dataesr/scientific-paragraphs-categorization huggingface dataset:
CSPDI patterns in the CE-IoT define the overall design goal of CbD and can set necessary and sufficient conditions not only for composing different components within IoT applications in ways that guarantee CSPDI properties but also for ensuring that these properties are guaranteed when IoT applications use the CE-IoT architecture. The CE-IoT's CSPDI patterns extend existing work on patterns  [8] , covering in an integrated manner not only security but also the connectivity, dependability, privacy, and interoperability properties of the IoT. In terms of semantic IoT interoperability, the focus is on the definition of semantic annotations for respective IoT architectural patterns and the development of data transformation and validation mechanisms  [9]. The semantic annotation process is based on the Web Service Specification Language validation mechanisms used for semantic interoperability, which rely on including interoperability conditions in the patterns and are based on the use of semantic reasoners or rule engines as well as logic programming.
VI. LCA AND CSPDI PATTERNS The key element enabling the proposed implementation of the CE-IoT approach is the use of architectural pat-terns. The patterns specify abstract and generic smart object interaction and orchestration protocols and, if necessary, define transformations to ensure data semantic interoperability. Furthermore, smart object interaction and orchestration protocols must have the proven ability to achieve the specific CSPDI or LCA properties that may be required. The compositions defined by patterns are both vertical and horizontal, i. e. , they can involve smart objects at the same (horizontal) or at different layers (vertical) of the IoT stack. Specifically, the CE-IoT agents implement a reasoning behavior for pattern evaluation that exhibits the following features: ■ composition structures for integrating smart objects and components of the IoT, enabling platforms while guaranteeing CSPDI properties ■ the end-to-end CSPDI properties that the compositions expressed by the pattern preserve ■ the component-level CSPDI properties that the types of smart objects and/or components orchestrated by the patterns must satisfy ■ additional conditions that need to be satisfied for guaranteeing end-to-end CSPDI properties ■ monitoring checks performed at runtime to verify that any assumptions about the individual smart objects and pattern-orchestrated components are true ■ adaptation actions that may be undertaken to adapt IoT applications and realize the runtime composition structure of the pattern.
VII. LOCALIZED, EMBEDDED INTELLIGENCE Although back-end analytics is a field that has been studied intensively in the past few years, the need for localized analytics at the edge devices has emerged  [10]. An International Data Corporation FutureScape report  [11]  for the IoT reported that, by 2018, 40% of IoT data will be stored, processed, analyzed, and acted upon locally for reducing both the volume of transmit-ted data and the reaction times because decisions are made locally. To implement an effective and efficient CE-IoT ecosystem as well as the adaptive and autonomic behavior needed to maintain the required LCA and CSPDI properties across all layers, intelligent analysis is required. Local analytics are required for semiautonomous reaction, but, considering smart objects' resource constraints, it should also be possible to involve local intelligence at higher levels, thereby enhancing the system's smart behavior (see the circular purple arrows depicting embedded intelligence in the architectural sketching presented in Figure  1 ).
The pat-tern monitors the location condition and the avail-ability of the components based on the agent data and identifies the required action to comply with a CE approach. When weak components are identified, the applied pattern proposes the relocation of unused or partially used components from other locations. In the event that existing components (after the relocation) are not able to cover the needs, the pattern application proposes the purchasing of new, necessary equipment. The results of these steps are presented in Figure  6. The lefthand side of the chart shows the status of the network as produced by the monitoring pattern. The status of network after component relocation is presented in the center of the graph. Finally, the right-hand side of the graph shows, the purchase proposal used to cope with the network needs.
ACKNOWLEDGMENTS The work presented in this article received funding from the  European Union 's  Horizon 2020 research and innova-tion program  under grant  780315  (SEMIoTICS); the  Marie Skłodowska-Curie Actions Research and Innovation Staff Exchange  under grants  777855  (circular economy and the  Internet of Things) ,  778229  ( Ideal Cities ), and  823951  ( Bio-Phoenix ); and from  SPOT5G  ( TEC2017-87456-P ) and  Generalitat de Catalunya  under grant  2017 SGR 891.
