esa-sceva/satcom-chunk-collection huggingface dataset:
# Geographic Routing Decision Method for Flying Ad Hoc Networks Based on Mobile Prediction
## 3 System Model
### Movement Prediction  
The extended Kalman filter is a commonly used state estimation algorithm for predicting and estimating the state of dynamic systems. In position prediction, it can be utilized to estimate the position and velocity of a target based on a series of observed  
Figure 1: The system model diagram.  
data. Assuming each flying node moves in three-dimensional space and is equipped with a global positioning system (GPS), the real-time position and velocity information of all nodes can be obtained. By estimating the states of nodes, such as position and velocity, the trajectory of nodes at the next time step can be derived. Assuming the state vector of a node is \(X_{t}\), which includes position coordinates and velocity information, the expression for \(X_{t}\) is as follows:  
\[X_{t}=[x_{t},y_{t},z_{t},v_{xt},v_{yt},v_{zt}] \tag{1}\]  
In this equation, \((x_{t},y_{t},z_{t})\), respectively, represent the coordinates of the node's position, while \((v_{xt},v_{yt},v_{zt})\) represent the velocity components of the node. The motion state of the node is defined according to the state transition equation and observation equation.  
The state transition equation is as follows:  
\[X_{t+1}=F_{t}X_{t}+W_{t} \tag{2}\]  
where \(X_{t+1}\) represents the position state at time \(t+1\), \(F_{t}\) represents the state transition matrix of \(X_{t}\), and \(W_{t}\) is the noise.  
The observation equation is as follows:  
\[Z_{t}=H_{t}X_{t}+V_{t} \tag{3}\]  
where \(Z_{t}\) is the observation vector covering the node's position coordinates which obtained through GPS measurements, \(H_{t}\) is the observation matrix describing the relationship between the observation vector and the state vector, and \(V_{t}\) is the observation noise.  
Predicted state:  
\[X_{t+1|t}=F_{t}X_{t|t} \tag{4}\]  
Predicted error covariance:  
\[P_{t+1|t}=F_{t}P_{t}F_{t}^{T}+Q_{t} \tag{5}\]  
Compute the Kalman gain:  
\[K_{t+1|t}=P_{t+1|t}H_{t+1}^{T}(H_{t+1}P_{t|t-1}H_{t+1}^{T}R_{t+1})^{-1} \tag{6}\]  
Update state estimation:  
\[X_{t+1|t+1}=X_{t+1|t}+K_{t+1}(Z_{t+1}-H_{t+1}X_{t+1|t}) \tag{7}\]  
Update error covariance:  
\[P_{t+1|t+1}=(I-K_{t+1}H_{t+1})P_{t+1|t} \tag{8}\]  
where \(Q_{t}\) is the process noise covariance matrix used to quantify the covariance of noise and uncertainty in the state prediction process, \(R_{t}\) is the observation noise covariance matrix, \(K_{t}\) represents the gain matrix, and \(P_{t}\) describes the variances and covariances among the elements of the state vector, reflecting the uncertainty in state prediction. \(P_{t+1|t}\) is the predicted covariance matrix used to predict the uncertainty of the state at the next time step. \(X_{t+1|t}\) is the result of measuring the node's position at the next time step.
## 3 Lateral Stability Tracking Control of Designed Rollator
### Lateral Controller Development
#### 3.3.3 Control Constraint Design  
In the previous section, an improved optimization function is designed for the trajectory tracking error model. Considering the safety and stability of the rollator, it is necessary to restrict the control limit and control increment, as following:  
\[\begin{array}{l}u_{\min}(t+k)\leq u(t+k)\leq u_{\max}(t+k)\\ \Delta U_{\min}(t+k)\leq\Delta U(t+k)\leq\Delta U_{\max}(t+k)\end{array} \tag{24}\]  
where \(k=1,2,\cdots,t+N_{c}-1\). To transform the Equation (24), we can get  
\[u(t+k)=u(t+k-1)+\Delta U(t+k) \tag{25}\]  
Define the following vectors: \(U_{\lambda}=1_{N_{c}}\otimes u(k-1)\) and \(H=I_{m}\otimes\left[\begin{array}{cccc}1&0&\cdots&\cdots&0\\ 1&1&0&\cdots&0\\ 1&1&1&\ddots&0\\ \vdots&\vdots&\ddots&\ddots&0\\ 1&1&\cdots&1&1\end{array}\right]\).  
