esa-sceva/satcom-chunk-collection huggingface dataset:
# Indoor PV Modeling Based on the One-Diode Model
## 2 Methodology
### One-Diode Model with Two Resistors  
The one-diode model was initially developed for one solar cell, but it can be easily extended to include PV panels with multiple cells by incorporating the number of cells in the model. The same model can also be used for both monocrystalline and polycrystalline solar cells [26; 27]. Figure 2 illustrates the equivalent circuit for the one-diode model with two resistors. The circuit is composed of the photocurrent source (\(I_{ph}\)), the diode, the parallel resistor (\(R_{p}\)), and the series resistor (\(R_{s}\)). Using [PERSON]'s current law, the output current (\(I\)) can be stated as follows with reference to Figure 2:  
\[I=I_{ph}-I_{d}-\frac{V+IR_{s}}{R_{p}} \tag{1}\]  
where \(I_{d}\) is the current through the diode and \(V\) is the voltage. The diode current can be further represented using the Shockley diode equation, which is as follows:  
\[I_{d}=I_{o}\ \bigg{[}\exp\bigg{(}\frac{q(V+IR_{s})}{N_{s}akT}\bigg{)}-1\bigg{]} \tag{2}\]  
Hence, the model's output current equation is described as follows by substituting Equation (2) into Equation (1):  
\[I=I_{ph}-I_{o}\bigg{[}\exp\bigg{(}\frac{q(V+IR_{s})}{N_{s}akT}\bigg{)}-1\bigg{]} -\frac{V+IR_{s}}{R_{p}} \tag{3}\]  
where \(I_{o}\) is the diode's saturation current, \(q\) is the charge of an electron, \(N_{s}\) is the number of PV cells that are connected in series, \(a\) is the diode's ideality factor, \(T\) is the temperature, and \(k\) is the Boltzmann constant.  
However, the addition of more variables to the one-diode equation presented in Equation (3) makes the analytical solution impractical. This means that variables \(I\) and \(V\) in each member cannot be separated and isolated using simple functions [28]. The equation is also known as a transcendental equation, since it is an expression of the form  
Figure 2: One-diode model with two resistors.  
\(I=f(I,V)\). Therefore, the Newton-Raphson numerical method is chosen. It is a relatively quick convergence algorithm for computing the roots of a function [29; 30; 31] and is expressed as:  
\[x_{n+1}=x_{n}-\frac{f(x)}{f^{\prime}(x)} \tag{4}\]  
where \(x_{n+1}\) is the present iteration's estimated value, \(x_{n}\) is the output of the previous iteration, \(f(x)\) is the initialized function with \(x_{n}\), and \(f^{\prime}(x)\) is the initialized derivative of \(x_{n}\).
# Advances of UAVs toward Future Transportation: The State-of-the-Art, Challenges, and Opportunities
## 3 State of the Art of Practice and Research
### Current Practice and Research
#### 3.1.1 Surveillance of Future Transportation Activities  
UAVs have been known to possess surveillance features because of their relatively inexpensive feature, manageable size, and programmable features. With a similar purpose, UAVs also have an immense potential to contribute particularly to surveillance activities in the logistics industry. Apart from delivering packages, UAVs can be used for monitoring ongoing logistics activities and transport infrastructure. Additionally, UAVs can be used for security and surveillance in warehousing and port facilities due to their ease of access to areas that are difficult to reach for humans. Aerial surveillance can monitor vast regions with lesser efforts compared to those that are man-powered. For instance, Abu Dhabi Ports company announced using UAVs to improve protection and protect ships carrying high-value or sensitive material. To help secure products on the rail network, Polish freightcarrier PKP Cargo tested surveillance UAVs and reported that the number of thefts on the network was reduced by 44% [71].  
[PERSON] et al. proposed panoramic UAV surveillance and recycling system for autonomous UAV recycling. The proposed approach was successfully tested in various challenging scenarios like poor weather conditions by employing multiple different UAVs [44]. [PERSON] et al. proposed a method for autonomous surveillance using UAVs considering various aspects of their operation [46]. They also developed a framework based on flight records for more precise controls of UAVs in complex environment surveillance.  
