esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
In this study, we solved one of the possible problems faced by DARTS [3], which is vote dispersion. With the proposed DG-DARTS, the total amount of computation is reduced, so the total computation compared to DARTS is decreased with significantly lower GPU days while reasonably increasing accuracy. The vote dispersion problem, which is latent in the DARTS methodology, is solved by the operations of the search space being grouped based on the criteria of the gradient of the architecture parameter \(\alpha\) over the training epochs. By the weights of the grouped operations and selections, useful operations that are discarded by DARTS survive and are used. With this AutoML approach, without manually changing the size of the search network, the test accuracy is increased to 97.49% on the CIFAR10 dataset [14]. For the same training epochs, the search cost is lower than that required for DARTS because DG-DARTS uses fewer operations in stage 1. In summary, DG-DARTS required 0.22 days in comparison to the 1.5 GPU days required for DARTS, representing a seven-fold increase in speed. Our future research will include experimental results on different datasets, such as CFAR100 [14] and ImageNet [24], to verify the effect of DG-DARTS on such datasets. In addition, we will apply DG-DART to other types of models, such as graph convolution [25] and RNN [3], to determine its effectiveness for search spaces with other types of operations. The application domains of DG-DARTS include natural language processing (NLP), EdgeML, and so on, with the concise models generated by DG-DARTS. For example, machine learning on edge devices may require simpler models given the restrictions of computational resources and communication bandwidth. Even though ARM Corex processors, which are usually used for edge devices, have relatively higher computation power, it is still impossible to achieve high-performance  
Figure 7: Architecture found by DG-DART on the CIFAR10 data set. (**a**) Normal cell. (**b**) Reduction cell.  
GPUs for edge devices; therefore, restrictions on computational power is still a problem for machine learning on edge devices. Additionally, low-power and low-bandwidth network technologies for IoT devices, such as LoRa, place another restriction on communication between edge devices. The simpler models generated by DG-DARTS provides one solution to these environmental restrictions.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MEST) (NRF-2019R1F1A1056123).  
Not applicable.  
The author is deeply appreciative of special help provided by [PERSON] for their efforts to initialize this research. The author also thanks those who helped with formatting, proofreading, and other support in preparing this research paper.  
To the best of my knowledge, regarding this research article, I have no conflict of interest with any other researcher or funding institute. My research is based on pure academic motivation. The funding institute did not provide any academic guidance and only performed management activities.
# SatGuard: Satellite Networks Penetration Testing and Vulnerability Risk Assessment Methods
## 1 Introduction
### Contributions  
This paper advances satellite network security through three principal contributions, with a focus on the innovative integration of large language models (LLMs) to address domain-specific challenges:  
1. To develop a three-dimensional penetration testing framework tailored to satellite networks, enabling systematic evaluation across space, ground, and user segments with configurable automation levels (manual, semi-automatic, and fully automatic).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Author** & **Domain** & **Research Scope** & **Satellite-Specific Limitations** & **Applicability to Satellite Networks** \\ \hline [PERSON] et al. (2022) [9] & General IT & Reinforcement learning applications & No adaptation to satellite & Limited \\  & & in penetration testing & network constraints & \\ [PERSON] et al. (2020) [10] & Mobile applications & Dynamic vulnerability analysis via & RF physical-layer vulnerability & No \\  & & mobile security platform & analysis omitted & \\ [PERSON] et al. (2018) [11] & IoT devices & Modular penetration testing & Satellite-terrestrial hybrid & Limited \\  & & frameworks for IoT ecosystems & protocol gaps & \\ [PERSON] (2023) [12] & Cloud services & Platform-specific penetration testing & Space protocol & \\  & & methodologies & vulnerabilities excluded & No \\ \hline [PERSON] et al. (2022) [13] & Power systems & Knowledge graph-based penetration & On-board power constraints and & \\  & & testing framework & radiation-induced software & Limited \\  & & & faults unaddressed & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Limitations of existing penetration testing frameworks.  
