esa-sceva/satcom-chunk-collection huggingface dataset:
# Real-Time Wave Energy Converter Control Using Instantaneous Frequency
## 6 Conclusions  
This study presented and compared several methods for real-time instantaneous frequency estimation for WEC control, focusing on PLL, CHT, and NCHT. The results demonstrated that the CHT method achieved superior energy capture performance and better cumulative energy accumulation compared to the baseline fixed parameter tuning and other real-time estimation methods. The NCHT approach also showed improved mean performance over the baseline, while the PLL-based control yielded a performance similar to the fixed parameter case but exhibited higher instantaneous peak powers.  
Figure 14: Histograms of CWR obtained from 1000 iterations of JONSWAP wave simulations for four different PTO parameter tuning strategies: (**a**) Fixed tuning (baseline), (**b**) NCHT-based estimation of the wave elevation frequency compared to the baseline, (**c**) PLL-based estimation compared to the baseline, and (**d**) CHT-based estimation compared to NCHT.  
A key finding of the study was that minimizing the NRMSE of frequency estimation alone does not guarantee better energy harvesting performance. Instead, the responsiveness of the estimation method played a critical role. Faster adaptation to wave frequency changes consistently led to higher CWR values, highlighting the importance of real-time estimator responsiveness in WEC control design.  
Additionally, practical aspects of implementation were emphasized. The proposed control strategies rely solely on standard WEC sensor measurements, without requiring external wave forecasting, making them well-suited for deployment on embedded hardware systems. Statistical validation using 1000 randomized JONSWAP simulations demonstrated the robustness and repeatability of the proposed methods under irregular sea conditions.  
Future work will focus on further enhancing the estimation methods by incorporating predictive modeling. In particular, the use of Long Short-Term Memory neural networks is being explored to forecast future wave signals at an affordable computational cost. Integrating such predictive capabilities with the proposed real-time control framework could further improve robustness under highly dynamic sea conditions. Moreover, the dynamic tuning strategies developed in this study are planned to be experimentally validated using the Laboratory Upgraded Point Absorber developed at Oregon State University [10].  
I.K. and T.K.A.B.: conception, simulation, application, writing, and editing; S.Y.: ideation, advising, and editing; B.J. and Y.C.: advising and editing; P.C.: editing. All authors have read and agreed to the published version of the manuscript.  
This material is based upon work supported by the U.S. Department of Energy's Office of Energy Efficiency and Renewable Energy (EERE) under Water Power Technology Office (WPTO) Agreement Number EE0009446.  
Not applicable.  
This work was supported by the US Department of Energy project DE-EE0009446 \"A Unified Multiphysics Approach for Modeling, Control, and Optimization of Wave Energy Converters\".  
The authors declare no conflicts of interest.
# An AC/DC Distribution Network DG Planning Problem: A Genetic-Ant Colony Hybrid Algorithm Approach
## 1 Introduction
[13] used the improved particle swarm optimization algorithm with simulated annealing algorithm to solve the problem of DG site selection and volume calculation based on the optimal network loss. [PERSON] et al. [14] used the adaptive mutation particle swarm optimization algorithm to plan the location and volume of DG without considering the load-added nodes. However, at this stage, distribution network planning with DG mainly stays in the AC power distribution stage, and there is very limited research content on AC/DC distribution network planning. At the same time, most of the literature on the planning process is mainly based on the rated capacity of DG, and does not take the timing characteristics of the DG and the load into account. This causes deviations in the actual planning problem, and it is necessary to fully consider the DG and load timing characteristics in the planning process. In recent years, more and more research including the application of heuristic intelligent optimization algorithms has been carried out. Genetic algorithms (GA) and ant colony algorithms (ACO) serve as two mainstream algorithms, and each of them has unique advantages. The genetic algorithm draws on the biological evolutionary law of nature to optimize the solution of the problem by simulating the evolution process of biological genes. The ant colony algorithm mimics real-world ant colony behavior. The algorithm simulates group behavior composed of simple individuals and seeks optimal results through group behavior. Therefore, this paper establishes a DG access AC/DC distribution network planning model that takes environmental costs and timing characteristics into account, and optimizes the type, location and capacity of DG in the distribution network. At the same time, this paper studies the ideas and methodsof GA and ACO to solve the problem, and analyzes the solution process. Then, by combining and improving the two algorithms, a GA-ACO algorithm is proposed and applied to the model solution.
