esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Satellite Observation-Relay Transmission-Downloading Coupling Scheduling Method
## 1 Introduction
In previous studies, [PERSON] [45] used the tabu search algorithm in the satellite observation mission scheduling problem, designing neighborhoods of insertion, deletion, exchange, and rearrangement. These types of search neighborhoods have good adaptability in solving the satellite observation scheduling problems that can be associated with the state changes of the satellite subsystem. Referring to the idea of the artificial potential field method [50; 51; 52; 53], the traditional tabu search method for satellite observation scheduling is improved in this article. In the searching process, different satellite cluster states are distinguished, while different neighborhood selection rules are adopted for different states. The state evaluation function of the cluster efficiency subsystem is established to control the search direction and adjust the state of each subsystem of the satellite cluster, which helps the traditional tabu search algorithm overcome the local optima problem, thus improving the search method. In this article, the multi-satellite observation-relay transmission-downloading coupling scheduling problem is described. Based on the tabu search algorithm for the multi-satellite observation scheduling problem [45], the data transmission path planning method for observation missions is incorporated to solve the scheduling problem. The contributions to this article are as follows:  
1. In the neighborhood selection process of the tabu search algorithm, a system state evaluation function is introduced, and local search improvement rules based on satellite states are formulated to improve the local search direction according to the artificial potential field method;
2. In the data transmission path planning process, two data transmission path selection strategies are designed based on the Dijkstra algorithm. Rules for selecting the problem-solving strategy are established based on the satellite cluster state, thereby enabling adaptive satellite data transmission path planning based on the satellite cluster's state. The remainder of this paper is organized as follows: Section 2 defines the multi-satellite observation-relay transmission-downloading scheduling problem, while Section 3 describes the improved tabu search method for multi-satellite observation-relay transmission-download coupling scheduling. Furthermore, Section 4 presents the simulation scenarios and results. Discussions are carried out in Section 5, and Section 6 concludes this paper.
## 2 Materials and Methods  
### Description of the Study Site  
The analyses concerned single-species and even-aged stands of the Scots pine (_Pinus sylvestris_ L.) in south-western Poland (extreme incidence coordinates: 50\({}^{\circ}\)32\({}^{\prime}\) N; 17\({}^{\circ}\)42\({}^{\prime}\) E-50\({}^{\circ}\)45\({}^{\prime}\) N; 17\({}^{\circ}\)44\({}^{\prime}\) E). Currently, 11,600 deer live in a forested area of 211,000 ha [38]. The compact forest complex we are analysing, called \"Bory Niemodlinskie\", covers approximately a quarter of this area and is currently administered by three forest districts: Opole, Proszkow and Tudowice [56; 57; 58]. Herds of deer are free to move from one forest complex to another. Depending on the duration of the winter period, the demand of deer on shoot fodder ranges from 638,000 to 899,000 kg of dry matter.  
In accordance with forest management plans [56; 57; 58], the analysed pine stands were of the same age (35 years) and located in a fresh mixed forest habitat. The habitat quality indicators (1 st class of habitat valuation) and soil type (podsolic soil) were identical in all the sites. The sites were situated on flat land ranging from 156 to 220 m a.s.l. in height. The mean total of precipitation amounted to 635 mm\(\cdot\)year\({}^{-1}\), with the heaviest precipitation recorded in July (91 mm max) and the lowest in February (30 mm). The duration of snow cover presence fluctuated between 50 and 70 days per year, with the mean thickness ranging from 5 to 15 centimetres. The average annual temperature was 8.5 \({}^{\circ}\)C. July was the warmest (18.1 \({}^{\circ}\)C) and January the coolest (\(-1.2\) \({}^{\circ}\)C). The growing season lasted for -248 days [56; 57; 58].  
