Abstract
Progressively more advanced and complex models are proposed to address problems related to computer vision, forecasting, Internet of Things, Big Data and so on. However, these disciplines require preprocessing steps to obtain meaningful results. One of the most common problems addressed in this stage is the presence of missing values. Understanding the reason why missingness occurs helps to select data imputation methods that are more adequate to complete these missing values. Missing at Random synthetic generation presents challenges such as achieving extreme missingness rates and preserving the consistency of the mechanism. To address these shortcomings, three new methods that generate synthetic missingness under the Missing at Random mechanism are proposed in this work and compared to a baseline model. This comparison considers a benchmark covering 33 data sets and five missingness rates $(10\%, 20\%, 40\%, 60\%, 80\%)$ . Seven data imputation methods are compared to evaluate the proposals, ranging from traditional methods to deep learning methods. The results demonstrate that the proposals are aligned with the baseline method in terms of the performance and ranking of data imputation methods. Thus, three new feasible and consistent alternatives for synthetic missingness generation under Missing at Random are presented.

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[No abstract available]

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Abstract
The European Union is pushing its members states to implement regulations that incentivize distribution system operators to procure flexibility to enhance grid operation and planning. Since flexibility should be obtained using market-based solutions, when possible, flexibility market platforms become essential tools to harness consumer-side flexibility, supporting its procurement, trading, dispatch, and settlement. These reasons have led to the appearance of multiple flexibility market platforms with different structure and functionalities. This work provides a comprehensive description of the main flexibility platforms operating in Europe and provides a concise review of the platform main characteristics and functionalities, including their user segment, flexibility trading procedures, settlement processes, and flexibility products supported.

Abstract
In this letter, we introduce the computation-limited (comp-limited) signals, a communication capacity regime where the computational complexity of signal processing is the primary constraint for communication performance, overriding factors such as power or bandwidth. We present the Spectro-Computational (SC) analysis, a novel mathematical framework designed to enhance classic concepts of information theory -such as data rate, spectral efficiency, and capacity - to accommodate the computational complexity overhead of signal processing. We explore a specific Shannon regime where capacity is expected to increase indefinitely with channel resources. However, we identify conditions under which the time complexity overhead can cause capacity to decrease rather than increase, leading to the definition of the comp-limited signal regime. Furthermore, we provide examples of SC analysis and demonstrate that the Orthogonal Frequency Division Multiplexing (OFDM) waveform falls under the comp-limited regime unless the lower-bound computational complexity of the N-point Discrete Fourier Transform (DFT) problem verifies as ohm (N)$ , which remains an open challenge in the theory of computation.

Abstract
Surface plasmon resonance (SPR) conventionally occurs at the interface of a thin metallic film and an external dielectric medium in fiber optics through core-guided light. However, this work introduces theoretical and experimental evidence suggesting that the SPR in optical fibers can also be induced through light scattering from Au nanoparticles (NPs) on the thin metallic film, defined as nanoparticle-induced SPR (NPI-SPR). This method adheres to phase-matching conditions between SPR dispersion curves and the wave vectors of scattered light from Au NPs. Experimentally, these conditions are met on an etched optical fiber, enabling direct interaction between light and immobilized Au NPs. Compared to SPR, NPI-SPR exhibits stronger field intensity in the external region and wavelength tuning capabilities (750 to 1250 nm) by varying Au NP diameters (20 to 90 nm). NPI-SPR demonstrates refractive index sensitivities of 4000 to 4416 nm per refractive index unit, nearly double those of typical SPR using the same optical fiber configuration sans Au NPs. Additionally, NPI-SPR fiber configuration has demonstrated its applicability for developing biosensors, achieving a remarkable limit of detection of 0.004 nm for thrombin protein evaluation, a twenty-fold enhancement compared to typical SPR. These findings underscore the intrinsic advantages of NPI-SPR for sensing. Surface plasmon resonance (SPR) typically occurs at the interface of a thin metallic film and a dielectric medium in fiber optics. This work presents evidence of nanoparticle-induced SPR (NPI-SPR) in optical fibers through light scattering from Au nanoparticles on the thin metallic film. NPI-SPR offers stronger field intensity, wavelength tuning, and enhanced refractive index sensitivities, making it advantageous for biosensing applications. image