esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Assessing the Effectiveness of the Proposed IDA Procedure
### IDA Curves Obtained Through Approximate Values of the Elongated Period  
The results obtained by calculating the IM parameter in reference to the approximate values of the elongated fundamental period obtained from Equations (1) and (2) ([PERSON] et al., 2018; [PERSON] and [PERSON], 2019), are presented in this section. Figure 8 shows the IDA curves obtained by considering \(T_{in}=2T_{el}\), which is the upper value proposed by [PERSON] et al. (2018) (see Equation (1)), while the curves derived through the approximate value given by [PERSON] and [PERSON] (2019) (see Equation (2)) are plotted in Figure 9. Despite referring to the inelastic period, the diagrams in Figures 8 and 9 still show a rather high scatter because the inelastic periods estimated by the methods of [PERSON] et al. and [PERSON] and [PERSON] are constants that can greatly differ from the actual values, as also highlighted by the diagrams in Figures 5 and 6.  
Figure 7: IDA curves obtained by referring to the elastic period: (**a**) \(x\)-direction; (**b**) \(y\)-direction.
## 5 Conclusions  
In this paper, NOMA networks with short packet communications were studied, and the effective capacity for non-orthogonal users was used as the performance evalution standard. We derived the exact expressions of effective capacity for non-orthogonal users. We then derived the asymptotic expressions of the effective capacity for non-orthogonal users when the SNR is infinite and concluded that the slope of the curve of the effective capacity for non-orthogonal users in the high SNR region is equal to zero. In addition,the relationship between the effective capacity of multiple users, delay parameters, the transmission error rate, the SNR, and the blocklength has been discussed in detail by simulations. The results show that, when the related parameters remain unchanged, the user's effective capacity decreases with the increase in the transmission error rate or the delay parameters, increases with the increase in SNR or blocklength, and tends to be stable in the high SNR region. Numerical results have also indicated that the NOMA networks can provide better an effective capacity in comparison with the OMA scheme.  
Methodology, X.Y.; Supervision, S.K.; Writing--review and editing, X.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data used to support the findings of this study are available from the corresponding author upon request.  
The authors declare no conflict of interest.
# Screen-Based Sports Simulation Using Acoustic Source Localization +
## 4 System Overview  
An accurate estimation of ball positions using acoustic source localization requires several preprocessing steps as follows: calibration for all MEMS microphones and impulsive sound detection and recognition to distinguish it from surrounding noises and other unwanted sounds. Further details of these steps are described in [10]. Figure 3 shows an overview of the proposed system to estimate the 3D location of an impulsive acoustic source. The proposed system is comprised of the following two steps: an estimation of two individual acoustic source directions, \(\overrightarrow{v_{1}}\) and \(\overrightarrow{v_{2}}\), which are obtained by DSBF controllers using a MEMS microphone array arranged in 2D space, and an estimation of the closest point of the two vectors by the host processor.  
Figure 4 shows the overview of the proposed estimation architecture for 3D acoustic source localization. When a sound is generated and its decibel exceeds a predefined threshold, the impulsive sound detector classifies it as a valid target sound based on a feedforward neural network (FFNN) [20]. If it is classified as a valid sound, micro control unit (MCU) calculates the delay between all the MEMS microphones (i.e., 49 in our system) and candidate degrees as discussed in Section 2. The MCU transfers it to the DSBF unit in the FPGA.  
The delay information is calculated only once before processing the DSBF and implementing a double precision \(\times\) angle between the sound and the microphones using the input acoustic sources and the delay in parallel for all microphones as described in Figure 5. The USB controller transfers the results of DSBF to a PC to evaluate the estimated acoustic source.  
Figure 3: Overview of the proposed system: Acoustic source localization using two delay and sun beamforming (DSBF) systems.
