esa-sceva/satcom-chunk-collection huggingface dataset:
# Iterative Parameter Estimation Algorithms for Dual-Frequency Signal Models
## 5 Examples  
* Case 1: The numerical simulations of the three iterative algorithms. Consider the following dual-frequency cosine signal model: \[y(t)=1.48\cos(0.06t)+2.25\cos(0.55t)+v(t).\] The parameter vector to be estimated is \[\mathbf{\theta}=[a,b,\omega_{1},\omega_{2}]^{\intercal}=[1.48,2.25,0.06,0.55]^{ \intercal}.\] In simulation, \(\{v(t)\}\) is taken as a white noise sequence (stationary stochastic noise) with zero mean and variances \(\sigma^{2}=0.50^{2}\), \(\sigma^{2}=1.50^{2}\) and \(\sigma^{2}=2.50^{2}\), respectively. Let \(t=kT\), the sampling period \(T=0.005\), \(k=2000\), the data length \(L=2000\). Apply the proposed GI algorithm using the observed data to estimate the parameters of this signal model. The parameter estimates and their estimation errors \(\delta:=\|\hat{\mathbf{\theta}}_{k}-\mathbf{\theta}\|/\|\mathbf{\theta}\|\times 100\%\) versus \(k\) are as shown in  
In order to validate the obtained models, we use the MDW-GI parameter estimates from next-to-last row in Table 3 with \(p=300\) to construct the MDW-GI estimated model  
\[\hat{y}(t)=1.46715\cos(0.07030t)+2.23197\cos(0.55621t).\]  
The estimated outputs \(\hat{y}(t)\) and the observed signal model \(y(t)\) are plotted in Figure 4, where the solid-line denotes the actual signal \(y(t)\) and the dot-line denotes the estimated signal \(\hat{y}(t)\).  
From the simulation results, we can draw the following conclusions:  
* The parameter estimation errors obtained by the presented three algorithms gradually decreasing trend as the iterative variable \(k\) increases.
* The parameter estimation errors given by three algorithms become smaller with the noise variance decreasing.
* Tables 1 and 2 and Figures 1 and 2 show that the proposed NI algorithm has faster convergence speed and more accurate parameter estimates than the GI algorithm under the same simulation conditions.
* The larger the length \(p\) of the moving data window is, the faster the parameter estimation converge speed is (see Figure 3). Moreover, the larger the length \(p\), the higher the accuracy is (see Table 3).  
Figure 3: The MDW-GI estimation errors \(\delta\) versus \(k\) (\(\sigma^{2}=0.50^{2}\)).  
Figure 2: The NI estimation errors \(\delta\) versus \(k\).  
* In the simulation, these three algorithms are fulfilled in the same conditions (\(\sigma^{2}=0.50^{2}\)), and the estimated models obtained by the MDW-GI and NI algorithms have higher accuracy than the GI algorithm.
* The outputs of estimated signal \(\hat{y}(t)\) are very close to the actual signal model \(y(t)\). In other words, the estimated model can capture the dynamics of the signal.
# A Survey of Visual SLAM Based on RGB-D Images Using Deep Learning and Comparative Study for VOE
## 3 Visual SLAM and Visual Odometry Using Deep Learning: Survey
### Deep Learning-Based Module for Visual SLAM and Visual Odometry
#### 3.1.5 End-to-End for the Visual SLAM Algorithm
The results of VOE in the environment based on end-to-end DL on the sequences of the KITTI 2012 dataset are shown in Table 12. In Table 12, the research results of end-to-end DL have also been compared with traditional machine learning methods such as ORB-SLAM-M, and the results show that the methods using end-to-end DL are better than traditional machine learning. \begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline \multirow{3}{*}{**Authors/Vears**} & \multirow{3}{*}{**Methods**} & \multicolumn{2}{c}{**KITTI**} & \multicolumn{2}{c}{**KITTI**} & \multicolumn{2}{c}{**KITTI**} \\  & & \multicolumn{2}{c}{2012} & \multicolumn{2}{c}{**2012**} & \multicolumn{2}{c}{**2012**} & \multicolumn{2}{c}{**2012**} \\  & & \multicolumn{2}{c}{**Dataset**} & \multicolumn{2}{c}{**Dataset**} & \multicolumn{2}{c}{**Dataset**} \\  & & \multicolumn{2}{c}{**Mean/**} & \multicolumn{2}{c}{**(00, 02, 05, 05, 09, 10)**} & \multicolumn{2}{c}{**(09, 10)**} & \multicolumn{2}{c}{**Seq.06**, **Seq.07**, **Seq.10**} \\ \cline{5-8}  & & \multicolumn{2}{c}{**\(t_{rel}\) (\%)**} & \multicolumn{2}{c}{\(r_{rel}\) (Degrees)**} & \multicolumn{2}{c}{\(t_{rel}\) (\%)} & \multicolumn{2}{c}{\(r_{rel}\) (Degrees)} & \multicolumn{2}{c}{\(t_{rel}\) (\%)} & \multicolumn{2}{c}{\(r_{rel}\) (Degrees)} \\ \hline
