esa-sceva/satcom-chunk-collection huggingface dataset:
# LEO Constellation-Augmented Multi-GNSS for 3D Water Vapor Tomography
###### Abstract  
Global navigation satellite systems (GNSS) water vapor tomography is an important technique to obtain the three-dimensional distribution of atmospheric water vapor. The rapid development of low Earth orbit (LEO) constellations has led to a richer set of observations, which brings new expectations for water vapor tomography. This paper analyzes the influence of LEO constellation-augmented multi-GNSS(LCAMG)on the tomography, in terms of ray distribution, tomography accuracy, and horizontal resolution, by simulating LEO constellation data. The results show that after adding 288 LEO satellites to GNSS, the 30-min ray distribution effect of GNSS can be achieved in 10 min, which can effectively shorten the observation time by 66.7%. In the 10-min observation time, the non-repetitive effective observation value of LCAMG is 238 times that of GNSS, and the accuracy is 1.27% higher than that of GNSS. Compared with GNSS and the global positioning system (GPS), at a horizontal resolution of 13 \(\times\) 14, the proportion of empty voxels in LCAMG reduces by 5.22% and 22.53%, respectively.  
low Earth orbit (LEO); global navigation satellite system (GNSS); water vapor tomography; simultaneous iterative reconstruction techniques (SIRT)
###### Abstract  
Alkali-activated materials have attracted increasing interest owing to their excellent properties and environmental protection. However, there have been few studies on their creep properties. The aim of this article is to investigate the effect of the stress-strength ratio on the creep property of sodium silicate-based alkali-activated slag (AAS) concrete. For this reason, five groups of AAS concrete with different stress-strength ratios (0.15, 0.3, 0.45, 0.6, and 0.75) were tested. The results indicate that the creep of AAS concrete has a convergent nonlinear stage and a non-convergent stage but not an obvious linear stage. The AAS concrete basically has a consistent creep coefficient and diverse specific creep under a stress-strength ratio of 0.15-0.6. The elasticity modulus of AAS is much smaller than that of ordinary Portland cement (OPC) concrete, which is the reason for the greater creep compared to that in OPC concrete, and the inaccuracy of the model prediction. By applying the actual elastic modulus, the models can predict the specific creep and stress-dependent strain of AAS concrete with a 0.3 stress-strength ratio, except for the B3 model. The secant modulus of AAS concrete decreases linearly with an increase in the stress-strength ratio. Finally, we propose an improved creep model for AAS concrete with a wide stress-strength ratio based on the GL2000 model.  
alkali activated; slag; creep; stress-strength ratio; creep model +
Footnote †: journal: Applied  
_Article_  
Effect of Stress-Strength Ratio on Creep Property of Sodium Silicate-Based Alkali-Activated Slag Concrete
# Hierarchical Mobile Edge Computing Architecture Based on Context Awareness
## 4 Performance Evaluation
In addition, if the number of hierarchy layers increases, the number of MECS capable of delegating content migration operations and performing caching functions can be increased, so that the proposed CoHEC architecture can download content quickly, even if the number of users increases. However, the proposed CoHEC architecture works well by delegating the computing load to the upper layer. However, when the number of users that can be processed by MEC increases, the performance is diluted due to the transmission of the migration request message of the neighboring MECS and the delay time of the neighboring MECS search. Nevertheless, since the proposed CoHEC architecture can delegate the computing load to the upper layer, it can operate more efficiently than the existing MEC architecture. Figure 11 shows the energy consumption according to computing operation type. We conducted an experiment on the energy consumption required for content transmission, video transcoding, and image processing. To do this, we performed video transcoding with 240p and 1080p resolution and experimented with face recognition application to measure the energy consumption of complex computing tasks [24]. Since the existing mobile cloud architecture and the flat MEC architecture simply provide computing resources without consideration of service type, they rely on their computing resources, regardless of the complexity of computation, and consume a lot of energy, making efficient energy use impossible. On the other hand, each MECS performs the Edge Performance Scoring process based on the profile, so it is possible to provide suitable services to users. That is, each MECS can perform the service that is suitable to them through the Performance Scoring process, which results in efficient