The optimization objective function can be represented as  
\[\delta[\phi(t),u(t),\Delta U(t)]=\mathcal{F}_{t}\left[\Delta U(t)^{T},\psi \right]^{T}+\left[\Delta U(t)^{T},\psi\right]^{T}\mathcal{D}_{t}\left[\Delta U (t)^{T},\psi\right]^{T} \tag{26}\]  
where \(|\mathcal{F}_{t}=\left[2e_{t}^{T}\mathcal{L}\mathrm{E}_{t},0\right]\) and \(\mathcal{D}_{t}=\left[\begin{array}{cc}\mathrm{E}_{t}^{T}\mathcal{L} \mathrm{E}_{t}+\mathcal{M}&0\\ 0&\sigma\end{array}\right]\). Therefore, the prediction horizon error can be obtained:  
\[\begin{array}{l}e_{t}=\Theta_{t}\phi(t|t)-Y_{\mathrm{ref}}(t)\\ Y_{\mathrm{ref}}(t)=\left[Y_{\mathrm{ref}}(t+1|t),\cdots,Y_{\mathrm{ref}}\left(t+ N_{p}|t\right)\right]^{T}\end{array} \tag{27}\]  
When the model predictive control completes the optimization objective function each time, the control input increment of the system in the control horizon is:\[\Delta U_{t}^{*}=\left[\Delta u_{t}^{*},\Delta u_{t+1}^{*},\cdots,\Delta u_{t+N_{c}- 1}^{*}\right]^{T} \tag{28}\]  
Finally, the first element of the control increment (Equation (28)) is applied to the control system as the actual control input increment  
\[u(t)=u(t-1)+\Delta u_{t}^{*} \tag{29}\]  
In order to achieve the ability of stability and smoothness for tracking performance, we define the constraint condition of control variable as  
\[\left[\begin{array}{c}-0.3\\ -25\end{array}\right]\leq u\leq\left[\begin{array}{c}0.3\\ 25\end{array}\right] \tag{30}\] \[\left[\begin{array}{c}-0.02\\ -0.04\end{array}\right]\leq\Delta U\leq\left[\begin{array}{c}0.02\\ 0.04\end{array}\right]\]
# Generating Activity-Based Mobility Plans from Trip-Based Models and Mobility Surveys
## 2 The Related Literature and Problem Statement
### The Related Literature
#### 2.1.3 Hybrid Mobility Models  
[PERSON] et al. [5] identified a research gap between activity-based and trip-based mobility models. They presented a hybrid form in between these types that overcame some of the limitations inherent to trip-based models. Their framework, called Microsimulation Transport Orchestrator (MITO), models the trip demand of individual persons using activity-based methods, yet the model is easier to implement than activity-based models. They thoroughly covered the entire mobility of the population (i.e., all modes of transport) in a large, heterogeneous area, including both dense urban and rural areas. However, this led to compromises with regard to trip assignment to reduce model complexity. Hence, while the trip generation is highly realistic on a disaggregated level regarding sociodemographic factors, and macroscopic traffic indicators are validated to represent the observed mobility behavior, the assignment is not necessarily spatially or temporally consistent on a personal level.
## A Novel Block-based Watermarking Scheme Using the SVD Transform  
**[PERSON]*, [PERSON], [PERSON], [PERSON] and [PERSON]**  
Department of Computer Science, Universita degli Studi di Torino, 10149, Torino, Italy  
E-mails: {[PERSON], bergadano, davide, pomponiu, [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed.  
_Received: 29 August 2008; in revised form: 11 December 2008 / Accepted: 14 January 2009 / Published: 22 January 2009_  
**Abstract:** In this paper, a block-based watermarking scheme based on the Singular Value Decomposition (SVD) is proposed. Our watermark, a pseudo-random Gaussian sequence, is embedded by modifying the angles formed by the right singular vectors of each block of the original image. The orthogonality property of the right singular vector matrix is preserved during the embedding process. Several experiments have been carried out to test the performance of the proposed scheme against different attack scenarios. We conclude that the proposed scheme is resistant against common signal processing operations and attacks, while it preserves the quality of the original image.  
