esa-sceva/satcom-chunk-collection huggingface dataset:
# Representing Deep Neural Networks Latent Space Geometries with Graphs
## 4 Applications
### Latent Embeddings  
We now present an objective function that consists of minimizing the label variation on the output of the considered DL architecture. The goal of our objective function is to train the DL architecture to be a good feature extractor for classification, as the LGGs generated by the features will have a very small label variation. This idea was originally proposed in Reference [14].  
**Methodology:** Let us consider the representations obtained at the output of a DL architecture. We build the corresponding LGG \(\mathcal{G}\) as described in Section 3.3. Then, we propose to use the label variation on this LGG as the objective function to train the network. By definition, minimizing the label variation leads to maximizing the distances between outputs of different classes. Compared to the classic cross entropy loss, we observe that label variation as an objective function does not suffer from the same drawbacks, notably: the proposed criterion does not need to force the output dimension to match the number of classes, it can result in distinct clusters in the output domain for a same class (as it only deals with distances between examples from different classes, which can be seen as a form of negative sampling), and it can leverage the initial distribution of representations at the output of the network function.  
**Experiments:** To evaluate the performance of label variation as an objective function, we perform experiments with the CIFAR-10 dataset [40] and using ResNet18 [17] as our DL architecture. In Table 2, we report the performance of the deep architectures trained with the proposed loss compared with cross-entropy. We also report the relative Mean Corruption Error (MCE), which is a standard measure of robustness towards corruptions of the inputs over the CIFAR-10 corruption benchmark [7], where smaller values of MCE are better. We observe that label variation is a viable alternative to cross-entropy in terms of raw test accuracy, as well as that it leads to significantly better robustness. More details and experiments can be found in Reference [14], where we particularly show how the initial distribution of data points is preserved throughout the learning process (We also make this result available in Appendix E).
# RBF Neural Network Based Backstepping Control for an Electrohydraulic Elastic Manipulator
## 4 Experimental Results  
### Electrohydraulic System Description  
An EEM as shown in Figure 6 is set up to verify the effectiveness of the proposed control scheme. It is an elastic manipulator that contains an adjustable stiffness mechanism and is powered by an electrohydraulic system. In the EHSs, a linear cylinder is used to control the inner link angle. A servo-valve (D633-317B of MOOG company) is used to adjust the flow rate of the cylinder from a hydraulic power unit (Kopack Engineering company). Additionally, an inverter (SINAMICS V20, SIEMENS company) is used to control the flow rate and displacement of the electrohydraulic unit to make differential working conditions. Furthermore, two pressure sensors with a pressure range of 0-160.10\({}^{5}\) (N/m\({}^{2}\)) (Kobold) are used to measure the pressures of the two chambers, and to estimate the feedback torque. The system pressure is limited to 150.10\({}^{5}\) (N/m\({}^{2}\)) by a relief valve for safety. Two high-resolution encoder sensors are used to measure the angle of the inner link and the outer link. The adjustable stiffness mechanism includes two springs, a DC motor, and ball screws, which are used to connect the inner link and the outer link. The stiffness of the system is influenced by the angular displacement of the DC motor. So, the desired stiffness can be achieved by controlling the angular displacement of the motor. Another encoder and a motor driver (MD03) are used to carry out this task.  
The detailed system parameters of the EEM are given as follows: \(J_{1}=0.45\) Nm\({}^{2}\), \(J_{2}=0.1\) Nm\({}^{2}\), \(m_{1}=7\) Kg, \(m_{2}=3\) Kg, \(l_{1}=0.245\) m, \(d_{1}=0.215\) m, \(d_{2}=0.245\) m, \(g=9.81\) ms\({}^{-2}\), \(P_{o}=0\left(\text{N/m}^{2}\right)\), \(k_{l}=6.83\times 10^{-11}\) m\({}^{4}\big{/}\big{(}\) sVN\({}^{1/2}\big{)}\), \(P_{s}=150.10^{5}\left(\text{N/m}^{2}\right)\), \(\beta=1.25\times 10^{9}\) Nm\({}^{-2}\), \(A_{1}=16.61\times 10^{-4}\) m\({}^{2}\), \(A_{2}=3.8\times 10^{-4}\) m\({}^{2}\).  
