dataesr/scientific-paragraphs-categorization huggingface dataset:
e. non-binding TCR-epitope pairs that allow the model to learn from a balanced binding set. Two common strategies to generate negative sets are utilizing a reference dataset of known non-interacting TCR-epitope and shuffling positive examples to generate negative instances with similar physicochemical properties. A study involving decoy epitopes, i. e. random epitope sequences generated by shuffling amino acid sequences, showed that using reference negatives results in over-optimistic performances compared to negatives generated via shuffling. Finally, Anna Weber (IBM Research Europe, Switzerland) continued to delve into the problem of TCR-epitope binding prediction and focused on understanding the features learned by black box models trained for this task. Anna presented DECODE, a computational pipeline designed to uncover the rules governing the decision process of any TCR binding prediction model  [10].
DECODE exploits Anchors, a widely used interpretability approach that utilizes perturbation analysis to identify the most crucial amino acids that impact binding outcomes. The selection of amino acids is further optimized via dynamic programming to ensure the extraction of reliable rules that locally approximate the decision boundary of any black-box TCR classifier. DECODE has been designed with flexibility in mind, allowing each component to be easily tailored to different research objectives and experimental conditions. It can be applied to any TCR binding prediction model, irrespective of its specific architecture or methodology, to generate human-understandable rules in the form of IF-THEN statements. This capability provides transparent and interpretable guidelines for understanding TCR-epitope binding.
DECODE offers various metrics to monitor and assess the quality of the generated explanations, such as clustering-based metrics, which gauge how well the explanations group together similar TCR sequences, or the coverage, which determines how generalizable the rules are to different TCR sequences. While these metrics contribute to the objective evaluation of the explanations, the explanations generated for different models can still vastly differ. For instance, the application of DECODE to two state-of-the-art TCR binding prediction models, TITAN  [11]  and pMtNET  [12] , resulted in markedly different binding rules. This previously observed behaviour reminds us that different ML models often use different features to arrive at the same conclusion, and it opens the interesting but unsolved question of how to compare and validate ML model explanations.
Acknowledgements MRM acknowledges funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No. 826121  and from the  Marie Sklodowska-Curie  actions No. 813545  and  675395. The generous support of these programmes enabled the organizers to cover the travel expenses of all invited speakers. MB acknowledges funding from the  Systems Biology  Grant of the  University of Surrey. AN was supported by a public-private partnership grant ( CIFRE  contract, no  2020/0766 ) with  SANOFI-AVENTIS R&D.
The purpose of this census is to assess the current status of the array in terms of data quality, how data quality has changed over time, and to provide a better understanding of the quality-controlled data to current and future users. Alongside increasing profile numbers and spatial coverage, we report that for most parameters between 80 and 95% of the profiles collected so far contain high-quality BGC data, with an exception for pH. The quality of pH profiles has seen a large improvement in the last five years and is on track to match the data quality of other BGC parameters. We highlight how BGC-Argo is improving and discuss strategies to increase the quality and quantity of BGC profiles available to users. This census shows that tracking percentages of high-quality data through time is useful for monitoring float sensor technology and helpful for ensuring the long-term success of BGC-Argo.
Materials and methods For each profile and BGC parameter used in this study, we recorded the WMO number of the float, date and location, cycle number, profile-and depth-level quality flags, and the presence of sensor data using Sprof files downloaded from March 16-17, 2023  (Argo, 2000). We also recorded information about each sensor's manufacturer and model from the metadata file associated with each float  (Argo Data Management Team, 2022). Data from 2023 was not included in this analysis. With the QC flags BGC-Argo provides, we applied two simplified flagging systems we created (described below). We only used ascending profiles (~96% of the database) because QC flags and BGC data were not consistently available from descending profiles. For the rare occasion where a single float had multiple sensors measuring the same BGC property (e.
