esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
A coupled model has been developed to simulate groundwater flow in fractured karst systems according to the complex geological and karst hydrogeological conditions of the dam site, where a 3D mathematical model based on Boussinesq equation was used to describe the movement of groundwater flow in fractured medium, and a 1D conduit model for karst medium. The model was solved with the continuous hydraulic heads at the common boundaries. The hydraulic conductivities of karst medium were determined by geometrical parameters and flux of pipes. Furthermore, the permeability parameters for fractured medium were calibrated by the measured and calculated groundwater levels. The calibrated model was employed to predict the variation of groundwater flow field and leakage from the karst pipes and underground powerhouse during the reservoir operation. The simulated results showed that the groundwater level of the powerhouse had decreased by about 2-5 m. The water level of conveyance pipeline had risen by 10-20 m, and the water level on both banks had risen by 15-25 m. The leakage of karst conduits for impervious failure was larger than that for normal seepage control. In addition, the leakage of the powerhouse was estimated to be about 1000-3000 m\({}^{3}\)/d, and the seepage control of karst pipes had little influence on the leakage of underground powerhouse.  
coupled model; fractured karst aquifer; karst pipes; hydraulic conductivity; leakage analysis +
Footnote †: journal: Applied Sciences  
11 January 2021  
Accepted: 16 February 2021  
1
## 3 Source Rocks Geochemical Characteristics and Distribution
### Organic Matter Maturity  
Vitrinite reflectance (R\({}_{\rm o}\)) is the most commonly used index to determine the maturity of organic matter. R\({}_{\rm o}\) of hydrocarbon source rocks varies widely in the central depression of the Junggar Basin, up to 2.0% or more [18]. The maturity of the source rocks from the Fengcheng Formation varies greatly and low to high maturity stage have been measured [18]. It is generally believed that source rocks in the Fengcheng Formation had entered the phase of oil generation in the Mahu, Penyijingxi, and Shawan sags.
# A Review on Data-Driven Condition Monitoring of Industrial Equipment
## 2 Data-Driven Condition Monitoring of Industrial Equipment
### Fault Diagnosis and Prediction
#### 2.2.2 Method Based on Deep Learning
propose a LSTM-based approach for fault diagnosis in three-phase asynchronous motors. Each type of vibration signal is first converted into a three-dimensional tensor sample matching to the current condition and utilized as input to the LSTM classifier to establish the link between the vibration signal and the fault condition. After testing the LSTM, linear regression (LR), SVM, multilayer neural network, and original RNN on six asynchronous motors with various failure circumstances, the LSTM demonstrated the highest classification accuracy at 98.28%. In [92], [PERSON] et al. presented a model based on complete ensemble empirical model decomposition (CEEMD) and LSTM for fault diagnostics of reciprocating pumps. The vibration signal is processed using CEEMD and singular spectral entropy to generate feature vectors for fault diagnostics using an LSTM classifier. In comparison to traditional neural network approaches, the LSTM has the best classification accuracy. Regarding fault prognosis and remaining useful life (RUL) estimation, [PERSON] et al. [185] utilized LSTM to estimate the RUL for gear pumps. Using the temporal and frequency domain features of the vibration signal obtained by signal analysis, the health index of the pump was characterized. The RUL estimate was then done using a Kalman filter to combine the health index and pressure data. Through training in a bi-directional LSTM model, it is able to predict the future of the RUL. Gate recurrent units (GRUs) were introduced by [PERSON] et al. [186] due to the varying contributions of various gating to learning ability. By removing the gates with minor contributions and their related weights, the model simplifies the structure of the LSTM network and enhances the efficiency of learning. It consists of two gating units, the reset gate and the update gate. This method has also been used to the monitoring of equipment condition [187; 188; 189]. The reset gate acts similarly to the input gate of the LSTM unit, but the update gate implements the forgetting gate and output gate. [PERSON] et al. [190] proposed a hybrid strategy for machine condition monitoring that combines manual feature creation with automatic feature learning. First, time domain features are retrieved manually from the device signals and utilized as input to an improved bidirectional GRU network. This is followed by learning to represent faults using the GRU network's produced local features. A supervised learning layer is then employed to predict the machine condition. The research demonstrates the efficacy and generalizability of the system through the prediction of tool wear, the diagnosis of gearbox faults, and the detection of early bearing faults. The relevant references for the RNN methods discussed in this section are summarized in Tables 21-23.
