esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The austempering conducted at 270 \({}^{\circ}\)C for 30 min and followed by water quenching, improved hardness, tensile and impact strength: these findings were associated with the optimal microstructure where a weak phase constrained by a hard phase matrix improved the mechanical properties as compared to a full hard single-phase. In addition to the above-mentioned issues regarding the heat-treatment route of bearing steels, a key factor is the amount of retained austenite (RA) within the matrix [20; 21; 22]. Based on the specific needs of each application, RA may be required to enhance rolling contact fatigue performance, particularly in contaminated lubricating conditions [3; 7; 23; 24]. Conversely, it may be avoided if dimensional stability is crucial, i.e., where bearings operate for extended durations at elevated temperatures [21]. Indeed, high RA contents decrease the ultimate strength of the material [23; 25; 26] and lead to a smaller dimensional stability due to phase transformation [22]. Within this context, the present study addresses the effects of heat treatment parameters, typically adopted in industrial-scale heat treatment routes, on the microstructure and hardness of EN 100 CrMo7 steel high-thickness rings. The paper opens with an overview of the literature concerning bearing steels, emphasizing the impact of temperature/time heat treatment parameters on the microstructural characteristics and mechanical behavior. Section 2 outlines the experimental methodologies employed in this study to assess the microstructural and hardness properties in response to the investigated isothermal heat treatment routes. Section 3 provides insights into the experimental findings, encompass ing microstructural characterization and hardness evolution for the high-thickness rings. Furthermore, the amount of retained austenite and the role of austenitizing temperature were considered. Finally, Section 4 delves into the obtained results from an industrial perspective, highlighting the strengths and weaknesses of the different austenitizing and isothermal heat treatment parameters. The novelty of the present study consists of an industrial-scale investigation of real components to improve the knowledge and control of heat treatment parameters with respect to the nominal guidelines provided by the supplier. Indeed, the geometry of the investigated EN 100 CrMo7 rings represents an attempt to use the material above the nominal range of application in terms of hardenability, since the ring wall thickness was 60% higher than the one suitable for the safe hardenability limit for the material. As a result, the heat treatment facility empowers customers to cut down on final expenses, steering away from more expensive materials with increased hardenability.
# Phase-Type Distributions of Animal Trajectories with Random Walks
## 3 System Model
### Trajectory Model Using Phase-Type Distributions
#### 3.1.4 Long-Tailed Duck  
From the long-tailed duck trajectories database, the parameters calculated are presented in Table 6, and the respective parameters are calculated using Equations (6)-(8). The pause time and movement time, \(T_{\text{fix}}\) and \(T_{\text{mov}}\), have \(CoVs>1\), hence a hyper-exponential distribution is chosen, and \(A_{\text{traj}}\) has a \(CoV<1\), so an Erlang distribution is selected. Finally, the Q-Q plots presented in Figure 11c show similar data quantile values in the left side of the plot for the theoretical distributions.  
Figure 11: Hypothetical and real PDFs and CDFs and Q–Q plots for the long-tailed duck. (**a**) PDF comparison, (**b**) CDF comparison, (**c**) Red and blue lines are the theoretical and practical quantiles respectively.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Random Variable** & \(\chi^{2}\) & \(\chi^{2}_{table}\) & \(D_{max}\) & \(D_{table}\) \\ \hline \(T_{fix}\) & 2.1452 & 62.9489 & 0.1516 & 0.2828 \\ \(T_{mov}\) & 2.1476 & 39.0882 & 0.1011 & 0.1050 \\ \(A_{traj}\) & 2.6301 & 62.9489 & 0.2284 & 0.2828 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Goodness of fit parameters for bats from Ghana.  
In summary, our conclusion is that the goodness of fit for the proposed and adjusted theoretical distributions has been validated. However, the Q-Q plots do not exhibit the desired straight line pattern. The presence of a concave downward curvature in a Q-Q plot indicates that the tails of the theoretical distribution are heavier than those of the sample data. While this can indicate a potential mismatch, its significance depends on factors such as the extent of deviation and the analysis's purpose. For practical considerations, considering the results of the goodness of fit tests, a sufficient match between distributions can be assumed.  
Next, we introduce an analysis involving different distributions that share similar coefficients of variation (\(CoV\)s). This comparison aims to evaluate the distributions, with particular emphasis on phase-type distributions, which are preferred due to their relevance in Markov chain models.
