esa-sceva/satcom-chunk-collection huggingface dataset:
Energy Efficient Hybrid Routing Protocol Based on the Artificial Fish Swarm Algorithm and Ant Colony Optimisation for WSNs  
[PERSON]  
1 Department of Computer Science, Hefei University, Hefei 230601, China  
2 School of Computing, Dublin Institute of Technology, Dublin 8, Ireland; brian.  
These routing protocols combine energy-efficient cluster-based routing with application-specific data aggregation, achieve improved lifetime and achieve Quality of Service (QoS) requirements for WSNs [4; 5].  
Recently, many new routing protocols based on Ant Colony Optimisation (ACO) [6] have been developed. The basic idea behind the ACO algorithm for routing is that it imitates an ant colony's foraging behaviour to define a nature-inspired meta-heuristic to discover the route to the destination. The first ACO-based routing protocol was presented for a wired network [7], then for ad hoc networks (MANETs) [8]. [PERSON] et al. [9] found that these protocols were not suitable for wireless sensor networks and subsequently presented a foundation framework for an ant-based WSN routing protocol. An Energy-Efficient Ant-Based Routing Algorithm (EEABR) [10] considers the energy factor of wireless sensor nodes on the basis of an ACO mechanism to extend network lifetime.  
Furthermore, many species in nature show similar self-organisation and decentralised behaviour to that of an ant colony. Inspired by such behaviour, researchers presented many Swarm Intelligence (SI) optimisation algorithms [11]. A new SI algorithm, Artificial Fish Swarm Algorithm (AFSA), was proposed by [PERSON] et al. [12], inspired by the natural social behaviour of schools of fish. The basic idea of AFSA is to imitate fish behaviour such as _preying_, _swarming_ and _following_ with the local search of an individual fish capable of reaching the global optimum [13]. AFSA has many advantages including high convergence speed, flexibility, fault tolerance and high accuracy. AFSA has found wide-spread applications in complex optimisation domains, currently considered a major research topic, and offers an alternative to the more established evolutionary computation techniques that may be applied in many of the same domains [14].  
The remaining sections of this paper are as follows: Section 2 provides a brief, but comprehensive overview of the state-of-the-art related works. In Section 3, we introduce our hybrid routing protocol based on AFSA and ACO in detail. In Section 4, a performance evaluation and discussion about the results are presented. Finally, the paper has been concluded in Section 5.
## 2 Materials and Methods
### Laser-Induced Breakdown Spectroscopy Experimental Model  
The experimental instruments mainly include a Laser-Induced Breakdown Spectroscopy (LIBS) device and 3D reconstruction, the schematic diagram of which is shown in Figure 3. The LIBS device uses a laser to generate plasma through an optical system, which is used for the spectrometer to resolve atomic spectra. The laser is generated by a Nd:YAG laser (Q switch), and the parameters of the Nd:YAG laser are the following: wavelength 532 nm, repetition frequency 10 Hz, pulse width 8 ns; the optical system consists of a laser reflector and a convex lens (focal length 50 mm). The laser reflector is used to reflect the laser and focus it on the sample surface through the convex lens. The sample is placed on a three-dimensional displacement platform so that the laser pulse can be focused on the sample surface every time. The plasma emission was collected by a light collector (Ocean Optics, Orlando, FL, USA, 84-UV-25, wavelength range: 200-2000 nm), and the emission spectrum was obtained by coupling a Czerny-Turner spectrometer (Shamrock 500i, Andor Tech., Belfast, UK, with a grating density of 3600 lines/mm) with an intensified charge-coupled device (ICCD) camera (iStar DH-334T, Andor Tech., UK). The laser and spectrometer were simultaneously controlled by a digital delay generator (SRS, Sunnyvale, CA, USA, DG535) to achieve the simultaneous acquisition of spectra and plasma images. Both ICCDs worked in gated mode, with the gate delay, gate width, and exposure time set to 2 us, 20 us, and 2 s, respectively, to obtain high signal-to-noise ratio spectra and high-quality images.  
