esa-sceva/satcom-chunk-collection huggingface dataset:
# Adaptive Pitch Control of Variable-Pitch PMSG Based Wind Turbine
## 1 Introduction
Recently, control methods based on observers have been successfully used to reinforce the robustness of disturbances and model uncertainties in power system [29], permanent magnet-synchronous motor [30,31], photovoltaics inverters [32] and WT [33]. In this paper, a nonlinear adaptive controller (NAC) based on observers is investigated for permanent magnet-synchronous generator-based WT (PMSG-WT) to limit the extracted wind power and provide high performance in Region 3. In the designed NAC, it contains one rotor speed controller and two stator current controllers. One third-order states and perturbation observer (SPO), and two second-order perturbation observers (POs) are employed for the estimations of perturbation terms, including parameter uncertainties, coupling nonlinear dynamics, and disturbances of the PMSG-WT. The estimated perturbations are used for compensating the real perturbation and obtaining adaptive linearizing control of the PMSG-WT. The comparisons of simulation studies among the proposed NAC,  
Figure 1: Main operating regions of the wind turbine. FLC, VC and GSPI under three different scenarios, e.g., ramp wind speed, random wind speed and field flux variation, are carried out to verify the effectiveness of the proposed NAC. The remaining parts of this paper is organized as follows. The model of the PMSG-WT is presented in Section 2. Section 3 presents the design of the NAC. In Section 4, simulation studies are carried out for verifying the effectiveness of the proposed NAC in comparing with the FLC, VC and GSPI. Finally, conclusions of this work are presented in Section 5.
## 2 Materials and Methods
### MultiSpectroANN Device Setup and Functionality
The device continually repeats this process at regular intervals, ensuring continuous and real-time monitoring of the samples [24]. Figure 2 illustrates the steps of the algorithm used for spectral data collection and processing from milk samples. The process begins with the initialization of the microcontroller and the AS7265x sensor. Next, the serial port settings are configured to ensure proper data communication, and a waiting time of 5 s is set to allow the sensor to stabilize. The microcontroller initiates communication with the sensor using the I2C protocol and sends a request to the sensor to start data collection. The sensor gathers spectral data from the milk sample and transfers these data to the microcontroller. The microcontroller then processes and formats the received data appropriately. The processed data are displayed  
Figure 1: Portable MultiSpectroANN (MSANN) Device. on the serial port screen in real-time for user observation. This procedure is repeated at specified intervals to ensure continuous monitoring and analysis. The entire process allows for real-time data acquisition and processing in a systematic manner. Portable MultiSpectroANN (MSANN) Device Mod:  
This mode enables the MSANN device to operate independently by uploading a model file, derived from computer analyses, onto the microcontroller. Consequently, the device can autonomously evaluate spectral data of milk samples based on the loaded model and relay the results to the user in real-time via Bluetooth messages. Figure 3 illustrates the operational steps of the MSANN device. The process begins with the model uploading phase, where the artificial neural network (ANN) model, developed through computer-based analyses, is saved as a file and uploaded to the Arduino Nano 33 BLE Sense microcontroller. Next, the microcontroller starts reading sensor values  
Figure 2: MultiSpectroANN (MSANN) Data Collection Algorithm. by gathering spectral data from the AS7265x Multispectral Sensor. These data are processed and evaluated based on the pre-loaded ANN model, which enables the microcontroller to assess the spectral data and derive valuable insights about the milk sample. Finally, the evaluation results are transmitted to the user's mobile device or computer via Bluetooth, providing real-time information about the milk sample's quality and potential adulteration. This systematic procedure enables effective and portable in-field analysis of milk samples.
