esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Design, Structure, and Initial Results of the R&D Agenda
### Overview of the Four Development Stages  
The R&D agenda is organised into four sequential stages, each of which builds upon the outputs and insights generated by the previous stage. These stages are designed to support iterative refinement, collaborative engagement, and alignment with small businesses' cybersecurity needs.  
1. **AI agent development**--focuses on building a custom AI agent (Custom GPT) that can draw on cybersecurity documents and other inputs to create a knowledge hub. This agent serves as a foundational tool for subsequent stages, providing accessible, relevant information for consulting tasks. Documents such as widely adopted frameworks and best practices [20; 21; 22; 23; 24; 25; 26], government resources [27; 28; 29; 30; 31] and industry resources [32; 33] are categorised and included in the training of the custom AI agent.
2. **Cybersecurity roadmap creation**--utilises Human-AI collaboration, integrating both GenAI and the AI agent to co-develop a cybersecurity roadmap and detailed capstone project specifications. The roadmap is structured as a flexible, five-phase maturity model that can be tailored to a variety of business contexts. Each phase is accompanied by modular templates and diagnostic tools that guide implementation according to the client's size, sector, and technical capacity. This customisation ensures accessibility for low-resourced businesses while preserving scalability for more mature organisations. Additionally, the roadmap is designed to incorporate feedback from industry partners and capstone participants, allowing it to evolve with changing cybersecurity threats and client needs.
3. **Resource development**--involves collaboration between students, GenAI, and the AI agent to create consulting resources aimed at supporting small businesses' cybersecurity needs.
4. **Industry application**--enables students to apply developed resources to real-world cybersecurity challenges faced by small businesses, adapting resources to fit specific needs and gathering feedback for further refinement.  
Each stage includes a set of development-oriented and research-oriented projects designed to achieve complementary outcomes. Table 3 provides an overview of development-oriented projects, while Table 4 summarises research-oriented projects within each stage.
# A Multi-Agent Deep Reinforcement Learning System for Government Interoperability
## 6 Discussion  
The results of our study demonstrate that integrating the JADE platform with Python-driven deep reinforcement learning (DRL) significantly improves the operational efficiency of municipal administrative processes. Key performance metrics, such as a task completion rate of 95% and a reduced communication latency of just 120 ms, suggest that our multi-agent system (MAS) enhances governmental interoperability, a critical factor for improving coordination across departments in complex administrative environments. The high average reward of 45 per episode indicates that agents are making better, more informed decisions, while a decision accuracy of 96% reflects the success of the DRL-based learning model in optimizing agent behaviors.  
The presented work, a multi-agent deep reinforcement learning system for governmental interoperability using the JADE, combines the strengths of the JADE (Java Agent Development Framework) and deep reinforcement learning (DRL) to tackle the challenge of governmental interoperability. While previous research has explored the use of the JADE and Python in isolated ways for multi-agent systems (MASs), our approach is distinguished by its integration of the JADE with DRL, specifically designed to address the unique interoperability challenges faced by government agencies. Agencies often work in silos with incompatible objectives, varying data formats, and disparate communication protocols. Our system, integrating the JADE's agent communication framework with DRL's adaptive learning capabilities, offers an innovative solution to harmonize these differences.  
By enabling agents to negotiate, exchange information, and optimize processes collaboratively, we facilitate smoother workflows, reduce inefficiencies, and foster greater coherence in inter-departmental operations. These findings underscore the potential of MAS in transforming public sector administration, providing a scalable and adaptable model for improving governance and public service delivery.
## 4 Validation of Numerical Model
### Accuracy of Numerical Calculations  
The accuracy of the numerical model was assessed at the external tunnel entrance facing the outside based on the empirical design charts in UFC 3-340-02 [21]. Four cell sizes were considered: 50 mm, 100 mm, 200 mm, and 400 mm. The overpressures were monitored at locations GL, GC, and GR, as shown in Figure 11. The calculated overpressures were considered acceptable when the differences in the cell size were less than 10%, as in the mesh sensitivity study for the main tunnel.  
Figure 11: Monitoring locations at the external tunnel entrance for evaluation of numerical accuracy.  
Figure 10: Stability of overpressure versus distance from the entrance of main tunnel; cell size = 100 mm.  
The overpressure histories calculated at the monitoring location GC for the four cell sizes are shown in Figure 12. The maximum overpressures at the external tunnel entrance appeared to converge for the 50-mm and 100-mm meshes (1.76 MPa and 1.60 MPa, respectively). In addition, these two mesh sizes led to differences of 4% and 6%, respectively, compared with that (1.70 MPa) estimated using the UFC 3-340-02 chart. Therefore, the numerical model was considered to be validated.
