dataesr/scientific-paragraphs-categorization huggingface dataset:
25 pressure tapings are used to best fit the pressure distribution along the profile at midspan against a database of numerical flow predictions obtained at angle increments of 0. 1. This enables to measure the inflow incidence with a precision of ± 0. 05⁰ for the investigated blade. The inlet turbulence is generated by a grid 280 bar widths upstream of the cascade and measured 0. 25c upstream of the leading edge of the blades.
e. with an available RANS result), an original and specific strategy for handling such computational failures is used. First, for each parametrisation, a large database of guide fins is generated using a Monte Carlo methodology and only the guide fins with viable CAD surfaces are kept. The main assumption in the algorithm used is that such large databases are representative of the set of all admissible geometries. The DOE is then built by picking a small subset which spans as best as possible the entire database. To achieve this, the "kernel herding" algorithm proposed by  (Chen et al. , 2012)  is used. It consists in minimising the distance between the empirical distribution defined on the DOE and the one on the entire dataset. For the first parametrisation with 4 parameters, the corresponding DOE (DOE-4) with 40 geometries is shown in Figure  7.
The red block is then replaced by an unstructured mesh containing the guide fin. This results in a hybrid mesh containing both structured an unstructured blocks. The unstructured red block is generated automatically using the software ANSA (Beta). It is made of 24 prisms layers that grow from and in a direction normal to the solid surfaces, of tetrahedra and of pyramids. Efforts are made to have elements as square as possible at the interface to enhance the quality of the pyramids. Prisms and pyramids automatically connect to the red boundaries, thus ensuring grid point coincidence. The growth rate at the interface is set to 1. 2 for both volumes to ensure its continuity, yielding an unstructured block of typically 3. 5 ' 10. cells with a guide fin, and 1. 7 ' 10. cells without. Figure  10  shows portions of a hybrid mesh with a guide fin and illustrate the consistence of the two meshes.
ACKNOWLEDGMENTS The authors would like to thank the  CIRT (Consortium Industrie-Recherche en Turbomachine ) and  Safran Tech  for supporting this work. In particular, thanks go to  Sebastien Da Veiga ,  Emma Croner  and  Etienne Tang  for their time and support. At Safran Engineering Services, the authors thank  Philippe Biagi  for his advices on the use of ANSA. At LMFA, the authors would also like to thank  Sebastien Goguey ,  Pierre Laucher ,  Benoit Paoletti ,  Gilbert Halter  and  Lionel Pierrard  for their advices on the experimental set up. Finally, special thanks to  Ivo Dawkins , for his many discussions and for sharing his experience on the baseline configuration.
Laser feedback-based self-mixing interferometry (SMI) is a promising technique for displacement sensing. However, commercial deployment of such sensors is being held back due to reduced performance in case of variable optical feedback which invariably happens due to optical speckle encountered when sensing the motion of non-cooperative remote target surfaces. In this work, deep neural networks have been trained under variable optical feedback conditions so that interferometric fringe detection and corresponding displacement measurement can be achieved. We have also proposed a method for automatic labelling of SMI fringes under variable optical feedback to facilitate the generation of a large training dataset. Specifically, we have trained two deep neural network models, namely Yolov5 and EfficientDet, and analysed the performance of these networks on various experimental SMI signals acquired by using different laser-diode-based sensors operating under different noise and speckle conditions.
Deep networks have also been used to improve computer-vision-based methods, such as for assessing steel tubular structures  [29]. In the microwave domain, localization of subwavelength objects has been successfully demonstrated by artificial neural networks that appear not only robust to noise but are also capable of achieving performances beyond those obtained using the training dataset  [30]. Subsequently, in the past five years, neural networks have also been used for many SMI applications, as detailed below. One approach presented in  [31]  uses a single layer-based artificial neural network for classification of multi-modal SMI signals. Traditional feature extraction was used on a dataset of only 250 fringes for this network. Another approach that uses neural networks to remove noise from SMI signal was presented in  [32].
