esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusion  
1. In this study, seabed silt was tested as a potential raw material for artificial reefs. The SAR specimens were composed purely of seabed silt and cement in four proportions. The compressive strength development and nutrient dissolution were examined in the SAR specimens. The hydration products of the SAR paste were investigated through XRD, SEM, and DSC techniques. The compressive strength of the SAR specimen was between that of C15 and C20, which was inversely proportional to their seabed silt content. The SAR specimens were able to continuously dissolve nitrogen-based nutrients, which is conducive to the improvement of primary productivity in the surrounding water. However, the effect on the N/P ratio in the surrounding water depends on the conditions of the seabed silt itself.
2. The results of compressive strength and microstructure tests showed that the strength of the specimens increased continuously during the hydration process of the mixture of seabed silt and cement. The ettrinigite became interlaced with C-S-H gel to form a compact structure, but the main hydration products of the SAR paste were SiO\({}_{2}\) and CaCO\({}_{3}\). Ca(OH)\({}_{2}\) was not detected in the SAR specimens, which may help to improve its adhesion and biological effects. This suggests that seabed silt can be partially mixed into AARs as a raw material.
3. The potential bioadhesion effect of seabed silt and cement mixture needs further experimental verification, especially the influence of interaction between microorganisms and concrete materials. In addition, seabed silt may also contain heavy metals and other organic materials. Whether these substances will be dissolved out or influence the attachment and growth of algae also needs further study.  
Figure 9: Differential scanning calorimetry (DSC) curves of SAR specimens at 28 days. (**a**) M2 paste, (**b**) M4 paste, (**c**) M6 paste, (**d**) M8 paste.  
**Author Contributions:** Conceptualization, Z.J. and Z.L.; methodology, J.Z.; software, Z.N.; validation, Y.C.; formal analysis, L.Z.; investigation, Z.G.; resources, Z.L.; data curation, J.Z.; writing--original draft preparation, Z.J.; writing--review and editing, Z.J.; visualization, W.C.; supervision, Z.L.; project administration, Z.J.; funding acquisition, Y.C. and Z.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Natural Science Foundation of China, grant number 41771544 and the Fundamental Research Funds for the Central Universities, grant number 2019 ZRJC006.  
**Acknowledgments:** The authors are grateful to [PERSON], [PERSON], and [PERSON] for technical assistance and discussions.  
**Conflicts of Interest:** The authors declare no financial and personal relationships with other people or organizations that can inappropriately influence our work, and there is no professional or other personal interest of any nature or kind in any product, service and/or company that could be construed as influencing the position presented in, or the review of, the manuscript entitled.
# Real-Time Circuit Breaker Availability Assessment in the Transmission Network
## 4 Input Variables of Decision Making and Conclusion Process Based on Fuzzy Regulator
### Circuit Breaker Alarm State  
The fourth input variable of the system is the state of a breaker alarm. To determine the availability of a breaker or the existence of an alarm state on the breaker, it is important to determine the alarm status indexes. The first input variable of the system constitutes the value defined by shifts in the breaker state in the network unit. Three states can appear:  
* Unchanged switch state: no alarm indicating the breaker is not ready;
* Intermediate position: changes in switching the state of a breaker requiring verification;
* Unavailability of the switch to operate: the alarm state indicates unavailability.  
Figure 5 shows the MF for the breaker input state on a network element. In the case of an unchanged state, the corresponding MF has a value of 0. The intermediate position has a value between 0 and 1, representing the indefinite state of the breaker availability. For an alarm state indicating unavailability for the breaker to operate, the corresponding MF has a value of 1. Figure 5 determines the corresponding MF.  
Index NA is marked with no alarm, IP indicates intermediate switch position, and PA indicates permanent alarm with high sensitivity.  
Figure 5: Membership functions of the NECB alarm state.  
The existence of the CB alarm does not necessarily correspond to its unavailability but rather marks a condition that, with the other three, determines the CB availability. The same will be applied in the rules of conclusion elaborated on in the following chapters.
## 3 6G State-of-the-Art
### Evolution of 6G
#### 3.2.2 mURLLC  
6G technology must scale the typical URLLC feature of 5G into a new service called mURLLC, which offers a high-reliability scalability-latency trade-off in comparison to average-based network designs.
