esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussions  
### Analysis of Routing Parameters Impacts on VANET Scenarios Performances  
In this study, we investigated the effects of varying key routing parameters on network performance, focusing on Maximum Jitter, Hello/Beacon Interval, and Route Timeout across nine experimental setups. The results demonstrate a trade-off between network responsiveness and resource consumption, with different configurations yielding optimal performance under specific network conditions. Lower jitter values (0.1 s in Experiment 1) led to more stable packet transmission times, improving performance in time-sensitive applications but increasing bandwidth usage due to tighter control over packet delivery variability. In contrast, a higher jitter (0.9 s in Experiment 9) allowed for greater flexibility in packet timing, reducing network overhead but at the cost of increased delay variability, potentially degrading performance in real-time communications. Similarly, shorter Hello/Beacon Intervals (4 s in Experiment 1) facilitated frequent updates of routing information, ensuring that nodes had accurate and timely data about network topology. However, this approach increased bandwidth consumption and power usage, which may not be suitable for resource-constrained environments. Longer intervals (e.g., 20 s in Experiment 9) reduced overhead but introduced the risk of stale routing information, particularly in dynamic networks, where nodes frequently move or topology changes occur. Route Timeout, another critical factor, influenced the network's adaptability to changes. Short timeouts (6 s in Experiment 1) ensured rapid route invalidation, reducing the chance of routing through outdated paths but at the cost of increased routing overhead. On the other hand, extended timeouts (e.g., 30 s in Experiment 9) decreased the frequency of route recalculations, making them better suited for stable networks but less effective in dynamic environments. Thus, the experimental results show that lower jitter, shorter Hello/Beacon Intervals, and quicker route timeouts are preferable in dynamic, time-sensitive networks, while higher jitter, longer intervals, and extended route timeouts optimize performance in static or low-mobility scenarios. Balancing these parameters is critical to achieving optimal network efficiency and responsiveness Table 3.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Parameter/Experiment Number** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** \\ \hline MAXIMUM\_Jitter & 0.1 s & 0.2 s & 0.3 s & 0.4 s & 0.5 s & 0.6 s & 0.7 s & 0.8 s & 0.9 s \\ \hline HELLO/BEACON\_INTERVAL & 4 s & 6 s & 8 s & 10 s & 12 s & 14 s & 16 s & 18 s & 20 s \\ \hline ROUTE\_TIME\_OUT & 6 s & 9 s & 12 s & 15 s & 18 s & 21 s & 24 s & 27 s & 30 s \\ \hline \hline \end{tabular}
\end{table}
Table 3: Routing parameter comparison values.
## 3 Hybrid Metaheuristic Algorithm
### System Model  
As aforementioned, this study proposes a new multi-strategy hybrid metaheuristic algorithm to address one of the np-hard problems, multi-level thresholding for color image segmentation. The aim of this problem is to partition a color image into meaningful regionsbased on pixel intensity values. Initially, it takes an RGB image as input and converts it to greyscale histograms. These histograms are then used to determine the optimal threshold values. This combinatorial optimization problem is modeled, and the goal is to select a set of threshold values that maximizes an image quality criterion. The proposed COSGO algorithm was employed to find these optimal thresholds. To evaluate the COSGO algorithm, a total of 10 real-world images were taken from the BSD500 dataset, which is one of the well-known datasets. The flowchart of the proposed COSGO algorithm is given in Figure 1.  
In addition, in the simulation, the main parameters used in the system and COSGO algorithm are explained below:  
* T: number of thresholds (3-5 in this study).
* MaxIter: maximum number of iterations (e.g., 100).
* PopulationSize: number of candidate solutions (e.g., 30).  
Figure 1: The flowchart of the COSGO algorithm.  
* Objective Functions: [PERSON] method and Kapur entropy.
* Dataset: BSD500 (10 selected color images).
* Channels: R, G, and B (treated independently for thresholding).
* Evaluation Metric: PSNR, SSIM, and computation time.  
