esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimizing Physics-Informed Neural Network in Dynamic System Simulation and Learning of Parameters
## 5 Computational Simulations of Dynamical Systems
### SIR Model
The graph displays the absolute errors for two datasets, \"I\" and \"R\" over time. After day 250, there is a noticeable spike in errors for both datasets. This surge might be attributed to data inconsistencies, external factors impacting the mea  
Figure 21: The learned parameters of SIR model using PINN Algorithm 5 on COVID-19 data. (**a**) The learned \(\beta\). (**b**) The learned \(\gamma\). Figure 20: The data and the learned SIR model using PINN Algorithm 5 on COVID-19 data. (**a**) The susceptible graph. (**b**) The data and the learned infectives. (**c**) The data and the learned recovered population. Figure 22: The phase space plot of the actual output of \(I\) against \(R\) and the predicted output of \(I\) against \(R\) of the SIR model from the COVID-19 data. surements, or potential limitations in the predictive model if one was used. Additionally, increased variability in the data or unforeseen shifts in the underlying system around day 250 could also be factors. A thorough examination of the data collection process and any external events during that period is essential for a definitive conclusion. The metrics include mean absolute error (MAE), mean squared error (MSE), and root mean squared error (RMSE). These metrics show that the PINN solution closely approximates the COVID-19 data, demonstrating the approach's effectiveness. Finally, learning time-varying parameters from real-life data using PINN enhances our understanding of disease dynamics and can aid in making informed decisions regarding public health interventions. This approach can be applied to various infectious diseases, allowing for more accurate modeling and prediction of their spread. Combining the SIR model and PINN presents a valuable tool for studying and managing epidemics.
# An Efficient Multistage Approach for Blind Source Separation of Noisy Convolutive Speech Mixture
###### Abstract
Forward Blind Source Separation (FBSS) combined with the Simplified Fast transversal filter (SFTF) method results in adoption gain from forwarding prediction [13]. Nevertheless, adaptive filtering methods face problems while canceling or suppressing the acoustic noise. This issue is tackled using the Modified Predator-prey particle swarm optimization (MPPPSO) approach. It also solves the problem of steady-state error of PPPSO for non-stationary inputs and a large filter length [14].The acoustic noise can also be suppressed by introducing variable step size in a two-channel sub-band forward algorithm (2 CSF) that improves the convergence speed and overcomes the fixed step size problem in the traditional 2 CSF method [15]. Another approach using variable step sizes is adaptive blind source separation through a two-channel forward-backward structure based on the normalized least-mean square (NLMS) method that uses variable step sizes for steady-state conditions [16]. The estimated source signal enhancement in the presence of acoustic noise is performed by Threshold Wavelet-based Forward Blind Source Separation (TWFBSS). This approach reduces the computational complexity from the Wavelet-based Forward Blind Source Separation (WFBSS) method [17]. Kalman filters can also be used with BSS techniques to deal with the noisy convolutive mixture. First, the BSS approach extracts the estimated source speech signal from the non-stationary noisy reverberant mixture. Then, Kalman filtering suppresses the noise components in the estimated speech signal [18]. Recently, new evolving techniques such as deep learning are also applied with the BSS approach in the reverberant noisy environment [19]. In general, the BSS methods are tested under non-Gaussian noise modeled by the fourth-order cumulant, and the singular value decomposition-total least square method [20]. Moreover, the speech signals are often corrupted by different types of noise produced in the surrounding environment that can be tackled by the Dual Recursive non-Quadratic (DRNQ) adaptive method combined with FBSS to enhance the speech quality [21].
# Explainable AI Frameworks: Navigating the Present Challenges and Unveiling Innovative Applications
## 1 Introduction  
As Artificial Intelligence (AI) rapidly evolves, new and powerful technologies emerge alongside unforeseen challenges. This rapid growth necessitates the continuous development of tools and methods to ensure AI's responsible and ethical use. One such crucial development is Explainable Artificial Intelligence (XAI) [1]. Imagine a machine learning (ML) model that predicts loan approvals. Traditionally, these models might be like a black box--where someone could input data and obtain an output (approved/rejected) without understanding why the model made that decision. XAI techniques aim to shed light on this process, helping users to understand the factors influencing the model's predictions. Some benefits of XAI are trust enhancement with transparency, improved model development, and better decision-making.  
