esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results
### MATLAB Simulation Results  
In Figure 9, the evolution of spread of parameter estimation throughout generations is presented. It is important to note that for different tracks, the evolution of spread of the estimations in the population changes; in other words, some knowledge related to the track and the spread of the parameters is available, and for different tracks the behaviour changes. The second point to note is that the solid green line attained with the proposed method has a greater spread in all the parameters, indicating the reduced sampling impoverishment achieved with the proposed method. The results are obtained after 100 Monte Carlo tests.  
The proposed method ends the system identification routine with convergence to the final estimates faster compared to classical time-domain estimation using each track individually. In addition to the reduced sampling impoverishment achieved with the proposed method, the estimates obtained of these parameters are closer to the true values. In Figure 9, one can see that even though the performance on some individual tracks (i.e., low-speed test on high-severity chassis-twist track takes 294 offspring) in terms of the number of offspring required for convergence is better than our method, which takes 397, but the combined prediction accuracy is inferior. (Some parameters are estimated better than others when we consider individual tracks, but when the track and response prioritization scheme is applied, then both the parameter estimation and convergence improve.) To compare our track and response prioritization scheme responses against individual test track estimation schemes, we report the average sum estimation error of our method against individual test track results in Table 8. This gives an idea of the overallperformance in estimating the twelve vehicle parameters, as some are estimated better than others on different tracks. The average estimation error with the track prioritization scheme is 9%, which is much lower compared to those of individual tracks.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Track** & **Severity** & **Speed** & **Average Error (\%)** \\ \hline \multirow{4}{*}{Chassis-twist} & Low & Low & 19 \\  & Low & High & 23 \\  & High & Low & 16 \\  & High & High & 14 \\  & Low & Low & 15 \\  & Low & High & 13 \\  & High & Low & 14 \\  & Low & Low & 23 \\  & High & High & 19 \\  & High & Low & 25 \\  & High & High & 13 \\  & Low method & High & 9 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Average estimation errors for different tracks against the proposed track prioritization scheme.  
Figure 9: Evolution of spread of the estimated parameters throughout offspring for different test tracks. Identifiers—black: chassis-twist; red: herringbone; blue: washboard; solid lines: low-severity–low-speed; dashed lines: low-severity–high-speed; dots: high-severity–low-speed; dash–dotted lines: high-severity–high-speed. The estimate spreads obtained after parameter-to-track weighing are shown in bold green lines.
# A Power-Optimized Cooperative MAC Protocol for Lifetime Extension in Wireless Sensor Networks
## 5 Power-Optimized Cooperative MAC Protocol
### Cooperative Node Selection Phase
#### 5.2.1 Contention Access Delay of HTS Packet Transmission  
If all the common neighbor nodes \(\text{R}_{\text{i}}\) satisfying the constraint conditions of cooperation send their HTS packets to the sender, there will be too much unnecessary delay, energy overhead and HTS packet collisions. To avoid this case, the maximum number of HTS packets sent to the sender during the cooperative node selection phase is set to be \(M\) in the PO-CMAC protocol. The value of \(M\) can be set in advance based on the network scale, the node density and the traffic load distribution of the network. Moreover, a mechanism is needed to evaluate the ability of all common neighbor nodes to take part in the cooperative transmissions, in order to guarantee the nodes with higher cooperative gain to access the channel and send the HTS packet earlier to participate in the cooperative transmissions, thus extending the network lifetime. The length of the network lifetime can be reflected by the total number of transmitted data packets \(n\) during the whole network lifetime. For simplicity, it can be formulated as follows:  
\[n=\frac{\eta E_{0}}{E_{\text{PKT}}}, \tag{17}\]  
Figure 2: Packet transmissions of power-optimized cooperative MAC (PO-CMAC) protocol. where \(E_{0}\) is the initial energy of the network, \(\eta\) is the energy utilization of the network, and \(E_{\text{PKT}}\) is the average energy consumption of each data packet transmission. Equation (17) shows that, with a certain initial energy, the total number of data packet transmissions is proportional to the energy utilization of the network and inversely proportional to the energy consumption of each data packet transmission. The LETPO algorithm balances the local network energy consumption by maximizing the minimum residual energy of all related nodes according to the greedy algorithm. In this process, the energy utilization is fully considered and all nodes in the network drain out their residual energy as evenly as possible, thus extending the network lifetime. However, it overlooks the energy consumption during each data packet transmission. In other words, the LETPO algorithm concerns the energy utilization more than the energy efficiency. Therefore, in the cooperative node selection phase, we should focus on the energy consumption of each data packet transmission cooperated by the selected cooperative nodes through the corresponding cooperative links. That is to say, we also consider the energy efficiency improvement in the cooperative node selection phase.
