esa-sceva/satcom-chunk-collection huggingface dataset:
# Vision-Guided Hierarchical Control and Autonomous Positioning for Aerial Manipulator
## 5 Experimental Results
### Simulation Experiment for LiDAR SLAM-Based Autonomous Navigation of UAVs  
In this study, an autonomous navigation and path planning experiment for a LiDAR-based SLAM system was built on the robot operating system (ROS) platform. The operating system used was Linux Ubuntu 18.04, and the experiment employed the physical simulation platform Gazebo and the data visualization tool rviz for real-time monitoring. The testing environment was set up and obstacles were added to simulate the autonomous navigation system of the aerial manipulator.  
First, in the simulator, the origin of the world coordinate system was taken as the initial position, along with a specified take-off height. At the beginning of the simulation, the UAV slowly ascended to the specified height and performed LiDAR scanner localization and map construction in the world coordinate system. The map data topic was visualized and displayed by rviz. The target object position was specified on the map and published as a topic. The UAV system in Gazebo then subscribed to this topic to obtain the optimized path planning algorithm for the shortest path and track to the position above the target object. Figure 10 shows the result of the SLAM autonomous navigation map building of a UAV from takeoff to near the target object in the Gazebo simulation environment. Figure 10 represents the laser radar scanning, where green and purple indicate obstacles. Figure 10 shows the UAV automatically planning its navigation based on the obstacles, with the planned flight path depicted as the red route. Based on the flight trajectory, the UAV readily avoided obstacles during flight, with no obvious swaying traces and a relatively smooth overall flight path.  
Based on the fused IMU laser SLAM algorithm proposed in this paper, the trajectory map of the UAV was constructed. This algorithm was compared with the classical laser SLAM algorithm Cartographer [39]. The Cartographer algorithm and the expected trajectory exhibited a relatively large cumulative drift error, while the trajectory obtained by the algorithm proposed in this paper had a relatively smaller cumulative drift error (Figure 11).
# The Cloud-to-Edge-to-IoT Continuum as an Enabler for Search and Rescue Operations
## 2 Related Work
### Situation Awareness and Perception with Mobile Robots
Especially in field robotic applications, including robotics to aid first responders in catastrophic incidents, which involve a much wider diversity of robots in terms of sensory capabilities and mobility and strict time response constraints, a more thorough and extensive treatment is needed to encompass all the dimensions of the cooperative perception problem, thus being able to fulfill the requirements posed by those complex real scenarios.
# Bounce: A High Performance Satellite-Based Blockchain System
## 6 Experiments, Results, and Analysis
### Slot Protocol Measurements  
Table 4 shows the time taken for each step of the slot protocol. The time from the Sending Stations to the Satellite and back to Broadcast Ground Stations are estimations based on the experiments with the International Space Station [3], which is approximately 0.5 millisecond closer to the earth based on the speed of light than a typical low earth orbiting satellite (altitude of 400 km vs. 550 km, with the 150 km taking 0.5 milliseconds at the speed of light) The total time for the slot protocol is around 1 s. Based on this timing, we can evaluate the sustained throughput and response time of our protocol.  
Section 6.3.1 presents the most naive design with one Merkle tree per slot. Section 6.3.2 then describes a design in which we collect multiple Merkle trees in a slot to reduce the response time and increase throughput.  
Figure 5: The time elapsed from the Sending Station starts sending a Sign Merkle Tree Request (1 million transactions) to receiving responses from Broadcast Ground Stations under a 10 Gbps network with different fanouts of the overlay broadcast tree. The results are averages over 20 runs.