[207]/2015 & OKVIS & Trajectory estimation & - & - & 13.535 & 2.895 & - & - \\ \hline
[42]/2017 & SFMLearner & Trajectory estimation & 36.232 & 4.562 & 21.085 & 7.25 & - & - \\ \hline
[208]/2017 & ROVIO & Trajectory estimation & - & - & 20.11 & 2.165 & - & - \\ \hline
[200]/2017 & DeepVO estimation & - & - & - & - & 5.96 & 6.12 \\ \hline
[204]/2018 & VIOLearner & Trajectory estimation & 5.574 & 2.31 & 1.775 & 1.135 & - & - \\ \hline
[202]/2018 & UnDeepVO estimation & 4.07 & 2.026 & - & - & - & - \\ \hline
[202]/2018 & VISO2-M & Trajectory estimation & 17.924 & 2.798 & - & - & 17.48 & 16.52 \\ \hline
[202]/2018 & ORB-SLAM-M & Trajectory estimation & 27.0875 & 10.2375 & - & - & - & - \\ \hline
[202]/2018 & VISO2-M & Trajectory estimation & - & - & - & - & 1.89 & 1.96 \\ \hline
[203]/2018 & Depth-VO-Feat & Trajectory estimation & - & - & 12.27 & 3.52 & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 12: The results of estimating the moving trajectory of camera/VOE on the sequences of KITTI 2012 dataset.
## 2 Investigation of Five Parametric Models
### MRE Models
M3 is a parametric magneto-viscoelastic model that includes four parallel elements, i.e., a spring element, a fractional derivative dashpot element, a nonlinear spring element and an additional analogous dashpot element, as shown in Figure 1c. In this model, the spring element together with the fractional derivative dashpot element represent the viscoelasticity of the matrix. Magnet field-dependent phenomenon is indicated by  
\begin{table}
\begin{tabular}{l c} \hline \hline \multicolumn{1}{c}{**No. **} & \multicolumn{1}{c}{**Mathematical Expression**} \\ \hline M1 [6] & \(\tau=\left[\frac{(k_{1}k_{0}+k_{0}k_{0}+k_{1}k_{2})([k_{1}+k_{2})^{2}+c_{2} \omega^{2}\omega^{2}]+c_{2}\omega^{2}k_{1}^{2}}{(k_{1}+k_{2})^{2}([k_{1}+k  
a nonlinear spring, and the damping variance with induced magnetic field is represented by the analogous dashpot element [8]. Model 4 (M4) is a model describing the experimental physical phenomenon that the polymer strain falls when it reaches a certain value. This physical phenomenon is called the Payne effect. When strain increases, the structural mesh is destroyed rapidly, and the elastic model decreases rapidly. M4 is an extended fractional derivative model with further modification of M3, as shown in Figure 1d. In this macroscopic model, the spring element represents static and elastic shear modulus. Viscosity coefficient along with fractional order element are considered determinant factors of viscosity of composite. The variable and nonlinear stiffness and damping elements describe magnetic-induced behavior. In addition, Payne effect is taken into consideration with a modified Kraus model that describes the influence of Van Der Waals interactions and friction between particles and matrix on breaking and rebuilding processes [9]. M2 is a mathematical model in the time domain. M1, M3 and M4 are mathematical models represented with Laplace transform, where the real and imaginary parts of the expression respectively represent the storage modulus and loss modulus of the complex modulus. Model 5 (M5) considers the pre-strain under the action of magnetic field. Starting from the chain structure of particles in the matrix, the dipole theory of the linear chain is used for modeling. The magnetic field direction is vertically upward, and the center distance of the dipole (magnetized particles) arranged along the magnetic field direction is d0, as shown in Figure 1e. When the dipole is deformed to the dotted circle position of Figure 1e, the two dipoles will generate energy. The conversion of magnetic field energy and matrix strain energy results in magnetic-induced modulus (including magnetic-induced Young's modulus and magnetic-induced shear modulus). Its size is related to particle spacing, particle volume ratio and saturation strength of magnetic field. So M5 is a microscopic model that simultaneously includes the effects of magnetic energy density, shear strain and pre-strain [12]. The parameters in these models have been identified and verified using an objective function based on the data from analytical and experimental work.