## 2 Numerical Model and Parameters
### Ground Roughness  
In the near-surface atmospheric boundary layer, the distribution of vegetation can have a large impact on the vertical distribution of wind speeds. The roughness height h is one of the parameters used to characterize vegetation roughness conditions. Alpine shrublands and alpine meadows are widely distributed in alpine cold regions of China, with plant roughness height approximately equal to plant height, which is taken as 15 cm to 20 cm. Aerodynamic roughness length \(z_{0}\) characterizes the interaction between the Earth's surface and the atmosphere, reflecting the impact of land surface on the reduction in wind speed. The linear relationship between the aerodynamic roughness and roughness height in the horizontal region is \(h_{0}=\alpha_{0}z_{0}\), where \(\alpha\) is a conversion factor and is related to land cover type; \(\alpha_{0}\) was taken as 7.5 [23]. Aerodynamic roughness is generally about 1/7-1/8 of the height of the land surface plant [23]. In this study, the plant height is taken as 0.2 m, the aerodynamic roughness is taken as 0.0285 m, and the roughness height used for FLUENT calculation is taken as 0.214 m.
# High-Resolution Multi-Scale Feature Fusion Network for Running Posture Estimation
## 2 Related Work
### Multi-Scale Feature Fusion  
Multi-scale feature fusion plays a pivotal role in deep neural networks. It can provide more comprehensive and rich feature representation, helping the network better understand the semantic information within images and thus improving performance on computer vision tasks. There are many different structures for multi-scale feature fusion, with the parallel multi-branch network structure and residual connection structure being relatively common. In reference [14], researchers proposed a parallel multi-branch heatmap regression network that inputs multiple images with different resolutions into multiple sub-networks separately and finally integrates the outputs of these sub-networks to obtain the final result. The hourglass network [2] and its extensions [12; 15] employ residual connections to progressively extract features at different scales during the downsampling process and gradually fuse the features during the upsampling process. RefineNet [16] combines the aforementioned structures and aims to improve the results of semantic segmentation through cascaded refinement. The approach first employs a base convolutional neural network to extract the feature representation of the input image. Subsequently, multiple parallel convolutional branches are utilized to process these features in order to capture semantic information at different scales. Each branch incorporates a series of residual connections to facilitate gradient propagation and retain detailed information. Our approach repeats multi-scale fusion, drawing partial design inspiration from RefineNet.
# Performance Evaluation of China's First Ocean Dynamic Environment Satellite Constellation
## 4 Discussion
This study evaluated the observation efficiency of China's first dynamic environment satellite constellation, as well as of the six other satellite networks. The findings demonstrated that, when compared to the six satellites from other countries, the observation efficiency of the HY-2B, HY-2C, and HY2D satellite networks was roughly comparable. This will provide an effective scientific basis for the accurate observation of ocean surface temperature, sea surface salinity, sea surface wind field, waves, surface current field, and other elements of the ocean dynamic environment. After the completion of China's first dynamic environment satellite constellation, the satellite radar altimeter obtained SWH as well as SSWS, but also in the global coastal wave disaster monitoring, it played an important role. Combining the altimeter data from other satellites, as well as the future planned satellite constellation of microwave remote sensing technology for detecting the ocean dynamic environment, will achieve higher accuracy in the calculation of SLA, SWH, SSWS, and other parameters, providing large amount of data services for ocean remote sensing, contributing to global ocean governance, and addressing climate change. Conceptualization, D.Q. and Y.J. ; methodology, S.L. ; validation, D.Q. and Y.J. ; formal analysis, M.L. and Y.J. ; writing--original draft preparation, Y.J. and S.L. ; writing--review and editing, D.Q. and Y.J. ; visualization, M.L. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China (No. 42192561, No. 42192531). Data Availability Statement: Data underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request. The authors thank the reviewers and editors for their positive and constructive comments, which have significantly improved the work. **Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
# Spatial Autocorrelation Incorporated Machine Learning Model for Geotechnical Subsurface Modeling
## 2 Study Area and Methodology
### Gaussian Process Regression (GPR)
#### 2.4.4 Principal Component Analysis (PCA) for Data Dimensionality Reduction  
PCA reduces data dimensionality while maintaining as much variance as possible [34; 35]. This is achieved by transforming datasets into a new group of variables called the principal components, which are uncorrelated, and then ordering them so that the first few of the components maintain the majority of variance included in the initial variables. As explained by [PERSON] et al. [35], when PCA is performed on the dataset, the weight vector for the \(k\)th principal component (PC) is obtained as follows:  
\[w_{k}=\underset{\|w\|=1}{\operatorname{argmax}}\mathrm{\Sigma}\bigg{\{}\Big{(}w ^{\mathrm{T}}\hat{D}_{k}\Big{)}^{2}\bigg{\}}, \tag{8}\]  
where \(w\) is the unit weight vector and \(\hat{D}_{k}\) is the dataset subtracting \(k-1\)th PC from the initial dataset \(D\). \(\hat{D}_{k}\), which is the same as the initial dataset, is provided by  
\[\hat{X}_{k}=X-\sum_{i=1}^{k-1}w_{i}w_{i}^{\mathrm{T}}X. \tag{9}\]  
The number of the dataset's PCs in this study was acquired by predefining the threshold into 80% and 90% of explained variance as the suggested value from the literatures [34; 35; 36; 37] instead of directly defining the numbers of PCs applied to the dataset.
