esa-sceva/satcom-chunk-collection huggingface dataset:
# Leveraging Blockchain Technology for Secure 5G Offloading Processes +
## 3 Blockchain-Based Secure 5G Offloading Mechanism
### Blockchain for a Secure Marketplace  
As already mentioned in Section 3, the offloading process in 5G networks involves the transfer of processing tasks and data from the 5G network to another one. This process is essential to improve the efficiency and capacity of the 5G network, especially in an environment where the demand for data and the number of connected devices are constantly increasing. In this context, a marketplace offers multiple benefits and is necessary for several reasons [24; 98]:  
* Resource optimization: A marketplace allows operators to select the best available options for offloading (i.e., right capacity, optimal location, lower costs, etc.). By having access to a variety of options, operators can make informed decisions that maximize performance and minimize costs.
* Flexibility: A marketplace allows operators to adapt to fluctuations in data and processing demand. This is crucial in situations where workloads can vary significantly (i.e., mass events).
* Competition: A marketplace promotes competition among operators as other operators can compare prices, which incentivizes providers to improve their offerings and reduce costs. This healthy competition results in better services and lower prices.  
* Ease of integration: Operators can access a centralized platform where they can find and offloading services without the need for complex individual negotiations, improving operational efficiency.
* Innovation: The presence of a marketplace encourages the development of new technologies and solutions as operators have an incentive to develop and offer new capabilities and improvements (i.e., better energy efficiency) to attract more users.
* Improved satisfaction: A marketplace allows the definition of filters in order to make the operators to select services that best meet their performance, cost, and location requirements, resulting in improved satisfaction.  
In summary, a marketplace in the 5G offloading process is crucial for optimizing resources, enhancing flexibility, promoting competition, facilitating integration, fostering innovation, and improving satisfaction. These benefits can even be amplified if Blockchain technology is considered as the backbone technology of the marketplace, as security and transparency are also enhanced by the design. As already mentioned in Section 2.1, improvements are even greater in terms of security, scalability, and performance when considering a permissioned Blockchain network such as Hyperledger Fabric. In addition, the use of Blockchain also allows different operators to communicate indirectly with each other without the need for prior communication.
# A Method to Reduce Non-Nominal Troposphere Error
###### Abstract  
Under abnormal troposphere, Ground-Based Augmentation System (GBAS) is unable to eliminate troposphere delay, resulting in non-nominal troposphere error. This paper analyzes the troposphere meteorological data of eight International GNSS Monitoring Assessment System (iGMAS) stations and 10 International GNSS Service (IGS) stations in China and records the most serious conditions during 2015 and 2016. Simulations show that the average increase in Vertical Protection Level (VPL) of all visible satellites under non-nominal troposphere is 2.32 m and that more satellites increase the VPL. To improve GBAS integrity, this paper proposes a satellite selection method to reduce the non-nominal troposphere error. First, the number of satellites in the optimal subset is determined to be 16 based on the relationship among VPL, non-nominal troposphere error and satellite geometry. Second, the distributions of the optimal satellites are determined. Finally, optimal satellites are selected in different elevation ranges. Results show that the average VPL increase caused by non-nominal troposphere error is 1.15 m using the proposed method. Compared with the brute method and greedy method, the running rate of the proposed method is improved by 390.91% and 111.65%, respectively. In summary, the proposed method balances the satellite geometry and non-nominal troposphere error while minimizing the VPL and improving the running rate.  
GBAS; troposphere delay; non-nominal troposphere; VPL; satellite geometry +
Footnote †: journal: Sensors
# Modeling and Analysis of Staged Constellation Deployment from a Single-Unit System
## 2 Defining the Deployment Problem
### Disengagement Sequence  
Through \(k_{max}\) successive separation events at perigee passes, the system transitions from a many-in-one satellite to a fully deployed constellation. At each deployment stage \(k\), every satellite from the previous stage undergoes a disengagement maneuver, increasing the total number of satellites to \(2^{k}\) until the final configuration of \(n\) satellites is reached. Each phase includes controlled phasing via differential drag control for constellation formation. Figure 1 illustrates a three-stage sequence, whilst the disengagement process is outlined as follows:  
1. Initial Deployment: The satellite system is deployed as a single unit made up of \(n\) satellites.
2. First Disengagement (\(k=1\)): Upon reaching the first perigee following the disengagement command, the primary unit undergoes a disengagement maneuver. This process results in the formation of two distinct sub-units.
