dataesr/scientific-paragraphs-categorization huggingface dataset:
This method referred to as CANYON for "CArbonate system and Nutrients concentration from hYdrological properties and Oxygen using a Neural network" predicts these key oceanographic variables, which cannot be measured independently or with sufficient accuracy, with much improved temporal and spatial resolution  (Chai et al. , 2020). This method as well as its subsequent improvement, CANYON-B  (Bittig et al. , 2018) , were trained on high-quality data collected over the past thirty years (the GLODAPv2 for Global Ocean Data Analysis Project version 2 database;  Olsen et al. , 2016). One of its potential applications is to generate virtual carbon and nutrients estimates from the large amount of data acquired by the Biogeochemical-Argo float array (BGC-Argo;  Johnson and Claustre, 2016; Claustre et al.
, 2020). The Mediterranean Sea is a semi-enclosed marginal sea characterized by high salinities and a rapid overturning circulation, rendering it capable of absorbing more CO 2 than adjacent oceanic regions  (Schneider et al. , 2010; Lee et al. , 2011; Touratier and Goyet, 2011; Álvarez et al. , 2014). Machine learning methods produce better results when trained on datasets representative of the considered case study. Therefore, both CANYON and CANYON-B approaches may underperform in this specific oceanic region making a regionalization of this approach highly desirable. Furthermore, the Mediterranean Sea is also a low nutrient concentration basin  (McGill, 1966; Krom et al. , 1991)  with an eastward-increasing oligotrophy gradient. Nutrients are particularly important as tracers of biological cycles, biomass production, and natural and anthropogenic inputs  (Béthoux, 1989; Béthoux et al.
, 2019). In this paper, we present the regional downscaling of the CANYON method for the Mediterranean Sea, CANYON-MED. A database of in situ measurements has been specifically assembled to constitute a new quality-controlled dataset used to train the regional neural networks. Neural network ensembles were trained and their results are compared to single neural network architectures. The resulting optimized CANYON-MED networks are validated with an independent dataset and compared to existing methods available for nutrients and carbonate system variables prediction in the Mediterranean Sea.
Training and Validation Datasets The CARIMED (CARbon in the MEDiterranean Sea) data synthesis initiative  (Sanleón-Bartolomé et al. , 2017)  aims to produce a consistent quality-controlled database for carbon relevant variables from hydrographic cruises covering the whole water column and the different basins of the Mediterranean Sea. As the validation of CARIMED data is still in progress, we performed secondary quality control through visual inspection of the profiles and by comparison with literature values, to remove outliers. After applying similar quality control, the data from ten other cruises were added to the database (i. e. , CARBOGIB, CASCADE, DEWEX,  GIFT, MOOSE_GE 2011 , MOOSE_GE 2013 , MOOSE_GE 2015 , PACIFIC-CELEBES, SOMBA, MSM72; Table  1 ). The spatial coverage of the complete dataset is shown in Figure  1.
, 2002; Pasqueron de Fommervault et al. , 2015; Coppola et al. , 2018). The dataset gathered for this study (Table  1 ) therefore includes 35 oceanic cruises and a time series, from 1976 to 2018 with samples from the surface to 4600 m depth of core variables such as O 2 , temperature, salinity as well as macronutrients [nitrates: NO - 3 , phosphates: PO 3- 4 , silicates: Si(OH) 4 ] and carbonate system variables (total alkalinity: A T , total carbon or dissolved inorganic carbon: C T , and pH on the total scale: pH T ). When A T and C T were available, pH T was calculated using CO2SYS-MATLAB  (Lewis et al. , 1998; van Heuven et al. , 2011). This dataset is openly accessible  (Fourrier, 2020). Thermodynamic calculations within the carbonate system used the carbonic acid dissociation constants of  Mehrbach et al.
Neural Network Development Artificial Neural Networks (ANNs) are approximate functions adapted to any dataset  (Marzban, 2009). One of the main advantages of these methods is their ability to recognize and exploit relationships in data that are not predefined (unlike regression techniques) and do not need to be made explicit by equations  (Marzban, 2009). This makes them particularly suitable for mapping non-linear relationships, provided that data are sufficiently available to "train" the neural network  (Lefevre et al. , 2005). Similarly to  Sauzède et al. (2017)  and  Bittig et al. (2018)  for CANYON and CANYON-B, respectively, an iterative statistical learning-based method, and more specifically an ANN was developed to predict carbon and nutrient variables. Separate the learning data into a training dataset for training the machine learning method and a validation dataset used to assess the performance of the trained method is a common practice.