# Future Scenarios for Software-Defined Metro and Access Networks and Software-Defined Photonics
## 3 Solutions for Software Defined Metro and Access Networks
### Physical Layer Re-Configurability
#### 3.2.3 Optical Routing at the Physical Layer  
The evolutionary traffic trends described in the Section 2.1 will drive greater need to route wavelengths optically between service endpoints in the metro and access segments, too. Efficiency, cost-effectiveness, scalability, and re-configurability will be the key requirements for wavelength routers: on-the-fly wavelength add-drop capacity and addition of more 'degrees', i.e., fiber directions, should be enabled by new types of equipment [11]. A recent survey of network providers indicates that the highest-ranking technologies required for metro networks are lower-cost 100 Gbit/s hardware (identical to long-haul form factor, but with reduced performance), pluggable C form-factor pluggable (CFP)-based 100 Gbit/s coherent module, flexible grid, and reconfigurable optical add-drop multiplexers (ROADMs), flexible coherent operation [12], as shown in Figure 7.  
In the past, the routing and wavelength assignment (RWA) problems has been widely investigated from both theoretical and practical points of view. The problem can be stated as follows: given the physical network structure and the required optical channels connections, the suitable path and wavelength have to be selected among the many possible choices for each connection, so that two paths sharing a link are not assigned the same wavelength. The RWA is an NP-complete problem that is usually divided to the more manageable routing and wavelength assignment sub-problems. For the routing sub-problem, three main approaches are known: fixed routing, adaptive routing, and semi-adaptive routing [59]. Efficient algorithms and protocols have been proposed with reference to complex mesh topologies. Since metro and access networks have simpler architectures, less complex solutions are expected to be proposed and developed to solve the RWA problem in the edge network segments.  
Wavelength routing has been proposed not only for traffic routing among metro nodes, but also inside the access segment. [PERSON] et al. [37] have proposed and demonstrated the Stanford Ultraflow access network, providing Intra-PON flow transmission with an optical software-defined reroute by using a quasi-passive reconfigurable (QPAR) node. Ultraflow architecture enables dual-mode  
Figure 7: Results of the Infonetics survey about technologies required for metro networks [12].  
service, conventional IP and Flow, and is based on the following devices: conventional GPON OLT and optical flow line terminal (OFLT) located at the central office (CO); conventional ONU and optical flow network units (OFNUs) located at the end-user premises; QPAR and coexistence elements (CEs) located at the remote node. Each CE is designed to combine/separate IP traffic (between OLT and ONUs) and Flow traffic (between OFLT and OFNUs). Flow routing is performed by the QPAR node and the Flow control plane, supported by the IP network, is completely decoupled from its data plane [37; 38].
# A Robust Timing Synchronization Algorithm Based on PSSS for LTE-V2X
## 4 Improved PSSS-Based Time Synchronization Algorithm  
The improved algorithm proposed in this paper is divided into coarse synchronization, frequency offset estimation and fine synchronization, as shown in Figure 7.  
The robustness and correctness of synchronization signal detection are improved from the following aspects while ensuring that the complexity of the algorithm is not too high:  
1. The block cross-correlation algorithm and selective summing algorithm are improved and merged in the existing symbol timing synchronization algorithm, and the threshold is designed to make it more robust to frequency offset and noise.
2. After coarse synchronization, the local synchronization signal and the received synchronization signal are used to estimate the frequency offset, and the local synchronization signal is compensated, which significantly improves the resistance of the algorithm to frequency offset.
3. Although the baseband signal is downsampled before coarse synchronization, the baseband signal is intercepted before fine synchronization, which significantly reduces the number of correlation operations at the receiving end and makes the algorithm less complex, ensuring that the reliability and accuracy of the algorithm are high.
# A Literature Review of Adhesive Systems in Density: Key Components and Their Clinical Applications
## 5 Resin-Dentin Bonding
### Factors Influencing Adhesion
#### 5.3.5 Age of the Patient (Sclerotic Dentin)  
Sclerotic dentin is a clinically challenging binding substrate in which dentin has been physiologically and pathologically altered, relatively as the body's natural defense mechanism, or as a result of colonization by the oral microflora. It is also characterized by a partial or complete obliteration of the dentinal tubules by mineral deposits. Dentin becomes transparent and insensitive, while the surface of lesions appears smooth. The thickness of the sclerotic dentin layer is variable [141].  
