esa-sceva/satcom-chunk-collection huggingface dataset:
# Fault-Tolerant Tripod Gait Planning and Verification of a Hexopod Robot
## 6 Conclusions  
In this paper, we focus on the fault-tolerant tripod gait of a hexapod robot with failure of one leg. A gait with different duty factors in different legs and a stability improvement method are proposed, which greatly improves the walking stability and the adaptability of the robot in complex environments. The specific research results are as follows.  
Based on the prototype structure of the hexapod robot, the geometric relationship between the links on the legs of the robot is obtained. The D-H method is used to establish a kinematic model of the hexapod robot, which provides a theoretical basis for the subsequent F-TT gait research.  
This research proposes a new gait generation mechanism of the tripod gait for a hexapod with a faulty leg, which results in a continuous gait. The advantage of the continuous tripod gait is that it has higher locomotion velocity, which helps to maintain the fast movement ability of the hexapod robot. An adjustment method based on pre-adjustment of the coxa joint is proposed to improve stability. This proposed method also provides an evaluation scheme to obtain the optimal adjustment. Through the coxa initialization angle adjustment method and the planned gait, the robot can move straight continuously without adding the adjustment phase during the movement.  
The trajectories of the COG and swing legs are planned, and the joint angles of the straight walking gait are calculated with the combination of kinematic models and the planned trajectories. The simulations and experiments of the F-TT gait of the hexapod under the failure of leg 1 and leg 2 conditions are investigated separately. If the failure occurs in other legs, it can be resolved according to symmetry.  
The F-TT gait effectively solves the forward motion problem of the hexapod under the condition of a single leg failure. However, this research was conducted only on even terrain. In the future, we will focus on the research of uneven terrain, and force control based on a dynamic model will also be considered. Furthermore, situations with locked legs that disturb the robot's motion should be analyzed.  
Writing--original draft preparation, Y.L., X.F.; writing--review and editing, Y.L., X.F.; supervision, L.D., H.G., Y.L.; funding acquisition, Y.L., J.W.; grammar check and language polish, T.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by National Natural Science Foundation of China (no. 51705097 and no. 91948202), Postdoctoral Science Foundation of China (no. 2017M621258), Self-Planned Task of State Key Laboratory of Robotics and System (HIT) (no. SKLRS201803C), the Fundamental Research Funds for the Central Universities (no. HIT.NSRF.202089), Scientific Research Foundation of Harbin Institute of Technology at Weihai (no. HIT(WH)201601), and Scientific Research Innovation Foundation of HIT(WH).  
The authors declare no conflict of interest.
# A Novel Method Using 3D Interest Points to Place Markers on a Large Object in Augmented Reality
## 3 Experiment and Discussion
### Preliminary Experiment  
The number of markers \(k\)  
Previous studies on marker-based AR mentioned that at least three markers are required to solve the perspective-n-point (PnP) problem for camera pose estimation [7; 43]. The ICP algorithm, which is primarily used for registration with multiple markers, also requires at least three markers [44]. Meanwhile, previous studies on markerless AR, which is relatively free from the PnP problem, often used more than three markers for the initial registration [45; 46; 47]. Therefore, in consideration of previous studies, the number of markers was used in this experiment from 4 to 2x as many as 8.  
Weight \(w\)  
The weights in this experiment were used with a total of 5 values, from 0 which excludes DoI to 1 which excludes CSA, split into quartiles:0, 1, 2, 3, and 1.  
This experiment was conducted by changing \(k\) and \(w\) and measuring the registration errors between the real object and the augmented virtual object. This process was repeated twice.  
Results of Preliminary Experiment  
As a result of the preliminary experiment, the mean of the registration errors (hereafter MRE), which are measured through all reference points, was calculated in Equation (7), as shown in Figure 22.  
\[\mathrm{MRE}=\frac{1}{n}\mathrm{\sum}\|\mathrm{p}_{\mathrm{r}}-\mathrm{p}_{ \mathrm{v}}\| \tag{7}\]  
where \(n\) denotes the number of reference points, \(\mathrm{p}_{\mathrm{r}}\) denotes the measured position of the reference point on the real object, and \(\mathrm{p}_{\mathrm{v}}\) denotes the measured position of the reference point on the virtual object.  
Figure 21: Preliminary experimental procedure for determining valid weight.  
Figure 22: Visualized results of the preliminary experiment.  
Impact of weights  
It was assumed that the MRE would be measured the least when a certain weight was used, regardless of the number of markers. Accordingly, the analysis was performed using 5 weight groups with a total of 20 MRE: 2 (number of models) \(\times\) 5 (number of markers) \(\times\) 2 (number of iterations).  
