esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Background of WUE Estimation by Remote Sensing
### Advances in Direct Estimation of Ecosystem WUE by Remote Sensing  
Indirect estimation of WUE by ground observations, process models and MODIS products (GPP and ET) have reported large discrepancies in terms of both threshold and spatiotemporal patterns [86]. Direct estimation methods reflecting the response of environmental factors are necessary to calculate the ecosystem WUE accurately. Both biotic and abiotic factors control the variability of WUE, especially for the optical response to VPD and LAI, providing a basis for WUE remote sensing. Biotic factors include percent Nitrogen (N) in canopy, LAI and vegetation indices, while abiotic factors include carbon dioxide (CO\({}_{2}\)) concentration, VPD, temperature (Ta), precipitation (P), soil moisture (SM), phenology, land use change, and so on. Seasonal and annual variability of WUE is controlled and driven by CO\({}_{2}\) concentration, LAI, and VPD, while it is very complex for shorter time scales. The annual increase in globally averaged atmospheric CO\({}_{2}\) concentrations along with climate variabilities strongly affect WUE in growing season with the physiological effect of rising CO\({}_{2}\) stomatal closure [4; 7; 87; 88]. The response of WUE to VPD varies under different atmospheric and soil conditions, while it is considered to decrease with the increasing VPD [22]. However, the response might be not obvious when VPD is high [89] and may not exist at all during drought events [90]. In comparison to climatic variables, the structural parameters of vegetation and LAI mainly drive the seasonal variability of WUE having a positive relationship with GPP, ET, and WUE across plant functions [91]. Studies also showed that monthly WUE weakly responds to VPD, while it is positively sensitive to air temperature, and is negatively correlated with PAR at the interannual scale [91]. However, response to environmental factors is unclear and not uniform across different types of ecosystem, while this provides a basis to build mechanistic WUE estimating methods. Few studies have reported a mechanistic estimation of WUE using remote sensing data. In this regard, previous studies have proved that MODIS-derived vegetation indices have a strong correlation with WUE at the ecosystem scale [75]. The existing empirical models are mainly based on a linear relationship of WUE with multiple factors such as MODIS-derived vegetation indices, meteorological data and flux observed data. Some existing empirical models are listed in Table 4.
# Concept, Design, Initial Tests and Prototype of Customized Upper Limb Prosthesis
## 3 Results
### Results of Command and Control System  
The command and control system is a complex one, briefly designed according to its block diagram presented in Figure 14. Basically, the EMG sensor [25] mounted on the existing part of the upper limb will generate the signal for motion toward and/or grabbing  
Figure 13: Mechatronic system design: (**a**) first design type, (**b**) intermediate design type, (**c**) up to date design—motion transmission components  
of an object (cup, ball, pencil, etc.). The resistive and infrared sensors [26] will acquire and send to the controller the data regarding prosthesis position, acceleration, pressure on the gripped object, temperature of the object, and motion towards the object. The motion toward the object is then available via micro-motors acting on mechanical structure (gears, joints, etc.) and assisted by the gyro sensors. Once the target is reached according to information on the software set values (psoft, Accsoft, \({}^{\circ}\)XTemp, asoft) and real values from the sensors, the prosthesis motion will stop or reverse.  
This paper presents research results focused on the concept, design, prototyping and initial tests of the prosthesis. The command and control system's testing refers to the preliminary testing of the sensors and micro-motors on a small platform, such as Arduino.  
One conclusion when testing the resistive sensors pointed out the need for careful selection of the base material, that of the fingers' surface. This is because it should not be extremely rigid--so as to allow for the correct fixture of flexible sensors, and not too soft--so that the material does not to overtake deformation and thus influence the resistive sensor's specific deformation. Images taken on the preliminary experiments are shown in Figure 15. In Appendix B the program customization for the display of conventional units for force [N] (Figure A3) and tilt angle [\({}^{\circ}\)] (Figure A4) is presented.  
Figure 14: Block diagram of the command and control system for the upper limb prosthesis.
