esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Category I: Energy Transaction
### Buyer-Seller Matching
#### 5.2.1 Stable Matching  
The stable matching problem, also known as the stable marriage problem, was first introduced in the economic background. It is a problem of matching users in different aspects of the bipartite graph. In this case, a group of equal numbers of men and women were matched according to their preferences. The goal is that eventually everyone has a partner, and everyone is happy with their partners. This problem was later applied to other fields, and the new matching pattern allows the two groups to be in different size. In energy trading, the process of matching trading objects can also be regarded as a stable matching problem [82].  
To protect the privacy of fixed residential charging piles, mobile electric cars and other charging equipment, a homomorphic encryption-based position hiding method is proposed in [82] so that the exact locations of the supplier and demanders can be known only if the stable matching is successful. Homomorphic encryption is a kind of cryptography technology which can process data under the premise of ensuring data privacy and security. When matched, the mobile suppliers need to send their updated location to the demander. After receiving the updated location information, the demander uses the homomorphic encryption algorithm to calculate the distance and get an ascending list in location preferences. Finally, based on the preference lists, the distributed stable matching algorithm obtains a matching such that every demander and supplier is satisfied.  
Based on the stable matching problem, there are also other studies using different methods to achieve the optimal solution. In [83], the symmetrical allocation problem based on native auction algorithm is used to match the buyers and sellers in the energy market. The naive auction algorithm is carried out in rounds, and only one buyer bids for the desired object in each round. An automatic matching mechanism is designed in [84] to ensure the prosumer privacy and system safety. Both the buyer and seller provide a vector and some matching criteria as the premise of matching. The objective is to find an optimal set of solutions that satisfy these criteria. With further consideration of time constraints, physical constraints, and more flexible pricing updates, the system can provide the most appropriate matching strategy.  
Power Ledger [46] is a blockchain based energy trading platform, which has its own trade matching algorithms to fairly deal with power trade between prosumers. In Power Ledger, the orders from producers and the orders from consumers are filled in equal increments in the energy market. When a new consumer joins the system, he will receive the equal allocation from the renewable energy pool in his area immediately, to reduce the transmission distance and carbon emissions.
## 3 Measurement Results and Discussion  
### Summer Season Experiments  
#### 3.1.1 Measurement Location and Environmental Conditions  
The measurements were carried out on sunny days in July and August, at three tourist spots in Japan, the Imperial Palace, Ginza, and Daimon, where many people gather, as the target for the urban environment. Figure 7 shows panoramic images in each location captured using a cell phone (iPhone Pro 11, Apple Inc.) as a reference to understand the measurement environment. The temperature and humidity were also recorded, and the experiment was started at noon. The measurements were taken twice per location, and the data were obtained using the visible, near-infrared, and far-infrared camera. The meteorological data are shown below in Table 1.
## 3 Data Coverage and Method
### Uncertainty Estimation
Furthermore, we also utilized the residual error between the estimated velocity and the single interferograms to estimate velocity uncertainty and found that both methods gave very similar results. So the uncertainty we estimated in this study is considered to be reliable.
## 3 Results and Discussion
### Yield Points Defined by the Critical Characteristic Energy \(E_{ch}\)  
To reduce the effect of \(\cos(3\theta)\), we try to use the elastic energy stored per unit RVE volume as a critical characteristic energy \(E_{ch}\) to judge the yield state of the foam material. Based on the micro-scale relationship between the three types of energy from Formulas (13) and (14), the elastic energy stored is given by Formula (16).  
\[E_{e}=\left(E_{t}-E_{p}\right) \tag{16}\]  
The critical character energy \(E_{ch}\) is defined as the \(E_{e}\) at the yield point B in Figure 2 divided by the volume of the RVE used in the simulation. It is given as Formula (14),  
\[E_{ch}=E_{e}/V_{RVE} \tag{17}\]  
where \(V_{RVE}\) is the volume of the RVE used in the simulation.  
Figure 8: Comparison of measured yield surfaces with predicted yield surfaces of the Kelvin model and random model using the yield definition method of critical plastic dissipation energy.  
