esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Absorption Lines and Laser Performance  
Figure 3a presents the absorption cross-section of chloroform near 1683 nm with five absorption peaks ranging from 1682 nm to 1684 nm according to the HITRAN data base [35]. The intensity of the absorption peak increased as the wavelength increased. The maximum intensity of the absorption peak almost achieved the order of \(10^{-19}\) cm\({}^{2}\)/molecule at 1683.15 nm, which was used as target peak for measuring chloroform to get the best PA signal. Furthermore, to observe the influence imposed on the PA signal by the adjacent absorption peak, we scanned the injected current of the laser in a large scale, leading to a wide wavelength scanning range, so as to completely cover the target and the adjacent absorption peak.  
The performances of the DFB laser used measured by an optical spectrum analyzer (AQ6317, ANDO Electric, Tokyo, Japan) and an optical power meter (PM100D, Thorlabs, Newton, MA, USA) were plotted in Figure 3b. As the injected current increased from 30 mA to 150 mA with a step length of 10 mA, the emission wavelength of the laser was gradually enlarged from 1682.74 nm to 1683.48 nm while the output power of the laser changed from 3.3 mW to 24.5 mW. Unlike common DFB lasers, its wavelength is not linearly tuned and the wavelength change accelerated with the increase of the injection current. During the experiment, we fixed the laser temperature at 19.8 \({}^{\circ}\)C and set the initial current at 84 mA  
Figure 1: Diagram of the experimental setup for chloroform gas detection.  
Figure 2: Frequency response of the acoustic resonate cavity inside the PA cell.  
to get an emitting wavelength of 1682.99 nm. A low-frequency sawtooth wave with an amplitude ranging from \(-\)1.25 V to 1.25 V, together with a high-frequency sine wave whose amplitude ranged from \(-\)240 mV to 240 mV, were imposed on the laser driver. The driver had a response coefficient of 40 mA/V, so the sawtooth wave led to a 100-mA change in the injected current that derived the wavelength varying from 1682.76 nm to 1683.34 nm, while the sine wave brought a 19.2-mA current change.
# Variable Speed Limit and Ramp Metering for Mixed Traffic Flows: A Review and Open Questions
## 2 Application of VSL and RM Control in HDV Traffic Flows
### Impact of VSL and RM on HDV Traffic Flows
#### 2.5.3 Impact of VSL and RM on Traffic Safety and Emissions  
The VSL system can monitor the propagation of lower speeds in the traffic flow opposite to the direction of traffic on the motorway caused, for example, by a traffic incident or an active bottleneck, and set a new speed limit to allow the arriving vehicles to gradually adjust their speed to the new traffic situation [66]. Safety improvement was observed in [30; 67; 68] with appropriate VSL actions that prevent sudden changes in vehicle speeds. A study in [69] confirmed an increase in safety on the M25 motorway in the United Kingdom, while study [70] found increased safety on the E4 motorway in Stockholm, Sweden. A significant reduction in accident risk when VSL was applied in free-flow traffic was observed in [29]. In contrast, no significant improvement was observed in increased traffic flow. As a consequence of the reduction in the speed limit, the application of VSL reduces the average speed of the vehicle and, therefore, the resistance to the movement of the vehicle (most significant is the air resistance, which is proportional to the square of the speed). This reduces the fuel consumption required to overcome the resistance to vehicle movement, and hence the harmful emissions of exhaust gases. In [68; 71; 72; 73], analyses have shown that the application of VSL can reduce emissions and fuel consumption by 4-6%.  
