esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The ability of some safflower lines to produce higher seed yield under drought conditions with higher leaf chlorophyll content, RWC and water potential was associated with their genetic traits [18]. Safflower is known to transfer a large percentage (65-92%) of its pre-flowering stored assimilates into seeds under drought stress at the end of the season [19]. The results of the evaluation of safflower cultivars under normal irrigation and drought stress during the seed-filling phase showed that water deficits significantly reduced seed yield in all genotypes by decreasing all components [20]. [PERSON] et al. [21] pointed out that water deficit stress in safflower plants reduces seed yield mainly by reducing all yield components due to reduced photosynthesis. [PERSON] et al. [22] reported that the number of branches and capitula per plant are the most important effective traits for safflower yield and that they have the highest positive indirect effects on seed yield. The results of a path analysis of traits related to safflower seed yield showed that under normal conditions, the number of seeds in the capitulum, 1000-seed weight and stem diameter and under water deficit conditions, the number of seeds in the capitulum, 1000-seed weight, days to 50% flowering and capitulum diameter had the highest positive direct effects on seed yield [23]. [PERSON] et al. [24], who studied safflower plants under water deficit, found that drought reduced seed yield by decreasing the number of seeds in the capitulum. The results of the evaluation of safflower genotypes under normal and saline field conditions showed that the number of capitula per plant, the number of seeds in the capitula, 1000-seed weight and seed yield decreased significantly under saline conditions. In addition, the number of capitula per plant was the strongest predictor of seed yield [25]. There are few reports on the response of safflower plants to drought stress in marginal areas early in the season. The aim of this study was to identify physiological traits for the selection of drought-tolerant safflower genotypes for cultivation in marginal and arid areas.
## 3 Results and Discussion
### The Influence of Shear Time
#### 3.3.2 MSCR Test  
In order to study the influence of shear time on the performance of SBS-modified asphalt, the above samples were examined with the MSCR test. The calculated R and Jnr are shown in Figure 8.  
At the temperature range of 70 \({}^{\circ}\)C to 82 \({}^{\circ}\)C, a 1 h shearing time shows the weakest elastic network, hence its MSCR results are also the worst (low R with high Jnr). SBS-modified asphalt that was sheared for 2 h and 4 h had better results, with 4 h shearing time exhibiting the best results.  
Figure 8: SBS-modified asphalt MSCR test results under different shear times (**a**) R, (**b**) Jnr.  
Figure 7: Fluorescence microscopy test of SBS-modified asphalt with different shearing times (100 times).  
Based on above results, the longer the shearing period, the more uniformly the SBS is dispersed in the plain asphalt, the greater the area ratio, and the stronger the network structure is [33]. Although the performance of SBS-modified asphalt sheared for 2 h is slightly lower than that of SBS-modified asphalt sheared for 4 h, the difference is insignificant and 4 h shearing might be too costly and ineffective during industrial production. Therefore, the recommended shearing time is 2 h.
# Research on Performance Evaluation and Optimization Theory for Thermal Microscope Imaging Systems
## 4 Analysis
### Evaluation Model Simulation Analysis
#### 4.2.2 Simulation Analysis of MRTD in the ITD  
Assuming the target emissivity difference is 0.01, the simulated MRTD (\(f\)) and MRTD (\(T_{B}\)) curves of ITD in different bands are shown in Figures 9 and 10.  
From Figure 9, it can be seen that the MRTD of the ITD increases with increasing spatial frequency, indicating that under the same conditions, the higher the spatial frequency, the more difficult it is to distinguish the object to be measured.  
By comparing Figures 8 and 10, it can be seen that the two trends are consistent, so the analysis and conclusions regarding Figure 8 are equally applicable to ITD, and therefore will not be repeated here.  
Figure 9: Relationship between spatial frequency \(f\) and MRTD (ITD).  
Both Figures 9 and 10 show that the expressions of MRTD about \(f\) and \(T_{B}\) are monotonically increasing functions. As the spatial frequency \(f\) and background temperature \(T_{B}\) increase, MRTD also tends to increase monotonically. The temperature resolution ability of ITD under the medium-wave is better than the long-wave. For example, when the spatial frequency \(f=0.2 cyc\times mrad^{-1}\), the MRTD of the ITD in the long-wave and medium-wave are1520.7 \(mk\) and 682.9 \(mk\). When \(T_{B}=300\) K, the MRTD of the ITD in the long-wave and medium-wave are 1617.8 \(mk\) and 721.3 \(mk\).  