If no QC is performed, a flag of "0" is used, while missing data are flagged as "9". Generally, data with flags "1", "2", "5", and "8" are treated as high-quality data, while data with flags "3" or "4" are treated as low-quality data. Profile-level quality flags, denoted as "PROFILE_<PARAM> _QC" in Argo, describe the percentage of high-quality data points out of all the available data points in a profile (N). For the calculation of N, the QC flags associated with the adjusted data (from either adjusted RTQC or DMQC) are used. If there are no adjusted data available, the QC flags associated with the unadjusted data are used in the calculation instead (from unadjusted RTQC processing). Depth-level data points with flags "0" or "9" are not used in the determination of the profile-level flag. The calculation for profile-level quality flags is as follows: N = n 1 + n 2 + n 5 + n 8 n 1 + n 2 + n 3 + n 4 + n 5 + n 8 Â 100 % where n is the number of QC flags in the profile with the subscript indicating the value of the depth-level QC flag.
Overall BGC-Argo profile quantity As of December 2022, the database of Sprof files has accumulated a total of 263,715 unique BGC profiles from 1,811 BGC-Argo floats (Table  S1 ). The most frequently measured property is O 2 (251,855 raw profiles from 1,754 floats) while the least frequently measured property is pH (39,483 raw profiles from 447 floats) after PAR/E d (46,028 raw profiles from 273 floats). Since 2002, the total number of floats deployed has steadily increased, alongside the total number of floats sustained in the array (Figure  1 ). How close the float array is to the annual float or profile targets is highly dependent on the BGC parameter because most floats do not measure the full suite of BGC parameters. For example, the number of radiometer-equipped floats deployed each year has not surpassed 50, while the other five BGC parameters see a minimum of 100 floats deployed per year as of 2022.
A B FIGURE 1 The number of floats deployed Due to their specialized QC procedures, which limit the quality flag of "1" to data that has been adjusted, we report only the QC metrics derived from adjusted data of O 2 , NO 3 -, pH, and Chla for the rest of this document, specifically referring to these variables as 'adjusted'. Otherwise, the QC metrics for the other parameters (b bp and PAR/E d ) include both adjusted and unadjusted data, unless specifically stated. Based on our first classification system, the number of highquality profiles has generally increased each year, except for profiles of PAR/E d (Figure  3 ). This lack of PAR/E d profiles is due to no sustained rise in the deployments of floats equipped with these sensors (Figure  1 ). We find that the database-wide P HQ was the highest for PAR/E d (92%, on average) and the lowest for adjusted pH (48%) (Table  S2   [490] ).
Discussion The success of BGC-Argo is dependent on collecting and providing high-quality BGC observations of the ocean. The data quality summary provided herein was based on the pre-existing flagging scheme that is used operationally within the Argo data management system. It offers a quick synopsis of the current extent of high-quality BGC-Argo data directly available to users for scientific applications. It is important to note that the various methods used in the quality control of different BGC parameters, as well as the capability to adopt and implement new methods uniformly across Argo Data Assembly Centers (DACs), are still maturing. For example, there remain floats within the data system for which QC flags are missing (Figure  2 ). This lack of QC is due, in part, to limited resources in addressing management requirements for legacy floats, as one example.
The most efficient way to bolster the number of profiles available is to process existing profiles that have not received QC flags, so such floats should not be overlooked. As we reported in the results, QC flags are missing for 4-15% of BGC profiles, excluding O 2 , which has QC flags assigned to almost all profiles. Processing these remaining profiles should increase the number of profiles labeled as high-quality, yet not significantly alter P HQ. Furthermore, efforts should be made to ensure all floats get processed (and reprocessed) with the most up-to-date QC procedures across DACs as protocols continue to be refined. This will result in improved consistency across the dataset into the future and strengthen its overall value for use in global analyses.