In [74], a local RM control strategy, PI-ALINEA, was proposed. Four scenarios were formulated based on congestion levels, where the first scenario has the biggest congestion, while scenario four is close to the free-flow condition, corresponding to decreasing congestion. The total emissions for the first three scenarios are reduced by 16.9%, 6.7% and 4.7%, respectively, for the RM queue length constraint of 100 vehicles. The total emissions for the first three scenarios are reduced by 12.9%, 7%, and 5.3%, respectively, for the RM queue length without constraint. In [75], RM was adopted to analyze changes in traffic flow and associated variations in CO\({}_{2}\) emissions. The simulation results show a reduction in CO\({}_{2}\) emissions of 818.4 kg/h. The effectiveness of RM was reduced by quantifying a decrease in CO\({}_{2}\) emissions by 7.3%. RM reduced the CO\({}_{2}\) emissions to 3273.6 kg/day and 1194.9 tons/year.
# Investigation into Influences of Hydraulic Fracturing for Hard Rock Weakening in Underground Mines
## 3 Numerical Simulation Results  
### Different Fracturing Intervals  
The failure situation and stress distribution of the surrounding rock in the hydraulic fracturing borehole in the working face under different fracturing interval conditions are shown in Figure 3. In the figures of fracture conditions, the blue areas represent the occurrence of shear failure, the pink areas represent the combination of shear and tension failure, and the olive area represents the occurrence of tension failure. In the figures of stress conditions, the color from blue to red caused an increase in the stress concentrations. From Figure 3, it can be seen that as the spacing between fracturing intervals in the borehole increased, the spacing of the fracture network formed between adjacent fracturing sections also increased. At the fracturing intervals between the boreholes at 1.1 m, the hard roof rock between the two fracturing sections was subjected to repeated fracturing. The cracks formed between the two fracturing sections were too close, which affected the generation of cracks in the next fracturing section. At fracturing intervals of 2.2 m, the fracture network that formed between the two fracturing sections was independent of each other and was conducive to the generation of fractures in the next fracturing section. At fracturing intervals of 3.3 m, the distance between the fracture networks formed between the two fracturing sections further increased, and the fracture networks that formed between the two fracturing sections were independent and did not interfere with each other. However, there was a large unfractured zone between the fracturing sections, and the number of fractures that could be performed within the drilling depth was reduced.  
Comparing the simulation results of different fracturing interval spacing, it could be concluded that a reasonable fracturing interval was one of the key parameters determining the hydraulic fracturing effects. If the spacing between fracturing sections is too large, the cracks generated by adjacent fracturing sections are independent of each other and cannot form a continuous fracturing transformation zone. If the spacing between fracturing sec  
Figure 2: Schematic diagram of hydraulic fracturing effects.  
tions is too small, although the fracture network generated by fracturing is continuous and the weakening effect is good, an overlap in the fracturing area is produced, which affects the formation of new fractures and significantly reduces the efficiency of the fracturing construction. Therefore, based on numerical simulation results and the in situ situation of the working face, a reasonable spacing of 2.2 m between fracturing sections can be determined, which can fully fracture the surrounding rock between the fracturing sections.
# State-Of-The-Art and Prospects for Peer-To-Peer Transaction-Based Energy System
## 5 Frameworks for TE
### Classification Based on Optimization Techniques  
In energy trading, the main goal is to optimize cost either through reduction in generation cost, transport cost, energy demand or profit maximization. Targets of optimization could also be the reliability and availability of energy, the minimization of losses, economic aspects, risk and stability criteria or various economic or ecological interests. Therefore, this section provides several optimization techniques adopted in the literature for prosumer energy trading and sharing.  
Optimization problems consist of selecting the best possible solution subject to some constraints from sets of available alternatives. It basically involves maximizing or minimizing some objective function by selecting some input value from a set of allowed function values [80]. The choice of optimization technique to apply at a particular time depends on the objective function.  
To minimize energy cost, some researchers used convex optimization [37; 38], stochastic optimization [81; 82] and/or particle swarm optimization [39]. In addition, linear programming (LP) was used to optimize energy cost in [40], although [41] proposed a multi-objective optimization model for annual cost and [42] used mixed integer linear programming (MILP). In [43], optimization was achieved by minimizing energy sharing losses and energy costs in distributed MGs. Furthermore, to reduce the total cost emanating from energy generation and transportation, Ref. [7] proposed distributed P2P energy trading where each peer must solve a local optimization problem.  