## 3 Results
### LUE at the Annual Time Scale and Influencing Factors  
The annual variations in the biophysical factors, LUE, and LUE\({}_{\rm max}\) during 2006-2019 are shown in Table 1. The annual PAR, T\({}_{\rm a}\), VPD, and P ranged from 7670.72 to 9807.15 mol m\({}^{-2}\), from 13.81 to 15.51 \({}^{\circ}\)C, from 7.98 to 10.04 hPa, and from 456.40 to 860.00 mm, respectively. The annual EF, CI, NDVI, and GPP ranged from 0.38 to 0.45, from 0.36 to 0.45, from 0.34 to 0.40, and from 842.37 to 1426.12 g C m\({}^{-2}\), respectively. The annual LUE ranged from 0.09 to 0.17 g C mol photon\({}^{-1}\), with a mean value of 0.12 g C mol photon\({}^{-1}\). The 8-day mean maximal LUE was regarded as the LUE\({}_{\rm max}\) to reduce the uncertainty caused by the noise data in each year. The annual LUE\({}_{\rm max}\) ranged from 0.21 to 0.41 g C mol photon\({}^{-1}\), with a mean value of 0.30 g C mol photon\({}^{-1}\).  
The results of the correlation analysis among the annual biophysical factors and LUE are shown in Figure 11a, where the annual GPP (\(r=0.90\)), PAR (\(r=-0.70\)), and CI (\(r=-0.60\)) influenced the LUE at significance levels of \(p<0.001\), \(p<0.01\), and \(p<0.05\), respectively. The remaining factors (T\({}_{\rm a}\), VPD, P, EF, and NDVI) did not influence the LUE because the significance level was \(p>0.05\). The effects of the GPP on the LUE were mainly direct; those of the PAR and CI on the LUE were mainly indirect, via the other two biophysical factors; the direct effect of the CI on was \(-0.24\) (Figure 11b). As shown in Figure 1, the annual Sr increased by 9.96 MJ y\({}^{-1}\) (\(p=0.064\)), the annual mean T\({}_{\rm a}\) increased by 0.05 \({}^{\circ}\)C y\({}^{-1}\) (\(p<0.001\)), and the annual P increased by 4.28 mm y\({}^{-1}\) (\(p=0.192\)) after 1990 in the study area. Under these trends of climate change, the annual LUE of the Chinese cork oak plantation might decrease in the future, based on the negative effect of the annual PAR, T\({}_{\rm a}\), and P on the annual LUE (Figure 11a).  
Figure 10: Variance inflation factors among monthly influencing factors (**a**), partial correlation coefficients between monthly biophysical factors and LUE (**b**), stepwise regression among monthly biophysical factors and LUE (**c**) in 2006–2019 in the Chinese cork oak plantation. *** significant at \(p<0.001\).
## 4 Experiment I--DSM Quality Evaluation
### Grid-Based Comparison  
In order to get a pixel-wise quality assessment in this section a grid-based evaluation was performed using the LiDAR 2001 and LiDAR2010 as reference data [20]. The HRSC2003 has been compared against LiDAR2001, while all other DSMs have been compared against LiDAR2010. Differing  
Figure 4: Profiles of the DSMs covering low-dense forest, high-dense forest and ground areas. (The DSMs are separated into two groups—above: moderately-high resolution DSMsl and below: very high resolution DSMs.)  
from the workflow in [20], we use two different grid cell sizes: one of 500 m\({}^{2}\), which corresponds to the size of the largest concentric circle of the forest inventory plots in Bavaria Germany (see Section 2.3), and another of 1 ha (10,000 m\({}^{2}\)), which is an important unit for forest management. Focusing on quality evaluation for the forest regions, we use only the grids that are full or partly covered by the forest mask (Figure 3) for the comparison. Figure 5 shows scatterplots for the 500 m\({}^{2}\) grid cells; for each cell, the mean height was derived from the DSM and compared to the corresponding mean height of the LiDAR2001 and LiDAR2010 data. In total, 6931 mean height values were compared. The names of the DSMs are reported below the \(y\)-axis.  
In this comparison, the Aerial 2012 data match the LiDAR data best, while WorldView2012 and HRSC2003 achieve similar results. In this grid-based comparison, ALOS2008 performs much better than in the profile comparison, and it yields similar results as the ones obtained by Cartosat2008 and TanDEM-X2013.  
