dataesr/scientific-paragraphs-categorization huggingface dataset:
B. Simulation results Both RLSA and CF-RLSA far-field patterns were computed. The complete simulation was performed with the commercial software Ansys HFSS, taking into account the finite size of the substrate. The simulated gains are illustrated in Figs. 10  and 11 , respectively. The RLSA exhibits a broadside 36. 8 dBi gain at the design frequency of 29 GHz, however, this gain rapidly degrades with frequency, reaching about 27 dBi at 28 GHz and 30 GHz. In comparison, the CF-RLSA shows a very stable radiation pattern over a larger bandwidth. The obtained radiation efficiencies at the central frequency for the RLSA and the CF-RLSA are 86% and 82%, respectively. The difference could be explained by the dielectric and ohmic losses in the propagation through the corporate feeder. While a slightly lower gain of 35.
One useful source used for indoor localization is image analysis and classification  [12]. Thanks to the success of convolutional neural networks (CNNs), starting with AlexNet in object classification with the large-scale ImageNet dataset  [13] , research focus on scene classification has been diverted from handcrafted feature extraction methods to deep learning (DL)  [14] [15] [16]. However, one significant drawback of CNNs is the requirement for a large-scale labeled dataset for training, which is not feasible in many applications, such as indoor scene recognition. By applying transfer learning, CNNs pretrained on large-scale datasets (such as ImageNet) are fine-tuned with target scene datasets by making the last layers more data-specific  [16]. With recent progress in computer vision techniques, visual place recognition (VPR) can now be considered a promising room-level localization solution.
e. , pointing direction). We assume that this additional information can be very informative, given that indoor scene recognition constitutes a complex task in computer vision. The proposed system contains four CNNs, each specific to a definite direction range. Given a query image, the system selects the corresponding CNNs for image classification depending on the magnetic heading of the user's smartphone camera. In the training phase, we propose overlapping direction ranges, which means that for a given image direction, the image is fed to two CNNs. This allows for more training images per CNN, since two CNNs may share a subset of the training dataset. In the inference phase, two CNNs are involved in query image classification depending on the magnetic heading of the smartphone camera to obtain more comprehensive features.
3. While several indoor and/or outdoor localization datasets exist in the literature, none of them integrates information other than images. To overcome this issue, we provide a dataset containing images with their respective magnetic heading direction in the metadata. We conducted experiments in five different indoor scenes and evaluated classification performance according to accuracy on the whole test set. Compared to the scene recognition method based solely on image features, which is a single-CNN-based classification system fine-tuned on an image training set, the proposed model enables significant improvement in recognition accuracy. The remainder of this paper is organized as follows. Section 2 introduces CNNs, VPR, and the use of magnetic field in localization, along with the advantages and disadvantages of the current computing approaches.
Convolutional Neural Networks with Transfer Learning In recent years, CNNs have become a very popular method for image classification and are therefore used in many applications due to their powerful feature extraction ability that allows them to outperform traditional approaches  [21]. A CNN is a hierarchical network composed mainly of convolutional layers, activation functions such as rectified linear units (ReLUs), pooling layers, and fully connected layers. The intermediate convolutional layers carry the important responsibility of feature extraction. CNNs can outperform handcrafted feature extraction methods, improving state-of-the-art recognition results  [22]. To this end, deep CNN models are necessary, requiring large-scale training datasets to properly estimate the underlying weights.
Two research directions have been undertaken to address issues, such as insufficient training datasets and the requirement for lighter architectures, as in the present work. With the evolution of DL, transfer learning has become a popular approach to solve new classification tasks with insufficient training datasets by fine-tuning pretrained CNNs  [23]. For example, a CNN model pretrained on large-scale datasets such as ImageNet  [13]  can be fine-tuned with a training dataset containing images representing the target task. Thus, the CNN weights are updated in an end-to-end manner in the training phase. Freezing the first layers refers to the process of fixing the weights and parameters of specific layers in a pretrained CNN while training on a new task during transfer learning, as shown in Figure  1.
