esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Data and Estimation Results  
### Data  
This study employs the EUA (European Union Allowances) spot price for the carbon price and the WTI (West Texas Intermediate) crude oil spot price for the energy market price. Next, we empirically use the data to test the non-uniform volatility-correlation relationships in carbon-energy markets. The testing period is between 16 March 2009 and 24 November 2021, consisting of 3311 daily observations (the WTI oil price on 20 April 2021 is negative, which causes the return rates (logarithmic change) on the 20 th and 21 st of April 2021 to be incalculable. Thus, we exclude the two days in our analysis). The data source is DATASTREAM.  
Table 1 presents the basic statistics of EUA (carbon market) and WTI (energy market). Panel A presents the results of the levels, and Panel B shows the results of the return rates (logarithmic change). Since the price levels of EUA and WTI are non-stationary (see Table 2), we use the return rates for the following analyses. Panel B of Table 1 shows that  
the EUA-WTI correlation is positive and significant (0.1791 with \(p\)-value \(<\) 0.01), implying that carbon and energy markets are connected.
# Response Analysis of the Tristable Energy Harvester with an Uncertain Parameter
## 3 The Approximation of the TEH with an Uncertain Parameter
### Equivalent Deterministic System
\begin{array}{c}\sum\limits_{i=0}^{4}\ddot{x}_{i}(t)H_{i}(\xi)+\xi \sum\limits_{i=0}^{4}\dot{x}_{i}(t)H_{i}(\xi)+\sum\limits_{i=0}^{4}x_{i}(t)H_ {i}(\xi)+\bar{\lambda}\sum\limits_{i=0}^{12}X_{i}(t)H_{i}(\xi)+\\ \frac{1}{2}\sigma\sum\limits_{i=0}^{12}\left[X_{i-1}(t)+X_{i+1}(t)\right]H_{i} (\xi)+\mu\sum\limits_{i=0}^{20}Y_{i}(t)H_{i}(\xi)-\alpha\sum\limits_{i=0}^{4} v_{i}(t)H_{i}(\xi)=A\cos(\omega t),\\ \sum\limits_{i=0}^{4}\dot{v}_{i}(t)H_{i}(\xi)+\beta\sum\limits_{i=0}^{4}v_{i}( t)H_{i}(\xi)+\alpha\sum\limits_{i=0}^{4}\dot{x}_{i}(t)H_{i}(\xi)=0.\end{array} \right. \tag{18}\]  
Multiplying both sides of Equation (18) by \(H_{i}(\xi)\) in sequence and taking expectations with respect to \(\xi\), in accordance with the orthogonality of the Chebyshev polynomials, the high-dimensional equivalent system of the TEH can be written as \[\left\{ \begin{array}{l}\ddot{x}_{0}(t)+\zeta\dot{x}_{0}(t)+x_{0}(t)+\bar{ \lambda}X_{0}(t)+\frac{1}{2}\sigma X_{1}(t)+\mu Y_{0}(t)-\alpha v_{0}(t)=A\cos( \omega t),\\ \dot{v}_{0}(t)+\beta v_{0}(t)+\alpha\dot{x}_{0}(t)=0,\\ \dot{x}_{1}(t)+\zeta\dot{x}_{1}(t)+x_{1}(t)+\bar{\lambda}X_{1}(t)+\frac{1}{2} \sigma[X_{0}(t)+X_{2}(t)]+\mu Y_{1}(t)-\alpha v_{1}(t)=0,\\ \dot{v}_{1}(t)+\beta v_{1}(t)+\alpha\dot{x}_{1}(t)=0,\\ \dot{x}_{2}(t)+\zeta\dot{x}_{2}(t)+x_{2}(t)+\bar{\lambda}X_{2}(t)+\frac{1}{2} \sigma[X_{1}(t)+X_{3}(t)]+\mu Y_{2}(t)-\alpha v_{2}(t)=0,\\ \dot{v}_{2}(t)+\beta v_{2}(t)+\alpha\dot{x}_{2}(t)=0,\\ \dot{x}_{3}(t)+\zeta\dot{x}_{3}(t)+x_{3}(t)+\bar{\lambda}X_{3}(t)+\frac{1}{2} \sigma[X_{2}(t)+X_{4}(t)]+\mu Y_{3}(t)-\alpha v_{3}(t)=0,\\ \dot{v}_{3}(t)+\beta v_{3}(t)+\alpha\dot{x}_{3}(t)=0,\\ \ddot{x}_{4}(t)+\zeta\dot{x}_{4}(t)+x_{4}(t)+\bar{\lambda}X_{4}(t)+\frac{1}{2} \sigma X_{3}(t)+\mu Y_{4}(t)-\alpha v_{4}(t)=0,\\ \dot{v}_{4}(t)+\beta v_{4}(t)+\alpha\dot{x}_{4}(t)=0,\end{array} \right. \tag{19}\]  
which is a kind of weighted average system. So the ensemble mean response (EMR) of the TEH with an uncertain parameter can be introduced to reflect the average characteristics of the original system (11), that is  
\[\left\{ \begin{array}{l}E[x(t,\xi)]=E\left[\sum\limits_{i=0}^{4}x_{i}(t)H_{i }(\xi)\right]=x_{0}(t),\\ E[v(t,\xi)]=E\left[\sum\limits_{i=0}^{4}v_{i}(t)H_{i}(\xi)\right]=v_{0}(t). \end{array} \right. \tag{20}\]