2. To propose a nonlinear vulnerability risk assessment formula that amplifies high-impact, low-probability threats, integrates dynamic time-decay mechanisms for residual risk quantification, and prioritizes critical segments through environmental weights.
3. To validate the framework through semi-automated experiments using GPT-4 and DeepSeek-R1, achieving a 73.3% success rate in vulnerability exploitation while embedding ethical safeguards to prevent service disruptions. The integration of prompt engineering and role-based constraints ensures ethical alignment, mitigating risks of unintended service disruptions--a critical innovation for sensitive space infrastructure.  
By bridging AI-driven automation with domain-specific risk modeling, this work pioneers the use of LLMs in satellite cybersecurity, offering scalable solutions for next-generation constellations.
# Application of Three-Dimensional Vision Technology in Dance
## 2 Three-Dimensional Vision Technology
### Three-Dimensional Vision Technology Types
#### 2.2.2 Traditional Three-Dimensional Vision Technology
Pointing light technology is less restricted by viewing distance and angle, has no resolution loss, is a natural and comfortable viewing experience, and has an excellent 3D vision effect [22]. However, the parallax effect is easily affected by the position of the light source and the ambient light, and the realization complexity is high, so the products are not mature. Integrated imaging, proposed by [PERSON] [23; 24], is a true 3D bare-vision autostereoscopic display technique for recording and reproducing a real 3D scene using an array of microlenses arranged in a periodic pattern in a 2D plane [25; 26; 27]. The traditional integrated imaging technique involves recording the elemental image array and reproducing the 3D image [25; 28]. The recording process is to record the microlens array to the object space scene imaging to obtain the spatial information of the object space scene from different viewpoints, forming the elemental image array. The reproduction process puts the elemental image array in the corresponding position of the object space with the same parameters as the reproducing microlens array. According to the principle of reversibility of the light path, the light is gathered through the reproducing microlens array to restore and reconstruct the original shape of the object space scene, which can be viewed from any direction within a limited viewing angle. Plenoptic imaging is usually based on a plenoptic camera. Plenoptic cameras capture a sampled version of the light map (often called the light field) emitted by a 3D scene [29]. These devices are used for various purposes, such as computing different sets of view maps of a 3D scene, removing occlusions, and changing the plane of focus of the scene. They can also capture images and project them onto an integral imaging display to show a 3D image with full parallax. However, it faces a similar problem to integrated imaging.
## 1 Introduction
A plethora of retrieval methods have been proposed for estimating spatially distributed ET and SSM utilising spectral information acquired in selected regions of the electromagnetic radiation spectrum; often combined with ancillary surface and atmospheric observations. Those techniques are well understood but each has atypical strengths and weaknesses, see recent reviews [20; 21; 22]. Some of those approaches have shown promising potential for providing accurate estimates of spatial-temporal estimates of surface heat fluxes and SSM, with accuracies reported in the ranges of 50 Wm\({}^{-2}\) and of 0.04 m\({}^{3}\cdot\)m\({}^{-3}\) respectively; accuracy levels required by many applications [23]. At present, there are several operational products available providing ET and SSM estimates, each with their own strengths and limitations. In light of the above, this paper aims at providing an overview of the state of the art of EO techniques to derive operational estimates of both ET and SSM. In this context, we critically discuss the challenges and caveats associated with the derivation of accurate estimates of ET and SSM parameters from space. We make suggestions on how future work should be directed to enhance the accuracy and operational capability of services developed using EO data. The paper closes with a reflective discussion on how existing or future EO operational products could lead to new applications related specifically to attain more sustainable agricultural practices, which is of the utmost importance since agriculture is the world's largest consumer of fresh water.