For the Kelvin model with 0.2 relative density, \(E_{ch}\) is 20.7 \(\times\) 10\({}^{-3}\) mJ/mm\({}^{3}\); with 0.43 relative density, \(E_{ch}\) is 47.8 \(\times\) 10\({}^{-3}\) mJ/mm\({}^{3}\); for the random model 6 (in Table 1) with 0.3 relative density, \(E_{ch}\) is 19.4 \(\times\) 10\({}^{-3}\) mJ/mm\({}^{3}\); with 0.4 relative density, \(E_{ch}\) of the random model 1 (in Table 1) is 38.3 \(\times\) 10\({}^{-3}\) mJ/mm\({}^{3}\). The yield point under multiaxial stress states is obtained when their \(E_{e}/V_{RVE}\) is equal to \(E_{ch}\).  
Figure 9 gives the yield surfaces defined by \(E_{ch}\) based on the micro-scale for the Kelvin model with 0.2 and 0.43 relative density and the random model with 0.3 and 0.4 relative density. Compared with the yield surfaces given above in Figures 6-8, the scattering of yield points for the same value of parameter k is significantly reduced. The effect of the value of \(\cos(30)\) is much smaller on the yield surface defined by \(E_{ch}\) than on the yield surfaces given by the previous three yield definition methods. Meanwhile, relative density does not affect the yield surface for random models. It is also shown that the yield surfaces using the Kelvin model and random model defined with \(E_{ch}\) are consistent with the experimental data, which covers the scatter of the experimental data. And the biaxial experimental data is given in [4], the triaxial experimental data is given in [9]. So this critical characteristic energy \(E_{ch}\) based on a micro-scale can be used to define the yielding of closed foam. This critical characteristic energy \(E_{ch}\) should be derived by finite simulation tests. The method of determining yield points is not sufficient for the experimental tests.
## 2 Materials and Methods  
### Materials  
The raw materials used comprised pea protein concentrate (PPC) (AMN 55% protein, 35% carbohydrates, and 3.8% fat on a dry weight basis (dm) (AM Nutrition, Stavanger, Norway) and pea protein isolate (PPI) (78.6% protein, 6.8% carbohydrates, and 7.8% fat, IsoPro80, ABS Food, China). Commercial oat protein concentrate PrOatein(OP) was obtained from Lantmannen Biofineries (Kimstad, Sweden). The proximate composition was 54.4% protein, 24.7% carbohydrate, and 18.7% fat (dm). Whole grain oat variety 'Active' was obtained from Lantmannen (Stockholm, Sweden). The latter was further processed at Nofima to produce the oat protein concentrate with 56% protein content (OA) via the dry fractionation method adapted from do [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] [22]. Briefly, the oat grains were non-heat treated, dehulled, and mechanically flattened to flakes in a Ferrell-Ross flaking mill (Ferrell-Ross, Hereford, TX, USA). SC-CO\({}_{2}\) extraction of lipids was performed on a pilot scale in a multi-use SFE plant with a pressure vessel of 5 L (SciMed, Stockport, UK). Oat flakes were defatted using SC-CO\({}_{2}\) with a flow of 200 mL CO\({}_{2}\)/min, pressure of 350 bar, and a temperature of 45 \({}^{\circ}\)C. The defatted oat flakes were pin milled at 20,000 rpm and air classified to obtain a fine fraction with 56% \(w/w\) protein content, 31% native starch, 2% non-soluble polysaccharides (NSP), and 6% total fat (dm) (Hosokawa Alpine 100 UPZ, Hosokawa Alpine, Augsburg, Germany). The mixtures used for high-moisture extrusion were 50% PPC: 50% PPI (PP), 45% PPC: 45% PPI: 10% OP (PP_OP), 45% PPC: 45% PPC: 45% PPI: 10% OA (PP_OA) targeting 66 \(\pm\) 2% protein content on a dry basis.
