esa-sceva/satcom-chunk-collection huggingface dataset:
# Investigation on the Measurement Method for Output Torque of a Spherical Motor
###### Abstract  
The output torque is an important performance indicator of a motor. Due to the special structure of a permanent magnet spherical motor (PMSM), it is difficult to measure its torque. This paper proposes a novel method to measure the output torque of the PMSM. The proposed method uses the microelectromechanical system (MEMS) gyroscope to measure the rotor motion acceleration, which is then used to calculate the output torque based on the rotor dynamics equation. In this paper, we firstly simulate and analyze the output torque of the PMSM. Secondly, we design a torque-measuring device to measure the output torque. Thirdly, we compare and discuss the experimental and simulation results. The comparison results show that the proposed method is feasible.  
permanent magnet spherical motor; measurement of output torque; MEMS +
Footnote †: journal: Applied Sciences
## 3 The Details of the Proposed LE-KCR Algorithm
### Cluster-Head Selection  
Before selecting cluster-heads, the network is clustered to reduce the energy consumed in the process of cluster formation. In this paper, the cluster-head selection policy satisfies the following conditions:  
* In-cluster position: The nodes are unevenly distributed, so we prefer to deploy the cluster-head in the area with higher node density. According to the proximity rule [31], the proposed algorithm calculates the sum of the Euclidean distance between candidate nodes and other nodes in the cluster, and selects the node with the smallest distance as the cluster-head, which can minimize the energy consumed by the sensor to transmit to the cluster-head. However, the selected cluster-head will likely be far from regions with sparse sensor deployment.
* Distance to anchor node: Some nodes close to AUVs will directly communicate and transmit with the AUVs without clustering. According to the energy consumption model of UWOC transmission as in Figure 3, the energy consumption increases with the transmission distance, especially with long transmission distance. When the transmission distance is short, the node transmission energy consumption is extremely low. Therefore, a small number of nodes close to the AUV can communicate directly with it to reduce the workload of the cluster-head. This can help reduce the cluster-head energy consumption, improve transmission efficiency, and avoid energy waste. Moreover, during cluster-head selection, nodes closer to an AUV are more likely to be selected as cluster-heads.
* Remaining energy level: Cluster-heads consume energy far more than other nodes. If a node is repeatedly selected as a cluster-head, it may die prematurely due to excessive energy consumption, resulting in a \"void area\". Therefore, the proposed algorithm takes the node remaining energy level into account. The node with more remaining energy is more likely to be selected as a cluster-head. In order to reduce the void area, an energy threshold is set for each node. When the remaining energy is less than a quarter of the initial energy, it can hardly be selected as the cluster-head. In this way, the node can work normally as an ordinary node when its remaining energy is low, and the phenomenon of \"void area\" can be avoided.  
Figure 2: Gap statistic value vs. different number of clusters.
# An Adaptive Model Predictive Voltage Control for LC-Filtered Voltage Source Inverters
## 4 Adaptive MPC  
As shown in Figure 1b, the proposed control method is based on an augmented model and an adaptive observer to include all model uncertainties and simultaneously eliminate load-current sensors. In the following, the proposed augmented model is presented, and after that, an adaptive observer to estimate model uncertainties and disturbances will be discussed.
## 5 Results and Discussion
### Dataset and Parameter Settings  
We used the Portland metropolitan area as a case study to apply MCGRH [20]. There were 122 patients in the study area. The patients are represented by the centroid of the ZIP code tabulated areas (ZCTAs). There are 104 potential facility locations, which are community centers in the Portland metropolitan area. Figure 3 shows the patient and potential facility locations for the case study.  
The total payloads requested by patients were 366.5 kg. It is worth mentioning that there were four patients (ZCTA points 97028, 97049, 97064, and 98616) with a total demand of 15.75 kg (their respective demands were 4.75, 2.25, 4, and 4.75) who could not be reached by any facility. This is because of the battery capacity required for one trip between each of them and any of the potential facilities, which is greater than the battery capacity of the drone (777 W h). This means that the optimal coverage of patients that could be reached was 350.75 kg (95.7%). \(U\) is the capacity that each facility can offer, which is calculated based on the total demand weight of patients \(w_{i}\) and the number of facilities to be opened \(p\)[20], as shown in Equation (1).  
