esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Analysis of Density of States of Gas Adsorption on Ag\({}_{2}\)O-InN  
Figure 5 presents the DOS (density of states) and PDOS (partial density of states) before and after NO\({}_{2}\), SOF\({}_{4}\), SOF\({}_{2}\), SO\({}_{2}\)F\({}_{2}\) are absorbed on Ag\({}_{2}\)O-InN monolayer. The outermost electrons of atoms have the greatest influence on the adsorption process, so their PDOS was analyzed and compared. For NO\({}_{2}\) adsorption, the redistribution of TDOS is dramatic. The clear hybridization of N-\(2p\) and O-\(2p\) at \(-12\) eV, \(-9\) eV, \(2\) eV is consistent with the phenomenon of bonding between N atom and O atom. Similarly, SOF\({}_{2}\) undergoes redistribution of TDOS and strong orbital hybridization. The strong orbital hybridization indicates that the adsorption reaction of NO\({}_{2}\) and SOF\({}_{2}\) is strong, which is consistent with the conclusion that they are chemisorption. As for the SOF\({}_{4}\) and SO\({}_{2}\)F\({}_{2}\) adsorption, a slight redistribution of TDOS occurred, and PDOS did not reflect the bonding information, which is consistent with Figure 4. The new peaks between \(-5\)\(-\)\(-\)\(12.5\) eV mainly caused by F-\(2p\), S-\(2p\) and O\({}_{\text{gas}}\)-\(2p\), while the contribution of Ag-\(4d\) is mainly located at the Fermi level. However, the adsorption of these two molecules on the substrate was strong physisorption. In short, after gas adsorption, the redistribution of TDOS proves the existence of charge transfer. The adsorption behavior will change the conductivity of adsorption system, which provide the feasibility of preparing Ag\(2\)O modified InN gas sensor.  
Figure 4: Adsorption structure of (**a**) NO\({}_{2}\), (**b**) SOF\({}_{4}\), (**c**) SOF\({}_{2}\) and (**d**) SO\({}_{2}\)F\({}_{2}\) molecules adsorbed on Ag\({}_{2}\)O-InN monolayer.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Gas Molecules** & **Figure** & \multicolumn{2}{c}{**Adsorption Distance**} & \multicolumn{1}{c}{**\(E_{\text{ads}}\) (eV)**} & \multicolumn{1}{c}{**Charge Transfer (Q\({}_{\text{d}}\))**} \\ \hline NO\({}_{2}\) & Figure 4a & \(1.383\) & \(-2.021\) & \(-0.174\)\(\epsilon\) \\ SOF\({}_{4}\) & Figure 4b & \(2.880\) & \(-0.185\) & \(-0.028\)\(\epsilon\) \\ SOF\({}_{2}\) & Figure 4c & \(2.143\) & \(-0.754\) & \(-0.279\)\(\epsilon\) \\ SO\({}_{2}\)F\({}_{2}\) & Figure 4d & \(3.110\) & \(-0.173\) & \(-0.015\)\(\epsilon\) \\ \hline \end{tabular}
\end{table}
Table 3: Adsorption parameters of NO\({}_{2}\), SOF\({}_{4}\), SOF\({}_{2}\) and SO\({}_{2}\)F\({}_{2}\) on Ag\({}_{2}\)O-InN monolayer.  
Figure 5: DOS (**a**,\(c\),**e**,**g**) and PDOS (**b**,\(d\),\(f\),**h**) of before and after gas molecules adsorption on Ag\({}_{2}\)O-InN. The dotted line represents the Fermi energy.
# A Lightweight Fire Detection Algorithm Based on the Improved YOLOv8 Model
## 3 Experiment and Discussion
### Comparison of Ablation Experiments  
To systematically analyze and validate the influence of each constituent part of the model on its final performance, ablation experiments were conducted on a custom fire dataset. Each experiment within the groups maintained consistent training environments and related training parameters. Refer to Table 2 for detailed experimental results.  
