Radiation Data Research Articles

Research on a three-dimensional radiation field reconstruction algorithm based on an improved 3D CNN

This article investigates the application of an improved three-dimensional convolutional neural network (3D CNN) for sparse data-based reconstruction of radiation fields. Sparse radiation data points are consolidated into structured three-dimensional matrices and fed into a self-attention integrated CNN, enabling the network to interpolate and produce complete radiation distribution grids. The model’s validity is assessed through experiments with randomly sourced radiation in scenarios both with and without shielding, as well as in refined grid configurations. Results indicate that in unshielded environments, a mere 5%(15 points) sampling yields an average relative error of 4%, while in shielded settings, a 7% (21 points) sampling maintains the error around 11%. In refined grid contexts, a 2% sampling rate suffices to limit the error to 6.58%. Thus, the improved 3D CNN is demonstrated to be highly effective for precise three-dimensional radiation field reconstruction in sparse data scenarios.

Analysis of Inverter Efficiency Using Photovoltaic Power Generation Element Parameters

Photovoltaic power generation is influenced not only by variable environmental factors, such as solar radiation, temperature, and humidity, but also by the condition of equipment, including solar modules and inverters. In order to preserve energy production, it is essential to maintain and operate the equipment in optimal condition, which makes it crucial to determine the condition of the equipment in advance. This paper proposes a method of determining a degradation of efficiency by focusing on photovoltaic equipment, especially inverters, using LSTM (Long Short-Term Memory) for maintenance. The deterioration in the efficiency of the inverter is set based on the power generation predicted through the LSTM model. To this end, a correlation analysis and a linear analysis were performed between the power generation data collected at the power plant to learn the power generation prediction model and the data collected by the environmental sensor. With this analysis, a model was trained using solar radiation data and power data that are highly correlated with power generation. The results of the evaluation of the model’s performance show that it achieves a MAPE of 7.36, an RMSE of 27.91, a MAE of 18.43, and an R2 of 0.97. The verified model is applied to the power generation data of the selected inverters for the years 2020, 2021, and 2022. Through statistical analysis, it was determined that the error rate in 2022, the third year of its operation, increased by 159.55W on average from the error rate of the power generation forecast in 2020, the first year of operation. This indicates a 0.75% decrease in the inverter’s efficiency compared to the inverter’s power generation capacity. Therefore, it is judged that it can be applied effectively to analyses of inverter efficiency in the operation of photovoltaic plants.

RadLab: An open science resource for radiation studies relevant to human spaceflight

In response to the growing need of the space life sciences community for a publicly available single access point for radiation physics data relevant to human space exploration, an open data repository and analysis platform, RadLab, has been developed. RadLab consists of a database and a user-friendly data retrieval, visualization, and analysis toolkit, including a graphical user interface (GUI) and an application programming interface (API). RadLab complements the space biology data in the NASA Open Science Data Repository (OSDR) and aims to provide open, centralized access to radiation physics data relevant to spaceflight. Analysis tools contained in RadLab will facilitate intercomparison among different detectors and radiation data sets to better understand the radiation environment in spacecraft and planetary habitats, and facilitate the use of space radiation data by space biology investigators and radiation instrument developers.

Integrating autoencoder and decision tree models for enhanced energy consumption forecasting in microgrids: A meteorological data-driven approach in Djibouti