## 7 Discussion
### Future Directions and Research Opportunities
#### 7.2.4 Policy and Privacy Considerations  
Consolidating transportation infrastructure data into a single repository, accessible by multiple DT systems throughout the lifecycle, can enhance data sharing and analytics. However, achieving complete interoperability is a multi-faceted challenge that involves not only technical issues but also regulatory concerns related to data privacy and security [61].  
The broad use of DT technology raises critical policy and privacy issues, particularly as it relies on the collection of vast amounts of data--from real-time sensors to GPS devices and cameras [112]. This opens the door to risks of sensitive data being compromised or misused. Addressing these concerns is fundamental for gaining public confidence and ensuring the acceptance of DT projects throughout their lifecycle [126].  
Another key policy consideration is the ethical use of DT technology. Scholars and policymakers must ensure that DT systems are designed and implemented in ways that promote equitable access to transportation services and avoid exacerbating existing inequalities in urban mobility. Ethical guidelines should guide the development and use of DTs, balancing innovation with societal values throughout all lifecycle stages [128].
###### Abstract
Uncertainty analysis aids in understanding the uncertainty in model outputs due to the uncertainties in the model inputs; whereas, sensitivity analysis gives insight into how uncertainties in the model inputs and outputs are related to each other. Most of the reported UA and SA methods in AM are experiment-based, which leads to a high material wastage and makes the process expensive [6]. In another approach, numerical models can be used to quantify the uncertainties in the SLM process. However, a full-scale (such as a finite elements-based) model could be computationally expensive and may take hours or days to complete depending on the simulation type. In addition, numerical models are deterministic, and usually do not consider the input variables' uncertainties [8]. This paper provides a framework to optimize the SLM process and study uncertainty and sensitivity analyses using a data-driven approach along with a 3D finite element model. As the discretization of SLM problems requires fine spatial meshes and a high number of time steps, the overwhelming demand for computational resources makes high-fidelity simulations too expensive computationally in the contexts of uncertainty propagation or inverse analysis studies. To alleviate this difficulty, we advocate the use of the data-driven approach, which consists of two major stages: (1) A database of high-fidelity solutions computed for a certain number of samples of the input data in the offline stage, which allows a surrogate model to be obtained using a regression method on a reduced basis; (2) Predictions on new data performed on the online stage using the surrogate model. It is possible to a priori prescribe a reduced basis such as in the well-known polynomial chaos expansion (PCE), or to use neural networks for which the reduced basis is obtained adaptively. The data-driven approach does not require any modification of the high-fidelity source codes; these are used as a black box. However, the solutions database should be computed in a reasonable timeframe, for which parallel computing is deemed essential. We used PCE and neural network methods to build an efficient machine learning model. Genetic and evolutionary optimization algorithms were then used to find the optimal parameters. These models were validated on a benchmark SLM test [9] for which the high-fidelity solutions were obtained using Ansys additive software. After this introduction, the second section contains a brief literature survey on the problem. Section 3 gives an overview of the mathematical methods used for statistical analysis, surrogate modeling, and optimization. The results of the benchmark test are discussed in Section 4. Finally, concluding remarks are given in Section 5.
# Design of a Partially Grid-Connected Photovoltaic Microgrid Using IoT Technology
## 3 Microgrid Modeling and Design
### Solid-State Transfer Switches  
Recent research has identified solid-state transfer switches (SSTS) as a potentially cost-effective solution to power control and power quality issues [60]. Figure 10 depicts the basic concept of the SSTS application, to switch selected loads between the microgrid and utility grid. The SSTS commonly contains a pair of thyristor switches for each phase, that assists in power transmission from the primary source, considered here as the microgrid, and the alternative source, considered as the utility-grid, to prevent load power disruptions. The coordinate system is created by instantly transforming the three-phase voltages \(v_{a,b,c}(t)\) into a bi-axial coordinate system \(v_{d,a,0}(t)\). The following equations are used to express the transfer switch operation [60]:  
\[\left[\begin{array}{c}v_{d}\\ v_{q}\\ v_{0}\end{array}\right]=\sqrt{\frac{2}{3}}\left[\begin{array}{cc}\sin(\theta )&\sin(\theta-\frac{2\tau}{3})&\sin(\theta+\frac{2\tau}{3})\\ \cos(\theta)&\cos(\theta-\frac{2\tau}{3})&\cos(\theta+\frac{2\tau}{3})\\ \frac{1}{\sqrt{2}}&\frac{1}{\sqrt{2}}&\frac{1}{\sqrt{2}}\end{array}\right] \left[\begin{array}{c}v_{a}\\ v_{b}\\ v_{c}\end{array}\right] \tag{26}\]  
\[\theta=\theta(0)+\int_{0}^{t}\omega(\tau)d\tau \tag{27}\]  
\[v_{dq}=\sqrt{v_{d}^{2}+v_{q}^{2}} \tag{28}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Rated Power & 100 kW \\ Line-to-line output voltage (_Vrms_) & 400 V \\ Switching frequency & 10 kHz \\ Filter Inductor values (\(L_{j}\)) & 0.25 mH \\ Filter capacitor values (\(C_{j}\)) & 100 μF \\ \hline \hline \end{tabular}
\end{table}
Table 4: Design parameters of the inverter.  
