dataesr/scientific-paragraphs-categorization huggingface dataset:
| PARTICIPANTS AND MODELING APPROACH As Table  2  shows, some participants decided to use more than one modeling approach and some used different codes. NREL used two different OpenFAST models (NREL 1 and NREL 2). NREL 1 models the tower by means of Euler-Bernoulli beams that account for the bending DOFs (ElastoDyn module) while NREL 2 models the tower by means of Timoshenko beams that account for axial, shear, bending, and torsion DOFs (SubDyn 15 module). A similar approach was adopted by eureka!. The EUREKA 1 model is equivalent to NREL 1, and EUREKA 2 is equivalent to NREL 2. The rest of the participants using OpenFAST adopted the same modeling approach as NREL 1. There are three participants using the AF method: DUT 2, NREL 2, and WyndTek. The fixity depth and beam properties for this approach must be determined according to the beam theory used.
Participant Code Soil-structure interaction approach AF CS DS REDWIN 4SUBSEA OrcaFlex X BVEX Samcef WT X X CGC Bladed X CSSC OpenFAST X DNV GL Bladed X X DUT 1 OpenFAST X DUT 2 HAWC2 X X DTU HAWC2 X EUREKA 1 OpenFAST X X EUREKA 2 OpenFAST X NREL 1 OpenFAST X NREL 2 OpenFAST X X X NTNU SIMA X X ORCINA OrcaFlex Xª PRINCIPIA DeepLines Wind X TECNALIA OpenFAST X X UC-IHC OpenFAST X URO OpenFAST X UOU OpenFAST X UPC FloaWDyn X X VULCAIN OpenFAST X WYNDTEK Ashes X X Note: Orcina uses a built-in capability in OrcaFlex with some of the inputs from the REDWIN approach. beams. Accordingly, NREL 2 and DUT 2 use the AF properties specified in the project definition document,  12  and WyndTek uses the properties corresponding to the improved AF method. The participants modeling the SSI by means of the CS method use the stiffness matrix from Equation  1 , and the participants using the DS approach use 61 discrete nonlinear springs defined every 0.
The new capability was verified across different tools and against industry standard methods for an example monopile offshore wind system (DTU 10-MW wind turbine). Two linear (AF and CS) and two nonlinear (DS and REDWIN) SSI approaches were used during this code-to-code verification. The REDWIN macroelement approach differs from traditional methods in the inclusion of plasticity and hysteretic damping. It requires more elaborate inputs (e. g. , stiffness matrix and load-displacement curves at the seabed) to characterize the SSI. However, it models the hysteretic damping internally. This is a great advantage because the energy dissipated by the SSI is not easy to quantify and include in a numerical model. On the one hand, traditional methods have often employed a viscous damping matrix at the seabed or dashpot elements along the monopile with viscous damping.
ACKNOWLEDGEMENTS The authors would like to thank the  Norwegian Geotechnical Institute  for their work in the  REDWIN  project to develop the capability being incorporated in OC6 Phase II and to provide the data to model the foundation as well as for their ongoing support. We would also like to thank the  Norwegian University of Science and Technology  for their support in developing the model for this project. This work was authored in part by the  National Renewable Energy Laboratory , operated by  Alliance for Sustainable Energy, LLC , for the  US Department of Energy (DOE)  under Contract No. DE-AC36-08GO28308. Funding is provided by the  US Department of Energy Office of Energy Efficiency and Renewable Energy Wind Energy Technologies Office. The views expressed in the article do not necessarily represent the views of the DOE or the  US Government.
Scope and Objective of The Survey This survey aims to provide a comprehensive view on state-of-the-art ML and DL practices in AMR domain for SISO and MIMO wireless communication systems. In this survey, a detailed and fair comparison between the latest methods in this area is provided along with emphasizing their limitations and key advantages. Each method is presented in a clear and concise manner along with all of its aspects. For instance, the features extraction and classification criteria along with channel assumption and modulation sets are pointed out for ML-AMR methods. Furthermore, DL-AMR methods are compared in terms of network architecture, network training and testing parameters, used datasets, classification accuracy, and concluding remarks. However, there are several architectural parameters that must be considered when designing a DL model.
