dataesr/scientific-paragraphs-categorization huggingface dataset:
Cerema-AWH Database The Cerema-AWH database (Cerema Adverse Weather Highway database) contains different weather conditions (Figure  1 ). These conditions are real conditions (unlike those of the Cerema-AWP database  [5] , which are artificially generated). This database was created using an AVT Pike F421B road camera installed at the Fageole highway on the E70 motorway, which belongs to "Direction interdépartementale des routes Massif-Central" to acquire images with a 1008 × 648 resolution at a 1. 9 Hz frequency. The specificity of this site is its location at an altitude of 1114 m, allowing natural degraded weather conditions observation (very heavy rain, heavy fog and frequent snowfall). This outdoor site is equipped with special-purpose meteorological sensors: an OTT PARSIVEL optical disdrometer to measure rainfall intensity, a visibilimeter Lufft VS20-UMB to measure the meteorological visibility distance and a Vaisala PWD12 weather sensor to measure both of them (Figure  2 ).
In this way, we will have the advantage of recording images and weather conditions in the same place unlike other databases where image acquisition is done in one place and meteorological data collection is done at the closest meteorological station. Upon completing the image acquisition phase, the data were automatically labelled by associating each image with the corresponding weather data. In this study, the snow case is not addressed. The used database contains over 195,000 images covering five weather conditions, distributed as indicated in Table  3 : day normal condition (DNC), day light fog (DF1), day heavy fog (DF2), day light rain (DR1) and day heavy rain (DR2). To avoid introducing bias into the results while training and testing the algorithm, images acquired during even days were used first to train the algorithm, then images acquired during odd days were used for the test.
Detailed Description of the Databases Used in This Study In our study, we used a new database called Cerema AWH. To be able to compare the efficiency of our database with others, we have selected two databases from the state-ofthe-art, which are the Image2Weather database  [28]  and the MWD database  [24]. These databases are, therefore, detailed in the following. Image2Weather (I2W)  [28]  is a meteorological database containing 183,798 images, of which 119,141 images are classified according to five classes: sunny, cloudy, rainy, foggy and snowy. This database contains a collection of large-scale images. I2W was built from a selection of already existing images belonging to the EC1M database (European City 1 Million). From there, the authors of  [28]  implemented an automatic image annotation procedure.
From the URL and image identifier available in EC1M, they use Flickr to obtain the image itself and its associated metadata, such as time and location. Depending on the position taken, the corresponding elevation information is acquired through Google Maps. Depending on the image longitude and latitude, they use Weather Underground to extract the corresponding weather properties. Weather information on the Weather Underground website comes from over 60,000 weather stations. For about 80% of images of the database, the distance between them and the nearest stations is less than four kilometres. By using these three tools, the I2W database was built with 28 properties available for each image, including date, time, temperature, weather condition, humidity, visibility, and rainfall rate. For the sake of meteorological estimation, the authors focus only on photos captured outside where the sky region occupies more than 10% of the entire image.
Overall, the images collected cover most of Europe. The Multi-class Weather database, noted MWD  [24] , was created from Flickr and Google images using the keywords "outdoor" and "weather". It contains 65,000 images of which 60,000 are divided into six weather classes: sunny, cloudy, rainy, snowy, foggy, and stormy weather. Image annotation is done manually by asking annotators to keep images containing outdoor scenes with reasonable resolutions. Visually similar images were rejected. The MWD database was divided into two groups. The first group contains annotated images with classes used for meteorological classification where images with critical meteorological conditions are identified. In this group, each image should belong to only one class. For this, the images that do not contain visible raindrops, snow, or fog are classified either in sunny or in cloudy classes.
So finally, the following three DCNNs selected are Inceptions-v4  [36] , ResNet-152  [37]  and DenseNet-121  [38]. All experiments were developed with Keras and Tensorflow using a GTX1080 Pascal Graphics Processing Unit (GPU). The learning parameters (learning rate, gamma, etc. ) used in our tests are given in the following publications of each selected architecture  [36] [37] [38]. In order to evaluate these different deep networks, we completed different analyses using the databases presented in Section 3. Section 5 explains the different methods implemented, while Section 6 presents the results.
Influence of Classes Number In the literature, different levels of detail exist regarding weather classes. When setting up our database, we chose to separate fog and rain into two sub-classes. This makes it possible to verify if the camera could be capable of graduating the meteorological condition detected, in addition to detecting it. Eventually, we could even imagine measuring the intensity of the meteorological condition encountered. We, therefore, propose here to test DCNN by confronting it with three or five classes of weather conditions. For this, we used the same architecture of our DCNN but with a different number of neurons on the last fully connected layer  (3 or 5). For the first case, we used just one sub-class for each meteorological condition, so the 3 classes are DNC, DF1 and DR2.