The ground cover contains: _Molina caruela_ L., _Deschampsia flexuosa L., Pteridium aquilinum L., Melamyrum pratense L., Vaccinium myrtillus_ L., intermittently _Vaccinium vitis-idaea_ L., and _Calluna vulgaris_ L. The mossy and lichenous layers on the analysed sampling sites are represented by Entodon schreberi [PERSON], Hylocomium splendens Hedw., Leucobryum glaucum Hedw. and Parmelia sulcata Taylor.
## 4 Discussion  
To accurately determine the mode structure in proposed BSW devices, we performed numerical calculations by the finite-difference time domain (FDTD) method. Model structure Figure 6a consisted of glass substrate (\(n\) = 1.52) with the infinite thickness, photonic crystal, the geometry and material that reproduced the experimental sample, and a polymer waveguide (\(n\) = 1.58) whose height end width was varied. The calculations performed for the sample 1 show that the waveguide sustains three BSW modes whose cross-sectional intensity distribution is presented in Figure 6b. The intensity pattern of the first mode \(TE_{00}\) has only one lobe and demonstrates the best confinement in the waveguide compared to other two modes. The results of calculation of modes effective refractive indices on waveguide height at fixed width and on waveguide width at fixed height are shown by solid lines on Figure 6c,d, respectively. The effective refractive indices values as well as number of modes grow with the increase of both the waveguide height and width. The experimental values of \(n_{eff}\) obtained for samples 1-3 are presented in the same figure by dots and demonstrate a good agreement with calculated ones.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Name** & **Height, nm** & **Width, nm** & \(\mathbf{n_{eff}/TE_{00}}\) & \(\mathbf{n_{eff}/TE_{1}}\) & \(\mathbf{n_{eff}/TE_{02}}\) \\ \hline sample 1 & 200 \(\pm\) 10 & 1800 \(\pm\) 100 & 1.106 \(\pm\) 0.018 & 1.053 \(\pm\) 0.020 & 1.000 \(\pm\) 0.015 \\ sample 2 & 200 \(\pm\) 10 & 1400 \(\pm\) 100 & 1.098 \(\pm\) 0.020 & 1.038 \(\pm\) 0.016 & - \\ sample 3 & 170 \(\pm\) 10 & 1800 \(\pm\) 100 & 1.040 \(\pm\) 0.008 & 1.000 \(\pm\) 0.008 & - \\ sample 4 & 215 \(\pm\) 10 & 2000 \(\pm\) 100 & 1.161 \(\pm\) 0.010 & 1.116 \(\pm\) 0.010 & 1.037 \(\pm\) 0.012 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Effective refractive index values obtained from experimental Back focal plane (BFP) images for different Bloch surface wave (BSW) modes propagating in the samples with different geometrical parameters of the waveguide.  
Figure 5: BFP image of sample 3. Two marked red concentric circles define the light line in air (inner) and NA of the collecting objective (external).
## 2 Geological Setting  
The Sichuan Basin in southwest China is bounded on the northeast by the Dabashan fold belt, on the southeast by the Qiyueshan thrust fault, on the northwest by the Longmenshan thrust belt, and on the southwest by the Emeishan-Liangshan thrust belt (Figure 1A) [20]. This basin has experienced the Caledonian, Hercynian, Indosinian, Yanshanian, and Himalayan tectonic movements, and can be divided into several tectonic units [16]. The Dingshan area is located in the southeast of the Sichuan Basin, and this area has developed a high steep anticlinal zone and a fault zone [21; 22; 23] (Figure 1B).  
Figure 1: (**A**) Geological map showing study area in the Sichuan Basin, southwest China. (**B**) Structural map showing the studied Dingshan area (modified from [20]).  