With limited energy efficiency, it is challenging to keep the surveillance system running. That is why [PERSON] et al. proposed a low-power wide-area network (LoRaWAN)-based communication strategy to improve the UAV coverage and ensure energy-efficient surveillance with low power [72]. In an attempt towards continuous surveillance using solar-powered hybrid UAVs, [PERSON] et al. [45] developed a photovoltaic power management system. They estimated the UAV flight times using the state of charge estimation technique. It was concluded that hybrid UAVs could obtain longer flight times during clear weather than traditional multi-rotor UAVs. A similar initiative was undertaken by [PERSON] et al. in which a detailed design and fabrication of solar-powered UAVs were laid out for continuous surveillance operation [47].
# Sensor-Data-Driven Prognosis Approach of Liquefied Natural Gas Satellite Plant
## 4 Methodology
### Sensor-Data-Driven Prognosis Methods  
The sensor-data-driven prognosis approach proposes forecasting the RUL on the basis of a predetermined end-of-life or failure threshold (FT). As proposed in [36; 37], the RUL is given by:  
\[\text{RUL}\in\mathbb{N}^{+}\ \mid\ \hat{y}(k+\text{RUL}|k)=\text{FT}, \tag{1}\]  
where \(\hat{y}(k+\text{RUL}|k)\) is the RUL step-ahead forecast at the sampled \(k\) of a given predictive model.  
A sensor-data-driven approach is used to derive the predictive models from the collected data. We propose a new method based on fuzzy-inductive-reasoning model for multistep forecast of the tank-storage level. This new methodology is compared with the [PERSON]'s double exponential smoothing method.
## 3 Methods
### Automated Geometric Correction Processing Method
#### 3.2.3 Step 3: Geometric Correction  
This study performed the geometric correction of the BNU-1 L1A product with the original spatial resolution (74 m) by using a quadratic polynomial (POLYORDER2) model in ArcGIS 10.6 software. We obtained the BNU-1 L1B product by resampling the geolocation error-corrected image to 80 m spatial resolution using the nearest resampling method.
## 4 Conclusions  
Heart rate variability (HRV) reflects cardiac activity and overall autonomic nervous system health. Proper HRV analysis aids in identifying diseases and managing heart conditions. Using ECG signals from the PhysioNet database, the HRV analysis employed the continuous wavelet transform (CWT) and discrete wavelet transform (DWT) methods.  
R peaks localization was performed using DWT methods: MODWPT and MODWT. MODWPT outperformed MODWT due to superior signal decomposition but was not continued due to computational limitations. Seven out of 60 recordings had missing R peaks due to data loss during initial localization.  
Figure 25: Scatter plot of LF/HF values of (**a**) Normal Sinus Rhythm dataset and (**b**) Arrhythmia dataset.  
CWT was applied to RR interval signals to generate spectrograms, revealing patterns related to heart conditions In these spectrograms high magnitude events were spotted. In some spectrograms, the power of the HF range was dominant, this was commonly observed in the arrhythmia ECG recordings. In some other spectrograms, the power of the LF range was dominant, this was commonly observed in the normal sinus rhythm ECG recordings. The CWT spectrogram of some recordings exhibited high magnitudes all over the spectrogram, while some other recordings displayed a reduction in brightness throughout the spectrogram. The reason is that every person has a unique pattern in ECG due to the different structures of their hearts and therefore, the ECG signal can vary in magnitude, having low or high intensities depending on the person.  
Frequency band extraction using ICWT reconstructed HF, LF, and VLF bands (0.003-0.5 Hz). Power estimation, calculated as the square of the RMS values of reconstructed signals, indicated significant differences in LF/HF ratios between the Normal Sinus Rhythm and Arrhythmia datasets. Normal sinus rhythm often showed abnormally low LF/HF ratios due to dominant HF power, while the Arrhythmia dataset exhibited abnormally high LF/HF ratios due to dominant LF power.  