As assumed in [20], the number of facilities to be opened ranges from 5 to 30. We computed the traveling distance between a patient and a potential facility in miles using the Euclidean distance based on the latitude and longitude of their locations. According to  
Figure 3: Patient locations and potential facility locations in the Portland Metropolitan Region.  
the average latitude of the dataset, 1-degree latitude (\(y\)-axis distance) = 111.13976776 km and 1-degree longitude (\(x\)-axis distance) = 78.000735479 km. It was assumed that there were no obstacles. The parameters we used for drones in this paper are based on reference [20] and are shown in Table 2.
# Map-Matching-Based Localization Using Camera and Low-Cost GPS for Lane-Level Accuracy +
## 2 Related Works
As consequence, the system can suffer from the pose update arriving out-of-sequence (OOS) because of the processing delay, and if this occurs, this would require the re-ordering and re-processing of the OOS-measurements which makes this system typically requiring a more complex architecture and slower in implementation. Localization based on sensor fusion has been applied in [26]. The GNSS, LiDAR, and IMU sensors have been fused adaptively. The system uses Kalman filter to integrate the GNSS, SLAM and inertial navigation. The local map matching method is used to eliminate the accumulated error and to correct the positioning system. Fusing the measurements from vision, GPS and LIDAR has also been proposed in [12; 13]. The high cost of LiDARs limits LiDAR-based localization for being widely used in wide-spread commodities [15]. On the other hand, the low-cost and promising performance in autonomous driving applications, vision-based approaches have shown to be appealing in solving the lane-level localization problems for autonomous driving vehicles. Therefore, we further explore vision-based technologies for developing a novel low cost lane-level localization approach.
# Influence of Synthesis Parameters in Obtaining KIT-6 Mesoporous Material
## 2 Materials and Methods
### Characterizations  
The X-ray diffraction (XRD) patterns were carried out on a Rigaku Mini Flex II equipment using CuK\(\alpha\) radiation at a voltage of 30 KV and a 15 mA tube current. The data were collected at low-angle 0.5-3.0\({}^{\circ}\) 20, with step of 0.005\({}^{\circ}\) and time of 0.4 s. Thermogravimetric analysis (TGA) was performed on the STA 449F3-Jupiter equipment. About 5 mg of material was used in an alumina crucible under heating from 298-1173 K, with a heating rate of 10 K\(\cdot\)min\({}^{-1}\), by a dynamic nitrogen atmosphere with flow of 25 mL\(\cdot\)min\({}^{-1}\). The experiments of adsorption and desorption of N\({}_{2}\) were performed at 77 K on ASAP 2020 Micromertics equipment. Prior to measurement, the samples were degassed at 573 K for 10 h under vacuum. The Brumauer-Emmett-Teller (BET) method was used to calculate the specific area [21] using the Rouquerol criterion to better apply this method [22]. The mesoporous volume was determined from the \(\alpha\)-plot method for ordered mesoporous materials (\(\alpha\)-plot OMM), and the total volume obtained by the Gurvich method [23]. The pore size distributions (PSD) were derived from the adsorption branch of the isotherms using the methods of Barrett-Joyner-Halanda (BJH), Villarroel-Bezerra-Sapag (VBS) [24] and the non-local density functional theory (NLDFT) [25]. For discussion of pore diameter, the VBS method was also applied in the desorption branch. Fourier-transform infrared spectroscopy (FTIR) spectra were obtained in the medium infrared region of 4000-400 cm\({}^{-1}\) with 4 cm\({}^{-1}\) resolution in a Bomem spectrophotometer model MB102. Transmission electron microscopy (TEM) was performed on Jeol microscope equipment, model JEM-2100 CM-200 (200 KV). Before analysis, the sample was  
Figure 1: Schematic of the modifications made in the KIT-6 synthesis.  
dispersed in ethanol using low frequency ultrasound and placed in a copper screen coated with carbon. Through the TEM images it was possible to determine the pore diameter for KDT4 and KHT12 samples. The values were measured from the ImageJ software, using the arithmetic mean for 50 points, with a standard deviation less than 20%.
# A Single Motor-Driven Focusing Mechanism with Flexure Hinges for Small Satellite Optical Systems
## 2 Materials and Methods
### Definition of Mis-Alignments of the Schmidt-Cassegrain Optical System
MTF should be over 30%, which is the minimum value that would allow the optical system to produce a high-resolution image. Using a DC motor with a rotary encoder, as it will be detailed in the next section, the displacement resolution of the linear screw was identified to 0.1 \(\upmu\)m. Since the control resolution for the De-space does not exceed 0.1 \(\upmu\)m, a minimum MTF control step of 0.22% is identified.
