esa-sceva/satcom-chunk-collection huggingface dataset:
# Acoustic-Based Engine Fault Diagnosis Using WPT, PCA and Bayesian Optimization
## 3 Methodology
### Principle Component Analysis  
Features once extracted can be a useful input for classification, but a large number of features can often result in less accuracy and longer execution time. Therefore, feature space dimension reduction techniques become necessary. PCA is such a technique which computes the PCs commonly by singular value decomposition of a data covariance matrix. PCs are new variables constructed as a linear combination of the initial features. They are uncorrelated due to being orthogonal to each other in the Cartesian space. Taking the ignition timing variation case as an example, Figure 5a shows the plot between two features (the mean values of the first and second sets of coefficients) for different conditions. It can be seen that differentiation among the three conditions is imperceptible. After applying PCA, the first two PCs are plotted as shown in Figure 5b for the three conditions, and it shows that the healthy and the retarded conditions are more distinguishable from each other.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Feature** & **Equations of the Detail \& Approximation Coefficients** \\ \hline Mean & \(\mu=\frac{1}{N}\sum\limits_{i=1}^{N}x_{i}\) \\ Standard Deviation & \(\sigma=\sqrt{\frac{1}{N}\sum\limits_{i=1}^{N}(x_{i}-\mu)^{2}}\) \\ Maximum & Maximum value or the peak of the given signal or its coefficients. \\ Sum of Peaks & Sum of first 5 peaks of the given signal or its coefficients. \\ Skewness & \(x_{skewness}=\frac{1}{N}\frac{\sum\
olimits_{i=1}^{N}(x_{i}-\mu)^{3}}{\sigma^{ 3}}\) \\ Kurtosis & \(x_{kurtosis}=\frac{1}{N}\frac{\sum\
olimits_{i=1}^{N}(x_{i}-\mu)^{4}}{\sigma^{ 4}}\) \\ Crest Factor & \(x_{restfactor}=\frac{kurtosis}{\text{RMS}}\) \\ Energy & \(E=\frac{1}{N}\sum\limits_{i=1}^{N}x_{i}^{2}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Description and formulae for important statistical features.  
The critical aspect of the PCA is choosing the number of PCs to be retained. This can be done by ranking the features in their order of significance. Ranking is done based on the eigenvalues. The percentage of total variance of each PC is calculated. For all datasets, the threshold of the total percentage of variance was chosen to be 99% for determining the optimal number of retained PCs.
# A Mathematical-Based Model for Estimating the Path Duration of the DSDV Routing Protocol in MANETs
## 3 Mathematical Model
### PDF of a Node Distance  
In realistic scenarios, two adjacent nodes can establish a direct link to exchange data if they are within their signals' ranges. Since the nodes in MANETs keep moving, the traveled distance to leave another node's coverage area mainly depends on two parameters: the node's direction taken by the node during its movement and the distance between the two nodes. As illustrated in Figure 5, node (A) broadcast's range is represented in the circular area with radius \(R\). The location of node (B) falls within the coverage area of node (A). In this case, the probability of the distance \(r\) between both nodes can be estimated by obtaining the Cumulative Density Function (CDF), as follows (Equation (8)):  
\[F_{R}(r)=\frac{\pi r^{2}}{\pi R^{2}} \tag{8}\]  
To obtain the PDF of the distance between the two nodes, we take the derivative of the CDF. Consequently, the PDF can be estimated using the following equation (Equation (9)):  
\[f_{R}(r)=\frac{2r}{R^{2}} \tag{9}\]  
From Equation (9), the PDF of the shortest distance of node (B) required to leave the coverage area of node (A) is expressed by Equation (10).  
\[f_{Y}(y)=\frac{2(R-y)}{R^{2}} \tag{10}\]  
As a consequence of moving the two nodes, node (B) can select any path from the list of paths (\(d_{1}\), \(d_{2}\), \(d_{3}\), \(d_{4}\),... \(d_{n}\)). The path probability being taken is a uniformly distributed function among all distances. It ranges from the shortest distance required from node (B) to leave the coverage area of node (A), which is denoted by \(y\) to the longest distance that node (B) can move before leaving node (A) coverage area, which is denoted by the distance \(2R\). Consequently, to choose the path to be followed by node (B), its PDF can be calculated using Equation (11).  
\[f_{M}(m)=\frac{1}{2R-y} \tag{11}\]  
Figure 5: Probability of the distance that neighbor node (B) is far from node (A), and the possible paths that can follow.  
