dataesr/scientific-paragraphs-categorization huggingface dataset:
Ground Truth Statistics Considering both datasets, ground truth comes in GIS vector file format containing a collection of polygons each attributed with a unique land cover class label. To ensure a precise spatial matching with image data, all geometries have been suitably corrected by hand using the corresponding VHSR image as a reference. Successively, the GIS vector file containing the polygon information has been converted in raster format at the spatial resolution of the PAN image (1. 5 m in our case). The final ground truth is constituted of 464,650 pixels distributed over 13 classes for the Reunion dataset (Table  1 ) and 400,970 pixels distributed over 8 classes for the Gard benchmark (Table  2 ). We remind that the ground truth, in both cases, was collected over large areas. Due to the practical constraints associated with such missions (time consumption, costs, and human effort), reference data are spatially sparse, noisy, and limited with respect to the study areas on which land cover classification is performed.
Competitors With the purpose to compare our approach (MultiResoLCC) to techniques tailored for the classification of VHSR images, different competitors are involved in the analysis. Firstly, we compare MultiResoLCC with other deep learning approaches and then, similarly to what is proposed in  [28] , we investigate the possibility to use the deep learning methods as feature extractor to obtain new data representation for the classification task. In this context, we compare the deep learning features to spatio-spectral representations commonly employed for the land cover classification of VHSR images  [7, 17]. Regarding deep learning competitors, the first method we consider is a CNN classifier that takes as input the pansharpened image and it has the same architecture structure of the P-CNN module (three convolutional, two max pooling and a final global max pooling layers) with 256, 512 and 1024 as number of filters for each convolutional layer respectively.
The number of filters is augmented with respect to the P-CNN architecture due to the amount of radiometric information (four bands) as input of such CNN. We refer to this competitor as CNN PS. The second deep learning competitor we consider is the model proposed in  [3] , named DMIL, since it can be trained from sparsely annotated data to produce classification at pixel-level from PAN and MS images. DMIL is a two-branch neural architecture that takes PAN and MS information separately as input. Internally, it up-samples the MS information, by means of deconvolution operators  [17, 22, 23] , at the same resolution of the PAN one. Successively, it employs a CNN to manage the spatial information carried out by the PAN image while it employs stacked autoencoder (SAE) to elaborate the multispectral data.
Experimental Setting All the deep learning methods are implemented using the Python Tensorflow library. The source code for the implementation of MultiResoLCC is available online (https://github. com/tanodino/ MultiResoLCC). We adopt standard Glorot initialization  [32] , also called Xavier uniform initialization, to initialize parameter weights. During the learning phase, we adopt the Adam optimizer  [33]  (Adaptive Moment Estimation) that is commonly employed in the parameter optimization of both CNN and Recurrent Neural Networks. Adam is an optimization algorithm, based on adaptive estimates of lower-order moments, that can be used instead of the classical stochastic gradient descent procedure to iteratively update network weights based on training data. We set the learning rate equal to 2 × 10 -4.
The training process is conducted over 250 epochs with a batch size of 64. The model that reaches the lowest value of the cost function (at training time) is used in the test phase. The dataset consists in pairs of patches (PAN, MS) associated with land cover class labels. We set the value of d, the PAN patch size, to 32. The patches sizes are respectively (32 × 32 × 1) and (8 × 8 × 4) for the PAN and MS images, as the physical pixel spacing ratio between SPOT6 PAN and MS is 4, and because the MS image has 4 spectral bands. Coherently to what explained in Section 3, each pair of patches is associated with the pixel in position  (16, 16)  of the PAN patch and its associated land cover class label. Prior to patch extraction, each spectral band is normalized in the interval [0, 1]. Considering the CNN PS and RF(PATCH) approaches, these methods take as input a patch of size (32 × 32 × 4) coming from the pansharpened image and, also in this case, the label information refers to the pixel in position  (16, 16).
