esa-sceva/satcom-chunk-collection huggingface dataset:
# Consumer Acceptance of Cultured Meat: An Updated Review (2018-2020)
## 7 Key Uncertainties
### Price  
The importance of cultured meat prices for consumers has been highlighted in several studies, and an anticipated high price was a perceived barrier to buying cultured meat identified by studies in our previous review [12; 40; 54]. Price was highlighted as the major concern for Brazilian consumers by [PERSON] et al. [30], whilst [PERSON] et al. [26] found that price was a predictor of the purchase intent in three of the four countries in their survey. The authors found that price tended to be more important to consumers than factors such as neophobia [25], commenting that overall, reducing the price of cultured meat will be crucial for mainstream consumers. Interestingly, their data showed that participants in the Dominican Republic were the most likely to expect cultured meat to be cheaper than conventional meat, while Brazilians were the most likely to expect it to be more expensive.  
There is mixed evidence on the willingness to pay a premium for cultured meat. [PERSON] & [PERSON] [28] found that roughly equal proportions of their sample were willing to pay a premium (23.2%), were \"maybe\" willing to pay a premium (20.8%), and were not willing to pay a premium (26.7%; those who indicated they would not try cultured meat did not answer this question). However, [PERSON] and [PERSON] [35] found that both rural and urban Irish consumers would expect to pay less money for cultured meat.  
Conversely, [PERSON] et al. [19] found that, with participants who had tasted and broadly liked \"cultured meat\", 58% were willing to pay a premium, averaging 37% above the price of conventional meat. This reflects a higher willingness to pay than other studies, most likely due to the circumstance of having a sensory experience with what participants believed to be cultured meat. However, the researchers also observed that price was by far the most common form of negative comment, accounting for 36% of negative remarks (not all participants made negative remarks). This suggests that some consumers are likely to be willing to pay a premium, but others certainly are not and will be price sensitive. [PERSON] and [PERSON] [18] found that the willingness to pay a premium was increased with additional information.
# Performance of the Transport Layer Security Handshake Over 6 TiSCH
## 2 Background
### Applicability of TCP in the IoT
#### 2.2.3 Delayed Acknowledgements  
Default TCP operation requires a corresponding acknowledgment for every received segment. Delayed acknowledgments try to reduce the number of acknowledgment packets on the wire. In a stream of full-sized segments, the receiver can delay the transmission of an acknowledgment. It can wait for the next segment to arrive. The delayed acknowledgment then acknowledges both segments cumulatively. There should be an acknowledgment for at least every second segment. In addition, the delay should not extend more than 0.5 s [17].
## 3 Results and Discussion
### Discussion
It displays balanced and moderate shrinkage: 11.11% in length and 10% in width, with a medium production time (5 min). The symmetry of the design and consistent loop repetition may contribute to its structural predictability. Among the five, Sample 5 offers a reasonable trade-off between design interest, production efficiency, and post-wash dimensional reliability. In summary, the analysis reveals the following:  
* Dense structures (Sample 1) are prone to vertical shrinkage;
* Balanced, repetitive structures (Sample 2) perform best in terms of dimensional stability;
* Openwork and decorative designs (Samples 3 and 4) may expand or distort due to loop relaxation;
* Geometric pointelle structures (Sample 5) can offer stable shrinkage ratios if well distributed. These findings demonstrate the critical role of design logic and module type in smart and sustainable knitted textile development. Future research may investigate the behaviour of complex knitted macrostructures under repeated washing cycles and mechanical stress using area shrinkage and geometric deformation as indicators of long-term dimensional stability. In addition, the incorporation of specific yarn types (e.g., elastane, high-twist, or recycled fibres) could be explored to optimise both dimensional resistance and functional performance. Further studies could also focus on the role of artificial intelligence and machine learning in the automatic generation of sustainable design alternatives, structural optimisation, or prediction of production outcomes based on selected materials. The application of digital twin technologies, supported by advanced simulation software, also presents a promising direction for quality control, predictive evaluation of product behaviour, and reduction in production waste. It is important to acknowledge that while simulation tools such as M1 plus V7.5 provide valuable predictive capabilities, they cannot fully replicate the complex interactions between yarn properties, stitch tension, and real-world finishing processes. A mismatch often arises between the simulated visual/textile structure and the final manufactured result, particularly in complex designs involving loop transfers or textured modules. This discrepancy can impact industrial adoption, as manufacturers must calibrate design parameters manually through sample testing. Further advancements in material behaviour modelling and digital twin technologies are required to bridge this gap and support fully predictive virtual development workflows.