From the Late Ordovician to the Early Silurian period, the Sichuan Basin experienced multi-stage tectonic movements, forming the Xuefeng, Qianzhong, and Central Sichuan paleo-uplifts. Two global transgressions led to sea level rise, resulting in the formation of widely distributed organic-rich shales, namely as the WF Formation and the LMX Formation (Figure 2A,B) [24; 25]. The upper Ordovician WF Formation mainly developed siliceous shales and calcareous shales with abundant graptolites [26]. Overlying the WF Formation, the Guanyinqiao Formation generally developed a thin shell-rich marl and lime mudstone due to the Hirnantian glaciation [27]. Overlying the Guanyinqiao Formation, the Lower Member of the LMX Formation is characterized by black organic-rich shales with numerous graptolites, whereas the Upper Member is dominated by siltstones, silty mudstones, and argillaceous mudstones. From the bottom to the top of the LMX Formation, TOC content decreases and silty materials increases gradually, suggesting a decreased depositional water depth [28]. Characterized by high TOC content, quartz content, high thermal maturity, and large thickness and area, the WF Formation and the Lower LMX Formation are the primary targets for shale gas development in south China [2].
# Optical Flow Estimation with Occlusion Detection
## 3 Occlusion Detection Strategy
### Edge-Based Strategy  
Since occlusion is caused by movement, so it would appear on the moving edges. The moving edge is the complex semantic information, so usually using image edge to approximate. By connecting the matching edges of two frames, we can obtain the occlusion candidate \(M_{e}\). The overall flow of our edge-based strategy is shown in Figure 5.  
Firstly, we calculate the edges of two frames using a recent state-of-the-art edge detector, namely the \"structured edge detector\" (SED) [37]. Then we use the descriptor matching algorithm mentioned above to calculate the matching relationships in the edge areas. By interpolating the retained matching points, we can get the occlusion candidate areas at image edge areas.
# A Linear-Arc Composite Beam Piezoelectric Energy Harvester Modeling and Finite Element Analysis
## 3 Finite-Element Simulation
### Finite Element Analysis
#### 3.4.2 Stress Distribution  
In order to explore the effect of curvature on the stress distribution, the curvature was unchanged. The acceleration magnitude was set to 5 m/s\({}^{2}\) and the direction was taken along the Z-axis. Taking the curvature of 100 m\({}^{-1}\) as an example, the stress cloud diagram is shown in Figure 8.  
The stress on the midline of the PEH-C straight beam was represented as a stress-length diagram. As a result, as shown in Figure 9, the stress gradually decreased from the fixed end to the free end of the beam. There was no significant difference between the PEH-C with a curvature of 20 m\({}^{-1}\) and a straight beam with a curvature of 0 m\({}^{-1}\) in the straight section. When the curvature of the PEH-C was increased from 20 m\({}^{-1}\) to 40 m\({}^{-1}\), the stress under the same load increased significantly. After this, as the curvature increased from 40 m\({}^{-1}\) to 100 m\({}^{-1}\), the stress decreased. The stress reached its peak when the curvature was 40 m\({}^{-1}\).  
Figure 7: Schematic diagram of resonant frequency.  
The stress on the midline of the PEH-C arc beam was also represented as a stress-length diagram. As a result, as shown in Figure 10, the stress gradually decreased from the fixed end to the free end of the beam. The application of the stress law of the PEH-C in the arc segment was the same as that in the straight segment. The stress was largest at 40 m\({}^{-1}\). The stress then decreased uniformly when the curvature changed from 60 m\({}^{-1}\) to 100 m\({}^{-1}\). The stress distribution with a curvature of 100 m\({}^{-1}\) was slightly uniform, but the average stress was small. Through finite element analysis, it was observed that, compared with straight beams, the PEH-C has obvious advantages in stress distribution. A straight beam with a curvature of 0 m\({}^{-1}\) and a PEH-C with a curvature of 20 m\({}^{-1}\) had similar stress distributions in the straight section, but the stress in the arc section was significantly different. The stress on straight beams decreased linearly and the stress on the PEH-C with a curvature of 20 m\({}^{-1}\) was significantly higher than that on straight beams.  
Figure 8: Stress cloud.  
Figure 9: Stress-length diagram of the straight segment.
