esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This article describes the effect of thermal radiation on the thin film nanofluid flow of a Williamson fluid over an unsteady stretching surface with variable fluid properties. The basic governing equations of continuity, momentum, energy, and concentration are incorporated. The effect of thermal radiation and viscous dissipation terms are included in the energy equation. The energy and concentration fields are also coupled with the effect of Dufour and Soret. The transformations are used to reduce the unsteady equations of velocity, temperature and concentration in the set of nonlinear differential equations and these equations are tackled through the Homotopy Analysis Method (HAM). For the sake of comparison, numerical (ND-Solve Method) solutions are also obtained. Special attention has been given to the variable fluid properties' effects on the flow of a Williamson nanofluid. Finally, the effect of non-dimensional physical parameters like thermal conductivity, Schmidt number, Williamson parameter, Brinkman number, radiation parameter, and Prandtl number has been thoroughly demonstrated and discussed.  
Keywords:Williamson fluid; unsteady flow; nanofluid film; HAM and numerical method +
Footnote †: journal: Applied
## 2 Methods
### Source Wavelet Inversion by Cross-Correlation Norm Elastic Waveform Inversion (CNEWI)
Therefore, we chose to directly invert the source wavelet from the near-offset direct waves, which can mitigate the cycle-skipping caused by the source wavelet errors and improve the robustness of the EFWI process. For elastic wave source wavelet inversion, the application of multi-component data can also theoretically improve the accuracy of the inversion. However, when the data are noisy or of poor quality, it is recommended to use single-component data inversion to avoid introducing interference. The initial source wavelet for both CNEWI and L2 EWI inversion can use the source wavelet estimated by wavefield backpropagation. Since the method discussed in this paper uses near-offset direct waves for inversion, it is necessary to assume that the shallow velocity model is relatively accurate. Therefore, before performing CNEWI and L2 EWI inversion, on the one hand, near-offset direct wave data should be intercepted, and on the other hand, a relatively accurate shallow velocity model should be obtained. Generally speaking, the velocity accuracy of first-arrival tomography can meet the requirements. To test the cross-talk between the P-wave velocity and S-wave velocity of the CNEWI method, we conducted a numerical test using a simple elastic model. The true P-wave and S-wave velocity models are shown in Figure 1a and 1b, respectively. There is a high-velocity layer in the model. The position of the layer is different in the P-wave velocity model and the S-wave velocity model. This may be rare in practice, but it is a good choice for the cross-talk test. The initial P-wave and S-wave velocity models are shown in Figure 1c and 1d, respectively. The source wavelet is a Ricker wavelet with a dominant frequency of 20 Hz. EFWI with a normalized cross-correlation objective function was conducted. The P-wave velocity and S-wave velocity are updated simultaneously, and the result after 100 iterations is shown in Figure 1e,f. We can see that both the high-velocity layers in the P-wave velocity model and the S-wave velocity model are recovered well, which demonstrates that the P-wave velocity and S-wave velocity can be decoupled well during inversion. Figure 1: \(Cont.\)
## 3 Detailed Operators
### Subsystem Weight Considering the Failure Diagnosis Based on FRPN
#### 3.2.2 Definition of Fuzzy Petri Nets  
The product fault tree analysis (FTA) model can be converted into the relevant FRPN model, and [PERSON] et al. [33] described the specific conversion methods and transformation rules which are omitted in this section to make this paper more concise and compact. In brief, the AND gate in the FTA model can be regarded as many places to one transition in the FRPN model. At the same time, the OR gate in the FTA model can be regarded as many places to many transitions in FRPN model. The work in [34; 35] explains in detail the particular structure of the FRPN model which is described by six elements as follows.  
\(P=\{p_{1},\,p_{2},\,\ldots,\,p_{n}\}\) is the finite set of the fuzzy places, and they represent a series of fuzzy propositions;_Trans \(=\{r_{1},r_{2},\ldots,r_{n}\}\)_ is the finite set of the transitions, and they represent the implementation of fuzzy rules;  
\(I\) is the directed arcs matrix from propositions to rules. \(I\) (\(p_{i}\), \(r_{j}\)) = 1 if there is a directed arc form the proposition \(p_{i}\) to the rule \(r_{j}\); otherwise, \(I\) (\(p_{i}\), \(r_{j}\)) = 0. \(i\) = 1, 2, \(\ldots,n\); \(j\) = 1, 2, \(\ldots,m\).  
