esa-sceva/satcom-chunk-collection huggingface dataset:
# Parametric Analysis of Control Techniques for 15 MW Semi-Submersible Floating Wind Turbine
## 3 Control Algorithms
### Feedforward Control  
The feedforward control algorithm was designed to mitigate the rotor speed fluctuations of the target FOWT in regions where the wind speed is higher than the rated wind speed. The block diagram of the feedforward control algorithm is shown in Figure 7.  
Figure 5: Frequency response function of (**a**) torque loop gain at 8 m/s and (**b**) pitch loop gain at 18 m/s.  
Figure 6: Comparison of time-domain data without and with detuning. The black line represents the wind speed input used in the simulation. The red and blue lines indicate the cases without and with detuning applied, respectively.  
The required blade pitch angle increment \(\delta\beta_{FF}\) to reduce the rotor speed and the power fluctuations is calculated based on the variation in the estimated wind speed \(\delta v\) and the variation in the measured rotor speed \(\delta\Omega_{r}\), utilizing each of the precomputed pitch control sensitivities in the algorithm [7 he classicalST \(\frac{\partial\beta}{\partial V}\), \(\frac{\partial\delta}{\partial\Omega_{r}}x\)th onf] obtained offline through a function minimization algorithm [23]. The obtained blade pitch angle increment is adjusted by the feedforward control gain \(k_{ff}\) as the control parameter. The wind speed estimator estimates the wind speed by using the relationship between the aerodynamic torque \(T_{r}\) derived from the equation of motion for the generator and that from the aerodynamic coefficients [24; 25].
## 3 Results and Discussion
### Optimization of the SI-HLIME Parameters Using Box-Belnken Design  
In order to optimize the three parameters (\(V_{\text{S}}\), \(V_{\text{ACN}}\), \(V_{\text{valt}}\)) selected by the 2-level FFD, an RSM consisting of a Box-Behnken design (BBD) was further utilized due to its widespread application for the optimization of sample preparation [32]. A total of 17 experimental runs were performed and each parameter was examined at three levels (\(-1\), \(0\), \(+1\)). The experiments were randomized to avoid systematic error. The examined parameters, their respective levels and the matrix of the BBD are shown in Table 2. Multivariate regression analysis was used to construct a fitted second-order polynomial quadratic model, as follows  
\[y(X_{1\leq i\leq 3})=\beta_{o}+\sum_{i=1}^{3}\beta_{i}X_{i}+\sum_{i=1}^{3}\beta_{ ii}X_{i}^{2}+\sum_{1}^{3}\sum_{j=1}^{i-1}\beta_{ij}X_{i}X_{j}+\varepsilon\]  
where the experimental response is indicated by \(y(X_{1\leq i\leq 3})\), the factor is indicated by \(X_{i}\), the constant is indicated by \(\beta_{\alpha}\), the linear coefficients are indicated by \(\beta_{i}\), the quadratic coefficients are indicated by \(\beta_{ii}\) and \(\beta_{ij}\) and the random error is indicated by \(\varepsilon\). Augmentation of the design was proposed for both analytes since high-order models were aliased.  
For the statistical analysis and the experimental designs, the Design-Expert(r) 13 software (Stat-Ease Inc, Minneapolis, MN, USA) was utilized. The estimates of the coefficients for the models were calculated using least squares multi-linear regression and analysis of variance (ANOVA) was used for model validation (Tables S1 and S2, Supplementary Material). In order to obtain a more \"realistic\" model, exclusion of the non-significant factors (\(p>0.05\)) was performed by means of the \"backward elimination\" process.  
The calculated \(R^{2}\) and the adjusted \(R^{2}\) were estimated to be 0.9033 and 0.8452 for PIR and 0.9149 and 0.8638 for MEL, respectively, showing that the resulted models adequately explain the response. The validity of the models was assessed through the examination of the plot of the residuals in comparison with the predicted values and the normal probability plot of residuals. As can be observed from Figures S2 and S3  
Derringer's desirability function (D) was used to find the optimum conditions for the SI-HLLME procedure. The desirability graphs and the prediction profiles are shown in Figure 3. The optimum values of \(V_{\text{ACN}}\), \(V_{\text{S}}\) and \(V_{\text{salt}}\) were estimated to be 598, 505 and 485 \(\mu\)L, respectively. For simplicity, the above values were rounded off to 600, 500 and 480 \(\mu\)L and adopted. The confirmation of this set of experimental conditions was conducted by performing six repetitive extractions. The variations between the experimental and the predicted values were <5%, which is considered satisfactory.
