Attitude Determination in Space with Ambient Light Sensors using Machine Learning for Solar Cell Characterization

Reb, Lennart K.; Böhmer, M.; Predeschly, Benjamin; Spanier, Lukas; Dreißigacker, C.; Meyer, Andreas; Müller‐Buschbaum, Peter

doi:10.1002/solr.202200537

Cited by 3 publications

(8 citation statements)

References 37 publications

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“…For each segment, the irradiance maximum‐likelihood‐estimate that is most time‐synchronous with the half‐time sweep is selected for the latter calculation. [ 50 ] Here, we note that the solar irradiance values used from previous work are derived without the consideration of soot on the windows and, thus, are upper limits to the in‐reality received somewhat lower solar power. Consequently, PCE values are conservative estimates throughout this work.…”

Section: Experiments and Datasetmentioning

confidence: 99%

“…Here, we use the irradiance values from our previous work, which do not account for soot and thus should be considered as upper limits to the effective received solar power. [50] Thus, the interpretation of the PCE In addition, the AM0 solar spectrum is shown in arbitrary scale in orange. [30,31] Note the dominant decrease of glass transmission in UV, where the glass appears clear in the photographs.…”

Section: In-flight Characterizationmentioning

confidence: 99%

“…[55,56] However, this visual trend should not be overinterpreted here since due to the unequal distribution of measurements as a function of irradiance and due to individual scattering of the j sc values, errors could enter in and further tests will be required. [50] c) PCE as a function of time after LO is shown together with the altitude above ground. d) PCE as a function of irradiance.…”

Section: In-flight Characterizationmentioning

confidence: 99%

“…Please refer to previous work for the irradiance evolution for each segment. [ 50 ] c) PCE as a function of time after LO is shown together with the altitude above ground. d) PCE as a function of irradiance.…”

Section: In‐flight Characterizationmentioning

confidence: 99%

“…From the sensor data parallelization, we determined the individual solar irradiance evolution for each SC at each point of the microgravity phase precisely in previous work. [50,51] Based on these developments, in the present study, we present a quantitative PCE analysis throughout the rocket flight and identify the influence of environmental conditions such as solar irradiance or temperature on photovoltaic performance parameters of perovskite and organic SCs. Correlations between solar irradiance and PV parameters can be identified, where the SCs behave similarly as known from terrestrial tests.…”

Section: Introductionmentioning

confidence: 99%

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Space‐ and Post‐Flight Characterizations of Perovskite and Organic Solar Cells

et al. 2023

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Perovskite and organic solar cells are promising for space applications for enabling higher specific powers or alternative deployment systems. However, terrestrial tests can only mimic space conditions to a certain extent. Herein, a detailed analysis of irradiation‐dependent photovoltaic parameters of perovskite and organic solar cells exposed to space conditions during a suborbital flight is presented. In orbital altitudes, perovskite and organic solar cells reach power‐conversion efficiencies of more than 13% and 6%, respectively. Based on postflight grazing‐incidence small‐angle and wide‐angle X‐ray scattering, the active layer morphology and crystalline structure of the returned space solar cells are studied and compared to those of reference solar cells that stayed in an inert atmosphere. Minor changes in the active layer morphology are induced by the sole transport, without causing significant performance loss. For the space solar cells, morphological changes are attributed to the flight experiment that includes rocket launch, spaceflight, and reentry, as well as short‐terrestrial environment exposure before and after launch. In contrast, no significant changes to the crystalline phase are observed. The notable performance during flight and high active layer stability, especially of perovskite solar cells, are promising results for further steps toward an orbital demonstration.

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Section: Experiments and Datasetmentioning

confidence: 99%

Section: In-flight Characterizationmentioning

confidence: 99%

Section: In-flight Characterizationmentioning

confidence: 99%

Section: In‐flight Characterizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Space‐ and Post‐Flight Characterizations of Perovskite and Organic Solar Cells

et al. 2023

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Meticulous research for design of plasmonics sensors for cancer detection and food contaminants analysis via machine learning and artificial intelligence

Jafrasteh,

Farmani,

Mohamadi

2023

Sci Rep

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Cancer is one of the leading causes of death worldwide, making early detection and accurate diagnosis critical for effective treatment and improved patient outcomes. In recent years, machine learning (ML) has emerged as a powerful tool for cancer detection, enabling the development of innovative algorithms that can analyze vast amounts of data and provide accurate predictions. This review paper aims to provide a comprehensive overview of the various ML algorithms and techniques employed for cancer detection, highlighting recent advancements, challenges, and future directions in this field. The main challenge is finding a safe, auditable and reliable analysis method for fundamental scientific publication. Food contaminant analysis is a process of testing food products to identify and quantify the presence of harmful substances or contaminants. These substances can include bacteria, viruses, toxins, pesticides, heavy metals, allergens, and other chemical residues. Machine learning (ML) and artificial intelligence (A.I) proposed as a promising method that possesses excellent potential to extract information with high validity that may be overlooked with conventional analysis techniques and for its capability in a wide range of investigations. A.I technology used in meta-optics can develop optical devices and systems to a higher level in future. Furthermore (M.L.) and (A.I.) play key roles as a health Approach for nano materials NMs safety assessment in environment and human health research. Beside, benefits of ML in design of plasmonic sensors for different applications with improved resolution and detection are convinced.

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