esa-sceva/satcom-chunk-collection huggingface dataset:
# Stability Analysis of Mixed Convection Flow towards a Moving Thin Needle in Nanofluid
## Problem Formulation
### Steady-State Solution  
In steady-state solutions, we assume \(\partial/\partial t=0\). Hence, to compute the basic Equations (1)-(3) under the boundary restrictions (4), we introduce the relevant similarity transformations as follows:  
\[\psi=vxf(\eta),\ \ \ \ \eta=\frac{Ur^{2}}{vx},\ \ \ \ \theta(\eta)=\frac{T-T_{ \infty}}{T_{w}-T_{\infty}} \tag{6}\]  
Here, \(\psi\) is defined as a stream function that satisfies Equation (1), and the velocity components are defined as \(u=r^{-1}\partial\psi/\partial r\) and \(v=-r^{-1}\partial\psi/\partial x\), respectively. Assuming \(\eta=c\) (refer to the wall of the needle), Equation (6) prescribes the size and shape of the needle where its surface is given by:  
\[R(x)=\left(\frac{\
u cx}{U}\right)^{1/2} \tag{7}\]  
Substituting Equations (5) and (6) into basic Equations (2)-(4) reduces this to the following nonlinear ordinary differential equations given by:  
\[\frac{2(\eta f^{\prime\prime})^{\prime}}{(1-\phi)^{2.5}\left[(1-\phi)+\phi \left(\rho_{s}/\rho_{f}\right)\right]}+ff^{\prime\prime}+\frac{(1-\phi)+\phi( \rho\beta)_{s}/(\rho\beta)_{f}}{(1-\phi)+\phi(\rho_{s}/\rho_{f})}\frac{ \lambda\theta}{4}=0, \tag{8}\]  
\[\frac{2(\eta\theta^{\prime})^{\prime}}{(1-\phi)+\phi\left(\rho C_{p}\right)_ {s}/\left(\rho C_{p}\right)_{f}}\frac{k_{nf}/k_{f}}{Pr}+f\theta^{\prime}=0, \tag{9}\]  
where prime represents the differentiation with respect to \(\eta\) (similarity variable). The appropriate boundary conditions are then given by:  
\[f(c)=\frac{\varepsilon}{2}c,\ \ f^{\prime}(c)=\frac{\varepsilon}{2},\ \ \theta(c)=1,\]  
\[f^{\prime}(\eta)\rightarrow\frac{1}{2}(1-\varepsilon),\ \ \theta(\eta) \to 0\ \ \text{as}\ \ \eta\rightarrow\infty, \tag{10}\]  
where \(\lambda\) is the mixed convection parameter, which can be expressed as:  
Figure 1: Physical models and coordinate systems: (**a**) assisting flow and (**b**) opposing flow.  
\[\lambda=\frac{g\beta_{f}(T_{w}-T_{\infty})x}{U^{2}}=\frac{Gr_{x}}{Re_{x}^{2}} \tag{11}\]  
Here, \(Gr_{x}=g\beta_{f}(T_{w}-T_{\infty})x^{3}/\
u^{2}\) is the local Grashof number, and \(Re_{x}=Ux/\
u\) is the local Reynolds number. It is worth mentioning that \(\lambda>0\) represents the assisting flow, while \(\lambda<0\) represents the opposing flow. \(Pr=\
u/a\) is the Prandtl number, and \(\varepsilon\) is the velocity ratio parameter between the needle and the free stream with \(U=U_{w}+U_{\infty}\).  
