esa-sceva/satcom-chunk-collection huggingface dataset:
# Can Blockchain Strengthen the Energy Internet?
## 5 Decentralized Scalability
### Future Directions  
Achieving distributed scalability at the cost of compromising information security and privacy of the user respectively defies the expectations of the future grids. The blockchain trilemma would receive attention in the pathway towards successful implementation of the futuristic grid infrastructure [64]. The vulnerabilities in grid security would expand beyond the current extent with the scalable, decentralized operations. Thus, proper measures would be a necessity to instate the trust in the EI grids.  
Further, connectivity has a major impact in reaching the scalability goals, which can be addressed through advancements such as 5G and 6G technologies. The former would enable low-latency communication channels while the latter would address the intermittent connectivity in rural geographical locations by utilizing edge devices [29].  
**Lessons:** Scalability of EI grids are partly addressed through off-chain and side-chain implementations [65] as well as suitable selection of the blockchain platform [21; 32; 66]. This includes scalable initiatives such as NRG Xchange [32] and analytical selection of the blockchain platform such as HyperLedger over Ethereum [64]. Energy data management with the increasing number of consumer connections would be a challenge to be addressed, while facilitating low-latency grid synchronization of DERs has not been discussed in the existing literature [65; 12; 67].
# Experimental Investigation of Task Performance and Human Vigilance in Different Noise Environments
## 3 Results
### Effects of Mental Workload  
Figure 6 presents the NASA-TLX scale scores, MATB task performance, and mean SampEn of HRV at different mental workloads. As can be seen from Figure 6a, with the increase in difficulty of the MATB task, the subjective mental load of subjects significantly increased, confirming that the workload design of the MATB tests was effective. As shown in Figure 6, the best performance of the overall task and three subtasks was found for MMW, whereas the worst performance was found for HMW. Both the overall performance and performance of the three subtasks had inverted U-shaped relationships with elevated mental workload, which was consistent with the findings of [PERSON] et al. [21] and [PERSON] et al. [30]. The lack of vigilance at LMW may have resulted in delayed responses. However, the demand for participant attention resources increased for HMW, which would not only delay response times, but also reduce task accuracy [31]. There was no significant difference in the mean SampEn of HRV among participants during the MATB task at different mental workloads, although the mean SampEn of HRV was slightly lower for MMW (Figure 6f).  
Figure 5: Boxplots of PVT metrics under the three noise exposure conditions. (**a**) PVT performance. (**b**) Response time. (**c**) Fastest 10% response time. (**d**) Slowest 10% response time (\({}^{\#}\)\(p\)\(<\) 0.1; \({}^{\star}\)\(p\)\(<\) 0.05; \({}^{\star\star}\)\(p\)\(<\) 0.01).  
mance was opposite for the LMW and MMW. At LMW, SYS performance was significantly higher under N75-S2 than that under N85-S1 (\(p\) = 0.019). In contrast, SYS performance was relatively lower under N75-S2 than that under N85-S1 (\(p\) = 0.099) and N80-S1 (\(p\) = 0.066) at MMW. As shown in Figure 7c, TRA performance was significantly higher under N85-S1 than that under N80-S1 (\(p\) = 0.028) and N75-S2 (\(p\) = 0.008).
## 2 Materials and Methods
### Data Collection  
#### 2.2.1 Satellite Imagery  
The satellite imagery dataset in this study consisted of a pair of PlanetScope multispectral images on similar dates in adjacent years. The first image was acquired on 22 October 2018, and the second image was acquired on 15 October 2019. PlanetScope is a satellite constellation consisting presently of 130+ CubeSats (4 kg satellites) operated by Planet  
Figure 1: Overview of the study area and field sites. (**a**) The study area located in Dangyang City, Hubei Province. (**b**) A thumbnail graph of the PlanetScope satellite image and the spatial distribution of the five field sites.  