use of their resources and a quick response time to the users' requests. Figure 10: Average content download time. Figure 9: Average latency time. Finally, Theorem 1 shows the processing method of the computing task requested by the user according to each computing architecture. The proposed hierarchical architecture performs the task of sending a task/content migration message to select a MEC server in the upper hierarchy. When there are many users connected to the MEC servers, the proposed hierarchical architecture is more efficient than the existing MEC, cloud computing and Fog Computing architectures because it delegates the task to the upper layer. However, if all the resources of MEC servers located in the upper hierarchy are exhausted, the network overhead increases and delays occur because of the task delegation message transfer behavior. **Theorem 1**. : \[Cloud=Task_{User\ Request}/Computing\ Power_{Cloud}\]  
\[\mathit{MEC}=Task_{User\ Request}/Computing\ Power_{\mathit{MEC}}+Task_{Migration}/Computing\ Power_{Cloud}\]  
\[CoHEC=\mathit{MEC}_{task}+Task_{Migration}/Computing\ Power_{Neighboring MEC}\]
## 4 QDAR Mechanism Overview
### Assumptions  
The following assumptions apply:  
1. Nodes hold their own depth information and can embed it in the packets;
2. Nodes in UWSNs implement Source_initiated Query [23];
3. The sink node keeps the successful and failed communication record of the nodes.  
In the Source_initiated Query, if a source node has a packet to send to the sink, it first broadcasts a DATA_READY packet for both communication request and information collection. This phase is defined as the data_ready phase in this paper. During the data_ready phase, a node only forwards the packet if its depth is smaller than that of the previous node.  
Once the sink node receives this packet, it can decide if it is interested in such data. If so, the sink node decides the routing path according to the QDAR algorithm and sends out an INTEREST packet to the source node along the decided path for path construction. This phase is defined as the interest phase. In this phase, each node records its previous hop and takes it as the next hop in the packet forwarding phase. Only nodes selected by QDAR algorithm join in the INTEREST packet forwarding, while the others turn into sleep state thereafter or take responsibilities for another communication task to save energy or improve network capacity.  
After the INTEREST packet reaches the source node, the path is constructed. Then, the source node transmits the data packet through the constructed path. Finally, the sink node responds acknowledgement (ACK) packet after the successful data reception. If the source node receives ACK successfully, this communication episode concludes. The coming packets from the same source node are sent along the same path without repeated data collection and routing decision phases until transmission failure occurs.
# Modeling and Performance Analysis of mmWave and WiFi-Based Vehicle Communications
## 1 Introduction
### Contribution
Given their respective roles in automotive applications--WiFi with its ubiquity but limited bandwidth, and mmWave with its higher bandwidth but reduced range--this analysis is crucial for optimizing the application of these technologies in future vehicular communication frameworks.
## 5 Conclusions
### Welding Process  
The conducted research on the possibility of welding high-carbon nanostructured CFB steels allowed for the determination of the requirements for welding parameters in order to achieve the high-strength parameters of welded joints without weld metals:  
* Before welding processes, pre-heating should be used at a temperature close to that of the bainitic transformation. This prevents cold cracking and allows for control of the cooling process after the welding process is finished.
* Welding process parameters should be characterized by low-input energy, which results in more advantageous structural changes in the HAZ, less blocky austenite in the weld (fusion zone and high-temperature affected-zone), and higher strength compared to high-input energy processes.
* In order to obtain high mechanical parameters, comparable to the base material, heat treatment should be performed after the welding process which will reconstruct the NB CFB structure in the welded joint. Regeneration should be designed similar to the previously conducted heat treatment on the base material. It should be noted that an appropriate regeneration design should be based on dilatometric research, because the regeneration technique requires the determination of real phases of transformation temperatures and times.
* Long-term regeneration can be reduced by introducing deformation into the FZ, HHAZ, and grain refinement. However, the implementation of these methods requires further research and the design of appropriate equipment.
