esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficient Time-Series Clustering through Sparse Gaussian Modeling
## 5 Experimental Evaluation
### Empirical Results: Time and Memory Considerations  
As mentioned in Section 5.1, we excluded certain UCR univariate datasets from our experimental evaluation, because it was impossible to successfully run the standard \(k\)-means algorithm on the original dataset in our computational infrastructure )and [22] does not provide running time estimations for the UCR datasets). The application of \(k\)-means to those datasets was terminated either due to memory issues, because of the very large size \(\Theta(NT)\) of the dataset, or due to running time exceeding two days without completing a single run of \(k\)-means.  
Nevertheless, using our Sparse Time-Series Clustering framework, we managed to obtain results for those datasets successfully, demonstrating its usefulness. For example, Table 5 reports the ARI achieved by our framework for the ElectricDevices dataset, a very large dataset consisting of 16637 time series of length 96 each and \(k=7\) clusters (this is one of the datasets for which we could not run \(k\)-means with the original time series in our computational infrastructure). We note that the best ARI is obtained by \(k\)-means with the original data, and the standard DTW distance is 0.19, as reported in [22].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameter \(a\)** & \(\mathbf{1\cdot log\,}T\) & \(\mathbf{2\cdot log\,}T\) & \(\mathbf{3\cdot log\,}T\) & \(\mathbf{4\cdot log\,}T\) & \(\mathbf{5\cdot log\,}T\) \\ \hline
**0** & 0.18 & **0.19** & 0.18 & 0.17 & 0.17 \\
**10\({}^{-4}\)** & **0.21** & 0.18 & 0.18 & **0.19** & **0.19** \\
**10\({}^{-3}\)** & 0.14 & **0.20** & 0.18 & 0.18 & 0.18 \\
**10\({}^{-2}\)** & 0.04 & 0.12 & **0.21** & 0.18 & **0.20** \\ \hline \hline \end{tabular}
\end{table}
Table 5: ARI results for the ElectricDevices dataset consisting of 16637 time series of length 96 each. The best ARI computed by the standard \(k\)-means in [22] is 0.19, while our framework achieves a best ARI of 0.21, which is 10% larger than that reported in [22].
# MPC for Optimized Energy Exchange between Two Renewable-Energy Prosumers
###### Abstract  
Renewable energy and information technologies are changing electrical energy distribution, favoring a move towards distributed production and trading between many buyers and sellers. There is new potential for trading between prosumers, entities which both consume and produce energy in small quantities. This work explores the optimization of energy trading between two prosumers, each of which consists of a load, renewable supply, and energy storage. The problem is described within a model predictive control (MPC) framework, which includes a single objective function to penalize undesirable behavior, such as the use of energy from a utility company. MPC integrates future predictions of supply and demand into current dispatch decisions. The control system determines energy flows between each renewable supply and load, battery usage, and transfers between the two prosumers. At each time step, future predictions are used to create an optimized power dispatch strategy between the system prosumers, maximizing renewable energy use. Modeling results indicate that this coordinated energy sharing between a pair of prosumers can improve their overall renewable energy penetration. For one specific choice of prosumers (mixed residential-commercial) penetration is shown to increase from 71% to 84%.  
model predictive control; renewable energy; prosumer; microgrid; energy storage; renewable penetration; blockchain technology +
Footnote †: journal: Applied Physics Letters  
_Article_  
MPC for Optimized Energy Exchange between Two Renewable-Energy Prosumers
# LEO Constellation-Augmented Multi-GNSS for 3D Water Vapor Tomography
## 3 Experiments and Results
### Horizontal Tomography Resolution
(**a**) Percentage of empty voxels to total voxels; (**b**) percentage of two-station voxels to total voxels; (**c**) comparison of accuracy of different strategies. We calculated the difference between GRECL288 and GREC and G under all grid divisions, and counted the differences of the maximum, minimum, and average values. The data are shown in Table 5. At a horizontal resolution of 13 \(\times\) 14, compared with GREC and G, GRECL288 improves the empty voxels by 5.22% and 22.53%, two-station voxels by 11.03% and 49.36%, and accuracy factor \(\eta\) by 2.06% and 6.32%, respectively. Interestingly, the largest difference appears in the 13 \(\times\) 14 grid division scheme. This shows that when the horizontal grid is more densely divided, the GRECL288 can effectively control the increase in empty voxels, and stabilize the decrease in the two-station voxels, so as to effectively restrain the influence of the increased horizontal resolution on the observation data.
