esa-sceva/satcom-chunk-collection huggingface dataset:
# PDDL Planning with Natural Language-Based Scene Understanding for UAV-UGV Cooperation
## 4 Experiment
### Experiment Setting  
The simulation environment was designed as an area around REDONE technologies cooperation, as shown in Figure 6. The size of the area was 110 m \(\times\) 100 m. We utilized three AGVs of REDONE technologies, each named Smart Cookie, and 1 UAV of REDONE technologies, named Beyond. Each Smart Cookie has 2D laser sensors and an RGB-D camera. Beyond is equipped with an RGB-D camera. The laser sensor is used for navigation on the execution part while the RGB-D cameras are used for cognition for the natural language-based scene understanding. Each robot navigated using the generated map and sensor. The platform was set up with Ubuntu 16.04, ROS Kinetic, and Gazebo 7. JENA-TDB is used as the semantic memory and Mongo DB is used as the episodic memory. DICQ.R is the control tower. We used tensorflow library and Python for the natural language processing, whereas JAVA was used for JENA-TDB, and C++ was used for the simulator. Socket communication was utilized to transfer information between processors. To train the neural network for scene understanding, we used the COCO dataset and visual genome dataset for language description and scene graph generation, respectively. Since these datasets use images for natural language processing, we manually generated a graph using bounding boxes and train the networks. The details of the trained network are shown in Appendix A.
## 5 Simulation Setup and Experiments
### Transmission Range  
We noted the sub-cluster effect on the transmission range in our next experiment. We formed a 400 m cluster with varying speeds. Through this method, we noted the average packet delivery ratio of three clusters (Congested, Sparse, and Intermediate) concerning the corresponding cluster range. For the Postman simulation, we supposed that the location of the base station is fixed at a certain point. The routing delay will occur as the vehicle moves in a corresponding cluster. We also supposed that the delay factor in routing would increase when the range is increased. Figure 5a compares Cluster Range and Packet Delivery. Our proposed Authentication and Communication Protocol (ACP) results perform much better than those obtained by [PERSON] et al. [58], as shown in Figure 5a. In Figure 5b, we compare  
Figure 4: Presents the Varying the Attackers (**a**) Authentication Delay vs. Number of Attackers, (**b**) Detection Accuracy vs. Number of Attackers, (**c**) Keying Overhead vs. Number of Attackers, (**d**) Packet Delivery Ratio vs. Number of Attackers.  
Authentication Delay, while Figure 5c shows Keying Overhead in the context of varying cluster ranges. In Figure 5d, we compare the Detection Accuracy with the results obtained by [58]. It is observable that our protocol has also provided much better performance in varying cluster ranges.
# Forecasting the Distortion in Solar Radiation during Midday
## 3 Results and Discussion
### Accuracy of the Proposed Approach  
As was shown in previous sections, the proposed approach failed to predict the distortion in solar radiation for a few days. In this section, we quantify the error of the proposed \(DI\) for the whole year in the first location (Riyadh city). Table 1 below shows the number of days in which the \(DI\) was not compatible with the solar radiation shape.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Month** & **Number of Errors** & **Date** \\ \hline January & 4 & 11, 17, 27, 30 \\ \hline February & 4 & 5, 9, 14, 24 \\ \hline March & 3 & 27, 29, 30 \\ \hline April & 3 & 16, 17, 29 \\ \hline May & 7 & 2, 13, 14, 15, 25, 18, 20 \\ \hline June & 3 & 6, 15, 16 \\ \hline July & 5 & 5, 9, 19, 23, 24 \\ \hline August & 0 \\ \hline \end{tabular}
\end{table}
Table 1: Accuracy of the proposed approach.  
Figure 21: The solar radiation of days 1–6 of December (**left**) and the distortion index (\(DI\)) (**right**).  
Figure 22: The solar radiation of days 16–21 of December (**left**) and the distortion index (\(DI\)) (**right**).  
The total number of errors is 54. Therefore, the error percent is 14.8. The approach accuracy is about 85.2%. It can be noted that the failure rate of the proposed approach is high in the months of October to December and low in March, April, June, July, and September.
