dataesr/scientific-paragraphs-categorization huggingface dataset:
Selection process and composition The MCSG would be co-chaired by NASA and ESA MSR lead scientists, with the remainder of members selected as follows:  Phase 1 (MCSG-1): NASA and ESA management would jointly evaluate and select members for the MCSG Phase 1 on the basis of an open competitive AO and may appoint additional ex officio members. The group will represent the science community, covering subject matter expertise relevant to sample collection and analysis. Additional expertise of some of the scientists in curation and data management is required. It is expected that the group would be selected to ensure that the major science domains are covered by more than one person. To avoid real or perceived conflict of interest, sitting MCSG-1 members should not be eligible to be PIs or Co-Is of objective-driven investigations at the time of proposal submission; any MCSG-1 member wishing to propose an investigation would step down at that time.
Key outputs The MCSG would serve as the integrating body for scientific elements of the MSR Campaign and would provide critical inputs into MSR Campaign-level reviews and overall Science Program management. Establishing the MSR Campaign's formal science objectives, Level 1 science requirements, and success criteria are of critical importance to maximize the MSR Campaign's scientific return. These products will be developed and maintained by the along with definition of a preliminary STM, which would be used as the basis for the objective-driven investigation competition.  Final: Based on final decision of what samples will be retrieved (date may be flexible depending on MSR Program element development). Beyond the products described above, the MCSG would also generate the following:  The MSR Data Management Plan.
While specifics of the overall data management approach will be described in the Data Management Plan, high level considerations may include:  All scientific datasets of the selected objective-driven science investigations are shared with the entire MSST.  Each scientific dataset has a well-defined owner (i. e. , a PI).  Within the proprietary period, scientific datasets can only be used by members of the MSST for publication after agreement provided by the owner of the scientific dataset.  After a proprietary period of X years (TBD), all standard scientific datasets become publicly available at the NASA PDS and ESA PSA archives.  Defined engineering datasets of the MSR program flight elements to support the scientific analyses and interpretation of the scientific datasets will be available to the MSR CSG.
 All defined engineering datasets of the MSR flight elements will become publicly available within six months of acquisition through the NASA PDS and ESA PSA archives. The aim should be to coordinate global scientific publications and conference abstracts to (i) help focus effort and attention in key areas and ensure that the entire scientific community has an opportunity to contribute; (ii) minimize duplication of effort or misuse of samples; (iii) ensure that appropriate credit is properly attributed; and (iv) make a longterm plan for the sample release that allows for new techniques to be developed and new resolutions/accuracies of the instrumentation in the future. This coordination will require specific procedures for the MSST to follow as they develop abstracts and manuscripts that describe scientific data.
A joint science management structure and documented agreements among the MSR Partners are required to coordinate the MSR Science Program elements that are not currently defined in existing structures or documents. FINDING SMP-2: A long-term ESA/NASA MSR Science Program, along with the necessary funding and human resources, will be required to accomplish the end-to-end scientific objectives of MSR. FINDING SMP-3:The MSR Science Management Plan should be linked to, but not encompass, other required functionalities within the MSR Campaign. Input will be needed to produce formal plans for (at a minimum) curation, planetary protection, data management, and public engagement. FINDING SMP-4: The guiding principles proposed in the MSR Science Planning Group (MSPG) Framework document (2019) remain appropriate and relevant and should be utilized in drafting the MSR Science Memorandum of Understanding (MOU) and Science Management Plan.
FINDING SMP- 1 : 1 A joint science management structure and documented agreements among the MSR Partners are required to coordinate the MSR Science Program elements that are not currently defined in existing structures or documents. FINDING SMP-2: A long-term ESA/NASA MSR Science Program, along with the necessary funding and human resources, will be required to accomplish the end-to-end scientific objectives of MSR. FINDING SMP-3:The MSR Science Management Plan should be linked to, but not encompass, other required functionalities within the MSR Campaign. Input will be needed to produce formal plans for (at a minimum) curation, planetary protection, data management, and public engagement. FINDING SMP-4: The guiding principles proposed in the MSR Science Planning Group (MSPG) Framework document (2019) remain appropriate and relevant and should be utilized in drafting the MSR Science Memorandum of Understanding (MOU) and Science Management Plan.
