esa-sceva/satcom-chunk-collection huggingface dataset:
# Design of Novel Laser Crosslink Systems Using Nanosatellites in Formation Flying: The VISION
## 3 Concept of the Laser Crosslink Systems
### Systems Design Specifications  
To ensure the design feasibility of the laser crosslink mission, the system design specifications, listed in Table 5, were evaluated using software simulations, and the systems meet the requirements. The total mass is 11.36 kg, and the dimensions satisfy the 6U CubeSat standard. The operation orbit was selected as the sun synchronous orbit with the local time of ascending node (LTAN) of 18:00 and altitude of 600 km. The laser crosslink can be accomplished with a sufficient SNR margin for all operational inter-satellite distances. At the end of the PAT sequence, the residual LOS jitter becomes smaller than 1 \(\upmu\)rad, as achieved by the FSM control. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & \multicolumn{3}{c}{**(d) COM#1—Communication**} \\ \multicolumn{1}{c}{**Elements**} & **Budget** & **Available** & **Unit** & **Remarks** \\ \hline Tx Power & \(>\)\(-\)3 & 0.00 & dBW & 1 W output \\ Tx Gain & \(>\)56 & 92.92 & & Beam divergence, 127.8 \(\upmu\)rad \\ Pointing Loss & \(>\)\(-\)5 & \(-\)0.03 & & Pointing error at Table 3 \\ Tx Optics Loss & \(>\)\(-\)7 & \(-\)2.00 & & Front-end, back-end optics \\ Path Loss & \(<\)\(-\)259 & \(-\)258.18 & dB & Max. range, 1000 km \\ Rx Gain & \(>\)111 & 111.71 & & Clear aperture diameter, 19 cm \\ Rx Optics Loss & \(>\)\(-\)7 & \(-\)3.50 & & APD \\ Rx Power & \(>\)\(-\)125 & \(-\)59.08 & dBW & Beam split, 99:1 \\ \hline
**SNR Margin** & \(>\)10 & 15.98 & dB & OOK signal and coded BER 1 \(\times\) 10\({}^{-3}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._
## 3 Method
### 3D Effect Model with Pipeline Formation  
To understand the boundary effect that may be caused by geological formations with pipelines, this study established a geological formation model with pipelines as shown in Figure 2. This study will establish and plan a geological formation model and testing parameters from the four aspects of resistivity ratio, spacing of the line and pipelines, depth of pipelines building, and pipeline size in the pipelines and strata.  
In the model, the resistivity of the pipelines is \(R2\), the soil layer resistivity is \(R1\), \(R1>R2\), and the ratio of the two is n (\(n=R2/R1\)). To discuss the influence of the resistivity ratio under this assumption, we fixed R1 as 1000 ohm-m, and set R2 as 10, 50, 100, and 250 ohm-m, respectively. The section size of the pipelines was \(dyp\times dzp\), and the depth of the embedding was \(dep=2\) m (the top of the pipeline). The parallel pipelines around the pipeline setting were 5 to 6 lines, each line was separated \(dy=3\), the spacing to electrode rods was \(dx=3\), and each line had a slight adjustment to the length of line L with the sounding requirement. Table 1 presents the explanatory table of the 3D effect model parameter with the pipeline formation.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Influence Factor**} & \(R1\) & \(R2\) & \(dx\) (m) & \(dy\) (m) & \(dep\) (m) & \(dyp\) (m) & \(dyp\) (m) & \(dyp\) (m) & \(L\) (m) \\ \hline \multirow{8}{*}{3D effect with pipeline} & Resistivity ratio & & 100 & & & & & & & \\  & (\(n=R2/R1\)) & 1000 & & 100 & 3 & 3 & 2 & 1.5 & 2 & 42 \\ \cline{1-1}  & & & 250 & & & & & & & & \\ \cline{1-1}  & Pipeline size & 10
# A More Efficient Conditional Private Preservation Scheme in Vehicular Ad Hoc Networks
## 2 Signature Security
### Further Security Analysis of The Proposed Scheme  
**1. Mutual Authentication**. The scheme realizes mutual authentication between the RSU and the OBU by the request-response protocol.  
