esa-sceva/satcom-chunk-collection huggingface dataset:
# Video Super Resolution Using a Selective Edge Aggregation Network
## 3 Method
### Edge Map Extraction  
Just as \(F_{t-k}\) is generated using \(LR_{t}\) and \(LR_{t-k}\), we use \(e_{t}\) and \(e_{t-k}\) to extract \(E_{t-k}\). Before extracting \(E_{t-k}\), we consider the motion difference between \(LR_{t}\) and \(LR_{t-k}\). If large-scale motion exists, noise occurs in \(E_{t-k}\) generated by fusing \(e_{t}\) and \(e_{t-k}\). Therefore, we proceed with the detection of blur frames to reduce noise.  
We use the Laplacian filter to get the variance of \(LR_{t-k}\), and if the variance is less than the threshold, we consider \(LR_{t-k}\) to be a blur frame. If \(LR_{t-k}\) is a blur frame, our model does not use \(e_{t-k}\) when generating \(E_{t-k}\), and \(e_{t}\) is used instead of \(e_{t-k}\). After detecting the blur frames, our model generates \(E_{t-k}\) as shown in Equation (1).  
\[E_{t-k}^{i,j}=\begin{cases}0&\text{if},e_{t-k}^{ij}+e_{t}^{ij}=0\\ 1&\text{otherwise}\end{cases} \tag{1}\]  
Figure 1: Architecture of our Video Super Resolution Using Selective Edge Aggregation Network (SEAN). It contains two stages: image reconstruction (Stage 1) and refinement (Stage 2).  
\(E_{t-k}^{i,j}\) and \(e_{t-k}^{i,j}\), which contain all the information on two edge maps (or a single edge map), provide the structural information corresponding to time \(t-k\) to our model. \(i\) and \(j\) are horizontal and vertical locations, respectively.
# Loosely Coupled GNSS and UWB with INS Integration for Indoor/Outdoor Pedestrian Navigation +
## 4 Test Setup
### Data Acquisition  
The data acquisition was performed assembling all the sensor in a handled platform together with an LCD screen for real time error checking and a prism mounted on the top. Each lever-arm between the sensors (GNSS antenna, UWB antenna, 360\({}^{\circ}\) prism) was measured with a caliber. The positioning algorithm was performed in real-time through the Raspberry Pi processor. The data acquisition, the communication protocols and the processing were undertaken all in the same Python environment.  
A major issue was to provide to the EKF the GNSS state vector already processed in a network real-time kinematic (NRTK) positioning mode. To do this, the u-blox receiver has been connected through USB port to the Raspberry board where a modified version of the RTKLIB 2.4.3 has been compiled. Here, through the _str2 str_ module the GNSS signal coming from the antenna is redirected on a Raspberry port through TCP/IP protocol. This flow is then split in order to allow both real-time and post-processing positioning: one data flow is saved as.ubx file for the quality control and eventually for post-processing positioning while the same flow split into another port has been used by the _rthrcv_ for the NRTK positioning. This module uses this flow as rover input and the differential corrections obtained from the SPIN (Servizio di Posizionamento Interregionale GNSS) network of permanent stations for performing the real-time positioning, considering the virtual reference station (VRS) correction. As demonstrated in [3; 5], this positioning method allows the achievement of centimetric precision and accuracy in real-time. Finally, this solution is used as input for the integrated positioning algorithm presented in this work. The same flow acquired by the TCP/IP port is also saved for a quality analysis and for the possibility of post-processing the data with other techniques (Figure 3).  
The presence of raw GNSS observations of code and phase allows the implementation of tightly coupled approaches subject of future work.
# Distance Estimation with a Stereo Camera and Accuracy Determination
## 1 Introduction
Mask R-CNN is a powerful tool for segmenting objects in images that not only identifies and classifies objects, but also accurately determines their contours by generating segmentation masks [7; 8; 9]. This makes it possible to isolate specific objects from the background, which is crucial for the accurate matching of correspondence points between images from the two cameras. The use of Mask R-CNN in the process of distance estimation not only allows for the improvement of the precision of object detection, but also significantly improves the entire process of 3D scene reconstruction, leading to more reliable and accurate distance measurements. Another advanced method that can be used to measure depth is the Spiking U-Net architecture, based on spiking neural networks (SNNs). These networks differ from traditional deep neural networks in that they mimic the action of biological neurons, processing information in the form of pulses, known as'spikes', rather than continuous signals. In the U-Net architecture, originally designed for the task of segmenting medical images, such spikes are used to more efficiently detect the edges and contours of objects in three-dimensional scenes. Spiking U-Net generates detailed segmentation masks, which in turn help with precise depth reconstruction. Spiking U-Net is an interesting alternative to classic networks such as Mask R-CNN, especially in the context of real-time applications. The use of this architecture can significantly improve precision in depth measurements, while enabling the more economical use of computing resources, which is important in the context of mobile or portable measurement systems [10; 11]. It was decided to conduct research to accurately determine the precision of the measurement system and identify specific distance ranges where the results obtained have the highest accuracy. Such a research goal allows for a better understanding of the capabilities and limitations of the systems used and helps determine the optimal operating conditions relevant to future practical applications of this technology.
