esa-sceva/satcom-chunk-collection huggingface dataset:
# Vision-Guided Autonomous Robot Navigation in Realistic 3D Dynamic Scenarios +
## 2 Problem Formulation
### Challenges in 3D Mapping and Navigation  
Mesh reconstruction techniques hold great potential for 3D mapping in multilevel environments. However, several challenges remain in achieving fully autonomous navigation. One of the primary difficulties is enabling robots to traverse unfamiliar terrains without prior knowledge. While integrating RGB-D cameras into SLAM systems has shown promise, these methods often encounter limitations, such as incomplete depth data and difficulties in accurately replicating real-world scenarios.  
Figure 1: Navigation on the wheelchair ramp. An unfeasible path (dotted arrow) is calculated on the 2D grid map, while the solid arrow is the path passing through multilevel environments.  
Although advancements in 3D navigation and pathfinding algorithms have been made [7], practical implementations frequently rely on 2D occupancy grids [8]. For example, the A* algorithm [9], widely adopted for global planning in the Robot Operating System (ROS) [10], suffers from high memory demands [11], especially in large-scale or complex environments [12]. This issue becomes critical during real-time path replanning. A 21 m path generated on a 2D grid map with a resolution of 5 cm typically involves over 400 nodes, leading to considerable computational overhead. This high node density not only increases memory usage but also poses challenges for real-time path planning, especially in large-scale or complex environments. Table 1 presents a comparative analysis of various pathfinding algorithms, highlighting their advantages and limitations in different scenarios. For more detailed pathfinding algorithms, please refer to [13].  
Research into 3D navigation methods has produced promising results. [PERSON] et al. employed the Fast Marching Method on triangular meshes to compute vector fields for pathfinding [17]. However, such approaches often lack comprehensive pipelines for mesh map construction and mechanisms for real-time obstacle avoidance. Furthermore, while voxel-based approaches can approximate walkable surfaces, they introduce a trade-off between accuracy and computational efficiency [18].  
When considering motion control, existing 2D planners, such as the dynamic window approach (DWA), face compatibility challenges in 3D settings [19]. For instance, while effective in addressing obstacles in 2D, DWA struggles in dynamic environments and narrow spaces. This highlights the necessity of extending collision avoidance to fully account for 3D spaces, ensuring robust and efficient navigation.
# Graphene Deposition on Glass Fibers by Triboelectrification
Separation or delivery of charges between the surfaces could be influenced by mechanical friction [35]. TE is often considered a negative phenomenon since it can result in dangerous electrical discharges, friction, energy losses, blending in pharmaceutical processes, etc. In the modern age, however, some applications of TE have been developed, e.g., xerography [36], laser printing [37], electrostatic separations [38], electrostatic [39] or triboelectric nanogenators [40; 41]. According to the conventional triboelectric series [34], glass is found on the top of the list, while polytetrafluoroethylene (PTFE) is at the bottom of the series. Rubbing or even touching these two materials results in a high charge transfer on both of them. On the basis of this natural phenomenon, in this manuscript, an innovative method is proposed to deposit graphene oxide (GO) nanosheets dispersed in a water solution onto glass fibers (GF) surface by contact electrification with PTFE. In particular, a glass fiber roving was passed at different velocities over one or two PTFE surfaces, and the amount of charge developed by CE on GF was analyzed. In another step, such triboelectrified fibers were passed through GO solutions at different concentrations. Next, GO coatings were chemically reduced to create rGO coated GF. Such coating, having an electrically conductive nature, could provide the possibility to achieve multi-functionality in composite materials. The morphological and electrical properties of the coated GF were analyzed to evaluate the quality of the coating. In particular, the influence of graphene-coated GF on the interfacial properties of epoxy/glass composites was investigated by the micro debonding test.