## 5 Limitations of This Study and Future Prospects  
This study highlights the impact of storage conditions on the microbiological quality and degradation of bioactive compound in fruit mousses, but it also revealed several limitations that should be addressed in future research. One key limitation is the lack of pH and acidity measurements, which are crucial for understanding the chemical factors that may influence both microbial growth and the stability of bioactive compounds during storage. The absence of these measurements limits the ability to fully assess the interactions between the chemical composition and microbiological characteristics of the fruit mousses. Another limitation lies in the relatively small sample size and limited diversity of fruit mousse types analyzed, which may restrict the generalizability of the findings. Including a broader range of formulations, brands, and production methods in future studies would allow for a more comprehensive evaluation. Furthermore, the impact of specific production processes on the preservation of bioactive compounds and microbial stability was not explored, which could be another area for further research.  
In future studies, the authors plan to incorporate measurements of pH and acidity to better understand the chemical dynamics affecting both bioactive compound retention and microbiological quality during storage. The inclusion of these parameters will help to correlate the chemical composition with microbial growth patterns more effectively. Additionally, expanding the study to include a larger variety of fruit mousse products and exploring different storage scenarios will enhance the applicability of the findings to a broader range of products and conditions. Moreover, evaluating the influence of manufacturing processes on product stability could yield valuable insights for optimizing the shelf life and enhancing the nutritional value of fruit mousses. By addressing these areas, future research can offer more targeted and practical recommendations for improving these products.
## 4 Attack Resistance
### Integrity Attacks  
Integrity attacks try to alter the normal behavior of the system by modifying the data stored in nodes. Although DoS attacks can be considered as integrity attacks as service interruption is one kind of bad behavior, we prefer to treat them separately because here the focus is on the data, instead of the communications.  
Tampering and homing  
These attacks are very difficult to avoid due to the weakness of wireless nodes. But these are clear cases of local attacks. Local or node attacks are not relevant for our network model, since redundancy allows losing nodes without any impact in the behavior. Negative reputation can be used from the servers in order to confine these attacks. Even if integrity of individual nodes is difficult to achieve, the use of redundancy can reduce or eliminate the impact on the global system.
# Controlled-Atmosphere Sintering of KNbO\({}_{3}\)
## 4 Discussion
If one considers an Ellingham diagram, the Gibbs free energy vs. temperature line for the reaction 4K + O\({}_{2}\rightleftarrows\) 2K\({}_{2}\)O lies above the line for the reaction 4 Na + O\({}_{2}\rightleftarrows\) 2 Na\({}_{2}\)O, indicating that potassium oxide is more easily reduced than sodium oxide [91]. [PERSON] and [PERSON] calculated the formation energy of oxygen vacancies in strongly reducing atmospheres to be lower in KNbO\({}_{3}\) (0.80 eV) than in NaNbO\({}_{3}\) (0.84 eV) [92; 93]. The vapor pressure of K over KNbO\({}_{3}\) is also higher than that of Na over NaNbO\({}_{3}\)[81; 94]. Therefore, KNbO\({}_{3}\) may be less stable than (K\({}_{0.5}\)Na\({}_{0.5}\))NbO\({}_{3}\) when sintered in reducing atmospheres. The appearance of the secondary phases may also explain the lower density of the H\({}_{2}\)-sintered samples. The density of K\({}_{4}\)Nb\({}_{6}\)O\({}_{17}\) is 3.897 g.cm\({}^{-3}\)[95] compared to 4.624 g.cm\({}^{-3}\) for KNbO\({}_{3}\)[48]. The present results show that KNbO\({}_{3}\) samples sintered in N\({}_{2}\)-5% H\({}_{2}\) underwent considerable abnormal grain growth. As abnormal grain growth is usually detrimental to the mechanical properties, KNbO\({}_{3}\) may not be suitable for co-firing in reducing atmospheres with base metal