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At least 235 records · Page 13

Reverse-Bias Resilience of Monolithic Perovskite/Silicon Tandem Solar Cells

Metal halide perovskites have rapidly enabled a range of high-performance photovoltaic technologies. However, catastrophic failure under reverse voltage bias hinders their commercialization. In this work, we demonstrate that by employing a monolithic perovskite/silicon tandem structure, the perovskite subcell can be effectively protected by the silicon subcell under reverse bias, owing to the low reverse-bias diode current of the silicon subcell. As a result, the tested perovskite/silicon tandem devices show superior reverse-bias resilience compared to perovskite single-junction devices in both long-term reverse voltage biasing tests at the single-cell level and partial shading tests at the module level. These results highlight that, compared to other perovskite technologies, monolithic perovskite/silicon tandems are at a higher technology readiness level in terms of tackling the reverse-bias and partial shading challenge, which is a considerable advantage towards commercialization.

commercialization↗

Bias-Modulated ALD of ZnO: Insights into Precursor-Surface Interactions for ZnO Films

Atomic layer deposition (ALD) is widely used to deposit conformal thin films but is often limited in the tunability of the resulting material’s properties. Substrate bias and electric fields alter precursor-surface interactions and provide means to tune material properties. To explore this, we performed zinc oxide (ZnO) ALD using diethylzinc (DEZ) and water on silicon native oxide substrates at 150 °C in a sample holder designed to create a static electrical field by biasing one plate of a parallel plate capacitor-style sample holder during deposition. ZnO films prepared in an electric field/on a biased sample holder were thinner, changed relative crystalline composition, and contained more carbon compared to samples grown in identical sample holders without bias. The thickness was independent of the magnitude of the eletric field between plates, indicating that the primary driver for the change was substrate biasing not the electric field between plates of the parallel plate capacitor-style sample holder. Density functional theory calculations showed enhanced electron migration between dissociatively adsorbed DEZ molecules and the ZnO (002) facet with increasing force from an electric field at the substrate surface, which strengthens the electronic interactions between the surface and the adsorbate. These models offer a compelling explanation for the inhibited growth, changes in the crystallinity, and increase in carbon content of films grown in an electric field/on biased plates.

Jones, Jessica C. (ORCID:0000000174754620)↗

SU-F-I-80: Correction for Bias in a Channelized Hotelling Model Observer Caused by Temporally Variable Non-Stationary Noise

Purpose: Application of a channelized Hotelling model observer (CHO) over a wide range of x-ray angiography detector target dose (DTD) levels demonstrated substantial bias for conditions yielding low detectability indices (d’), including low DTD and small test objects. The purpose of this work was to develop theory and methods to correct this bias. Methods: A hypothesis was developed wherein the measured detectability index (d’b) for a known test object is positively biased by temporally variable non-stationary noise in the images. Hotelling’s T2 test statistic provided the foundation for a mathematical theory which accounts for independent contributions to the measured d’b value from both the test object (d’o) and non-stationary noise (d’ns). Experimental methods were developed to directly estimate d’o by determining d’ns and subtracting it from d’b, in accordance with the theory. Specifically, d’ns was determined from two sets of images from which the traditional test object was withheld. This method was applied to angiography images with DTD levels in the range 0 to 240 nGy and for disk-shaped iodine-based contrast targets with diameters 0.5 to 4.0 mm. Results: Bias in d’ was evidenced by d’b values which exceeded values expected from a quantum limited imaging system and decreasing object size and DTD. d’ns increased with decreasing DTD, reaching a maximum of 2.6 for DTD = 0. Bias-corrected d’o estimates demonstrated sub-quantum limited performance of the x-ray angiography for low DTD. Findings demonstrated that the source of non-stationary noise was detector electronic readout noise. Conclusion: Theory and methods to estimate and correct bias in CHO measurements from temporally variable non-stationary noise were presented. The temporal non-stationary noise was shown to be due to electronic readout noise. This method facilitates accurate estimates of d’ values over a large range of object size and detector target dose.

