esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep Learning-Based Estimation of Muckpile Fragmentation Using Simulated 3D Point Cloud Data
## 2 Related Work
### Estimation Methods Using 2D Images  
The rapid advancement of image analysis technology has enabled the development of methods for estimating particle size distribution using image analysis. WipFrag, for instance, is a system that estimates the particle size distribution of blast muckpile using digital image analysis and videotape images [4]. Despite its ease of use and widespread application in mining sites, several drawbacks have been identified with this estimation method. The automatic contouring tool built into the 2D image processing system requires manual correction, making it less user-friendly. This process can be time-consuming and prone to human error, leading to potential inaccuracies in the estimated particle size distribution [11]. Additionally, the quality of the input images has a significant impact on the accuracy of the estimation. Factors such as lighting conditions, camera angle, and image resolution can all influence the reliability of the results [12].  
Furthermore, estimating the particle size distribution of the entire blast muckpile necessitates the analysis of multiple moving photographs, each requiring calibration. The process of calibrating multiple images can be cumbersome, and the complexity increases with the number of images involved [13]. The overlapping nature of blast muckpile and the potential for particles to be too small for recognition can lead to overestimation of particle size distribution [14]. In an effort to address these limitations, researchers have proposed alternative methods, such as those based on stereoscopic imaging and 3D reconstruction [2; 15]. These approaches aim to improve the accuracy of particle size distribution estimation by leveraging the additional depth information provided by multiple images or 3D models. However, these methods can also be more computationally intensive and may require specialized equipment, potentially limiting their widespread adoption in the mining industry.
# On-Orbit Absolute Radiometric Calibration and Validation of ZY3-02 Satellite Multispectral Sensor
## 2 Materials
### ZY3-02 Multispectral Imager (MUX)  
The ZY3-02 multispectral imager produces a GSD equal to 5.8 m at nadir in all four multispectral bands and a swath width of 51 km from a nominal altitude of 506 km. The normalized spectral response function of ZY3-02 MUX is shown in Figure 1. Several of the key characteristics of ZY3-02 MUX are listed in Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**ZY3-02 Satellite** & **Technical Specification** \\ \hline \multirow{4}{*}{Spatial resolution} & Panchromatic: nadir-view: 2.1 m (GSD) \\ \cline{2-3}  & forward-view (+22\({}^{\circ}\)): 2.5 m (GSD) \\ \cline{2-3}  & backward-view (\(-22\)\({}^{\circ}\)): 2.5 m (GSD) \\ \cline{2-3}  & Multi-spectral: nadir-view: 5.8 m (GSD) \\ \hline Dynamic Range & 10 bits/pixel; time-delayed integral imaging \\ \hline Swath width & 51 km \\ \hline \multirow{2}{*}{Attitude determination and control} & Three-axis stabilization \\ \cline{2-3}  & Sensors: star sensor, solid inertia reference: GPS \\ \hline Pointing Accuracy & 0.1\({}^{\circ}\) \\ \hline Side-sway Ability & Steer up to \(\pm\)32\({}^{\circ}\) \\ \hline \multirow{2}{*}{Cycle} & Repeat cycle time: 59 days \\ \cline{2-3}  & Revisit time: 5 days \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._  
Figure 1: Normalized spectral response function of the ZY3 MUX.
# Study of the Effect of Exploiting 3D Semantic Segmentation in LiDAR Odometry
## 4 Results and Discussion
### Discussion
Furthermore, it may also appear that all points removed in this test case are prone to introduce noise to the system, as is the case for highly dynamic objects and road points, and therefore they may worsen the estimation from other configurations. Additionally, this configuration also shows adequate results in Sequence 05. This sequence also includes some kind of small walls close to the road, in a very similar way as the guardrails on the highway. We believe that having these close structures provides benefits in the matching. Nevertheless, the good results are obtained only in very specific cases, while most of the times, the algorithm gets lost because of the lack of information due to the high downsizing. Finally, we can clearly observe in Figure 3 and Tables 1 and 2 that removing the ground points prior to computing the odometry generally produces better results. Among the 44 test cases (including all metrics and all sequences), this configuration gives the best results in 24 of them. The only sequence that presents a problem for this configuration is Sequence 01, because of the challenging highway environment. The negative effect of ground points in the odometry algorithm is due to the uniformity of the road. When the LiDAR laser beams hit the road, they create rings of points. In a flat road, these rings will always have the same radius, which depends on the height and the characteristics of the sensor. Consequently, the points that are created always have the same coordinates, even if the vehicle is actually moving. This effect is simplified in Figure 5, where it can be observed how the distance to the object reflects the translation of the vehicle, while the distances to the points laying on the ground remain static. This absence of movement in the points may mislead the odometry algorithm into believing that there is no translation movement from the car. However, as shown in Figure 3, removing the ground points does affect the rotation error, greatly increasing the variance, while also slightly increasing the median. This leads to the conclusion that the ground points induce a negative effect in the translation component, while still being beneficial for the rotation angle. However, this configuration is the one that produces better results overall, as presented in Tables 1 and 2.