The scatter plot analysis confirmed lower LF/HF ratios in the Arrhythmia dataset, indicating parasympathetic dominance, whereas the Normal Sinus Rhythm dataset showed sympathetic dominance. This highlights the use of wavelet-based HRV analysis for distinguishing autonomic regulation patterns in cardiac health and disease.  
As a future direction, taking a more statistical approach together with MODWPT-based HRV analysis will provide a more quantitative and reliable interpretation of HRV with deeper insight into the autonomic nervous system. Also, DWT feature extraction could improve with machine learning to enhance the interpretation of CWT spectrograms in HRV analysis.  
Writing--original draft, A.M.N.; Writing--review & editing, C.H.M.; Supervision, N.G.S.S.G.; Funding acquisition, T.N. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflict of interest.
# A Comprehensive Experimental Emulation For OTFS Waveform RF-Impairments
## 3 Channel Effects Furthermore, RF-Impairments
### Phase Noise  
When a local oscillator in a transceiver is unable to create pure sinusoidal waves in conformance with the Dirac spectrum, phase noise is produced, as shown in Figure 13a,b. The frequency spectrum and timing properties of the oscillator output induce large adjustments as a direct result of PN's influence [47].  
In most cases, designers typically define PN in the frequency domain, using a bandwidth of one Hz and an offset of one \(\Delta f\) from the carrier [47]. The power of the PN signal throughout this bandwidth is normalized in relation to the power of the carrier in dBc/Hz units, as illustrated in Figure 13c.  
Both the N5172B VSG and the N9010A EXA Keysight X-Series VSA had amazingly low phase noise in our experiment. The phase noise of the VSA's local oscillator is shown in Figure 14. According to what has been seen, the phase noise of the local oscillator is equivalent to \(-137.06\) dBm/Hz at a 30 Hz frequency offset.  
Figure 12: BER performance for OTFS waveform with \(M=32,N=32\) under different values of DC-offset in both I-component (\(\gamma_{I}\)) and Q-component (\(\gamma_{Q}\)) at 2.4 GHz carrier frequency  
Figure 13: Phase noise of the local oscillator. (**a**) Ideal oscillator, (**b**) practical oscillator, and (**c**) phase noise power level in dBc/Hz with \(\Delta f\) offset.  
The influence of phase noise will not be seen on the performance of the waveform systems because the level of phase noise is very low. Therefore, we have introduced phase noise at the receiver, as illustrated in Figure 15. Furthermore, this modeling of the phase noise already exists on the 2.4 GHz complementary metal-oxide-semiconductor voltage control oscillator (2.4 GHz CMOS VCO) [54]. The phase noise impact on OTFS and OFDM BER performance is shown in Figure 16 Phase noise has been proven to have a detrimental effect on both the system's performance and the orthogonality of the subcarriers, resulting in ICI. In contrast to OFDM, the OTFS waveform is more resistant to the phase noise effect. The observed results are consistent with the conclusions presented in [32].  
Figure 14: The phase noise of the VSA local oscillator.  
Figure 15: Phase noise model for 2.4 GHz CMOS VCO.
## 3 Analysis and Correction of Main Errors
### Error Calibration and Correction of Interferometers' Reflection Mirrors  
The reflection mirrors of the three interferometers are fixed on the moving table. However, the reflection surfaces of mirrors themselves are not ideal flat and the surface deformation of each mirror is inevitable after the mirrors are fixed on the moving table. The straightness in \(X\)- and \(Y\)-mirrors and the flatness in \(Z\)-mirror will contribute to the final measuring result directly. Therefore, the errors of reflection mirrors should be separated and corrected. Figure 7 shows the schematic diagram of the straightness error separation of reflection mirror of \(X\)-interferometer. After the error calibration and correction, the measurement straightness of the optical flat is about 57 nm.  
