esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Materials and Methods  
### Acquisition of the ECG Signals and Extraction of the HRV Signals  
The present study involved the analysis of the HRV signals of 200 paddy-field workers who had given consent to volunteer in this study. All of them were residents of the Sambalpur district in Odisha, India [38]. Written consent from the volunteers was taken as  
\begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline  
per the World Health Organization (WHO) guidelines. Initially, there were 218 paddy-field workers, out of which 207 paddy-field workers gave written permission for their voluntary participation in the study after detailed information about the study was provided to them. Out of the 207 interested volunteers, 200 volunteers were chosen for this study based on our inclusion criteria of 18-60 years of age and no occurrence of cardiovascular diseases [38]. Prior ethical approval was received from the Institute Ethical Committee (IEC) of NIT Rourkela, India (Ref.# NIRTKL/IEC/FORM-2/002; dated 16/8/2017) to acquire the ECG signals. The ECG signals were acquired using an ECG machine (VESTA 121i ECG machine, RMS Pvt. Ltd., India) for 5 min in the lead-I configuration. The sampling rate of the ECG machine was 500.6 Hz. The acquired signals were divided into two categories, i.e., Category-C (control group) and Category-B (bhang consuming group) [38]. Each of the categories comprised 100 signals. The ECG signals were processed through the Biomedical Workbench toolkit of LabVIEW (V2017, National Instruments, USA) and were further analyzed using the Pan-Tompkins algorithm to extract the RR interval signals (also known as HRV Signals). The ECG Feature Extractor tool of the Biomedical Workbench toolkit initially performs the signal enhancement through bandpass filtering (10-25 Hz). The employment of the bandpass filter allows for the extraction of the QRS complexes from the ECG signals. After the filtering process, the processed signal is rectified by squaring the signal. The combined processing technique allows the ECG Feature Extractor tool to extract the R waves accurately [46]. The use of default settings has also been suggested in numerous studies like [PERSON] et al. (2017) [47] and [PERSON] et al. (2019) [48]. The tool subsequently uses a threshold adjustment factor for the identification of the R peaks. In other words, a threshold value is used to detect the peak of the R-wave. The default threshold adjustment factor of 0.1 was used in our study. Furthermore, the \"remove noise from raw signal\" box was ticked to obtain a noise-free measurement, as recommended by [PERSON] et al. (2019) [48]. The HRV signals were resampled at 4 Hz before subjecting them to decomposition using the EMD, DWT, and WPD methods [49].
# Research on Data Link Channel Decoding Optimization Scheme for Drone Power Inspection Scenarios
## 2 Materials and Methods
### Air-to-Air Data Transmission Link Model  
The large amount of collected power data needs to be efficiently and reliably transmitted between the drone and the drone relay, or between the drone relay executing inspection tasks. Therefore, the drone channel adopts LDPC channel encoding and decoding to improve the anti-interference ability of the drone data link. The drone data link transmission model based on LDPC code is shown in Figure 2. \(UAV_{i}\) (\(i=1,2,3,...,n-1\)) serves as the transmitter, taking captured images, videos, and other power data as input. The input data are channel encoded using LDPC code. Then, BPSK digital modulation is used to map the binary data into constellation symbols, which are transmitted through the communication channel between unmanned aerial vehicles (i.e., the small-scale fading Rician channel of the air-to-air link). \(UAV_{i+1}\) serves as the receiver, performing the opposite operation on the drone relay compared to the transmitter, namely, digital demodulation, LDPC decoding, and outputting data.  
Within the data link system transmission model, this article focuses on the design of the LDPC decoder. The aim is to design a channel decoding algorithm that satisfies the characteristics of unmanned aerial vehicles and fulfills the high-reliability-communication requirements of air-to-air links.
## 4 Experiments and Results
### Step Response Tests
#### 4.3.1 Step Response Tests at Reference Velocity 10 rad/s  
In the first experiment, a comparison between the linear velocity PI-controller and proposed OAC-DES controller was conducted according to the three previously mentioned step responses. These responses are depicted in Figure 10, which is divided into two diagrams denoted as Figure 10,b. The former presents the step responses of the linear PI-controller, the latter those of the OAC-DES. Each diagram consists of three graphs depicting the corresponding step responses numbered from 1 to 3, respectively.  
The graphs for the step response 1 show improvements of the OAC-DES compared to the linear PI-controller according to the velocity feedback loop. As can be seen in Figure 10, the overshoot, denoted as a transient peak in the figure representing the linear PI-controller, is much higher than for the OAC-DES. The former amounts to 40% of the whole step signal, while the latter is only 10%.  