# Time Slotted Channel Hopping and ContikiMAC for IPv6 Multicast-Enabled Wireless Sensor Networks
## 6 Results
### Energy Consumption per Hop
Placing nodes randomly and creating a lot of interference between them resulted in an increased energy consumption when using ContikiMAC, whereas this had no effect when using TSCH; see Figure 17. This phenomenon is explained by the fact that for TSCH, most transmissions were scheduled on different timeslots and on different frequency channels, so the interference between nodes had a smaller effect on the performance parameters. Figure 16: Energy comparison between LL unicast and LL broadcast and between ContikiMAC and TSCH on topologies with interference between same rank nodes. (**a**) Results for LL unicast in BMRF over TSCH. (**b**) Results for LL broadcast in BMRF over TSCH. (**c**) Results for LL unicast in BMRF over ContikiMAC. (**d**) Results for LL broadcast in BMRF over ContikiMAC. However, because of the high energy consumption of the synchronization process, TSCH still had a significant higher overall energy consumption for the traffic levels considered in this study.
# F127/Cisplatin Microemulsions: _In Vitro_, _In Vivo_ and Computational Studies
## 3 Results and Discussion
### Computational Results: The Interplay between Cis and Surfactant F127
We changed the concentration of drug as changed in cells to study the role of drug concentration on the thermodynamical properties of the complex formation process. Changes of thermal Gibbs free energy (\(\Delta\)G), thermal enthalpy (\(\Delta\)H), and thermal entropy (\(\Delta\)S) are reported in Table 3. As can be seen, all \(\Delta\)S values are positive, which is in agreement with spontaneous processes. Results show that an increase in drug concentration is followed by a decrease in \(\Delta\)S values. In fact, irregularity decreases to some extent at higher concentrations. This outcome implies that there are positive interplays between drug and surfactant at higher concentrations. It seems that such interactions have helpful roles in decreasing the toxicity of drug-surfactant in comparison to free Cis. Consequently, Cis forms HB interactions with electron-rich positions on F127 via its ammonia groups. Charges of atoms that take part in HB interaction between Cis and F127 influence the stability of the drug-surfactant system. Indeed, the electron charge transfers between molecular orbitals of the Cis and F127 control such stability. Formation of BCPs between HB donors and HB acceptors contributes to enhancing the stability of the drug-surfactant bond. Indeed, the decrease of irregularity occurs at higher concentrations of drugs, and there are acceptable interplays between Cis and F127. It seems that such intermolecular interactions may lead to less toxicity of drug-surfactant systems (i.e., microemulsions) in comparison to free Cis. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Concentration** & **2.5** & **5.0** & **10.0** & **20.0** & **40.0** \\ \hline \(\mathrm{{}^{*}\Delta G}\) & \(-3.486\) & 328.313 & 988.026 & 2304.883 & 4937.124 \\ \(\mathrm{{}^{*}\Delta H}\) & 10.139 & 337.308 & 993.932 & 2308.857 & 4940.136 \\ \(\mathrm{{}^{**}\Delta S}\) & 0.046 & 0.03 & 0.020 & 0.013 & 0.010 \\ \hline \hline \end{tabular} \({}^{*}\) In kcal mol\({}^{-1}\) and \(\mathrm{{}^{**}}\) in kcal mol\({}^{-1}\)K\({}^{-1}\).  
\end{table} 
Table 3: Variation of thermal Gibbs free energies, enthalpies, and entropies of the complex formation process with change of drug concentration.
# New Advances and Challenges of Fall Detection Systems: A Survey
## 5 Conclusions  
The threat of fall concerns not only the elderly but also researchers working in related areas. As one of the hottest topics in health-care, it has made great progress in several aspects in recent years. We surveyed 6380 papers discussed twenty representative and highly cited works in a profound way, and summarized the developments of fall detection from three main angles:  
1. Revolution of sensors: Depth camera, especially the Kinect, has been widely used; RF sensors, such as Wi-Fi, appear on the stage of fall detection.
2. Changes of algorithms: Machine learning becomes the mainstream in fall detection, but feature extraction does not have an obvious innovation.
3. Performance of system: Accuracy remains at the high level; however, it is still far from becoming a practical application.  
Four main challenges on theories and practice were discussed in a fine-grained way:  
1. How to define the fall based on universal features
2. How to apply this study to practice
3. How to protect privacy
4. How to from detection to prediction  
Although there are many issues and challenges, as smart sensors and IoT advance and new technologies are introduced, we envision that this field will have a promising future.  
**Acknowledgments:** This work was supported by the National Natural Science Foundation of China (61702417 and 61703259), and the Shaanxi Natural Science Foundation (2017 JM6097).  
