esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Chinese Network Security Named Entity Recognition Method Based on CSBERT-IDCNN-BiLSTM-CRF
### CSBERT-IDCNN-BiLSTM-CRF Model Structure and Algorithm Flow  
After analyzing and filtering each module in Section 3.1, this paper proposes a joint entity recognition model based on the CSBERT network security pre-training model, called CSBERT-IDCNN-BiLSTM-CRF. The structure diagram is as Figure 6.  
The algorithm flow of the model during its working process is as Algorithm 1:  
Figure 6: Model flowchart of this paper.  
The input to the model is a text corpus sequence \(X=\{c_{1},c_{2}...,c_{n}\}\), and the output is a predicted label sequence \(\hat{y}=\{\hat{y}_{1},\hat{y}_{2},.......,\hat{y}_{n}\}\).  
Step 1: Extract a batch size of text data from the dataset for processing. Tokenize the text sequence and perform character-level segmentation on it. Vectorize the text sequence at the character level using a dictionary to generate unique encoding sequences.  
Step 2: Input the unique encoding sequence into the BERT model. The model combines context and semantic information to dynamically generate word vectors \(x_{B}\), which are then passed to downstream task models.  
Step 3: IDCNN performs local information modeling for the target task by extracting local features from the text sequence. It outputs a local feature sequence \(W=\{w_{1},w_{2}...,w_{n}\}\).  
Step 4: Bi-LSTM is used to perform context and sequential information modeling for the target task. It extracts contextual features and sequential order features contained in the local feature sequence \(W=\{w_{1},w_{2}......,w_{n}\}\), which are used for sequence labeling.  
Step 5: Use CRF (Conditional Random Field) to select the predicted label sequence with maximum probability \(\hat{y}=\{\hat{y}_{1},\hat{y}_{2},...,\hat{y}_{n}\}\) as an output of the model.
# Fault Detection of Stator Inter-Turn Short-Circuit in PMSM on Stator Current and Vibration Signal
## 3 Results  
In the experiment, a stator winding motor with a single phase of 15% inter-turn short circuit fault was compared with the normal motor. Both the current signal and vibration signal were analyzed to extracting the fault features.
## 2 Materials and Methods
### Traditional Feature Extraction  
In this study, we employed four widely used traditional feature extraction methods to represent protein sequences:  
(i) Amino acid composition (AAC) [15] calculates the frequency of each amino acid in a protein sequence, resulting in a 20-dimensional feature vector.  
\[\text{AAC}_{i}=\frac{n_{i}}{N}\quad\text{for }1\leq i\leq 20\]  
where \(n_{i}\) is the number of occurrences of amino acid \(i\) and \(N\) is the total number of amino acids in the sequence.  
(ii) Composition of k-Spaced Amino Acid Pairs (CKSAAP) [16] captures the composition of amino acid pairs separated by \(k\) residues, providing information regarding short-range interactions.  
\[\text{CKSAAP}_{ij}^{k}=\frac{n_{ij}^{k}}{N_{ij}^{k}}\quad\text{for }1\leq i,j \leq 20\]  
where \(n_{ij}^{k}\) is the number of occurrences of pair \((i,j)\) separated by \(k\) residues and \(N_{ij}^{k}\) is the total number of possible pairs in the sequence.  
(iii) Dipeptide Deviation from Expected Mean (DDE) [17] measures the deviation of observed dipeptide frequencies from expected mean frequencies, reflecting preferences in local amino acid order.  
\[\text{DDE}_{ij}=\frac{f_{ij}-\mu_{ij}}{\sigma_{ij}}\quad\text{for }1\leq i,j \leq 20\]  
where \(f_{ij}\) is the observed frequency of dipeptide \(ij\), \(\mu_{ij}\) is the expected frequency of dipeptide \(ij\), and \(\sigma_{ij}\) is the standard deviation of dipeptide \(ij\).  
(iv) Grouped Tripeptide Composition (GTPC) [18] groups amino acids based on physicochemical properties and calculates the frequency of tripeptides formed by these groups.  
\[\text{GTPC}_{G_{i}G_{j}G_{k}}=\frac{n_{G_{i}G_{j}G_{k}}}{N_{G_{i}G_{j}G_{k}} }\quad\text{for }1\leq i,j,k\leq\text{number of groups}\]  
where \(n_{G_{i}G_{j}G_{k}}\) is the number of occurrences of tripeptides formed by groups \(G_{i}\), \(G_{j}\), and \(G_{k}\), and \(N_{G_{i}G_{j}G_{k}}\) is the total number of tripeptides in the sequence.