XAI is a field dedicated to making the decisions and outputs of AI models understandable and transparent for humans. This transparency is crucial for several reasons. First, it helps to build trust. When people understand how AI systems reach conclusions, they are more likely to trust and accept those results. This is particularly important in critical domains like healthcare, finance, and law, where AI decisions can significantly impact people's lives. Second, XAI fosters improved understanding. Deeper insights into AI models' inner workings can help in understanding the decision-making processes. This knowledge is invaluable for debugging and improving models, also ensuring that they align with human values and ethical guidelines. Finally, XAI techniques play a crucial role in identifying and mitigating bias. These techniques can be used to detect and addressthe potential biases that may be present in AI systems, leading to fairer and more ethical development [2].  
According to [3], the future of XAI lies in methods that capture causal relationships. This necessitates the development of metrics to assess the explanation quality, ensuring that users grasp causality effectively, efficiently, and with satisfaction in specific use cases. New human-AI interfaces are foreseen, enabling contextual understanding and user interaction through questions and counterfactuals. By fostering explainability, XAI plays a vital role in navigating the challenges of the rapidly evolving AI landscape. It paves the way for building trust, ensuring responsible AI development, and, ultimately, unlocking AI's full potential for society's benefit.
# Frequency Comb-Based WDM Transmission Systems Enabling Joint Signal Processing
## 4 Joint Digital Signal Processing Schemes for Carrier Recovery
### Experimental Evaluation
#### 4.3.1 Master-Slave  
The joint carrier recovery was implemented by doing coarse frequency offset estimation and phase estimation on the master channel. The estimated frequency offset and phase noise was then applied to the slave channel. Since the combs were unsynchronized, they had a small spacing difference of \(\sim\)30 kHz. This difference was measured once and used as a correction factor when the master frequency offset was applied to the slave channels, as illustrated in Figure 7a. After applying the master phase to the slave channels, a slow feedback-based phase tracker was used to compensate the small phase variations remaining.
# Angle-of-Arrival Estimation Using Difference Beams in Localized Hybrid Arrays
## 1 Introduction
In [13], the optimal sum and difference beamformers based AoA estimator was constructed by exploiting the ratio of difference pattern to sum pattern with two overlapping subarrays, which can achieve the minimum estimation variance under Gaussian noise, regardless of any nulling performed. The work in [14] uses hierarchical search in the designed multi-resolution codebook to promptly identify one single multipath component (MPC) and thus the AoA. A compressed sensing based method was further investigated in [15] to find multiple MPCs, exploiting the sparse nature of mmWave channels. The beam needs to be recurrently narrowed down according to the codebook, which incurs additional overhead. In this paper, we propose a novel phase shift design to enable unambiguous AoA estimation using a localized array. Instead of generating multiple single beams as proposed in [3; 4; 5; 6; 7], a difference beam based phase shift configuration is designed to steer each subarray in two directions. This can effectively improve the performance in terms of mean square error (MSE) of \(Nu\) estimation and detection probability of the expected subarray index by providing better coverage of the directions of interest. Based on the derivation in terms of the even and odd ratios of \(N/K\) where \(K\) is the different phase shifts per symbol, two IDFT-and-correlation based estimation schemes are proposed to directly estimate the AoA. Simulation results show the effectiveness of the proposed approach in estimation accuracy.
# Rapid Target Extraction in LiDAR Sensing and Its Application in Rocket Launch Phase Measurement
## 3 LiDAR Target Detection Experiment
### Method Effectiveness Verification  
#### 3.2.1 Time Complexity Evaluation  
In this study, by converting the 3D point cloud data into 2D for target extraction, the method achieves a more efficient processing time. Using data from preliminary validation experiments, the point cloud is divided into different levels, while ensuring the inclusion of the target points. The processing times for varying point cloud quantities are then compared. Finally, the proposed method is evaluated against other target clustering and segmentation techniques for point cloud extraction.  
The proposed method in this paper is compared with the DBSCAN clustering segmentation algorithm [23] and the Minimum Spanning Tree (MST) segmentation algorithm [24] for target extraction. In the experiments, the algorithms process 3D point cloud data with point cloud quantities of 1000, 10,000, 20,000, 30,000, 50,000, 100,000, and 152,701 (the total number of points). The time results for processing point clouds with different numbers of points using the three methods are as the Table 2.  