[8]  (the 3rd section) A brief overview of emerging ML and DL approaches for signal recognition  10 25  This survey  [8]  A comprehensive state-of-the-art review of the existing ML-AMR and DL-AMR methods for SISO and MIMO systems  19 75  scheme classification. Equally, using an insufficient or badly constructed dataset can distort the hyperplanes of classifiers and, hence, affect badly its accuracy. Trainable parameters are very essential since they reflect the computational complexity and the required storage of the model and, thus, reveal the possibilities of implementing the model on real hardware  [10]. Similarly, there are many other parameters to be considered during the model training process such as the batch normalization, activation functions, cost functions, and training configurations  [10].
Organization Of The Survey This paper is structured in a top-down manner as shown in Fig. 1. The outline of this paper can be summarised as: • First, we begin, in section II, by presenting a basic background of the traditional process of FB-AMR followed by providing a basic ML and DL background to illustrate the main required concepts for the rest of this article. Thereafter, we discuss the main key factors that encourage the implementation of ML and DL models in AMR domain, aiming to clarify why such models can outperform the traditional AMR methods. • Later in section III, a discussion on the integration of ML models into AMR methods is provided for SISO and MIMO systems. The overall organization of the paper • Since DL models require large-scale datasets, we begin section IV by presenting the specifications of some existing radio signals datasets.
This provides the required information about the signal parameters and channel characteristics of these datasets. Therefore, it will help to understand their usage in several of the presented DL-AMR methods. Then, we review the recent DL algorithms for AMR in SISO systems. We then review the existing DL-AMR methods for MIMO systems. • Finally, we wrap up this article by providing open challenges and potential research directions along with concluding remarks in section V and in section VI, respectively. In Fig. 1 , a chart of the overall organization of the paper is provided. Furthermore, a list of main abbreviations is provided in Table 2.
A. DL-AMR METHODS FOR SISO SYSTEMS We categorize the DL-AMR methods in this subsection based on the employed deep network models, which simplify the comparison among them. First, we outline some of the exiting signal datasets in the literature. Second, the methods that exploit CNN are discussed. Then, those using RNN are explored. Next, DNN-based AMR methods are summarized. Thereafter, we review the methods proposed for AMR based on AE models.
1) Related Signals Datasets Signals dataset are particularly required for training, validating, and testing the networks in ML-AMR and DL-AMR models. Some researchers used their own simulated datasets, and others prefer to employ those introduced in the literature. Some of the datasets are presented as follows. In  [40] , 5 modulation families passing through 5 different wireless channels (ideal, static, Rayleigh, Rician, and Nakagami-m) were considered in the generated dataset. This dataset was generated using MATLAB2017. It includes 780k of In-Phase and Quadrature (IQ) samples spreading in the same SNR range of the aforementioned RadioML. 2016. 10A. Table  5  summarizes the modulation schemes, covered SNR range, dataset's size, and the synthetic simulated channel effects of the datasets.
The authors created two datasets to be used in AMR. The first dataset was created by IQ samples in order to train the former CNN, while the second one adopted constellation diagrams with a density window to train the latter CNN. Simulation results demonstrated a considerable improvement when compared to the benchmark methods. CNN and LSTM were employed in  [46]  for modulation scheme classification. The method in  [47]  was proved to have higher robustness against SNR variation and less memory consuming than the benchmark methods. Similarly, the algorithm in  [48]  was proposed for varying noise regimes with less computational complexity and likewise smaller model sizes. In particular, the redundant neurons were pruned through a compressive sensing based neuron pruning technology. Simulations showed that the proposed method not only reduces the computational time but also diminishes the required device memories under an acceptable performance loss.
In their work, Zheng et al. [49]  aimed to solve the problem of variable CNN's input size and to make full use of the complete signal burst. Thus, three fusion methods were explored: voting-based fusion, confidencebased fusion, and feature-based fusion. First, the signal is divided into multiple segments of the same length. Then, each segment is sent to the CNN to make a decision by the corresponding fusion method. Finally, all results will be fused based on classification results, confidence, or intermediate features of these segmented signals. Simulations revealed that all of the proposed fusion methods overcome the non-fusion method. In  [50]  Many researchers chose to use one of the existing IQ signals datasets  3  in order to form the input of CNN. In  [38] , the adaptation of CNN to the dataset in  [37]  was studied along with a performance comparison between the proposed CNN, and those of the expert HOM-based methods.