The graphs for the step response 2 show much higher disturbance peaks for the linear PI-controller than for the OAC-DES. This means that the linear PI-controller initiates a periodic perturbations cannot be eliminated, while the OAC-DES adapts to the oscillations and eliminates the steady state errors almost perfectly.  
Interestingly, neither controller had any problem controlling steady state error when the reference value was \(\omega_{r}=0\), although the response of the OAC-DES was a bit more restless.  
The graphs illustrating step response 3 are very important, as they directly react to velocity error \(\omega_{r}-\omega_{a}\) and therefore dictate the actual velocity \(\omega_{a}\). When promptness of the reactions is compared, we find that the linear PI-controller reacts more quickly and intensively, and consequently obtains a higher overshoot. The current ripples \(i_{r}\) for this controller are almost twice as high as those of the OAC-DES. On the other hand, the OAC-DES reacts more prudently and therefore causes less  
Figure 10: Step response at reference velocity \(\omega_{r}=10\) rad/s.  
stress on the non-linear robotic mechanism. This is reflected in the reduced reference current ripples \(i_{r}\) and reduced transient peaks. Although the OAC-DES does have advantages compared to the linear PI-controller, the high-frequency noise has been caused by reference current \(i_{r}\) when using the OAC-DES, especially at \(\omega_{r}=0\).
# Impacts of Drought and Heatwave on the Vegetation and Ecosystem in the Yangtze River Basin in 2022
## 4 Discussion
### Correlations between Vegetation Indices and Environmental Factors  
We found that the correlation strength between SIF anomalies and anomalies in VPD and SM are comparable. The influence of extreme climates on vegetation cannot be decisively attributed to either low SM dominance or high VPD dominance. This contrasts with some previous studies advocating for either SM dominance [38] or VPD dominance [54; 55]. On one hand, during drought and heatwave, the decrease in soil moisture imposes severe water limitations on vegetation, leading to the partial closure of plant stomata to prevent hydraulic conductivity loss [56]. On the other hand, under drought and heatwave conditions, atmospheric evaporative demand (VPD) may increase sharply, prompting vegetation to increase transpiration to cope with environmental water stress. As the rate of water evaporation increases, vegetation tends to reduce stomatal conductance and water loss, resulting in decreased photosynthetic activity and consequently reduced intensity of vegetation photosynthetic products and SIF signals.
## 5 Implementation Challenges for the Smart Water Grid
### Funding and Investment  
The current U.S. water system has much deteriorating infrastructure due for replacement. It is estimated that it will take 325 billion dollars over the next 20 years to install needed replacements in the U.S. system, including new pipes and meters. One side effect of deteriorating infrastructure is water leaks. It is estimated that municipalities lose 3.4 billion dollars each year to water loses,which are mainly leaks [15]. Presumably, some infrastructure replacements will include upgrades with smart technology, particularly smart water meters. However, many of the benefits of a networked system, like a comprehensive smart water grid, requires scale to be realized--scale that requires an investment that is difficult in the capital constrained environment of most U.S. water utilities.  
One reason for the lack of investment in smart water grids is a lack of a federal agency's mission to support its research and development. The electricity sector is supported by the U.S. Department of Energy and the U.S. Department of Defense. The societal benefits of smart water grids span environmental, energy and security issues, but smart water grids have yet to be recognized as important enough to merit significant federal attention.  
Another reason for the lack of investment and funding in smart water grids is due to institutional and political legacies in the water sector. First, U.S. water utilities were founded on principles of delivering an invisible service at the lowest cost possible [51]. Smart technology can have large upfront costs that many water utilities would be reluctant to pay. Second, water prices are often determined by boards whose membership is determined by popular election. Increasing water prices, even for sensible infrastructure maintenance, is often viewed negatively by voters. The general public tends to take supply of clean and inexpensive water as given. These factors constrain availability of capital.
