esa-sceva/satcom-chunk-collection huggingface dataset:
# Handover Scheme in LEO Satellite Networks Based on QoE for Streaming Media Services
###### Abstract  
The development of satellite communications has received considerable attention in recent years. Early satellite communications were dominated by voice and low-speed data services, but now they must support high-speed multimedia services. Low Earth Orbit (LEO) satellites, because of their lower altitude orbits, have much smaller transmission loss and delay than Geostationary Earth Orbit (GEO) satellites, and they are an important part of the future realization of high-bandwidth and low-latency multimedia services. Among them, the on-demand streaming service has a large number of users in terrestrial communication and is also an important service component that will be in satellite communication environments in the future. However, LEO satellites face many challenges in handover and accessing due to their fast moving speed. Although many handover and access schemes for LEO satellites have been proposed and evaluated in existing studies, most of them stay at the level of quality of service (QoS), and few of them have been studied at the level of quality of experience (QoE). These studies also rarely consider the performance of multimedia services, including streaming services, in satellite communication environments, and there is no relevant simulation system to evaluate and examine them. Therefore, this paper builds a simulation system for streaming services in LEO satellite communication environments in order to simulate the initial buffering, rebuffering, and idle state of the users during service. Then, access and handover schemes for the QoE level of streaming service are proposed. Finally, our proposed scheme is evaluated based on this simulation system. From the simulation results, the simulation system proposed in this paper can successfully realize the various functions of users in on-demand streaming services and record the initial buffering and rebuffering events of users. And the streaming QoE-based access and handover scheme proposed in this paper can perform well in satellites, which operate within a resource-constrained environment.  
streaming media; QoE; LEO satellite; handover +
Footnote †: journal: Sensors
## 5 Conclusions  
Our study revealed some cell functional clusters and signaling pathways associated with ESCC by re-annotating a gene chip dataset with prognostic information and subsequent bioinformatics and medical statistical analysis. ELN, MMP13, SPP1, MMP10, COL1A1, CSF2, MMP1, SERPINE1, CXCL8, and MMP3 were identified as the key genes driving the occurrence and development of esophageal squamous carcinoma. By combining clinical prognosis information and Western blot analysis, we found four genes with diagnostic and prognostic significance, including MMP13, SPP1, MMP10, and COL1A1. Additionally, MMP13, SPP1, MMP10, and COL1A1 in ESCC also had significant correlations with immune markers. Follow-up studies on the mechanism and clinical value of these genes will vigorously promote the ESCC diagnosis and treatment.  
The following are available online at [[https://www.mdpi.com/article/10.3390/app11073229/s1](https://www.mdpi.com/article/10.3390/app11073229/s1)]([https://www.mdpi.com/article/10.3390/app11073229/s1](https://www.mdpi.com/article/10.3390/app11073229/s1)), Figure S1: Expression of MMP13, MMP10, COL1A1 and SPP1 in different types of human cancers in the Oncomine database, Figure S2: Expression of DEGs in different types of human cancers in the TIMER database, Figure S3: Based on the TCGA data, the expression levels of the MMP13, MMP10, COL1A1 and SPP1 gene were analyzed by the main pathological stages (stage I, stage II, stage III and stage IV) of ESCA. Log2 (TPM + 1) was applied for log-scale. Violin plots showed that stage II and stage III had higher gene expression level than stage I, which suggested an association of increased key gene expression with tumor progression, Table S1: Clinical characteristics of ESCC patients in the research, Table S2: GO enrichment results, Table S3: The enriched pathways of DEGs, Table S4: The node scores of top 10 DEGs, Table S5: Correlation analysis between DEGs and immune cell infiltrations in ESCC samples using GEPIA2, Table S6: Correlation analysis between DEGs and HPV marker in ESCC samples using GEPIA2.  