3. First Sub-Unit Phase: Following the initial disengagement, the two newly formed sub-units undergo a controlled phasing process, gradually achieving a predefined in-plane separation angle.
4. Subsequent Disengagement Sequences (\(k=2,\ 3,\ \ldots,\ k_{max}\)): Following the phasing process and at the subsequent perigee pass, each satellite from the previous stage undergoes a disengagement maneuver, progressively increasing the total number of orbiting satellites to \(2^{k}\). Following each disengagement, each sub-unit is phased to a predefined in-plane separation angle.
5. Final Disengagement at (\(k=k_{max}\)): At the last deployment stage, each of the satellites from the previous phase undergoes a final separation, completing the deployment and reaching the total constellation size \(n\).
6. Final Phasing and Constellation Formation: With all \(n\) satellites deployed, one is designated as the reference unit, while the remaining \(n-1\) satellites are phased into their final constellation configuration using differential drag control to achieve the desired spatial distribution with respect to the designated reference.  
Figure 1: Multi-stage satellite deployment with \(k_{max}=3\).
## 4 Results and Discussion  
### Analysis of Measured Flow Method  
The researchers continuously observed CBM extraction of each group for 110 days and plotted observation results on the coordinate chart with extraction time as the abscissa and the daily pure volume as the ordinate. According to Equation (10), the variation law of the CBM extraction volume \(q_{t}\) with extraction time is fitted, and then initial CBM extraction volume \(q_{0}\) and attenuation coefficient \(\alpha\) are obtained.  
\[q_{t}=q_{0}e^{-\alpha t} \tag{10}\]  
Based on initial CBM extraction volume and attenuation coefficients obtained from the Equation (10), cumulative CBM extraction volume \(Q_{t}\) in each group at different extraction time is obtained by Equation (11), as shown in Figure 7.  
\[Q_{t}=\frac{q_{0}}{\alpha}\big{(}1-e^{-\alpha t}\big{)} \tag{11}\]  
Equation (12) is used to calculate the extraction ratio from target coalbeds 6u, 6m and 6l (\(\eta_{6u}\), \(\eta_{6m}\), \(\eta_{6l}\)) in different zones at different extraction times. The calculation results are shown in Figure 8.  
\[\left\{\begin{array}{l}\eta_{6u}=\dfrac{1}{n}\sum\limits_{i=1}^{n}\left( \dfrac{Q_{t2}-Q_{t3}}{Q_{t1}}\right)_{i}\\ \eta_{6m}=\dfrac{1}{n}\sum\limits_{i=1}^{n}\left(\dfrac{Q_{t3}}{Q_{t1}}\right) _{i}\\ \eta_{6l}=\dfrac{1}{n}\sum\limits_{i=1}^{n}\left(\dfrac{Q_{t4}}{Q_{t1}}\right) _{i}\end{array}\right. \tag{12}\]  
In the equation, \(n\) is the number of zones, \(n=2\); \(Q_{t1}\), \(Q_{t2}\), \(Q_{t3}\) and \(Q_{t4}\) are the CBM extraction volumes of the 1 st, 2 nd, 3 rd and 4 th groups at different extraction times, respectively.  
It can be observed from Figure 8 that the extraction ratio changes with the extraction time increase. For the 6u coalbed, the extraction ratio in zone A, zone B and their arithmetic means both show a linear increasing trend. For the 6m coalbed, the extraction ratio in zone A follows a stable trend, while the ratio in zone B and their arithmetic means both show a linear decreasing trend. For the 6l coalbed, the extraction ratio in zone A, zone B and their arithmetic means both show a linear decreasing trend.  
Based on the variation trend of the extraction time in the first 110 days, it can be expected that the data of zone A and zone B will converge after a certain subsequent extraction time.  
Figure 6: Arrangement of sampling boreholes.  