**Keywords:** digital image watermarking, Singular Value Decomposition, singular vectors, orthogonality, security.
# Multi-Satellite Task Parallelism via Priority-Aware Decomposition and Dynamic Resource Mapping
## 1 Introduction
On one hand, many algorithms have high computational complexity and struggle to meet immediate response requirements. On the other hand, most research has been confined to cooperation within satellite networks alone, lacking consideration of integrated satellite-ground computing, which limits their applicability to broader collaborative scenarios. In response to the abovementioned challenges, we introduce a comprehensive approach that integrates three main layers to achieve efficient task decomposition and allocation. The task decomposition layer utilizes a graph-theoretical model to represent complex task dependency and priority relationships. Then, it is addressed by mathematical tools such as Integer Linear Programming to derive optimal or near-optimal task decomposition solutions. The resource mapping and allocation layer models the heterogeneous resource competition within the multi-satellite system as a game-theoretic process by combining game theory with Deep Reinforcement Learning (DRL), gradually converging toward Nash equilibrium. Finally, the dynamic scheduling and temporal constraints layer formalizes task execution order and time constraints using Temporal Logic theory. It solves the dynamic scheduling problem to ensure feasibility and optimality during execution. The proposed method effectively overcomes the challenges of real-time dynamic environments and complex non-convex optimizations. The main contributions of this paper are as follows:  
1. The task dependencies and priorities are explicitly represented through a directed acyclic graph (DAG) model based on graph theory, and the NP-hard decomposition problem caused by task dependencies and resource constraints is solved by integer linear programming. This method can reasonably allocate subtasks among multiple heterogeneous satellites, effectively reduce the overall execution time, and ensure the optimization of task decomposition. 2. We propose a model based on game theory to construct a resource competition game between satellites. We combine this with a Deep Q-Learning Network (DQN), and the Nash equilibrium of the game is gradually approximated through the dynamic learning of intelligence, thus achieving the efficient adaptive allocation of resources. This method can adapt to changes in system resources in real time and optimize resource utilization efficiency. 3. We introduce an LTL-based model to formally describe the dependencies and time-frames of tasks, and the dynamic scheduling problem can be solved through a mixed integer nonlinear programming (MINLP) model, which incorporates real-time feedback for online updating of the scheduling scheme, which adapts to environmental changes and resource fluctuations in the execution process, ensuring the optimality of task scheduling.
## 1 Introduction
The OPA process is symmetric in the frequency domain, and hence, the parametric pump wavelengths, powers and fiber lengths must be chosen properly to cover this large bandwidth in the wavelength domain (which is not symmetric). In addition, to achieve minimum ripple, the Raman pump must be placed at a position in which the Raman gain compliments the OPA gain. In particular, its highest gain coincides with the dip in the OPA gain. However, the optimum Raman pump wavelength is required to be at 1270 nm, which is also the wavelength of the first CWDM channel. Hence, such a configuration generates strong in-band crosstalk for the first signal channel, and it is impossible to make the amplifier functional at this signal wavelength. Therefore, to overcome the aforementioned challenges, we modified our previous HROPA design and present a theoretical and simulation analysis of the HROPA performance. For the first time, to our knowledge, we show error-free performance at this entire bandwidth. The design of such an amplifier and its error-free performance operation will enable CWDM PONs. The paper is structured as follows: Section 2 presents the amplifier configuration and the mathematical model for the HROPA design. In Section 3, we present the modeling results and discuss the design performance. Finally, in Section 4, we summarize the outcome of the proposed configuration.
# Pose Estimation of Primitive-Shaped Objects from a Depth Image Using Superquadric Representation
## 3 Methodology
### Superquadric Fitting  
The \(i\)-th superquadric surface \(\mathcal{O}_{i}\), which best represents the object, is estimated from given \(K\) 3D points \(p_{k}=(x_{k},y_{k},z_{k})\) of the \(i\)-th object's 3D points cloud. The superquadric surface \(\mathcal{O}_{i}\) is represented by \(\Lambda_{i}\) and \(\Phi_{i}\). The minimization of the algebraic distance from points to the superquadric surface can be solved by defining a non-linear least-squares minimization problem:  
\[\min_{\Lambda_{i},\Phi_{i}}\sum_{k=0}^{K}(\sqrt{s_{x}s_{y}s_{z}}(F^{\epsilon_{ 1}}(\mathrm{Tr}_{\Phi_{i}}(p_{k});\Lambda_{i})-1))^{2}, \tag{3}\]  
where \((F(\mathrm{Tr}_{\Phi}(p_{i});\Lambda)-1)^{2}\) imposes the point to superquadric surface distance minimization, where the term \(\sqrt{s_{x}s_{y}s_{z}}\) is proportional to the superquadric volume, compensates for the fact that the previous equation is biased toward larger superquadric surfaces. \(\mathrm{Tr}_{\Phi}(\cdot)\) is a rigid transformation by 6 DoF pose \(\Phi\). The Levenberg-Marquardt algorithm [32] is used to minimize the non-linear function Equation (3).  