Labs [31]. The company has established a data-sharing program to provide PlanetScope data with high spatial and temporal resolution to users [32]. PlanetScope imagery contains four spectral bands, i.e., blue (455-515 nm), green (500-590 nm), red (590-670 nm), and near-infrared (NIR, 780-860 nm). We employed the Level-3B surface reflectance products that have been atmospherically corrected by Planet Labs. The spatial resolution of these products is 3 m, which could meet the requirements for tree-scale monitoring in the study area according to the canopy width of pine trees from the field survey. We checked the co-registration of the two PlanetScope acquisitions and found that they were already well co-registered. A reference image (date: 15 October 2019) was selected, and the other image was radiometrically normalized to this reference image following the approach of [PERSON] and [PERSON] [33]. Based on these operations, we finally obtained the ready-to-use images for the five field sites.
## 4 Results and Discussion
### Aerodynamic Heat
#### 4.3.3 The Effect of Different Relative Air Speeds on Aerodynamic Heat Errors  
To study the effect of the relative air flow rate on aerodynamic heat, the ambient temperature was set to 233.15 K; the air pressure was set to 5 Pa; and the airflow rates were set to 100 m/s, 150 m/s, 200 m/s, 250 m/s, and 300 m/s, respectively.  
Figure 14: Variation in aerodynamic heat with air pressure at two points of the sensor.  
Figure 13: Aerodynamic heat distribution of temperature sensor at altitude of (**a**) 60,000 m, (**b**) 50,000 m, (**c**) 40,000 m, and (**d**) 30,000 m during high-speed falling.  
As shown in Figure 15, as the air velocity increases, the temperature at the two points then rises, and the rate of warming increases. This is because the more significant the air flow rate, the greater the aerodynamic heat, and according to the aerodynamic heat error model, it can be seen that the aerodynamic heat error is proportional to the square of the relative air flow rate. Hence, the slope of the curve increases.  
The above analysis shows that aerodynamic heat has the greatest effect on the temperature error, followed by the temperature error caused by Joule heat. Additionally, solar radiation has the most minor effect on the sensor temperature error. The error model formula corrected the detection temperature. The correction was the sum of the thermal errors of aerodynamic heat, solar radiation, and Joule heat, dramatically improving the temperature's accuracy.
## 4 Results, Discussions and Achievements
### Variation Analysis of RMSE  
Variation analysis of RMSE is one of the popular tools to express the distribution of error concerning \(Q_{1}\),\(Q_{2}\),\(Q_{3}\) min and max values of given data. These values are distinguished using outlier, where the performance of estimation algorithm can be easily identified of used parameters. The performance of proposed CS and PSO are compared in Figure 10 for maximum RMSE of FPA using 40, 55 and 60 at 0 dB, \(-5\) dB and \(-10\) dB, respectively. The result elaborates the outcomes of FPA against the current PSO system.  
Figure 8: Estimation of robustness using nine sources in terms of snapshots.  
Figure 9: Estimation of robustness using twelve sources in terms of snapshots.  
As for nine targets, the RMSE of FPA has less expansion than PSO at \(0\), \(-5\) and \(10\) dB as mentioned in Figure 11. Similar performance of FPA as compared to PSO can be seen in Figure 12 at \(0\), \(-5\) and \(-10\) dB.  
both algorithms. RMSE rises in CS algorithm after 60% of Monte Carlo simulation runs, and in PSO algorithm, it raised after 40% of Monte Carlo simulation tunes. At \(-10\) dB, RMSE occurred at 40% of Monte Carlo simulation runs, but an increase in RMSE is more rapid in PSO than CS. So the overall performance of CS is strongly preferable concerning PSO in the case of six sources estimation.  
In the case of nine and twelve sources estimation, the performance of CS is significantly better than PSO, as shown in Figures 14 and 15. We have successfully achieved the maximum DOF of coprime arrays up to \(O(MN)\) by estimating twelve sources through the CS algorithm.
