esa-sceva/satcom-chunk-collection huggingface dataset:
# Timing Performance Testing and Regularity Analysis of eLoran System
## 3 Measuring Equipment and Tests
### Internal Delay Calibration  
For Receiver 1, the internal delay was caused by the internal circuit and signal processing of the receiver. Following calibration by users, the internal delay of the equipment was 78.21 us. Therefore, this part of the error should be removed from the timing results obtained from subsequent measurements. For the self-developed domestic equipment, the internal delay system was calibrated to zero at all stations before leaving the factory. Thus, the internal delay of Receiver 2 was zero by default.
# An Unequal Clustering and Multi-Hop Routing Protocol Based on Fuzzy Logic and Q-Learning in WSNs
## 5 Results and Discussion
### Performance Evaluation in Scenario 1
#### 5.2.4 Throughput  
Figure 21 presents a comparative analysis of network throughput for the EEUC, CHEF, and FQ-UCR protocols across different node counts. As illustrated in Figure 21, the proposed FQ-UCR protocol demonstrated a substantial performance advantage over the other protocols. The improvement was due to fuzzy logic clustering and Q-learning routing, which optimized inter-cluster transmission and CH selection, resulting in at least an 98.8% increase in network throughput across various sizes.
## 5 Numerical Validation of the Model
### Modal Analysis
#### 5.1.2 Five-Wall Structure with Openings  
The layout of the five-wall structure, as illustrated in Figure 12, featured a symmetric design comprising four distinct wall types. One of these, termed the \"Window wall\", included  
Figure 11: Evolution of the natural frequency value of the 3 rd mode analyzed in relation to the mesh size.  
Figure 10: Evolution of error in determining the modes, as depicted in Figure 9, which were analyzed in relation to the mesh size.  
a centrally positioned window. Additionally, two walls, labeled \"Door wall 1\" and \"Door wall 2\", each featured a centrally positioned door, though with differing dimensions. The fourth wall, which had no opening, shared the same dimensions as \"Door wall 2\".  
For the five-wall structure, a different naming convention was used for the discretization. All of the meshes consisted of square macro-elements and were identified by a single number \(n\), which represents the level of mesh refinement. \(n=1\) corresponds to the coarsest mesh, where the DFM\({}_{-}\)1 macro-elements were twice as wide as the DFM\({}_{-}\)2 macro-elements.  
Figure 13 illustrates the modeling of the structure using the DFM (a) and the refined 3D cubic FE model (b).  
Figure 12: Schemes of the five-wall structure for the modal analysis.  
Figure 13: Mesh for the five-wall structure with openings: **(a)** DFM\({}_{-}\)2; **(b)** 3D FE model.  
The comparison of mode shapes derived from the DFM and the 3D cubic FE model, shown in Figure 14, highlights similar dynamic behavior that was predicted by both methods. However, minor differences in the displacement amplitudes were observed, particularly in OOP displacements, mirroring the findings from the square wall analysis.  
Figure 15 illustrates the DFM's ability to predict the natural frequencies of the complete structure compared to those obtained from the reference model. The results show that the coarsest mesh provided less accurate predictions, particularly for the first mode, where the error reached 22%. In contrast, the finer meshes considerably improved the accuracy, reducing the maximum error to a more acceptable level of 8%.  
This analysis confirms that the DFM, especially when applied with an appropriate mesh density, can yield satisfactory results in the modal analysis of complex structures. Despite the inherent simplifications of the DFM, its ability to closely replicate the dynamic behavior predicted by the reference model underscores its effectiveness as a structural analysis tool for evaluating the seismic performance of masonry buildings.  
Figure 14: Mode shapes of the five-wall structure with openings.  
Figure 15: Evolution of the error for the determination of the primary modes with the mesh size.