# A Probabilistic Forecast Algorithm of Nonconvective Turbulence over the Tibetan Plateau
## 2 Materials and Methods  
Kelvin-Helmholtz (K-H) instability is thought to be the root cause of CAT, and a Ri smaller than 0.25 is typically thought to allow K-H instability [25]. However, due to the numerical model's resolution, Ri rarely approaches 0.25 when calculating grid data; hence, the turbulence threshold should be adjusted [1]. Here, we use 17 turbulence indices, including 11 CAT indices and 6 MWT indices. A list of the 11 CAT and 6 MWT indices can be found in Appendix A.  
Recent studies have mapped indices to a benchmark index [12; 17; 18]. The percentile division method in Ref. [18] is used. We choose -Ri as the benchmark index and calculate its threshold percentile (Table 1), taking the thresholds \(-\)20, \(-\)5, \(-\)2, \(-\)0.5, and 0.5. The threshold percentile of -Ri is then used to calculate the turbulence intensity threshold of the other 16 indices (Table 2).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Turbulence Intensity** & **Null** & **Light** & **Light–Moderate** & **Moderate–Severe** & **Severe** \\ \hline -Ri & \(<\)\(-\)20 & \(-\)20\(\ldots\)\(-\)5 & \(-\)5\(\ldots\)\(-\)2 & \(-\)2\(\ldots\)\(-\)0.5 & \(-\)0.5\(-\)0.5 & \(>\)0.5 \\ Percentile & 0–69.25 & 69.25–95.84 & 95.84–99.60 & 99.60–99.97 & 99.97–99.99 & 99.99–100 \\ \hline \end{tabular}
\end{table}
Table 1: The range of turbulence intensity percentiles according to the Richardson number.  
Previous studies on CAT used the output data of numerical weather prediction models such as NCEP's 20 km horizontal resolution model (RUC20), the earlier 40 km version (RUC40), and a 30 km horizontal resolution version of MM5 [26]. [PERSON] et al. (1988, 27) investigated the clear-air turbulence in response to climate change by using the data of the GFDL-CM2.1 coupled atmosphere-ocean model, with a resolution of 2.5\({}^{\circ}\) longitude and 2.0\({}^{\circ}\) latitude. Here, the ERA5 reanalysis data on pressure levels are used, with a horizontal resolution of 0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\) and a temporal resolution of 1 h [28]. ERA5 is the fifth generation ECMWF reanalysis for the global climate and weather and has replaced the ERA-Interim reanalysis. It combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. ERA5 data have high spatial and temporal resolution and reliability of data quality. The ERA5 data is assumed to accurately represent the state of the atmosphere to support this study. When calculating the threshold percentile of -Ri, we used the data for the altitude of 200 hPa from 2012 to 2021 (only August) in the airspace within 74-104\({}^{\circ}\) E and 25-40\({}^{\circ}\) N, with a temporal resolution of 3 h, and a total of 61 \(\times\) 121 \(\times\) 31 \(\times\) 8 \(\times\) 10 = 18,304,880 data points.
