esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Study Area and Datasets
### Datasets  
The LT-1 satellite, integral to China's civilian space infrastructure, is a scientific research satellite featuring two L-band multi-polarization SAR satellites. These satellites, with identical performance indicators, constitute China's first L-band Differential Interferometric SAR satellite constellation, emphasizing interferometry as the primary mission. The LT-1 constellation, comprising satellites LT-1A and LT-1B, operates in near-sun-synchronous orbit at an altitude of 607 km. Both satellites are identically designed and equipped with advanced L-band multi-polarization, multi-channel SAR payloads offering a variety of imaging modes, a maximum resolution of 3 m, and an observation swath of up to 400 km. The satellites function in a dual formation and spiral flight pattern, with intervals from 700 to 7000 m, positioning the LT-1 constellation as China's inaugural civilian satellite with operational capabilities in interferometric topographic mapping [25; 26; 27; 28]. The LT-1 data used in this study were sourced from the Shaanxi Satellite Application Center for Natural Resources and the Shaanxi Institute of Geological Survey. These institutions provided the necessary datasets, facilitating the analysis and research presented in this paper.  
The Sentinel-1 satellite series, launched by the European Space Agency (ESA) under the Copernicus program, consists of two satellites dedicated to Earth observation. The Sentinel-1A satellite, launched on 3 April 2014, is outfitted with a 12 m long C-band Synthetic Aperture Radar antenna and dual 10 m long solar panels. Operating in a sun-synchronous orbit with a 12-day repeat cycle, it provides continuous imaging across a 5.6 cm wavelength in all weather conditions, both day and night [29; 30].  
Given the varying revisit periods of LT-1 and Sentinel-1 data, two interferometric pairs with comparable temporal baselines--32 and 24 days--were selected for analysis. The spatial baselines for these pairs are 262.9 m and 196.8 m, respectively, detailed in Table 1. Table 2 outlines the primary parameters of the SAR images from both LT-1 and Sentinel-1 data.  
Figure 1: (a) Geographical location of the study area; (b) desert grass beach area; (c) open-pit mining area.
# Comparative Study of Energy Efficient Routing Techniques in Wireless Sensor Networks
## 3 The Proposed Taxonomy
### Initiator of Communication  
When any end-point of the network, whether the sensing nodes or the sinks, need to communicate with the other end-point, it will initiate the routing paths. In this way, we can classify the routing protocols based on whether they are source or destination initiated depending on whether the initiator of the communication is, respectively, the sensors (source) or the sink (destination).
## 2 Background
### Existing Protocols to Support VSN  
Currently there are different protocols that support VSN. The Melete system is based on the Mate virtual machine that enables reliable storage and execution of concurrent applications on a single sensor node [16, 25]. Most recently the Federated Secure Sensor Network Laboratory (FRESnel) at the Cambridge University aims to build a large scale federated sensor network framework with multiple applications sharing the same sensor node resources [26]. The primary aim of this project is to offer an environment that can support multiple applications running on each sensor node [27]. It provides an execution environment that hides the system details from the running applications. The system operates in a shared environment. The key characteristics of this approach are: A virtualization layer that is running on each sensor node abstracts access to sensor resources and allows the management of these resources through policies expressed by the infrastructure owner. A runtime environment on each node allows multiple applications to run inside the node. A policy based application deployment that enables multiple applications to be deployed over the shared infrastructure.  
Figure 1: Business model of sensor network virtualization.