# Evaluation of Five Reanalysis Products over France: Implications for Agro-Climatic Studies
## 3 Results
### Evaluating the RPs for Vine Crop Indicators  
On a global scale, all RPs show a high agreement with the reference dataset in reproducing WI, with LCCC values greater than 0.95 (Table 3). For most of the stations (40%, 70%, 80%, 90%, and 90% for SCOPE Climate, ERA5-Land, ERA5, FYRE Climate, and RFHR, respectively), RPs exhibit high correlations (LCCC values \(\geqslant\) 0.8), showing their reliability at reproducing WI (Figure 2d). RFHR has the lowest IQD value, indicating a closer match with the reference dataset, followed by FYRE Climate and ERA5, which exhibit similar levels of agreement. Meanwhile ERA5-Land and SCOPE Climate show relatively higher dissimilarity (Table 3). The spatial distribution of IQD values reveals varying levels of agreement with the reference dataset across the study sites (Figure 2f). Furthermore, as shown in Table 3 and Figure 2b, FYRE Climate and RFHR slightly overestimate WI values (the kernel density estimation (KDE) curve is right-shifted), while the other RPs tend to underestimate WI values. Figure 2e further confirms that the performance of the RPs varies across the study sites. Overall, RPs are able to reproduce the observed WI values. However, some discrepancies are seen at the Bordeaux-Merignac station, where SCOPE Climate shows poor performances, and at Montelimar, where a significant dissimilarity compared to the reference dataset is seen for ERA5-Land. According to Table 3 and Figure 3d, FYRE Climate shows strong similarity with the reference dataset when reproducing HyI values; FYRE Climate consistently shows the highest level of agreement, when considering all study sites collectively (with LCCC = 0.92) and for each individual location (LCCCs \(\geqslant\) 0.8). ERA5, ERA5-Land, RFHR, and SCOPE Climate, however, have weaker correlations with the reference dataset, with SCOPE Climate displaying the lowest agreement with the observations. IQD values affirm the earlier finding (Table 3 and Figure 3f); FYRE Climate consistently displays superior agreement with the reference dataset for when reproducing HyI values. However, SCOPE Climate demonstrates a closer match with the reference dataset, in terms of IQD, compared to ERA5, ERA5-Land, and RFHR. The three latter exhibit similar results, suggesting a comparable level of agreement with the reference dataset. All RPs tend to overestimate the HyI values (Table 3 and Figure 3e), with FYRE Climate and SCOPE Climate showing a low degree of overestimation compared to the other RPs. However, a significant variability in the bias values is seen which suggests a different level of agreement between the RPs and the reference dataset across the study period and sites.
## 2 End-to-End Speech Recognition
### Hybrid CTC/Attention Encoder-Decoder  
To address the irregular alignments problem in attention mechanism, the authors in [17] propose the hybrid CTC/Attention architecture. Unlike the attention encoder-decoder model, it utilizes a CTC objective function as an auxiliary task to train the encoder network within a multitask learning (MTL) framework. During training, the forward-backward algorithm of CTC can enforce monotonic alignment between speech and label sequences. The objective to be maximized is a logarithmic linear combination of the CTC and attention objectives:  
\[L_{MTL}=\lambda logp_{ctc}(\mathbf{y}|\mathbf{x})+\lambda logp_{att}(\mathbf{y}|\mathbf{x}) \tag{12}\]  
with a tunable parameter \(\lambda:0\leq\lambda\leq 1\).
###### Abstract  
At the very end of the year 2020, on 29 December, a hazardous earthquake of M\({}_{w}\) = 6.2 hit the area of Petrinja and its surroundings, in the NW of Croatia. The earthquake was felt across the area of 400 km, leaving an inconceivable damage in the vicinity of the epicenter, devastated towns and ruined lives. In order to map the spreading of earthquake waves and to determine the coseismic ground displacement after the mainshock, we have analyzed open satellite radar images of Sentinel-1 and the GNSS data from the nearest CORS station related to the epicenter, along with the seismic faults. In this paper, we addressed and mapped the displacement linear surface ruptures detected by the SAR interferometry. The results show the vertical ground displacement to the extent of \(-12\) cm in the southern area and up to 22 cm in the north-western part of a wide area struck by the earthquake impact, related to the epicenter. Subsidence and uplift in a range of \(\pm 5\) cm over a wider affected area indicate a spatial extent and hazardous impact made by the earthquake. The ground displacement of 30 cm to the West and 40 cm to the East has been identified considering the intersection of Pokupsko and Petrinja strike-slip fault system in the seismic zone of Pannonian basin. Accordingly, we obtained matching results of 5 cm south-eating shift and \(-3\) cm subsidence on Sisak GNSS COPOS station, addressing the tectonic blocks movement along the activated complex fault system. The results compared with the geology data confirm the existence of two main faults; the Pokupsko and the Petrinja strike-slip faults and interpret the occurrence of secondary post-seismic events over the observed area.  