A similar work for noise removal was performed in  [33]  by using a generative adversarial network (GAN). In this work, the authors used a pre-processing technique to process the signals which are corrupted with white-noise and amplitudefluctuations in all major optical regimes. They used an unlabelled, simulated training dataset obtained for different constant values of optical feedback coupling parameter, denoted as C, and signal to noise ratio (SNR). A one dimensional input is used. A generator is used in between an encoder and a decoder consisting of 22 convolution layers each to enhance the signal. The work presented in  [34]  uses a convolutional neural network (CNN) to directly retrieve displacement from SMI signals without detection of fringes. It uses 11 layers of which 4 layers are one dimensional convolution layers, 4 max-pooling layers, and 1 dropout layer with two fully connected layers at the end.
They trained their network in a restricted way because of limitation of dataset. This method appears to work well for the moderate feedback regime. Weak feedback regime SMI signals (with C < 1) were not explored. Consequently, their network cannot correctly predict displacement for small C cases where SMI signal tends to become symmetrical, as stated in  [34]. A recent work in SMI-based vibration sensing uses joint time-frequency analysis to extract vibration information. Certain limitations in this sensing are then removed by using a generalized regression neural network (GRNN)  [35]. Another work  [36]  uses a shallow (single hidden layer) and 1D neural network to detect the SMI fringes under constant and weak optical feedback conditions by using the fringe slope as the defining feature of a fringe.
An optical attenuator was used to ensure a specific amount of optical feedback. This neural network relies on pre-processed SMI signals by using auto-correlation, amplitude-thresholding, and peak-detection operations. Detected fringes were then manually labelled (total dataset includes five SMI signals with 50 fringes each). Importantly, it does not address possible occurrence of optical speckle. Furthermore, use of fixed number of samples to represent the fringe slope restricts the method as any significant change in frequency or amplitude of remote motion needs to be measured followed by appropriate interpolation or decimation operations  [36]. Another recent work by authors using a DNN for FD is currently under review  [37]. This work converts 1D SMI signals to images in which SMI fringes are manually labelled for training purposes.
Then, this small manually labelled dataset is vastly increased by using a novel anisotropic augmentation method that preserves the labels for augmented images. This network was trained by using constant C-based SMI signals, simulated by using the mathematical model reported in  [38]. On the other hand, this work specifically focuses on VOF conditions. In addition to performing FD, the present work measures remote displacement along with FD by using the detected labels corresponding to correctly processed variable C signals. Furthermore, we remove the limitation of manual labelling of SMI fringes by proposing in this present work an automatic segmentation and labelling technique that places correct labels on the training dataset. Lastly, we have explored two different DNNs for this purpose.
• The other network that we have used for our variable feedback conditions is Yolov5s  [41]. It is based on Darknet architecture on Pytorch framework  [42]. Yolov5 is able to process 140 frames per second so can be used for real time processing of SMI  [43]. We have trained both these networks by using our dataset of variable optical feedbackbased SMI signals, and then tested their performance on unseen experimental SMI signals with and without optical speckle. The rest of the paper is organized as follows. Section 2 presents a very brief overview of SMI-based displacement sensing. Section 3 presents the methodology that we have followed for this work. Performance analysis of explored networks is presented in Section 4 for simulated as well as experimental SMI signals. Section 5 presents Discussion, followed by the Conclusion.
Methodology In this work, we explore object detecting DNNs to detect and classify SMI fringes. These networks first perform FD and then classify each detected fringe in two classes: positive fringe (corresponding to forward motion) and negative fringe (corresponding to backward motion). To train our networks for fringe detection of variable optical feedbackbased SMI signals, first we have to create a dataset that includes many SMI signals under different variable optical feedback conditions. The schematic block diagram of the complete system is shown in Figure  3. First, we generate noiseless SMI signals using a variable C-based SMI simulator  [17]. Noise is then added to these signals. We have used the additive white Gaussian noise (AWGN) model to represent the typical experimental noise affecting the SMI signals  [47].