The skin friction coefficients, \(C_{f}\), and the local Nusselt number, \(Nu_{x}\), are given by the following equations:  
\[C_{f}=\mu_{nf}(\partial u/\partial r)_{r=c}/\rho_{f}U^{2}=\frac{4}{(1-\varphi )^{2.5}}Re_{x}^{-1/2}c^{1/2}f^{\prime\prime}(c), \tag{12}\]  
\[Nu_{x}=-xk_{nf}(\partial T/\partial r)_{r=c}/k_{f}(T_{w}-T_{\infty})=-2\frac{ k_{nf}}{k_{f}}Re_{x}^{1/2}c^{1/2}\theta^{\prime}(c), \tag{13}\]
## 3 Proposed Method
### Domain Adaptation  
The general concept of domain adaptation is illustrated in Figure 2. First, let us denote \(X\) as a feature space and \(x\) as a feature vector. In particular, a vector having \(d\) attributes is denoted as \(x=[x^{(1)},x^{(2)},\ldots,x^{(d)}]\), and \(x\in X=X^{(1)}\times X^{(2)}\times\cdots\times X^{(d)}\).  
The idea behind the proposed domain adaptation method is to project the feature vectors \(x_{i}^{(d)}\) recorded for Network B, where \(i\in[1,m]\), onto a feature space of Network A, for which the original classifier has been trained. In other words, this approach resembles the batch normalization concept widely used when training artificial neural networks. In principle, after applying the inner-bracket part of Equation (1), we obtain a zero mean and unit variance matrix of feature vectors. Next, we transform this matrix (using the outer part of Equation (1)), so that the feature vectors become aligned with the source domain, where the original classifier has been trained. These two steps are implemented by sequentially executing transposition and scaling operations according to Equation (1), where \(T_{A,B}\) and \(S_{A,B}\) indicate transposition and scaling applied for network \(A\) or \(B\), and \(x\) represents the collection of feature vectors.  
\[T_{B\to A}(x)=\Big{[}(x-T_{B})S_{B}^{-1}\Big{]}S_{A}+T_{A} \tag{1}\]  
In this way, the \(T\) and \(S\) can be estimated separately for different networks. Here, \(T\) and \(S\) are considered as transformations implementing the standardization process, so that the mean becomes zero and the standard deviation becomes one. More precisely, \(T_{\{A,B\}}\) and \(S_{\{A,B\}}\) are calculated using Equations (2) and (3).  
\[T_{\{A,B\}}=1_{m}\mu_{\{A,B\}}^{T} \tag{2}\]  
\[S_{\{A,B\}}=diag(\sigma_{\{A,B\}}) \tag{3}\]  
Figure 2: Model transfer—general overview of domain adaptation for traffic recorded for Network B to feature space of Network A.  
In both formulas, \(\mu\) and \(\sigma\) are calculated as the classical mean and standard deviation using Formulas (4) and (5), respectively. Moreover, \(1_{m}\) is \(m\times 1\) vector of ones.  
\[\mu=\tfrac{1}{m}\sum_{i=1}^{m}x_{i} \tag{4}\]  
\[\sigma=\tfrac{1}{m}\sum_{i=1}^{m}(x_{i}-\mu)^{2} \tag{5}\]
## 3 Mathematical Model of Geolocation and Tracking Scenarios
### Conventional Geolocation Measurements
#### 3.1.1 Tdoa  
The TOA measurement at \(i\)th sensor is given by  
\[\begin{cases}\tau_{i,k}^{o}=\frac{\|\mathbf{d}_{i,k}^{o}\|}{c}\,\\ d_{i,k}^{o}=(\mathbf{u}_{k}^{o}-\mathbf{s}_{i}^{o})\,\qquad\{i=1,2,\ldots,M\},\end{cases} \tag{2}\]  
where \(\|\cdot\|\) represents the 2-norm, and \(c\) is the propagation speed \(\approx 2.998\times 10^{8}\) m/s, \(d_{i,k}^{o}\) is range between emitter and \(i\)th sensors in (m), as well \(\mathbf{s}_{i}^{o}=[x_{i}^{o},y_{i}^{o},z_{i}^{o}]^{\prime}\) is initial location vector of \(i\)th sensor.  