The Shapiro-Wilk test was performed to examine whether each weight group followed a normal distribution (with a 95% confidence interval), and the results are listed in Table 7.  
As a result of this test, \(p\)-values for all groups were less than 0.05, indicating that the normal distribution was not valid. Therefore, the non-parametric Kruskal-Wallis test was performed to confirm significant differences between the groups (with a 95% confidence interval). This test confirmed a statistically significant difference between the weight groups (H = 35.772, \(p\) = 3.22 \(\times\) 10\({}^{-7}\) \(<\) 0.001). As listed in Table 8, it was confirmed that the MRE was the lowest when the weight was 0.75, and accordingly, the proposed method used \(w\) = 0.75 as the default value.
## 2 Basic Concepts
### Rsa  
RSA (Rivest-Shamir-Adleman) is a well-known encryption algorithm that has been implemented in different security scenarios. This algorithm was created by [PERSON], [PERSON] and [PERSON] and was patented in 1983 [33]; it is based on the generation of keys from two seed values (\(p\) and \(q\)), which are usually chosen as very high value prime numbers. This selection is based on the complexity involved in factoring very large numbers, which becomes more complex when the entry is a prime number. This method is an asymmetric algorithm, i.e., it uses different public and private keys. The encryption process of this algorithm can be summarized in the following six steps.  
1. Select two different prime numbers, named \(p\) and \(q\).
2. Calculate \(n=p\times q\).
3. Calculate the function \(\phi\left(n\right)=\left(p-1\right)\times\left(q-1\right)\).
4. Randomly select an integer \(e\), where \(1<e<\phi\left(n\right)\) and \(e\) is coprime with \(\phi(n)\).
5. Calculate a private exponent \(d\), where \(1<d<\phi(n)\).
6. Apply module operation: \(e\times\ d\ mod\ \phi(n)=1\).
7. Finally, public keys are \(e\) and \(n\), and the private key is \(d\).
# A Spectral Gap-Based Topology Control Algorithm for Wireless Backhaul Networks
## 4 SGTC: The Spectral Gap-Based Topology Control Algorithm
### Computational Complexity  
The following theorem establishes the temporal complexity of the SGTC algorithm.  
Theorem 1: _The temporal complexity of the SGTC algorithm (Algorithm 2) is \(O(Max\_itera\times|V|^{2})\)._  
Proof of Theorem 1.: The main loop of Algorithm 2 (lines 7-18) iterates \(Max\_itera\) times and contains calls to isFeasible() (Algorithm 3), getFeasibleRandomNeighbor() (Algorithm 4), and LSG() to compute the LSG of a network \(G=(V,E)\).  
The complexity of Algorithm 3 is \(O(|V|^{2})\) because it checks whether all the \(e\in E\) are feasible, and the maximum number of edges in a graph is in \(O(|V|^{2})\). Similarly, the complexity of Algorithm 4 is \(O(|V|^{2})\) because, in the worst case, it performs \(|V|^{2}\) iterations to identify a feasible link \(e\in V\times V\) that will be either added or removed from \(E\). Computing the LSG of a graph \(G=(V,E)\) is dominated by the cost of determining its eigenvalues, which is \(O(|V|^{2})\) because the Laplacian Matrices are symmetric. Therefore, the total complexity of the main loop is \(O(Max\_itera\times|V|^{2})\).  
The complexity of computing the Pareto front of \(N\) data points and three objective functions is \(O(N\log N)\) when we use [PERSON]'s algorithm [46]. So, if we select \(N\in\Theta(|V|)\) we obtain \(O(|V|\log|V|)\).  
Therefore, the overall complexity of the SGTC algorithm is \(O(Max\_itera\times|V|^{2}+|V|\log|V|)\) which is the same as \(O(Max\_itera\times|V|^{2})\).