Sensors_**2009**, \(9\), 175-195; doi:10.3390/s90100175  
_Sensors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
**Effects of Orbit and Pointing Geometry of a Spaceborne Formation for Monostatic-Bistatic Radargrammetry on Terrain Elevation Measurement Accuracy**  
**[PERSON] * and [PERSON]**  
Department of Aerospace Engineering, University of Naples Federico II, 80 Piazzale Tecchio, 80125, Naples, Italy; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +39-081-768-2160; Fax: +39-081-768-2160  
_Received: 11 November 2008; in revised form: 29 December 2008 / Accepted: 30 December 2008 / Published: 8 January 2009_  
**Abstract:** During the last decade a methodology for the reconstruction of surface relief by Synthetic Aperture Radar (SAR) measurements - SAR interferometry - has become a standard. Different techniques developed before, such as stereo-radargrammetry, have been experienced from space only in very limiting geometries and time series, and, hence, branded as less accurate. However, novel formation flying configurations achievable by modern spacecraft allow fulfillment of SAR missions able to produce pairs of monostatic-bistatic images gathered simultaneously, with programmed looking angles. Hence it is possible to achieve large antenna separations, adequate for exploiting to the utmost the stereoscopic effect, and to make negligible time decorrelation, a strong limiting factor for repeat-pass stereo-radargrammetric techniques. This paper reports on design of a monostatic-bistatic mission, in terms of orbit and pointing geometry, and taking into account present generation SAR and technology for accurate relative navigation. Performances of different methods for monostatic-bistatic stereo-radargrammetry are then evaluated, showing the possibility to determine the local surface relief with a metric accuracy over a wide range of Earth latitudes.  
**Keywords:** Spaceborne Monostatic-Bistatic Synthetic Aperture Radar; Single-Pass Stereo-Radargrammetry; Space Mission Design; Terrain Elevation Measurement Accuracy.
## 4 Discussion of Results
### Concerning the Platform Application (Phase 3): 3D Building Facilities Maintenance Management
OmniClass is a comprehensive BIM classification system for the construction industry, primarily designed to offer a classificationstructure for electronic databases and software, thereby enriching the information within these resources [39]. In reference to the results regarding resource consumption (time, costs, personnel, tools, etc.) of the proposed 3D management model in this research (excluding the cost of the GIS and BIM modeling process) and in comparison with the existing 2D management model for the case study, Table 3 presents, based on the authors' experience, the relative savings (in percentage) for different topics. These values were obtained by considering the actual time and resources consumed for each management task using both systems (2D and 3D). However, if 3D models are not already available, creating these models in the short term may result in 3D facility management being even more costly than 2D management; nevertheless, neither the quality of information handled nor the efficiency of this management is comparable. Furthermore, in the medium to long term, the initial cost of model creation would be justified as the associated operational resources are reduced. On the other hand, it is important to note that these results are not entirely transferable to all contexts, as aspects such as, among others, the institution's interest in digitalizing its infrastructure in line with current and future needs, as well as the potential existence of administrative regulations, such as in Europe [3; 40], that may mandate building digitalization, could impact the resulting returns. For example, in the case of Spain, in compliance with European regulations, there is an obligation to present public projects in BIM [41], and public administrations are even encouraged to adopt GIS tools [42]. Some strategies of the Spanish government emphasize the need to promote and implement the use of BIM methodology in the life cycle analysis of buildings. This approach aims to efficiently calculate their sustainability, including aspects such as refurbishment, thereby contributing to improvements in climate change mitigation and the sustainability of constructions, including infrastructure [43]. For this reason, 3D digitalization and management are more advantageous for public buildings in Spain, such as the case study presented.
## 3 Results and Discussion
### Modification of Surface Bearing Capacity of Ti6 Al4V Using Inorganic Thermal Oxide Coating
Above 2.5 N, which corresponds to a stress of 22 MPa, the wear of the inorganic thermal oxide coating was always lower than that of the bare Ti6 Al4V at the same load, as shown in Figure 6c-j, and the inorganic thermal oxide coating increased the bearing capacity of Ti6 Al4V from 22 to 352 MPa. The increase in bearing capacity is primarily due to the increased surface hardness of the inorganic thermal oxide coating, as shown in Figure 5a. Therefore, the higher the hardness of the surface, the higher the bearing capacity. The friction characteristics are closely related to the composition of the inorganic thermal oxide coating. For example, the inorganic thermal oxide coating mainly composed of rutile phase shows a higher friction coefficient. For the same inorganic thermal oxide coating composition, the friction properties are closely related to the elastic modulus of the surface. An ideal temperature value exists at which the inorganic thermal oxide coating has excellent tribological properties. In this study, a temperature of 300 \({}^{\circ}\)C was selected for thermal oxidation treatment. Through friction experiments at different loads, the inorganic thermal oxide coating was found to increase the bearing capacity by a factor of 16. Essentially, inorganic thermal oxide coatings enhance the surface hardness, thereby increasing the bearing capacity of the surface. In addition, inorganic thermal oxide coatings can act as an intermediate boundary layer in composite coatings, improving the adhesion between the organic layer and the substrate. This has been demonstrated in previous studies as well [27]. Figure 5: **(a)** Hardness and (b) elastic modulus of bare Ti6 Al4V and inorganic thermal oxide coatings formed at 100 \({}^{\circ}\)C, 300 \({}^{\circ}\)C, and 500 \({}^{\circ}\)C.  