## 1 Introduction
Some examples include image segmentation of bones to diagnose and quantify osteoporosis [38], or estimation of phase contrast signal from X-ray images acquired with two energy spectra [39]. Regarding CXRs, the combination of dual energy and deep learning has been used to obtain two separate images with bone and soft-tissue components [40]. However, to the best of our knowledge, the application of dual-energy images to obtain the scatter signal has not yet been examined. In this work, we present a study of the robustness of three U-Net-like CNNs that estimate and correct the scatter contribution using either single-energy CXRs or dual-energy CXRs to perform the network training. All images were simulated with a Monte Carlo code from actual CT scans of patients affected by COVID-19. The scatter-corrected CXRs were obtained by subtracting the estimation of the scatter contribution from the image affected by the scattered rays, which we call an uncorrected-scatter image. Two different analyses were performed to evaluate the robustness of the models. First, several metrics were calculated to assess the accuracy of the scatter correction, taking the MC simulations as ground truth, after the algorithms were applied to images simulated with various source-to-detector distances (SDD), including the original SDD with which the training images were simulated. The accuracy of the models with CXRs acquired with the original SDD was taken as a reference, and it was compared with the values obtained for other distances. Second, the contrast between the area of the lesion (COVID-19) and the healthy area of the lung was evaluated on soft-tissue dual-energy subtraction (DES) images to quantify how scatter removal helps to better identify the affected region. Then, values of contrast in the ground truth were compared with the ones obtained on the estimated scatter-corrected images only with the original SDD. Finally, the single-energy neural network was tested with a cohort of varied real CXRs, and the ratio between the values of two regions in the lung (with and without ribs) was calculated to determine how the image contrast improves after applying the scatter correction method.
## 11 Conservation of Energy  
The first term \(-\Big{[}1-\frac{\varphi_{m}^{2}}{c^{2}}\Big{]}.\frac{\partial U_{es}}{ \partial s}\) in the right hand side of Equation (12) will now be discussed. In view of the physical meaning of \(U_{es}\) (energy of formation), the term \(-\frac{\partial U_{es}}{\partial s}\) evidently expresses the expansion tendency in the s-direction for the static droplet (\(v_{m}=0\)). An alternative expression, in view of Equation (4), is:  
\[Q_{s,el.static}\big{|}_{v_{m}=0}\ =\ -\frac{\partial U_{es}}{\partial s}\ =\ \frac{U_{es}}{s}=\ \frac{1}{2}\ \frac{q^{2}}{4\pi \varepsilon_{0}}\ \frac{1}{s^{2}}\ \alpha \tag{17}\]  
which, in the complete equation of motion, Equation (12), is multiplied by the typically relativistic factor \(\Big{[}1-\frac{\varphi_{m}^{2}}{c^{2}}\Big{]}\). This is one of the few relativistic effects that should be kept in the equation of motion, because it has an asymptotic order \(O(v/c)^{2}\). The complete term \(Q_{s,el.static}=-\Big{[}1-\frac{\varphi_{m}^{2}}{c^{2}}\Big{]}.\frac{\partial U _{es}}{\partial s}\) indicates a coupling between the translation motion and the pulsation. Its physical meaning can be understood when considering the simplified case of a droplet where an artificial external force cancels the radiation resistance. This is a situation where the loss of energy by radiation is compensated by an external energy input, and the droplet itself must have a constant value of its energy. According to Equation (10), the momentum \(p\) then is constant, say \(p_{0}\). The energy associated with the translation velocity is purely kinetic energy:  
\[E_{transl}=\ \frac{1}{2}\ \frac{{p_{0}}^{2}}{\Big{[}m_{bare}+2\ \frac{U_{es}(s)}{c^{2}} \Big{]}} \tag{18}\]  
It is seen that \(E_{transl}\) fluctuates when \(s\) is variable. Changes of \(E_{transl}\) cannot come from outside the system, and can only happen if there is at the same time an energy transfer to or from \(E_{puls}\), the energy in the pulsation. The power that is transferred to the translation mode equals the time derivative:  
\[\frac{dE_{transl}}{dt}=\ \frac{1}{2}\ \frac{{p_{0}}^{2}}{\Big{[}m_{bare}+2\ \frac{U_{es}(s)}{c^{2}}\Big{]}^{2}}\ \frac{2}{c^{2}}\ \frac{U_{es}(s)}{s}\dot{s}=\ \frac{v_{m} ^{2}}{c^{2}}\ \frac{U_{es}(s)}{s}\dot{s} \tag{19}\]  
The equation of motion for the pulsation, Equation (12), can be written as a power equation through multiplication by \(\dot{s}\). The equation is then written in the form:  
\[\Big{[}m_{bare}^{*}+2\ \frac{U_{es}^{*}(s)}{c^{2}}\Big{]}\ddot{s}\ \dot{s}=\Big{[}Q_{s,surf.tension}-\frac{ \partial U_{es}}{\partial s}\Big{]}\dot{s}-\frac{{v_{m}}^{2}}{c^{2}}\frac{U_{ es}}{s}\ \dot{s} \tag{20}\]It is seen that the coupling term \(-\frac{v_{n}s^{2}}{c^{2}}\frac{U_{ss}}{s}\ \dot{s}\) in Equation (20) is in agreement with the energy transferred to the translation according to Equation (19). In other words, the presence of the coupling term in the pulsation equation of motion guarantees that the total energy of the droplet is conserved.
