dataesr/scientific-paragraphs-categorization huggingface dataset:
Given the known stability of a cdom (440) over extended periods at our matchup sites, i. e. , boreal lakes  (Cardille et al. , 2013) , and to incorporate an adequate number of matchups, we adopted a +/-3 Fig. 2. Frequency distribution (top) of WQ indicators for which co-located R rs spectra covering the MSI visible-Near-InfraRed spectral bands (< 800 nm) were available. 2D histograms (bottom) exhibit correlations between co-located sample pairs. The number of available data samples was the highest for OLI (410 < λ < 700 nm) and slightly lower for OLCI due to a broader spectral coverage. The R-squared (R 2 ) and Pearson Correlation Coefficient (PCC) are annotated to give insights into the correlations between WQ indicators. day time window for this parameter  (Brezonik et al. , 2015). Note that a large portion of our dataset originates from narrow ecosystems or nearshore coastal waters making it difficult to obtain enough matchups for OLCI's 300 m nominal spatial resolution.
Estimating missing values For generating simultaneous WQ indicators, several modifications were made to the basic MDN model. First, the data itself necessitates special handling, due to hundreds of missing samples from one or two categories (Fig. 2 ). For instance, within the in situ Chla dataset suitable for OLI, there are 5680 available measurements out of a total of 6475 samples. For TSS and a cdom (440), the number of available measurements is even smaller (3847 and 2910 samples, respectively), with only 1613 samples encompassing all three quantities -just over 20% of the total dataset. To make use of the remaining 80% of the data with missing values, a method is needed to allow the MDN model to learn from partial information. To fill in the missing values, we apply a method referred to as multiple imputation  (Rubin, 2004) , which has been utilized in inverse problems related to geophysical parameter retrievals  (Hudak et al.
, 2008; Junninen et al. , 2004; Kalteh and Hjorth, 2009). Simply put, the missing values are replaced by values randomly drawn from a distribution that represents the dataset. After m values are drawn, each of those values is independently used to complete the analysis as if the value were not missing and subsequently pooled into a final analysis output. This process assumes the missing values are either Missing Completely At Random (MCAR), or as a slightly weaker assumption, Missing At Random (MAR)  (King et al. , 2001). Concretely, this means that we are assuming a missing Chla is not dependent upon the Chla value itself -it is, at worst, dependent only on the other available measurements for that sample. Suppose the missing value is instead conditionally missing upon its value (or the value of other missing variables).
In that case, the data is considered Missing Not At Random (MNAR) and requires additional care in handling  (King et al. , 2001). MDNs inherently model the joint probability of all output variables, leading to a multiple imputation approach which is capable of handling even MNAR variables in certain circumstances (in addition to the usual MAR or MCAR) by allowing us to draw directly from the learned posterior  (Buuren and Groothuis-Oudshoorn, 2010; Galimard et al. , 2018). Moreover, the multiple imputation estimates are improved continuously during the training of the MDN, i. e. , as the MDN learns to better model the target variables, the accuracy of the imputed samples improves, thus leading to further improvements in the target variable estimates. Separate parts of this type of chained bootstrap learning have been documented in the literature  (Ghahramani and Jordan, 1995).
These metrics are similar to those applied in  Seegers et al. (2018)  with slight modifications for enhanced interpretability and robustness. Previous studies  (Smith et al. 2021)  have shown that these median measures, along with the slope of linear regressions (Type I), and root mean squared logarithmic difference (RMSLD), are adequate, offering a comprehensive perspective for evaluating algorithms. Two different schemes for the performance assessment were adopted. The first method was the commonly used hold-out method that entails a random split of the entire dataset. Similar to our prior research  (Pahlevan et al. , 2021b) , we chose a 50-50 random split to allow equal datasets for testing and training. This approach, however, may favorably impact the model performance as the test data may not be entirely independent of the training samples (e.
g. , data acquired in one lake through one or multiple field campaigns may be correlated). To overcome this drawback and provide a more realistic (or even pessimistic) view of the performance, our second assessment strategy was the leaveone-out (LOO) approach where the model is trained with all the data sources (Appendix A) except one against which its performance is evaluated. This process is iterated until each data source is utilized once as a test set. The ultimate performance for MDN and the rest of the benchmarking algorithms is reported as the median or weighted average of all the individual performances (N = 35 for Chla in Fig. 5 ). This approach would deduce the expected performance of the model when an out-of-sample (unseen) dataset is applied to the model. In general, these two approaches (hold-out and LOO) are anticipated to provide a practical range for algorithm uncertainties and elucidate how well the model can estimate optical WQ indicators in globally diverse aquatic ecosystems.
