esa-sceva/satcom-chunk-collection huggingface dataset:
# Releasing Differentially Private Trajectories with Optimized Data Utility
## Appendix A
### Parameter Settings  
Parameters in our experiments are listed in Table 11. (i) In Algorithm 1, the (total) privacy budget of UDPT takes values in the interval \([0.1,10]\), which is a widely-accepted convention in the literature. The division of the (total) budget is \(1/3:1/2:1/6\) for Algorithm 2, 3 and 4, respectively. Note that a larger privacy budget (piece) usually brings about better preservation of data utility. We allocate larger budget piece to Algorithms 2 and 3, because their iterations consume more budgets for noise injecting. In particular, we allocate the largest budget piece to Algorithm 3 for simultaneously preserving the geographical and semantic utility as much as possible. (ii) In Algorithm 2, we set the number of cluster as 100. In this case, the diameter of a single cluster is around one to two kilometers, which can represent a real-world point-of-interest, e.g., a hospital. The maximum number of iterations in clustering, \(p\), depends on the experimental datasets. In this work, \([20,50]\) is a suitable interval for \(p\). (iii) In Algorithm 3, we set the number of candidate obfuscated locations, \(M\), as 6. A larger value results in better data utility while larger time cost. In our experiments, \(M=6\) can achieve an acceptable trade-off between the time cost and data utility. The previous work [29] related to genetic algorithm has demonstrated that without domain-specific information, the setting of \(m=200\), \(m^{\prime}=10\), and \(r\in[20,40]\) generally leads to good results. We follow this setting in our experiments. The weight of geographical utility, \(\alpha\), indicates the data curator's preference for the geographical utility compared with the semantic utility; thus, it actually should be determined by the data curator. In this work, to demonstrate that UDPT's outperformance, in terms of semantic utility, we tune \(\alpha\) until UDPT achieves similar geographical utility with its competitors. In this case, \(\alpha\) is around 0.5. (iv) In Algorithm 4, the number of candidate obfuscated trajectories is set as 10. Since UDPT synthesizes \(N\) obfuscated trajectories for each actual trajectory in \(D_{A}\), the data curator can exploit \(N\) to control the size of \(D_{O}\). In this work, 10 is a moderate value for \(N\)
## 4 Performance of the Evaluated Photodetector  
The FBR2178 GB photodetector under test is an SMF-coupled APD-TIA produced by FiBest and operating at 1550 nm wavelength. It is soldered on a customized printed circuit board (PCB). Since this is a typical PD deployed in next-generation OGSs for near-Earth FSO communication, the supported 3 dB bandwidth is 8.4 GHz offering up to 10 Gbps. The maximum breakdown PD supply voltage (\(V_{\text{pd}}\)) is 31.5 V, and TIA supply voltage is 3.3 V with 1.021 A/W maximum responsivity offering high sensitivity. In order to achieve an APD \(M\)-factor = 9, the setup \(V_{\text{pd}}\) is 30 V providing maximum sensitivity (\(<\)\(-\)27 dBm) for a given data rate with a damage threshold \(P_{\text{th}}\) of \(-\)5.5 dBm.  
In order to validate the maximum performance of the used FBR2178 GB APD-TIA photodetector, ideal atmospheric conditions without any atmospheric-induced fading or attenuation and a maximum data rate of 10 Gbps are selected. The experimental setup is shown in Figure 10, and it is based on 10 Gbps MZM fed by a 1550 nm tunable laser source.  
Figure 9: Joint PDF characterizing a space-ground FSO system that operates into Mie scattering (clouds) and atmospheric-turbulence-induced fading based on 12 months data.  
For maximum MZM efficiency, the in-house prepared MZM driver operates at a bias point of 4.2 V, while the driving voltage is 2 V.  
In addition, its extinction ratio at DC is 20 dB, which is a low value compared to the minimum 25 dB value given in the recommendations for space-qualified modulators [20]. Moreover, the channel emulator is used as a tunable VOA, allowing step measurements of the BER curve at 10 Gbps. The performance measurements of APD-TIA operating at 10 Gbps are provided in Section 5, where they are compared with the operation of APD-TIA at 1 Gbps in the presence of atmospheric-turbulence-induced fading.
