dataesr/scientific-paragraphs-categorization huggingface dataset:
The methods with +CMG gain improvement over the others at around recall level of 0. 5 for the VEDAI datasets and both +MG and +CMG outperforms vanilla method on the xView dataset. Some qualitative results on the VEDAI25 and xView dataset can be found in Figure  5  and Figure  6 , respectively. Several objects are missing in the second and third columns while the CMG strategy (last column) is able to recognize objects of complex shapes and appearances. The methods with +CMG gain an improvement over the others at around a recall level of 0. 5 for the VEDAI datasets and both +MG and +CMG outperform the vanilla method on the xView dataset. Some qualitative results on the VEDAI25 and xView datasets can be found in Figures  5  and 6 , respectively. Several objects are missing in the second and third columns, while the CMG strategy (last column) is able to recognize objects of complex shape and appearance.
Comparison to the state-of-the-art. In Table  5 , we compare our method with several state-of-the-art methods on the three datasets. Our YOLOX backbone with the CMG strategy outperforms others on the VEDAI datasets and is on par with YOLO-fine on xView. From the qualitative results in Figures  7  and 8 , respectively, for the VEDAI and xView, it can be seen that although the xView dataset contains extremely small objects, our method, without deliberate operations for tiny object detection, can approach the state-of-the-art method specifically designed for small vehicle detection  [28]. A breakdown of performance for each class of VEDAI is shown in Table  6. Two failure cases are shown in the last columns of Figures  7  and 8. We can see that our method has difficulty in recognizing the "other" class (VEDAI), which comprises various object types, and might wrongly detect objects of extreme resemblance (xView).
Discussion Although supervised contrastive loss has been shown to be able to replace crossentropy for classification problems  [17] , in this paper, contrastive loss is applied as an auxiliary loss besides the main localization and classification losses. This is because only a small number of anchors are involved in the contrastive process due to the large number of anchors, especially negative anchors. However, contrastive loss shows weakness when the annotations are noisy, such as those of the xView dataset. Several boxes are missing for (what appear to be) legitimate objects, as shown in Figure  9. Comparison to the state-of-the-art. In Table  5 , we compare our method with several state-of-the-art methods on the 3 datasets. Our YOLOX backbone with CMG strategy outperforms on the VEDAI datasets and on par with YOLO-fine on xView.
From qualitative results in Figure  8  and Figure  9 , respectively for the VEDAI and xView, it can be seen that although the xView dataset contains extremely small objects, our method, without deliberate operations for tiny object detection, can approach the state-of-the-art method specifically designed for small vehicle detection  [29]. A breakdown performance for each class of VEDAI is shown in Table  6. It is shown from the experimental results that inward contrastive loss is not always inferior to its outward counterpart, as shown in  [17]. We speculate that this could be due to the auxiliary role of contrastive loss in the detection problem and/or the characteristics of small objects in remote sensing images.
That data is filename data_s3_mapped brightness as part of the Supplementary Information (SI). The third step is to measure cloud displacements in the reference plane using the Tracker3 software from JPL. The software automatically searches for the best correlation of brightness patterns between two images. This is done between images in series n01 and n03 and between images in series n02 and n04. Velocity is the displacement in km divided by the time interval, which is always close to 16 minutes but depends on which image in each series was used. Correlation is done within a square box in the reference plane. After experimenting we settled on 15 km/pixel in the reference plane and a 15 x 15 pixel correlation box for the Tracker3 software. In other words, we use a square 225 km on a side to define a feature.
Therefore the resolution of the wind measurement is ±112. 5 km. We oversample it by a factor of 2. 5 to obtain wind vectors on a 45 x 45 km grid. That data set is filename data_s4_velocity vectors in the SI. Finally, we determine vorticity and divergence at every grid point by integrating around the 8 surrounding points using Stokes's theorem and Gauss's law, respectively. Those data are filename data_s5_ figure_2_data in the SI. The error in the velocity estimate sv depends crucially on the granularity of the scene at the scale of the resolution element d, which on average is about 18 km. Except for no features at all, for which there is no estimate, the worst case is a single cloud feature of size £ d, for which the variance σ ( # = 2d 2 /Dt 2 , where Dt is the 16-minute time step and the factor of 2 arises because we are subtracting position in two images.