29 December 2020 hazardous earthquake; M\({}_{w}\)6.2 earthquake in Croatia; Sentinel-1; SAR processing; coseismic ground displacement; GNSS +
Footnote †: journal: Physics Letters B  
1
Footnote 1: [[https://doi.org/10.3390/geosciences11040170](https://doi.org/10.3390/geosciences11040170)]([https://doi.org/10.3390/geosciences11040170](https://doi.org/10.3390/geosciences11040170))
## 1 Introduction
[22] proposed a Bayesian framework for integrating CPT and MASW data for the probabilistic characterization of 2D soil profiles. The above methods require consideration of site-specific empirical equations. Therefore, [PERSON] et al. [24] proposed a multi-source Bayesian compressive sampling method for 2D data fusion, fusing geotechnical and geophysical data for 2D subsurface site characterization. Based on the above theory, this study proposes a data-driven adaptive fusion sampling strategy that automatically develops an assessment model of the spatial distribution of soil liquefaction potential from spatially sparse geotechnical data, performs monitoring of liquefaction-induced settlement, and integrates spatiotemporally unconstrained geophysical data to update the model systematically and quantitatively. It contains three major components, site modeling using sparse geotechnical data from CPT soundings via 2D Bayesian compressive sampling (2D BCS), consolidation settlement modeling combining domain knowledge with CPT-based liquefaction procedures, and multi-source fusion Bayesian compressive sampling (MSF-BCS) coupled with information entropy to automatically fuse sampling samples of geotechnical and geophysical data in a self-adaptive and determine the optimal sampling locations. The reliability level of the spatial assessment target of liquefaction potential is finally achieved, and the interpolation uncertainty and soil spatial variability are quantified. The remainder of this study is organized as follows. Firstly, the simplified CPT-based procedure and site settlement model, as well as the 2D BCS theory, are presented. In addition, the proposed MSF-BCS is presented in detail. Finally, real data are used to illustrate and validate the proposed sampling strategy.
# A Co-Design-Based Reliable Low-Latency and Energy-Efficient Transmission Protocol for UWSNs
## 3 Design Challenges
### Mobility  
Objects residing in an underwater environment commonly exhibit passive mobility from water currents or active mobility exhibited by autonomous platforms [28]. Therefore, the neighborhood of a node will constantly change due to this feature of an underwater environment. That is to say, it is difficult for a node to obtain the geographic information of its neighbors. However, frequent neighborhood discovery is necessary in UWSNs to achieve an efficient selection of next-hop candidate nodes. Critically, it is unrealistic to find a fixed routing path with dynamic network topology in a complex underwater propagation environment. The routing process demands frequent updates and maintenance of the routing table, which will undoubtedly increase the end-to-end latency and energy consumption. Due to the long propagation delays, the route information would soon become stale. It is difficult to maintain fresh route information without enough update control packets.  
In summary, underwater data transmission protocols require cutting back the number of control packets and reducing the redundant packets to improve the QoS of time-critical aquatic applications. Meanwhile, the protocol needs to overcome the challenges brought by node movement to ensure reliability.