## 2 Methodology
### Watermark Embedding
#### 2.3.3 Optimizing PSO Parameters for Computational Efficiency  
Particle swarm optimization (PSO) plays a critical role in determining the coefficients used for watermark embedding in the proposed method. However, the choice of PSO parameters significantly impacts not only the quality of the watermarking results but also the computational efficiency of the optimization process. To address this, we conducted a focused analysis of the key parameters involved in PSO: inertia weight (\(\omega\)), cognitive component (C1), social component (C2), swarm size, and the number of iterations.  
The optimization of PSO parameters was approached with the dual objectives of minimizing computational time while maintaining high performance in terms of imperceptibility and robustness. The following parameter ranges and configurations were examined based on insights from the existing literature and empirical observations:  
* Inertia Weight (\(\omega\)): Tested within the range [0.4, 0.9] to balance exploration and exploitation.
* Cognitive Component (C1): Varied between 0.5 and 2.0 to evaluate the effect of individual particle learning.
* Social Component (C2): Varied between 0.5 and 2.0 to assess the influence of swarm-wide cooperation.
* Swarm Size: Analyzed for sizes ranging from 20 to 50 particles to explore trade-offs between computational cost and optimization performance.
* Number of Iterations: Evaluated over a range of 50 to 200 iterations to identify the point of convergence for the objective function.
## 5 Results
### EO-Based Terrain Motion Measurements
Further investigation by means of spatial profiles across fault zones clearly shows maximum motion occurring, not at the edge of the mountain ranges, but relatively shifted towards the center of the sedimentary basin (Figure 11). Deviations between measured PSI displacement rates from different viewing geometries should be, at least partially, related to the contribution of horizontal components, especially at the margins of regions showing relatively high subsidence rates. Hence, apart from the co-interpretation of independent results from different SAR geometries, the investigation of the actual motion components (vertical and E-W) provides valuable insights on the nature of motion and the corresponding deformation sources. It is worth noting that most of the deformation patterns recognized in the different SAR geometries are maintained in the vertical motion component, whereas motion in the E-W direction is practically insignificant, with rates ranging within 1-2 mm/yr (Supplementary Figures S4 and S5). Figure 11: Spatial profiles (**a**–**e** and **f**–**k**) showing LoS displacement rates across major fault zones from both ascending and descending satellite tracks (see Figures 6 and 7). For the calculations, average motion grids of 100 m spacing were used. Bars indicate the variability of motion within a buffer of 1-2 km, whereas dots the average calculated rates along the profiles. The absence of a pronounced tectonic-related signal in the InSAR measurements poses no requirement for processing complementary satellite tracks (i.e., different satellite orbits) or PSI reprocessing at a higher spatial resolution. This is also valid since a majority of the surface discontinuities (i.e., basin margins and terrain slopes) are well sampled by the already acquired results.
# Optimization of Clustering in Wireless Sensor Networks: Techniques and Protocols
## 5 Clustering Process Optimization
### Data Aggregation Phase  
Data from multiple sensors are gathered to remove redundancy during the transmission stage and provide fused information to the BS. Most data aggregation schemes aim at data gathering and aggregation in an energy-aware manner. Considering the limited energy of the SNs, allowing direct data transmission by all the SNs to the BS may not be energy-efficient. Additionally, the BS may not be capable of processing all the data generated by all the SNs. The data aggregation process is shown in Figure 8.
# A Time-Frequency Image Quality Evaluation Method Based on Improved LIME
## 2 Materials and Methods  
### Lime  
LIME (local interpretable model-agnostic explanations) is an interpretive technique for deep learning. By learning a locally interpretable model, the classifier's predictions are interpreted in an explainable and trustworthy way [20]. The basic principle of LIME is shown in Figure 1. LIME is a general paradigm of interpretable methods rather than an algorithm, and its process can be expressed as follows. In order to explain a complex model \(f\), a sample \(x\) is selected first, and several samples \(\{z_{1}\), \(z_{2}\), \(\ldots\}\) are generated by perturbing the sample \(x\) to form a perturbed sample data set \(Z\), and the corresponding predicted values \(\{f(z_{1}),f(z_{2}),\ldots\}\) of the original model \(f\) are obtained. Next, interpretable features are extracted from samples \(\{z_{1},z_{2},\ldots\}\) to obtain interpretable samples \(\{z_{1}\)', \(z_{2}\)', \(\ldots\}\). Then, an interpretable model \(g\) is selected and trained with \(\{z_{1}\)', \(z_{2}\)', \(\ldots\}\), which are taken as the input, and \(\{f(z_{1}),f(z_{2}),\ldots\}\), which are taken as the labels. Finally, a model with similar decision-making ability to the original model can be obtained in the neighborhood of sample \(x\). By describing the interpretable model \(g\), the interpretation of the original model \(f\) can be obtained.  