Moreover, Equation (3) is numerically unstable when \(\epsilon_{1},\epsilon_{2}<0.1\) and the superquadric surface has concavities with \(\epsilon_{1},\epsilon_{2}>2\). We apply constraints when minimizing Equation (3) for shape parameters \(0.1\leq\epsilon_{1},\epsilon_{2}\leq 2\) and for the scale parameters \(s_{1},s_{2},s_{3}>0\).  
As the function in Equation (3) is not a convex function, the initial parameters determine on which local minimum the minimization converges. It is important to estimate the rough parameters: translation, rotation, scale, and shape parameters. First, as it is difficult to roughly estimate the shape of the object, the initial shape parameters \(\epsilon_{1}\) and \(\epsilon_{2}\) are set to 1, which implies that the shape of the initial model is an ellipsoid. Second, the centroid of all 3D data points can be used to estimate the initial translation. Third, to compute the initial rotation, we gauge the covariance matrix of all 3D data points. From this covariance matrix, we can compute three pairs of eigenvectors and eigenvalues. The largest eigenvector of the covariance matrix always points in the direction of the largest variance of the data, while the magnitude of this largest vector equals the corresponding eigenvalue. The second-largest eigenvector is always orthogonal to the largest and points in the direction of the second largest spread of the data, which is the same as the third vector. Therefore, the eigenvectors can be used as the initial rotation parameters, and the eigenvalues can be used as the initial scale parameters.
## 3 Methods  
### Grasp Configuration  
To facilitate the representation of robot grasp detection, we abstract the grasp into the determination of a grasp point and a line segment, which offers a more concise and efficient representation compared with the use of grasp rectangle [4; 6]. Specifically, in the image space, the grasp configuration is detected from a depth image \(D=\mathbb{R}^{h\times w}\), which can be described as:  
\[G_{i}=(x_{i},y_{i},\theta_{i},W_{i},Q) \tag{1}\]  
where \((x_{i},y_{i})\) is the grasp center, \(\theta_{i}\) is the rotation angle in image coordinates, with a range of \([-\frac{\pi}{2},\frac{\pi}{2}]\), and \(W_{i}\) is the required grasp width in the image, within a range of \([0,W_{\text{max}}]\). \(Q\) is the grasp confidence corresponding to each pixel, with values between 0 and 1. The goal of robot grasping is to infer the grasp configuration \(G_{i}^{*}\) with the maximum confidence from the detected grasp configuration, which can be described as:  
\[G_{i}^{*}=\text{argmax}_{Q}\{G_{i}\} \tag{2}\]  
In the robot's 3D workspace, a grasp pose can be described as:  
\[G_{\text{r}}=(\mathbf{P}_{\text{r}},\theta_{r},W_{r},Q) \tag{3}\]  
where \(\mathbf{P}_{\text{r}}=(x,y,z)\) represents the center position of the gripper tip, \(z\) is derived from the depth value of predicted grasp point by coordinate transformation [30], \(\theta_{r}\) denotes the gripper orientation around the \(Z\)-axis, \(W_{r}\) denotes the closing width of the robot gripper, and \(Q\) is the grasp quality score used to predict the probability of successful grasping.  
To effectively perform robot grasping tasks in real-world scenarios, it is essential to translate the grasp configurations from the image space to the robot workspace. We utilize the hand-eye calibration method [30] to compute the transformation matrix from the camera coordinate system to the robot base coordinate system. This transformation is shown in Figure 1 and described in Equation (4) [6].  
\[G_{r}=T_{cr}(T_{ic}(G_{i}^{*})) \tag{4}\]  
where \(T_{ic}\) refers to converting the grasp configuration from the image coordinate system to the camera's 3D coordinate system, while \(T_{cr}\) refers to converting the camera's 3D coordinate system to the robot's world coordinate system.  
In the experiment, within the robot's workspace, the robot approached the object based on the predicted grasp position \(\mathbf{P}_{\text{r}}=(x,y,z)\), adjusted the gripper orientation according to the predicted angle \(\theta_{r}\), and closed the gripper to grasp the object based on the predicted width \(W_{r}\).
