esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Fractional PMSM Model  
PMSM model has been mathematically formulated as a system of non linear differential equations. The fractional calculus is a new trend that is used to provide a better emulation of the motor physical performance and to increase the model flexibility. Thereby, the PMSM can be modeled mathematically as fractional differential equations with derivative orders lower or greater than 1 [2; 9]. In this work, two fractional PMSM models are introduced. The first one is the variable order fractional model [2], while the other one is the equal order factional model [9]. The general system of differential equations of PMSM is described as in a system of equations (Equation (1)) [2].  
\[\frac{d^{q_{1}}i_{d}}{dt} =-i_{d}+\omega i_{q}+u_{d}, \tag{1}\] \[\frac{d^{q_{2}}i_{q}}{dt} =-i_{d}-\omega i_{q}+\gamma\omega+u_{q},\] \[\frac{d^{q_{3}}\omega}{dt} =\sigma(i_{q}-\omega)-T_{L}\]  
where \(q_{i}\) (\(i\) = 1,2,3) are the fractional derivatives, the \(i_{q}\) and \(i_{d}\) and \(\omega\) are the state variables, which represent currents and motor angular frequency. The \(u_{q}\) and \(u_{d}\) are the quadrature and direct-axis stator voltage component [9; 32]. The \(\gamma\) and \(\sigma\) are the system dimensionless operating parameters. The load torque is \(T_{L}\).  
When the inputs of the system are 0 where \(T_{L}=u_{d}=u_{q}=0\), the equations of the system in 1 is reformulated as follows in the system of equations (Equation (2)) [2].  
\[\frac{d^{q_{1}}i_{d}}{dt} =-i_{d}+\omega i_{q}, \tag{2}\] \[\frac{d^{q_{2}}i_{q}}{dt} =-i_{d}-\omega i_{q}+\gamma\omega,\] \[\frac{d^{q_{3}}\omega}{dt} =\sigma(i_{q}-\omega)\]  
The PMSM behaves chaotically when the equal order fractional model parameters \(\gamma\) and \(\sigma\) have values equal to 100, 10 respectively and \(q_{1}=q_{2}=q_{3}\)\(q=0.95\) as reported in Ref. [9]. While, for the variable order fractional model, the parameters \(\gamma\), \(\sigma\), \(q_{1}\), \(q_{2}\) and \(q_{3}\) is equal to 50, 4, 0.99, 1 and 0.98, respectively as in Ref. [2]. The initial conditions at the chaotic region for equal and variable order fractional models are [\(i_{d}\), \(i_{q}\), \(\omega\)] = [2.5, 3, 1].
## 2 Physical Model Design
### Data Acquisition  
The fabricated physical model was placed in the seismic data acquisition equipment to collect seismic data (Figure 5). The physical model was placed in a pool and the distance from the water surface to the top surface of the model was controlled to be 245 mm to model the seismic propagation path of a real earthquake from the ground to the target stratum. The ultrasonic transducer used to excite the source and receive the seismic data was located 0.5 mm below the water surface. The main frequency of the source was set to 500 kHz, corresponding to 25 Hz in the real acquisition. The model acquisition 3D observation system was designed based on the real exploration observation system, and its parameters are shown in Table 2. Based on raw seismic data, conventional seismic processing procedures were applied, including noise attenuation, amplitude compensation, multiple wave attenuation, stacking, and post-stack migration [2; 41]. Unlike the real industrial seismic data, no static corrections were required because all geophones and sources were placed on the same horizontal plane and the surface of the physical model was flat.  
Figure 4: Fracture reservoirs’ locations and modeling plans with the major formations and lithologies labeled.  
Figure 3: The design of the north–south 5000 profile in the physical model. A1–A7, B1–B6, and C1–C4 show the fractured reservoir in the top of the Yingshan Formation, the fourth member of the Yingshan Formation, and the Penglaiba Formation, respectively. The specific physical parameters are shown in Table 1.
# XBeats: A Real-Time Electrocardiogram Monitoring and Analysis System
## 3 XBeats System Architecture
### Hardware Specifications
#### 3.1.1 Data Acquisition  
The data acquisition module is set to acquire the following leads purely in the analog format: two of the limb leads and the six chest leads (i.e., Leads I, II, V1, V2, V3, V4, V5, and V6), while the augmented leads (i.e., aVR, aVL, and aVF) and Lead III are computed digitally. Most importantly, to capture at least one complete heartbeat signal, a sliding window of 2 s interval is used based on the fact that the minimum and maximum heart rates are recorded between 30 and 240 beats per minute (bpm) [31].  