###### Abstract  
Estimates of regional net primary productivity (NPP) are useful in modeling regional and global carbon cycles, especially in karst areas. This work developed a new method to study NPP characteristics and changes in Chongqing, a typical karst area. To estimate NPP accurately, the model which integrated an ecosystem process model (CEVSA) with a light use efficiency model (GLOPEM) called GLOPEM-CEVSA was applied. The fraction of photosynthetically active radiation (fPAR) was derived from remote sensing data inversion based on moderate resolution imaging spectroradiometer atmospheric and land products. Validation analyses showed that the PAR and NPP values, which were simulated by the model, matched the observed data well. The values of other relevant NPP models, as well as the MOD17A3 NPP products (NPP MOD17), were compared. In terms of spatial distribution, NPP decreased from northeast to southwest in the Chongqing region. The annual average NPP in the study area was approximately 534 gC/m2a (Std. = 175.53) from 2001 to 2011, with obvious seasonal variation characteristics. The NPP from April to October accounted for 80.1% of the annual NPP, while that from June to August accounted for 43.2%. NPP changed with the fraction of absorbed PAR, and NPP was also significantly correlated to precipitation and temperature at monthly temporal scales, and showed stronger sensitivity to interannual variation in temperature.  
net primary productivity (NPP); MODIS; GLOPEM-CEVSA; FPAR; karst area +
Footnote †: journal: Remote Sens. **2017**, 9, 477; doi:10.3390/rs9050477
## 2 Related Works
### Literature Review
#### 2.2.2 IOTA-Based Approaches  
The IOTA Tangle, a DAG-based DLT, offers a compelling solution to these limitations. Unlike traditional blockchains, IOTA's architecture supports feeless transactions and improved scalability, making it particularly suitable for resource-constrained IoT devices. [PERSON] et al. [20] demonstrated the integration of DIDs with IOTA Chrysalis, demonstrating its efficiency in managing identities for heterogeneous IoT networks. In addition, the authors of [21] explored the application of IOTA in IoT identity management, highlighting its potential to reduce operational costs while ensuring secure, real-time data exchange. The paper [22] highlights the use of IOTA's Tangle architecture for Identity and Access Management in IoT ecosystems, emphasizing its scalability, security, and cost-effectiveness. IOTA's fee-free transactions and decentralized structure support unique node identities, enabling the secure and traceable management of IoT devices. The ongoing Coordicide transition further enhances decentralization and eliminates central points of failure. IOTAalso employs public key cryptography, a reputation-based system to prevent Sybil attacks, and cryptographic techniques to ensure secure identity management. These features, combined with IOTA's energy efficiency and interoperability, make it a promising solution for IoT identity management, especially in resource-constrained environments.  
Other innovative approaches leverage IOTA's capabilities for specific use cases. IHIBE, a Hierarchical Identity-based Encryption framework built on IOTA, combines secure access control with scalability in IoT systems [23]. The study validated IOTA's adaptability to hierarchical access structures, achieving significant performance improvements in scenarios involving access token delegation. Similarly, a decentralized trust management framework using IOTA demonstrated how Web of Trust mechanisms could enhance trustworthiness and interoperability in IoT networks [24].  
In terms of theoretical advances, ref. [7] provides a comprehensive survey of DIDs and Verifiable Credentials (VCs), analyzing their applications in IoT, healthcare, and smart cities. They emphasize that the successful adoption of decentralized identity systems requires addressing key challenges such as interoperability, efficiency, and compliance with regulations such as GDPR. In addition, [PERSON] et al. [10] highlighted the importance of lightweight frameworks for edge IoT devices and proposed a decentralized identity model validated through simulation tools.  