The imaging principle of IPD (refigerated detector) is based on the fact that the detector absorbs the photons of the target or background radiation to produce a photoconductive or photovoltaic effect. It is suitable for working at a low background temperature (70-200 K). The simulation results show that the IPD maintains a high temperature resolving ability in both the medium-wave infrared thermal imaging and the long-wave infrared thermal imaging in a low background temperature environment. The imaging principle of ITD (non-refrigerated detector) is based on changes in the resistivity or polarity of the detector caused by the absorption of energy by thermal radiation. Simulation results show that the temperature resolution of ITD in the medium-wave infrared band is higher than that of the long-wave infrared band. Figures 9 and 10 show that as \(f\) and \(T_{B}\) increase, the gap between the MRTD in the medium-wave and the long-wave becomes gradually larger. This shows that ITD is more sensitive to the non-uniformity of emissivity in the long-wave infrared band and has higher sensitivity for observing natural objects that do not generate heat with a small difference in emissivity, and it is easier to obtain the difference in target emissivity.
###### Abstract  
The parameters of electromagnetic emissions from the antenna of a wearable radio communication module (parameterizing device functionality) were investigated at different positions near the body where an antenna is located. The specific absorption rate (SAR) coefficient was also investigated as a way of parameterizing the absorption of electromagnetic radiation in the user's body adjacent to the antenna in various locations. The modeled exposure scenarios concerned a body-worm device with a 2.45 GHz ISM band antenna (used, e.g., for Wi-Fi 2G/Bluetooth applications). The antennas were modeled as follows: (1) located directly on the body (considered to be a model of a disposable, adhesive device) or (2) next to the body (considered to be a model of a classic, reusable, wearable electronic device located inside a plastic housing). Several body sections adjacent to the antenna were considered: head, arm, forearm, and chest (simplified and anatomical body models were used). The numerical models of the exposure scenarios were verified by relevant laboratory tests using physical models. It was found that the use of simplified models of the human body (numerical or physical) may be sufficient when analyzing antenna performance and SAR in a user's body, such as in studies regarding microwave imaging and sensing, wireless implantable devices, wireless body-area networks or SAR estimation.  
Antenna performance; electromagnetic hazards; general public exposure; numerical simulations; occupational exposure; specific absorption rate (SAR); wearables; wireless communication +
Footnote †: journal: Applied sciences  
Article
# Fault Analysis of a Small PV/Wind Farm Hybrid System Connected to the Grid
## 1 Introduction
In this reference, the model maintains its stable behavior after disturbances in the active and reactive power of the load as well as the solar and wind energy input. More difficulties have recently arisen with a system that combines solar and wind power generation connected to the main power grid. Improved power quality injection, maximum power extraction, and issues with hybrid power systems' connections to the electrical grid under all circumstances are some of these difficulties [13; 14]. This work investigates the detail in creating a model that simulates the behavior and changes over time, design, and control of a hybrid power generation system that combines wind and solar power. Two PV arrays and two sets of 1.5 MW wind turbines are connected into the main AC bus of the proposed hybrid system to increase system efficiency. To maximize power under standard test conditions (STCs), the method of obtaining the highest level of power output is used for both PV arrays. SCIGs, which are utilized as constant speed generators and are connected directly to the grid, are the foundation of the wind system. A pitch angle control approach is suggested to keep the wind turbine's rotor speed stable. The goal of this research is to is to evaluate and analyze the functioning of a combined solar and wind power system under fault scenarios such as the LLLG fault. In brief, the current work delves into the intricacies of a hybrid PV/wind farm system connected to the electrical network:  
* The paper presents a novel design for the PI regulator that takes into account the time constant of the pitch angle and the process of the quantity to be regulated;
* The proposed system is tested under high perturbation scenarios, such as the LLLG fault, and is found to have a robust controller that allows the system to quickly recover from faults;
* Furthermore, to enhance the robustness of the proposed method, the maximum peak value of power is taken into consideration. The rest of this document is organized in the following manner: The proposed hybrid system architectural design is shown in Section 2. The Model of PV Array connected to the grid is covered in Section 3. The model of a SCIG connected to the grid was outlined in Section 4. The simulations conducted with MATLAB/Simulink to verify the effectiveness and performance of the suggested architecture are then described in Section 5. The study's conclusions are presented in Section 6.