Pearson's correlation coefficients were also derived for the mean, maximum and 68.3% and 95% quantiles of each DSM height metrics as derived from the grid cells [20; 43; 44]. Table 3 shows the correlation coefficients for the 500 m\({}^{2}\) and the 1 ha grid cells, in which mean height values yield the highest correlation coefficients for all DSMs. Aerial2012 achieved the highest correlation coefficients, while WorldView2012 showed satisfactory results, and RapidEye2009 had the lowest correlation coefficients. For the larger (1 ha) grid size the correlation coefficients are overall slightly higher than for the 500 m\({}^{2}\) grid. The correction coefficients values are generally higher than those in the next section, as only grids intersecting the forest mask have been taken into account, as described above.  
Figure 5: Comparison of mean DSM height values for 500 m\({}^{2}\) grid cells from HRSC2003 (**a**) ALOS2008 (**b**), Cartosat2008 (**c**), RapidEye2009 (**d**), WorldView2012 (**e**), Aerial2012 (**f**) and TanDEM-X2013 (**g**) with the corresponding heights as derived from the LiDAR2001 and LiDAR2010 DSM (\(n=6931\)).
# Research on Corrosion Damage Evolution of Aluminum Alloy for Aviation
## 3 Results and Discussion
### Depth of Corrosion Pits
#### 3.2.2 Distribution Law  
According to the research results, the distribution forms of the depth of corrosion pits of aluminum alloy materials generally included Gumbel (type I maximum) distribution, normal distribution, lognormal distribution, and two-parameter Weibull distribution [28; 29; 30]. In this section, statistical methods were applied to analyze the depth of corrosion pits, and linear regression equation fitting was conducted for the four distribution forms. When the significance level is \(\alpha\) = 0.05, that is, within the confidence level of 0.95, the linear correlation critical value of each corrosion cycle satisfying the assumed distribution can be solved by Formula (4). \[r_{\mathrm{C}}=\frac{t_{\alpha}(n-1)}{\sqrt{n-2+t_{\alpha}^{2}(n-1)}} \tag{4}\]  
The number N of corrosion pits selected for each corrosion time was 45, which resulted in \(t_{0.05}\) (45-2) = 1.681 and \(t_{0.05}\) (45-1) = 1.680, and the linear correlation critical value \(r_{\mathrm{C}}\) was calculated to be 0.248. It can be seen from Table 4 that the correlation coefficients of the four distributions were much greater than the critical coefficient \(r_{\mathrm{C}}\). It was obtained that, under accelerated corrosion conditions in the laboratory, the depth of corrosion pits of the specimens with different corrosion times could better follow the normal distribution and the Gumbel distribution, lognormal distribution, and two-parameter Weibull distribution. Further comparing the advantages and disadvantages of the three distributions, we obtained that, under different corrosion times, when the corrosion time was 8h, the absolute value of the correlation coefficient of the Gumbel distribution was 0.991 (the highest) and when the corrosion time was 24 h, the Weibull distribution of the correlation coefficient was 0.939 (the highest). So, the best distributions of the first two corrosion cycles were the Gumbel distribution and the two-parameter Weibull distribution. When the corrosion time was 48 h, 72 h, 96 h, and 120 h, the correlation coefficients of the normal distribution were above 0.990, better than the other three distributions. So, the optimal distribution was the normal distribution. Figure 8: The depth of corrosion pits under different corrosion time. Through the above analysis, we statistically obtained the optimal distribution linear regression equation combination corresponding to the depth of corrosion pits under different corrosion times, as shown in Table 5.  
The equations and experimental data in Table 5 are plotted in Figure 9. From the figure, it can also be seen that the linear regression equation combination under different corrosion times fitted in this paper can better reflect the distribution law of the depth of corrosion pits.
