esa-sceva/satcom-chunk-collection huggingface dataset:
# Superior Control of Spacecraft Re-Entry Trajectory
## 4 Conclusions and Discussion  
In the past, research on control algorithms did not intentionally reduce the role of weights. This article innovatively refines [PERSON]'s concept of superior control without weights into a specific theory and designs corresponding algorithms for programming and simulations. This article applies the designed superior control algorithm to the trajectory optimization of lunar return and re-entry, and optimizes the re-entry point velocity of 10.8 km/s using both the superior control algorithm and the weighted optimal control algorithm. Research has shown that both algorithms have optimized feasible re-entry trajectories, with the maximum heating rate of the superior control being smaller than that of the weighted optimal control. In terms of overload, the maximum overload of superior control slightly exceeds 80% of the maximum allowable limit, which slightly greater than the maximum overload value of weighted optimal control.  
From the simulation results in this article, it can be seen that both the superior control algorithm and the weighted class algorithm can obtain usable re-entry trajectories. However, the performance indexes in the superior control algorithm will be optimized more evenly and will not be prioritized for important indexes, nor will their limits be ignored because they are not the most important indexes. On the other hand, superior control better utilizes the floating range of performance indexes that do not exceed 80% of the maximum allowable limit, which provides greater optimization space for performance indexes that exceed 80% of the maximum allowable limit.  
With the rapid development of contemporary computer computing power, there will be more and more performance indexes in the field of aerospace re-entry trajectory optimization, and there will be more refined requirements for optimization constraints. We believe that researchers will gradually stop using known weights to limit performance indexes. Moreover, we suggest that more simulations should be conducted for experimentation. In addition, superior control can be applied in the field of aerospace re-entry trajectory optimization and in the optimization calculation of other multi-optimization objectives using superior control algorithms. This is also worthy of further research.  
Conceptualization, Y.C. and Y.N.; Methodology, Y.C. and Y.N.; Software, Y.C.; Validation, Y.C.; Formal analysis, Y.C.; Investigation, Y.C., X.S. and T.T.; Resources, Y.C.; Data curation, Y.N.; Writing--original draft, Y.C.; Writing--review and editing, Y.C., Y.N., X.S. and T.T.; Visualization, Y.C.; Supervision, Y.N., X.S. and T.T.; Project administration, Y.N., X.S. and T.T.; Funding acquisition, Y.N., X.S. and T.T. All authors have read and agreed to the published version of the manuscript.  
This research was funded by a cooperative project with China Aerospace Science and Technology Corporation (No. FdJX-YY-22-282).  
Not applicable.
# Effective Attention-Based Feature Decomposition for Cross-Age Face Recognition
###### Abstract
We propose an efficient channel attention (ECA) block-based feature decomposition module (EFDM) to decompose mixed features into identity-specific and age-specific features. We also propose a novel loss function based on a direct sum to sufficiently separate age information and identity information from facial features. Through that direct sum loss function, we achieve a significant separation between identity-specific and age-specific features, which allows us to notably reduce the age information in the identity features. The main contributions of this paper are three-fold:  
Figure 1: Example images from the FG-NET dataset showing the same person at different ages, illustrating the significant changes caused by facial aging. * We propose an efficient end-to-end training model called the age-invariant features extraction network (AFEN) to learn age-invariant features for CAFR. Our end-to-end framework learns the age-invariant features directly, which is more convenient and can greatly reduce training complexity compared with existing multi-stage training methods. As the well pre-trained backbone does not require training, we only train the feature decompose model, which greatly reduces the number of training parameters. * Based on the attention mechanism, we propose a novel EPDM to separate the age- and identity-related feature on high-level features maps. Age classification and face recognition tasks are incorporated to supervise the decomposition process. On the one hand, by minimizing the direct sum loss between features from the two subnetworks, the face-recognition branch is forced to generate identity-specific facial representations, with the age information removed as much as possible. * We report the results of extensive experiments conducted to compare our proposed approach with state-of-the-art models. Analyses using the developed model are conducted on popular public datasets to demonstrate the robustness of the proposed method, and it verifies that the proposed method obtains the relative improvements up to 2.2%. The rest of this paper is organized as follows. Related works on the generative and discriminative approaches are introduced in Section 2. The proposed method is described in Section 3. The experimental results and analyses of four public CAFR databases and one general face database are reported in Section 4, and conclusions are given in Section 5.