A typical SMI signal that we used comprises 100 k samples. This typical SMI signal is converted into 140 images. In the next step, we perform automated bounding box placement and labelling of fringes contained inside these SMI signals' images. These labels and SMI signals' images are fed to DNNs to train them. Our proposed work uses DNNs including Yolov5s and EfficientDet (detailed ahead) to correctly detect and classify multiple fringes present in an image at a frame-rate of 140 frames per second. At the end, displacement is reconstructed based on output of these models. The next subsections will explain these steps of dataset generation for variable optical feedback conditions, automated labelling, training of our DNN models, and displacement reconstruction.
VOF-Based SMI Signal Generation and Automated Labelling In this subsection, generation and automatic labelling of variable optical feedback SM signal is explained. The training dataset that we have created includes SMI signals having different continuously varying spans of C (one such signal is shown in Figure  4 ). For training data, C values range from 0. 5 to 8 and SNR was varied from 15 dB to 22 dB. For all these SMI signals, we created a label file (containing the class and bounding box of each fringe existing in the corresponding image of the SMI signal) in an automated manner. One such signal with additive noise is shown in Figure  5. We have generated these SMI signals in MATLAB by using the variable C-based SMI simulator  [17]. It requires information about remote motion's frequency, peak to peak amplitude, initial phase φ, sampling frequency f s , and variation in C.
We have used multiple vibration frequencies for training dataset ranging from 2 Hz to 10 Hz and f s was set to 100 kHz. Furthermore, different peak to peak amplitude values, ranging from 2 to 8 µm, were used. For C(t), we used different variations including sinusoidal and arbitrary variations. Next step is to detect transitions of this SMI signal. For this purpose, we use the simple derivative-based method proposed in  [48]  to detect the transitions (indicative of fringes) in the simulated and noiseless SMI signal. In this method  [48] , derivative is compared with a positive and a negative threshold to detect a positive or a negative fringe. This simple derivative-based FD method is giving correct FD results for simulated VOF conditions only in absence of any noise. Any significant presence of noise results in poor FD performance by this method as it was proposed for constant and moderate feedback regime only  [48].
At this point, we have the information that is required to be saved in a text file, as needed by object detectors. The labels on each fringe appear as shown in Figure  6. The label file contains information of bounding boxes and class name in column format. Yolov5 model uses. txt annotation for the label file containing the information of bounding boxes. EfficientDet model uses these text files to generate a large annotation file of complete dataset. So we needed to devise a method that can generate an SMI signal along with the corresponding. txt file. Once all the dataset is generated and labelled, we use augmentation techniques to expand our dataset. We have used vertical and horizontal concatenation for our signals and their corresponding labels. Augmentation of datasets is performed because these machine learning models perform very well for larger datasets and result in better accuracy.
Now, our dataset is complete for training on Yolov5. To train our data on EfficientDet model, we reuse the same dataset. EfficientDet model uses another label format called. json for training. So, to use this dataset and its labels, we have to convert. txt format to. json format so that we can use the same dataset and label files for our EfficientDet model. An annotation file in. json format was created for our complete augmented dataset. We have partitioned our dataset in training and validation folders. Annotation files for both training and validation datasets are created. For EfficientDet model, we shall use training images, their respective annotation file, validation images, and their annotation file.
Fringe Detection for Both Models We have used EfficientDet D0 model to train and detect fringes for variable feedbackbased SMI signals. We have trained model on simulated data with a batch-size of 8. The batch-size indicates the number of samples that are processed in one pass, be it forward or backward during training of the model. There were 130,000 images in our complete augmented dataset. We have tested our model on experimental data that is unseen for the model shown in Figure  9. For training, we have used simulated signals while for testing, we used unseen experimental signals. We have implemented and trained a Yolov5s model on our dataset of Variable feedback conditions. Yolov5 model takes images and its corresponding labels in. txt format as training data. We trained our model by using a batch-size of 8.