Conceptualization, M.W.; Formal analysis, M.W.; Investigation, M.W. and L.M.; Methodology, M.W.; Project administration, Y.C. and L.M.; Software, M.W.; Validation, D.L., Y.H. and F.W.; Writing--original draft, M.W. and D.L.; Writing--review and editing, D.L., Y.H. and Z.J. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Shenzhen Strategic Emerging Industry Development Special Funds, grant number JCYJ20170816143646446, and Shenzhen Science and Technology Research and Development Funds, grant number JCYJ20200109143018683.  
Not applicable.  
Data Availability Statement: Data is contained within the article.  
We thank [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] of the Lab for assistance during the study. We thank Tsinghua University for its generous support of necessary experiment instruments.  
The authors declare no conflict of interest.
# Graphene-Based Membranes for CO2/CH4 Separation: Key Challenges and Perspectives
## 3 Few- to Multi-Layer Graphene-Based Stacked Laminates
Researchers have sought several strategies to overcome this drawback, which include using porous (holey) graphene nanosheets to facilitate a more direct CO\({}_{2}\) transport (Figure 3b), chemical functionalization to increase the affinity for CO\({}_{2}\) (Figure 3c) and intercalation by spacers to increase the interlayer spacing between the GO nanosheets (Figure 3d) [5; 40; 41]. To this end, the most commonly used spacers include ionic liquids, various ions (such as metal and borate ions), as well as a multitude of CO\({}_{2}\)-philic amine-based crosslinkers [5; 33; 42; 43; 44].
## 4 Proposed Experimental Results and Comparative Performance
### Comparative Performances on Delhi Dataset  
To test the applicability of our ensemble framework on the dataset acquired in other countries, we chose the dataset used in [32] which applies time series analysis and regression to forecast the hourly PM\({}_{2.5}\) concentration in the R.K. Puram area in Delhi, India. The time span of the dataset is from 1 January 2018 to 30 November 2019, and it is separated to a training set and a test set by respectively using 80% and 20% of the entire dataset. The features contained in the dataset include solar radiance, air pressure, temperature,  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Models** & **RMSE** & **MAE** & **MAPE** & **R\({}^{2}\)** \\ \hline Averaged & 8.02 & 5.92 & 0.44 & 0.54 \\ Weighted & 7.70 & 5.64 & 0.41 & 0.57 \\ Max\_Avg\_Min & 8.01 & 5.91 & 0.44 & 0.54 \\
**Max\_Wgt\_Min** & **7.69** & **5.63** & **0.41** & **0.57** \\ Adpt\_Wgt & 8.31 & 6.20 & 0.51 & 0.50 \\ Mean & 7.95 & 5.86 & 0.44 & 0.54 \\ Lower Bound & 4.73 & 2.92 & 0.21 & 0.84 \\ \hline LSTM & 8.09 & 5.50 & 0.40 & 0.59 \\ CNN & 9.20 & 6.97 & 0.58 & 0.39 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Three-year forecasting performance obtained by various multi-model strategies and deep-learning models.  
wind speed, wind direction and PM\({}_{2.5}\) time series. [PERSON] et al. [32] conducted a comprehensive comparison on several state-of-the-art machine learning techniques. We use the same experimental settings and apply our ensemble framework on Delhi dataset. Table 8 shows the comparative performances of various methods on Delhi dataset. We can see that all of the single models used in our framework, if they are executed as a stand-alone predictor, are inferior to all the compared models in [PERSON] et al. [32]. Our long-term or short-term ensemble can improve their elementary models, but still cannot be fairly comparable to the leading group. With our multi-model ensemble strategies, the forecasting performance obtained by using weighted, Max_Wgt_Min, and Adpt_Wgt is comparable to the leading methods in [PERSON] et al. [32]. The best ensemble, weighted strategy, can surpass all but one method, the ET + AdaBoost, in [PERSON] et al. [32]. This result indicates that our ensembles adapt very well to the Delhi dataset. It is worthy to note that the formation and transportation of PM\({}_{2.5}\) concentration are dependent on the geographical terrains and local anthropogenic activities, so the set of effective meteorological variables for forecasting PM\({}_{2.5}\) could vary with the studied location, as revealed by many previous researchers [7; 15; 16; 20; 29]. The features used in our ensemble models for Puli dataset are different to those used in [32]. Consider this situation, it is a promising result to note that our ensemble framework can achieve the best performance of almost all compared models for Delhi dataset.