The sclerotic deposits which obliterate the dentinal tubules were always present after the acidic conditioning of the sclerotic dentin, resulting in minimal formation even of zero \"tags\". In addition, theclerotic dentin area impregnated with resin was found to be thinner compared to normal dentin [142].  
Another morphological alteration of sclerotic dentin is the formation of a hypermineralized layer on the surface. This layer resists etching agents, thereby preventing the formation of the HL, which is essential for the adhesion process [143]. To overcome the diffusion barrier, the use of a rotary instrument can be a solution to remove the hypermineralized layer, and thus obtain intertubular retention [144]. However, this method could be detrimental when the lesion is near the pulp.  
All in all, adhesion to sclerotic dentin was lower than the normal dentin which ultimately influenced the longevity of restorations [142; 143]. A recommendation has been done by [PERSON] et al. [144] suggesting that the optimal approach to restore such a lesion is by removing a thin layer from the surface of the hypermineralized dentin. This method effectively removes the adherent bacteria layer on the surface as well, facilitating better adhesion of the dentinal substrate.
## 2 Assimilation Method and Experiment Configuration  
### POD-4 DEnVar Algorithm  
The POD-4 DEnVar method is developed from the traditional 4 DVar method. The incremental format of the 4 DVar cost function is as follows:  
\[J(\mathbf{x}^{\prime})=\frac{1}{2}(\mathbf{x}^{\prime})^{T}\mathbf{B}^{-1}( \mathbf{x}^{\prime})+\frac{1}{2}\sum_{t=0}^{s-1}\left[\mathbf{y}_{t}^{\prime}( \mathbf{x}^{\prime})-\left(\mathbf{y}_{t}^{obs}\right)^{\prime}\right]^{T} \mathbf{R}_{t}^{-1}\left[\mathbf{y}_{t}^{\prime}(\mathbf{x}^{\prime})-\left( \mathbf{y}_{t}^{obs}\right)^{\prime}\right], \tag{1}\]  
where \(\mathbf{x}^{\prime}=\mathbf{x}-\mathbf{x}_{b}\) represents the perturbation of the background field \(\mathbf{x}_{b}\), \(\mathbf{B}\) and \(\mathbf{R}\) are the error covariances of background and observations. \(s\) is the total number of observations in the assimilation window. \(\mathbf{y}_{t}^{\prime}(\mathbf{x}^{\prime})\) is the perturbation of the observations at the analysis time \(t\), and \(\left(\mathbf{y}_{t}^{obs}\right)^{\prime}\) is the observation innovation, which are  
\[\mathbf{y}_{t}^{\prime}(\mathbf{x}^{\prime})=\left[ \begin{array}{c}\mathbf{ y}_{1}^{\prime}(\mathbf{x}^{\prime})\\ \mathbf{y}_{2}^{\prime}(\mathbf{x}^{\prime})\\ \ldots\\ \mathbf{y}_{s}^{\prime}(\mathbf{x}^{\prime})\end{array} \right], \tag{2}\]  
\[\mathbf{y}_{t}^{\prime}(\mathbf{x}^{\prime})=\mathbf{y}_{t}^{\prime}(\mathbf{ x}_{b}+\mathbf{x}^{\prime})-\mathbf{y}_{t}^{\prime}(\mathbf{x}_{b}),\;t=1,2, \ldots,s, \tag{3}\]  
\[(\mathbf{y}_{t}^{obs})^{\prime}=\left[ \begin{array}{c}\left(\mathbf{y}_{1}^ {obs}\right)^{\prime}\\ \left(\mathbf{y}_{2}^{obs}\right)^{\prime}\\ \ldots\\ \left(\mathbf{y}_{s}^{obs}\right)^{\prime}\end{array} \right], \tag{4}\]  
Unlike the traditional 4 DVar method, POD-4 DEnVar solve the cost function in (1) by constructing ensemble members, which avoids the development of the tangent linear and adjoint model. \(N\) ensemble members \((\mathbf{x}_{1},\mathbf{x}_{2},\cdots,\mathbf{x}_{N})\) are obtained by ensemble forecast, which generate \(N\) ensemble perturbations \((\mathbf{x}_{1}^{\prime},\mathbf{x}_{2}^{\prime},\cdots,\mathbf{x}_{N}^{ \prime})\) to the background field \(\mathbf{x}_{b}\), where \(\mathbf{x}_{i}^{\prime}=\mathbf{x}_{i}-\mathbf{x}_{b}(i=1,2,\cdots,N)\). Then the ensemble perturbations make up the model perturbations (MPs) matrix \(\mathbf{X}^{p}\). In the meantime, \(N\) observation perturbations \((\mathbf{y}_{1}^{\prime},\mathbf{y}_{2}^{\prime},\cdots,\mathbf{y}_{N}^{ \prime})\) are constructed by applying the observation operator \(H\) in ensemble members and background field \(\mathbf{y}_{t}=H_{t}(M_{1\to t}\mathbf{x})\) (\(M_{1\to t}\) is the forecast model), making up the observation perturbation (OPs) matrix \(\mathbf{Y}^{p}\). Since the dimension of the observed variable space is much smaller than that of the model space [6], the POD decomposition is performed on the observation perturbation matrix \(\mathbf{Y}^{p}\). \[(\mathbf{Y}^{p})^{T}(\mathbf{Y}^{p})=\mathbf{V}\Lambda^{2}\mathbf{V}^{T}, \tag{5}\]  
Here \(\mathbf{V}\) and \(\Lambda\) are the decomposed eigenvector and eigenvalue.