# Historical Eco-Environmental Quality Mapping in China with Multi-Source Data Fusion
## 3 Results
### EEQ of the Yangtze and Yellow River Basins  
#### 3.3.1 Monitoring of the EEQ of the Yangtze River Basin  
The Yangtze River [47], known as the \"mother river\" of China, is the third longest river in the world and the first hydroelectric river. Spanning a length of 6,397 km, it traverses Qinghai, the Tibet Autonomous Region, Sichuan, Yunnan, Chongqing, Hubei, Hunan, Jiangxi, Anhui, Jiangsu, and Shanghai, playing a crucial role in China's energy, food, and economy. The Yangtze River basin is renowned for its abundant biological resources and stands as the most ecologically diverse region in China [48]. However, since the 21 st century, the rapid economic development of the Yangtze River economic zone has led to a decline in the EEQ of the Yangtze River basin, necessitating long-term monitoring efforts [49].  
As depicted in Figure 7, the distribution of EEQ in the Yangtze River basin in 2000, 2008, and 2017 exhibits a consistent pattern characterized by \"worse in the east and better in the west.\" Certain regions in Chongqing consistently displayed poor EEQ over the three-year period, indicating insufficient implementation of ecological environmental protection measures in the area. Additionally, the Yangtze River Delta region also exhibited poor EEQ, highlighting the lack of synchronized development between urbanization and EEQ in the region, thereby demonstrating a low coupling phenomenon.  
Figures 7 and 8 reveal that significant changes in EEQ primarily occurred in the Sichuan basin and certain areas of Chongqing city during the period of 2000-2017. Notably, Chongqing exhibited a larger region transitioning from poor to bad EEQ, suggesting efforts were made to improve the environmental quality. Conversely, the regions experiencing EEQ deterioration were concentrated in Sichuan Province, Hubei Province, and Shanghai.  
According to Table 5, the majority of the Yangtze River basin, encompassing an area of 2,021,800 km\({}^{2}\), or 83.40% of the total, maintained an unchanged EEQ from 2000 to 2017. The area with decreased EEQ accounted for 144,400 km\({}^{2}\), equivalent to 5.96% of the total basin area. Conversely, the area with increased EEQ covered 257,900 km\({}^{2}\), representing 10.64% of the total basin area. Overall, the EEQ of the Yangtze River basin exhibited improvement.  
Figure 8: Change map of M-RSEQI in the Yangtze River Basin from 2000 to 2017.  
Figure 7: M-RSEQI distribution map and M-RSEQI classification map of the Yangtze River Basin in 2000, 2008 and 2017.
# Digitalization Processes in Distribution Grids: A Comprehensive Review of Strategies and Challenges
## 2 Grid Planning
### Sustainability and Environmental Planning
#### 2.6.4 Energy Community Planning  
The transition to sustainable, localized energy generation and consumption is increasingly powered by grassroots energy communities, blending democratic participation with renewable energy efforts, which is significantly aided by digital platforms for energy sharing and management.  
The research in [173] explores smart energy community development, spotlighting the role of smart technologies like blockchain in optimizing community-based energy systems. Another study [135], discusses the planning of a sustainable district in Sweden and examines diverse stakeholder views on citizen energy communities (CECs) in decentralized energy solutions through a sociotechnical perspective, highlighting the potential for decentralized energy models.  
The critical function of ICT in supporting energy communities is the focus of [174], which illustrates how digital tools aid energy management and market participation, emphasizing ICT's role in facilitating a sustainable and decentralized energy transition.  
Additionally, Ref. [175] presents a digital platform with a one-stop-shop (OSS) architecture aimed at enhancing community energy projects by simplifying stakeholder interactions and project management, further advancing the energy transition agenda.  
Together, these discussions spotlight the shift towards community-driven, decentralized energy systems, stressing the merger of digital and renewable technologies for improved energy management and distribution. Future directions will involve enhancing digital sharing platforms and community engagement strategies, navigating regulatory landscapes to support the global shift towards more sustainable decentralized energy systems.