# A New Sensor System for Accurate 3D Surface Measurements and Modeling of Underwater Objects
## 2 Materials and Methods
### Data Recording in Movement
#### 2.2.3 Motion Velocity and Direction Estimation  
The estimation of the sensor motion can be realized using several methods.  
The simplest method is the use of constants for \(v\) and \(d\) according to predefined control inputs (for example, if the velocity of the carrying ROV is known) and a default standard value for \(d\). Alternatively, data obtained by the IMU and current 3D measurement data from the last scan (real-time measurement) can be used for estimation. This would require additional calculation, which must be provided with low latency. This kind of estimation has the advantage of quick parameter modifications to actual ROV speed and direction changes.  
In this work, we estimated the full six-degree-of-freedom sensor trajectory using color camera and IMU data. The developed motion compensation assumes the simplified case of a constant object distance. The position of the sensor is extrapolated by visual-inertial odometry [48] from the average pixel shift \(\Delta s\) = (\(\Delta x\), \(\Delta y\)) over all structured image points of the color camera, combined with IMU data and 3D data iterative closest point (ICP) alignment [49]. The calculated pixel shift, \(\Delta s\), is transformed by back projection to the expected shift in the rectified images of the measurement cameras for correlation determination using a standard distance and color camera frame rate.  
Using the above assumptions and the approximated motion velocity and direction, the recorded object points in motion in the same image sequence are mapped onto the same image point. Then, all subsequent calculation steps, such as filtering, correlation, triangulation, etc. (see [40]), are applied in the same way as for static measurements. To avoid the additional performance requirement for shifting of images, the correlation determination is extended by offset \(\Delta s\). This means that the temporal correlation calculation is processed without performance degradation.
## 1 Introduction
In addition, it allows the assessment of only a small fragment of the skin, and during the sample preparation, some dye may be displaced [21; 22]. However, it should be emphasized that the effectiveness of removing pigments implemented into the skin is often an individual matter because obtaining a satisfactory final effect depends not only on the composition of the pigments, location, and depth but also on the age, skin phototype, general health, medications, and supplements, health history as well as immunity level. Therefore, it is necessary to be able to adjust the laser operating parameters to individual skin characteristics [17]. So, the hyperspectral analysis proposed in this study is free of the disadvantages of other methods. Hyperspectral imaging (HSI--hyperspectral imaging) as the method of optimizing permanent makeup laser removal may be helpful in improving efficiency and reducing the risk of side effects [14; 17]. Hyperspectral imaging is a method that combines two non-invasive techniques: imaging and spectroscopy. As a result, it is possible to determine both the optical (image) and spectral (reflectance/absorption profile) properties of the analyzed objects in relation to each pixel of the image [23]. The hyperspectral camera acquires the image by registering radiation with a specific energy intensity (I) in a specific range of spatial coordinates (x,y) and wavelength (\(\lambda\)). The values of \(\Delta\)x and \(\Delta\)y determine the spatial resolution of the image, while the value of \(\Delta\lambda\) determines the spectral resolution [24]. As a result, a series of images of the same object is obtained--in this case, pig skin with dyes implanted in it--each recorded at a different wavelength. A complex analysis of the optical spectra of the tested object is possible, as well as the analysis of the entire object using the methods of image analysis and processing in a wide spectral range resulting from the spectral range of the camera. Molecular hyperspectral imaging, on the other hand, is a technique where each pixel that contains a complete spectral signature in the entire spectral range is additionally analyzed. It is possible to obtain not only a spatial image of the skin with pigments (up to the depth of photon penetration in the spectral range of 400-1000 nm) but also spectral information, which is extremely useful in the quantitative assessment of the content and distribution of pigments [14; 25; 26]. The aim of the study was to verify the hypothesis about the functionality of a hyperspectral camera to assess the effectiveness of dye removal in permanent makeup.
###### Abstract  
As a clean and renewable energy source, wave energy is of great significance in solving primary energy shortages and environmental pollution. Direct-drive wave power systems consisting of linear generators have attracted the attention of researchers from various countries. Linear Switched Reluctance Generator has the advantages of simple structure, sturdiness, reliable operation, suitable for harsh environments, and easy maintenance, aiming at the problem of single-sided magnetic pull force and serious coupling of phase winding of traditional linear switched reluctance generator, a Double-sided Linear Switched Reluctance Generator (DLSRG) for wave power generation is designed, and its electromagnetic characteristics (including coupling characteristics, magnetic saturation characteristics, and magnetic tension characteristics) are analyzed to verify the rationality of the structure and parameter selection. Finally, the power generation performance is studied. The joint simulation results show that the structure design of DLSRG is reasonable, overcomes the problem of single-sided magnetic pull force, the phase-to-phase coupling is negligible, and it has continuous power generation capability, and the power generation efficiency is as high as 80.6%. Therefore, DLSRG designed in this paper is suitable for wave power generation.  