The device runs on the default operation mode until a new instruction is received from a connected paired BLE device as shown in Figure 2. Once the connection is established, the instructions are received through the connected BLE device. Then, the ECG patch starts a service routine that listens for new instructions to update the current operation mode. The service routine expects one of three instructions to be received at a time. One of the expected instructions is to activate the continuous mode to perform a 12-lead ECG test. This instruction comes directly from the user through the mobile application or as instructed by the healthcare provider through the backend system. The second instruction involves handling the results of the classification module. The classification module analyzes the ECG data continuously on an edge device upon receiving it from the ECG patch. The classification results are transmitted to the mobile application, and then the mobile application sends instructions to the ECG patch with the classification results. The ECG patch will continue in the triggered operation mode with three leads (i.e., Leads I, II,  
Figure 1: Higher-level architecture of the XBeats framework.  
and V1) when the heart activity is normal. On the other hand, in case of abnormal heart conditions, the classification module will return the number of leads to be activated and start the triggered mode.
## 4 Radiometric Processing Workflow
### Scene Reflectance Generation
#### 4.3.1 Reflectance Generation Based on Incident Irradiance
A further requirement is that the upward-looking detector must be properly leveled to allow accurate measurements of the downwelling irradiance. LibRadtran radiative transfer modeling ([PERSON] et al., 2015; [PERSON] et al., 2015) with a solar zenith angle of 55.66\({}^{\circ}\) with a 10\({}^{\circ}\) zenith angle showed approximately 20% differences depending on whether the sensor was facing toward or away from the sun ([PERSON] et al., 2015). When flying under cloud cover, the influence is weaker ([PERSON] et al., 2015). Examples of the impact of illumination changes on broadband irradiance measurements when using an irradiance sensor fixed on the UAV frame during eight flights carried out under sunny, partially cloudy, and cloudy conditions were presented by [PERSON] et al. (2015). In sunny conditions, the tilting of the sensor toward and away from the sun caused manifold impacts in the irradiance recordings. Both the stationary and mobile approaches allow the illumination changes to be determined during the data capture. However, the mobile solution allows the irradiance changes to be tracked at the measurement place, which has benefits in case of non-homogeneous sky conditions (still one needs to regard that due to the oblique illumination from the sun, the ground could receive a different amount of energy to the reference device on the UAV. Ideally, the second spectrometer (and eventually also including the reflectance of the radiometric reference panel) is radiometrically and spectrally cross-calibrated with the primary spectrometer. Often, this is done simultaneously, cross-calibrating both devices to a third radiometrically calibrated device [22; 27; 151].
# Internet of Things-Based Healthcare Systems: An Overview of Privacy-Preserving Mechanisms
## 10 Open Issues and Future Directions
### Bridging the Gap Between Privacy and Utility  
* Differential Privacy and Federated Learning: While these techniques offer promising avenues for secure data analysis, further research is crucial on practical challenges in deployment and exploring strategies for adapting differential privacy to smaller datasets. Addressing federated learning scalability issues with multiple healthcare providers and exploring advanced aggregation and encryption methods specifically tailored to sensitive health data [53] can unlock their full potential without compromising patient anonymity.
* Homomorphic Encryption and Secure Multi-Party Computation: Further, real-world use cases should be elaborated to illustrate the potential of these techniques. Future work should investigate how homomorphic encryption can be optimized for low-power healthcare IoT devices and consider hybrid approaches that balance computation between trusted and untrusted nodes to reduce bottlenecks [54].
## 2 Biological Signal Extraction and Processing
### EMG and EEG Signal Abstraction
#### 2.1.2 EEG Measurement and Synchronization of Two Signals  
An electroencephalograph (EEG) is typically composed of signals below 50 Hz [13], because the output of a signal has amplitude and energy output characteristics significantly smaller than EMG.  
Figure 3: Block diagram for raw EMG measurement.  