One of the implementation methods for optimization is game theory, amongst many algorithms. Therefore, a brief introduction and classification of game theory techniques whilst analysing different pieces of work that propose game theory to drive energy trading and sharing is provided.
# Surface Plasmon Resonance-Based Fiber Optic Sensors Utilizing Molecular Imprinting
## 3 Fiber Optic Surface Plasmon Resonance Sensors
### Probe Modification  
The performance of a fiber optic sensor can be increased by modifying the structural configuration of the fiber. A tapered core configuration of the sensing region can be used instead of the flat model shown in Figure 2, which increases the sensitivity. Here the penetration depth of the evanescent field varies through the sensing region [48; 49]. The tapering can be done in a parabolic, linear and exponential-linear way [50]. A high increase in sensitivity has been observed in exponential-linear tapered fiber sensor probes since there would be a decrease in the angle of incidence at the interface which increases the sensitivity. Similarly, the sensitivity also increases with an increase in the tapering ratio [32]. The asymmetric metal coating over a single fiber of uniform waist has been reported to generate multiple resonance dips which boost the dynamic range of sensors [51]. A U-shaped fiber probe profile was reported initially for SPR in 2008 and later the idea was also extended to localized plasmons (Section 4) [52; 53]. Sensitivity has been reported to increase with the decrease in the bending radius of fiber up to a certain extent, because further decreases in bending radius cause the angle of incidence to be less than the critical angle, which fails to generate the required condition for SPR. Another configuration is the D-shaped fiber probe which achieves a high resolution and sensitivity due to the multiple peaks/dips [54].  
Different designs of SPR-based probes have also been reported using bimetallic coatings which improve the sensitivity, figure of merit and detection accuracy [55; 56]. An enhancement in sensitivity of a guided wave SPR sensor has been reported in 2008 by using a dielectric layer having a higher real part over the metal layer coated over a core of optical fiber [57; 58]. Thus using different combinations of metals, high index layers, structural configurations and fiber profiles, a great enhancement in fiber optic sensor probe performance can be achieved. In addition to these, the presence of metal nanostructures near the fiber core in the structure of the probe produces localized plasmons instead of the propagating ones, resulting in what termed as localized surface plasmons (LSP), which enhances the sensor performance multiple times.
## 9 Conclusions  
As cyber threats continue to advance, the development of next-generation C2 servers is crucial for bolstering cybersecurity resilience, enhancing automation, and mitigating risks. The proposed C3 framework introduces a secure, scalable, and adaptable approach to command and control, addressing key limitations of traditional frameworks. By incorporating enhanced stealth, optimized performance, adaptability, expandability, and decentralized communication, this system offers a novel approach to C2 infrastructure in cybersecurity operations. This work contributes to the ongoing discourse in offensive security, penetration testing, and red team operations, providing a foundation for researchers and security professionals to advance the fields of ethical hacking and network security.  
For future research, offensive C2 infrastructures must evolve to maintain effectiveness, resilience, and stealth against increasingly sophisticated defenses. A key direction lies in enhanced automation and self-adaptive C2 frameworks, leveraging artificial intelligence and machine learning to optimize command execution, lateral movement, and real-time decision-making. This would minimize manual operator intervention, enabling more autonomous operations. Furthermore, exploring multi-layered and redundant C2 architectures is essential to counter detection and takedown attempts. This includes investigating peer-to-peer (P2P) C2, domain fronting, and the use of blockchain or decentralized services to ensure uninterrupted command execution, even in contested environments. Integrating serverless and ephemeral infrastructure, such as cloud functions and containerized C2 nodes, can further enhance operational security by minimizing forensic footprints and dynamically shifting C2 endpoints.  