# Evaluating the High Frequency Behavior of the Modified Grounding Scheme in Wind Farms
###### Abstract  
Wind generators are exposed to numerous destructive forces such as lightning and are therefore vulnerable to these phenomena. To evaluate the transient behavior of a wind power plant during direct and indirect strikes, modeling of all relevant components is required. Among the protective and control components of wind turbines, the grounding system is the most important element for protection against lightning strikes. This paper examines the impact of nonlinear soil ionization behavior and frequency dependency on a wind turbine in order to model a sufficient protection scheme to reduce overvoltage and make the system tolerable against transitions. The high frequency models of other equipment such as transformers, horizontal conductors, vertical rods, surge arresters and underground cables must also be taken into account to design the grounding system. Our Proposed Modified Grounding Scheme (PMGS) is to reduce the maximum transient overvoltages. We simulate the model in a restructured version of the Electromagnetic Transient Program (EMTP-RV) software to examine the effectiveness of the system. We then apply the simulated results to pair of turbines that are interconnected with a frequency-dependent cable. We carry out the simulation for direct and indirect lightning strikes. The results indicate that the MGS can lead to considerably more than a 50% reduction in transient voltages for lightning and thus leads to more reliable networks.  
wind turbine; lightning; high frequency model; overvoltages; soil ionization; grounding scheme +
Footnote †: journal: Applied Physics Letters
## 2 Data and Pre-Processing
### Pre-Processing  
By adopting spectral band combinations 7/5/4, 7/4/3, 6/5/4, and 4/3/2 combined with visual interpretation, a sample dataset is selected from Landsat images for classification; the sample set contains 340 water samples and 454 non-water samples. To avoid the influences of heterogeneous categories in the subsequent classification, the ratio of other ground object samples to the water body samples remains at 1.3:1.  
The characteristics of the data, such as a large correlation between multiple spectral bands in the original images and similar information and structures between different spectral bands, generally bring significant amounts of redundancy. For this reason, principal component analysis (PCA) for dimensionality reduction is applied to remove repetitive and redundant information between various spectral bands [56]. The first and second principal components in the PCA with a cumulative variance contribution of 99% are selected as classification characteristics.  
Based on the PCA, four generally used texture features, i.e., contrast, autocorrelation, dissimilarity, and entropy are extracted. The distance is set to be 1 pixel (distance of 30 m), 2 pixels (distance of 60 m), and 3 pixel (distance of 90 m), and 3 \(\times\) 3, 5 \(\times\) 5, 7 \(\times\) 7, and 9 \(\times\) 9 are selected as windows with orientations of 0\({}^{\circ}\), 45\({}^{\circ}\), 90\({}^{\circ}\), and 135\({}^{\circ}\). Optimal combined features are selected as the characteristic spectral bands for water body extraction. When the two parameters--i.e., window size and distance--increase, the edges of the images get fuzzy, and the window size shows more effects than distance. Considering the factors of ground objects correlation and image resolution, we set the distance to 1 pixel and select a 3 \(\times\) 3 window with four orientations of 0\({}^{\circ}\), 45\({}^{\circ}\), 90\({}^{\circ}\), and 135\({}^{\circ}\).  
Figure 1: Landsat-8 remote sensing images are displayed in false color in bands 7, 5, and 3. Three local areas are extracted from this image. Areal has a large area of water distribution with a simple ground environment and is only affected by vegetation; Area2 is affected by mountain shadow and vegetation; Area3 is located in the urban built-up area with scattered water distribution and is affected by roads and buildings.  
After the size and window parameters are determined, J-M distance [57; 58] and transformed divergence [59] (T-D) in many extracted texture features are used for studying the separability of ground objects; thus, the characteristics ultimately used for classification are determined as well. As shown in Table 2, the separability of the first component (PCA1) and the second component (PCA2) is compared in detail, and the separability of J-M dissimilarity in PCA2 is the optimal. Therefore, in later classifications, a total of six characteristics are selected.