double-side linear switched reluctance generator; wave power generation; electromagnetic characteristics; generation characteristics
# Synchronization of Heterogeneous Multi-Robotic Cell with Emphasis on Low Computing Power
## 4 Validation of the Proposed Solution
### Implementation of the Proposed Solution and Discussion
#### 4.2.1 Master--Mitsubishi Melfa RV-2 FB-D  
In the case of the Mitsubishi CR750-D controller of the robotic manipulator Mitsubishi Melfa RV-2 FB-D, the algorithm application was implemented in MELFA BASIC V language [29]. Communication with the slave object was realized through the initiated TCP/IP channel (Box 1).
# PV-Powered Electric Vehicle Charging Stations: Preliminary Requirements and Feasibility Conditions
## 3 Driving Characteristics and Charging Profiles  
People have different attitudes and living styles, and therefore, they differ in their driving patterns, which significantly affects the spatial-temporal distribution of the charging load (e.g., EV battery). However, the EV charging load profiles vary and depend mainly on the type of charging preferences, EV user habits, and energy consumption rates. In [34] the driving data for different users are analyzed in time and space dimensions to understand driving patterns of different populations, grouped by age as a demographic attribute. The daily driving distance is the factor to compare the behavior of different EV users. The U.S. National Household Travel Survey dataset, as in [34], shows the daily driving distance and where the elderly drive for a short distance. Based on these data, the daily average urban/peri-urban trip can be deduced as 20-40 km. With two driving modes, normal drive with 15 kWh/100 km and eco-drive with 10 kWh/100 km, therefore, the daily energy consumption rate is 3-6 kWh for a normal drive and 2-4 kWh for an eco-drive. Considering the above and an average EV battery, e.g., 50 kWh, Figure 2 shows the required time for 80% charging (green lines) and 10% charging (orange lines) depending on power delivered by the terminal and accepted by the EV battery.  
For delivered and accepted power of 1.8 kW, the time required is more than 24 h to charge 80% and around 3 h to charge 10% of EV battery capacity. On the other hand, the time required is around 1 h to charge 80% and around 6 min to charge 10% of EV battery capacity, for delivered power of 100 kW and accepted power of 50 kW. Thus, a 10% increase of charge, e.g., 5 kWh, is possible with reasonable charging time depending on delivered/accepted power. Therefore, the EV charging load profile must be built to increase PV benefits for PV-powered EV charging stations.
## 1 Introduction  
Phonophobia and photophobia are symptoms often associated with migraine [1], affecting over 80% of the patients [2, 3]. Although the pathophysiology related to these symptoms in migraine is unclear, it is well-known that there is overall increased responsiveness to auditory and visual stimuli during the attacks [4, 5]. Overall, a functional change of the hypothalamic-thalamo-brainstem networks is suggested as a source of photophobia and phonophobia in migraineurs [4]. Also, these symptoms remain at a lower level during the headache-free period [6, 7, 8, 9].  
Photopbobia and phonophobia have been studied through questionnaires ascertaining the presence of these symptoms during the headache attack, with a focus on the diagnostic improvement of the migraine-related photophobia and phonophobia [10, 11, 12]. Previous studies have also investigated the level of these symptoms, especially photophobia, in different subtypes of migraine, but with conflicting findings [3, 13, 14, 15, 16]. Based on neuroimaging studies showing higher visual network connectivity and visual cortex activation in migraineurs with aura in contrast to those without aura [13, 14], as well a visual-nociceptiveintegration at the brainstem level in chronic migraineurs [17], it is expected that there will be higher photophobia in patients with aura and chronic migraine. [PERSON] et al. [13] and [PERSON] et al. [16] confirmed this hypothesis, in contrast to other studies [14; 15], but they did not differentiate these symptoms in the ictal and interictal period. Regarding phonophobia, it has been suggested to be associated with migraine frequency [3; 16] but seems not related to the presence of aura [16].  
Visual and auditory discomfort may lead to behavioral consequences and a negative impact on migraineurs' quality of life, affecting basic tasks and social activities. Patients with interictal photophobia are more likely to manifest symptoms of depression and anxiety compared to migraine patients without interictal photophobia [18]. Phonophobia, besides also being associated with anxiety, is associated with stress that may induce emotional and cognitive reactions, such as difficulties in concentration [19]. Furthermore, both visual and auditory stimuli are likely to trigger a migraine attack [5; 20]. Therefore, the knowledge of the photo- and phono-phobia perception in different migraine subtypes can contribute to understanding how the sensory stimuli can affect the daily routine of migraine sufferers even on a headache-free day.  
