esa-sceva/satcom-chunk-collection huggingface dataset:
# A GNSS-Cellular Network Hybridization Strategy for Robust Positioning
## 3 Results
### Position-Level Hybridization Method results
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Obs** & **E (m)** & **N (m)** & **U (m)** & \(\sigma_{\text{E }}\)**(m)** & \(\sigma_{\text{N }}\)**(m)** & \(\sigma_{\text{U }}\)**(m)** & **RSS** \\ \hline Hybrid & 729,009.80 & 4,373,590.02 & 73.22 & 0.34 & 0.49 & 0.11 & 0.61 \\
4G & 729,006.45 & 4,373,576.40 & 72.39 & 2.24 & 1.97 & 0.35 & 3.00 \\ GNSS & 729,009.63 & 4,373,590.20 & 72.54 & 0.04 & 0.17 & 0.21 & 0.27 \\ \hline \end{tabular}
\end{table} 
Table 5: Estimated coordinates and standard deviations for the urban canyon scenario using the hybrid, GNSS-only, and 4G-only solutions. The RSS column indicates the combined 3D precision via the root sum square of standard deviations for the three spatial axes. To provide a complete evaluation of the position-level hybridization method, two additional synthetic indicators were calculated using the root sum square (RSS) of the east, north, and up components. Figure 8 shows the RSS of the standard deviations, representing the internal precision of each method under the test conditions. Figure 9 presents the RSS of the positioning errors relative to the reference coordinates, summarizing the overall accuracy of the GNSS-only, 4G-only, and hybrid solutions across the three experimental scenarios.
# Nano- and Micropatterning on Optical Fibers by Bottom-Up Approach: The Importance of Being Ordered
## 4 Methods for Order Quantification
### Order Quantification by Pair Correlation Function  
Another method for quantifying the order of 2D periodic structures is based on the pair correlation function (PCF). The PCF is typically used to describe quantitatively the internal structure of fluids and quantifies how the particle of interest is surrounded by other particles. For example, for a rarefied gas in which there is no internal structure, the PCF has a constant value for all radial distances. In contrast, for a solid system in which molecules are almost regularly located, the PCF has sharply peaked values at the positions of particles, while is nearly zero at the positions where particles are seldom located. Therefore, the PCF provides a useful method to investigate the degree of order of a self-assembled structure [104].  
The PCF is expressed by g(r), and it describes the probability of finding a particle at a distance r from a given particle in the 2D space. To calculate it, an initial central particle \(a\) is chosen as the origin. A circular shell of width \(dr\) expands from the center to a radius distance \(r\). Any particles inside a circular shell of width \(\Delta r\) are counted together and binned to produce the neighbor probability \(n_{n}(r)\) as a function of distance, weighted by the particle density, \(\rho\):  
\[g(r)=\frac{n_{n}(r)}{2\pi r\Delta r\rho} \tag{1}\]  
A schematic representation of g(r) is shown in Figure 6(c-ii). The particle coordinates must be determined by analyzing a microscopy image (SEM, TEM, AFM) of the periodic structure with an image analysis software such as ImageJ [105]. A practical example of the application of the PCF to colloidal patterns is provided in a recent article by [PERSON] et al. [99]. In this paper, the authors developed a mathematical method, named disLocate, to rapidly assess and quantify the two-dimensional order of PS-b-P2 VP diblock copolymer micelles assembled by spin-coating at different speeds. When the AFM image shows that micelles are organized in a hexagonally ordered pattern, as in the top frame of Figure 6(c-i), the corresponding PCF finds particles at regular intervals as the distance from the central particle increases, as schematized by Figure 6(c-ii) top, and plotted in Figure 6(c-iii) top. These intervals are specific for the expected geometry and can therefore be predicted by calculation. For instance, for the hexagonal pattern, if \(d_{p}\) is the particle diameter, the positions of these peaks correspond to \(d_{p}\), \(\sqrt{3}d_{p}\), \(2d_{p}\), \(\sqrt{7}d_{p}\), \(3d_{p}\), \(2\sqrt{3}d_{p}\), \(\sqrt{13}d_{p}\), \(4d_{p}\) etc, represented by the bottom plot of Figure 6(c-iii). As the periodicity of the pattern is weakened or lost, like in the bottom case of Figure 6c, the main g(r) peaks get further from these predicted positions.