Due to the necessity of training an MDN for each data source in the LOO scheme, we used the Amazon SageMaker Studio (https://aws. am azon. com/sagemaker/) to train the models simultaneously. Thus, each data split was sent as a separate training job ten times, with a different random seed used to initialize network weights and training data shuffling for each of these ten trials. The results of the ten trials were then averaged, and the final performance for each data source is used in the overall LOO weighted performance assessment (Table  3 ). In total, this led to several hundred training jobs necessary for each sensor.
Reported Slopes slightly less than unity suggest degraded performance at the two tails of data distributions (e. g. , Chla < 1 mg m -3 and Chla > 100 mg m -3 ) which may be alleviated with a larger dataset or by augmenting the existing dataset to fill in the gaps further. The equivalent metrics for OLI-and OLCI-like spectra are shown in Table  2. While the uncertainty measures from the MSI and OLCI spectra are similar, the estimates through OLI-like band settings contain uncertainties 30 to 100% larger for the three variables. The reason for the lower accuracy of Chla estimates is OLI's lack of spectral bands within the 700-800 nm range  (Gitelson, 1992) , underscoring that MSI and OLCI spectral band configurations in this region introduce more degrees of freedom for quantifying TSS and a cdom (440), despite a cdom (440) being a UV-blue absorbing component  (Kirk, 1994).
It should also be noted that the OLCI's spectral band configuration appears to enable 10-30% more accurate products over those from MSI, which corroborates the benefits of additional spectral bands for the optical sensing of WQ indicators  (Lee et al. , 2007). The LOO assessment of MDN and other best-performing algorithms for an MSI-like instrument is provided in bar charts for all the available data sources and each WQ component in Fig. 5. Again, MDN produces the most accurate estimates for most datasets. There are exceptions to that, however, where other algorithms like GI2B, Moses-2B, SOLID, and Ficek outperform MDN -though it should be noted that some of these occasionally generate invalid retrievals (e. g. , GI2B for Chla < 8 mg m -3 ), rendering them less ideal for global applications.
We note that there are datasets for which predictions are associated with considerable uncertainties (e. g. , Dataset #39 for Chla and TSS). For these scenarios, MDN typically provides the best approximations. The results do not imply that the dataset is of lower quality; instead, they may signify that the range of measured values from a specific dataset is not well represented within the rest of the dataset. A summary of the LOO performance assessment for the MDN model and the second-best algorithms in terms of a median and weighted average of ε is included in Table  3. Here, medians correspond to the median of all ε from all the datasets, and weighted averages represent sum of all ε weighed by the number of samples per dataset normalized by N. The reported uncertainties are often >50% higher than those denoted in Table  2  and bear similar conclusions.
For example, the performance metrics suggest that OLCI TSS and a cdom (440) products are expected to be of higher quality than those from MSI and OLI. Analogous to the hold-out analyses (Table  2 ), it is found that OLI can produce viable TSS and a cdom (440) estimates -although the derived Chla in highly eutrophic waters may be questionable, with more than 80% estimated uncertainties. The large differences in median and weighted average pertain to an unequal sample size of different data sources. For instance, large uncertainties (ε) for small datasets (<10 samples) do not significantly contribute to ultimate uncertainties when reporting weighted average ε, whereas median ε (computed over all datasets) is equally affected by uncertainties in each data source. Among the three sensors and WQ indicators, the largest uncertainties ranging from 47 to 85% correspond to a cdom (440), which may be attributable to the availability of fewer samples and a possible presence of outliers.