## 3 Problem Formulation and Proposed Solution
### Problem Formulation  
To answer the aforementioned question, we propose matching to assign each RF chain at \(m_{th}\) AP to its suitable number of selected antennas instead of assuming a single predefined value of active antennas as mentioned in [22]. Furthermore, we consider half of the RF chains at each AP have selected antennas exceeding 50% of the total number of antennas based on channel condition. On the other hand, each of the remaining RF chains might be connected to 50% or less of the total number of \(N_{r}\) at \(m_{th}\) based on channel condition, as mentioned in [22]. Thus, this will be considered a compromise between maximum EE and avoiding significant SE loss. To achieve this aim, we formulate an assignment optimization problem to match each RF chain at \(m_{th}\) AP to its suitable predefined value of the selected antennas, as shown in Figure 2. The proposed linear assignment optimization problem to compute the optimal match between \(K\) RF chains and \(\mathcal{Z}_{m}\) in this paper can be formulated as  
\[\max_{x_{u,b}\in\{0,1\}} \sum_{u=1}^{K}\sum_{b=1}^{Z_{K,m}}\mathrm{R}_{k,m}^{(Z_{k,m})}x_ {u,b}\] \[\text{s.t.} \sum_{u=1}^{K}x_{u,b}=1,\text{for }u=1,\ldots,K,\] \[\sum_{b=1}^{K}x_{u,b}=1,\text{for }b=\mathcal{Z}_{1,m},\ldots, \mathcal{Z}_{K,m}.\]  
where \(R_{k,m}^{(Z_{k,m})}=\log_{2}(1+\text{SINR}_{k,m})\) is the reward function of the above linear program, \(x_{u,b}^{K\times K}\) gives the binary matrix, where \(x_{u,b}=1\) if and only if \(u_{th}\) RF chain is assigned to \(b_{th}\)\(\mathcal{Z}_{k,m}\) switches. In addition, \(\mathcal{Z}_{k,m}=N_{r}-\varkappa\kappa\), where \(\kappa=\frac{N_{r}}{K}\) and \(0\leq\varkappa\leq K\). Thus, \(\mathcal{Z}_{m}=[N_{r}^{(1)},N_{r}-\kappa^{(2)},N_{r}-2\kappa^{(3)},\ldots,N_ {r}-\varkappa\kappa^{(K)}]\) based on the number of RF chains at each AP.
## 2 Materials and Methods
### PET Feature-Based Measures  
Similar to the dose-volume histogram (DVH), which is the radiation dose histogram for tumor treatment [30], the CSH uses the SUV derived from PET imaging instead of the dose value derived from CT imaging. Specifically, the SUV is the widely used PET semiquantitative parameter calculated as the ratio of the tissue radioactivity concentration (RC) in kBq/mL and the MET injected dose (ID) in MBq at the time of injection divided by the body weight in kilograms [31]:  
\[\text{SUV}=\text{ }\frac{\text{RC}}{\text{ID}}\times\text{ M}_{\text{P}} \tag{1}\]  
where RC is calculated as the ratio between the image intensity and the image scale factor. ID is the product between actual activity and dose calibration factor. Therefore, in the case of PET imaging, the image intensity values were normalized in SUVs. While the SUV\({}_{\text{mean}}\) is the mean intensity value in the region of interest (ROI), the SUV\({}_{\text{max}}\) is defined as the voxel with the highest SUV within a specified ROI. It is the most common PET parameter because it is both a ROI and user independent [32]. A disadvantage is that it represents a small portion of the tumor that may not be a statistically reliable representation of the whole-tumor biology. It does not take into account the SUV distribution within the tumor. Starting from these considerations, the CSH is the representation of the percentage of the tumor volume with a certain SUV [22; 33]. The CSH summarizes the 3D functional imaging intensity information in a single curve for the structure of interest, which will be used to derive intensity-volume metrics, such as the area under the CSH (AUC) to take into account the tumor uptake heterogeneity [22] (see Figure 1 for an example of CSH and AUC). In this way, it is possible to analyze changes in the uptake distribution within the tumor due to nonuniform dose distribution during GK treatments. Finally, in addition to the aforementioned PET feature-based measures, MTV and TLG were also calculated [21]. These parameters provide information on the tumor extension but no information on the uptake heterogeneity. MTV is the active volume of oncological lesions obtained using a segmentation algorithm, e.g., [34; 35], while TLG was calculated to acquire a simultaneous estimate of volumetric and metabolic information:  
\[\text{TLG}=\text{MTV}\times\text{SUV}_{\text{mean}} \tag{2}\]  
Consequently, TLG is also a segmentation-dependent parameter.
# Virtualization Airborne Trusted General Computing Technology
## 4 Experimental Results and Analysis  
### Application Hosting Function Test  
During the verification of application hosting function, multiple airborne applications with different safety levels are selected, such as the aircraft parameter distribution and file transfer service application of the airborne information system, the aircraft parameter acquisition application of airborne maintenance system, and the cabin parameter distribution and cabin lighting control applications of the cabin management system, to reside in multiple VMs of the VAGCSP, thereby verifying the VAGCSP's ability to host avionics system applications with low security levels. Figure 4 displays the verification environment.  