# Superelastic Shape Memory Alloy Honeycomb Damper
## 2 Superelastic SMA Honeycomb Damper  
### Design Principle and Configuration  
The honeycomb, as depicted in Figure 1, possesses a hexagonal configuration. Due to its thin walls and porous interiors, it exhibits a substantial capacity for deformation when stretched or compressed transversely due to its geometric nonlinearity. Research has demonstrated that a thin-walled SMA honeycomb structure is capable of achieving an overall strain of 60% [10]. Motivated by this phenomenon, we proposed a superelastic SMA honeycomb damper in an effort to enhance the deformation of superelastic SMA restrainers through their geometrical nonlinearity.  
Figure 1: A honeycomb.  
Figure 2 depicts a schematic of the proposed superelastic shape memory alloy (SMA) honeycomb damper (SHD). It consists of two steel plates that encase the superelastic SMA honeycomb plate, which provides energy dissipation and self-centering capabilities. When the restrainer is compressed, the out-of-plane deformation of the SHD will be restricted by the steel plates. The dimensions of the SHD are correlated with the cells in the longitudinal and transverse orientations.
# Tele-Assessment of Executive Functions in Young Adults with ADHD: A Pilot Study
## 11 Outlook  
The present pilot study offers preliminary yet meaningful insights into the executive functioning profile of young adults with ADHD using a tele-assessment approach. While several group differences were detected--particularly in the response inhibition, high-load working memory, and time estimation--other components appeared less sensitive to group differences, possibly due to individual variability or task specificity [92, 93, 94].  
These findings highlight both the promise and limitations of current tele-assessment methods. Future research should aim to expand the sample size, include a broader age range, and differentiate between treatment subgroups (e.g., medication, psychotherapy, and untreated), which would allow for more robust analyses of TeleFE's sensitivity and ecological validity across subpopulations.  
Moreover, integrating additional standardized tools--such as the MOXO d-CPT--could provide a complementary profile of attentional and timing processes, enriching the interpretive power of the assessment. Given the feasibility and diagnostic utility demonstrated, longitudinal studies could also explore how executive function profiles evolve over time and in response to intervention.  
The diagnosis of ADHD was based on multiple tools. Future research should identify a primary diagnostic instrument to reduce heterogeneity and further validate the applicability of TeleFE in a sample with unified diagnostic criteria. In the long term, the standardization of TeleFE may contribute to the development of shared protocols for ADHD evaluation, helping clinicians implement more individualized, efficient, and data-informed interventions for adults with ADHD.  
**Author Contributions:** Conceptualization, A.C. and V.O.; methodology, A.C.; validation, C.T., M.C., A.M., A.Z. and G.G.; formal analysis, A.C. and V.O.; investigation and resources, all authors; data curation, C.T., M.C., A.M., A.Z. and G.G.; writing--original draft preparation, A.C.; writing--review and editing, V.O., C.T., M.C., A.M., A.Z. and G.G.; visualization and supervision, A.C. and V.O. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** This study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Ethics Committee of IUSVE of 2 February 2024.  
**Informed Consent Statement:** Informed consent was obtained from all subjects involved in this study.  
**Data Availability Statement:** Data are available upon request to interested researchers.  
**Acknowledgments:** The authors would like to express their sincere gratitude to [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] for their valuable contribution in collecting data and all participants for their availability and commitment.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
optimal technique (the particle swarm optimisation (PSO) algorithm), it focuses exclusively on minimising the system error. Thus, aspects such as stability and disturbance rejection capability are not considered. This paper seeks to extend the application of fractional proportional-resonant (FPR) controllers from AC current control to AC voltage regulation. The primary contribution is a stability analysis of ideal FPR controllers, based on the Nyquist criterion, conducted in the continuous-time domain and applied to the capacitor voltage control of a voltage source converter (VSC). Additionally, the paper proposes a method for determining the controller gains for specific fractional values of the FPR controllers based on the phase margin criterion. This controller gain tuning criterion is applied for a case study that offers a sensitivity analysis, examining the effect of selecting different fractional values for the FPR controller. The analysis also contrasts the results with the classical idea (no damped) PR and PRHC controllers. This study evaluates reference tracking capability, disturbance rejection, robustness, and the impact of modelling inaccuracies on stability margins. The results indicate that the fractional term is critical in achieving various control objectives. By selecting appropriate fractional terms, improvements in both tracking and disturbance rejection can be realised. Thus, FPR controllers offer a viable solution for high-accuracy AC voltage systems, such as AC power supplies, grid-forming converters, or series active filters. Furthermore, the paper shows that this type of controller is well suited for applications with computational constraints, particularly in terms of execution time and memory usage. The paper layout is as follows. Section 2 introduces the system and the design considerations and develops the controller stability by applying the Nyquist criterion. Section 3 proposes the tuning strategy for the next sections. Section 4 presents and validates a control analysis based on the system sensitivity and the tolerable modelling uncertainty. Section 5 shows the experimental tests. Section 6 presents a discussion the results. Finally, in Section 7 we draw the conclusions.