All three methods are capable of accurately extracting the target 3D point cloud, with processing time comparisons shown in Table 2. The results reveal that for smaller datasets, the DBSCAN and MST algorithms can extract the target point cloud more quickly. However, as the dataset size increases, the size of the distance matrix grows exponentially, leading to a significant increase in processing time. When the data exceeds 50,000 points, the distance matrix becomes too large, making it difficult for these algorithms to deliver results efficiently. In contrast, the method proposed in this paper converts the 3D point cloud data into 2D before performing target extraction. As a result, the processing time remains relatively stable even as the data size increases. Even when processing the entire point cloud, the method only takes 0.53 s. This demonstrates that the proposed algorithm offers a substantial speed advantage over traditional 3D target extraction methods, underscoring the practicality and efficiency of the point cloud target processing approach introduced in this paper.
# An Experimental Tethered UAV-Based Communication System with Continuous Power Supply
## 2 Background
### The Role of UAVs in Wireless Communications
#### 2.2.2 IoT Device Data Collection  
Implementing IoT systems requires wireless facilities to collect data and retransmit it [23]. However, a drawback arises in rural areas or areas without wireless network infrastructure, such as in the case of data collection in agricultural areas. UAVs can be used to address this situation. In the field of IoT, it is possible to integrate UAV technology and wireless energy transfer. The objective is for the UAV to transmit radio waves to IoT nodes. These nodes receive and store energy, then use it to transmit information back to the UAV. To achieve efficient data acquisition from the UAV, it is essential to ensure that the energy is supplied in a directional manner [24].
## 3 Proposed MISO Converter
### Unequal Currents between the Two Inputs  
This subsection will show that the proposed structure has an automatic current sharing element embedded but does not have an automatic current sharing feature. To simplify the analysis, it is assumed that there is no loss in power conversion in this circuit, i.e., the input power is equal to the output power. According to (17), the power of the two inputs can be split and their DC output currents can be assumed to be the same, equal to \(I_{o}\).  
As shown in Figure 5, the input currents \(I_{d1}\) and \(I_{d2}\) can be expressed as  
\[I_{d1}=I_{Lm1}-I_{Np1} \tag{18}\]\[I_{d2}=I_{Lm2}-I_{Np2} \tag{19}\]  
Next, the DC component of the coupling inductor is analyzed. According to the ampere-second balance, the average current in the capacitor is zero, so the following equations can be obtained as follows:  
\[I_{Np1}=\frac{N_{s1}}{N_{p1}}\times I_{Ns1}=\frac{N_{s1}}{N_{p1}}\times I_{o}=nI _{o} \tag{20}\]  
\[I_{Ns2}=I_{Np2}=0 \tag{21}\]  
By substituting (18) into (20), the expression for the current \(I_{d1}\) can be obtained as  
\[I_{d1}=I_{Lm1}-nI_{o} \tag{22}\]  
Additionally, substituting (19) into (21) yields the expression for current \(I_{d2}\) as  
\[I_{d2}=I_{Lm2} \tag{23}\]  
Because there is no loss of power conversion in the circuit, the following equation can be obtained as  
\[V_{o}I_{o}=V_{d1}I_{d1}+V_{d2}I_{d2} \tag{24}\]  
and also, multiplying (17) by \(I_{o}\) yields  
\[V_{o}I_{o}=\frac{2+n(1+D_{a})}{1-D_{a}}V_{d1}I_{o}+\frac{2(1+n)}{1-D_{b}}V_{ d2}I_{o} \tag{25}\]  
By comparing (24) and (25) to find the relationship between the input currents \(I_{d1}\) and \(I_{d2}\) and the output current \(I_{o}\), the following Equations (22) and (23) can be obtained as  
\[I_{d1}=\frac{2+n(1+D_{a})}{1-D_{a}}I_{o}=I_{Lm1}-nI_{o} \tag{26}\]  
\[I_{d2}=\frac{2(1+n)}{1-D_{b}}I_{o}=I_{Lm2} \tag{27}\]  
From (26), \(I_{Lm1}\) can be obtained as  
\[I_{Lm1}=\frac{2(1+n)}{1-D_{a}}I_{o} \tag{28}\]  
By substituting (23) and (26) into (18) and (19), the DC components of the input currents can be found as  
\[I_{d1}=\frac{2(1+n)}{1-D_{a}}I_{o}-nI_{o} \tag{29}\]  
\[I_{d2}=\frac{2(1+n)}{1-D_{b}}I_{o} \tag{30}\]  
The two input currents will not be identical even if \(D_{a}\) is equal to \(D_{b}\), according to (29) and (30).