# Visible Measurement of Terahertz Power Based on Capsulized Cholesteric Liquid Crystal Film
## 3 Setup and Results
Figure 3: (**a**) Experimental setup. Broadband THz waves are generated by the tilted-pump-pulse-front method with a LiNbO\({}_{3}\) crystal. The THz beam is tightly focused into the CCLCF through off-axis parabolic mirrors (PM). (**b**) Measured THz temporal waveform and (**c**) its Fourier components. The inset shows the THz beam profile image at the focused point after PM3. The increasing diameter looks similar to a target, which suggests an apparent temperature growth due to the increase in THz intensity. The response time is shown in Figure 4c. We can see that the CCLCF clearly has different responses with different THz powers. The entire system has corresponding images in thermodynamic equilibrium with different THz powers. With the high THz power, thermal diffusion is faster than that at low power, but the stabilization diameter is much larger, thus, the response time is slow. As can be seen, at the THz power maximum of 2.6 mW in our system, it takes about 30 sec to reach equilibrium, while at 1.3 mW, the stabilization time is about 15 sec. The images from the video captured by the camera under 2.6 mW at radiation times of 2, 5, 28, and 30 sec shown in the inset demonstrate how the diameter increases with time. For rapid THz power measurement, we switched to transient detection which means THz power can be obtained by the measurement at one point in time before the thermal equilibrium. The transient performance of the CCLCF for 1 sec of THz radiation was demonstrated in Figure 5. We varied the power by rotating the wire grid and detected the color change of the area. When the angle of the wire grid polarizer \(\theta=0^{\circ}\), which is parallel to the polarization of the THz wave, there is no THz transmission. Then, the angle changes to 40\({}^{\circ}\), 60\({}^{\circ}\), and 80\({}^{\circ}\) to obtain the radiation images from the video taken by the same smartphone shown in the inset. We can see that the CCLCF clearly differs for different THz powers and the response in the central small active area is more rapid than in the large active area shown in Figure 4. Here, the images are still analyzed based on hue of which the digital value we chose is 122. The THz intensity is proportional to the area of the color change. A quantitative evaluation with an approximately parabolic relation between THz power with the color change area is also obtained based on this fast measurement method. Figure 4: (**a**) Visible pictures are taken under different THz intensities by a smartphone camera with Bluetooth. (**b**) Increase in the diameter of the color change with different THz powers in thermal equilibrium, similar to the target shown in the inset. (**c**) Increase in the diameters as a function of response time with 1.3 and 2.6-mW THz radiation. The inset shows image changes under different THz radiation times.
## 2 Related Works
### Fault Model
#### 2.2.4 Lightning Fault Model  
The lightning current is represented by a Norton circuit with a current source (Iy) of 40 kA in parallel with an impedance (Ry) in Figure 5. The impedance of the lightning channel is considered to be of about 400 ohms, although the CIGRE, IEC, and IEEE standards assume higher values [25; 26]. The tower electrical parameters for an overhead line of 275 kV are presented in Table 3, considering also a tower footing resistance in series with the entire structure. The parameters are established considering the model proposed by [26].  
The multistory type tower model is used in this study where it is composed of four main parts representing the tower section between the cross arm, as illustrated in Figure 6. Each section consists of a lossless line in series with a parallel R-L circuit, included for attenuation of the traveling waves along the tower, \(a_{1}\). The propagation velocity of a traveling wave along the tower, \(C_{0}\) is assumed equal to \(300\,\mathrm{m}/\mathrm{\SIUnitSymbolMicro s}\). Note that the overvoltage that can be obtained by means of simulation, when the simplest models are used, should be the same between terminals of all insulator strings since these models do not distinguish between line phases. In fact, some differences will be expected due to the different coupling between the shield wires and the phase conductors located at different heights above the ground. Meanwhile, this study will not vary the tower height as we maintained the use for 275 kV system voltage from Tenaga  
Figure 5: Norton circuit.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{4}{c}{**Tower Height/Geometry (m)**} & \multicolumn{3}{c}{**Surge**} & \multicolumn{2}{c}{**Footing**} & \multirow{2}{*}{**Lightning Speed**} & \multirow{2}{*}{**Attenuation**} \\  & & & & **Impedance (f1)** & & & **Resistance (f1)** & \\ \hline \(h_{t}\) & \(h_{1}\) & \(h_{2}\) & \(h_{3}\) & \(h_{4}\) & \(Z_{t1}\) & \(Z_{t4}\) & \(R_{f}\) & \(c_{0}\,\mathrm{m}/\mathrm{\SIUnitSymbolMicro s}\) & \(a_{1}\) \\ \hline
31.76 & 2.70 & 5.55 & 5.55 & 17.96 & 220 & 150 & 5 & 300 & 0.89 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Tower model parameters for 275 kV system voltage [26].  