# A Framework for Bluetooth-Based Real-Time Audio Data Acquisition in Mobile Robotics
## 6 Results and Discussion
### Integrated Performance Evaluation  
The combined results indicate that the Sound Recording Rover successfully achieves its primary objective of enabling mobile audio acquisition with concurrent teleoperation capabilities. The dual-thread architecture effectively partitions the computational resources, preventing command processing from interrupting audio acquisition while maintaining responsive control (Figure 8).  
Figure 9 visualizes the relationship between audio quality (measured as the packet delivery rate) and control responsiveness (measured as the command latency) across the distance conditions. This visualization illustrates the system's ability to maintain an acceptable performance in both domains simultaneously, with graceful degradation at the edges of its operational envelope.  
The observed increase in the command latency with distance (from 105 ms at 2 m to 142 ms at 10 m) provides indirect evidence of Bluetooth signal degradation, though dedicated RF signal strength measurements were not conducted in this study. Future work should include a comprehensive Bluetooth SNR analysis across varying distances and environmental conditions to better characterize wireless communication performance.  
The noise floor calibration and attenuation algorithms implemented in the Android application significantly improved the subjective audio quality in stationary recording scenarios. However, during movement, mechanical vibration coupling and motor noise presented challenges that were only partially mitigated by the current implementation. Figure 10 illustrates the effectiveness of the noise attenuation algorithm in both stationary and mobile recording scenarios.  
These results suggest several potential improvements for future iterations:  
1. Enhanced mechanical isolation for the microphone module.  
Figure 8: Dual-thread processing performance analysis.  
Active noise cancellation techniques specifically targeting motor frequency bands, including LMS (Least Mean Squares) adaptive filtering for dynamic interference suppression of the identified 120 Hz and 250 Hz peaks.
* 3. Adaptive packet sizing based on a real-time connection quality assessment.
* 4. Directional microphone implementation to focus audio capture while reducing ambient noise.
* 5. Comprehensive Bluetooth SNR analysis across varying transmission distances and environmental conditions.
* 6. Comparative evaluation against existing mobile audio sensing solutions where applicable.  
Figure 10: Effectiveness of noise floor calibration and attenuation.  
Figure 9: Relationship between audio quality and control responsiveness.
## 2 System Model and Analysis
### Snr  
The transmitted signal of the LED can be expressed as  
\[x=\sqrt{\xi}s+I_{\mathrm{DC}}, \tag{9}\]  
where \(s\) is the input message.  
The transmitted signal must be real and non-negative in VLC, and the optical power must be limited to human eye safety and illumination requirement. Generally, we assume that the signal \(s\) satisfies the following conditions:  
\[-A\leq s\leq A, \tag{10a}\] \[\mathbb{E}(s)=0,\] (10b) \[\mathbb{E}\left(s^{2}\right)=\varepsilon,\] (10c) \[A>0,\] (10d) \[\varepsilon>0,\] (10e) \[\sqrt{\xi}s\leq I_{\mathrm{DC}}, \tag{10f}\]The total electrical power of the LED driver can be expressed as  
\[\mathbb{E}\bigg{(}\Big{(}\sqrt{\xi}s+I_{\text{DC}}\Big{)}^{2}\bigg{)}=\mathbb{E} \Big{(}\zeta s^{2}+2\sqrt{\xi}sI_{\text{DC}}+I_{\text{DC}}^{2}\Big{)}=\zeta \varepsilon+I_{\text{DC}}^{2}, \tag{11}\]  
where \(P_{i}=\zeta\varepsilon\) is the power of the signal \(s\).  
The total electrical power should be limited, i.e.,  
\[\zeta\varepsilon+I_{\text{DC}}^{2}\leq P_{max}. \tag{12}\]  
The received signal can be expressed as  
\[y=\eta H^{IRS}x+w, \tag{13}\]  
where \(w\) is the additive white Gaussian noise obeys a distribution \(\mathcal{N}\big{(}0,\sigma^{2}\big{)}\) with mean zero and variance \(\sigma^{2}\).  