# A Prototype Mechatronic Device for Upper Limb Rehabilitation and Analysis of Its Functionality
## 2 Motivation and Contribution
[67] employed a robotic arm from the Neuro-X(r) system in a study involving BBT tasks. During testing, the impaired limb remained in a fixed position relative to the participant's body. Contemporary rehabilitation strategies increasingly incorporate VR technology [68], which has the potential to improve patients' ability to manage complex scenarios. Adaptive VR-based training may be further enhanced by integrating real-time kinematic and kinetic data, physiological measurements, and offline analytical tools that allow the adaptive control of simulated elements [69]. [PERSON] et al. [70] presented a rehabilitation study in which participants observed a virtual representation of their limb on a screen. The task involved moving virtual blocks between boxes using hand movements, simulating the Box and Block Test (BBT). A notable limitation of this method is the absence of physical resistance--participants do not engage all upper limb muscles, particularly those in the hand, nor do they experience any haptic feedback or reaction force. Another device based on the BBT was described in [71], where the authors evaluated its effectiveness in rehabilitation across three age groups. In this system, participants manipulated a gripper linked to a mechanical lever within a mechatronic setup, transferring virtual objects from one box to another while bypassing an obstacle. However, a key limitation of this approach is the uniformity of the grip mechanism--participants consistently employed the same grasp pattern in relation to the lever and the finger sensor mechanism. Several studies [72; 73; 74; 75; 76] address modifications and improvements to virtual systems for performing BBT-based exercises. However, in the absence of haptic rendering, grasping motions in virtual environments differ significantly from natural physical interactions [77]. Another BBT-related development employed proximity sensors and a cube-counting system integrated with a mobile application [78].
## 3 Proposed Approach
### Evaluation of Model Performance
#### 3.4.1 AP  
In object detection, AP is widely used as a numerical metric for the evaluation of accuracy. AP is the average precision across all recall values between 0 and 1 at different thresholds of Intersection over Union (IOU), referring to the proportion of the area that is common to both the predicted and ground truth bounding boxes, relative to the total area covered by both boxes. AP is calculated using the formula shown in Equation (1), where \(n\) is the total number of bounding boxes detected, i is the rank of a specific detected bounding box, \(p(i)\) is the precision value between the first and ith detection, and \(\Delta r(i)\) is the change in recall values between the (\(i\)-1)th and \(i\)th detection [2].  
\[AP=\sum_{i=1}^{n}p(i)\Delta r(i) \tag{1}\]  
In this study, the following three IOU thresholds were set: AP50, AP75, and AP50:AP95; AP50, and AP75. In particular, AP50:AP95 denotes that the mean AP ranges from 0.5 to 0.95 with an incremental step size of 0.05.  
To assess the model's performance at different object scales, \(AP\) was further calculated for small (\(AP_{S}\)), medium (\(AP_{M}\)), and large (\(AP_{L}\)) objects, categorized by their pixel area in the images:  
\(AP_{S}\): occupying less than 32 \(\times\) 32 pixels,  
\(AP_{M}\): ranging from 32 \(\times\) 32 to 96 \(\times\) 96 pixels,  
\(AP_{L}\): exceeding 96 \(\times\) 96 pixels in area.  
By using these size-based categories, the evaluation specifically examined the model's detection capabilities for heavy equipment of various scales within the images.
