esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
In this module, a signal processing algorithm based on discrete Poisson distribution channel models and signal correlation detection is used for decoding optical pulse signal data. The theoretical analysis explores the relationship between ideal scenarios, tailing effects, and varying signal duty cycles in relation to system BER. Subsequently, system performance is evaluated through computer simulations, FPGA-based hardware systems are developed to implement UV communication, the impact of duty cycle changes on system BER is tested, and the effectiveness of our approach is validated through real-time outdoor communication experiments. Through derivations, the relationship between different duty cycles and BER under the influence of tailing effects is established. Furthermore, an analysis is conducted on how changes in transmission rate and distance affect BER when altering the signal duty cycle. By elucidating these relationships, insights are provided for optimizing UV communication systems operating under the influence of LED tailing effects, and guidance is offered for improving BER in UV communication systems employing OOK modulation. In Section 2, scattering models and discrete Poisson distribution are analyzed, and formulas for theoretical states, LED tailing effects, and BER under varying duty cycle conditions are derived. Section 3 employs computer simulations to examine the relationship between ideal scenarios, tailing effects, and varying signal duty cycles with respect to system BER. In Section 4, outdoor experiments were conducted, and the experimental results were discussed. Section 5 provides a comprehensive summary of the entire paper.
###### Abstract  
Accurate modeling of the hysteretic behavior of reinforced concrete columns with non-seismic detailing is crucial for assessing the seismic performance of reinforced concrete structures. While shear spring models are commonly used to capture nonlinear shear deformation in shear-critical reinforced concrete columns, determining appropriate parameters for these models remains challenging and often relies on expert judgment. This study proposes predictive equations for shear spring parameters that can be integrated into fiber-section elements to enhance the accuracy of modeling reinforced concrete columns. Specifically, empirical equations are developed for the nonlinear modeling parameters of the Pinching4 model implemented in OpenSees. Experimental data of flexural-shear and shear-critical reinforced concrete columns are used to calibrate the modeling parameters for predicting strength, stiffness, pinching, and cyclic degradation behavior. The predictive equations, derived through regression analysis, are validated against experimental data and demonstrated relatively high accuracy in capturing the shear behavior. To evaluate the impact of the shear behavior of reinforced concrete columns on the seismic fragility of non-conforming reinforced concrete frames, two types of numerical models are established with and without the Pinching4 material using the proposed predictive equations. Analytical results indicate that neglecting the appropriate shear behavior of reinforced concrete columns could lead to an overestimation of the seismic performance of non-conforming reinforced concrete frames.  
hypotheses; nonlinear modeling parameters; non-seismic details; reinforced concrete columns; seismic fragility analysis Article
## 4 Conclusions  
In this paper, a performance comparison of statistical methods and ANN-based hybrid methods for daily average wind speed and wind power estimation in Canakkale is presented. Four estimation techniques were used: Weibull distribution, Rayleigh distribution, ANN-GA, and ANN-PSO algorithms. The accuracy of the proposed hybrid ANN-based methods was calculated using the statistical error indices R\({}^{2}\), RMSE, MSE, MAE, and MAPE. Among the statistical methods presented, the Weibull distribution yielded better results than the Rayleigh distribution, whereas the ANN-GA gave better results than ANN-PSO. The obtained results of R\({}^{2}\), MSE, RMSE, MAE, and MAPE were 0.896, 0.3653, 0.6044, 0.3994, and 16.66%, respectively, for the ANN-GA model. It was seen that the ANN-GA model realized the wind speed and wind power density estimation of Canakkale Province with 94.839% accuracy. The results indicate that the proposed hybrid approaches provide reasonable wind speed and wind power density predictions. Consequently, thanks to the presented study, it is expected that better planning and a more effective evaluation can be made for investments in the wind sector in the region. In further studies, by using different  
Figure 10: Prediction of daily wind power density for ANN-GA, ANN-PSO, Weibull distribution, and Rayleigh distribution.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{6}{c}{**Statistical Error Performance Result**} \\ \cline{2-7}  & **R** & **R\({}^{2}\)** & **MSE** & **RMSE** & **MAE** & **MAPE** \\ \hline Presented ANN-PSO & 0.94042 & 0.866 & 0.4721 & 0.6871 & 0.4304 & 17.07\% \\ Presented ANN-GA & 0.94839 & 0.896 & 0.3653 & 0.6044 & 0.3994 & 16.66\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: The statistical error performance result of ANN-based hybrid models.  