# An Architecture to Manage Incoming Traffic of Inter-Domain Routing Using OpenFlow Networks
## 4 Controlling Inbound Traffic
### Reactive Round-Robin Load Balancer with Threshold (RRT)  
The reactive load balancer with Round-Robin and threshold is an extension of the aforementioned strategy where a threshold of bandwidth usage is provided for each path in the list. Thus, if the bandwidth of a path overcomes this threshold, the algorithm must consider the associate path congested and not able to be used for flow creation until the usage becomes lower than the threshold value. Algorithm 3 depicts the RRT algorithm.  
To be clear, all the already created flows assigned to the path that has overcome the threshold will continue to use that path, even when the path state is congested. This decision will avoid degradation in the transport level protocols that require keeping the state of established connections.  
```
0: The list of valid and available next hop to a given prefix \(L\), the BGP best hop best for the prefix, and the bandwidth usage list of outbound links in the domain \(D\), a threshold for bandwidth utilization \(t\).
0: Select the next hop for a given destination.
1: length_of_L \(\leftarrow\)\(get\_length(L)\)
2: last_next_hop \(\leftarrow\)\(L\)[length_of_L]
3:fornext_hop in \(L\)do
4: next_hop \(\leftarrow\)\(L\_pop(0)\)
5:\(L\_append(\text{next\_hop})\)
6:if\(D[\text{next\_hop}]\leq t\)then
7:returnnext_hop
8:endif
9:ifnext_hop equal last_next_hop then
10:return best
11:endif
12:endfor
```  
**Algorithm 3** Reactive Round-Robin Load Balancer with Threshold
# Research on Satellite Network Traffic Prediction Based on Improved GRU Neural Network
## 2 Literature Review
In this scheme, LSTM is used to extract the time characteristics of network traffic, a genetic algorithm is used to get the hyperparameters of the proposed network, and finally, the proposed new network traffic prediction model is used to predict the network traffic. [PERSON] et al. compared SVM, MPL, and MLPWD in the same network environment [13]. The simulation results show that the prediction effect of SVM is better than MPL and MLPWD. [PERSON] et al. proposed a new graph convolution network model [14], which considered the static spatial dependence of traffic and the influence of dynamic traffic on spatial relationships. Simulation results show that the prediction error of the proposed scheme is small. [PERSON] et al. proposed a traffic forecasting method using a deep neural network to model traffic [15]. It is realized by treating the data flow as a tensor and then transmitting it to the convolutional neural network. Finally, the results of the proposed network were verified using the Telecom Italia data set. [PERSON] et al. proposed a neural network traffic prediction algorithm based on transfer learning [16]. Combined with transfer learning, the problem of insufficient online traffic data is solved. Experiments show that the proposed scheme can reduce the error of traffic prediction.