# Concurrent Transmission Based on Distributed Scheduling for Underwater Acoustic Networks
## 3 Concurrent Transmission Based on Distributed Scheduling
### Joint Optimization of Sending Sequence and Time  
In previous studies, scheduling was usually implemented in a sender- and/or receiver-initiated manner for content-based networks, where a sender or receiver can receive multiple packets from different nodes. In these protocols, the effect of the sending sequence was insignificant. However, in the proposed protocol, multiple senders send packets to their corresponding receivers in a transmission cycle, which means the sending sequence is important to the transmission efficiency due to the unequal propagation delays among nodes. Figure 2 provides an example of two concurrent transmissions with different sending sequences. The total transmission time is much shorter when S\({}_{1}\) sends the packet before S\({}_{2}\). Therefore, in the proposed protocol, the sending sequence and times of the source nodes both must be optimized. In this section, we first consider the optimization of the sending time of the source node when the sending sequence is given. Then, a dynamic programming-based algorithm is proposed for the joint optimization of the sending sequence and time of the source nodes. To satisfy the synchronization requirement, no stochastic algorithm is used in the optimization.
## 4 Data-Driven Framework for Building Energy Consumption Prediction
### Feature Engineering
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Num.** & **Feature Types** & **Contents** \\ \hline
1 & Meteorological information & Outdoor temperature, humidity, wind speed, solar radiation, rain- fall, air pressure, and other factors. \\
2 & Indoor environmental information & Set temperature, indoor temperature, humidity, carbon dioxide concentration. \\
3 & Occupancy-related data & The number of occupants and types of activities. \\
4 & Time indices & Date, day of the week, time of day, and holidays. \\
5 & Building characteristic data & Compactness, surface area, wall area, roof area, height, orientation, glass area, thermal transmittance coefficient, and other factors. \\
6 & Socio-economic information & Income levels, electricity prices, GDP, population. \\
7 & Historical data & Historical meteorological data, historical energy consumption data. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The primary features that influence building loads. important inputs for the prediction model. Different prediction models demonstrate their unique performances precisely based on different ways of handling these factors and the ability to mine the internal patterns of the data. For example, in polynomial regression models, factors such as outdoor temperature and humidity in meteorological information may be used to make predictions by establishing linear or polynomial relationships with energy consumption. In SVR, these factors will be mapped to a high-dimensional space, and the relationship between energy consumption and these factors is fitted by finding the optimal hyperplane. Other factors such as indoor environmental information and living patterns also play a key role in different models. The way they are combined with the model determines the model's ability to capture the complex patterns of building energy consumption.
# Detection of Induced Activity in Social Networks: Model and Methodology
## 3 Methodology for Identifying and Diagnosing Induced Support for a Political Project
### The Algorithm of Segmentation of the Audience of a Political Project  
Let us consider an algorithm that assesses the audience of a political project by their activity on the social network. By activity we mean the number of comments and/or \"likes\" left by the social network user.  
At the first step, the \"Passive\" segment is distinguished from the whole audience of the political project, which includes users who have viewed the posts, comments, but have never shown communicative activity. Users who are not in the \"Passive\" segment will be referred to as communicative core (Figure 2).  
The second step in the communication core summarizes the activity of each social network user and then calculates the average of the number of messages left and \"likes\". All communicators with activity below the average are classified as \"Observer\".  
The third step follows the same steps as the second step for users who are not in the \"Passive\" and \"Observer\" segments. Users with below average activity are placed in the \"Sub-Activist\" segment.  
The fourth step follows the same steps as the third step for users who are not in the \"Passive\", \"Observer\", and \"Sub-Activist\" segments. Users with below average activity will be placed in the \"Activist\" segment.  
The fifth step proceeds in the same way as the fourth step for users who are not in the \"Passive\", \"Observer\", \"Sub-Activist\", and \"Activist\" segments. Users with below average activity are placed in the \"Sub-Influential\" segment and above average users are placed in the \"Influential\" segment.  
The algorithm results in six user segments. The segmentation significantly reduces the number of profiles for analysis and comparison when identifying NC. The algorithm identifies the most active group of users who form the online social capital artificially created for a political project. They create induced content traffic and thereby influence public opinion.  
The proposed algorithm is an elaboration of the authors' previous studies, which presented the results of an analysis of information dissemination sources in social networks [41; 42].