At this time, as the world and nations move to reduce the use of fossil fuels, research is oriented toward improving the energy consumption of people and buildings. Recent methods, mainly computing techniques such as deep learning, are proposed in the literature. This paper proposes a model that integrates the autoencoder with the decision tree model using six months of meteorological data, including solar radiation, wind speed, temperature, and other meteorological data. This study aims to advance the energy consumption prediction of loads connected to the microgrid. A case study of a microgrid park of a campus in Djibouti is presented to test the proposed model. Autoencoders were exploited to extract the main features of the meteorological data. Then the decision tree is proposed to predict the energy consumption using the resulting encoded features. The evaluation of the training of the autoencoder model gave a favorable result with a mean squared error of 0.002 and an R2 value of 0.99. Hyperparameter tuning scenarios facilitated the exploration of the decision tree model. Ensemble decision trees performed better than individual trees in this model, achieving a mean absolute error of 1.4 % and an R2 value of 0.997. It showed that hyperparameter tuning improved the results due to the best architecture fit for the decision tree. Moreover, the proposed model was validated by comparing it with the literature on KNN, SVR, and MLP models commonly used in microgrid energy management. The autoencoder decision tree outperformed other compared methods, achieving an explained variance of 0.997 and an MAE of 1.7 % in the standard decision tree regressor scenario. These results demonstrated the capabilities of machine learning and weather data. Combining an autoencoder and the decision tree model will open a new door to energy management improvement.

Fueling Costa Rica’s green hydrogen future: A financial roadmap for global leadership

This study evaluates the financial viability and scalability of green hydrogen production in Costa Rica, focusing on solar and wind energy. The research analyzes seven key provinces using Global Horizontal Irradiance (GHI) and wind speed data to assess energy potential. Monte Carlo simulations were employed to calculate the Net Present Value (NPV), hydrogen production costs, and economic sustainability over multiple project lifetimes. Guanacaste, with solar irradiance of 5.49 kWh/m2/day and wind speeds of 6.59 m/s, emerges as the most favorable region. The analysis reveals an NPV of $1,519.79 USD for onshore wind and $2,320.24 USD for offshore wind over 10 years. These values increase significantly for longer lifetimes, with 25-year NPVs of $3,687.40 USD for onshore wind and $5,890.72 USD for offshore wind, and 50-year NPVs reaching $7,456.21 USD and $11,560.38 USD, respectively. Hydrogen production costs are estimated at $49,696.75 USD from solar and $14,923.19 USD from wind energy. Despite its potential, high costs remain a challenge, requiring policy incentives, international cooperation, and green bonds to drive down costs and scale production. The study offers crucial insights for policymakers, investors, and researchers to support Costa Rica’s leadership in the global green hydrogen economy. Statement of SignificanceThe global shift toward renewable energy is critical for meeting decarbonization goals, and green hydrogen is emerging as a vital solution for sectors that are hard to electrify, such as transportation and heavy industry. However, the high cost of green hydrogen production poses a significant barrier to its widespread adoption. This study tackles the critical research problem of how to make green hydrogen economically viable by evaluating the potential of Costa Rica’s renewable energy resources—specifically solar PV and wind energy—to support large-scale hydrogen production.By integrating Monte Carlo simulations with region-specific financial analysis, this study provides a thorough assessment of hydrogen production costs and infrastructure requirements across Costa Rica’s provinces. The significance of this work lies in its localized approach, offering actionable insights into how a developing country with abundant renewable energy can position itself as a global leader in green hydrogen production. This research provides evidence that while current hydrogen production costs are higher than global targets, Costa Rica’s renewable resources present a strong foundation for future growth with the right policy interventions.The study’s findings have substantial implications for future research, policy, and investment strategies. By highlighting the financial and technological challenges, as well as offering potential solutions through international partnerships and innovative financing models, this research paves the way for Costa Rica and similar countries to contribute meaningfully to the global green hydrogen economy.

Accurate solar radiation site adaptation: Harnessing satellite data and in situ measurements

Accurate solar radiation data are essential to optimize solar energy systems and assess their feasibility. In this study, we propose a site-adaptation procedure based on a machine learning model trained to enhance the accuracy of solar radiation data using a combination of the National Solar Radiation Database (NSRDB) and in situ data collected in southern Colombia. The NSRDB provides high temporal and spatial resolution data, while in situ data offer accurate localized measurements specific to the study area. Our machine learning models were trained to learn the relationships between NSRDB data and in situ meteorological station data. The results demonstrate promising predictive capabilities, with the extreme grading boosting model effectively reducing mean absolute error, while a neural network model trained with the triplet loss function proved effective in minimizing mean bias error (MBE) and improving correlation between model-adjusted and in situ collected data. These findings make significant contributions to the field of solar radiation prediction, highlighting the effectiveness of amalgamating NSRDB and in situ data for precise solar radiation estimation, and promote the advancement of solar energy system design and decision-making processes.