60 APPLIED LIFE SCIENCES↗

Deep Learning-based Non-Stationary Bias Correction (NSBC)

This work develops the NSBC (non-stationary bias correction) methodology to correct temperature projection bias from E3SM. The NSBC deep learning framework consists of a three-part architecture: an auto-encoder for compressing the spatial information, an LSTM for predicting annual temperature mean, and a U-Net for capturing the residual bias in temperature. The non-stationary bias correction (NSBC) framework can correct the non-stationarity of the biases of the climate models, which significantly improves the accuracy of future temperature prediction and improves the overestimation of extreme high temperatures that many existing bias correction methods suffer from. Getting started 1. Obtain the historical climate simulation and observation data. The E3SM simulation data are available through https://aims2.llnl.gov/search/cmip6/. The pseudo observations, the Geophysical Fluid Dynamics Laboratory (GFDL)-ESM4 model (Krasting et al., 2018) are available through https://aims2.llnl.gov/search/cmip6/. The spatial resolution of E3SM and pseudo observation datasets are both regridded to a common 1° resolution grid using conservative interpolation. The regridded E3SM and pseudo observation with 1° resolution can be found throught ./data/. 2. Train the Auto-encoder model. Python 0-autoencoder.py 3. Train the LSTM Python 1-LSTM.py 4. Generate the annual mean temperature based on trained LSTM Python 2-generate_annual_mean_LSTM.py 5. Train the U-Net. Python 3-unet.py 6. Evaluation and compared with the baseline Python 4_evaluation.py Is there a deadline approaching that requires the release of yo

Lucas, Donald↗

Assessment of Warm and Dry Bias over ARM SGP Site in E3SMv2 and E3SM-MMF

Abstract Many climate models exhibit a dry and warm bias over the central United States during the summer months, including the Energy Exascale Earth System Model (E3SM) and its Multiscale Modeling Framework (MMF) configuration. Understanding the causes of this bias is important to shine a light on this common model error and reduce the uncertainty in future projections. In this study, we use E3SMv2 and E3SM-MMF to assess how parameterized and resolved convection affect temperature and precipitation biases over the Southern Great Plains site of the Atmospheric Radiation Measurement program. Both configurations overestimate near-surface temperature and underestimate precipitation at the ARM SGP site. The bias is associated with a lack of low-level clouds during days without precipitation and too much incoming solar radiation causing the surface to warm. Low-level cloud fraction in E3SM-MMF during the nonprecipitating days is lower in comparison to E3SMv2 and observation, consistent with the larger warm bias. We also find that the underestimated precipitation can be characterized as “too frequent, too weak” in E3SMv2 and “too rare, too intense” in E3SM-MMF. These deficiencies conspire to sustain the warm and dry bias over the central United States.

54 ENVIRONMENTAL SCIENCES↗

Bias Estimates Used in Lieu of Validation of Fission Products and Minor Actinides in MCNP K eff Calculations for PWR Burnup Credit Casks

The U.S. Nuclear Regulatory Commission (NRC) Division of Spent Fuel Storage and Transportation recently issued Interim Staff Guidance (ISG) 8, Revision 3. This ISG provides guidance for burnup credit (BUC) analyses supporting transport and storage of PWR pressurized water reactor (PWR) fuel in casks. Revision 3 includes guidance for addressing validation of criticality (k eff ) calculations crediting the presence of a limited set of fission products and minor actinides (FP&MA). Based on previous work documented in NUREG/CR-7109, recommendation 4 of ISG-8, Rev. 3, includes a recommendation to use 1.5 or 3% of the FP&MA worth to conservatively cover the bias due to the specified FP&MAs. This bias is supplementary to the bias and bias uncertainty resulting from validation of k eff calculations for the major actinides in SNF and does not address extension to actinides and fission products beyond those identified herein. The work described in this report involves comparison of FP&MA worths calculated using SCALE and MCNP with ENDF/B-V, -VI, and -VII based nuclear data and supports use of the 1.5% FP&MA worth bias when either SCALE or MCNP codes are used for criticality calculations, provided the other conditions of the recommendation 4 are met. The method used in this report may also be applied to demonstrate the applicability of the 1.5% FP&MA worth bias to other codes using ENDF/B V, VI or VII based nuclear data. The method involves use of the applicant s computational method to generate FP&MA worths for a reference SNF cask model using specified spent fuel compositions. The applicant s FP&MA worths are then compared to reference values provided in this report. The applicants FP&MA worths should not exceed the reference results by more than 1.5% of the reference FP&MA worths.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Biased deposition of Nb-ON-Cu for SRF accelerators, Final Report