# Geophysical Coupling Before Three Earthquake Doublets Around the Arabian Plate
## 3 Results
### Lithosphere  
In order to investigate the ionosphere, the earthquake catalogue from USGS was retrieved ([[https://earthquake.usgs.gov/earthquakes](https://earthquake.usgs.gov/earthquakes)]([https://earthquake.usgs.gov/earthquakes](https://earthquake.usgs.gov/earthquakes)), last access, 12 September 2024). Each doublet earthquake has been considered as one phenomenon with the mean epicentral coordinates and the equivalent magnitude, and the lithospheric analysis was performed just before the first of the two earthquakes (both excluded). In particular, the following parameters have been considered:  
1. M = 6.3, on 18 August 2014 at 32.643\({}^{\circ}\) N, 47.700\({}^{\circ}\) E
2. M = 6.2 on 15 July 2018 at 13.956\({}^{\circ}\) N, 51.727\({}^{\circ}\) E
3. M = 6.2 on 1 July 2022 at 26.897\({}^{\circ}\) N, 55.280\({}^{\circ}\) EFor each earthquake doublet, the seismic events that occurred six months before the doublet earthquake (excluded from the analysis) inside [PERSON]'s area [80], defined according to the homonymous radius \"\(\tau_{\text{Dob}}\)\":  
\[\tau_{\text{Dob}}[\text{km}]=10^{(0.43\cdot\text{M})}\]  
The catalogue has been overviewed by ZMap Software Version 7 [81], and in particular, the completeness magnitude for each case study has been estimated by the \"maximum curvature\" criterion. It was found to be Mc = 4.3 for Dehloran (Iran), 2014, Mc = 4.4 for Kilmia (Yemen), 2018 and Mc = 4.1 for Bandar-e Lengeh (Iran), 2022  
Figure 2 shows the results of the computation of the cumulative Benioff strain for the three case studies. It's worth noting that each case presents a peculiar behaviour. In fact, acceleration can be underlined for Dehloran, Iran, 2014, about three months before the doublet case, and after that, there is an almost linear trend. A linear trend is one that is expected in the absence of partial phenomena and is produced by the basic/background seismicity. A very different case was recorded before the Kilmia, Yemen, 2018 case, as there is a linear trend for the whole period except for the last 15 days before the earthquake doublet, where a sudden increase in the strain is well visible. This can be considered a seismic activation two weeks before the main events. It's worth mentioning that the same behaviour was present also before the Bandar-e Lengeh, Iran, 2022 earthquake doublet. In fact, in this case, a sudden acceleration in strain release is present from 14 June 2022, i.e., 17 days before the main events. In addition, another clear and sudden acceleration in seismicity was recorded from 15 to 22 March 2022, i.e., about 3.5 months before the main events.  
Considering these results, the three earthquake doublets have been preceded by different trends in the cumulative Benioff strain, which means a different accumulation of stress on the fault before the main events. However, some similarities have also been noted. The lithospheric trend will be considered as a base for interpreting the following analyses in the atmosphere and the ionosphere.
## 4 Case Study
### Sensitivity Analysis  
#### 4.4.1 On-grid Price of Wind Power  
There are differences in on-grid price of wind power for different wind sources. Therefore, the effects of different on-grid price from 0.084 $/kW\(\cdot\)h to 0.064 $/kW\(\cdot\)h on the optimal storage capacity of BESS are studied, as shown in Table 5.  