EMD-based ultraviolet radiation prediction for sport events recommendation with environmental constraint

With the rising awareness of health and wellness, accurate ultraviolet (UV) radiation forecasts have become crucial for planning and conducting outdoor activities safely, particularly in the context of global sporting events arrangement and recommendation with definite constraint on environmental conditions. The dynamic nature of UV exposure, influenced by factors such as solar zenith angles, cloud cover, and atmospheric conditions, makes accurate UV radiation data forecasting difficult and challenging. To cope with these challenges, we present an innovative approach for predicting the UV radiation levels of a certain region during a certain time period using Empirical Mode Decomposition (EMD), a robust method for analyzing non-linear and non-stationary data. Our model is specifically designed for urban areas, where outdoor events are common, and integrates meteorological data with historical UV radiation records from specific geographic regions and time periods. The EMD-based model offers precise predictions of UV levels, essential for event organizers and city planners to make informed decisions regarding the scheduling, relocation and recommendation of events to minimize health risks associated with UV exposure. At last, the effectiveness of this model is validated through various experiments across different spatial and temporal contexts based on the Urban-Air dataset (recording 2,891,393 Air Quality Index data that cover four major Chinese cities), demonstrating its adaptability and accuracy under diverse environmental conditions.

The solar beryllium abundance revisited with 3D non-LTE models

The present-day abundance of beryllium in the solar atmosphere provides clues about mixing mechanisms within stellar interiors. However, abundance determinations based on the Be II313.107 nm line are prone to systematic errors due to imperfect model spectra. These errors arise from missing continuous opacity in the UV, a significant unidentified blend at 313.102 nm, departures from local thermodynamic equilibrium (LTE), and microturbulence and macroturbulence fudge parameters associated with one-dimensional (1D) hydrostatic model atmospheres. Although these factors have been discussed in the literature, no study has yet accounted for all of them simultaneously. To address this, we present 3D non-LTE calculations for neutral and ionised beryllium in the Sun. We used these models to derive the present-day solar beryllium abundance, calibrating the missing opacity on high resolution solar irradiance data and the unidentified blend on the centre-to-limb variation. We find a surface abundance of 1.21 ± 0.05 dex, which is significantly lower than the value of 1.38 dex that has been commonly adopted since 2004. Taking the initial abundance via CI chondrites, our result implies that beryllium has been depleted from the surface by an extra 0.11 ± 0.06 dex, or 22 ± 11%, on top of any effects of atomic diffusion. This is in tension with standard solar models, which predict negligible depletion, as well as with contemporary solar models that have extra mixing calibrated on the abundances of helium and lithium, which predict excessive depletion. These discrepancies highlight the need for further improvements to the physics in solar and stellar models.

The added value of combining solar irradiance data and forecasts: A probabilistic benchmarking exercise