## 2 Literature Review
### Predictive Modeling for Neurodegenerative Diseases
Utilizing a range of data sources, predictive models integrate genetic markers linked to MS susceptibility, along with clinical assessments, like the expanded disability status scale (EDSS), to track motor and cognitive impairments over time [17]. Imaging techniques, such as MRIs, provide insights into lesion formation and progression within the central nervous system [21]. In the context of home-health monitoring systems, predictive modeling facilitates continuous evaluation of mobility changes, physical activity levels, respiratory function, speech abilities and cognitive functions [15], all important for optimizing treatment plans and enhancing quality of life for individuals living with MS [21]. Being able to predict the onset, progression, and exacerbations of conditions like PD, AD, and MS involves the use of statistical and ML techniques:  
* _Time series analysis_: These models analyze time series data from IoMT devices to forecast future health states [42];
* _ML models_: Supervised and unsupervised learning techniques are used to develop models that can predict the onset or progression of neurodegenerative diseases based on historical data and real-time monitoring. Deep Learning Techniques and neural networks are utilized to examine intricate patterns in huge datasets, increasing prediction accuracy. Convolutional neural networks (CNN) and VGG16 models have been used for predicting AD from MRI images and PD from spiral drawings with high accuracy [43]. Recurrent neural networks (RNNs), convolutional neural networks (CNNs), and transformer-based models have been used for building different predictive models. These techniques exhibit excellent accuracy, have the potential for early diagnosis, and improve patient outcomes [43; 44];
* _Multimodal data integration_: Prediction accuracy is increased by combining data from multiple sources such as genetic information, medical records, and wearable technology. To forecast the course of a disease and comprehend biological trends in neurodegenerative disorders, data-driven models of disease progression incorporate multiple sources, for example imaging and cognitive testing. These models allow for accurate patient staging and offer fine-grained longitudinal patterns [41]. The NeuroPredict platform integrates predictive modeling algorithms tailored for each specific disease (AD, PD, and MS) using the data provided by the IoMT devices integrated into the home environment and/or worn by the monitored subjects in order to provide personalized healthcare.
## 2 Electrohydrodynamic Model
### Plasma Kinetics  
To simulate the plasma behavior under EHD body forces, we consider the migration of all the particles species involved, such as the electrons, ions, and neutrals, as they are fundamental to the calculation of the space charge density. The set of equations that are responsible for the evolution of each species are the continuity equation: for the electron density, \(n_{e}\); for the electron energy density, \(n_{e}\), and for the gas particles, which are calculated, using the mass fraction of the \(k\)-th species, \(\omega_{k}\)[27]  
\[\frac{\partial}{\partial t}n_{e}+(\mathbf{u}\boldsymbol{\cdot}\boldsymbol{ \
abla})n_{e}=-\boldsymbol{\
abla}\boldsymbol{\cdot}\boldsymbol{\Gamma}_{e}+R _{e} \tag{7}\]  
\[\frac{\partial}{\partial t}n_{e}+(\mathbf{u}\boldsymbol{\cdot}\boldsymbol{ \
abla})n_{e}=-\boldsymbol{\
abla}\boldsymbol{\cdot}\boldsymbol{\Gamma}_{e}+R _{e} \tag{8}\]  
\[\rho_{f}\frac{\partial}{\partial t}\omega_{k}+(\mathbf{u}\boldsymbol{\cdot} \boldsymbol{\
abla})\omega_{k}=-\boldsymbol{\
abla}\boldsymbol{\cdot} \boldsymbol{\Gamma}_{k}+R_{k} \tag{9}\]  
Here, \(\mathbf{u}\) is the gas fluid velocity; \(\rho_{f}\) is the gas mass density; \(\boldsymbol{\Gamma}_{e}\) is the electron flux, \(\boldsymbol{\Gamma}_{e}\) is the electron energy flux, and \(\boldsymbol{\Gamma}_{k}\) is the diffusive flux vector. Finally, the densities sources are \(R_{e}\), \(R_{e}\) and \(R_{k}\) for the electron, electron energy, and the \(k\)-th species, respectively [24].  
The source terms are obtained with the chemical reactions and associated rate coefficients. The set of kinetic reactions considered is shown in Table 1. The collisional processes of gain and loss of electron energy are adequately described using the Boltzmann equation [35], but as here we are only interested in the average influence of plasma parameters on thruster performance, we use a Maxwellian electron energy distribution function (EEDF), \(f_{M}(\varepsilon)\), for electron energy to find the coefficients for the electron-impact collision processes (elastic, inelastic, and ionization), with a set of cross-section data (for references, see Table 1), through  
\[C_{j}=\left(\frac{2e}{m_{e}}\right)^{1/2}\int_{0}^{\infty}e\sigma_{j}( \varepsilon)f_{M}(\varepsilon)d\varepsilon \tag{10}\]  
where \(\varepsilon\) is the electron energy (in eV units), \(m_{e}\) is the electron mass, and \(\sigma_{j}(\varepsilon)\) represents the cross-section data for the considered reaction. As this information regarding electrons is necessary to predict the populations of the various species present in the corona discharge, the Maxwellian EEDF must be solved simultaneously with a system of rate balance equations for the various heavy species of particles (neutrals and ions) considered. Although the use in this work of a Maxwellian-type EEDF is a simplified form when compared to a more realistic EEDF obtained from solving the Boltzmann integro-differential equation, it allows us to obtain computational solutions faster in the framework of a two-dimensional finite element model with a dense mesh.