# Prediction of Harvest Time of Apple Trees: An RNN-Based Approach
## 6 Conclusions  
This work proposed a new approach to predict the harvest date in apple trees. The computational model PredHarv uses a multivariable approach, using historical and meteorological data with a strategy that uses RNN. The proposed evaluation scenarios and the comparative tests between the models show that the PredHarv model is a useful alternative that is capable of improving the accuracy of the prediction results. This work contributes to fruit growing, enabling anticipating information about the harvest date, generating financial savings for the fruit grower, avoiding unnecessary costs, and improving planning and productivity. The PredHarv model contributes with a methodology that makes the predictive capacity and applicability more effective, enabling the prospecting of future scenarios.  
A prototype of the PredHarv model was developed and submitted to evaluation scenarios. The results were evaluated according to the RMSE and MAE metrics. In the tests of the first PredHarv evaluation scenario, it demonstrated a generalization capacity when submitted to new data. In the second scenario, PredHarv showed results very close to the real values in tests with real data. In the third scenario, when submitted to synthetic data, the results showed that the PredHarv model showed a positive response to the stimuli caused by the changes made to the input data, adjusting the prediction as the data are being introduced. We performed tests with other supervised learning methods, and the average of the RMSE metric results obtained with LSTM was superior to the results obtained with other algorithms.  
Conceptualization, T.B.; Investigation, T.B. and L.M.P.; Methodology, T.B. and L.M.P.; Supervision, G.O.R. and R.d.R.R.; Validation, T.B.; Writing--original draft, T.B.; Writing--review & editing, L.M.P., G.d.O.R., R.d.R.R. and D.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** In order to contribute to the reproducibility of the results of this article, the data set is available in a publicly available repository at [[https://github.com/tboechel/dataset/](https://github.com/tboechel/dataset/)]([https://github.com/tboechel/dataset/](https://github.com/tboechel/dataset/)) (accessed on 3 February 2022).  
**Acknowledgments:** This study was supported by the Federal Institute of Education, Science and Technology of Rio Grande do Sul (IFRS). The authors would like to thank the following Brazilian agencies: CNPq, FAPERGS, FAPESP (grant 2020/05165-1), and CAPES.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 10: PredHarv model and linear model prediction results—DS2 dataset.
## 2 Proposed Approach and Its Verification
### Combined Metrics and Discussion of Results

Table 1: [PERSON]’s linear correlation coefficient (PLCC) of individual IQA metrics with the Levenshtein Distance for the considered 1760 images. The two best results are marked with bold font. particularly for the weighted sum denoted as \(CM_{2}\). Further experiments related to the additional use of the third metric in both combinations have not led to significant improvements of the computed PLCC values, including the use of neural networks, as well as the extension of the proposed approach for video sequences. Additionally, a simple combination of the abovementioned two models, leading to the combined metric \(CM_{3}\), has also been considered, which may be expressed in the following form:  
\[CM_{3}=a_{1}\cdot Q_{1}{}^{w_{1}}+a_{2}\cdot Q_{2}{}^{w_{2}}+a_{3}\cdot Q_{1}{}^ {w_{3}}\cdot Q_{2}{}^{w_{4}}\, \tag{3}\]  
where \(Q_{1}\) is the F-Measure and \(Q_{2}\) is the pseudo-F-Measure in this case. Nevertheless, its application has led to a small improvement of the achieved correlation to 0.9085, as shown in Table 2.  
