esa-sceva/satcom-chunk-collection huggingface dataset:
# The Evolution of Service Ecosystems Based on the Lotka-Volterra Model
## 1 Introduction  
The diversification and expansion of businesses have become essential strategies for modern enterprises, with large corporations increasingly transitioning from singular business models to diversified ecosystems. In these ecosystems, services are interconnected and collaboratively evolve to meet dynamic market demands [1]. The service ecosystem is multi-layered, with foundational services at the base providing stability and support, while interactions between different service sectors at the top drive innovation [2] and resource integration [3]. This interconnectedness enables the development of composite services that generate value through cross-field collaboration. As consumer demands become more complex, enterprises must innovate by integrating diverse resources and exploring new service compositions to maintain competitiveness in a rapidly evolving market [4].  
Despite extensive research on individual services, comprehensive studies on service ecosystems remain underdeveloped [5]. At present, research in this field faces many challenges. Firstly, computational experimental results lack a theoretical foundation. In the few relevant studies, the lack of theoretical support for computational experimental results is a common problem, which may reduce the credibility and interpretability of the experiments. In addition, it is difficult to design or implement computational experimental systems. Mostprevious work in this area has focused only on the supply-demand relationship between service providers and demanders, ignoring that the service ecosystem is a complex system influenced by multiple factors [6]. Therefore, the Lotka-Volterra model is introduced to explore the competitive and cooperative relationships among services. Considering that the characteristics of services in service ecosystems are more complex than populations in biology, we also extend the binary Lotka-Volterra model to a ternary model and improve the service evolution in response to social environmental factors and service dynamics. In addition, an adversarial game algorithm is utilized as the decision-making method for service agent nodes to experiment the evolutionary patterns of service ecosystems, and a genetic algorithm is designed as the model for service evolution.  
The contributions of this paper can be summarised as follows:  
1. The Lotka-Volterra model is introduced from biology into the field of service ecosystem research, and a novel extended ternary Lotka-Volterra model is proposed to address the influence of social environmental factors and service dynamics on service evolution.
2. A computational experimental system integrating adversarial game algorithms was designed to study the competition-cooperative evolution model of services.
3. Combining the experimental results with the analysis of the real world, it is concluded that moderate competition among the original businesses is conducive to the development of new composite businesses, while excessive cooperation will hinder the survival and growth of new businesses in the market.
## 5 Cross-Contextual Studies
Figure 11 shows a visual comparison in ablation studies on the CT lung dataset, whereby the ACNCL-based networks subdued the sole predictors in all metrics used. A representative case is where the ACNCL-U-Net had an SSIM value of 0.979, RMSE of 0.032, and PSNR of 31.353 dB compared to the sole predictor for content learning, U-Net, which had an SSIM value of 0.862, RMSE of 0.035, and PSNR of 28.653 dB. Similarly, in Figure 12, for the MRI dataset, the ACNCL-based networks demonstrated superior denoising, excelling in both content preservation and noise reduction. Among these networks, ACNCL-U-Net and ACNCL-CA-AGF outperformed ACNCL-DnCNN, ACNCL-DenseNet, and ACNCL-DWT as they had the highest PSNR values and SSIM values closer to 1. This means that there were better-quality images that evaluated how well features like tumors were preserved after noise reduction. An SSIM value closer to 1 indicates that structural information and the fidelity of tumor shape and texture were preserved. The ACNCL-DnCNN and ACNCL-DenseNet had better RMSE and PSNR compared to ACNCL-DWT, as shown in Table 5, but this came at the cost of some blurring in structural features. When compared with comparison methods like BM3D, RED-CNN, WGAN-GP, and VAE, all the proposed ACNCL-based networks achieved better quantitative results. In general, the quantitative evaluation in Table 5 confirmed our visual observations. Regarding MRI comparison of different methods for under-sampling MRI denoising and tumor detection, the NLM, SCNN, U-Net, and DnCNN were used as reference methods, as shown in Figure 14. All the ACNCL-based networks displayed better results across all metrics used. Non-local means, while effective at low-to-moderate noise levels, were not competent at higher noise levels. The SCNN struggled with non-uniform noise, such as periodic patterns or correlated noise, which required more sophisticated contextual information. Both U-Net and DnCNN were effective in noise reduction but at the cost of blurriness and over-smoothed textures, leading to output that was clean but lacked natural sharpness. The ACNCL-CA-AGF displayed better results compared to ACNCL-DnCNN and ACNCL-U-NET in image restoration and tumor detection, as confirmed in Table 6. In general, the proposed ACNCL-based networks were superior to the comparison methods both in noise removal and structural restoration.