Subjects Two days before the experiment, the animal's pure-tone audiogram was determined by testing auditory brainstem responses (ABR) under isoflurane anesthesia (2. 5%) as described in  Gourévitch and colleagues (2009). A software (RTLab, Echodia, Clermont-Ferrand, France) allowed averaging 500 responses during the presentation of nine pure-tone frequencies (between 0. 5 and 32 kHz) delivered by a speaker (Knowles Electronics) placed in the animal right ear canal. The auditory threshold of each ABR was the lowest intensity where a small ABR wave can still be detected (usually wave III). For each frequency, the threshold was determined by gradually decreasing the sound intensity (from 80 dB down to -10 dB SPL). All animals used in this study had normal puretone audiograms  (Gourévitch et al.
Acoustic stimuli The acoustic stimuli were the same as in  Souffi and colleagues (2020, 2021). They were generated using MatLab, transferred to a RP2. 1-based sound delivery system (TDT) and sent to a Fostex speaker (FE87E). The speaker was placed at 2 cm from the guinea pig's right ear, a distance at which the speaker produced a flat spectrum (± 3 dB) between 140 Hz and 36 kHz. Calibration of the speaker was made using noise and pure tones recorded by a Bruel and Kjaer microphone 4133 coupled to a preamplifier BandK 2169 and a digital recorder Marantz PMD671. The Time-Frequency Response Profiles (TFRP) were determined using 129 pure-tone frequencies covering eight octaves (0. 14-36 kHz) and presented at 75 dB SPL. The tones had a gamma envelop given by () = ( ! ! )²𝑒 !! ! ,where t is time in ms.
Recording procedures Data from multi-unit recordings were collected in 5 auditory structures, the non-primary cortical area VRB, the primary cortical area A1, the medial geniculate body (MGB), the inferior colliculus (IC) and the cochlear nucleus (CN). In a given animal, neuronal recordings were only collected in one auditory structure. Cortical extracellular recordings were obtained from arrays of 16 tungsten electrodes (TDT, TuckerDavis Technologies; ø: 33 µm, <1 MΩ) composed of two rows of 8 electrodes separated by 1000 µm (350 µm between electrodes of the same row). A silver wire, used as ground, was inserted between the temporal bone and the dura mater on the contralateral side. The location of the primary auditory cortex was estimated based on the pattern of vasculature observed in previous studies  (Wallace et al.
All thalamic recordings were from the ventral part of MGB (see above surgical procedures) and all displayed latencies < 9ms. At the collicular level, we distinguished the lemniscal and non-lemniscal divisions of IC based on depth and the latencies of pure tone responses. We excluded the most superficial recordings (until a depth of 1500µm) and those exhibiting latency >= 20ms in an attempt to select recordings from the central nucleus of IC (CNIC). At the level of the cochlear nucleus, the recordings were collected from both the dorsal and ventral divisions. The raw signal was amplified 10,000 times (TDT Medusa). It was then processed by an RX5 multichannel data acquisition system (TDT). The signal collected from each electrode was filtered (610-10000 Hz) to extract multi-unit activity (MUA).
The value of 8 ms was selected for the data analysis because in each structure the MI reached its maximum at this value of temporal precision. The MI estimates are subject to non-negligible positive sampling biases. Therefore, as in  Schnupp and colleagues (2006) , we estimated the expected size of this bias by calculating MI values for "shuffled" data, in which the response patterns were randomly reassigned to stimulus classes. The shuffling was repeated 100 times, resulting in 100 MI estimates of the bias (MI bias ). These MI bias estimates are then used as estimators for the computation of the statistical significance of the MI estimate for the real (unshuffled) datasets: the real estimate is considered as significant if its value is statistically different from the distribution of MI bias shuffled estimates.