* The highest mechanical properties of welded joints can be obtained after welding in the delivery (softened) state, and then after the conducted isothermal heat treatment that is aimed at obtaining a nanocrystalline structure. There are no problems with the precipitation of cementite in the heat-affected zone due to the complete recrystallization of the weld and the base material. However,this requires an additional technological process. In addition, due to the fact of their dimensions, not all welded constructions can be heat treated.
# Real-Time Evaluation of the Improved Eagle Strategy Model in the Internet of Things
## 6 Conclusions  
This research introduced the Improved Eagle Strategy (IES), a novel hybrid method for optimizing service selection in cloud-based environments, with promising results regarding both functional and non-functional requirements. While the approach demonstrates superiority over traditional bio-inspired methods, its potential extends beyond the immediate findings.  
Future applications of the IES could explore its integration into other dynamic and critical IoT-driven environments, such as healthcare, smart cities, and autonomous systems. By adapting the model to sector-specific requirements, the IES could address unique challenges, such as real-time decision-making in life-critical applications or efficient resource management in large-scale IoT ecosystems.  
Additionally, future advancements in this research could focus on incorporating machine learning techniques to enhance the adaptability of the IES in handling more complex, large-scale service compositions. Exploring the integration of reinforcement learning or other bio-inspired algorithms may further improve its accuracy and speed in varying scenarios.  
Security enhancements within the IES framework are also critical, given the growing concerns regarding the data privacy and integrity of IoT networks. Developing a more robust, secure infrastructure will ensure that the method is sustainable in environments requiring stringent data protection.  
In conclusion, the contributions of this research lay a solid foundation for further advancements, positioning the IES as a flexible and adaptive tool with broad applicability across industries. The continuous development of this approach could significantly contribute to the evolution of IoT services, facilitating the implementation of more efficient, secure, and scalable service-oriented architectures.  
[PERSON] contributed to the conceptualization of the study and the design of experiments. During the testing phase, [PERSON] actively participated in conducting experiments, ensuring the accuracy of data collection, and managing the experimental procedures. Additionally, [PERSON] was involved in the analysis of data, interpretation of results, and drafting of the manuscript. [PERSON] played a significant role in the testing phase of the research and provided valuable insights during the final review of the manuscript. [PERSON]'s contributions included conducting experiments, analyzing data, providing critical feedback on the manuscript drafts, and ensuring the accuracy and clarity of the final version. All authors have read and agreed to the published version of the manuscript.  
The APC was funded by Multimedia University, Malaysia.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflict of interest.
# A Real Case of Implementation of the Future 5G City
## 3 Future 5G City Use Cases
### The 5G Project Use Cases
#### 3.2.5 Video Surveillance  
One goal is to create a capillary video surveillance system in a municipal territory: every point at which a power supply is available becomes a possible installation point for a camera. The high throughput of 5G technology enables the use of ultra-high-resolution cameras, and their control/management can be performed remotely in real time. The dynamic use of non-sanctioning car-plate visual control will allow for a more widespread and targeted control over the territory. At the same time, together with the use of thermal cameras, the abandonment of hazardous waste can be monitored generating alerts with the exact position of the area.
## 2 Establishment of a Simulation Model of the Cable Terminal Sheath Structure
### Defining Material Parameters and Boundary Conditions  
After establishing the geometric model, it is necessary to configure the electric field-related electrical parameters of each material, including the relative dielectric constant \(\varepsilon\) and conductivity \(\sigma\). COMSOL software users can add empty materials by themselves, and can then configure the parameters of the relevant materials. The umbrella skirt and insulating sheath are made from the same silicone rubber material, and the cable core and shielding layer are made from copper. The electrical parameters of the simulation model are set as shown in Table 1.  
The current field of the COMSOL software defines the boundary conditions by adding the terminals, and since the normal operating voltage of an electric locomotive is 25 kV, a voltage value with an effective value of 25 kV is applied to the fittings at the top of the cable termination. The frequency is defined as 50 Hz in the study, and the flange and the shielding at the bottom of the cable termination are set as being grounded.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Name of Material** & **Conductivity (S/m)** & **Relative Permitivity** \\ \hline hardware & \(1\times 10^{7}\) & \(1\times 10^{50}\) \\ copper & \(5.998\times 10^{7}\) & 1 \\ semiconductor layer & 2 & 100 \\ lagging & \(1\times 10^{-15}\) & 3.5 \\ stress pipe & \(1\times 10^{-8}\) & 30 \\ heat-shrink tubing & \(1\times 10^{-12}\) & 3.34 \\ PE layer & \(5\times 10^{-12}\) & 3.07 \\ silicone rubber & \(5\times 10^{-12}\) & 3.34 \\ filler glue & \(5\times 10^{-9}\) & 7 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Electrical parameter settings of the model.  