## 1 Introduction
In early studies, the outputs were limited to ship detection from the image without localization information due to the low image resolution and image processing power [11]. Parallel to the advances in optical satellite sensors, computational hardware, and software, classification of single ship images, localization of ships from the images, and multi-level classification like recognition and identification of ships have been performed using the threshold-based, salient-based, shape-and-texture-based, statistical, transform domain, anomaly detection, computer vision, or deep learning-based methods [12]. As is also the case in many other engineering disciplines, deep learning-based approaches are being increasingly used in the DLRI process of ships, especially with the success of Convolutional Neural Network (CNN) structures.
# Application and Evaluation of Energy Conservation Technologies in Wastewater Treatment Plants
## 1 Introduction
### Current Energy Consumption of WWTPs
#### 1.2.3 The Distribution of Energy Consumption in WWTPs  
In order to find opportunities to save energy, it is necessary to find out where the power is consumed. WWTPs usually have primary, secondary and advanced treatment stages. Compared with other stages, energy consumption in the primary treatment stage is lower [18]. The consumption of secondary treatment depends on the method used, which is shown in Table 1. Advanced treatment requires much energy consumption to remove nutrients.  
The energy of WWTPs which use activated sludge is mainly consumed in the three processes, the oxygen supply and aeration (50-60%), the treatment and disposal of sludge (10-25%), and the secondary sedimentation (15%) [43]. Auxiliary buildings and the lighting of factories also consume small amounts of electricity. The energy consumption of aeration and wastewater upgrade accounts for the vast majority of WWTPs, which is also the key point of energy performance optimization for WWTPs. Generally speaking, most of the energy of wastewater treatment is spent on the treatment of some major pollutants, especially the stabilization and harmlessness of organic matter.
###### Abstract  
The use of renewable energy resources has created some problems for power systems. One of the most important of these is load frequency control (LFC). In this study, in order to solve the LFC problem, modern control methods were applied to a two area multi source interconnected power system. A photovoltaic solar power plant (PV-SPP) was also connected, in order to identify the harmful effects on the frequency of the system. A new Genetic-based Fuzzy Logic (GA-FL) controller was designed to control the frequency of the system. For comparison, conventional proportional-integral-derivative (PID), fuzzy logic (FL), and Genetic Algorithm (GA)-PID controllers were also designed. The new control method exhibited a better performance than the conventional and other modern control methods, because of the low overshoot and short settling time. All simulations were realized with the Matlab-Simulink program.  
Keywords:genetic algorithm; fuzzy logic controller; load frequency control +
Footnote †: journal: Applied Sciences
## 6 Conclusions  
In the context of estimating the SNRs of remote sensors on orbit and, more specifically, making an estimation based on CNN, we proposed a lightweight CNN and a novel training method of multi-branch training and parameter-reconstructed inference. The proposed CNN and the novel method are evaluated with other reference methods on three experiments such as the comparison of training results based on neural networks, accuracy validation with known noise levels, and SNR estimations on blind imagery. The first experiment proves that CNNs that are similar to VGG and trained by the novel method are faster at convergence and show a smaller RMSE than the traditional training methods. The second experiment demonstrates that the proposed CNN performs the best in the accuracy test and is less affected by texture features than the reference methods. The third experiment also proves the accuracy from another side. The results show that the proposed model has provided an increase in the evaluation performance when estimated with average accuracy and spectrum characteristics. In the future, the proposed model needs to be improved in ways such as combining the advantages of traditional algorithms and considering multiplicative noise. More tests need to be performed to validate the effectiveness of the proposed CNN and the novel training method.  
Conceptualization, B.Z., X.L. and J.Z.; methodology, B.Z., X.L. and C.T.; software, X.L., J.Z. and C.T.; validation, B.Z. and Y.X.; formal analysis, X.L., C.T. and Y.X.; investigation, B.Z., X.L., Y.X. and C.T.; resources, J.Z. and C.T.; data curation, C.T.; writing--original draft preparation, X.L.; writing--review and editing, B.Z. and J.Z.; visualization, Y.X.; supervision, X.L. and C.T.; project administration, J.Z. and Y.X.; funding acquisition, Y.X. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (no. 62027801).  
Not applicable.  
The authors declare no conflict of interest.