esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Background and Related Concepts
### Access Control Models  
Access control ensures that only authorised users are granted the appropriate access permissions to protect system resources' privacy and integrity. There are two types of access control models, which are traditional and dynamic [3]. Traditional approaches to access control rely on policies that are inflexible and static. These policies are predetermined and always produce the same result, regardless of the circumstances. Consequently, this rigid approach to making access decisions is inflexible in changing and variable conditions [4]. Three primary traditional access control models exist: Discretionary Access Control, Mandatory Access Control and Role-based Access Control (RBAC).  
Discretionary Access Control (DAC) models were created for multi-user databases and systems with a few previously known users. The user has complete control over every system resource. DAC gives access based on the identity and authorization of the user, as specified by open policies. Any user can be granted access to a resource by its owner [3]. The Mandatory Access Control model (MAC) is concerned with the security and integrity of information and is primarily employed in military and government applications. In MAC, a security policy administrator controls the policy, and the user cannot override it [3]. Next, Role-based Access Control (RBAC) consists of three components: users (subjects requesting access), roles (permission collection) and operations (actions on the target resource). Access permissions are associated with roles, and the proper role is assigned to the user. A user can be associated with one or more roles, and a role can contain one or more users. RBAC enables the categorization of users according to their jobs [5]. Dynamic access control models are distinguished by their use of access policies and dynamic contextual variables that are assessed in real-time at the moment of the request [6].
# Single-Pixel Imaging Based on Enhanced Multi-Network Prior
## 3 Multi-Network Prior Reconstruction Method  
### Compressed Sensing  
Compressed sensing, also known as compressive sampling or sparse sampling, is a method of finding sparse solutions for underdetermined systems. The image reconstruction process for solving the uncertain equation y = Ax is based on the measurement matrix  
Figure 1: This is the structure of the single-pixel imaging system. After modulation by the parallel light tube, the light source becomes a single photon horizontal parallel light, and the object is imaged on the DMD under the irradiation of parallel light and then collected by the PMT. The imaged object selects the Chinese character “\(\%\)” on the resolution board. The signal processed by the FPGA is the measured value Y, and the reconstructed image is obtained after reconstruction by the EMNP network.  
A and known measurement values y and is widely used in single-pixel imaging technology. We define prior information as prior (x), and the reconstructed model can be expressed as:  
\[\underset{x}{Min}\|Ax-y\|^{2}+L\,*\,prior(x), \tag{1}\]  
where \(x\in\mathbb{R}^{mn}\) is the reconstructed image, \(A\in\mathbb{R}^{r\times mn}\) represents a partially sampled random matrix, \(y\in\mathbb{R}^{r}\) represents the original data obtained in the single photon imaging system, and L is a hyperparameter that balances the effects of image fidelity and prior constraints.
## 3 Energy-Saving Principle of HPCA
### Pressure Characteristics of HPCA
#### 3.2.2 Static Pressure Characteristic  
For the asymmetric hydraulic cylinder with control of the symmetric servo valve shown in Figure 4, the force balance on the cylinder can be calculated as [29]:  
\[A_{pis}P_{pis}-\alpha A_{pis}P_{r}=f_{h}=f_{l}. \tag{7}\]  
Again, ignoring the leakage and oil compressibility, the following relationship will be satisfied according to the flow continuity equation :  
\[\left\{\begin{array}{ll}\dfrac{K\sqrt{P_{S}-P_{pis}}-Q_{accu}}{A_{pis}}= \dfrac{K\sqrt{P_{r}}}{\alpha A_{pis}}&\dot{x}_{p}>0\\ \dfrac{K\sqrt{P_{pis}}+Q_{accu}}{A_{pis}}=\dfrac{K\sqrt{P_{S}-P_{r}}}{\alpha A _{pis}}&\dot{x}_{p}<0,\end{array}\right. \tag{8}\]  
with  
\[K=C_{d}x_{p}A, \tag{9}\]  
where \(C_{d}\) and \(A\) are the flow coefficient and area gradient, respectively; the positive and negative of \(\dot{x}_{p}\) indicate the extension and retraction of the piston; \(Q_{accu}\) can be described as:  
\[Q_{accu}=\dfrac{\dot{p}_{accu}P_{g0}^{1/n}V_{g0}}{nP_{accu}^{(n+1)/n}}. \tag{10}\]  
Since the chamber pressures \(P_{pis}\) and \(P_{r}\) cannot be accurately solved by directly implementing Equation (8), only pressure characteristics at the moment of piston reversal are considered. Since the working volume of the accumulator \(V_{accu}\) cannot change during this extremely short time, the following relationships are still satisfied before and after the reversal:  
\[\left\{\begin{array}{ll}P_{accu}(t-\Delta t)=P_{accu}(t+\Delta t)\\ \dot{P}_{accu}(t-\Delta t\leq t\leq t+\Delta t)=0,\end{array}\right. \tag{11}\]  
where \(t\) is the moment of reversal, and \(\Delta t\to 0\).  