Figure 7: _Cont._
# Gas-Liquid Mixability Study in a Jet-Stirred Tank for Mineral Flotation
## 3 Results and Discussion
### Influence of Nozzle Height on Mixing Uniformity  
To investigate the effect of nozzle height on the gas-liquid mixing homogeneity of the jet-stirred tank, five different nozzle heights are explored: 40 mm, 60 mm, 80 mm, 100 mm, and 120 mm from the bottom surface of the vessel body. The nozzles in these models are oriented circumferentially, each with a nozzle radius of 5 mm. Figure 16 shows the contours of gas volume fraction distribution for different nozzle heights.  
Figure 14: The contour of gas volume fraction distribution for different nozzle directions.  
Figure 13: Geometric modelling of structural parameters (**a**) nozzle direction; (**b**) nozzle height; (**c**) nozzle inner diameter; (**d**) nozzle radius ratio.  
Additionally, to illustrate the variation in the gas volume fraction under different operational conditions, the gas volume fraction profile concerning height is depicted in Figure 17. From this plot, it is evident that the gradient of the gas volume fraction is less pronounced at nozzle heights of 80 mm and 100 mm from the tank bottom. This observation suggests a relatively minor variation in the gas volume fraction under these specific conditions.  
Figure 16: The contour of gas volume fraction distribution for different nozzle heights.  
Figure 17: Gas volume fraction curve with height for different nozzle heights; **(a)** all working conditions; **(b)** local zoom in grey area.  
Figure 15: Gas volume fraction curve with height for different nozzle directions; **(a)** all working conditions; **(b)** local zoom in grey area.  
Table 9 presents the mean, variance, and dimensionless variance of the gas volume fraction in the ZY plane at various nozzle heights. It is evident from the table that at a nozzle height (H) of 80 mm, both the variance and dimensionless variance are minimized, indicating the most uniform distribution of the gas volume fraction. Considering all factors, the condition with H at 80 mm appears to be the most favorable. This is significant because achieving the most uniform gas distribution at this nozzle height enhances the efficiency of the flotation process. The improved gas-liquid mixing uniformity at 80 mm contributes to more effective mineral separation, leading to higher recovery rates and better overall flotation performance.
# A Framework to Model Bursty Electronic Data Interchange Messages for Queueing Systems +
## 1 Introduction
### Background and Previous Work
#### 1.1.1 Performance of Message Queueing Systems  
LinkedIn wanted to move away from batch-oriented systems towards real-time publish-subscribe systems. Their requirement was a system capable of processing 10 billion messages per day, with demand peaking at 172,000 messages per second. Initially, they considered ActiveMQ, but decided to develop Kafka [12], which was subsequently made open source.  
The performance of Kafka was studied, and the impact of tuning specific parameters was considered [13]. The study found that performance was variable, depending on both system infrastructure and messages processed. A correlation between packet sizes and sending intervals was also observed.  
The performance of the IBM MQ JMS Server was also undertaken [14]. This study considered the study of the capacity of the system, considering filters, message sizes and number of publishers, subscribers and topics. Message size influenced server performance, in terms of both message and data throughput. Likewise, message replication grade and the number of filters used also influenced server capacity. On the other hand, the number of topics had limited impact on capacity.
## 3 Applying Bioinspired Networking
### Time Synchronization  
A common clock reference is essential for sensing applications for reasons such as an accurate time-stamping of sampled data. Somewhat unfortunate, time synchronization is one of the most difficult problems that an engineer of a distributed system will encounter [3].  
Prior effort in time synchronization over multi-hop wireless sensor networks has mainly focused on centralized solutions using beacon signals as timing references. However, centralized solutions do not scale well and for very large-scale, distributed networks the use of a central timing references becomes impractical. Another important aspect of time synchronization is the possibility to synchronize the transmission and reception phase of nodes and hereby reducing the energy consumption by minimizing the idle listening period of the nodes. Finally, the idea is to optimize the energy-efficiency for periodic data gathering in sensor networks by a strict control of the network timing.  