**Author Contributions:** [PERSON] and [PERSON] analyzed the data, concluded the tendency of fall detection fields, and wrote the paper. [PERSON] searched and collected literature related to fall detection.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 3 Experimental Results
### Effect of Vibration on DC Resistance
#### 3.2.2 Hypothesis Testing for Samples after Vibration  
Similarly, before the hypothesis testing, it is necessary to test the population distribution of the internal resistance based on samples after vibration in the same way as described in Section 3.1 for the capacity distribution test. Finally, the distribution of the internal resistance based on the samples after vibration is obtained as normal distribution \(N\) (38.09, 1.2442).  
Hypothesis Testing for the Internal Resistance Mean \(\mu\) after Vibration  
Confidence intervals of the population mean \(\mu_{0}\) and the standard variation \(\sigma_{0}\) have been obtained in the previous section. According to the theorem related to statistics, based on the normal distribution \(N\) (\(\mu_{0 max}\), \(\sigma_{0 max}^{2}\)) the \(Z\)-test is adopted to test whether the internal resistance mean \(\mu\) after vibration exhibits a significant increase compared to that before vibration. Therefore, the null hypothesis and the alternative hypothesis are expressed as follows:  
The null hypothesis \(H_{0}\):  
\[\mu\leq\mu_{0\max}. \tag{16}\]  
The alternative hypothesis \(H_{1}\):  
\[\mu>\mu_{0\max}. \tag{17}\]  
Then, the statistic \(Z=\frac{X-\mu_{0 max}}{\sigma_{0 max}/\sqrt{n}}\) is adopted to determine the rejection region. Thus, the rejection region can be expressed as follows:  
\[Z=\frac{X-\mu_{0\max}}{\sigma_{0\max}/\sqrt{n}}\geq Z_{\alpha}, \tag{18}\]  
where \(n=32\), \(X=38.09\), \(\mu_{0 max}=37.58\) and \(\sigma_{0 max}=1.411\), respectively, from the sections above.  
Based on Equation (18), \(Z=2.0023\) is obtained and then, according to the standard normal distribution table, the corresponding \(P\) is obtained as 0.0226. Given the significant level \(\alpha=0.05\), there is \(P<\alpha\). Therefore, there are good reasons to refuse the null hypothesis, and it is accepted that the internal resistance mean \(\mu\) after vibration increased at significant level \(\alpha=0.05\).  
Hypothesis Testing for the Resistance Standard Variation \(\sigma\) after Vibration  
According to the theorem related to statistics, the chi-square test is adopted to test whether the internal resistance standard variation \(\sigma\) after vibration exhibits a significant increase compared to that before vibration. Therefore, the null hypothesis and the alternative hypothesis are expressed as follows:  
The null hypothesis \(H_{0}\):  
\[\sigma^{2}\leq\sigma_{0}^{2}. \tag{19}\]  
The alternative hypothesis \(H_{1}\):  
\[\sigma^{2}>\sigma_{0}^{2}. \tag{20}\]  
Then, the statistic \(\chi^{2}=\frac{(n-1)S^{2}}{\sigma_{0}^{2}}\) is adopted to determine the rejection region. Thus, the rejection region can be expressed as follows:  
\[\chi^{2}=\frac{(n-1)S^{2}}{\sigma_{0}^{2}}\\(\chi_{\alpha}^{2}(n-1)=41.422\). Consequently, \(\chi^{2}>\chi_{\alpha}^{2}(n-1)\). Therefore, there are good reasons to refuse the null hypothesis, and it is accepted that the internal resistance standard variation \(\sigma\) after vibration increased at significant level \(\alpha=0.1\).
# Performance Investigation of Active, Semi-Active and Passive Suspension Using Quarter Car Model
## 2 Methodology
### Passive Suspension System and Semi-Active Suspension System Modelling  
Mathematical equations were derived from first principles to describe the response of the passive suspension system under forced vibration.  
The quarter car passive suspension system is represented as a damped two-degree-of-freedom spring mass system under a forced vibration described by the displacement of the sprung and unsprung masses, \(x_{1}\) and \(x_{2}\), respectively, and input road displacement \(w\), as shown in Figure 4.  