The Phase I project objectives were: Task 1: Design, assemble, and commission a new coating facility that uses the RF cavity as the vacuum chamber Task 3: Coat and RF test biased Cu cavity Task 4: Evaluate test results and prepare Ph-II bid. As for project accomplishments, AASC has completed all of the tasks of Ph-I. However, for Task 2, we found a limit to the bias voltage on a substrate placed at the radius of the beam pipe. A bias voltage higher than -50 V can induce arcing from the substrate to the grounded anode. Arc spots form on the substrate and damage the film. The main problem addressed by this SBIR project is that the price of niobium dominates the cost of superconducting RF cavity production. The most effective route to significant cost reduction is to use cavities that have a thin layer of niobium deposited on a less expensive substrate. However, all attempts to use thin films of niobium in superconducting RF cavities have to date resulted in cavity performance that does not meet the requirements of modern linear accelerators. Alameda Applied Sciences Corporation (AASC) has developed a novel deposition method that can produce high-purity films of niobium. For superconducting radio-frequency (SRF) accelerator cavities, films of niobium on another base material could provide good performance at lower cost than for bulk niobium. The primary goal of all thin film coated approaches must be to demonstrate RF performance that matches that of bulk Nb cavities. This Ph-I was not expected to accomplish this major milestone for thin film coated SRF cavities in just 9 months with limited resources. We set less ambitious goals of demonstrating that using our CED process to coat a biased substrate could result in an increase of RF performance over previous tests with no applied bias voltage. In this Ph-1, we obtained preliminary RF data from Nb coated biased cavity structures so as to move ahead in Ph-II to demonstration of performance that approaches that of bulk Nb. Our task has been made more daunting recently as several labs (Fermilab, Cornell, and JLab) have demonstrated significantly improved performance of bulk Nb cavities using nitrogen doping and rapid cool-down techniques. The bar has just been raised for thin films. At the 2013 SRF thin film workshop held at INFN in Padua Italy, leading scientists from the LHC tossed a gauntlet to the thin film community. The LHC plans a high luminosity upgrade starting in about 4 years. Upper management at CERN has stated that they would prefer not to use bulk Nb for the upgrade, which will require hundreds of new SRF cavities. But the thin film community has a short window in which to demonstrate that a Cu (or Al) cavity that is coated with Nb delivers the kind of performance that we have come to expect from bulk Nb: Q o >10 10 that stays flat up to 20 MV/m or higher, good statistics from multiple cavities and reliable operation over thousands of hours. Breakthroughs are needed to meet these daunting goals. AASC has persisted with this challenge for more than five years now, with the benefit of DOE SBIR funding. We aim to preserve our momentum and build upon our base to try to reach the lofty goals within four more years.

36 MATERIALS SCIENCE↗

Foreground bias from parametric models of far-IR dust emission

We use simple toy models of far-IR dust emission to estimate the accuracy to which the polarization of the cosmic microwave background can be recovered using multi-frequency fits, if the parametric form chosen for the fitted dust model differs from the actual dust emission. Commonly used approximations to the far-IR dust spectrum yield CMB residuals comparable to or larger than the sensitivities expected for the next generation of CMB missions, despite fitting the combined CMB + foreground emission to precision 0.1% or better. The Rayleigh–Jeans approximation to the dust spectrum biases the fitted dust spectral index by Δβ{sub d}=0.2 and the inflationary B-mode amplitude by Δr=0.03. Fitting the dust to a modified blackbody at a single temperature biases the best-fit CMB by Δr>0.003 if the true dust spectrum contains multiple temperature components. A 13-parameter model fitting two temperature components reduces this bias by an order of magnitude if the true dust spectrum is in fact a simple superposition of emission at different temperatures, but fails at the level Δr=0.006 for dust whose spectral index varies with frequency. Restricting the observing frequencies to a narrow region near the foreground minimum reduces these biases for some dust spectra but can increase the bias for others. Data at THz frequencies surrounding the peak of the dust emission can mitigate these biases while providing a direct determination of the dust temperature profile.