# An Algorithm to Find the Shortest Path through Obstacles of Arbitrary Shapes and Positions in 2D
## 4 Details of the Implementation
### Preprocessing  
The path construction algorithm requires the following data that are extracted from the Map.  
1. Identify the blobs in Map by writing in them an integer identity number and call the resulting image \"etiquettes\". The Matlab function \"_bvlabel_\" can be used for this. Then,the Matlab function \"_regionprops_\" can provide the center of mass of these blobs. These are stored in a cell array called \"centre\", such that centre\([i]\) gives the 2D coordinates of the center of mass of blob \"\(i\)\".
2. Calculate the outside contour of each blob. This can be done by drawing a straight line from the corner of the image [1; 1] toward the center of mass of the selected blob, until this line hits the blob. Upon backing up to a neighboring pixel in free space, a walk is then performed on the outside contour of the obstacle in the positive direction around the obstacle. The list of N pixels so traveled is stored in a 2 \(\times\) N cell-array we call \"contour\".
3. Calculate the list of the obstacles that have another obstacle penetrating into their convex hull. The non-overlapping of the bounding box of obstacles is an efficient first test to rule out such an occurrence. Calculating this list before running the algorithm saves having to check constantly during its execution whether obstacles are embedded in others.
## 5 Performance Analysis
### Energy Efficiency  
Figure 17 shows the average energy consumption per round in the network stability period. When the network size was 100 \(\times\) 100 m\({}^{2}\), the energy efficiency of D-LEACH increased by 13.5% and 2.7%, respectively, relative to LEACH and MH-LEACH. When the network size was 200 \(\times\) 200 m\({}^{2}\), the energy efficiency of the proposed algorithm increased by 64.5% and 13.3%, respectively, relative to LEACH and MH-LEACH. When the network size was 300 \(\times\) 300 m\({}^{2}\), an increase of 86% and 33.8%, respectively, can be observed compared with the other two algorithms. Therefore, the D-LEACH has higher energy efficiency than the other protocols.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**(FND Time Radio Based on 100 \(\times\) 100 m\({}^{2}\))** & **100 \(\times\) 100 m\({}^{2}\)** & **200 \(\times\) 200 m\({}^{2}\)** & **300 \(\times\) 300 m\({}^{2}\)** \\ \hline D-LEACH & 1777 & 1458 & 761 \\  & (100\%) & (82\%) & (43\%) \\ LEACH & 1750 & 264 & 60 \\  & (100\%) & (15\%) & (3\%) \\ MH-LEACH & 1766 & 1261 & 359 \\  & (100\%) & (71\%) & (20\%) \\ \hline \hline \end{tabular}
\end{table}
Table 2: FND time ratio according to network size.  
Figure 16: Time of occurrence of death of the first node (FND).  
Figure 15: The number of dead sensor nodes in WSNs (300 \(\times\) 300 m\({}^{2}\)).
## 3 Methodology
### BATMAN Algorithm
#### 3.1.3 Hop Penalty  
In an ad hoc network, each node acts as a router and is responsible for processing each packet, computing \(TQ\), and forwarding OGM to neighbors, other than receiving and forwarding data packets. The processing delay also affects the transmission quality and is yet to be included as it is not easy to map the processing delay of the node as it will vary depending on the configuration/specification of the node, queue length, and number of free resources that a node possesses when processing a data packet or OGM. Therefore, many routing algorithms utilize a parameter called hop counter or hop penalty to affect the metric depending on the number of nodes/hops a data packet takes in reaching its destination. Hop penalty in BATMAN IV is a pre-defined and configurable parameter that can be altered at will to achieve desired results, e.g., enhancing the effect of hops into the metric or marring so that path transmission quality is less or more depending on the  
Figure 4: Best path \(TQ\) calculation.  
parameter, respectively. To compute the hop penalty effect on \(TQ\), another parameter is introduced to show the maximum achievable \(TQ\) for any path set to 255 by default. This ensures that paths with an increasing number of hops are penalized regardless of their local transmission quality, and it is provided below [17]:  
\[TQ_{HP}=TQ_{path}\times\frac{TQ_{\max}-HP}{TQ_{\max}} \tag{5}\]  
where \(HP\) is the hop penalty that is set to 30 by default in BATMAN.