* _The RSU can quickly authenticate the OBU_. In Step 2 of Section 5.2, if the verification equation \(R_{2}^{\prime}\cdot e(g_{1},g_{2})^{f(R_{2}^{\prime}||T_{i}||Y)}=e(Sig_{OBU},g _{2}^{h(PID_{i})}\cdot U)\) holds, the OBU can be authenticated with pseudo-id \(PID_{i}\). Since the private key is secure according to **Lemma 1**, therefore, \(Sig_{OBU}\) is unforgeable, and no adversary can launch an impersonation's attack on the RSU.
* _The OBU can also efficiently authenticate the RSU at location \(L_{j}\)_. In Step 3 of Section 5.2, if the equation \(e(h(L_{j})P_{2}+U,Sig_{RSU})=e(g_{1},g_{2})^{f(R_{2}||T_{i}||Y||PID_{i}^{\prime})}\) holds, the RSU is authenticated. Because the adversary is not feasible to recover the correct \(R_{2}\) without knowing the RSU's private key \(A_{j}=g_{1}^{\frac{1}{\pi(L_{j})+\pi}}\).
## 3 Proposed Approach
### Ortho-Rectification of GPI  
Ideally, GPI is the ortho-rectified image obtained from the virtual top-view camera. Therefore, there should be no distortion in the image of the ground plane. However, due to the inherent errors in the EUCM calibration parameters and homography transformation from the front-view face of the cubemap model to the ground, there exists image distortion in GPI. An example of image distortion in GPI is shown in Figure 10. From the left in the figure, an original fisheye image is converted to a cubemap image and finally to a GPI. In this figure, we intentionally place the chessboard pattern in front of the robot to identify the distortion easily. In the generated GPI, we can notice that there is image distortion of the chessboard pattern. This kind of image distortion must be corrected. Otherwise, the visual odometry using the distorted GPI yields erroneous results.  
To correct the image distortion in GPI, we transform the GPI again to a new image, ortho-rectified GPI. The method of distortion correction is as follows. We place the chessboard at the front of the robot, obtain a fisheye image, and generate the GPI, as shown in Figure 11. Then, we select four corner corners in the image, as shown in the red-colored rectangle in the figure. Finally, a homography matrix is calculated so that the red-colored rectangle area is transformed into a real rectangle, and the area of the rectangle is the same as the real metric space of the 2 \(\times\) 4 squares area. To convert the pixel to metric space unit, we use the pixel-to-meter scale factor, which is used in Section 3.6.  
To compare the ortho-rectification accuracy of the GPI, we measure the side length and area of the 2 \(\times\) 4 chessboard squares, as shown in Table 1. As shown in Figure 11, four sides of the red-colored rectangle, a, b, c, and d, are compared before and after ortho-rectification. The area of the rectangle S is also compared in Table 1. As shown in the table, the ortho-rectification corrects image distortion in GPI, and the dimension of the ortho-rectified GPI is almost close to the true values.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Measurement** & **Chessboard** & **GPI** & **Ortho-Rectified GPI** \\ (**Figure 11**) & **Ground Truth** & **(Error)** & **(Error)** \\ \hline \multirow{3}{*}{(a + c)/2 (mm)} & \multirow{3}{*}{4 \(\times\) 20.007} & 84.664 & 80.085 \\  & & & (4.636) & (0.058) \\ \cline{1-1}  & & 36.660 & 40.036 \\ \cline{1-1}  & & (3.44) & (0.064) \\ \cline{1-1}  & & 3209.139 & 3127.102 & 3207.881 \\ \cline{1-1}  & & (82.037) & (1.258) \\ \hline \end{tabular}
\end{table}
Table 1: Comparison of chessboard dimension in GPI and ortho-rectified GPI.  
Figure 10: The distorted ground plane image of a chessboard placed at the front of the robot.
## 3 Results and Discussion
### NO\({}_{\rm X}\) Prediction in an Italian City  
In this experiment, we took the NO\({}_{\rm X}\) concentration of an Italian city as the regression target. Data from the last three months were used to test the model, and the remaining data were used for the model training.  
For the SVR-based model training, RBF was also chosen as the kernel function. The kernel parameter gamma (\(\gamma\)) and the penalty parameter (\(C\)) were also selected by a grid search method. The experimental results showed that the best combination for the SVR model was \(C=20\) and \(\gamma=0.15\). Table 1 summarizes the statistical parameters of the SVR regression model for the AQI prediction. In the testing set, the statistical parameters were as follows: \(r=0.8923\), \(R^{2}=0.7960\), and RMSE = 94.4918.  
