This study explores the forecasting accuracy of SARIMAX, LSTM, and XGBoost models in predicting solar PV output using one-year data from three solar PV installations in the Philippines. The research aims to compare the performance of these models with their hybrid counterparts and investigate their performance. The study utilizes the adjusted shortwave radiation (SWR) product in the Advanced Himawari Imager 8 (AHI-8), as a proxy for in situ solar irradiance, and weather parameters, to improve the accuracy of the forecasting models. The results show that SARIMAX outperforms LSTM, XGBoost, and their combinations for Plants 1 and 2, while XGBoost performs best for Plant 3. Contrary to previous studies, the hybrid models did not provide more accurate forecasts than the individual methods. The performance of the models varied depending on the forecasted month and installation site. Using adjusted SWR and other weather parameters, as inputs in forecasting solar PV output, adds novelty to this research. Future research should consider comparing the accuracy of using adjusted SWR alone and combined with other weather parameters. This study contributes to solar PV output forecasting by utilizing adjusted satellite-derived solar radiation, and combining SARIMAX, LSTM, and XGBoost models, including their hybrid counterparts, in a single and comprehensive analysis.

Location information is the most crucial information used in context-aware applications, e-commerce and IoT-based consumer applications. Traditional methods doesn’t focus on network coverage, accuracy, hardware cost, and noise in dense environment. To defeat these issues, this paper presents a novel localization algorithm for UWB nodes adopting self-calibration and ToA measurement for context-aware applications. The Link quality induction values are used instead of RSSI for distance estimation by costing technique. A calibration factor (CF) is further introduce to automatically update the location information in mobility. As the signal strength can be distorted heavily due to shadowing and multi-path fading, the localization is estimated in noisy condition and extended Kalman filtering (EKF) is applied to refine the node coordinates. Simulation results shows that the positioning error is decreased with an overall accuracy of 0.23m and standard-deviation of 0.76m.

In the past few decades, the transmission of data over an unsecure channel has resulted in an increased rate of hacking. The requirement to make multimedia data more secure is increasing day by day. Numerous algorithms have been developed to improve efficiency and robustness in the encryption process. In this article, a novel and secure image encryption algorithm is presented. It is based on a modified chaotic logistic map (CLM) that provides the advantage of taking less computational time to encrypt an input image. The encryption algorithm is based on Shannon’s idea of using a substitution–permutation and one-time pad network to achieve ideal secrecy. The CLM is used for substitution and permutation to improve randomness and increase dependency on the encryption key. Various statistical tests are conducted, such as keyspace analysis, complexity analysis, sensitivity analysis, strict avalanche criteria (SAC), histogram analysis, entropy analysis, mean of absolute deviation (MAD) analysis, correlation analysis, contrast analysis and homogeneity, to give a comparative analysis of the proposed algorithm and verify its security. As a result of various statistical tests, it is evident that the proposed algorithm is more efficient and robust as compared to previous ones.