Acknowledgments JME was supported by grants from the  French Agence Nationale de la Recherche (ANR)  ( ANR-14-CE30-0019-01 ). SS was supported by the  Fondation pour la Recherche Médicale (FRM)  grant number  ECO20160736099  and by the  Entendre Foundation. We thank  Nihaad Paraouty  for training us on the cochlear nucleus surgery. We thank  Prf. Christian Lorenzi  and  Prf. Shihab Shamma  for helpful comments on a previous version of the MS, and are particularly grateful to  Dr. Virginie van Wassenhove  for suggesting several improvements of the MS. We also wish to thank  Céline Dubois ,  Mélanie Dumont  and  Aurélie Bonilla , for taking care of the guinea-pig colony.
| Digestate input The harvested energy cover crop biomass was converted into the quantity of C using a C content of 0. 44 g g -1 dry biomass  (Clivot et al. , 2019). We used the formalisms (1) C crop_AG = Y × 1 -HI HI × 0. 44, ( 2 ) C crop_BG = 1. 65 × Y SRR × HI × 1 -depth × 0. 4, and the database of the Sys-Metha model  (Bareha et al. , 2021)  to estimate on average the remaining amount of C in the digestate after the AD of the energy cover crop and the storage of the digestate (mean estimation for all case studies). Sys-Metha is a simple mass balance tool used to predict carbon and nitrogen fluxes in AD systems. It is composed of an exhaustive substrate database and of three submodels related to AD, phase separation of digestate and digestate storage. Input data are the main digester characteristics and the digester feedstock.
| Input data For each case study, C input (Section 2. 3) and soil and climate characteristics (Section 2. 2) were used as input data in AMG. In addition to the reference C input, we tested alternative C input from cover crops to study its effect on SOC stocks: from 0 to 5 t DM ha -1 for nonharvested cover crops and from 0 to 15 t DM ha -1 for energy cover crops. For all case studies, we considered an initial SOC stock of 50 t C ha -1 over 0-25 cm, which roughly represented an average SOC stock in arable fields in France  (Pellerin et al. , 2020). Owing to the formalism of AMG, the difference in simulated SOC stocks between the no-AD and AD scenarios is not impacted by this initial SOC stock value. SOC stocks were simulated over 30 years for each case study for the no-AD and AD scenarios, with past average yearly climate data.
DATA AVAILABILITY STATEMENT The data that support the findings of this study areopenly available in: The Agreste database at https://agres te. agric ulture. gouv. fr/agres te-web/disar on/Chd20 09/detai l/ (main crop yield, nonharvested cover crop biomass). The Meteo France database at https://meteo france. com/clima t/norma les/france (climatic data). The  Carton et al. (2022)  report at https://agric ulture. gouv. fr/perfo rmanc es-agron omiqu es-et-envir onnem ental es-de-la-metha nisat ion-agric ole-sans-eleva ge-analyse (energy cover crop management and biomass, humification coefficient ofdigestate). The Opticive project report at https://libra irie. ademe. fr/deche ts-econo mie-circu laire/ 3993-optic ive. html (energy cover crop management and biomass, energy cover crop root biomass).
Participants and study design We previously published the full description of the study design and assessments used in this cohort clinical study. 11 Patients with breast cancer from the Institut de Cancérologie Strasbourg Europe were included in the PROTECT-01 study (NCT03956641). All patients provided written informed consent before enrollment, and the study was conducted in accordance with the Declaration of Helsinki and ethics approval received from the national ethics committee (2019-A00848-49). The patients had three visits: (1) before the first CT administration (pre-CT), (2) 8 weeks after, and (3) within 2 weeks after CT completion (post-CT). All assessments are detailed here. The eligibility criteria included nonpregnant women at least 18 years old with a World Health Organization performance status of 0 to 2 who were diagnosed with early-stage breast cancer and treated with (neo)adjuvant CT.
Machine learning algorithms demonstrated promising results for hyperspectral semantic segmentation. However, they strongly rely on the quality of training datasets. As far as the annotation of hyperspectral images is often expensive and time-consuming, only a few thousand pixels can be labeled. In this context, active learning algorithms select the most informative pixels to be labeled. In the machine learning community, recent active learning methods have overcome the performance of conventional algorithms but do not always scale to large remote sensing images. Therefore, we introduce in this paper a preprocessing method that allows the use of computationally intensive active learning algorithms without significant impacts on their effectiveness.