Acknowledgments This research was carried out at the  California Institute of Technology  under a contract with the  National Aeronautics and Space Administration (NASA) , Grant/Cooperative Agreement Number  80NSSC20K0555 , and a contract with the Juno mission, which is administered for NASA by the  Southwest Research Institute. CL was supported by the 51 peg b Postdoctoral Fellowship. JIRAM was supported by the  Italian Space Agency through ASI-INAF  agreements n. I/ 010/10/0 ,  2014-050-R. 0 , 2016-23-H. 0and 2016-23-H. 1-2018. AA, AM, DG, and FT were supported by  INAF. CP and GS were supported by  ASI. LS is funded by the  Scripps Institution of Oceanography Postdoctoral Fellowship.
Although already two algorithms exist and provide quite good data, this field is new and has not yet been fully explored, while the long-term perspectives are exciting. In particular, all developments undertaken now using IASI data will be the basis for the future algorithms for IASI-NG, the new generation instrument that will succeed IASI in the coming years. Even though the current IASI products, including the one presented in this manuscript, do not reach sufficient accuracy and precision to precisely and reliably observe the tiny geographical and seasonal variations of N 2 O, a clear improvement is expected with IASI-NG due to improved spectral resolution and signal-to-noise ratio. In the next section, we describe the different data sets used in this paper. Section 3 provides a full description of the NOPIR retrieval algorithm and a summary of the differences with SOFRID and MUSICA.
3°viewing angle) with 30 elementary fields of view, each composed of 4 instantaneous fields of view (also often referred to as pixels). A pixel has a diameter of 12 km at sub-satellite point (pure nadir), and its size increases with the viewing angle to reach an ellipse with axes of 39 km by 20 km at the extremities of the swath. The scanning and orbital parameters of IASI/Metop allow almost global coverage twice per day by each IASI instrument. The radiometric noise is about 0. 2 K in the spectral range used in this work (see later)  [23]. In this work, we use the Metop-A IASI level 1c radiance from the Principal Components Analysis (PCA, started on 22 February 2011), the Metop-A IASI level 2 atmospheric profiles of temperature and humidity and the cloud fraction. The version of these data depends on the observation date and follows updates in the EUMETSAT operational processing.
2% difference) when the same constraints are applied in both algorithms  [32]. The N 2 O retrievals are done using four micro-windows in the 2480-2540 cm -1 spectral range, and have 2. 5 to 3 DOF in the full profile, from which about 1. 5 in the vertical range of the IASI NOPIR retrievals (as reported in the data files). Table  2  lists the NDACC stations used in this work. The systematic (reported to be mostly linked to spectroscopy  [33] ) and random uncertainties are reported in the data files, and their mean value is listed in the Table. Reported systematic uncertainties range from 2 to 4. 4%, while reported random uncertainties range from 0. 13 to 2. 4%. The NDACC data are used to validate our retrieved N 2 O data, with an additional outlier filtering, following the procedure described in the validation Section 4.
The atmosphere is set up using the EUMETSAT IASI level 2 temperature and humidity profiles (and those are not retrieved within NOPIR), a GEOS-Chem-based monthly climatology for N 2 O (as a priori), and the standard RTTOV profiles for all other gases (not retrieved). The N 2 O vmr climatology is obtained from the GEOS-Chem monthly data by interpolating on our radiative transfer pressure grid, then averaging the data for all years by 4°latitude bands. That way, a monthly climatology (average of GEOS-Chem data during 2006 to 2016) of vertical profiles is obtained in latitude bands. Before each retrieval, for each pixel the a priori is obtained from that climatology using a linear interpolation as a function of the latitude to avoid any discontinuity. As an atmospheric constituent, only N 2 O is part of the state vector, as a profile, and modified during the retrieval.