Figure 3: Schematic diagram of the experimental setup combining laser-induced breakdown spectroscopy (LIBS) and microscopic 3D reconstruction. The system includes a Nd:YAG laser source, optical focusing components, a three-dimensional translation stage for sample positioning, and an optical emission collection system composed of a spectrometer and a camera. A digital delay generator synchronizes the laser firing and spectral acquisition.  
Figure 2: Surface morphology of pressed tungsten carbide (WC) samples with varying Cobalt (Co) contents (4–32%) prepared under different compaction pressures (40–110 MPa). The images illustrate how Co content and compaction pressure jointly influence the surface uniformity, densification, and structural integrity of the pellets, with higher Co content and greater compaction pressures resulting in smoother, more homogeneous surfaces, and reduced porosity.
## 3 Results and Discussion
### Phenolic Compound Profile
[4] reported a decrease in the content of caffe acid, \(p\)-coumaric acid, chlorogenic acid, ferulic acid, quercetin, and kaempfer as a result of steaming carrots, while the levels of sinapic acid and luteolin increased after this process. Similarly, [PERSON] et al. [28] found that boiling carrots led to a complete loss of chlorogenic acid, caffe acid, and \(p\)-coumaric acid due to their diffusion into the cooking water. The authors also observed a decrease in chlorogenic acid content and an increase in caffeic acid content when carrots were steamed or fried compared to raw samples. This increase in caffeic acid could be attributed to the hydrolysis of chlorogenic acid into caffeic and quinic acids, explaining the significant increase in caffeic acid observed with these cooking methods. Additionally, polyphenol losses may be due to covalent bonds between oxidized phenols and proteins or amino acids, as well as the polymerization of oxidized phenols [28]. Analyzing the total phenolic compounds detected by UHPLC-DAD-MS, the phenolic content in the carrot samples increased in the following order: raw sample < sous-vide < steaming (combi oven) < baking < steaming (steel pot) < boiling. The increase in phenolic compounds observed during cooking can be explained by their localization and structural association within plant tissues. Phenolic compounds are typically stored in vacuoles and the apoplast, while others are bound to cell wall components as insoluble phenolic compounds. Cooking processes, particularly those involving heat and moisture, disrupt cellular structures such as membranes and cell walls. This breakdown facilitates the release of bound phenolic compounds into a soluble and extractable form, thereby increasing their detectable concentration in cooked samples [4; 55].
# Fusion Classification Method Based on Audiovisual Information Processing
## 4 Experimental Result and Analysis  
### Dataset Description and Experimental Platform  
The self-built dataset used to measure the performance of M-AVFA is a combination of common objects in life, consisting of four categories: automobiles, buses, people, and bicycles. The camera provided by our laboratory captures images, and the microphone array collects the actual sound signals. In the sound dataset, the car's driving, braking, and whistle sounds are marked as car-related; the bus's driving sounds and the sounds from the station are categorized as bus-related; the bicycle's brake sounds, chain drive sounds, and bicycle bell sounds are categorized as bicycle-related; and the sounds of people walking and speaking are categorized as people-related. Regarding the image dataset, we label cars, buses, bicycles and people according to our visual observations. The dataset was primarily collected outdoors, and the data we sampled outdoors are shown in Figure 3.  
To strengthen the nonlinearity and robustness of the image dataset, we adopted a series of operations, such as adding noise, resizing images, and adjusting contrast ratios, for data augmentation, thereby expanding the image dataset to a total of 8408 samples across four object categories. For the audio dataset, each sound file was segmented into 3 s clips for storage, with incomplete segments (shorter than 3 s) retained to ensure temporal alignment with visual counterparts. Consequently, the audio dataset was correspondingly expanded to 8408 samples. (To maintain modality consistency, the number of image and audio samples per target category was strictly matched.)