To illustrate the properties of the proposed combined metrics based on the averaged mutual similarities of binary images, a sample image from the WEZUT OCR Dataset and its three binary versions are presented in Figure 6, together with the values of three combined metrics and the respective values of the Levenshtein Distance. The relations between the visual quality of the binary images, Levenshtein Distance values, and the results obtained using the three proposed combined metrics may be easily noticed. The best results among three presented methods may be observed for the NICK thresholding. \begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Metric** & **PLCC** & \(\mathbf{a_{1}}\) & \(\mathbf{a_{2}}\) & \(\mathbf{a_{3}}\) & \(\mathbf{w_{1}}\) & \(\mathbf{w_{2}}\) & \(\mathbf{w_{3}}\) & \(\mathbf{w_{4}}\) \\ \hline CM\({}_{1}\) & 0.8960 & \(-\) & \(-\) & \(-\) & \(-\)0.9643 & 2.3779 & \(-\) & \(-\) \\ CM\({}_{2}\) & 0.9077 & 0.9098 & 0.0537 & \(-\) & \(-\)0.7213 & 0.6592 & \(-\) & \(-\) \\ CM\({}_{3}\) & 0.9085 & 1.1595 & \(-\)0.9666 & \(-\)1.1790 & 1.0414 & \(-\)0.1376 & 1.0231 & 0.0157 \\ \hline \end{tabular}
\end{table} 
Table 2: Pearson’s linear correlation coefficient (PLCC) values of two combined IQA metrics with the Levenshtein Distance for the considered 1760 images together with the optimized weights for the combined metrics. Figure 6: _Cont._
## 4 Non-Vision-Based HAR
### Multimodal HAR
\begin{table}
\begin{tabular}{p{42.7 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**S/N** & **Paper** & **Dataset** & **Model** & **Accuracy** & **Contribution to Knowledge** \\ \hline \multirow{6}{*}{1} & Human Activity Recognition Through & \multirow{3}{*}{WISDM} & Ensemble of three CNN models & \multirow{3}{*}{93.66\%} & Proposed an ensemble of CNN models \\  & Ensemble Learning of Multiple & & & & CNN models \\  & Convolutional & & & & CNN models \\  & Neural Networks [68] & & & & \\ \hline \multirow{6}{*}{2} & MMHAR-EnsemNet: & \multirow{6}{*}{MMHAR-EnsemNet} & \multirow{6}{*}{93.66\%} & \multirow{6}{*}{Proposed a novel deep learning based ensemble model} \\  & A multimodal & UTD-MHAD and & & UTD-MHAD and & \\  & Human Activity Recognition & & Berkeley-MHAD & 0.996 on & \\  & Model [67] & & & Berkley-MHAD & MMHAR-EnsemNet \\ \hline \multirow{6}{*}{3} & A Deep & \multirow{6}{*}{NTU RGB and Multimodal Data Fusion in Action Recognition [69]} & \multirow{6}{*}{MTU RGB and HMDB51} & Twin Delayed Deep Deterministic (TD3) & 94.8\% on NTU RGB+D and 70.3\% on HMDB51 dataset & Proposed a reinforcement learning based \\  & A multimodal Data Fusion in Action Recognition [69] & & & HMDB51 & multimodal data fusion method. \\ \hline \multirow{6}{*}{4} & Adaptive multimodal & \multirow{6}{*}{C-MHAD, UTD-MHAD and M-MHAD} & ALSTGCN and LSTM-FCN models using an Adaptive Weight Learning (AWL) features fusion. & \multirow{6}{*}{91.18\% and the recall rate of falling activity is 100\%} & Proposed a multistream HAR model called \\  & Fast Convolution of People With Mobility [57] &
# Optimizing Viscoelastic Properties of Rubber Compounds for Ballistic Applications
## 2 Materials and Methods
### Rubber Compounds  
Rubber compounds are conventional formulations of butyl rubber (X_Butyl RB 301; Arlanxeo, Maastricht, The Netherlands), styrene-butadiene rubber (Buna SBR 2382; Arlanxeo, Maastricht, The Netherlands), butadiene rubber (BUNA cis-132-Schkopau; Trinseo, Berwyn, PA, USA) and natural rubber (SVR CV60; Mai Thao Rubber, Ho Chi Minh City, Vietnam) using commercially available additives. Carbon black (Corax N550; Safic Alcan, Paris, France) is used as a reinforcing filler. Tetramethylenethiuram disulfide (TMTD; Safic Alcan, Paris, France) and mercaptobenzothiazzole disulfide (MBTS; Safic Alcan, Paris, France) are used as accelerators for sulfur vulcanization. Zink oxide (Safic Alcan, Paris, France) and Stearic acid (Carl Roth GmbH, Karlsruhe, Germany) are used as activators. Paraffinic oils (Tudalen 3912, Vivatec 200) are obtained from Hansen & Rosenthal (Hamburg, Germany). The composition of all rubber formulations is listed in Table 1. The rubber-to-metal adhesive system consists of an adhesive primer (Megum 3276; Safic Alcan, Paris, France) and a cover coat (Megum 538; Safic Alcan, Paris, France).  