## 4 Results and Discussion  
The practical effect of modifying the four GEKO coefficients under consideration, which are \(C_{\text{sep}}\), \(C_{\text{mix}}\), \(C_{\text{mw}}\), and \(C_{\text{corner}}\), was systematically examined by carrying out a series of different steady RANS simulations. Specifically, a parametric study, where only one parameter was varied while keeping the others constant, was performed. The different RANS solutions were compared with each other as well as with the reference high-fidelity SRS. This allowed for a clear understanding of the influence of each model coefficient.
# Improved PPP Ambiguity Resolution with the Assistance of Multiple LEO Constellations and Signals
## 1 Introduction
[23] show that LEO-augmented GNSS (GPS+BDS+Galileo) can decrease the PPP convergence time to 5 min. [PERSON] et al. [24] investigated the performance of LEO-augmented multi-GNSS (GPS + GLONASS + BDS + Galileo) PPP with different LEO constellations. The results indicated that more LEO satellites lead to a shorter convergence time and the convergence time can be shortened from 8.2 to 0.8 min by introducing observations from 288 polar-orbiting LEO satellites. As previous studies all focused on the contribution of the LEO constellation to dual-frequency GNSS float solutions, in this paper, for the first time, multi-frequency LEO-augmented GNSS PPPambiguity resolution was investigated. The augmentation performance of different LEO constellations was evaluated in terms of TTFF and positioning accuracy. The benefits of multi-frequency LEO observations for PPP AR were also analyzed. After this introduction, this paper is organized as follows: the constellation design, data simulation, and LEO-augmented PPP AR methods are introduced in Section 2. Then, the results of uncalibrated phase delay (UPD) products, and the performance of the GNSS PPP AR with multiple LEO constellations and signals are evaluated in Section 3. Finally, the discussions and conclusions are provided in Sections 4 and 5, respectively.
# High Q-Factor Resonance in a Symmetric Array of All-Dielectric Bars
## 3 Conclusions  
In summary, sharp electric resonance with extremely high Q-factor over several thousands are achieved in a simple all-dielectric silicon rod arrays without asymmetric structure. In the mean time, large electric field enhancement both inside and surrounding the rods is observed. A plane wave with its electric field polarized perpendicular to the rod (instead of parallel) illuminates on the metasurface, hence strong high order ER (TE\({}_{12\delta}\)) with high Q value is excited. The electric field coupling between adjacent rods is enhanced by increasing the length of the rod, thus the radiation loss of the ER is significantly depressed, which further explains the source of the high Q value. The resonant wavelength, the Q value and the field enhancement factors versus various geometrical parameters are calculated. The proposed all-dielectric metasurface has many advantages, such as low loss, simple structure, and CMOS-compatible. This structure can be widely used in applications, such as label-free bio-chemical sensing, narrowband filters, optical modulations and nonlinear interactions.  
This work was supported by the National Natural Science Foundation of China under Grant Nos. 61377108, 61405182.  
[PERSON] designed the structure and performed the numerical simulations; [PERSON], [PERSON], [PERSON] and [PERSON] analyzed the simulations and modified the article; [PERSON] putted forward the idea, analyzed the date and wrote the article.  
The authors declare no conflict of interest.
# Generating Bessel-Gaussian Beams with Controlled Axial Intensity Distribution
## 4 Conclusions  
The diffraction of a Gaussian beam on a binary mask and its propagation through an axicon to form a quasi-uniform axial intensity distribution of a zero-order Bessel beam were studied. With an external or internal diaphragm of a Gaussian beam, only one edge of the axial intensity distribution of the Bessel beam field changed. The use of the mask with annular aperture allowed to obtain two sharp edges in the distribution of the axial field intensity. In addition, the internal diaphragm of the input Gaussian beam allowed to exclude the influence of axicon apex angle quality on the formed Bessel beam. Thus, the choice of the parameters of the mask and the axicon allows one to control the length of the Bessel zone of the formed zero-order Bessel beam with a sharp drop in one or both edges of the axial field intensity. This method and the laser optical system that implements it use a commercially available element base and do not have significant restrictions on radiation resistance when working with high-power radiation. The obtained research results will find application in various technologies of photonics and laser technology, such as optical tweezers, visualization of objects, material processing, etc.  