# Non-Rotationally Symmetric Field Mapping for Back-Scanned Step/Stare Imaging System
## 3 Optical System Design
For the above optical system, the distortion weight should be magnified as much as possible while the MTF is close to the diffraction limit. For this optical system, the maximum distortion is less than 0.46% within the FPA. Figure 6 shows the grid distortion map. In order to visualize the distortion characteristics, the distortion deviation is enlarged by 20 times. As can be seen from Figure 6, the distortion is similar to pincushion distortion, but the distortion is not rotationally symmetric. The deviation of the actual image with the theoretical field point in \(x\) direction is larger than that in \(y\) direction. During the exposure of the FPA, the back-scan is over \(\pm 0.4\) deg in object space. The curves in Figure 7 indicate motion (in units of pixel IFOV) of the chief ray at nine image points during the back-scan operation. As we can see in Figure 7, except for the central image point, the other off-axis image points have image wander. The image wander at the four edge fields of view is the largest. Compared with the case when the fast steering mirror has no motion, the maximum image blur during the back-scan is only 0.22 pixels. The image point motion caused by projection distortion is well controlled. Figure 5: Optical system according to the field mapping conditions \(h_{x}=f^{\prime}\delta_{x},h_{y}=f^{\prime}\theta_{y}\)
## 3 System Model and Problem Statement
### Power Grid System  
In this paper, we focus on the interaction of datacenters and renewable power plants in the transmission network of the smart grid. Take the IEEE 30 Bus system [56] as an example of the transmission network and the topology of case_ieee30 is shown in Figure 1. The case_ieee30 dataset was converted from an IEEE common data format. The data of renewable energy generation is the photovoltaic generation from the Green Power Network [57], which is a third-party monitoring data management service platform for photovoltaic power stations. It realizes intelligent housekeeping services of photovoltaic power stations with high-quality products and technologies.  
1. Load model: To model the load of this system, we used constant power according to the specifications in Reference [49]. Loads are connected to 21 buses respectively as shown by the arrows in Figure 1.
2. Generator model: There are six generators, which are connected to buses 1, 2, 5, 8, 11 and 13 respectively. Specifically, Gen1 is a balancing bus node.
3. Branch model: There are 41 branches each connecting two buses in the system. Each branch has its own line capacity limit and the voltage limitation of all branches is 0.95 pu to 1.1 pu. We will check and try to avoid the possible violations based on these limits. Moreover, when power is transmitted along the branches, there will be some losses on the line. We will use the summarized losses as the main metric in the experiments in later sections.  
Figure 1: Model of the IEEE 30-bus test system.
## 2 Study Area and Materials  
The selected study area is the Ebre River Basin, located in Spain. The Ebre river, one of the most significant rivers in the Iberian Peninsula, spans approximately 930 km in length and covers a drainage area of 85,550 km\({}^{2}\). Throughout the year, the river flow exhibits irregular patterns, with low levels typically occurring at the end of summer and peak levels during spring, coinciding with the thawing season of the Pyrenees' snow. Notably, the Ebre Basin is among the largest and most extensively irrigated basins in Spain, with approximately 924,424 km\({}^{2}\) of land under irrigation, meeting 92% of the basin's total water demand. The basin is equipped with over 100 reservoirs, boasting a combined water storage capacity exceeding 6800 km\({}^{3}\), alongside a network of canals utilized for agricultural irrigation, electricity generation, and domestic water supply. Continuous monitoring of the basin's hydrological resources is facilitated by the Ebre Hydrographic Confederation (_Confederacion Hidrografica del Ebre_, CHE), the local authority responsible for managing the basin's water resources.  
The selected targets comprise a collection of reservoirs and a narrow river within the Ebre basin, detailed in Table 1 and depicted in Figure 1. The satellite's nominal ground tracks traversing the water surface of these reservoirs, referred to as transects, have approximate lengths ranging from 50 to 2000 m. Consequently, these targets can be classified as small- to medium-sized inland water targets.  
Figure 1: Collection of reservoirs studied in the Ebre Basin along with the corresponding Sentinel-6 nominal tracks represented as red lines. Maps provided by Esri satellite imagery. The purple dots indicate the location of the hydrological stations.