Then in  [39] , the authors extended their prior work on using deep CNNs for AMR  [38] ,  [44] ,  [51]. They investigated the performance of DL-AMR considering a rigorous baseline method using HOMs and strongly boosted gradient tree classification. Besides, the effects of a wide range of design parameters, channel impairment conditions, and training dataset parameters were studied. They conducted several simulations including over-the-air measurement of AMR performance. Moreover, they built an improved version of the tool described in  [37]  to be used in dataset generation 3. In  [52] , a signal distortion correction module was proposed to improve the accuracy of CNN-based AMR. The proposed module employed an ANN in order to estimate both CFO and PO of received signals. In contrast to  [41] , this module eliminates the effects of both CFO and PO by shifting the signal frequency and phase prior to modulation recognition.
In addition, an Auxiliary Classification Generative Adversarial Network (ACGAN) was proposed to expand the training data set. PCA procedure was used in  [55]  to create a set of uncorrelated features and, therefore, reduce the features vector length required for AMR. In  [40] , HisarMod2019. 1 dataset 3 was used to evaluate the proposed CNN-based AMR method. The modulation type of each signal was firstly identified, then the signals were classified based on modulation order. The model in  [56]  was dedicated for beyond 5th-generation communication systems. Therefore, the main objective was to reduce the computing time below 0. 01 millisecond to comply with the future communications standards. In  [57] , the signals were identified whether they are wideband frequency modulation or not by using two expert features: (1) the maximum value of the power spectral density of the normalizedcentered instantaneous amplitude, and (2) the kurtosis of the normalized instantaneous frequency.
Then, the signals were fed into a CNN-LSTM classifier where QAM16 and QAM64 schemes were considered as the same class. Finally, QAM16 and QAM64 were classified by using Haar-wavelet transform crest searching. In  [58] , an adversarial transfer learning architecture was introduced using the RadioML2016. 10A dataset. In order to improve the AMR overall performance, both adversarial training and knowledge transfer were exploited. The computational complexity of this architecture was found to be slightly larger than CNN and DNN. However, it was acceptable since the performance improvement was significant. Table  6  summarizes the network configuration, modulation pool, maximum achieved accuracy per SNR, and remarks of the previously discussed CNN-based AMR based on IQ samples. c: Classification Using Image Representations DL technologies in the last decades have achieved outstanding achievements in the field of image processing.
Therefore, many researchers in the field of wireless communication are very keen on the idea of converting the signal recognition problem into image recognition one. For example, in  [68] , a CNN-based AMR was presented in which various signal spectrograms were converted into an image dataset using Short-Time Fourier Transform (STFT). The authors introduced two image classification approaches in order to examine the AMR accuracy. The first one is by optimizing activation functions, while the second one is by using optimization functions. Similarly, Sun et al. [64]  and Zhang et al. [69] , adopted the same methodology. However, the authors in  [64]  implemented VGG-16  [70] , a widely known CNN model, for recognizing 10 different modulation schemes. Similarly, the algorithm in  [69]  exploited both smooth pseudo Wigner-Ville and Born-Jordan distributions to obtain a different kind of images.
Traditional Generative Adversarial Nets  [78]  suffers from several training issues such as generator disconverge, discriminator overfitting, and mode collapse. Hence, the authors presented several measures to overcome these issues. Results showed a 0. 1-6% increase in the classification accuracy using the extended dataset when comparing to the original one. Waveform identification is considered a major part of CR technology. Therefore, Zhang et al. [79] , proposed a blind method for CR waveforms recognition using Choi-Williams time-frequency Distribution (CWD). The received signals were also transformed to 2D images through CWD. Then, they were converted into a binary image by using image binarization and image denoising algorithm. In  [80] , an AMR method for PSK/QAM schemes of different orders was proposed based on constellation structure and k-medoids clustering in the slow and flat fading channel.
In  [83] , constellation diagram and transfer adaptation was adopted in features extraction subsystem, while SVM was used for classification. 1 g - 2 h 2 g - 4 h - - - {2ASK, Zha et al. [84] , introduced a DL framework for multisignals detection and modulation recognition. The proposed scheme can obtain the signal modulation format, center frequency, and start-stop time. Both single shot multi-box detector networks and multi-inputs CNNs were built for signal detection and modulation recognition, respectively. The time-frequency spectrum was employed as the signal characteristic expression. Hence, MFSK modulation schemes were identified during the signal detection phase while MPSK, MAPSK, and MQAM schemes were identified in the same categories to be then recognized using multi-inputs CNNs. In  [85] , a comparative analysis was provided in order to clarify the effects of choosing the features, classifiers, and datasets.