# Mobile Data-Mule Optimal Path Planning for Wireless Sensor Networks
## 5 Simulation Studies
### Data Mule for Medium Complexity WSN (\(M=3,n=13\))--No Priorities Assigned  
In this section, we consider the aforementioned case study having deactivated the prioritized nodes scheme. Hence, despite the estimation of priority nodes set \(Pr\) via the MES scheme after each iteration, the corresponding re-calculation of each static node's profit based on its priority (Algorithm 4) is not applied. The profit \(p_{j}\) is equal for all the static nodes, and equalities (3) and (4) and inequalities set (12), which account for the prioritization constraints not being used anymore. In Figure 5 and Table 4, the extracted routes along with the estimated values for the optimization problem variables for the first 10 iterations are stored. The optimization extracts exactly the same routes in each iteration, as shown in this Figure and in the second column of the aforementioned Table. This behavior resulted from the assumption that the prioritized nodes are no longer considered as input for the next iteration of the algorithm. Thus, the solution of the MILP problem provides the same routes, which tend to visit as many nodes as possible in order to collect higher profit and concurrently meet the time budget limit \(B\) that is assigned for each route. Due to this profit collection, the MoEl 'prefers' to visit more nodes within its route rather than visiting just one distant node (e.g., node 9 or 11). In a similar way, the extracted routes do not include node 3, despite its closeness compared to nodes 9 and 11, because this node has a capacity of 8 Kbytes requiring a visiting time of \(v_{t_{j}}=68\) s, thus resulting in higher travel and service cost. The priority nodes and the estimated expiration time for all the nodes appear in the fifth and sixth columns of Table 4 for each iteration. However, this priority is not considered in the next iteration, resulting in these nodes' data loss in the upcoming iteration (last column of the Table). Figure 4: Mobile Element Scheduling Routes with 3 MoEls and 13 Nodes.
## 3 Numerical Discussion
### Analysis of Vibration Power Flow  
The vibration power flow of the beam can be expressed as [39]:  
\[P=\frac{1}{2}\text{Re}\{FV^{*}\}=\frac{1}{2}[\text{Re}\{F\}\text{Re}\{V\}+\text{ Im}\{F\}\text{Im}\{V\}] \tag{26}\]  
Because the real and imaginary parts of dynamic displacement and force are all verified above, the vibration power flow is assumed correct here. Due to the excitation force in the plane, there are only three kinds of power flow in the X, Y, and rotate Z direction. The in-plane three kinds of vibration power flow of point P2 are shown in Figure 9. They have some common peak frequencies, and in the low and high-frequency domains, power flow in the Z direction is the largest. In the middle-frequency range, all three kinds of power flow are almost the same level.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Modes No.** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** \\ \hline Zuo [23] & 4.71 & 27.58 & 70.09 & 85.58 & 122.55 & 176.45 & 224.25 & 232.45 \\ FEM (400 elements) & 4.71 & 27.58 & 70.16 & 85.68 & 122.94 & 177.81 & 228.60 & 236.88 \\ FEM (800 elements) & 4.71 & 27.58 & 70.11 & 85.60 & 122.65 & 176.73 & 225.24 & 233.45 \\ The present & 4.71 & 27.58 & 70.08 & 85.57 & 122.55 & 176.38 & 224.10 & 232.30 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of the natural frequency with literature.  
Figure 8: Force comparison of ISM and FEM of point P7 in the Z direction.  
Figure 9: Vibration power flow of P2 in the z-direction.  
The vibration power flow of point P2 can also be divided into three sub-structure beams, A1/A2 and A5. The vibration power flow of three substructures in the Z direction is compared in Figure 10. The harmonic excitation force is applied at point P1 in the z-direction, so the input vibration power for beam A1 is mainly the axial direction. However, there exists a vibration wave transfer phenomenon on point P2; due to the geometry discontinue, we can easily see that the axial vibration power of beam A2 is smaller than the other two, especially in the low-frequency domain.  
On the contract, the compared three flexural vibration power flows in the Y direction are shown in Figure 11. The vibration power flow in beam A1 is the smallest, almost in the whole frequency range. However, the vibration powers of points A2 and A5 in the Y direction are almost overlapped.  