After removing the constant DC-offset, the SNR \(\gamma\) can be expressed as  
\[\gamma=\frac{\big{(}\eta H^{IRS}\big{)}^{2}\cdot P_{i}}{\sigma^{2}}, \tag{14}\]  
The BER of the optical OOK modulation is given by  
\[BER=Q\Big{(}\sqrt{SNR}\Big{)}, \tag{15}\]  
where  
\[Q(x)=\frac{1}{\sqrt{2\pi}}\int_{x}^{\infty}e^{-y^{2}/2}dy. \tag{16}\]
## 1 Introduction
### Motivation
The results from these tests are subsequently used in combination with a device profile to replicate the device's behavior in a real-time simulation framework, OMNeT++, enabling reliable network simulations. This approach allows the replication of individual devices and components within the simulation, providing a platform for evaluating the behavior of OT devices in a complex, integrated IIoT network, with a focus on identifying vulnerabilities and implementation challenges. By using this framework, it is, therefore, possible to test protocol compliance, performance, and security in a controlled environment before deploying devices in live production networks.
## 7 Conclusions  
In this paper, a fractional-order robust backstepping control system combined with fractional-order tracking differentiator and fractional-order disturbance observer utilizing  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Axis** & **Levant** & **Zong** & **Proposed** \\ \hline x (m) & 0.132 (100\%) & 0.091 (69\%) & 0.065 (49\%) \\ y (m) & 0.419 (100\%) & 0.154 (38\%) & 0.125 (30\%) \\ z (m) & 0.206 (100\%) & 0.063 (31\%) & 0.032 (16\%) \\ \hline Average value & 0.252 (100\%) & 0.103 (41\%) & 0.074 (32\%) \\ \hline \hline \end{tabular}
\end{table}
Table 7: RMS value of the positioning error of three systems.  
Figure 6: Conducted simulation results of the unknown system for 300% increase in the load mass with the controller equipped with the disturbance observer: (**a**) 3–D positioning result; (**b**) positioning error on x–axis; (**c**) positioning error on y–axis; and (**d**) positioning error on z–axis.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Axis** & **Levant** & **Zong** & **Proposed** \\ \hline x (m) & 0.303 (100\%) & 0.259 (85\%) & 0.048 (16\%) \\ y (m) & 0.432 (100\%) & 0.261 (60\%) & 0.028 (11\%) \\ z (m) & 0.310 (100\%) & 0.179 (58\%) & 0.010 (3\%) \\ \hline Average value & 0.348 (100\%) & 0.233 (67\%) & 0.029 (10\%) \\ \hline \hline \end{tabular}
\end{table}
Table 8: MAE value of the positioning error of three systems.  
the sine hyperbolic function was designed to achieve robust positioning performance of a quadrotor UAV system. The fractional-order filtered error surfaces and the fractional-order tracking differentiator were blended into the recursive design procedure in traditional backstepping control scheme to enforce robustness to high nonlinearities and alleviate the repeated differentiation issue resulting in a smooth derivative of the virtual controller.  
A new nonlinear disturbance observer based on the fractional-order sine hyperbolic function was then designed to obtain the enhanced estimation performance and unknown perturbations of quadrotor UAV systems. Equipping this observer enabled the outperformed control system over the traditional control scheme for the uncertainties of the quadrotor UAV system. Sequential simulations for the nominal system and perturbed cases with load mass variation and wind gust were conducted and the efficacy of the proposed control system was demonstrated from the obtained results. A further experimental examination is needed to ensure that the proposed controller and disturbance observer are implemented into more feasible control systems under various real flight conditions of UAVs.  
Data curation, validation, software, and writing, S.P.; methodology, formal analysis, review and editing, S.H. All authors have read and agreed to the published version of the manuscript.  
This work was funded by Korea Hydro & Nuclear Power Co. (2021) and the Dongguk University Research Fund of 2020.  
Data Availability StatementData sharing is not applicable.  
This work was supported by Korea Hydro & Nuclear Power Co (2021) and was supported by the Dongguk University Research Fund of 2020.  
The authors declare no conflict of interest.