Figure 6: Tribological properties of bare Ti6 Al4V and inorganic thermal oxide-coated Ti6 Al4V at different loads: (**a**) friction coefficient of bare Ti6 Al4V versus time; (**b**) friction coefficient of inorganic thermal oxide-coated Ti6 Al4V versus time; (**c\({}_{1}\)–f\({}_{1}\)**) wear morphologies of bare Ti6 Al4V with applied loads of (**c\({}_{1}\)**) 5 N, (**d\({}_{1}\)**) 10 N, (**e\({}_{1}\)**) 20 N, and (**f\({}_{1}\)**) 40 N; (**c\({}_{2}\)–f\({}_{2}\)**) wear morphologies of PTFE balls (tribo-pairs of bare Ti6 Al4V) with applied loads of (**c\({}_{2}\)**) 5 N, (**d\({}_{2}\)**) 10 N, (**e\({}_{2}\)**) 20 N, and (**f\({}_{2}\)**) 40 N; (**g\({}_{1}\)–j\({}_{1}\)**) wear morphologies of inorganic thermal oxide-coated Ti6 Al4V with applied loads of (**g\({}_{1}\)**) 5 N, (**h\({}_{1}\)**) 10 N, (**i\({}_{1}\)**) 20 N, and (**j\({}_{1}\)**) 40 N; (**j\({}_{2}\)**) wear morphologies of PTFE balls ( tribo-pairs of inorganic thermal oxide-coated Ti6 Al4V) with applied loads of (**g\({}_{2}\)**) 5 N, (**h\({}_{2}\)**) 10 N, (**i\({}_{2}\)**) 20 N, and (**j\({}_{2}\)**) 40 N.
# Analysis of the Dynamic Characteristics of Coaxial Counter-Rotating Planetary Transmission System
## 3 Dynamic Model of the Transmission System
### Derivation of the Motion Differential Equations
#### 3.3.4 Motion Differential Equation for the Sun Gear s  
The support force of the bearing acting on the sun gear, \(s\), is as follows:  
\[\left\{\begin{array}{c}F_{bxs}=c_{bxs}(\dot{x}_{s}-\omega_{h}y_{s})+k_{ bxs}x_{s}\\ F_{bys}=c_{bys}(\dot{y}_{s}+\omega_{h}x_{s})+k_{bys}y_{s}\\ F_{bxs}=c_{bxs}\dot{z}_{s}+k_{bxs}z_{s}\\ M_{bbzs}=c_{bxs}\dot{\theta}_{zs}+k_{bxs}\theta_{zs}\\ \end{array}\right., \tag{20}\]  
where \(k_{bxs}\), \(k_{bys}\), \(k_{txs}\), and \(k_{bxs}\) represent the support stiffnesses of the bearing in the respective directions; \(c_{bxs}\), \(c_{bys}\), \(c_{bxs}\), and \(c_{bxs}\) represent the support dampings of the bearing; and \(w_{h}\) represents the angular velocity of the carrier \(h\).  
The support force of the shaft acting on the sun gear, \(s\), is as follows:  
\[\left\{\begin{array}{c}F_{xsa}=c_{xas}(\dot{x}_{s}-\dot{x}_{a})+k_{xas}(x_{ s}-x_{a})\\ F_{ysa}=c_{yas}(\dot{y}_{s}-\dot{y}_{a})+k_{yas}(y_{s}-y_{a})\\ F_{zsa}=c_{zas}(\dot{z}_{s}-\dot{z}_{a})+k_{zas}(z_{s}-z_{a})\\ M_{\theta 2 sa}=c_{bxs}(\dot{\theta}_{zs}-\dot{\theta}_{za})+k_{\theta 2 as}(\theta_{zs}-\dot{ \theta}_{za})\\ \end{array}\right., \tag{21}\]  
The force acting on the sun gear, \(s\), as a result of its mesh with the planet gear, \(pi\), is as follows:  
\[\left\{\begin{array}{c}F_{spiL}=k_{spiL}\delta_{spiL}+c_{spiL}\dot{\delta}_{ spiL}\\ F_{spiR}=k_{spiR}\delta_{spiR}+c_{spiR}\delta_{spiR}\\ \end{array}\right., \tag{22}\]where \(k_{spiL}\) and \(k_{spiR}\) represent the mesh stiffnesses of the gear pairs between the left and right sun gears, \(s\), and planet gears, \(p\); and \(c_{spilL}\) and \(c_{spiR}\) represent the mesh dampings.  