Figure 12 shows the in-plane torsional vibration power flow. We can see that the three beams are almost at the same level.  
Figure 10: Vibration power flow of P2 in the Z direction.  
Compared to the above three figures, there are some common peak frequencies in all three kinds of vibration power flow; these are the natural frequencies of the whole dynamic truss-like model.
# Extrinsic Bayesian Optimization on Manifolds
## 4 Examples and Applications  
To illustrate the broad applications of our eBO framework, we utilize a large class of examples with data domains on different manifolds, including spheres, positive definite matrices, and the Grassmann manifold. We construct the extrinsic kernels for eGPs based on the corresponding embedding map on the specific manifold from [45]. In this section, we show the simulation studies in Sections 4.1 and 4.2 and the main application on real data in Section 4.3. In Section 4.1, the Frechet mean estimation is carried out with data distributed on the sphere. In Section 4.2, the eBO method is applied to the matrix approximation problem on Grassmannians. Lastly, Section 4.3 considers a kernel regression problem on the manifold of positive definite matrices, which has essential applications in neuroimaging. We use the eBO method to solve the regression problem and show the difference between healthy samples and HIV+ samples. For all examples in this section, we consider the squared exponential kernel from [45] as follows:\[K_{ext}(x,z)=\alpha exp(-\beta\rho(x,z)^{2}), \tag{12}\]  
where \(\rho(x,z)=\|J(x)-J(z)\|\). The explicit form of embedding \(J\) depends on the specific manifolds, which would be illustrated in the simulation cases. We initially set kernel coefficients as \(\alpha=1,\beta=0.5\), and the regularization coefficient \(\sigma=0.01\). After adding a new point to the data in Algorithm 2, we also update those coefficients by maximizing the likelihood of all data.
# Quality Assessment of Photogrammetric Methods--A Workflow for Reproducible UAS Orthomosaics
## 5 Conclusions  
The rising popularity of UAS imagery in all fields of spatial research led to a variety of processing approaches. The supposedly ease of use and low cost of ready-to-fly UAS opened up some pitfalls in the image acquisition and processing which this study addressed. The evaluation of the orthomosaic accuracy aimed at the reproducibility of the final product. The presented optimization of the georeferencing accuracy based on the checkpoint error and the quantification of the orthomosaic reproducibility enhance the UAS workflow with the necessary quality assessment. This complements the standardized acquisition of high quality UAS time series.  
In forest environments, there are still some shortcomings of UAS orthomosaic reproducibility that quantitative analyses need to consider. In grassland environments, these issues are marginal, which supports the validity of UAS in agricultural applications. The novel approaches of this study and their incorporation into a workflow are promising for validated and transparent UAS research.  
[[https://github.com/envima/UASreproducibility](https://github.com/envima/UASreproducibility)]([https://github.com/envima/UASreproducibility](https://github.com/envima/UASreproducibility)).  
**Author Contributions:** [PERSON] analyzed data, contributed to the study design, developed the methodology, and was the main writer of the manuscript; [PERSON] analyzed data (grassland), and contributed to the manuscript; [PERSON] contributed to the study design (forest), and reviewed the manuscript; [PERSON] contributed to the study design, and reviewed the manuscript; [PERSON] provided methods; [PERSON] performed the flights (forest), and provided methods; [PERSON] provided methods; [PERSON] contributed to the study design (grassland), analyzed data, reviewed the manuscript, and supervised the project; [PERSON]-T.S. contributed to the grassland study design, reviewed the manuscript, and supervised the project; [PERSON] provided methods, contributed to the study design, contributed to the manuscript, and supervised the project; [PERSON] contributed to the study design, reviewed the manuscript, and supervised the project; All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Hessian State Ministry for Higher Education, Research and the Arts, Germany, as part of the LOEWE priority project Nature 4.0--Sensing Biodiversity. The grassland study was funded by the Spanish Science Foundation FECYT-MINECO through the BIOGEI (GL2013- 49142-C2-1-R) and IMAGINE (CGL2017-85490-R) projects, and by the University of Lleida; and supported by a FI Fellowship to C.M.R. (2019 FI_B 01167) by the Catalan Government.  