# Automating Stimulation Frequency Selection for SSVEP-Based Brain-Computer Interfaces
## 2 Related Work
### BCI Usability and User Satisfaction  
While the safety of modern BCIs is well-established and performance remains a primary research focus, calibration algorithms that consider interface usability are relative new. BCI-related studies that follow a user-centered approach define usability in terms of effectiveness (accuracy), efficiency (ITR and subjective workloads), and user satisfaction [44].  
Selection algorithms that consider the user's emotional state and subjective comfort during BCI interactions can help lead to new levels of user experience. However, there is a certain disparity in how exactly the registered EEG signals should be used to automatically infer various dimensions of user satisfaction. [PERSON] and co-authors have exhaustively addressed this in their works, recently culminating in a connection between potential EEG signal characteristics and usability measures, as presented in [45].  
As mentioned in [46], using flicker frequencies in the range of 4-30 Hz can lead to visual fatigue. Thus, it is important to find frequencies at which the interaction will be the most effective and the person experiences minimal discomfort in using BCIs [47]. Various methods are employed to minimize the discomfort; for example, [46] proposed using a chessboard stimulus, which allows for reducing user discomfort without compromising performance. In turn, we suggest taking user satisfaction into account when selecting an individual frequency for BCI.  
The safety of using brain-computer interfaces is a determining factor that allows them to be integrated into various aspects of our lives; therefore, this factor should be approached with the utmost seriousness and attention. In experiments based on SSVEP, the presentation of rhythmic visual stimuli potentially has the ability to induce epileptic activity and trigger seizures. However, in our study, participants over 18 years old without a history of epileptic seizures in their medical records were involved, and the likelihood that the current experimental protocol could elicit such pathological brain activity was extremely low. Additionally, after undergoing experimental stimulation without any negative effects, the probability of experiencing seizures during subsequent long-term use of BCIs becomes nearly zero. This is why we deemed it unnecessary to account for this factor or develop a special index that could consider its influence.
## 2 Current Issues, System Rationale and Objectives
### System Design Rationale  
Adhering to the above issues and observations, we have evolved our previous AR HUD interface designed to attend to driver's requests, yet to provide the incoming infotainment data in a safe and accessible manner [2; 23; 24].  
Due to the provided windshield estate and the proximity to driver's FOV, a HUD solution was deemed essential for a complex set of infotainment systems' functionalities aiming to minimise driver distraction by controlling the multiple stimuli provided by in-vehicle infotainment devices [2; 4; 14; 15]. To mitigate the aforementioned issue, we designed and implemented a prototype medium-size, HUD interface which aims to improve driver performance and reduce significantly the eyes-off-the-road effect resulting from the use of infotainment systems as presented in Figure 1.  
Being aware of the limitations posed by the small-factor HUD and the required interactions through dashboard devices, our medium-sized HUD design utilises a more significant segment of the windshield. Previous evaluation results provided an insight into the HUD requirements that improve driver's response times (RT) and situational awareness during driving under adverse weather conditions and low visibility. They highlighted the improved efficiency of the large-factor and full-windshield HUD interfaces [1; 23; 28; 2].  
Prior studies in the development of efficient and user-friendly AR-HUD interfaces provided a crucial background for the currently proposed system and identified in advance potential design and development issues. A recurrent issue was the lack of interaction with the incoming infotainment data which were presented in the HUD interface [28; 29]. Similarly, the existing HUD interfaces provided by a large number of automotive manufacturers are plagued with complicated menus and buttons positioned in the HDD space or in awkward to reach positions, around the steering wheel.  
The proposed novel HUD interface aims to keep the driver in touch with the incoming information (i.e., mobile phone and navigation system data) whilst maintaining a driver's focus on the primary task of driving and alleviating the cognitive load of the in-vehicle infotainment systems.
## 4 Results and Discussion
### Time-Domain Characteristics
#### 4.1.1 Aerodynamic Lift  
The panhead of the pantograph is composed of contact strips, supports, and a bracket. Aerodynamic lift is an important factor affecting the contact force between the pantograph and catenary. Comparison curves of the aerodynamic lift of the panhead passing through tunnel at different speeds are shown in Figure 11.  