## 7 Conclusions  
The space observation radar TIRA is undergoing a transformative upgrade in order to fulfill upcoming requirements for space domain awareness, space safety, and space sustainability. For this, the current imaging radar working in the Ku band will be replaced by a new imaging radar which will operate in the Ka band. With this new frequency band, a higher bandwidth will enable a higher spatial resolution for the radar images. The new Ka-band amplifiers will also provide significantly increased transmit power compared to the current Ku-band system. Together with the higher center frequency, a gain in sensitivity will be reached. Please note that some radar parameters are classified and cannot be listed in this paper. Another benefit of the new imaging radar will be the availability of fully polarimetric radar images. This will allow a much more detailed analysis of space objects. In addition, the complete processing control infrastructure of the current tracking radar in the L band is facing a major overhaul, which will enable continuous incremental upgrades in the future through a modular and open design. Tracking will be carried out in a new combined mode fusing the data acquired by the tracking and the imaging radars. Also, new signal processing techniques are under development to improve the characterization of space objects by improving the imaging quality and exploiting the polarimetric information.  
Writing--original draft, review and editing, J.K., F.B., C.C., D.C.-M., L.F., C.H., V.K., M.L., A.M., R.P., S.R. and F.R. All authors have read and agreed to the published version of the manuscript.  
The projects detailed in this article are funded by the German Federal Ministry of Defence (BMVg).  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
ADC Analog-to-digital conversion  
ARCADIA ARchitecture Analysis and Design Integrated Approach  
ASAT Anti-satellite  
COTS Commercial off-the-shelf  
DAC Digital-to-analog conversion  
DDC Digital down-conversion  
DOA Direction of arrival  
DUC Digital up-conversion  
ERPs Earth rotation parameters  
ESA European Space Agency  
FFT Fast Fourier transform
# RSS Indoor Localization Based on a Single Access Point
## 2 Related Work
### RTLS Performance Evaluation Techniques
Based on the analysis of this algorithm, the Correlation Score (\(CS_{pairAB}\)) of any pair of random fingerprint entries, A \((x_{A},y_{A})\) and B \((x_{B},y_{B})\), for any active AP (\(AP_{i}\)), without introducing the \(RSS_{tol}\) parameter, is given by the formula:  
\[CS_{pairAB}=\frac{1}{\sigma_{\overline{RSS}_{AP_{i}}}\sqrt{2\pi}}e^{-\frac{1}{ 2}(\frac{\overline{RSS}_{B}-\overline{RSS}_{A}}{\overline{RSS}_{AP_{i}}})^{2}} \tag{3}\]  
where random fingerprint entries \(A,B\in Area_{radiomap}\), and \(\sigma_{\overline{RSS}_{AP_{i}}}\) is the standard deviation of the \(\overline{RSS}\) values observed from each \(AP_{i}\) as:  
\[\sigma_{\overline{RSS}_{AP_{i}}}=\sqrt{\frac{\sum_{i=1}^{n}\overline{RSS}_{i}^ {2}-\left(\frac{\sum_{i=1}^{n}\overline{RSS}_{i}}{n}\right)^{2}}{n-1}}, \tag{4}\]  
where \(n\) is the number of fingerprint entries in the radio map and \(\overline{RSS}_{i}\geq MS_{Sensitivity}\). By introducing the \(RSS_{tol}\) parameter in Formula (3), the correlation score \(CS_{TBNPD}\), is formulated. The \(CS_{TBNPD}\) for a pair of random points \(A,B\in Area_{radiomap}\) and for any \(AP_{i}\) is calculated by (5) below:  
\[CS_{TBNPD_{pairAB}}=\frac{1}{\sigma_{\overline{RSS}_{AP_{i}}}\sqrt{2\pi}}e^{- \frac{1}{2}(\frac{\overline{RSS}_{diff}}{\overline{RSS}_{AP_{i}}})^{2}} \tag{5}\]  
where  