INTRODUCTION Airborne hyperspectral imaging with high spectral and spatial resolutions is well suited to map the land cover of urban areas. Applications of land cover maps include the mitigation of urban heat island effects  (Zhou et al. , 2017)  or urban management  (Fox et al. , 2012). Many machine learning models have been developed to automatically segment hyperspectral images and have demonstrated interesting results  (Audebert et al. , 2019). However, the diversity of soil materials, the spectral intra-class variabilities, the inter-class similarities, the presence of shadows or of small irrelevant objects (cf fig. 1a ) typically hinder the use of machine learning models to large and complex images. In order to further simplify the automation of land cover mapping with machine learning models, representative training datasets are crucial.
Therefore, building an optimal training dataset is a critical step. Labeling hyperspectral images, though, is often hard and expensive. Field campaigns, alongside timeconsuming photo-interpretation by experts, can be necessary. Thus, the annotation of images is often limited to a few thousand pixels. In this context, active learning (AL) methods guide the data annotation, answering the following question: out of millions of pixels, which ones to annotate to quickly improve classification performances? Active learning methods are iterative algorithms that select at each step the most informative samples to be labeled, given an initial training dataset and a classifier  (Settles, 2012). An oracle (a user) then labels the given pixels that are added to the training dataset. Many strategies have been developed  (Tuia et al.
, 2011)  with various computational requirements. Recently,  (Sener and Savarese, 2017)  tackled the active learning problem as a coreset problem, showing very high performance on several state-of-the-art machine learning datasets. However, it does not scale to larger hyperspectral datasets such as the Houston image  (Prasad et al. , 2020) , because of its high memory footprint and computational burden. Thus, we introduce in the present paper a preprocessing method that allows the use of such computationally intensive active learning algorithms on large hyperspectral scenes, without significant loss of effectiveness. The paper is organized as follows. In section 2, we describe the active learning framework and present our preprocessing method. In section 3, we present the results of our numerical experiments.
Active Learning Framework First, we rigorously describe the active learning framework. We denote the reflectance spectrum of one pixel by s ∈ S = [0, 1] B and its class y ∈ {1,. , c} where B is the number of spectral bands and c is the number of classes. The active learning algorithm is defined by a number of steps Nsteps, a budget b of pixels to be labeled at each step and an acquisition function a : S b -→ R that takes as input a subset of b pixels and measures how much information its annotation brings. The active learning process  (Settles, 2012)  works as follows. From a dataset S = (si) i∈(1,. ,N ) of N pixels, an initial training dataset L0 = (S0, Y0) = (si, yi) i∈(1,. ,N 0 ) is manually labeled. Then, at each step t, b unlabeled pixels {s * 1 ,. , s * b } are queried from the current unlabeled pool Ut = S\St so that the acquisition function a is maximized.
Data preprocessing: computational cost reduction To deal with large datasets, we introduce a preprocessing method that relies on the segmentation of the image in superpixels. The most informative superpixels are selected. Then, a subset of randomly drawn pixels within these superpixels are labeled and added to the training dataset. To perform the image segmentation, we chose the SLIC algorithm  (Achanta et al. ,   In the following, we present the preprocessing algorithm in detail and briefly describe the SLIC algorithm.
Active learning methods Three active learning methods were studied. Coreset  (Sener and Savarese, 2017)  is the method of main interest because it showed impressive results  (Sener and Savarese, 2017)  on CIFAR  (Krizhevsky et al. , 2009)  and SVHN  (Netzer et al. , 2011)  but is not usable on the Houston dataset that is approximately five times larger. Coreset has large memory and time requirements as it computes and stores distances between labeled and unlabeled points. It has a subroutine (a k-center greedy algorithm) with a O(|U | • b) time complexity  (Gonzalez, 1985) , where |U | is the size of the unlabeled pool. In practice, it could not be applied within a reasonable time on the Houston dataset with our hardware 1. In order to assess the impact of our preprocessing routine on the performance of active learning methods, we also studied Breaking Tie (Tong  Luo et al.