Due to the frequency band also being low, it is difficult to identify signals delivered to the muscle through simple comparisons or signals received from the muscles. Therefore, it is necessary to limit the point of special events or stimuli occurrences, and experiments should be organized to compare data before and after the stimulation to ensure the user behavior and willingness. Electroencephalography is divided into areas in which motion or emotion are deduced according to the region. Surely, it is difficult to present a pattern that precisely matches the distribution and situation of the pattern, but in general it is possible to define output characteristics that show intensity step by step according to the site. Figure 4 shows regions of the brain according to the output of the EEG signal. The area of brain waves related to movement or physical activity in humans was measured mainly by designating areas of C3 and C4 and eight points around them based on Cz as the center of the brain.
# A Photometric Stereo Using Re-Projected Images for Active Stereo Vision System
## 5 Conclusions  
In this study, we implemented a photometric stereo method using re-projected images under a stereo vision system, which does not use any external light sources. Comparing the normal vectors obtained from the traditional photometric stereo method (which uses external light sources), we confirmed that our re-projection method also works well in obtaining the normal vectors, even without external light sources. We applied each of these normal vectors to [PERSON]'s method to improve the quality of 3D scan data, and found that the precision of the 3D scan data and surface details are better than those of the stereo vision system. Our proposed method requires no external light sources; thus, it can be implemented in the existing stereo vision system without any additional equipment. This brings an advantage in size and complexity compared to the traditional photometric stereo system or [PERSON]'s configuration. Another advantage of our method is that time for taking images required for the algorithm is shorter, while having no significant difference in terms of computational cost. We expect that this study can help improve the quality of 3D scan data with minimal additional cost.  
This research was supported by the Technology Innovation Program (10065150, Development for Low-Cost and Small LIDAR System Technology Based on 3D Laser scanning for 360 Real-time Monitoring), funded by the Ministry of Trade, Industry & Energy (MOTIE, Korea) and the Korea Evaluation Institute of Industrial Technology (KEIT, Korea).  
[PERSON] and [PERSON] proposed the method of the research, designed the experiments and wrote the manuscript. [PERSON] and [PERSON] performed the experiments. [PERSON] provided the expertise in 3D measurement. All authors approved the final version of the manuscript.  
The authors declare no conflict of interest.  
Figure 12: Angular deviation of the normal vector.
## 4 Results and Discussion
### Pattern Reconfigurable Characteristics  
Figure 9 gives the curves of measured radiating patterns of reconfigurable antenna. As shown in Figure 9, the proposed antenna can switch among four operating modes (\(-17.2^{\circ}\), \(-0.6^{\circ}\), \(-1.5^{\circ}\) and \(17.3^{\circ}\)) and obtain three reconfigurable radiating patterns--i.e., left (approximately \(-17^{\circ}\)), middle (approximately \(0^{\circ}\)), and right (approximately \(+17^{\circ}\)), respectively.  
Figure 8: The measured return losses of all modes. (**a**) Simulation; (**b**) Measurement.  
Figure 9: Measurement patterns of reconfigurable antenna for four modes. (**a**) Mode 1; (**b**) Mode 2; (**c**) Mode 3; (**d**) Mode 4.  
The simulated patterns of the reconfigurable antenna are shown in Figure 6b. Comparing the simulated results with measured results, good agreements can be achieved. The errors in fabrication, such as the resolution of lithographic, the residual polyimide, thickness inhomogeneity, the asymmetry between the left RF MEMS switch and the right RF MEMS switch, and the conductor loss are the primary causes for any discrepancies observed in the measurements. When the manufacture process has a good release and flatness, the discrepancies will decrease rapidly. The measured patterns have back lobes due to coupling between the main and assistant patches.