electrodes. On the other hand, an increase in grain size is known to improve the piezoelectric properties of (K\({}_{0.5}\)Na\({}_{0.5}\))NbO\({}_{3}\)-based ceramics due to reduced domain wall pinning (extrinsic contribution) [96; 97; 98]. Control of the orthorhombic-tetragonal phase transition temperature, which controls the relative amounts of orthorhombic and tetragonal phases, is also very important in improving the piezoelectric properties due to enhanced polarization extension and rotation (intrinsic contribution) [96; 99; 100; 101], as well as enhanced extrinsic contributions [102]. By sintering KNbO\({}_{3}\) in reducing atmospheres, it may be possible to control both grain size and orthorhombic-tetragonal phase transition temperature simultaneously, thereby enhancing the piezoelectric properties. The present study focused only on the effect of sintering atmosphere on the density, structure, and microstructure of KNbO\({}_{3}\). The samples sintered in reducing atmospheres would have relatively high conductivity due to the free electrons formed by Equation (2). When sintering ferroelectric materials such as BaTiO\({}_{3}\) or (K\({}_{0.5}\)Na\({}_{0.5}\))NbO\({}_{3}\) in reducing atmospheres, an acceptor dopant such as MnO\({}_{2}\) or MnO is often added to trap these free electrons, enabling the materials to maintain high resistivity [103; 104]. In the future, the effect of MnO\({}_{2}\) doping on the grain growth behavior and electrical properties of KNbO\({}_{3}\) sintered in reducing atmospheres needs to be studied.
## 5 Experiments
### Experimental Results and Discussions
Regarding cross-language adaptability, models developed by Meta, OpenAI, and Google, such as GPT-4o and GPT-3.5-turbo, exhibited high adaptability across languages. For instance, GPT-4o maintained high performance across all prompt types, achieving 86.00% accuracy with English prompts and slightly lower, yet still impressive, accuracy with Chinese prompts. This adaptability is essential for applications requiring multilingual support. Chinese LLMs, such as Qwen-turbo and ERNIE-4.0-8k, demonstrated a strong preference for their native language prompts. Qwen-turbo exhibited a drop in accuracy from 59.92% with Chinese prompts to 55.41% with English prompts in the English MCQ scenario. This suggests that these models may be more optimized for their native language, due to the training corpus. There is a clear linear relationship between the number of model parameters and accuracy. As observed, models with higher parameter counts, such as GPT-4o and Meta-Llama-3-70B, consistently outperformed others in both test scenarios. This indicates that larger models tend to better handle complexity in multiple languages. The number of output tokens varied significantly across models and prompt languages. Models such as Qianfan-Chinese-Llama-2-70B and Meta-Llama-3-70B tended to generate excessive tokens when using English prompts, suggesting a need for better prompt adherence. Conversely, GPT-4o adhered closely to prompt instructions, boosting its overall efficiency. While our results show that most models improved their performance with English prompts, this cannot conclusively demonstrate that LLMs perform better on English MCQs than on Chinese MCQs. The datasets contain different content, which likely influences the performance of the models. Figure 4: Results of testing the same set of Chinese ship-handling theory MCQs using Chinese and English prompts, respectively. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**\# Ques.**} & \multirow{2}{*}{**\# Corr.**} & \multirow{2}{*}{**Acc.**} & \multirow{2}{*}{**Time (s)**} & \multicolumn{2}{c}{**\# Total Tokens**} \\ \cline{5-6}  & & & & & **\# Input Tokens** & **\# Output Tokens** \\ \hline Qwen-turbo & 814 & 451 & 55.41\% & 767.11 & 244,152 & 845 \\ \hline ERNIE-4.0-8k & 814 & 549 & 67.44\% & 3788.96 & 237,234 & 6011 \\ \hline GPT-3.5-turbo & 814 & 467 & 57.37\% & 352.25 & 243,151 & 832 \\ \hline CPT-4 & 814 & 613 & 75.31\% & 547.76 & 243,151 & 814 \\ \hline GPT-4o & 814 & 700 & 86.00\% & 366.41 & 243,703 & 814 \\ \hline GLM-3-turbo & 814 & 476 & 58.48\% & 1533.61 & 252,842 & 2481 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Using English prompts to query multiple LLMs with English ship-handling theory MCQs.