There are many different types of indoor scene images, including scenes of houses, offices, hospitals, and classrooms. Researchers have been working on diversifying datasets to make classification algorithms more effective. The majority of the available scene datasets include images of external and internal environments, such as MIT Indoor67  [32] , Scene15  [33] , SUN  [34] , SUN Attribute  [35] , and Places  [14]. These datasets do not account for changes in rotation, luminance variation, or point of view. As the common well-known datasets from the computer vision community cannot be used in practice, variations of practical experiments in real-world case scenarios led to the creation of datasets based on available information  [36]. As indoor scene recognition is a key component of vision-based indoor localization, the created datasets directly support real-world applications.
The global architecture illustrated in Figure  4a  consists of three main components: the selection block for direction-driven model selection, the image classification model defining four CNN models, and the fusion and decision block for combination of the obtained results. These three components are described in detail in Sections 3. 1. 1-3. 1. 3, respectively. In order to cover the four ranges of orientations (A, B, C, and D in Figure  5a ), the proposed classification system contains four CNNs, denoted as A, B, C, and D. The use of four ranges of orientations is motivated by small datasets and the need to avoid underfitting or overfitting. Dividing the heading directions into more ranges would necessitate a greater number of images for each range to provide adequate representation. This strategy allows us to assign a fair number of images to each range, guaranteeing that the CNNs can efficiently learn distinguishing features.
Image Classification Models We propose a generic system that can include all types of CNNs used for image classification. Pretrained CNNs are trained on more than a million images from the Ima-geNet dataset  [13]. Consequently, these networks learn rich feature representations from a wide range of images. Instead of building CNN models from scratch, we investigated mobile-compatible pretrained CNNs, namely SqueezeNet  [24] , ShuffleNet  [25] , and Mo-bileNet  [26, 27]. These light CNN models have demonstrated a good tradeoff between accuracy and efficiency while addressing resource-constrained environments, including low memory and hardware requirements. By transfer learning, we fine-tuned these pretrained CNN models as follows. The layer directly preceding the classification layer (fully connected layer or convolutional layer) of the pretrained CNN is replaced with a new layer having a number of outputs equal to the number of categories in the target dataset.
Dataset Preparation To construct and evaluate the proposed indoor scene classification system, we first created a dataset of images with their respective magnetic headings with respect to magnetic north. The prepared dataset includes informative images of the indoor environment with different perspectives of the studied rooms. To ensure an efficient data collection process, we designed an Android application that uses the smartphone's built-in sensors. When capturing images using this application, each image is collected with the corresponding magnetic heading saved in its metadata. For data collection, the smartphone was held in the portrait/vertical position. The RGB images were cropped and saved at a size of 1088 × 1088 pixels to avoid distorting the shapes of the objects in the images when resized.
The dataset was prepared using the main rear camera of a Samsung Galaxy A51. The indoor environment studied in this work has five rooms: coffee break room, office 1, office 2, office 3, and storage room. To provide diverse and representative data, the data collection process was conducted over several days. We took precautions to maintain consistency during the data collection period. Two data collection rounds were conducted. In the first round, we took eight images per position (i. e. , a given standing location) in each room with different orientations. Each position used to collect images results in a distinct perspective. Images were collected at orientations of 0°(North), 45°, 90°, 135°, 180°, 225°, 270°, and 315°. These images were used for offline training of the proposed direction-driven multi-CNN system.
In the second round, we took an average of 20 images per position in each room, with different positions than the first round and a full 360-degree rotation in each position to take all the heading viewpoints into consideration. The entire dataset was then cleaned by deleting uninformative images (i. e. , images constituted mostly of walls, windows, etc. ). We obtained between 100 and 200 images per class depending on the room dimensions and complexity. A part of these collected images was used for training and validation of the system, and the rest were used for assessment of the classification accuracy (50% for the training phase and 50% for the testing phase). Figure  6  shows some examples from the collected dataset.
