dataesr/scientific-paragraphs-categorization huggingface dataset:
Randomization and blinding procedure The study was proposed to patients with a breast lesion who were scheduled to undergo preoperative wire placement. Patients were informed that the objective of the study was the comparison of two groups: (1) preoperative wire placement using the standard procedure (control group) and (2) preoperative wire placement with CH (hypnosis group). After providing informed consent, patients were randomized on an equal basis between two groups (i. e. 1:1 ratio). Randomization was centralized by computergenerated random numbers in blocks of 4, with stratification by center, using CleanWeb © software. Allocation concealment mechanism was based on a randomization list created before the start of the trial. The list was thus used when a patient was included into the trial (via Cleanweb) and the result of this randomization was available on Cleanweb.
Data recorded Anxiety was evaluated in both groups on a visual analog scale (VAS), ranging from 0 to  10 (Millar et al. , 1995; Rossi and Pourtois, 2012). Anxiety was measured immediately prior to the procedure (as soon as the patient was set up in the examination room), and immediately after marker placement, with 0 corresponding to the absence of anxiety, and 10 to maximal anxiety. The primary endpoint of the study was the percentage of patients in each group with a score of 6 or more on the anxiety scale immediately after marker placement  (Lang et al. , 2006). Patient anxiety was also measured using the State-Trait Anxiety Inventory (STAI) Y-A form (state anxiety) when the patient was in the waiting room before and after marker placement. The STAI-Y-A form refers to how the patient feels "right now, that is, at this moment, " and the score ranges from 20 to 80, as follows: (1) severe anxiety (≥ 66); (2) high anxiety (56-65); (3) moderate anxiety (46-55); (4) mild anxiety (36-45); and (5) very mild anxiety (≤ 35).
When the normality of residuals was not stratified, a Box-Cox transformation was performed by choosing the optimal power parameter according to the maximum likelihood criterion. For these mixed models, the within-subject correlation was modelized with a compound-symmetry covariance matrix. The same analyses were performed for the VAS pain score, the STAI Y-A score, and the duration of the procedure. Radiologist-reported scores regarding the ease of performance of the procedure and the technician-reported satisfaction scores were compared between groups using the Student t test or the Mann-Whitney U test. All analyses were performed using SAS version 9. 4 (SAS Institute Inc. , Cary, NC, United States). A value of p < 0. 001 was considered statistically significant. SAS code for the main outcomes is publicly available via an open repository at https://osf.
io/8e4xd/. Qualitative analysis of the semi-structured interviews was performed with the aid of the text analysis software Tropes (a free software programme developed by P. Molette and A. Landré based on work by R. Ghiglione). 1 Audio-recorded interviews were fully transcribed. Our study was based on conventional content analysis technique  (Vaismoradi et al. , 2013). The interviews were analyzed separately for each group and blinded to trial allocation. To ensure the robustness of analyses, our study was triangulated data in two ways: data source triangulation for the transcription (VA and RE) and triangulation to compare alternative interpretations and reveal any inconsistencies (VA and RE)  (Patton, 1999).
The ML Payload The first and simplest component of a machine learning enabled satellite network is the ML payload. This is a self-contained machine learning software module that can be considered analogous to a hardware payload, such as a camera or sensor. ML payloads are typically encapsulated in a virtualised software container, isolated from the base computing environment and adjacent software. PodMan  13  and Docker 14 are two of the most popular virtualisation software that enable guest operating system with custom environments to be distributed as single 'image' files, with all dependencies included. Usually containers are compiled in a layered fashion, such that multiple containers can extend a shared 'base' image that contains common dependencies. This greatly simplifies the process of developing software for onboard processing, allowing the satellite computing module to offer a familiar and consistent base system (e.