[PERSON] and [PERSON] [21] demonstrated a distributed synchronization by using nearest neighbor communication. The mechanism was inspired by natural synchronization in colonies of fireflies and was implemented in an embedded wireless network. Similarly, [PERSON] and [PERSON] [22] reported on a distributed clock synchronization. The authors pointed out that synchronization is not always obtained in a network because normal nodes are \"deaf\" while transmitting and hence cannot always receive synchronization messages from reference nodes.  
[PERSON] _et al._[35] reported on the study of a decentralized time synchronization based on the Reachback Firefly algorithm. The algorithm was implemented in a sensor testbed and results were compared with simulations. In their work, the authors were able to apply and evaluate realistic effects of radio wave communication to the bioinspired synchronization mechanism and to provide a robust time-synchronized networked system.  
The foreseeable scale of future networked sensor systems is encouraging scientists and engineers to use asynchronous methods, mimicking the biological systems, for the establishment of clock references. Instead of assuming that the network system is in a well-defined state at any point in time one must instead rely on a distributed system design that rely on probability.
## 2 Finite Element Model of Micro-Cutting  
### Geometric Model of Micro-Cutting  
The commercial software, Abaqus/Explicit, was used to establish an orthogonal micro-cutting model to accurately analyze the effect of J-C constitutive parameters on the formation mechanism of Ti6 Al4V serrated chips. The model adopts temperature-displacement coupling in an eight-node element. Temperature-displacement coupling, a four-node element, was used by the tool model. In general, the cutting tool is rigid due to the significant hardness between the tool and working materials [57]. To balance the accuracy and efficiency of the model, the Lagrangian meshing technique used in micro-cutting models and local mesh refinement is required to reproduce the serrated chip better and decrease calculation time. The workpiece in the micro-cutting model is divided into two parts: the upper part that mainly corresponds to the chip layer and a grid with dimensions of 1 \(\upmu\)m \(\times\) 1 \(\upmu\)m \(\times\) 1 \(\upmu\)m. The grid size decreases gradually and increases away from the cutting layer. All material properties of the workpiece are identical. The rake angle of the tool is 10\({}^{\circ}\), the flank angle is 6\({}^{\circ}\), and the radius of cutting wedge is 1 \(\upmu\)m. The micro-cutting simulation geometry model of Ti6 Al4V is shown in Figure 1.  
Boundary constraints correspond to the bottom and sides of the workpiece; the \(Z\) and \(Y\) directions of the tool are fixed. The degree of freedom in the \(X\)-direction of the tool is retained such that the tool can move in the \(X\)-direction. Simultaneously, the rake face of the tool is defined as the main contact surface, and the workpiece is defined as the slave contact surface. Therefore, the grid of the rake surface of the tool must exceed the grid size of the workpiece. The initial temperature of the entire model is set to 25 \({}^{\circ}\)C. Notably, compared with traditional machining parameters, the amount of material removal in the micro-cutting process is less, which leads to the redistribution of thermal softening and hardening effects in the micro-cutting process, the influence of the J-C constitutive parameters on serrated chips is not entirely similar to traditional cutting. Although the tool radius have a certain effect on the results, this article focuses on the analysis of the influence of the JC parameters on the constant tool radius. Therefore, the effect of size on the serrated chip was not considered in the simulation.
# Corn Plant In-Row Distance Analysis Based on Unmanned Aerial Vehicle Imagery and Row-Unit Dynamics
## Materials and Methods
### Methodology for Corn Plant Detection and Spacing Estimation Based on UAV Orthomosaic
\begin{table}
\begin{tabular}{c c} \hline \hline
**Metrics** & **Equation** \\ \hline Accuracy & \(\frac{TP+TN}{TP+TN+FP+FN}\) \\ Recall & \(\frac{TP}{TP+FN}\) \\ Precision & \(\frac{TP}{TP+FP}\) \\ F1 score & \(\frac{2\cdot(Recall\cdot Precision)}{Recall+Precision}\) \\ \hline \hline \end{tabular} Note: \(TP\) is a true positive and correctly detected plant; \(TN\) is a true negative, a non-plant accurately detected as the ‘other’ class; \(FP\) represents the ‘other’ class detected as a plant; and \(FN\) is a missed plant.  