# A Review of Oil Spill Remote Sensing
## 4 Slick Thickness Measurements
### Visual Appearance  
Historically, several workers tried to correlate the thickness of oil to its visual appearance and color. Further study indicates that this technique is limited to sheens and rainbow-colored slicks [57]. Physically based thicknesses of about 0.7-2.5 mm appear as rainbow colors as a consequence of multiple constructive and destructive light interferences. Sheen is the result of sunlight reflection from thinner oil layers. Some workers note that visual appearance and colors should be limited to thin oil or sheen, and thick oil categories [61].
## 2 Materials and Methods
### Test System Design and Test
#### 2.3.5 Image Processing  
In order to improve the generalization ability and robustness of the model, the 9546 tea sample images were augmented. In particular, affine transformations were used to adjust the angle of the image to simulate different shooting angles, and brightness adjustment was used to simulate the effect of different lighting conditions, thus increasing the light adaptability of the model. Noise was added to simulate possible image interference, such as sensor noise. In addition, a random cropping technique was used to enhance the model's ability to recognize the local features of tea. This series of image processing techniques was carried out to enhance each image three times in order to simulate the complex and variable field environment.  
In the image annotation stage, LabelImg 1.7.1 software was used to label the target region of the tea bud samples in the image. A total of 186,580 tea bud targets were marked, which provided an accurate database for subsequent target detection. With regard to data set preparation, the tea sample data set was split into a training set and a test set following an 8:2 ratio in order to ensure that the model was able to learn the tea characteristics completely, as well as to allow for an effective performance evaluation on an independent test set.  
The experimental platform adopts an industrial control computer equipped with an Intel Core i5-12450H CPU (main frequency 3.3 GHz, L3 cache 12 MB, produced by Intel Corporation, Santa Clara, CA, USA), an NVIDIA Geforce RTX 3050 GPU (produced by NVIDIA Corporation, Santa Clara, CA, USA), and 16 GB of memory. Using the PyTorch 2.1 deep-learning framework, advanced YOLOv8 and YOLOv5 algorithms were used for model training. To ensure a fair comparison of model performance and the validity of subsequent comparison analyses, hyperparameters were uniformly set during training: the number of training cycles (epochs) was set to 600, and the batch size was set to 32. Through using these settings, it was expected that a tea target detection model with excellent performance would be obtained.
# Toward Integrated Satellite Operations and Network Management: A Review and Novel Framework
## 2 State of the Art
### Satellite Operations and Management Systems
#### 2.2.3 Security Considerations in Integrated Satellite-Terrestrial Networks
& Extends traditional CCSDS task scheduling to incorporate telecommunications constraints including QoS enforcement, network metrics, and 3 GPP procedural requirements. Enables fully autonomous operations with minimal human intervention through closed-loop optimization and adaptive scheduling. & Extends traditional CCSDS task scheduling to incorporate telecommunications constraints including QoS enforcement, network metrics, and 3 GPP procedural requirements. Enables fully autonomous operations with minimal human intervention through closed-loop optimization and adaptive scheduling. & Extends traditional CCSDS task scheduling to incorporate telecommunications constraints including QoS enforcement, network metrics, and 3 GPP procedural requirements. Enables fully autonomous operations with minimal human intervention through closed-loop optimization and adaptive scheduling. & Extends traditional CCSDS task scheduling to incorporate telecommunications constraints including QoS enforcement, network metrics, and 3 GPP procedural requirements. Enables fully autonomous operations with minimal human intervention through closed-loop optimization and adaptive scheduling. & Extends traditional CCSDS task scheduling to incorporate telecommunications constraints including QoS enforcement, network metrics, and 3 GPP procedural requirements. & Extends traditional CCSDS task scheduling to incorporate telecommunications constraints including QoS enforcement, network metrics, and 3 GPP procedural requirements. and communication links. These frameworks provide systematic approaches for identifying vulnerabilities across the complete satellite ecosystem [87]. While these security considerations are crucial for integrated satellite-terrestrial network deployments, a comprehensive security analysis of the proposed CMS framework and operational architectures is beyond the scope of this paper. Future work should address security requirements for task-scheduling systems, authentication mechanisms for multi-operator environments, and secure communication protocols for distributed constellation management.