###### Abstract  
A low-noise and -power readout integrated circuit (IC) for two-electrode electrocardiogram (ECG) recording is developed in this study using a common-mode charge pump (CMCP) for a robust 20-V\({}_{\text{PP}}\) common-mode interference (CMI). Two-electrode ECG recording offers more comfort than three-electrode ECG recording. Contrasting to the three-electrode ECG recording, the two-electrode ECG recording is affected by CMI during measurements; the intervention of a large CMI will distort the ECG signal measurement. To achieve robustness for the CMI, the proposed ECG readout IC adopts CMCP--it uses switched capacitors that store and subtract CMI by control logic. In this paper, a window comparator structure is applied to CMCP to obtain a signal with less distortion. The window voltage ranges were set between the input common-mode ranges in which IA can operate. Therefore, a signal with less distortion was obtained by stopping the operation of CMCP between the window voltage ranges. It also reduced additional current consumption. To achieve this, the proposed circuit is implemented using a chopper stabilization technique. The chopper implemented in the amplifier can reduce low-frequency noise components, such as 1/f noise, and it comprises a CMCP, current feedback instrumentation amplifier, QRS peak detector, relaxation oscillator, voltage reference, timing generator, and serial peripheral interface on a single chip. The proposed circuit was designed using a standard 0.18 \(\upmu\)m CMOS process with an active area of 0.54 mm\({}^{2}\). The proposed CMCP achieves a CMI robustness of 20 V\({}_{\text{PP}}\) at 60 Hz. The measured input-referred noise level was 129 nV/\(\surd\)Hz at 1 Hz, and the power consumption was 23.83 \(\upmu\)W with a 1.8 V power supply.  
low-noise; low-power; common-mode charge pump (CMCP); common-mode interference (CMI); window comparator; current feedback instrumentation amplifier (CFIA)
## 6 Conclusions  
This study integrates the petrophysical properties of conventional well logs and XRD results to predict the illite wt.% using an FF-ANN with a backpropagation algorithm and tangent sigmoid hyperbolic transfer function. Additionally, increasing the conventional log parameter inputs significantly improves the performance of the considered algorithm, which is also evident from correlation values; the results showed that the ability of this method to predict the values of illite wt.% was excellent and reliable after applying the model to wells that were outside the training and testing processes. The application of PCA supported the reliability of the results. The following are some of the conclusions that are derived from this study:  
1. PCA is an excellent method for reducing the number of input parameters since it focuses on the parameters that mainly influence the output and provides a high level of matching predictivity.
2. The utilization of artificial intelligence methods, specifically FF-ANNs, in calculating illite wt.%, along with integrating petrophysical log data, proved highly advantageous. This approach yielded results that exhibited a remarkable level of accuracy, as seen by the excellent match rate observed during both the training and testing stages, with an R\({}^{2}\) value of 92%. This strategy conferred a notable advantage.
3. The tests conducted on a data set kept hidden from the testers for three wells demonstrate that the developed model had an exceptional capacity to forecast, giving  
Figure 8: A regression model comparing observed and forecasted illite wt.% values in test wells.  
R\({}^{2}\) = 88.5% for illite wt.% in test wells. These findings demonstrate the exceptional efficacy of the designed model.
* The findings indicate that ANNs have the potential to be a practical, speedy, and low-cost method for calculating the illite weight percentage in the absence of core samples.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app14010318/s1](https://www.mdpi.com/article/10.3390/app14010318/s1)]([https://www.mdpi.com/article/10.3390/app14010318/s1](https://www.mdpi.com/article/10.3390/app14010318/s1)), Table S1: Weights and biases of the optimized ANN model, between the input layer, the first hidden layer; Table S2: Weights and biases of the optimized ANN model, between the second hidden and output layers (first part from W2-1 to W2-15, second part from W2-16 to W2-30).  
Methodology, A.B.; Software, A.B.; Validation, N.S.Z.A.; Resources, N.S.Z.A.; Data curation, A.B.; Writing-original draft, A.B.; Writing-review & editing, N.S.Z.A.; Funding acquisition, N.S.Z.A. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Yayasan Universiti Teknologi PETRONAS grant number YUTP-FRG 015 LC0-433.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
The authors declare no conflict of interest.