\[\overline{RSS}_{diff}=\left|\left(\overline{RSS}_{A}\right)_{AP_{i}}-\left( \overline{RSS}_{B}\right)_{AP_{i}}\right|-2 RSS_{tol}. \tag{6}\]  
In Formula (6), \(RSS_{diff}\geq 0\). For \(RSS_{diff}<0\), the value was set to 0, since the range of the RSS values of the two fingerprint entries overlap, indicating a high level of correlation. The total correlation score (\(CS_{TBNPD_{total}}\)) to be utilized to assess any candidate radiomap is the product of the correlation scores of all active APs:  
\[CS_{TBNPD_{total}}=\prod_{AP=1}^{m}\left(CS_{TBNPD_{pairAB}}\right). \tag{7}\]
## 3 Model Structure
### Construct Spatiotemporal Prediction Model  
We designed an orbital satellite region tracking prediction model composed of two parts using graph convolutional networks. In the first part of the model, we designed two graph neural networks, each with two layers of graph convolutions. Accordingly, we input the intra-frame graph structure data of two adjacent frames into each graph neural network with two layers of graph convolutions, then concatenated the two output results to obtain the final output of the first part of the network. Thus, the feature extraction process of one of the graph neural networks in the first part could be expressed as follows:  
\[f_{\text{gen}}(G)=F_{\text{gen2}}(F_{\text{gen1}}(G)) \tag{17}\]  
\[F_{\text{gen1}}(G)=\text{ReLU}\Big{(}F_{\text{gcf}}(\hat{A},D,F)W\Big{)} \tag{18}\]  
where \(W\) was the graph convolution kernel. \(F_{\text{gcf}}\) was the graph convolution function, and could be expressed as follows:  
\[F_{\text{gcf}}=\Big{(}\tilde{D}^{-\frac{1}{2}}\hat{A}\tilde{D}^{-\frac{1}{2}} \Big{)}F \tag{19}\]  
where \(\tilde{A}=A+I,\tilde{D}=D+I\), \(I\) was the identity matrix, and F was the feature matrix, as shown in Equation (10).  
The second part of the model consisted of a two-layer graph convolutional network, and the computation process is referred to in Equations (17)-(19). Unlike the previous section, we used the inter-frame graph structure as the input to the second part of the model, as described in Section 3.2.2. The detailed structure of the model that predicted the third frame based on information from two consecutive frames is shown in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Layer** & **Input Size** & **Output Size** & **Activation** & **Source of Data** & **Operate** \\ \hline
1\_1 & \(N\times 1\) & \(N\times 4\) & ReLU & \(F_{t-1}\) and \(\hat{A}_{t-1}\) & \\ \hline
1\_2 & \(N\times 4\) & \(N\times 1\) & ReLU & & \\ \hline
2\_1 & \(N\times 1\) & \(N\times 4\) & ReLU & \(F_{t}\) and \(\hat{A}_{t}\) & \\ \hline
2\_2 & \(N\times 4\) & \(N\times 1\) & ReLU & \(F_{t}\) and \(\hat{A}_{t}\) & \\ \hline
2\_3 & \(2\times N\times 1\) & \(N\times 2\) & – & Output of 1\_2 and 2\_2 & Cat \\ \hline
3\_1 & \(N\times 2\) & \(N\times 4\) & ReLU & \(F_{t-1,t}\) of 2\_3 and \(\hat{A}_{t-1,t}\) & \\ \hline
3\_2 & \(N\times 4\) & \(N\times 1\) & ReLU & \(F_{t-1,t}\) of 3\_1 and \(\hat{A}_{t-1,t}\) & \\ \hline \hline \end{tabular}
\end{table}
Table 1: The specific structure of the model.
# CubeSat Mission Scheduling Method Considering Operational Reliability
## 4 CubeSat Scheduling Model
### Solution by the Branch and Bound (BB) Method  
The CubeSat task scheduling model in this paper is a mixed integer programming (MIP) model, and its standard form is as follows:  
\[\left\{\begin{array}{c}max^{T}x+h^{T}y+k^{T}z\\ s.t.\ \ \ A_{i}x+B_{i}y+C_{i}z\leq b,\forall i\in\{1,2,\dots,m\}\\ x\in\{0,1\}\\ y,z\in R\end{array}\right. \tag{26}\]  
where \(max^{T}x+h^{T}y+k^{T}z\) is the objective function; \(x\) is the decision variable, which only takes \(0\) or \(1\); \(A_{i}x+B_{i}y+C_{i}z\leq b\) is the constraint; and \(m\) is the number of constraints.  