## 2 Related Work and Design Challenges
### Related Work on Privacy Preserving Wireless Sensor Network including Secure Data Aggregation
The node failure is due to limitations in secure authentication and authorization in the sensed data, which lead to privacy, reliability, and integrity of data concerns in the network. * SD, EEHA, HAS, IIF, and RHC secure data protocols incur scalability regarding large-scale sensor deployment concerns. Scalability of legitimate sensor nodes to be part of the WSN requires availability of well authenticated and authorization security control mechanism. Therefore, it is important to solve these security challenges in WSN secure data aggregation and privacy-preserving protocols, including well-known WSN approaches such as SD, EEHA, HAS, IIF, and RHC. We first provide an overview of privacy preservation and design objectives, including secure authentication and authorization. After that, we describe the design of a new system model, specifically a mathematical and analytical secure data aggregation model, using our proposed SDAAA protocol methods to assess new QoS performance metrics in the network.
## 2 Zero-Trust Overview  
### Principles of Zero Trust  
There is no single reason for implementing zero trust, it is considered an improvement over the traditional perimeter-based security model which operates on the concept of trust [2]. Every user, device, and network traffic inside the border is trusted by default and this gives rise to easy lateral access to the attackers once they successfully cross the border, whereas in zero-trust everything is considered hostile, and trust must be gained through multiple parameters including (but not limited to) user authentication, authorization, verification of devices and services [10]. The main principles of zero-trust include:  
* All critical data, assets, and services (DAAS) must be secured irrespective of their location (inside or outside of the corporate network) [10].
* No components are trusted by default and access communications must be encrypted at all times, even for channels within the intranet [11].
* Just-in-time access (JITA), where the authentication and authorization verification are done based on the set access policy exactly at the time of access request and will be valid for one session only [2].
* Just enough access (JEA) which corresponds to least privilege access; should be applied such that only enough amount of access required to carry out the task is provided and no more [2].
* Access control policies must be configured based on the data received from a maximum number of sources such as device health, type of resource accessed, etc. [11].
* Access is never granted based on history and must be evaluated every time it is requested [11].
# Spread Spectrum Image Watermarking Through Latent Diffusion Model
## 3 Experiments
### Discussions  
In this section, we discuss some of the key parameters that affect the robustness and imperceptibility of the proposed watermarking scheme. In traditional watermarking schemes, selecting an appropriate strength factor to balance the invisibility and the robustness of  
Figure 5: Performance of different prompts for image editing. The first group is the result with Instruct-Pix2 Pix, the second is with UltraEdit.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Transparency**} & \multicolumn{4}{c}{**Robustness**} \\ \cline{3-8}  & **Capacity** & **PSNR \(\uparrow\)** & **SSIM \(\uparrow\)** & **LPIPS \(\downarrow\)** & **Common** & **Regeneration** & **Editing** \\ \hline \multirow{3}{*}{Proposed} & 32 & **30.85** & **0.90** & **0.05** & 0.042 & 0.052 & 0.045 \\  & 64 & 28.28 & 0.88 & 0.10 & 0.041 & 0.071 & 0.039 \\ \cline{1-1}  & 96 & 22.73 & 0.78 & 0.27 & **0.026** & **0.022** & **0.026** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance comparison of the quantitative image quality and robustness under different embedding capacities in COCO dataset. The average BER of the three categories of attacks in Table 2 is computed to reflect the robustness. The best result is marked in **bold**.  