# Rapid Target Extraction in LiDAR Sensing and Its Application in Rocket Launch Phase Measurement
## 1 Introduction
These studies indicate that, although deep learning methods and other algorithms show significant advantages over traditional point cloud processing methods in terms of improving processing speed and reducing computational resource consumption, they may not guarantee consistent performance when dealing with complex or dynamic environments. Moreover, current approaches still face challenges when handling high-density, large-scale point cloud data, as they must strike a balance between processing speed and data accuracy, making it difficult to maintain measurement precision while enhancing efficiency. In light of the limitations of existing technologies, particularly the challenge of rapidly acquiring target point clouds in cluttered environments during LiDAR-based detection, this paper proposes a fast 3D point cloud target extraction method. The method begins with a cylindrical unfolding projection, converting the 3D point cloud acquired by LiDAR into a 2D grayscale image while establishing the conversion relationship between the two. Subsequently, image processing operations such as edge extraction, region segmentation, and area filtering are performed on the grayscale image to extract the 2D target region. Finally, using the conversion relationship between the projection image and the 3D point cloud, along with a target backtracking search algorithm, the 2D target region is accurately mapped back to 3D space, achieving efficient and precise target extraction. The effectiveness of the proposed method is validated through near-field experiments and rocket launch segment measurements. This method aims to strike a balance between data processing speed and accuracy, providing a new technical pathway for the rapid processing of the LiDAR point cloud data. The main contributions of this paper are as follows:  
1. To enable the rapid extraction of target point clouds from large-scale LiDAR data, this study proposes a method that projects 3D point cloud data into a 2D projection map. This approach significantly enhances the efficiency of target point cloud extraction, greatly improving the real-time applicability of LiDAR technology. 2. To ensure the accuracy of target extraction, a combination of inverse projection operations and a target backtracking search algorithm is employed. This method achieves precise target point cloud extraction in cluttered environments while maintaining high processing speed without compromising accuracy. The rest of this paper is structured as follows. In Section II, the principles of the proposed LiDAR-based rapid target point cloud extraction method are introduced, including cylindrical projection, image filtering, and target extraction. In Section III, the effectiveness of the proposed method is first validated through near-field experiments, followed by an analysis of its time complexity and accuracy. Finally, the method is experimentally applied to a real rocket launch scenario. The last section is the conclusion of this paper.
# Waste Cork in Metakaolin-Geopolymer Matrix: Physico-Mechanical Characterization
## 2 Materials and Methods
### Preparation of Geopolymer Specimens  
To a certain amount of MK dry powder, the addition of 8 M NaOH plus sodium silicate solution was carried out under mechanical stirring to obtain the reference geopolymer formulation, hereafter indicated as GP0. To this formulation, different percentages of cork waste powder (namely those equal to 2.44, 4.76 and 9.09 wt% calculated upon a dry MK basis) were added to produce the geopolymer composites labelled GP-2.4 CW, GP-4.8 CW and GP-9.1 CW, respectively (Table 1). This range of CW additions was chosen after some preliminary tests during which we found out that the addition of an amount around 10 wt% was creating workability problems and required too much water.  
Details of the geopolymer mixes are reported in Table 1. It should be noted that in the cases of two formulations, the addition of water was necessary to maintain the workability of the fresh pastes. As a result of the water addition, the liquid (L) (NaOH and sodium silicate solutions plus water) to solid (S) ratio (MK and cork powder) was kept around 0.65-0.68 (see Table 1).  
All the fresh pastes were prepared by mixing powders and liquids in a planetary mixer (Aucma 1400W, Acuma CO., LTD., Shandong, China). The fresh paste was poured into silicone cubic molds (25 \(\times\) 25 \(\times\) 25 mm\({}^{3}\)) and into plastic molds (245.54 \(\times\) 233.82 \(\times\) 26.37 mm\({}^{3}\)) for thermal conductivity testing. After removing all the bubbles with the vibrating table, the molds were carefully sealed and the geopolymers were cured at room temperature at 100% relative humidity. The silicone molds were opened after 1 day of curing time. A minimum of 6 samples per each formulation was obtained. The characterization of all the samples was carried out after 28 and 90 ageing days.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multirow{2}{*}{**MK (g)**} & \multirow{2}{*}{**NaOH (g)**} & **Na** & **Cork** & \multirow{2}{*}{**H\({}_{2}\)O (g)**} & **L/S** \\  & & & **Silicate (g)** & **Waste (g)** & & **(wt/wt)** \\ \hline GP0 & 200 & 66.67 & 66.67 & 0 & 0 & 0.67 \\ \hline GP-2.4 CW & 200 & 66.67 & 66.67 & 5 & 0 & 0.65 \\ \hline GP-4.8 CW & 200 & 66.67 & 66.67 & 10 & 8 & 0.67 \\ \hline GP-9.1 CW & 200 & 66.67 & 66.67 & 20 & 17 & 0.68 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Geopolymer formulations (L/S = liquid to solid ratio, see text for description).  