# Caching Transient Contents in Vehicular Named Data Networking: A Performance Analysis
## 4 V-NDN: Design Concepts and Caching Strategies
### Autonomous Schemes for Non-Transient Contents  
CEE and CEA are the simplest autonomous caching schemes available in the literature. To limit their intrinsic cache redundancy effects while maintaining a simple decision criterion, random schemes with a static caching probability were devised [30], where nodes cache Data packets with a pre-defined probability \(p\), with \(0\leq p\leq 1\). If \(p=0\) the node never caches packets, while if \(p=1\) the scheme behaves like CEE. The most common value used in NDN implementations is \(p=0.5\), which limits the cache redundancy without underusing the available storage space [31]. The caching probability can be also computed dynamically at each node based on the perceived information about the network status and the content demands in the neighbourhood. In this context, decision strategies largely vary depending on the communication type, namely V2I or V2V.  
In [32], an autonomous probabilistic caching scheme, for RSUs only, is deployed, with the targets of minimizing the average number of hops to retrieve the requested content and maximizing the cache hit ratio. The caching probability is computed according to the content popularity evaluated adaptively to the distinct request patterns. However, the strategy is deployed in an infrastructured network of RSUs that retrieve contents from a remote server and it does not consider caching at vehicles. Conversely, in [33], the focus is on V2V communications and the caching probability is computed by vehicles according to three parameters: _(i)_ the content popularity, inferred from the received Interest packets, _(ii)_ the vehicle's topological centrality, and _(iii)_ the relative movement of the receiver and the sender. Performance evaluation shows that the strategy outperforms CEE and probabilistic caching with \(p=0.5\). In [34], a cache probability utility function is defined that takes into account the content popularity and two new defined parameters, the _moving similarity_ and the _content similarity_. The moving similarity indicates if a content is requested by vehicles moving on the same route of the potential cache. The higher the moving similarity the longer the connectivity between vehicles, and therefore the higher the caching probability. Vice versa, the content similarity indicates if drivers/passengers request similar contents. The higher the content similarity the higher the probability that a vehicle will cache a received Data packet.
# Evaluation of Remote Sensing-Based Evapotranspiration
## 2 Study Area and Data
### Data Sources
#### 2.2.4 Gleam  
Central to the methodology of the GLEAM dataset is the use of the Priestley and Taylor evaporation model [34]. The dataset consists of four modules, namely the evaporation of intercepted rainfall from forest canopies, the water budget distributing the incoming  
Figure 1: The Jinhua River Basin and location of meteorological and hydrological stations used in this study.  
precipitation (rain and snow) over the root zone, the stress conditions parameterized as a function of the root-zone available water and dynamic vegetation information, and the evaporation from each of the three surface components [23; 34]. The GLEAM_v3.3b dataset is utilized in this study, which is provided on a 0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\) latitude-longitude grid with a daily temporal resolution. Actual ET in GLEAM is estimated as the sum of different ET components, including transpiration, interception loss, bare soil evaporation, snow sublimation, and open water evaporation. In this study, the daily GLEAM_v3.3b dataset with 0.25\({}^{\circ}\) resolution from 2004 to 2010 is used and can be freely downloaded ([[https://www.gleam.eu/](https://www.gleam.eu/)]([https://www.gleam.eu/](https://www.gleam.eu/)), accessed on 30 November 2020).