As shown in Figure 11, aerodynamic lift values of the contact strip-support and bracket increase as running speed increases. At the same speed, the direction of aerodynamic lift of the contact strip-support is opposite to that of the bracket and the values are smaller than that of the bracket. At a certain distance from the entrance or the exit of the tunnel, fluctuations in panhead aerodynamic lift are relatively flat at different running speeds; the cases are open air, which validates the calculation results given above. For the contact strip-support aerodynamic lift, the fluctuations and values are smaller at 280 km/h than at other speeds; the differences between at 320 km/h and at 360 km/h are relatively small as the differences between at 400 km/h and at 420 km/h are smaller; the fluctuations and values between 400 km/h and 420 km/h are larger than at other speeds. For the bracket aerodynamic lift, the fluctuations and values are smaller at 280 km/h; the differences at 320 km/h and between 360 km/h and 400 km/h are relatively small; the fluctuations and values are much larger at 420 km/h than that at other speeds. Figure 10: Contact forces between pantograph and catenary simulated and fitted under open air at 360 km/h. The aerodynamic lift of the panhead is the sum of that of the contact strip-support and bracket, so it does not strictly increase with increase in running speed. For example, its values at 320 km/h are greater than that at 400 km/h at many positions, and its values at 360 km/h are greater than that at 420 km/h at many positions. This is also a main reason why the contact force does not simply increase with running speed in subsequent tests. The statistical results of the aerodynamic lift of the panhead passing through tunnel at different speeds are listed in Table 2. The standard deviation and mean value of panhead aerodynamic lift are largest at 420 km/h at 43.33% and 156.72% higher compared to 280 km/h, respectively. No performance index of the aerodynamic lift of the panhead increases strictly with increase in speed. Compared with that at 360 km/ h, at 400 km/h, the contact strip-support aerodynamic lift increases more in the positive direction than the negative while that of the bracket increases less in the negative direction. The resultant force of the two, namely, the aerodynamic lift of the panhead, decreases; this is also the main factor that affects the subsequent contact force between pantograph and catenary. Compared with that at other speeds, at 420 km/h, the contact strip-support aerodynamic lift increases more in the positive direction while that of the bracket increases more in the negative direction. So the resultant force of the two, namely, the aerodynamic lift of the panhead, is not always maximal.
# All-in-Focus Three-Dimensional Reconstruction Based on Edge Matching for Artificial Compound Eye
## 5 Experiments
### Evaluation  
#### 5.2.1 Image Correction  
To achieve accurate depth estimation, image correction plays a crucial role. To verify the effect of image correction, in Figure 10, the calibration board and another test pattern are placed parallel to eCley, and the sub-images are fused and reconstructed at a certain distance. If the sub-images are corrected and reconstructed at the right distance, the reconstructed image will be all-in-focus. As shown in Figure 10a,b, the reconstructed image without correction is blurry at the border of the image, while the image is all-in-focus after correction. Figure 10c,d show the MSE of each pixel in reconstructed image of (a) and (b), where the white area has larger MSE than the black area. As one can see, the reconstructed image after correction has larger MSE in the transition area. The MSE of the whole image of (a) and (b) are 0.0427 and 0.0205, respectively. The sub-images of the calibration board before and after correction are shown in (e) and (f), respectively. The sub-images of different channels may have different distortions, which are corrected with our method. Moreover, with the test pattern, the sub-images are fused and reconstructed in two resolutions--480 \(\times\) 480 and 640 \(\times\) 640--and the reconstructed images in both resolutions are blurry before correction, while all-in-focus after correction.
## 3 User Study
### Participants  
We engaged 25 participants in the experiment. The sample of 25 psychology students was recruited from the Department of Psychology, Faculty of Arts, Masaryk University, where we expected a rather low general experience with head-mounted display (HMD) technologies. We initially excluded two individuals with visual impairments. Three other participants were excluded because of incomplete experimental runs caused by VR-induced motion sickness. The final number of analyzed participants was 20 (10F/10M; aged 20-26, \(m=21.33\), \(sd=1.602\)).
## 3 Results and Discussion
### SPAD Index
However, it was important that very high doses of nitrogen did not progressively increase chlorophyll content in leaves, as observed by other authors [37; 38]. The content of chlorophyll expressed in the SPAD index value can be a good indicator of nitrogen nutrition of plants, but only when doses of this nutrient applied with fertilizer do not exceed 190 kg N\(\cdot\)ha\({}^{-1}\). Above this limit, the SPAD leaf greenness index is less useful for the evaluation of plant nutrition with nitrogen, because nitrogen is not completely converted to chlorophyll at large amounts [39]. The content of chlorophyll pigments in plants is not only a species-specific feature, but also variety-specific feature, as observed by [PERSON] et al. [20]. In our study, the analysis of variance showed that the plants of the variety Magitpo were characterized by significantly higher SPAD index values regardless of the fertilization level and year of the experiment. These values at subsequent measurement dates amounted to 616, 634, and 649, respectively, whereas the SPAD index values of the variety Delitpo were significantly lower and amounted to 587, 607, and 614, respectively (Figure 3). These results clearly indicate that nitrogen nutrition of these plants was significantly worse. This reaction may have been caused by the slower dynamics of this nutrient uptake. Nutrition of plants with nitrogen also influenced the morphological properties and yield of both varieties (Figures 4-6). The variety Magitpo (FAO 270) was characterized by a higher yield, and its cobs were heavier than those of the typical grain variety Delitpo (FAO  
Figure 3: Boxplot of the SPAD index at successive measurement dates of in relation to maize variety. 240) (Table 4). Chlorophyll content in the leaves and the SPAD value were significantly influenced not only by the soil abundance in minerals, but also by total rainfall, thermal conditions, and light intensity [29]. The results of our study confirmed this observation. Figure 4: Boxplot of DMY of two maize varieties in the first year of study. Figure 5: Boxplot of DMY of two maize varieties in the second year of the study. The SPAD leaf greenness index values of both maize varieties were significantly lower in the first than in the second year, regardless of the dose of nitrogen fertilizer. On average, these differences in both varieties were the largest in the second time point (21 SPAD units), smaller in the first time point (19 SPAD units), and the smallest in the third time point (14 SPAD units). They may have been caused by different weather conditions during the growing season in both years of the experiment. The distribution of rainfall was better in the second year (Table 1), and it influenced the availability and uptake of nutrients from the soil by maize plants. In consequence, the nutrition of the plants with nitrogen was better. [PERSON] et al. [25] attempted to eliminate the influence of water stress on the nutrition of maize plants by irrigating them with sprinklers. It significantly increased the SPAD index values regardless of the nitrogen fertilization level.