**Acknowledgments:** Thanks to [PERSON] for letting us use his meadow as a landing site.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Results and Evaluation  
### Visual Evaluation  
Based on the improved model presented in this paper, two Landsat-5 TM images (referred to as spring scene and autumn scene, respectively) acquired under different solar elevation angles were processed. The topographic correction effects were evaluated by comparing both with the original image (after atmospheric correction, serve as benchmark) and correction results from Sandmeier model (representative of models with Lambertian assumption) and Li model (representative of models without Lambertian assumption). Figure 2 shows the overall visual effect of the two TM scenes, both before and after topographic correction. Also, smaller sub-scenes in typical areas with large variations of topography were chosen and enlarged to reveal the subtle differences of correction results by different methods. It can be seen from Figure 2B,D that topographic effects were greatly suppressed after correction, with reflectance in sun-averted slopes being evidently enhanced and information in shaded areas being recovered. Reflectance of the same land cover type on sun-facing and sun-averted slopes became much more identical, and overall quality of the images were significantly improved. Moreover, the proposed model performed consistently well for both spring and autumn images.  
Performances of the three models were quite similar in gentle-slope areas. However, notable distinctions existed for severely rugged areas. From the illumination condition maps (a1 and b1) for the enlarged windows, we may partly feel the considerable ruggedness in chosen areas. Fairly serious overcorrection can be seen in faintly-illuminated area corrected by Sandmeier model (\(\zeta\)1 in a3 and \(\lambda\)1 in b3). This may be owing to the Lambertian assumption in Sandmeier model, which simplifies the anisotropic scattering behavior of land surface. And this simplification does not impact correction result too much with low incident angles, while it leads to large discrepancy from reality with high incident angles; this is consistent with [PERSON]'s conclusion [4]. Compared with Sandmeier model, Li model reduced the overcorrection to a large extent (a4 _vs._ a3, b4 _vs._ b3), by roughly considering the land surface BRDF effect. Model proposed in this paper slightly outperformed Li model (\(\zeta\)2 in a4 _vs._\(\zeta\)3 in a5, \(\lambda\)2 in b4 _vs._\(\lambda\)3 in b5) and further reduced the overcorrection phenomenon. Nevertheless, the visually detectable improvements of correction results from model proposed, when compared with that from Li model, mainly come from the over-correction adjustment of the shadow areas. The improvements for other areas are minor and inconspicuous, and hard to detect by visual evaluation. However, the improvements can be confirmed by the following quantitative analysis.  
Figure 2: _Cont._
## 3 Proposed Sensor Fusion Framework Model Using PDR and W-Fi Localization Systems
### PDR Positioning  
The PDR positioning in the proposed model uses our previous work [17] for user position estimation. In [17], we proposed a sensor fusion technique for pitch and roll estimation, a pitch-based step detector algorithm for step detection, step length estimation from step detector, a sensor fusion technique for heading estimation and a position estimator. The pitch values from the proposed sensor fusion technique are used for user step detection. The pitch-based step detector identifies the user steps from pitch amplitude. A step is detected when the pitch amplitude crosses the threshold level. The step length estimator uses the results from step detector and the step length estimator follows a model presented in [34]. The sensor fusion technique for heading estimation uses the heading results from the magnetometer and gyroscope sensors. The individual heading results of the magnetometer and gyroscope are not free from smartphone sensor errors and these heading results are not sufficient for position estimation. To compensate the magnetometer and gyroscope sensor errors, the PDR model combines the gyroscope heading with the magnetometer heading and the combined results are more accurate than individual sensor heading results. The LKF used in [33] is used for heading sensor fusion. The position estimation algorithm proposed in [35] is used for current user position estimation.  
Figure 1: Proposed sensor fusion framework for PDR and Wi-Fi.  
The position estimation algorithm takes the user step length and heading values and estimates the current user position. For more details of PDR implementation, refer to our previous work in [17].