CNN Training and System Testing For the proposed indoor scene recognition system based on the direction-driven multi-CNN architecture, the four CNN models need to be trained and validated in order to be implemented. To assess its performance, the proposed classification system was evaluated on the totality of the testing set. We also used the testing set to examine the relevance of the fusion strategies described in Section 3. 1. 3. As previously mentioned, since we had few real data points, we relied on CNN models pretrained on ImageNet  [13] , and fine-tuned them using the real dataset. We examined three well-known mobile-compatible pretrained CNNs: SqueezeNet  [24] , ShuffleNet  [25] , and MobileNet version 2  [27]. In order to provide a baseline system, we trained and finetuned a single CNN model with the totality of the training and validation sets.
In order to provide a fair comparative analysis, we considered the same pretrained CNN used for our proposed recognition system. Models were optimized using a batch gradient descent optimizer with a learning rate equal to 0. 001. Note that pretrained CNNs take fixed image sizes and a defined number of input channels; therefore, all the images in the dataset were preprocessed (Images from our real dataset were scaled to 224 × 224 × 3 to respect the dimensions accepted by the input layer of the pretrained CNNs ShuffleNet and MobileNet and to 227 × 227 × 3 when working with SqueezeNet). We trained the models for a maximum of 500 epochs. In order to avoid overfitting, the CNN training stopped automatically when the validation loss starts began increasing while the training loss was still decreasing.
Model Analysis and Partitioning for Inference In a CNN model, each layer has its own set of learnable weights that are optimized during training by minimizing the classification loss. These parameters are typically saved in a model file that can be loaded into memory during inference. ONNX (Open Neural Network Exchange; GitHub repository: https://github. com/onnx/onnx, accessed on 26 January 2022. ) is an important DL model format because it provides a common standard for representing DL models, making it easier to develop and deploy them across multiple frameworks and devices. We compared the model file size of each implementation (with MobileNet-v2) as shown in Table  4. The proposed computing strategy (composed of frozen layers and trained layers) is lighter than the four complete direction-driven CNNs by about 15.
Conclusions and Future Work In this paper, we propose a direction-driven multi-CNN indoor scene recognition system for room-level localization that uses embedded smartphone sensors to account for the camera heading direction if the user smartphone relative to magnetic north. We created our own dataset, which includes images with corresponding magnetic heading values. We also used and compared two heading-based weighted fusion techniques. Experiments showed that the proposed system outperforms the baseline system based solely on images. When dealing with indoor scene image data, the proposed system outperformed the traditional CNN image classification system. The proposed system relies on built-in smartphone sensors, which vary in quality and accuracy across different devices and environments and may have an impact on the overall performance of the system.
RESULTS AND DISCUSSION Multimaterial active composite With the proposed computational design framework, we consider and study an active composite in the form of a simple cantilever beam subjected to temperature changes in order to induce a shape change. First, the user-modelized beam is partitioned to acquire a voxelized structure that is then affected by a certain material distribution. This generated distribution consists of a smart material, that is selected from the database and modified by the user, an inert material, and void elements. Void elements are thus the novelty that is introduced in this paper and corresponds to the deletion of voxels within the structure to eventually achieve a solution that contains the correct placement of smart and inert materials to achieve the target shape with added benefits.
This is done automatically, and the process is generally very fast and computationally negligible relative to the simulation time. The FE simulation and analysis is done with ABAQUS software (Version 2022, Dassault Systèmes Simulia Corp. , Johnston, RI, USA) and the settings and steps depend on the chosen active material. This will be discussed in depth in the following section. Once the simulation is complete, the displacement of each node at the vertices on the top surface of every voxel is calculated relative to their initial position at peak deformation. This data is extracted and compared to the target deformation after, which a fitness can be calculated for the simulated individual based on the fitness function. Subsequently, after a score is assigned to every individual in the population the next step is begun.