## 3 Application
### Artificial Intelligence Model Results  
#### 3.2.1 Multiple Linear Regression Model  
For broken rock zone prediction, the MLR method was utilized and the following empirical formula was created after using SPSS software.  
\[BRT=0.001\times ED+0.113\times DS-0.003\times RS+0.384\times JI-0.697 \tag{7}\]  
By using the above formula, the predicted \(BRZT\) can be calculated and the predicting results with _VAF_ of 76.2155, \(R^{2}\) of 0.7639 and _RMSE_ of 0.2973 for training datasets and _VAF_ of 88.6696, \(R^{2}\) of 0.8899 and _RMSE_ of 0.1678 for testing datasets can be found in Figure 4.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Variable** & **Maximum** & **Minimum** & **Mean** & **Median** &
\begin{tabular}{c} **Standard** \\ **Deviation** \\ \end{tabular} & **Skew** & **Kurtosis** \\ \hline \(ED\) & 780.00 & 97.00 & 455.78 & 424.00 & 180.14 & 0.08 & \(-\)1.19 \\ \(DS\) & 10.00 & 2.40 & 3.78 & 3.40 & 1.29 & 2.93 & 10.59 \\ RMS & 110.20 & 1.10 & 27.03 & 16.80 & 21.76 & 1.71 & 2.78 \\ \(JI\) & 5.00 & 1.00 & 3.25 & 3.00 & 0.92 & 0.07 & \(-\)0.39 \\ \(BRZT\) & 3.45 & 0.30 & 1.43 & 1.40 & 0.56 & 0.63 & 0.71 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Descriptive statistical values of the five collected variables in this paper.  
Figure 3: Scatter plot matrix of the broken rock zone (BRZ) database.
## 3 Methodology
### Network Architecture  
Our proposed network follows a UNet-inspired encoder-decoder design, optimized for lightweight deployment and real-time performance. The architecture includes the following:  
* Encoder: A series of convolutional blocks with downsampling, designed to extract hierarchical low-level to mid-level features. Each encoder block contains two convolutional layers with kernel size 3 \(\times\) 3, batch normalization, and ReLU activation. We use depthwise separable convolutions to reduce the number of parameters and computational cost, inspired by MobileNet [22]. The feature map sizes progressively reduce from 256 \(\times\) 256 to 64 \(\times\) 64.
* Attention Modules: Integrated between encoder and decoder stages, we adopt channel attention mechanisms (similar to SE-Blocks [25]) to emphasize informative feature maps and suppress noise.
* Decoder: Uses transposed convolutions (or bilinear upsampling + convolution) to reconstruct the image. Skip connections from the encoder ensure high-frequency detail is preserved.
* Final Output Layer: A 1 \(\times\) 1 convolution with a sigmoid activation outputs a 3-channel RGB image normalized between 0 and 1.  
The overall architecture of the proposed low-light enhancement network is illustrated in Figure 1, highlighting the encoder-decoder backbone, attention modules, and skip connections that facilitate feature preservation and noise suppression.  
Each stage in the architecture is designed with computational efficiency and enhancement quality in mind:  
* Encoder Blocks: Each block contains depthwise separable convolutions followed by batch normalization and ReLU activation. These layers capture hierarchical features while keeping parameter count low. Downsampling is achieved via strided convolutions.  
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline  
* Attention Module: We integrate Squeeze-and-Excitation (SE) blocks [25] between encoder and decoder stages to perform dynamic channel-wise feature recalibration. The SE module learns to suppress noisy or irrelevant channels by modeling interdependencies, which helps reduce amplification of low-light noise during upsampling.