# The Effects of Fertilization on the Growth and Physiological Characteristics of _Ginkgo biloba_ L.
## 4 Discussion
In N-poor sites, particularly during cold growing seasons, the availability of N for trees may also be low and N deficiencies may be quite common [60; 61]. Thus, it is an adaptation to store N in excess of current requirements for photosynthesis that can later be recycled and utilized during periods of lower N supply from the soil. P fertilization improved foliar P contents, and a high P fertilizer dosage had an inhibiting effect on K content. Our findings were consistent with the trend of a previous study of _Ploebe bournei_ seedlings, where foliar N and P concentrations increased, but K concentration decreased as the phosphorus addition increased [62]. The results from [PERSON] et al. (2008) showed a trend of increasing foliar K with an increase in the application of K when no phosphate fertilizer was applied. When P was applied with K on the plots with residual fertilizer, no such trend was observed [63]. [PERSON] (1992) reported nitrogen utilization of maize was accentuated when K application was supplemented [64]. We found that foliar K concentration was significantly correlated with K fertilizer application, which might owe to the high mobility of K in plants and soil. Additionally, light affects the internal redistribution of potassium, leading to higher concentrations in sun-exposed branches than in shaded ones [65; 66]. The previous studies reported that potassium plays an important role in stomatal function by maintaining turgor pressure [67]. Growth is expected to improve in response to added K as shown in sandy soils [49]. According to the nutrient content contained in the _Ginkgo_ leaves, the largest demand is N, followed by K, then P in this study. These results suggest that a new compound ratio of fertilizer is needed to optimize the growth of _Ginkgo_ timber forest planted in coastal sandy saline-alkali soil because of different foliar N, P, and K concentrations under different fertilization treatments. According to the actual situation, we can suggest an optimal fertilization formula for improving the yield and quality of _Ginkgo_ timber forests in Yellow Sea Forest Park.
# Total Internal Reflection Lens for Optical Wireless Receivers
## 2 Concept, Design, and Assembly  
### Concept  
The optical concentration ratio \(g_{\mathrm{o}}\) can be defined as the ratio of the detected power with the lens divided by the detected power without the lens. The maximum concentration ratio \(g_{\mathrm{o\ max}}\) due to the conservation of Etendue can be expressed according to Equation (1) [16] (pp. 18-22). Therefore, \(n_{1}\) and \(n_{2}\) denote the refractive indices of the mediums at the concentrator's input and output. The angles \(\theta_{\mathrm{in}}\) and \(\theta_{\mathrm{out}}\) denote the maximum input and output angles, respectively.  
\[g_{\mathrm{o\ max}}=\left(\frac{n_{2}\sin(\theta_{\mathrm{out}})}{n_{1}\sin( \theta_{\mathrm{in}})}\right)^{2} \tag{1}\]  
Figure 1 shows a cross-sectional concept render of the TIR lens and sketches incident rays for \(-5^{\circ}\), \(0^{\circ}\), and \(+5^{\circ}\) that reach the PD. The lens has a central refracting region and a surrounding TIR groove. TIR allows for larger deflection angles without large Fresnel losses in contrast to refraction [32]. Thus, it is suited to couple rays under larger angles \(\theta_{\mathrm{out}}\) to the PD chip. Therefore, the TIR lens has the potential to achieve a high concentration ratio.  
In our setup, an APD is employed. However, the TIR lens is also compatible to other types of PDs. The PD features a cuboidal surface-mounted device (SMD) package. The PD chip has a diameter of 0.5 mm, which corresponds to an active area of approximately 0.2 mm\({}^{2}\). The chip is bonded onto a carrier and molded into a transparent polymer. The polymer top is flat. Because the polymer has no integrated lens, the receiver performance is prone to misalignments between the PD and the lens. Even a displacement of 200 \(\mathrm{\SIUnitSymbolMicro m}\) takes a strong influence due to the small chip size. The problem is addressed by mounting the lens directly on the SMD package. For this purpose, the lens has mounting structures at the bottom that fit the SMD package. The mounting structures are located between the center and the TIR region. The size of the center region is determined by the SMD package. For handling and manufacturing, the lens features a cylindrical-shaped top. The flat top surface makes the lens robust against dirt particles.
## 5 Experiment Results  
To address the research problem of dynamic resource allocation and task offloading in STIN, we constructed multiple experimental scenarios on our self-developed Common Satellite Tool Kit (CSTK) platform. These experimental scenarios simulate the dynamic changes of satellite nodes, ground stations, user terminals, and inter-satellite communication links, aiming to validate the effectiveness of the proposed task scheduling and resource optimization model in minimizing system latency and maximizing resource utilization. Figure 2 illustrates the satellite network topology used in our simulations, showing the distribution of satellites, ground stations, and their communication links. The yellow coverage areas represent the effective communication regions of different satellites, while the red and blue lines depict inter-satellite and ground communication links, respectively. The CSTK platform provides comprehensive orbital simulation and mission planning functionalities, enabling precise simulation of satellite trajectories and their communication contact windows. This ensures that accurate satellite trajectory and communication delay information are provided for the experiments, thereby supporting the implementation and validation of the model.