Applying [PERSON]'s 2 nd law,  
\[-k_{1}(x_{1}-x_{2})-b_{1}\big{(}\dot{x_{1}}-\dot{x_{2}}\big{)}=m_{1}\big{(} \ddot{x_{1}}\big{)} \tag{5}\]  
\[-k_{2}(x_{2}-w)-b_{2}\big{(}\dot{x_{2}}-\ddot{w}\big{)}+k_{1}(x_{1}-x_{2})+b_{1 }\big{(}\dot{x_{1}}-\dot{x_{2}}\big{)}=m_{2}\big{(}\ddot{x_{2}}\big{)} \tag{6}\]  
Figure 3: Road profile 3: Consecutive bumps.  
Figure 2: Road profile 2: Sudden step.  
Equations (5) and (6) are derived by considering the forces acting on the sprung mass, \(m_{1}\), and unsprung mass, \(m_{2}\), respectively.  
The semi-active suspension system was developed by the implementation of a PID closed feedback loop on the original passive system. Figure 5 shows the block diagram of the semi-active suspension system that contains a PID feedback loop and the variable damper \(b_{1}(t)\).  
Figure 6 highlights the input and output parameters of the PID controller in the feedback control loop. The output of the PID controller is equal to the control input \(b_{1}(t)\) for the quarter car suspension system. The control input, or variable damping coefficient, \(b_{1}(t)\), is therefore the sum of the proportional control, integral control, and derivative control terms, which act on the error signal \(e(t)\). This is represented mathematically in Equation (7), as follows:  
\[b_{1}(t)=K_{p}e(t)+K_{i}\int_{0}^{t}e(t)dt+K_{d}\frac{de(t)}{dt} \tag{7}\]  
Figure 4: Quarter car passive suspension system diagram.  
Figure 5: Block diagram of the closed feedback loop used in the semi-active suspension system.
# OFDM Network Optimization Using a QPSK Based on a Wind-Driven Genetic Algorithm
## 2 Materials and Methods
### Research Questions  
Based on the literature reviewed in Section 2, the quest to improve error performance, increase data throughput, raise bandwidth efficiency, step up channel capacity, and elevate OFDM network reliability creates a need to address the following questions [6; 8; 9; 10; 11; 14; 15; 19]:  
* How can the WDO be successfully incorporated into the GA to help mitigate the weaknesses of the GA, e.g., selecting the most elite parents for subsequent populations thus mitigating premature convergence and promoting diverse exploration [10; 11]?
* How can the developed hybrid GAWDO be used to deduce and assign optimum phase values for QPSK in an attempt to mitigate QPSK phase ambiguity [6; 17; 18]?
* How can the QPSK based on the GAWDO be further modified to eliminate carrier offset drift?
* How can OFDM channel efficiency be increased by optimizing a QPSK signal [8; 9]?
## 5 Experimental Results
### Collision Avoidance Performance
* Front-left direction case: When obstacles are detected in both the front and left directions, each generates a repulsive force away from its location. The vector summation of these forces results in a resultant force directed toward the rear-right direction, steering the drone away from the obstacles, as illustrated in Figure 25a. * Front-right direction case: When obstacles are present in the front and right directions, the resulting repulsive force is directed towards the rear-left direction, as shown in Figure 25b. This response ensures the drone moves away from both detected threats. * Rear-left direction case: When obstacles are detected in the rear and left directions, as shown in Figure 24, the corresponding repulsive forces combine to produce a resultant force toward the front-right direction, enabling the drone to avoid both obstacles, as illustrated in Figure 25c. Figure 25: Summary of resultant repulsive force of different cases and directions: (**a**) for obstacle in the front and left side, repulsive force towards back–right; (**b**) for obstacle in the front and right side, repulsive force towards back–left; (**c**) for obstacle in the back and left side, repulsive force towards front–right; (**d**) for obstacle in the back and right side, repulsive force towards front–left. * Rear-right direction case: When obstacles are detected in both the rear and right directions, a resultant force is generated toward the front-left direction, guiding the drone away from the obstacles, as shown in Figure 25d. Following the generation of virtual repulsive forces tested in various scenarios, the corresponding modification of user commands was also verified through a series of experiments. Figure 26 illustrates two different case scenarios in which the drone detects surrounding obstacles. Figure 26a,c show two distinct experimental setups with various obstacle arrangements surrounding the drone (positioned at the center), while Figure 26b,d display the corresponding 2D RViz visualizations. In both cases, the original user control command (blue arrow) is adjusted by the virtual repulsive force (red arrow), resulting in a modified control command (green arrow) that directs the drone along a safer path. The virtual repulsive force is represented by a red arrow, the original user command by a blue arrow, and the resulting modified control command by a green arrow. In both scenarios, the initial user commands would direct the drone toward nearby obstacles; however, the system dynamically adjusts the control signal in real time to steer the drone away from potential collisions. This demonstrates the effectiveness of the repulsive force mechanism in modifying the drone's trajectory for safe navigation, while still maintaining responsiveness to user input. The visualizations clearly illustrate the real-time adaptation of control commands based on obstacle proximity. Figure 26: User command modification based on virtual repulsive force for two different obstacle scenarios.