## 3 Results
### Adsorption Studies  
#### 3.2.1 Effect of pH on Adsorption  
The influence of pH on the removal of two sulfonic acid-based dyes, MO and E110 by Fe\({}_{3}\)O\({}_{4}\)@MSN-PDMAEMA and Fe\({}_{3}\)O\({}_{4}\)@MSN-QPDMAEMA were examined, (Figure 7). Each sample (10 mg) was mixed with 200 mg/cm\({}^{-3}\) of the targeted analytes at pH ranging from 4 to 9. Fe\({}_{3}\)O\({}_{4}\)@MSN-PDMAEMA sample exhibited a weak adsorption performance toward MO with 40% extraction efficiency at pH \(<\) 6, whereas 100% extraction efficiency was achieved using Fe\({}_{3}\)O\({}_{4}\)@MSN-QPDMAEMA. The observed behavior can be explained by knowing the actual pKa value of PDMAEMA (pKa = 6.8). As the pH increased above pKa, the polymer brush became deprotonated and collapsed. When the pH was below the pKa, the polymer was positively charged and swollen due to the protonation of the tertiary amine groups. However, previous investigations confirmed that the pKa value of PDMAEMA can be shifted to below 5 depending upon the polymer's grafting density and thickness [57; 58; 59]. Therefore, it is possible that the grafting density of the sample was high, causing a significant pKa shift, especially when silane as initiator was used. Hence, it should not be surprising that Fe\({}_{3}\)O\({}_{4}\)@MSN-PDMAEMA was less efficient in removing the negatively charged dyes, due to the deprotonation of polymer chains. E110 dye exhibited a better adsorption performance which might be attributed to the presence of two sulfonic acid moieties compared with the one in MO.
## 2 Materials and Methods
### The Classifiers
#### 2.2.3 Support Vector Machines (SVM)  
The SVM classifier is particularly suitable for classifying large dimensional datasets [35; 36]. The basic optimization goal in this classifier is to maximize the margin, i.e., the between-class separation region. The margin is defined as the distance between the decision boundary (separation hyperspace) and the nearest learning samples, called the support vectors. The aim is to obtain decision boundaries with the widest possible margins. In our implementation of the SVM classifier for the multiclass problem, a scheme in which the one-vs-rest classification approach was adopted, we tested the performance of these classifiers for regularization parameter values \(C\in\{0.1,1.0,5.0,10.0\}\) for the two types of kernels, a RBF kernel and a 3 rd degree polynomial. The best classification results were obtained for \(C=1.0\) for the parametrized signals and for \(C=10.0\) for time domain analysis. Thus, we present the classification results for the regularization parameter values for which the corresponding SVM classifiers performed best.
# Multi-WiIR: Multi-User Identity Legitimacy Authentication Based on WiFi Device
## 3 Perception Principles and Key Technologies
### Principle of WiFi Sensing  
The foundational premise of WiFi sensing is predicated on exploiting the effects that targets have on transmitted signals for the purpose of identification. As signals are emitted from a transmitter and navigate through the environment, they encounter a range of static and dynamic obstacles and entities, experiencing various phenomena such as reflection, refraction, and diffraction. Gait information quintessentially mirrors the behavioral traits correlated with an individual's identity; divergent walking patterns among users induce specific alterations in signal propagation. By meticulously examining the characteristics of these altered signals, we are able to infer the legitimacy of the user. By analyzing these signal characteristics, one can infer human activity states. This analytical process is  
Figure 1: CSI amplitude features of user walking.  
delineated through the intricate analysis of CSI, which represents the link state from the transmitter to the receiver.  
Assuming \(X\) and \(Y\) denote the transmitted and received signals, this can be modeled as follows:  
\[Y=HX+N \tag{2}\]  
Here, \(H\) denotes the channel matrix and \(N\) denotes the environmental noise. WiFi signals undergo reflection from diverse objects within the environment, generating a multipath effect, as illustrated in Figure 2. The signal exhibits delay, fading, and frequency spreading on different paths, thus showing distortion at the receiver.  