## 4 Results
### Experimental Results  
Figure 9 describes the reconstructed conductivity image of the phantom experiment. Figure 9a,b represent the SE magnitude and phase images, respectively. The SNR of magnitude is 132. In the magnitude image, the region between the blue circles is region 1, while the red circle describes region 2. The black circle in the magnitude image is the material used to separate these two compartments. Figure 9b illustrates the conductivity using the CM, DRM, and proposed method. The conductivity of the proposed method exhibits more homogeneity in two regions than CM and DRM. Table 2 shows the calculated mean and SD of these two regions. The proposed method has more accurate mean values than CM and DRM, indicating its feasibility to reconstruct accurate conductivity.  
Figure 8: The RMSE of reconstructed conductivity images under noise conditions using the conventional method (CM), double regularization method (DRM), and proposed method (PM).  
Figure 7: Reconstructed conductivity images of the Ella head model using the conventional method (CM), double regularization method (DRM) and proposed method (PM) under a signal to noise ratio (SNR) = 200 and SNR = 100.  
Figure 10 shows the magnitude, phase, and reconstructed conductivity images of the in vivo experiment. The SNR of the magnitude image is 75. The proposed method shows a clearer structure than the results of the CM and DRM. The CSF region offers more errors, which are possibly caused by low-phase data.
# Research on the Dynamics of the Space Tubular Expandable Structure Driving Deployment Unit
## 3 Finite Element Analysis  
### Boundary Conditions  
The first step involves modeling the variable-radius inner-drive unit using SolidWorks. Figure 16 presents the complete geometric model of the variable-radius inner-drive unit, with the right side representing the unit in its fully expanded state, and the left side depicting the initial stage of axial deployment of the variable-radius inner-drive unit inside the cylindrical structure. From the figure, it can be observed that the variable-radius inner-drive unit consists of three groups of axially symmetrical driving and stowing units, divided into upper and lower layers. During the axial deployment inside the cylindrical structure, the angle between each group of driving and stowing units remains fixed at 120 degrees. However, as the diameter of the cylindrical structure decreases, the angle between the two layers of axially symmetrical driving and stowing units gradually increases.  
We constructed the explicit dynamic coupling field under the radial pre-tensioning load state using the Ansys-Workbench Ls-Dyna module [45]. Figure 17 displays the finite element model of the variable-radius inner-drive unit, comprising 7429 beam elements used to simulate the driving and stowing units experiencing tensile, compressive, and bending loads simultaneously. Additionally, 1847 nonlinear elastic elements are employed to simulate the flexible connection components.  
Figure 16: The geometric model of the variable-radius inner-drive unit.  
To ensure effective analysis and validation, we carefully chose boundary conditions that align with the real operating state of the actual prototype. Additionally, the computational conditions used in the theoretical phase were maintained. The material properties of the driving and stowing units can be found in Table 1.  
Axial forward speed is constant at 5 mm/s;  
Radial pre-tensioning load: 100-150 N. The radial pre-tensioning load is adjusted within this range based on the variation of the cylindrical structure diameter. For instance, the radial pre-tensioning load is higher during the transition between the first-level stages compared to the transition between the second-level stages, and so on;  
Top load: 40 N. The total top load in the full structure state is 120 N, and the designed variable-radius inner-drive unit follows a 120-degree axially symmetrical structure. Therefore, one-third of the structure is used for validating the driving and stowing units;  
Boundary contact: Face-to-face contact is used, and the contact options include sliding friction, rolling friction, and viscoelastic friction in a mixed contact mode. The contact state remains non-offset in the axial direction;  
Deformation setting: Large deformation (flexural deformation) is enabled. The elastic material, JSR, undergoes certain elastic deformation when in contact with the discontinuous rigid surface;  
Ambient temperature: 26 degrees Celsius.