From Table 5, it can be seen that the capacity of BESS has changed slowly and only reflects on the net benefit of the wind-energy union system before the on-grid price down to 0.076 $/kW\(\cdot\)h. However, when the on-grid price of wind power falls to 0.064 $/kW\(\cdot\)h, the benefit that is contributed by wind energy union operation cannot balance the investment cost of BESS, and it is no longer suitable for configuring BESS in this wind farm.
# Thermal Characterization of Phase Change Materials by Differential Scanning Calorimetry: A Review
## 2 Phase Change Materials and Base Concepts  
PCMs are materials that can store and release a significant amount of heat during their phase transition [6]. For example, water (ice) can be considered as a PCM, as 1 kg of ice needs around 333 kJ of heat to melt at around 0 \({}^{\circ}\)C [13]. A phase change occurs between different states of a material such as solid/solid, solid/liquid, solid/gas or liquid/gas, in which the solid/liquid transition is more promising compared to others due to the ability to store a high amount of energy in a small volume. Thermal energy is stored mainly by three types of storage systems, including sensible, latent and thermochemical heat storage [14]. Each of these technologies has its own advantages and disadvantages. For example, latent thermal energy storages have larger energy density compared to sensible storages, and lower development complexity compared to thermochemical storages. PCMs can be categorized into three groups: organic, inorganic and metallic. They can also be obtained by a eutectic combination of the mentioned groups [13]. When the technical properties of PCMs, including the operating temperature and the latent heat, overlap, among the following types of PCMs the preferred option would be organic ones, as they are often less hazardous and less corrosive [15]. According to the unique thermal storage capacity of PCMs, they are already applied in two categories of thermal storage and thermal control systems at low temperatures. Furthermore, the PCMs have high potential at higher temperature applications (100-400 \({}^{\circ}\)C). Several examples for each application are listed in Table 1. More information about PCMs' applications can be found in the review proposed by [PERSON] and [PERSON] [16]. In the present one, the focus is on the determination of some thermophysical properties of PCMs during their solid-liquid phase change. PCMs provide a high amount of energy in a narrow temperature range. In order to integrate a PCM in a given application, one has to select a suitable PCM based on the application's requirements considering the technical, economic and environmental aspects [17]. Table 1 displays the properties of a suitable PCM. As can be seen in Figure 1, the dashed line shows a phase change characteristic for an ideal PCM, while the phase change phenomenon occurs over a temperature range instead of a single temperature point for most of the PCMs (continuous line). Equation (1) describes the contribution of each sensible heat of solid and liquid, as well as the latent heat to the total stored thermal energy. \[Q=m\left[\int\limits_{T_{1}}^{T_{m}}C_{p,solid}\cdot dT+\Delta h+\int\limits_{T _{m}}^{T_{2}}C_{p,liquid}\cdot dT\right] \tag{1}\]  
where \(Q\) represents the thermal energy (J), \(h\) the enthalpy (J), \(C_{p}\) the specific heat capacity (J\(\cdot\)g\({}^{-1}\cdot^{\circ}\)C\({}^{-1}\)), \(m\) the mass (kg), \(T\) the temperature (K).
# Design of Multi-Modal Ship Mobile Ad Hoc Network under the Guidance of an Autonomous Ship
## 5 Analysis of Node Transmission Capacity
### Find the Distance and Number of Nodes in Each Radius  
During sea navigation, ships can discover other ships through radar and other navigational aids, and check the number, position, and distance of target ships. However, from an interactive perspective, we need to search for the target ship through communication devices in the ship MANET. Therefore, in a marine scene, we need to determine the number of nodes in the network and their distance from OS. The experimental parameters are shown in Table 2. We take the location of the ship as the central node, that is, the coordinates of our ship are \(X_{OS}=0,\ Y_{OS}=0\), and, then, some nodes are randomly distributed within a certain range of the coordinate axis, which represents a sea area, and these nodes represent TSs in the sea area, thus simulating a scene in which ships encounter at sea at random. This allows us to calculate and list the distance between the center node and each other node. Suppose that in a circular sea area with a radius of 20 nautical miles, for the central node, that is OS, its transmission coverage radius can cover the farthest node in the above range in steps of 1 nautical mile. As shown in Figure 9, we can find the number of nodes under different transmission coverage radii. Through several groups of random tests, we can see that with the increase of the launch radius, the number of other ships that this ship can connect in a sea area will increase.