## 3 Problem Description  
The issues of the insecure and non-trusted underwater environment [2] discourage localization of nodes, data sharing and forwarding for underwater communication. Previous techniques used central authorities to determined node positions. The central authorities (e.g., base and master station) [14; 15; 16] are not capable of ensuring cross-validation of node positions and their selection due to the absence of an authentication mechanism. Moreover, several collateral problems occur when an unknown object is detected as a known object (such as location information privacy and reporting). The whole communication is exposed and disturb with bogus routing, as well as data gathering communication situations. Conventional underwater methodologies were not able to provide authentication and proper visualization of unexplored ocean areas in order to accommodate a wide range of underwater applications. For independent secure communication in terms of centralized authority, the use of BC underwater has been revolutionized in terms of localization, as well as end-to-end. The beacon nodes are used for localization with its trust value which is subsequently shared with adjacent nodes via BC technology to successfully perform the localization process. The solutions of waiting time (with different base loads), number of received packets and PDR are discussed in Section 7. Its preliminary results are also discussed. The solution of the identification of the surviving nodes and its impact is discussed in Section 7. The solutions of the number of rounds for energy exhausted, energy consumption for received packets and residual energy are discussed in Section 7. The simulation of the number of alive and dead nodes are also discussed in Section 7 respectively.
# GraphDBSCAN: Optimized DBSCAN for Noise-Resistant Community Detection in Graph Clustering
## 3 Proposed Method
### Identification and Removal of Noise Points  
Utilizing the optimal DBSCAN parameters (bestEps and minPts) determined in Section 3.3, we apply the DBSCAN algorithm to identify and remove noise points (outliers) from the graph, preparing it for subsequent community detection. This process leverages the compressed 5-dimensional spatial proximity matrix to classify nodes into core points, border points, and noise based on density thresholds. Core points, surrounded by at least minPts neighbors within distance bestEps, form the backbone of clusters, while border points lie within the Eps neighborhood of a core point but lack sufficient neighbors to be cores. Nodes failing these criteria are labeled as noise--typically low-density outliers that disrupt community structure--and are removed from the graph. This adaptive outlier detection, driven by KNN-optimized parameters, ensures precise identification of noisy nodes, enhancing the graph's density and coherence for the improved Newman-Girvan clustering in Section 3.5. By filtering out these outliers, our method mitigates their distorting effect on community boundaries, as demonstrated in our evaluation, thereby improving overall clustering accuracy and robustness.
# Evolution of System Embedded Optical Interconnect in Sub-Top-of-Rack Data Center Systems
## 4 ThunderValley2--Second-Generation Optically Enabled Data Center System
### Electro-Optical Midplane  
The electro-optical midplane design was a modified version of the standard 2U midplane, in which all high-speed electrical transmission lines had been removed and provision made for a separate fiber flexible circuit laminate to be attached to the reduced electronic midplane PCB (Figure 9a). Consequently, the number of electronic layers in the midplane PCB was reduced by 55% and the open area available for airflow increased by 20%. The fiber flexplane comprised separate right-hand and left-hand sections (Figure 9b), each providing connectivity to 12 of the 24 slots.  
The high-availability interconnect topology, in a passive dual-star configuration, required that each disk drive support two duplex data links situated on the midplane, one to each controller module. As a result, the midplane of the 24 SAS drive enclosure needed to support at least 48 duplex links (96 multimode OM3 fibers). To fully exploit the density advantages of optical interconnect, the midplane was designed for up to 96 duplex links (192 multimode OM3 fibers), thus providing for the future possibility of quad-lane drive interfaces (four independent duplex links per drive), for example through the deployment of PCIe drives, MultiLink SAS or Quad Ethernet interfaces. The midplane provided each drive with a separate, small, electrical connector for the purpose of supplying power and low-speed control signals with an optical midplane receptacle for high-speed SAS signals. The 192-fiber flexplane was terminated with four 2 \(\times\) 12 MT ferrules on the controller side and twenty-four 1 \(\times\) 12 MT ferrules on the drive side (of which only eight fibers per ferrule were populated). The flexplane was produced by TE Connectivity as part of the PhoxTroT project, and the optical link performance on all 192 fibers was measured using the same launch and measurement set-up described in Section 3.2 (Figure 9c). The optical  
Figure 8: Optically enabled data storage controller cards.  
link performance showed an average insertion loss of 0.32 dB with six fibers exhibiting abnormally high losses, due to partial damage during assembly. This shows that fiber, due to its maturity, is still far more suited for on-board interconnect than polymer waveguides and will be so for the foreseeable future.