# Physicochemical Properties and Sensory Attributes of Yanggaeng Treated with Citrus Peel Powder
## 3 Results and Discussion
### Textural Properties of Yanggaeng  
Table 7 presents the findings regarding the textural properties of Yanggaeng contating CP. The incorporation of CP into Yanggaeng led to an increase in its hardness compared with that of CON. CP4 exhibited the highest hardness (CON \(<\) CP2 \(<\) CP6 \(<\) CP4; \(p\)\(<\) 0.0001). Conversely, adhesiveness was higher in CON Yanggaeng; however, CP treatment resulted in decreased adhesiveness (\(p\)\(<\) 0.01). In particular, CP4 had the lowest adhesiveness compared with the Yanggaeng-containing groups of CP2 or CP6 (CON \(>\) CP6 \(>\) CP2 \(>\) CP4). Gumminess and chewiness levels decreased in CON, whereas CP treatment increased both gumminess and chewiness (\(p\)\(<\) 0.001). Interestingly, the values of adhesiveness, resilience, cohesion, and springiness remained consistent across all treatment groups. The addition of CP leads to a decrease in adhesiveness, accompanied by an increase in gumminess, hardness, and chewiness, owing to the effect of the powder particles on the formation of the solid-like structure. However, based on textural properties, resilience, cohesion, and springiness remained unaffected by the introduction of CP.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & \multicolumn{3}{c}{**Treatments (\%)**} \\ \cline{2-4}  & **CON 1** [FOOTNOTE:1]Footnote 1: Treatments are shown in Table 1. Data are presented as means \(\pm\) standard deviation (\(n=3\)). Different lowercase letters in the same column are significantly
## 4 Results
### Accuracy Comparisons between the Fine-Tuned Models and Retained Models  
We found that the fine-tuned model based on a small sample size could realize inter-continental transfer of the pre-trained model and obtain high rice classification accuracy. However, the gap between the rice classification accuracy of the fine-tuned model based on a small sample size and that of the model trained from scratch based on a large number of samples also affected the application potential of model transfer based on the fine-tuning method in large-scale rice mapping. Therefore, we compared the rice mapping accuracy of the model fine-tuned based on 10 samples with that of the 10-fold cross-validation based on all samples in the Shuangcheng area in 2017; the results are shown in Figure 23. The rice mapping accuracy based on the fine-tuned model was based on the accuracy of retraining a model using all samples from scratch. For example, the F-score and kappa coefficient of the retrained model, which represent the overall classification accuracy, were only 0.58% and 0.63% higher, respectively, than those of the fine-tuned model based on a small sample size. The results showed that it was completely feasible to transfer the rice mapping model trained in areas with abundant samples to areas lacking samples based on the fine-tuning method and a small number of samples, such that we could realize low-cost, large-scale rice mapping, which has high application value.  
Figure 23: Accuracy comparison between the fine-tuned models and retained models. The fine-tuned models were based on 10 randomly selected samples in Shuangcheng in 2017. The retained models were based on 10-fold cross validation of the Shuangcheng 2017 dataset.  
Figure 22: Feature visualization based on t-SNE method. The input data of the t-SNE is the time-series SAR data in Shuangcheng from 2017 to 2020. The farther the separation between rice pixels and other pixels in the t-SNE map, the more effective the features are for rice classification, and vice versa.
# Data Analytics in Smart Healthcare: The Recent Developments and Beyond
## 1 Introduction  
In the light of the promotion of the smart city [1], many smart applications have been raised, for instance, smart energy [2], smart education [3], smart transportation [4] and smart healthcare [5]. Guest Editors have proposed a special issue on the theme of \"Data Analytics in Smart Healthcare\" which aims at collecting innovative applications in smart healthcare via data analytic techniques.  
The tremendous growth of Internet-of-Things devices enable the (big) data collection of health-related parameters (e.g., body temperature, blood pressure, heart beat, respiratory rate, oxygen saturation, blood glucose level, wrist pulse signal, magnetoencephalogram (MEG), galvanic skin response (GSR), electrooculography (EOG), mechanomyogram (MMG), electromyogram (EMG), electrocardiogram (ECG) and electroencephalogram (EEG)). Numerous data analytic techniques are applied to analyze the data in order to realize smart healthcare applications.  
The world has been seeking effective measures to relieve the issues of population ageing as well as inadequate amounts of medical staff. The World Health Organization (WHO) reported that the world requirement and actual number of medical staff are about 60 million and 43 million, respectively, in 2013 [6]. These figures will be increased to 82 million and 67 million, respectively, by 2030. It can be seen that the percentage of medical shortages will be decreased; unfortunately, more than 15 million of shortage in medical staff is an unimaginable figure which requires some alternatives.  
This editorial is organized as follows. Section 2 summarized the applications, methodologies and key results of the published articles. In Section 3, guest editors discuss emergent topics in smart healthcare. Finally, a conclusion is drawn.
# Towards the Feasibility of Long Range Wireless Power Transfer over an Ocean Surface
## 3 Results
### Transmission Efficiency Versus Distance Test under Different Antenna Heights  
The objective of this test was to measure the transmission efficiency under three different antenna heights, namely 1 m, 1.6 m, and 2 m. During this test, the proposed setup as shown in Figure 2 was used throughout the experiment. The transmitting end was excited with the spark transmitter at 44 MHz. The powers at the transmitting and receiving ends were individually measured using two oscilloscopes. Finally, the measured results were checked against the values calculated according to [PERSON]'s law of transmission.  