It is possible to explain the problem formulation. Let h(i) be an image histogram for intensity level i \(\in\) [0, 255]. The objective is to determine a threshold vector for a multi-level thresholding problem with T thresholds:  
\[T=\{t1,t2,\ldots,t_{T}\}\;\;with\qquad 0<t1<t2<\cdots<t_{T}<255\]  
The goal is to maximize f(T), a fitness function that can be either [PERSON]'s method or [PERSON]'s entropy. Thus, the optimization problem is formulated as:  
\[max(T)\;subject\;to\;\;t_{1}<t_{2}<\cdots<t_{T}\]  
In order to determine the optimal threshold set T that produces the highest segmentation quality based on the chosen objective function, the COSGO algorithm searches the solution space.
## 2 Analysis Method for Direct Solar Heat Flux of Pointing Attitude
The corresponding rotation transformation matrix is given by:  
\[M_{x}= \begin{bmatrix}1&0&0\\ 0&1&0\\ 0&0&1\end{bmatrix}  \tag{11}\]  
\[M_{y}= \begin{bmatrix}\cos\theta&0&\sin\theta\\ 0&1&0\\ -\sin\theta&0&\cos\theta\end{bmatrix}  \tag{12}\]  
\[M_{z}= \begin{bmatrix}\cos\phi&-\sin\phi&0\\ \sin\phi&\cos\phi&0\\ 0&0&1\end{bmatrix}  \tag{13}\]  
In the equation, \(M_{x}\), \(M_{y}\), and \(M_{z}\) represent the rotation transformation matrices around the X, Y, and Z axes, respectively. As described earlier, the transformation from the Earth-pointing attitude frame to the pointing attitude frame involves a rotation around the Z-axis, then a rotation around the Y-axis. Thus, the total rotation matrix \(M\) is expressed as:  
\[M=M_{z}\bullet M_{y}\bullet M_{x} \tag{14}\]  
For surfaces of different geometries, the unit normal vector [_x,y,z_]T can be calculated using the method described in [23]. By combining Equations (10) and (14), the direct solar heat flux on any surface at any given time can be computed. It should be noted that this analytical method is only applicable to calculating the external heat flux on convex surfaces, and the computed results represent the first-arrival heat flux rather than the absorbed heat flux by the surface. For concave surfaces, where different faces are mutually visible, direct solar heat flux undergoes multiple reflections after reaching the surface. Consequently, the final incident heat flux becomes closely related to both the geometric configuration between surfaces and their optical properties, making the calculation more complex. Regarding absorbed heat flux, the surface absorptivity must also be considered. However, for typical spacecraft external surfaces, particularly radiator surfaces, they are generally designed to be unobstructed to achieve optimal thermal dissipation performance and can therefore be treated as convex surfaces. Using the aforementioned analytical method, the incident heat flux on various spacecraft surfaces can be evaluated, providing valuable references for the selection of radiator surfaces on spacecraft. In commercial software, data interfaces are typically provided to allow direct input of constructed arrays or expressions--composed of yaw/pitch angles calculated according to Equations (8) and (9) and orbital time (or true anomaly)--into the orbital setup module of the model. By utilizing the software's generic external heat flux solver, the transient heat flux on spacecraft/payload surfaces under inter-satellite pointing attitudes can then be computed. These software packages commonly employ the Monte Carlo method for external heat flux calculations.
## 7 LPWAN Topologies and Architecture
### Major Architectures in LPWAN Technologies  
The basic LPWAN architecture requires wireless access and connectivity to the Internet and the cloud. Typical LPWAN technology may require a selected set of architectural entities, as shown in Figure 4[4].  
The basic function of an LPWAN device is to collect data and respond to inputs from the LPWAN. Collected data are sent on a specific radio link to the wireless access station and on to the IoT network. The access station provides the radio link for device management and device traffic exchange. It maintains the integrity of the radio link by handling acceptable bit error rates, admissions, security, and so on. The access station interfaces with the gateway/concentrator, which, in some instances, is also called a core. There are different implementations depending upon the proprietary or standards-based LPWAN technology being considered. The core is responsible for handling control and user plane traffic. It provides a conduit for information exchange between the access station and the IoT network and supports translation between the protocols supported by the access station on one side and by the network on the other. Depending upon the technology, a concentrator may provide edge computing and storage facilities to offload the cloud. Because of its relative proximity to the end devices, this is particularly applicable to cases where the device requires real-time support with low latency. For selected LPWAN technologies, the core may provide robust admission, priority treatment, and mobility support if applicable. The LPWAN server is responsible for provisioning, registering, and operations for the LPWAN entities. It may also share or augment core functionalities like routing of traffic, security, and priority handling with the core. The application servers and the cloud support the LPWAN in carrying out the management of the database that contains the messages received from all the connected objects. It may use big data analytics to analyze and act upon the data.  