# Metal-Coordinated Polymer-Inorganic Hybrids: Synthesis, Properties, and Application
## 2 Methods of Synthesizing Coordinated Polymer-Inorganic Hybrids
### Homogeneous Synthesis  
The synthesis of polymer-metal complexes, which forms the basis for creating organic-inorganic hybrids (OIHs), revolves around the coordination chemistry between metal ions and functional groups embedded within polymer chains. Typically, polymers equipped with functional groups such as pyridine, amine, or carboxylate groups are used because of their ability to coordinate strongly with metal ions. During synthesis, the polymer is dissolved in a compatible solvent, and a solution of the metal precursor is gradually introduced. The metal ions diffuse into the polymer matrix, interacting and binding with the available functional groups through coordination bonds. This method encompasses the early studies on polymer hybrids, and a more comprehensive review can be found elsewhere [44, 45]. Nonetheless, examples of the most common polymers and metal precursors used to create these hybrids are provided in Section 3.
## 3 Measurement Results
### Respiration Detection  
In Figure 10, the multimode radar system is used to measure the respiration of a target using CW, PSK, and PMCW waveforms. For this setup, the same three modes measured in Section 3.1 are used with \(F_{Clk}\) at 1 kHz, while the oscilloscope records 60 s of data. The limiting factor in this measurement setup is the memory depth of the oscilloscope, which is 3 million points. Across a measurement window of 60 s, this allows for a sampling rate of 50 ksps and a maximum of 50 points per encoded symbol. The four measurements recorded in each setup are the \(I/Q\) signals from the output of the mixer, the modulation scheme from the FPGA to the BPS, and the output of the respiration belt to act as the GT. As human respiration responses most commonly fall between 0.2 Hz and 2 Hz, this longer time window of 60 s was chosen to ensure more than 10 breathing cycles are captured and analyzed.  
Figure 10: Respiration measurement setup. Target’s chest is 1 m away from the radar system.  
\begin{table}
\begin{tabular}{c c c} \hline \hline Max Distance & 4 m & @ 1 mm motion, 2 Hz \\ Min Amplitude & 50 \(\upmu\)m & @ 1 m distance, 2 Hz \\ Min Frequency & 0.05 Hz & @ 1 mm motion, 1 m distance \\ \hline \hline \end{tabular}
\end{table}
Table 3: Radar Doppler motion Sensitivity Parameters.  
Figure 9: Sensitivity test measurement setup (**a**) and baseline Doppler frequency response for a 1 m target with 1 mm motion at 2 Hz (**b**). The distance, oscillation frequency, and oscillation motion were isolated and taken to their respective response limits to determine the Doppler sensitivity.  
From the equations in Section 2.3, the baseband response of the CW data contains the respiration information, while the baseband PSK and PMCW data need to be decoded following the algorithm outlined in Section 2.2 to recover the low-frequency information. As stated previously, the 50 ksps of the oscilloscope is not fast enough to measure the phase delay of the carrier from the target for PMCW range measurements but is well above the Nyquist sampling rate needed for the 1 kHz modulation rate used. The 1 kHz clock is also significantly higher than any potential frequency information of interest. The measured respiration rates for each mode are compared to their GTs in Figure 11.  
Figure 11: _Cont._
# Noise Reduction Mechanisms of an Airfoil with Trailing Edge Serrations at Low Mach Number
## 7 Conclusions  
The turbulent flow over a NACA0012 airfoil with a straight trailing edge and serrated trailing edge at zero-angle of attack in low Mach number range and the resulting sound field and trailing edge noise were studied to investigate the underlying noise reduction mechanism. Especially, a variation in the hydrodynamic field due to the varying geometry at trailing edge was studied. The flow field was computed by LES with an improved one-equation dynamic SGS model. The weak compressibility at low Mach number was taken into account by modifying the pressure equation. The acoustic far-field was obtained by means of the Lighthill-Curle method [45]. A grid resolution study and comparison against experimental data was used to assess the computational set-up. The computational results were validated against experimental data [47; 48]. It was confirmed that the serrated airfoil reduces the trailing edge noise, while the flow around a serrated airfoil shows an expansion of the separation area near the trailing edge, resulting in slightly increasing pressure drag. It was found that the trailing edge serrations of an airfoil impede the growth of the spanwise vortices near the trailing edge and promote the development of the streamwise vortices near the trailing edge and the wake. Meanwhile, the velocity fluctuations in the vertical cross-section of the mainstream direction near the trailing edge are reduced by the serrated airfoil in comparison with the baseline NACA0012 airfoil. As a result, the strength of the pressure fluctuation is reduced near the trailing edge. Especially, in the flow around the baseline airfoil, the location where the strong sound source distribution begins to appear is in good agreement with the location where the separated boundary layer reattaches. Thus, application of the trailing edge serrations on the upstream of the reattachment point for an airfoil is effective to reduce the strong sound source and then consequently is effective for noise reduction.  