\(O\) is the directed arcs matrix from rules to propositions. \(O\) (\(p_{i}\), \(r_{j}\)) = 1 if there is a directed arc form the rule \(r_{j}\) to the proposition \(p_{i}\); otherwise, \(O\) (\(p_{i}\), \(r_{j}\)) = 0. \(i\) = 1, 2, \(\ldots,n\); \(j\) = 1, 2, \(\ldots,m\).  
\(U\) is the true degree matrix of propositions, \(u\) = (u_1, \(u\)2, \(\ldots,u_{n}\))\({}^{T}\), and _u_\({}_{i}\) is within (0, 1) (_i_ = 1, 2, \(\ldots,n\)).  
\(C\) = diag \(\{c_{1},c_{2},\ldots,c_{m}\}\) is the confidence matrix of fuzzy production rules, and \(c_{j}\) is the confidence of the fuzzy production rule \(r_{j}\) (_j_ = 1, 2, \(\ldots,n\)).
# A High Dynamic Weak Spread Spectrum Signal Acquisition Strategy Based on Iterative Local Search
## 3 ILS Acquisition Strategy
### Doppler Frequency and Code Phase Transfer Model  
In the acquisition strategy based on ILS, multiple searches are used to complete the acquisition. Each search in the acquisition process uses the Doppler and code phase information obtained from the previous search to deduce the starting frequency and the starting code phase of the next search and constantly reduces the search's scope. Thus, the single search time is successively reduced to meet the requirement of real-time acquisition.  
Using the ILS strategy in the acquisition process, the \(k\)th iteration search signal state vector is given by:  
\[x_{k}=\left[x_{f}\,x_{fv}\,x_{fa}\,x_{\tau}\right]_{k} \tag{7}\]  
where \(x_{f}\) is the Doppler frequency result of the \(k\)th search; \(x_{fv}\) is the estimation of the Doppler change rate of the \(k\)th search, when \(k<2\), \(x_{fv_{0}}=0\); \(x_{fa}\) is the estimation result for the second-order change rate of the Doppler frequency of the \(k\)th search, when \(k<3\), \(x_{fa}=0\); \(x_{\tau}\) is the result of the code phase search of the \(k\)th search.  
In the ILS strategy, the state model of the signal's Doppler frequency and code phase transfer can be expressed as follows:  
\[\left[\begin{array}{c}x_{f}\\ x_{fv_{0}}\\ x_{fa}\\ x_{\tau}\end{array}\right]_{k}=\left[\begin{array}{ccc}1&T&\frac{T^{2}}{2}&0 \\ 0&1&\frac{T}{2}&0\\ 0&0&\frac{2T}{T+T_{o}}&0\\ 0&0&0&1\end{array}\right]_{k-1}\times\left[\begin{array}{c}x_{f}\\ x_{fv_{0}}\\ x_{fa}\\ x_{\tau}\end{array}\right]_{k-1}+\left[\begin{array}{ccc}1&0&0\\ 0&\frac{1}{T+T_{o}}&0\\ 0&0&1\end{array}\right]_{k-1}\times\left[\begin{array}{c}x_{\Delta f}\\ x_{\Delta f}\\ x_{\Delta f}\\ x_{\Delta\tau}\end{array}\right]_{k} \tag{8}\]  
where \(T\) is the elapsed time for the \(k\)th search, \(T_{o}\) is the elapsed time for the \((k-1)\)th search, \(x_{\Delta f}\) is the correction of the predicted value for the \(k\)th carrier Doppler frequency search, and \(x_{\Delta\tau}\) is the correction of the predicted value for the \(k\)th code phase search.  
For example, when \(k=4\), in the signal state vector \([x_{f}\chi_{fp}\chi_{fa}\chi_{\tau}]_{4}\), each variable can be expressed as:  
Doppler frequency: \([x_{f}]_{4}=[x_{f}]_{3}+[x_{f}]_{3}[T]_{3}+\frac{[x_{fa}]_{3}[T]_{3}^{2}}{2}+ [x_{\Delta f}]_{4}\);  
Doppler change rate: \([x_{fv_{0}}]_{4}=\frac{[x_{f}]_{4}-[x_{f}]_{3}}{[T]_{3}}\);  
Doppler second-order change rate: \([x_{fa}]_{4}=2\frac{[x_{f}]_{4}-[x_{f}]_{3}}{[T]_{3}+[T]_{2}}\);  
Code phase: \([x_{\tau}]_{4}=[x_{\tau}]_{3}+[x_{\Delta\tau}]_{4}\).