In situ measured Chla and TSS data  (Schraga et al. , 2020)  available through the U. S. Geological Survey's field cruise (https://sfbay. wr. usgs. gov/water-quality-database /) two days prior to the overpass date are in general in agreement with those depicted in our maps. The reported near-surface Chla and TSS measurements in the South Bay at station #24 were ~10. 7 mg m -3 and 5 g m -3 , respectively, while they are 9 mg m -3 and 7 g m -3 , respectively, for station #30. The mid-section of Chesapeake Bay is mapped by the three missions ~15 min apart from each other on Nov 7 th , 2016 (Fig. 7 ). In the main stem of the bay, OLI and MSI exhibit very similar spatial patterns of Chla, with OLCI showing slightly elevated concentrations in the bay and along the Potomac River. A single point measurement by the Maryland Department of Natural Resources (MD DNR) within the Patuxent River discharge (CB5.
Discussion We showed that the MDN model has the potential for producing consistent, high-quality WQ products from the past, present, and future satellite observations at a global scale. The model was demonstrated to be capable of generating WQ products spanning a wide range of Chla, TSS, and a cdom (440) that conform to previous studies and expectations. It can also be readily adapted to heritage instruments by applying the respective RSRs. These missions and/or instruments include, but are not limited to, the Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), Medium Resolution Imaging Spectrometer (MERIS), and Moderate Resolution Imaging Spectroradiometer (MODIS). The extension of MDNs to these instruments will enable the construction of a longterm record of WQ variability across scales from watersheds to rivers and to nearshore and offshore areas that represent distinct optical regimes and offers a historical dataset to quantify trends and shifts in WQ indicators.
To inform a plausible range of uncertainties for the model performance, two commonly used approaches in the machine-learning domain were employed. The LOO method is possibly the preferred assessment subject to the availability of high-quality, methodically measured multisource datasets, as well as datasets with overlapping distributions. This is not currently feasible and, owing to the diversity in in situ measurement methodologies and laboratory analyses likely presents the worstcase model performance per category (Fig. 5 ). Our reported weighted average and median statistics are, however, reliable metrics to gauge the overall performance, and in combination with the hold-out method, offer a viable range of uncertainties (e. g. , 26 to 65%). This implies that if the model is tested with unseen datasets that resemble our development data, the performance would compare with the statistical descriptors reported through the hold-out method (Table  2 ).
Nevertheless, distinct test datasets would yield performances bearing a resemblance to the LOO approach (Table  3 ). We further demonstrated that depending on the AC processor, uncertainties in MDN estimates vary differently for each WQ component. We presented two instances over the San Francisco Bay and Chesapeake Bay, for which MDN generated relatively consistent WQ products across three sensors. Given the range of model uncertainties, however, the difference in products from one sensor to another may reach >100% even when a perfect AC processor is applied (e. g. , when ε for a Chla estimate from MSI is -50% and the corresponding ε in an OLCI-derived estimate is 60%). Hence, we surmise that the products are consistent within the algorithm uncertainty and caution users on the interpretation and application until pixel-level uncertainties are provided (see below).
For a spectral band configuration like that of OLI, 37 to 85% uncertainties are expected for TSS and a cdom (440), whereas this range spans from 59 to ~100% for Chla. Our demonstration maps exhibited that our model enables consistent multi-mission products within model uncertainties. Chla products for less eutrophic waters (e. g. , large estuaries) are anticipated to agree well across OLI, MSI, and OLCI. In contrast, across more eutrophic waters (Chla >5 mg m -3 ), MSI and OLCI should provide consistent products assuming negligible differences in the respective TOA observations. Given high-quality R rs products, we expect TSS and a cdom (440) maps from the three missions to match very well within the uncertainty of the model across a diverse spectrum of optical regimes represented in our development database.
Our matchup assessments through samples representing an acceptable range of WQ conditions (< 20 mg m -3 of Chla, < 20 g m -3 of TSS and a cdom (440) < 2. 5 m -1 ) suggested that the sensitivity of MDN to σ(R rs ) is subject to the quality of R rs across all the bands and requires further studies at global and regional scales. Although our MDN model is anticipated to offer practical global multi-mission WQ products, local and regional ML models developed using relevant data or tied to a specific AC processor may well be advantageous over our global model; hence, such exercises, particularly in areas with challenging atmospheric and/or aquatic conditions, are immensely encouraged. These developments will likely better serve local communities and water resource managers with high demands for precise and timely WQ monitoring.