The time difference of signal arrival (TDOA) between \(i\)th sensors when we use sensor #1 as reference is calculated as  
\[\tau_{i,1,k}=\tau_{i,k}^{o}-\tau_{1,k}^{o}+n_{i,1,k}\,\quad\{i=2,3,\ldots,M; \ k=1,2,\ldots,K\}, \tag{3}\]  
where \(n_{i,1,k}\sim N(0,\sigma_{\tau}^{2})\). The vector form of TDOA can be represented as  
\[\mathbf{\tau}_{k}=\mathbf{\tau}_{k}^{o}+\mathbf{n}_{k}, \tag{4}\]  
where  
\[\begin{cases}\mathbf{\tau}_{k}=[\tau_{2,1,k},\tau_{3,1,k},\cdots\tau_{M,1,k}]^{ \prime}\,\\ \mathbf{\tau}_{k}^{o}=[\tau_{2,1,k}^{o},\tau_{3,1,k}^{o},\cdots\tau_{M,1,k}^{o}]^{ \prime}\,\\ \mathbf{n}_{k}=[n_{2,1,kCovariance matrix \((\mathbf{R}_{n_{k}})\) of TDOA noise is determined as  
\[\mathbf{R}_{n_{k}}=\mathrm{E}[\mathbf{n}_{k}\mathbf{n}_{k}^{\prime}]=\left[\underbrace{ \sigma_{\tau}^{2}\cdots\sigma_{\tau}^{2}}_{(M-1)(K+1)}\right], \tag{6}\]  
where \(\mathrm{E}[\cdot]\) represents the statistical expectation; and \(\sigma_{\tau}^{2}\) is the variance of TDOA measurement noise.  
\[\sigma_{\tau}=\frac{1}{B_{S}}\frac{1}{\sqrt{B_{N}T}}\frac{1}{\sqrt{S/N}}, \tag{7}\]  
where \(B_{S}\) is signal bandwidth, \(B_{N}\) is noise bandwidth, \(T\) is integration time, and \(S/N\) is interference power [20].
# A Fast and Simple Method for Absolute Orientation Estimation Using a Single Vanishing Point
###### Abstract
The known roll angle can simplify the rotation matrix, and this simplification results in being able to estimate the other two orientations independently, which also enormously improve both the efficiency and accuracy, because the system of non-linear equations could decrease both. Then, our proposed method uses a vanishing point of parallel lines whose direction vector is known in order to estimate the camera orientation. Actually, the vanishing point contains the rotation between the camera frame and world frame, and it does not contain the information about the translation; thus, the coarse orientation can be estimated independently. Last, a non-linear optimization algorithm is given to refine the orientation and it improves the accuracy of our proposed method. In addition, the usable range of our proposed method is given, which is the vanishing point that cannot be used to estimate orientation in this method, and the algebraic derivation is given. The rest of this paper is organized as follows: Section 2 provides the Materials and Methods, including the vanishing point and method statement, orientation refining, and limitations of parallel lines and their algebraic derivations. Section 3 is the Experiments and Results, including the numerical stability, noise sensitivity, computational speed in synthetic data, and testing in real images, compared with several state-of-the-art orientation estimation solvers. Section 4 provides the Discussion and Section 5 is the Conclusions.
## 2 The Proposed Method
### Residual Time-Frequency Mixed Attention Module Network
#### 2.2.2 Loss Function  
Compared to other loss functions, cross entropy loss can avoid gradient dispersion in gradient descent calculation, leading to the decrease of learning rate. So the cross entropy loss function is a common objective function that can be divided into binary and multi-classification cross-entropy loss functions. The proposed network model realizes a multi-classification fault diagnosis based on TFCF time-frequency images of rolling bearing vibration signals. Therefore, the multi-classification cross-entropy loss function is adopted, and its expression is as follows:  
\[Loss=\sum_{i=1}^{N}y_{i}\log{\hat{y}_{i}} \tag{16}\]  
where \(N\) indicates the number of types of rolling bearing faults, \(y_{i}\) indicates the actual label value of category \(i\), and \(\hat{y}_{i}\) indicates the predicted value of category \(i\).