Figure 2: Geometric model of the cable’s terminal-insulating sheath structure.
# Mixed-Carrier Communication for Technology Division Multiplexing
## 3 Complexity Analysis  
An essential feature of MCC is its unique transmitter and receiver design. As illustrated in Figure 1, the transmitter has more blocks compared to conventional ACO-OFDM in order to transmit more bits for low-end receivers. For the ACO-OFDM waveform generation, the largest computational complexity is associated with the IFFT block. According to [16], a radix-2 FFT/FFT algorithm of length \(N\) requires \(N\log_{2}(N)\) complex additions, \(A_{c}(N)\), and \(N/2\log_{2}(N-N+1)\) complex multiplications, \(M_{c}(N)\). A complex multiplication can be implemented by four real multiplications, \(M_{r}(N)\) and two real additions, \(A_{r}(N)\) while a complex addition includes two real additions. Therefore, an IFFT/FFT algorithm requires real multiplication operations of \(2N\log_{2}(N)-4N+4\) and real addition operations of \(3N\log_{2}(N)-2N+2\). Thus, the IFFT/FFT computational complexity is dominated by \(\mathcal{O}(\log(N))\). According to Equation (2), \(L_{2}+1\) additions and \(L_{2}\) multiplications are required to calculate \(T_{H}(n)\) for \(L_{2}\) beacon samples, while the arithmetic complexity of obtaining the minimum or maximum sample is \(\mathcal{O}(L_{2})\). As for the PWM-sampled BPM envelope generation based on Equation (8), \(L_{1}\) multiplications and \(L_{1}-1\) additions are needed. To  
Figure 4: BPM receiver.  
Figure 3: BnPSK receiver.  
Figure 2: MCC receiver.  
embed the PWM dimming control duty cycles, two additions and one multiplication are required as in Equation (11). Subsequently, the transmitter complexity is dominated by \(\mathcal{O}(\max(L_{2},L_{1},\log(N)))\).  
Similarly, the high-speed receiver is dominated by the FFT block arithmetic complexity, \(\mathcal{O}(\log(N))\). The interesting fact about the MCC high-end receiver is that it is being built upon the same building blocks of a conventional ACO-OFDM receiver due to the half-wave symmetry nature of ACO-OFDM, i.e., \(x_{\text{ACO-OFDM}}(n)=-x_{\text{ACO-OFDM}}(\frac{N}{2}+n-1)\), as mentioned in the previous section. Thus, the output of an FFT of the NACO-OFDM symbols the same as an FFT of the original symbols [22]. Additionally, the DC-bias only impacts the DC sub-carrier which is not actively carrying any data. As for the BPM receiver, its arithmetic complexity is dominated by the energy detector \(L_{1}\) multiplication operations and obtaining the maximum energy which requires an arithmetic complexity of \(\mathcal{O}(L_{1})\). Additionally, a BnPSK receiver requires \(L_{2}\) multiplications and integration operations, as well as obtaining the maximum value after \(L_{2}\) integrator and dump blocks. Consequently, the high-speed receiver arithmetic complexity is \(\mathcal{O}(\log(N))\), while, for the low-speed receivers, it is \(\mathcal{O}(max(L_{1},L_{2}))\). In the following Table 3, the computational complexity of the transmitter and receiver are summarized.