In Figure 7, an optical flat with the accuracy of grade one is placed on the stage. The maximum flatness error of the optical flat's measuring surface is less than 30 nm. Here, the measuring surface of the optical flat is used as the reference and is adjusted parallel to the long reflection mirror surface to the best we can. In other words, the angle of \(\alpha\) should be as small as possible. Then, the measuring surface of the optical flat is measured by the micro CMM. Using the obtained data, the straightness error of the \(X\)-interferometer's reflection mirror can be calibrated and its systematic error can be corrected. By this approach, the straightness of the \(Y\)-mirror and flatness of the \(Z\)-mirror are calibrated and corrected. After the error calibration and correction, all residual errors are less than 50 nm (2\(\sigma\)).
###### Abstract
[PERSON] et al. [24] researched the two-dimensional waveguide problem governed by the Helmholtz equation and estimated the unknown wave number with the help of PINN. [PERSON] et al. [25] utilized PINN to predict the scattered wavefield function. [PERSON] et. al. [26] used PINN to solve the nonlinear Eikonal equation and demonstrated the transfer learning possibilities of PINN. [PERSON] et al. [27] verified PINN's ability in solving the wave equation, the KdV-Burgers equation, and the KdV equation. In addition, PINN was also applied to the uncertainty quantification problem [28; 29] and the atomic simulation of materials [30]. Since the introduction of PINN, only few have worked on the computation of multi-physics problems [31]. In this article, we propose to use the PINN to solve a Joule heating problem, which consists of a coupling problem of the electrokinetic field and steady-state thermal field. The study is carried out with the help of the deep learning framework, PyTorch. To compute the electric field or the current density at a point of the domain, the calculation of the gradient of the state variable, i.e., the electric scalar potential, is needed. The automatic differential function of PyTorch makes the computation convenient for this solution. Therefore, PINN has the capability of handling the electro-thermal coupling problem. To explore the feasibility and effectiveness of this method, we employ two neural networks to approximate those two fields, namely the steady electric field and steady-state thermal field, and then combine them with a sequential coupling. The rest of the paper is organized as follows. Section 2 recalls the idea behind the PINN framework and presents the method we use for applying PINN on electro-thermal analysis. Section 3 utilizes a concrete case to solve the electro-thermal coupling problem. Then, we compare the accuracy and continuity of the gradient field between the first-order FEM and the PINN. Section 4 discusses the experimental results and provides some prospects for future work. Section 5 concludes this work.
# Development and Evaluation of a Fluctuating Plume Model for Odor Impact Assessment
## 2 Model Development
### Conceptual Bases of the Fluctuating Plume Model
In practice, if we were to sample at the point (\(x\), \(y\), \(z\)) at high frequency (i.e., at successive instants very close to each other), what we would obtain would be a sequence of instantaneous concentration \(c_{i}\), each corresponding to a possible position of the centroid (\(y_{m}\), \(z_{m}\)). These statistical realizations will be different from each other and, on average, they will give the average concentration \(C\), which corresponds to the visible and overall result of an absolute dispersion process. Figure 1 reports a graphical example of the average and the instantaneous plume dispersion. Despite being very simplified, the [PERSON]'s Fluctuating Plume model captures in an immediate fashion the phenomenology of the instantaneous dispersion of a passive substance in the PBL. Despite the fact that the ground reflections of instantaneous plumes in the original model are neglected, the biggest restriction of the model in its original formulation is that it assumes the transverse and vertical distribution of the concentration in an instantaneous plume to be deterministic (described by a Gaussian distribution) and not a stochastic process as the experimental evidence has confirmed. Further studies [63; 64; 65], more than 30 years after the model was proposed, extended [PERSON]'s model to include fluctuations within the instantaneous plume. However, even though [PERSON]'s Fluctuating Plume model was formulated on a predominantly phenomenological basis, in [PERSON] and [PERSON] (2000) [65] it was shown to be theoretically justified, describing in a simple and immediate way the fact that the absolute (average) total dispersion of a passive substance in the turbulent PBL is the result of two different and practically independent stochastic processes: a dispersion due to the meandering of the plume centroid and a relative dispersion of the centroid itself. This conceptual model has been the key to a number of experimental campaigns aimed at highlighting the absolute dispersion of the plume, the relative dispersion, and the meandering of the centroid. In this regard it is worth referring to the works of [PERSON] et al. (2005), [PERSON] et al. (1999), [PERSON] (1995, 1992), and [PERSON] et al. (1994) [66; 67; 68; 69; 70].