Figure 4: Typical low power wide area network (LPWAN) entities.
# Multi-Body Dynamics Modeling and Simulation of Maglev Satellites
## 3 Design of Three Body Multi Closed Loop Control Law
### Design of Payload-Centered Attitude Pointing Control Law
\begin{array}{c}\ddot{\mathbf{q}}_{pev}=\dot{\mathbf{Q}}(\mathbf{q}_{pe})\mathbf{\omega}_{pe }+\mathbf{Q}(\mathbf{q}_{pe})\dot{\mathbf{\omega}}_{pe}=-\frac{1}{4}\mathbf{q}_{pe}\mathbf{\omega} _{pe}^{\mathsf{T}}\mathbf{\omega}_{pe}+\mathbf{Q}(\mathbf{q}_{pe})\\ (-\dot{\mathbf{\omega}}_{pr}+\mathbf{J}_{p}^{l-1}\mathbf{u}_{pl}+\mathbf{J}_{p}^{l-1}( \widetilde{\tau}_{su}\mathbf{C}_{p}^{r}\mathbf{I}_{ur1}+\widetilde{\tau}_{sa}\mathbf{C}_{ p}^{r}\mathbf{F}_{ar}+\widetilde{\tau}_{sl}\mathbf{C}_{p}^{r}\mathbf{F}_{lr1})+\mathbf{J}_{p}^{l-1} \mathbf{D}_{1})\end{array}  \tag{42}\]  
Note that \(\mathbf{B}_{p}=\mathbf{Q}(\mathbf{q}_{pe})\mathbf{I}_{p}^{l-1}\), \(\mathbf{D}_{p}=\mathbf{B}_{p}\mathbf{D}_{1}\), therefore the above equation can be organized as:  
\[\ddot{\mathbf{q}}_{pev}=\mathbf{h}_{p}+\mathbf{B}_{p}\mathbf{u}_{pl}+\mathbf{D}_{p}+\mathbf{B}_{p}\mathbf{D} \tag{43}\]  
\[\mathbf{h}_{p}=-\frac{1}{4}\mathbf{q}_{pev}\mathbf{\omega}_{pe}^{\mathsf{T}}\mathbf{\omega}_{ pe}-\mathbf{Q}(\mathbf{q}_{pe})\dot{\mathbf{\omega}}_{pr} \tag{44}\]  
Under the influence of unknown bounded interference \(\mathbf{D}_{p}\), design a control law \(\mathbf{u}_{pl}\) so that the system states \(\mathbf{q}_{p}\) and \(\mathbf{\omega}_{p}\) can track the expected states \(\mathbf{q}_{pd}\) and \(\mathbf{\omega}_{pd}\) within a fixed time; that is, there exists a time constant \(T_{\max}>0\), and at \(t>T_{\max}\), there are error states \(\mathbf{q}_{pev}=\mathbf{0}_{3\times 1}\) and \(\mathbf{\omega}_{pe}=\mathbf{0}_{3\times 1}\) that hold. Before designing the control law, provide the following assumptions and related lemmas. For nonlinear systems:  
\[\dot{\mathbf{x}}=\mathbf{f}(\mathbf{x}(t)),\mathbf{x}(0)=\mathbf{x}_{0},\mathbf{f}(\mathbf{0}_{n\times 1})= \mathbf{0}_{n\times 1} \tag{45}\]  
where \(\mathbf{f}\colon\mathbf{R}^{n}\to\mathbf{R}^{n}\) is a nonlinear function. **Lemma 1**: _For a system, if there exists a Lyapunov function \(V(\mathbf{x})\) with scalars \(\alpha_{1},\ \beta_{1},p,q,k\in\ R^{+}\) that satisfy \(pk<1\) and \(qk>1\), and \(\dot{V}(x)\leq-(\alpha_{1}V(x)^{p}+\beta_{1}V(x)^{q})^{k}\) holds, then the system is fixed time stable, and its convergence time satisfies the following inequality relationship:_  
\[T\leq\frac{1}{\alpha_{1}^{k}(1-pk)}+\frac{1}{\beta_{1}^{k}(qk-1)} \tag{46}\]  
**Lemma 2**: _For scalars \(x_{i}\in R\) and \(i=1,2,\ldots,n\), the following inequality relationship holds for \(0<a\leq 1\) and \(b>1\):_  
\[\left(\sum_{i=1}^{n}\lvert x_{i}\rvert\right)^{a}\leq\sum_{i=1}^{n}\lvert x_{i} \rvert^{a},\left(\sum_{i=1}^{n}\lvert x_{i}\rvert\right)^{b}\leq n^{b-1}\sum_{ i=1}^{n}\lvert x_{i}\rvert^{b} \tag{47}\]  
**Assumption 1**: _The mass parameters of the payload subsystem are known. In order to estimate the unknown disturbance \(\mathbf{D}_{p}=\mathbf{B}_{p}\mathbf{D}_{1}\), the state variable \(\mathbf{x}_{1}=\hat{\mathbf{q}}_{pev}\) is defined, and the extended state is \(\mathbf{x}_{2}=\mathbf{D}_{p}\).