# Optimization of Submodularity and BBO-Based Routing Protocol for Wireless Sensor Deployment
## 2 Wireless Sensor Deployment Optimization
### IHACA-COpSPIEL Deployment Method
#### 2.2.3 IHACA-COpSPIEL Algorithm
```
0: Position set \(V\) and covariance matrix
0: Solution set \(A\)
1: Initialize parameters: \(\alpha\), \(\beta\), \(w\), \(\bar{\xi}\), \(\tau_{0}\), \(\epsilon\), \(n_{\max}\)
2: Divide V into \(m_{cmax}\) clusters \(\{C_{i}|i\in[1,m_{c\max}]\}\)
3:for each cluster \(C_{i}\)do
4: Sort position points in \(C_{i}\) by greedy algorithm and then get the ranks of \(g_{i}{}_{1},g_{i}{}_{2},\cdots,g_{i}{}_{n_{i}}\)
5: Connect \(g_{i}{}_{1},g_{i}{}_{2},\ldots,g_{i}{}_{n_{i}}\) to form a chain which is then included into \(G_{i}^{{}^{\prime}}\)
6:end
7: Uses \(G^{{}^{\prime}}\) as input of block-oriented algorithm to solve \(F(A)\) and then get the solution \(A^{{}^{\prime\prime}}\), where \(G^{{}^{\prime}}=\{G_{i}{}^{{}^{\prime}}|i\in[1,m_{c\max}]\}\)
8:while a given maximum mutual information in \(A^{{}^{\prime\prime}}\) is not reached do
9: Select nodes for \(A^{{}^{\prime\prime}}\) with greedy algorithm
10:end
11:\(A^{{}^{\prime}}=A^{{}^{\prime\prime}}\)
12:for n=1:\(m_{max}\)do
13:while a given maximum mutual information in \(A_{n}{}^{{}^{\prime}}\) is not reached do
14: Select IHACA initial points in \(A_{n}{}^{{}^{\prime}}\) from head nodes in \(A^{{}^{\prime}}\)
15: Select next point with Equation (14)
16: Update local pheromone \(\tau_{ij}(n)\) with Equation (11)
17:end
18:if\(C(A_{n}{}^{{}^{\prime}})\leq B\)then
19:\(A\)=\(A_{n}{}^{{}^{\prime}}\) and output \(A\)
20:return
21:end
22: Update global pheromone \(\tau_{ij}(n)\) with Equation (12)
23:end
24:\(A\)=\(A_{n}{}^{{}^{\prime}}\) and output \(A\)
25:return
```  
**Algorithm 1** Improved Heuristic Ant Colony Algorithm-Chaos Optimization of Padded Sensor Placements at Informative and cost-Effective Locations (IHACA-COpSPIEL).
## Formulation of the Problem
### FE Models  
The dynamic parameters of the structure, that is, the natural frequencies and modal shapes, were obtained by using the commercial FE code ADINA [40]. The FE model was built using multi-layered shell 4-node MITC4 elements (first-order shear theory), the number of which equals 80 along the circumferential direction and 120 in the axial direction (each shell element is thus about 5 cm long and 4.8 cm wide). The overall number of FE elements, nodes, and degrees of freedom equals 6900, 9680, and 58,080, respectively. The described FE model parameters are based on the authors' previous experience [31]. For the second structure, namely, the three-segment MODEL2 shell, in addition to the above-described dense model, a sparse model was prepared, with a number of finite elements that is four times smaller (40 elements along the circumferential direction and 60 in the axial direction).  
The numbers of elements in MODEL1 and MODEL2 were selected carefully after detailed studies of FE convergence. For MODEL1, the adopted FE mesh gives very precise results, and an increase in the number of elements does not change the results of FE analysis. The so-called _sparse_ FE model adopted in MODEL2 FE calculations enables FE calculations that are several-fold faster while the computed natural frequencies remain precise. The maximum difference between the values obtained from the _dense_ and _sparse_ models does not exceed 2-3%. The mode shapes, even when using the _sparse_ model, are computed precisely.