## 2 Problem Formulation  
Before posing our synchronization problem and establishing its control strategy, let us first recall some of the required definitions. Let us denote the Riemann-Liouville fractional integral operator [19] and the Caputo fractional derivative [20] of a function \(f(t)\) for \(t>0\), respectively, by  
\[J^{p}f(t)=\frac{1}{\Gamma(p)}\int_{0}^{t}(t-\tau)^{p-1}f(\tau)d\tau,\ \ \ p>0, \tag{1}\]  
and  
\[D_{t}^{p}f(t)=J^{m-p}\left(\frac{d^{m}}{dt^{m}}f(t)\right)=\frac{1}{\Gamma(m-p )}\int_{0}^{t}\frac{f^{(m)}(\tau)}{(t-\tau)^{p-m+1}}d\tau,\ \ \ m-1<p\leq m,\,m\in\mathbb{N}. \tag{2}\]  
As defined in [21], the Laplace transform of the Riemann-Liouville operator (1) is given by  
\[\mathbf{L}\left\{J^{q}f(t)\right\}=s^{-q}F\left(s\right),\ \ \ \left(q>0\right). \tag{3}\]  
Similarly, according to [22], the Laplace transform of \(D_{t}^{p}f(t)\) is defined as  
\[\mathbf{L}\left\{D_{t}^{p}f\left(t\right)\right\}=s^{p}F\left(s\right)-\sum_{ k=0}^{n-1}s^{q-k-1}f^{(k)}\left(0\right), \tag{4}\]  
for \(p>0\) and \(n-1<p\leq n\). Obviously, if \(p\in(0,1]\), then Label (4) simplifies to  
\[\mathbf{L}\left\{D_{t}^{p}f\left(t\right)\right\}=s^{p}F\left(s\right)-s^{p-1 }f\left(0\right).\]  
One of the interesting properties of the Caputo fractional derivative \(D_{t}^{p}\), which will come in handy in our study [23] is the fact that subject to \(f(t)\) having a continuous \(k^{\text{th}}\) derivative on \([0,t]\) for \(k\in N\) and \(t>0\), then by choosing \(p,q>0\) in a way that there exists \(\ell\in N\) satisfying  
\[D_{t}^{p}D_{t}^{q}f\left(t\right)=D_{t}^{p+q}f\left  
\[\lim_{t\rightarrow+\infty}\left\|e\left(t\right)=\mathbf{F}\left(Y\left(t\right) \right)-\mathbf{M}\left(t\right)X(t)\right\|=0. \tag{7}\]  
**Remark 1**.: _Depending on the pair \(\left(\mathbf{F}\left(.\right),\mathbf{M}\right)\), different synchronization types may arise:_  
1. _Complete synchronization for_ \(\left(\mathbf{F}\left(.\right),\mathbf{M}\right)=\left(I,X(t)\right)\)_._
2. _Anti-synchronization for_ \(\left(\mathbf{F}\left(.\right),\mathbf{M}\right)=\left(I,-X(t)\right)\)_._
3. _Matrix projective synchronization for_ \(\left(\mathbf{F}\left(.\right),\mathbf{M}\right)=\left(I,\mathbf{M}(t)\right)\)_._
4. _Inverse generalized synchronization for_ \(\left(\mathbf{F}\left(.\right),\mathbf{M}\right)=\left(\mathbf{F}\left(Y \left(t\right)\right),I\right)\)_._
## 2 Design of IEP-EBG Structure  
The proposed EBG structure with an inductance-enhanced patch (IEP-EBG) consists of three conducting layers, namely the layers for a power plane, an IEP, and a ground plane, as shown in Figure 1. The power plane is connected to the IEP through a via. In previous EBG structures such as the mushroom-type EBG structure, the via mainly provided inductive characteristics [16]. However, the IEP in the proposed EBG structure further enhances the inductive characteristics.  
Figure 1: A proposed EBG structure using an inductance-enhanced patch (IEP-EBG) within the design parameters of a unit cell.  
The IEP is an EBG patch including a U-shape slot. The U-shape slot surrounds the via and generates a narrow bridge connecting the via and a position on the edge of the patch. In practice, an inductive path on the patch is formed. The increase in patch inductance not only results in downsizing of the EBG UC, but also reduces the low cut-off frequency (\(f_{L}\)) of the stopband. This feature will be thoroughly analyzed in Section 3.  
The proposed IEP technique can be simply realized by etching the patch in a manufacturing process, thus it does not require complicated or additional processes. Moreover, it has the advantages of simple design, cost-effectiveness, and convenience for the analysis of the PPW mode suppression.  