# A Study of Cislunar-Based Small Satellite Constellations with Sustainable Autonomy
## 7 Conclusions and Future Work
This paper makes a sincere effort towards that direction and forms a major foundation towards building autonomous systems and operations that are significantly contributing but also sustainable for a long term. This will bring synergy of various factors together and avoid unknown anomalies and conflicts in the Cislunar space, boosting its economy and allowing missions to be safe, intelligent, and organized while meeting their mission objectives as planned. The limitations that are being worked on currently with this proposed methodology are the complexity of computations to be optimally reduced to a significantly lower level. It doesn't mean that it is not simple in computation now, but further efforts in this regard to reduce the complexity overall in a system of operations towards more precision in implementation will be desired. The computation time for each simulation (including the iterations of Monte Carlo) remained under 10 s, making the algorithms compact and significantly reliable. Even during several trials, the overall accuracy in the execution of the algorithms remained simple, compact, and accurate without any major anomalies detected. This can be further improved with upgraded accuracy and reduced computation efforts, as simple but effective systems are the key to the NewSpace challenges. In the near future, these results will be studied further in various real-time operating scenarios. The space environment is dynamic and changes rapidly over time. Hence, more rigorous testing and evaluation of the proposed methodology will be aligned specifically to small satellites and their constellations. The Cislunar operations also will be the center of the focus to make its operations effective, safe, continuous, and enduring for a long period of time. Conceptualization, M.I.R. ; methodology, M.I.R. ; software, M.I.R. ; validation, M.I.R. and H.B. ; formal analysis, M.I.R. ; investigation, M.I.R. ; resources, M.I.R. ; data curation, M.I.R. ; writing--original draft preparation, M.I.R. ; writing--review and editing, M.I.R. ; visualization, M.I.R. ; supervision, H.B. ; project administration, M.I.R. and H.B. ; funding acquisition, M.I.R. and H.B. All authors have read and agreed to the published version of the manuscript. **Funding:** This research received no external funding. **Data Availability Statement:** The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author. **Acknowledgments:** I'd like to specifically thank KAIST for providing me with relevant material for research and also reference downloads. I'm also highly indebted to my seniors and Freeflyer for their seamless support and encouragement always. I thank national space agencies globally for their continued efforts and aspiring students like me to research and develop systems that are sustainable for the Cislunar space. **Conflicts of Interest:** The authors declare no conflicts of interest.
# Integrated Design of Moon-to-Earth Transfer Trajectory Considering Re-Entry Constraints
## 1 Introduction
Aiming at the problems above, an integrated design method for the opportunity and precise transfer trajectory is proposed in this paper. The application of geometric principles for re-entry and window and precise trajectory determination based on maneuverability are the main innovations. First, an analytical geometry method is brought up to determine the state of the probe at the re-entry point according to the re-entry constraints to ensure an accurate landing. Second, the obtained re-entry state is inversely integrated to acquire the daily transfer trajectory with a certain perilune altitude, and the Moon-to-Earth transfer opportunity is determined by delimiting the maximum perilune altitude as a boundary, which is closely related to the maneuverability of the probe. The opportunity obtained is more accurate and with a wider range compared to the patched-conic method. Then, a three-impulse correction strategy is brought forward to rapidly obtain the precise transfer trajectory within the opportunity, which enhances the emergency capability of the trajectory. Furthermore, the effects of several important design parameters on the Moon-to-Earth transfer opportunity and the performance of the transfer trajectory are also exhaustively analyzed to provide a more explicit reference for future Moon exploration missions. The proposed integrated design method obtains the transfer opportunity, which efficiently provides a reliable initial value for precise transfer trajectory. The structure of the paper is as follows. The precise dynamical model and the definition of the re-entry coordinate frame are presented in Section 2. The Moon-to-Earth transfer opportunity determination method with strict re-entry constraints is described in Section 3. The precise fuel-saving three-impulse transfer trajectory determination is shown in Section 4. In Section 5, the numerical simulation and parameter analysis is presented. Finally, conclusions are drawn in Section 6.