As shown in Table 2, incorporating GhostConv and Ghost Bottleneck structures from GhostNet into the YOLOv8n baseline model optimizes the feature extraction network, significantly reducing the number of parameters and computational load. Compared to the baseline model, the parameters and FLOPs are reduced by 2.8 M and 2.5 G, respectively, while the [EMAIL_ADDRESS] (mean average precision at IoU threshold of 0.5) of this model only decreases by 0.1 percentage points. Adding the CARAFE upsampling detection module to the baseline model increases the attention to global feature information, improving the [EMAIL_ADDRESS] by 1.0%. Model 3 introduces the MLCA attention mechanism into the baseline model, enhancing the network's perception of both local and global information, which boosts the [EMAIL_ADDRESS] by 0.3% without additional computational cost. Model 4, which combines GhostNet and CARAFE modules, reduces computational load while enhancing feature resampling effectiveness, achieving an [EMAIL_ADDRESS] of 82.3%. This model shows a 38.3% reduction in parameters and a 34.6% reduction in FLOPs compared to the baseline model. Finally, the GCM-YOLO model integrates GhostNet, CARAFE, and MLCA, reaching an [EMAIL_ADDRESS] of 82.9% and a recall rate of 76.9%. It maintains a low level of parameters and FLOPs, demonstrating optimal overall performance. The synergistic interaction between these three modules maximizes the model's detection effectiveness while minimizing computational resource consumption.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Network** & **GhostNet** & **CARAFE** & **MLCA** & **[EMAIL_ADDRESS]** & **Recall/\%** & **Parameters** & **FLOPS/G** & **Model Size/MB** \\ \hline YOLOv8n & - & - & - & 81.7 & 75.8 & 3,006,038 & 8.1 & 6.3 \\ Module 1 & \(\surd\) & - & - & 81.6 & 75.8 & 1,714,466 & 5.0 & 3.8 \\ Module 2 & - & \(\surd\) & - & 82.7 & 77.6 & 3,146,142 & 8.4 & 6.6 \\ Module 3 & - & - & \(\surd\) & 82.0 & 76.8 & 3,006,064 & 8.1 & 6.3 \\ Module 4 & \(\surd\) & \(\surd\) & - & 82.3 & 76.0 & 1,854,570 & 5.3 & 4.1 \\ Module 5 & \(\surd\) & - & \(\surd\) & 82.2 & 76.0 & 1,714,476 & 5.0 & 3.8 \\ Module 6 & - & \(\surd\) & \(\surd\) & 82.6 & 76.3 & 3,146,168 & 8.4 & 6.6 \\ GCM-OLO & \(\surd\) & \(\surd\) & \(\surd\) & 82.9 & 76.9 & 1,854,596 & 5.3 & 4.1 \\ \hline \hline \end{tabular} Note. “\(\surd\)” means that an improvement module has been added to the model, “-” means that this improvement has not been added.  
\end{table}
Table 2: Ablation experiment results.
## 2 Related Work
### Federated Modeling Mechanisms for Heterogeneous Edge Environments  
In real-world scenarios, federated systems are commonly deployed across heterogeneous edge environments, facing non-ideal conditions such as device heterogeneity, resource imbalance, communication disruptions, and dynamic client availability. These factors significantly amplify challenges in fairness and robustness [38; 39; 40].  
To address system-level heterogeneity, FedProx [41] introduces a regularization term into the local training objective to limit model divergence and improve global convergence. FedNova [42] normalizes updates to unify contribution scales across clients, while FedCS [43] proposes a bandwidth-aware client selection strategy to optimize training efficiency under communication constraints. Other works have focused on the timeliness of information, proposing Age of Information (AoI)-aware client selection or update weighting schemes to prioritize fresher updates from clients with better connectivity, thereby mitigating the negative impact of stragglers and stale models [22]. While these approaches have achieved progress in system optimization, they largely ignore the dynamics of client participation and strategic evolution, making them less effective in open edge environments with frequent malicious behaviors.  
In particular, under the presence of strategic participants, clients may evade detection and manipulate rewards through mimicry attacks, intermittent poisoning, or frequent switching, ultimately undermining long-term stability [44; 45]. Therefore, \"behavioral trustworthiness\" must be considered a core constraint in federated learning systems to enable multi-objective optimization under trustworthy guidance.  
This work integrates edge heterogeneity modeling, dynamic trust evaluation, and incentive-response mechanisms to construct a trust-driven game-theoretic regulation framework at the strategic level. A soft suppression strategy is incorporated during aggregation, enabling robustness, incentive compatibility, and resource adaptation. This provides a systematic modeling paradigm for building secure and controllable federated learning systems at the edge.