## 3 Edge Computing and Related Paradigms
### Edge Computing  
Edge computing is a computing paradigm that involves deploying computing resources at the edge of the network, closer to where the data are generated, processed, and consumed. This technique aims to reduce latency, ease traffic on the network, and meet the computational requirements of applications that demand low latencies [47]. It is considered an open platform that extends cloud computing capabilities by providing services close to users through IT infrastructure at the network edge [48]. Edge computing architecture involves placing tiny data centers at the network edge to enhance and extend cloud computing capabilities [49].  
Edge computing utilizes advanced technologies such as deep learning to improve its applications. Researchers are increasingly exploring the intersection of deep learning and edge computing, as evidenced by existing surveys [50]. Moreover, edge computing is closely linked to other concepts such as fog computing, mobile edge computing (MEC), and mobile cloud computing (MCC), each with its own unique architectural features and areas of focus [29; 51]. Security and privacy protection are crucial aspects of edge computing. Ensuring the security and privacy of computation and data management at the edge and in the cloud is a critical requirement [52]. Researchers have highlighted the importance of addressing security issues in supporting technologies for edge computing, including challenges and opportunities related to security and convergence with blockchain technologies [53].
## 1 Introduction
This work is a complete effort to verify the merits of the 3-dimensional constellation proposed in [20] for the MA-SM system. To this end, we derive the analytical formulae of the upper bound on the pair-wise error probability (PEP) of the MA-SM for the system parameters provided in Table I of [20]. The derived formulae are functions of the rotation angles introduced through the 3-dimensional constellation. We model the search of the rotation angles in [20] as a multi-objective optimization that is solved to obtain the optimal rotation angles that reduce the error probability. Based on the provided analytical analysis and simulation results, we conclude that the 3-dimensional constellation obtained from the optimal angles derived in this paper slightly improve the BER performances of the MA-SM with signal-to-noise ratio (SNR) gains of less than 0.5 dB, as compared to the performance of MA-SM when the rotation angles that were given in [20] are used. Overall, we recommend that the 3-dimensional constellation should not be used with the MA-SM due to the marginal improvement in the BER performance. In his paper, we used exactly the same transmitter structure described in [20]. Specifically, we used the same number of transmit antennas, the same values of the angles, and the same number of active antennas at each channel use. The work in [20] is pioneering and of great importance to the development of spatial modulation techniques. Our work should be regarded as a continuity rather than a replacement. The rest of this paper is organized as follows. In Section 2 and 3, we introduce the related work and system model of MA-SM, respectively. In Section 4, we derived the upper bound on the PEP of the MA-SM in terms of the rotation angles for two system configurations and support the analytical performance with numerical results. Simulation results were presented in Section 5 and we draw the paper's conclusions in Section 6.