Despite these advances, critical gaps remain in integrating IOTA's unique features--such as Decentralized Identifiers, Verifiable Credentials, and secure streams--into comprehensive frameworks tailored for IoT ecosystems. Existing studies focus primarily on theoretical models or specific applications, often lacking a cohesive approach to scalability and real-time identity verification.
# Using Auto-ML on Synthetic Point Cloud Generation
### Data Generators and Synthetic Training Data  
Synthetic training data is data generated by a computer program and not collected from real sources. Synthetic data can be used to create training datasets, fill gaps in data, and increase the diversity of the data. Due to progress in computer graphics, simulation environments gather a growing interest for industrial and scientific purposes and are recently used in a variety of applications including object detection [5; 6; 7], semantic segmentation [8; 9], data augmentation [10; 11], pose estimation [12; 13] or robot control [14; 15]. Current game engines such as Unreal Engine provide state-of-the-art technologies for realistic visualization and physics simulation that can be used to produce almost photo-realistic data.  
A domain gap is a mismatch between the distribution of data in the synthetic dataset and the distribution of data in the real-world dataset. This can lead to poor performance when a synthetically trained model is applied to real-world data. Domain adaptation techniques are used to address this issue by modifying the synthetic data to better match the distribution of the real-world data. Some common domain adaptation techniques include transfer learning [16], data augmentation, domain randomization and generative adversarial networks (GANs) [17]. For our approach we chose to generate synthetic data with variations of real-world properties of point clouds to better fit the target distribution. The parameters of these properties are being tuned with the presented Auto-ML technique. However, there are overlapping approaches to synthetic 2D image generation. One of them is Meta-Sim, a method proposed for automatically generating synthetic labeled datasets relevant for a downstream task [18]. The method learns a generative model of synthetic scenes and obtains images as well as their corresponding ground-truth via a graphics engine. The proposed method aims to improve the synthetic scene content in a dataset over a human-engineered probabilistic scene grammar. Another approach is Structured Domain Randomization (SDR), which adds context to the generated data by randomly placing objects and distractors according to specific problem probability distributions [19]. SDR generates images that allow neural networks to consider the context around an object during detection. We argue that the aforementioned work is limited to optimize the synthetic scene content, but to successfully bridge the domain gap, more domain characteristics such as lighting, coloring, or sensor behaviours are relevant.
# A Study of Network Intrusion Detection Systems Using Artificial Intelligence/Machine Learning
## 6 Literature Review
They created these two groups to have better accuracy of the U2R and R2L classes which are often the weakness of most of the IDS solutions that we have seen. Then, they performed feature extraction in both groups. They first used Intersectional Correlated Feature Selection (ICFS). In ICFS, the Pearson Correlation Coefficient (PCC) is used to select important features between two random variables. PCC can determine how much two variables vary from each other. Once ICFS is done, they performed Principal Component Analysis (PCA) to reduce the dimension of the data. Finally, to have a ratio of 1:1 between attacks and normal data in the second group they randomly choose the same amount of data as R2L and U2R combined. Then, they used those two groups to train their model which is composed of a first layer using Naive Bayes classifier and a second layer using SVM. The first layer is used only to detect DoS and Probe. If the outcome is not one of those two classes, then the data goes through the second layer to detect if it is a R2L, U2R or Normal data. They tested their solution on the NSL-KDD dataset. They obtained an overall accuracy of 93.11% and detection of 96.67% for R2L and 100% for U2R classes. Their solution outperformed other solutions when identifying the small classes, however, contrary to other efficient solutions, their accuracy for the large classes was not as good. [PERSON] et al. [42] proposed a Hybrid Nested Genetic-Fuzzy Algorithm (HNGFA) to detect attacks. They first performed feature selection using Naive Bayes. Major features and Minor features are split into two groups. Their model is composed of two genetic-fuzzy algorithms. The first one is the Outer Genetic-Fuzzy Algorithm (OGFA) and the second one is the Inner Genetic-Fuzzy Algorithm (IGFA). Each of these algorithms used two nestedgenetic algorithms. The outer one is used for the fuzzy sets and the inner one is used for the fuzzy rules. The OGFA is used for classifying data with major features, whereas the IGFA is used for classifying data with minor features. The two genetic-fuzzy algorithms interact with each other to develop new solutions to have better accuracy. The goal is to make the interaction between the best results of the OGFA with weak results from the IGFA to have the best model possible. They tested their solution on the KDDcup99 and UNSW-NB15 datasets and they obtained an overall accuracy of 98.19% and 80.54%, respectively. In addition, their solution got a good accuracy for detecting small classes such as R2L and U2R. However, due to the complexity of their model, the training time is high. A summary of this study is provided in Table 4. In addition, the strengths, and weaknesses of each solution are presented in Table 5.