In this paper, all experiments are carried out using MATLAB with the real-time target tool which is supported to embed in two data acquisition (DAQ) cards, (PCI1711 and PCI-Quad04; Measurement Computing and Advance-Tech companies, respectively). The sample time for the signal processing and for implementing the control algorithms was 10\({}^{-3}\) (s).
# Sparse Representation and SVM Diagnosis Method for Inter-Turn Short-Circuit Fault in PMSM
###### Abstract  
Permanent magnet synchronous motors (PMSM) has the advantages of simple structure, small size, high efficiency, and high power factor, and a key dynamic source and is widely used in industry, equipment and electric vehicle. Aiming at its inter-turn short-circuit fault, this paper proposes a fault diagnosis method based on sparse representation and support vector machine (SVM). Firstly, the sparse representation is used to extract the first and second largest sparse coefficients of both current signal and vibration signals, and then they are composed into four-dimensional feature vectors. Secondly, the feature vectors are input into the support vector machine for fault diagnosis, which is suitable for small sample. Experiments on a permanent magnet synchronous motor with artificially set inter-turn short-circuit fault and a normal one showed that the method is feasible and accurate.  
Keywords:fault diagnosis; inter-turn short circuit; sparse representation; support vector machine; PMSM
## 2 Materials and Methods
### Deep Siamese CNN Change Detection Framework  
To integrate multi-scale feature information, this study combines Siamese neural networks and encoder-decoder structures commonly used in semantic segmentation to build a deep Siamese CNN change detection framework (Figure 2).  
The encoder comprises two Siamese neural network layers with shared weights for feature extraction, which support ResNet, Xception, Efficientnet, and other typical backbone networks. The input of the encoder is images of different temporalities, which are processed by the Siamese neural network and atrous spatial pyramid pooling (ASPP), to obtain multi-level difference feature maps for integration. Through deconvolution by the decoder, concatenation, upsampling, and other calculations, detailed information on the feature maps and its spatial dimension is gradually recovered. The probability distribution of change is calculated, as well as the cross-entropy loss between the change prediction map and reference value to continuously update the network parameters. Finally, a pixel-level change prediction map with the same dimensions as the input is obtained.
## 2 The Proposed Approach
### Concepts and Rules for Multi-Attribute Three-Way Decisions
#### 2.2.2 Relative Loss Function of Attribute Values  
The relative loss function of attribute values is given in Table 3.  
\(\lambda_{BP}^{{}^{\prime}}=\sigma_{j}\Big{(}x_{ij}^{{}^{\prime\prime}}-x_{min} ^{{}^{\prime\prime}}\Big{)}\) represents alternative \(A_{i}\), which has attributes \(C_{j}(P)\) to make the noncommitment decision \(a_{B}\), and the loss value is \(\sigma_{j}\Big{(}x_{ij}^{{}^{\prime\prime}}-x_{min}^{{}^{\prime\prime}}\Big{)}\);  
\(\lambda_{NP}^{{}^{\prime}}=x_{ij}^{{}^{\prime\prime}}-x_{min}^{{}^{\prime\prime}}\) represents alternative \(A_{i}\), which has attributes \(C_{j}(P)\) to make the rejection decision \(a_{N}\), and the loss value is \(x_{ij}^{{}^{\prime\prime}}-x_{min}^{{}^{\prime\prime}}\);  
