esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Numerical Analysis
### Test Cases  
There are three different models used for tests, as shown in Table 1: Case 1 is the dam-foundation single-body model, which assumes the undamaged state of the dam. Case 2 is the dam-foundation contact model with a fixed pre-existing fractured area at the upstream dam base, as depicted in Figure 1b. Case 3 is the dam-foundation contact model with a pre-existing fractured area which can be dynamically propagated along its entire dam-foundation interface to the downstream toe. In Cases 2 and 3, pre-existing fractures at the dam base are set to consider the strong hydraulic pressure or earthquakes of the past. The initial ratio of the fractured area to the total area of the dam base is assumed as 50 percent in Cases 2 and 3. Before being fractured, the undamaged area resists against the external force by the shear strength of the dam-foundation interface. After being fractured, the resistant force against the external force follows Coulomb's friction law.  
Figure 4: Numerical model for a dam-foundation system: (**a**) non-overflow monolith geometry; (**b**) finite element mesh.  
In Table 2, there is a list of Peak Ground Acceleration (PGA) data from the recent strong earthquakes [28; 29; 30]. The highest PGA of an earthquake is 3 g and the lowest one is 0.5 g. In this study, two artificial earthquakes are used, in which the PGAs are 0.64 g and 0.7 g, respectively, as depicted in Figure 5. The effective duration of Artificial earthquake I is 15 s and Artificial earthquake II is 20 s.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Name** & **Country** & **Year** & **Magnitude** & **PGA (g)** \\ \hline Kaikoura & New Zealand & 2016 & 7.8 & 3 \\ Tohoku & Japan & 2011 & 9.0 & 2.7 \\ Canterbury & New Zealand & 2010 & 7.1 & 1.26 \\ Kobe & Japan & 1995 & 6.8 & 0.8 \\ Chile & Chile & 2010 & 8.8 & 0.78 \\ Athens & Greece & 1999 & 6.0 & 0.6 \\ Zarand & Iran & 2005 & 6.4 & 0.51 \\ Haiti & Haiti & 2010 & 7.0 & 0.5 \\ \hline \end{tabular}
\end{table}
Table 2: Significant high-PGA earthquakes.  
Figure 5: Artificial earthquake accelerations: (**a**) Artificial earthquake I, PGA = 0.64 g; (**b**) Artificial earthquake II, PGA = 0.7 g.
## 5 Conclusions  
Based on the indoor simulation platform, the present study was conducted to evaluate the light environment in the middle section of the tunnel under dynamic traffic conditions with experimental and convolutional neural network models for the nonlinear relationship of sensitivity of light environment parameters. Sensitivity analysis of the light environment parameters in the middle section of the tunnel was conducted by means of the average pupil area of the testers, and the experimental results were verified by a CNN. The main findings could be summarized as follows.  
Figure 11: A comprehensive comparative computing analysis of four factors of tunnel light environment.  
Figure 10: Cross-contrast analysis of tunnel sidewall material color and light source color temperature.  
(1) Through analysis of experimental data, findings were made that there was a nonlinear relationship between the sensitivity of light environment parameters, and a CNN judging model with adaptive learning capability was constructed in the present study, which can make comprehensive judging of the data related to the sensitivity of light environment parameters. The judging results are in high compliance with the design requirements of the national standard.  
(2) The pupil change test results show that the driver's pupil diameter was most affected by the way the lamps being laid out during the driving process, followed by the color temperature of the LED light source, the height of the sidewall reflective paint laying and the color of the sidewall reflective paint.  
(3) Analysis of the test results dataset using a CNN revealed a ranked order of factors influencing pupil diameter, as follows: lamp layout with staggered arrangement on both sides > symmetrical arrangement on both sides > symmetrical arrangement on the midline > symmetrical arrangement on the midline. Other factors include reflective coating laying height (3 m > 3.5 m > 2.5 m > 2 m), sidewall reflective coating color (green > blue > yellow > white), and LED light source color temperature (6500 K > 3500 K > 5500 K > 4500 K). Furthermore, blue > yellow > white was observed under different sidewall reflective coating color conditions, and 6500 K > 3500 K > 5500 K > 4500 K under different LED light source color temperature conditions.  