## 2 VMD-ESN Model
### Echo State Network  
ESN serves as a dynamical system approximator in the VMD-ESN model, learning and predicting the temporal evolution of decomposed aerodynamic noise modes by leveraging its high-dimensional reservoir states to resolve temporal correlations in the time series. As shown in Figure 1, the ESN architecture comprises three fundamental components: an input layer, a high-dimensional processing reservoir, and an output layer. The network operates by projecting the input vector \(\mathbf{u}(t)\) into a higher-dimensional state space, creating a rich set of features that capture the temporal dynamics. Supposing that the dynamic state of the reservoir at a discrete time \(t(t=0,\Delta t,2\Delta t,\ldots)\) is characterized by a state vector \(\mathbf{r}(t)\in\mathbf{R}^{N\times 1}\), the state update of the ESN reservoir is described by the equation below:  
\[\mathbf{r}(t+\Delta t)=f(\mathbf{A}\cdot\mathbf{r}(t)+\mathbf{W}_{\mathrm{in}}\cdot\mathbf{u}(t)), \tag{6}\]  
where \(f\) is the activation function, \(\mathbf{A}\) is an adjacency matrix which reflects the connection relationships between elements within the vector \(\mathbf{r}(t)\) and \(\mathbf{W}_{\mathrm{in}}\) is the mapping matrix from the input state to the reservoir state space. The output vector \(\hat{\mathbf{v}}(t)\) can be obtained from  
\[\hat{\mathbf{v}}(t)=f_{\mathrm{out}}(\mathbf{W}_{\mathrm{out}},\mathbf{r}(t)), \tag{7}\]  
where \(\mathbf{W}_{\mathrm{out}}\) is the mapping matrix from the reservoir state space to the output state, and \(f_{\mathrm{out}}\) is an element-wise output function. Generally, using the ridge regression algorithm can improve the stability of numerical calculations, and then obtain the output connection matrix \(W_{\text{out}}\), so that the output vector \(\mathbf{v}(t)\) approximates the desired output as closely as possible. During the training phase for a time period \(-T\leq t<0\), \(W_{\text{out}}\) is optimized via ridge regression to minimize the prediction error:  
\[\hat{\mathbf{W}}_{\text{out}}=\underset{\hat{\mathbf{W}}_{\text{out}}}{\text{argmin}} \left[\sum_{-T\leq t<0}\|f_{\text{out}}(\mathbf{W}_{\text{out}},\mathbf{r}(t))-v(t)\|^{ 2}+\beta\|\mathbf{W}_{\text{out}}\|^{2}\right], \tag{8}\]  
where \(\beta>0\) represents the regularization parameter. The solution of Equation (8) can be expressed in matrix form:  
\[\hat{\mathbf{W}}_{\text{out}}=\mathbf{V}\mathbf{R}^{\text{T}}(\mathbf{R}\mathbf{R}^{\text{T}}+\beta \mathbf{I})^{-1}, \tag{9}\]  
where \(\mathbf{R}=(\mathbf{r}(-T),\mathbf{r}(-T+\Delta t),\ldots,\mathbf{r}(-\Delta t))\), \(\mathbf{V}=(\mathbf{v}(-T),\mathbf{v}(-T+\Delta t),\ldots,\mathbf{v}(-\Delta t))\), \(\mathbf{v}(t)\) is the desired output, and \(\mathbf{I}\) is the identity matrix. During the prediction phase (\(t>0\)), the output \(\mathbf{v}(t)\) is computed through Equation (7). Since only the output weights need to be trained, the computational complexity is significantly reduced compared to other recurrent networks, which allows for less training time.