The Wild Ride mission -a ML payload testbed Trillium has partnered with D-Orbit 15 , Unibap 16 and ESA Φ-Lab 17 to build and test a ML payload on a prototype satellite constellation node. D-Orbit is a space logistics and transportation company offering MicroSat and CubeSat deployment services through their ION Satellite Carrier  18. The D-Orbit Wild Ride mission for the carrier ION SCV Dauntless David successfully launched into LEO on a SpaceX Falcon 9 rocket on June 30 th 2021 (see Figure  1 ). In addition to seven satellites destined for deployment to multiple orbits, the carrier also included three internal demonstrator payloads, including D-Orbit's Cloud Computing in Space module -the first iteration of an on-orbit cloud computing module being developed by Unibap. The Cloud Computing in Space module can be considered a precursor to a fully-fledged space cloud node, offering a quad-core x86 64-bit processor, a Microsemi SmartFusion2 FPGA and an Intel Movidius Myriad X VPU.
The WorldFloods ML payload For this project we choose to deploy the WorldFloods ML payload, which was developed in partnership with ESA during Frontier Development Lab (FDL) Europe 2019  20. WorldFloods 12 is a comprehensive dataset and suite of machine learning models that can be used to create flood masks from multi-spectral Earth-observation images. The segmentation models can distinguish between cloud, land and water, and were trained on multi-band images from ESA's Sentinel-2 (S2) satellite, including the infrared bands. The multi-spectral instrument of S2 is a push-broom sensor with high radiometric resolution (12-bit). Its spectral response covers the visible, near-infrared and shortwave-infrared ranges (490 nm -2380 nm) with 13 bands, with a spatial resolution varying from 10 m to 60 m depending on the band.
If the multi-spectral data can be processed in orbit instead, a vectorised polygonal outline of the flooded region could quickly be transmitted to the ground. This data product is potentially tens of times smaller in size, making it feasible to push directly onto mobile devices in the field -within minutes of being acquired. At present, the cost of downloading data from orbit dominates most operational budgets, so even modest decreases in file size offer potentially significant savings. Table  1. Different models tested to segment flood water in Sentinel-2 images. Here, IR stands for Intermediate Representation, a compiled model format used for edge deployment. Most of these models were proposed in Mateo-Garcia et al. 12. Models ordered in increasing order of complexity. The PyTorch implementation of all the models is open-sourced at https://github.
Model development The WorldFloods segmentation models created during FDL Europe 2019  12  have recently been open-sourced in a public python package called 'ML4Floods'  26. In this framework, users can train segmentation models using the WorldFloods dataset and different S2 band combinations. These models can subsequently be benchmarked using a dedicated set of test images from WorldFloods, or applied to new S2 images that can also be downloaded with the assistance of the ML4Floods package. For this work, we trained models using all thirteen S2 bands as in Mateo-Garcia et al. 2021  12  , but also using only three visible bands B2 -B4, to approximate a standard RGB camera (e. g. , like the D-Sense camera on the compute module). It is well-known that infrared (IR) and short-wave infrared (SWIR) bands are the dominant discriminators of water in optical EO data  23, 27, 28  , so we expect the RGB-only models to perform worse than multi-band models.
In Section 4 we directly compare the performance of the RGB models against the all-band models. The available model variants are presented in Table  1. The architectures of the Linear, Simple CNN and U-Net models are the same as presented in Mateo-Garcia et al. 2021  12  , but we added HRNet  25, 29, 30  as an example of a modern architecture that has produced state-of-the-art results in several semantic segmentation tasks, including remote sensing problems (see e. g. , Etten and Horgan 2021 1 ). The implementation of all the model-training pipelines is open-sourced in the ML4Floods GitHub package  31.
g. , Mateo-Garcia et al. 2020  36  or Tasar et al. 2020  37  ). Hence, in order to build a sufficiently good model for processing D-Sense camera data, we incorporated supervised information on native D-Sense images. To build a training dataset we downloaded four D-Sense acquisitions of the Earth (size 2, 500 × 1950 pixels) and annotated regions of water, land and cloud with manually drawn polygons. We trained new models to segment D-Sense images, both by using the S2 RGB WorldFloods models as a starting point (called 'fine-tuning' in the literature) and by training from randomly initialised weights. The performance of the SCNN model displayed the best trade-off in accuracy vs model size and was chosen for uplinking to the satellite. We present the validation metrics of all models and some representative examples in Section 4.