# A Hybrid Multi-Agent Reinforcement Learning Approach for Spectrum Sharing in Vehicular Networks
## 5 Simulation Results and Findings
### Discussion  
In this study, we investigated the challenge of multiple Vehicle-to-Vehicle (V2V) links interfering with the spectrum used by Vehicle-to-Infrastructure (V2I) links. This can significantly reduce the data capacity available for V2I communication and hinder the reliability of data transmission for V2V communication. To address this issue, we aimed to improve both spectrum efficiency and message transmission reliability using the Manhattan urban simulation model [29]. Our approach involved employing the multi-agent reinforcement learning method with QMIX, where agents autonomously learn and share insights with the central QMIX network, thereby fostering collaboration towards a common goal.  
From the simulation, we observed that the RL techniques initially showed lower performance than baseline schemes. However, as iterations progressed and the RL methods learned, their performance became exceptional. Specifically, as we increased the V2V payload size, our proposed approach maintained superior performance with higher V2I data transfer capability and V2V data transmission reliability.  
It is worth noting that implementing the hybrid QMIX approach requires substantial computational resources. This is due to the initial learning phase of the agents, followed by the sharing of insights with the central network, which necessitates additional computational settings and resources.
## 1 Introduction
In contrast, the lower-priority domain, sometimes called the BLACK domain, handles less sensitive, often unclassified information. While still important, communications in this domain can tolerate brief interruptions or delays without catastrophic consequences. This tiered approach allows for more efficient resource allocation, focusing the most robust protections on the most critical information flows. In normal operations, the domains remain strictly segregated, maintaining clear boundaries between sensitive and non-sensitive information. However, in emergencies or when resources are stretched thin, there is potential for controlled, secure sharing of resources between domains. The reliability model for these systems must account for several key factors:  
* The model needs to accurately represent the independence of multiple communication channels. * The models of high-priority (RED) and lower-priority (BLACK) domains must account for cross-domain resource sharing in emergency situations. * Reliability models must incorporate maintenance operations and repair times. * Reliability models need to accurately represent how mechanisms of redundancy and failover mechanisms perform under various failure scenarios. This paper aims to develop a comprehensive reliability model for advanced communication systems that operate with multiple channels and distinct priority levels. To ground this theoretical work in practical reality, we will use air traffic control (ATC) communication systems as our primary case study. Air traffic control serves as an excellent example for our modeling efforts due to its critical nature and complex operational requirements. While our focus is on air traffic control, the principles underlying this model can be applicable to other critical communication systems that share similar characteristics. The remainder of this paper is structured as follows. Section 2 provides a review of the current literature in the field of two priority communication systems with RED/BLACK architectures. Section 3 introduces the concepts and methodologies employed in the study, describing in detail the architecture of RED/BLACK communication systems, discussing the reliability issues inherent in these architectures, and introducing the concept of cross-domain redundancy for enhancing their reliability. Section 4 presents two distinct algorithms for managing cross-domain redundancy in RED/BLACK systems and provides detailed Markov chain models for each approach. Mathematical expressions for steady-state probabilities and channel availability are derived for both strategies. Section 5 discusses the results. It compares the performance of the two proposed algorithms and examines the reliability improvement achieved through cross-domain redundancy. This Section includes a detailed interpretation of the graphical results, exploring the implications of varying system parameters on overall reliability. Section 6 summarizes the key findings of the study, discusses their implications for the design and operation of critical communication systems, and suggests directions for future research in this field.