An equilibrium contraction is assumed such that the CTE values depend purely on the current temperature of the structure T and the reference temperature of the material T ref , where the thermal strain is null. This material constitutes the active material used in all the examples and case studies presented in this paper and represented in red voxels. For FEM simulations, each voxel was meshed with C3D8R elements in the commercial software ABAQUS. A linear elastic material is considered instead of a hyperelastic material as the latter is limited solely to isotropic thermal expansions in ABAQUS. Hydrogel. A simplistic version of a temperature-sensitive hydrogel  42, 43  is modelled with an incompressible neo-Hookean material model such that the strain energy potential is defined as below: U ¼ C 10 ðI 1 À 3Þ þ 1 D 1 ðJ el À 1Þ 2 ; ( 2 ) where U is the strain energy per unit of reference volume, C 10 and D 1 are temperature-dependent material parameters, I 1 is the first deviatoric strain invariant defined as: I 1 ¼ λ 2 1 þ λ 2 2 þ λ 2 3 ( 3 ) where the deviatoric stretches λ i is expressed as follows: λ i ¼ J À 1 3 λ i (4) J is the total volume ratio, J el is the elastic volume ratio, and λ i are the principal stretches.
The elastic volume ratio J el relates the total volume ratio J and the thermal volume ratio J th , as follows: J el ¼ J J th Fig. 5  Schematic flowchart of the full design process drivenby an EA. By considering a voxel-based representation of a design space, the computational design process starts from a target shape (or displacement) and uses a FEA-based EA. It combines the advantages of optimizing both the materials distribution and material layout via TO to solve the inverse design problem of finding an optimal design to achieve a target shape by integrating void voxels. The thermal volume ratio J th can be given by: J th ¼ 1 þ ε th À Á 3 (5) where ε th is the linear thermal expansion strain that is obtained from the temperature and the isotropic CTE. An incompressible model would give us the following simplification: U ¼ C 10 ðI 1 À 3Þ (6) For FEM simulations in the ABAQUS software, hybrid C3D8RH elements were used.
6  Known target distribution to attain a parabola target shape after deformation. In this voxel-based model, active material and passive material are distributed through red voxels and blue voxels, respectively. sinusoidal equations. However, to maximize the capacity of the framework to solve for 2D shape changes, it is essential to introduce multivariable displacement equations. Since it is complicated to specify the target displacements in both directions, especially in the context of large deformations, a novel approach is adopted. A user-friendly method to design the target shape is via Bezier curves. Bezier curves are parametric curves that allows the user to define a smooth, continuous curve using discrete control points. Although these curves can be represented via specific functions, a more hands-on strategy is opted for, via a module designed on the graphical algorithm editor Grasshopper® (GH), an add-on of the commercial computer-aided design (CAD) software Rhinoceros© (RH).
METHOD The proposed computational framework has been implemented in a program written in Python 2. 7 and adapted to be launched via ABAQUS CAE's python scripting within the ABAQUS software and the settings and steps depend on the chosen active material. The program is structured into two principal classes, VoxAbaqus, that contains the methods used to run commands directly in ABAQUS, such as assigning material distributions to a structure or creating a material, and EvolutionaryAlgorithm which contains the methods that govern the EA processes, such as crossover and mutation. The latter class pilots and calls methods from the former class. The slightly outdated version of Python limits the number of libraries that can be imported and utilized.
ACKNOWLEDGEMENTS The research work is part of a much larger project in the field of design for 4D printing. The authors would like to thank the  French 'Investissements d'Avenir' program , project  ISITE-BFC 4D-META  (contract  ANR-15-IDEX-0003 ) as the main financial support of this research activity, the  EIPHI Graduate School  (contract  ANR-17-EURE-0002 ), and the  S. mart academic society  for their contribution.