## 4 Model Hyperbolicity
### Numerical Stability  
For the proposed and PW traffic systems, the first order centered (FORCE) scheme [56] is used. This scheme can approximate impulsive changes and can accurately estimate traffic evolution. Therefore, this is used for the solution of traffic systems. This merges Richtmyer and Lax-Friedrichs schemes to accurately asses second order traffic systems. The benefit of the FORCE scheme is its lower computational cost and easy implementation compared to the ROE scheme [57]. For the stability of this numerical scheme employed to a hyperbolic traffic system [58], the spatial size of a road segment \(\Delta x\) should be larger than the temporal step \(\Delta t\). That is, the space acquired by the traffic in temporal steps should be smaller than the spatial steps. This is mandatory to ensure that the solution should not grow or decay due to hyperbolic traffic system nature. Therefore, the Courant-Friedrich-Levy (CFL) condition [17; 59] is employed, and is given as  
\[\max|\lambda|\frac{\Delta t}{\Delta x}\leq 1. \tag{17}\]  
This is the maximum spatial road segment that can be covered in a temporal step. For numerical accuracy and stability, the distance covered during the traffic evolution over the temporal step should be smaller than a spatial road segment, therefore  
\[\Delta t\leq\frac{\Delta x}{\max|\lambda|}. \tag{18}\]  
For the proposed traffic system, the maximum eigenvalue from (14) at maximum velocity is  
\[\max|\lambda|=\bigg{|}v_{m}+\frac{(d_{t}-d_{s})\tau}{l^{2}}\bigg{|}, \tag{19}\]  
and the corresponding stability condition is  
\[\Delta t<\frac{\Delta x}{\max\bigg{|}v_{m}+\frac{(d_{t}-d_{s})\tau}{l^{2}} \bigg{|}}. \tag{20}\]  
For the PW traffic system, the maximum from (16) is  
\[\max|\lambda|=|v+A|, \tag{21}\]  
and the corresponding stability condition is  
\[\Delta t<\frac{\Delta x}{\max|v+A|}. \tag{22}\]  
If (20) and (22) are guaranteed in the numerical solution, the performance results of both traffic systems in the next section will be stable.
## 3 Methodology
### Data Formatting and Validation  
Before fine-tuning, a two-stage automated validation pipeline was developed to ensure data integrity and model compatibility. Each entry was formatted into OpenAI's required JSONL schema, consisting of a system message, user prompt, and assistant response--specifically, as follows:  
* The system role was set to define the assistant's task.
* The user message included all structured crash attributes (e.g., location, light condition, vehicle type).
* The assistant message encoded the target labels--crash severity and construction zone involvement.  
This structure, based on OpenAI's supervised fine-tuning schema, ensures that the assistant is guided by both structured input (user) and task description (system). Each record was then validated through the following checks:  
* Confirmed valid JSONL structure with correctly nested fields.
* Verified role consistency (system, user, assistant) across messages.  
* Used the Tiktoken library to calculate token lengths and ensure compliance with the 4096-token limit. To ensure scalability, the dataset (\(n=6400\) records) was processed programmatically. The validation script flagged malformed examples, messages with missing roles or content, and examples that exceeded the token limit. These entries were automatically logged and excluded from the training set to avoid truncation or model instability. This process ensured that only structurally correct, role-complete, and token-safe entries were retained for fine-tuning, resulting in a clean and efficient dataset suitable for high-quality model adaptation.
## 1 Introduction
So too are DT, which require accurate data on the state and functionality of the physical system that it represents. While DT can be developed using an existing library of data, the best applications of DT require real-time data, enabling analysis on the impact of the system's current state. Figure 1: Basic Concept of the Digital Twin Development Lifecycle. One notable area where integrating digital twin software solutions has become increasingly crucial is in conjunction with industrial collaborative robotics applications [16]. Collaborative robots, also known as cobots, are designed to work alongside human operators, enhancing productivity, safety, and flexibility in various industrial settings [17]. By combining digital twin technology with cobot applications, organizations can achieve a higher level of automation, efficiency, and performance. Integrating digital twin software solutions and industrial robot applications offers numerous benefits across different industries [18]. Real-time monitoring of the physical systems through digital twins enables predictive maintenance, identifying potential issues before they escalate into costly failures. Additionally, digital twins promote optimization by simulating and analyzing different scenarios, allowing for data-driven decision-making and performance improvement. Other benefits of digital twins include improved design and development, efficient maintenance and monitoring, predictive analytics, risk reduction, remote operation, data-driven insights, optimized performance, and lifecycle management. They lead to cost savings, support customization, facilitate training, encourage innovation, and contribute to sustainability. Moreover, digital twins enable cross-disciplinary collaboration and real-time decision-making, transforming industries with their holistic and versatile advantages [19]. Before technology enabled the development and application of DT, simulations and analysis were conducted using pre-existing data sources. Physical systems undergo operational verification and validation. Predictive analysis required a large dataset. Prediction only applied to the system as it was when the data was captured. Parameters and constraints were defined by design and not by operation or actual capability.