## 4 Discussion
### Limitation and Future Research Perspectives
Future research should aim to incorporate rainfall time series, sewer system topologies, and detention infrastructure into the CD/CCD framework, creating a fully integrated and hydrologically informed model of urban flood resilience. Despite these methodological strengths, the study recognizes inherent limitations in the hierarchical evaluation process. Variations and potential biases in weighting methods--such as those introduced by expert judgment in the AHP or entropy weighting--may affect the consistency and accuracy of the coupling and coordination analysis. Additionally, the scope of this work is primarily confined to assessing GSI demand. Although all districts in the GBA were clustered based on indicators such as social infrastructure and population vulnerability, the analysis did not incorporate variations in GSI supply within the clustering framework. Future investigations should aim to include supply-side parameters alongside demand factors and also explore related infrastructure typologies--such as grey-greenhybrid systems and three-dimensional urban forms--to develop a more holistic framework for urban stormwater resilience.
# Multi-Agent Simulation Approach for Modular Integrated Construction Supply Chain
## 2 Related Works
### Overview of Modular Integrated Construction (MiC)  
As illustrated in Figure 2, the MiC process involves a series of planned steps to ensure efficiency and accuracy from design to completion [28]. The process begins with design approval and the engineering process, where the architectural design is conceptualized and refined, followed by rigorous engineering calculations to ensure structural integrity and conformity [29]. Permit applications and approvals are then required. The necessary documentation is submitted to meet local codes and standards [30]. After architectural, conceptual, and regulatory approval, the MiC process progresses to the site development and plant fabrication phase [31]. During this stage, decision-makers must carefully consider the optimal locations for these fabrication sites [32]. Key criteria for site selection include proximity to the final construction site and to the suppliers of the module components. Additionally, the size of the fabrication plant is crucial, as it influences the production rate. Accessibility is another important factor, particularly the adequacy of road networks for transporting the integrated modules to the construction site [33]. The next phase, factory production and module assembly, involves assembling the structural elements, integrating the mechanical, electrical, and plumbing (MEP) systems, and carrying out thorough quality checks. This stage is crucial, as it determines the overall quality and integrity of the modules [34]. Once the modules are ready, they are transported to the project site. Logistics are carefully planned, and the modules are transported safely and on schedule [3]. During the installation phase, the integrated modules arrive at the construction site where they are precisely assembled, and finishing touches are applied followed by a rigorous final inspection to ensure compliance with all standards. This phase requires optimal planning to ensure continuous operations. Firstly, adequate on-site storage capacity is crucial and must be aligned with the delivery schedule of the integrated modules to prevent logistical bottlenecks. Secondly, appropriate lifting and assembly resources need to be available, scaled to the size of the construction project, and designed to meet project timelines efficiently [35]. Lastly, connectivity capabilities must be robust, ensuring that all modules integrate seamlessly and function as intended once assembled, maintaining the integrity of the entire construction project. Each of these steps is crucial, requiring close coordination between the various stakeholders, ultimately resulting in an efficient construction process that takes advantage of the benefits of OSC to reduce lead times, costs, and environmental impact [28]. Furthermore, the decision-making in the MiC process is influenced by a variety of factors, ranging from logistical considerations to regulatory compliance and a skilled and experienced factory labor force. These factors are comprehensively reviewed and discussed in the review article by [34].
