esa-sceva/satcom-chunk-collection huggingface dataset:
# A New Signal Processing Method for Time-of-Flight and Center Frequency Estimation
## 2 Methodology for Estimation of Time-of-Flight and Center Frequency of Signals  
### Signal Processing of Ultrasound Signals with Noises  
The measured ultrasonic noisy signal \(u(t)\) was a linear combination of the desired clean signal \(s(t)\) and the additive noise \(n(t)\)[22]. A complex ultrasound signal \(u(t)\) was chosen for the explanation of the proposed algorithm. The ultrasonic signal was composed of the sum of two three-period harmonic signals of different center frequencies with a Gaussian envelope (Figure 1, (0))  
\[u(t)=s(t)+n(t)=\left\{ \begin{array}{l}n(t),\ 0<t<t_{1},\\ Ae^{-\frac{t^{2}}{2\
u^{2}}}\sin{(2\pi f_{1}t)}+n(t),\ t_{1}<t<t_{2}\,,\\ Ae^{-\frac{t^{2}}{2\
u^{2}}}\sin{(2\pi f_{2}t)}+n(t),\ t_{2}<t<t_{3},\end{array} \right. \tag{1}\]  
where \(n(t)\) is the additive white Gaussian noise; \(A\) is the signal amplitude, \(A=1\); \(s\) is the pulse width factor; \(f_{1}\) is the center frequency of the first signal, \(f_{1}=300\) kHz; \(f_{2}\) is the center frequency of the second signal, \(f_{2}=500\) kHz; \(t_{1}\) is the delay time of the first signal, \(t_{1}=40\) us; and \(t_{2}\) is the delay time of the second signal, \(t_{2}=65\) us. Figure 1: The block diagram of the proposed algorithm for the digital signal processing of ultrasonic signals with noise. Such a signal was chosen with the idea that it would be a complex signal with different frequencies and different time-of-flights. The initial stage of signal processing is the calculation of its frequency spectra and the determination of its width \(\Delta f_{0}\) at the \(-10\) dB level (Figure 1, (1))  
\[U(f)=\mathrm{FT}[u(t)], \tag{2}\]  
\[\Delta f_{0}=\max(\arg\{U(f)>0.3\})-\min(\arg\{U(f)>0.3\}), \tag{3}\]  
where FT is the Fourier transform. According to the set bandwidth \(\Delta f_{0}\), a bandpass filter is formed, the maximum amplitude variation of which covers this bandwidth (Figure 1, (1), red line). Filtering with this filter eliminates part of the spectrum of the noisy signal and highlights the original signal \(s(t)\) (Figure 1, (2)). The envelope \(e(t)\) of this signal and its extremes (\(min\) and \(max\)) are determined below  
\[e(t)=\mathrm{HT}[u(t)], \tag{4}\]  
where HT is the Hilbert transform. Separate signals \(u_{1}(t)\) (Figure 1, (3)) and \(u_{2}(t)\) (Figure 1, (4)) are distinguished with the help of the extremes \(min_{1}\) and \(min_{2}\)  
\[u_{1}(t)=u(t)\ \mathrm{if}\ t<\arg\{min_{1}(\mathrm{HT}[u(t)])\}, \tag{5}\]  
\[u_{2}(t)=u(t)\ \mathrm{ifarg}\{min_{1}(\mathrm{HT}[u(t)])\}<t<\arg\{min_{2}( \mathrm{HT}[u(t)])\}. \tag{6}\]  
where the extreme values \"\(min_{1}\)\" and \"\(min_{2}\)\" are the first minimums of the signal envelopes following each maximum of the envelopes (Figure 1, (2)). Frequency spectra \(U_{1}(f)\) (Figure 1, (5), black line) and \(U_{2}(f)\) (Figure 1, (6), black line) are calculated for the extracted signals.
# Multi-Sensor Fusion Positioning Method Based on Batch Inverse Covariance Intersection and IMM
## 3 Fusion Algorithms Considering Accuracy and Robustness  
Different from [33], the multi-sensor fusion positioning method with unknown covariance is more practical, because covariance is inessential. The fusion method with unknown covariance is mainly based on the CI method, which is a conservative global estimation method for local filters. Although it can guarantee a robust estimation, the accuracy of positioning is poor.  