Influence of Scene Change In a concrete application, the DCNN that will have to classify the weather will be deployed on surveillance cameras installed at different locations. The scene filmed will not be the same for each camera and this may have an impact on the classification results obtained. It is, therefore, necessary to determine whether it is possible for a single learning process to measure the weather from different cameras, or whether it is necessary to consider an independent learning process for each camera. To do this, we propose to verify the impact of a scene change on the classification results obtained. As we have only one camera in the Cerema AWH database, we propose to extract sub-images from it, either fixed (to simulate a single fixed scene) or random (to simulate various scenes).
For this, we will fix the test database and only change the trained one. In this part, we study two variants of scenes: fixed scene and random scene. The fixed scene consists of extracting an area of 224 × 224 pixels (299 × 299 pixels for learning Inception-v4) in the centre of the image of the Cerema-AWH database with 640 × 480 pixels as initial size, which will be the neural network input (Figure  3 ). The training phase and the test phase are completed in this same area. The random scene consists of extracting a random area of 224 × 224 pixels (299 × 299 pixels for learning Inception-v4) from the original image of the Cerema-AWH database. In this case, the network input images will no longer be the same in terms of the scene. The test phase is completed on the area extracted in the centre of the image, which is the same test database as the case of a fixed scene.
Databases Transfer between Cerema-AWH and Public Databases The two previous analyses were performed on our database: Cerema-AWH database. It is interesting to test our classification methods on other public meteorological databases to study their contribution and their impact and to compare the results obtained on our database with those obtained on the other databases. The use of these databases will allow us to learn about global scenes and introduce a variety of scenes since Cerema-AWH images belong to scenes acquired by a fixed camera. Thus, we are interested in this section in the transfer of databases. It is about learning on a database and then testing on another one. Again, this method makes it possible to study the portability of our system for detecting degraded weather conditions from one site to another.
• As I2W and MWD images are in colour and those of Cerema-AWH are in grayscale, the public databases images were converted into grayscale; • The snow class was removed from the I2W and MWD databases since Cerema-AWH does not contain this class; • The "storm" class was removed from the MWD database because I2W and Cerema-AWH do not contain it; • The two classes "sunny" and "cloudy" of I2W and MWD were merged into one class, which will present a normal conditions class. Taking into account all these constraints, we will no longer be able to classify with five meteorological classes using the I2W and MWD databases, and therefore the classification will be made according to three meteorological classes (JCN, JB, JP).
Influence of Classes Number In this part, we are interested in studying the influence of changing the number of meteorological classes on the classification result. Let us start with a classification into three classes. In this case, the used train database contains 75,000 images and the test database contains 45,000 images, knowing that the number of images per class is the same. The classification scores are presented in Table  5. As our objective is to measure weather by camera, a more refined classification is proposed and, therefore, an increase in classes number is implemented. Thus, the second case consists of categorising the weather according to five meteorological classes. In this case, the used train database contains 125,000 images and the test database contains 75,000 images. The classification scores in five classes are also presented in Table  5.
Influence of Scene Change In this section, we try to simulate a scene change by randomly extracting sub-images of the whole image. We can then analyse if the learning phase always leads to a good detection of the weather conditions. In this part, we use the database with five classes. From Table  6 , it is clear that switching from a fixed scene to a random scene decreases classification scores for the three neural networks. Indeed, for the case of a fixed scene, the algorithm only takes into account the change in weather conditions, which allows it to learn the characteristics specific to each class. This is not the case for the random scene, where the algorithm must take into account all scene changes, including weather changes. Concerning the different architectures, we can see that the ResNet-152 network obtains better results for random scenes than the other two (0.
82). However, ResNet-152 and DenseNet-121 had the same scores for fixed scenes (0. 90). For the moment, ResNet-152 can, therefore, be evaluated as the best architecture for weather detection by camera. This result has to be confirmed in the following analyses of this study. Although scene changes limit the classification networks performance, it is necessary to use this methodology since the scene changes exist in real life. Indeed, the advantage of having a neural network trained on random scenes is its adaptability to test databases different from those of the training phase in terms of the observed scene. This will be useful when applying the database transfer method. In this case, an improvement of classification results with random scenes is required. Therefore, in the following, we propose to use other databases, with many scenes, in order to try to improve these results.