Parameter-specific improvements In addition to refining quality flags through time and initiating data adjustments, there are further parameter-specific procedures that should be adopted by the data management team to further improve the absolute accuracy of BGC parameters. While high-quality profiles already represent more than 90% of adjusted O 2 profiles collected thus far, O 2 optode sensors suffer from slow response times, which are not routinely corrected within the data system. Slow sensor response times lead to a lag in true O 2 concentrations and can create errors on the order of ~10 mmol kg -1  (Bittig et al. , 2014; Gordon et al. , 2020). Fortunately, various methods exist for calculating the in situ response time of O 2 optodes, given the existence of certain ancillary data and/or sampling conditions  (Bittig et al.
However, this approach is not the most cost-effective, and any relationships determined at the time of deployment will likely not be the same once seasons change or the float drifts into an ecologically distinct region. Currently, each fluorometer is calibrated in a laboratory setting, using a reference fluorometer that was initially calibrated to a single culture of Thalassiosira weissflogii. This calibration has introduced a bias in the determination of Chla in situ. Roesler et al. (2017)  quantified this bias with WET Labs ECO sensors and Chla concentrations determined by high pressure liquid chromatography (HPLC). Using their analysis, BGC-Argo adjusts Chla data by dividing unadjusted Chla by two to reduce this global bias. While this adjustment does not account for regional or seasonal changes in the fluorescence-Chla relationship nor greatly affects how QC flags are applied, it is a step forward for gaining more accurate estimates.
An important effort is made by the Argo Data Management Team to calibrate and qualify fluorescence data measured from BGC-Argo floats in order to deliver Chla with the best possible accuracy. It has been shown that using radiometric data associated with fluorescence measurements is an effective way to improve the fluorescence calibration  (Xing et al. , 2011; Xing et al. , 2018). However, not all BGC-Argo floats are equipped with radiometers, making it impossible to calibrate the entire fleet homogeneously. Recently, new methods based on machine learning have made it possible to derive radiometric profiles, from merged satellite ocean color observations and hydrological data, for any BGC-Argo float, whether equipped with a radiometer or not. These synthetic radiometric profiles can thus be assigned to the fluorescence profiles to consistently calibrate the whole BGC-Argo fleet.
g. , from oligotrophic subtropical gyres to high latitude environments strong seasonal changes in biomass). This cross-calibration could then be used to convert all Chla measurements to a common unit, assuming that differences between the same models of fluorometers are negligible. Similarly, a standard calibration procedure could be developed for BGC-Argo floats to ensure all Chla derived from fluorescence measurements from various models are intercomparable (e. g. , with diffuse attenuation coefficient estimates from irradiance profiles). Regardless of which technique is applied, great care will need to be taken to ensure the interoperability of the resulting Chla concentration data so that the researchers can leverage the full database at once. Measurements of b bp from floats currently have a limited QC procedure that includes processing for range filters, data spikes, and bad offsets.
Once implemented, these added real-time QC procedures will reduce the number of falsely identified highquality profiles available, but also erroneously identify good data as bad. Efforts should be made in developing a DMQC procedure to improve data quality flagging. We found that high-quality PAR/E d profiles represent ~90% of the entire database (Table  S2 ). The data from radiometers are generally good without much QC. (Radiometry RTQC currently involves a global range test, and all radiometry data are given a QC flag of "2" unless the test is failed. ) Previously tested improvements to the float radiometry processing include a dark offset correction, temperature correction, spike removal, and sensor drift detection  (Organelli et al. , 2016; Organelli et al. , 2017; Jutard et al. , 2021; O'Brien and Boss, 2022).
About half of the database has DMQC processing correcting for temperature and dark values  (Jutard et al. , 2021) but  has not yet been implemented on all floats. Future float deployments should prioritize collecting nighttime profiles of PAR/ E d , at least once a year by each float, for these temperature and dark offset corrections  (Jutard et al. , 2021). In a similar sense, collecting these nighttime profiles could also prove beneficial for assessing NPQ corrections. To reduce space-time biases in such an assessment without changing the standard profiling interval, average daily cycles could be extracted  (Johnson and Bif, 2021; Stoer and Fennel, 2023). Unstable sea states, clouds, and wave focusing can also momentarily alter light conditions which can lead to large fluctuations in the downwelling light profile.