(ii) The reference electrode was located in the bulk solution, at a distance ensuring that the polarization of the active electrode did not affect the solution around the Pt reference. (iii) a circular Pt counter electrode (with Pt black deposited on the top of Pt wire) was used to improve the uniformity of current distribution and to assure that less current reached the reference electrode. To ensure reliability of SIET measurements, a special experimental methodology was used and described in detail in the Supporting Information. Micro-potentiometry. -Micro-potentiometric pH and pNa (i. e. pNa = -loga Na+ ) measurements were performed using equipment and corresponding software from Applicable Electronics™ and Science Wares™. pH-selective glass-capillary microelectrodes were employed. Silanized glass micropipettes were back-filled with the inner filling solution and tip-filled with the corresponding ion-selective membrane.
Acknowledgments This work has been performed in scope of  International Doctoral Program in Functional Materials (IDS-FunMat, Erasmus Mundus)  and funded by the  European Union. We would like to express our gratitude to the Fundação para a Ciência e  Tecnologia (FCT)  for the funding under the contracts  M-ERA. NET/0004/2014  and  UID/QUI/ 00100/2013 ,  UID/QUI/00100/2019  and  UIDB/00100/2020. We would like to acknowledge  Tuyen Nguyen ,  Sonia Eugénio  and  Isabel Dias Nogueira  for their assistance during characterization.
Bulk and surface porosity The bulk porosity, 3 b of the substrate layer was determined through its dry-wet weight. The samples were immersed in deionized water for 24 h. Then, the weight of the wet samples was measured aer wiping off excess water on the outer surfaces using a lter paper. Aerwards, the wet samples were dried in an oven for another 24 h before they were weighted again. The bulk porosity can hence be calculated using the following equation: 3 b ð%Þ ¼ h W w À W d r w h W w À W d r w þ W d r p Â 100% (1) where 3 b is the membrane bulk porosity, W w is the wet membrane weight (g), W d is the dry membrane weight (g), r w is the water density (1. 00 g cm À3 ), and r p is the polymer density (1. 37 g cm À3 ). On the other hand, surface porosity, 3 s was calculated based on the data gathered from the SEM analysis.
Acknowledgements The authors would like to acknowledge the nancial support by  Universiti Sains Malaysia USM-RUI  grant (Grant no:  1001/ PJKIMIA/8014063 ). Zulda Mohamad Has Mohd Shae  would like to express his thanks to  USM Fellowship and the Government of France  through the  French Embassy in Malaysia/Campus France  for nancing his study and research mobility.
Acknowledgements The authors are indebted to the grant given by the  CEA  for V. V. The authors are also very grateful to  Dr M. D. Allendorf  for the fruitful discussions review on this manuscript, to  Dr T. -H. Tran-Thi  and  C. Rivron  for assistance with surface area measurements and to  Dr P. Thuery  and  Dr S. Coste  for X-ray diffraction measurements.
In  [15] , they tried to effectively address a novel and accurate method for wind turbine gearboxes fault detection based on both peak values and RMS of vibration signals. They presented well in their study that the overall accuracy of each CM technique depends on the physics of failure. Their studies suggested that a technology that benefits multiple techniques is essential for a holistic and reliable health assessment of wind turbine components  [15]. In  [32] , an innovative investigation of jerk data extracted directly from vibration acceleration (VA) data of the WT gearbox was presented. The vibration magnitude can be measured by either displacement or velocity or acceleration. In  [32] , the vibration acceleration (VA) data were actually gathered. In order to do test the gearbox, several vibration accelerometers had mounted on various locations of the drive-train system without hub and blades to measure the VA.
Scores are generated for each object category in every of the default bounding boxes. In order to better fit the ground truth boxes, adjustment offsets are calculated for each box. In addition, this network merges all predictions obtained from several feature maps with divers resolutions to handle large number of objects with various sizes. In fact, SSD strongly benefits from multi-scale feature maps, which are generated from a backbone network such as VGG-16  [48]  to detect objects having various sizes. The SSD fully eliminates proposal generation and subsequent pixel or feature resampling stages. It also encapsulates all computations in just one network. As a result, it is easy to train SSD and easy to integrate into other systems. Experimental outcomes on the ILSVRC , PASCAL VOC, and COCO datasets verify that in comparison to methods that utilize some additional object proposal steps, the SSD not only has competitive accuracy but also has much lower runtime while providing a consistent framework for training and inference.