Conceptualization, B.L. and M.X.; methodology, M.X.; software, Z.L.; validation, J.N., M.X. and Z.L.; formal analysis, M.X.; investigation, B.L.; resources, M.X.; data curation, J.N.; writing--original draft preparation, B.L.; writing--review and editing, M.X.; visualization, Z.L.; supervision, M.X.; project administration, B.L.; funding acquisition, B.L. All authors have read and agreed to the published version of the manuscript.  
National Natural Science Foundation of China (51878107); Project of Chongqing Talent Team (2019-9-95).  
Not applicable.  
The authors declare no conflict of interest.
## 5 Issues Relating to the Fundamental Design Aspects
### Energy Management
#### 5.1.2 Factors That Affect Energy Consumption in AoI-Aware UAV-Assisted Data Gathering  
Proper consideration of the factors that affect energy consumption in UAV-assisted data gathering would significantly help to optimize energy efficiency, thereby reducing AoI. These factors are discussed below.  
Number of Sensor Nodes Visited  
The number of IoT devices a UAV visits in a time round affects the AoI and should thus be planned appropriately and well-optimized. In large-scale networks, using CPs has proven to be an efficient alternative to optimize the UAV trajectory since the UAVs would not need to visit all the sensor nodes individually, but rather the CPs. Another energy-saving technique could involve timing the data collection task for each sensor or CP. For instance, in [1], IoT devices monitor UAV signals to determine the presence of UAVs for data collection. If the signals are received, and the UAV switches to a hovering status, the sensors then upload data to the UAV.  
In specific applications where data are collected continually, energy consumption might have to be traded off for minimal AoI. In such cases, optimizing energy use as much as possible is still very important. For instance, a UAV continually collects data from sensor nodes in [1], and thus an optimization problem is formulated to minimize energy consumption and the weighted sum expected AoI while incorporating UAV distance, flight speed, direction, and IoT device scheduling. The most important issue here is ensuring that UAVs have sufficient energy levels to sustain their missions effectively within the least possible time.  
UAV Cooperation  
A cooperative deployment of UAVs is advantageous when UAVs are assigned the task of collaboratively collecting data. In large-scale IoT networks, a single battery-constrained UAV will usually be unable to efficiently cater to the entire network, especially given the strict time and performance constraints of AoI-sensitive applications. In this respect, ref. [91] considers a multi-UAV cooperative data collection framework for time-sensitive dense WSNs. The aim is to find the AoI-optimal trajectory for each energy-constrained UAV.
## 4 Proposed CPCROK Scheme
### Fbg  
This module generates fake beacons according to the predictions provided by the RNN-FGTGM. When a new vehicle approaches a mix zone that has deployed the RNNbased fake beacon scheme, the mRSU first collects all beacons before this vehicle enters the mix zone. If this vehicle requires a fake trace, the mRSU will continue with the algorithm.  
At this time, the direction of this vehicle has been detected by either indicator light information from beacons or steering angle analysis. Thus, the mRSU chooses another random exit and uses the angle of deflection to this exit as the system command to build an IKF and make preliminary predictions. Each time the mRSU receives a new interval beacon before the vehicle leaves the mix zone, an iteration is updated in the IKF.  
Next, the data from the IKF are put into the RNN model to make final predictions. To enhance privacy protection in VANET simulations, asynchronous timestamps are used, meaning each vehicle is randomly assigned a start time to broadcast beacons. Vehicles decide whether to encrypt their beacon based on their location relative to the mix zone. Upon entering the mix zone, vehicles continue to broadcast using their previous timestamp and pseudonym, but only RSUs can receive these encrypted beacons. As a vehicle reaches the edge of the mix zone, the RSU assigns a new pseudonym to the vehicle and begins broadcasting at a new random time. This strategy is also applied to fake beacons. Since the length of the RNN prediction sequence is the same as that of the real trajectory, the last state of the prediction sequence becomes the first fake beacon, i.e., the start of the fake trace driving out of the mix zone. Starting from the first point of the fake trace, if the position indicated in the fake beacon is still within the range of the mix zone, this fake beacon will be encrypted by the mRSU.  