Validation Statistics Metrics Results were validated using four statistical metrics chosen to evaluate the performance of the CANYON-MED algorithms on the validation datasets: the MAE (Mean Absolute Error, Equation  5 ), the RMSE (Root Mean Squared Error, Equation  6 ), the coefficient of determination (r 2 , Equation  7 ) as well as the slope (Equation  8 ) of the linear regression between the CANYON-MED-retrieved values and the corresponding in situ measured values. The absolute uncertainties are expressed as concentrations for NO - 3 , PO 3- 4 , Si(OH) 4 , A T , and C T (in µmol. kg -  ) and pH T (pH unit) parameters. MAE = 1 N * N i=1 |X CANYON-MEDi -X DATAi | (5) RMSE = 1 N * N i=1 (X CANYON-MEDi -X DATAi ) 2 (6) r 2 = 1 N N i=1 X DATAi -X DATAi X CANYON-MEDi -X CANYON-MEDi σ X DATA * σ XCANYON-MED 2 (7) Slope = X CANYON-MED -CANYON -MED intercept X DATA (8) RESULTS AND DISCUSSION.
The low MAE values suggest that the ANN-E model is not biased, although the slopes are slightly less than 1, resulting in an underestimation of neural network outputs. Using the validation dataset, the performance of the CANYON-MED method was evaluated by comparing CANYON-MED's results with the corresponding in situ values. Figure  4  shows these results as a function of pressure while the corresponding statistics are in Table  2. The accuracies obtained are very satisfactory with, for example, an accuracy of NO 3- extracted from the neural network method (0. 73 µmol. kg -  ) comparable to that obtained with optical sensors such as those mounted on BGC-Argo floats (1 µmol. kg -  ;  Johnson et al. , 2017). Owing to the lower number of data presently available in our training dataset for C T and pH T , the ability of the corresponding neural networks to generalize correctly is lowered, explaining the less robust statistics for C T and pH T with slopes around 0.
Validation on Independent Time Series: ANTARES The ANTARES time series was chosen as an additional independent validation dataset because, among the few time series in the deep offshore Mediterranean, it is one of the few where measurements of nutrients and carbonate system variables are performed semi-regularly and over the entire water column  (Lefevre, 2010). The vertical profiles of the differences between in situ measurements of the ANTARES dataset and the values predicted by CANYON-MED are represented (Figure  5 ) along with their mean value and associated standard deviation. In general, the accuracies (Table  3 ) of each variable are comparable, albeit slightly worse, to those determined on the CANYON-MED validation dataset (Table  2 ). These accuracies are even lower for A T and C T probably due to the smaller ranges covered by the ANTARES site.
The errors seem quite homogeneous over the whole water column (Figure  5 ), with higher errors when lacking data at specific depths [e. g. , NO - 3 and Si(OH) 4 at around 1250 dbar]. In addition, we can also note the clear overestimation of PO 3- 4 at the ANTARES time series, evident from the slope of 1. 06 (Table  3 ) as well as from the observation of a clear shift in Figure  5B. However, it is important to note that the ANTARES dataset, although subject to quality control, still exhibits a high nutrient dispersion along the water column. This variability may result from natural phenomena or potential issues in the measurement accuracy (e. g. , measurement uncertainty, sampling procedure, change of operator, evolution of techniques). In the latter case, it can explain the dispersion of errors found for nutrients in Figure  5.
CANYON Methods The performance of the CANYON, CANYON-B, and CANYON-MED methods were compared using the validation dataset (the remaining 20% of the database not used for training). The performances were computed by comparing the neural network outputs for nutrients and carbonate system parameters with the in situ measurements according to the metrics defined in section "Validation Statistics Metrics. " Scatterplots of neural network-retrieved variables against their corresponding in situ measurements (Figure  6 ) reveal that the CANYON-MED method gives much better results than its global counterparts (i. e. , CANYON and CANYON-B). The accuracies (RMSE) have been reduced, for each variable, by more than half between CANYON-B and CANYON-MED and by a third for nutrients and A T between CANYON and CANYON-MED, as shown by statistics in Table  4.