## 3 Results and Discussion
### Results Analysis of Single-Factor Experiments
#### 3.1.2 Effect of Ultrasonication Time on NFC Goji Juice Quality  
Ultrasonic treatment can enhance fruit juice quality and delay its deterioration [30]. As shown in Figure 3, the NFC goji juice exhibited the highest TSS/TA ratio (11.15) and sensory score (79%) at an ultrasonication time of 2 min, along with increased turbidity. This improvement is attributed to ultrasonic cavitation effects, which break down large molecules such as pectin in the juice, thereby reducing viscosity and enhancing juice quality [31]. This finding aligns with the results of [PERSON] [32], who reported improved stability and quality of blackberry and tangerine juices through ultrasonic treatment.  
However, with prolonged ultrasonication, both the TSS/TA ratio and sensory score of the juice tended to decrease. At an ultrasonication time of 5 min, the TSS/TA ratio dropped to 10.6, the sensory score fell to 60 points, and turbidity increased to 1589 NTU. The decline in the TSS/TA ratio may be related to the mild disruption of cell structures by ultrasonic treatment [33], leading to the release of intracellular substances such as sugars (glucose,  
Figure 3: Effect of ultrasonication time on fruit juice quality.  
Figure 2: Effect of homogenisation time on fruit juice quality.  
-fructose, and sucrose) [34]. This result is consistent with previous findings on the impact of ultrasonic treatment on strawberry juice quality [35].
# Quantitative Ultrafast Spectroscopy and Microscopy of Traditional and Soft Condensed Matter
## 1 Introduction
For solid state, detailed knowledge of Raman active vibrational properties in intrinsic and doped materials, as well as in thin films, is of high value from the standpoint of identifying mechanisms that limit transport parameters which are critical for device applications. For biological media, this can become a powerful tool in detecting small spectral changes that can be translated to variations in bio-molecular composition and ultimately lead to disease diagnoses on a molecular level. In this report, we discuss and implement a method that is applied to characterize crystal phonons and Raman active vibrations in soft matter. We show how to obtain high spectral resolution dispersion data for the resonant part of the third-order optical nonlinearity (\(\chi^{(3)}\)), for both real and imaginary parts, within experiments when the medium is probed with sub-micron spatial resolution. Time-resolved coherent anti-Stokes Raman scattering (CARS) replicas are detected with femtosecond resolution and can be traced within orders of magnitude of the signal decay. This enables resolution of fine spectral features in dispersion of the resonant third-order nonlinearity that cannot be reliably detected by Raman based spectroscopy techniques. For solid state medium, we focused our study on phonon modes in potassium titanyl phosphate crystal (KTiOPO\({}_{4}\) or KTP) as the crystal has the most complex vibrational spectra represented by vibrations of different interatomic groups. The material has been shown in a variety of applications as an efficient solid state medium for generation of Raman shifted components within source lasers. The crystal is also a good candidate within applications developing a few femtosecond laser source based on stimulated Raman scattering. Complex phonon lines were analyzed in the KTP crystal where we focused on vibrations within titanium octahedron and Ti-O-P intergroup vibrations. For the TiO\({}_{6}\) octahedron, we resolved a Raman doublet with a 62 cm\({}^{-1}\) frequency difference and slightly different phonon decay times of 495 fs and 515 fs for the two closely spaced vibrations. For phonons representing Ti-O-P intergroup vibrations, we found three components with decay times within the 0.95-1.2 ps range that translate to phonon linewidths of 9.1 \(\pm\) 0.4 cm\({}^{-1}\), 7.5 \(\pm\) 0.6 cm\({}^{-1}\), and 11.2 \(\pm\) 0.5 cm\({}^{-1}\). Comparing Raman active C-C stretching vibrations in fat tissue and oil, we found close similarity between the two. Picosecond dephasing times within the 1.6-2.7 ps range has been detected. Based on the time-domain CARS data, we are able to deduce linewidths as narrow as 4.1 cm\({}^{-1}\) in fat tissue which has not been resolved in prior works that used spontaneous scattering and frequency domain coherent Raman spectroscopy methods.