## Instrument Description
### Implemented Instrument  
The implemented instrument is shown in Figure 9 and has been used in several airborne campaigns (not described in this paper) in support of space-borne GNSS-R research, in particular the GEROS-ISS mission [4]. A compact version of SPIR, called SPIR-UAV, has also been implemented on-board an unmanned aerial vehicle (UAV) to gather GNSS reflections over sea ice in the Arctic. To reduce the mass and power consumption, the antenna arrays of SPIR-UAV consist of six elements each, instead of the eight elements of SPIR, and the control and recording computer has been integrated close to the RF receivers. The SPIR-UAV is also shown in Figure 9.
## 5 Discussion
### Impact of Neglecting Rivers and Smaller Lakes that Are Not Available in WGHM  
When investigating the percentage of monitored surface water storage in Section 4.2, smaller lakes that are not available in WGHM as well as water available in rivers have been neglected.  
Rivers only store 0.006% of global freshwater. Most of the world's freshwater is stored in icecaps and glaciers and as groundwater (nearly 99%) [32]. From the remaining 1.2% of global freshwater, river water storage comprises less than 0.5%, whereas lakes store nearly 21% [1]. The small percentage of river water justifies the focus on lakes and reservoirs. Even if the Mississippi contains more water than most other rivers, the entire MRB is so large that global conditions can be assumed.  
The impact of neglecting lakes smaller than 0.1 km\({}^{2}\) can be assessed using the analyses performed by [PERSON] et al. [33] based on statistical extrapolations. They estimated the number of lakes with areas from 0.001 to 0.1 km\({}^{2}\) to be 99% of all lakes and to cover about 30% of the overall global lake area. When assuming that this relation can be transferred to the MRB, 30% of the total lake area would have been missed as lakes smaller than 0.1 km\({}^{2}\) were neglected: i.e., 12,472 km\({}^{2}\). The lower percentage published by [PERSON] et al. [26] (about 4.8% of the area of all lakes, leading to around 2000 km\({}^{2}\) being negleged) based on remote sensing data is at least partly due to the spatial resolution and detection limit of the used satellite imagery. However, this documents the continuing lack of knowledge regarding the number and area of small lakes on Earth well. Current remote sensing instruments are not able to shed light on this since the spatial resolution of satellite techniques is limited. Moreover, the lake abundance is changing rapidly in some regions [26], and each study can only present a snapshot of the respective situation.  
With respect to lake volume changes, the relation presented by [PERSON] et al. [34] suggests that 20% of global storage changes are due to lakes smaller than 0.1 km\({}^{2}\). Due to the resolution limits of satellite altimetry (including SWOT), these percentages cannot be monitored from space in the foreseeable future. Assuming that WGHM includes all water bodies with areas larger about 3 km\({}^{2}\), slightly more than 50% of storage changes should be captured. Consequently, nearly 50% (about  
Figure 8: Percentage of missed lakes/reservoirs for different altimetry configurations and different water occurrences. Left: all water bodies larger than 0.1 km\({}^{2}\); right: water bodies larger than 10 km\({}^{2}\).  