Accordingly, this study aimed to evaluate the self-perception of photophobia and phonophobia intensity of migraineurs with and without aura, chronic migraineurs, and control individuals during daily activities. We hypothesize that during the pain-free period, patients with migraine with aura and chronic migraine would be more sensitive than migraineurs without aura and the controls.
## 3 Results and Discussion
### CO\({}_{2}\) Adsorption Performance
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Support Materials** & \begin{tabular}{c} **Surface** \\ **Modification** \\ \end{tabular} & \multirow{2}{*}{\(\mathbf{S_{SET}}\) (\(\mathbf{m^{2}/g}\))} & \begin{tabular}{c} **Concentration** \\ **of CO\({}_{2}\)** \\ \end{tabular} & \
\begin{tabular}{c} **Adsorption** \\ **Temp. (\(\lx@sectionsign\)c)** \\ \end{tabular} &
\begin{tabular}{c} **CO\({}_{2}\) Uptake** \\ **(mmole/g)** \\ \end{tabular} & **Reference** \\ \hline ACFs & KOH & 1565 & 1 atm & 25 & 2.74 & This study \\ \hline ACFs & KOH & 527–1720 & 1 bar & 25 & 1.9–2.6 & [34] \\ \hline ACFs & KOH & 780 & 1 atm & 25 & –1.0 & [37] \\ \hline ACFs & & 1461 & 1 atm & 25 & 2.80 & [38] \\ \hline ACFs & & 140–925 & 1 atm & 20 & 0.55–2.28 & [39] \\ \hline ACFs & & 907–1914 & 1 bar & 25 & 0.6–0.7 & [40] \\ \hline Carbon & & 1343 & 1 bar & 25 & 1.50 & [14] \\ \hline Hierarchical carbon & \multirow{2}{*}{KOH} & \multirow{2}{*}{1556} & \multirow{2}{*}{1 bar} & \multirow{2}{*}{25} & \multirow{2}{*}{3.10} & [25] \\ nanosheet & & & & & & \\ \hline Carbon sphere & Ethylenediamine & 550 & 1 atm & 25 & 2.90 & [41] \\ \hline Carbon & SOCI\({}_{2}\), TEPA & 823 & 1 bar & 25 & 3.2 & [20] \\ \hline Carbon & KOH, urea & 862 & 1 bar & 25 & 2.91 & [29] \\ \hline Silica-coated & \multirow{2}{*}{Polyethylene-imine} & \multirow{2}{*}{375} & \multirow{2}{*}{1 bar} & \multirow{2}{*}{25} & \multirow{2}{*}{1.41} & [42] \\ MWCNTs & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparisons of CO\({}_{2}\) uptake on the adsorbents in this study with various supporting materials from the literature. Figure 7: Correlation of equilibrium CO\({}_{2}\) uptake at CO\({}_{2}\) = 0.15 atm and 25 \({}^{\circ}\)C with the **(a)** BET surface area (\(\mathrm{S_{SET}}\)) and micropore area (\(\mathrm{S_{mi}}\)); **(b)** micropore volume (\(\mathrm{V_{mi}}\)) and ultramicropore volume (\(\mathrm{V_{ultra}}\)); **(c)** surface N and O content; **(d)** NO\({}_{2}\) (%) in N-groups; **(e)** percentage of O-functionalities.
## 3 Results
### Mechanical Properties  
Figure 9 represents mechanical properties of ADC12 alloys with different Y contents. It can be observed from the figure that the UTS of the unmodified alloy was 172.4 MPa, the elongation was 1.9%, and the hardness was 75.9 HV. When the amount of Y addition was \(<\)0.2 wt%, the UTS, elongation, and hardness of the alloys were significantly improved with the increase of Y addition. The 0.2 wt% Y/ADC12 alloy showed the best UTS (255.6 MPa), elongation (3.3%), and hardness (94.6 HV), which were 48.3%, 72.9%, and 24.6% higher than those of the matrix, respectively. An appropriate increase in the hardness of the alloy is beneficial to the subsequent secondary processing due to the softness of the aluminum alloy [24; 25]. However, further increasing the Y content caused a decline in the UTS, elongation, and hardness. Moreover, the UTS and hardness of the alloys with different Y contents were all significantly improved after heat treatment. The UTS and microhardness of the alloy with 0.2 wt% Y were enhanced by 57.9 MPa and 38.1 HV, respectively. A further increase in temperature of the solid solution to 540 \({}^{\circ}\)C resulted in a decrease in both UTS and elongation. After the rare earth modification and solid solution process, the 0.2 wt% Y/ADC12 alloy revealed the best UTS (313.5 MPa), elongation (3.2%), and hardness (132.7 HV), which were 81.9%, 69.7%, and 74.8% higher than those of the as-cast ADC12 alloys without any treatment, respectively.  