\(\lambda_{PN}^{{}^{\prime}}=x_{max}^{{}^{\prime\prime}}-x_{ij}^{{}^{\prime\prime}}\) represents alternative \(A_{i}\), which has no attribute \(C_{j}(P)\) to make the acceptance decision \(a_{P}\), and the loss value is \(x_{max}^{{}^{\prime\prime}}-x_{ij}^{{}^{\prime\prime}}\);  
\(\lambda_{BN}^{{}^{\prime}}=\sigma_{j}\Big{(}x_{max}^{{}^{\prime\prime}}-x_{ ij}^{{}^{\prime\prime}}\Big{)}\) represents alternative \(A_{i}\), which has no attribute \(C_{j}(P)\) to make the noncommitment decision \(a_{B}\), and the loss value is \(\sigma_{j}\Big{(}x_{max}^{{}^{\prime\prime}}-x_{ij}^{{}^{\prime\prime}}\Big{)}\);  
\(x_{min}^{{}^{\prime\prime}}\) is in the normalized fuzzy decision matrix \(M^{{}^{\prime\prime}}\), \(x_{min}^{{}^{\prime\prime}}=0\);  
\(x_{max}^{{}^{\prime\prime}}\) is in the normalized fuzzy decision matrix \(M^{{}^{\prime\prime}}\), \(x_{max}^{{}^{\prime\prime}}=1\);  
\(\sigma_{j}\) is the risk coefficient of attributes given by a user, \(\sigma_{j}\in[0,\,0.5]\), when \(\sigma_{j}=0.5\), multi-attribute three-way decisions will reduce to multi-attribute two-way decisions; if the user prefers accurate results, a large value can be assigned; if the user prefers fuzzy results, a small value can be assigned;  
\(C_{j}(P)\), \(\
eg C_{j}(N)\) indicate two states such that \(C_{j}(P)\cup\
eg C_{j}(N)=\Omega\); \(A_{i}\)\(\in\)\(C_{j}(P)\) indicates that \(A_{i}\) has attribute \(C_{j}(P)\); and \(A_{i}\in\
eg C_{j}(N)\) indicates that \(A_{i}\) does not have attribute \(C_{j}(P)\).  
\(a_{P}\), \(a_{B}\), \(a_{N}\), respectively, represent the decisions made: acceptance (\(POS(C)\)), noncommitment (\(BND(C)\)), and rejection (\(NEG(C)\)).
## 3 Case Study and Parametric Analysis
### Factors Influencing the Sealing Capacity of the EPDM Gasket
#### 3.3.6 Sensitivity Analyses of Influencing Factors  
In order to compare and analyze the influence of various factors on the shield lining joint leakage, the orthogonal array testing is designed here with four factors including the joint gap, the surface roughness, the service time, and groundwater pressure. The joint offset is not considered here, because its influence is meager. Each of those factors was calculated in five levels as Table 1. The value of each level is selected according to the possible value range of each factor in engineering practice.  
According to the range analysis, the influential degree of each factor on the leakage is in the order of surface roughness \(>\) service time \(>\) groundwater pressure \(>\) joint gap. Among them, instead of the joint gap and groundwater pressure, the surface roughness of the sealing material is the most important factor affecting the joint leakage of shield tunnels. A rougher contact surface can directly result in a less -real contact region, which makes it easier to form percolation channels. The service time and the joint gap can reduce the real contact region by changing the contact stress, which indirectly affects the formation of percolation channels. The groundwater pressure can determine the leakage in the observation scales. If the water pressure is large enough, the leakage will occur through finer percolation channels.