Micro-Windows Selection The retrieval micro-windows were selected within the ν 3 asymmetric stretch vibration mode around 2200 cm -1 and are listed in Table  4. The precise IASI channel selection was based on the following steps (details are given in the next paragraphs): • Channels with significant absorption signature of N 2 O (including isotopes) only were selected, and in addition some channels as transparent as possible; • Only channels with post-retrieval root-mean-square of spectral residuals (RMSSR) mostly below 0. 4 K were allowed; • Only micro-windows of at least 4 spectral channels were kept. For the first step, we used data from the high-resolution transmission molecular absorption (HITRAN) 2016 database to model the atmospheric transmittance, at high spectral resolution (0. 002 cm -1 ), in total and separately for each molecule, for a typical atmospheric state at the Saint-Denis station (Reunion Island).
We validate integrated N 2 O columns (instead of only the layer with the highest sensitivity) with the available quality controlled data from NDACC and TCCON stations. 5. NOPIR retrieved products have about 2 degrees of freedom (DOF) (more than MU-SICA, less than SOFRID) except at high latitudes where the DOF is reduced to about 1 (similar for MUSICA, not considered in SOFRID).
Results The N 2 O retrieval as described in the previous section is undertaken on each available IASI cloud-free scene for which good quality data is available, since 22 February 2011 (the start of the level 1 PCA data). Then, a quality control is undertaken, to ensure selecting only the highest quality retrieval results (described in Section 4. 1). This filtered dataset is analyzed in this section, first in terms of its intrinsic properties, then by comparing it to reference ground-based network data.
The analysis on the quality control of our N 2 O data also brings interesting information on the evolution of the quality over time. As our retrieval method, N 2 O a priori and surface emissivity do not change at all over the years (but do vary seasonally), the only possibility for a changed quality over the years is the other input data we use: the IASI level 1c PCA spectra, the IASI level 2 Ts (used as a priori), temperature and humidity profiles (not retrieved) or even the cloud product (used for selecting cloud-free pixels). Indeed, the clear improvements in the number of good quality N 2 O retrievals are all but one (mid-May 2013) linked to a reported change in the IASI level 2 processor version (see also Table  1 ). End October 2011, the update (cloud screening) causes a smaller ratio of converging retrievals, which is then corrected at the next update (cloud product) end February 2012.
The lower ratio of converging N 2 O retrievals between these two changes is most probably due to a higher number of unreported cloudy pixels, for which our retrieval is attempted and fails. Mid-May 2013 our ratio of good retrievals is improved, with no change in the EUMETSAT level 2 data processor. However, a calibration change in the IASI level 1c spectra occurred in the processing chain on 16 May 2013 (the IASI point spread function was improved  [76] ), to which we attribute the improvement in our retrieval residuals. The switch to the IASI level 2 processor version 6 in September 2014 leads to a clear increase in the ratio of converging and of good quality N 2 O retrievals, attributed mostly to the improved quality in the IASI temperature profiles but also partially to the improved cloud product (improving the convergence ratio).
García et al. [17]  have also shown that, in the MUSICA algorithm, the EUMETSAT IASI level 2 version 6 IASI data lead to higher quality N 2 O retrievals than the level 2 version 5 data. The next update in September 2015 shows also some improvements in our ratio of good retrievals, although it is less clear because of the seasonal cycle in that ratio. The next level 2 updates do not seem to have a strong impact on the ratio of good quality results, except for the last one in December 2019, which actually leads to an increase of the spectral residuals in our retrieval. In addition to the changes in the quality statistics linked to changes in the IASI level 2 processor, there is a seasonal cycle in the ratio of good quality N 2 O retrievals. This cycle is mostly due to the high latitudes: at high North latitudes there are little good retrievals during the winter and much more during the summer, while at high south latitudes there are few good retrievals all year long.