As mentioned above, the distance required by node (B) to be out of node's A range mainly depends on two factors: \(P(r)\); the distance probability between node (B) to node (A) and \(P(m)\); the path probability to be taken by node (B). Considering the fact that both factors are independent, the PDF for the traveled distance (\(z\)) can be calculated using the following equation (Equation (12)):  
\[f_{Z}(z)=\frac{1}{2R-y}\frac{2(R-y)}{R^{2}} \tag{12}\]  
And the expected distance is represented by Equation (13).  
\[E_{Z}(z)=\int_{y}^{2R}z\cdot f_{z}(z)\text{d}z \tag{13}\]
# Computational Acceleration of Topology Optimization Using Deep Learning
## 2 Topology Optimization Theory  
Topology optimization (TO) is a method for optimizing the distribution of material within a given space for a given set of initial and boundary conditions. The method is based on steps of finite element analysis, sensitivity, filtering, optimization, and updating until convergence to the final topology [20]. The overall steps of the TO are shown in Figure 1. Thus, the TO problem can be described as a search for a material distribution that minimizes the objective function F under a volume constraint. The distribution of material is described by a variable density \(\rho(x)\), which can take value 0 (emptiness) or 1 (solid material) at any point in the design plane \(\Omega\). The optimization problem for state function \(u\) can be written in mathematical form as given below [20]:  
\[\begin{array}{rl}\min:&F=F(u(\rho),\rho)=\int_{\Omega}^{a}f(u(\rho),\rho)dV\\ &\rho\\ \text{subject to}:&G_{0}(\rho)=\int_{\Omega}^{a}\rho(x)dV-V_{0}\leq 0\\ &:&G_{i}(u(\rho),\rho)\leq 0,i=1,\ \ldots,\ M\\ &:&\rho(x)=0\ or\ 1,\ \forall x\in\Omega\end{array} \tag{1}\]  
where \(G_{0}\)--volume constraint and \(G_{i}\)--other possible \(M\) constraints. The first introduced homogenization approach for the topology approach was developed by [PERSON] and [PERSON] [21], which later led to the evolution of other methods.  
So far, several methods have been developed for solving TO problems: the density approach, the level-set approach, the phase field approach, and the discrete approaches [20; 22; 23]. Among them, the density approach method, and, in particular, the SIMP method (solid isotropic material with penalization) [24] is commonly used by researchers/engineers and it is included in commercial software such as SolidWorks/COMSOL/ANSYS and so on. Thus, a common task of TO, based on the SIMP approach, is to minimize the compliance (i.e., maximize the stiffness) of a given structure under a given mechanical equilibrium, and it can be written as [22]:  
\[\begin{array}{rl}\min:&c(x)=U^{T}KU=\sum_{x}^{N}(x_{e})^{p}u_{e}^{T}k_{0}u_{e}\\ &\text{subject to: }\begin{array}{l}V(x)\\ V_{0}\end{array}=f\\ &:KU=F\\ &:0<x_{min}\leq x\leq 1\end{array} \tag{2}\]  
where \(U\)--displacement vector, \(F\)--force vector, \(K\)--stiffness matrix, \(u_{e}\)--element displacement, \(k_{e}\)--element stiffness matrix.  
The optimization problem can be solved using various iterative methods, such as the optimality criteria (OC) method, the sequential linear programming (SLP) method, or the moving asymptotes (MMA) method [25]. Any of these methods require significant running time. Thus, considering the computational cost of the conventional TO methods, acceleration techniques that permit the obtaining of optimized structures are of interest. Below, we propose ML-based solutions that are reliable, highly accurate, and promising for TO applications.