Despite the growing awareness in academia and industry of the importance of solar probabilistic forecasting for further enhancing the integration of variable photovoltaic power generation into electrical power grids, there is still no benchmark study comparing a wide range of solar probabilistic methods across various local climates. Having identified this research gap, experts involved in the activities of IEA PVPS T1611International Energy Agency - Photovoltaic Power Systems - Solar Resource for High Penetration and Large Scale Applications. agreed to establish a benchmarking exercise to evaluate the quality of intra-hour and intra-day probabilistic irradiance forecasts.The tested forecasting methodologies are based on different input data including ground measurements, satellite-based forecasts and Numerical Weather Predictions (NWP), and different statistical methods are employed to generate probabilistic forecasts from these. The exercise highlights different forecast quality depending on the method used, and more importantly, on the input data fed into the models.In particular, the benchmarking procedure reveals that the association of a point forecast that blends ground, satellite and NWP data with a statistical technique generates high-quality probabilistic forecasts. Therefore, in a subsequent step, an additional investigation was conducted to assess the added value of such a blended point forecast on forecast quality. Three new statistical methods were implemented using the blended point forecast as input.To ensure a fair evaluation of the different methods, we calculate a skill score that measures the performance of the proposed model relative to that of a trivial baseline model. The closer the skill score is to 100%, the more efficient the method is. Overall, skill scores of methods that use the blended point forecast ranges from 42% to 46% for the intra-hour scenario and 27% to 32% for the intra-day scenario. Conversely, methods that do not use the blended point forecast exhibit skill scores ranging from 33% to 43% for intra-hour forecasts and 8% to 16% for intra-day forecasts.These results suggest that using (a) blended point forecasts that optimally combine different sources of input data and (b) a post-processing with a statistical method to produce the quantile forecasts is an effective and consistent way to generate high-quality intra-hour or intra-day probabilistic forecasts.

A precise and efficient K-means-ELM model to improve ultra-short-term solar irradiance forecasting

To address the problems of intermittent and uncontrollable solar irradiance faced by grid-connected photovoltaic power plants, this paper proposes an ultra-short-term solar irradiance prediction model that combines K-means clustering with the Extreme Learning Machine (ELM). The K-means algorithm is first applied to cluster solar irradiance data from different seasons in Shanghai based on spatial similarity. Subsequently, the ELM algorithm is employed to train the model, significantly improving both prediction accuracy and training speed. Compared with the prediction accuracy of the model before clustering, the root-mean-square error (RMSE) of the clustered model is significantly reduced by 28.75 % in spring, and 0.30 %, 6.70 % and 5.92 % in summer, Autumn and winter, respectively. In addition, the model demonstrates stable predictive performance at different time resolutions (5, 10 and 15 minutes) with R2 values close to 1, confirming its accuracy and stability under small sample conditions. In terms of training speed, ELM’s training time is more than 100 times faster than Support Vector Regression (SVR) and significantly shorter than traditional models such as PSO-BP and Random Forest (RF), showing its great advantage in application scenarios that require fast response. Overall, the K-means-ELM model is able to accurately capture the overall trend and sudden changes in solar irradiance, which is of great application value for enhancing the efficiency and stability of solar power generation systems.

Uncertainty of evaluation of spectral mismatch correction factor

Abstract Addressing the effect of partially correlated components of spectral irradiance data on spectral integrals poses a substantial challenge. This study employs Monte Carlo methods to conduct an uncertainty analysis of spectral integrals, with a focus on the uncertainty of the spectral mismatch correction factor. The investigation encompasses the primary types of uncertainty contributors of spectral irradiance, based on carefully calibrated FEL lamp measurements. A novel approach is introduced to estimate the impacts of partial correlations among spectral irradiance values at different wavelengths. The findings reveal that uncertainty components arising from partial correlations significantly outweigh those associated with full spectral correlation or complete lack of correlation at different wavelengths. This insight advances our understanding of uncertainty analysis of spectral integrals and underscores the importance of accounting for partial correlations in accurate measurements.

Cross-device Portability of Machine Learning Models in Electromagnetic Side-Channel Analysis for Forensics

The possession of smart devices has ingrained itself into daily life. Therefore, smart devices, such as IoT and smartphones, are crucial sources of evidence in instances where criminal activity occurs. Due to the challenges in traditional digital forensic techniques involving smart devices, it has been recently proposed in the literature to leverage electromagnetic side-channel analysis (EM-SCA) for the purpose. This paper identifies and discusses an important barrier that exists in the application of EM-SCA for digital forensics that hinders its successful use, namely, the issue of cross-device portability of machine learning (ML) models that are used for EM-SCA. Firstly, the paper empirically evaluates the possibility of using trained ML models to extract forensic insights from EM radiation data of IoT devices. During this empirical study, the inability to reuse a trained ML model across different devices is identified. Secondly, the paper surveys the literature in search of related work that has studied the use of EM-SCA to gather information from smart devices. The purpose of the survey is to identify whether any existing work has been able to introduce potential approaches to enable cross-device portability of ML models in EM-SCA. The findings of this survey point to the fact that the identified problem still exists and requires further studies opening the door to future research.