AI-based hybrid methods to predict the wind speed of the region and more favorable datasets, results with higher accuracy can be achieved.  
Conceptualization, T.K.; Methodology, T.K. and E.E.; Software, T.K. and E.E.; Validation, T.K.; Writing--original draft, T.K. and E.E.; Writing--review & editing, T.K. and E.E.; Project administration, T.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy and ethical restrictions.  
The authors declare no conflict of interest.
# Context-Aware Complex Human Activity Recognition Using Hybrid Deep Learning Models
## 3 Methodology
### The CNN-LSTM Hybrid Model  
The proposed CNN-LSTM ensemble model uses the _time-distributed_ CNN sub model for automatic learning and extraction of discriminatory features from raw input signals and the LSTM networks for frequency and temporal modeling. First, a CNN is made up of the following components: an input layer, multiple hidden layers, and an output layer (Figure S2). The input layer is determined by the input signals. Each of the multiple hidden layers can either be a convolutional layer followed by an activation function, a pooling layer, or a fully connected layer (FC). The main difference between the two models is that at the _time-distributed_ input layer, the baseline model trains on 9 input features, while the context-aware model trains on 11.
# High-Precision Tea Plantation Mapping with Multi-Source Remote Sensing and Deep Learning
## 3 Methods
### Accuracy Assessment  
This study simultaneously employed both polygonal samples and sample points for accuracy assessment. Among them, polygonal samples were mainly used for the validation dataset used in the training process of deep learning models. They served the purpose of early stopping to eliminate overfitting during model training and for selecting the optimal model. Sample points were primarily used to test the testing accuracy of the model at the scale of the entire Anji County to test the model's spatial generalization ability. In terms of accuracy indicators, this study selected four indicators: overall accuracy (OA), recall, precision, and F1-score. The calculation of these four indicators was based on the confusion matrix, and the calculation formulas were defined in Equations (16)-(19).  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{16}\]  
\[Precision=\frac{TP+TN}{TP+FP} \tag{17}\]  
\[Recall=\frac{TP}{TP+FN} \tag{18}\]  
\[F1 score=\frac{2\times Precision\times Recall}{Precision+Recall} \tag{19}\]  
where _TP_ represented the number of tea garden samples correctly classified, _TN_ represented the number of non-tea garden samples correctly classified, _FP_ represented the number of samples that were actually non-tea garden but incorrectly classified as tea garden, and _FN_ represented the number of samples that were actually tea garden but incorrectly classified as non-tea garden.  
It is noteworthy that this study utilized four-fold cross-validation with polygonal samples for model training and validation. Four-fold cross-validation was a widely used technique in machine learning for evaluating the performance of a predictive model. In this method, the original dataset was randomly divided into four equal-sized subsets. The model was then trained on two of the subsets and validated on the remaining subset. This process was repeated three times, each time with a different subset designated for validation. Finally, the performance metrics obtained from the three validation sets were averaged to provide an overall assessment of the model's accuracy and generalization ability. Three-fold cross-validation helped mitigate the risk of overfitting by providing multiple independent assessments of the model's performance.