# Discrete-Time Asymptotic Tracking Control System for a Satellite with a Solar Panel
## 3 Plant State Space Models and Their Properties
### Properties of the Discrete-Time State Space Model  
The described transformation from the continuous-time state space model to the discrete-time one is known to preserve several system-theoretic properties of the model (see, e.g., [21]). The most important features--which we make use of in our considerations--are described below. We provide proofs for these, as the results are of independent interest in the general context of regulation theory.
## 4 Range-Based Algorithms  
Having extracted ranges or bearing from the RF signal, intuitive mathematical or geometrical approaches are then leveraged to infer the receiver's position. The most important methods include Multi-Lateration/Triangulation, Min-Max, Multidimensional scaling (MDS), Least Squares (LS), Maximum Likelihood (ML), the Bayesian inference method, and Bayesian Filters. Table 2 provides a comparison of range-based methods.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Range-Based Methods** & **Scenario** & **Advantages** & **Disadvantages** \\ \hline Multi-lateration Triangulation & fast and rough & simple calculation & limited accuracy, sensitive to measurement error \\ \hline Min-Max & fast and rough & low complexity, easy implementation & limited accuracy \\ \hline Multidimensional Scaling (MDS) & cooperative localisation & reduce the complexity & difficult to include the knowledge about unequal measurement error \\ \hline Least Square (LS) & high accuracy & easier implementation and less demanding than ML and Bayesian, gives estimation uncertainty & computationally demanding, less optimal compared to ML and Bayesian \\ \hline Maximum Likelihood (ML) & high accuracy, inaccurate prior information & gives estimation uncertainty & computationally demanding \\  & (outperform Bayesian) & & computationally demanding \\ \hline Bayesian Inference & higher accuracy, sparse observations & gives estimation uncertainty & computationally demanding (more demanding than LS and ML) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison between range-based methods.
## 2 Methods  
This section explains our SPFSAT algorithm, which is depicted in Figure 1. An encoder is used to extract the features of the input fundus structure image, and three decoders with different weights are used to generate three different fake FFA images from the encoder's extracted features. To discriminate the real FFA images from the fake FFA images, the fundus structure image and three-phase FFA images are inputted to a single discriminator. Thus, the single discriminator judge FFA images not only by single FFA image information but also by mutual information among fundus structure image and three-phase FFA images. The loss function is determined by adding \(L1\) loss and adversarial loss between real FFA images and fake FFA images to optimize the network performance.
## 4 Results
### Simulated Data  
When generating simulated data, the dimension of the SAR stack is set to \(N~{}=~{}20\), \(21,\cdots,108\). The number of samples was set to \(M=50\). The following steps were adopted to simulate a group of \(M\) sample vectors with respect to a certain point. Firstly, \(2N\) uniform distributed random numbers between zero and one were generated. They were used to form an \(N\)-dimensional vector. This vector is denoted as \(\mathbf{w}\), which is considered to be the \"real\" interferometric signals. Next, the latent vector \(\mathbf{z}\), which contains \(M\) complex numbers, was generated based on Gaussian distribution. Based on a pre-configured standard deviation \(\sigma^{2}=0.01\), a zero-mean vector \(\epsilon\) was simulated. Finally, a set of \(M\) observation samples was obtained based on \(\mathbf{w}\), \(\mathbf{z}\) and \(\sigma^{2}\) by using Equation (14).  
For a given stack dimension \(N\), \(1000\) groups of simulated samples were generated, which were processed by the EVD and the CPPCA method, respectively. In order to evaluate the accuracy of the two methods, the root mean square errors (RMSE) of the results were calculated, as demonstrated in Figure 1(a). No apparent differences between the two methods can be observed from this figure, which implies that the proposed method has an equivalent accuracy to the conventional EVD method. The execution times of the two methods are presented in Figure 1(b). It can be clearly observed that the EVD method exhibited a super non-linear computational complexity. On the other hand, the execution time required by the CPPCA method was virtually never influenced by the stack dimension. When \(N=108\), the EVD method spent around 19.75 s on the reconstruction process. In contrast, the CPPCA method only required about 1.37 s. Apparently, the proposed CPPCA method was far superior to the EVD method in the aspect of computational efficiency.
# An ECC-Based Anonymous and Fast Handover Authentication Protocol for Internet of Vehicles
## 4 Proposed Protocol
### Registration Phase
#### 4.2.2 \(Rsu\) Registration  
The registration process of \(R\)SU is shown in Table 4. In a similar manner, during this phase, the \(TA\) provides the registration services to \(RSU_{j}\) through the security channel, ultimately enabling \(RSU_{j}\) to generate its private key \(d_{RSU_{j}}\) and public key \(P_{RSU_{j}}\).  