# Airborne Fungi in Show Caves from Southern Spain
## 1 Introduction  
The study of the behavior of airborne fungi in caves is of interest because many caves contain Paleolithic paintings that should be protected from fungal outbreaks. In the past, fungal outbreaks were noticed in a number of caves, of which Lascaux and Castanar de lbor caves were noteworthy [1; 2]. In Lascaux Cave, the outbreak was difficult to control using biocides such as different benzalkonium chloride derivatives [3], while in the Castanar de lbor Cave it was easily controlled by the adoption of an appropriate management (mechanical removal of the polluted sediment and sterilization of the lying materials with hydrogen peroxide) [4].  
[PERSON] et al. [5] recognized that preventive conservation of cultural heritage includes aerobiological analysis in order to assess biological risk and propose strategies aimed at controlling biodeteration. In the last decade, aerobiology has proved to be a suitable approach for studying the dispersion of airborne fungi in caves [6; 7; 8; 9]. Fungal spores represent a potential risk to the conservation of cultural heritage since, in favorable cave environmental conditions (high relative humidity, stable temperature, nutrient inputs, etc.), fungi colonize speleothems and other mineral substrata in addition to the cadavers of animals and arthropods populating the cavity [2; 10].  
Airborne fungi are transported from outside the cave to the inside due to the fact that a door opening favors communication with the external atmosphere and the passage of warm air moves towards the interior of the cave. This implies that the rooms and galleries near a cave entrance present a higher concentration of spores than in deeper areas. This was also confirmed by a study of fungal sequences from spores inside and outside a cave, which resulted in being identical [8].  
The aerobiology of caves in Southern Spain possesses special characteristics, different from those located in Northern Spain. Previous studies demonstrated the influence of exterior air on the cave and the existence of two different patterns, depending on the season, in La Garma Cave. In summer there was an abundance of _Ascomycota_, whereas in winter _Basidiomycota_ predominated [11], which are related to periods of stagnation and ventilation, respectively. In Southern Spain the presence of airborne _Basidiomycota_ in caves is scarce and _Ascomycota_ represented the main group of fungi, which was widely distributed across the caves [6; 12].  
In this study we present aerobiological data on three show caves located in Andalusia, Southern Spain, recorded in four seasons over a year and discuss the origin of airborne fungi.
## 4 Results and Discussion
### Model Validation with Statistical Yield and Production at the County Level
From the above results, we found that the optimal yield estimation models for maize and sunflower were different. However, there were many studies on the yield estimation of maize [11; 19; 42; 64], a food crop, and few studies on sunflower, an economic crop. Sunflower was an important economic crop in HID because of its obvious drought-tolerance characteristics. This study was the first to develop a regional sunflower yield estimation model, which was of great significance to the management of sunflower planting in HID. Moreover, there were many studies on estimating maize yield in areas where precipitation and temperature were the dominant factors for maize yield while irrigation was not a major influencing factor [7; 11; 42; 64]. Based on the growth conditions of maize in arid areas, the phenological characteristics were added to the yield estimation model in this study, and the yield estimation model suitable for maize in arid area was obtained.
## 3 Problem Statement
### Orchestration Based on IoT Data  
Smart cities [30] or production plants 4.0 [31; 32; 33] are environments in which automation and automatic orchestration is crucial. They need to orchestrate services or devices based on a secure and trusted source of data.  
Each decision is made based on data from other devices and control parameters stored in scripts and programs. A more complicated scenario assumes that devices need to coordinate actions using choreography [34] in which we do not distinguish the central coordinator service.  
Smart cities face many challenges related to ensuring data privacy [35; 36] and the anonymity [37] of measurements or registration of decisions made. This is especially important when integrating heterogeneous data providers to ensure the native independence of a coherent system of entitlements.  
Classical platforms [38; 39] for gathering events are mainly based on SQL databases and distributed file systems (Apache HDFS, Amazon S3). If the data should be immutable then we usually use some blockchain network (e.g., Hyperledger Fabric, R3 Corda, private network of Ethereum). Improvements based on SQL databases have the same weak point, which is a central point of privilege management.  
On the other hand, we have decentralized solutions [40; 41] based on popular blockchain networks. Those systems usually struggle with low efficiency or do not have native mechanisms that separate information about the source from reading metric values. By default, the architecture offers full transparency of the transactions performed.  
Finally, data stored in the blockchain increases over time, and if we want to have insight into some metrics stored in the data, then the process becomes very slow. To find some data registered by a specific device in some period, we have to analyze each branch--readingblock after block, from leaf to root. For a large-scale environment, this quickly becomes an infeasible approach.