# The Design and FPGA-Based Implementation of a Stream Cipher Based on a Secure Chaotic Generator
## 1 Introduction
The main contributions of the proposed chaotic system are: First all, the introduction of some countermeasures to fix side channel attacks (SCAs) which is done using the masking technique on the recursive cells, and to fix division and conquer attacks on the initial vector (IV), using a weakly coupling matrix. Second, its hardware implementation on a Xilinx XC7Z020 PYNQ-Z2 FPGA platform and evaluation of its performance in terms of computational complexity and security. The remainder of this paper is organized as follows. The next Section 2 presents the architecture of the proposed secure chaos-based stream cipher. Section 3 presents the hardware implementation on the Xilinx XC7Z020 PYNQ-Z2 FPGA platform of the proposed secure pseudo-chaotic number generator (SPCNG) and analyzes its performance. Section 4, investigates the performance of the proposed SCbSC in terms of hardware metrics and cryptanalytic analysis. Finally, Section 5 summarizes the whole paper.
## 3 Influence of Lithology on Fracture Propagation Morphology
### Analysis of Fracture Propagation Morphology of Different Mineral Combinations
#### 3.2.1 Fracture Morphology Analysis of Two Mineral Combinations  
(a) Models of different proportions of quartz and clay  
In this section, geological models of different proportions of quartz and clay are established. It includes three models with a quartz proportion of 30%, 50%, and 70% to explore the influence of different proportions of quartz on hydraulic fracture morphology. The results of the model calculations are shown in Figure 8.  
In Figure 8a, the quartz content in the model is low, and the fractures extend in multiple directions in the clay area, which is similar to the properties of a single clay reservoir. When the quartz proportion is 30%, the fracture length is 140 m, the fracture width is 60 m, and many main fractures are developed. When the quartz proportion increases to 50%, with the fracture extending to the quartz boundary, the fractures can pass through the quartz better. Due to the high brittleness of quartz, quartz will be squeezed and ruptured by the stress at the fracture tip, and the fracture will extend towards the area where quartz accumulates.  
Figure 8: Fracture morphology of different proportions of quartz and clay.  
Figure 7: Fracture morphology of a single mineral.  
When the quartz proportion is 70%, the fracture propagation in the direction of the quartz is more obvious. At the same time, the fracture length in quartz also increases accordingly. The fracture length is 160 m, and the induced fracture density is about 70% of the fracture in the 30% quartz reservoir. Quartz is a good medium to guide the fracture direction.  
In this section, geological models of different proportions of pyrite and clay are established. It includes three models with a pyrite proportion of 30%, 50%, and 70% to explore the influence of different proportions of pyrite on hydraulic fracture morphology. The results of the model calculations are shown in Figure 9.  
It can be seen from the fracture morphology in Figure 7 that the hydraulic fracture always attempts to extend around pyrite. More often, fractures gather at the interface between pyrite and clay. The hindering effect of pyrite on fractures is obvious, and some fractures will terminate the extension within pyrite. Moreover, with the increase in pyrite proportion, the fracture length is small and the induced fractures are less developed. The reservoir with a higher pyrite proportion is not conducive to the formation of complex fractures.
# A Low-Complexity Volterra Filtered-Error LMS Algorithm with a Kronecker Product Decomposition
## 2 Problem Formulation  
### Linear Feedforward ANC System  
Before describing the nonlinear active noise control problem, the standard linear feedforward active noise control structure is first depicted in Figure 1[1]. \(u(n)\) is the reference signal at the time index \(n\) recorded by the reference microphone, and \(y(n)\) is the output of the secondary actuator, which can be expressed as  
\[y(n)=w(n)*u(n), \tag{1}\]  
where \(w(n)\) is the coefficient of the active noise control filter, and \(*\) denotes the linear convolution. \(e(n)\) is the residual error sensed by the error microphone, which can be expressed as  
\[e(n)=d(n)+y(n)*s(n), \tag{2}\]  
where \(S(z)\) and \(\hat{S}(z)\) are the secondary path transfer function and its estimation, respectively. \(P(z)\) is the primary path transfer function, and \(d(n)\) is the primary noise signal at the error microphone.  
In a standard linear feedforward active noise control structure, our goal is to find a suitable output signal \(y(n)\) to minimize the mean square of the error signal \(e(n)\). All transfer functions and control filters are assumed to be linear, which may be violated in many circumstances, as mentioned above.