1. \(RSU_{j}\) randomly selects a secret parameter \(y_{r}\in\mathbb{Z}_{p}^{*}\), computes \(Y_{r}=y_{r}\cdot P\), and transmits the registration message \(\{ID_{RSU_{j}},Y_{r}\}\) to the \(TA\) through the security channel.
2. Upon receiving the registration information from \(RSU_{j}\), the \(TA\) selects a random number \(v\in\mathbb{Z}_{p}^{*}\), computes \(X_{r}=v\cdot P\), generates a partial private key \(x_{r}=v+s\cdot h(ID_{RSU_{j}}||X_{r}||Y_{r})\) for \(RSU_{j}\), and sends the response message \(\{x_{r},X_{r}\}\).
3. Upon receiving the information, \(RSU_{j}\) first verifies whether \(x_{r}\cdot P=X_{r}+P_{Pub}\cdot h(ID_{RSU_{j}}||X_{r}||Y_{r})\) holds true. If satisfied, \(RSU_{j}\) generates the private key \(d_{RSU_{j}}=x_{r}+y_{r}\) and public key \(P_{RSU_{j}}=\{X_{r},Y_{r}\}\). Subsequently, \(RSU_{j}\) stores \(\{d_{RSU_{j}},P_{RSU_{j}}\}\) in memory and publishes its public key \(P_{RSU_{j}}=\{X_{r},Y_{r}\}\).  
\begin{table}
\begin{tabular}{c c} \hline \(RSU_{j}\) & \(TA\) \\ \hline Randomly choose \(y_{r}\) & \\ Compute \(Y_{r}=y_{r}\cdot P\) & \\ send \(\{ID_{RSU_{j}},Y_{r}\}\) & \\ \hline Randomly choose \(v\) & \\ Compute \(X_{r}=v\cdot P\) & \\ \(x_{r}=v+s\cdot h(ID_{RSU_{j}}||X_{r}||Y_{r})\) & \\ \(\longleftarrow\) & \\ Verify \(x_{r}\cdot P\stackrel{{?}}{{=}}X_{r}+P_{Pub}\cdot h(ID_{RSU_{j}}||X_{ r}||Y_{r})\) & \\ Compute \(d_{RSU_{j}}=x_{r}+y_{r}\), \(P_{RSU_{j}}=\{X_{r},Y_{r}\}\) & \\ store \(\{d_{RSU_{j}},P_{RSU_{j}}\}\) & \\ \hline \end{tabular}
\end{table}
Table 4: RSU registration stage.  
\begin{table}
\begin{tabular}{c c} \hline \(V_{i}\) & \(TA\) \\ \hline Randomly choose \(y_{v}\) & \\ Compute \(Y_{v}=y_{v}\cdot P\) & \\ send \(\{ID_{V_{i}},Y_{v}\}\) & \\ \hline Randomly choose \(u\) & \\ Compute \(X_{v}=u\cdot P\) & \\ \(x_{v}=u+s\cdot h(ID_{V_{i}}||X_{v}||Y_{v})\) & \\ \(\longleftarrow\) & \\ Compute \(C=h(ID_{V_{i}}||X_{v}||Y_{v})\) & \\ Verify \(x_{v}\cdot P\stackrel{{?}}{{=}}X_{v}+P_{Pub}\cdot C\) & \\ Compute \(d_{V_{i}}=x_{v}+y_{v}\), \(P_{V_{i}}=\{X_{v},Y_{v}\}\) & \\ store \(\{d_{V_{i}},P_{V_{i}},C\}\) & \\ \hline \end{tabular}
\end{table}
Table 3: Vehicle registration stage.