## 2 Related Work
### Membership Inference Attack  
Membership Inference Attacks (MIA) are adversarial attacks closely related to privacy protection, aiming to determine whether a specific sample is part of the training dataset of a machine learning model. Even with only black-box access to the model, MIA has demonstrated strong performance across numerous machine learning tasks. Therefore, MIA has significant potential for application and research value in revealing privacy vulnerabilities in machine learning models [16].  
Shadow training [17] is one of the commonly used techniques in MIA. It involves training a model with the same architecture as the target model but using its own data samples to approximate the training set of the target model. Since current attack methods often require access to the model's prediction confidence scores, [PERSON] et al. introduced a label-only attack to evaluate the robustness of model prediction labels under input perturbations to infer membership status [18]. [PERSON] et al. developed a likelihood ratio attack (LiRA), which improved the efficacy of membership inference attacks by tenfold at lower false positive rates [15].  
There remains a lack of consensus on whether existing MIA algorithms will result in significant privacy leakage in practical large language models. Existing MIAs designed for LLMs can be categorized into two types: reference-free attacks and reference-based attacks. Reference-based attacks appear to be promisingly effective in LLMs, as they measure more reliable membership signals by comparing probability differences between the target model and a reference model. However, the performance of reference-based attacks highly depends on the availability of a reference dataset that closely resembles the training dataset, which is often inaccessible in real-world scenarios. [PERSON] et al. [19] proposed a membership inference attack based on Self-Calibrated Probability Variation (SPV-MIA), which constructs a dataset to fine-tune a reference model by prompting the target LLM itself. This approach enables the attacker to collect datasets with similar distributions from public APIs. [PERSON] et al. developed a neighborhood-based membership inference attack [20], which achieves the effectiveness of reference-based attacks without requiring a reference dataset to train the reference model.
## 7 Discussion and Open Questions
### Underutilized Potential of Abstraction Layers (L0-L4)  
The L0-L4 abstraction model provides a structured framework for progressive system validation, from conceptual modeling to complete system integration in real-world environments. However, its practical implementation is often inconsistent:  
* L0 models are disconnected from downstream validation efforts and rarely reused.
* L1 (SiL) environments are limited to isolated logic testing, without direct traceability to requirements or outcomes at later levels.
* L3 and L4 stages are rarely implemented due to a lack of tools, data collection capabilities, or test infrastructure.  
Recent studies further substantiate these findings and expand the contextual relevance of DT-based automotive validation. [PERSON] et al. [38] implement a digital twin for ground-vehicle predictive maintenance, integrating real telemetry, closely matching our assertion of necessary bi-directional data loops at abstraction levels L3-L4. A review in Advanced Engineering Informatics [39] emphasizes methodological requirement traceability in DT predictive maintenance systems, reaffirming our concerns regarding gaps in gate-based processes and edge-case coverage. In the area of cybersecurity, a survey in [40] outlines layered DT security challenges, offering systematic threat categorizations that align with and expand our discussion in Section 8.3. The issue that increasing connectivity of DT platforms exposes the validation environment to cybersecurity threats, such as unauthorized access and data falsification, has been notably addressed in another recent systematic review by [PERSON] et al. [41]. They analyzed 67 studies and highlighted common attack vectors, intrusion detection, spoofed telemetry, and vulnerabilities in IIoT-to-DT communication, as well as methods for ensuring data confidentiality and authentication. These findings substantiate our calls for proactive security measures, including lightweight secure communication, integrated intrusion detection, and robust encryption, even in resource-constrained validation setups.