## 2 Data and Methods
### Asymptotic Theory
Although we do not know the distribution of the estimator \(\Theta^{u}_{OLS}\), we can approximate it under _asymptotic theory (as \(n\to\infty\))_ by the multivariate Gaussian distribution [10; 11; 12]  
\[\Theta^{u}_{OLS}\sim N(\theta_{0},\Sigma^{u}_{0}) \tag{7}\]  
where, based on previous assumptions, the covariance matrix \(\Sigma^{u}_{0}\) is approximated by  
\[\Sigma^{u}_{0}\approx\hat{\Sigma}^{n}=\hat{\sigma}^{2}_{OLS}\big{[}\chi^{uT}( \hat{\theta})\chi^{u}(\hat{\theta})\big{]}^{-1}. \tag{8}\]  
Here \(\chi^{u}\) is the sensitivity matrix  
\[\chi^{u}_{jk}(\theta)=\frac{\partial f(t_{i},\theta)}{\partial\theta_{k}},i=1,\ldots,n;k=1,\ldots,p, \tag{9}\]  
where \(\theta_{k}\) is the \(k^{th}\) component of the vector \(\theta\in\mathbb{R}^{1\times p}\). The unbiased estimator for \(\sigma^{2}_{0}\) is  
\[\hat{\sigma}^{2}_{OLS}=\frac{1}{n-p}\sum_{i=1}^{n}[y_{i}-f(t_{i},\hat{\theta}^ {u}_{OLS})]^{2} \tag{10}\]  
where, for our own examples, \(n=23\) and \(p=3\) or \(4\) is the number of model parameters. Both \(\hat{\theta}^{u}_{OLS}\) and \(\hat{\sigma}^{2}_{OLS}\) are then used in Equation (8) (_i.e.,_\(\hat{\theta}=\hat{\theta}_{OLS}\) and \(\hat{\sigma}^{2}=\hat{\sigma}^{2}_{OLS}\)). In our calculations, the sensitivity equations are calculated analytically by solving the differential equation at \(\hat{\theta}\)  
\[\frac{d}{dt}\left(\frac{\partial y}{\partial\theta}\right)=\frac{\partial g}{ \partial y}\frac{\partial y}{\partial\theta}+\frac{\partial g}{\partial\theta}. \tag{11}\]  
Note that \(\frac{dy}{dt}=g(t,y(t),\hat{\theta})\) is the differential equation for the green algae model and \(f(t_{j},\hat{\theta})=y(t_{j},\hat{\theta})\) is the forward solution of each model. Because we know analytical formulas that provide solutions for \(\frac{dy}{dt}=g(t,y(t),\hat{\theta})\), we can solve Equation (11) by setting up a differential equation in terms of the sensitivity [11]. The \(\chi^{n}\) matrix provides a measure for how sensitive the mathematical model is to each of its parameters. This can be used to estimate the \(p\times p\) covariance matrix, \(\Sigma^{n}_{0}\),  
\[f(t_{i},\theta)=Cy(t_{i},\theta),i=1,\ldots,n. \tag{3}\]0  
In order to determine the confidence we have in the parameter estimates, we also compute the asymptotic theory based standard error \(SE(\hat{\theta}_{k})=\sqrt{\hat{\Sigma}^{n}_{kk}}\) for the \(k^{th}\) parameter.
## 2 Materials and Methods
### Inhibition of ARG1-Polyamine Axis Leads to Accelerated HSC Activation
The number and diameter of droplets decreased during activation of the untreated cells, still, the cell shape and the characteristic of the lipid droplets showed that the HSCs were not fully activated at day 4. L-NIO treated cells exhibited smaller and more stellate-shaped cell morphology and contained a comparable high amount of lipid droplets at day 4. Nor-NOHA treated cells showed a bulky cell shape from day 3 onwards and a notable loss of lipid droplets. DFMO treated cells exhibited a significant reduction in droplet diameter and a myofiblat-like cell shape (Figure 4D). The lipid droplet area was used to quantify the state of HSC activation (Figure 4E). At day 1, the cells of all conditions displayed a similar phenotype. At day 3 the difference between untreated cells and arginase inhibited cells was significant (\(p\) = 0.0308). Due to the limitations of contrast in light microscopy, other factors like cell shape and total cell size could not be considered. An exemplary picture is shown in Supplementary Figure S5, displaying the variable cell morphology of the HSCs with different treatments. The cotreatment of HSCs with nor-NOHA and L-NIO exhibited the phenotype of nor-NOHA treated cells, rather than L-NIO mono-treatment (Supplementary Figure S6). The results indicated an accelerated activation under nor-NOHA and DFMO treatment, which was absent when L-NIO was added to the HSCs. The data implied that ARG1 activity and the downstream polyamine synthesis impacted on the maintenance of quiescent HSCs, raising the question about the impact of the ERas interaction with ARG1 on its enzyme activity, in this context.