* Decoder Blocks: These use transposed convolutions or bilinear upsampling + convolution to restore spatial resolution. Skip connections from the encoder are fused to preserve texture and structural detail.  
This modular design enables compactness while maintaining the visual fidelity of restored images. The SE module works by globally pooling feature maps, passing them through a bottleneck fully connected layer (squeeze), and reweighting the channels based on learned importance (excitation). This mechanism is particularly effective in low-light scenarios where some channels may represent structured noise or overamplified darkness. A detailed layer-by-layer breakdown of the network architecture, including output shapes and activation functions, is provided in Appendix B.
## 4 Discussion
### Outer Torso Volume and Surface Area  
On average, the torso volume and surface area increased as each garment layer was added, except for torso volume when the SCBA was donned. Minimal, insignificant differences were identified (Figure 1) between the CO and the BL, which is to be expected as base layers are often single layer garments worn close to the skin in a more form fitting fashion [25]. The TS, however, is a multilayered garment comprised of multiple fabric and clothing layers including the outer shell, moisture barrier, and thermal liner [20]. These layers create additional air gaps in between, contributing to the significantly large jump in torso volume from 11% (CO-BL) to 76% (CO-TS) when wearing a turnout suit. A similar increase in torso surface area, from 5% (CO-BL) to 50% (CO-TS), was also determined.  
When donning the SCBA, torso volume slightly decreased, on average, compared to the TS (\(-1.2\%\)). This reduction was both statistically and meaningfully insignificant. These findings may be explained, in part, by the synching of the SCBA straps around the shoulders, waist, and front chest which compresses internal air gaps, reducing overall torso volume compared to the TS alone. Interestingly, torso volume increased for four of the nine individual participants when donning the SCBA. This may be caused by the addition of the SCBA straps and pack, which may also explain the increase in torso surface area, on average, as well. These findings highlight the need for additional research as 3D scanning limitations remain when trying to distinguish between various parts of the SCBA (e.g., straps, pack, and tank). Overall, results demonstrate minimal impacts to overall torso volume and surface area when donning an SCBA in a static, standing position. However, significant changes to internal microclimate air gaps may still be present. Therefore, air gap volume and distance were calculated.
## 3 Regularization Method
### ADMM Algorithm
Finally, we iteratively solve Equation (10)  
\[\frac{\sigma_{n}^{k}-\sigma_{n}^{k-1}}{\Delta\theta} =\frac{\sigma_{n+1}^{k}-2\sigma_{n}^{k}+\sigma_{n-1}^{k}}{(\Delta t )^{2}}-\frac{1}{2}\sum\limits_{i=1}^{N}\sum\limits_{j=1}^{M_{i}}\mu_{ij}^{k} \frac{\partial V}{\partial\sigma}(\tau_{n},r_{0},S_{0},T_{i},K_{ij},\alpha^{ k},\sigma^{k}(\tau_{n}))\] \[-\lambda\sum\limits_{i=1}^{N}\sum\limits_{j=1}^{M_{i}}\frac{ \partial V}{\partial\sigma}(\tau_{n},r_{0},S_{0},T_{i},K_{ij},\alpha^{k}, \sigma^{k}(\tau_{n}))[V(\tau_{n},r_{0},S_{0},T_{i},K_{ij},\alpha^{k},\sigma^{k }(\tau_{n}))-\hat{V_{ij}}].\]  
For step 2  
\[\alpha^{k+1} =\operatorname*{arg\,min}_{\alpha}\{||\alpha||_{2}^{2}+\lambda \sum\limits_{i=1}^{N}\sum\limits_{j=1}^{M_{i}}[V(\tau,r_{0},S_{0},T_{i},K_{ij},\alpha^{k},\sigma^{k+1})-\hat{V_{ij}}]^{2}\] \[+\sum\limits_{i=1}^{N}\sum\limits_{j=1}^{M_{i}}\mu_{ij}[V(\tau,r_ {0},S_{0},T_{i},K_{ij},\alpha^{k},\sigma^{k+1})-V_{ij}]\},\]  
this optimization problem can be tackled by employing the Particle Swarm Optimization (PSO) algorithm (Algorithm 1). Alternatively, a direct exploration of \(\alpha\) from 0 to 1 can also be conducted to solve the problem. ```