For the RF-based model in our experiment, we also used 100 regression trees to build the regression model. The model achieved the criteria for good performance, with \(r=0.9180\), \(R^{2}=0.8401\), and RMSE = 83.6716, which is also shown in Table 1. The results showed that the RFR-based method was a better substitute for analyzing the regression on the NO\({}_{\rm X}\).  
As shown in Figure 5, the NOx of the testing samples was estimated using SVR and RFR, with the \(x\)-axis denoting the sequence number of the testing samples and the \(y\)-axis (target value) representing the NO\({}_{\rm X}\) of the Italian city. In Figure 5, the line chart of the actual values and the values predicted using different regression models demonstrates that the regression model based on RFR is a better predictor than the model based on SVR.  
Figure 4: Scatterplots of the actual values and the predicted values: (**a**) SVR model, (**b**) RFR model.  
Figure 5: Scatterplots of the actual values and the predicted values (10% testing instances randomly): (**a**) SVR model, (**b**) RFR model.
# Monitoring Vegetation Change and Its Potential Drivers in Inner Mongolia from 2000 to 2019
## 1 Introduction
However, current studies on the driving mechanisms of vegetation growth focus on natural factors, and anthropogenic factors are often neglected. Therefore, in this study, we use the Geodetector model combined with geospatial data and climate and human activity factor data, for a comprehensive investigation of the mechanisms that influence NDVI spatial patterns. DEM, slope and land type, and precipitation and temperature data were selected for analyzing the driving factors, after considering the special topographic and climatic conditions of the Inner Mongolia region [53]. In terms of anthropogenic factors, the impact of human activities on the ecological environment is becoming more and more complex as urbanization accelerates, and we chose GDP, land use type, and population data to explore this aspect [54]. In addition, as Inner Mongolia is a major livestock province in China, the impact of grazing intensity on vegetation change should not be neglected. We chose the number of livestock as a representative driver to examine its influence [55]. Overall, the objectives of this study were: (1) To study the interannual variations of the NDVI characteristics during 2000-2019 for each vegetation type in Inner Mongolia using time series analyses and Mann-Kendall tests; (2) to assess the NDVI stability over the last 20 years in Inner Mongolia by calculating the coefficient of variation; (3) to detect breakpoints in the NDVI at the pixel scale using the STM method and to conduct a trend analysis at each stage; (4) to predict future trends in vegetation dynamics in Inner Mongolia using the Hurst exponent and R/S analysis; and (5) to quantify the mechanisms of single and compound influences of natural and anthropogenic factors on the NDVI using Geode-tector. The results of this study can provide a basis for understanding vegetation trends and ecosystem evolution and for predicting the future direction of vegetation growth in Inner Mongolia. These results are of great theoretical and practical significance for guiding future ecological conservation policies and managing the effects of climatic and anthropogenic factors on vegetation growth in Inner Mongolia.
## 4 Modeling and Implementations of Software-Defined Vehicular Networks for V2X
### Software-Defined Networks and their Concept in Vehicular Networks for Deploying V2X Services  
The control layer plane is responsible for collecting and maintaining the status of all SDN cellular network devices, RSUs, and the vehicles [8]. An example of such SDN deployment in V2X services could be the route prediction on demand. The application could monitor vehicles on the roads and provides additional route prediction paths at a certain time of the day or when the vehicles are temporarily disconnected due to the high speed of the vehicles. The control layer would have to provide with the information about the vehicle's future route based on the Global Positioning System (GPS) or a navigation system [11]. The ability to deploy V2X services through SDN concepts is perhaps the most significant for automakers to solve the challenges of the no-direct interoperability of vehicle's wireless interface. Today, deployment of V2X services demands higher agility in network restoration, massive scalability, faster deployment, and operating expense optimization [36]. Therefore, V2X services cannot simply afford to be slowed down by the lack of speed in human-driven processes.  
Automakers' onboard wireless communication interfaces have been traditionally specific to their vehicles. Automakers offer limited support for allowing external network devices to make decisions based on the logic and constraints across the vehicular networks. SDN offers a solution by linking V2X services to the vehicular network and bridging the challenge that existed with manual control and management processes. In addition, maximum use of automated tools and application have become a necessity to meet the V2X service demands. Automation and programmability capability are needed to support the provisioning of V2X services, the monitoring, and interpreting of V2X networks devices data. Therefore, automated tools implement run-time changes based on high mobility of vehicular networks, road traffic loads, and disconnection due to a high speed.  