The mechanical behavior of the alloy primarily depends on a sound microstructure. The refinement of the \(\beta\)-phases reduces the probability of these particles tearing the matrix under applied stress. On the other hand, the internal defects of the fine-grained eutectic Si phases were less than the coarse-grained Si phases, which leads to the maintenance of intrinsic fracture stress. As mentioned before, the Si, Fe, and \(\alpha\)-Al phases were refined due to the addition of Y. When the amount of added Y was 0.2 wt%, the eutectic Si phases completely transformed into fibrous or granular structures. The flaky Fe-rich phases exhibited the smallest size compared to the others. Thus, the mechanical properties were greatly improved. Meanwhile, the mechanical properties of the alloy also been improved due to the refinement of \(\alpha\)-Al phases. According to the Hall-Petch equation, grain boundaries are obstacles to dislocation movement. Therefore, the finer the grain,  
Figure 8: The distribution of Si, Cu and Fe elements of alloys with Y addition on SEM-EDS elemental maps: (**a**) 0 wt%; and, (**b**) 0.2 wt%.  
Figure 9: Mechanical properties of alloys with different concentrations of Y.  
the higher the mechanical properties of the alloy. Thus, the mechanical properties of the alloy after heat treatment were further improved. However, the excessively high temperature led to a decrease in mechanical properties due to the rapid growth of Si phases, grains, and even the appearance of cavities. This phenomenon weakens the effect of the particle's morphology and the distribution of the mechanical properties.
# A Phase Error Correction System for Bioimpedance Measurement Circuits
## 3 Experimental Results
### Frequency Response  
Figure 8 further justifies the phase difference observed between the measured signal Vsense and demodulation signal V\({}_{\text{in}}\), as in Figure 6a, for a load of 1 k\(\Omega\) and frequency of 300 kHz. Whilst the phase difference observed in Figure 6a is about 19.44\({}^{\circ}\), as calculated from the phase detector pulse width, the erroneous phase observed in Figure 8 for 1 k\(\Omega\) was notably 23\({}^{\circ}\). As such, the additional phase difference of 4\({}^{\circ}\) may be due to non-idealities of the lock-in amplifier.  
Figure 9a,c showed the impedance magnitude as a function of frequencies for RC loads of (1 k\(\Omega\), 244 pF) and (3 k\(\Omega\), 33.5 nF), respectively. Figure 9a,c shows no significant improvement with phase compensation unlike the counterpart figures in Figure 9b,d, which showed significant improvement in phase error correction. The percentage phase errors for Figure 9b,d were further illustrated in Figure 10b, where the SD system without compensation for RC loads of (1 k\(\Omega\), 244 pF) and (3 k\(\Omega\), 33.5 nF) exhibited phase errors of approximately 19% and 21% at 300 kHz, respectively. After phase compensation, however, they were significantly reduced to 2\({}^{\circ}\) and 4%. These findings underscore the critical role of phase compensation in enhancing accuracy. The clinical significance of these phase errors becomes evident considering previous research [28], where phase differences between healthy and cancerous tissues were approximately 20\({}^{\circ}\), 17\({}^{\circ}\), and 11\({}^{\circ}\) at frequencies of 100 kHz, 200 kHz, and 300 kHz, respectively. As such, without proper phase correction, there is a substantial risk of diagnostic unreliability, potentially leading to misidentifying healthy tissue as cancerous. Therefore, accurate phase compensation in diagnostic procedures using SD systems is needed.  
Figure 9: Impedance magnitude and phase for RC load (3 k\(\Omega\), 33.5 nF) as a function of frequency. **(a)** Impedance magnitude for RC load (1 k\(\Omega\), 244 pF) as a function of frequency. **(b)** Impedance phase for RC load (1 k\(\Omega\) & 244 pF) as a function of frequency. **(c)** Impedance magnitude for RC load (3 k\(\Omega\), 33.5 nF) as a function of frequency. **(d)** Impedance phase for RC load (3 k\(\Omega\), 33.5 nF) as a function of frequency.