Figure 1: Comparison of particle size cumulative distribution curves of pure metakaolin (MK) and cork waste (CW).
## 4 The Performance Parameters and Application Scenarios of 6G Networks
### Application Scenarios
#### 4.2.4 Emergency Rescue Communication  
Since the advent of 1G through to the present 5G, terrestrial mobile communication systems have made substantial progress in achieving broader coverage, increased bandwidth, faster speeds, lower latency, and denser networks. Nonetheless, in the face of widespread natural disasters such as earthquakes, floods, mudslides, or severe human-induced accidents, the local terrestrial communication network could potentially become entirely incapacitated. This could result in individuals in need of assistance being unable to send distress signals promptly, impeding external rescue efforts. Moreover, certain scenarios, such as oceans and deserts, lack sufficient communication network coverage. The crucial first 72 h following accidents and emergencies are pivotal for saving lives. The advent of 6G, with its realization of 3D full space coverage, will enable the rapid deployment of unmanned aerial vehicles (UAVs) and satellite communication networks on demand during crises. This deployment will facilitate emergency communications to aid in swift search and rescue operations. Given the urgency of rescue operations, it is imperative to swiftly deploy a high-bandwidth network with extensive coverage.  
Furthermore, the 6G network can help with real-time dynamic monitoring of desert, ocean, and river regions that are vulnerable to natural disasters. By providing early warning services in reaction to events like sandstorms, typhoons, and floods, this capability helps to lower the amount of money lost as a result of disasters.
# A Population-Based Search Approach to Solve Continuous Distributed Constraint Optimization Problems
## 2 Background
### Distributed Population  
The distributed population is an extension of the traditional population, which has become a basic technology in the population-based algorithm for DCOPs and C-DCOPs [30; 31; 35; 38] in recent years. The distributed population is held collaboratively by all agents. Table 1 shows a distributed population of \(n\) agents and \(K\) solutions, the agent \(x_{i}\) holds one dimension of each solution, and multiple solutions are composed of a population, denoted as \(S\).  
One of the solutions, solution \(k=\{S_{k}.x_{1},S_{k}.x_{2},\dots,S_{k}.x_{n}\}\), represents an assignment combination of all agents. For solution \(k\), we use \(S_{k}.x_{i}\) to denote the variable held by agent \(x_{i}\), and we define that \(S_{k}(U_{i})\) represents the sum of local utilities of agent \(x_{i}\), calculated by Equation (2).  
\[S_{k}(U_{i})=\sum_{j\in N_{i}}f_{ij}(S_{k}.x_{i},S_{k}.x_{j}) \tag{2}\]
## 4 Remote Sensing Platforms Used in Peatland Research
### Airborne-Based Remote Sensing
#### 4.2.2. Monitoring Peatland Dynamics, Degradation, and Hydrological Changes  
Monitoring peatland hydrological dynamics is crucial for understanding carbon fluxes and ecosystem health. Airborne-based remote sensing techniques like LiDAR have been utilised to estimate water table levels and monitor peatland moisture content. The research by [PERSON] and [PERSON] (2017) and [PERSON] et al. (2017) focus on monitoring peatland dynamics, degradation, and hydrological changes [99; 100]. [PERSON] and [PERSON] (2017) investigate the connections between the El Nino/Southern Oscillation (ENSO) index and permafrost loss in a watershed, highlighting the accelerated permafrost loss and associated hydrological changes [100]. [PERSON] et al. (2017) explore the uncertainties in digital elevation models (DEMs) and their impact on water level assessments in riparian and mire ecosystems [99]. They emphasise the need to address the quality of DEMs and the associated uncertainties to ensure accurate and reliable DEM uncertainty in model-based water level assessments for wetland management and ecological assessments.
# An Effectively Finite-Tailed Updating for Multiple Object Tracking in Crowd Scenes
## 3 Problem Formulation and the Approach
### Revisiting FairMOT
#### 3.1.2 Object Detection Branch  
The detection branch is based on CenterNet. Three parallel heads are appended to DLA-34 to estimate heatmaps, object center offsets, and bounding box sizes, respectively. Each head is implemented by applying a 3 \(\times\) 3 convolution (with 256 channels) to the output features of DLA-34, followed by a 1 \(\times\) 1 convolutional layer that generates the final objects.  