On the basis of the estimation values from the RESOs, a feedback controller is derived by using the recursive backstepping methodology, where three second-order command filters (SCFs) are incorporated to overcome the problem of \"explosion of complexity\". In addition, an error compensation dynamic system is designed to reduce the filtering errors caused by the SCFs. By utilizing Lyapunov stability theory, it rigorously proves that all the error signals in the closed-loop control system are uniformly ultimately bounded, and converge to a small neighbourhood of the origin. Numerical simulations with comparisons to existing methods are finally presented to verify the effectiveness and efficiency of the proposed approach. The novel features and main contributions of this paper are highlighted as follows. 1. The RESOs constructed with partial known model information are capable of estimating and compensating the matched and mismatched disturbances simultaneously. This is much different from the existing ESO-based methods where complex coordinate transformations are required to convert a mismatched disturbance into a matched one. The developed control algorithm is thus robust and efficient;
2. The inherent complexity problem of backstepping is addressed by employing the SCFB control, where the derivatives of the virtual control laws are obtained through integrations instead of differentiations. The transient control performance of the controller is thus improved;
3. The potential filtering errors caused by the command filters are taken into account, and they are reduced by the error compensation dynamic system, which improves the steady-state tracking control accuracy. The remainder of this paper is organized as follows. The dynamic model of a single-link FJM with disturbances and the corresponding control problem are presented in Section 2. The detailed control design including two RESOs, a backstepping controller, and an error compensation system is given in Section 3. The stability of the resulting control system is analysed in Section 4. Simulation results are presented in Section 5 to verify the effectiveness and efficiency of the proposed approach. The concluding remarks are finally discussed in Section 6.
# Review of sEMG for Robot Control: Techniques and Applications
## 4 Application of sEMG Interface in Robot Control
### Classification of Robot Control Strategies
#### 4.1.1 Full-Human Continuous Control Strategy (S1)
Both human and robot models were simulated and experimentally validated using OpenSim [62; 98]. In [4], a 6-DOF parallel robot was controlled by four channels of sEMG signals from the lower limb and applied to lower-limb rehabilitation. [PERSON] used a fuzzy neural network to identify a user's motion intent based on sEMG and interactive forces [18]. The lower-limb exoskeleton robot uses the estimated human motion as the control command and feedback with the exoskeleton joint angle, forming a closed-loop rehabilitation system for human-machine collaboration. [PERSON] designed a spherical parallel 3-DOF robot for ankle-foot joint rehabilitation using sEMG and interactive force [2]. [PERSON] et al. combined binary intent estimation based on a SVM for sEMG classification and continuous intent estimation based on linear regression for force prediction for the control of a rehabilitation robot, which can help improve joint mobility in the human body [112]. [PERSON] et al. studied an artificial finger that could perform biomimetic movements using sEMG signals from the index finger. This artificial finger was made of an ion-polymer metal composite material [128].
# A Parallel Two-Stage Iteration Method for Solving Continuous Sylvester Equations
## 3 Parallel Implementation of the Two-Stage Iteration Method
### Improved Parallel Implementation of the SYMMLQ Algorithm  
Clearly, when computing \(a_{k}\), \(b_{k}\) in each step of the inner iteration, all processors need to apply the reduce operator twice in the parallel implementation of the SYMMLQ algorithm in Section 3.3. Therefore, we should rearrange Step 2 in the cycle process, while the remaining steps remain the same. The detailed parallel process of the algorithm can be expressed as follows.  
Processor \(p_{i}(i=1,2,\cdots,p)\) computes  
\[\begin{split}& Q_{i}^{(k)}=(H_{i}^{(k-1)}-a_{k-1}Q_{i}^{(k-1)})/b_{k-1 },\;G_{i}^{(k)}=A_{i}Q^{(k)}+Q_{i}^{(k)}B,\\ & H_{i}^{(k)}=G_{i}^{(k)}-b_{k-1}Q_{i}^{(k-1)}\text{ and }L_{i}^{(k)}=H_{i}^{(k)}-a_{k-1}Q_{i}^{(k)},\end{split}\]  
then compute \([G_{i}^{(k)},Q_{i}^{(k)}]\) and \([L_{i}^{(k)},L_{i}^{(k)}]\), get \(a_{k}=[G^{(k)},Q^{(k)}]\) and \(e_{k}=[L^{(k)},L^{(k)}]\) after one all-reduce, compute \(b_{k}=\sqrt{e_{k}-(a_{k}-a_{k-1})^{2}}\).  
In this way, computing \(a_{k}\), \(e_{k}\) only needs to all-reduce once, so as to reduce the frequency of communication and thus reduce the parallel time. Eventually, we obtain an improved parallel implementation of the SYMMLQ algorithm.