## 2 Study Area and Materials
### Reference Data  
The reference dataset was collected by National Forestland Change Survey (NFCS) of China in 2018 and TimeSync-plus v4.6 tool [52]. The NFCS produces a forestland database annually for recording forest management activities and the spatial-temporal changes of forestland utilization and land cover types by two steps: (1) visual interpretation based on the remote sensing images with the resolution higher than 5 m (e.g., GF-1, GF-2 and ZY-3) and (2) verifying the results by field survey of each change plot (accuracy over 95%). The changes of the forestland larger than 667 m\({}^{2}\) (smaller than one Landsat pixel) caused by the following reasons are included in the database: afforestation and regeneration, forest harvesting, planning adjustment, requisition and occupation, deforestation, fire, geologic hazard, other disasters and other natural factors. A total of 8960 regions with changes in the study area were extracted from the NFCS database of 2018. In addition to NFCS data, 420 90 \(\times\) 90 m\({}^{2}\) sampling plots (3 \(\times\) 3 Landsat pixels) with a spacing of 5 km were extracted by TimeSync based on time-series Landsat and Google Earth images for the validation of the detected changepoints in the last 20 years. The spatial and temporal changes information of every sample plot was recorded monthly. These changes were classified as vegetation degradation (browning), recovery (greening) and stability. Figure 3 depicts the distribution of sampling plots and the forestland changes in 2018 over the study area.
# Hierarchical Task Assignment and Path Finding with Limited Communication for Robot Swarms
## 4 Experimental Evaluation  
We evaluate our approach empirically by exploiting a custom 2D simulation environment written in C++ implementing a grid world (Section 4.1). On this setup, we compare results for the proposed algorithm against two baselines: (i) a greedy task selection with task swapping, and (ii) an auction-based mechanism relying on the Contract Net Protocol (CNP) framework [44] (see Section 4.2). To ensure a fair comparison, all algorithms rely on the same motion planner and have access to the same information. The experimental setup is detailed in Section 4.3, and the obtained results are analyzed in Section 4.4.
# Range-Free Localization Approaches Based on Intelligent Swarm Optimization for Internet of Things
## 2 Related Works and Background
The typical DV-Hop technique is then modified, allowing it to be used as an online sequential localization technique in the next phase. To determine the locations of target nodes, the final phase applies a sequential technique in conjunction with a specified anchor set. In simulations, this scheme outperformed the original DV-Hop in terms of localization accuracy. Another enhanced scheme based on DV-Hop was presented in [35]. The proposed technique applies a new method to report the hop size distance, and uses Hyperbolic 2D instead of multilateration to estimate node locations. Simulations demonstrated that this scheme outperforms the original DV-Hop. In [36], the authors proposed various enhancements based on DV-Hop, which they denoted iDV Hop1, iDV-Hop2, and Quad DV-Hop. Geometrical approaches are used in all three variants. The performance of these algorithms was evaluated using C-shaped and uniform network topologies. In terms of localization accuracy, their findings revealed that the iDV-Hop1, iDV-Hop2, and Quad DV-Hop algorithms all outperform DV-Hop. In [37], a new DV-Hop-based scheme was presented that incorporates the PSO metaheuristic technique to reduce the localization error. The authors described two ways to reduce localization errors in a range-free localization approach, and addressed node localization faults by applying a well-defined set of equations. Their improved node local ization technique resulted in a lower localization. Simulations revealed that this approach outperforms the original DV-Hop. In [38], the authors presented a new optimized scheme that uses CSO to enhance localization precision when estimating node locations. They conducted an experiment in which they varied the network features and the number of hens in the algorithm to evaluate the performance of their new approach. They assessed its ability to improve on conventional CSO-based approaches and carried out a comparison between their scheme and PSO, finding that their technique can outperform PSO. This confirms the significance and potential of CSO-based approaches for enhancing network node localization. Table 1 below provides a comprehensive summary of DV-Hop localization algorithm variants. In the next section, we provide an extensive overview of the localization process in DV-Hop-based approaches and briefly discuss our previous weight-based DV-HOP localization algorithms.
# Dynamic Aeroelastic Response of Stall-Controlled Wind Turbine Rotors in Turbulent Wind Conditions
## 3 Reduced-Order Characterization of the Aero-Structural Interaction
### Characterization of Pulses Representing Typical Atmospheric Flow Oscillations  
Our objective was to identify and quantify pulses with a characteristic time span and amplitude that represent typical oscillations in wind speed in a statistical sense. We collected samples of anemometry data from different sources and classified them in an amplitude versus characteristic time span chart (see Figure 4). We found three distinguishable regions in this chart, which are indicated by encircling elliptical dashed lines.  
1. Short pulses (associated with wind flow turbulence): These are characterized by pulses that are short enough that they end before the first peak in the blade oscillation occurs. After a very short initial time when the blades start displacing and accumulating energy by their own inertia, they deflect to a maximum, and the kinetic energy content of the wind pulse is accumulated as blade elastic deformation. The energy transfer and subsequent evolution can be characterized only by measuring the instantaneous blade deflection.