Rubber formulations are compounded on an ERMAFA TMI internal mixer (ERMAFA Sondermaschinen- und Anlagenbau GmbH, Chemnitz, Germany) using an upside-down process. After putting all additives (carbon black, oil, zinc oxide and stearic acid) into the internal mixer, raw rubber is added. The compound is mixed for 5 min at a temperature of 40 \({}^{\circ}\)C and a rotor speed of 80 rpm. Afterwards, the compound is processed on a Vogt rolling mill to incorporate the crosslinking agents (sulfur, accelerator). Vulcanization is then carried out through compression molding on a Schmidt HB 20 300 (Schmidt Maschinentechnik, Bretten-Bauerbach, Germany) laboratory press.  
For the production of the specimen used for dynamic mechanical analysis (DMA), between 7 and 9 g of the raw butyl rubber compound is compression molded for 6 min at a temperature of 180 \({}^{\circ}\)C and a pressure of 50 bar. The resulting rubber plates have a length and width of 4.5 cm and a thickness of 2 mm. Specimens with an edge length of 1 cm get punched out of the rubber plate.
# Fabrication of Hydrophobic Coatings Using Sugarcane Bagsase Waste Ash as Silica Source
## 3 Results and Discussion
### Water Contact Angle Test  
The floor tiles coated with acid treated SBA-750 \({}^{\circ}\)C showed the best hydrophobicity. This is ascribed to the increase of surface roughness and maximum silica content. The high surface roughness imparts a tortuous three-phase contact line, which increases the hydrophobicity of the floor tiles. The wettability of the SBA coated surfaces was assessed in terms of WCA. SBA coated floor tiles (Figure 2a-d) show a WCA of 135\({}^{\circ}\), 105\({}^{\circ}\), 124\({}^{\circ}\), and 93\({}^{\circ}\) for pH 3, 5, 7, and 10 solutions, respectively. SBA solution with pH 3 showed a high WCA compared to others. SBA with enriched SiO\({}_{2}\) and long alkyl chain of DMDEOS influence the surface roughness and reduce the surface energy of the floor.  
\begin{table}
\begin{tabular}{c c c c} \hline  & & \multicolumn{2}{c}{**Weight\%**} \\ \hline \multirow{2}{*}{**Components**} & \multirow{2}{*}{**Raw Sample**} & **After Acid Treatment** & **After Acid Treatment** \\ \cline{3-4}  & & **SBA 500 \({}^{\circ}\)C** & **SBA 750 \({}^{\circ}\)C** \\ \hline SiO\({}_{2}\) & 54.20 & 87.63 & 94.2 \\ CaO & 3.77 & 1.26 & 2.16 \\ SO\({}_{3}\) & 16.10 & 0.45 & 1.34 \\ Al\({}_{2}\)O\({}_{3}\) & 0.20 & 0.40 & 0.94 \\ K\({}_{2}\)O & 1.26 & 0.28 & 0.41 \\ MgO & 20.72 & 0.19 & 0.39 \\ P\({}_{2}\)O\({}_{5}\) & 7.36 & 0.07 & 0.20 \\ Fe\({}_{2}\)O\({}_{3}\) & 0.78 & 0.06 & 0.11 \\ TiO\({}_{2}\) & 0.01 & 0.01 & 0.03 \\ MnO & 1.45 & - & 0.01 \\ CuO & 0.06 & - & - \\ \hline \end{tabular}
\end{table}
Table 1: Elemental composition of raw sugarcane bagasse ash (SBA), acid treated SBA-500 \({}^{\circ}\)C and acid treated SBA-750 \({}^{\circ}\)C.  
According to the principle of wettability of the surface [12,13], the coating composition on the surface of tiles, which consisted of silica and low surface free energy DMDEOS, could effectively prevent the water penetration. Therefore, the treated floor tiles were highly hydrophobic. SBA mixture with pH 3 was further tested on various substrates such as brick, glass, and cotton fabric. The WCA of SBA coated tiles, brick, glass and cotton fabric are \(135^{\circ}\), \(136^{\circ}\), \(138^{\circ}\), and \(137^{\circ}\), respectively. Almost all of the SBA coated substrates showed good hydrophobicity. The substrates were kept at open air conditions (RTP) and the WCA was measured after 30 days (Figure 2i-l) to evaluate the stability of the coating. The WCAs of SBA coated tiles, brick, glass and cotton fabric are \(111^{\circ}\), \(123^{\circ}\), \(115^{\circ}\) and \(121^{\circ}\), respectively. These results revealed that a single layer of SBA coating is not enough for long-term usage. This could be rectified by increasing the number of coating layers on the substrates.