# Prognostics and Health Management Based on Next-Generation Technologies: A Literature Review
## 6 Challenges and Potential
### Potential and Directions of Development  
1. After summarizing, we find that PHM needs to be improved, especially in terms of prediction, which needs to be further solved through continuous improvement and exploration of the algorithms, which need to be deployed in practical research with reasonable and adequate arrangements.
2. With the emergence of some new technologies and new methods which provide more selective ways for PHM to be realized, including drones, the IoT, digital twins, AR, VR, MR, mobile assistive devices, etc., the research on PHM should be focused on its combination with new technologies and improvement in specific situations, which will surely obtain great results.
3. PHM based on DTs should focus more on data cleaning and dimensionality reduction and solve the problem of vast computation volumes to achieve real-time prediction and maintenance accuracy.
4. With the development of AR technology, digital twins integrated with AR technology help to enhance the interaction between digital twins and the real world, and more and more scholars have been engaged in this research. Optimizing their technical and logical relationships further and exploring more efficient and reasonable PHM techniques and methods based on AR-DT is necessary for combining AR and digital twins. In addition, the lightweight design of digital twins is an issue that deserves profound consideration, related to whether the operator can accurately and quickly perform whole life cycle fault monitoring and diagnosis for equipment using AR technology.
## 3 Results and Discussion
### Bubble Growth Rates  
To advance the understanding of bubble evolution, significant research has focused on nucleation, growth, and departure mechanisms. In previous work, Professor [PERSON]'s group [54; 55; 56] examined critical aspects of hydrogen nanobubble nucleation, including activation energy, critical nucleus size, and the stability of nanobubbles [29]. These findings are pivotal in describing the initial stages of bubble formation on electrode surfaces.  
In Figure 19, the measured bubble radius versus time is presented alongside the theoretically predicted growth rates using different exponents according to Equations (2)-(4) for applied potentials of 2.9, 3.0, and 3.1 V. The growth coefficients were determined to achieve the best fit with the first part of the measured data for Equation (2), the middle part of the data for Equation (3), and the last part of the data for Equation (4). For a detailed frame-by-frame bubble radius analysis, high-speed video recordings using a 50\(\times\) magnification lens were used. As previously shown in Figure 17, when a potential of 3.2 V is applied, the bubble diameter starts to exceed the size of the observable region of interest, resulting in higher uncertainty related to bubble radius detection.  
Figure 19a presents the bubble growth versus time for the 2.9 V case. Bubble radius increases in steps due to the coalescence of pre-coalesced (i.e., intermediate) bubbles of similar diameters, causing a rapid increase in the observed diameter of the main (i.e., the largest) bubble. For the 3.0 V case, (Figure 19b), this non-monotonic increase in bubble radius is even more pronounced. The curve according to Equation (4) provides the best fit to the experimental data in both cases, when the entire bubble growth cycle is taken into account. In Figure 19c,d, the bubble growth rate is shown for the potential of 3.1 V and exhibits favorable fits with predictions according to Equations (2)-(4). The initial region of linear growth is clearly distinguishable in the first 3 recorded frames, soon switching to a diffusion-controlled regime due to bubble coalescence. Considering greater deviations due to the stepwise coalescence of multiple bubbles, the linear model could be used instead of  
Figure 19: Measured and calculated bubble growth at electrical potentials of 2.9 V (**a**), 3.0 V (**b**), and 3.1 V (**c**,**d**).  
the square root relation for the measured data within the diffusion-controlled regime. In the reaction-limited growth stage (represented by the blue curve), the measured bubble growth clearly follows the theoretical prediction. In Figure 19d, additional measured points are presented, showing the measured bubble size after the detachment from the electrode surface, designated with the light blue diagram background. Our findings regarding the growth rate in the diffusion-controlled regime (Equation (3), i.e., \(r_{b}\propto t^{\frac{1}{2}}\)), confirm conclusions of previous studies proposing that dissolved hydrogen is not the rate-limiting factor.