Comparison Methodology The methodology for the validation of the IASI N 2 O data against NDACC and TCCON data is very similar, with small differences linked to the fact that NDACC provides vertical profiles of N 2 O vmr while TCCON provides dry-air column-averaged mole fractions. We first describe in detail the method for NDACC comparisons, then we will highlight the differences for the TCCON comparisons. Co-locations are done between IASI and NDACC observations, with maximum 100 km distance and 6 h time difference. This maximum time difference is long, but necessary because the NDACC observations are done only a few times per day. This should, however, not be a problem because the N 2 O amount does not have a pronounced diurnal cycle. The allowed distance between IASI and the ground-based instrument has to be considered in comparison to the IASI pixel size (reminder: 12 km circle at nadir, 39 by 20 km ellipse at scan edges).
Acknowledgments: The IASI data used in this publication was obtained from the  EUMETSAT , and is freely available. The ground-based data used in this publication were obtained as part of the  Network for the Detection of Atmospheric Composition Change (NDACC)  and the  Total Carbon Column Observing Network (TCCON). The NDACC data is available through the website www. ndacc. org. The TCCON data were obtained from the  TCCON Data Archive  hosted by CaltechDATA available online (accessed on 21 January 2022): https://tccondata. org. The IT service at  BIRA-IASB  is acknowledged for support in the processing of the data.
In the context of decision-making in healthcare organizations, issues related to obtaining, combining and processing data often have a profound impact on projects to improve the quality of healthcare services  (5). On the one hand, successful cases clearly demonstrate the potential of massive use of data, such as the "Google Flu", capable of predicting flu rates in the USA population by massively analyzing Google search patterns (and achieving a degree of accuracy comparable to the much more complex models of CDC, that country's center for disease control)  (6). On the other hand, there are also numerous cases of failure, often experienced by professionals who work in the routine of organizations that provide healthcare services  (5, 7). Ironically, the latter case is the most common, especially for health decision support systems (DSS) projects, whose scope encompasses precisely the treatment of large databases to support the quality of decision making  (9).
According to Towbin (2019)  (5) , data collection is the decisive step for the success or the failure of healthcare service-improvement projects. At this stage, critical questions about the nature of the data often go unnoticed by project creators, subsequently impacting their course of development. In the present work, based on the findings of other authors  (7, 8, 9) , we propose that this is the main stage of decision support systems implementation projects. Consequently, we also propose that this step should not be cryptic, but instead explicitly allows for the active participation of healthcare professionals. All other steps should be focused on facilitating this one and in the simplest possible way. In this context, in the present work our aim is to present a conceptual model of DSSs, prioritizing simple, translucid and performative algorithms, while contemplating data collection and database maintenance from the beginning as well as the delivery of recommendations.
Methods The conceptual model of the proposed decision support system (DSS) is based on an architecture composed of simple and easy-to-implement algorithms. To make this possible, we assume the existence of repositories of professional practice protocols, accessible via the internet. Obtaining the protocol files is performed by a web crawler, actively triggered by the user, whenever the need arises. A second web crawler monitors for updates to the protocol database. We assume that all users have an email account, which can be added to (or removed from) the receiver's list of updates, to be eventually emailed when modifications are found in the protocols repository. The information from these documents (protocols) is manually tabulated by a team of specialist professionals, forming a database that will be used to parameterize a decision table algorithm (see  (11) ), which will serve as a knowledge representation model.
Recommendations are obtained through user interaction with the decision table algorithm through an interface algorithm, in which basic and clinical patient data are provided by the user (usually, a healthcare professional), who will receive the recommendation of action to be taken. Figure  1  illustrates the proposed conceptual model. In the present work, we computationally implemented the proposed conceptual model for the practical case of radiological exams prescription. Thus, the American College of Radiologists (ACR) protocol repository, called ACR Appropriateness Criteria (accessible via the URL https://acsearch. acr. org/list), was used as our document source. The implementation of the web crawlers (FilesFetcher and Monitor, in Figure  2 ) and of the decision table (DecisionTable, in Figure  02 ) was carried out as algorithm pipelines using the python programming language, version 3.