Engineering the ML payload The ML4Floods Python toolbox produces trained network definitions and weights in the PyTorch format, and these comprised our starting point. The PyTorch files must be converted to the Intel OpenVINO intermediate representation (IR) format to run in the Myriad X chip. This conversion process quantises the weights and intermediate tensor representations to 16 bit floats (FP16 or 'half precision'), shrinking the size of the weights on-disk file size and speeding up inference in the Myriad X processor. Table  1  shows the size of the model definition files, which vary between 8 KB and 15 MB for the quantized versions in IR format. Deploying these models on the Unibap SpaceCloud hardware required further development steps: • Finalise and test the tool chain to convert models from PyTorch to IR format via the Open Neural Network eXchange (ONNX) format.
• Build an inference pipeline that ingests a multi-band image and produces a vectorised mask outlining cloud, land and water. • Encapsulate the inference pipeline in a ML payload software container and integrate into the Unibap SpaceCloud Framework. • Test and tune the ML Payload so that it functions within the processing envelope of the hardware for the mission: a wall-time under 600 s and using less than 2 GB of memory. The Unibap SpaceCloud Framework (SCFW) is a software platform running on the satellite payload computer and providing a Docker host for deploying custom containerised applications. The SCFW abstracts access to satellite sensors and application management routines via a simple API that supports multiple languages via protocol buffer definitions. The containerised environment is based on Ubuntu Linux (for this mission, version 18.
04), meaning that SCFW applications can be developed on commodity x86 hardware using popular languages, rather than specialised languages designed for embedded programming. This system greatly accelerated development, which took place over ∼ 4 weeks during May -June 2021. Our WorldFloods payload application targeted the Myriad X processor to speed up machine learning, meaning that it was restricted to using the inference engine provided by the Intel OpenVINO Toolkit  38. However, this proved to be a boon as the inference engine can be called from the Python language in which previous development had been done. Myriad X processors are also readily available off-the-shelf with USB interfaces 39 so testing of network architectures could be done directly on the target hardware -essential for space-qualifying the ML payload.
A schematic diagram of the ML payload is shown in Figure  2. As a prototype SCFW application, it is currently designed to be triggered from the ground when data becomes available in the input directory. The application detects and normalises the data cubes (depending on the pre-processing required by the requested model) and then pushes the data through the neural network in a forward pass, producing spatial per-pixel masks that classify the image into 'land', 'cloud' and 'water' categories. These intermediate pixel masks are written to a temporary directory before being further processed into polygonal mask outlines. The integer masks are converted to polygons using the rasterio python module, which offers a suitable algorithm in the 'features. shapes()' method. Under the hood, the method calls the C routine 'GDALPolygonize' of the Geospatial Data Abstraction Library (GDAL)  40.
The final masks and meta-data were then compressed into a ZIP file, ready for download. The ML payload application is controlled by running a custom Docker command and feeding the controlling script with different parameters. These specify the file system directories to access input and output data, the model name and weight definition directory, the processor device (e. g. , Myriad X or CPU) and the processing time-limit. Different experiments can be performed by changing these inputs and -crucially -the application can be pointed towards completely new weight definition files, allowing the models to be updated without significant infrastructure changes.
A progressive validation process for the ML Payload was implemented to test the developed software. Firstly, the payload was tested locally on our development machines using USB-format Myriad X devices. Once our tests indicated that the ML payload was running with the imposed constrains we were given access to a SpaceCloud test device (a 'flatsat') with similar  hardware characteristics as the computing payload. Using these machines, we ran a comprehensive suite of benchmarks to obtain performance statistics of the different models. Finally, based on those benchmarks, we selected the models and the experiments to run in orbit. For benchmarking the models, we compare four critical variables: processing time (which includes the inference time of the network plus the time taken to vectorise the resultant water mask), the compression ratio (ratio between the size on disk of the input image and the vectorised output product), the Intersection over Union (IoU) of the water mask -this is the metric to measure the accuracy of segmentations used in  12  -and the weight size in bytes, which is a critical cost factor when uploading the model back to the satellite.