Since the SDN puts the intelligence of the vehicular networks in a central controlling software called SDN controller which conveys vehicular routing protocols to VANET's wireless nodes (such vehicles, RSUs). In fact, the vehicular routing protocols automatically react to the vehicle's mobility since the global view of the network is permanently available on SDN Controller. Therefore, the dissemination of routing path based on the vehicle's speed could be built directly into the SDN controller. Alternatively, the open protocols to manage the V2X applications can run on the top of the SDN controller using the northbound bound APIs [30] to proceed down the routing policies and rules to the controller and southbound APIS [30] to convey routing policies from SDN controller to the V2X forwarding devices. In conclusion, features of the SDN should handle the issue of high mobility and then improve V2X messages exchanged in a heterogeneous VANET architecture.
# A Parametric Study of Wind Pressure Distribution on Facades Using Computational Fluid Dynamics
## 3 Model Convergence
### Convergence of Peak Pressures
To explain why there is no converged solution, it is necessary to study the reasons why Mesh 2 has a lower value with respect to Mesh 3. From Figure 7a, there are four distinct points from 0.36 m and 0.45 m along the fin where the net pressure of Mesh 3 exceeds Mesh 2, representing a discontinuity. This is explained by looking at the graph for minimum outliers that occur for the leeward face (Figure 7b). For the same locations (between 0.36 m and 0.45 m), Mesh 3 has pressure at the leeward side of approximately zero. For Mesh 2 however, the pressure equates on average to approximately 0.22 kPa. Due to its action on the leeward edge, the net pressures were reduced by this amount. As net forces and net moments are both based on net pressures, it was apparent that averages for these values are more skewed for Mesh 2 than Mesh 3 due to the small pressure outliers found in Mesh 2. Based on the findings above, modeling was carried out with a 1 mm minimum cell size for all meshes. This mesh size was chosen because there is both improved accuracy in comparison to using a 2 mm minimum cell size mesh and strong agreement with the results of the finer 0.5 mm mesh. Also, the tendency of the 1 mm mesh to overestimate values for peak wind loading by between 13% and 14% (Table 4) ensured that the findings of the research are conservative without requiring the greater computational time demanded for calculating wind pressures and velocities in a more refined mesh. The decision was also taken to adjust the time step (\(\Delta\)t) used in the 1 mm mesh case. The value of \(\Delta\)t equal to \(4\times 10^{-4}\) s is revised down to \(2.5\times 10^{-4}\) s, to account for increased wind speed in subsequent models. This adjustment is made to maintain Courant Number (Co) values less than the upper limit of 40 for solution stability. As a result of this change, the solutions were found to have sufficient stability. Solution data were acquired every 160 time-steps, or 0.04 s, to maintain the frequency of the previous studies. Figure 6: (**a**) Differences in peak net force (per meter fin-height) due to changes in mesh resolution, (**b**) Differences in peak net moment (per meter fin-height) due to changes in mesh resolution. Peak net force has a 95% confidence interval of not being exceeded within the experiment.
## 3 Dual-Stack Single-Radio Communication Architecture (DSSRCA)
### Hardware Prototype  
In order to experiment with DSSRCA in real scenarios, a hardware prototype was developed and embedded in our UAV, as depicted in Figure 3. The core of the proposed solution is the MRF24J40 MC transceiver (center-left part of the UAV), which implements an extended range version (and the standard version) of the IEEE 802.15.4 protocol. It was selected because the great majority of the WSNs are implemented using the IEEE 802.15.4 standard. It is a protocol suitable for connecting different devices at a short/mid-range, focusing on low-cost, low-speed (250 kbps), and low-power applications. The adopted radio transceiver contains a Power Amplifier (PA) and a Low Noise Amplifier (LNA) that allows communication ranges up to 1200 m. The adopted external antenna (at the center of the UAV) is a full wave +5 dBi antenna manufactured by Aristotle Enterprises. The antenna perpendicular, to the ground, is used for the 72 MHz RC.  
Figure 3: UAV equipped with the communication prototype.  