Inspired by SCI and BCI, we deduced the batch form of the ICI fusion positioning algorithm. Then the BICI fusion positioning algorithm is proposed and the robustness is certified. Meanwhile, the robustness of SICI is also verified. The BICI and SICI algorithms are divided into two phases: local filtering and global fusion. In the local filtering phase, the local optimal result \(\hat{x}_{k}^{(h)}\) and variance \(P_{k}^{h}\) of each sensor are obtained by a local filter in Section 2. Then, the global fusion is employed for the local result \(\hat{x}_{k}^{(h)}\) and \(P_{k}^{h}\), which is described in detail as follows.
## 1 Introduction
The main research contents are as follows: firstly, a reliability model for ship electronic information system tasks was established from the perspectives of system structure, system behavior, and fault impact using architecture analysis and AADL, laying the foundation for subsequent reliability allocation and analysis; then, the mission reliability formula based on the AADL model was extracted and the AADL model mission reliability formula extraction software was developed. Research on the methods of mission reliability allocation for reconfigurable ship electronic information systems under multiple constraints, and the realization of the mission reliability index allocation for the constituent units of the reconfigurable ship electronic information system; finally, based on the mapping relationship and mapping method from the AADL to GO method, the transformation module software was developed. The accurate quantitative prediction of mission reliability of reconfigurable ship electronic information systems was achieved under the influence of common cause failure. The rest of the paper is organized as follows: Section 2 describes the methodology used in the study and its principles; Section 3 analyzes the structure and model of the ship electronic information system; Section 4 presents the overall program design for the design and analysis of the mission reliability of the ship electronic information system. Specifically, it includes the construction of the mission reliability model of the ship electronic information system, the study of the mission reliability allocation method, and the study of the mission reliability analysis method. Section 5 tests application validation based on a typical ship electronic information system mission and discusses and analyzes the research results. Finally, in the Conclusions and Prospects section, the main research of this study is summarized, the shortcomings of this paper are analyzed, and the prospect of subsequent research is detailed.
## 5 Algorithm
### Basis of the Algorithm
In the secondary wave, the ids of the neighbors of the boundary, the \(ids\) of the boundary processes, and the \(id\)'s of the leaf processes (level \(l\)) are gathered and sent to the root process in a bottom-up manner. When the root collects the abovementioned information, it identifies the level \(l+2\) processes as those that are neighbors of the boundary processes that are neither boundary nor leaf processes. Subsequently, the root assigns each level \(l+2\) process \(i\) to a neighboring boundary process \(j\) such that \(j\) is designated as the parent of process \(i\). Then, these designations are sent to boundary processes as the secondary wave proceeds in a top-down manner in broadcast tree \(T\). When the top-down secondary wave reaches a boundary process \(j\) with the designations, the process waits until all its designated level \(l+2\) children assume \(j\) as their parent, enter the cleaning phase and lock themselves. When all the designated children of \(j\) assume \(j\) as their parent, enter the cleaning phase, and are locked, the secondary wave is completed in the branch of the broadcast tree extending from root \(r\) to process \(j\). Subsequently, the primary wave, extends the broadcast to process \(j\) as process \(j\) enters the broadcast phase. Subsequently, the secondary wave initiated by the new boundary processes (level \(l+2\) processes in the previous secondary wave) proceed in a bottom-up manner as described above, and so on. The secondary wave consists of two bottom-up phases, called \(f\)- and \(c\)-phases, and one top-down phase referred to as \(b\)-phase. A process is in state \(f\), \(c\), and \(b\) where it is in f-, c-, and b-phases, respectively. When the broadcast phase of the primary wave reaches process \(i\) such that the broadcast phase cannot be extended to any neighbor of \(i\) (i.e., all the neighbors of \(i\) have already received the broadcast), \(i\) enters the feedback phase instead of the broadcast phase. Subsequently, each internal process enters the feedback phase upon discovering that all its children are in the feedback phase. The feedback phase proceeds in a bottom-up manner in the constructed tree and eventually reaches the root process. After a process is involved in a broadcast phase followed by a feedback phase, upon discovering that all its children are in the cleaning phase, it is removed from the normal tree by entering the cleaning phase. A process in the broadcast phase and the feedback phase is in state \(B\) and \(F\), respectively, while a process in the cleaning phase is in state \(C\). Notice that any process not included in the normal broadcast tree can be in an arbitrary state and such processes may form abnormal trees due to arbitrary initialization. Our algorithm ensures that all such processes are eventually included in the normal broadcast tree and receive the broadcast.