While there is nothing inherently wrong with the sensor during such events (as it represents natural light variability), stable light conditions are needed to better characterize the optical properties of the water column (e. g. , estimation of the light diffuse attenuation coefficient). Procedures to identify these effects have been successfully applied to floats before  (Organelli et al. , 2016; Organelli et al. , 2017)  and could be further developed to create a reference dataset for use in the ocean optics community.
Pre-deployment strategies A few pre-deployment strategies can also be to help improve data quality in the future. For one, testing the float from a dock could help identify issues with sensors, communications, and mechanics before deployment in the open ocean. Ideally, dock testing would prevent the deployment of floats that would otherwise quickly fail in the field. This strategy has proven to be very effective for the SOCCOM and GO-BGC projects. Secondly, while not always logistically feasible, reducing the time between when the float is manufactured and deployed may improve float survival and functionality by limiting exposure to possible damage. Finally, larger batteries can also be purchased for some float models to increase the float's lifetime, allowing the float to collect more highquality profiles (assuming all sensors continue to function).
Conclusion BGC-Argo is a rapidly expanding global ocean observation network that offers an independent, cost-effective approach to studying ocean biogeochemistry. The success of BGC-Argo depends on ensuring that scientific-quality data can be retrieved and delivered to the user in near real-time. Delivering scientificquality data requires rigorous QC. Alongside the increasing profile numbers and spatial coverage, we report that for most of the key BGC parameters, high-quality profiles make up at least 80% of all profiles collected thus far, with the exception of pH. We find that pH profiles have drastically improved in quality in the past five years and are on track to have a similar data quality as the other BGC parameters. We suggest several ways in which the BGC-Argo data system is improving, which should lead to both an increase in the number of high-quality profiles available to users, as well as improvements to the absolute accuracy of key parameters in the future.
Data availability statement Publicly available datasets were analyzed in this study. The Sprof files used in this study are available from the Argo Global Data Assembly Centers in Brest, France (ftp://ftp. ifremer. fr/ifremer/argo/ dac/) and Monterey, California (ftp://usgodae. org/pub/outgoing/ argo/dac/). The marine region data is from Flanders Marine Institute (2021) and is publicly available online (https:// www. marineregions. org/). The Python software and the data in each figure are freely available on Zenodo (Stoer et al. , 2023).
Dataset The forecast of daily demand in tons for each retailer was randomly generated from the total predicted daily data of agricultural products in the northern region of Thailand  (Kantasa-ard et al. , 2021b). The demand interval of each retailer was the range 15-30 tons, and the stock interval of each hub was the range 50-100 tons. In this case, a single agricultural product, pineapple, was considered. For the delivery process, PI containers distribute new pineapples Multiple-depot vehicle ruting problem in the PI to retailers. For the pickup process, PI containers pick up some overripe pineapples and take them back to PI hubs. Accurate positions of the PI hubs and the retailer were established in the main cities and some random supermarkets in the northern region based on the positions in Google Maps, as shown in Figure  4.
The unit price of transportation was equal to V0. 053   (2021b) , was equal to [5. 2, 2. 6, 1. 3] euros for all hubs based on the location of each hub. The fuel emission rate (FE) was 2,621 g/l, and the fuel consumption rate (FC) was 0. 3462 l/km based on a 70%-80% load in rural areas  (Hoen et al. , 2014). The total distance (D) was the main input factor to calculate total carbon emissions. In this study, the total distance was the sum of the This model was validated using IBM CPLEX (Version 12. 8) for the exact method and java programming language for the heuristic and metaheuristic methods on an Intel Core i5 CPU with 4 GB of DDR3 RAM. The global time limit in CPLEX was 7,200 s. For the metaheuristics, the tests were replicated five times and the average values are presented. According to the background and dataset of the case study, the results of each model, including the performance comparisons between the classical supply chain and PI, are presented in the results analysis and discussion section.