Heatmap Head: This head is responsible for predicting an object heatmap \(H\in[0,1]^{\frac{H}{K}\times\frac{W}{K}\times 1}\) with a default down-sampling factor \(R\) of 4. The response at a location in the heatmap is expected to be 1 if it collapses with the ground-truth object center. The response decays exponentially as the distance between the heatmap location and the object center increases.  
Center Offset Head: This head is responsible for positioning the object more accurately. Since the stride of the final feature map is four, it will introduce quantization errors up to four pixels. Because the Re-ID feature needs to be extracted based on the accurate object center, the careful alignment of the Re-ID feature with the object center is critical to performance. To solve the problem of quantization errors, the branch estimates the continuous offset of each pixel relative to the center of the object to reduce the impact of down-sampling.  
Box Size Head: This head is responsible for estimating the height and width of the bounding box of the object at each anchor point. The head has no direct relationship with the Re-ID feature, but the positioning accuracy will affect the evaluation of the object detection performance.
# Improving DFT-Based Image Watermarking Using Particle Swarm Optimization Algorithm
## 5 Experimental Results and Discussion
### Watermark Robustness
#### 5.2.1 Signal Processing Distortions  
Considering 1000 color images extracted from the COCO dataset [30], and a binary watermark of \(L=32\) bits, in Figure 11 we summarize the average BCR obtained by PSO-DFT and conventional DFT watermarking respectively, against the several signal processing distortions, such as Joint Photographic Experts Group (JPEG) compression, among others, given in Table 5. The values of a pair of radiuses \(r_{1}\) and \(r_{2}\) are fixed at 50 and 150, respectively, and the value of the watermark strength \(\alpha\) is 1.5, according to the conventional proposal previously reported in [6]. For the sake of brevity, in this testing we include aggressive tolerances for some attacks, assuming that the minor tolerances are detected in a successful manner.  
As shown in Figure 11, the optimized 2D DFT-based watermarking by PSO algorithm outperforms to the conventional 2D DFT image watermarking in terms of watermark robustness against several signal processing. The relevant improvements are when the watermarked image is distorted by the attacks in Table 6. Italics in Table 6 indicate that the detection has failed.  
Figure 10: The PSO convergence of 50 images from the COCO dataset.  
Figure 9: The PSO convergence history of the proposed method using an image with predominantly plain content.  
From Figure 11, we show that against the signal processing distortions described in Table 5, the PSO-DFT watermarking keep the robustness obtained by the conventional 2D DFT watermarking, obtaining average BCR values over the threshold value \(T\!h=0.78\).
# Simulation of Crack Propagation in Reinforced Concrete Elements
## 5 Comparison with Experiments
The comparison of the experimental and calculated crack spacing shows the following results:  
* In general, the test results can be reproduced very well with the CPM (see Figure 15 and Table 4);
* Varying the coefficient of variation and correlation length to determine the distribution of the tensile strength along the model axis has only a limited effect on the results;
* The range of very small crack spacings (\(s_{\rm r}\leq 100\) mm) is underestimated for \(v_{\rm k}=10\%\) and \(v_{\rm k}=15\%\), as well as for \(L_{\rm x}=20\) mm and \(L_{\rm x}=10\) mm (see Figure 15); \begin{table}
\begin{tabular}{c c c c c c} \hline  & **Naotunna**[36] & \multicolumn{2}{c}{**CPM: \(v_{\rm k}=10\%\)**} & \multicolumn{2}{c}{**CPM: \(v_{\rm k}=15\%\)**} \\ \hline  & & \(L_{\rm x}=20\) mm & \(L_{\rm x}=10\) mm & \(L_{\rm x}=20\) mm & \(L_{\rm x}=10\) mm \\ \hline Total number \(n\)\({}^{1}\) & 230 & 214 & 217 & 215 & 224 \\ Mean (section) \(s_{\rm r,m}\) & 131–140 mm & 141–150 mm & 141–150 mm & 141–150 mm & 131–140 mm \\ Median (section) & 121–130 mm & 131–140 mm & 131–140 mm & 131–140 mm & 121–130 mm \\
95 th percentile & 191–200 mm & 191–200 mm & 191–200 mm & 191–200 mm & 181–190 mm \\ \(s_{\rm c0,95}/s_{\rm r,m}\) & \(\sim\)1.5 & \(\sim\)1.4 & \(\sim\)1.4 & \(\sim\)1.4 & \(\sim\)1.4 \\ \hline \end{tabular} \({}^{1}\) Recorded crack spacings for 3 specimens, 3 surfaces, and 2 bars each (6 sets per specimen).  