The transceiver is connected to the embedded computing platform through a 4-wire SPI interface. As a computing platform, a low-cost credit-card-sized Linux computer equipped with a 1 GHz super-scalar ARM Cortex-A8 processor was selected (Beaglebone black [34]). The Angstrom Linux distribution [35] is used as the OS. This embedded platform, located in the center-right of the UAV, also interfaces with a GPS and additional sensors/actuators.
# Web Objects Based Contextual Data Quality Assessment Model for Semantic Data Application
## 6 Result and Discussion
For the ASC application set, the model classification quality assessment with four and three VOs was 99.2% and 98.0% on dataset A and 91% and 90% on dataset B respectively. In the TMC application set, the model classification capability with respect to the appropriate amount of data quality metric is 98.8% and 97.5% for three VOs set and four VOs set respectively when the input data was from dataset A. During the data quality analytics experimentations, the semantic data repositories of VOs, CVOs, and services have been created. The instances of dataset A and B have been transformed and represented as semantic data with VOs and CVOs ontologies [52] and stored in the repositories. Then this data has been collected and analyzed in terms of contextual data quality. The results of semantic data acquisition costs are shown in Figure 29. To demonstrate the features in data acquisition and analytics we simulated semantic data up to 800 CVOs and 1000 VOs. On 600 CVOs and 600 VOs semantic data, the acquisition cost has been observed averagely at 14.45 s and 12.2 s respectively. The data acquisition model for retrieving of VOs and CVOs data have been tested with many concurrent requests by using the Apache HTTP server benchmarking tool [65]. Figure 28: Appropriate amount of data for the ASC (**a**) and TMC (**b**) applications CVOs. Figure 27: Completeness assessment of Teens’ Mood Care (TMC) application semantic data. The developed data quality assessment mechanism has also been analyzed with respect to the overall data quality analytics of developed WoO enabled ASC and TMC healthcare applications. The CVOs semantic data of both applications have been evaluated with the incoming data from dataset A and B; The average of all analyzed data quality metric parameters and data transformation and acquisition cost on VOs and CVOs has been considered in the overall data quality of the ASC and TMC applications as shown in Figure 30.  
The ASC application VOs and CVOs semantic data quality remains at 88.5% and 79.4% with dataset A and B respectively and for the TMC applications, the data quality was 87.1% and 76.7% respectively. The trend of data quality assessment results of the ASC and TMC application remains the same in all the data quality metric parameters analytics. Assessment results of both applications re different although the data is similar, because both applications are using different ontology set of VOs, and CVOs with respect to the data quality usage context and requirements. The results of our approach prove that once a deep learning model is trained on high quality semantic data, it can be used to classify the incoming data quality for the task in hand. Moreover, when the quality of data decreases to a certain level, the model's learning quality also decreases. Figure 30: Overall data quality assessment for the ASC and TMC application. Figure 29: Semantic CVOs (**a**) and VOs (**b**) ontological data acquisition cost during the analytics.
# Enhanced Conjunction Assessment in LEO: A Hybrid Monte Carlo and Spline-Based Method Using TLE Data
## 3 Physical Characterization and Results
### Collision Probability Estimation
Future studies will aim to evaluate this trade-off by benchmarking the hybrid framework under precise orbital conditions and assessing its suitability for operational deployment within advanced space situational awareness (SSA) systems. Despite the practical advantages of using TLE data, namely, global availability and compatibility with standard propagation models, it remains inherently limited in both accuracy and dynamical representation. The lack of formal covariance information restricts uncertainty modeling, and the empirical nature of the B* drag term prevents the propagation model from responding accurately to rapid changes in the space environment. These constraints become more pronounced during periods of elevated solar activity or in low-altitude orbits where atmospheric drag is highly variable. Moreover, the discrete and delayed nature of TLE updates reduces the reliability of short-term collision predictions, particularly for agile or actively maneuvered satellites. Looking ahead, advancements in space tracking infrastructure are expected to mitigate many of these limitations. The deployment of space-based optical and radar sensors will enable continuous object tracking with higher resolution, while the integration of GNSS-derived ephemerides and the routine sharing of formal covariance data through standardized formats such as CDMs will allow for real-time, uncertainty-aware propagation. These developments would significantly enhance the reliability of conjunction assessments and allow methods like the one proposed here to operate on richer, more responsive datasets with broader applicability across orbital regimes.