# DTGCF: Diversified Tag-Aware Recommendation with Graph Collaborative Filtering
## 4 Methodology
### Tag-Aware GCN
#### 4.1.3 Model Optimization  
Based on the final representation of users and items, we take the inner product to predict the interaction probability between user \(u\) and item \(i\):  
\[\hat{y}_{u,i}=e_{u}^{T}e_{i} \tag{6}\]  
where \(\hat{y}_{u,i}\) is also considered as the ranking score for providing recommendations. In our tag-aware GCN, the model parameters to be trained are only the embeddings at the 0 thlayer, including \(e_{u}^{(0)}\) for all users, \(e_{i}^{(0)}\) for all items, and \(e_{i}^{(0)}\) for all tags. To optimize the model parameters, we adopt the log loss as the loss function, which is formulated as:  
\[L_{G}=-\sum_{(u,i)\in R^{+}\cup R^{-}}(y_{u,i}\log\hat{y}_{u,i}+(1-y_{u,i})\log( 1-\hat{y}_{u,i}) \tag{7}\]  
where \(R^{+}\) and \(R^{-}\) are the positive and negative instance set, respectively. \(y_{u,i}\) is the ground truth of the user-item interaction, which is set to 1 if the pair \((u,i)\in R^{+}\). Otherwise, we set \(y_{u,i}=0\). By optimizing the loss function, positive item representations are learned to be close to user representations, while negative item representations are pushed farther.
###### Abstract  
The infrared image of power equipment plays a crucial role in identifying faults, monitoring equipment condition, and so on. The low resolution and low definition of infrared images in applications contribute to the low accuracy of infrared diagnosis. A super-resolution reconstruction method of infrared image, based on compressed sensing theory, is proposed. Firstly, by analyzing the variation of high-frequency information in infrared images with different blurring degrees, the image gradient norm ratio is introduced to estimate the blur kernel matrix in the degradation model a priori. Then, in the process of image reconstruction, we add the full variational regularization term to the traditional compressed sensing model, and design a two-step full variational sparse reconstruction algorithm. Experimental results verify the effectiveness of the method. Compared with the existing classical super-resolution methods, this method offers improvement in subjective visual effect and objective evaluation index. In addition, the final image recognition and infrared diagnosis experiments show that this method is helpful to improve the accuracy of infrared diagnosis of power equipment.  
compressed sensing; super-resolution; fault diagnosis; infrared image; power equipment
# A Survey of Target Detection and Recognition Methods in Underwater Turbid Areas
## 4 Research on Target Detection and Recognition in Turbid Waters
### Other Methods  
Other methods for target detection and recognition in turbid waters also exist, which are not restricted to inertial thinking. It is necessary to explore other methods. For example, for subsea pipelines and underwater pipelines, sonar can effectively identify targets, and electronic communications can also be used for target detection and positioning.  