The aircraft parameter distribution application resides in VM 1, which uses Ubuntu18.04 as the guest operating system. The aircraft parameter acquisition application resides in VM 2, with CentOS7 as the guest operating system. The cabin parameter distribution and lighting control applications reside in VM 3, whose guest operating system is Ubuntu18.04. The file transfer service application resides in VM 4, with Ubuntu18.04 as the guest operating system. To achieve isolation, each VM is configured with exclusive computing resources, including the processor cores, memory, and Ethernet interface. Table 1 lists the test items of the application hosting function. Adopting automated testing, the test case code developed based on Python was run on the testing equipment, and the application interaction data received during the test process were compared with the expected results to determine the functional correctness. Taking the test items for an aircraft parameter distribution  
Figure 3: Virtualization security protection architecture.  
application airborne information system as an example, the functional test results displayed in the testing equipment are shown in Figure 5. The outcomes of the VAGCSP's application hosting function test were consistent with the expected results.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Systems** & **Typical Applications** & **Function Test Items** \\ \hline \multirow{4}{*}{airborne information system} & aircraft parameter distribution application & distributing aircraft parameters \\  & file transfer service application & downloading files \\  & & uploading files \\ \hline airborne maintenance system & aircraft parameter acquisition application & acquiring aircraft parameters and information \\ \hline \multirow{4}{*}{cabin management system} & cabin passenger address management application & enabling passenger address \\  & management application & disabling passenger address \\  & & turning on the no smoking and fasten seat belt \\  & & lights at terminal \\  & cabin lighting control service application & turning off the no smoking and fasten seat belt \\  & & lights at terminal \\  & & turning on the reading light \\  & & turning off the reading light \\ \hline \hline \end{tabular}
\end{table}
Table 1: Test items of the application hosting function.  
Figure 4: Verification environment of the application hosting function.
###### Abstract  
The presented research addresses the problem of dependency analysis of the ultrasonic signal measured by a sensor in an engine oil bath. The dependency analysis is performed on a selected ultrasonic signal sensor solution containing its own generator and an ultrasonic signal receiver detecting the level of the oil in which it is immersed. The influence of the resulting amplitude of the received ultrasonic signal is mainly due to the level of the measured oil level and the oil temperature, as shown by the regression analysis and ANOVA (Analysis of Variance) testing performed. The analyzed dependence of the time determination of the length of the generated ultrasonic signal envelope is given by a set threshold value, which can be dynamically adjusted based on the backtracking evaluation. The analysis results in the form of approximation by the dependency algorithm confirm the assumption of possible standardization of the envelope parameters with the achievement of accuracy up to 99.02%. The analyzed parameters approximated by the temperature and oil level dependence algorithms include the amplitude of the measured signal, steepness of the rising edge, duration of the envelope, and the digitally processed amplitude value.  
ANOVA; automotive; level measurement; oil sensor; temperature dependency; regression analysis Article
## 2 Materials and Methods
### Tidal Effect Analysis on GPP, SIF, ET, and Their Interrelations  
Figure 2 presents the comprehensive analysis framework of this study, which aims to investigate the tidal effects on SIF, GPP, ET, and their interrelationships within coastal wetland ecosystems. This study employs a multi-scale approach encompassing leaf-scale field measurements (parameters such as F\({}_{\rm o}\), F\({}_{\rm m}\), F\({}_{\rm m}\), F\({}_{\rm t}\), and NPQ), site-scale field observations of GPP, SIF\({}_{760}\), and ET, as well as the corresponding model simulations. Specifically, Content a in Figure 2 illustrates the application of the SCOPE model, where the parameters of the fluorescence photosynthesis module have been calibrated based on field measurements from coastal wetlands to better align the simulation results with the vegetation characteristics of these ecosystems. However, the original model does not account for inputs specific to tidal processes in coastal wetlands. Thus, in this study, the model is primarily employed to simulate the GPP, SIF\({}_{760}\), and ET of vegetation under non-tidal inundation conditions. Content b in Figure 2 analyzes the responses of GPP, SIF, and ET to changes in meteorological factors (such as T\({}_{\rm a}\), T\({}_{\rm s}\), and VPD) by comparing SCOPE model simulations with field observation data. It further isolates the components influ  
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & \multicolumn{3}{c}{**Parameter**} \\ \cline{2-4}  & \(\alpha\) & \(\beta\) & \(K_{N}^{0}\) \\ \hline
**All the measured data** & 3.818 & 0.04989 & 1.997 \\ \hline
**Measured data grouped by time period** & & & \\ \hline June–August & 4.161 & 0.03096 & 2.079 \\ \hline August–November & 3.474 & 0.08238 & 1.997 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Calibrated values of \(\alpha\), \(\beta\), and \(K_{N}^{0}\) in the fluorescence parameterization based on all the measured leaf gas exchange and fluorescence data, and the measured data in June–August and August–November, respectively.  