The amount of power being transmitted at the transmitting end was approximately 196 watts.  
Figure 5a-c respectively show the transmission efficiencies as a function of the propagation distance for an antenna height of 1 m, 1.6 m, and 2 m.  
Table 2 shows a comparison between our estimated maximum propagation distance and the maximum signaling distance based on [PERSON]'s law of transmission. In our estimation, the propagation distance that led to the first maxima from the left axis was assumed to be 80% of maximum propagation distance. With this assumption, the antenna heights together with their respective maximum propagation distance were tabulated in Table 2.  
The right-most column of Table 2 displays the maximum signaling distance estimated using [PERSON]'s law of transmission. By comparing the figures in the second and third columns of Table 2, it is obvious that our experimental outcome was consistent with [PERSON]'s law.  
Figure 5: Transmission efficiency as a function of transmission distance: (**a**) when the antenna height was 1 m; (**b**) when the antenna height was 1.6 m; (**c**) when the antenna height was 2 m.
## 4 Results and Discussions
### Emerging Digital Technologies
#### 4.1.2 Industrial Metaverse
Figure 9 provides a concise overview of the enabling technologies, key applications, and core functions of IM. The core functions represent the foundational capabilities of IM, including the development of immersive and interactive virtual environments, facilitation of value co-creation and collaboration through shared digital workspaces, and integration of physical, cyber, and social spaces to achieve virtual-real symbiosis. Similarly, the key applications demonstrate how these functions are operationalized, such as in the collaborative design, commissioning, and execution of manufacturing processes, enhancement of human-machine interaction involving humans, COBOTS, and process management systems, and optimization of operations through improved planning, task execution, and strategic decision-making. Supporting these applications are enabling technologies, which include DTs, CPS, Big Data Analytics, Cyber Infrastructure, Human-Machine Interfaces, Metaverse Configuration tools, AI, and Edge Computing. These technologies collectively enable immersive experiences, seamless integration between real and virtual environments, operational efficiency, low latency, and robust data security, forming the technological backbone of IM. Table 3 shows the key differences and synergies between DTs and IM in manufacturing OHS. Figure 9: Key applications and core functions of IM. To sum up, IM builds upon the foundations of Industry 4.0 and DTs by integrating CPS, immersive environments, and intelligent automation into unified, human-centric digital spaces. These environments support remote safety training, scenario-based planning, and cross-functional engagement. It creates a seamless bridge between physical production environments and immersive virtual realities, enabling users to simulate, plan, and manage complex operations through real-time interactions. While DTs provide precision simulations, IM enables experiential learning and collaboration, allowing remote users to rehearse coordinated safety responses and decision-making. It delivers immersive multi-sensory experiences that strengthen real-time decision-making, enhance virtual collaboration, and improve situational awareness. For instance, its convergence with DT and Extended Reality (XR) technologies drives the evolution of intelligent safety ecosystems. It enhances the effectiveness of traditional DTs by embedding human presence and interactions into virtual models. In summary, IM enhances DT functionality by enabling shared immersive experiences for training and hazard response. Its integration into OHS frameworks represents a shift toward collaborative and experiential safety designs.
# UVsq-Sat NG, a New CubeSat Pathfinder for Monitoring Earth Outgoing Energy and Greenhouse Gases
## 2 Scientific Objectives
### Earth's Radiation Budget
The present development of an autonomous and cost-effective observing system inspired by Uvsq-Sat and Inspire-Sat 7 is a contribution towards that goal.
## 3 Methodology
### Phase 2: Optimal Architecture Synthesis
#### 3.2.6 Evaluation and Validation Methodology  
Dataset Selection and Preprocessing  
Three different FN datasets are used to ensure comprehensive evaluation in different domains and linguistic patterns. FakeNewsNet provides social media-based FN with associated metadata and propagation patterns [31]. The ISOT dataset offers the classification of traditional news articles with clear fake/real distinctions [32]. The LIAR dataset presents fine-grained FND with multiple levels of veracity and political context [33]. Each dataset undergoes standardized that includes text normalization, tokenization using model-specific tokenizers, and sequence truncation to maximum lengths determined by computational constraints. Stratified sampling ensures balanced train-validation-test splits while maintaining original class distributions.  