## 2 Materials and Methods
### Monotonic Triaxial Tests  
Figure 1 presents the result of monotonic triaxial tests with different confining pressures. The parameters on the figures are the deviator stress \(q=\sigma^{\prime}{}_{1}-\sigma^{\prime}{}_{3}\), mean effective stress \(p^{\prime}=(\sigma^{\prime}{}_{1}+2\sigma^{\prime}{}_{3})/3\), axial vertical strain \(\varepsilon_{1}\), and excess pore water pressure \(\Delta u\). On Figure 1a the effective stress paths on the \(p^{\prime}\)-\(q\) plane are presented. The slope of the critical state line (CSL) is given as \(M=1.33\). The monotonic triaxial tests results indicate the typical compacted soil behavior. This type of soil response can be called quasi-overconsolidated due to the reverse curvature of the stress path. The failure points were estimated based on the intersection between the stress path and the CSL. The deviator stress at failure \(q_{max}\) was 46.04, 93.66, and 153.55 kPa for confining pressures of 45, 90, and 135 kPa respectively. The pore pressure presented in Figure 1c shows peak values of the pore pressure and therefore indicates the maximum axial strain \(\varepsilon_{1,\ max}\) achieved by sample when failure  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Index Properties** & **Value** \\ \hline Specific gravity, \(G_{s}\) (g/cm\({}^{3}\)) & 2.67 \\ Optimal moisture content, \(w_{opt}\) (\%) & 12.3 \\ Maximal dry density, \(\rho_{d\ max}\) (g/cm\({}^{3}\)) & 1.96 \\ Liquid limit, \(w_{L}\) (\%) & 42.0 \\ Plasticity index, \(I_{P}\) (\%) & 18.4 \\ Clay fraction, \(f_{C\Omega}\) (\%) & 19 \\ Silt fraction, \(f_{Si}\) (\%) & 41 \\ Sand fraction, \(f_{Sa}\) (\%) & 40 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Index properties of tested sandy silty clay.  
occurred the value of \(\varepsilon_{1,\,max}\) was 1.102%, 2.277%, and 3.439% and the maximal excess pore water pressure \(\Delta u\) was 27.39, 47.83, and 71.32 kPa for confining pressure 45, 90, and 135 kPa respectively.
# AUTOSAR Runnable Periods Optimization for DAG-Based Complex Automobile Applications
## 3 Background and Problem Description
### Control Performance Model  
A control system's performance can be defined in many different aspects. For example, its robustness to external disturbances, control stability, and control error can be such performance metrics. In general, a control system's performance is affected by its timing behavior as well as the control algorithm itself [31]. In this paper, we assume that the control algorithm is given as a fixed system property. Thus, our control performance model is about how the system's temporal properties affect the control performance. More specifically, we consider two distinct temporal properties: control period and end-to-end delay of the target control system.  
In the AUTOSAR timing extensions, two latency constraints are defined: (i) data age timing constraint and (ii) reaction time constraint [20; 32; 33]. The specification states that when an actuator command is periodically produced, its source input (sensing) value's age should be maintained within a specified timing constraint. The reaction time constraint is also considered when an external event, such as pressing a button, should be reacted within a specified timing constraint. In this paper, since we are considering periodic workloads, the data age timing constraint is considered.  
Based on the timing model, there are several ways to build a control performance model. One is to measure the resulting performance of the system by artificially controlling the temporal parameters. Simulation tools [34; 35] can also be used to predict the control performance when we cannot directly measure the system under investigation. To provide a more general model, [PERSON] and [PERSON] [6] introduced a linear control cost function as  
\[J(T,\Delta)=\alpha T+\beta\Delta \tag{10}\]  
where \(T\) is the control period, and \(\Delta\) is the end-to-end delay from the sensors to the actuators. Note that \(\alpha\) and \(\beta\) are constants that define the characteristics of the control target plant. Figure 2 shows an example control cost function. The intuition behind it is that if we give control commands to the actuator more often (frequently), its control cost gets smaller, and in the same manner, if we decide the control command with more fresh (recent) sensor data with shorter delays, the cost gets smaller, again. In general, the cost function \(J\) can be a nonlinear function of \(T\) and \(\Delta\), however, it can be approximated as a linear function as in [6; 7; 8; 9; 10; 16]. In this paper, we use this linear approximate control cost function as the optimization objective.  
Figure 2: Control cost function, which is a linear function of the system’s control period and delay [5].