Future work should prioritize enhancing autonomous tasking and action execution. Scripted playbooks and intelligent decision trees can reduce operator workload while maximizing operational efficiency. Next-generation C2 frameworks should dynamically adapt their tactics--for example, by implementing interactive shell communication--to ensure persistent access within complex target networks. Additionally, exploring alternative communication channels between the C2 server and compromised hosts, such as WebSockets and WebRTC, can provide further avenues for communication. By incorporating these advancements, offensive C2 capabilities can achieve greater stealth, automation, and resilience, thereby increasing their effectiveness in contested cybersecurity environments.  
Conceptualization, E.C. and G.K.; methodology, E.C. and G.K.; software, E.C.; validation, E.C. and G.K.; formal analysis, E.C. and G.K.; investigation, E.C.; resources, E.C.; data curation, E.C.; writing--original draft preparation, E.C.; writing--review and editing, E.C. and G.K.; visualization, E.C.; supervision, G.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The relevant implementation of C3 is available in the following public GitHub repository [4].  
The authors declare no conflicts of interest.
# Sustainable Power Consumption for Variance-Based Integration Model in Cellular 6G-IoT System
## 3 System Model
### Convergence Analysis  
The convergence analysis is presented in Figure 7. The graph is plotted for the iteration-vs-cost function. The iteration value ranges among 0 to 25. The proposed model exhibits the minimum value for ACO and IACO-C. Thus, the superiority of the proposed model is proved over other existing models.  
Figure 7 clearly shows that convergence analysis of proposed IACO is much better when compared to existing models. This study presented a variance-based integrating model for the 6G-IoT approach that takes Energy, Communication and Computation (ECC) into account in order to jointly handle the important concerns of 6G cellular IoT, namely information transfer, data aggregation and energy supply. In the beginning, the base station (BS) simultaneously charges numerous large IoT devices using WPT in the downlink. Then, using the energy that was collected, IoT devices carried out communication and computation tasks in the uplink in a similar spectrum. To balance system performance and computational complexity, the model also incorporates transmit-beam optimization using the Improved-Ant-Colony-Optimization (IACO) model. The results of the simulation compare the performance of the suggested work to the traditional models in terms of error analysis. The MSE of the IACO task was 0.011 according to the analysis, whereas ZFBF, Fixed MMSE, UFBF, JBDA, ACO and IACO-C achieved comparatively high errors of 0.021, 0.028, 0.015 and 0.012. Although ZFBF, Fixed MMSE, UFBF, JBDA, ACO and IACO-C generated comparatively large errors of 0.091, 0.093, 0.093 and 0.090, the MAE of the IACO task was 0.086. Here in this research, the complexity analysis is computed in terms of time. The required time for completing the entire simulation is up to 3 min (nearly 180 s). The analysis clearly shows IACO performs with better stability; however, when working with an extensive amount of data, it has some issues with convergence speed and accuracy.
## 6 Overcoming Barriers to Integration
### Policy Recommendations and Stakeholder Collaboration  
Policy recommendations to enhance the use of secondary resources should aim to create an enabling environment that balances safety, performance, and sustainability. For example, mandatory requirements for incorporating a minimum percentage of recycled content in construction materials, alongside incentives for companies that exceed these targets, could significantly boost the use of secondary resources [288]. Clear labeling and certification programs could also play a vital role in building consumer confidence and driving market demand for sustainable construction products [289]. Collaboration among industry, academia, and government is essential for overcoming the barriers to integrating secondary resources. Industry can offer practical insights into the feasibility and challenges of using secondary materials, while academic researchers contribute advanced knowledge and innovations in processing and application technologies. Government support, through funding and regulatory adjustments, can bridge the gap between research and practical implementation, ensuring that new technologies reach the market effectively. Public-private partnerships, in particular, provide a valuable platform for piloting the new uses of secondary materials, allowing for the refinement of processes and the validation of economic and environmental benefits under real-world conditions.  