## 3 Results and Discussion
### Analysis of Flavor Compounds
(**a**) Moisture, (**b**) Acidity, (**c**) pH value, (**d**) Starch content, (**e**) Reducing sugars content. Figure 4: Heatmap analysis of flavor compounds in fermented grains on day 42. Cluster analysis is a classification method and can be used to automatically classify a batch of sample data according to their intimate nature without prior knowledge [33]. To investigate the similarity and divergence of the flavor compound compositions of each sample, heatmaps of the flavor compounds of the final fermented grains were clustered. The results of the heatmap analysis revealed that samples 2, 3, and 4 were closer in terms of metabolite profiles of fermented grains compared to sample 1 (Figure 4). Cluster analysis revealed that the flavor compounds of fermented grains on day 42 can be divided into four clusters, namely cluster I, cluster II, cluster III, and cluster IV (Figure 4). In cluster III, the flavor compounds in sample 4 showed the highest relative contents, followed by sample 3 and sample 2, including 3-methyl-1-butanol, phenylethyl alcohol, butane-dioic acid, diethyl ester; octanoic acid, ethyl ester; decanoic acid, ethyl ester; benzoic acid, ethyl ester; 3-phenylpropionic acid ethyl ester; tetradecanoic acid, 13-methyl-, ethyl ester; (Z)-ethyl pentadec-9-enoate, dodecanoic acid, ethyl ester; tetradecanoic acid, ethyl ester; 9-hexadecenoic acid, ethyl ester; heptadecanoic acid, 15-methyl-, ethyl ester; pentadecanoic acid, ethyl ester; 9,12-octadecenoate, ethyl ester; and 9-octadecenoic acid, ethyl ester (Figure 4). 9,12-Octadecatione acid ethyl ester has the function of reducing cholesterol and blood lipids, and preventing atherosclerosis [34]. In addition, butyrate esters contribute to the flavor of Baijiu [34], for example, phenylethyl butyrate has the aroma of floral, green with a tropical winev nuance; ethyl butyrate has the aroma of sweet, apple like, fresh and lifting, ethereal; isoamyl butyrate has the aroma of wax, green apple, fruit, sweet, berry; propyl butyrate has the aroma of sweet, bubble gum and pineapple like with a light green nuance; and isobutyl butyrate has sweet, fruity, pineapple, apple, bubble gum, and tropical fruit flavors [2;35]. As for cluster I, these flavor compounds showed the highest content in sample 3, followed by sample 4, and hardly any in sample 2. These results showed that the combination of key functional microorganisms (sample 3) could realize the synthesis of important flavor compounds in Baijiu, and the use of the functional microbial combination in place of some Daqu (sample 4) could not only give full play to the function of Daqu, but also promoted the stable production of many more flavor compounds together with the functional microbial combination.
# Machine Learning Method for Prediction of Hearing Improvement After Stapedotomy
## 2 Materials and Methods
### Experimental Setting 1: Hold-Out Method  
In this setting, the model training and evaluation were performed with a separate training and test set. Feature importance assessment was performed on the testing set. The experimental scheme is illustrated in the Figure 2, with detailed steps outlined below. We describe only the processes specific to the hold-out method; general approaches are described in the previous sections.  
1. Data preprocessing Value differences can be caused by different measurement techniques or units, which may magnify or obscure the importance of certain features. Data scaling was used to  
Figure 2: Flow diagram of the experimental scheme of the hold-out method.  
establish a consistent scale for the features. For categorical features, namely, gender and ear side, binary representation was used (e.g., male = 0 and female = 1). All other continuous values, both basic and constructed, were standardized to have a mean of 0 and a SD of 1. A portion of 20% of the data was randomly separated from the rest and used as a test dataset. Feature selection and training was performed on the remaining 80% of the data. The test dataset was used in the final evaluation.
2. Feature selection The reduction of redundancy introduced in feature engineering is achieved through the feature selection process, which aims to include only the most pertinent features for the given target. Limiting the number of features is also beneficial for decreasing computational time and improving interpretability [13]. In the Lasso regularization process, some coefficients are shrinked to zero. If there are highly correlated values, the Lasso tends to select one variable from each group and ignore the others [20]. This is essential, especially for feature importance estimation, since the permutation importance (PFI) [18] is estimated lower for strongly correlated features than for independent features [21; 22]. Similarly, the presence of multicollinearity leads to unreliable estimation of Shapley values [23]. Shapley values are a core component of SHapley Additive exPlanations (SHAP) [24], another explainability method we used. Additionally, Spearman correlation was computed on the Lasso-selected features to perform correlation analysis [25]. Collinear feature pairs were detected and the feature with the lower Lasso coefficient from the pair was removed from the selection.
3. Hyperparameter tuning The entire training set was used for hyperparameter tuning. The setting of tuning was cross-validated. The training set was split in 10 folds. Then, hyperparameters were selected based on CV performance.