This kind of problem can be solved by the BB method, cut plane method, implicit enumeration method, etc. Considering the small scale of the model, the BB method is used to obtain the global optimal solution, which can obtain its relaxed problem by relaxing the constraints of the integer programming problem. If the optimal solution to the relaxed problem guarantees that each variable is an integer, then the original problem is solved. If the value of the variable in the optimal solution of the relaxed problem is not an integer, further \"branching\" and \"bounding\" operations are required. In this paper, the BB method is used to solve the CubeSat scheduling problem [35], and the optimal scheduling scheme of the task is obtained to realize the scheduling of the CubeSat task. The BB method in this paper is the same as the method described in [35], and the flowchart is shown in Figure 1.  
As shown in Figure 1, the CubeSat scheduling model in this paper can be solved by the BB method. First, relax the constraints on \(x_{j,t}\), \(\phi_{j,t}\), and \(\phi^{\prime}_{j,t}\) to solve the linear programming problem and obtain the optimal solution. If the optimal solution is a 0-1 integer and \"branching\" is completed, then we obtain the final result; otherwise, continue to carry out the \"branching\" operation until the optimal solution meets the constraints.  
Compared with intelligent algorithms, this algorithm has higher solving accuracy and better ability to solve real-time problems, but when the scale of operation is large, the algorithm's solving efficiency is relatively low.  
Figure 1: Flow chart of the BB method.
## 3 Results and Discussion
### Spectral Radius of the Jacobian Matrix  
Next, the variation of the spectral radius of the Jacobian matrix in the Poincare application (Equation (47)) is evaluated at the equilibrium points given by Equation (29) (see Figure 5).  
Figure 4: Evolution of voltage and current.  
Figure 5: Spectral radius as a function of \(k_{1}\) to determine the stability limit of 1T-periodic orbits.  
Figure 3: A 1T-periodic orbit.  
In this figure, \(T=0.18\), \(k_{2}=0.5\), and \(\gamma=0.35\) have been chosen. As the spectral radius values move from less than 1 to greater than 1 just at \(k_{1}=-0.26\), there is a stability change in the \(1T\)-periodic orbit. Then, for \(k_{1}=-0.26\), the system presents a bifurcation point.
# U-SSD: Improved SSD Based on U-Net Architecture for End-to-End Table Detection in Document Images
## 4 Experiment
### Evaluation Method and Parameter Settings  
As it can calculate the overlap range between the predicted target and the ground-truth target, Intersection over Union (IoU) is one of the basic measurement standards for object detection. When the overlap area is larger, the IoU value is higher, which indicates a more accurate prediction, as shown in Figure 5. As it is actually quite a strict index, the results will be relatively poor if there is a slight deviation. In general, good prediction results can be obtained if the IoU is greater than 0.5.  
True Positive (TP) is actually positive; if it is predicted to be positive, the prediction result is correct. False Positive (FP) is actually negative; if it is predicted to be positive,  
Figure 4: Data pre-processing.  
Figure 5: The measurement of intersection over union (IoU).  
the prediction result is wrong. False Negative (FN) is actually positive; if it is predicted to be negative, the prediction result is wrong. True Negative (TN) is actually negative; if it is predicted to be negative, the prediction result is correct. Precision and recall are used as the evaluation indices in this paper.  
There is a tradeoff between precision and recall. Usually, when precision is high, recall is low, and vice versa. When there is a contradiction between the two indices, the F1-score is the most commonly used measurement index, as shown in Equation (4), as it can fully consider precision and recall, especially in the case of category imbalance.  
\[F1\text{-}score=2*\frac{Precision*Recall}{Precision+Recall} \tag{4}\]  
The Precision-Recall Curve takes Precision as the Y-axis and Recall as the X-axis, and each point represents Precision and Recall under different threshold values. Average Precision (AP), which calculates the average accuracy of a single category, is the area under the PR curve.  
The experimental environment settings are shown in Table 1. This paper scaled the image input of the dataset to 300 \(\times\) 300 pixels, the same as SSD, and used the Adam optimizer. After 100 times of Epochs training, the learning rate of the first 50 times was 0.0005, and the batch size was 8, and the learning rate of the last 50 times was 0.0001, and the batch size was 4.