Data: Initialize swarm of particles (\(\alpha_{i}\)) Result: Optimal solution Initialize particle positions and velocities randomly; Initialize historical best position as current positions; Initialize global best position as one of the historical best position; whilestopping criterion not metdo foreachparticledo  Update velocities using Equation (12);  Update positions using Equation (13);  Evaluate fitness of the current position; ifcurrent position is better thanpersonal bestthen  Update personal best position; ifcurrent position is better than global bestthen  Update global best position;  Output the global best position as the optimal solution;
```  
**Algorithm 1**Particle Swarm Optimization (PSO)  
Equations for the velocity and the position updates:  
\[v_{i}(t+1)=w\cdot v_{i}(t)+c_{1}\cdot r_{1}\cdot(\mathbf{p}_{i}-\alpha_{i}( t))+c_{2}\cdot r_{2}\cdot(\mathbf{pg}-\alpha_{i}(t)), \tag{12}\]Position update: \(\alpha_{i}(t+1)=\alpha_{i}(t)+v_{i}(t+1)\), (13)  
where \(w\) is the inertia weight, \(c_{1}\) and \(c_{2}\) are acceleration constants typically set to 0.2, \(r_{1}\) and \(r_{2}\) are random values in the range [0, 1], \(\textbf{p}_{i}\) represents the historical best position of particle \(\alpha_{i}\), and \(\textbf{pg}\) is the global best position among all particles. For step 3  
Directly update the Lagrange multiplier \(\mu_{ij}\).
## 3 Results and Discussion
### Adsorption Study
#### 3.2.3 Impact of nZVISLP Dosage  
The effect of nZVISLP dosage on MB adsorption was also systematically investigated by varying the amount of nZVISLP from 0.4 to 1.2 g/L, keeping other process parameters at fixed values (pH: 8, C\({}_{0}\): 10 mg/L, and temperature: 25 \({}^{\circ}\)C). Figure 5c shows that the adsorptive removal of MB increased from 71-86% with the increase in nZVISLP dosage from 0.4 to 1.0 g/L, as evidenced by the C\({}_{0}\) value changing from 3.4 \(\pm\) 0.2 to 1.6 \(\pm\) 0.1 mg/L. With a further increase in dosage (up to 1.2 g/L), there was no substantial increase observed in MB removal. Upon increasing nZVISLP dosage from 0.4 to 1.2 g/L, q\({}_{\text{max}}\) values increased from 6.5 \(\pm\) 0.2 to 8.2 \(\pm\) 0.01 mg/g. Upon increasing adsorbent dosage, the surface area available for adsorption increases, which then enhances the adsorption [41]. [PERSON] et al. [39] also reported a similar trend for MB adsorption. However, a further increase in dosage did not influence the removal rate, due to the unavailability of adsorption sites [38].
# Research on Application of Improved Quantum Optimization Algorithm in Path Planning
## 4 Analysis of Evacuation Path Planning Model Experiment
### Experimental Analysis of Multi-Robots Path Planning
#### 4.4.3 Influence Analysis of QABC, ACA and ABC Stability Results  
According to the data obtained by the three algorithms, the relationship between iteration times and fitness can be drawn, as shown in Figure 14. The use of QABC reduces the possibility of collision between robots, and improves the safety of robots. When the number of location points is large, the running times of ABC and ACA are obviously longer than that of QABC. Comparing the stability of the three algorithms, it is found that QABC is more stable.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & **Robot** & **Estimate Path Length** & **Actual Path Length** & **Time/s** \\ \hline \multirow{2}{*}{ACA} & S1 & 11.983 & 11.983 & 1.291 \\  & S2 & 14.678 & 14.678 & 1.339 \\ \hline \multirow{2}{*}{ABC} & S1 & 11.346 & 12.237 & 1.343 \\  & S2 & 12.327 & 12.438 & 1.433 \\ \hline \multirow{2}{*}{QABC} & S1 & 12.628 & 12.682 & 1.153 \\  & S2 & 15.729 & 15.720 & 1.127 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of ACA, ABA and QABC.  