Database Transfer between Cerema-AWH and Public Databases We are working toward the portability of our weather classification system to different external sites having a variety of images and different scene-related features. For that, we applied the transfer of databases on the public databases I2W and MWD, which were described previously. The following sections present classification results. Table  7  presents the results obtained with neural networks for the three databases, with training and testing on the same set. From Table  7 , for MWD and I2W databases, the classification scores are quite close for all architectures (around 0. 75). However, the results of Cerema-AWH are much better (around 0. 99). This can be explained by the fact that:.
• Public database images present a diversity to the level of the scenes (images acquired from webcams installed everywhere in the world); • Cerema-AWH images belong to scenes acquired by a fixed road camera. This confirms what we have seen previously: the change of scenes decreases the performance of meteorological classification of neural networks (See Section 6. 2). However, we find in the case of public databases (MWD and I2W) scores quite similar to those obtained with our random scene method (around 0. 70-0. 80). This reinforces our method of creating random scenes, which is quite relevant to generalize a fixed scene and bring variability to our database. After comparing the public databases to our Cerema AWH database independently (train and test on the same set), it is possible to go further by performing the train and test phases on different databases.
This makes it possible to verify the feasibility of carrying out laboratory training on academic databases before a real site deployment. In a way, we are taking the random scene approach a step further by now completely changing the database. The first case concerns the transfer from the Cerema-AWH database to each of the public databases. Table  8  presents the obtained classification results. We are quite close to a uniform random draw for the three classes. The table shows that for an algorithm trained on the Cerema-AWH database and tested on another one, the transfer is not feasible, given the low obtained scores. This result was more or less predictable because the network learns on a database whose scene is fixed, and therefore the image descriptors extracted will only concern changes of weather conditions.
However, when it is going to be tested on another database, where the climatic scenes and phenomena are different, it will find difficulties in weather classification. The second case concerns the transfer from one of the public databases to the Cerema-AWH database. Unlike the first case, the transfer of databases from MWD to Cerema-AWH presents better classification scores (Table  9 ). This suggests the possibility of learning in the laboratory, using a variety of images, for further application in the field. Comparing Tables  8  and 9 , we can see that the MWD database gives better results than the I2W database. It would therefore appear to be a better choice for learning for real site deployment. This can be explained by the reduced images number of I2W (357 images per class) compared to the MWD database (1700 images per class).
Furthermore, the I2W database contains a lot of images with light rain compared to MWD, where the AWH database contains rainy images with a higher rainfall rate. So it is important to use two databases with close meteorological condition intensities. Concerning the different architectures, they all obtain similar results when analysing the scores on the databases taken independently (Table  7 ). However, we notice that ResNet-152 has much better adaptability to database change than the other two architectures (Tables  8  and 9 ). ResNet-152-like architectures would therefore be better to adapt to scene changes and to detect weather conditions. It is, therefore, towards this type of architecture that we must continue to work.
Conclusions A new method that uses cameras to detect weather conditions has been developed. This method is designed to be implemented on traffic monitoring cameras. It is based on convolutional neural networks in order to address a set of clearly-defined goals: fast and simple operation, classify all weather conditions using a single method, determine multiple intensity levels for each observed weather condition, and minimise false positives. The method's training and testing phases were performed by using a new database containing numerous degraded weather conditions and convolutional neural networks. This method has a broader scope than existing camera-based weather condition measurement solutions. Although it was only tested with day, fog and rain conditions for this study, the method is able to uniformly detect all weather conditions and measure multiple intensity levels in real-time.
Furthermore, it was tested for real weather conditions at a real-life site, for which measurements with reference sensors are available for day, fog and rain conditions. As far as we are aware, no equivalent research exists in the literature. When only the type of weather condition was considered (three classes), regardless of its intensity, the accuracy was 0. 99, higher than the scores previously reported in the literature for comparable measurements (0. 92 for the most similar method  [16] ). This study has, therefore, shown that measuring weather conditions by camera is feasible in a simple case. It is, therefore, a very important proof of concept, especially since this score is obtained on a database that has been labelled not with the human eye but with real meteorological sensors. This is, therefore, a first very positive step forward.
Indeed, network managers need to have real alerts in case of adverse weather conditions in order to warn road users. They could, therefore, use the existing camera network for this detection task. However, when it comes to fine-tuning the results, the scores decrease quite significantly. This is the case, for example, by going from three simple classes to five classes, with a gradation of weather conditions (score of 0. 83). In the same way, by moving closer to a use case where only one training would be done and that would allow it to be deployed everywhere, the results are much worse (score around 0. 63). An important development work remains to be done. For this, we think we already have to build a database sim-ilar to AWH but with many more sites instrumented, which would allow learning on various scenes.