The proposed IEP-EBG structure periodically arranges UCs in packages and PCBs. Although Figure 1 shows an IEP-EBG array with 3-by-2 (3 \(\times\) 2) UCs, the array size is not limited and depends on the board size. The geometric design of the IEP-EBG structure can be explained by determining the physical parameters of the UC. The design parameters associated with the IEP are the \(w_{s}\), \(d_{s}\), \(g_{s}\), and \(\theta_{s}\) of the bridge width, the slot length, the slot width, and the slot rotation angle, respectively. The UC and patch size are depicted as \(d_{c}\) and \(d_{p}\), respectively. The distance between the power plane and an IEP is \(h_{v}\), which is equivalent to the via length. \(h_{c}\) is the distance between the power plane and the ground plane. Conducting layers (e.g., the power plane) are separated by a dielectric material with relative dielectric permittivity \(\varepsilon_{r}\).
# Software Defect Prediction Using Heterogeneous Ensemble Classification Based on Segmented Patterns
## 4 Proposed Approach
### Testing Phase  
In the testing phase, we are concerned with the testing data generated in the first phase. For each instance in the testing data, we must specify to which cluster it belongs by calculating the distance between the instance and each centroid of the clusters. As a result, the instance will belong to the closest cluster (most similar), and it will be given to the model that has been assigned to the cluster  
Figure 2: Example of the model development phase of the proposed approach.  
in the training phase for final prediction. To determine the similarity, we use the Euclidean distance between the testing instance \(I\) and the centroid \(C\), which can be defined as follows:  
\[d(I,C)=\sqrt{\sum_{i=1}^{d}(I_{i}-C_{i})^{2}} \tag{2}\]  
where \(d\) is the number of input features in the dataset.  
After classifying all instances in the testing data, we can use the predictions against the actual values of classes to evaluate the performance of the given hybrid algorithm. The procedure of the algorithm is explained in Algorithm 1.  
```
input :D = {\(x_{1},x_{2},...,x_{n}\)} //Training dataset, D, which contains a set of training instances and their associated class labels.  Select \(F\) classifiers {\(f_{1},f_{2},...,f_{i}\)}  Set \(k\) //\(k\) number of clusters.  Split data into training data (\(\lambda\)) and testing data (\(\Gamma\))  Cluster \(\lambda\) into \(k\) clusters = \(>\) {\(C_{1},C_{2},...,C_{k}\)} //Training phase. foreach classifier \(f_{i}\) in \(F\)do for\(j\gets 1\) to \(k\)do  Split \(C_{j}\) to \(cv\) folds//inner cross-validation  cvError = 0 for\(l\gets 1\) to \(cv\)do  Train \(f_{i}\) on \(cv-1\) folds  compute error \(E_{l}\) on the held out fold  cvError = cvError+\(E_{l}\) \(avgError_{i}\) = cvError/cv  For each \(C_{j}\) select \(f_{i}\) with the lowest avgError //Testing phase. foreachinstance \(I\) in \(\Gamma\)do  Calculate the distance between \(I\) and each \(C_{j}\)  Find the closest \(C_{j}\) to \(I\) and its corresponding \(f_{i}\)  Prediction [I] = \(f_{i}(I)\)
```  
**Algorithm 1**Ensemble with clustering.
###### Abstract  
Turbine blades are crucial components exposed to harsh conditions, such as high temperatures, high pressures, and high rotational speeds. It is of great significance to accurately predict the life of blades for reducing maintenance cost and improving the reliability of gas turbine systems. A rapid and accurate blade life assessment method holds significant importance in the maintenance plan of gas turbine engines. In this paper, a novel on-line remaining useful life (RUL) prediction method for high-temperature blades is proposed based on 3D reconstruction technology and data-driven surrogate mode. Firstly, the 3D reconstruction technology was employed to establish the geometric model of real turbine blades, and the fluid-thermal-solid analysis under actual operational conditions was carried out in ANSYS software. Six checkpoints were selected to estimate the RUL according to the stress-strain distribution of the blade surface. The maximum equivalent stress was 1481.51 MPa and the highest temperature was 1393.42 K. Moreover, the fatigue-creep lifetime was calculated according to the parameters of the selected checkpoints. The RUL error between the simulation model and commercial software (Control and Engine Health Management (CEHM)) was less than 0.986%. Secondly, different data-driven surrogate models (BP, DNN, and LSTM algorithms) were developed according to the results from numerical simulation. The maximum relative errors of BP, DNN, and LSTM models were 0.030%, 0.019%, and 0.014%. LSTM demonstrated the best performance in predicting the RUL of turbine blades with time-series characteristics. Finally, the LSTM model was utilized for predicting the RUL within a gas turbine real operational process that involved five start-stop cycles.  