A transmission tower is represented by four distributed-parameter lines as defined in Table 3, where \(Z_{t1}\) is surge impedance of tower top to the upper phase arm which is equivalent to the upper to middle and middle to lower. Meanwhile, \(Z_{t4}\) is the surge impedance of tower to tower bottom. The value of R and L are defined using the following expression in (11) to (13) where the \(h_{i}\) were indicated in Figure 6.  
\[R_{i}=\Delta R_{i}\cdot h_{i}\,\ \ L_{i}=2\tau R_{i}\ \ (i=1-4) \tag{11}\]  
where \(\tau=h_{t}/c_{0}\): travelling time along the tower.  
\[\Delta R_{1}=\Delta R_{2}=\Delta R_{3}=\frac{2Z_{t1}\cdot\ln\left(\frac{1}{a_{ 1}}\right)}{h_{t}-h_{4}} \tag{12}\]  
\[\Delta R_{4}=2Z_{t4}\cdot\ln\left(\frac{1}{\alpha_{1}}\right)/h_{t} \tag{13}\]
## 5 Experiment Validation  
For the purpose of validating the efficiency and precision of the proposed roll angle detection method, experiments were carried out. An intermediate frequency (IF) signal acquisition system was designed to collect satellite signals received by a single-patch antenna installed on a spinning vehicle. The L1 radio frequency signal is down-converted to the IF signal, sampled and stored in a flash memory unit, which can be processed by an SDR (Software Defined Radio) receiver on PC to obtain the roll modulation signals and test the roll angle detection module. The GNSS SDR software receiver we used was initiated by the Danish GPS Center at Aalborg University and was improved by the GNSS Laboratory at University of Colorado. A Hall Effect sensor was also installed on the spinning vehicle to record the zero-phase point of the roll angle as measurement datum to determine the measurement accuracy of the roll angle measurements. An Analog-to-Digital Converter (ADC) was designed to collect the output of the Hall Effect sensor as well.
# Optimal Prediction of Individual Vessel Trajectories Based on Sparse Gaussian Processes
## 4 Methodology
### Trajectory Observation  
To predict the future trajectory of a vessel, observing its trajectory patterns is necessary. A motion model [6; 40] is used to map from the vessel's position \(\mathrm{p}=(x,\ y)\) in local Cartesian coordinates to a distribution over trajectory derivatives (\(\frac{\Delta x}{\Delta t},\frac{\Delta y}{\Delta t}\)) to measure the pattern of the vessel trajectory. Because the derivative of the trajectory is the vessel velocity, given the current position \((x_{t},y_{t})\) of the vessel at time \(t\), the position at the next time step in the future can be obtained as follows:  
\[x_{t+1}=x_{t}+\frac{\Delta x_{t}}{\Delta t}\Delta t, \tag{28}\]  
\[y_{t+1}=y_{t}+\frac{\Delta y_{t}}{\Delta t}\Delta t. \tag{29}\]  
Therefore, in the process of modeling the trajectory pattern, the explanatory variable of \(\mathrm{p}=(x,\ y)\) was used to predict the trajectory derivative for each of the two dimensions. The Gaussian process for each dimension is expressed as  
\[\frac{\Delta x}{\Delta t}\ \sim\ \mathcal{GP}_{x}\big{(}m(p),\ k\big{(}p,p^{ \prime}\big{)}\big{)}, \tag{30}\]  
\[\frac{\Delta y}{\Delta t}\ \sim\ \mathcal{GP}_{y}\big{(}m(p),\ k\big{(}p,p^{ \prime}\big{)}\big{)}. \tag{31}\]  
Figure 2 depicts the cases of Gaussian process regression for an individual trajectory in each dimension. The red line denotes the mean of the Gaussian process predictive distribution, and the shaded region represents the prediction's uncertainty, which is plus or minus two standard deviations around the mean. The observation data, represented in  
Figure 1: Flowchart for the proposed method.  
blue circles, are real AIS data points with noise. The uncertainty increases in proportion to the prediction error.
## 3 Results
### Future Simulation of GPP and NEE  
The simulation results of ATQ and PRR sites in the Arctic ecosystem have different results under the three future scenarios. Under future climate conditions, both the GPP at the tundra and needle leaf forest sites showed increasing trends (Figure 8). The average GPP values from 2020 to 2100 at the tundra site under SSP1-2.6, SSP2-4.5, and SSP5-8.5 were 22, 31, and \(40\) gC/m\({}^{2}\)/yr, respectively. The average GPP values from 2020 to 2100 at the needle leaf forest site under the three pathways were 651, 574, and (831 gC/m\({}^{2}\)/yr. For the NEE values, the tundra sites from 2020 to 2100 under SSP1-2.6, SSP2-4.5, and SSP5-8.5 were \(-36\), \(-43\), and \(-48\) gC/m\({}^{2}\)/yr, respectively. The average NEE values at the needle leaf forest site from 2020 to 2100 were \(-229\), \(-226\), and \(-276\) gC/m\({}^{2}\)/yr, respectively (Figure 8). The rate of decline of NEE at the ATQ tundra site was the fastest under the SSP5-8.5 pathway from 2020 to 2100. For the PRR needle leaf forest site, the carbon uptake rate was the fastest under the SSP1-2.6 pathway, followed by SSP5-8.5. For the GPP values,  
Figure 6: Monthly values of NEE and GPP values at the ATQ tundra site (**a,b**) and PRR needle leaf forest site (**c,d**).  