# Boosted Reptile Search Algorithm for Engineering and Optimization Problems
## 4 Experimental Results and Discussion
### Series of Analysis 1: Engineering Problems
#### 4.1.3 Pressure Vessel Design Problem  
This section presents the performance of developed RSRFT to solve another engineering problem, named the pressure vessel design (PVD) problem (see Figure 4), by determining the parameters that lead to minimizing the cost of the pressure cylinder. Those parameters are the thickness of the head \(Th\), the length of the cylindrical section of the vessel \(L\), the thickness \(Ts\), and the inner radius \(R\). The mathematical representation of PVD is given as  
\[\begin{split}\text{{\it Consider}}&\vec{u}=[u_{1} \ u_{2}\ u_{3}\ u_{4}]=[T_{s}\ T_{h}\ R\ L],\\ \text{{\it Minimize}}& 0.6224u_{1}u_{3}u_{4}+1.7781u_{2}u_{3}^{2}\\ &+3.1661u_{1}^{2}u_{4}+19.84u_{1}^{2}u_{3},\\ \text{{\it Subject to}}& g_{1}(\vec{u})=-u_{1}+0.019 3u_{3}\leqslant 0,\\ & g_{2}(\vec{u})=-u_{2}+0.00954u_{3}\leqslant 0,\\ & g_{3}(\vec{u})=-\pi u_{3}^{2}u_{4}-\frac{4}{3}\pi u_{3}^{3}+1296 000\leqslant 0,\\ & g_{4}(\vec{u})=u_{4}-240\leqslant 0,\\ \text{{\it Variables}}^{\prime}\ \text{range}& 0\leqslant u_{1}\leqslant 9 9,\\ & 0\leqslant u_{2}\leqslant 99,\\ & 10\leqslant u_{3}\leqslant 200,\\ & 10\leqslant u_{4}\leqslant 200\end{split} \tag{28}\]  
The outcomes of the comparisons are listed in Table 3. Those approaches are HPSO [56], ACO [57], the ES [53], the GA [52], PSO-DE [58], the GSA [41], CDE [44], and LSH-SPCM [49]. From these results, we noticed that RSRFT was able to reach the minimum objective value (as given in the column Optimal Objective), which outperformed the other compared techniques.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{6}{c}{**Estimated Values for Parameters**} \\ \hline \hline
**Method** & \(Th\) & \(Ts\) & \(R\) & \(L\) &
\begin{tabular}{c} **Optimal** \\ **Objective** \\ \end{tabular} \\ \hline RSRFT & 0.81612257 & 0.403409949 & 42.2861349 & 174.325078 & 5953.4364 \\ RFO & 0.81425 & 0.44521 & 42.20231 & 176.62145 & 6113.3195 \\ RSA & 0.8400693 & 0.4189594 & 43.38117 & 161.5556 & 6034.7591 \\ LSHSFCM & 0.9808 & 0.5251 & 50.1435 & 99.5532 & 6654.8902747 \\ OBLGOA [50] & 0.81622 & 0.40350 & 42.291138 & 174.811191 & 5966.67160 \\ PSO-DE [58] & 0.8125 & 0.4375 & 42.098446 & 176.6366 & 6059.71433 \\ HPSO [56] & 0.8125 & 0.4375 & 42.098353 & 176.637751 & 6059.7258 \\ ACO [57] & 0.8125 & 0.4375 & 42.098411 & 176.63769 & 6059.734 \\ CBE [44] & 0.8125 & 0.4375 & 42.098087 & 176.640518 & 6059.7456 \\ ES [53] & 0.8125 & 0.4375 & 42.097398 & 176.65405 & 6059.94634 \\ GA [52] & 1.125 & 0.625 & 55.9886598 & 84.4542025 & 8538.8359 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The value of the estimated parameters using RSRFT and other approaches to solve the PVD problem.
# An Empirical Study of the Code Generation of Safety-Critical Software Using LLMs
## 8 Conclusions  
Pre-training large models such as ChatGPT and GPT-4 have revolutionized software development by providing a new possibility for automated program code generation. We focus on the demand for automatic code generation in safety-critical domains such as nuclear energy and automotive industries. We use real-world engineering cases to conduct a case study on generating safety-critical software code using GPT-4 as the code generation tool. By exploring different approaches and methods, a novel Prompt engineering (Prompt-FDC) suitable for generating code for safety-critical software is introduced, and a new software development process and lifecycle V-model for safety software are proposed. Through the practical experiments described in this paper, we demonstrate that, with appropriate prompt engineering, LLMs can auto-generate safety-critical software code that can be applied in practical engineering. The LLMs are still evolving, aiming to improve the understanding of requirements and increase code quality. Applying LLMs-based code generation techniques to more engineering domains to address efficiency and safety issues in software development is an area that deserves attention and further research in the future.  
There are several directions for future work. The code generation accomplished in this paper mainly focuses on functional components in safety-critical software. Researching code generation on a larger scale would bring greater convenience to industrial applications. Another important aspect to consider is the efficient and accurate verification of code generated by LLMs. So far, software verification relies mainly on manual code review and testing. It is crucial to explore verification methods that are more suitable for automatically generated code. Additionally, to facilitate the refinement and enhancement of safety-critical software requirements, prompt and code plugins based on GPT-4 can be developed to better support new software development processes.  
Conceptualization, M.L. and J.W.; methodology, Q.M.; software, Z.F.; validation, Y.W. and T.L.; formal analysis, M.L.; investigation, Y.W.; resources, J.W. and Q.M.; data curation, Z.F.; writing--original draft preparation, M.L.; writing--review and editing, J.W.; visualization, Y.W.; supervision, T.L.; project administration, Q.M.; funding acquisition, J.W. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the National Key Research and Development Program under Grant 2022 YFB3305200; and in part by the Major Science and Technology Special Project of Sichuan Province under Grant 2022 ZDZX0008.  