The differences are primarily due to the under-representation of Mediterranean Sea cruises in CANYON and CANYON-B's training dataset (Mediterranean data poorly represented in the GLODAPv2), as opposed to the training dataset specifically designed for the Mediterranean Sea in CANYON-MED. Specifically, for the CANYON and CANYON-B networks, a higher scatter is observed for NO - 3 and PO 3- 4 near-zero values (Figures  6A, B ). CANYON-MED's MAE and RMSE are halved compared to CANYON and CANYON-B's (Table  4 ). In addition, a significant number of values are predicted to be negative (around -0. 1 µmol. kg -  ). This feature is mainly caused by the difference in the nutrient concentrations for the oligotrophic to ultra-oligotrophic Mediterranean Sea which are close to the detection limits of the nutrients analysis method  (Krom et al.
Other Methods A key advantage of CANYON-MED lies in the few inputs required to use it, but other methodologies exist to predict nutrients and carbonate system variables. CANYON-MED and the methods described in this section were applied to our validation dataset and their results evaluated by the MAE and RMSE (as defined in section "Validation Statistics Metrics") are presented in Table  5. First of all,  Carter et al. (2018)  developed methods for locally interpolated estimations of NO - 3 , PO 3- 4 , Si(OH) 4 , A T , and pH T (LINR, LIPR, LISIR, LIARv2, and LIPHR, respectively). These methods base their computations on equations requiring salinity, Apparent Oxygen Utilization (derived from O 2 ), depth, temperature, as well as nutrients concentrations. For comparability to our method and its possible application on BGC-Argo floats, these regressions were applied on our validation dataset using only the equations with similar inputs as our neural networks (i.
Generally, CANYON-MED neural networks are more accurate than other methods with the same inputs. This is not  surprising as it was built specifically for the Mediterranean Sea whereas CANYON, CANYON-B, CONTENT, and the LIRs were developed for the global ocean. However, high MAE and RMSE were obtained using A T -S and C T -S from  Hassoun et al. (2015). This can be explained by the fact that, while having been developed specifically for the Mediterranean Sea, these equations were derived using data collected only in May whereas our validation dataset covers as much as possible the whole year. Furthermore, we only used the global equations developed for the entire Mediterranean Sea and for all depths. It is acknowledged that the equations derived for specific areas and depth layers would have produced more accurate results in targeted areas, but the aim was to compare the methods on a global basin scale.
Therefore, it is suggested that, while allowing for easy computations of carbonate system variables in cases where they are lacking, the simple approximation from salinity might not always produce the best results on a varied dataset. Overall, CANYON-MED stands out as the most robust method for the prediction of nutrients and carbonate system parameters in the Mediterranean Sea. The variables required are limited to systematically measured variables such as temperature, pressure, and salinity as well as high-quality O 2 measurements as inputs which are also widely measured  (Bittig and Körtzinger, 2015). Thus, with the increased densification of high-quality O 2 measurements from BGC-Argo floats, as well as ocean gliders and moorings in the Mediterranean Sea  (Testor et al. , 2019; Tintoré et al.
But these methods are often developed for the global ocean and might therefore not be as satisfactory in the semi-enclosed Mediterranean Sea. Furthermore, they often require a larger amount of inputs to predict a single variable. For example,  Broullón et al. (2019)  developed a neural network method called NNGv2 to derive A T from geolocation, depth, temperature, salinity, and O 2 as well as nitrate, phosphate and silicate concentrations. NNGv2 predicts A T, for the global ocean, with a RMSE of 5-6 µmol. kg -  , about half the RMSE obtained when retrieving A T using CANYON-MED. However, it is highly likely that those results would worsen in the Mediterranean Sea as NNGv2 was trained using the GLODAPv2 database  (Olsen et al. , 2016) , similarly to CANYON, CANYON-B, and CONTENT, in which the Mediterranean Sea is poorly described.
Example of Application: Mediterranean Deep Values Using our 20% validation dataset, the averages of CANYON-MED outputs were calculated for each variable [i. e. , NO - 3 , PO 3- 4 , Si(OH) 4 , A T , C T , and pH T ] from 1000 m depth to the bottom of the water column (4000 m) and averaged. The corresponding in situ measurements were averaged in the same way. The differences between in situ values and those predicted by CANYON-MED are presented in Figure  7. The choice of deep values relies on their seasonal stability. Overall, the variables provided by CANYON-MED and the corresponding in situ measurements are in satisfactory agreement, i. e. , the difference is close to zero. No spatial trend is observed in the differences between the in situ data and neural network's outputs indicating that CANYON-MED adequately predicts without bias values along the known oligotrophy and acidity gradients between the Eastern and Western basins  (Krom et al.