Figure 13: (**a**) Conflict type II; (**b**) conflict type III; (**c**) conflict type I.
# Spaceborne GNSS-R for Sea Ice Classification Using Machine Learning Classifiers
## 2 Dataset Description  
### TDS-1 Mission and Dataset  
The TDS-1 satellite began its data acquisition in September 2014 after its launch in July 2014. As one of eight instruments placed on the TDS-1 satellite, the Space GNSS Receiver-Remote Sensing Instrument (SGR-ReSI) was turned on only two days of an eight-day cycle until January 2018 [45]. The SGR-ReSI started its full-time operation in February 2018, and it came to the end in December 2018. The TDS-1 has provided a large amount of data at a global scale as the satellite runs on a quasi-Sun synchronous orbit with an inclination of 98.4\({}^{\circ}\). Currently, the TDS-1 data are freely available on the Measurement of Earth Reflected Radio-navigation Signals by Satellite (MERRBYS) website (www.mertbys.co.uk, accessed on 11 September 2021). The TDS-1 data are processed into three levels, including Level 0 (L0), Level 1 (L1), and Level 2 (L2). Among them, L1 data are usually adopted in scientific research. L0 refers to the raw data, which are not accessible except for a small amount of sample data. L2 includes wind speed, mean square slope, and sea ice products. One of the most important GNSS-R observables is DDM, which is generated by the SGR-ReSI through the cross-correlation between scattered signals and locally generated code replicas with different delays and Doppler shifts. The orbit and instrument specifications of the TDS-1 mission are presented in Table 2[45].  
\begin{table}
\begin{tabular}{l l l l l} \hline
**Source** & **Algorithms** & **Purpose** & **Data Time Span** & **Overall Performance** \\ \hline [PERSON] et al. & & & & & Seawater 97\% \\ (2019) [30] & CART & Sea ice classification & 10 days of TDS-1 data in 2015 & First-year ice 70\% \\  & & & & Multi-year ice 82\% \\ [PERSON] et al. (2017) [36] & NN & Sea ice detection and & & & Youn ice 81\% \\  & & SIC retrieval & 15 days of TDS-1 data in 2015 & ice detection:98.4\% \\ [PERSON] and [PERSON] (2018) [37] & CNN & Sea ice detection and & & & \\ [PERSON] and [PERSON] (2019) [38] & SVM & Sea ice detection & & & \\ [PERSON] et al. (2020) [39] & DT and RF & Sea ice detection & & & \\  & & & & & \\  & & & & \\ \end{tabular}
\end{table}
Table 1: Applications of machine learning-aided sea ice sensing using spaceborne GNSS-R data.
## 3 System Description
### Unmanned Aerial Vehicle  
As previously explained, the system heavily relies on UAVs as a tool to ensure that measurements can be obtained from locations that could not be reached without it. In this way, the drone's flying capabilities make it possible to cover extensive areas while requiring considerably lower amounts of time. What is more, as a single piece of equipment is used to take measurements rather than a team that could potentially require several people, it becomes a comparably inexpensive solution.  
In order to design such a subsystem, it was necessary to conceive a series of functional and non-functional requirements that would cover all the aspects that would be required by the drone to be fully functional. This list is as shown in Table 4.  
The UAV that was built makes use of several parts with different functionalities, as they are all required to be integrated in a single development. Some of these components are purely hardware oriented and consist of the parts used to build a physical UAV from scratch, so that it is tailored for the needs of the system. The other parts are the software associated with the UAV so that it can be used for actual mobile network monitoring missions.