Intermediate inflation in a generalized non-minimal derivative coupling model

In this work, we consider intermediate inflation in the context of the generalized non-minimal derivative coupling (GNMDC) model. In the GNMDC model, inflation is driven by a canonical scalar field that is coupled not only to gravity but also to the derivative of the scalar field. The model introduces new dynamics and features during the inflationary epoch. We find inflationary solutions with a power law scalar field for the power law coupling function. Additionally, we determine the inflaton potential that generates intermediate expansion of the scale factor. We also discuss the background equations in the high friction limit and derive constraints on the parameters of our model. Furthermore, we investigate the cosmological perturbations in the slow roll approximation within the GNMDC model, and we calculate the scalar and tensor spectral index and the tensor-to-scalar ratio during intermediate inflation. We compare the results of this model with observational data that can be used to test the model using cosmic microwave background radiation data. Overall, we establish conditions for the inflaton potential that ensure the continuation of accelerated expansion during slow roll inflation. We numerically analyze the power spectrum and spectral index for scalar and tensor modes in intermediate inflation in the high friction limit, and we use Planck 2018 data to obtain constraints on the parameters of the model. We demonstrate that intermediate inflation in the GNMDC model is successful in evaluation and explanation of background and perturbation quantities using observational data.

Linear energy transfer dependent variation in viability and proliferation along the Bragg peak curve in sarcoma and normal tissue cells

Objective.The energy deposition of photons and protons differs. It depends on the position in the proton Bragg peak (BP) and the linear energy transfer (LET) leading to a variable relative biological effectiveness (RBE). Here, we investigate LET dependent alterations on metabolic viability and proliferation of sarcoma and endothelium cell lines following proton irradiation in comparison to photon exposure.Approach.Using a multi-step range shifter, each column of a 96-well plate was positioned in a different depth along four BP curves with increasing intensities. The high-throughput experimental setup covers dose, LET, and RBE changes seen in a treatment field. Photon irradiation was performed to calculate the RBE along the BP curve. Two biological information out of one experiment were extracted allowing a correlation between metabolic viability and proliferation of the cells.Main results.The metabolic viability and cellular proliferation were column-wise altered showing a depth-dose profile. Endothelium cell viability recovers within 96 h post BP irradiation while sarcoma cell viability remains reduced. Highest RBE values were observed at the BP distal fall-off regarding proliferation of the sarcoma and endothelial cells.Significance.The high-throughput experimental setup introduced here (I) covers dose, LET, and RBE changes seen in a treatment field, (II) measures short-term effects within 48 h to 96 h post irradiation, and (III) can additionally be transferred to various cell types without time consuming experimental adaptations. Traditionally, RBE values are calculated from clonogenic cell survival. Measured RBE profiles strongly depend on physical characteristics such as dose and LET and biological characteristics for example cell type and time point. Metabolic viability and proliferation proofed to be in a similar effect range compared to clonogenic survival results. Based on limited data of combined irradiation with doxorubicin, future experiments will test combined treatment with systemic therapies applied in clinics e.g. cyclin-dependent inhibitors.

Spatial and temporal variability of temperature, precipitation, and solar radiation in Magdalena department, Colombia from 2000 to 2022

Climate variability analysis is crucial for understanding current patterns and predicting future climate behavior. This knowledge is essential for decision-making about mitigation and reduction activities to combat climate change from those activities that may cause abrupt changes in climate dynamics. The present study analyzed climate variability in the Magdalena department, Colombia, over a 22-year period. Temperature, solar radiation, and precipitation data from NASA's Giovanni reanalysis and simulation are used with data from local meteorological stations. Statistical homogeneity and trend tests were applied to find climate patterns using RStudio software. The results showed that the years 2010 and 2011 were the wettest in the time series. Conversely, 2015 was the warmest year on record in the time series. This analysis provides a more comprehensive understanding of the climate variability in the region, laying the foundation for future studies on this subject.