## 3 Methodology  
We present the methodological procedure followed for the realization of our hybrid optimization scheme, which integrates genetic algorithms and Q-learning, an RL-based methodology, in order to improve drone fleet coordination. Our system employs a multi-objective fitness function that tries to maximize coverage and network connectivity while, simultaneously, maintaining effective energy consumption. The GA module achievesglobal search via the evolutionary optimization of drone locations by selection, crossover, and mutation iteratively, and the Q-learning module learns mutation corrections online to cope with time-varying real-world environmental factors.  
The GA initiates with a population of candidate drone fleet configurations generated randomly, with each being encoded as a chromosome of positional coordinates. The fitness function evaluates each configuration on a weighted sum of objectives: coverage area, network connectivity, and energy efficiency. After selection and crossover, the mutation phase applies Q-learning, where drones learn to update their positions by selecting actions from a discrete set of movement increments. The Q-learning agent learns its policy based on state representations that involve the current position, quality of coverage, connectivity, and available battery capacity.  
The reward function of Q-learning is designed to reward movements that increase fitness values, and penalize actions that increase energy consumption without high-coverage rewards. In this way, we enable the system to optimize exploration (via GAs' population diversity) and exploitation (via RL's goal-directed tuning). By integrating Q-learning with GA, we enable the fleet to reposition itself dynamically based on evolving environmental dynamics such as fire propagation in disaster relief or sudden signal interference in urban environments.  
Moreover, constraint handling mechanisms are employed to maintain the feasibility of the optimized solutions. They comprise constraints on drone flight capabilities, network stability prerequisites, and battery performance limitations. The approach is verified via a set of simulation experiments, wherein the hybrid solution is compared against a standalone genetic algorithm and particle swarm optimization under various scenarios, including tracking wildfires, disaster response, and urban connectivity. Its performance is analyzed based on critical metrics like the area covered, connection stability, and rate of convergence.
## 5 Experiments
### Flower Classification  
We finally evaluate the proposed LPLC-HDL on the Oxford 102 Flowers dataset [32] for fine-grained image classification, which consists of 8189 images from 102 categories, and each category contains at least 40 images. This dataset is very challenging because there exist large variations within the same category but small differences across several categories. The flowers appear at different scales, poses and lighting conditions; some flower images are shown in Figure 5. For each category, 10 images are used for training, 10 for validation, and the rest for testing, as in [32]. For ease of comparison, we take the convolutional neural network (CNN) features provided by [PERSON] et al. [35] as the image-level features.  
The parameters of LPLC-HDL on the Oxford 102 Flowers dataset can be seen in Table 1. The size of hybrid dictionary is set to be \(102\times 9+100=1018\), which means nine atoms for each category and 100 atoms as the shared amount. We compare LPLC-HDL with different kinds of representative methods for flower classification, such as the basic classifiers (Softmax and linear SVM [38]), the recent ProCRC [35], the related DDL methods and the deep learning models. Following the common measurement [41; 42], we evaluate the comparison methods by the average classification accuracy of all the categories, and the results are reported in Table 6. From it we can see that, taking the CNN features as the image-level features, the proposed LPLC-HDL method outperforms the basic classifiers and the competing DL methods. Compared with the other kinds of methods, LPLC-HDL is greatly superior to SparBases [43] and SMP [44]. Moreover, our method outperforms the recent deep learning models including GoogLeNet-GAP [45], ASPD [46] and AugHS [47], which need to design the special CNN architectures for flower classification.
## 3 Applications to a Waste Separation System
### Parameter Estimation by Importance Sampling  
MC simulation has already been used for the chosen waste separation technology to represent the expert-based distributions of separation efficiencies by discrete sampling and estimation of the outflows.It was shown that, if the sample size is large enough, the resulting distributions of the outflows are the same for all runs of the simulation. These distributions were also compared with experts' intervals about the outflows where they were available, thus validating the model [29].  