# Vibration Signal Classification Using Stochastic Configuration Networks Ensemble
###### Abstract
[32] proposed an ensemble model with the base models, selecting from a set of SCN-based learners, and it has been used for large-scale and/or online stream data modeling. Meanwhile, for the fault diagnosis problem, [PERSON] et al. [33] built a SCN model to classify the types of fault by using the fault features extracted from the vibration signals. [PERSON] et al. [34] adopted a self-attention long short-term memory (LSTM) network and self-attention residual network to extract fault features, then a SCN-based method was built with these fault features for bearing fault classification. Although experimental results have demonstrated the advantages of SCN-based models for application in fault diagnosis, the recognition performance is strongly dependent on the early feature extraction methods. This paper proposes a SCNs ensemble model, which aims to combine different fault characters extracted from multiple source signals for the classification task. The proposed model makes use of the fault vibration information in the frequency domain and the time-frequency domain; the extracted features are fused in different ways to improve the classification effect. The ensemble algorithm randomly initializes the hidden layers parameters in the base SCNs, and the global output weight is calculated by employing the least square method with the \(L_{1}\) norm constraint, which is easy and simple to implement. The experimental results show that the proposed SCNs ensemble model outperforms the compared stochastic models and obtains better classification performance for vibration fault signal. The remainder of this paper is organized as follows. Section 2 overviews the stochastic configuration networks, including the SCN framework and 2 DSCN with image inputs. Section 3 details the classification model based on SCN with hybrid data and the proposedensemble SCNs with algorithmic descriptions. Section 4 presents the experimental study on industry fault dataset. Finally, the conclusion is presented in Section 5.
## 2 Materials and Methods
### ATP, Mitochondrial Membrane Potential (MMP), and Oxygen Consumption Assay  
ATP detection was performed using a luminescent ATP detection assay kit (CAT: ab113849, Abcam). HUVECs were seeded into 6 well plate. The number of seeded cells was 2 \(\times\) 10\({}^{5}\) cells per well. To assess the effect of 4 HR administration on ATP levels, 1, 10, and 100 uM 4 HR were applied, and cellular lysates were collected after 8 and 24 h. The subsequent procedure was in accordance with the manufacturer's protocol. Briefly, ATP standard was added into standard wells, and medium was added into control wells. The detergent solution was added and incubated for 5 min. Then, the substrate solution was added and incubated for 5 min. The plate was stored in a dark room for 10 min. The luminescence was measured using a plate reader.  
MMP was measured using a commercially available kit (CAT: MAK159, Sigma-Aldrich). Cells were prepared on a 96-well plate. The number of seeded cells was 5 \(\times\) 10\({}^{4}\) cells per well. The JC-10 Dye Loading Solution was prepared by adding 50 \(\upmu\)L of 100\(\times\) JC-10 to 5 mL of Assay Buffer A. Cells were treated with 10 \(\upmu\)L of 10\(\times\) test compounds to induce apoptosis. In parallel, we set up negative (vehicle only) and 4 HR-treated samples. The cells were incubated for 24 h. JC-10 Dye Loading Solution (50 \(\upmu\)L) was added to each well. Cells were protected from light and incubated in a 5% CO\({}_{2}\) incubator at 37 \({}^{\circ}\)C for 30 min. Assay Buffer B was added at 50 mL to each well. The fluorescence intensity was measured at 490 and 540 nm. The ratio of red/green intensity was used to determine MMP.  
Oxygen consumption assay was performed using an oxygen consumption assay kit (CAT:ab197243, Abcam). Briefly, HUVECs (4.0 \(\times\) 10\({}^{4}\) cells/well) were plated on 96-well cell culture plates and incubated overnight. After removing the media from all wells, they were replaced with 150 \(\upmu\)L of fresh culture media. Reconstituted extracellular O\({}_{2}\) consumption reagent (10 \(\upmu\)L) was added to each sample well. For the blank control wells, 10 \(\upmu\)L of fresh culture media was added. Each well was sealed by adding 100 \(\upmu\)L of pre-warmed high sensitivity mineral oil. For the measurement, the prepared plate was inserted into a fluorescence plate reader pre-set to the measurement temperature (37 \({}^{\circ}\)C).