From Figure 2, we can see that the signal at the receiver is reflected from multiple paths, which can be categorized into two types: static paths and dynamic paths. Therefore, the channel frequency response (CFR) can be derived as follows:  
\[H(f,t)=\sum\limits_{s=1}^{S}a_{s}^{k}(f,t)+\sum\limits_{d=1}^{D}a_{d}^{k}(f,t) \tag{3}\]  
where \(H(f,t)\) is the CFR at moment \(t\), carrier frequency \(f\), and \(\sum\limits_{s=1}^{S}a_{s}^{k}(f,t)\) and \(\sum\limits_{d=1}^{D}a_{d}^{k}(f,t)\) represent the CFR of the static and dynamic environments, respectively. \(a_{s}^{k}(f,t)\) and \(a_{d}^{k}(f,t)\) denote the initial signal attenuation and phase shift of the \(k\)-th path on the static and dynamic path at moment \(t\), the carrier frequency \(f\), and \(S\) and \(D\) are the number of static and dynamic paths.
# Benchmarking Numerical Methods for Impact and Cratering Applications
## 3 Code Verification Problem
### HOSS Setup  
The 2D HOSS set up consisted of a circular impactor with diameter 1 km and a 7.5 km radius semicircular target (Figure 1). The simulations concluded shortly after the shock wave reflected off of the boundary of the target domain, 7.5 km from the point of impact. The mesh consisted of evenly sized triangular elements that varied with the cppr of interest (5, 10, 20).  
Figure 3 illustrates the 3D HOSS simulation domain that consisted of a spherical aluminum impactor with diameter 1 km and a 7.5 km radius hemispherical target. Both the aluminum impactor and target were considered to be strengthless, and a Tillotson EOS [49] was used to describe the aluminum's bulk response. The HOSS implementation of the Tillotson EOS, which follows the procedure outlined by [PERSON] [2], is shown in Equation (4).  
\[P=\left(a+\frac{b}{(E/E_{0}\eta^{2})+1}\right)\rho E+A\mu+B\mu^{2} \tag{4}\]  
where \(E\) is the energy density, \(\rho\) is the density, \(\eta=\rho/\rho_{0}\), and \(\mu=\eta-1\). Relevant parameters for the EOS are included in Table 1. Tracer points were equally spaced throughout the domain to record the temporal evolution of displacement, velocity, and pressure as functions of distance and time. A resolution of \(\sim\)5 cppr was selected for consistency in the code comparison. The computational cost for HOSS is also prohibitively expensive for high-resolution 3D simulations. For 5 cppr, the HOSS computational domain consisted of 2.8 million tetrahedral elements divided across 1961 cores. If the resolution were 40 cppr, the same computational domain would have roughly 4.5 billion tetrahedral elements and would require 3 million cores assuming the same load balancing.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Description [49, 50]** & **Value** & **Units** \\ \hline \(\rho_{0}\) & Density & 2700 & kg m\({}^{-3}\) \\ \(a\) & Tillotson parameter & 0.5 & – \\ \(b\) & Tillotson parameter & 1.63 & – \\ \(A\) & Bulk modulus & 75.2 & GPa \\ \(B\) & Tillotson parameter & 65 & GPa \\ \(E_{0}\) & Tillotson parameter & 5 & MJ kg\({}^{-1}\) \\ \(a\) & Tillotson parameter & 5 & – \\ \(\beta\) & Tillotson parameter & 5 & – \\ \(E_{\text{iv}}\) & Energy of incipient vaporization & 3.0 & MJ kg\({}^{-1}\) \\ \(E_{\text{cv}}\) & Energy of complete vaporization & 13.9 & MJ kg\({}^{-1}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Tillotson equation of state parameters for aluminum.
## 8 The Effect of Alloying Elements on Electrical Steel
### Effect of Chromium  
Research on Fe-6.5% Si alloys manufactured through hot-rolling and warm-rolling has explored the impact of chromium additives on microstructure, desired phase, and magnetic properties. The findings indicate that the inclusion of chromium effectively reduces the size of the alloy bead, leading to a decrease in extraction density with increasing chromium content. The required amount of the phase in the alloys was also reduced by 1.0 wt%. Despite these changes, the magnetic properties of the alloy with chromium were not negatively affected [92].  
In another study by [PERSON] et al. [94], an innovative magnetic material was developed for high-frequency appliances and compared with straight electrical steel and 6.5% Si steel. This new material exhibited remarkably low iron loss at high frequencies above 5 kHz similar to that of 6.5% Si steel. This low iron loss was achieved by increasing electric resistivity or decreasing eddy current loss. Typically, increasing the resistivity of steels results in increased brittleness and reduced workability. However, this new material contributed to its satisfactory workability thanks to the addition of chromium. Additionally, the material demonstrated favourable pulse response properties and excelled in applications for power electronic devices, particularly for active filters operating at 15 kHz.