Then, Equation (8) can be rewritten as:  
\[\left\{\begin{array}{ll}\dfrac{K\sqrt{P_{S}-P_{pis}}}{A_{pis}}=\dfrac{K \sqrt{P_{r}}}{\alpha A_{pis}}&\dot{x}_{p}>0\\ \dfrac{K\sigma\sqrt{P_{pis}}}{A_{pis}}=\dfrac{K\sqrt{P_{S}-P_{r}}}{\alpha A_{pis }}&\dot{x}_{p}<0\end{array}\right.. \tag{12}\]  
Figure 5: Parameter comparison.  
Taking into account Equations (7) and (12), the pressure in the working chambers can be described as:  
For \(\dot{x}_{p}>0\),  
\[\left\{\begin{array}{lll}P_{pis}&=&\frac{\alpha^{3}A_{pis}P_{S}+f_{l}}{A_{pis}(1 +\alpha^{3})}\\ P_{r}&=&\frac{\alpha^{2}(A_{pis}P_{S}-f_{l})}{A_{pis}(1+\alpha^{3})}.\\ \end{array}\right. \tag{13}\]  
For \(\dot{x}_{p}<0\),  
\[\left\{\begin{array}{lll}P_{pis}&=&\frac{\alpha A_{pis}P_{S}+f_{l}}{A_{pis}(1 +\alpha^{3})}\\ P_{r}&=&\frac{A_{pis}P_{S}-\alpha^{2}f_{l}}{A_{pis}(1+\alpha^{3})}.\\ \end{array}\right. \tag{14}\]  
For asymmetric hydraulic cylinders, \(\alpha\) is less than 1, and the following rules will be met:  
\[\left\{\begin{array}{lll}P_{pis}(\dot{x}_{p}>0)&<&P_{pis}(\dot{x}_{p}<0)\\ P_{r}(\dot{x}_{p}>0)&<&P_{r}(\dot{x}_{p}<0).\\ \end{array}\right. \tag{15}\]  
This means that a pressure transition will be generated in the working chamber of the HPCA due to the piston reversal. When the piston changes from extracting to retracting, both the chamber pressures (\(P_{pis}\) and \(P_{r}\)) increase, and vice versa.