The authors declare no conflicts of interest.
## 3 Experimental Results and Analysis
### Fabrication and Assembly of the CPFRA  
The primary reflector surface and the sub-reflector surface were fabricated using the standard printed circuit board (PCB) process. The fabricated parts of the CPFRA are shown in Figure 11. Figure 11a is a photo of the transmissive linear-to-circular converter, which consists of the sub-reflector, and Figure 11b is a photo of the metasurface-based element, which consists of the primary reflector. The LP horn antenna was fabricated with metal materials. To assemble the FRA with the linearly polarized horn antenna, a substrate, several nylon struts, and screws were used, as shown in Figure 11c.  
Figure 10: **(a)** Reflection coefficient and (**b**) normalized radiation pattern of the LP horn antenna.
## 3 Numerical Case Study
### Time Efficiency  
Time efficiency is an important factor in implementation of an identification process. The elapsed computation time to identify a set of analysis process is used to compare the computation time of the CVA, PC, and UPC subspace algorithms. Time efficiency evaluation was carried out in a MSI Dual Core CPU desktop personal computer (3 GHz dual core CPU and 2 GB RAM) having Windows 7 as its operating system. Matlab software is used for all simulations, programing and analysis in this study. In order to simulate the response data obtained from the ambient excitation, random noise with ratio of 30% is added to the response signal of the numerical model. One hundred different noise-induced response signals are used in the evaluation process. The average elapsed time for identification of 100 sets of signals using CVA, PC and UPC algorithm was recorded as presented in Table 1.  
The mean values for identification of the system matrices using UPC, PC, and CVA are recorded 5.2, 10.8, and 11.3, respectively. The computation time for running CVA and PC algorithms is almost two fold higher than that of UPC for identification of the same set of input data. Higher performance of the UPC can be due to using unit weight matrix in the identification process.
## 3 Results
### Volatile Se Compounds in _G. lucidum_  
With the extension of culture time, the mycelia color and odor changed, especially in _G. lucidum_ supplemented with 200 ppm Na\({}_{2}\)SeO\({}_{3}\). Therefore, to fully explore the odor changes of _G. lucidum_ under Na\({}_{2}\)SeO\({}_{3}\) culture, headspace solid-phase microextraction combined with gas chromatography-mass spectrometry was used to analyze the volatile Se compounds in the liquid culture process (Supplementary Figure S1). Significant differences in the volatile Se compound profiles were observed in response to Se treatments, and the kinds and contents of volatile Se compounds in the 200 ppm Na\({}_{2}\)SeO\({}_{3}\) group were remarkably higher than those in the 50 ppm Na\({}_{2}\)SeO\({}_{3}\) group. Ten volatile Se compounds were identified in the culture broth of Se-enriched _G. lucidum_, and carbo selenide, dimethyl selenodisulfide, and dimethyl diselenenyl sulfide were sulfur-containing compounds only detected in the 200 ppm Na\({}_{2}\)SeO\({}_{3}\) group. No volatile Se compounds were found in the control condition, whereas three and ten volatile Se compounds were identified in the 50 ppm and 200 ppm Na\({}_{2}\)SeO\({}_{3}\) groups, respectively (Table 1).  
Figure 3: Changes in nucleic acid (**a**) and protein (**b**) and the cell wall and cell membrane components chitin (**c**), \(\beta\)-1,3-glucan (**d**), and ergosterol (**e**) in _Ganoderma lucidum_ with selenite at different concentrations. The bars indicate the standard error of the mean (different letters indicate a significant difference on the same culture day at \(p<0.05\)).  
The volatile Se compound types and contents all increased with increasing Na\({}_{2}\)SeO\({}_{3}\) levels, which was not only the main cause of the odor change but also the detoxification mechanism of _G. lucidum_ to alleviate high Se concentration stress. The most abundant volatile Se compound was dimethyl selenide in the 50 ppm Na\({}_{2}\)SeO\({}_{3}\) group, accounting for more than 90% of all volatile Se compounds. Carbon diselenide, methyl diselenide, dimethyl diselenenylsulfide, and triselenothane were the major volatile Se compounds in the 200 ppm Na\({}_{2}\)SeO\({}_{3}\) group, and carbon diselenide was principally produced on the 4 th day during the culture process, whereas dimethyl diselenenylsulfide, methyl diselenide, and triselenothane were largely generated on the 5 th and 6 th days.