# An Intelligent Method for C++ Test Case Synthesis Based on a Q-Learning Agent
## 1 Introduction
### State of the Art
#### 1.2.6 Comparison and Research Gap  
Table 1 summarizes a comparative analysis of representative TSO methods across several evaluation dimensions. The precision refers to the selectivity with which a method eliminates redundant test fragments without degrading effectiveness. Computational cost characterizes the expected resource consumption during optimization, including instrumentation overhead and runtime complexity. Scalability denotes the ability of the method to handle large and complex test suites. Finally, input requirement indicates the necessary information or artifacts required for the method to operate effectively. Empirical results illustrate the practical trade-offs between these dimensions. For example, greedy reduction methods achieve up to a 70% test suite size reduction on average without compromising fault detection capability, as shown in [8], yet remain sensitive to TC ordering and coverage redundancy. ILP-based approaches provide formally optimal solutions across multiple criteria (e.g., coverage, fault detection, execution cost), but suffer from scalability constraints when applied to large, complex test suites [13; 14]. Delta debugging is highly effective for isolating failure-inducing input fragments and has been shown to reduce test cases to minimal failure-inducing subsets, but it operates at a coarse level and lacks support for semantic preservation in API-based scenarios [9]. Slicing-based methods such as HSFal are precise and maintain only semantically relevant code segments yet are dependent on source-level analysis and are infeasible when source code is unavailable [10]. Dynamic slicing-based methods, such as the one proposed in [11], support fine-grained test suite minimization by identifying and removing non-essential instructions or operations. Their evaluation on five real-world Java projects demonstrated that up to 52% of partially redundant test cases could be eliminated without reducing fault detection capability, confirming the potential of instruction-level optimization for improving test suite maintainability. Reinforcement learning, particularly Q-learning, has shown strong results in test prioritization [16; 17], fuzzing [19], and debugging [21]. Other RL-based classifiers, such as the XCSF approach combined with a replay buffer [20], show promise in TSP by using experience replay to improve policy stability and generalization. While methods like RLFUZZ [16; 18] demonstrate success in specific domains, most operate at the level of whole test cases or byte streams, lacking support for API-level structural reasoning. Deep learning-based methods demonstrate high fault-detection precision, particularly when modeling test behavior with temporal dependencies [24; 25; 27], but are constrained by data availability and scalability, especially for sequence-based models like LSTM. Reinforcement learning, particularly its deep variants, has shown potential in test prioritization and fuzzing, but fine-grained TSM as test case synthesis remains largely unexplored.
## 2 Methodology
### Texture Detection in Spatial Domain  
Currently, the state-of-the-art methods of universal object detection are usually based on the data-driven CNN. Taking the commonly used 16-depth network by Visual Geometry Group (VGG16-Net) as an example, the convolutional neural network has about 138 million parameters, so thousands of training samples are required to solve the over-fitting problem. However, due to the diversity and rarity of floating objects on different water surfaces, only hundreds or even dozens of effective source images can be collected, which makes it difficult to robustly detect floating objects with CNN-based methods. Although there are a series of Transfer Learning methods [32; 33; 34] and Data Augmentation strategies [35; 36] that help CNN models fit methods without plenty of source images, these training skills cannot essentially improve the detection precision and maximize the performance of CNN. Therefore, the popular CNN-based methods may not be the best choices of floating object detection in the case of small sample acquisition.  
To solve the problem of over-fitting caused by insufficient training samples, we adopted Latent SVMs as the classifiers instead of convolutional neural networks. Unlike neural networks, Latent SVMs adopt the optimization goal of minimizing structural risk rather than empirical risk, and use the principle of maximum margin to reduce the requirements of data size and data distribution [26; 27; 28]. Therefore, small sample training with SVM is able to complete a rough detection task. More importantly, SVM is insensitive to the problem of sample imbalance when all background windows are treated as negative samples. Experiments shown in Section 3 will prove that SVM is effective enough for the rough texture detection task, and more details of SVM training are provided in the later experiment section.  
In order to ensure that our classifier has a high hit rate (while high false alarm rate is acceptable at this stage), it is necessary to extract very reliable features which helps to clearly distinguish foregrounds and backgrounds. To this end, the combination of FHOG and GLCM features is adopted. The new texture features have good grayscale invariance and rotation invariance, which effectively describe global and local information of floating objects in complex water scenes.
## 2 System  
The integrated detection framework combines the unmanned aerial vehicle magnetic (UAVMAG) measurement system and the unmanned aerial vehicle time-domain electromagnetic (UAV-TDEM) detection system, operating sequentially to prevent mutual interference. Both systems are equipped with RTK GPS, specifically the T300 unit, manufactured by Shanghai Sinan Satellite Navigation Technology Co., Ltd. (Shanghai, China),which provides centimeter-level positioning accuracy and delivers timing precision up to 20 ns (pulse per second, pps). The RTK GPS ensures precise spatial registration within a unified coordinate framework, enabling high-fidelity synchronization between magnetic and electromagnetic measurements.