## 3 Problem Research
### Ship Control Technology Problem
#### 3.2.1 Linear Matrix Inequalities in Use
On the basis of the Pareto curve, the relationship between the fixed value of the \(H_{\infty}\) norm (which is the minimum value of \(\gamma_{\infty}\)) and the minimized value of the \(H_{2}\) norm (which is the value of \(\gamma_{2}\)) is obtained. The third LMI condition of the form: \[\left\{ \begin{array}{ccc}\left[\begin{array}{ccc}\mathbf{A}\mathbf{X}_{2 }+\mathbf{X}_{2}\mathbf{A}^{T}&\mathbf{B}\\ \mathbf{B}^{T}&-\mathbf{I}\end{array}\right]\prec 0\\ \left[\begin{array}{ccc}\mathbf{Q}&\mathbf{C}^{T}\\ \mathbf{C}&\mathbf{X}_{2}\end{array}\right]\succ 0\\ Tr(\mathbf{Q})<\gamma_{2}^{2},\end{array} \right.\] (17) is satisfied if and only if there exists a symmetric and positively defined \(\mathbf{X}_{2}\) matrix and a symmetric \(\mathbf{Q}\) matrix. The state controller synthesis problem finds a solution if the third condition of LMI, \(\gamma_{2}\), is satisfied. Invoking the property about the existence of the inverse of the matrix \(\mathbf{Y}_{2}=\mathbf{X}_{2}^{-1}\), the matrix \(\mathbf{K}_{2}\) of the state controller is written by the following relation: \[\mathbf{K}_{2}=\mathbf{Y}_{2}\cdot\mathbf{X}_{2}^{-1}\] (18)The fourth step is equivalent to the notation in which the authors of [47; 49] use the statement (25 in [47]) and assume that the matrix \(H_{\mathbf{X}}\) is equivalent to the notation \(\mathbf{X}=\mathbf{X}_{2}=\mathbf{X}_{\infty}=\mathbf{X}_{zone}\). The LMI conditions, defined for \(X_{\infty}\), \(X_{2}\), and for the area of pole placement in the left half-plane of the complex variable s, are satisfied if there is a symmetric and positively defined Lyapunov matrix. The assumption (forcing) that all Lyapunov matrices are equal in all variants, and that all three LMI conditions are treated as a single constraint, allows for the synthesis of the state controller. Regarding the final form of state space controller gain matrix \(K\), which stabilizes the whole control system and minimizes \(H_{\infty}\) and \(H_{2}\) norms, the assumptions are of the form:  
\[\mathbf{K}=\left[ \begin{array}{cccccc}1595.3&0.000&-0.100&-807.800&-0.000&0.0 00\\ -0.100&1664.800&-0.036&0.000&-897.900&0.200\\ -6.100&-0.280&435.400&0.350&0.160&-234.4\end{array} \right] \tag{19}\]  
The controller from condition K was obtained based on the conditions and assumptions. The result is often called a suboptimal solution, which means that the listed matrices \(\mathbf{X}=\mathbf{X}_{2}=\mathbf{X}_{\infty}=\mathbf{X}_{zone}\) are the same for the three LMI conditions. The K-matrix calculated using the aforementioned four steps is inserted into the scheme shown in Figure 5.
# Analysis of Score-Level Fusion Rules for Deepfake Detection
## 4 Experimental Results
### Fusion Time Analysis  
The analysis of the computational time required for performing the fusion of the individual models' scores of a single frame, reported in Table 4, highlighted that the Complement NB was the fastest approach in the range \([2,6]\) of individual classifiers, requiring about 0.5 \(\upmu\)s per sample on a system with an AMD EPYC 7B12 CPU and 12 GB memory.  
The SVM showed the highest computation time, for example, over 50 times the Complement NB in the fusion of six models. However, it is interesting to notice that this tended to decrease as the number of detection models included in the merger increased from two to six (Figure 8).  
Hence, with reference to the previous analysis (Tables 2 and 3), these results show that the best performing model was not necessarily the one with the greatest computational complexity. Therefore, this highlights how the choices between the processing speed of the fusion methods and the performance of the same are not necessarily in opposition.  
Figure 7: Score distributions related to the DFDC dataset from single methods (**a**,**b**), non-parametric fusion methods (**c**,**d**), weighted averages (**e**,**f**), and Machine Learning-based methods (**g**,**h**).
# Improving Smartphone GNSS Positioning Accuracy Using Inequality Constraints
## 2 Positioning Model and Inequality Constraints
#### 2.2.2 Direction Constraint
\begin{array}{l}\overline{\mathbf{E}}_{1}=\left[\begin{array}{ccc}-\sin l& \cos l&0\\ -\sin b\cos l&-\sin b\sin l&\cos b\end{array}\right]\\ \\ \mathbf{C_{1}}=\left[\begin{array}{ccc}0&-1\\ 1&-\tan\delta\\ -1&-\tan\delta\end{array}\right]\left[\begin{array}{ccc}\cos\alpha_{t}&- \sin\alpha_{t}\\ \sin\alpha_{t}&\cos\alpha_{t}\end{array}\right]\left[\begin{array}{ccc}0_{2 \times 3}&\overline{\mathbf{E}}_{1}\Delta t&0.5\overline{\mathbf{E}}_{1}\Delta t^{2} \end{array}\right]\\ \\ \mathbf{d_{1}}=\mathbf{0}_{3\times 1}\end{array} \right. \tag{18}\]  
where \(\overline{\mathbf{E}}_{1}\) contains the first and second row of \(\mathbf{E_{0}}\), and \(\mathbf{C_{1}}\) and \(\mathbf{d_{1}}\) are the constraint matrices for applying direction information. A flowchart of general direction inequality constraint is shown in Figure 3.