both the ATQ tundra site and PRR needle leaf forest site had the greatest increasing value under the SSP5-8.5 pathway.  
Figure 8: Changes in GPP and NEE from 2020 to 2100 under future climate conditions at the ATQ tundra site (**a,b**) and PRR needle leaf forest site (**c,d**).  
Figure 7: Changes in NEE and GPP from 1992 to 2014 at the ATQ tundra (**a,b**) and PRR needle leaf forest (**c,d**) sites.  
The carbon exchange of the Arctic ecosystem in a future climate is simulated (Table 5). In the future scenario, the carbon exchange of the PRR site is higher than that of the ATQ site, in which the SSP5-8.5 pathway is higher than that of the SSP1-2.6 pathway, but the transformation rate of the SSP1-2.6 pathway is faster than that of SSP5-8.5 pathway. The net vegetation carbon exchange rate was higher than the net soil carbon exchange rate.
# An Evasion Attack against Stacked Capsule Autoencoder
## 2 Related Works
### Poisoning Attacks and Evasion Attacks  
The major security threats in machine learning are adversarial attacks, which can be categorized into two types: poisoning attacks and evasion attacks.  
Poisoning attacks, which aim at degrading the performance of the model or creating a backdoor in the model so as to control its behavior, occur during the training phase. The attacker adds elaborately constructed malicious samples to the training set, thus causing the trained model to output the attacker's expected results for specific samples or reducing the classification accuracy of the model at test time [10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20].  
Evasion attacks, which aim at creating adversarial samples to deceive the model, occur during the testing phase. The attacker adds a carefully constructed perturbation to the clean sample to form a malicious sample. Its appearance is generally consistent with that of the clean sample, but the model will misclassify it or make a prediction specified by the attacker [21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33]. The adversarial attack proposed in this paper is an evasion attack.  
According to the information held by the attacker, adversarial attacks can also be categorized into white-box attacks and black-box attacks. In white-box attacks, the attacker can obtain the detail of the model, including the structure and parameters of the model. While in black-box attacks, only the inputs and outputs are available for the attacker, and the model itself remains sealed.
# A Survey of the Real-Time Metaverse: Challenges and Opportunities +
## 5 Technological Infrastructure
### Edge and Fog Computing  
Edge and fog computing are paradigms that enhance data processing and analysis capabilities closer to the source of data generation, reducing latency and improving efficiency [137]. Edge computing involves processing data directly on devices or near the data source, such as sensors, IoT devices, or local servers [138]. The edge approach minimizes the need to send data to centralized cloud servers, reducing latency and bandwidth usage. Edge computing applications include real-time analytics, autonomous vehicles, and smart cities, where immediate data processing is crucial. For instance, in autonomous cars, edge computing allows for rapid decision-making based on real-time data from sensors, enhancing safety and performance.  
Fog computing, on the other hand, extends the concept of edge computing by providing a distributed computing infrastructure that includes edge devices, local servers, and potentially the cloud [139]. The fog architecture acts as an intermediary layer that processes data before it reaches the cloud, providing additional storage and computational resources closer to the data source [140]. The fog-edge layered approach benefits applications requiring real-time processing and more substantial computational power or data aggregation. Fog computing is beneficial in scenarios like industrial IoT, where data from numerous devices needs to be aggregated and analyzed swiftly to optimize operations and maintenance. By distributing resources across multiple layers, fog computing improves overall system efficiency, scalability, and reliability.  
Figure 6 illustrates a comprehensive architecture that integrates cloud computing, fog computing, and edge computing. At the center of the diagram is the cloud, symbolizing the centralized and extensive data processing capabilities of cloud computing. Cloud computing is depicted with multiple connections, including data centers and satellites, highlighting its broad reach and ability to handle significant computational tasks and data storage. Servers and network infrastructure placed closer to the data sources, such as IoT devices and sensors, represent fog computing. The fog layer aims to reduce latency by processing data near its origin before sending it to the cloud. Edge computing, depicted with smaller, decentralized servers and network nodes, is even closer to the end-users and devices. It emphasizes real-time data processing and immediate response actions, crucial for applications requiring low latency and high reliability. Integrating these computing paradigms ensures a balanced and efficient data handling from the core cloud to the network's edge, optimizing performance and resource utilization.