The expected solar performance and ramp rate tool: a decision-making tool for planning prospective photovoltaic systems

The Expected Solar Performance and Ramp Rate tool (ESPRR) is an open-source interactive web-based application that reliably calculates ramp rate (RR) statistics and an expected power generation time series for prospective photovoltaic (PV) systems. Users create PV systems by defining site parameters. ESPRR uses those parameters with irradiance data from the National Solar Radiation Database (NSRDB) to create a time series of power output from which RR statistics are calculated. This study rigorously evaluates ESPRR’s performance using 5 years of measured power output from a fleet of utility-scale systems and finds that ESPRR calculates stress-case RRs within an error of 0.05 MW/min and 0.42 MW/min for the worst-case RRs. We evaluate the expected AC power output in clear-sky conditions and find an NRMSE of less than 10% and an NMBE of less than 6% for the fleet’s largest system. The NRMSE is 10%–15% of system capacity for non-clear-sky conditions, and the NMBE is about zero. The evaluation shows that ESPRR can estimate PV output and RRs that are representative of operational systems, meaning users can use the results from ESPRR in the decision-making process for designing new systems or when adding systems to an existing fleet. Since only system parameters are required to site a proposed system anywhere on a map, users can site and reposition a fleet of PV systems in a way that reduces significant RRs. As the grid-tied PV capacity continues to increase, the mitigation of significant RRs grows in importance. ESPRR can help developers and utilities create geographically diverse fleets of PV systems that will promote grid reliability and avoid significant RRs. ESPRR source code is available at https://github.com/UARENForecasting/ESPRR.

Resveratrol enhances the antiliver cancer effect of cisplatin by targeting the cell membrane protein PLA2.

In this study, we aimed to explore the mechanism by which resveratrol promotes cisplatin-induced death of HepG2 cells and to provide a potential strategy for resveratrol in the treatment of cancer. HepG2 cells were exposed to a range of drug concentrations for 24 h: resveratrol (2.5 μg/mL [10.95 μM], 5 μg/mL [21.91 μM], 10 μg/mL [43.81 μM], 20 μg/mL [87.62 μM], 40 μg/mL [175.25 μM], and 80 μg/mL [350.50 μM]), cisplatin (0.625 μg/mL [2.08 μM], 1.25 μg/mL [4.17 μM], 2.5 μg/mL [8.33 μM], 4.5 μg/mL [15.00 μM], and 10 μg/mL [33.33 μM]), 24 μg/mL (105.15 μM) resveratrol + 9 μg/mL (30.00 μM) cisplatin, and 12 μg/mL (52.57 μM) resveratrol + 4.5 μg/mL (15.00 μM) cisplatin. The interaction of two drugs was evaluated by coefficient of drug interaction (CDI), which was based on the Pharmacological Additivity model. The MTT 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide assay was used to detect the effect of different concentrations of drugs on cell viability, whiletranscriptome sequencing was used to identify pathways associated with higher gene enrichment. Synchrotron radiation FTIR microspectroscopy experiments and data analysis were conducted to obtain detailed spectral information. The second-derivative spectra were calculated using the Savitzky-Golay algorithm. Single-cell infrared spectral absorption matrices were constructed to analyze the spectral characteristics of individual cells. The Euclidean distance between cells was calculated to assess their spectral similarity. The cell-to-cell Euclidean distance was computed to evaluate the spatial relationships between cells. The target protein of resveratrol was verified by performing a Western blot analysis. After 24 h of treatment with resveratrol, HepG2 cell growth was inhibited in a dose-dependent manner. Resveratrol promotes cisplatin-induced HepG2 cell death through membrane-related pathways. It also significantly changes the membrane components of HepG2 cells. Additionally, resveratrol changes the morphology of the HepG2 cell membrane by decreasing the expression of PLA2G2. Resveratrol changes the morphology of the HepG2 cell membrane by decreasing the expression of PLA2G2 and promotes cisplatin-induced HepG2 cell death. The combination of cisplatin and resveratrol can play a synergistic therapeutic effect on HepG2 cells.