# MANET Routing Protocols' Performance Assessment Under Dynamic Network Conditions
## 5 Results and Comparative Analysis
### Overall Results
As network size increases, reactive protocols become more effective in managing dynamic changes while reducing the overhead of maintaining routing tables. For large and very large networks, hybrid protocols are the optimal choice, as they combine the strengths of proactive and reactive approaches to balance scalability, efficiency, and reliability. This strategic selection ensures that the chosen protocol aligns with the network's scale and complexity, leading to improved communication performance, reduced energy consumption, and enhanced overall efficiency. In the context of MANET, selecting the appropriate routing protocol is crucial for optimizing performance based on specific application scenarios. Proactive protocols, such as OLSR and DSDV, are advantageous in low-density, stable networks like military operations or emergency response scenarios, as they provide low latency due to readily available routes and maintain consistent routing information. However, their high overhead from frequent routing table updates and increased energy consumption can be significant drawbacks. Conversely, reactive protocols, such as AODV and DSR, are better suited for dynamic environments with high node mobility, like disaster recovery operations, where they minimize overhead by establishing routes on demand. Despite their adaptability, these protocols may experience increased latency during the route discovery process. Hybrid protocols, such as ZRP and GRP, effectively balance the strengths and weaknesses of both proactive and reactive approaches, making them ideal for applications like sensor networks. In such scenarios, hybrid protocols can maintain stability while also accommodating varying latency needs, thus ensuring efficient data transmission and energy usage in dynamic environments.
# A Review of Research on Secure Aggregation for Federated Learning
## 2 Federated Learning
### Applications  
Federated learning is currently exhibiting its great potential in various fields, and applying federated learning to real-life applications can protect user data, improve the accuracy of models, and provide higher-quality services.  
The main application areas of federated learning demand robust privacy protection, the ability to handle data heterogeneity, suitability for real-time scenarios, and efficient management of widely distributed data.  
In the field of mobile devices, mobile applications are typically latency-sensitive. Federated learning supports model updates directly on local devices, reducing communication with central servers and enabling low-latency responses. Given the heterogeneity of mobile applications due to diverse user behaviors, federated learning can aggregate individualized device models to deliver personalized services.  
Figure 5: The network structures for centralized federated learning.  
Figure 6: The network structures for decentralized federated learning.  
In agriculture, data is widely distributed across various sources such as sensors and drones. Federated learning minimizes the communication overhead associated with centralized data transmission, leverages data diversity, and enhances model generalization. Agricultural applications often require real-time predictive analytics, such as yield forecasting and disease classification. Federated learning supports edge device-based model updates, improving real-time performance and alleviating cloud computing burdens.  
In healthcare, sensitive data (e.g., medical records, imaging data, genetic information) poses significant privacy risks if exposed. Federated learning provides robust privacy protection by enabling multi-institutional collaborative modeling without sharing raw data. It addresses the challenges of data ownership and privacy in clinical research, drug development, and other fields by facilitating the integration of multi-center data while safeguarding patient confidentiality.  
In the field of renewable energy, it is challenging for a single model to adapt to complex environments due to significant regional variations in meteorological conditions, such as wind speed and solar irradiance. Therefore, improving model generalization capability is essential. Federated learning has been widely adopted as it enables the sharing of modeling experience and enhances both the generalization and prediction accuracy of models across diverse scenarios.  