Prior to the real vehicle exiting the mix zone, each new beacon received by the mRSU is used to update the IKF and the RNN model. Regardless of where the fake beacon generated by the RNN model terminates within the mix zone, the generation of fake beacons will continue following the protocol outlined in [24] to form a complete fake trajectory. As the RNN eventually generates beacons at the edge of the mix zone, the RSU also assigns new pseudonyms and new random times to these fake beacons. Attackers cannot trace vehicles by merely analyzing time intervals. If a fake beacon terminates inside the mix zone, all subsequent fake beacons generated within the mix zone will need to be encrypted to maintain security. This ensures that the continuation of fake beacons adheres to the established security protocols whether or not the real vehicle has left the mix zone.  
With the help of the RNN model, the fake trace behaves very closely to a real vehicle, with similar patterns of steering angle and speed transformation in the junction. Therefore, fake traces are accurate enough to deceive attackers, and a small number of fake traces can effectively protect the privacy of vehicles under a low-vehicle-density situation.
# Characterization of Carrier Phase-Based Positioning in Real-World Jamming Conditions
## 3 Cases for Investigation
### Selected Jamming Incidents
#### 3.2.1 Case 1  
The first case is recorded on TA03 on 27 May 2020, and it is presented in Figure 4, which shows C/N\({}_{0}\) plots for the incident. Here, it is seen that the duration is more than 1 h, and that the jamming primarily affects GPS L1 and Galileo E1 (both at 1575.42 MHz), with BeiDou B1I (at 1561.098 MHz) also taking a major hit. GLONASS G1 (at 1602 MHz) signals, however, are, due to the offset in center frequency, left unaffected except for a small drop at the beginning of the incident. The same goes for signals on any other frequency. For simplicity, only GPS L1 signals are shown in the plot.
# A Survey on Underwater Acoustic Sensor Network Routing Protocols
## 5 The Cross-Layer Design Method
### The Optimization-Based Method
#### 5.1.3 Summary  
In this subsection, we have introduced two different kinds of traditional cross-layer design routing protocols: location information-free routing protocols and location information-based routing protocols. As shown in Section 5.1, the location information-free routing protocols do not need node location information. These protocols use flooding, depth information, or hierarchy models to transmit data packets from the source nodes to the destination nodes. From Section 5.1, we can conclude that the disadvantage of location information-free routing protocol is that they are blind in routing discovery due to the absence of location information, which will waste vast amounts of energy and cause serious propagation delays. Unlike the location information-free routing protocols, the location information-based routing protocols can utilize the location information of nodes to assist the routing discovery. The location information can be obtained by underwater GPS or location algorithms. The challenge of location information-based routing protocols is that underwater GPS is expensive and energy intensive; moreover, the location algorithms are usually complex, which will waste large amounts of node resources.
## 5 Conclusions and Future Research  
In this study, we first analyzed the correlation between six VWC-related indices and the NMRI product, based on GNSS-IR. The three machine learning methods of BPNN, GRNN, and RF were used to construct point-surface fusion models using data from 2007 to 2016. The results showed that the machine learning methods outperformed the traditional methods of MLR in the cross-validation results. Among the three machine learning methods, the results of RF were the best, followed by those of GRNN and BPNN. Then, by using the RF model, we obtained an NMRI product with a spatial resolution of 500 m, which compensate for the spatial limitations of the NMRI product in the PBO H2O sites. Finally, maps of the 500 m spatial resolution NMRI product for the summer from 2010 to 2016 were obtained. The results showed that, during the period from 2012 to 2014, when drought occurred in the western part of the CONUS, the NMRI value was also significantly reduced, which is consistent with the drought distribution map. In conclusion, this paper proves the effectiveness of using machine learning methods to acquire the spatially continuous NMRI product with a point-surface fusion technique, and verifies the feasibility of analyzing and predicting drought events by using spatially continuous products with a finer resolution.  