# FedCO: Communication-Efficient Federated Learning via Clustering Optimization +
## 3 Preliminaries and Definitions
### FL State-of-the-Art Algorithms
#### 3.3.3 Cmf  
Communication-Mitigated Federated Learning (CMFL) [PERSON] et al. (2016) improves the communication efficiency of Federated Learning while at the same time providing guaranteed learning convergence.
# A High-Resolution, Wide-Swath SAR Imaging System Based on Tandem SAR Satellites
## 4 Verification and Analysis
### System Design Example and Imaging Performance
The evaluated results are shown in Table 5.  
Consistent with the description above, the evaluation results in Table 5 show that the proposed algorithm has the highest SSIM, PSLR, and ISLR; the imaging quality of the L1 algorithm is slightly worse; and the performance of the BP algorithm is the worst. Since the simulation scene in Figure 9 is sparse, and cannot verify the imaging performance sufficiently, the next section uses real airborne SAR data to further measure the performance of the three algorithms. Figure 11: Illustration of the azimuth profile. Figure 10: Illustration of the imaging results, where (**a–c**) are the results of the BP algorithm, the L1 algorithm, and the proposed algorithm, respectively. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **BP Algorithm** & **L1 Algorithm** & **Proposed Algorithm** \\ \hline Azimuth resolution (m) & 2.94 & 2.73 & 2.73 \\ PSLR (dB) & \(-\)16.96 & \(-\)20.55 & \(-\)22.62 \\ ISLR (dB) & \(-\)9.64 & \(-\)15.16 & \(-\)15.74 \\ SSIM & 0.61 & 0.80 & 0.81 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Evaluation results.
## 4 Design
### Topic Data Quality Assessment Module
For every \(w_{ij}\), the TDQAM calculates the new association coefficients based on the last completed window of all \(w\in R\) and stores them in the ledger. At any point in time for a group, the corresponding quality can be calculated based on the last available association scores, that is, \(Q=\left\{q_{1},\,q_{2},\,q_{3}\right\}\). The final score for quality is then normalized within the group to obtain the _Normalized Group Specific Quality_ for each quality score in \(Q\).
# Bridge Model Updating Based on Wavelet Neural Network and Wind-Driven Optimization
## 5 Finite Element Model Updating of Ningbo Bund Bridge
### Selection of Updating Parameters
When conducting parameter sensitivity analysis [50], each of the 12 updating parameters was individually increased by 1% and brought into the finite element model to approximately obtain the sensitivity values of each parameter corresponding to each order of natural frequency, as shown in Figure 12.  
Through sensitivity analysis of 12 updating parameters, 7 parameters with large sensitivity values were finally selected as updating parameters, which include the elastic modulus of the main beam \(E_{1}\), the elastic modulus of the front tower column \(E_{2}\), the elastic modulus of the rear-inclined rod \(E_{3}\), the elastic modulus of the stay cable \(E_{5}\), the density of the main beam \(\rho_{1}\), the density of the front tower column \(\rho_{2}\), and the density of the rear-inclined rod \(\rho_{3}\). The initial values of the final updating parameters in the finite element model are shown in Table 8.  
In the table, the elastic modulus of the main beam \(E_{1}\), the elastic modulus of the front tower column \(E_{2}\), the elastic modulus of the rear-inclined rod \(E_{3}\), and the elastic modulus  
Figure 12: Sensitivity analysis of updating parameters. \begin{table}
\begin{tabular}{c c c c c c} \hline \(E_{1}\) & \(E_{2}\) & \(E_{3}\) & \(E_{5}\) & \(\rho_{1}\) & \(\rho_{2}\) & \(\rho_{3}\) \\ (\(\times 10^{4}\) MPa) & (\(\times 10^{4}\) MPa) & (\(\times 10^{4}\) MPa) & (\(\times 10^{4}\) MPa) & (\(\times 10^{3}\) kg/m\({}^{3}\)) & (\(\times 10^{3}\) kg/m\({}^{3}\)) & (\(\times 10^{3}\) kg/m\({}^{3}\)) \\ \hline
2.06 & 2.06 & 2.06 & 2.06 & 12.59 & 8.05 & 8.05 \\ \hline \end{tabular}
\end{table} 
Table 8: Initial values of updating parameters. of the stay cable \(E_{5}\) are all the elastic modulus values of steel: 2.06 \(\times\) 10\({}^{4}\) MPa; the density of the main beam \(\rho_{1}\), the density of the front tower column \(\rho_{2}\), and the density of the rear-inclined rod \(\rho_{3}\) are the values after mass conversion by the equivalent density method; hence, their densities are all greater than the density of steel, which is 7.85 \(\times\) 10\({}^{3}\) kg/m\({}^{3}\).