# Evaluating the High Frequency Behavior of the Modified Grounding Scheme in Wind Farms
## 2 Wind Turbine Model Description  
The electrical scheme of a Low/High Voltage (LV/HV) substation close to the tower. The wind turbine model is validated with the following components:  
* a synchronous 0.69-kV generator that is sufficiently stable at 50 Hz,
* an auxiliary transformer, 0.69/0.4 kV, for the control equipment,
* a boost transformer, 0.69/20 kV, inside the tower for which electromagnetic transfer will be considered, but static transfer is neglected, and
* a 20/60 kV transformer that connects the wind turbine to the grid.  
This study assumes that the rated power of the wind turbine is 2 MW with an approximately 70-m hub height and 82-m rotor diameter. The construction of the wind turbine is based on a direct drive system with the purpose of eliminating gears and making a fixed unit by directly connecting the rotor hub and annular generator to each other. Because there is no direct grid coupling in an annular generator, in order to connect such a low-speed synchronous generator to the grid, changes in the amount of output voltage and the frequency alter the speed, so that there is a high degree of speed variability. As shown at the bottom of the figure, a 2500-kVA transformer that fits the specific working conditions of the tower is situated in its vicinity. The following subsections discuss the high-frequency-dependent models for the wind turbine.
## 5 Analysis of Behavior and Results
### Simulation Results at Spring/Summer ToU Tariff
#### 5.1.2 Summer Day Scenario  
Summer day simulated scenario is implemented by taking under consideration the power consumption profile (Figure 8) for an average day of summer and the ToU tariff of spring/summer (Figure 1a). Over and above, the weights for the 16 time slots of the discharging zone are set as: \(w_{i}=[1\ 1\ 1\ 1\ 1\ 1\ 1\ 1\ 1\ 1\ 1\ 1\ 1\ 2\ 2\ 2\ 2\ 2]\). In that context, the optimization algorithm will prioritize the load cover at the last five hourly slots of the discharging zone (6 p.m.-11 p.m.).  
Considering that the day-ahead load demand matched the actual load demand Figure 12a is obtained. It is observed that the EMS distributed the stored energy just at the slots with low uncertainty for load mismatching (6 p.m.-11 p.m.). More specifically, 807 Wh were supplied from 6 p.m. to 7 p.m., 807.9 Wh from 7 p.m. to 8 p.m., 808.8 Wh from 8 p.m. to 9 p.m., 809.4 Wh from 9 p.m. to 10 p.m. and 809.2 Wh from 10 p.m. to 11 p.m. Additionally, the overall power supplied to the household was 4042 W and the load at the BESS at the end of the discharging zone was 16.08 Ah. In order to fully charge the battery stack during the 8 h charging zone the needed current was calculated and found 10.49 A. Figure 12b depicts the discharging and charging currents by starting from the first time slot (7 a.m.) of the discharging zone and ending to the end of the charging zone (7 a.m. of the next day).  
Furthermore, Figure 13 depicts the BESS parameters which were defined by the KiBaM model. Moreover, the daily energy cost for the household with and without the BESS was calculated. With the BESS the cost is 1.746 \(\upepsilon\) while without the BESS is 1.864 \(\upepsilon\). This implies that the daily profit was 0.118 \(\upepsilon\) and for the whole summer would be 10.738 \(\upepsilon\). Similarly, with the spring day simulated scenario, the profit for the consumer is not high since the ToU tariff permits only one full charge/discharge cycle per day. Finally, the estimated capacity loss for the whole day is 0.1022 Ah while the capacity loss set point is 0.0984 Ah.  
Figure 11: BESS parameters.  
Figure 12: BESS operation schedule: (**a**) Energy distribution; (**b**) Charging and discharging currents.