## 5 Attenuation Method Experiment
### Phase II: Attenuation of Service Channels  
The following measurement that is shown in Figure 3 directly follows the previous phase and is aimed at finding the minimum possible attenuation of the service channels so that they do not interfere with the quantum channel after coupling, so that the system remains functional. However, unlike the first phase, this measurement requires a working QKD system. To evaluate something like this, it is necessary to connect the service channels directly so that they do not interfere with the quantum channel and at the same time their synchronization is not disturbed when they are attenuated. For this purpose, a second pair of SFP modules must be used.  
The other pair of SFPs is plugged into an arbitrary device and is used solely for interference testing. Thus, it does not transmit any useful signal and can be attenuated at will by using a variable optical attenuator (VOA). This adds additional attenuation to the 20 dB already designed. Since the quantum channel is not functional at a 20 dB attenuation, there is no point in reducing the attenuation.  
As in the previous phase, the transmitted power is monitored. The measurement principle itself consists of gradually adding attenuation to the VOA and monitoring changes in the QKD system parameters. In total, testing is performed for both service channels used simultaneously in 10 steps. Subsequently, the values of the key rate, visibility and quantum-bit error rate (QBER) are obtained. Their averages for the individual attenuation values can be found in Table 2.  
Table 2 contains the quantum channel performance values for both connected service channels simultaneously. The results confirm the expectation that as the value of service channel attenuation decreases, the QBER increases, and visibility decreases with the transmission rate. Thus, the minimum route configuration should contain at least 5 dB of added attenuation.  
Figure 3: Initial route from Phase I, modified for service channel attenuation testing. The main difference is the addition of a second SFP pair.
## 3 RUL Prediction Method Based on Hierarchical Clustering and Transformer-GRU
### Hierarchical Clustering Adaptively Removes Redundant Features  
If the similarity between features is excessively high, the information they contain is largely redundant. This not only increases computational complexity but may also negatively affect the prediction performance of subsequent models. Therefore, it is essential to perform classification and reduction on the initially selected feature set. In this study, a redundancy elimination method based on the combination of hierarchical clustering and the elbow method is proposed. Given a feature set with a total of n features and a target number of clusters \(K\), the procedure is illustrated in Figure 5, with the specific steps outlined as follows:  
Step 1: Compute the distance matrix as defined in Section 2.1.  
Step 2: Use the distance matrix as the input to the hierarchical clustering algorithm to perform agglomerative clustering. Obtain clustering results for different values of \(K\) (where \(K\) = 2, 3, 4, \(\ldots\), n) from the resulting dendrogram.  
Step 3: Based on the clustering results from Step 2 and Formula (2), calculate the sum of squared errors for \(K\) clusters and plot the elbow graph (where \(K\) = 2, 3, 4, \(\ldots\), n).  
Step 4: Determine the optimal number of clusters \(K\) by identifying the \"elbow point\" on the curve using a combination of visual inspection and slope change analysis.  
Step 5: Trim the dendrogram to obtain \(K\) clusters according to the determined number of clusters.  
Step 6: Select the optimal feature within each cluster as the cluster representative and construct the optimal feature set from these representatives.