## 5 Results of Tests and Analyses  
### Methods Based on Contrast Assessments  
Attempts to estimate the line length based on the image contrast analysis as a function of changes in the length \(d_{\Delta}\) of the line for the given accuracy \(\Delta\) were carried out based on the dependencies (2), (8), and (11). The analysis was performed with the use of revealing emission signals, the source of which was the primary images presented in Figure 7a,d. The obtained results were visualized in the form of variations of the normalized values of the calculated contrasts as a function of the parameter \(d_{\Delta}\) (Figures 10 and 11). This made it possible to observe the lack of any dependence of contrast on the image line length, and thus prevent the correct estimation of the image line length for the given accuracies.  
The changing of the value of the image line length does not affect the quality of the analyzed image. The pixel amplitude values are not modified. Only the number of pixels with given amplitudes changes, which affects the average value of this parameter and the variance of the gray levels of the image ((3), (12)). In the case of the contrast evaluation method described by the dependence (9), the values of \(l_{\Delta\_max}\) and \(l_{\Delta\_max}\) remain constant as a function of changes in \(d_{\Delta}\). Hence, the calculated value of the contrast is constant, making it impossible to estimate the length \(d_{\Delta}\) of the reconstructed image line.  
Figure 9: Algorithm of the determining the correct line length \(d_{\Delta}\) of the reconstructed image.  
Figure 10: Normalized values of changing of images contrasts calculated according to Equations (2), (8) and (11) in domain of image line length \(d_{\Delta}\) for different accuracies \(\Delta\): (**a**) \(\Delta=1.0\), (**b**) \(\Delta=0.1\), (**c**) \(\Delta=0.01\), (**d**) \(\Delta=0.001\), (**e**) \(\Delta=0.0001\), (**f**) \(\Delta=0.00001\), original image Figure 7a.
## 2 Seafarer Fatigue, Sleep and Ship Noise
### Seafarers' Fatigue and Ship Noise
#### 2.3.1 Sleep Disturbance by Type of Noise on Board  
(1) Main Engine Noise  
The continuous operation of ship engines produces substantial noise levels that can interfere with seafarers' sleep. The low-frequency vibrations and noise emitted by engines can penetrate cabin walls and disturb sleep patterns. The study measured noise levels on merchant ships and found that engine noise often exceeded recommended exposure limits, reaching an average level of 84 dB in the engine rooms. Seafarers working near the engine rooms were exposed to even higher noise levels, with some areas reaching 100 dB or more. The low-frequency component of engine noise, characterized by vibrations below 200 Hz, was particularly concerning as it could penetrate cabin walls and significantly disrupt sleep patterns. Sleep disturbance due to engine noise was reported by seafarers, with a considerable number experiencing difficulties falling asleep or being awakened during sleep due to the noise [38].  
(2) Machinery Space Noise  
A study assessed noise exposure levels among seafarers working on merchant vessels and found that machinery noise exceeded recommended exposure limits, reaching levels up to 86 dB during daytime operations. Seafarers exposed to high levels of machinery noise reported significant sleep disturbances, with 41% of participants experiencing difficulties falling asleep and 35% being awakened during sleep due to the noise. The noise generated by ship machinery, including generators, pumps, and compressors, was identified as a major contributor to sleep disruptions, causing discomfort and hindering seafarers' sleep quality. Living quarters onboard were susceptible to the penetration of machinery noise, as noise levels in cabins often exceeded guidelines, leading to increased discomfort and difficulties in obtaining restful sleep [39].  
(3) Ventilation System Noise  
Through ventilation system noise, inadequately designed or maintained ventilation systems can generate noise that disrupts seafarers' sleep. Noise from fans, air conditioning units, and ductwork can be intrusive, leading to sleep disturbances and reduced sleep quality. The study involved 327 seafarers from various vessels, and their noise exposure levels were assessed using personal dosimeters. Seafarers exposed to higher levels of ventilation system noise (\(\geq\)70 dB(A)) reported significantly more sleep disturbances compared to those exposed to lower levels (\(<\)70 dB(A)). The prevalence of sleep disturbances, such as difficulties falling asleep and frequent awakenings, was higher among seafarers exposed to high ventilation system noise levels (\(\geq\)70 dB(A)). Noise from fans, air conditioning units, and ductwork accounted for a considerable proportion of the overall noise exposure among seafarers [40].