# Stability Analysis of the Exploitation System with Room and Pillar by Analytical Methods
## 3 Methods
### Stability Analysis of the Rooms and Pillars Exploitation System through Analytical Method  
The way of appearance, distribution, and manifestation of the secondary stress state, \(\left|\mathrm{T}_{\sigma}\right|\,\), as well as its size, is not only a characteristic of the salt massif, because it is not dependent only on its quality but is a function of the complete and inseparable interaction of a complex of natural, technical, mining, and organizational factors. Knowing the size and distribution of the secondary stress-deformation state is an imperative that can lead to the design and realization of stable underground excavations. The problem of evaluating this secondary stress-strain state in the elements of the room-pillar-floor system has been widely studied worldwide. In this paper, we tried to find the possibility of an analytical assessment of the secondary stress state in the pillars between the operating rooms, as well as in the whole room-pillar-floor system, based on the results obtained from laboratory research through modeling and also the results of measurements performed in situ [11; 45; 46]. Such a problem was approached based on the following principles: (1) based on the principles and theories of the mechanics of the continuous medium; (2) a mathematical model based on the principle of the function of a complex variable. 2.1 Analytical Model for Assessing the Stress State in the Pillar Based on the Principles and Theories of the Mechanics of the Continuous Medium  
We considered the room-pillar mining system (rooms and rectangular pillars, Figure 2, respectively, small rooms and square pillars, Figure 3) used in the solid exploitation of rock salt. Applying the theories of the mechanics of the continuous medium, it was possible to specify that the stress state of a pillar can be established starting from its assimilation with a cuboid under stress. This stress is created by the secondary stress state that manifests itself in this cuboid. Figure 2: Rooms and rectangular pillars exploitation system. The problem was addressed spatially in the Oxyz coordinate system. The stress state of the elementary cuboid implies the existence of the nine unknowns: three normal components (\(\sigma_{\rm z}\), \(\sigma_{\rm y}\), and \(\sigma_{\rm x}\)) and six tangential components, which, due to [PERSON]'s principle, are reduced to three (\(\tau_{\rm xy}\), \(\tau_{\rm yz}\), and \(\tau_{\rm zx}\)). As a result, the evaluation of the secondary stress state requires the obtaining of the analytical expressions of these components. These expressions were determined and are given by the relations (3):  
\[
# A Review of Distribution Grid Consumption Strategies Containing Distributed Photovoltaics
## 3 Distribution Grid Consumption Strategy with Distributed PV
### Voltage Control Strategies for Distribution Grids Containing Distributed Photovoltaics
#### 3.3.2 Voltage Coordination Control Methods
It was shown that the single switch converter has a lower size and cost, but its control strategy is limited. [PERSON] et al. from Canada proposed a multi-port converter architecture for PV cell systems [120]. In the proposed design, the battery charging and discharging circuits are integrated with the PV power optimizer through a high-frequency AC link, and the converter system is able to regulate the output voltage and use variable switching frequency for maximum power point tracking. The literature [121] proposes a novel power-sharing voltage regulation strategy based on solar PV power microgrids using advanced power management techniques for microgrid systems. This technique improves the stability of the distribution network based on voltage ride-through capability and optimizes the active and reactive currents of the distribution network. Iranian scholars [PERSON] et al. proposed a backstepping sliding mode control method based on the Liapunov criterion [122]. The proposed method provides faster adaptation to changes in temperature and solar radiation and ensures faster convergence to the MPP. Finally, it is verified that the method has good robustness. Thai scholars [PERSON] et al. used a control strategy involving the coordination between the central control unit and local control functions of the PV system [123]. An adaptive forbidden search technique was applied to search for the optimal LCF parameters of the PV system and adjusted weekly. The optimization problem is to maximize the true power generation of all PV systems under varying sunlight and three-phase unbalanced loads. By summarizing the study, it can be seen that the PV voltage control strategy can be mainly divided into the passive control strategy, which regulates the reactive power in the grid through the physical components, thus controlling the voltage level; the active control strategy utilizes advanced control systems and algorithms to dynamically adjust the reactive power in the grid in order to achieve a more accurate voltage control; and the inverter-based control strategy of three kinds. In addition, the different quasi-controllocations are mainly divided into three ways, which are in situ, centralized, and distributed, and their specific concepts and advantages and disadvantages are shown in Table 4[124]. Directions for future work on voltage control strategy include further optimizing the design of the control system to improve response speed and accuracy, exploring more intelligent and adaptive voltage control strategies to cope with the complex and changing grid environment, and carrying out large-scale empirical studies to verify the effectiveness and feasibility of the strategy.