79 ASTRONOMY AND ASTROPHYSICS↗

Revealing the Cosmic Web-dependent Halo Bias

Halo bias is the one of the key ingredients of the halo models. It was shown at a given redshift to be only dependent, to the first order, on the halo mass. In this study, four types of cosmic web environments—clusters, filaments, sheets, and voids—are defined within a state-of-the-art high-resolution N-body simulation. Within these environments, we use both halo-dark matter cross correlation and halo-halo autocorrelation functions to probe the clustering properties of halos. The nature of the halo bias differs strongly between the four different cosmic web environments described here. With respect to the overall population, halos in clusters have significantly lower biases in the 10{sup 11.0}∼10{sup 13.5} h{sup −1} M{sub ⊙} mass range. In other environments, however, halos show extremely enhanced biases up to a factor 10 in voids for halos of mass ∼10{sup 12.0} h{sup −1} M{sub ⊙}. Such a strong cosmic web environment dependence in the halo bias may play an important role in future cosmological and galaxy formation studies. Within this cosmic web framework, the age dependency of halo bias is found to be only significant in clusters and filaments for relatively small halos ≲10{sup 12.5} h{sup −1} M{sub ⊙}.

79 ASTRONOMY AND ASTROPHYSICS↗

Emission or atmospheric processes? An attempt to attribute the source of large bias of aerosols in eastern China simulated by global climate models

Here, global climate models often underestimate aerosol loadings in China, and these biases can have significant implications for anthropogenic aerosol radiative forcing and climate effects. The biases may be caused by either the emission inventory or the treatment of aerosol processes in the models, or both, but so far no consensus has been reached. In this study, a relatively new emission inventory based on energy statistics and technology, Multi-resolution Emission Inventory for China (MEIC), is used to drive the Community Atmosphere Model version 5 (CAM5) to evaluate aerosol distribution and radiative effects against observations in China. The model results are compared with the model simulations with the widely used Intergovernmental Panel on Climate Change Fifth Assessment Report (IPCC AR5) emission inventory. We find that the new MEIC emission improves the aerosol optical depth (AOD) simulations in eastern China and explains 22–28 % of the AOD low bias simulated with the AR5 emission. However, AOD is still biased low in eastern China. Seasonal variation of the MEIC emission leads to a better agreement with the observed seasonal variation of primary aerosols than the AR5 emission, but the concentrations are still underestimated. This implies that the atmospheric loadings of primary aerosols are closely related to the emission, which may still be underestimated over eastern China. In contrast, the seasonal variations of secondary aerosols depend more on aerosol processes (e.g., gas- and aqueous-phase production from precursor gases) that are associated with meteorological conditions and to a lesser extent on the emission. It indicates that the emissions of precursor gases for the secondary aerosols alone cannot explain the low bias in the model. Aerosol secondary production processes in CAM5 should also be revisited. The simulation using MEIC estimates the annual-average aerosol direct radiative effects (ADREs) at the top of the atmosphere (TOA), at the surface, and in the atmosphere to be –5.02, –18.47, and 13.45 W m –2 , respectively, over eastern China, which are enhanced by –0.91, –3.48, and 2.57 W m –2 compared with the AR5 emission. The differences of ADREs by using MEIC and AR5 emissions are larger than the decadal changes of the modeled ADREs, indicating the uncertainty of the emission inventories. This study highlights the importance of improving both the emission and aerosol secondary production processes in modeling the atmospheric aerosols and their radiative effects. Yet, if the estimations of MEIC emissions in trace gases do not suffer similar biases to those in the AOD, our findings will help affirm a fundamental error in the conversion from precursor gases to secondary aerosols as hinted in other recent studies following different approaches.