## 4 Experimental Validation
### CASE 2: Fault Diagnosis of a Roller Bearing Unit  
#### 4.2.1 Fault Data Description of Roller Bearing  
The Case Western Reserve University (CWRU) bearing fault simulation platform consists of bearings, a motor, a torque sensor, an encoder, a power tester, and other electronic control equipment [35]. Its platform is shown in Figure 4. Single-point faults were introduced on the bearings using electric discharge machining, with fault sizes ranging from 0.007 inches to 0.040 inches in diameter. The fault types include inner ring faults, rolling element faults, and outer ring faults. Faulty bearings were mounted onto the test motor. Accelerometers were placed on the drive end, fan end, and base of the motor casing to record vibration data under four load conditions (from 0 to 3 horsepower) at motor speeds ranging from 1797 to 1720 RPM.  
The vibration data consist of four datasets. The normal baseline data dataset contains baseline vibration signals from the base under normal operating conditions. The 12 \(k\) drive-end bearing fault data contain drive-end fault data collected at a 12 kHz sampling rate. The 12k  fan-end bearing fault data comprise fan-end fault data collected at a 12 kHz sampling rate, and the 48 \(k\)  drive-end bearing fault data comprise drive-end fault data collected at a 48 kHz sampling rate.
# Comparison of Oil Spill Classifications Using Fully and Compact Polarimetric SAR Images
## 2 Methods
### Feature Extraction from Quad-Polarimetric SAR Data
#### Correlation and Coherence Coefficients  
The correlation and coherence coefficients that are derived from the coherence matrix are also used for oil slick discrimination [34].  
\[\rho_{HH/VV}=\left|\frac{\left\langle S_{HH}S_{VV}^{*}\right\rangle}{\left\langle S _{HH}^{2}\right\rangle\left\langle S_{VV}^{2}\right\rangle}\right| \tag{16}\]  
\[Coh=\frac{\left|\left\langle T_{12}\right\rangle\right|}{\sqrt{\left\langle T _{11}\right\rangle\left\langle T_{22}\right\rangle}} \tag{17}\]  
where \(T_{ij}\) are elements of the coherence matrix \(\mathbf{T}\).  
These two parameters both lie between 0 and 1. For a slick-free area, where Bragg scattering is dominant, \(HH\) and \(VV\) channels are highly correlatable, so they are expected to be very close to 1. For an oil-covered sea surface, a much lower \(HH/VV\) correlation is expected, so both the correlation and coherence coefficients are much lower.  
The polarimetric SAR features above and their relative behaviors in the presence of different ocean surface targets are summarized in Table 1.
## 2 Materials and Methods
### Fairness Definitions
#### 2.3.1 Equalized Odds  
Equalized odds is a fairness measurement for predictive models, ensuring that a predictor \(\hat{Y}\) is independent of any protected attribute \(A\) given the true outcome \(Y\). The measurement requires equal true positive and false positive rates across demographics in binary and multi-class settings. The purpose of equalized odds is to ensure that no group is unfairly advantaged or disadvantaged by the predictions.  
**Definition 1**.: _For binary variables, equalized odds is defined as:_  
\[Pr(\hat{Y}=1|A=0,Y=y)=Pr(\hat{Y}=1|A=1,Y=y),\quad y\in\{0,1\} \tag{1}\]  
This metric aligns with the goal of training classifiers that perform equitably across all demographics [18].  
Figure 2: The workflow and architecture of our Slide Level Classifier: feature extraction, classification, and fairness-centered post-processing.