The time taken by this model was 2 iterations per sec. For one epoch, this model took 2 h to train. Similarly to the EfficientDet D0 model, this trained Yolov5s model is then tested on experimental data that was not used in training at any stage. Our trained model performed very well on these signals and correctly predicted and classified fringes as positive and negative fringe. This prediction on one of the speckle affected, amplitude modulated SMI signal is shown in Figure  9. Once the model is trained, we tested VOF-based speckle affected experimental signals to check the performance. We noted that both the models can process 140 frames per second in terms of fringe detection and classification. After fringes are detected, displacement reconstruction is performed by using generated labels of fringes, as detailed below.
Results We have trained our dataset on Yolov5 and EfficientDet models for weak and moderate feedback regime such that values of C vary from 0. 5 to 8 and value of SNR varies from 15 dB to 22 dB with a sampling frequency of 100 ksamples/s. Both the models use same dataset with same characteristics of SMI signals for training. We have also used same image size for both the models so that we can fairly compare their performances. The next subsections will explain simulated and experimental results on unseen data.
Simulated Results We have measured the performance on simulated data to quantify the range of C and SNR for correct detection and classification of fringes, as shown in Figure  11. We have obtained these SMI signals under simulated environment similar to the one that we have used to generate our training data. The trained models correctly detected and classified fringes contained in simulated noisy and variable C-based signals. The results for these simulated data are shown in Table  1. We have also quantified the relationship between SNR of noisy SMI signals with variable C and fringe detection accuracy of these methods. We have tested various simulated SMI signals under variable optical feedback conditions subjected to different levels of noise. We have used the SMI signals with SNR values from 6 dB to 20 dB while variation in C ranges from 0.
Performance Analysis As we trained both models with the same dataset, we can compare the training time that both these models consumed for our dataset. Our dataset was divided into training and validation datasets. Total images after augmentation were 130,000. We have trained both of these models on NVIDIA GPU GE FORCE RTX 2080 8GB by using the same batchsize of 8. The impact of network architecture of both the detectors can be explained in terms of training time and accuracy. Batch-size and training time have inverse relation. Higher batch-size leads to smaller time but very high batch-size may negatively affect the generalization of the model. So, we used the batch-size of 8 for our training. Hence to complete one epoch on dataset of 130,000 images with a batch-size of 8 means that there would be 16,214 iterations (130,000/8) in one epoch.
We trained both the DNNs for 60 epochs. For Yolov5, the training time that it took was 1 iterations per second and the time consumed was 4 h approximately for one epoch. Hence Yolov5 took total time of 240 h for 60 epochs. For EfficientDet model, the training time was 2 iterations per second for same batch-size. So, the total time for one epoch was approximately 2 h. It took around 120 h to complete training for 60 epochs. Hence, we can conclude that EfficientDet requires less training time as compared to Yolov5 for our augmented dataset. This difference in training time is due to compound scaling and BiFPN that EfficientDet uses  [43]. We have also measured the inference time for both DNNs. The inference time is the time taken by the trained model to process (i. e. , infer the result of) a test image.
Discussion In this paper, we have proposed a method for automatic labelling of our dataset of SMI signals. We have trained and tested two models namely EfficientDet and Yolov5, for fringe detection in case of variable optical feedback conditions. We have also retrieved displacement for the case of Yolov5 by using the labels that it creates for test signals for fringe detection and classification of variable C-based SMI signals. Both the models performed well on the unseen test data. EfficientDet model has shown significant training time reduction by approximately 50 percent as compared to Yolov5. It is recalled that SMI is based on a laser under optical feedback. This results in a nonlinear relation between the remote displacement and the laser power signal, relation that is strongly affected by speckle that induces changes in optical feedback and thus in OFI fringe shape.