## 1 Introduction
However, the experimentally measured magnetic properties of all alnico compositions are estimated to be less than half the calculated energy product theoretically possible by refining and controlling the nanostructure and by composition and processing optimization, guided by modern techniques [11]. Thus, enhanced alnico permanent magnets with an energy product of \(\sim\)20 MGOe could be achieved that would match, or outperform, both Dy-enriched Nd-Fe-B and Sm-Co-based permanent magnets operating at 150-200 \({}^{\circ}\)C [11]. Additive manufacturing (AM) has great potential to be applied in the permanent magnet industry for near-net shape complex parts, system integration, functionally graded magnetic materials and complicated magnetic field generation [15; 16; 17; 18]. In most AM fusion processes, parts are built up one layer at a time as new material is melted and solidified onto the layer below using a laser or electron beam as a localized heat source. AM eliminates expensive dies and holds, and reduces material waste drastically by requiring only minimal machining to final shape [15]. AM broadens the available processing space and empowers geometries not possible by traditional manufacturing, providing exciting possibilities for grain texture control for tailored isotropic or anisotropic properties [15; 19]. In particular, alnico magnetic properties can be increased if a textured, columnar grain microstructure results, thus motivating exploration of an AM processing route for alnico magnet fabrication [10; 19]. EBM/PBF of alnico has not been performed to date due to concerns about electron beam createdmagnetic fluxes within the AM build chamber and potential for interactions with the magnetic AM feedstock powders. Tight control of the localized heat input and pre-heat temperature relative to the Curie temperature of the material is required in order to be able to AM process magnetic materials. AM processing of Nd-Fe-B has been attempted in [PERSON]'s and [PERSON]'s studies showing the inability to obtain non-bonded magnets of these RE magnet alloys with good properties [17; 18]. This work describes AM processing of alnico magnets made from high-pressure gas atomized (HPGA) pre-alloyed powders [16; 20; 21] of custom compositions using both laser melted directed energy deposition (DED) and electron beam melted powder bed fusion (EBM/PBF) AM systems. Custom compositions include a full-Co alnico designed to increase coercivity and a Co-lean alnico designed to also increase coercivity, but with reduced Co content, an element that is likely to see a supply strain in the future due to its use in batteries [22; 23; 24]. The resulting alnico samples were characterized with scanning electron microscopy (SEM), electron backscatter diffraction (EBSD), and closed loop hysteresisraph measurements. The buildability of alnico and resulting magnetic properties can help establish the case for making alnico magnet components by AM for next generation motors and other high temperature, functionally graded or complex engineered part geometry applications.
# Polarization in Political Rallies: A Markovian Agent-Based Model for Opinion Dynamics
## 3 The Markovian Agent Modelling Paradigm
#### Comparison with Classical Opinion Dynamics Models  
To contextualize our MAM within the broader field of opinion dynamics, it is crucial to compare its foundations and features with existing modeling paradigms. While traditional approaches such as the DeGroot model, bounded confidence models (e.g., Hegselmann-Krause and Deffuant-Weisbuch), and voter models have significantly advanced our understanding, MAM introduces a distinct set of core assumptions and operational mechanisms. A key differentiating aspect lies in its foundation on CTMCs, where agents stochastically transit between discrete opinion states according to specified transition rates. This contrasts with most established models that operate in discrete time and rely on deterministic or probabilistic update rules based on opinion distances or network topology.  