## 4 Vehicle Dynamics Model
### Half-Car Dynamics Model
#### 4.1.1 Tire Modelling  
The Magic Formula (MF) Tire Model established by [PERSON] [17] is utilized here. MF is a commonly used nonlinear tire model which has the general form shown below:  
\[y=D\sin[\text{Carctan}\{Bx-E(Bx-\text{arctan}(Bx))\}] \tag{17}\]  
\[\begin{array}{c}Y(X)=\\ x=X+S_{\text{H}}\end{array}y(x)+S_{\text{V}} \tag{18}\]  
where \(X\) is input state, which represents slip ratio or slip angle; \(Y\) is output state and represents longitudinal force, lateral force or aligning torque. Factors \(C\), \(D\), \(B\) and \(E\) are usually derived from experiments. \(D\) is peak value of the curve; \(C\) determines shape of the obtained curve; \(B\) represents stiffness value of the curve; \(E\) represents curvature of the curve; \(S_{H}\) and \(S_{v}\) are offset values of input and output states.  
In combined slip conditions, lateral force will decrease due to longitudinal slip, and the longitudinal force will decrease due to lateral slip. Force and moments in this situation are obtained by multiplication of some weighing parameters with pure slip characteristics. However, tire longitudinal slip is defined to zero under constant vehicle longitudinal velocity. Various scaling factors are available  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Vehicle Model Parameters** & **Symbol** & **Unit** \\ \hline Lateral acceleration & \(a_{y}\) & m/s\({}^{2}\) \\ Roll angle of sprung mass & \(\varphi\) & deg (\({}^{\circ}\)) \\ Front tire cornering stiffness & \(C_{f}\) & N/\({}^{\circ}\) \\ Rear tire cornering stiffness & \(C_{r}\) & N/\({}^{\circ}\) \\ Total mass of the vehicle & \(M_{1}\) & kg \\ Sprung mass of vehicle body & \(m_{s}\) & kg \\ Unsprung mass of vehicle left side & \(m_{u1}\) & kg \\ Unsprung mass of vehicle right side & \(m_{u2}\) & kg \\ Suspension stiffness of left side & \(k_{s1}\) & N/m \\ Suspension stiffness of right side & \(k_{s2}\) & N/m \\ Suspension damping coefficient of left side & \(c_{p1}\) & Ns/m \\ Suspension damping coefficient of right side & \(c_{p2}\) & Ns/m \\ Tire stiffness of left side & \(k_{t1}\) & N/m \\ Tire stiffness of right side & \(k_{t2}\) & N/m \\ Sprung mass displacement of vehicle body & \(z_{b}\) & m \\ Unsprung mass displacement of left side & \(z_{w1}\) & m \\ Unsprung mass displacement of right side & \(z_{w2}\) & m \\ Road profile of left side & \(z_{r1}\) & m \\ Road profile of right side & \(z_{r2}\) & m \\ Track width & \(B\) & m \\ Distance between roll center to C.G. of sprung mass & \(h_{r}\) & m \\ Roll damping coefficient & \(C_{\text{roll}}\) & Ns/m \\ Roll stiffness coefficient & \(K_{\text{roll}}\) & N/m \\ \hline \hline \end{tabular}
\end{table}
Table 2: Description of symbols for 6-DOF half-car dynamics model.  
in the MF model to check influence of changing tire properties without using real MF coefficients [18]. The details are stated in [17].  
For known empirical formula, tire models for wheels of the vehicle can be obtained as shown in Figure 6.
## 2 Literature Review
### VRP and Map APIs  
Apart from endeavoring to create benchmark instances, recent VRP studies have also employed map APIs to produce more realistic and practical solutions. For instance, utilizing the Google Maps API, a dynamic vehicle routing system based on online map service was designed [24]. The map service was able to help accommodate dynamic customer demands and traffic information in the research. In addition, a study on school bus routing and scheduling, which could be treated as a multi-objective VRP, used the map service to obtain economical routing results that met time window constraints [25]. The OpenStreetMap API has been another option for accurate travel time estimations. A case study on the development of a commercial dynamic vehicle routing system made good use of the map service [26].  
With accumulated data and knowledge, it is common that local map service companies provide the best quality information for their region. For example, Baidu's map service [27] would be the most appropriate choice in China. Many studies on VRP variants, including green VRPs [28], multi-depot VRPs with shared resources [29], or VRPs for emergency cold chain logistics [30], have been carried out using the map service in China. In case of Korea, T map, owned by SK Telecom, is the most popular and the most used map service in the country. Holding the largest telecommunications market share in the country, the map service has over ten thousand users [5]. In Seoul, Korea, the T map API service reflects actual travel patterns and travel times better than foreign map APIs [6]. However, as of writing, no cases could be found in the VRP literature that incorporated T map.