Figure 9: Schematic diagram of three-phase VSI and its controller.  
As shown in the above equations, \(\theta(t)\) is the angle of conversion utilized to determine the amplitude of the \(v_{d,q,0}(t)\) vector. The voltage \(v_{dq}(t)\) is the continuously monitored voltage that has to be compared to a predetermined threshold voltage in the control algorithm of the microgrid operation. In case the microgrid fails to power the connected loads, a control signal is used to start the transfer process of the assigned loads from the microgrid to the utility grid. Algorithm 2 illustrates the pseudocode used to activate the transfer process by the SSTS.  
```
1:Initialization; threshold voltage of \(v_{dq+th}\)
2:Read actual \(v_{dq}\) voltage of the microgrid
3:Is \(V_{dq}\leq V_{dq+min}\)?
4:If YES \(\rightarrow\) select the loads to be shed from the microgrid \(\rightarrow\) generate transfer signals \(\rightarrow\) transfer the loads
5:Goto step 2
6:If NO \(\rightarrow\)Goto step 2
```  
**Algorithm 2** Pseudocode of SSTS transfer process
## 3 Case Study
### Results
#### 3.3.3 Demand-Side Response Analysis  
As shown in Figure 10, multi-objective optimization incurs lower costs under time-of-use electricity pricing compared to fixed-rate pricing under the same comfort level. By incorporating demand-side response and optimizing the scheduling of the climate control system based on dynamic electricity pricing, the operational costs are significantly reduced compared to optimization under fixed pricing. Specifically, the dynamic pricing-based model achieves cost reductions of 25.3% on typical summer days, 24.4% during transitional seasons, and 18.7% on typical winter days. Moreover, the electricity costs are reduced by 27.4%, 25.5%, and 20.6%, respectively. By shifting equipment operation to periods with lower electricity prices, overall costs can be effectively reduced.
# Detecting and Processing Unuspected Sensitive Variables for Robust Machine Learning
However, we want to emphasise that studies focusing on the presence of bias in more general industrial applications based on complex data, like images, have mostly been left out of the spotlight. We intend to raise awareness about these kinds of problems for safety-critical and/or industrial applications, where trained models may be discriminating against a certain group (or situation) in the form of a biased decision or diminished performance. We point out that a team developing an NN-based application might simply be unaware of this behaviour until the application is deployed. In this case, specific groups of end-users may observe that it does not work as intended. A typical example of undesired algorithmic bias in image analysis applications is the one that was made popular by the paper presenting the LIME explainability technique [22]. Indeed, the authors trained a neural network to discriminate images representing wolves and huskies. Despite the NN's reasonable accuracy, it was still basing itself off of spurious correlations, i.e., the presence or not of snow in the background, to detect whether the image contained a wolf or a husky. Another example that will be at the heart of this paper is a blue veil effect in satellite images, which will be discussed in Section 7. When present, these biases provide a shortcut for the models to achieve a higher accuracy score both in the training and test datasets, although the logic behind the decision rules is generally false. This phenomenon is often modelled by the use of confounding variables in statistics, which hinder the models' performance when predicting a sample from the disadvantaged group. This makes it completely clear that all harmful biases must be addressed in industrial and safety-critical applications, as algorithmic biases might render the general performance guarantees useless in specific or uncommon situations. In order to address these concerns, our paper is structured as follows:  
* In Section 2, we rigorously define which types of algorithmic biases are commonly observed in machine learning applications based on images, and what their causes are. * In Sections 3-5, we then give a comprehensive overview of various methods to measure, to detect and to mitigate algorithmic biases. Note that Section 4 distinguishes the cases where the potential algorithmic biases are either due to suspected or to unsuspected sensitive variables, the second case being of particular interest in our paper. * In Sections 6 and 7, we finally propose and illustrate a generic pipeline to detect and to mitigate the algorithmic biases observed on unsuspected sensitive variables.