# An Automatic Geometric Registration Method for Multi Temporal 3D Models
## 2 Study Area and Data  
### Study Area  
The study area is located within the Xinjiang province (79\({}^{\circ}\)43\({}^{\prime}\)26\({}^{\prime\prime}\) E and 39\({}^{\circ}\)28\({}^{\prime}\)57\({}^{\prime\prime}\) N), 81 km east north from Akesu in China, as shown in Figure 1a. The average annual precipitation is 80.4 mm, and the main rainfall period is concentrated in May to September. The annual evaporation is 1643-2202 mm, which is 27 times the average annual precipitation. The dry climate results in low vegetation coverage and a large number of stones on the surface of the study area. As shown in Figure 1a,b the study site is located on the Akesu River and is crossed by a road. There is a steep rock with a height difference of 100 m in the west of the study area, as shown in Figure 1c. The stones often become loose from the steep rock and fall to the road, which causes economic loss, property damage, and loss of life [26]. The goal of our team was to rely on UAV technology to periodically acquire UAV images of the study site and to generate 3D models by photogrammetry. The multi-temporal 3D models identified the rocks that were at risk of falling, or had moved. To achieve this goal, the first problem to be solved was how to make multi-temporal 3D models have a unified spatial reference.
# Meta-Learning Approach for Adaptive Anomaly Detection from Multi-Scenario Video Surveillance
## 3 Methodology
### Model Agnostic Meta-Learning
#### 3.5.4 MAML Architecture  
Figure 5 illustrates the core concept of MAML, where the objective is to find an optimal set of initial parameters \(\theta\) that can quickly adapt to a variety of tasks with minimal training. The solid curve represents the trajectory of meta-learning, which optimizes the initial parameters across multiple tasks. Each red arrow (\(\
abla L_{1}\), \(\
abla L_{2}\), \(\
abla L_{3}\)) indicates the task-specific gradient computed during the inner loop of training for different tasks. These gradients guide the adaptation of the initial parameters \(\theta\) toward task-specific optima (\(\theta_{1}\)*, \(\theta_{2}\)*, \(\theta_{3}\)*), shown as blue dashed arrows labeled as \"Learning/Adaptation\". The goal of MAML is to optimize \(\theta\) such that a small number of gradient steps can lead to effective task-specific models, enabling rapid learning on new tasks. The figure highlights how meta-learning integrates feedback from multiple tasks to update the shared initialization in a way that supports efficient fine-tuning.  
To implement the meta-learning approach in a task-agnostic manner, the following algorithm outlines the generic MAML procedure. This algorithm is designed to optimize an initial model that can quickly adapt to new video frame-based anomaly detection tasks with only a few gradient steps. The steps below detail the inner and outer learning loops required to achieve this fast adaptation (see Algorithm 1):  
```
Require: \(D(T)\): Distribution over tasks (video frames) Require: \(\alpha\) and \(\beta\): step size hyper parameters Step 1. Randomly initialize parameter \(\theta\) Step 2. While not done do Step 3. Sample batch of task \(Ti\sim D(T)\) Step 4. For all \(Ti\) do Step 5. Evaluate \(\
abla_{\theta}L_{Ti}(f_{\theta})\) with respect to k samples Step 6. Calculate adapted parameters using gradient descent: \(\theta^{\prime}{}_{i}=\theta\)-\(\pi\
abla_{\theta}L_{Ti}(f_{\theta})\) Step 7. End for Step 8. Update \(\theta\)\(\leftarrow\)\(\theta-\beta\
abla_{\theta}\)\(\sum_{Ti}\)\(\sim\)\(D(T)\)\(L_{Ti}(f_{\theta})\) Step 9. End while
```  
**Algorithm 1**Generic MAML algorithm.
# Adaptive Binaural Cue-Based Amplitude Panning in Irregular Loudspeaker Configurations
## 2 Methods
### Magnitude Compensation Stage  
#### 2.1.1 Inverse Filtering  
The inverse filtering problem can be conceptualized from a model-matching perspective [30]. For a system with \(S\) loudspeakers, \(T\) program input channels, and \(R\) receiver points, it can be expressed as the following optimization problem:  
\[\min_{C}\lVert\mathbf{M}-\mathbf{H}\mathbf{C}\rVert_{\mathrm{F}}^{2}, \tag{1}\]  
Figure 1: Schematic diagram of amplitude panning method.  