enced by tidal inundation, providing an in-depth exploration of how tidal inundation modulates these physiological parameters' responses to meteorological factor variations. Additionally, it assesses the responses of GPP, ET, and SIF to changes in tidal factors (e.g., T\({}_{\text{w}}\) and salinity), thereby revealing the direct impacts of tides on them. Finally, Content c in Figure 2 establishes correlations between SIF and GPP, as well as between SIF and ET, based on field observation data in coastal wetlands, and examines how these correlations respond to changes in meteorological factors. By separating the components affected by tidal inundation, it further examines the regulatory role of tidal inundation on the responses to meteorological factors. Simultaneously, it analyzes the responses of these two sets of correlations to tidal factor changes, thus providing a thorough investigation into how tides affect the effectiveness of using SIF to track the dynamic changes in GPP and ET in coastal wetland ecosystems.
# Deployment Protection for Interference of 5G Base Stations with Aeronautical Radio Altimeters
## 4 Results and Discussion
### Discussion
#### 4.6.3 The Application of the Deployment Protection Method  
The conclusions drawn from this research can be applied to delineate the scope of prohibited zones. Figure 26 (not to scale) provides an illustration of the application of prohibited zones. As the aircraft climbs after takeoff, the radius of the horizontal and vertical prohibited zones gradually increases for altitudes below 2000 ft. Base stations deployed in these zones may interfere with radio altimeters, affecting flight safety. Second, in the prohibited zones, base stations can implement interference mitigation measures, including power control and angle shutoff methods. For example, in Figure 25c, at a range of 5 km at \(AGL=2000\) ft, base stations need only shut off transmission angles of no more than 45\({}^{\circ}\) to avoid such interference. This can serve as a technical requirement for base stations in the prohibited zones.
# Historical Eco-Environmental Quality Mapping in China with Multi-Source Data Fusion
## 2 Materials and Methods
### Datasets  
The data in this paper are all remote sensing data (Table 1), including precipitation (PRE), temperature (TEMP), net primary productivity (NPP), vegetation coverage (VC), digital elevation model (DEM), land-use and cover change (LUCC), population (POP) and potential evapotranspiration (PET).  
The data processing process mainly includes annual synthesis of indicator data, resampling, indicator standardization (selection of uniform extreme values), and weighted summation of indicators (Figure 2). The details are shown below:  
1. Synthesis of annual data: MOD17A3 (NPP) data [30] are annual data synthesized by averaging the monthly data. MOD13A2 (NDVI) data [31] are annual data synthesized by averaging the growing season data. MCD12Q1 (LUCC) data [32] are annual data synthesized by averaging the monthly data. MOD16A3 (PET) data [33] are annual data synthesized by averaging the monthly data. Precipitation (PRE) data [34] are annual data synthesized by summing the monthly data. Temperature (TEMP) data [34] are annual data synthesized by averaging the monthly data.
2. Projection transformation: MRT software was used to perform the projection transformation NPP, NDVI, LUCC and PET data and unify them into the WGS 1984 geographic coordinate system.
3. Resampling: all data was resampled to 1000 m resolution.
4. Data format transformation: MRT software was used to perform data format transformation of NPP, NDVI, LUCC and PET data, and HDF was transformed to TIFF  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Data** & **Time Range** & **Spatial Resolution** & **Time Resolution** & **Source** \\
**Name** & **Time Range** & **Spatial Resolution** & **Time Resolution** & **Source** \\ \hline Precipitation & 2000–2017 & 1000 m & Monthly & NTPDC a [FOOTNOTE:a]Footnote a: National Tibetan Plateau Data Center ([[https://data.tpdc.ac.cn/en/news](https://data.tpdc.ac.cn/en/news)]([https://data.tpdc.ac.cn/en/news](https://data.tpdc.ac.cn/en/news)), accessed on 1 March 2023). b National Aeronautics and Space Administration ([[https://www.nasa.gov/](https://www.nasa.gov/)]([https://www.nasa.gov/](https://www.nasa.gov/)), accessed on 1 March 2023). c United States Geological Survey ([[https://www.usgs.gov/](https://www.usgs.gov/)]([https://www.usgs.gov/](https://www.usgs.gov/)), accessed on 1 March 2023). d Oak Ridge National Laboratory ([[https://www.ornl.gov/](https://www.ornl.gov/)]([https://www.ornl.gov/](https://www.ornl.gov/)), accessed on 1 March 2023). d Chinese Academy of Sciences ([[http://www.resdc.cn/](http://www.resdc.cn/)]([http://www.resdc.cn/](http://www.resdc.cn/)), accessed on 1 March 2023). \\ \hline \hline \end{tabular}
\end{table}
Table 1: Detailed description of the data.  