## 4 Results
### The Second Research Question Results  
In the second research problem, it was examined whether there was a statistically significant relationship between the real scores calculated from the teachers' responses to the Belief Scale for Mathematical Problem Solving and the artificial scores predicted by ANFIS and ANN approaches. Spearman-Brown correlation coefficients and confidence intervals were used to determine the relationship between real and artificial results. Correlation analyses were presented in Table 5.  
Based on the results obtained from the Spearman rho correlation coefficient, there is a statistically significant, positive, and moderate relationship between teachers' real MPSB scores and the artificial MPSB scores obtained with the ANN (r = 0.422; \(p<0.05\)). Also, there is a statistically significant, positive, and moderate relationship between teachers' real MPSB scores and the artificial MPSB scores obtained with the ANFIS (r = 0.564; \(p<0.05\)).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Gender** & **Age** & \begin{tabular}{c} **Level of** \\ **Education** \\ \end{tabular} & **School Level** & \begin{tabular}{c} **Teaching** \\ **Experience** \\ \end{tabular} & **CTD Scores** & \
\begin{tabular}{c} **Real** \\ **MPSB Scores** \\ \end{tabular} &
\begin{tabular}{c} **ANFIS** \\ **Scores** \\ \end{tabular} \\ \hline  
5 & 0.5 & 1 & 1 & 0.47 & 0.40 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Normalized ANN data (20 samples out of 133).  
Figure 7: Neural network diagram (w and b are weight and biases respectively).  
Confidence intervals were also considered for the correlation coefficient because calculating the lower and upper limits of correlation coefficients can provide useful inferences. According to the value of the upper bound of the coefficient, the size of the relationship can be estimated. Similarly, the lower bound of the coefficient indicates the extent to which the relationship may weaken [117].  
Table 5 shows that the real correlation coefficient between teachers' real MPSB scores and artificial ANFIS MPSB scores has a 95% probability of being within the confidence interval [0.397, 0.718]. That is, there is only a 5% chance that the real correlation coefficient between the real and artificial ANFIS MPSB scores of teachers is less than 0.397 or greater than 0.718. Similarly, the real correlation coefficient between teachers' real MPSB scores and artificial ANN MPSB scores has a 95% probability of being within the confidence interval [0.254, 0.559]. That is, there is only a 5% chance that the real correlation coefficient between the real and artificial ANFIS MPSB scores of teachers is less than 0.254 or greater than 0.559.
## 5 Discussion  
In the cut-out to the cycle waveform performed in the evaluation experiment, the range of the norm at the ball screw stop was defined manually from the measurement data. It is assumed that the range of the norm varies depending on the industrial machine and product to be measured. Therefore, when the number of industrial machines handled by the proposed system increases, the definition and management of these ranges becomes a burden for the operator. Therefore, it is necessary to automatically detect the periodicity of the measured waveforms, taking into account the autocorrelation. Norm ranges are also used in sensor devices that perform edge processing. To change these values, the firmware needs to be written again. Therefore, the downlink function of LPWA communication is used to transmit the norm range set in the server to the sensor device, so that the range can be updated remotely.  
In the proposed system, the sensor devices used in the sensor data collection phase are different from those used in the industrial machine monitoring phase. Therefore, two types of sensor devices need to be prepared for each industrial machine in order to install the proposed system. In factories where there are several industrial machines to be installed, the number of sensor devices required increases, which increases production costs and the burden on workers when changing the firmware. Therefore, we design a sensor device that integrates the functions of these two devices. This makes it easy to switch from the sensor data collection phase to the industrial machine monitoring phase.  
[PERSON] et al. [21] proposed the development of an industrial Internet of Things (IIoT) platform for information integration and data analysis using open-source technologies. They compared the proposed method with several industrial commercial platforms and conducted tests related to data communication and analysis for information support. They also demonstrated a web interface for system implementation. [PERSON] et al. [22] proposed a data acquisition and monitoring system based on IIoT for a Polymer Electrolyte Membrane hydrogen generators. Parameters such as current, voltage, pressure, and hydrogen flow are sensed and acquired by industrial controllers and stored in a database. This enables users to access the information online in real time through a web-based interface. These works provide a good idea for further improvement of our research in the future.