On one hand, the CNN classifier was found to be insensitive to the input formats and capture similar radio features. On the other hand, the LSTM classifier discriminates different modulation schemes in a similar manner to the knowledge of human experts. Moreover, the LeNet-based and the ResNet-based > 95% for SNR > -2 dB  [77]  and for SNR>8 dB  [74]  • Input:contour stellar images  [77]  & constellation diagram  [74]. • Benchmarks: GoogleNet, SVM & Cumulant. • Simulation SNR Range: [-6, +14][77], [ -4, +14][74]. • Data Augmentation was applied using ACGAN  [77]. • Iterations < 50k  [77] , 100k  [74]  • Batch size: 64 • AlexNet  [75]  was adopted. [ {4ASK, BSPK, QPSK, OQPSK, 8PSK, 16QAM, 32QAM, 64QAM} >90% for SNR>-4dB • Input: contour stellar images. • Benchmarks: (AlexNet,SVM-5,AVM-7,Cumulant)  [94] , Orginal AlexNet.
• Dropout were used. • Antennas number: Nr × Nt ∈ {4×1, 4×2, 4x4}. • Benchmark: CNN/ZF-AMR  [125]. • Classification accuracy: >90%, >82%, and >80% for SNR>-5dB when Nr × Nt ∈ {4×1,4×2,4x4}, respectively. ts per type per SNR Discussion and Learned Lessons DL models play an role in AMR and the performance demonstrated by prototypes is remarkable. This is because DL can prove advantageous with regard to feature learning, performance, complexity, and generalization capabilities  [2]. On one hand, training DL models on a wide range of SNR can eliminate the requirement of SNR estimation and generalize the classification process in the different environmental conditions  [39]. However, maintaining low computational complexity, in such models, remains challenging as well as constructing and gathering a large dataset of signals in order to train the model and to guarantee the performance under varying noise regimes.
D. SIGNALS DATASETS Even though DL-AMR can provide significant performance, it requires a large amount of training data. In practice, collecting a sufficient amount of reliable training samples is usually costly and difficult. Therefore, the used largescale training dataset should be constructed adequately and carefully with more realistic signals under a wide range of SNR  [39]. Furthermore, data augmentation methods can make the classification of radio modulation schemes more successful by using shorter radio samples  [77] ,  [102] , which will provide a simplified DL model with a smaller classification response time.
E. ENHANCED DL MODELS As mentioned before, the DL-based methods can operate under a wide range of SNR. Hence, large-scale datasets will be needed in order to achieve the expected results. This may result in a high number of training parameters and, hence, high computational complexity along with high storage requirements. Besides, the training process may consume a long time and suffers from over-fitting issues. Therefore, optimized and hybrid DL methods  [46]  can be studied in order to avoid over-fitting and reduce the computational complexity as well as to improve the classification accuracy  [25] ,  [109]. Moreover, the samples-labeling process in the training phase in supervised learning methods is very difficult to accomplish in addition to be time-consuming. Hence, the idea of semi-supervised  [115]  and unsupervised learning methods is being adopted  [127].
H. DL-AMR IN MIMO SYSTEMS DL-AMR models are scarcely explored for MIMO and massive MIMO communication systems. In this direction, further investigation can be done in order to design new methods or even extend prior works which were dedicated for SISO systems. Although some signals datasets already exist in the literature for SISO systems, but yet there are seldom datasets for MIMO and massive MIMO systems. Hence, it is very important future researches to build robust and sufficient signals datasets for such systems.
[6]]ary of Dataset used in the literature Normal Classes: OOK, 4ASK, BPSK, QPSK, 8PSK, 16QAM, AM-SSB-SC, AM-DSB-SC, FM, GMSK, OQPSK Difficult Classes: OOK, 4ASK, 8ASK, BPSK, QPSK, 8PSK, 16PSK, 32PSK, 16APSK, 32APSK, 64APSK, 128APSK, 16QAM, 32QAM, 64QAM, 128QAM, 256QAM, AM-SSB-WC, AM-SSB-SC, AM-DSB-WC, AM-DSB-SC, FM, GMSK, OQPSK IQ samples convey the changes in magnitude and phase of a wireless signal, and they are often used in RF applications. Many researchers simply used their own simulated IQ signals as the input of CNN. JIE et al. [41]conducted some preprocessing techniques and SNR estimation in order to design a CNN-based AMR method. This method aims at eliminating the bad effects of PO in uncooperative OFDM systems and, therefore, achieving a high classification accuracy. The algorithm in[6]can learn to extract features automatically from the long symbol-rate signals but still requires high SNR to achieve high classification accuracy.