4. Feature importance PFI was used to evaluate the importance of features. PFI does not suffer from the feature importance bias of the impurity importance, which can favor features with high cardinality [26]. To provide additional insight into the influence of each feature, SHAP analysis [24] was performed. Both explainability methods were applied to RF predictions on the test set.
# MIMO Radar Imaging Method with Non-Orthogonal Waveforms Based on Deep Learning
## 3 Imaging Method of Non-Orthogonal Waveform MIMO Radar Based on Deep Learning  
In this paper, the deep learning technology is introduced to solve the problem of non-orthogonal waveform coupling suppression, and a new non-orthogonal waveform MIMO radar imaging framework based on a deep network is proposed. The main idea is to realize the mapping from the complex echo of non-orthogonal waveform MIMO radar to the target high-resolution image through the deep network, and the key is to design a network structure suitable for non-orthogonal waveform MIMO radar imaging. The network structure based on deep expansion of the sparse reconstruction algorithm can effectively deal with complex echoes, but for non-orthogonal MIMO radar imaging, the dimension of the sparse reconstruction model is too high, and the network is difficult to realize. Convolutional neural networks and recurrent neural networks, which are widely used in the field of general deep learning, are not suitable for the data characteristics of non-orthogonal waveform MIMO radar echo, and it is difficult to learn the complex mapping from complex echo to target image. The proposed framework organically combines the MIMO radar imaging theory with the advanced deep network, provides the basis for network construction and parameter initialization through the imaging theory, and uses the advanced deep network module to improve the nonlinear fitting ability of the imaging network. The overall network structure is divided into two parts, which are called the 'echo transformation module' and 'feature imaging module'. The 'echo transformation module' serves to extract artificial features, transform the complex echo using the traditional MIMO radar imaging theories such as matched filtering, sparse recovery, time-frequency analysis, etc., and realize the transformation from the complex echo domain of 'implicit expression' of information to the feature domain of 'explicit expression' of information. The function of the 'feature imaging module' is to carry out automatic feature extraction, use advanced achievements in the field of deep learning to build a network structure, and complete the nonlinear mapping from echo features to target images. The overall structure of the framework is shown in Figure 1, which mainly includes three parts: training data generation based on the MIMO radar imaging model, deep network forward propagation,and error back-propagation based on reconstruction loss. Based on the above framework, a non-orthogonal waveform MIMO radar imaging method based on deep learning is designed in this paper. The proposed imaging method is introduced from three aspects: deep network structure, training data generation, and network training strategy.
## 4 Parametric Analysis
### Gap Parameter  
Figure 14 shows the settlements, rotation angles, bending moments and shear forces of the existing building under different gap parameters. Contrary to the effect of the soil Poisson's ratio on the building response, the increase in the gap parameter greatly increases the settlement and the differential settlement of the building (Figure 14a). Figure 14b indicates that the larger the gap parameter is, the larger the rotation angles are. As the gap parameter increases from 10.0 to 50.0 mm, the absolute value of the maximum rotation angle increases from 1.7 \(\times\) 10\({}^{-4}\) to 8.7 \(\times\) 10\({}^{-4}\) rad, while that of the minimum rotation angle increases from 0.7 \(\times\) 10\({}^{-4}\) to 3.5 \(\times\) 10\({}^{-4}\) rad. It can be found that the bending moments achieve rapid increase when the gap parameter becomes larger, and the magnitude of the increase in the bending moments increases as the gap parameter increases (Figure 14c). With an increase in the gap parameter, the maximum positive and negative shear forces gradually increase according to Figure 14d, and always appear at \(y\) = 4.0 m and \(y\) = 16.0 m, respectively.  
Figure 13: Building response with different bending stiffness: (**a**) settlement; (**b**) rotation angle; (**c**) bending moment; (**d**) shear force.