Federated learning is more frequently applied in these fields due to its inherent advantages in privacy protection, real-time processing, support for training on distributed data, and the maturity of its technologies and ecosystem.
## 3 Results and Discussion
### Screening Analysis (ELISA) Results
#### 3.1.3 Ruggedness  
The samples were spiked with 62.5 uL of a standard solution of HT-2 at a concentration of 20 mg L\({}^{-1}\) to obtain the fortified products at 250 ug kg\({}^{-1}\). The variables were modified using the experimental plan according to the procedure developed by [PERSON] and [PERSON] [23]. The Youden fractional factorial design adopted in this part of method validation and related results are reported in Supplementary Tables S1 and S2. Method ruggedness was demonstrated under minor changes conditions, since the SD of difference was lower than the SD of the intra-laboratory reproducibility.
# BEVCorner: Enhancing Bird's-Eye View Object Detection with Monocular Features via Depth Fusion
## 2 Related Work
### Fusion Techniques in 3D Object Detection  
Fusion techniques in 3D object detection aim to combine information from different data sources or methods to enhance detection accuracy, addressing the limitations of single-modal approaches. While most research focuses on fusing camera and LiDAR data, either by early fusion [36; 37], late fusion [38], or cascade fusion [39; 40; 41], camera-only fusion, particularly combining monocular and multi-view methods, is relevant to the proposed work. DPFusion [42] proposes fusing camera and LiDAR data with dense depth map-guided BEV transform and multi-modal feature adaptive fusion, achieving competitive results on NuScenes [3]. However, papers exploring camera-only fusion, especially combining monocular and multi-view information, are less common. View-to-Label [43] proposes self-supervising monocular 3D detection using multi-view consistency, leveraging RGB sequences alone for improved performance. MVC-MonoDet [44] introduces a semi-supervised framework, enforcing consistency across unlabeled multi-view data (stereo or video) with box-level and object-level regularizers, enhancing monocular detection. These works suggest that combining monocular and multi-view information can improve detection accuracy, particularly in data-scar scenarios. This aligns with the proposed method that aims to leverage the strengths from both monocular and BEV object detection, by fusing their outputs through depth map enhancement, providing a novel approach to improve overall detection accuracy.
# Recent Progress on Vegetation Remote Sensing Using Spaceborne GNSS-Reflectometry
## 2 Research Status
### Spaceborne GNSS-R Observation Experiment
The local and global inversion results indicate that this inversion method is better for biomass and tree height. Their algorithm was tested on both the selected areas and globally, and it has shown a promising correlation coefficient, r \(\approx\) 0.8.
## 2 Materials and Methods
### Statistics, Test Screening, and Analysis of Geographical Factors
#### 2.5.3 Correlation Test of Topographic Factors  
A Pearson correlation coefficient test was run following the computation of the topographic factors. Table 2 shows the resulting correlation matrix of the five topographic factors after normalization. In such a matrix, a correlation is considered significant if its absolute value is larger than 0.7, moderately significant if it is between 0.3 and 0.7, and marginally significant if it is below 0.3.  
Catchment size was so strongly correlated to stream length that the researchers elected to omit the latter from further analyses. The remaining four topographic factors, however, could be deemed independent of one another because their eigenvalues were all less than 0.7, and all were, therefore, retained.
# Breakthrough and Challenging Application: Mixed Reality-Assisted Intracardiac Surgery
## 2 Materials and Methods
### Heart Segmentation
Dynamic Region GrosSimilar to Region Grow, but it accepts multiple seed points across multiple layers, allowing for varied gray levels to control connectivity analysis. Threshold values on gray level similarity and mask expansion can also be applied. Particularly beneficial when standard Region Grow yields unsatisfactory results or when contrast agent effects vary significantly across the blood pool. Useful for generating valid starting points for chamber segmentation. 3D InterpolatePerforms interpolation akin to the Multiple Slice Edit, but utilizes seed masks from every principal anatomical plane. It is suitable for structures with complex shapes that cannot be described from a single perspective, such as left atrium/right atrium and left ventricle/right ventricle shapes. It is particularly advantageous for ventricle volumes due to their intricate shape. Smart FillAnalyzes an input mask and fills enclosed groups of inactive pixels, known as holes. The maximum hole size filled can be controlled by selecting the number of voxels. Each step of the segmentation process was discussed with the radiologist and approved by the cardiac surgery team. The statistical data analysis has showed a standard deviation level for the methodology of segmentation applied about 7% lower than the gold standard, i.e., the manual segmentation performed by the radiologist.