The motion differential equation for the sun gear, \(s\), is as follows:  
\[\left\{\begin{array}{l}m_{s}\big{(}\ddot{x}_{s}-2\omega_{h}\dot{y}_{s}-\omega _{h}^{2}x_{s}\big{)}+\sum\limits_{i=1}^{N}(F_{spiL}+F_{spiR})\cos\beta_{b} \sin\psi_{spi}+F_{bsx}+F_{xsa}=0\\ m_{s}\big{(}\ddot{y}_{s}+2\omega_{h}\dot{x}_{s}-\omega_{h}^{2}y_{s}\big{)}+ \sum\limits_{i=1}^{N}(F_{spiL}+F_{spiR})\cos\beta_{b}\cos\psi_{spi}+F_{bys}+F_ {ysa}=0\\ m_{s}\ddot{z}_{s}-\sum\limits_{i=1}^{N}(F_{spiL}-F_{spiR})\sin\beta_{b}+F_{bzs} +F_{zsa}=0\\ f_{s}\ddot{\theta}_{zs}+\sum\limits_{i=1}^{N}(F_{spiL}+F_{spiR})r_{bs}\cos \beta_{b}+M_{\theta zs}+M_{\theta zsa}=0\end{array}\right., \tag{23}\]
## 3 Results and Discussion  
### Effect of Different Brewing Methods on VOC Composition by HS-SPME/GC Mass Spectrometry Analysis  
The volatile profiles of the six replicates of four filter coffee brewing methods were analyzed using the previously described method (Section 2.2) in full scan mode. The relative peak area percentage (RPA) of each scanned ion was calculated, and a total of 49 volatile compounds were identified (Table 2), representing 80.98 to 86.99% of the total headspace composition. The chemical classes detected were aldehydes (8), ketones (5), furans (11), phenolic compounds (3), pyridines (3), pyrazines (11), acids (2), terpene alcohols (3), and pyrroles (3). In this study, some molecules were found to have a very high RPA value, as already confirmed in the previous study [16]. Some of them are characterized by their capacity to stimulate odor receptors, also called \"key odorants\", and they are highlighted in Table 2 with a \"K\". Furans and pyrazines are quantitatively highly abundant in coffee, as they are produced during the roasting process. Furans promote the characteristic malty, sweet, and roasted flavors with a high sensory threshold. This class is formed due to the Maillard reaction between amino acids or proteins and reducing sugars during the thermal degradation of carbohydrates, the oxidation of polyunsaturated fatty acids, and the degradation of ascorbic acid or its derivatives [24; 25]. The highest 2-furanmethanol RPA values (9.45 \(\pm\) 0.45%) [26] were found in PB (9.54 \(\pm\) 0.45%). This molecule is related to candied, burnt, and smoky descriptors. Another important furan derivative is 2-furan methanol acetate, mainly related to fruity and banana flavors, which presented the highest RPA (7.90 \(\pm\) 0.21%) in V60 samples. Despite their low sensory threshold, pyrazines are potent aroma compounds that contribute to the characteristic coffee flavor. Pyrazines exhibit a nutty, earthy, roasted, and green aroma produced during the Maillard reaction [25; 27]. Among pyrazines, methylpyrazine was the most prevalent. It showed an RPA range between 2.16 \(\pm\) 0.11% in FP samples and 2.91 \(\pm\) 0.06% in PB filter coffees. These molecules contribute to a chocolaty, corn-like, and nutty aroma in coffee cups [28]. 2,5-Dimethylpyrazine was the second-most abundant pyrazine in the analyzed samples. It was reported to be related to a nutty, peanut, musty, earthy, powdery, and slightly roasted cocoa powder nuance [5]. This compound was found with an RPA of 2.77 \(\pm\) 0.25% in PB samples, resulting in the richest group. A similar trend was reported for 2,6-dimethylpyrazine, related to a cocoa and roasted nuts descriptor. The highest amount was found in PB and FP filter coffees (2.23 \(\pm\) 0.02% and 2.05 \(\pm\) 0.02%, respectively), while it was not detected in V60 or AP samples. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Compounds Name and Classes** & **Sens.