# Heavy Metal Ion Detection Platforms Based on a Glutathione Probe: A Mini Review
## 3 Heavy Metal Ion Detection Platforms Using Glutathione
### Optical Techniques
#### 3.1.2 Semiconductor Quantum-Dot-Based Platforms
Sulfur- and nitrogen-co-doped carbon nanoparticles (SNCNs) by GSH were reported to detect Hg\({}^{2+}\) specifically [39]. A one-pot solid-phase thermal treatment of GSH was adopted to obtain the functionalized fluorescent carbon dots. Fluorescence spectra were recorded to quantify the fluorescence quenching degree related to Hg\({}^{2+}\) concentration. Using the synthetic method of a one-pot solid-phase thermal treatment, there were plenty of (-SH) on the carbon dots' surface, which was presumed to provide dominant affinity to Hg\({}^{2+}\) rather than other HMIs, leading to a good selectivity to other non-target ions (Ag\({}^{+}\), Al\({}^{3+}\), Ba\({}^{2+}\), Cu\({}^{2+}\), Mn\({}^{2+}\), Mg\({}^{2+}\), Na\({}^{+}\), Ni\({}^{2+}\), Pb\({}^{2+}\), Zn\({}^{2+}\), Ca\({}^{2+}\), Fe\({}^{3+}\), K\({}^{+}\), Cd\({}^{2+}\), and Fe\({}^{2+}\), 50.0 M) under the same conditions. This carbon-dot-based assay demonstrated an LOD of 0.05 nM. One test ran for about 20 min.
## 3 Optimal OBC Cooling Plate Design and Results
### Optimization and Numerical Validation
Table 6 lists the design variable values as well as the composite desirability values obtained via this optimization. In each case, the composite desirability value after optimization was higher than approximately 0.94, which is close to the maximum value of 1; in particular, it was highest when the product function was used for optimization. Next, in the verification stage, the variables (A, B, C) obtained for each optimization case were applied to the design model; this modified model was then verified through simulation. These simulation results are listed in Table 7 and as shown Figures 6 and 7. Furthermore, the velocity and pressure optimization analysis results obtained using the multi-response object function composed of all functions for Case M6 are shown in Figure 8. On comparing the initial model in Figure 3 with the optimized M6 model in Figure 6, it is clear that the velocity distribution difference related to flow rate significantly improved. The optimization results were evaluated based on the standard deviation of the responses for each channel (\(R^{\prime}_{1}\sim R^{\prime}_{6}\)), difference between maximum and minimum values, product function values, and error distance function values. Based on the values listed in Table 7 and as shown Figures 7 and 8, the different cases can be arranged in descending order of prediction accuracy for the optimal variables: M1 \(<\) M2 \(<\) M4 \(<\) M3 \(<\) M5 \(<\) M6. Thus, for the single-response optimization, optimization using the product function (\(E(R)_{mod}\)) proposed in this study showed a higher uniformity than using the error distance function (\(E(R)_{dist}\)). Furthermore, prediction that is more accurate is possible via optimization based on the multi-response object function than using the single-response object function. In particular, in the case of the multi-response surface optimization design, the prediction accuracy of the design variables can be improved by additionally introducing the system response functions \(E(R)_{dist}\) and \(E(R)_{mod}\) in the multi-response object function proposed in this study, rather than using the multi-response object function composed only of direct response functions. As previously mentioned, the maximum-minimum difference in the volume flow ratio (Table 2) for the initial model is 0.54 with a standard deviation is 0.21; in contrast, in the case of the optimized model (Table 5), the maximum-minimum difference is 0.464 for M1, and 0.048 for M6, with the reduced standard deviations of 0.164 and 0.019, respectively. These results suggest that the optimal design with the most suitable design conditions can be obtained using the proposed objective function (Equation (13)). _Appl. Sci._**2019**, \(9\), 4844