The explanation produced by LIME is obtained by the following:  
\[\xi(x)=arg\min_{g\in\mathbb{G}}L(f,g,\pi_{x})+\Omega(g), \tag{1}\]  
where \(f\) represents the original model, that is, the model that needs to be explained. \(g\) is a selected interpretable model for the interpretation of the original model. \(G\) is a class of potentially interpretable models. \(\pi\) is the weight function.  
The expression consists of two parts: a fidelity function \(L\) and a complexity measure \(\Omega\)The fidelity function is defined as follows:  
\[L(f,g,\pi_{x})=\sum_{z,z^{\prime}\in Z}\pi_{x}(z)\times(f(z)-g(z^{\prime}))^{2}, \tag{2}\]  
where \(z\) is the perturbed sample, \(z^{\prime}\) is the interpretable version of \(z\), and \(Z\) is a data set of perturbed samples. \(\pi\) is an exponential kernel defined based on a distance function. For images, it is \(L2\) distance with width \(\sigma\):  
\[\pi_{x}=exp\Big{(}-D(x,z)^{2}/\sigma^{2}\Big{)}, \tag{3}\]  
The complexity measure \(\Omega\) is a measure of complexity (as opposed to interpretability) of the explanation \(g\). For linear models, \(\Omega\) is the number of weights.
## 2 Materials and Methods
### Effectiveness of Spraying and Fogging on Total Bacterial Load Reduction on Eggshell Surface  
For total bacterial counts, intact and visibly clean eggs were selected for each of the treatment groups (three eggs per treatment): unwashed control, NaCl spray (~200 mg/L of FAC, positive control), water spray (45 s), ECAS spray (150 mg/L of FAC, 45 s), water fog (60 and 120 s) and ECAS fog (150 mg/L of FAC; 60 and 120 s). The disinfection procedures were performed in a biosafety cabinet (BSC) and eggs were placed in separate compartments on an oscillating tray for uniform exposure during spray-wash and fogging treatment. Each treatment was independently repeated for at least two times. In addition, ECAS (150 mg/L of FAC) fog treatment was compared for one and three eggs per oscillating tray, for 60 s and 120 s, respectively.  
After treatment, individual eggs were immediately placed into a sterile Whirl-Pak bags (Nasco, Fort Atkinson, WI, USA) containing 5 mL of buffered peptone water (BPW), massaged gently (without breaking the egg) for 1 min, then the broth was transferred into 10 mL sterile tubes (SARSTEDT Australia Pty Ltd., Adelaide, Australia). Samples were centrifuged at 5444\(\times\)\(g\) (MPW-351e Centrifuge, Med Instruments, Adelab Scientific, Adelaide, Australia) for 10 min, supernatant discarded, and the pellet was resuspended in 200 \(\upmu\)L of 1 \(\times\) PBS. Aliquots (100 \(\upmu\)L) of 10-fold dilutions (up to 10\({}^{-5}\)) of the samples were spread plated on plate count agar (PCA; CM0325, Oxoid, Melbourne, Australia) in duplicates and incubated overnight at 37 \({}^{\circ}\)C for the enumeration of colonies. Plates with 25 to 300 colonies were used for colony forming unit (CFU) calculation and data presented as log\({}_{10}\) CFU/egg.
## 5 Conclusions  
In this paper, a suitable discretization method was used to solve a real-time NMPC problem. Then, a SQP method was used for the solution. Monte Carlo simulations were performed to analyze the robustness, which is rare in autonomous vehicle trajectory tracking. The algorithm was verified with both a HiL simulation platform and a real vehicle, and the calculation accuracy and speed were well balanced. The experimental results showed that the algorithm could effectively solve trajectory tracking problems. The mean square error was maintained at approximately \(10^{-3}\) m\({}^{2}\) and the calculation time was 100 ms. It can be concluded that this method can guarantee the accuracy and real-time performance of an algorithm during a driving process, which is of great significance in practical vehicle applications. However, there are still some aspects which can be improved in this paper. The vehicle model in Monte Carlo simulation can be more complex a small number of parameters were random sampled with the limitations of experimental conditions. And the application research and development are based on the three-degree-of-freedom nonlinear kinematics model. In fact, the vehicle is a high-degree-of-freedom nonlinear system. In the future, it can be extended to a dynamic vehicle model with higher degrees of freedom to explore the performance of algorithmic path tracking combined with vehicle dynamics. The experiment item is the lane change and return  
Figure 14: Results of the real vehicle experiment.  