## 1 Introduction
The latter obtains less accurate pseudoranges without ambiguity, and therefore is used to resolve the ambiguity of former. In summary, the BOC signal receiving process has two strategies: the traditional BPSK-like receiving method, and reconstruction of the correlation function. To obtain a multifunctional signal, the paper adds the BPSK signals at the central frequency point of the BOC signal. At present, the combination of BPSK and BOC modulation is mostly used for global navigation satellite system (GNSS) navigation signals. [PERSON] uses interplay modulation to combine three signals at the same frequency [24]. The interplay modulator forms the signal of the in-phase (I) channel and the quadrature (Q) channel signal as a constant envelope signal by phase modulation. [PERSON] et al. use phase-optimized constant envelope a transmission method to achieve multiplexing at the same frequency [25]. The method can finish phase modulation through optimized algorithm. However, these methods should modulate PN code on each signal to distinguish each other, and cannot achieve high-speed data transmission. [PERSON] and [PERSON] propose an integrated signal based on orthogonal frequency division multiplex (OFDM) and BOC modulation [26]. The OFDM signal can accomplish high-speed data transmission, and the BOC signal achieves measurement and navigation. The power of the BOC signal is lower than the OFDM signal in order to protect the OFDM signal from interference. The multifunctional signal has the characteristics of both a BOC signal and a central frequency functional signal. By carrying two different signals at the same frequency point, the two signals assist and optimize each other, reduce hardware complexity, and simplify transmitter design. Since a BOC signal has better anti-interference performance and Doppler tracking performance than a BPSK signal, the parameters of a BOC signal (such as the PN code offset and Doppler frequency) can assist the carrier synchronization and bit synchronization of a BPSK signal. In addition, the BOC + BPSK multifunctional signal is suitable for fast switching and communication in spacecraft formation and UAV groups. The outline of the remainder of this paper is as follows. First, the composite signals are designed in Section 2. The design of the receiving and multinode fast switching algorithm of the composite signal are described in Section 3. The application scenarios and performance analysis of the system are given in Section 4. In Sections 5 and 6, we provide the discussion and conclusion of the composite signal, respectively.
## 5 Results and Discussion
Figure 9 gives a view of the GUI; the output of the GUI is feedback to the operator. The feedback is NO GO for tools that the target model predicts will produce a non-conforming part, and GO for tools that the model predicts will produce a conforming part. The machine operator is encouraged to replace the tool when the GUI displays NO GO. The prediction is generated within 5 s, facilitating the mass production without interruptions from quality inspections. Figure 8: Results for target model training run on validation and test datasets. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Actual** & **Prediction** & **GO** & **NO GO** \\ \hline GO & 7 & 0 \\ NO GO & 3 & 10 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Confusion matrix for run 5. The proposed system directs the TCM from a quantitative to a qualitative approach using a TCP. This is the reason the study does not consider quantification of the wear using any measuring system, and this makes the system more flexible to accommodate different quality requirements seen in machine shops around the world. The system uses the feature extraction capabilities of CNN and the ability of these models to learn new features using TL. Future development in the proposed system has three fronts. First, the development of the camera system to be integrated into the machine: the image acquisition in the study was independent of the machine, but the system has demonstrated that the images acquired at a reasonable distance away from the cutting operation can be used and classified by the system. The system uses standard room lighting; there are systems discussed in studies done by [PERSON] et al. [14], which are capable of generating the required light intensity and protecting the camera from cutting oil. Second, improvement of the intelligence and accuracy of the system by integrating more diverse wear images into the base model: since the base model is the central nervous system and target models have similar data distribution, the accuracy of the GO/NO GO model can be improved with fewer iteration requirements, and with a more robust base model. Finally, the proposed system is designed for the turning process: considering that the wear mechanisms in milling are different, there is a need to develop a similar system for milling applications. A similar neural network can be trained with milling insert data to repurpose the framework for a milling application. The CNN architecture is a standard approach when it comes to image recognition and identification-related neural network architectures. The area of deep learning also continues to evolve; therefore, there is a need to keep an eye out for new techniques that can improve the training time and accuracy of the system.
## 5 Experimental Verification
### Parameter Identification  
The PC computer inputs the model identification system program, and the Target PC computer generates a voltage signal that excites the piezoelectric ceramic driver, and collects the displacement information of the tool holder movement of the FTS system in real time.  
Using a sine wave with an amplitude of 10 mV and a frequency range from 0.1 Hz to 1000 Hz as the input signal, the corresponding open-loop system input signal and output signal are obtained. The amplitude-frequency and phase-frequency curves of the system are calculated through the input signal and output signal [31].  