# Stress Analysis in Damaged Pipeline with Composite Coating
## 1 Introduction
Another approach to the analysis is to present the coated pipeline as a contact pair: \"insulating coating-pipe surface\". Usually, when formulating contact problems, rearchers pay close attention to modeling the strength, rigidity, and durability of the contacting  
Figure 1: Winding insulating coating on a pipe. elements. Approaches to investigate the contact interaction of cylindrical pipes with elastic bodies by means of virtual and laboratory experiments were presented in [19]. A class of problems concerning the modeling of contact of the edges of cuts or cracks in cylindrical shells, including the arbitrary combination of tensile and bending loads, was presented in a number of works [20; 21; 22; 23; 24]. Two-dimensional mixed bending problems for plates and shells with through faults, including cases where cracks start to grow from the slots, were considered in works [25; 26]. In the context of the outlined problems, particular attention has been paid to models investigating the effect of flexible coatings on the strength of thin-walled structures with through crack-like defects. In particular, the authors of the works [27; 28; 29] investigated the stress concentration in defective plates supported by films on one of the face surfaces. In studies [30; 31; 32; 33] similar problems were solved for coated shells. Publications [34; 35] presented some approaches for numerical analysis of repaired surface cracks with a composite patch. Estimation models for the treatment of defects such as cracks by injecting a compliant material have also been proposed [36; 37; 38]. Simulation of the solidity restoration of a cracked body using a contrastless filler was described by [39]. The authors of the article [40] successfully developed this technique to investigate the effectiveness of the treatment of elastic bodies with surface cracks. Thus, in the theoretical approaches used to solve the problems of the fracture mechanics of nonhomogeneous elements of thin-walled structures with crack-like defects, models of through cracks in flexible coated shells are now known and have been sufficiently developed. However, similar models are required for coated thin-walled pipes with non-through damage. We know of only a few publications [41; 42] in which finite element analysis was used to investigate the effectiveness of fiber-reinforced polymer bandages in rehabilitating misaligned welded pipes with outer circumferential surface cracks. The purpose of this paper was to investigate the stress state of a steel pipeline covered with a three-layer composite coating. Cases of a defect-free pipe and a pipe with damage in the form of surface cracks were considered. To achieve this goal, the following tasks were undertaken:  
* A study of the effects of structural anisotropy caused by the spiral winding of the bearing fiberglass layer;
* An investigation of the stress state in the defect-free pipe and in the structural elements of the layered coating;
* A finite element analysis of the stress concentration near the surface crack in the coated pipe at different repair pressure values.
# Sound Environments in Large Public Buildings for Crowd Transit: A Systematic Review
## 4 Discussion
### Methodologies  
The methodologies involved in this review are mainly four approaches: (1) measurement; (2) questionnaire/interview; (3) listening test; (4) software simulation. The former two were in general applied to collect in-situ data, and the latter two were mainly adopted to interpret or solve the issues that were considered to be unreasonable to reach by the former. This review did not find any experiment using listening test for shopping malls, all of which were developed as in situ measurements, or questionnaire, or interviews. However, for transportation hubs/stations, in collecting the subjective data, the method of listening test was more frequently to be adopted rather than in-situ survey. In terms of museums/exhibition spaces, there were more experiments dependent on software simulation [52]. Some sites were functioning as performing spaces and simulated for exhibition configurations.  
Normally, the methodologies of in situ investigation on such a large public environment would inevitably consider survey time and locations. The measurements, in obtaining the physical outcomes of the site, were conducted in three or four blocks, as morning, midday, and evening, and take the entire seven-day period in order to cover weekends and weekdays, especially for shopping malls. Some studies [41; 49] lasted four seasons. The selection of measuring locations was to cover a representative sample of the case, otherwise, it was intended for the specific research questions, such as space types, or practical problems. The questionnaire and interviews usually use five-point Likert scales, with either open or close end questions, probably associated with indoor soundwalk (a soundscape method that implies a walk in an indoor area with a focus on listening to the acoustic environment [14]). Aside from this feature, the divisions between the three selected building types and the other ones which were not targeted in this review of the methodologies were in general subtle to spot at present.  
Comparatively, the methodologies of listening test and software simulation were found to be less focused on the temporal and spatial features of the site in this context. Most studies were addressing the prediction accuracy of theoretical solutions in such spaces through software simulation exploring the potentials in using decay and attenuation of objective parameters. Furthermore, listening tests were aimed to find the exploratory factors related and unrelated to the dynamics of acoustic environment, such as acoustic sequence and users' attendance. These attempts were gradually filling the vacancy in this area.  