2. Pulse-duration transitional zone: This is characterized by pulses that are long enough that energy dissipation by aerodynamic damping occurs during the duration of the pulse itself (i.e., only part of the pulse energy goes into elastic energy). Energy transfer and evolution can no longer be characterized only by measuring blade deflection.
3. Long pulses: These are characterized by pulses that are long enough that they act as gradual variations in the kinetic energy of the flow that are absorbed by the rotor, inducing very small (or even negligible) oscillations.  
Figure 4: Amplitude versus characteristic time span chart for typical oscillations in wind speed.
# A Novel Sucrose Isomerase Producing Isomaltulose from _Raoultella terrigena_
## 3 Results and Discussion
### Optimization of Isomaltulose Production
#### 3.4.3 Optimization of Reaction Time for Isomaltulose Biosynthesis  
As shown in Figure 4c, the conversion rate of isomaltulose increased significantly within 6 h. After 6 h, the substrate was utterly transformed and the conversion rate of isomaltulose kept stable. Therefore, the reaction time was set as 6 h for the further study. The yield reached 81.7% and the monosaccharide content was less than 2% after 6 h reaction. However, the proportion of monosaccharides in the catalytic products carried out by Slase from _S. plymuttica_ ATCC 15928 [25] and _K. plantiola_ UQ14S [39] was 18% and 19%. The high content of monosaccharides makes it difficult to separate products and reduces economic benefits.
# Toward Low Time Fragmentation of Equipment: A Double-DQN Based TT&C Task Planning Approach
###### Abstract  
With the increase of the number of satellites in space, satellite tracking, telemetry, and command (TT&C) is becoming more and more important for aerospace. This paper proposes a method for a low time fragmentation oriented. TT&C task planning method based on Double Deep Q-Network (DDQN). This method mainly solves the problem of poor responses to emergency tasks caused by the large amount of time fragments of equipment under traditional TT&C task-planning methods. Firstly, a multi-objective optimization model aiming at minimizing time fragments and maximizing task revenue is formulated. Then, according to the conflict characteristics of tasks, a task-planning conflict graph is proposed, based on which a TT&C task-planning problem is transferred into an independent set problem. Finally, DDQN is combined with graph structure embedding to solve the transferred independent set problem. The experimental results demonstrate that the proposed method reduces the time fragment of TT&C equipment by 32% and shortens the response time of emergency tasks by 36% compared to existing methods.  
deep reinforcement learning; low time fragmentation; satellite tracking, telemetry, and command; task planning Article
# Supporting Immersive Video Streaming via V2X Communication
## 2 Bandwidth Allocation for Diverse Streaming Services in V2X Communication
### Vehicle Fleet Bandwidth Coordination
\(urb_{t_{l}^{\sigma,p}}^{\mu^{\sigma,p}_{f,1}}\) and \(drb_{t_{l}^{\sigma,p}}^{\mu^{\sigma,p}_{f,1}}\) denote the remaining bandwidth that can be uploaded and downloaded from vehicle \(r_{f,l}^{\sigma,p}\) to \(r_{f,l+1}^{\sigma,p}\) at time \(t_{l}^{\sigma,p}\), respectively, while \(wvb_{t_{l}^{\sigma,p}}^{\mu^{\sigma,p}_{f,1},\mu^{\sigma,p}_{f,1}}\) and \(wb_{t_{l}^{\sigma,p}}^{\mu^{\sigma,p}_{f,1},\mu^{\sigma,p}_{f,1}}\) represent the total bandwidth required to upload and download from vehicle \(r_{f,l}^{\sigma,p}\) to vehicle \(r_{f,l+1}^{\sigma,p}\) at time \(t_{l}^{\sigma,p}\), respectively. * If the fleet connection establishment fails, record the insufficient bandwidth for \(v\) and proceed to Step 4. Otherwise, continue to execute the next step. * Once the fleet is formed, bandwidth is forwarded to \(v\) using V2V communication. This module evaluates whether the CF XL-MIMO access points supporting the fleet meet \(v\)'s bandwidth requirements as follows: \[du_{t_{l}^{\sigma,p}}= \begin{cases}\sum\limits_{f=1}^{\mathbb{F}^{\sigma,p}} \sum\limits_{\phi^{f,1}}w_{t_{l}^{\sigma,p}}^{\phi^{\sigma,p}_{f,1}}-du_{t_{l}^ {\sigma,p}}&\ \ \text{if}\ \beta^{\sigma,p}=1\ \&\gamma^{\sigma,p}=1\\ 0&\ \ \text{others}\end{cases} ,\] (61) \[dd_{t_{l}^{\sigma,p}}= \begin{cases}\sum\limits_{f=1}^{\mathbb{F}^{\sigma,p}} \sum\limits_{\phi^{f,1}}db_{t_{l}^{\sigma,p}}^{\phi^{\sigma,p}_{f,1}}-dd_{t_{l} ^{\sigma,p}}&\ \ \text{if}\ \gamma^{\sigma,p}=1\\ 