the watermark is a vital concern. Generally, a larger strength factor would lead to better robustness, while compromising the image quality. In Figure 6a, the impact of the strength factor on image quality and watermark robustness is plotted, measured in terms of PSNR and BER. As Figure 6a suggests, the proposed scheme is fragile given a mild strength factor of less than 50, even when no attack is imposed. This is because watermark embedding is performed in the noisy latent space and the noise predictor might be inaccurate when the amplitude is small. Drawing on this, Figure 6b presents the range of BER under different attacks, by applying the strength factor from 50 to 100. It is demonstrated that an optimal option exists at \(S=70\) since \(S\) larger than 70 can bring about severe image degradation. Considering the available editing techniques and the potential distortions brought by channel transmission, we prefer to have higher priority on robustness of the watermark given an acceptable quality of the watermarked image.  
In addition, the parameter \(\lambda\) in Equation (8) controls the weight of raw data to further enhance image quality. Meanwhile, \(\lambda\) also affects the robustness of the watermark, so we sample at equal intervals in the range of [0,0.5] to determine the choice of \(\lambda\). From Figure 7, a value of 0.3 is opted for considering the tradeoff between image quality and robustness against attacks.
# Water Quality Evaluation and Analysis by Integrating Statistical and Machine Learning Approaches
## 3 Methods
### Statistical Analysis
#### 3.2.3 Analysis of Learning Curve  
Learning curve analysis evaluates the Time Series Regression Forest (TSRF) model's performance and generalization across varying training data sizes. It helps identify underfitting, overfitting, and optimal performance conditions. The analysis is conducted separately for the regression (DTR) and classification (RF) components of the hybrid model.  
Step 1: Incremental Training  
The TSRF model was iteratively retrained using progressively larger training subsets. For regression, the Decision Tree Regressor was trained on increasing dataset portions, while for classification, the Random Forest Classifier followed a similar approach. Training began with 10% of the total data, increasing in 10% increments until the full dataset was utilized. A consistent validation set (30% of the dataset) was used to evaluate generalization ability.  
Step 2: Error Metrics  
For regression tasks:  
* Training error was calculated using MSE on the training subset.
* Validation error was calculated with the same metrics on the validation set. For classification tasks:
* Training error represented the proportion of misclassified samples in the training subset.
* Encountered a validation error regarding the proportion of misclassified samples in the validation set or 1 minus the accuracy score.  
Step 3: Training and Validation Curves Were Analyzed  
The training error and validation error were plotted against the size of the training dataset to construct the learning curves.  
* Training Curve: Shows how the error decreases as the model receives more data to learn from. An initial rapid decline in error suggests effective learning, while a flattening indicates saturation of model performance [44].
* Validation Curve: Demonstrates the model's generalization ability. A high validation error that is compared to the training error suggests overfitting, while consistently high errors in both curves indicate underfitting [44].  
To further address overfitting and improve model generalization, k-fold cross-validation (k = 10) is implemented during model training. This approach ensures that the model's performance is validated across multiple data splits, reducing the risk of bias from a single train-test partition. For the tree-based models, additional complexity control measures are applied where maximum tree depth is limited, minimum samples per split are increased, and pruning techniques are explored where applicable. These hyperparameters are optimized using grid search. Model performance is evaluated using multiple error metrics, including MSE, Mean Absolute Error (MAE), and R\({}^{2}\) score, computed for both training and validation sets. To assess result stability, bootstrapped confidence intervals (95%) are generated for the error metrics, ensuring the robustness of the outcomes.
## 5 Analysis of the Experiment and Simulation
### Ride Comfort of the Vehicle  
In order to verify the correctness of the design, the change curves of the vehicle load-bearing platform center are collected as shown in Figure 17. When the accumulator is on, it can be seen that the height of the vehicle platform changes smoothly, and the displacement change of the vehicle platform in the vertical direction is obviously reduced.