Experimental Data To test and benchmark our ML Payload we selected six flood products from the WorldFloods testing dataset consisting of a S2 image and its corresponding ground truth mask (see Figure  3 ). We refer to these products as the WF-6 dataset and these were choose to be of similar size and sample a range of different surface conditions. None of the WF-6 data were used for training the models. Additionally, we also selected a full S2 product (10,980×10,980 pixels) to measure the current processing capabilities of the payload for its operational use-case. Figure  3  shows the location of these and an example of a product with its ground truth mask. All these products were pre-loaded in the computing payload to test the system in-orbit. Four D-Sense camera acquisitions were downlinked by the D-Orbit and UNIBAP teams to support re-training of models for that sensor.
We manually labelled these scenes with cloud, water and land classes (i. e. , pixel masks) using open-source computer vision software  42. Figure  4  shows the acquisitions together with the manually derived masks, hereafter referred to as the D-Sense-4 dataset. During training we follow the same approach as in Mateo-Garcia et al. 2021  12  : we divide these large acquisitions into at total of 5,048 overlapping tiles of 256×256 pixels each, which are sampled in mini-batches to feed to the training loop. In order to test the generalisation capabilities of those models we used a leave-one-acquisition-out scheme (LOO); that is, we iteratively train with three out of the four D-Sense image and test the model on the remaining one.
Results and Discussion In this section we present the experimental results of the WorldFloods ML Payload and discuss their implications. In § 4. 1, we dive into the results of processing the WF-6 dataset and compare different critical variables affecting operations on Unibap SpaceCloud hardware. In § 4. 2 we present the results of processing a full S2 product, both on the ground-based 'flatsat' and in-orbit, demonstrating that the inference time is within reasonable constraints. In § 4. 3 we show the results of re-training of the ML Payload for the D-Sense camera, comparing the models trained on WorldFloods with those trained on the labeled D-Sense-4 dataset. Additionally, we show the segmentation results of one of these models run on D-Orbit's Cloud Computing in Space module, in orbit. Finally, we conclude this section with some complementary benchmarking experiments, showing the performance of the payload on other edge devices ( § 4.
Results in Sentinel-2 flooding tiles Figure  5  illustrates the performance of our models (Table  1 ) applied to the WF-6 dataset. In the top-left plot we see that the compression ratio is between 400 -800 in these products and tends to be greater for the more complex CNN architectures than for the baseline models (Linear, NDWI and MNDWI). This indicates that the polygons created by convolutional models are less complex (i. e. , have fewer vertices) than the baselines. We hypothesize that this is because CNN models are able to understand the spatial context to produce simplified as-human-drawn polygons whereas the baselines, working in a pixelwise mode, produce more 'salt-and-pepper' (i. e. , noisy) outputs, resulting in more complex polygons. The top-right plot in Figure  5  shows the averaged intersection-over-union of the predicted versus ground-truth water mask for the WF-6 images.
Results on D-Sense images We showed in the previous section that the performance of the RGB models trained on the WorldFloods dataset is low (Figure  5 , top-right). This is likely because detecting flood water in RGB imagery is difficult due to the presence of debris and suspended matter that mainly affects the visible bands. This factor, together with the huge difference in resolution and radiometric quality between the S2 and D-Sense instruments, makes RGB models trained in WorldFloods perform poorly at detecting water in D-Sense images. Figure  7  shows a D-Sense acquisition (top left) and the results of applying the SCNN RGB model trained on the WorldFloods dataset. We see that this model produces mostly random noise as output. It is worth mentioning that these results contrast with other deep learning models transferred across multispectral instruments that produced reasonable results.