format. In addition, Matlab was used to transform the data format of PRE and TEMP from NETCDF (.nc) to TIFF.
* Data stitching and cropping: 19 sheets MODIS data covering the Chinese region were stitched together.
# Operation Risk Assessment of Urban Dense Cable Passageway Based on Fuzzy-Analytic Hierarchy Process
## 2 Operational Risk Parameters for Urban Dense Cable Corridors
### Cable Body Risk Parameters
#### 2.1.2 Ageing of Cables and Accessories  
Ageing of cables and their accessories refers to the process by which cables and their connecting parts gradually deteriorate over time due to a variety of factors, which may  
Figure 1: Common urban dense cableway operational risks: (**a**) cable channel excess capacity; (**b**) accumulation of combustible debris; (**c**) non-standard cable laying; (**d**) cable overheating.  
ultimately affect their normal function. Aging cables and their accessories may lead to safety hazards such as unstable power transmission, increased risk of leakage, heat generation, and even fires.
## 5 Conclusions  
This paper proposes an optimal energy-saving strategy for HEMS, which considers bounded customer rationality. Three crucial contributions are made by this paper, including a general electric appliance utility function, a novel concept called the energy-saving cost incorporating prospect theory, and an energy-saving optimization model. The algorithm is first tested in a simulation system, and a case study is also set up based on real field tests from a pilot project in Guangdong province, China, in which air-conditioners, lighting, and some other popular electric appliances are included. The total energy-saving rate reaches 65.5% after the proposed energy-saving program is deployed. The benchmark test shows our optimal strategy is able to considerably save electric energy for residents, while ensuring customer comfort satisfaction. Limitations of this paper are as follows. First, the uncertainty of the environment is not considered. For example, temperature spikes can occur which may impact the results. Additionally, different electric retail rates are not considered in this  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Appliances** & \begin{tabular}{c} **Before** \\ **Optimization/kWh** \\ \end{tabular} & \begin{tabular}{c} **After** \\ **Optimization/kWh** \\ \end{tabular} & \begin{tabular}{c} **Total Energy** \\ **Savings/kWh** \\ \end{tabular} & \
\begin{tabular}{c} **Energy-Saving** \\ **Rate** \\ \end{tabular} &
\begin{tabular}{c} **Ratio of** \\ **Energy-Saving** \\ \end{tabular} \\ \hline Air conditioner in the & & & & & \\ equipment room & 459.41 & 149.69 & 309.72 & 67.4\% & 48.9\% \\ \hline Air conditioner in the office & 356.04 & 131.13 & 224.91 & 63.2\% & 35.5\% \\ \hline Water dispenser & 18.46 & 10.78 & 7.68 & 41.6\% & 1.2\% \\ \hline Indoor lighting & 10.89 & 7.86 & 3.03 & 27.8\% & 0.5\% \\ \hline Outdoor lighting & 121.48 & 33.5 & 87.98 & 72.4\% & 13.9\% \\ \hline Total amount & 966.28 & 332.96 & 633.32 & 65.5\% & 100\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Energy saving statistics.  
Figure 8: Energy saving optimization for air-conditioners in the central control room. (**a**) The operation of air conditioners and temperature before optimization. (**b**) The operation of air conditioners and temperature after optimization.  
paper, such as time of use and real time price. As a result, considering more practical environment conditions should be the focus of future research.  
Writing--original draft, G.L.; Writing--review & editing, F.W. and S.F.; Data curation, Y.Y.; Methodology, F.P.; Software, S.Z.; Funding acquisition, G.L.; Project administration, G.L.; Supervision, S.F.  
This research was funded by Science and Technology Project of China Southern Power Grid Co., Ltd. (GDKJXM20161607) and The National Natural Science Foundation of China (51877134).  
The authors declare no conflict of interest.