[PERSON] and [PERSON] conceived the original ideal; [PERSON] and [PERSON] wrote and edited the manuscript; and [PERSON] supervised the study.  
This research was funded by the project of Chinese National Natural Science Foundation (grant number: 51575220), and the project of the Key Scientific and Technological Project of Jilin Province (grant numbers: 20160519008 JH and 20170204073 GX).  
The authors thank [PERSON] and [PERSON] for their help with the numerical method proposed and the simulation.  
The authors declare no conflict of interest.
Sensors_**2012**, _12_, 1312-1335; doi:10.3390/s120201312  
_Sensors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
**Underwater Wireless Sensor Networks: How Do Acoustic Propagation Models Impact the Performance of Higher-Level Protocols?**  
**[PERSON] * and [PERSON]**  
Physics and Computer Engineering Department, Miguel Hernandez University, Ave. Universidad S/N, Ed. Alcudia, 03202 Elche (Alicante), Spain; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +34-96-665-8393; Fax: +34-96-665-8814.  
_Received: 12 December 2011; in revised form: 13 January 2012 / Accepted: 29 January 2012 / Published: 31 January 2012_  
**Abstract:** Several Medium Access Control (MAC) and routing protocols have been developed in the last years for Underwater Wireless Sensor Networks (UWSNs). One of the main difficulties to compare and validate the performance of different proposals is the lack of a common standard to model the acoustic propagation in the underwater environment. In this paper we analyze the evolution of underwater acoustic prediction models from a simple approach to more detailed and accurate models. Then, different high layer network protocols are tested with different acoustic propagation models in order to determine the influence of environmental parameters on the obtained results. After several experiments, we can conclude that higher-level protocols are sensitive to both: (a) physical layer parameters related to the network scenario and (b) the acoustic propagation model. Conditions like ocean surface activity, scenario location, bathymetry or floor sediment composition, may change the signal propagation behavior. So, when designing network architectures for UWSNs, the role of the physical layer should be seriously taken into account in order to assert that the obtained simulation results will be close to the ones obtained in real network scenarios.  
**Keywords:** underwater wireless sensor networks; network simulation; acoustic propagation models; MAC and routing protocols
## 4 Results and Discussion  
The results presented in this study directly apply the methodological framework described in the previous section. Each finding is connected to a specific methodological step, ensuring that the improvements proposed in space allocation, route optimization, traffic control, and boarding management are systematically supported by the multi-agent system architecture developed.  
The evaluation of the results obtained in this study is structured according to the methodological steps previously defined. Each finding responds to a specific phase of the development of the multi-agent system, from the identification of needs to the benchmarking with traditional models. The main results are presented below, pointing out the relationship between the methodology applied and the benefits obtained in terms of operational efficiency, route optimization, reduction of transit times, and improvements in the synchronization of shipment.  
The results obtained from the design and analysis of the multi-agent system for the optimization of management in Ro-Ro terminals reflect a series of theoretical and operational benefits compared to traditional management models. The main findings are presented below and organized according to the defined methodology.  
A Ro-Ro terminal is designed to manage vehicles and Ro-Ro units that are loaded and disembarked using their own wheels, thus optimizing cargo handling and reducing operating times compared to other loading systems. The main activities and processes that characterize the operation of these terminals are described below and structured in stages.  
The operation of a Ro-Ro (Roll-on/Roll-off) terminal follows a structured flow that allows the efficient loading and unloading of vehicles. This process is made up of three main stages:  
Arrival and parking in waiting areas: vehicles enter the terminal and are directed to designated areas where they await their turn for loading or unloading. At this stage, documentation is checked, and traffic is organized.  
Loading/unloading: vehicles are guided to or from the ship using their own wheels. This occurs through specially designed ramps that connect the dock to the vessel.  
Storage or departure: After disembarking, vehicles are directed to temporary storage areas or directly outside the terminal, depending on their final destination.  
The use of technologies such as the Internet of Vehicles (IoV) and automation improves coordination, reduces waiting times, and optimizes vehicular flow at each stage (Figure 2). This diagram provides contextual understanding for readers unfamiliar with the sequential logistics of vehicle movement within the terminal and is not part of the proposed MAS model.