# Assessment of _Bacillus pumilus_ Isolated from Fresh Water Milieu for Bioflocculant Production
## 3 Results and Discussion  
### Isolation of Bioflocculant-Producing Bacteria  
About 13 bacterial isolates were obtained from rock scrapings and screened for bioflocculant production. Isolate F45 was among the isolates demonstrated a flocculating activity above 50% when tested against a kaolin suspension. The 16S rDNA analysis of the bacteria revealed it to have 99% similarity to _Bacillus pumilus_ strain ZAP 028. The genus _Bacillus_ is ubiquitous in nature and includes a variety of species that are commercially useful [30]. Compared with other industrially important Bacilli such as _B. subtilis_ or _B. licheniformis_, _Bacillus pumilus_ is known for high resistance for oxidative stress [31]. This natural potential and resistances displayed by _B. pumilus_ could be a major benefit for the development of industrial production processes, since oxidative stress can take place in all phases of fermentation processes [32; 33; 34].  
In another study, a novel _Bacillus pumilis_ strain was used for the production of xylanase at alkaline pH and temperatures above 40 \({}^{\circ}\)C [31]. In bioflocculation, a number of Bacillus species have been documented for their bioflocculant-producing potential, including _B. licheniformis_[35],B. mucilaginousus[36; 37], B. circulans[38], B. subtilis[39] and B. alvei[40]. However, Bacillus pumilus has not been reported in literature as a biof flocculant producer. Continuous screening for new biof flocculant-producing strains is highly imperative because of the low flocculating activity and low yields of biofLoculants produced by microorganisms reported in the literature, which are some of the problems hindering their large-scale production. Hence, Bacillus pumilus reported in the studies is a new strain that has not been implicated in biofLoculant production before. The high flocculating activity observed with the biofoculant produced by this strain is an added advantage over those reported in the previous studies.
## 2 Preliminaries  
In this study, we use bold font style to represent vectors, matrices and tensors. Let \(\mathbf{x}=(x_{1},x_{2},x_{3},...,x_{n})^{T}\), \(\mathbf{x}^{*}=(x_{1}^{*},x_{2}^{*},x_{3}^{*},...,x_{n}^{*})^{T}\) and \(\mathbf{F}(\mathbf{x})=(f_{1}(x_{1},...,x_{n}),f_{2}(x_{1},...,x_{n}),...,f_{ n}(x_{1},...,x_{n}))^{T}\) be \(n\times 1\) vectors. The Jacobian \(\mathbf{F}^{\prime}(\mathbf{x})\), is an \(n\times n\) matrix.  
The matrix multiplication \(\mathbf{F}^{\prime}(\mathbf{x})(\mathbf{x}-\mathbf{x}^{*})\) is a \(n\times 1\) vector. The Hessian \(\mathbf{F}^{\prime\prime}(\mathbf{x})\) is a third-order tensor (\(n\times n\times n\) matrix) and the notation \(\mathbf{F}^{\prime\prime}(\mathbf{x})(\mathbf{x}-\mathbf{x}^{*})^{2}\) means the matrix multiplication \(\mathbf{F}^{\prime\prime}(\mathbf{x})(\mathbf{x}-\mathbf{x}^{*})(\mathbf{x}- \mathbf{x}^{*})\), which results in a \(n\times 1\) vector as well. The same notations are applied to the higher order derivatives. Furthermore, we let  
\[\mathbf{c}_{j}(\mathbf{x})=\frac{1}{j!}\mathbf{F}^{\prime}(\mathbf{x})^{-1} \mathbf{F}^{(j)}(\mathbf{x}),\,j=2,3,\cdots \tag{1}\]  
which is \(\underline{n\times\ldots\times n}\) tensor.  