maneuvers, which is classic in a stability test, and we will test more complicated conditions on the basis of this experiment. And in the real vehicle experiment, the speed was kept at about 50 km/h considering the safety for all participants in the competition. More complex conditions and higher speeds will be tested in the future, and the overlap for a nonlinear system is a potential subject. In this paper, a feasible method and some problems are presented which may inspire other researchers and promote the development of unmanned vehicle trajectory tracking.  
Conceptualization, K.Z. and H.L.; methodology, K.Z.; software, G.X.; validation, K.Z., C.W. and G.X.; resources, J.Y.; data curation, H.L.; writing--original draft preparation, C.W.; writing--review and editing, K.Z.; supervision, Y.L.; project administration, K.Z. and Y.L.; funding acquisition, K.Z. and Y.L. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the Key-Area Research and Development Program of Guangdong Province (No. 2019B090912001) and Natural Science Foundation of Guangdong Province (No. 2019A1515110562).  
The authors declare no conflict of interest. The funders had no role in the design of the study, in the collection, analyses, or interpretation of data, in the writing of the manuscript, or in the decision to publish the results.
## 3 Experimental Results
### Experiment Settings
#### 3.1.2 Experimental Environment  
The experiments in this paper were conducted on a 64-bit Windows 10 operating system. The CPU used was an 11 th Gen Intel Core i5-11300H, and the deep learning framework employed was PyTorch. During the training process, Adam [35] was used as the network optimizer, with an initial learning rate of 0.001. The batch size was set to 40, and the number of epochs was set to 500. An exponential decay learning rate [36] was utilized, where the learning rate decayed by a factor of 0.2 every 15 epochs.  
The objective function proposed in [12] was introduced as the loss function for predicting speech quality. It incorporates frame-level prediction errors to obtain speech-level predictions closer to human scores, as shown in Equation (5).  
\[O=\frac{1}{S}\sum_{s=1}^{S}\left[\left(X_{s}-X_{s}\right)^{2}+\frac{1}{T_{s}} \sum_{t=1}^{T_{s}}\left(X_{s}-x_{s,t}\right)^{2}\right] \tag{5}\]  
where \(S\) represents the total number of speech samples; and \(X_{s}\), \(X_{s}\), \(T_{s}\), and \(x_{s,t}\) denote the true MOS, the predicted MOS, the total number of frames, and the frame-level prediction for the \(t\)-th frame of the \(s\)-th speech sample, respectively.
## 4 Results and Discussion
### Acoustic Attenuation along Exposed MDPE Pipe
The influence of pipe inner radius, wall thickness, elastic modulus and Poisson's ratio on acoustic attenuation along the MDPE was also investigated using the numerical model. The results are summarized in Table 4.  
Table 4 shows that the acoustic attenuation prediction along the MDPE pipe is sensitive to changes in pipe inner radius, wall thickness, elastic modulus and Poisson's ratio. To accurately predict acoustic attenuation along the exposed MDPE pipe described in Section 3, the choice of pipe inner radius, wall thickness, elastic modulus and Poisson's ratio for the numerical model must therefore be within \(\pm\)1%, \(\pm\)1%, \(\pm\)0.4% and \(\pm\)1%, respectively, of their corresponding values in Table 1.
## 2 Theoretical Basis
### Navier-Stokes Equation  
In the study of structural wind engineering, lower-speed air is generally considered as a Newtonian fluid. According to the constitutive equation of a Newtonian fluid, the equation of motion of the fluid can be obtained, that is, the Navier-Stokes equation [28].  