Hyperparameters We ran the experiments with the following hyperparameters: Nsteps = 15, b = 250, N th = 0 and m = 1, where we recall 1 Intel Cascade Lake CLX-6230 20c 2,1GHz and 64GB memory that Nsteps is the number of AL steps, b is the number of queried superpixels at each step, N th is the minimal size of a superpixel and m is the number of randomly queried pixels within a superpixel. We performed 15 steps with 250-superpixels queries because it was enough for accuracy metrics to converge. As far as there are no abnormal objects in the Houston dataset, we left N th to 0 but we argue that a non zero value would be interesting in life-like scenarios. Moreover, we have varied the number of superpixels within {20000, 10000, 5000}. We found that it yielded quite spectrally homogeneous and spatially consistent regions while dramatically reducing the size of the pool.
DISCUSSION AND CONCLUSION We introduced a preprocessing method that allows to use computationally intensive active learning algorithms on very large hyperspectral datasets without loss of effectiveness. This allows the use of methods that would normally have long calculation time during field campaigns, facilitating the annotation of experts. Besides, we argue that our method could be used on other kind of data, such as very high resolution satellite images. The main side effect of our method is a reduction of redundancy alongside additional randomness. Firstly, the preprocessing reduces redundancy, because when a superpixel is highly informative according to the acquisition function, only few pixels within the superpixel (one in our experiments) are labeled. Without the preprocessing, almost every pixels within the superpixel would be labeled.
It is a hyperspectral image acquired by ITRES CASI 1500 that covers the 380-1050 nm spectral range with 48 bands at a 1m ground sampling distance. More than four hundred thousands pixels were labeled over 20 classes (see table1). However, we only kept in the initial training dataset 200 labeled pixels per class, i. e. 4000 pixels in total, which is representative of an operational use case. Five sets of different initial training dataset, unlabeled pools and test datasets were selected from disjoint regions. Here we emphasize that, in this experiment, active learning methods are applied on unlabeled pools with well-defined labels only. In a life-like scenario, active learning methods could query mixed pixels (pixels at the frontier of two materials) or pixels whose classes do not belong to the classes of the initial training dataset.
A subset of the snow pit data (from 2012 to 2015) is presented in  Domine et al. (2015). For each snow pit, the grain types of the layers were identified, and the density and temperature profiles were measured. For some snow pits, the thermal conductivity profiles were also measured with a portable instrument equipped with a TP02 heated needle. A 100 cm 3 box cutter  (Conger and McClung, 2009)  and a field scale were used to measure the density profiles. The vertical spacing was mostly 3 cm between measurements but was increased to 5 cm for some snow pits, essentially those with deeper snow. At FOR-EST, snow pits were dug at some distance from trees, so snow interception can be neglected.
Forcing data and model setup The meteorological forcing variables of ISBA-Crocus are typical of those of a land surface model: air temperature, specific humidity, wind speed, incoming shortwave and longwave radiation, atmospheric pressure, and (solid and liquid) precipitation rates. Hourly observations of these variables at each of the two sites have been collected since 2012, except for atmospheric pressure, which has been available since June 2017, and precipitation, which was available since May 2016. The closest grid point data of ERA5  (Hersbach et al. , 2020)  to the study site data were used for the pressure before 2017 and were adjusted by applying a simple regression obtained with data from after 2017 between the measured data and the ERA5 data (R 2 = 0. 99 after the adjustment). We corrected the observed precipitation data for undercatch using the transfer function of  Kochendorfer et al.
The soil composition was set to 95 % sand and 5 % silt  (Gagnon et al. , 2019)  for TUNDRA and to 80 % sand, 15 % silt, and 5 % clay for FOREST based on estimates from several soil pits dug around the station where higher fractions of fine particles were found compared to TUNDRA. At both sites, the vegetation consisted of shrubs of 0. 4 and 1. 3 m height at TUNDRA and FOREST, respectively. To ensure the equilibrium of soil moisture and temperature, we initialized the model with a spin-up of 5 years  (2012) (2013) (2014) (2015) (2016) (2017). Note that because observations of precipitation from before 2016 were not available, raw ERA5 data had to be used for the 2012-2016 period, and for this reason the evaluation period is from 2017 to 2020. We used a different forcing data set for each station.