Furthermore, the work in  [37]  uses a dataset containing non-variable optical feedback regime SMI signals in which the C value remains constant whereas we have dealt with VOF-based signals in which the amplitude and shape of SMI fringes varies over time, even within a single SMI signal. Although this model  [37]  also shows fair accuracy for moderate and weak feedback SMI signals but this occurs only when the feedback regime does not change over time during SMI signal acquisition. This work  [37]  shows cases of incorrect detection and classification when the SMI signal is significantly affected by speckle. Another aspect that is different is in terms of segmentation of SMI signals. We have used automated labelling instead of manual labelling used in  [37]. We have performed the segmentation of SM signals automatically whereas this work  [37]  uses manual segmentation in which each SMI signal is treated differently.
Assessment of Immunological Markers In all treated groups we observed a strong inflammation, and therefore in the next step we assessed the recruitment of lymphocytes. The CD3+ inflammatory marker and two subsets of T cells, namely, CD4+ T helper cells and CD8+ cytotoxic T lymphocytes were quantified and their localization was assessed in extracted tumors at different times after tumor grafting. Expression of CD3+ in untreated control tumors (CTR ND NL) at 12 days post grafting was 17% (data not shown) and increased significantly to 29% at 30 days post tumor grafting (p < 0. 01) (Figure  4 ). This increase in CD3+ expression in untreated tumors confirms a chronic inflammatory status inherent to tumor presence. CD3+ expression in rat bladders subjected to HAL BL was significantly lower (19%; p < 0.
Acknowledgements This manuscript is dedicated to the memory of  Prof. Richard A. Andersen. The authors also thank  Dr Michael Wörle  and  Darryl Nater  for assistance with crystallographic measurements;  Dr Zachariah J. Berkson  for the SS NMR measurements;  Prof. Richard A. Andersen ,  Prof. Keith Searles  and  Dr Florian Allouche  for scientic discussions. Dr Romain Berthoud  and  Dr John Severn  are acknowledged for enabling HT-SEC measurements. D. T. , A. N. , and Y. K. are grateful to the  Swiss National Foundation (SNF)  for nancial support of this work (grant no. 200021_169134 ,  200020B_192050 , and 200020B_192050 respectively).
The design of this facility and the commissioning results are described in detail in a recent publication  [15]. After implantation, the samples were studied using several experimental techniques, including Thermal Desorption Spectroscopy (TDS) and Transmission Electron Microscopy (TEM). Our aim is to identify peaks in the TDS spectra of irradiated tungsten, to investigate the dependence of deuterium inventory on irradiation damage, and to compare the data with earlier experimental results. Next, we performed Positron Annihilation Spectroscopy (PAS) analysis of the samples, as PAS is an established technique for the detection of vacancy-type defects  [16] [17] [18]. PAS is sensitive to small defects from a single vacancy to vacancy clusters and has lower detection limit (depending on the material and the nature of the defects) in the range from about 10 23 m -3 down to about 10 20 m -3 , hence enabling a fairly precise determination of the density of defects.
Acknowledgments Some of the research used equipment at the  UKAEA's Materials Research Facility (MRF)  which is part of the  UK's Henry Royce Institute and National Nuclear User Facility initiatives. We thank the  MRF  staff for their support. This work was supported by  EUROfusion Enabling Research  project  TRiCEM, Tritium Retention in Controlled and Evolving Microstructure. This work has been carried out within the framework of the  EUROfusion Consortium  and has received funding from the  Euratom  research and training programme 2014-2018 and  2019-2020  under Grant Agreement No. 633053. The views and opinions expressed herein do not necessarily reflect those of the  European Commission. We acknowledge funding by the  RCUK   Energy Programme  (Grant No. EP/T012250/1 ). Acknowledgments Some of the research used equipment at the  UKAEA's Materials Research Facility (MRF)  which is part of the  UK's Henry Royce Institute and National Nuclear User Facility initiatives.