Based on the results obtained, we can affirm that the ResNet architecture is the best to make the classification of weather conditions by camera. Indeed, although it obtains similar results to other architectures for simple examples, it has proven to be more efficient both in increasing the number of classes and in database transfer between learning and testing. It is, therefore, this type of architecture that needs to be further worked on with regard to weather classification, as it seems to have a much greater power of generalisation. Finally, our work could be extended to car-embedded cameras, which are present in all new cars nowadays. This challenging extension would be of high interest for autonomous cars, whose road perception tools are strongly impacted by adverse weather conditions.
Funding: This work was sponsored by the  French government research program  " Investissements d'Avenir " through the  IMobS3 Laboratory of Excellence  ( ANR-10-LABX-16-01 ) and the  RobotEx Equipment of Excellence  ( ANR-10-EQPX-44 ), by the  European Union  through the  Regional Competitiveness and Employment program 2014-2020 (ERDF-AURA region ), and by the AURA region. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable.
Additionally, ring emissions were measured in the breathing zone of instructors using a suite of real-time monitoring instruments and o ine methods. Lung function, urinary 8-OHdG, and urinary Cu were measured before and after each work shift. Additionally, epidemiological questionnaires captured symptoms and lifestyle exposures that may impact biological measurements. Biomarker data were compared with emissions data and questionnaire outcomes to determine a method to monitor range instructors for exposure and to support updates to exposure standards. Finally, observational studies were utilized to correlate peaks in exposure to biomarkers of oxidative stress along with symptoms reported by range instructors to obtain a more complete picture of exposure.
The instructor group, overall, was signi cantly older than the control group (Table  1 ). Daily pre-shift questionnaires asked subjects about their engagement in certain activities outside of their work shifts due to their potential to produce symptoms similar to those of interest in this study (Table  2 ). More WPAFB instructors reported recreational shooting than the other subject groups, reporting an average of two additional hours and 170 rounds per week. Other off-duty hobbies were reported too infrequently to assess their potential impact on symptom reports. In addition to external activities, health behavior data was also collected. Use of tobacco products was more prevalent among WPAFB subjects than JBC subjects, and WPAFB controls consumed less caffeine than other subject groups, who all used it daily (Table  2 ).
Data on seasonal, pet, or environmental allergies were also collected among the other lifestyle factors. Allergies were more prevalent in JBC subjects than in WPAFB subjects. Among the 7 individuals across both bases with allergies, only 42. 9% reported that their allergy symptoms were completely controlled by medication. However, allergies were not correlated with symptom outcomes. Firing range class duration and rounds red Eight classes were assessed at WPAFB versus four classes at JBC. The hours spent on the line per weapon and class by range instructors did not differ signi cantly between bases (Table  3 ). The average class size at JBC was signi cantly larger than that of WPAFB (18. 3 ± 0. 96 vs 8. 25 ± 3. 77, p < 0. 001) (Table  3 ). Classes from both bases red approximately the same number of rounds per student for both the M9 (90 rounds) and M4 (206-286 rounds) with the exception of 07 July 19 at JBC where the M4 class was canceled due to weather after ring 8 rounds.
The total number of rounds red during assessments at WPAFB were approximately 4200 rounds of M9 and 9200 rounds of M4. The total number of rounds red during assessments at JBC were approximately 3200 rounds of M9 and 9000 rounds of M4. Range instructors report signi cantly more adverse symptoms compared to control participants Range instructors at both WPAFB and JBC reported experiencing a variety of symptoms during the data collection period while only one control subject reported symptoms of a headache (Table  4 ). Being a range instructor was signi cantly correlated with reporting post-shift symptoms during the study period (p < 0. 0001). Thus, only symptoms reported by instructors were examined further. On the initial questionnaire, 54. 5% of all range instructors reported experiencing one or more symptoms during or within 24 h of exposure to LFF ring emissions in the past month.
At WPAFB, there was a trend for increased 8-OHdG levels in instructors on the line compared to controls (Fig. 8A , 1. 24 ng/mg uCr vs 0. 91 ng/mg uCr, p = 0. 22). On the other hand, at JBC where exposure was higher, the 8-OHdG levels in line instructors compared to controls was signi cantly higher (Fig. 8B, 2. 07 ng/mg uCr vs 0. 76 ng/mg uCr, p = 0. 045). Finally, when the data from both bases was analyzed together, line instructors displayed signi cantly higher 8-OHdG levels compared to controls (Fig. 8C  1. 57 ng/mg uCr vs 0. 84 ng/mg uCr, p = 0. 022). Cu increased in urine collected from instructors compared to controls In order to determine whether the evaluation of metals could be a viable biomarker of exposure, urine was collected both before (pre) and after (post) the work shift from controls and instructors and analyzed for metals.