g 2023 13 11079 10.3390/app131911079
# Seismic Fragility Analysis of the Transmission Tower-Line System Considering Bol Slippage
###### Abstract  
Seismic fragility analysis can directly reflect the damage probability of the transmission tower-line system and effectively evaluate the seismic performance. To accurately estimate the failure probability of transmission tower-line systems under earthquakes, the tower-line system model considering bolt slippage was established in this paper. Subsequently, through the pushover analysis, different limit states of transmission lines subjected to earthquakes were determined. Finally, 15 seismic waves were selected to study the seismic fragility of a tower-line system by conducting an incremental dynamic analysis on the tower-line system, and seismic fragility curves were drawn. The influences of bolt slippage and foundation deformation on the seismic fragility of the tower are discussed. The results show that the bolt slippage behavior can affect the seismic fragility of tower-line systems and has opposite effects on the transmission tower with a fixed foundation and the transmission tower with foundation deformation.  
seismic fragility; transmission tower-line system; bolt slippage; foundation deformation; pushover analysis +
Footnote †: journal: Applied sciences  
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Footnote 0: Copyright © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
# Kleene Algebra to Compute Invariant Sets of Dynamical Systems
## 3 Kleene Algebra
### Intervals  
Consider a star-continuous Kleene algebra \((\mathcal{K},+,\cdot,*)\). It is equipped with the order relation \(\leq\) defined by \(a\leq b\Leftrightarrow a+b=b\) (see Table 1). We consider \((\mathcal{K}_{M},\leq,\wedge,\vee)\) a machine lattice of \(\mathcal{K}\) with respect to \(\leq\), i.e.,  
\[(i) a\wedge b = \max\{c\in\mathcal{K}_{M}|c\leq a,c\leq b\}\] \[= \sum\{c\in\mathcal{K}_{M}|c\leq a,c\leq b\}\] \[(ii) a\lor b = \min\{c\in\mathcal{K}_{M}|a\leq c,b\leq c\}\] \[= a+b.\]  
The max and min are used since \(\mathcal{K}_{M}\) is finite. To prove (i) we consider \(c,d\) in \(\mathcal{K}_{M}\). We have  
\[\left\{\begin{array}{lcl}c\leq a\wedge b&\Rightarrow&\left\{ \begin{array}{l}c\leq a,c\leq b\\ d\leq a,d\leq b\end{array}\right.\\ &\Rightarrow&\left\{\begin{array}{l}c+d\leq a+a\\ c+d\leq b+b\end{array}\right.\\ &\Rightarrow&\left\{\begin{array}{l}c+d\leq a\\ c+d\leq b\end{array}\right.\\ &\Rightarrow&\left\{\begin{array}{l}c+d\leq a\\ c+d\leq b\end{array}\right.\\ &\Rightarrow&c+d\leq a\wedge b\end{array}\right.\end{array}\]  
\begin{table}
\begin{tabular}{c c} \hline
**Kleene algebra** & \((\mathcal{K},+,\cdot,*)\) \\ \hline Addition & \(a+b\) \\ \hline Product & \(a\cdot b\) \\ \hline Associativity & \(a+(b+c)=(a+b)+c\) \\ & \(a\cdot(b\cdot c)=(a\cdot b)\cdot c\) \\ \hline Commutativity & \(a+b=b+a\) \\ \hline Distributivity & \(a\cdot(b+c)=(a\cdot b)+(a\cdot c)\) \\ & \((b+c)\cdot a=(b\cdot a)+(c\cdot a)\) \\ \hline Zero & \(a+0=a\) \\ \hline One & \(a\cdot 1=1\cdot a=a\) \\ \hline Annihilation & \(a\cdot 0=0\cdot a=0\) \\ \hline Idempotence & \(a+a=a\) \\ \hline Partial order & \(a\leq b\Leftrightarrow a+b=b\) \\ \hline Kleene star & \(a^{*}=1+a+a\cdot a+a\cdot a+\ldots\) \\ \hline \end{tabular}
\end{table}
Table 1: Axioms\(/\)definitions of a star-continuous Kleene algebra.  