It is well-known that the [PERSON] method (\(2^{nd}NM\)) in multi-dimensional space is given by  
\[\mathbf{x}^{(k+1)}=\mathbf{G}_{2^{nd}NM}(\mathbf{x}^{(k)})=\mathbf{x}^{(k)}- \mathbf{u}(\mathbf{x}^{(k)}),\,\,where\,\,\mathbf{u}(\mathbf{x}^{(k)})=\mathbf{ F}^{\prime}(\mathbf{x}^{(k)})^{-1}\mathbf{F}(\mathbf{x}^{(k)}) \tag{2}\]  
Research on systems of nonlinear equations has widely expanded over the last few decades [7,8]. As is well known, the iteration (2) and its variants coupled with some direct solution technique such as Gaussian elimination are good solvers for challenging nonlinear systems in case one has a sufficiently good initial guess \(\mathbf{x}^{(0)}\) and the dimension of system is not too large. When the Jacobian is large and sparse, inexact Newton methods or high-order methods may be used. For further reading one may refer to [9].
# Simulation Framework for Detection and Localization in Integrated Sensing and Communication Systems
## 4 Sensing Algorithms Generation: Target Detection and Localization  
This section presents two algorithms that will be used to illustrate the simulation framework: one for target localization and another for target detection. As mentioned in the previous section, the received sensing signal in (1) includes all MPCs, even those that do not involve the target. In this case, it is essential to differentiate the part of the received sensing signal that contains relevant information about the target from the rest of the received signal. However, this is not an easy task. One way to address this problem is to perform background subtraction, as in [21]. To allow for this, the sensing BS needs to acquire previous sensing knowledge of the scenario without the target, i.e., to estimate the sensing channel corresponding to MPCs of only scatterers that are not the target of interest. This sensing channel comprises only the black arrows in Figure 2a and can be denoted as \(\mathrm{H}_{\mathrm{pre}}(k)\in\mathbb{C}^{M\times M}\) for the \(k\)-th sub-carrier. This channel can be obtained through environment reconstruction and channel estimation techniques, as proposed in [22, 23]. Then, the effective sensing channel matrix can be defined as  
\[\mathrm{H}_{\mathrm{eff}}(k)=\mathrm{H}_{\mathrm{sen}}(k)-\mathrm{H}_{ \mathrm{pre}}(k), \tag{4}\]where \(\mathrm{H}_{\mathrm{eff}}(k)\in\mathbb{C}^{M\times M}\) contains only those MPCs involving the target, i.e., only the red arrows in Figure 2a. The following section presents a method to estimate \(\mathrm{H}_{\mathrm{eff}}(k)\).
# Unmanned Aerial Vehicle Landing on Rugged Terrain by On-Board LIDAR-Camera Positioning System
## 5 Experimental Results
### Simulation Experiments
The trajectories from each method were compared with the expected trajectory by calculating the average overall trajectory deviation \(E_{tra}\) and the variance of the overall trajectory deviation \(\sigma_{tra}^{2}\). Table 2 shows that the proposed method performed well, with a 29.31% improvement in \(E_{tra}\) and a stable \(\sigma_{tra}^{2}\). Hybrid Optical Flow is the integration of Optical Flow and GPS, which enhances location accuracy, thus becoming a prevalent approach for outdoor positioning. The proposed method provided UAVs with positioning accuracy comparable to traditional GPS-based methods in the event of GPS component failure, demonstrating that it could serve as a viable alternative for UAV positioning in GPS-denied conditions.