Forecasting solar irradiance for the strategic integration of hybrid hydro and solar photovoltaic systems in rural Indian regions

ABSTRACT Conversion of the conventional electrical grid into a smart and sustainable grid involves several considerations. The primary factors, however, are renewable energy penetration, associated storage systems, and energy generation costs. This research endeavors to conduct a thorough survey and analysis of the solar irradiance on various hydropower locations in India, including Run-of-River (RoR), Run-of-River with Pondage (RoRP), Reservoir Storage (S), Multi-Purpose Storage (MP) and Pumped Storage Systems (PSS). The hydroelectric projects in rural Indian regions have been the subject of the proposed case study. As a preliminary study, the probabilistic variables like minimum, maximum, and mean solar irradiance are calculated for 252 High-Scale Hydropower Plant locations (HSHPs) using the past 40 years day ahead solar radiation data to identify the high-irradiance hydropower plant location in each state of India. This study concludes that the maximum mean solar irradiance location in each state as these sites are well suited for hybrid PV-hydro systems. The identified high-irradiance locations 40 years day ahead data sets are analyzed employing 8 machine learning models and 2 deep learning models. This analysis aims to forecast solar irradiance, serving as a crucial foundation for the initial phase of the implementation of hybrid PV-hydro.

A novel solar radiation forecasting model based on time series imaging and bidirectional long short‐term memory network

AbstractThe instability of solar energy is the biggest challenge to its successful integration with modern power grids, and accurate prediction of long‐term solar radiation can effectively solve this problem. In this study, we proposed a novel long‐term solar radiation prediction model based on time series imaging and bidirectional long short‐term memory network. First, inspired by the computer vision algorithm, the recursive graph algorithm is used to transform the one‐dimensional time series into two‐dimensional images, and then convolutional neural network is used to extract the features from the images, thus, the deeper features in the original solar radiation data can be mined. Second, to solve the problem of low accuracy of long‐term solar radiation prediction, a hybrid model BiLSTM‐Transformer is used to predict long‐term solar radiation. The hybrid prediction model can capture the long‐term dependencies, thereby further improving the accuracy of the prediction model. The experimental results show that the hybrid model proposed in this study is superior to other single models and hybrid models in long‐term solar radiation prediction accuracy. The accuracy and stability of the hybrid model are verified by many tests.

Space radiation measurements during the Artemis I lunar mission

Space radiation is a notable hazard for long-duration human spaceflight1. Associated risks include cancer, cataracts, degenerative diseases2 and tissue reactions from large, acute exposures3. Space radiation originates from diverse sources, including galactic cosmic rays4, trapped-particle (Van Allen) belts5 and solar-particle events6. Previous radiation data are from the International Space Station and the Space Shuttle in low-Earth orbit protected by heavy shielding and Earth’s magnetic field7,8 and lightly shielded interplanetary robotic probes such as Mars Science Laboratory and Lunar Reconnaissance Orbiter9,10. Limited data from the Apollo missions11–13 and ground measurements with substantial caveats are also available14. Here we report radiation measurements from the heavily shielded Orion spacecraft on the uncrewed Artemis I lunar mission. At differing shielding locations inside the vehicle, a fourfold difference in dose rates was observed during proton-belt passes that are similar to large, reference solar-particle events. Interplanetary cosmic-ray dose equivalent rates in Orion were as much as 60% lower than previous observations9. Furthermore, a change in orientation of the spacecraft during the proton-belt transit resulted in a reduction of radiation dose rates of around 50%. These measurements validate the Orion for future crewed exploration and inform future human spaceflight mission design.