Overall, these methods have potential advancements, and they are supposed to develop with the theoretical advances, technique innovations, or social changes, such as the issues with Covid-19.
# Storage Capacity in Dependency of Supercooling and Cycle Stability of Different PCM Emulsions
###### Abstract  
Phase-change materials (PCM) play off their advantages over conventional heat storage media when used within narrow temperature ranges. Many cooling and temperature buffering applications, such as cold storage and battery cooling, are operated within small temperature differences, and therefore, they are well-suited for the application of these promising materials. In this study, the storage capacities of different phase-change material emulsions are analysed under consideration of the phase transition behaviour and supercooling effect, which are caused by the submicron size scale of the PCM particles in the emulsion. For comparison reasons, the same formulation for the emulsions was used to emulsify 35 wt.% of different paraffins with different purities and melting temperatures between 16 and 40 \({}^{\circ}\)C. Enthalpy curves based on differential scanning calorimeter (DSC) measurements are used to calculate the storage capacities within the characteristic and defined temperatures. The enthalpy differences for the emulsions, including the first phase transition, are in a range between 69 and 96 kJ/kg within temperature differences between 6.5 and 10 K. This led to an increase of the storage capacity by a factor of 2-2.7 in comparison to water operated within the same temperature intervals. The study also shows that purer paraffins, which have a much higher enthalpy than blends, reveal, in some cases, a lower increase of the storage capacity in the comparison due to unfavourable crystallisation behaviour when emulsified. In a second analysis, the stability of emulsions was investigated by applying 100 thermal cycles with defined mechanical stress at the same time. An analysis of the viscosity, particle size and melting crystallisation behaviour was done by showing the changes in each property due to the cycling.  
phase-change material; dispersion; thermal-mechanical stability; viscosity; supercooling; nucleating agent; cold storage; battery cooling +
Footnote †: journal: Applied Sciences
## 4 Experimental Results and Discussion
### Hyperparameters Search  
We determined the various parameters for the proposed relief extraction method before comparing the performance with existing methods. For clarity, RF models that were trained using the SI and CSI information are denoted as RF(SI) and RF(CSI), respectively.
# Multi-Class Strategies for Joint Building Footprint and Road Detection in Remote Sensing
## 2 Methods
### One-vs.-All Strategy  
The OVA [31] decomposition scheme, like OVO, can be used to divide a multi-class semantic segmentation problem into multiple binary ones. Considering a scenario with \(N\) classes, the OVA strategy divides it into \(N\) binary sub-problems. In this strategy, each model is trained to discriminate between one class and the remaining ones. In this regard, it is common to have several more negative samples than positive ones, resulting in a highly imbalanced binary dataset. In this work, given the problem statement, three binary models are learned to detect buildings, roads and the background, respectively, as shown in Figure 3.  
Again, the outputs of the binary models must be aggregated to generate the multi-class prediction. To simplify notation, we will use \(P_{i}\) to denote the confidence of the binary semantic segmentation model discriminating the class \(i\) from the rest of classes for a given pixel. Accordingly, as Figure 3 depicts, each binary model contributes to the confidence of the positive class (\(i\)) with \(P_{i}\). Unlike OVO, the confidence in favor of the negative class is no longer used since each class is covered by a different model. To finally aggregate these values, the maximum confidence strategy [42] is used, that is, the class with the largest confidence is predicted.  
Figure 2: OVO decomposition scheme adapted to a multi-class semantic segmentation problem, including building footprints and road networks.
# Exploiting the Capture Effect to Enhance RACH Performance in Cellular-Based M2M Communications
## 5 Simulation Results  
In this section, the simulation results are provided to show the performance of the proposed Msg3 PR scheme. A discrete event simulator is built to analyze the performance of the RA procedure and the effect of the capture. The simulator implements the RA procedure defined in Section 2.1, and alsoimplements the capture effects by adjusting the capture condition as Equation (1). Table 2 summarizes the commonly used simulation parameters in Sections 5.1 and 5.2.  
Through the analysis results defined in Section 4 and the simulation results, the steady-state performances in terms of the RA throughput, the connection failure probability and the average energy consumption are presented in Section 5.1. In Section 5.2, the possibility of coexistence of the Msg3 PR scheme with other typical RA-related schemes is presented by showing simulation results.