such that \(c\leq a,c\leq b\) and \(d\leq a,d\leq b\). To check that \(a\lor b=a+b\) (in (ii)), it suffices to observe that \(a\leq a+b\) and that \(b\leq a+b\).  
It can be shown that \((\mathcal{K}_{M},+,\cdot,\ast)\) is also a Kleene algebra. Note that since \(\mathcal{K}_{M}\) is finite, it is obviously star-continuous.  
Definition 1: An interval of \((\mathcal{K},+,\cdot,\ast)\) is a subset \([a]\) of \(\mathcal{K}\) that can be written as  
\[[a]=\big{[}a^{-},a^{+}\big{]}=\big{\{}a\in\mathcal{K}|a^{-}\leq a\leq a^{+}\big{\}}\]  
where \(a^{-},a^{+}\) belong to \(\mathcal{K}_{M}\).  
Note that both \(\emptyset\) and \(\mathcal{K}\) are intervals of \(\mathcal{K}\). Thus, an interval arithmetic similar to that proposed by [PERSON] [43] for real numbers can be derived. This will be used later to compute with quantities defined as expressions of fixed point operators. As a consequence, if \(a\in[a]=[a^{-},a^{+}]\), \(b\in[b]=[b^{-},b^{+}]\), we have  
\[\begin{array}{rll}[a^{-},a^{+}]^{\ast}&=&\big{[}(a^{-})^{\ast},(a^{+})^{\ast }\big{]}\\ [a^{-},a^{+}]+[b^{-},b^{+}]&=&[a^{-}+b^{-},a^{+}+b^{+}]\\ [a^{-},a^{+}]\cdot[b^{-},b^{+}]&=&[a^{-}\cdot b^{-},a^{+}\cdot b^{+}].\end{array} \tag{6}\]  
due to the monotonicity of the operators \(+,\cdot,\ast\).
## 4 Materials and Methods
### De Novo Assembly and Functional Annotation  
After removing the adaptor and low-quality sequences, a de novo assembly of the remaining high-quality clean reads was performed using Trinity, as previously described for use without a reference genome [36]. After assembly, unigenes were obtained and functional annotation was carried out using BLASTx program (Available online: [[http://www.ncbi.nlm.nih.gov/BLAST/](http://www.ncbi.nlm.nih.gov/BLAST/)]([http://www.ncbi.nlm.nih.gov/BLAST/](http://www.ncbi.nlm.nih.gov/BLAST/))) (accessed on 30 April 2021) with an E-value threshold of 1e\(-\)5. Public databases including the NCBI non-redundant protein database (Nr) (Available online: [[http://www.ncbi.nlm.nih.gov](http://www.ncbi.nlm.nih.gov)]([http://www.ncbi.nlm.nih.gov](http://www.ncbi.nlm.nih.gov))) (accessed on 30 April 2021), the SwissProt database (Available online: [[http://www.expasy.ch/sprot](http://www.expasy.ch/sprot)]([http://www.expasy.ch/sprot](http://www.expasy.ch/sprot))) (accessed on 30 April 2021), the Kyoto Encyclopedia of Genes and Genomes database (KEGG) (Available online: [[http://www.genome.jp/kegg](http://www.genome.jp/kegg)]([http://www.genome.jp/kegg](http://www.genome.jp/kegg))) (accessed on 30 April 2021), and the COG/KOG database (Available online: [[http://www.ncbi.nlm.nih.gov/COG](http://www.ncbi.nlm.nih.gov/COG)]([http://www.ncbi.nlm.nih.gov/COG](http://www.ncbi.nlm.nih.gov/COG))) (accessed on 30 April 2021), were used to annotate the M. canadensis unigenes. TFs were identified by aligning unigenes to the PlantTFDB database [37].