In several countries, green public procurement (GPP) frameworks are already being used to promote SRMs by prioritizing materials with verified environmental benefits in public works. For example, France mandates minimum recycled content thresholds in state-funded building projects, and South Korea has implemented financial incentives and pilot schemes for the use of industrial by-products in concrete and asphalt. Moreover, policy instruments, such as tax incentives, dedicated SRM certification schemes, and national registries of valorized materials have proven useful in reducing market risk and encouraging innovation. Integrating these tools within broader circular economy strategies can help align construction policies with climate and waste reduction targets.  
In conclusion, overcoming the barriers to integrating secondary resources into construction requires a multi-faceted approach that addresses economic, technological, and regulatory challenges. By investing in research and development, updating regulations, and fostering innovative business models and economic incentives, the construction sector can make significant strides towards sustainability. Collaborative efforts between industry, academia, and government will be pivotal in unlocking the full potential of secondary resources, driving the transition to more circular and resource-efficient construction practices.
# A Distributed Autonomous Mission Planning Method for the Low-Orbit Imaging Constellation
## 6 Discussion
### Parameter Testing of TPS  
The effect of TPS is influenced by parameter \(\alpha\). This experiment tests two scenarios, local-1080-90 and global-1080-90, as examples. The algorithm results are shown in Table 8 (local) and Table 9 (global). As parameter \(\alpha\) changes, the relationship between communication cost and global total benefit is shown in Figure 11.  
The basic CBBA algorithm determines the winning satellite for one task after global communication. For each task's bidding process, it belongs to the global optimal strategy. TPS is essentially a local optimal choice to preempt a task when the satellite still believes it has the highest bid after communicating with neighbors \(\alpha\) times in a row. \(\alpha\) represents the \"local\" range, and the larger it is, the more times the satellite communicates with its neighbors, the more information it obtains, and the closer its scheduling results are to CBBA. The purpose of TPS is to significantly reduce communication volume at the expense of a small amount of profit. From the experiment, it can be seen that the local-1080-90 and global-1080-90 scenarios exhibit similar trends. When \(\alpha=1\), the communication  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(\alpha\) & **Iterations** & **Comm Volume** & **Comm (\%)** & **TS** & **TP** & **P (\%)** \\ \hline
0 * & 55 & 70,533 & 1 & 765 & 59,501.6 & 1 \\
1 & 14 & 15,671 & 22.22 & 593 & 47,296.6 & 7  
volume significantly decreased, accounting for 22.22% and 33.74% of the basic CBBA, respectively. However, there was also a certain degree of decrease in revenue, accounting for 79.49% and 74.06% of the basic CBBA, respectively. In addition, when \(\alpha=2\), the revenue both reach over 95% of the basic CBBA, and the communication volume is 38.45% and 56.53% of the basic CBBA, respectively. As \(\alpha\) continues to increase, the scheduling profit is almost equal to that of the basic CBBA, while the communication volume is significantly lower. Therefore, the TPS policy can significantly reduce the communication volume while effectively guaranteeing the scheduling quality.
# EEG-Based Attention Classification for Enhanced Learning Experience
## 2 Materials and Methods
### Feature Reduction and Classification  
**PCA and k-NN Classifier:** After feature reduction, the extracted principal components were used to train a k-nearest neighbors (k-NN) classifier. Principal Component Analysis (PCA) was employed to reduce the dimensionality of EEG signals, extracting key features while minimizing noise. This process is essential for managing high-dimensional data and enhancing the efficacy of subsequent classification algorithms.  
Analyzing these properties across frequency bands and attentional states (HA and LA) allowed us to identify significant variations in network organization and synchronization. For this reason, we performed network analysis (NA) using 30 EEG channels, calculating six key metrics: clustering coefficient, path length, network degree, network density, global efficiency, and small-worldness (SWN).  
Classification performance was evaluated using 5-fold cross-validation, ensuring that training and testing sets remained stratified to balance high and low attention conditions across folds. Metrics including accuracy, precision, recall, and F1-score were computed as averages across folds.