## 5 Results and Discussion
### Packets Dropped  
Packets dropped is the overall number of packets that have been dropped because of factors such as time limitation, collisions, and queue congestion. When a Black Hole node is present in the network, the packet drop is quite high because the Black and Gray Hole nodes swallow the packets. Figure 12 shows the total number of packets dropped. However, as Black Hole and Gray Hole detection is getting better with OEAODV (our approach), the packet drop rate in our proposed technique is much less as compared to previous techniques, as shown in Figure 11.  
Figure 11: Generated packets.  
Figure 10: Packet loss ratio in each technique.
## 5 Experiments
### Experimental Setup
#### 5.1.1 Baselines  
We selected methodologies from the following categories to be competitors: (1) statistics-based approaches, (2) feature-based approaches, and (3) deep-learning-based approaches.  
**Statistics-based approaches:**  
* Node_num_at_T: We counted the number of posts observed within the observation time and used the values as the predictive cascade size.  
**Feature-based approaches:**  
* Future-linear: We fed the hand-crafted features outlined in Sections 4.1.1-4.1.4 into a linear regression model.
* Future-deep: We fed the hand-crafted features into a MLP model consisting of three layers of nodes.  
**Deep-learning-based approaches:**  
* Topo-LSTM [24]: A DAG (directed acyclic graph)-structured recurrent neural network was used to model diffusion topologies. The original application of Topo-LSTM is tailored for tasks related to node activation, so we replaced the logistic classifier with a regressor to predict the cascade size. Additionally, Topo-LSTM only depends on the order of nodes in each cascade. To ensure fairness in comparison, richer node features were incorporated by taking into account user information.
* CasCN [22]: CasCN leverages GCN and LSTM to extract both structural and temporal information from the cascade graph. By considering the cascade graph as a sequence of sub-cascade graphs, it studies the local structure of each sub-cascade through graph convolutions. After that, it applies LSTM to model the development of the cascade structure.
# Environmental Impacts on Hardware-Based Link Quality Estimators in Wireless Sensor Networks
## 5 Environmental Impacts on Hardware-Based LQEs
### Environmental Impacts on LQI Based LQEs  
#### 5.4.1 LQI and PRR in Different Environments  
Figure 19 shows the relationship between LQI and \(PRR\) in different environments, including the minimum, maximum, and mean value of LQI. It can be seen that the trend of change between LQI and \(PRR\) is basically the same for the three different environments. In terms of \(\mu_{iji}\), \(PRR\) increases as \(\mu_{iji}\) increases. To describe the impact of environmental changes on the fluctuation of LQI, the fluctuation ranges of LQI in different environments were statistically obtained, and their CDFs are shown in  
Figure 17: Effects of the theoretical model after correction.  
Figure 18: Effects of the LR model in different environments.  
Figure 20. The fluctuation range of LQI is calculated by subtracting the minimum LQI from the maximum one. It can be seen that the maximum fluctuation ranges of LQI in the runway and corridor are close to 60, while the maximum fluctuation range of LQI in the artificial lawn is less than 50. The fluctuation of LQI in the corridor is the most violent, while the fluctuation in the artificial lawn is the gentlest. The fluctuation of LQI in the runway is between them. These relationships are consistent with the fluctuation of \(RSSI\) in the three environments. Considering the linear relationship between LQI and \(SNR\)[32], this consistency is not difficult to understand.  
To observe the environmental impact on the relationship between LQI and \(PRR\) more clearly, the relationship between \(\mu_{lqi}\) and \(PRR\) is shown in Figure 21. There already exist some studies which utilize the relationship between LQI and \(PRR\) to estimate link quality [6; 15; 16; 17; 18; 26; 27; 28; 30]. It can be seen that there is no obvious difference among the relationships between \(\mu_{lqi}\) and \(PRR\) in different environments. This means that the relationship between \(\mu_{lqi}\) and \(PRR\) is almost unaffected when the environment changes.  
Figure 19: LQI vs. \(PRR\) in different environments.  
Figure 20: CDFs of the fluctuation range of LQI in different environments.