###### Abstract
It can be seen that, due to the same orbital inclination of these two satellites, the distribution of the relative value of the velocity in the look direction of the two beams is approximately symmetrical; however, the absolute value is different due to the influence of wind and wave motion. Figure 7c,d shows the wave velocity when the current field is set to zero under the same wind field conditions, in which the absolute value of the velocity changes due to the change of the relationship between wind direction and beam look direction. When the wind direction is perpendicular to the beam look direction, the wave velocity is zero, which is consistent with the research conclusion in the literature [11]. Figure 6: Simulation input sea-surface environment data. (**a**) current field and (**b**) wind field. Figure 7: (**a**) Sea-surface radial velocity along the ascending beam. (**b**) Sea-surface radial velocity along the descending beam. (**c**) Wave velocity along the ascending beam. (**d**) Wave velocity along the descending beam. The statistical analysis of the experimental data is shown in Figure 10. Figure 10 shows a scatterplot of the velocity of the simulated current field compared with that of the measured current field from SAR data, with a correlation coefficient greater than 0.9 and a root mean square error (RMSE) of 0.028 m/s. Figure 10 shows the scatterplot comparing the direction of the simulated current compared with that of the measurement data. The correlation coefficient is greater than 0.98 and the root mean square error (RMSE) is 6.05 degrees. It can be seen that the simulated current field is in good agreement with the measured current of SAR data. We also notice that there is a visible deviation between these two data, which will not only come from the difference of input data but also be limited by the model. For example, the wave spectrum is different from the real sea-surface state. Nevertheless, we believe that the simulation results are consistent with the measurement results. Therefore, the proposed method in this paper can effectively simulate the two-dimensional sea-surface-current field in the given spaceborne SAR system and marine environment parameters. The results also show that the proposed simulation method is conducive to the parameter analysis process of the trajectory-crossing method in the application of current measurement in the future.
## 4. Scope and Applicability
### Business Critical Applications: Initial Results
This efficiency exists, as without this tool the user would have to search each CSP individually to identify matches and reach a decision about the best solution on the information that was discovered manually;
2. The **operation and self-healing** phase enhances the fulfilment of the application's Service Level Agreement (SLA). As the SLA, and the underlying SLAs of the running cloud services, can continuously be monitored and the violations are automatically detected, so the application's SLA is hardly broken  
Based on these initial results we can envision greater impacts in future implementations of the proposed CloudOps framework, in the two proposed test cases in Sections 4.1 and 4.2. Especially when we will incorporate into the validation process new infrastructural elements (such as edge or IoT agents) and the complete functionality described.
# Reversible Data Hiding Scheme in Homomorphic Encrypted Image Based on EC-EG
## 4 Simulation Experiment and Theoretical Analysis
### Analysis of Security
#### 4.3.2 Correlation Analysis of Adjacent Pixels  
To test the correlation of two pixels that are vertically adjacent, horizontally adjacent, and diagonally adjacent, the following test was performed. In order not to lose generality, we used a random function to select any 1000 pairs of adjacent pixels in the image. The correlation coefficient of the original image should be close to 1, and the correlation coefficient of the encrypted image should be much smaller than 1, indicating that the correlation between the cover image and the encrypted image is completely separated. The correlation coefficient (\(r_{xy}\in[-1,1]\)) (\(-1\) indicates a negative correlation, and 1 indicates a positive correlation) of the adjacent pixels of the image is:  
\[r_{xy}=\frac{\text{cov}(x,y)}{\sqrt{D(x)}\sqrt{D(y)}} \tag{25}\]  
In Equation (25),  
\[\begin{split} E(x)&=\frac{1}{N}\sum_{i=1}^{N}x_{i} \\ D(x)&=\frac{1}{N-1}\sum_{i=1}^{N}\left(x_{i}-E(x) \right)\\ \text{cov}(x,y)&=\frac{1}{N-1}\sum_{i=1}^{N}\left(x _{i}-E(x)\right)(y_{i}-E(y))\end{split} \tag{26}\]  
In the above equation, the gray values of two adjacent pixels of the image are represented by \(x\) and \(y\), respectively. Table 5 shows adjacent pixel correlation coefficients of the images in Figure 6a,b. The adjacent pixel correlation of the encrypted image is much smaller than the original image. Besides, horizontal adjacent pixel correlation scatter plot of the images in Figure 6a,b,f,g,k,l,p,q are draw in Figure 9. The pixels of the encrypted image exhibit irregular characteristics.