In MAM, _message passing_ is the fundamental interaction mechanism: agents emit messages either during transitions or while residing in a state, and reception by other agents can probabilistically induce state changes, modulated by _perception functions_ that reflect individual openness to influence. This explicit modeling of communication processes distinguishes MAM from models that assume direct averaging (DeGroot), bounded confidence thresholds (Hegselmann-Krause), or simple mimicry (voter models). Another key advantage of MAM lies in its capacity for _analytical computation of opinion distributions over time_, without relying exclusively on large-scale simulations. In contrast, most other models offer either simulation-based insights or closed-form analytical results only under restrictive or highly symmetric assumptions. Furthermore, MAM's inherent scalability and flexibility enable the inclusion of heterogeneous agents and spatial locality--both essential for capturing real-world dynamics. Its ability to capture _transient evolution_ makes it particularly suited to time-bounded scenarios such as _political rallies, public health campaigns, or emergency communication_.  
To clarify these distinctions, we present in Table 1 a structured comparison of representative models, including their assumptions, features, limitations, and application domains. This highlights how MAM complements existing approaches, offering a versatile and analytically tractable framework for studying opinion dynamics in temporally and spatially structured settings. Our focus here is to demonstrate the efficacy of this paradigm in modeling _opinion polarization_ in dynamic assemblies, with broader potential across social, political, and economic systems where belief evolution and heterogeneous communication are central.
# Antioxidant Properties of Soybean Oil Supplemented with Ginger and Turmeric Powders
## 3 Results and Discussion
### Antioxidant Activity of Ginger- and Turmeric-Supplemented Soybean Oils  
As previously reported, the total phenolic content, and also the antioxidant activity of the refined soybean oil, used as control, was not detected (0.00 mg TE/g), as confirmed by DPPH and FRAP assays. Therefore, the antioxidant activity increased only after adding ginger and turmeric powders containing natural antioxidants [41; 42; 43; 44; 45; 46]. The antioxidant activity was significantly different (\(p<0.001\)) among oil samples at each enrichment cycle in both DPPH and FRAP assays (Table 2). All of the ginger- and turmeric-supplemented soybean oils lead to greater antioxidant properties than the reference BHT. Oil samples such as TR and TP had the highest antioxidant activity at Cycle 1 and Cycle 2, while GP achieved the best performance at Cycle 3 because of their greater phenolic yields (Table 1). In this regard, previous research works associated the antioxidant activity of agro-food products and wastes with their phenolic composition [6; 17; 22; 25; 27; 28]. Other authors found that phytochemicals such as 6-gingerol and curcumin contributed to the antioxidant effects of ginger and turmeric rhizomes and peels [41]. Regarding turmeric-supplemented soybean oils, TR and TP had greater antioxidant activity than that of TC only for the first two cycles where achieved the highest yields of total polyphenols and curcumin (Table 1). The percentage reductions of DPPH and FRAP values in TR (respectively 68% and 66% at Cycle 2; 92% and 90% at Cycle 3) and TP (respectively 59% and 55% at Cycle 2, 94% at Cycle 3) were higher than those of TC (respectively 25% and 30% at Cycle 2, 67% and 59% at Cycle 3) as a consequence of their greater percentage reductions of curcumin content (Table 1). Regarding ginger-supplemented soybean oils, GP, which achieved the best antioxidant performance at each oil enrichment cycle, had lower percentage reductions of DPPH and FRAP values (respectively 44% and 56% at Cycle 2; 78% and 75% at Cycle 3) than those of GR (respectively 63% and 79% at Cycle 2; 91% and 96% at Cycle 3) and GC (respectively 59% and 72% at Cycle 2; 92% and 91% at Cycle 3) as a consequence of its lower percentage reduction of 6-gingerol content (Table 1). Moreover, a previous study confirmed that the antioxidant activity of ginger peel was greater than that of the corresponding peeled rhizome because of its higher content of total polyphenols and 6-gingerol [41]. As shown in Figure 1, the positive correlation between antioxidant and phenolic values in soybean oils with ginger (\(r=0.94\), \(r^{2}=0.88\); \(r=0.97\), \(r^{2}=0.94\)) and turmeric (\(r=0.99\), \(r^{2}=0.97\); \(r=0.96\), \(r^{2}=0.92\)) powders was statistically relevant (\(p<0.001\)). Hence, the antioxidant activity linearly increased at increasing concentration of 6-gingerol and curcumin as confirmed by several studies [43; 44; 55].