# Shadow Fleets: A Growing Challenge in Global
## 4 Approaches to Detecting Dark and Gray Fleet Characteristics
### Unusual Behavior  
Dark fleet vessels may display behavior patterns that deviate from the norm, including operating in restricted areas, shunning designated ports or coastlines, or conducting suspicious meetings with other vessels at sea. Ship-to-ship (STS) transfers have become a key tactic in dark fleet operations. While STS transfers are a legitimate practice in the oil trade, their frequency and locations have changed significantly in response to sanctions, often occurring in international waters (waters beyond national jurisdiction as defined under UNCLOS) to avoid regulatory scrutiny [43; 44; 45; 46]. Such atypical actions are key indicators of involvement in covert operations. Regulatory bodies have increasingly focused on these risky STS operations, recognizing them as a critical weak point in sanctions enforcement. The proliferation of these transfers in areas like the Mediterranean and Southeast Asian waters has raised concerns about both safety and legal compliance [47].  
These vessels often avoid well-monitored routes and ports to evade scrutiny, opting instead for remote areas where oversight is minimal. Meetings between vessels at sea, especially in international waters, can facilitate the transfer of illegal cargo, including drugs, weapons, and sanctioned goods, without detection. Such rendezvous are challenging to monitor and regulate, requiring surveillance from air and space, as well as coordination between different countries' naval and coastguard services.
# HCLE: Hybrid Clustering Approach for Extending WBAN Lifetime
## 4 Materials and Methods
### HCEL Routing Protocol
#### 4.2.3 Data Transmission  
After the parent node selection and network organization, the WBAN enters the data transmission phase. This phase considers the specific requirements of one-hop sensor nodes, which transmit their critical data directly to the sink. Meanwhile, two-hop sensor nodes transmit their packets to the chosen parent node before reaching the sink node.  
After each round of data transmission, the two-hop sensor nodes send their updated residual energy (RE) and RSSI values to the sink. This information is used to update the cost function calculations and, consequently, to select the parent nodes. During this process, the sink actively monitors the residual energy level of the parent node. If the parent node's residual energy reaches a threshold (\(Th\)), the sink temporarily suspends its involvement in data forwarding until the residual energy of all two-hop sensor nodes reaches the threshold (\(Th\)). At that moment, the sink node selects another two-hop sensor node as a parent node. This ensures that no individual parent node becomes excessively drained of energy, maintaining a balanced traffic load distribution and prolonging the overall network stability.  
Upon reaching the threshold energy level for all two-hop sensor nodes, a new round of parent node selection is initiated. At this stage, all two-hop sensor nodes are eligible to serve as parent nodes and participate in data forwarding. This approach promotes fairness by giving each two-hop sensor node an opportunity to contribute as a parent node, which in turn extends the lifetime of the network as a whole. The sequential steps and the pseudo-code of the data transmission phase are shown in Figure 3 and Algorithm 2, respectively.  
Figure 2: Flowchart of the parent node selection process.  
```
0:\(N\) (Number of nodes), \(R\) (Number of rounds), \(PN[1...N]\) (parent node), \(E[1...N]\) (initial node energy), \(E_{tx}\) (transmitted energy consumption as in Equation (1), \(E_{rx}\) (received energy consumption as in Equation (2), one-hop nodes' id.
0: Transmission of all node packets to the sink (single-hop, two-hop)
1:for\(round=1\) to \(R\)do
2:for\(i=1\) to \(N\)do
3:if\(i\) in one-hop nodes' id then\(\triangleright\) Critical Packet
4:if\(E[i]>0\)then\(\triangleright\) Enough Energy
5: Send packet directly to the sink
6:\(E[i]=E[i]-E_{tx}\)
7:else\(\triangleright\) Not Enough Energy
8: Cannot send the packet
9:endif
10:else\(\triangleright\) Normal Packet
11:if\(E[i]>0\)then\(\triangleright\) Enough Energy
12: Send packet to \(PN[i]\)
13:\(E[i]=E[i]-E_{tx}\)
14:\(E[PN[i]]=E[PN[i]]-E_{rx}\)
15: Send packet from \(PN[i]\) to the sink
16:\(E[PN[i]]=E[PN[i]]-E_{tx}\)
17:else\(\triangleright\) Not Enough Energy
18: Cannot send the packet
19:endif
20:endif
21:endfor
22:endfor
```  
**Algorithm 2** Data Transmission  
Figure 3: Sequence diagram of the data transmission phase.