\end{table} 
Table 2: Evaluation metrics for model performance. the distance between the nearest connected components exceeded 75 cm (interrow space), the connection to the nearest remaining plant was skipped. Consequently, polylines were formed within the rows, linking nearby plants. These polylines were extended until they intersected with the nearest line. Connected polylines present the row lines and serve to ensure accurate plant detection. The row lines were intersected with the prior plant-detected positions, which filter out plants located within interrow spacing (Figure 3). We further analyzed created polylines to identify plant spacings.
# Genesis and Magmatic Evolution of the Gejiu Complex in Southeastern Yunnan, China
## 5 Discussion  
### Fractional Crystallization and Mineralogical Evolution of the Gejiu Granites  
In the Harker variation diagrams, the granites exhibit distinct linear trends (Figure 7). These trends may result from various processes, including closed-system behavior, fractional crystallization, partial melting, and magma mixing [37]. Closed-system behavior refers to a geological system that is chemically isolated from its surroundings, meaning that elements are neither lost nor gained within the system. The linear decreases in TiO\({}_{2}\), CaO, P\({}_{2}\)O\({}_{5}\), Fe\({}_{2}\)O\({}_{3}\)T, and Al\({}_{2}\)O\({}_{3}\) with increasing SiO\({}_{2}\) content rule out the influence of closed-system behavior [38].  
Additionally, the flat REE distribution patterns, strong negative Eu anomalies, and low total rare earth element (REE) abundances in the synegogranites suggest plagioclase crystallization in the magma source region. As the geochemical characteristics of the rocks in the previous text show, the significant depletion of Ba, Nb, P, and Ti indicates that magmatic differentiation played a dominant role in the formation of the granite. Further crystallization vector studies suggest that plagioclase, potassium feldspar, amphibole, homblende, apatite, and monazite played important roles in the magmatic evolution process. Therefore, fractional crystallization is likely the primary petrogenetic process governing magma evolution, and the granites likely formed through fractional crystallization during magmatic evolution.  
The variations in major elements can be attributed to a two-stage fractional crystallization process in the Gejiu granite. Whole-rock geochemical indicators (e.g., Nb/Ta, Zr/Hf) (Figure 8b,c) reveal that the magmatic evolution progressed from lowly evolved porphyritic granite to moderately evolved synegoranite, K-feldspar granite, and equigranular biotite granite, culminating in highly evolved equigranular biotite monzogranite (Figure 7). The Sr/Y ratio initially increased and then sharply decreased, confirming a two-stage fractional crystallization process dominated by amphibole and feldspar (Figure 8a). In the Harker diagrams, Al\({}_{2}\)O\({}_{3}\), TiO\({}_{2}\), Fe\({}_{2}\)O\({}_{3}\), MgO, MnO, CaO, and P\({}_{2}\)O\({}_{5}\) can be seen to decrease with increasing SiO\({}_{2}\), while Na\({}_{2}\)O remains nearly constant, indicating intense fractional crystallization or metasomatic processes. This suggests the fractional crystallization of mafic minerals, plagioclase, Ti-Fe oxides, and apatite. The presence of varying negative Eu anomalies in the chondrite-normalized REE patterns further supports this interpretation (Figure 6a). [PERSON] et al. [17] suggested that these features reflect the highly fractionated nature of I-type granite, resulting from the differentiation of apatite, monazite, and Ti-bearing minerals.  
Figure 7: Harker diagram for Gejiu granites (the dotted line represents the turning point of the evolution trend, the arrow indicates the direction of evolution).
# Space Debris In-Orbit Detection with Commercial LiDAR Sensors
###### Abstract  
This article presents an alternative approach to detecting and mapping space debris in low Earth orbit by utilizing commercially available automotive LiDAR sensors mounted on CubeSats. The main objective is to leverage the compact size, low weight, and minimal power consumption of these sensors to create a \"Large Cosmic LiDAR\" (LCL) system. This LCL system would operate similarly to a giant radar circling the Earth, with strategically positioned LiDAR sensors along the target orbit. The article examines the feasibility of this concept by analyzing the relative orbital velocity between the sensor and debris objects, and calculating the time required to scan a complete orbit.  
space debris; LiDAR; mapping; LASER; sensors +
Footnote †: journal: Sensors  
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