## 3 Leveraging DL of Mm-Wave in B5G
### Enhancing Channel Prediction Accuracy
Furthermore, the model uses spatial domain quantization signals and HAD architecture for effective beamforming and channel prediction, which reduces the errors brought on by low-resolution ADCs. The proposed approach provides a reliable solution for mm-wave situations since it closely matches realistic channel behaviors, as confirmed by extensive validation using MSE optimization. It also enhances the predictability of the inferred channels by improving prediction accuracy and it streamlines and expedites the channel estimation process by avoiding the costly neural network training procedure using a DL-based beam training strategy. Training a DNN using historical beam measurements involves maximizing the expected long-term reward. In DNN, the rewards are obtained using a configurable reward mechanism that considers the power or data rate needs of various users. A DNN is taught to maximize the predicted reward over time by applying DRL approaches. Using DRL approaches allows the system to perform better in a variety of applications and adjust to changing needs. Decision-making is made more effective and the system's overall efficiency is increased through the application of ML algorithms and prior data. To enhance the balance between prediction accuracy and channel estimation overhead, a straightforward training approach  
Figure 3: Massive MIMO system, the BS receiver employs 1-bit ADC converters with DL [32]. can be utilized. Instead of relying on traditional pilot-based channel estimation, an M-based predictor can be employed. This predictor can detect the channel aging pattern using an autoregressive model. Enhancing the supervised DL-based hybrid beam's highest sum rate under a specified transmit power limit HB. To reduce computing complexity for multi-user mMIMO systems presented, which are used to reduce the number of RF chains and enhance channel estimation overhead, the method has suggested the use of a learning approach that is the closest to modeling the hybrid beamformer [63, 64]. The BS deployed the antenna arrays and studied the channel estimation for an mm-wave huge mMIMO system with a mixed-resolution c architecture. The approach based on compressed sensing can recover the channel. The sparsity of the mm-wave channel may be used to formulate the beamspace channel estimation as a sparse signal-recovery problem.
## 6 Analysis of Results and Discussion
### Analysis of Results
\begin{table}
\begin{tabular}{l c c c} \hline \hline \multirow{2}{*}{**Environmental Metric**} & **Before** & **After** & \multirow{2}{*}{**Improvement**} \\  & **Implementation** & & \\ \hline Energy & & & \\ Consumption & 10,000 kWh/month & 7500 kWh/month & \(-\)25\% \\ \hline CO\({}_{2}\) Emissions & 5000 kg/month & 3000 kg/month & \(-\)40\% \\ \hline Waste Produced & 500 kg/month & 350 kg/month & \(-\)30\% \\ \hline Material Usage & 8000 kg/month & 6500 kg/month & \(-\)18.75\% \\ \hline Recycling Rate & 70\% & 85\% & +21.43\% \\ \hline \hline \end{tabular}
\end{table} 
Table 16: Environmental and sustainability improvements. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Financial Metric**} & **Before** & **After** & \multirow{2}{*}{**Improvement**} \\  & **Implementation** & & \\ \hline Profit Margin & 5\% & 12\% & +7\% \\ \hline \hline \end{tabular}
\end{table} 
Table 17: Financial impact. repair times, downtime events, and energy consumption manually, using spreadsheets and daily reports to monitor equipment performance and operations. With the implementation of the predictive model, the team began to compare the data from each month with the records from the previous year, now with the adoption of the predictive system, which included greater accuracy in failure predictions, optimization of repair time, and better energy management practices. The comparison between the periods before and after the application of the model allowed for the quantification of improvements in terms of reduced operating costs, maintenance efficiency, and sustainability, such as energy consumption and CO\({}_{2}\) emissions, validating the positive impacts of the adopted solution.