, 2021). The experimental area was a permanent grassland site consisting of a predominantly perennial ryegrass sward (Lolium perenne L. ) and white clover (Trifolium repens); the swards were, on average, 5 yr old at the start of the experiment. Weather data were collected from a weather station situated at the Moorepark research farm. Maximum distance between the weather station and the pasture was 1. 5 km. The station measured air temperature using a platinum resistance thermometer (Sensing Devices) placed 1 m above the soil. A tipping-bucket rain gauge (Casella, UK) was used to monitor rainfall. All sensors were connected to a data logger (CR series, Campbell Scientific) that processed all the readings and transmitted them to the Irish National Meteorological Service (Met Éireann) server via a broad-band connection.
Total lactation yields were calculated using the first 35-wk data from the experiment, as all cows were lactating for at least 35 wk. Somatic cell score was calculated as the natural logarithm of SCC. The following dry-off criteria to determine if cows should cease lactating were implemented at the end of lactation: cows milking less than 5 kg/d, cows within 8 wk of calving-predicted calving date based on ultrasound scanning for pregnancy diagnosis at 120 d, cows with a BCS <2. 75 within 10 wk of calving, cows with a SCC >500,000 for 2 consecutive weeks. BW and BCS. Bodyweight and BCS were recorded weekly following morning milking throughout the experimental period. An electronic portable-weighing scale with Winweigh software package (Tru-test Limited, Auckland, New Zealand) was used to record BW.
Five aspects of the cows' locomotor ability were scored (spine curvature, tracking, ab-or adduction, speed, and head bob) between 1 (perfect) and 5 (most impaired), using the system adopted by  O'Driscoll et al. (2010). Following each measurement, the 5 aspects were summed to give 1 score for each cow, a greater score representing worse locomotion. Complete data sets were available for yr 1 and 3. Due to Covid-19 restrictions, measurements were only undertaken for the last 4 mo of lactation in yr 2. Fertility. The reproductive measurements that were calculated were as follows: pregnant to first service (proportion of cows that became pregnant during the first 21 d of the breeding season, confirmed by ultrasound scans during and after the end of the breeding season); calving within 42 d (proportion of cows that became pregnant during the first 42 d of the breeding season, confirmed by ultrasound scans during and after the end of the breeding season); overall pregnancy rate (proportion of cows confirmed pregnant by ultrasound scans at 120 d); number of services (during the 11-wk breeding season, the number of times a cow was observed in estrus and inseminated); calving to service interval (number of days from calving date to date of first service); calving to conception interval (number of days from calving to conception, confirmed by ultrasound scans during and after the end of the breeding season).
Data Editing and Statistical Analysis All data were analyzed using SAS (v9. 4; SAS Institute). All variables were first tested for normality (UNIVARIATE procedure); P-values <0. 05 were considered statistically significant. All herbage data were analyzed using a mixed model procedure (PROC MIXED) in SAS. Variables included in the model were milking frequency (n = 1 to 2), paddock nested within week, rotation (n = 1 to 10), and year (n = 1 to 3). Paddock was nested within week, as there was a hierarchical data structure; paddock and week are not mutually independent but have a correlation structure. Pasture quality was analyzed using a mixed model procedure (PROC MIXED) in SAS. Variables included in the model were milking frequency (n = 1 to 2), month of year (n = 1 to 11), and year (n = 1 to 3).
Cow was included as a random effect, and measurement period within year was included as a repeated measure. A heterogeneous variance compound symmetry covariance structure was used. Days in milk and PTA for milk kilograms were included as covariates, and a Tukey adjustment was also included. Fertility data were analyzed using the GLIMMIX procedure in SAS (PROC GLIMMIX). Similar to previous models, variables included were milking frequency (n = 1 to 2), parity (n = 1 to 2), year (n = 1 to 3). The interactions of milking frequency and parity and milking frequency and year were also included; however, both were not significant for any variables and were removed from the final model. Days in milk in each respective year and the EBI subindex for fertility were included as covariates in the model, and a Tukey adjustment was also included.