## 2 Methodological Evolution
### Foundational Framework Development
** Recent theoretical advancements provide rigorous foundations:  
* Convergence Analysis: For linear elliptic PDEs, ref. [13] proves error bounds: \[|u_{\theta}-u^{*}|L^{2}(\Omega)\leq C\Big{(}m^{-k/d}+\epsilon_{\text{opt}} \Big{)}\] (2) where \(u_{\theta}\) represents the solution or prediction result obtained by the neural network model(parameter \(\theta\)), \(u^{*}\) represents the exact solution or ideal solution to the problem; \(m\) denotes network width, \(d\) denotes the dimension of the space \(\Omega\), \(k\) solution regularity, \(C\) is a constant, and \(\epsilon_{\text{opt}}\) optimization error. * Generalization Bounds: Through Rademacher complexity theory [27]: \[\mathcal{E}_{g}\leq\sqrt{\frac{\log\mathcal{N}(\mathcal{H},\epsilon,n)}{n}}+e\] (3) where \(\mathcal{N}\) represents hypothesis space covering number, \(\mathcal{E}_{g}\) denotes generalization error, \(\mathcal{H}\) is the hypothesis space, \(\epsilon\) is a scale parameter of the cover, and \(n\) is the number of training samples. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Architecture** & **NN Layers** & **Activation** & **Data Generation** & **Distinctive Feature** \\ \hline \hline \begin{tabular}{c} VPINN \\ XPINN \\ \end{tabular} & 4–8 Dense & Tanh/Legendre & Sobol Sequence & Variational Residual Form \\ \begin{tabular}{c} XPINN \\ \end{tabular} & 3–5 Subnets & Swish & Adaptive RAR & Domain Decomposition \\ \
\begin{tabular}{c} HFD-PINN \\ \end{tabular} & 6–10 CNN-MLP & ReLU & Physics-Initiated & Hybrid Finite-Difference \\
\begin{tabular}{c} TFE-PINN \\ \end{tabular} & 7–12 LSTM-MLP & GELU & LES Data Assimilation & Turbulence Closure Modeling \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Core PINN Architectures: Properties and Specialization. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Module** & **Function** & **Implementation** & **Performance Gain** \\ \hline \begin{tabular}{c} Residual Connections \\ Adaptive Activation \\ Mixed-Precision Training \\ \end{tabular} & \begin{tabular}{c} Zhitage gradient pathology \\ Enhance nonlinear expressivity \\ Accelerate tensor operations \\ Balance multi-physics constraints \\ Enable high-dimensional solutions \\ \end{tabular} & \
\begin{tabular}{c} DenseNet [11] \\ Learnable Tanh [21] \\ FP16-FP32 hybrid [24] \\ NITK theory [25] \\ NITK theory [25] \\ XPINN [26] \\ \end{tabular} &
\begin{tabular}{c} 40\% faster convergence \\ 50\% accuracy boost \\ 55\% memory reduction \\ 46\% success rate improvement \\ 5\% speedup in 3D turbulence \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Core architectural components of PINNs
## 2 Materials and Methods  
### Medium and Bacterial Strain  
The full-length gene coding for the CMCase of _B. velezensis_ A-68 was amplified using two specific primers, 5'-AGGAGGAAAAGATCAGATATGAAACGGTCAATC-3' (Forward) and 5'-TCCAGATTTCATCCACAACGCAAAACCTCC-3' (Rever), which were designed based on DNA sequences of previously cloned cellulase genes of _Bacillus_ species. The amplified genes were ligated with the T-tail site of pGEM-T Easy Vector System (Promega Co., Madison, WI, USA) and constructed plasmids were transformed into _E. coli_ JM109. The constructed recombinant was named _E. coli_ Korean Agricultural Culture Collection (KACC) 91335P [17]. This strain was grown at 37 \({}^{\circ}\)C in a Luria-Bertani (LB) broth supplemented with 100 ug/mL ampicillin. The carbon and nitrogen sources in the medium for the production of CMCase by _E. coli_ KACC 91335P were 132.3 g/L rice bran and 4.68 g/L ammonium chloride, respectively and mineral components in the medium were 5.0 g/L K\({}_{2}\)HPO\({}_{4}\), 0.6 g/L MgSO\({}_{4}\).7H\({}_{2}\)O, 1.5 g/L NaCl and 0.6 g/L (NH\({}_{4}\))\({}_{2}\)SO\({}_{4}\)[17].