We divide the dataset into two parts, one for learning and the other one for testing the performance of the supervised classification methods. We used 30% of the objects for the training phase while the remaining 70% are employed for the test phase, to force a relative parsimony in the training stage with respect to the available reference data while ensuring a more robust validation. We impose that pixels of the same object belong exclusively to the training or to the test set to avoid spatial bias in the evaluation procedure  [34]. More in detail, the training data for the Gard (respectively Reunion) study site involves around 977 objects (respectively 1859) while the test data for the Gard (respectively Reunion) study site involves around 419 (respectively 797) objects. Table  3  reports the training time of each deep learning method on a workstation with an Intel (R) Xeon (R) CPU E5-2667 v4@3.
We can also note that the RF classifier combined with a patch-based input (RF(PATCH)) and with multi-resolution spatio-spectral features (RF(MRSSF)) supplies results that are competitive with respect to CNN PS and DMIL on the Gard study site while, on the Reunion dataset, RF(MRSSF) obtains the second-best average performances while RF(PATCH) is outperformed by the other methods. Finally, we can highlight that MultiResoLCC still provides better performances than all the other competing approaches considering it as direct classification strategy as well as feature extraction tool. Generally, performance trends are similar between the two study sites. We can note that better results are achieved on the Reunion Island study with respect to those obtained on the Gard. This can be explained by the fact that, the SPOT6 image acquired on the Reunion Island depicts this site during a period in which contrasts among the considered classes are more evident.
On the Reunion benchmark, we can note that most of the information seems to be carried out by the features generated by the P-CNN branch while on the Gard study site the features obtained by the MS-CNN branch are more effective for the classification task. The proposed two-branch CNN architecture can exploit the complementarity of PAN and MS information adapting itself to the underlying data distribution. Importantly, we remind that the features extracted by a deep learning model are the results of multiple nonlinear combinations of the input data. Due to the nature of the learning process, the neural network adapts the generated features (neural activations) with the objective to spread the useful information as much as possible over all the connections. This aspect is also enforced using Dropout at training time.
Per-Class Classification Results Tables  7  and 8  depict the per-class F-Measure results for the Reunion Island and the Gard study sites, respectively. For each study site, we differentiate between the main competing methods (CNN PS , DMIL and MultiResoLCC) and experiments with random forest classifier learned on hand-crafted or deep learning features. For both study sites, MultiResoLCC obtains better or very similar per class F-Measure with respect to the others competing approaches. For the classification of the Reunion Island dataset, we can note significant improvement in classes (1), (3), (  5 ), (  8 ) and (11) (respectively Crop Cultivations, Orchards, Meadow, Herbaceous savannah and Greenhouse crops). Here, the improvement ranges between six points (Meadow) and twelve points (Greenhouse crops) with respect to the second-best method.
The analysis of per-class results on the Gard site also shows improvement for certain classes: (1) and (3) (Cereal Crops and Tree Crops) with an average gain of 7 points of Accuracy. We can also note that the random forest approach coupled with the features learned by the different methods (lower part of Tables  7  and 8 ) provides systematic improvement with respect to almost all the land cover classes compared to the pure deep learning classification approaches. To further advance the understanding of our method, we report in Figure  6  the confusion matrices associated with the CNN PS , DMIL and MultiResoLCC methods respectively on the two study sites. Figure  6a-c    Figure  6d -f represent the confusion matrices of CNN PS , DMIL and MultiResoLCC, respectively, on the Gard study site. Here, the different confusion matrices share a more similar appearance with respect to those shown for the Reunion Island dataset.
The use of a bigger patch size (e. g. , 44 × 44) allows to gain around two points of accuracy in both cases. An exception can be highlighted considering the patch value 40 × 40 on the Gard dataset. In this case, the performance slightly decreases with respect to the previous value of patch size. Probably, this size of patch, for this dataset, introduces some spatial noisy affecting the stability of our approach. This is also supported by the fact that the standard deviation associated with this prediction is much higher than in the other cases. The batch size experiments are reported in Table  11. Here, we can note that the performance results are quite stable. Nevertheless, we can underline that batch constituted of 128 examples supplying the best performances while batch size of 256 examples still exhibits a more than reasonable behavior.