# Dynamic UAV Inspection Boosted by Vehicle Collaboration Under Harsh Conditions in the IoT Realm
Lastly, due to environmental uncertainties and dynamic task changes, the scheduling system must respond in real time to new demands and emergencies, ensuring that tasks are completed within the required time frame. This study proposes a dynamic adjustment method to address UAV speed fluctuations in complex weather environments. Such weather conditions lead to continuous fluctuations in flight speed, increasing the uncertainty in task execution. To mitigate this, this paper introduces a dynamic adjustment approach that periodically monitors UAV status, combining real-time flight data with task execution conditions, without relying on weather prediction models. At each time interval, the UAV's current status is evaluated, completed task points are removed, and flight routes for remaining tasks are re-planned. The initial flight plan is adjusted based on multiple real-time monitoring results and gradually optimized through static optimization to accommodate speed fluctuations. This method effectively addresses the speed variations caused by complex weather, ensuring that UAVs can perform tasks efficiently and stably in dynamic environments, thus improving task reliability and efficiency. The main contributions of this paper are summarized as follows:  
1. We propose a dynamic model for the multi-UAV-vehicle cooperative point patrol problem in dynamic scenarios. First, for the initial static task of the system, this paper establishes mathematical models for UAV flight routes and vehicle travel routes. Then, these two parts are combined as the optimization objective function in this paper, i.e., the research problem in this paper is modeled as a 0-1 integer programming problem. On the basis of the static model, this paper establishes a dynamic task processing problem model. 2. In the dynamic task processing scenario considering the speed variations of UAVs and vehicles under initial static tasks, we propose KGTSA (K-means clustering, hybrid genetic algorithm, and tabu search). The KGTSA is an iterative heuristic approach that first uses K-means clustering to group initial static task points and plan vehicle and UAV routes, assigning routes to multiple UAVs to create an initial feasible solution. Then, a hybrid genetic algorithm optimizes this solution to obtain a static optimized solution. For dynamic tasks, the algorithm performs real-time local dynamic optimization based on the current flight status of the UAVs, including position, power, and remaining endurance, to refine the UAV route planning and allocation within a single anchor point. The rest of this paper is organized as follows: Section 2 discusses the research work related to the multi-UAV-vehicle cooperative problem; Section 3 establishes a model for the multi-UAV-vehicle cooperative inspection problem; Section 4 elaborates on the multi-UAV-vehicle cooperative data acquisition problem based on dynamic task processing and the KGTSA; Section 5 conducts simulation experiments for validation; and Section 6 draws conclusions.
# An Algebraic Approach to Identifiability
## 4 Examples
### The Lotka-Volterra Model  
We consider the Lotka-Volterra system given by  
\[\dot{x}_{1} =ax_{1}-bx_{1}x_{2}\] \[\dot{x}_{2} =-cx_{2}+dx_{1}x_{2}\] \[y =x_{1}\]  
with measurement of the prey population \(x_{1}\). The parameters \((a,b,c,d)\in\Theta=\mathbb{R}_{+}^{4}\) are all positive.  
As the system is autonomous, there is a single extended vector field. Evaluating the ideal defined by Equation (8) yields an ideal with 18 prime components. We have used a more efficient method to compute the minimal associated primes of the stabilized chain directly, as evaluating the chain of ideals defined by Equation (5) using the simple algorithm was not possible in reasonable time. Thus, we do not know up to which order Lie derivatives are required to stabilize the chain.  
Most of these corresponding varieties of the prime ideals lie in a plane where at least one parameter equals zero. These components can be removed since they do not lie in the parameter space. The remaining components read  
\[\hat{\mathfrak{J}}_{1} =\langle x_{1},\bar{x}_{1}\rangle\] \[\hat{\mathfrak{J}}_{2} =\langle x_{1}-\bar{x}_{1},x_{2},\bar{x}_{2},a-\bar{a}\rangle\] \[\hat{\mathfrak{J}}_{3} =\langle x_{1}-\bar{x}_{1},bx_{2}-\bar{b}\bar{x}_{2},a-\bar{a},c -\bar{c},d-\bar{d}\rangle\] \[\hat{\mathfrak{J}}_{4} =\langle x_{1}-\bar{x}_{1},dx_{1}-c,\bar{d}\bar{x}_{1}-\bar{c},bx _{2}-a,\bar{b}\bar{x}_{2}-\bar{a}\rangle.\]  
As one can see, the ideal  
\[\mathfrak{J}=\big{\langle}x_{1}-\bar{x}_{1},x_{2}-\bar{x}_{2},a-\bar{a},b-\bar {b},c-\bar{c},d-\bar{d}\big{\rangle}\]  
is not under these primes, which means that the system is not locally identifiable. However, the component \(\hat{\mathfrak{J}}_{3}\) suggests that at least a subset of the parameters are locally identifiable. Indeed, eliminating the state \((x_{1},x_{2})\), the parameter \(b\) and their copies yields  
\[\hat{\mathfrak{J}}_{3}\cap\mathbb{R}[a,c,d;\bar{a},\bar{c},\bar{d}]=\mathfrak{ J}\cap\mathbb{R}[a,c,d;\bar{a},\bar{c},\bar{d}]=\big{\langle}a-\bar{a},c-\bar{c},d- \bar{d}\big{\rangle}.\]  
This means that the parameters \(a\), \(c\), and \(d\) are locally identifiable, while \(b\) is not.  