As a consequence, various commercial and academic HLS tools, such as Xilinx Vivado HLS, Intel OpenCL, Catapult-C, and ROCCC, are being developed. Thus, the goal of this research is to build several hardware designs for the NFMO's proposed denoising algorithm utilizing HLS flow. When creating these designs, the cost and speed of the FPGA were considered. Using the Xilinx Zynq FPGA, the best-designed architecture will be built and evaluated. This paper is organized as follows: In Section 2, MLVMF-based impulsive noise detection is presented. This algorithm will be used to detect corrupted pixels in the image. Proposed local data enhancement by nested filtering is the subject of Section 3, while Section 4 explains the missing information estimation process performed by the morphological tools.
In Table  2 , the chosen methods are listed, along with a description of how they work. The Lena standard image is used in this step. Table  3  shows the PSNR performances of selected methods as well as the performances of the proposed NFMO method at different noise densities (from 10% to 90%) on the Lena standard image. Figure  5  illustrates the PSNR variation as a function of the impulsive noise density injected into the Lena image. The PSNR variations of the 12 selected methods are presented in grayscale. The proposed NFMO method's PSNR variation is displayed in red. The proposed method works, as shown by the results in Table  3  and Figure  5. Beyond 50% impulsive noise, the NFMO gives the best PSNR and, therefore, the best denoising quality of all the methods. The local data enhancement proposed in the case of an image too poor in useful pixels succeeds in providing the best estimate of the corrupted pixels.
The proposed NFMO method's PSNR variation is displayed in red. Figures  9  and 10  show a visual qualitative evaluation of the performance of the NFMO applied, respectively, to the Boat and Baboon 512 × 512 images with impulsive noise of density 10%, 30%, 50%, 70%, and 90%. These two test sets confirm the results obtained on the Lena image. The NFMO is efficient in the case of a low noise density and becomes more and more efficient when the noise density increases, giving the best values of PSNR among the other methods. It is, however, necessary to specify that a statistical validation of the proposed method to confirm the very satisfactory results found would be important. This requires further statistical study using a dataset of at least 100 images. Figure  7. The variation of the PSNR according to the impulsive noise density injected into the Boat image.
NFMO Performances Evaluation with Medical Images The objective of this work is to provide a real-time filtering tool for medical images presenting strong impulsive noise. We have proposed a new method for replacing noisy pixels capable of restoring highly noisy images with very good quality. We are interested here in the quantitative and qualitative evaluation of the performance of the NFMO algorithm applied to a set of medical images. Medical images are selected from the Retinal Fundus Multi-Disease Image Dataset (RFMiD) 2. 0  [59] , the Alzheimer's Dataset available in the Kaggle platform  [60] , and the COVID-19 Image Repository  [61]. Six images were chosen to perform the experimental tests. CI1 and CI2 consist of brain MRI images with moderate dementia. CI3 and CI4 show color retina fundus: CI3 shows an anterior ischemic optic neuropathy, while CI4 shows coloboma in the macula and optic disc.
NFMO Real-Time Implementation When combined with FPGA, the HLS process gives engineers a useful tool for quickly exploring the design space based on a high-level programming language (such as SystemC or C/C++) that describes how the system works. So, the HLS has turned into a useful and effective tool that could increase the amount of work that can be done during the design process and shorten the time it takes to finish a design cycle. In this situation, a number of HLS tools have been made, such as the Xilinx Vivado HLS tool, which gives a number of steps or directives for making the best hardware design for every algorithm. In fact, the RESOURCE directive makes it possible to construct arrays either as registers or as memory. In addition to this, the ALLOCATION directive may be used to improve the efficiency with which the arithmetic operation performed.