Using importance sampling (IS), feedback is gained: sample elements can be weighted on the basis of the available distributions about the outflows and separation efficiencies resampled according to these weights. Therefore, the parameter estimation of the system can be executed by the iterative circle introduced earlier in Figure 1. In the present case, the separation efficiencies (\(r_{i}^{j}\)) serve as parameters (\(\mathbf{\Theta}\)), and the yields form the outputs (\(\mathbf{y}\)) of the system.  
To apply the method, first the structure of the system model is defined in Section 3.3.1. Then, the estimable parameters are identified in Section 3.3.2.
## 3 Description of Problem Phenomenon and Theoretical Analysis
### Analysis of the Causes of Phenomenon 2  
The logical chain of causes for the phenomenon in the above figure is as follows:  
1. Negative Y-wing current decreases \(\rightarrow\) Positive Y-wing power supply mode switches \(\rightarrow\) Shunt stage temperature changes \(\rightarrow\) Output current fluctuates \(\rightarrow\) Total current responsiveness changes;
2. Changes in illumination conditions \(\rightarrow\) Synergetic changes in Negative Y-wing current and temperature \(\rightarrow\) Extremely low temperature without illumination. The specific elaboration is as follows: As shown in the figure, under normal conditions, the satellite load current stabilizes in the range of 29 A-37 A, and the normal solar array current of both wings fluctuates around 32 A. When the Negative Y-wing solar array current drops from the maximum value of 32 A to the minimum value (approximately 0 A), the Positive Y-wing power supply mode switches from \"stages 12-9 supplying the load, stage 8 adjusting (MEA 16.65 V)\" to \"stages 12-1 supplying the load (MEA 10 V)\". The temperature rises when the shunt circuit shunts to ground and decreases when supplying the load. For stages 1, 3, 5, and 7 of the +Y wing transition from the shunt state to the load supply state, causing a temperature decrease. This temperature reduction subsequently leads to a decline in the output current of the corresponding shunt levels, ultimately resulting in a decrease in the total current of the +Y wing. Stage 9 of the +Y wing remains in the load supply state, with no significant temperature change.  
Figure 5: Trend diagram of load, solar array current, and MEA for Satellite B.  
Figure 6: Trend diagram of solar array shunt stage temperature and load current for Satellite B.  
The Negative Y-wing solar array current decreases as the solar incidence angle increases, and the reduction in the light intensity factor causes the output current of a single sub-array to drop. The illumination factor decreases synchronously, and the temperature change trend is consistent with the Negative Y-wing current; the temperature can drop to \(-120\)\({}^{\circ}\)C without illumination. When the Negative Y-wing solar array current recovers from the minimum value to the maximum value, the shunt circuits of the Positive Y-wing sequentially re-enter the shunt state, the temperature gradually rises, and the total current recovers accordingly.
# Model Validation and Scenario Selection for Virtual-Based Homologation of Automated Vehicles
## 2 Literature Overview
### Scenario Selection Methods
#### 2.4.2 Data-Driven Methods  
Data-driven methods extract scenarios from data that are normally recorded during test drives with a fleet of vehicles equipped with special measurement technology. In the post-processing step, machine learning and pattern recognition techniques are applied to the recorded data. Some papers extract concrete scenarios from the data that have a high novelty value. For example, [PERSON] et al. [10] use an autoencoder, as its reproduction error can be interpreted as a novelty indicator. [PERSON] et al. [11] extend the idea and use generative neural networks such as a Generative Adversarial Network (GAN) and a Variational Autoencoder (VAE) to not only extract existing scenarios, but also to generate new ones from them.  
Further papers aimed to parameterize scenarios with parameter ranges and distributions. Therefore, similar scenarios are grouped together in a first processing step. Either specific scenario classes such as following, lane change or cutting-in are defined to use supervised learning techniques to assign the data to the classes [43; 44], or the data are clustered based on similarity using unsupervised learning techniques [45; 46]. Afterwards, the grouped data can be used to parameterize the scenarios. [PERSON] and [PERSON] [47] parameterize a lane-change maneuver with a hyperbolic tangent function. Similar work fitting a distribution to a car-following scenario using Kernel Density Estimation (KDE) can be found in [48].