\end{table} 
Table 4: The statistical parameters from Figure 15.  
Figure 15: Distribution of experimental and calculated crack spacings in the test conducted by [PERSON] [36]: (a) \(v_{\rm k}=10\%\); (b) \(v_{\rm k}=15\%\). * The experimental value \(s_{\text{r},0.95}/s_{\text{r},\text{m}}=1.5\) is estimated with adequate accuracy by the CPM (\(s_{\text{r},0.95}/s_{\text{r},\text{m}}=1.4\)) (see Table 4);
* The comparison of the median and mean values of the experimental results show a positive skew (skewed to the right) (see Table 4) indicating a log-normal distribution of the crack spacing \(s_{\text{r}}\). The high number of very small crack spacings (\(s_{\text{r}}\leq 100\) mm) can be attributed to the fact that complete separation cracks do not always form in testing and that overlapping cracks can occur. Both phenomena can be observed in [36]. Furthermore, the effects of shrinkage strains have not been taken into account in the calculation with the CPM, as the respective input values were not documented in [36]. However, the model can consider shrinkage by adding shrinkage strains as an additional load component to reduce the effective tensile concrete strength [6; 7]. As a result, the crack spacing would be reduced.
# Support Vector Machine-Based Transmit Antenna Allocation for Multiuser Communication Systems
## 3 SVM-Based Antenna Allocation
### Task 1: Designing a Training Sample Set
#### 3.1.1 Subtask 1-1: KPI Design  
A KPI is designed to label the training set. In general, a KPI can be defined as any metric used in communications, such as spectral efficiency, energy efficiency, BER, effective signal-to-noise ratio (SNR), communications latency, and any combination thereof [53]. In this study, we use the sum rate of a system, \(R_{\text{sum}}\), as the target KPI.
# The Improvement and Application of the Geoelectrochemical Exploration Method
## 5 Conclusions  
1. The DL-CHIM method, characterized by simple equipment, easy operation and low cost, is therefore suitable particularly for field surveys.
2. The DL-CHIM method enables a simultaneous extraction of anionic and cationic species of pathfinder elements, from which more information can be derived with a higher extraction efficiency.  
Figure 5: Results obtained by employing the DL-CHIM method and geochemical soil survey over Daiyinzhang gold deposit from Shanxi, China. 1—Chlorite schist; 2—Seriatric schist 3—Metomorphic diabase; 4—Plagioclase granite; 5—Gold mineralization; 6—Gold ore body; 7—Drill hole; (**a**) Soil geochemical anomalies; (**b**) Anion anomalies of Au (Anode extraction); (**c**) Cation anomalies of Au (Cathode extraction); (**d**) geological base map [35; 36].  
3. In thick overburden areas, deep-seated concealed ore bodies are difficult to identify by conventional geochemical exploration methods, such as geochemical soil survey. However, the DL-CHIM method can detect obvious anomalies of pathfinder elements for anionic or cationic species. Additionally, anion anomalies of elements can show better results for concealed gold deposits in depth because gold may mainly exist as micro-nano particles in surface soils, which are negatively charged.
4. The prospecting depth of this technique is much greater than that of conventional geochemical exploration methods because the detecting objects of this technique are those mobile forms of elements and ions.  
M.K., conceptualization, methodology, data curation, writing--original draft, investigation, project administration; H.G., investigation, data curation; W.Z., investigation, funding acquisition; X.L., supervision; J.Y., writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
This work was financially supported by the National Natural Science Foundation of China (Grant No. 40743018).  
Not applicable.  
The authors declare no conflict of interest.