# Uplink NOMA-MIMO Systems with Rayleigh Power Distribution
## 2 System Model
As a result, for model (2), the capacity of continuous channel will be determined by the following equation:  
\[C=\log\det\biggl{(}I_{N_{rx}}+\frac{p}{2\sigma_{\eta}^{2}N_{tx}}\mathbf{H} \mathbf{H}^{{}^{\prime}}\biggr{)} \tag{5}\]  
Capacity of MIMO channel is a function of the channel matrix, i.e., \(C(\mathbf{H})\) which consists of random elements that depend on the type of fading and their correlation. Therefore, to analyze the average capacity (ergodic capacity) of the system with the MIMO channel, an averaging procedure is used for the distribution of random channel multipliers:  
\[C_{E}=E_{H}\biggl{\{}\log\det\Biggl{(}I_{N_{rx}}+\frac{p}{2\sigma_{\eta}^{2}N_ {tx}}\mathbf{H}\mathbf{H}^{{}^{\prime}}\Biggr{)}\biggr{\}} \tag{6}\]  
Expression (6) can be written in another form, known as the multiple access capacity [14, 15, 17]:  
\[C_{E}=E_{H}\biggl{\{}\log\det\Biggl{(}I_{N_{rx}}+\frac{p}{2\sigma_{\eta}^{2}N _{tx}}\sum\limits_{u=1}^{U}H_{u}H_{u}{{}^{\prime}}\Biggr{)}\biggr{\}} \tag{7}\]  
Figure 1 shows the capacity dependencies on the SNR ratio for a different number of users (Figure 1a) and on the number of users for different values of the SNR ratio (Figure 1b). Figures are obtained for values \(N_{rx}=4\) and \(N_{tx}=2\). Figure 1 shows if \(N_{tx,\Sigma}=UN_{tx}>N_{rx}\) the growth of capacity with an increase of number of users slows down and at high SNR ratios it becomes proportional to \(\log U\). These results are known, and from them, as a rule, it follows that the use of systems with an MIMO channel for the case is inefficient and is not advisable. This conclusion is valid if we limit ourselves to considering the maximum capacity of a continuous channel as a criterion. In real systems, for information transmission, we use a  
Figure 1: Dependencies of the MU-MIMO uplink channel capacity for continuous channel: (**a**) capacity dependencies on the SNR ratio for a different number of users, (**b**) capacity dependencies on the number of users for different values of the SNR ratio. discrete alphabet, i.e., the real channel is discrete-continuous. Therefore, it is interesting to analyze the capacity of the discrete-continuous channel.
# Two-Stage Single-Channel Speech Enhancement with Multi-Frame Filtering
## 5 Results and Discussion
### Comparison with Baselines on WHAM!  
Table 5 shows the performance of the proposed model and baseline models on the test set of WHAM!. From the table, we can see that compared to noisy speech, speech after enhancement achieves significant improvement in WER, which to some extent illustrates the help of speech enhancement for back-end ASR tasks. The proposed model performs significantly better than the LSTM model in all the metrics, even though both of them are time-frequency domain models and the number of parameters of them is close to each other.  
The proposed model has a larger number of parameters than Conv-TasNet, which is reasonable as the latter is a time domain model and usually has a smaller parameter size. Though Conv-TasNet contains half as many parameters as the proposed model, there is still a large gap between its performance and the proposed one, especially on the WER score.  
Thanks to its dual-path structure, DPRNN can deliver high-quality enhancement results with a limited number of parameters and performs well in terms of WER metrics. Despite having a larger number of parameters than DPRNN, the proposed model requires less memory and time to train a single epoch during the training phase. Moreover, it outperforms DPRNN in all metrics.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Framework** & **\#Para (M)** & **PESQ-NB** & **STOI** & **SI-SNR (dB)** & **SDR (dB)** & **WER (\%)** \\ \hline Two DNNs & 15.44 & 2.84 & 0.94 & 13.50 & 13.92 & 19.60 \\ Single DNN & 7.72 & 2.75 & 0.94 & 12.69 & 13.11 & 23.90 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance of systems using different frameworks.  
Figure 4: The framework of the system with a single DNN.
# Iterated Orthogonal Simplex Cubature Kalman Filter and Its Applications in Target Tracking
## 5 Simulations and Results
### CV Target Tracking Simulation
While the filtering accuracy is further enhanced by the repetitive calculation of the measurement update, the IOSCKF's accuracy is 0.5% higher than that of the OSCKF. Through the analysis of the simulation results, the validity of the proposed method is verified.