8, using traditional libraries for this purpose. (beautifulsoup4 version 4. 11. 1, pylightxl version 1. 60, plus standard python library features). The document's data tabulation (into the DSS database), for our experimental purposes here, was performed by the authors themselves -being defined head and neck radiology as our focal case. For the interface between system and users, an API was implemented, also in python, using the Flask library (version 1. 1. 4). two endpoints were implemented, the first being for consultations to the decision table (acepted parameters: sex, age, clinical indication and subcategory of clinical indication). The second endpoint is dedicated to facilitate the search for clinical indications (and their respective subcategories) in the system's database, using an conventional inverted index for that (which was also implemented by ourselves).
This API was made available through the AWS Lambda cloud computing service. For this, we employ the open source Serverless Framework tool (see https://www. serverless. com/), which automates the publication of the API as a function at AWS Lambda and creates a monitoring dashboard. For the purposes of this work, we do not implement any graphical user interfaces, being used the postman tool for this purpose (see https://www. postman. com/). Figure  02  illustrates the implemented DSS. All steps of this work were carried out using python version 3. 8, on a personal computer with an Intel® Core™ i5-10210U processor, 1. 60GHz 2. 11GHz CPU and 16GB RAM. In view of the replicability of our experimental implementation, the complete source code was made available on the GitHub repository https://github.
Results and Discussion The implemented version for the proposed conceptual model proved to be stable and suitable for quick deployment, using the python language, combined with the open source Serverless Framework tool. The FilesFetcher algorithm pipeline proved to be capable of retrieving the files published in the repository used for the experimental implementation (i. e. , the ACR Appropriateness Criteria). A sample of the results is shown in Figure  3. The Monitor pipeline also behaved as expected, performing the checking notifications for updates at the specified frequency (which, for our experimental implementation, was once a week, on Mondays at 6 AM). An illustration of this result can be seen in Figure  4. In this image, the email was sent on Monday, August 1, 2022, with no new content found in the monitored radiology protocols web-source.
This is an aspect that commonly impacts projects involving the implementation of such systems, being reported as one of the main factors of failure of the initiatives  (7, 8). The heterogeneity in the data to be used in the knowledge representation algorithm (here, the decision table algorithm, in the proposed conceptual model) is also a critical point commonly pointed out by different authors  (5, 7, 8)  and that can be mitigated in systems that adopt the KISS principle, as the conceptual model proposed here. This potential should be realized when the DSS to be implemented is specific to a particular area or healthcare providing (as in the case of prescribing radiology exams, explored in this paper). This should happen because the system's database formation relies on the semi-automated compilation of data from documented protocols for professional practice, already validated and agreed by the focal professional community (see the Methods section).
Firstly, the adoption of our model is limited to contexts in which there is a source of protocols duly documented and accessible via the internet (see the Methods section). Another relevant point is the hybrid nature of the database formation (for more details regarding hybrid databases, see(  5 )). As already mentioned in this section, this is an aspect that brings benefits (direct involvement of healthcare professionals throughout the implementation of the DSS), but also limits the implementation of highly complex systems, encompassing too heterogeneous, ambiguous and large-volume information contents. In these situations (which are beyond the scope of the model proposed in the present work), more complex systems, specially involving artificial intelligence algorithms, may be more indicated, inevitably (see  (8)  and (  12 )).
Participants Fifteen active and healthy participants (11 men and four women; age: 26 ± 5 yr; body weight: 70 ± 10 kg; height: 174 ± 9 cm) participated in the present experiment. They self-reported their main strength as either explosive (n = 9) or enduring (n = 6). They reported no history of neurological or musculoskeletal impairment. Participants were asked to refrain from strenuous and unaccustomed physical activity for 48 h before testing to minimize the risk of prior fatigue or muscle damage. The study protocol was approved by the local Ethics Committee and was in accordance with the latest update of the Helsinki Declaration (except for registration in a database). All subjects gave their written informed consent before participation.