## 3 Materials and Methods
### GeoDetector  
To further identify the driving mechanisms behind the coupling coordination between HF and EQ, this study applies the GeoDetector model. Unlike traditional regression methods, GeoDetector does not rely on linear assumptions and is unaffected by multicollinearity. It effectively reveals the explanatory power of spatial factors on the distribution of CCD and their interaction mechanisms. As a result, it has been widely used in studies of complex human-environment systems. This study utilized the R package (R version 4.3.3, [[https://cran.r-project.org](https://cran.r-project.org)]([https://cran.r-project.org](https://cran.r-project.org)), accessed on 25 February 2025) developed by [PERSON] [39], applying the factor detection and interaction detection modules to identify each driving factor's stress intensity and interaction mechanisms. Factor detection examines the spatial heterogeneity of influencing factors and their explanatory power about the spatial differentiation of CCD between HF and HQ, with results quantified by the q-value. The calculation formula is as follows:  
\[q=1-\frac{\sum_{h=1}^{L}N_{h}\sigma_{h}^{2}}{N\sigma^{2}} \tag{8}\]  
where \(h=1\),... ;L indicates the stratification or classification of variable \(Y\) or factor \(X\); \(N_{h}\) denotes the number of units in layer \(h\); \(N\) is the number of units in the entire region; \(\sigma_{h}^{2}\) represents the variance of \(Y\) values in layer \(h\); \(\sigma^{2}\) indicates the variance of \(Y\) values across the entire region.  
Interaction detection assesses the combined impact of two factors on CCD, examining whether the interaction between factors \(X_{1}\) and \(X_{2}\) strengthens or reduces their explanatory  
\begin{table}
\begin{tabular}{c c c} \hline
**Level** & **Coupling Coordination Interval** & **Type** \\ \hline I & \textless{}0.20 & Very unbalanced \\ II & 0.20–0.30 & Less unbalanced \\ III & 0.30–0.40 & Neighborhood balance \\ IV & 0.40–0.50 & Less balanced \\ V & 0.50–0.60 & Moderately balanced \\ VI & 0.60–0.70 & Higher equilibrium \\ VII & \textgreater{}0.70 & High equilibrium \\ \hline \end{tabular}
\end{table}
Table 2: Coupling coordination categories of HF and EQ in lake-ring urban agglomerations.  
power for CCD and whether their effects are independent. Table 3 shows the five types of interaction effects between the two factors.
# Dynamic Information Flow Tracking: Taxonomy, Challenges, and Opportunities
## 6 Discussion and Prospect
### Future Research  
Software and hardware co-design is a good future direction. This type of design can combine the benefits of both hardware-based design and software-based design. Further, off-core-based design can package all DIFT operations into one dedicated hardware component and introduce few modifications to the processor core. The target software is instrumented through binary instrumentation mechanisms or compiler aided tools. The inserted auxiliary instructions are utilized to instruct the dedicated hardware component to accomplish tag propagation and checking. In addition, the problem of implicit flow can be processed at the software side.  
The common tag designs include single-bit, multi-bit and policy oriented. In order to enforce security policy in terms of combinations of tags, the granularity of tags should be defined according to the types of system resources, e.g., process, even, file and variable. Additionally, the tag should record the lifetime and the specific activity of the corresponding resource. It can serve as a log and enable the programmer or user to replay the action on specified resource. Therefore, the possible security risks can be analyzed.  
Along with protecting the target software at runtime, the security of SoC should also be considered. Modern SoC includes a lot of third-party IPs which may compromise the security of the whole system. Tracking the information flow on the whole SoC can help the system designer define suitable security policy. For the bus, the tag can be transmitted in parallel with the shake signal between IPs. Additionally, the bus can be extended to support the tag transmission. For security checking, the corresponding operations can be packaged into one dedicated hardware component or accomplished in IP wrapper.
## 2 Target Area and Data
### Data  
The data used in this study were collected from geotechnical surveys during the construction and design stages located at the target areas. The geotechnical data collected included the soils' average sampling depth, natural water content, specific gravity, liquid limit plasticity index, plastic limit, initial void ratio, saturated unit weight, uniaxial compressive strength, compression index, and pre-consolidation pressure. The natural water content was measured by drying samples collected in the field in a laboratory. Samples collected closer to the coast and at lower depths have higher natural water contents [14; 15; 16].  