## 3 Methods  
### Trip Assignment Probability  
The trip assignment probability is calculated by extracting the accessible HSR stations for each zone by route. The trip assignment probability concept is shown in Figure 3. Unlike the optimal strategy algorithm, this method can simulate the passenger behavior of selecting different HSR stations in a zone. For zone \(i\), stations \(s_{A}\) and \(s_{B}\) within the  
Figure 2: Standard long-term HSR demand-forecasting procedure in Korea.  
accessible distance are considered accessible stations, and station \(s_{\mathbb{C}}\) is excluded because it is outside the accessible distance. Thus, the HSR trips from zone \(i\) are assigned to \(s_{A}\) and \(s_{B}\). The passengers are assigned in the same way for the departure and arrival zones. If the departure and arrival stations are on the same line, then X = 1; otherwise, X = 0, and the passenger volume between the two stations equals zero. For example, in Figure 3, \(X_{s_{A}\to s_{D}}=1\), but \(X_{s_{A}\to s_{\mathbb{C}}}=0\). The inter-station passenger volume, \(V_{s_{A}\to s_{D}}\), is equal to the sum of all stations multiplied by \(P_{i_{SA}}\), the probability of using station \(s_{A}\) in zone \(i\); \(P_{i_{D}}\), the probability of using station \(s_{D}\) in zone \(j\); and \(T_{ij}\), the O/D trips from zone \(i\) to zone \(j\). If \(s_{A}\) and \(s_{D}\) are not connected on the same line (\(X_{s_{A}\to s_{D}}=0\)), \(V_{s_{A}\to s_{D}}\) becomes 0.  
Passengers can access different stations, even within the same zone, based on impedances experienced in traveling to the station or the service level of the station. [PERSON] and [PERSON] found that induced demand increases with decreased travel time, travel costs, and access time, and increased service frequency [41]. [PERSON] et al. used walking distance to the station as an influential factor for metro ridership [42]. Moreover, the vehicle frequency of public transportation is the most crucial factor of the trip assignment in the optimal strategy algorithm [38]. Therefore, we considered \(d_{is}\), the Euclidean distance from zone \(i\) to station \(s\), to be a travel impedance factor, and \(f_{sl}\), the train frequency at station \(s\), to be a travel attractiveness factor.
## 2 Experimental and Numerical Setup
### Laser Doppler Anementometry  
Point-wise velocity measurements inside the swirl chamber were carried out using a 2D LDA FlowExplorer (Dantec Dynamics A/S, Skovlunde, Denmark), for setup details see [25]. The measurements were performed in four axial distances from the atomizer cap, see Figure 2. The distance of \(d_{ac2}\), \(d_{ac8}\), \(d_{ac11}\) and \(d_{ac13}\) from the top wall of the swirl chamber is 2, 8, 11 and 13 mm, respectively. The refractive index of the atomizer body and working liquid differed by less than 0.005 for both wavelengths involved in the measurement. Therefore, the velocity error is expected to be less than 0.5% [14; 22]. Nevertheless, the real position of the measurement volume inside the atomizer body had to be corrected as \(S_{2}=nS_{1}\), where \(S_{2}\) is the real distance of the measurement volume from the atomizer wall and \(S_{1}\) is the traversed distance of the measurement volume from the atomizer wall. The measurements near the air-core surface were affected by light reflections, which generate noise on the LDA signals and subsequent velocity estimates. Therefore, these data were processed by a filtration algorithm which seeks a Gaussian distribution in the velocity histogram and calculates the mean velocity only from the data which satisfies the Gaussian distribution.  
Particles SL75 e-spheres with a mean diameter of 45 \(\upmu\)m were used as flow tracers for the LDA measurements. Their Stokes number, based on the highest swirl velocity and air-core diameter, which is the worst scenario case for the particle movement, was less than 0.3 for the \(Re=4000\) regime, which ensured a sufficient flow fidelity. Measurement repeatability error was lower than 2% for swirl velocity.