CNN PS exhibits similar artifacts also on the second example. This behavior is not shared by the other approaches, which probably mean that such artifacts are due to some slight radiometric inconsistency of the pansharpened source. Concerning the Reunion Island dataset, the first example (Figure  8a-d ) depicts a coastal area of the west coast of the island. Here, we highlight a zone that is characterized by an underwater coral reef. DMIL and CNN PS have some troubles classifying this zone as water; more in detail they confused the true class and the Bare Rocks and Urban areas classes. Conversely, MultiResoLCC does not have any issue with this point and it supplies a coherent water classification. The second example (Figure  8e -h) represents a zone mainly characterized by forest. In this case, both DMIL and CNN PS provide a noisy classification mixing Forest with Sugar Cane and Orchards.
Conversely, when we analyze the land cover map produced by MultiResoLCC (Figure  8h ), we observe that the Forest classification is more spatially homogeneous and consistent with the reference data available in the corresponding VHSR image. The third and last example related to the Reunion Island dataset is supplied in Figure  8i -l. This area is mainly characterized by an urban settlement surrounded by some agricultural plots. The three zones highlighted in this example involve zones belonging to Urban Areas and Bare rocks. Comparing the maps provided by DMIL and CNN PS with respect to the one provided by MultiResoLCC, we can note that the formers made more confusion between Urban Areas/Bare rocks and Water Surfaces than the latter. DMIL and CNN PS tend to overestimate the prediction of the Water Surfaces class.
Improving the quality of LULC maps will positively impacts the quality of the services built upon such thematic layer. For instance, the retrieval of zonal statistics on a geographical area as well as information retrieved querying a geospatial data warehouse to support environmental and urban planning. As future work, we plan to extend the approach on other optical remote sensing images, for instance dealing with classification on Sentinel-2 satellite images where the spectral information is available at different spatial resolutions.
REUNION Dataset We use a SPOT6 image, acquired on 6 April 2016 consisting of a 1. 5 m Panchromatic band and 4 multi-spectral bands (blue, green, red, and near infrared) at 6 m resolution Top of Atmosphere reflectance. The Panchromatic image has a size of 44,374 × 39,422 while the Multi-Spectral one has a size of 11,094 × 9,856. Panchromatic and Multi-Spectral satellite images are reported in Figure 4. The field database constituting the ground truth has been built from various sources: (i) the Registre parcellaire graphique (RPG) reference data of 2014 (RPG is part of the EU Land Parcel Identification System (LPIS) provided by the French Agency for services and payment), (ii) GPS records from June 2017 and (iii) photo interpretation of the VHSR image conducted by an expert, with knowledge of the territory.
GARD Dataset The SPOT6 image, acquired on 12 March 2016 consists of a 1. 5 m Panchromatic band and 4 Multi-Spectral bands (Blue, Green, Red and Near Infrared) at 6 m resolution Top of Atmosphere reflectance. The Panchromatic image has a size of 24,110 × 33,740 while the Multi-Spectral image has a size of 6028 × 8435. Panchromatic and Multi-Spectral satellite images are reported in Figure 5.
4. 2. 2 GARD Dataset The SPOT6 image, acquired on 12 March 2016 consists of a 1. 5 m Panchromatic band and 4 Multi-Spectral bands (Blue, Green, Red and Near Infrared) at 6 m resolution Top of Atmosphere reflectance. The Panchromatic image has a size of 24,110 × 33,740 while the Multi-Spectral image has a size of 6028 × 8435. Panchromatic and Multi-Spectral satellite images are reported in Figure 5.
The stand-alone map-making software, HFTS_mapping, is written in Python and uses utilities publicly available in astropy 2  (version 1. 1. 1, Robitaille et al. 2013) , scipy (version 0. 17. 0) and numpy (version 1. 12. 1) 3. HFTS_mapping is now available for public use 4. Our map-making procedure can be laid out in three steps: -First, before coadding the level-2 bolometer spectra on the map, a continuum level is estimated between two frequencyintervals, (ν line -15 GHz < ν < ν line -5 GHz) and (ν line + 5 GHz < ν < ν line + 15 GHz), and removed as a parabola function. Weighted by the uncertainties, which are estimated as the standard deviation from all scans, the continuumremoved bolometer spectra are then assigned to the pixels, according to the bolometer pointings, to create spectral cubes over the FOV for each line listed in Table  2.