The constitutive relationship of Newtonian fluids is expressed in Equation (4):  
\[\sigma_{ij}=(-p+\lambda S_{ik})\delta_{ij}+2\mu S_{ij}, \tag{4}\]  
where \(\sigma_{ij}\) and \(\delta_{ij}\) are second-order symmetric tensors, \(p\) is the fluid pressure, \(S_{kk}\) is the trace of fluid, \(\mu\) is the viscosity coefficient, and \(\lambda\) is a scale. According to the Navier-Stokes hypothesis, a simplified form can be obtained:  
\[\sigma_{ij}=-p\delta_{ij}+2\Big{(}S_{ij}-\frac{1}{3}S_{kk}\delta_{ij}\Big{)}. \tag{5}\]  
Substituting Equation (5) into the momentum Equation (3), the resultant equation of motion is called the Navier-Stokes equation:  
\[\frac{Du}{Dt}=f-\frac{1}{\rho}\triangledown p-\frac{1}{\rho}\triangledown \left(\frac{2}{3}\mu\triangledown\cdot U\right)+\frac{1}{\rho}\triangledown \cdot(2\mu S), \tag{6}\]where the left side is the acceleration of the fluid particle, \(f\) is the body force acting on unit mass, \(\frac{1}{\rho}\triangledown p\) is the resulted pressure per unit mass acting on a microelement, \(\frac{1}{\rho}\triangledown\left(\frac{2}{3}\mu\triangledown U\right)\) is the viscous volumetric expansion force acting on the unit mass of fluid, and \(\frac{1}{\rho}\triangledown\cdot(2\mu S)\) is the resultant force of the viscous partial stress tensor of a unit mass fluid.  
Due to the nature of the incompressible fluid and ignoring the expansion of its volume, \(u\) is a constant. According to the above equation, the continuity equation and momentum equation can be expressed in Equations (7) and (8) respectively:  
\[\frac{\partial u_{i}}{\partial x_{i}}=0 \tag{7}\]  
\[\frac{Du_{i}}{Dt}=f_{i}-\frac{1}{\rho}\frac{\partial p}{\partial x_{i}}-\frac {u}{\rho}\frac{\partial}{\partial x_{i}}\Big{(}\frac{\partial u_{i}}{ \partial x_{i}}\Big{)}\,(i\,=\,1,\,2,\,3). \tag{8}\]
# Towards Explainable Artificial Intelligence for GNSS Multipath LSTM Training Models
## 1 Introduction
The findings of this study have the potential to enhance the robustness of GNSS systems against multipath effects and pave the way for more trustworthy AI-driven navigation solutions. The remainder of this paper is organized as follows: Section 2 presents a brief theoretical background of multipath estimation in GNSS and describes our RNN architecture using LSTM cells and its implementation. Section 3 details the adaptation of LRP for GNSSapplications. Section 4 presents our experimental results, followed by discussion and conclusions in Sections 5 and 6, respectively.
# Optical Design of a Miniaturized Airborne Push-Broom Spectrometer
## 2 System Specification
### Optical System Parameters
#### 2.2.1 Sensor Selection and Frame Rate  
The image sensor is the core component of an airborne spectrometer. After researching existing commercial products, we selected the GSENSE2011 CMOS of Gpixel. Its specific parameters are shown in Table 2.  
According to the spectrometer's instantaneous field of view (\(iFOV\)), flight speed (\(v\)), and flying altitude (\(H\)), the frame frequency of the sensor (\(fps\)) can be calculated, and its expression is as follows:  
\[\frac{1}{fps}=iFOV\cdot\frac{H}{v}. \tag{1}\]  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Spectral Range** & **Spectral** & **Spatial** & **Coverage** & **Total** & **SNR** \\  & **Resolution** & **Resolution** & **Width** & **Weight** & \\ \hline
0.4\(\sim\)1.0 \(\upmu\)m & 15 nm & 0.5 m@1 km & 1 km@1 km & \textless{3} kg & \textgreater{}100 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specifications of the spectrometer. The signal-to-noise ratio (SNR) is meaningful only at a given reference radiance, which is provided by the application.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Pixel number & \(2048\times 1152\) \\ Pixel size (\(\upmu\)m) & \(6.5\times 6.5\) \\ Dark Noise (e\({}^{-}\)) & \textless{6} \\ Dark Current (e\({}^{-}\)/p/s) & \textless{1} \\ Maximum frame rate (fps) & 167 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The parameters of GSENSE2011 CMOS.  
According to the spatial resolution (0.5 m@1 km) in Table 1, the \(iFOV\) is 0.5 mrad. When the aircraft's altitude is 1 km and the speed is 100 km/h, the _fps_ should be 56 frames, and the corresponding integration time is 0.018 s.