# Detection of Hidden Communities in Twitter Discussions of Varying Volumes
## 3 Models and Methods in SNA
### Description of Community Detection Methods
#### 3.2.3 The Directed Label Propagation Algorithm  
The directed label propagation algorithm (DLPA) [28] is a development for directed graphs of the label propagation algorithm [14] method. LPA is one of the fastest community  
Figure 1: Visual representation of the structure of a networked discussion, by user type.  
finding algorithms for undirected graphs. It can be used with large graphs, relies on topology, and is easy to implement:  
1. The algorithm assigns a unique label to each node;
2. Each node selects a label among its neighbors based on the frequency of occurrence;
3. If the distribution of labels reaches a steady state, the algorithm stops, otherwise it returns to step 2;  
The principle of label selection looks like this:  
\[l_{v}^{new}=\Big{|}N^{l}(v)\Big{|},\]  
where \(v\)--the node, \(l\)--the node label, and \(N(v)\)--neighbors of node \(v\).  
DLPA differs from the original algorithm by the weighting rule for each edge:  
\[1-\frac{E_{S}^{out}E_{T}^{in}}{k_{S}k_{T}}\]  
where \(E_{S}^{out}\)--the outdegree of the source node, \(E_{S}^{in}\)--the indegree of the target node, \(k_{S}\)--the degree of the source node, and \(k_{T}\)--the degree of the target node.
# A Novel Cloud Approach for Connected Vehicles
## 3 GeoVCDN Approach
### Centralized Context Cloud Architecture
#### 3.1.5 Sequence Number  
The sequence number allows us to avoid sending non-updated data. It is an additional value that causes an overhead, but in some cases this overhead is compensated. When \(Pv*Nv*F_{CAM}<(Pd+Pi)*C*F_{CAM}\), the sequence number mechanism reduces the overhead related to the ICN exchanges on the G5 channel.  
Here is another example, with \(Pv=4\), \(Nv=20\), and \(C=10\):  
\[\left\{\begin{array}{c}Pv*Nv*F_{CAM}~{}~{}~{}~{}=800\text{ bytes}\\ (Pd+Pi)*C*F_{CAM}=500\text{ bytes}\end{array}\right.\]  
In this case, the sequence number will not be good for the overhead.  
Now, if \(C=50\), then \((Pd+Pi)*C*F_{CAM}=2500\) bytes, so the sequence number helps to reduce the overhead.
# Multi-Modal Spatio-Temporal Knowledge Graph of Ship Management
## 3 Method
### Multi-Modal Spatio-Temporal Knowledge Graph Generation
#### 3.2.2 Knowledge Extraction from Structured Data  
Ship communication data are mostly stored in the structured form, and extracting knowledge from these data manually requires considerable human cost, and expertise and can be error-prone. The mapping between the data source and the domain ontology can be represented as a semantic network, also known as a semantic model, which describes the implicit semantic relations in the data source according to the concepts and relations defined in the domain ontology. The constructed semantic model can be used to automatically transform the data source to RDF triples for publishing to the knowledge graph. In this paper, we apply an automatic semantic modeling algorithm including seed semantic model generation and seed semantic model amending these two steps to obtain the most plausible semantic model [19], and further complete the task of extracting knowledge from structured data.  
**Seed Semantic Model Generation**  
For the input ship communication data source, we first find all candidate semantic types for each attribute in the data source, and then generate a candidate semantic model for it by using the Steiner tree algorithm. In summary, there are two sub-steps, that is semantic labeling and relation discovery to obtain the initial seed semantic model. In the semantic labeling phase, we employ the SemanticTyper algorithm proposed by [PERSON] et al. [20] to annotate the semantic types for source attributes. Based on the annotated semantic types, we obtain a candidate semantic model by modeling the relations between them using the Steiner tree algorithm [21].  
**Seed Semantic Model Amending**  
There are some missed substructures and wrong relations in the obtained seed semantic model after the first step. To improve the quality of the generated semantic model for the input data source, we use TF-IDF cosine similarity and other measurement machine learning methods to distinguish some ambiguous relations by analyzing data source information. Meanwhile, some incorrect substructures in the seed semantic model can be detected by matching model fragments in an existing relevant knowledge graph. After removing incorrect relations and substructures, with the help of the existing relevant knowledge graph, we can obtain the most plausible semantic model by adding potentially missed substructures using the modified frequent subgraph mining algorithm [22].  
As shown in Figure 8, we have completed the construction of a multi-modal spatio-temporal knowledge graph based on ship communication through the processing steps of unstructured knowledge extraction based on transformer and structured data knowledge extraction based on automatic semantic modeling.