# A Novel Optimal Control Strategy of Four Drive Motors for an Electric Vehicle
## 2 Simulation Platform and Optimal Energy Management
### Electric Vehicle Simulation Platform
#### 2.1.6 Hardware-in-the-Loop System Architecture  
The HIL is a method used in the development of algorithms, where parameters from physical systems are input into a program, and various computational modules are constructed. This builds the physical models used in this research to test whether the control logic in the controller functions correctly under real-world conditions, and it can also reduce development costs [30]. This study employs the rapid prototyping controller (Micro-Box 2200(r) /Terasoft, company in Hsinchu County, Taiwan) to construct a HIL system platform, providing a real-time model of the entire vehicle platform and its controller. This system requires capturing data from the entire vehicle to adapt to the construction of the HIL system. Starting with the initial driving cycle model, the demanded vehicle velocity is input into the driver model, and after PI computation, the total vehicle demanded torque is sent to the drive motor model. The vehicle dynamics model outputs the motor speed, which passes through the Energy Management System (EMS) model. The EMS allocates the output torque to the drive motor model based on control strategies and then sends it back to the receiving end of the Micro-Box 2200 rapid prototyping controller, as shown in Figure 5. During the signal transmission process between the vehicle system and the controller, the vehicle signal variables are continuously converted into voltage signals, which are then converted into the physical signals required by the vehicle, ensuring accurate signal transmission between the vehicle system and the controller. Simulations of various control strategies were conducted within both the Simulink environment and an HIL platform. Initially, the complete vehicle model was deployed onto the second hardware platform, functioning as the plant. This platform received voltage signals from the first hardware platform, converting analog voltages into digital signals. After computations within the vehicle model, the demanded power and actual motor speed were converted between digital and analog forms, transforming physical quantities into voltage signals sent back to the first hardware platform. At this stage, the first platform acted as the controller, executing control strategy calculations. Upon receiving analog voltage signals, it immediately converted them into digital signals, performed control strategy computations, and translated various motor torque demands from physical quantities into analog voltage signals, transmitting them to the second hardware platform. Finally, by inputting the NEDC or FTP-75 driving cycles, real-time tests demonstrated the proposed optimization strategy's high applicability in online energy management.
## Materials and Methods
### Data Pre-Processing  
The stage of data pre-processing and feature selection process is crucial in the initial stage of the machine learning model building. This process can significantly affect the prediction accuracy in any type of data [21]. The overall data pre-processing and feature selection are summarized as follows:  
* Data randomization: the data were randomized to enhance the diversity of the data before splitting into training and testing datasets.
* Data partition/splitting: datasets were randomly partitioned into training and testing datasets consisting of 260 data samples (\(\approx\)75%) for model training and 88 data samples (\(\approx\)25%) for model validation test.
* Data normalization: ANNs benefit from data normalization as do some other machine learning algorithms. The input data are normalized to standardize the scale of each variable. In this study, the data are normalized to the range [0,1] before the ANNs training.
* Feature selection: feature removal for considerably low correlation score to the output variable. Normally, if the correlation score is less than \(|0.5|\), these variables indicate a low correlation, i.e., a weak association between the specific input variable with the target variable. This process is the most important for predicting the accuracy of this study. It is also useful for model parsimony, especially when the input features are large. The reduced number of input features will give benefit for model simplicity and data reduction in the absence of data collection/sensor measurement. However, the experimentation results of this process are discussed in Section 3. The correlation coefficient (\(r_{xy}\)) between two variables was calculated by dividing the covariance with the product of the standard deviations of the two variables as follows: \[r_{xy}\ =\ \frac{Cov(x,y)}{\sigma_{x}\sigma_{y}}\] (1)