The explored physical space is the thermal pressure, the intensity of incident UV radiation fields and the depth of the cloud. The thermal pressure, P, ranges from 10 5 to 10 9 K cm -3. The UV field scaling factor, G UV , ranges from 1 to 10 5 in the Mathis units  (Mathis et al. 1983). The visual extinction, A V goes from 1 to 40 magnitudes. Other parameters, common to all models are presented in Tables 3. Elementary abundances used for the chemistry are the standard values in the ISM (Table 4). These models are available in the Interstellar Medium Data Base (ISMDB) 6  at http://ismdb. obspm. fr.
Acknowledgements -We offer our sincere gratitude to  Dr. I. Sakon ,  Dr. D. Ishihara ,  Dr. T. Shimonishi ,  Dr. R. Ohsawa , and,  Dr. K. Arimatsu  for their success in obtaining the Herschel observations presented in this work and to  Dr. T. I. Mori  for investing her time in the ISO data analysis. The authors sincerely thank  Dr. D. Rebolledo  for sharing the Mopra data,  Dr. C. Kramer  for sharing the NANTEN2 data,  Dr. L. K. Townsley  and  Dr. P. Broos  for sharing the Chandra data, and  Dr. T. Preibisch  for sharing the Herschel photometry data. We thank  Dr. A. Heays  and  Dr. S. Hony  for useful discussions and the anonymous referee for useful suggestions. -This research was supported in part by the  Grant-in-Aid for Scientific Research  for the  Japan Society of Promotion of Science (140500000638) , the  PRC 1311 between France (CNRS)  and  Japan (JSPS) , the  Agence Nationale de la Recherche (ANR)  through the  programme SYMPA-TICO (Program Blanc Projet  ( NR-11-BS56-0023 ), the  Programme National Physique et Chimie du Milieu Interstellaire (PCMI)  of  CNRS/INSU  with  INC/INP  co-funded by  CEA  and  CNES , and the  EU   FP7  project  DustPedia  (Grant No.
Communication Dedicated Software In this section, the firmware of the BLE113 IC will be detailed. The scripting language provided by Silicon Labs, BGScript, was used to program this firmware. A custom GATT server was implemented with respect to the heart rate profile detailed in the Bluetooth 4 specification: the mandatory generic access profile, device information, heart rate and battery services were implemented. The heart-rate service was augmented with a customized heart rate variability characteristic. This characteristic is of length 5, and the SSDN, RMSSD, LF/HF ratio and normalized LF components ratio are encoded as single-byte unsigned integers. An additional service was implemented for the respiration along with another service dedicated to IoT-related information (i. e. , information about the sensor nonfunctional states).
Gateway Software and IoT-Based Framework Android phones were chosen as the main gateways to our sensor. This choice was motivated principally because phones can be carried by the subjects, resulting in a mobile gateway with perpetual connection to the Internet. Consequently, an Android application was developed to connect to the sensor, but also to plot the data, store it locally on the phone or transmit them over the Internet. The application is based on the nRF Toolbox open-source application 1  developed by Nordic Semiconductor (Oslo, Norway), and screenshots of the application are given in Figure  5. Because data is collected in real time and sensor configuration can be triggered at any moment, a publish/subscribe type protocol was chosen. Practically, This protocol is built around the notion of topics.
Data Acquisition In this section, various data acquisition are presented. This first experiment to be performed was the validation of RR-interval acquisition using synthetic ECG signals generated using an Agilent 33220A (Santa Clara, CA, USA) arbitrary waveform generator. The objective of this test is twofold: it validates the computation of RR-interval and the reliability of the BLE connection. In order to simulate heart rate variability, triangular frequency modulation was applied to the synthetic ECG signal: The sensor was then connected to a Raspberry Pi used as a BLE data logger to record RR-intervals and HRV parameters, and an excerpt of the collected RR interval values is given in Figure  7. The experiment was performed during 1 day, 17 hours and 9 minutes. A total of 185153 were logged.
Data was analyzed for records violating the theoretical limits (with a tolerance of 1% over the limits), and no violations were found. The next experiment was data acquisition during short respiratory exercises. During this exercise, a young healthy patient was asked to perform a series of forced inspiration and expiration during 1 minute in order to trigger sinus arrhythmia. Results from this experiment are given in Figure  8 , where both the instantaneous heart rate and the normalized respiratory waveform are plotted. This figure clearly illustrates sinus arrhythmia, with increasing heart rate during  The final experiment consisted on a short-term ambulatory testing of the sensor. For this experiment, a patient was asked to wear the sensor during one hour, while performing normal daily activities.