•Recognition accuracy: ≈95% for SNR>5dB and ≈97% for SNR>10dB. a: RadioML 2016. 10A Dataset In [37], a survey on the emerging applications of ML in radio signal processing domain was proposed along with a synthetic dataset. It was generated with GNU's Not Unix (GNU) Radio and it includes eight digital modulations and three analog ones. 220k signals for 20 different SNRs were generated and divided as 1k signals per modulation per SNR. In fact, it represents a cleaner and more normalized version of the RadioML 2016. 04C dataset [38]. However, there is a larger version of this dataset, called RadioML 2016. 10B. [39]E4: b: RadioML 2018. 01ADatasetThisdataset is one of the most challenging datasets of modulation classification which was presented in[39]2. It includes over-the-air measurements of 24 digital and analog modulation schemes spreading in a wide range of SNR2A discussion on[39]will be provided later in this paper, in section IV.
A unit classifier was applied to deal with the changes in input dimensions. Dataset Modulation Pool SNR range Size Frame length Included effects [39] [-20,+30] 2. 5M 1024 CFO, symbol rate offset, multipath fading, and thermal noise [37] BPSK, QPSK, 8PSK, 16QAM, 64QAM, GFSK, CPFSK, 4PAM, WB-FM, AM-SSB, and AM-DSB [-20,+18] 220k 128 CFO, sample rate offset, AWGN, and fading [40] BPSK, QPSK, 8PSK, 16PSK, 32PSK, 64PSK, 4QAM, 8QAM, 16QAM, 32QAM, 64QAM, 128QAM, 256QAM, 4PAM, 8PAM, 16PAM, 2FSK, 4FSK, 8FSK, 16FSK, FM, PM, AM-LSB, AM-USB, AM-SC, and AM-DSB [-20,+18] 780k 1024 multipath fading with different number of channel taps a: Classification Using IQ Samples TABLE5:.
Summary of network configuration, modulation pool, achieved accuracy, and remarks of CNN-based AMR Methods Paper Dense Conv Configuration Flatten/BN Pooling Dropout Training dataset size Testing/validating dataset size Modulation Pool Accuracy per SNR Remark [39] 3 7 7 (*) (*) RadioML 2018. 01A (**) • Input: IQ samples. • Epochs: [0-50] • SNR Range: [-20, +20] • Benchmarks: Statistical Features in XGBoost[59]. • Transfer learning was investigated. • Refer to Table 8 for RNN configuration. • (*) 80% training data and 20% for testing where dataset size∈{240k,1M,1. 4M,2M}. • (**) >98. 3%, > 60%, and >67% at high SNR for the normal classes with 1M training samples, the difficult classes with 240k training samples, and with unsynchronized LOs and Doppler frequency offset with 240k training samples, respectively.
TABLE7 : TABLE7 Summary of network configuration, modulation pool, achieved accuracy, and remarks of CNN-based AMR Methods Paper Dense Conv Configuration Flatten/BN Pooling Dropout Training dataset size Testing/validating dataset size Modulation Pool Accuracy per SNR Remark • Input: STFT spectrogram images. • Iterations < 80k • Adam solver was used. [68] 3 2 2 36k t 16. 8k t {AM, BPSK, FSK, FM} >80% for SNR>-6dB • Benchmarks: SGD[65], Adam[92] & RMSProp[93]. • The recognition accuracy under different activation functions (ReLU, Sigmoid, softplus, • SNR Range: [-20, +20] tanh) was studied. [72] 1 2 2 4k t 0. 2k t {Const, LFM, SFM, PFM, and FSK} >93% when SNR>0dB • Input: time-frequency images using Wigner-Ville formula. • Benchmarks: Methods in [4]. • SNR Range: [-4, +15] • Mini-batch gradient descent was used to improve efficiency.
Numerical experiments. In this section, we report the results of several experiments performed in order to assess the efficiency and the robustness of Algorithm 2. 2. We implemented all the algorithms as Matlab m-files. Our tests include small-scale standard test cases, a challenging nonlinear nonconvex data assimilation task, and two large-scale inverse problems with systems governed by PDE-based dynamics. Our main goal is to understand the behavior of our method on generic least squares problems compared to standard alternatives, and to observe how it handles additional challenges such as nonlinearity in both the constraints and the objective functions. 4. 1. Implementation details. Our parameter values follow that previously adopted for matrix-free trust region SQP  [12]  and nonmonotone trust-region methods  [28].