54 ENVIRONMENTAL SCIENCES↗

Satellite-based evaluation of AeroCom model bias in biomass burning regions

Global models are widely used to simulate biomass burning aerosol (BBA). Exhaustive evaluations on model representation of aerosol distributions and properties are fundamental to assess health and climate impacts of BBA. Here we conducted a comprehensive comparison of Aerosol Comparisons between Observations and Models (AeroCom) project model simulations with satellite observations. A total of 59 runs by 18 models from three AeroCom Phase-III experiments (i.e., biomass burning emissions, CTRL16, and CTRL19) and 14 satellite products of aerosols were used in the study. Aerosol optical depth (AOD) at 550 nm was investigated during the fire season over three key fire regions reflecting different fire dynamics (i.e., deforestation-dominated Amazon, Southern Hemisphere Africa where savannas are the key source of emissions, and boreal forest burning in boreal North America). The 14 satellite products were first evaluated against AErosol RObotic NETwork (AERONET) observations, with large uncertainties found. But these uncertainties had small impacts on the model evaluation that was dominated by modeling bias. Through a comparison with Polarization and Directionality of the Earth’s Reflectances measurements with the Generalized Retrieval of Aerosol and Surface Properties algorithm (POLDER-GRASP), we found that the modeled AOD values were biased by –93 % to 152 %, with most models showing significant underestimations even for the state-of-the-art aerosol modeling techniques (i.e., CTRL19). By scaling up BBA emissions, the negative biases in modeled AOD were significantly mitigated, although it yielded only negligible improvements in the correlation between models and observations, and the spatial and temporal variations in AOD biases did not change much. For models in CTRL16 and CTRL19, the large diversity in modeled AOD was in almost equal measures caused by diversity in emissions, lifetime, and the mass extinction coefficient (MEC). We found that in the AeroCom ensemble, BBA lifetime correlated significantly with particle deposition (as expected) and in turn correlated strongly with precipitation. Additional analysis based on Cloud-Aerosol LIdar with Orthogonal Polarization (CALIOP) aerosol profiles suggested that the altitude of the aerosol layer in the current models was generally too low, which also contributed to the bias in modeled lifetime. Modeled MECs exhibited significant correlations with the Ångström exponent (AE, an indicator of particle size). Comparisons with the POLDER-GRASP-observed AE suggested that the models tended to overestimate the AE (underestimated particle size), indicating a possible underestimation of MECs in models. The hygroscopic growth in most models generally agreed with observations and might not explain the overall underestimation of modeled AOD. Our results imply that current global models contain biases in important aerosol processes for BBA (e.g., emissions, removal, and optical properties) that remain to be addressed in future research.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Variance and bias confidence criteria for ERA modal parameter identification

For the ERA system identification algorithm, perturbation methods are used to develop expressions for variance and bias of the identified modal parameters. Based on the statistics of the measurement noise, the variance results serve as confidence criteria by indicating how likely the true parameters are to lie within any chosen interval about their identified values. This replaces the use of expensive and time-consuming Monte Carlo computer runs to obtain similar information. The bias estimates help guide the ERA user in his choice of which data points to use and how much data to use in order to obtain the best results, performing the trade-off between the bias and scatter. Also, when the uncertainty in the bias is sufficiently small, the bias information can be used to correct the ERA results. In addition, expressions for the variance and bias of the singular values serve as tools to help the ERA user decide the proper modal order.

Longman, Richard W.↗

The effect of small-wave modulation on the electromagnetic bias

The effect of the modulation of small ocean waves by large waves on the physical mechanism of the EM bias is examined by conducting a numerical scattering experiment which does not assume the applicability of geometric optics. The modulation effect of the large waves on the small waves is modeled using the principle of conservation of wave action and includes the modulation of gravity-capillary waves. The frequency dependence and magnitude of the EM bias is examined for a simplified ocean spectral model as a function of wind speed. These calculations make it possible to assess the validity of previous assumptions made in the theory of the EM bias, with respect to both scattering and hydrodynamic effects. It is found that the geometric optics approximation is inadequate for predictions of the EM bias at typical radar altimeter frequencies, while the improved scattering calculations provide a frequency dependence of the EM bias which is in qualitative agreement with observation. For typical wind speeds, the EM bias contribution due to small-wave modulation is of the same order as that due to modulation by the nonlinearities of the large-scale waves.

Rodriguez, Ernesto↗

Experimental and theoretical determination of sea-state bias in radar altimetry

The major unknown error in radar altimetry is due to waves on the sea surface which cause the mean radar-reflecting surface to be displaced from mean sea level. This is the electromagnetic bias. The primary motivation for the project was to understand the causes of the bias so that the error it produces in radar altimetry could be calculated and removed from altimeter measurements made from space by the Topex/Poseidon altimetric satellite. The goals of the project were: (1) observe radar scatter at vertical incidence using a simple radar on a platform for a wide variety of environmental conditions at the same time wind and wave conditions were measured; (2) calculate electromagnetic bias from the radar observations; (3) investigate the limitations of the present theory describing radar scatter at vertical incidence; (4) compare measured electromagnetic bias with bias calculated from theory using measurements of wind and waves made at the time of the radar measurements; and (5) if possible, extend the theory so bias can be calculated for a wider range of environmental conditions.