The FTS system we designed is based on a compliant mechanism. Compliant mechanisms inherently possess infinite dimensions characteristics. Thus, from the perspective of control system design, it is simplified into a second-order system. The significant discrepancy between the red and blue lines at high frequencies in Figure 5 is attributed to the presence of an unmodeled modal (unmodeled dynamic characteristics) at high frequencies. These modalities are treated as disturbances and compensated for as part of the total disturbance during the controller design.  
Figure 4: The experimental system.  
Therefore, based on the experimental results, a second-order transfer function is used to approximate the dynamic response of the FTS system. The sampling time of the entire system is T = 0.0001 s. MATLAB data are used to simulate the combined command, and FTS plant recognition is  
\[G(s)=\frac{X(s)}{U(s)}=\frac{2.229\times 10^{7}}{s^{2}+70.16s+2.413\times 10^{6}} \tag{39}\]  
For controller design, the parameters of the electromechanical modeling model defined by Equation (2) can be determined, from which we obtain the following model parameters: \(a_{1}=2.413\times 10^{6},a_{2}=70.16,b=2.229\times 10^{7}\). Since the load conditions are not considered in the system identification, the cutting forces in actual machining will affect the performance of the PZT driver. Due to the robustness of the SMC controller, fluctuations in cutting force are also considered as an external disturbance and compensated in the controller.  
The parameters of the proposed controller are initially selected through MATLAB/Simulink simulation and then adjusted through experimental methods. Specifically, the value of the parameter \(\lambda_{1},\lambda_{2}\) related to the selected sliding surface in Equation (6) is related to the rate at which the error converges to zero when the system reaches the sliding surface, so it is selected as follows: \(\lambda_{1}=500,\lambda_{2}=0.3\). Parameter \(k_{n}\) in Equation (9) that affects the arrival speed of the sliding phase is positively correlated with the amplitude of the control signal.
## 5 Results and Discussion
### Discussion
Experiments showed that for presented here systems there was no need to specify the parameter determining the frequency of mutations. It was initially set to the value 2, meaning two mutations per genotype, and by the end of the study, there was no need to change it. Results were obtained in acceptable time from a fraction of a second to tens of seconds. Therefore, in reality, there was no need for a specific analysis of the influence of thisparameter. Also, in this case, there are no significant differences between M-CGP and CGP; so in both methods, the impact is exactly the same. To conclude, the previously described limitations for more complex systems are related to the difficulty of their descriptions in the form of answer tables and not to computational difficulties. It is important to emphasize that none of the presented experiments could have been carried out using classic CGP alone without employing the mechanisms proposed in M-CGP. Therefore, M-CGP consistently outperforms CGP in all these cases. Within the M-CGP model, any number of function type groups can be established, for which there will be different input data and the output data saved in a different place. If only one group is defined, the model reduces to classic CGP. Therefore, M-CGP retains all the capabilities of CGP while enabling calculations for multiple function groups. Wherever CGP can be used, M-CGP can also be applied, offering additional flexibility in handling diverse function groups. Thus, compared to other methods, M-CGP provides all the advantages of CGP with enhanced computational versatility. The examples provided in this work demonstrate that it is possible to design digital systems effectively based on tables describing their responses without needing detailed information about their internal structure. For instance, a system could be designed to generate sequential output values without requiring knowledge of counters. This practical significance underlines the value of the approach presented. Future research directions include developing alternative system description methods to handle more complex digital systems, as discussed earlier.
## 3 Results and Discussion
### Removal Efficiencies of PhACs by WWTPs
The \(pKa\) = 4.15 of DCF is also lower than the \(pH\) of wastewater (\(pH\) around 8); therefore, it is in ionized form, with more affinity to the aqueous phase. The slight increase of DCF in effluents can be explained probably due to the movement from the adsorbed to the dissolved phase during the treatment process and due to the degradation of organic matter, as reported by other authors [23; 46]. Our findings are in agreement with previous studies found in the scientific literature, in which incomplete removal of a wide range of pharmaceuticals in conventional WWTPs has been described [3; 5; 23; 35; 36; 38; 40].