where F symbolizes the Frobenius norm, \(\mathbf{H}\) is an \(R\times S\) plant transfer matrix, \(\mathbf{M}\) is an \(R\times T\) matching model matrix, and \(\mathbf{C}\) is an \(S\times T\) inverse filter matrix. By utilizing Tikhonov regularization, the inverse filter matrix can be obtained as  
\[\mathbf{C}\textbf{=}(\mathbf{H}^{\mathrm{H}}\mathbf{H}\textbf{+}\beta\mathbf{I})^{-1}\mathbf{H}^{ \mathrm{H}}\mathbf{M}, \tag{2}\]  
where \(\beta\) is the regularization factor. To simplify the system and reduce filter length, the inverse filter is calculated individually for each loudspeaker. For a single-input single-output system, the magnitude compensation algorithm is applied to each loudspeaker, expressed as  
\[C(k)=\frac{|H^{*}(k)|}{|H(k)|^{2}+\beta}\cdot M(k), \tag{3}\]  
where \(k\) is the frequency index. By performing the inverse Fourier transform and applying circular shifts, a causal FIR filter is obtained.
# Predicting Putting Development of Pavement with Flexible Overlay Using Artificial Neural Network
## 1 Introduction
[17] used three years of rut-depth data to predict the rut depth in a time series for 10 years by applying a recurrent neural network (RNN) model; [PERSON] [18] established an RNN model to predict rutting by taking traffic and climate as input variables. Based on the LTPP database, [PERSON] et al. [19] selected 29 input variables from 50 influencing factors and took traffic, climatic conditions, pavement thickness, and pavement material properties as inputs to establish a deep ANN model. Neural networks have been widely used in the service performance prediction of asphalt pavement [20; 21; 22; 23], but there is little research on applying ANNs to pavement overlay rutting prediction. [PERSON] [24] applied an ANN to predict the appropriate pavement overlay thickness. [PERSON] [25] and [PERSON] [1] proposed an ANN model to predict the rutting depth of asphalt pavement. However, these input variables do not include the thickness of the overlay layer, the overlay material, or repave measures. [PERSON] [26] analyzed the impact of different maintenance measures on the smoothness index, rutting, and cracks of flexible pavement. It is believed that the overlay thickness is the most important influencing factor besides climate and the axial load. It is well known that the accumulated damage of the original pavement and the pavement repair plan have a great influence on the rutting of the pavement after repair. This article establishes an artificial neural network model based on the LTPP database to predict the rutting development of flexible overlay pavement. The features affecting the pavement performance were selected as input variables, including the overlay thickness, overlay materials, overlay measures, pavement structure, climate, and traffic. Finally, the article proposes maintenance strategies based on the curve of the rutting depth over time. The overall research scope of this study is shown in Figure 1.
## 3 Results and Discussion
### Characterization and Analysis of Results
#### 3.1.2 XPS Analysis  
Figure 2a presents the comprehensive X-ray photoelectron spectroscopy (XPS) spectrum of the O-MWCNT\({}_{\text{S}}\). The figure illustrates that the O-MWCNTs' surface is composed primarily of two elements, carbon (C) and oxygen (O), which accounted for 96.03% and 3.97% of the composition, respectively, and had binding energies of 286.1 and 530.6 eV. In Figure 2b, the XPS fine spectra of the C 1s peak in the O-MWCNT\({}_{\text{S}}\) are presented. The distinct binding energies of 284.8, 286.0, and 289.3 eV were attributed to the C-C, C-O-C, and O-C=O peaks, respectively. The corresponding percentages of these peaks were determined to be 55.18, 21.63, and 23.20%, respectively. Figure 2c illustrates the XPS fine spectra of the O 1s orbital in the O-MWCNT\({}_{\rm S}\). The distinct peak patterns associated with C=O and C-O correspond to the combining energies of 531.9 and 533.4 eV, respectively. The relative percentages of these peaks have been reported as 70.47% and 29.53% [33]. Evidence for oxygen-carrying functional moieties on the surface of O-MWCNT\({}_{\rm S}\) is provided by these observations in conjunction with Figure 2b,c.