## 2 Proposed STPA Configuration for JCAS, Its Design Pipeline, and Performance
### JCAS Scenarios and Performance Metrics
#### 2.3.1 JCAS Scenario 1: Non-Overlapping Communication and Sensing Beam Directions
When the tracked target is at the angle \((\theta_{s},\phi_{s})\), according to (8), the radar channel is related to \(\mathbf{w}_{JCAS}^{T}\mathbf{a}_{T}(\theta_{s},\phi_{s})\approx 1/\sqrt{2} \mathbf{w}_{s}^{T}\mathbf{a}_{T}(\theta_{s},\phi_{s})\) in case of no overlapping and orthogonality of beams, and \(\mathbf{w}_{JCAS}^{T}\mathbf{a}_{T}(\theta_{s},\phi_{s})\approx\sqrt{2} \mathbf{w}_{s}^{T}\mathbf{a}_{T}(\theta_{s},\phi_{s})\) when the beams are overlapping, therefore \(SNR\) in (10), when beams are overlapping, is four times larger than that of no overlapping. A decrease in BCRLB is expected in the overlapping of beams, and the magnitude of the decrease depends on the prior information as well, which we do not study in detail.
# Stepped Spillways and Energy Dissipation: A Non-Uniform Step Length Approach
## 3 Results and Discussion
### Mesh-Independence Test and Model Validation
#### 3.1.3 Energy Dissipation  
For different volumetric flow rates within the range of \(0.9\leq d_{o}h\leq 1.7\) the energy dissipation over a stepped chute was simulated. The results indicated energy dissipation of up to 60%. As can be seen in Figure 7, the numerically obtained results are very similar to the experimental results reported by [PERSON] and [PERSON] [16], indicating a high degree of accuracy of the proposed method.  
Figure 5: Comparison of velocity profiles between the numerical results and experimental data at some longitudinal stations.  
Figure 6: Air–water flow on a uniform stepped spillway at slope 1V:1H for \(d_{o}h=1.3\). Part (**a**) is the water volume fraction and part (**b**) is the velocity vector distribution.  
Figure 7: _Cont._  
recirculating flow in the step cavities. The results reveal that, as the step length increases, the air entrainment increases, which is simply caused by the increased length of the aerated flow region.  
It was observed that the rate of energy dissipation increases slightly when the spillway slope decreases, and this has an inverse relationship with the discharge (Figure 10). This can be explained by the formation of a recirculation flow at the step cavities, which creates large vortexes. For instance, for step lengths of 0.1, 0.15, and 0.2 m, a recirculation vortex fills the entire step cavities, acting as a roller and consequently conveying the flow. However, a part of this flow hits a step length which is larger than 0.2 m and generates a slight increase in the energy dissipation for slopes smaller than 1V:2H (Figure 11). The flat slope 1V:2.2H yielded a slight increase in the energy dissipation by up to 1.3% compared to the slope 1V:2H, while the slope 1V:2.5 showed similar results against the flat slope 1V:2.2. Furthermore, the flat slopes 1V:3H and 1V:3.5 H yielded a slight increase in the energy dissipation by up to 2% compared to the slope 1V:2.2H, but the design would yield long and uneconomical designs. Among all uniform configurations tested here, the uniform stepped spillway with the 1V:2.2H slope was the optimum design in terms of the energy dissipation performance.  
As shown in Figure 10 the results of energy dissipation for uniform configurations seem to have the same tendency. The energy dissipation is directly proportional to \(d_{o}\)/\(h\) and can be estimated (\(R^{2}>0.94\)) by:  
\[\frac{\Delta E}{E_{t}}=-0.1864\frac{d_{c}}{h}+0.8679\;\;for\;0.285\leq\frac{h}{ L}\leq 0.5 \tag{9}\]  
\[\frac{\Delta E}{E_{t}}=-0.1629\frac{d_{c}}{h}+0.7917\;,\;for\;0.667\leq\frac{h} {L}\leq 1 \tag{10}\]