The not locally observable points include the degenerate case \(x_{1}=0\), where not one parameter can be identified at all due to \(\hat{\mathfrak{J}}_{1}\). The other degenerate case \(x_{2}=0\) is also special. Eliminating all variables except \(a\) and \(\bar{a}\) in \(\mathfrak{J}_{2}\) and \(\mathfrak{J}_{3}\) yields \(\langle a-\bar{a}\rangle\); thus, \(a\) is identifiable on this line. However, only \(\hat{\lambda}_{3}\) equals \(\langle c-\tilde{c}\rangle\) or \(\langle d-\tilde{d}\rangle\) when eliminating all but these variables, respectively. This shows that parameters \(c\) and \(d\) are not local identifiable at \(x_{2}=0\). Another point for that identification fails for all parameters is the nontrivial equilibrium, indicated by \(\hat{\lambda}_{4}\). Note that one still can get the ratio of the parameters \(c\) and \(d\); however, these identifiable functions are beyond the scope of this article.
# Large-Scale Multi-Objective Imaging Satellite Task Planning Algorithm for Vast Area Mapping
## 3 Proposed Method
### Efficient Competition Learning Particle Update Strategy
#### 3.1.2 Flight Time  
For Equation (15), the position update equation of the loser can be abstracted as  
\[x_{l}(t+1)=x_{l}(t)+V_{l}(t+1) \tag{17}\]  
where  
\[V_{l}(t+1)=(1+r_{0})v(t+1)-R_{3}v(t)-R_{4}(x_{w}-x_{l}(t)) \tag{18}\]  
Other algorithms based on PSO can also perform similar abstractions, but the specific expression \(V_{l}(t+1)\) varies among different algorithms. It can be seen that the flight time of each particle in evolution is fixed, and the value is 1. However, in the early stages of evolution, the loser individuals are far away from the winners, and the losers should have a longer flight time to ensure that they are close to the winners. On the contrary, in the later stages of evolution, the losers should have a shorter flight time to ensure that they do not fly over the winners. Thus, the appropriate flight time can avoid 'oscillation phenomena' during evolution. Therefore, the flight time is introduced into ECLUS, enabling the ECLUS to converge quickly in large steps in the early stage of evolution and converge in small steps in the later stage of evolution, preventing the algorithm from falling into local optima. The calculation equation for the flight time designed in this paper is as follows:  
\[T(t)=T_{\max}*(1-k\frac{t}{t_{\max}}) \tag{19}\]  
where \(T_{\max}\) is the maximum flight time, \(k\) is the adjustment coefficient, \(t\) is the current evolution generation, and \(t_{\max}\) is the maximum evolution generation.  
Thus, the position update equation for the loser in ECLUS is  
\[x_{l}(t+1)=x_{l}(t)+T(t)\times V_{l}(t+1) \tag{20}\]  
By introducing (18) into (20), the following is obtained:  
\[x_{l}(t+1)=x_{l}(t)+T(t)\times[(1+r_{0})v(t+1)-R_{3}v(t)-R_{4}(x_{w}-x_{l}(t))] \tag{21}\]  
Finally, Equations (14), (19), and (21) together constitute the update strategy for the loser's real variables in ECLUS.