Table 2 shows the range and mean values of the data. The dataset is based on CH or CL groups, which are in the clay series under the unified soil classification system.  
\begin{table}
\begin{tabular}{c c c} \hline
**Geotechnical Properties** & **Range** & **Mean** \\ \hline Average Depth (m) & 0.050–66.000 & 10.652 \\ \hline Natural Water Content (W\({}_{c}\),\%) & 7.400–147.100 & 58.996 \\ \hline Specific Gravity (Gs) & 2.530–2.900 & 2.692 \\ \hline Liquid Limit (LL, \%) & 22.200–142.500 & 58.554 \\ \hline Plasticity Index (PL, \%) & 1.400–118.90 & 24.707 \\ \hline Plasticity Limit (PL, \%) & 1.40–91.80 & 25.866 \\ \hline Initial Void Ratio (\(\mathrm{e_{0}}\)) & 0.607–3.825 & 1.675 \\ \hline Saturated Unit Weight (\(\mathrm{r_{t}}\), \(\mathrm{tf/m^{3}}\)) & 1.063–4.155 & 1.648 \\ \hline Uniaxial Compressive Strength (\(\mathrm{q_{\mu}}\), \(\mathrm{kgf/cm^{2}}\)) & 0.012–2.323 & 0.346 \\ \hline Compression Index (C\({}_{c}\)) & 0.119–2.614 & 0.711 \\ \hline Pre-consolidation Pressure (P\({}_{c}\), \(\mathrm{kgf/cm^{2}}\)) & 0.000–4.900 & 0.732 \\ \hline \end{tabular}
\end{table}
Table 2: Data characteristics.  
Figure 1: Target area. (Source: National Geographic Information Institute).
## 5 Computing and Networking Integration
### Computing Supporting Networking  
The computational system may enhance the networking system in several situations; for instance, several environmental information, such as altitude or terrain changes may lead to changes in the used channel models, which will have an impact in the performance of the networking system. In this scenario, the networking performance can be improved by the computational system, since it can measure and model the wireless channel, and classify that channel according to predefined rules.  
Several research attempts have been conducted in an attempt to embed computation into a networking system, aiming to improve the overall system performance. Instances of such intelligence algorithms (e.g., through P4-assisted constraint operations) [99] helps to mitigate the limitations brought by high latency and low transmission rates. These computational functions are normally focused on data plane operations, such as aggregation of acknowledgement messages, better support for multicast, improved routing decision, scalability of forwarding depending on current conditions (e.g., link quality, drone mobility), reduction of the communication overhead in the whole system considering communication costs constraints.  
In a drone swarm, and due to the potential hazards brought by the usage of wireless channels, communication security and data protection are significant for a successful deployment of drone swarms. Misbehavior and information leakage can lead to physical damage and endanger the operation of the swarm. In this context embedding computing in the swarm would allow the processing of traffic and data directly in the network and at line-rate. Hence, an integration of computational and networking systems creates the needed support for providing the needed security and privacy mechanisms, namely: first by supporting a mechanism for preventing attacks and intrusion. Second for detecting intrusion and undesired behavior when it has already taken place. Third, embedding computing in the network augments the swarm capacity for analyzing potential incidents, preventing future attacks.  
By introducing a computational system, the decision-making process can help drones to better control their networking considering their own properties as well as the status of the environment.
## 2 FMCW LiDAR
### FMCW LiDAR Key Parameters
#### 2.3.1 Detection Distance and Accuracy
Although the operating principles of silicon-based OPA are relatively mature, optimizing power consumption costs and device structures remains a crucial consideration in designing silicon-based solid-state FMCW LiDAR systems for long-range, high-resolution detection. Currently, the detection range and accuracy are relatively limited, which does not yet meet the needs for long-range, high-precision detection. In the short term, such technology may find applications in areas like vehicular LiDAR. Future research directions include the continuous improvement and high-level integration of high-power semiconductor lasers, low-loss passive optical transmission devices, high-performance on-chip OPA architectures, and balanced receiver arrays. If the issue of losses can be addressed, this technology could be further applied in long-range, high-precision target detection.