## 3 Results
### Submerged vs. Emerged Primary Production and Its Predictors
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & **Term** & **df** & **Pseudo-F** & **p (perm)** & \multicolumn{2}{c}{**Post-Hoc Pairwise Tests**} \\ \cline{2-6}  & & & & & **Site** & **Time** & **Tide** \\ \hline NPP & Site & 2 & 8.05 & **0.006** & & \\ \cline{2-6}  & Time & 1 & 1.10 & 0.323 & & \\ \cline{2-6}  & Tide & 1 & 5.87 & **0.025** & & \\ \cline{2-6}  & Site \(\times\) Time & 2 & 3.78 & **0.026** & & \\ \cline{2-6}  & Site \(\times\) Tide & 2 & 33.43 & **0.001** & & \\ \cline{2-6}  & Time \(\times\) Tide & 1 & 9.06 & **0.007** & & \\ \cline{2-6}  & Site \(\times\) Time \(\times\) Tide & 2 & 7.18 & **0.006** & \begin{tabular}{c} **Sub T2, R \(<\) T \(<\) O, T3, O \(=\) (R \(<\) T)** \\ **Em T2, T3, R \(>\) (O \(=\) T)** \\ \end{tabular} & \begin{tabular}{c} **R, T sub**, T \(2<\) T3, \\ **em T2, T2 \(=\) T3** \\ **O sub, em,** \\ **T2** = T3 \\ \end{tabular} & \begin{tabular}{c} **R, T sub**, T \(2<\) T3, \\ **em T2, T3**, sub \(>\) em \\ **T2**, sub \(=\) em, \\ **T3**, sub \(>\) em \\ \end{tabular} \\ \hline GPP & Site & 2 & 5.73 & **0.007** & & \\ \cline{2-6}  & Time & 1 & 12.10 & **0.002** & \\ \cline{2-6}  & Tide & 1 & 51.88 & **0.001** & \\ \cline{2-6}  & Site \(\times\) Time & 2 & 0.96 & 0.404 & & \\ \cline{2-6}  & Site \(\times\) Tide & 2 & 25.59 & **0.001** & \begin{tabular}{c} **Sub, R \(<\) T \(<\) O** \\ **Em, R \(>\) (O \(=\) T)** \\ \end{tabular} & \begin{tabular}{c} **R, sub \(=\) em** \\ **O, T, sub \(>\) em** \\ \end{tabular} \\ \hline Time \(\times\) Tide & 1 & 33.06 & **0.001** & & \
\begin{tabular}{c} **Sub, T2 \(<\) T3, \\ **em, T2 \(>\) T3** \\ \end{tabular} &
\begin{tabular}{c} **T2, T3**, sub \(>\) em \\ **T2**, T3**, sub \(>\) em \\ \end{tabular} \\ \hline Site \(\times\) Time \(\times\) Tide & 2 & 1.26 & 0.282 & & \\ \hline \end{tabular}
\end{table} 
Table 5: Results of repeated measures PERMANOVAs testing the effect of site and tidal state at two different sampling dates on measures of primary production. Site (3 levels: RAG, TAU-O and TAU-T), time (2 levels: T2 and T3) and tide (2 levels; submerged and emerged) were treated as fixed factors and replicate plot nested within site. Site names have been further abbreviated as: R = RAG, O = TAU-O and T = TAU-T and submerged and emerged tidal periods to sub and em, respectively. Significant effects (\(p<0.05\)) are given in bold.
# Recent Development of High-Energy Short-Pulse Lasers with Cryogenically Cooled Yb:YAG
## 2 Changes in Crystal Properties with Temperature  
At room temperature, due to the serious self-absorption effect of Yb:YAG, the pump threshold is high, and the small emission cross-section result in a low gain. It requires a high pump power to obtain a high gain, a condition that easily damages the crystal, which limits the continued increase in the laser power [8; 9]. It was found that with the decrease in temperature, the thermodynamic and spectral properties of Yb:YAG changed significantly, which was beneficial to the expansion of high-power lasers.
# On the Performance of Partial LIS for 6G Systems
## 4 Conclusions  
This article performs a comparison between the results with the full LIS, in different scenarios of antennas, and with the partial LIS, with and without LDPC codes. In the case of the partial LIS, different numbers of antennas are considered. It can be seen that, as long as the number of antennas that compose the LIS system is sufficiently high to provide a high level of diversity, the partial LIS with one panel tends to achieve the best overall performance. Different receiver types were analysed in the above-described scenarios, while the ZF/MMSE are those that tend to achieve the best performance and the MRC is the one that, performing slightly worse than ZF/MMSE, is very simple. This is because it avoids the computation of the channel inversion, for each frequency component of the channel, that is required for ZF/MMSE receivers.  
Finally, one can see that the use of the partial LIS, associated with LDPC codes, tends to achieve the best overall performance, as long as the number of antennas that compose the LIS is sufficiently high to provide a high level of diversity. The partial LIS tends to outperform the full LIS because it allows for the mitigation of interference.  
Considering the downlink scenario, future research should focus on the use of OFDM transmission techniques, instead of the SC-FDE adopted in this study.  
Investigation, M.M.d.S., A.G., R.D., G.P., A.C. and J.G. All authors have read and agreed to the published version of the manuscript.  
Figure 9: Performance results of the 4 \(\times\) 225 LIS system, with 2 users, with and without LDPC codes, and with the partial LIS.  
Funding: This work is funded by FCT/MCTES through national funds and, when applicable, co-funded EU funds under the projects UIDB/EEA/50008/2020 and 2022.03897.PTDC.  