[PERSON] [54] found that forward-looking sonar is prone to interference when imaging linear targets in water. The geometric, grayscale, and statistical features of such interferences are similar to real targets, which easily leads to misjudgment and omission of the visual system. Therefore, underwater linear target detection technology combining the Hough transform and threshold segmentation has been proposed. This method combines the Hough transform and threshold segmentation and uses the Hough transform to restrict the orientation of threshold segmentation. Meanwhile, threshold segmentation provides a detection basis for the Hough transform, and the two interact. When the statistical characteristics of the change in the segmentation area with the attitude are used as the objective evaluation conditions, this method can effectively extract linear objects, such as pipes, and the straight-line part of the target area is neat, with less external noise. For sonar images from other angles that are not displayed, the segmentation effect of this method is still better than some other segmentation methods.  
In nature, weak-current fish generate electric fields, which are used for navigation and target detection, and the recognition of terrain and prey. Inspired by this biology, [PERSON] et al. [5] studied the effect of obstacles on electronic communication in quasi-two-dimensional water environments with bionic electronic communication systems. They first applied the Fresnel zone theory to theoretically analyze the influence of obstacles on electronic communication, simplified the marine terrain obstacles, and used ANSYS Maxwell to simulate the impact of these obstacles on electronic communication. The simulation and experiment results showed that the material, relative position, geometry, and size of the obstacles have different effects on electronic communication. This work illustrated the possibility of underwater target recognition based on the obstacle effect, which indicates that electronic communication could represent a new and feasible underwater target detection method, and further proves that a robot integrated with an electronic communication system can detect underwater targets using the obstacle effect.
# Modeling and Control of an Articulated Multibody Aircraft
## 2 Development of the Multibody Equations of Motion
### Preliminaries  
This section briefly introduces the concept of the rotational time derivative operator, which is used throughout the text. A brief description is presented below; more details about the rotational time derivative and its application in modeling can be found in [125]. Consider two arbitrary frames \(A\) and \(O\), with relative angular velocity of \(\omega^{OA}\), and a vector \(a\), associated with frame \(A\). For clarity, a vector associated with a frame implies it is constant, as it is fixed relative to the associated frame. In addition, vector \(a\) transforms similar to a first-order tensor given the coordinate systems of frames \(A\) and \(O\) as in  
\[[a]^{O}=[R]^{OA}[a]^{A} \tag{1}\]  
where \([R]^{OA}\) is the transformation matrix from the coordinate system of frame \(A\) to frame \(O\). Transformation matrices are applicable to second-order tensors (equivalent to matrices) as well.  
Given that the time rate of change of vector \(a\) with respect to frame \(A\) is given by \([\frac{da}{dt}]^{A}\), the time rate of change of vector \(a\), expressed in frame \(O\), can be obtained by applying the classical _Euler transformation_ using _ordinary time derivatives_ as  
\[\left[\frac{da}{dt}\right]^{A}=\left[\frac{da}{dt}\right]^{O}+\Omega^{OA}\;a \tag{2}\]  
where \(\Omega^{OA}\) is the skew symmetric matrix of the angular velocity vector \(\omega^{OA}\). The resulting expression on the right-hand side (RHS) of Equation (2) does not preserve the tensorial formulation due to the time-dependent nature of the derivative of vector \(a\).  
To preserve the tensor formulation during Euler transformation, a time operator, called the _rotational time derivative_, is used instead of the _ordinary time derivative_, and was first introduced in [PERSON]'s textbook [126] on vector and tensor analysis. The operator depends only on a frame of reference such that the _rotational time derivative_ of a vector \(a\) regarding frame \(A\) is expressed as \(D^{A}\;a\). A change in reference frame of vector \(a\) from frame \(A\) to \(O\) is obtained through _Euler transformation_[125] with the _rotational time derivative_, using the following relationship  
\[D^{A}a=D^{O}a+\Omega^{OA}a \tag{3}\]  
Thus, the vector \(a\) can be expressed in the coordinate system of any frame due to the tensor properties the _rotational time derivative_ possesses as in  
\[[D^{A}\;a]^{O}=[R]^{OA}[D^{A}\;a]^{A} \tag{4}\]  
The time rate of change of vector \(a\) with respect to its associated frame \(A\) can be written as \([D^{A}\;a]^{A}\). It is important to note that rotational time derivatives expressed in their own associated frame become ordinary time derivatives ready for numerical integration as in  
\[[D^{A}a]^{A}=\frac{da}{dt}. \tag{5}\]  
In the case of a multibody aircraft, the use of fixed body reference frames and displacement vectors between points in the associated reference frames enables the equations to be derived in tensor form, and to be both compact and coordinate-independent (invariant).