180 km\({}^{3}\)) of all lakes are not included in WGHM. If one aims at smaller lakes, regional hydrological models must be used.
## 5 Experiment Simulation
### Performance on the Tree Topology
#### 5.2.2 Comparison in Content Distribution and Cache Replacement  
As mentioned above, the ideal content distribution in the cache is that contents with higher popularity are cached closer to users, which makes more requests hit at the edge routers. To verify the results of content distribution, we calculate the cache hit ratio of caching nodes at the same depth of the tree topology and calculate the average. Figure 5 shows the average cache hit ratio of different cache strategies when node depths changes. The root node has a depth of 1, and the leaf nodes have a depth of 4. From Figure 5, we can find out, in LCE, nodes of different depths have a similar cache hit ratio. This is because LCE does not take into account the location of the node or content information. LCD and CL4M place content more at the core nodes, resulting in the low cache hit ratio at the edge nodes. In PROB, more requests are hit at nodes with larger depth, which improves the network performance. By contrast, in PB-NCC, most requests are served by leaf nodes, which would significantly reduce the content transfer latency. The average cache hit ratio grows when the depth grows in PB-NCC. This also reflects the hierarchical content distribution in the network: popular contents are cached at the edge, and less popular contents are cached near the core.  
Figure 4: Performance of PB-NCC with different \(n_{base}\) when the size of cache space changes. **(a)** the cache hit ratio under a different cache size ratio; **(b)** the average latency under a different cache size ratio; (c) the server load under a different cache size ratio.  
Figure 3: Performance of PB-NCC with different \(n_{base}\) when the Zipf parameter \(\alpha\) changes. **(a)** the cache hit ratio under different Zipf parameter \(\alpha\); **(b)** the average latency under different Zipf parameter \(\alpha\); **(c)** the server load under different Zipf parameter \(\alpha\).  
Next, we count the number of cache replacement that occurs under different caching strategies. Frequent cache replacement will affect the forwarding and content service capabilities of the cache router. Figure 6 shows the average number of cache replacement per node in different caching strategies. PB-NCC has the smallest number of cache replacement because both popularity-based process and number-of-copies control reduce unnecessary content placement operations. PROB also has a relatively low number of cache replacement because it caches contents selectively. The number of cache replacement in LCE, LCD, and CL4M is larger since they place more copies during content transmission, leading to more energy consumption.
# Estimation of Chlorophyll Content in Wheat Based on Optimal Spectral Index
## 5 Conclusions  
In this study, based on the measured hyperspectral data and four types of machine learning models, the correlation matrix method was used to optimize the spectral index of the 400-1000 nm band. We discovered that the optimal spectral index was highly correlated with the chlorophyll content in wheat at different growth stages, but the optimal vegetation index with a high correlation between chlorophyll content at different growth stages was different. The GCI vegetation index had the highest correlation with chlorophyll during the flag-picking period. The correlation coefficients between NDVI and RDVI vegetation index and wheat chlorophyll were greater than 0.6 at the flowering stage, and the chlorophyll content had the strongest correlation with GCI at the grain-filling stage, and NDVI and GCI showed a higher correlation with chlorophyll at maturity stage. Among them, the chlorophyll content in wheat at the grain-filling stage was the most correlated. Compared with the estimation models of chlorophyll content at different growth stages, the RF model based on NDVI, RDVI, and NDVI, the RVI and GCI vegetation indices achieved good estimation results during both the flowering and irrigation stages of wheat. The optimal vegetation indices were NDVI and GCI at both the flag-picking stage and the maturity stage, among which PR was found to be the best in estimating chlorophyll content at the flag-picking stage, and the accuracy R\({}^{2}\) of the validation set was 0.792. The ANN model was the most ideal for estimating chlorophyll content at the maturity stage.  
G.G.: Methodology, Formal analysis, Writing--original draft, Visualization. L.Z.: Methodology, Software. L.W.: Data curation, Writing--review and editing. D.Y.: Supervision, Funding acquisition, Data curation, Supervision, Project administration. All authors have read and agreed to the published version of the manuscript. This study was supported by the National Natural Science Foundation of China (No. 52174160).