Data Availability Statement: Data was obtained using Monte Carlo simulations and implemented using Matlab.  
We acknowledge the support of FCT/MCTES, as described above in the funding section. We also acknowledge the support of Autonoma TechLab for providing an interesting environment in which to carry out this research.  
The authors declare no conflicts of interest.
# Performance Evaluation of Routing Protocols for Underwater Wireless Sensor Networks
## 3 Performance Evaluation and Simulation Results
### Results
#### 3.2.3 Packet Delivery Ratio  
Figure 4 shows the PDR for all tested routing schemes. The graph shows that the RACE-SM has a much higher PDR than any other method. The reason for obtaining a higher PDR is that BER has decreased. The other reason is that the function's parameters, such as the shortest distance and energy consumption, are used to choose the relay node that will carry the signal from sender to receiver.  
EH-UWSN and MuLSi-Co have approximately the same packet delivery ratio at the start of simulation but slowly reduced when crossed the 4000 rounds. MuLSi-Co uses two methods: transferring data directly to the mobile sink node when the mobile sink is in the communication range and through multiple relay nodes. MuLSi is a non-cooperative routing strategy that searches for the best forwarder node while it does not have the location information of the forwarder and the sink node. It also uses multiple hops for data transmission from source to destination, consuming more energy, and the nodes decrease  
Figure 3: End-to-End Delay.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Rounds** & **RACE-SM** & **EH-UWSN** & **MulSi-Co** & **EERBCR** & **MulSi** & **GCORP** & **EEDG** \\ \hline
0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\
1000 & 0.1 & 0.6 & 0.4 & 0.5 & 0.5 & 0.4 & 0.5 \\
2000 & 0.1 & 0.7 & 0.7 & 0.9 & 0.5 & 0.6 & 0.6 \\
3000 & 0.2 & 0.9 & 1 & 1 & 0.6 & 0.8 & 0.6 \\
4000 & 0.3 & 1 & 1 & 1.2 & 0.6 & 1.1 & 0.6 \\
5000 & 0.5 & 1 & 1.2 & 1.4 & 0.7 & 1.2 & 0.9 \\
6000 & 0.5 & 1.3 & 1.2 & 1.5 & 0.9 & 1.4 & 1.1 \\
7000 & 0.6 & 1.5 & 1.5 & 1.7 & 1.1 & 1.6 & 1.3 \\
8000 & 0.6 & 1.6 & 1.7 & 1.9 & 1.2 & 1.6 & 1.3 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The end-to-end delay of all evaluated routing protocols.  
the energy quickly. Therefore, PDR is significantly smaller than other routing strategies. RACE-SM has a greater PDR from the beginning to the completion of the simulation than existing routing systems. The PDR of all protocols mentioned here is for the same data rate. However, it will affect if the data rate has changed. Table 4 addresses the PDR of all evaluated routing schemes.
## 4 Experimental Results Analysis
### Error Impact Analysis  
This study employs a mathematical model based on angle and distance observations to optimize the terminal trajectory of anti-ship missiles. Analyzing the impact of observational errors on optimization outcomes is essential. In scenario 1, the binding angle error \(\delta_{\varepsilon_{d}}\) varies within the interval \([0.01\;\mathrm{rad},0.05\;\mathrm{rad}]\), while the ranging error \(\delta_{\varepsilon_{d}}\) takes values of 1 m, 2 m, and 5 m. The variation curve of MMD is presented in Figure 7. When the binding angle error reaches 0.05 rad, ensuring that the optimal trajectory hits the target (\(d\leq D=5\;\mathrm{m}\)) becomes challenging. Moreover, when the ranging error exceeds 5 m, the missile's hit accuracy significantly decreases.  
Figure 4: Multi-stage terminal trajectories of the typical scenarios.  
Figure 5: The missile–target distance reduction chart.  
Figure 6: Speed variation charts of the missile (**left**) and overload variation (**right**).  
With the parameters in Table 1 unchanged, the azimuth angles error \(\delta_{\ell_{\phi}}\) and elevation angles error \(\delta_{\ell_{\phi}}\) are allowed to vary within the interval \([0,0.05\;\mathrm{rad}]\). The resulting MMD surface is shown in Figure 8. Observations reveal that the influence of \(\delta_{\ell_{\phi}}\) is more pronounced than \(\delta_{\ell_{\phi}}\). When \(\delta_{\ell_{\phi}}\geq 0.026\;\mathrm{rad}\) occurs, the missile loses all reliability in achieving the target strike.