In the future, NMRI products can be fused with other VWC-related microwave remote sensing data to obtain an NMRI product with higher accuracy. Furthermore, other meteorological factors related to vegetation growth, such as LST, will be added into the model. Statistical distance approaches, such as the Jeffries Matusita distance [55; 56; 57; 58], can be used to assess the statistical separability of variables and dataset selection. Other machine learning models, or deeper neural networks, will be used to study the relationship between NMRI and these vegetation indices, to further improve the accuracy of the model. Due to the fusion with optical remote sensing data, the temporal resolution of the final fusion result is limited by the optical remote sensing data. In our future work, we will consider  
Figure 15: 500 m enlarged NMRI results of the four frame areas in Figure 13, from 2011 to 2014.  
the idea of combining point-surface fusion and spatial-temporal fusion to improve the temporal resolution of the NMRI products for the monitoring and prediction of more unexpected disaster events.  
Conceptualization, Q.Y.; Data curation, S.L.; Formal analysis, S.L.; Methodology, Q.Y. and S.L.; Resources, Q.Y. and T.L.; Supervision, L.Y., H.S. and L.Z.; Validation, S.L., L.Y., T.L., H.S. and L.Z.; Visualization, S.L. and L.Y.; Writing--original draft, S.L.; Writing--review & editing, Q.Y., L.Y., T.L., H.S. and L.Z.  
This work was supported by the National Key R&D Program of China (No. 2016 YFC0200900) in part of the Fundamental Research Funds for the Central Universities under Grant 2042019k60213. Sincere thanks to anonymous reviewers and members of the editorial team, for the comments and contributions.  
The authors declare no conflict of interest.
# Research on the Posture Control Method of Hexapod Robot for Rugged Terrain
## 3 Posture Maintenance Strategy for Mildly Rugged Terrain  
The configuration and motion mode characteristics of the hexapod robot determine that it has a high stability margin. The stability of the walking robot is evaluated by the size of the stability margin (SM) (Zero Moment Point). The widely used criterion of the stability margin is the ZMP (Zero Moment Point) principle, which is defined as the minimum distance between the projection point of the center of gravity vector on the supporting polygon and each side of the polygon. The motion characteristics of the hexapod robot determine that it can be analyzed in a quasi-static way. Therefore, the minimum distance of the center of gravity from the supporting polygon to each side of the polygon is taken as the evaluation standard of the stability margin, ignoring the effect of inertia. As shown in Figure 8, the stability margin for n-side shape is shown as follows.  
Figure 8: Diagram of the stability margin.  
Figure 7: The flow chart of single leg control.  
The interference of mildly rugged terrain is not enough to cause its motion instability, but it will still affect the posture. Therefore, a posture maintenance strategy, which keeps the level and height of the torso unchanged based on the initial posture during walking, is proposed for mild ruggedness terrain. In order to obtain higher leg space, the hexapod robot was required to walk with TG (tripod gait) as the starting gait [29]. Adjust the position of the center of mass of the torso on the projection of the supporting polygon within the range of motion and take the minimum distance between the projection point and each side of the supporting polygon as the initial attitude of the hexapod robot. So, the initial level and height of the torso depends on the terrain.