For instance, López-Puigdollers et al. 2021  43  and Mateo-Garcia et al. 2020  44  show that deep learning cloud-detection models trained on Landsat-8 work well when applied to Proba-V and S2 images. In our case, we hypothesize that the lack of calibration of the D-Sense camera together with the huge difference in spatial resolution and bad per se performance of the RGB models contributed to these results. As outlined in Section 3, we trained each model on the D-Sense-4 data following a leave-one-image-out validation strategy. Figure  8  presents the intersection-over-union (IoU) for each RGB model, measured from the excluded images. We compare the models trained on WorldFloods (blue bars) against two different training strategies: fine-tuning from the pre-trained weights of the WorldFloods models and training from scratch (i.
e. , from randomly initialised weights). As we have seen before, the IoU of the original WorldFloods models is very low on D-Sense images. However, when those models are fine-tuned or trained from scratch on the small D-Sense-4 dataset their IoU is comparable to, or even higher than, the WorldFloods models on S2 images reported previously (Figure  5 ). Results in Figure  8  show that models trained from scratch have similar segmentation accuracy to those trained by fine-tuning. Finally, the two plots at the bottom of Figure  7  show the output of the re-trained model (bottom left) and the vectorised water mask (in purple) overlayed on the unseen RGB D-Sense test image (bottom right). The segmentation result displays good accuracy overall, with some false-detections of water pixels at the top left of the image, where vignetting artefacts are evident.
This must be accounted for in any system design. 5. Data from low-resolution RGB cameras will likely be of limited value -on its own or as a trigger for tasking other space assets. Our results suggest that WorldFloods models rely heavily on the infrared bands for water detection (as expected). 6. The main motivator of the WorldFloods ML payload is to decrease the lag time to deliver flood maps by creating smaller data products for download. The compression factor for the data is typically between 200 -10, 000, but this is not guaranteed and it can be much smaller for complex scenes, or small images. 7. If the telecommunication link is no longer a limiting factor then the advantage of the WorldFloods payload is diminished. However, the demonstration of the ML processing capability is still valid.
Acknowledgements The Trillium Technology team would like to thank the European Space Agency Φ-Lab for their excellent support and encouragement. We are also incredibly grateful to the D-Orbit and Unibap teams for teaching us how to develop for the 'NewSpace' paradigm and look forward to working with them in the future. G. M. -G. has been partially supported by the Spanish Ministry of Science and Innovation (project PID2019-109026RB-I00, ERDF) and the European Social Fund.
Annotation of pectinid genomes Out of the 29 Pectinidae and 1 outgroup (O. lurida) mitogenomes retrieved from Genbank, only two were incomplete. All protein coding and rRNA genes identified in previous descriptions of these genomes were found and reannotated in this study. The atp8 gene was annotated in all 25 genomes, in which it was not previously annotated, including two paralogous copies in Mizuhopecten yessoensis and Pl. magellanicus, and unusually long versions of the gene in two Ar. irradians GenBank accessions-with alternative start codons-showing only 78. 5% sequence similarity with conspecific atp8 genes (Table  3 , Additional file 3). Between 20 and 40 tRNA genes were annotated per mitogenome in the 29 pectinid species. Most mitogenomes contained 23 tRNA genes, compared to the 22 commonly found in animals.
Mito-phylogenomics All four Subfamilies and 7 out of 9 Tribes of the Pectinidae family are represented in this study (Table  4 ). Two different topologies can be observed among the ML trees reconstructed from the eight datasets (Fig. 3 ). The two rRNA datasets (G and H) show a distinct topology compared to the other 6 datasets, but the topology based on the rRNA datasets is not well-supported. All PCG ML trees had very similar topology. Mim. varia is placed within the same clade with the other two congeneric species only in the tree based on the E dataset (PCG amino acid sequences). In every other tree Mim. varia is placed in a clade with species in the Crassadomini and Chlamydini tribes. The monophyly of neither of these clades is supported. Similarly, although the topology suggests that Pl.