(Werdell et al. , 2013)mi, 2007)m tuning of similar singleparameter models(O'Shea et al. , 2021)suggest that due to non-linear covariations between R rs and WQ indicators(Defoin-Platel and Chami, 2007), adjusting one or two single (fixed) coefficients does not offer significant performance improvements when a globally applicable model is sought. On the other hand, most multi-parameter retrieval algorithms suitable for inland and nearshore coastal waters (e. g. , LMI) would have required further calibration data to adapt their specific IOPs unavailable for most of our dataset. When possible, equations for the empirical models listed in Table1are provided in Appendix C. It should be noted that generic ocean-color algorithms (e. g. , default Generalized IOP model;(Werdell et al. , 2013)have been attempted in prior research efforts(Pahlevan et al.
(continued ) Lake Linh Dam,(Smith et al. , 2021)Quan(Vietnam)α 1 = (ϕ -0. 75)/(1. 15-0. 75)α 2 = (1. 15ϕ)/(1. 15-0. 75)Chl=α 1 Chl G2B + α 2 Chl OCxMDN (Pahlevan et al. , 2020;(Smith et al. , 2021)). Codes are accessible via: https://github. com/STREAM-RS/STREAM-RS SOLID (Balasubramanian et al. , 2020) b bp (865) = (R rs (865) (a w (865) + b bw (865) + 0. 5 ) -0. 105 b bw (865) ) / (0. 105 -R rs (865) ) TSS = { 53. 736 b bp MDN (665) 0. 8559 if R rs (492). < R rs (665). < R rs (560) 224. 43 b bp (865) -12. 575 if R rs (560). < R rs (665)&R rs (740). > 0. 01 53. 736 b bp QAA (665) 0. 8559 if R rs (560). < R rs (492) 53. 736 b bp MDN (665) 0. 8559 else } *b bp MDN is derived from a MDN model trained with a synthetic dataset (Balasubramanian et al. , 2020). Code is available via: https://github.
Acknowledgement The principal investigators providing the development data (Section 3. 1) included  Ronghua Ma ,  John Schalles ,  Anatoly Gitelson ,  Wesley Moses ,  Tim Moore ,  Natascha Oppelt ,  Mike Ondrusek ,  Steve Greb ,  Cedric Fichot , and  Deepak Mishra. The data in Lake Kummerow were partly produced within the scope of the LAKESAT (grant #  50EE1340 ) funded by the  Federal Ministry for Economic Affairs and Energy, Germany. We also acknowledge  NASA's Ocean Ecology Lab  for creating and maintaining  SeaBASS and Amazon Web Services (AWS)  for providing computing resources through  SageMaker Studio  as part of the  Goddard Commercial Cloud (GCC) Mission Cloud Platform (MCP)  and  NASA Goddard Artificial Intelligence Center of Excellence (AICOE)  pilot projects. This work was primarily accomplished with funding from the  NASA  ROSES contract # 80HQTR19C0015 ,  Remote Sensing  of  Water Quality element , and the  USGS Landsat Science Team  Award # 140G0118C0011.
There is therefore an apparent discrepancy between blocking as a persistent atmospheric feature  (Petoukhov et al. , 2013; Legras and Ghil, 1985)  and blocking as a transient feature of an atmosphere whose persistent state is one of zonal flow  (Faranda et al. , 2016 (Faranda et al. , , 2017;; Messori et al. , 2017; Hochman et al. , 2021). In this study, we adopt a dynamical systems viewpoint to illustrate the link between both summertime heatwaves and wintertime warm spells in Europe, and atmospheric persistence in the Euro-Atlantic sector. In doing so, we will attempt to reconcile the different views highlighted above on the persistence of blocked and zonal flows. The remainder of the paper is structured as follows: Section 2 details the datasets and methods used for the analysis, Section 3 shows composites of the atmospheric configurations associated with heatwaves, an analysis of the persistence of these configurations and composites of possible driving mechanisms for heatwaves, namely temperature advection and radiative forcing.