## 4 Computational Experience
### Real networks case studies  
Simulations were performed on real urban networks to test the suitability of this prototype in order to reproduce real traffic situations, and to test the scalability of the code on large size networks. In  
Figure 4: Computational gain when embedding the PL solver algorithm in the code (Godunov numerical approximation scheme): computational time (seconds) vs simulated period (minutes) (top); computational time (seconds) vs (number of segments)/meter (bottom).  
particular, the code was tested on the whole traffic network extracted from the city of Salerno, Italy, formed by about \(1200\) roads. GIS data of roads placement are courtesy of Salerno City Hall.  
In Figure 5, we report the result of a \(30\) real minutes simulation. More precisely in Figure 5 (top) the load is reported at the beginning of the simulation. The load was artificially set to be low on every road, corresponding to \(0.3\) density. Arcs not connected to any node \(n\) in \(FS(n)\), _i.e._, as exiting road, are interpreted as sources from outside the network and a constant boundary datum of \(0.3\) density is assigned. Then the load evolves in the whole city, see Figure 5 (bottom) and higher densities are represented by darker red segments. It is possible to appreciate how the network structure of the whole city naturally gives rise to high density zones, where bottlenecks effects occur.  
It is important to notice that such simulation has the meaning of illustrating the potentiality of the method, however one has to be cautious in deriving conclusions about the real traffic load have. Indeed,  
Figure 5: The full network of Salerno: before (top) and after (bottom) the simulation.  
one should make use of real data to set the initial load. Also, source terms and absorption terms should be present due to parking lots, private and public sites, _etc._  
Then we focus on the junction of Salerno Fratte. At this crossing traffic exiting the highway A3, connecting Salerno to the Salerno Univerity area and the city of Avellino, meets that from the center city. Unfortunately, there is no priority for the traffic coming from the highway. As a result, long queues form at this junction and move backs into the highway. We report the simulation of this are in Figure 6. One can notice that the model well reproduce the real traffic behaviour. Also in this case dark red corresponds to high density.  
Various other simulations were run with the present model and simulation prototype: Some movies can be downloaded from [28].
# Effect of Curing Conditions on Microstructure and Pore-Structure of Brown Coal Fly Ash Geopolymers
## 3 Experimental Procedure
### Testing  
The compressive strength of mortar specimens was tested using a Technotest concrete testing machine in accordance with AS 1012.9 [38] with a loading rate of 0.34 N/mm\({}^{2}\)/s. The compressive strength test on concrete specimens was performed with a MTS machine with a loading rate of 20 MPa/min according to AS 1012.9 [38]. In each mix design, three specimens were tested for the 7-day compressive strength and the mean value was reported.  
The scanning electron microscope (SEM) with backscatter electron imaging (15 kV of energy) (Olympus Corporation, Tokyo, Japan) and an energy-dispersive X-ray spectroscopic (EDS) were used to examine the microstructure and the elemental distribution of brown coal fly ash geopolymer mortar, respectively. All tested specimens were 5 mm in height and 12 mm in diameter, and carbon coated. The pore structure was investigated using mercury intrusion porosimetry (MIP) and X-ray computed tomography (CT) [39]. The porosity distribution analysis was conducted using an Auto pore IV 9500 (V1.09) micrometric mercury intrusion porosimeter. Prior to the analysis, the geopolymer mortar monoliths (1 cm \(\times\) 1 cm \(\times\) 2 cm) were dried in an oven at 105 \({}^{\circ}\)C (221 \({}^{\circ}\)F) for 48 h.  