Conceptualization, N.S. and P.N.; methodology, M.K. and S.K.; software, M.G.; validation, N.S. and G.K.; formal analysis, P.N. and S.K.; investigation, N.S. and P.N.; resources, P.N. and M.K.; data curation, M.G.; writing--original draft preparation, N.S., P.N. and G.K.; writing--review and editing, G.K. and M.K.; visualization, N.S.; supervision, S.K.; project administration, N.S. and P.N.; funding acquisition, P.N. and M.K. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Russian Foundation for Basic Research, grant number 18-38-20155, and Ministry of Science and Higher Education of the Russian Federation, grant number 0705-2020-0041.  
The authors declare no conflict of interests.
## 4 Results and Discussion
### Numerical Simulation of Precipitation Infiltration in the Field Test Site
#### 4.2.2. Mathematic Model  
The land use type of the field test site was bare land, and the water absorption rate of the root system (S) was set to 0. One-dimensional vertical flow was mainly considered for vertical groundwater recharge. The Richards equation used to describe the vertical percolation in a vadose zone is as follows.  
\[\mathrm{c}(h)\frac{\partial h}{\partial t}=\frac{\partial}{\partial z}\bigg{(} K(h)\frac{\partial h}{\partial z}\bigg{)}+\frac{\partial K(h)}{\partial z} \tag{2}\]  
where \(h\) is pressure head (L); \(c(h)\) is the specific moisture capacity (L\({}^{-1}\)); \(K(h)\) is the unsaturated hydraulic conductivity (LT\({}^{-1}\)); \(z\) is the vertical coordinate (L) with zero referring to the ground and upwards being positive; \(t\) is time (T).  
The Van Genuchten-Mualem soil hydraulic functions were used to describe the unsaturated hydraulic properties in the models.  
\[\theta(h)=\begin{cases}\theta_{r}+\frac{\theta_{s}-\theta_{r}}{\big{[}1+|ah|^{ n}\big{]}^{m}},&h<0\ \ \ (m=1-n^{-1},\ n>1)\\ \theta_{s},&h\geq 0\end{cases} \tag{3}\]  
\[K(h)=\begin{cases}K_{s}S_{e}^{l}\Big{[}1-\Big{(}1-S_{e}^{1/m}\Big{)}^{m}\Big{]} ^{2},&h<0\\ K_{s},&h\geq 0\end{cases} \tag{4}\]  
\[S_{e}=\frac{\theta-\theta_{r}}{\theta_{s}-\theta_{r}}=\bigg{[}\frac{1}{1+( \alpha h)^{n}}\bigg{]}^{m} \tag{5}\]  
where \(\theta_{r}\) is the residual water content (-); \(\theta_{s}\) is the saturated water content (-); \(\alpha\) (L\({}^{-1}\)), \(n\) (-), m (-), and \(l\) (-) are empirical shape factors that depend on soil type; \(K_{s}\) is the saturated hydraulic conductivity (LT\({}^{-1}\)); \(S_{e}\) is the effective saturation (-).
## 1 Introduction
### Impact-Echo Method  
Impact-echo is an acoustic method for the nondestructive evaluation of concrete, which is based on the use of impact-generated elastic waves that propagate through concrete, and they are reflected by internal flaws or external surfaces [9]. Impact-echo can be used to not only determine the location and extent of flaws, such as cracks, delamination, and voids in the targeted concrete, but also to provide thickness measurements of concrete slabs as well [10].  
As shown in Figure 1, in the impact-echo system, an induced elastic wave at a surface of concrete propagates and reflected by defects or the surfaces in concrete. The received signal is monitored by a transducer coupled to the surface near the elastic wave source. The time-domain waveform is transformed into the frequency-domain one using the fast Fourier transform (FFT). The frequency spectrum contains those resonance frequencies due to reflections at such interfaces as the internal defects and the surfaces of the material of interest. When the P-wave velocity in the concrete is known, the detected peak frequency can be expressed as in Equations (1) and (2), respectively.  
\[f_{T}=\frac{C_{p}}{2T} \tag{1}\]  
\[f_{d}=\frac{C_{p}}{2d} \tag{2}\]  
where \(f_{T}\) is resonance frequency due to material thickness, \(f_{d}\) is resonance frequency due to internal damage (defect), \(T\) is the thickness of targeted member, \(d\) is defect depth, and \(C_{p}\) is P-wave velocity in concrete.