## 4 Results and Discussion
### Further Discussion  
In the course of the study, we found that project life (\(L\)), pipeline construction costs (\(C(d_{i})\)), and electricity prices (\(C_{e}\)) were the main factors determining the total cost. To ensure the flexibility and adaptability of this design method, we carried out sensitivity analysis of the upper and lower 25% of the above parameters. The comparison with the scheme in Figure 13 is shown in Table 3. It can be found that has the greatest impact on the results, as high as 21.33%, which seems incredible. This is because we use it not only for the initial investment cost, but also for depreciation and maintenance. The second is \(L\). It can be seen  
Figure 14: Comparison of two schemes before and after optimization.  
Figure 13: The network scheme with minimum cost as the objective of optimization.  
from the formula of average annual cost that the impact on the results will decrease with increasing values of \(L\). has the least impact, and only determines the cost of heat losses and pumping costs.  
The essence of the design optimization method (Pipe Diameter Grading) proposed in this paper is to minimize the total cost of the project on the basis of meeting the requirements of the system by flexibly using the differences between the pipelines of each classification. In other words, the core of this method is the grading of pipelines into different classifications, as well as there being sufficient differences between different classifications. If the differences between the different classifications of pipelines are too small, the PDG will fail. In this regard, we selected the parameter \(C(d_{i})\) with the largest influence weight ratio, and tried to narrow the gap of \(C(d_{DN100})\), \(C(d_{DN150})\) and \(C(d_{DN200})\). The results show that when \(C(d_{DN100})\) is increased by 33.1% or \(C(d_{DN200})\) is decreased by 37.8%, the optimized topology changes. The final result (shown in Figure 15) is exactly the same as that (Figure 11) obtained by ESLP before introducing PDG. In both cases, \(C(d_{DN100})\) and \(C(d_{DN200})\) are almost identical to \(C(d_{DN150})\). This indicates that, in order to ensure the effectiveness of PDG, it is necessary to ensure that the characteristics of each pipeline classification are sufficiently different.
# Nitrate-Polluted Waterbodies Remediation: Global Insights into Treatments for Compliance
## 4 Nitrate Removal Technologies in Waterbodies
### Separation-Based Technologies
#### 4.1.4 Electrocoagulation  
Electrocoagulation (EC) is an innovative technology in water treatment to remove pollutants. It is an electrochemical process that removes suspended, emulsified, or dissolved pollutants from water using electric current [72]. The goal of EC is that contaminants present in the water form aggregates, producing solid particles that are less colloidal and less emulsified (or soluble) than those in the equilibrium state. When this occurs, the contaminants form hydrophobic components that precipitate and/or float and can be easily removed by some secondary type of separation method. The coagulant is released by electrolytic dissolution of a sacrificial electrode (anode). The dissolution of this anode generates a gaseous flow (O2, H2) that aids the flotation of the aggregates. Sometimes, it may be necessary to add a (supporting) flocculant to enhance flotation performance [73].  
Key factors influencing EC performance are the electrode metal (usually Fe or Al), current density applied, electric charge per unit volume, and pH of the solution. Commercial EC systems are typically powered at a steady current (5-20 mA/cm2) to achieve an efficient result [74].  
Figure 3: Schematic view of the electrodialysis (ED) process.  
The most critical aspect of the EC process is the coagulant-pollutant interaction. After the coagulant metal is solubilized in water, it undergoes various hydrolysis processes, generating different hydroxometallic compounds, some of which eventually polymerise, as well as precipitating metal hydroxides. [75]. The species and quantities formed (speciation) mostly are dependent on pH and metal concentration [76]. The coagulant substances occurring in the cell will determine the coagulation/destabilization mechanisms and the performance of the overall removal process [77]. Nitrate ions can be effectively removed from waterbodies through adsorption on the particles of metal hydroxides produced from Fe or Al electrodes [75; 78].  
The strengths of the EC for water pollutant removal are its wide applicability, easy scaling, minimal chemical dosing requirements, reduced sludge production, operation at atmospheric pressure and ambient temperature, simple equipment, and low-medium capital investment costs [74; 79]. Moreover, the versatility of adjusting the applied intensity to improve treatment intensity simplifies process control and automation [80]. The weaknesses of this technology lie in the anode passivation through time and sludge deposits on the electrodes, which negatively affect the electrolytic process when operating continuously Furthermore, sacrificial anodes are consumed and need to be renewed regularly [81].