From the morphological point of view, a blade and consequently a random part of it are directional objects. In traditional approaches, there are a lot of methods such as Radon transform  [37, 45, 50]  and Gabor wavelet transform (GWT)  [6, [51] [52] [53]  that can be used for directional pattern analysis. In our previous work  [6] , we applied GWT to solve the issue. However, these approaches have deficiencies and suffer from scale and illumination variations. Therefore, in this work, we develop our classification model based on deep neural networks  [54]. To tackle this issue, we define a binary classification problem. We propose to deploy transfer learning and fulfill the task by fine-tuning some strong classification models such as DenseNet, SqueezeNet, ResNet50, and InceptionV3 for our developed dataset including 10,000 images with high precision.
DenseNet Densely Connected Convolutional Network (DenseNet) was introduced in  [57]. Outstanding results on image classification tasks are obtained by DenseNet. The idea of DenseNet is based on the observation that if each layer is directly connected to every other layer in a feed-forward fashion then the network will be more accurate and easier to train  [58]. Due to all improvements applied on DenseNets architecture, it has one of the lowest error rates on CIFAR/SVHN datasets  [59]. Although DenseNets have several compelling advantages, they alleviate the vanishing-gradient problem, strengthen feature propagation, encourage feature reuse, and substantially reduce the number of parameters; they struggle to make use of more and more memory. In comparison to Wide ResNet, DenseNet is less memory/speed-efficient.
InceptionV3 One example of very deep convolutional networks is InceptionV3. It is a widely-used image recognition model that has been shown to attain greater than 78. 1% accuracy on the ImageNet dataset  [56, 60]. The model is the culmination of many ideas developed by multiple researchers over the years. The model itself is made up of symmetric and asymmetric building blocks, including convolutions, average pooling, max pooling, concats, dropouts, and fully connected layers. Batch-norm is used extensively throughout the model and applied to activation inputs. The loss is also computed via Softmax  [60].
Dataset In our research, two types of dataset have been developed. The first type includes still images (snapshot images), and the second one includes the video dataset. We developed two snapshot image datasets named DB1 and DB2 along with the video dataset named DB3. DB1 includes 6000 images that have been captured by us from different angles, distances, and backgrounds in a real wind turbine farm. In this wind turbine farm, more than 300 WTTs exist, and on average, 20 images/WTT are available in DB1. This dataset is used for the object detection problem (Hub detection). In addition, DB2 includes about 10,000 snapshot images uniformly distributed in two different classes (Blade/Non-Blade) for the second problem (Binary classification). DB2 includes 5000 blade images with diverse angles (0 to 360 degrees), different thicknesses and lengths, as well as 5000 non-blade images (please see Figure  5 ).
For velocity estimation, we have deployed our third dataset DB3 including 4 video files. These four videos were captured with 30 frames per second (fps) with different distances and angles from a shared object (a wind tower target), synchronously. Each of these includes 3600 images with 120 s time duration. It is worth mentioning that we measured the ground truth velocity (reference rotational speed) in a time window about 120 s (time duration of each video file) using Long Range Finder Sensor UART(TTL) with USB RB605B 703A U81. The resolution of our B605B laser distance meter sensor was 1mm, with measuring range from 0. 03 to 100 meters for constant conditions.
Hub Object Detection In this section, we intend to examine the result of applying the SSD7 algorithm to find the hub object in an input image. For the learning process, the bounding box ground truth information for all images should be created and applied to the algorithm. To handle that, we use the labelImg software  [64]  to label our image dataset. For this purpose, first we created a data set including 10,000 images; then, for convenience, we randomly sub-sampled it to have just 1000 images. Then, we augmented all these images by labelImg to feed bounding box information to SSD7. Table  2  shows the results of the implementation of the SSD7 algorithm on this reduced dataset. In this experiment, in order to reduce the time overhead, the dimensions of the input images from the data are reduced to the values (300, 480), then the algorithm is run for confidence threshold = 0.