Rigorous statistical validation ensures the reliability and significance of the experimental findings. Multiple independent runs with different random seeds generate performance distributions for each architectural configuration. Paired t-tests compare optimal model performance with baseline architectures, providing statistical significance measures. Effect size calculations using Cohen's d quantify the practical significance of performance improvements beyond statistical significance. The confidence intervals in 95% provide uncertainty estimates for all performance metrics. Cross-validation using stratified k-fold sampling validates the results across different data partitions. This comprehensive statistical framework ensures that the improvements reported represent genuine architectural advantages rather than random variation.  
Model consistency is evaluated through variance analysis on multiple training runs and datasets. Standard deviation calculations across repeated experiments quantify the result stability, with thresholds established for acceptable performance variation. The cross-dataset evaluation assesses the generalizability of the model by training on one dataset and evaluating others. The consistency of the contribution of the components is measured by comparing the results of the ablation study in different datasets and training conditions. This multifaceted consistency assessment ensures that the optimal architecture provides reliable performance across diverse conditions and datasets, supporting its practical applicability for FND tasks.
## 3 Experimental Results and Discussions
### Analysis of Test Results
#### 3.2.2 Analysis of Influencing Factors  
The effects of the block and mortar strength on the compressive strength of RAC block masonry structure are shown in Figures 7 and 8, respectively.  
It can be observed from Figure 7 that the compressive strength of RAC block masonry was enhanced with the increase of block strength. The difference between NAC and RAC blocks masonry may be attributed to more ITZs present in RAC blocks. The weak ITZs in concrete were more likely to generate micro-cracks under the pressure, which led to the destruction of the concrete masonry.  
According to Figure 8, it can be found that the mortar strength also had a significant effect on the strength of concrete block masonry. The increase of mortar strength was encouraging to improve the strength of concrete masonry. Moreover, the lower the concrete block strength was, the more sensitive  
Figure 6: Stress-strain curves of RAC block masonry under compression: (**a**) effect of RA replacement ratio; (**b–d**) effect of mortar strength.  
Figure 7: Effect of RAC block strength on the compressive strength of RAC blocks masonry.  
the concrete block masonry strength was to mortar strength. The results may be due to the characteristic of recycled concrete block being unstable, which weakened the contribution of block strength to the compressive strength of concrete masonry and reinforced the effect of mortar strength relatively.  
This paper compared the effects of mortar strength and block strength on masonry structure performance. A 40% increase in block strength will result in a 39% increase in compressive strength of concrete block masonry, but a 40% increase in mortar strength will only increase the compressive strength of concrete block masonry by 8%. It can be found that the strength of block is more sensitive to the compressive strength value of concrete block masonry than that of mortar.
# A High-Utility Differentially Private Mechanism for Space Information Networks
###### Abstract  
Nowadays, Space Information Networks represented by the satellite internet are developing rapidly. For example, the Starlink of SpaceX plans to provide network access services worldwide and has drawn much attention. To operate and maintain Space Information Networks, e.g., performing collision avoidance maneuvers and forensic investigation, statistic information on networks such as the average of orbital inclination needs to be shared with analysts. However, for some particular reasons, such as safety or confidentiality, accurate information on networks cannot be shared with analysts. To solve this contradiction, we design a differentially private mechanism for the Space Information Network so that the entities of a network can keep accurate information privacy while sharing statistic information. In particular, we extend differentially private mechanisms based on personalized sampling to distributed communication systems such as Space Information Networks. In comparison with other mechanisms, the proposed mechanism has better data utility. Moreover, the proposed mechanism has a hierarchical privacy guarantee. In particular, there are differences between the privacy guarantees made between system entities and between the system and the user.  
space information network; differential privacy; privacy protection; data utility; personalized sampling 2022