# Measuring Torque and Temperature in a Rotating Shaft Using Commercial SAW Sensors
## 3 Results
### Measurements under Rotation
#### 3.4.2 Influence of the Rotation in the Torque Measurements  
Similarly to what was performed with the temperature sensor, 1000 frequency readings of both strain sensors were recorded and the measured torque was determined using Equation (10). The standard deviation of the array of torque readings is shown in Figure 12a. As the load cell begins to rotate, the standard deviation increases, but the effect of rotation speed is not quite clear since the values are scattered. The maximum values recorded were \(\approx\)7.2 and 8.4 N\(\cdot\)m at 25 and 42 \({}^{\circ}\)C, respectively. The effect of rotation in the stability of the electromagnetic coupler is similar to the effects in measuring the temperature described in the previous section. However, there is an additional source of error related with the configuration of the SAW strain sensors. The W.FL connector is located at the extremity of the strain sensor; as a consequence, the centrifugal force felt by the cable \"pulls\" the  
Figure 11: (**a**) Standard deviation and (**b**) temperature measured at different rotation speeds. Black data points: values calculated with all frequency readings. Red data points: values calculated with frequency readings that fall within the standard deviation.  
sensor away from the load cell surface, making it behave like a cantilever and inducing the appearance of a measurable strain. This is more evident in Figure 12b (black and green data points) that shows the average value of the torque calculated from the average frequency readings. At 25 \({}^{\circ}\)C, the torque increases with rotation speed in a clockwise direction, reaching a maximum value of \(\approx\) -20.9 N\(\cdot\)m. In the counterclockwise direction, the torque is more or less stable around \(-\)5 N\(\cdot\)m. At 42 \({}^{\circ}\)C, the average value again stabilizes around \(-\)12.5 N\(\cdot\)m. Measurements obtained at 65 \({}^{\circ}\)C are not shown since the W.FL cables became very soft and the electrical connection was too loose to give reliable signals. Similarly to what was done for the temperature, the average torque was calculated again using only the frequency readings that fall within the standard deviation--Figure 12b (red and blue data points). As a result, the absolute value of the calculated torque generically decreases; the maximum absolute values at 25 and 42 \({}^{\circ}\)C decrease from 20.9 (at 2100 rpm) and 15.3 N\(\cdot\)m (at 1200 rpm) to 12.4 and 7.5 N\(\cdot\)m, respectively. This reinforces the fact that the mechanical instability of the link budget, together with the cantilever effect introduced by the wires (which also vibrate during the rotation), are responsible for the low resolution of the measurements.  
Figure 12: (**a**) Standard deviation and (**b**) torque measured at different rotation speeds. Black and green data points: values calculated with all frequency readings. Red and blue data points: values calculated with frequency readings that fall within the standard deviation.
## 3 Results
### Analysis of Ventilation Rate Results
Lower-Upper configuration with an outlet opening of 60 mm exhibited the highest performance with a ventilation rate of 0.003021 m\({}^{3}\)/s. In contrast, the Lower-Lower configuration with both inlet and outlet openings of 20 mm had the lowest ventilation rate of 0.002932 m\({}^{3}\)/s, colored in dark purple. This implied that a higher outlet opening increased the airflow while the inlet opening was constant. A similar trend was observed for the models with a fixed inlet opening position of 40 mm. The Middle-Upper building configuration achieved the highest ventilation rate of 0.003065 m\({}^{3}\)/s with an outlet opening height of 60 mm. On the other hand, the lowest ventilation rate of 0.002959 m\({}^{3}\)/s was recorded in the model with an outlet opening position of 20 mm (Middle-Lower). This result shows that a medium height inlet opening better supports the airflow when paired with higher outlet openings. Figure 31: Heat map of Q for an isolated building with various inlet and outlet window opening positions. Figure 30: Heat map of Q and Q/Q\({}_{\text{ref}}\) distribution for an isolated building with various outlet window opening sizes. For the building models with the highest inlet opening position of 60 mm, the building model with a 20 mm outlet opening position (Upper-Lower) achieved the highest ventilation rate of 0.003129 m\({}^{3}\)/s, highlighted in yellow, while the model with a 60 mm outlet position (Upper-Upper) reached the lowest ventilation rate of 0.003012 m\({}^{3}\)/s. The best ventilation performance among all the configurations related to the opening positions was obtained in the Upper-Lower building model.