The results indicate that the higher the con f idence thresh, the lower the accuracy while the effect of IoU variation on accuracy is not very noticeable. Based on achieved results, we set the con f idence thresh and IoU values to be 0. 3 and 0. 50, respectively, in our work. In Figure  10 , the results of hub object detection based on the SSD7 algorithm with con f idence thresh = 0. 21 are shown for two sample images from our dataset. In these experiments, for testing, the NMS algorithm is not applied, so several hub objects are identified and located for just an image. In Figure  11 , another example is shown where some false positives can be seen for new sample images. False positives can be either due to the low accuracy of the algorithm or the lower threshold for confidence. Therefore, it can be expected that with increasing con f idence thresh or improving accuracy, this problem will be solved.
In these experiments, the variable parameters are: database size (1000 and 10, 000), image size (32 × 32, 64 × 64, and 480 × 480), epochs (10, 20, 50, and 100) for four selected models. The implementation results are presented in Table  4. It indicates that after the tenth epoch, the DenseNet fine-tuned model, which is trained on our dataset with 10,000 images can achieve the highest validation accuracy of about 0. 9912 among all scenarios. comparison between estimated wind turbine velocity ( VB ) and true one (V B ) (in RPM) for our proposed model and  [6]. It clearly indicates our new algorithm can achieve about 99. 05% accuracy. velocity estimation for the proposed system. Our algorithm is loaded by all these 3600 images/video 354 but for better visualization, obtained results for just 200 consecutive images are plotted in Figure  14.
As a result, the accuracy of about 99. 5% achieved during the run time/Prediction Time per Image (PTPI) was just 0. 01500 s. Then, at the appropriate angle and distance from the hub center, our proposed binary classification algorithms such as DenseNet, SqueezeNet, ResNet50, and InceptionV3 should be deployed to distinguish between a blade and non-blade object. The results obtained indicate that DenseNet (Densely Connected Convolutional Networks) fine-tuned model, which was trained on our dataset with 10,000 images, can achieve the highest validation accuracy, about 99. 12%, among all other classifiers. After executing the classification algorithm, the blade probability is recorded for the current frame. Then, this process is repeated for all frames in a specified time window, and it can be used to estimate the angular speed.
Acknowledgements The study presented in this article was supported by  General Research Fund No. CityU   11272916  and  CityU 11213517  from the  Research Grant Council of the Hong Kong SAR and Research Grant  No. 51779213  from the  National Science Foundation of China. Laboratoire 3SR is part of the  LabEx Tec21  ( Investissements d'Avenir -grant  agreement  nANR-11-LABX-0030 ).
Markov models to explore patterns in the design activity Transitions between design issues can be modeled with a first-order Markov model, which expresses the probability of transitioning from one state to another. Markov models normalize data and lose magnitude effects since they are based on percentage occurrences. The interest in using first-order Markov models is to reveal design patterns in the datasets collected from empirical data  (Yu & Gero 2016; Kan & Gero 2017; Milovanovic & Gero 2018)  or participants' design patterns in the case of a team design  (Gero et al. 2014) ; see Figure  9. Using a similar tool, Stempfle & Badke-Schaub (2002) explored transitions of team mental focus, either on content (solution generation, analysis, evaluation, etc. ) or the collaborative processes engaged (planning, decision, control, etc.
Therefore, studying emotions while designing with automatic facial expression analysis promises to increase our understanding of design processes. The Imotion software (https://imotions. com/) integrates multiple biosensors such as eye tracking, EDA, electroencephalography (EEG) and facial expression analysis modules, which can be recorded simultaneously. Emotion changes across time can be measured with the AFFDEX module in the Imotion software as illustrated in Figure  15   (Abdellahi 2020).