, 1991; Flecha et al. , 2015). However, a few outlier points stand out for each parameter. These larger differences may be due to seasonal imprints (the data presented in Figure  7  refer to the complete validation dataset, regardless of their date and time). In the Algero-Provencal basin, a deep A T value stands out with a high difference (Figure  7D ). The same occurs for a few C T and pH T values in the Gulf of Lion (Figures  7E, F ). These extreme values could be explained by local phenomena also impacting temperature, salinity and/or O 2 , the neural network's inputs, consequently impacting the retrieved values. Indeed, these areas are known to be dynamic with an eddy-driven mesoscale circulation, where the anomalous biogeochemical profiles could originate from  (Pessini et al. , 2018).
We therefore hypothesize that discrete data may not sufficiently reflect the vertical distribution of these variables in a dynamic region such as the Gulf of Lion, owing to the lack of adequate observational resolution. High differences can also stem from erroneous stations not detected by the quality controls but being highlighted in CANYON-MED's outputs, causing the differences to deviate from zero values. Additionally, some points standing out as high differences (exceptionally low values) for C T (and A T ) in the Alboran Sea, Gulf of Lion and Algero-Provencal basin appear, after further investigation, to correspond to the first sampling campaign of the carbonate system in our database  (i. e. , in 1981). We, therefore, hypothesize that a difference in the quality of the measurements could explain some of the extreme deviations.
The difference between basins is mainly caused by the disparity in spatial and temporal coverage in our training database, stemming from a lack of cruises in the Eastern part of the Mediterranean Sea. CANYON-MED remains unsatisfactory in areas where training data are too scarce (e. g. , South Ionian Sea, off the Libyan coasts) and results accuracy might be lowered during anomalous events (extreme meteorological conditions impacting physical variables, such as deep convection). It should also be recalled that CANYON-MED is not suited for coastal areas.
73, 0. 045, and 0. 70 µmol. kg -  for NO - 3 , PO 3- 4 and Si(OH) 4 , respectively, and 0. 016 units, 11 µmol. kg -  and 10 µmol. kg -  for pH T , A T , and C T , respectively]. With such accuracy, CANYON-MED can produce estimates of variables that are not currently measured by autonomous platforms, as is the case for PO 3- 4 , Si(OH) 4 , A T , and C T. CANYON-MED can also help identify periods and areas in the Mediterranean Sea where the data density remains too low in space and/or time, which limits the understanding of some processes and the assessment of long-term variability. Indeed, the spatial and temporal domains where the method provides the least satisfactory results are related to weaknesses in the training database that does not sufficiently capture variability in space and time.
g. ,  Johnson et al. , 2016 Johnson et al. , , 2015;; Sauzède et al. , 2020). In line with this, multiple Eulerian moorings acquire and provide high-quality and high-frequency measurements of temperature, salinity, and O 2 over the water column at fixed locations in the Mediterranean Sea (e. g. , HYDROCHANGES, EMSO, and OceanSITES networks). CANYON-MED can be applied to data collected at these sites, complementing the measured core variables, and generating high-frequency biogeochemical datasets, hence supplementing temporally limited oceanographic cruises. Finally, since the accuracy of the virtual data obtained for NO - 3 by this approach is comparable to that obtained with autonomous platforms, their use in oceanography would be beneficial, in particular by increasing the datasets used for the assimilation of some regional models that still lack crucial reference data  (Doney et al.
ACKNOWLEDGMENTS Thanks are due to  Henry C. Bittig  ( IOW, Rostock ) for helpful discussions on neural network development. We are extremely grateful to  Marta Álvarez  ( IEO, La Coruña ) and collaborators for making available to us the first version of the CARIMED database. We deeply acknowledge the work from analysts, investigators, and crew who collected the data at sea. We are thankful to the  MOOSE program  financially supported by  CNRS-INSU , for providing annual cruises and fixed time series dataset for CANYON-MED training.