The surface coverage is linearly proportional to the amount of PEO added to the pristine AuNPs solution between 0 and 3. 2 µl of volumes. The proportionality factor, k, between volumes was added and the PEO surface coverage was calculated as follows: k ¼ 100% 3:2 ¼ 31:25%=μl ð1Þ The AuNPs samples A, B, C, D, F, G and L, corresponding to the PEO surface coverage indicated in Table  1  were used to study the binding affinity with the different collectors. Nanoparticles binding study. The AuNPs affinity has been calculated by directly measuring the real-time binding kinetics of the NPs to the different collectors. The binding curves are built by using a script created in the software ImageJ allowing to automatically detecting the NPs as round objects in the video sequence. Typical sequence images of the adsorption of NPs on the T0 collectors are shown in Fig.
The interaction energy maps were calculated using the function wizard included in the software OriginPro 2015. The values for the interaction energy are given in kT units (1 kT = 4 × 10 -21 J). The total interaction energy is expressed in Eq. 10 for the interaction of a NP and a surface as a function of the distance and of the hydrophobic correlation length λ. Even if the range of possible and measured value in literature for λ is broad (between 0. 6 nm and 13 nm), we decided to keep it in the range between 0. 6 nm and 2 nm, in order to take into account possible influences of the radius of curvature of the NPs and of the roughness of the collectors  24. The heat-maps for the 15 nm, 60 nm and 200 nm hydrophobic NPs on hydrophobic collector are shown in Supplementary Figure  7  and illustrate the influence of the value of λ on the potential profile as a function of the NP size (Supplementary Note 3).
Digital images were analysed with the ImageJ software, using a custom macro to perform smoothing (3 × 3 or 5 × 5 median filter), a manual global threshold and the automatic particle analysis of ImageJ. The programme can be downloaded from http://code. google. com/p/psa-macro. A circularity filter of 0. 8 was used to exclude agglomerates that could occur during drying. UV-vis adsorption spectra were recorded with an Evolution 300 UV-vis spectrophotometer (Thermo Scientific, USA) at room temperature. DLS and Zeta-potential measurements were obtained using a Zetasizer Nano ZS instrument (Malvern Instruments, UK). Hydrodynamic diameters were calculated using the internal software analysis from the DLS intensity-weighted particle size distribution. Differential centrifugal sedimentation. DCS measurements (instrument model DC24000UHR, DCS Instruments Inc, USA) were performed in an 8-24 wt% sucrose density gradient with a disc speed of 22,000 rpm.
The mixture was centrifuged (10,000 rcf, 5 min, 4 °C) and the supernatant carefully removed. The NP pellet was subsequently washed with 1× PBS (Gibco). This washing procedure was repeated three times. The final pellet was suspended in 20 µL Pierce Lane Marker reducing sample buffer (ThermoFisher) and incubated at 95 °C for 5 min. After a short spin down, the supernatant was loaded in 12% SDS polyacrylamide gel and run at 110 V, 25 mA in 1× SDS running buffer. After electrophoresis, the gel was Coomassie stained. Scanned gel images were analysed with the ImageJ software using sharp + smooth + brightness/contrast adjustment, followed by splitting of colour channels (best contrast for Red and Green). Quantification of the proteins was made by calculating pixel intensity in the central band (lower intensity = dark = more proteins) and plotted as the inverse of the pixel intensity.
Nanoparticles adsorption study by dark-field microscopy. Dark field microscopy (DFM) videos were recorded to measure the NP adsorption rates on the different collectors. Image analysis was performed using the open source graphics software ImageJ (http://rsb. info. nih. gov/ij/). A special data processing protocol was developed to enable automatic frame-by-frame analysis of the collector surface coverage by the NPs. The coverage boundaries were identified as red and green stains on a black background and only those with a size between 7 and 200 pixel units and circularity value between 0. 20 and 1. 00 were taken into account in calculating the degree of surface coverage (using the Analyse Particles function of ImageJ). After background correction, noise reduction (Despeckle, Kalmann Stack Filter with values 0.