## 3 Results and Discussion
### Evaluation of Antioxidant Activities
#### 3.4.3 Reducing Potential  
Antioxidant activity of different species of _Brassica_ genus is also determined in terms of reducing power and results are reported in Figure 2. The reducing potential of cauliflower, red cabbage, Chinese cabbage, white cabbage and broccoli extracts was determined up to 0-10 mg/mL extract concentration. The antioxidant activity in terms of reducing power increases with respect to the increasing concentration. On comparing the reducing potential of all these vegetables, red cabbage showed maximum reducing power while the Chinese cabbage showed minimum reducing potential. The high reducing power of red cabbage might be ascribed to the fact that reducing power is strongly correlated with the antioxidant activities, i.e., there is a good association between phenolic contents and reducing power [20]. As the red cabbage showed strong total phenolic contents, thus it also exhibited the maximum reducing power. Our results are contrary to the previous reports published by [19] who reported the good correlation of reducing power between cabbage and cauliflower. Another report published by [26] explained the reducing power of fermented Chinese cabbage.
# Support of Advanced Technologies in Supply Chain Processes and Sustainability Impact
## 1 Introduction
In the new context of SC digitalization, where sustainability is also a concern, it is important to identify a set of suitable performance indicators for assessing the economic, environmental and social sustainability of SCs. In this process, the SCOR model developed by the Supply Chain Council makes an important contribution since it is a process reference model that serves as a diagnostic tool for supply chain management in general and for assessing metrics, best practices, and technology in particular. It is focused on Plan, Make, Source, and Deliver as the stages of the supply chain [24,25]. Attending to this contextualization, it is important to know the type of advanced technology used in supporting a set of important manufacturing SC processes, particularly, what stages of the SCOR model these technologies are supporting. It is important also to recognize which performance indicators are best suited to assess SC4.0's impact on sustainability. Therefore, the main objective of this paper is to study the type of advanced technology used across manufacturing supply chains in different countries that support the main processes of the SCOR model. It also intends to identify the performance indicators most suitable for assessing SC4.0 performance in terms of sustainability. Through a multiple case study, the authors contribute to knowledge of which advanced technologies have a higher level of implementation and at which stage of the SCOR model such technologies are being implemented. Moreover, the key performance indicators related to sustainability of SC4.0 are identified. The paper is structured in seven sections. Section 2 provides a theoretical background to the SCOR model, supply chain sustainability performance and the digitalization of SCs. Section 3 presents the conceptual model developed in this research attending to the literature review. In Section 4, the research methodology and methods are explained; after that,in Section 5 the analysis and discussion of the results of the research are explored, grounded in the literature and the achieved results. Section 6 presents the managerial implications of the results obtained. The last section is Section 7, where the main conclusions of this study are presented, as well as future research.
# A Novel Deep Reinforcement Learning Approach for Task Offloading in MEC Systems
## 3 System Model
### Task Queue Model  
The task queue model is based on one episode composed of a list of time slots \(\mathcal{T}=\{1,2,\dots,T\}\), where every slot has \(\mu\) seconds. We use \(u_{m}(t)\) to denote the arriving task at the MD \(m\in\mathcal{M}\). At the beginning of each time slot, there are \(u_{m}(t)\in\{1,2,\dots\}\) tasks held and waiting for allocation. Furthermore, we choose \(\lambda_{m}(t)\) to present the user \(m\)'s task size arrived at slot \(t\), which is supposed to be independent and identically distributed (i.i.d) over all slots. The queues in our system operate in a first-in first-out (FIFO) manner. Every task for executing has a deadline, \(\pi_{m}\) (in time slot). If the task has not been completely executed at the time slot, \(t+\pi_{m}-1\), it will be dropped by the queue.  
Let \(d_{m,\pi}^{edge}(t)\) denote the length of the queue of the MD, \(m\), at the ED, \(n\), in time slot \(t\). At the edge servers, given that these queuing states always keep changing at EDs since some EDs have tasks to process while others can be free, we use \(K_{n}(t)\) to indicate the amount of active queues of the ED, \(n\); it will evolve as follows:  
\[K_{n}(t)=\{m|\lambda_{m,\pi}^{edge}(t)>0\quad\text{or}\quad d_{m,\pi}^{edge}( t)>0,m\in M\} \tag{1}\]