0&\ \ \text{others}\end{cases} ,\] (62) \[dnr_{t_{l}^{\sigma,p}}= \begin{cases}\sum\limits_{f=1}^{\mathbb{F}^{ \sigma,p}}\delta_{\pi^{\sigma,p}}\cdot\Delta\cdot\sum\limits_{\phi^{f,1}}db_{t_{l}^ {\sigma,p}}^{\phi^{\sigma,p}_{f,1}}-sl_{s}^{\sigma,p}\cdot dvr_{t_{l}^{\sigma,p}}^{ \mathbb{B}}\cdot\text{ft}^{\sigma,p}&\ \ \text{if}\ \gamma^{\sigma,p}=0\\ \ \ \text{others}\end{cases} ,\] (63)where \(t_{j}^{\sigma,v}\) denotes the time period during which the application faces a bandwidth deficit. If \(du_{t_{l}^{\sigma,v}}\) is negative, it indicates that the real-time application does not obtain sufficient bandwidth for uploading video at time \(t_{l}^{\sigma,v}\). Similarly, if \(dd_{t_{l}^{\sigma,v}}\) is negative, it signifies that the application does not obtain sufficient bandwidth for downloading video at that time. If \(dnr_{t_{l}^{\sigma,v}}\) is negative, it implies that the non-real-time video segment's bandwidth is insufficient at that time. 4. If the fleet formed in Step 1 has successfully met the expected bandwidth for \(v\) after forwarding the bandwidth, notify the vehicle passenger of the result, and this module will terminate its execution. Otherwise, notify the vehicle passenger of the result, and this module will enter background execution mode. At the next intersection on the vehicle's route, it will resume at Step 1 and continue its execution.
## 2 Isolated Single Tree Radio Channel Characterization
### Model of Tree  
For the presented analysis, the radio propagation through both pines (_Pinus pinaster_) and oaks (_Quercus robur_) has been assessed and specific models have been created in order for them to be considered in the 3D RL simulation code. The structure of oak tree is complex to make a 3D model with RL. Therefore, an approach has been made to respect the main shape of the tree, modelling the mass of leaves as a cube. Usually, three fifths of the height of the tree is mass of leaves and two fifths trunk, although this will depend on the particular oak tree. The oak tree simulation model implemented is composed by a solid trunk and a homogeneous leaf canopy, as depicted in Figure 1a. In the case of the pine simulation model, homogeneous branches with air gaps have been considered. The pine tree branches have a cone-shaped structure with superposition of greater to less length layers starting from the branches closer to the ground (see Figure 1b). The size of the branches depends on the particular pine tree. It is worth noting that both the widths and the heights of the tree models have been parameterized in order to create a computational tree model as closer to the real trees. Moreover, the forest could have a strong influence on the propagation of radio waves. The substrates generally are basic, and rich in acids. In addition, they are usually wet and with accumulation of little leaves and flora remains. Therefore, an appropriate ground has been modelled and included for simulations, since it is an inherent part of a real scenario, which also generates reflections on the propagating electromagnetic wave. The implemented oak and pine tree models are depicted in Figure 1, whereas the corresponding simulation parameters are detailed in Table 1, obtained from [42].
# Potential of a SAR Small-Satellite Constellation for Rapid Monitoring of Flood Extent
## 3 Results
### Evaluation of Observation Performance
19 from 13:00 on October 12 to 11:00 on 13 October 2019). observation performance is fairly dependent on temporal and spatial flood fluctuation in each event. As another interesting point, in fact, the whole area of damage extent was larger in case 2 (69.49 km\({}^{2}\)) than that in case 1 (53.82 km\({}^{2}\)), but the capture ratio was generally higher in case 2, so it seems unnatural. This is because there is a water channel network unique to the region (Saga city) in case 1. Due to that geographical feature, the flood extent was distributed more widely, but the total area resulted in a smaller value, which means it was difficult to capture the whole area of flood extent at one time (but conversely it provided more opportunities to observe a part of the flood extent). This kind of flood feature also impacted on the observation performance. Therefore, the required observation performance would vary depending on how dynamically and widely each flood process would occur on a spatial and temporal scale, which would be determined by meteorological conditions and topographical features.