The \(X\)-ray CT was used to investigate pore connectivity and pore-structure of the brown coal fly ash geopolymer concrete. The duplicate 10 mm \(\times\) 10 mm \(\times\) 50 mm specimens were scanned at 20 \(\upmu\)m resolution at 180 kV and 100 \(\upmu\)A, and 1000 images recorded during a complete rotation. The samples (prepared with same concrete mix) were scanned at 4, 8, 12 and 24 h to identify changes in the  
Figure 1: The concrete specimen (100 mm cube) with thermocouples embedded.  
pore structure with time. Finally, VGSTudioMax 2.2 software (Volume Graphics GmbH, Heidelberg, Germany) was used to analyse the porosity of tested concrete specimens.
# Demand Flexibility: A Review of Quantification Methods, Models, and Required Data
## 4 Discussion
### Applications of Flexibility Quantification
#### 4.1.2 Power Grid Stability and Security  
The stable and secure operation of the electric power grid relies on the availability of adequate and efficient ancillary services. These are typically provided in the form of frequency and voltage control at different timescales. They may consist of frequency regulation, load following, capacity reserves, and voltage support. While generation sources have traditionally been considered providers of ancillary services, demand flexibility can be viewed as virtual power generation, offering similar support. Different DR programs can offer demand reduction at various timescales ranging from a few seconds (for instance, in DLC-type programs and emergency DR) to a few minutes (for instance, in interruptible DR). Not only can this reduce overall operational costs, but it may also lower the stress on generation resources that would otherwise have to go through ramp-up and ramp-down cycles. In fact, demand flexibility has been proposed by researchers for capacity reserves, frequency support, ease of ramping for generation resources, load shaping, peak demand reduction, and improved utilization of generation capacities [1, 10, 11, 18, 32, 34, 40, 43, 64]. Some have also proposed integrating DR and demand flexibility with generation planning.  
However, the criticality of ancillary services in ensuring power grid stability and security necessitates an accurate assessment of the amount of energy or demand curtailment available, the timing, the duration, and the associated probabilities. This requires a precise model for quantifying demand flexibility potential subject to all associated uncertainties. Higher accuracy levels can result in improved performance efficiency and lower operational costs across the grid.
## 4 Results and Discussion
### Results on Language Process Tasks
Firstly, the user attention mechanism dynamically adjusts attention weights, enabling the model to accurately identify and protect sensitive information based on the characteristics of the input data and user privacy needs. The combination of these techniques not only enhances the model's privacy protection capabilities but also ensures efficient data processing and accurate output. For example, the Transformer model, when applying our method, dynamically adjusts weights in the self-attention calculation through the user attention mechanism, allowing it to protect sensitive information while efficiently processing long texts. The BERT model, within its bidirectional Transformer architecture, uses differential privacy techniques by adding noise during parameter updates, effectively preventing data leakage while maintaining high precision and high recall. The CLIP model, combining multimodal information from language and images, performs excellently in protecting user privacy and processing multimodal data with our method. The BLIP and BLIP2 models, further optimized for multimodal pre-training tasks and datasets, show significant improvements in precision, recall, and accuracy in multimodal tasks while protecting user privacy. Overall, the experimental results and theoretical analysis demonstrate that the proposed user-centered data \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Model** & **Our Method** & **FHE** & **FL** & **DP** & **DP + FL** \\ \hline Transformer & 0.78 & 0.72 & 0.70 & 0.69 & 0.66 \\ BERT & 0.80 & 0.74 & 0.72 & 0.71 & 0.68 \\ CLIP & 0.83 & 0.77 & 0.74 & 0.73 & 0.70 \\ BLIP & 0.85 & 0.78 & 0.76 & 0.74 & 0.72 \\ BLIP2 & 0.88 & 0.80 & 0.77 & 0.76 & 0.73 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Results on LLMs of accuracy for different methods. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Model** & **Our Method** & **FHE** & **FL** & **DP** & **DP + FL** \\ \hline Transformer & 0.76 & 0.70 & 0.68 & 0.67 & 0.64 \\ BERT & 0.78 & 0.72 & 0.70 & 0.69 & 0.66 \\ CLIP & 0.81 & 0.74 & 0.72 & 0.70 & 0.68 \\ BLIP & 0.84 & 0.76 & 0.74 & 0.72 & 0.70 \\ BLIP2 & 0.87 & 0.78 & 0.75 & 0.74 & 0.71 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results on LLMs of recall for different methods. privacy protection framework performs excellently across different large language models. This proves the framework's broad potential and significant practical value in real-world applications, providing a strong solution for the field of data privacy protection.