Similarly,  Kim et al. (2012)  explored the correlation of skin conductance response (SCR), self-assessment emotions and semantic descriptors to measure emotions related to the evaluation and perception of an industrial design object (vacuums). The EDA accounts for an emotional arousal but does not give a qualitative measure of the valence of the arousal. In Figure  21   Nguyen et al. (2015 Nguyen et al. ( , 2019) )  pointed out the limits of manually segmented design protocols and explored how to use EEG data to perform that task. In their study, they compared manual segmentation to a set of algorithms that automatically segment the EEG dataset. Eight design sessions of up to 2-hour length were used for the analysis. The study showed that transient microstate algorithms worked well to describe the temporal aspects of design moves.
Post-processing: highlighting patterns In their study,  Goucher-Lambert & McComb (2019)  provide an example of post-processing results of an fMRI using Hidden Markov Models to decipher brain activation patterns during ideation tasks. This technique uses machine learning algorithms to automatically infer cognitive states in fMRI datasets. Twelve states were analyzed over the 2-min sequence, where designers produced design solutions while in the MRI. Each of the 12 states represents average modes of brain activation during the solution generation sequence. The study explores differences between high performing designers and low performing designers in terms of the activations of each state over the time period.
Using physiological or neurocognitive data to study design protocols can be a more efficient way to analyze design situations. As explored by  Nguyen et al. (2015 Nguyen et al. ( , 2019)) , EEG-based protocol segmentation is relevant to revealing design actions over time. Another direction to explore concerns the automation of protocol analysis using speech recognition and machine learning using tools from natural language processing. Already coded protocols form a database for pattern classification that can be used by a machine learning algorithm to encode new datasets. To obtain a near-to real-time coding of the data, speech recognition could be added to the automated coding of the protocols.
Physiological and neurocognitive tools can measure design thinking at the microscale and provide opportunities to explore whether there are activities specific to designing that exists at this smaller scale. In the previous section, we identified the emergence of new tools to accompany the design process. By acquiring increased knowledge of design thinking, we can develop more refined computational models of design activity based not only on design cognition but also on design physiology and design neurocognition. The integration of active computational agents into the design process provides a new kind of AI design tool. The BCI and 'thinking caps' are another direction to explore in terms of tool development based on results from design neurocognition studies. Design software (CAD, BIM) and design software interfaces (Graphical User Interface, Tangible User Interface) affect the design activity, which raises another question: What will be the effects of AI design tools and BCI on the design process and outcomes?.
Real-Life GPS Data Experiment The proposed CRB expression was also validated in a real-life GPS scenario. The signal acquisition was performed using a static L1 frequency patch antenna in open sky conditions, and recorded using an USRP X310 at F s = 20 MHz. To test standard front-end configurations, such a signal was converted to the equivalent F s = 5 MHz and F s = 2. 5 MHz signals by averaging and decimating 4 and 8 samples, respectively. In a real-life data test it is not possible to perform MC runs, therefore the MSE was obtained via 100 different correlations, each one using a coherent integration time of 10 ms (i. e. , s (η) being 10 GPS L1 C/A PRN codes of length 1 ms), and separated by 20 ms. This leads to an overall observation interval of 1s during which the hypothesis of relative uniform radial movements (  4 ) is regarded as valid  [51].
The beam of rays is dense (e. g. , we cannot graphically distinguish one ray from another one). lead to an architectural variation, whose consequences have been explored in Sect. 3. 2. 1. In order to keep the illustration simple, in this example we take into consideration only the number of rays per cycle while keeping the other parameters of Table  1  fixed. Our goal is to find the ideal instrument, i. e. a LIDAR whose acquired data have enough quality to allow the rover to act properly while keeping the energy consumption limited. Hokuyo LIDAR UTM-30LX consumes up to 8 W for each capture(Hokuyo UTM-30LX, not dated). By fixing the range measurement to 120 • (from -π/3 to +π/3, in radians), an update rate of 20 Hz and a time window for the analysis of 20 min, we have evaluated the behaviour of LIDAR in two case studies: In the first case, we favour a precise reconstruction of the image by fixing the number of rays per cycle to 640 (Table 1).