# Energy Efficiency for 5G and Beyond 5G: Potential, Limitations, and Future Directions
## 5 Conclusions  
The exploration of energy efficiency solutions for 5G and beyond 5G networks has revealed a diverse range of innovative approaches, each with its own strengths and limitations. The surveyed research underscores the concerted effort to address these networks' escalating energy demands, driven by the surge in data traffic, device proliferation, and the integration of heterogeneous technologies. The proposed solutions leverage a spectrum of techniques, including sleep modes, traffic offloading, renewable energy integration, clustering, the hybridization of solutions, and sophisticated algorithms such as reinforcement learning and genetic algorithms.  
While these solutions offer promising avenues for energy conservation, they also underscore the inherent complexities and challenges in attaining optimal energy efficiency. The trade-offs between energy savings, Quality of Service (QoS), computational complexity, and infrastructure requirements necessitate careful consideration and adaptation to specific network scenarios. For instance, reinforcement learning-based approaches, while adaptive, require a learning phase that could impact immediate deployment. Similarly, certain algorithms, though effective, may exhibit high computational complexity, potentially hindering scalability in large-scale networks.  
The future trajectory of energy-efficient 5G and beyond 5G networks hinges on the continuous exploration and refinement of these solutions. Addressing limitations such as the learning phase in reinforcement learning, computational complexity of certain algorithms, and need for specialized infrastructure will be pivotal. The integration of online algorithms, which can adapt to real-time network dynamics, alongside clustering techniques, renewable energy sources, traffic offloading to lower consumption networks, and the hybridization of all these, presents a promising direction for further research. The dynamic clustering of base stations based on real-time traffic patterns and cooperation factors can enable more granular control over energy-saving mechanisms, while the incorporation of renewable energy sources can contribute to a greener and more sustainable network ecosystem.  
The successful realization of energy-efficient strategies will necessitate a holistic approach that considers the dynamic nature of networks, users' diverse and evolving demands, and the continuous advancements in technology. The pursuit of sustainable and high-performing networks will remain a central focus as we navigate the complexities of the ever-evolving telecommunications landscape. The convergence of cutting-edge research, real-world data-driven insights, and collaborative efforts across industry and academia will be instrumental in shaping the future of energy-efficient 5G and beyond 5G networks.
# UV System Architecture: Challenges and Evolution
## 2 UV System Architecture: Version 1
While using Arduino Mega or Leonardo greatly limits the number of relatively complex sensors and actuators, such as the camera, in our test, it was shown to be functional, and the architecture also allowed the Arduino board to be swapped out for one with greater capacity. In any case, it should be noted that more computing power implies greater energy consumption, so it is a trade-off between the two. Using a single processing device like Arduino involves limits, and one of these limits is the memory capacity. During the proof of concept for tasks of capturing and transmitting multimedia content (in this case, consecutive image captures), memory was an important limit. This limit forced lower image resolutions in order to not saturate the available  
Figure 2: Proof of concept for Architecture V1. memory. Although there was the possibility of expanding the memory with an SD card, for the process of capturing and storing the image on the SD card, it is necessary to have all the memory space to process it. Additionally, Zigbee was used in the same tests. These communications impose their own limit: in this case, it was the maximum payload allowed per message, which involved fragmenting the image into small packets. This is a common practice but it involves additional processing in the Arduino, as well as additional wait times. Taking the above factors into account, low-resolution images (160 \(\times\) 120 pixels) could be sent. It is always possible to overcome the limits imposed by each technology and hardware used. Communications can be replaced, Arduino-based boards can be replaced by more powerful ones with greater memory capacity, etc., but in all cases, there will also be limits imposed by each design and implementation decision, including the energy costs required. This is an example of the challenges involved in selecting the processing unit. In the case of sending, for example, multimedia data, the system presented up to this point does not have sufficient capabilities, so it is necessary to review and adapt the processing subsystem.