# Effects and Mechanisms of Microbial Remediation of Heavy Metals in Soil: A Critical Review
## 5 Microbial Remediation of Heavy Metal Pollution in Soil and Water
### Composite Reclamation System  
The current proposed composite system uses microbial-plant combination and electric-microbial combination technology. These two technologies are combined by using mycorrhizal fungi to enhance metal removal at plant roots and a DC power plant in combination with bacteria to achieve restoration [48; 77; 81]. In soil washing and soil flushing processes, a composite reclamation system is also applied. [PERSON] showed that microbial-plant remediation technology can enhance microbial removal [82]; the combination of sulfuric acid (0.05 mol/L), _Bacteroides_, and ryegrass joints has shown Cu removal of up to 30% [83]. Studies have shown that results with a composite repair system are better than those using either pure plant or microbial reclamation. [PERSON] et al. [84] showed that the removal rate of mycorrhizal fungi and Festuca can be 64-72%, whereas the control group showed only 31% of the plant removal rate. The removal rate of mycorrhizal fungi and _Polygonum aviculare_ L. was 54-88%, whereas the control groups with only plants or fungi showed removal rates of 24% and 52-76%, respectively [85]. The removal rate with an electric field increased by more than 10% compared to that without the field, and the removal rate increased to 85-88% by using a DC electric field and adding the matrix and a stationary electrode [47; 86]. This is mainly because the electric effect can accelerate the movement of microorganisms and metal ions in the environment and the electrode reaction can provide favorable conditions for microbial degradation. Soil washing and soil flushing combine physical and chemical methods. For example, [PERSON] [16] presented a soil washing method that involved screening, gravity, concentration, hydrocyclone, froth, and floatation technology. Removal reached 50%, and the capacity reached 3 m\({}^{3}\)/h. Another experiment included vibrating, screen, and magnetic separation technologies. The concentration of heavy metal ions was 52 \(\upmu\)g/g initially and only 14 \(\upmu\)g/g in the end. However, this composite technology is often affected by a variety of environmental conditions, such as soil moisture, temperature, nutrients, and high cost, preventing it from being widely used [86].
## 2 Finite Element Model Verification
### Building Up Finite Element Model
However, this method was too time-consuming because it required a lot of time to analyze the full 3D model and extract the data in the circumferential direction. For the 3D model, after several attempts to obtain a stable value for the mesh size, the mesh size was reduced until the finite element analysis results converged and set, as shown in Table 5. For the element type, the hexahedral mesh was used as much as possible, and the tetrahedral mesh was used in areas where it was impossible to use hexahedral mesh due to its complex geometry shape. Denser meshes were used in the contact area. When considering the analysis time based on a computer (Intel Core-i7, 64 GB RAM), enough number of elements could not be used in the case of 3D models, so, in this paper, as shown in Figure 9, a two-dimensional, axisymmetric model was used to reduce the analysis time and use a sufficient number of elements. In Figure 10, the results of analysis with the 2D axisymmetric model and 3D model are compared. For the analysis results of the 3D model, the calculated data obtained through arithmetic averaging after extracting analysis results in the circumferential direction for elements located at the same distance from the wafer center mentioned above is used. In Figure 10, the analysis result of the 2D axisymmetric model has no significant difference from the analysis result of the 3D model, and this paper intends to improve the WIWNU through trend analysis using the finite element analysis results, so the finite element analysis was performed using a 2D axisymmetric model. Figure 7: Three-dimensional model for finite element model. Figure 8: Finite element analysis data extraction in the circumferential direction. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \begin{tabular}{c} **Water** \\ **(Model \#1)** \\ \end{tabular} & \begin{tabular}{c} **Retainer Ring** \\ **(Model \#1)** \\ \end{tabular} & \begin{tabular}{c} **Pad** \\ \end{tabular} & \
\begin{tabular}{c} **Platen** \\ \end{tabular} \\ \hline \multirow{4}{*}{3D model} & No. of node \# & 79,456 & 422,558 & 82,622 & 13,416 \\  & No. of element & 39,463 & 244,576 & 40,991 & 27,198 \\  & Element type & C3D8R & C3D10/C3D8R & C3D8R & C3D8R \\  & Approx. size & 1.5 mm & 2 mm & 4 mm & 7 mm \\  & No. of node \# & 3780 & 15654 & 34527 & 4477 \\  & No. of element & 3020 & 15324 & 32480 & 4060 \\ \multirow{4}{*}{
\begin{tabular}{c} axisymmetric \\ model \\ \end{tabular} } & Element type & CAX4R & CAX4R & CAX4R & CAX4R \\  & Approx. size & 0.2 mm & 0.2 mm & 1 mm \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Element types and number of elements for finite element model. Figure 10: Finite element analysis result of full 3D model and 2D axisymmetric model. Figure 9: Schematic diagram of 2D axisymmetric model.