83 0. 014 0. 020 0. 86 0. 93 0. 010 0. 016 TABLE 5 | Performance indicators on the 20% validation dataset of CANYON (Sauzède et al. , 2017), CANYON-B (Bittig et al. , 2018), CANYON-MED (this paper), CONTENT (Bittig et al. , 2018), A T -S and C T -S (Hassoun et al. , 2015), LIARv2, LINR, and LIPHR (Carter et al. , 2018) for NO -3 , PO 3-4 , Si(OH) 4 , A T , C T , pH T. Method MAE RMSE NO -3 (µmol kg -1 ) CANYON 1. 37 1. 81 CANYON-B 0. 95 1. 34 CANYON-MED 0. 47 0. 74 LINR 4. 14 5. 90 PO 3-4 (µmol kg -1 ) CANYON 0. 076 0. 107 CANYON-B 0. 049 0. 075 CANYON-MED 0. 026 0. 045 LIPR 0. 228 0. 308 Si(OH) 4 (µmol kg -1 ) CANYON 2. 28 2. 98 CANYON-B 0. 97 1. 30 CANYON-MED 0. 43 0. 66 LISIR 4. 32 7. 93 A T (µmol kg -1 ) CANYON 21. 53 32. 74 CANYON-B 11. 18 20. 07 CANYON-MED 6. 51 11. 09 CONTENT 11. 52 21.
A. THE ARCHITECTURE OVERVIEW Figure  5  shows the architecture for automatic conformance testing of the IoT applications using the test stimulus. We designed AUTOCON-IoT based on the testing system standardized in the oneM2M conformance testing. Both the TS and SUT in AUTOCON-IoT contain the upper tester (UT) (ut-TS for the TS and ut-SUT for the SUT), which is an essential software component coordinating all the testing procedures, to support the automated conformance testing without human intervention. AUTOCON-IoT starts its testing process with an input of a control file, testConfig, that provides all the necessary information to perform testing. testConfig contains a type of SUT, features implemented in the SUT, communication information to set up a test connection, testing protocol, a list of test cases to perform, and serialization formats.
However, not all IoT applications have such resources, and they are not equipped to deal with any additional software component such as the ut-SUT. In these resource-constrained IoT devices, the ut-SUT cannot reside inside the SUT. To address this particular issue of testing the IoT applications that run in resource-constrained devices, AUTOCON-IoT supports two configurations based on where to locate the ut-SUT. If an SUT has enough computing power and resources, as shown in Fig. 6  (a), ut-SUT is hosted in the SUT. However, for constraint IoT devices, ut-SUT  can exist outside the SUT, which is depicted in Fig. 6 (b). For this situation, ut-SUT communicates with the TS in an indistinguishable manner. However, to communicate with the IoT application in the SUT, the ut-SUT needs to know the IP address of the target IoT application to establish a network connection for testing.
IV. EXPERIMENT This section shows the experimental results of an automatic conformance testing process for the IoT applications. To show the feasibility of AUTOCON-IoT, we compare the proposed automated IoT testing with conventional conformance testing. For testing purposes, a normal IoT application to be tested needs to support ut-SUT. We implemented a constrained IoT application with ut-SUT based on Profile 1 from Tabl 1 to examine the proposed mechanism. The test code implemented for the testable IoT application contains the generateOperation()function to receive a command to be executed from ut-SUT. The command is then executed in the IoT application according to the declared operation in the message. In addition, we implemented a TS system embedded with ut-TS using an IoT conformance testing system called oneM2MTester (https://github.
com/IoTKETI/oneM2MTester), which was developed based on open source Eclipse Titan for oneM2M platform developers. In the experiment, ut-SUT receives a triggering message from ut-TS and parses its data to obtain the values for intended operation, target, resource type primitive contents. After validating the triggering message, ut-SUT responds with an acknowledgement message to the TS. ut-SUT also creates an SSH connection with the IoT application to communicate and invoke operations, such as the generateOperation() function. Finally, the generateOperation() function executes the testing procedure from its IoT application according to the received operation value from ut-SUT. TS then evaluates the test oracle after receiving the triggering request from the IoT application and determines a verdict.
In addition, as shown in Fig. 10 , we conducted a performance evaluation to show the feasibility and practicality of the proposed conformance testing mechanism. Conformance testing for AE was performed on a computer with UBUNTU 16. 04 LTS OS, an Intel Core i7-7800X CPU @ 3. 50 GHz X2 processor, and 8. 0 GB of memory. For the testing, we prepared a total of sixty test cases for general IoT service layer capability (20 test cases), registration of IoT devices (9 test cases), data management-related features (27 test cases), and management of repository functions (4 test cases). Such test cases were developed by the oneM2M Test Working Group as sections of testing specifications. We participated and contributed to the development of the test cases together with other testing experts from industry.