\) & 2.5694 & \(-10.153\) & 0.0069855 & \(-10.153\) & \(7.76\times 10^{-5}\) \\ \(f_{22}(p)\) & \(-8.27682\) & 2.64851 & \(-8.10100\) & 2.6773 & \(-10.4025\) & 0.0049065 & \(-10.402\) & 1.6218 \\ \(f_{38}(p)\) & \(-8.19300\) & 2.7256 & \(-9.63512\) & 2.0499 & \(-10.5336\) & 0.0068874 & \(-5.1\) & 0.987257 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Statistical outcomes of fixed dimensional multimodal benchmark test functions.
## 3 Results and Discussion
### Going from NIPS to VIPS: A Parametric Study
#### 3.2.2 Effect of Humidity  
Higher RH provides larger chemical potential for water to absorb on the cast film. Therefore, for a similar period of time, a higher amount of water uptake is expected for a higher RH (later discussed in Section 3.3). By considering the high miscibility of water and DMAC, the transport of DMAC from the deeper zone of the cast film to the top of the film and influx of water from the top of the film is expected, leading to lower concentration of PVDF at the top of the film. This is due to the transport of DMAC from deep in the bulk of the casted film to the top layer, leaving behind a PVDF-rich phase at the bottom layers of the cast film and a PVDF-lean phase at the top layers. Increasing humidity and/or exposure time thus promotes a higher concentration gradient of PVDF that promotes a change of surface hydrophilicity, like the one observed as an effect of PVDF concentration. This is in agreement with the obtained CA results seen in Table 4. CA increased from 91.0\({}^{\circ}\) to 104.5\({}^{\circ}\) and 93.6\({}^{\circ}\) to 98.2\({}^{\circ}\) by increasing exposure time from 2 to 10 min for RHs of 60% and 80%, respectively.  
As the RH and exposure time increased, the presence of droplet-like pore mouths on the membrane surface increased (see SEM images of 80% at 10 min, Figure 5), which led to the creation of larger pores, especially at higher RH. These observations are in agreement with BP and MPS results, both of which increased with an increase in RH (Table 4). It is worth noting that some large pores on the membrane surface were a result of localized water vapor condensation on the top of the polymer film that was unavoidable sometimes during RH tests. Whenever the ambient humidity was below the RH set-point, the humidifier created extra water vapor that could potentially precipitate on the top of polymer film, leading to local concentration of water.  
Figure 4: Surface SEM images of membrane samples prepared using polymer solutions of different PVDF concentrations (casting thickness: 500 \(\upmu\)m, RH: 60%, exposure time: 30 min).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**PVDF Concentration (_w/w_)** & **MPS (\(\upmu\)m)** & **BP (\(\upmu\)m)** & **CA (\({}^{\circ}\))** & **LEP (kPa)** & **Volume Porosity** \\ \hline
8\% & 0.2076 & 7.5956 & 123.2 & 108.30 & 48\% \\
10\% & 0.1196 & 4.0325 & 113.1 & 111.21 & 70\% \\
12\% & 0.089 & 0.210 & 94.6 & 143.93 & 88\% \\
15\% & 0.064 & 0.652 & 88.9 & 96.64 & 67\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Properties of membranes cast using PVDF solutions of different concentrations at cast thickness: 500 \(\upmu\)m, relative humidity (RH): 60% and exposure time: 30 min.