Data We use the ERA5 reanalysis dataset from the European Centre for Medium Range Weather Forecasts (ECMWF)  (Hersbach et al. , 2020 (Hersbach et al. , ) over 1978 (Hersbach et al. , -2018. The specific variables used were temperature at 2m (t2m), geopotential at 500 hPa (Z500), surface sensible heat flux (SSHF) and mean sea level pressure (SLP), all at a horizontal spatial resolution of 0. 25 •. The SSHF was selected for the noon time step and the accumulated values divided by the accumulation period as per ECMWF's online data documentation. Daily means were calculated from hourly data, except for Z500, which was calculated from 6-hourly data due to the much larger size of pressure-level variables. Our Euro-Atlantic domain spans 30-70 • N, 345-45 • W. The anomalies for each variable were computed relative to a daily climatology smoothed with a 31 day running mean.
As a larger global EP community, it is our collective responsibility to target these disparities and identify concepts that may be ready for widespread adoption. No concept seems better positioned, tested, and primed for widespread adoption than CIED recycling for exportation and reuse in LMIC. Given the relative contributions of conduction disease vs sudden cardiac death, our discussion of CIED recycling will focus on pacing as a first step mirroring efforts and data to date. However, in time many of these concepts may be applied to broader CIED reuse. In this manuscript, we will characterize the need, review the evidence, and describe the regulatory framework around CIED recycling for export. In our experiences and based on the literature, the widespread practice of CIED reuse is hindered by conceptual misunderstanding and delays in learning to navigate the regulations rather than any inherent risks of the practice.
The history The concept of pacemaker recycling originated in the 1970s and 1980s when a number of industrialized countries, including Canada, Australia, France, Sweden, and Norway, embraced pacemaker domestic generator reuse for domestic health care. Recognizing the potential economic benefits of such a practice, the North American Society of Pacing and Electrophysiology (now the Heart Rhythm Society [HRS]) in 1985 endorsed the concept of CIED recycling as long as it adhered to a rigorous process of testing, resterilization, informed consent, data analytics, and tracking. 12 The tide against domestic CIED reuse began in 1980 when the United States Food and Drug Administration (FDA) published guidance against pacemaker reuse, citing concerns about the feasibility of device header resterilization.
15 In 2018, Sinha and colleagues  16  performed a meta-analysis of recent trial data for pacemaker and implantable cardioverter-defibrillator (ICD) reuse, including cardiac resynchronization therapy devices. The analysis included 9 observational studies, 5 with control groups, published between 2009 and 2017. All of the studies were performed in LMIC using comparable CIED screening interrogation, resterilization, and repackaging protocols using ethylene oxide gas. This analysis found that the rates of infection, device malfunction, and premature battery depletion were not significantly different from controls. 16 A.
KEY FINDINGS -Cardiac implantable electronic device (CIED) recycling has the potential to make a significant impact on the leading cause of mortality in low-and middle-income countries (LMIC) worldwide. -As science, technology, education, and regulatory framework have improved, CIED recycling for exportation and reuse in LMIC has become possible and primed for widespread adoption. -CIED recycling is medically feasible, is ethical, and will have a positive economic downstream effect on developing nations. summary of trials included in these meta-analyses is shown in Table  1. Most recently, Khairy and colleagues  17  published the data from a multinational controlled prospective registry study further supporting the safety and efficacy of CIED reuse. This study analyzed the outcomes of recycled CIEDs    5, 6  demonstrated a low 2-year infection rate (2.
Sterilization process The potential for device infection owing to inadequate resterilization techniques seems to be the greatest source of concern from a regulatory and public acceptance standpoint. Data from the University of Michigan outlined a rigorous process that met medical industry standards and achieved a 12-log reduction of inoculated product and a sterility assurance of 10 -24 while maintaining a high rate of appropriate device function. 19 An illustration of this process is shown in Figure  2. An important concept to address is a standardized approach to headers and set screws, which represented the FDA's original objection to the concept of CIED recycling. A published report characterized in detail the process of screw removal, inspection, discarding of malfunctional or damaged apparatuses, and rigorous cleaning of those still acceptable for reuse.