Stewart, Robert H.↗

Temporal study of the current collected by a Langmuir probe biased to high voltages in the wake behind a conducting body

The temporal evolution of the voltage-current characteristic of a spherical Langmuir probe in the near-wake region of a conducting body is investigated in a pulsed plasma device. Measurements of the current collected by a sphere placed on axis behind a conducting disk are presented as a function of the bias voltage and time. The bias voltage on the collecting sphere is large enough, ranging from +500 to -3000 V, to attract ions or electrons across the wake region. In addition, plasma potential profiles as a function of sphere bias voltage and time throughout the nearwake and midwake region are presented. It is found that for a negative sphere bias a threshold voltage exists for current collection. This threshold voltage decreases during the formation of the wake behind the conducting disk. Potential measurements indicate that the sheath region around the negatively biased sphere contracts as the wake region establishes itself. This contraction of the sheath region increases the electric field attracting ions to the sphere and decreases the threshold voltage for current collection. For positive sphere bias voltage the current collected by the sphere does not exhibit a threshold voltage but is monotonically increasing.

Meassick, S.↗

Differential sea-state bias: A case study using TOPEX/POSEIDON data

We used selected data from the NASA altimeter TOPEX/POSEIDON to calculate differences in range measured by the C and Ku-band altimeters when the satellite overflew 5 to 15 m waves late at night. The range difference is due to free electrons in the ionosphere and to errors in sea-state bias. For the selected data the ionospheric influence on Ku range is less than 2 cm. Any difference in range over short horizontal distances is due only to a small along-track variability of the ionosphere and to errors in calculating the differential sea-state bias. We find that there is a barely detectable error in the bias in the geophysical data records. The wave-induced error in the ionospheric correction is less than 0.2% of significant wave height. The equivalent error in differential range is less than 1% of wave height. Errors in the differential sea-state bias calculations appear to be small even for extreme wave heights that greatly exceed the conditions on which the bias is based. The results also improved our confidence in the sea-state bias correction used for calculating the geophysical data records. Any error in the correction must influence Ku and C-band ranges almost equally.

Stewart, Robert H.↗

Symmetry as Bias: Rediscovering Special Relativity

This paper describes a rational reconstruction of Einstein's discovery of special relativity, validated through an implementation: the Erlanger program. Einstein's discovery of special relativity revolutionized both the content of physics and the research strategy used by theoretical physicists. This research strategy entails a mutual bootstrapping process between a hypothesis space for biases, defined through different postulated symmetries of the universe, and a hypothesis space for physical theories. The invariance principle mutually constrains these two spaces. The invariance principle enables detecting when an evolving physical theory becomes inconsistent with its bias, and also when the biases for theories describing different phenomena are inconsistent. Structural properties of the invariance principle facilitate generating a new bias when an inconsistency is detected. After a new bias is generated. this principle facilitates reformulating the old, inconsistent theory by treating the latter as a limiting approximation. The structural properties of the invariance principle can be suitably generalized to other types of biases to enable primal-dual learning.

Lowry, Michael R.↗

Publication Bias ( The "File-Drawer Problem") in Scientific Inference

Publication bias arises whenever the probability that a study is published depends on the statistical significance of its results. This bias, often called the file-drawer effect since the unpublished results are imagined to be tucked away in researchers' file cabinets, is potentially a severe impediment to combining the statistical results of studies collected from the literature. With almost any reasonable quantitative model for publication bias, only a small number of studies lost in the file-drawer will produce a significant bias. This result contradicts the well known Fail Safe File Drawer (FSFD) method for setting limits on the potential harm of publication bias, widely used in social, medical and psychic research. This method incorrectly treats the file drawer as unbiased, and almost always miss-estimates the seriousness of publication bias. A large body of not only psychic research, but medical and social science studies, has mistakenly relied on this method to validate claimed discoveries. Statistical combination can be trusted only if it is known with certainty that all studies that have been carried out are included. Such certainty is virtually impossible to achieve in literature surveys.

Scargle, Jeffrey D.↗