## 6 Experiments
### Experiment II: Models Tested with Wind-Speed Data From Site 1
\begin{table}
\begin{tabular}{l l c c c c c} \hline \hline \multicolumn{2}{c}{**Number of Observations**} & \multicolumn{3}{c}{**Number of Rows without Any NaN Values.**} \\ \hline \multirow{4}{*}{Error degrees of freedom} & \(n-p\), where \(n\) is the number of observations, and \(p\) is the number of coefficients in the model, including the intercept. & \\  & & & Coefficient of determination and adjusted coefficient of determination, respectively & \\ \multirow{2}{*}{\(R\)-squared and Adjusted \(R\)-squared} & \multirow{2}{*}{Test statistic for the \(F\)-test on the regression model. It tests for a significant regression relationship between the response variable} & \multirow{2}{*}{and the predictor variables} & \multirow{2}{*}{\(p\)-value for the F statistic of the hypotheses test that the} \\  & & & & & & \\ \multirow{2}{*}{\(p\)-Value} & \multirow{2}{*}{\(p\)-value} & \multirow{2}{*}{corresponding coefficient is equal to zero or not.} \\ \end{tabular}
\end{table} 
Table 3: The explanations of the test parameters. (a) The results of Table 4 show the following:  
(1) CS-BPNN reached the best results in Tuesday, Wednesday, and Thursday compared to other branch models, with MAPE values being 3.726%, 4.081%, and 5.173%, respectively. (2) On Monday and Sunday, DE-OSELM obtained the lowest MAPE compared with other branch models. (3) HW achieved the most accurate forecasting value of all branch models on Friday and Saturday. (4) Although ARIMA could achieve higher forecasting precision, their forecasting performance was worse than nonlinear models and HW  
(5) Our combined model had a significant improvement in forecasting accuracy with a lower MAPE compared with all branch models. The MAPE values from Monday to Sunday are 5.691%, 3.698%, 4.078%, 5.166%, 5.692%, 6.443%, and 5.082%, respectively. (b) The results of Figure 4 show the following:  
(1) Part A shows the MAE, MSE, and MAPE of five models, although our combined model did not achieve the lowest MAE every day; the lowest MSE and MAPE were obtained by our proposed model. (2) Part B shows the forecasting results of CS-BPNN, DE-OSELM, ARIMA, HW, and the combined model. (3) Part C also shows the 95% confidence intervals (CIs) obtained by each model; the figure indicates that both the upper and lower CIs were close between four branch models but, for linear models, there were more points in the confidence interval. As Part C shows, the errors of the combined model were very small, and our combined model also achieved a small CI. **Remark 1. **_From Table 4 and Figure 4, the results indicate that our proposed model showed better performance than the other branch models. In brief, it can be explained that SSA, which could denoise the time-series  
Figure 4: The forecasting results from Site 1.  
wind-speed data as a preprocessing method, and the combined model, which took advantage of the branch models, could improve forecasting accuracy._
## 5 Comparative Analysis of Incremental Launching Monitoring Results
### Steel Box Girder Displacement Monitoring  
The theoretical displacements at the leading and trailing edges of the steel box girder for five operational scenarios are provided by the finite element analysis and contrasted with the monitoring data. Figure 11 shows the overall vertical deformation cloud map of the finite element simulation of the steel box girder. Table 2 shows the calculated and monitored values of the displacement of the steel box girder under five working conditions, and the displacement limit is given according to reference [12]. According to the data in the table, the displacement is within the limit value, indicating that the displacement of the steel box girder meets the safety design requirements.  
Figure 11: Overall deformation under five working conditions. (**a–e**) Corresponding working conditions 1–5.
# Towards a Collection of Security and Privacy Patterns
## 4 Privacy Patterns
### Undetectability
#### 4.3.1 Cover Traffic  
Considering the importance of generating dummy traffic to increase the unlinkability set and negate any attempts for linking users to their actions, a valuable addition in any orchestration requiring unlinkability is one of a proxy generating fake requests and dummy traffic. Such a proxy will typically be placed between the user and the services of interest (Figure 22), ensuring that third parties cannot map specific users to specific requests of access to a service or a resource.  
Figure 21: Conceptual overview of Unlinkability and Undetectability process pattern [49].  
A similar strategy can be followed to protect the data at rest--e.g., leveraging dummy files' generation through the technique provided in [82], whereby authors describe a fake online repository generation engine for cyber deception.